Swat 2000
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VERSION 2000
S.L. NEITSCH, J.G. ARNOLD, J.R. KINIRY, J.R. WILLIAMS DRAFT-April, 2001
GRASSLAND, SOIL AND WATER RESEARCH LABORATORY ○ AGRICULTURAL RESEARCH SERVICE 808 EAST BLACKLAND ROAD ○ TEMPLE, TEXAS 76502 BLACKLAND RESEARCH CENTER ○ TEXAS AGRICULTURAL EXPERIMENT STATION 720 EAST BLACKLAND ROAD ○ TEMPLE, TEXAS 76502
ACKNOWLEDGEMENTS The authors and developers of the Soil and Water Assessment Tool wish to acknowledge the support of the Wisconsin Department of Natural Resources and WDNR staff member Mr. John Panuska for assistance in the development and testing of SWAT.
CONTENTS
CHAPTER 1 INTRODUCTION 1.1 1.2
DEVELOPMENT OF SWAT OVERVIEW OF SWAT LAND PHASE OF THE HYDROLOGIC CYCLE ROUTING PHASE OF THE HYDROLOGIC CYCLE
1.3
REFERENCES
1 3 6 8 21 24
PART 1: MODEL DESCRIPTION SECTION 1: CLIMATE CHAPTER 2 EQUATIONS: ENERGY 2.1
SUN-EARTH RELATIONSHIPS
31 32
DISTANCE BETWEEN EARTH AND SUN SOLAR DECLINATION SOLAR NOON, SUNRISE, SUNSET, AND DAYLENGTH
32 32 33
2.2
2.3
2.4 2.5 2.6
SOLAR RADIATION
34
EXTRATERRESTRIAL RADIATION SOLAR RADIATION UNDER CLOUDLESS SKIES DAILY SOLAR RADIATION HOURLY SOLAR RADIATION DAILY NET RADIATION
34 35 36 37 38
TEMPERATURE
41
DAILY AIR TEMPERATURE HOURLY AIR TEMPERATURE SOIL TEMPERATURE WATER TEMPERATURE
41 42 42 47
WIND SPEED NOMENCLATURE REFERENCES
48 49 51
CHAPTER 3 EQUATIONS: ATMOSPHERIC WATER 3.1 3.2 3.3 3.4 3.5 3.6 3.7
PRECIPITATION MAXIMUM HALF-HOUR RAINFALL WATER VAPOR SNOW COVER SNOW MELT
53 54 55 55 58 61
SNOW PACK TEMPERATURE SNOW MELT EQUATION
61 62
NOMENCLATURE REFERENCES
63 64
CHAPTER 4 EQUATIONS: WEATHER GENERATOR 4.1 4.2
4.3
4.4
PRECIPITATION
67 68
OCCURRENCE OF WET OR DRY DAY AMOUNT OF PRECIPITATION
68 69
SOLAR RADIATION & TEMPERATURE
70
DAILY RESIDUALS GENERATED VALUES ADJUSTMENT FOR CLEAR/OVERCAST CONDITIONS
70 72 73
RELATIVE HUMIDITY
75
MEAN MONTHLY RELATIVE HUMIDITY GENERATED DAILY VALUE ADJUSTMENT FOR CLEAR/OVERCAST CONDITIONS
75 76 77
MAXIMUM HALF-HOUR RAINFALL
78
4.5 4.6 4.7
MONTHLY MAXIMUM HALF-HOUR RAIN GENERATED DAILY VALUE
79 79
WIND SPEED NOMENCLATURE REFERENCES
81 81 83
CHAPTER 5 EQUATIONS: CLIMATE CUSTOMIZATION 5.1 5.2 5.3
ELEVATION BANDS CLIMATE CHANGE NOMENCLATURE
85 86 88 90
SECTION 2: HYDROLOGY CHAPTER 6 EQUATIONS: SURFACE RUNOFF 6.1 6.2 6.3
6.4 6.5 6.6 6.7
RUNOFF VOLUME: SCS CURVE NUMBER PROCEDURE
93 94
SCS CURVE NUMBER
95
RUNOFF VOLUME: GREEN & AMPT INFILTRATION METHOD 101 103 PEAK RUNOFF RATE TIME OF CONCENTRATION RUNOFF COEFFICIENT RAINFALL INTENSITY MODIFIED RATIONAL FORMULA
104 107 107 108
SURFACE RUNOFF LAG TRANSMISSION LOSSES NOMENCLATURE REFERENCES
109 110 113 114
CHAPTER 7 EQUATIONS: EVAPOTRANSPIRATION 7.1 7.2
7.3
CANOPY STORAGE POTENTIAL EVAPOTRANSPIRATION
117 118 119
PENMAN-MONTEITH METHOD PRIESTLEY-TAYLOR METHOD HARGREAVES METHOD
120 126 127
ACTUAL EVAPOTRANSPIRATION
128
EVAPORATION OF INTERCEPTED RAINFALL TRANSPIRATION
128 129
7.4 7.5
SUBLIMATION AND EVAPORATION FROM THE SOIL
129
NOMENCLATURE REFERENCES
134 136
CHAPTER 8 EQUATIONS: SOIL WATER 8.1 8.2 8.3 8.4 8.5 8.6
SOIL STRUCTURE PERCOLATION BYPASS FLOW LATERAL FLOW
139 140 144 145 151
LATERAL FLOW LAG
154
NOMENCLATURE REFERENCES
156 157
CHAPTER 9 EQUATIONS: GROUNDWATER 9.1 9.2
9.3 9.4 9.5
GROUNDWATER SYSTEMS SHALLOW AQUIFER
159 160 162
RECHARGE GROUNDWATER/BASE FLOW REVAP PERCOLATION TO DEEP AQUIFER PUMPING GROUNDWATER HEIGHT
163 164 166 167 168 168
DEEP AQUIFER NOMENCLATURE REFERENCES
169 170 171
SECTION 3: NUTRIENTS/PESTICIDES CHAPTER 10 EQUATIONS: NITROGEN 10.1 NITROGEN CYCLE INITIALIZATION OF SOIL NITROGEN LEVELS
10.2 MINERALIZATION & DECOMPOSITION/ IMMOBILIZATION HUMUS MINERALIZATION RESIDUE DECOMPOSITION & MINERALIZATION
10.3 NITRIFICATION & AMMONIA VOLATILIZATION 10.4 DENITRIFICATION
175 176 178
180 181 182
184 187
10.5 10.6 10.7 10.8 10.9 10.10
NITROGEN IN RAINFALL FIXATION UPWARD MOVEMENT OF NITRATE IN WATER LEACHING NOMENCLATURE REFERENCES
CHAPTER 11 EQUATIONS: PHOSPHORUS 11.1 PHOSPHORUS CYCLE INITIALIZATION OF SOIL PHOSPHORUS LEVELS
11.2 MINERALIZATION & DECOMPOSITION/ IMMOBILIZATION 11.3 11.4 11.5 11.6
193 194 195
197
HUMUS MINERALIZATION RESIDUE DECOMPOSITION & MINERALIZATION
198 199
SORPTION OF INORGANIC P LEACHING NOMENCLATURE REFERENCES
201 203 204 205
CHAPTER 12 EQUATIONS: PESTICIDES 12.1 12.2 12.3 12.4 12.5
188 188 189 189 190 191
WASH-OFF DEGRADATION LEACHING NOMENCLATURE REFERENCES
207 209 209 211 211 211
SECTION 4: EROSION CHAPTER 13 EQUATIONS: SEDIMENT 13.1 MUSLE SOIL ERODIBILITY FACTOR COVER AND MANAGEMENT FACTOR SUPPORT PRACTICE FACTOR TOPOGRAPHIC FACTOR COARSE FRAGMENT FACTOR
215 216 216 219 220 222 223
13.2 USLE 13.3 13.4 13.5 13.6 13.7
223
RAINFALL ERODIBILITY INDEX
223
SNOW COVER EFFECTS SEDIMENT LAG IN SURFACE RUNOFF SEDIMENT IN LATERAL & GROUNDWATER FLOW NOMENCLATURE REFERENCES
226 226 227 228 230
CHAPTER 14 EQUATIONS: NUTRIENT TRANSPORT 14.1 NITRATE MOVEMENT 14.2 ORGANIC N IN SURFACE RUNOFF ENRICHMENT RATIO
14.3 SOLUBLE PHOSPHORUS MOVEMENT 14.4 ORGANIC & MINERAL P ATTACHED TO SEDIMENT IN SURFACE RUNOFF ENRICHMENT RATIO
14.5 NUTRIENT LAG IN SURFACE RUNOFF AND LATERAL FLOW 14.6 NOMENCLATURE 14.7 REFERENCES
CHAPTER 15 EQUATIONS: PESTICIDE TRANSPORT 15.1 PHASE DISTRIBUTION OF PESTICIDE 15.2 MOVEMENT OF SOLUBLE PESTICIDES 15.3 TRANSPORT OF SORBED PESTICIDE ENRICHMENT RATIO
15.4 PESTICIDE LAG IN SURFACE RUNOFF AND LATERAL FLOW 15.5 NOMENCLATURE 15.6 REFERENCES
CHAPTER 16 EQUATIONS: WATER QUALITY PARAMETERS 16.1 ALGAE 16.2 CARBONACEOUS BIOLOGICAL OXYGEN DEMAND ENRICHMENT RATIO
231 232 234 234
235 236 237
238 239 241
243 244 245 248 249
250 251 252
253 254 254 255
16.3 DISSOLVED OXYGEN OXYGEN SATURATION CONCENTRATION
16.4 NOMENCLATURE 16.5 REFERENCES
256 256
257 257
SECTION 5: LAND COVER/PLANT CHAPTER 17 EQUATIONS: GROWTH CYCLE 17.1 HEAT UNITS 17.2 17.3 17.4 17.5
261 262
HEAT UNIT SCHEDULING
264
DORMANCY PLANT TYPES NOMENCLATURE REFERENCES
267 268 269 269
CHAPTER 18 EQUATIONS: OPTIMAL GROWTH 18.1 POTENTIAL GROWTH
271 272
BIOMASS PRODUCTION CANOPY COVER AND HEIGHT ROOT DEVELOPMENT MATURITY
272 275 277 278
18.2 WATER UPTAKE BY PLANTS 18.3 NUTRIENT UPTAKE BY PLANTS
279 283
NITROGEN UPTAKE PHOSPHORUS UPTAKE
18.4 CROP YIELD 18.5 NOMENCLATURE 18.6 REFERENCES
CHAPTER 19 EQUATIONS: ACTUAL GROWTH
283 287
291 293 296
19.1 GROWTH CONSTRAINTS
299 300
WATER STRESS TEMPERATURE STRESS NITROGEN STRESS PHOSPHORUS STRESS
300 300 301 302
19.2 ACTUAL GROWTH
303
BIOMASS OVERRIDE
19.3 ACTUAL YIELD HARVEST INDEX OVERRIDE HARVEST EFFICIENCY
19.4 NOMENCLATURE
303
304 304 305
306
SECTION 6: MANAGEMENT PRACTICES CHAPTER 20 EQUATIONS: GENERAL MANAGEMENT 20.1 20.2 20.3 20.4 20.5 20.6 20.7 20.8 20.9 20.10 20.11 20.12
PLANTING/BEGINNING OF GROWING SEASON HARVEST OPERATION GRAZING OPERATION HARVEST & KILL OPERATION KILL/END OF GROWING SEASON TILLAGE
311 312 313 314 315 316 316
BIOLOGICAL MIXING
317
FERTILIZER APPLICATION AUTO-APPLICATION OF FERTILIZER PESTICIDE APPLICATION FILTER STRIPS NOMENCLATURE REFERENCES
318 320 323 325 325 327
CHAPTER 21 EQUATIONS: WATER MANAGEMENT 21.1 IRRIGATION 21.2 21.3 21.4 21.5 21.6 21.7
329 330
AUTO-APPLICATION OF IRRIGATION
331
TILE DRAINAGE IMPOUNDED/DEPRESSIONAL AREAS WATER TRANSFER CONSUMPTIVE WATER USE POINT SOURCE LOADINGS NOMENCLATURE
331 332 332 333 334 334
CHAPTER 22 EQUATIONS: URBAN AREAS 22.1 22.2 22.3 22.4
CHARACTERISTICS OF URBAN AREAS SURFACE RUNOFF FROM URBAN AREAS USGS REGRESSION EQUATIONS BUILD UP/WASH OFF
335 336 337 337 339
STREET CLEANING
341
22.5 NOMENCLATURE 22.3 REFERENCES
343 344
SECTION 7: MAIN CHANNEL PROCESSES CHAPTER 23 EQUATIONS: WATER ROUTING 23.1 23.2 23.3 23.4 23.5 23.6 23.7 23.8 23.9 23.10
CHANNEL CHARACTERISTICS FLOW RATE AND VELOCITY VARIABLE STORAGE ROUTING METHOD MUSKINGUM ROUTING METHOD TRANSMISSION LOSSES EVAPORATION LOSSES BANK STORAGE CHANNEL WATER BALANCE NOMENCLATURE REFERENCES
CHAPTER 24 EQUATIONS: SEDIMENT ROUTING 24.1 SEDIMENT CHANNEL ROUTING CHANNEL ERODIBILITY FACTOR CHANNEL COVER FACTOR
24.2 CHANNEL DOWNCUTTING AND WIDENING 24.3 NOMENCLATURE 24.4 REFERENCES
349 350 352 354 356 359 360 360 362 363 364
367 368 370 371
371 373 374
CHAPTER 25 EQUATIONS: IN-STREAM NUTRIENT PROCESSES 25.1 ALGAE CHLOROPHYLL A ALGAL GROWTH
25.2 NITROGEN CYCLE ORGANIC NITROGEN AMMONIUM NITRITE NITRATE
25.3 PHOSPHORUS CYCLE ORGANIC PHOSPHORUS INORGANIC/SOLUBLE PHOSPHORUS
25.4 CARBONACEOUS BIOLOGICAL OXYGEN DEMAND 25.5 OXYGEN OXYGEN SATURATION CONCENTRATION REAERATION
25.6 NOMENCLATURE 25.7 REFERENCES
CHAPTER 26 EQUATIONS: PESTICIDE 26.1 PESTICIDE IN THE WATER SOLID-LIQUID PARTITIONING DEGRADATION VOLATILIZATION SETTLING OUTFLOW
26.2 PESTICIDE IN THE SEDIMENT SOLID-LIQUID PARTITIONING DEGRADATION RESUSPENSION DIFFUSION BURIAL
26.3 MASS BALANCE 26.4 NOMENCLATURE 26.5 REFERENCES
375 376 376 376
383 383 384 386 387
388 388 389
390 391 393 393
396 399
401 402 402 403 403 405 405
406 406 407 408 408 409
409 410 411
SECTION 8: WATER BODIES CHAPTER 27 EQUATIONS: IMPOUNDMENT WATER ROUTING 27.1 RESERVOIRS
415 416
SURFACE AREA PRECIPITATION EVAPORATION SEEPAGE OUTFLOW
417 417 418 418 418
27.2 PONDS/WETLANDS
422
SURFACE AREA PRECIPITATION INFLOW EVAPORATION SEEPAGE OUTFLOW
423 424 424 424 425 425
27.3 DEPRESSIONS/POTHOLES SURFACE AREA PRECIPITATION INFLOW EVAPORATION SEEPAGE OUTFLOW
27.4 NOMENCLATURE
CHAPTER 28 EQUATIONS: SEDIMENT IN WATER BODIES 28.1 28.2 28.3 28.4
MASS BALANCE SETTLING SEDIMENT OUTFLOW NOMENCLATURE
CHAPTER 29 EQUATIONS: NUTRIENTS IN WATER BODIES 29.1 NUTRIENT TRANSFORMATIONS 29.2 TOTAL BALANCE 29.3 EUTROPHICATION PHOSPHORUS/CHLOROPHYLL a CONCENTRATIONS
427 427 428 428 429 429 430
431
433 434 434 436 436
437 438 442 442 443
CHLOROPHYLL a/SECCHI DEPTH CORRELATIONS
29.4 NOMENCLATURE 29.5 REFERENCES
CHAPTER 30 EQUATIONS: PESTICIDES IN WATER BODIES 30.1 PESTICIDE IN THE WATER SOLID-LIQUID PARTITIONING DEGRADATION VOLATILIZATION SETTLING OUTFLOW
30.2 PESTICIDE IN THE SEDIMENT SOLID-LIQUID PARTITIONING DEGRADATION RESUSPENSION DIFFUSION BURIAL
30.3 MASS BALANCE 30.4 NOMENCLATURE 30.5 REFERENCES
444
445 445
447 448 448 449 450 451 451
452 452 454 454 455 456
456 457 458
PART 2: MODEL OPERATION CHAPTER 31 SWAT INPUT: WATERSHED CONFIGURATION 31.1 DISCRETIZATION SCHEMES 31.2 WATERSHED CONFIGURATION FILE (.FIG) INCORPORATION OF COMMENTS COMMAND LINES 31.3 REFERENCES
CHAPTER 32 SWAT INPUT: SIMULATION MANAGEMENT 32.1 CONTROL INPUT/OUTPUT FILE (FILE.CIO) 32.2 INPUT CONTROL CODE FILE (.COD)
CHAPTER 33 SWAT INPUT: GENERAL WATERSHED ATTRIBUTES 33.1 BASIN INPUT FILE (.BSN)
CHAPTER 34 SWAT INPUT: CLIMATE 34.1 34.2 34.3 34.4 34.5 34.6 34.7 34.8
WEATHER GENERATOR INPUT FILE (.WGN) PRECIPITATION INPUT FILE (.PCP) TEMPERATURE INPUT FILE (.TMP) SOLAR RADIATION INPUT FILE (.SLR) RELATIVE HUMIDITY INPUT FILE (.HMD) WIND SPEED INPUT FILE (.WND) POTENTIAL EVAPOTRANSPIRATION INPUT FILE (.PET) MULTIPLE RECORDS IN PRECIP/TEMP FILES
CHAPTER 35 SWAT INPUT: GENERAL HRU/SUBBASIN ATTRIBUTES 35.1 SUBBASIN GENERAL INPUT FILE (.SUB) 35.2 HRU GENERAL INPUT FILE (.HRU)
CHAPTER 36 SWAT INPUT: SOIL 36.1 SOIL INPUT FILE (.SOL) 36.2 SOIL CHEMICAL INPUT FILE (.CHM)
CHAPTER 37 SWAT INPUT: LAND/WATER MANAGEMENT 37.1 MANAGEMENT INPUT FILE (.MGT) GENERAL MANAGEMENT VARIABLES SCHEDULED MANAGEMENT OPERATIONS 37.2 WATER USE INPUT FILE (.WUS)
CHAPTER 38 SWAT INPUT: GROUNDWATER 38.1 GROUNDWATER INPUT FILE (.GW)
CHAPTER 39 SWAT INPUT: MAIN CHANNEL 39.1 MAIN CHANNEL INPUT FILE (.RTE)
CHAPTER 40 SWAT INPUT: RESERVOIRS/PONDS 40.1 40.2 40.3 40.4
RESERVOIR INPUT FILE (.RES) DAILY RESERVOIR OUTFLOW FILE MONTHLY RESERVOIR OUTFLOW FILE POND INPUT FILE (.PND)
CHAPTER 41 SWAT INPUT: WATER QUALITY 41.1 GENERAL WATER QUALITY INPUT FILE (.WWQ) 41.2 STREAM WATER QUALITY INPUT FILE (.SWQ) 41.3 RESERVOIR WATER QUALITY INPUT FILE (.LWQ)
CHAPTER 42 SWAT INPUT: DATABASES 42.1 42.2 42.3 42.4 42.5
LAND COVER/PLANT GROWTH DATABASE FILE (CROP.DAT) TILLAGE DATABASE FILE (TILL.DAT) PESTICIDE/TOXIN DATABASE FILE (PEST.DAT) FERTILIZER DATABASE FILE (FERT.DAT) URBAN DATABASE FILE (URBAN.DAT)
CHAPTER 43 SWAT INPUT: MEASURED 43.1 43.2 43.3 43.4
DAILY RECORDS (RECDAY) DATA FILE MONTHLY RECORDS (RECMON) DATA FILE ANNUAL RECORDS (RECYEAR) DATA FILE AVERAGE ANNUAL RECORDS (RECCNST) DATA FILE
CHAPTER 44 SWAT OUTPUT FILES 44.1 44.2 44.3 44.4 44.5 44.6 44.7 44.8 44.9
INPUT SUMMARY FILE (INPUT.STD) STANDARD OUTPUT FILE (OUTPUT.STD) HRU OUTPUT FILE (.SBS) SUBBASIN OUTPUT FILE (.BSB) MAIN CHANNEL OUTPUT FILE (.RCH) HRU IMPOUNDMENT OUTPUT FILE (.WTR) RESERVOIR OUTPUT FILE (.RSV) PESTICIDE OUTPUT FILE (.PSO) EVENT OUTPUT FILE (.EVE)
CHAPTER 45 SWAT OUTPUT ANALYSIS 45.1 45.2 45.3 45.4
STREAM FLOW CALIBRATION SEDIMENT CALIBRATION NUTRIENT CALIBRATION PESTICIDE CALIBRATION
PART 3: APPENDICES APPENDIX A MODEL DATABASES A.1 A.2 A.3 A.4 A.5 A.6
LAND COVER/PLANT GROWTH DATABASE TILLAGE DATABASE PESTICIDE DATABASE FERTILIZER DATABASE URBAN LAND TYPE DATABASE REFERENCES
APPENDIX B EXAMPLE WATERSHED CONFIGURATIONS B.1
B.2
SUBWATERSHED DISCRETIZATION SUBWATERSHED DISCRETIZATION: 3 SUBBASINS SUBWATERSHED DISCRETIZATION: SAVING RESULTS FOR DOWNSTREAM RUNS SUBWATERSHED DISCRETIZATION: INCORPORATING POINT SOURCE/UPSTREAM SIMULATION DATA SUBWATERSHED DISCRETIZATION: INCORPORATING RESERVOIRS SUBWATERSHED DISCRETIZATION: SAVING SIMULATION RESULTS FOR ONE LOCATION HILLSLOPE DISCRETIZATION HILLSLOPE DISCRETIZATION: MODELING A DAIRY OPERATION HILLSLOPE DISCRETIZATION: COMBINING WITH SUBWATERSHED DISCRETIZATION
B.3
GRID CELL DISCRETIZATION GRID CELL DISCRETIZATION: 9 CELLS
APPENDIX C EXAMPLE MANAGEMENT SCENARIOS C.1
LAND COVER/PLANT GROWTH DATABASE
APPENDIX D HYDROLOGIC GROUPS FOR U.S. SOILS
SOIL AND WATER ASSESSMENT TOOL
CHAPTER 1
INTRODUCTION
SWAT is the acronym for Soil and Water Assessment Tool, a river basin, or watershed, scale model developed by Dr. Jeff Arnold for the USDA Agricultural Research Service (ARS). SWAT was developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long periods of time. To satisfy this objective, the model ♦ is physically based. Rather than incorporating regression equations to describe the relationship between input and output variables, SWAT requires
specific
information
about
weather,
soil
properties,
topography, vegetation, and land management practices occurring in the watershed. The physical processes associated with water 1
2
SWAT USER’S MANUAL, VERSION 2000
movement, sediment movement, crop growth, nutrient cycling, etc. are directly modeled by SWAT using this input data. Benefits of this approach are ♦
watersheds with no monitoring data (e.g. stream gage data) can be modeled
♦
the relative impact of alternative input data (e.g. changes in management practices, climate, vegetation, etc.) on water quality or other variables of interest can be quantified
♦ uses readily available inputs. While SWAT can be used to study more specialized processes such as bacteria transport, the minimum data required to make a run are commonly available from government agencies. ♦ is computationally efficient. Simulation of very large basins or a variety of management strategies can be performed without excessive investment of time or money. ♦ enables users to study long-term impacts. Many of the problems currently addressed by users involve the gradual buildup of pollutants and the impact on downstream water bodies. To study these types of problems, results are needed from runs with output spanning several decades. SWAT is a continuous time model, i.e. a long-term yield model. The model is not designed to simulate detailed, single-event flood routing.
CHAPTER 1: INTRODUCTION
3
1.1 DEVELOPMENT OF SWAT SWAT incorporates features of several ARS models and is a direct outgrowth of the SWRRB1 model (Simulator for Water Resources in Rural Basins) (Williams et al., 1985; Arnold et al., 1990). Specific models that contributed significantly to the development of SWAT were CREAMS2 (Chemicals, Runoff, and Erosion from Agricultural Management Systems) (Knisel, 1980), GLEAMS3 (Groundwater Loading Effects on Agricultural Management Systems) (Leonard et al., 1987), and EPIC4 (Erosion-Productivity Impact Calculator) (Williams et al., 1984). Development of SWRRB began with modification of the daily rainfall hydrology model from CREAMS. The major changes made to the CREAMS hydrology model were: a) the model was expanded to allow simultaneous computations on several subbasins to predict basin water yield; b) a groundwater or return flow component was added; c) a reservoir storage component was added to calculate the effect of farm ponds and reservoirs on water and sediment yield; d) a weather simulation model incorporating data for rainfall, solar radiation, and temperature was added to facilitate long-term simulations and provide temporally and spatially representative weather; e) the method for predicting the peak runoff rates was improved; f) the EPIC crop growth model was added to account for annual variation in growth; g) a simple flood routing component was added; h) sediment transport components were added to simulate sediment movement 1
SWRRB is a continuous time step model that was developed to simulate nonpoint source loadings from watersheds. 2 In response to the Clean Water Act, ARS assembled a team of interdisciplinary scientists from across the U.S. to develop a process-based, nonpoint source simulation model in the early 1970s. From that effort CREAMS was developed. CREAMS is a field scale model designed to simulate the impact of land management on water, sediment, nutrients and pesticides leaving the edge of the field. A number of other ARS models such as GLEAMS, EPIC, SWRRB and AGNPS trace their origins to the CREAMS model. 3 GLEAMS is a nonpoint source model which focuses on pesticide and nutrient groundwater loadings. 4 EPIC was originally developed to simulate the impact of erosion on crop productivity and has now evolved into a comprehensive agricultural management, field scale, nonpoint source loading model.
4
SWAT USER’S MANUAL, VERSION 2000
through ponds, reservoirs, streams and valleys; and i) calculation of transmission losses was incorporated. The primary focus of model use in the late 1980s was water quality assessment and development of SWRRB reflected this emphasis. Notable modifications of SWRRB at this time included: a) incorporation of the GLEAMS pesticide fate component; b) optional SCS technology for estimating peak runoff rates; and c) newly developed sediment yield equations. These modifications extended the model’s capability to deal with a wide variety of watershed management problems. In the late 1980s, the Bureau of Indian Affairs needed a model to estimate the downstream impact of water management within Indian reservation lands in Arizona and New Mexico. While SWRRB was easily utilized for watersheds up to a few hundred square kilometers in size, the Bureau also wanted to simulate stream flow for basins extending over several thousand square kilometers. For an area this extensive, the watershed under study needed to be divided into several hundred subbasins. Watershed division in SWRRB was limited to ten subbasins and the model routed water and sediment transported out of the subbasins directly to the watershed outlet. These limitations led to the development of a model called ROTO (Routing Outputs to Outlet) (Arnold et al., 1995), which took output from multiple SWRRB runs and routed the flows through channels and reservoirs. ROTO provided a reach routing approach and overcame the SWRRB subbasin limitation by “linking” multiple SWRRB runs together. Although this approach was effective, the input and output of multiple SWRRB files was cumbersome and required considerable computer storage. In addition, all SWRRB runs had to be made independently and then input to ROTO for the channel and reservoir routing. To overcome the awkwardness of this arrangement, SWRRB and ROTO were merged into a single model, SWAT. While allowing simulations of very extensive areas, SWAT retained all the features which made SWRRB such a valuable simulation model.
CHAPTER 1: INTRODUCTION
5
Since SWAT was created in the early 90s, it has undergone continued review and expansion of capabilities. The most significant improvements of the model between releases include: ♦ SWAT94.2: Multiple hydrologic response units (HRUs) incorporated. ♦ SWAT96.2:
Auto-fertilization
and
auto-irrigation
added
as
management options; canopy storage of water incorporated; a CO2 component added to crop growth model for climatic change studies; Penman-Monteith potential evapotranspiration equation added; lateral flow of water in the soil based on kinematic storage model incorporated; in-stream nutrient water quality equations from QUAL2E added; in-stream pesticide routing. ♦ SWAT98.1: Snow melt routines improved; in-stream water quality improved; nutrient cycling routines expanded; grazing, manure applications, and tile flow drainage added as management options; model modified for use in Southern Hemisphere. ♦ SWAT99.2: Nutrient cycling routines improved, rice/wetland routines improved, reservoir/pond/wetland nutrient removal by settling added; bank storage of water in reach added; routing of metals through reach added; all year references in model changed from last 2 digits of year to 4-digit year; urban build up/wash off equations from SWMM added along with regression equations from USGS. ♦ SWAT2000: Bacteria transport routines added; Green & Ampt infiltration added; weather generator improved; allow daily solar radiation, relative humidity, and wind speed to be read in or generated; allow potential ET values for watershed to be read in or calculated; all potential ET methods reviewed; elevation band processes improved; enabled simulation of unlimited number of reservoirs; Muskingum routing method added; modified dormancy calculations for proper simulation in tropical areas.
6
SWAT USER’S MANUAL, VERSION 2000
In addition to the changes listed above, interfaces for the model have been developed in Windows (Visual Basic), GRASS, and ArcView. SWAT has also undergone extensive validation.
1.2 OVERVIEW OF SWAT SWAT allows a number of different physical processes to be simulated in a watershed. These processes will be briefly summarized in this section. For more detailed discussions of the various procedures, please consult the chapter devoted to the topic of interest.
. Figure 1.1: Map of the Lake Fork Watershed in Northeast Texas showing the land use distribution and stream network
For modeling purposes, a watershed may be partitioned into a number of subwatersheds or subbasins. The use of subbasins in a simulation is particularly beneficial when different areas of the watershed are dominated by land uses or
CHAPTER 1: INTRODUCTION
7
soils dissimilar enough in properties to impact hydrology. By partitioning the watershed into subbasins, the user is able to reference different areas of the watershed to one another spatially. Figure 1.2 shows a subbasin delineation for the watershed shown in Figure 1.1.
Figure 1.2: Subbasin delineation of the Lake Fork Watershed.
Input information for each subbasin is grouped or organized into the following
categories:
climate;
hydrologic
response
units
or
HRUs;
ponds/wetlands; groundwater; and the main channel, or reach, draining the subbasin. Hydrologic response units are lumped land areas within the subbasin that are comprised of unique land cover, soil, and management combinations.
No matter what type of problem studied with SWAT, water balance is the driving force behind everything that happens in the watershed. To accurately predict the movement of pesticides, sediments or nutrients, the hydrologic cycle as
8
SWAT USER’S MANUAL, VERSION 2000
simulated by the model must conform to what is happening in the watershed. Simulation of the hydrology of a watershed can be separated into two major divisions. The first division is the land phase of the hydrologic cycle, depicted in Figure 1.3. The land phase of the hydrologic cycle controls the amount of water, sediment, nutrient and pesticide loadings to the main channel in each subbasin. The second division is the water or routing phase of the hydrologic cycle which can be defined as the movement of water, sediments, etc. through the channel network of the watershed to the outlet.
Figure 1.3: Schematic representation of the hydrologic cycle.
CHAPTER 1: INTRODUCTION
9
1.2.1 LAND PHASE OF THE HYDROLOGIC CYCLE The hydrologic cycle as simulated by SWAT is based on the water balance equation: t
SWt = SW0 + å (Rday − Qsurf − E a − wseep − Q gw ) i =1
where SWt is the final soil water content (mm H2O), SW0 is the initial soil water content on day i (mm H2O), t is the time (days), Rday is the amount of precipitation on day i (mm H2O), Qsurf is the amount of surface runoff on day i (mm H2O), Ea is the amount of evapotranspiration on day i (mm H2O), wseep is the amount of water entering the vadose zone from the soil profile on day i (mm H2O), and Qgw is the amount of return flow on day i (mm H2O). The subdivision of the watershed enables the model to reflect differences in evapotranspiration for various crops and soils. Runoff is predicted separately for each HRU and routed to obtain the total runoff for the watershed. This increases accuracy and gives a much better physical description of the water balance.
Figure 1.4: HRU/Subbasin command loop
10
SWAT USER’S MANUAL, VERSION 2000
Figure 1.4 shows the general sequence of processes used by SWAT to model the land phase of the hydrologic cycle. The different inputs and processes involved in this phase of the hydrologic cycle are summarized in the following sections.
1.2.1.1 CLIMATE The climate of a watershed provides the moisture and energy inputs that control the water balance and determine the relative importance of the different components of the hydrologic cycle. The climatic variables required by SWAT consist of daily precipitation, maximum/minimum air temperature, solar radiation, wind speed and relative humidity. The model allows values for daily precipitation, maximum/minimum air temperatures, solar radiation, wind speed and relative humidity to be input from records of observed data or generated during the simulation. WEATHER GENERATOR. Daily values for weather are generated from average monthly values. The model generates a set of weather data for each subbasin. The values for any one subbasin will be generated independently and there will be no spatial correlation of generated values between the different subbasins. GENERATED PRECIPITATION. SWAT uses a model developed by Nicks (1974) to generate daily precipitation for simulations which do not read in measured data. This precipitation model is also used to fill in missing data in the measured records. The precipitation generator uses a first-order Markov chain model to define a day as wet or dry by comparing a random number (0.0-1.0) generated by the model to monthly wet-dry probabilities input by the user. If the day is classified as wet, the amount of precipitation is generated from a skewed distribution or a modified exponential distribution. GENERATED AIR TEMPERATURE AND SOLAR RADIATION. Maximum and minimum air temperatures and solar radiation are generated from a normal distribution. A continuity equation is incorporated into the generator to account for temperature and radiation variations caused by dry vs. rainy conditions. Maximum air temperature and solar radiation are adjusted downward when simulating rainy conditions and upwards when simulating dry conditions. The adjustments are made so that the long-term generated values for the average monthly maximum temperature and monthly solar radiation agree with the input averages.
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GENERATED WIND SPEED. A modified exponential equation is used to generate daily mean wind speed given the mean monthly wind speed. GENERATED RELATIVE HUMIDITY. The relative humidity model uses a triangular distribution to simulate the daily average relative humidity from the monthly average. As with temperature and radiation, the mean daily relative humidity is adjusted to account for wet- and dry-day effects. SNOW. SWAT classifies precipitation as rain or freezing rain/snow using the average daily temperature. SNOW COVER. The snow cover component of SWAT has been updated from a simple, uniform snow cover model to a more complex model which allows non-uniform cover due to shading, drifting, topography and land cover. The user defines a threshold snow depth above which snow coverage will always extend over 100% of the area. As the snow depth in a subbasin decreases below this value, the snow coverage is allowed to decline non-linearly based on an areal depletion curve. SNOW MELT. Snow melt is controlled by the air and snow pack temperature, the melting rate, and the areal coverage of snow. If snow is present, it is melted on days when the maximum temperature exceeds 0°C using a linear function of the difference between the average snow packmaximum air temperature and the base or threshold temperature for snow melt. Melted snow is treated the same as rainfall for estimating runoff and percolation. For snow melt, rainfall energy is set to zero and the peak runoff rate is estimated assuming uniformly melted snow for a 24 hour duration. ELEVATION BANDS. The model allows the subbasin to be split into a maximum of ten elevation bands. Snow cover and snow melt are simulated separately for each elevation band. By dividing the subbasin into elevation bands, the model is able to assess the differences in snow cover and snow melt caused by orographic variation in precipitation and temperature. SOIL TEMPERATURE. Soil temperature impacts water movement and the decay rate of residue in the soil. Daily average soil temperature is calculated at the soil surface and the center of each soil layer. The temperature of the soil surface is a function of snow cover, plant cover and residue cover, the bare soil surface temperature, and the previous day’s soil surface temperature. The temperature of a soil layer is a function of the surface temperature, mean annual air temperature and the depth in the soil at which variation in temperature due to changes in climatic conditions no longer occurs. This depth, referred to as the damping depth, is dependent upon the bulk density and the soil water content.
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SWAT USER’S MANUAL, VERSION 98.1
1.2.1.2 HYDROLOGY As precipitation descends, it may be intercepted and held in the vegetation canopy or fall to the soil surface. Water on the soil surface will infiltrate into the soil profile or flow overland as runoff. Runoff moves relatively quickly toward a stream channel and contributes to short-term stream response. Infiltrated water may be held in the soil and later evapotranspired or it may slowly make its way to the surface-water system via underground paths. The potential pathways of water movement simulated by SWAT in the HRU are illustrated in Figure 1.5. CANOPY STORAGE. Canopy storage is the water intercepted by vegetative surfaces (the canopy) where it is held and made available for evaporation. When using the curve number method to compute surface runoff, canopy storage is taken into account in the surface runoff calculations. However, if methods such as Green & Ampt are used to model infiltration and runoff, canopy storage must be modeled separately. SWAT allows the user to input the maximum amount of water that can be stored in the canopy at the maximum leaf area index for the land cover. This value and the leaf area index are used by the model to compute the maximum storage at any time in the growth cycle of the land cover/crop. When evaporation is computed, water is first removed from canopy storage. Infiltration refers to the entry of water into a soil profile INFILTRATION. from the soil surface. As infiltration continues, the soil becomes increasingly wet, causing the rate of infiltration to decrease with time until it reaches a steady value. The initial rate of infiltration depends on the moisture content of the soil prior to the introduction of water at the soil surface. The final rate of infiltration is equivalent to the saturated hydraulic conductivity of the soil. Because the curve number method used to calculate surface runoff operates on a daily time-step, it is unable to directly model infiltration. The amount of water entering the soil profile is calculated as the difference between the amount of rainfall and the amount of surface runoff. The Green & Ampt infiltration method does directly model infiltration, but it requires precipitation data in smaller time increments. Redistribution refers to the continued movement of water REDISTRIBUTION. through a soil profile after input of water (via precipitation or irrigation) has ceased at the soil surface. Redistribution is caused by differences in water content in the profile. Once the water content throughout the entire profile is uniform, g
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SWAT USER’S MANUAL, VERSION 98.1
redistribution will cease. The redistribution component of SWAT uses a storage routing technique to predict flow through each soil layer in the root zone. Downward flow, or percolation, occurs when field capacity of a soil layer is exceeded and the layer below is not saturated. The flow rate is governed by the saturated conductivity of the soil layer. Redistribution is affected by soil temperature. If the temperature in a particular layer is 0°C or below, no redistribution is allowed from that layer. EVAPOTRANSPIRATION. Evapotranspiration is a collective term for all processes by which water in the liquid or solid phase at or near the earth's surface becomes atmospheric water vapor. Evapotranspiration includes evaporation from rivers and lakes, bare soil, and vegetative surfaces; evaporation from within the leaves of plants (transpiration); and sublimation from ice and snow surfaces. The model computes evaporation from soils and plants separately as described by Ritchie (1972). Potential soil water evaporation is estimated as a function of potential evapotranspiration and leaf area index (area of plant leaves relative to the area of the HRU). Actual soil water evaporation is estimated by using exponential functions of soil depth and water content. Plant transpiration is simulated as a linear function of potential evapotranspiration and leaf area index. POTENTIAL EVAPOTRANSPIRATION. Potential evapotranspiration is the rate at which evapotranspiration would occur from a large area completely and uniformly covered with growing vegetation which has access to an unlimited supply of soil water. This rate is assumed to be unaffected by micro-climatic processes such as advection or heat-storage effects. The model offers three options for estimating potential evapotranspiration: Hargreaves (Hargreaves et al., 1985), Priestley-Taylor (Priestley and Taylor, 1972), and Penman-Monteith (Monteith, 1965). LATERAL SUBSURFACE FLOW. Lateral subsurface flow, or interflow, is streamflow contribution which originates below the surface but above the zone where rocks are saturated with water. Lateral subsurface flow in the soil profile (0-2m) is calculated simultaneously with redistribution. A kinematic storage model is used to predict lateral flow in each soil layer. The model accounts for variation in conductivity, slope and soil water content. SURFACE RUNOFF. Surface runoff, or overland flow, is flow that occurs along a sloping surface. Using daily or subdaily rainfall amounts, SWAT simulates surface runoff volumes and peak runoff rates for each HRU. SURFACE RUNOFF VOLUME is computed using a modification of the SCS curve number method (USDA Soil Conservation Service, 1972) or the Green & Ampt infiltration method (Green and Ampt, 1911). In the curve number method, the curve number varies non-linearly with the moisture content of the soil. The curve number drops as the soil approaches the wilting point and increases to near 100 as the soil approaches saturation. The Green & Ampt method requires sub-daily precipitation data and calculates infiltration as a function of the wetting front matric potential
CHAPTER 1: INTRODUCTION
15
and effective hydraulic conductivity. Water that does not infiltrate becomes surface runoff. SWAT includes a provision for estimating runoff from frozen soil where a soil is defined as frozen if the temperature in the first soil layer is less than 0°C. The model increases runoff for frozen soils but still allows significant infiltration when the frozen soils are dry. PEAK RUNOFF RATE predictions are made with a modification of the rational method. In brief, the rational method is based on the idea that if a rainfall of intensity i begins instantaneously and continues indefinitely, the rate of runoff will increase until the time of concentration, tc, when all of the subbasin is contributing to flow at the outlet. In the modified Rational Formula, the peak runoff rate is a function of the proportion of daily precipitation that falls during the subbasin tc, the daily surface runoff volume, and the subbasin time of concentration. The proportion of rainfall occurring during the subbasin tc is estimated as a function of total daily rainfall using a stochastic technique. The subbasin time of concentration is estimated using Manning’s Formula considering both overland and channel flow. PONDS. Ponds are water storage structures located within a subbasin which intercept surface runoff. The catchment area of a pond is defined as a fraction of the total area of the subbasin. Ponds are assumed to be located off the main channel in a subbasin and will never receive water from upstream subbasins. Pond water storage is a function of pond capacity, daily inflows and outflows, seepage and evaporation. Required inputs are the storage capacity and surface area of the pond when filled to capacity. Surface area below capacity is estimated as a nonlinear function of storage. TRIBUTARY CHANNELS. Two types of channels are defined within a subbasin: the main channel and tributary channels. Tributary channels are minor or lower order channels branching off the main channel within the subbasin. Each tributary channel within a subbasin drains only a portion of the subbasin and does not receive groundwater contribution to its flow. All flow in the tributary channels is released and routed through the main channel of the subbasin. SWAT uses the attributes of tributary channels to determine the time of concentration for the subbasin. Transmission losses are losses of surface TRANSMISSION LOSSES. flow via leaching through the streambed. This type of loss occurs in ephemeral or intermittent streams where groundwater contribution occurs only at certain times of the year, or not at all. SWAT uses Lane’s method described in Chapter 19 of the SCS Hydrology Handbook (USDA Soil Conservation Service, 1983) to estimate transmission losses. Water losses from the channel are a function of channel width and length and flow duration. Both runoff volume and peak rate are adjusted when transmission losses occur in tributary channels.
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SWAT USER’S MANUAL, VERSION 98.1
RETURN FLOW. Return flow, or base flow, is the volume of streamflow originating from groundwater. SWAT partitions groundwater into two aquifer systems: a shallow, unconfined aquifer which contributes return flow to streams within the watershed and a deep, confined aquifer which contributes return flow to streams outside the watershed (Arnold et al., 1993). Water percolating past the bottom of the root zone is partitioned into two fractions—each fraction becomes recharge for one of the aquifers. In addition to return flow, water stored in the shallow aquifer may replenish moisture in the soil profile in very dry conditions or be directly removed by plant. Water in the shallow or deep aquifer may be removed by pumping.
1.2.1.3 LAND COVER/PLANT GROWTH SWAT utilizes a single plant growth model to simulate all types of land covers. The model is able to differentiate between annual and perennial plants. Annual plants grow from the planting date to the harvest date or until the accumulated heat units equal the potential heat units for the plant. Perennial plants maintain their root systems throughout the year, becoming dormant in the winter months. They resume growth when the average daily air temperature exceeds the minimum, or base, temperature required. The plant growth model is used to assess removal of water and nutrients from the root zone, transpiration, and biomass/yield production. POTENTIAL GROWTH. The potential increase in plant biomass on a given day is defined as the increase in biomass under ideal growing conditions. The potential increase in biomass for a day is a function of intercepted energy and the plant's efficiency in converting energy to biomass. Energy interception is estimated as a function of solar radiation and the plant’s leaf area index. The process used to calculate POTENTIAL AND ACTUAL TRANSPIRATION. potential plant transpiration is described in the section on evapotranspiration. Actual transpiration is a function of potential transpiration and soil water availability. NUTRIENT UPTAKE. Plant use of nitrogen and phosphorus are estimated with a supply and demand approach where the daily plant nitrogen and phosphorus demands are calculated as the difference between the actual concentration of the element in the plant and the optimal concentration. The optimal concentration of the elements varies with growth stage as described by Jones (1983).
CHAPTER 1: INTRODUCTION
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GROWTH CONTRAINTS. Potential plant growth and yield are usually not achieved due to constraints imposed by the environment. The model estimates stresses caused by water, nutrients and temperature.
1.2.1.4 EROSION Erosion and sediment yield are estimated for each HRU with the Modified Universal Soil Loss Equation (MUSLE) (Williams, 1975). While the USLE uses rainfall as an indicator of erosive energy, MUSLE uses the amount of runoff to simulate erosion and sediment yield. The substitution results in a number of benefits: the prediction accuracy of the model is increased, the need for a delivery ratio is eliminated, and single storm estimates of sediment yields can be calculated. The hydrology model supplies estimates of runoff volume and peak runoff rate which, with the subbasin area, are used to calculate the runoff erosive energy variable. The crop management factor is recalculated every day that runoff occurs. It is a function of above-ground biomass, residue on the soil surface, and the minimum C factor for the plant. Other factors of the erosion equation are evaluated as described by Wischmeier and Smith (1978).
1.2.1.5 NUTRIENTS SWAT tracks the movement and transformation of several forms of nitrogen and phosphorus in the watershed. In the soil, transformation of nitrogen from one form to another is governed by the nitrogen cycle as depicted in Figure 1.6. The transformation of phosphorus in the soil is controlled by the phosphorus cycle shown in Figure 1.7. Nutrients may be introduced to the main channel and transported downstream through surface runoff and lateral subsurface flow. NITROGEN. The different processes modeled by SWAT in the HRUs and the various pools of nitrogen in the soil are depicted in Figure 1.6. Plant use of nitrogen is estimated using the supply and demand approach described in the section on plant growth. In addition to plant use, nitrate and organic N may be removed from the soil via mass flow of water. Amounts of NO3-N contained in runoff, lateral flow and percolation are estimated as products of the volume of water and the average concentration of nitrate in the layer. Organic N transport
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SWAT USER’S MANUAL, VERSION 98.1
with sediment is calculated with a loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978) for application to individual runoff events. The loading function estimates the daily organic N runoff loss based on the concentration of organic N in the top soil layer, the sediment yield, and the enrichment ratio. The enrichment ratio is the concentration of organic N in the sediment divided by that in the soil.
Figure 1.6: Partitioning of Nitrogen in SWAT
PHOSPHORUS. The different processes modeled by SWAT in the HRUs and the various pools of phosphorus in the soil are depicted in Figure 1.7. Plant use of phosphorus is estimated using the supply and demand approach described in the section on plant growth. In addition to plant use, soluble phosphorus and organic P may be removed from the soil via mass flow of water. Phosphorus is not a mobile nutrient and interaction between surface runoff with solution P in the top 10 mm of soil will not be complete. The amount of soluble P removed in runoff is predicted using solution P concentration in the top 10 mm of soil, the runoff volume and a partitioning factor. Sediment transport of P is simulated with a loading function as described in organic N transport.
CHAPTER 1: INTRODUCTION
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Figure 1.7: Partitioning of Phosphorus in SWAT
1.2.1.6 PESTICIDES Although SWAT does not simulate stress on the growth of a plant due to the presence of weeds, damaging insects, and other pests, pesticides may be applied to an HRU to study the movement of the chemical in the watershed. SWAT simulates pesticide movement into the stream network via surface runoff (in solution and sorbed to sediment transported by the runoff), and into the soil profile and aquifer by percolation (in solution). The equations used to model the movement of pesticide in the land phase of the hydrologic cycle were adopted from GLEAMS (Leonard et al., 1987). The movement of the pesticide is controlled by its solubility, degradation half-life, and soil organic carbon adsorption coefficient. Pesticide on plant foliage and in the soil degrade exponentially according to the appropriate half-life. Pesticide transport by water and sediment is calculated for each runoff event and pesticide leaching is estimated for each soil layer when percolation occurs.
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SWAT USER’S MANUAL, VERSION 98.1
Figure 1.8 Pesticide fate and transport in SWAT
1.2.1.7 MANAGEMENT SWAT allows the user to define management practices taking place in every HRU. The user may define the beginning and the ending of the growing season, specify timing and amounts of fertilizer, pesticide and irrigation applications as well as timing of tillage operations. At the end of the growing season, the biomass may be removed from the HRU as yield or placed on the surface as residue. In addition to these basic management practices, operations such as grazing, automated fertilizer and water applications, and incorporation of every conceivable management option for water use are available. The latest improvement to land management is the incorporation of routines to calculate sediment and nutrient loadings from urban areas.
CHAPTER 1: INTRODUCTION
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ROTATIONS. The dictionary defines a rotation as the growing of different crops in succession in one field, usually in a regular sequence. A rotation in SWAT refers to a change in management practices from one year to the next. There is no limit to the number of years of different management operations specified in a rotation. SWAT also does not limit the number of land cover/crops grown within one year in the HRU. However, only one land cover can be growing at any one time. WATER USE. The two most typical uses of water are for application to agricultural lands or use as a town's water supply. SWAT allows water to be applied on an HRU from any water source within or outside the watershed. Water may also be transferred between reservoirs, reaches and subbasins as well as exported from the watershed.
1.2.2 ROUTING PHASE OF THE HYDROLOGIC CYCLE Once SWAT determines the loadings of water, sediment, nutrients and pesticides to the main channel, the loadings are routed through the stream network of the watershed using a command structure similar to that of HYMO (Williams and Hann, 1972). In addition to keeping track of mass flow in the channel, SWAT models the transformation of chemicals in the stream and streambed. Figure 1.9 illustrates the different in-stream processes modeled by SWAT.
Figure 1.9: In-stream processes modeled by SWAT
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SWAT USER’S MANUAL, VERSION 98.1
1.2.2.1 ROUTING IN THE MAIN CHANNEL OR REACH Routing in the main channel can be divided into four components: water, sediment, nutrients and organic chemicals.
FLOOD ROUTING. As water flows downstream, a portion may be lost due to evaporation and transmission through the bed of the channel. Another potential loss is removal of water from the channel for agricultural or human use. Flow may be supplemented by the fall of rain directly on the channel and/or addition of water from point source discharges. Flow is routed through the channel using a variable storage coefficient method developed by Williams (1969) or the Muskingum routing method. SEDIMENT ROUTING. The transport of sediment in the channel is controlled by the simultaneous operation of two processes, deposition and degradation. Previous versions of SWAT used stream power to estimate deposition/degradation in the channels (Arnold et al, 1995). Bagnold (1977) defined stream power as the product of water density, flow rate and water surface slope. Williams (1980) used Bagnold’s definition of stream power to develop a method for determining degradation as a function of channel slope and velocity. In this version of SWAT, the equations have been simplified and the maximum amount of sediment that can be transported from a reach segment is a function of the peak channel velocity. Available stream power is used to reentrain loose and deposited material until all of the material is removed. Excess stream power causes bed degradation. Bed degradation is adjusted for stream bed erodibility and cover. NUTRIENT ROUTING. Nutrient transformations in the stream are controlled by the in-stream water quality component of the model. The in-stream kinetics used in SWAT for nutrient routing are adapted from QUAL2E (Brown and Barnwell, 1987). The model tracks nutrients dissolved in the stream and nutrients adsorbed to the sediment. Dissolved nutrients are transported with the water while those sorbed to sediments are allowed to be deposited with the sediment on the bed of the channel. CHANNEL PESTICIDE ROUTING. While an unlimited number of pesticides may be applied to the HRUs, only one pesticide may be routed through the channel network of the watershed due to the complexity of the processes simulated. As with the nutrients, the total pesticide load in the channel is partitioned into dissolved and sediment-attached components. While the dissolved pesticide is transported with water, the pesticide attached to sediment is affected by sediment transport and deposition processes. Pesticide transformations in the dissolved and sorbed phases are governed by first-order decay relationships. The
CHAPTER 1: INTRODUCTION
23
major in-stream processes simulated by the model are settling, burial, resuspension, volatilization, diffusion and transformation.
1.2.2.2 ROUTING IN THE RESERVOIR The water balance for reservoirs includes inflow, outflow, rainfall on the surface, evaporation, seepage from the reservoir bottom and diversions. RESERVOIR OUTFLOW. The model offers three alternatives for estimating outflow from the reservoir. The first option allows the user to input measured outflow. The second option, designed for small, uncontrolled reservoirs, requires the users to specify a water release rate. When the reservoir volume exceeds the principle storage, the extra water is released at the specified rate. Volume exceeding the emergency spillway is released within one day. The third option, designed for larger, managed reservoirs, has the user specify monthly target volumes for the reservoir. Sediment inflow may originate from transport SEDIMENT ROUTING. through the upstream reaches or from surface runoff within the subbasin. The concentration of sediment in the reservoir is estimated using a simple continuity equation based on volume and concentration of inflow, outflow, and water retained in the reservoir. Settling of sediment in the reservoir is governed by an equilibrium sediment concentration and the median sediment particle size. The amount of sediment in the reservoir outflow is the product of the volume of water flowing out of the reservoir and the suspended sediment concentration in the reservoir at the time of release. A simple model for nitrogen and phosphorus mass RESERVOIR NUTRIENTS. balance was taken from Chapra (1997). The model assumes: 1) the lake is completely mixed; 2) phosphorus is the limiting nutrient; and, 3) total phosphorus is a measure of the lake trophic status. The first assumption ignores lake stratification and intensification of phytoplankton in the epilimnon. The second assumption is generally valid when non-point sources dominate and the third assumption implies that a relationship exists between total phosphorus and biomass. The phosphorus mass balance equation includes the concentration in the lake, inflow, outflow and overall loss rate. The lake pesticide balance model is taken from RESERVOIR PESTICIDES. Chapra (1997) and assumes well mixed conditions. The system is partitioned into a well mixed surface water layer underlain by a well mixed sediment layer. The pesticide is partitioned into dissolved and particulate phases in both the water and sediment layers. The major processes simulated by the model are loading, outflow, transformation, volatilization, settling, diffusion, resuspension and burial.
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1.3 REFERENCES Arnold, J.G., P.M. Allen, and G. Bernhardt. 1993. A comprehensive surfacegroundwater flow model. J. Hydrol. 142:47-69. Arnold, J.G., J.R. Williams, A.D. Nicks, and N.B. Sammons. 1990. SWRRB: A basin scale simulation model for soil and water resources management. Texas A&M Univ. Press, College Station, TX. Arnold, J.G., J.R. Williams and D.R. Maidment. 1995. Continuous-time water and sediment-routing model for large basins. Journal of Hydraulic Engineering 121(2):171-183. Bagnold, R.A. 1977. Bedload transport in natural rivers. Water Resources Res. 13(2):303-312. Brown, L.C. and T.O. Barnwell, Jr. 1987. The enhanced water quality models QUAL2E and QUAL2E-UNCAS documentation and user manual. EPA document EPA/600/3-87/007. USEPA, Athens, GA. Chapra, S.C. 1997. Surface water-quality modeling. McGraw-Hill, Boston. Green, W.H. and G.A. Ampt. 1911. Studies on soil physics, 1. The flow of air and water through soils. Journal of Agricultural Sciences 4:11-24. Hargreaves, G.L., G.H. Hargreaves, and J.P. Riley. 1985. Agricultural benefits for Senegal River Basin. J. Irrig. and Drain. Engr. 111(2):113-124. Jones, C.A. 1983. A survey of the variability in tissue nitrogen and phosphorus concentrations in maize and grain sorghum. Field Crops Res. 6:133-147. Knisel, W.G. 1980. CREAMS, a field scale model for chemicals, runoff and erosion from agricultural management systems. USDA Conservation Research Rept. No. 26. Leonard, R.A. and R.D. Wauchope. 1980. Chapter 5: The pesticide submodel. p. 88-112. In Knisel, W.G. (ed). CREAMS: A field-scale model for chemicals, runoff, and erosion from agricultural management systems. U.S. Department of Agriculture, Conservation research report no. 26.
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Leonard, R.A., W.G. Knisel, and D.A. Still. 1987. GLEAMS: Groundwater loading effects on agricultural management systems. Trans. ASAE 30(5):1403-1428. McElroy, A.D., S.Y. Chiu, J.W. Nebgen, and others. 1976. Loading functions for assessment of water pollution from nonpoint sources. EPA document EPA 600/2-76-151. USEPA, Athens, GA. Monteith, J.L. 1965. Evaporation and the environment. p. 205-234. In The state and movement of water in living organisms. 19th Symposia of the Society for Experimental Biology. Cambridge Univ. Press, London, U.K. Nicks, A.D. 1974. Stochastic generation of the occurrence, pattern and location of maximum amount of daily rainfall. p. 154-171. In Proc. Symp. Statistical Hydrology, Tucson, AZ. Aug.-Sept. 1971. USDA Misc. Publ. 1275. U.S. Gov. Print. Office, Washington, DC. Priestley, C.H.B. and R.J. Taylor. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev. 100:8192. Ritchie, J.T. 1972. A model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res. 8:1204-1213. USDA Soil Conservation Service. 1983. National Engineering Handbook Section 4 Hydrology, Chapter 19. USDA Soil Conservation Service. 1972. National Engineering Handbook Section 4 Hydrology, Chapters 4-10. Williams, J.R. 1980. SPNM, a model for predicting sediment, phosphorus, and nitrogen yields from agricultural basins. Water Resour. Bull. 16(5):843848. Williams, J.R. 1975. Sediment routing for agricultural watersheds. Water Resour. Bull. 11(5):965-974. Williams, J.R. 1969. Flood routing with variable travel time or variable storage coefficients. Trans. ASAE 12(1):100-103.
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Williams, J.R., A.D. Nicks, and J.G. Arnold. 1985. Simulator for water resources in rural basins. Journal of Hydraulic Engineering 111(6): 970-986. Williams, J.R. and R.W. Hann. 1978. Optimal operation of large agricultural watersheds with water quality constraints. Texas Water Resources Institute, Texas A&M Univ., Tech. Rept. No. 96. Williams, J.R. and R.W. Hann. 1972. HYMO, a problem-oriented computer language for building hydrologic models. Water Resour. Res. 8(1):79-85. Williams, J.R., C.A. Jones and P.T. Dyke. 1984. A modeling approach to determining the relationship between erosion and soil productivity. Trans. ASAE 27(1):129-144. Wischmeier, W.H., and D.D. Smith. 1978. Predicting rainfall losses: A guide to conservation planning. USDA Agricultural Handbook No. 537. U.S. Gov. Print. Office, Washington, D. C.
PART 1
MODEL DESCRIPTION
CLIMATE The climatic inputs to the model are reviewed first because it is these inputs that provide the moisture and energy that drive all other processes simulated in the watershed. The climatic processes modeled in SWAT consist of precipitation, air temperature, soil temperature and solar radiation. Depending on the method used to calculate potential evapotranspiration, wind speed and relative humidity may also be modeled.
CHAPTER 2
EQUATIONS: ENERGY
Once water is introduced to the system as precipitation, the available energy, specifically solar radiation, exerts a major control on the movement of water in the land phase of the hydrologic cycle. Processes that are greatly affected by temperature and solar radiation include snow fall, snow melt and evaporation. Since evaporation is the primary water removal mechanism in the watershed, the energy inputs become very important in reproducing or simulating an accurate water balance.
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2.1 SUN-EARTH RELATIONSHIPS A number of basic concepts related to the earth's orbit around the sun are required by the model to make solar radiation calculations. This section summarizes these concepts. Iqbal (1983) provides a detailed discussion of these and other topics related to solar radiation for users who require more information.
2.1.1 DISTANCE BETWEEN EARTH AND SUN The mean distance between the earth and the sun is 1.496 x 108 km and is called one astronomical unit (AU). The earth revolves around the sun in an elliptical orbit and the distance from the earth to the sun on a given day will vary from a maximum of 1.017 AU to a minimum of 0.983 AU. An accurate value of the earth-sun distance is important because the solar radiation reaching the earth is inversely proportional to the square of its distance from the sun. The distance is traditionally expressed in mathematical form as a Fourier series type of expansion with a number of coefficients. For most engineering applications a simple expression used by Duffie and Beckman (1980) is adequate for calculating the reciprocal of the square of the radius vector of the earth, also called the eccentricity correction factor, E0, of the earth's orbit:
E0 = (r0 r ) = 1 + 0.033 cos[(2πd n 365)] 2
2.1.1
where r0 is the mean earth-sun distance (1 AU), r is the earth-sun distance for any given day of the year (AU), and dn is the day number of the year, ranging from 1 on January 1 to 365 on December 31. February is always assumed to have 28 days, making the accuracy of the equation vary due to the leap year cycle.
2.1.2 SOLAR DECLINATION The solar declination is the earth's latitude at which incoming solar rays are normal to the earth's surface. The solar declination is zero at the spring and fall equinoxes, approximately +23½° at the summer solstice and approximately -23½° at the winter solstice. A simple formula to calculate solar declination from Perrin de Brichambaut (1975) is:
CHAPTER 2: EQUATIONS—ENERGY
ì é 2π (d n − 82)ùú üý δ = sin −1 í0.4 sin ê ë 365 ûþ î
33
2.1.2
where δ is the solar declination reported in radians, and dn is the day number of the year.
2.1.3 SOLAR NOON, SUNRISE, SUNSET AND DAYLENGTH The angle between the line from an observer on the earth to the sun and a vertical line extending upward from the observer is called the zenith angle, θz (Figure 2-1). Solar noon occurs when this angle is at its minimum value for the day.
Figure 2-1: Diagram illustrating zenith angle
For a given geographical position, the relationship between the sun and a horizontal surface on the earth's surface is:
cos θ z = sin δ sin φ + cos δ cos φ cos ωt
2.1.3
where δ is the solar declination in radians, φ is the geographic latitude in radians, ω is the angular velocity of the earth's rotation (0.2618 rad h-1 or 15û h-1), and t is the solar hour. t equals zero at solar noon, is a positive value in the morning and is a negative value in the evening. The combined term ωt is referred to as the hour angle. Sunrise, TSR, and sunset, TSS, occur at equal times before and after solar noon. These times can be determined by rearranging the above equation as:
34
SWAT USER'S MANUAL, VERSION 2000
TSR = +
cos −1 [− tan δ tan φ ] ω
2.1.4
cos −1 [− tan δ tan φ ] ω
2.1.5
and TSS = −
Total daylength, TDL, is calculated: TDL =
2 cos −1 [− tan δ tan φ ] ω
2.1.6
At latitudes above 66.5° or below -66.5°, the absolute value of [-tanδ tanφ] can exceed 1 and the above equation cannot be used. When this happens, there is either no sunrise (winter) or no sunset (summer) and TDL must be assigned a value of 0 or 24 hours, respectively. To determine the minimum daylength that will occur during the year, equation 2.1.6 is solved with the solar declination set to -23.5° (-0.4102 radians) for the northern hemisphere or 23.5° (0.4102 radians) for the southern hemisphere. The only SWAT input variable used in the calculations reviewed in Section 2.1 is given in Table 2-1. Table 2-1: SWAT input variables that used in earth-sun relationship calculations. Variable name Definition LATITUDE Latitude of the subbasin (degrees).
File Name .sub
2.2 SOLAR RADIATION 2.2.1 EXTRATERRESTRIAL RADIATION The radiant energy from the sun is practically the only source of energy that impacts climatic processes on earth. The solar constant, ISC, is the rate of total solar energy at all wavelengths incident on a unit area exposed normally to rays of the sun at a distance of 1 AU from the sun. Quantifying this value has been the object of numerous studies through the years. The value officially adopted by the Commission for Instruments and Methods of Observation in October 1981 is
CHAPTER 2: EQUATIONS—ENERGY
35
ISC = 1367 W m-2 = 4.921 MJ m-2 h-1 On any given day, the extraterrestrial irradiance (rate of energy) on a surface normal to the rays of the sun, I0n, is: I 0 n = I SC E0
2.2.1
where E0 is the eccentricity correction factor of the earth's orbit, and I0n has the same units as the solar constant, ISC. To calculate the irradiance on a horizontal surface, I0, I 0 = I 0 n cos θ z = I SC E0 cos θ z
2.2.2
where cos θz is defined in equation 2.1.3. The amount of energy falling on a horizontal surface during a day is given by ss
ss
sr
0
H 0 = ò I 0 dt = 2 ò I 0 dt
2.2.3
where H0 is the extraterrestrial daily irradiation (MJ m-2 d-1), sr is sunrise, and ss is sunset. Assuming that E0 remains constant during the one day time step and converting the time dt to the hour angle, the equation can be written H0 =
ωTSR 24 I SC E0 ò (sin δ sin φ + cos δ cos φ cos ωt )dωt 0 π
2.2.4
H0 =
24 I SC E0 [ωTSR (sin δ sin φ ) + (cos δ cos φ sin(ωTSR ))] π
2.2.5
or
where ISC is the solar constant (4.921 MJ m-2 h-1), E0 is the eccentricity correction factor of the earth's orbit, ω is the angular velocity of the earth's rotation (0.2618 rad h-1), the hour of sunrise, TSR, is defined by equation 2.1.4, δ is the solar declination in radians, and φ is the geographic latitude in radians. Multiplying all the constants together gives H 0 = 37.59 E0 [ωTSR sin δ sin φ + cos δ cos φ sin (ωTSR )]
2.2.6
2.2.2 SOLAR RADIATION UNDER CLOUDLESS SKIES When solar radiation enters the earth's atmosphere, a portion of the energy is removed by scattering and adsorption. The amount of energy lost is a function of the transmittance of the atmosphere, the composition and concentration of the
36
SWAT USER'S MANUAL, VERSION 2000
constituents of air at the location, the path length the radiation travels through the air column, and the radiation wavelength. Due to the complexity of the process and the detail of the information required to accurately predict the amount of radiant energy lost while passing through the atmosphere, SWAT makes a broad assumption that roughly 20% of the extraterrestrial radiation is lost while passing through the atmosphere under cloudless skies. Using this assumption, the maximum possible solar radiation, HMX, at a particular location on the earth's surface is calculated as: H MX = 30.0 E0 [ωTSR sin δ sin φ + cos δ cos φ sin (ωTSR )]
2.2.7
where the maximum possible solar radiation, HMX, is the amount of radiation reaching the earth's surface under a clear sky (MJ m-2 d-1).
2.2.3 DAILY SOLAR RADIATION The solar radiation reaching the earth's surface on a given day, Hday, may be less than HMX due to the presence of cloud cover. The daily solar radiation data required by SWAT may be read from an input file or generated by the model. The variable SLRSIM in the input control code (.cod) file identifies the method used to obtain solar radiation data. To read in daily solar radiation data, the variable is set to 1 and the name of the solar radiation data file and the number of solar radiation records stored in the file are set in the control input/output (file.cio) file. To generate daily solar radiation values, SLRSIM is set to 2. The equations used to generate solar radiation data in SWAT are reviewed in Chapter 4. SWAT input variables that pertain to solar radiation are summarized in Table 2-2. Table 2-2: SWAT input variables used in solar radiation calculations. Variable name Definition LATITUDE Latitude of the subbasin (degrees). SLRSIM Solar radiation input code: 1-measured, 2-generated NSTOT Number of solar radiation records within the .slr file (required if SLRSIM = 1) SLRFILE Name of measured solar radiation input file (.slr) (required if SLRSIM = 1) ISGAGE Number of solar radiation record used within the subbasin (required if
File Name .sub .cod file.cio file.cio file.cio
SLRSIM = 1)
see description of .slr file in the User’s Manual for input and format requirements if measured daily solar radiation data is being used
CHAPTER 2: EQUATIONS—ENERGY
37
2.2.4 HOURLY SOLAR RADIATION The extraterrestrial radiation falling on a horizontal surface during one hour is given by the equation: I 0 = I SC E0 (sin δ sin φ + cos δ cos φ cos ωt )
2.2.8
where I0 is the extraterrestrial radiation for 1 hour centered around the hour angle
ωt. An accurate calculation of the radiation for each hour of the day requires a knowledge of the difference between standard time and solar time for the location. SWAT simplifies the hourly solar radiation calculation by assuming that solar noon occurs at 12:00pm local standard time. When the values of I0 calculated for every hour between sunrise and sunset are summed, they will equal the value of H0. Because of the relationship between I0 and H0, it is possible to calculate the hourly radiation values by multiplying H0 by the fraction of radiation that falls within the different hours of the day. The benefit of this alternative method is that assumptions used to estimate the difference between maximum and actual solar radiation reaching the earth’s surface can be automatically incorporated in calculations of hourly solar radiation at the earth’s surface. SWAT calculates hourly solar radiation at the earth’s surface with the equation: I hr = I frac ⋅ H day
2.2.9
where Ihr is the solar radiation reaching the earth’s surface during a specific hour of the day (MJ m-2 hr-1), Ifrac is the fraction of total daily radiation falling during that hour, and Hday is the total solar radiation reaching the earth’s surface on that day. The fraction of total daily radiation falling during an hour is calculated I frac =
(sin δ sin φ + cos δ cos φ cos ωti ) SS
å (sin δ sin φ + cos δ cos φ cos ωt )
t = SR
where ti is the solar time at the midpoint of hour i.
2.2.10
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SWAT USER'S MANUAL, VERSION 2000
2.2.5 DAILY NET RADIATION Net radiation requires the determination of both incoming and reflected short-wave radiation and net long-wave or thermal radiation. Expressing net radiation in terms of the net short-wave and long-wave components gives: H net = H day ↓ −α ⋅ H day ↑ + H L ↓ − H L ↑
2.2.11
H net = (1 − α ) ⋅ H day + H b
2.2.12
or
where Hnet is the net radiation (MJ m-2 d-1), Hday is the short-wave solar radiation reaching the ground (MJ m-2 d-1), α is the short-wave reflectance or albedo, HL is the long-wave radiation (MJ m-2 d-1), Hb is the net incoming long-wave radiation (MJ m-2 d-1) and the arrows indicate the direction of the radiation flux.
2.2.5.1 NET SHORT-WAVE RADIATION Net short-wave radiation is defined as
(1 − α ) ⋅ H day .
SWAT
calculates a daily value for albedo as a function of the soil type, plant cover, and snow cover. When the snow water equivalent is greater than 0.5 mm,
α = 0 .8
2.2.13
When the snow water equivalent is less than 0.5 mm and no plants are growing in the HRU,
α = α soil
2.2.14
where αsoil is the soil albedo. When plants are growing and the snow water equivalent is less than 0.5 mm,
α = α plant ⋅ (1 − cov sol ) + α soil ⋅ cov sol
2.2.15
where αplant is the plant albedo (set at 0.23), and covsol is the soil cover index. The soil cover index is calculated cov sol = exp(− 5.0 × 10 −5 ⋅ CV )
where CV is the aboveground biomass and residue (kg ha-1).
2.2.16
CHAPTER 2: EQUATIONS—ENERGY
39
2.2.5.2 NET LONG-WAVE RADIATION Long-wave radiation is emitted from an object according to the radiation law: H R = ε ⋅ σ ⋅ TK
4
2.2.17
where HR is the radiant energy (MJ m-2 d-1), ε is the emissivity, σ is the Stefan-Boltzmann constant (4.903 × 10-9 MJ m-2 K-4 d-1), and TK is the mean air temperature in Kelvin (273.15 + °C). Net long-wave radiation is calculated using a modified form of equation 2.2.17 (Jensen et al., 1990): H b = f cld ⋅ (ε a − ε vs ) ⋅ σ ⋅ TK
4
2.2.18
where Hb is the net long-wave radiation (MJ m-2 d-1), fcld is a factor to adjust for cloud cover, εa is the atmospheric emittance, and εvs is the vegetative or soil emittance. Wright and Jensen (1972) developed the following expression for the cloud cover adjustment factor, fcld: f cld = a ⋅
H day H MX
−b
2.2.19
where a and b are constants, Hday is the solar radiation reaching the ground surface on a given day (MJ m-2 d-1), and HMX is the maximum possible solar radiation to reach the ground surface on a given day (MJ m-2 d-1). The two emittances in equation 2.2.18 may be combined into a single term, the net emittance ε′. The net emittance is calculated using an equation developed by Brunt (1932):
(
ε ′ = ε a − ε vs = − a1 + b1 ⋅ e
)
2.2.20
where a1 and b1 are constants and e is the vapor pressure on a given day (kPa). The calculation of e is given in Chapter 3. Combining equations 2.2.18, 2.2.19, and 2.2.20 results in a general equation for net long-wave radiation:
[
]
é H day ù 4 H b = − êa ⋅ − bú ⋅ a1 + b1 e ⋅ σ ⋅ TK ë H MX û
2.2.21
40
SWAT USER'S MANUAL, VERSION 2000
Experimental values for the coefficients a, b, a1 and b1 are presented in Table 2.3. The default equation in SWAT uses coefficent values proposed by Doorenbos and Pruitt (1977):
[
]
H day é ù 4 H b = − ê0.9 ⋅ + 0.1ú ⋅ 0.34 − 0.139 e ⋅ σ ⋅ TK H MX ë û
2.2.22
Table 2-3: Experimental coefficients for net long-wave radiation equations (from Jensen et al., 1990) b1) Region (a, b) (a1, Davis, California (1.35, -0.35) (0.35, -0.145) Southern Idaho (1.22, -0.18) (0.325, -0.139) England not available (0.47, -0.206) England not available (0.44, -0.253) Australia not available (0.35, -0.133) General (1.2, -0.2) (0.39, -0.158) General-humid areas (1.0, 0.0) General-semihumid areas (1.1, -0.1) Table 2-4: SWAT input variables used in net radiation calculations. Variable name Definition SOL_ALB αsoil: moist soil albedo MAX TEMP Tmx: Daily maximum temperature (°C) MIN TEMP Tmn: Daily minimum temperature (°C) SOL_RAD Hday: Daily solar radiation reaching the earth’s surface (MJ m-2 d-1)
File Name .sol .tmp .tmp .slr
CHAPTER 2: EQUATIONS—ENERGY
41
2.3 TEMPERATURE Temperature influences a number of physical, chemical and biological processes. Plant production is strongly temperature dependent, as are organic matter decomposition and mineralization. Daily air temperature may be input to the model or generated from average monthly values. Soil and water temperatures are derived from air temperature.
2.3.1 DAILY AIR TEMPERATURE SWAT requires daily maximum and minimum air temperature. This data may be read from an input file or generated by the model. The user is strongly recommended to obtain measured daily temperature records from gages in or near the watershed if at all possible. The accuracy of model results is significantly improved by the use of measured temperature data. The variable TMPSIM in the input control code (.cod) file identifies the method used to obtain air temperature data. To read in daily maximum and minimum air temperature data, the variable is set to 1 and the name of the temperature data file(s) and the number of temperature records stored in the file are set in the control input/output (file.cio) file. To generate daily air temperature values, TMPSIM is set to 2. The equations used to generate air temperature data in SWAT are reviewed in Chapter 4. SWAT input variables that pertain to air temperature are summarized in Table 2-5. Table 2-5: SWAT input variables that pertain to daily air temperature. Variable name Definition TMPSIM Air temperature input code: 1-measured, 2-generated NTGAGE Number of temperature gage (.tmp) files used in simulation file (required if
File Name .cod file.cio
TMPSIM = 1)
NTTOT NTFIL
Number of temperature records used in simulation (required if TMPSIM = 1) Number of temperature records within each .tmp file file (required if
TFILE
Name of measured temperature input file (.tmp) Up to 18 files may be used. (required if TMPSIM = 1) Number of temperature record used within the subbasin (required if TMPSIM
file.cio file.cio
TMPSIM = 1)
ITGAGE
file.cio file.cio
= 1)
see description of .tmp file in the User’s Manual for input and format requirements if measured temperature data is being used
42
SWAT USER'S MANUAL, VERSION 2000
2.3.2 HOURLY AIR TEMPERATURE Air temperature data are usually provided in the form of daily maximum and minimum temperature. A reasonable approximation for converting these to hourly temperatures is to assume a sinusoidal interpolation function between the minimum and maximum daily temperatures. The maximum daily temperature is assumed to occur at 1500 hours and the minimum daily temperature at 300 hours (Campbell, 1985). The temperature for the hour is then calculated with the equation: Thr = T av +
(Tmx − Tmn ) ⋅ cos(0.2618 ⋅ (hr − 15)) 2
2.3.1
where Thr is the air temperature during hour hr of the day (°C), T av is the average temperature on the day (°C), Tmx is the daily maximum temperature (°C), and Tmn is the daily minimum temperature (°C). Table 2-6: SWAT input variables that pertain to hourly air temperature. Variable name Definition MAX TEMP Tmx: Daily maximum temperature (°C) MIN TEMP Tmn: Daily minimum temperature (°C)
File Name .tmp .tmp
2.3.3 SOIL TEMPERATURE Soil temperature will fluctuate due to seasonal and diurnal variations in temperature at the surface. Figure 2-2 plots air temperature and soil temperature at 5 cm and 300 cm below bare soil at College Station, Texas.
Figure 2-2: Four-year average air and soil temperature at College Station, Texas.
CHAPTER 2: EQUATIONS—ENERGY
43
This figure illustrates several important attributes of temperature variation in the soil. First, the annual variation in soil temperature follows a sinusoidal function. Second, the fluctuation in temperature during the year (the amplitude of the sine wave) decreases with depth until, at some depth in the soil, the temperature remains constant throughout the year. Finally, the timing of maximum and minimum temperatures varies with depth. Note in the above graph that there is a three month difference between the recording of the minimum temperature at the surface (January) and the minimum temperature at 300 cm (March). Carslaw and Jaeger (1959) developed an equation to quantify the seasonal variation in temperature: Tsoil (z, d n ) = T AA + Asurf exp(− z / dd ) sin (ω tmp d n − z / dd )
2.3.2
where Tsoil(z,dn) is the soil temperature (°C) at depth z (mm) and day of the year dn, T AA is the average annual soil temperature (°C), Asurf is the amplitude of the surface fluctuations (°C), dd is the damping depth (mm), and ωtmp is the angular frequency.
When
z
=
0
(soil
surface),
equation
2.3.2
reduces
to
Tsoil (0, d n ) = T AA + Asurf sin (ω tmp d n ) . As z → ∞, equation 2.3.2 becomes Tsoil (∞, d n ) = T AA . In order to calculate values for some of the variables in this equation, the heat capacity and thermal conductivity of the soil must be known. These are properties not commonly measured in soils and attempts at estimating values from other soil properties have not proven very effective. Consequently, an equation has been adopted in SWAT that calculates the temperature in the soil as a function of the previous day’s soil temperature, the average annual air temperature, the current day’s soil surface temperature, and the depth in the profile. The equation used to calculate daily average soil temperature at the center of each layer is:
[ [
]
Tsoil (z,d n ) = l ⋅ Tsoil (z,d n − 1) + [1.0 − l] ⋅ df ⋅ T AAair − Tssurf + Tssurf
]
2.3.3
44
SWAT USER'S MANUAL, VERSION 2000
where Tsoil(z,dn) is the soil temperature (°C) at depth z (mm) and day of the year dn, l is the lag coefficient (ranging from 0.0 to 1.0) that controls the influence of the previous day’s temperature on the current day’s temperature, Tsoil(z,dn-1) is the soil temperature (°C) in the layer from the previous day, df is the depth factor that quantifies the influence of depth below surface on soil temperature, T AAair is the average annual air temperature (°C), and Tssurf is the soil surface temperature on the day. SWAT sets the lag coefficient, l , to 0.80. The soil temperature from the previous day is known and the average annual air temperature is calculated from the long-term monthly maximum and minimum temperatures reported in the weather generator input (.wgn) file. This leaves the depth factor, df, and the soil surface temperature, Tssurf, to be defined. The depth factor is calculated using the equation: df =
zd zd + exp(− 0.867 − 2.078 ⋅ zd )
2.3.4
where zd is the ratio of the depth at the center of the soil layer to the damping depth: zd =
z dd
2.3.5
where z is the depth at the center of the soil layer (mm) and dd is the damping depth (mm). From the previous three equations (2.3.3, 2.3.4 and 2.3.5) one can see that at depths close to the soil surface, the soil temperature is a function of the soil surface temperature. As the depth increases, soil temperature is increasingly influenced by the average annual air temperature, until at the damping depth, the soil temperature is within 5% of T AAair . The damping depth, dd, is calculated daily and is a function of the maximum damping depth, bulk density and soil water. The maximum damping depth, ddmax, is calculated: dd max = 1000 +
2500 ρ b ρ b + 686 exp(− 5.63ρ b )
2.3.6
CHAPTER 2: EQUATIONS—ENERGY
45
where ddmax is the maximum damping depth (mm), and ρb is the soil bulk density (Mg/m3). The impact of soil water content on the damping depth is incorporated via a scaling factor,ϕ, that is calculated with the equation:
ϕ=
SW (0.356 − 0.144 ρ b ) ⋅ ztot
2.3.7
where SW is the amount of water in the soil profile expressed as depth of water in the profile (mm H2O), ρb is the soil bulk density (Mg/m3), and ztot is the depth from the soil surface to the bottom of the soil profile (mm). The daily value for the damping depth, dd, is calculated: é æ 500 ö æ 1 − ϕ ö 2 ù ÷÷ ⋅ çç dd = dd max ⋅ exp êlnçç ÷÷ ú dd 1 ϕ + è ø ûú max ø ëê è
2.3.8
where ddmax is the maximum damping depth (mm), and ϕ is the scaling factor for soil water. The soil surface temperature is a function of the previous day’s temperature, the amount of ground cover and the temperature of the surface when no cover is present. The temperature of a bare soil surface is calculated with the equation: Tbare = T av + ε sr
(Tmx − Tmn ) 2
2.3.9
where Tbare is the temperature of the soil surface with no cover (°C), T av is the average temperature on the day (°C), Tmx is the daily maximum temperature (°C), Tmn is the daily minimum temperature (°C), and εsr is a radiation term. The radiation term is calculated with the equation:
ε sr =
H day ⋅ (1 − α ) − 14 20
2.3.10
where Hday is the solar radiation reaching the ground on the current day (MJ m-2 d-1), and α is the albedo for the day.
46
SWAT USER'S MANUAL, VERSION 2000
Any cover present will significantly impact the soil surface temperature. The influence of plant canopy or snow cover on soil temperature is incorporated with a weighting factor, bcv, calculated as: CV ì ü ï CV + exp(7.563 − 1.297 × 10 −4 ⋅ CV )ï ï ï bcv = max í ý SNO ï ï ï SNO + exp(6.055 − 0.3002 ⋅ SNO ) ï î þ
2.3.11
where CV is the total aboveground biomass and residue present on the current day (kg ha-1) and SNO is the water content of the snow cover on the current day (mm H2O). The weighting factor, bcv, is 0.0 for a bare soil and approaches 1.0 as cover increases. The equation used to calculate the soil surface temperature is: Tssurf = bcv ⋅ Tsoil (1, d n − 1) + (1 − bcv ) ⋅ Tbare
2.3.12
where Tssurf is the soil surface temperature for the current day (°C), bcv is the weighting factor for soil cover impacts, Tsoil(1,dn-1) is the soil temperature of the first soil layer on the previous day (°C), and Tbare is the temperature of the bare soil surface (°C). The influence of ground cover is to place more emphasis on the previous day’s temperature near the surface. SWAT input variables that directly impact soil temperature calculations are listed in Table 2-7. There are several other variables that initialize residue and snow cover in the subbasins or HRUs (SNO_SUB and SNOEB in .sub; RSDIN in .hru). The influence of these variables will be limited to the first few months of simulation. Finally, the timing of management operations in the .mgt file will affect ground cover and consequently soil temperature.
CHAPTER 2: EQUATIONS—ENERGY
47
Table 2-7: SWAT input variables that pertain to soil temperature. Variable name TMPMX TMPMN SOL_Z SOL_BD SOL_ALB MAX TEMP
MIN TEMP
File Name .wgn .wgn .sol .sol .sol .tmp .tmp
Definition Average maximum air temperature for month (°C) Average minimum air temperature for month (°C) z: Depth from soil surface to bottom of layer (mm) ρb: Moist bulk density (Mg m-3 or g cm-3) Moist soil albedo. Tmx: Daily maximum temperature (°C) Tmn: Daily minimum temperature (°C)
2.3.4 WATER TEMPERATURE Water temperature is required to model in-stream biological and water quality processes. SWAT uses an equation developed by Stefan and Preud’homme (1993) to calculate average daily water temperature for a well-mixed stream: Twater = 5.0 + 0.75T av
2.3.13
where Twater is the water temperature for the day (°C), and T av is the average temperature on the day (°C). Due to thermal inertia of the water, the response of water temperature to a change in air temperature is dampened and delayed. When water and air temperature are plotted for a stream or river, the peaks in the water temperature plots usually lag 3-7 hours behind the peaks in air temperature. As the depth of the river increases, the lag time can increase beyond this typical interval. For very large rivers, the lag time can extend up to a week. Equation 2.3.13 assumes that the lag time between air and water temperatures is less than 1 day. In addition to air temperature, water temperature is influenced by solar radiation, relative humidity, wind speed, water depth, ground water inflow, artificial heat inputs, thermal conductivity of the sediments and the presence of impoundments along the stream network. SWAT assumes that the impact of these other variables on water temperature is not significant. Table 2-8: SWAT input variables that pertain to water temperature. Variable name Definition MAX TEMP Tmx: Daily maximum temperature (°C) MIN TEMP Tmn: Daily minimum temperature (°C)
File Name .tmp .tmp
48
SWAT USER'S MANUAL, VERSION 2000
2.4 WIND SPEED Wind speed is required by SWAT if the Penman-Monteith equation is used to estimate potential evapotranspiration and transpiration. SWAT assumes wind speed information is collected from gages positioned 1.7 meters above the ground surface. When using the Penman-Monteith equation to estimate transpiration, the wind measurement used in the equation must be above the canopy. In SWAT, a minimum difference of 1 meter is specified for canopy height and wind speed measurements. When the canopy height exceeds 1 meter, the original wind measurements is adjusted to: z w = hc + 100
2.4.1
where zw is the height of the wind speed measurement (cm), and hc is the canopy height (cm). The variation of wind speed with elevation near the ground surface is estimated with the equation (Haltiner and Martin, 1957): uz2
éz ù = u z1 ⋅ ê 2 ú ë z1 û
aa
2.4.2
where uz1 is the wind speed (m s-1) at height z1 (cm), uz2 is the wind speed (m s-1) at height z2 (cm), and aa is an exponent between 0 and 1 that varies with atmospheric stability and surface roughness. Jensen (1974) recommended a value of 0.2 for aa and this is the value used in SWAT. The daily wind speed data required by SWAT may be read from an input file or generated by the model. The variable WNDSIM in the input control code (.cod) file identifies the method used to obtain wind speed data. To read in daily wind speed data, the variable is set to 1 and the name of the wind speed data file and the number of different records stored in the file are set in the control input/output (file.cio) file. To generate daily wind speed values, WNDSIM is set to 2. The equations used to generate wind speed data in SWAT are reviewed in Chapter 4.
CHAPTER 2: EQUATIONS—ENERGY Table 2-9: SWAT input variables used in wind speed calculations. Variable name Definition WNDSIM Wind speed input code: 1-measured, 2-generated NWTOT Number of wind speed records within the .wnd file (required if WNDSIM = 1) WNDFILE Name of measured wind speed input file (.wnd) (required if WNDSIM = 1) IWGAGE Number of wind speed record used within the subbasin (required if WNDSIM
49 File Name .cod file.cio file.cio file.cio
= 1)
see description of .wnd file in the User’s Manual for input and format requirements if measured daily wind speed data is being used
2.5 NOMENCLATURE Asurf AU CV E0 H0 Hb Hday HL HMX Hnet HR Ifrac Ihr ISC I0 I0n SNO SW Tbare TDL Thr TK Tmn Tmx Tsoil Tssurf TSR TSS Twater T AA
Amplitude of the surface fluctuations in soil temperature (°C) Astronomical unit (1 AU = 1.496 x 108 km) Total aboveground biomass and residue present on current day (kg ha-1) Eccentricity correction factor of earth (r0/r)2 Extraterrestrial daily irradiation (MJ m-2 d-1) Net outgoing long-wave radiation (MJ m-2 d-1) Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) Long-wave radiation (MJ m-2 d-1) Maximum possible solar radiation (MJ m-2 d-1) Net radiation on day (MJ m-2 d-1) Radiant energy (MJ m-2 d-1) Fraction of daily solar radiation falling during specific hour on current day of simulation Solar radiation reaching ground during specific hour on current day of simulation (MJ m-2 h-1) Solar constant (4.921 MJ m-2 h-1) Extraterrestrial daily irradiance incident on a horizontal surface (MJ m-2 h-1) Extraterrestrial daily irradiance incident on a normal surface (MJ m-2 h-1) Water content of snow cover on current day (mm H2O) Amount of water in soil profile (mm H2O) Temperature of soil surface with no cover (°C) Daylength (h) Air temperature during hour (°C) Mean air temperature in Kelvin (273.15 + °C) Minimum air temperature for day (°C) Maximum air temperature for day (°C) Soil temperature (°C) Soil surface temperature (°C) Time of sunrise in solar day (h) Time of sunset in solar day (h) Average daily water temperature (°C) Average annual soil temperature (°C)
50
SWAT USER'S MANUAL, VERSION 2000
T AAair Average annual air temperature (°C) T av Average air temperature for day (°C) a a1 aa b b1 bcv covsol dn dd ddmax df e fcld hc hr r r0 t ti uz1 uz2 z z1 z2 ztot zw zd
α αplant αsoil δ ε ε′ εa εsr εvs l
σ θz
Constant in equation used to calculate the cloud cover adjustment factor Constant in equation used to calculate net emissivity Exponent between 0 and 1 that varies with atmospheric stability and surface roughness that is used in calculating wind speed at different heights Constant in equation used to calculate the cloud cover adjustment factor Constant in equation used to calculate net emissivity weighting factor for impact of ground cover on soil surface temperature Soil cover index for albedo determination Day number of year, 1 on January 1 and 365 on December 31 Damping depth (mm) Maximum damping depth (mm) Depth factor used in soil temperature calculations Vapor pressure (actual) on a given day (kPa) Factor to adjust for cloud cover in net long-wave radiation calculation Canopy height (cm) Hour of day (1-24) Actual earth-sun distance (AU) Mean earth-sun distance, 1 AU Number of hours before (+) or after (-) solar noon Solar time at the midpoint of the hour i Wind speed (m s-1) at height z1 (cm) Wind speed (m s-1) at height z2 (cm) Depth below soil surface (mm) Height of wind speed measurement (cm) Height of wind speed measurement (cm) Depth to bottom of soil profile (mm) Height of the wind speed measurement (cm) Ratio of depth in soil to damping depth Short-wave reflectance or albedo Plant albedo (set at 0.23) Soil albedo Solar declination (radians) Emissivity Net emittance Atmospheric emittance Radiation term for bare soil surface temperature calculation Vegetative or soil emittance Lag coefficient that controls influence of previous day’s temperature on current days temperature Stefan-Boltzmann constant (4.903 × 10-9 MJ m-2 K-4 d-1) Zenith angle (radians)
CHAPTER 2: EQUATIONS—ENERGY
φ ρb ϕ ω ωtmp
51
Latitude in radians Soil bulk density (Mg m-3) Scaling factor for impact of soil water on damping depth Angular velocity of the earth's rotation (0.2618 radians h-1) Angular frequency in soil temperature variation
2.6 REFERENCES Brunt, D. 1932. Notes on radiation in the atmosphere. Quart. J. Roy. Meteorol. Soc. 58: 389-418. Campbell, G.S. 1985. Soil physics with BASIC: transport models for soil-plant systems. Elsevier, Amsterdam. Carslaw, H.S. and J.C. Jaeger. 1959. Conduction of heat in solids. Oxford University Press, London. Doorenbos, J. and W.O. Pruitt. 1977. Guidelines for predicting crop water requirements. FAO Irrig. and Drain. Paper No. 24, 2nd ed. FAO, Rome. Duffie, J.A. and W.A. Beckman. 1980. Solar engineering of thermal processes. Wiley, N.Y. Haltiner, G.J. and F.L. Martin. 1957. Dynamical and physical meteorology. McGraw-Hill, New York. Iqbal, M. 1983. An introduction to solar radiation. Academic Press, N.Y. Jensen, M.E. (ed.) 1974. Consumptive use of water and irrigation water requirements. Rep. Tech. Com. on Irrig. Water Requirements, Irrig. and Drain. Div., ASCE. Jensen, M.E., R.D. Burman, and R.G. Allen (ed). 1990. Evapotranspiration and irrigation water requirements. ASCE Manuals and Reports on Engineering Practice No. 70, ASCE, N.Y. Perrin de Brichambaut, Chr. 1975. Cahiers A.F.E.D.E.S., supplément au no 1. Editions Européennes Thermique et Industrie, Paris. Stefan, H.G. and E.B. Preud’homme. 1993. Stream temperature estimation from air temperature. Water Resources Bulletin 29(1): 27-45.
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SWAT USER'S MANUAL, VERSION 2000
Wright, J.L. and M.E. Jensen. 1972. Peak water requirements of crops in Southern Idaho. J. Irrig. and Drain. Div., ASCE, 96(IR1):193-201.
CHAPTER 3
EQUATIONS: ATMOSPHERIC WATER
Precipitation is the mechanism by which water enters the land phase of the hydrologic cycle. Because precipitation controls the water balance, it is critical that the amount and distribution of precipitation in space and time is accurately simulated by the model.
53
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SWAT USER’S MANUAL, VERSION 2000
3.1 PRECIPITATION The precipitation reaching the earth's surface on a given day, Rday, may be read from an input file or generated by the model. Users are strongly recommended to incorporate measured precipitation into their simulations any time the data is available. The ability of SWAT to reproduce observed stream hydrographs is greatly improved by the use of measured precipitation data. Unfortunately, even with the use of measured precipitation the model user can expect some error due to inaccuracy in precipitation data. Measurement of precipitation at individual gages is subject to error from a number of causes and additional error is introduced when regional precipitation is estimated from point values. Typically, total or average areal precipitation estimates for periods of a year or longer have relative uncertainties of 10% (Winter, 1981). Point measurements of precipitation generally capture only a fraction of the true precipitation. The inability of a gage to capture a true reading is primarily caused by wind eddies created by the gage. These wind eddies reduce the catch of the smaller raindrops and snowflakes. Larson and Peck (1974) found that deficiencies of 10% for rain and 30% for snow are common for gages projecting above the ground surface that are not designed to shield wind effects. Even when the gage is designed to shield for wind effects, this source of error will not be eliminated. For an in-depth discussion of this and other sources of error as well as methods for dealing with the error, please refer to Dingman (1994). The variable PCPSIM in the input control code (.cod) file identifies the method used to obtain precipitation data. To read in daily precipitation data, the variable is set to 1 and the names of the precipitation data files and the number of precipitation records stored in the files are defined in the control input/output (file.cio) file. To generate daily precipitation values, PCPSIM is set to 2. The equations used to generate precipitation data in SWAT are reviewed in Chapter 4. SWAT input variables that pertain to precipitation are summarized in Table 3-1.
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 55 Table 3-1: SWAT input variables used in precipitation calculations. Variable name Definition PCPSIM Precipitation input code: 1-measured, 2-generated NRGAGE Number of precipitation gage files (.pcp) used (required if PCPSIM = 1) NRTOT Total number of precipitation records used in simulation (required if PCPSIM
File Name .cod file.cio file.cio
= 1)
NRGFIL RFILE IRGAGE
Number of precipitation records in each .pcp file (required if PCPSIM = 1) Name of measured precipitation input file(s) (.pcp) (required if PCPSIM = 1) Number of precipitation record used within the subbasin (required if PCPSIM
file.cio file.cio file.cio
= 1)
see description of .pcp file in the User’s Manual for input and format requirements if measured daily precipitation data is being used
3.2 MAXIMUM HALF-HOUR RAINFALL The maximum half-hour rainfall is required by SWAT to calculate the peak runoff rate. The maximum half-hour rainfall is reported as a fraction of the total daily rainfall, α0.5. If sub-daily precipitation data is used in the model, SWAT will calculate the maximum half-hour rainfall fraction directly from the precipitation data. If daily precipitation data is used, SWAT generates a value for
α0.5 using the equations summarized in Chapter 4.
3.3 WATER VAPOR Relative humidity is required by SWAT if the Penman-Monteith or Priestley-Taylor equation is used to estimate potential evapotranspiration. The Penman-Monteith equation includes terms that quantify the effect of the amount of water vapor in the air near the evaporative surface on evaporation. Both Penman-Monteith and Priestley-Taylor require the actual vapor pressure, which is calculated from the relative humidity. Relative humidity is the ratio of an air volume’s actual vapor pressure to its saturation vapor pressure:
Rh =
e eo
3.3.1
where Rh is the relative humidity on a given day, e is the actual vapor pressure on a given day (kPa), and e o is the saturation vapor pressure on a given day (kPa).
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SWAT USER’S MANUAL, VERSION 2000
The saturation vapor pressure is the maximum vapor pressure that is thermodynamically stable and is a function of the air temperature. SWAT calculates saturation vapor pressure using an equation presented by Tetens (1930) and Murray (1967):
é16.78 ⋅ T av − 116.9 ù e o = exp ê ú ë T av + 237.3 û
3.3.2
where e o is the saturation vapor pressure on a given day (kPa) and T av is the mean daily air temperature (°C). When relative humidity is known, the actual vapor pressure can be calculated by rearranging equation 3.3.1: e = Rh ⋅ e o
3.3.3
The saturation vapor pressure curve is obtained by plotting equation 3.3.2. The slope of the saturation vapor pressure curve can be calculated by differentiating equation 3.3.2: ∆=
(T
4098 ⋅ e o av
)
+ 237.3
2
3.3.4
where ∆ is the slope of the saturation vapor pressure curve (kPa °C-1), e o is the saturation vapor pressure on a given day (kPa) and T av is the mean daily air temperature (°C). The rate of evaporation is proportional to the difference between the vapor pressure of the surface layer and the vapor pressure of the overlying air. This difference is termed the vapor pressure deficit:
vpd = e o − e
3.3.5
where vpd is the vapor pressure deficit (kPa), e o is the saturation vapor pressure on a given day (kPa), and e is the actual vapor pressure on a given day (kPa). The greater the value of vpd the higher the rate of evaporation. The latent heat of vaporization, λ, is the quantity of heat energy that must be absorbed to break the hydrogen bonds between water molecules in the liquid state to convert them to gas. The latent heat of vaporization is a function of temperature and can be calculated with the equation (Harrison, 1963):
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 57
λ = 2.501 − 2.361 × 10 −3 ⋅ T av
3.3.6
where λ is the latent heat of vaporization (MJ kg-1) and T av is the mean daily air temperature (°C). Evaporation involves the exchange of both latent heat and sensible heat between the evaporating body and the air. The psychrometric constant, γ, represents a balance between the sensible heat gained from air flowing past a wet bulb thermometer and the sensible heat converted to latent heat (Brunt, 1952) and is calculated:
γ =
cp ⋅ P 0.622 ⋅ λ
3.3.7
where γ is the psychrometric constant (kPa °C-1), cp is the specific heat of moist air at constant pressure (1.013 × 10-3 MJ kg-1 °C-1), P is the atmospheric pressure (kPa), and λ is the latent heat of vaporization (MJ kg-1). Calculation of the psychrometric constant requires a value for atmospheric pressure. SWAT estimates atmospheric pressure using an equation developed by Doorenbos and Pruitt (1977) from mean barometric pressure data at a number of East African sites: P = 101.3 − 0.01152 ⋅ EL + 0.544 × 10 −6 ⋅ EL2
3.3.8
where P is the atmospheric pressure (kPa) and EL is the elevation (m). The daily relative humidity data required by SWAT may be read from an input file or generated by the model. The variable RHSIM in the input control code (.cod) file identifies the method used to obtain relative humidity data. To read in daily relative humidity data, the variable is set to 1 and the name of the relative humidity data file and the number of different records stored in the file are set in the control input/output (file.cio) file. To generate daily relative humidity values, RHSIM is set to 2. The equations used to generate relative humidity data in SWAT are reviewed in Chapter 4.
58
SWAT USER’S MANUAL, VERSION 2000 Table 3-2: SWAT input variables used in relative humidity calculations. Variable name Definition RHD Rh: daily average relative humidity TMP_MX Tmx: maximum temperature for day (°C) TMP_MN Tmn: minimum temperature for day (°C) ELEV EL: elevation (m) RHSIM Relative humidity input code: 1-measured, 2-generated NHTOT Number of relative humidity records within the .hmd file (required if RHSIM
File Name .hmd .tmp .tmp .sub .cod file.cio
= 1)
RHFILE IHGAGE
Name of measured relative humidity input file (.hmd) (required if RHSIM = 1) Number of relative humidity record used within the subbasin (required if
file.cio file.cio
RHSIM = 1)
see description of .hmd file in the User’s Manual for input and format requirements if measured relative humidity data is being used
3.4 SNOW COVER SWAT classifies precipitation as rain or freezing rain/snow by the mean daily air temperature. The boundary temperature, Ts-r, used to categorize precipitation as rain or snow is defined by the user. If the mean daily air temperature is less than the boundary temperature, then the precipitation within the HRU is classified as snow and the water equivalent of the snow precipitation is added to the snow pack. Snowfall is stored at the ground surface in the form of a snow pack. The amount of water stored in the snow pack is reported as a snow water equivalent. The snow pack will increase with additional snowfall or decrease with snow melt or sublimation. The mass balance for the snow pack is: SNO = SNO + Rday − E sub − SNOmlt
3.4.1
where SNO is the water content of the snow pack on a given day (mm H2O), Rday is the amount of precipitation on a given day (added only if T av ≤ Ts − r ) (mm H2O), Esub is the amount of sublimation on a given day (mm H2O), and SNOmlt is the amount of snow melt on a given day (mm H2O). The amount of snow is expressed as depth over the total HRU area. Due to variables such as drifting, shading and topography, the snow pack in a subbasin will rarely be uniformly distributed over the total area. This results
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 59
in a fraction of the subbasin area that is bare of snow. This fraction must be quantified to accurately compute snow melt in the subbasin. The factors that contribute to variable snow coverage are usually similar from year to year, making it possible to correlate the areal coverage of snow with the amount of snow present in the subbasin at a given time. This correlation is expressed as an areal depletion curve, which is used to describe the seasonal growth and recession of the snow pack as a function of the amount of snow present in the subbasin (Anderson, 1976). The areal depletion curve requires a threshold depth of snow, SNO100, to be defined above which there will always be 100% cover. The threshold depth will depend on factors such as vegetation distribution, wind loading of snow, wind scouring of snow, interception and aspect, and will be unique to the watershed of interest. The areal depletion curve is based on a natural logarithm. The areal depletion curve equation is: snocov
SNO = SNO100
æ SNO æ SNO ö ö ÷÷ ÷÷ ⋅ çç + expçç cov1 − cov 2 ⋅ SNO SNO 100 100 è øø è
−1
3.4.2
where snocov is the fraction of the HRU area covered by snow, SNO is the water content of the snow pack on a given day (mm H2O), SNO100 is the threshold depth of snow at 100% coverage (mm H2O), cov1 and cov2 are coefficients that define the shape of the curve. The values used for cov1 and cov2 are determined by solving equation 3.4.2 using two known points: 95% coverage at 95% SNO100; and 50% coverage at a user specified fraction of SNO100. Example areal depletion curves for various fractions of SNO100 at 50% coverage are shown in the following figures.
1
1
0.9
0.9
0.8
0.8 Fraction areal coverage
Fraction areal coverage
SWAT USER’S MANUAL, VERSION 2000
0.7 0.6 0.5 0.4 0.3 0.2 0.1
0.7 0.6 0.5 0.4 0.3 0.2 0.1
0
0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
Snow volume (fraction of SNO100 )
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Snow volume (fraction of SNO100 )
Figure 3-1:10% SNO100 = 50% coverage
Figure 3-2: 30% SNO100 = 50%
1
1
0.9
0.9
0.8
0.8
0.7
0.7
Fraction areal coverage
Fraction areal coverage
0.6 0.5 0.4 0.3
0.5 0.4 0.3 0.2
0.1
0.1
0
coverage
0.6
0.2
0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
S now volum e (fra ction of S NO100 )
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Snow volum e (fra ction of S NO100)
Figure 3-3: 50% SNO100 = 50% coverage
Figure 3-4: 70% SNO100 = 50% coverage
1 0.9 0.8 Fraction areal coverage
60
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Snow volum e (fra ction of SNO100)
Figure 3-5: 90% SNO100 = 50% coverage
It is important to remember that once the volume of water held in the snow pack exceeds SNO100 the depth of snow over the HRU is assumed to be uniform, i.e. snocov = 1.0. The areal depletion curve affects snow melt only when the snow
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 61
pack water content is between 0.0 and SNO100. Consequently if SNO100 is set to a very small value, the impact of the areal depletion curve on snow melt will be minimal. As the value for SNO100 increases, the influence of the areal depletion curve will assume more importance in snow melt processes. Table 3-3: SWAT input variables used in snow cover calculations. Variable name Definition SFTMP Ts-r: Mean air temperature at which precipitation is equally likely to be rain as snow/freezing rain (°C) SNOCOVMX SNO100: Threshold depth of snow, above which there is 100% cover SNO50COV Fraction of SNOCOVMX that provides 50% cover SNO_SUB Initial snow water content in subbasin (mm H2O) SNOEB Initial snow water content in subbasin elevation band (mm H2O)
File Name .bsn .bsn .bsn .sub .sub
3.5 SNOW MELT Snow melt is controlled by the air and snow pack temperature, the melting rate, and the areal coverage of snow. Snow melt is included with rainfall in the calculations of runoff and percolation. When SWAT calculates erosion, the rainfall energy of the snow melt fraction of the water is set to zero. The water released from snow melt is assumed to be evenly distributed over the 24 hours of the day.
3.5.1 SNOW PACK TEMPERATURE The snow pack temperature is a function of the mean daily temperature during the preceding days and varies as a dampened function of air temperature (Anderson, 1976). The influence of the previous day’s snow pack temperature on the current day’s snow pack temperature is controlled by a lagging factor, l sno . The lagging factor inherently accounts for snow pack density, snow pack depth, exposure and other factors affecting snow pack temperature. The equation used to calculate the snow pack temperature is: Tsnow (dn ) = Tsnow (d n −1) ⋅ (1 − l sno ) + T av ⋅ l sno
3.5.1
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SWAT USER’S MANUAL, VERSION 2000
where Tsnow(d n ) is the snow pack temperature on a given day (°C), Tsnow(d n −1) is the snow pack temperature on the previous day (°C), l sno is the snow temperature lag factor, and T av is the mean air temperature on the current day (°C). As l sno approaches 1.0, the mean air temperature on the current day exerts an increasingly greater influence on the snow pack temperature and the snow pack temperature from the previous day exerts less and less influence. The snow pack will not melt until the snow pack temperature exceeds a threshold value, Tmlt. This threshold value is specified by the user.
3.5.2 SNOW MELT EQUATION The snow melt in SWAT is calculated as a linear function of the difference between the average snow pack-maximum air temperature and the base or threshold temperature for snow melt: + Tmx éT ù SNOmlt = bmlt ⋅ snocov ⋅ ê snow − Tmlt ú 2 ë û
3.5.2
where SNOmlt is the amount of snow melt on a given day (mm H2O), bmlt is the melt factor for the day (mm H2O/day-°C), snocov is the fraction of the HRU area covered by snow, Tsnow is the snow pack temperature on a given day (°C), Tmx is the maximum air temperature on a give day (°C), and Tmlt is the base temperature above which snow melt is allowed (°C). The melt factor is allowed a seasonal variation with maximum and minimum values occurring on summer and winter solstices: bmlt =
(bmlt 6 + bmlt12 ) + (bmlt 6 − bmlt12 ) ⋅ sinæ 2π ⋅ (d − 81)ö ç ÷ n 2
2
è 365
ø
3.5.3
where bmlt is the melt factor for the day (mm H2O/day-°C), bmlt6 is the melt factor for June 21 (mm H2O/day-°C), bmlt12 is the melt factor for December 21 (mm H2O/day-°C), and dn is the day number of the year. In rural areas, the melt factor will vary from 1.4 to 6.9 mm H2O/day-°C (Huber and Dickinson, 1988). In urban areas, values will fall in the higher end of the range due to compression of the snow pack by vehicles, pedestrians, etc.
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 63
Urban snow melt studies in Sweden (Bengston, 1981; Westerstrom, 1981) reported melt factors ranging from 3.0 to 8.0 mm H2O/day-°C. Studies of snow melt on asphalt (Westerstrom, 1984) gave melt factors of 1.7 to 6.5 mm H2O/day°C. Table 3-4: SWAT input variables used in snow melt calculations. Variable name Definition TIMP l sno : Snow temperature lag factor SMTMP SMFMX SMFMN
Tmlt: Threshold temperature for snow melt (°C) bmlt6: Melt factor on June 21 (mm H2O/day-°C) bmlt12: Melt factor on December 21 (mm H2O/day-°C)
3.6 NOMENCLATURE Esub Amount of sublimation on a given day (mm H2O) EL Elevation (m) P Atmospheric pressure (kPa) Rday Amount of rainfall on a given day (mm H2O) Rh Average relative humidity for the day SNO Water content of snow cover on current day (mm H2O) SNO100 Amount of snow above which there is 100% cover (mm H2O) SNOmlt Amount of snow melt on a given day (mm H2O) Tmlt Threshold temperature for snow melt (°C) Tmx Maximum air temperature for day (°C) Ts-r Rain/snow boundary temperature (°C) Tsnow Snow pack temperature on a given day (°C) T av
Average air temperature for day (°C)
bmlt bmlt6 bmlt12 cp cov1 cov2 dn e eo snocov vpd
Melt factor for the day (mm H2O/day-°C) Melt factor for June 21 (mm H2O/day-°C) Melt factor for December 21 (mm H2O/day-°C) Specific heat of moist air at constant pressure (1.013 × 10-3 MJ kg-1 °C-1) Snow cover areal depletion curve shape coefficient Snow cover areal depletion curve shape coefficient Day number of year, 1 on January 1 and 365 on December 31 Actual vapor pressure on a given day (kPa) Saturation vapor pressure on a given day (kPa) Fraction of the HRU area covered by snow Vapor pressure deficit (kPa)
File Name .bsn .bsn .bsn .bsn
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SWAT USER’S MANUAL, VERSION 2000
α0.5 ∆ γ λ l sno
Maximum half-hour rainfall expressed as a fraction of daily rainfall Slope of the saturation vapor pressure curve (kPa °C-1) Psychrometric constant (kPa °C-1) Latent heat of vaporization (MJ kg-1) Snow temperature lag factor
3.7 REFERENCES Anderson, E.A. 1976. A point energy and mass balance model of snow cover. NOAA Technical Report NWS 19, U.S. Dept. of Commerce, National Weather Service. Bengston, L. 1981. Snowmelt-generated runoff in urban areas. p. 444-451. In B.C. Yen (ed.) Urban stormwater hydraulics and hydrology: proceedings of the Second International Conference on Urban Storm Drainage, held at Urbana, Illinois, USA, 15-19 June 1981. Water Resources Publications, Littleton, CO. Brunt, D. 1952. Physical and dynamical meteorology, 2nd ed. University Press, Cambridge. Dingman, S.L. 1994. Physical hydrology. Prentice-Hall, Inc., Englewood Cliffs, NJ. Doorenos, J. and W.O. Pruitt. 1977. Guidelines for predicting crop water requirements. FAO Irrig. and Drain. Paper No. 24, 2nd ed. FAO, Rome. Harrison, L.P. 1963. Fundamental concepts and definitions relating to humidity. In A. Wexler (ed.) Humidity and moisture, Vol. 3. Reinhold Publishing Company, N.Y. Huber, W.C. and R.E. Dickinson. 1988. Storm water management model, version 4: user’s manual. U.S. Environmental Protection Agency, Athens, GA. Larson, L.L., and E.L. Peck. 1974. Accuracy of precipitation measurements for hydrologic modeling. Water Resources Research 10:857-863. Murray, F.W. 1967. On the computation of saturation vapor pressure. J. Appl. Meteor. 6:203-204. Tetens, O. 1930. Uber einige meteorologische Begriffe. Z. Geophys. 6:297-309. Westerstrom, G. 1984. Snowmelt runoff from Porson residential area, Lulea, Sweden. p. 315-323. In Proceedings of the Third International Conference on Urban Storm Drainage held at Chalmers University, Goteborg, Sweden, June 1984.
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 65
Westerstrom, G. 1981. Snowmelt runoff from urban plot. p. 452-459. In B.C. Yen (ed.) Urban stormwater hydraulics and hydrology: proceedings of the Second International Conference on Urban Storm Drainage, held at Urbana, Illinois, USA, 15-19 June 1981. Water Resources Publications, Littleton, CO. Winter, T.C. 1981. Uncertainties in estimating the water balance of lakes. Water Resources Bulletin 17:82-115.
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CHAPTER 4
EQUATIONS: WEATHER GENERATOR
SWAT requires daily values of precipitation, maximum and minimum temperature, solar radiation, relative humidity and wind speed. The user may choose to read these inputs from a file or generate the values using monthly average data summarized over a number of years. SWAT includes the WXGEN weather generator model (Sharpley and Williams, 1990) to generate climatic data or to fill in gaps in measured records. This weather generator was developed for the contiguous U.S. If the user prefers a different weather generator, daily input values for the different weather parameters may be generated with an alternative model and formatted for input to SWAT.
67
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SWAT USER'S MANUAL, VERSION 2000
The occurrence of rain on a given day has a major impact on relative humidity, temperature and solar radiation for the day. The weather generator first independently generates precipitation for the day. Maximum temperature, minimum temperature, solar radiation and relative humidity are then generated based on the presence or absence of rain for the day. Finally, wind speed is generated independently.
4.1 PRECIPITATION The precipitation generator is a Markov chain-skewed (Nicks, 1974) or Markov chain-exponential model (Williams, 1995). A first-order Markov chain is used to define the day as wet or dry. When a wet day is generated, a skewed distribution or exponential distribution is used to generate the precipitation amount. Table 4.1 lists SWAT input variables that are used in the precipitation generator.
4.1.1 OCCURRENCE OF WET OR DRY DAY With the first-order Markov-chain model, the probability of rain on a given day is conditioned on the wet or dry status of the previous day. A wet day is defined as a day with 0.1 mm of rain or more. The user is required to input the probability of a wet day on day i given a wet day on day i – 1, Pi(W/W), and the probability of a wet day on day i given a dry day on day i – 1, Pi(W/D), for each month of the year. From these inputs the remaining transition probabilities can be derived: Pi (D W ) = 1 − Pi (W W )
4.1.1
Pi (D D ) = 1 − Pi (W D )
4.1.2
where Pi(D/W) is the probability of a dry day on day i given a wet day on day i – 1 and Pi(D/D) is the probability of a dry day on day i given a dry day on day i – 1. To define a day as wet or dry, SWAT generates a random number between 0.0 and 1.0. This random number is compared to the appropriate wet-dry probability, Pi(W/W) or Pi(W/D). If the random number is equal to or less than the
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
69
wet-dry probability, the day is defined as wet. If the random number is greater than the wet-dry probability, the day is defined as dry.
4.1.2 AMOUNT OF PRECIPITATION Numerous probability distribution functions have been used to describe the distribution of rainfall amounts. SWAT provides the user with two options: a skewed distribution and an exponential distribution. The skewed distribution was proposed by Nicks (1974) and is based on a skewed distribution used by Fiering (1967) to generate representative streamflow. The equation used to calculate the amount of precipitation on a wet day is:
Rday = µ mon
3 æ éæ ö ç ç SND − g mon ö÷ ⋅ æç g mon ö÷ + 1ù − 1 ÷ day ç êè ÷ 6 ø è 6 ø úû + 2 ⋅ σ mon ⋅ ç ë ÷ g mon ç ÷ ç ÷ è ø
4.1.3
where Rday is the amount of rainfall on a given day (mm H2O), µmon is the mean daily rainfall (mm H2O) for the month, σmon is the standard deviation of daily rainfall (mm H2O) for the month, SNDday is the standard normal deviate calculated for the day, and gmon is the skew coefficient for daily precipitation in the month. The standard normal deviate for the day is calculated: SNDday = cos(6.283 ⋅ rnd 2 ) ⋅ − 2 ln (rnd 1 )
4.1.4
where rnd1 and rnd2 are random numbers between 0.0 and 1.0. The exponential distribution is provided as an alternative to the skewed distribution. This distribution requires fewer inputs and is most commonly used in areas where limited data on precipitation events is available. Daily precipitation is calculated with the exponential distribution using the equation: Rday = µ mon ⋅ (− ln (rnd 1 ))
r exp
4.1.5
where Rday is the amount of rainfall on a given day (mm H2O), µmon is the mean daily rainfall (mm H2O) for the month, rnd1 is a random number between 0.0 and 1.0, and rexp is an exponent that should be set between 1.0 and 2.0. As the value of rexp is increased, the number of extreme rainfall events during the year will
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SWAT USER'S MANUAL, VERSION 2000
increase. Testing of this equation at locations across the U.S. have shown that a value of 1.3 gives satisfactory results. Table 4-1: SWAT input variables that pertain to generation of precipitation. Variable name Definition PCPSIM Precipitation input code: 1-measured, 2-generated PR_W(1,mon) Pi(W/D): probability of a wet day following a dry day in month PR_W(2,mon) Pi(W/W): probability of a wet day following a wet day in month IDIST Rainfall distribution code: 0-skewed, 1-exponential REXP rexp: value of exponent (required if IDIST = 1) PCPMM(mon) average amount of precipitation falling in month (mm H2O) PCPD(mon) average number of days of precipitation in month (µmon = PCPMM / PCPD) PCPSTD(mon) σmon: standard deviation for daily precipitation in month (mm H2O) PCPSKW(mon) gmon: skew coefficient for daily precipitation in month
File Name .cod .wgn .wgn .cod .cod .wgn .wgn .wgn .wgn
4.2 SOLAR RADIATION & TEMPERATURE The procedure used to generate daily values for maximum temperature, minimum temperature and solar radiation (Richardson, 1981; Richardson and Wright, 1984) is based on the weakly stationary generating process presented by Matalas (1967).
4.2.1 DAILY RESIDUALS Residuals for maximum temperature, minimum temperature and solar radiation are required for calculation of daily values. The residuals must be serially correlated and cross-correlated with the correlations being constant at all locations. The equation used to calculate residuals is:
χ i ( j ) = Aχ i −1 ( j ) + Bε i ( j )
4.2.1
where χi(j) is a 3 × 1 matrix for day i whose elements are residuals of maximum temperature (j = 1), minimum temperature (j = 2) and solar radiation (j = 3), χi-1(j) is a 3 × 1 matrix of the previous day’s residuals, εi is a 3 × 1 matrix of independent random components, and A and B are 3 × 3 matrices whose elements are defined such that the new sequences have the desired serial-correlation and cross-correlation coefficients. The A and B matrices are given by
A = M1 ⋅ M 0
−1
4.2.2
CHAPTER 4: EQUATIONS—WEATHER GENERATOR −1
B ⋅ BT = M 0 − M 1 ⋅ M 0 ⋅ M 1
T
71
4.2.3
where the superscript –1 denotes the inverse of the matrix and the superscript T denotes the transpose of the matrix. M0 and M1 are defined as
ρ 0 (1,2 ) ρ 0 (1,3)ù é 1 ê ρ 0 (2,3)ú M 0 = ρ 0 (1,2 ) 1 ú ê êë ρ 0 (1,3) ρ 0 (2,3) 1 úû
4.2.4
é ρ1 (1,1) ρ1 (1,2 ) ρ1 (1,3)ù M 1 = ê ρ1 (2,1) ρ1 (2,2 ) ρ1 (2,3)ú ú ê ëê ρ1 (3,1) ρ1 (3,2 ) ρ1 (3,3)úû
4.2.5
ρ0(j,k) is the correlation coefficient between variables j and k on the same day where j and k may be set to 1 (maximum temperature), 2 (minimum temperature) or 3 (solar radiation) and ρ1(j,k) is the correlation coefficient between variable j and k with variable k lagged one day with respect to variable j. Correlation coefficients were determined for 31 locations in the United States using 20 years of temperature and solar radiation data (Richardson, 1982). Using the average values of these coefficients, the M0 and M1 matrices become 0.186 ù é1.000 0.633 ê M 0 = 0.633 1.000 − 0.193ú ú ê êë0.186 − 0.193 1.000 úû
4.2.6
0.087 ù é0.621 0.445 ê M 1 = 0.563 0.674 − 0.100ú ú ê êë0.015 − 0.091 0.251 úû
4.2.7
Using equations 4.2.2 and 4.2.3, the A and B matrices become 0.086 − 0.002ù é 0.567 ê A = 0.253 0.504 − 0.050ú ú ê êë − 0.006 − 0.039 0.244 úû
4.2.8
0 0 ù é 0.781 ê B = 0.328 0.637 0 ú ú ê ëê0.238 − 0.341 0.873ûú
4.2.9
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SWAT USER'S MANUAL, VERSION 2000
The A and B matrices defined in equations 4.2.8 and 4.2.9 are used in conjunction with equation 4.2.1 to generate daily sequences of residuals of maximum temperature, minimum temperature and solar radiation.
4.2.2 GENERATED VALUES The daily generated values are determined by multiplying the residual elements generated with equation 4.2.1 by the monthly standard deviation and adding the monthly average value. Tmx = µmx mon + χ i (1) ⋅ σmxmon
4.2.10
Tmn = µmnmon + χ i (2 ) ⋅ σmnmon
4.2.11
H day = µrad mon + χ i (3) ⋅ σrad mon
4.2.12
where Tmx is the maximum temperature for the day (°C), µmxmon is the average daily maximum temperature for the month (°C), χi(1) is the residual for maximum temperature on the given day, σmxmon is the standard deviation for daily maximum temperature during the month (°C), Tmn is the minimum temperature for the day (°C), µmnmon is the average daily minimum temperature for the month (°C), χi(2) is the residual for minimum temperature on the given day, σmnmon is the standard deviation for daily minimum temperature during the month (°C), Hday is the solar radiation for the day (MJ m-2), µradmon is the average daily solar radiation for the month (MJ m-2), χi(3) is the residual for solar radiation on the given day, and σradmon is the standard deviation for daily solar radiation during the month (MJ m-2). The user is required to input standard deviation for maximum and minimum temperature. For solar radiation the standard deviation is estimated as ¼ of the difference between the extreme and mean value for each month.
σrad mon =
H mx − µrad mon 4
4.2.13
where σradmon is the standard deviation for daily solar radiation during the month (MJ m-2), Hmx is the maximum solar radiation that can reach the earth’s surface on
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
73
a given day (MJ m-2), and µradmon is the average daily solar radiation for the month (MJ m-2).
4.2.3 ADJUSTMENT FOR CLEAR/OVERCAST CONDITIONS Maximum temperature and solar radiation will be lower on overcast days than on clear days. To incorporate the influence of wet/dry days on generated values of maximum temperature and solar radiation, the average daily maximum temperature, µmxmon, and average daily solar radiation, µradmon, in equations 4.2.10 and 4.2.12 are adjusted for wet or dry conditions.
4.2.3.1 MAXIMUM TEMPERATURE The
continuity
equation
relates
average
daily
maximum
temperature adjusted for wet or dry conditions to the average daily maximum temperature for the month:
µmx mon ⋅ daystot = µWmxmon ⋅ days wet + µDmx mon ⋅ days dry
4.2.14
where µmxmon is the average daily maximum temperature for the month (°C), daystot are the total number of days in the month, µWmxmon is the average daily maximum temperature of the month on wet days (°C), dayswet are the number of wet days in the month, µDmxmon is the average daily maximum temperature of the month on dry days (°C), and daysdry are the number of dry days in the month. The wet day average maximum temperature is assumed to be less than the dry day average maximum temperature by some fraction of (µmxmon - µmnmon):
µWmxmon = µDmxmon − bT ⋅ (µmx mon − µmnmon )
4.2.15
where µWmxmon is the average daily maximum temperature of the month on wet days (°C), µDmxmon is the average daily maximum temperature of the month on dry days (°C), bT is a scaling factor that controls the degree of deviation in temperature caused by the presence or absence of precipitation, µmxmon is the average daily maximum temperature for the
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SWAT USER'S MANUAL, VERSION 2000
month (°C), and µmnmon is the average daily minimum temperature for the month (°C). The scaling factor, bT, is set to 0.5 in SWAT. To calculate the dry day average maximum temperature, equations 4.2.14 and 4.2.15 are combined and solved for µDmxmon:
µDmx mon = µmxmon + bT ⋅
days wet ⋅ (µmx mon − µmnmon ) daystot
4.2.16
Incorporating the modified values into equation 4.2.10, SWAT calculates the maximum temperature for a wet day using the equation: Tmx = µWmxmon + χ i (1) ⋅ σmx mon
4.2.17
and the maximum temperature for a dry day using the equation: Tmx = µDmx mon + χ i (1) ⋅ σmx mon
4.2.18
4.2.3.2 SOLAR RADIATION The continuity equation relates average daily solar radiation adjusted for wet or dry conditions to the average daily solar radiation for the month:
µrad mon ⋅ daystot = µWrad mon ⋅ days wet + µDrad mon ⋅ daysdry 4.2.19 where µradmon is the average daily solar radiation for the month (MJ m-2), daystot are the total number of days in the month, µWradmon is the average daily solar radiation of the month on wet days (MJ m-2), dayswet are the number of wet days in the month, µDradmon is the average daily solar radiation of the month on dry days (MJ m-2), and daysdry are the number of dry days in the month. The wet day average solar radiation is assumed to be less than the dry day average solar radiation by some fraction:
µWrad mon = bR ⋅ µDrad mon
4.2.20
where µWradmon is the average daily solar radiation of the month on wet days (MJ m-2), µDradmon is the average daily solar radiation of the month on dry days (MJ m-2), and bR is a scaling factor that controls the degree of
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
75
deviation in solar radiation caused by the presence or absence of precipitation. The scaling factor, bR, is set to 0.5 in SWAT. To calculate the dry day average solar radiation, equations 4.2.19 and 4.2.20 are combined and solved for µDradmon:
µDrad mon =
µrad mon ⋅ daystot bR ⋅ days wet + daysdry
4.2.21
Incorporating the modified values into equation 4.2.12, SWAT calculated the solar radiation on a wet day using the equation: H day = µWrad mon + χ i (3) ⋅ σrad mon
4.2.22
and the solar radiation on a dry day using the equation: H day = µDrad mon + χ i (3) ⋅ σrad mon
4.2.23
Table 4-2: SWAT input variables that pertain to generation of temperature and solar radiation. Variable name Definition TMPSIM Temperature input code: 1-measured, 2-generated SLRSIM Solar radiation input code: 1-measured, 2-generated TMPMX(mon) µmxmon: average maximum air temperature for month (°C) TMPSTDMX(mon) σmxmon: standard deviation for maximum air temperature in month (°C) TMPMN(mon) µmnmon: average minimum air temperature for month (°C) TMPSTDMN(mon) σmnmon: standard deviation for minimum air temperature in month (°C) SOLARAV(mon) µradmon: average daily solar radiation for month (MJ m-2) PCPD(mon) dayswet: average number of days of precipitation in month
File Name .cod .cod .wgn .wgn .wgn .wgn .wgn .wgn
4.3 RELATIVE HUMIDITY Relative humidity is required by SWAT when the Penman-Monteith equation is used to calculate potential evapotranspiration. Daily average relative humidity values are calculated from a triangular distribution using average monthly relative humidity.
4.3.1 MEAN MONTHLY RELATIVE HUMIDITY Relative humidity is defined as the ratio of the actual vapor pressure to the saturation vapor pressure at a given temperature: Rhmon =
emon o emon
4.3.1
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SWAT USER'S MANUAL, VERSION 2000
where Rhmon is the average relative humidity for the month, emon is the actual vapor o is the saturation vapor pressure at the mean monthly temperature (kPa), and emon
pressure at the mean monthly temperature (kPa). The saturation vapor pressure, o emon , is related to the mean monthly air temperature with the equation:
é16.78 ⋅ µtmpmon − 116.9 ù o = exp ê emon ú ë µtmpmon + 237.3 û
4.3.2
o is the saturation vapor pressure at the mean monthly temperature where emon
(kPa), and µtmpmon is the mean air temperature for the month (°C). The mean air temperature for the month is calculated by averaging the mean maximum monthly temperature, µmxmon, and the mean minimum monthly temperature, µmnmon. The dew point temperature is the temperature at which the actual vapor pressure present in the atmosphere is equal to the saturation vapor pressure. Therefore, by substituting the dew point temperature in place of the average monthly temperature in equation 4.3.2, the actual vapor pressure may be calculated: é16.78 ⋅ µdewmon − 116.9 ù emon = exp ê ú ë µdewmon + 237.3 û
4.3.3
where emon is the actual vapor pressure at the mean month temperature (kPa), and
µdewmon is the average dew point temperature for the month (°C).
4.3.2 GENERATED DAILY VALUE The triangular distribution used to generate daily relative humidity values requires four inputs: mean monthly relative humidity, maximum relative humidity value allowed in month, minimum relative humidity value allowed in month, and a random number between 0.0 and 1.0. The maximum relative humidity value, or upper limit of the triangular distribution, is calculated from the mean monthly relative humidity with the equation: RhUmon = Rhmon + (1 − Rhmon ) ⋅ exp(Rhmon − 1)
4.3.4
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
77
where RhUmon is the largest relative humidity value that can be generated on a given day in the month, and Rhmon is the average relative humidity for the month. The minimum relative humidity value, or lower limit of the triangular distribution, is calculated from the mean monthly relative humidity with the equation: RhLmon = Rhmon ⋅ (1 − exp(− Rhmon ))
4.3.5
where RhLmon is the smallest relative humidity value that can be generated on a given day in the month, and Rhmon is the average relative humidity for the month. The triangular distribution uses one of two sets of equations to generate a æ R − RhLmon ö ÷÷ then relative humidity value for the day. If rnd1 ≤ çç hmon è RhUmon − RhLmon ø Rh = RhLmon + [rnd1 ⋅ (RhUmon − RhLmon ) ⋅ (Rhmon − RhLmon )]
0.5
4.3.6
æ R − RhLmon ö ÷÷ then If rnd1 > çç hmon è RhUmon − RhLmon ø Rh = RhUmon − (RhUmon
éR (1 − rnd1 ) − RhLmon (1 − rnd1 )ù − Rhmon ) ⋅ ê hUmon ú RhUmon − Rhmon ë û
0.5
4.3.7
where Rh is the average relative humidity calculated for the day, rnd1 is a random number generated by the model each day, Rhmon is the average relative humidity for the month, RhLmon is the smallest relative humidity value that can be generated on a given day in the month, and RhUmon is the largest relative humidity value that can be generated on a given day in the month.
4.3.3 ADJUSTMENT FOR CLEAR/OVERCAST CONDITIONS To incorporate the effect of clear and overcast weather on generated values of relative humidity, monthly average relative humidity values can be adjusted for wet or dry conditions. The continuity equation relates average relative humidity adjusted for wet or dry conditions to the average relative humidity for the month: Rhmon ⋅ daystot = RhWmon ⋅ days wet + RhDmon ⋅ daysdry
4.3.8
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SWAT USER'S MANUAL, VERSION 2000
where Rhmon is the average relative humidity for the month, daystot are the total number of days in the month, RhWmon is the average relative humidity for the month on wet days, dayswet are the number of wet days in the month, RhDmon is the average relative humidity of the month on dry days, and daysdry are the number of dry days in the month. The wet day average relative humidity is assumed to be greater than the dry day average relative humidity by some fraction: RhWmon = RhDmon + bH ⋅ (1 − RhDmon )
4.3.9
where RhWmon is the average relative humidity of the month on wet days, RhDmon is the average relative humidity of the month on dry days, and bH is a scaling factor that controls the degree of deviation in relative humidity caused by the presence or absence of precipitation. The scaling factor, bH, is set to 0.9 in SWAT. To calculate the dry day relative humidity, equations 4.3.8 and 4.3.9 are combined and solved for RhDmon: RhDmon
æ days wet = çç Rhmon − bH ⋅ daystot è
ö æ days wet ÷÷ ⋅ çç 1.0 − bH ⋅ daystot ø è
ö ÷÷ ø
−1
4.3.10
To reflect the impact of wet or dry conditions, SWAT will replace Rhmon with RhWmon on wet days or RhDmon on dry days in equations 4.3.4 through 4.3.7. Table 4-3: SWAT input variables that pertain to generation of relative humidity. Variable name Definition RHSIM Relative humidity input code: 1-measured, 2-generated TMPMN(mon) µmnmon: average minimum air temperature for month (°C) TMPMX(mon) µmxmon: average maximum air temperature for month (°C) DEWPT(mon) µdewmon: average dew point temperature for month (°C) PCPD(mon) dayswet: average number of days of precipitation in month
File Name .cod .wgn .wgn .wgn .wgn
4.4 MAXIMUM HALF-HOUR RAINFALL Maximum half-hour rainfall is required by SWAT to calculate the peak flow rate for runoff. When daily precipitation data is used by the model, the maximum half-hour rainfall is calculated from a triangular distribution using monthly maximum half-hour rainfall data. The maximum half-hour rainfall is calculated only on days where surface runoff has been generated.
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
79
4.4.1 MONTHLY MAXIMUM HALF-HOUR RAIN For each month, users provide the maximum half-hour rain observed over the entire period of record. These extreme values are used to calculate representative monthly maximum half-hour rainfall fractions. Prior to calculating the representative maximum half-hour rainfall fraction for each month, the extreme half-hour rainfall values are smoothed by calculating three month average values: R0.5sm ( mon ) =
R0.5 x ( mon −1) + R0.5 x ( mon ) + R0.5 x ( mon +1)
4.4.1
3
where R0.5sm(mon) is the smoothed maximum half-hour rainfall for a given month (mm H2O) and R0.5x is the extreme maximum half-hour rainfall for the specified month (mm H2O). Once the smoothed maximum half-hour rainfall is known, the representative half-hour rainfall fraction is calculated using the equation:
α 0.5mon = adj0.5α
é æ ç ê R0.5 sm ( mon ) ç ⋅ ê1 − expç ê 0.5 ç µ mon ⋅ ln æç ê ç yrs ⋅ days ç êë wet è è
öù ÷ú ÷ú ö ÷÷ú ÷÷ ÷ú ø øúû
4.4.2
where α0.5mon is the average half-hour rainfall fraction for the month, adj0.5α is an adjustment factor, R0.5sm is the smoothed half-hour rainfall amount for the month (mm H2O), µmon is the mean daily rainfall (mm H2O) for the month, yrs is the number of years of rainfall data used to obtain values for monthly extreme halfhour rainfalls, and dayswet are the number of wet days in the month. The adjustment factor is included to allow users to modify estimations of half-hour rainfall fractions and peak flow rates for runoff.
4.4.2 GENERATED DAILY VALUE The triangular distribution used to generate the maximum half-hour rainfall fraction requires four inputs: average monthly half-hour rainfall fraction, maximum value for half-hour rainfall fraction allowed in month, minimum value for half-hour rainfall fraction allowed in month, and a random number between 0.0 and 1.0.
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SWAT USER'S MANUAL, VERSION 2000
The maximum half-hour rainfall fraction, or upper limit of the triangular distribution, is calculated from the daily amount of rainfall with the equation: æ − 125 ö ÷ α 0.5U = 1 − expçç ÷ + 5 R è day ø
4.4.3
where α0.5U is the largest half-hour fraction that can be generated on a given day, and Rday is the precipitation on a given day (mm H2O). The minimum half-hour fraction, or lower limit of the triangular distribution, α0.5L, is set at 0.02083. The triangular distribution uses one of two sets of equations to generate a æα − α 0.5 L ö ÷÷ then maximum half-hour rainfall fraction for the day. If rnd1 ≤ çç 0.5mon è α 0.5U − α 0.5 L ø
α 0.5 = α 0.5 L + [rnd1 ⋅ (α 0.5U − α 0.5 L ) ⋅ (α 0.5mon − α 0.5 L )]0.5
4.4.4
æα − α 0.5 L ö ÷÷ then If rnd1 > çç 0.5mon − α α 0.5 L ø è 0.5U
α 0.5 = α 0.5U − (α 0.5U
é α (1 − rnd 1 ) − α 0.5 L (1 − rnd 1 )ù − α 0.5mon ) ⋅ ê 0.5U ú α 0.5U − α 0.5mon ë û
0.5
4.4.5
where α0.5 is the maximum half-hour rainfall fraction for the day, α0.5mon is the average maximum half-hour rainfall fraction for the month, rnd1 is a random number generated by the model each day, α0.5L is the smallest half-hour rainfall fraction that can be generated, and α0.5U is the largest half-hour fraction that can be generated. Table 4-4: SWAT input variables that pertain to generation of maximum half-hour rainfall. Variable name Definition RAINHHMX(mon) R0.5x: extreme half-hour rainfall for month (mm H2O) APM adj0.5α: peak rate adjustment factor PCPMM(mon) average amount of precipitation falling in month (mm H2O) PCPD(mon) dayswet: average number of days of precipitation in month (µmon = PCPMM / PCPD) RAIN_YRS yrs: number of years of data used to obtain values for RAINHHMX PRECIPITATION Rday: amount of rain falling on a given day (mm H2O)
File Name .wgn .bsn .wgn .wgn .wgn .pcp
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81
4.5 WIND SPEED Wind speed is required by SWAT when the Penman-Monteith equation is used to calculate potential evapotranspiration. Mean daily wind speed is generated in SWAT using a modified exponential equation:
µ10 m = µwnd mon ⋅ (− ln(rnd1 ))0.3
4.5.1
where µ10m is the mean wind speed for the day (m s-1), µwndmon is the average wind speed for the month (m s-1), and rnd1 is a random number between 0.0 and 1.0. Table 4-5: SWAT input variables that pertain to generation of wind speed. Variable name Definition WNDSIM Wind speed input code: 1-measured, 2-generated WNDAV(mon) µwndmon: Average wind speed in month (m/s)
File Name .cod .wgn
4.6 NOMENCLATURE 3 × 3 matrix of elements defined to ensure serial and cross correlation of −1 generated temperature and radiation values A = M 1 ⋅ M 0 3 × 3 matrix of elements defined to ensure serial and cross correlation of B −1 T generated temperature and radiation values B ⋅ B T = M 0 − M 1 ⋅ M 0 ⋅ M 1 Hday Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) HMX Maximum possible solar radiation (MJ m-2 d-1) M0 3 × 3 matrix of correlation coefficients between maximum temperature, minimum temperature and solar radiation on same day M1 3 × 3 matrix of correlation coefficients between maximum temperature, minimum temperature and solar radiation on consecutive days Pi(D/D) Probability of a dry day on day i given a dry day on day i – 1 Pi(D/W) Probability of a dry day on day i given a wet day on day i – 1 Pi(W/D) Probability of a wet day on day i given a dry day on day i – 1 Pi(W/W) Probability of a wet day on day i given a wet day on day i – 1 R0.5sm Smoothed maximum half-hour rainfall for a given month (mm H2O) R0.5x Extreme maximum half-hour rainfall for the specified month (mm H2O) Rday Amount of rainfall on a given day (mm H2O) Rh Average relative humidity for the day RhDmon Average relative humidity of the month on dry days A
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SWAT USER'S MANUAL, VERSION 2000
RhLmon Smallest relative humidity value that can be generated on a given day in the month RhUmon Largest relative humidity value that can be generated on a given day in the month RhWmon Average relative humidity for the month on wet days Rhmon Average relative humidity for the month SNDday Standard normal deviate for the day Tmn Minimum air temperature for day (°C) Tmx Maximum air temperature for day (°C) adj0.5α Peak rate adjustment factor bH Scaling factor that controls the degree of deviation in relative humidity caused by the presence or absence of precipitation bR Scaling factor that controls the degree of deviation in solar radiation caused by the presence or absence of precipitation bT Scaling factor that controls the degree of deviation in temperature caused by the presence or absence of precipitation daysdry Number of dry days in the month daystot Total number of days in the month dayswet Number of wet days in the month emon Actual vapor pressure at the mean monthly temperature (kPa) o emon Saturation vapor pressure at the mean monthly temperature (kPa) gmon Skew coefficient for daily precipitation in the month rexp Exponent for exponential precipitation distribution rnd1 Random number between 0.0 and 1.0 rnd2 Random number between 0.0 and 1.0 yrs Number of years of rainfall data used to obtain values for monthly extreme halfhour rainfalls
α0.5 Maximum half-hour rainfall expressed as a fraction of daily rainfall α0.5L Smallest half-hour rainfall fraction that can be generated on a given day α0.5mon Average maximum half-hour rainfall fraction for the month α0.5U Largest half-hour rainfall fraction that can be generated on a given day εi 3 × 1 matrix of independent random components σmon Standard deviation of daily rainfall (mm H2O) for the month σmnmonStandard deviation for daily minimum temperature during the month (°C) σmxmon Standard deviation for daily maximum temperature during the month (°C) σradmon Standard deviation for daily solar radiation during the month (MJ m-2) ρ0(j,k) Correlation coefficient between variables j and k on the same day where j and k may be set to 1 (maximum temperature), 2 (minimum temperature) or 3 (solar radiation) ρ1(j,k) Correlation coefficient between variable j and k with variable k lagged one day with respect to variable j µmon Mean daily rainfall (mm H2O) for the month µDmxmon Average daily maximum temperature of the month on dry days (°C) µDradmon Average daily solar radiation of the month on dry days (MJ m-2)
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83
µWmxmonAverage daily maximum temperature of the month on wet days (°C) µWradmon Average daily solar radiation of the month on wet days (MJ m-2) µdewmon Average dew point temperature for the month (°C) µmnmon Average daily minimum temperature for the month (°C) µmxmon Average daily maximum temperature for the month (°C) µradmon Average daily solar radiation for the month (MJ m-2) µtmpmon Mean air temperature for the month (°C) µwndmon Average wind speed for the month (m s-1) µ10m Mean wind speed for the day at height of 10 meters (m s-1) χi(j) 3 × 1 matrix for day i whose elements are residuals of maximum temperature (j = 1), minimum temperature (j = 2) and solar radiation (j = 3),
4.7 REFERENCES Fiering, M.B. 1967. Streamflow synthesis. Harvard University Press, Cambridge. Matalas, N.C. 1967. Mathematical assessment of synthetic hydrology. Water Resources Res. 3(4):937-945. Nicks, A.D. 1974. Stochastic generation of the occurrence, pattern, and location of maximum amount of daily rainfall. p. 154-171. In Proc. Symp. Statistical Hydrology, Aug.-Sept. 1971, Tuscon, AZ. U.S. Department of Agriculture, Misc. Publ. No. 1275. Richardson, C.W. 1982. Dependence structure of daily temperature and solar radiation. Trans. ASAE 25(3):735-739. Richardson, C.W. 1981. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Res. 17(1):182-190. Richardson, C.W. and D.A. Wright. 1984. WGEN: a model for generating daily weather variables. U.S. Department of Agriculture, Agricultural Research Service, ARS-8. Sharpley, A.N. and J.R. Williams, eds. 1990. EPIC-Erosion Productivity Impact Calculator, 1. model documentation. U.S. Department of Agriculture, Agricultural Research Service, Tech. Bull. 1768. Williams, J.R. 1995. Chapter 25. The EPIC Model. p. 909-1000. In Computer Models of Watershed Hydrology. Water Resources Publications. Highlands Ranch, CO.
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CHAPTER 5
EQUATIONS: CLIMATE CUSTOMIZATION
SWAT is capable of simulating orographic impacts on temperature and precipitation for watersheds in mountainous regions. The model will also modify climate inputs for simulations that are looking at the impact of climatic change in a given watershed.
85
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SWAT USER'S MANUAL, VERSION 2000
5.1 ELEVATION BANDS Orographic precipitation is a significant phenomenon in certain areas of the world. To account for orographic effects on both precipitation and temperature, SWAT allows up to 10 elevation bands to be defined in each subbasin. Precipitation and maximum and minimum temperatures are calculated for each band as a function of the respective lapse rate and the difference between the gage elevation and the average elevation specified for the band. For precipitation, Rband = Rday + (ELband − ELgage ) ⋅
plaps 1000
when Rday > 0.01
5.1.1
where Rband is the precipitation falling in the elevation band (mm H2O), Rday is the precipitation recorded at the gage or generated from gage data (mm H2O), ELband is the mean elevation in the elevation band (m), ELgage is the elevation at the recording gage (m), plaps is the precipitation lapse rate (mm H2O/km), and 1000 is a factor needed to convert meters to kilometers. For temperature, Tmx ,band = Tmx + (ELband − ELgage ) ⋅
tlaps 1000
5.1.2
Tmn ,band = Tmn + (ELband − ELgage ) ⋅
tlaps 1000
5.1.3
T av ,band = T av + (ELband − ELgage ) ⋅
tlaps 1000
5.1.4
where Tmx,band is the maximum daily temperature in the elevation band (°C), Tmn,band is the minimum daily temperature in the elevation band (°C), T av ,band is the mean daily temperature in the elevation band (°C), Tmx is the maximum daily temperature recorded at the gage or generated from gage data (°C), Tmn is the minimum daily temperature recorded at the gage or generated from gage data (°C), T av is the mean daily temperature recorded at the gage or generated from gage data (°C), ELband is the mean elevation in the elevation band (m), ELgage is the elevation at the recording gage (m), tlaps is the temperature lapse rate (°C/km), and 1000 is a factor needed to convert meters to kilometers.
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87
Once the precipitation and temperature values have been calculated for each elevation band in the subbasin, new average subbasin precipitation and temperature values are calculated: Rday = Tmx = Tmn = T av =
b
åR
band
bnd =1
⋅ frbnd
b
åT
bnd =1
mx ,band
b
åT
bnd =1
mn ,band
b
åT
bnd =1
av ,band
5.1.5
⋅ frbnd
5.1.6
⋅ frbnd
5.1.7
⋅ frbnd
5.1.8
where Rday is the daily average precipitation adjusted for orographic effects (mm H2O), Tmx is the daily maximum temperature adjusted for orographic effects (°C),
Tmn is the daily minimum temperature adjusted for orographic effects (°C), T av is the daily mean temperature adjusted for orographic effects (°C), Rband is the precipitation falling in elevation band bnd (mm H2O), Tmx,band is the maximum daily temperature in elevation band bnd (°C), Tmn,band is the minimum daily temperature in elevation band bnd (°C), T av ,band is the mean daily temperature in elevation band bnd (°C), frbnd is the fraction of subbasin area within the elevation band, and b is the total number of elevation bands in the subbasin. The only processes modeled separately for each individual elevation band are the accumulation, sublimation and melting of snow. As with the initial precipitation and temperature data, after amounts of sublimation and snow melt are determined for each elevation band, subbasin average values are calculated. These average values are the values that are used in the remainder of the simulation and reported in the output files.
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Table 5-1: SWAT input variables that pertain to orographic effects. Variable Name ELEVB ELEVB_FR WELEV ELEVATION ELEVATION PLAPS TLAPS PRECIPITATION
MAX TEMP MIN TEMP
Definition ELband: Elevation at center of the elevation band (m) frbnd: Fraction of subbasin area within the elevation band. ELgage: Elevation of recording gage whose data is used to calculate values in .wgn file (m) ELgage: Elevation of precipitation recording gage (m) ELgage: Elevation of temperature recording gage (m) plaps: Precipitation lapse rate (mm H2O/km) tlaps: Temperature lapse rate (°C/km) Rday: Daily precipitation (mm H2O) Tmx: Daily maximum temperature (°C) Tmn: Daily minimum temperature (°C)
Input File .sub .sub .wgn .pcp .tmp .sub .sub .pcp .tmp .tmp
5.2 CLIMATE CHANGE The impact of global climate change on water supply is a major area of research. Climate change can be simulated with SWAT by manipulating the climatic input that is read into the model (precipitation, temperature, solar radiation, relative humidity, wind speed, potential evapotranspiration and weather generator parameters). A less time-consuming method is to set adjustment factors for the various climatic inputs. SWAT will allow users to adjust precipitation, temperature, solar radiation, relative humidity, and carbon dioxide levels in each subbasin. The alteration of precipitation, temperature, solar radiation and relative humidity are straightforward:
adj pcp ö æ ÷ Rday = Rday ⋅ çç1 + 100 ÷ø è
5.2.1
where Rday is the precipitation falling in the subbasin on a given day (mm H2O), and adjpcp is the % change in rainfall. Tmx = Tmx + adjtmp
5.2.2
where Tmx is the daily maximum temperature (°C), and adjtmp is the change in temperature (°C). Tmn = Tmn + adjtmp
5.2.3
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89
where Tmn is the daily minimum temperature (°C), and adjtmp is the change in temperature (°C). T av = T av + adjtmp
5.2.4
where T av is the daily mean temperature (°C), and adjtmp is the change in temperature (°C). H day = H day + adjrad
5.2.5
where Hday is the daily solar radiation reaching the earth’s surface (MJ m-2), and adjrad is the change in radiation (MJ m-2 d-1). Rh = Rh + adjhmd
5.2.6
where Rh is the relative humidity for the day expressed as a fraction, and adjhmd is the change in relative humidity expressed as a fraction. SWAT allows the adjustment terms to vary from month to month so that the user is able to simulate seasonal changes in climatic conditions. Changes in carbon dioxide levels impact plant growth. As carbon dioxide levels increase, plant productivity increases and plant water requirements go down. The equations used to account for the impact of carbon dioxide levels on plant water requirements are reviewed in Chapters 7 and 18. When carbon dioxide climate change effects are being simulated, the Penman-Monteith equation must be used to calculate potential evapotranspiration. This method has been modified to account for CO2 impacts on potential evapotranspiration levels. Table 5-2: SWAT input variables that pertain to climate change. Variable Name RFINC(mon) TMPINC(mon) RADINC(mon) HUMINC(mon) CO2 IPET
Definition adjpcp: % change in rainfall for month adjtmp: increase or decrease in temperature for month (°C) adjrad: increase or decrease in solar radiation reaching earth’s surface for month (MJ m-2) adjhmd: increase or decrease in relative humidity for month CO2: carbon dioxide level in subbasin (ppmv) Potential evapotranspiration method
Input File .sub .sub .sub .sub .sub .cod
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5.3 NOMENCLATURE CO2 Concentration of carbon dioxide in the atmosphere (ppmv) ELband Mean elevation in the elevation band (m) ELgage Elevation at the precipitation, temperature, or weather generator data recording gage (m) Hday Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) Rband Precipitation falling in the elevation band (mm H2O) Rday Amount of rainfall on a given day (mm H2O) Rh Average relative humidity for the day Tmn Minimum air temperature for day (°C) Tmn,band Minimum daily temperature in the elevation band (°C) Tmx Maximum air temperature for day (°C) Tmx,band Maximum daily temperature in the elevation band (°C) T av Mean air temperature for day (°C) T av ,band Mean daily temperature in the elevation band (°C) adjhmd adjpcp adjrad adjtmp frbnd plaps tlaps
Change in relative humidity expressed as a fraction % change in rainfall Change in radiation (MJ m-2 d-1) Change in temperature (°C) Fraction of subbasin area within the elevation band Precipitation lapse rate (mm H2O/km) Temperature lapse rate (°C/km)
HYDROLOGY The land phase of the hydrologic cycle is based on the water balance equation: t
SWt = SW0 + å (Rday − Q surf − E a − w seep − Q gw ) i =1
where SWt is the final soil water content (mm H2O), SW0 is the initial soil water content (mm H2O), t is the time (days), Rday is the amount of precipitation on day i (mm H2O), Qsurf is the amount of surface runoff on day i (mm H2O), Ea is the amount of evapotranspiration on day i (mm H2O), wseep is the amount of percolation and bypass flow exiting the soil profile bottom on day i (mm H2O), and Qgw is the amount of return flow on day i (mm H2O).
CHAPTER 6
EQUATIONS: SURFACE RUNOFF
Surface runoff occurs whenever the rate of water application to the ground surface exceeds the rate of infiltration. When water is initially applied to a dry soil, the application rate and infiltration rates may be similar. However, the infiltration rate will decrease as the soil becomes wetter. When the application rate is higher than the infiltration rate, surface depressions begin to fill. If the application rate continues to be higher than the infiltration rate once all surface depressions have filled, surface runoff will commence. SWAT provides two methods for estimating surface runoff: the SCS curve number procedure (SCS, 1972) and the Green & Ampt infiltration method (1911). 93
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6.1 RUNOFF VOLUME: SCS CURVE NUMBER PROCEDURE The SCS runoff equation is an empirical model that came into common use in the 1950s. It was the product of more than 20 years of studies involving rainfall-runoff relationships from small rural watersheds across the U.S. The model was developed to provide a consistent basis for estimating the amounts of runoff under varying land use and soil types (Rallison and Miller, 1981). The SCS curve number equation is (SCS, 1972): Qsurf =
(R
(R
day
− Ia )
2
day
− Ia + S )
6.1.1
where Qsurf is the accumulated runoff or rainfall excess (mm H2O), Rday is the rainfall depth for the day (mm H2O), Ia is the initial abstractions which includes surface storage, interception and infiltration prior to runoff (mm H2O), and S is the retention parameter (mm H2O). The retention parameter varies spatially due to changes in soils, land use, management and slope and temporally due to changes in soil water content. The retention parameter is defined as: æ 1000 ö S = 25.4ç − 10 ÷ CN è ø
6.1.2
where CN is the curve number for the day. The initial abstractions, Ia, is commonly approximated as 0.2S and equation 6.1.1 becomes Qsurf
(R = (R
day day
− 0.2 S )
2
+ 0.8S )
6.1.3
Runoff will only occur when Rday > Ia. A graphical solution of equation 6.1.3 for different curve number values is presented in Figure 6-1.
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95
Figure 6-1: Relationship of runoff to rainfall in SCS curve number method.
6.1.1 SCS CURVE NUMBER The SCS curve number is a function of the soil’s permeability, land use and antecedent soil water conditions. Typical curve numbers for moisture condition II are listed in tables 6-1, 6-2 and 6-3 for various land covers and soil types (SCS Engineering Division, 1986). These values are appropriate for a 5% slope. Table 6-1: Runoff curve numbers for cultivated agricultural lands Cover Hydrologic Soil Group Land Use Fallow
Treatment or practice Bare soil Crop residue cover∗
Row crops
Straight row Straight row w/ residue Contoured Contoured w/ residue Contoured & terraced Contoured & terraced w/ residue
Small grains
Straight row Straight row w/ residue
∗
Hydrologic condition ---Poor
A 77 76
B 86 85
C 91 90
D 94 93
Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor
74 72 67 71 64 70 65 69 64 66 62 65 61 65 63 64
83 81 78 80 75 79 75 78 74 74 71 73 70 76 75 75
88 88 85 87 82 84 82 83 81 80 78 79 77 84 83 83
90 91 89 90 85 88 86 87 85 82 81 81 80 88 87 86
Crop residue cover applies only if residue is on at least 5% of the surface throughout the year.
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Table 6-1, cont.: Runoff curve numbers for cultivated agricultural lands Cover Hydrologic Soil Group Land Use
Hydrologic condition Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor Good
Treatment or practice Contoured Contoured w/ residue Contoured & terraced Contoured & terraced w/ residue
Close-seeded or broadcast legumes or rotation
Straight row Contoured Contoured & terraced
A 60 63 61 62 60 61 59 60 58 66 58 64 55 63 51
B 72 74 73 73 72 72 70 71 69 77 72 75 69 73 67
C 80 82 81 81 80 79 78 78 77 85 81 83 78 80 76
D 84 85 84 84 83 82 81 81 80 89 85 85 83 83 80
Table 6-2: Runoff curve numbers for other agricultural lands Cover Hydrologic Soil Group Hydrologic Cover Type condition Pasture, grassland, or range—continuous forage for grazing1 Poor Fair Good Meadow—continuous grass, protected from grazing and generally mowed for hay - - - Brush—brush-weed-grass mixture with brush the major element2 Poor Fair Good Woods—grass combination (orchard or tree farm) Poor Fair Good Woods3 Poor Fair Good Farmsteads—buildings, lanes, driveways, and surrounding lots. ----
A 68 49 39 30 48 35 30 57 43 32 45 36 30 59
B 79 69 61 58 67 56 48 73 65 58 66 60 55 74
C 86 79 74 71 77 70 65 82 76 72 77 73 70 82
D 89 84 80 78 83 77 73 86 82 79 83 79 77 86
Table 6-3: Runoff curve numbers for urban areas§ Cover Hydrologic Soil Group Cover Type Fully developed urban areas Open spaces (lawns, parks, golf courses, cemeteries, etc.)†
Hydrologic condition
Impervious areas: Paved parking lots, roofs, driveways, etc. (excl. right-of-way) Paved streets and roads; open ditches (incl. right-of-way) Gravel streets and roads (including right-of-way) Dirt streets and roads (including right-of way) 1
Average % impervious area
A
B
C
D
Poor Fair Good
68 49 39
79 69 61
86 79 74
89 84 80
-------------
98 83 76 72
98 89 85 82
98 92 89 87
98 93 91 89
Poor: < 50% ground cover or heavily grazed with no mulch; Fair: 50 to 75% ground cover and not heavily grazed; Good: > 75% ground cover and lightly or only occasionally grazed 2 Poor: < 50% ground cover; Fair: 50 to 75% ground cover; Good: > 75% ground cover 3 Poor: Forest litter, small trees, and brush are destroyed by heavy grazing or regular burning; Fair: Woods are grazed but not burned, and some forest litter covers the soil; Good: Woods are protected from grazing, and litter and brush adequately cover the soil. § SWAT will automatically adjust curve numbers for impervious areas when IURBAN and URBLU are defined in the .hru file. Curve numbers from Table 6-3 should not be used in this instance. † Poor: grass cover < 50%; Fair: grass cover 50 to 75%; Good: grass cover > 75%
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97
Table 6-3, continued: Runoff curve number for urban areas Cover Hydrologic Soil Group Cover Type Urban districts: Commercial and business Industrial Residential Districts by average lot size: 1/8 acre (0.05 ha) or less (town houses) 1/4 acre (0.10 ha) 1/3 acre (0.13 ha) 1/2 acre (0.20 ha) 1 acre (0.40 ha) 2 acres (0.81 ha) Developing urban areas: Newly graded areas (pervious areas only, no vegetation)
Hydrologic condition
Average % impervious area
A
B
C
D
85% 72%
89 81
92 88
94 91
95 93
65% 38% 30% 25% 20% 12%
77 61 57 54 51 46
85 75 72 70 68 65
90 83 81 80 79 77
92 87 86 85 84 82
77
86
91
94
6.1.1.1 SOIL HYDROLOGIC GROUPS The U.S. Natural Resource Conservation Service (NRCS) classifies soils into four hydrologic groups based on infiltration characteristics of the soils. NRCS Soil Survey Staff (1996) defines a hydrologic group as a group of soils having similar runoff potential under similar storm and cover conditions. Soil properties that influence runoff potential are those that impact the minimum rate of infiltration for a bare soil after prolonged wetting and when not frozen. These properties are depth to seasonally high water table, saturated hydraulic conductivity, and depth to a very slowly permeable layer. Soil may be placed in one of four groups, A, B, C, and D, or three dual classes, A/D, B/D, and C/D. Definitions of the classes are: A: (Low runoff potential). The soils have a high infiltration rate even when thoroughly wetted. They chiefly consist of deep, well drained to excessively drained sands or gravels. They have a high rate of water transmission. B: The soils have a moderate infiltration rate when thoroughly wetted. They chiefly are moderately deep to deep, moderately well-drained to well-drained soils that have moderately fine to moderately coarse textures. They have a moderate rate of water transmission. C: The soils have a slow infiltration rate when thoroughly wetted. They chiefly have a layer that impedes downward movement of water or have moderately fine to fine texture. They have a slow rate of water transmission.
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D. (High runoff potential). The soils have a very slow infiltration rate when thoroughly wetted. They chiefly consist of clay soils that have a high swelling potential, soils that have a permanent water table, soils that have a claypan or clay layer at or near the surface, and shallow soils over nearly impervious material. They have a very slow rate of water transmission. Dual hydrologic groups are given for certain wet soils that can be adequately drained. The first letter applies to the drained condition, the second to the undrained. Only soils that are rated D in their natural condition are assigned to dual classes. A summary of U.S. soils and their hydrologic group is given in Appendix D.
6.1.1.2 ANTECEDENT SOIL MOISTURE CONDITION SCS defines three antecedent moisture conditions: I—dry (wilting point), II—average moisture, and III—wet (field capacity). The moisture condition I curve number is the lowest value the daily curve number can assume in dry conditions. The curve numbers for moisture conditions I and III are calculated with the equations: CN 1 = CN 2 −
20 ⋅ (100 − CN 2 ) (100 − CN 2 + exp[2.533 − 0.0636 ⋅ (100 − CN 2 )])
CN 3 = CN 2 ⋅ exp[0.00673 ⋅ (100 − CN 2 )]
6.1.4 6.1.5
where CN1 is the moisture condition I curve number, CN2 is the moisture condition II curve number, and CN3 is the moisture condition III curve number. The retention parameter varies with soil profile water content according to the following equation:
æ ö SW ÷÷ S = S max ⋅ çç1 − è [SW + exp(w1 − w2 ⋅ SW )] ø
6.1.6
where S is the retention parameter for a given moisture content (mm), Smax is the maximum value the retention parameter can achieve on any given day (mm), SW is the soil water content of the entire profile excluding the amount of water held in the profile at wilting point (mm H2O), and w1 and
CHAPTER 6: EQUATIONS—SURFACE RUNOFF
99
w2 are shape coefficients. The maximum retention parameter value, Smax, is calculated by solving equation 6.1.2 using CN1. The shape coefficients are determined by solving equation 6.1.6 assuming that 1) the retention parameter for moisture condition I curve number corresponds to wilting point soil profile water content, 2) the retention parameter for moisture condition III curve number corresponds to field capacity soil profile water content, and 3) the soil has a curve number of 99 (S = 2.54) when completely saturated. é ù FC w1 = ln ê − FC ú + w2 ⋅ FC −1 ë1 − S 3 ⋅ S max û
6.1.7
æ é ù é ùö FC SAT ç ln ê − − − FC SAT ln ú ê ú ÷÷ −1 ç 1 − S ⋅ S −1 − ⋅ S 1 2 . 54 3 max max ë û ë ûø w2 = è 6.1.8 (SAT − FC )
where w1 is the first shape coefficient, w2 is the second shape coefficient, FC is the amount of water in the soil profile at field capacity (mm H2O), S3 is the retention parameter for the moisture condition III curve number, Smax is the retention parameter for the moisture condition I curve number, SAT is the amount of water in the soil profile when completely saturated (mm H2O), and 2.54 is the retention parameter value for a curve number of 99. When the top layer of the soil is frozen, the retention parameter is modified using the following equation: S frz = S max ⋅ [1 − exp(− 0.000862 ⋅ S )]
6.1.9
where Sfrz is the retention parameter adjusted for frozen conditions (mm), Smax is the maximum value the retention parameter can achieve on any given day (mm), and S is the retention parameter for a given moisture content calculated with equation 6.1.6 (mm).
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SWAT USER'S MANUAL, VERSION 2000
The daily curve number value adjusted for moisture content is calculated by rearranging equation 6.1.2 and inserting the retention parameter calculated for that moisture content: CN =
25400 (S + 254)
6.1.10
where CN is the curve number on a given day and S is the retention parameter calculated for the moisture content of the soil on that day.
6.1.1.3 SLOPE ADJUSTMENTS The moisture condition II curve numbers provided in the tables are assumed to be appropriate for 5% slopes. Williams (1995) developed an equation to adjust the curve number to a different slope: CN 2 s =
(CN 3 − CN 2 ) ⋅ [1 − 2 ⋅ exp(− 13.86 ⋅ slp )] + CN 3
2
6.1.11
where CN2s is the moisture condition II curve number adjusted for slope, CN3 is the moisture condition III curve number for the default 5% slope, CN2 is the moisture condition II curve number for the default 5% slope, and slp is the average percent slope of the subbasin. SWAT does not adjust curve numbers for slope. If the user wishes to adjust the curve numbers for slope effects, the adjustment must be done prior to entering the curve numbers in the management input file. Table 6-1: SWAT input variables that pertain to surface runoff calculated with the SCS curve number method. Input Variable Name Definition File IEVENT Rainfall, runoff, routing option. .cod PRECIPITATION Rday: Daily precipitation (mm H2O) .pcp CN2 CN2: Moisture condition II curve number .mgt CNOP CN2: Moisture condition II curve number .mgt
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101
6.2 RUNOFF VOLUME: GREEN & AMPT INFILTRATION METHOD The Green & Ampt equation was developed to predict infiltration assuming excess water at the surface at all times (Green & Ampt, 1911). The equation assumes that the soil profile is homogenous and antecedent moisture is uniformly distributed in the profile. As water infiltrates into the soil, the model assumes the soil above the wetting front is completely saturated and there is a sharp break in moisture content at the wetting front. Figure 6-2 graphically illustrates the difference between the moisture distribution with depth modeled by the Green & Ampt equation and what occurs in reality.
Figure 6-2: Comparison of moisture content distribution modeled by Green & Ampt and a typical observed distribution.
Mein and Larson (1973) developed a methodology for determining ponding time with infiltration using the Green & Ampt equation. The GreenAmpt Mein-Larson excess rainfall method was incorporated into SWAT to provide an alternative option for determining surface runoff. This method requires sub-daily precipitation data supplied by the user. The Green-Ampt Mein-Larson infiltration rate is defined as: æ Ψwf ⋅ ∆θ v f inf ,t = K e ⋅ ç1 + ç Finf ,t è
ö ÷ ÷ ø
6.2.1
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SWAT USER'S MANUAL, VERSION 2000
where finf is the infiltration rate at time t (mm/hr), Ke is the effective hydraulic conductivity (mm/hr), Ψwf is the wetting front matric potential (mm), ∆θv is the change in volumetric moisture content across the wetting front (mm/mm) and Finf is the cumulative infiltration at time t (mm H2O). When the rainfall intensity is less than the infiltration rate, all the rainfall will infiltrate during the time period and the cumulative infiltration for that time period is calculated: Finf ,t = Finf ,t −1 + R∆t
6.2.2
where and Finf,t is the cumulative infiltration for a given time step (mm H2O), Finf,t-1 is the cumulative infiltration for the previous time step (mm H2O), and R∆t is the amount of rain falling during the time step (mm H2O). The infiltration rate defined by equation 6.2.1 is a function of the infiltrated volume, which in turn is a function of the infiltration rates in previous time steps. To avoid numerical errors over long time steps, finf is replaced by dFinf dt in equation 6.2.1 and integrated to obtain
é Finf ,t + Ψwf ⋅ ∆θ v ù Finf ,t = Finf ,t −1 + K e ⋅ ∆t + Ψwf ⋅ ∆θ v ⋅ ln ê ú êë Finf ,t −1 + Ψwf ⋅ ∆θ v úû
6.2.3
Equation 6.2.3 must be solved iteratively for Finf,t, the cumulative infiltration at the end of the time step. A successive substitution technique is used. The Green-Ampt effective hydraulic conductivity parameter, Ke, is approximately equivalent to one-half the saturated hydraulic conductivity of the soil, Ksat (Bouwer, 1969). Nearing et al. (1996) developed an equation to calculate the effective hydraulic conductivity as a function of saturated hydraulic conductivity and curve number. This equation incorporates land cover impacts into the calculated effective hydraulic conductivity. The equation for effective hydraulic conductivity is: 56.82 ⋅ K sat −2 1 + 0.051 ⋅ exp(0.062 ⋅ CN ) 0.286
Ke =
6.2.4
where Ke is the effective hydraulic conductivity (mm/hr), Ksat is the saturated hydraulic conductivity (mm/hr), and CN is the curve number.
CHAPTER 6: EQUATIONS—SURFACE RUNOFF
103
Wetting front matric potential, Ψwf, is calculated as a function of porosity, percent sand and percent clay (Rawls and Brakensiek, 1985):
[
Ψwf = 10 ⋅ exp 6.5309 − 7.32561 ⋅ φ soil + 0.001583 ⋅ mc + 3.809479 ⋅ φ soil + 2
2
0.000344 ⋅ ms ⋅ mc − 0.049837 ⋅ ms ⋅ φ soil + 0.001608 ⋅ ms ⋅ φ soil + 2
2
0.001602 ⋅ mc ⋅ φ soil − 0.0000136 ⋅ ms ⋅ mc − 0.003479 ⋅ mc ⋅ φ soil − 2
0.000799 ⋅ ms ⋅ φ soil 2
2
2
2
]
6.2.5
where φsoil is the porosity of the soil (mm/mm), mc is the percent clay content, and ms is the percent sand content. For each time step, SWAT calculates the amount of water entering the soil. The water that does not infiltrate into the soil becomes surface runoff. Table 6-2: SWAT input variables that pertain to Green & Ampt infiltration calculations. Variable Name IEVENT IDT PRECIPITATION SOL_K CN2 CNOP SOL_BD CLAY SAND
Definition Rainfall, runoff, routing option. Length of time step (min): ∆t=IDT/60 R∆t: Precipitation during time step (mm H2O) Ksat: Saturated hydraulic conductivity of first layer (mm/hr) CN: Moisture condition II curve number CN: Moisture condition II curve number ρb: Moist bulk density (Mg/m3): φsoil=1 - ρb / 2.65 mc: % clay content ms: % sand content
Input File .cod .cod .pcp .sol .mgt .mgt .sol .sol .sol
6.3 PEAK RUNOFF RATE The peak runoff rate is the maximum runoff flow rate that occurs with a given rainfall event. The peak runoff rate is an indicator of the erosive power of a storm and is used to predict sediment loss. SWAT calculates the peak runoff rate with a modified rational method. The rational method is widely used in the design of ditches, channels and storm water control systems. The rational method is based on the assumption that if a rainfall of intensity i begins at time t = 0 and continues indefinitely, the rate of runoff will increase until the time of concentration, t = tconc, when the entire subbasin area is contributing to flow at the outlet. The rational formula is:
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SWAT USER'S MANUAL, VERSION 2000
q peak =
C ⋅ i ⋅ Area 3 .6
6.3.1
where qpeak is the peak runoff rate (m3 s-1), C is the runoff coefficient, i is the rainfall intensity (mm/hr), Area is the subbasin area (km2) and 3.6 is a unit conversion factor.
6.3.1 TIME OF CONCENTRATION The time of concentration is the amount of time from the beginning of a rainfall event until the entire subbasin area is contributing to flow at the outlet. In other words, the time of concentration is the time for a drop of water to flow from the remotest point in the subbasin to the subbasin outlet. The time of concentration is calculated by summing the overland flow time (the time it takes for flow from the remotest point in the subbasin to reach the channel) and the channel flow time (the time it takes for flow in the upstream channels to reach the outlet): t conc = tov + t ch
6.3.2
where tconc is the time of concentration for a subbasin (hr), tov is the time of concentration for overland flow (hr), and tch is the time of concentration for channel flow (hr).
6.3.1.1 OVERLAND FLOW TIME OF CONCENTRATION The overland flow time of concentration, tov, can be computed using the equation tov =
Lslp 3600 ⋅ vov
6.3.3
where Lslp is the subbasin slope length (m), vov is the overland flow velocity (m s-1) and 3600 is a unit conversion factor. The overland flow velocity can be estimated from Manning’s equation by considering a strip 1 meter wide down the sloping surface: vov =
qov
0.4
⋅ slp 0.3
n 0.6
6.3.4
CHAPTER 6: EQUATIONS—SURFACE RUNOFF
105
where qov is the average overland flow rate (m3 s-1), slp is the average slope in the subbasin (m m-1), and n is Manning’s roughness coefficient for the subbasin. Assuming an average flow rate of 6.35 mm/hr and converting units vov =
0.005 ⋅ Lslp
0.4
⋅ slp 0.3
n 0.6
6.3.5
Substituting equation 6.3.5 into equation 6.3.3 gives tov =
Lslp
0.6
⋅ n 0.6
18 ⋅ slp 0.3
6.3.6
Table 6-3: Values of Manning’s roughness coefficient, n, for overland flow (Engman, 1983). Characteristics of Land Surface Median Range Fallow, no residue 0.010 0.008-0.012 Conventional tillage, no residue 0.090 0.060-0.120 Conventional tillage, residue 0.190 0.160-0.220 Chisel plow, no residue 0.090 0.060-0.120 Chisel plow, residue 0.130 0.100-0.160 Fall disking, residue 0.400 0.300-0.500 No till, no residue 0.070 0.040-0.100 No till, 0.5-1 t/ha residue 0.120 0.070-0.170 No till, 2-9 t/ha residue 0.300 0.170-0.470 Rangeland, 20% cover 0.600 Short grass prairie 0.150 0.100-0.200 Dense grass 0.240 0.170-0.300 Bermudagrass 0.410 0.300-0.480
6.3.1.2 CHANNEL FLOW TIME OF CONCENTRATION The channel flow time of concentration, tch, can be computed using the equation: t ch =
Lc 3 .6 ⋅ v c
6.3.7
where Lc is the average flow channel length for the subbasin (km), vc is the average channel velocity (m s-1), and 3.6 is a unit conversion factor. The average channel flow length can be estimated using the equation Lc = L ⋅ Lcen
6.3.8
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where L is the channel length from the most distant point to the subbasin outlet (km), and Lcen is the distance along the channel to the subbasin centroid (km). Assuming Lcen = 0.5 ⋅ L , the average channel flow length is Lc = 0.71 ⋅ L
6.3.9
The average velocity can be estimated from Manning’s equation assuming a trapezoidal channel with 2:1 side slopes and a 10:1 bottom width-depth ratio. 0.489 ⋅ qch ⋅ slpch n 0.75 0.25
vc =
0.375
6.3.10
where vc is the average channel velocity (m s-1), qch is the average channel flow rate (m3 s-1), slpch is the channel slope (m m-1), and n is Manning’s roughness coefficient for the channel. To express the average channel flow rate in units of mm/hr, the following expression is used qch =
* qch ⋅ Area 3.6
6.3.11
* is the average channel flow rate (mm hr-1), Area is the subbasin where qch
area (km2), and 3.6 is a unit conversion factor. The average channel flow rate is related to the unit source area flow rate (unit source area = 1 ha) * qch = q0* ⋅ (100 ⋅ Area )
−0.5
6.3.12
where q0* is the unit source area flow rate (mm hr-1), Area is the subbasin area (km2), and 100 is a unit conversion factor. Assuming the unit source area flow rate is 6.35 mm/hr and substituting equations 6.3.11 and 6.3.12 into 6.3.10 gives 0.317 ⋅ Area 0.125 ⋅ slpch vc = n 0.75
0.375
6.3.13
Substituting equations 6.3.9 and 6.3.13 into 6.3.7 gives t ch =
0.62 ⋅ L ⋅ n 0.75 0.375 Area 0.125 ⋅ slpch
6.3.14
where tch is the time of concentration for channel flow (hr), L is the channel length from the most distant point to the subbasin outlet (km), n is
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Manning’s roughness coefficient for the channel, Area is the subbasin area (km2), and slpch is the channel slope (m m-1). Table 6-4: Values of Manning’s roughness coefficient, n, for channel flow (Chow, 1959).1 Characteristics of Channel Median Range Excavated or dredged Earth, straight and uniform 0.025 0.016-0.033 Earth, winding and sluggish 0.035 0.023-0.050 Not maintained, weeds and brush 0.075 0.040-0.140 Natural streams Few trees, stones or brush 0.050 0.025-0.065 Heavy timber and brush 0.100 0.050-0.150 1 Chow (1959) has a very extensive list of Manning’s roughness coefficients. These values represent only a small portion of those he lists in his book.
Although some of the assumptions used in developing equations 6.3.6 and 6.3.14 may appear liberal, the time of concentration values obtained generally give satisfactory results for homogeneous subbasins. Since equations 6.3.6 and 6.3.14 are based on hydraulic considerations, they are more reliable than purely empirical equations.
6.3.2 RUNOFF COEFFICIENT The runoff coefficient is the ratio of the inflow rate, i ⋅ Area , to the peak discharge rate, qpeak. The coefficient will vary from storm to storm and is calculated with the equation: C=
Q surf Rday
6.3.15
where Qsurf is the surface runoff (mm H2O) and Rday is the rainfall for the day (mm H2O).
6.3.3 RAINFALL INTENSITY The rainfall intensity is the average rainfall rate during the time of concentration. Based on this definition, it can be calculated with the equation: i=
Rtc t conc
6.3.16
where i is the rainfall intensity (mm/hr), Rtc is the amount of rain falling during the time of concentration (mm H2O), and tconc is the time of concentration for the subbasin (hr).
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An analysis of rainfall data collected by Hershfield (1961) for different durations and frequencies showed that the amount of rain falling during the time of concentration was proportional to the amount of rain falling during the 24-hr period. Rtc = α tc ⋅ Rday
6.3.17
where Rtc is the amount of rain falling during the time of concentration (mm H2O), αtc is the fraction of daily rainfall that occurs during the time of concentration, and Rday is the amount of rain falling during the day (mm H2O). For short duration storms, all or most of the rain will fall during the time of concentration, causing αtc to approach its upper limit of 1.0. The minimum value of αtc would be seen in storms of uniform intensity (i24 = i). This minimum value can be defined by substituting the products of time and rainfall intensity into equation 6.3.17
α tc ,min =
Rtc i ⋅ t conc t conc = = Rday i24 ⋅ 24 24
6.3.18
Thus, αtc falls in the range tconc/24 ≤ αtc ≤ 1.0. SWAT estimates the fraction of rain falling in the time of concentration as a function of the fraction of daily rain falling in the half-hour of highest intensity rainfall.
α tc = 1 − exp[2 ⋅ t conc ⋅ ln(1 − α 0.5 )]
6.3.19
where α0.5 is the fraction of daily rain falling in the half-hour highest intensity rainfall, and tconc is the time of concentration for the subbasin (hr). The determination of a value for α0.5 is discussed in Chapters 3 and 4.
6.3.4 MODIFIED RATIONAL FORMULA The modified rational formula used to estimate peak flow rate is obtained by substituting equations 6.3.15, 6.3.16, and 6.3.17 into equation 6.3.1 q peak =
α tc ⋅ Qsurf ⋅ Area 3.6 ⋅ t conc
6.3.20
where qpeak is the peak runoff rate (m3 s-1), αtc is the fraction of daily rainfall that occurs during the time of concentration, Qsurf is the surface runoff (mm H2O),
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Area is the subbasin area (km2), tconc is the time of concentration for the subbasin (hr) and 3.6 is a unit conversion factor. Table 6-5: SWAT input variables that pertain to peak rate calculations. Variable Name DA_KM HRU_FR SLSUBBSN SLOPE OV_N CH_L(1) CH_S(1) CH_N(1)
Definition Area of the watershed (km2) Fraction of total watershed area contained in HRU Lslp: Average slope length (m) slp: Average slope steepness (m/m) n: Manning’s “n” value for overland flow L: Longest tributary channel length in subbasin (km) slpch: Average slope of tributary channels (m/m) n: Manning’s “n” value for tributary channels
Input File .bsn .hru .hru .hru .hru .sub .sub .sub
6.4 SURFACE RUNOFF LAG In large subbasins with a time of concentration greater than 1 day, only a portion of the surface runoff will reach the main channel on the day it is generated. SWAT incorporates a surface runoff storage feature to lag a portion of the surface runoff release to the main channel. Once surface runoff is calculated with the curve number or Green & Ampt method, the amount of surface runoff released to the main channel is calculated: æ é − surlag ù ö ′ + Q stor ,i −1 ) ⋅ ç1 − exp ê Q surf = (Q surf ú ÷÷ ç t conc ûø ë è
6.4.1
where Qsurf is the amount of surface runoff discharged to the main channel on a ′ is the amount of surface runoff generated in the given day (mm H2O), Q surf
subbasin on a given day (mm H2O), Qstor,i-1 is the surface runoff stored or lagged from the previous day (mm H2O), surlag is the surface runoff lag coefficient, and tconc is the time of concentration for the subbasin (hrs).
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æ é − surlag ù ö The expression çç1 − exp ê ú ÷÷ in equation 6.4.1 represents the t conc ë ûø è
fraction of the total available water that will be allowed to enter the reach on any one day. Figure 6-3 plots values for this expression at different values for surlag and tconc.
Figure 6-3: Influence of surlag and tconc on fraction of surface runoff released.
Note that for a given time of concentration, as surlag decreases in value more water is held in storage. The delay in release of surface runoff will smooth the streamflow hydrograph simulated in the reach. Table 6-6: SWAT input variables that pertain to surface runoff lag calculations. Variable Name SURLAG
Definition surlag: surface runoff lag coefficient
Input File .bsn
6.5 TRANSMISSION LOSSES Many semiarid and arid watersheds have ephemeral channels that abstract large quantities of streamflow (Lane, 1982). The abstractions, or transmission losses, reduce runoff volume as the flood wave travels downstream. Chapter 19 of the SCS Hydrology Handbook (Lane, 1983) describes a procedure for estimating transmission losses for ephemeral streams which has been incorporated into SWAT. This method was developed to estimate transmission losses in the absence
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of observed inflow-outflow data and assumes no lateral inflow or out-of-bank flow contributions to runoff. The prediction equation for runoff volume after transmission losses is 0 ì volQsurf , f = í îa x + bx ⋅ volQsurf ,i
volQsurf ,i ≤ volthr volQsurf ,i > volthr
6.5.1
where volQsurf,f is the volume of runoff after transmission losses (m3), ax is the regression intercept for a channel of length L and width W (m3), bx is the regression slope for a channel of length L and width W, volQsurf,i is the volume of runoff prior to transmission losses (m3), and volthr is the threshold volume for a channel of length L and width W (m3). The threshold volume is vol thr = −
ax bx
6.5.2
The corresponding equation for peak runoff rate is q peak , f =
1 [ ( ) ] (3600 ⋅ durflw ) ⋅ a x − 1 − bx ⋅ volQsurf ,i + bx ⋅ q peak ,i
6.5.3
where qpeak,f is the peak rate after transmission losses (m3/s), durflw is the duration of flow (hr), ax is the regression intercept for a channel of length L and width W (m3), bx is the regression slope for a channel of length L and width W, volQsurf,i is the volume of runoff prior to transmission losses (m3), qpeak,i is the peak rate before accounting for transmission losses (m3/s). The duration of flow is calculated with the equation: dur flw =
Q surf ⋅ Area
3.6 ⋅ q peak
6.5.4
where durflw is the duration of runoff flow (hr), Qsurf is the surface runoff (mm H2O), Area is the area of the subbasin (km2), qpeak is the peak runoff rate (m3/s), and 3.6 is a conversion factor. In order to calculate the regression parameters for channels of differing lengths and widths, the parameters of a unit channel are needed. A unit channel is defined as a channel of length L = 1 km and width W = 1 m. The unit channel parameters are calculated with the equations:
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é K ch ⋅ dur flw ù k r = −2.22 ⋅ ln ê1 − 2.6466 ⋅ ú volQsurf ,i úû êë
6.5.5
a r = −0.2258 ⋅ K ch ⋅ durflw
6.5.6
br = exp[− 0.4905 ⋅ k r ]
6.5.7
where kr is the decay factor (m-1 km-1), ar is the unit channel regression intercept (m3), br is the unit channel regression slope, Kch is the effective hydraulic conductivity of the channel alluvium (mm/hr), durflw is the duration of runoff flow (hr), and volQsurf,i is the initial volume of runoff (m3). The regression parameters are bx = exp[− k r ⋅ L ⋅ W ]
6.5.8
ar ⋅ (1 − bx ) (1 − br )
6.5.9
ax =
where ax is the regression intercept for a channel of length L and width W (m3), bx is the regression slope for a channel of length L and width W, kr is the decay factor (m-1 km-1), L is the channel length from the most distant point to the subbasin outlet (km), W is the average width of flow, i.e. channel width (m) ar is the unit channel regression intercept (m3), and br is the unit channel regression slope. Transmission losses from surface runoff are assumed to percolate into the shallow aquifer. Table 6-7: SWAT input variables that pertain to transmission loss calculations. Variable Name DA_KM HRU_FR CH_K(1) CH_W(1) CH_L(1)
Definition Area of the watershed (km2) Fraction of total watershed area contained in HRU Kch: effective hydraulic conductivity (mm/hr) W: average width of tributary channel (m) L: Longest tributary channel length in subbasin (km)
Input File .bsn .hru .sub .sub .sub
CHAPTER 6: EQUATIONS—SURFACE RUNOFF
6.6 NOMENCLATURE
Kch Ke Ksat L Lc Lcen Lslp Qstor Qsurf R∆t Rday Rtc S S3 Sfrz Smax SAT SW W
Subbasin area (km2) Runoff coefficient in peak runoff rate calculation Curve number Moisture condition I curve number Moisture condition II curve number Moisture condition II curve number adjusted for slope Moisture condition III curve number Cumulative infiltration at time t (mm H2O) Water content of soil profile at field capacity (mm H2O) Initial abstractions which includes surface storage, interception and infiltration prior to runoff (mm H2O) Effective hydraulic conductivity of the channel alluvium (mm/hr) Effective hydraulic conductivity (mm/hr) Saturated hydraulic conductivity (mm/hr) Channel length from the most distant point to the subbasin outlet (km) Average flow channel length for the subbasin (km) Distance along the channel to the subbasin centroid (km) Subbasin slope length (m) Surface runoff stored or lagged (mm H2O) Accumulated runoff or rainfall excess (mm H2O) Amount of rain falling during the time step (mm H2O) Amount of rainfall on a given day (mm H2O) Amount of rain falling during the time of concentration (mm H2O) Retention parameter in SCS curve number equation (mm) Retention parameter for the moisture condition III curve number Retention parameter adjusted for frozen conditions (mm) Maximum value the retention parameter can achieve on any given day (mm) Amount of water in the soil profile when completely saturated (mm H2O), Amount of water in soil profile (mm H2O) Average width of flow, i.e. channel width (m)
ar ax br bx durflw finf i kr mc ms n q0*
Unit channel regression intercept (m3) Regression intercept for a channel of length L and width W (m3) Unit channel regression slope Regression slope for a channel of length L and width W Duration of flow (hr) Infiltration rate (mm/hr) Rainfall intensity (mm/hr) Decay factor (m-1 km-1) Percent clay content Percent sand content Manning’s roughness coefficient for the subbasin or channel Unit source area flow rate (mm hr-1)
Area C CN CN1 CN2 CN2s CN3 Finf FC Ia
113
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qch Average channel flow rate (m3 s-1) * qch Average channel flow rate (mm hr-1) qov Average overland flow rate (m3 s-1) qpeak Peak runoff rate (m3/s) qpeak,f Peak rate after transmission losses (m3/s) qpeak,i Peak rate before accounting for transmission losses (m3/s) slp Average slope of the subbasin (% or m/m) slpch Average channel slope (m m-1) surlag Surface runoff lag coefficient tch Time of concentration for channel flow (hr) tconc Time of concentration for a subbasin (hr) tov Time of concentration for overland flow (hr) vc Average channel velocity (m s-1) vov Overland flow velocity (m s-1) volQsurf,f Volume of runoff after transmission losses (m3) volQsurf,i Volume of runoff prior to transmission losses (m3) volthr Threshold volume for a channel of length L and width W (m3) w1 Shape coefficient in retention parameter adjustments for soil moisture content w2 Shape coefficient in retention parameter adjustments for soil moisture content
α0.5 αtc φsoil Ψwf θv
Fraction of daily rain falling in the half-hour highest intensity rainfall, Fraction of daily rainfall that occurs during the time of concentration Porosity of the soil (mm/mm) Wetting front matric potential (mm) Volumetric moisture content (mm/mm)
6.7 REFERENCES Bouwer, H. 1969. Infiltration of water into nonuniform soil. Journal Irrigation and Drainage Div., ASCE 95(IR4):451-462. Chow, V.T. 1959. Open-channel hydraulics. McGraw-Hill, New York. Engman, E.T. 1983. Roughness coefficients for routing surface runoff. Proc. Spec. Conf. Frontiers of Hydraulic Engineering. Green, W.H. and G.A. Ampt. 1911. Studies on soil physics, 1. The flow of air and water through soils. Journal of Agricultural Sciences 4:11-24. Hershfield, D.M. 1961. Rainfall frequency atlas of the United States for durations from 30 minutes to 24 hours and return periods from 1 to 100 years. U.S. Dept. Commerce Tech. Paper No. 40.
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Lane, L.J. 1983. Chapter 19: Transmission Losses. p.19-1–19-21. In Soil Conservation Service. National engineering handbook, section 4: hydrology. U.S. Government Printing Office, Washington, D.C. Lane, L.J. 1982. Distributed model for small semi-arid watersheds. J. Hydraulic Eng., ASCE, 108(HY10):1114-1131. Mein, R.G. and C.L. Larson. 1973. Modeling infiltration during a steady rain. Water Resources Research 9(2):384-394. Natural Resources Conservation Service Soil Survey Staff. 1996. National soil survey handbook, title 430-VI. U.S. Government Printing Office, Washington, D.C. Nearing, M.A., B.Y. Liu, L.M. Risse, and X. Zhang. 1996. Curve number and Green-Ampt effective hydraulic conductivities. Water Resources Bulletin 32:125-136. Rallison, R.E. and N. Miller. 1981. Past, present and future SCS runoff procedure. p. 353-364. In V.P. Singh (ed.). Rainfall runoff relationship. Water Resources Publication, Littleton, CO. Rawls, W.J. and D.L. Brakensiek. 1985. Prediction of soil water properties for hydrologic modeling. p. 293-299. In E.B. Jones and T.J. Ward (eds). Watershed management in the 80’s. ASCE, New York, N.Y. Soil Conservation Service. 1972. Section 4: Hydrology In National Engineering Handbook. SCS. Soil Conservation Service Engineering Division. 1986. Urban hydrology for small watersheds. U.S. Department of Agriculture, Technical Release 55. Williams, J.R. 1995. Chapter 25: The EPIC model. p. 909-1000. In V.P. Singh (ed). Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, CO.
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CHAPTER 7
EQUATIONS: EVAPOTRANSPIRATION
Evapotranspiration is a collective term that includes all processes by which water at the earth’s surface is converted to water vapor. It includes evaporation from the plant canopy, transpiration, sublimation and evaporation from the soil. Evapotranspiration is the primary mechanism by which water is removed from a watershed. Roughly 62% of the precipitation that falls on the continents is evapotranspired. Evapotranspiration exceeds runoff in most river basins and on all continents except Antarctica (Dingman, 1994). The difference between precipitation and evapotranspiration is the water available for human use and management. An accurate estimation of
117
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evapotranspiration is critical in the asssessment of water resources and the impact of climate and land use change on those resources.
7.1 CANOPY STORAGE The plant canopy can significantly affect infiltration, surface runoff and evapotranspiration. As rain falls, canopy interception reduces the erosive energy of droplets and traps a portion of the rainfall within the canopy. The influence the canopy exerts on these processes is a function of the density of plant cover and the morphology of the plant species. When calculating surface runoff, the SCS curve number method lumps canopy interception in the term for initial abstractions. This variable also includes surface storage and infiltration prior to runoff and is estimated as 20% of the retention parameter value for a given day (see Chapter 6). When the Green and Ampt infiltration equation is used to calculate surface runoff and infiltration, the interception of rainfall by the canopy must be calculated separately. SWAT allows the maximum amount of water that can be held in canopy storage to vary from day to day as a function of the leaf area index: can day = canmx ⋅
LAI LAI mx
7.1.1
where canday is the maximum amount of water that can be trapped in the canopy on a given day (mm H2O), canmx is the maximum amount of water that can be trapped in the canopy when the canopy is fully developed (mm H2O), LAI is the leaf area index for a given day, and LAImx is the maximum leaf area index for the plant. When precipitation falls on any given day, the canopy storage is filled before any water is allowed to reach the ground: ′ and Rday = 0 R INT ( f ) = RINT ( i ) + Rday ′ ≤ canday − R INT (i ) when Rday
7.1.2
′ − (canday − R INT (i ) ) RINT ( f ) = canday and Rday = Rday ′ > canday − R INT (i ) when Rday
7.1.3
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119
where RINT(i) is the initial amount of free water held in the canopy on a given day (mm H2O), RINT(f) is the final amount of free water held in the canopy on a given ′ is the amount of precipitation on a given day before canopy day (mm H2O), Rday
interception is removed (mm H2O), Rday is the amount of precipitation on a given day that reaches the soil surface (mm H2O), and canday is the maximum amount of water that can be trapped in the canopy on a given day (mm H2O). Table 7-1: SWAT input variables used in canopy storage calculations. Variable name Definition CANMX canmx: maximum canopy storage
File Name .hru
7.2 POTENTIAL EVAPOTRANSPIRATION Potential evapotranspiration (PET) was a concept originally introduced by Thornthwaite (1948) as part of a climate classification scheme. He defined PET is the rate at which evapotranspiration would occur from a large area uniformly covered with growing vegetation that has access to an unlimited supply of soil water and that was not exposed to advection or heat storage effects. Because the evapotranspiration rate is strongly influenced by a number of vegetative surface characteristics, Penman (1956) redefined PET as “the amount of water transpired ... by a short green crop, completely shading the ground, of uniform height and never short of water”. Penman used grass as his reference crop, but later researchers (Jensen, et al., 1990) have suggested that alfalfa at a height of 30 to 50 cm may be a more appropriate choice. Numerous methods have been developed to estimate PET. Three of these methods have been incorporated into SWAT: the Penman-Monteith method (Monteith, 1965; Allen, 1986; Allen et al., 1989), the Priestley-Taylor method (Priestley and Taylor, 1972) and the Hargreaves method (Hargreaves et al., 1985). The model will also read in daily PET values if the user prefers to apply a different potential evapotranspiration method. The three PET methods included in SWAT vary in the amount of required inputs. The Penman-Monteith method requires solar radiation, air temperature, relative humidity and wind speed. The Priestley-Taylor method requires solar
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radiation, air temperature and relative humidity. The Hargreaves method requires air temperature only.
7.2.1 PENMAN-MONTEITH METHOD The Penman-Monteith equation combines components that account for energy needed to sustain evaporation, the strength of the mechanism required to remove the water vapor and aerodynamic and surface resistance terms. The Penman-Monteith equation is:
λE =
[
]
∆ ⋅ (H net − G ) + ρ air ⋅ c p ⋅ e zo − ez ra ∆ + γ ⋅ (1 + rc ra )
7.2.1
where λE is the latent heat flux density (MJ m-2 d-1), E is the depth rate evaporation (mm d-1), ∆ is the slope of the saturation vapor pressure-temperature curve, de/dT (kPa ûC-1), Hnet is the net radiation (MJ m-2 d-1), G is the heat flux density to the ground (MJ m-2 d-1), ρair is the air density (kg m-3), cp is the specific heat at constant pressure (MJ kg-1 ûC-1), e zo is the saturation vapor pressure of air at height z (kPa), ez is the water vapor pressure of air at height z (kPa), γ is the psychrometric constant (kPa ûC-1), rc is the plant canopy resistance (s m-1), and ra is the diffusion resistance of the air layer (aerodynamic resistance) (s m-1). For well-watered plants under neutral atmospheric stability and assuming logarithmic wind profiles, the Penman-Monteith equation may be written (Jensen et al., 1990): ∆ ⋅ (H net − G ) + γ ⋅ K1 ⋅ (0.622 ⋅ λ ⋅ ρ air P ) ⋅ (e zo − ez ) ra λE t = ∆ + γ ⋅ (1 + rc ra )
7.2.2
where λ is the latent heat of vaporization (MJ kg-1), Et is the maximum transpiration rate (mm d-1), K1 is a dimension coefficient needed to ensure the two terms in the numerator have the same units (for uz in m s-1, K1 = 8.64 x 104), and P is the atmospheric pressure (kPa). The calculation of net radiation, Hnet, is reviewed in Chapter 2. The calculations for the latent heat of vaporization, λ, the slope of the saturation vapor
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pressure-temperature curve, ∆, the psychrometric constant, γ, and the saturation and actual vapor pressure, e zo and ez, are reviewed in Chapter 3. The remaining undefined terms are the soil heat flux, G, the combined term K10.622λρ/P, the aerodynamic resistance, ra, and the canopy resistance, rc.
7.2.1.1 SOIL HEAT FLUX Soil heat storage or release can be significant over a few hours, but is usually small from day to day because heat stored as the soil warms early in the day is lost when the soil cools late in the day or at night. Since the magnitude of daily soil heat flux over a 10- to 30-day period is small when the soil is under a crop cover, it can normally be ignored for most energy balance estimates. SWAT assumes the daily soil heat flux, G, is always equal to zero.
7.2.1.2 AERODYNAMIC RESISTANCE The aerodynamic resistance to sensible heat and vapor transfer, ra, is calculated: ra =
ln[(z w − d ) zom ]ln[(z p − d ) zov ] k 2uz
7.2.3
where zw is the height of the wind speed measurement (cm), zp is the height of the humidity (psychrometer) and temperature measurements (cm), d is the zero plane displacement of the wind profile (cm), zom is the roughness length for momentum transfer (cm), zov is the roughness length for vapor transfer (cm), k is the von Kármán constant, and uz is the wind speed at height zw (m s-1). The von Kármán constant is considered to be a universal constant in turbulent flow. Its value has been calculated to be near 0.4 with a range of 0.36 to 0.43 (Jensen et al., 1990). A value of 0.41 is used by SWAT for the von Kármán constant. Brutsaert (1975) determined that the surface roughness parameter, zo, is related to the mean height (hc) of the plant canopy by the relationship
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hc/zo = 3e or 8.15 where e is the natural log base. Based on this relationship, the roughness length for momentum transfer is estimated as: zom = hc 8.15 = 0.123 ⋅ hc
hc ≤ 200cm
7.2.4
zom = 0.058 ⋅ (hc )
hc > 200cm
7.2.5
1.19
where mean height of the plant canopy (hc) is reported in centimeters. The roughness length for momentum transfer includes the effects of bluff-body forces. These forces have no impact on heat and vapor transfer, and the roughness length for vapor transfer is only a fraction of that for momentum transfer. To estimate the roughness length for vapor transfer, Stricker and Brutsaert (1978) recommended using: zov = 0.1 ⋅ zom
7.2.6
The displacement height for a plant can be estimated using the following relationship (Monteith, 1981; Plate, 1971): d = 2 / 3 ⋅ hc
7.2.7
The height of the wind speed measurement, zw, and the height of the humidity (psychrometer) and temperature measurements, zp, are always assumed to be 170 cm.
7.2.1.3 CANOPY RESISTANCE Studies in canopy resistance have shown that the canopy resistance for a well-watered reference crop can be estimated by dividing the minimum surface resistance for a single leaf by one-half of the canopy leaf area index (Jensen et. al, 1990): rc = rl (0.5 ⋅ LAI )
7.2.8
where rc is the canopy resistance (s m-1), rl is the minimum effective stomatal resistance of a single leaf (s m-1), and LAI is the leaf area index of the canopy. The distribution of stomates on a plant leaf will vary between species. Typically, stomates are distributed unequally on the top and bottom of plant leaves. Plants with stomates located on only one side are
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classified as hypostomatous while plants with an equal number of stomates on both sides of the leaf are termed amphistomatous. The effective leaf stomatal resistance is determined by considering the stomatal resistance of the top (adaxial) and bottom (abaxial) sides to be connected in parallel (Rosenburg, et al., 1983). When there are unequal numbers of stomates on the top and bottom, the effective stomatal resistance is calculated: rl =
rl −ad ⋅ rl −ab rl − ab + rl −ad
7.2.9
where rl is the minimum effective stomatal resistance of a single leaf (s m-1), rl−ad is the minimum adaxial stomatal leaf resistance (s m-1), and rl−ab is the minimum abaxial stomatal leaf resistance (s m-1). For amphistomatous leaves, the effective stomatal resistance is: rl =
rl − ad rl −ab = 2 2
7.2.10
For hypostomatous leaves the effective stomatal resistance is: rl = rl−ad = rl−ab
7.2.11
Leaf conductance is defined as the inverse of the leaf resistance: gl =
1 rl
7.2.12
where g l is the maximum effective leaf conductance (m s-1). When the canopy resistance is expressed as a function of leaf conductance instead of leaf resistance, equation 7.2.8 becomes: rc = (0.5 ⋅ g l ⋅ LAI )
−1
7.2.13
where rc is the canopy resistance (s m-1), g l is the maximum conductance of a single leaf (m s-1), and LAI is the leaf area index of the canopy. For climate change simulations, the canopy resistance term can be modified to reflect the impact of change in CO2 concentration on leaf conductance. The influence of increasing CO2 concentrations on leaf
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conductance was reviewed by Morison (1987). Morison found that at CO2 concentrations between 330 and 660 ppmv, a doubling in CO2 concentration resulted in a 40% reduction in leaf conductance. Within the specified range, the reduction in conductance is linear (Morison and Gifford, 1983). Easterling et al. (1992) proposed the following modification to the leaf conductance term for simulating carbon dioxide concentration effects on evapotranspiration: g l,CO2 = g l ⋅ [1.4 − 0.4 ⋅ (CO2 330 )]
7.2.14
where g l,CO2 is the leaf conductance modified to reflect CO2 effects (m s-1) and CO2 is the concentration of carbon dioxide in the atmosphere (ppmv). Incorporating this modification into equation 7.2.8 gives CO2 öù é æ rc = rl ⋅ ê(0.5 ⋅ LAI ) ⋅ ç1.4 − 0.4 ⋅ ÷ 330 øúû è ë
−1
7.2.15
SWAT will default the value of CO2 concentration to 330 ppmv if no CO2 ö æ value is entered by the user. With this default, the term ç1.4 − 0.4 ⋅ ÷ 330 ø è
reduces to 1.0 and the canopy resistance equation becomes equation 7.2.8. When calculating actual evapotranspiration, the canopy resistance term is modified to reflect the impact of high vapor pressure deficit on leaf conductance (Stockle et al, 1992). For a plant species, a threshold vapor pressure deficit is defined at which the plant’s leaf conductance begins to drop in response to the vapor pressure deficit. The adjusted leaf conductance is calculated:
[
]
g l = g l,mx ⋅ 1 − ∆g l ,dcl (vpd − vpd thr ) if vpd > vpd thr
7.2.16
if vpd ≤ vpd thr
7.2.17
g l = g l,mx
where g l is the conductance of a single leaf (m s-1), g l,mx is the maximum conductance of a single leaf (m s-1), ∆g l,dcl is the rate of decline in leaf conductance per unit increase in vapor pressure deficit (m s-1 kPa-1), vpd is the vapor pressure deficit (kPa), and vpdthr is the threshold vapor pressure
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deficit above which a plant will exhibit reduced leaf conductance (kPa). The rate of decline in leaf conductance per unit increase in vapor pressure deficit is calculated by solving equation 7.2.16 using measured values for stomatal conductance at two different vapor pressure deficits: ∆g l ,dcl =
(1 − fr )
(vpd
g ,mx
fr
− vpd thr )
7.2.18
where ∆g l,dcl is the rate of decline in leaf conductance per unit increase in vapor pressure deficit (m s-1 kPa-1), frg,mx is the fraction of the maximum stomatal conductance, g l,mx , achieved at the vapor pressure deficit vpdfr, and vpdthr is the threshold vapor pressure deficit above which a plant will exhibit reduced leaf conductance (kPa). The threshold vapor pressure deficit is assumed to be 1.0 kPa for all plant species.
7.2.1.4 COMBINED TERM For wind speed in m s-1, Jensen et al. (1990) provided the following relationship to calculate K10.622λρ/P: K1 ⋅ 0.622 ⋅ λ ⋅ ρ P = 1710 − 6.85 ⋅ T av
7.2.19
where T av is the mean air temperature for the day (ûC).
To calculate potential evapotranspiration, the Penman-Monteith equation must be solved for a reference crop. SWAT uses alfalfa at a height of 40 cm with a minimum leaf resistance of 100 s m-1 for the reference crop. Using this canopy height, the equation for aerodynamic resistance (7.2.3) simplifies to: ra =
114. uz
7.2.20
The equation for canopy resistance requires the leaf area index. The leaf area index for the reference crop is estimated using an equation developed by Allen et al. (1989) to calculate LAI as a function of canopy height. For nonclipped grass and alfalfa greater than 3 cm in height:
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LAI = 1.5 ⋅ ln (hc ) − 1.4
7.2.21
where LAI is the leaf area index and hc is the canopy height (cm). For alfalfa with a 40 cm canopy height, the leaf area index is 4.1. Using this value, the equation for canopy resistance simplifies to: CO2 ö æ rc = 49 ç1.4 − 0.4 ⋅ ÷ 330 ø è
7.2.22
The most accurate estimates of evapotranspiration with the PenmanMonteith equation are made when evapotranspiration is calculated on an hourly basis and summed to obtain the daily values. Mean daily parameter values have been shown to provide reliable estimates of daily evapotranspiration values and this is the approach used in SWAT. However, the user should be aware that calculating evapotranspiration with the Penman-Monteith equation using mean daily values can potentially lead to significant errors. These errors result from diurnal distributions of wind speed, humidity, and net radiation that in combination create conditions which the daily averages do not replicate.
7.2.2 PRIESTLEY-TAYLOR METHOD Priestley and Taylor (1972) developed a simplified version of the combination equation for use when surface areas are wet. The aerodynamic component was removed and the energy component was multiplied by a coefficient, αpet = 1.28, when the general surroundings are wet or under humid conditions
λEo = α pet ⋅
∆ ⋅ (H net − G ) ∆ +γ
7.2.23
where λ is the latent heat of vaporization (MJ kg-1), Eo is the potential evapotranspiration (mm d-1), αpet is a coefficient, ∆ is the slope of the saturation vapor pressure-temperature curve, de/dT (kPa ûC-1), γ is the psychrometric constant (kPa ûC-1), Hnet is the net radiation (MJ m-2 d-1), and G is the heat flux density to the ground (MJ m-2 d-1).
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The Priestley-Taylor equation provides potential evapotranspiration estimates for low advective conditions. In semiarid or arid areas where the advection component of the energy balance is significant, the Priestley-Taylor equation will underestimate potential evapotranspiration.
7.2.3 HARGREAVES METHOD The Hargreaves method was originally derived from eight years of coolseason Alta fescue grass lysimeter data from Davis, California (Hargreaves, 1975). Several improvements were made to the original equation (Hargreaves and Samani, 1982 and 1985) and the form used in SWAT was published in 1985 (Hargreaves et al., 1985):
(
λEo = 0.0023 ⋅ H 0 ⋅ (Tmx − Tmn )0.5 ⋅ T av + 17.8
)
7.2.24
where λ is the latent heat of vaporization (MJ kg-1), Eo is the potential evapotranspiration (mm d-1), H0 is the extraterrestrial radiation (MJ m-2 d-1), Tmx is the maximum air temperature for a given day (°C), Tmn is the minimum air temperature for a given day (°C), and T av is the mean air temperature for a given day (°C). Table 7-2: SWAT input variables used in potential evapotranspiration calculations summarized in this section. Variable File name Definition Name IPET Potential evapotranspiration method .cod WND_SP uz: Daily wind speed (m/s) .wnd .sub CO2 CO2: Carbon dioxide concentration (ppmv) MAX TEMP .tmp Tmx: Daily maximum temperature (°C) MIN TEMP Tmn: Daily minimum temperature (°C) .tmp -1 crop.dat GSI g : maximum leaf conductance (m s ) l ,mx
FRGMAX VPDFR
frg,mx: Fraction of maximum leaf conductance achieved at the vapor pressure deficit specified by vpdfr vpdfr: Vapor pressure deficit corresponding to value given for frg,mx (kPa)
crop.dat crop.dat
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7.3 ACTUAL EVAPOTRANSPIRATION Once total potential evapotranspiration is determined, actual evaporation must be calculated. SWAT first evaporates any rainfall intercepted by the plant canopy. Next, SWAT calculates the maximum amount of transpiration and the maximum amount of sublimation/soil evaporation using an approach similar to that of Richtie (1972). The actual amount of sublimation and evaporation from the soil is then calculated. If snow is present in the HRU, sublimation will occur. Only when no snow is present will evaporation from the soil take place.
7.3.1 EVAPORATION OF INTERCEPTED RAINFALL Any free water present in the canopy is readily available for removal by evapotranspiration. The amount of actual evapotranspiration contributed by intercepted rainfall is especially significant in forests where in some instances evaporation of intercepted rainfall is greater than transpiration. SWAT removes as much water as possible from canopy storage when calculating actual evaporation. If potential evapotranspiration, Eo, is less than the amount of free water held in the canopy, RINT, then
E a = E can = Eo
7.3.1
RINT ( f ) = RINT ( i ) − E can
7.3.2
where Ea is the actual amount of evapotranspiration occurring in the watershed on a given day (mm H2O), Ecan is the amount of evaporation from free water in the canopy on a given day (mm H2O), Eo is the potential evapotranspiration on a given day (mm H2O), RINT(i) is the initial amount of free water held in the canopy on a given day (mm H2O), and RINT(f) is the final amount of free water held in the canopy on a given day (mm H2O). If potential evapotranspiration, Eo, is greater than the amount of free water held in the canopy, RINT, then E can = RINT ( i )
7.3.3
RINT ( f ) = 0
7.3.4
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129
Once any free water in the canopy has been evaporated, the remaining evaporative water demand ( Eo′ = Eo − Ecan ) is partitioned between the vegetation and snow/soil.
7.3.2 TRANSPIRATION If
the Penman-Monteith equation is selected as the
potential
evapotranspiration method, transpiration is also calculated with the equations summarized in Section 7.2.1. For the other potential evapotranspiration methods, transpiration is calculated as: Et =
Eo′ ⋅ LAI 3 .0
Et = Eo′
0 ≤ LAI ≤ 3.0
7.3.5
LAI > 3.0
7.3.6
where Et is the maximum transpiration on a given day (mm H2O), Eo′ is the potential evapotranspiration adjusted for evaporation of free water in the canopy (mm H2O), and LAI is the leaf area index. The value for transpiration calculated by equations 7.3.5 and 7.3.6 is the amount of transpiration that will occur on a given day when the plant is growing under ideal conditions. The actual amount of transpiration may be less than this due to lack of available water in the soil profile. Calculation of actual plant water uptake and transpiration is reviewed in Chapters 18 and 19.
7.3.3 SUBLIMATION AND EVAPORATION FROM THE SOIL The amount of sublimation and soil evaporation will be impacted by the degree of shading. The maximum amount of sublimation/soil evaporation on a given day is calculated as:
E s = Eo′ ⋅ cov sol
7.3.7
where Es is the maximum sublimation/soil evaporation on a given day (mm H2O),
Eo′ is the potential evapotranspiration adjusted for evaporation of free water in the canopy (mm H2O), and covsol is the soil cover index. The soil cover index is calculated cov sol = exp(− 5.0 × 10 −5 ⋅ CV )
7.3.8
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where CV is the aboveground biomass and residue (kg ha-1). If the snow water content is greater than 0.5 mm H2O, the soil cover index is set to 0.5. The maximum amount of sublimation/soil evaporation is reduced during periods of high plant water use with the relationship: é E s′ = min ê E s , ë
E s ⋅ Eo′ ù E s + Et úû
7.3.9
where E s′ is the maximum sublimation/soil evaporation adjusted for plant water use on a given day (mm H2O), Es is the maximum sublimation/soil evaporation on a given day (mm H2O), Eo′ is the potential evapotranspiration adjusted for evaporation of free water in the canopy (mm H2O), and Et is the transpiration on a given day (mm H2O). When Et is low E s′ → E s . However, as Et approaches Eo′ , E s′ →
Es . 1 + cov sol
7.3.3.1 SUBLIMATION Once the maximum amount of sublimation/soil evaporation for the day is calculated, SWAT will first remove water from the snow pack to meet the evaporative demand. If the water content of the snow pack is greater than the maximum sublimation/soil evaporation demand, then
E sub = E s′
7.3.10
SNO( f ) = SNO( i ) − E s′
7.3.11
E s′′ = 0.
7.3.12
where Esub is the amount of sublimation on a given day (mm H2O), E s′ is the maximum sublimation/soil evaporation adjusted for plant water use on a given day (mm H2O), SNO(i) is the amount of water in the snow pack on a given day prior to accounting for sublimation (mm H2O), SNO(f) is the amount of water in the snow pack on a given day after accounting for sublimation (mm H2O), and E s′′ is the maximum soil water evaporation on
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131
a given day (mm H2O). If the water content of the snow pack is less than the maximum sublimation/soil evaporation demand, then E sub = SNO( i )
7.3.13
SNO( f ) = 0.
7.3.14
E s′′ = E s′ − E sub
7.3.15
7.3.3.2 SOIL WATER EVAPORATION When an evaporation demand for soil water exists, SWAT must first partition the evaporative demand between the different layers. The depth distribution used to determine the maximum amount of water allowed to be evaporated is:
E soil , z = E s′′ ⋅
z z + exp(2.374 − 0.00713 ⋅ z )
7.3.16
where Esoil,z is the evaporative demand at depth z (mm H2O), E s′′ is the maximum soil water evaporation on a given day (mm H2O), and z is the depth below the surface. The coefficients in this equation were selected so that 50% of the evaporative demand is extracted from the top 10 mm of soil and 95% of the evaporative demand is extracted from the top 100 mm of soil. The amount of evaporative demand for a soil layer is determined by taking the difference between the evaporative demands calculated at the upper and lower boundaries of the soil layer: E soil ,ly = E soil , zl − E soil , zu
7.3.16
where Esoil,ly is the evaporative demand for layer ly (mm H2O), Esoil,zl is the evaporative demand at the lower boundary of the soil layer (mm H2O), and Esoil,zu is the evaporative demand at the upper boundary of the soil layer (mm H2O). Figure 7-1 graphs the depth distribution of the evaporative demand for a soil that has been partitioned into 1 mm layers assuming a total soil evaporation demand of 100 mm.
SWAT USER'S MANUAL, VERSION 2000 E v a p o r a tio n a llo w e d w ith d e p t h a s s u m in g 1 0 0 m m d e m a n d 9 .0 0
8 .0 0
7 .0 0 Maximum Evaporation (mm H2O)
132
6 .0 0
5 .0 0
4 .0 0
3 .0 0
2 .0 0
1 .0 0
0 .0 0 1
51
101
151
201
251
301
351
401
451
D ep th (m m )
Figure 7-1: Soil evaporative demand distribution with depth.
As mentioned previously, the depth distribution assumes 50% of the evaporative demand is met by soil water stored in the top 10 mm of the soil profile. With our example of a 100 mm total evaporative demand, 50 mm of water is 50%. This is a demand that the top layer cannot satisfy. SWAT does not allow a different layer to compensate for the inability of another layer to meet its evaporative demand. The evaporative demand not met by a soil layer results in a reduction in actual evapotranspiration for the HRU. A coefficient has been incorporated into equation 7.3.16 to allow the user to modify the depth distribution used to meet the soil evaporative demand. The modified equation is: E soil ,ly = E soil , zl − E soil , zu ⋅ esco
7.3.17
where Esoil,ly is the evaporative demand for layer ly (mm H2O), Esoil,zl is the evaporative demand at the lower boundary of the soil layer (mm H2O),
Esoil,zu is the evaporative demand at the upper boundary of the soil layer
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133
(mm H2O), and esco is the soil evaporation compensation coefficient. Solutions to this equation for different values of esco are graphed in Figure 7-2. The plot for esco = 1.0 is that shown in Figure 7-1.
E v a p o r a t io n a llo w e d w it h d e p t h a s s u m in g 1 0 0 m m d e m a n d
3 5 .0 0
Maximum Evaporation (mm H2O)
3 0 .0 0
2 5 .0 0
2 0 .0 0
1 5 .0 0
1 0 .0 0
5 .0 0
0 .0 0 1
51
101
151
201
251
301
351
401
451
D ep th (m m ) esc o = 1.0
e s c o = 0 .9
e sc o = 0 .8
esc o = 0.7
Figure 7-2: Soil evaporative demand distribution with depth
As the value for esco is reduced, the model is able to extract more of the evaporative demand from lower levels. When the water content of a soil layer is below field capacity, the evaporative demand for the layer is reduced according to the following equations: æ 2.5 ⋅ (SWly − FCly ) ö ÷ when SWly < FCly 7.3.18 ′ ,ly = E soil ,ly ⋅ expç E soil ç FC − WP ÷ ly ly è ø ′ ,ly = E soil ,ly E soil
when SWly ≥ FCly 7.3.19
′ ,ly is the evaporative demand for layer ly adjusted for water where E soil
content (mm H2O), Esoil,ly is the evaporative demand for layer ly (mm
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H2O), SWly is the soil water content of layer ly (mm H2O), FCly is the water content of layer ly at field capacity (mm H2O), and WPly is the water content of layer ly at wilting point (mm H2O). In addition to limiting the amount of water removed by evaporation in dry conditions, SWAT defines a maximum value of water that can be removed at any time. This maximum value is 80% of the plant available water on a given day where the plant available water is defined as the total water content of the soil layer minus the water content of the soil layer at wilting point (-1.5 MPa). ′′ ,ly = min(E soil ′ ,ly E soil
0.8 ⋅ ( SWly − WPly ) )
7.3.20
′′ ,ly is the amount of water removed from layer ly by evaporation where E soil ′ ,ly is the evaporative demand for layer ly adjusted for (mm H2O), E soil
water content (mm H2O), SWly is the soil water content of layer ly (mm H2O), and WPly is the water content of layer ly at wilting point (mm H2O). Table 7-3: SWAT input variables used in soil evaporation calculations. Variable name Definition ESCO esco: soil evaporation compensation coefficient
File Name .bsn, .hru
7.4 NOMENCLATURE CO2 CV E Ea Ecan Eo Eo′ Es E s′ E s′′ Esoil,ly
Concentration of carbon dioxide in the atmosphere (ppmv) Total aboveground biomass and residue present on current day (kg ha-1) Depth rate evaporation (mm d-1) Actual amount of evapotranspiration on a given day (mm H2O) Amount of evaporation from free water in the canopy on a given day (mm H2O) Potential evapotranspiration (mm d-1) Potential evapotranspiration adjusted for evaporation of free water in the canopy (mm H2O) Maximum sublimation/soil evaporation on a given day (mm H2O) Maximum sublimation/soil evaporation adjusted for plant water use on a given day (mm H2O) Maximum soil water evaporation on a given day (mm H2O) Evaporative demand for layer ly (mm H2O)
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135
′ ,ly Evaporative demand for layer ly adjusted for water content (mm H2O) E soil ′′ ,ly E soil Esoil,z Esub Et FCly G H0 Hnet K1 LAI LAImx P Rday ′ Rday
RINT SNO SWly Tmn Tmx T av WPly
Amount of water removed from layer ly by evaporation (mm H2O) Evaporative demand at depth z (mm H2O) Amount of sublimation on a given day (mm H2O) Transpiration rate (mm d-1) Water content of layer ly at field capacity (mm H2O) Heat flux density to the ground (MJ m-2 d-1) Extraterrestrial daily irradiation (MJ m-2 d-1) Net radiation on day (MJ m-2 d-1) Dimension coefficient in Penman-Monteith equation Leaf area index of the canopy Maximum leaf area index for the plant Atmospheric pressure (kPa) Amount of rainfall on a given day (mm H2O) Amount of precipitation on a given day before canopy interception is removed (mm H2O) Amount of free water held in the canopy on a given day (mm H2O) Water content of snow cover on current day (mm H2O) Soil water content of layer ly (mm H2O) Minimum air temperature for day (°C) Maximum air temperature for day (°C) Mean air temperature for day (°C) Water content of layer ly at wilting point (mm H2O).
cp Specific heat of moist air at constant pressure (1.013 × 10-3 MJ kg-1 °C-1) canday Maximum amount of water that can be trapped in the canopy on a given day (mm H2O) canmx Maximum amount of water that can be trapped in the canopy when the canopy is fully developed (mm H2O) covsol Soil cover index d Zero plane displacement of the wind profile (cm) e Actual vapor pressure on a given day (kPa) o e Saturation vapor pressure on a given day (kPa) esco Soil evaporation compensation coefficient frg,mx Fraction of the maximum stomatal conductance, g l,mx , achieved at the vapor pressure deficit, vpdfr gl Leaf conductance (m s-1) g l,mx Maximum conductance of a single leaf (m s-1) hc Canopy height (cm) k Von Kármán constant ra Diffusion resistance of the air layer (aerodynamic resistance) (s m-1) rc Plant canopy resistance (s m-1) rl Minimum effective resistance of a single leaf (s m-1)
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SWAT USER'S MANUAL, VERSION 2000
Minimum abaxial stomatal leaf resistance (s m-1) Minimum adaxial stomatal leaf resistance (s m-1) Wind speed at height zw (m s-1) Vapor pressure deficit (kPa) Vapor pressure deficit corresponding to frg,mx (kPa) Threshold vapor pressure deficit above which a plant will exhibit reduced leaf conductance (kPa) Depth below soil surface (mm) Roughness length for momentum transfer (cm) Roughness length for vapor transfer (cm) Height of the humidity (psychrometer) and temperature measurements (cm) Height of the wind speed measurement (cm)
αpet Coefficient in Priestley-Taylor equation ∆ Slope of the saturation vapor pressure curve (kPa °C-1) ∆g l,dcl Rate of decline in leaf conductance per unit increase in vapor pressure deficit (m s-1 kPa-1) ρair Air density (kg m-3) γ Psychrometric constant (kPa °C-1) λ Latent heat of vaporization (MJ kg-1)
7.5 REFERENCES Allen, R.G. 1986. A Penman for all seasons. J. Irrig. and Drain Engng., ASCE, 112(4): 348-368. Allen, R.G., M.E. Jensen, J.L. Wright, and R.D. Burman. 1989. Operational estimates of evapotranspiration. Agron. J. 81:650-662. Brutsaert, W. 1975. Comments on surface roughness parameters and the height of dense vegetation. J. Meterol. Soc. Japan 53:96-97. Dingman, S.L. 1994. Physical hydrology. Prentice-Hall, Inc., Englewood Cliffs, NJ. Easterling, W.E., N.J. Rosenburg, M.S. McKenney, C.A. Jones, P.T. Dyke, and J.R. Williams. 1992. Preparing the erosion productivity impact calculator (EPIC) model to simulate crop response to climate change and the direct effects of CO2. Agricultural and Forest Meteorology 59:17-34.
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Hargreaves, G.H. 1975. Moisture availability and crop production. Trans. ASAE 18: 980-984. Hargreaves, G.H. and Z.A. Samani. 1985. Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture 1:96-99. Hargreaves, G.H. and Z.A. Samani. 1982. Estimating potential evapotranspiration. Tech. Note, J. Irrig. and Drain. Engr. 108(3):225-230. Hargreaves, G.L., G.H. Hargreaves, and J.P. Riley. 1985. Agricultural benefits for Senegal River Basin. J. Irrig. and Drain. Engr. 111(2):113-124. Jensen, M.E., R.D. Burman, and R.G. Allen (ed). 1990. Evapotranspiration and irrigation water requirements. ASCE Manuals and Reports on Engineering Practice No. 70, ASCE, N.Y. 332 pp. Monteith, J.L. 1965. Evaporation and the environment. p. 205-234. In The state and movement of water in living organisms, XIXth Symposium. Soc. for Exp. Biol., Swansea, Cambridge University Press. Monteith, J.L. 1981. Evaporation and surface temperature. Quart. J. Roy. Meteorol. Soc. 107:1-27. Morison, J.I.L. 1987. Intercellular CO2 concentration and stomatal response to CO2. p. 229-251. In E. Zeiger, G.D. Farquhar and I.R. Cowan (ed.) Stomatal function. Standford University Press, Palo Alto, CA. Morison, J.I.L. and R.M. Gifford. 1983. Stomatal sensitivity tocarbon dioxide and humidity. Plant Physiol. 71:789-796. Penman, H.L. 1956. Evaporation: An introductory survey. Netherlands Journal of Agricultural Science 4:7-29. Plate, E.J. 1971. Aerodynamic characteristics of atmospheric boundary layers. U.S. Atomic Energy Comm., Critical Review Series, TID-25465. 190 pp. Priestley, C.H.B. and R.J. Taylor. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather. Rev. 100:81-92. Ritchie, J.T. 1972. Model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res. 8:1204-1213.
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Rosenburg, N.J., B.L. Blad, and S.B. Verma. 1983. Microclimate: the biological environment, 2nd ed. John Wiley & Sons, New York. Stockle, C.O., J.R. Williams, N.J. Rosenberg, and C.A. Jones. 1992. A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part 1—Modification of the EPIC model for climate change analysis. Agricultural Systems 38:225-238. Stricker, H. and W. Brutsaert. 1978. Actual evapotranspiration over summer period in the 'Hupsel Catchment.' J. Hydrol. 39:139-157. Thornthwaite, C.W. 1948. An approach toward a rational classification of climate. Geographical Review 38:55-94.
CHAPTER 8
EQUATIONS: SOIL WATER
Water that enters the soil may move along one of several different pathways. The water may be removed from the soil by plant uptake or evaporation. It can percolate past the bottom of the soil profile and ultimately become aquifer recharge. A final option is that water may move laterally in the profile and contribute to streamflow. Of these different pathways, plant uptake of water removes the majority of water that enters the soil profile.
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8.1 SOIL STRUCTURE Soil is comprised of three phases—solid, liquid and gas. The solid phase consists of minerals and/or organic matter that forms the matrix or skeleton of the soil. Between the solid particles, soil pores are formed that hold the liquid and gas phases. The soil solution may fill the soil pores completely (saturated) or partially (unsaturated). When the soil is unsaturated, the soil solution is found as thin films along particle surfaces, as annular wedges around contact points of particles and as isolated bodies in narrow pore passages. The soil’s bulk density defines the relative amounts of pore space and soil matrix. Bulk density is calculated:
ρb =
MS VT
8.1.1
where ρb is the bulk density (Mg m-3), MS is the mass of the solids (Mg), and VT is the total volume (m3). The total volume is defined as VT = V A + VW + VS
8.1.2
where VA is the volume of air (m3), VW is the volume of water (m3), and VS is the volume of solids (m3). The relationship between soil porosity and soil bulk density is
φ soil = 1 −
ρb ρs
8.1.3
where φsoil is the soil porosity expressed as a fraction of the total soil volume, ρb is the bulk density (Mg m-3), and ρs is the particle density (Mg m-3). The particle density, or density of the solid fraction, is a function of the mineral composition of the soil matrix. Based on research, a default value of 2.65 Mg m-3 is used for particle density. Storage, transport and availability of soil solution and soil air are not nearly as dependent on the total amount of porosity as they are on the arrangement of pore space. Soil pores vary in size and shape due to textural and structural arrangement. Based on the diameter of the pore at the narrowest point, the pores may be classified as macropores (narrowest diameter > 100 µm),
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mesopores (narrowest diameter 30-100 µm), and micropores (narrowest diameter < 30 µm) (Koorevaar et al, 1983). Macropores conduct water only during flooding or ponding rain and drainage of water from these pores is complete soon after cessation of the water supply. Macropores control aeration and drainage processes in the soil. Mesopores conduct water even after macropores have emptied, e.g. during non-ponding rain and redistribution. Micropores retain soil solution or conduct it very slowly. When comparing soils of different texture, clay soils contain a greater fraction of mesopores and micropores while sand soils contain mostly macropores. This is evident when the hydraulic conductivities of clay and sand soils are compared. The conductivity of a sand soil can be several orders of magnitude greater than that for a clay soil. The water content of a soil can range from zero when the soil is oven dried to a maximum value (φsoil) when the soil is saturated. For plant-soil interactions, two intermediate stages are recognized: field capacity and permanent wilting point. Field capacity is the water content found when a thoroughly wetted soil has drained for approximately two days. Permanent wilting point is the water content found when plants growing in the soil wilt and do not recover if their leaves are kept in a humid atmosphere overnight. To allow these two stages to be quantified more easily, they have been redefined in terms of tensions at which water is held by the soil. Field capacity is the amount of water held in the soil at a tension of 0.033 MPa and the permanent wilting point is the amount of water held in the soil at a tension of 1.5 MPa. The amount of water held in the soil between field capacity and permanent wilting point is considered to be the water available for plant extraction. Table 8-1: Water contents for various soils at different moisture conditions. Water content (fraction total soil volume) Clay Content Permanent Texture (% Solids) Saturation Field capacity wilting point sand 3% 0.40 0.06 0.02 loam 22 % 0.50 0.29 0.05 clay 47 % 0.60 0.41 0.20
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Table 8-1 lists the water content for three soils as a fraction of the total volume for different moisture conditions. Note that the total porosity, given by the water content at saturation, is lowest for the sand soil and highest for the clay soil. The sand soil drains more quickly than the loam and clay. Only 15% of the water present in the sand soil at saturation remains at field capacity. 58% of the water present at saturation in the loam remains at field capacity while 68% of the water present at saturation in the clay soil remains at field capacity. The reduction of water loss with increase in clay content is cause by two factors. As mentioned previously, clay soils contain more mesopores and micropores than sand soils. Also, unlike sand and silt particles, clay particles possess a net negative charge. Due to the polar nature of water molecules, clay particles are able to attract and retain water molecules. The higher water retention of clay soils is also seen in the fraction of water present at permanent wilting point. In the soils listed in Table 8-1, the volumetric water content of the clay is 0.20 at the wilting point while the sand and loam have a volumetric water content of 0.02 and 0.05 respectively. The plant available water, also referred to as the available water capacity, is calculated by subtracting the fraction of water present at permanent wilting point from that present at field capacity. AWC = FC − WP
8.1.4
where AWC is the plant available water content, FC is the water content at field capacity, and WP is the water content at permanent wilting point. For the three soil textures listed in Table 8-1, the sand has an available water capacity of 0.04, the loam has an available water capacity of 0.24 and the clay has an available water capacity of 0.21. Even though the clay contains a greater amount of water than the loam at all three tensions, the loam has a larger amount of water available for plant uptake than the clay. This characteristic is true in general. SWAT estimates the permanent wilting point volumetric water content for each soil layer as: WPly = 0.40 ⋅
mc ⋅ ρ b 100
8.1.5
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where WPly is the water content at wilting point expressed as a fraction of the total soil volume, mc is the percent clay content of the layer (%), and ρb is the bulk density for the soil layer (Mg m-3). Field capacity water content is estimated FCly = WPly + AWCly
8.1.6
where FCly is the water content at field capacity expressed as a fraction of the total soil volume, WPly is the water content at wilting point expressed as a fraction of the total soil volume, and AWCly is the available water capacity of the soil layer expressed as a fraction of the total soil volume. AWCly is input by the user. Water in the soil can flow under saturated or unsaturated conditions. In saturated soils, flow is driven by gravity and usually occurs in the downward direction. Unsaturated flow is caused by gradients arising due to adjacent areas of high and low water content. Unsaturated flow may occur in any direction. SWAT directly simulates saturated flow only. The model records the water contents of the different soil layers but assumes that the water is uniformly distributed within a given layer. This assumption eliminates the need to model unsaturated flow in the horizontal direction. Unsaturated flow between layers is indirectly modeled with the depth distribution of plant water uptake (equation 18.2.1) and the depth distribution of soil water evaporation (equation 7.3.16). Saturated flow occurs when the water content of a soil layer surpasses the field capacity for the layer. Water in excess of the field capacity water content is available for percolation, lateral flow or tile flow drainage unless the temperature of the soil layer is below 0°C. When the soil layer is frozen, no water movement is calculated. Table 8-1: SWAT input variables used in percolation calculations. Variable name Definition CLAY mc: Percent clay content SOL_BD ρb: Bulk density (Mg m-3) SOL_AWC AWCly: available water capacity
File Name .sol .sol .sol
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8.2 PERCOLATION Percolation is calculated for each soil layer in the profile. Water is allowed to percolate if the water content exceeds the field capacity water content for that layer. When the soil layer is frozen, no water flow out of the layer is calculated. The volume of water available for percolation in the soil layer is calculated: SWly ,excess = SWly − FC ly
if
SWly > FCly
8.2.1
SWly ,excess = 0
if
SWly ≤ FCly
8.2.2
where SWly,excess is the drainable volume of water in the soil layer on a given day (mm H2O), SWly is the water content of the soil layer on a given day (mm H2O) and FCly is the water content of the soil layer at field capacity (mm H2O). The amount of water that moves from one layer to the underlying layer is calculated using storage routing methodology. The equation used to calculate the amount of water that percolates to the next layer is: æ é − ∆t ù ö w perc ,ly = SWly ,excess ⋅ ç1 − exp ê ú ÷÷ ç TT êë perc úû ø è
8.2.3
where wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O), SWly,excess is the drainable volume of water in the soil layer on a given day (mm H2O), ∆t is the length of the time step (hrs), and TTperc is the travel time for percolation (hrs). The travel time for percolation is unique for each layer. It is calculate TT perc =
SATly − FC ly K sat
8.2.4
where TTperc is the travel time for percolation (hrs), SATly is the amount of water in the soil layer when completely saturated (mm H2O), FCly is the water content of the soil layer at field capacity (mm H2O), and Ksat is the saturated hydraulic conductivity for the layer (mm·h-1). Water that percolates out of the lowest soil layer enters the vadose zone. The vadose zone is the unsaturated zone between the bottom of the soil profile
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and the top of the aquifer. Movement of water through the vadose zone and into the aquifers is reviewed in Chapter 9. Table 8-2: SWAT input variables used in percolation calculations. Variable name Definition SOL_K Ksat: Saturated hydraulic conductivity (mm/hr)
File Name .sol
8.3 BYPASS FLOW One of the most unique soil orders is the Vertisols. These soils are characterized by a propensity to shrink when dried and swell when moistened. When the soil is dry, large cracks form at the soil surface. This behavior is a result of the type of soil material present and the climate. Vertisols contain at least 30% clay with the clay fraction dominated by smectitic mineralogy and occur in areas with cyclical wet and dry periods. Vertisols are found worldwide (Figure 8-1). They have a number of local names, some of which are listed in Table 8-3.
Figure 8-1: Soil associations of Vertisols (From Dudal and Eswaran, 1988)
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Table 8-3: Alternative names for Vertisols or soils with Vertic properties (Dudal and Eswaran, 1988). Names Countries Names that include the word “black” Barros pretos Black clays Black cotton soils Black cracking clays Black earths Black turf soils Dark clay soils Subtropical black clays Sols noirs tropicaux Terra nera Terres noires tropicales Terras negras tropicais Tierras negras de Andalucia Tropical black earths Tropical black clays
Portugal South Africa, Australia Africa, India Uganda Australia, Africa South Africa United States Africa Africa Italy Africa Mozambique Spain Angola, Ghana Africa
Names that reflect the black color Karail Melanites Teen Suda Tropical Chernozems Impact Chernozems
India Ghana Sudan Africa, India Russia
Vernacular names Adobe soils Badobes Dian Pere Gilgai soils Firki Mbuga Kahamba Makande Morogan Regur Rendzina Shachiang soils Smolnitza Smonitza Sols de paluds Tirs Vlei grond Sonsocuite
United States, Philippines Sudan French West Africa Australia Nigeria Tanzania Congo Malawi Romania India United States China Bulgaria, Romania Austria, Yugoslavia France Morocco, North Africa South Africa Nicaragua
Coined names Densinigra soils Gravinigra soils Grumusols Margalite soils Vertisols
Angola Angola United States Indonesia United States
One criteria used to classify a soil as a Vertisol is the formation of shrinkage cracks in the dry season that penetrate to a depth of more than 50 cm and are at least 1 cm wide at 50 cm depth. The cracks can be considerably wider at the surface—30 cm cracks at the surface are not unusual although 6-15 cm cracks are more typical.
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To accurately predict surface runoff and infiltration in areas dominated by soils that exhibit Vertic properties, the temporal change in soil volume must be quantified. Bouma and Loveday (1988) identified three soil moisture conditions for which infiltration needs to be defined (Figure 8-2).
Figure 8-2: Diagram showing the effect of wetting and drying on cracking in Vertisols (from Bouma and Loveday, 1988)
Traditional models of infiltration are applicable to soils in which cracks have been closed by swelling and the soil acts as a relatively homogenous porous medium (Condition 3 in Figure 8-2). Condition 1 in Figure 8-2 represents the driest state with cracks at maximum width, a condition present at the end of the dry season/beginning of the rainy season. Condition 2 in Figure 8-2 represents the crack development typical with an actively growing crop requiring multiple irrigation or rainfall events to sustain growth. Bypass flow, the vertical movement of free water along macropores through unsaturated soil horizons, will occur in conditions 1 and 2. Bypass flow (finf,2 in Figure 8-2) occurs when the rate of rainfall or irrigation exceeds the vertical infiltration rate into the soil peds (finf,1 in Figure 8-2). When bypass flow is modeled, SWAT calculates the crack volume of the soil matrix for each day of simulation by layer. On days in which precipitation events occur, infiltration and surface runoff is first calculated for the soil peds (finf,1 in Figure 8-2) using the curve number or Green & Ampt method. If any surface runoff is generated, it is allowed to enter the cracks. A volume of water
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equivalent to the total crack volume for the soil profile may enter the profile as bypass flow. Surface runoff in excess of the crack volume remains overland flow. Water that enters the cracks fills the soil layers beginning with the lowest layer of crack development. After cracks in one layer are filled, the cracks in the overlying layer are allowed to fill.
The crack volume initially estimated for a layer is calculated: crk ly ,i = crk max ,ly ⋅
coef crk ⋅ FC ly − SWly coef crk ⋅ FCly
8.3.1
where crkly,i is the initial crack volume calculated for the soil layer on a given day expressed as a depth (mm), crkmax,ly is the maximum crack volume possible for the soil layer (mm), coefcrk is an adjustment coefficient for crack flow, FCly is the water content of the soil layer at field capacity (mm H2O), and SWly is the water content of the soil layer on a given day (mm H2O). The adjustment coefficient for crack flow, coefcrk, is set to 0.10. When the moisture content of the entire profile falls below 90% of the field capacity water content for the profile during the drying stage, the crack volume for a given day is a function of the crack volume estimated with equation 8.3.1 and the crack volume of the layer on the previous day. When the soil is wetting and/or when the moisture content of the profile is above 90% of the field capacity water content, the crack volume for a given day is equal to the volume calculated with equation 8.3.1. crk ly = l crk ⋅ crk ly ,d −1 + (1.0 − l crk ) ⋅ crk ly ,i
when SW < 0.90 ⋅ FC and crk ly ,i > crk ly ,d −1
8.3.2
when SW ≥ 0.90 ⋅ FC or crk ly ,i ≤ crk ly ,d −1
8.3.3
crk ly = crk ly ,i
where crkly is the crack volume for the soil layer on a given day expressed as a depth (mm), l crk is the lag factor for crack development during drying, crkly,d-1 is the crack volume for the soil layer on the previous day (mm), crkly,i is the initial crack volume calculated for the soil layer on a given day using equation 8.3.1
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(mm), SW is the water content of the soil profile on a given day (mm H2O), and FC is the water content of the soil profile at field capacity (mm H2O). As the tension at which water is held by the soil particles increases, the rate of water diffusion slows. Because the rate of water diffusion is analogous to the coefficient of consolidation in classical consolidation theory (Mitchell, 1992), the reduction in diffusion will affect crack formation. The lag factor is introduced during the drying stage to account for the change in moisture redistribution dynamics that occurs as the soil dries. The lag factor, l crk , is set to a value of 0.99. The maximum crack volume for the layer, crkmax,ly, is calculated: crk max,ly = 0.916 ⋅ crk max ⋅ exp[− 0.0012 ⋅ z l ,ly ]⋅ depthly
8.3.4
where crkmax,ly is the maximum crack volume possible for the soil layer (mm), crkmax is the potential crack volume for the soil profile expressed as a fraction of the total volume, zl,ly is the depth from the soil surface to the bottom of the soil layer (mm), and depthly is the depth of the soil layer (mm). The potential crack volume for the soil profile, crkmax, is input by the user. Those needing information on the measurement of this parameter are referred to Bronswijk (1989; 1990). Once the crack volume for each layer is calculated, the total crack volume for the soil profile is determined. n
crk = å crk ly
8.3.5
ly =1
where crk is the total crack volume for the soil profile on a given day (mm), crkly is the crack volume for the soil layer on a given day expressed as a depth (mm), ly is the layer, and n is the number of layers in the soil profile.
After surface runoff is calculated for rainfall events using the curve number or Green & Ampt method, the amount of runoff is reduced by the volume of cracks present that day: Qsurf = Qsurf ,i − crk
if Qsurf ,i > crk
8.3.6
Qsurf = 0
if Qsurf ,i ≤ crk
8.3.7
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where Qsurf is the accumulated runoff or rainfall excess for the day (mm H2O),
Qsurf,i is the initial accumulated runoff or rainfall excess determined with the Green & Ampt or curve number method (mm H2O), and crk is the total crack volume for the soil profile on a given day (mm). The total amount of water entering the soil is then calculated: winf = Rday − Qsurf
8.3.8
where winf is the amount of water entering the soil profile on a given day (mm H2O), Rday is the rainfall depth for the day adjusted for canopy interception (mm H2O), and Qsurf is the accumulated runoff or rainfall excess for the day (mm H2O). Bypass flow past the bottom of the profile is calculated: æ crk ly = nn ö ÷ wcrk ,btm = 0.5 ⋅ crk ⋅ ç ç depth ÷ ly = nn è ø
8.3.9
where wcrk,btm is the amount of water flow past the lower boundary of the soil profile due to bypass flow (mm H2O), crk is the total crack volume for the soil profile on a given day (mm), crkly=nn is the crack volume for the deepest soil layer (ly=nn) on a given day expressed as a depth (mm), and depthly=nn is the depth of the deepest soil layer (ly=nn) (mm). After wcrk,btm is calculated, each soil layer is filled to field capacity water content beginning with the lowest layer and moving upward until the total amount of water entering the soil, winf, has been accounted for. Table 8-4: SWAT input variables used in bypass flow calculations. Variable name Definition ICRK Bypass flow code: 0-do not model bypass flow; 1-model bypass flow SOL_CRK crkmax: Potential crack volume for soil profile
File Name .cod .sol
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8.4 LATERAL FLOW Lateral flow will be significant in areas with soils having high hydraulic conductivities in surface layers and an impermeable or semipermeable layer at a shallow depth. In such a system, rainfall will percolate vertically until it encounters the impermeable layer. The water then ponds above the impermeable layer forming a saturated zone of water, i.e. a perched water table. This saturated zone is the source of water for lateral subsurface flow. SWAT incorporates a kinematic storage model for subsurface flow developed by Sloan et al. (1983) and summarized by Sloan and Moore (1984). This model simulates subsurface flow in a two-dimensional cross-section along a flow path down a steep hillslope. The kinematic approximation was used in its derivation. This model is based on the mass continuity equation, or mass water balance, with the entire hillslope segment used as the control volume. The hillslope segment has a permeable soil surface layer of depth Dperm and length Lhill with an impermeable soil layer or boundary below it as shown in Figure 8-3. The hillslope segment is oriented at an angle αhill to the horizontal.
Figure 8-3: Conceptual representation of the hillslope segment.
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The kinematic wave approximation of saturated subsurface or lateral flow assumes that the lines of flow in the saturated zone are parallel to the impermeable boundary and the hydraulic gradient equals the slope of the bed.
Figure 8-4: Behavior of the water table as assumed in the kinematic storage model.
From Figure 8-4, the drainable volume of water stored in the saturated zone of the hillslope segment per unit area, SWly,excess, is SWly ,excess =
1000 ⋅ H o ⋅ φ d ⋅ Lhill 2
8.4.1
where SWly,excess is the drainable volume of water stored in the saturated zone of the hillslope per unit area (mm H2O), Ho is the saturated thickness normal to the hillslope at the outlet expressed as a fraction of the total thickness (mm/mm), φd is the drainable porosity of the soil (mm/mm), Lhill is the hillslope length (m), and 1000 is a factor needed to convert meters to millimeters. This equation can be rearranged to solve for Ho:
Ho =
2 ⋅ SWly ,excess 1000 ⋅ φ d ⋅ Lhill
8.4.2
The drainable porosity of the soil layer is calculated:
φ d = φ soil − φ fc
8.4.3
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where φd is the drainable porosity of the soil layer (mm/mm), φsoil is the total porosity of the soil layer (mm/mm), and φfc is the porosity of the soil layer filled with water when the layer is at field capacity water content (mm/mm). A soil layer is considered to be saturated whenever the water content of the layer exceeds the layer’s field capacity water content. The drainable volume of water stored in the saturated layer is calculated: SWly ,excess = SWly − FC ly
if
SWly > FCly
8.4.4
SWly ,excess = 0
if
SWly ≤ FCly
8.4.5
where SWly is the water content of the soil layer on a given day (mm H2O) and
FCly is the water content of the soil layer at field capacity (mm H2O). The net discharge at the hillslope outlet, Qlat, is given by
Qlat = 24 ⋅ H o ⋅ vlat
8.4.6
where Qlat is the water discharged from the hillslope outlet (mm H2O/day), Ho is the saturated thickness normal to the hillslope at the outlet expressed as a fraction of the total thickness (mm/mm), vlat is the velocity of flow at the outlet (mm·h-1), and 24 is a factor to convert hours to days. Velocity of flow at the outlet is defined as
vlat = K sat ⋅ sin(α hill )
8.4.7
where Ksat is the saturated hydraulic conductivity (mm·h-1) and αhill is the slope of the hillslope segment. The slope is input to SWAT as the increase in elevation per unit distance (slp) which is equivalent to tan (α hill ) . Because tan (α hill ) ≅ sin (α hill ) , equation 8.4.3 is modified to use the value for the slope as input to the model:
vlat = K sat ⋅ tan (α hill ) = K sat ⋅ slp
8.4.8
Combining equations 8.4.2 and 8.4.8 with equation 8.4.6 yields the equation
æ 2 ⋅ SWly ,excess ⋅ K sat ⋅ slp ö ÷÷ Qlat = 0.024 ⋅ çç φ d ⋅ Lhill è ø where all terms are previously defined.
8.4.9
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8.4.1 LATERAL FLOW LAG In large subbasins with a time of concentration greater than 1 day, only a portion of the lateral flow will reach the main channel on the day it is generated. SWAT incorporates a lateral flow storage feature to lag a portion of lateral flow release to the main channel. Once lateral flow is calculated, the amount of lateral flow released to the main channel is calculated: æ é − 1 ùö ′ + Qlatstor ,i −1 ) ⋅ ç1 − exp ê Qlat = (Qlat ú ÷÷ ç TT lat ûø ë è
8.4.10
where Qlat is the amount of lateral flow discharged to the main channel on a given ′ is the amount of lateral flow generated in the subbasin on a day (mm H2O), Qlat given day (mm H2O), Qlatstor,i-1 is the lateral flow stored or lagged from the previous day (mm H2O), and TTlag is the lateral flow travel time (days). The model will calculate lateral flow travel time or utilize a user-defined travel time. In the majority of cases, the user should allow the model to calculate the travel time. If drainage tiles are present in the HRU, lateral flow travel time is calculated: TTlag =
tilelag
8.4.11
24
where TTlag is the lateral flow travel time (days) and tilelag is the drain tile lag time (hrs). In HRUs without drainage tiles, lateral flow travel time is calculated: TTlag = 10.4 ⋅
Lhill K sat ,mx
8.4.12
where TTlag is the lateral flow travel time (days), Lhill is the hillslope length (m), and Ksat,mx is the highest layer saturated hydraulic conductivity in the soil profile (mm/hr). æ é − 1 ùö The expression ç1 − exp ê ú ÷÷ in equation 8.4.10 represents the fraction ç êë TTlag úû ø è
of the total available water that will be allowed to enter the reach on any one day. Figure 8-5 plots values for this expression at different values of TTlag.
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Figure 8-5: Influence of TTlag on fraction of lateral flow released.
The delay in release of lateral flow will smooth the streamflow hydrograph simulated in the reach. Table 8-5: SWAT input variables used in lateral flow calculations. Variable name Definition SLSOIL Lhill: Hillslope length (m) SOL_K Ksat: Saturated hydraulic conductivity (mm/hr) SLOPE slp: Average slope of the subbasin (m/m) LAT_TTIME TTlag: Lateral flow travel time (days) GDRAIN tilelag: Drain tile lag time (hrs)
File Name .hru .sol .hru .hru .hru
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8.5 NOMENCLATURE AWC AWCly FC FCly Ho
Available water capacity (fraction or mm H2O) Available water capacity of soil layer (fraction or mm H2O) Water content of soil profile at field capacity (fraction or mm H2O) Water content of layer ly at field capacity (fraction or mm H2O) Saturated thickness normal to the hillslope at the outlet expressed as a fraction of the total thickness (mm/mm) Ksat Saturated hydraulic conductivity (mm/hr) Lhill Hillslope length (m) MS Mass of the solids (Mg) Qlat Lateral flow; water discharged from the hillslope outlet (mm H2O/day) Qlatstor,i-1 Lateral flow stored or lagged from the previous day (mm H2O) Qsurf Accumulated runoff or rainfall excess (mm H2O) Rday Amount of rainfall on a given day (mm H2O) SATly Amount of water in the soil layer when completely saturated (mm H2O) SW Amount of water in soil profile (mm H2O) SWly Soil water content of layer ly (mm H2O) SWly,excess Drainable volume of water stored layer (mm H2O) TTlag Lateral flow travel time (days) TTperc Travel time for percolation (hrs) VA Volume of air (m3) VS Volume of solids (m3) VT Total soil volume (m3) VW Volume of water (m3) WP Water content at wilting point (fraction or mm H2O) WPly Water content of the soil layer at wilting point (fraction or mm H2O) coefcrk Adjustment coefficient for crack flow crk Total crack volume for the soil profile on a given day (mm) crkly Crack volume for the soil layer on a given day expressed as a depth (mm) crkly,d-1 Crack volume for the soil layer on the previous day (mm) crkly,i Initial crack volume calculated for the soil layer on a given day expressed as a depth (mm) crkmax Potential crack volume for the soil profile expressed as a fraction of the total volume crkmax,ly Maximum crack volume possible for the soil layer (mm) depthly Depth of the soil layer (mm) mc Percent clay content slp Average slope of the subbasin (% or m/m) tilelag Drain tile lag time (hrs). vlat Velocity of flow at the hillslope outlet (mm·h-1) wcrk,btm Amount of water flow past the lower boundary of the soil profile due to bypass flow (mm H2O) winf Amount of water entering the soil profile on a given day (mm H2O)
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wperc,ly Amount of water percolating to the underlying soil layer on a given day (mm H2O) zl,ly Depth from the surface to the bottom of the soil layer (mm)
αhill ∆t l crk ρb ρs φd φfc φsoil
Slope of the hillslope segment (degrees) Length of the time step (hrs) Lag factor for crack development during drying Bulk density (Mg m-3) Particle density (Mg m-3) Drainable porosity of the soil (mm/mm) Porosity of the soil layer filled with water when the layer is at field capacity water content (mm/mm) Porosity of the soil (mm/mm)
8.6 REFERENCES Bouma, J. and J. Loveday. 1988. Chapter 5: Characterizing soil water regimes in swelling clay soils. p. 83-96. In L.P. Wilding and R. Puentes (ed). Vertisols: their distribution, properties, classification and management. Texas A&M University Printing Center, College Station, TX. Bronswijk, J.J.B. 1989. Prediction of actual cracking and subsidence in clay soils. Soil Science 148:87-93. Bronswijk, J.J.B. 1990. Shrinkage geometry of a heavy clay soil at various stresses. Soil Science Soc. Am. J. 54:1500-1502. Dudal, R. and H. Eswaran. 1988. Chapter 1: Distribution, properties and classification of vertisols. p. 1-22. In L.P. Wilding and R. Puentes (ed). Vertisols: their distribution, properties, classification and management. Texas A&M University Printing Center, College Station, TX. Koorevaar, P., G. Menelik, and C. Dirksen. 1983. Elements of Soil Physics. Elsevier, Amsterdam. Mitchell, A.R. 1992. Shrinkage terminology: escape from ‘normalcy’. Soil. Sci. Soc. Am. J. 56:993-994.
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Sloan, P.G. and I.D. Moore. 1984. Modeling subsurface stormflow on steeply sloping forested watersheds. Water Resources Research. 20(12): 18151822. Sloan, P.G., I.D. Morre, G.B. Coltharp, and J.D. Eigel. 1983. Modeling surface and subsurface stormflow on steeply-sloping forested watersheds. Water Resources Inst. Report 142. Univ. Kentucky, Lexington.
CHAPTER 9
EQUATIONS: GROUNDWATER
Groundwater is water in the saturated zone of earth materials under pressure greater than atmospheric, i.e. positive pressure. Water enters groundwater storage primarily by infiltration/percolation, although recharge by seepage from surface water bodies may occur. Water leaves groundwater storage primarily by discharge into rivers or lakes, but it is also possible for water to move upward from the water table into the capillary fringe.
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9.1 GROUNDWATER SYSTEMS Within the saturated zone of groundwater, regions of high conductivity and low conductivity will be found. The regions of high conductivity are made up of coarse-grained particles with a large percentage of macropores that allow water to move easily. The regions of low conductivity are made up of fine-grained particles with a large percentage of mesopores and micropores that restrict the rate of water movement. An aquifer is “a geologic unit that can store enough water and transmit it at a rate fast enough to be hydrologically significant” (Dingman, 1994). An unconfined aquifer is an aquifer whose upper boundary is the water table. The water table is defined as the depth at which the water pressure equals atmospheric pressure. A confined aquifer is an aquifer bounded above and below by geologic formations whose hydraulic conductivity is significantly lower than that of the aquifer. Figure 9-1 illustrates the two types of aquifers.
Figure 9-1: Unconfined and confined aquifers (from Dingman, 1994).
Recharge to unconfined aquifers occurs via percolation to the water table from a significant portion of the land surface. In contrast, recharge to confined aquifers by percolation from the surface occurs only at the upstream end of the
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confined aquifer, where the geologic formation containing the aquifer is exposed at the earth’s surface, flow is not confined, and a water table is present. Topography exerts an important influence on groundwater flow. The flow of groundwater in an idealized hilly upland area is depicted in Figure 9-2. The landscape can be divided into areas of recharge and areas of discharge. A recharge area is defined as a portion of a drainage basin where ground water flow is directed away from the water table. A discharge area is defined as a portion of the drainage basin where ground water flow is directed toward the water table. The water table is at or near the surface in discharge areas and surface water bodies are normally located in discharge areas.
Figure 9-2: Groundwater flow net in an idealized hilly region with homogenous permeable material resting on an impermeable base (from Hubbert, 1940)
Streams may be categorized by their relationship to the groundwater system. A stream located in a discharge area that receives groundwater flow is a gaining or effluent stream (Figure 9-3a). This type of stream is characterized by an increase in discharge downstream. A stream located in a recharge area is a losing or influent stream. This type of stream is characterized by a decrease in discharge downstream. A losing stream may be connected to (Figure 9-3b) or perched above (Figure 9-3c) the groundwater flow area. A stream that simultaneously receives and loses groundwater is a flow-through stream (Figure 9-3d).
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Figure 9-3: Stream-groundwater relationships: a) gaining stream receiving water from groundwater flow; b) losing stream connected to groundwater system; c) losing stream perched above groundwater system; and d) flow-through stream (from Dingman, 1994).
SWAT simulates two aquifers in each subbasin. The shallow aquifer is an unconfined aquifer that contributes to flow in the main channel or reach of the subbasin. The deep aquifer is a confined aquifer. Water that enters the deep aquifer is assumed to contribute to streamflow somewhere outside of the watershed (Arnold et al., 1993).
9.2 SHALLOW AQUIFER The water balance for the shallow aquifer is: aq sh ,i = aqsh ,i −1 + wrchrg − Q gw − wrevap − wdeep − w pump ,sh
9.2.1
where aqsh,i is the amount of water stored in the shallow aquifer on day i (mm H2O), aqsh,i-1 is the amount of water stored in the shallow aquifer on day i-1 (mm H2O), wrchrg is the amount of recharge entering the aquifer on day i (mm H2O), Qgw is the groundwater flow, or base flow, into the main channel on day i (mm H2O), wrevap is the amount of water moving into the soil zone in response to water deficiencies on day i (mm H2O), wdeep is the amount of water percolating from the
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shallow aquifer into the deep aquifer on day i (mm H2O), and wpump,sh is the amount of water removed from the shallow aquifer by pumping on day i (mm H2O).
9.2.1 RECHARGE Water that moves past the lowest depth of the soil profile by percolation or bypass flow enters and flows through the vadose zone before becoming shallow aquifer recharge. The lag between the time that water exits the soil profile and enters the shallow aquifer will depend on the depth to the water table and the hydraulic properties of the geologic formations in the vadose and groundwater zones. An exponential decay weighting function proposed by Venetis (1969) and used by Sangrey et al. (1984) in a precipitation/groundwater response model is utilized in SWAT to account for the time delay in aquifer recharge once the water exits the soil profile. The delay function accommodates situations where the recharge from the soil zone to the aquifer is not instantaneous, i.e. 1 day or less. The recharge to the aquifer on a given day is calculated: wrchrg ,i = (1 − exp[− 1 δ gw ]) ⋅ wseep + exp[− 1 δ gw ]⋅ wrchrg ,i −1
9.2.2
where wrchrg,i is the amount of recharge entering the aquifer on day i (mm H2O),
δgw is the delay time or drainage time of the overlying geologic formations (days), wseep is the total amount of water exiting the bottom of the soil profile on day i (mm H2O), and wrchrg,i-1 is the amount of recharge entering the aquifer on day i-1 (mm H2O). The total amount of water exiting the bottom of the soil profile on day i is calculated: wseep = w perc ,ly =n + wcrk ,btm
9.2.3
where wseep is the total amount of water exiting the bottom of the soil profile on day i (mm H2O), wperc,ly=n is the amount of water percolating out of the lowest layer, n, in the soil profile on day i (mm H2O), and wcrk,btm is the amount of water flow past the lower boundary of the soil profile due to bypass flow on day i (mm H2O).
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The delay time, δgw, cannot be directly measured. It can be estimated by simulating aquifer recharge using different values for δgw and comparing the simulated variations in water table level with observed values. Johnson (1977) developed a simple program to iteratively test and statistically evaluate different delay times for a watershed. Sangrey et al. (1984) noted that monitoring wells in the same area had similar values for δgw, so once a delay time value for a geomorphic area is defined, similar delay times can be used in adjoining watersheds within the same geomorphic province.
9.2.2 GROUNDWATER/BASE FLOW The shallow aquifer contributes base flow to the main channel or reach within the subbasin. Base flow is allowed to enter the reach only if the amount of water stored in the shallow aquifer exceeds a threshold value specified by the user, aqshthr,q. The steady-state response of groundwater flow to recharge is (Hooghoudt, 1940): Q gw =
8000 ⋅ K sat ⋅ hwtbl 2 Lgw
9.2.4
where Qgw is the groundwater flow, or base flow, into the main channel on day i (mm H2O), Ksat is the hydraulic conductivity of the aquifer (mm/day), Lgw is the distance from the ridge or subbasin divide for the groundwater system to the main channel (m), and hwtbl is the water table height (m). Water table fluctuations due to non-steady-state response of groundwater flow to periodic recharge is calculated (Smedema and Rycroft, 1983):
dhwtbl wrchrg − Q gw = 800 ⋅ µ dt where
9.2.5
dhwtbl is the change in water table height with time (mm/day), wrchrg is the dt
amount of recharge entering the aquifer on day i (mm H2O), Qgw is the groundwater flow into the main channel on day i (mm H2O), and µ is the specific yield of the shallow aquifer (m/m).
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Assuming that variation in groundwater flow is linearly related to the rate of change in water table height, equations 9.2.5 and 9.2.4 can be combined to obtain: dQ gw dt
= 10 ⋅
K sat ⋅ (wrchrg − Q gw ) = α gw ⋅ (wrchrg − Q gw ) µ ⋅ Lgw 2
9.2.6
where Qgw is the groundwater flow into the main channel on day i (mm H2O), Ksat is the hydraulic conductivity of the aquifer (mm/day), µ is the specific yield of the shallow aquifer (m/m), Lgw is the distance from the ridge or subbasin divide for the groundwater system to the main channel (m), wrchrg is the amount of recharge entering the aquifer on day i (mm H2O) and αgw is the baseflow recession constant or constant of proportionality. Integration of equation 9.2.6 and rearranging to solve for Qgw yields:
Q gw,i = Q gw,i −1 ⋅ exp[− α gw ⋅ ∆t ] + wrchrg ⋅ (1 − exp[− α gw ⋅ ∆t ])
9.2.7
where Qgw,i is the groundwater flow into the main channel on day i (mm H2O), Qgw,i-1 is the groundwater flow into the main channel on day i-1 (mm H2O), αgw is the baseflow recession constant, ∆t is the time step (1 day), and wrchrg is the amount of recharge entering the aquifer on day i (mm H2O). The baseflow recession constant, αgw, is a direct index of groundwater flow response to changes in recharge (Smedema and Rycroft, 1983). Values vary from 0.1-0.3 for land with slow response to recharge to 0.9-1.0 for land with a rapid response. Although the baseflow recession constant may be calculated, the best estimates are obtained by analyzing measured streamflow during periods of no recharge in the watershed. When the shallow aquifer receives no recharge, equation 9.2.7 simplifies to:
Q gw = Qgw,0 ⋅ exp[− α gw ⋅ t ]
9.2.8
where Qgw is the groundwater flow into the main channel at time t (mm H2O), Qgw,0 is the groundwater flow into the main channel at the beginning of the recession (time t=0) (mm H2O), αgw is the baseflow recession constant, and t is
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the time lapsed since the beginning of the recession (days). The baseflow recession constant is measured by rearranging equation 9.2.8.
α gw =
é Q gw, N ù 1 ⋅ ln ê ú N êë Q gw,0 ûú
9.2.9
where αgw is the baseflow recession constant, N is the time lapsed since the start of the recession (days), Qgw,N is the groundwater flow on day N (mm H2O), Qgw,0 is the groundwater flow at the start of the recession (mm H2O). It is common to find the baseflow days reported for a stream gage or watershed. This is the number of days for base flow recession to decline through one log cycle. When baseflow days are used, equation 9.2.9 can be further simplified:
α gw =
é Q gw , N ù 1 1 2 .3 ⋅ ln ê ⋅ ln[10] = ú= N BFD êë Q gw,0 ûú BFD
9.2.10
where αgw is the baseflow recession constant, and BFD is the number of baseflow days for the watershed.
9.2.3 REVAP Water may move from the shallow aquifer into the overlying unsaturated zone. In periods when the material overlying the aquifer is dry, water in the capillary fringe that separates the saturated and unsaturated zones will evaporate and diffuse upward. As water is removed from the capillary fringe by evaporation, it is replaced by water from the underlying aquifer. Water may also be removed from the aquifer by deep-rooted plants which are able to uptake water directly from the aquifer. SWAT models the movement of water into overlying unsaturated layers as a function of water demand for evapotranspiration. To avoid confusion with soil evaporation and transpiration, this process has been termed ‘revap’. This process is significant in watersheds where the saturated zone is not very far below the surface or where deep-rooted plants are growing. Because the type of plant cover will affect the importance of revap in the water balance, the parameters governing revap are usually varied by land use. Revap is allowed to occur only if the amount
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of water stored in the shallow aquifer exceeds a threshold value specified by the user, aqshthr,rvp. The maximum amount of water than will be removed from the aquifer via ‘revap’ on a given day is: wrevap ,mx = β rev ⋅ Eo
9.2.11
where wrevap,mx is the maximum amount of water moving into the soil zone in response to water deficiencies (mm H2O), βrev is the revap coefficient, and Eo is the potential evapotranspiration for the day (mm H2O). The actual amount of revap that will occur on a given day is calculated: wrevap = 0
if aq sh ≤ aqshthr ,rvp
wrevap = wrevap ,mx − aqshthr ,rvp
if aqshthr ,rvp < aqsh < (aqshthr ,rvp + wrevap ,mx ) 9.2.13
wrevap = wrevap ,mx
if aqsh ≥ (aqshthr ,rvp + wrevap ,mx )
9.2.12
9.2.14
where wrevap is the actual amount of water moving into the soil zone in response to water deficiencies (mm H2O), wrevap,mx is the maximum amount of water moving into the soil zone in response to water deficiencies (mm H2O), aqsh is the amount of water stored in the shallow aquifer at the beginning of day i (mm H2O) and aqshthr,rvp is the threshold water level in the shallow aquifer for revap or percolation to deep aquifer to occur (mm H2O).
9.2.4 PERCOLATION TO DEEP AQUIFER A fraction of the total daily recharge can be routed to the deep aquifer. Percolation to the deep aquifer is allowed to occur only if the amount of water stored in the shallow aquifer exceeds a threshold value specified by the user, aqshthr,rvp. The maximum amount of water than will be removed from the shallow aquifer via percolation to the deep aquifer on a given day is: wdeep ,mx = β deep ⋅ wrchrg
9.2.15
where wdeep,mx is the maximum amount of water moving into the deep aquifer on day i (mm H2O), βdeep is the aquifer percolation coefficient, and wrchrg is the amount of recharge entering the aquifer on day i (mm H2O). The actual amount of percolation to the deep aquifer that will occur on a given day is calculated:
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wdeep = 0
if aq sh ≤ aqshthr ,rvp
wdeep = wdeep ,mx − aqshthr ,rvp
if aqshthr ,rvp < aqsh < (aqshthr ,rvp + wrevap ,mx ) 9.2.13
wdeep = wdeep ,mx
if aqsh ≥ (aqshthr ,rvp + wrevap ,mx )
9.2.12
9.2.14
where wdeep is the actual amount of water moving into the deep aquifer on day i (mm H2O), wdeep,mx is the maximum amount of water moving into the deep aquifer on day i (mm H2O), aqsh is the amount of water stored in the shallow aquifer at the beginning of day i (mm H2O) and aqshthr,rvp is the threshold water level in the shallow aquifer for revap or percolation to deep aquifer to occur (mm H2O).
9.2.5 PUMPING If the shallow aquifer is specified as the source of irrigation water or water removed for use outside the watershed, the model will allow an amount of water up to the total volume of the shallow aquifer to be removed on any given day. Detailed information on water management may be found in Chapter 21.
9.2.6 GROUNDWATER HEIGHT Although SWAT does not currently print groundwater height in the output files, the water table height is updated daily by the model. Groundwater height is related to groundwater flow by equation 9.2.4. Q gw =
8000 ⋅ K sat 8000 ⋅ µ 10 ⋅ K sat ⋅ hwtbl = ⋅ ⋅ hwtbl = 800 ⋅ µ ⋅ α gw ⋅ hwtbl 2 10 Lgw µ ⋅ Lgw 2
9.2.15
where Qgw is the groundwater flow into the main channel on day i (mm H2O), Ksat is the hydraulic conductivity of the aquifer (mm/day), Lgw is the distance from the ridge or subbasin divide for the groundwater system to the main channel (m), hwtbl is the water table height (m), µ is the specific yield of the shallow aquifer (m/m), and αgw is the baseflow recession constant. Substituting this definition for Qgw into equation 9.2.7 gives hwtbl ,i = hwtbl ,i −1 ⋅ exp[− α gw ⋅ ∆t ] +
wrchrg ⋅ (1 − exp[− α gw ⋅ ∆t ]) 800 ⋅ µ ⋅ α gw
9.2.16
where hwtbl,i is the water table height on day i (m), hwtbl,i-1 is the water table height on day i-1 (m), αgw is the baseflow recession constant, ∆t is the time step (1 day),
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wrchrg is the amount of recharge entering the aquifer on day i (mm H2O), and µ is the specific yield of the shallow aquifer (m/m). Table 9-1: SWAT input variables used in shallow aquifer calculations. Variable name Definition GW_DELAY δgw: Delay time for aquifer recharge (days) GWQMN aqshthr,q: Threshold water level in shallow aquifer for base flow (mm H2O) ALPHA_BF αgw: Baseflow recession constant REVAPMN aqshthr,rvp: Threshold water level in shallow aquifer for revap or percolation to deep aquifer (mm H2O) GW_REVAP βrev: Revap coefficient RCHRG_DP βdeep: Aquifer percolation coefficient GW_SPYLD µ: Specific yield of the shallow aquifer (m/m)
File Name .gw .gw .gw .gw .gw .gw .gw
9.3 DEEP AQUIFER The water balance for the deep aquifer is: aqdp ,i = aqdp ,i −1 + wdeep − w pump ,dp
9.3.1
where aqdp,i is the amount of water stored in the deep aquifer on day i (mm H2O), aqdp,i-1 is the amount of water stored in the deep aquifer on day i-1 (mm H2O), wdeep is the amount of water percolating from the shallow aquifer into the deep aquifer on day i (mm H2O), and wpump,dp is the amount of water removed from the deep aquifer by pumping on day i (mm H2O). The amount of water percolating into the deep aquifer is calculated with the equations reviewed in section 9.2.4. If the deep aquifer is specified as the source of irrigation water or water removed for use outside the watershed, the model will allow an amount of water up to the total volume of the deep aquifer to be removed on any given day. Water entering the deep aquifer is not considered in future water budget calculations and can be considered to be lost from the system.
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9.4 NOMENCLATURE BFD Eo Ksat Lgw N Qgw Qgw,0 Qgw,N
Number of baseflow days for the watershed Potential evapotranspiration (mm d-1) Hydraulic conductivity of the aquifer (mm/day) Distance from the ridge or subbasin divide for the groundwater system to the main channel (m) Time lapsed since the start of the recession (days) Groundwater flow, or base flow, into the main channel (mm H2O) Groundwater flow at the start of the recession (mm H2O) Groundwater flow on day N (mm H2O)
aqdp Amount of water stored in the deep aquifer (mm H2O) aqsh Amount of water stored in the shallow aquifer (mm H2O) aqshthr,q Threshold water level in shallow aquifer for base flow (mm H2O) aqshthr,rvp Threshold water level in shallow aquifer for revap or percolation to deep aquifer (mm H2O) hwtbl Water table height (m) wcrk,btm Amount of water flow past the lower boundary of the soil profile due to bypass flow (mm H2O) wdeep Amount of water percolating from the shallow aquifer into the deep aquifer (mm H2O) wdeep,mx Maximum amount of water moving into the deep aquifer (mm H2O) wpump,dp Amount of water removed from the deep aquifer by pumping (mm H2O) wpump,sh Amount of water removed from the shallow aquifer by pumping (mm H2O) wrchrg Amount of water entering the aquifer via recharge (mm H2O) wrevap Amount of water moving into the soil zone in response to water deficiencies (mm H2O) wrevap,mx Maximum amount of water moving into the soil zone in response to water deficiencies on day i (mm H2O) wseep Total amount of water exiting the bottom of the soil profile (mm H2O)
αgw βdeep βrev δgw µ
Baseflow recession constant Aquifer percolation coefficient Revap coefficient Delay time or drainage time for aquifer recharge (days) Specific yield of the shallow aquifer (m/m)
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9.5 REFERENCES Arnold, J.G., P.M. Allen, and G. Bernhardt. 1993. A comprehensive surfacegroundwater flow model. Journal of Hydrology 142: 47-69. Dingman, S.L. 1994. Physical hydrology. Prentice-Hall, Inc., Englewood Cliffs, NJ. Hooghoudt, S.B. 1940. Bijdrage tot de kennis van enige natuurkundige grootheden van de grond. Versl. Landbouwkd. Onderz. 46: 515-707. Hubbert, M.K. 1940. The theory of groundwater motion. Journal of Geology 48: 785-944. Johnson, K.H. 1977. A predictive method for ground water levels. Master’s Thesis, Cornell University, Ithica, N.Y. Sangrey, D.A., K.O. Harrop-Williams, and J.A. Klaiber. 1984. Predicting groundwater response to precipitation. ASCE J. Geotech. Eng. 110(7): 957-975. Smedema, L.K. and D.W. Rycroft. 1983. Land drainage—planning and design of agricultural drainage systems, Cornell University Press, Ithica, N.Y. Venetis, C. 1969. A study of the recession of unconfined aquifers. Bull. Int. Assoc. Sci. Hydrol. 14(4): 119-125.
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NUTRIENTS/PESTICIDES The fate and transport of nutrients and pesticides in a watershed depend on the transformations the compounds undergo in the soil environment. SWAT models the complete nutrient cycle for nitrogen and phosphorus as well as the degradation of any pesticides applied in an HRU. The following three chapters review the methodology used by SWAT to simulate nutrient and pesticide processes in the soil.
CHAPTER 10
EQUATIONS: NITROGEN
The complexity of the nitrogen cycle and nitrogen’s importance in plant growth have made this element the subject of much research. The nitrogen cycle is a dynamic system that includes the water, atmosphere and soil. Plants require nitrogen more than any other essential element, excluding carbon, oxygen and hydrogen.
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10.1 NITROGEN CYCLE The three major forms of nitrogen in mineral soils are organic nitrogen associated with humus, mineral forms of nitrogen held by soil colloids, and mineral forms of nitrogen in solution. Nitrogen may be added to the soil by fertilizer, manure or residue application, fixation by symbiotic or nonsymbiotic bacteria, and rain. Nitrogen is removed from the soil by plant uptake, leaching, volatilization, denitrification and erosion. Figure 10-1 shows the major components of the nitrogen cycle.
Figure 10-1: The nitrogen cycle
Nitrogen is considered to be an extremely reactive element. The highly reactive nature of nitrogen results from its ability to exist in a number of valance states. The valence state or oxidation state describes the number of electrons orbiting the nucleus of the nitrogen atom relative to the number present in an
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electronically neutral atom. The valence state will be positive as the atom looses electrons and will be negative as the atom gains electrons. Examples of nitrogen in different valence states are: most oxidized
+5
NO -3
nitrate
NO 2 NO -2 NO N 2O N2 NH 4 OH N2H4
nitrogen dioxide nitrite nitrogen monoxide (gas) nitrous oxide (laughing gas) N2 gas or elemental N hydroxylamine hydrozine
most reduced
+4 +3 +2 +1 0 -1 -2 -3
NH3 or NH 4+ ammonia gas or ammonium
The ability of nitrogen to vary its valence state makes it a highly mobile element. Predicting the movement of nitrogen between the different pools in the soil is critical to the successful management of this element in the environment. SWAT monitors five different pools of nitrogen in the soil (Figure 10-2). Two pools are inorganic forms of nitrogen, NH4+ and NO -3 , while the other three pools are organic forms of nitrogen. Fresh organic N is associated with crop residue and microbial biomass while the active and stable organic N pools are associated with the soil humus. The organic nitrogen associated with humus is partitioned into two pools to account for the variation in availability of humic substances to mineralization.
Figure 10-2: SWAT soil nitrogen pools and processes that move nitrogen in and out of pools.
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10.1.1 INITIALIZATION OF SOIL NITROGEN LEVELS Users may define the amount of nitrate and organic nitrogen contained in humic substances for all soil layers at the beginning of the simulation. If the user does not specify initial nitrogen concentrations, SWAT will initialize levels of nitrogen in the different pools. Initial nitrate levels in the soil are varied by depth using the relationship: æ −z ö NO3conc , z = 7 ⋅ expç ÷ è 1000 ø
10.1.1
where NO3conc,z is the concentration of nitrate in the soil at depth z (mg/kg or ppm), and z is the depth from the soil surface (mm). The nitrate concentration with depth calculated from equation 10.1.1 is displayed in Figure 10-3. The nitrate concentration for a layer is calculated by solving equation 10.1.1 for the horizon’s lower boundary depth.
Figure 10-3: Nitrate concentration with depth.
Organic nitrogen levels are assigned assuming that the C:N ratio for humic materials is 14:1. The concentration of humic organic nitrogen in a soil layer is calculated: æ orgCly ö ÷÷ orgN hum ,ly = 10 4 ⋅ çç è 14 ø
10.1.2
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179
where orgNhum,ly is the concentration of humic organic nitrogen in the layer (mg/kg or ppm), and orgCly is the amount of organic carbon in the layer (%). The humic organic N is partitioned between the active and stable pools using the following equations: orgN act ,ly = orgN hum ,ly ⋅ fractN
10.1.3
orgN sta ,ly = orgN hum ,ly ⋅ (1 − fractN )
10.1.4
where orgNact,ly is the concentration of nitrogen in the active organic pool (mg/kg), orgNhum,ly is the concentration of humic organic nitrogen in the layer (mg/kg), fractN is the fraction of humic nitrogen in the active pool, and orgNsta,ly is the concentration of nitrogen in the stable organic pool (mg/kg). The fraction of humic nitrogen in the active pool, fractN, is set to 0.02. Nitrogen in the fresh organic pool is set to zero in all layers except the top 10 mm of soil. In the top 10 mm, the fresh organic nitrogen pool is set to 0.15% of the initial amount of residue on the soil surface. orgN frsh ,surf = 0.0015 ⋅ rsd surf
10.1.5
where orgNfrsh,surf is the nitrogen in the fresh organic pool in the top 10 mm (kg N/ha), and rsdsurf is material in the residue pool for the top 10 mm of soil (kg/ha). The ammonium pool for soil nitrogen, NH4ly, is initialized to 0 ppm. While SWAT allows nutrient levels to be input as concentrations, it performs all calculations on a mass basis. To convert a concentration to a mass, the concentration is multiplied by the bulk density and depth of the layer and divided by 100: conc N ⋅ ρ b ⋅ depthly 100
=
kg N ha
10.1.6
where concN is the concentration of nitrogen in a layer (mg/kg or ppm), ρb is the bulk density of the layer (Mg/m3), and depthly is the depth of the layer (mm).
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Table 10-1: SWAT input variables that pertain to nitrogen pools. Variable Name SOL_NO3 SOL_ORGN RSDIN SOL_BD SOL_CBN
Definition NO3conc,ly: Initial NO3 concentration in soil layer (mg/kg or ppm) orgNhum,ly: Initial humic organic nitrogen in soil layer (mg/kg or ppm) rsdsurf: Material in the residue pool for the top 10mm of soil (kg ha-1) ρb: Bulk density of the layer (Mg/m3) orgCly: Amount of organic carbon in the layer (%)
Input File .chm .chm .hru .sol .sol
10.2 MINERALIZATION & DECOMPOSITION / IMMOBILIZATION Decomposition is the breakdown of fresh organic residue into simpler organic components. Mineralization is the microbial conversion of organic, plantunavailable nitrogen to inorganic, plant-available nitrogen. Immobilization is the microbial conversion of plant-available inorganic soil nitrogen to plantunavailable organic nitrogen. Bacteria decompose organic material to obtain energy for growth processes. Plant residue is broken down into glucose which is then converted to energy: energy released C 6 H 12 O 6 + O 2 → 6CO 2 + 6 H 2 O
The energy released by the conversion of glucose to carbon dioxide and water is used for various cell processes, including protein synthesis. Protein synthesis requires nitrogen. If the residue from which the glucose is obtained contains enough nitrogen, the bacteria will use nitrogen from the organic material to meet the demand for protein synthesis. If the nitrogen content of the residue is too low to meet the bacterial demand for nitrogen, the bacteria will use NH4+ and NO -3 from the soil solution to meet its needs. If the nitrogen content of the residue exceeds the bacterial demand for nitrogen, the bacterial will release the excess nitrogen into soil solution as NH4+. A general relationship between C:N ratio and mineralization/immobilization is: C:N > 30:1
immobilization occurs, a net decrease in soil NH4+ and NO -3
20:1 ≤ C:N ≤ 30:1
expect no net change; immobilization mineralization processes are at equilibrium
and
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C:N < 20:1
181
mineralization occurs, a net gain in soil NH4+ and NO -3
The nitrogen mineralization algorithms in SWAT are net mineralization algorithms which incorporate immobilization into the equations. The algorithms were adapted from the PAPRAN mineralization model (Seligman and van Keulen, 1981). Two sources are considered for mineralization: the fresh organic N pool associated with crop residue and microbial biomass and the active organic N pool associated with soil humus. Mineralization and decomposition are allowed to occur only if the temperature of the soil layer is above 0°C. Mineralization and decomposition are dependent on water availability and temperature. Two factors are used in the mineralization and decomposition equations to account for the impact of temperature and water on these processes. The nutrient cycling temperature factor is calculated:
γ tmp ,ly = 0.9 ⋅
Tsoil ,ly
Tsoil ,ly + exp[9.93 − 0.312 ⋅ Tsoil ,ly ]
+ 0 .1
10.2.1
where γtmp,ly is the nutrient cycling temperature factor for layer ly, and Tsoil,ly is the temperature of layer ly (°C). The nutrient cycling temperature factor is never allowed to fall below 0.1. The nutrient cycling water factor is calculated:
γ sw,ly =
SWly FCly
10.2.2
where γsw,ly is the nutrient cycling water factor for layer ly, SWly is the water content of layer ly on a given day (mm H2O), and FCly is the water content of layer ly at field capacity (mm H2O). The nutrient cycling water factor is never allowed to fall below 0.05.
10.2.1 HUMUS MINERALIZATION Nitrogen is allowed to move between the active and stable organic pools in the humus fraction. The amount of nitrogen transferred from one pool to the other is calculated: æ 1 ö N trns ,ly = β trns ⋅ orgN act ,ly ⋅ çç − 1÷÷ − orgN sta ,ly è fractN ø
10.2.3
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Ntrns,ly is the amount of nitrogen transferred between the active and stable organic pools (kg N/ha), βtrns is the rate constant (1×10-5), orgNact,ly is the amount of nitrogen in the active organic pool (kg N/ha), fractN is the fraction of humic nitrogen in the active pool (0.02), and orgNsta,ly is the amount of nitrogen in the stable organic pool (kg N/ha). When Ntrns,ly is positive, nitrogen is moving from the active organic pool to the stable organic pool. When Ntrns,ly is negative, nitrogen is moving from the stable organic pool to the active organic pool. Mineralization from the humus active organic N pool is calculated: N mina,ly = β min ⋅ (γ tmp ,ly ⋅ γ sw,ly ) ⋅ orgN act ,ly 12
10.2.4
where Nmina,ly is the nitrogen mineralized from the humus active organic N pool (kg N/ha), βmin is the rate coefficient for mineralization of the humus active organic nutrients, γtmp,ly is the nutrient cycling temperature factor for layer ly, γsw,ly is the nutrient cycling water factor for layer ly, orgNact,ly is the amount of nitrogen in the active organic pool (kg N/ha). Nitrogen mineralized from the humus active organic pool is added to the nitrate pool in the layer.
10.2.2 RESIDUE DECOMPOSITION & MINERALIZATION Decomposition and mineralization of the fresh organic nitrogen pool is allowed only in the first soil layer. Decomposition and mineralization are controlled by a decay rate constant that is updated daily. The decay rate constant is calculated as a function of the C:N ratio and C:P ratio of the residue, temperature and soil water content. The C:N ratio of the residue is calculated:
ε C:N =
0.58 ⋅ rsd ly orgN frsh ,ly + NO3ly
10.2.5
where εC:N is the C:N ratio of the residue in the soil layer, rsdly is the residue in layer ly (kg/ha), 0.58 is the fraction of residue that is carbon, orgNfrsh,ly is the nitrogen in the fresh organic pool in layer ly (kg N/ha), and NO3ly is the amount of nitrate in layer ly (kg N/ha). The C:P ratio of the residue is calculated:
CHAPTER 10: EQUATIONS—NITROGEN
ε C :P =
0.58 ⋅ rsd ly orgPfrsh ,ly + Psolution,ly
183
10.2.6
where εC:P is the C:P ratio of the residue in the soil layer, rsdly is the residue in layer ly (kg/ha), 0.58 is the fraction of residue that is carbon, orgPfrsh,ly is the phosphorus in the fresh organic pool in layer ly (kg P/ha), and Psolution,ly is the amount of phosphorus in solution in layer ly (kg P/ha). The decay rate constant defines the fraction of residue that is decomposed. The decay rate constant is calculated:
δ ntr ,ly = β rsd ⋅ γ ntr ,ly ⋅ (γ tmp ,ly ⋅ γ sw,ly )1 2
10.2.7
where δntr,ly is the residue decay rate constant, βrsd is the rate coefficient for mineralization of the residue fresh organic nutrients, γntr,ly is the nutrient cycling residue composition factor for layer ly, γtmp,ly is the nutrient cycling temperature factor for layer ly, and γsw,ly is the nutrient cycling water factor for layer ly. The nutrient cycling residue composition factor is calculated:
γ ntr ,ly
(ε C:N − 25)ù ì é ï exp ê− 0.693 ⋅ úû 25 ë ï ï (ε − 200)ù ï é = min íexp ê− 0.693 ⋅ C:P úû 200 ï ë ï 1.0 ï ï î
10.2.8
where γntr,ly is the nutrient cycling residue composition factor for layer ly, εC:N is the C:N ratio on the residue in the soil layer, and εC:P is the C:P ratio on the residue in the soil layer. Mineralization from the residue fresh organic N pool is then calculated: N minf,ly = 0.8 ⋅ δ ntr ,ly ⋅ orgN frsh ,ly
10.2.9
where Nminf,ly is the nitrogen mineralized from the fresh organic N pool (kg N/ha),
δntr,ly is the residue decay rate constant, and orgNfrsh,ly is the nitrogen in the fresh organic pool in layer ly (kg N/ha). Nitrogen mineralized from the fresh organic pool is added to the nitrate pool in the layer.
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Decomposition from the residue fresh organic N pool is calculated: N dec,ly = 0.2 ⋅ δ ntr ,ly ⋅ orgN frsh ,ly
10.2.9
where Ndec,ly is the nitrogen decomposed from the fresh organic N pool (kg N/ha),
δntr,ly is the residue decay rate constant, and orgNfrsh,ly is the nitrogen in the fresh organic pool in layer ly (kg N/ha). Nitrogen decomposed from the fresh organic pool is added to the humus active organic pool in the layer. Table 10-2: SWAT input variables that pertain to mineralization. Variable Name CMN RSDCO RSDCO_PL
Definition βmin: Rate coefficient for mineralization of the humus active organic nutrients βrsd: Rate coefficient for mineralization of the residue fresh organic nutrients βrsd: Rate coefficient for mineralization of the residue fresh organic nutrients
Input File .bsn .bsn crop.dat
10.3 NITRIFICATION & AMMONIA VOLATILIZATION Nitrification is the two-step bacterial oxidation of NH4+ to NO -3 . −
−12 e → 2 NO -2 + 2 H 2 O + 4H + step 1: 2 NH +4 + 3O 2
(Nitrosomonas)
-4e 2NO -3 step 2: 2 NO -2 + O 2 →
(Nitrobacter)
-
Ammonia volatilization is the gaseous loss of NH3 that occurs when ammonium, NH4+, is surface applied to a calcareous soil or when urea, (NH2)2CO, is surface applied to any soil. NH4+ surface applied to a calcareous soil: →(NH 4 )2 CO 3 + CaX 2 step 1: CaCO 3 + 2 NH 4+ X ←
step 2: (NH 4 )CO 3 ← → 2NH 3 + CO 2 + H 2 O Urea surface applied to any soil: urease enzyme →(NH 4 )2 CO 3 step 1: (NH 2 )2 CO + 2 H 2 O ← step 2: (NH 4 )2 CO 3 ← → 2NH 3 + CO 2 + H 2 O
SWAT simulates nitrification and ammonia volatilization using a combination of the methods developed by Reddy et al. (1979) and Godwin et al. (1984). The total amount of nitrification and ammonia volatilization is calculated, and then partitioned between the two processes. Nitrification is a function of soil
CHAPTER 10: EQUATIONS—NITROGEN
185
temperature and soil water content while ammonia volatilization is a function of soil
temperature
and
depth.
Three
coefficients
are
used
in
the
nitrification/volatilization algorithms to account for the impact of these parameters. Nitrification/volatilization occurs only when the temperature of the soil layer exceeds 5°C. The nitrification/volatilization temperature factor is calculated:
ηtmp ,ly = 0.41 ⋅
(T
soil ,ly
− 5)
if Tsoil ,ly > 5
10
10.3.1
where ηtmp,ly is the nitrification/volatilization temperature factor, and Tsoil,ly is the temperature of layer ly (°C). The nitrification soil water factor is calculated:
η sw,ly =
SWly − WPly
0.25 ⋅ (FCly − WPly )
if SWly − WPly < 0.25 ⋅ (FC ly − WPly ) 10.3.2 if SWly − WPly ≥ 0.25 ⋅ (FC ly − WPly ) 10.3.3
η sw,ly = 1.0
where ηsw,ly is the nitrification soil water factor, SWly is the soil water content of layer ly on a given day (mm H2O), WPly is the amount of water held in the soil layer at wilting point water content (mm H2O), and FCly is the amount of water held in the soil layer at field capacity water content (mm H2O). The volatilization depth factor is calculated:
η midz ,ly = 1 −
z mid ,ly
z mid ,ly + exp[4.706 − 0.305 ⋅ z mid ,ly ]
10.3.4
where ηmidz,ly is the volatilization depth factor, and zmid,ly is the depth from the soil surface to the middle of the layer (mm). The impact of environmental factors on nitrification and ammonia volatilization in a given layer is defined by the nitrification regulator and volatilization regulator. The nitrification regulator is calculated:
η nit ,ly = η tmp ,ly ⋅ η sw,ly
10.3.5
and the volatilization regulator is calculated:
η vol ,ly = η tmp ,ly ⋅ η midz ,ly
10.3.6
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SWAT USER'S MANUAL, VERSION 2000
where ηnit,ly is the nitrification regulator, ηvol,ly is the volatilization regulator, ηtmp,ly is the nitrification/volatilization temperature factor, ηsw,ly is the nitrification soil water factor, and ηmidz,ly is the volatilization depth factor. The total amount of ammonium lost to nitrification and volatilization is calculated using a first-order kinetic rate equation (Reddy et al., 1979): N nit vol ,ly = NH4ly ⋅ (1 − exp[− η nit ,ly − η vol ,ly ])
10.3.7
where N nit vol ,ly is the amount of ammonium converted via nitrification and volatilization in layer ly (kg N/ha), NH4ly is the amount of ammonium in layer ly (kg N/ha), ηnit,ly is the nitrification regulator, and ηvol,ly is the volatilization regulator. N nit vol ,ly
To partition
between nitrification and volatilization, the
expression by which NH4ly is multiplied in equation 10.3.7, is solved using each regulator individually to obtain a fraction of ammonium removed by each process:
frnit ,ly = 1 − exp[− η nit ,ly ]
10.3.8
frvol ,ly = 1 − exp[− η vol ,ly ]
10.3.9
where frnit,ly is the estimated fraction of nitrogen lost by nitrification, frvol,ly is the estimated fraction of nitrogen lost by volatilization, ηnit,ly is the nitrification regulator, and ηvol,ly is the volatilization regulator. The amount of nitrogen removed from the ammonium pool by nitrification is then calculated: N nit ,ly =
( fr
frnit ,ly
nit ,ly
+ frvol ,ly )
⋅ N nit vol ,ly
10.3.10
and the amount of nitrogen removed from the ammonium pool by volatilization is: N vol ,ly =
( fr
frvol ,ly
nit ,ly
+ frvol ,ly )
⋅ N nit vol ,ly
10.3.11
where Nnit,ly is the amount of nitrogen converted from NH4+ to NO -3 in layer ly (kg N/ha), Nvol,ly is the amount of nitrogen converted from NH4+ to NH3 in layer ly (kg
CHAPTER 10: EQUATIONS—NITROGEN
187
N/ha), frnit,ly is the estimated fraction of nitrogen lost by nitrification, frvol,ly is the estimated fraction of nitrogen lost by volatilization, and N nit vol ,ly is the amount of ammonium converted via nitrification and volatilization in layer ly (kg N/ha)
10.4 DENITRIFICATION Denitrification is the bacterial reduction of nitrate, NO -3 , to N2 or N2O gases under anaerobic (reduced) conditions. Denitrification is a function of water content, temperature, presence of a carbon source and nitrate. In general, when the water-filled porosity is greater than 60% denitrification will be observed in a soil. As soil water content increases, anaerobic conditions develop due to the fact that oxygen diffuses through water 10,000 times slower than through air. Because the rate of oxygen diffusion through water slows as the water temperature increases, temperature will also influence denitrification. Cropping systems where water is ponded, such as rice, can lose a large fraction of fertilizer by denitrification. For a regular cropping system, an estimated 10-20% of nitrogen fertilizer may be lost to denitrification. Under a rice cropping system, 50% of nitrogen fertilizer may be lost to denitrification. In a flooded cropping system, the depth of water plays an important role because it controls the amount of water oxygen has to diffuse through to reach the soil. SWAT determines the amount of nitrate lost to denitrification with the equation:
N denit ,ly = NO3ly ⋅ (1 − exp[− 1.4 ⋅ γ tmp ,ly ⋅ orgCly ]) if γ sw,ly ≥ 0.95
10.4.1
if γ sw,ly < 0.95
10.4.2
N denit ,ly = 0.0
where Ndenit,ly is the amount of nitrogen lost to denitrification (kg N/ha), NO3ly is the amount of nitrate in layer ly (kg N/ha), γtmp,ly is the nutrient cycling temperature factor for layer ly calculated with equation 10.2.1, γsw,ly is the nutrient cycling water factor for layer ly calculated with equation 10.2.2, orgCly is the amount of organic carbon in the layer (%).
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Table 10-3: SWAT input variables that pertain to denitrification. Variable Name SOL_CBN
Input File .sol
Definition orgCly: Amount of organic carbon in the layer (%)
10.5 NITROGEN IN RAINFALL Lightning discharge converts atmospheric N2 to nitric acid which can then be transferred to the soil with precipitation. The chemical steps involved are: arc of electricity step 1: N 2 + O 2 → 2NO (monoxide)
→ NO 2 (dioxide) step 2: 2NO + O 2 → 2HNO 2 + NO (nitric acid and monoxide) step 3: 3NO 2 + H 2 O More nitrogen will be added to the soil with rainfall in areas with a high amount of lightning activity than in areas with little lightning. The amount of nitrate added to the soil in rainfall is calculated: N rain = 0.01 ⋅ RNO 3 ⋅ Rday
10.5.1
where Nrain is nitrate added by rainfall (kg N/ha), RNO3 is the concentration of nitrogen in the rain (mg N/L), and Rday is the amount of precipitation on a given day (mm H2O). The nitrogen in rainfall is added to the nitrate pool in the top 10 mm of soil. Table 10-4: SWAT input variables that pertain to nitrogen in rainfall. Variable Name RCN
Definition RNO3: Concentration of nitrogen in the rain (mg N/L)
Input File .bsn
10.6 FIXATION Legumes are able to obtain a portion of their nitrogen demand through fixation of atmospheric N2 performed by rhizobia living in association with the plant. In exchange for nitrogen, the plant supplies the bacteria with carbohydrates. SWAT simulates nitrogen fixation by legumes when the soil does not supply the plant with the amount of nitrogen needed for growth. The nitrogen obtained by fixation is incorporated directly into the plant biomass and never enters the soil (unless plant biomass is added to the soil as residue after the plant
CHAPTER 10: EQUATIONS—NITROGEN
189
is killed). The equations for nitrogen fixation by legumes are reviewed in Chapter 18.
10.7 UPWARD MOVEMENT OF NITRATE IN WATER As water evaporates from the soil surface, the water content at the surface drops, creating a gradient in the profile. Water from lower in the profile will move upward in response to the gradient, carrying dissolved nutrients with it. SWAT allows nitrate to be transported from the first soil layer defined in the .sol file to the surface top 10 mm of soil with the equation: N evap = 0.1 ⋅ NO3ly ⋅
′′ ,ly E soil SWly
where Nevap is the amount of nitrate moving from the first soil layer to the soil surface zone (kg N/ha), NO3ly is the nitrate content of the first soil layer (kg ′′ ,ly is the amount of water removed from the first soil layer as a result N/ha), E soil
of evaporation (mm H2O), and SWly is the soil water content of the first soil layer (mm H2O).
10.8 LEACHING The majority of plant-essential nutrients are cations which are attracted and sorbed to negatively-charged soil particles. As plants extract these cations from soil solution, the soil particles release bound cations into soil solution to bring the ratio of nutrients in solution and on soil particles back into equilibrium. In effect, the soil buffers the concentration of cations in solution. In contrast, nitrate is an anion and is not attracted to or sorbed by soil particles. Because retention of nitrate by soils is minimal, nitrate is very susceptible to leaching. The algorithms used by SWAT to calculated nitrate leaching simultaneously solve for loss of nitrate in surface runoff and lateral flow also. These algorithms are reviewed in Chapter 14.
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SWAT USER'S MANUAL, VERSION 2000
10.9 NOMENCLATURE ′′ ,ly E soil FCly Ndec,ly Ndenit,ly Nevap
Amount of water removed from layer ly by evaporation (mm H2O) Water content of layer ly at field capacity (mm H2O) Nitrogen decomposed from the fresh organic N pool (kg N/ha) Amount of nitrogen lost to denitrification (kg N/ha) Amount of nitrate moving from the first soil layer to the soil surface zone (kg N/ha) Nmina,ly Nitrogen mineralized from the humus active organic N pool (kg N/ha) Nminf,ly Nitrogen mineralized from the fresh organic N pool (kg N/ha) Nnit,ly Amount of nitrogen converted from NH4+ to NO -3 in layer ly (kg N/ha) N nit vol ,ly Amount of ammonium converted via nitrification and volatilization in layer ly
(kg N/ha) Nrain Nitrate added by rainfall (kg N/ha) Ntrns,ly Amount of nitrogen transferred between the active and stable organic pools (kg N/ha) Nvol,ly Amount of nitrogen converted from NH4+ to NH3 in layer ly (kg N/ha) NH4ly Ammonium content of layer ly (kg NH4-N/ha) NO3conc,z Concentration of nitrate in the soil at depth z (mg/kg or ppm) NO3ly Nitrate content of soil layer ly (kg NO3-N/ha) Psolution,ly Solution phosphorus content of soil layer ly (kg P/ha) Rday Amount of rainfall on a given day (mm H2O) RNO3 Concentration of nitrogen in the rain (mg N/L) SWly Soil water content of layer ly (mm H2O) Tsoil,ly Temperature of layer ly (°C) WPly Water content of layer ly at wilting point (mm H2O) concN Concentration of nitrogen in a layer (mg/kg or ppm) depthly Depth of the layer (mm) fractN Fraction of humic nitrogen in the active pool frnit,ly Estimated fraction of nitrogen lost by nitrification frvol,ly Estimated fraction of nitrogen lost by volatilization orgCly Amount of organic carbon in the layer (%) orgNact,ly Nitrogen in the active organic pool in layer ly (mg/kg or kg N/ha) orgNfrsh,ly Nitrogen in the fresh organic pool in layer ly (kg N/ha) orgNhum,ly Concentration of humic organic nitrogen in the layer (mg/kg or ppm) orgNsta,ly Nitrogen in the stable organic pool in layer ly (mg/kg or kg N/ha) orgPfrsh,ly Phosphorus in the fresh organic pool in layer ly (kg P/ha) rsdly Residue in layer ly (kg/ha) z Depth below soil surface (mm) zmid,ly Depth from the soil surface to the middle of the layer (mm)
βmin βrsd
Rate coefficient for mineralization of the humus active organic nutrients Rate coefficient for mineralization of the residue fresh organic nutrients
CHAPTER 10: EQUATIONS—NITROGEN
βtrns δntr,ly εC:N εC:P γntr,ly γsw,ly γtmp,ly ηmidz,ly ηnit,ly ηsw,ly ηtmp,ly ηvol,ly ρb
191
Rate constant for nitrogen transfer between active and stable organic pools (1×10-5) Residue decay rate constant Residue C:N ratio in the soil layer Residue C:P ratio in the soil layer Nutrient cycling residue composition factor for layer ly Nutrient cycling water factor for layer ly Nutrient cycling temperature factor for layer ly Volatilization depth factor Nitrification regulator Nitrification soil water factor Nitrification/volatilization temperature factor Volatilization regulator Bulk density of the layer (Mg/m3)
10.10 REFERENCES Godwin, D.C., C.A. Jones, J.T. Ritchie, P.L.G. Vlek, and L.G. Youngdahl. 1984. The water and nitrogen components of the CERES models. p. 95-100. In Proc. Intl. Symp. on Minimum Data Sets for Agrotechnology Transfer, March 1983, Patancheru, India. Intl. Crops Research Institute for the Semi-Arid Tropics. Reddy, K.R., R. Khaleel, M.R. Overcash, and P.W. Westerman. 1979. A nonpoint source model for land areas receiving animal wastes: II. Ammonia volatilization. Trans. ASAE 22:1398-1404. Seligmand, N.G. and H. van Keulen. 1981. PAPRAN: A simulation model of annual pasture production limited by rainfall and nitrogen. p. 192-221. In M.J. Frissel and J.A. van Veeds. (eds) Simulation of nitrogen behaviour of soil-plant systems, Proc. Workshop. Wageningen, Jan.-Feb. 1980.
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 11
EQUATIONS: PHOSPHORUS
Although plant phosphorus demand is considerably less than nitrogen demand, phosphorus is required for many essential functions. The most important of these is its role in energy storage and transfer. Energy obtained from photosynthesis and metabolism of carbohydrates is stored in phosphorus compounds for later use in growth and reproductive processes.
193
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SWAT USER'S MANUAL, VERSION 2000
11.1 PHOSPHORUS CYCLE The three major forms of phosphorus in mineral soils are organic phosphorus associated with humus, insoluble forms of mineral phosphorus, and plant-available phosphorus in soil solution. Phosphorus may be added to the soil by fertilizer, manure or residue application. Phosphorus is removed from the soil by plant uptake and erosion. Figure 11-1 shows the major components of the phosphorus cycle.
Figure 11-1: The phosphorus cycle
Unlike nitrogen which is highly mobile, phosphorus solubility is limited in most environments. Phosphorus combines with other ions to form a number of insoluble compounds that precipitate out of solution. These characteristics contribute to a build-up of phosphorus near the soil surface that is readily available for transport in surface runoff. Sharpley and Syers (1979) observed that
CHAPTER 11: EQUATIONS—PHOSPHORUS
195
surface runoff is the primary mechanism by which phosphorus is exported from most catchments. SWAT monitors six different pools of phosphorus in the soil (Figure 112). Three pools are inorganic forms of phosphorus while the other three pools are organic forms of phosphorus. Fresh organic P is associated with crop residue and microbial biomass while the active and stable organic P pools are associated with the soil humus. The organic phosphorus associated with humus is partitioned into two pools to account for the variation in availability of humic substances to mineralization. Soil inorganic P is divided into solution, active, and stable pools. The solution pool is in rapid equilibrium (several days or weeks) with the active pool. The active pool is in slow equilibrium with the stable pool.
Figure 11-2: SWAT soil phosphorus pools and processes that move P in and out of pools.
11.1.1 INITIALIZATION OF SOIL PHOSPHORUS LEVELS Users may define the amount of soluble P and organic phosphorus contained in humic substances for all soil layers at the beginning of the simulation. If the user does not specify initial phosphorus concentrations, SWAT will initialize levels of phosphorus in the different pools. The concentration of solution phosphorus in all layers is initially set to 5 mg/kg soil. This concentration is representative of unmanaged land under native vegetation. A concentration of 25 mg/kg soil in the plow layer is considered representative of cropland (Cope et al., 1981).
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SWAT USER'S MANUAL, VERSION 2000
The concentration of phosphorus in the active mineral pool is initialized to (Jones et al., 1984): minPact ,ly = Psolution,ly ⋅
1 − pai pai
11.1.1
where minPact,ly is the amount of phosphorus in the active mineral pool (mg/kg), Psolution,ly is the amount of phosphorus in solution (mg/kg), and pai is the phosphorus availability index. The concentration of phosphorus in the stable mineral pool is initialized to (Jones et al., 1984): minPsta ,ly = 4 ⋅ minPact ,ly
11.1.2
where minPsta,ly is the amount of phosphorus in the stable mineral pool (mg/kg), and minPact,ly is the amount of phosphorus in the active mineral pool (mg/kg). Organic phosphorus levels are assigned assuming that the N:P ratio for humic materials is 8:1. The concentration of humic organic phosphorus in a soil layer is calculated: orgPhum ,ly = 0.125 ⋅ orgN hum ,ly
11.1.3
where orgPhum,ly is the concentration of humic organic phosphorus in the layer (mg/kg) and orgNhum,ly is the concentration of humic organic nitrogen in the layer (mg/kg). Phosphorus in the fresh organic pool is set to zero in all layers except the top 10mm of soil. In the top 10 mm, the fresh organic phosphorus pool is set to 0.03% of the initial amount of residue on the soil surface. orgPfrsh ,surf = 0.0003 ⋅ rsd surf
11.1.4
where orgPfrsh,surf is the phosphorus in the fresh organic pool in the top 10mm (kg P/ha), and rsdsurf is material in the residue pool for the top 10mm of soil (kg/ha). While SWAT allows nutrient levels to be input as concentrations, it performs all calculations on a mass basis. To convert a concentration to a mass, the concentration is multiplied by the bulk density and depth of the layer and divided by 100:
CHAPTER 11: EQUATIONS—PHOSPHORUS
conc P ⋅ ρ b ⋅ depthly 100
=
kg P ha
197
11.1.5
where concP is the concentration of phosphorus in a layer (mg/kg or ppm), ρb is the bulk density of the layer (Mg/m3), and depthly is the depth of the layer (mm). Table 11-1: SWAT input variables that pertain to nitrogen pools. Variable Name SOL_SOLP SOL_ORGP PSP RSDIN SOL_BD
Definition Psolution,ly: Initial soluble P concentration in soil layer (mg/kg or ppm) orgPhum,ly: Initial humic organic phosphorus in soil layer (mg/kg or ppm) pai: Phosphorus availability index rsdsurf: Material in the residue pool for the top 10mm of soil (kg ha-1) ρb: Bulk density of the layer (Mg/m3)
Input File .chm .chm .bsn .hru .sol
11.2 MINERALIZATION & DECOMPOSITION / IMMOBILIZATION Decomposition is the breakdown of fresh organic residue into simpler organic components. Mineralization is the microbial conversion of organic, plantunavailable phosphorus to inorganic, plant-available phosphorus. Immobilization is the microbial conversion of plant-available inorganic soil phosphorus to plantunavailable organic phosphorus. The
phosphorus
mineralization
algorithms
in
SWAT
are
net
mineralization algorithms which incorporate immobilization into the equations. The phosphorus mineralization algorithms developed by Jones et al. (1984) are similar in structure to the nitrogen mineralization algorithms. Two sources are considered for mineralization: the fresh organic P pool associated with crop residue and microbial biomass and the active organic P pool associated with soil humus. Mineralization and decomposition are allowed to occur only if the temperature of the soil layer is above 0°C. Mineralization and decomposition are dependent on water availability and temperature. Two factors are used in the mineralization and decomposition equations to account for the impact of temperature and water on these processes. The nutrient cycling temperature factor is calculated:
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SWAT USER'S MANUAL, VERSION 2000
γ tmp ,ly = 0.9 ⋅
Tsoil ,ly
Tsoil ,ly + exp[9.93 − 0.312 ⋅ Tsoil ,ly ]
+ 0 .1
11.2.1
where γtmp,ly is the nutrient cycling temperature factor for layer ly, and Tsoil,ly is the temperature of layer ly (°C). The nutrient cycling temperature factor is never allowed to fall below 0.1. The nutrient cycling water factor is calculated:
γ sw,ly =
SWly
11.2.2
FCly
where γsw,ly is the nutrient cycling water factor for layer ly, SWly is the water content of layer ly on a given day (mm H2O), and FCly is the water content of layer ly at field capacity (mm H2O). ). The nutrient cycling water factor is never allowed to fall below 0.05.
11.2.1 HUMUS MINERALIZATION Phosphorus in the humus fraction is partitioned between the active and stable organic pools using the ratio of humus active organic N to stable organic N. The amount of phosphorus in the active and stable organic pools is calculated: orgPact ,ly = orgPhum ,ly ⋅ orgPsta ,ly = orgPhum ,ly ⋅
orgN act ,ly orgN act ,ly + orgN sta ,ly orgN sta ,ly orgN act ,ly + orgN sta ,ly
11.2.3
11.2.4
where orgPact,ly is the amount of phosphorus in the active organic pool (kg P/ha), orgPsta,ly is the amount of phosphorus in the stable organic pool (kg P/ha), orgPhum,ly is the concentration of humic organic phosphorus in the layer (kg P/ha), orgNact,ly is the amount of nitrogen in the active organic pool (kg N/ha), and orgNsta,ly is the amount of nitrogen in the stable organic pool (kg N/ha). Mineralization from the humus active organic P pool is calculated: Pmina,ly = 1.4 ⋅ β min ⋅ (γ tmp ,ly ⋅ γ sw,ly ) ⋅ orgPact ,ly 12
11.2.5
where Pmina,ly is the phosphorus mineralized from the humus active organic P pool (kg P/ha), βmin is the rate coefficient for mineralization of the humus active organic nutrients, γtmp,ly is the nutrient cycling temperature factor for layer ly, γsw,ly
CHAPTER 11: EQUATIONS—PHOSPHORUS
199
is the nutrient cycling water factor for layer ly, and orgPact,ly is the amount of phosphorus in the active organic pool (kg P/ha). Phosphorus mineralized from the humus active organic pool is added to the solution P pool in the layer.
11.2.2 RESIDUE DECOMPOSITION & MINERALIZATION Decomposition and mineralization of the fresh organic phosphorus pool is allowed only in the first soil layer. Decomposition and mineralization are controlled by a decay rate constant that is updated daily. The decay rate constant is calculated as a function of the C:N ratio and C:P ratio of the residue, temperature and soil water content. The C:N ratio of the residue is calculated:
ε C:N =
0.58 ⋅ rsd ly orgN frsh ,ly + NO3ly
11.2.6
where εC:N is the C:N ratio of the residue in the soil layer, rsdly is the residue in layer ly (kg/ha), 0.58 is the fraction of residue that is carbon, orgNfrsh,ly is the nitrogen in the fresh organic pool in layer ly (kg N/ha), and NO3ly is the amount of nitrate in layer ly (kg N/ha). The C:P ratio of the residue is calculated:
ε C :P =
0.58 ⋅ rsd ly orgPfrsh ,ly + Psolution,ly
11.2.7
where εC:P is the C:P ratio of the residue in the soil layer, rsdly is the residue in layer ly (kg/ha), 0.58 is the fraction of residue that is carbon, orgPfrsh,ly is the phosphorus in the fresh organic pool in layer ly (kg P/ha), and Psolution,ly is the amount of phosphorus in solution in layer ly (kg P/ha). The decay rate constant defines the fraction of residue that is decomposed. The decay rate constant is calculated:
δ ntr ,ly = β rsd ⋅ γ ntr ,ly ⋅ (γ tmp ,ly ⋅ γ sw,ly )1 2
11.2.8
where δntr,ly is the residue decay rate constant, βrsd is the rate coefficient for mineralization of the residue fresh organic nutrients, γntr,ly is the nutrient cycling
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SWAT USER'S MANUAL, VERSION 2000
residue composition factor for layer ly, γtmp,ly is the nutrient cycling temperature factor for layer ly, and γsw,ly is the nutrient cycling water factor for layer ly. The nutrient cycling residue composition factor is calculated:
γ ntr ,ly
(ε C:N − 25)ù ì é ï exp ê− 0.693 ⋅ úû 25 ë ï ï (ε − 200)ù ï é = min íexp ê− 0.693 ⋅ C:P úû 200 ï ë ï 1.0 ï ï î
11.2.9
where γntr,ly is the nutrient cycling residue composition factor for layer ly, εC:N is the C:N ratio on the residue in the soil layer, and εC:P is the C:P ratio on the residue in the soil layer. Mineralization from the residue fresh organic P pool is then calculated: Pminf,ly = 0.8 ⋅ δ ntr ,ly ⋅ orgPfrsh ,ly
11.2.10
where Pminf,ly is the phosphorus mineralized from the fresh organic P pool (kg P/ha), δntr,ly is the residue decay rate constant, and orgPfrsh,ly is the phosphorus in the fresh organic pool in layer ly (kg P/ha). Phosphorus mineralized from the fresh organic pool is added to the solution P pool in the layer. Decomposition from the residue fresh organic P pool is calculated: Pdec,ly = 0.2 ⋅ δ ntr ,ly ⋅ orgPfrsh ,ly
11.2.11
where Pdec,ly is the phosphorus decomposed from the fresh organic P pool (kg P/ha), δntr,ly is the residue decay rate constant, and orgPfrsh,ly is the phosphorus in the fresh organic pool in layer ly (kg P/ha). Phosphorus decomposed from the fresh organic pool is added to the humus organic pool in the layer. Table 11-2: SWAT input variables that pertain to mineralization. Variable Name CMN RSDCO RSDCO_PL
Definition βmin: Rate coefficient for mineralization of the humus active organic nutrients βrsd: Rate coefficient for mineralization of the residue fresh organic nutrients βrsd: Rate coefficient for mineralization of the residue fresh organic nutrients
Input File .bsn .bsn crop.dat
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11.3 SORPTION OF INORGANIC P Many studies have shown that after an application of soluble P fertilizer, solution P concentration decreases rapidly with time due to reaction with the soil. This initial “fast” reaction is followed by a much slower decrease in solution P that may continue for several years (Barrow and Shaw, 1975; Munns and Fox, 1976; Rajan and Fox, 1972; Sharpley, 1982). In order to account for the initial rapid decrease in solution P, SWAT assumes a rapid equilibrium exists between solution P and an “active” mineral pool. The subsequent slow reaction is simulated by the slow equilibrium assumed to exist between the “active” and “stable” mineral pools. The algorithms governing movement of inorganic phosphorus between these three pools are taken from Jones et al. (1984). Equilibration between the solution and active mineral pool is governed by the phosphorus availability index. This index specifies the fraction of fertilizer P which is in solution after an incubation period, i.e. after the rapid reaction period. A number of methods have been developed to measure the phosphorus availability index. Jones et al. (1984) recommends a method outlined by Sharpley et al. (1984) in which various amounts of phosphorus are added in solution to the soil as K2HPO4. The soil is wetted to field capacity and then dried slowly at 25°C. When dry, the soil is rewetted with deionized water. The soil is exposed to several wetting and drying cycles over a 6-month incubation period. At the end of the incubation period, solution phosphorus is determined by extraction with anion exchange resin. The P availability index is then calculated: pai =
Psolution, f − Psolution,i fert minP
11.3.1
where pai is the phosphorus availability index, Psolution,f is the amount of phosphorus in solution after fertilization and incubation, Psolution,i is the amount of phosphorus in solution before fertilization, and fertminP is the amount of soluble P fertilizer added to the sample.
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The movement of phosphorus between the solution and active mineral pools is governed by the equilibration equations: æ pai ö Psol act ,ly = Psolution,ly − minPact ,ly ⋅ çç ÷÷ è 1 − pai ø æ pai ö if Psolution,ly > minPact ,ly ⋅ çç ÷÷ è 1 − pai ø
11.3.2
æ æ pai ö ö Psol act ,ly = 0.1 ⋅ çç Psolution,ly − minPact ,ly ⋅ çç ÷÷ ÷÷ è 1 − pai ø ø è æ pai ö if Psolution,ly < minPact ,ly ⋅ çç ÷÷ è 1 − pai ø
11.3.3
where Psol act ,ly is the amount of phosphorus transferred between the soluble and active mineral pool (kg P/ha), Psolution,ly is the amount of phosphorus in solution (kg P/ha), minPact,ly is the amount of phosphorus in the active mineral pool (kg P/ha), and pai is the phosphorus availability index. When Psol act ,ly is positive, phosphorus is being transferred from solution to the active mineral pool. When
Psol act ,ly is negative, phosphorus is being transferred from the active mineral pool to solution. Note that the rate of flow from the active mineral pool to solution is 1/10th the rate of flow from solution to the active mineral pool. SWAT simulates slow phosphorus sorption by assuming the active mineral phosphorus pool is in slow equilibrium with the stable mineral phosphorus pool. At equilibrium, the stable mineral pool is 4 times the size of the active mineral pool. When not in equilibrium, the movement of phosphorus between the active and stable mineral pools is governed by the equations:
Pact sta ,ly = β eqP ⋅ (4 ⋅ minPact ,ly − minPsta ,ly ) if minPsta ,ly < 4 ⋅ minPact ,ly
11.3.4
Pact sta ,ly = 0.1 ⋅ β eqP ⋅ (4 ⋅ minPact ,ly − minPsta ,ly ) if minPsta ,ly > 4 ⋅ minPact ,ly
11.3.5
CHAPTER 11: EQUATIONS—PHOSPHORUS
203
where Pact sta ,ly is the amount of phosphorus transferred between the active and stable mineral pools (kg P/ha), βeqP is the slow equilibration rate constant (0.0006 d-1), minPact,ly is the amount of phosphorus in the active mineral pool (kg P/ha), and minPsta,ly is the amount of phosphorus in the stable mineral pool (kg P/ha). When Pact sta ,ly is positive, phosphorus is being transferred from the active mineral pool to the stable mineral pool. When Pact sta ,ly is negative, phosphorus is being transferred from the stable mineral pool to the active mineral pool. Note that the rate of flow from the stable mineral pool to the active mineral pool is 1/10th the rate of flow from the active mineral pool to the stable mineral pool. Table 11-3: SWAT input variables that pertain to inorganic P sorption processes. Variable Name PSP
Definition pai: Phosphorus availability index
Input File .bsn
11.4 LEACHING The primary mechanism of phosphorus movement in the soil is by diffusion. Diffusion is the migration of ions over small distances (1-2 mm) in the soil solution in response to a concentration gradient. The concentration gradient is created when plant roots remove soluble phosphorus from soil solution, depleting solution P in the root zone. Due to the low mobility of phosphorus, SWAT allows soluble P to leach only from the top 10 mm of soil into the first soil layer. The amount of solution P moving from the top 10 mm into the first soil layer is: Pperc =
Psolution,surf ⋅ w perc ,surf 10 ⋅ ρ b ⋅ depthsurf ⋅ k d , perc
11.4.1
where Pperc is the amount of phosphorus moving from the top 10 mm into the first soil layer (kg P/ha), Psolution,surf is the amount of phosphorus in solution in the top 10 mm (kg P/ha), wperc,surf is the amount of water percolating to the first soil layer from the top 10 mm on a given day (mm H2O), ρb is the bulk density of the top 10 mm (Mg/m3) (assumed to be equivalent to bulk density of first soil layer),
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depthsurf is the depth of the “surface” layer (10 mm), and kd,perc is the phosphorus percolation coefficient (10 m3/Mg). The phosphorus percolation coefficient is the ratio of the phosphorus concentration in the surface 10 mm of soil to the concentration of phosphorus in percolate. Table 11-4: SWAT input variables that pertain to phosphorus leaching. Variable Name SOL_BD PPERCO
Definition ρb: Bulk density of the layer (Mg/m3) kd,perc: Phosphorus percolation coefficient (10 m3/Mg)
Input File .sol .bsn
11.5 NOMENCLATURE FCly Water content of layer ly at field capacity (mm H2O) NO3ly Nitrate content of soil layer ly (kg NO3-N/ha) Pact sta ,ly Amount of phosphorus transferred between the active and stable mineral pools (kg P/ha) Phosphorus decomposed from the fresh organic P pool (kg P/ha) Phosphorus mineralized from the humus active organic P pool (kg P/ha) Phosphorus mineralized from the fresh organic P pool (kg P/ha), Amount of phosphorus moving from the top 10 mm into the first soil layer (kg P/ha) Psolution,ly Amount of phosphorus in solution (mg/kg) Psol act ,ly Amount of phosphorus transferred between the soluble and active mineral pool
Pdec,ly Pmina,ly Pminf,ly Pperc
SWly Tsoil,ly
(kg P/ha) Soil water content of layer ly (mm H2O) Temperature of layer ly (°C)
concP Concentration of phosphorus in a layer (mg/kg or ppm) depthly Depth of the layer (mm) kd,perc Phosphorus percolation coefficient (10 m3/Mg) minPact,ly Amount of phosphorus in the active mineral pool (mg/kg or kg P/ha) minPsta,ly Amount of phosphorus in the stable mineral pool (mg/kg or kg P/ha) orgNact,ly Nitrogen in the active organic pool in layer ly (mg/kg or kg N/ha) orgNfrsh,ly Nitrogen in the fresh organic pool in layer ly (kg N/ha) orgNhum,ly Amount of nitrogen in humic organic pool in the layer (mg/kg or kg N/ha) orgNsta,ly Nitrogen in the stable organic pool in layer ly (mg/kg or kg N/ha) orgPact,ly Amount of phosphorus in the active organic pool (kg P/ha) orgPfrsh,ly Phosphorus in the fresh organic pool in layer ly (kg P/ha) orgPhum,ly Amount of phosphorus in humic organic pool in the layer (mg/kg or kg P/ha) orgPsta,ly Amount of phosphorus in the stable organic pool (kg P/ha) pai Phosphorus availability index rsdly Residue in layer ly (kg/ha)
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wperc,ly Amount of water percolating to the underlying soil layer on a given day (mm H2O)
βeqP βmin βrsd δntr,ly εC:N εC:P γntr,ly γsw,ly γtmp,ly ρb
Slow equilibration rate constant (0.0006 d-1) Rate coefficient for mineralization of the humus active organic nutrients Rate coefficient for mineralization of the residue fresh organic nutrients Residue decay rate constant Residue C:N ratio in the soil layer Residue C:P ratio in the soil layer Nutrient cycling residue composition factor for layer ly Nutrient cycling water factor for layer ly Nutrient cycling temperature factor for layer ly Bulk density of the layer (Mg/m3)
11.6 REFERENCES Barrow, N.J. and T.C. Shaw. 1975. The slow reactions between soil and anions. 2. Effect of time and temperature on the decrease in phosphate concentration in soil solution. Soil Sci. 119:167-177. Cope, J.T., C.E. Evans, and H.C. Williams. 1981. Soil test fertility recommendations for Alabama crops. Alabama Agric. Station Circular No. 251. Jones, C.A. C.V. Cole, A.N. Sharpley, and J.R. Williams. 1984. A simplified soil and plant phosphorus model. I. Documentation. Soil Sci. Soc. Am. J. 48:800-805. Munns, D.N. and R.L. Fox. 1976. The slow reaction which continues after phosphate adsorption: Kinetics and equilibrium in some tropical soils. Soil Sci. Soc. Am. J. 40:46-51. Rajan, S.S.S. and R.L. Fox. 1972. Phosphate adsorption by soils. 1. Influence of time and ionic environment on phosphate adsorption. Commun. Soil. Sci. Plant Anal. 3:493-504. Sharpley, A.N. 1982. A prediction of the water extractable phosphorus content of soil following a phosphorus addition. J. Environ. Qual. 11:166-170.
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Sharpley, A.N., C. Gray, C.A. Jones, and C.V. Cole. 1984. A simplified soil and plant phosphorus model. II. Prediction of labile, organic, and sorbed P amounts. Soil Sci. Soc. Am. J. 48:805-809. Sharpley, A.N. and J.K. Syers. 1979. Phosphorus inputs into a stream draining an agricultural watershed: II. Amounts and relative significance of runoff types. Water, Air and Soil Pollution 11:417-428.
CHAPTER 12
EQUATIONS: PESTICIDES
One of the primary purposes of tillage and harvesting practices in early farming systems was to remove as much plant residue from the field as possible so that pests had no food source to sustain them until the next growing season. As research linked erosion to lack of soil cover, farmers began to perform fewer tillage operations and altered harvesting methods to leave more residue. As mechanical methods of pest control were minimized or eliminated, chemical methods of pest control began to assume a key role in the management of unwanted organisms. 207
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Pesticides are toxic by design, and there is a natural concern about the impact of their presence in the environment on human health and environmental quality. The fate and transport of a pesticide are governed by properties such as solubility in water, volatility and ease of degradation. The algorithms in SWAT used to model pesticide movement and fate are adapted from GLEAMS (Leonard et al., 1987). Pesticide may be aerially applied to an HRU with some fraction intercepted by plant foliage and some fraction reaching the soil. Pesticide may also be incorporated into the soil through tillage. SWAT monitors pesticide amounts on foliage and in all soil layers. Figure 12-1 shows the potential pathways and processes simulated in SWAT.
Figure 12-1: Pesticide fate and transport in SWAT.
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209
12.1 WASH-OFF A portion of the pesticide on plant foliage may be washed off during rain events. The fraction washed off is a function of plant morphology, pesticide solubility, and the timing and intensity of the rainfall event. Wash-off will occur when the amount of precipitation on a given day exceeds 2.54 mm. The amount of pesticide washing off plant foliage during a precipitation event on a given day is calculated: pst f ,wsh = frwsh ⋅ pst f
12.1.1
where pstf,wsh is the amount of pesticide on foliage that is washed off the plant and onto the soil surface on a given day (kg pst/ha), frwsh is the wash-off fraction for the pesticide, and pstf is the amount of pesticide on the foliage (kg pst/ha). The wash-off fraction represents the portion of the pesticide on the foliage that is dislodgable. Table 12-1: SWAT input variables that pertain to pesticide wash-off. Variable Name WOF
Definition frwsh: Wash-off fraction for the pesticide
Input File pest.dat
12.2 DEGRADATION Degradation is the conversion of a compound into less complex forms. A compound in the soil may degrade upon exposure to light (photo degradation), reaction with chemicals present in the soil (chemical degradation) or through use as a substrate for organisms (biodegradation). The majority of pesticides in use today are organic compounds. Because organic compounds contain carbon, which is used by microbes in biological reactions to produce energy, organic pesticides may be susceptible to microbial degradation. In contrast, pesticides that are inorganic are not susceptible to microbial degradation. Examples of pesticides that will not degrade are lead arsenate, a metallic salt commonly applied in orchards before DDT was invented, and arsenic acid, a compound formerly used to defoliate cotton.
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Pesticides vary in their susceptibility to degradation. Compounds with chain structures are easier to break apart than compounds containing aromatic rings or other complex structures. The susceptibility of a pesticide to degradation is quantified by the pesticide’s half-life. The half-life for a pesticide defines the number of days required for a given pesticide concentration to be reduced by one-half. The soil half-life entered for a pesticide is a lumped parameter that includes the net effect of volatilization, photolysis, hydrolysis, biological degradation and chemical reactions in the soil. Because pesticide on foliage degrades more rapidly than pesticide in the soil, SWAT allows a different half-life to be defined for foliar degradation. Pesticide degradation or removal in all soil layers is governed by firstorder kinetics:
pst s ,ly ,t = pst s ,ly ,o ⋅ exp[− k p ,soil ⋅ t ]
12.2.1
where psts,ly,t is the amount of pesticide in the soil layer at time t (kg pst/ha), psts,ly,o is the initial amount of pesticide in the soil layer (kg pst/ha), kp,soil is the rate constant for degradation or removal of the pesticide in soil (1/day), and t is the time elapsed since the initial pesticide amount was determined (days). The rate constant is related to the soil half-life as follows:
t1 2,s =
0.693 k p ,soil
12.2.2
where t1/2,s is the half-life of the pesticide in the soil (days). The equation governing pesticide degradation on foliage is: pst f ,t = pst f ,o ⋅ exp[− k p , foliar ⋅ t ]
12.2.3
where pstf,t is the amount of pesticide on the foliage at time t (kg pst/ha), pstf,o is the initial amount of pesticide on the foliage (kg pst/ha), kp,foliar is the rate constant for degradation or removal of the pesticide on foliage (1/day), and t is the time elapsed since the initial pesticide amount was determined (days). The rate constant is related to the foliar half-life as follows: t1 2, f =
0.693 k p , foliar
where t1/2,f is the half-life of the pesticide on foliage (days).
12.2.4
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211
Table 12-2: SWAT input variables that pertain to pesticide degradation. Variable Name HLIFE_S HLIFE_F
Definition t1/2,s: Half-life of the pesticide in the soil (days) t1/2,f: Half-life of the pesticide on foliage (days)
Input File pest.dat pest.dat
12.3 LEACHING Highly water-soluble pesticides can be transported with percolation deep into the soil profile and potentially pollute shallow groundwater systems. The algorithms used by SWAT to calculated pesticide leaching simultaneously solve for loss of pesticide in surface runoff and lateral flow also. These algorithms are reviewed in Chapter 15.
12.4 NOMENCLATURE frwsh kp,foliar kp,soil pstf pstf,wsh psts,ly t t1/2,f t1/2,s
Wash-off fraction for the pesticide Rate constant for degradation or removal of the pesticide on foliage (1/day) Rate constant for degradation or removal of the pesticide in soil (1/day) Amount of pesticide on the foliage (kg pst/ha) Amount of pesticide on foliage that is washed off the plant and onto the soil surface on a given day (kg pst/ha) Amount of pesticide in the soil (kg pst/ha) Time elapsed since the initial pesticide amount was determined (days) Half-life of the pesticide on foliage (days) Half-life of the pesticide in the soil (days)
12.5 REFERENCES Leonard, R.A., W.G. Knisel., and D.A. Still. 1987. GLEAMS: Groundwater loading effects of agricultural management systems. Trans. ASAE. 30:1403-1418.
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EROSION Transport of sediment, nutrients and pesticides from land areas to water bodies is a consequence of weathering that acts on landforms. Soil and water conservation planning requires knowledge of the relations between factors that cause loss of soil and water and those that help to reduce such losses. The following three chapters review the methodology used by SWAT to simulate erosion processes.
CHAPTER 13
EQUATIONS: SEDIMENT
Erosion caused by rainfall and runoff is computed with the Modified Universal Soil Loss Equation (MUSLE) (Williams, 1975). MUSLE is a modified version of the Universal Soil Loss Equation (USLE) developed by Wischmeier and Smith (1965, 1978). USLE predicts average annual gross erosion as a function of rainfall energy. In MUSLE, the rainfall energy factor is replaced with a runoff factor. This improves the sediment yield prediction, eliminates the need for delivery ratios, and allows the equation to be applied to individual storm events. Sediment yield 215
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prediction is improved because runoff is a function of antecedent moisture condition as well as rainfall energy. Delivery ratios (the sediment yield at any point along the channel divided by the source erosion above that point) are required by the USLE because the rainfall factor represents energy used in detachment only. Delivery ratios are not needed with MUSLE because the runoff factor represents energy used in detaching and transporting sediment.
13.1 MUSLE The modified universal soil loss equation (Williams, 1995) is: sed = 11.8 ⋅ (Qsurf ⋅ q peak ⋅ area hru )
0.56
⋅ KUSLE ⋅ CUSLE ⋅ PUSLE ⋅ LSUSLE ⋅ CFRG
13.1.1
where sed is the sediment yield on a given day (metric tons), Qsurf is the surface runoff volume (mm H2O/ha), qpeak is the peak runoff rate (m3/s), areahru is the area of the HRU (ha), KUSLE is the USLE soil erodibility factor (0.013 metric ton m2 hr/(m3-metric ton cm)), CUSLE is the USLE cover and management factor, PUSLE is the USLE support practice factor, LSUSLE is the USLE topographic factor and CFRG is the coarse fragment factor. Surface runoff and peak rate calculations are reviewed in Chapter 6. The USLE factors are discussed in the following sections.
13.1.1 SOIL ERODIBILITY FACTOR Some soils erode more easily than others even when all other factors are the same. This difference is termed soil erodibility and is caused by the properties of the soil itself. Wischmeier and Smith (1978) define the soil erodibility factor as the soil loss rate per erosion index unit for a specified soil as measured on a unit plot. A unit plot is 22.1-m (72.6-ft) long, with a uniform length-wise slope of 9percent, in continuous fallow, tilled up and down the slope. Continuous fallow is defined as land that has been tilled and kept free of vegetation for more than 2 years. The units for the USLE soil erodibility factor in MUSLE are numerically equivalent to the traditional English units of 0.01 (ton acre hr)/(acre ft-ton inch).
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Wischmeier and Smith (1978) noted that a soil type usually becomes less erodible with decrease in silt fraction, regardless of whether the corresponding increase is in the sand fraction or clay fraction. Direct measurement of the erodibility factor is time consuming and costly. Wischmeier et al. (1971) developed a general equation to calculate the soil erodibility factor when the silt and very fine sand content makes up less than 70% of the soil particle size distribution. KUSLE =
0.00021 ⋅ M 1.14 ⋅ (12 − OM ) + 3.25 ⋅ (csoilstr − 2 ) + 2.5 ⋅ (c perm − 3) 100
13.1.2
where KUSLE is the soil erodibility factor, M is the particle-size parameter, OM is the percent organic matter (%), csoilstr is the soil structure code used in soil classification, and cperm is the profile permeability class. The particle-size parameter, M, is calculated M = (msilt + mvfs ) ⋅ (100 − mc )
13.1.3
where msilt is the percent silt content (0.002-0.05 mm diameter particles), mvfs is the percent very fine sand content (0.05-0.10 mm diameter particles), and mc is the percent clay content (< 0.002 mm diameter particles). The percent organic matter content, OM, of a layer can be calculated: OM = 1.72 ⋅ orgC
13.1.4
where orgC is the percent organic carbon content of the layer (%). Soil structure refers to the aggregation of primary soil particles into compound particles which are separated from adjoining aggregates by surfaces of weakness. An individual natural soil aggregate is called a ped. Field description of soil structure notes the shape and arrangement of peds, the size of peds, and the distinctness and durability of visible peds. USDA Soil Survey terminology for structure consists of separate sets of terms defining each of these three qualities. Shape and arrangement of peds are designated as type of soil structure; size of peds as class; and degree of distinctness as grade. The soil-structure codes for equation 13.1.2 are defined by the type and class of soil structure present in the layer. There are four primary types of structure:
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-Platy, with particles arranged around a plane, generally horizontal -Prismlike, with particles arranged around a verticle line and bounded by relatively flat vertical surfaces -Blocklike or polyhedral, with particles arranged around a point and bounded by flat or rounded surfaces which are casts of the molds formed by the faces of surrounding peds -Spheroidal or polyhedral, with particles arranged around a point and bounded by curved or very irregular surfaces that are not accomodated to the adjoining aggregates Each of the last three types has two subtypes: -Prismlike Prismatic: without rounded upper ends Columnar: with rounded caps -Blocklike Angular Blocky: bounded by planes intersecting at relatively sharp angles Subangular Blocky: having mixed rounded and plane faces with vertices mostly rounded -Spheroidal Granular: relatively non-porous Crumb: very porous The size criteria for the class will vary by type of structure and are summarized in Table 13-1. The codes assigned to csoilstr are: 1 2 3 4
very fine granular fine granular medium or coarse granular blocky, platy, prismlike or massive
Table 13-1: Size classes of soil structure
Size Classes Very fine Fine Medium Coarse Very coarse
Platy < 1 mm 1-2 mm 2-5 mm 5-10 mm > 10 mm
Shape of structure Prismatic and Columnar Blocky < 10 mm < 5 mm 10-20 mm 5-10 mm 20-50 mm 10-20 mm 50-100 mm 20-50 mm > 100 mm > 50 mm
Granular < 1 mm 1-2 mm 2-5 mm 5-10 mm > 10 mm
Permeability is defined as the capacity of the soil to transmit water and air through the most restricted horizon (layer) when moist. The profile permeability classes are based on the lowest saturated hydraulic conductivity in the profile. The codes assigned to cperm are:
CHAPTER 13: EQUATIONS—SEDIMENT
1 2 3 4 5 6
219
rapid (> 150 mm/hr) moderate to rapid (50-150 mm/hr) moderate (15-50 mm/hr) slow to moderate (5-15 mm/hr) slow (1-5 mm/hr) very slow (< 1 mm/hr)
Williams (1995) proposed an alternative equation: KUSLE = f csand ⋅ f cl − si ⋅ f orgc ⋅ f hisand
13.1.5
where fcsand is a factor that gives low soil erodibility factors for soils with high coarse-sand contents and high values for soils with little sand, fcl-si is a factor that gives low soil erodibility factors for soils with high clay to silt ratios, forgc is a factor that reduces soil erodibility for soils with high organic carbon content, and fhisand is a factor that reduces soil erodibility for soils with extremely high sand contents. The factors are calculated:
æ é æ m öù ö f csand = çç 0.2 + 0.3 ⋅ exp ê − 0.256 ⋅ ms ⋅ ç1 − silt ÷ ú ÷÷ 100 ø û ø è ë è f cl − si
æ msilt ö ÷÷ = çç è mc + msilt ø
0.3
æ ö 0.25 ⋅ orgC f orgc = çç1 − ÷÷ è orgC + exp[3.72 − 2.95 ⋅ orgC ] ø
f hisand
13.1.6
æ ö m ö æ ç ÷ 0.7 ⋅ ç1 − s ÷ 100 ø ç ÷ è = ç1 − é m m ù÷ çç æç1 − s ö÷ + exp ê − 5.51 + 22.9 ⋅ æç1 − s ö÷ú ÷÷ è 100 øû ø ë è è 100 ø
13.1.7
13.1.8
13.1.9
where ms is the percent sand content (0.05-2.00 mm diameter particles), msilt is the percent silt content (0.002-0.05 mm diameter particles), mc is the percent clay content (< 0.002 mm diameter particles), and orgC is the percent organic carbon content of the layer (%).
13.1.2 COVER AND MANAGEMENT FACTOR The USLE cover and management factor, CUSLE, is defined as the ratio of soil loss from land cropped under specified conditions to the corresponding loss
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from clean-tilled, continuous fallow (Wischmeier and Smith, 1978). The plant canopy affects erosion by reducing the effective rainfall energy of intercepted raindrops. Water drops falling from the canopy may regain appreciable velocity but it will be less than the terminal velocity of free-falling raindrops. The average fall height of drops from the canopy and the density of the canopy will determine the reduction in rainfall energy expended at the soil surface. A given percentage of residue on the soil surface is more effective that the same percentage of canopy cover. Residue intercepts falling raindrops so near the surface that drops regain no fall velocity. Residue also obstructs runoff flow, reducing its velocity and transport capacity. Because plant cover varies during the growth cycle of the plant, SWAT updates CUSLE daily using the equation: CUSLE = exp([ln (0.8) − ln (CUSLE ,mn )]⋅ exp[− 0.00115 ⋅ rsd surf ] + ln[CUSLE ,mn ])
13.1.10
where CUSLE,mn is the minimum value for the cover and management factor for the land cover, and rsdsurf is the amount of residue on the soil surface (kg/ha). The minimum C factor can be estimated from a known average annual C factor using the following equation (Arnold and Williams, 1995): CUSLE ,mn = 1.463 ln[CUSLE ,aa ] + 0.1034
13.1.11
where CUSLE,mn is the minimum C factor for the land cover and CUSLE,aa is the average annual C factor for the land cover.
13.1.3 SUPPORT PRACTICE FACTOR The support practice factor, PUSLE, is defined as the ratio of soil loss with a specific support practice to the corresponding loss with up-and-down slope culture. Support practices include contour tillage, stripcropping on the contour, and terrace systems. Stabilized waterways for the disposal of excess rainfall are a necessary part of each of these practices. Contour tillage and planting provides almost complete protection against erosion from storms of low to moderate intensity, but little or no protection against occasional severe storms that cause extensive breakovers of contoured
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rows. Contouring is most effective on slopes of 3 to 8 percent. Values for PUSLE and slope-length limits for contour support practices are given in Table 13-2. Table 13-2: P factor values and slope-length limits for contouring (Wischmeier and Smith, 1978). Land slope (%) PUSLE Maximum length (m) 1 to 2 0.60 122 3 to 5 0.50 91 6 to 8 0.50 61 9 to 12 0.60 37 13 to 16 0.70 24 17 to 20 0.80 18 21 to 25 0.90 15
Stripcropping is a practice in which contoured strips of sod are alternated with equal-width strips of row crop or small grain. Recommended values for contour stripcropping are given in Table 13-3. Table 13-3: P factor values, maximum strip width and slope-length limits for contour stripcropping (Wischmeier and Smith, 1978). PUSLE values1 Land slope Strip width Maximum (%) (m) length (m) A B C 1 to 2 0.30 0.45 0.60 40 244 3 to 5 0.25 0.38 0.50 30 183 6 to 8 0.25 0.38 0.50 30 122 9 to 12 0.30 0.45 0.60 24 73 13 to 16 0.35 0.52 0.70 24 49 17 to 20 0.40 0.60 0.80 18 37 21 to 25 0.45 0.68 0.90 15 30 1
P values: A: For 4-year rotation of row crop, small grain with meadow seeding, and 2 years of meadow. A second row crop can replace the small grain if meadow is established in it. B: For 4-year rotation of 2 years row crop, winter grain with meadow seeding, and 1-year meadow. C: For alternate strips of row crop and winter grain
Terraces are a series of horizontal ridges made in a hillside. There are several types of terraces. Broadbase terraces are constructed on gently sloping land and the channel and ridge are cropped the same as the interterrace area. The steep backslope terrace, where the backslope is in sod, is most common on steeper land. Impoundment terraces are terraces with underground outlets. Terraces divide the slope of the hill into segments equal to the horizontal terrace interval. With terracing, the slope length is the terrace interval. For broadbase terraces, the horizontal terrace interval is the distance from the center of the ridge to the center of the channel for the terrace below. The horizontal terrace interval for steep backslope terraces is the distance from the point where
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cultivation begins at the base of the ridge to the base of the frontslope of the terrace below. Values for PUSLE for contour farming terraced fields are listed in Table 134. These values apply to broadbase, steep backslope and level terraces. Keep in mind that the values given in Table 13-4 do not account for all erosion control benefits of terraces. The shorter slope-length used in the calculation of the lengthslope factor will produce additional reduction. Table 13-4: P factor values for contour-farmed terraced fields1 Computing sediment yield3 Land Farm planning slope (%) Contour P Stripcrop P Graded channels Steep backslope factor2 factor sod outlets underground outlets 1 to 2 0.60 0.30 0.12 0.05 3 to 8 0.50 0.25 0.10 0.05 9 to 12 0.60 0.30 0.12 0.05 13 to 16 0.70 0.35 0.14 0.05 17 to 20 0.80 0.40 0.16 0.06 21 to 25 0.90 0.45 0.18 0.06 1
Slope length is the horizontal terrace interval. The listed values are for contour farming. No additional contouring factor is used in the computation. Use these values for control of interterrace erosion within specified soil loss tolerances. 3 These values include entrapment efficiency and are used for control of offsite sediment within limits and for estimating the field’s contribution to watershed sediment yield. 2
13.1.4 TOPOGRAPHIC FACTOR The topographic factor, LSUSLE, is the expected ratio of soil loss per unit area from a field slope to that from a 22.1-m length of uniform 9 percent slope under otherwise identical conditions. The topographic factor is calculated: m
LSUSLE
æL ö = ç hill ÷ ⋅ (65.41 ⋅ sin 2 (α hill ) + 4.56 ⋅ sin α hill + 0.065) è 22.1 ø
13.1.12
where Lhill is the slope length (m), m is the exponential term, and αhill is the angle of the slope. The exponential term, m, is calculated:
m = 0.6 ⋅ (1 − exp[− 35.835 ⋅ slp ])
13.1.13
where slp is the slope of the HRU expressed as rise over run (m/m). The relationship between αhill and slp is: slp = tan α hill
13.1.14
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223
13.1.5 COARSE FRAGMENT FACTOR The coarse fragment factor is calculated: CFRG = exp(− 0.053 ⋅ rock )
13.1.15
where rock is the percent rock in the first soil layer (%). Table 13-5: SWAT input variables that pertain to sediment yield. Variable Name USLE_K USLE_C USLE_P SLSUBBSN SLOPE ROCK
Definition KUSLE: USLE soil erodibility factor (0.013 metric ton m2 hr/(m3metric ton cm)) CUSLE,mn: Minimum value for the cover and management factor for the land cover PUSLE: USLE support practice factor Lhill: Slope length (m) slp: Average slope of the subbasin (% or m/m) rock: Percent rock in the first soil layer (%)
Input File .sol crop.dat .mgt .hru .hru .sol
13.2 USLE For comparative purposes, SWAT prints out sediment loadings calculated with USLE. These values are not used by the model, they are for comparison only. The universal soil loss equation (Williams, 1995) is:
sed = 1.292 ⋅ EI USLE ⋅ KUSLE ⋅ CUSLE ⋅ PUSLE ⋅ LSUSLE ⋅ CFRG
13.2.1
where sed is the sediment yield on a given day (metric tons/ha), EIUSLE is the rainfall erosion index (0.017 m-metric ton cm/(m2 hr)), KUSLE is the USLE soil erodibility factor (0.013 metric ton m2 hr/(m3-metric ton cm)), CUSLE is the USLE cover and management factor, PUSLE is the USLE support practice factor, LSUSLE is the USLE topographic factor and CFRG is the coarse fragment factor. The factors other than EIUSLE are discussed in the preceeding sections.
13.2.1 RAINFALL ERODIBILITY INDEX The value of EIUSLE for a given rainstorm is the product, total storm energy times the maximum 30 minute intensity. The storm energy indicates the volume of rainfall and runoff while the 30 minute intensity indicates the prolonged peak rates of detachment and runoff. EI USLE = E storm ⋅ I 30
13.2.2
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where EIUSLE is the rainfall erosion index (0.017 m-metric ton cm/(m2 hr)), Estorm is the total storm energy (0.0017 m-metric ton/m2), and I30 is the maximum 30minute intensity (mm/hr). The energy of a rainstorm is a function of the amount of rain and of all the storm’s component intensities. Because rainfall is provided to the model in daily totals, an assumption must be made about variation in rainfall intensity. The rainfall intensity variation with time is assumed to be exponentially distributed: æ t ö it = imx ⋅ expçç − ÷÷ è ki ø
13.2.3
where it is the rainfall intensity at time t (mm/hr), imx is the maximum rainfall intensity (mm/hr), t is the time (hr), and ki is the decay constant for rainfall intensity (hr). The USLE energy equation is æ é ∆Rday ù ö E storm = ∆Rday ⋅ çç12.1 + 8.9 ⋅ log10 ê ú ÷÷ t ∆ ë ûø è
13.2.4
where ∆Rday is the amount of rainfall during the time interval (mm H2O), and ∆t is the time interval (hr). This equation may be expressed analytically as: ∞
∞
0
0
E storm = 12.1ò it dt + 8.9 ò it log10 it dt
13.2.5
Combining equation 13.2.5 and 13.2.3 and integrating gives the equation for estimating daily rainfall energy: E storm =
Rday 1000
⋅ (12.1 + 8.9 ⋅ (log10 [imx ] − 0.434))
13.2.6
where Rday is the amount of precipitation falling on a given day (mm H2O), and imx is the maximum rainfall intensity (mm/hr). To compute the maximum rainfall intensity, imx, equation 13.2.3 is integrated to give Rday = imx ⋅ k i
13.2.7
æ é t ùö Rt = Rday ⋅ çç 1 − exp ê − ú ÷÷ ë ki û ø è
13.2.8
and
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225
where Rday is the amount of precipitation falling on a given day (mm H2O), imx is the maximum rainfall intensity (mm/hr), ki is the decay constant for rainfall intensity (hr), Rt is the amount of rain falling during a time interval (mm H2O), and t is the time interval (hr). The maximum half-hour rainfall for the precipitation event is known: R0.5 = α 0.5 ⋅ Rday
13.2.9
where R0.5 is the maximum half-hour rainfall (mm H2O), α0.5 is the maximum half-hour rainfall expressed as a fraction of daily rainfall, and Rday is the amount of precipitation falling on a given day (mm H2O). Calculation of α0.5 is reviewed in Chapter 4. Substituting equation 13.2.9 and 13.2.7 into 13.2.8 and solving for the maximum intensity gives: imx = −2 ⋅ Rday ⋅ ln (1 − α 0.5 )
13.2.10
where imx is the maximum rainfall intensity (mm/hr), Rday is the amount of precipitation falling on a given day (mm H2O), and α0.5 is the maximum half-hour rainfall expressed as a fraction of daily rainfall. The maximum 30 minute intensity is calculated: I 30 = 2 ⋅ α 0.5 ⋅ Rday
13.2.3
where I30 is the maximum 30-minute intensity (mm/hr), α0.5 is the maximum halfhour rainfall expressed as a fraction of daily rainfall, and Rday is the amount of precipitation falling on a given day (mm H2O). Table 13-6: SWAT input variables that pertain to USLE sediment yield. Variable Name USLE_K USLE_C USLE_P SLSUBBSN SLOPE ROCK
Definition KUSLE: USLE soil erodibility factor (0.013 metric ton m2 hr/(m3metric ton cm)) CUSLE,mn: Minimum value for the cover and management factor for the land cover PUSLE: USLE support practice factor Lhill: Slope length (m) slp: Average slope of the subbasin (% or m/m) rock: Percent rock in the first soil layer (%)
Input File .sol crop.dat .mgt .hru .hru .sol
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13.3 SNOW COVER EFFECTS The erosive power of rain and runoff will be less when snow cover is present than when there is no snow cover. During periods when snow is present in an HRU, SWAT modifies the sediment yield using the following relationship: sed =
sed ′ é 3 ⋅ SNO ù exp ê ë 25.4 úû
13.3.1
where sed is the sediment yield on a given day (metric tons), sed ′ is the sediment yield calculated with MUSLE (metric tons), and SNO is the water content of the snow cover (mm H2O).
13.4 SEDIMENT LAG IN SURFACE RUNOFF In large subbasins with a time of concentration greater than 1 day, only a portion of the surface runoff will reach the main channel on the day it is generated. SWAT incorporates a surface runoff storage feature to lag a portion of the surface runoff release to the main channel. Sediment in the surface runoff is lagged as well. Once the sediment load in surface runoff is calculated, the amount of sediment released to the main channel is calculated: æ é − surlag ù ö sed = (sed ′ + sed stor ,i −1 ) ⋅ çç 1 − exp ê ú ÷÷ t conc ûø ë è
13.4.1
where sed is the amount of sediment discharged to the main channel on a given day (metric tons), sed ′ is the amount of sediment load generated in the HRU on a given day (metric tons), sedstor,i-1 is the sediment stored or lagged from the previous day (metric tons), surlag is the surface runoff lag coefficient, and tconc is the time of concentration for the HRU (hrs).
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227
æ é − surlag ù ö The expression çç1 − exp ê ú ÷÷ in equation 13.4.1 represents the t conc ë ûø è
fraction of the total available sediment that will be allowed to enter the reach on any one day. Figure 13-1 plots values for this expression at different values for surlag and tconc.
Figure 13-1: Influence of surlag and tconc on fraction of surface runoff and sediment released.
Note that for a given time of concentration, as surlag decreases in value more sediment is held in storage. Table 13-7: SWAT input variables that pertain to sediment lag calculations. Variable Name SURLAG
Definition surlag: surface runoff lag coefficient
Input File .bsn
13.5 SEDIMENT IN LATERAL & GROUNDWATER FLOW SWAT allows the lateral and groundwater flow to contribute sediment to the main channel. The amount of sediment contributed by lateral and groundwater flow is calculated: sed lat =
(Q
lat
+ Q gw ) ⋅ area hru ⋅ conc sed 1000
13.5.1
where sedlat is the sediment loading in lateral and groundwater flow (metric tons), Qlat is the lateral flow for a given day (mm H2O), Qgw is the groundwater flow for
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a given day (mm H2O), areahru is the area of the HRU (km2), and concsed is the concentration of sediment in lateral and groundwater flow (mg/L). Table 13-8: SWAT input variables that pertain to sediment lag calculations. Variable Name LAT_SED
Definition concsed: Concentration of sediment in lateral and groundwater flow (mg/L)
Input File .hru
13.6 NOMENCLATURE CUSLE USLE cover and management factor CUSLE,aa Average annual C factor for the land cover CUSLE,mn Minimum value for the cover and management factor for the land cover CFRG Coarse fragment factor Estorm Total storm energy (0.0017 m-metric ton/m2), EIUSLE Rainfall erosion index (0.017 m-metric ton cm/(m2 hr)) I30 Maximum 30 minute intensity (mm/hr) KUSLE USLE soil erodibility factor (0.013 metric ton m2 hr/(m3-metric ton cm)) Lhill Slope length (m) LSUSLE USLE topographic factor M Particle-size parameter for estimation of USLE K factor OM Percent organic matter (%) PUSLE USLE support practice factor Qgw Groundwater flow for a given day (mm H2O) Qlat Lateral flow (mm H2O) Qsurf Surface runoff volume (mm H2O/ha) Rday Amount of rainfall on a given day (mm H2O) SNO Water content of the snow cover (mm H2O) areahru HRU area (ha or km2) cperm Profile-permeability class csoilstr Soil-structure code used in soil classification concsed Concentration of sediment in lateral and groundwater flow (mg/L) fcl-si Factor that gives low soil erodibility factors for soils with high clay to silt ratios fcsand Factor that gives low soil erodibility factors for soils with high coarse-sand contents and high values for soils with little sand fhisand Factor that reduces soil erodibility for soils with extremely high sand contents forgc Factor that reduces soil erodibility for soils with high organic carbon content imx Maximum rainfall intensity (mm/hr) it Rainfall intensity at time t (mm/hr) ki Decay constant for rainfall intensity (hr) m Exponential term in USLE LS factor calculation mc Percent clay content (< 0.002 mm diameter particles) ms Percent sand content
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msilt Percent silt content (0.002-0.05 mm diameter particles) mvfs Percent very fine sand content (0.05-0.10 mm diameter particles) orgCly Amount of organic carbon in the layer (%) qpeak Peak runoff rate (m3/s) rock Percent rock in soil layer (%) rsdsurf Amount of residue on the soil surface (kg/ha) sed Sediment yield on a given day (metric tons) sedlat Sediment loading in lateral and groundwater flow (metric tons) sedstor,i-1 Sediment stored or lagged from the previous day (metric tons) slp Average slope of the subbasin (m/m) surlag Surface runoff lag coefficient t Time (hr) tconc Time of concentration for a subbasin (hr)
α0.5 αhill
Maximum half-hour rainfall expressed as a fraction of daily rainfall Angle of the slope
13.7 REFERENCES Arnold, J.G. and J.R. Williams. 1995. SWRRB—A watershed scale model for soil and water resources management. p. 847-908. In V.P. Singh (ed) Computer models of watershed hydrology. Water Resources Publications. Williams, J.R. 1975. Sediment-yield prediction with universal equation using runoff energy factor. p. 244-252. In Present and prospective technology for predicting sediment yield and sources: Proceedings of the sedimentyield workshop, USDA Sedimentation Lab., Oxford, MS, November 2830, 1972. ARS-S-40. Williams, J.R. 1995. Chapter 25: The EPIC model. p. 909-1000. In V.P. Singh (ed.) Computer models of watershed hydrology. Water Resources Publications. Wischmeier, W.H., C.B. Johnson, and B.V. Cross. 1971. A soil erodibility nomograph for farmland and construction sites. Journal of Soil and Water Conservation 26:189-193. Wischmeier, W.H. and D.D. Smith. 1965. Predicting rainfall-erosion losses from cropland east of the Rocky Mountains. Agriculture Handbook 282. USDA-ARS
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Wischmeier, W.H. and D.D. Smith. 1978. Predicting rainfall erosion losses: a guide to conservation planning. Agriculture Handbook 282. USDA-ARS
CHAPTER 14
EQUATIONS: NUTRIENT TRANSPORT
The transport of nutrients from land areas into streams and water bodies is a normal result of soil weathering and erosion processes. However, excessive loading of nutrients into streams and water bodies will accelerate eutrophication and render the water unfit for human consumption. This chapter reviews the algorithms governing movement of mineral and organic forms of nitrogen and phosphorus from land areas to the stream network.
231
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14.1 NITRATE MOVEMENT Most soil minerals are negatively charged at normal pH and the net interaction with anions such as nitrate is a repulsion from particle surfaces. This repulsion is termed negative adsorption or anion exclusion. Anions are excluded from the area immediately adjacent to mineral surfaces due to preferential attraction of cations to these sites. This process has a direct impact on the transport of anions through the soil for it effectively excludes anions from the slowest moving portion of the soil water volume found closest to the charged particle surfaces (Jury et al, 1991). In effect, the net pathway of the anion through the soil is shorter than it would be if all the soil water had to be used (Thomas and McMahon, 1972). Nitrate may be transported with surface runoff, lateral flow or percolation. To calculate the amount of nitrate moved with the water, the concentration of nitrate in the mobile water is calculated. This concentration is then multiplied by the volume of water moving in each pathway to obtain the mass of nitrate lost from the soil layer. The concentration of nitrate in the mobile water fraction is calculated:
conc NO 3,mobile
é − wmobile ù NO3ly ⋅ exp ê ú ê (1 − θ e ) ⋅ SATly ûú ë = wmobile
14.1.2
where concNO3,mobile is the concentration of nitrate in the mobile water for a given layer (kg N/mm H2O), NO3ly is the amount of nitrate in the layer (kg N/ha), wmobile is the amount of mobile water in the layer (mm H2O), θe is the fraction of porosity from which anions are excluded, and SATly is the saturated water content of the soil layer (mm H2O). The amount of mobile water in the layer is the amount of water lost by surface runoff, lateral flow or percolation: wmobile = Qsurf + Qlat ,ly + w perc ,ly
for top 10 mm
14.1.3
wmobile = Qlat ,ly + w perc ,ly
for lower soil layers
14.1.4
where wmobile is the amount of mobile water in the layer (mm H2O), Qsurf is the surface runoff generated on a given day (mm H2O), Qlat,ly is the water discharged
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233
from the layer by lateral flow (mm H2O), and wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O). Surface runoff is allowed to interact with and transport nutrients from the top 10 mm of soil. Nitrate removed in surface runoff is calculated: NO3surf = β NO 3 ⋅ conc NO 3,mobile ⋅ Qsurf
14.1.5
where NO3surf is the nitrate removed in surface runoff (kg N/ha), βNO3 is the nitrate percolation coefficient, concNO3,mobile is the concentration of nitrate in the mobile water for the top 10 mm of soil (kg N/mm H2O), and Qsurf is the surface runoff generated on a given day (mm H2O). The nitrate percolation coefficient allows the user to set the concentration of nitrate in surface runoff to a fraction of the concentration in percolate. Nitrate removed in lateral flow is calculated: NO3lat ,ly = β NO 3 ⋅ conc NO 3,mobile ⋅ Qlat ,ly
for top 10 mm
14.1.6
NO3lat ,ly = conc NO 3,mobile ⋅ Qlat ,ly
for lower layers
14.1.7
where NO3lat,ly is the nitrate removed in lateral flow from a layer (kg N/ha), βNO3 is the nitrate percolation coefficient, concNO3,mobile is the concentration of nitrate in the mobile water for the layer (kg N/mm H2O), and Qlat,ly is the water discharged from the layer by lateral flow (mm H2O). Nitrate moved to the underlying layer by percolation is calculated: NO3 perc ,ly = conc NO 3,mobile ⋅ w perc ,ly
14.1.8
where NO3perc,ly is the nitrate moved to the underlying layer by percolation (kg N/ha), concNO3,mobile is the concentration of nitrate in the mobile water for the layer (kg N/mm H2O), and wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O). Table 14-1: SWAT input variables that pertain to nitrate transport. Variable Name ANION_EXCL NPERCO
Definition θe: Fraction of porosity from which anions are excluded βNO3: Nitrate percolation coefficient
Input File .sol .bsn
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14.2 ORGANIC N IN SURFACE RUNOFF Organic N attached to soil particles may be transported by surface runoff to the main channel. This form of nitrogen is associated with the sediment loading from the HRU and changes in sediment loading will be reflected in the organic nitrogen loading. The amount of organic nitrogen transported with sediment to the stream is calculated with a loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978). orgN surf = 0.001 ⋅ concorgN ⋅
sed ⋅ ε N :sed area hru
14.2.1
where orgNsurf is the amount of organic nitrogen transported to the main channel in surface runoff (kg N/ha), concorgN is the concentration of organic nitrogen in the top 10 mm (g N/ metric ton soil), sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and εN:sed is the nitrogen enrichment ratio. The concentration of organic nitrogen in the soil surface layer, concorgN, is calculated: concorgN = 100 ⋅
(orgN
frsh , surf
+ orgN sta ,surf + orgN act ,surf )
ρ b ⋅ depthsurf
14.2.2
where orgNfrsh,surf is nitrogen in the fresh organic pool in the top 10mm (kg N/ha), orgNsta,surf is nitrogen in the stable organic pool (kg N/ha), orgNact,surf is nitrogen in the active organic pool in the top 10 mm (kg N/ha), ρb is the bulk density of the first soil layer (Mg/m3), and depthsurf is the depth of the soil surface layer (10 mm).
14.2.1 ENRICHMENT RATIO As surface runoff flows over the soil surface, part of the water’s energy is used to pick up and transport soil particles. The smaller particles weigh less and are more easily transported than coarser particles. When the particle size distribution of the transported sediment is compared to that of the soil surface layer, the sediment load to the main channel has a greater proportion of clay sized particles. In other words, the sediment load is enriched in clay particles. Organic
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235
nitrogen in the soil is attached primarily to colloidal (clay) particles, so the sediment load will also contain a greater proportion or concentration of organic N than that found in the soil surface layer. The enrichment ratio is defined as the ratio of the concentration of organic nitrogen transported with the sediment to the concentration in the soil surface layer. SWAT will calculate an enrichment ratio for each storm event, or allow the user to define a particular enrichment ratio for organic nitrogen that is used for all storms during the simulation. To calculate the enrichment ratio, SWAT uses a relationship described by Menzel (1980) in which the enrichment ratio is logarithmically related to sediment concentration. The equation used to calculate the nitrogen enrichment ratio, εN:sed, for each storm event is:
ε N :sed = 0.78 ⋅ (conc sed ,surq )−0.2468
14.2.3
where concsed,surq is the concentration of sediment in surface runoff (Mg sed/m3 H2O). The concentration of sediment in surface runoff is calculated:
conc sed ,surq =
sed 10 ⋅ area hru ⋅ Qsurf
14.2.4
where sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and Q,surf is the amount of surface runoff on a given day (mm H2O). Table 14-2: SWAT input variables that pertain to organic N loading. Variable Name SOL_BD ERORGN
Definition
ρb: Bulk density (Mg/m3) εN:sed: Organic nitrogen enrichment ratio
Input File .sol .hru
14.3 SOLUBLE PHOSPHORUS MOVEMENT The primary mechanism of phosphorus movement in the soil is by diffusion. Diffusion is the migration of ions over small distances (1-2 mm) in the soil solution in response to a concentration gradient. Due to the low mobility of solution phosphorus, surface runoff will only partially interact with the solution P stored in the top 10 mm of soil. The amount of solution P transported in surface runoff is:
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SWAT USER'S MANUAL, VERSION 2000
Psurf =
Psolution,surf ⋅ Qsurf
14.3.1
ρ b ⋅ depthsurf ⋅ k d ,surf
where Psurf is the amount of soluble phosphorus lost in surface runoff (kg P/ha), Psolution,surf is the amount of phosphorus in solution in the top 10 mm (kg P/ha), Q,surf is the amount of surface runoff on a given day (mm H2O), ρb is the bulk density of the top 10 mm (Mg/m3) (assumed to be equivalent to bulk density of first soil layer), depthsurf is the depth of the “surface” layer (10 mm), and kd,surf is the phosphorus soil partitioning coefficient (m3/Mg). The phosphorus soil partitioning coefficient is the ratio of the soluble phosphorus concentration in the surface 10 mm of soil to the concentration of soluble phosphorus in surface runoff. Table 14-3: SWAT input variables that pertain to soluble P runoff. Variable Name SOL_BD PHOSKD
Definition
ρb: Bulk density (Mg/m3) kd,surf: Phosphorus soil partitioning coefficient (m3/Mg)
Input File .sol .bsn
14.4 ORGANIC & MINERAL P ATTACHED TO SEDIMENT IN SURFACE RUNOFF Organic and mineral P attached to soil particles may be transported by surface runoff to the main channel. This form of phosphorus is associated with the sediment loading from the HRU and changes in sediment loading will be reflected in the loading of these forms of phophorus. The amount of phosphorus transported with sediment to the stream is calculated with a loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978). sedPsurf = 0.001 ⋅ conc sedP ⋅
sed ⋅ ε P:sed area hru
14.4.1
where sedPsurf is the amount of phosphorus transported with sediment to the main channel in surface runoff (kg P/ha), concsedP is the concentration of phosphorus attached to sediment in the top 10 mm (g P/ metric ton soil), sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and εP:sed is the phosphorus enrichment ratio.
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237
The concentration of phosphorus attached to sediment in the soil surface layer, concsedP, is calculated: conc sedP = 100 ⋅
(minP
act ,surf
+ minPsta ,surf + orgPhum ,surf + orgPfrsh ,surf )
ρ b ⋅ depthsurf
14.4.2
where minPact,surf is the amount of phosphorus in the active mineral pool in the top 10 mm (kg P/ha), minPsta,surf is the amount of phosphorus in the stable mineral pool in the top 10 mm (kg P/ha), orgPhum,surf is the amount of phosphorus in humic organic pool in the top 10 mm (kg P/ha), orgPfrsh,surf is the amount of phosphorus in the fresh organic pool in the top 10 mm (kg P/ha), ρb is the bulk density of the first soil layer (Mg/m3), and depthsurf is the depth of the soil surface layer (10 mm).
14.4.1 ENRICHMENT RATIO The enrichment ratio is defined as the ratio of the concentration of phosphorus transported with the sediment to the concentration of phosphorus in the soil surface layer. SWAT will calculate an enrichment ratio for each storm event, or allow the user to define a particular enrichment ratio for phosphorus attached to sediment that is used for all storms during the simulation. To calculate the enrichment ratio, SWAT uses a relationship described by Menzel (1980) in which the enrichment ratio is logarithmically related to sediment concentration. The equation used to calculate the phosphorus enrichment ratio, εP:sed, for each storm event is:
ε P:sed = 0.78 ⋅ (concsed ,surq )−0.2468
14.4.3
where concsed,surq is the concentration of sediment in surface runoff (Mg sed/m3 H2O). The concentration of sediment in surface runoff is calculated: conc sed ,surq =
sed 10 ⋅ area hru ⋅ Qsurf
14.4.4
where sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and Qsurf is the amount of surface runoff on a given day (mm H2O).
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Table 14-4: SWAT input variables that pertain to loading of P attached to sediment. Variable Name SOL_BD ERORGP
Definition
ρb: Bulk density (Mg/m3) εP:sed: Phosphorus enrichment ratio
Input File .sol .hru
14.5 NUTRIENT LAG IN SURFACE RUNOFF AND LATERAL FLOW In large subbasins with a time of concentration greater than 1 day, only a portion of the surface runoff and lateral flow will reach the main channel on the day it is generated. SWAT incorporates a storage feature to lag a portion of the surface runoff and lateral flow release to the main channel. Nutrients in the surface runoff and lateral flow are lagged as well. Once the nutrient load in surface runoff and lateral flow is determined, the amount of nutrients released to the main channel is calculated: æ é − surlag ù ö ′ + NO3surstor ,i −1 ) ⋅ ç1 − exp ê NO3surf = (NO3surf ú ÷÷ ç ë t conc û ø è
14.5.1
æ é − 1 ùö ′ + NO3latstor ,i −1 ) ⋅ ç1 − exp ê NO3lat = (NO3lat ú ÷÷ ç TT ë lat û ø è
14.5.2
æ é − surlag ù ö ′ + orgN stor , i −1 ) ⋅ ç1 − exp ê orgN surf = (orgN surf ú ÷÷ ç t ë conc û ø è
14.5.3
æ é − surlag ù ö ′ + Pstor ,i −1 ) ⋅ ç 1 − exp ê Psurf = (Psurf ú ÷÷ ç t conc ûø ë è
14.5.4
æ é − surlag ù ö ′ + sedPstor ,i −1 ) ⋅ ç 1 − exp ê sedPsurf = (sedPsurf ú ÷÷ ç ë t conc û ø è
14.5.5
where NO3surf is the amount of nitrate discharged to the main channel in surface ′ is the amount of surface runoff nitrate runoff on a given day (kg N/ha), NO3surf
generated in the HRU on a given day (kg N/ha), NO3surstor,i-1 is the surface runoff nitrate stored or lagged from the previous day (kg N/ha), NO3lat is the amount of nitrate discharged to the main channel in lateral flow on a given day (kg N/ha),
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′ is the amount of lateral flow nitrate generated in the HRU on a given day NO3lat (kg N/ha), NO3latstor,i-1 is the lateral flow nitrate stored or lagged from the previous day (kg N/ha), orgNsurf is the amount of organic N discharged to the main channel ′ is the organic N loading in surface runoff on a given day (kg N/ha), orgN surf
generated in the HRU on a given day (kg N/ha), orgNstor,i-1 is the organic N stored or lagged from the previous day (kg N/ha), Psurf is the amount of solution P ′ is discharged to the main channel in surface runoff on a given day (kg P/ha), Psurf
the amount of solution P loading generated in the HRU on a given day (kg P/ha), Pstor,i-1 is the solution P loading stored or lagged from the previous day (kg P/ha), sedPsurf is the amount of sediment-attached P discharged to the main channel in ′ is the amount of sedimentsurface runoff on a given day (kg P/ha), sedPsurf
attached P loading generated in the HRU on a given day (kg P/ha), sedPstor,i-1 is the sediment-attached P stored or lagged from the previous day (kg P/ha), surlag is the surface runoff lag coefficient, tconc is the time of concentration for the HRU (hrs) and TTlag is the lateral flow travel time (days). Table 14-5: SWAT input variables that pertain to nutrient lag calculations. Variable Name SURLAG LAT_TTIME
Definition surlag: surface runoff lag coefficient TTlag: Lateral flow travel time (days)
Input File .bsn .hru
14.6 NOMENCLATURE NO3lat,ly Nitrate removed in lateral flow from a layer (kg N/ha) ′ Amount of lateral flow nitrate generated in HRU on a given day (kg N/ha) NO3lat NO3latstor,i-1 Lateral flow nitrate stored or lagged from the previous day (kg N/ha) NO3ly Amount of nitrate in the layer (kg N/ha) NO3perc,ly Nitrate moved to the underlying layer by percolation (kg N/ha) NO3surf Nitrate removed in surface runoff (kg N/ha) ′ Amount of surface runoff nitrate generated in HRU on a given day (kg N/ha) NO3surf NO3surstor,i-1 Surface runoff nitrate stored or lagged from the previous day (kg N/ha) Psolution,surf Amount of phosphorus in solution in the top 10 mm (kg P/ha) Pstor,i-1 Solution P loading stored or lagged from the previous day (kg P/ha) Psurf Amount of soluble phosphorus lost in surface runoff (kg P/ha) ′ Psurf Amount of solution P loading generated in HRU on a given day (kg P/ha)
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Lateral flow from soil layer (mm H2O) Accumulated runoff or rainfall excess (mm H2O) Saturated water content of the soil layer (mm H2O) Lateral flow travel time (days)
areahru HRU area (ha) concNO3,mobile Concentration of nitrate in the mobile water for a given layer (kg N/mm H2O) concorgN Concentration of organic nitrogen in the soil surface top 10 mm (g N/ metric ton soil) concsed,surq Concentration of sediment in surface runoff (Mg sed/m3 H2O) concsedP Concentration of phosphorus attached to sediment in the top 10 mm (g P/ metric ton soil) depthsurf Depth of the “surface” layer (10 mm) kd,surf Phosphorus soil partitioning coefficient (m3/Mg) minPact,ly Amount of phosphorus in the active mineral pool (kg P/ha) minPsta,ly Amount of phosphorus in the stable mineral pool (kg P/ha) orgNact,ly Nitrogen in the active organic pool (mg/kg or kg N/ha) orgNfrsh,surf Nitrogen in the fresh organic pool in the top 10mm (kg N/ha) orgNsta,ly Nitrogen in the stable organic pool (mg/kg or kg N/ha) orgNstor,i-1 Surface runoff organic N stored or lagged from the previous day (kg N/ha) orgNsurf Amount of organic nitrogen transport to the main channel in surface runoff (kg N/ha) ′ orgN surf Amount of surface runoff organic N generated in HRU on a given day (kg N/ha) orgPfrsh,ly Phosphorus in the fresh organic pool in layer ly (kg P/ha) orgPhum,ly Amount of phosphorus in humic organic pool in the layer (kg P/ha) sed Sediment yield on a given day (metric tons) sedPstor,i-1 Sediment-attached P stored or lagged from the previous day (kg P/ha) sedPsurf Amount of phosphorus transported with sediment to the main channel in surface runoff (kg P/ha) ′ Amount of sediment-attached P loading generated in HRU on a given day (kg sedPsurf P/ha) surlag Surface runoff lag coefficient tconc Time of concentration for a subbasin (hr) wmobile Amount of mobile water in the layer (mm H2O) wperc,ly Amount of water percolating to the underlying soil layer on a given day (mm H2O)
βNO3 θe εN:sed εP:sed ρb
Nitrate percolation coefficient Fraction of porosity from which anions are excluded Nitrogen enrichment ratio Phosphorus enrichment ratio Bulk density (Mg/m3)
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14.7 REFERENCES Jury, W.A., W.R. Gardner, and W.H. Gardner. 1991. Soil physics, 5th edition. John Wiley & Sons, Inc. New York, N.Y. McElroy, A.D., S.Y. Chiu, J.W. Nebgen, A. Aleti, and F.W. Bennett. 1976. Loading functions for assessment of water pollution from nonpoint sources. Environ. Prot. Tech. Serv., EPA 600/2-76-151. Menzel, R.G. 1980. Enrichment ratios for water quality modeling. p. 486-492. In W.G. Knisel (ed.) CREAMS, A field scale model for chemicals, runoff, and erosion from agricultural management systems. U.S. Dept. Agric. Conserv. Res. Rept. No. 26. Thomas, G.W. and M. McMahon. 1972. The relation between soil characteristics, water movement and nitrate concentration of ground water. Univ. of Kentucky Water Resources Institute Research Report No. 52, Lexington, KY. Williams, J.R. and R.W. Hann. 1978. Optimal operation of large agricultural watersheds with water quality constraints. Texas Water Resources Institute, Texas A&M Univ., Tech. Rept. No. 96.
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CHAPTER 15
EQUATIONS: PESTICIDE TRANSPORT
The transport of pesticide from land areas into streams and water bodies is a result of soil weathering and erosion processes. Excessive loading of pesticides in streams and water bodies can produce toxic conditions that harm aquatic life and render the water unfit for human consumption. This chapter reviews the algorithms governing movement of soluble and sorbed forms of pesticide from land areas to the stream network. Pesticide transport algorithms in SWAT were taken from EPIC (Williams, 1995). 243
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15.1 PHASE DISTRIBUTION OF PESTICIDE Pesticide in the soil environment can be transported in solution or attached to sediment. The partitioning of a pesticide between the solution and soil phases is defined by the soil adsorption coefficient for the pesticide. The soil adsorption coefficient is the ratio of the pesticide concentration in the soil or solid phase to the pesticide concentration in the solution or liquid phase: Kp =
C solidphase C solution
15.1.1
where Kp is the soil adsorption coefficient ((mg/kg)/(mg/L) or m3/ton), Csolidphase is the concentration of the pesticide sorbed to the solid phase (mg chemical/kg solid material or g/ton), and Csolution is the concentration of the pesticide in solution (mg chemical/L solution or g/ton). The definition of the soil adsorption coefficient in equation 15.1.1 assumes that the pesticide sorption process is linear with concentration and instantaneously reversible. Because the partitioning of pesticide is dependent upon the amount of organic material in the soil, the soil adsorption coefficient input to the model is normalized for soil organic carbon content. The relationship between the soil adsorption coefficient and the soil adsorption coefficient normalized for soil organic carbon content is: K p = K oc ⋅
orgC 100
15.1.2
where Kp is the soil adsorption coefficient ((mg/kg)/(mg/L)), Koc is the soil adsorption coefficient normalized for soil organic carbon content ((mg/kg)/(mg/L) or m3/ton), and orgC is the percent organic carbon present in the soil. Table 15-1: SWAT input variables that pertain to pesticide phase partitioning. Variable Name SOL_CBN SKOC
Definition orgCly: Amount of organic carbon in the layer (%) Koc: Soil adsorption coefficient normalized for soil organic carbon content (ml/g or (mg/kg)/(mg/L) or L/kg)
Input File .sol pest.dat
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245
15.2 MOVEMENT OF SOLUBLE PESTICIDE Pesticide in the soluble phase may be transported with surface runoff, lateral flow or percolation. The change in the amount of pesticide contained in a soil layer due to transport in solution with flow is a function of time, concentration and amount of flow: dpst s ,ly dt
= 0.01 ⋅ C solution ⋅ wmobile
15.2.1
where psts,ly is the amount of pesticide in the soil layer (kg pst/ha), Csolution is the pesticide concentration in solution (mg/L or g/ton), and wmobile is the amount of mobile water on a given day (mm H2O). The amount of mobile water in the layer is the amount of water lost by surface runoff, lateral flow or percolation: wmobile = Qsurf + Qlat ,surf + w perc ,surf
for top 10 mm
15.2.2
wmobile = Qlat ,ly + w perc ,ly
for lower soil layers
15.2.3
where wmobile is the amount of mobile water in the layer (mm H2O), Qsurf is the surface runoff generated on a given day (mm H2O), Qlat,ly is the water discharged from the layer by lateral flow (mm H2O), and wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O). The total amount of pesticide in the soil layer is the sum of the adsorbed and dissolved phases: pst s ,ly = 0.01 ⋅ (C solution ⋅ SATly + C solidphase ⋅ ρ b ⋅ depthly )
15.2.4
where psts,ly is the amount of pesticide in the soil layer (kg pst/ha), Csolution is the pesticide concentration in solution (mg/L or g/ton), SATly is the amount of water in the soil layer at saturation (mm H2O), Csolidphase is the concentration of the pesticide sorbed to the solid phase (mg/kg or g/ton), ρb is the bulk density of the soil layer (Mg/m3), and depthly is the depth of the soil layer (mm). Rearranging equation 15.1.1 to solve for Csolidphase and substituting into equation 15.2.4 yields: pst s ,ly = 0.01 ⋅ (C solution ⋅ SATly + C solution ⋅ K p ⋅ ρ b ⋅ depthly )
which rearranges to
15.2.5
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C solution =
pst s ,ly
0.01 ⋅ (SATly + K p ⋅ ρ b ⋅ depthly )
15.2.6
Combining equation 15.2.6 with equation 15.2.1 yields dpst s ,ly dt
=
(SAT
ly
pst s ,ly ⋅ wmobile
+ K p ⋅ ρ b ⋅ depthly )
15.2.7
Integration of equation 15.2.7 gives é ù − wmobile pst s ,ly ,t = pst s ,ly ,o ⋅ exp ê ú êë (SATly + K p ⋅ ρ b ⋅ depthly )úû
15.2.8
where psts,ly,t is the amount of pesticide in the soil layer at time t (kg pst/ha), psts,ly,o is the initial amount of pesticide in the soil layer (kg pst/ha), wmobile is the amount of mobile water in the layer (mm H2O), SATly is the amount of water in the soil layer at saturation (mm H2O), Kp is the soil adsorption coefficient ((mg/kg)/(mg/L)), ρb is the bulk density of the soil layer (Mg/m3), and depthly is the depth of the soil layer (mm). To obtain the amount of pesticide removed in solution with the flow, the final amount of pesticide is subtracted from the initial amount of pesticide: æ é ùö − wmobile pst flow = pst s ,ly ,o ⋅ ç1 − exp ê ú ÷÷ ç ( ) + ⋅ ⋅ SAT K ρ depth ê úû ø ly p b ly ë è
15.2.9
where pstflow is the amount of pesticide removed in the flow (kg pst/ha) and all other terms were previously defined. The pesticide concentration in the mobile water is calculated:
conc pst , flow
ì pst flow / wmobile ï = min í ï pst / 100. sol î
15.2.10
where concpst,flow is the concentration of pesticide in the mobile water (kg pst/hamm H2O), pstflow is the amount of pesticide removed in the flow (kg pst/ha), wmobile is the amount of mobile water in the layer (mm H2O), and pstsol is the solubility of the pesticide in water (mg/L). Pesticide moved to the underlying layer by percolation is calculated: pst perc ,ly = conc pst , flow ⋅ w perc ,ly
15.2.11
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247
where pstperc,ly is the pesticide moved to the underlying layer by percolation (kg pst/ha), concpst,flow is the concentration of pesticide in the mobile water for the layer (kg pst/mm H2O), and wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O). Pesticide removed in lateral flow is calculated: pstlat ,surf = β pst ⋅ conc pst , flow ⋅ Qlat ,surf
for top 10 mm
15.2.12
pstlat ,ly = conc pst , flow ⋅ Qlat ,ly
for lower layers
15.2.13
where pstlat,ly is the pesticide removed in lateral flow from a layer (kg pst/ha), βpst is the pesticide percolation coefficient, concpst,flow is the concentration of pesticide in the mobile water for the layer (kg pst/mm H2O), and Qlat,ly is the water discharged from the layer by lateral flow (mm H2O). The pesticide percolation coefficient allows the user to set the concentration of pesticide in runoff and lateral flow from the top 10 mm to a fraction of the concentration in percolate. Pesticide removed in surface runoff is calculated: pst surf = β pst ⋅ conc pst , flow ⋅ Qsurf
15.2.14
where pstsurf is the pesticide removed in surface runoff (kg pst/ha), βpst is the pesticide percolation coefficient, concpst,flow is the concentration of pesticide in the mobile water for the top 10 mm of soil (kg pst/mm H2O), and Qsurf is the surface runoff generated on a given day (mm H2O). Table 15-2: SWAT input variables that pertain to pesticide transport in solution. Variable Name SOL_BD WSOL PERCOP
Definition ρb: Soil bulk density (Mg m-3) pstsol: Solubility of the pesticide in water (mg/L) βpst: Pesticide percolation coefficient
Input File .sol pest.dat .bsn
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15.3 TRANSPORT OF SORBED PESTICIDE Pesticide attached to soil particles may be transported by surface runoff to the main channel. This phase of pesticide is associated with the sediment loading from the HRU and changes in sediment loading will be reflected in the loading of sorbed pesticide. The amount of pesticide transported with sediment to the stream is calculated with a loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978). pst sed = 0.001 ⋅ C solidphase ⋅
sed ⋅ ε pst:sed area hru
15.3.1
where pstsed is the amount of sorbed pesticide transported to the main channel in surface runoff (kg pst/ha), Csolidphase is the concentration of pesticide on sediment in the top 10 mm (g pst/ metric ton soil), sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and εpst:sed is the pesticide enrichment ratio. The total amount of pesticide in the soil layer is the sum of the adsorbed and dissolved phases: pst s ,ly = 0.01 ⋅ (C solution ⋅ SATly + C solidphase ⋅ ρ b ⋅ depthly )
15.3.2
where psts,ly is the amount of pesticide in the soil layer (kg pst/ha), Csolution is the pesticide concentration in solution (mg/L or g/ton), SATly is the amount of water in the soil layer at saturation (mm H2O), Csolidphase is the concentration of the pesticide sorbed to the solid phase (mg/kg or g/ton), ρb is the bulk density of the soil layer (Mg/m3), and depthly is the depth of the soil layer (mm). Rearranging equation 15.1.1 to solve for Csolution and substituting into equation 15.3.2 yields: æ C solidphase ö pst s ,ly = 0.01 ⋅ ç ⋅ SATly + C solidphase ⋅ ρ b ⋅ depthly ÷ ç K ÷ p è ø
15.3.3
which rearranges to C solidphase =
100 ⋅ K p ⋅ pst s ,ly
(SAT
ly
+ K p ⋅ ρ b ⋅ depthly )
15.3.4
where Csolidphase is the concentration of the pesticide sorbed to the solid phase (mg/kg or g/ton), Kp is the soil adsorption coefficient ((mg/kg)/(mg/L) or m3/ton)
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249
psts,ly is the amount of pesticide in the soil layer (kg pst/ha), SATly is the amount of water in the soil layer at saturation (mm H2O), , ρb is the bulk density of the soil layer (Mg/m3), and depthly is the depth of the soil layer (mm).
15.3.1 ENRICHMENT RATIO As surface runoff flows over the soil surface, part of the water’s energy is used to pick up and transport soil particles. The smaller particles weigh less and are more easily transported than coarser particles. When the particle size distribution of the transported sediment is compared to that of the soil surface layer, the sediment load to the main channel has a greater proportion of clay sized particles. In other words, the sediment load is enriched in clay particles. The sorbed phase of pesticide in the soil is attached primarily to colloidal (clay) particles, so the sediment load will also contain a greater proportion or concentration of pesticide than that found in the soil surface layer. The enrichment ratio is defined as the ratio of the concentration of sorbed pesticide transported with the sediment to the concentration in the soil surface layer. SWAT will calculate an enrichment ratio for each storm event, or allow the user to define a particular enrichment ratio for sorbed pesticide that is used for all storms during the simulation. To calculate the enrichment ratio, SWAT uses a relationship described by Menzel (1980) in which the enrichment ratio is logarithmically related to sediment concentration. The equation used to calculate the pesticide enrichment ratio, εpst:sed, for each storm event is:
ε pst:sed = 0.78 ⋅ (concsed ,surq )−0.2468
15.3.5
where concsed,surq is the concentration of sediment in surface runoff (Mg sed/m3 H2O). The concentration of sediment in surface runoff is calculated:
conc sed ,surq =
sed 10 ⋅ area hru ⋅ Qsurf
15.3.6
where sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and Qsurf is the amount of surface runoff on a given day (mm H2O).
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Table 15-3: SWAT input variables that pertain to sorbed pesticide loading. Variable Name SOL_BD PSTENR
Definition
ρb: Bulk density (Mg/m3) εpst:sed: Pesticide enrichment ratio
Input File .sol .chm
15.4 PESTICIDE LAG IN SURFACE RUNOFF AND LATERAL FLOW In large subbasins with a time of concentration greater than 1 day, only a portion of the surface runoff and lateral flow will reach the main channel on the day it is generated. SWAT incorporates a storage feature to lag a portion of the surface runoff and lateral flow release to the main channel. Pesticides in the surface runoff and lateral flow are lagged as well. Once the pesticide load in surface runoff and lateral flow is determined, the amount of pesticide released to the main channel is calculated: æ é − surlag ù ö ′ + pst surstor,i −1 ) ⋅ ç1 − exp ê pst surf = ( pst surf ú ÷÷ ç ë tconc û ø è
15.4.1
æ é −1 ùö ′ + pstlatstor ,i −1 ) ⋅ ç1 − exp ê pstlat = ( pstlat ú ÷÷ ç TT ë lat û ø è
15.4.2
æ é − surlag ù ö ′ + pst sedstor ,i −1 ) ⋅ ç1 − exp ê pst sed = ( pst sed ú ÷÷ ç t ë conc û ø è
15.4.3
where pstsurf is the amount of soluble pesticide discharged to the main channel in ′ is the amount of surface runoff surface runoff on a given day (kg pst/ha), pst surf
soluble pesticide generated in HRU on a given day (kg pst/ha), pstsurstor,i-1 is the surface runoff soluble pesticide stored or lagged from the previous day (kg pst/ha), pstlat is the amount of soluble pesticide discharged to the main channel in ′ is the amount of lateral flow soluble lateral flow on a given day (kg pst/ha), pstlat pesticide generated in HRU on a given day (kg pst/ha), pstlatstor,i-1 is the lateral flow pesticide stored or lagged from the previous day (kg pst/ha), pstsed is the amount of sorbed pesticide discharged to the main channel in surface runoff on a
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251
′ is the sorbed pesticide loading generated in HRU on given day (kg pst/ha), pst sed a given day (kg pst/ha), pstsedstor,i-1 is the sorbed pesticide stored or lagged from the previous day (kg pst/ha), surlag is the surface runoff lag coefficient, tconc is the time of concentration for the HRU (hrs) and TTlag is the lateral flow travel time (days). Table 15-4: SWAT input variables that pertain to pesticide lag calculations. Variable Name SURLAG LAT_TTIME
Definition surlag: surface runoff lag coefficient TTlag: Lateral flow travel time (days)
Input File .bsn .hru
15.5 NOMENCLATURE Csolidphase Concentration of the pesticide sorbed to the solid phase (mg/kg or g/ton) Csolution Concentration of the pesticide in solution (mg/L or g/ton) Soil adsorption coefficient normalized for soil organic carbon content (ml/g or Koc (mg/kg)/(mg/L) or L/kg) Soil adsorption coefficient ((mg/kg)/(mg/L)) Kp Lateral flow from soil layer (mm H2O) Qlat Qsurf Accumulated runoff or rainfall excess (mm H2O) SATly Soil water content of layer ly at saturation (mm H2O) TTlag Lateral flow travel time (days) areahru HRU area (ha) concpst,flow Concentration of pesticide in the mobile water (kg pst/ha-mm H2O) concsed,surq Concentration of sediment in surface runoff (Mg sed/m3 H2O) depthly Depth of the soil layer (mm) orgCly Amount of organic carbon in the layer (%) pstflow Amount of pesticide removed in the flow (kg pst/ha) pstlat,ly Pesticide removed in lateral flow from a layer (kg pst/ha) ′ Amount of lateral flow soluble pesticide generated in HRU on a given day (kg pstlat pst/ha) pstlatstor,i-1 Lateral flow pesticide stored or lagged from the previous day (kg pst/ha) pstperc,ly Pesticide moved to the underlying layer by percolation (kg pst/ha) psts,ly Amount of pesticide in the soil (kg pst/ha) pstsed Amount of sorbed pesticide transported to the main channel in surface runoff (kg pst/ha) ′ Sorbed pesticide loading generated in HRU on a given day (kg pst/ha) pst sed pstsedstor,i-1 Sorbed pesticide stored or lagged from the previous day (kg pst/ha) pstsol Solubility of the pesticide in water (mg/L) pstsurf Pesticide removed in surface runoff (kg pst/ha)
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′ Amount of surface runoff soluble pesticide generated in HRU on a given day (kg pst surf pst/ha) pstsurstor,i-1 Surface runoff soluble pesticide stored or lagged from the previous day (kg pst/ha) sed Sediment yield on a given day (metric tons) surlag Surface runoff lag coefficient tconc Time of concentration for a subbasin (hr) wmobile Amount of mobile water in the layer (mm H2O) wperc,ly Amount of water percolating to the underlying soil layer on a given day (mm H2O)
βpst εpst:sed ρb ρw
Pesticide percolation coefficient Pesticide enrichment ratio Soil bulk density (Mg m-3) Density of water (1 Mg m-3)
15.6 REFERENCES McElroy, A.D., S.Y. Chiu, J.W. Nebgen, A. Aleti, and F.W. Bennett. 1976. Loading functions for assessment of water pollution from nonpoint sources. Environ. Prot. Tech. Serv., EPA 600/2-76-151. Menzel, R.G. 1980. Enrichment ratios for water quality modeling. p. 486-492. In W.G. Knisel (ed.) CREAMS, A field scale model for chemicals, runoff, and erosion from agricultural management systems. U.S. Dept. Agric. Conserv. Res. Rept. No. 26. Williams, J.R. 1995. Chapter 25: The EPIC model. p. 909-1000. In V.P. Singh (ed.). Computer models of watershed hydrology. Water Resources Publications. Williams, J.R. and R.W. Hann. 1978. Optimal operation of large agricultural watersheds with water quality constraints. Texas Water Resources Institute, Texas A&M Univ., Tech. Rept. No. 96.
CHAPTER 16
EQUATIONS: WATER QUALITY PARAMETERS
In addition to sediment, nutrients and pesticides, SWAT calculates the amount of algae, dissolved oxygen and carbonaceous biological oxygen demand (CBOD) entering the main channel with surface runoff. Loadings of these three parameters are required to monitor the quality of stream water. This chapter reviews the algorithms governing movement of algae, dissolved oxygen and CBOD from land areas to the stream network.
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16.1 ALGAE Suspended algal biomass is assumed to be directly proportional to chlorophyll a. Therefore, the algal biomass loading to the stream can be estimated as the chlorophyll a loading from the land area. Cluis et al. (1988) developed a relationships between the nutrient enrichment index (total N: total P), chlorophyll a, and algal growth potential in the North Yamaska River, Canada.
( AGP + chla ) ⋅ v surf
æ TN ö = f ⋅ç ÷ è TP ø
g
16.1.1
where AGP is the algal growth potential (mg/L), chla is the chlorophyll a concentration in the surface runoff (µg/L), vsurf is the surface runoff flow rate (m3/s), TN is the total Kjeldahl nitrogen load (kmoles), TP is the total phosphorus load (kmoles), f is a coefficient and g is an exponent. The chlorophyll a concentration in surface runoff is calculated in SWAT using a simplified version of Cluis et al.’s exponential function (1988): chla = 0
if ( v surf < 10 −5 m 3 /s ) or ( TP and TN < 10 −6 )
16.1.2
chla =
0.5 ⋅ 10 2.7 v surf
if v surf > 10 −5 m 3 /s , and ( TP and TN > 10 −6 )
16.1.3
chla =
0.5 ⋅ 10 0.5 v surf
if v surf > 10 −5 m 3 /s , TP < 10 −6 and TN > 10 −6
16.1.4
16.2 CARBONACEOUS BIOLOGICAL OXYGEN DEMAND Carbonaceous biological oxygen demand (CBOD) defines the amount of oxygen required to decompose the organic matter transported in surface runoff. The SWAT loading function for the ultimate CBOD is based on a relationship given by Thomann and Mueller (1987): cbod surq =
2.7 ⋅ orgC surq Qsurf ⋅ area hru
16.2.1
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255
where cbodsurq is the CBOD concentration in surface runoff (mg CBOD/L), orgCsurq is the organic carbon in surface runoff (kg orgC), Qsurf is the surface runoff on a given day (mm H2O), and areahru is the area of the HRU (km2). The amount of organic carbon in surface runoff is calculated: orgC surq = 1000 ⋅
orgC surf 100
⋅ sed ⋅ ε C:sed
16.2.2
where orgCsurq is the organic carbon in surface runoff (kg orgC), orgCsurf is the percent organic carbon in the top 10 mm of soil (%), sed is the sediment loading from the HRU (metric tons), and εC:sed is the carbon enrichment ratio.
16.2.1 ENRICHMENT RATIO As surface runoff flows over the soil surface, part of the water’s energy is used to pick up and transport soil particles. The smaller particles weigh less and are more easily transported than coarser particles. When the particle size distribution of the transported sediment is compared to that of the soil surface layer, the sediment load to the main channel has a greater proportion of clay sized particles. In other words, the sediment load is enriched in clay particles. Organic carbon in the soil is attached primarily to colloidal (clay) particles, so the sediment load will also contain a greater proportion or concentration of organic carbon than that found in the soil surface layer. The enrichment ratio is defined as the ratio of the concentration of organic carbon transported with the sediment to the concentration in the soil surface layer. SWAT will calculate an enrichment ratio for each storm event. To calculate the enrichment ratio, SWAT uses a relationship described by Menzel (1980) in which the enrichment ratio is logarithmically related to sediment concentration. The equation used to calculate the carbon enrichment ratio, εC:sed, for each storm event is:
ε C:sed = 0.78 ⋅ (concsed ,surq )−0.2468
16.2.3
where concsed,surq is the concentration of sediment in surface runoff (Mg sed/m3 H2O). The concentration of sediment in surface runoff is calculated:
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conc sed ,surq =
sed 10 ⋅ area hru ⋅ Qsurf
16.2.4
where sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and Qsurf is the amount of surface runoff on a given day (mm H2O). Table 16-1: SWAT input variables that pertain to CBOD in surface runoff. Variable Name SOL_CBN
Definition orgCly: Percent organic carbon in the top 10 mm of soil (%)
Input File .sol
16.3 DISSOLVED OXYGEN Rainfall is assumed to be saturated with oxygen. To determine the dissolved oxygen concentration of surface runoff, the oxygen uptake by the oxygen demanding substance in runoff is subtracted from the saturation oxygen concentration. Ox surf = Ox sat − κ 1 ⋅ cbod surq ⋅
tov 24
16.3.1
where Oxsurf is the dissolved oxygen concentration in surface runoff (mg O2/L), Oxsat is the saturation oxygen concentration (mg O2/L), κ1 is the CBOD deoxygenation rate (day-1), cbodsurq is the CBOD concentration in surface runoff (mg CBOD/L), and tov is the time of concentration for overland flow (hr). For loadings from HRUs, SWAT assumes κ1 = 1.047 day-1.
16.3.1 OXYGEN SATURATION CONCENTRATION The amount of oxygen that can be dissolved in water is a function of temperature, concentration of dissolved solids, and atmospheric pressure. An equation developed by APHA (1985) is used to calculate the saturation concentration of dissolved oxygen: é 1.575701 × 105 6.642308 × 10 7 Ox sat = exp ê − 139.34410 + − T (Twat ,K )2 wat K , ë
+
1.243800 × 1010 8.621949 × 1011 ù − (Twat ,K )3 (Twat ,K )4 úúû
16.3.2
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257
where Oxsat is the equilibrium saturation oxygen concentration at 1.00 atm (mg O2/L), and Twat,K is the water temperature in Kelvin (273.15+°C).
16.4 NOMENCLATURE AGP Oxsat Oxsurf Qsurf Twat,K TN TP
Algal growth potential (mg/L) Saturation oxygen concentration (mg O2/L) Dissolved oxygen concentration in surface runoff (mg O2/L) Surface runoff on a given day (mm H2O) Water temperature in Kelvin (273.15+°C) Total Kjeldahl nitrogen load (moles) Total phosphorus load (moles)
areahru Area of the HRU (km2) cbodsurq CBOD concentration in surface runoff (mg CBOD/L) chla Chlorophyll a concentration in the surface runoff (µg/L) concsed,surq Concentration of sediment in surface runoff (Mg sed/m3 H2O) f Coefficient g Exponent orgCsurf Percent organic carbon in the top 10 mm of soil (%) orgCsurq Organic carbon in surface runoff (kg orgC), sed Sediment loading from the HRU (metric tons) Time of concentration for overland flow (hr) tov Surface runoff flow rate (m3/s) vsurf
εC:sed Carbon enrichment ratio κ1 CBOD deoxygenation rate (day-1)
16.5 REFERENCES American Public Health Association. 1985. Standard methods for the examination of water and wastewater, 16th edition. American Public Health Association, Inc. Cluis, D., P. Couture, R. Bégin, and S.A. Visser. 1988. Potential eutrophication assessment in rivers; relationship between produced and exported loads. Schweiz. Z. Hydrol. 50:166-181. Menzel, R.G. 1980. Enrichment ratios for water quality modeling. p. 486-492. In W.G. Knisel (ed.) CREAMS, A field scale model for chemicals, runoff,
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and erosion from agricultural management systems. U.S. Dept. Agric. Conserv. Res. Rept. No. 26. Thomann, R.V. and J.A. Mueller. 1987. Principles of surface water quality modeling and control. Harper & Row Publishers, New York.
LAND COVER/PLANT The plant growth component of SWAT is a simplified version of the EPIC plant growth model. As in EPIC, phenological plant development is based on daily accumulated heat units, potential biomass is based on a method developed by Monteith, a harvest index is used to calculate yield, and plant growth can be inhibited by temperature, water, nitrogen or phosphorus stress. Portions of the EPIC plant growth model that were not incorporated into SWAT include detailed root growth, micronutrient cycling and toxicity responses, and the simultaneous growth of multiple plant species in the same HRU.
CHAPTER 17
EQUATIONS: GROWTH CYCLE
The growth cycle of a plant is controlled by plant attributes summarized in the plant growth database and by the timing of operations listed in the management file. This chapter reviews the heat unit theory used to regulate the growth cycle of plants. Chapter 20 focuses on the impact of user inputs in management operations on the growth and development of plants.
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17.1 HEAT UNITS Temperature is one of the most important factors governing plant growth. Each plant has its own temperature range, i.e. its minimum, optimum, and maximum for growth. For any plant, a minimum or base temperature must be reached before any growth will take place. Above the base temperature, the higher the temperature the more rapid the growth rate of the plant. Once the optimum temperature is exceeded the growth rate will begin to slow until a maximum temperature is reached at which growth ceases. In the 1920s and 1930s, canning factories were searching for ways to time the planting of sweet peas so that there would be a steady flow of peas at the peak of perfection to the factory. Crops planted at weekly intervals in the early spring would sometimes come to maturity with only a 1- or 2-day differential while at other times there was a 6- to 8-day differential (Boswell, 1926; 1929). A heat unit theory was suggested (Boswell, 1926; Magoon and Culpepper, 1932) that was revised and successfully applied (Barnard, 1948; Phillips, 1950) by canning companies to determine when plantings should be made to ensure a steady harvest of peas with no “bunching” or “breaks”. The heat unit theory postulates that plants have heat requirements that can be quantified and linked to time to maturity. Because a plant will not grow when the mean temperature falls below its base temperature, the only portion of the mean daily temperature that contributes towards the plant’s development is the amount that exceeds the base temperature. To measure the total heat requirements of a plant, the accumulation of daily mean air temperatures above the plant’s base temperature is recorded over the period of the plant’s growth and expressed in terms of heat units. For example, assume sweet peas are growing with a base temperature of 5°C. If the mean temperature on a given day is 20°C, the heat units accumulated on that day are 20 – 5 = 15 heat units. Knowing the planting date, maturity date, base temperature and mean daily temperatures, the total number of heat units required to bring a crop to maturity can be calculated.
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The heat index used by SWAT is a direct summation index. Each degree of the daily mean temperature above the base temperature is one heat unit. This method assumes that the rate of growth is directly proportional to the increase in temperature. It is important to keep in mind that the heat unit theory without a high temperature cutoff does not account for the impact of harmful high temperatures. SWAT assumes that all heat above the base temperature accelerates crop growth and development. 30
Temperature (deg C)
20
10
0
-10 Mean daily temperature
Base temperature, corn
1/ 1/ 9 1/ 2 15 /9 1/ 2 29 /9 2/ 2 12 /9 2/ 2 26 /9 3/ 2 11 /9 3/ 2 25 /9 2 4/ 8/ 9 4/ 2 22 /9 2 5/ 6/ 92 5/ 20 /9 2 6/ 3/ 9 6/ 2 17 /9 2 7/ 1/ 92 7/ 15 /9 7/ 2 29 /9 8/ 2 12 /9 8/ 2 26 /9 2 9/ 9/ 92 9/ 23 /9 10 2 /7 / 10 92 /2 1/ 9 11 2 /4 /9 11 2 /1 8/ 9 12 2 /2 / 12 92 /1 6/ 12 92 /3 0/ 92
-20
Date
Figure 17-1: Mean daily temperature recorded for Greenfield, Indiana
The mean daily temperature during 1992 for Greenfield, Indiana is plotted in Figure 17-1 along with the base temperature for corn (8°C). Crop growth will only occur on those days where the mean daily temperature exceeds the base temperature. The heat unit accumulation for a given day is calculated with the equation:
HU = T av − Tbase
when T av > Tbase
17.1.1
where HU is the number of heat units accumulated on a given day (heat units),
T av is the mean daily temperature (°C), and Tbase is the plant’s base or minimum
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temperature for growth (°C). The total number of heat units required for a plant to reach maturity is calculated: m
PHU = å HU
17.1.2
d =1
where PHU is the total heat units required for plant maturity (heat units), HU is the number of heat units accumulated on day d where d = 1 on the day of planting and m is the number of days required for a plant to reach maturity. PHU is also referred to as potential heat units. When calculating the potential heat units for a plant, the number of days to reach maturity must be known. For most crops, these numbers have been quantified and are easily accessible. For other plants, such as forest or range, the time that the plants begin to develop buds should be used as the beginning of the growing season and the time that the plant seeds reach maturation is the end of the growing season. For the Greenfield Indiana example, a 120 day corn hybrid was planted on May 15. Summing daily heat unit values, the total heat units required to bring the corn to maturity was 1456.
17.1.1 HEAT UNIT SCHEDULING As the heat unit theory was proven to be a reliable predictor of harvest dates for all types of crops, it was adapted by researchers for prediction of the timing of other plant development stages such as flowering (Cross and Zuber, 1972). The successful adaptation of heat units to predict the timing of plant stages has subsequently led to the use of heat units to schedule management operations. SWAT allows management operations to be scheduled by day or by fraction of potential heat units. For each operation the model checks to see if a month and day has been specified for timing of the operation. If this information is provided, SWAT will perform the operation on that month and day. If the month and day are not specified, the model requires a fraction of potential heat units to be specified. As a general rule, if exact dates are available for scheduling operations, these dates should be used. Scheduling by heat units allows the model to time operations as a function of temperature. This method of timing is useful for several situations. When very
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large watersheds are being simulated where the climate in one portion of the watershed is different enough from the climate in another section of the watershed to affect timing of operations, heat unit scheduling may be beneficial. By using heat unit scheduling, only one generic management file has to be made for a given land use. This generic set of operations can then be used wherever the land use is found in the watershed. Also, in areas where the climate can vary greatly from year to year, heat unit scheduling will allow the model to adjust the timing of operations to the weather conditions for each year. To schedule by heat units, the timing of the operations are expressed as fractions of the potential heat units for the plant or fraction of maturity. Let us use the following example for corn in Indiana. Date
Operation
April 24 April 30 May 7 May 15 June 3 June 17 October 15 October 29 November 5
Tandem disk Tandem disk Field cultivator Plant corn (PHU = 1456) Row cultivator Row cultivator Harvest & Kill Tandem disk Chisel
Heat Units Accumulated
Fraction of PHU
0 165 343 1686
.00 .11 .24 1.16
The number of heat units accumulated for the different operation timings is calculated by summing the heat units for every day starting with the planting date (May 15) and ending with the day the operation takes place. To calculate the fraction of PHU at which the operation takes place, the heat units accumulated is divided by the PHU for the crop (1456). Note that the fraction of PHU for the harvest operation is 1.16. The fraction is greater than 1.0 because corn is allowed to dry down prior to harvesting. The model will simulate plant growth until the crop reaches maturity (where maturity is defined as PHU = 1456). From that point on, plants will not transpire or take up nutrients and water. They will stand in the HRU until converted to residue or harvested. While the operations after planting have been scheduled by fraction of
PHU, operations—including planting—which occur during periods when no crop
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is growing must still be scheduled. To schedule these operations, SWAT keeps track of a second heat index where heat units are summed over the entire year using Tbase = 0°C. This heat index is solely a function of the climate and is termed the base zero heat index. For the base zero index, the heat units accumulated on a given day are:
HU 0 = T av
when T av > 0°C
17.1.3
where HU0 is the number of base zero heat units accumulated on a given day (heat units), and T av is the mean daily temperature (°C). The total number of heat units for the year is calculated: 365
PHU 0 = å HU 0
17.1.4
d =1
where PHU0 is the total base zero heat units (heat units), HU0 is the number of base zero heat units accumulated on day d where d = 1 on January 1 and 365 on December 31. Unlike the plant PHU which must be provided by the user, PHU0 is the average calculated by SWAT using long-term weather data provided in the .wgn file. For the example watershed in Indiana, PHU0 = 4050. The heat unit fractions for the remaining operations are calculated using this value for potential heat units. Date
Operation
Base Zero Heat Units Accumulated
April 24 April 30 May 7 May 15 June 3 June 17 October 15 October 29 November 5
Tandem disk Tandem disk Field cultivator Plant corn (PHU = 1456) Row cultivator Row cultivator Harvest & Kill Tandem disk Chisel
564 607 696 826 1136 1217 3728 3860 3920
Plant Heat Units Accumulated
0 165 343 1686
Fraction of PHU0 (PHU0 = 4050)
Fraction of PHU (PHU = 1456)
.14 .15 .17 .20 .11 .24 1.16 .95 .97
As stated previously, SWAT always keeps track of base zero heat units. The base zero heat unit scheduling is used any time there are no plants growing in the HRU (before and including the plant operation and after the kill operation). Once plant growth is initiated, the model switches to plant heat unit scheduling until the plant is killed.
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The following heat unit fractions have been found to provide reasonable timings for the specified operations: 0.15 1.0 1.2 0.6
planting harvest/kill for crops with no dry-down harvest/kill for crops with dry-down hay cutting operation
fraction of PHU0 fraction of PHU fraction of PHU fraction of PHU
Table 17-1: SWAT input variables that pertain to heat units. Variable Name Definition PHU PHU: potential heat units for plant that is growing at the beginning of the simulation in an HRU HEAT UNITS PHU: potential heat units for plant whose growth is initiated with a planting operation. HUSC Fraction of potential heat units at which operation takes place. T_BASE Tbase: Minimum temperature for plant growth (°C)
Input File .mgt .mgt .mgt crop.dat
17.2 DORMANCY SWAT assumes trees, perennials and cool season annuals can go dormant as the daylength nears the shortest or minimum daylength for the year. During dormancy, plants do not grow. The beginning and end of dormancy are defined by a threshold daylength. The threshold daylength is calculated: TDL,thr = TDL ,mn + t dorm
17.2.1
where TDL,thr is the threshold daylength to initiate dormancy (hrs), TDL,mn is the minimum daylength for the watershed during the year (hrs), and tdorm is the dormancy threshold (hrs). When the daylength becomes shorter than TDL,thr in the fall, plants other than warm season annuals that are growing in the watershed will enter dormancy. The plants come out of dormancy once the daylength exceeds
TDL,thr in the spring. The dormancy threshold, tdorm, varies with latitude.
t dorm = 1.0 t dorm =
φ − 20 20
t dorm = 0.0
if φ > 40 º N or S
17.2.2
if 20 º N or S ≤ φ ≤ 40 º N or S
17.2.3
if φ < 20 º N or S
17.2.4
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where tdorm is the dormancy threshold used to compare actual daylength to minimum daylength (hrs) and φ is the latitude expressed as a positive value (degrees). At the beginning of the dormant period for trees, leaf biomass is converted to residue and the leaf area index for the tree species is set to the minimum value allowed (defined in the plant growth database). At the beginning of the dormant period for perennials, 95% of the biomass is converted to residue and the leaf area index for the species is set to the minimum value allowed. For cool season annuals, none of the biomass is converted to residue.
17.3 PLANT TYPES SWAT categorizes plants into seven different types: warm season annual legume, cold season annual legume, perennial legume, warm season annual, cold season annual, perennial and trees. The differences between the different plant types, as modeled by SWAT, are as follows: 1
2
3
4 5
warm season annual legume: • simulate nitrogen fixation • root depth varies during growing season due to root growth cold season annual legume: • simulate nitrogen fixation • root depth varies during growing season due to root growth • fall-planted land covers will go dormant when daylength is less than the threshold daylength perennial legume: • simulate nitrogen fixation • root depth always equal to the maximum allowed for the plant species and soil • plant goes dormant when daylength is less than the threshold daylength warm season annual: • root depth varies during growing season due to root growth cold season annual: • root depth varies during growing season due to root growth • fall-planted land covers will go dormant when daylength is less than the threshold daylength
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6
perennial: • root depth always equal to the maximum allowed for the plant species and soil • plant goes dormant when daylength is less than the threshold daylength 7 trees: • root depth always equal to the maximum allowed for the plant species and soil • partitions new growth between leaves/needles (30%) and woody growth (70%). At the end of each growing season, biomass in the leaf fraction is converted to residue
17.4 NOMENCLATURE HU
Number of heat units accumulated on a given day where base temperature is dependant on the plant species (heat units) HU0 Number of base zero heat units accumulated on a given day (heat units) PHU Potential heat units or total heat units required for plant maturity where base temperature is dependant on the plant species (heat units) PHU0 Total base zero heat units or potential base zero heat units (heat units) Tbase Plant’s base or minimum temperature for growth (°C) TDL,mn Minimum daylength for the watershed during the year (hrs) TDL,thr Threshold daylength to initiate dormancy (hrs) T av Mean air temperature for day (°C)
tdorm
Dormancy threshold (hrs)
φ
Latitude expressed as a positive value (degrees)
17.5 REFERENCES Barnard, J.D. 1948. Heat units as a measure of canning crop maturity. The Canner 106:28. Boswell, V.G. 1929. Factors influencing yield and quality of peas—Biophysical and biochemical studies. Maryland Agr. Exp. Sta. Bul. 306. Boswell, V.G. 1926. The influence of temperature upon the growth and yield of garden peas. Proc. Amer. Soc. Hort. Sci. 23:162-168.
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Cross, H.Z. and M.S. Zuber. 1972. Prediction of flowering dates in maize based on different methods of estimating thermal units. Agronomy Journal 64:351-355. Magoon, C.A. and C.W. Culpepper. 1932. Response of sweet corn to varying temperatures from time of planting to canning maturity. U.S.D.A. tech. Bull. 312. Phillips, E.E. 1950. Heat summation theory as applied to canning crops. The Canner 27:13-15.
CHAPTER 18
EQUATIONS: OPTIMAL GROWTH
For each day of simulation, potential plant growth, i.e. plant growth under ideal growing conditions, is calculated. Ideal growing conditions consist of adequate water and nutrient supply and a favorable climate. Differences in growth between plant species are defined by the parameters contained in the plant growth database.
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18.1 POTENTIAL GROWTH Plant growth is modeled by simulating leaf area development, light interception and conversion of intercepted light into biomass assuming a plant species-specific radiation-use efficiency.
18.1.1 BIOMASS PRODUCTION The amount of daily solar radiation intercepted by the leaf area of the plant is calculated using Beer’s law (Monsi and Saeki, 1953): H phosyn = 0.5 ⋅ H day ⋅ (1 − exp(k l ⋅ LAI ))
18.1.1
where Hphosyn is the amount of intercepted photosynthetically active radiation on a given day (MJ m-2), Hday is the incident total solar (MJ m-2), 0.5 ⋅ H day is the incident photosynthetically active radiation (MJ m-2), k l is the light extinction coefficient, and LAI is the leaf area index. In SWAT, the light extinct coefficient is –0.65 for all plants. Photosynthetically active radiation is radiation with a wavelength between 400 and 700 mm (McCree, 1972). Direct solar beam radiation contains roughly 45% photosynthetically active radiation while diffuse radiation contains around 60% photosynthetically active radiation (Monteith, 1972; Ross, 1975). The fraction of photosynthetically active radiation will vary from day to day with variation in overcast conditions but studies in Europe and Israel indicate that 50% is a representative mean value (Monteith, 1972; Szeicz, 1974; Stanhill and Fuchs, 1977). Radiation-use efficiency is the amount of dry biomass produced per unit intercepted solar radiation. The radiation-use efficiency is defined in the plant growth database and is assumed to be independent of the plant’s growth stage. The maximum increase in biomass on a given day that will result from the intercepted photosynthetically active radiation is estimated (Monteith, 1977): ∆bio = RUE ⋅ H phosyn
18.1.2
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273
where ∆bio is the potential increase in total plant biomass on a given day (kg/ha), RUE is the radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), and Hphosyn is the amount of intercepted photosynthetically active radiation on a given day (MJ m-2). Equation 18.1.2 assumes that the photosynthetic rate of a canopy is a linear function of radiant energy. The total biomass on a given day, d, is calculated as: d
bio = å ∆bioi
18.1.3
i =1
where bio is the total plant biomass on a given day (kg ha-1), and ∆bioi is the increase in total plant biomass on day i (kg/ha).
18.1.1.1 IMPACT OF CLIMATE ON RADIATION-USE EFFICIENCY Radiation-use efficiency is sensitive to variations in atmospheric CO2 concentrations and equations have been incorporated into SWAT to modify the default radiation-use efficiency values in the plant database for climate change studies. The relationship used to adjust the radiation-use efficiency for effects of elevated CO2 is (Stockle et al., 1992): RUE =
100 ⋅ CO2 CO2 + exp(r1 − r2 ⋅ CO2 )
18.1.4
where RUE is the radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), CO2 is the concentration of carbon dioxide in the atmosphere (ppmv), and r1 and r2 are shape coefficients. The shape coefficients are calculated by solving equation 18.1.4 using two known points (RUEamb, CO2amb) and (RUEhi, CO2hi):
é ù CO2 amb r1 = ln ê − CO2 amb ú + r2 ⋅ CO2 amb ë (0.01 ⋅ RUE amb ) û
18.1.5
æ é ù é ùö CO2 amb CO2 hi ç ln ê ÷ ç (0.01 ⋅ RUE ) − CO2 amb ú − ln ê (0.01 ⋅ RUE ) − CO2 hi ú ÷ amb hi ë û ë ûø è r2 = 18.1.6 CO2 hi − CO2 amb
where r1 is the first shape coefficient, r2 is the second shape coefficient, CO2amb is the ambient atmospheric CO2 concentration (ppmv), RUEamb is
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the radiation-use efficiency of the plant at ambient atmospheric CO2 concentration (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), CO2hi is an elevated atmospheric CO2 concentration (ppmv), RUEhi is the radiation-use efficiency of the plant at the elevated atmospheric CO2 concentration, CO2hi, (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ). Equation 18.1.4 was developed when the ambient atmospheric CO2 concentration was 330 ppmv and is valid for carbon dioxide concentrations in the range 330-660 ppmv. Even though the ambient atmospheric concentration of carbon dioxide is now higher than 330 ppmv, this value is still used in the calculation. If the CO2 concentration used in the simulation is less than 330 ppmv, the model defines RUE = RUEamb. Stockle and Kiniry (1990) have shown that a plant’s radiation-use efficiency is affected by vapor pressure deficit. For a plant, a threshold vapor pressure deficit is defined at which the plant’s radiation-use efficiency begins to drop in response to the vapor pressure deficit. The adjusted radiation-use efficiency is calculated: RUE = RUE vpd =1 − ∆ruedcl ⋅ (vpd − vpd thr )
if vpd > vpd thr
18.1.7
RUE = RUE vpd =1
if vpd ≤ vpd thr
18.1.8
where RUE is the radiation-use efficiency adjusted for vapor pressure deficit (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), RUEvpd=1 is the radiation-use efficiency for the plant at a vapor pressure deficit of 1 kPa (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), ∆ruedcl is the rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit (kg/ha⋅(MJ/m2)-1⋅kPa-1 or (10-1 g/MJ)⋅kPa-1), vpd is the vapor pressure deficit (kPa), and vpdthr is the threshold vapor pressure deficit above which a plant will exhibit reduced radiation-use efficiency (kPa). The radiation-use efficiency value reported for the plant in the plant growth database, RUEamb, or adjusted for elevated carbon dioxide levels (equation 18.1.4) is the value used for RUEvpd=1. The threshold vapor pressure deficit for reduced radiation-use efficiency is assumed to be 1.0 kPa for all plants ( vpd thr = 1.0 ).
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The radiation-use efficiency is never allowed to fall below 27% of RUEamb. This minimum value was based on field observations (Kiniry, personal communication, 2001).
18.1.2 CANOPY COVER AND HEIGHT The change in canopy height and leaf area through the growing season as modeled by SWAT is illustrated using parameters for Alamo Switchgrass in Figures 18-1 and 18-2.
Figure 18-1: Seasonal change in plant canopy height during growing season.
Figure 18-2: Seasonal change in plant leaf area index during growing season.
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In the initial period of plant growth, canopy height and leaf area development are controlled by the optimal leaf area development curve: frLAImx =
frPHU
frPHU + exp(l 1 − l 2 ⋅ frPHU )
18.1.9
where frLAImx is the fraction of the plant’s maximum leaf area index corresponding to a given fraction of potential heat units for the plant, frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and l 1 and l 2 are shape coefficients. The fraction of potential heat units accumulated by a given date is calculated: d
frPHU =
å HU i =1
i
PHU
18.1.10
where frPHU is the fraction of potential heat units accumulated for the plant on day d in the growing season, HU is the heat units accumulated on day i (heat units), and PHU is the total potential heat units for the plant (heat units). The shape coefficients are calculated by solving equation 18.1.9 using two known points (frLAI,1,frPHU,1) and (frLAI,2,frPHU,2): é fr ù l 1 = ln ê PHU ,1 − frPHU ,1 ú + l 2 ⋅ frPHU ,1 ë frLAI ,1 û
æ é frPHU ,1 ù é fr ùö ç ln ê − frPHU ,1 ú − ln ê PHU , 2 − frPHU , 2 ú ÷ ç ÷ ë frLAI ,1 û ë frLAI , 2 ûø l2 = è frPHU , 2 − frPHU ,1
18.1.11
18.1.12
where l 1 is the first shape coefficient, l 2 is the second shape coefficient, frPHU,1 is the fraction of the growing season (i.e. fraction of total potential heat units) corresponding to the 1st point on the optimal leaf area development curve, frLAI,1 is the fraction of the maximum plant leaf area index (i.e. fraction of LAImx) corresponding to the 1st point on the optimal leaf area development curve, frPHU,2 is the fraction of the growing season corresponding to the 2nd point on the optimal leaf area development curve, and frLAI,2 is the fraction of the maximum plant leaf area index corresponding to the 2nd point on the optimal leaf area development curve.
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The canopy height on a given day is calculated: hc = hc ,mx ⋅
frLAImx
18.1.13
where hc is the canopy height for a given day (m), hc,mx is the plant’s maximum canopy height (m), and frLAImx is the fraction of the plant’s maximum leaf area index corresponding to a given fraction of potential heat units for the plant. As can be seen from Figure 18-1, once the maximum canopy height is reached, hc will remain constant until the plant is killed. The amount of canopy cover is expressed as the leaf area index. The leaf area added on day i is calculated: ∆LAI i = ( frLAImx ,i − frLA Im x ,i −1 ) ⋅ LAI mx ⋅ (1 − exp(5 ⋅ (LAI i −1 − LAI mx ))) 18.1.14
And the total leaf area index is calculated: LAI i = LAI i −1 + ∆LAI i
18.1.15
where ∆LAIi is the leaf area added on day i, LAIi and LAIi-1 are the leaf area indices for day i and i-1 respectiviely, frLAImx,i and frLAImx,i-1 are the fraction of the plant’s maximum leaf area index calculated with equation 18.1.9 for day i and i-1, and LAImx is the maximum leaf area index for the plant. Leaf area index is defined as the area of green leaf per unit area of land (Watson, 1947). As shown in Figure 18-2, once the maximum leaf area index is reached, LAI will remain constant until leaf senescence begins to exceed leaf growth. Once leaf senescence becomes the dominant growth process, the leaf area index is calculated:
LAI = 16 ⋅ LAI mx ⋅ (1 − frPHU )
2
frPHU > frPHU ,sen
18.1.16
where LAI is the leaf area index for a given day, LAImx is the maximum leaf area index, frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and frPHU,sen is the fraction of growing season (PHU) at which senescence becomes the dominant growth process.
18.1.3 ROOT DEVELOPMENT The amount of total plant biomass partitioned to the root system is 30-50% in seedlings and decreases to 5-20% in mature plants (Jones, 1985). SWAT varies
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the fraction of total biomass in roots from 0.40 at emergence to 0.20 at maturity. The daily root biomass fraction is calculated with the equation: frroot = 0.40 − 0.20 ⋅ frPHU
18.1.17
where frroot is the fraction of total biomass partitioned to roots on a given day in the growing season, and frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season. Calculation of root depth varies according to plant type. SWAT assumes perennials and trees have roots down to the maximum rooting depth defined for the soil throughout the growing season: z root = z root ,mx
18.1.18
where zroot is the depth of root development in the soil on a given day (mm), and zroot,mx is the maximum depth for root development in the soil (mm). The simulated root depth for annuals varies linearly from 0.0 mm at the beginning of the growing season to the maximum rooting depth at frPHU = 0.40 using the equation: z root = 2.5 ⋅ frPHU ⋅ z root ,mx
if frPHU ≤ 0.40
18.1.19
z root = z root ,mx
if frPHU > 0.40
18.1.20
where zroot is the depth of root development in the soil on a given day (mm), frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and zroot,mx is the maximum depth for root development in the soil (mm). The maximum rooting depth is defined by comparing the maximum potential rooting depth for the plant from the plant growth database (RDMX in crop.dat), and the maximum potential rooting depth for the soil from the soil input file (SOL_ZMX in .sol—if no value is provided for this variable the model will set it to the deepest depth specified for the soil profile). The shallower of these two depths is the value used for zroot,mx.
18.1.4 MATURITY Plant maturity is reached when the fraction of potential heat units accumulated, frPHU, is equal to 1.00. Once maturity is reached, the plant ceases to
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transpire and take up water and nutrients. Simulated plant biomass remains stable until the plant is harvested or killed via a management operation. Table 18-1: SWAT input variables that pertain to optimal plant growth. Variable Name Definition BIO_E RUEamb: Radiation use efficiency in ambient CO2((kg/ha)/(MJ/m2)) CO2HI CO2hi: Elevated CO2 atmospheric concentration (ppmv) BIOEHI RUEhi: Radiation use efficiency at elevated CO2 atmospheric concentration value for CO2HI ((kg/ha)/(MJ/m2)) WAVP ∆ruedcl: Rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit (kg/ha⋅(MJ/m2)-1⋅kPa-1 or (10-1 g/MJ)⋅kPa-1) PHU PHU: potential heat units for plant growing at beginning of simulation (heat units) HEAT UNITS PHU: potential heat units for plant whose growth is initiated in a planting operation (heat units) FRGRW1 frPHU,1: Fraction of the growing season corresponding to the 1st point on the optimal leaf area development curve LAIMX1 frLAI,1: Fraction of the maximum plant leaf area index corresponding to the 1st point on the optimal leaf area development curve FRGRW2 frPHU,2: Fraction of the growing season corresponding to the 2nd point on the optimal leaf area development curve LAIMX2 frLAI,2: Fraction of the maximum plant leaf area index corresponding to the 2nd point on the optimal leaf area development curve CHTMX hc,mx: Plant’s potential maximum canopy height (m) BLAI LAImx: Potential maximum leaf area index for the plant DLAI frPHU,sen: Fraction of growing season at which senescence becomes the dominant growth process SOL_ZMX zroot,mx: Maximum rooting depth in soil (mm) RDMX zroot,mx: Maximum rooting depth for plant (mm)
Input File crop.dat crop.dat crop.dat crop.dat .mgt .mgt crop.dat crop.dat crop.dat crop.dat crop.dat crop.dat crop.dat .sol crop.dat
18.2 WATER UPTAKE BY PLANTS The potential water uptake from the soil surface to any depth in the root zone is estimated with the function: wup , z =
é æ Et z ⋅ ê1 − expçç − β w ⋅ [1 − exp(− β w )] ë z root è
öù ÷÷ú øû
18.2.1
where wup,z is the potential water uptake from the soil surface to a specified depth, z, on a given day (mm H2O), Et is the maximum plant transpiration on a given day (mm H2O), βw is the water-use distribution parameter, z is the depth from the soil surface (mm), and zroot is the depth of root development in the soil (mm). The potential water uptake from any soil layer can be calculated by solving equation
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18.2.1 for the depth at the top and bottom of the soil layer and taking the difference. wup ,ly = wup , zl − wup , zu
18.2.2
where wup,ly is the potential water uptake for layer ly (mm H2O), wup,zl is the potential water uptake for the profile to the lower boundary of the soil layer (mm H2O), and wup,zu is the potential water uptake for the profile to the upper boundary of the soil layer (mm H2O). Since root density is greatest near the soil surface and decreases with depth, the water uptake from the upper layers is assumed to be much greater than that in the lower layers. The water-use distribution parameter, βw, is set to 10 in SWAT. With this value, 50% of the water uptake will occur in the upper 6% of the root zone. Figure 18-3 graphically displays the uptake of water at different depths in the root zone.
Figure 18-3: Depth distribution of water uptake
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The amount of water uptake that occurs on a given day is a function of the amount of water required by the plant for transpiration, Et, and the amount of water available in the soil, SW. Equations 18.2.1 and 18.2.2 calculate potential water uptake solely as a function of water demand for transpiration and the depth distribution defined in equation 18.2.1. SWAT modifies the initial potential water uptake from a given soil layer to reflect soil water availability in the following ways. If upper layers in the soil profile do not contain enough water to meet the potential water uptake calculated with equation 18.2.2, users may allow lower layers to compensate. The equation used to calculate the adjusted potential water uptake is: ′ ,ly = wup ,ly + wdemand ⋅ epco wup
18.2.3
′ ,ly is the adjusted potential water uptake for layer ly (mm H2O), wup,ly is where wup
the potential water uptake for layer ly calculated with equation 18.2.2 (mm H2O), wdemand is the water uptake demand not met by overlying soil layers (mm H2O), and epco is the plant uptake compensation factor. The plant uptake compensation factor can range from 0.01 to 1.00 and is set by the user. As epco approaches 1.0, the model allows more of the water uptake demand to be met by lower layers in the soil. As epco approaches 0.0, the model allows less variation from the depth distribution described by equation 18.2.1 to take place. As the water content of the soil decreases, the water in the soil is held more and more tightly by the soil particles and it becomes increasingly difficult for the plant to extract water from the soil. To reflect the decrease in the efficiency of the plant in extracting water from dryer soils, the potential water uptake is modified using the following equations: é æ öù SWly ′′ ,ly = wup ′ ,ly ⋅ exp ê5 ⋅ ç wup − 1÷ú when SWly < (.25 ⋅ AWCly ) ç ÷ êë è (.25 ⋅ AWCly ) øúû
18.2.4
′′ ,ly = wup ′ ,ly wup
18.2.5
when SWly ≥ (.25 ⋅ AWCly )
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′′ ,ly is the potential water uptake adjusted for initial soil water content where wup ′ ,ly is the adjusted potential water uptake for layer ly (mm H2O), (mm H2O), wup
SWly is the amount of water in the soil layer on a given day (mm H2O), and AWCly is the available water capacity for layer ly (mm H2O). The available water capacity is calculated: AWCly = FCly − WPly
18.2.6
where AWCly is the available water capacity for layer ly (mm H2O), FCly is the water content of layer ly at field capacity (mm H2O), and WPly is the water content of layer ly at wilting point (mm H2O). Once the potential water uptake has been modified for soil water conditions, the actual amount of water uptake from the soil layer is calculated: ′′ ,ly , (SWly − WPly )] wactualup ,ly = min[wup
18.2.7
where wactualup,ly is the actual water uptake for layer ly (mm H2O), SWly is the amount of water in the soil layer on a given day (mm H2O), and WPly is the water content of layer ly at wilting point (mm H2O). The total water uptake for the day is calculated: n
wactualup = å wactualup ,ly
18.2.8
ly =1
where wactualup is the total plant water uptake for the day (mm H2O), wactualup,ly is the actual water uptake for layer ly (mm H2O), and n is the number of layers in the soil profile. The total plant water uptake for the day calculated with equation 18.2.8 is also the actual amount of transpiration that occurs on the day. Et ,act = wactualup
18.2.9
where Et,act is the actual amount of transpiration on a given day (mm H2O) and wactualup is the total plant water uptake for the day (mm H2O). Table 18-2: SWAT input variables that pertain to plant water uptake. Variable Name Definition EPCO epco: Plant uptake compensation factor
Input File .bsn, .hru
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18.3 NUTRIENT UPTAKE BY PLANTS SWAT monitors plant uptake of nitrogen and phosphorus.
18.3.1 NITROGEN UPTAKE Plant nitrogen uptake is controlled by the plant nitrogen equation. The plant nitrogen equation calculates the fraction of nitrogen in the plant biomass as a function of growth stage given optimal growing conditions. é ù frPHU frN = ( frN ,1 − frN ,3 ) ⋅ ê1 − + frN ,3 frPHU + exp(n1 − n2 ⋅ frPHU )úû ë
18.3.1
where frN is the fraction of nitrogen in the plant biomass on a given day, frN,1 is the normal fraction of nitrogen in the plant biomass at emergence, frN,3 is the normal fraction of nitrogen in the plant biomass at maturity, frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and n1 and n2 are shape coefficients. The shape coefficients are calculated by solving equation 18.3.1 using two known points (frN,2, frPHU,50%) and (frN,3, frPHU,100%): ù é ú ê frPHU ,50% ê n1 = ln ê − frPHU ,50% úú + n2 ⋅ frPHU ,50% æ ( fr − frN ,3 ) ö÷ ú ê ç1 − N ,2 ç ( frN ,1 − frN ,3 ) ÷ø úû êë è
18.3.2
æ é ù é ùö ç ê ú ê ú÷ ç ê ÷ frPHU ,50% fr PHU ,100% − frPHU ,50% úú − ln êê − frPHU ,100% úú ÷ ç ln ê æ ( frN ,2 − frN ,3 ) ö÷ ( fr − frN ,3 ) ö÷ ç ê æç ú ê ç 1 − N ,~ 3 ú÷ ç ê ç 1 − ( fr − fr ) ÷ ç ÷ ( frN ,1 − frN ,3 ) ø úû êë è úû ÷ø N ,1 N ,3 ø ëè è n2 = 18.3.3 frPHU ,100% − frPHU ,50%
where n1 is the first shape coefficient, n2 is the second shape coefficient, frN,1 is the normal fraction of nitrogen in the plant biomass at emergence, frN,2 is the normal fraction of nitrogen in the plant biomass at 50% maturity, frN,3 is the normal fraction of nitrogen in the plant biomass at maturity, frN,~3 is the normal fraction of nitrogen in the plant biomass near maturity, frPHU,50% is the fraction of
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potential heat units accumulated for the plant at 50% maturity (frPHU,50%=0.5), and frPHU,100% is the fraction of potential heat units accumulated for the plant at maturity (frPHU,100%=1.0). The normal fraction of nitrogen in the plant biomass near maturity (frN,~3) is used in equation 18.3.3 to ensure that the denominator term
æ ( fr − frN ,3 ) ö÷ ç 1 − N ,~ 3 ç ( frN ,1 − frN ,3 ) ÷ø è
( fr
− frN ,3 ) = 0.00001 .
N ,~ 3
does
not
equal
1.
The
model
assumes
To determine the mass of nitrogen that should be stored in the plant biomass on a given day, the nitrogen fraction is multiplied by the total plant biomass: bio N ,opt = frN ⋅ bio
18.3.4
where bioN,opt is the optimal mass of nitrogen stored in plant material for the current growth stage (kg N/ha), frN is the optimal fraction of nitrogen in the plant biomass for the current growth stage, and bio is the total plant biomass on a given day (kg ha-1). The plant nitrogen demand for a given day is determined by taking the difference between the nitrogen content of the plant biomass expected for the plant’s growth stage and the actual nitrogen content: N up = bio N ,opt − bio N
18.3.5
where Nup is the potential nitrogen uptake (kg N/ha), bioN,opt is the optimal mass of nitrogen stored in plant material for the current growth stage (kg N/ha), and bioN is the actual mass of nitrogen stored in plant material (kg N/ha). The depth distribution of nitrogen uptake is calculated with the function: N up , z =
é æ z öù ÷÷ú ⋅ ê1 − expçç − β n ⋅ [1 − exp(− β n )] ë z root ø û è
N up
18.3.6
where Nup,z is the potential nitrogen uptake from the soil surface to depth z (kg N/ha), Nup is the potential nitrogen uptake (kg N/ha), βn is the nitrogen uptake distribution parameter, z is the depth from the soil surface (mm), and zroot is the depth of root development in the soil (mm). Note that equation 18.3.6 is similar in form to the depth distribution for water uptake described by equation 18.2.1. The
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285
potential nitrogen uptake for a soil layer is calculated by solving equation 18.3.6 for the depth at the upper and lower boundaries of the soil layer and taking the difference. N up ,ly = N up , zl − N up , zu
18.3.7
where Nup,ly is the potential nitrogen uptake for layer ly (kg N/ha), Nup,zl is the potential nitrogen uptake from the soil surface to the lower boundary of the soil layer (kg N/ha), and Nup,zu is the potential nitrogen uptake from the soil surface to the upper boundary of the soil layer (kg N/ha). Root density is greatest near the surface, and nitrogen uptake in the upper portion of the soil will be greater than in the lower portion. The depth distribution of nitrogen uptake is controlled by βn, the nitrogen uptake distribution parameter, a variable users are allowed to adjust. Figure 18-4 illustrates nitrogen uptake as a function of depth for four different uptake distribution parameter values.
Figure 18-4: Depth distribution of nitrogen uptake
Nitrogen removed from the soil by plants is taken from the nitrate pool. The importance of the nitrogen uptake distribution parameter lies in its control
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over the maximum amount of nitrate removed from the upper layers. Because the top 10 mm of the soil profile interacts with surface runoff, the nitrogen uptake distribution parameter will influence the amount of nitrate available for transport in surface runoff. The model allows lower layers in the root zone to fully compensate for lack of nitrate in the upper layers, so there should not be significant changes in nitrogen stress with variation in the value used for βn. The actual amount if nitrogen removed from a soil layer is calculated: N actualup ,ly = min[N up ,ly + N demand , NO3ly ]
18.3.8
where Nactualup,ly is the actual nitrogen uptake for layer ly (kg N/ha), Nup,ly is the potential nitrogen uptake for layer ly (kg N/ha), Ndemand is the nitrogen uptake demand not met by overlying soil layers (kg N/ha), and NO3ly is the nitrate content of soil layer ly (kg NO3-N/ha).
18.3.1.1 NITROGEN FIXATION If nitrate levels in the root zone are insufficient to meet the demand of a legume, SWAT allows the plant to obtain additional nitrogen through nitrogen fixation. Nitrogen fixation is calculated as a function of soil water, soil nitrate content and growth stage of the plant. N fix = N demand ⋅ f gr ⋅ min( f sw ,
f no 3 , 1)
18.3.9
where Nfix is the amount of nitrogen added to the plant biomass by fixation (kg N/ha), Ndemand is the plant nitrogen demand not met by uptake from the soil (kg N/ha), fgr is the growth stage factor (0.0-1.0), fsw is the soil water factor (0.0-1.0), and fno3 is the soil nitrate factor (0.0-1.0). The maximum amount of nitrogen that can be fixed by the plant on a given day is Ndemand. Growth stage exerts the greatest impact on the ability of the plant to fix nitrogen. The growth stage factor is calculated: f gr = 0
when frPHU ≤ 0.15
18.3.10
f gr = 6.67 ⋅ frPHU − 1
when 0.15 < frPHU ≤ 0.30
18.3.11
f gr = 1
when 0.30 < frPHU ≤ 0.55
18.3.12
f gr = 3.75 − 5 ⋅ frPHU
when 0.55 < frPHU ≤ 0.75
18.3.13
CHAPTER 18: EQUATIONS—OPTIMAL GROWTH
f gr = 0
when frPHU > 0.75
287
18.3.14
where fgr is the growth stage factor and frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season. The growth stage factor is designed to reflect the buildup and decline of nitrogen fixing bacteria in the plant roots during the growing season. The soil nitrate factor inhibits nitrogen fixation as the presence of nitrate in the soil goes up. The soil nitrate factor is calculated: f no 3 = 1
when NO 3 ≤ 100
18.3.15
f no 3 = 1.5 − 0.0005 ⋅ NO 3
when 100 < NO 3 ≤ 300
18.3.16
f no 3 = 0
when NO 3 > 300
18.3.17
where fno3 is the soil nitrate factor and NO3 is the nitrate content of the soil profile (kg NO3-N/ha). The soil water factor inhibits nitrogen fixation as the soil dries out. The soil water factor is calculated: f sw =
SW .85 ⋅ FC
18.3.18
where fsw is the soil water factor, SW is the amount of water in soil profile (mm H2O), and FC is the water content of soil profile at field capacity (mm H2O).
18.3.2 PHOSPHORUS UPTAKE Plant phosphorus uptake is controlled by the plant phosphorus equation. The plant phosphorus equation calculates the fraction of phosphorus in the plant biomass as a function of growth stage given optimal growing conditions.
é ù frPHU frP = ( frP ,1 − frP ,3 ) ⋅ ê1 − + frP ,3 frPHU + exp( p1 − p2 ⋅ frPHU )úû ë
18.3.19
where frP is the fraction of phosphorus in the plant biomass on a given day, frP,1 is the normal fraction of phosphorus in the plant biomass at emergence, frP,3 is the normal fraction of phosphorus in the plant biomass at maturity, frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and p1 and p2 are shape coefficients.
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The shape coefficients are calculated by solving equation 18.3.19 using two known points (frP,2, frPHU,50%) and (frP,3, frPHU,100%): ù é ú ê frPHU ,50% ê p1 = ln ê − frPHU ,50% úú + p2 ⋅ frPHU ,50% æ ( fr − frP ,3 ) ö ú ê ç1 − P , 2 ÷ ( frP,1 − frP,3 ) ÷ø úû êë çè
18.3.20
æ é ù ùö é ç ê ú ú÷ ê ÷ ç ê frPHU ,50% fr PHU ,100% − frPHU ,50% úú − ln êê − frPHU ,100% úú ÷ ç ln ê æ ( fr − frP ,3 ) ö ç ê æç ( frP , 2 − frP ,3 ) ö÷ ÷ ú ú÷ ê ç 1 − P ,~ 3 1 − ç êç ç ÷ ÷ ( frP,1 − frP,3 ) ø ( frP,1 − frP,3 ) ø úû úû ÷ø êë è ëè è p2 = frPHU ,100% − frPHU ,50%
18.3.21
where p1 is the first shape coefficient, p2 is the second shape coefficient, frP,1 is the normal fraction of phosphorus in the plant biomass at emergence, frP,2 is the normal fraction of phosphorus in the plant biomass at 50% maturity, frP,3 is the normal fraction of phosphorus in the plant biomass at maturity, frP,~3 is the normal fraction of phosphorus in the plant biomass near maturity, frPHU,50% is the fraction of potential heat units accumulated for the plant at 50% maturity (frPHU,50%=0.5), and frPHU,100% is the fraction of potential heat units accumulated for the plant at maturity (frPHU,100%=1.0). The normal fraction of phosphorus in the plant biomass near maturity (frN,~3) is used in equation 18.3.21 to ensure that the denominator term
( fr
P , ~3
æ ( frP ,~3 − frP ,3 ) ö ÷ ç1 − ÷ ç ( ) fr − fr P ,1 P ,3 ø è
does
not
equal
1.
The
model
assumes
− frP ,3 ) = 0.00001 .
To determine the mass of phosphorus that should be stored in the plant biomass for the growth stage, the phosphorus fraction is multiplied by the total plant biomass: bioP ,opt = frP ⋅ bio
18.3.22
where bioP,opt is the optimal mass of phosphorus stored in plant material for the current growth stage (kg P/ha), frP is the optimal fraction of phophorus in the
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plant biomass for the current growth stage, and bio is the total plant biomass on a given day (kg ha-1). The plant phosphorus demand for a given day is a function of the difference between the phosphorus content of the plant biomass expected for the plant’s growth stage and the actual phosphorus content: Pup = 1.5 ⋅ (bio P ,opt − bioP )
18.3.23
where Pup is the potential phosphorus uptake (kg P/ha), bioP,opt is the optimal mass of phosphorus stored in plant material for the current growth stage (kg P/ha), and bioP is the actual mass of phosphorus stored in plant material (kg P/ha). The difference between the phosphorus content of the plant biomass expected for the plant’s growth stage and the actual phosphorus content is multiplied by 1.5 to simulate luxury phosphorus uptake. The depth distribution of phosphorus uptake is calculated with the function: Pup , z =
é æ z öù ÷ú ⋅ ê1 − expçç − β p ⋅ [1 − exp(− β p )] ë z root ÷ø û è Pup
18.3.24
where Pup,z is the potential phosphorus uptake from the soil surface to depth z (kg P/ha), Pup is the potential phosphorus uptake (kg P/ha), βP is the phosphorus uptake distribution parameter, z is the depth from the soil surface (mm), and zroot is the depth of root development in the soil (mm). The potential phosphorus uptake for a soil layer is calculated by solving equation 18.3.24 for the depth at the upper and lower boundaries of the soil layer and taking the difference. Pup ,ly = Pup ,zl − Pup , zu
18.3.25
where Pup,ly is the potential phosphorus uptake for layer ly (kg P/ha), Pup,zl is the potential phosphorus uptake from the soil surface to the lower boundary of the soil layer (kg P/ha), and Pup,zu is the potential phosphorus uptake from the soil surface to the upper boundary of the soil layer (kg P/ha). Root density is greatest near the surface, and phosphorus uptake in the upper portion of the soil will be greater than in the lower portion. The depth distribution of phosphorus uptake is controlled by βp, the phosphorus uptake
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distribution parameter, a variable users are allowed to adjust. The illustration of nitrogen uptake as a function of depth for four different uptake distribution parameter values in Figure 18-4 is valid for phosphorus uptake as well. Phosphorus removed from the soil by plants is taken from the solution phosphorus pool. The importance of the phosphorus uptake distribution parameter lies in its control over the maximum amount of solution P removed from the upper layers. Because the top 10 mm of the soil profile interacts with surface runoff, the phosphorus uptake distribution parameter will influence the amount of labile phosphorus available for transport in surface runoff. The model allows lower layers in the root zone to fully compensate for lack of solution P in the upper layers, so there should not be significant changes in phosphorus stress with variation in the value used for βp. The actual amount if phosphorus removed from a soil layer is calculated: Pactualup ,ly = min[Pup ,ly + Pdemand , Psolution ,ly ]
18.3.26
where Pactualup,ly is the actual phosphorus uptake for layer ly (kg P/ha), Pup,ly is the potential phosphorus uptake for layer ly (kg P/ha), Pdemand is the phosphorus uptake demand not met by overlying soil layers (kg P/ha), and Psolution,ly is the phosphorus content of the soil solution in layer ly (kg P/ha). Table 18-3: SWAT input variables that pertain to plant nutrient uptake. Variable Name Definition BN(1) frN,1: Normal fraction of N in the plant biomass at emergence BN(2) frN,2: Normal fraction of N in the plant biomass at 50% maturity BN(3) frN,3: Normal fraction of N in the plant biomass at maturity UBN βn: Nitrogen uptake distribution parameter BP(1) frP,1: Normal fraction of P in the plant biomass at emergence BP(2) frP,2: Normal fraction of P in the plant biomass at 50% maturity BP(3) frP,3: Normal fraction of P in the plant biomass at maturity UBP βp: Phosphorus uptake distribution parameter
Input File crop.dat crop.dat crop.dat .bsn crop.dat crop.dat crop.dat .bsn
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18.4 CROP YIELD When a harvest or harvest/kill operation is performed, a portion of the plant biomass is removed from the HRU as yield. The nutrients and plant material contained in the yield are assumed to be lost from the system (i.e. the watershed) and will not be added to residue and organic nutrient pools in the soil with the remainder of the plant material. In contrast, a kill operation converts all biomass to residue. The fraction of the above-ground plant dry biomass removed as dry economic yield is called the harvest index. For the majority of crops, the harvest index will be between 0.0 and 1.0. However, plants whose roots are harvested, such as sweet potatoes, may have a harvest index greater than 1.0. The economic yield of most commercial crops is the reproductive portion of the plant. Decades of crop breeding have lead to cultivars and hybrids having maximized harvest indices. Often, the harvest index is relatively stable across a range of environmental conditions. SWAT calculates harvest index each day of the plant’s growing season using the relationship: HI = HI opt ⋅
(100 ⋅ frPHU
100 ⋅ frPHU + exp[11.1 − 10 ⋅ frPHU ])
18.4.1
where HI is the potential harvest index for a given day, HIopt is the potential harvest index for the plant at maturity given ideal growing conditions, and frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season. The variation of the optimal harvest index during the growing season is illustrated in Figure 18-5. The crop yield is calculated as: yld = bioag ⋅ HI
when HI ≤ 1.00
18.4.2
æ 1 ö yld = bio ⋅ çç1 − ÷÷ è (1 + HI ) ø
when HI > 1.00
18.4.3
where yld is the crop yield (kg/ha), bioag is the aboveground biomass on the day of harvest (kg ha-1), HI is the harvest index on the day of harvest, and bio is the
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total plant biomass on the day of harvest (kg ha-1). The aboveground biomass is calculated: bio ag = (1 − frroot ) ⋅ bio
18.4.4
where frroot is the fraction of total biomass in the roots the day of harvest, and bio is the total plant biomass on the day of harvest (kg ha-1).
Figure 18-5: Variation in optimal harvest index (HIi/HIopt) with fraction of growing season (frPHU)
The amount of nutrients removed in the yield are calculated: yld N = frN , yld ⋅ yld
18.4.5
yld P = frP , yld ⋅ yld
18.4.6
where yldN is the amount of nitrogen removed in the yield (kg N/ha), yldP is the amount of phosphorus removed in the yield (kg P/ha), frN,yld is the fraction of nitrogen in the yield, frP,yld is the fraction of phosphorus in the yield, and yld is the crop yield (kg/ha). If the harvest index override is used in the harvest only operation, the model assumes that a significant portion of the plant biomass is being removed in addition to the seed. Therefore, instead of using the nitrogen and phosphorus yield fractions from the plant growth database, the model uses the total biomass nitrogen and phosphorus fractions to determine the amount of nitrogen and phosphorus removed:
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yld N = frN ⋅ yld
18.4.7
yld P = frP ⋅ yld
18.4.8
where yldN is the amount of nitrogen removed in the yield (kg N/ha), yldP is the amount of phosphorus removed in the yield (kg P/ha), frN is the fraction of nitrogen in the plant biomass calculated with equation 18.3.1, frP is the fraction of phosphorus in the plant biomass calculated with equation 18.3.19, and yld is the crop yield (kg/ha). Table 18-4: SWAT input variables that pertain to crop yield. Variable Name Definition HVSTI HIopt: Potential harvest index for the plant at maturity given ideal growing conditions CNYLD frN,yld: Fraction of nitrogen in the yield CPYLD frP,yld: Fraction of phosphorus in the yield
Input File crop.dat crop.dat crop.dat
18.5 NOMENCLATURE AWCly Available water capacity for layer ly (mm H2O) CO2 Concentration of carbon dioxide in the atmosphere (ppmv) CO2amb Ambient atmospheric CO2 concentration (330 ppmv) CO2hi Elevated atmospheric CO2 concentration (ppmv) Et Maximum transpiration rate (mm d-1) Et,act Actual amount of transpiration on a given day (mm H2O) FC Water content of soil profile at field capacity (mm H2O) FCly Water content of layer ly at field capacity (mm H2O) Hday Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) Hphosyn Intercepted photosynthetically active radiation on a given day (MJ m-2) HI Potential harvest index for a given day HIopt Potential harvest index for the plant at maturity given ideal growing conditions HU Number of heat units accumulated on a given day (heat units) LAI Leaf area index of the canopy LAImx Maximum leaf area index for the plant Nactualup,ly Actual nitrogen uptake for layer ly (kg N/ha) Ndemand Nitrogen uptake demand not met by overlying soil layers (kg N/ha) Nfix Amount of nitrogen added to the plant biomass by fixation (kg N/ha) Nup Potential nitrogen uptake (kg N/ha) Nup,ly Potential nitrogen uptake for layer ly (kg N/ha) Nup,z Potential nitrogen uptake from the soil surface to depth z (kg N/ha) Nup,zl Potential nitrogen uptake from the soil surface to the lower boundary of the soil layer (kg N/ha)
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Nup,zu Potential nitrogen uptake from the soil surface to the upper boundary of the soil layer (kg N/ha) NO3 Nitrate content of the soil profile (kg NO3-N/ha) NO3ly Nitrate content of soil layer ly (kg NO3-N/ha) Pactualup,ly Actual phosphorus uptake for layer ly (kg P/ha) Pdemand Phosphorus uptake demand not met by overlying soil layers (kg P/ha) Pup Potential phosphorus uptake (kg P/ha) Pup,ly Potential phosphorus uptake for layer ly (kg P/ha) Pup,z Potential phosphorus uptake from the soil surface to depth z (kg P/ha) Pup,zl Potential phosphorus uptake from the soil surface to the lower boundary of the soil layer (kg P/ha) Pup,zu Potential phosphorus uptake from the soil surface to the upper boundary of the soil layer (kg P/ha) PHU Potential heat units or total heat units required for plant maturity (heat units) Psolution,ly Phosphorus content of soil solution in layer ly (kg P/ha) RUE Radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) RUEamb Radiation-use efficiency of the plant at ambient atmospheric CO2 concentration (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) RUEhi Radiation-use efficiency of the plant at the elevated atmospheric CO2 concentration, CO2hi, (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) RUEvpd=1 Radiation-use efficiency for the plant at a vapor pressure deficit of 1 kPa (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) SW Amount of water in soil profile (mm H2O) SWly Soil water content of layer ly (mm H2O) WPly Water content of layer ly at wilting point (mm H2O). bio Total plant biomass on a given day (kg/ha) bioag Aboveground biomass on the day of harvest (kg ha-1) bioN Actual mass of nitrogen stored in plant material (kg N/ha) bioN,opt Optimal mass of nitrogen stored in plant material for the growth stage (kg N/ha) bioP Actual mass of phosphorus stored in plant material (kg P/ha) bioP,opt Optimal mass of phosphorus stored in plant material for the current growth stage (kg P/ha) epco Plant uptake compensation factor fgr Growth stage factor in nitrogen fixation equation fno3 Soil nitrate factor in nitrogen fixation equation fsw Soil water factor in nitrogen fixation equation frLAI,1 Fraction of the maximum plant leaf area index corresponding to the 1st point on the optimal leaf area development curve frLAI,2 Fraction of the maximum plant leaf area index corresponding to the 2nd point on the optimal leaf area development curve frLAImx Fraction of the plant’s maximum leaf area index corresponding to a given fraction of potential heat units for the plant frN Optimal fraction of nitrogen in the plant biomass for current growth stage frN,1 Normal fraction of nitrogen in the plant biomass at emergence frN,2 Normal fraction of nitrogen in the plant biomass at 50% maturity
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frN,3 frN,~3 frN,yld frP frP,1 frP,2 frP,3 frP,~3 frP,yld frPHU
Normal fraction of nitrogen in the plant biomass at maturity Normal fraction of nitrogen in the plant biomass near maturity Fraction of nitrogen in the yield Fraction of phosphorus in the plant biomass Normal fraction of phosphorus in the plant biomass at emergence Normal fraction of phosphorus in the plant biomass at 50% maturity Normal fraction of phosphorus in the plant biomass at maturity Normal fraction of phosphorus in the plant biomass near maturity Fraction of phosphorus in the yield Fraction of potential heat units accumulated for the plant on a given day in the growing season frPHU,1 Fraction of the growing season corresponding to the 1st point on the optimal leaf area development curve frPHU,2 Fraction of the growing season corresponding to the 2nd point on the optimal leaf area development curve frPHU,50% Fraction of potential heat units accumulated for the plant at 50% maturity (frPHU,50%=0.5) frPHU,100% Fraction of potential heat units accumulated for the plant at maturity (frPHU,100%=1.0) frPHU,sen Fraction of growing season at which senescence becomes the dominant growth process frroot Fraction of total biomass in the roots on a given day in the growing season hc Canopy height (cm) hc,mx Plant’s maximum canopy height (m) kl Light extinction coefficient n1 First shape coefficient in plant nitrogen equation n2 Second shape coefficient in plant nitrogen equation p1 First shape coefficient in plant phosphorus equation p2 Second shape coefficient in plant phosphorus equation r1 First shape coefficient for radiation-use efficiency curve r2 Second shape coefficient for radiation-use efficiency curve vpd Vapor pressure deficit (kPa) vpdthr Threshold vapor pressure deficit above which a plant will exhibit reduced radiation-use efficiency (kPa) wactualup Total plant water uptake for the day (mm H2O) wactualup,ly Actual water uptake for layer ly (mm H2O) wdemand Water uptake demand not met by overlying soil layers (mm H2O) wup,ly Potential water uptake for layer ly (mm H2O) ′ ,ly Adjusted potential water uptake for layer ly (mm H2O) wup ′′ ,ly Potential water uptake when the soil water content is less than 25% of plant wup available water (mm H2O) wup,z Potential water uptake from the soil surface to a specified depth, z, on a given day (mm H2O) wup,zl is the potential water uptake for the profile to the lower boundary of the soil layer (mm H2O)
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wup,zu is the potential water uptake for the profile to the upper boundary of the soil layer (mm H2O) yld Crop yield (kg/ha) yldN Amount of nitrogen removed in the yield (kg N/ha) yldP Amount of phosphorus removed in the yield (kg P/ha) z Depth below soil surface (mm) zroot Depth of root development in the soil (mm) zroot,mx Maximum depth for root development in the soil (mm)
βn Nitrogen uptake distribution parameter βp Phosphorus uptake distribution parameter βw Water-use distribution parameter ∆LAIi Leaf area added on day i ∆bio Potential increase in total plant biomass on a given day (kg/ha) ∆ruedcl Rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit (kg/ha⋅(MJ/m2)-1⋅kPa-1 or (10-1 g/MJ)⋅kPa-1) l1 First shape coefficient for optimal leaf area development curve l2 Second shape coefficient for optimal leaf area development curve
18.6 REFERENCES Jones, C.A. 1985. C-4 grasses and cereals. John Wiley & Sons, Inc., New York. 419 pp. McCree, K.J. 1972. The action spectrum, absorption and quantum yield of photosynthesis in crop plants. Agric. Met. 9:191-216. Monsi, M. and T. Saeki. 1953. Uber den Lictfaktor in den Pflanzengesellschaften und sein Bedeutung fur die Stoffproduktion. Japan J. Bot. 14:22-52. Monteith, J.L. 1972. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9:747-766. Monteith, J.L. 1977. Climate and the efficiency of crop production in Britian. Phil. Trans. Res. Soc. London Ser. B 281:277-329. Ross, J. 1975. Radiative transfer in plant communities. p. 13-55 In J.L. Monteith (ed.) Vegetation and the atmosphere, Vol. 1. Academic Press, London. Stanhill, G. and M. Fuchs. 1977. The relative flux density of photosynthetically active radiation. J. Appl. Ecol. 14:317-322.
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Stockle, C.O. and J.R. Kiniry. 1990. Variability in crop radiation-use efficiency associated with vapor-pressure deficit. Field Crops Res. 25:171-181. Stockle, C.O., J.R. Williams, N.J. Rosenburg, and C.A. Jones. 1992. A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part 1—Modification of the EPIC model for climate change analysis. Agricultural Systems 38:225-238. Szeicz, G. 1974. Solar radiation for plant growth. J. Appl. Ecol. 11:617-636. Watson, D.J. 1947. Comparative physiological studies on the growth of field crops. 1. Variation in net assimilation rate and leaf area index between species and varieties and within and between years. Ann. Bot. N.S. 11:4176. Williams, J. R., C.A. Jones, and P.T. Dyke. 1984. A modeling approach to determining the relationship between erosion and soil productivity. Trans. ASAE 27:129-144.
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CHAPTER 19
EQUATIONS: ACTUAL GROWTH
Actual growth varies from potential growth due to extreme temperatures, water deficiencies and nutrient deficiencies. This chapter reviews growth constraints as well as overrides that the user may implement to ignore growth constraints.
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19.1 GROWTH CONSTRAINTS Plant growth may be reduced due to extreme temperatures, and insufficient water, nitrogen or phosphorus. The amount of stress for each of these four parameters is calculated on a daily basis using the equations summarized in the following sections.
19.1.1 WATER STRESS Water stress is simulated by comparing actual and potential plant transpiration: wstrs = 1 −
w Et ,act = 1 − actualup Et Et
19.1.1
where wstrs is the water stress for a given day, Et is the maximum plant transpiration on a given day (mm H2O), Et,act is the actual amount of transpiration on a given day (mm H2O) and wactualup is the total plant water uptake for the day (mm H2O). The calculation of maximum transpiration is reviewed in Chapter 7 and the determination of actual plant water uptake/transpiration is reviewed in Chapter 18.
19.1.2 TEMPERATURE STRESS Temperature stress is a function of the daily average air temperature and the optimal temperature for plant growth. Near the optimal temperature the plant will not experience temperature stress. However as the air temperature diverges from the optimal the plant will begin to experience stress. The equations used to determine temperature stress are: tstrs = 1
when Tav ≤ Tbase
é − 0.1054 ⋅ (Topt − Tav )2 ù tstrs = 1 − exp ê ú when Tbase < Tav ≤ Topt (Tav − Tbase )2 úû êë
19.1.2
19.1.3
é − 0.1054 ⋅ (Topt − Tav )2 ù tstrs = 1 − exp ê when Topt < Tav ≤ 2 ⋅ Topt − Tbase 19.1.4 2 ú êë (2 ⋅ Topt − Tav − Tbase ) úû tstrs = 1
when Tav > 2 ⋅ Topt − Tbase
19.1.5
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where tstrs is the temperature stress for a given day expressed as a fraction of optimal plant growth, T av is the mean air temperature for day (°C), Tbase is the plant’s base or minimum temperature for growth (°C), and Topt is the plant’s optimal temperature for growth (°C). Figure 19-1 illustrates the impact of mean daily air temperature on plant growth for a plant with a base temperature of 0°C and an optimal temperature of 15°C.
Figure 19-1: Impact of mean air temperature on plant growth for a plant with Tbase= 0°C and Topt=15°C
19.1.3 NITROGEN STRESS Nitrogen stress is calculated only for non-legumes. SWAT never allows legumes to experience nitrogen stress. Nitrogen stress is quantified by comparing actual and optimal plant nitrogen levels. Nitrogen stress varies non-linearly between 0.0 at optimal nitrogen content and 1.0 when the nitrogen content of the plant is 50% or less of the optimal value. Nitrogen stress is computed with the equation: nstrs = 1 −
ϕn ϕ n + exp[3.535 − 0.02597 ⋅ ϕ n ]
19.1.6
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where nstrs is the nitrogen stress for a given day, and ϕn is a scaling factor for nitrogen stress. The scaling factor is calculated: æ bio N ö − 0 .5 ÷ ϕ n = 200 ⋅ çç ÷ è bio N ,opt ø
19.1.7
where bioN,opt is the optimal mass of nitrogen stored in plant material for the current growth stage (kg N/ha) and bioN is the actual mass of nitrogen stored in plant material (kg N/ha).
19.1.4 PHOSPHORUS STRESS As with nitrogen, phosphorus stress is quantified by comparing actual and optimal plant phosphorus levels. Phosphorus stress varies non-linearly between 0.0 at optimal phosphorus content and 1.0 when the phosphorus content of the plant is 50% or less of the optimal value. Phosphorus stress is computed with the equation: pstrs = 1 −
ϕp ϕ p + exp[3.535 − 0.02597 ⋅ ϕ p ]
19.1.8
where pstrs is the phosphorus stress for a given day, and ϕp is a scaling factor for phosphorus stress. The scaling factor is calculated: æ bio P ö ϕ p = 200 ⋅ çç − 0 .5 ÷ ÷ è bioP ,opt ø
19.1.9
where bioP,opt is the optimal mass of phosphorus stored in plant material for the current growth stage (kg N/ha) and bioP is the actual mass of phosphorus stored in plant material (kg N/ha). Table 19-1: SWAT input variables that pertain to stress on plant growth. Variable Name Definition T_BASE Tbase: Base temperature for plant growth (°C) T_OPT Topt: Optimal temperature for plant growth (°C)
Input File crop.dat crop.dat
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19.2 ACTUAL GROWTH The plant growth factor quantifies the fraction of potential growth achieved on a given day and is calculated:
γ reg = 1 − max (wstrs, tstrs, nstrs,
pstrs )
19.2.3
where γreg is the plant growth factor (0.0-1.0), wstrs is the water stress for a given day, tstrs is the temperature stress for a given day expressed as a fraction of optimal plant growth, nstrs is the nitrogen stress for a given day, and pstrs is the phosphorus stress for a given day. The potential biomass predicted with equation 18.1.2 is adjusted daily if one of the four plant stress factors is greater than 0.0 using the equation: ∆bioact = ∆bio ⋅ γ reg
19.2.1
where ∆bioact is the actual increase in total plant biomass on a given day (kg/ha), ∆bio is the potential increase in total plant biomass on a given day (kg/ha), and
γreg is the plant growth factor (0.0-1.0). The potential leaf area added on a given day is also adjusted daily for plant stress: ∆LAI act ,i = ∆LAI i ⋅ γ reg
19.2.2
where ∆LAIact,i is the actual leaf area added on day i, ∆LAIi is the potential leaf area added on day i that is calculated with equation 18.1.14, and γreg is the plant growth factor (0.0-1.0).
19.2.1 BIOMASS OVERRIDE The model allows the user to specify a total biomass that the plant will produce each year. When the biomass override is set in the plant operation (.mgt), the impact of variation in growing conditions from year to year is ignored, i.e. γreg is always set to 1.00 when biomass override is activated in an HRU. When a value is defined for the biomass override, the change in biomass is calculated: ∆bioact = ∆bioi ⋅
(bio
trg
− bioi −1 )
biotrg
19.2.4
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where ∆bioact is the actual increase in total plant biomass on day i (kg/ha), ∆bioi is the potential increase in total plant biomass on day i calculated with equation 18.1.2 (kg/ha), biotrg is the target biomass specified by the user (kg/ha), and bioi-1 is the total plant biomass accumulated on day i-1 (kg/ha). Table 19-2: SWAT input variables that pertain to actual plant growth. Variable Name Definition BIO_TARG biotrg/1000: Biomass target (metric tons/ha)
Input File .mgt
19.3 ACTUAL YIELD The harvest index predicted with equation 18.4.1 is affected by water deficit using the relationship: HI act = (HI − HI min ) ⋅
γ wu
γ wu + HI min + exp[6.13 − 0.883 ⋅ γ wu ]
19.3.1
where HIact is the actual harvest index, HI is the potential harvest index on the day of harvest calculated with equation 18.4.1, HImin is the harvest index for the plant in drought conditions and represents the minimum harvest index allowed for the plant, and γwu is the water deficiency factor. The water deficiency factor is calculated: m
γ wu = 100 ⋅
åE i =1 m
åE
a
19.3.2 o
i =1
where Ea is the actual evapotranspiration on a given day, Eo is the potential evapotranspiration on a given day, i is a day in the plant growing season, and m is the day of harvest if the plant is harvested before it reaches maturity or the last day of the growing season if the plant is harvested after it reaches maturity.
19.3.1 HARVEST INDEX OVERRIDE In the plant and harvest only operations (.mgt), the model allows the user to specify a target harvest index. The target harvest index set in a plant operation is used when the yield is removed using a harvest/kill operation. The target
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harvest index set in a harvest only operation is used only when that particular harvest only operation is executed. When a harvest index override is defined, the override value is used in place of the harvest index calculated by the model in the yield calculations. Adjustments for growth stage and water deficiency are not made. HI act = HI trg
19.3.3
where HIact is the actual harvest index and HItrg is the target harvest index.
19.3.2 HARVEST EFFICIENCY In the harvest only operation (.mgt), the model allows the user to specify a harvest efficiency. The harvest efficiency defines the fraction of yield biomass removed by the harvesting equipment. The remainder of the yield biomass is converted to residue and added to the residue pool in the top 10 mm of soil. If the harvest efficiency is not set or a 0.00 is entered, the model assumes the user wants to ignore harvest efficiency and sets the fraction to 1.00 so that the entire yield is removed from the HRU. yld act = yld ⋅ harv eff
19.3.4
where yldact is the actual yield (kg ha-1), yld is the crop yield calculated with equation 18.4.2 or 18.4.3 (kg ha-1), and harveff is the efficiency of the harvest operation (0.01-1.00). The remainder of the yield biomass is converted to residue: ∆rsd = yld ⋅ (1 − harv eff )
19.3.5
rsd surf ,i = rsd surf ,i −1 + ∆rsd
19.3.6
where ∆rsd is the biomass added to the residue pool on a given day (kg ha-1), yld is the crop yield calculated with equation 18.4.2 or 18.4.3 (kg ha-1) and harveff is the efficiency of the harvest operation (0.01-1.00) rsdsurf,i is the material in the residue pool for the top 10 mm of soil on day i (kg ha-1), and rsdsurf,i-1 is the material in the residue pool for the top 10 mm of soil on day i-1 (kg ha-1).
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Table 19-3: SWAT input variables that pertain to actual plant yield. Variable Name Definition WSYF HImin: Harvest index for the plant in drought conditions, the minimum harvest index allowed for the plant HITAR HItrg: Harvest index target HIOVR HItrg: Harvest index target HARVEFF harveff: Efficiency of the harvest operation
Input File crop.dat .mgt .mgt .mgt
19.4 NOMENCLATURE Ea Eo Et Et,act HI HIact HImin HItrg Tbase Topt T av
Actual amount of evapotranspiration on a given day (mm H2O) Potential evapotranspiration (mm d-1) Maximum transpiration rate (mm d-1) Actual amount of transpiration on a given day (mm H2O) Potential harvest index for a given day Actual harvest index Harvest index for the plant in drought conditions and represents the minimum harvest index allowed for the plant Target harvest index Plant’s base or minimum temperature for growth (°C) Plant’s optimal temperature for growth (°C) Mean air temperature for day (°C)
bioN Actual mass of nitrogen stored in plant material (kg N/ha) bioN,opt Optimal mass of nitrogen stored in plant material for the growth stage (kg N/ha) bioP Actual mass of phosphorus stored in plant material (kg P/ha) bioP,opt Optimal mass of phosphorus stored in plant material for the current growth stage (kg P/ha) biotrg Target biomass specified by the user (kg/ha) harveff Efficiency of the harvest operation nstrs Nitrogen stress for a given day pstrs Phosphorus stress for a given day rsdsurf,i Material in the residue pool for the top 10mm of soil on day i (kg ha-1) tstrs Temperature stress for a given day expressed as a fraction of optimal plant growth wactualup Total plant water uptake for the day (mm H2O) wstrs Water stress for a given day yldact Actual yield (kg ha-1) ∆LAIi Leaf area added on day i (potential) ∆LAIact,i Actual leaf area added on day i ∆bio Potential increase in total plant biomass on a given day (kg/ha) ∆bioact Actual increase in total plant biomass on a given day (kg/ha) ∆rsd Biomass added to the residue pool on a given day (kg ha-1) γreg Plant growth factor (0.0-1.0)
CHAPTER 19: EQUATIONS—ACTUAL GROWTH
γwu ϕn ϕp
Water deficiency factor Scaling factor for nitrogen stress equation Scaling factor for phosphorus stress equation
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MANAGEMENT PRACTICES Quantifying the impact of land management and land use on water supply and quality is a primary focus of environmental modeling. SWAT allows very detailed management information to be incorporated into a simulation. The following three chapters review the methodology used by SWAT to simulate water management, tillage and urban processes.
CHAPTER 20
EQUATIONS: GENERAL MANAGEMENT
Management operations that control the plant growth cycle, the timing of fertilizer and pesticide and the removal of plant biomass are explained in this chapter. Water management and the simulation of urban areas are summarized in subsequent chapters.
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20.1 PLANTING/BEGINNING OF GROWING SEASON The plant operation initiates plant growth. This operation can be used to designate the time of planting for agricultural crops or the initiation of plant growth in the spring for a land cover that requires several years to reach maturity (forests, orchards, etc.). The plant operation will be performed by SWAT only when no land cover is growing in an HRU. Before planting a new land cover, the previous land cover must be removed with a kill operation or a harvest and kill operation. If two plant operations are placed in the management file and the first land cover is not killed prior to the second plant operation, the second plant operation is ignored by the model. Information required in the plant operation includes the timing of the operation (month and day or fraction of base zero potential heat units), the total number of heat units required for the land cover to reach maturity, and the specific land cover to be simulated in the HRU. If the land cover is being transplanted, the leaf area index and biomass for the land cover at the time of transplanting must be provided. Also, for transplanted land covers, the total number of heat units for the land cover to reach maturity should be from the period the land cover is transplanted to maturity (not from seed generation). Heat units are reviewed in Chapter 17. The user has the option of varying the curve number in the HRU throughout the year. New curve number values may be entered in a plant operation, tillage operation and harvest and kill operation. The curve number entered for these operations are for moisture condition II. SWAT adjusts the entered value daily to reflect change in water content. For simulations where a certain amount of crop yield and biomass is required, the user can force the model to meet this amount by setting a harvest index target and a biomass target. These targets are effective only if a harvest and kill operation is used to harvest the crop.
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Table 20-1: SWAT input variables that pertain to planting. Variable Name
Definition
Variables in plant operation line: MONTH/DAY or HUSC Timing of planting operation.
MGT_OP HEAT UNITS NCR Optional inputs: HITAR BIO_TARG ALAINIT BIOINIT CNOP
Operation code. MGT_OP = 1 for plant operation PHU : Total heat units required for plant maturity (heat units) Plant/land cover code from crop.dat HItrg: Target harvest index biotrg: Target biomass specified by the user (kg/ha) LAI: Leaf area index of the canopy for transplanted species bio: Total plant biomass on a given day (kg/ha) CN2: Moisture condition II curve number
Variables in second line of .mgt file IGRO Land cover status code Inputs for plants growing at the beginning of the simulation NCRP Plant/land cover code from crop.dat ALAI LAI: Leaf area index of the canopy BIO_MS biotrg: Target biomass specified by the user (kg/ha) PHU PHU : Total heat units required for plant maturity (heat
Input File .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt
units)
20.2 HARVEST OPERATION The harvest operation will remove plant biomass without killing the plant. This operation is most commonly used to cut hay or grass. The only information required by the harvest operation is the date. However, a harvest index override and a harvest efficiency can be set. When no harvest index override is specified, SWAT uses the plant harvest index from the plant growth database to set the fraction of biomass removed. The plant harvest index in the plant growth database is set to the fraction of the plant biomass partitioned into seed for agricultural crops and a typical fraction of biomass removed in a cutting for hay. If the user prefers a different fraction of biomass to be removed, the harvest index override should be set to the desired value. A harvest efficiency may also be defined for the operation. This value specifies the fraction of harvested plant biomass removed from the HRU. The remaining fraction is converted to residue on the soil surface. If the harvest efficiency is left blank or set to zero, the model assumes this feature is not being
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used and removes 100% of the harvested biomass (no biomass is converted to residue). After biomass is removed in a harvest operation, the plant’s leaf area index and accumulated heat units are set back by the fraction of biomass removed. Reducing the number of accumulated heat units shifts the plant’s development to an earlier period in which growth is usually occurring at a faster rate. Table 20-2: SWAT input variables that pertain to harvest. Variable Name
Definition
Variables in harvest operation line: MONTH/DAY or HUSC Timing of harvest operation.
Input File
MGT_OP
Operation code. MGT_OP = 7 for harvest operation
.mgt .mgt
Optional inputs: HIOVR HARVEFF
HItrg: Harvest index override or target harvest index harveff: Efficiency of the harvest operation
.mgt .mgt
20.3 GRAZING OPERATION The grazing operation simulates plant biomass removal and manure deposition over a specified period of time. This operation is used to simulate pasture or range grazed by animals. Information required in the grazing operation includes the time during the year at which grazing begins (month and day or fraction of plant potential heat units), the length of the grazing period, the amount of biomass removed daily, the amount of manure deposited daily, and the type of manure deposited. The amount of biomass trampled is an optional input. Biomass removal in the grazing operation is similar to that in the harvest operation. However, instead of a fraction of biomass being specified, an absolute amount to be removed every day is given. In some conditions, this can result in a reduction of the plant biomass to a very low level that will result in increased erosion in the HRU. To prevent this, a minimum plant biomass for grazing may be specified (BIO_MIN in the second line of the management file). When the plant biomass falls below the amount specified for BIO_MIN, the model will not graze, trample, or apply manure in the HRU on that day.
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If the user specifies an amount of biomass to be removed daily by trampling, this biomass is converted to residue. Nutrient fractions of the manure applied during grazing must be stored in the fertilizer database. The manure nutrient loadings are added to the topmost 10 mm of soil. This is the portion of the soil with which surface runoff interacts. After biomass is removed by grazing and/or trampling, the plant’s leaf area index and accumulated heat units are set back by the fraction of biomass removed. Table 20-3: SWAT input variables that pertain to grazing. Variable Name
Definition
Variables in grazing operation line: MONTH/DAY or HUSC Time grazing operation is initiated (1st day of grazing)
MGT_OP NDGRAZ BMEAT IGFTYP WMANURE Optional inputs: BMTRMP
Input File
Operation code. MGT_OP = 9 for grazing operation Number of days of grazing. bio: Total plant biomass consumed daily (kg/ha) Manure code from fert.dat fert: Amount of manure applied—dry weight (kg/ha)
.mgt .mgt .mgt .mgt .mgt .mgt
bio: Total plant biomass trampled daily (kg/ha)
.mgt
Variables in second line of .mgt file BIO_MIN bio: Minimum plant biomass for grazing to occur (kg/ha)
.mgt
20.4 HARVEST & KILL OPERATION The harvest and kill operation stops plant growth in the HRU. The fraction of biomass specified in the land cover’s harvest index (in the plant growth database) is removed from the HRU as yield. The remaining fraction of plant biomass is converted to residue on the soil surface. The only information required by the harvest and kill operation is the timing of the operation (month and day or fraction of plant potential heat units). The user also has the option of updating the moisture condition II curve number in this operation. Table 20-4: SWAT input variables that pertain to harvest & kill. Variable Name
Definition
Variables in harvest & kill operation line: MONTH/DAY or HUSC Timing of harvest and kill operation.
Input File
MGT_OP
Operation code. MGT_OP = 5 for harvest/kill operation
.mgt .mgt
Optional inputs: CNOP
CN2: Moisture condition II curve number
.mgt
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20.5 KILL/END OF GROWING SEASON The kill operation stops plant growth in the HRU. All plant biomass is converted to residue. The only information required by the kill operation is the timing of the operation (month and day or fraction of plant potential heat units). Table 20-5: SWAT input variables that pertain to kill. Variable Name
Definition
Variables in kill operation line: MONTH/DAY or HUSC Timing of kill operation.
MGT_OP
Operation code. MGT_OP = 8 for kill operation
Input File .mgt .mgt
20.6 TILLAGE The tillage operation redistributes residue, nutrients, pesticides and bacteria in the soil profile. Information required in the tillage operation includes the timing of the operation (month and day or fraction of base zero potential heat units), and the type of tillage operation. The user has the option of varying the curve number in the HRU throughout the year. New curve number values may be entered in a plant operation, tillage operation and harvest and kill operation. The curve number entered for these operations are for moisture condition II. SWAT adjusts the entered value daily to reflect change in water content. The mixing efficiency of the tillage implement defines the fraction of a residue/nutrient/pesticide/bacteria pool in each soil layer that is redistributed through the depth of soil that is mixed by the implement. To illustrate the redistribution of constituents in the soil, assume a soil profile has the following distribution of nitrate. Layer # surface layer 1 2 3 4
Depth of Layer 0-10 mm 10-100 mm 100-400 mm 400-1050 mm 1050-2000 mm
NO3 Content 50 kg/ha 25 kg/ha 20 kg/ha 10 kg/ha 10 kg/ha
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If this soil is tilled with a field cultivator, the soil will be mixed to a depth of 100 mm with 30% efficiency. The change in the distribution of nitrate in the soil is:
Layer # surface layer 1 2 3 4
Mixed NO3 (30%)
Redistribution of Mixed NO3
Final NO3
35 kg/ha 17.5 kg/ha 20 kg/ha 10 kg/ha 10 kg/ha
15 kg/ha 7.5 kg/ha
22.5×10mm/100mm = 2.25 kg/ha 22.5×90mm/100mm = 20.25 kg/ha
37.25 kg/ha 37.75 kg/ha 20 kg/ha 10 kg/ha 10 kg/ha
Total mixed:
22.5 kg/ha
Depth of Layer
Initial NO3
Unmixed NO3 (70%)
0-10 mm 10-100 mm 100-400 mm 400-1050 mm 1050-2000 mm
50 kg/ha 25 kg/ha 20 kg/ha 10 kg/ha 10 kg/ha
Because the soil is mixed to a depth of 100 mm by the implement, only the nitrate in the surface layer and layer 1 is available for redistribution. To calculated redistribution, the depth of the layer is divided by the tillage mixing depth and multiplied by the total amount of nitrate mixed. To calculate the final nitrate content, the redistributed nitrate is added to the unmixed nitrate for the layer. All nutrient/pesticide/bacteria/residue pools are treated in the same manner as the nitrate example above. Bacteria mixed into layers below the surface layer is assumed to die.
20.6.1 BIOLOGICAL MIXING Biological mixing is the redistribution of soil constituents as a result of the activity of biota in the soil (e.g. earthworms, etc.). Studies have shown that biological mixing can be significant in systems where the soil is only infrequently disturbed. In general, as a management system shifts from conventional tillage to conservation tillage to no-till there will be an increase in biological mixing. SWAT allows biological mixing to occur to a depth of 300 mm (or the bottom of the soil profile if it is shallower than 300 mm). The efficiency of biological mixing is defined by the user. The redistribution of nutrients by biological mixing is calculated using the same methodology as that used for a tillage operation.
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Table 20-6: SWAT input variables that pertain to tillage. Variable Name
Definition
Variables in tillage operation line: MONTH/DAY or HUSC Timing of planting operation.
MGT_OP TILLAGE_ID Optional inputs: CNOP
Input File
Operation code. MGT_OP = 6 for tillage operation Tillage implement code from till.dat
.mgt .mgt .mgt
CN2: Moisture condition II curve number
.mgt
Variables in second line of .mgt file BIOMIX Biological mixing efficiency Variable in tillage database: EFFMIX Mixing efficiency of tillage operation. DEPTIL Depth of mixing by tillage operation.
.mgt till.dat till.dat
20.7 FERTILIZER APPLICATION The fertilizer operation applies fertilizer or manure to the soil. Information required in the fertilizer operation includes the timing of the operation (month and day or fraction of plant potential heat units), the type of fertilizer/manure applied, the amount of fertilizer/manure applied, and the depth distribution of fertilizer application. SWAT assumes surface runoff interacts with the top 10 mm of soil. Nutrients contained in this surface layer are available for transport to the main channel in surface runoff. The fertilizer operation allows the user to specify the fraction of fertilizer that is applied to the top 10 mm. The remainder of the fertilizer is added to the first soil layer defined in the HRU .sol file. In the fertilizer database, the weight fraction of different types of nutrients and bacteria are defined for the fertilizer. The amount of nutrient added to the different pools in the soil are calculated: NO3 fert = fert minN ⋅ (1 − fert NH 4 ) ⋅ fert
20.7.1
NH4 fert = fert minN ⋅ fert NH 4 ⋅ fert
20.7.2
orgN frsh , fert = 0.5 ⋅ fertorgN ⋅ fert
20.7.3
orgN act , fert = 0.5 ⋅ fertorgN ⋅ fert
20.7.4
Psolution , fert = fert minP ⋅ fert
20.7.5
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orgPfrsh , fert = 0.5 ⋅ fertorgP ⋅ fert
20.7.6
orgPhum , fert = 0.5 ⋅ fertorgP ⋅ fert
20.7.7
bact lpsol , fert = fert lpbact ⋅ k bact ⋅ fert
20.7.8
bact lpsorb, fert = fert lpbact ⋅ (1 − k bact ) ⋅ fert
20.7.9
bact psol , fert = fert pbact ⋅ k bact ⋅ fert
20.7.10
bact psorb, fert = fert pbact ⋅ (1 − k bact ) ⋅ fert
20.7.11
where NO3fert is the amount of nitrate added to the soil in the fertilizer (kg N/ha), NH4fert is the amount of ammonium added to the soil in the fertilizer (kg N/ha), orgNfrsh,fert is the amount of nitrogen in the fresh organic pool added to the soil in the fertilizer (kg N/ha), orgNact,fert is the amount of nitrogen in the active organic pool added to the soil in the fertilizer (kg N/ha), Psolution,fert is the amount of phosphorus in the solution pool added to the soil in the fertilizer (kg P/ha), orgPfrsh,fert is the amount of phosphorus in the fresh organic pool added to the soil in the fertilizer (kg P/ha), orgPhum,fert is the amount of phosphorus in the humus organic pool added to the soil in the fertilizer (kg P/ha), bactlpsol,fert is the amount of less persistent bacteria in the solution pool added to the soil in the fertilizer (# bact/ha), bactlpsorb,fert is the amount of less persistent bacteria in the sorbed pool added to the soil in fertilizer (# bact/ha), bactpsol,fert is the amount of persistent bacteria in the solution pool added to the soil in the fertilizer (# bact/ha), bactpsorb,fert is the amount of persistent bacteria in the sorbed pool added to the soil in fertilizer (# bact/ha), fertminN is the fraction of mineral N in the fertilizer, fertNH4 is the fraction of mineral N in the fertilizer that is ammonium, fertorgN is the fraction of organic N in the fertilizer, fertminP is the fraction of mineral P in the fertilizer, fertorgP is the fraction of organic P in the fertilizer, fertlpbact is the concentration of less persistent bacteria in the fertilizer (# bact/kg fert), fertpbact is the concentration of persistent bacteria in the fertilizer (# bact/kg fert), kbact is the bacterial partition coefficient, and fert is the amount of fertilizer applied to the soil (kg/ha).
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Table 20-7: SWAT input variables that pertain to fertilizer application. Variable Name
Definition
Variables in fertilizer operation line: MONTH/DAY or HUSC Timing of fertilizer operation.
MGT_OP FERT_ID FRT_KG FRT_LY1
Operation code. MGT_OP = 3 for fertilizer operation Type of fertilizer/manure applied (code from fert.dat). fert: Amount of fertilizer/manure applied (kg/ha) Fraction of fertilizer applied to top 10 mm
Variables in fertilizer database: FMINN fertminN: Fraction of mineral nitrogen in the fertilizer FMINP fertminP: Fraction of mineral P in the fertilizer FORGN fertorgN: Fraction of organic N in the fertilizer FORGP fertorgP: Fraction of organic P in the fertilizer FNH3N fertNH4: Fraction of mineral N in the fertilizer that is BACTPDB BACTLPDB BACTKDDB
ammonium fertpbact: Concentration of persistent bacteria in manure (# bact/kg) fertlpbact: Concentration of less-persistent bacteria in manure (# bact/kg) kbact: Bacterial partition coefficient
Input File .mgt .mgt .mgt .mgt .mgt fert.dat fert.dat fert.dat fert.dat fert.dat fert.dat fert.dat fert.dat
20.8 AUTO-APPLICATION OF FERTILIZER Fertilization in an HRU may be scheduled by the user or automatically applied by SWAT. When the user selects auto-application of fertilizer in an HRU, a nitrogen stress threshold must be specified. The nitrogen stress threshold is a fraction of potential plant growth. Anytime actual plant growth falls below this threshold fraction due to nitrogen stress, the model will automatically apply fertilizer to the HRU. The user specifies the type of fertilizer, the fraction of total fertilizer applied to the soil surface, the maximum amount of fertilizer that can be applied during the year, the maximum amount of fertilizer that can be applied in any one application, and the application efficiency. To determine the amount of fertilizer applied, an estimate of the amount of nitrogen that will be removed in the yield is needed. For the first year of simulation, the model has no information about the amount of nitrogen removed from the soil by the plant. The nitrogen yield estimate is initially assigned a value using the following equations: yld est , N = 350 ⋅ frN , yld ⋅ RUE
if HI opt < 1.0
20.8.1
yld est , N = 1000 ⋅ frN , yld ⋅ RUE
if HI opt ≥ 1.0
20.8.2
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where yldest,N is the nitrogen yield estimate (kg N/ha), frN,yld is the fraction of nitrogen in the yield, RUE is the radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), and HIopt is the potential harvest index for the plant at maturity given ideal growing conditions. The nitrogen yield estimate is updated at the end of every simulation year using the equation: yld est , N =
yld est , Nprev ⋅ yrsim + yld yr , N yrsim + 1
20.8.3
where yldest,N is the nitrogen yield estimate update for the current year (kg N/ha), yldest,Nprev is the nitrogen yield estimate from the previous year (kg N/ha), yrsim is the year of simulation, yldyr,N is the nitrogen yield target for the current year (kg N/ha). The nitrogen yield target for the current year is calculated at the time of harvest using the equation: yld yr , N = bioag ⋅ frN ⋅ fert eff
20.8.4
where yldyr,N is the nitrogen yield target for the current year (kg N/ha), bioag is the aboveground biomass on the day of harvest (kg ha-1), frN is the fraction of nitrogen in the plant biomass calculated with equation 18.3.1, and ferteff is the fertilizer application efficiency assigned by the user. The fertilizer application efficiency allows the user to modify the amount of fertilizer applied as a function of plant demand. If the user would like to apply additional fertilizer to adjust for loss in runoff, ferteff will be set to a value greater than 1. If the user would like to apply just enough fertilizer to meet the expected demand, ferteff will be set to 1. If the user would like to apply only a fraction of the demand, ferteff will be set to a value less than 1. The optimal amount of mineral nitrogen to be applied is calculated: minN app = yld est , N − (NO3 + NH4 ) − bio N
20.8.5
where minNapp is the amount of mineral nitrogen applied (kg N/ha), yldest,N is the nitrogen yield estimate (kg N/ha), NO3 is the nitrate content of the soil profile (kg NO3-N/ha), NH4 is the ammonium content of the soil profile (kg NH4-N/ha), and bioN is the actual mass of nitrogen stored in plant material (kg N/ha). If the amount of mineral nitrogen calculated with equation 20.8.5 exceeds the maximum
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amount allowed for any one application, minNapp is reset to the maximum value ( minN app = minN app ,mx ). The total amount of nitrogen applied during the year is also compared to the maximum amount allowed for the year. Once the amount applied reaches the maximum amount allowed for the year (minNapp,mxyr), SWAT will not apply any additional fertilizer regardless of nitrogen stress. Once the amount of mineral nitrogen applied is determined, the total amount of fertilizer applied is calculated by dividing the mass of mineral nitrogen applied by the fraction of mineral nitrogen in the fertilizer: fert =
minN app
20.8.6
fertminN
where fert is the amount of fertilizer applied (kg/ha), minNapp is the amount of mineral nitrogen applied (kg N/ha), and fertminN is the fraction of mineral nitrogen in the fertilizer. The type of fertilizer applied in the HRU is specified by the user. In addition to mineral nitrogen, organic nitrogen and phosphorus and mineral phosphorus are applied to the HRU. The amount of each type of nutrient is calculated from the amount of fertilizer and fraction of the various nutrient types in the fertilizer as summarized in Section 20.7. While the model does not allow fertilizer to be applied as a function of phosphorus stress, the model does monitor phosphorus stress in the autofertilization subroutine. If phosphorus stress causes plant growth to fall below 75% of potential growth, the model ignores the fraction of mineral phosphorus in 1 the fertilizer and applies an amount of mineral phosphorus equal to ( ⋅ minN app ). 7 Table 20-8: SWAT input variables that pertain to auto-fertilization. Variable Name
Definition
Variables in auto-fertilizer operation line: MONTH/DAY or HUSC Timing of fertilizer operation.
MGT_OP FERT_ID AFRT_LY1 AUTO_NSTR AUTO_EFF AUTO_NMXS
Operation code. MGT_OP = 11 for auto-fertilizer operation Type of fertilizer/manure applied (code from fert.dat). Fraction of fertilizer applied to top 10 mm nstrs: Nitrogen stress that triggers fertilizer application ferteff: Application efficiency minNapp,mx: Maximum amount of mineral N allowed to be applied on any one day (kg N/ha)
Input File .mgt .mgt .mgt .mgt .mgt .mgt .mgt
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Table 20-8, cont.: SWAT input variables that pertain to auto-fertilization. Variable Name AUTO_NMXA
Definition minNapp,mxyr: Maximum amount of mineral N allowed to be applied during a year (kg N/yr)
Input File .mgt
Other variables: CNYLD
frN,yld: Fraction of nitrogen in the yield RUE: Radiation use efficiency ((kg/ha)/(MJ/m2)) HIopt: Potential harvest index for the plant at maturity given ideal growing conditions fertminN: fraction of mineral N in the fertilizer
BIO_E HVSTI FMINN
crop.dat crop.dat crop.dat fert.dat
20.9 PESTICIDE APPLICATION The pesticide operation applies pesticide to the HRU. Information required in the pesticide operation includes the timing of the operation (month and day or fraction of plant potential heat units), the type of pesticide applied, and the amount of pesticide applied. Field studies have shown that even on days with little or no wind, a portion of pesticide applied to the field is lost. The fraction of pesticide that reaches the foliage or soil surface is defined by the pesticide’s application efficiency. The amount of pesticide that reaches the foliage or ground is: pest ′ = ap ef ⋅ pest
20.9.1
where pest ′ is the effective amount of pesticide applied (kg pst/ha), apef is the pesticide application efficiency, and pest is the actual amount of pesticide applied (kg pst/ha). The amount of pesticide reaching the ground surface and the amount of pesticide added to the plant foliage is calculated as a function of ground cover. The ground cover provided by plants is: gc =
1.99532 − erfc[1.333 ⋅ LAI − 2] 2 .1
20.9.2
where gc is the fraction of the ground surface covered by plants, erfc is the complementary error function, and LAI is the leaf area index.
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The complementary error function frequently occurs in solutions to advective-dispersive equations. Values for erfc(β) and erf(β) (erf is the error function for β), where β is the argument of the function, are graphed in Figure 201. The figure shows that erf(β) ranges from –1 to +1 while erfc(β) ranges from 0 to +2. The complementary error function takes on a value greater than 1 only for negative values of the argument.
Figure 20-1: erf(β) and erfc(β) plotted versus β (from Domenico and Schwartz, 1990)
Once the fraction of ground covered by plants is known, the amount of pesticide applied to the foliage is calculated: pest fol = gc ⋅ pest ′
20.9.3
and the amount of pesticide applied to the soil surface is pest surf = (1 − gc ) ⋅ pest ′
20.9.4
where pestfol is the amount of pesticide applied to foliage (kg pst/ha), pestsurf is the amount of pesticide applied to the soil surface (kg pst/ha), gc is the fraction of the ground surface covered by plants, and pest ′ is the effective amount of pesticide applied (kg pst/ha). Table 20-9: SWAT input variables that pertain to pesticide application. Variable Name
Definition
Variables in pesticide operation line: MONTH/DAY or HUSC Timing of pesticide operation.
MGT_OP PEST_ID PST_KG
Operation code. MGT_OP = 4 for pesticide operation Type of pesticide applied (code from pest.dat). pest: Amount of pesticide applied (kg/ha)
Variables in pesticide database: AP_EF apef: Pesticide application efficiency
Input File .mgt .mgt .mgt .mgt pest.dat
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20.10 FILTER STRIPS Edge-of field filter strips may be defined in an HRU. Sediment, nutrient, pesticide and bacteria loads in surface runoff are reduced as the surface runoff passes through the filter strip. The filter strip trapping efficiency for bacteria is calculated: trapef ,bact = 1 −
(12 + 4.5 ⋅ width
filtstrip
)
100
20.10.1
where trapef,bact is the fraction of the bacteria loading trapped by the filter strip, and widthfiltstrip is the width of the filter strip (m). The filter strip trapping efficiency for sediment, nutrients and pesticides is calculated: trapef = 0.367 ⋅ (width filtstrip )
0.2967
20.10.2
where trapef is the fraction of the constituent loading trapped by the filter strip, and widthfiltstrip is the width of the filter strip (m). Table 20-10: SWAT input variables that pertain to filter strips. Variable Name FILTERW
Definition widthfiltstrip: Width of filter strip (m)
Input File .hru
20.11 NOMENCLATURE CN2 Moisture condition II curve number HIopt Potential harvest index for the plant at maturity given ideal growing conditions HItrg Target harvest index LAI Leaf area index of the canopy NH4 Ammonium content of the soil profile (kg NH4-N/ha) NH4fert Amount of ammonium added to the soil in the fertilizer (kg N/ha) NO3 Nitrate content of the soil profile (kg NO3-N/ha) NO3fert Amount of nitrate added to the soil in the fertilizer (kg N/ha) Psolution,fert Amount of phosphorus in the solution pool added to the soil in the fertilizer (kg P/ha) PHU Potential heat units or total heat units required for plant maturity where base temperature is dependant on the plant species (heat units) RUE Radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) apef
Pesticide application efficiency
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bactlpsol,fert Amount of less persistent bacteria in the solution pool added to the soil in the fertilizer (# bact/ha) bactlpsorb,fert Amount of less persistent bacteria in the sorbed pool added to the soil in fertilizer (# bact/ha) bactpsol,fert Amount of persistent bacteria in the solution pool added to the soil in the fertilizer (# bact/ha) bactpsorb,fert Amount of persistent bacteria in the sorbed pool added to the soil in fertilizer (# bact/ha) bio Total plant biomass on a given day (kg/ha) bioag Aboveground biomass on the day of harvest (kg ha-1) bioN Actual mass of nitrogen stored in plant material (kg N/ha) biotrg Target biomass specified by the user (kg/ha) fert Amount of fertilizer applied (kg/ha) ferteff Fertilizer application efficiency assigned by the user fertlpbact Concentration of less persistent bacteria in the fertilizer (# bact/kg fert) fertminN Fraction of mineral nitrogen in the fertilizer fertminP Fraction of mineral P in the fertilizer fertNH4 Fraction of mineral N in the fertilizer that is ammonium fertorgN Fraction of organic N in the fertilizer fertorgP Fraction of organic P in the fertilizer fertpbact Concentration of persistent bacteria in the fertilizer (# bact/kg fert) frN Optimal fraction of nitrogen in the plant biomass for current growth stage frN,yld Fraction of nitrogen in the yield gc Fraction of the ground surface covered by plants harveff Efficiency of the harvest operation kbact Bacterial partition coefficient minNapp Amount of mineral nitrogen applied (kg N/ha) minNapp,mx Maximum amount of mineral N allowed to be applied on any one day (kg N/ha) minNapp,mxyr Maximum amount of mineral N allowed to be applied during a year (kg N/ha) nstrs Nitrogen stress for a given day orgNact,fert Amount of nitrogen in the active organic pool added to the soil in the fertilizer (kg N/ha) orgNfrsh,fert Amount of nitrogen in the fresh organic pool added to the soil in the fertilizer (kg N/ha) orgPfrsh,fert Amount of phosphorus in the fresh organic pool added to the soil in the fertilizer (kg P/ha) orgPhum,fert Amount of phosphorus in the humus organic pool added to the soil in the fertilizer (kg P/ha) pest Actual amount of pesticide applied (kg pst/ha) pest ′ Effective amount of pesticide applied (kg pst/ha) pestfol Amount of pesticide applied to foliage (kg pst/ha) pestsurf Amount of pesticide applied to the soil surface (kg pst/ha) trapef Fraction of the constituent loading trapped by the filter strip trapef,bact Fraction of the bacteria loading trapped by the filter strip
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widthfiltstrip Width of filter strip (m) yldest,N Nitrogen yield estimate (kg N/ha) yldest,Nprev Nitrogen yield estimate from the previous year (kg N/ha) yldyr,N Nitrogen yield target for the current year (kg N/ha) yrsim Year of simulation
20.12 REFERENCES Domenico, P.A. and F.W. Schwartz. 1990. Physical and chemical hydrology. John Wiley & Sons, New York, NY.
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CHAPTER 21
EQUATIONS: WATER MANAGEMENT
Accurately reproducing water management practices can be one of the most complicated portions of data input for the model. Because water management affects the hydrologic balance, it is critical that the model is able to accommodate a variety of management practices. Water management options modeled by SWAT include irrigation, tile drainage, impounded/depressional areas, water transfer, consumptive water use, and loadings from point sources.
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21.1 IRRIGATION Irrigation in an HRU may be scheduled by the user or automatically applied by SWAT. In addition to specifying the timing and application amount, the user must specify the source of irrigation water. Water applied to an HRU is obtained from one of five types of water sources: a reach, a reservoir, a shallow aquifer, a deep aquifer, or a source outside the watershed. In addition to the type of water source, the model must know the location of the water source (unless the source is outside the watershed). For the reach, shallow aquifer or deep aquifer, SWAT needs to know the subbasin number in which the source is located. If a reservoir is used to supply water, SWAT must know the reservoir number. If the source of the irrigation water is a reach, SWAT allows additional input parameters to be set. These parameters are used to prevent flow in the reach from being reduced to zero as a result of irrigation water removal. Users may define a minimum in-stream flow, a maximum irrigation water removal amount that cannot be exceeded on any given day, and/or a fraction of total flow in the reach that is available for removal on a given day. For a given irrigation event, SWAT determines the amount of water available in the source. The amount of water available is compared to the amount of water specified in the irrigation operation. If the amount available is less than the amount specified, SWAT will only apply the available water. Water applied to an HRU is used to fill the soil layers up to field capacity beginning with the soil surface layer and working downward until all the water applied is used up or the bottom of the profile is reached. If the amount of water specified in an irrigation operation exceeds the amount needed to fill the soil layers up to field capacity water content, the excess water is returned to the source. For HRUs that are defined as potholes or depressional areas, the irrigation water is added to the ponded water overlying the soil surface.
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21.1.1 AUTO-APPLICATION OF IRRIGATION When the user selects auto-application of irrigation water in an HRU, a water stress threshold must be specified. The water stress threshold is a fraction of potential plant growth. Anytime actual plant growth falls below this threshold fraction due to water stress the model will automatically apply water to the HRU. If enough water is available from the irrigation source, the model will add water to the soil until it is at field capacity. The water stress threshold is usually set somewhere between 0.90 and 0.95. Table 21-1: SWAT input variables that pertain to irrigation. Variable Name
Definition
Variables in irrigation operation line: MONTH/DAY or HUSC Timing of irrigation operation.
Input File
IRR_AMT
Operation code. MGT_OP = 2 for irrigation operation Depth of irrigation water applied on HRU (mm)
.mgt .mgt .mgt
Variables in .hru file IRR IRRNO FLOWMIN DIVMAX FLOWFR
Type of water body from which irrigation water is obtained Source location Minimum in-stream flow (m3/s) Maximum daily irrigation diversion (mm or 104 m3) Fraction of available flow allowed to be used for irrigation
.hru .hru .hru .hru .hru
MGT_OP
Variables in auto-irrigation operation line: MONTH/DAY or HUSC Initialization of auto-irrigation
MGT_OP AUTO_WSTR
Operation code. MGT_OP = 10 for auto-irrigation Water stress that triggers irrigation
.mgt .mgt .mgt
21.2 TILE DRAINAGE To simulate tile drainage in an HRU, the user must specify the depth from the soil surface to the drains, the amount of time required to drain the soil to field capacity, and the amount of lag between the time water enters the tile till it exits the tile and enters the main channel. Tile drainage occurs when the soil water content exceeds field capacity. In the soil layer where the tile drains are installed, the amount of water entering the drain on a given day is calculated:
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æ é − 24 ù ö tilewtr = (SWly − FCly ) ⋅ çç 1 − exp ê ú ÷÷ t drain ë ûø è
if SWly > FCly
21.2.1
where tilewtr is the amount of water removed from the layer on a given day by tile drainage (mm H2O), SWly is the water content of the layer on a given day (mm H2O), FCly is the field capacity water content of the layer (mm H2O), and tdrain is the time required to drain the soil to field capacity (hrs). Water entering tiles is treated like lateral flow. The flow is lagged using equations reviewed in Chapter 8. Table 21-2: SWAT input variables that pertain to tile drainage. Variable Name DDRAIN TDRAIN GDRAIN
Definition Depth to subsurface drain (mm). tdrain: Time to drain soil to field capacity (hrs) tilelag: Drain tile lag time (hrs)
Input File .hru .hru .hru
21.3 IMPOUNDED/DEPRESSIONAL AREAS Impounded/depressional areas are simulated as a water body overlying a soil profile in an HRU. This type of ponded system is needed to simulate the growth of rice, cranberries or any other plant that grows in a waterlogged system. The simulation and management operations pertaining to impounded/depressional areas is reviewed in Chapter 27.
21.4 WATER TRANSFER While water is most typically removed from a water body for irrigation purposes, SWAT also allows water to be transferred from one water body to another. This is performed with a transfer command in the watershed configuration file. The transfer command can be used to move water from any reservoir or reach in the watershed to any other reservoir or reach in the watershed. The user must input the type of water source, the location of the source, the type of water body receiving the transfer, the location of the receiving water body, and the amount of water transferred.
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Three options are provided to specify the amount of water transferred: a fraction of the volume of water in the source; a volume of water left in the source; or the volume of water transferred. The transfer is performed every day of the simulation. The transfer of water from one water body to another can be accomplished using other methods. For example, water could be removed from one water body via consumptive water use and added to another water body using point source files. Table 21-3: SWAT input variables that pertain to water transfer. Variable Name DEP_TYPE DEP_NUM DEST_TYPE DEST_NUM TRANS_AMT TRANS_CODE
Definition Water source type Water source location Destination type Destination location Amount of water transferred Rule code governing water transfer.
Input File .fig .fig .fig .fig .fig .fig
21.5 CONSUMPTIVE WATER USE Consumptive water use is a management tool that removes water from the basin. Water removed for consumptive use is considered to be lost from the system. SWAT allows water to be removed from the shallow aquifer, the deep aquifer, the reach or the pond within any subbasin in the watershed. Water also may be removed from reservoirs for consumptive use. Consumptive water use is allowed to vary from month to month. For each month in the year, an average daily volume of water removed from the source is specified. For reservoirs, the user may also specify a fraction of the water removed that is lost during removal. The water lost in the removal process becomes outflow from the reservoir.
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Table 21-4: SWAT input variables that pertain to consumptive water use. Variable Name WUPND(1-12) WURCH(1-12) WUSHAL(1-12) WUDEEP(1-12) WURESN(1-12) WURTNF
Definition Average daily water removal from pond in subbasin (104 m3) Average daily water removal from reach in subbasin (104 m3) Average daily water removal from shallow aquifer in subbasin (104 m3) Average daily water removal from deep aquifer in subbasin (104 m3) Average daily water removal from reservoir (104 m3) Fraction of water removal lost in transfer and returned as reservoir outflow.
Input File .wus .wus .wus .wus .res .res
21.6 POINT SOURCE LOADINGS SWAT directly simulates the loading of water, sediment and other constituents off of land areas in the watershed. To simulate the loading of water and pollutants from sources not associated with a land area (e.g. sewage treatment plants), SWAT allows point source information to be read in at any point along the channel network. The point source loadings may be summarized on a daily, monthly, yearly, or average annual basis. Files containing the point source loads are created by the user. The loads are read into the model and routed through the channel network using recday, recmon, recyear, or reccnst commands in the watershed configuration file. SWAT will read in water, sediment, organic N, organic P, nitrate, soluble P, ammonium, nitrite, metal, and bacteria data from the point source files. Chapter 31 reviews the format of the command lines in the watershed configuration file while Chapter 43 reviews the format of the point source files.
21.7 NOMENCLATURE SWly FCly
Water content of the layer on a given day (mm H2O) Field capacity water content of the layer (mm H2O)
tdrain Time required to drain the soil to field capacity (hrs) tilewtr Amount of water removed from the layer on a given day by tile drainage (mm H2O)
CHAPTER 22
EQUATIONS: URBAN AREAS
Most large watersheds and river basins contain areas of urban land use. Estimates of the quantity and quality of runoff in urban areas are required for comprehensive management analysis. SWAT calculates runoff from urban areas with the SCS curve number method or the Green & Ampt equation. Loadings of sediment and nutrients are determined using one of two options. The first is a set of linear regression equations developed by the USGS (Driver and Tasker, 1988) for estimating storm runoff volumes and constituent loads. The other option is to simulate the buildup and washoff mechanisms, similar to SWMM - Storm Water Management Model (Huber and Dickinson, 1988).
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22.1 CHARACTERISTICS OF URBAN AREAS Urban areas differ from rural areas in the fraction of total area that is impervious. Construction of buildings, parking lots and paved roads increases the impervious cover in a watershed and reduces infiltration. With development, the spatial flow pattern of water is altered and the hydraulic efficiency of flow is increased through artificial channels, curbing, and storm drainage and collection systems. The net effect of these changes is an increase in the volume and velocity of runoff and larger peak flood discharges. Impervious areas can be differentiated into two groups: the area that is hydraulically connected to the drainage system and the area that is not directly connected. As an example, assume there is a house surrounded by a yard where runoff from the roof flows into the yard and is able to infiltrate into the soil. The rooftop is impervious but it is not hydraulically connected to the drainage system. In contrast, a parking lot whose runoff enters a storm water drain is hydraulically connected. Table 22-1 lists typical values for impervious and directly connected impervious fractions in different urban land types. Table 22-1: Range and average impervious fractions for different urban land types. Average directly Range directly connected connected Average total Range total impervious impervious Urban Land Type impervious impervious Residential-High Density (> 8 unit/acre or unit/2.5 ha) .60 .44 - .82 .44 .32 - .60 Residential-Medium Density (1-4 unit/acre or unit/2.5 ha) .38 .23 - .46 .30 .18 - .36 Residential-Med/Low Density (> 0.5-1 unit/acre or unit/2.5 ha) .20 .14 - .26 .17 .12 - .22 Residential-Low Density (< 0.5 unit/acre or unit/2.5 ha) .12 .07 - .18 .10 .06 - .14 Commercial .67 .48 - .99 .62 .44 - .92 Industrial .84 .63 - .99 .79 .59 - .93 Transportation .98 .88 - 1.00 .95 .85 – 1.00 Institutional .51 .33 - .84 .47 .30 - .77
During dry periods, dust, dirt and other pollutants build up on the impervious areas. When precipitation events occur and runoff from the impervious areas is generated, the runoff will carry the pollutants as it moves through the drainage system and enters the channel network of the watershed.
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337
22.2 SURFACE RUNOFF FROM URBAN AREAS In urban areas, surface runoff is calculated separately for the directly connected impervious area and the disconnected impervious/pervious area. For directly connected impervious areas, a curve number of 98 is always used. For disconnected impervious/pervious areas, a composite curve number is calculated and used in the surface runoff calculations. The equations used to calculate the composite curve number for disconnected impervious/pervious areas are (Soil Conservation Service Engineering Division, 1986): impdcon ö æ æ impdcon ö CN p ⋅ ç1 − imptot + ÷ + 98 ⋅ ç ÷ 2 ø 2 ø è è if imptot ≤ 0.30 CN c = 1 − impcon
CN c =
CN p ⋅ (1 − imptot ) + 98 ⋅ impdcon 1 − impcon
if imptot > 0.30
22.2.1
22.2.2
where CNc is the composite moisture condition II curve number, CNp is the pervious moisture condition II curve number, imptot is the fraction of the HRU area that is impervious (both directly connected and disconnected), impcon is the fraction of the HRU area that is impervious and hydraulically connected to the drainage system, impdcon is the fraction of the HRU area that is impervious but not hydraulically connected to the drainage system. Table 22-2: SWAT input variables that pertain to surface runoff calculations in urban areas. Input Variable Name Definition File CN2 CNp: SCS moisture condition II curve number for pervious areas .mgt CNOP CNp: SCS moisture condition II curve number for pervious areas .mgt specified in plant, harvest/kill and tillage operation urban.dat FIMP imptot: fraction of urban land type area that is impervious FCIMP impcon: fraction of urban land type area that is connected urban.dat impervious
22.3 USGS REGRESSION EQUATIONS The linear regression models incorporated into SWAT are those described by Driver and Tasker (1988). The regression models were developed from a national urban water quality database that related storm runoff loads to urban
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physical, land use, and climatic characteristics. USGS developed these equations to predict loadings in ungaged urban watersheds. The regression models calculate loadings as a function of total storm rainfall, drainage area and impervious area. The general equation is
β 0 ⋅ (Rday 25.4 )β ⋅ (DA 2.59 )β ⋅ (imptot ⋅ 100 + 1)β ⋅ β 4 Y = 2.205 1
2
3
22.3.1
where Y is the total constituent load (kg), Rday is precipitation on a given day (mm H2O), DA is the HRU drainage area (km2), imptot is the fraction of the total area that is impervious, and the β variables are regression coefficients. The regression equations were developed in English units, so conversion factors were incorporated to adapt the equations to metric units: 25.4 mm/inch, 2.59 km2/mi2, and 2.205 lb/kg. USGS derived three different sets of regression coefficients that are based on annual precipitation. Category I coefficients are used in watersheds with less than 508 mm of annual precipitation. Category II coefficients are used in watersheds with annual precipitation between 508 and 1016 mm. Category III coefficients are used in watersheds with annual precipitation greater than 1016 mm. SWAT determines the annual precipitation category for each subbasin by summing the monthly precipitation totals provided in the weather generator input file. Regression coefficients were derived to estimate suspended solid load, total nitrogen load, total phosphorus load and carbonaceous oxygen demand (COD). SWAT calculates suspended solid, total nitrogen, and total phosphorus loadings (the carbonaceous oxygen demand is not currently calculated). Regression coefficients for these constituents are listed in Table 22-3. Once total nitrogen and phosphorus loads are calculated, they are partitioned into organic and mineral forms using the following relationships from Northern Virginia Planning District Commission (1979). Total nitrogen loads consist of 70 percent organic nitrogen and 30 percent mineral (nitrate). Total phosphorus loads are divided into 75 percent organic phosphorus and 25 percent orthophosphate.
CHAPTER 22: EQUATIONS—URBAN AREAS Table 22-3: Urban regression coefficients (from Driver and Tasker, 1988). Precipitation Loading Category β0 β1 β2 β3 suspended solids I 1778.0 0.867 0.728 0.157 II 812.0 1.236 0.436 0.202 III 97.7 1.002 1.009 0.837
β4 2.367 1.938 2.818
total nitrogen
I II III
20.20 4.04 1.66
0.825 0.936 0.703
1.070 0.937 0.465
0.479 0.692 0.521
1.258 1.373 1.845
total phosphorus
I II III
1.725 0.697 1.618
0.884 1.008 0.954
0.826 0.628 0.789
0.467 0.469 0.289
2.130 1.790 2.247
I 407.0 0.626 II 151.0 0.823 III 102.0 0.851 I = annual precipitation < 508 mm II = 508 mm < annual precipitation < 1,016 mm III = annual precipitation > 1,016 mm
0.710 0.726 0.601
0.379 0.564 0.528
1.518 1.451 1.978
COD
339
Table 22-4: SWAT input variables that pertain to urban modeling with regression equations. Input Variable Name Definition File IURBAN Urban simulation code .hru URBLU Urban land type identification number from urban database .hru FIMP urban.dat Fraction of HRU that is impervious. imptot = FIMP ⋅ 100 PRECIPITATION Rday: Precipitation on a given day (mm H2O) .pcp HRU_FR Fraction of total watershed area in HRU .hru DA_KM Area of watershed (km2) .bsn PCPMM(mon) Average amount of precipitation falling in month (mm H2O) .wgn
22.4 BUILD UP/WASH OFF In an impervious area, dust, dirt and other constituents are built up on street surfaces in periods of dry weather preceding a storm. Build up may be a function of time, traffic flow, dry fallout and street sweeping. During a storm runoff event, the material is then washed off into the drainage system. Although the build up/wash off option is conceptually appealing, the reliability and credibility of the simulation may be difficult to establish without local data for calibration and validation (Huber and Dickinson, 1988). When the build up/wash off option is used in SWAT, the urban hydrologic response unit (HRU) is divided into pervious and impervious areas. Management operations other than sweep operations are performed in the pervious portion of
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the HRU. Sweep operations impact build up of solids in the impervious portion of the HRU. For the pervious portion of the HRU, sediment and nutrient loadings are calculated using the methodology summarized in Chapters 13 and 14. The impervious portion of the HRU uses the build up/wash off algorithm to determine sediment and nutrient loadings. The build up/wash off algorithm calculates the build up and wash off of solids. The solids are assumed to possess a constant concentration of organic and mineral nitrogen and phosphorus where the concentrations are a function of the urban land type. Build up of solids is simulated on dry days with a Michaelis-Menton equation: SED =
SEDmx ⋅ td (thalf + td )
22.4.1
where SED is the solid build up (kg/curb km) td days after the last occurrence of SED = 0 kg/curb km, SEDmx is the maximum accumulation of solids possible for the urban land type (kg/curb km), and thalf is the length of time needed for solid build up to increase from 0 kg/curb km to ½ SEDmx (days). A dry day is defined as a day with surface runoff less than 0.1 mm. An example build-up curve is shown in Figure 22-1. As can be seen from the plot, the Michaelis-Menton function will initially rise steeply and then approach the asymptote slowly. 100 90 80 70 60 50 40 30 20 10 0
Figure 22-1: Build-up function for solids in urban areas.
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341
The two parameters that determine the shape of this curve are SEDmx and thalf. These parameters are a function of the urban land type. Wash off is the process of erosion or solution of constituents from an impervious surface during a runoff event. An exponential relationship is used to simulate the wash off process (Huber and Dickinson, 1988): Ysed = SED0 ⋅ (1 − e − kk ⋅t ) 22.4.2 where Ysed is the cumulative amount of solids washed off at time t (kg/curb km),
SED0 is the amount of solids built up on the impervious area at the beginning of the precipitation event (kg/curb km), and kk is a coefficient. The coefficient, kk, may be estimated by assuming it is proportional to the peak runoff rate: kk = urbcoef ⋅ q peak
22.4.3
where urbcoef is the wash off coefficient (mm-1) and qpeak is the peak runoff rate (mm/hr). The original default value for urbcoef was calculated as 0.18 mm-1 by assuming that 13 mm of total runoff in one hour would wash off 90% of the initial surface load. Later estimates of urbcoef gave values ranging from 0.002-0.26 mm-1. Huber and Dickinson (1988) noted that values between 0.039 and 0.390 mm-1 for urbcoef give sediment concentrations in the range of most observed values. They also recommended using this variable to calibrate the model to observed data. To convert the sediment loading from units of kg/curb km to kg/ha, the amount of sediment removed by wash off is multiplied by the curb length density. The curb length density is a function of the urban land type. Nitrogen and phosphorus loadings from the impervious portion of the urban land area are calculated by multiplying the concentration of nutrient by the sediment loading.
22.4.1 STREET CLEANING Street cleaning is performed in urban areas to control buildup of solids and trash. While it has long been thought that street cleaning has a beneficial effect on the quality of urban runoff, studies by EPA have found that street sweeping has
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little impact on runoff quality unless it is performed every day (U.S. Environmental Protection Agency, 1983). SWAT performs street sweeping operations only when the build up/wash off algorithm is specified for urban loading calculations. Street sweeping is performed only on dry days, where a dry day is defined as a day with less than 0.1 mm of surface runoff. The sweeping removal equation (Huber and Dickinson, 1988) is: SED = SED0 ⋅ (1 − frav ⋅ reff )
22.4.4
where SED is amount of solids remaining after sweeping (kg/curb km), SED0 is the amount of solids present prior to sweeping (kg/curb km), frav is the fraction of the curb length available for sweeping (the availability factor), and reff is the removal efficiency of the sweeping equipment. The availability factor and removal efficiency are specified by the user. Table 22-5: Removal efficiencies (fraction removed) from street cleaner path (from Pitt, 1979) COD KN PO4 Pesticides Street Cleaning Program and Total BOD5 Street Surface Loading Conditions Solids Vacuum Street Cleaner (5.5-55 kg/curb km)
1 pass 2 passes 3 passes
.31 .45 .53
.24 .35 .41
.16 .22 .27
.26 .37 .45
.08 .12 .14
.33 .50 .59
1 pass 2 passes 3 passes
.37 .51 .58
.29 .42 .47
.21 .29 .35
.31 .46 .51
.12 .17 .20
.40 .59 .67
1 pass 2 passes 3 passes
.48 .60 .63
.38 .50 .52
.33 .42 .44
.43 .54 .57
.20 .25 .26
.57 .72 .75
1 pass 2 passes 3 passes
.54 .75 .85
.40 .58 .69
.31 .48 .59
.40 .58 .69
.20 .35 .46
.40 .60 .72
Flusher
.30
a
a
a
a
a
Mechanical Street Cleaner followed by a Flusher
.80
b
b
b
b
b
Vacuum Street Cleaner (55-280 kg/curb km)
Vacuum Street Cleaner (280-2820 kg/curb km)
Mechanical Street Cleaner (50-500 kg/curb km)
a: efficiency fraction estimated .15 to .40 b: efficiency fraction estimated .35 to 1.00
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343
The availability factor, frav, is the fraction of the curb length that is sweepable. The entire curb length is often not available for sweeping due to the presence of cars and other obstacles. The removal efficiency of street sweeping is a function of the type of sweeper, whether flushing is a part of the street cleaning process, the quantity of total solids, the frequency of rainfall events and the constituents considered. Removal efficiency can vary depending on the constituent being considered, with efficiencies being greater for particulate constituents. The removal efficiencies for nitrogen and phosphorus are typically less than the solid removal efficiency (Pitt, 1979). Because SWAT assumes a set concentration of nutrient constituents in the solids, the same removal efficiency is in effect used for all constituents. Table 225 provides removal efficiencies for various street cleaning programs. Table 22-6: SWAT input variables that pertain to build up/wash off. Variable Name IURBAN URBLU DIRTMX THALF URBCOEF CURBDEN TNCONC TPCONC TNO3CONC SWEEPEFF AVWSP
Definition Urban simulation code Urban land type identification number from urban database SEDmx: maximum amount of solids allowed to build up on impervious areas (kg/curb km) thalf: number of days for amount of solids on impervious area to build up from 0 kg/curb km to ½ SEDmx urbcoef: wash off coefficient (mm-1) curb length density in urban land type (km/ha) concentration of total nitrogen in suspended solid load (mg N/kg) concentration of total phosphorus in suspended solid load (mg N/kg) concentration of nitrate in suspended solid load (mg N/kg) reff: removal efficiency of the sweeping equipment frav: fraction of the curb length that is sweepable.
Input File .hru .hru urban.dat urban.dat urban.dat urban.dat urban.dat urban.dat urban.dat .mgt .mgt
22.5 NOMENCLATURE CN DA Rday SED SEDmx Y Ysed
Curve number HRU drainage area (km2) Amount of rainfall on a given day (mm H2O) Solid build up (kg/curb km) Maximum accumulation of solids possible for the urban land type (kg/curb km) Total constituent load (kg) Cumulative amount of solids washed off at time t (kg/curb km)
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frav Fraction of the curb length available for sweeping (the availability factor) impcon Fraction of the HRU area that is impervious and hydraulically connected to the drainage system impdcon Fraction of the HRU area that is impervious but not hydraulically connected to the drainage system imptot Fraction of the HRU area that is impervious (both connected and disconnected) kk Coefficient in urban wash off equation qpeak Peak runoff rate (mm/hr) reff Removal efficiency of the sweeping equipment Length of time needed for solid build up to increase from 0 kg/curb km to ½ thalf SEDmx (days) urbcoef Wash off coefficient (mm-1)
β0 β1 β2 β3 β4
Coefficient for USGS regression equations for urban loadings Coefficient for USGS regression equations for urban loadings Coefficient for USGS regression equations for urban loadings Coefficient for USGS regression equations for urban loadings Coefficient for USGS regression equations for urban loadings
22.6 REFERENCES Driver, N.E. and G.D. Tasker. 1988. Techniques for estimation of storm-runoff loads, volumes, and selected constituent concentrations in urban watersheds in the United States. U.S. Dept. of the Interior, U.S. Geological Survey: Books and Open-File Reports Section 88-191. Huber, W.C. and R.E. Dickinson. 1988. Storm water management model, version 4: user’s manual. U.S. Environmental Protection Agency, Athens, GA. Northern Virginia Planning District Commission. 1979. Guidebook for screening urban nonpoint pollution management strategies: a final report prepared for Metropolitan Washington Council of Governments. Northern Virginia Planning District Commission, Falls Church, VA. Pitt, R. 1979. Demonstration of non-point pollution abatement through improved street cleaning practices. EPA-600/2-79-161 (NTIS PB80-108988), U.S. Environmental Protection Agency, Cincinnati, OH. Soil Conservation Service Engineering Division. 1986. Urban hydrology for small watersheds. U.S. Department of Agriculture, Technical Release 55.
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U.S. Environmental Protection Agency. 1983. Results of the nationwide urban runoff program; Volume 1 final report. NTIS PB84-185552, U.S. Environmental Protection Agency, Washington, D.C.
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MAIN CHANNEL PROCESSES Flow in a watershed is classified as overland or channelized. The primary difference between the two flow processes is that water storage and its influence on flow rates is considered in channelized flow. Main channel processes modeled by SWAT include the movement of water, sediment and other constituents (e.g. nutrients, pesticides) in the stream network, in-stream nutrient cycling, and in-stream pesticide transformations. Optional processes include the change in channel dimensions with time due to downcutting and widening.
CHAPTER 23
EQUATIONS: WATER ROUTING
Open channel flow is defined as channel flow with a free surface, such as flow in a river or partially full pipe. SWAT uses Manning’s equation to define the rate and velocity of flow. Water is routed through the channel network using the variable storage routing method or the Muskingum river routing method. Both the variable storage and Muskingum routing methods are variations of the kinematic wave model. A detailed discussion of the kinematic wave flood routing model can be found in Chow et al. (1988).
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23.1 CHANNEL CHARACTERISTICS SWAT assumes the main channels, or reaches, have a trapezoidal shape (Figure 23-1).
Figure 23-1: Trapezoidal channel dimensions
Users are required to define the width and depth of the channel when filled to the top of the bank as well as the channel length, slope along the channel length and Manning’s “n” value. SWAT assumes the channel sides have a 2:1 run to rise ratio (zch = 2). The slope of the channel sides is then ½ or 0.5. The bottom width is calculated from the bankfull width and depth with the equation: Wbtm = Wbnkfull − 2 ⋅ z ch ⋅ depthbnkfull
23.1.1
where Wbtm is the bottom width of the channel (m), Wbnkfull is the top width of the channel when filled with water (m), zch is the inverse of the channel side slope, and depthbnkfull is the depth of water in the channel when filled to the top of the bank (m). Because of the assumption that zch = 2, it is possible for the bottom width calculated with equation 23.1.1 to be less than or equal to zero. If this occurs, the model sets Wbtm = 0.5 ⋅ Wbnkfull and calculates a new value for the channel side slope run by solving equation 23.1.1 for zch: z ch =
(W
bnkfull
− Wbtm )
2 ⋅ depthbnkfull
23.1.2
For a given depth of water in the channel, the width of the channel at water level is:
CHAPTER 23: EQUATIONS—WATER ROUTING
W = Wbtm + 2 ⋅ z ch ⋅ depth
351
23.1.3
where W is the width of the channel at water level (m), Wbtm is the bottom width of the channel (m), zch is the inverse of the channel slope, and depth is the depth of water in the channel (m). The cross-sectional area of flow is calculated: Ach = (Wbtm + z ch ⋅ depth ) ⋅ depth
23.1.4
where Ach is the cross-sectional area of flow in the channel (m2), Wbtm is the bottom width of the channel (m), zch is the inverse of the channel slope, and depth is the depth of water in the channel (m). The wetted perimeter of the channel is defined as Pch = Wbtm + 2 ⋅ depth ⋅ 1 + z ch
2
23.1.5
where Pch is the wetted perimeter for a given depth of flow (m). The hydraulic radius of the channel is calculated Rch =
Ach Pch
23.1.6
where Rch is the hydraulic radius for a given depth of flow (m), Ach is the crosssectional area of flow in the channel (m2), and Pch is the wetted perimeter for a given depth of flow (m). The volume of water held in the channel is Vch = 1000 ⋅ Lch ⋅ Ach
23.1.7
where Vch is the volume of water stored in the channel (m3), Lch is the channel length (km), and Ach is the cross-sectional area of flow in the channel for a given depth of water (m2). When the volume of water in the reach exceeds the maximum amount that can be held by the channel, the excess water spreads across the flood plain. The flood plain dimensions used by SWAT are shown in Figure 23-2.
Figure 23-2: Illustration of flood plain dimensions.
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The bottom width of the floodplain, Wbtm,fld, is Wbtm , fld = 5 ⋅ Wbnkfull . SWAT assumes the flood plain side slopes have a 4:1 run to rise ratio (zfld = 4). The slope of the flood plain sides is then ¼ or 0.25. When flow is present in the flood plain, the calculation of the flow depth, cross-sectional flow area and wetting perimeter is a sum of the channel and floodplain components: depth = depthbnkfull + depth fld
23.1.8
Ach = (Wbtm + z ch ⋅ depthbnkfull ) ⋅ depthbnkfull + (Wbtm , fld + z fld ⋅ depth fld ) ⋅ depth fld 23.1.9 Pch = Wbtm + 2 ⋅ depthbnkfull ⋅ 1 + z ch + 4 ⋅ Wbnkfull + 2 ⋅ depth fld ⋅ 1 + z fld 2
2
23.1.10
where depth is the total depth of water (m), depthbnkfull is the depth of water in the channel when filled to the top of the bank (m), depthfld is the depth of water in the flood plain (m), Ach is the cross-sectional area of flow for a given depth of water (m2), Wbtm is the bottom width of the channel (m), zch is the inverse of the channel side slope, Wbtm,fld is the bottom width of the flood plain (m), zfld is the inverse of the flood plain side slope, Pch is the wetted perimeter for a given depth of flow (m), and Wbnkfull is the top width of the channel when filled with water (m). Table 23-1: SWAT input variables that pertain to channel dimension calculations. Variable name Definition CH_W(2) Wbnkfull: Width of channel at top of bank (m) CH_D depthbnkfull: Depth of water in channel when filled to bank (m) CH_L(2) Lch: Length of main channel (km)
File Name .rte .rte .rte
23.2 FLOW RATE AND VELOCITY Manning’s equation for uniform flow in a channel is used to calculate the rate and velocity of flow in a reach segment for a given time step: qch = vc =
Ach ⋅ Rch
23
⋅ slpch
n Rch
23
⋅ slpch n
12
23.2.1
12
23.2.2
where qch is the rate of flow in the channel (m3/s), Ach is the cross-sectional area of flow in the channel (m2), Rch is the hydraulic radius for a given depth of flow (m),
CHAPTER 23: EQUATIONS—WATER ROUTING
353
slpch is the slope along the channel length (m/m), n is Manning’s “n” coefficient for the channel, and vc is the flow velocity (m/s). SWAT routes water as a volume. The daily value for cross-sectional area of flow, Ach, is calculated by rearranging equation 23.1.7 to solve for the area: Ach =
Vch 1000 ⋅ Lch
23.2.3
where Ach is the cross-sectional area of flow in the channel for a given depth of water (m2), Vch is the volume of water stored in the channel (m3), and Lch is the channel length (km). Equation 23.1.4 is rearranged to calculate the depth of flow for a given time step: 2
depth =
Ach æ Wbtm ö W ÷÷ − btm + çç 2 ⋅ z ch z ch è 2 ⋅ z ch ø
23.2.4
where depth is the depth of flow (m), Ach is the cross-sectional area of flow in the channel for a given depth of water (m2), Wbtm is the bottom width of the channel (m), and zch is the inverse of the channel side slope. Equation 23.2.4 is valid only when all water is contained in the channel. If the volume of water in the reach segment has filled the channel and is in the flood plain, the depth is calculated: depth = depthbnkfull +
(A
ch
− Ach ,bnkfull ) æ Wbtm , fld +ç ç 2⋅z z fld fld è
2
ö W ÷ − btm , fld ÷ 2 ⋅ z fld ø
23.2.5
where depth is the depth of flow (m), depthbnkfull is the depth of water in the channel when filled to the top of the bank (m), Ach is the cross-sectional area of flow in the channel for a given depth of water (m2), Ach,bnkfull is the cross-sectional area of flow in the channel when filled to the top of the bank (m2), Wbtm,fld is the bottom width of the flood plain (m), and zfld is the inverse of the flood plain side slope. Once the depth is known, the wetting perimeter and hydraulic radius are calculated using equations 23.1.5 (or 23.1.10) and 23.1.6. At this point, all values required to calculate the flow rate and velocity are known and equations 23.2.1 and 23.2.2 can be solved.
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Table 23-2: SWAT input variables that pertain to channel flow calculations. Variable name Definition CH_S(2) slpch: Average channel slope along channel length (m m-1) CH_N(2) n: Manning’s “n” value for the main channel CH_L(2) Lch: Length of main channel (km)
File Name .rte .rte .rte
23.3 VARIABLE STORAGE ROUTING METHOD The variable storage routing method was developed by Williams (1969) and used in the HYMO (Williams and Hann, 1973) and ROTO (Arnold et al., 1995) models. For a given reach segment, storage routing is based on the continuity equation: Vin − Vout = ∆Vstored
23.3.1
where Vin is the volume of inflow during the time step (m3 H2O), Vout is the volume of outflow during the time step (m3 H2O), and ∆Vstored is the change in volume of storage during the time step (m3 H2O). This equation can be written as æ q + qin , 2 ö æ q + qout , 2 ö ∆t ⋅ çç in ,1 ÷÷ − ∆t ⋅ çç out ,1 ÷÷ = Vstored ,2 − Vstored ,1 2 2 è ø è ø
23.3.2
where ∆t is the length of the time step (s), qin,1 is the inflow rate at the beginning of the time step (m3/s), qin,2 is the inflow rate at the end of the time step (m3/s), qout,1 is the outflow rate at the beginning of the time step (m3/s), qout,2 is the outflow rate at the end of the time step (m3/s), Vstored,1 is the storage volume at the beginning of the time step (m3 H2O), and Vstored,2 is the storage volume at the end of the time step (m3 H2O). Rearranging equation 23.3.2 so that all known variables are on the left side of the equation, qin ,ave +
Vstored ,1 ∆t
−
qout ,1 2
=
Vstored , 2 ∆t
+
qout , 2
23.3.3
2
where qin,ave is the average inflow rate during the time step: qin ,ave =
qin ,1 + qin , 2 2
.
Travel time is computed by dividing the volume of water in the channel by the flow rate.
CHAPTER 23: EQUATIONS—WATER ROUTING
TT =
Vstored Vstored ,1 Vstored , 2 = = qout qout ,1 qout , 2
355
23.3.4
where TT is the travel time (s), Vstored is the storage volume (m3 H2O), and qout is the discharge rate (m3/s). To obtain a relationship between travel time and the storage coefficient, equation 23.3.4 is substituted into equation 23.3.3: qin ,ave +
Vstored ,1 æ ∆t ö æç Vstored ,1 ö÷ ç ÷⋅ è TT ø çè qout ,1 ÷ø
−
qout ,1 Vstored , 2 q = + out , 2 2 2 æ ∆t ö æç Vstored , 2 ö÷ ç ÷⋅ç è TT ø è qout , 2 ÷ø
23.3.5
which simplifies to 2 ⋅ ∆t ö æ 2 ⋅ ∆t ö æ qout , 2 = ç ÷ ⋅ qin ,ave + ç 1 − ÷ ⋅ qout ,1 2 ⋅ TT + ∆t ø è 2 ⋅ TT + ∆t ø è
23.3.6
This equation is similar to the coefficient method equation qout , 2 = SC ⋅ qin ,ave + (1 − SC ) ⋅ qout ,1
23.3.7
where SC is the storage coefficient. Equation 23.3.7 is the basis for the SCS convex routing method (SCS, 1964) and the Muskingum method (Brakensiek, 1967; Overton, 1966). From equation 23.3.6, the storage coefficient in equation 23.3.7 is defined as SC =
2 ⋅ ∆t 2 ⋅ TT + ∆t
23.3.8
It can be shown that
(1 − SC ) ⋅ qout
= SC ⋅
Vstored ∆t
23.3.9
Substituting this into equation 23.3.7 gives V æ ö qout , 2 = SC ⋅ çç qin ,ave + stored ,1 ÷÷ ∆t ø è
23.3.10
To express all values in units of volume, both sides of the equation are multiplied by the time step Vout ,2 = SC ⋅ (Vin + Vstored ,1 )
23.3.11
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23.4 MUSKINGUM ROUTING METHOD The Muskingum routing method models the storage volume in a channel length as a combination of wedge and prism storages (Figure 23-3).
Figure 23-3: Prism and wedge storages in a reach segment (from Chow et al., 1988)
When a flood wave advances into a reach segment, inflow exceeds outflow and a wedge of storage is produced. As the flood wave recedes, outflow exceeds inflow in the reach segment and a negative wedge is produced. In addition to the wedge storage, the reach segment contains a prism of storage formed by a volume of constant cross-section along the reach length. As defined by Manning’s equation (equation 23.2.1), the cross-sectional area of flow is assumed to be directly proportional to the discharge for a given reach segment. Using this assumption, the volume of prism storage can be expressed as a function of the discharge, K ⋅ qout , where K is the ratio of storage to discharge and has the dimension of time. In a similar manner, the volume of wedge storage can be expressed as K ⋅ X ⋅ (qin − qout ) , where X is a weighting factor that controls the relative importance of inflow and outflow in determining the storage in a reach. Summing these terms gives a value for total storage Vstored = K ⋅ qout + K ⋅ X ⋅ (qin − qout )
23.4.1
CHAPTER 23: EQUATIONS—WATER ROUTING
357
where Vstored is the storage volume (m3 H2O), qin is the inflow rate (m3/s), qout is the discharge rate (m3/s), K is the storage time constant for the reach (s), and X is the weighting factor. This equation can be rearranged to the form Vstored = K ⋅ ( X ⋅ qin + (1 − X ) ⋅ qout )
23.4.2
This format is similar to equation 23.3.7. The weighting factor, X, has a lower limit of 0.0 and an upper limit of 0.5. This factor is a function of the wedge storage. For reservoir-type storage, there is no wedge and X = 0.0. For a full-wedge, X = 0.5. For rivers, X will fall between 0.0 and 0.3 with a mean value near 0.2. The definition for storage volume in equation 23.4.2 can be incorporated into the continuity equation (equation 23.3.2) and simplified to qout , 2 = C1 ⋅ qin , 2 + C 2 ⋅ qin ,1 + C3 ⋅ qout ,1
23.4.3
where qin,1 is the inflow rate at the beginning of the time step (m3/s), qin,2 is the inflow rate at the end of the time step (m3/s), qout,1 is the outflow rate at the beginning of the time step (m3/s), qout,2 is the outflow rate at the end of the time step (m3/s), and C1 =
∆t − 2 ⋅ K ⋅ X 2 ⋅ K ⋅ (1 − X ) + ∆t
23.4.4
C2 =
∆t + 2 ⋅ K ⋅ X 2 ⋅ K ⋅ (1 − X ) + ∆t
23.4.5
C3 =
2 ⋅ K ⋅ (1 − X ) − ∆t 2 ⋅ K ⋅ (1 − X ) + ∆t
23.4.6
where C1 + C2 + C3 = 1 . To express all values in units of volume, both sides of equation 23.4.3 are multiplied by the time step Vout , 2 = C1 ⋅ Vin , 2 + C2 ⋅ Vin ,1 + C3 ⋅ Vout ,1
23.4.7
To maintain numerical stability and avoid the computation of negative outflows, the following condition must be met:
2 ⋅ K ⋅ X < ∆t < 2 ⋅ K ⋅ (1 − X )
23.4.8
The value for the weighting factor, X, is input by the user. The value for the storage time constant is estimated as:
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K = coef 1 ⋅ K bnkfull + coef 2 ⋅ K 0.1bnkfull
23.4.9
where K is the storage time constant for the reach segment (s), coef1 and coef2 are weighting coefficients input by the user, Kbnkfull is the storage time constant calculated for the reach segment with bankfull flows (s), and K0.1bnkfull is the storage time constant calculated for the reach segment with one-tenth of the bankfull flows (s). To calculate Kbnkfull and K0.1bnkfull, an equation developed by Cunge (1969) is used: K=
1000 ⋅ Lch ck
23.4.10
where K is the storage time constant (s), Lch is the channel length (km), and ck is the celerity corresponding to the flow for a specified depth (m/s). Celerity is the velocity with which a variation in flow rate travels along the channel. It is defined as ck =
d (qch ) dAch
23.4.11
where the flow rate, qch, is defined by Manning’s equation. Differentiating equation 23.2.1 with respect to the cross-sectional area gives ck =
23 12 5 æ Rch ⋅ slpch ö 5 ÷ = ⋅ vc ⋅ çç ÷ 3 3 è n ø
23.4.12
where ck is the celerity (m/s), Rch is the hydraulic radius for a given depth of flow (m), slpch is the slope along the channel length (m/m), n is Manning’s “n” coefficient for the channel, and vc is the flow velocity (m/s). Table 23-3: SWAT input variables that pertain to Muskingum routing. Variable name Definition MSK_X X: weighting factor MSK_CO1 coef1: weighting factor for influence of normal flow on storage time constant value MSK_CO2 coef2: weighting factor for influence of low flow on storage time constant
File Name .bsn .bsn .bsn
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359
23.5 TRANSMISSION LOSSES The classification of a stream as ephemeral, intermittent or perennial is a function of the amount of groundwater contribution received by the stream. Ephemeral streams contain water during and immediately after a storm event and are dry the rest of the year. Intermittent streams are dry part of the year, but contain flow when the groundwater is high enough as well as during and after a storm event. Perennial streams receive continuous groundwater contributions and flow throughout the year. During periods when a stream receives no groundwater contributions, it is possible for water to be lost from the channel via transmission through the side and bottom of the channel. Transmission losses are estimated with the equation tloss = K ch ⋅ TT ⋅ Pch ⋅ Lch
23.5.1
where tloss are the channel transmission losses (m3 H2O), Kch is the effective hydraulic conductivity of the channel alluvium (mm/hr), TT is the flow travel time (hr), Pch is the wetted perimeter (m), and Lch is the channel length (km). Transmission losses from the main channel are assumed to enter bank storage or the deep aquifer. Typical values for Kch for various alluvium materials are given in Table 23-4. For perennial streams with continuous groundwater contribution, the effective conductivity will be zero. Table 23-4: Example hydraulic conductivity values for various bed materials (from Lane, 1983). Bed material Bed material characteristics Hydraulic group conductivity 1 Very high loss rate 2 High loss rate 3 Moderately high loss rate 4 Moderate loss rate 5 Insignificant to low loss rate
Very clean gravel and large sand
> 127 mm/hr
Clean sand and gravel, field conditions
51-127 mm/hr
Sand and gravel mixture with low silt-clay content
25-76 mm/hr
Sand and gravel mixture with high silt-clay content
6-25 mm/hr
Consolidated bed material; high silt-clay content
0.025-2.5 mm/hr
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Table 23-5: SWAT input variables that pertain to transmission losses. Variable name Definition CH_K(2) Kch: Effective hydraulic conductivity of channel (mm/hr) CH_L(2) Lch: Length of main channel (km)
File Name .rte .rte
23.6 EVAPORATION LOSSES Evaporation losses from the reach are calculated: E ch = coef ev ⋅ Eo ⋅ Lch ⋅ W ⋅ fr∆t
23.6.1
where Ech is the evaporation from the reach for the day (m3 H2O), coefev is an evaporation coefficient, Eo is potential evaporation (mm H2O), Lch is the channel length (km), W is the channel width at water level (m), and fr∆t is the fraction of the time step in which water is flowing in the channel. The evaporation coefficient is a calibration parameter for the user and is allowed to vary between 0.0 and 1.0. The fraction of the time step in which water is flowing in the channel is calculated by dividing the travel time by the length of the time step. Table 23-6: SWAT input variables that pertain to evaporation losses. Variable name Definition EVRCH coefev: Reach evaporation adjustment factor CH_L(2) Lch: Length of main channel (km)
File Name .bsn .rte
23.7 BANK STORAGE The amount of water entering bank storage on a given day is calculated: bnk in = tloss ⋅ (1 − frtrns )
23.7.1
where bnkin is the amount of water entering bank storage (m3 H2O), tloss are the channel transmission losses (m3 H2O), and frtrns is the fraction of transmission losses partitioned to the deep aquifer. Bank storage contributes flow to the main channel or reach within the subbasin. Bank flow is simulated with a recession curve similar to that used for groundwater. The volume of water entering the reach from bank storage is calculated:
CHAPTER 23: EQUATIONS—WATER ROUTING
Vbnk = bnk ⋅ (1 − α bnk )
361
23.7.2
where Vbnk is the volume of water added to the reach via return flow from bank storage (m3 H2O), bnk is the total amount of water in bank storage (m3 H2O), and
αbnk is the bank flow recession constant or constant of proportionality. Water may move from bank storage into an adjacent unsaturated zone. SWAT models the movement of water into adjacent unsaturated areas as a function of water demand for evapotranspiration. To avoid confusion with soil evaporation and transpiration, this process has been termed ‘revap’. This process is significant in watersheds where the saturated zone is not very far below the surface or where deep-rooted plants are growing. ‘Revap’ from bank storage is governed by the groundwater revap coefficient defined for the last HRU in the subbasin. The maximum amount of water than will be removed from bank storage via ‘revap’ on a given day is: bnk revap ,mx = β rev ⋅ Eo ⋅ Lch ⋅ W
23.7.3
where bnkrevap,mx is the maximum amount of water moving into the unsaturated zone in response to water deficiencies (m3 H2O), βrev is the revap coefficient, Eo is the potential evapotranspiration for the day (mm H2O), Lch is the channel length (km), and W is the width of the channel at water level (m). The actual amount of revap that will occur on a given day is calculated: bnk revap = bnk
if bnk < bnk revap ,mx
23.7.4
bnk revap = bnk revap ,mx
if bnk ≥ bnk revap ,mx
23.7.5
where bnkrevap is the actual amount of water moving into the unsaturated zone in response to water deficiencies (m3 H2O), bnkrevap,mx is the maximum amount of water moving into the unsaturated zone in response to water deficiencies (m3 H2O), and bnk is the amount of water in bank storage at the beginning of day i (m3 H2O).
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Table 23-7: SWAT input variables that pertain to bank storage. Variable name Definition TRNSRCH frtrns: Fraction of transmission losses partitioned to the deep aquifer ALPHA_BNK αbnk: Bank flow recession constant or constant of proportionality GW_REVAP βrev: Revap coefficient
File Name .bsn .rte .gw
23.8 CHANNEL WATER BALANCE Water storage in the reach at the end of the time step is calculated: Vstored , 2 = Vstored ,1 + Vin − Vout − tloss − E ch + div + Vbnk
23.8.1
where Vstored,2 is the volume of water in the reach at the end of the time step (m3 H2O), Vstored,1 is the volume of water in the reach at the beginning of the time step (m3 H2O), Vin is the volume of water flowing into the reach during the time step (m3 H2O), Vout is the volume of water flowing out of the reach during the time step (m3 H2O), tloss is the volume of water lost from the reach via transmission through the bed (m3 H2O), Ech is the evaporation from the reach for the day (m3 H2O), div is the volume of water added or removed from the reach for the day through diversions (m3 H2O), and Vbnk is the volume of water added to the reach via return flow from bank storage (m3 H2O). SWAT treats the volume of outflow calculated with equation 23.3.11 or 23.4.7 as the net amount of water removed from the reach. As transmission losses, evaporation and other water losses for the reach segment are calculated, the amount of outflow to the next reach segment is reduced by the amount of the loss. When outflow and all losses are summed, the total amount will equal the value obtained from 23.3.11 or 23.4.7.
CHAPTER 23: EQUATIONS—WATER ROUTING
363
23.9 NOMENCLATURE Ach Cross-sectional area of flow in the channel (m2) Ach,bnkfull Cross-sectional area of flow in the channel when filled to the top of the bank (m2) C1 Coefficient in Muskingum flood routing equation C2 Coefficient in Muskingum flood routing equation C3 Coefficient in Muskingum flood routing equation Ech Evaporation from the reach for the day (m3 H2O) Eo Potential evapotranspiration (mm d-1) K Storage time constant for the reach (s) K0.1bnkfull Storage time constant calculated for the reach segment with one-tenth of the bankfull flows (s) Kbnkfull Storage time constant calculated for the reach segment with bankfull flows (s) Kch Effective hydraulic conductivity of the channel alluvium (mm/hr) Lch Length of main channel (km) Pch Wetted perimeter for a given depth of flow (m) Rch Hydraulic radius for a given depth of flow (m) SC Storage coefficient for variable storage flow routing TT Travel time (s) Vbnk Volume of water added to the reach via return flow from bank storage (m3 H2O) Vch Volume of water stored in the channel (m3) Vin Volume of inflow during the time step (m3 H2O) Vout Volume of outflow during the time step (m3 H2O) Vstored Volume of water stored in water body or channel (m3 H2O) W Width of channel at water level (m) Wbnkfull Top width of the channel when filled with water (m) Wbtm Bottom width of the channel (m) Wbtm,fld Bottom width of the flood plain (m) X Weighting factor in Muskingum routing bnk Total amount of water in bank storage (m3 H2O) bnkin Amount of water entering bank storage (m3 H2O) bnkrevap,mx Maximum amount of water moving into the unsaturated zone in response to water deficiencies (m3 H2O) ck Celerity corresponding to the flow for a specified depth (m/s) coef1 Weighting coefficient for storage time constant calculation coef2 Weighting coefficient for storage time constant calculation coefev Evaporation coefficient depth Depth of water in the channel (m) depthbnkfull Depth of water in the channel when filled to the top of the bank (m) depthfld Depth of water in the flood plain (m) div Volume of water added or removed from the reach for the day through diversions (m3 H2O) frtrns Fraction of transmission losses partitioned to the deep aquifer
364
fr∆t n qch qin qout slpch tloss vc
SWAT USER'S MANUAL, VERSION 2000
Zch Zfld
Fraction of the time step in which water is flowing in the channel Manning’s roughness coefficient for the subbasin or channel Average channel flow rate (m3 s-1) Inflow rate (m3/s) Outflow rate (m3/s) Average channel slope along channel length (m m-1) Channel transmission losses (m3 H2O) Average channel velocity (m s-1) Inverse of the channel side slope Inverse of the flood plain side slope
αbnk βrev ∆t
Bank flow recession constant or constant of proportionality Revap coefficient Length of the time step (s)
23.10 REFERENCES Arnold, J.G., J.R. Williams, and D.R. Maidment. 1995. Continuous-time water and sediment routing model for large basins. Journal of Hydraulic Engineering 121(2): 171-183. Brakensiek, D.L. 1967. Kinematic flood routing. Transactions of the ASAE 10(3):340-343. Chow, V.T., D.R. Maidment, and L.W. Mays. 1988. Applied hydrology. McGraw-Hill, Inc., New York, NY. Cunge, J.A. 1969. On the subject of a flood propagation method (Muskingum method). J. Hydraulics Research 7(2):205-230. Lane, L.J. 1983. Chapter 19: Transmission Losses. p.19-1–19-21. In Soil Conservation Service. National engineering handbook, section 4: hydrology. U.S. Government Printing Office, Washington, D.C. Overton, D.E. 1966. Muskingum flood routing of upland streamflow. Journal of Hydrology 4:185-200. Soil Conservation Service. 1964. Chapter 17: Flood routing, Section 4, Hydrology, National engineering handbook. U.S. Department of Agriculture. U.S. Gov’t Printing Office, Washington, D.C.
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365
Williams, J.R. 1969. Flood routing with variable travel time or variable storage coefficients. Transactions of the ASAE 12(1):100-103. Williams, J.R. and R.W. Hann. 1973. HYMO: Problem-oriented language for hydrologic modeling—User’s manual. USDA, ARS-S-9.
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CHAPTER 24
EQUATIONS: SEDIMENT ROUTING
Sediment transport in the channel network is a function of two processes, deposition and degradation, operating simultaneously in the reach. SWAT will compute deposition and degradation using the same channel dimensions for the entire simulation. Alternatively, SWAT will simulate downcutting and widening of the stream channel and update channel dimensions throughout the simulation.
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24.1 SEDIMENT CHANNEL ROUTING Previous versions of SWAT used stream power to predict degradation and fall velocity to estimate deposition in the channels (Arnold et al, 1995). Williams (1980) used Bagnold’s (1977) definition of stream power to develop a method for determining degradation as a function of channel slope and velocity. In this version, the equations have been simplified and the maximum amount of sediment that can be transported from a reach segment is a function of the peak channel velocity. The peak channel velocity, vch,pk, is calculated: v ch , pk =
qch , pk
24.1.1
Ach
where qch,pk is the peak flow rate (m3/s) and Ach is the cross-sectional area of flow in the channel (m2). The peak flow rate is defined as: qch , pk = prf ⋅ qch
24.1.2
where prf is the peak rate adjustment factor, and qch is the average rate of flow (m3/s). Calculation of the average rate of flow, qch, and the cross-sectional area of flow, Ach, is reviewed in Chapter 23. The maximum amount of sediment that can be transported from a reach segment is calculated: conc sed ,ch ,mx = c sp ⋅ vch , pk
spexp
24.1.3
where concsed,ch,mx is the maximum concentration of sediment that can be transported by the water (ton/m3 or kg/L), csp is a coefficient defined by the user, vch,pk is the peak channel velocity (m/s), and spexp is an exponent defined by the user. The exponent, spexp, normally varies between 1.0 and 2.0 and was set at 1.5 in the original Bagnold stream power equation (Arnold et al., 1995). The maximum concentration of sediment calculated with equation 24.1.3 is compared to the concentration of sediment in the reach at the beginning of the time step, concsed,ch,i. If conc sed ,ch ,i > conc sed ,ch ,mx , deposition is the dominant process in the reach segment and the net amount of sediment deposited is calculated:
CHAPTER 24: EQUATIONS—SEDIMENT ROUTING
sed dep = (conc sed ,ch ,i − conc sed ,ch ,mx ) ⋅ Vch
369
24.1.4
where seddep is the amount of sediment deposited in the reach segment (metric tons), concsed,ch,i is the initial sediment concentration in the reach (kg/L or ton/m3), concsed,ch,mx is the maximum concentration of sediment that can be transported by the water (kg/L or ton/m3), and Vch is the volume of water in the reach segment (m3 H2O). If conc sed ,ch ,i < conc sed ,ch ,mx , degradation is the dominant process in the reach segment and the net amount of sediment reentrained is calculated: sed deg = (conc sed ,ch ,mx − conc sed ,ch ,i ) ⋅ Vch ⋅ K CH ⋅ CCH
24.1.5
where seddeg is the amount of sediment reentrained in the reach segment (metric tons), concsed,ch,mx is the maximum concentration of sediment that can be transported by the water (kg/L or ton/m3), concsed,ch,i is the initial sediment concentration in the reach (kg/L or ton/m3), Vch is the volume of water in the reach segment (m3 H2O), KCH is the channel erodibility factor (cm/hr/Pa), and CCH is the channel cover factor. Once the amount of deposition and degradation has been calculated, the final amount of sediment in the reach is determined: sed ch = sed ch ,i − sed dep + sed deg
24.1.6
where sedch is the amount of suspended sediment in the reach (metric tons), sedch,i is the amount of suspended sediment in the reach at the beginning of the time period (metric tons), seddep is the amount of sediment deposited in the reach segment (metric tons), and seddeg is the amount of sediment reentrained in the reach segment (metric tons). The amount of sediment transported out of the reach is calculated: sed out = sed ch ⋅
Vout Vch
24.1.7
where sedout is the amount of sediment transported out of the reach (metric tons), sedch is the amount of suspended sediment in the reach (metric tons), Vout is the volume of outflow during the time step (m3 H2O), and Vch is the volume of water in the reach segment (m3 H2O).
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24.1.1 CHANNEL ERODIBILITY FACTOR The channel erodibility factor is conceptually similar to the soil erodibility factor used in the USLE equation. Channel erodibility is a function of properties of the bed or bank materials. Channel erodibility can be measured with a submerged vertical jet device. The basic premise of the test is that erosion of a vegetated or bare channel and local scour beneath an impinging jet are the result of hydraulic stresses, boundary geometry, and the properties of the material being eroded. Hanson (1990) developed a method for determining the erodibility coefficient of channels in situ with the submerged vertical jet. Allen et al. (1999) utilized this method to determine channel erodibility factors for thirty sites in Texas. A submerged, vertical jet of water directed perpendicularly at the channel bed causes erosion of the bed material in the vicinity of the jet impact area (Figure 24-1). Important variables in the erosion process are: the volume of material removed during a jetting event, elevation of the jet above the ground surface, diameter of the jet nozzle, jet velocity, time, mass density of the fluid and coefficient of erodibility.
Figure 24-1: Simplified cross-section of submerged jet test.
CHAPTER 24: EQUATIONS—SEDIMENT ROUTING
371
Hanson (1991) defined a jet index, Ji, to relate erodibility to scour created by the submerged jet. The jet index is a function of the depth of scour beneath the jet per unit time and the jet velocity. The jet index is determined by a least squares fit following the procedures outlined in ASTM standard D 5852-95. Once the jet index is determined, the channel erodibility coefficient is calculated: K CH = 0.003 ⋅ exp[385 ⋅ J i ]
24.1.8
where KCH is the channel erodibility coefficient (cm/h/Pa) and Ji is the jet index. In general, values for channel erodibility are an order of magnitude smaller than values for soil erodibility.
24.1.2 CHANNEL COVER FACTOR The channel cover factor, CCH, is defined as the ratio of degradation from a channel with a specified vegetative cover to the corresponding degradation from a channel with no vegetative cover. The vegetation affects degradation by reducing the stream velocity, and consequently its erosive power, near the bed surface. Table 24-1: SWAT input variables that pertain to sediment routing. Variable Name PRF SPCON SPEXP CH_COV CH_EROD
Definition prf: Peak rate adjustment factor csp: Coefficient in sediment transport equation spexp: Exponent in sediment transport equation CCH: Channel cover factor KCH: Channel erodibility factor (cm/hr/Pa)
Input File .bsn .bsn .bsn .rte .rte
24.2 CHANNEL DOWNCUTTING AND WIDENING While sediment transport calculations have traditionally been made with the same channel dimensions throughout a simulation, SWAT will model channel downcutting and widening. When channel downcutting and widening is simulated, channel dimensions are allowed to change during the simulation period. Three channel dimensions are allowed to vary in channel downcutting and widening simulations: bankfull depth, depthbnkfull, channel width, Wbnkfull, and
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channel slope, slpch. Channel dimensions are updated using the following equations when the volume of water in the reach exceeds 1.4 × 106 m3. The amount of downcutting is calculated (Allen et al., 1999): depthdcut = 358 ⋅ depth ⋅ slpch ⋅ K CH
24.2.1
where depthdcut is the amount of downcutting (m), depth is the depth of water in channel (m), slpch is the channel slope (m/m), and KCH is the channel erodibility coefficient (cm/h/Pa). The new bankfull depth is calculated: depthbnkfull = depthbnkfull ,i + depthdcut
24.2.2
where depthbnkfull is the new bankfull depth (m), depthbnkfull,i is the previous bankfull depth, and depthdcut is the amount of downcutting (m). The new bank width is calculated: Wbnkfull = ratioWD ⋅ depthbnkfull
24.2.3
where Wbnkfull is the new width of the channel at the top of the bank (m), ratioWD is the channel width to depth ratio, and depthbnkfull is the new bankfull depth (m). The new channel slope is calculated: slpch = slpch ,i −
depthdcut 1000 ⋅ Lch
24.2.4
where slpch is the new channel slope (m/m), slpch,i is the previous channel slope (m/m), depthbnkfull is the new bankfull depth (m), and Lch is the channel length (km). Table 24-2: SWAT input variables that pertain to channel downcutting and widening. Variable Name IDEG CH_WDR
Definition Channel degradation code ratioWD: Channel width to depth ratio
Input File .cod .rte
CHAPTER 24: EQUATIONS—SEDIMENT ROUTING
24.3 NOMENCLATURE Ach CCH Ji KCH Lch Vch Vout Wbnkfull
Cross-sectional area of flow in the channel (m2) Channel cover factor Jet index used to calculate channel erodibility Channel erodibility factor (cm/hr/Pa) Channel length (km) Volume of water in the reach segment (m3 H2O) Volume of outflow during the time step (m3 H2O) Top width of the channel when filled with water (m)
Coefficient in sediment transport equation csp concsed,ch,i Initial sediment concentration in the reach (kg/L or ton/m3) concsed,ch,mx Maximum concentration of sediment that can be transported by the water (kg/L or ton/m3) depth Depth of water in channel (m) depthbnkfull Depth of water in the channel when filled to the top of the bank (m) depthdcut Amount of downcutting (m) prf Peak rate adjustment factor Average rate of flow in the channel (m3/s) qch qch,pk Peak flow rate (m3/s) ratioWD Channel width to depth ratio sedch Amount of suspended sediment in the reach (metric tons) seddeg Amount of sediment reentrained in the reach segment (metric tons) seddep Amount of sediment deposited in the reach segment (metric tons) sedout Amount of sediment transported out of the reach (metric tons) slpch Average channel slope along channel length (m m-1) spexp Exponent in sediment transport equation vch,pk Peak channel velocity (m/s)
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24.4 REFERENCES Allen, P.M., J. Arnold, E. Jakubowski. 1999. Prediction of stream channel erosion potential. Environmental and Engineering Geoscience 5:339-351. American Society for Testing and Materials. 1995. Annual book of ASTM standards, Vol. 04.08, Designation: D 5852-5. American Society for Testing and Materials, Philidelphia, PA. p. 653-657. Arnold, J.G., J.R. Williams, and D.R. Maidment. 1995. Continuous-time water and sediment-routing model for large basins. Journal of Hydraulic Engineering. 121:171-183. Bagnold, R.A. 1977. Bedload transport in natural rivers. Water Resour. Res. 13:303-312. Hanson, G.J. 1990. Surface erodibility of earthen channels at high stresses. Part II-Developing an in situ testing device. Trans. ASAE 33:132-137. Hanson, G.J. 1991. Development of a jet index method to characterize erosion resistance of soils in earthen spillways. Trans. ASAE 34:2015-2020. Williams, J.R. 1980. SPNM, a model for predicting sediment, phosphorus, and nitrogen yields from agricultural basins. Water Resour. Bull. 16:843-848.
CHAPTER 25
QUATIONS IN-STREAM NUTRIENT PROCESSES
Parameters which affect water quality and can be considered pollution indicators include nutrients, total solids, biological oxygen demand, nitrates, and microorganisms (Loehr, 1970; Paine, 1973). Parameters of secondary importance include odor, taste, and turbidity (Azevedo and Stout, 1974). The SWAT in-stream water quality algorithms incorporate constituent interactions and relationships used in the QUAL2E model (Brown and Barnwell, 1987). The documentation provided in this chapter has been taken from Brown 375
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and Barnwell (1987). The modeling of in-stream nutrient transformations has been made an optional feature of SWAT. To route nutrient loadings downstream without simulating transformations, the variable IWQ in the input control code (.cod) file should be set to 0. To activate the simulation of in-stream nutrient transformations, this variable should be set to 1.
25.1 ALGAE During the day, algae increase the stream’s dissolved oxygen concentration via photosynthesis. At night, algae reduce the concentration via respiration. As algae grow and die, they form part of the in-stream nutrient cycle. This section summarizes the equations used to simulate algal growth in the stream.
25.1.1 CHLOROPHYLL a Chlorophyll a is assumed to be directly proportional to the concentration of phytoplanktonic algal biomass. chla = α 0 ⋅ algae
25.1.1
where chla is the chlorophyll a concentration (µg chla/L), α0 is the ratio of chlorophyll a to algal biomass (µg chla/mg alg), and algae is the algal biomass concentration (mg alg/L).
25.1.2 ALGAL GROWTH Growth and decay of algae/chlorophyll a is calculated as a function of the growth rate, the respiration rate, the settling rate and the amount of algae present in the stream. The change in algal biomass for a given day is:
æ æ σ öö ∆algae = çç (µ a ⋅ algae ) − (ρ a ⋅ algae ) − çç 1 ⋅ algae ÷÷ ÷÷ ⋅ TT è depth øø è
25.1.2
where ∆algae is the change in algal biomass concentration (mg alg/L), µa is the local specific growth rate of algae (day-1), ρa is the local respiration or death rate of algae (day-1), σ1 is the local settling rate for algae (m/day), depth is the depth of water in the channel (m), algae is the algal biomass concentration at the beginning
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
377
of the day (mg alg/L), and TT is the flow travel time in the reach segment (day). The calculation of depth and travel time are reviewed in Chapter 23.
25.1.2.1 LOCAL SPECIFIC GROWTH RATE OF ALGAE The local specific growth rate of algae is a function of the availability of required nutrients, light and temperature. SWAT first calculates the growth rate at 20°C and then adjusts the growth rate for water temperature. The user has three options for calculating the impact of nutrients and light on growth: multiplicative, limiting nutrient, and harmonic mean. The multiplicative option multiplies the growth factors for light, nitrogen and phosphorus together to determine their net effect on the local algal growth rate. This option has its biological basis in the mutiplicative effects of enzymatic processes involved in photosynthesis:
µ a , 20 = µ max ⋅ FL ⋅ FN ⋅ FP
25.1.3
where µa,20 is the local specific algal growth rate at 20°C (day-1), µmax is the maximum specific algal growth rate (day-1), FL is the algal growth attenuation factor for light, FN is the algal growth limitation factor for nitrogen, and FP is the algal growth limitation factor for phosphorus. The maximum specific algal growth rate is specified by the user. The limiting nutrient option calculates the local algal growth rate as limited by light and either nitrogen or phosphorus. The nutrient/light effects are multiplicative, but the nutrient/nutrient effects are alternate. The algal growth rate is controlled by the nutrient with the smaller growth limitation factor. This approach mimics Liebig’s law of the minimum:
µ a , 20 = µ max ⋅ FL ⋅ min(FN , FP )
25.1.4
where µa,20 is the local specific algal growth rate at 20°C (day-1), µmax is the maximum specific algal growth rate (day-1), FL is the algal growth attenuation factor for light, FN is the algal growth limitation factor for nitrogen, and FP is the algal growth limitation factor for phosphorus. The maximum specific algal growth rate is specified by the user.
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The harmonic mean is mathematically analogous to the total resistance of two resistors in parallel and can be considered a compromise between equations 25.1.3 and 25.1.4. The algal growth rate is controlled by a multiplicative relation between light and nutrients, while the nutrient/nutrient interactions are represented by a harmonic mean.
µ a , 20 = µ max ⋅ FL ⋅
2
25.1.5
1 ö æ 1 + ç ÷ è FN FP ø
where µa,20 is the local specific algal growth rate at 20°C (day-1), µmax is the maximum specific algal growth rate (day-1), FL is the algal growth attenuation factor for light, FN is the algal growth limitation factor for nitrogen, and FP is the algal growth limitation factor for phosphorus. The maximum specific algal growth rate is specified by the user. Calculation of the growth limiting factors for light, nitrogen and phosphorus are reviewed in the following sections. ALGAL GROWTH LIMITING FACTOR FOR LIGHT. A
number
of
mathematical
relationships
between
photosynthesis and light have been developed. All relationships show an increase in photosynthetic rate with increasing light intensity up to a maximum or saturation value. The algal growth limiting factor for light is calculated using a Monod half-saturation method. In this option, the algal growth limitation factor for light is defined by a Monod expression: FLz =
I phosyn , z K L + I phosyn , z
25.1.6
where FLz is the algal growth attenuation factor for light at depth z,
Iphosyn,z is the photosynthetically-active light intensity at a depth z below the water surface (MJ/m2-hr), and KL is the half-saturation coefficient for light (MJ/m2-hr). Photosynthetically-active light is radiation with a wavelength between 400 and 700 nm. The halfsaturation coefficient for light is defined as the light intensity at
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379
which the algal growth rate is 50% of the maximum growth rate. The half-saturation coefficient for light is defined by the user. Photosynthesis is assumed to occur throughout the depth of the water column. The variation in light intensity with depth is defined by Beer’s law: I phosyn , z = I phosyn ,hr exp(− k l ⋅ z )
25.1.7
where Iphosyn,z is the photosynthetically-active light intensity at a depth z below the water surface (MJ/m2-hr), Iphosyn,hr is the photosynthetically-active solar radiation reaching the ground/water surface during a specific hour on a given day (MJ/m2-hr), k l is the light extinction coefficient (m-1), and z is the depth from the water surface (m). Substituting equation 25.1.7 into equation 25.1.6 and integrating over the depth of flow gives: ù K L + I phosyn ,hr ö é æ 1 ÷÷ ⋅ ln ê FL = çç ú è k l ⋅ depth ø ëê K L + I phosyn ,hr exp(− k l ⋅ depth )ûú
25.1.8
where FL is the algal growth attenuation factor for light for the water column, KL is the half-saturation coefficient for light (MJ/m2-hr), Iphosyn,hr is the photosynthetically-active solar radiation reaching the ground/water surface during a specific hour on a given day (MJ/m2-hr), k l is the light extinction coefficient (m-1), and depth is the depth of water in the channel (m). The photosynthetically-active solar radiation is calculated: I phosyn ,hr = I hr ⋅ frphosyn
25.1.9
where Ihr is the solar radiation reaching the ground during a specific hour on current day of simulation (MJ m-2 h-1), and frphosyn is the fraction of solar radiation that is photosynthetically active. The calculation of Ihr is reviewed in Chapter 2. The fraction of solar radiation that is photosynthetically active is user defined. For daily simulations, an average value of the algal growth attenuation factor for light calculated over the diurnal cycle must
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be used. This is calculated using a modified form of equation 25.1.8: ù K L + I phosyn ,hr ö é æ 1 ÷÷ ⋅ ln ê FL = 0.92 ⋅ frDL ⋅ çç ú 25.1.10 è k l ⋅ depth ø ëê K L + I phosyn ,hr exp(− k l ⋅ depth )ûú
where frDL is the fraction of daylight hours, I phosyn ,hr is the daylight average photosynthetically-active light intensity (MJ/m2-hr) and all other variables are defined previously. The fraction of daylight hours is calculated: frDL =
TDL 24
25.1.11
where TDL is the daylength (hr). I phosyn ,hr is calculated:
I phosyn ,hr =
frphosyn ⋅ H day
25.1.12
TDL
where frphosyn is the fraction of solar radiation that is photosynthetically active, Hday is the solar radiation reaching the water surface in a given day (MJ/m2), and TDL is the daylength (hr). Calculation of Hday and TDL are reviewed in Chapter 2. The light extinction coefficient, k l , is calculated as a function of the algal density using the nonlinear equation:
k l = k l,0 + k l,1 ⋅ α 0 ⋅ algae + k l, 2 ⋅ (α 0 ⋅ algae )
23
25.1.13
where k l,0 is the non-algal portion of the light extinction coefficient (m-1), k l,1 is the linear algal self shading coefficient (m1
(µg-chla/L)-1), k l, 2 is the nonlinear algal self shading coefficient
(m-1 (µg-chla/L)-2/3), α0 is the ratio of chlorophyll a to algal biomass (µg chla/mg alg), and algae is the algal biomass concentration (mg alg/L). Equation 25.1.13 allows a variety of algal, self-shading, light extinction relationships to be modeled. When k l ,1 = k l, 2 = 0 , no
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381
algal self-shading is simulated. When k l,1 ≠ 0 and k l, 2 = 0 , linear algal self-shading is modeled. When k l,1 and k l, 2 are set to a value other than 0, non-linear algal self-shading is modeled. The Riley equation (Bowie et al., 1985) defines k l,1 = 0.0088 m -1 (µg - chla/L )
−1
and k l, 2 = 0.054 m -1 (µg - chla/L )
−2 3
.
ALGAL GROWTH LIMITING FACTOR FOR NUTRIENTS. The algal growth limiting factor for nitrogen is defined by a Monod expression. Algae are assumed to use both ammonia and nitrate as a source of inorganic nitrogen. FN =
(C NO 3 + C NH 4 ) (C NO 3 + C NH 4 ) + K N
25.1.14
where FN is the algal growth limitation factor for nitrogen, CNO3 is the concentration of nitrate in the reach (mg N/L), CNH4 is the concentration of ammonium in the reach (mg N/L), and KN is the Michaelis-Menton half-saturation constant for nitrogen (mg N/L). The algal growth limiting factor for phosphorus is also defined by a Monod expression. FP =
C solP C solP + K P
25.1.15
where FP is the algal growth limitation factor for phosphorus, CsolP is the concentration of phosphorus in solution in the reach (mg P/L), and KP is the Michaelis-Menton half-saturation constant for phosphorus (mg P/L). The Michaelis-Menton half-saturation constant for nitrogen and phosphorus define the concentration of N or P at which algal growth is limited to 50% of the maximum growth rate. Users are allowed to set these values. Typical values for KN range from 0.01 to 0.30 mg N/L while KP will range from 0.001 to 0.05 mg P/L.
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Once the algal growth rate at 20°C is calculated, the rate coefficient is adjusted for temperature effects using a Streeter-Phelps type formulation:
µ a = µ a , 20 ⋅ 1.047 (T
water − 20
)
25.1.16
where µa is the local specific growth rate of algae (day-1), µa,20 is the local specific algal growth rate at 20°C (day-1), and Twater is the average water temperature for the day (°C).
25.1.2.2 LOCAL RESPIRATION RATE OF ALGAE The local respiration or death rate of algae represents the net effect of three processes: the endogenous respiration of algae, the conversion of algal phosphorus to organic phosphorus, and the conversion of algal nitrogen to organic nitrogen. The user defines the local respiration rate of algae at 20°C. The respiration rate is adjusted to the local water temperature using the relationship:
ρ a = ρ a , 20 ⋅ 1.047 (T
water − 20
)
25.1.17
where ρa is the local respiration rate of algae (day-1), ρa,20 is the local algal respiration rate at 20°C (day-1), and Twater is the average water temperature for the day (°C).
25.1.2.3 LOCAL SETTLING RATE OF ALGAE The local settling rate of algae represents the net removal of algae due to settling. The user defines the local settling rate of algae at 20°C. The settling rate is adjusted to the local water temperature using the relationship:
σ 1 = σ 1, 20 ⋅ 1.024 (T
water − 20
)
25.1.18
where σ1 is the local settling rate of algae (m/day), σ1,20 is the local algal settling rate at 20°C (m/day), and Twater is the average water temperature for the day (°C).
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES Table 25-1: SWAT input variables used in algae calculations. Variable name Definition AI0 α0: Ratio of chlorophyll a to algal biomass (µg chla/mg alg) MUMAX µmax: Maximum specific algal growth rate (day-1) K_L KL: Half-saturation coefficient for light (MJ/m2-hr) TFACT frphosyn: Fraction of solar radiation that is photosynthetically active LAMBDA0 k l,0 : Non-algal portion of the light extinction coefficient (m-1)
383 File Name .wwq .wwq .wwq .wwq .wwq
LAMBDA1
k l,1 : Linear algal self shading coefficient (m-1 (µg-chla/L)-1)
.wwq
LAMBDA2
k l, 2 : Nonlinear algal self shading coefficient (m-1 (µg-chla/L)-2/3)
.wwq
K_N
KN: Michaelis-Menton half-saturation constant for nitrogen (mg N/L) KP: Michaelis-Menton half-saturation constant for phosphorus (mg P/L) ρa,20: Local algal respiration rate at 20°C (day-1) σ1,20: Local algal settling rate at 20°C (m/day)
.wwq .wwq .wwq .swq
K_P RHOQ RS1
25.2 NITROGEN CYCLE In aerobic water, there is a stepwise transformation from organic nitrogen to ammonia, to nitrite, and finally to nitrate. Organic nitrogen may also be removed from the stream by settling. This section summarizes the equations used to simulate the nitrogen cycle in the stream.
25.2.1 ORGANIC NITROGEN The amount of organic nitrogen in the stream may be increased by the conversion of algal biomass nitrogen to organic nitrogen. Organic nitrogen concentration in the stream may be decreased by the conversion of organic nitrogen to NH4+ or the settling of organic nitrogen with sediment. The change in organic nitrogen for a given day is: ∆orgN str = (α1 ⋅ ρ a ⋅ algae − β N ,3 ⋅ orgN str − σ 4 ⋅ orgN str ) ⋅ TT
25.2.1
where ∆orgNstr is the change in organic nitrogen concentration (mg N/L), α1 is the fraction of algal biomass that is nitrogen (mg N/mg alg biomass), ρa is the local respiration or death rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), βN,3 is the rate constant for hydrolysis of organic nitrogen to ammonia nitrogen (day-1), orgNstr is the organic nitrogen concentration at the beginning of the day (mg N/L), σ4 is the rate coefficient for organic nitrogen settling (day-1), and TT is the flow travel time in the reach
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SWAT USER'S MANUAL, VERSION 2000
segment (day). The fraction of algal biomass that is nitrogen is user-defined. Equation 25.1.17 describes the calculation of the local respiration rate of algae. The calculation of travel time is reviewed in Chapter 23. The user defines the local rate constant for hydrolysis of organic nitrogen to NH4+ at 20°C. The organic nitrogen hydrolysis rate is adjusted to the local water temperature using the relationship:
β N ,3 = β N , 3, 20 ⋅ 1.047 (T
water − 20
)
25.2.2
where βN,3 is the local rate constant for hydrolysis of organic nitrogen to NH4+ (day-1), βN,3,20 is the local rate constant for hydrolysis of organic nitrogen to NH4+ at 20°C (day-1), and Twater is the average water temperature for the day (°C). The user defines the rate coefficient for organic nitrogen settling at 20°C. The organic nitrogen settling rate is adjusted to the local water temperature using the relationship:
σ 4 = σ 4, 20 ⋅ 1.024 (T
water − 20
)
25.2.3
where σ4 is the local settling rate for organic nitrogen (day-1), σ4,20 is the local settling rate for organic nitrogen at 20°C (day-1), and Twater is the average water temperature for the day (°C).
25.2.2 AMMONIUM The amount of ammonium (NH4+) in the stream may be increased by the mineralization of organic nitrogen and diffusion of ammonium from the streambed sediments. The ammonium concentration in the stream may be decreased by the conversion of NH4+ to NO -2 or the uptake of NH4+ by algae. The change in ammonium for a given day is:
σ3 æ ö ∆NH4str = çç β N ,3 ⋅ orgN str − β N ,1 ⋅ NH4str + − frNH 4 ⋅ α1 ⋅ µ a ⋅ algae ÷÷ ⋅ TT 25.2.4 (1000 ⋅ depth ) è ø where ∆NH4str is the change in ammonium concentration (mg N/L), βN,3 is the rate constant for hydrolysis of organic nitrogen to ammonia nitrogen (day-1), orgNstr is the organic nitrogen concentration at the beginning of the day (mg N/L),
βN,1 is the rate constant for biological oxidation of ammonia nitrogen (day-1),
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
385
NH4str is the ammonium concentration at the beginning of the day (mg N/L), σ3 is the benthos (sediment) source rate for ammonium (mg N/m2-day), depth is the depth of water in the channel (m), frNH4 is the fraction of algal nitrogen uptake from ammonium pool, α1 is the fraction of algal biomass that is nitrogen (mg N/mg alg biomass), µa is the local growth rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), and TT is the flow travel time in the reach segment (day). The local rate constant for hydrolysis of organic nitrogen to NH4+ is calculated with equation 25.2.2. Section 25.1.2.1 describes the calculation of the local growth rate of algae. The calculation of depth and travel time is reviewed in Chapter 23. The rate constant for biological oxidation of ammonia nitrogen will vary as a function of in-stream oxygen concentration and temperature. The rate constant is calculated:
β N ,1 = β N ,1,20 ⋅ (1 − exp[− 0.6 ⋅ Ox str ]) ⋅ 1.083(T
water − 20
)
25.2.5
where βN,1 is the rate constant for biological oxidation of ammonia nitrogen (day-1),
βN,1,20 is the rate constant for biological oxidation of ammonia nitrogen at 20°C (day-1), Oxstr is the dissolved oxygen concentration in the stream (mg O2/L), and Twater is the average water temperature for the day (°C). The second term on the right side of equation 25.2.5, (1 − exp[− 0.6 ⋅ Ox str ]) , is a nitrification inhibition correction factor. This factor inhibits nitrification at low dissolved oxygen concentrations. The user defines the benthos source rate for ammonium at 20°C. The benthos source rate for ammonium nitrogen is adjusted to the local water temperature using the relationship:
σ 3 = σ 3, 20 ⋅ 1.074 (T
water − 20
)
25.2.6
where σ3 is the benthos (sediment) source rate for ammonium (mg N/m2-day),
σ3,20 is the benthos (sediment) source rate for ammonium nitrogen at 20°C (mg N/m2-day), and Twater is the average water temperature for the day (°C). The fraction of algal nitrogen uptake from ammonium pool is calculated:
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SWAT USER'S MANUAL, VERSION 2000
frNH 4 =
f NH 4 ⋅ NH4str ( f NH 4 ⋅ NH4str + (1 − f NH 4 ) ⋅ NO3str )
25.2.7
where frNH4 is the fraction of algal nitrogen uptake from ammonium pool, fNH4 is the preference factor for ammonia nitrogen, NH4str is the ammonium concentration in the stream (mg N/L), and NO3str is the nitrate concentration in the stream (mg N/L).
25.2.3 NITRITE The amount of nitrite ( NO -2 ) in the stream will be increased by the conversion of NH4+ to NO -2 and decreased by the conversion of NO -2 to NO -3 . The conversion of NO -2 to NO -3 occurs more rapidly than the conversion of NH4+ to NO -2 , so the amount of nitrite present in the stream is usually very small. The change in nitrite for a given day is: ∆NO2str = (β N ,1 ⋅ NH4str − β N , 2 ⋅ NO2str ) ⋅ TT
25.2.8
where ∆NO2str is the change in nitrite concentration (mg N/L), βN,1 is the rate constant for biological oxidation of ammonia nitrogen (day-1), NH4str is the ammonium concentration at the beginning of the day (mg N/L), βN,2 is the rate constant for biological oxidation of nitrite to nitrate (day-1), NO2str is the nitrite concentration at the beginning of the day (mg N/L), and TT is the flow travel time in the reach segment (day). The local rate constant for biological oxidation of ammonia nitrogen is calculated with equation 25.2.5. The calculation of travel time is reviewed in Chapter 23. The rate constant for biological oxidation of nitrite to nitrate will vary as a function of in-stream oxygen concentration and temperature. The rate constant is calculated:
β N ,2 = β N , 2, 20 ⋅ (1 − exp[− 0.6 ⋅ Ox str ]) ⋅ 1.047 (T
water − 20
)
25.2.9
where βN,2 is the rate constant for biological oxidation of nitrite to nitrate (day-1),
βN,2,20 is the rate constant for biological oxidation of nitrite to nitrate at 20°C (day-1), Oxstr is the dissolved oxygen concentration in the stream (mg O2/L), and Twater is the average water temperature for the day (°C). The second term on the
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
387
right side of equation 25.2.9, (1 − exp[− 0.6 ⋅ Ox str ]) , is a nitrification inhibition correction factor. This factor inhibits nitrification at low dissolved oxygen concentrations.
25.2.4 NITRATE The amount of nitrate ( NO -3 ) in the stream may be increased by the oxidation of NO -2 . The nitrate concentration in the stream may be decreased by the uptake of NO -3 by algae. The change in nitrate for a given day is: ∆NO3str = (β N , 2 ⋅ NO2str − (1 − frNH 4 ) ⋅ α1 ⋅ µ a ⋅ algae ) ⋅ TT
25.2.10
where ∆NO3str is the change in nitrate concentration (mg N/L), βN,2 is the rate constant for biological oxidation of nitrite to nitrate (day-1), NO2str is the nitrite concentration at the beginning of the day (mg N/L), frNH4 is the fraction of algal nitrogen uptake from ammonium pool, α1 is the fraction of algal biomass that is nitrogen (mg N/mg alg biomass), µa is the local growth rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), and TT is the flow travel time in the reach segment (day). The local rate constant for biological oxidation of nitrite to nitrate is calculated with equation 25.2.9 while the fraction of algal nitrogen uptake from ammonium pool is calculated with equation 25.2.7. Section 25.1.2.1 describes the calculation of the local growth rate of algae. The calculation of travel time is reviewed in Chapter 23.
Table 25-2: SWAT input variables used in in-stream nitrogen calculations. Variable name Definition AI1 α1: Fraction of algal biomass that is nitrogen (mg N/mg alg biomass) RHOQ ρa,20: Local algal respiration rate at 20°C (day-1) BC3 βN,3,20: Local rate constant for hydrolysis of organic nitrogen to NH4+ at 20°C (day-1 or hr-1) RS4 σ4,20: Local settling rate for organic nitrogen at 20°C (day-1) BC1 βN,1,20: Rate constant for biological oxidation of ammonia nitrogen at 20°C (day-1) RS3 σ3,20: Benthos (sediment) source rate for ammonium nitrogen at 20°C (mg N/m2-day or mg N/m2-hr) P_N fNH4: Preference factor for ammonia nitrogen BC2 βN,2,20: Rate constant for biological oxidation of nitrite to nitrate at 20°C (day-1 or hr-1)
File Name .wwq .wwq .swq .swq .swq .swq .wwq .swq
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SWAT USER'S MANUAL, VERSION 2000
25.3 PHOSPHORUS CYCLE The phosphorus cycle is similar to the nitrogen cycle. The death of algae transforms algal phosphorus into organic phosphorus. Organic phosphorus is mineralized to soluble phosphorus which is available for uptake by algae. Organic phosphorus may also be removed from the stream by settling. This section summarizes the equations used to simulate the phosphorus cycle in the stream.
25.3.1 ORGANIC PHOSPHORUS The amount of organic phosphorus in the stream may be increased by the conversion of algal biomass phosphorus to organic phosphorus. Organic phosphorus concentration in the stream may be decreased by the conversion of organic phosphorus to soluble inorganic phosphorus or the settling of organic phosphorus with sediment. The change in organic phosphorus for a given day is: ∆orgPstr = (α 2 ⋅ ρ a ⋅ algae − β P , 4 ⋅ orgPstr − σ 5 ⋅ orgPstr ) ⋅ TT
25.3.1
where ∆orgPstr is the change in organic phosphorus concentration (mg P/L), α2 is the fraction of algal biomass that is phosphorus (mg P/mg alg biomass), ρa is the local respiration or death rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), βP,4 is the rate constant for mineralization of organic phosphorus (day-1), orgPstr is the organic phosphorus concentration at the beginning of the day (mg P/L), σ5 is the rate coefficient for organic phosphorus settling (day-1), and TT is the flow travel time in the reach segment (day). The fraction of algal biomass that is phosphorus is user-defined. Equation 25.1.17 describes the calculation of the local respiration rate of algae. The calculation of travel time is reviewed in Chapter 23. The user defines the local rate constant for mineralization of organic phosphorus at 20°C. The organic phosphorus mineralization rate is adjusted to the local water temperature using the relationship:
β P , 4 = β P , 4, 20 ⋅ 1.047 (T
water − 20
)
25.3.2
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
389
where βP,4 is the local rate constant for organic phosphorus mineralization (day-1),
βP,4,20 is the local rate constant for organic phosphorus mineralization at 20°C (day-1), and Twater is the average water temperature for the day (°C). The user defines the rate coefficient for organic phosphorus settling at 20°C. The organic phosphorus settling rate is adjusted to the local water temperature using the relationship:
σ 5 = σ 5, 20 ⋅ 1.024 (T
water − 20
)
25.3.3
where σ5 is the local settling rate for organic phosphorus (day-1), σ5,20 is the local settling rate for organic phosphorus at 20°C (day-1), and Twater is the average water temperature for the day (°C).
25.3.2 INORGANIC/SOLUBLE PHOSPHORUS The amount of soluble, inorganic phosphorus in the stream may be increased by the mineralization of organic phosphorus and diffusion of inorganic phosphorus from the streambed sediments. The soluble phosphorus concentration in the stream may be decreased by the uptake of inorganic P by algae. The change in soluble phosphorus for a given day is: æ ö σ2 ∆solPstr = çç β P , 4 ⋅ orgPstr + − α 2 ⋅ µ a ⋅ algae ÷÷ ⋅ TT (1000 ⋅ depth ) è ø
25.3.4
where ∆solPstr is the change in solution phosphorus concentration (mg P/L), βP,4 is the rate constant for mineralization of organic phosphorus (day-1), orgPstr is the organic phosphorus concentration at the beginning of the day (mg P/L), σ2 is the benthos (sediment) source rate for soluble P (mg P/m2-day), depth is the depth of water in the channel (m), α2 is the fraction of algal biomass that is phosphorus (mg P/mg alg biomass), µa is the local growth rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), and TT is the flow travel time in the reach segment (day). The local rate constant for mineralization of organic phosphorus is calculated with equation 25.3.2. Section 25.1.2.1 describes the calculation of the local growth rate of algae. The calculation of depth and travel time is reviewed in Chapter 23.
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SWAT USER'S MANUAL, VERSION 2000
The user defines the benthos source rate for soluble P at 20°C. The benthos source rate for soluble phosphorus is adjusted to the local water temperature using the relationship:
σ 2 = σ 2, 20 ⋅ 1.074 (T
water − 20
)
25.3.5
where σ2 is the benthos (sediment) source rate for soluble P (mg P/m2-day), σ2,20 is the benthos (sediment) source rate for soluble phosphorus at 20°C (mg P/m2day), and Twater is the average water temperature for the day (°C). Table 25-3: SWAT input variables used in in-stream phosphorus calculations. Variable name Definition AI2 α2: Fraction of algal biomass that is phosphorus (mg P/mg alg biomass) RHOQ ρa,20: Local algal respiration rate at 20°C (day-1) BC4 βP,4,20: Local rate constant for organic phosphorus mineralization at 20°C (day-1) RS5 σ5,20: Local settling rate for organic phosphorus at 20°C (day-1) RS2 σ2,20: Benthos (sediment) source rate for soluble phosphorus at 20°C (mg P/m2-day)
File Name .wwq .wwq .swq .swq .swq
25.4 CARBONACEOUS BIOLOGICAL OXYGEN DEMAND The carbonaceous oxygen demand (CBOD) of the water is the amount of oxygen required to decompose the organic material in the water. CBOD is added to the stream with loadings from surface runoff or point sources. Within the stream, two processes are modeled that impact CBOD levels, both of which serve to reduce the carbonaceous biological oxygen demand as the water moves downstream. The change in CBOD within the stream on a given day is calculated: ∆cbod = −(κ 1 ⋅ cbod + κ 3 ⋅ cbod ) ⋅ TT
25.4.1
where ∆cbod is the change in carbonaceous biological oxygen demand concentration (mg CBOD/L), κ1 is the CBOD deoxygenation rate (day-1), cbod is the carbonaceous biological oxygen demand concentration (mg CBOD/L), κ3 is the settling loss rate of CBOD (day-1), and TT is the flow travel time in the reach segment (day). The calculation of travel time is reviewed in Chapter 23.
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The user defines the carbonaceous deoxygenation rate at 20°C. The CBOD deoxygenation rate is adjusted to the local water temperature using the relationship:
κ 1 = κ 1, 20 ⋅ 1.047 (T
water − 20
)
25.4.2
where κ1 is the CBOD deoxygenation rate (day-1), κ1,20 is the CBOD deoxygenation rate at 20°C (day-1), and Twater is the average water temperature for the day (°C). The user defines the settling loss rate of CBOD at 20°C. The settling loss rate is adjusted to the local water temperature using the relationship:
κ 3 = κ 3, 20 ⋅ 1.024 (T
water − 20
)
25.4.3
where κ3 is the settling loss rate of CBOD (day-1), κ3,20 is the settling loss rate of CBOD at 20°C (day-1), and Twater is the average water temperature for the day (°C). Table 25-4: SWAT input variables used in in-stream CBOD calculations. Variable name RK1 RK3
Definition κ1,20: CBOD deoxygenation rate at 20°C (day-1) κ3,20: Settling loss rate of CBOD at 20°C (day-1)
File Name .swq .swq
25.5 OXYGEN An adequate dissolved oxygen concentration is a basic requirement for a healthy aquatic ecosystem. Dissolved oxygen concentrations in streams are a function of atmospheric reareation, photosynthesis, plant and animal respiration, benthic (sediment) demand, biochemical oxygen demand, nitrification, salinity, and temperature. The change in dissolved oxygen concentration on a given day is calculated: ∆Ox str = (κ 2 ⋅ (Ox sat − Ox str ) + (α 3 ⋅ µ a − α 4 ⋅ ρ a ) ⋅ algae − κ 1 ⋅ cbod − − α 5 ⋅ β N ,1 ⋅ NH4str − α 6 ⋅ β N , 2 ⋅ NO2str ) ⋅ TT
κ4 1000 ⋅ depth 25.5.1
where ∆Oxstr is the change in dissolved oxygen concentration (mg O2/L), κ2 is the reaeration rate for Fickian diffusion (day-1), Oxsat is the saturation oxygen
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concentration (mg O2/L), Oxstr is the dissolved oxygen concentration in the stream (mg O2/L), α3 is the rate of oxygen production per unit of algal photosynthesis (mg O2/mg alg), µa is the local specific growth rate of algae (day-1), α4 is the rate of oxygen uptake per unit of algae respired (mg O2/mg alg), ρa is the local respiration or death rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), κ1 is the CBOD deoxygenation rate (day-1), cbod is the carbonaceous biological oxygen demand concentration (mg CBOD/L), κ4 is the sediment oxygen demand rate (mg O2/(m2⋅day)), depth is the depth of water in the channel (m), α5 is the rate of oxygen uptake per unit NH4+ oxidation (mg O2/mg N), βN,1 is the rate constant for biological oxidation of ammonia nitrogen (day-1), NH4str is the ammonium concentration at the beginning of the day (mg N/L), α6 is the rate of oxygen uptake per unit NO -2 oxidation (mg O2/mg N), βN,2 is the rate constant for biological oxidation of nitrite to nitrate (day-1), NO2str is the nitrite concentration at the beginning of the day (mg N/L) and TT is the flow travel time in the reach segment (day). The user defines the rate of oxygen production per unit algal photosynthesis, the rate of oxygen uptake per unit algal respiration, the rate of oxygen uptake per unit NH4+ oxidation and rate of oxygen uptake per unit NO -2 oxidation. Section 25.1.2.1 describes the calculation of the local growth rate of algae while equation 25.1.17 describes the calculation of the local respiration rate of algae. The rate constant for biological oxidation of NH4+ is calculated with equation 25.2.5 while the rate constant for NO -2 oxidation is determined with equation 25.2.9. The CBOD deoxygenation rate is calculated using equation 25.4.2. The calculation of depth and travel time are reviewed in Chapter 23. The user defines the sediment oxygen demand rate at 20°C. The sediment oxygen demand rate is adjusted to the local water temperature using the relationship:
κ 4 = κ 4, 20 ⋅ 1.060 (T
water − 20
)
25.5.2
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393
where κ4 is the sediment oxygen demand rate (mg O2/(m2⋅day)), κ4,20 is the sediment oxygen demand rate at 20°C (mg O2/(m2⋅day)), and Twater is the average water temperature for the day (°C).
25.5.1 OXYGEN SATURATION CONCENTRATION The amount of oxygen that can be dissolved in water is a function of temperature, concentration of dissolved solids, and atmospheric pressure. An equation developed by APHA (1985) is used to calculate the saturation concentration of dissolved oxygen: Ox sat
é 1.575701 × 105 6.642308 × 10 7 − = exp ê − 139.34410 + 2 Twat ,K Twat ,K ë +
1.243800 × 1010 8.621949 × 1011 ù − ú 3 4 Twat Twat ,K ,K û
25.5.3
where Oxsat is the equilibrium saturation oxygen concentration at 1.00 atm (mg O2/L), and Twat,K is the water temperature in Kelvin (273.15+°C).
25.5.2 REAERATION Reaeration occurs by diffusion of oxygen from the atmosphere into the stream and by the mixing of water and air that occurs during turbulent flow.
25.5.2.1 REAERATION BY FICKIAN DIFFUSION The user defines the reaeration rate at 20°C. The reaeration rate is adjusted to the local water temperature using the relationship:
κ 2 = κ 2, 20 ⋅ 1.024 (T
water − 20
)
25.5.4
where κ2 is the reaeration rate (day-1), κ2,20 is the reaeration rate at 20°C (day-1), and Twater is the average water temperature for the day (°C). Numerous methods have been developed to calculate the reaeration rate at 20°C, κ2,20. A few of the methods are listed below. Brown and Barnwell (1987) provide additional methods. Using field measurements, Churchill, Elmore and Buckingham (1962) derived the relationship:
κ 2, 20 = 5.03 ⋅ vc 0.969 ⋅ depth −1.673
25.5.5
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where κ2,20 is the reaeration rate at 20°C (day-1), v c is the average stream velocity (m/s), and depth is the average stream depth (m). O’Connor and Dobbins (1958) incorporated stream turbulence characteristics into the equations they developed. For streams with low velocities and isotropic conditions,
κ 2, 20 = 294 ⋅
(Dm ⋅ vc )0.5 depth1.5
25.5.6
where κ2,20 is the reaeration rate at 20°C (day-1), Dm is the molecular diffusion coefficient (m2/day), v c is the average stream velocity (m/s), and depth is the average stream depth (m). For streams with high velocities and nonisotropic conditions,
κ 2, 20
D ⋅ slp 0.25 = 2703 ⋅ m depth1.25 0.5
25.5.7
where κ2,20 is the reaeration rate at 20°C (day-1), Dm is the molecular diffusion coefficient (m2/day), slp is the slope of the streambed (m/m), and depth is the average stream depth (m). The molecular diffusion coefficient is calculated Dm = 177 ⋅ 1.037Twater −20
25.5.8
where Dm is the molecular diffusion coefficient (m2/day), and Twater is the average water temperature (°C). Owens et al. (1964) developed an equation to determine the reaeration rate for shallow, fast moving streams where the stream depth is 0.1 to 3.4 m and the velocity is 0.03 to 1.5 m/s.
κ 2, 20
0.67
v = 5.34 ⋅ c 1.85 depth
25.5.9
where κ2,20 is the reaeration rate at 20°C (day-1), v c is the average stream velocity (m/s), and depth is the average stream depth (m).
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25.5.2.2 REAERATION BY TURBULENT FLOW OVER A DAM Reareation will occur when water falls over a dam, weir, or other structure in the stream. To simulate this form of reaeration, a “structure” command line is added in the watershed configuration file (.fig) at every point along the stream where flow over a structure occurs. The amount of reaeration that occurs is a function of the oxygen deficit above the structure and a reaeration coefficient: 1 ö æ ∆Ox str = Da − Db = Da ç 1 − ÷ rea ø è
25.5.10
where ∆Oxstr is the change in dissolved oxygen concentration (mg O2/L), Da is the oxygen deficit above the structure (mg O2/L), Db is the oxygen deficit below the structure (mg O2/L), and rea is the reaeration coefficient. The oxygen deficit above the structure, Da, is calculated: D a = Ox sat − Ox str
25.5.11
where Oxsat is the equilibrium saturation oxygen concentration (mg O2/L), and Oxstr is the dissolved oxygen concentration in the stream (mg O2/L). Butts and Evans (1983) documents the following relationship that can be used to estimate the reaeration coefficient: rea = 1 + 0.38 ⋅ coef a ⋅ coef b ⋅ h fall ⋅ (1 − 0.11 ⋅ h fall ) ⋅ (1 + 0.046 ⋅ Twater ) 25.5.12 where rea is the reaeration coefficient, coefa is an empirical water quality factor, coefb is an empirical dam aeration coefficient, hfall is the height through which water falls (m), and Twater is the average water temperature (°C). The empirical water quality factor is assigned a value based on the condition of the stream: coefa = 1.80 in clean water coefa = 1.60 in slightly polluted water coefa = 1.00 in moderately polluted water coefa = 1.00 in moderately polluted water coefa = 0.65 in grossly polluted water
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The empirical dam aeration coefficient is assigned a value based on the type of structure: coefb = 0.70 to 0.90 for flat broad crested weir coefb = 1.05 for sharp crested weir with straight slope face coefb = 0.80 for sharp crested weir with vertical face coefb = 0.05 for sluice gates with submerged discharge Table 25-5: SWAT input variables used in in-stream oxygen calculations. Variable name RK2 AI3 AI4 RHOQ RK1 RK4 AI5 AI6 AERATION_COEF
Definition κ2,20: Reaeration rate at 20°C (day-1) α3: Rate of oxygen production per unit algal photosynthesis (mg O2/mg alg) α4: Rate of oxygen uptake per unit algal respiration (mg O2/mg alg) ρa,20: Local algal respiration rate at 20°C (day-1) κ1,20: CBOD deoxygenation rate at 20°C (day-1) κ4,20: Sediment oxygen demand rate at 20°C (mg O2/(m2⋅day)) α5: Rate of oxygen uptake per unit NH4+ oxidation (mg O2/mg N) α6: Rate of oxygen uptake per unit NO2 oxidation (mg O2/mg N) rea: Reaeration coefficient
File Name .swq .wwq .wwq .wwq .swq .swq .wwq .wwq .fig
25.6 NOMENCLATURE CNH4 CNO3 CsolP Da Db Dm FL FLz FN FP Hday Ihr
Concentration of ammonium in the reach (mg N/L) Concentration of nitrate in the reach (mg N/L) Concentration of phosphorus in solution in the reach (mg P/L) Oxygen deficit above the structure (mg O2/L) Oxygen deficit below the structure (mg O2/L) Molecular diffusion coefficient for oxygen (m2/day) Algal growth attenuation factor for light for the water column Algal growth attenuation factor for light at depth z Algal growth limitation factor for nitrogen Algal growth limitation factor for phosphorus Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) Solar radiation reaching ground during specific hour on current day of simulation (MJ m-2 h-1) Iphosyn,hrPhotosynthetically-active solar radiation reaching ground during specific hour on current day of simulation (MJ m-2 h-1) Iphosyn,z Photosynthetically-active light intensity at a depth z below the water surface (MJ/m2-hr) I phosyn ,hr Daylight average photosynthetically-active light intensity (MJ/m2-hr) Half-saturation coefficient for light (MJ/m2-hr) KL KN Michaelis-Menton half-saturation constant for nitrogen (mg N/L) KP Michaelis-Menton half-saturation constant for phosphorus (mg P/L)
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
NH4str NO2str NO3str Oxsat Oxstr TDL Twater Twat,K Twater TT
Ammonium concentration in the stream (mg N/L) Nitrite concentration in the stream (mg N/L) Nitrate concentration in the stream (mg N/L) Saturation oxygen concentration (mg O2/L) Dissolved oxygen concentration in the stream (mg O2/L) Daylength (h) Average daily water temperature (°C) Water temperature in Kelvin (273.15+°C) Average water temperature (°C) Travel time (day)
algae cbod chla coefa coefb depth fNH4 frDL frNH4 frphosyn hfall kl
Algal biomass concentration (mg alg/L) Carbonaceous biological oxygen demand concentration (mg CBOD/L) Chlorophyll a concentration (µg chla/L) Empirical water quality factor Empirical dam aeration coefficient Depth of water in the channel (m) Preference factor for ammonia nitrogen Fraction of daylight hours Fraction of algal nitrogen uptake from ammonium pool, Fraction of solar radiation that is photosynthetically active Height through which water falls (m) Light extinction coefficient (m-1)
k l,0
Non-algal portion of the light extinction coefficient (m-1)
k l,1
Linear algal self shading coefficient (m-1 (µg-chla/L)-1)
397
k l,2 Nonlinear algal self shading coefficient (m-1 (µg-chla/L)-2/3) orgNstr Organic nitrogen concentration in the stream (mg N/L) orgPstr Organic phosphorus concentration in the stream (mg P/L) rea Reaeration coefficient slp Slope of the streambed (m/m) vc Average stream velocity (m/s) z Depth from the water surface (m)
α0 α1 α2 α3 α4 α5 α6 βN,1 βN,1,20 βN,2
Ratio of chlorophyll a to algal biomass (µg chla/mg alg) Fraction of algal biomass that is nitrogen (mg N/mg alg biomass), Fraction of algal biomass that is phosphorus (mg P/mg alg biomass) Rate of oxygen production per unit algal photosynthesis (mg O2/mg alg) Rate of oxygen uptake per unit algal respiration (mg O2/mg alg) Rate of oxygen uptake per unit NH4+ oxidation (mg O2/mg N) Rate of oxygen uptake per unit NO2 oxidation (mg O2/mg N) Rate constant for biological oxidation of ammonia nitrogen (day-1 or hr-1) Rate constant for biological oxidation of ammonia nitrogen at 20°C (day-1 or hr-1) Rate constant for biological oxidation of nitrite to nitrate (day-1 or hr-1)
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βN,2,20 Rate constant for biological oxidation of nitrite to nitrate at 20°C (day-1 or hr-1) βN,3 Rate constant for hydrolysis of organic nitrogen to ammonia nitrogen (day-1 or hr-1) βN,3,20 Local rate constant for hydrolysis of organic nitrogen to NH4+ at 20°C (day-1 or hr-1) βP,4 Rate constant for mineralization of organic phosphorus (day-1 or hr-1) βP,4,20 Local rate constant for organic phosphorus mineralization at 20°C (day-1 or hr-1) ∆algae Change in algal biomass concentration (mg alg/L) ∆NH4str Change in ammonium concentration (mg N/L) ∆NO2str Change in nitrite concentration (mg N/L) ∆orgNstr Change in organic nitrogen concentration (mg N/L) ∆orgPstr Change in organic phosphorus concentration (mg P/L) ∆Oxstr Change in dissolved oxygen concentration (mg O2/L) ∆solPstr Change in solution phosphorus concentration (mg P/L) κ1 CBOD deoxygenation rate (day-1 or hr-1) κ1,20 CBOD deoxygenation rate at 20°C (day-1 or hr-1) κ2 Reaeration rate for Fickian diffusion (day-1 or hr-1) κ2,20 Reaeration rate at 20°C (day-1 or hr-1) κ3 Settling loss rate of CBOD (day-1 or hr-1) κ3,20 Settling loss rate of CBOD at 20°C (day-1 or hr-1) κ4 Sediment oxygen demand rate (mg O2/(m2⋅day)) κ4,20 Sediment oxygen demand rate at 20°C (mg O2/(m2⋅day) or mg O2/(m2⋅hr)) ρa Local respiration rate of algae (day-1 or hr-1) ρa,20 Local algal respiration rate at 20°C (day-1 or hr-1) σ1 Local settling rate for algae (m/day or m/hr) σ1,20 Local algal settling rate at 20°C (m/day or m/hr) σ2 Benthos (sediment) source rate for soluble P (mg P/m2-day or mg P/m2-hr) σ2,20 Benthos (sediment) source rate for soluble phosphorus at 20°C (mg P/m2-day or mg P/m2-hr) σ3 Benthos (sediment) source rate for ammonium (mg N/m2-day or mg N/m2-hr) σ3,20 Benthos (sediment) source rate for ammonium nitrogen at 20°C (mg N/m2-day or mg N/m2-hr) σ4 Rate coefficient of organic nitrogen settling (day-1 or hr-1) σ4,20 Local settling rate for organic nitrogen at 20°C (day-1 or hr-1) σ5 Rate coefficient for organic phosphorus settling (day-1 or hr-1) σ5,20 Local settling rate for organic phosphorus at 20°C (day-1 or hr-1) µa Local specific growth rate of algae (day-1 or hr-1) µa,20 Local specific algal growth rate at 20°C (day-1 or hr-1) µmax Maximum specific algal growth rate (day-1 or hr-1)
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25.7 REFERENCES American Public Health Association. 1985. Standard methods for the examination of water and wastewater, 16th edition. American Public Health Association, Inc. Azevedo, J., and P.R. Stout. 1974. Farm animal manures: An overview of their role in the agricultural environment (Service Manual no. 44). University of California, Agricultural Experiment Station Extension. Bowie, G.L. W.B. Mills, D.B. Porcella, C.L. Campbell, J.R. Pagenkopt, G.L. Rupp, K.M. Johnson, P.W.H. Chan, and S.A. Gherini. 1985. Rates, constants, and kinetic formulations in surface water quality modeling, 2nd ed. EPA/600/3-85/040, U.S. Environmental Protection Agency, Athens, GA. Brown, L.C. and T.O. Barnwell, Jr. 1987. The enhanced water quality models QUAL2E and QUAL2E-UNCAS documentation and user manual. EPA document EPA/600/3-87/007. USEPA, Athens, GA. Churchill, M.A., H.L. Elmore, and R.A. Buckingham. 1962. The prediction of stream reaeration rates. International Journal of Air and Water Pollution. 6: 467-504. Loehr, R.C. 1970. Drainage and pollution from beef cattle feedlots (Proceedings paper No. 7726). Journal of the Sanitary Engineers Division, American Society of Civil Engineers, 96 (SA6): 1295-1309. O’Connor, D.J. and W.E. Dobbins. 1958. Mechanism of reaeration in natural streams. Trans. ASCE. 123:641-684. Owens, M. R.W. Edwards, and J.W. Gibbs. 1964. Some reaeration studies in streams. International Journal of Air and Water Pollution 8:469-486. Paine, M.D. 1973. Confined animals and public environment (Great Plains Beef Cattle Feeding Handbook, GPE-7000/EC-72-246). Lincoln: University of Nebraska, College of Agriculture, Cooperative Extension Service.
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CHAPTER 26
EQUATIONS: IN-STREAM PESTICIDE TRANSFORMATIONS
SWAT incorporates a simple mass balance developed by Chapra (1997) to model the transformation and transport of pesticides in streams. The model assumes a well-mixed layer of water overlying a sediment layer. Only one pesticide can be routed through the stream network. The pesticide to be routed is defined by the variable IRTPEST in the .bsn file.
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26.1 PESTICIDE IN THE WATER Pesticide in a reach segment is increased through addition of mass in inflow as well as resuspension and diffusion of pesticide from the sediment layer. The amount of pesticide in a reach segment is reduced through removal in outflow as well as degradation, volatilization, settling and diffusion into the underlying sediment.
26.1.1 SOLID-LIQUID PARTITIONING Pesticides will partition into particulate and dissolved forms. The fraction of pesticide in each phase is a function of the pesticide’s partition coefficient and the reach segment’s suspended solid concentration: Fd =
1 1 + K d ⋅ conc sed
26.1.1
Fp =
K d ⋅ conc sed = 1 − Fd 1 + K d ⋅ conc sed
26.1.2
where Fd is the fraction of total pesticide in the dissolved phase, Fp is the fraction of total pesticide in the particulate phase, Kd is the pesticide partition coefficient (m3/g), and concsed is the concentration of suspended solids in the water (g/m3). The pesticide partition coefficient can be estimated from the octanol-water partition coefficient (Chapra, 1997): K d = 3.085 × 10 −8 ⋅ K ow
26.1.3
where Kd is the pesticide partition coefficient (m3/g) and Kow is the pesticide’s −3 3 (mg m −water ) ). Values for the octanol-water partition coefficient ( mg m octanol −1
octanol-water partition coefficient have been published for many chemicals. If a published value cannot be found, it can be estimated from solubility (Chapra, 1997): ′ ) log(K ow ) = 5.00 − 0.670 ⋅ log( pst sol
26.1.4
′ is the pesticide solubility (µmoles/L). The solubility in these units is where pst sol calculated:
CHAPTER 26: EQUATIONS—IN-STREAM PESTICIDE TRANSFORMATIONS
′ = pst sol
pst sol ⋅ 10 3 MW
403
26.1.5
′ is the pesticide solubility (µmoles/L), pstsol is the pesticide solubility where pst sol (mg/L) and MW is the molecular weight (g/mole).
26.1.2 DEGRADATION Pesticides in both the particulate and dissolved forms are subject to degradation. The amount of pesticide that is removed from the water via degradation is: pstdeg,wtr = k p ,aq ⋅ pst rchwtr ⋅ TT
26.1.6
where pstdeg,wtr is the amount of pesticide removed from the water via degradation (mg pst), kp,aq is the rate constant for degradation or removal of pesticide in the water (1/day), pstrchwtr is the amount of pesticide in the water at the beginning of the day (mg pst), and TT is the flow travel time (days). The rate constant is related to the aqueous half-life:
k p ,aq =
0.693 t1 / 2,aq
26.1.7
where kp,aq is the rate constant for degradation or removal of pesticide in the water (1/day), and t1/2,aq is the aqueous half-life for the pesticide (days).
26.1.3 VOLATILIZATION Pesticide in the dissolved phase is available for volatilization. The amount of pesticide removed from the water via volatilization is: pstvol ,wtr =
vv ⋅ Fd ⋅ pst rchwtr ⋅ TT depth
26.1.8
where pstvol,wtr is the amount of pesticide removed via volatilization (mg pst), vv is the volatilization mass-transfer coefficient (m/day), depth is the flow depth (m), Fd is the fraction of total pesticide in the dissolved phase, pstrchwtr is the amount of pesticide in the water (mg pst), and TT is the flow travel time (days). The volatilization mass-transfer coefficient can be calculated based on Whitman’s two-film or two-resistance theory (Whitman, 1923; Lewis and Whitman, 1924 as described in Chapra, 1997). While the main body of the gas and liquid phases are assumed to be well-mixed and homogenous, the two-film
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theory assumes that a substance moving between the two phases encounters maximum resistance in two laminar boundary layers where transfer is a function of molecular diffusion. In this type of system the transfer coefficient or velocity is: vv = K l ⋅
He H e + R ⋅ TK ⋅ (K l K g )
26.1.9
where vv is the volatilization mass-transfer coefficient (m/day), Kl is the masstransfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), He is Henry’s constant (atm m3 mole-1), R is the universal gas constant (8.206 × 10-5 atm m3 (K mole)-1), and TK is the temperature (K). For rivers where liquid flow is turbulent, the transfer coefficients are estimated using the surface renewal theory (Higbie, 1935; Danckwerts, 1951; as described by Chapra, 1997). The surface renewal model visualizes the system as consisting of parcels of water that are brought to the surface for a period of time. The fluid elements are assumed to reach and leave the air/water interface randomly, i.e. the exposure of the fluid elements to air is described by a statistical distribution. The transfer velocities for the liquid and gaseous phases are calculated: K l = rl ⋅ Dl
K g = rg ⋅ Dg
26.1.10
where Kl is the mass-transfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), Dl is the liquid molecular diffusion coefficient (m2/day), Dg is the gas molecular diffusion coefficient (m2/day), rl is the liquid surface renewal rate (1/day), and rg is the gaseous surface renewal rate (1/day). O’Connor and Dobbins (1956) defined the surface renewal rate as the ratio of the average stream velocity to depth. rl =
86400 ⋅ vc depth
26.1.11
where rl is the liquid surface renewal rate (1/day), vc is the average stream velocity (m/s) and depth is the depth of flow (m).
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405
26.1.4 SETTLING Pesticide in the particulate phase may be removed from the water layer by settling. Settling transfers pesticide from the water to the sediment layer. The amount of pesticide that is removed from the water via settling is: pst stl ,wtr =
vs ⋅ Fp ⋅ pst rchwtr ⋅ TT depth
26.1.12
where pststl,wtr is the amount of pesticide removed from the water due to settling (mg pst), vs is the settling velocity (m/day), depth is the flow depth (m), Fp is the fraction of total pesticide in the particulate phase, pstrchwtr is the amount of pesticide in the water (mg pst), and TT is the flow travel time (days).
26.1.5 OUTFLOW Pesticide is removed from the reach segment in outflow. The amount of dissolved and particulate pesticide removed from the reach segment in outflow is: pst sol ,o = Q ⋅
Fd ⋅ pst rchwtr V
pst sorb,o = Q ⋅
Fp ⋅ pst rchwtr V
26.1.13 26.1.14
where pstsol,o is the amount of dissolved pesticide removed via outflow (mg pst), pstsorb,o is the amount of particulate pesticide removed via outflow (mg pst), Q is the rate of outflow from the reach segment (m3 H2O/day), Fd is the fraction of total pesticide in the dissolved phase, Fp is the fraction of total pesticide in the particulate phase, pstrchwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the reach segment (m3 H2O). Table 26-1: SWAT input variables that pesticide partitioning. Variable Name Definition CHPST_KOC Kd: Pesticide partition coefficient (m3/g) CHPST_REA kp,aq: Rate constant for degradation or removal of pesticide in the water (1/day) CHPST_VOL vv: Volatilization mass-transfer coefficient (m/day) CHPST_STL vs: Pesticide settling velocity (m/day)
Input File .swq .swq .swq .swq
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26.2 PESTICIDE IN THE SEDIMENT Pesticide in the sediment layer underlying a reach segment is increased through addition of mass by settling and diffusion from the water. The amount of pesticide in the sediment layer is reduced through removal by degradation, resuspension, diffusion into the overlying water, and burial.
26.2.1 SOLID-LIQUID PARTITIONING As in the water layer, pesticides in the sediment layer will partition into particulate and dissolved forms. Calculation of the solid-liquid partitioning in the sediment layer requires a suspended solid concentration. The “concentration” of solid particles in the sediment layer is defined as: * conc sed =
M sed Vtot
26.2.1
* where conc sed is the “concentration” of solid particles in the sediment layer
(g/m3), Msed is the mass of solid particles in the sediment layer (g) and Vtot is the total volume of the sediment layer (m3). Mass and volume are also used to define the porosity and density of the sediment layer. In the sediment layer, porosity is the fraction of the total volume in the liquid phase:
φ=
Vwtr Vtot
26.2.2
where φ is the porosity, Vwtr is the volume of water in the sediment layer (m3) and Vtot is the total volume of the sediment layer (m3). The fraction of the volume in the solid phase can then be defined as: 1−φ =
Vsed Vtot
26.2.3
where φ is the porosity, Vsed is the volume of solids in the sediment layer (m3) and Vtot is the total volume of the sediment layer (m3). The density of sediment particles is defined as:
ρs =
M sed Vsed
26.2.4
CHAPTER 26: EQUATIONS—IN-STREAM PESTICIDE TRANSFORMATIONS
407
where ρs is the particle density (g/m3), Msed is the mass of solid particles in the sediment layer (g), and Vsed is the volume of solids in the sediment layer (m3). Solving equation 26.2.3 for Vtot and equation 26.2.4 for Msed and substituting into equation 26.2.1 yields: * conc sed = (1 − φ ) ⋅ ρ s
26.2.5
* is the “concentration” of solid particles in the sediment layer where conc sed
(g/m3), φ is the porosity, and ρs is the particle density (g/m3). Assuming φ = 0.5 and ρs = 2.6 × 106 g/m3, the “concentration” of solid particles in the sediment layer is 1.3 × 106 g/m3. The fraction of pesticide in each phase is then calculated: Fd ,sed =
1 φ + (1 − φ ) ⋅ ρ s ⋅ K d
Fp ,sed = 1 − Fd ,sed
26.2.6 26.2.7
where Fd,sed is the fraction of total sediment pesticide in the dissolved phase, Fp,sed is the fraction of total sediment pesticide in the particulate phase, φ is the porosity,
ρs is the particle density (g/m3), and Kd is the pesticide partition coefficient (m3/g). The pesticide partition coefficient used for the water layer is also used for the sediment layer.
26.2.2 DEGRADATION Pesticides in both the particulate and dissolved forms are subject to degradation. The amount of pesticide that is removed from the sediment via degradation is: pstdeg,sed = k p ,sed ⋅ pst rchsed
26.2.8
where pstdeg,sed is the amount of pesticide removed from the sediment via degradation (mg pst), kp,sed is the rate constant for degradation or removal of pesticide in the sediment (1/day), and pstrchsed is the amount of pesticide in the sediment (mg pst). The rate constant is related to the sediment half-life: k p ,sed =
0.693 t1 / 2,sed
26.2.9
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where kp,sed is the rate constant for degradation or removal of pesticide in the sediment (1/day), and t1/2,sed is the sediment half-life for the pesticide (days).
26.2.3 RESUSPENSION Pesticide in the sediment layer is available for resuspension. The amount of pesticide that is removed from the sediment via resuspension is: pst rsp ,wtr =
vr ⋅ pst rchsed ⋅ TT depth
26.2.10
where pstrsp,wtr is the amount of pesticide removed via resuspension (mg pst), vr is the resuspension velocity (m/day), depth is the flow depth (m), pstrchsed is the amount of pesticide in the sediment (mg pst), and TT is the flow travel time (days). Pesticide removed from the sediment layer by resuspension is added to the water layer.
26.2.4 DIFFUSION Pesticide in the dissolved phase is available for diffusion. Diffusion transfers pesticide between the water and sediment layers. The direction of movement is controlled by the pesticide concentration. Pesticide will move from areas of high concentration to areas of low concentration. The amount of pesticide that is transferred between the water and sediment by diffusion is: pstdif =
vd ⋅ (Fd ,sed ⋅ pst rchsed − Fd ⋅ pst rchwtr ) ⋅ TT depth
26.2.11
where pstdif is the amount of pesticide transferred between the water and sediment by diffusion (mg pst), vd is the rate of diffusion or mixing velocity (m/day), depth is the flow depth (m), Fd,sed is the fraction of total sediment pesticide in the dissolved phase, pstrchsed is the amount of pesticide in the sediment (mg pst), Fd is the fraction of total water layer pesticide in the dissolved phase, pstrchwtr is the amount of pesticide in the water (mg pst), and TT is the flow duration (days). If Fd ,sed ⋅ pst rchsed > Fd ⋅ pst rchwtr , pstdif is transferred from the sediment to the water
layer. If, Fd ,sed ⋅ pst rchsed < Fd ⋅ pst rchwtr , pstdif is transferred from the water to the sediment layer.
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The diffusive mixing velocity, vd, can be estimated from the empirically derived formula (Chapra, 1997): vd =
69.35 ⋅ φ ⋅ MW − 2 / 3 365
26.2.12
where vd is the rate of diffusion or mixing velocity (m/day), φ is the sediment porosity, and MW is the molecular weight of the pesticide compound.
26.2.5 BURIAL Pesticide in the sediment layer may be lost by burial. The amount of pesticide that is removed from the sediment via burial is: pstbur =
vb ⋅ pst rchsed Dsed
26.2.13
where pstbur is the amount of pesticide removed via burial (mg pst), vb is the burial velocity (m/day), Dsed is the depth of the active sediment layer (m), and pstrchsed is the amount of pesticide in the sediment (mg pst). Table 26-2: SWAT input variables related to pesticide in the sediment. Variable Name Definition CHPST_KOC Kd: Pesticide partition coefficient (m3/g) SEDPST_REA kp,sed: Rate constant for degradation or removal of pesticide in the sediment (1/day) CHPST_RSP vr: Resuspension velocity (m/day) SEDPST_ACT Dsed: Depth of the active sediment layer (m) CHPST_MIX vd: Rate of diffusion or mixing velocity (m/day) SEDPST_BRY vb: Pesticide burial velocity (m/day)
Input File .swq .swq .swq .swq .swq .swq
26.3 MASS BALANCE The processes described above can be combined into mass balance equations for the well-mixed reach segment and the well-mixed sediment layer: ∆pst rchwtr = pstin − ( pst sol ,o + pst sorb,o ) − pstdeg ,wtr − pstvol ,wtr − pst stl ,wtr + pst rsp ,wtr ± pst dif 26.3.1 ∆pst rchsed = − pstdeg,sed + pst stl ,wtr − pst rsp ,wtr − pstbur ± pstdif
26.3.2
where ∆pstrchwtr is the change in pesticide mass in the water layer (mg pst), ∆pstrchsed is the change in pesticide mass in the sediment layer (mg pst), pstin is the
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pesticide added to the reach segment via inflow (mg pst), pstsol,o is the amount of dissolved pesticide removed via outflow (mg pst), pstsorb,o is the amount of particulate pesticide removed via outflow (mg pst), pstdeg,wtr is the amount of pesticide removed from the water via degradation (mg pst), pstvol,wtr is the amount of pesticide removed via volatilization (mg pst), pststl,wtr is the amount of pesticide removed from the water due to settling (mg pst), pstrsp,wtr is the amount of pesticide removed via resuspension (mg pst), pstdif is the amount of pesticide transferred between the water and sediment by diffusion (mg pst), pstdeg,sed is the amount of pesticide removed from the sediment via degradation (mg pst), pstbur is the amount of pesticide removed via burial (mg pst)
26.4 NOMENCLATURE Dg Dl Dsed Fd Fd,sed Fp Fp,sed He Kd Kg Kl Msed MW Q R Vsed Vtot Vwtr
Gas molecular diffusion coefficient (m2/day) Liquid molecular diffusion coefficient (m2/day) Depth of the active sediment layer (m) Fraction of total pesticide in the dissolved phase Fraction of total sediment pesticide in the dissolved phase Fraction of total pesticide in the particulate phase Fraction of total sediment pesticide in the particulate phase Henry’s constant (atm m3 mole-1) Pesticide partition coefficient (m3/g) Mass-transfer velocity in the gaseous laminar layer (m/day) Mass-transfer velocity in the liquid laminar layer (m/day) Mass of solid phase in the sediment layer (g) Molecular weight of the pesticide compound Rate of outflow from the reach segment (m3 H2O/day) Universal gas constant (8.206 × 10-5 atm m3 (K mole)-1) Volume of solids in the sediment layer (m3) Total volume of the sediment layer (m3) Volume of water in the sediment layer (m3)
concsed Concentration of suspended solids in the water (g/m3) * conc sed “Concentration” of solid particles in the sediment layer (g/m3) depth Depth of flow (m) kp,aq Rate constant for degradation or removal of pesticide in the water (1/day) kp,sed Rate constant for degradation or removal of pesticide in the sediment (1/day) pstbur Amount of pesticide removed via burial (mg pst) pstdeg,sed Amount of pesticide removed from the sediment via degradation (mg pst)
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pstdeg,wtr Amount of pesticide removed from the water via degradation (mg pst) pstdif Amount of pesticide transferred between the water and sediment by diffusion (mg pst) pstrchsed Amount of pesticide in the sediment (mg pst) pstrchwtr Amount of pesticide in the water (mg pst) pstrsp,wtr Amount of pesticide removed from sediment via resuspension (mg pst) pstsol,o Amount of dissolved pesticide removed via outflow (mg pst) pstsorb,o Amount of particulate pesticide removed via outflow (mg pst) pststl,wtr Amount of pesticide removed from the water due to settling (mg pst) pstvol,wtr Amount of pesticide removed via volatilization (mg pst) Gaseous surface renewal rate (1/day) rg Liquid surface renewal rate (1/day) rl t1/2,aq Aqueous half-life for the pesticide (days) t1/2,sed Sediment half-life for the pesticide (days) Pesticide burial velocity (m/day) vb Average stream velocity (m/s) vc Rate of diffusion or mixing velocity (m/day) vd Resuspension velocity (m/day) vr Settling velocity (m/day) vs Volatilization mass-transfer coefficient (m/day) vv ∆pstrchwtr Change in pesticide mass in the water layer (mg pst) ∆pstrchsed Change in pesticide mass in the sediment layer (mg pst) φ Porosity ρs Particle density (g/m3)
26.5 REFERENCES Chapra, S.C. 1997. Surface water-quality modeling. WCB/McGraw-Hill, Boston, MA. Danckwerts, P.V. 1951. Significance of liquid-film coefficients in gas absorption. Ind. Eng. Chem. 43:1460-1467. Higbie, R. 1935. The rate of adsorption of a pure gas into a still liquid during short periods of exposure. Trans. Amer. Inst. Chem. Engin. 31:365-389. Lewis, W.K. and W.G. Whitman. 1924. Principles of gas absorption. Ind. Eng. Chem. 16:1215-1220. Whitman, W.G. 1923. The two-film theory of gas adsorption. Chem. Metallurg. Eng. 29:146-148.
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WATER BODIES Impoundment structures modify the movement of water in the channel network by lowering the peak flow and volume of flood discharges. Because impoundments slow down the flow of water, sediment will fall from suspension, removing nutrient and chemicals adsorbed to the soil particles.
CHAPTER 27
EQUATIONS: IMPOUNDMENT WATER ROUTING
Impoundments play an important role in water supply and flood control. SWAT models four types of water bodies: ponds, wetlands, depressions/potholes, and reservoirs. Ponds, wetlands, and depressions/potholes are located within a subbasin off the main channel. Water flowing into these water bodies must originate from the subbasin in which the water body is located. Reservoirs are located on the main channel network. They receive water from all subbasins upstream of the water body.
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27.1 RESERVOIRS A reservoir is an impoundment located on the main channel network of a watershed. No distinction is made between naturally-occurring and man-made structures. The features of an impoundment are shown in Figure 27.1.
Figure 27.1: Components of a reservoir with flood water detention features.
The water balance for a reservoir is: V = Vstored + V flowin − V flowout + V pcp − Vevap − Vseep
27.1.1
where V is the volume of water in the impoundment at the end of the day (m3 H2O), Vstored is the volume of water stored in the water body at the beginning of the day (m3 H2O), Vflowin is the volume of water entering the water body during the day (m3 H2O), Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), Vpcp is the volume of precipitation falling on the water body during the day (m3 H2O), Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), and Vseep is the volume of water lost from the water body by seepage (m3 H2O).
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27.1.1 SURFACE AREA The surface area of the reservoir is needed to calculate the amount of precipitation falling on the water body as well as the amount of evaporation and seepage. Surface area varies with change in the volume of water stored in the reservoir. The surface area is updated daily using the equation: SA = β sa ⋅ V expsa
27.1.2
where SA is the surface area of the water body (ha), βsa is a coefficient, V is the volume of water in the impoundment (m3 H2O), and expsa is an exponent. The coefficient, βsa, and exponent, expsa, are calculated by solving equation 27.1.2 using two known points. The two known points are surface area and volume information provided for the principal and emergency spillways. expsa =
β sa
log10 (SAem ) − log10 (SApr ) log10 (Vem ) − log10 (V pr )
æ SA ö = çç em ÷÷ è Vem ø
27.1.3
expsa
27.1.4
where SAem is the surface area of the reservoir when filled to the emergency spillway (ha), SApr is the surface area of the reservoir when filled to the principal spillway (ha), Vem is the volume of water held in the reservoir when filled to the emergency spillway (m3 H2O), and Vpr is the volume of water held in the reservoir when filled to the principal spillway (m3 H2O).
27.1.2 PRECIPITATION The volume of precipitation falling on the reservoir during a given day is calculated: V pcp = 10 ⋅ Rday ⋅ SA
27.1.5
where Vpcp is the volume of water added to the water body by precipitation during the day (m3 H2O), Rday is the amount of precipitation falling on a given day (mm H2O), and SA is the surface area of the water body (ha).
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27.1.3 EVAPORATION The volume of water lost to evaporation on a given day is calculated: Vevap = 10 ⋅ η ⋅ Eo ⋅ SA
27.1.6
where Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), η is an evaporation coefficient (0.6), Eo is the potential evapotranspiration for a given day (mm H2O), and SA is the surface area of the water body (ha).
27.1.4 SEEPAGE The volume of water lost by seepage through the bottom of the reservoir on a given day is calculated: Vseep = 240 ⋅ K sat ⋅ SA
27.1.7
where Vseep is the volume of water lost from the water body by seepage (m3 H2O), Ksat is the effective saturated hydraulic conductivity of the reservoir bottom (mm/hr), and SA is the surface area of the water body (ha).
27.1.5 OUTFLOW The volume of outflow may be calculated using one of four different methods: measured daily outflow, measured monthly outflow, average annual release rate for uncontrolled reservoir, controlled outflow with target release.
27.1.5.1 MEASURED DAILY OUTFLOW When measured daily outflow (IRESCO = 3) is chosen as the method to calculate reservoir outflow, the user must provide a file with the outflow rate for every day the reservoir is simulated in the watershed. The volume of outflow from the reservoir is then calculated: V flowout = 86400 ⋅ qout
27.1.8
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), and qout is the outflow rate (m3/s).
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419
27.1.5.2 MEASURED MONTHLY OUTFLOW When measured monthly outflow (IRESCO = 1) is chosen as the method to calculate reservoir outflow, the user must provide a file with the average daily outflow rate for every month the reservoir is simulated in the watershed. The volume of outflow from the reservoir is then calculated using equation 27.1.8.
27.1.5.3 AVERAGE ANNUAL RELEASE RATE FOR UNCONTROLLED RESERVOIR
When the average annual release rate (IRESCO = 0) is chosen as the method to calculate reservoir outflow, the reservoir releases water whenever the reservoir volume exceeds the principal spillway volume, Vpr. If the reservoir volume is greater than the principal spillway volume but less than the emergency spillway volume, the amount of reservoir outflow is calculated: V flowout = V − V pr
if V − V pr < qrel ⋅ 86400
27.1.9
V flowout = qrel ⋅ 86400
if V − V pr > qrel ⋅ 86400
27.1.10
If the reservoir volume exceeds the emergency spillway volume, the amount of outflow is calculated: V flowout = (V − Vem ) + (Vem − V pr )
if Vem − V pr < qrel ⋅ 86400
27.1.11
V flowout = (V − Vem ) + qrel ⋅ 86400
if Vem − V pr > qrel ⋅ 86400
27.1.12
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the reservoir (m3 H2O), Vpr is the volume of water held in the reservoir when filled to the principal spillway (m3 H2O), Vem is the volume of water held in the reservoir when filled to the emergency spillway (m3 H2O), and qrel is the average daily principal spillway release rate (m3/s).
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27.1.5.4 TARGET RELEASE FOR CONTROLLED RESERVOIR When target release (IRESCO = 2) is chosen as the method to calculate reservoir outflow, the reservoir releases water as a function of the desired target storage. The target release approach tries to mimic general release rules that may be used by reservoir operators. Although the method is simplistic and cannot account for all decision criteria, it can realistically simulate major outflow and low flow periods. For the target release approach, the principal spillway volume corresponds to maximum flood control reservation while the emergency spillway volume corresponds to no flood control reservation. The model requires the beginning and ending month of the flood season. In the nonflood season, no flood control reservation is required, and the target storage is set at the emergency spillway volume. During the flood season, the flood control reservation is a function of soil water content. The flood control reservation for wet ground conditions is set at the maximum. For dry ground conditions, the flood control reservation is set at 50% of the maximum. The target storage may be specified by the user on a monthly basis or it can be calculated as a function of flood season and soil water content. If the target storage is specified: Vtarg = starg
27.1.13
where Vtarg is the target reservoir volume for a given day (m3 H2O), and starg is the target reservoir volume specified for a given month (m3 H2O). If the target storage is not specified, the target reservoir volume is calculated: Vtarg = Vem
Vtarg
if mon fld ,beg < mon < mon fld ,end
æ é SW ùö , 1ú ÷ ç1 − min ê ë FC ûø = V pr + è ⋅ (Vem − V pr ) 2
27.1.14
CHAPTER 27: EQUATIONS—IMPOUNDMENT WATER ROUTING
if mon ≤ mon fld ,beg or mon ≥ mon fld ,end
421
27.1.15
where Vtarg is the target reservoir volume for a given day (m3 H2O), Vem is the volume of water held in the reservoir when filled to the emergency spillway (m3 H2O), Vpr is the volume of water held in the reservoir when filled to the principal spillway (m3 H2O), SW is the average soil water content in the subbasin (mm H2O), FC is the water content of the subbasin soil at field capacity (mm H2O), mon is the month of the year, monfld,beg is the beginning month of the flood season, and monfld,end is the ending month of the flood season. Once the target storage is defined, the outflow is calculated: V flowout =
V − Vtarg
27.1.16
NDtarg
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the reservoir (m3 H2O), Vtarg is the target reservoir volume for a given day (m3 H2O), and NDtarg is the number of days required for the reservoir to reach target storage. Once outflow is determined using one of the preceding four methods, the user may specify maximum and minimum amounts of discharge that the initial outflow estimate is checked against. If the outflow doesn’t meet the minimum discharge or exceeds the maximum specified discharge, the amount of outflow is altered to meet the defined criteria. ′ V flowout = V flowout
′ if qrel ,mn ⋅ 86400 ≤ V flowout ≤ qrel ,mx ⋅ 86400
27.1.17
V flowout = qrel ,mn ⋅ 86400
′ if V flowout < qrel ,mn ⋅ 86400
27.1.18
V flowout = qrel ,mx ⋅ 86400
′ if V flowout > qrel ,mx ⋅ 86400
27.1.19
where Vflowout is the volume of water flowing out of the water body during the day ′ (m3 H2O), V flowout is the initial estimate of the volume of water flowing out of the
water body during the day (m3 H2O), qrel,mn is the minimum average daily outflow for the month (m3/s), and qrel,mx is the maximum average daily outflow for the month (m3/s).
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Table 27-1: SWAT input variables that pertain to reservoirs. Variable name Definition RES_ESA SAem: Surface area of the reservoir when filled to the emergency spillway (ha) RES_PSA SApr: Surface area of the reservoir when filled to the principal spillway (ha) RES_EVOL Vem: Volume of water held in the reservoir when filled to the emergency spillway (104 m3 H2O) RES_PVOL Vpr: Volume of water held in the reservoir when filled to the principal spillway (104 m3 H2O) RES_K Ksat: Effective saturated hydraulic conductivity of the reservoir bottom (mm/hr) IRESCO Outflow method RES_OUTFLOW qout: Outflow rate (m3/s) RESOUT qout: Outflow rate (m3/s) RES_RR qrel: Average daily principal spillway release rate (m3/s) STARG(mon) starg: Target reservoir volume specified for a given month (m3 H2O) IFLOD1R monfld,beg: Beginning month of the flood season IFLOD2R monfld,end: Ending month of the flood season NDTARGR NDtarg: Number of days required for the reservoir to reach target storage OFLOWMN(mon) qrel,mn: Minimum average daily outflow for the month (m3/s) OFLOWMX(mon) qrel,mx: Maximum average daily outflow for the month (m3/s)
File Name .res .res .res .res .res .res resdayo.dat resmono.dat .res .res .res .res .res .res .res
27.2 PONDS/WETLANDS Ponds and wetlands are water bodies located within subbasins that received inflow from a fraction of the subbasin area. The algorithms used to model these two types of water bodies differ only in the options allowed for outflow calculation. The water balance for a pond or wetland is: V = Vstored + V flowin − V flowout + V pcp − Vevap − Vseep
27.2.1
where V is the volume of water in the impoundment at the end of the day (m3 H2O), Vstored is the volume of water stored in the water body at the beginning of the day (m3 H2O), Vflowin is the volume of water entering the water body during the day (m3 H2O), Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), Vpcp is the volume of precipitation falling on the water body during the day (m3 H2O), Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), and Vseep is the volume of water lost from the water body by seepage (m3 H2O).
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27.2.1 SURFACE AREA The surface area of the pond or wetland is needed to calculate the amount of precipitation falling on the water body as well as the amount of evaporation and seepage. Surface area varies with change in the volume of water stored in the impoundment. The surface area is updated daily using the equation: SA = β sa ⋅ V expsa
27.2.2
where SA is the surface area of the water body (ha), βsa is a coefficient, V is the volume of water in the impoundment (m3 H2O), and expsa is an exponent. The coefficient, βsa, and exponent, expsa, are calculated by solving equation 27.1.2 using two known points. For ponds, the two known points are surface area and volume information provided for the principal and emergency spillways. expsa =
β sa
log10 (SAem ) − log10 (SApr ) log10 (Vem ) − log10 (V pr )
æ SA ö = çç em ÷÷ è Vem ø
27.2.3
expsa
27.2.4
where SAem is the surface area of the pond when filled to the emergency spillway (ha), SApr is the surface area of the pond when filled to the principal spillway (ha), Vem is the volume of water held in the pond when filled to the emergency spillway (m3 H2O), and Vpr is the volume of water held in the pond when filled to the principal spillway (m3 H2O). For wetlands, the two known points are surface area and volume information provided for the maximum and normal water levels. expsa =
log10 (SAmx ) − log10 (SAnor ) log10 (Vmx ) − log10 (Vnor )
æ SA ö β sa = çç mx ÷÷ è Vmx ø
27.2.5
expsa
27.2.6
where SAmx is the surface area of the wetland when filled to the maximum water level (ha), SAnor is the surface area of the wetland when filled to the normal water level (ha), Vmx is the volume of water held in the wetland when filled to the
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maximum water level (m3 H2O), and Vnor is the volume of water held in the wetland when filled to the normal water level (m3 H2O).
27.2.2 PRECIPITATION The volume of precipitation falling on the pond or wetland during a given day is calculated: V pcp = 10 ⋅ Rday ⋅ SA
27.2.7
where Vpcp is the volume of water added to the water body by precipitation during the day (m3 H2O), Rday is the amount of precipitation falling on a given day (mm H2O), and SA is the surface area of the water body (ha).
27.2.3 INFLOW The volume of water entering the pond or wetland on a given day is calculated: V flowin = frimp ⋅ 10 ⋅ (Qsurf + Q gw + Qlat ) ⋅ ( Area − SA)
27.2.8
where Vflowin is the volume of water flowing into the water body on a given day (m3 H2O), frimp is the fraction of the subbasin area draining into the impoundment, Qsurf is the surface runoff from the subbasin on a given day (mm H2O), Qgw is the groundwater flow generated in a subbasin on a given day (mm H2O), Qlat is the lateral flow generated in a subbasin on a given day (mm H2O), Area is the subbasin area (ha), and SA is the surface area of the water body (ha). The volume of water entering the pond or wetland is subtracted from the surface runoff, lateral flow and groundwater loadings to the main channel.
27.2.4 EVAPORATION The volume of water lost to evaporation on a given day is calculated: Vevap = 10 ⋅ η ⋅ Eo ⋅ SA
27.2.9
where Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), η is an evaporation coefficient (0.6), Eo is the potential evapotranspiration for a given day (mm H2O), and SA is the surface area of the water body (ha).
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27.2.5 SEEPAGE The volume of water lost by seepage through the bottom of the pond or wetland on a given day is calculated: Vseep = 240 ⋅ K sat ⋅ SA
27.2.10
where Vseep is the volume of water lost from the water body by seepage (m3 H2O), Ksat is the effective saturated hydraulic conductivity of the pond or wetland bottom (mm/hr), and SA is the surface area of the water body (ha).
27.2.6 OUTFLOW The primary difference between ponds and wetlands is the method in which the outflow is calculated.
27.2.6.1 POND OUTFLOW Pond outflow is calculated as a function of target storage. The target storage varies based on flood season and soil water content. The target pond volume is calculated: Vtarg = Vem
Vtarg
if mon fld ,beg < mon < mon fld ,end
27.2.11
æ é SW ùö , 1ú ÷ ç1 − min ê ë FC ûø = V pr + è ⋅ (Vem − V pr ) 2
if mon ≤ mon fld ,beg or mon ≥ mon fld ,end
27.2.12
where Vtarg is the target pond volume for a given day (m3 H2O), Vem is the volume of water held in the pond when filled to the emergency spillway (m3 H2O), Vpr is the volume of water held in the pond when filled to the principal spillway (m3 H2O), SW is the average soil water content in the subbasin (mm H2O), FC is the water content of the subbasin soil at field capacity (mm H2O), mon is the month of the year, monfld,beg is the beginning month of the flood season, and monfld,end is the ending month of the flood season. Once the target storage is defined, the outflow is calculated:
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V flowout =
V − Vtarg
27.2.13
NDtarg
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the pond (m3 H2O), Vtarg is the target pond volume for a given day (m3 H2O), and NDtarg is the number of days required for the pond to reach target storage.
27.2.6.2 WETLAND OUTFLOW The wetland releases water whenever the water volume exceeds the normal storage volume, Vnor. Wetland outflow is calculated: V flowout = 0 V flowout =
V − Vnor 10
V flowout = V − Vmx
if V < Vnor
27.2.14
if Vnor ≤ V ≤ Vmx
27.2.15
if V > Vmx
27.2.16
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the wetland (m3 H2O), Vmx is the volume of water held in the wetland when filled to the maximum water level (m3 H2O), and Vnor is the volume of water held in the wetland when filled to the normal water level (m3 H2O). Table 27-2: SWAT input variables that pertain to ponds and wetlands. Variable name Definition PND_ESA SAem: Surface area of the pond when filled to the emergency spillway (ha) PND_PSA SApr: Surface area of the pond when filled to the principal spillway (ha) PND_EVOL Vem: Volume of water held in the pond when filled to the emergency spillway (104 m3 H2O) PND_PVOL Vpr: Volume of water held in the pond when filled to the principal spillway (104 m3 H2O) WET_MXSA SAmx: Surface area of the wetland when filled to the maximum water level (ha) WET_NSA SAnor: Surface area of the wetland when filled to the normal water level (ha) WET_MXVOL Vmx: Volume of water held in the wetland when filled to the maximum water level (m3 H2O) WET_NVOL Vnor: Volume of water held in the wetland when filled to the normal water level (m3 H2O) PND_FR frimp: Fraction of the subbasin area draining into the pond WET_FR frimp: Fraction of the subbasin area draining into the wetland
File Name .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd
CHAPTER 27: EQUATIONS—IMPOUNDMENT WATER ROUTING Table 27-2, cont.: SWAT input variables that pertain to ponds and wetlands Variable name Definition PND_K Ksat: Effective saturated hydraulic conductivity of the pond bottom (mm/hr) WET_K Ksat: Effective saturated hydraulic conductivity of the wetland bottom (mm/hr) IFLOD1 monfld,beg: Beginning month of the flood season IFLOD2 monfld,end: Ending month of the flood season NDTARG NDtarg: Number of days required for the reservoir to reach target storage
427
File Name .pnd .pnd .pnd .pnd .pnd
27.3 DEPRESSIONS/POTHOLES In areas of low relief and/or young geologic development, the drainage network may be poorly developed. Watersheds in these areas may have many closed depressional areas, referred to as potholes. Runoff generated within these areas flows to the lowest portion of the pothole rather than contributing to flow in the main channel. Other systems that are hydrologically similar to potholes include playa lakes and fields that are artifically impounded for rice production. The algorithms reviewed in this section are used to model these types of systems. To define an HRU as a pothole, the user must set IPOT (.hru) to the HRU number. To initiate water impoundment, a release/impound operation must be placed in the .mgt file. The water balance for a pothole is: V = Vstored + V flowin − V flowout + V pcp − Vevap − Vseep
27.3.1
where V is the volume of water in the impoundment at the end of the day (m3 H2O), Vstored is the volume of water stored in the water body at the beginning of the day (m3 H2O), Vflowin is the volume of water entering the water body during the day (m3 H2O), Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), Vpcp is the volume of precipitation falling on the water body during the day (m3 H2O), Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), and Vseep is the volume of water lost from the water body by seepage (m3 H2O).
27.3.1 SURFACE AREA The surface area of the pothole is needed to calculate the amount of precipitation falling on the water body as well as the amount of evaporation and
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seepage. Surface area varies with change in the volume of water stored in the impoundment. For surface area calculations, the pothole is assumed to be coneshaped. The surface area is updated daily using the equation:
π SA = 4 10
æ 3 ⋅V ö ⋅ çç ÷÷ è π ⋅ slp ø
2/3
27.3.2
where SA is the surface area of the water body (ha), V is the volume of water in the impoundment (m3 H2O), and slp is the slope of the HRU (m/m).
27.3.2 PRECIPITATION The volume of precipitation falling on the pothole during a given day is calculated: V pcp = 10 ⋅ Rday ⋅ SA
27.3.3
where Vpcp is the volume of water added to the water body by precipitation during the day (m3 H2O), Rday is the amount of precipitation falling on a given day (mm H2O), and SA is the surface area of the water body (ha).
27.3.3 INFLOW Water entering the pothole on a given day may be contributed from any HRU in the subbasin. To route a portion of the flow from an HRU into a pothole, the variable IPOT (.hru) is set to the number of the HRU containing the pothole and POT_FR (.hru) is set to the fraction of the HRU area that drains into the pothole. This must be done for each HRU contributing flow to the pothole. Water routing from other HRUs is performed only during the period that water impoundment has been activated (release/impound operation in .mgt). Water may also be added to the pothole with an irrigation operation in the management file (.mgt). Chapter 21 reviews the irrigation operation. The inflow to the pothole is calculated: V flowin = irr +
å [ fr n
hru =1
pot ,hru
⋅ 10 ⋅ (Qsurf ,hru + Q gw ,hru + Qlat ,hru ) ⋅ area hru ]
27.3.4
where Vflowin is the volume of water flowing into the pothole on a given day (m3 H2O), irr is the amount of water added through an irrigation operation on a given day (m3 H2O), n is the number of HRUs contributing water to the pothole, frpot,hru
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429
is the fraction of the HRU area draining into the pothole, Qsurf,hru is the surface runoff from the HRU on a given day (mm H2O), Qgw,hru is the groundwater flow generated in the HRU on a given day (mm H2O), Qlat,hru is the lateral flow generated in the HRU on a given day (mm H2O), and areahru is the HRU area (ha).
27.3.4 EVAPORATION The volume of water lost to evaporation on a given day is calculated: æ LAI ö÷ Vevap = 10 ⋅ ç1 − ⋅ Eo ⋅ SA ç LAI evap ÷ø è
if LAI < LAI evap
27.3.5
Vevap = 0
if LAI ≥ LAI evap
27.3.6
where Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), LAI is the leaf area index of the plants growing in the pothole, LAIevap is the leaf area index at which no evaporation occurs from the water surface, Eo is the potential evapotranspiration for a given day (mm H2O), and SA is the surface area of the water body (ha).
27.3.5 SEEPAGE The volume of water lost by seepage through the bottom of the pothole on a given day is calculated as a function of the water content of the soil profile beneath the pothole. Vseep = 240 ⋅ K sat ⋅ SA
if SW < 0.5 ⋅ FC
27.3.7
SW ö æ Vseep = 240 ⋅ ç1 − ÷ ⋅ K sat ⋅ SA FC ø è
if 0.5 ⋅ FC ≤ SW < FC
27.3.8
Vseep = 0
if SW ≥ FC
27.3.9
where Vseep is the volume of water lost from the water body by seepage (m3 H2O), Ksat is the effective saturated hydraulic conductivity of the 1st soil layer in the profile (mm/hr), SA is the surface area of the water body (ha), SW is the soil water content of the profile on a given day (mm H2O), and FC is the field capacity soil water content (mm H2O). Water lost from the pothole by seepage is added to the soil profile.
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27.3.6 OUTFLOW Water may be removed from the pothole in three different types of outflow. When the volume of water in the pothole exceeds the maximum storage, the excess water is assumed to overflow and enter the main channel in the subbasin. When the retaining wall or berm is removed (this is done with a release/impound operation in the management file), all water stored in the pothole enters the main channel. The third type of flow from the pothole is via drainage tiles installed in the pothole.
27.3.6.1 OVERFLOW Pothole outflow caused by overflow is calculated: V flowout = V − V pot ,mx
if V > V pot . mx
27.3.10
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the pothole (m3 H2O), and Vpot,mx is the maximum amount of water that can be stored in the pothole (m3 H2O).
27.3.6.2 RELEASE OPERATION When a release operation is scheduled, all water in the pothole becomes outflow: V flowout = V
27.3.11
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), and V is the volume of water stored in the pothole (m3 H2O).
27.3.6.3 TILE FLOW When drainage tiles are installed in a pothole, the pothole will contribute water to the main channel through tile flow. The pothole outflow originating from tile drainage is: V flowout = qtile ⋅ 86400
if V > qtile ⋅ 86400
27.3.12
V flowout = V
if V ≤ qtile ⋅ 86400
27.3.13
CHAPTER 27: EQUATIONS—IMPOUNDMENT WATER ROUTING
431
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), qtile is the average daily tile flow rate (m3/s), and V is the volume of water stored in the pothole (m3 H2O). Table 27-3: SWAT input variables that pertain to potholes. Variable name Definition IPOT Number of HRU that is impounding water (that contains the pothole) Variables in release/impound operation line: MONTH/DAY or HUSC Timing of release/impound operation.
MGT_OP IREL_IMP
SLOPE POT_FR EVLAI POT_VOLX POT_TILE
Operation code. MGT_OP = 13 for release/impound operation Release/impound action code: 0: impound, 1: release slp: Slope of the HRU (m/m) frpot: Fraction of the HRU area draining into the pothole LAIevap: Leaf area index at which no evaporation occurs from the water surface Vpot,mx: Maximum amount of water that can be stored in the pothole (m3 H2O) qtile: Average daily tile flow rate (m3/s)
File Name .hru .mgt .mgt .mgt .hru .hru .bsn .hru .hru
27.4 NOMENCLATURE Area Subbasin area (ha) Potential evapotranspiration for a given day (mm H2O) Eo FC Water content of the soil at field capacity (mm H2O) Ksat Effective saturated hydraulic conductivity of the reservoir bottom (mm/hr) LAI Leaf area index of the plants growing in the pothole LAIevap Leaf area index at which no evaporation occurs from the water surface NDtarg Number of days required for the reservoir to reach target storage Qgw Groundwater flow generated in a subbasin on a given day (mm H2O) Lateral flow generated in a subbasin on a given day (mm H2O) Qlat Qsurf Surface runoff from the subbasin on a given day (mm H2O) Rday Amount of precipitation falling on a given day (mm H2O) SA Surface area of the water body (ha) SAem Surface area of the reservoir or pond when filled to the emergency spillway (ha) SAmx Surface area of the wetland when filled to the maximum water level (ha) SAnor Surface area of the wetland when filled to the normal water level (ha) SApr Surface area of the reservoir when filled to the principal spillway (ha) SW Average soil water content (mm H2O) V Volume of water in the impoundment at the end of the day (m3 H2O) Volume of water held in the reservoir when filled to the emergency spillway (m3 Vem H2O) Vevap Volume of water removed from the water body by evaporation during the day (m3 H2O) Vflowin Volume of water entering the water body during the day (m3 H2O)
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Vflowout Volume of water flowing out of the water body during the day (m3 H2O) ′ V flowout Initial estimate of the volume of water flowing out of the water body during the day (m3 H2O) Vmx Volume of water held in the wetland when filled to the maximum water level (m3 H2O) Vnor Volume of water held in the wetland when filled to the normal water level (m3 H2O) Vpcp Volume of precipitation falling on the water body during the day (m3 H2O) Vpot,mx Maximum amount of water that can be stored in the pothole (m3 H2O) Vpr Volume of water held in the reservoir when filled to the principal spillway (m3 H2O) Vseep Volume of water lost from the water body by seepage (m3 H2O) Vstored Volume of water stored in the water body at the beginning of the day (m3 H2O) Vtarg Target reservoir volume for a given day (m3 H2O) areahru HRU area (ha) expsa Exponent for impoundment surface area calculation frimp Fraction of the subbasin area draining into the impoundment frpot Fraction of the HRU area draining into the pothole irr Amount of irrigation water added on a given day (m3 H2O) mon Month of the year monfld,beg Beginning month of the flood season monfld,end Ending month of the flood season Outflow rate (m3/s) qout qrel Average daily principal spillway release rate (m3/s) qrel,mn Minimum average daily outflow for the month (m3/s) qrel,mx Maximum average daily outflow for the month (m3/s) qtile Average daily tile flow rate (m3/s) slp Slope of the HRU (m/m) starg Target reservoir volume specified for a given month (m3 H2O)
βsa η
Coefficient for impoundment surface area equation Evaporation coefficient (0.6)
CHAPTER 28
EQUATIONS: SEDIMENT IN WATER BODIES
SWAT incorporates a simple mass balance model to simulate the transport of sediment into and out of water bodies. SWAT defines four different types of water bodies: ponds, wetlands, reservoirs and potholes. Sediment processes modeled in ponds, wetlands, reservoirs, and potholes are identical. When calculating sediment movement through a water body, SWAT assumes the system is completely mixed. In a completely mixed system, as sediment enters the water body it is instantaneously distributed throughout the volume. 433
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28.1 MASS BALANCE The mass balance equation for sediment in a water body is: sed wb = sed wb,i + sed flowin − sed stl − sed flowout
28.1.1
where sedwb is the amount of sediment in the water body at the end of the day (metric tons), sedwb,i is the amount of sediment in the water body at the beginning of the day (metric tons), sedflowin is the amount of sediment added to the water body with inflow (metric tons), sedstl is the amount of sediment removed from the water by settling (metric tons), sedflowout is the amount of sediment transported out of the water body with outflow (metric tons).
28.2 SETTLING The amount of suspended solid settling that occurs in the water body on a given day is calculated as a function of concentration. The initial suspended solid concentration is: conc sed ,i =
(sed (V
wb ,i
stored
+ sed flowin ) + V flowin )
28.2.1
where concsed,i is the initial concentration of suspended solids in the water (Mg/m3), sedwb,i is the amount of sediment in the water body at the beginning of the day (metric tons), sedflowin is the amount of sediment added to the water body with inflow (metric tons), Vstored is the volume of water stored in water body or channel at the beginning of the day (m3 H2O), and Vflowin is the volume of water entering water body on given day (m3 H2O). Settling occurs only when the sediment concentration in the water body exceeds the equilibrium sediment concentration specified by the user, concsed,eq. The concentration of sediment in the water body at the end of the day is calculated: conc sed , f = (conc sed ,i − conc sed ,eq ) ⋅ exp[− k s ⋅ t ⋅ d 50 ] + conc sed ,eq
conc sed , f = conc sed ,i
if conc sed ,i > conc sed ,eq
28.2.2
if conc sed ,i ≤ conc sed ,eq
28.2.3
CHAPTER 28: EQUATIONS—SEDIMENT IN WATER BODIES
435
where concsed,f is the final sediment concentration in the water body (Mg/m3), concsed,i is the initial concentration of suspended solids in the water body (Mg/m3), concsed,eq is the equilibrium concentration of suspended solids in the water body (Mg/m3), ks is the decay constant (1/day), t is the length of the time step (1 day), and d50 is the median particle size of the inflow sediment (µm). Assuming 99% of the 1 µm size particles settle out of solution within 25 days, ks is equal to 0.184. The median particle size of the inflow sediment is calculated: m m m ö æ d 50 = expç 0.41 ⋅ c + 2.71 ⋅ silt + 5.7 ⋅ s ÷ 100 100 100 ø è
28.2.4
where d50 is the median particle size of the inflow sediment (µm), mc is percent clay in the surface soil layer in the subbasin, msilt is the percent silt in the surface soil layer in the subbasin, ms is the percent sand in the surface soil layer in the subbasin. The amount of sediment settling out of solution on a given day is then calculated: sed stl = (conc sed ,i − conc sed , f ) ⋅ V
28.2.5
where sedstl is the amount of sediment removed from the water by settling (metric tons), concsed,i is the initial concentration of suspended solids in the water body (Mg/m3), concsed,f is the final sediment concentration in the water body (Mg/m3), and V is the volume of water in the impoundment (m3 H2O). Table 28-1: SWAT input variables that pertain to sediment settling. Variable Name Definition RES_NSED concsed,eq: Equilibrium sediment concentration in water body (mg/L) PND_NSED concsed,eq: Equilibrium sediment concentration in water body (mg/L) WET_NSED concsed,eq: Equilibrium sediment concentration in water body (mg/L) POT_NSED concsed,eq: Equilibrium sediment concentration in water body (mg/L) CLAY mc: Percent clay in the surface soil layer in the subbasin SILT msilt: Percent silt in the surface soil layer in the subbasin SAND ms: Percent sand in the surface soil layer in the subbasin
Input File .res .pnd .pnd .hru .sol .sol .sol
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28.3 SEDIMENT OUTFLOW The amount of sediment transported out of the water body on a given day is calculated as a function of the final concentration. The initial suspended solid concentration is: sed flowout = conc sed , f ⋅ V flowout
28.3.1
where sedflowout is the amount of sediment transported out of the water body with outflow (metric tons), concsed,f is the final sediment concentration in the water body (Mg/m3), and Vflowout is the volume of outflow from the impoundment (m3 H2O).
28.4 NOMENCLATURE V Vflowin Vflowout Vstored
Volume of water in the impoundment (m3 H2O) Volume of water entering water body on given day (m3 H2O) Volume of outflow from the impoundment (m3 H2O) Volume of water stored in water body or channel (m3 H2O)
concsed Concentration of suspended solids in the water (Mg/m3) concsed,eq Equilibrium concentration of suspended solids in the water body (Mg/m3) d50 Median particle size of the inflow sediment (µm) ks Decay constant (1/day) t Length of the time step (1 day) mc Percent clay in the surface soil layer in the subbasin Percent sand in the surface soil layer in the subbasin ms msilt Percent silt in the surface soil layer in the subbasin sedflowin Amount of sediment added to the water body with inflow (metric tons) sedflowout Amount of sediment transported out of the water body (metric tons) sedstl Amount of sediment removed from the water by settling (metric tons) sedwb Sediment in the water body (metric tons)
CHAPTER 29
EQUATIONS: NUTRIENTS IN WATER BODIES
SWAT incorporates a simple empirical model to predict the trophic status of water bodies. For studies that require detailed modeling of lake water quality, SWAT has been linked to distributed lake water quality models such as WASP. SWAT defines four different types of water bodies: ponds, wetlands, reservoirs and depressional/impounded areas (potholes). Nutrient processes modeled in ponds, wetlands and reservoirs are identical. Nutrient processes are not yet modeled in potholes.
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29.1 NUTRIENT TRANSFORMATIONS When calculating nutrient transformations in a water body, SWAT assumes the system is completely mixed. In a completely mixed system, as nutrients enter the water body they are instantaneously distributed throughout the volume. The assumption of a completely mixed system ignores lake stratification and intensification of phytoplankton in the epilimnion. The initial amount of nitrogen and phosphorus in the water body on the given day is calculated by summing the mass of nutrient entering the water body on that day with the mass of nutrient already present in the water body. M initial = M stored + M flowin
29.1.1
where Minitial is the initial mass of nutrient in the water body for the given day (kg), Mstored is the mass of nutrient in the water body at the end of the previous day (kg), and Mflowin is the mass of nutrient added to the water body on the given day (kg). In a similar manner, the initial volume of water in the water body is calculated by summing the volume of water entering the water body on that day with the volume already present in the water body. Vinitial = Vstored + V flowin
29.1.2
where Vinitial is the initial volume of water in the water body for a given day (m3 H2O), Vstored is the volume of water in the water body at the end of the previous day (m3 H2O), and Vflowin is the volume of water entering the water body on the given day (m3 H2O). The initial concentration of nutrients in the water body is calculated by dividing the initial mass of nutrient by the initial volume of water. Nutrient transformations simulated in ponds, wetlands and reservoirs are limited to the removal of nutrients by settling. Transformations between nutrient pools (e.g. NO3 ⇔ NO2 ⇔ NH4) are ignored.
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439
Settling losses in the water body can be expressed as a flux of mass across the surface area of the sediment-water interface (Figure 29-1) (Chapra, 1997).
Figure 29-1: Settling losses calculated as flux of mass across the sediment-water interface.
The mass of nutrient lost via settling is calculated by multiplying the flux by the area of the sediment-water interface. M settling = ν ⋅ c ⋅ As ⋅ dt
29.1.3
where Msettling is the mass of nutrient lost via settling on a day (kg), ν is the apparent settling velocity (m/day), As is the area of the sediment-water interface (m2), c is the initial concentration of nutrient in the water (kg/m3 H2O), and dt is the length of the time step (1 day). The settling velocity is labeled as “apparent” because it represents the net effect of the different processes that deliver nutrients to the water body’s sediments. The water body is assumed to have a uniform depth of water and the area of the sediment-water interface is equivalent to the surface area of the water body. The apparent settling velocity is most commonly reported in units of m/year and this is how the values are input to the model. For natural lakes, measured phosphorus settling velocities most frequently fall in the range of 5 to 20 m/year although values less than 1 m/year to over 200 m/year have been reported (Chapra, 1997). Panuska and Robertson (1999) noted that the range in apparent settling velocity values for man-made reservoirs tends to be significantly greater than for natural lakes. Higgins and Kim (1981) reported phosphorus
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apparent settling velocity values from –90 to 269 m/year for 18 reservoirs in Tennessee with a median value of 42.2 m/year. For 27 Midwestern reservoirs, Walker and Kiihner (1978) reported phosphorus apparent settling velocities ranging from –1 to 125 m/year with an average value of 12.7 m/year. A negative settling rate indicates that the reservoir sediments are a source of N or P; a positive settling rate indicates that the reservoir sediments are a sink for N or P. A number of inflow and impoundment properties affect the apparent settling velocity for a water body. Factors of particular importance include the form of phosphorus in the inflow (dissolved or particulate) and the settling velocity of the particulate fraction. Within the impoundment, the mean depth, potential for sediment resuspension and phosphorus release from the sediment will affect the apparent settling velocity (Panuska and Robertson, 1999). Water bodies with high internal phosphorus release tend to possess lower phosphorus retention and lower phosphorus apparent settling velocities than water bodies with low internal phosphorus release (Nürnberg, 1984). Table 29-1 summarizes typical ranges in phosphorus settling velocity for different systems. Table 29-1: Recommended apparent settling velocity values for phosphorus (Panuska and Robertson, 1999) Range in settling velocity Nutrient Dynamics values (m/year) Shallow water bodies with high net internal phosphorus flux ν≤0 Water bodies with moderate net internal phosphorus flux 1<ν<5 Water bodies with minimal net internal phosphorus flux 5 < ν < 16 Water bodies with high net internal phosphorus removal ν > 16
SWAT input variables that pertain to nutrient settling in ponds, wetlands and reservoirs are listed in Table 29-2. The model allows the user to define two settling rates for each nutrient and the time of the year during which each settling rate is used. A variation in settling rates is allowed so that impact of temperature and other seasonal factors may be accounted for in the modeling of nutrient settling. To use only one settling rate for the entire year, both variables for the nutrient may be set to the same value. Setting all variables to zero will cause the model to ignore settling of nutrients in the water body.
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441
After nutrient losses in the water body are determined, the final concentration of nutrients in the water body is calculated by dividing the final mass of nutrient by the initial volume of water. The concentration of nutrients in outflow from the water body is equivalent to the final concentration of the nutrients in the water body for the day. The mass of nutrient in the outflow is calculated by multiplying the concentration of nutrient in the outflow by the volume of water leaving the water body on that day. Table 29-2: SWAT input variables that control settling in ponds, wetlands and reservoirs. Variable Name Definition IPND1 Beginning month of mid-year nutrient settling period for pond and wetland modeled in subbasin IPND2 Ending month of mid-year nutrient settling period for pond and wetland modeled in subbasin PSETL1 Phosphorus settling rate in pond during mid-year nutrient settling period (IPND1 ≤ month ≤ IPND2) (m/year) PSETL2 Phosphorus settling rate in pond during time outside mid-year nutrient settling period ( month < IPND1 or month > IPND2) (m/year) NSETL1 Nitrogen settling rate in pond during mid-year nutrient settling period (IPND1 ≤ month ≤ IPND2) (m/year) NSETL2 Nitrogen settling rate in pond during time outside mid-year nutrient settling period ( month < IPND1 or month > IPND2) (m/year) PSETLW1 Phosphorus settling rate in wetland during mid-year nutrient settling period (IPND1 ≤ month ≤ IPND2) (m/year) PSETLW2 Phosphorus settling rate in wetland during time outside mid-year nutrient settling period ( month < IPND1 or month > IPND2) (m/year) NSETLW1 Nitrogen settling rate in wetland during mid-year nutrient settling period (IPND1 ≤ month ≤ IPND2) (m/year) NSETLW2 Nitrogen settling rate in wetland during time outside mid-year nutrient settling period ( month < IPND1 or month > IPND2) (m/year) IRES1 Beginning month of mid-year nutrient settling period for reservoir IRES2 Ending month of mid-year nutrient settling period for reservoir PSETLR1 Phosphorus settling rate in reservoir during mid-year nutrient settling period (IRES1 ≤ month ≤ IRES2) (m/year) PSETLR2 Phosphorus settling rate in reservoir during time outside mid-year nutrient settling period ( month < IRES1 or month > IRES2) (m/year) NSETLR1 Nitrogen settling rate in reservoir during mid-year nutrient settling period (IRES1 ≤ month ≤ IRES2) (m/year) NSETLR2 Nitrogen settling rate in reservoir during time outside mid-year nutrient settling period ( month < IRES1 or month > IRES2) (m/year)
Input File .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .lwq .lwq .lwq .lwq .lwq .lwq
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29.2 TOTAL BALANCE Assuming that the volume of the water body remains constant over time, the processes described above (inflow, settling, outflow) can be combined into the following mass balance equation for a well-mixed water body: V⋅
dc = W (t ) − Q ⋅ c − ν ⋅ c ⋅ As dt
29.2.1
where V is the volume of the system (m3 H2O), c is the concentration of nutrient in the system (kg/m3 H2O), dt is the length of the time step (1 day), W(t) is the amount of nutrient entering the water body during the day (kg/day), Q is the rate of water flow exiting the water body (m3 H2O/day), ν is the apparent settling velocity (m/day), and As is the area of the sediment-water interface (m2).
29.3 EUTROPHICATION Under favorable conditions of light and temperature, excess amounts of nutrients in water can increase the growth of algae and other plants. The result of this growth is an increase in the rate of eutrophication, which is a natural ecological process of change from a nutrient-poor to a nutrient-rich environment. Eutrophication is defined as the process by which a body of water becomes enriched in dissolved nutrients (as phosphates) that stimulate the growth of aquatic plant life, usually resulting in the depletion of dissolved oxygen (Merriam-Webster, Inc., 1996). Nutrient enrichment of moving waters and lakes is a normal result of soil weathering and erosion processes. The gradual evolution of Ice Age lakes into marshes and, eventually, organic soils is a result of eutrophication. However, this process can be accelerated by the discharge of wastes containing high levels of nutrients into lakes or rivers. One example of this is Lake Erie, which is estimated to have aged the equivalent of 150 natural years in a 15-year span of accelerated eutrophication. Excessive plant growth caused by accelerated eutrophication can lead to stagnation of the water. The stagnation is caused by an increased biological
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oxygen demand created by decaying plant remains. The result of this increased oxygen demand is a tendency toward anaerobic conditions and the inability of the water body to support fish and other aerobic organisms. Nitrogen, carbon and phosphorus are essential to the growth of aquatic biota. Due to the difficulty of controlling the exchange of nitrogen and carbon between the atmosphere and water and fixation of atmospheric nitrogen by some blue-green algae, attempts to mitigate eutrophication have focused on phosphorus inputs. In fresh-water systems, phosphorus is often the limiting element. By controlling phosphorus loading, accelerated eutrophication of lake waters can be reduced. In systems where phosphorus is the primary, controllable limiting nutrient of water body eutrophication, the amount of phosphorus present in the water body can be used to estimate the amount of eutrophication present in the water body.
29.3.1 PHOSPHORUS/CHLOROPHYLL a CORRELATIONS A number of empirically derived equations have been developed to calculate chlorophyll a level as a function of total phosphorus concentration. SWAT uses an equation developed by Rast and Lee (1978) to calculate the chlorophyll a concentration in the water body. Chla = 0.551 ⋅ p 0.76
29.3.1
where Chla is the chlorophyll a concentration (µg/L) and p is the total phosphorus concentration (µg/L). The equation has been modified to include a user-defined coefficient: Chla = Chlaco ⋅ 0.551 ⋅ p 0.76
29.3.2
The user-defined coefficient, Chlaco, is included to allow the user to adjust the predicted chlorophyll a concentration for limitations of nutrients other than phosphorus. When Chlaco is set to 1.00, equation 29.3.2 is equivalent to equation 29.3.1. For most water bodies, the original equation will be adequate..
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29.3.2 CHLOROPHYLL a/SECCHI-DISK DEPTH CORRELATION The secchi-disk depth is another measure of the trophic status of a water body. The secchi-disk depth quantifies the clarity of the water, an attribute easily perceived by the general public. The secchi-disk depth can be calculated from chlorophyll levels using the equation (Chapra, 1997): SD = 6.35 ⋅ Chla −0.473
29.3.3
where SD is the secchi-disk depth (m) and Chla is the chlorophyll a concentration (µg/L). For incorporation into SWAT, equation 29.3.3 was modified to include a user-defined coefficient: SD = SDco ⋅ 6.35 ⋅ Chla −0.473
29.3.4
The user-defined coefficient, SDco, is included to allow the user to adjust the predicted secchi-disk depth for impacts of suspended sediment and other particulate matter on water clarity that are ignored by the original equation. When SDco is set to 1.00, equation 29.3.4 is equivalent to equation 29.3.3. For most water bodies, the original equation will be adequate. While evaluation of water quality by secchi-disk depth measurements is subjective, some general correlations between secchi-disk depth and public perception of water quality have been made. One such correlation made for Annebessacook Lake in Maine (EPA, 1980) is given in Table 29-3. Table 29-3: Relationship between secchi-disk depth and public perception of water quality. Secchi-disk depth (m) Public perception of water quality 0.0 – 0.9 gross pollution; water body totally unsuitable for recreation 1.0 – 1.9 algae blooms still evident; quality unacceptable for most uses 2.0 – 2.9 some complaints of declining water quality; some impairment of water use 3.0 – 3.9 satisfactory quality; no impairment of water use 4.0 – 4.9 excellent water quality; a positive factor encouraging use of lake 5.0 + exceptional quality
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Table 29-4: SWAT input variables that impact eutrophication calculations in ponds, wetlands and reservoirs. Variable Input Name Definition File CHLA Chlaco variable for calculation of chlorophyll a concentration in a pond .pnd CHLAW Chlaco variable for calculation of chlorophyll a concentration in a .pnd wetland CHLAR Chlaco variable for calculation of chlorophyll a concentration in a .lwq reservoir .pnd SECCI SDco variable for calculation of secchi-disk depth in a pond SECCIW SDco variable for calculation of secchi-disk depth in a wetland .pnd SECCIR SDco variable for calculation of secchi-disk depth in a reservoir .lwq
29.4 NOMENCLATURE As Area of sediment-water interface (m2) Chla Chlorophyll a concentration (µg/L) Chlaco User-defined coefficient to adjust predicted chlorophyll a concentration Mflowin Mass of nutrient entering water body on the given day (kg) Minitial Initial mass of nutrient in water body for the given day (kg) Msettling Mass of nutrient lost via settling on a given day (kg) Mstored Mass of nutrient in water body at end of previous day (kg) Q Volumetric flow rate for water exiting water body (m3 H2O/day) SD Secchi-disk depth (m) SDco User-defined coefficient to adjust predicted secchi-disk depth V Volume of water in water body (assumed constant) (m3 H2O) Vflowin Volume of water entering water body on given day (m3 H2O) Vinitial Initial volume of water in water body on given day (m3 H2O) Vstored Volume of water in water body at end of previous day (m3 H2O) W(t) Rate of nutrient loading (kg/day) c dt p
Concentration of nutrient in the water (kg/m3 H2O) Length of time step (1 day) Total phosphorus concentration (µg P/L)
ν
Apparent settling velocity (m/day)
29.5 REFERENCES Chapra, S.C. 1997. Surface water-quality modeling. WCB/McGraw-Hill, Boston, MA. Higgins, J.M. and B.R. Kim. 1981. Phosphorus retention models for the Tennessee Valley Authority reservoirs. Wat. Resour. Res. 17:571-576.
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Merriam-Webster, Inc. 1996. Merriam-Webster’s collegiate dictionary, 10th edition. Merriam-Webster, Inc. Springfield, MA. Nürnberg, G.K. 1984. The prediction of internal phosphorus load in lakes with anoxic hypolimnia. Limnol. Oceanogr. 29:111-124. Panuska, J.C. and D.M. Robertson. 1999. Estimating phosphorus concentration following alum treatment using apparent settling velocity. Lake and Reserv. Manage. 15:28-38. Rast, W. and Lee, G.F. 1978. Summary analysis of the North American project (US portion) OECD eutrophication project: nutrient loading-lake response relationships and trophic state indices. USEPA Corvallis Environmental Research Laboratory, Corvallis, OR. EPA-600/3-78-008. USEPA. 1980. Lake restoration in Cobbossee watershed. Capsule Rept. Office of Water Planning and Standards Div., ORD, Cincinnati, OH. EPA-624/280-027. Walker, W.W. and J. Kiihner. 1978. An empirical analysis of factors controlling eutrophication in midwestern impoundments. Paper presented at the International Symposium on the Environmental Effects of Hydraulic Engineering Works, Univ. of Tenn., Knoxville.
CHAPTER 30
EQUATIONS: PESTICIDES IN WATER BODIES
SWAT incorporates a simple mass balance developed by Chapra (1997) to model the transformation and transport of pesticides in water bodies. The model assumes a well-mixed layer of water overlying a sediment layer. Figure 30-1 illustrates the mechanisms affecting the pesticide mass balance in water bodies. SWAT defines four different types of water bodies: ponds, wetlands, reservoirs and depressional/impounded areas (potholes). Pesticide processes are modeled only in reservoirs.
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Figure 30-1: Pesticide mass balance for well-mixed water body with sediment layer.
30.1 PESTICIDE IN THE WATER Pesticide in a well-mixed water body is increased through addition of mass in inflow, resuspension and diffusion from the sediment layer. The amount of pesticide in a well-mixed water body is reduced through removal in outflow, degradation, volatilization, settling and diffusion into the underlying sediment.
30.1.1 SOLID-LIQUID PARTITIONING Pesticides will partition into particulate and dissolved forms. The fraction of pesticide in each phase is a function of the pesticide’s partition coefficient and the water body’s suspended solid concentration: Fd =
1 1 + K d ⋅ conc sed
30.1.1
Fp =
K d ⋅ conc sed = 1 − Fd 1 + K d ⋅ conc sed
30.1.2
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where Fd is the fraction of total pesticide in the dissolved phase, Fp is the fraction of total pesticide in the particulate phase, Kd is the pesticide partition coefficient (m3/g), and concsed is the concentration of suspended solids in the water (g/m3). The pesticide partition coefficient can be estimated from the octanol-water partition coefficient (Chapra, 1997): K d = 3.085 × 10 −8 ⋅ K ow
30.1.3
where Kd is the pesticide partition coefficient (m3/g) and Kow is the pesticide’s −3 3 (mg m −water ) ). Values for the octanol-water partition coefficient ( mg m octanol −1
octanol-water partition coefficient have been published for many chemicals. If a published value cannot be found, it can be estimated from solubility (Chapra, 1997): ′ ) log(K ow ) = 5.00 − 0.670 ⋅ log( pst sol
30.1.4
′ is the pesticide solubility (µmoles/L). The solubility in these units is where pst sol calculated: ′ = pst sol
pst sol ⋅ 10 3 MW
30.1.5
′ is the pesticide solubility (µmoles/L), pstsol is the pesticide solubility where pst sol (mg/L) and MW is the molecular weight (g/mole).
30.1.2 DEGRADATION Pesticides in both the particulate and dissolved forms are subject to degradation. The amount of pesticide that is removed from the water via degradation is: pstdeg,wtr = k p ,aq ⋅ pstlkwtr
30.1.6
where pstdeg,wtr is the amount of pesticide removed from the water via degradation (mg pst), kp,aq is the rate constant for degradation or removal of pesticide in the water (1/day), and pstlkwtr is the amount of pesticide in the water at the beginning of the day (mg pst). The rate constant is related to the aqueous half-life:
k p ,aq =
0.693 t1 / 2,aq
30.1.7
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where kp,aq is the rate constant for degradation or removal of pesticide in the water (1/day), and t1/2,aq is the aqueous half-life for the pesticide (days).
30.1.3 VOLATILIZATION Pesticide in the dissolved phase is available for volatilization. The amount of pesticide removed from the water via volatilization is: pstvol ,wtr = vv ⋅ SA ⋅
Fd ⋅ pstlkwtr V
30.1.8
where pstvol,wtr is the amount of pesticide removed via volatilization (mg pst), vv is the volatilization mass-transfer coefficient (m/day), SA is the surface area of the water body (m2), Fd is the fraction of total pesticide in the dissolved phase, pstlkwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the water body (m3 H2O). The volatilization mass-transfer coefficient can be calculated based on Whitman’s two-film or two-resistance theory (Whitman, 1923; Lewis and Whitman, 1924 as described in Chapra, 1997). While the main body of the gas and liquid phases are assumed to be well-mixed and homogenous, the two-film theory assumes that a substance moving between the two phases encounters maximum resistance in two laminar boundary layers where transfer is a function of molecular diffusion. In this type of system the transfer coefficient or velocity is: vv = K l ⋅
He H e + R ⋅ TK ⋅ (K l K g )
30.1.9
where vv is the volatilization mass-transfer coefficient (m/day), Kl is the masstransfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), He is Henry’s constant (atm m3 mole-1), R is the universal gas constant (8.206 × 10-5 atm m3 (K mole)-1), and TK is the temperature (K). For lakes, the transfer coefficients are estimated using a stagnant film approach: Kl =
Dl zl
Kg =
Dg zg
30.1.10
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where Kl is the mass-transfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), Dl is the liquid molecular diffusion coefficient (m2/day), Dg is the gas molecular diffusion coefficient (m2/day), zl is the thickness of the liquid film (m), and zg is the thickness of the gas film (m). Alternatively, the transfer coefficients can be estimated with the equations: æ 32 ö K l = K l ,O 2 ⋅ ç ÷ è MW ø
0.25
30.1.11
æ 18 ö K g = 168 ⋅ µ w ⋅ ç ÷ è MW ø
0.25
30.1.12
where Kl is the mass-transfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), K l ,O2 is the oxygen transfer coefficient (m/day), MW is the molecular weight of the compound, and
µw is the wind speed (m/s). Chapra (1997) lists several different equations that can be used to calculate K l ,O2 .
30.1.4 SETTLING Pesticide in the particulate phase may be removed from the water layer by settling. Settling transfers pesticide from the water to the sediment layer. The amount of pesticide that is removed from the water via settling is: pst
= v ⋅ SA ⋅ stl , wtr s
F ⋅ pst p lkwtr V
30.1.13
where pststl,wtr is the amount of pesticide removed from the water due to settling (mg pst), vs is the settling velocity (m/day), SA is the surface area of the water body (m2), Fp is the fraction of total pesticide in the particulate phase, pstlkwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the water body (m3 H2O).
30.1.5 OUTFLOW Pesticide is removed from the water body in outflow. The amount of dissolved and particulate pesticide removed from the water body in outflow is:
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pst sol ,o = Q ⋅
Fd ⋅ pstlkwtr V
pst sorb,o = Q ⋅
Fp ⋅ pstlkwtr V
30.1.14 30.1.15
where pstsol,o is the amount of dissolved pesticide removed via outflow (mg pst), pstsorb,o is the amount of particulate pesticide removed via outflow (mg pst), Q is the rate of outflow from the water body (m3 H2O/day), Fd is the fraction of total pesticide in the dissolved phase, Fp is the fraction of total pesticide in the particulate phase, pstlkwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the water body (m3 H2O). Table 30-1: SWAT input variables that pesticide partitioning. Variable Name Definition LKPST_KOC Kd: Pesticide partition coefficient (m3/g) LKPST_REA kp,aq: Rate constant for degradation or removal of pesticide in the water (1/day) LKPST_VOL vv: Volatilization mass-transfer coefficient (m/day) LKPST_STL vs: Pesticide settling velocity (m/day)
Input File .lwq .lwq .lwq .lwq
30.2 PESTICIDE IN THE SEDIMENT Pesticide in the sediment layer underlying a water body is increased through addition of mass by settling and diffusion from the water. The amount of pesticide in the sediment layer is reduced through removal by degradation, resuspension, diffusion into the overlying water, and burial.
30.2.1 SOLID-LIQUID PARTITIONING As in the water layer, pesticides in the sediment layer will partition into particulate and dissolved forms. Calculation of the solid-liquid partitioning in the sediment layer requires a suspended solid concentration. The “concentration” of solid particles in the sediment layer is defined as: * conc sed =
M sed Vtot
30.2.1
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* where conc sed is the “concentration” of solid particles in the sediment layer
(g/m3), Msed is the mass of solid particles in the sediment layer (g) and Vtot is the total volume of the sediment layer (m3). Mass and volume are also used to define the porosity and density of the sediment layer. In the sediment layer, porosity is the fraction of the total volume in the liquid phase:
φ=
Vwtr Vtot
30.2.2
where φ is the porosity, Vwtr is the volume of water in the sediment layer (m3) and Vtot is the total volume of the sediment layer (m3). The fraction of the volume in the solid phase can then be defined as: 1−φ =
Vsed Vtot
30.2.3
where φ is the porosity, Vsed is the volume of solids in the sediment layer (m3) and Vtot is the total volume of the sediment layer (m3). The density of sediment particles is defined as:
ρs =
M sed Vsed
30.2.4
where ρs is the particle density (g/m3), Msed is the mass of solid particles in the sediment layer (g), and Vsed is the volume of solids in the sediment layer (m3). Solving equation 30.2.3 for Vtot and equation 30.2.4 for Msed and substituting into equation 30.2.1 yields: * conc sed = (1 − φ ) ⋅ ρ s
30.2.5
* where conc sed is the “concentration” of solid particles in the sediment layer
(g/m3), φ is the porosity, and ρs is the particle density (g/m3). Typical values of porosity and particle density for fine-grained sediments are φ = 0.8-0.95 and ρs = 2.4-2.7 × 106 g/m3 (Chapra, 1997). Assuming φ = 0.8 and ρs = 2.6 × 106 g/m3, the “concentration” of solid particles in the sediment layer is 5.2 × 105 g/m3.
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The fraction of pesticide in each phase is then calculated: Fd ,sed =
1 φ + (1 − φ ) ⋅ ρ s ⋅ K d
Fp ,sed = 1 − Fd ,sed
30.2.6 30.2.7
where Fd,sed is the fraction of total sediment pesticide in the dissolved phase, Fp,sed is the fraction of total sediment pesticide in the particulate phase, φ is the porosity,
ρs is the particle density (g/m3), and Kd is the pesticide partition coefficient (m3/g). The pesticide partition coefficient used for the water layer is also used for the sediment layer.
30.2.2 DEGRADATION Pesticides in both the particulate and dissolved forms are subject to degradation. The amount of pesticide that is removed from the sediment via degradation is: pstdeg,sed = k p ,sed ⋅ pstlksed
30.2.8
where pstdeg,sed is the amount of pesticide removed from the sediment via degradation (mg pst), kp,sed is the rate constant for degradation or removal of pesticide in the sediment (1/day), and pstlksed is the amount of pesticide in the sediment (mg pst). The rate constant is related to the sediment half-life: k p ,sed =
0.693 t1 / 2,sed
30.2.9
where kp,sed is the rate constant for degradation or removal of pesticide in the sediment (1/day), and t1/2,sed is the sediment half-life for the pesticide (days).
30.2.3 RESUSPENSION Pesticide in the sediment layer is available for resuspension. The amount of pesticide that is removed from the sediment via resuspension is: pst rsp ,wtr = v r ⋅ SA ⋅
pstlksed Vtot
30.2.10
where pstrsp,wtr is the amount of pesticide removed via resuspension (mg pst), vr is the resuspension velocity (m/day), SA is the surface area of the water body (m2),
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pstlksed is the amount of pesticide in the sediment (mg pst), and Vtot is the volume of the sediment layer (m3). The volume of the sediment layer is calculated: Vtot = SA ⋅ Dsed
30.2.11
where Vtot is the volume of the sediment layer (m3), SA is the surface area of the water body (m2), Dsed is the depth of the active sediment layer (m). Pesticide removed from the sediment layer by resuspension is added to the water layer.
30.2.4 DIFFUSION Pesticide in the dissolved phase is available for diffusion. Diffusion transfers pesticide between the water and sediment layers. The direction of movement is controlled by the pesticide concentration. Pesticide will move from areas of high concentration to areas of low concentration. The amount of pesticide that is transferred between the water and sediment by diffusion is: ⋅ pstlksed Fd ⋅ pstlkwtr ö æF ÷÷ pstdif = vd ⋅ SA ⋅ çç d ,sed − Vtot V ø è
30.2.12
where pstdif is the amount of pesticide transferred between the water and sediment by diffusion (mg pst), vd is the rate of diffusion or mixing velocity (m/day), SA is the surface area of the water body (m2), Fd,sed is the fraction of total sediment pesticide in the dissolved phase, pstlksed is the amount of pesticide in the sediment (mg pst), Vtot is the volume of the sediment layer (m3), Fd is the fraction of total water layer pesticide in the dissolved phase, pstlkwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the water body (m3 H2O). If Fd ,sed ⋅ pstlksed Vtot layer. If
>
Fd ⋅ pstlkwtr , pstdif is transferred from the sediment to the water V
Fd ,sed ⋅ pstlksed Vtot
<
Fd ⋅ pstlkwtr , pstdif is transferred from the water to the V
sediment layer. The diffusive mixing velocity, vd, can be estimated from the empirically derived formula (Chapra, 1997): vd =
69.35 ⋅ φ ⋅ MW − 2 / 3 365
30.2.13
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where vd is the rate of diffusion or mixing velocity (m/day), φ is the sediment porosity, and MW is the molecular weight of the pesticide compound.
30.2.5 BURIAL Pesticide in the sediment layer may be lost by burial. The amount of pesticide that is removed from the sediment via burial is: pstbur = vb ⋅ SA ⋅
pstlksed Vtot
30.2.14
where pstbur is the amount of pesticide removed via burial (mg pst), vb is the burial velocity (m/day), SA is the surface area of the water body (m2), pstlksed is the amount of pesticide in the sediment (mg pst), and Vtot is the volume of the sediment layer (m3). Table 30-2: SWAT input variables related to pesticide in the sediment. Variable Name Definition LKPST_KOC Kd: Pesticide partition coefficient (m3/g) LKSPST_REA kp,sed: Rate constant for degradation or removal of pesticide in the sediment (1/day) LKPST_RSP vr: Resuspension velocity (m/day) LKSPST_ACT Dsed: Depth of the active sediment layer (m) LKPST_MIX vd: Rate of diffusion or mixing velocity (m/day) LKSPST_BRY vb: Pesticide burial velocity (m/day)
Input File .lwq .lwq .lwq .lwq .lwq .lwq
30.3 MASS BALANCE The processes described above can be combined into mass balance equations for the well-mixed water body and the well-mixed sediment layer: ∆pstlkwtr = pstin − ( pst sol ,o + pst sorb,o ) − pstdeg ,wtr − pstvol ,wtr − pst stl ,wtr + pst rsp ,wtr ± pst dif 30.3.1 ∆pstlksed = pstdeg,sed + pst stl ,wtr − pst rsp ,wtr − pstbur ± pstdif
30.3.2
where ∆pstlkwtr is the change in pesticide mass in the water layer (mg pst), ∆pstlksed is the change in pesticide mass in the sediment layer (mg pst), pstin is the pesticide added to the water body via inflow (mg pst), pstsol,o is the amount of dissolved pesticide removed via outflow (mg pst), pstsorb,o is the amount of particulate pesticide removed via outflow (mg pst), pstdeg,wtr is the amount of
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pesticide removed from the water via degradation (mg pst), pstvol,wtr is the amount of pesticide removed via volatilization (mg pst), pststl,wtr is the amount of pesticide removed from the water due to settling (mg pst), pstrsp,wtr is the amount of pesticide removed via resuspension (mg pst), pstdif is the amount of pesticide transferred between the water and sediment by diffusion (mg pst), pstdeg,sed is the amount of pesticide removed from the sediment via degradation (mg pst), pstbur is the amount of pesticide removed via burial (mg pst)
30.4 NOMENCLATURE Dg Dl Dsed Fd Fd,sed Fp Fp,sed He Kd Kg Kl K l ,O 2 Msed MW Q R SA V Vsed Vtot Vwtr
Gas molecular diffusion coefficient (m2/day) Liquid molecular diffusion coefficient (m2/day) Depth of the active sediment layer (m) Fraction of total pesticide in the dissolved phase Fraction of total sediment pesticide in the dissolved phase Fraction of total pesticide in the particulate phase Fraction of total sediment pesticide in the particulate phase Henry’s constant (atm m3 mole-1) Pesticide partition coefficient (m3/g) Mass-transfer velocity in the gaseous laminar layer (m/day) Mass-transfer velocity in the liquid laminar layer (m/day) Oxygen transfer coefficient (m/day) Mass of solid phase in the sediment layer (g) Molecular weight of the pesticide compound Rate of outflow from the water body (m3 H2O/day) Universal gas constant (8.206 × 10-5 atm m3 (K mole)-1) Surface area of the water body (m2) Volume of water in the water body (m3 H2O) Volume of solids in the sediment layer (m3) Total volume of the sediment layer (m3) Volume of water in the sediment layer (m3)
concsed Concentration of suspended solids in the water (g/m3) * conc sed “Concentration” of solid particles in the sediment layer (g/m3) kp,aq Rate constant for degradation or removal of pesticide in the water (1/day) kp,sed Rate constant for degradation or removal of pesticide in the sediment (1/day) pstbur Amount of pesticide removed via burial (mg pst) pstdeg,sed Amount of pesticide removed from the sediment via degradation (mg pst) pstdeg,wtr Amount of pesticide removed from the water via degradation (mg pst) pstdif Amount of pesticide transferred between the water and sediment by diffusion (mg pst)
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pstin Pesticide added to the water body via inflow (mg pst) pstlksed Amount of pesticide in the sediment (mg pst) pstlkwtr Amount of pesticide in the water (mg pst) pstrsp,wtr Amount of pesticide removed from sediment via resuspension (mg pst) pstsol,o Amount of dissolved pesticide removed via outflow (mg pst) pstsorb,o Amount of particulate pesticide removed via outflow (mg pst) pststl,wtr Amount of pesticide removed from the water due to settling (mg pst) pstvol,wtr Amount of pesticide removed via volatilization (mg pst) t1/2,aq Aqueous half-life for the pesticide (days) t1/2,sed Sediment half-life for the pesticide (days) vb Pesticide burial velocity (m/day) vd Rate of diffusion or mixing velocity (m/day) vr Resuspension velocity (m/day) vs Settling velocity (m/day) vv Volatilization mass-transfer coefficient (m/day) zg Thickness of the gas film (m) zl Thickness of the liquid film (m)
φ Porosity ∆pstlkwtr Change in pesticide mass in the water layer (mg pst) ∆pstlksed Change in pesticide mass in the sediment layer (mg pst) ρs Particle density (g/m3) µw Wind speed (m/s)
30.5 REFERENCES Chapra, S.C. 1997. Surface water-quality modeling. WCB/McGraw-Hill, Boston, MA. Lewis, W.K. and W.G. Whitman. 1924. Principles of gas absorption. Ind. Eng. Chem. 16:1215-1220. Whitman, W.G. 1923. The two-film theory of gas adsorption. Chem. Metallurg. Eng. 29:146-148.
PART 2
MODEL OPERATION
CHAPTER 31
SWAT INPUT DATA: WATERSHED CONFIGURATION
The first step in setting up a watershed simulation is to define the relative arrangement of the parts or elements, i.e. the configuration, of the watershed. If the watershed has only one primary channel and there is very little variation in topography and climate across the watershed, there may not be a need to partition the watershed into smaller units. However, the majority of watersheds will exhibit enough complexity in the stream network, topography or climate to warrant subdivision for modeling purposes. There are several techniques used to discretize a watershed. In the past, models could only apply one type of discretization scheme to a watershed. This
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SWAT USER’S MANUAL, VERSION 2000
resulted in the development of several models that differ only in the watershed discretization scheme used.
31.1 DISCRETIZATION SCHEMES The three most common techniques used to discretize a watershed are: ♦ Grid cell. This configuration allows the user to incorporate significant spatial detail into a simulation. Models which use this technique include AGNPS (Young et al., 1987), ANSWERS (Beasley et al., 1980) and the WEPP grid version (Foster, 1987). ♦ Representative hillslope. This configuration is useful for modeling hillslope processes. This technique is used in APEX (Williams, et al., 1998) and the WEPP hillslope version (Lane and Nearing, 1989). ♦ Subwatershed.
This configuration preserves the natural channels
and flow paths of the watershed. This technique is used in the WEPP watershed version (Foster, 1987), HYMO (Williams and Hann, 1973) and SWRRB (Arnold et al., 1990).
All of these schemes have strengths and weaknesses and applications for which they are most appropriate. SWAT uses the subwatershed configuration as the primary discretization scheme for a watershed. However, because of the routing command language utilized in SWAT, it is possible to use any of these three, alone or in combination, to model a watershed.
31.2 WATERSHED CONFIGURATION FILE (.FIG) The watershed configuration file contains information used by SWAT to simulate processes occurring within the HRU/subbasin and to route the stream
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
31
loadings through the channel network of the watershed. A reach routing command structure, similar to that developed for HYMO (Williams and Hann, 1973), is utilized to route and add flows through the watershed. The following sections review the different features of the watershed configuration file.
31.2.1 INCORPORATION OF COMMENTS To assist the user in interpreting the watershed configuration file, an unlimited number of comment lines are allowed. These comments can be used to isolate the routing commands for different reaches, etc. To included comments in the watershed configuration file, a line must have an asterisk (*) in the 1st space on the line. When SWAT reads the asterisk, it will skip to the next line.
31.2.2 COMMAND LINES Thirteen different commands may be used in the watershed configuration file. The commands, along with their numeric codes, are: finish subbasin route routres transfer add recmon recyear save recday reccnst structur saveconc
0 1 2 3 4 5 7 8 9 10 11 12 14
The most commonly used commands are: subbasin, route, add, and finish. In brief, these commands simulated the land phase of the hydrologic cycle and determine the loadings to the reach (subbasin), model the movement and transformations occurring in the reach (route), allow the output from different
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SWAT USER’S MANUAL, VERSION 2000
subbasins to be summed together (add), and identify the end of the routing command sequence (finish). The remaining commands are utilized to model more unique configurations. This set of commands can be divided several subgroups: routing of water through a reservoir (routres), humanly contrived movement of water (transfer), aeration of water resulting from flow through structures along the channel (structur), incorporation of point source data (recday, recmon, recyear, reccnst), formatting of watershed outflow for input into a different SWAT simulation (save), and formatting of water quality simulation results at specified points in the reach network (saveconc).
31.2.2.1 SUBBASIN COMMAND (1) The subbasin command simulates all processes involved in the land phase of the hydrologic cycle and computes runoff, sediment, and chemical loadings from each HRU within the subbasin. Variables required on the subbasin command line are: Variable name
Definition
COMMAND
The command code = 1 for the subbasin command.
HYD_STOR
The hydrograph storage location number. After a command is executed, the results are stored in an array at the position defined by this number. It is crucial that all hydrograph storage location numbers are unique. If the same number is used twice, output from one command line will be overwritten by that from another and simulation results will be incorrect.
SUB_NUM
Subbasin number. This number is assigned in file.cio. Every subbasin in the watershed has a different number.
GIS_CODE
GIS code printed to output files (optional)
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
33
The format of the subbasin command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_STOR
space 17-22
6-digit integer
i6
SUB_NUM
space 23-28
6-digit integer
i6
GIS_CODE
space 47-55
9-digit integer
i9
31.2.2.2 ROUTE COMMAND (2) The route command routes the water, sediment, and chemical loadings through a main channel or reach. Variables required on the route command line are: Variable name
Definition
COMMAND
The command code = 2 for the route command.
HYD_STOR
The hydrograph storage location number. After a command is executed, the results are stored in an array at the position defined by this number. It is crucial that all hydrograph storage location numbers are unique. If the same number is used twice, output from one command line will be overwritten by that from another and simulation results will be incorrect.
RCH_NUM
Reach number. The reach number is the same as the subbasin number.
HYD_NUM
Inflow hydrograph storage location number. The data that is to be routed through the reach.
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SWAT USER’S MANUAL, VERSION 2000
Variable name
Definition
FLOW_OVN
Fraction of overland flow (0.000 to 1.000). If flow leaving a subbasin is completely channelized, FLOW_OVN = 0.000. In cases where a hillslope is being simulated, overland flow from one subbasin to another occurs and the value of FLOW_OVN can be increased to account for the amount of non-channelized overland flow taking place between the subbasins. The overland flow to the next subbasin is added to the rainfall of the receiving subbasin and allowed to infiltrate or run off. The sediment and chemical loadings associated with the overland flow are assumed to be deposited on the upper soil layer of the receiving subbasin. The fraction of the flow in the channel is routed directly to the reach of the receiving subbasin.
The format of the route command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_STOR
space 17-22
6-digit integer
i6
RCH_NUM
space 23-28
6-digit integer
i6
HYD_NUM
space 29-34
6-digit integer
i6
FLOW_OVN
space 41-46
decimal (xx.xxx)
f6.3
31.2.2.3 ADD COMMAND (5) The add command is used to sum the water, sediment, and chemical loadings of any two flows. Variables required on the add command line are: Variable name
Definition
COMMAND
The command code = 5 for the add command.
HYD_STOR
The hydrograph storage location number for the results.
HYD_NUM1
The hydrograph storage location number of the 1st set of data to be added.
HYD_NUM2
The hydrograph storage location number of the 2nd set of data to be added.
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
35
The format of the add command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_STOR
space 17-22
6-digit integer
i6
HYD_NUM1
space 23-28
6-digit integer
i6
HYD_NUM2
space 29-34
6-digit integer
i6
31.2.2.4 FINISH COMMAND (0) The last command line in the .fig file must be a finish command line. The finish command notifies the model that the end of the command lines in the watershed configuration file has been reached. Variables required on the finish command line are: Variable name
Definition
COMMAND
The command code = 0 for the finish command
The format of the finish command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
31.2.2.5 ROUTRES COMMAND (3) The routres command routes water, sediment, and chemical loadings through a reservoir. The routres command requires two lines. Variables required on the routres command lines are: Variable name
Definition
COMMAND
The command code = 3 for the routres command.
HYD_STOR
The hydrograph storage location number for results.
RES_NUM
Reservoir number. Each reservoir modeled in the watershed must be assigned a unique consecutive number beginning at 1.
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SWAT USER’S MANUAL, VERSION 2000
Variable name
Definition
HYD_NUM
Inflow hydrograph storage location number. The data that is to be routed through the reservoir.
RESFILE
Name of reservoir input file (.res)
LWQFILE
Name of reservoir water quality input file (optional) (.lwq)
The format of the routres command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
RES_NUM
1
space 23-28
6-digit integer
i6
HYD_NUM
1
space 29-34
6-digit integer
i6
RESFILE
2
space 11-23
character
a13
LWQFILE
2
space 24-36
character
a13
31.2.2.6 TRANSFER COMMAND (4) Originally, the transfer command was the only method available to irrigate an HRU. While the irrigation scenarios are now handled primarily in the management files, the transfer command was retained for flexibility. The transfer command moves water from any reach or reservoir to any other reach or reservoir. Variables required on the transfer command line are: Variable name
Definition
COMMAND
The command code = 4 for the transfer command.
DEP_TYPE
Water source type: 1 reach 2 reservoir
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
Variable name
Definition
DEP_NUM
Water source number. The number of the reach or reservoir from which the flow will be diverted.
DEST_TYPE
Destination type. Defines the receiving body. 1 reach 2 reservoir Destination number. Number of reach or reservoir receiving the water.
DEST_NUM
37
TRANS_AMT
The flow amount transferred. (defined by TRANS_CODE)
TRANS_CODE
The rule code governing the transfer of water: 1 A fraction of the flow or volume to be transferred out of the reach or reservoir is specified 2 A minimum flow (reach) or volume (reservoir) to leave in the reach or reservoir is specified (m3/day) 3 An exact amount of water to be transferred is specified (m3/day)
The format of the transfer command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
DEP_TYPE
space 17-22
6-digit integer
i6
DEP_NUM
space 23-28
6-digit integer
i6
DEST_TYPE
space 29-34
6-digit integer
i6
DEST_NUM
space 35-40
6-digit integer
i6
TRANS_AMT
space 41-46
decimal (xx.xxx)
f6.3
TRANS_CODE
space 47-55
9-digit integer
i9
31.2.2.7 STRUCTURE COMMAND (12) The structure command simulates aeration caused by the tumbling of water as it moves over weirs or other structures along the stream network. In highly polluted streams, the aeration of the stream by this method is a significant source of oxygen. The structure command alters the dissolved oxygen content based on the aeration coefficient input by the user. Variables required on the structure command line are:
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SWAT USER’S MANUAL, VERSION 2000
Variable name
Definition
COMMAND
The command code = 12 for the structur command.
HYD_STOR
The hydrograph storage location number for data.
HYD_NUM
Inflow hydrograph storage location number. The data that is to be adjusted to reflect aeration. (Dissolved oxygen content is the only value that is altered with this command.)
AERATION_COEF
Aeration coefficient.
The format of the structure command is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_STOR
space 17-22
6-digit integer
i6
HYD_NUM
space 23-28
6-digit integer
i6
AERATION_COEF
space 41-46
decimal (xx.xxx)
f6.3
31.2.2.8 RECMON COMMAND (7) The recmon command is one of four routing commands that reads in flow, sediment and chemical loading records from a file and routes the input through the watershed. The recmon command is used to read in data summarized by month. The recmon command requires two lines. Variables required on the recmon command lines are: Variable name
Definition
COMMAND
The command code = 7 for the recmon command.
HYD_STOR
The hydrograph storage location number for data.
FILEMON_NUM
The file number. Unique file numbers should be used for each recmon command.
DRAINAGE_AREA Drainage area associated with records (km2) (optional) FILE_MON
Name of the file containing the monthly records
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
39
The format of the recmon command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
FILEMON_NUM
1
space 23-28
6-digit integer
i6
DRAINAGE_AREA
1
space 41-46
decimal (xx.xxx)
f6.3
FILE_MON
2
space 11-23
character
a13
31.2.2.9 RECYEAR COMMAND (8) The recyear command is one of four routing commands that reads in flow, sediment and chemical loading records from a file and routes the input through the watershed. The recyear command is used to read in annual output. The recyear command requires two lines. Variables required on the recyear command lines are: Variable name
Definition
COMMAND
The command code = 8 for the recyear command.
HYD_STOR
The hydrograph storage location number for data.
FILEYR_NUM
The file number. Unique file numbers should be used for each recyear command.
DRAINAGE_AREA Drainage area associated with records (km2) (optional) FILE_YR
Name of file containing annual output.
The format of the recyear command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
FILEYR_NUM
1
space 23-28
6-digit integer
i6
DRAINAGE_AREA
1
space 41-46
decimal(xx.xxx)
f6.3
FILE_YR
2
space 11-23
character
a13
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SWAT USER’S MANUAL, VERSION 2000
31.2.2.10 RECDAY COMMAND (10) The recday command is one of four routing commands that reads in flow, sediment and chemical loading records from a file and routes the input through the watershed. This command is useful for reading in point source data or data from simulations of upstream areas. The recday command is used to read in data summarized on a daily basis. The recday command requires two lines. Variables required on the recday command lines are: Variable name
Definition
COMMAND
The command code = 10 for the recday command.
HYD_STOR
The hydrograph storage location number for data.
FILEDAY_NUM
The file number. Unique file numbers should be used for each recday command.
DRAINAGE_AREA Drainage area associated with records (km2) (optional) FILE_DAY
Name of file containing daily records.
The format of the recday command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
FILEDAY_NUM
1
space 23-28
6-digit integer
i6
DRAINAGE_AREA
1
space 41-46
decimal (xx.xxx)
f6.3
FILE_DAY
2
space 11-23
character
a13
31.2.2.11 RECCNST COMMAND (11) The reccnst command is one of four routing commands that reads in flow, sediment and chemical loading records from a file and routes the input through the watershed. This command is useful for reading in point source data. The reccnst command is used to read in average annual data. The reccnst command requires two lines. Variables required on the reccnst command lines are:
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
Variable name
Definition
COMMAND
The command code = 11 for the reccnst command.
HYD_STOR
The hydrograph storage location number for data.
FILECNST_NUM
The file number. Unique file numbers should be used for each reccnst command.
41
DRAINAGE_AREA Drainage area associated with records (km2) (optional) FILE_CNST
Name of file containing average annual records.
The format of the reccnst command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
FILECNST_NUM
1
space 23-28
6-digit integer
i6
DRAINAGE_AREA
1
space 41-46
decimal(xx.xxx)
f6.3
FILE_CNST
2
space 11-23
character
a13
31.2.2.12 SAVE COMMAND (9) The save command allows the user to print daily SWAT output to the event output file specified in file.cio. This output file can then be read into another SWAT run using the recday command. Variables required on the save command line are: Variable name
Definition
COMMAND
The command code = 9 for save command.
HYD_NUM
The hydrograph storage location number of the data to be printed to file.
The format of the save command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_NUM
space 17-22
6-digit integer
i6
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SWAT USER’S MANUAL, VERSION 2000
31.2.2.13 SAVECONC COMMAND (14) The saveconc command saves flow, sediment and water quality indicator information from a specified point on the reach network to a file. The water quality information is reported as concentrations. This command is useful for isolating reach information at a particular point on the channel network. Up to 50 saveconc commands can be specified in the watershed configuration file. The saveconc command requires two lines. Variables required on the saveconc command lines are: Variable name
Definition
COMMAND
The command code = 14 for the saveconc command.
HYD_NUM
The hydrograph storage location number of the data to be printed to file.
FILECONC_NUM
The file number. Unique file numbers should be used for each saveconc command.
PRINT_FREQ
Printing frequency. For simulations using a sub-daily time step, water quality information may be summarized and printed for every hour or every day. Simulations using a daily time step will always print daily average values. 0 1
report daily averages report hourly averages (currently not operational)
If no printing frequency is specified, the model will print daily averages. FILE_CONC
Name of file to which the water quality information is written.
The format of the saveconc command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_NUM
1
space 17-22
6-digit integer
i6
FILECONC_NUM
1
space 23-28
6-digit integer
i6
PRINT_FREQ
1
space 29-34
6-digit integer
i6
FILE_CONC
2
space 11-23
character
a13
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
43
31.3 REFERENCES Arnold, J.G., J.R. Williams, A.D. Nicks, and N.B. Sammons. 1990. SWRRB, a basin scale simulation model for soil and water resources management. Texas A&M University Press, College Station, TX. Beasley, D.B., L.F. Huggins, and E.J. Monke. 1980. ANSWERS: A model for watershed planning. Trans. of the ASAE 23(4): 938-944. Foster, G.R. 1987. User requirements: USDA-Water erosion prediction project. Lane, L.J. and M.A. Nearing (ed.). 1989. USDA-Water erosion prediction project: hillslope profile model documentation. NSERL Report No. 2. National Soil Erosion Research Laboratory. USDA-Agricultural Research Service. W. Lafayette, IN. Williams, J.R. J.G. Arnold, R. Srinivasan, and T.S. Ramanarayanan. 1998. Chapter 33. APEX: a new tool for predicting the effects of climate and CO2 changes on erosion and water quality. p. 441-449. In J. Boardman and D. Favis-Mortlock (ed.) Modeling soil erosion by water. Springer-Verlag, Berlin. Williams, J.R. and R.W. Hann. 1973. HYMO: Problem oriented computer language for hydrologic modeling. USDA ARS-S-9. 76 pp. Young, R.A. et al. 1987. AGNPS, Agricultural non-point source pollution model: a watershed analysis tool. USDA Agricultural Research Service.
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SWAT USER’S MANUAL, VERSION 2000
CHAPTER 32
SWAT INPUT DATA: SIMULATION MANAGEMENT
Two different files contain information used by SWAT to govern the processing of input data and the formatting and type of output data produced by the simulation. These files also include parameters that identify any specialized processes to be simulated. They are the control input/output file (file.cio) and the input control code file (.cod).
45
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SWAT USER'S MANUAL, VERSION 2000
32.1 CONTROL INPUT/OUTPUT FILE (FILE.CIO) File management is performed with the control input/output file (file.cio). The control input/output file contains the name of files associated with the subbasins, database files, climate files and watershed-level input files accessed by SWAT during a simulation as well as the name of the files in which output will be stored. While the user may adopt any file naming scheme, we recommend that the file extensions listed in the manual are used to facilitate identification of the different file types. Files required by the model that are not listed in the control input/output file include HRU files listed in the subbasin general input (.sub) file, reservoir and point source input files listed in the watershed configuration (.fig) file and "unique" files which contain input for uncommon or specialized processes not typically simulated. The control input/output file can be divided into a number of different sections. Figure 32.1 illustrates the different groupings of files within file.cio.
Figure 32.1: General organization of control input/output files
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
47
Following is a brief description of the variables in the control input/output file. They are listed in the order they appear within the file.
32.1.1 TITLE SECTION (FILE.CIO) Variable name
Definition
TITLE
The first three lines of ‘file.cio’ are reserved for a description of the simulation run. The description may take up to 80 spaces per line. The title given in file.cio is printed to every output file. (optional)
32.1.2 GENERAL INPUT/OUTPUT SECTION (FILE.CIO)
Variable name
Definition
BIGSUB
Name of subbasin output file (.bsb). Loadings to the reach (water, sediment and nutrients) are summarized for each subbasin.
SBSOUT
Name of hydrologic response unit (HRU) output file (.sbs). Loadings to the reach (water, sediment and nutrients) and crop growth are summarized for each HRU.
RCHOUT
Name of main channel, or reach, output file (.rch). Summarizes the amount of water, sediment and pollutants entering and leaving the reach and provides data on in-steam processes (e.g. sediment deposition, evaporation occurring in the channel)
RSVOUT
Name of reservoir output file (.rsv). Summarizes the amount of water, sediment and pollutants entering and leaving the reservoir and quantifies processes occurring in the reservoir (e.g. sediment deposition).
WTROUT
Name of HRU impoundment output file (.wtr). Summarizes the amount of water, sediment and pollutants entering and leaving ponds, wetlands and depressional areas (potholes) and quantifies processes occurring in the different impoundments.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PESTOUT
Name of pesticide output file (.pso). The movement and transformation of the different pesticides used in the simulation are summarized for each HRU.
EVENT
Name of event output file (.eve). When very large basins are being simulated, it is often easier to split them into several SWAT runs. This file writes daily output which can be read into a different SWAT run.
ROUTIN
Name of watershed configuration file (.fig). Contains the commands to add and route flows through the watershed.
CODEDAT
Name of input control code file (.cod). The input control code file summarizes information that affects the operation of SWAT (e.g. print codes, weather generator codes, number of years being simulated, number of subbasins being simulated)
BASNDAT
Name of basin input file (.bsn). Contains inputs for processes modeled at the watershed level.
WATQAL
Name of watershed water quality input file (.wwq)
CROPDB
Name of land cover/plant growth database file (crop.dat). This file contains growth parameters for the different land covers.
TILLDAT
Name of tillage database file (till.dat). This file contains mixing efficiencies for different tillage implements.
PESTIDAT
Name of pesticide database file (pest.dat). This file contains parameters governing movement and degradation of pesticides.
FERTDAT
Name of fertilizer/manure database file (fert.dat). This file contains nutrient content data for fertilizers.
URBDAT
Name of urban land type database file (urban.dat). This file contains data required to model build-up/wash-off in urban areas.
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
49
32.1.3 CLIMATE INPUT SECTION (FILE.CIO) Variable name
Definition
NRGAGE
Number of precipitation gage (.pcp) files used in the simulation. Up to 18 files may be used.
NTGAGE
Number of temperature gage (.tmp) files used in the simulation. Up to 18 files may be used.
NRTOT
Total number of precipitation gage records used in the simulation. If each .pcp file contains only one precipitation gage record, NRTOT = NRGAGE. Otherwise, NRTOT > NRGAGE. A maximum of 5400 precipitation gage records may be used in a simulation.
NTTOT
Total number of temperature gage records used in the simulation. If each .tmp file contains only one temperature gage record, NTTOT = NTGAGE. Otherwise, NTTOT > NTGAGE. A maximum of 2700 temperature gage records may be used in a simulation.
NRGFIL
Number of precipitation gage records within each .pcp file. A maximum of 300 precipitation gage records may be placed in each .pcp file.
NTGFIL
Number of temperature gage records within each .tmp file. a maximum of 150 temperature gage records may be placed in each .tmp file
NSTOT
Number of solar radiation records within the .slr file. A maximum of 300 solar radiation records may be placed in the .slr file.
NHTOT
Number of relative humidity records within the .hmd file. A maximum of 300 relative humidity records may be placed in the .hmd file.
NWTOT
Number of wind speed records within the .wnd file. A maximum of 300 wind speed records may be placed in the .wnd file.
RFILE(1)
Name of measured precipitation input file #1 (.pcp).
RFILE(2)
Name of measured precipitation input file #2 (.pcp).
RFILE(3)
Name of measured precipitation input file #3 (.pcp).
RFILE(4)
Name of measured precipitation input file #4 (.pcp).
RFILE(5)
Name of measured precipitation input file #5 (.pcp).
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
RFILE(6)
Name of measured precipitation input file #6 (.pcp).
RFILE(7)
Name of measured precipitation input file #7 (.pcp).
RFILE(8)
Name of measured precipitation input file #8 (.pcp).
RFILE(9)
Name of measured precipitation input file #9 (.pcp).
RFILE(10)
Name of measured precipitation input file #10 (.pcp).
RFILE(11)
Name of measured precipitation input file #11 (.pcp).
RFILE(12)
Name of measured precipitation input file #12 (.pcp).
RFILE(13)
Name of measured precipitation input file #13 (.pcp).
RFILE(14)
Name of measured precipitation input file #14 (.pcp).
RFILE(15)
Name of measured precipitation input file #15 (.pcp).
RFILE(16)
Name of measured precipitation input file #16 (.pcp).
RFILE(17)
Name of measured precipitation input file #17 (.pcp).
RFILE(18)
Name of measured precipitation input file #18 (.pcp).
TFILE(1)
Name of measured temperature input file #1 (.tmp).
TFILE(2)
Name of measured temperature input file #2 (.tmp).
TFILE(3)
Name of measured temperature input file #3 (.tmp).
TFILE(4)
Name of measured temperature input file #4 (.tmp).
TFILE(5)
Name of measured temperature input file #5 (.tmp).
TFILE(6)
Name of measured temperature input file #6 (.tmp).
TFILE(7)
Name of measured temperature input file #7 (.tmp).
TFILE(8)
Name of measured temperature input file #8 (.tmp).
TFILE(9)
Name of measured temperature input file #9 (.tmp).
TFILE(10)
Name of measured temperature input file #10 (.tmp).
TFILE(11)
Name of measured temperature input file #11 (.tmp).
TFILE(12)
Name of measured temperature input file #12 (.tmp).
TFILE(13)
Name of measured temperature input file #13 (.tmp).
TFILE(14)
Name of measured temperature input file #14 (.tmp).
TFILE(15)
Name of measured temperature input file #15 (.tmp).
TFILE(16)
Name of measured temperature input file #16 (.tmp).
TFILE(17)
Name of measured temperature input file #17 (.tmp).
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
Definition
TFILE(18)
Name of measured temperature input file #18 (.tmp).
SLRFILE
Name of measured solar radiation input file (.slr).
RHFILE
Name of measured relative humidity input file (.hmd).
WNDFILE
Name of measured wind speed input file (.wnd).
PETFILE
Name of potential evapotranspiration input file (.pet).
51
32.1.4 SUBBASIN INPUT SECTION (FILE.CIO) Variable name
Definition
ISB
Subbasin number. This number is present to assist the user— the model ignores this number. Input files for the different subbasins must be listed consecutively in the subbasin input section.
SUBDAT
Name of subbasin general input data file (.sub).
RTEDAT
Name of subbasin routing input data file (.rte). This file contains parameters for the main channel.
PNDDAT
Name of subbasin pond input data file (.pnd).
WUSDAT
Name of subbasin water use management data file (.wus).
WGNDAT
Name of subbasin weather generator data file (.wgn).
SWQDAT
Name of subbasin stream water quality data file (.swq).
IRGAGE
Number of the measured precipitation record used within subbasin. Optional.
ITGAGE
Number of the measured temperature record used within the subbasin. Optional.
ISGAGE
Number of the solar radiation record used within the subbasin. Optional.
IHGAGE
Number of the relative humidity record used within the subbasin. Optional.
IWGAGE
Number of the wind speed record used within the subbasin. Optional.
52
SWAT USER'S MANUAL, VERSION 2000
32.1.5 FILE FORMAT (FILE.CIO) Variable name
Position
Format
F90 Format
1-3
space 1-80
character
a80
BIGSUB
4
space 1-13
character
a13
SBSOUT
4
space 14-26
character
a13
RCHOUT
4
space 27-39
character
a13
RSVOUT
4
space 40-52
character
a13
empty location
4
space 53-65
character
a13
WTROUT
4
space 66-78
character
a13
PESTOUT
5
space 1-13
character
a13
EVENT
5
space 14-26
character
a13
ROUTIN
5
space 27-39
character
a13
CODEDAT
5
space 40-52
character
a13
BASNDAT
5
space 53-65
character
a13
WATQAL
5
space 66-78
character
a13
CROPDB
6
space 1-13
character
a13
TILLDAT
6
space 14-26
character
a13
PESTIDAT
6
space 27-39
character
a13
FERTDAT
6
space 40-52
character
a13
URBDAT
6
space 53-65
character
a13
NRGAGE
7
space 1-4
integer
i4
NTGAGE
7
space 5-8
integer
i4
NRTOT
7
space 9-12
integer
i4
NTTOT
7
space 13-16
integer
i4
NRGFIL
7
space 17-20
integer
i4
NTGFIL
7
space 21-24
integer
i4
NSTOT
7
space 25-28
integer
i4
NHTOT
7
space 29-32
integer
i4
NWTOT
7
space 33-36
integer
i4
RFILE(1)
8
space 1-13
character
a13
RFILE(2)
8
space 14-26
character
a13
RFILE(3)
8
space 27-39
character
a13
RFILE(4)
8
space 40-52
character
a13
RFILE(5)
8
space 53-65
character
a13
TITLE
Line #
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT Variable name
Line #
Position
Format
F90 Format
RFILE(6)
8
space 66-78
character
a13
RFILE(7)
9
space 1-13
character
a13
RFILE(8)
9
space 14-26
character
a13
RFILE(9)
9
space 27-39
character
a13
RFILE(10)
9
space 40-52
character
a13
RFILE(11)
9
space 53-65
character
a13
RFILE(12)
9
space 66-78
character
a13
RFILE(13)
10
space 1-13
character
a13
RFILE(14)
10
space 14-26
character
a13
RFILE(15)
10
space 27-39
character
a13
RFILE(16)
10
space 40-52
character
a13
RFILE(17)
10
space 53-65
character
a13
RFILE(18)
10
space 66-78
character
a13
TFILE(1)
11
space 1-13
character
a13
TFILE(2)
11
space 14-26
character
a13
TFILE(3)
11
space 27-39
character
a13
TFILE(4)
11
space 40-52
character
a13
TFILE(5)
11
space 53-65
character
a13
TFILE(6)
11
space 66-78
character
a13
TFILE(7)
12
space 1-13
character
a13
TFILE(8)
12
space 14-26
character
a13
TFILE(9)
12
space 27-39
character
a13
TFILE(10)
12
space 40-52
character
a13
TFILE(11)
12
space 53-65
character
a13
TFILE(12)
12
space 66-78
character
a13
TFILE(13)
13
space 1-13
character
a13
TFILE(14)
13
space 14-26
character
a13
TFILE(15)
13
space 27-39
character
a13
TFILE(16)
13
space 40-52
character
a13
TFILE(17)
13
space 53-65
character
a13
TFILE(18)
13
space 66-78
character
a13
SLRFILE
14
space 1-13
character
a13
RHFILE
14
space 14-26
character
a13
WNDFILE
14
space 27-39
character
a13
PETFILE
14
space 40-52
character
a13
53
54
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
The remaining lines provide input data for all of the subbasins. Two lines are devoted to each subbasin's input data. In the equation for the line number, i is the subbasin number. ISB
13 + 2i
space 1-5
integer
i5
SUBDAT
13 + 2i
space 7-19
character
a13
RTEDAT
13 + 2i
space 21-33
character
a13
PNDDAT
13 + 2i
space 35-47
character
a13
WUSDAT
13 + 2i
space 49-61
character
a13
WGNDAT
14 + 2i
space 7-19
character
a13
SWQDAT
14 + 2i
space 21-33
character
a13
IRGAGE
14 + 2i
space 49-52
integer
i4
ITGAGE
14 + 2i
space 53-56
integer
i4
ISGAGE
14 + 2i
space 57-60
integer
i4
IHGAGE
14 + 2i
space 61-64
integer
i4
IWGAGE
14 + 2i
space 65-68
integer
i4
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
55
32.2 INPUT CONTROL CODE FILE (.COD) The input control code file regulates the general operation of SWAT. In addition to setting output file formats, the input control code file defines which processes are/are not modeled in the SWAT simulation. Following is a brief description of the variables in the input control file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .cod file is reserved for user comments. The comments may take up to 80 spaces. (optional)
NBYR
Number of calendar years simulated. The number of years simulated in a SWAT run can vary from 1 to 9,999 years. If a simulation is begun on August 1st of the year 1995 and ends July 30th of the year 1997, the model will be simulating 3 calendar years (1995, 1996 and 1997).
IYR
Beginning year of simulation (for example, 1980). The value entered for this variable is not important unless measured data (e.g. weather) is used in the run. When measured data is used, the model uses this variable to locate the beginning year within the data file.
IDAF
Beginning julian day of simulation. With this variable, SWAT is able to begin a simulation at any time of the year. If the variable is left blank or set to zero, the model starts the simulation on January 1st.
IDAL
Ending julian day of simulation. With this variable, SWAT will end the simulation on the date specified. If the variable is left blank or set to zero, the model ends the simulation on December 31st.
56
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
IPD
Print code. This variable governs the frequency that model results are printed to output files. There are three options: 0 1 2
monthly daily annually
If you choose to print results on a daily basis, the number of years simulated should be limited and/or the variables printed to the output file should be restricted. If these precautions are not taken, the output files will be too large to view. NYSKIP
Number of years to not print output. The options are 0 print output for all years of the simulation 1 print output after the first year of simulation 2 print output after the second year of simulation $ nbyr no output will be printed If initial conditions are not well known, a warm-up or equilibration period may be needed to get the values of soil water, residue, etc. to representative amounts. NYSKIP defines the duration of the equilibration period. In addition to not writing data to the output files, annual averages are not computed for the skipped years. Averages for the entire simulation period will also exclude data from the skipped years. The default value for NYSKIP is 0.
IPRN
Print code for input.std file. There are two options: 0 1
ILOG
entire input.std file is printed condensed version of input.std file is printed
Streamflow print code. This variable allows the user to take the log10 of the flow prior to printing streamflow values to the .rch file. There are two options: 0 1
print streamflow in .rch file print log of streamflow in .rch file
In large basins (for example, the Mississippi River basin), streamflow values printed to the .rch file may exceed the range allowed by the file format statements. This variable will eliminate print errors caused by very large values.
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
Definition
IPRP
Print code for .pso file. There are two options: 0 1
IGN
57
do not print pesticide output (.pso file will be empty) print pesticide output
Random generator seed code. A set of random numbers is needed by SWAT to generate weather data. SWAT has a set of default random numbers embedded in the code. To use the default random numbers, the user should set IGN = 0. This is the default value for IGN. In some situations, a user may wish to vary the weather sequence between runs. This is done by setting IGN to a different number every time the model is run. This code will activate a random number generator, which will replace the default set of random numbers with a new set. The value to which IGN is set determines the number of times the random number generator is cycled before the simulation begins. The seeds produced by the random number generator are then utilized by the weather generator instead of the default values. Measured weather data read into the model is not affected by this variable. However, if the measured data contains missing values, the weather generator is activated to produce data to replace the missing values. The data produced to replace missing values will be affected by this variable.
PCPSIM
Rainfall input code. This variable identifies the method the model will use to process rainfall data. There are two options: 1 measured data read for each subbasin 2 rainfall generated for each subbasin
IDT
Time step used to report measured rainfall data (minutes). Required if IEVENT = 2 or 3. One of the following should be chosen: 1 min, 2 min, 3 min, 4 min, 5 min, 6 min, 10 min, 12 min, 15 min, 20 min, 30 min.
IDIST
Rainfall distribution code. There are two options: 0 1
skewed distribution mixed exponential distribution
58
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
REXP
Value of exponent for mixed exponential rainfall distribution. A value for REXP must be entered if IDIST = 1. The model will set REXP = 1.3 if no value is entered.
TMPSIM
Temperature input code. This variable identifies the method the model will use to process temperature data. There are two options: 1 2
SLRSIM
Solar radiation input code. This variable identifies the method the model will use to process solar radiation data. There are two options: 1 2
RHSIM
measured data read for each subbasin relative humidity generated for each subbasin
Wind speed input code. This variable identifies the method the model will use to process wind speed data. There are two options: 1 2
IPET
measured data read for each subbasin solar radiation generated for each subbasin
Relative humidity input code. This variable identifies the method the model will use to process relative humidity data. There are two options: 1 2
WNDSIM
measured date read for each subbasin daily max/min generated for each subbasin
measured data read for each subbasin wind speed generated for each subbasin
Potential evapotranspiration method. There are four options for potential ET calculations: 0 1 2 3
Priestley-Taylor method Penman/Monteith method Hargreaves method read in potential ET values
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
Definition
IEVENT
Rainfall/runoff/routing option:
59
0 1
daily rainfall/curve number runoff/daily routing daily rainfall/Green & Ampt runoff/daily routing (sub-hourly rainfall required for Green & Ampt is generated from daily) this option not yet operational 2 sub-hourly rainfall/Green & Ampt runoff/daily routing 3 sub-hourly rainfall/Green & Ampt runoff/hourly routing Option 0 was the only active option in prior versions of the model and is the default. ICRK
Crack flow code. There are two options: 0 1
do not model crack flow in soil model crack flow in soil
The default option is ICRK=0. IRTE
Channel water routing method: 0 1
variable storage method Muskingum method
The default option is IRTE=0. The Muskingum method is a new option available with SWAT2000. The user must be careful to define MSK_CO1, MSK_CO2 and MSK_X (in .bsn) when the Muskingum method is chosen. IDEG
Channel degradation code. There are two options: 0
1
channel dimensions are not updated as a result of degradation (the dimensions remain constant for the entire simulation) channel dimensions are updated as a result of degradation
Channel degradation refers to the downcutting and widening of the channel with time and is always calculated. The default option is IDEG=0 (the channel width and depth remain constant).
60
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
IRESQ
Lake water quality code. The variable identifies whether or not lake water quality is simulated in the reservoirs. There are two options: 0 1
do not model lake water quality model lake water quality
The default option is IRESQ=0. IWQ
In-stream water quality code. The variable identifies whether in-stream transformation of nutrients is allowed to occur. 0 1
do not model in-stream nutrient transformations model in-stream nutrient transformations
The default option is IWQ=0. ISPROJ
Special project flag. SWAT includes sections of code specific to particular projects. This variable flags the code used in the particular simulation. There are three options: 0 1 2
not a special project HUMUS project Missouri River climate change project
A user will set this variable to something other than zero only if the SWAT programmers have told him to do so.
For long runs, the output files can get so large that the user may have difficulty in opening the files to look at output. The user has the option of customizing the output printed to the output files. Lines of the .cod file are used to specify the variables to be printed to the reach output file (.rch), the subbasin output file (.bsb), and the HRU output file (.sbs). If these lines contain only zeros, the model will print all the output variables to the file.
Variable name IPDVAR(:)
Definition Output variables printed to the .rch file. (up to 20 variables may be chosen in customized output.) The codes for the output variables are: 1 2
FLOW_IN: Average daily streamflow into reach (m3/s) FLOW_OUT: Average daily streamflow out of reach (m3/s)
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
61
Definition continued from previous page: 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
EVAP: Average daily loss of water from reach by evaporation (m3/s) TLOSS: Average daily loss of water from reach by transmission (m3/s) SED_IN: Sediment transported with water into reach (metric tons) SED_OUT: Sediment transported with water out of reach (metric tons) SEDCONC: Concentration of sediment in reach (mg/L) ORGN_IN: Organic nitrogen transported with water into reach (kg N) ORGN_OUT: Organic nitrogen transported with water out of reach (kg N) ORGP_IN: Organic phosphorus transported with water into reach (kg P) ORGP_OUT: Organic phosphorus transported with water out of reach (kg P) NO3_IN: Nitrate transported with water into reach (kg N) NO3_OUT: Nitrate transported with water out of reach (kg N) NH4_IN: Ammonium transported with water into reach (kg N) NH4_OUT: Ammonium transported with water out of reach (kg N) NO2_IN: Nitrite transported with water into reach (kg N) NO2_OUT: Nitrite transported with water out of reach (kg N) MINP_IN: Mineral phosphorus transported with water into reach (kg P) MINP_OUT: Mineral phosphorus transported with water out of reach (kg P) CHLA_IN: Chlorophyll-a transported with water into reach (kg) CHLA_OUT: Chlorophyll-a transported with water out of reach (kg) CBOD_IN: Carbonaceous biochemical oxygen demand transported into reach (kg O2) CBOD_OUT: Carbonaceous biochemical oxygen demand transported out of reach (kg O2) DISOX_IN: Dissolved oxygen transported into reach (kg O2) DISOX_OUT: Dissolved oxygen transported out of reach (kg O2) SOLPST_IN: Soluble pesticide transported with water into reach (mg a.i.) SOLPST_OUT: Soluble pesticide transported with water out of reach (mg a.i.) SORPST_IN: Pesticide sorbed to sediment transported with water into reach (mg a.i.) SORPST_OUT: Pesticide sorbed to sediment transported with water out of reach (mg a.i.) REACTPST: Loss of pesticide from water by reaction (mg a.i.) VOLPST: Loss of pesticide from water by volatilization (mg a.i.) SETTLPST: Transfer of pesticide from water to river bed sediment by settling (mg a.i.) RESUSP_PST: Transfer of pesticide from river bed sediment to water by resuspension (mg a.i.) DIFFUSEPST: Transfer of pesticide from water to river bed sediment by diffusion (mg a.i.)
62
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition continued from previous page: 35 REACBEDPST: Loss of pesticide from river bed sediment by reaction (mg a.i.) 36 BURYPST: Loss of pesticide from river bed sediment by burial (mg a.i.) 37 BED_PST: Pesticide in river bed sediment (mg a.i.) 38 BACTP_OUT: Number of persistent bacteria transported out of reach 39 BACTLP_OUT: Number of less persistent bacteria transported out of reach 40 CMETAL#1: Conservative metal #1 transported out of reach (kg) 41 CMETAL#2: Conservative metal #2 transported out of reach (kg) 42 CMETAL#3: Conservative metal #3 transported out of reach (kg)
IPDVAB(:)
Output variables printed to the .bsb file (up to 15 variables may be chosen in customized output.) The codes for the output variables are: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
IPDVAS(:)
PRECIP: Average total precipitation on subbasin (mm H2O) SNOMELT: Snow melt (mm H2O) PET: Potential evapotranspiration (mm H2O) ET: Actual evapotranspiration (mm H2O) SW: Soil water content (mm H2O) PERC: Amount of water percolating out of root zone (mm H2O) SURQ: Surface runoff (mm H2O) GW_Q: Groundwater discharge into reach (mm H2O) WYLD: Net water yield to reach (mm H2O) SYLD: Sediment yield (metric tons/ha) ORGN: Organic N released into reach (kg/ha) ORGP: Organic P released into reach (kg/ha) NSURQ: Nitrate released into reach (kg/ha) SOLP: Soluble P released into reach (kg/ha) SEDP: Mineral P attached to sediment released into reach (kg/ha)
Output variables printed to the .sbs file (up to 20 variables may be chosen in customized output.) The codes for the output variables are: 1 2
PRECIP: Total precipitation on HRU (mm H2O) SNOFALL: Precipitation falling as snow, sleet, or freezing rain (mm H2O) 3 SNOMELT: Amount of snow or ice melting (mm H2O) 4 IRR: Amount of irrigation water applied to HRU (mm H2O) 5 PET: Potential evapotranspiration (mm H2O) 6 ET: Amount of water removed by evapotranspiration (mm H2O) 7 SW: Soil water content at end of time period (mm H2O) 8 PERC: Amount of water percolating out of the root zone (mm H2O) 9 GW_RCHG: Amount of water entering both aquifers (mm H2O) 10 DA_RCHG: Amount of water entering deep aquifer from root zone (mm H2O) 11 REVAP: Water in shallow aquifer returning to root zone (mm H2O)
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
63
Definition continued from previous page: 12 SA_IRR: Amount of water removed from shallow aquifer for irrigation (mm H2O) 13 DA_IRR: Amount of water removed from deep aquifer for irrigation (mm H2O) 14 SA_ST: Amount of water in shallow groundwater storage at end of time period (mm H2O) 15 DA_ST: Amount of water in deep groundwater storage at end of time period (mm H2O) 16 SURQ: Surface runoff contribution to reach (mm H2O) 17 TLOSS: Amount of water removed from tributary channels by transmission (mm H2O) 18 LATQ: Lateral flow contribution to reach (mm H2O) 19 GW_Q: Groundwater discharge into reach (mm H2O) 20 WYLD: Net amount of water contributed by the HRU to the reach (mm H2O) 21 SYLD: Amount of sediment contributed by the HRU to the reach (metric tons/ha) 22 USLE: USLE soil loss (metric tons/ha) 23 N_APP: Amount of N fertilizer applied (kg N/ha) 24 P_APP: Amount of P fertilizer applied (kg P/ha) 25 NAUTO: Amount of N fertilizer applied automatically (kg N/ha) 26 PAUTO: Amount of P fertilizer applied automatically (kg P/ha) 27 NGRZ: Nitrogen applied to HRU in grazing operation during time step (kg N/ha) 28 PGRZ: Phosphorus applied to HRU in grazing operation during time step (kg P/ha) 29 NRAIN: Nitrate added in rainfall (kg N/ha) 30 NFIX: Amount of N fixed by legumes (kg N/ha) 31 F-MN: Transformation of N from fresh organic to mineral pool (kg N/ha) 32 A-MN: Transformation of N from active organic to mineral pool (kg N/ha) 33 A-SN: Transformation of N from active organic to stable organic pool (kg N/ha) 34 F-MP: Transformation of P from fresh organic to mineral pool (kg P/ha) 35 AO-LP: Transformation of P from organic to labile pool (kg P/ha) 36 L-AP: Transformation of P from labile to active mineral pool (kg P/ha) 37 A-SP: Transformation of P from active mineral to stable mineral pool (kg P/ha) 38 DNIT: Amount of N removed from soil by denitrification (kg N/ha) 39 NUP: Nitrogen uptake by plants (kg N/ha) 40 PUP: Phosphorus uptake by plants (kg P/ha) 41 ORGN: Organic N contributed by HRU to reach (kg N/ha) 42 ORGP: Organic P contributed by HRU to reach (kg P/ha) 43 SEDP: Mineral P attached to sediment contributed by HRU to reach (kg P/ha) 44 NSURQ: NO3 contributed by HRU in surface runoff to reach (kg N/ha)
64
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition continued from previous page: 45 NLATQ: NO3 contributed by HRU in lateral flow to reach (kg N/ha) 46 NO3L: NO3 leached below the soil profile (kg N/ha) 47 NO3GW: NO3 contributed by HRU in groundwater flow to reach (kg N/ha) 48 SOLP: Soluble phosphorus contributed by HRU in surface runoff to reach (kg P/ha) 49 P_GW: Soluble phosphorus contributed by HRU in groundwater flow to reach (kg P/ha) 50 W_STRS: Number of water stress days. 51 TMP_STRS: Number of temperature stress days 52 N_STRS: Number of nitrogen stress days. 53 P_STRS: Number of phosphorus stress days. 54 BIOM: Total plant biomass (metric tons/ha) 55 LAI: Leaf area index 56 YLD: Harvested yield (metric tons/ha) 57 BACTP: Number of persistent bacteria in surface runoff (count) 58 BACTLP: Number of less persistent bacteria in surface runoff (count)
The input control code file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
NBYR
2
integer
free
IYR
3
integer
free
IDAF
4
integer
free
IDAL
5
integer
free
IPD
6
integer
free
NYSKIP
7
integer
free
IPRN
8
integer
free
ILOG
9
integer
free
IPRP
10
integer
free
IGN
11
integer
free
PCPSIM
12
integer
free
IDT
13
integer
free
IDIST
14
integer
free
REXP
15
real
free
TMPSIM
16
integer
free
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT Variable name
Line #
Format
F90 Format
SLRSIM
17
integer
free
RHSIM
18
integer
free
WNDSIM
19
integer
free
IPET
20
integer
free
IEVENT
21
integer
free
ICRK
22
integer
free
IRTE
23
integer
free
IDEG
24
integer
free
IRESQ
25
integer
free
IWQ
26
integer
free
ISPROJ
27
integer
free
COMMENT LINE
28
character
a80
IPDVAR(1)
29
integer
free
IPDVAR(2)
29
integer
free
IPDVAR(3)
29
integer
free
IPDVAR(4)
29
integer
free
IPDVAR(5)
29
integer
free
IPDVAR(6)
29
integer
free
IPDVAR(7)
29
integer
free
IPDVAR(8)
29
integer
free
IPDVAR(9)
29
integer
free
IPDVAR(10)
29
integer
free
IPDVAR(11)
29
integer
free
IPDVAR(12)
29
integer
free
IPDVAR(13)
29
integer
free
IPDVAR(14)
29
integer
free
IPDVAR(15)
29
integer
free
IPDVAR(16)
29
integer
free
IPDVAR(17)
29
integer
free
IPDVAR(18)
29
integer
free
IPDVAR(19)
29
integer
free
IPDVAR(20)
29
integer
free
COMMENT LINE
30
character
a80
IPDVAB(1)
31
integer
free
IPDVAB(2)
31
integer
free
65
66
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
IPDVAB(3)
31
integer
free
IPDVAB(4)
31
integer
free
IPDVAB(5)
31
integer
free
IPDVAB(6)
31
integer
free
IPDVAB(7)
31
integer
free
IPDVAB(8)
31
integer
free
IPDVAB(9)
31
integer
free
IPDVAB(10)
31
integer
free
IPDVAB(11)
31
integer
free
IPDVAB(12)
31
integer
free
IPDVAB(13)
31
integer
free
IPDVAB(14)
31
integer
free
IPDVAB(15)
31
integer
free
COMMENT LINE
32
character
a80
IPDVAS(1)
33
integer
free
IPDVAS(2)
33
integer
free
IPDVAS(3)
33
integer
free
IPDVAS(4)
33
integer
free
IPDVAS(5)
33
integer
free
IPDVAS(6)
33
integer
free
IPDVAS(7)
33
integer
free
IPDVAS(8)
33
integer
free
IPDVAS(9)
33
integer
free
IPDVAS(10)
33
integer
free
IPDVAS(11)
33
integer
free
IPDVAS(12)
33
integer
free
IPDVAS(13)
33
integer
free
IPDVAS(14)
33
integer
free
IPDVAS(15)
33
integer
free
IPDVAS(16)
33
integer
free
IPDVAS(17)
33
integer
free
IPDVAS(18)
33
integer
free
IPDVAS(19)
33
integer
free
IPDVAS(20)
33
integer
free
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
67
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 33
SWAT INPUT DATA: GENERAL WATERSHED ATTRIBUTES
General watershed attributes are defined in the basin input file. These attributes control physical processes at the watershed level. Other than the variable DA_KM, the area of the watershed, the variables in this file are calibration variables or variables normally modified only for advanced or research applications.
69
70
SWAT USER'S MANUAL, VERSION 2000
33.1 BASIN INPUT FILE (.BSN) Following is a brief description of the variables in the basin input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line is reserved for a description. The description may take up to 80 spaces. Optional.
DA_KM
Area of the watershed (km2)
DT
Variable not currently used.
SFTMP
Snowfall temperature (ºC). Mean air temperature at which precipitation is equally likely to be rain as snow/freezing rain. The snowfall temperature should be between –5 ºC and 5 ºC. A default recommended for this variable is SFTMP = 1.0. Optional.
SMTMP
Snow melt base temperature (ºC). Snow pack temperature above which snow melt will occur. The snow melt base temperature should be between –5 ºC and 5 ºC. A default recommended for this variable is SMTMP = 0.50. Optional.
SMFMX
Melt factor for snow on June 21 (mm H2O/ºC-day). If the watershed is in the Northern Hemisphere, SMFMX will be the maximum melt factor. If the watershed is in the Southern Hemisphere, SMFMX will be the minimum melt factor. SMFMX and SMFMN allow the rate of snow melt to vary through the year. The variables account for the impact of snow pack density on snow melt. If no value for SMFMX is entered, the model will set SMFMX = 4.5. Optional.
SMFMN
Melt factor for snow on December 21 (mm H2O/ºC-day). If the watershed is in the Northern Hemisphere, SMFMN will be the minimum melt factor. If the watershed is in the Southern Hemisphere, SMFMN will be the maximum melt factor. SMFMX and SMFMN allow the rate of snow melt to vary through the year. The variables account for the impact of snow pack density on snow melt. If no value for SMFMN is entered, the model will set SMFMN = 4.5. Optional.
CHAPTER 33: SWAT INPUT—GENERAL WATERSHED ATTRIBUTES
71
Variable name
Definition
TIMP
Snow pack temperature lag factor. This parameter controls the impact of the current day's air temperature on the snow pack temperature. TIMP can vary between 0.01 and 1.0. When TIMP=1.0 there is no lag, i.e. the snow pack temperature is the same as the current day's air temperature. As TIMP goes to zero, the snow pack's temperature will be less influenced by the current day's air temperature. If no value for TIMP is entered, the model will set TIMP = 1.0. Optional.
SNOCOVMX
Minimum snow water content that corresponds to 100% snow cover (mm H2O). If the snow water content is less than SNOCOVMX, then a certain percentage of ground cover will be bare. If no value for SNOCOVMX is entered, the model will set SNOCOVMX = 1.00. Optional.
SNO50COV
Fraction of snow volume represented by SNOCOVMX that corresponds to 50% snow cover. SWAT assumes a nonlinear relationship between snow water and snow cover. SNO50COV can vary between 0.01 and 0.99. If no value for SNO50COV is entered, the model will set SNO50COV = 0.50, i.e. 50% of SNOCOVMX. Optional.
RCN
Concentration of nitrogen in rainfall (mg N/L). If no value for RCN is entered, the model will set RCN = 1.0. Optional.
SURLAG
Surface runoff lag coefficient. This parameter is needed in subbasins where the time of concentration is greater than 1 day. SURLAG is used to create a "storage" for surface runoff to allow runoff to take longer than one day to reach the subbasin outlet. If no value for SURLAG is entered, the model will set SURLAG = 4.0. Optional.
APM
Peak rate adjustment factor for sediment routing in the subbasin (tributary channels). Sediment routing is a function of peak flow rate and mean daily flow. Because SWAT can not directly calculate the sub-daily hydrograph due to the use of precipitation summarized on a daily basis, this variable was incorporated to allow adjustment for the effect of the peak flow rate on sediment routing. This factor is used in the MUSLE equation and impacts the amount of erosion generated in the HRUs. If no value for APM is entered, the model will set APM=1.0. Optional.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PRF
Peak rate adjustment factor for sediment routing in the main channel. Sediment routing is a function of peak flow rate and mean daily flow. Because SWAT can not directly calculate the sub-daily hydrograph, this variable was incorporated to allow adjustment for the effect of the peak flow rate on sediment routing. This variable impacts channel degradation. If no value for PRF is entered, the model will set PRF = 1.0. Optional. Linear parameter for calculating the maximum amount of sediment that can be reentrained during channel sediment routing ( DEGr=SPCON × (Vc)SPEXP where DEGr is the amount of sediment reentrained and Vc is stream velocity in the channel). SPCON should be between 0.0001 and 0.01. If no value for SPCON is entered, the model will set SPCON = 0.0001. Optional.
SPCON
SPEXP
Exponent parameter for calculating sediment reentrained in channel sediment routing ( DEGr=SPCON × (Vc)SPEXP where DEGr is the amount of sediment reentrained and Vc is stream velocity in the channel). SPEXP should be between 1.0 and 1.5. If no value for SPEXP is entered, the model will set SPEXP = 1.0. Optional.
EVRCH
Reach evaporation adjustment factor. If no value for EVRCH is entered, the model will set EVRCH = 1.00. Optional
EVLAI
Leaf area index at which no evaporation occurs from water surface. EVLAI is used in HRUs where a plant is growing in a ponded environment (e.g. rice). Evaporation from the water surface is allowed until the leaf area of the plant reaches the value specified for EVLAI. EVLAI should be between 0.0 and 10.0. If no value for EVLAI is entered, the model will set EVLAI = 3.0. Optional.
FFCB
Initial soil water storage expressed as a fraction of field capacity water content. All soils in the watershed will be initialized to the same fraction. If FFCB is not set to a value, the model will calculate it as a function of average annual precipitation. FFCB should be between 0.0 and 1.0. The default method is to allow the model to calculate FFCB (set FFCB = 0.0). Optional.
CMN
Rate factor for humus mineralization of active organic nutrients (N and P). If no value for CMN is specified, the model will set CMN = 0.0003. Optional.
CHAPTER 33: SWAT INPUT—GENERAL WATERSHED ATTRIBUTES
73
Variable name
Definition
UBN
Nitrogen uptake distribution parameter. This parameter controls the amount of nitrogen removed from the different soil layers by the plant. In particular, this parameter controls the amount of nitrogen removed from the surface layer via plant uptake. While the relationship between UBN and nitrogen removed from the surface layer is affected by the depth of the soil profile, in general as UBN increases the amount of N removed from the surface layer relative to the amount removed from the entire profile increases. If no value for UBN is entered, the model will set UBN = 20.0. Optional.
UBP
Phosphorus uptake distribution parameter. This parameter controls plant uptake of phosphorus from the different soil horizons in the same way that UBN controls nitrogen uptake. If no value for UBP is entered, the model will set UBP = 20.0. Optional.
NPERCO
Nitrate percolation coefficient. NPERCO controls the amount of nitrate removed from the surface layer in runoff relative to the amount removed via percolation. The value of NPERCO can range from 0.01 to 1.0. As NPERCO → 0.0, the concentration of nitrate in the runoff approaches 0. As NPERCO → 1.0, surface runoff has the same concentration of nitrate as the percolate. If no value for NPERCO is entered, the model will set NPERCO = 0.20. Optional.
PPERCO
Phosphorus percolation coefficient (10 m3/Mg). The phosphorus percolation coefficient is the ratio of the solution phosphorus concentration in the surface 10 mm of soil to the concentration of phosphorus in percolate. The value of PPERCO can range from 10.0 to 17.5 If no value for PPERCO is entered, the model will set PPERCO = 10.0. Optional.
PHOSKD
Phosphorus soil partitioning coefficient (m3/Mg). The phosphorus soil partitioning coefficient is the ratio of the soluble phosphorus concentration in the surface 10 mm of soil to the concentration of soluble phosphorus in surface runoff. If no value for PHOSKD is entered, the model will set PHOSKD = 175.0. Optional.
PSP
Phosphorus availability index. The fraction of mineral phosphorus remaining in solution after initial rapid sorption to soil. If no value for PSP is entered, the model will set PSP = 0.40. Optional.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
RSDCO
Residue decomposition coefficient. The fraction of residue which will decompose in a day assuming optimal moisture, temperature, C:N ratio and C:P ratio. If no value for RSDCO is entered, the model will set RSDCO = 0.05. Optional.
PERCOP
Pesticide percolation coefficient. PERCOP controls the amount of pesticide removed from the surface layer in runoff relative to the amount removed via percolation. The value of PERCOP can range from 0.01 to 1.0. As PERCOP → 0.0, the concentration of pesticide in the runoff approaches 0. As PERCOP → 1.0, surface runoff has the same concentration of pesticide as the percolate. If no value for PERCOP is entered, the model will set PERCOP = 0.50. Optional.
IRTPEST
Number of pesticide to be routed through the watershed channel network. This is the pesticide ID number from the pesticide database. While more than one type of pesticide may be applied to the HRUs, the model will monitor the movement of only one pesticide through the channel network. Optional.
WDPQ
Die-off factor for persistent bacteria in soil solution. (1/day) Optional.
WGPQ
Growth factor for persistent bacteria in soil solution. (1/day) Optional.
WDLPQ
Die-off factor for less persistent bacteria in soil solution. (1/day) Optional.
WGLPQ
Growth factor for less persistent bacteria in soil solution. (1/day) Optional.
WDPS
Die-off factor for persistent bacteria adsorbed to soil particles. (1/day) Optional.
WGPS
Growth factor for persistent bacteria adsorbed to soil particles. (1/day) Optional.
WDLPS
Die-off factor for less persistent bacteria adsorbed to soil particles. (1/day) Optional.
WGLPS
Growth factor for less persistent bacteria adsorbed to soil particles. (1/day) Optional.
BACTKDQ
Bacteria partition coefficient. Partition coefficient for bacteria between soluble and sorbed phase in surface runoff. If no value for BACTKDQ is entered, the model will set BACTKDQ = 175.0. Optional.
CHAPTER 33: SWAT INPUT—GENERAL WATERSHED ATTRIBUTES
75
Variable name
Definition
THBACT
Temperature adjustment factor for bacteria die-off/growth. If no value for THBACT is entered, the model will set THBACT = 1.07. Optional.
MSK_CO1
Calibration coefficient used to control impact of the storage time constant (Km) for normal flow (where normal flow is when river is at bankfull depth) upon the Km value calculated for the reach. Required only if IRTE = 1 in .cod file.
MSK_CO2
Calibration coefficient used to control impact of the storage time constant (Km) for low flow (where low flow is when river is at 0.1 bankfull depth) upon the Km value calculated for the reach. Required only if IRTE = 1 in .cod file.
MSK_X
Weighting factor controlling relative importance of inflow rate and outflow rate in determining water storage in reach segment. The values for MSK_X can range from 0.01 - 0.30. If no value for MSK_X is entered, the model will set MSK_X = 0.2. Required only if IRTE = 1 in .cod file.
ESCO
Soil evaporation compensation factor. This factor adjusts the depth distribution for evaporation from the soil to account for the effect of capillary action, crusting and cracks. ESCO must be between 0.01 and 1.0. If no value for ESCO is entered, the model will set ESCO = 0.95. The value for ESCO may be set at the watershed or HRU level (ESCO in .hru).
EPCO
Plant uptake compensation factor. This factor adjusts the depth distribution for plant uptake of water from the soil to account for the variation in root density with depth. EPCO must be between 0.01 and 1.0. If no value for EPCO is entered, the model will set EPCO = 1.0. The value for EPCO may be set at the watershed or HRU level (EPCO in .hru).
The basin input file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
DA_KM
2
real
free
DT
3
real
free
SFTMP
4
real
free
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
SMTMP
5
real
free
SMFMX
6
real
free
SMFMN
7
real
free
TIMP
8
real
free
SNOCOVMX
9
real
free
SNO50COV
10
real
free
RCN
11
real
free
SURLAG
12
real
free
APM
13
real
free
PRF
14
real
free
SPCON
15
real
free
SPEXP
16
real
free
BLANK LINE
17
real
free
BLANK LINE
18
real
free
BLANK LINE
19
real
free
BLANK LINE
20
real
free
BLANK LINE
21
real
free
EVRCH
22
real
free
EVLAI
23
real
free
FFCB
24
real
free
CMN
25
real
free
UBN
26
real
free
UBP
27
real
free
NPERCO
28
real
free
PPERCO
29
real
free
PHOSKD
30
real
free
PSP
31
real
free
RSDCO
32
real
free
PERCOP
33
real
free
IRTPEST
34
integer
free
WDPQ
35
real
free
WGPQ
36
real
free
WDLPQ
37
real
free
WGLPQ
38
real
free
WDPS
39
real
free
CHAPTER 33: SWAT INPUT—GENERAL WATERSHED ATTRIBUTES Variable name
Line #
Format
F90 Format
WGPS
40
real
free
WDLPS
41
real
free
WGLPS
42
real
free
BACTKDQ
43
real
free
THBACT
44
real
free
MSK_CO1
45
real
free
MSK_CO2
46
real
free
MSK_X
47
real
free
ESCO
48
real
free
EPCO
49
real
free
77
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 34
SWAT INPUT DATA: CLIMATE SWAT requires daily precipitation, maximum/minimum air temperature, solar radiation, wind speed and relative humidity. Values for all these parameters may be read from records of observed data or they may be generated. Seven types of files store the climatic data required by SWAT—the weather generator input file (.wgn), the measured precipitation file (.pcp), the measured temperature file (.tmp), the solar radiation input file (.slr), the wind speed input file (.wnd), the relative humidity input file (.hmd), and the potential evapotranspiration input file (.pet). Up to 18 precipitation files and 18 temperature files may be utilized in a simulation. The precipitation and temperature files are able to hold records for more than one gage, so there is not a limitation on the number of gages that can be used in a simulation. One solar radiation file, wind 77
78
SWAT USER'S MANUAL, VERSION 2000
speed file and relative humidity input file may be used in a simulation. These files are able to hold records for more than one gage, so there is not a limitation on the number of gages that can be used in a simulation. The potential evapotranspiration file holds only one record that is used for the entire watershed.
34.1 WEATHER GENERATOR INPUT FILE (.WGN) The weather generator input file contains the statistical data needed to generate representative daily climate data for the subbasins. Climatic data will be generated in two instances: when the user specifies that simulated weather will be used or when measured data is missing. Following is a brief description of the variables in the weather generator input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .wgn file is reserved for user comments. The comments may take up to 80 spaces. (optional)
WLATITUDE
Latitude of weather station used to create statistical parameters (degrees). The latitude is expressed as a real number with minutes and seconds converted to fractions of a degree. Optional.
WLONGITUDE
Longitude of weather station (degrees). This variable is not used by the model. Optional.
WELEV
Elevation of weather station (m). Optional.
RAIN_YRS
The number of years of maximum monthly 0.5 h rainfall data used to define values for RAIN_HHMX(1,:) RAIN_HHMX(12,:).
TMPMX(mon)
Average daily maximum air temperature for month (ºC).
TMPMN(mon)
Average daily minimum air temperature for month (ºC).
TMPSTDMX(mon)
Standard deviation for daily maximum air temperature in month (ºC).
CHAPTER 34: SWAT INPUT—CLIMATE
79
Variable name
Definition
TMPSTDMN(mon)
Standard deviation for daily minimum air temperature in month (ºC).
PCPMM(mon)
Average amount of precipitation falling in month (mm H2O).
PCPSTD(mon)
Standard deviation for daily precipitation in month (mm H2O/day ).
PCPSKW(mon)
Skew coefficient for daily precipitation in month.
PR_W(1,mon)
Probability of a wet day following a dry day in the month.
PR_W(2,mon)
Probability of a wet day following a wet day in the month.
PCPD(mon)
Average number of days of precipitation in month.
RAINHHMX(mon)
Maximum 0.5 hour rainfall in entire period of record for month (mm).
SOLARAV(mon)
Average daily solar radiation for month (MJ/m2/day).
DEWPT(mon)
Average daily dew point temperature in month (ºC).
WNDAV(mon)
Average daily wind speed in month (m/s).
The format of the weather generator input file is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
space 1-80
character
a80
WLATITUDE
2
space 13-19
decimal(xxxx.xx)
f7.2
WLONGITUDE
2
space 32-38
decimal(xxxx.xx)
f7.2
WELEV
3
space 13-19
decimal(xxxx.xx)
f7.2
RAIN_YRS
4
space 13-19
decimal(xxxx.xx)
f7.2
TMPMX(1)
5
space 1-6
decimal(xxx.xx)
f6.2
TMPMX(2)
5
space 7-12
decimal(xxx.xx)
f6.2
TMPMX(3)
5
space 13-18
decimal(xxx.xx)
f6.2
TMPMX(4)
5
space 19-24
decimal(xxx.xx)
f6.2
TMPMX(5)
5
space 25-30
decimal(xxx.xx)
f6.2
TMPMX(6)
5
space 31-36
decimal(xxx.xx)
f6.2
TMPMX(7)
5
space 37-42
decimal(xxx.xx)
f6.2
TMPMX(8)
5
space 43-48
decimal(xxx.xx)
f6.2
TMPMX(9)
5
space 49-54
decimal(xxx.xx)
f6.2
TMPMX(10)
5
space 55-60
decimal(xxx.xx)
f6.2
80
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
TMPMX(11)
5
space 61-66
decimal(xxx.xx)
f6.2
TMPMX(12)
5
space 67-72
decimal(xxx.xx)
f6.2
TMPMN(1)
6
space 1-6
decimal(xxx.xx)
f6.2
TMPMN(2)
6
space 7-12
decimal(xxx.xx)
f6.2
TMPMN(3)
6
space 13-18
decimal(xxx.xx)
f6.2
TMPMN(4)
6
space 19-24
decimal(xxx.xx)
f6.2
TMPMN(5)
6
space 25-30
decimal(xxx.xx)
f6.2
TMPMN(6)
6
space 31-36
decimal(xxx.xx)
f6.2
TMPMN(7)
6
space 37-42
decimal(xxx.xx)
f6.2
TMPMN(8)
6
space 43-48
decimal(xxx.xx)
f6.2
TMPMN(9)
6
space 49-54
decimal(xxx.xx)
f6.2
TMPMN(10)
6
space 55-60
decimal(xxx.xx)
f6.2
TMPMN(11)
6
space 61-66
decimal(xxx.xx)
f6.2
TMPMN(12)
6
space 67-72
decimal(xxx.xx)
f6.2
TMPSTDMX(1)
7
space 1-6
decimal(xxx.xx)
f6.2
TMPSTDMX(2)
7
space 7-12
decimal(xxx.xx)
f6.2
TMPSTDMX(3)
7
space 13-18
decimal(xxx.xx)
f6.2
TMPSTDMX(4)
7
space 19-24
decimal(xxx.xx)
f6.2
TMPSTDMX(5)
7
space 25-30
decimal(xxx.xx)
f6.2
TMPSTDMX(6)
7
space 31-36
decimal(xxx.xx)
f6.2
TMPSTDMX(7)
7
space 37-42
decimal(xxx.xx)
f6.2
TMPSTDMX(8)
7
space 43-48
decimal(xxx.xx)
f6.2
TMPSTDMX(9)
7
space 49-54
decimal(xxx.xx)
f6.2
TMPSTDMX(10)
7
space 55-60
decimal(xxx.xx)
f6.2
TMPSTDMX(11)
7
space 61-66
decimal(xxx.xx)
f6.2
TMPSTDMX(12)
7
space 67-72
decimal(xxx.xx)
f6.2
TMPSTDMN(1)
8
space 1-6
decimal(xxx.xx)
f6.2
TMPSTDMN(2)
8
space 7-12
decimal(xxx.xx)
f6.2
TMPSTDMN(3)
8
space 13-18
decimal(xxx.xx)
f6.2
TMPSTDMN(4)
8
space 19-24
decimal(xxx.xx)
f6.2
TMPSTDMN(5)
8
space 25-30
decimal(xxx.xx)
f6.2
TMPSTDMN(6)
8
space 31-36
decimal(xxx.xx)
f6.2
TMPSTDMN(7)
8
space 37-42
decimal(xxx.xx)
f6.2
TMPSTDMN(8)
8
space 43-48
decimal(xxx.xx)
f6.2
TMPSTDMN(9)
8
space 49-54
decimal(xxx.xx)
f6.2
CHAPTER 34: SWAT INPUT—CLIMATE Variable name
Line #
Position
Format
F90 Format
TMPSTDMN(10)
8
space 55-60
decimal(xxx.xx)
f6.2
TMPSTDMN(11)
8
space 61-66
decimal(xxx.xx)
f6.2
TMPSTDMN(12)
8
space 67-72
decimal(xxx.xx)
f6.2
PCPMM(1)
9
space 1-6
decimal(xxx.xx)
f6.2
PCPMM(2)
9
space 7-12
decimal(xxx.xx)
f6.2
PCPMM(3)
9
space 13-18
decimal(xxx.xx)
f6.2
PCPMM(4)
9
space 19-24
decimal(xxx.xx)
f6.2
PCPMM(5)
9
space 25-30
decimal(xxx.xx)
f6.2
PCPMM(6)
9
space 31-36
decimal(xxx.xx)
f6.2
PCPMM(7)
9
space 37-42
decimal(xxx.xx)
f6.2
PCPMM(8)
9
space 43-48
decimal(xxx.xx)
f6.2
PCPMM(9)
9
space 49-54
decimal(xxx.xx)
f6.2
PCPMM(10)
9
space 55-60
decimal(xxx.xx)
f6.2
PCPMM(11)
9
space 61-66
decimal(xxx.xx)
f6.2
PCPMM(12)
9
space 67-72
decimal(xxx.xx)
f6.2
PCPSTD(1)
10
space 1-6
decimal(xxx.xx)
f6.2
PCPSTD(2)
10
space 7-12
decimal(xxx.xx)
f6.2
PCPSTD(3)
10
space 13-18
decimal(xxx.xx)
f6.2
PCPSTD(4)
10
space 19-24
decimal(xxx.xx)
f6.2
PCPSTD(5)
10
space 25-30
decimal(xxx.xx)
f6.2
PCPSTD(6)
10
space 31-36
decimal(xxx.xx)
f6.2
PCPSTD(7)
10
space 37-42
decimal(xxx.xx)
f6.2
PCPSTD(8)
10
space 43-48
decimal(xxx.xx)
f6.2
PCPSTD(9)
10
space 49-54
decimal(xxx.xx)
f6.2
PCPSTD(10)
10
space 55-60
decimal(xxx.xx)
f6.2
PCPSTD(11)
10
space 61-66
decimal(xxx.xx)
f6.2
PCPSTD(12)
10
space 67-72
decimal(xxx.xx)
f6.2
PCPSKW(1)
11
space 1-6
decimal(xxx.xx)
f6.2
PCPSKW(2)
11
space 7-12
decimal(xxx.xx)
f6.2
PCPSKW(3)
11
space 13-18
decimal(xxx.xx)
f6.2
PCPSKW(4)
11
space 19-24
decimal(xxx.xx)
f6.2
PCPSKW(5)
11
space 25-30
decimal(xxx.xx)
f6.2
PCPSKW(6)
11
space 31-36
decimal(xxx.xx)
f6.2
PCPSKW(7)
11
space 37-42
decimal(xxx.xx)
f6.2
PCPSKW(8)
11
space 43-48
decimal(xxx.xx)
f6.2
81
82
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
PCPSKW(9)
11
space 49-54
decimal(xxx.xx)
f6.2
PCPSKW(10)
11
space 55-60
decimal(xxx.xx)
f6.2
PCPSKW(11)
11
space 61-66
decimal(xxx.xx)
f6.2
PCPSKW(12)
11
space 67-72
decimal(xxx.xx)
f6.2
PR_W(1,1)
12
space 1-6
decimal(xxx.xx)
f6.2
PR_W(1,2)
12
space 7-12
decimal(xxx.xx)
f6.2
PR_W(1,3)
12
space 13-18
decimal(xxx.xx)
f6.2
PR_W(1,4)
12
space 19-24
decimal(xxx.xx)
f6.2
PR_W(1,5)
12
space 25-30
decimal(xxx.xx)
f6.2
PR_W(1,6)
12
space 31-36
decimal(xxx.xx)
f6.2
PR_W(1,7)
12
space 37-42
decimal(xxx.xx)
f6.2
PR_W(1,8)
12
space 43-48
decimal(xxx.xx)
f6.2
PR_W(1,9)
12
space 49-54
decimal(xxx.xx)
f6.2
PR_W(1,10)
12
space 55-60
decimal(xxx.xx)
f6.2
PR_W(1,11)
12
space 61-66
decimal(xxx.xx)
f6.2
PR_W(1,12)
12
space 67-72
decimal(xxx.xx)
f6.2
PR_W(2,1)
13
space 1-6
decimal(xxx.xx)
f6.2
PR_W(2,2)
13
space 7-12
decimal(xxx.xx)
f6.2
PR_W(2,3)
13
space 13-18
decimal(xxx.xx)
f6.2
PR_W(2,4)
13
space 19-24
decimal(xxx.xx)
f6.2
PR_W(2,5)
13
space 25-30
decimal(xxx.xx)
f6.2
PR_W(2,6)
13
space 31-36
decimal(xxx.xx)
f6.2
PR_W(2,7)
13
space 37-42
decimal(xxx.xx)
f6.2
PR_W(2,8)
13
space 43-48
decimal(xxx.xx)
f6.2
PR_W(2,9)
13
space 49-54
decimal(xxx.xx)
f6.2
PR_W(2,10)
13
space 55-60
decimal(xxx.xx)
f6.2
PR_W(2,11)
13
space 61-66
decimal(xxx.xx)
f6.2
PR_W(2,12)
13
space 67-72
decimal(xxx.xx)
f6.2
PCPD(1)
14
space 1-6
decimal(xxx.xx)
f6.2
PCPD(2)
14
space 7-12
decimal(xxx.xx)
f6.2
PCPD(3)
14
space 13-18
decimal(xxx.xx)
f6.2
PCPD(4)
14
space 19-24
decimal(xxx.xx)
f6.2
PCPD(5)
14
space 25-30
decimal(xxx.xx)
f6.2
PCPD(6)
14
space 31-36
decimal(xxx.xx)
f6.2
PCPD(7)
14
space 37-42
decimal(xxx.xx)
f6.2
CHAPTER 34: SWAT INPUT—CLIMATE Variable name
Line #
Position
Format
F90 Format
PCPD(8)
14
space 43-48
decimal(xxx.xx)
f6.2
PCPD(9)
14
space 49-54
decimal(xxx.xx)
f6.2
PCPD(10)
14
space 55-60
decimal(xxx.xx)
f6.2
PCPD(11)
14
space 61-66
decimal(xxx.xx)
f6.2
PCPD(12)
14
space 67-72
decimal(xxx.xx)
f6.2
RAINHHMX(1)
15
space 1-6
decimal(xxx.xx)
f6.2
RAINHHMX(2)
15
space 7-12
decimal(xxx.xx)
f6.2
RAINHHMX(3)
15
space 13-18
decimal(xxx.xx)
f6.2
RAINHHMX(4)
15
space 19-24
decimal(xxx.xx)
f6.2
RAINHHMX(5)
15
space 25-30
decimal(xxx.xx)
f6.2
RAINHHMX(6)
15
space 31-36
decimal(xxx.xx)
f6.2
RAINHHMX(7)
15
space 37-42
decimal(xxx.xx)
f6.2
RAINHHMX(8)
15
space 43-48
decimal(xxx.xx)
f6.2
RAINHHMX(9)
15
space 49-54
decimal(xxx.xx)
f6.2
RAINHHMX(10)
15
space 55-60
decimal(xxx.xx)
f6.2
RAINHHMX(11)
15
space 61-66
decimal(xxx.xx)
f6.2
RAINHHMX(12)
15
space 67-72
decimal(xxx.xx)
f6.2
SOLARAV(1)
16
space 1-6
decimal(xxx.xx)
f6.2
SOLARAV(2)
16
space 7-12
decimal(xxx.xx)
f6.2
SOLARAV(3)
16
space 13-18
decimal(xxx.xx)
f6.2
SOLARAV(4)
16
space 19-24
decimal(xxx.xx)
f6.2
SOLARAV(5)
16
space 25-30
decimal(xxx.xx)
f6.2
SOLARAV(6)
16
space 31-36
decimal(xxx.xx)
f6.2
SOLARAV(7)
16
space 37-42
decimal(xxx.xx)
f6.2
SOLARAV(8)
16
space 43-48
decimal(xxx.xx)
f6.2
SOLARAV(9)
16
space 49-54
decimal(xxx.xx)
f6.2
SOLARAV(10)
16
space 55-60
decimal(xxx.xx)
f6.2
SOLARAV(11)
16
space 61-66
decimal(xxx.xx)
f6.2
SOLARAV(12)
16
space 67-72
decimal(xxx.xx)
f6.2
DEWPT(1)
17
space 1-6
decimal(xxx.xx)
f6.2
DEWPT(2)
17
space 7-12
decimal(xxx.xx)
f6.2
DEWPT(3)
17
space 13-18
decimal(xxx.xx)
f6.2
DEWPT(4)
17
space 19-24
decimal(xxx.xx)
f6.2
DEWPT(5)
17
space 25-30
decimal(xxx.xx)
f6.2
DEWPT(6)
17
space 31-36
decimal(xxx.xx)
f6.2
83
84
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
DEWPT(7)
17
space 37-42
decimal(xxx.xx)
f6.2
DEWPT(8)
17
space 43-48
decimal(xxx.xx)
f6.2
DEWPT(9)
17
space 49-54
decimal(xxx.xx)
f6.2
DEWPT(10)
17
space 55-60
decimal(xxx.xx)
f6.2
DEWPT(11)
17
space 61-66
decimal(xxx.xx)
f6.2
DEWPT(12)
17
space 67-72
decimal(xxx.xx)
f6.2
WNDAV(1)
18
space 1-6
decimal(xxx.xx)
f6.2
WNDAV(2)
18
space 7-12
decimal(xxx.xx)
f6.2
WNDAV(3)
18
space 13-18
decimal(xxx.xx)
f6.2
WNDAV(4)
18
space 19-24
decimal(xxx.xx)
f6.2
WNDAV(5)
18
space 25-30
decimal(xxx.xx)
f6.2
WNDAV(6)
18
space 31-36
decimal(xxx.xx)
f6.2
WNDAV(7)
18
space 37-42
decimal(xxx.xx)
f6.2
WNDAV(8)
18
space 43-48
decimal(xxx.xx)
f6.2
WNDAV(9)
18
space 49-54
decimal(xxx.xx)
f6.2
WNDAV(10)
18
space 55-60
decimal(xxx.xx)
f6.2
WNDAV(11)
18
space 61-66
decimal(xxx.xx)
f6.2
WNDAV(12)
18
space 67-72
decimal(xxx.xx)
f6.2
34.2 PRECIPITATION INPUT FILE (.PCP) Measured precipitation data is read into the model from the .pcp file. The precipitation data may be read into the model in daily or sub-daily time increments. The following sections describe the format for a daily and a subdaily precipitation file.
34.2.1 DAILY PRECIPITATION DATA Daily precipitation data is used when the SCS curve number method is chosen to model surface runoff (Set by IEVENT in the .cod file). While the input file must contain data for the entire period of simulation, the record does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the file, saving editing time on the user's part. Once SWAT locates the record for the beginning day of simulation, it no longer
CHAPTER 34: SWAT INPUT—CLIMATE
85
processes the year and date. Because it does not check the subsequent dates, it is very important that the data for the remaining days in the simulation are listed sequentially. (If no year and date are entered for any of the records, the model assumes the first line data corresponds to the first day of simulation.) A negative 99.0 (-99.0) should be inserted for missing data. This value tells SWAT to generate precipitation for that day. Following is a brief description of the variables in the precipitation input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the precipitation file is reserved for comments.
LATITUDE
Latitude of precipitation recording gage location. This value is not used by the model.
LONGITUDE
Longitude of precipitation recording gage location. This value is not used by the model.
ELEVATION
Elevation of precipitation recording gage station (m). Precipitation values are adjusted for elevation in subbasins where elevation bands are defined.
YEAR
Year (4-digit)
DATE
Julian date
PRECIPITATION
Amount of precipitation falling in the time period (mm)
The format of the daily precipitation file with one record is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
unrestricted
character
unrestricted
LATITUDE
2
space 8-12
free
unrestricted
LONGITUDE
3
space 8-12
free
unrestricted
ELEVATION
4
space 8-12
integer
i5
YEAR
5-END
space 1-4
integer
i4
DATE
5-END
space 5-7
integer
i3
PRECIPITATION
5-END
space 8-12
decimal(xxx.x)
f5.1
86
SWAT USER'S MANUAL, VERSION 2000
To place more than one data record within the .pcp file, repeat the original formatting for the recorded data to the right of the existing data. Simulations have been run with 200 records placed in the precipitation files. For example, assume there are records for six different rain gages stored in the daily .pcp. The formatting of the .pcp file is Gage
Variable name
ALL
TITLE
1
Line #
Position
Format
F90 Format
1
unrestricted
character
unrestricted
LATITUDE
2
space 8-12
free
unrestricted
2
LATITUDE
2
space 13-17
free
unrestricted
3
LATITUDE
2
space 18-22
free
unrestricted
4
LATITUDE
2
space 23-27
free
unrestricted
5
LATITUDE
2
space 28-32
free
unrestricted
6
LATITUDE
2
space 33-37
free
unrestricted
1
LONGITUDE
3
space 8-12
free
unrestricted
2
LONGITUDE
3
space 13-17
free
unrestricted
3
LONGITUDE
3
space 18-22
free
unrestricted
4
LONGITUDE
3
space 23-27
free
unrestricted
5
LONGITUDE
3
space 28-32
free
unrestricted
6
LONGITUDE
3
space 33-37
free
unrestricted
1
ELEVATION
4
space 8-12
integer
i5
2
ELEVATION
4
space 13-17
integer
i5
3
ELEVATION
4
space 18-22
integer
i5
4
ELEVATION
4
space 23-27
integer
i5
5
ELEVATION
4
space 28-32
integer
i5
6
ELEVATION
4
space 33-37
integer
i5
ALL
YEAR
5-END
space 1-4
4-digit integer
i4
ALL
DATE
5-END
space 5-7
3-digit integer
i3
1
PRECIPITATION
5-END
space 8-12
decimal(xxx.x)
f5.1
2
PRECIPITATION
5-END
space 13-17
decimal(xxx.x)
f5.1
3
PRECIPITATION
5-END
space 18-22
decimal(xxx.x)
f5.1
4
PRECIPITATION
5-END
space 23-27
decimal(xxx.x)
f5.1
5
PRECIPITATION
5-END
space 28-32
decimal(xxx.x)
f5.1
6
PRECIPITATION
5-END
space 33-37
decimal(xxx.x)
f5.1
CHAPTER 34: SWAT INPUT—CLIMATE
87
34.2.2 SUB-DAILY PRECIPITATION Sub-daily precipitation data is required if the Green & Ampt infiltration method is being used (Set by IEVENT in the .cod file). When the Green & Ampt infiltration method is used to calculate surface runoff, SWAT is unable to generate precipitation data. Because of this, PCPSIM in the .cod file must be set to 1 and negative 99.0 (-99.0) should not be inserted for missing data. An independent weather generator or extrapolation from adjacent weather stations should be used to fill in missing data. While the input file must contain data for the entire period of simulation, the record does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the file, saving editing time on the user’s part. Unlike the daily precipitation data, SWAT verifies that the date is correct on all lines. If the model reads in an incorrect date, it will print an error message to the input.std file stating the day and year in the precipitation record where the inconsistency is located and the program will stop. The number of lines of precipitation data per day is governed by the time step used (IDT in the .cod file). To save space, only one line is required for days with no rain at all. When SWAT reads a blank for the delimiter (see variable list below), it knows that all time steps on the day have no precipitation and that there are no more lines of precipitation data for that day. Following is a brief description of the variables in the sub-daily precipitation input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the precipitation file is reserved for comments.
LATITUDE
Latitude of precipitation recording gage location. This value is not used by the model.
LONGITUDE
Longitude of precipitation recording gage location. This value is not used by the model.
88
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
ELEVATION
Elevation of precipitation recording gage station (m). Precipitation values are adjusted for elevation in subbasins where elevation bands are defined.
YEAR
Year (4-digit)
DATE
Julian date
HOUR
Hour of day (0-23). The hour and minute are at the end of the time step.
DELIMITER
Space is allowed on the line for a colon to separate the hour and minute readings. The delimiter is used by the model to identify days where there is no rain and only one line is present for the day in the .pcp file. If a blank space is inserted instead of the colon, the model will assign zero precipitation to all time steps on the day.
MINUTE
Minute of hour (0-59). The hour and minute are at the end of the time step.
PRECIPITATION
Amount of precipitation falling in the time period (mm)
The format of the sub-daily precipitation file with one record is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
unrestricted
character
unrestricted
LATITUDE
2
space 13-17
free
unrestricted
LONGITUDE
3
space 13-17
free
unrestricted
ELEVATION
4
space 13-17
integer
i5
YEAR
5-END
space 1-4
integer
i4
DATE
5-END
space 5-7
integer
i3
HOUR
5-END
space 8-9
integer
i2
DELIMITER
5-END
space 10
character
a1
MINUTE
5-END
space 11-12
integer
i2
PRECIPITATION
5-END
space 13-17
decimal(xxx.x)
f5.1
To place more than one data record within the .pcp file, repeat the original formatting for the recorded data to the right of the existing data. Simulations have been run with 200 records placed in the precipitation files. For example, assume there are records for six different rain gages stored in the sub-daily .pcp. The formatting of the .pcp file is
CHAPTER 34: SWAT INPUT—CLIMATE Gage
Variable name
ALL
TITLE
1
Line #
89
Position
Format
F90 Format
1
unrestricted
character
unrestricted
LATITUDE
2
space 13-17
free
unrestricted
2
LATITUDE
2
space 18-22
free
unrestricted
3
LATITUDE
2
space 23-27
free
unrestricted
4
LATITUDE
2
space 28-32
free
unrestricted
5
LATITUDE
2
space 33-37
free
unrestricted
6
LATITUDE
2
space 38-42
free
unrestricted
1
LONGITUDE
3
space 13-17
free
unrestricted
2
LONGITUDE
3
space 18-22
free
unrestricted
3
LONGITUDE
3
space 23-27
free
unrestricted
4
LONGITUDE
3
space 28-32
free
unrestricted
5
LONGITUDE
3
space 33-37
free
unrestricted
6
LONGITUDE
3
space 38-42
free
unrestricted
1
ELEVATION
4
space 13-17
integer
i5
2
ELEVATION
4
space 18-22
integer
i5
3
ELEVATION
4
space 23-27
integer
i5
4
ELEVATION
4
space 28-32
integer
i5
5
ELEVATION
4
space 33-37
integer
i5
6
ELEVATION
4
space 38-42
integer
i5
ALL
YEAR
5-END
space 1-4
4-digit integer
i4
ALL
DATE
5-END
space 5-7
3-digit integer
i3
ALL
HOUR
5-END
space 8-9
integer
i2
ALL
DELIMITER
5-END
space 10
character
a1
ALL
MINUTE
5-END
space 11-12
integer
i2
1
PRECIPITATION
5-END
space 13-17
decimal(xxx.x)
f5.1
2
PRECIPITATION
5-END
space 18-22
decimal(xxx.x)
f5.1
3
PRECIPITATION
5-END
space 23-27
decimal(xxx.x)
f5.1
4
PRECIPITATION
5-END
space 28-32
decimal(xxx.x)
f5.1
5
PRECIPITATION
5-END
space 33-37
decimal(xxx.x)
f5.1
6
PRECIPITATION
5-END
space 38-42
decimal(xxx.x)
f5.1
90
SWAT USER'S MANUAL, VERSION 2000
34.3 TEMPERATURE INPUT FILE (.TMP) Measured temperature data is read into the model from the .tmp file. A negative 99.0 (-99.0) should be inserted for missing maximum or minimum temperatures. This value tells SWAT to generate the missing value(s). As with the precipitation file, the record in the temperature input file does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the temperature file and all the comments made for this feature in the discussion of the precipitation file pertain to the temperature file as well. Following is a brief description of the variables in the temperature input file. They are listed in the order they appear within the file.
Variable name
Definition
TITLE
The first line of the temperature file is reserved for comments.
LATITUDE
Latitude of temperature recording gage location. This value is not used by the model.
LONGITUDE
Longitude of temperature recording gage location. This value is not used by the model.
ELEVATION
Elevation of temperature recording gage station (m). Temperature values are adjusted for elevation in subbasins where elevation bands are defined.
YEAR
Year (4-digit)
DATE
Julian date
MAX TEMP
Daily maximum temperature (ºC).
MIN TEMP
Daily minimum temperature (ºC).
CHAPTER 34: SWAT INPUT—CLIMATE
91
The format of the temperature file with one record is: Variable name
Line #
Position
Format
F90 Format unrestricted
TITLE
1
unrestricted
character
LATITUDE
2
space 8-17
free
LONGITUDE
3
space 8-17
free
ELEVATION
4
space 8-17
integer
i10
YEAR
5-END
space 1-4
4-digit integer
i4
DATE
5-END
space 5-7
3-digit integer
i3
MAX TEMP
5-END
space 8-12
decimal(xxx.x)
f5.1
MIN TEMP
5-END
space 13-17
decimal(xxx.x)
f5.1
To place more than one data record within the .tmp file, repeat the original formatting for the recorded data to the right of the existing data. Simulations have been run with 150 records placed in the temperature files. For example, assume there are records for three different temperature gages stored in the .tmp. The formatting of the .tmp file is Gage
Variable name
ALL
TITLE
1
Line #
Position
Format
F90 Format
1
unrestricted
character
unrestricted
LATITUDE
2
space 8-17
free
unrestricted
2
LATITUDE
2
space 18-27
free
unrestricted
3
LATITUDE
2
space 28-37
free
unrestricted
1
LONGITUDE
3
space 8-17
free
unrestricted
2
LONGITUDE
3
space 18-27
free
unrestricted
3
LONGITUDE
3
space 28-37
free
unrestricted
1
ELEVATION
4
space 8-17
integer
i10
2
ELEVATION
4
space 18-27
integer
i10
3
ELEVATION
4
space 28-37
integer
i10
ALL
YEAR
5-END
space 1-4
4-digit integer
i4
ALL
DATE
5-END
space 5-7
3-digit integer
i3
1
MAX TEMP
5-END
space 8-12
decimal(xxx.x)
f5.1
1
MIN TEMP
5-END
space 13-17
decimal(xxx.x)
f5.1
2
MAX TEMP
5-END
space 18-22
decimal(xxx.x)
f5.1
2
MIN TEMP
5-END
space 23-27
decimal(xxx.x)
f5.1
3
MAX TEMP
5-END
space 28-32
decimal(xxx.x)
f5.1
3
MIN TEMP
5-END
space 33-37
decimal(xxx.x)
f5.1
92
SWAT USER'S MANUAL, VERSION 2000
34.4 SOLAR RADIATION INPUT FILE (.SLR) Measured solar radiation data is read into the model from the .slr file. A negative 99.0 (-99.0) should be inserted for missing radiation values. This value tells SWAT to generate the missing value(s). As with the precipitation file, the record in the solar radiation input file does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the solar radiation file and all the comments made for this feature in the discussion of the precipitation file pertain to the solar radiation file as well. Following is a brief description of the variables in the solar radiation input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the solar radiation file is reserved for comments.
YEAR
Year (4-digit)
DATE
Julian date
SOL_RAD
Daily total solar radiation (MJ/m2).
The format of the solar radiation input file with one record is: Variable name
Line #
Position
Format
F90 Format
unrestricted
character
unrestricted
TITLE
1
YEAR
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
SOL_RAD
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
To place more than one data record within the .slr file, repeat the original formatting for the recorded data to the right of the existing data. For example, assume there are records for ten different solar radiation gages stored in the .slr. The formatting of the .slr file is
CHAPTER 34: SWAT INPUT—CLIMATE
Gage
Variable name
Line #
ALL
TITLE
1
ALL
YEAR
ALL
93
Position
Format
F90 Format
unrestricted
character
unrestricted
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
1
SOL_RAD
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
2
SOL_RAD
2-END
space 16-23
decimal(xxxx.xxx)
f8.3
3
SOL_RAD
2-END
space 24-31
decimal(xxxx.xxx)
f8.3
4
SOL_RAD
2-END
space 32-39
decimal(xxxx.xxx)
f8.3
5
SOL_RAD
2-END
space 40-47
decimal(xxxx.xxx)
f8.3
6
SOL_RAD
2-END
space 48-55
decimal(xxxx.xxx)
f8.3
7
SOL_RAD
2-END
space 56-63
decimal(xxxx.xxx)
f8.3
8
SOL_RAD
2-END
space 64-71
decimal(xxxx.xxx)
f8.3
9
SOL_RAD
2-END
space 72-79
decimal(xxxx.xxx)
f8.3
10
SOL_RAD
2-END
space 80-87
decimal(xxxx.xxx)
f8.3
34.5 WIND SPEED INPUT FILE (.WND) Measured wind speed data is read into the model from the .wnd file. A negative 99.0 (-99.0) should be inserted for missing wind speed values. This value tells SWAT to generate the missing value(s). As with the precipitation file, the record in the wind speed input file does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the wind speed file and all the comments made for this feature in the discussion of the precipitation file pertain to the wind speed file as well. Following is a brief description of the variables in the wind speed input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the wind speed radiation file is reserved for comments.
YEAR
Year (4-digit)
DATE
Julian date
WND_SP
Daily average wind speed (m/s).
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SWAT USER'S MANUAL, VERSION 2000
The format of the wind speed input file with one record is: Variable name
Line #
Position
Format
F90 Format
unrestricted
character
unrestricted
TITLE
1
YEAR
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
WND_SP
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
To place more than one data record within the .wnd file, repeat the original formatting for the recorded data to the right of the existing data. For example, assume there are records for ten different wind speed gages stored in the .wnd. The formatting of the .wnd file is Gage
Variable name
Line #
Position
Format
F90 Format
ALL
TITLE
1
unrestricted
character
unrestricted
ALL
YEAR
2-END
space 1-4
4-digit integer
i4
ALL
DATE
2-END
space 5-7
3-digit integer
i3
1
WND_SP
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
2
WND_SP
2-END
space 16-23
decimal(xxxx.xxx)
f8.3
3
WND_SP
2-END
space 24-31
decimal(xxxx.xxx)
f8.3
4
WND_SP
2-END
space 32-39
decimal(xxxx.xxx)
f8.3
5
WND_SP
2-END
space 40-47
decimal(xxxx.xxx)
f8.3
6
WND_SP
2-END
space 48-55
decimal(xxxx.xxx)
f8.3
7
WND_SP
2-END
space 56-63
decimal(xxxx.xxx)
f8.3
8
WND_SP
2-END
space 64-71
decimal(xxxx.xxx)
f8.3
9
WND_SP
2-END
space 72-79
decimal(xxxx.xxx)
f8.3
10
WND_SP
2-END
space 80-87
decimal(xxxx.xxx)
f8.3
34.6 RELATIVE HUMIDITY INPUT FILE (.HMD) Measured relative humidity data is read into the model from the .hmd file. A negative 99.0 (-99.0) should be inserted for missing relative humidity values. This value tells SWAT to generate the missing value(s). As with the precipitation file, the record in the relative humidity input file does not have to begin with the first day of simulation. SWAT is able to search
CHAPTER 34: SWAT INPUT—CLIMATE
95
for the beginning date in the relative humidity file and all the comments made for this feature in the discussion of the precipitation file pertain to the relative humidity file as well. Following is a brief description of the variables in the relative humidity input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the wind speed radiation file is reserved for comments.
YEAR
Year (4-digit)
DATE
Julian date
RHD
Daily average relative humidity expressed as a fraction.
The format of the relative humidity input file with one record is: Variable name
Line #
Position
Format
F90 Format
unrestricted
character
unrestricted
TITLE
1
YEAR
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
RHD
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
To place more than one data record within the .hmd file, repeat the original formatting for the recorded data to the right of the existing data. For example, assume there are records for seven different relative humidity gages stored in the .hmd. The formatting of the .hmd file is Gage
Variable name
Line #
Position
Format
F90 Format
ALL
TITLE
1
unrestricted
character
unrestricted
ALL
YEAR
2-END
space 1-4
4-digit integer
i4
ALL
DATE
2-END
space 5-7
3-digit integer
i3
1
RHD
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
2
RHD
2-END
space 16-23
decimal(xxxx.xxx)
f8.3
3
RHD
2-END
space 24-31
decimal(xxxx.xxx)
f8.3
4
RHD
2-END
space 32-39
decimal(xxxx.xxx)
f8.3
5
RHD
2-END
space 40-47
decimal(xxxx.xxx)
f8.3
6
RHD
2-END
space 48-55
decimal(xxxx.xxx)
f8.3
7
RHD
2-END
space 56-63
decimal(xxxx.xxx)
f8.3
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SWAT USER'S MANUAL, VERSION 2000
34.7 POTENTIAL EVAPOTRANSPIRATION INPUT FILE (.PET) Daily potential evapotranspiration data is read into the model from the .pet file. As with the precipitation file, the record in the potential evapotranspiration input file does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the potential evapotranspiration input file and all the comments made for this feature in the discussion of the precipitation file pertain to the potential evapotranspiration file as well. Following is a brief description of the variables in the potential evapotranspiration input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the wind speed radiation file is reserved for comments.
YEAR
Year (4-digit)
DATE
Julian date
PETMEAS
Daily potential evapotranspiration for watershed (mm H2O).
The format of the potential evapotranspiration input file is: Variable name
Line #
Position
Format
F90 Format
unrestricted
character
unrestricted
TITLE
1
YEAR
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
PETMEAS
2-END
space 8-12
decimal(xxx.x)
f5.1
CHAPTER 34: SWAT INPUT—CLIMATE
97
34.8 MULTIPLE RECORDS IN CLIMATE FILES Multiple records may be placed in all measured climatic data files except the potential evapotranspiration file. To assign the different gages to the subbasins in file.cio, a gage number is specified on the subbasin input lines for each type of measured climate data file used in the simulation. Unique gage numbers are assigned to the individual records in the following manner: Using the precipitation files as an example, numbering is begun in the file assigned to RFILE(1). If ten gages are present in this file, they are numbered from left to right: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Moving to the next precipitation file which also contains 10 files, RFILE(2), the gages are numbered from left to right beginning where the previous file left off: 11, 12, 13, 14, 15, 16, 17, 18, 19, 20. In the same manner, the gages in RFILE(3), RFILE(4), RFILE(5), RFILE(6), etc. will be processed.
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 35
SWAT INPUT DATA: GENERAL ATTRIBUTES
The subbasin and HRU general input files contain information related to a diversity of features within the HRU and its subbasin. Data contained in the subbasin input file can be grouped into the following categories: properties of tributary channels within the subbasin, the amount of topographic relief within the subbasin and its impact on the climate, variables related to climate change, the number of HRUs in the subbasin and the names of HRU input files. Data contained in the HRU input file can be grouped into the following categories: area contained in HRU, parameters affecting surface and subsurface water flow, parameters affecting erosion and management inputs related to the simulation of urban areas, irrigation, tile drains and potholes. 89
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SWAT USER'S MANUAL, VERSION 2000
35.1 SUBBASIN GENERAL INPUT FILE (.SUB) Following is a brief description of the variables in the subbasin general input file. They are listed in the order they appear within the file.
Variable name
Definition
TITLE
The first line of the .sub file is reserved for user comments. The comments may take up to 80 spaces. Optional.
HRUTOT
Total number of HRUs modeled in the subbasin.
LATITUDE
Latitude of subbasin (degrees). The latitude is expressed as a real number with minutes and seconds converted to fractions of a degree.
ELEV
Elevation of subbasin (m).
ELEVB(band)
Elevation at the center of the elevation band (m). Up to 10 zones may be specified. Optional.
ELEVB_FR(band)
Fraction of subbasin area within the elevation band. Values for ELEVB_FR should be between 0.0 and 1.0. Optional.
SNOEB(band)
Initial snow water content in elevation band (mm H2O). Optional.
PLAPS
Precipitation lapse rate (mm H2O/km). A positive value denotes an increase in precipitation with an increase in elevation while a negative value denotes a decrease in precipitation with an increase in elevation. The lapse rate is used to adjust precipitation for elevation bands in the subbasin. If no elevation bands are defined, the precipitation generated or read in from the .pcp file is used for the subbasin with no adjustment. To adjust the precipitation, the elevation of the recording station or the weather station is compared to the elevation specified for the elevation band. Optional.
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
91
Variable name
Definition
TLAPS
Temperature lapse rate (ºC/km). A positive value denotes an increase in temperature with an increase in elevation while a negative value denotes a decrease in temperature with an increase in elevation. If no value is entered for TLAPS, the model sets TLAPS = -6 ºC/km. The lapse rate is used to adjust temperature for elevation bands in the subbasin. If no elevation bands are defined, the temperature generated or read in from the .tmp file is used for the subbasin with no adjustment. To adjust the temperature, the elevation of the recording station or the weather station is compared to the elevation specified for the elevation band. Optional.
SNO_SUB
Initial snow water content (mm H2O). This value is not needed if the subbasin is divided into elevation bands (see variables ELEVB, ELEVB_FR and SNOEB in this file). Optional.
CH_L(1)
Longest tributary channel length in subbasin (km). The channel length is the distance along the channel from the subbasin outlet to the most distant point in the subbasin.
CH_S(1)
Average slope of tributary channels (m/m). The average channel slope is computed by taking the difference in elevation between the subbasin outlet and the most distant point in the subbasin and dividing by CH_L.
CH_W(1)
Average width of tributary channels (m).
CH_K(1)
Effective hydraulic conductivity in tributary channel alluvium (mm/hr).
CH_N(1)
Manning's "n" value for the tributary channels.
CO2
Carbon dioxide concentration (ppmv). If no value for CO2 is entered the model will set CO2 = 330 ppmv (ambient CO2 concentration). (Optional—used in climate change studies only)
RFINC(mon)
Rainfall adjustment (% change). (Optional—used in climate change studies only). Daily rainfall within the month is adjusted by the specified percentage. For example, setting RFINC = 10 will make rainfall equal to 110% of the original value.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
TMPINC(mon)
Temperature adjustment (ºC). (Optional—used in climate change studies only). Daily maximum and minimum temperatures within the month are raised or lowered by the specified amount.
RADINC(mon)
Radiation adjustment (MJ/m2-day). (Optional—used in climate change studies only). Daily radiation within the month is raised or lowered by the specified amount.
HUMINC(mon)
Humidity adjustment. (Optional—used in climate change studies only). Daily values for relative humidity within the month are raised or lowered by the specified amount. The relative humidity in SWAT is reported as a fraction.
HRUDAT
Name of HRU general input data file (.hru).
MGTDAT
Name of HRU land use management data file (.mgt).
SOILDAT
Name of HRU soil data file (.sol).
CHEMDAT
Name of HRU soil chemical data file (.chm).
GWDAT
Name of HRU groundwater data file (.gw).
The subbasin general input file is partially free format and partially fixed format. The variables that are free format will have free listed in the F90Format column and will not have a position defined. The variables that are fixed format will have a FORTRAN format and position specified. The free format variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The fixed format variables must be entered using the specified format and positioning on the line in order for the model to read them properly.
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
The format for the subbasin general input file is: Variable name
Line #
Position
Format
F90 Format
space 1-80
character
a80
TITLE
1
HRUTOT
2
integer
free
LATITUDE
3
real
free
ELEV
4
real
free
COMMENT LINE
5
space 1-80
character
a80
ELEVB(1)
6
space 1-8
decimal (xxxx.xxx)
f8.3
ELEVB(2)
6
space 9-16
decimal (xxxx.xxx)
f8.3
ELEVB(3)
6
space 17-24
decimal (xxxx.xxx)
f8.3
ELEVB(4)
6
space 25-32
decimal (xxxx.xxx)
f8.3
ELEVB(5)
6
space 33-40
decimal (xxxx.xxx)
f8.3
ELEVB(6)
6
space 41-48
decimal (xxxx.xxx)
f8.3
ELEVB(7)
6
space 49-56
decimal (xxxx.xxx)
f8.3
ELEVB(8)
6
space 57-64
decimal (xxxx.xxx)
f8.3
ELEVB(9)
6
space 65-72
decimal (xxxx.xxx)
f8.3
ELEVB(10)
6
space 73-80
decimal (xxxx.xxx)
f8.3
COMMENT LINE
7
space 1-80
character
a80
ELEVB_FR(1)
8
space 1-8
decimal (xxxx.xxx)
f8.3
ELEVB_FR(2)
8
space 9-16
decimal (xxxx.xxx)
f8.3
ELEVB_FR(3)
8
space 17-24
decimal (xxxx.xxx)
f8.3
ELEVB_FR(4)
8
space 25-32
decimal (xxxx.xxx)
f8.3
ELEVB_FR(5)
8
space 33-40
decimal (xxxx.xxx)
f8.3
ELEVB_FR(6)
8
space 41-48
decimal (xxxx.xxx)
f8.3
ELEVB_FR(7)
8
space 49-56
decimal (xxxx.xxx)
f8.3
ELEVB_FR(8)
8
space 57-64
decimal (xxxx.xxx)
f8.3
ELEVB_FR(9)
8
space 65-72
decimal (xxxx.xxx)
f8.3
ELEVB_FR(10)
8
space 73-80
decimal (xxxx.xxx)
f8.3
COMMENT LINE
9
space 1-80
character
a80
SNOEB(1)
10
space 1-8
decimal (xxxx.xxx)
f8.3
SNOEB(2)
10
space 9-16
decimal (xxxx.xxx)
f8.3
SNOEB(3)
10
space 17-24
decimal (xxxx.xxx)
f8.3
SNOEB(4)
10
space 25-32
decimal (xxxx.xxx)
f8.3
SNOEB(5)
10
space 33-40
decimal (xxxx.xxx)
f8.3
93
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
SNOEB(6)
10
space 41-48
decimal (xxxx.xxx)
f8.3
SNOEB(7)
10
space 49-56
decimal (xxxx.xxx)
f8.3
SNOEB(8)
10
space 57-64
decimal (xxxx.xxx)
f8.3
SNOEB(9)
10
space 65-72
decimal (xxxx.xxx)
f8.3
SNOEB(10)
10
space 73-80
decimal (xxxx.xxx)
f8.3
PLAPS
11
real
free
TLAPS
12
real
free
SNO_SUB
13
real
free
CH_L(1)
14
real
free
CH_S(1)
15
real
free
CH_W(1)
16
real
free
CH_K(1)
17
real
free
CH_N(1)
18
real
free
CO2
19
real
free
COMMENT LINE
20
space 1-80
character
a80
RFINC(1)
21
space 1-8
decimal (xxxx.xxx)
f8.3
RFINC(2)
21
space 9-16
decimal (xxxx.xxx)
f8.3
RFINC(3)
21
space 17-24
decimal (xxxx.xxx)
f8.3
RFINC(4)
21
space 25-32
decimal (xxxx.xxx)
f8.3
RFINC(5)
21
space 33-40
decimal (xxxx.xxx)
f8.3
RFINC(6)
21
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
22
space 1-80
character
a80
RFINC(7)
23
space 1-8
decimal (xxxx.xxx)
f8.3
RFINC(8)
23
space 9-16
decimal (xxxx.xxx)
f8.3
RFINC(9)
23
space 17-24
decimal (xxxx.xxx)
f8.3
RFINC(10)
23
space 25-32
decimal (xxxx.xxx)
f8.3
RFINC(11)
23
space 33-40
decimal (xxxx.xxx)
f8.3
RFINC(12)
23
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
24
space 1-80
character
a80
TMPINC(1)
25
space 1-8
decimal (xxxx.xxx)
f8.3
TMPINC(2)
25
space 9-16
decimal (xxxx.xxx)
f8.3
TMPINC(3)
25
space 17-24
decimal (xxxx.xxx)
f8.3
TMPINC(4)
25
space 25-32
decimal (xxxx.xxx)
f8.3
TMPINC(5)
25
space 33-40
decimal (xxxx.xxx)
f8.3
TMPINC(6)
25
space 41-48
decimal (xxxx.xxx)
f8.3
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES Variable name
Line #
Position
Format
F90 Format
COMMENT LINE
26
space 1-80
character
a80
TMPINC(7)
27
space 1-8
decimal (xxxx.xxx)
f8.3
TMPINC(8)
27
space 9-16
decimal (xxxx.xxx)
f8.3
TMPINC(9)
27
space 17-24
decimal (xxxx.xxx)
f8.3
TMPINC(10)
27
space 25-32
decimal (xxxx.xxx)
f8.3
TMPINC(11)
27
space 33-40
decimal (xxxx.xxx)
f8.3
TMPINC(12)
27
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
28
space 1-80
character
a80
RADINC(1)
29
space 1-8
decimal (xxxx.xxx)
f8.3
RADINC(2)
29
space 9-16
decimal (xxxx.xxx)
f8.3
RADINC(3)
29
space 17-24
decimal (xxxx.xxx)
f8.3
RADINC(4)
29
space 25-32
decimal (xxxx.xxx)
f8.3
RADINC(5)
29
space 33-40
decimal (xxxx.xxx)
f8.3
RADINC(6)
29
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
30
space 1-80
character
a80
RADINC(7)
31
space 1-8
decimal (xxxx.xxx)
f8.3
RADINC(8)
31
space 9-16
decimal (xxxx.xxx)
f8.3
RADINC(9)
31
space 17-24
decimal (xxxx.xxx)
f8.3
RADINC(10)
31
space 25-32
decimal (xxxx.xxx)
f8.3
RADINC(11)
31
space 33-40
decimal (xxxx.xxx)
f8.3
RADINC(12)
31
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
32
space 1-80
character
a80
HUMINC(1)
33
space 1-8
decimal (xxxx.xxx)
f8.3
HUMINC(2)
33
space 9-16
decimal (xxxx.xxx)
f8.3
HUMINC(3)
33
space 17-24
decimal (xxxx.xxx)
f8.3
HUMINC(4)
33
space 25-32
decimal (xxxx.xxx)
f8.3
HUMINC(5)
33
space 33-40
decimal (xxxx.xxx)
f8.3
HUMINC(6)
33
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
34
space 1-80
character
a80
HUMINC(7)
35
space 1-8
decimal (xxxx.xxx)
f8.3
HUMINC(8)
35
space 9-16
decimal (xxxx.xxx)
f8.3
HUMINC(9)
35
space 17-24
decimal (xxxx.xxx)
f8.3
HUMINC(10)
35
space 25-32
decimal (xxxx.xxx)
f8.3
HUMINC(11)
35
space 33-40
decimal (xxxx.xxx)
f8.3
HUMINC(12)
35
space 41-48
decimal (xxxx.xxx)
f8.3
95
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Position
Format
F90 Format
36
space 1-80
character
a80
HRUDAT
37-END
space 1-13
character
a13
MGTDAT
37-END
space 14-26
character
a13
SOILDAT
37-END
space 27-39
character
a13
CHEMDAT
37-END
space 40-52
character
a13
GWDAT
37-END
space 53-65
character
a13
COMMENT LINE
Line #
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
97
35.2 HRU GENERAL INPUT FILE (.HRU) Following is a brief description of the variables in the HRU general input file. They are listed in the order they appear within the file.
Variable name
Definition
TITLE
The first line of the .hru file is reserved for user comments. The comments may take up to 80 spaces. (optional)
HRU_FR
Fraction of total watershed area contained in HRU (km2/km2). If no value for HRU_FR is entered, the model will set HRU_FR = 0.0000001.
SLSUBBSN
Average slope length (m). If no value for SLSUBBSN is entered, the model will set SLSUBBSN = 50. The GIS interfaces will assign the same value to this variable for all HRUs within a subbasin. However, some users like to vary this value by soil type and land cover.
SLOPE
Average slope steepness (m/m). The GIS interfaces will assign the same value to this variable for all HRUs within a subbasin. However, some users like to vary this value by soil type and land cover.
OV_N
Manning's "n" value for overland flow.
LAT_TTIME
Lateral flow travel time (days). Setting LAT_TTIME = 0.0 will allow the model to calculate the travel time based on soil hydraulic properties. This variable should be set to a specific value only by hydrologists familiar with the base flow characteristics of the watershed.
LAT_SED
Sediment concentration in lateral and groundwater flow (mg/L). Sediment concentration in lateral and groundwater flow is usually very low and does not contribute significantly to total sediment yields unless return flow is very high.
SLSOIL
Slope length for lateral subsurface flow (m). If no value is entered for SLSOIL, the model sets SLSOIL = SLSUBBSN. The GIS interfaces will assign the same value to this variable for all HRUs within a subbasin. However, some users like to vary this value by soil type and land cover.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
CANMX
Maximum canopy storage (mm H2O). (Optional)
ESCO
Soil evaporation compensation factor. This factor adjusts the depth distribution for evaporation from the soil to account for the effect of capillary action, crusting and cracks. ESCO must be between 0.0 and 1.0. If no value for ESCO is entered, the model will set ESCO = 0.95. The value for ESCO may be set at the watershed or HRU level (ESCO in .bsn).
EPCO
Plant uptake compensation factor. This factor adjusts the depth distribution for plant uptake of water from the soil to account for the variation in root density with depth. EPCO must be between 0.0 and 1.0. If no value for EPCO is entered, the model will set EPCO = 1.0. The value for EPCO may be set at the watershed or HRU level (EPCO in .bsn).
RSDIN
Initial residue cover (kg/ha). (Optional)
ERORGN
Organic N enrichment ratio. If the value for ERORGN is set to zero, the model will calculate an enrichment ratio for every storm event. The default option is to allow the model to calculate the enrichment ratio.
ERORGP
Organic P enrichment ratio. If the value for ERORGP is set to zero, the model will calculate an enrichment ratio for every storm event. The default option is to allow the model to calculate the enrichment ratio.
FILTERW
Width of edge-of-field filter strip.
IURBAN
Urban simulation code: 0 no urban sections in HRU 1 urban sections in HRU, simulate using USGS regression equations 2 urban sections in HRU, simulate using build up/wash off algorithm.
URBLU
Urban land type identification number from urban.dat.
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
99
Variable name
Definition
IRR
Irrigation code. This variable, along with IRRNO, specifies the source of irrigation water applied in the HRU. Irrigation water may be diverted from anywhere in the watershed or outside the watershed. IRR tells the model what type of water body the irrigation water is being diverted from. The options are: 0 no irrigation 1 divert water from reach 2 divert water from reservoir 3 divert water from shallow aquifer 4 divert water from deep aquifer 5 divert water from unlimited source outside watershed
IRRNO
Irrigation source location. The definition of this variable depends on the setting of IRR. If IRR = 1, IRRNO is the number of the reach that water is removed from. If IRR = 2, IRRNO is the number of the reservoir that water is removed from. If IRR = 3 or 4, IRRNO is the number of the subbasin that water is removed from. If IRR = 0 or 5, this variable is not used.
FLOWMIN
Minimum in-stream flow for irrigation diversions (m3/s). If irrigation water being applied in the HRU is from a reach (IRR = 1), a threshhold level of streamflow can be specified. Irrigation water will be diverted from the reach only if flow in the reach is at or above FLOWMIN.
DIVMAX
Maximum daily irrigation diversion from the reach (if value entered for DIVMAX is positive the units are mm, if the value entered for DIVMAX is negative the units are 104 m3). If irrigation water being applied in the HRU is from a reach (IRR = 1), a value can be defined which specifies the maximum amount of water that can be removed from the reach and applied to the HRU on any one day.
FLOWFR
Fraction of available flow (total flow in reach – FLOWMIN) that is allowed to be applied to the HRU. If FLOWMIN is left at zero, the fraction of available flow becomes the fraction of total flow in reach that is allowed to be applied to the reach. The value for FLOWFR should be between 0.0 and 1.0. The model will default FLOWFR = 1.0 if no value is entered. Used only if IRR = 1.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
DDRAIN
Depth to subsurface drain (mm). If drainage tiles are installed in the HRU, the depth to the tiles is needed (optional).
TDRAIN
Time to drain soil to field capacity (hours). If tile drainage is installed in the HRU, the time required to drain the soil from saturation to field capacity is needed (optional).
GDRAIN
Drain tile lag time (hours). The amount of time between the transfer of water from the soil to the drain tile and the release of the water from the drain tile to the reach (optional).
IPOT
Number of HRU (must be located in subbasin) that is ponding water, i.e. the number of the HRU into which surface runoff from the current HRU drains. This variable identifies closed depressional areas or impounded areas used to grow plants in water (e.g. rice paddies). These areas are commonly referred to as potholes. Optional.
POT_FR
Fraction of HRU area that drains into the pothole. Required if IPOT is set to a number other than zero.
POT_TILE
Average daily outflow to main channel from tile flow if drainage tiles are installed in the pothole (m3/s). Required only for the HRU that is ponding water (IPOT = current HRU number).
POT_VOLX
Maximum volume of water stored in the pothole (104 m3 H2O). Required only for the HRU that is ponding water (IPOT = current HRU number).
POT_VOL
Initial volume of water stored in the pothole (104 m3 H2O). Required only for the HRU that is ponding water (IPOT = current HRU number).
POT_NSED
Normal sediment concentration in pothole (mg/L). Required only for the HRU that is ponding water (IPOT = current HRU number).
POT_NO3L
Not currently active. Nitrate decay rate in pothole (1/day). Required only for the HRU that is ponding water (IPOT = current HRU number).
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
101
The HRU general input file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format for the HRU general input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
HRU_FR
2
real
free
SLSUBBSN
3
real
free
SLOPE
4
real
free
OV_N
5
real
free
LAT_TTIME
6
real
free
LAT_SED
7
real
free
SLSOIL
8
real
free
CANMX
9
real
free
ESCO
10
real
free
EPCO
11
real
free
RSDIN
12
real
free
ERORGN
13
real
free
ERORGP
14
real
free
FILTERW
15
real
free
IURBAN
16
integer
free
URBLU
17
integer
free
IRR
18
integer
free
IRRNO
19
integer
free
FLOWMIN
20
real
free
DIVMAX
21
real
free
FLOWFR
22
real
free
DDRAIN
23
real
free
TDRAIN
24
real
free
GDRAIN
25
real
free
NO VARIABLE
26
free
free
IPOT
27
integer
free
POT_FR
28
real
free
POT_TILE
29
real
free
102
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
POT_VOLX
30
real
free
POT_VOL
31
real
free
POT_NSED
32
real
free
POT_NO3L
33
real
free
CHAPTER 36
SWAT INPUT DATA: SOIL
The soils data used by SWAT can be divided into two groups, physical characteristics and chemical characteristics. The physical properties of the soil govern the movement of water and air through the profile and have a major impact on the cycling of water within the HRU. Inputs for chemical characteristics are used to set initial levels of the different chemicals in the soil. While the physical properties are required, information on chemical properties is optional. Two files, the soil input file (.sol) and the soil chemical input file (.chm), contain the soil properties used by SWAT. The soil input file defines the physical properties and initializes chemical quantities for all layers in the soil. The soil chemical input file initializes additional chemical quantities for the first soil layer. 97
98
SWAT USER'S MANUAL, VERSION 2000
36.1 SOIL INPUT FILE (.SOL) Following is a brief description of the variables in the soil input file. They are listed in the order they appear within the file. The soil input file will hold data for up to 10 layers. Variable name
Definition
TITLE/TEXT
The first line of the .sol file is reserved for user comments. The comments may take up to 80 spaces. (optional)
SNAM
Soil name. If given, the soil name is used in data summarization.
HYDGRP
Soil hydrologic group (A, B, C, or D (required by the SWAT ArcView interface) The criteria for placement in hydrologic groups are: A
Minimum saturated hydraulic conductivity in the uppermost 0.5 m is > 110 mm/hr and internal free water occurrence is below 1.5 m.
B
Minimum saturated hydraulic conductivity in the uppermost 0.5 m is between 11 and 110 mm/hr and internal free water occurrence is below 1.0 m.
C
Minimum saturated hydraulic conductivity in the uppermost 0.5 m is between 1.1 and 11 mm/hr and internal free water occurrence is below 0.25 m.
D
Minimum saturated hydraulic conductivity in the uppermost 0.5 m is below 1.1 mm/hr and internal free water occurrence may be at any depth.
SOL_ZMX
Maximum rooting depth of soil profile (mm). If no depth is specified, the model assumes the roots can develop throughout the entire depth of the soil profile.
ANION_EXCL
Fraction of porosity (void space) from which anions are excluded. This parameter is currently used only in nitrate transport. If no value for ANION_EXCL is entered, the model will set ANION_EXCL = 0.50
CHAPTER 36: SWAT INPUT—SOIL
99
Variable name
Definition
SOL_CRK
Crack volume potential of soil (optional)
TEXTURE
Texture of soil layer (optional). This data is not used by the model.
SOL_Z(layer #)
Depth from soil surface to bottom of layer (mm).
SOL_BD(layer #)
Moist bulk density (Mg/m3 or g/cm3). The soil bulk density expresses the ratio of the mass of solid particles to the total volume of the soil, ρb = MS /VT. In moist bulk density determinations, the mass of the soil is the oven dry weight and the total volume of the soil is determined when the soil is at or near field capacity. Bulk density values should fall between 1.1 and 1.9 Mg/m3.
SOL_AWC(layer #)
Available water capacity of the soil layer (mm H2O/mm soil). This is the volume of water that should be available to plants if the soil, inclusive of rock fragments, was at field capacity. Available water capacity is estimated by determining the amount of water released between in situ field capacity (the soil water content at soil matric potential of -0.033 MPa) and the permanent wilting point (the soil water content at soil matric potential of -1.5 MPa).
SOL_K(layer #)
Saturated hydraulic conductivity (mm/hr). The saturated hydraulic conductivity, Ksat, relates soil water flow rate (flux density) to the hydraulic gradient and is a measure of the ease of water movement through the soil. Ksat is the reciprocal of the resistance of the soil matrix to water flow.
SOL_CBN(layer #)
Organic carbon content (% soil weight). When defining by soil weight, the soil is the portion of the sample that passes through a 2 mm sieve.
CLAY(layer #)
Clay content (% soil weight). The percent of soil particles which are < 0.002 mm in equivalent diameter.
SILT(layer #)
Silt content (% soil weight). The percentage of soil particles which have an equivalent diameter between 0.05 and 0.002 mm.
SAND(layer #)
Sand content (% soil weight). The percentage of soil particles which have a diameter between 2.0 and 0.05 mm.
100
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
ROCK(layer #)
Rock fragment content (% total weight). The percent of the sample which has a particle diameter > 2 mm, i.e. the percent of the sample which does not pass through a 2 mm sieve.
SOL_ALB(layer #)
Moist soil albedo. The ratio of the amount of solar radiation reflected by a body to the amount incident upon it, expressed as a fraction. The value for albedo should be reported when the soil is at or near field capacity.
USLE_K(layer #)
USLE equation soil erodibility (K) factor (units: 0.013 (metric ton m2 hr)/(m3-metric ton cm)). The units given are numerically equivalent to the traditional English units (0.01 (ton acre hr)/(acre ft-ton inch)). The values for the metric units will be exactly the same as those for the English units.
SOL_EC(layer #)
Not currently active Electrical conductivity (dS/m).
The format of the soil input file is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
space 1-80
character
a80
SNAM
2
space 13-28
character
a16
HYDGRP
3
space 25
character
a1
SOL_ZMX
4
space 29-35
decimal(xxxxxxxxx.xx)
f12.2
ANION_EXCL
5
space 52-56
decimal(x.xxx)
f5.3
SOL_CRK
6
space 34-38
decimal(x.xxx)
f5.3
COMMENT LINE
7
space 1-147
character
a80
SOL_Z(1)
8
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(2)
8
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(3)
8
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(4)
8
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(5)
8
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(6)
8
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(7)
8
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(8)
8
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(9)
8
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(10)
8
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(1)
9
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
CHAPTER 36: SWAT INPUT—SOIL Variable name
Line #
101
Position
Format
F90 Format
SOL_BD(2)
9
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(3)
9
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(4)
9
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(5)
9
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(6)
9
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(7)
9
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(8)
9
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(9)
9
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(10)
9
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(1)
10
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(2)
10
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(3)
10
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(4)
10
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(5)
10
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(6)
10
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(7)
10
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(8)
10
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(9)
10
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(10)
10
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(1)
11
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(2)
11
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(3)
11
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(4)
11
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(5)
11
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(6)
11
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(7)
11
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(8)
11
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(9)
11
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(10)
11
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(1)
12
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(2)
12
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(3)
12
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(4)
12
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(5)
12
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(6)
12
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
102
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
SOL_CBN(7)
12
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(8)
12
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(9)
12
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(10)
12
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
CLAY(1)
13
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
CLAY(2)
13
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
CLAY(3)
13
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
CLAY(4)
13
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
CLAY(5)
13
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
CLAY(6)
13
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
CLAY(7)
13
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
CLAY(8)
13
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
CLAY(9)
13
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
CLAY(10)
13
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SILT(1)
14
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SILT(2)
14
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SILT(3)
14
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SILT(4)
14
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SILT(5)
14
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SILT(6)
14
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SILT(7)
14
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SILT(8)
14
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SILT(9)
14
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SILT(10)
14
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SAND(1)
15
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SAND(2)
15
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SAND(3)
15
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SAND(4)
15
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SAND(5)
15
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SAND(6)
15
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SAND(7)
15
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SAND(8)
15
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SAND(9)
15
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SAND(10)
15
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
ROCK(1)
16
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
CHAPTER 36: SWAT INPUT—SOIL Variable name
Line #
103
Position
Format
F90 Format
ROCK(2)
16
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
ROCK(3)
16
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
ROCK(4)
16
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
ROCK(5)
16
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
ROCK(6)
16
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
ROCK(7)
16
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
ROCK(8)
16
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
ROCK(9)
16
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
ROCK(10)
16
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(1)
17
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(2)
17
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(3)
17
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(4)
17
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(5)
17
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(6)
17
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(7)
17
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(8)
17
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(9)
17
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(10)
17
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(1)
18
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(2)
18
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(3)
18
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(4)
18
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(5)
18
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(6)
18
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(7)
18
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(8)
18
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(9)
18
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(10)
18
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(1)
19
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(2)
19
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(3)
19
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(4)
19
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(5)
19
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(6)
19
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
SOL_EC(7)
19
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(8)
19
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(9)
19
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(10)
19
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
CHAPTER 36: SWAT INPUT—SOIL
105
36.2 SOIL CHEMICAL INPUT FILE (.CHM) Following is a brief description of the variables in the soil chemical input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .chm file is reserved for user comments. The comments may take up to 80 spaces. Optional.
NUTRIENT TITLE
The second line of the .chm file is reserved for the nutrient data title. This is not used by the model. Optional.
SOIL LAYER
Number of soil layer. This line in the .chm file is not used by the model and may be left blank.
SOL_NO3(layer #)
Initial NO3 concentration (mg/kg) in the soil layer Optional.
SOL_ORGN(layer #)
Initial organic N concentration in the soil layer (mg/kg). Optional.
SOL_SOLP(layer #)
Initial soluble P concentration in soil layer (mg/kg). Optional.
SOL_ORGP(layer #) Initial organic P concentration in soil layer (mg/kg). Optional. PESTICIDE TITLE
Lines 8-11 are reserved for pesticide data titles. The data on these lines are not used by the model.
PESTNUM
Number of pesticide from pesticide database (pest.dat) Required if pesticide amounts are given.
PLTPST
Initial pesticide amount on foliage (kg/ha). Optional.
SOLPST
Initial pesticide amount in soil (mg/kg). The pesticide is assumed to be found at this concentration in all soil layers. Optional.
PSTENR
Enrichment ratio for pesticide in the soil. This is the ratio of the pesticide concentration on the sediment transported in surface runoff to the pesticide concentration in the soil. Optional.
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SWAT USER'S MANUAL, VERSION 2000
The format of the soil chemical input file is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
space 1-80
character
a80
NUTRIENT TITLE
2
space 1-80
character
a80
SOIL LAYERS
3
space 1-80
character
a80
SOL_NO3(1)
4
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(2)
4
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(3)
4
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(4)
4
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(5)
4
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(6)
4
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(7)
4
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(8)
4
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(9)
4
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(10)
4
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(1)
5
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(2)
5
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(3)
5
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(4)
5
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(5)
5
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(6)
5
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(7)
5
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(8)
5
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(9)
5
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(10)
5
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(1)
6
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(2)
6
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(3)
6
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(4)
6
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(5)
6
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(6)
6
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(7)
6
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(8)
6
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(9)
6
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(10)
6
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
CHAPTER 36: SWAT INPUT—SOIL Variable name
Line #
SOL_ORGP(1)
107
Position
Format
F90 Format
7
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(2)
7
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(3)
7
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(4)
7
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(5)
7
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(6)
7
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(7)
7
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(8)
7
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(9)
7
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(10)
7
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
space 1-80
character
a80
PESTICIDE TITLE
8-11
PSTNUM
12-END
integer
free
PLTPST
12-END
real
free
SOLPST
12-END
real
free
PSTENR
12-END
real
free
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 37
SWAT INPUT DATA: LAND/WATER MANAGEMENT
A primary goal of environmental modeling is to assess the impact of anthropogenic activities on a given system. Central to this assessment is the itemization of the land and water management practices taking place within the system. SWAT utilizes two files to summarize these practices within the HRUs, the HRU management file (.mgt) and the subbasin water use file (.wus).
105
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SWAT USER'S MANUAL, VERSION 2000
37.1 MANAGEMENT INPUT FILE (.MGT) This file contains input data for planting, harvest, irrigation applications, nutrient applications, pesticide applications, and tillage operations. Operations may be scheduled by month and day or by heat units. If both month/day and heat units are given for a scheduled operation, the model will implement the operation on whichever occurs first. For example, assume a fertilizer operation is scheduled for April 1 or a heat unit index of 0.3. In year one, the heat unit index of the crop reaches 0.3 on April 14 while in year two the heat unit index of the crop reaches 0.3 on March 25. SWAT will apply the fertilizer on April 1 the first year and March 25 the second year. SWAT calculates two different heat unit indices throughout a year. The first is a base zero, or annual, heat unit index for the HRU. The total heat units that can be accumulated annually is equal to the sum of degrees C above 0˚C in the daily mean temperature for every day of the year. Throughout each year of simulation, an index is calculated which tells the fraction of the total heat units accumulated. The base zero, or annual, heat unit index is reset to zero on January 1st. This heat unit index is used to schedule operations when no plant is growing. The second heat unit index calculated by SWAT is the plant heat unit index. The base temperature for the plant heat unit index is the minimum temperature required by the plant for growth. The total number of heat units required to bring the plant to maturity, referred to as the potential heat units, is equal to the sum of degrees C above the plant base temperature in the daily mean temperature for every day of the plant growing season. The plant heat unit index is calculated only when something is growing in the HRU.
37.1.1 GENERAL MANAGEMENT VARIABLES The first two lines of the management input file contain general management variables. The remaining lines in the file list the sequence of management operations which occur throughout a year of simulation.
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
107
The general management variables are: Variable name
Definition
TITLE
The first line of the .mgt file is reserved for user comments. The comments may take up to 80 spaces. (optional)
IGRO
Land cover status code. This code informs the model whether or not a land cover is growing at the beginning of the simulation. 0 no land cover growing 1 land cover growing
NROT
Number of years of rotation. This code identifies the number of years of management practices given in the .mgt file. (A blank line should be inserted between each different year of management.) If the management doesn't change from year to year, the management operations for only one year are needed. Two land covers/crops may not be grown simultaneously, but they may be grown in the same year. For two or more crops grown in the same year, NROT is equal to 1 for 1 year of management practices listed. NROT has nothing to do with the number of different crops grown.
NMGT
Management code. Used by SWAT/GRASS (GIS) interface. The model doesn't use this variable.
NCRP
Land cover identification number. If a land cover is growing at the beginning of the simulation (IGRO = 1), this variable defines the type of land cover. The identification number is the numeric code for the land cover given in the crop.dat file.
ALAI
Initial leaf area index. If a land cover is growing at the beginning of the simulation (IGRO = 1), the leaf area index of the land cover must be defined.
BIO_MS
Initial dry weight biomass (kg/ha). If a land cover is growing at the beginning of the simulation (IGRO = 1), the initial biomass must be defined.
PHU
Total number of heat units or growing degree days needed to bring plant to maturity. This value is needed only if a land cover is growing at the beginning of the simulation (IGRO = 1).
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
BIO_MIN
Minimum plant biomass for grazing (kg/ha). Grazing will not be simulated unless the biomass is at or above BIO_MIN.
BIOMIX
Biological mixing efficiency. Mixing of the soil due to activity of earthworms and other soil biota. Mixing is performed at the end of every calendar year. If no value for BIOMIX is entered, the model will set BIOMIX = 0.20
CN2
Initial SCS runoff curve number for moisture condition II. The curve number may be updated in plant, tillage, and harvest/ kill operations. If CNOP is never defined for these operations, the value set for CN2 will be used throughout the simulation. If CNOP is defined for an operation, the value for CN2 is used until the time of the operation containing the first CNOP value. From that point on, the model only uses operation CNOP values to define the curve number for moisture condition II. Values for CN2 and CNOP should be entered for pervious conditions. In HRUs with urban areas, the model will adjust the curve number to reflect the impact of the impervious areas.
USLE_P
USLE equation support practice (P) factor. The ratio of soil loss with a support practice like contouring, stripcropping, or terracing to that with straight-row farming up and down the slope.
The format of the first two lines in the management file are: Variable name
Line #
Position
Format
F90 Format
TITLE
1
space 1-80
character
a80
IGRO
2
space 1
1-digit integer
i1
NROT
2
space 2-4
3-digit integer
i3
NMGT
2
space 5-8
4-digit integer
i4
NCRP
2
space 9-12
4-digit integer
i4
ALAI
2
space 13-20
decimal (xxxxx.xx)
f8.2
BIO_MS
2
space 21-28
decimal (xxxxx.xx)
f8.2
PHU
2
space 29-36
decimal (xxxxx.xx)
f8.2
BIO_MIN
2
space 37-44
decimal (xxxxx.xx)
f8.2
BIOMIX
2
space 45-52
decimal (xxxxx.xx)
f8.2
CN2
2
space 53-60
decimal (xxxxx.xx)
f8.2
USLE_P
2
space 61-68
decimal (xxxxx.xx)
f8.2
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
109
37.1.2 SCHEDULED MANAGEMENT OPERATIONS SWAT will simulate 14 different types of management operations. The first four variables on all management lines are identical while the remaining ten are operation specific. The variables for the different operations will be defined in separate sections. The type of operation simulated is identified by the code given for the variable MGT_OP. The different codes for MGT_OP are: 1 2 3 4 5
6 7
8
9 10
11
12
13 0
planting/beginning of growing season: this operation initializes the growth of a specific land cover/plant type in the HRU irrigation operation: this operation applies water to the HRU fertilizer application: this operation adds nutrients to the soil in the HRU pesticide application: this operation applies a pesticide to the plant and/or soil in the HRU harvest and kill operation: this operation harvests the portion of the plant designated as yield, removes the yield from the HRU and converts the remaining plant biomass to residue on the soil surface. It also sets IGRO = 0 which allows the next crop to be planted. tillage operation: this operation mixes the upper soil layers and redistributes the nutrients/chemicals/etc. within those layers harvest only operation: this operation harvests the portion of the plant designated as yield and removes the yield from the HRU, but allows the plant to continue growing. (this operation is used for hay cuttings) kill/end of growing season: this operation stops all plant growth and converts all plant biomass to residue. It also sets IGRO = 0 which allows the next crop to be planted. grazing operation: this operation removes plant biomass at a specified rate and allows simultaneous application of manure. auto irrigation initialization: this operation initializes auto irrigation within the HRU. Auto irrigation applies water whenever the plant experiences a user-specified level of water stress. auto fertilization initialization: this operation initializes auto fertilization within the HRU. Auto fertilization applies nutrients whenever the plant experiences a user-specified level of nitrogen stress. street sweeping operation: this operation removes sediment and nutrient build-up on impervious areas in the HRU. This operation can only be used when the urban build up/wash off routines are activated for the HRU. release/impound: this operation releases/impounds water in HRUs growing rice or other plants end of year rotation flag: this operation identifies the end of the operation scheduling for the year.
The operations must be listed in chronological order starting in January.
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.1 PLANTING/BEGINNING OF GROWING SEASON The variables which may be entered on the planting line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total base zero heat units at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 1 for planting/beginning of growing season
HEAT UNITS
Total heat units for cover/plant to reach maturity.
NCR
Land cover/plant identification number from crop.dat.
HITAR
Harvest index target ((kg/ha)/(kg/ha)) (optional). This variable along with BIO_TARG allow the user to specify the harvest index and biomass produced by the plant every year. The model will then simulate plant growth to meet these specified values. If you are studying the effect of management practices on yields or you want the biomass to vary in response to different weather conditions, you would not want to use HITAR or BIO_TARG.
BIO_TARG
Biomass (dry weight) target (metric tons/ha) (optional). This variable along with HITAR allow the user to specify the harvest index and biomass produced by the plant every year. The model will then simulate plant growth to meet these specified values. If you are studying the effect of management practices on yields or you want the biomass to vary in response to different weather conditions, you would not want to use HITAR or BIO_TARG.
ALAINIT
Initial leaf area index (optional). This variable would be used only for covers/plants which are transplanted rather than established from seeds.
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
111
Variable name
Definition
BIO_INIT
Initial dry weight biomass (kg/ha) (optional). This variable would be used only for covers/plants that are transplanted rather than established from seeds.
CNOP
SCS runoff curve number for moisture condition II (optional). The initial curve number for the HRU is input in the second line of the .mgt file. If you wish to use one moisture condition II curve number for the entire year, place that value in the second line of the .mgt file and do not enter values for CNOP in the operation lines. If you want the moisture condition II value to vary through the year, management operations 1, 5, and 6 allow new runoff curve numbers to be entered. The curve number value for CNOP should be for pervious ground. In HRUs with urban areas, the model will adjust the curve number to reflect the impact of the impervious areas.
The format of the planting operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
HEAT UNITS
space 21-28
decimal (xxxx.xxx)
f8.3
NCR
space 29-32
4-digit integer
i4
HITAR
space 33-40
decimal (xxxx.xxx)
f8.3
BIO_TARG
space 41-48
decimal (xxxx.xxx)
f8.3
ALAINIT
space 49-56
decimal (xxxx.xxx)
f8.3
BIO_INIT
space 61-66
decimal (xx.xxx)
f6.3
CNOP
space 67-72
decimal (xx.xxx)
f6.3
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.2 IRRIGATION OPERATION The variables which may be entered on the irrigation line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 2 for irrigation operation
IRR_AMT
Depth of irrigation water applied on HRU (mm).
IRR_SALT
Concentration of salt in irrigation water (mg/L) (optional). not currently active
The format of the irrigation operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
IRR_AMT
space 33-40
decimal (xxxx.xxx)
f8.3
IRR_SALT
space 41-48
decimal (xxxx.xxx)
f8.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
113
37.1.2.3 FERTILIZER APPLICATION The variables which may be entered on the fertilization line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 3 for fertilizer application
FRT_LY1
Fraction of fertilizer applied to top 10mm of soil. The remaining fraction is applied to the 1st soil layer below 10 mm. If FRT_LY1 is set to 0, the model applies 20% of the fertilizer to the top 10mm and the remainder to the 1st soil layer below 10mm.
FERT_ID
Fertilizer identification number. This corresponds to the line number of the fertilizer in fert.dat. If no identification number is provided, the model assumes 28-10-10 is being applied.
FRT_KG
Amount of fertilizer applied to HRU (kg/ha).
The format of the fertilizer application line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
FRY_LY1
space 21-28
decimal (xxxx.xxx)
f8.3
FERT_ID
space 29-32
4-digit integer
i4
FRT_KG
space 33-40
decimal (xxxx.xxx)
f8.3
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.4 PESTICIDE APPLICATION The variables which may be entered on the pesticide application line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 4 for pesticide application
PEST_ID
Pesticide identification code from pesticide database (pest.dat).
PST_KG
Amount of pesticide applied to HRU (kg/ha).
The format of the pesticide application line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
PEST_ID
space 57-60
4-digit integer
i4
PST_KG
space 61-66
decimal (xx.xxx)
f6.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
115
37.1.2.5 HARVEST AND KILL OPERATION The variables which may be entered on the harvest and kill line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 5 for harvest and kill operation
CNOP
SCS runoff curve number for moisture condition II (optional). The initial curve number for the HRU is input in the second line of the .mgt file. If you wish to use one moisture condition II curve number for the entire year, place that value in the second line of the .mgt file and do not enter values for CNOP in the operation lines. If you want the moisture condition II value to vary through the year, management operations 1, 5, and 6 allow new runoff curve numbers to be entered. The curve number value for CNOP should be for pervious ground. In HRUs with urban areas, the model will adjust the curve number to reflect the impact of the impervious areas.
The format of the harvest and kill line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
CNOP
space 67-72
decimal (xx.xxx)
f6.3
116
SWAT USER'S MANUAL, VERSION 2000
37.1.2.6 TILLAGE OPERATION The variables which may be entered on the tillage line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 6 for tillage operation
TILLAGE_ID
Tillage implement code from till.dat
CNOP
SCS runoff curve number for moisture condition II (optional). The initial curve number for the HRU is input in the second line of the .mgt file. If you wish to use one moisture condition II curve number for the entire year, place that value in the second line of the .mgt file and do not enter values for CNOP in the operation lines. If you want the moisture condition II value to vary through the year, management operations 1, 5, and 6 allow new runoff curve numbers to be entered. The curve number value for CNOP should be for pervious ground. In HRUs with urban areas, the model will adjust the curve number to reflect the impact of the impervious areas.
The format of the tillage operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
TILLAGE_ID
space 29-32
4-digit integer
i4
CNOP
space 67-72
decimal (xx.xxx)
f6.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
117
37.1.2.7 HARVEST OPERATION The variables which may be entered on the harvest line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 7 for the harvest only operation
HIOVR
Harvest index override ((kg/ha)/(kg/ha)) Optional. This variable will force the ratio of yield to total aboveground biomass to the specified value. The harvest index in the plant growth database (crop.dat) assumes only the seed is being harvested. If biomass is cut and removed (for example, in hay cuttings), HIOVR must be used to specify the amount of biomass removed.
HARVEFF
Harvest efficiency. Optional. This variable defines the efficiency of the harvest operation and is set to a value between 0.0 and 1.0. If the efficiency is set to a value less than 1.0 the fraction of biomass or yield removed is the fraction defined by the harvest efficiency. The remainder is converted to residue. If no value is defined for HARVEFF, the model assumes all of the biomass/yield is removed.
The format of the harvest operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
HIOVR
space 21-28
decimal (xxxx.xxx)
f8.3
HARVEFF
space 33-40
decimal (xxxx.xxx)
f8.3
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.8 KILL OPERATION The variables which may be entered on the kill line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 8 for kill operation
The format of the kill line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
119
37.1.2.9 GRAZING OPERATION The variables which may be entered on the grazing line are listed and described below Variable name
Definition
MONTH
Month grazing begins.
DAY
Day grazing begins.
HUSC
Fraction of total heat units for the year at which grazing begins. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 9 for grazing operation
BMEAT
Dry weight of biomass consumed daily ((kg/ha)/day).
NDGRAZ
Number of consecutive days grazing takes place in the HRU.
BMTRMP
Dry weight of biomass trampled daily ((kg/ha)/day) (optional). Trampling becomes significant as the number of animals grazed per hectare increases. This is a very subjective value which is typically set equal to BMEAT, i.e. the animals trample as much as they eat.
WMANURE
Dry weight of manure deposited daily ((kg/ha)/day).
IGFTYP
Manure identification code from fert.dat.
The format of the grazing operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
BMEAT
space 21-28
decimal (xxxx.xxx)
f8.3
NDGRAZ
space 29-32
4-digit integer
i4
BMTRMP
space 33-40
decimal (xxxx.xxx)
f8.3
WMANURE
space 49-56
decimal (xxxx.xxx)
f8.3
IGFTYP
space 57-60
4-digit integer
i4
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.10 AUTO IRRIGATION INITIALIZATION The variables which may be entered on the auto irrigation line are listed and described below Variable name
Definition
MONTH
Month auto irrigation is initialized.
DAY
Day auto irrigation is initialized.
HUSC
Fraction of total heat units for the year at which auto irrigation is initialized. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 10 for auto irrigation initialization
AUTO_WSTR
Water stress factor of cover/plant that triggers irrigation. The water stress factor is calculated by dividing the growth of the plant undergoing water stress by the growth of the plant if there was no water stress. This factor ranges from 0.0 to 1.0 where 0.0 indicates there is no growth of the plant due to water stress and 1.0 indicates there is no reduction of plant growth due to water stress.
The format of the auto irrigation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
AUTO_WSTR
space 33-40
decimal (xxxx.xxx)
f8.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
121
37.1.2.11 AUTO FERTILIZATION INITIALIZATION Auto fertilization needs to be initialized only once in the management file. The variables which may be entered on the auto fertilization line are listed and described below. Variable name
Definition
MONTH
Month initialization takes place.
DAY
Day initialization takes place.
HUSC
Fraction of total heat units for the year at which initialization takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 11 for auto fertilization initialization
AUTO_NSTR
Nitrogen stress factor of cover/plant that triggers fertilization. The nitrogen stress factor is calculated by dividing the growth of the plant undergoing nitrogen stress by the growth of the plant if there was no nitrogen stress. This factor ranges from 0.0 to 1.0 where 0.0 indicates there is no growth of the plant due to nitrogen stress and 1.0 indicates there is no reduction of plant growth due to nitrogen stress.
FERT_ID
Fertilizer identification number. This corresponds to the line number of the fertilizer in fert.dat. If this variable is left blank or set to zero, the model will apply the commercial fertilizer 28-10-10.
AUTO_NMXS
Maximum amount of mineral N allowed in any one application (kg N/ha). If this variable is left blank, the model will set AUTO_NMXS = 200.
AUTO_NMXA
Maximum amount of mineral N allowed to be applied in any one year (kg N/ha). If this variable is left blank, the model will set AUTO_NMXA = 300.
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Variable name
Definition
AUTO_EFF
Application efficiency. The amount of fertilizer applied in auto fertilization is based on the amount of nitrogen removed at harvest. If you set AUTO_EFF = 1.0, the model will apply enough fertilizer to replace the amount of nitrogen removed at harvest. If AUTO_EFF > 1.0, the model will apply fertilizer to meet harvest removal plus an extra amount to make up for nitrogen losses due to surface runoff/leaching. If AUTO_EFF < 1.0, the model will apply fertilizer at the specified fraction below the amount removed at harvest. If this variable is left blank, the model will set AUTO_EFF = 1.3.
AFRT_LY1
Fraction of fertilizer applied to top 10mm of soil. The remaining fraction is applied to the 1st soil layer below 10mm. If this variable is left blank, the model will set AFRT_LY1 = 0.2.
The format of the auto fertilization line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
AUTO_NSTR
space 21-28
decimal (xxxx.xxx)
f8.3
FERT_ID
space 29-32
4-digit integer
i4
AUTO_NMXS
space 33-40
decimal (xxxx.xxx)
f8.3
AUTO_NMXA
space 41-48
decimal (xxxx.xxx)
f8.3
AUTO_EFF
space 61-66
decimal (xx.xxx)
f6.3
AFRT_LY1
space 67-72
decimal (xx.xxx)
f6.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
123
37.1.2.12 STREET SWEEPING OPERATION The street sweeping operation can be used only if the urban build up/wash off routines have been selected for the HRU. The variables which may be entered on the street sweeping line are listed and described below. Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of heat units at which street sweeping takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 12 for street sweeping
SWEEPEFF
Removal efficiency of sweeping operation. SWEEPEFF is a fraction that ranges between 0.0 and 1.0. A value of 0.0 indicates that none of the built-up sediments are removed while a value of 1.0 indicates that all of the built-up sediments are removed.
AVWSP
Fraction of curb length available for sweeping. The amount of curb length available for sweeping may be less than the total length due to the presence of parked cars and other obstructions. AVWSP can range from 0.01 to 1.00. If no value is entered for AVWSP (AVWSP left blank or set to 0.0, the model will assume 100% of the curb length is available for sweeping.
The format of the street sweeping line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
SWEEPEFF
space 21-28
decimal (xxxx.xxx)
f8.3
AVWSP
space 33-40
decimal (xxxx.xxx)
f8.3
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.13 RELEASE/IMPOUND OPERATION The release/impound operation can be used only in the HRU designated as a depressional/impounded area in the subbasin (IPOT in .hru). The variables which may be entered on the release/impound line are listed and described below. Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units at which release/impounding takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 13 for release/impoundment of water
IREL_IMP
Release/impound action code: 0 initiate water impoundment 1 initiate water release
The format of the release/impound line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
IREL_IMP
space 29-32
4-digit integer
i4
37.1.2.14 END OF YEAR OPERATION SWAT requires a blank line to be inserted after all operations for a single year are listed. The blank line lets the model know that there will be no more operations in the year. If a rotation is being simulated in which the land is left fallow for one of the years with no operations occurring, a blank line should be entered for the fallow year.
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
125
37.2 WATER USE INPUT FILE (.WUS) The water use file quantifies consumptive water use in the watershed. The water removed is considered to be lost from the system. This file is used to simulate removal of water for irrigation outside the watershed or removal of water for urban/industrial use. Following is a brief description of the variables in the water use input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first three lines of the .wus file are reserved for user comments. The comments may take up to 80 spaces on each line. (optional)
WUPND(mon)
Average daily water removal from the pond for the month (104 m3/day). (optional)
WURCH(mon)
Average daily water removal from the reach for the month (104 m3/day). (optional)
WUSHAL(mon)
Average daily water removal from the shallow aquifer for the month (104 m3/day). (optional)
WUDEEP(mon)
Average daily water removal from the deep aquifer for the month (104 m3/day). (optional)
The format of the water use file is: Variable name
Position
Format
F90 Format
1-3
space 1-80
character
a80
WUPND(1)
4
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUPND(2)
4
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUPND(3)
4
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUPND(4:)
4
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUPND(5)
4
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUPND(6)
4
space 51-60
decimal (xxxxxxxx.x)
f10.1
TITLE
Line #
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
WUPND(7)
5
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUPND(8)
5
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUPND(9)
5
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUPND(10)
5
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUPND(11)
5
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUPND(12)
5
space 51-60
decimal (xxxxxxxx.x)
f10.1
WURCH(1)
6
space 1-10
decimal (xxxxxxxx.x)
f10.1
WURCH(2)
6
space 11-20
decimal (xxxxxxxx.x)
f10.1
WURCH(3)
6
space 21-30
decimal (xxxxxxxx.x)
f10.1
WURCH(4)
6
space 31-40
decimal (xxxxxxxx.x)
f10.1
WURCH(5)
6
space 41-50
decimal (xxxxxxxx.x)
f10.1
WURCH(6)
6
space 51-60
decimal (xxxxxxxx.x)
f10.1
WURCH(7)
7
space 1-10
decimal (xxxxxxxx.x)
f10.1
WURCH(8)
7
space 11-20
decimal (xxxxxxxx.x)
f10.1
WURCH(9)
7
space 21-30
decimal (xxxxxxxx.x)
f10.1
WURCH(10)
7
space 31-40
decimal (xxxxxxxx.x)
f10.1
WURCH(11)
7
space 41-50
decimal (xxxxxxxx.x)
f10.1
WURCH(12)
7
space 51-60
decimal (xxxxxxxx.x)
f10.1
WUSHAL(1)
8
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUSHAL(2)
8
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUSHAL(3)
8
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUSHAL(4)
8
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUSHAL(5)
8
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUSHAL(6)
8
space 51-60
decimal (xxxxxxxx.x)
f10.1
WUSHAL(7)
9
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUSHAL(8)
9
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUSHAL(9)
9
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUSHAL(10)
9
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUSHAL(11)
9
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUSHAL(12)
9
space 51-60
decimal (xxxxxxxx.x)
f10.1
WUDEEP(1)
10
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUDEEP(2)
10
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUDEEP(3)
10
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUDEEP(4)
10
space 31-40
decimal (xxxxxxxx.x)
f10.1
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT Variable name
Line #
127
Position
Format
F90 Format
WUDEEP(5)
10
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUDEEP(6)
10
space 51-60
decimal (xxxxxxxx.x)
f10.1
WUDEEP(7)
11
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUDEEP(8)
11
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUDEEP(9)
11
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUDEEP(10)
11
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUDEEP(11)
11
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUDEEP(12)
11
space 51-60
decimal (xxxxxxxx.x)
f10.1
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 38
SWAT INPUT DATA: GROUNDWATER
SWAT partitions groundwater into two aquifer systems: a shallow, unconfined aquifer which contributes return flow to streams within the watershed and a deep, confined aquifer which contributes return flow to streams outside the watershed. The properties governing water movement into and out of the aquifers are initialized in the groundwater input file.
127
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38.1 GROUNDWATER INPUT FILE (.GW) Following is a brief description of the variables in the groundwater input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .gw file is reserved for user comments. The comments may take up to 80 spaces. (optional)
SHALLST
Initial depth of water in the shallow aquifer (mm H2O).
DEEPST
Initial depth of water in the deep aquifer (mm H2O). If no value for DEEPST is entered, the model sets DEEPST = 1000.0.
GW_DELAY
Groundwater delay (days). The time required for water leaving the bottom of the root zone to reach the shallow aquifer.
ALPHA_BF
Baseflow alpha factor (days). The baseflow alpha factor, or recession constant, characterizes the groundwater recession curve. This constant will be some number less than 1.0, and will be large (approach one) for flat recessions and small (approach zero) for steep recessions.
GWQMN
Threshold depth of water in the shallow aquifer required for return flow to occur (mm H2O). Groundwater flow to the reach is allowed only if the depth of water in the shallow aquifer is equal to or greater than GWQMN.
GW_REVAP
Groundwater "revap" coefficient. This variable controls the amount of water which will move from the shallow aquifer to the root zone as a result of soil moisture depletion and the amount of direct water uptake from deep rooted trees and shrubs. As GW_REVAP approaches 0, movement of water from the shallow aquifer to the root zone is restricted. As GW_REVAP approaches 1, the rate of transfer from the shallow aquifer to the root zone approaches the rate of potential evapotranspiration. The value for GW_REVAP should be between 0.02 and 0.20.
CHAPTER 38: SWAT INPUT—GROUNDWATER
129
Variable name
Definition
REVAPMN
Threshold depth of water in the shallow aquifer for "revap" or percolation to the deep aquifer to occur (mm H2O). Movement of water from the shallow aquifer to the unsaturated zone or to the deep aquifer is allowed only if the volume of water in the shallow aquifer is equal to or greater than REVAPMN.
RCHRG_DP
Deep aquifer percolation fraction. The fraction of percolation from the root zone which recharges the deep aquifer. The value for RCHRG_DP should be between 0.0 and 1.0.
GWHT
Initial groundwater height (m). Optional.
GW_SPYLD
Specific yield of the shallow aquifer (m3/m3). Specific yield is defined as the ratio of the volume of water that drains by gravity to the total volume of rock. Optional.
GWNO3
Concentration of nitrate in groundwater contribution to streamflow from subbasin (mg N/L). Optional.
GWSOLP
Concentration of soluble phosphorus in groundwater contribution to streamflow from subbasin (mg P/L). Optional.
The groundwater file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
SHALLST
2
real
free
DEEPST
3
real
free
GW_DELAY
4
real
free
ALPHA_BF
5
real
free
GWQMN
6
real
free
GW_REVAP
7
real
free
REVAPMN
8
real
free
RCHRG_DP
9
real
free
GWHT
10
real
free
GW_SPYLD
11
real
free
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
GWNO3
12
real
free
GWSOLP
13
real
free
CHAPTER 39
SWAT INPUT DATA: MAIN CHANNEL
In order to simulate the physical processes affecting the flow of water and transport of sediment in the channel network of the watershed, SWAT requires information on the physical characteristics of the main channel within each subbasin. The main channel input file (.rte) summarizes the physical characteristics of the channel which affect water flow and sediment and pesticide transport.
131
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SWAT USER'S MANUAL, VERSION 2000
39.1 MAIN CHANNEL INPUT FILE (.RTE) Following is a brief description of the variables in the main channel input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .rte file is reserved for user comments. The comments may take up to 80 spaces. (optional)
CH_W(2)
Average width of main channel at top of bank (m).
CH_D
Depth of main channel from top of bank to bottom (m).
CH_S(2)
Average slope of main channel along the channel length (m/m).
CH_L(2)
Length of main channel (km). If no value for CH_L is entered, the model will set CH_L = 0.001.
CH_N(2)
Manning's "n" value for the main channel.
CH_K(2)
Effective hydraulic conductivity in main channel alluvium (mm/hr).
CH_EROD
Channel erodibility factor. CH_EROD is set to a value between 0.0 and 1.0. A value of 0.0 indicates a nonerosive channel while a value of 1.0 indicates no resistance to erosion.
CH_COV
Channel cover factor. CH_COV is set to a value between 0.0 and 1.0. A value of 0.0 indicates that the channel is completely protected from degradation by cover while a value of 1.0 indicates there is no vegetative cover on the channel.
CH_WDR
Channel width-depth ratio (m/m). Required only if channel degradation is being modeled (IDEG = 1 in .cod).
ALPHA_BNK
Baseflow alpha factor for bank storage (days). The baseflow alpha factor, or recession constant, characterizes the bank storage recession curve. This constant will be some number less than 1.0, and will be large (approach one) for flat recessions and small (approach zero) for steep recessions. If no value is entered for ALPHA_BNK, the variable will be set to the same value as ALPHA_BF from the groundwater (.gw) file.
CHAPTER 39: SWAT INPUT—MAIN CHANNEL
133
The main channel file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the main channel input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
CH_W(2)
2
real
free
CH_D
3
real
free
CH_S(2)
4
real
free
CH_L(2)
5
real
free
CH_N(2)
6
real
free
CH_K(2)
7
real
free
CH_EROD
8
real
free
CH_COV
9
real
free
CH_WDR
10
real
free
ALPHA_BNK
11
real
free
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 40
SWAT INPUT DATA: RESERVOIRS/PONDS
Impoundment structures modify the movement of water in the channel network by lowering the peak flow and volume of flood discharges. Because impoundments slow down the flow of water, sediment will fall from suspension, removing nutrient and chemicals adsorbed to the soil particles. SWAT is able to model three types of impoundments. The first type is a small structure with one spillway. Releases occur only when the storage volume of the structure is exceeded and the excess volume is released within one day. The second type of impoundment is a small, uncontrolled reservoir with a principal and emergency spillway. Water is released at a specified rate when the volume of the reservoir exceeds the principal spillway volume. Volume exceeding the
135
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SWAT USER'S MANUAL, VERSION 2000
emergency spillway storage is released within one day. The third type of impoundment is a managed reservoir. Water may be released from the managed reservoir based on measured outflow or target reservoir volumes. The features of an impoundment are shown in Figure 40.1.
Figure 40.1: Components of a reservoir with flood water detention features.
SWAT uses two files to store information on impoundments, the reservoir input file (.res) and the pond input file (.pnd). An unlimited number of reservoirs may be modeled in a watershed, while a pond may be modeling in each subbasin. Originally, the pond input file contained data for simple impoundment structures with only one spillway while data for the more complex impoundments was stored in the reservoir input file. Due to user needs, the options allowed for impoundments directly linked to subbasins have been increased over the years and the distinction between "reservoirs" and "ponds" as simulated by SWAT has become somewhat blurred. The key difference between the two structures is that a reservoir is located on the main channel network of the watershed while a pond is located off of the main channel within a subbasin. Consequently, water contribution from a pond is limited to the water loading generated within a subbasin. The following table has been prepared to assist the user in identifying the input file and required variables to be used for different situations. The structures are identified on the basis of how water is released.
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
137
Structure to be modeled: impoundment with one spillway where water is released when the storage capacity is exceeded. There is no control on the rate of water release from the impoundment. File: .pnd Additional comments: values are entered for the principal spillway only—no data should be entered for the emergency spillway (PND_ESA, PND_EVOL) or target storage (IFLOD1, IFLOD2, NDTARG). Structure to be modeled: small, uncontrolled reservoir with a principal and emergency spillway where water release is passive (e.g. gravity driven). Water is not released until the level stored in the reservoir is above the height of the principal spillway. If the water level is above the principal spillway, water is released at a known rate. If the level of the reservoir rises above the emergency spillway, the volume of water exceeding the storage capacity of the emergency spillway is released within a day. File: .res Additional comments: the variable IRESCO should be set to 0 and the data required for this option entered. Structure to be modeled: large, managed reservoir where the daily or monthly values for outflow are known. File: .res Additional comments: the variable IRESCO should be set to 1 or 3 depending on the outflow data available. Structure to be modeled: a reservoir which is managed to maintain a certain volume of water (the target storage) at all times File: .pnd or .res Additional comments: In the .pnd file, the target storage is calculated by the model. The target storage varies between the principal and emergency storage as a function of the time of year—the target storage approaches the principal spillway storage in the flood season while during the non-flood season it approaches the emergency spillway storage. In the .res file, the user specifies the target storage for each month of the year. To use target storage in the .pnd file, values must be entered for PND_ESA, PND_EVOL, IFLOD1, IFLOD2 and NDTARG. To use target storage in the .res file, IRESCO is set to 2 and the data required for this option is entered.
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SWAT USER'S MANUAL, VERSION 2000
40.1 RESERVOIR INPUT FILE (.RES) Following is a brief description of the variables in the reservoir input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the file is reserved for user comments. The comments may take up to 80 spaces. (optional)
RES_SUB
Number of the subbasin the reservoir is in (weather for subbasin is used for the reservoir). If no subbasin number is assigned to RES_SUB, the model uses weather data from subbasin 1 to model climatic processes on the reservoir.
MORES
Month the reservoir became operational (1-12). If 0 is input for MORES and IYRES, the model assumes the reservoir is in operation at the beginning of the simulation.
IYRES
Year of the simulation the reservoir became operational (eg 1980). If 0 is input for MORES and IYRES, the model assumes the reservoir is in operation at the beginning of the simulation.
RES_ESA
Reservoir surface area when the reservoir is filled to the emergency spillway (ha)
RES_EVOL
Volume of water needed to fill the reservoir to the emergency spillway (104 m3).
RES_PSA
Reservoir surface area when the reservoir is filled to the principal spillway (ha).
RES_PVOL
Volume of water needed to fill the reservoir to the principal spillway (104 m3).
RES_VOL
Initial reservoir volume. If the reservoir is in existence at the beginning of the simulation period, the initial reservoir volume is the volume on the first day of simulation. If the reservoir begins operation in the midst of a SWAT simulation, the initial reservoir volume is the volume of the reservoir the day the reservoir becomes operational (104 m3).
RES_SED
Initial sediment concentration in the reservoir (mg/L).
RES_NSED
Normal sediment concentration in the reservoir (mg/L).
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
Variable name
Definition
RES_K
Hydraulic conductivity of the reservoir bottom (mm/hr).
IRESCO
Outflow simulation code: 0
1 2
3
139
compute outflow for uncontrolled reservoir with average annual release rate (if IRESCO=0, need RES_RR) measured monthly outflow (if IRESCO=1, need RESOUT) simulated controlled outflowtarget release (if IRESCO=2, need STARG, IFLOD1R, IFLOD2D, and NDTARGR measured daily outflow (if IRESCO=3, need RESDAYO)
OFLOWMX(mon)
Maximum daily outflow for the month (m3/s). Set all months to zero if you do not want to trigger this requirement.
OFLOWMN(mon)
Minimum daily outflow for the month (m3/s). Set all months to zero if you do not want to trigger this requirement.
RES_RR
Average daily principal spillway release rate (m3/s). Needed if IRESCO = 0.
RESMONO
Name of monthly reservoir outflow file. Required if IRESCO = 1.
IFLOD1R
Beginning month of non-flood season. Needed if IRESCO = 2.
IFLOD2R
Ending month of non-flood season. Needed if IRESCO = 2.
NDTARGR
Number of days to reach target storage from current reservoir storage. Needed if IRESCO = 2.
STARG(mon)
Monthly target reservoir storage (104 m3). Needed if IRESCO = 2.
RESDAYO
Name of daily reservoir outflow file. Required if IRESCO = 3.
WURESN(mon,:)
Average amount of water withdrawn from reservoir each day in the month for consumptive use (104 m3). This variable allows water to be removed from the reservoir for use outside the watershed. (optional)
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
WURTNF(:)
Fraction of water removed from the reservoir via WURESN that is returned and becomes flow out of reservoir (m3/m3). (optional)
The reservoir file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the reservoir input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
RES_SUB
2
integer
free
MORES
3
integer
free
IYRES
4
integer
free
RES_ESA
5
real
free
RES_EVOL
6
real
free
RES_PSA
7
real
free
RES_PVOL
8
real
free
RES_VOL
9
real
free
RES_SED
10
real
free
RES_NSED
11
real
free
RES_K
12
real
free
IRESCO
13
integer
free
COMMENT LINE
14
character
a80
OFLOWMX(1)
15
real
free
OFLOWMX(2)
15
real
free
OFLOWMX(3)
15
real
free
OFLOWMX(4)
15
real
free
OFLOWMX(5)
15
real
free
OFLOWMX(6)
15
real
free
COMMENT LINE
16
character
a80
OFLOWMX(7)
17
real
free
OFLOWMX(8)
17
real
free
OFLOWMX(9)
17
real
free
OFLOWMX(10)
17
real
free
OFLOWMX(11)
17
real
free
CHAPTER 40: SWAT INPUT—RESERVOIR/POND Variable name
Line #
Format
F90 Format
OFLOWMX(12)
17
real
free
COMMENT LINE
18
character
a80
OFLOWMN(1)
19
real
free
OFLOWMN(2)
19
real
free
OFLOWMN(3)
19
real
free
OFLOWMN(4)
19
real
free
OFLOWMN(5)
19
real
free
OFLOWMN(6)
19
real
free
COMMENT LINE
20
character
a80
OFLOWMN(7)
21
real
free
OFLOWMN(8)
21
real
free
OFLOWMN(9)
21
real
free
OFLOWMN(10)
21
real
free
OFLOWMN(11)
21
real
free
OFLOWMN(12)
21
real
free
RES_RR
22
real
free
RESMONO
23
character (len=13)
a13
IFLOD1R
24
integer
free
IFLOD2R
25
integer
free
NDTARGR
26
integer
free
COMMENT LINE
27
character
a80
STARG(1)
28
real
free
STARG(2)
28
real
free
STARG(3)
28
real
free
STARG(4)
28
real
free
STARG(5)
28
real
free
STARG(6)
28
real
free
COMMENT LINE
29
character
a80
STARG(7)
30
real
free
STARG(8)
30
real
free
STARG(9)
30
real
free
STARG(10)
30
real
free
STARG(11)
30
real
free
STARG(12)
30
real
free
RESDAYO
31
character (len=13)
a13
141
142
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
COMMENT LINE
32
character
a80
WURESN(1)
33
real
free
WURESN(2)
33
real
free
WURESN(3)
33
real
free
WURESN(4)
33
real
free
WURESN(5)
33
real
free
WURESN(6)
33
real
free
COMMENT LINE
34
character
a80
WURESN(7)
35
real
free
WURESN(8)
35
real
free
WURESN(9)
35
real
free
WURESN(10)
35
real
free
WURESN(11)
35
real
free
WURESN(12)
35
real
free
WURTNF
36
real
free
40.2 DAILY RESERVOIR OUTFLOW FILE When measured daily outflow is used for a reservoir, the name of the file containing the data is assigned to the variable RESDAYO. The daily outflow file contains the flow rate for every day of operation of the reservoir, beginning with the first day of operation in the simulation. The daily outflow file contains one variable: Variable name
Definition
TITLE
The first line of the file is reserved for a description. The description may take up to 80 spaces. (optional)
RES_OUTFLOW
The water release rate for the day (m3/sec).
The format of the daily reservoir outflow file is: Variable name TITLE RES_OUTFLOW
Line #
Position
Format
F90 Format
1
space 1-80
character
a80
2-END
space 1-8
decimal(xxxxx.xx)
f8.2
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
143
40.3 MONTHLY RESERVOIR OUTFLOW FILE When outflow data average over a month is used for a reservoir, the name of the file containing the data is assigned to the variable RESMONO. The monthly outflow file contains the average daily flow rate for every month of operation of the reservoir, beginning with the first month of operation in the simulation. The monthly outflow file contains the following variables: Variable name
Definition
TITLE
The first line of the file is reserved for a description. The description may take up to 80 spaces. (optional)
RESOUT(mon,yr)
Measured average daily outflow from the reservoir for the month (m3/s). Needed when IRESCO = 1. There must be a line of input for every year of simulation.
Variable name TITLE
Line #
Format
F90 Format
1
character
a80
If IRESCO = 1, the model will read the input data for RESOUT. There should be one line for data for RESOUT for each year of simulation beginning with the 1st year of simulation. RESOUT(1,yr)
2-END
real
free
RESOUT(2,yr)
2-END
real
free
RESOUT(3,yr)
2-END
real
free
RESOUT(4,yr)
2-END
real
free
RESOUT(5,yr)
2-END
real
free
RESOUT(6,yr)
2-END
real
free
RESOUT(7,yr)
2-END
real
free
RESOUT(8,yr)
2-END
real
free
RESOUT(9,yr)
2-END
real
free
RESOUT(10,yr)
2-END
real
free
RESOUT(11,yr)
2-END
real
free
RESOUT(12,yr)
2-END
real
free
144
SWAT USER'S MANUAL, VERSION 2000
40.4 POND INPUT FILE (.PND) Following is a brief description of the variables in the subbasin pond input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the file is reserved for user comments. The comments may take up to 80 spaces. Optional.
POND SECTION TITLE
The second line of the file is reserved for a section title for the pond data. The model does not process this line. The title may take up to 80 spaces. Optional.
PND_FR
Fraction of subbasin area that drains into ponds. The value for PND_FR should be between 0.0 and 1.0.
PND_PSA
Surface area of ponds when filled to principal spillway (ha).
PND_PVOL
Volume of water stored in ponds when filled to the principal spillway (104 m3 H2O).
PND_ESA
Surface area of ponds when filled to emergency spillway (ha). Optional.
PND_EVOL
Volume of water stored in ponds when filled to the emergency spillway (104 m3 H2O). Optional.
PND_VOL
Initial volume of water in ponds (104 m3 H2O).
PND_SED
Initial sediment concentration in pond water (mg/L).
PND_NSED
Normal sediment concentration in pond water (mg/L).
PND_K
Hydraulic conductivity through bottom of ponds (mm/hr).
IFLOD1
Beginning month of non-flood season. Optional.
IFLOD2
Ending month of non-flood season. Optional.
NDTARG
Number of days needed to reach target storage from current pond storage. The default value for NDTARG is 15 days. Optional.
PSETL1
Phosphorus settling rate in pond for months IPND1 through IPND2 (m/year). Optional.
PSETL2
Phosphorus settling rate in pond for months other than IPND1-IPND2 (m/year). Optional.
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
145
Variable name
Definition
NSETL1
Nitrogen settling rate in pond for months IPND1 through IPND2 (m/year). Optional.
NSETL2
Nitrogen settling rate in pond for months other than IPND1-IPND2 (m/year). Optional.
CHLA
Chlorophyll a production coefficient for ponds. Chlorophyll a concentration in the pond is calculated from the total phosphorus concentration. The equation assumes the system is phosphorus limited. The chlorophyll a coefficient was added to the equation to allow the user to adjust results to account for other factors ignored by the basic equation such as nitrogen limitations. The default value for CHLA is 1.00, which uses the original equation. Optional.
SECCI
Water clarity coefficient for ponds. The clarity of the pond is expressed by the secci-disk depth (m) which is calculated as a function of chlorophyll a. Because suspended sediment also can affect water clarity, the water clarity coefficient has been added to the equation to allow users to adjust for the impact of factors other than chlorophyll a on water clarity. The default value for SECCI is 1.00, which uses the original equation. Optional.
PND_NO3
Initial concentration of NO3-N in pond (mg N/L). Optional.
PND_SOLP
Initial concentration of soluble P in pond (mg P/L). Optional.
PND_ORGN
Initial concentration of organic N in pond (mg N/L). Optional.
PND_ORGP
Initial concentration of organic P in pond (mg P/L). Optional.
COMMON VARIABLES SECTION TITLE
The 25th line of the file is reserved for a section title for data used for ponds and wetlands. The model does not process this line. The title may take up to 80 spaces. Optional.
IPND1
Beginning month of mid-year nutrient settling “season”. Optional.
IPND2
Ending month of mid-year nutrient settling “season”. Optional.
146
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
WETLAND SECTION TITLE
The 28th line of the file is reserved for a section title for the wetland data. The model does not process this line. The title may take up to 80 spaces. Optional.
WET_FR
Fraction of subbasin area that drains into wetlands.
WET_NSA
Surface area of wetlands at normal water level (ha).
WET_NVOL
Volume of water stored in wetlands when filled to normal water level (104 m3 H2O).
WET_MXSA
Surface area of wetlands at maximum water level (ha).
WET_MXVOL
Volume of water stored in wetlands when filled to maximum water level (104 m3 H2O).
WET_VOL
Initial volume of water in wetlands (104 m3 H2O).
WET_SED
Initial sediment concentration in wetland water (mg/L).
WET_NSED
Normal sediment concentration in wetland water (mg/L).
WET_K
Hydraulic conductivity of bottom of wetlands (mm/hr).
PSETLW1
Phosphorus settling rate in wetland for months IPND1 through IPND2 (m/year). Optional.
PSETLW2
Phosphorus settling rate in wetlands for months other than IPND1-IPND2 (m/year). Optional.
NSETLW1
Nitrogen settling rate in wetlands for months IPND1 through IPND2 (m/year). Optional.
NSETLW2
Nitrogen settling rate in wetlands for months other than IPND1-IPND2 (m/year). Optional.
CHLAW
Chlorophyll a production coefficient for wetlands. Chlorophyll a concentration in the wetland is calculated from the total phosphorus concentration. The equation assumes the system is phosphorus limited. The chlorophyll a coefficient was added to the equation to allow the user to adjust results to account for other factors ignored by the basic equation such as nitrogen limitations. The default value for CHLA is 1.00, which uses the original equation. Optional.
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
147
Variable name
Definition
SECCIW
Water clarity coefficient for wetlands. The clarity of the wetland is expressed by the secci-disk depth (m) which is calculated as a function of chlorophyll a. Because suspended sediment also can affect water clarity, the water clarity coefficient has been added to the equation to allow users to adjust for the impact of factors other than chlorophyll a on water clarity. The default value for SECCI is 1.00, which uses the original equation. Optional.
WET_NO3
Initial concentration of NO3-N in wetland (mg N/L). Optional.
WET_SOLP
Initial concentration of soluble P in wetland (mg P/L). Optional.
WET_ORGN
Initial concentration of organic N in wetland (mg N/L). Optional.
WET_ORGP
Initial concentration of organic P in wetland (mg P/L). Optional.
The pond input file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the pond input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
POND SECT. TITLE
2
character
a80
PND_FR
3
real
free
PND_PSA
4
real
free
PND_PVOL
5
real
free
PND_ESA
6
real
free
PND_EVOL
7
real
free
PND_VOL
8
real
free
PND_SED
9
real
free
PND_NSED
10
real
free
PND_K
11
real
free
IFLOD1
12
integer
free
IFLOD2
13
integer
free
148
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
NDTARG
14
integer
free
PSETL1
15
real
free
PSETL2
16
real
free
NSETL1
17
real
free
NSETL2
18
real
free
CHLA
19
real
free
SECCI
20
real
free
PND_NO3
21
real
free
PND_SOLP
22
real
free
PND_ORGN
23
real
free
PND_ORGP
24
real
free
POND/WETLAND SECT. TITLE
25
character
a80
IPND1
26
integer
free
IPND2
27
integer
free
WETLAND SECT. TITLE
28
character
a80
WET_FR
29
real
free
WET_NSA
30
real
free
WET_NVOL
31
real
free
WET_MXSA
32
real
free
WET_MXVOL
33
real
free
WET_VOL
34
real
free
WET_SED
35
real
free
WET_NSED
36
real
free
WET_K
37
real
free
PSETLW1
38
real
free
PSETLW2
39
real
free
NSETLW1
40
real
free
NSETLW2
41
real
free
CHLAW
42
real
free
SECCIW
43
real
free
WET_NO3
44
real
free
WET_SOLP
45
real
free
WET_ORGN
46
real
free
WET_ORGP
47
real
free
CHAPTER 41
SWAT INPUT DATA: WATER QUALITY
While water quality is a broad subject, the primary areas of concern are nutrients, organic chemicals—both agricultural (pesticide) and industrial, heavy metals, bacteria and sediment levels in streams and large water bodies. SWAT is able to model processes affecting nutrient, pesticide and sediment levels in the main channels and reservoirs. The data used by SWAT for water quality is primarily contained within three files: the stream water quality input file (.swq), the general water quality input file (.wwq), and the lake water quality input file (.lwq). The stream water quality input file and the general water quality input file contain input data used in the QUAL2E subroutines in the model.
147
148
SWAT USER'S MANUAL, VERSION 2000
41.1 GENERAL WATER QUALITY INPUT FILE (.WWQ) The general water quality input file contains information used by SWAT to initialize stream water quality variables that apply to the entire watershed. Following is a brief description of the variables in the general water quality input file. The variables are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line is reserved for user comments. This line is not processed by the model.
LAO
Qual2E light averaging option. Qual2E defines four light averaging options. 1
Depth-averaged algal growth attenuation factor for light (FL) is computed from one daylight average solar radiation value calculated in the steady state temperature heat balance.
2
FL is computed from one daylight average solar radiation value supplied by the user.
3
FL is obtained by averaging the hourly daylight values of FL computed from the hourly daylight values of solar radiation calculated in the steady state temperature heat balance.
4
FL is obtained by averaging the hourly daylight values of FL computed from the hourly daylight values of solar radiation calculated from a single value of total daily, photosynthetically active, solar radiation and an assumed cosine function.
The only option currently active in SWAT is 2. IGROPT
Qual2E algal specific growth rate option. Qual2E provides three different options for computing the algal growth rate. 1
Multiplicative: the effects of nitrogen, phosphorus and light are multiplied together to calculate the net effect on the local algal growth rate
CHAPTER 41: SWAT INPUT—WATER QUALITY
Variable name
Definition
IGROPT, cont.
2
3
149
Limiting nutrient: the local algal growth rate is limited by light and one of the nutrients (nitrogen or phosphorus) Harmonic mean: the local algal growth rate is limited by light and the harmonic mean of the nutrient interactions
The default option is the limiting nutrient option (2). AI0
Ratio of chlorophyll-a to algal biomass (µg-chla/mg algae). Values for AI0 should fall in the range 10-100. If no value for AI0 is entered, the model will set AI0 = 50.0.
AI1
Fraction of algal biomass that is nitrogen (mg N/mg alg). Values for AI1 should fall in the range 0.07-0.09. If no value for AI1 is entered, the model will set AI1 = 0.08.
AI2
Fraction of algal biomass that is phosphorus (mg P/mg alg). Values for AI2 should fall in the range 0.01-0.02. If no value for AI2 is entered, the model will set AI2 = 0.015.
AI3
The rate of oxygen production per unit of algal photosynthesis (mg O2/mg alg). Values for AI3 should fall in the range 1.4-1.8. If no value for AI3 is entered, the model will set AI3 = 1.6.
AI4
The rate of oxygen uptake per unit of algal respiration (mg O2/mg alg). Values for AI4 should fall in the range 1.6-2.3. If no value for AI4 is entered, the model will set AI4 = 2.0.
AI5
The rate of oxygen uptake per unit of NH3-N oxidation (mg O2/mg NH3-N). Values for AI5 should fall in the range 3.0-4.0. If no value for AI5 is entered, the model will set AI5 = 3.5.
AI6
The rate of oxygen uptake per unit of NO2-N oxidation (mg O2/mg NO2-N). Values for AI6 should fall in the range 1.00-1.14. If no value for AI6 is entered, the model will set AI6 = 1.07.
MUMAX
Maximum specific algal growth rate at 20º C (day-1). Values for MUMAX should fall in the range 1.0-3.0. If no value for MUMAX is entered, the model will set MUMAX = 2.0.
RHOQ
Algal respiration rate at 20º C (day-1). Values for RHOQ should fall in the range 0.05-0.50. If no value for RHOQ is entered, the model will set RHOQ = 0.30.
150
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
TFACT
Fraction of solar radiation computed in the temperature heat balance that is photosynthetically active. Values for TFACT should fall in the range 0.01-1.0. If no value for TFACT is entered, the model will set TFACT = 0.3.
K_L
Half-saturation coefficient for light (kJ/(m2·min)). Values for K_L should fall in the range 0.2227-1.135. If no value for K_L is entered, the model will set K_L = 0.75.
K_N
Michaelis-Menton half-saturation constant for nitrogen (mg N/L). Values for K_N should fall in the range 0.010.30. If no value for K_N is entered, the model will set K_N = 0.02.
K_P
Michaelis-Menton half-saturation constant for phosphorus (mg P/L). Values for K_P should fall in the range 0.0010.050. If no value for K_P is entered, the model will set K_P = 0.025.
LAMBDA0
Non-algal portion of the light extinction coefficient (m-1). If no value for LAMBDA0 is entered, the model will set LAMBDA0 = 1.0.
LAMBDA1
Linear algal self-shading coefficient (m-1·(µg chla/L)-1). Values for LAMBDA1 should fall in the range 0.00650.065. If no value for LAMBDA1 is entered, the model will set LAMBDA1 = 0.03.
LAMBDA2
Nonlinear algal self-shading coefficient (m-1·(µg chla/L)-2/3). The recommended value for LAMBDA2 is 0.0541. If no value for LAMBDA2 is entered, the model will set LAMBDA2 = 0.054.
P_N
Algal preference factor for ammonia. Values for P_N should fall in the range 0.01-1.0. If no value for P_N is entered, the model will set P_N = 0.5.
The watershed water quality file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the general water quality input file is:
CHAPTER 41: SWAT INPUT—WATER QUALITY
Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
LAO
2
integer
free
IGROPT
3
integer
free
AI0
4
real
free
AI1
5
real
free
AI2
6
real
free
AI3
7
real
free
AI4
8
real
free
AI5
9
real
free
AI6
10
real
free
MUMAX
11
real
free
RHOQ
12
real
free
TFACT
13
real
free
K_L
14
real
free
K_N
15
real
free
K_P
16
real
free
LAMBDA0
17
real
free
LAMBDA1
18
real
free
LAMBDA2
19
real
free
P_N
20
real
free
151
152
SWAT USER'S MANUAL, VERSION 2000
41.2 STREAM WATER QUALITY INPUT FILE (.SWQ) The stream water quality input file contains information used by SWAT to set main channel water quality attributes in the subbasins. Following is a brief description of the variables in the stream water quality input file. The variables are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line is reserved for user comments. This line is not processed by the model.
NUTRIENT TITLE
The second line is reserved for the nutrient section title. This line is not processed by the model.
DISOX
Initial dissolved oxygen concentration in the reach (mg O2/L). If no value for DISOX is entered, the model sets DISOX = 0.0.
BOD
Initial carbonaceous biochemical oxygen demand in the reach (mg O2/L). If no value for BOD is entered, the model sets BOD = 0.0.
ALGAE
Initial chlorophyll-a concentration in the reach (mg chla/L). If no value for ALGAE is entered, the model sets ALGAE = 0.0.
ORGANICN
Initial organic nitrogen concentration in the reach (mg org N-N/L). If no value for ORGANICN is entered, the model sets ORGANICN = 0.0.
AMMONIAN
Initial ammonia concentration in the reach (mg NH3-N/L). If no value for AMMONIAN is entered, the model sets AMMONIAN = 0.0.
NITRITEN
Initial nitrite concentration in the reach (mg NO2-N/L). If no value for NITRITEN is entered, the model sets NITRITEN = 0.0.
NITRATEN
Initial nitrate concentration in the reach (mg NO3-N/L). If no value for NITRATEN is entered, the model sets NITRATEN = 0.0.
CHAPTER 41: SWAT INPUT—WATER QUALITY
153
Variable name
Definition
ORGANICP
Initial organic phosphorus concentration in the reach (mg org P-P/L). If no value for ORGANICP is entered, the model sets ORGANICP = 0.0.
DISOLVP
Initial dissolved phosphorus concentration in the reach (mg sol P-P/L). If no value for DISOLVP is entered, the model sets DISOLVP = 0.0.
RS1
Local algal settling rate in the reach at 20º C (m/day). Values for RS1 should fall in the range 0.15 to 1.82. If no value for RS1 is entered, the model sets RS1 = 1.0.
RS2
Benthic (sediment) source rate for dissolved phosphorus in the reach at 20º C (mg dissolved P/(m2·day)). If no value for RS2 is entered, the model sets RS2 = 0.05.
RS3
Benthic source rate for NH4-N in the reach at 20º C (mg NH4-N/(m2·day)). If no value for RS3 is entered, the model sets RS3 = 0.5.
RS4
Rate coefficient for organic N settling in the reach at 20º C (day-1). Values for RS4 should fall in the range 0.001 to 0.10. If no value for RS4 is entered, the model sets RS4 = 0.05.
RS5
Organic phosphorus settling rate in the reach at 20º C (day-1). Values for RS5 should fall in the range 0.001 to 0.1. If no value for RS5 is entered, the model sets RS5 = 0.05.
RS6
Rate coefficient for settling of arbitrary non-conservative constituent in the reach at 20º C (day-1). If no value for RS6 is entered, the model sets RS6 = 2.5. (not currently used by the model)
RS7
Benthic source rate for arbitrary non-conservative constituent in the reach at 20º C (mg ANC/(m2·day)). If no value for RS7 is entered, the model sets RS7 = 2.5. (not currently used by the model)
RK1
Carbonaceous biological oxygen demand deoxygenation rate coefficient in the reach at 20º C (day-1). Values for RK1 should fall in the range 0.02 to 3.4. If no value for RK1 is entered, the model sets RK1 = 1.71.
RK2
Oxygen reaeration rate in accordance with Fickian diffusion in the reach at 20º C (day-1). Values for RK2 should fall in the range 0.01 to 100.0. If no value for RK2 is entered, the model sets RK2 = 50.0.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
RK3
Rate of loss of carbonaceous biological oxygen demand due to settling in the reach at 20º C (day-1). Values for RK3 should fall in the range -0.36 to 0.36. The recommended default for RK3 is 0.36 (not set by model).
RK4
Benthic oxygen demand rate in the reach at 20º C (mg O2/(m2·day)). If no value for RK4 is entered, the model sets RK4 = 2.0.
RK5
Coliform die-off rate in the reach at 20º C (day-1). Values for RK5 should fall in the range 0.05 to 4.0. If no value for RK5 is entered, the model sets RK5 = 2.0.
RK6
Decay rate for arbitrary non-conservative constituent in the reach at 20º C (day-1). If no value for RK6 is entered, the model sets RK6 = 1.71. (not currently used by the model)
BC1
Rate constant for biological oxidation of NH4 to NO2 in the reach at 20º C (day-1). Values for BC1 should fall in the range 0.1 to 1.0. If no value for BC1 is entered, the model sets BC1 = 0.55.
BC2
Rate constant for biological oxidation of NO2 to NO3 in the reach at 20º C (day-1). Values for BC2 should fall in the range 0.2 to 2.0. If no value for BC2 is entered, the model sets BC2 = 1.1.
BC3
Rate constant for hydrolysis of organic N to NH4 in the reach at 20º C (day-1). Values for BC3 should fall in the range 0.2 to 0.4. If no value for BC3 is entered, the model sets BC3 = 0.21.
BC4
Rate constant for mineralization of organic P to dissolved P in the reach at 20º C (day-1). Values for BC4 should fall in the range 0.01 to 0.70. If no value for BC4 is entered, the model sets BC4 = 0.35.
PESTICIDE TITLE
This line is reserved for the pesticide section title. This line is not processed by the model.
CHPST_CONC
Initial pesticide concentration in reach (mg/m3 H2O). (Optional)
CHPST_REA
Pesticide reaction coefficient in reach (day-1). If no value for CHPST_REA is entered, the model will set CHPST_REA = 0.007. (Optional)
CHAPTER 41: SWAT INPUT—WATER QUALITY
155
Variable name
Definition
CHPST_VOL
Pesticide volatilization coefficient in reach (m/day). If no value for CHPST_VOL is entered, the model will set CHPST_VOL = 0.01. (Optional)
CHPST_KOC
Pesticide partition coefficient between water and air in reach (m3/day). If no value for CHPST_KOC is entered, the model will set CHPST_KOC = 0. (Optional)
CHPST_STL
Settling velocity for pesticide sorbed to sediment (m/day). If no value for CHPST_STL is entered, the model will set CHPST_STL = 1.0. (Optional)
CHPST_RSP
Resuspension velocity for pesticide sorbed to sediment (m/day). If no value for CHPST_RSP is entered, the model will set CHPST_RSP = 0.002. (Optional)
CHPST_MIX
Mixing velocity (diffusion/dispersion) for pesticide in reach (m/day). If no value for CHPST_MIX is entered, the model will set CHPST_MIX = 0.001. (Optional)
SEDPST_CONC
Initial pesticide concentration in reach bed sediment (mg/m3 sediment). (Optional)
SEDPST_REA
Pesticide reaction coefficient in reach bed sediment (day-1). If no value for SEDPST_REA is entered, the model will set SEDPST_REA = 0.05. (Optional)
SEDPST_BRY
Pesticide burial velocity in reach bed sediment (m/day). If no value for SEDPST_BRY is entered, the model will set SEDPST_BRY = 0.002. (Optional)
SEDPST_ACT
Depth of active sediment layer for pesticide (m). If no value for SEDPST_ACT is entered, the model will set SEDPST_ACT = 0.03. (Optional)
The stream water quality file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the stream water quality input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
NUTRIENT TITLE
2
character
a80
DISOX
3
real
free
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Variable name
Line #
Format
F90 Format
BOD
4
real
free
ALGAE
5
real
free
ORGANICN
6
real
free
AMMONIAN
7
real
free
NITRITEN
8
real
free
NITRATEN
9
real
free
ORGANICP
10
real
free
DISOLVP
11
real
free
RS1
12
real
free
RS2
13
real
free
RS3
14
real
free
RS4
15
real
free
RS5
16
real
free
RS6
17
real
free
RS7
18
real
free
RK1
19
real
free
RK2
20
real
free
RK3
21
real
free
RK4
22
real
free
RK5
23
real
free
RK6
24
real
free
BC1
25
real
free
BC2
26
real
free
BC3
27
real
free
BC4
28
real
free
PESTICIDE TITLE
29
character
a80
CHPST_CONC
30
real
free
CHPST_REA
31
real
free
CHPST_VOL
32
real
free
CHPST_KOC
33
real
free
CHPST_STL
34
real
free
CHPST_RSP
35
real
free
CHPST_MIX
36
real
free
SEDPST_CONC
37
real
free
SEDPST_REA
38
real
free
CHAPTER 41: SWAT INPUT—WATER QUALITY Variable name
Line #
Format
F90 Format
SEDPST_BRY
39
real
free
SEDPST_ACT
40
real
free
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41.3 LAKE WATER QUALITY INPUT FILE (.LWQ) The lake water quality input file contains information used by SWAT to model nutrient and pesticide water quality in reservoirs. Following is a brief description of the variables in the lake water quality input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line is reserved for user comments. This line is not processed by the model.
NUTRIENT TITLE
The second line is reserved for the nutrient section title. This line is not processed by the model.
IRES1
Beginning month of mid-year nutrient settling period. Optional.
IRES2
Ending month of mid-year nutrient settling period. Optional.
PSETLR1
Phosphorus settling rate in reservoir for months IRES1 through IRES2 (m/year). Optional.
PSETLR2
Phosphorus settling rate in reservoir for months other than IRES1-IRES2 (m/year). Optional.
NSETLR1
Nitrogen settling rate in reservoir for months IRES1 through IRES2 (m/year). Optional.
NSETLR2
Nitrogen settling rate in reservoir for months other than IRES1-IRES2 (m/year). Optional.
CHLAR
Chlorophyll a production coefficient for reservoir. Chlorophyll a concentration in the reservoir is calculated from the total phosphorus concentration. The equation assumes the system is phosphorus limited. The chlorophyll a coefficient was added to the equation to allow the user to adjust results to account for other factors not taken into account by the basic equation such as nitrogen limitations. The default value for CHLA is 1.00, which uses the original equation. Optional.
CHAPTER 41: SWAT INPUT—WATER QUALITY
159
Variable name
Definition
SECCIR
Water clarity coefficient for the reservoir. The clarity of the reservoir is expressed by the secci-disk depth (m) which is calculated as a function of chlorophyll a. Because suspended sediment also can affect water clarity, the water clarity coefficient has been added to the equation to allow users to adjust for the impact of factors other than chlorophyll a on water clarity. The default value for SECCI is 1.00, which uses the original equation. Optional.
RES_ORGP
Initial concentration of organic P in reservoir (mg P/L). Optional.
RES_SOLP
Initial concentration of soluble P in reservoir (mg P/L). Optional.
RES_ORGN
Initial concentration of organic N in reservoir (mg N/L). Optional.
RES_NO3
Initial concentration of NO3-N in reservoir (mg N/L). Optional.
RES_NH3
Initial concentration of NH3-N in reservoir (mg N/L). Optional.
RES_NO2
Initial concentration of NO2-N in reservoir (mg N/L). Optional.
PESTICIDE TITLE
This line is reserved for the pesticide section title. This line is not processed by the model.
LKPST_CONC
Initial pesticide concentration in the reservoir water for the pesticide defined by IRTPEST (.bsn). While up to ten pesticides may be applied in a SWAT simulation, only one pesticide (IRTPEST) is routed through the channel network. (mg/m3)
LKPST_REA
Reaction coefficient of the pesticide in reservoir water (day-1)
LKPST_VOL
Volatilization coefficient of the pesticide from the reservoir water (m/day)
LKPST_KOC
Partition coefficient (m3/day)
LKPST_STL
Settling velocity of pesticide sorbed to sediment (m/day)
LKPST_RSP
Resuspension velocity of pesticide sorbed to sediment (m/day).
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Variable name
Definition
LKPST_MIX
Mixing velocity (m/day)
LKSPST_CONC
Initial pesticide concentration in the reservoir bottom sediments. (mg/m3)
LKSPST_REA
Reaction coefficient of pesticide in reservoir bottom sediment (day-1)
LKSPST_BRY
Burial velocity of pesticide in reservoir bottom sediment (m/day)
LKSPST_ACT
Depth of active sediment layer in reservoir (m)
The lake water quality file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the lake water quality input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
NUTRIENT TITLE
2
character
a80
IRES1
3
integer
free
IRES2
4
integer
free
PSETLR1
5
real
free
PSETLR2
6
real
free
NSETLR1
7
real
free
NSETLR2
8
real
free
CHLAR
9
real
free
SECCIR
10
real
free
RES_ORGP
11
real
free
RES_SOLP
12
real
free
RES_ORGN
13
real
free
RES_NO3
14
real
free
RES_NH3
15
real
free
RES_NO2
16
real
free
CHAPTER 41: SWAT INPUT—WATER QUALITY
Variable name
Line #
Format
F90 Format
PESTICIDE TITLE
17
character
a80
LKPST_CONC
18
real
free
LKPST_REA
19
real
free
LKPST_VOL
20
real
free
LKPST_KOC
21
real
free
LKPST_STL
22
real
free
LKPST_RSP
23
real
free
LKPST_MIX
24
real
free
LKSPST_CONC
25
real
free
LKSPST_REA
26
real
free
LKSPST_BRY
27
real
free
LKSPST_ACT
28
real
free
161
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CHAPTER 42
SWAT INPUT DATA: DATABASES
SWAT uses five databases to store information required for plant growth, urban land characteristics, tillage, fertilizer components and pesticide properties. These databases are supplied with the model. The following sections summarize the required information needed within the five databases.
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42.1 LAND COVER/PLANT GROWTH DATABASE FILE (CROP.DAT) Following is a brief description of the variables in the land cover/plant growth database file. They are listed in the order they appear within the file. Variable name
Definition
ICNUM
Land cover/plant code. The different plants listed in crop.dat must have consecutive values for ICNUM. ICNUM is the numeric code used in the management file to identify the land cover to be modeled.
CPNM
A four character code to represent the land cover/plant name. This code is printed to the output files.
IDC
Land cover/plant classification: 1 2 3 4 5 6 7
warm season annual legume cold season annual legume perennial legume warm season annual cold season annual perennial trees
DESCRIPTION
Full land cover/plant name. This description is not used by the model and is present to assist the user in differentiating between plant species.
BIO_E
Radiation-use efficiency or biomass-energy ratio ((kg/ha)/(MJ/m2)). This is the potential (unstressed) growth rate (including roots) per unit of intercepted photosynthetically active radiation. This parameter can greatly change the rate of growth, incidence of stress during the season and the resultant yield. This parameter should be one of the last to be adjusted. Adjustments should be based on research results. Care should be taken to make adjustments based only on data with no drought, nutrient or temperature stress.
HVSTI
Harvest index. This is the plant yield divided by the total aboveground biomass ((kg/ha)/(kg/ha)). This plant parameter should be based on experimental data where crop stresses have been minimized to allow the crop to attain its potential. SWAT will adjust the harvest index if water stress occurs near flowering.
CHAPTER 42: SWAT INPUT—DATABASES
159
Variable name
Definition
BLAI
Maximum potential leaf area index. The values for BLAI in the plant growth database are based on average plant densities in dryland (rainfed) agriculture. BLAI may need to be adjusted for drought-prone regions where planting densities are much smaller or irrigated conditions where densities are much greater.
FRGRW1
Fraction of the plant growing season or fraction of total potential heat units corresponding to the 1st point on the optimal leaf area development curve. The total potential heat units are the number of heat units required to bring the plant to maturity.
LAIMX1
Fraction of the maximum leaf area index corresponding to the 1st point on the optimal leaf area development curve.
FRGRW2
Fraction of the plant growing season or fraction of total potential heat units corresponding to the 2nd point on the optimal leaf area development curve. The total potential heat units are the number of heat units required to bring the plant to maturity.
LAIMX2
Fraction of the maximum leaf area index corresponding to the 2nd point on the optimal leaf area development curve.
DLAI
Fraction of growing season when leaf area declines (heat units/heat units).
CHTMX
Maximum canopy height (m).
RDMX
Maximum root depth (m).
T_OPT
Optimal temperature for plant growth (ºC). Both optimal and base temperatures are very stable for cultivars within a species.
T_BASE
Minimum (base) temperature for plant growth (ºC).
CNYLD
Normal fraction of nitrogen in yield (kg N/kg yield). This value is estimated on a dry weight basis.
CPYLD
Normal fraction of phosphorus in yield (kg P/kg yield). This value is estimated on a dry weight basis.
BN(1)
Nitrogen uptake parameter #1: normal fraction of nitrogen in plant biomass at emergence (kg N/kg biomass)
BN(2)
Nitrogen uptake parameter #2: normal fraction of nitrogen in plant biomass at 50% maturity (kg N/kg biomass)
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
BN(3)
Nitrogen uptake parameter #3: normal fraction of nitrogen in plant biomass at maturity (kg N/kg biomass)
BP(1)
Phosphorus uptake parameter #1: normal fraction of phosphorus in plant biomass at emergence (kg P/kg biomass)
BP(2)
Phosphorus uptake parameter #2: normal fraction of phosphorus in plant biomass at 50% maturity (kg P/kg biomass)
BP(3)
Phosphorus uptake parameter #3: normal fraction of phosphorus in plant biomass at maturity (kg P/kg biomass)
WSYF
Lower limit of harvest index ((kg/ha)/(kg/ha)). The value between 0.0 and HVSTI which represents the lowest harvest index expected due to water stress.
USLE_C
Minimum value of USLE C factor for water erosion applicable to the land cover/plant.
GSI
Maximum stomatal conductance at high solar radiation and low vapor pressure deficit (m·s-1). Used in PenmanMonteith evapotranspiration calculations.
VPDFR
Vapor pressure deficit (kPa) corresponding to the second point on the stomatal conductance curve. (The first point on the stomatal conductance curve is comprised of a vapor pressure deficit of 1 kPa and the fraction of maximum stomatal conductance equal to 1.00.)
FRGMAX
Fraction of maximum stomatal conductance corresponding to the second point on the stomatal conductance curve. (The first point on the stomatal conductance curve is comprised of a vapor pressure deficit of 1 kPa and the fraction of maximum stomatal conductance equal to 1.00.)
WAVP
Rate of decline in radiation use efficiency per unit increase in vapor pressure deficit. The value of WAVP varies among species, but a value of 6 to 8 is suggested as an approximation for most plants.
CO2HI
Elevated CO2 atmospheric concentration (µL CO2/L air) corresponding the 2nd point on the radiation use efficiency curve. (The 1st point on the radiation use efficiency curve is comprised of the ambient CO2 concentration, 330 µL CO2/L air, and the biomass-energy ratio reported for BIO_E)
CHAPTER 42: SWAT INPUT—DATABASES
161
Variable name
Definition
BIOEHI
Biomass-energy ratio corresponding to the 2nd point on the radiation use efficiency curve. (The 1st point on the radiation use efficiency curve is comprised of the ambient CO2 concentration, 330 µL CO2/L air, and the biomassenergy ratio reported for BIO_E.)
RSDCO_PL
Plant residue decomposition coefficient. The fraction of residue that will decompose in a day assuming optimal moisture, temperature, C:N ratio, and C:P ratio. Optional. If no value is entered for RSDCO_PL, it is set equal to the value given for RSDCO in the watershed general attribute (.bsn) file.
42.1.1 FILE FORMAT (CROP.DAT) Four lines are required to store the plant growth parameters for a land cover/plant in the database (crop.dat) file. The plant growth database file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. Variable name
Line #
Format
F90 Format
ICNUM
1
integer
free
CPNM
1
character
a4
IDC
1
integer
free
BIO_E
2
real
free
HVSTI
2
real
free
BLAI
2
real
free
FRGRW1
2
real
free
LAIMX1
2
real
free
FRGRW2
2
real
free
LAIMX2
2
real
free
DLAI
2
real
free
CHTMX
2
real
free
RDMX
2
real
free
T_OPT
3
real
free
T_BASE
3
real
free
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Variable name
Line #
Format
F90 Format
CNYLD
3
real
free
CPYLD
3
real
free
BN(1)
3
real
free
BN(2)
3
real
free
BN(3)
3
real
free
BP(1)
3
real
free
BP(2)
3
real
free
BP(3)
3
real
free
WSYF
4
real
free
USLE_C
4
real
free
GSI
4
real
free
VPDFR
4
real
free
FRGMAX
4
real
free
WAVP
4
real
free
CO2HI
4
real
free
BIOEHI
4
real
free
RSDCO_PL
4
real
free
CHAPTER 42: SWAT INPUT—DATABASES
163
42.2 TILLAGE DATABASE FILE (TILL.DAT) Following is a brief description of the variables in the tillage database file. They are listed in the order they appear within the file. Variable name
Definition
ITNUM
Tillage number. The different tillage operations in till.dat must have consecutive values for ITNUM. ITNUM is the numeric code used in the management file to identify the tillage practice to be modeled.
TILLNM
An eight character code representing the tillage operation name.
EFFMIX
Mixing efficiency of tillage operation. The mixing efficiency specifies the fraction of materials (residue and nutrients) on the soil surface which are mixed uniformly throughout the soil depth mixed by the implement. The remaining fraction of residue and nutrients is left in the original location (soil surface or layer).
DEPTIL
Depth of mixing caused by the tillage operation (mm).
The format of the tillage database file is: Variable name
Line #
Position
Format
F90 Format
ITNUM
ALL
space 1-4
4-digit integer
i4
TILLNM
ALL
space 9-16
character
a8
EFFMIX
ALL
space 25-32
decimal(xxxx.xxx)
f8.3
DEPTIL
ALL
space 41-48
decimal(xxxx.xxx)
f8.3
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42.3 PESTICIDE/TOXIN DATABASE FILE (PEST.DAT) Following is a brief description of the variables in the pesticide/toxin database file. They are listed in the order they appear within the file. Variable name
Definition
IPNUM
Pesticide/toxin number. The different toxins in pest.dat must have consecutive values for IPNUM. IPNUM is the numeric code used in the management file to identify the pesticide/toxin to be applied.
PNAME
Name of pesticide/toxin. (up to 17 characters allowed)
SKOC
Soil adsorption coefficient normalized for soil organic carbon content (mg/kg)/(mg/L). Koc is equal to the soil adsorption coefficient, Kp, divided by the fraction of organic carbon in the soil. Kp is calculated by dividing Csolid phase by Csolution where Csolid phase is the concentration of the chemical sorbed to the solid phase (expressed as mg chemical/kg solid material) and Csolution is the concentration of the chemical in the solution (expressed as mg chemical/L solution). Koc and Kp have the same units.
WOF
Wash-off fraction. Fraction of pesticide on foliage available for wash off by rainfall (0.0-1.0). The pesticide is deposited on the soil surface.
HLIFE_F
Degradation half-life of the chemical on the foliage (days).
HLIFE_S
Degradation half-life of the chemical in the soil (days).
AP_EF
Application efficiency. The fraction of pesticide applied which is deposited on the foliage and soil surface (0.11.0). The remainder is lost.
WSOL
Solubility of the chemical in water (mg/L or ppm)
CHAPTER 42: SWAT INPUT—DATABASES
165
Variable name
Definition
HENRY
Henry's Law Constant (KH) for the chemical (unitless). Henry's Law Constant characterizes the partitioning of the chemical between the air and water. Values for Henry's Law are reported in several different units and care must be taken when obtaining values to make sure they are in the correct units. For calculations in SWAT, KH is defined as Cgas/Csolution where Cgas is the concentration of the chemical in the gas phase (mg/L) and Csolution is defined as the concentration of the chemical in solution. (not currently operational)
The format of the pesticide/toxin database file is: Variable name
Line #
Position
Format
F90 Format
IPNUM
ALL
space 1-3
integer
i3
PNAME
ALL
space 4-20
character
a17
SKOC
ALL
space 21-30
decimal(xxxxxxxx.x)
f10.1
WOF
ALL
space 31-35
decimal(xx.xx)
f5.2
HLIFE_F
ALL
space 36-43
decimal(xxxxxx.x)
f8.1
HLIFE_S
ALL
space 44-51
decimal(xxxxxx.x)
f8.1
AP_EF
ALL
space 52-56
decimal(xx.xx)
f5.2
WSOL
ALL
space 57-67
decimal(xxxxxxx.xxx)
f11.3
HENRY
ALL
space 68-79
exponential(x.xxxxExxx)
e12.4
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42.4 FERTILIZER DATABASE FILE (FERT.DAT) Following is a brief description of the variables in the fertilizer database file. They are listed in the order they appear within the file. Variable name
Definition
IFNUM
Number of fertilizer in database. This number should be equivalent to the line number and is the reference number used in the management file to identify the fertilizer type being applied.
FERTNM
Name of fertilizer/manure (up to 8 characters allowed).
FMINN
Fraction of mineral N (NO3 and NH4) in fertilizer (kg min-N/kg fertilizer). Value should be between 0.0 and 1.0.
FMINP
Fraction of mineral P in fertilizer (kg min-P/kg fertilizer). Value should be between 0.0 and 1.0.
FORGN
Fraction of organic N in fertilizer (kg org-N/kg fertilizer). Value should be between 0.0 and 1.0.
FORGP
Fraction of organic P in fertilizer (kg org-P/kg fertilizer). Value should be between 0.0 and 1.0.
FNH3N
Fraction of mineral N in fertilizer applied as ammonia (kg NH3-N/kg min-N). Value should be between 0.0 and 1.0.
BACTPDB
Concentration of persistent bacteria in manure/fertilizer (# bacteria/kg manure). Optional.
BACTLPDB
Concentration of less-persistent bacteria in manure/fertilizer (# bacteria/kg manure). Optional.
BACTKDDB
Bacteria partition coefficient. Value should be between 0.0 and 1.0. When the bacteria partition coefficient is 0.0, all bacteria are sorbed to soil particles. When the bacteria partition coefficient is 1.0 all bacteria is in solution. Optional.
CHAPTER 42: SWAT INPUT—DATABASES
The format of the fertilizer database file is: Variable name
Line #
Position
Format
F90 Format
IFNUM
ALL
space 1-4
integer
i4
FERTNM
ALL
space 6-13
character
a8
FMINN
ALL
space 14-21
decimal(xxxx.xxx)
f8.3
FMINP
ALL
space 22-29
decimal(xxxx.xxx)
f8.3
FORGN
ALL
space 30-37
decimal(xxxx.xxx)
f8.3
FORGP
ALL
space 38-45
decimal(xxxx.xxx)
f8.3
FNH3N
ALL
space 46-53
decimal(xxxx.xxx)
f8.3
BACTPDB
ALL
space 54-61
decimal(xxxx.xxx)
f8.3
BACTLPDB
ALL
space 62-69
decimal(xxxx.xxx)
f8.3
BACTKDDB
ALL
space 70-77
decimal(xxxx.xxx)
f8.3
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42.5 URBAN DATABASE FILE (URBAN.DAT) Following is a brief description of the variables in the urban database file. They are listed in the order they appear within the file. Variable name
Definition
IUNUM
Number of urban land type. This value should be equivalent to the line number.
URBNAME
4-character code for urban land type.
URBFLNM
Full description for urban land type—may take up to 54 characters. (not used by SWAT)
FIMP
Fraction total impervious area in urban land type. This includes directly and indirectly connected impervious areas.
FCIMP
Fraction directly connected impervious area in urban land type.
CURBDEN
Curb length density in urban land type (km/ha).
URBCOEF
Wash-off coefficient for removal of constituents from impervious area (mm-1)
DIRTMX
Maximum amount of solids allowed to build up on impervious areas (kg/curb km).
THALF
Number of days for amount of solids on impervious areas to build up from 0 kg/curb km to half the maximum allowed, i.e. 1/2 DIRTMX (days).
TNCONC
Concentration of total nitrogen in suspended solid load from impervious areas (mg N/kg sed).
TPCONC
Concentration of total phosphorus in suspended solid load from impervious areas (mg P/kg sed).
TNO3CONC
Concentration of nitrate in suspended solid load from impervious areas (mg NO3-N/kg sed).
CHAPTER 42: SWAT INPUT—DATABASES
169
Every urban land type uses two lines in the urban.dat file to store input values. The format of every set of two lines is described below. Variable name
Line #
Position
Format
F90 Format
IUNUM
1
space 1-3
integer
i3
URBNAME
1
space 5-8
character
a4
URBFLNM
1
space 10-64
character
a54
FIMP
1
space 65-72
decimal(xxxx.xxx)
f8.3
FCIMP
1
space 73-80
decimal(xxxx.xxx)
f8.3
CURBDEN
2
space 5-12
decimal(xxxx.xxx)
f8.3
URBCOEF
2
space 13-20
decimal(xxxx.xxx)
f8.3
DIRTMX
2
space 21-28
decimal(xxxx.xxx)
f8.3
THALF
2
space 29-36
decimal(xxxx.xxx)
f8.3
TNCONC
2
space 37-44
decimal(xxxx.xxx)
f8.3
TPCONC
2
space 45-52
decimal(xxxx.xxx)
f8.3
TNO3CONC
2
space 53-60
decimal(xxxx.xxx)
f8.3
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CHAPTER 43
SWAT INPUT DATA: MEASURED
Four different file formats may be used to store stream loading data that is incorporated directly into the watershed routing. This stream loading data may come from a point source, such as a town's sewage treatment discharge, or it may be output from simulation of an upstream area. The four different file formats allow the user to summarize the data in one of four ways: daily, monthly, yearly, or average annual.
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43.1 DAILY RECORDS (RECDAY .DAT FILE) The recday command in the watershed configuration (.fig) file requires a file containing SWAT input data summarized on a daily time step. An unlimited∗ number of files with daily flow data are allowed in the simulation. The file numbers assigned to the recday files in the watershed configuration file (.fig) must be ≥ 1 and numbered sequentially. Following is a brief description of the variables in the recday input file. They are listed in the order they appear within the file.
∗
Variable name
Definition
TITLE
The first six lines of the file are reserved for user comments. The comments may take up to 80 spaces per line.
DAY
Julian date for record (optional). If the julian date and year are provided for the records, SWAT will search for the beginning day of simulation in the record. If the julian date and year are left blank, SWAT assumes that the first line of record corresponds to the first day of simulation. (SWAT uses the date and year to locate the record corresponding to the first day of simulation. From that point on, the day and year are ignored.)
YEAR
Four-digit year for record (optional). See description of DAY for more information.
FLODAY
Contribution to streamflow for the day (m3)
SEDDAY
Sediment loading to reach for the day (metric tons)
ORGNDAY
Organic N loading to reach for the day (kg N)
ORGPDAY
Organic P loading to reach for the day (kg P)
NO3DAY
NO3 loading to reach for the day (kg N)
MINPDAY
Mineral P loading to reach for the day (kg P)
NH3DAY
NH3 loading to reach for the day (kg N)
NO2DAY
NO2 loading to reach for the day (kg N)
CMTL1DAY
Loading of conservative metal #1 to reach for the day (kg)
Please keep in mind that FORTRAN limits the total number of files that can be open at one time to something in the neighborhood of 250. The input files containing daily data (.pcp, .tmp, and recday) remain open throughout the simulation.
CHAPTER 43: SWAT INPUT—MEASURED
Variable name
Definition
CMTL2DAY
Loading of conservative metal #2 to reach for the day (kg)
CMTL3DAY
Loading of conservative metal #3 to reach for the day (kg)
BACTPDAY
Loading of persistent bacteria to reach for the day (# bact)
BACTLPDAY
Loading of less persistent bacteria to reach for the day (# bact)
169
One line of data is required for every day of the simulation period. The recday data file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the recday data file is: Variable name TITLE
Line #
Format
F90 Format
1-6
character
a80
DAY
7-END
integer
free
YEAR
7-END
integer
free
FLODAY
7-END
real or exponential
free
SEDDAY
7-END
real or exponential
free
ORGNDAY
7-END
real or exponential
free
ORGPDAY
7-END
real or exponential
free
NO3DAY
7-END
real or exponential
free
MINPDAY
7-END
real or exponential
free
NH3DAY
7-END
real or exponential
free
NO2DAY
7-END
real or exponential
free
CMTL1DAY
7-END
real or exponential
free
CMTL2DAY
7-END
real or exponential
free
CMTL3DAY
7-END
real or exponential
free
BACTPDAY
7-END
real or exponential
free
BACTLPDAY
7-END
real or exponential
free
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SWAT USER'S MANUAL, VERSION 2000
43.2 MONTHLY RECORDS (RECMON .DAT FILE) The recmon command in the watershed configuration (.fig) file requires a file containing input data summarized on a monthly time step. SWAT will accept an unlimited number of data files with monthly flow data. The file numbers assigned to the files in the watershed configuration file (.fig) must be numbered sequentially and begin at 1. Following is a brief description of the variables in the recmon data file. They are listed in the order they appear within the file.
Variable name
Definition
TITLE
The first 6 lines of the data file is reserved for user comments. The comments may take up to 80 spaces.
MONTH
Month of measured data. This variable is provided for the user—it is ignored by SWAT. The model assumes the first line of measured data in the file contains data for January of the first year of simulation. The monthly data file must contain a line of data for every month of simulation in consecutive order.
YEAR
4-digit year of measured data. This variable is provided for the user—it is ignored by SWAT. The model assumes the first line of measured data in the file contains data for January of the first year of simulation. The monthly data file must contain a line of data for every month of simulation in consecutive order.
FLOMON
Average daily water loading for month (m3/day).
SEDMON
Average daily sediment loading for month (metric tons/day).
ORGNMON
Average daily organic nitrogen loading for month (kg N/day).
ORGPMON
Average daily organic phosphorus loading for month (kg P/day).
NO3MON
Average daily nitrate loading for month (kg N/day).
MINPMON
Average daily mineral (soluble) P loading for month (kg P/day).
CHAPTER 43: SWAT INPUT—MEASURED
171
Variable name
Definition
NH3MON
Average daily ammonia loading for month (kg N/day).
NO2MON
Average daily nitrite loading for month (kg N/day).
CMTL1MON
Average daily loading of conservative metal #1 for month (kg/day).
CMTL2MON
Average daily loading of conservative metal #2 for month (kg/day).
CMTL3MON
Average daily loading of conservative metal #3 for month (kg /day).
BACTPMON
Average daily loading of persistent bacteria for month (# bact/day).
BACTLPMON
Average daily loading of less persistent bacteria for month (# bact/day).
The file must contain one line of data for every month of simulation (Even if the simulation begins in a month other than January, the file must contain lines for every month of the first year.) The recmon data file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the recmon data file is: Variable name TITLE
Line #
Format
F90 Format
1-6
character
a80
MONTH
7 - END
integer
free
YEAR
7 - END
integer
free
FLOMON
7 - END
real or exponential
free
SEDMON
7 - END
real or exponential
free
ORGNMON
7 - END
real or exponential
free
ORGPMON
7 - END
real or exponential
free
NO3MON
7 - END
real or exponential
free
MINPMON
7 - END
real or exponential
free
NH3MON
7 - END
real or exponential
free
NO2MON
7 - END
real or exponential
free
CMTL1MON
7 - END
real or exponential
free
CMTL2MON
7 - END
real or exponential
free
CMTL3MON
7 - END
real or exponential
free
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
BACTPMON
7 - END
real or exponential
free
BACTLPMON
7 - END
real or exponential
free
43.3 YEARLY RECORDS (RECYEAR .DAT FILE) The recyear command in the watershed configuration (.fig) file requires a file containing SWAT input data summarized on an annual time step. SWAT will accept an unlimited number of data files with yearly flow data. The file numbers assigned to the recyear files in the watershed configuration file (.fig) must be numbered sequentially and begin at 1. Following is a brief description of the variables in the recyear data file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first six lines of the data file are reserved for user comments. The comments may take up to 80 spaces per line.
YEAR
4-digit year of measured data. This variable is provided for the user—it is ignored by SWAT. The model assumes the first line of measured data in the file contains data for the first year of simulation. The yearly data file must contain a line of data for every year of simulation in consecutive order.
FLOYR
Average daily water loading for year (m3/day).
SEDYR
Average daily sediment loading for year (metric tons/day).
ORGNYR
Average daily organic nitrogen loading for year (kg N/day).
ORGPYR
Average daily organic phosphorus loading for year (kg P/day).
NO3YR
Average daily nitrate loading for year (kg N/day).
CHAPTER 43: SWAT INPUT—MEASURED
173
Variable name
Definition
MINPYR
Average daily mineral (soluble) P loading for year (kg P/day).
NH3YR
Average daily ammonia loading for year (kg N/day).
NO2YR
Average daily nitrite loading for year (kg N/day).
CMTL1YR
Average daily loading of conservative metal #1 for year (kg/day).
CMTL2YR
Average daily loading of conservative metal #2 for year (kg/day).
CMTL3YR
Average daily loading of conservative metal #3 for year (kg/day).
BACTPYR
Average daily loading of persistent bacteria for year (# bact/day).
BACTLPYR
Average daily loading of less persistent bacteria for year (# bact/day).
The recyear data file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line.The format of the recyear data file is: Variable name
Line #
Format
F90 Format
TITLE
1-6
character
a80
YEAR
7 - END
integer
free
FLOYR
7 - END
real or exponential
free
SEDYR
7 - END
real or exponential
free
ORGNYR
7 - END
real or exponential
free
ORGPYR
7 - END
real or exponential
free
NO3YR
7 - END
real or exponential
free
MINPYR
7 - END
real or exponential
free
NH3YR
7 - END
real or exponential
free
NO2YR
7 - END
real or exponential
free
CMTL1YR
7 - END
real or exponential
free
CMTL2YR
7 - END
real or exponential
free
CMTL3YR
7 - END
real or exponential
free
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
BACTPYR
7 - END
real or exponential
free
BACTLPYR
7 - END
real or exponential
free
43.4 AVERAGE ANNUAL RECORDS (RECCNST .DAT FILE) The reccnst command in the watershed configuration (.fig) file requires a file containing average annual SWAT input data. SWAT will accept an unlimited number of data files with average annual flow data. The file numbers assigned to the reccnst files in the watershed configuration file (.fig) must be numbered sequentially and begin at 1. Following is a brief description of the variables in the reccnst data file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first six lines of the data file are reserved for user comments. The comments may take up to 80 spaces on each line.
FLOCNST
Average daily water loading (m3/day)
SEDCNST
Average daily sediment loading (metric tons/day)
ORGNCNST
Average daily organic N loading (kg N/day)
ORGPCNST
Average daily organic P loading (kg P/day)
NO3CNST
Average daily NO3 loading (kg N/day)
MINPCNST
Average daily mineral P loading (kg P/day)
NH3CNST
Average daily NH3 loading (kg N/day)
NO2CNST
Average daily NO2 loading (kg N/day)
CMTL1CNST
Average daily loading of conservative metal #1 (kg/day)
CMTL2CNST
Average daily loading of conservative metal #2 (kg/day)
CMTL3CNST
Average daily loading of conservative metal #3 (kg/day)
BACTPCNST
Average daily loading of persistent bacteria (# bact/day)
BACTLPCNST
Average daily loading of less persistent bacteria (# bact/day)
CHAPTER 43: SWAT INPUT—MEASURED
The format of the reccnst data file is: Variable name TITLE
Line #
Format
F90 Format
1-6
character
a80
FLOCNST
7
real or exponential
free
SEDCNST
7
real or exponential
free
ORGNCNST
7
real or exponential
free
ORGPCNST
7
real or exponential
free
NO3CNST
7
real or exponential
free
MINPCNST
7
real or exponential
free
NH3CNST
7
real or exponential
free
NO2CNST
7
real or exponential
free
CMTL1CNST
7
real or exponential
free
CMTL2CNST
7
real or exponential
free
CMTL3CNST
7
real or exponential
free
BACTPCNST
7
real or exponential
free
BACTLPCNST
7
real or exponential
free
175
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 44
SWAT OUTPUT DATA: PRIMARY OUTPUT FILES
A number of output files are generated in every SWAT simulation. These files are: the summary output file (output.std), the HRU output file (.sbs), the subbasin output file (.bsb), and the main channel or reach output file (.rch). The detail of the data printed out in each file is controlled by the print codes in the input control code (.cod) file. Average daily values are always printed in the HRU, subbasin and reach files, but the time period they are summarized over will vary. Depending on the print code selected, the output files may include all daily values, daily amounts averaged over the month, daily amounts averaged over the year, or daily amounts averaged over the entire simulation period.
173
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SWAT USER'S MANUAL, VERSION 2000
44.1 INPUT SUMMARY FILE (INPUT.STD) The input summary file prints summary tables of important input values. This file provides the user with a mechanism to spot-check input values. All model inputs are not printed, but the file does contain some of the most important.
44.2 OUTPUT SUMMARY FILE (OUTPUT.STD) The output summary file provides watershed average loadings from the HRUs to the streams. Tables are also included that present average annual HRU and subbasin values for a few parameters.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
175
44.3 HRU OUTPUT FILE (.SBS) The HRU output file contains summary information for each of the hydrologic response units in the watershed. The file is written in spreadsheet format. Following is a brief description of the output variables in the HRU output file. Variable name
Definition
LULC
Four letter character code for the cover/plant on the HRU. (code from crop.dat file)
HRU
Hydrologic response unit number
GIS
GIS code reprinted from watershed configuration file (.fig). See explanation of subbasin command.
SUB
Topographically-defined subbasin to which the HRU belongs.
MGT
Management number. This is pulled from the management (.mgt) file. Used by the SWAT/GRASS interface to allow development of output maps by landuse/management type.
MON
Daily time step: the julian date Monthly time step: the month (1-12) Annual time step: four-digit year Average annual summary lines: total number of years averaged together
AREA
Drainage area of the HRU (km2).
PRECIP
Total amount of precipitation falling on the HRU during time step (mm H2O).
SNOFALL
Amount of precipitation falling as snow, sleet or freezing rain during time step (water-equivalent mm H2O).
SNOMELT
Amount of snow or ice melting during time step (waterequivalent mm H2O).
IRR
Irrigation (mm H2O). Amount of irrigation water applied to HRU during the time step.
PET
Potential evapotranspiration (mm H2O). Potential evapotranspiration from the HRU during the time step.
ET
Actual evapotranspiration (soil evaporation and plant transpiration) from the HRU during the time step (mm H2O).
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
SW
Soil water content (mm H2O). Amount of water in the soil profile at the end of the time period.
PERC
Water that percolates past the root zone during the time step (mm H2O). There is usually a lag between the time the water leaves the bottom of the root zone and reaches the shallow aquifer. Over a long period of time, this variable should equal groundwater recharge (PERC = GW_RCHG as time → ∞).
GW_RCHG
Recharge entering aquifers during time step (total amount of water entering shallow and deep aquifers during time step) (mm H2O).
DA_RCHG
Deep aquifer recharge (mm H2O). The amount of water from the root zone that recharges the deep aquifer during the time step. (shallow aquifer recharge = GW_RCHG - DA_RCHG)
REVAP
Water in the shallow aquifer returning to the root zone in response to a moisture deficit during the time step (mm H2O). The variable also includes water uptake directly from the shallow aquifer by deep tree and shrub roots.
SA_IRR
Irrigation from shallow aquifer (mm H2O). Amount of water removed from the shallow aquifer for irrigation during the time step.
DA_IRR
Irrigation from deep aquifer (mm H2O). Amount of water removed from the deep aquifer for irrigation during the time step.
SA_ST
Shallow aquifer storage (mm H2O). Amount of water in the shallow aquifer at the end of the time period.
DA_ST
Deep aquifer storage (mm H2O). Amount of water in the deep aquifer at the end of the time period.
SURQ
Surface runoff contribution to streamflow in the main channel during time step (mm H2O).
TLOSS
Transmission losses (mm H2O). Water lost from tributary channels in the HRU via transmission through the bed. This water becomes recharge for the shallow aquifer during the time step. Net surface runoff contribution to the main channel streamflow is calculated by subtracting TLOSS from SURQ.
LATQ
Lateral flow contribution to streamflow (mm H2O). Water flowing laterally within the soil profile that enters the main channel during time step.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
177
Variable name
Definition
GW_Q
Groundwater contribution to streamflow (mm H2O). Water from the shallow aquifer that enters the main channel during the time step. Groundwater flow is also referred to as baseflow.
WYLD
Water yield (mm H2O). Total amount of water leaving the HRU and entering main channel during the time step. (WYLD = SURQ + LATQ + GWQ – TLOSS – pond abstractions)
SYLD
Sediment yield (metric tons/ha). Sediment from the HRU that is transported into the main channel during the time step.
USLE
Soil loss during the time step calculated with the USLE equation (metric tons/ha). This value is reported for comparison purposes only.
N_APP
Nitrogen fertilizer applied (kg N/ha). Total amount of nitrogen (mineral and organic) applied in fertilizer during the time step.
P_APP
Phosphorus fertilizer applied (kg P/ha). Total amount of phosphorus (mineral and organic) applied in fertilizer during the time step.
NAUTO
Nitrogen fertilizer auto-applied (kg N/ha). Total amount of nitrogen (mineral and organic) auto-applied during the time step.
PAUTO
Phosphorus fertilizer auto-applied (kg P/ha). Total amount of phosphorus (mineral and organic) auto-applied during the time step.
NGRZ
Nitrogen applied during grazing operation (kg N/ha). Total amount of nitrogen (mineral and organic) added to soil by grazing operation during the time step.
PGRZ
Phosphorus applied during grazing operation (kg P/ha). Total amount of phosphorus (mineral and organic) added to soil by grazing operation during the time step.
NRAIN
Nitrate added to soil profile by rain (kg N/ha).
NFIX
Nitrogen fixation (kg N/ha). Amount of nitrogen fixed by legumes during the time step.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
F-MN
Fresh organic to mineral N (kg N/ha). Mineralization of nitrogen from the fresh residue pool to the nitrate (80%) pool and active organic nitrogen (20%) pool during the time step. A positive value denotes a net gain in the nitrate and active organic pools from the fresh organic pool while a negative value denotes a net gain in the fresh organic pool from the nitrate and active organic pools.
A-MN
Active organic to mineral N (kg N/ha). Movement of nitrogen from the active organic pool to the nitrate pool during the time step.
A-SN
Active organic to stable organic N (kg N/ha). Movement of nitrogen from the active organic pool to the stable organic pool during the time step.
F-MP
Fresh organic to mineral P (kg P/ha). Mineralization of phosphorus from the fresh residue pool to the "active" mineral (80%) pool (P sorbed to soil surface) and the active organic (20%) pool. A positive value denotes a net gain in the active mineral and active organic pools from the fresh organic pool while a negative value denotes a net gain in the fresh organic pool from the active mineral and active organic pools.
AO-LP
Organic to labile mineral P (kg P/ha). Movement of phosphorus between the organic pool and the labile mineral pool during the time step. A positive value denotes a net gain in the labile pool from the organic pool while a negative value denotes a net gain in the organic pool from the labile pool.
L-AP
Labile to active mineral P (kg P/ha). Movement or transformation of phosphorus between the "labile" mineral pool (P in solution) and the "active" mineral pool (P sorbed to the surface of soil particles) during the time step. A positive value denotes a net gain in the active pool from the labile pool while a negative value denotes a net gain in the labile pool from the active pool.
A-SP
Active to stable P (kg P/ha). Movement or transformation of phosphorus between the "active" mineral pool (P sorbed to the surface of soil particles) and the "stable" mineral pool (P fixed in soil) during the time step. A positive value denotes a net gain in the stable pool from the active pool while a negative value denotes a net gain in the active pool from the stable pool.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
179
Variable name
Definition
DNIT
Denitrification (kg N/ha). Transformation of nitrate to gaseous compounds during the time step.
NUP
Plant uptake of nitrogen (kg N/ha). Nitrogen removed from soil by plants during the time step.
PUP
Plant uptake of phosphorus (kg P/ha). Phosphorus removed from soil by plants during the time step.
ORGN
Organic N yield (kg N/ha). Organic nitrogen transported out of the HRU and into the reach during the time step.
ORGP
Organic P yield (kg P/ha). Organic phosphorus transported with sediment into the reach during the time step.
SEDP
Sediment P yield (kg P/ha). Mineral phosphorus sorbed to sediment transported into the reach during the time step.
NSURQ
NO3 in surface runoff (kg N/ha). Nitrate transported with surface runoff into the reach during the time step.
NLATQ
NO3 in lateral flow (kg N/ha). Nitrate transported by lateral flow into the reach during the time step.
NO3L
NO3 leached from the soil profile (kg N/ha). Nitrate that leaches past the bottom of the soil profile during the time step. The nitrate is not tracked through the shallow aquifer.
NO3GW
NO3 transported into main channel in the groundwater loading from the HRU (kg N/ha).
SOLP
Soluble P yield (kg P/ha). Soluble mineral forms of phosphorus transported by surface runoff into the reach during the time step.
P_GW
Soluble phosphorus transported by groundwater flow into main channel during the time step (kg P/ha).
W_STRS
Water stress days during the time step (days).
TMP_STRS
Temperature stress days during the time step (days).
N_STRS
Nitrogen stress days during the time step (days).
P_STRS
Phosphorus stress days during the time step (days).
BIOM
Biomass (metric tons/ha). Total biomass, i.e. aboveground and roots at the end of the time period reported as dry weight.
LAI
Leaf area index at the end of the time period.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
YLD
Harvested yield (metric tons/ha). The model partitions yield from the total biomass on a daily basis (and reports it). However, the actual yield is not known until it is harvested. The harvested yield is reported as dry weight.
BACTP
Number of persistent bacteria in surface runoff entering reach (count).
BACTLP
Number of less persistent bacteria in surface runoff entering reach (count).
The file format for the HRU output file (.sbs) is: Variable name
Line #
Position
Format
F90 Format
LULC
All
space 1-4
character
a4
HRU
All
space 5-8
4-digit integer
i4
GIS
All
space 10-17
8-digit integer
i8
SUB
All
space 19-22
4-digit integer
i4
MGT
All
space 24-27
4-digit integer
i4
MON
All
space 29-32
4-digit integer
i4
AREA
All
space 33-42
decimal(xxxxxx.xxx)
f10.3
PRECIP
All
space 43-52
decimal(xxxxxx.xxx)
f10.3
SNOFALL
All
space 53-62
decimal(xxxxxx.xxx)
f10.3
SNOMELT
All
space 63-72
decimal(xxxxxx.xxx)
f10.3
IRR
All
space 73-82
decimal(xxxxxx.xxx)
f10.3
PET
All
space 83-92
decimal(xxxxxx.xxx)
f10.3
ET
All
space 93-102
decimal(xxxxxx.xxx)
f10.3
SW
All
space 103-112
decimal(xxxxxx.xxx)
f10.3
PERC
All
space 113-122
decimal(xxxxxx.xxx)
f10.3
GW_RCHG
All
space 123-132
decimal(xxxxxx.xxx)
f10.3
DA_RCHG
All
space 133-142
decimal(xxxxxx.xxx)
f10.3
REVAP
All
space 143-152
decimal(xxxxxx.xxx)
f10.3
SA_IRR
All
space 153-162
decimal(xxxxxx.xxx)
f10.3
DA_IRR
All
space 163-172
decimal(xxxxxx.xxx)
f10.3
SA_ST
All
space 173-182
decimal(xxxxxx.xxx)
f10.3
DA_ST
All
space 183-192
decimal(xxxxxx.xxx)
f10.3
SURQ
All
space 193-202
decimal(xxxxxx.xxx)
f10.3
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES Variable name
Line #
Position
Format
F90 Format
TLOSS
All
space 203-212
decimal(xxxxxx.xxx)
f10.3
LATQ
All
space 213-222
decimal(xxxxxx.xxx)
f10.3
GW_Q
All
space 223-232
decimal(xxxxxx.xxx)
f10.3
WYLD
All
space 233-242
decimal(xxxxxx.xxx)
f10.3
SYLD
All
space 243-252
decimal(xxxxxx.xxx)
f10.3
USLE
All
space 253-262
decimal(xxxxxx.xxx)
f10.3
N_APP
All
space 263-272
decimal(xxxxxx.xxx)
f10.3
P_APP
All
space 273-282
decimal(xxxxxx.xxx)
f10.3
NAUTO
All
space 283-292
decimal(xxxxxx.xxx)
f10.3
PAUTO
All
space 293-302
decimal(xxxxxx.xxx)
f10.3
NGRZ
All
space 303-312
decimal(xxxxxx.xxx)
f10.3
PGRZ
All
space 313-322
decimal(xxxxxx.xxx)
f10.3
NRAIN
All
space 323-332
decimal(xxxxxx.xxx)
f10.3
NFIX
All
space 333-342
decimal(xxxxxx.xxx)
f10.3
F-MN
All
space 343-352
decimal(xxxxxx.xxx)
f10.3
A-MN
All
space 353-362
decimal(xxxxxx.xxx)
f10.3
A-SN
All
space 363-372
decimal(xxxxxx.xxx)
f10.3
F-MP
All
space 373-382
decimal(xxxxxx.xxx)
f10.3
AO-LP
All
space 383-392
decimal(xxxxxx.xxx)
f10.3
L-AP
All
space 393-402
decimal(xxxxxx.xxx)
f10.3
A-SP
All
space 403-412
decimal(xxxxxx.xxx)
f10.3
DNIT
All
space 413-422
decimal(xxxxxx.xxx)
f10.3
NUP
All
space 423-432
decimal(xxxxxx.xxx)
f10.3
PUP
All
space 433-442
decimal(xxxxxx.xxx)
f10.3
ORGN
All
space 443-452
decimal(xxxxxx.xxx)
f10.3
ORGP
All
space 453-462
decimal(xxxxxx.xxx)
f10.3
SEDP
All
space 463-472
decimal(xxxxxx.xxx)
f10.3
NSURQ
All
space 473-482
decimal(xxxxxx.xxx)
f10.3
NLATQ
All
space 483-492
decimal(xxxxxx.xxx)
f10.3
NO3L
All
space 493-502
decimal(xxxxxx.xxx)
f10.3
NO3GW
All
space 503-512
decimal(xxxxxx.xxx)
f10.3
SOLP
All
space 513-522
decimal(xxxxxx.xxx)
f10.3
P_GW
All
space 523-532
decimal(xxxxxx.xxx)
f10.3
W_STRS
All
space 533-542
decimal(xxxxxx.xxx)
f10.3
TMP_STRS
All
space 543-552
decimal(xxxxxx.xxx)
f10.3
181
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
N_STRS
All
space 553-562
decimal(xxxxxx.xxx)
f10.3
P_STRS
All
space 563-572
decimal(xxxxxx.xxx)
f10.3
BIOM
All
space 573-582
decimal(xxxxxx.xxx)
f10.3
LAI
All
space 583-592
decimal(xxxxxx.xxx)
f10.3
YLD
All
space 593-602
decimal(xxxxxx.xxx)
f10.3
BACTP
All
space 603-612
decimal(xxxxxx.xxx)
f10.3
BACTLP
All
space 613-622
decimal(xxxxxx.xxx)
f10.3
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
183
44.4 SUBBASIN OUTPUT FILE (.BSB) The subbasin output file contains summary information for each of the subbasins in the watershed. The reported values for the different variables are the total amount or weighted average of all HRUs within the subbasin. The subbasin output file is written in spreadsheet format. Following is a brief description of the output variables in the subbasin output file.
Variable name
Definition
SUB
Subbasin number.
GIS
GIS code reprinted from watershed configuration file (.fig). See explanation of subbasin command.
MON
Daily time step: julian date Monthly time step: the month (1-12) Annual time step: four-digit year Average annual summary lines: total number of years averaged together
AREA
Area of the subbasin (km2).
PRECIP
Total amount of precipitation falling on the subbasin during time step (mm H2O).
SNOMELT
Amount of snow or ice melting during time step (waterequivalent mm H2O).
PET
Potential evapotranspiration from the subbasin during the time step (mm H2O).
ET
Actual evapotranspiration from the subbasin during the time step (mm).
SW
Soil water content (mm). Amount of water in the soil profile at the end of the time period.
184
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PERC
Water that percolates past the root zone during the time step (mm). There is potentially a lag between the time the water leaves the bottom of the root zone and reaches the shallow aquifer. Over a long period of time, this variable should equal groundwater percolation.
SURQ
Surface runoff contribution to streamflow during time step (mm H2O).
GW_Q
Groundwater contribution to streamflow (mm). Water from the shallow aquifer that returns to the reach during the time step.
WYLD
Water yield (mm H2O). The net amount of water that leaves the subbasin and contributes to streamflow in the reach during the time step. (WYLD = SURQ + LATQ + GWQ – TLOSS – pond abstractions)
SYLD
Sediment yield (metric tons/ha). Sediment from the subbasin that is transported into the reach during the time step.
ORGN
Organic N yield (kg N/ha). Organic nitrogen transported out of the subbasin and into the reach during the time step.
ORGP
Organic P yield (kg P/ha). Organic phosphorus transported with sediment into the reach during the time step.
NSURQ
NO3 in surface runoff (kg N/ha). Nitrate transported by the surface runoff into the reach during the time step.
SOLP
Soluble P yield (kg P/ha). Phosphorus that is transported by surface runoff into the reach during the time step.
SEDP
Mineral P yield (kg P/ha). Mineral phosphorus attached to sediment that is transported by surface runoff into the reach during the time step.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
The format of the subbasin output file (.bsb) is: Variable name
Line #
Position
Format
F90 Format
SUB
All
space 7-10
4-digit integer
i4
GIS
All
space 12-19
8-digit integer
i8
MON
All
space 21-24
4-digit integer
i4
AREA
All
space 25-34
decimal(xxxxxx.xxx)
f10.3
PRECIP
All
space 35-44
decimal(xxxxxx.xxx)
f10.3
SNOMELT
All
space 45-54
decimal(xxxxxx.xxx)
f10.3
PET
All
space 55-64
decimal(xxxxxx.xxx)
f10.3
ET
All
space 65-74
decimal(xxxxxx.xxx)
f10.3
SW
All
space 75-84
decimal(xxxxxx.xxx)
f10.3
PERC
All
space 85-94
decimal(xxxxxx.xxx)
f10.3
SURQ
All
space 95-104
decimal(xxxxxx.xxx)
f10.3
GW_Q
All
space 105-114
decimal(xxxxxx.xxx)
f10.3
WYLD
All
space 115-124
decimal(xxxxxx.xxx)
f10.3
SYLD
All
space 125-134
decimal(xxxxxx.xxx)
f10.3
ORGN
All
space 135-144
decimal(xxxxxx.xxx)
f10.3
ORGP
All
space 145-154
decimal(xxxxxx.xxx)
f10.3
NSURQ
All
space 155-164
decimal(xxxxxx.xxx)
f10.3
SOLP
All
space 165-174
decimal(xxxxxx.xxx)
f10.3
SEDP
All
space 175-184
decimal(xxxxxx.xxx)
f10.3
185
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SWAT USER'S MANUAL, VERSION 2000
44.5 MAIN CHANNEL OUTPUT FILE (.RCH) The main channel output file contains summary information for each routing reach in the watershed. The file is written in spreadsheet format. Following is a brief description of the output variables in the .rch file. Variable name
Definition
RCH
Reach number. The reach number is also the hydrograph number of the subbasin as defined in the .fig file.
GIS
GIS number reprinted from watershed configuration (.fig) file. See explanation of subbasin command.
MON
Daily time step: the julian date Monthly time step: the month (1-12) Annual time step: 4-digit year Average annual summary lines: number of years averaged together
AREA
Area drained by reach (km2).
FLOW_IN
Average daily streamflow into reach during time step (m3/s).
FLOW_OUT
Average daily streamflow out of reach during time step (m3/s).
EVAP
Average daily rate of water loss from reach by evaporation during time step (m3/s).
TLOSS
Average daily rate of water loss from reach by transmission through the streambed during time step (m3/s).
SED_IN
Sediment transported with water into reach during time step (metric tons).
SED_OUT
Sediment transported with water out of reach during time step (metric tons).
SEDCONC
Concentration of sediment in reach during time step (mg/L).
ORGN_IN
Organic nitrogen transported with water into reach during time step (kg N).
ORGN_OUT
Organic nitrogen transported with water out of reach during time step (kg N).
ORGP_IN
Organic phosphorus transported with water into reach during time step (kg P).
ORGP_OUT
Organic phosphorus transported with water out of reach during time step (kg P).
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
187
Variable name
Definition
NO3_IN
Nitrate transported with water into reach during time step (kg N).
NO3_OUT
Nitrate transported with water out of reach during time step (kg N).
NH4_IN
Ammonium transported with water into reach during time step (kg N).
NH4_OUT
Ammonium transported with water out of reach during time step (kg N).
NO2_IN
Nitrite transported with water into reach during time step (kg N).
NO2_OUT
Nitrite transported with water out of reach during time step (kg N).
MINP_IN
Mineral phosphorus transported with water into reach during time step (kg P).
MINP_OUT
Mineral phosphorus transported with water out of reach during time step (kg P).
ALGAE_IN
Algal biomass transported with water into reach during time step (kg).
ALGAE_OUT
Algal biomass transported with water out of reach during time step (kg).
CBOD_IN
Carbonaceous biochemical oxygen demand of material transported into reach during time step (kg O2).
CBOD_OUT
Carbonaceous biochemical oxygen demand of material transported out of reach during time step (kg O2).
DISOX_IN
Amount of dissolved oxygen transported into reach during time step (kg O2).
DISOX_OUT
Amount of dissolved oxygen transported out of reach during time step (kg O2).
While more than one pesticide may be applied to the HRUs, due to the complexity of the pesticide equations only the pesticide listed in .bsn is routed through the stream network. SOLPST_IN
Soluble pesticide transported with water into reach during time step (mg active ingredient)
SOLPST_OUT
Soluble pesticide transported with water out of reach during time step (mg active ingredient).
188
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
SORPST_IN
Pesticide sorbed to sediment transported with water into reach during time step (mg active ingredient).
SORPST_OUT
Pesticide sorbed to sediment transported with water out of reach during time step (mg active ingredient).
REACTPST
Loss of pesticide from water by reaction during time step (mg active ingredient).
VOLPST
Loss of pesticide from water by volatilization during time step (mg active ingredient).
SETTLPST
Transfer of pesticide from water to river bed sediment by settling during time step (mg active ingredient).
RESUSP_PST
Transfer of pesticide from river bed sediment to water by resuspension during time step (mg active ingredient).
DIFFUSEPST
Transfer of pesticide from water to river bed sediment by diffusion during time step (mg active ingredient).
REACBEDPST
Loss of pesticide from river bed sediment by reaction during time step (mg active ingredient).
BURYPST
Loss of pesticide from river bed sediment by burial during time step (mg active ingredient).
BED_PST
Pesticide in river bed sediment during time step (mg active ingredient).
BACTP_OUT
Number of persistent bacteria transported out of reach during time step.
BACTLP_OUT
Number of less persistent bacteria transported out of reach during time step.
CMETAL#1
Conservative metal #1 transported out of reach (kg).
CMETAL#2
Conservative metal #2 transported out of reach (kg).
CMETAL#3
Conservative metal #3 transported out of reach (kg).
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
The format of the main channel output file (.rch) is: Variable name
Line #
Position
Format
F90 Format
RCH
All
space 7-10
4-digit integer
i4
GIS
All
space 12-19
8-digit integer
i8
MON
All
space 21-25
5-digit integer
i5
AREA
All
space 26-37
exponential
e12.4
FLOW_IN
All
space 38-49
exponential
e12.4
FLOW_OUT
All
space 50-61
exponential
e12.4
EVAP
All
space 62-73
exponential
e12.4
TLOSS
All
space 74-85
exponential
e12.4
SED_IN
All
space 86-97
exponential
e12.4
SED_OUT
All
space 98-109
exponential
e12.4
SEDCONC
All
space 110-121
exponential
e12.4
ORGN_IN
All
space 122-133
exponential
e12.4
ORGN_OUT
All
space 134-145
exponential
e12.4
ORGP_IN
All
space 146-157
exponential
e12.4
ORGP_OUT
All
space 158-169
exponential
e12.4
NO3_IN
All
space 170-181
exponential
e12.4
NO3_OUT
All
space 182-193
exponential
e12.4
NH4_IN
All
space 194-205
exponential
e12.4
NH4_OUT
All
space 206-217
exponential
e12.4
NO2_IN
All
space 218-229
exponential
e12.4
NO2_OUT
All
space 230-241
exponential
e12.4
MINP_IN
All
space 242-253
exponential
e12.4
MINP_OUT
All
space 254-265
exponential
e12.4
CHLA_IN
All
space 266-277
exponential
e12.4
CHLA_OUT
All
space 278-289
exponential
e12.4
CBOD_IN
All
space 290-301
exponential
e12.4
CBOD_OUT
All
space 302-313
exponential
e12.4
DISOX_IN
All
space 314-325
exponential
e12.4
DISOX_OUT
All
space 326-337
exponential
e12.4
SOLPST_IN
All
space 338-349
exponential
e12.4
SOLPST_OUT
All
space 350-361
exponential
e12.4
SORPST_IN
All
space 362-373
exponential
e12.4
SORPST_OUT
All
space 374-385
exponential
e12.4
REACTPST
All
space 386-397
exponential
e12.4
189
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
VOLPST
All
space 398-409
exponential
e12.4
SETTLPST
All
space 410-421
exponential
e12.4
RESUSP_PST
All
space 422-433
exponential
e12.4
DIFFUSEPST
All
space 434-445
exponential
e12.4
REACBEDPST
All
space 446-457
exponential
e12.4
BURYPST
All
space 458-469
exponential
e12.4
BED_PST
All
space 470-481
exponential
e12.4
BACTP_OUT
All
space 482-493
exponential
e12.4
BACTLP_OUT
All
space 494-505
exponential
e12.4
CMETAL#1
All
space 506-517
exponential
e12.4
CMETAL#2
All
space 518-529
exponential
e12.4
CMETAL#3
All
space 530-541
exponential
e12.4
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
191
44.6 HRU IMPOUNDMENT OUTPUT FILE (.WTR) The HRU impoundment output file contains summary information for ponds, wetlands and depressional/impounded areas in the HRUs. The file is written in spreadsheet format. Following is a brief description of the output variables in the HRU impoundment output file. Variable name
Definition
LULC
Four letter character code for the cover/plant on the HRU. (code from crop.dat file)
HRU
Hydrologic response unit number
GIS
GIS code reprinted from watershed configuration file (.fig). See explanation of subbasin command.
SUB
Topographically-defined subbasin to which the HRU belongs.
MGT
Management number. This is pulled from the management (.mgt) file. Used by the SWAT/GRASS interface to allow development of output maps by landuse/management type.
MON
Daily time step: the julian date Monthly time step: the month (1-12) Annual time step: year Average annual summary lines: total number of years averaged together
AREA
Drainage area of the HRU (km2).
PNDPCP
Precipitation falling directly on the pond during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
PND_IN
Pond inflow (mm H2O). Surface runoff entering the pond during the time step. The depth of water is the volume divided by the area of the HRU.
PSED_I
Pond sediment inflow (metric tons/ha). Sediment transported into the pond during the time step. The loading is the mass divided by the area of the HRU.
PNDEVP
Evaporation from the pond surface during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
192
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PNDSEP
Water that seeps through the bottom of the pond and recharges the shallow aquifer during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
PND_OUT
Pond outflow (mm H2O). Water leaving the pond and entering the reach during the time step. The depth of water is the volume divided by the area of the HRU.
PSED_O
Pond sediment outflow (metric tons/ha). Sediment transported out of the pond and entering the reach during the time step. . The loading is the mass divided by the area of the HRU.
PNDVOL
Volume of water in pond at end of time step (m3 H2O).
PNDORGN
Concentration of organic N in pond at end of time step (mg N/L or ppm).
PNDNO3
Concentration of nitrate in pond at end of time step (mg N/L or ppm).
PNDORGP
Concentration of organic P in pond at end of time step (mg P/L or ppm).
PNDMINP
Concentration of mineral P in pond at end of time step (mg P/L or ppm).
PNDCHLA
Concentration of chlorophyll-a in pond at end of time step (mg chl-a/L or ppm).
PNDSECI
Secchi-disk depth of pond at end of time step (m).
WETPCP
Precipitation falling directly on the wetland during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
WET_IN
Wetland inflow (mm H2O). Surface runoff entering the wetland during the time step. The depth of water is the volume divided by the area of the HRU.
WSED_I
Wetland sediment inflow (metric tons/ha). Sediment transported into the wetland during the time step. The loading is the mass divided by the area of the HRU.
WETEVP
Evaporation from the wetland during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
WETSEP
Water that seeps through the bottom of the wetland and recharges the shallow aquifer during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
193
Variable name
Definition
WET_OUT
Wetland outflow (mm H2O). Water leaving the wetland and entering the reach during the time step. The depth of water is the volume divided by the area of the HRU.
WSED_O
Wetland sediment outflow (metric tons/ha). Sediment transported out of the wetland and entering the reach during the time step. . The loading is the mass divided by the area of the HRU.
WET_VOL
Volume of water in wetland at end of time step (m3 H2O).
WETORGN
Concentration of organic N in wetland at end of time step (mg N/L or ppm).
WETNO3
Concentration of nitrate in wetland at end of time step (mg N/L or ppm).
WETORGP
Concentration of organic P in wetland at end of time step (mg P/L or ppm).
WETMINP
Concentration of mineral P in wetland at end of time step (mg P/L or ppm).
WETCHLA
Concentration of chlorophyll-a in wetland at end of time step (mg chl-a/L or ppm).
WETSECI
Secchi-disk depth of wetland at end of time step (m).
POTPCP
Precipitation falling directly on the pothole during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
POT_IN
Pothole inflow (mm H2O). Surface runoff entering the pothole during the time step. The depth of water is the volume divided by the area of the HRU.
OSED_I
Pothole sediment inflow (metric tons/ha). Sediment transported into the pothole during the time step. The loading is the mass divided by the area of the HRU.
POTEVP
Evaporation from the pothole during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
POTSEP
Water that seeps through the bottom of the pothole and enters the underlying soil during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
POT_OUT
Pothole outflow (mm H2O). Water leaving the pothole and entering the reach during the time step. The depth of water is the volume divided by the area of the HRU.
194
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
OSED_O
Pothole sediment outflow (metric tons/ha). Sediment transported out of the pothole and entering the reach during the time step. . The loading is the mass divided by the area of the HRU.
POTVOL
Volume of water in pothole at end of time step (m3 H2O).
POT_SA
Surface area of pothole at end of time step (ha).
HRU_SURQ
Surface runoff contribution to streamflow in the main channel from entire HRU during the time step (mm H2O).
PLANT_ET
Amount of water removed by transpiration from plants during the time step (mm H2O).
SOIL_ET
Amount of water removed by evaporation from the soil during the time step (mm H2O).
The format of the HRU impoundment output file (.wtr) is: Variable name
Line #
Position
Format
F90 Format
LULC
All
space 1-4
character
a4
HRU
All
space 5-8
4-digit integer
i4
GIS
All
space 10-17
8-digit integer
i8
SUB
All
space 19-22
4-digit integer
i4
MGT
All
space 24-27
4-digit integer
i4
MON
All
space 29-32
4-digit integer
i4
AREA
All
space 33-42
decimal(xxxxxx.xxx)
f10.3
PNDPCP
All
space 43-52
decimal(xxxxxx.xxx)
f10.3
PND_IN
All
space 53-62
decimal(xxxxxx.xxx)
f10.3
PSED_I
All
space 63-72
decimal(xxxxxx.xxx)
f10.3
PNDEVP
All
space 73-82
decimal(xxxxxx.xxx)
f10.3
PNDSEP
All
space 83-92
decimal(xxxxxx.xxx)
f10.3
PND_OUT
All
space 93-102
decimal(xxxxxx.xxx)
f10.3
PSED_O
All
space 103-112
decimal(xxxxxx.xxx)
f10.3
PNDVOL
All
space 113-122
exponential
e10.4
PNDORGN
All
space 123-132
decimal(xxxxxx.xxx)
f10.3
PNDNO3
All
space 133-142
decimal(xxxxxx.xxx)
f10.3
PNDORGP
All
space 143-152
decimal(xxxxxx.xxx)
f10.3
PNDMINP
All
space 153-162
decimal(xxxxxx.xxx)
f10.3
PNDCHLA
All
space 163-172
decimal(xxxxxx.xxx)
f10.3
PNDSECI
All
space 173-182
decimal(xxxxxx.xxx)
f10.3
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES Variable name
Line #
Position
Format
F90 Format
WETPCP
All
space 183-192
decimal(xxxxxx.xxx)
f10.3
WET_IN
All
space 193-202
decimal(xxxxxx.xxx)
f10.3
WSED_I
All
space 203-212
decimal(xxxxxx.xxx)
f10.3
WETEVP
All
space 213-222
decimal(xxxxxx.xxx)
f10.3
WETSEP
All
space 223-232
decimal(xxxxxx.xxx)
f10.3
WET_OUT
All
space 233-242
decimal(xxxxxx.xxx)
f10.3
WSED_O
All
space 243-252
decimal(xxxxxx.xxx)
f10.3
WET_VOL
All
space 253-262
exponential
e10.4
WETORGN
All
space 263-272
decimal(xxxxxx.xxx)
f10.3
WETNO3
All
space 273-282
decimal(xxxxxx.xxx)
f10.3
WETORGP
All
space 283-292
decimal(xxxxxx.xxx)
f10.3
WETMINP
All
space 293-302
decimal(xxxxxx.xxx)
f10.3
WETCHLA
All
space 303-312
decimal(xxxxxx.xxx)
f10.3
WETSECI
All
space 313-322
decimal(xxxxxx.xxx)
f10.3
POTPCP
All
space 323-332
decimal(xxxxxx.xxx)
f10.3
POT_IN
All
space 333-342
decimal(xxxxxx.xxx)
f10.3
OSED_I
All
space 343-352
decimal(xxxxxx.xxx)
f10.3
POTEVP
All
space 353-362
decimal(xxxxxx.xxx)
f10.3
POTSEP
All
space 363-372
decimal(xxxxxx.xxx)
f10.3
POT_OUT
All
space 373-382
decimal(xxxxxx.xxx)
f10.3
OSED_O
All
space 383-392
decimal(xxxxxx.xxx)
f10.3
POTVOL
All
space 393-402
exponential
e10.4
POT_SA
All
space 403-412
decimal(xxxxxx.xxx)
f10.3
HRU_SURQ
All
space 413-422
decimal(xxxxxx.xxx)
f10.3
PLANT_ET
All
space 423-432
decimal(xxxxxx.xxx)
f10.3
SOIL_ET
All
space 433-442
decimal(xxxxxx.xxx)
f10.3
195
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SWAT USER'S MANUAL, VERSION 2000
44.7 RESERVOIR OUTPUT FILE (.RSV) The reservoir output file contains summary information for reservoirs in the watershed. The file is written in spreadsheet format. Following is a brief description of the output variables in the reservoir output file. Variable name
Definition
RES
Reservoir number (assigned in .fig file)
MON
Daily time step: the julian date Monthly time step: the month (1-12) Annual time step: four-digit year
VOLUME
Volume of water in reservoir at end of time step (m3 H2O).
FLOW_IN
Average flow into reservoir during time step (m3/s H2O).
FLOW_OUT
Average flow out of reservoir during time step (m3/s H2O).
PRECIP
Precipitation falling directly on the reservoir during the time step (m3 H2O).
EVAP
Evaporation from the reservoir during the time step (m3 H2O).
SEEPAGE
Water that seeps through the bottom of the reservoir and enters the shallow aquifer during the time step (m3 H2O).
SED_IN
Reservoir sediment inflow (metric tons). Sediment transported into the reservoir during the time step.
SED_OUT
Reservoir sediment outflow (metric tons). Sediment transported out of the reservoir during the time step.
ORGN_IN
Amount of organic nitrogen transported into reservoir during the time step (kg N).
ORGN_OUT
Amount of organic nitrogen transported out of reservoir during the time step (kg N).
ORGP_IN
Amount of organic phosphorus transported into reservoir during the time step (kg P).
ORGP_OUT
Amount of organic phosphorus transported out of reservoir during the time step (kg P).
NO3_IN
Amount of nitrate transported into reservoir during the time step (kg N).
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
197
Variable name
Definition
NO3_OUT
Amount of nitrate transported out of reservoir during the time step (kg N).
NO2_IN
Amount of nitrite transported into reservoir during the time step (kg N).
NO2_OUT
Amount of nitrite transported out of reservoir during the time step (kg N).
NH3_IN
Amount of ammonia transported into reservoir during the time step (kg N).
NH3_OUT
Amount of ammonia transported out of reservoir during the time step (kg N).
MINP_IN
Amount of mineral phosphorus transported into reservoir during the time step (kg P).
MINP_OUT
Amount of mineral phosphorus transported out of reservoir during the time step (kg P).
CHLA_IN
Amount of chlorophyll a transported into reservoir during the time step (kg chla).
CHLA_OUT
Amount of chlorophyll a transported out of reservoir during the time step (kg chla).
SECCHIDEPTH
Secchi-disk depth of reservoir at end of time step (m).
PEST_IN
Amount of pesticide transported into reservoir during the time step (mg pesticide active ingredient).
REACTPST
Loss of pesticide from water by reaction during time step (mg active ingredient).
VOLPST
Loss of pesticide from water by volatilization during time step (mg active ingredient).
SETTLPST
Transfer of pesticide from water to reservoir bed sediment by settling during time step (mg active ingredient).
RESUSP_PST
Transfer of pesticide from reservoir bed sediment to water by resuspension during time step (mg active ingredient).
DIFFUSEPST
Transfer of pesticide from water to reservoir bed sediment by diffusion during time step (mg active ingredient).
REACBEDPST
Loss of pesticide from reservoir bed sediment by reaction during time step (mg active ingredient).
BURYPST
Loss of pesticide from reservoir sediment by burial during time step (mg active ingredient).
198
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PEST_OUT
Amount of pesticide transported out of reservoir during the time step (mg pesticide active ingredient).
PSTCNCW
Average concentration of pesticide in reservoir water during time step (mg active ingredient/m3 H2O or ppb).
PSTCNCB
Average concentration of pesticide in reservoir bed sediment during time step (mg active ingredient/m3 H2O or ppb).
The format of the reservoir output file (.rsv) is: Variable name
Line #
Position
Format
F90 Format
RES
All
space 7-14
integer
i8
MON
All
space 16-19
integer
i4
VOLUME
All
space 20-31
exponential
e12.4
FLOW_IN
All
space 32-43
exponential
e12.4
FLOW_OUT
All
space 44-55
exponential
e12.4
PRECIP
All
space 56-67
exponential
e12.4
EVAP
All
space 68-79
exponential
e12.4
SEEPAGE
All
space 80-91
exponential
e12.4
SED_IN
All
space 92-103
exponential
e12.4
SED_OUT
All
space 104-115
exponential
e12.4
ORGN_IN
All
space 116-127
exponential
e12.4
ORGN_OUT
All
space 128-139
exponential
e12.4
ORGP_IN
All
space 140-151
exponential
e12.4
ORGP_OUT
All
space 152-163
exponential
e12.4
NO3_IN
All
space 164-175
exponential
e12.4
NO3_OUT
All
space 176-187
exponential
e12.4
NO2_IN
All
space 188-199
exponential
e12.4
NO2_OUT
All
space 200-211
exponential
e12.4
NH3_IN
All
space 212-223
exponential
e12.4
NH3_OUT
All
space 224-235
exponential
e12.4
MINP_IN
All
space 236-247
exponential
e12.4
MINP_OUT
All
space 248-259
exponential
e12.4
CHLA_IN
All
space 260-271
exponential
e12.4
CHLA_OUT
All
space 272-283
exponential
e12.4
SECCHIDEPTH
All
space 284-295
exponential
e12.4
PEST_IN
All
space 296-307
exponential
e12.4
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES Variable name
Line #
Position
Format
F90 Format
REACTPST
All
space 308-319
exponential
e12.4
VOLPST
All
space 320-331
exponential
e12.4
SETTLPST
All
space 332-343
exponential
e12.4
RESUSP_PST
All
space 344-355
exponential
e12.4
DIFFUSEPST
All
space 356-367
exponential
e12.4
REACBEDPST
All
space 368-379
exponential
e12.4
BURYPST
All
space 380-391
exponential
e12.4
PEST_OUT
All
space 392-403
exponential
e12.4
PSTCNCW
All
space 404-415
exponential
e12.4
PSTCNCB
All
space 416-427
exponential
e12.4
199
200
SWAT USER'S MANUAL, VERSION 2000
SWAT MODEL CALIBRATION Calibration of a model run can be divided into several steps: ♦ water balance and stream flow ♦ sediment ♦ nutrients ♦ pesticides
WATER BALANCE AND STREAM FLOW To calibrate the water balance and stream flow you need to have some understanding of the actual conditions occurring in the watershed. Ideally, you have data from a stream gage located within or at the outlet of your watershed. The U.S. Geological Survey maintains a website (http://water.usgs.gov/) with daily records for all stream gages in the U.S. available for downloading.
Calibration for water balance and stream flow is first done for average annual conditions. Once the run is calibrated for average annual conditions, the user can shift to monthly or daily records to fine-tune the calibration. The average annual observed and simulated results should be summarized in a manner similar to the following table:
Actual SWAT
Total Water Yield 200 mm 300 mm
Baseflow 80 mm 20 mm
Surface Flow 120 mm 280 mm
(When calibrating, we usually summarize data as depth of water in millimeters over the drainage area. Feel free to use whatever units you prefer.) If you are calibrating at the watershed outlet, the SWAT values for the table are provided in the .std file. These values are listed in the table titled "Ave Annual Basin Values" located near the end of the file. If you are calibrating a gage located within the watershed, the total water yield can be calculated from the FLOW_OUT variable in the reach (.rch) file. The values for Baseflow and Surface Flow have to be estimated from the HRU output (.sbs) file or the subbasin output file (.bsb). To estimate the contributions by baseflow and streamflow, the average annual values for GWQ, SURQ and WYLD need to be averaged so that an areally weighted value for the drainage area of interest is obtained. The surface flow and baseflow then need to be converted to fractions by dividing by the total water yield (WYLD). These fractions are then multiplied by the total water yield obtained from the reach output file. The values for GWQ and SURQ cannot be used directly because in-stream precipitation, evaporation, transmission losses, etc. will alter the net water yield from that predicted by the WYLD variable in the HRU or Subbasin Output files.
There are a number of methods available for partitioning observed stream flow into fractions contributed by baseflow and surface runoff. If daily steam flow is available, a baseflow filter program can be run which performs this analysis.
I. BASIC WATER BALANCE & TOTAL FLOW CALIBRATION CALIBRATE SURFACE RUNOFF: Step 1: Adjust the curve number (CN2 in .sub or .mgt) until surface runoff is acceptable. Appendix A contains tables of curve number values for a wide variety of land covers. Appendix B contains tables summarizing ranges for the general categories of land cover and lists the land cover category for all plants in the SWAT Land Cover/Plant database. If surface runoff values are still not reasonable after adjusting curve numbers, adjust: -soil available water capacity (±0.04) (SOL_AWC in .sol) and/or -soil evaporation compensation factor (ESCO in .sub). CALIBRATE SUBSURFACE FLOW: Step 2: Once surface runoff is calibrated, compare measured and simulated values of baseflow. If simulated baseflow is too high: -increase the groundwater "revap" coefficient (GW_REVAP in .gw)—the maximum value that GW_REVAP should be set at is 0.20. -decrease the threshold depth of water in the shallow aquifer for "revap" to occur (REVAPMN in .gw)—the minimum value that REVAPMN should be set at is 0.0. -increase the threshold depth of water in the shallow aquifer required for base flow to occur (GWQMN in .gw)—the maximum value that GWQMN should be set at is left to user discretion. If simulated baseflow is too low, check the movement of water into the aquifer. If groundwater recharge (GWQ in .sbs or .bsb) is greater than or equal to the desired baseflow: -decrease the groundwater "revap" coefficient (GW_REVAP in .gw)—the minimum value that GW_REVAP should be set at is 0.02. -increase the threshold depth of water in the shallow aquifer for "revap" to occur (REVAPMN in .gw). -decrease the threshold depth of water in the shallow aquifer required for base flow to occur (GWQMN in .gw)—the minimum value that GWQMN should be set at is 0.0. Step 3: Repeat steps 1 and 2 until values are acceptable. It may take several reiterations to get the surface runoff and baseflow correct. II. TEMPORAL FLOW CALIBRATION
Flow
Once average annual and annual surface runoff and baseflow are realistic, the temporal flow should look reasonable as well. A few problems that may still be present include:
Observed Simulated
Time (days)
1) Peaks are reasonable, but the recessions "bottom out": Check the transmission losses/values for channel hydraulic conductivity (CH_K in .rte). The value for channel hydraulic conductivity is an effective hydraulic conductivity for movement of water out of the stream bed. For perennial streams receiving groundwater contribution to flow, the groundwater enters the stream through the sides and bottom of the stream bed, making the effective hydraulic conductivity of the channel beds to water losses equal to zero. The only time the channel hydraulic conductivity would be greater than zero is for ephemeral and transient streams that do not receive continuous groundwater contributions to streamflow.
Flow
A second variable that will affect the shape of the hydrograph is the baseflow alpha factor (ALPHA_BF in .gw).
Observed Simulated
Time (days)
2) In snow melt months, the peaks are too high and recessions are too low: Check the values for maximum and minimum melt rates for snow (SMFMX and SMFMN in .bsn). These values may need to be lowered. Another variable that will impact snow melt is the temperature lapse rate (TLAPS in .sub). These values may need to be increased. Finally, the baseflow alpha factor may need to be modified (ALPHA_BF in .gw).
III. SPATIAL FLOW CALIBRATION If you are calibrating a watershed with multiple stream gages, calibrate streamflow for the gage furthest upstream. Once that gage is calibrated, move downstream to the next gage and calibrate for that area. It is important that, as you calibrate downstream gages, you do not change parameters within the files associated with the drainage area of the upstream gages already calibrated.
SEDIMENT There are two sources of sediment in the SWAT simulation: loadings from HRUs/subbasins and channel degradation/deposition. Once the ratio of surface runoff to baseflow contribution to streamflow is being simulated correctly, the sediment contribution (loadings from HRUs/subbasins) should be close to measured values. In most situations, the user will probably have little information about channel degradation/deposition. For those unable to go out and assess the channel, we suggest that you adjust the loadings from the subbasins until they look reasonable and then assume that the remaining difference between actual and observed is due to channel degradation/deposition. The average annual observed and simulated results should be summarized in a manner similar to the following table. A more detailed table which contains the loadings by land use on a given soil type may be used also.
Loadings from mixed forest Loadings from bermuda pasture Loadings from range Loading from HRUs/subbasins Amount of sediment leaving reach
Sediment 187 metric tons/yr 354 metric tons/yr 1459 metric tons/yr 2,000 metric tons/yr 2,873 metric tons/yr
Actual
2,321 metric tons/yr
0.14 metric tons/ha/yr 0.23 metric tons/ha/yr 0.35 metric tons/ha/yr 0.28 metric tons/ha/yr
Sediment loadings from the HRUs/subbasins can be calculated by summing values for SYLD in either the .sbs or .bsb file. The amount of sediment leaving the reach can be obtained from values reported for SED_OUT in the .rch file.
CHECK RESERVOIR/POND SIMULATION:
Reservoirs and ponds have a big impact on sediment loadings. If the amount of sediment being simulated in the watershed is off, first verify that you are accounting for all the ponds and reservoirs in the watershed and that they are being simulated properly. CALIBRATE SUBBASIN LOADINGS: While surface runoff is the primary factor controlling sediment loadings to the stream, there are a few other variables that affect sediment movement into the stream. 1) Tillage has a great impact on sediment transport. With tillage, plant residue is removed from the surface causing erosion to increase. Verify that the tillage practices are being accurately simulated. 2) USLE equation support practices (P) factor (USLE_P in the .sub file): Verify that you have accurately accounted for contouring and terracing in agricultural areas. In general, agricultural land with a slope greater that 5% will be terraced. 3) USLE equation slope length factor (SLSUBBSN in .sub file): There is usually a large amount of uncertainty in slope length measurements. The slope length will also be affected by support practices used in the HRU. 4) Slope in the HRUs (SLOPE in .sub file): Verify that the slopes given for the subbasin are correct. 5) USLE equation cropping practices (C) factor (USLE_C in crop.dat): In some cases, the minimum C value reported for the plant cover may not be accurate for your area. CALIBRATE CHANNEL DEGRADATION/DEPOSITION: Channel degradation will be significant during extreme storm events and in unstable subbasins. Unstable subbasins are those undergoing a significant change in land use patterns such as urbanization. Variables that affect channel degradation/deposition include: 1) The linear and exponential parameters used in the equation to calculate sediment reentrained in channel sediment routing (SPCON and SPEXP in .bsn file). These variables affect sediment routing in the entire watershed. 2) The channel erodibility factor (CH_EROD in .rte) 3) The channel cover factor (CH_COV in .rte)
NUTRIENTS The nutrients of concern in SWAT are nitrate, soluble phosphorus, organic nitrogen and organic phosphorus. When calibrating for a nutrient, keep in mind that changes made will have an effect all the nutrient levels. Nutrient calibration can be divided into two steps: calibration of nutrient loadings and calibration of in-stream water quality processes.
CALIBRATE NUTRIENT LOADINGS (ALL NUTRIENTS): 1) Check that the initial concentrations of the nutrients in the soil are correct. These are set in the soil chemical input file (.chm) and in the soil input file (.sol): nitrate (SOL_NO3 in .sol) soluble P (SOL_MINP in .chm) organic N (SOL_ORGN in .chm) organic P (SOL_ORGP in .chm) 2) Verify that fertilizer applications are correct. Check amounts and the soil layer that the fertilizer is applied to. The fertilizer may be applied to the top 10mm of soil or incorporated in the first soil layer. The variable FRT_LY1 identifies the fraction of fertilizer applied to the top 10mm of soil. (If this variable is left at zero, the model will set FRT_LY1 = 0.20). 3) Verify that tillage operations are correct. Tillage redistributes nutrients in the soil and will alter the amount available for interaction or transport by surface runoff. 4) Alter the biological mixing efficiency (BIOMIX in .bsn file). Biological mixing acts the same as a tillage operation in that it incorporates residue and nutrients into the soil. This variable controls mixing due to biological activity in the entire watershed. CALIBRATE NUTRIENT LOADINGS (NITRATE): In addition to the variables mentioned above, 1) Modify the nitrogen percolation coefficient (NPERCO in .bsn file) CALIBRATE NUTRIENT LOADINGS (SOLUBLE P): In addition to the variables mentioned in the section for all nutrients, 1) Modify the phosphorus percolation coefficient (PPERCO in .bsn file) 2) Modify the phosphorus soil partitioning coefficient (PHOSKD in .bsn file). CALIBRATE NUTRIENT LOADINGS (ORGANIC N & P): Organics are transported to the stream attached to sediment, so the movement of sediment will greatly impact the movement of organics.
CALIBRATE IN-STREAM NUTRIENT PROCESSES: SWAT includes in-stream nutrient cycling processes as described in the QUAL2E documentation. Variables in the watershed water quality (.wwq) and stream water quality (.swq) files control these processes.
Tables of Runoff Curve Number Values‡ Table 1: Runoff curve numbers for cultivated agricultural lands Cover Hydrologic Soil Group Land Use
Treatment or practice
Fallow
Bare soil Crop residue cover
Row crops
∗
Straight row Straight row w/ residue Contoured Contoured w/ residue Contoured & terraced Contoured & terraced w/ residue
Small grains
Straight row Straight row w/ residue Contoured Contoured w/ residue Contoured & terraced Contoured & terraced w/ residue
Close-seeded or broadcast legumes or rotation
Straight row Contoured Contoured & terraced
‡
Hydrologic condition
A
B
C
D
---Poor
77 76
86 85
91 90
94 93
Good
74
83
88
90
Poor
72
81
88
91
Good
67
78
85
89
Poor
71
80
87
90
Good
64
75
82
85
Poor
70
79
84
88
Good
65
75
82
86
Poor
69
78
83
87
Good
64
74
81
85
Poor
66
74
80
82
Good
62
71
78
81
Poor
65
73
79
81
Good
61
70
77
80
Poor
65
76
84
88
Good
63
75
83
87
Poor
64
75
83
86
Good
60
72
80
84
Poor
63
74
82
85
Good
61
73
81
84
Poor
62
73
81
84
Good
60
72
80
83
Poor
61
72
79
82
Good
59
70
78
81
Poor
60
71
78
81
Good
58
69
77
80
Poor
66
77
85
89
Good
58
72
81
85
Poor
64
75
83
85
Good
55
69
78
83
Poor
63
73
80
83
Good
51
67
76
80
These tables are reproduced from Urban Hydrology for Small Watersheds, USDA Soil Conservation Service Engineering Division, Technical Release 55, June 1986. ∗ Crop residue cover applies only if residue is on at least 5% of the surface throughout the year.
Table 2: Runoff curve numbers for other agricultural lands Cover Hydrologic Soil Group Cover Type Pasture, grassland, or range—continuous forage for grazing
1
Hydrologic condition
A
B
C
D
Poor Fair
68 49
79 69
86 79
89 84
Good
39
61
74
80
Meadow—continuous grass, protected from grazing and generally mowed for hay.
----
30
58
71
78
Brush—brush-weed-grass mixture with brush the major element2
Poor
48
67
77
83
Fair
35
56
70
77
Good
30
48
65
73
Poor
57
73
82
86
Fair
43
65
76
82
Good
32
58
72
79
Poor
45
66
77
83
Woods—grass combination (orchard or tree farm)
3
Woods
Farmsteads—buildings, lanes, driveways, and surrounding lots.
1
Fair
36
60
73
79
Good ----
30 59
55 74
70 82
77 86
Poor: < 50% ground cover or heavily grazed with no mulch Fair: 50 to 75% ground cover and not heavily grazed Good: > 75% ground cover and lightly or only occasionally grazed 2 Poor: < 50% ground cover Fair: 50 to 75% ground cover Good: > 75% ground cover 3 Poor: Forest litter, small trees, and brush are destroyed by heavy grazing or regular burning Fair: Woods are grazed but not burned, and some forest litter covers the soil. Good: Woods are protected from grazing, and litter and brush adequately cover the soil.
Table 3: Runoff curve numbers for urban areas Cover Hydrologic Soil Group
Cover Type
Hydrologic condition
Fully developed urban areas Open spaces (lawns, parks, golf courses, cemeteries, etc.)4
Average % impervious area
A
B
C
D
Poor
68
79
86
89
Fair
49
69
79
84
Good
39
61
74
80
Paved parking lots, roofs, driveways, etc. (excluding right-of-way)
----
98
98
98
98
Paved streets and roads; curbs and storm sewers (excluding right-of-way)
----
98
98
98
98
Paved streets and roads; open ditches (including right-of-way)
----
83
89
92
93
Gravel streets and roads (including right-ofway)
----
76
85
89
91
Dirt streets and roads (including right-of way)
----
72
82
87
89
Impervious areas:
Urban districts: Commercial and business
85%
89
92
94
95
Industrial
72%
81
88
91
93
Residential Districts by average lot size: 1/8 acre (0.05 ha) or less (town houses)
65%
77
85
90
92
1/4 acre (0.10 ha)
38%
61
75
83
87
1/3 acre (0.13 ha)
30%
57
72
81
86
1/2 acre (0.20 ha)
25%
54
70
80
85
1 acre (0.40 ha)
20%
51
68
79
84
2 acres (0.81 ha)
12%
46
65
77
82
77
86
91
94
Developing urban areas: Newly graded areas (pervious areas only, no vegetation)
4
Poor: grass cover < 50% Fair: grass cover 50 to 75% Good: grass cover > 75%
Table 4: Runoff curve numbers for arid and semiarid rangelands Cover Hydrologic Soil Group Cover Type
Hydrologic condition5
Herbaceous—mixture of grass, weeds, and low-growing brush, with brush the minor element.
Poor
Oak-aspen—mountain brush mixture of oak brush, aspen, mountain mahogany, bitter brush, maple, and other brush.
Pinyon-juniper—pinyon, juniper, or both: grass understory.
Sagebrush with grass understory.
Desert shrub—major plants include saltbrush, greasewood, creosotebush, blackbrush, bursage, palo verde, mesquite, and cactus.
5
A
B
C
D
80
87
93
Fair
71
81
89
Good
62
74
85
Poor
66
74
79
Fair
48
57
63
Good
30
41
48
Poor
75
85
89
Fair
58
73
80
Good
41
61
71
Poor
67
80
85
Fair
51
63
70
Good
35
47
55
Poor
63
77
85
88
Fair
55
72
81
86
Good
49
68
79
84
Poor: < 30% ground cover (litter, grass, and brush overstory) Fair: 30 to 70% ground cover Good: > 70% ground cover
Curve Number Calibration Table 1: Guideline runoff curve number ranges Land Cover Category Row crop Small grain/close grown crop Perennial grasses Annual grasses (close-seeded legumes) Range Semiarid/arid range Brush Woods Orchard/tree farm Urban
A 61-72 58-65 30-68 51-66 39-68 39-74 30-48 25-45 32-57 46-89
Hydrologic Soil Group B C 70-81 77-88 69-76 77-84 58-79 71-86 67-77 76-85 61-79 74-86 62-80 74-87 48-67 65-77 55-66 70-77 58-73 72-82 65-92 77-94
D 80-91 80-88 78-89 80-89 80-89 85-93 73-83 77-83 79-86 82-95
APPENDIX A
DATABASES
The following sections describe the source of input for databases included with the model and any assumptions used in compilation of the database. Also, a methodology for appending additional information to the various databases is summarized.
29
30
SWAT USER’S MANUAL, VERSION 2000
A.1 LAND COVER/PLANT GROWTH DATABASE The land cover/plant growth database contains information needed by SWAT to simulate the growth of a particular land cover. The growth parameters in the plant growth database define plant growth under ideal conditions and quantify the impact of some stresses on plant growth. Table A-1 lists all the default plant species and Table A-2 lists all the generic land covers included in the database. When adding a new plant/land cover to the database, a review of existing literature should provide most of the parameter values needed to simulate plant growth. For users that plan to collect the data directly, the following sections briefly describe the methods used to obtain the plant growth parameters needed by SWAT. Table A-1: Plants included in plant growth database. Plant Common Name Code Taxonomic Name Corn Zea mays L. CORN Corn silage Zea mays L. CSIL Zea mays L. saccharata Sweet corn SCRN Tripsacum dactyloides (L.) L. Eastern gamagrass EGAM Sorghum bicolor L. (Moench) Grain sorghum GRSG
Plant type warm season annual warm season annual warm season annual perennial warm season annual
Sorghum hay Johnsongrass Sugarcane Spring wheat Winter wheat
SGHY JHGR SUGC SWHT WWHT
Sorghum bicolor L. (Moench) Sorghum halepense (L.) Pers. Saccharum officinarum L. Triticum aestivum L. Triticum aestivum L.
warm season annual perennial perennial cool season annual cool season annual
Durum wheat Rye Spring barley Oats Rice
DWHT RYE BARL OATS RICE
Triticum durum Desf. Secale cereale L. Hordeum vulgare L. Avena sativa L. Oryza sativa L.
cool season annual cool season annual cool season annual cool season annual warm season annual
Pearl millet Timothy Smooth bromegrass Meadow bromegrass Tall fescue
PMIL TIMO BROS BROM FESC
Pennisetum glaucum L. Phleum pratense L. Bromus inermis Leysser Bromus biebersteinii Roemer & Schultes Festuca arundinacea
warm season annual perennial perennial perennial perennial
Kentucky bluegrass Bermudagrass Crested wheatgrass Western wheatgrass Slender wheatgrass
BLUG BERM CWGR WWGR SWGR
Poa pratensis Cynodon dactylon Agropyron cristatum (L.) Gaertner Agropyron smithii (Rydb.) Gould Agropyron trachycaulum Malte
perennial perennial perennial perennial perennial
APPENDIX A: DATABASES
Italian (annual) ryegrass Russian wildrye Altai wildrye Sideoats grama Big bluestem
Plant Code RYEG RYER RYEA SIDE BBLS
Little bluestem Alamo switchgrass Indiangrass Alfalfa Sweetclover
Common Name
31
Taxonomic Name
Plant type
Lolium multiflorum Lam. Psathyrostachys juncea (Fisch.) Nevski Leymus angustus (Trin.) Pilger Bouteloua curtipendula (Michaux) Torrey Andropogon gerardii Vitman
cool season annual perennial perennial perennial perennial
LBLS SWCH INDN ALFA CLVS
Schizachyrium scoparium (Michaux) Nash Panicum virgatum L. Sorghastrum nutans (L.) Nash Medicago sativa L. Melilotus alba Med.
perennial perennial perennial perennial legume perennial legume
Red clover Alsike clover Soybean Cowpeas Mung bean
CLVR CLVA SOYB CWPS MUNG
Trifolium pratense L. Trifolium hybridum L. Glycine max L., Merr. Vigna sinensis Phaseolus aureus Roxb.
cool season annual legume perennial legume warm season annual legume warm season annual legume warm season annual legume
Lima beans Lentils Peanut Field peas Garden or canning peas
LIMA LENT PNUT FPEA PEAS
Phaseolus lunatus L. Lens esculenta Moench J. Arachis hypogaea L. Pisum arvense L. Pisum sativum L. ssp. sativum
warm season annual legume warm season annual legume warm season annual legume cool season annual legume cool season annual legume
Sesbania Flax Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco
SESB FLAX COTS
Sesbania macrocarpa Muhl [exaltata] Linum usitatissum L. Gossypium hirsutum L.
warm season annual legume cool season annual warm season annual
COTP
Gossypium hirsutum L.
warm season annual
TOBC
Nicotiana tabacum L.
warm season annual
Sugarbeet Potato Sweetpotato Carrot Onion
SGBT POTA SPOT CRRT ONIO
Beta vulgaris (saccharifera) L. Solanum tuberosum L. Ipomoea batatas Lam. Daucus carota L. subsp. sativus (Hoffm.) Arcang. Allium cepa L. var cepa
warm season annual cool season annual warm season annual cool season annual cool season annual
Sunflower Spring canola-Polish Spring canola-Argentine Asparagus Broccoli
SUNF CANP CANA ASPR BROC
Helianthus annuus L. Brassica campestris Brassica napus Asparagus officinalis L. Brassica oleracea L. var italica Plenck.
warm season annual cool season annual cool season annual perennial cool season annual
Cabbage Cauliflower Celery Head lettuce Spinach
CABG CAUF CELR LETT SPIN
Brassica oleracea L. var capitata L. Brassica oleracea L. var botrytis L. Apium graveolens L. var dulce (Mill.) Pers. Lactuca sativa L. var capitata L. Spinacia oleracea L.
perennial cool season annual perennial cool season annual cool season annual
Green beans Cucumber
GRBN CUCM
Phaseolus vulgaris Cucumis sativus L.
warm season annual legume warm season annual
32
SWAT USER’S MANUAL, VERSION 2000
Eggplant Cantaloupe Honeydew melon Watermelon Bell pepper
Plant Code EGGP CANT HMEL WMEL PEPR
Strawberry Tomato Apple Pine Oak Poplar Honey mesquite
Common Name
Taxonomic Name
Plant Type
Solanum melongena L. Cucumis melo L. Cantaloupensis group Cucumis melo L. Inodorus group Citrullus lanatus (Thunb.) Matsum and Nakai Capsicum annuum L. Grossum group
warm season annual warm season annual warm season annual warm season annual warm season annual
STRW TOMA APPL PINE OAK
Fragaria X Ananassa Duchesne. Lycopersicon esculentum Mill. Malus domestica Borkh. Pinus Quercus
perennial warm season annual trees trees trees
POPL MESQ
Populus Prosopis glandulosa Torr. var. glandulosa
trees trees
Table A-2: Generic Land Covers included in database. Plant Name Code Origin of Plant Growth Values Agricultural Land-Generic AGRL use values for Grain Sorghum Agricultural Land-Row Crops AGRR use values for Corn Agricultual Land-Close-grown AGRC use values for Winter Wheat Orchard ORCD use values for Apples use values for Bermudagrass Hay‡ HAY use values for Oak Forest-mixed FRST use values for Oak Forest-deciduous FRSD use values for Pine Forest-evergreen FRSE Wetlands WETL use values for Alamo Switchgrass Wetlands-forested WETF use values for Oak Wetlands-nonforested WETN use values for Alamo Switchgrass use values for Bermudagrass Pasture‡ PAST use values for Bermudagrass Summer pasture SPAS Winter pasture WPAS use values for Fescue Range-grasses RNGE use values for Little Bluestem (LAImax=2.5) Range-brush RNGB use values for Little Bluestem (LAImax=2.0) Range-southwestern US SWRN use values for Little Bluestem (LAImax=1.5) Water∗ WATR
‡
Plant Type warm season annual warm season annual cool season annual trees perennial trees trees trees perennial trees perennial perennial perennial perennial perennial perennial perennial not applicable
The Bermudagrass parameters input for Hay and Pasture are valid only in latitudes less than 35 to 37°. At higher latitudes, Fescue parameters should be used to model generic Hay and Pasture. ∗ Water was included in the plant growth database in order to process USGS map layers in the HUMUS project. This land cover should not be used as a land cover in an HRU. To model water bodies, create ponds, wetlands or reservoirs.
APPENDIX A: DATABASES
33
A.1.1 LAND COVER/PLANT TYPES IN DATABASE When compiling the list of plants in the default database, we attempted to include the most economically important plants as well as those that are widely distributed in the landscape. This list is by no means exhaustive and users may need to add plants to the list. A number of generic land cover types were also compiled to facilitate linkage of land use/land cover maps to SWAT plant categories. Because of the broad nature of the some of the categories, a number of assumptions had to be made when compiling the plant growth parameter values. The user is strongly recommended to use parameters for a specific plant rather than those of the generic land covers any time information about plant types is available for the region being modeled. Plant code (CPNM): The 4-letter codes in the plant growth and urban databases are used by the GIS interfaces to link land use/land cover maps to SWAT plant types. When adding a new plant species or land cover category, the four letter code for the new plant must be unique. Land cover/plant classification (IDC): SWAT groups plants into seven categories: warm season annual legume, cold season annual legume, perennial legume, warm season annual, cold season annual, perennial and trees. (Biannual plants are classified as perennials.) The differences between the categories as modeled by SWAT are summarized in Chapter 17. Plant classifications can be easily found in horticulture books that summarize characteristics for different species. The classifications assigned to the plants in Table A-1 were obtained from Martin et al. (1976) and Bailey (1935).
A.1.2 TEMPERATURE RESPONSES SWAT uses the base temperature (T_BASE) to calculate the number of heat units accrued every day. The minimum or base temperature for plant growth varies with growth stage of the plant. However, this variation is ignored by the model—SWAT uses the same base temperature throughout the growing season. The optimal temperature (T_OPT) is used to calculate temperature stress for the plant during the growing season (temperature stress is the only calculation
34
SWAT USER’S MANUAL, VERSION 2000
in which optimal temperature is used). Chapter 19 reviews the influence of optimal temperature on plant growth. Base temperature is measured by growing plants in growth chambers at several different temperatures. The rate of leaf tip appearance as a function of temperature is plotted. Extrapolating the line to the leaf tip appearance rate of 0.0 leaves/day gives the base or minimum temperature for plant growth. Figure A-1 plots data for corn. (Note that the line intersects the x-axis at 8°C.)
Figure A-1: Rate of leaf tip appearance as a function of temperature for corn.
Optimal temperature for plant growth is difficult to measure directly. Looking at Figure A-1, one might be tempted to select the temperature corresponding to the peak of the plot as the optimal temperature. This would not be correct. The peak of the plot defines the optimal temperature for leaf development—not for plant growth. If an optimal temperature cannot be obtained through a review of literature, use the optimal temperature listed for a plant already in the database with similar growth habits. Review of temperatures for many different plants have provided generic values for base and optimal temperatures as a function of growing season. In situations, where temperature information is unavailable, these values may be
APPENDIX A: DATABASES
35
used. For warm season plants, the generic base temperature is ~8ºC and the generic optimal temperature is ~25ºC. For cool season plants, the generic base temperature is ~0ºC and the generic optimal temperature is ~13ºC. Base and optimal temperatures for the plants included in the database are listed in Table A-3. Table A-3: Temperature parameters for plants included in plant growth database. Plant Common Name Code Tbase Topt Corn 8 25 CORN Corn silage 8 25 CSIL 12 24 Sweet corn SCRN 12 25 Eastern gamagrass EGAM 11 30 Grain sorghum GRSG
Reference (Kiniry et al, 1995) (Kiniry et al, 1995) (Hackett and Carolane, 1982) (Kiniry, personal comm., 2001) (Kiniry et al, 1992a)
Sorghum hay Johnsongrass Sugarcane Spring wheat Winter wheat
SGHY JHGR SUGC SWHT WWHT
11 11 11 0 0
30 30 25 18 18
Durum wheat Rye Spring barley Oats Rice
DWHT RYE BARL OATS RICE
0 0 0 0 10
15 12.5 25 15 25
Pearl millet Timothy Smooth bromegrass Meadow bromegrass Tall fescue
PMIL TIMO BROS BROM FESC
10 8 8 6 0
30 25 25 25 15
(Kiniry et al, 1991) estimated estimated (Kiniry et al, 1995) estimated
Kentucky bluegrass Bermudagrass Crested wheatgrass Western wheatgrass Slender wheatgrass
BLUG BERM CWGR WWGR SWGR
12 12 6 6 8
25 25 25 25 25
(Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry et al, 1995) (Kiniry et al, 1995) estimated
Italian (annual) ryegrass Russian wildrye Altai wildrye Sideoats grama Big bluestem
RYEG RYER RYEA SIDE BBLS
0 0 0 12 12
18 15 15 25 25
estimated (Kiniry et al, 1995) (Kiniry et al, 1995) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001)
Little bluestem Alamo switchgrass Indiangrass Alfalfa Sweetclover
LBLS SWCH INDN ALFA CLVS
12 12 12 4 1
25 25 25 20 15
(Kiniry, personal comm., 2001) (Kiniry et al, 1996) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) estimated
(Kiniry et al, 1992a) (Kiniry et al, 1992a) (Kiniry and Williams, 1994) (Kiniry et al, 1995) (Kiniry et al, 1995) estimated estimated (Kiniry et al, 1995) (Kiniry, personal comm., 2001) (Martin et al, 1976)
36
SWAT USER’S MANUAL, VERSION 2000
Red clover Alsike clover Soybean Cowpeas
Plant Code CLVR CLVA SOYB CWPS
Mung bean
Common Name
Tbase
Topt
1 1 10 14
15 15 25 28
MUNG
15
30
estimated estimated (Kiniry et al, 1992a) (Kiniry et al, 1991; Hackett and Carolane, 1982) (Hackett and Carolane, 1982)
Lima beans Lentils Peanut Field peas Garden or canning peas
LIMA LENT PNUT FPEA PEAS
18 3 14 1 5
26 20 27 15 14
(Hackett and Carolane, 1982) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982) estimated (Hackett and Carolane, 1982)
Sesbania Flax Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco
SESB FLAX COTS
10 5 15
25 22.5 30
estimated estimated (Martin et al, 1976)
COTP
15
30
(Martin et al, 1976)
TOBC
10
25
(Martin et al, 1976)
Sugarbeet Potato Sweetpotato
SGBT POTA SPOT
4 7 14
18 22 24
Carrot Onion
CRRT ONIO
7 7
24 19
(Kiniry and Williams, 1994) (Hackett and Carolane, 1982) (estimated; Hackett and Carolane, 1982) (Kiniry and Williams, 1994) (Hackett and Carolane, 1982; Kiniry and Williams, 1994)
Sunflower
SUNF
6
25
Spring canola-Polish Spring canola-Argentine Asparagus Broccoli
CANP CANA ASPR BROC
5 5 10 4
21 21 24 18
(Kiniry et al, 1992b; Kiniry, personal communication, 2001) (Kiniry et al, 1995) (Kiniry et al, 1995) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982)
Cabbage Cauliflower Celery Head lettuce Spinach
CABG CAUF CELR LETT SPIN
1 5 4 7 4
18 18 22 18 24
(Hackett and Carolane, 1982) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982) (Kiniry and Williams, 1994)
Green beans Cucumber Eggplant Cantaloupe
GRBN CUCM EGGP CANT
10 16 15 15
19 32 26 35
Honeydew melon
HMEL
16
36
(Hackett and Carolane, 1982) (Kiniry and Williams, 1994) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982; Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Watermelon Bell pepper Strawberry Tomato Apple
WMEL PEPR STRW TOMA APPL
18 18 10 10 7
35 27 32 22 20
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982)
Reference
APPENDIX A: DATABASES
Plant Code PINE OAK POPL MESQ
Common Name Pine Oak Poplar Honey mesquite
Tbase
Topt
0 10 10 10
30 30 30 30
37
Reference (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001)
A.1.3 LEAF AREA DEVELOPMENT Leaf area development is a function of the plant’s growing season. Plant growth database variables used to quantify leaf area development are: BLAI, FRGRW1, LAIMX1, FRGRW2, LAIMX2, and DLAI. Figure A-2 illustrates the relationship of the database parameters to the leaf area development modeled by SWAT.
Figure A-2: Leaf area index as a function of fraction of growing season for Alamo switchgrass.
To identify the leaf area development parameters, record the leaf area index and number of accumulated heat units for the plant species throughout the growing season and then plot the results. For best results, several years worth of field data should be collected. At the very minimum, data for two years is recommended. It is important that the plants undergo no water or nutrient stress during the years in which data is collected.
38
SWAT USER’S MANUAL, VERSION 2000
The leaf area index incorporates information about the plant density, so field experiments should either be set up to reproduce actual plant densities or the maximum LAI value for the plant determined from field experiments should be adjusted to reflect plant densities desired in the simulation. Maximum LAI values in the default database correspond to plant densities associated with rainfed agriculture. The leaf area index is calculated by dividing the green leaf area by the land area. Because the entire plant must be harvested to determine the leaf area, the field experiment needs to be designed to include enough plants to accommodate all leaf area measurements made during the year. Although measuring leaf area can be laborious for large samples, there is no intrinsic difficulty in the process. The most common method is to obtain an electronic scanner and feed the harvested green leaves and stems into the scanner. Older methods for estimating leaf area include tracing of the leaves (or weighed subsamples) onto paper, the use of planimeters, the punch disk method of Watson (1958) and the linear dimension method of Duncan and Hesketh (1968). Chapter 17 reviews the methodology used to calculate accumulated heat units for a plant at different times of the year as well as determination of the fraction of total, or potential, heat units that is required for the plant database. Leaf area development parameter values for the plants included in the database are listed in Table A-4 (LAImx = BLAI; frPHU,1 = FRGRW1; frLAI,1 = LAIMX1; frPHU,2 = FRGRW2; frLAI,2 = LAIMX2; frPHU,sen = DLAI). Table A-4: Leaf area development parameters for plants included in plant growth database. Plant Common Name Code LAImx frPHU,1 frLAI,1 frPHU,2 frLAI,2 frPHU,sen Corn 3 0.15 0.05 0.50 0.95 0.70 CORN Corn silage
CSIL
4
0.15
0.05
0.50
0.95
0.70
Sweet corn
SCRN
2.5
0.15
0.05
0.50
0.95
0.50
Eastern gamagrass Grain sorghum
EGAM GRSG
2.5 3
0.05 0.15
0.18 0.05
0.25 0.50
0.90 0.95
0.40 0.64
Sorghum hay
SGHY
4
0.15
0.05
0.50
0.95
0.64
Reference (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; Kiniry and Williams, 1994) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; Kiniry and Bockholt, 1998) (Kiniry, personal comm., 2001; Kiniry and Bockholt, 1998)
APPENDIX A: DATABASES
39
LAImx
frPHU,1
frLAI,1
frPHU,2
frLAI,2
frPHU,sen
Reference
Johnsongrass
Plant Code JHGR
2.5
0.15
0.05
0.57
0.95
0.50
Sugarcane Spring wheat
SUGC SWHT
6 4
0.15 0.15
0.01 0.05
0.50 0.50
0.95 0.95
0.75 0.60
Winter wheat
WWHT
4
0.05
0.05
0.45
0.95
0.50
Durum wheat
DWHT
4
0.15
0.01
0.50
0.95
0.80
(Kiniry, personal comm., 2001; Kiniry et al, 1992a) (Kiniry and Williams, 1994) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal communication, 2001; estimated)
Rye
RYE
4
0.15
0.01
0.50
0.95
0.80
Spring barley
BARL
4
0.15
0.01
0.45
0.95
0.60
Oats Rice
OATS RICE
4 5
0.15 0.30
0.02 0.01
0.50 0.70
0.95 0.95
0.80 0.80
Pearl millet
PMIL
2.5
0.15
0.01
0.50
0.95
0.85
Timothy
TIMO
4
0.15
0.01
0.50
0.95
0.85
Smooth bromegrass
BROS
5
0.15
0.01
0.50
0.95
0.85
Meadow bromegrass
BROM
3
0.45
0.02
0.80
0.95
0.85
Tall fescue
FESC
4
0.15
0.01
0.50
0.95
0.80
Kentucky bluegrass
BLUG
2
0.05
0.05
0.30
0.70
0.35
Bermudagrass Crested wheatgrass
BERM CWGR
4 4
0.05 0.35
0.05 0.02
0.49 0.62
0.95 0.95
0.99 0.85
Western wheatgrass
WWGR
4
0.50
0.02
0.89
0.95
0.85
Slender wheatgrass
SWGR
4
0.15
0.01
0.50
0.95
0.85
Italian (annual) ryegrass
RYEG
4
0.20
0.32
0.45
0.95
0.50
Russian wildrye Altai wildrye Sideoats grama Big bluestem Little bluestem
RYER RYEA SIDE BBLS LBLS
3 3 1.7 3 2.5
0.35 0.35 0.05 0.05 0.05
0.02 0.02 0.05 0.10 0.10
0.62 0.62 0.30 0.25 0.25
0.95 0.95 0.70 0.70 0.70
0.80 0.80 0.35 0.35 0.35
(Kiniry et al, 1995) (Kiniry et al, 1995) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001)
Alamo switchgrass
SWCH
6
0.10
0.20
0.20
0.95
0.70
Indiangrass Alfalfa Sweetclover
INDN ALFA CLVS
3 4 4
0.05 0.15 0.15
0.10 0.01 0.01
0.25 0.50 0.50
0.70 0.95 0.95
0.35 0.90 0.75
Red clover
CLVR
4
0.15
0.01
0.50
0.95
0.75
(Kiniry, personal comm., 2001; Kiniry et al, 1996) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated)
Alsike clover
CLVA
4
0.15
0.01
0.50
0.95
0.75
Common Name
(Kiniry, personal communication, 2001; estimated) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal comm, 2001; estimated) (Kiniry, personal comm., 2001) (Kiniry, personal comm, 2001) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated)
(Kiniry, personal comm., 2001; estimated)
40
SWAT USER’S MANUAL, VERSION 2000
LAImx
frPHU,1
frLAI,1
frPHU,2
frLAI,2
frPHU,sen
Soybean
Plant Code SOYB
3
0.15
0.05
0.50
0.95
0.60
Cowpeas
CWPS
4
0.15
0.01
0.50
0.95
0.80
Mung bean
MUNG
4
0.15
0.01
0.50
0.95
0.90
Lima beans Lentils
LIMA LENT
2.5 4
0.10 0.15
0.05 0.02
0.80 0.50
0.95 0.95
0.90 0.90
Peanut
PNUT
4
0.15
0.01
0.50
0.95
0.75
Field peas
FPEA
4
0.15
0.01
0.50
0.95
0.75
Garden or canning peas Sesbania
PEAS SESB
2.5 5
0.10 0.15
0.05 0.01
0.80 0.50
0.95 0.95
0.60 0.90
Flax
FLAX
2.5
0.15
0.02
0.50
0.95
0.90
Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco Sugarbeet Potato
COTS
4
0.15
0.01
0.50
0.95
0.95
COTP
4
0.15
0.01
0.50
0.95
0.95
TOBC SGBT POTA
4.5 5 4
0.15 0.05 0.15
0.05 0.05 0.01
0.50 0.50 0.50
0.95 0.95 0.95
0.70 0.60 0.60
Sweetpotato
SPOT
4
0.15
0.01
0.50
0.95
0.60
Carrot Onion Sunflower
CRRT ONIO SUNF
3.5 1.5 3
0.15 0.15 0.15
0.01 0.01 0.01
0.50 0.50 0.50
0.95 0.95 0.95
0.60 0.60 0.62
Spring canola-Polish
CANP
3.5
0.15
0.02
0.45
0.95
0.50
(Kiniry, personal comm., 2001; estimated) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; Kiniry et al, 1992b) (Kiniry et al, 1995)
Spring canola-Argentine Asparagus Broccoli Cabbage Cauliflower
CANA ASPR BROC CABG CAUF
4.5 4.2 4.2 3 2.5
0.15 0.25 0.25 0.25 0.25
0.02 0.23 0.23 0.23 0.23
0.45 0.40 0.40 0.40 0.40
0.95 0.86 0.86 0.86 0.86
0.50 1.00 1.00 1.00 1.00
(Kiniry et al, 1995) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Celery Head lettuce Spinach Green beans Cucumber
CELR LETT SPIN GRBN CUCM
2.5 4.2 4.2 1.5 1.5
0.25 0.25 0.10 0.10 0.15
0.23 0.23 0.05 0.05 0.05
0.40 0.40 0.90 0.80 0.50
0.86 0.86 0.95 0.95 0.95
1.00 1.00 0.95 0.90 0.60
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Eggplant Cantaloupe Honeydew melon Watermelon Bell pepper
EGGP CANT HMEL WMEL PEPR
3 3 4 1.5 5
0.15 0.15 0.15 0.15 0.15
0.05 0.05 0.05 0.05 0.05
0.50 0.50 0.50 0.50 0.50
0.95 0.95 0.95 0.95 0.95
0.60 0.60 0.60 0.60 0.60
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Common Name
Reference (Kiniry, personal comm., 2001; Kiniry et al, 1992a) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; Kiniry and Williams, 1994)
APPENDIX A: DATABASES
Strawberry Tomato Apple
Plant Code STRW TOMA APPL
Pine Oak
PINE OAK
Poplar Honey mesquite
POPL MESQ
Common Name
LAImx
frPHU,1
frLAI,1
frPHU,2
frLAI,2
frPHU,sen
3 3 4
0.15 0.15 0.10
0.05 0.05 0.15
0.50 0.50 0.50
0.95 0.95 0.75
0.60 0.95 0.99
5 5
0.15 0.05
0.70 0.05
0.25 0.40
0.99 0.95
0.99 0.99
5 1.25
0.05 0.05
0.05 0.05
0.40 0.40
0.95 0.95
0.99 0.99
41
Reference (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, 1998; Kiniry, personal communication, 2001)
A.1.4 ENERGY-BIOMASS CONVERSION Radiation-use efficiency (RUE) quantifies the efficiency of a plant in converting light energy into biomass. Four variables in the plant growth database are used to define the RUE in ideal growing conditions (BIO_E), the impact of reduced vapor pressure on RUE (WAVP), and the impact of elevated CO2 concentration on RUE (CO2HI, BIOEHI). Determination of RUE is commonly performed and a literature review will provide those setting up experiments with numerous examples. The following overview of the methodology used to measure RUE was summarized from Kiniry et al (1998) and Kiniry et al (1999). To calculate RUE, the amount of photosynthetically active radiation (PAR) intercepted and the mass of aboveground biomass is measured several times throughout a plant’s growing season. The frequency of the measurements taken will vary but in general 4 to 7 measurements per growing season are considered to be adequate. As with leaf area determinations, the measurements should be performed on non-stressed plants. Intercepted radiation is measured with a light meter. Whole spectrum and PAR sensors are available and calculations of RUE will be performed differently depending on the sensor used. A brief discussion of the difference between whole spectrum and PAR sensors and the difference in calculations is given in Kiniry (1999). The use of a PAR sensor in RUE studies is strongly encouraged.
42
SWAT USER’S MANUAL, VERSION 2000
When measuring radiation, three to five sets of measurements are taken rapidly for each plant plot. A set of measurements consists of 10 measurements above the leaf canopy, 10 below, and 10 more above. The light measurements should be taken between 10:00 am and 2:00 pm local time. The measurements above and below the leaf canopy are averaged and the fraction of intercepted PAR is calculated for the day from the two values. Daily estimates of the fraction of intercepted PAR are determined by linearly interpolating the measured values. The fraction of intercepted PAR is converted to an amount of intercepted PAR using daily values of incident total solar radiation measured with a standard weather station. To convert total incident radiation to total incident PAR, the daily solar radiation values are multiplied by the percent of total radiation that has a wavelength between 400 and 700 mm. This percent usually falls in the range 45 to 55% and is a function of cloud cover. 50% is considered to be a default value. Once daily intercepted PAR values are determined, the total amount of PAR intercepted by the plant is calculated for each date on which biomass was harvested. This is calculated by summing daily intercepted PAR values from the date of seedling emergence to the date of biomass harvest. To determine biomass production, aboveground biomass is harvested from a known area of land within the plot. The plant material should be dried at least 2 days at 65°C and then weighed. RUE is determined by fitting a linear regression for aboveground biomass as a function of intercepted PAR. The slope of the line is the RUE. Figure A-3 shows
the
plots
of
aboveground
biomass
and
summed
intercepted
photosynthetically active radiation for Eastern gamagrass. (Note that the units for RUE values in the graph, as well as values typically reported in literature, are different from those used by SWAT. To obtain the value used in SWAT, multiply by 10.)
APPENDIX A: DATABASES
43
Figure A-3: Aboveground biomass and summed intercepted photosynthetically active radiation for Eastern gamagrass (from Kiniry et al.,1999).
Stockle and Kiniry (1990) first noticed a relationship between RUE and vapor pressure deficit and were able to explain a large portion of within-species variability in RUE values for sorghum and corn by plotting RUE values as a function of average daily vapor pressure deficit values. Since this first article, a number of other studies have been conducted that support the dependence of RUE on vapor pressure deficit. However, there is still some debate in the scientific community on the validity of this relationship. If the user does not wish to simulate a change in RUE with vapor pressure deficit, the variable WAVP can be set to 0.0 for the plant. To define the impact of vapor pressure deficit on RUE, vapor pressure deficit values must be recorded during the growing seasons that RUE determinations are being made. It is important that the plants are exposed to no other stress than vapor pressure deficit, i.e. plant growth should not be limited by lack of soil water and nutrients. Vapor pressure deficits can be calculated from relative humidity (see Chapter 3) or from daily maximum and minimum temperatures using the technique of Diaz and Campbell (1988) as described by Stockle and Kiniry (1990). The change in RUE with vapor pressure deficit is determined by fitting a
44
SWAT USER’S MANUAL, VERSION 2000
linear regression for RUE as a function of vapor pressure deficit. Figure A-4 shows a plot of RUE as a function of vapor pressure deficit for grain sorghum.
Figure A-4: Response of radiation-use efficiency to mean daily vapor pressure deficit for grain sorghum.
From Figure A-4, the rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit, ∆ruedcl, for sorghum is 8.4×10-1 g⋅MJ-1⋅kPa-1. When RUE is adjusted for vapor pressure deficit, the model assumes the RUE value reported for BIO_E is the radiation-use efficiency at a vapor pressure deficit of 1 kPa. In order to assess the impact of climate change on agricultural productivity, SWAT incorporates equations that adjust RUE for elevated atmospheric CO2 concentrations. Values must be entered for CO2HI and BIOEHI in the plant database whether or not the user plans to simulate climate change. For simulations in which elevated CO2 levels are not modeled, CO2HI should be set to some number greater than 330 ppmv and BIOEHI should be set to some number greater than BIO_E. To obtain radiation-use efficiency values at elevated CO2 levels for plant species not currently in the database, plants should be established in growth chambers set up in the field or laboratory where CO2 levels can be controlled. RUE values are determined using the same methodology described previously.
APPENDIX A: DATABASES
45
Radiation-use efficiency parameter values for the plants included in the database are listed in Table A-5 (RUE = BIO_E; ∆ruedcl = WAVP; RUEhi = BIOEHI; CO2hi = CO2HI).
Table A-5: Biomass production parameters for plants included in plant growth database. Plant Common Name Code RUE ∆ruedcl RUEhi CO2hi Reference Corn 39 7.2 45 660 (Kiniry et al, 1998; Kiniry et al, 1997; Kiniry, CORN personal communication, 2001) (Kiniry et al, 1998; Kiniry et al, 1997; Kiniry, personal communication, 2001) (Kiniry and Williams, 1994; Kiniry et al, 1997; Kiniry, personal communication, 2001) (Kiniry et al, 1999; Kiniry, personal communication, 2001) (Kiniry et al, 1998; Kiniry, personal communication, 2001)
Corn silage
CSIL
39
7.2
45
660
Sweet corn
SCRN
39
7.2
45
660
Eastern gamagrass
EGAM
21
10
58
660
Grain sorghum
GRSG
33.5
8.5
36
660
Sorghum hay
SGHY
33.5
8.5
36
660
Johnsongrass
JHGR
35
8.5
36
660
Sugarcane
SUGC
25
10
33
660
Spring wheat
SWHT
35
8
46
660
Winter wheat
WWHT
30
6
39
660
(Kiniry et al, 1998; Kiniry, personal communication, 2001) (Kiniry et al, 1992a; Kiniry, personal communication, 2001) (Kiniry and Williams, 1994; Kiniry, personal communication, 2001) (Kiniry et al, 1992a; Kiniry, personal communication, 2001; estimated) (Kiniry et al, 1995; estimated)
Durum wheat Rye Spring barley Oats Rice
DWHT RYE BARL OATS RICE
30 35 35 35 22
7 7 7 10 5
45 45 45 45 31
660 660 660 660 660
(estimated) (estimated) (Kiniry et al, 1995; estimated) (Kiniry, personal communication, 2001) (Kiniry et al, 1989; estimated)
Pearl millet Timothy Smooth bromegrass Meadow bromegrass Tall fescue
PMIL TIMO BROS BROM FESC
35 35 35 35 30
8 8 8 8 8
40 45 45 45 39
660 660 660 660 660
(estimated) (estimated) (estimated) (Kiniry et al, 1995; estimated) (estimated)
Kentucky bluegrass Bermudagrass Crested wheatgrass
BLUG BERM CWGR
18 35 35
10 10 8
31 36 38
660 660 660
Western wheatgrass Slender wheatgrass
WWGR SWGR
35 35
8 8
45 45
660 660
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al, 1995; Kiniry, personal communication, 2001) (Kiniry et al, 1995; estimated) (estimated)
Italian (annual) ryegrass Russian wildrye Altai wildrye
RYEG RYER RYEA
30 30 30
6 8 8
39 39 46
660 660 660
Sideoats grama
SIDE
11
10
21
660
(estimated) (Kiniry et al, 1995; estimated) (Kiniry et al, 1995; Kiniry, personal communication, 2001) (Kiniry et al, 1999; Kiniry, personal communication, 2001)
46
SWAT USER’S MANUAL, VERSION 2000 RUE
∆ruedcl
RUEhi
CO2hi
Big bluestem
Plant Code BBLS
14
10
39
660
Little bluestem Alamo switchgrass
LBLS SWCH
34 47
10 8.5
39 54
660 660
Indiangrass Alfalfa
INDN ALFA
34 20
10 10
39 35
660 660
Sweetclover Red clover Alsike clover Soybean
CLVS CLVR CLVA SOYB
25 25 25 25
10 10 10 8
30 30 30 34
660 660 660 660
Cowpeas
CWPS
35
8
39
660
(estimated) (estimated) (estimated) (Kiniry et al, 1992a; Kiniry, personal communication, 2001) (estimated)
Mung bean Lima beans Lentils Peanut Field peas
MUNG LIMA LENT PNUT FPEA
25 25 20 20 25
10 5 10 4 10
33 34 33 25 30
660 660 660 660 660
(estimated) (Kiniry and Williams, 1994; estimated) (estimated) (estimated) (estimated)
Garden or canning peas Sesbania Flax Upland cotton (harvested with stripper) Upland cotton (harvested with picker)
PEAS SESB FLAX COTS
25 50 25 15
5 10 10 3
34 60 33 19
660 660 660 660
(Kiniry and Williams, 1994; estimated) (estimated) (estimated) (estimated)
COTP
15
3
19
660
(estimated)
Tobacco Sugarbeet Potato Sweetpotato Carrot
TOBC SGBT POTA SPOT CRRT
39 30 25 15 30
8 10 14.8 3 10
44 35 30 19 35
660 660 660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Manrique et al, 1991; estimated) (estimated) (Kiniry and Williams, 1994; estimated)
Onion Sunflower
ONIO SUNF
30 46
10 32.3
35 59
660 660
Spring canola-Polish Spring canola-Argentine Asparagus
CANP CANA ASPR
34 34 90
10 10 5
39 40 95
660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry et al, 1992b; Kiniry, personal communication, 2001) (Kiniry et al, 1995; estimated) (Kiniry et al, 1995; estimated) (Kiniry and Williams, 1994; estimated)
Broccoli Cabbage Cauliflower Celery Head lettuce
BROC CABG CAUF CELR LETT
26 19 21 27 23
5 5 5 5 8
30 25 25 30 25
660 660 660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated)
Spinach Green beans Cucumber Eggplant Cantaloupe
SPIN GRBN CUCM EGGP CANT
30 25 30 30 30
5 5 8 8 3
35 34 39 39 39
660 660 660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated)
Common Name
Reference (Kiniry et al, 1999; Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al, 1996; Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
APPENDIX A: DATABASES
Honeydew melon Watermelon Bell pepper Strawberry Tomato
Plant Code HMEL WMEL PEPR STRW TOMA
Apple Pine Oak Poplar Honey mesquite
APPL PINE OAK POPL MESQ
Common Name
47
RUE
∆ruedcl
RUEhi
CO2hi
30 30 30 30 30
3 3 8 8 8
39 39 39 39 39
660 660 660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated)
15 15 15 30 16.1
3 8 8 8 8
20 16 16 31 18
660 660 660 660 660
(estimated) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, 1998; Kiniry, personal comm., 2001)
Reference
A.1.5 STOMATAL CONDUCTANCE Stomatal conductance of water vapor is used in the Penman-Monteith calculations of maximum plant evapotranspiration. The plant database contains three variables pertaining to stomatal conductance that are required only if the Penman-Monteith equations are chosen to model evapotranspiration: maximum stomatal conductance (GSI), and two variables that define the impact of vapor pressure deficit on stomatal conductance (FRGMAX, VPDFR). Körner et al (1979) defines maximum leaf diffusive conductance as the largest value of conductance observed in fully developed leaves of well-watered plants under optimal climatic conditions, natural outdoor CO2 concentrations and sufficient nutrient supply. Leaf diffusive conductance of water vapor cannot be measured directly but can be calculated from measurements of transpiration under known climatic conditions. A number of different methods are used to determine diffusive conductance: transpiration measurements in photosynthesis cuvettes, energy balance measurements or weighing experiments, ventilated diffusion porometers and non-ventilated porometers. Körner (1977) measured diffusive conductance using a ventilated diffusion porometer. To obtain maximum leaf conductance values, leaf conductance is determined between sunrise and late morning until a clear decline or no further increase is observed. Depending on phenology, measurements are taken on at least three bright days in late spring and summer, preferably just after a rainy period.
48
SWAT USER’S MANUAL, VERSION 2000
The means of maximum leaf conductance of 5 to 10 samples each day are averaged, yielding the maximum diffusive conductance for the species. Due to the variation of the location of stomata on plant leaves for different plant species, conductance values should be calculated for the total leaf surface area. Körner et al (1979) compiled maximum leaf diffusive conductance data for 246 plant species. The data for each individual species was presented as well as summarized by 13 morphologically and/or ecologically comparable plant groups. All maximum stomatal conductance values in the plant growth database were based on the data included in Körner et al (1979) (see Table A-6). As with radiation-use efficiency, stomatal conductance is sensitive to vapor pressure deficit. Stockle et al (1992) compiled a short list of stomatal conductance response to vapor pressure deficit for a few plant species. Due to the paucity of data, default values for the second point on the stomatal conductance vs. vapor pressure deficit curve are used for all plant species in the database. The fraction of maximum stomatal conductance (FRGMAX) is set to 0.75 and the vapor pressure deficit corresponding to the fraction given by FRGMAX (VPDFR) is set to 4.00 kPa. If the user has actual data, they should use those values, otherwise the default values are adequate.
A.1.6 CANOPY HEIGHT/ROOT DEPTH Maximum canopy height (CHTMX) is a straightforward measurement. The canopy height of non-stressed plants should be recorded at intervals throughout the growing season. The maximum value recorded is used in the database. To determine maximum rooting depth (RDMX), plant samples need to be grown on soils without an impermeable layer. Once the plants have reached maturity, soil cores are taken for the entire depth of the soil. Each 0.25 m increment is washed and the live plant material collected. Live roots can be differentiated from dead roots by the fact that live roots are whiter and more elastic and have an intact cortex. The deepest increment of the soil core in which live roots are found defines the maximum rooting depth. Table A-6 lists the
APPENDIX A: DATABASES
49
maximum canopy height and maximum rooting depths for plants in the default database. Table A-6: Maximum stomatal conductance ( g l ,mx ), maximum canopy height (hc,mx), maximum root depth (zroot,mx), minimum USLE C factor for land cover (CUSLE,mn). Plant g l,mx Common Name Code hc,mx Corn .0071 2.5 CORN
zroot,mx
CUSLE,mn
Reference
2.0
.20
(Körner et al, 1979; Martin et al, 1976; Kiniry et al, 1995; Kiniry, personal comm., 2001) (Körner et al, 1979; Martin et al, 1976; Kiniry et al, 1995; Kiniry, personal comm., 2001) (Körner et al, 1979, Kiniry and Williams, 1994; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001)
Corn silage
CSIL
.0071
2.5
2.0
.20
Sweet corn
SCRN
.0071
2.5
2.0
.20
Eastern gamagrass
EGAM
.0055
1.7
2.0
.003
Grain sorghum
GRSG
.0050
1.0
2.0
.20
Sorghum hay
SGHY
.0050
1.5
2.0
.20
Johnsongrass Sugarcane
JHGR SUGC
.0048 .0055
1.0 3.0
2.0 2.0
.20 .001
Spring wheat
SWHT
.0056
0.9
2.0
.03
Winter wheat
WWHT
.0056
0.9
1.3
.03
Durum wheat
DWHT
.0056
1.0
2.0
.03
Rye
RYE
.0100
1.0
1.8
.03
Spring barley
BARL
.0083
1.2
1.3
.01
Oats
OATS
.0055
1.5
2.0
.03
Rice
RICE
.0078
0.8
0.9
.03
Pearl millet
PMIL
.0143
3.0
2.0
.20
Timothy Smooth bromegrass
TIMO BROS
.0055 .0025
0.8 1.2
2.0 2.0
.003 .003
Meadow bromegrass
BROM
.0055
0.8
1.3
.003
Tall fescue
FESC
.0055
1.5
2.0
.03
Kentucky bluegrass
BLUG
.0055
0.2
1.4
.003
Bermudagrass
BERM
.0055
0.5
2.0
.003
Crested wheatgrass
CWGR
.0055
0.9
1.3
.003
Western wheatgrass
WWGR
.0083
0.6
1.3
.003
Slender wheatgrass
SWGR
.0055
0.7
2.0
.003
(Körner et al, 1979; Martin et al, 1976; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry et al, 1992a) (Körner et al, 1979; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001; Kiniry et al, 1995) (Körner et al, 1979; estimated; Kiniry, personal comm., 2001) (Körner et al, 1979; estimated; Martin et al, 1976; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry and Williams, 1994; Kiniry et al, 1995) (Körner et al, 1979; Martin et al, 1976; Kiniry, personal comm., 2001) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; Kiniry, personal comm., 2001; estimated) (Körner et al, 1979; estimated) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; estimated; Kiniry et al, 1995) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Martin et al, 1976; Kiniry et al, 1995) (Körner et al, 1979; Martin et al, 1976; Kiniry et al, 1995; estimated) (Körner et al, 1979; estimated)
50
SWAT USER’S MANUAL, VERSION 2000
g l,mx
hc,mx
zroot,mx
CUSLE,mn
Italian (annual) ryegrass Russian wildrye
Plant Code RYEG RYER
.0055 .0065
0.8 1.0
1.3 1.3
.03 .03
Altai wildrye
RYEA
.0055
1.1
1.3
.03
Sideoats grama
SIDE
.0055
0.4
1.4
.003
Big bluestem
BBLS
.0055
1.0
2.0
.003
Little bluestem
LBLS
.0055
1.0
2.0
.003
Alamo switchgrass
SWCH
.0055
2.5
2.2
.003
Indiangrass
INDN
.0055
1.0
2.0
.003
Alfalfa
ALFA
.0100
0.9
3.0
.01
Sweetclover
CLVS
.0055
1.5
2.4
.003
Red clover
CLVR
.0065
0.75
1.5
.003
Alsike clover
CLVA
.0055
0.9
2.0
.003
Soybean Cowpeas Mung bean
SOYB CWPS MUNG
.0071 .0055 .0055
0.8 1.2 1.5
1.7 2.0 2.0
.20 .03 .20
Lima beans
LIMA
.0055
0.6
2.0
.20
Lentils
LENT
.0055
0.55
1.2
.20
Peanut Field peas
PNUT FPEA
.0063 .0055
0.5 1.2
2.0 1.2
.20 .01
Garden or canning peas
PEAS
.0055
0.6
1.2
.20
Sesbania
SESB
.0055
2.0
2.0
.20
Flax
FLAX
.0055
1.2
1.5
.20
Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco
COTS
.0091
1.0
2.5
.20
COTP
.0091
1.0
2.5
.20
TOBC
.0048
1.8
2.0
.20
Sugarbeet
SGBT
.0071
1.2
2.0
.20
Potato
POTA
.0050
0.6
0.6
.20
Common Name
Reference (Körner et al, 1979; estimated) (Körner et al, 1979; estimated; Kiniry et al, 1995) (Körner et al, 1979; Kiniry, personal comm., 2001; Kiniry et al, 1995) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001; Kiniry et al, 1996) (Körner et al, 1979; Kiniry, personal comm., 2001) (Jensen et al, 1990; Martin et al, 1976; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001; Martin et al, 1976; estimated) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; Kiniry et al, 1992a) (Körner et al, 1979; estimated) (Körner et al, 1979; estimated) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Martin et al, 1976; Maynard and Hochmuth, 1997) (Körner et al, 1979; estimated) (Körner et al, 1979; Martin et al, 1976; Maynard and Hochmuth, 1997; estimated) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001; estimated) (Körner et al, 1979; Martin et al, 1976; Jensen et al, 1990; estimated) (Monteith, 1965; Kiniry, personal comm., 2001; Martin et al, 1976) (Monteith, 1965; Kiniry, personal comm., 2001; Martin et al, 1976) (Körner et al, 1979; Martin et al, 1976; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry and Williams, 1994) (Körner et al, 1979; Martin et al, 1976; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994)
APPENDIX A: DATABASES
g l,mx
hc,mx
zroot,mx
CUSLE,mn
Sweetpotato
Plant Code SPOT
.0065
0.8
2.0
.05
Carrot
CRRT
.0065
0.3
1.2
.20
Onion
ONIO
.0065
0.5
0.6
.20
Sunflower
SUNF
.0077
2.5
2.0
.20
Spring canola-Polish
CANP
.0065
0.9
0.9
.20
Spring canola-Argentine
CANA
.0065
1.3
1.4
.20
Asparagus
ASPR
.0065
0.5
2.0
.20
Broccoli
BROC
.0065
0.5
0.6
.20
Cabbage
CABG
.0065
0.5
0.6
.20
Cauliflower
CAUF
.0065
0.5
0.6
.20
Celery
CELR
.0065
0.5
0.6
.20
Head lettuce
LETT
.0025
0.2
0.6
.01
Spinach
SPIN
.0065
0.5
0.6
.20
Green beans
GRBN
.0077
0.6
1.2
.20
Cucumber
CUCM
.0033
0.5
1.2
.03
Eggplant
EGGP
.0065
0.5
1.2
.03
Cantaloupe
CANT
.0065
0.5
1.2
.03
Honeydew melon
HMEL
.0065
0.5
1.2
.03
Watermelon
WMEL
.0065
0.5
2.0
.03
Bell pepper
PEPR
.0053
0.5
1.2
.03
Strawberry
STRW
.0065
0.5
0.6
.03
Common Name
51
Reference (Körner et al, 1979; estimated; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; estimated; Kiniry et al, 1995) (Körner et al, 1979; estimated; Kiniry et al, 1995) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994)
52
SWAT USER’S MANUAL, VERSION 2000
g l,mx
hc,mx
zroot,mx
CUSLE,mn
Tomato
Plant Code TOMA
.0077
0.5
2.0
.03
Apple
APPL
.0071
3.5
2.0
.001
Pine
PINE
.0019
10.0
3.5
.001
Oak
OAK
.0020
6.0
3.5
.001
Poplar
POPL
.0036
7.5
3.5
.001
Honey mesquite
MESQ
.0036
6.0
3.5
.001
Common Name
Reference (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; estimated; Jensen et al, 1990) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001)
A.1.7 PLANT NUTRIENT CONTENT In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: BN(1), BN(2), BN(3), BP(1), BP(2), and BP(3). Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. The plant samples can be sent to testing laboratories to obtain the fraction of nitrogen and phosphorus in the biomass. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured. The fractions of nitrogen and phosphorus for the plants included in the default database are listed in Table A-7.
Table A-7: Nutrient parameters for plants included in plant growth database. Plant Common Name Code frN,1 frN,2 frN,3 frP,1 frP,2 Corn .0470 .0177 .0138 .0048 .0018 CORN Corn silage .0470 .0177 .0138 .0048 .0018 CSIL .0470 .0177 .0138 .0048 .0018 Sweet corn SCRN .0200 .0100 .0070 .0014 .0010 Eastern gamagrass EGAM
frP,3 .0014 .0014 .0014 .0007
Reference (Kiniry et al., 1995) (Kiniry et al., 1995) (Kiniry and Williams, 1994) (Kiniry, personal communication, 2001)
APPENDIX A: DATABASES
53
frN,1
frN,2
frN,3
frP,1
frP,2
frP,3
Grain sorghum Sorghum hay Johnsongrass Sugarcane Spring wheat
Plant Code GRSG SGHY JHGR SUGC SWHT
.0440 .0440 .0440 .0100 .0600
.0164 .0164 .0164 .0040 .0231
.0128 .0128 .0128 .0025 .0134
.0060 .0060 .0060 .0075 .0084
.0022 .0022 .0022 .0030 .0032
.0018 .0018 .0018 .0019 .0019
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al., 1992a) (Kiniry and Williams, 1994) (Kiniry et al., 1992a)
Winter wheat Durum wheat Rye Spring barley Oats
WWHT DWHT RYE BARL OATS
.0663 .0600 .0600 .0590 .0600
.0255 .0231 .0231 .0226 .0231
.0148 .0130 .0130 .0131 .0134
.0053 .0084 .0084 .0057 .0084
.0020 .0032 .0032 .0022 .0032
.0012 .0019 .0019 .0013 .0019
(Kiniry et al., 1995) estimated estimated (Kiniry et al., 1995) (Kiniry, personal communication, 2001)
Rice Pearl millet Timothy Smooth bromegrass Meadow bromegrass
RICE PMIL TIMO BROS BROM
.0500 .0440 .0314 .0400 .0400
.0200 .0300 .0137 .0240 .0240
.0100 .0100 .0103 .0160 .0160
.0060 .0060 .0038 .0028 .0028
.0030 .0022 .0025 .0017 .0017
.0018 .0012 .0019 .0011 .0011
estimated estimated estimated (Kiniry et al., 1995) (Kiniry et al., 1995)
Tall fescue Kentucky bluegrass Bermudagrass Crested wheatgrass Western wheatgrass
FESC BLUG BERM CWGR WWGR
.0560 .0200 .0600 .0300 .0300
.0210 .0100 .0231 .0200 .0200
.0120 .0060 .0134 .0120 .0120
.0099 .0014 .0084 .0020 .0020
.0022 .0010 .0032 .0015 .0015
.0019 .0007 .0019 .0013 .0013
estimated (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al., 1995) (Kiniry et al., 1995)
Slender wheatgrass Italian (annual) ryegrass Russian wildrye Altai wildrye Sideoats grama
SWGR RYEG RYER RYEA SIDE
.0300 .0660 .0226 .0226 .0200
.0200 .0254 .0180 .0180 .0100
.0120 .0147 .0140 .0140 .0060
.0020 .0105 .0040 .0040 .0014
.0015 .0040 .0040 .0040 .0010
.0013 .0024 .0024 .0024 .0007
estimated estimated (Kiniry et al., 1995) (Kiniry et al., 1995) (Kiniry, personal communication, 2001)
Big bluestem Little bluestem Alamo switchgrass Indiangrass Alfalfa
BBLS LBLS SWCH INDN ALFA
.0200 .0200 .0350 .0200 .0417
.0120 .0120 .0150 .0120 .0290
.0050 .0050 .0038 .0050 .0200
.0014 .0014 .0014 .0014 .0035
.0010 .0010 .0010 .0010 .0028
.0007 .0007 .0007 .0007 .0020
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al., 1996) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
Sweetclover Red clover Alsike clover Soybean Cowpeas
CLVS CLVR CLVA SOYB CWPS
.0650 .0650 .0600 .0524 .0600
.0280 .0280 .0280 .0265 .0231
.0243 .0243 .0240 .0258 .0134
.0060 .0060 .0060 .0074 .0049
.0024 .0024 .0025 .0037 .0019
.0024 .0024 .0025 .0035 .0011
estimated estimated estimated (Kiniry et al., 1992a) estimated
Mung bean Lima beans Lentils Peanut Field peas
MUNG LIMA LENT PNUT FPEA
.0524 .0040 .0440 .0524 .0515
.0265 .0030 .0164 .0265 .0335
.0258 .0015 .0128 .0258 .0296
.0074 .0035 .0074 .0074 .0033
.0037 .0030 .0037 .0037 .0019
.0035 .0015 .0023 .0035 .0014
estimated (Kiniry and Williams, 1994) estimated estimated estimated
Garden or canning peas Sesbania Flax
PEAS SESB FLAX
.0040 .0500 .0482
.0030 .0200 .0294
.0015 .0150 .0263
.0030 .0074 .0049
.0020 .0037 .0024
.0015 .0035 .0023
(Kiniry and Williams, 1994) estimated estimated
Common Name
Reference
54
SWAT USER’S MANUAL, VERSION 2000 Plant Code COTS
frN,1
frN,2
frN,3
frP,1
frP,2
frP,3
.0580
.0192
.0177
.0081
.0027
.0025
estimated
COTP
.0580
.0192
.0177
.0081
.0027
.0025
estimated
TOBC SGBT POTA
.0470 .0550 .0550
.0177 .0200 .0200
.0138 .0120 .0120
.0048 .0060 .0060
.0018 .0025 .0025
.0014 .0019 .0019
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Sweetpotato Carrot Onion Sunflower Spring canola-Polish
SPOT CRRT ONIO SUNF CANP
.0450 .0550 .0400 .0500 .0440
.0160 .0075 .0300 .0230 .0164
.0090 .0012 .0020 .0146 .0128
.0045 .0060 .0021 .0063 .0074
.0019 .0030 .0020 .0029 .0037
.0015 .0020 .0019 .0023 .0023
estimated (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry, personal communication, 2001) (Kiniry et al., 1995)
Spring canola-Argentine Asparagus Broccoli Cabbage Cauliflower
CANA ASPR BROC CABG CAUF
.0440 .0620 .0620 .0620 .0620
.0164 .0500 .0090 .0070 .0070
.0128 .0400 .0070 .0040 .0040
.0074 .0050 .0050 .0050 .0050
.0037 .0040 .0040 .0035 .0035
.0023 .0020 .0030 .0020 .0020
(Kiniry et al., 1995) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Celery Head lettuce Spinach Green beans Cucumber
CELR LETT SPIN GRBN CUCM
.0620 .0360 .0620 .0040 .0663
.0150 .0250 .0400 .0030 .0075
.0100 .0210 .0300 .0015 .0048
.0060 .0084 .0050 .0040 .0053
.0050 .0032 .0040 .0035 .0025
.0030 .0019 .0035 .0015 .0012
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Eggplant Cantaloupe Honeydew melon Watermelon Bell pepper
EGGP CANT HMEL WMEL PEPR
.0663 .0663 .0070 .0663 .0600
.0255 .0255 .0040 .0075 .0350
.0075 .0148 .0020 .0048 .0250
.0053 .0053 .0026 .0053 .0053
.0020 .0020 .0020 .0025 .0020
.0015 .0012 .0017 .0012 .0012
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Strawberry Tomato Apple Pine Oak
STRW TOMA APPL PINE OAK
.0663 .0663 .0060 .0060 .0060
.0255 .0300 .0020 .0020 .0020
.0148 .0250 .0015 .0015 .0015
.0053 .0053 .0007 .0007 .0007
.0020 .0035 .0004 .0004 .0004
.0012 .0025 .0003 .0003 .0003
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) estimated (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
Poplar Honey mesquite
POPL MESQ
.0060 .0200
.0020 .0100
.0015 .0080
.0007 .0007
.0004 .0004
.0003 .0003
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
Common Name Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco Sugarbeet Potato
Reference
A.1.8 HARVEST Harvest operations are performed on agricultural crops where the yield is sold for a profit. Four variables in the database provide information used by the model to harvest a crop: HVSTI, WSYF, CNYLD, and CPYLD. The harvest index defines the fraction of the aboveground biomass that is removed in a harvest operation. This value defines the fraction of plant biomass
APPENDIX A: DATABASES
55
that is “lost” from the system and unavailable for conversion to residue and subsequent decomposition. For crops where the harvested portion of the plant is aboveground, the harvest index is always a fraction less than 1. For crops where the harvested portion is belowground, the harvest index may be greater than 1. Two harvest indices are provided in the database, the harvest index for optimal growing conditions (HVSTI) and the harvest index under highly stressed growing conditions (WSYF). To determine the harvest index, the plant biomass removed during the harvest operation is dried at least 2 days at 65°C and weighed. The total aboveground plant biomass in the field should also be dried and weighed. The harvest index is then calculated by dividing the weight of the harvested portion of the plant biomass by the weight of the total aboveground plant biomass. Plants will need to be grown in two different plots where optimal climatic conditions and stressed conditions are produced to obtain values for both harvest indices. In addition to the amount of plant biomass removed in the yield, SWAT needs to know the amount of nitrogen and phosphorus removed in the yield. The harvested portion of the plant biomass is sent to a testing laboratory to determine the fraction of nitrogen and phosphorus in the biomass. Table A-8 lists values for the optimal harvest index (HIopt), the minimum harvest index (HImin), the fraction of nitrogen in the harvested portion of biomass (frN,yld), and the fraction of phosphorus in the harvested portion of biomass (frP,yld).
Table A-8: Harvest parameters for plants included in the plant growth database. Plant Common Name Code HIopt HImin frN,yld frP,yld Reference Corn 0.50 0.30 .0140 .0016 (Kiniry, personal communication, 2001; CORN Corn silage
CSIL
0.90
0.90
.0140
.0016
Sweet corn
SCRN
0.50
0.30
.0214
.0037
Eastern gamagrass Grain sorghum
EGAM GRSG
0.90 0.45
0.90 0.25
.0160 .0199
.0022 .0032
Kiniry et al, 1995) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984a) (Kiniry, personal communication, 2001) (Kiniry and Bockholt, 1998; Nutrition Monitoring Division, 1984b)
56
SWAT USER’S MANUAL, VERSION 2000 HIopt
HImin
frN,yld
frP,yld
Sorghum hay
Plant Code SGHY
0.90
0.90
.0199
.0032
Johnsongrass
JHGR
0.90
0.90
.0200
.0028
Sugarcane Spring wheat Winter wheat
SUGC SWHT WWHT
0.50 0.42 0.40
0.01 0.20 0.20
.0000 .0234 .0250
.0000 .0033 .0022
Durum wheat
DWHT
0.40
0.20
.0263
.0057
Rye
RYE
0.40
0.20
.0284
.0042
Spring barley Oats
BARL OATS
0.54 0.42
0.20 0.175
.0210 .0316
.0017 .0057
Rice
RICE
0.50
0.25
.0136
.0013
Pearl millet
PMIL
0.25
0.10
.0200
.0028
Timothy
TIMO
0.90
0.90
.0234
.0033
Smooth bromegrass
BROS
0.90
0.90
.0234
.0033
Meadow bromegrass
BROM
0.90
0.90
.0234
.0033
Tall fescue
FESC
0.90
0.90
.0234
.0033
Kentucky bluegrass Bermudagrass Crested wheatgrass
BLUG BERM CWGR
0.90 0.90 0.90
0.90 0.90 0.90
.0160 .0234 .0500
.0022 .0033 .0040
Western wheatgrass
WWGR
0.90
0.90
.0500
.0040
Slender wheatgrass
SWGR
0.90
0.90
.0500
.0040
Italian (annual) ryegrass
RYEG
0.90
0.90
.0220
.0028
Russian wildrye
RYER
0.90
0.90
.0230
.0037
Altai wildrye
RYEA
0.90
0.90
.0230
.0037
Sideoats grama Big bluestem
SIDE BBLS
0.90 0.90
0.90 0.90
.0160 .0160
.0022 .0022
Little bluestem Alamo switchgrass Indiangrass Alfalfa Sweetclover
LBLS SWCH INDN ALFA CLVS
0.90 0.90 0.90 0.90 0.90
0.90 0.90 0.90 0.90 0.90
.0160 .0160 .0160 .0250 .0650
.0022 .0022 .0022 .0035 .0040
(Kiniry, personal communication, 2001) (Kiniry et al, 1996) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001; estimated)
Red clover
CLVR
0.90
0.90
.0650
.0040
(Kiniry, personal communication, 2001; estimated)
Common Name
Reference (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry, personal communication, 2001; Kiniry et al, 1992a) (Kiniry and Williams, 1994) (Kinry et al, 1995; Kiniry et al, 1992a) (Kiniry et al, 1995) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry et al, 1995) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
APPENDIX A: DATABASES
HIopt
HImin
frN,yld
frP,yld
Alsike clover
Plant Code CLVA
0.90
0.90
.0600
.0040
Soybean Cowpeas
SOYB CWPS
0.31 0.42
0.01 0.05
.0650 .0427
.0091 .0048
Mung bean
MUNG
0.31
0.01
.0420
.0040
Lima beans
LIMA
0.30
0.22
.0368
.0046
Lentils
LENT
0.61
001
.0506
.0051
Peanut
PNUT
0.40
0.30
.0505
.0040
Field peas Garden or canning peas
FPEA PEAS
0.45 0.30
0.10 0.22
.0370 .0410
.0021 .0051
Sesbania
SESB
0.31
0.01
.0650
.0091
Flax Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco Sugarbeet
FLAX COTS
0.54 0.50
0.40 0.40
.0400 .0140
.0033 .0020
COTP
0.40
0.30
.0190
.0029
TOBC SGBT
0.55 2.00
0.55 1.10
.0140 .0130
.0016 .0020
Potato
POTA
0.95
0.95
.0246
.0023
Sweetpotato
SPOT
0.60
0.40
.0097
.0010
Carrot
CRRT
1.12
0.90
.0135
.0036
Onion
ONIO
1.25
0.95
.0206
.0032
Sunflower
SUNF
0.30
0.18
.0454
.0074
Spring canola-Polish Spring canola-Argentine Asparagus
CANP CANA ASPR
0.23 0.30 0.80
0.01 0.01 0.95
.0380 .0380 .0630
.0079 .0079 .0067
Broccoli
BROC
0.80
0.95
.0512
.0071
Cabbage
CABG
0.80
0.95
.0259
.0031
Cauliflower
CAUF
0.80
0.95
.0411
.0059
Celery
CELR
0.80
0.95
.0199
.0049
Head lettuce
LETT
0.80
0.01
.0393
.0049
Spinach
SPIN
0.95
0.95
.0543
.0058
Common Name
Reference (Kiniry, personal communication, 2001; estimated) (Kiniry et al, 1992a) (estimated; Nutrition Monitoring Division, 1984c) (estimated; Nutrition Monitoring Division, 1984c) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (estimated; Nutrition Monitoring Division, 1984c) (estimated; Nutrition Monitoring Division, 1984c) estimated (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) estimated estimated (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; estimated) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (estimated; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry et al, 1992b; Nutrition Monitoring Division, 1984d) (Kiniry et al, 1995) (Kiniry et al, 1995) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a)
57
58
SWAT USER’S MANUAL, VERSION 2000 HIopt
HImin
frN,yld
frP,yld
Green beans
Plant Code GRBN
0.10
0.10
.0299
.0039
Cucumber
CUCM
0.27
0.25
.0219
.0043
Eggplant
EGGP
0.59
0.25
.0218
.0041
Cantaloupe
CANT
0.50
0.25
.0138
.0017
Honeydew melon
HMEL
0.55
0.25
.0071
.0010
Watermelon
WMEL
0.50
0.25
.0117
.0011
Bell pepper
PEPR
0.60
0.25
.0188
.0030
Strawberry
STRW
0.45
0.25
.0116
.0023
Tomato
TOMA
0.33
0.15
.0235
.0048
Apple
APPL
0.10
0.05
.0019
.0004
(Kiniry and Williams, 1994; Consumer Nutrition Center, 1982) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Consumer Nutrition Center, 1982) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (estimated; Consumer Nutrition Center, 1982)
Pine Oak Poplar Honey mesquite
PINE OAK POPL MESQ
0.76 0.76 0.76 0.05
0.60 0.01 0.01 0.01
.0015 .0015 .0015 .0015
.0003 .0003 .0003 .0003
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
Common Name
Reference (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Consumer Nutrition Center, 1982) (Kiniry and Williams, 1994; Consumer Nutrition Center, 1982)
A.1.9 USLE C FACTOR The USLE C factor is the ratio of soil loss from land cropped under specified conditions to the corresponding loss from clean-tilled, continuous fallow. This factor measures the combined effect of all the interrelated cover and management variables. SWAT calculates the actual C factor based on the amount of soil cover and the minimum C factor defined for the plant/land cover. The minimum C factor quantifies the maximum decrease in erosion possible for the plant/land cover. Because the USLE C factor is influenced by management, this variable may be adjusted by the user to reflect management conditions in the watershed of interest. The minimum C factor can be estimated from a known average annual C factor using the following equation (Arnold and Williams, 1995): CUSLE ,mn = 1.463 ln[CUSLE ,aa ] + 0.1034
APPENDIX A: DATABASES
59
where CUSLE,mn is the minimum C factor for the land cover and CUSLE,aa is the average annual C factor for the land cover. The minimum C factor for plants in the database are listed in Table A-6.
A.1.10 RESIDUE DECOMPOSITION The plant residue decomposition coefficient is the fraction of residue that will decompose in a day assuming optimal moisture, temperature, C:N ratio, and C:P ratio. This variable was originally in the basin input file (.bsn), but was added to the crop database so that users could vary decomposition by land cover. A default value of 0.05 is used for all plant species in the database.
60
SWAT USER’S MANUAL, VERSION 2000
A.2 TILLAGE DATABASE The tillage database contains information needed by SWAT to simulate the redistribution of nutrients and pesticide that occurs in a tillage operation. Table A-9 lists all the default tillage implements. This list was summarized from a farm machinery database maintained by the USDA Economic Research Service. Depth of tillage for each implement was also obtained from the USDA Economic Research Service. The fraction of residue mixed into the soil was estimated for each implement from a ‘Residue Scorecard’ provided by NACD’s (National Association of Conservation Districts) Conservation Technology Information Center. Table A-9: Implements included in the tillage database. Implement Tillage Code Duckfoot Cultivator DUCKFTC Field Cultivator FLDCULT Furrow-out Cultivator FUROWOUT Marker (Cultivator) MARKER Rolling Cultivator ROLLCULT Row Cultivator ROWCULT Discovator DISCOVAT Leveler LEVELER Harrow (Tines) HARROW Culti-mulch Roller CULMULCH Culti-packer Pulverizer CULPKPUL Land Plane-Leveler LANDLEVL Landall, Do-All LANDALL Laser Planer LASRPLAN Levee-Plow-Disc LEVPLDIS Float FLOAT Field Conditioner (Scratcher) FLDCDSCR Lister (Middle-Buster) LISTRMID Roller Groover ROLLGROV Roller Packer Attachment ROLPKRAT Roller Packer Flat Roller ROLPKRFT Sand-Fighter SANDFIGT Seedbed Roller SEEDROLL Crust Buster CRUSTBST Roller Harrow ROLLHRRW Triple K TRIPLE K Finishing Harrow FINHARRW Flex-Tine Harrow CL FLEXHARW Powered Spike Tooth Harrow SPIKETTH Spike Tooth Harrow SPIKTOTH Springtooth Harrow SPRGTOTH Soil Finisher SOILFINS
Mixing Depth
Mixing Efficiency
100 mm 100 mm 25 mm 100 mm 25 mm 25 mm 25 mm 25 mm 25 mm 25 mm 40 mm 75 mm 150 mm 150 mm 25 mm 60 mm 60 mm 40 mm 60 mm 40 mm 40 mm 100 mm 100 mm 60 mm 60 mm 100 mm 100 mm 25 mm 75 mm 25 mm 25 mm 75 mm
0.55 0.30 0.75 0.45 0.50 0.25 0.50 0.50 0.20 0.25 0.35 0.50 0.30 0.30 0.75 0.10 0.10 0.15 0.25 0.05 0.35 0.70 0.70 0.10 0.40 0.40 0.55 0.20 0.40 0.25 0.35 0.55
APPENDIX A: DATABASES
Implement Rotary Hoe Roterra Roto-Tiller Rotovator-Bedder Rowbuck Ripper Middle Buster Rod Weeder Rubber-Wheel Weed Puller Multi-Weeder Moldboard Plow Reg Chisel Plow Coulter-Chisel Disk Plow Stubble-mulch Plow Subsoil Chisel Plow Row Conditioner Hipper Rice Roller Paraplow Subsoiler-Bedder Hip-Rip Deep Ripper-Subsoiler V-Ripper Bed Roller Bedder (Disk) Bedder Disk-Hipper Bedder Disk-Row Bedder Shaper Disk Border Maker Disk Chisel (Mulch Tiller) Offset Disk-Heavy Duty Offset Disk-Light Duty One-Way (Disk Tiller) Tandem Disk Plow Tandem Disk Reg Single Disk Power Mulcher Blade 10 ft Furrow Diker Beet Cultivator Cultiweeder Packer
Tillage Code ROTHOE ROTERRA ROTOTILL ROTBEDDR ROWBUCK RIPPER MIDBST1R RODWEEDR RUBWHWPL MULTIWDR MLDBOARD CHISPLOW CCHPLOW DISKPLOW STUBMLCH SUBCHPLW ROWCOND HIPPER RICEROLL PARAPLOW SBEDHIPR RIPRSUBS VRIPPER BEDROLLR BEDDER D BEDDHIPR BEDDKROW BEDDER S DSKBRMKR DKCHMTIL OFFSETHV OFFSETLT ONE-WAYT TANDEMPL TANDEMRG SINGLDIS PWRMULCH BLADE 10 FURWDIKE BEETCULT CLTIWEED PACKER
61
Mixing Depth
Mixing Efficiency
5 mm 5 mm 5 mm 100 mm 100 mm 350 mm 100 mm 25 mm 5 mm 25 mm 150 mm 150 mm 150 mm 100 mm 75 mm 350 mm 25 mm 100 mm 50 mm 350 mm 350 mm 350 mm 350 mm 50 mm 150 mm 150 mm 100 mm 150 mm 150 mm 150 mm 100 mm 100 mm 100 mm 75 mm 75 mm 100 mm 50 mm 75 mm 100 mm 25 mm 100 mm 40 mm
0.10 0.80 0.80 0.80 0.70 0.25 0.70 0.30 0.35 0.30 0.95 0.30 0.50 0.85 0.15 0.45 0.50 0.50 0.10 0.15 0.70 0.25 0.25 0.25 0.55 0.65 0.85 0.55 0.55 0.55 0.70 0.55 0.60 0.55 0.60 0.45 0.70 0.25 0.70 0.25 0.30 0.35
In addition to information about specific implements, the tillage database includes default information for the different crop residue management categories. Table A-10 summarizes the information in the database on the different residue management categories.
62
SWAT USER’S MANUAL, VERSION 2000
Table A-10: Generic management scenarios included in the tillage database. Implement Tillage Code Mixing Depth Generic Fall Plowing Operation 150 mm FALLPLOW Generic Spring Plowing Operation 125 mm SPRGPLOW 100 mm Generic Conservation Tillage CONSTILL 25 mm Generic No-Till Mixing ZEROTILL
Mixing Efficiency 0.95 0.50 0.25 0.05
ASAE (1998b) categorizes tillage implements into five different categories—primary tillage, secondary tillage, cultivating tillage, combination primary tillage, and combination secondary tillage. The definitions for the categories are (ASAE, 1998b): Primary tillage: the implements displace and shatter soil to reduce soil strength and bury or mix plant materials, pesticides, and fertilizers in the tillage layer. This type of tillage is more aggressive, deeper, and leaves a rougher soil surface relative to secondary tillage. Examples include plows—moldboard, chisel, disk, bedder; moldboard listers; disk bedders; subsoilers; disk harrows—offset disk, heavy tandem disk; and powered rotary tillers. Secondary tillage: the implements till the soil to a shallower depth than primary tillage implements, provide additional pulverization, mix pesticides and fertilizers into the soil, level and firm the soil, close air pockets, and eradicate weeds. Seedbed preparation is the final secondary tillage operation. Examples include harrows—disk, spring, spike, coil, tine-tooth, knife, packer, ridger, leveler, rotary ground driven; field or field conditioner cultivators; rod weeders; rollers; powered rotary tillers; bed shapers; and rotary hoes. Cultivating tillage: the implements perform shallow post-plant tillage to aid the crop by loosening the soil and/or by mechanical eradication of undesired vegetation. Examples include row crop cultivators—rotary ground-driven, spring tooth, shank tooth; rotary hoes; and rotary tillers. Combination primary tillage: the implements perform primary tillage functions and utilize two or more dissimilar tillage components as integral parts of the implement. Combination secondary tillage: the implements perform secondary tillage functions and utilize two or more dissimilar tillage components as integral parts of the implement. ASAE (1998b) provides detailed descriptions and illustrations for the major implements. These are very helpful for those who are not familiar with farm implements.
APPENDIX A: DATABASES
63
A.3 PESTICIDE DATABASE The pesticide database file (pest.dat) summarizes pesticide attribute information for various pesticides. The pesticide data included in the database was originally compiled for the GLEAMS model in the early nineties (Knisel, 1993). The following table lists the pesticides included in the pesticide database. Table A-11: SWAT Pesticide Database Washoff Frac.
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
Trade Name
Common Name
Koc (ml/g)
2,4,5-TP 2 Plus 2 Aatrex Abate Acaraben
Silvex Mecoprop Amine Atrazine Temephos Chlorobenzilate
2600 20 100 100000 2000
0.40 0.95 0.45 0.65 0.05
5.0 10.0 5.0 5.0 10.0
20.0 21.0 60.0 30.0 20.0
2.5 660000 33 0.001 13
Accelerate Acclaim Alanap Alar Aldrin
Endothall Salt Fenoxaprop-Ethyl Naptalam Sodium Salt Daminozide Aldrin
20 9490 20 10 300
0.90 0.20 0.95 0.95 0.05
7.0 5.0 7.0 4.0 2.0
7.0 9.0 14.0 7.0 28.0
100000 0.8 231000 100000 0.1
Aliette Ally Amiben Amid-Thin W Amitrol T
Fosetyl-Aluminum Metsulfuron-Methyl Chloramben Salts NAA Amide Amitrole
20 35 15 100 100
0.95 0.80 0.95 0.60 0.95
0.1 30.0 7.0 5.0 5.0
0.1 120.0 14.0 10.0 14.0
120000 9500 900000 100 360000
Ammo Antor A-Rest Arsenal Arsonate
Cypermethrin Diethatyl-Ethyl Ancymidol Imazapyr Acid MSMA
100000 1400 120 100 10000
0.40 0.40 0.50 0.90 0.95
5.0 10.0 30.0 30.0 30.0
30.0 21.0 120.0 90.0 100.0
0.004 105 650 11000 1000000
Asana Assert (m) Assert (p) Assure Asulox
Esfenvalerate Imazamethabenz-m Imazamethabenz-p Quizalofop-Ethyl Asulam Sodium Salt
5300 66 35 510 40
0.40 0.65 0.65 0.20 0.95
8.0 18.0 18.0 15.0 3.0
35.0 35.0 35.0 60.0 7.0
0.002 1370 875 0.31 550000
Avenge Azodrin Balan Banol Banvel
Difenzoquat Monocrotophos Benefin Propamocarb Dicamba
54500 1 9000 1000000 2
0.95 0.95 0.20 0.95 0.65
30.0 2.0 10.0 15.0 9.0
100.0 30.0 30.0 30.0 14.0
817000 1000000 0.1 1000000 400000
Basagran Basta Bayleton
Bentazon Glufosinate Ammonia Triadimefon
34 100 300
0.60 0.95 0.30
2.0 4.0 8.0
20.0 7.0 26.0
2300000 1370000 71.5
64
SWAT USER’S MANUAL, VERSION 2000
Washoff Frac.
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
Trade Name
Common Name
Koc (ml/g)
Baytex Baythroid Benlate Benzex Betamix
Fenthion Cyfluthrin Benomyl BHC Phenmedipham
1500 100000 1900 55000 2400
0.65 0.40 0.25 0.05 0.70
2.0 5.0 6.0 3.0 5.0
34.0 30.0 240.0 600.0 30.0
4.2 0.002 2 0.1 4.7
Betanex Bidrin Bladex Bolero Bolstar
Desmedipham Dicrotophos Cyanazine Thiobencarb Sulprofos
1500 75 190 900 12000
0.70 0.70 0.60 0.70 0.55
5.0 20.0 5.0 7.0 0.5
30.0 28.0 14.0 21.0 140.0
8 1000000 170 28 0.31
Bordermaster Botran Bravo Buctril Butyrac Ester
MCPA Ester DCNA (Dicloran) Chlorothalonil Bromoxynil Octan. Ester 2,4-DB Ester
1000 1000 1380 10000 500
0.50 0.50 0.50 0.20 0.45
8.0 4.0 5.0 3.0 7.0
25.0 10.0 30.0 7.0 7.0
5 7 0.6 0.08 8
Caparol Carbamate Carsoron Carzol Cerone
Prometryn Ferbam Dichlobenil Formetanate Hydrochlor Ethephon
400 300 400 1000000 100000
0.50 0.90 0.45 0.95 0.95
10.0 3.0 5.0 30.0 5.0
60.0 17.0 60.0 100.0 10.0
33 120 21.2 500000 1239000
Chem-Hoe Chlordane Chopper Classic Cobra
Propham (IPC) Chlordane Imazapyr Amine Chlorimuron-ethyl Lactofen
200 100000 100 110 100000
0.50 0.05 0.80 0.90 0.20
2.0 2.5 30.0 15.0 2.0
10.0 100.0 90.0 40.0 3.0
250 0.1 500000 1200 0.1
Comite Command Cotoran Counter Crossbow
Propargite Clomazone Fluometuron Terbufos Triclopyr Amine
4000 300 100 500 20
0.20 0.80 0.50 0.60 0.95
5.0 3.0 30.0 2.5 15.0
56.0 24.0 85.0 5.0 46.0
0.5 1000 110 5 2100000
Curacron Cygon Cyprex Cythion Dacamine
Profenofos Dimethoate Dodine Acetate Malathion 2,4-D Acid
2000 20 100000 1800 20
0.90 0.95 0.50 0.90 0.45
3.0 3.0 10.0 1.0 5.0
8.0 7.0 20.0 1.0 10.0
28 39800 700 130 890
Dacthal Dalapon Dasanit DDT Dedweed
DCPA Dalapon Sodium Salt Fensulfothion DDT MCPA Amine
5000 1 10000 240000 20
0.30 0.95 0.90 0.05 0.95
10.0 37.0 4.0 10.0 7.0
100.0 30.0 24.0 120.0 25.0
0.5 900000 0.01 0.1 866000
DEF Dessicant L-10 Devrinol
Tribufos Arsenic Acid Napropamide
5000 100000 400
0.25 0.95 0.60
7.0 10000.0 15.0
30.0 10000.0 70.0
2.3 17000 74
APPENDIX A: DATABASES
Trade Name
Common Name
Di-Syston Dibrom Dieldrin Dimilin Dinitro
Disulfoton Naled Dieldrin Diflubenzuron Dinoseb Phenol
Diquat Dithane Dowpon Dropp DSMA
Diquat Dibromide Mancozeb Dalapon Thidiazuron Methanearsonic Acid Na
Du-ter Dual Dyfonate Dylox Dymid
Koc (ml/g)
Washoff Frac.
65
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
600 180 50000 10000 500
0.50 0.90 0.05 0.05 0.60
3.0 5.0 5.0 27.0 3.0
30.0 1.0 1400.0 10.0 20.0
25 2000 0.1 0.08 50
1000000 2000 4 110 100000
0.95 0.25 0.95 0.40 0.95
30.0 10.0 37.0 3.0 30.0
1000.0 70.0 30.0 10.0 1000.0
718000 6 1000 20 1400000
Triphenyltin Hydroxide Metolachlor Fonofos Trichlorfon Diphenamid
23000 200 870 10 210
0.40 0.60 0.60 0.95 0.80
18.0 5.0 2.5 3.0 5.0
75.0 90.0 40.0 10.0 30.0
1 530 16.9 120000 260
Dyrene Elgetol EPN Eradicane Ethanox
Anilazine DNOC Sodium Salt EPN EPTC Ethion
3000 20 13000 200 10000
0.50 0.95 0.60 0.75 0.65
5.0 8.0 5.0 3.0 7.0
1.0 20.0 5.0 6.0 150.0
8 100000 0.5 344 1.1
Evik Evital Far-Go Fenatrol Fenitox
Ametryn Norflurazon Triallate Fenac Fenitrothion
300 600 2400 20 2000
0.65 0.50 0.40 0.95 0.90
5.0 15.0 15.0 30.0 3.0
60.0 90.0 82.0 180.0 8.0
185 28 4 500000 30
Fruitone CPA Fundal Funginex Furadan Fusilade
3-CPA Sodium Salt Chlordimeform Hydroclo. Triforine Carbofuran Fluazifop-P-Butyl
20 100000 540 22 5700
0.95 0.90 0.80 0.55 0.40
3.0 1.0 5.0 2.0 4.0
10.0 60.0 21.0 50.0 15.0
200000 500000 30 351 2
Glean Goal Guthion Harmony Harvade
Chlorsulfuron Oxyfluorfen Azinphos-Methyl Thifensulfuron-Methyl Dimethipin
40 100000 1000 45 10
0.75 0.40 0.65 0.80 0.80
30.0 8.0 2.0 3.0 3.0
160.0 35.0 10.0 12.0 10.0
7000 0.1 29 2400 3000
Hoelon Hyvar Imidan Isotox Karate
Diclofop-Methyl Bromacil Phosmet Lindane Lambda-Cyhalothrin
16000 32 820 1100 180000
0.45 0.75 0.90 0.05 0.40
8.0 20.0 3.0 2.5 5.0
37.0 60.0 19.0 400.0 30.0
0.8 700 20 7.3 0.005
Karathane Karmex Kelthane
Dinocap Diuron Dicofol
550 480 180000
0.30 0.45 0.05
8.0 30.0 4.0
20.0 90.0 60.0
4 42 1
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SWAT USER’S MANUAL, VERSION 2000
Trade Name
Common Name
Kerb Krenite Lannate Larvadex Larvin
Pronamide Fosamine Ammon. Salt Methomyl Cyromazine Thiodicarb
Lasso Limit Lontrel Lorox Lorsban
Alachlor Amidochlor Clopyralid Linuron Clorpyrifos
Manzate Marlate Matacil Mavrik Metasystox
Maneb Methoxychlor Aminocarb Fluvalinate Oxydemeton-Methyl
Milogard Miral Mitac Modown Monitor
Propazine Isazofos Amitraz Bifenox Methamidophos
Morestan Nemacur Nemacur Sulfone Nemacur Sulfoxide Norton
Oxythioquinox Fenamiphos Fenamiphos Sulfone Fenamiphos Sulfoxide Ethofumesate
Octave Oftanol Orthene Orthocide Oust
Prochloraz Isofenphos Acephate Captan Sulfometuron-Methyl
Pay-Off Penncap-M Phenatox Phosdrin Phoskil
Flucythrinate Methyl Parathion Toxaphene Mevinphos Parathion (Ethyl)
Pipron Pix Plantvax Poast Polyram
Piperalin Mepiquat Chlor. Salt Oxycarboxin Sethoxydim Metiram
Pounce Pramitol Prefar
Permethrin Prometon Bensulide
Koc (ml/g)
Washoff Frac.
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
200 150 72 200 350
0.30 0.95 0.55 0.95 0.70
20.0 4.0 0.5 30.0 4.0
60.0 8.0 30.0 150.0 7.0
15 1790000 58000 136000 19.1
170 1000 6 400 6070
0.40 0.70 0.95 0.60 0.65
3.0 8.0 2.0 15.0 3.3
15.0 20.0 30.0 60.0 30.0
240 10 300000 75 0.4
1000 80000 100 1000000 10
0.65 0.05 0.90 0.40 0.95
3.0 6.0 4.0 7.0 3.0
12.0 120.0 6.0 30.0 10.0
6 0.1 915 0.005 1000000
154 100 1000 10000 5
0.45 0.65 0.45 0.40 0.95
5.0 5.0 1.0 3.0 4.0
135.0 34.0 2.0 7.0 6.0
8.6 69 1 0.4 1000000
2300 240 45 40 340
0.50 0.70 0.70 0.70 0.65
10.0 5.0 18.0 42.0 10.0
30.0 5.0 18.0 42.0 30.0
1 400 400 400 50
500 600 2 200 78
0.50 0.65 0.70 0.65 0.65
30.0 30.0 2.5 9.0 10.0
120.0 150.0 3.0 2.5 20.0
34 24 818000 5.1 70
100000 5100 100000 44 5000
0.40 0.90 0.05 0.95 0.70
5.0 3.0 2.0 0.6 4.0
21.0 5.0 9.0 3.0 14.0
0.06 60 3 600000 24
5000 1000000 95 100 500000
0.60 0.95 0.70 0.70 0.40
10.0 30.0 10.0 3.0 7.0
30.0 1000.0 20.0 5.0 20.0
20 1000000 1000 4390 0.1
100000 150 1000
0.30 0.75 0.40
8.0 30.0 30.0
30.0 500.0 120.0
0.006 720 5.6
APPENDIX A: DATABASES
Trade Name
Common Name
Prelude Prime Princep Probe Prowl
Paraquat Flumetralin Simazine Methazole Pendimethalin
Pursuit Pydrin Pyramin Ramrod Reflex
AC 263,499 Fenvalerate Pyrazon Propaclor Fomesafen Salt
Rescue Ridomil Ro-Neet Ronstar Roundup
2,4-DB Sodium Amine Metalaxyl Cycloate Oxadiazon Glyphosate Amine
Rovral Royal Slo-Gro Rubigan Sancap Savey
Iprodione Maleic Hydrazide Fenarimol Dipropetryn Hexythiazox
Scepter Sencor Sevin Sinbar Slug-Geta
Imazaquin Ammonium Metribuzin Carbaryl Terbacil Methiocarb
Sonalan Spectracide Spike Sprout Nip Stam
Koc (ml/g)
Washoff Frac.
67
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
1000000 10000 130 3000 5000
0.60 0.40 0.40 0.40 0.40
30.0 7.0 5.0 5.0 30.0
1000.0 20.0 60.0 14.0 90.0
620000 0.1 6.2 1.5 0.275
10 5300 120 80 60
0.90 0.25 0.85 0.40 0.95
20.0 10.0 5.0 3.0 30.0
90.0 35.0 21.0 6.0 100.0
200000 0.002 400 613 700000
20 50 430 3200 24000
0.45 0.70 0.50 0.50 0.60
9.0 30.0 2.0 20.0 2.5
10.0 70.0 30.0 60.0 47.0
709000 8400 95 0.7 900000
700 20 600 900 6200
0.40 0.95 0.40 0.40 0.40
5.0 10.0 30.0 5.0 5.0
14.0 30.0 360.0 30.0 30.0
13.9 400000 14 16 0.5
20 60 300 55 300
0.95 0.80 0.55 0.70 0.70
20.0 5.0 7.0 30.0 10.0
60.0 40.0 10.0 120.0 30.0
160000 1220 120 710 24
Ethalfluralin Diazinon Tebuthiuron Chlorpropham Propanil
4000 1000 80 400 149
0.40 0.90 0.90 0.90 0.70
4.0 4.0 30.0 8.0 1.0
60.0 40.0 360.0 30.0 1.0
0.3 60 2500 89 200
Supracide Surflan Sutan Swat Tackle
Methidathion Oryzalin Butylate Phosphamidon Acifluorfen
400 600 400 7 113
0.90 0.40 0.30 0.95 0.95
3.0 5.0 1.0 5.0 5.0
7.0 20.0 13.0 17.0 14.0
220 2.5 44 1000000 250000
Talstar Tandem Tanone Tattoo TBZ
Bifenthrin Tridiphane Phenthoate Bendiocarb Thiabendazole
240000 5600 250 570 2500
0.40 0.40 0.65 0.85 0.60
7.0 8.0 2.0 3.0 30.0
26.0 28.0 40.0 5.0 403.0
0.1 1.8 200 40 50
Temik Temik Sulfone Temik Sulfoxide
Aldicarb Aldicarb Sulfone Aldicarb Sulfoxide
40 10 30
0.70 0.70 0.70
7.0 20.0 30.0
7.0 20.0 30.0
6000 6000 6000
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SWAT USER’S MANUAL, VERSION 2000
Trade Name
Common Name
Tenoran Terbutrex Terrachlor Terraneb Terrazole
Chloroxuron Terbutryn PCNB Chloroneb Etridiazole
Thimet Thiodan Thiram Thistrol Tillam
Koc (ml/g)
Washoff Frac.
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
3000 2000 5000 1650 1000
0.40 0.50 0.40 0.50 0.60
15.0 5.0 4.0 30.0 3.0
60.0 42.0 21.0 130.0 20.0
2.5 22 0.44 8 50
Phorate Endosulfan Thiram MCPB Sodium Salt Pebulate
1000 12400 670 20 430
0.60 0.05 0.50 0.95 0.70
2.0 3.0 8.0 7.0 4.0
60.0 50.0 15.0 14.0 14.0
22 0.32 30 200000 100
Tilt Tolban Topsin Tordon Tralomethrin
Propiconazole Profluralin Thiophanate-Methyl Picloram Tralomethrin
1000 2240 1830 16 100000
0.70 0.35 0.40 0.60 0.40
30.0 1.0 5.0 8.0 1.0
110.0 140.0 10.0 90.0 27.0
110 0.1 3.5 200000 0.001
Treflan Tre-Hold Tupersan Turflon Velpar
Trifluralin NAA Ethyl Ester Siduron Triclopyr Ester Hexazinone
8000 300 420 780 54
0.40 0.40 0.70 0.70 0.90
3.0 5.0 30.0 15.0 30.0
60.0 10.0 90.0 46.0 90.0
0.3 105 18 23 3300
Vendex Vernam Volck oils Vydate Weedar
Fenbutatin Oxide Vernolate Petroleum oil Oxamyl 2,4-D amine
2300 260 1000 25 20
0.20 0.80 0.50 0.95 0.45
30.0 2.0 2.0 4.0 9.0
90.0 12.0 10.0 4.0 10.0
0.013 108 100 282000 796000
Weed-B-Gon Wedone Zolone
2,4,5-T Amine Dichlorprop Ester Phosalone
80 1000 1800
0.45 0.45 0.65
10.0 9.0 8.0
24.0 10.0 21.0
500000 50 3
Knisel (1993) cites Wauchope et al. (1992) as the source for water solubility, soil half-life and Koc values. Wash-off fraction and foliar half-life were obtained from Willis et al. (1980) and Willis and McDowell (1987).
A.3.1 WATER SOLUBILITY The water solubility value defines the highest concentration of pesticide that can be reached in the runoff and soil pore water. While this is an important characteristic, researchers have found that the soil adsorption coefficient, Koc, tends to limit the amount of pesticide entering solution so that the maximum
APPENDIX A: DATABASES
69
possible concentration of pesticide in solution is seldom reached (Leonard and Knisel, 1988). Reported solubility values are determined under laboratory conditions at a constant temperature, typically between 20°C and 30°C.
A.3.2 SOIL ADSORPTION COEFFICIENT The pesticide adsorption coefficient reported in the pesticide database can usually be obtained from a search through existing literature on the pesticide.
A.3.3 SOIL HALF-LIFE The half-life for a pesticide defines the number of days required for a given pesticide concentration to be reduced by one-half. The soil half-life entered for a pesticide is a lumped parameter that includes the net effect of volatilization, photolysis, hydrolysis, biological degradation and chemical reactions. The pesticide half-life for a chemical will vary with a change in soil environment (e.g. change in soil temperature, water content, etc.). Soil half-life values provided in the database are “average” or representative values. Half-life values reported for a chemical commonly vary by a factor of 2 to 3 and sometimes by as much as a factor of 10. For example, the soil half-life for atrazine can range from 120 to 12 days when comparing values reported in cool, dry regions to those from warm, humid areas. Another significant factor is soil treatment history. Repeated soil treatment by the same or a chemically similar pesticide commonly results in a reduction in half-life for the pesticide. This reduction is attributed to the preferential build-up of microbial populations adapted to degrading the compound. Users are encouraged to replace the default soil half-life value with a site-specific or region-specific value whenever the information is available.
A.3.4 FOLIAR HALF-LIFE As with the soil half-life, the foliar half-life entered for a pesticide is a lumped parameter describing the loss rate of pesticides on the plant canopy. For most pesticides, the foliar half-life is much less than the soil half-life due to enhanced volatilization and photodecomposition. While values for foliar half-life
70
SWAT USER’S MANUAL, VERSION 2000
were available for some pesticides in the database, the majority of foliar half-life values were calculated using the following rules: 1) Foliar half-life was assumed to be less than the soil half-life by a factor of 0.5 to 0.25, depending on vapor pressure and sensitivity to photodegradation. 2) Foliar half-life was adjusted downward for pesticides with vapor pressures less than 10-5 mm Hg. 3) The maximum foliar half-life assigned was 30 days.
A.3.5 WASH-OFF FRACTION The wash-off fraction quantifies the fraction of pesticide on the plant canopy that may be dislodged. The wash-off fraction is a function of the nature of the leaf surface, plant morphology, pesticide solubility, polarity of the pesticide molecule, formulation of the commercial product and timing and volume of the rainfall event. Some wash-off fraction values were obtained from Willis et al. (1980). For the remaining pesticides, solubility was used as a guide for estimating the wash-off fraction.
A.3.6 APPLICATION EFFICIENCY The application efficiency for all pesticides listed in the database is defaulted to 0.75. This variable is a calibration parameter.
APPENDIX A: DATABASES
71
A.4 FERTILIZER DATABASE The fertilizer database file (fert.dat) summarizes nutrient fractions for various fertilizers and types of manure. The following table lists the fertilizers and types of manure in the fertilizer database. Table A-12: SWAT Fertilizer Database Name Name Code
Min-N
Min-P
Org-N
Org-P
NH3-N/ Min N
Elemental Nitrogen Elemental Phosphorous Anhydrous Ammonia Urea 46-00-00
Elem-N Elem-P ANH-NH3 UREA 46-00-00
1.000 0.000 0.820 0.460 0.460
0.000 1.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 1.000 1.000 0.000
33-00-00 31-13-00 30-80-00 30-15-00 28-10-10
33-00-00 31-13-00 30-80-00 30-15-00 28-10-10
0.330 0.310 0.300 0.300 0.280
0.000 0.057 0.352 0.066 0.044
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
28-03-00 26-13-00 25-05-00 25-03-00 24-06-00
28-03-00 26-13-00 25-05-00 25-03-00 24-06-00
0.280 0.260 0.250 0.250 0.240
0.013 0.057 0.022 0.013 0.026
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
22-14-00 20-20-00 18-46-00 18-04-00 06-24-24
22-14-00 20-20-00 18-46-00 18-04-00 16-24-24
0.220 0.200 0.180 0.180 0.060
0.062 0.088 0.202 0.018 0.106
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
16-20-20 15-15-15 15-15-00 13-13-13 12-20-00
16-20-20 15-15-15 15-15-00 13-13-13 12-20-00
0.160 0.150 0.150 0.130 0.120
0.088 0.066 0.066 0.057 0.088
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
11-52-00 11-15-00 10-34-00 10-28-00 10-10-10
11-52-00 11-15-00 10-34-00 10-28-00 10-10-10
0.110 0.110 0.100 0.100 0.100
0.229 0.066 0.150 0.123 0.044
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
08-15-00 08-08-00 07-07-00 07-00-00 05-10-15
08-15-00 08-08-00 07-07-00 07-00-00 05-10-15
0.080 0.080 0.070 0.070 0.050
0.066 0.035 0.031 0.000 0.044
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
05-10-10
05-10-10
0.050
0.044
0.000
0.000
0.000
72
SWAT USER’S MANUAL, VERSION 2000
Name
Name Code
Min-N
Min-P
Org-N
Org-P
NH3-N/ Min N
05-10-05 04-08-00 03-06-00 02-09-00 10-20-20
05-10-05 04-08-00 03-06-00 02-09-00 00-20-20
0.050 0.040 0.030 0.020 0.100
0.044 0.035 0.026 0.040 0.088
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
00-15-00 00-06-00 Dairy-Fresh Manure Beef-Fresh Manure Veal-Fresh Manure
00-15-00 00-06-00 DAIRY-FR BEEF-FR VEAL-FR
0.000 0.000 0.007 0.010 0.023
0.066 0.026 0.005 0.004 0.006
0.000 0.000 0.031 0.030 0.029
0.000 0.000 0.003 0.007 0.007
0.000 0.000 0.990 0.990 0.990
Swine-Fresh Manure Sheep-Fresh Manure Goat-Fresh Manure Horse-Fresh Manure Layer-Fresh Manure
SWINE-FR SHEEP-FR GOAT-FR HORSE-FR LAYER-FR
0.026 0.014 0.013 0.006 0.013
0.011 0.003 0.003 0.001 0.006
0.021 0.024 0.022 0.014 0.040
0.005 0.005 0.005 0.003 0.013
0.990 0.990 0.990 0.990 0.990
Broiler-Fresh Manure BROIL-FR 0.010 0.004 Turkey-Fresh Manure TRKEY-FR 0.007 0.003 Duck-Fresh Manure DUCK-FR 0.008 0.023 Values in bold italics are estimated (see section A.4.2)
0.040 0.045 0.025
0.010 0.016 0.009
0.990 0.990 0.990
A.4.1 COMMERCIAL FERTILIZERS In compiling the list of commercial fertilizers in the database, we tried to identify and include commonly used fertilizers. This list is not comprehensive, so users may need to append the database with information for other fertilizers used in their watersheds. When calculating the fractions of N and P for the database, it is important to remember that the percentages reported for a fertilizer are %N-%P2O5-%K2O. The fraction of mineral N in the fertilizer is equal to %N divided by 100. To calculate the fraction of mineral P in the fertilizer, the fraction of P in P2O5 must be known. The atomic weight of phosphorus is 31 and the atomic weight of oxygen is 16, making the molecular weight of P2O5 equal to 142. The fraction of P in P2O5 is 62/142 = 0.44 and the fraction of mineral P in the fertilizer is equal to 0.44 (%P2O5 / 100).
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A.4.2 MANURE The values in the database for manure types were derived from manure production and characteristics compiled by the ASAE (1998a). Table A-13 summarizes the levels of nitrogen and phosphorus in manure reported by the ASAE. The data summarized by ASAE is combined from a wide range of published and unpublished information. The mean values for each parameter are determined by an arithmetic average consisting of one data point per reference source per year and represent fresh (as voided) feces and urine. Table A-13: Fresh manure production and characteristics per 1000 kg live animal mass per day (from ASAE, 1998a) Parameter Total Manure
kg†
Total Solids
kg
mean std dev mean std dev mean std dev mean std dev mean std dev mean std dev
Dairy 86 17 12 2.7 0.45 0.096 0.079 0.083 0.094 0.024 0.061 0.0058
Beef 58 17 8.5 2.6 0.34 0.073 0.086 0.052 0.092 0.027 0.030 **
Veal 62 24 5.2 2.1 0.27 0.045 0.12 0.016 0.066 0.011 ** **
Swine 84 24 11 6.3 0.52 0.21 0.29 0.10 0.18 0.10 0.12 **
Animal Type‡ Sheep Goat Horse 40 41 51 11 8.6 7.2 11 13 15 3.5 1.0 4.4 0.42 0.45 0.30 0.11 0.12 0.063 ** ** ** ** ** ** 0.087 0.11 0.071 0.030 0.016 0.026 0.032 ** 0.019 0.014 ** 0.0071
Layer 64 19 16 4.3 0.84 0.22 0.21 0.18 0.30 0.081 0.092 0.016
Broiler 85 13 22 1.4 1.1 0.24 ** ** 0.30 0.053 ** **
Turkey 47 13 12 3.4 0.62 0.13 0.080 0.018 0.23 0.093 ** **
Total Kjeldahl kg nitrogen║ Ammonia kg nitrogen Total kg phosphorus Orthokg phosphorus ** Data not found. † All values wet basis. ‡ Typical live animal masses for which manure values represent are: dairy, 640 kg; beef, 360 kg; veal, 91 kg; swine, 61 kg; sheep, 27 kg; goat, 64 kg; horse, 450 kg; layer, 1.8 kg; broiler, 0.9 kg; turkey, 6.8 kg; and duck, 1.4 kg. ║ All nutrient values are given in elemental form.
The fractions of the nutrient pools were calculated on a Total Solids basis, i.e. the water content of the manure was ignored. Assumptions used in the calculations are: 1) the mineral nitrogen pool is assumed to be entirely composed of NH3/NH4+, 2) the organic nitrogen pool is equal to total Kjeldahl nitrogen minus ammonia nitrogen, 3) the mineral phosphorus pool is equal to the value given for orthophosphorus, and 4) the organic phosphorus pool is equal to total phosphorus minus orthophosphorus. Total amounts of nitrogen and phosphorus were available for all manure types. For manure types with either the ammonia nitrogen or orthophosphorus value missing, the ratio of organic to mineral forms of the provided element were used to partition the total amount of the other element. For example, in Table A-
Duck 110 ** 31 15 1.5 0.54 ** ** 0.54 0.21 0.25 **
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SWAT USER’S MANUAL, VERSION 2000
13 amounts of total Kjeldahl N, ammonia N, and total P are provided for veal but data for orthophosphorus is missing. To partition the total P into organic and mineral pools, the ratio of organic to mineral N for veal was used. If both ammonia nitrogen and orthophosphorus data are missing, the ratio of the organic to mineral pool for a similar animal were used to partition the total amounts of element into different fractions. This was required for goat and broiler manure calculations. The ratio of organic to mineral pools for sheep was used to partition the goat manure nutrient pools while layer manure nutrient ratios were used to partition the broiler manure nutrient pools. As can be seen from the standard deviations in Table A-13, values for nutrients in manure can vary widely. If site specific data are available for the region or watershed of interest, those values should be used in lieu of the default fractions provided in the database.
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75
A.5 URBAN DATABASE The urban database file (urban.dat) summarizes urban landscape attributes needed to model urban areas. These attributes tend to vary greatly from region to region and the user is recommended to use values specific to the area being modeled. The following tables list the urban land types and attributes that are provided in the urban database. Numerous urban land type classifications exist. For the default urban land types included in the database, an urban land use classification system created by Palmstrom and Walker (1990) was simplified slightly. Table A-14 lists the land type classifications used by Palmstrom and Walker and those provided in the database. Table A-14: Urban land type classification systems Palmstrom and Walker (1990) SWAT Urban Database Residential-High Density Residential-High Density Residential-Med/High Density Residential-Medium Density Residential-Med/Low Density Residential-Med/Low Density Residential-Low Density Residential-Low Density Residential-Rural Density Commercial Commercial Industrial Industrial-Heavy Transportation Industrial-Medium Institutional Transportation Institutional
The urban database includes the following information for each urban land type: 1) fraction of urban land area that is impervious (total and directly connected); 2) curb length density; 3) wash-off coefficient; 4) maximum accumulated solids; 5) number of days for solid load to build from 0 kg/curb km to half of the maximum possible load; 6) concentration of total N in solid loading; 7) concentration of total P in solid loading; and 8) concentration of total NO3-N in solid loading. The fraction of total and directly connected impervious areas is needed for urban surface runoff calculations. The remaining information is used only when the urban build up/wash off algorithm is chosen to model sediment and nutrient loading from the urban impervious area.
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SWAT USER’S MANUAL, VERSION 2000
A.5.1 DRAINAGE SYSTEM CONNECTEDNESS When modeling urban areas the connectedness of the drainage system must be quantified. The best methods for determining the fraction total and directly connected impervious areas is to conduct a field survey or analyze aerial photographs. However these methods are not always feasible. An alternative approach is to use data from other inventoried watersheds with similar land types. Table A-15 contains ranges and average values calculated from a number of different individual surveys (the average values from Table A-15 are the values included in the database). Table A-16 contains data collected from the cities of Madison and Milwaukee, Wisconsin and Marquett, Michigan. Table A-15: Range and average impervious fractions for different urban land types. Range Average Range Average total total connected connected Urban Land Type impervious impervious impervious impervious Residential-High Density .60 .44 - .82 .44 .32 - .60 (> 8 unit/acre or unit/2.5 ha) Residential-Medium Density .38 .23 - .46 .30 .18 - .36 (1-4 unit/acre or unit/2.5 ha) Residential-Med/Low Density .20 .14 - .26 .17 .12 - .22 (> 0.5-1 unit/acre or unit/2.5 ha) Residential-Low Density .12 .07 - .18 .10 .06 - .14 (< 0.5 unit/acre or unit/2.5 ha) Commercial .67 .48 - .99 .62 .44 - .92 Industrial .84 .63 - .99 .79 .59 - .93 Transportation .98 .88 - 1.00 .95 .85 – 1.00 Institutional .51 .33 - .84 .47 .30 - .77 Table A-16: Impervious fractions for different urban land types in Madison and Milwaukee, WI and Marquett, MI. Directly Indirectly connected connected Urban Land Type impervious impervious Pervious Residential-High Density .51 .00 .49 Residential-Medium Density .24 .13 .63 Residential-Low Density .06 .10 .84 Regional Mall .86 .00 .14 Strip Mall .75 .00 .25 Industrial-Heavy .80 .02 .18 Industrial-Light .69 .00 .31 Airport .09 .25 .66 Institutional .41 .00 .59 Park .08 .06 .86
APPENDIX A: DATABASES
77
A.5.2 CURB LENGTH DENSITY Curb length may be measured directly by scaling the total length of streets off of maps and multiplying by two. To calculate the density the curb length is divided by the area represented by the map. The curb length densities assigned to the different land uses in the database were calculated by averaging measured curb length densities reported in studies by Heaney et al. (1977) and Sullivan et al. (1978). Table A-17 lists the reported values and the averages used in the database. Table A-17: Measured curb length density for various land types Location: Tulsa, 10 Ontario Average of OK Cities two values Land type km/ha km/ha km/ha Residential 0.30 0.17 0.24 Commercial 0.32 0.23 0.28 Industrial 0.17 0.099 0.14 Park 0.17 -0.17 Open 0.063 0.059 0.06 Institutional -0.12 0.12
SWAT database categories using average value: All Residential Commercial Industrial Transportation, Institutional
A.5.3 WASH-OFF COEFFICENT The database assigns the original default value, 0.18 mm-1, to the wash-off coefficient for all land types in the database (Huber and Heaney, 1982). This value was calculated assuming that 13 mm of total runoff in one hour would wash off 90% of the initial surface load. Using sediment transport theory, Sonnen (1980) estimated values for the wash-off coefficient ranging from 0.002-0.26 mm-1. Huber and Dickinson (1988) noted that values between 0.039 and 0.390 mm-1 for the wash-off coefficient give sediment concentrations in the range of most observed values. This variable is used to calibrate the model to observed data.
A.5.4 MAXIMUM SOLID ACCUMULATION AND RATE OF ACCUMULATION The shape of the solid build-up equation is defined by two variables: the maximum solid accumulation for the land type and the amount of time it takes to build up from 0 kg/curb km to one-half the maximum value. The values assigned
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SWAT USER’S MANUAL, VERSION 2000
to the default land types in the database were extrapolated from a study performed by Sartor and Boyd (1972) in ten U.S. cities. They summarized the build-up of solids over time for residential, commercial, and industrial land types as well as providing results for all land types combined (Figure A-5).
Figure A-5: Solid loading as a function of time (Sartor and Boyd, 1972)
The lines plotted in Figure A-5 were adapted for use in the database. Table A-18 lists maximum load values and time to accumulate half the maximum load that were derived from the graph. The assignment of values to the different land types is provided in the table also. Table A-18: Maximum solid load and accumulation time (from Sartor and Boyd, 1972). Maximum time to accumulate SWAT database categories loading ½ maximum load Land type kg/curb km days using value: Residential 225 0.75 All Residential Commercial 200 1.60 Commercial Industrial 400 2.35 Industrial All land types 340 3.90 Transportation/Institutional
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79
A.5.5 NUTRIENT CONCENTRATION IN SOLIDS For the default land types in the database, nutrient concentrations in the solids were extrapolated from a nationwide study by Manning et al. (1977). The data published by Manning is summarized in Table A-19. Three concentration values are required: total nitrogen (mg N/kg), nitrate nitrogen (mg NO3-N/kg), and total phosphorus (mg P/kg). Manning provided total nitrogen values for all of his land use categories, nitrate values for one land use category and mineral phosphorus values for all the land use categories. To obtain nitrate concentrations for the other land use categories, the ratio of NO3-N to total N for commercial areas was assumed to be representative for all the categories. The nitrate to total N ratio for commercial land was multiplied by the total N concentrations for the other categories to obtain a nitrate concentration. The total phosphorus concentration was estimated by using the ratio of organic phosphorus to orthophosphate provided by the Northern Virginia Planning District Commission (1979). Total phosphorus loads from impervious areas are assumed to be 75 percent organic and 25 percent mineral. Table A-20 summarizes the assignment of values to the default land types in the urban database. Table A-19: Nationwide dust and dirt build-up rates and pollutant fractions (Manning et al., 1977) Pollutant Land Use Category Dust & Dirt Accumulation (kg/curb km/day)
mean range # obs.
Single Family Residential 17 1-268 74
Mult. Family Residential 32 2-217 101
Commercial 47 1-103 158
Industrial 90 1-423 67
All Data 45 1-423 400
Total N-N (mg/kg)
mean range # obs.
460 325-525 59
550 356-961 93
420 323-480 80
430 410-431 38
480 323-480 270
NO3 (mg/kg)
mean range # obs.
----
----
24 10-35 21
----
24 10-35 21
PO4-P (mg/kg)
mean range # obs.
49 20-109 59
58 20-73 93
60 0-142 101
26 14-30 38
53 0-142 291
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SWAT USER’S MANUAL, VERSION 2000
Table A-20: Nutrient concentration assignments for default land types Manning et al (1977) Modifications: Final Value:
SWAT database categories using value:
Total Nitrogen-N Single Fam Res. 460 ppm -460 ppm Residential: Med/Low & Low Mult. Fam. Res. 550 ppm -550 ppm Residential: Med. & High Commercial 420 ppm -420 ppm Commercial Industrial 430 ppm -430 ppm Industrial All Data 480 ppm -480 ppm Transportation/Institutional Nitrate-N: multiply reported value by fraction of weight that is nitrogen to get NO3-N Single Fam Res. (5.5/420) x 460 6.0 ppm Residential: Med/Low & Low Mult. Fam. Res. (5.5/420) x 550 7.2 ppm Residential: Med. & High Commercial 5.5 ppm -5.5 ppm Commercial Industrial (5.5/420) x 430 5.6 ppm Industrial All Data (5.5/420) x 480 6.3 ppm Transportation/Institutional Total Phosphorus-P: assume PO4-P is 25% of total P Single Fam Res. 49/(.25) 196 ppm Residential: Med/Low & Low 49 ppm PO4-P 58/(.25) 232 ppm Residential: Med. & High Mult. Fam. Res. 58 ppm PO4-P 60/(.25) 240 ppm Commercial Commercial 60 ppm PO4-P 26/(.25) 104 ppm Industrial Industrial 26 ppm PO4-P 53/(.25) 212 ppm Transportation/Institutional All Data 53 ppm PO4-P
A.6 REFERENCES American Society of Agricultural Engineers, 1998a. Manure production and characteristics, p. 646-648. In ASAE Standards 1998, 45th edition, Section D384.1. ASAE, St. Joseph. American Society of Agricultural Engineers, 1998b. Terminology and definitions for agricultural tillage implements, p. 261-272. In ASAE Standards 1998, 45th edition, Section S414.1. ASAE, St. Joseph. Arnold, J.G. and J.R. Williams. 1995. SWRRB—A watershed scale model for soil and water resources management. p. 847-908. In V.P. Singh (ed) Computer models of watershed hydrology. Water Resources Publications. Bailey, L.H. 1935. The Standard cyclopedia of horticulture. The Macmillan Publishing Co., New York, N.Y. Consumer Nutrition Center. 1982. Composition of foods: Fruit and fruit juices. USDA Human Nutrition Information Service. Agricultural Handbook 8-9. Diaz, R.A. and G.S. Campbell. 1988. Assessment of vapor density deficit from available air temperature information. ASA Annual Meetings, Anaheim, CA, Agron. Abstr., 1988, 16.
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Duncan, W.G. and Hesketh, J.D. 1968. Net photosynthesis rates, relative leaf growth rates and leaf numbers of 22 races of maize grown at eight temperatures. Crop Sci. 8:670-674. Hackett, C. and J. Carolane. 1982. Edible horticultural crops, a compendium of information on fruit, vegetable, spice and nut species, Part II: Attribute data. Division of Land Use Research, CSIRO, Canberra. Heaney, J.P., W.C. Huber, M.A. Medina, Jr., M.P. Murphy, S.J. Nix, and S.M. Haasan. 1977. Nationwide evaluation of combined sewer overflows and urban stormwater discharges—Vol. II: Cost assessment and impacts. EPA600/2-77-064b (NTIS PB-266005), U.S. Environmental Protection Agency, Cincinnati, OH. Huber, W.C. and R.E. Dickinson. 1988. Storm water management model, version 4: user’s manual. U.S. Environmental Protection Agency, Athens, GA. Huber, W.C. and J.P. Heaney. 1982. Chapter 3: Analyzing residual discharge and generation from urban and non-urban land surfaces. p. 121-243. In D.J. Basta and B.T. Bower (eds). Analyzing natural systems, analysis for regional residuals—environmental quality management. John Hopkins University Press, Baltimore, MD. Jensen, M.E., R.D. Burman, and R.G. Allen. 1990. Evapotranspiration and Irrigation Water Requirements. ASCE Manuals and Reports on Engineering Practice No. 70. ASCE, New York, N.Y. Kiniry, J.R. 1998. Biomass accumulation and radiation use efficiency of honey mesquite and eastern red cedar. Biomass and Bioenergy 15:467-473. Kiniry, J.R. 1999. Response to questions raised by Sinclair and Muchow. Field Crops Research 62:245-247. Kiniry, J.R., R. Blanchet, J.R. Williams, V. Texier, C.A. Jones, and M. Cabelguenne. 1992b. Sunflower simulation using EPIC and ALMANAC models. Field Crops Res., 30:403-423. Kiniry, J.R. and A.J. Bockholt. 1998. Maize and sorghum simulation in diverse Texas environments. Agron. J. 90:682-687.
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Kiniry, J.R. C.A. Jones, J.C. O'Toole, R. Blanchet, M. Cabelguenne and D.A. Spanel. 1989. Radiation-use efficiency in biomass accumulationprior to grain-filling for five grain-crop species. Field Crops Research 20:51-64. Kiniry, J.R., J.A. Landivar, M. Witt, T.J. Gerik, J. Cavero, L.J. Wade. 1998. Radiation-use efficiency response to vapor pressure deficit for maize and sorghum. Field Crops Research 56:265-270. Kiniry, J.R., D.J. Major, R.C. Izaurralde, J.R. Williams, P.W. Gassman, M. Morrison, R. Bergentine, and R.P. Zentner. 1995. EPIC model parameters for cereal, oilseed, and forage crops in the northern Great Plains region. Can. J. Plant Sci. 75: 679-688. Kiniry, J.R., W.D. Rosenthal, B.S. Jackson, and G. Hoogenboom. 1991. Chapter 5: Predicting leaf development of crop plants. p. 30-42. In Hodges (ed.) Predicted crop phenology. CRC Press, Boca Raton, FL. Kiniry, J.R., M.A. Sanderson, J.R. Williams, C.R. Tischler, M.A. Hussey, W.R. Ocumpaugh, J.C. Read, G.V. Esbroeck, and R.L. Reed. 1996. Simulating Alamo switchgrass with the Almanac model. Agron. J. 88:602-606. Kiniry, J.R., C.R. Tischler and G.A. Van Esbroeck. 1999. Radiation use efficiency and leaf CO2 exchange for diverse C4 grasses. Biomass and Bioenergy 17:95-112. Kiniry, J.R. and J.R. Williams. 1994. EPIC Crop Parameters for Vegetables for the Nitrogen and Phosphorus Portions of the RCA Analysis. Memorandum. Kiniry, J.R., J.R. Williams, P.W. Gassman, P. Debaeke. 1992a. A general, process-oriented model for two competing plant species. Transactions of the ASAE 35:801-810. Kiniry, J.R., J.R. Williams, R.L. Vanderlip, J.D. Atwood, D.C. Reicosky, J. Mulliken, W.J. Cox, H.J. Mascagni, Jr., S.E. Hollinger and W.J. Wiebold. 1997. Evaluation of two maize models for nine U.S. locations. Agron. J. 89:421-426.
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Knisel, W.G. (ed). 1993. GLEAMS: Groundwater loading effects of agricultural management systems, Version 2.10. UGA-CPES-BAED Publication No. 5. University of Georgia, Tifton, GA. Körner, Ch. 1977. Blattdiffusionswiderstände verschiedener Pflanzen in der zentralalpinen Grasheide der Hohen Tauren. p. 69-81. In Cernusca, A. (ed.) Alpine Grasheide Hohe Tauern. Ergebnisse der Ökosystemstudie 1976. Veröff. Österr. MaB-Hochgebirgsprogr. ,,Hohe Tauern“. Vol 1. Universitätsverlag Wagner, Innsbruck. Körner, Ch., J.A. Scheel and H. Bauer. 1979. Maximum leaf diffusive conductance in vascular plants. Photosynthetica 13:45-82. Leonard, R.A. and W.G. Knisel. 1988. Evaluating groundwater contamination potential from herbicide use. Weed Tech. 2:207-216. Manning, M.J., R.H. Sullivan, and T.M. Kipp. 1977. Nationwide evaluation of combined sewer overflows and urban stormwater discharges—Vol. III: Characterization of discharges. EPA-600/2-77-064c (NTIS PB-272107) U.S. Environmental Protection Agency, Cincinnati, OH. Manrique, L.A., J.R. Kiniry, T. Hodges, and D.S. Axness. 1991. Dry matter production and radiation interception of potato. Crop Sci. 31: 1044-1049. Martin, J.H., W.H. Leonard and D.L. Stamp. 1976. Principles of field crop production, 3rd edition. Macmillan Publishing Co., Inc., New York. Maynard, D.N. and Hochmuth. 1997. Knott's handbook for vegetable growers, 4th edition. John Wiley & Sons, Inc., New York. Monteith, J.L. 1965. Evaporation and the environment. p. 205-234. In The state and movement of water in living organisms, XIXth Symposium. Soc. for Exp. Biol., Swansea. Cambridge University Press. Northern Virginia Planning District Commission. 1979. Guidebook for screening urban nonpoint pollution management strategies: a final report prepared for Metropolitan Washington Council of Governments. Northern Virginia Planning District Commission, Falls Church, VA.
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Nutrition Monitoring Division. 1984b. Composition of food: Cereal grains and pasta. USDA Human Nutrition Information Service. Agricultural Handbook 8-20. Nutrition Monitoring Division. 1984c. Composition of food: Legumes and legume products.
USDA
Human
Nutrition
Information
Service.
AgriculturalHandbook 8-16. Nutrition Monitoring Division. 1984d. Composition of food: Nut and seed products.
USDA
Human
Nutrition
Information
Service.
AgriculturalHandbook 8-12. Nutrition Monitoring Division. 1984a. Composition of food: Vegetables and vegetable products. USDA Human Nutrition Information Service. Agricultural Handbook 8-11. Palmstrom, N. and W.W. Walker, Jr. 1990. P8 Urban Catchment Model: User’s guide, program documentation, and evaluation of existing models, design concepts and Hunt-Potowomut data inventory. The Narragansett Bay Project Report No. NBP-90-50. Sartor, J.D. and G.B. Boyd. 1972. Water pollution aspects of street surface contaminants. EPA-R2-72-081 (NTIS PB-214408) U.S. Environmental Protection Agency, Washington, DC. Sonnen, M.B. 1980. Urban runoff quality: information needs. ASCE Journal of the Technical Councils 106(TC1): 29-40. Stockle, C.O. and J.R. Kiniry. 1990. Variability in crop radiation-use efficiency associated with vapor pressure deficit. Field Crops Research 25:171-181. Stockle, C.O., J.R. Williams, N.J. Rosenberg, and C.A. Jones. 1992. A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part 1—Modification of the EPIC model for climate change analysis. Agricultural Systems 38:225-238. Sullivan, R.H., W.D. Hurst, T.M. Kipp, J.P. Heaney, W.C. Huber, and S.J. Nix. 1978. Evaluation of the magnitude and significance of pollution from urban storm water runoff in Ontario. Research Report No. 81, Canada-
APPENDIX A: DATABASES
Ontario
Research
Program,
Environmental
Protection
85
Service,
Environment Canada, Ottawa, Ontario. Watson, D.J. 1958. The dependence of net assimilation rate on leaf area index. Ann. Bot. N.S. 22:37-54. Wauchope, R.D., T.M. Buttler, A.G. Hornsby, P.W.M. Augustijn-Beckers, and J.P. Burt. 1992. The SCS/ARS/CES pesticide properties database for environmental decision-making. Environ. Contam. Toxicol. Reviews 123:1-164. Willis, G.H. and L.L. McDowell. 1987. Pesticide persistence on foliage. Environ. Contam. Toxicol. Reviews 100:23-73. Willis, G.H., W.F. Spencer, and L.L. McDowell. 1980. Chapter 18: The interception of applied pesticides by foliage and their persistence and washoff potential. p. 595-606. In W.G. Knisel (ed). CREAMS: A field scale model for chemicals, runoff, and erosion from agricultural management systems, Vol. 3. U.S. Dept. of Agri., Sci., and Education Adm., Conservation Research Report No. 26. U.S. Government Printing Office, Washington, D.C.
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APPENDIX B
EXAMPLE WATERSHED CONFIGURATIONS
The watershed configuration file defines the spatial relationship of objects within the watershed. The three techniques used to subdivide a watershed are the subwatershed discretization, the hillslope discretization, and the grid cell discretization. The following sections describe how to set up the watershed configuration file for each of the different discretization techniques.
B.1 SUBWATERSHED DISCRETIZATION The subwatershed discretization divides the watershed into subbasins based on topographic features of the watershed. This technique preserves the natural flow paths, boundaries, and channels required for realistic routing of water, sediment and chemicals. All of the GIS interfaces developed for SWAT use the subwatershed discretization to divide a watershed. The number of subbasins chosen to model the watershed depends on the size of the watershed, the spatial detail of available input data and the amount of detail required to meet the goals of the project. When subdividing the watershed, keep in mind that topographic attributes (slope, slope length, channel length, channel width, etc.) are calculated or summarized at the subbasin level. The subbasin delineation should be detailed enough to capture significant topographic variability within the watershed. Once the subbasin delineation has been completed, the user has the option of modeling a single soil/land use/management scheme for each subbasin or partitioning the subbasins into multiple hydrologic response units (HRUs). Hydrologic response units are unique soil/land use/management combinations within the subbasin which are modeled without regard to spatial positioning. When multiple HRUs are modeled within a subbasin, the land phase of the hydrologic cycle is modeled for each HRU and then the loadings from all HRUs within the subbasin are summed. The net loadings for the subbasin are then routed through the watershed channel network. HRUs are set up in the subbasin general attribute file (.sub). The following sections demonstrate how to create a SWAT watershed configuration file using the subwatershed discretization.
B.1.1 SUBWATERSHED DISCRETIZATION: 3 SUBBASINS Assume we have a watershed with 3 subbasins as illustrated in Figure B-1.
Figure B-1: Subwatershed delineation
Step 1: Write the subbasin command for each subbasin. (This command simulates the land phase of the hydrologic cycle.) column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1
1 2 3
1 2 3
Writing subbasin in space 1-10 is optional. The model identifies the configuration command by the code in column 1. The option of writing the command in space 1-10 is provided to assist the user in interpreting the configuration file. Column 2 is the hydrograph storage location number (array location) where data for the loadings (water, sediment, chemicals) from the subbasin are stored. Column 3 is the subbasin number. The subbasin number tells SWAT which input files listed in file.cio contain the data used to model the subbasin. Subbasin numbers are assigned sequentially in file.cio to each pair of lines after line 14. The files listed on lines 15 & 16 of file.cio are used to model subbasin 1, the files listed on lines 17 & 18 of file.cio are used to model subbasin 2, the files listed on lines 19 & 20 of file.cio are used to model subbasin 3, and so on.
Step 2a: Route the stream loadings through the reach network. Begin by routing the headwater subbasin loadings through the main channel of the respective subbasin. (Headwater subbasins are those with no subbasins upstream.) Referring to Figure B-1, assume that subbasins 1 and 2 are upstream of subbasin 3. This would make subbasins 1 and 2 headwater subbasins. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 2
1 2 3 4 5
1 2 3 1 2
1 2
0.000 0.000
As mentioned in the last step, column 1 is used to identify the command. Column 2 is the hydrograph storage location number identifying the location where results from the route simulation are placed. Column 3 provides the number of the reach, or main channel, the inputs are routed through. The number of the reach in a particular subbasin is the same as the number of the subbasin. Column 4 lists the number of the hydrograph storage location containing the data to be routed through the reach. The loadings from subbasin 1 are stored in hydrograph storage #1 and the loadings from subbasin 2 are stored in hydrograph storage #2. Column 6 lists the fraction of overland flow. For the subwatershed discretization, this value will always be zeroflow is always considered to be channelized before entering the next subbasin. Step 2b: Route the stream loadings through the reach network. Use the add and route commands to continue routing through the watershed. For this example, the water, sediment and chemicals flowing out of subbasins 1 and 2 and the loadings from subbasin 3 must be added together and routed through the main channel of subbasin 3. The loadings from the outlet of subbasin 1 are stored in hydrograph location #4; the loadings from the outlet of subbasin 2 are stored in hydrograph location #5; and the loadings from subbasin 3 are stored in hydrograph location #3.
column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route add add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 2 5 5 2
1 2 3 4 5 6 7 8
1 2 3 1 2 4 6 3
1 2 5 3 7
0.000 0.000
0.000
The add command is specified in column 1 by the number 5. The hydrograph storage location numbers of the 2 data sets to be added are listed in columns 3 and 4. The summation results are stored in the hydrograph location number given in column 2. Step 3: Once the stream loadings have been routed to the watershed outlet, append a finish command line to signify the end of the watershed routing file. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 2 5 5 2 0
1 2 3 4 5 6 7 8
1 2 3 1 2 4 6 3
1 2 5 3 7
0.000 0.000
0.000
B.1.2 SUBWATERSHED DISCRETIZATION: SAVING SIMULATION RESULTS FOR DOWNSTREAM RUNS If the watershed of interest is split up into subwatersheds that are modeled with separate SWAT runs, the outflow from the upstream subwatersheds must be saved in a file using the save command. This data will then be input into the SWAT simulation of the downstream portion of the watershed using a recday command. In example B.1.1, the outflow from the watershed is stored in hydrograph location #8, so this is the data we need to store in a daily file for use in another SWAT simulation. The watershed configuration modified to store outflow data is: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route add add route save finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 2 5 5 2 9 0
1 2 3 4 5 6 7 8 8
1 2 3 1 2 4 6 3
1 2 5 3 7
0.000 0.000
0.000
The save command is specified in column 1 by the number 9. Column 2 lists the hydrograph storage location of the data to be saved in the event output file. The name of the event output file is listed in file.cio and usually possesses the .eve file extension. Only one save command is allowed in a simulation. The event file output is described in Chapter 44.
B.1.3 SUBWATERSHED DISCRETIZATION: INCORPORATING POINT SOURCE/UPSTREAM SIMULATION DATA Point source and upstream simulation data may be incorporated into a run using one of four record commands: recday, recmon, recyear, and reccnst. The recday command reads data from a file containing loadings of different constituents for each day of simulation. The recmon command reads data from a file containing average daily loadings for each month. The recyear command reads data from a file containing average daily loadings for each year. The reccnst command reads in average annual daily loadings. The record command chosen to read in the data is a function of the detail of data available. To read in upstream simulation data, the recday command is always used. Assuming the subbasin delineation in Figure B-1 is used with one point source (sewage treatment plants) per subbasin, the watershed configuration file is: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin recday add route recday add route add recday add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 1 3 10 4 sub1.pnt 5 5 2 6 10 7 sub2.pnt 5 8 2 9 5 10 10 11 sub3.pnt 5 12 5 13 2 14 0
1 2 3 1 4 1 2
1 5
7 2 6 3
2 8 9
11 12 3
10 3 13
0.000
0.000
0.000
All of the record commands require 2 lines. On the first line, column 1 contains the command code for the specific record command, column 2 contains the hydrologic storage location where the data from the file is stored, and column 3 contains the file number. A different file number must be used for each point source of a specific type (e.g., all recday commands must have unique file numbers). The second line lists the name of the file containing the input data. A description of the four types of record files is given in Chapter 43.
B.1.4 SUBWATERSHED DISCRETIZATION: INCORPORATING RESERVOIRS Water bodies located along the main channel are modeled using reservoirs. To incorporate a reservoir into a simulation, a routres command is used. There is no limitation on the number of reservoirs modeled. Assuming the subbasin delineation in Figure B-1 is used with one reservoir located at the outlet, the watershed configuration file is: space 1-10
subbasin subbasin subbasin route route add add route routres finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 1 3 2 4 2 5 5 6 5 7 2 8 3 9 lakefork.res 0
1 2 3 1 1 2 2 4 5 6 3 3 7 1 8 lakefork.lwq
0.000 0.000
0.000 3
The routres command requires 2 lines. On the first line, the routres command is identified with the number 3 in column 1. Column 2 gives the hydrograph storage location where outflow data from the reservoir is stored. Column 3 lists the reservoir number. Column 4 gives the hydrograph storage location of the data to be routed through the reservoir. Column 5 lists the subbasin with which the reservoir is associated. A different reservoir number must be assigned to each reservoir and the numbers should be sequential beginning with 1. The second line lists two file names, the reservoir input file (.res) and the reservoir water quality file (.lwq).
B.1.5 SUBWATERSHED DISCRETIZATION: SAVING SIMULATION RESULTS FOR ONE LOCATION Users often need to compare streamflow, sediment, nutrient and/or pesticide levels predicted by the model with levels measured in the stream. To save daily or hourly model output data for a particular location on the stream, the saveconc command is used. Assume there is a stream gage at the outlet of the watershed shown in Figure B-1 and that we want to compare simulated and measured streamflow for this location. Hydrograph storage location #8 stores the flow data for this location in the watershed, so this is the data we need to process to create the saveconc output file. The watershed configuration modified to process data for this location is: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route add add route saveconc finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 1 3 2 4 2 5 5 6 5 7 2 8 14 8 strgage.out 0
1 2 3 1 2 4 6 3 1
1 2 5 3 7 0
0.000 0.000
0.000
The saveconc command requires 2 lines. On the first line, the saveconc command is identified with the number 14 in column 1. Column 2 gives the hydrograph storage location of the data to be processed for the saveconc output file. Column 3 lists the file number. Column 4 gives the print frequency (daily or hourly). More than one saveconc command may be used in a simulation. A different file number must be assigned to each saveconc output file and the file numbers should be sequential beginning with 1. The second line lists the name of the saveconc output file. The saveconc command differs from the save command in that it converts the mass amounts of water, sediment, and chemicals to units that are commonly used to report measured values. Output files produced by the saveconc command cannot be read into another SWAT run—the save command must be used to produce input for another simulation.
B.2 HILLSLOPE DISCRETIZATION The hillslope discretization allows overland flow from one subbasin to flow onto the land area of another subbasin. As the name implies, this discretization allows SWAT to model hillslope processes. The hillslope discretization incorporates more detail into the watershed configuration file than the subwatershed discretization. The number of subbasins chosen to model the watershed will depend on the size of the watershed, the spatial relationship of different land uses to one another, the spatial detail of available input data and the amount of detail required to meet the goals of the project. Because this discretization scheme places more emphasis on land use, the subbasins are delineated so that there is one land use and soil per subbasin. The hillslope discretization can be combined with the subwatershed discretization to provide detailed modeling of particular land use areas while modeling the remaining land use areas with the more generalized approach. Useful applications of this discretization include: watersheds with concentrated animal feeding operations, watersheds where detailed modeling of filter strips is desired, and microwatersheds where the scale of the simulation allows detail about relative land use positions to be incorporated.
B.2.1 HILLSLOPE DISCRETIZATION: MODELING A DAIRY OPERATION Assume a microwatershed containing a concentrated animal feeding operation with several different areas of land use and management is being modeled. Milking cows are confined in stalls. All waste produced by the milking cows is collected and applied over manure application fields also located in the microwatershed. The dry cows are kept in pastures. The pastured cows keep the areas adjacent to the farm buildings denuded of grass. Runoff from the denuded areas flows onto the pasture. Runoff from the pasture flows into a filter strip or buffer zone. Runoff exiting the filter strip enters the stream. The manure application fields are isolated from the rest of the dairy operation. Runoff from the application fields flows into a filter strip, and then enters the steam. Figure B-2 illustrates the relationship of land areas in the dairy operation. Areas of the microwatershed outside of the daily operation are forested.
Figure B-2: Spatial positioning of land areas in dairy operation.
This microwatershed will be divided into 6 subbasins: Subbasin 1: loafing area Subbasin 2: pasture Subbasin 3: filter strip associated with pasture Subbasin 4: waste application area Subbasin 5: filter strip associated with waste application area Subbasin 6: completely channelized stream and forest in microwatershed
Step 1: Write the subbasin command for each subbasin. (This command simulates the land phase of the hydrologic cycle.) column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
subbasin subbasin subbasin subbasin subbasin subbasin
1 1 1 1 1 1
1 2 3 4 5 6
1 2 3 4 5 6
Writing subbasin in space 1-10 is optional. The model identifies the configuration command by the code in column 1. The option of writing the command in space 1-10 is provided to assist the user in interpreting the configuration file. Column 2 is the hydrograph storage location number (array location) where data for the loadings (water, sediment, chemicals) from the subbasin are stored. Column 3 is the subbasin number. The subbasin number tells SWAT which input files listed in file.cio contain the data used to model the subbasin. Subbasin numbers are assigned sequentially in file.cio to each pair of lines after line 14. The files listed on lines 15 & 16 of file.cio are used to model subbasin 1, the files listed on lines 17 & 18 of file.cio are used to model subbasin 2, the files listed on lines 19 & 20 of file.cio are used to model subbasin 3, and so on. Step 2: Route the stream loadings. The hillslope discretization differs from the subwatershed discretization primarily in the method used to route loadings through the watershed. Loadings from subbasins are not routed through the subbasin if the flow leaving the subbasin is not completely channelized. For our example, subbasin 6 is the only subbasin completely channelized. Assume that runoff from the denuded areas (subbasin 1) is sheet flow, i.e. there are no rills, gullies or any other evidence of channelized flow in the denuded area. Runoff from the denuded area will be routed to the pasture (subbasin 2) using the route command: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
2
7
2
1
1.000
As mentioned in the last step, column 1 is used to identify the command. Column 2 is the hydrograph storage location number identifying the location where results from the channelized portion of the route simulation are placed. In this instance, because there is no channelized flow, this storage location will contain no data.
Column 3 provides the number of the reach or subbasin the inputs are routed through. (The number of the reach in a particular subbasin is the same as the number of the subbasin.) The fraction of the loadings classified as overland flow are applied to the subbasin land area while the fraction of the loadings classified as channelized flow are routed through the main channel of the subbasin and are exposed to in-stream processes. Channelized flow has no interaction with the land area in the subbasin. Column 4 lists the number of the hydrograph storage location containing the data to be routed through the reach. The loadings from subbasin 1 are stored in hydrograph storage #1. Column 6 lists the fraction of overland flow. For completely channelized flow this fraction is zero. For 100% overland flow, this fraction is 1.00. The entire watershed configuration to this point looks like: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2
1 2 3 4 5 6 7
1 2 3 4 5 6 2
1
1.000
Assume that runoff from the pasture is slightly channelized (10% channels). Flow from the pasture is routed to the filter strip (subbasin 3) using the next route command: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2
1 2 3 4 5 6 7 8
1 2 3 4 5 6 2 3
1 2
1.000 0.900
As mentioned previously, hydrograph storage location #7 contains no data because none of the runoff entering subbasin 2 is channelized. Consequently, when routing runoff leaving subbasin 2, this hydrograph storage location can be ignored. For subbasin 3, however, there will be data in hydrograph storage location #8 from the 10% of flow that is channelized in that subbasin. Loadings from subbasin 3 will enter the main stream in subbasin 6. The total loadings from the denuded area/pasture/filter strip section of the microwatershed are determined
by adding the runoff generated from subbasin 3 and the channelized flow routing results. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 2 3 3
1 2 8
1.000 0.900
The loadings from simulation of the land phase of the hydrologic cycle in subbasin 3 are stored in hydrograph storage location #3 and the loadings from simulation of the channelized flow in subbasin 3 are stored in hydrograph location #8. The add command is specified in column 1 by the number 5. The hydrograph storage location numbers of the 2 data sets to be added are listed in columns 3 and 4. The summation results are stored in the hydrograph location number given in column 2. Net loadings from the denuded area/pasture/filter strip is stored in hydrograph location #9. Assume that the manure application area (subbasin 4) is well managed and all runoff from this area is overland flow (no channelized flow). To route flow from the application area to the associated filter strip (subbasin 5) a route command will be appended to the end of the configuration: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 2 3 3 5
1 2 8 4
1.000 0.900 1.000
Hydrograph storage location #10 contains no data because none of the runoff entering subbasin 5 is channelized. Consequently, when routing runoff leaving subbasin 5, this hydrograph storage location can be ignored. Net loadings from the waste application area/filter strip section of the watershed is stored in hydrograph location #5.
Flow through subbasin 6, which contains the stream, is completely channelized. All of the loadings for the stream must be summed together and then routed through the stream. There are 3 sources of loading to the stream: the denuded area/pasture/filter strip (hydrograph location #9), the waste application area/filter strip (hydrograph location #10), and the forest land area (hydrograph location #6). Add commands are used to sum the loadings. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route add add
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2 5 5
1 2 3 4 5 6 7 8 9 10 11 12
1 2 3 4 5 6 2 3 3 5 9 6
1 2 8 4 5 11
1.000 0.900 1.000
Flow is routed through the stream using a route command: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route add add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2 5 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13
1 2 3 4 5 6 2 3 3 5 9 6 6
1 2 8 4 5 11 12
1.000 0.900 1.000
0.000
Step 3: Once the stream loadings have been routed to the watershed outlet, append a finish command line to signify the end of the watershed routing file. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2 5 5 2 0
1 2 3 4 5 6 7 8 9 10 11 12 13
1 2 3 4 5 6 2 3 3 5 9 6 6
1 2 8 4 5 11 12
1.000 0.900 1.000
0.000
B.2.2 HILLSLOPE DISCRETIZATION: COMBINING WITH SUBWATERSHED DISCRETIZATION The hillslope discretization is a very detailed discretization scheme and is suited to small watersheds. However, it can be used in combination with the subwatershed discretization to provide detailed simulation of certain land uses in a large watershed whose spatial relationships are important to the study. As an example, assume that the dairy operation described in Section B.2.1 is located in a headwater region of the watershed example used in Section B.1. Figure B-3 illustrates the location of the dairy in the larger watershed. (Assume the microwatershed modeled in Section B.2.1 is subbasin B in Figure B-3.)
Figure B-3: Watershed with dairy operation
There are two options that may be used to combine the detailed modeling of the dairy with the less detailed modeling of the other land uses in the watershed. The first option is to model the dairy in a separate simulation and save the loadings from the microwatershed using the save command. These daily loadings will then be read into the simulation of the larger watershed using a recday command. The second option is to merge the watershed configuration given in Section B.2.1 with the watershed configuration given Section B.1.1
Option 1: Two separate runs. The watershed configuration file for simulation of the microwatershed with the dairy will be modified to save the outflow data to an event file. The name of the event file is specified as “dairy.eve” in the file.cio for the microwatershed simulation. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route add add route save finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2 5 5 2 9 0
1 2 3 4 5 6 7 8 9 10 11 12 13 13
1 2 3 4 5 6 2 3 3 5 9 6 6
1 2 8 4 5 11 12
1.000 0.900 1.000
0.000
Because the area in subbasin B is modeled in the microwatershed simulation, the area will not be directly modeled in the large watershed simulation. Instead, the data in the file dairy.eve will be read in and routed. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin route recday add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 2 10 dairy.eve 5 5 2 0
1 2 3 4
1 2 1 1
5 6 7
3 5 2
1
0.000
4 2 6
0.000
In the above configuration, subbasin A is subbasin 1, subbasin C is subbasin 2 and outflow from subbasin B is read in with the recday command.
Option 2: A combined simulation. In this simulation, the routing for the entire watershed is contained in one configuration file. We will include comment lines in this watershed configuration to identify the different portions of the watershed being simulated. Subbasin B will be divided into 6 separate subbasins numbered 1-6 with the same land use assignments listed in section B.2.1. Subbasin A is subbasin 7 in this simulation while subbasin C is subbasin 8. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
* land phase for subbasin B subbasin 1 1 1 subbasin 1 2 2 subbasin 1 3 3 subbasin 1 4 4 subbasin 1 5 5 subbasin 1 6 6 * land phase for subbasin A subbasin 1 7 7 * land phase for subbasin C subbasin 1 8 8 * route flow through subbasin A route 2 9 7 7 0.000 * route flow through subbasin B route 2 10 2 1 1.000 route 2 11 3 2 0.900 add 5 12 3 11 route 2 13 5 4 1.000 add 5 14 12 5 add 5 15 6 14 route 2 16 6 15 0.000 * add outflow from subbasin A and B to loadings from subbasin C add 5 17 9 16 add 5 18 8 17 * route flow through subbasin C route 2 19 8 18 0.000 finish 0
Comment lines are denoted by an asterisk in the first space. When SWAT reads an asterisk in this location it knows the line is a comment line and does not process the line.
B.3 GRID CELL DISCRETIZATION The grid cell discretization allows a user to capture a high level of spatial heterogeneity or variability in the simulation. The grid cells should be small enough to obtain homogenous land use, soil, and topographic characteristics for the area in each cell but large enough to keep the amount of data required for the run at a reasonable level. The routing methodology for the grid cell discretization is the same as that for the subwatershed discretization. The difference between the two discretization schemes lies in the average size of the subbasin and the method used to define subbasin boundaries. The GIS interfaces are currently not able to delineate a watershed using a grid cell discretization. However, there are plans to create a GIS tool capable of generating a grid cell discretization.
B.3.1 GRID CELL DISCRETIZATION: 9 CELLS To illustrate the grid cell discretization, a simple nine-cell example will be used.
Figure B-4: Grid cell delineation with flow paths shown.
Step 1: Write the subbasin command for each cell. (This command simulates the land phase of the hydrologic cycle.) column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
Writing subbasin in space 1-10 is optional. The model identifies the configuration command by the code in column 1. The option of writing the command in space 1-10 is provided to assist the user in interpreting the configuration file. Column 2 is the hydrograph storage location number (array location) where data for the loadings (water, sediment, chemicals) from the subbasin are stored. Column 3 is the subbasin number. The subbasin number tells SWAT which input files listed in file.cio contain the data used to model the subbasin. Subbasin numbers are assigned sequentially in file.cio to each pair of lines after line 14.
The files listed on lines 15 & 16 of file.cio are used to model subbasin 1, the files listed on lines 17 & 18 of file.cio are used to model subbasin 2, the files listed on lines 19 & 20 of file.cio are used to model subbasin 3, and so on. Step 2a: Route the stream loadings through the flow path network. Begin by routing the headwater subbasin loadings through the main channel of the respective subbasin. (Headwater subbasins are those with no subbasins upstream.) Referring to Figure B-4, subbasins 1, 3, 6 and 7 are headwater subbasins. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2
1 2 3 4 5 6 7 8 9 10 11 12 13
1 2 3 4 5 6 7 8 9 1 3 6 7
1 3 6 7
0.000 0.000 0.000 0.000
As mentioned in the last step, column 1 is used to identify the command. Column 2 is the hydrograph storage location number identifying the location where results from the route simulation are placed. Column 3 provides the number of the reach, or main channel, the inputs are routed through. The number of the reach in a particular subbasin is the same as the number of the subbasin. Column 4 lists the number of the hydrograph storage location containing the data to be routed through the reach. Column 6 lists the fraction of overland flow. For the grid cell discretization, this value will always be zero. Step 2b: Route the stream loadings through the reach network. Use the add and route commands to continue routing through the watershed. First, add the outflow from subbasin 1 to the loadings from subbasin 4 and route the total through the channel in subbasin 4.
column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4
1 3 6 7 4 14
0.000 0.000 0.000 0.000 0.000
The loadings from the outlet of subbasin 1 are stored in hydrograph location #10; the loadings from subbasin 4 are stored in hydrograph location #4. The add command is specified in column 1 by the number 5. The hydrograph storage location numbers of the 2 data sets to be added are listed in columns 3 and 4. The summation results are stored in the hydrograph location number given in column 2. Next, add the outflow from subbasin 3 to the loadings from subbasin 2 and route the total through the channel in subbasin 2. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4 11 2
1 3 6 7 4 14 2 16
0.000 0.000 0.000 0.000 0.000 0.000
Next, add the outflow from subbasin 2 and 4 to the loadings from subbasin 5 and route the total through the channel in subbasin 5. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route add route add add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2 5 2 5 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4 11 2 15 18 5
1 3 6 7 4 14 2 16 17 5 19
0.000 0.000 0.000 0.000 0.000 0.000
0.000
Next, add the outflow from subbasin 5 and 7 to the loadings from subbasin 8 and route the total through the channel in subbasin 8. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route add route add add route add add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2 5 2 5 5 2 5 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4 11 2 15 18 5 20 21 8
1 3 6 7 4 14 2 16 17 5 19 13 8 22
0.000 0.000 0.000 0.000 0.000 0.000
0.000
0.000
Next, add the outflow from subbasin 8 and 6 to the loadings from subbasin 9, route the total through the channel in subbasin 9, and append a finish command line to signify the end of the watershed routing file. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route add route add add route add add route add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2 5 2 5 5 2 5 5 2 5 5 2 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4 11 2 15 18 5 20 21 8 23 24 9
1 3 6 7 4 14 2 16 17 5 19 13 8 22 12 9 25
0.000 0.000 0.000 0.000 0.000 0.000
0.000
0.000
0.000
As illustrated in section B.2.2 for the hillslope discretization, it is possible to combine the grid cell discretization with the subwatershed discretization to provide detailed modeling of portions of a large watershed while treating less significant areas in the more generalized approach used in the subwatershed discretization.
S.L. NEITSCH, J.G. ARNOLD, J.R. KINIRY, J.R. WILLIAMS DRAFT-April, 2001
GRASSLAND, SOIL AND WATER RESEARCH LABORATORY ○ AGRICULTURAL RESEARCH SERVICE 808 EAST BLACKLAND ROAD ○ TEMPLE, TEXAS 76502 BLACKLAND RESEARCH CENTER ○ TEXAS AGRICULTURAL EXPERIMENT STATION 720 EAST BLACKLAND ROAD ○ TEMPLE, TEXAS 76502
ACKNOWLEDGEMENTS The authors and developers of the Soil and Water Assessment Tool wish to acknowledge the support of the Wisconsin Department of Natural Resources and WDNR staff member Mr. John Panuska for assistance in the development and testing of SWAT.
CONTENTS
CHAPTER 1 INTRODUCTION 1.1 1.2
DEVELOPMENT OF SWAT OVERVIEW OF SWAT LAND PHASE OF THE HYDROLOGIC CYCLE ROUTING PHASE OF THE HYDROLOGIC CYCLE
1.3
REFERENCES
1 3 6 8 21 24
PART 1: MODEL DESCRIPTION SECTION 1: CLIMATE CHAPTER 2 EQUATIONS: ENERGY 2.1
SUN-EARTH RELATIONSHIPS
31 32
DISTANCE BETWEEN EARTH AND SUN SOLAR DECLINATION SOLAR NOON, SUNRISE, SUNSET, AND DAYLENGTH
32 32 33
2.2
2.3
2.4 2.5 2.6
SOLAR RADIATION
34
EXTRATERRESTRIAL RADIATION SOLAR RADIATION UNDER CLOUDLESS SKIES DAILY SOLAR RADIATION HOURLY SOLAR RADIATION DAILY NET RADIATION
34 35 36 37 38
TEMPERATURE
41
DAILY AIR TEMPERATURE HOURLY AIR TEMPERATURE SOIL TEMPERATURE WATER TEMPERATURE
41 42 42 47
WIND SPEED NOMENCLATURE REFERENCES
48 49 51
CHAPTER 3 EQUATIONS: ATMOSPHERIC WATER 3.1 3.2 3.3 3.4 3.5 3.6 3.7
PRECIPITATION MAXIMUM HALF-HOUR RAINFALL WATER VAPOR SNOW COVER SNOW MELT
53 54 55 55 58 61
SNOW PACK TEMPERATURE SNOW MELT EQUATION
61 62
NOMENCLATURE REFERENCES
63 64
CHAPTER 4 EQUATIONS: WEATHER GENERATOR 4.1 4.2
4.3
4.4
PRECIPITATION
67 68
OCCURRENCE OF WET OR DRY DAY AMOUNT OF PRECIPITATION
68 69
SOLAR RADIATION & TEMPERATURE
70
DAILY RESIDUALS GENERATED VALUES ADJUSTMENT FOR CLEAR/OVERCAST CONDITIONS
70 72 73
RELATIVE HUMIDITY
75
MEAN MONTHLY RELATIVE HUMIDITY GENERATED DAILY VALUE ADJUSTMENT FOR CLEAR/OVERCAST CONDITIONS
75 76 77
MAXIMUM HALF-HOUR RAINFALL
78
4.5 4.6 4.7
MONTHLY MAXIMUM HALF-HOUR RAIN GENERATED DAILY VALUE
79 79
WIND SPEED NOMENCLATURE REFERENCES
81 81 83
CHAPTER 5 EQUATIONS: CLIMATE CUSTOMIZATION 5.1 5.2 5.3
ELEVATION BANDS CLIMATE CHANGE NOMENCLATURE
85 86 88 90
SECTION 2: HYDROLOGY CHAPTER 6 EQUATIONS: SURFACE RUNOFF 6.1 6.2 6.3
6.4 6.5 6.6 6.7
RUNOFF VOLUME: SCS CURVE NUMBER PROCEDURE
93 94
SCS CURVE NUMBER
95
RUNOFF VOLUME: GREEN & AMPT INFILTRATION METHOD 101 103 PEAK RUNOFF RATE TIME OF CONCENTRATION RUNOFF COEFFICIENT RAINFALL INTENSITY MODIFIED RATIONAL FORMULA
104 107 107 108
SURFACE RUNOFF LAG TRANSMISSION LOSSES NOMENCLATURE REFERENCES
109 110 113 114
CHAPTER 7 EQUATIONS: EVAPOTRANSPIRATION 7.1 7.2
7.3
CANOPY STORAGE POTENTIAL EVAPOTRANSPIRATION
117 118 119
PENMAN-MONTEITH METHOD PRIESTLEY-TAYLOR METHOD HARGREAVES METHOD
120 126 127
ACTUAL EVAPOTRANSPIRATION
128
EVAPORATION OF INTERCEPTED RAINFALL TRANSPIRATION
128 129
7.4 7.5
SUBLIMATION AND EVAPORATION FROM THE SOIL
129
NOMENCLATURE REFERENCES
134 136
CHAPTER 8 EQUATIONS: SOIL WATER 8.1 8.2 8.3 8.4 8.5 8.6
SOIL STRUCTURE PERCOLATION BYPASS FLOW LATERAL FLOW
139 140 144 145 151
LATERAL FLOW LAG
154
NOMENCLATURE REFERENCES
156 157
CHAPTER 9 EQUATIONS: GROUNDWATER 9.1 9.2
9.3 9.4 9.5
GROUNDWATER SYSTEMS SHALLOW AQUIFER
159 160 162
RECHARGE GROUNDWATER/BASE FLOW REVAP PERCOLATION TO DEEP AQUIFER PUMPING GROUNDWATER HEIGHT
163 164 166 167 168 168
DEEP AQUIFER NOMENCLATURE REFERENCES
169 170 171
SECTION 3: NUTRIENTS/PESTICIDES CHAPTER 10 EQUATIONS: NITROGEN 10.1 NITROGEN CYCLE INITIALIZATION OF SOIL NITROGEN LEVELS
10.2 MINERALIZATION & DECOMPOSITION/ IMMOBILIZATION HUMUS MINERALIZATION RESIDUE DECOMPOSITION & MINERALIZATION
10.3 NITRIFICATION & AMMONIA VOLATILIZATION 10.4 DENITRIFICATION
175 176 178
180 181 182
184 187
10.5 10.6 10.7 10.8 10.9 10.10
NITROGEN IN RAINFALL FIXATION UPWARD MOVEMENT OF NITRATE IN WATER LEACHING NOMENCLATURE REFERENCES
CHAPTER 11 EQUATIONS: PHOSPHORUS 11.1 PHOSPHORUS CYCLE INITIALIZATION OF SOIL PHOSPHORUS LEVELS
11.2 MINERALIZATION & DECOMPOSITION/ IMMOBILIZATION 11.3 11.4 11.5 11.6
193 194 195
197
HUMUS MINERALIZATION RESIDUE DECOMPOSITION & MINERALIZATION
198 199
SORPTION OF INORGANIC P LEACHING NOMENCLATURE REFERENCES
201 203 204 205
CHAPTER 12 EQUATIONS: PESTICIDES 12.1 12.2 12.3 12.4 12.5
188 188 189 189 190 191
WASH-OFF DEGRADATION LEACHING NOMENCLATURE REFERENCES
207 209 209 211 211 211
SECTION 4: EROSION CHAPTER 13 EQUATIONS: SEDIMENT 13.1 MUSLE SOIL ERODIBILITY FACTOR COVER AND MANAGEMENT FACTOR SUPPORT PRACTICE FACTOR TOPOGRAPHIC FACTOR COARSE FRAGMENT FACTOR
215 216 216 219 220 222 223
13.2 USLE 13.3 13.4 13.5 13.6 13.7
223
RAINFALL ERODIBILITY INDEX
223
SNOW COVER EFFECTS SEDIMENT LAG IN SURFACE RUNOFF SEDIMENT IN LATERAL & GROUNDWATER FLOW NOMENCLATURE REFERENCES
226 226 227 228 230
CHAPTER 14 EQUATIONS: NUTRIENT TRANSPORT 14.1 NITRATE MOVEMENT 14.2 ORGANIC N IN SURFACE RUNOFF ENRICHMENT RATIO
14.3 SOLUBLE PHOSPHORUS MOVEMENT 14.4 ORGANIC & MINERAL P ATTACHED TO SEDIMENT IN SURFACE RUNOFF ENRICHMENT RATIO
14.5 NUTRIENT LAG IN SURFACE RUNOFF AND LATERAL FLOW 14.6 NOMENCLATURE 14.7 REFERENCES
CHAPTER 15 EQUATIONS: PESTICIDE TRANSPORT 15.1 PHASE DISTRIBUTION OF PESTICIDE 15.2 MOVEMENT OF SOLUBLE PESTICIDES 15.3 TRANSPORT OF SORBED PESTICIDE ENRICHMENT RATIO
15.4 PESTICIDE LAG IN SURFACE RUNOFF AND LATERAL FLOW 15.5 NOMENCLATURE 15.6 REFERENCES
CHAPTER 16 EQUATIONS: WATER QUALITY PARAMETERS 16.1 ALGAE 16.2 CARBONACEOUS BIOLOGICAL OXYGEN DEMAND ENRICHMENT RATIO
231 232 234 234
235 236 237
238 239 241
243 244 245 248 249
250 251 252
253 254 254 255
16.3 DISSOLVED OXYGEN OXYGEN SATURATION CONCENTRATION
16.4 NOMENCLATURE 16.5 REFERENCES
256 256
257 257
SECTION 5: LAND COVER/PLANT CHAPTER 17 EQUATIONS: GROWTH CYCLE 17.1 HEAT UNITS 17.2 17.3 17.4 17.5
261 262
HEAT UNIT SCHEDULING
264
DORMANCY PLANT TYPES NOMENCLATURE REFERENCES
267 268 269 269
CHAPTER 18 EQUATIONS: OPTIMAL GROWTH 18.1 POTENTIAL GROWTH
271 272
BIOMASS PRODUCTION CANOPY COVER AND HEIGHT ROOT DEVELOPMENT MATURITY
272 275 277 278
18.2 WATER UPTAKE BY PLANTS 18.3 NUTRIENT UPTAKE BY PLANTS
279 283
NITROGEN UPTAKE PHOSPHORUS UPTAKE
18.4 CROP YIELD 18.5 NOMENCLATURE 18.6 REFERENCES
CHAPTER 19 EQUATIONS: ACTUAL GROWTH
283 287
291 293 296
19.1 GROWTH CONSTRAINTS
299 300
WATER STRESS TEMPERATURE STRESS NITROGEN STRESS PHOSPHORUS STRESS
300 300 301 302
19.2 ACTUAL GROWTH
303
BIOMASS OVERRIDE
19.3 ACTUAL YIELD HARVEST INDEX OVERRIDE HARVEST EFFICIENCY
19.4 NOMENCLATURE
303
304 304 305
306
SECTION 6: MANAGEMENT PRACTICES CHAPTER 20 EQUATIONS: GENERAL MANAGEMENT 20.1 20.2 20.3 20.4 20.5 20.6 20.7 20.8 20.9 20.10 20.11 20.12
PLANTING/BEGINNING OF GROWING SEASON HARVEST OPERATION GRAZING OPERATION HARVEST & KILL OPERATION KILL/END OF GROWING SEASON TILLAGE
311 312 313 314 315 316 316
BIOLOGICAL MIXING
317
FERTILIZER APPLICATION AUTO-APPLICATION OF FERTILIZER PESTICIDE APPLICATION FILTER STRIPS NOMENCLATURE REFERENCES
318 320 323 325 325 327
CHAPTER 21 EQUATIONS: WATER MANAGEMENT 21.1 IRRIGATION 21.2 21.3 21.4 21.5 21.6 21.7
329 330
AUTO-APPLICATION OF IRRIGATION
331
TILE DRAINAGE IMPOUNDED/DEPRESSIONAL AREAS WATER TRANSFER CONSUMPTIVE WATER USE POINT SOURCE LOADINGS NOMENCLATURE
331 332 332 333 334 334
CHAPTER 22 EQUATIONS: URBAN AREAS 22.1 22.2 22.3 22.4
CHARACTERISTICS OF URBAN AREAS SURFACE RUNOFF FROM URBAN AREAS USGS REGRESSION EQUATIONS BUILD UP/WASH OFF
335 336 337 337 339
STREET CLEANING
341
22.5 NOMENCLATURE 22.3 REFERENCES
343 344
SECTION 7: MAIN CHANNEL PROCESSES CHAPTER 23 EQUATIONS: WATER ROUTING 23.1 23.2 23.3 23.4 23.5 23.6 23.7 23.8 23.9 23.10
CHANNEL CHARACTERISTICS FLOW RATE AND VELOCITY VARIABLE STORAGE ROUTING METHOD MUSKINGUM ROUTING METHOD TRANSMISSION LOSSES EVAPORATION LOSSES BANK STORAGE CHANNEL WATER BALANCE NOMENCLATURE REFERENCES
CHAPTER 24 EQUATIONS: SEDIMENT ROUTING 24.1 SEDIMENT CHANNEL ROUTING CHANNEL ERODIBILITY FACTOR CHANNEL COVER FACTOR
24.2 CHANNEL DOWNCUTTING AND WIDENING 24.3 NOMENCLATURE 24.4 REFERENCES
349 350 352 354 356 359 360 360 362 363 364
367 368 370 371
371 373 374
CHAPTER 25 EQUATIONS: IN-STREAM NUTRIENT PROCESSES 25.1 ALGAE CHLOROPHYLL A ALGAL GROWTH
25.2 NITROGEN CYCLE ORGANIC NITROGEN AMMONIUM NITRITE NITRATE
25.3 PHOSPHORUS CYCLE ORGANIC PHOSPHORUS INORGANIC/SOLUBLE PHOSPHORUS
25.4 CARBONACEOUS BIOLOGICAL OXYGEN DEMAND 25.5 OXYGEN OXYGEN SATURATION CONCENTRATION REAERATION
25.6 NOMENCLATURE 25.7 REFERENCES
CHAPTER 26 EQUATIONS: PESTICIDE 26.1 PESTICIDE IN THE WATER SOLID-LIQUID PARTITIONING DEGRADATION VOLATILIZATION SETTLING OUTFLOW
26.2 PESTICIDE IN THE SEDIMENT SOLID-LIQUID PARTITIONING DEGRADATION RESUSPENSION DIFFUSION BURIAL
26.3 MASS BALANCE 26.4 NOMENCLATURE 26.5 REFERENCES
375 376 376 376
383 383 384 386 387
388 388 389
390 391 393 393
396 399
401 402 402 403 403 405 405
406 406 407 408 408 409
409 410 411
SECTION 8: WATER BODIES CHAPTER 27 EQUATIONS: IMPOUNDMENT WATER ROUTING 27.1 RESERVOIRS
415 416
SURFACE AREA PRECIPITATION EVAPORATION SEEPAGE OUTFLOW
417 417 418 418 418
27.2 PONDS/WETLANDS
422
SURFACE AREA PRECIPITATION INFLOW EVAPORATION SEEPAGE OUTFLOW
423 424 424 424 425 425
27.3 DEPRESSIONS/POTHOLES SURFACE AREA PRECIPITATION INFLOW EVAPORATION SEEPAGE OUTFLOW
27.4 NOMENCLATURE
CHAPTER 28 EQUATIONS: SEDIMENT IN WATER BODIES 28.1 28.2 28.3 28.4
MASS BALANCE SETTLING SEDIMENT OUTFLOW NOMENCLATURE
CHAPTER 29 EQUATIONS: NUTRIENTS IN WATER BODIES 29.1 NUTRIENT TRANSFORMATIONS 29.2 TOTAL BALANCE 29.3 EUTROPHICATION PHOSPHORUS/CHLOROPHYLL a CONCENTRATIONS
427 427 428 428 429 429 430
431
433 434 434 436 436
437 438 442 442 443
CHLOROPHYLL a/SECCHI DEPTH CORRELATIONS
29.4 NOMENCLATURE 29.5 REFERENCES
CHAPTER 30 EQUATIONS: PESTICIDES IN WATER BODIES 30.1 PESTICIDE IN THE WATER SOLID-LIQUID PARTITIONING DEGRADATION VOLATILIZATION SETTLING OUTFLOW
30.2 PESTICIDE IN THE SEDIMENT SOLID-LIQUID PARTITIONING DEGRADATION RESUSPENSION DIFFUSION BURIAL
30.3 MASS BALANCE 30.4 NOMENCLATURE 30.5 REFERENCES
444
445 445
447 448 448 449 450 451 451
452 452 454 454 455 456
456 457 458
PART 2: MODEL OPERATION CHAPTER 31 SWAT INPUT: WATERSHED CONFIGURATION 31.1 DISCRETIZATION SCHEMES 31.2 WATERSHED CONFIGURATION FILE (.FIG) INCORPORATION OF COMMENTS COMMAND LINES 31.3 REFERENCES
CHAPTER 32 SWAT INPUT: SIMULATION MANAGEMENT 32.1 CONTROL INPUT/OUTPUT FILE (FILE.CIO) 32.2 INPUT CONTROL CODE FILE (.COD)
CHAPTER 33 SWAT INPUT: GENERAL WATERSHED ATTRIBUTES 33.1 BASIN INPUT FILE (.BSN)
CHAPTER 34 SWAT INPUT: CLIMATE 34.1 34.2 34.3 34.4 34.5 34.6 34.7 34.8
WEATHER GENERATOR INPUT FILE (.WGN) PRECIPITATION INPUT FILE (.PCP) TEMPERATURE INPUT FILE (.TMP) SOLAR RADIATION INPUT FILE (.SLR) RELATIVE HUMIDITY INPUT FILE (.HMD) WIND SPEED INPUT FILE (.WND) POTENTIAL EVAPOTRANSPIRATION INPUT FILE (.PET) MULTIPLE RECORDS IN PRECIP/TEMP FILES
CHAPTER 35 SWAT INPUT: GENERAL HRU/SUBBASIN ATTRIBUTES 35.1 SUBBASIN GENERAL INPUT FILE (.SUB) 35.2 HRU GENERAL INPUT FILE (.HRU)
CHAPTER 36 SWAT INPUT: SOIL 36.1 SOIL INPUT FILE (.SOL) 36.2 SOIL CHEMICAL INPUT FILE (.CHM)
CHAPTER 37 SWAT INPUT: LAND/WATER MANAGEMENT 37.1 MANAGEMENT INPUT FILE (.MGT) GENERAL MANAGEMENT VARIABLES SCHEDULED MANAGEMENT OPERATIONS 37.2 WATER USE INPUT FILE (.WUS)
CHAPTER 38 SWAT INPUT: GROUNDWATER 38.1 GROUNDWATER INPUT FILE (.GW)
CHAPTER 39 SWAT INPUT: MAIN CHANNEL 39.1 MAIN CHANNEL INPUT FILE (.RTE)
CHAPTER 40 SWAT INPUT: RESERVOIRS/PONDS 40.1 40.2 40.3 40.4
RESERVOIR INPUT FILE (.RES) DAILY RESERVOIR OUTFLOW FILE MONTHLY RESERVOIR OUTFLOW FILE POND INPUT FILE (.PND)
CHAPTER 41 SWAT INPUT: WATER QUALITY 41.1 GENERAL WATER QUALITY INPUT FILE (.WWQ) 41.2 STREAM WATER QUALITY INPUT FILE (.SWQ) 41.3 RESERVOIR WATER QUALITY INPUT FILE (.LWQ)
CHAPTER 42 SWAT INPUT: DATABASES 42.1 42.2 42.3 42.4 42.5
LAND COVER/PLANT GROWTH DATABASE FILE (CROP.DAT) TILLAGE DATABASE FILE (TILL.DAT) PESTICIDE/TOXIN DATABASE FILE (PEST.DAT) FERTILIZER DATABASE FILE (FERT.DAT) URBAN DATABASE FILE (URBAN.DAT)
CHAPTER 43 SWAT INPUT: MEASURED 43.1 43.2 43.3 43.4
DAILY RECORDS (RECDAY) DATA FILE MONTHLY RECORDS (RECMON) DATA FILE ANNUAL RECORDS (RECYEAR) DATA FILE AVERAGE ANNUAL RECORDS (RECCNST) DATA FILE
CHAPTER 44 SWAT OUTPUT FILES 44.1 44.2 44.3 44.4 44.5 44.6 44.7 44.8 44.9
INPUT SUMMARY FILE (INPUT.STD) STANDARD OUTPUT FILE (OUTPUT.STD) HRU OUTPUT FILE (.SBS) SUBBASIN OUTPUT FILE (.BSB) MAIN CHANNEL OUTPUT FILE (.RCH) HRU IMPOUNDMENT OUTPUT FILE (.WTR) RESERVOIR OUTPUT FILE (.RSV) PESTICIDE OUTPUT FILE (.PSO) EVENT OUTPUT FILE (.EVE)
CHAPTER 45 SWAT OUTPUT ANALYSIS 45.1 45.2 45.3 45.4
STREAM FLOW CALIBRATION SEDIMENT CALIBRATION NUTRIENT CALIBRATION PESTICIDE CALIBRATION
PART 3: APPENDICES APPENDIX A MODEL DATABASES A.1 A.2 A.3 A.4 A.5 A.6
LAND COVER/PLANT GROWTH DATABASE TILLAGE DATABASE PESTICIDE DATABASE FERTILIZER DATABASE URBAN LAND TYPE DATABASE REFERENCES
APPENDIX B EXAMPLE WATERSHED CONFIGURATIONS B.1
B.2
SUBWATERSHED DISCRETIZATION SUBWATERSHED DISCRETIZATION: 3 SUBBASINS SUBWATERSHED DISCRETIZATION: SAVING RESULTS FOR DOWNSTREAM RUNS SUBWATERSHED DISCRETIZATION: INCORPORATING POINT SOURCE/UPSTREAM SIMULATION DATA SUBWATERSHED DISCRETIZATION: INCORPORATING RESERVOIRS SUBWATERSHED DISCRETIZATION: SAVING SIMULATION RESULTS FOR ONE LOCATION HILLSLOPE DISCRETIZATION HILLSLOPE DISCRETIZATION: MODELING A DAIRY OPERATION HILLSLOPE DISCRETIZATION: COMBINING WITH SUBWATERSHED DISCRETIZATION
B.3
GRID CELL DISCRETIZATION GRID CELL DISCRETIZATION: 9 CELLS
APPENDIX C EXAMPLE MANAGEMENT SCENARIOS C.1
LAND COVER/PLANT GROWTH DATABASE
APPENDIX D HYDROLOGIC GROUPS FOR U.S. SOILS
SOIL AND WATER ASSESSMENT TOOL
CHAPTER 1
INTRODUCTION
SWAT is the acronym for Soil and Water Assessment Tool, a river basin, or watershed, scale model developed by Dr. Jeff Arnold for the USDA Agricultural Research Service (ARS). SWAT was developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long periods of time. To satisfy this objective, the model ♦ is physically based. Rather than incorporating regression equations to describe the relationship between input and output variables, SWAT requires
specific
information
about
weather,
soil
properties,
topography, vegetation, and land management practices occurring in the watershed. The physical processes associated with water 1
2
SWAT USER’S MANUAL, VERSION 2000
movement, sediment movement, crop growth, nutrient cycling, etc. are directly modeled by SWAT using this input data. Benefits of this approach are ♦
watersheds with no monitoring data (e.g. stream gage data) can be modeled
♦
the relative impact of alternative input data (e.g. changes in management practices, climate, vegetation, etc.) on water quality or other variables of interest can be quantified
♦ uses readily available inputs. While SWAT can be used to study more specialized processes such as bacteria transport, the minimum data required to make a run are commonly available from government agencies. ♦ is computationally efficient. Simulation of very large basins or a variety of management strategies can be performed without excessive investment of time or money. ♦ enables users to study long-term impacts. Many of the problems currently addressed by users involve the gradual buildup of pollutants and the impact on downstream water bodies. To study these types of problems, results are needed from runs with output spanning several decades. SWAT is a continuous time model, i.e. a long-term yield model. The model is not designed to simulate detailed, single-event flood routing.
CHAPTER 1: INTRODUCTION
3
1.1 DEVELOPMENT OF SWAT SWAT incorporates features of several ARS models and is a direct outgrowth of the SWRRB1 model (Simulator for Water Resources in Rural Basins) (Williams et al., 1985; Arnold et al., 1990). Specific models that contributed significantly to the development of SWAT were CREAMS2 (Chemicals, Runoff, and Erosion from Agricultural Management Systems) (Knisel, 1980), GLEAMS3 (Groundwater Loading Effects on Agricultural Management Systems) (Leonard et al., 1987), and EPIC4 (Erosion-Productivity Impact Calculator) (Williams et al., 1984). Development of SWRRB began with modification of the daily rainfall hydrology model from CREAMS. The major changes made to the CREAMS hydrology model were: a) the model was expanded to allow simultaneous computations on several subbasins to predict basin water yield; b) a groundwater or return flow component was added; c) a reservoir storage component was added to calculate the effect of farm ponds and reservoirs on water and sediment yield; d) a weather simulation model incorporating data for rainfall, solar radiation, and temperature was added to facilitate long-term simulations and provide temporally and spatially representative weather; e) the method for predicting the peak runoff rates was improved; f) the EPIC crop growth model was added to account for annual variation in growth; g) a simple flood routing component was added; h) sediment transport components were added to simulate sediment movement 1
SWRRB is a continuous time step model that was developed to simulate nonpoint source loadings from watersheds. 2 In response to the Clean Water Act, ARS assembled a team of interdisciplinary scientists from across the U.S. to develop a process-based, nonpoint source simulation model in the early 1970s. From that effort CREAMS was developed. CREAMS is a field scale model designed to simulate the impact of land management on water, sediment, nutrients and pesticides leaving the edge of the field. A number of other ARS models such as GLEAMS, EPIC, SWRRB and AGNPS trace their origins to the CREAMS model. 3 GLEAMS is a nonpoint source model which focuses on pesticide and nutrient groundwater loadings. 4 EPIC was originally developed to simulate the impact of erosion on crop productivity and has now evolved into a comprehensive agricultural management, field scale, nonpoint source loading model.
4
SWAT USER’S MANUAL, VERSION 2000
through ponds, reservoirs, streams and valleys; and i) calculation of transmission losses was incorporated. The primary focus of model use in the late 1980s was water quality assessment and development of SWRRB reflected this emphasis. Notable modifications of SWRRB at this time included: a) incorporation of the GLEAMS pesticide fate component; b) optional SCS technology for estimating peak runoff rates; and c) newly developed sediment yield equations. These modifications extended the model’s capability to deal with a wide variety of watershed management problems. In the late 1980s, the Bureau of Indian Affairs needed a model to estimate the downstream impact of water management within Indian reservation lands in Arizona and New Mexico. While SWRRB was easily utilized for watersheds up to a few hundred square kilometers in size, the Bureau also wanted to simulate stream flow for basins extending over several thousand square kilometers. For an area this extensive, the watershed under study needed to be divided into several hundred subbasins. Watershed division in SWRRB was limited to ten subbasins and the model routed water and sediment transported out of the subbasins directly to the watershed outlet. These limitations led to the development of a model called ROTO (Routing Outputs to Outlet) (Arnold et al., 1995), which took output from multiple SWRRB runs and routed the flows through channels and reservoirs. ROTO provided a reach routing approach and overcame the SWRRB subbasin limitation by “linking” multiple SWRRB runs together. Although this approach was effective, the input and output of multiple SWRRB files was cumbersome and required considerable computer storage. In addition, all SWRRB runs had to be made independently and then input to ROTO for the channel and reservoir routing. To overcome the awkwardness of this arrangement, SWRRB and ROTO were merged into a single model, SWAT. While allowing simulations of very extensive areas, SWAT retained all the features which made SWRRB such a valuable simulation model.
CHAPTER 1: INTRODUCTION
5
Since SWAT was created in the early 90s, it has undergone continued review and expansion of capabilities. The most significant improvements of the model between releases include: ♦ SWAT94.2: Multiple hydrologic response units (HRUs) incorporated. ♦ SWAT96.2:
Auto-fertilization
and
auto-irrigation
added
as
management options; canopy storage of water incorporated; a CO2 component added to crop growth model for climatic change studies; Penman-Monteith potential evapotranspiration equation added; lateral flow of water in the soil based on kinematic storage model incorporated; in-stream nutrient water quality equations from QUAL2E added; in-stream pesticide routing. ♦ SWAT98.1: Snow melt routines improved; in-stream water quality improved; nutrient cycling routines expanded; grazing, manure applications, and tile flow drainage added as management options; model modified for use in Southern Hemisphere. ♦ SWAT99.2: Nutrient cycling routines improved, rice/wetland routines improved, reservoir/pond/wetland nutrient removal by settling added; bank storage of water in reach added; routing of metals through reach added; all year references in model changed from last 2 digits of year to 4-digit year; urban build up/wash off equations from SWMM added along with regression equations from USGS. ♦ SWAT2000: Bacteria transport routines added; Green & Ampt infiltration added; weather generator improved; allow daily solar radiation, relative humidity, and wind speed to be read in or generated; allow potential ET values for watershed to be read in or calculated; all potential ET methods reviewed; elevation band processes improved; enabled simulation of unlimited number of reservoirs; Muskingum routing method added; modified dormancy calculations for proper simulation in tropical areas.
6
SWAT USER’S MANUAL, VERSION 2000
In addition to the changes listed above, interfaces for the model have been developed in Windows (Visual Basic), GRASS, and ArcView. SWAT has also undergone extensive validation.
1.2 OVERVIEW OF SWAT SWAT allows a number of different physical processes to be simulated in a watershed. These processes will be briefly summarized in this section. For more detailed discussions of the various procedures, please consult the chapter devoted to the topic of interest.
. Figure 1.1: Map of the Lake Fork Watershed in Northeast Texas showing the land use distribution and stream network
For modeling purposes, a watershed may be partitioned into a number of subwatersheds or subbasins. The use of subbasins in a simulation is particularly beneficial when different areas of the watershed are dominated by land uses or
CHAPTER 1: INTRODUCTION
7
soils dissimilar enough in properties to impact hydrology. By partitioning the watershed into subbasins, the user is able to reference different areas of the watershed to one another spatially. Figure 1.2 shows a subbasin delineation for the watershed shown in Figure 1.1.
Figure 1.2: Subbasin delineation of the Lake Fork Watershed.
Input information for each subbasin is grouped or organized into the following
categories:
climate;
hydrologic
response
units
or
HRUs;
ponds/wetlands; groundwater; and the main channel, or reach, draining the subbasin. Hydrologic response units are lumped land areas within the subbasin that are comprised of unique land cover, soil, and management combinations.
No matter what type of problem studied with SWAT, water balance is the driving force behind everything that happens in the watershed. To accurately predict the movement of pesticides, sediments or nutrients, the hydrologic cycle as
8
SWAT USER’S MANUAL, VERSION 2000
simulated by the model must conform to what is happening in the watershed. Simulation of the hydrology of a watershed can be separated into two major divisions. The first division is the land phase of the hydrologic cycle, depicted in Figure 1.3. The land phase of the hydrologic cycle controls the amount of water, sediment, nutrient and pesticide loadings to the main channel in each subbasin. The second division is the water or routing phase of the hydrologic cycle which can be defined as the movement of water, sediments, etc. through the channel network of the watershed to the outlet.
Figure 1.3: Schematic representation of the hydrologic cycle.
CHAPTER 1: INTRODUCTION
9
1.2.1 LAND PHASE OF THE HYDROLOGIC CYCLE The hydrologic cycle as simulated by SWAT is based on the water balance equation: t
SWt = SW0 + å (Rday − Qsurf − E a − wseep − Q gw ) i =1
where SWt is the final soil water content (mm H2O), SW0 is the initial soil water content on day i (mm H2O), t is the time (days), Rday is the amount of precipitation on day i (mm H2O), Qsurf is the amount of surface runoff on day i (mm H2O), Ea is the amount of evapotranspiration on day i (mm H2O), wseep is the amount of water entering the vadose zone from the soil profile on day i (mm H2O), and Qgw is the amount of return flow on day i (mm H2O). The subdivision of the watershed enables the model to reflect differences in evapotranspiration for various crops and soils. Runoff is predicted separately for each HRU and routed to obtain the total runoff for the watershed. This increases accuracy and gives a much better physical description of the water balance.
Figure 1.4: HRU/Subbasin command loop
10
SWAT USER’S MANUAL, VERSION 2000
Figure 1.4 shows the general sequence of processes used by SWAT to model the land phase of the hydrologic cycle. The different inputs and processes involved in this phase of the hydrologic cycle are summarized in the following sections.
1.2.1.1 CLIMATE The climate of a watershed provides the moisture and energy inputs that control the water balance and determine the relative importance of the different components of the hydrologic cycle. The climatic variables required by SWAT consist of daily precipitation, maximum/minimum air temperature, solar radiation, wind speed and relative humidity. The model allows values for daily precipitation, maximum/minimum air temperatures, solar radiation, wind speed and relative humidity to be input from records of observed data or generated during the simulation. WEATHER GENERATOR. Daily values for weather are generated from average monthly values. The model generates a set of weather data for each subbasin. The values for any one subbasin will be generated independently and there will be no spatial correlation of generated values between the different subbasins. GENERATED PRECIPITATION. SWAT uses a model developed by Nicks (1974) to generate daily precipitation for simulations which do not read in measured data. This precipitation model is also used to fill in missing data in the measured records. The precipitation generator uses a first-order Markov chain model to define a day as wet or dry by comparing a random number (0.0-1.0) generated by the model to monthly wet-dry probabilities input by the user. If the day is classified as wet, the amount of precipitation is generated from a skewed distribution or a modified exponential distribution. GENERATED AIR TEMPERATURE AND SOLAR RADIATION. Maximum and minimum air temperatures and solar radiation are generated from a normal distribution. A continuity equation is incorporated into the generator to account for temperature and radiation variations caused by dry vs. rainy conditions. Maximum air temperature and solar radiation are adjusted downward when simulating rainy conditions and upwards when simulating dry conditions. The adjustments are made so that the long-term generated values for the average monthly maximum temperature and monthly solar radiation agree with the input averages.
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GENERATED WIND SPEED. A modified exponential equation is used to generate daily mean wind speed given the mean monthly wind speed. GENERATED RELATIVE HUMIDITY. The relative humidity model uses a triangular distribution to simulate the daily average relative humidity from the monthly average. As with temperature and radiation, the mean daily relative humidity is adjusted to account for wet- and dry-day effects. SNOW. SWAT classifies precipitation as rain or freezing rain/snow using the average daily temperature. SNOW COVER. The snow cover component of SWAT has been updated from a simple, uniform snow cover model to a more complex model which allows non-uniform cover due to shading, drifting, topography and land cover. The user defines a threshold snow depth above which snow coverage will always extend over 100% of the area. As the snow depth in a subbasin decreases below this value, the snow coverage is allowed to decline non-linearly based on an areal depletion curve. SNOW MELT. Snow melt is controlled by the air and snow pack temperature, the melting rate, and the areal coverage of snow. If snow is present, it is melted on days when the maximum temperature exceeds 0°C using a linear function of the difference between the average snow packmaximum air temperature and the base or threshold temperature for snow melt. Melted snow is treated the same as rainfall for estimating runoff and percolation. For snow melt, rainfall energy is set to zero and the peak runoff rate is estimated assuming uniformly melted snow for a 24 hour duration. ELEVATION BANDS. The model allows the subbasin to be split into a maximum of ten elevation bands. Snow cover and snow melt are simulated separately for each elevation band. By dividing the subbasin into elevation bands, the model is able to assess the differences in snow cover and snow melt caused by orographic variation in precipitation and temperature. SOIL TEMPERATURE. Soil temperature impacts water movement and the decay rate of residue in the soil. Daily average soil temperature is calculated at the soil surface and the center of each soil layer. The temperature of the soil surface is a function of snow cover, plant cover and residue cover, the bare soil surface temperature, and the previous day’s soil surface temperature. The temperature of a soil layer is a function of the surface temperature, mean annual air temperature and the depth in the soil at which variation in temperature due to changes in climatic conditions no longer occurs. This depth, referred to as the damping depth, is dependent upon the bulk density and the soil water content.
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SWAT USER’S MANUAL, VERSION 98.1
1.2.1.2 HYDROLOGY As precipitation descends, it may be intercepted and held in the vegetation canopy or fall to the soil surface. Water on the soil surface will infiltrate into the soil profile or flow overland as runoff. Runoff moves relatively quickly toward a stream channel and contributes to short-term stream response. Infiltrated water may be held in the soil and later evapotranspired or it may slowly make its way to the surface-water system via underground paths. The potential pathways of water movement simulated by SWAT in the HRU are illustrated in Figure 1.5. CANOPY STORAGE. Canopy storage is the water intercepted by vegetative surfaces (the canopy) where it is held and made available for evaporation. When using the curve number method to compute surface runoff, canopy storage is taken into account in the surface runoff calculations. However, if methods such as Green & Ampt are used to model infiltration and runoff, canopy storage must be modeled separately. SWAT allows the user to input the maximum amount of water that can be stored in the canopy at the maximum leaf area index for the land cover. This value and the leaf area index are used by the model to compute the maximum storage at any time in the growth cycle of the land cover/crop. When evaporation is computed, water is first removed from canopy storage. Infiltration refers to the entry of water into a soil profile INFILTRATION. from the soil surface. As infiltration continues, the soil becomes increasingly wet, causing the rate of infiltration to decrease with time until it reaches a steady value. The initial rate of infiltration depends on the moisture content of the soil prior to the introduction of water at the soil surface. The final rate of infiltration is equivalent to the saturated hydraulic conductivity of the soil. Because the curve number method used to calculate surface runoff operates on a daily time-step, it is unable to directly model infiltration. The amount of water entering the soil profile is calculated as the difference between the amount of rainfall and the amount of surface runoff. The Green & Ampt infiltration method does directly model infiltration, but it requires precipitation data in smaller time increments. Redistribution refers to the continued movement of water REDISTRIBUTION. through a soil profile after input of water (via precipitation or irrigation) has ceased at the soil surface. Redistribution is caused by differences in water content in the profile. Once the water content throughout the entire profile is uniform, g
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SWAT USER’S MANUAL, VERSION 98.1
redistribution will cease. The redistribution component of SWAT uses a storage routing technique to predict flow through each soil layer in the root zone. Downward flow, or percolation, occurs when field capacity of a soil layer is exceeded and the layer below is not saturated. The flow rate is governed by the saturated conductivity of the soil layer. Redistribution is affected by soil temperature. If the temperature in a particular layer is 0°C or below, no redistribution is allowed from that layer. EVAPOTRANSPIRATION. Evapotranspiration is a collective term for all processes by which water in the liquid or solid phase at or near the earth's surface becomes atmospheric water vapor. Evapotranspiration includes evaporation from rivers and lakes, bare soil, and vegetative surfaces; evaporation from within the leaves of plants (transpiration); and sublimation from ice and snow surfaces. The model computes evaporation from soils and plants separately as described by Ritchie (1972). Potential soil water evaporation is estimated as a function of potential evapotranspiration and leaf area index (area of plant leaves relative to the area of the HRU). Actual soil water evaporation is estimated by using exponential functions of soil depth and water content. Plant transpiration is simulated as a linear function of potential evapotranspiration and leaf area index. POTENTIAL EVAPOTRANSPIRATION. Potential evapotranspiration is the rate at which evapotranspiration would occur from a large area completely and uniformly covered with growing vegetation which has access to an unlimited supply of soil water. This rate is assumed to be unaffected by micro-climatic processes such as advection or heat-storage effects. The model offers three options for estimating potential evapotranspiration: Hargreaves (Hargreaves et al., 1985), Priestley-Taylor (Priestley and Taylor, 1972), and Penman-Monteith (Monteith, 1965). LATERAL SUBSURFACE FLOW. Lateral subsurface flow, or interflow, is streamflow contribution which originates below the surface but above the zone where rocks are saturated with water. Lateral subsurface flow in the soil profile (0-2m) is calculated simultaneously with redistribution. A kinematic storage model is used to predict lateral flow in each soil layer. The model accounts for variation in conductivity, slope and soil water content. SURFACE RUNOFF. Surface runoff, or overland flow, is flow that occurs along a sloping surface. Using daily or subdaily rainfall amounts, SWAT simulates surface runoff volumes and peak runoff rates for each HRU. SURFACE RUNOFF VOLUME is computed using a modification of the SCS curve number method (USDA Soil Conservation Service, 1972) or the Green & Ampt infiltration method (Green and Ampt, 1911). In the curve number method, the curve number varies non-linearly with the moisture content of the soil. The curve number drops as the soil approaches the wilting point and increases to near 100 as the soil approaches saturation. The Green & Ampt method requires sub-daily precipitation data and calculates infiltration as a function of the wetting front matric potential
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15
and effective hydraulic conductivity. Water that does not infiltrate becomes surface runoff. SWAT includes a provision for estimating runoff from frozen soil where a soil is defined as frozen if the temperature in the first soil layer is less than 0°C. The model increases runoff for frozen soils but still allows significant infiltration when the frozen soils are dry. PEAK RUNOFF RATE predictions are made with a modification of the rational method. In brief, the rational method is based on the idea that if a rainfall of intensity i begins instantaneously and continues indefinitely, the rate of runoff will increase until the time of concentration, tc, when all of the subbasin is contributing to flow at the outlet. In the modified Rational Formula, the peak runoff rate is a function of the proportion of daily precipitation that falls during the subbasin tc, the daily surface runoff volume, and the subbasin time of concentration. The proportion of rainfall occurring during the subbasin tc is estimated as a function of total daily rainfall using a stochastic technique. The subbasin time of concentration is estimated using Manning’s Formula considering both overland and channel flow. PONDS. Ponds are water storage structures located within a subbasin which intercept surface runoff. The catchment area of a pond is defined as a fraction of the total area of the subbasin. Ponds are assumed to be located off the main channel in a subbasin and will never receive water from upstream subbasins. Pond water storage is a function of pond capacity, daily inflows and outflows, seepage and evaporation. Required inputs are the storage capacity and surface area of the pond when filled to capacity. Surface area below capacity is estimated as a nonlinear function of storage. TRIBUTARY CHANNELS. Two types of channels are defined within a subbasin: the main channel and tributary channels. Tributary channels are minor or lower order channels branching off the main channel within the subbasin. Each tributary channel within a subbasin drains only a portion of the subbasin and does not receive groundwater contribution to its flow. All flow in the tributary channels is released and routed through the main channel of the subbasin. SWAT uses the attributes of tributary channels to determine the time of concentration for the subbasin. Transmission losses are losses of surface TRANSMISSION LOSSES. flow via leaching through the streambed. This type of loss occurs in ephemeral or intermittent streams where groundwater contribution occurs only at certain times of the year, or not at all. SWAT uses Lane’s method described in Chapter 19 of the SCS Hydrology Handbook (USDA Soil Conservation Service, 1983) to estimate transmission losses. Water losses from the channel are a function of channel width and length and flow duration. Both runoff volume and peak rate are adjusted when transmission losses occur in tributary channels.
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SWAT USER’S MANUAL, VERSION 98.1
RETURN FLOW. Return flow, or base flow, is the volume of streamflow originating from groundwater. SWAT partitions groundwater into two aquifer systems: a shallow, unconfined aquifer which contributes return flow to streams within the watershed and a deep, confined aquifer which contributes return flow to streams outside the watershed (Arnold et al., 1993). Water percolating past the bottom of the root zone is partitioned into two fractions—each fraction becomes recharge for one of the aquifers. In addition to return flow, water stored in the shallow aquifer may replenish moisture in the soil profile in very dry conditions or be directly removed by plant. Water in the shallow or deep aquifer may be removed by pumping.
1.2.1.3 LAND COVER/PLANT GROWTH SWAT utilizes a single plant growth model to simulate all types of land covers. The model is able to differentiate between annual and perennial plants. Annual plants grow from the planting date to the harvest date or until the accumulated heat units equal the potential heat units for the plant. Perennial plants maintain their root systems throughout the year, becoming dormant in the winter months. They resume growth when the average daily air temperature exceeds the minimum, or base, temperature required. The plant growth model is used to assess removal of water and nutrients from the root zone, transpiration, and biomass/yield production. POTENTIAL GROWTH. The potential increase in plant biomass on a given day is defined as the increase in biomass under ideal growing conditions. The potential increase in biomass for a day is a function of intercepted energy and the plant's efficiency in converting energy to biomass. Energy interception is estimated as a function of solar radiation and the plant’s leaf area index. The process used to calculate POTENTIAL AND ACTUAL TRANSPIRATION. potential plant transpiration is described in the section on evapotranspiration. Actual transpiration is a function of potential transpiration and soil water availability. NUTRIENT UPTAKE. Plant use of nitrogen and phosphorus are estimated with a supply and demand approach where the daily plant nitrogen and phosphorus demands are calculated as the difference between the actual concentration of the element in the plant and the optimal concentration. The optimal concentration of the elements varies with growth stage as described by Jones (1983).
CHAPTER 1: INTRODUCTION
17
GROWTH CONTRAINTS. Potential plant growth and yield are usually not achieved due to constraints imposed by the environment. The model estimates stresses caused by water, nutrients and temperature.
1.2.1.4 EROSION Erosion and sediment yield are estimated for each HRU with the Modified Universal Soil Loss Equation (MUSLE) (Williams, 1975). While the USLE uses rainfall as an indicator of erosive energy, MUSLE uses the amount of runoff to simulate erosion and sediment yield. The substitution results in a number of benefits: the prediction accuracy of the model is increased, the need for a delivery ratio is eliminated, and single storm estimates of sediment yields can be calculated. The hydrology model supplies estimates of runoff volume and peak runoff rate which, with the subbasin area, are used to calculate the runoff erosive energy variable. The crop management factor is recalculated every day that runoff occurs. It is a function of above-ground biomass, residue on the soil surface, and the minimum C factor for the plant. Other factors of the erosion equation are evaluated as described by Wischmeier and Smith (1978).
1.2.1.5 NUTRIENTS SWAT tracks the movement and transformation of several forms of nitrogen and phosphorus in the watershed. In the soil, transformation of nitrogen from one form to another is governed by the nitrogen cycle as depicted in Figure 1.6. The transformation of phosphorus in the soil is controlled by the phosphorus cycle shown in Figure 1.7. Nutrients may be introduced to the main channel and transported downstream through surface runoff and lateral subsurface flow. NITROGEN. The different processes modeled by SWAT in the HRUs and the various pools of nitrogen in the soil are depicted in Figure 1.6. Plant use of nitrogen is estimated using the supply and demand approach described in the section on plant growth. In addition to plant use, nitrate and organic N may be removed from the soil via mass flow of water. Amounts of NO3-N contained in runoff, lateral flow and percolation are estimated as products of the volume of water and the average concentration of nitrate in the layer. Organic N transport
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SWAT USER’S MANUAL, VERSION 98.1
with sediment is calculated with a loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978) for application to individual runoff events. The loading function estimates the daily organic N runoff loss based on the concentration of organic N in the top soil layer, the sediment yield, and the enrichment ratio. The enrichment ratio is the concentration of organic N in the sediment divided by that in the soil.
Figure 1.6: Partitioning of Nitrogen in SWAT
PHOSPHORUS. The different processes modeled by SWAT in the HRUs and the various pools of phosphorus in the soil are depicted in Figure 1.7. Plant use of phosphorus is estimated using the supply and demand approach described in the section on plant growth. In addition to plant use, soluble phosphorus and organic P may be removed from the soil via mass flow of water. Phosphorus is not a mobile nutrient and interaction between surface runoff with solution P in the top 10 mm of soil will not be complete. The amount of soluble P removed in runoff is predicted using solution P concentration in the top 10 mm of soil, the runoff volume and a partitioning factor. Sediment transport of P is simulated with a loading function as described in organic N transport.
CHAPTER 1: INTRODUCTION
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Figure 1.7: Partitioning of Phosphorus in SWAT
1.2.1.6 PESTICIDES Although SWAT does not simulate stress on the growth of a plant due to the presence of weeds, damaging insects, and other pests, pesticides may be applied to an HRU to study the movement of the chemical in the watershed. SWAT simulates pesticide movement into the stream network via surface runoff (in solution and sorbed to sediment transported by the runoff), and into the soil profile and aquifer by percolation (in solution). The equations used to model the movement of pesticide in the land phase of the hydrologic cycle were adopted from GLEAMS (Leonard et al., 1987). The movement of the pesticide is controlled by its solubility, degradation half-life, and soil organic carbon adsorption coefficient. Pesticide on plant foliage and in the soil degrade exponentially according to the appropriate half-life. Pesticide transport by water and sediment is calculated for each runoff event and pesticide leaching is estimated for each soil layer when percolation occurs.
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SWAT USER’S MANUAL, VERSION 98.1
Figure 1.8 Pesticide fate and transport in SWAT
1.2.1.7 MANAGEMENT SWAT allows the user to define management practices taking place in every HRU. The user may define the beginning and the ending of the growing season, specify timing and amounts of fertilizer, pesticide and irrigation applications as well as timing of tillage operations. At the end of the growing season, the biomass may be removed from the HRU as yield or placed on the surface as residue. In addition to these basic management practices, operations such as grazing, automated fertilizer and water applications, and incorporation of every conceivable management option for water use are available. The latest improvement to land management is the incorporation of routines to calculate sediment and nutrient loadings from urban areas.
CHAPTER 1: INTRODUCTION
21
ROTATIONS. The dictionary defines a rotation as the growing of different crops in succession in one field, usually in a regular sequence. A rotation in SWAT refers to a change in management practices from one year to the next. There is no limit to the number of years of different management operations specified in a rotation. SWAT also does not limit the number of land cover/crops grown within one year in the HRU. However, only one land cover can be growing at any one time. WATER USE. The two most typical uses of water are for application to agricultural lands or use as a town's water supply. SWAT allows water to be applied on an HRU from any water source within or outside the watershed. Water may also be transferred between reservoirs, reaches and subbasins as well as exported from the watershed.
1.2.2 ROUTING PHASE OF THE HYDROLOGIC CYCLE Once SWAT determines the loadings of water, sediment, nutrients and pesticides to the main channel, the loadings are routed through the stream network of the watershed using a command structure similar to that of HYMO (Williams and Hann, 1972). In addition to keeping track of mass flow in the channel, SWAT models the transformation of chemicals in the stream and streambed. Figure 1.9 illustrates the different in-stream processes modeled by SWAT.
Figure 1.9: In-stream processes modeled by SWAT
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SWAT USER’S MANUAL, VERSION 98.1
1.2.2.1 ROUTING IN THE MAIN CHANNEL OR REACH Routing in the main channel can be divided into four components: water, sediment, nutrients and organic chemicals.
FLOOD ROUTING. As water flows downstream, a portion may be lost due to evaporation and transmission through the bed of the channel. Another potential loss is removal of water from the channel for agricultural or human use. Flow may be supplemented by the fall of rain directly on the channel and/or addition of water from point source discharges. Flow is routed through the channel using a variable storage coefficient method developed by Williams (1969) or the Muskingum routing method. SEDIMENT ROUTING. The transport of sediment in the channel is controlled by the simultaneous operation of two processes, deposition and degradation. Previous versions of SWAT used stream power to estimate deposition/degradation in the channels (Arnold et al, 1995). Bagnold (1977) defined stream power as the product of water density, flow rate and water surface slope. Williams (1980) used Bagnold’s definition of stream power to develop a method for determining degradation as a function of channel slope and velocity. In this version of SWAT, the equations have been simplified and the maximum amount of sediment that can be transported from a reach segment is a function of the peak channel velocity. Available stream power is used to reentrain loose and deposited material until all of the material is removed. Excess stream power causes bed degradation. Bed degradation is adjusted for stream bed erodibility and cover. NUTRIENT ROUTING. Nutrient transformations in the stream are controlled by the in-stream water quality component of the model. The in-stream kinetics used in SWAT for nutrient routing are adapted from QUAL2E (Brown and Barnwell, 1987). The model tracks nutrients dissolved in the stream and nutrients adsorbed to the sediment. Dissolved nutrients are transported with the water while those sorbed to sediments are allowed to be deposited with the sediment on the bed of the channel. CHANNEL PESTICIDE ROUTING. While an unlimited number of pesticides may be applied to the HRUs, only one pesticide may be routed through the channel network of the watershed due to the complexity of the processes simulated. As with the nutrients, the total pesticide load in the channel is partitioned into dissolved and sediment-attached components. While the dissolved pesticide is transported with water, the pesticide attached to sediment is affected by sediment transport and deposition processes. Pesticide transformations in the dissolved and sorbed phases are governed by first-order decay relationships. The
CHAPTER 1: INTRODUCTION
23
major in-stream processes simulated by the model are settling, burial, resuspension, volatilization, diffusion and transformation.
1.2.2.2 ROUTING IN THE RESERVOIR The water balance for reservoirs includes inflow, outflow, rainfall on the surface, evaporation, seepage from the reservoir bottom and diversions. RESERVOIR OUTFLOW. The model offers three alternatives for estimating outflow from the reservoir. The first option allows the user to input measured outflow. The second option, designed for small, uncontrolled reservoirs, requires the users to specify a water release rate. When the reservoir volume exceeds the principle storage, the extra water is released at the specified rate. Volume exceeding the emergency spillway is released within one day. The third option, designed for larger, managed reservoirs, has the user specify monthly target volumes for the reservoir. Sediment inflow may originate from transport SEDIMENT ROUTING. through the upstream reaches or from surface runoff within the subbasin. The concentration of sediment in the reservoir is estimated using a simple continuity equation based on volume and concentration of inflow, outflow, and water retained in the reservoir. Settling of sediment in the reservoir is governed by an equilibrium sediment concentration and the median sediment particle size. The amount of sediment in the reservoir outflow is the product of the volume of water flowing out of the reservoir and the suspended sediment concentration in the reservoir at the time of release. A simple model for nitrogen and phosphorus mass RESERVOIR NUTRIENTS. balance was taken from Chapra (1997). The model assumes: 1) the lake is completely mixed; 2) phosphorus is the limiting nutrient; and, 3) total phosphorus is a measure of the lake trophic status. The first assumption ignores lake stratification and intensification of phytoplankton in the epilimnon. The second assumption is generally valid when non-point sources dominate and the third assumption implies that a relationship exists between total phosphorus and biomass. The phosphorus mass balance equation includes the concentration in the lake, inflow, outflow and overall loss rate. The lake pesticide balance model is taken from RESERVOIR PESTICIDES. Chapra (1997) and assumes well mixed conditions. The system is partitioned into a well mixed surface water layer underlain by a well mixed sediment layer. The pesticide is partitioned into dissolved and particulate phases in both the water and sediment layers. The major processes simulated by the model are loading, outflow, transformation, volatilization, settling, diffusion, resuspension and burial.
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1.3 REFERENCES Arnold, J.G., P.M. Allen, and G. Bernhardt. 1993. A comprehensive surfacegroundwater flow model. J. Hydrol. 142:47-69. Arnold, J.G., J.R. Williams, A.D. Nicks, and N.B. Sammons. 1990. SWRRB: A basin scale simulation model for soil and water resources management. Texas A&M Univ. Press, College Station, TX. Arnold, J.G., J.R. Williams and D.R. Maidment. 1995. Continuous-time water and sediment-routing model for large basins. Journal of Hydraulic Engineering 121(2):171-183. Bagnold, R.A. 1977. Bedload transport in natural rivers. Water Resources Res. 13(2):303-312. Brown, L.C. and T.O. Barnwell, Jr. 1987. The enhanced water quality models QUAL2E and QUAL2E-UNCAS documentation and user manual. EPA document EPA/600/3-87/007. USEPA, Athens, GA. Chapra, S.C. 1997. Surface water-quality modeling. McGraw-Hill, Boston. Green, W.H. and G.A. Ampt. 1911. Studies on soil physics, 1. The flow of air and water through soils. Journal of Agricultural Sciences 4:11-24. Hargreaves, G.L., G.H. Hargreaves, and J.P. Riley. 1985. Agricultural benefits for Senegal River Basin. J. Irrig. and Drain. Engr. 111(2):113-124. Jones, C.A. 1983. A survey of the variability in tissue nitrogen and phosphorus concentrations in maize and grain sorghum. Field Crops Res. 6:133-147. Knisel, W.G. 1980. CREAMS, a field scale model for chemicals, runoff and erosion from agricultural management systems. USDA Conservation Research Rept. No. 26. Leonard, R.A. and R.D. Wauchope. 1980. Chapter 5: The pesticide submodel. p. 88-112. In Knisel, W.G. (ed). CREAMS: A field-scale model for chemicals, runoff, and erosion from agricultural management systems. U.S. Department of Agriculture, Conservation research report no. 26.
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Leonard, R.A., W.G. Knisel, and D.A. Still. 1987. GLEAMS: Groundwater loading effects on agricultural management systems. Trans. ASAE 30(5):1403-1428. McElroy, A.D., S.Y. Chiu, J.W. Nebgen, and others. 1976. Loading functions for assessment of water pollution from nonpoint sources. EPA document EPA 600/2-76-151. USEPA, Athens, GA. Monteith, J.L. 1965. Evaporation and the environment. p. 205-234. In The state and movement of water in living organisms. 19th Symposia of the Society for Experimental Biology. Cambridge Univ. Press, London, U.K. Nicks, A.D. 1974. Stochastic generation of the occurrence, pattern and location of maximum amount of daily rainfall. p. 154-171. In Proc. Symp. Statistical Hydrology, Tucson, AZ. Aug.-Sept. 1971. USDA Misc. Publ. 1275. U.S. Gov. Print. Office, Washington, DC. Priestley, C.H.B. and R.J. Taylor. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev. 100:8192. Ritchie, J.T. 1972. A model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res. 8:1204-1213. USDA Soil Conservation Service. 1983. National Engineering Handbook Section 4 Hydrology, Chapter 19. USDA Soil Conservation Service. 1972. National Engineering Handbook Section 4 Hydrology, Chapters 4-10. Williams, J.R. 1980. SPNM, a model for predicting sediment, phosphorus, and nitrogen yields from agricultural basins. Water Resour. Bull. 16(5):843848. Williams, J.R. 1975. Sediment routing for agricultural watersheds. Water Resour. Bull. 11(5):965-974. Williams, J.R. 1969. Flood routing with variable travel time or variable storage coefficients. Trans. ASAE 12(1):100-103.
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Williams, J.R., A.D. Nicks, and J.G. Arnold. 1985. Simulator for water resources in rural basins. Journal of Hydraulic Engineering 111(6): 970-986. Williams, J.R. and R.W. Hann. 1978. Optimal operation of large agricultural watersheds with water quality constraints. Texas Water Resources Institute, Texas A&M Univ., Tech. Rept. No. 96. Williams, J.R. and R.W. Hann. 1972. HYMO, a problem-oriented computer language for building hydrologic models. Water Resour. Res. 8(1):79-85. Williams, J.R., C.A. Jones and P.T. Dyke. 1984. A modeling approach to determining the relationship between erosion and soil productivity. Trans. ASAE 27(1):129-144. Wischmeier, W.H., and D.D. Smith. 1978. Predicting rainfall losses: A guide to conservation planning. USDA Agricultural Handbook No. 537. U.S. Gov. Print. Office, Washington, D. C.
PART 1
MODEL DESCRIPTION
CLIMATE The climatic inputs to the model are reviewed first because it is these inputs that provide the moisture and energy that drive all other processes simulated in the watershed. The climatic processes modeled in SWAT consist of precipitation, air temperature, soil temperature and solar radiation. Depending on the method used to calculate potential evapotranspiration, wind speed and relative humidity may also be modeled.
CHAPTER 2
EQUATIONS: ENERGY
Once water is introduced to the system as precipitation, the available energy, specifically solar radiation, exerts a major control on the movement of water in the land phase of the hydrologic cycle. Processes that are greatly affected by temperature and solar radiation include snow fall, snow melt and evaporation. Since evaporation is the primary water removal mechanism in the watershed, the energy inputs become very important in reproducing or simulating an accurate water balance.
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SWAT USER'S MANUAL, VERSION 2000
2.1 SUN-EARTH RELATIONSHIPS A number of basic concepts related to the earth's orbit around the sun are required by the model to make solar radiation calculations. This section summarizes these concepts. Iqbal (1983) provides a detailed discussion of these and other topics related to solar radiation for users who require more information.
2.1.1 DISTANCE BETWEEN EARTH AND SUN The mean distance between the earth and the sun is 1.496 x 108 km and is called one astronomical unit (AU). The earth revolves around the sun in an elliptical orbit and the distance from the earth to the sun on a given day will vary from a maximum of 1.017 AU to a minimum of 0.983 AU. An accurate value of the earth-sun distance is important because the solar radiation reaching the earth is inversely proportional to the square of its distance from the sun. The distance is traditionally expressed in mathematical form as a Fourier series type of expansion with a number of coefficients. For most engineering applications a simple expression used by Duffie and Beckman (1980) is adequate for calculating the reciprocal of the square of the radius vector of the earth, also called the eccentricity correction factor, E0, of the earth's orbit:
E0 = (r0 r ) = 1 + 0.033 cos[(2πd n 365)] 2
2.1.1
where r0 is the mean earth-sun distance (1 AU), r is the earth-sun distance for any given day of the year (AU), and dn is the day number of the year, ranging from 1 on January 1 to 365 on December 31. February is always assumed to have 28 days, making the accuracy of the equation vary due to the leap year cycle.
2.1.2 SOLAR DECLINATION The solar declination is the earth's latitude at which incoming solar rays are normal to the earth's surface. The solar declination is zero at the spring and fall equinoxes, approximately +23½° at the summer solstice and approximately -23½° at the winter solstice. A simple formula to calculate solar declination from Perrin de Brichambaut (1975) is:
CHAPTER 2: EQUATIONS—ENERGY
ì é 2π (d n − 82)ùú üý δ = sin −1 í0.4 sin ê ë 365 ûþ î
33
2.1.2
where δ is the solar declination reported in radians, and dn is the day number of the year.
2.1.3 SOLAR NOON, SUNRISE, SUNSET AND DAYLENGTH The angle between the line from an observer on the earth to the sun and a vertical line extending upward from the observer is called the zenith angle, θz (Figure 2-1). Solar noon occurs when this angle is at its minimum value for the day.
Figure 2-1: Diagram illustrating zenith angle
For a given geographical position, the relationship between the sun and a horizontal surface on the earth's surface is:
cos θ z = sin δ sin φ + cos δ cos φ cos ωt
2.1.3
where δ is the solar declination in radians, φ is the geographic latitude in radians, ω is the angular velocity of the earth's rotation (0.2618 rad h-1 or 15û h-1), and t is the solar hour. t equals zero at solar noon, is a positive value in the morning and is a negative value in the evening. The combined term ωt is referred to as the hour angle. Sunrise, TSR, and sunset, TSS, occur at equal times before and after solar noon. These times can be determined by rearranging the above equation as:
34
SWAT USER'S MANUAL, VERSION 2000
TSR = +
cos −1 [− tan δ tan φ ] ω
2.1.4
cos −1 [− tan δ tan φ ] ω
2.1.5
and TSS = −
Total daylength, TDL, is calculated: TDL =
2 cos −1 [− tan δ tan φ ] ω
2.1.6
At latitudes above 66.5° or below -66.5°, the absolute value of [-tanδ tanφ] can exceed 1 and the above equation cannot be used. When this happens, there is either no sunrise (winter) or no sunset (summer) and TDL must be assigned a value of 0 or 24 hours, respectively. To determine the minimum daylength that will occur during the year, equation 2.1.6 is solved with the solar declination set to -23.5° (-0.4102 radians) for the northern hemisphere or 23.5° (0.4102 radians) for the southern hemisphere. The only SWAT input variable used in the calculations reviewed in Section 2.1 is given in Table 2-1. Table 2-1: SWAT input variables that used in earth-sun relationship calculations. Variable name Definition LATITUDE Latitude of the subbasin (degrees).
File Name .sub
2.2 SOLAR RADIATION 2.2.1 EXTRATERRESTRIAL RADIATION The radiant energy from the sun is practically the only source of energy that impacts climatic processes on earth. The solar constant, ISC, is the rate of total solar energy at all wavelengths incident on a unit area exposed normally to rays of the sun at a distance of 1 AU from the sun. Quantifying this value has been the object of numerous studies through the years. The value officially adopted by the Commission for Instruments and Methods of Observation in October 1981 is
CHAPTER 2: EQUATIONS—ENERGY
35
ISC = 1367 W m-2 = 4.921 MJ m-2 h-1 On any given day, the extraterrestrial irradiance (rate of energy) on a surface normal to the rays of the sun, I0n, is: I 0 n = I SC E0
2.2.1
where E0 is the eccentricity correction factor of the earth's orbit, and I0n has the same units as the solar constant, ISC. To calculate the irradiance on a horizontal surface, I0, I 0 = I 0 n cos θ z = I SC E0 cos θ z
2.2.2
where cos θz is defined in equation 2.1.3. The amount of energy falling on a horizontal surface during a day is given by ss
ss
sr
0
H 0 = ò I 0 dt = 2 ò I 0 dt
2.2.3
where H0 is the extraterrestrial daily irradiation (MJ m-2 d-1), sr is sunrise, and ss is sunset. Assuming that E0 remains constant during the one day time step and converting the time dt to the hour angle, the equation can be written H0 =
ωTSR 24 I SC E0 ò (sin δ sin φ + cos δ cos φ cos ωt )dωt 0 π
2.2.4
H0 =
24 I SC E0 [ωTSR (sin δ sin φ ) + (cos δ cos φ sin(ωTSR ))] π
2.2.5
or
where ISC is the solar constant (4.921 MJ m-2 h-1), E0 is the eccentricity correction factor of the earth's orbit, ω is the angular velocity of the earth's rotation (0.2618 rad h-1), the hour of sunrise, TSR, is defined by equation 2.1.4, δ is the solar declination in radians, and φ is the geographic latitude in radians. Multiplying all the constants together gives H 0 = 37.59 E0 [ωTSR sin δ sin φ + cos δ cos φ sin (ωTSR )]
2.2.6
2.2.2 SOLAR RADIATION UNDER CLOUDLESS SKIES When solar radiation enters the earth's atmosphere, a portion of the energy is removed by scattering and adsorption. The amount of energy lost is a function of the transmittance of the atmosphere, the composition and concentration of the
36
SWAT USER'S MANUAL, VERSION 2000
constituents of air at the location, the path length the radiation travels through the air column, and the radiation wavelength. Due to the complexity of the process and the detail of the information required to accurately predict the amount of radiant energy lost while passing through the atmosphere, SWAT makes a broad assumption that roughly 20% of the extraterrestrial radiation is lost while passing through the atmosphere under cloudless skies. Using this assumption, the maximum possible solar radiation, HMX, at a particular location on the earth's surface is calculated as: H MX = 30.0 E0 [ωTSR sin δ sin φ + cos δ cos φ sin (ωTSR )]
2.2.7
where the maximum possible solar radiation, HMX, is the amount of radiation reaching the earth's surface under a clear sky (MJ m-2 d-1).
2.2.3 DAILY SOLAR RADIATION The solar radiation reaching the earth's surface on a given day, Hday, may be less than HMX due to the presence of cloud cover. The daily solar radiation data required by SWAT may be read from an input file or generated by the model. The variable SLRSIM in the input control code (.cod) file identifies the method used to obtain solar radiation data. To read in daily solar radiation data, the variable is set to 1 and the name of the solar radiation data file and the number of solar radiation records stored in the file are set in the control input/output (file.cio) file. To generate daily solar radiation values, SLRSIM is set to 2. The equations used to generate solar radiation data in SWAT are reviewed in Chapter 4. SWAT input variables that pertain to solar radiation are summarized in Table 2-2. Table 2-2: SWAT input variables used in solar radiation calculations. Variable name Definition LATITUDE Latitude of the subbasin (degrees). SLRSIM Solar radiation input code: 1-measured, 2-generated NSTOT Number of solar radiation records within the .slr file (required if SLRSIM = 1) SLRFILE Name of measured solar radiation input file (.slr) (required if SLRSIM = 1) ISGAGE Number of solar radiation record used within the subbasin (required if
File Name .sub .cod file.cio file.cio file.cio
SLRSIM = 1)
see description of .slr file in the User’s Manual for input and format requirements if measured daily solar radiation data is being used
CHAPTER 2: EQUATIONS—ENERGY
37
2.2.4 HOURLY SOLAR RADIATION The extraterrestrial radiation falling on a horizontal surface during one hour is given by the equation: I 0 = I SC E0 (sin δ sin φ + cos δ cos φ cos ωt )
2.2.8
where I0 is the extraterrestrial radiation for 1 hour centered around the hour angle
ωt. An accurate calculation of the radiation for each hour of the day requires a knowledge of the difference between standard time and solar time for the location. SWAT simplifies the hourly solar radiation calculation by assuming that solar noon occurs at 12:00pm local standard time. When the values of I0 calculated for every hour between sunrise and sunset are summed, they will equal the value of H0. Because of the relationship between I0 and H0, it is possible to calculate the hourly radiation values by multiplying H0 by the fraction of radiation that falls within the different hours of the day. The benefit of this alternative method is that assumptions used to estimate the difference between maximum and actual solar radiation reaching the earth’s surface can be automatically incorporated in calculations of hourly solar radiation at the earth’s surface. SWAT calculates hourly solar radiation at the earth’s surface with the equation: I hr = I frac ⋅ H day
2.2.9
where Ihr is the solar radiation reaching the earth’s surface during a specific hour of the day (MJ m-2 hr-1), Ifrac is the fraction of total daily radiation falling during that hour, and Hday is the total solar radiation reaching the earth’s surface on that day. The fraction of total daily radiation falling during an hour is calculated I frac =
(sin δ sin φ + cos δ cos φ cos ωti ) SS
å (sin δ sin φ + cos δ cos φ cos ωt )
t = SR
where ti is the solar time at the midpoint of hour i.
2.2.10
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SWAT USER'S MANUAL, VERSION 2000
2.2.5 DAILY NET RADIATION Net radiation requires the determination of both incoming and reflected short-wave radiation and net long-wave or thermal radiation. Expressing net radiation in terms of the net short-wave and long-wave components gives: H net = H day ↓ −α ⋅ H day ↑ + H L ↓ − H L ↑
2.2.11
H net = (1 − α ) ⋅ H day + H b
2.2.12
or
where Hnet is the net radiation (MJ m-2 d-1), Hday is the short-wave solar radiation reaching the ground (MJ m-2 d-1), α is the short-wave reflectance or albedo, HL is the long-wave radiation (MJ m-2 d-1), Hb is the net incoming long-wave radiation (MJ m-2 d-1) and the arrows indicate the direction of the radiation flux.
2.2.5.1 NET SHORT-WAVE RADIATION Net short-wave radiation is defined as
(1 − α ) ⋅ H day .
SWAT
calculates a daily value for albedo as a function of the soil type, plant cover, and snow cover. When the snow water equivalent is greater than 0.5 mm,
α = 0 .8
2.2.13
When the snow water equivalent is less than 0.5 mm and no plants are growing in the HRU,
α = α soil
2.2.14
where αsoil is the soil albedo. When plants are growing and the snow water equivalent is less than 0.5 mm,
α = α plant ⋅ (1 − cov sol ) + α soil ⋅ cov sol
2.2.15
where αplant is the plant albedo (set at 0.23), and covsol is the soil cover index. The soil cover index is calculated cov sol = exp(− 5.0 × 10 −5 ⋅ CV )
where CV is the aboveground biomass and residue (kg ha-1).
2.2.16
CHAPTER 2: EQUATIONS—ENERGY
39
2.2.5.2 NET LONG-WAVE RADIATION Long-wave radiation is emitted from an object according to the radiation law: H R = ε ⋅ σ ⋅ TK
4
2.2.17
where HR is the radiant energy (MJ m-2 d-1), ε is the emissivity, σ is the Stefan-Boltzmann constant (4.903 × 10-9 MJ m-2 K-4 d-1), and TK is the mean air temperature in Kelvin (273.15 + °C). Net long-wave radiation is calculated using a modified form of equation 2.2.17 (Jensen et al., 1990): H b = f cld ⋅ (ε a − ε vs ) ⋅ σ ⋅ TK
4
2.2.18
where Hb is the net long-wave radiation (MJ m-2 d-1), fcld is a factor to adjust for cloud cover, εa is the atmospheric emittance, and εvs is the vegetative or soil emittance. Wright and Jensen (1972) developed the following expression for the cloud cover adjustment factor, fcld: f cld = a ⋅
H day H MX
−b
2.2.19
where a and b are constants, Hday is the solar radiation reaching the ground surface on a given day (MJ m-2 d-1), and HMX is the maximum possible solar radiation to reach the ground surface on a given day (MJ m-2 d-1). The two emittances in equation 2.2.18 may be combined into a single term, the net emittance ε′. The net emittance is calculated using an equation developed by Brunt (1932):
(
ε ′ = ε a − ε vs = − a1 + b1 ⋅ e
)
2.2.20
where a1 and b1 are constants and e is the vapor pressure on a given day (kPa). The calculation of e is given in Chapter 3. Combining equations 2.2.18, 2.2.19, and 2.2.20 results in a general equation for net long-wave radiation:
[
]
é H day ù 4 H b = − êa ⋅ − bú ⋅ a1 + b1 e ⋅ σ ⋅ TK ë H MX û
2.2.21
40
SWAT USER'S MANUAL, VERSION 2000
Experimental values for the coefficients a, b, a1 and b1 are presented in Table 2.3. The default equation in SWAT uses coefficent values proposed by Doorenbos and Pruitt (1977):
[
]
H day é ù 4 H b = − ê0.9 ⋅ + 0.1ú ⋅ 0.34 − 0.139 e ⋅ σ ⋅ TK H MX ë û
2.2.22
Table 2-3: Experimental coefficients for net long-wave radiation equations (from Jensen et al., 1990) b1) Region (a, b) (a1, Davis, California (1.35, -0.35) (0.35, -0.145) Southern Idaho (1.22, -0.18) (0.325, -0.139) England not available (0.47, -0.206) England not available (0.44, -0.253) Australia not available (0.35, -0.133) General (1.2, -0.2) (0.39, -0.158) General-humid areas (1.0, 0.0) General-semihumid areas (1.1, -0.1) Table 2-4: SWAT input variables used in net radiation calculations. Variable name Definition SOL_ALB αsoil: moist soil albedo MAX TEMP Tmx: Daily maximum temperature (°C) MIN TEMP Tmn: Daily minimum temperature (°C) SOL_RAD Hday: Daily solar radiation reaching the earth’s surface (MJ m-2 d-1)
File Name .sol .tmp .tmp .slr
CHAPTER 2: EQUATIONS—ENERGY
41
2.3 TEMPERATURE Temperature influences a number of physical, chemical and biological processes. Plant production is strongly temperature dependent, as are organic matter decomposition and mineralization. Daily air temperature may be input to the model or generated from average monthly values. Soil and water temperatures are derived from air temperature.
2.3.1 DAILY AIR TEMPERATURE SWAT requires daily maximum and minimum air temperature. This data may be read from an input file or generated by the model. The user is strongly recommended to obtain measured daily temperature records from gages in or near the watershed if at all possible. The accuracy of model results is significantly improved by the use of measured temperature data. The variable TMPSIM in the input control code (.cod) file identifies the method used to obtain air temperature data. To read in daily maximum and minimum air temperature data, the variable is set to 1 and the name of the temperature data file(s) and the number of temperature records stored in the file are set in the control input/output (file.cio) file. To generate daily air temperature values, TMPSIM is set to 2. The equations used to generate air temperature data in SWAT are reviewed in Chapter 4. SWAT input variables that pertain to air temperature are summarized in Table 2-5. Table 2-5: SWAT input variables that pertain to daily air temperature. Variable name Definition TMPSIM Air temperature input code: 1-measured, 2-generated NTGAGE Number of temperature gage (.tmp) files used in simulation file (required if
File Name .cod file.cio
TMPSIM = 1)
NTTOT NTFIL
Number of temperature records used in simulation (required if TMPSIM = 1) Number of temperature records within each .tmp file file (required if
TFILE
Name of measured temperature input file (.tmp) Up to 18 files may be used. (required if TMPSIM = 1) Number of temperature record used within the subbasin (required if TMPSIM
file.cio file.cio
TMPSIM = 1)
ITGAGE
file.cio file.cio
= 1)
see description of .tmp file in the User’s Manual for input and format requirements if measured temperature data is being used
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SWAT USER'S MANUAL, VERSION 2000
2.3.2 HOURLY AIR TEMPERATURE Air temperature data are usually provided in the form of daily maximum and minimum temperature. A reasonable approximation for converting these to hourly temperatures is to assume a sinusoidal interpolation function between the minimum and maximum daily temperatures. The maximum daily temperature is assumed to occur at 1500 hours and the minimum daily temperature at 300 hours (Campbell, 1985). The temperature for the hour is then calculated with the equation: Thr = T av +
(Tmx − Tmn ) ⋅ cos(0.2618 ⋅ (hr − 15)) 2
2.3.1
where Thr is the air temperature during hour hr of the day (°C), T av is the average temperature on the day (°C), Tmx is the daily maximum temperature (°C), and Tmn is the daily minimum temperature (°C). Table 2-6: SWAT input variables that pertain to hourly air temperature. Variable name Definition MAX TEMP Tmx: Daily maximum temperature (°C) MIN TEMP Tmn: Daily minimum temperature (°C)
File Name .tmp .tmp
2.3.3 SOIL TEMPERATURE Soil temperature will fluctuate due to seasonal and diurnal variations in temperature at the surface. Figure 2-2 plots air temperature and soil temperature at 5 cm and 300 cm below bare soil at College Station, Texas.
Figure 2-2: Four-year average air and soil temperature at College Station, Texas.
CHAPTER 2: EQUATIONS—ENERGY
43
This figure illustrates several important attributes of temperature variation in the soil. First, the annual variation in soil temperature follows a sinusoidal function. Second, the fluctuation in temperature during the year (the amplitude of the sine wave) decreases with depth until, at some depth in the soil, the temperature remains constant throughout the year. Finally, the timing of maximum and minimum temperatures varies with depth. Note in the above graph that there is a three month difference between the recording of the minimum temperature at the surface (January) and the minimum temperature at 300 cm (March). Carslaw and Jaeger (1959) developed an equation to quantify the seasonal variation in temperature: Tsoil (z, d n ) = T AA + Asurf exp(− z / dd ) sin (ω tmp d n − z / dd )
2.3.2
where Tsoil(z,dn) is the soil temperature (°C) at depth z (mm) and day of the year dn, T AA is the average annual soil temperature (°C), Asurf is the amplitude of the surface fluctuations (°C), dd is the damping depth (mm), and ωtmp is the angular frequency.
When
z
=
0
(soil
surface),
equation
2.3.2
reduces
to
Tsoil (0, d n ) = T AA + Asurf sin (ω tmp d n ) . As z → ∞, equation 2.3.2 becomes Tsoil (∞, d n ) = T AA . In order to calculate values for some of the variables in this equation, the heat capacity and thermal conductivity of the soil must be known. These are properties not commonly measured in soils and attempts at estimating values from other soil properties have not proven very effective. Consequently, an equation has been adopted in SWAT that calculates the temperature in the soil as a function of the previous day’s soil temperature, the average annual air temperature, the current day’s soil surface temperature, and the depth in the profile. The equation used to calculate daily average soil temperature at the center of each layer is:
[ [
]
Tsoil (z,d n ) = l ⋅ Tsoil (z,d n − 1) + [1.0 − l] ⋅ df ⋅ T AAair − Tssurf + Tssurf
]
2.3.3
44
SWAT USER'S MANUAL, VERSION 2000
where Tsoil(z,dn) is the soil temperature (°C) at depth z (mm) and day of the year dn, l is the lag coefficient (ranging from 0.0 to 1.0) that controls the influence of the previous day’s temperature on the current day’s temperature, Tsoil(z,dn-1) is the soil temperature (°C) in the layer from the previous day, df is the depth factor that quantifies the influence of depth below surface on soil temperature, T AAair is the average annual air temperature (°C), and Tssurf is the soil surface temperature on the day. SWAT sets the lag coefficient, l , to 0.80. The soil temperature from the previous day is known and the average annual air temperature is calculated from the long-term monthly maximum and minimum temperatures reported in the weather generator input (.wgn) file. This leaves the depth factor, df, and the soil surface temperature, Tssurf, to be defined. The depth factor is calculated using the equation: df =
zd zd + exp(− 0.867 − 2.078 ⋅ zd )
2.3.4
where zd is the ratio of the depth at the center of the soil layer to the damping depth: zd =
z dd
2.3.5
where z is the depth at the center of the soil layer (mm) and dd is the damping depth (mm). From the previous three equations (2.3.3, 2.3.4 and 2.3.5) one can see that at depths close to the soil surface, the soil temperature is a function of the soil surface temperature. As the depth increases, soil temperature is increasingly influenced by the average annual air temperature, until at the damping depth, the soil temperature is within 5% of T AAair . The damping depth, dd, is calculated daily and is a function of the maximum damping depth, bulk density and soil water. The maximum damping depth, ddmax, is calculated: dd max = 1000 +
2500 ρ b ρ b + 686 exp(− 5.63ρ b )
2.3.6
CHAPTER 2: EQUATIONS—ENERGY
45
where ddmax is the maximum damping depth (mm), and ρb is the soil bulk density (Mg/m3). The impact of soil water content on the damping depth is incorporated via a scaling factor,ϕ, that is calculated with the equation:
ϕ=
SW (0.356 − 0.144 ρ b ) ⋅ ztot
2.3.7
where SW is the amount of water in the soil profile expressed as depth of water in the profile (mm H2O), ρb is the soil bulk density (Mg/m3), and ztot is the depth from the soil surface to the bottom of the soil profile (mm). The daily value for the damping depth, dd, is calculated: é æ 500 ö æ 1 − ϕ ö 2 ù ÷÷ ⋅ çç dd = dd max ⋅ exp êlnçç ÷÷ ú dd 1 ϕ + è ø ûú max ø ëê è
2.3.8
where ddmax is the maximum damping depth (mm), and ϕ is the scaling factor for soil water. The soil surface temperature is a function of the previous day’s temperature, the amount of ground cover and the temperature of the surface when no cover is present. The temperature of a bare soil surface is calculated with the equation: Tbare = T av + ε sr
(Tmx − Tmn ) 2
2.3.9
where Tbare is the temperature of the soil surface with no cover (°C), T av is the average temperature on the day (°C), Tmx is the daily maximum temperature (°C), Tmn is the daily minimum temperature (°C), and εsr is a radiation term. The radiation term is calculated with the equation:
ε sr =
H day ⋅ (1 − α ) − 14 20
2.3.10
where Hday is the solar radiation reaching the ground on the current day (MJ m-2 d-1), and α is the albedo for the day.
46
SWAT USER'S MANUAL, VERSION 2000
Any cover present will significantly impact the soil surface temperature. The influence of plant canopy or snow cover on soil temperature is incorporated with a weighting factor, bcv, calculated as: CV ì ü ï CV + exp(7.563 − 1.297 × 10 −4 ⋅ CV )ï ï ï bcv = max í ý SNO ï ï ï SNO + exp(6.055 − 0.3002 ⋅ SNO ) ï î þ
2.3.11
where CV is the total aboveground biomass and residue present on the current day (kg ha-1) and SNO is the water content of the snow cover on the current day (mm H2O). The weighting factor, bcv, is 0.0 for a bare soil and approaches 1.0 as cover increases. The equation used to calculate the soil surface temperature is: Tssurf = bcv ⋅ Tsoil (1, d n − 1) + (1 − bcv ) ⋅ Tbare
2.3.12
where Tssurf is the soil surface temperature for the current day (°C), bcv is the weighting factor for soil cover impacts, Tsoil(1,dn-1) is the soil temperature of the first soil layer on the previous day (°C), and Tbare is the temperature of the bare soil surface (°C). The influence of ground cover is to place more emphasis on the previous day’s temperature near the surface. SWAT input variables that directly impact soil temperature calculations are listed in Table 2-7. There are several other variables that initialize residue and snow cover in the subbasins or HRUs (SNO_SUB and SNOEB in .sub; RSDIN in .hru). The influence of these variables will be limited to the first few months of simulation. Finally, the timing of management operations in the .mgt file will affect ground cover and consequently soil temperature.
CHAPTER 2: EQUATIONS—ENERGY
47
Table 2-7: SWAT input variables that pertain to soil temperature. Variable name TMPMX TMPMN SOL_Z SOL_BD SOL_ALB MAX TEMP
MIN TEMP
File Name .wgn .wgn .sol .sol .sol .tmp .tmp
Definition Average maximum air temperature for month (°C) Average minimum air temperature for month (°C) z: Depth from soil surface to bottom of layer (mm) ρb: Moist bulk density (Mg m-3 or g cm-3) Moist soil albedo. Tmx: Daily maximum temperature (°C) Tmn: Daily minimum temperature (°C)
2.3.4 WATER TEMPERATURE Water temperature is required to model in-stream biological and water quality processes. SWAT uses an equation developed by Stefan and Preud’homme (1993) to calculate average daily water temperature for a well-mixed stream: Twater = 5.0 + 0.75T av
2.3.13
where Twater is the water temperature for the day (°C), and T av is the average temperature on the day (°C). Due to thermal inertia of the water, the response of water temperature to a change in air temperature is dampened and delayed. When water and air temperature are plotted for a stream or river, the peaks in the water temperature plots usually lag 3-7 hours behind the peaks in air temperature. As the depth of the river increases, the lag time can increase beyond this typical interval. For very large rivers, the lag time can extend up to a week. Equation 2.3.13 assumes that the lag time between air and water temperatures is less than 1 day. In addition to air temperature, water temperature is influenced by solar radiation, relative humidity, wind speed, water depth, ground water inflow, artificial heat inputs, thermal conductivity of the sediments and the presence of impoundments along the stream network. SWAT assumes that the impact of these other variables on water temperature is not significant. Table 2-8: SWAT input variables that pertain to water temperature. Variable name Definition MAX TEMP Tmx: Daily maximum temperature (°C) MIN TEMP Tmn: Daily minimum temperature (°C)
File Name .tmp .tmp
48
SWAT USER'S MANUAL, VERSION 2000
2.4 WIND SPEED Wind speed is required by SWAT if the Penman-Monteith equation is used to estimate potential evapotranspiration and transpiration. SWAT assumes wind speed information is collected from gages positioned 1.7 meters above the ground surface. When using the Penman-Monteith equation to estimate transpiration, the wind measurement used in the equation must be above the canopy. In SWAT, a minimum difference of 1 meter is specified for canopy height and wind speed measurements. When the canopy height exceeds 1 meter, the original wind measurements is adjusted to: z w = hc + 100
2.4.1
where zw is the height of the wind speed measurement (cm), and hc is the canopy height (cm). The variation of wind speed with elevation near the ground surface is estimated with the equation (Haltiner and Martin, 1957): uz2
éz ù = u z1 ⋅ ê 2 ú ë z1 û
aa
2.4.2
where uz1 is the wind speed (m s-1) at height z1 (cm), uz2 is the wind speed (m s-1) at height z2 (cm), and aa is an exponent between 0 and 1 that varies with atmospheric stability and surface roughness. Jensen (1974) recommended a value of 0.2 for aa and this is the value used in SWAT. The daily wind speed data required by SWAT may be read from an input file or generated by the model. The variable WNDSIM in the input control code (.cod) file identifies the method used to obtain wind speed data. To read in daily wind speed data, the variable is set to 1 and the name of the wind speed data file and the number of different records stored in the file are set in the control input/output (file.cio) file. To generate daily wind speed values, WNDSIM is set to 2. The equations used to generate wind speed data in SWAT are reviewed in Chapter 4.
CHAPTER 2: EQUATIONS—ENERGY Table 2-9: SWAT input variables used in wind speed calculations. Variable name Definition WNDSIM Wind speed input code: 1-measured, 2-generated NWTOT Number of wind speed records within the .wnd file (required if WNDSIM = 1) WNDFILE Name of measured wind speed input file (.wnd) (required if WNDSIM = 1) IWGAGE Number of wind speed record used within the subbasin (required if WNDSIM
49 File Name .cod file.cio file.cio file.cio
= 1)
see description of .wnd file in the User’s Manual for input and format requirements if measured daily wind speed data is being used
2.5 NOMENCLATURE Asurf AU CV E0 H0 Hb Hday HL HMX Hnet HR Ifrac Ihr ISC I0 I0n SNO SW Tbare TDL Thr TK Tmn Tmx Tsoil Tssurf TSR TSS Twater T AA
Amplitude of the surface fluctuations in soil temperature (°C) Astronomical unit (1 AU = 1.496 x 108 km) Total aboveground biomass and residue present on current day (kg ha-1) Eccentricity correction factor of earth (r0/r)2 Extraterrestrial daily irradiation (MJ m-2 d-1) Net outgoing long-wave radiation (MJ m-2 d-1) Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) Long-wave radiation (MJ m-2 d-1) Maximum possible solar radiation (MJ m-2 d-1) Net radiation on day (MJ m-2 d-1) Radiant energy (MJ m-2 d-1) Fraction of daily solar radiation falling during specific hour on current day of simulation Solar radiation reaching ground during specific hour on current day of simulation (MJ m-2 h-1) Solar constant (4.921 MJ m-2 h-1) Extraterrestrial daily irradiance incident on a horizontal surface (MJ m-2 h-1) Extraterrestrial daily irradiance incident on a normal surface (MJ m-2 h-1) Water content of snow cover on current day (mm H2O) Amount of water in soil profile (mm H2O) Temperature of soil surface with no cover (°C) Daylength (h) Air temperature during hour (°C) Mean air temperature in Kelvin (273.15 + °C) Minimum air temperature for day (°C) Maximum air temperature for day (°C) Soil temperature (°C) Soil surface temperature (°C) Time of sunrise in solar day (h) Time of sunset in solar day (h) Average daily water temperature (°C) Average annual soil temperature (°C)
50
SWAT USER'S MANUAL, VERSION 2000
T AAair Average annual air temperature (°C) T av Average air temperature for day (°C) a a1 aa b b1 bcv covsol dn dd ddmax df e fcld hc hr r r0 t ti uz1 uz2 z z1 z2 ztot zw zd
α αplant αsoil δ ε ε′ εa εsr εvs l
σ θz
Constant in equation used to calculate the cloud cover adjustment factor Constant in equation used to calculate net emissivity Exponent between 0 and 1 that varies with atmospheric stability and surface roughness that is used in calculating wind speed at different heights Constant in equation used to calculate the cloud cover adjustment factor Constant in equation used to calculate net emissivity weighting factor for impact of ground cover on soil surface temperature Soil cover index for albedo determination Day number of year, 1 on January 1 and 365 on December 31 Damping depth (mm) Maximum damping depth (mm) Depth factor used in soil temperature calculations Vapor pressure (actual) on a given day (kPa) Factor to adjust for cloud cover in net long-wave radiation calculation Canopy height (cm) Hour of day (1-24) Actual earth-sun distance (AU) Mean earth-sun distance, 1 AU Number of hours before (+) or after (-) solar noon Solar time at the midpoint of the hour i Wind speed (m s-1) at height z1 (cm) Wind speed (m s-1) at height z2 (cm) Depth below soil surface (mm) Height of wind speed measurement (cm) Height of wind speed measurement (cm) Depth to bottom of soil profile (mm) Height of the wind speed measurement (cm) Ratio of depth in soil to damping depth Short-wave reflectance or albedo Plant albedo (set at 0.23) Soil albedo Solar declination (radians) Emissivity Net emittance Atmospheric emittance Radiation term for bare soil surface temperature calculation Vegetative or soil emittance Lag coefficient that controls influence of previous day’s temperature on current days temperature Stefan-Boltzmann constant (4.903 × 10-9 MJ m-2 K-4 d-1) Zenith angle (radians)
CHAPTER 2: EQUATIONS—ENERGY
φ ρb ϕ ω ωtmp
51
Latitude in radians Soil bulk density (Mg m-3) Scaling factor for impact of soil water on damping depth Angular velocity of the earth's rotation (0.2618 radians h-1) Angular frequency in soil temperature variation
2.6 REFERENCES Brunt, D. 1932. Notes on radiation in the atmosphere. Quart. J. Roy. Meteorol. Soc. 58: 389-418. Campbell, G.S. 1985. Soil physics with BASIC: transport models for soil-plant systems. Elsevier, Amsterdam. Carslaw, H.S. and J.C. Jaeger. 1959. Conduction of heat in solids. Oxford University Press, London. Doorenbos, J. and W.O. Pruitt. 1977. Guidelines for predicting crop water requirements. FAO Irrig. and Drain. Paper No. 24, 2nd ed. FAO, Rome. Duffie, J.A. and W.A. Beckman. 1980. Solar engineering of thermal processes. Wiley, N.Y. Haltiner, G.J. and F.L. Martin. 1957. Dynamical and physical meteorology. McGraw-Hill, New York. Iqbal, M. 1983. An introduction to solar radiation. Academic Press, N.Y. Jensen, M.E. (ed.) 1974. Consumptive use of water and irrigation water requirements. Rep. Tech. Com. on Irrig. Water Requirements, Irrig. and Drain. Div., ASCE. Jensen, M.E., R.D. Burman, and R.G. Allen (ed). 1990. Evapotranspiration and irrigation water requirements. ASCE Manuals and Reports on Engineering Practice No. 70, ASCE, N.Y. Perrin de Brichambaut, Chr. 1975. Cahiers A.F.E.D.E.S., supplément au no 1. Editions Européennes Thermique et Industrie, Paris. Stefan, H.G. and E.B. Preud’homme. 1993. Stream temperature estimation from air temperature. Water Resources Bulletin 29(1): 27-45.
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SWAT USER'S MANUAL, VERSION 2000
Wright, J.L. and M.E. Jensen. 1972. Peak water requirements of crops in Southern Idaho. J. Irrig. and Drain. Div., ASCE, 96(IR1):193-201.
CHAPTER 3
EQUATIONS: ATMOSPHERIC WATER
Precipitation is the mechanism by which water enters the land phase of the hydrologic cycle. Because precipitation controls the water balance, it is critical that the amount and distribution of precipitation in space and time is accurately simulated by the model.
53
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SWAT USER’S MANUAL, VERSION 2000
3.1 PRECIPITATION The precipitation reaching the earth's surface on a given day, Rday, may be read from an input file or generated by the model. Users are strongly recommended to incorporate measured precipitation into their simulations any time the data is available. The ability of SWAT to reproduce observed stream hydrographs is greatly improved by the use of measured precipitation data. Unfortunately, even with the use of measured precipitation the model user can expect some error due to inaccuracy in precipitation data. Measurement of precipitation at individual gages is subject to error from a number of causes and additional error is introduced when regional precipitation is estimated from point values. Typically, total or average areal precipitation estimates for periods of a year or longer have relative uncertainties of 10% (Winter, 1981). Point measurements of precipitation generally capture only a fraction of the true precipitation. The inability of a gage to capture a true reading is primarily caused by wind eddies created by the gage. These wind eddies reduce the catch of the smaller raindrops and snowflakes. Larson and Peck (1974) found that deficiencies of 10% for rain and 30% for snow are common for gages projecting above the ground surface that are not designed to shield wind effects. Even when the gage is designed to shield for wind effects, this source of error will not be eliminated. For an in-depth discussion of this and other sources of error as well as methods for dealing with the error, please refer to Dingman (1994). The variable PCPSIM in the input control code (.cod) file identifies the method used to obtain precipitation data. To read in daily precipitation data, the variable is set to 1 and the names of the precipitation data files and the number of precipitation records stored in the files are defined in the control input/output (file.cio) file. To generate daily precipitation values, PCPSIM is set to 2. The equations used to generate precipitation data in SWAT are reviewed in Chapter 4. SWAT input variables that pertain to precipitation are summarized in Table 3-1.
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 55 Table 3-1: SWAT input variables used in precipitation calculations. Variable name Definition PCPSIM Precipitation input code: 1-measured, 2-generated NRGAGE Number of precipitation gage files (.pcp) used (required if PCPSIM = 1) NRTOT Total number of precipitation records used in simulation (required if PCPSIM
File Name .cod file.cio file.cio
= 1)
NRGFIL RFILE IRGAGE
Number of precipitation records in each .pcp file (required if PCPSIM = 1) Name of measured precipitation input file(s) (.pcp) (required if PCPSIM = 1) Number of precipitation record used within the subbasin (required if PCPSIM
file.cio file.cio file.cio
= 1)
see description of .pcp file in the User’s Manual for input and format requirements if measured daily precipitation data is being used
3.2 MAXIMUM HALF-HOUR RAINFALL The maximum half-hour rainfall is required by SWAT to calculate the peak runoff rate. The maximum half-hour rainfall is reported as a fraction of the total daily rainfall, α0.5. If sub-daily precipitation data is used in the model, SWAT will calculate the maximum half-hour rainfall fraction directly from the precipitation data. If daily precipitation data is used, SWAT generates a value for
α0.5 using the equations summarized in Chapter 4.
3.3 WATER VAPOR Relative humidity is required by SWAT if the Penman-Monteith or Priestley-Taylor equation is used to estimate potential evapotranspiration. The Penman-Monteith equation includes terms that quantify the effect of the amount of water vapor in the air near the evaporative surface on evaporation. Both Penman-Monteith and Priestley-Taylor require the actual vapor pressure, which is calculated from the relative humidity. Relative humidity is the ratio of an air volume’s actual vapor pressure to its saturation vapor pressure:
Rh =
e eo
3.3.1
where Rh is the relative humidity on a given day, e is the actual vapor pressure on a given day (kPa), and e o is the saturation vapor pressure on a given day (kPa).
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SWAT USER’S MANUAL, VERSION 2000
The saturation vapor pressure is the maximum vapor pressure that is thermodynamically stable and is a function of the air temperature. SWAT calculates saturation vapor pressure using an equation presented by Tetens (1930) and Murray (1967):
é16.78 ⋅ T av − 116.9 ù e o = exp ê ú ë T av + 237.3 û
3.3.2
where e o is the saturation vapor pressure on a given day (kPa) and T av is the mean daily air temperature (°C). When relative humidity is known, the actual vapor pressure can be calculated by rearranging equation 3.3.1: e = Rh ⋅ e o
3.3.3
The saturation vapor pressure curve is obtained by plotting equation 3.3.2. The slope of the saturation vapor pressure curve can be calculated by differentiating equation 3.3.2: ∆=
(T
4098 ⋅ e o av
)
+ 237.3
2
3.3.4
where ∆ is the slope of the saturation vapor pressure curve (kPa °C-1), e o is the saturation vapor pressure on a given day (kPa) and T av is the mean daily air temperature (°C). The rate of evaporation is proportional to the difference between the vapor pressure of the surface layer and the vapor pressure of the overlying air. This difference is termed the vapor pressure deficit:
vpd = e o − e
3.3.5
where vpd is the vapor pressure deficit (kPa), e o is the saturation vapor pressure on a given day (kPa), and e is the actual vapor pressure on a given day (kPa). The greater the value of vpd the higher the rate of evaporation. The latent heat of vaporization, λ, is the quantity of heat energy that must be absorbed to break the hydrogen bonds between water molecules in the liquid state to convert them to gas. The latent heat of vaporization is a function of temperature and can be calculated with the equation (Harrison, 1963):
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 57
λ = 2.501 − 2.361 × 10 −3 ⋅ T av
3.3.6
where λ is the latent heat of vaporization (MJ kg-1) and T av is the mean daily air temperature (°C). Evaporation involves the exchange of both latent heat and sensible heat between the evaporating body and the air. The psychrometric constant, γ, represents a balance between the sensible heat gained from air flowing past a wet bulb thermometer and the sensible heat converted to latent heat (Brunt, 1952) and is calculated:
γ =
cp ⋅ P 0.622 ⋅ λ
3.3.7
where γ is the psychrometric constant (kPa °C-1), cp is the specific heat of moist air at constant pressure (1.013 × 10-3 MJ kg-1 °C-1), P is the atmospheric pressure (kPa), and λ is the latent heat of vaporization (MJ kg-1). Calculation of the psychrometric constant requires a value for atmospheric pressure. SWAT estimates atmospheric pressure using an equation developed by Doorenbos and Pruitt (1977) from mean barometric pressure data at a number of East African sites: P = 101.3 − 0.01152 ⋅ EL + 0.544 × 10 −6 ⋅ EL2
3.3.8
where P is the atmospheric pressure (kPa) and EL is the elevation (m). The daily relative humidity data required by SWAT may be read from an input file or generated by the model. The variable RHSIM in the input control code (.cod) file identifies the method used to obtain relative humidity data. To read in daily relative humidity data, the variable is set to 1 and the name of the relative humidity data file and the number of different records stored in the file are set in the control input/output (file.cio) file. To generate daily relative humidity values, RHSIM is set to 2. The equations used to generate relative humidity data in SWAT are reviewed in Chapter 4.
58
SWAT USER’S MANUAL, VERSION 2000 Table 3-2: SWAT input variables used in relative humidity calculations. Variable name Definition RHD Rh: daily average relative humidity TMP_MX Tmx: maximum temperature for day (°C) TMP_MN Tmn: minimum temperature for day (°C) ELEV EL: elevation (m) RHSIM Relative humidity input code: 1-measured, 2-generated NHTOT Number of relative humidity records within the .hmd file (required if RHSIM
File Name .hmd .tmp .tmp .sub .cod file.cio
= 1)
RHFILE IHGAGE
Name of measured relative humidity input file (.hmd) (required if RHSIM = 1) Number of relative humidity record used within the subbasin (required if
file.cio file.cio
RHSIM = 1)
see description of .hmd file in the User’s Manual for input and format requirements if measured relative humidity data is being used
3.4 SNOW COVER SWAT classifies precipitation as rain or freezing rain/snow by the mean daily air temperature. The boundary temperature, Ts-r, used to categorize precipitation as rain or snow is defined by the user. If the mean daily air temperature is less than the boundary temperature, then the precipitation within the HRU is classified as snow and the water equivalent of the snow precipitation is added to the snow pack. Snowfall is stored at the ground surface in the form of a snow pack. The amount of water stored in the snow pack is reported as a snow water equivalent. The snow pack will increase with additional snowfall or decrease with snow melt or sublimation. The mass balance for the snow pack is: SNO = SNO + Rday − E sub − SNOmlt
3.4.1
where SNO is the water content of the snow pack on a given day (mm H2O), Rday is the amount of precipitation on a given day (added only if T av ≤ Ts − r ) (mm H2O), Esub is the amount of sublimation on a given day (mm H2O), and SNOmlt is the amount of snow melt on a given day (mm H2O). The amount of snow is expressed as depth over the total HRU area. Due to variables such as drifting, shading and topography, the snow pack in a subbasin will rarely be uniformly distributed over the total area. This results
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 59
in a fraction of the subbasin area that is bare of snow. This fraction must be quantified to accurately compute snow melt in the subbasin. The factors that contribute to variable snow coverage are usually similar from year to year, making it possible to correlate the areal coverage of snow with the amount of snow present in the subbasin at a given time. This correlation is expressed as an areal depletion curve, which is used to describe the seasonal growth and recession of the snow pack as a function of the amount of snow present in the subbasin (Anderson, 1976). The areal depletion curve requires a threshold depth of snow, SNO100, to be defined above which there will always be 100% cover. The threshold depth will depend on factors such as vegetation distribution, wind loading of snow, wind scouring of snow, interception and aspect, and will be unique to the watershed of interest. The areal depletion curve is based on a natural logarithm. The areal depletion curve equation is: snocov
SNO = SNO100
æ SNO æ SNO ö ö ÷÷ ÷÷ ⋅ çç + expçç cov1 − cov 2 ⋅ SNO SNO 100 100 è øø è
−1
3.4.2
where snocov is the fraction of the HRU area covered by snow, SNO is the water content of the snow pack on a given day (mm H2O), SNO100 is the threshold depth of snow at 100% coverage (mm H2O), cov1 and cov2 are coefficients that define the shape of the curve. The values used for cov1 and cov2 are determined by solving equation 3.4.2 using two known points: 95% coverage at 95% SNO100; and 50% coverage at a user specified fraction of SNO100. Example areal depletion curves for various fractions of SNO100 at 50% coverage are shown in the following figures.
1
1
0.9
0.9
0.8
0.8 Fraction areal coverage
Fraction areal coverage
SWAT USER’S MANUAL, VERSION 2000
0.7 0.6 0.5 0.4 0.3 0.2 0.1
0.7 0.6 0.5 0.4 0.3 0.2 0.1
0
0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
Snow volume (fraction of SNO100 )
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Snow volume (fraction of SNO100 )
Figure 3-1:10% SNO100 = 50% coverage
Figure 3-2: 30% SNO100 = 50%
1
1
0.9
0.9
0.8
0.8
0.7
0.7
Fraction areal coverage
Fraction areal coverage
0.6 0.5 0.4 0.3
0.5 0.4 0.3 0.2
0.1
0.1
0
coverage
0.6
0.2
0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
S now volum e (fra ction of S NO100 )
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Snow volum e (fra ction of S NO100)
Figure 3-3: 50% SNO100 = 50% coverage
Figure 3-4: 70% SNO100 = 50% coverage
1 0.9 0.8 Fraction areal coverage
60
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Snow volum e (fra ction of SNO100)
Figure 3-5: 90% SNO100 = 50% coverage
It is important to remember that once the volume of water held in the snow pack exceeds SNO100 the depth of snow over the HRU is assumed to be uniform, i.e. snocov = 1.0. The areal depletion curve affects snow melt only when the snow
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 61
pack water content is between 0.0 and SNO100. Consequently if SNO100 is set to a very small value, the impact of the areal depletion curve on snow melt will be minimal. As the value for SNO100 increases, the influence of the areal depletion curve will assume more importance in snow melt processes. Table 3-3: SWAT input variables used in snow cover calculations. Variable name Definition SFTMP Ts-r: Mean air temperature at which precipitation is equally likely to be rain as snow/freezing rain (°C) SNOCOVMX SNO100: Threshold depth of snow, above which there is 100% cover SNO50COV Fraction of SNOCOVMX that provides 50% cover SNO_SUB Initial snow water content in subbasin (mm H2O) SNOEB Initial snow water content in subbasin elevation band (mm H2O)
File Name .bsn .bsn .bsn .sub .sub
3.5 SNOW MELT Snow melt is controlled by the air and snow pack temperature, the melting rate, and the areal coverage of snow. Snow melt is included with rainfall in the calculations of runoff and percolation. When SWAT calculates erosion, the rainfall energy of the snow melt fraction of the water is set to zero. The water released from snow melt is assumed to be evenly distributed over the 24 hours of the day.
3.5.1 SNOW PACK TEMPERATURE The snow pack temperature is a function of the mean daily temperature during the preceding days and varies as a dampened function of air temperature (Anderson, 1976). The influence of the previous day’s snow pack temperature on the current day’s snow pack temperature is controlled by a lagging factor, l sno . The lagging factor inherently accounts for snow pack density, snow pack depth, exposure and other factors affecting snow pack temperature. The equation used to calculate the snow pack temperature is: Tsnow (dn ) = Tsnow (d n −1) ⋅ (1 − l sno ) + T av ⋅ l sno
3.5.1
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SWAT USER’S MANUAL, VERSION 2000
where Tsnow(d n ) is the snow pack temperature on a given day (°C), Tsnow(d n −1) is the snow pack temperature on the previous day (°C), l sno is the snow temperature lag factor, and T av is the mean air temperature on the current day (°C). As l sno approaches 1.0, the mean air temperature on the current day exerts an increasingly greater influence on the snow pack temperature and the snow pack temperature from the previous day exerts less and less influence. The snow pack will not melt until the snow pack temperature exceeds a threshold value, Tmlt. This threshold value is specified by the user.
3.5.2 SNOW MELT EQUATION The snow melt in SWAT is calculated as a linear function of the difference between the average snow pack-maximum air temperature and the base or threshold temperature for snow melt: + Tmx éT ù SNOmlt = bmlt ⋅ snocov ⋅ ê snow − Tmlt ú 2 ë û
3.5.2
where SNOmlt is the amount of snow melt on a given day (mm H2O), bmlt is the melt factor for the day (mm H2O/day-°C), snocov is the fraction of the HRU area covered by snow, Tsnow is the snow pack temperature on a given day (°C), Tmx is the maximum air temperature on a give day (°C), and Tmlt is the base temperature above which snow melt is allowed (°C). The melt factor is allowed a seasonal variation with maximum and minimum values occurring on summer and winter solstices: bmlt =
(bmlt 6 + bmlt12 ) + (bmlt 6 − bmlt12 ) ⋅ sinæ 2π ⋅ (d − 81)ö ç ÷ n 2
2
è 365
ø
3.5.3
where bmlt is the melt factor for the day (mm H2O/day-°C), bmlt6 is the melt factor for June 21 (mm H2O/day-°C), bmlt12 is the melt factor for December 21 (mm H2O/day-°C), and dn is the day number of the year. In rural areas, the melt factor will vary from 1.4 to 6.9 mm H2O/day-°C (Huber and Dickinson, 1988). In urban areas, values will fall in the higher end of the range due to compression of the snow pack by vehicles, pedestrians, etc.
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 63
Urban snow melt studies in Sweden (Bengston, 1981; Westerstrom, 1981) reported melt factors ranging from 3.0 to 8.0 mm H2O/day-°C. Studies of snow melt on asphalt (Westerstrom, 1984) gave melt factors of 1.7 to 6.5 mm H2O/day°C. Table 3-4: SWAT input variables used in snow melt calculations. Variable name Definition TIMP l sno : Snow temperature lag factor SMTMP SMFMX SMFMN
Tmlt: Threshold temperature for snow melt (°C) bmlt6: Melt factor on June 21 (mm H2O/day-°C) bmlt12: Melt factor on December 21 (mm H2O/day-°C)
3.6 NOMENCLATURE Esub Amount of sublimation on a given day (mm H2O) EL Elevation (m) P Atmospheric pressure (kPa) Rday Amount of rainfall on a given day (mm H2O) Rh Average relative humidity for the day SNO Water content of snow cover on current day (mm H2O) SNO100 Amount of snow above which there is 100% cover (mm H2O) SNOmlt Amount of snow melt on a given day (mm H2O) Tmlt Threshold temperature for snow melt (°C) Tmx Maximum air temperature for day (°C) Ts-r Rain/snow boundary temperature (°C) Tsnow Snow pack temperature on a given day (°C) T av
Average air temperature for day (°C)
bmlt bmlt6 bmlt12 cp cov1 cov2 dn e eo snocov vpd
Melt factor for the day (mm H2O/day-°C) Melt factor for June 21 (mm H2O/day-°C) Melt factor for December 21 (mm H2O/day-°C) Specific heat of moist air at constant pressure (1.013 × 10-3 MJ kg-1 °C-1) Snow cover areal depletion curve shape coefficient Snow cover areal depletion curve shape coefficient Day number of year, 1 on January 1 and 365 on December 31 Actual vapor pressure on a given day (kPa) Saturation vapor pressure on a given day (kPa) Fraction of the HRU area covered by snow Vapor pressure deficit (kPa)
File Name .bsn .bsn .bsn .bsn
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SWAT USER’S MANUAL, VERSION 2000
α0.5 ∆ γ λ l sno
Maximum half-hour rainfall expressed as a fraction of daily rainfall Slope of the saturation vapor pressure curve (kPa °C-1) Psychrometric constant (kPa °C-1) Latent heat of vaporization (MJ kg-1) Snow temperature lag factor
3.7 REFERENCES Anderson, E.A. 1976. A point energy and mass balance model of snow cover. NOAA Technical Report NWS 19, U.S. Dept. of Commerce, National Weather Service. Bengston, L. 1981. Snowmelt-generated runoff in urban areas. p. 444-451. In B.C. Yen (ed.) Urban stormwater hydraulics and hydrology: proceedings of the Second International Conference on Urban Storm Drainage, held at Urbana, Illinois, USA, 15-19 June 1981. Water Resources Publications, Littleton, CO. Brunt, D. 1952. Physical and dynamical meteorology, 2nd ed. University Press, Cambridge. Dingman, S.L. 1994. Physical hydrology. Prentice-Hall, Inc., Englewood Cliffs, NJ. Doorenos, J. and W.O. Pruitt. 1977. Guidelines for predicting crop water requirements. FAO Irrig. and Drain. Paper No. 24, 2nd ed. FAO, Rome. Harrison, L.P. 1963. Fundamental concepts and definitions relating to humidity. In A. Wexler (ed.) Humidity and moisture, Vol. 3. Reinhold Publishing Company, N.Y. Huber, W.C. and R.E. Dickinson. 1988. Storm water management model, version 4: user’s manual. U.S. Environmental Protection Agency, Athens, GA. Larson, L.L., and E.L. Peck. 1974. Accuracy of precipitation measurements for hydrologic modeling. Water Resources Research 10:857-863. Murray, F.W. 1967. On the computation of saturation vapor pressure. J. Appl. Meteor. 6:203-204. Tetens, O. 1930. Uber einige meteorologische Begriffe. Z. Geophys. 6:297-309. Westerstrom, G. 1984. Snowmelt runoff from Porson residential area, Lulea, Sweden. p. 315-323. In Proceedings of the Third International Conference on Urban Storm Drainage held at Chalmers University, Goteborg, Sweden, June 1984.
CHAPTER 3: EQUATIONS—ATMOSPHERIC WATER 65
Westerstrom, G. 1981. Snowmelt runoff from urban plot. p. 452-459. In B.C. Yen (ed.) Urban stormwater hydraulics and hydrology: proceedings of the Second International Conference on Urban Storm Drainage, held at Urbana, Illinois, USA, 15-19 June 1981. Water Resources Publications, Littleton, CO. Winter, T.C. 1981. Uncertainties in estimating the water balance of lakes. Water Resources Bulletin 17:82-115.
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CHAPTER 4
EQUATIONS: WEATHER GENERATOR
SWAT requires daily values of precipitation, maximum and minimum temperature, solar radiation, relative humidity and wind speed. The user may choose to read these inputs from a file or generate the values using monthly average data summarized over a number of years. SWAT includes the WXGEN weather generator model (Sharpley and Williams, 1990) to generate climatic data or to fill in gaps in measured records. This weather generator was developed for the contiguous U.S. If the user prefers a different weather generator, daily input values for the different weather parameters may be generated with an alternative model and formatted for input to SWAT.
67
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SWAT USER'S MANUAL, VERSION 2000
The occurrence of rain on a given day has a major impact on relative humidity, temperature and solar radiation for the day. The weather generator first independently generates precipitation for the day. Maximum temperature, minimum temperature, solar radiation and relative humidity are then generated based on the presence or absence of rain for the day. Finally, wind speed is generated independently.
4.1 PRECIPITATION The precipitation generator is a Markov chain-skewed (Nicks, 1974) or Markov chain-exponential model (Williams, 1995). A first-order Markov chain is used to define the day as wet or dry. When a wet day is generated, a skewed distribution or exponential distribution is used to generate the precipitation amount. Table 4.1 lists SWAT input variables that are used in the precipitation generator.
4.1.1 OCCURRENCE OF WET OR DRY DAY With the first-order Markov-chain model, the probability of rain on a given day is conditioned on the wet or dry status of the previous day. A wet day is defined as a day with 0.1 mm of rain or more. The user is required to input the probability of a wet day on day i given a wet day on day i – 1, Pi(W/W), and the probability of a wet day on day i given a dry day on day i – 1, Pi(W/D), for each month of the year. From these inputs the remaining transition probabilities can be derived: Pi (D W ) = 1 − Pi (W W )
4.1.1
Pi (D D ) = 1 − Pi (W D )
4.1.2
where Pi(D/W) is the probability of a dry day on day i given a wet day on day i – 1 and Pi(D/D) is the probability of a dry day on day i given a dry day on day i – 1. To define a day as wet or dry, SWAT generates a random number between 0.0 and 1.0. This random number is compared to the appropriate wet-dry probability, Pi(W/W) or Pi(W/D). If the random number is equal to or less than the
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
69
wet-dry probability, the day is defined as wet. If the random number is greater than the wet-dry probability, the day is defined as dry.
4.1.2 AMOUNT OF PRECIPITATION Numerous probability distribution functions have been used to describe the distribution of rainfall amounts. SWAT provides the user with two options: a skewed distribution and an exponential distribution. The skewed distribution was proposed by Nicks (1974) and is based on a skewed distribution used by Fiering (1967) to generate representative streamflow. The equation used to calculate the amount of precipitation on a wet day is:
Rday = µ mon
3 æ éæ ö ç ç SND − g mon ö÷ ⋅ æç g mon ö÷ + 1ù − 1 ÷ day ç êè ÷ 6 ø è 6 ø úû + 2 ⋅ σ mon ⋅ ç ë ÷ g mon ç ÷ ç ÷ è ø
4.1.3
where Rday is the amount of rainfall on a given day (mm H2O), µmon is the mean daily rainfall (mm H2O) for the month, σmon is the standard deviation of daily rainfall (mm H2O) for the month, SNDday is the standard normal deviate calculated for the day, and gmon is the skew coefficient for daily precipitation in the month. The standard normal deviate for the day is calculated: SNDday = cos(6.283 ⋅ rnd 2 ) ⋅ − 2 ln (rnd 1 )
4.1.4
where rnd1 and rnd2 are random numbers between 0.0 and 1.0. The exponential distribution is provided as an alternative to the skewed distribution. This distribution requires fewer inputs and is most commonly used in areas where limited data on precipitation events is available. Daily precipitation is calculated with the exponential distribution using the equation: Rday = µ mon ⋅ (− ln (rnd 1 ))
r exp
4.1.5
where Rday is the amount of rainfall on a given day (mm H2O), µmon is the mean daily rainfall (mm H2O) for the month, rnd1 is a random number between 0.0 and 1.0, and rexp is an exponent that should be set between 1.0 and 2.0. As the value of rexp is increased, the number of extreme rainfall events during the year will
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SWAT USER'S MANUAL, VERSION 2000
increase. Testing of this equation at locations across the U.S. have shown that a value of 1.3 gives satisfactory results. Table 4-1: SWAT input variables that pertain to generation of precipitation. Variable name Definition PCPSIM Precipitation input code: 1-measured, 2-generated PR_W(1,mon) Pi(W/D): probability of a wet day following a dry day in month PR_W(2,mon) Pi(W/W): probability of a wet day following a wet day in month IDIST Rainfall distribution code: 0-skewed, 1-exponential REXP rexp: value of exponent (required if IDIST = 1) PCPMM(mon) average amount of precipitation falling in month (mm H2O) PCPD(mon) average number of days of precipitation in month (µmon = PCPMM / PCPD) PCPSTD(mon) σmon: standard deviation for daily precipitation in month (mm H2O) PCPSKW(mon) gmon: skew coefficient for daily precipitation in month
File Name .cod .wgn .wgn .cod .cod .wgn .wgn .wgn .wgn
4.2 SOLAR RADIATION & TEMPERATURE The procedure used to generate daily values for maximum temperature, minimum temperature and solar radiation (Richardson, 1981; Richardson and Wright, 1984) is based on the weakly stationary generating process presented by Matalas (1967).
4.2.1 DAILY RESIDUALS Residuals for maximum temperature, minimum temperature and solar radiation are required for calculation of daily values. The residuals must be serially correlated and cross-correlated with the correlations being constant at all locations. The equation used to calculate residuals is:
χ i ( j ) = Aχ i −1 ( j ) + Bε i ( j )
4.2.1
where χi(j) is a 3 × 1 matrix for day i whose elements are residuals of maximum temperature (j = 1), minimum temperature (j = 2) and solar radiation (j = 3), χi-1(j) is a 3 × 1 matrix of the previous day’s residuals, εi is a 3 × 1 matrix of independent random components, and A and B are 3 × 3 matrices whose elements are defined such that the new sequences have the desired serial-correlation and cross-correlation coefficients. The A and B matrices are given by
A = M1 ⋅ M 0
−1
4.2.2
CHAPTER 4: EQUATIONS—WEATHER GENERATOR −1
B ⋅ BT = M 0 − M 1 ⋅ M 0 ⋅ M 1
T
71
4.2.3
where the superscript –1 denotes the inverse of the matrix and the superscript T denotes the transpose of the matrix. M0 and M1 are defined as
ρ 0 (1,2 ) ρ 0 (1,3)ù é 1 ê ρ 0 (2,3)ú M 0 = ρ 0 (1,2 ) 1 ú ê êë ρ 0 (1,3) ρ 0 (2,3) 1 úû
4.2.4
é ρ1 (1,1) ρ1 (1,2 ) ρ1 (1,3)ù M 1 = ê ρ1 (2,1) ρ1 (2,2 ) ρ1 (2,3)ú ú ê ëê ρ1 (3,1) ρ1 (3,2 ) ρ1 (3,3)úû
4.2.5
ρ0(j,k) is the correlation coefficient between variables j and k on the same day where j and k may be set to 1 (maximum temperature), 2 (minimum temperature) or 3 (solar radiation) and ρ1(j,k) is the correlation coefficient between variable j and k with variable k lagged one day with respect to variable j. Correlation coefficients were determined for 31 locations in the United States using 20 years of temperature and solar radiation data (Richardson, 1982). Using the average values of these coefficients, the M0 and M1 matrices become 0.186 ù é1.000 0.633 ê M 0 = 0.633 1.000 − 0.193ú ú ê êë0.186 − 0.193 1.000 úû
4.2.6
0.087 ù é0.621 0.445 ê M 1 = 0.563 0.674 − 0.100ú ú ê êë0.015 − 0.091 0.251 úû
4.2.7
Using equations 4.2.2 and 4.2.3, the A and B matrices become 0.086 − 0.002ù é 0.567 ê A = 0.253 0.504 − 0.050ú ú ê êë − 0.006 − 0.039 0.244 úû
4.2.8
0 0 ù é 0.781 ê B = 0.328 0.637 0 ú ú ê ëê0.238 − 0.341 0.873ûú
4.2.9
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SWAT USER'S MANUAL, VERSION 2000
The A and B matrices defined in equations 4.2.8 and 4.2.9 are used in conjunction with equation 4.2.1 to generate daily sequences of residuals of maximum temperature, minimum temperature and solar radiation.
4.2.2 GENERATED VALUES The daily generated values are determined by multiplying the residual elements generated with equation 4.2.1 by the monthly standard deviation and adding the monthly average value. Tmx = µmx mon + χ i (1) ⋅ σmxmon
4.2.10
Tmn = µmnmon + χ i (2 ) ⋅ σmnmon
4.2.11
H day = µrad mon + χ i (3) ⋅ σrad mon
4.2.12
where Tmx is the maximum temperature for the day (°C), µmxmon is the average daily maximum temperature for the month (°C), χi(1) is the residual for maximum temperature on the given day, σmxmon is the standard deviation for daily maximum temperature during the month (°C), Tmn is the minimum temperature for the day (°C), µmnmon is the average daily minimum temperature for the month (°C), χi(2) is the residual for minimum temperature on the given day, σmnmon is the standard deviation for daily minimum temperature during the month (°C), Hday is the solar radiation for the day (MJ m-2), µradmon is the average daily solar radiation for the month (MJ m-2), χi(3) is the residual for solar radiation on the given day, and σradmon is the standard deviation for daily solar radiation during the month (MJ m-2). The user is required to input standard deviation for maximum and minimum temperature. For solar radiation the standard deviation is estimated as ¼ of the difference between the extreme and mean value for each month.
σrad mon =
H mx − µrad mon 4
4.2.13
where σradmon is the standard deviation for daily solar radiation during the month (MJ m-2), Hmx is the maximum solar radiation that can reach the earth’s surface on
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
73
a given day (MJ m-2), and µradmon is the average daily solar radiation for the month (MJ m-2).
4.2.3 ADJUSTMENT FOR CLEAR/OVERCAST CONDITIONS Maximum temperature and solar radiation will be lower on overcast days than on clear days. To incorporate the influence of wet/dry days on generated values of maximum temperature and solar radiation, the average daily maximum temperature, µmxmon, and average daily solar radiation, µradmon, in equations 4.2.10 and 4.2.12 are adjusted for wet or dry conditions.
4.2.3.1 MAXIMUM TEMPERATURE The
continuity
equation
relates
average
daily
maximum
temperature adjusted for wet or dry conditions to the average daily maximum temperature for the month:
µmx mon ⋅ daystot = µWmxmon ⋅ days wet + µDmx mon ⋅ days dry
4.2.14
where µmxmon is the average daily maximum temperature for the month (°C), daystot are the total number of days in the month, µWmxmon is the average daily maximum temperature of the month on wet days (°C), dayswet are the number of wet days in the month, µDmxmon is the average daily maximum temperature of the month on dry days (°C), and daysdry are the number of dry days in the month. The wet day average maximum temperature is assumed to be less than the dry day average maximum temperature by some fraction of (µmxmon - µmnmon):
µWmxmon = µDmxmon − bT ⋅ (µmx mon − µmnmon )
4.2.15
where µWmxmon is the average daily maximum temperature of the month on wet days (°C), µDmxmon is the average daily maximum temperature of the month on dry days (°C), bT is a scaling factor that controls the degree of deviation in temperature caused by the presence or absence of precipitation, µmxmon is the average daily maximum temperature for the
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SWAT USER'S MANUAL, VERSION 2000
month (°C), and µmnmon is the average daily minimum temperature for the month (°C). The scaling factor, bT, is set to 0.5 in SWAT. To calculate the dry day average maximum temperature, equations 4.2.14 and 4.2.15 are combined and solved for µDmxmon:
µDmx mon = µmxmon + bT ⋅
days wet ⋅ (µmx mon − µmnmon ) daystot
4.2.16
Incorporating the modified values into equation 4.2.10, SWAT calculates the maximum temperature for a wet day using the equation: Tmx = µWmxmon + χ i (1) ⋅ σmx mon
4.2.17
and the maximum temperature for a dry day using the equation: Tmx = µDmx mon + χ i (1) ⋅ σmx mon
4.2.18
4.2.3.2 SOLAR RADIATION The continuity equation relates average daily solar radiation adjusted for wet or dry conditions to the average daily solar radiation for the month:
µrad mon ⋅ daystot = µWrad mon ⋅ days wet + µDrad mon ⋅ daysdry 4.2.19 where µradmon is the average daily solar radiation for the month (MJ m-2), daystot are the total number of days in the month, µWradmon is the average daily solar radiation of the month on wet days (MJ m-2), dayswet are the number of wet days in the month, µDradmon is the average daily solar radiation of the month on dry days (MJ m-2), and daysdry are the number of dry days in the month. The wet day average solar radiation is assumed to be less than the dry day average solar radiation by some fraction:
µWrad mon = bR ⋅ µDrad mon
4.2.20
where µWradmon is the average daily solar radiation of the month on wet days (MJ m-2), µDradmon is the average daily solar radiation of the month on dry days (MJ m-2), and bR is a scaling factor that controls the degree of
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
75
deviation in solar radiation caused by the presence or absence of precipitation. The scaling factor, bR, is set to 0.5 in SWAT. To calculate the dry day average solar radiation, equations 4.2.19 and 4.2.20 are combined and solved for µDradmon:
µDrad mon =
µrad mon ⋅ daystot bR ⋅ days wet + daysdry
4.2.21
Incorporating the modified values into equation 4.2.12, SWAT calculated the solar radiation on a wet day using the equation: H day = µWrad mon + χ i (3) ⋅ σrad mon
4.2.22
and the solar radiation on a dry day using the equation: H day = µDrad mon + χ i (3) ⋅ σrad mon
4.2.23
Table 4-2: SWAT input variables that pertain to generation of temperature and solar radiation. Variable name Definition TMPSIM Temperature input code: 1-measured, 2-generated SLRSIM Solar radiation input code: 1-measured, 2-generated TMPMX(mon) µmxmon: average maximum air temperature for month (°C) TMPSTDMX(mon) σmxmon: standard deviation for maximum air temperature in month (°C) TMPMN(mon) µmnmon: average minimum air temperature for month (°C) TMPSTDMN(mon) σmnmon: standard deviation for minimum air temperature in month (°C) SOLARAV(mon) µradmon: average daily solar radiation for month (MJ m-2) PCPD(mon) dayswet: average number of days of precipitation in month
File Name .cod .cod .wgn .wgn .wgn .wgn .wgn .wgn
4.3 RELATIVE HUMIDITY Relative humidity is required by SWAT when the Penman-Monteith equation is used to calculate potential evapotranspiration. Daily average relative humidity values are calculated from a triangular distribution using average monthly relative humidity.
4.3.1 MEAN MONTHLY RELATIVE HUMIDITY Relative humidity is defined as the ratio of the actual vapor pressure to the saturation vapor pressure at a given temperature: Rhmon =
emon o emon
4.3.1
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SWAT USER'S MANUAL, VERSION 2000
where Rhmon is the average relative humidity for the month, emon is the actual vapor o is the saturation vapor pressure at the mean monthly temperature (kPa), and emon
pressure at the mean monthly temperature (kPa). The saturation vapor pressure, o emon , is related to the mean monthly air temperature with the equation:
é16.78 ⋅ µtmpmon − 116.9 ù o = exp ê emon ú ë µtmpmon + 237.3 û
4.3.2
o is the saturation vapor pressure at the mean monthly temperature where emon
(kPa), and µtmpmon is the mean air temperature for the month (°C). The mean air temperature for the month is calculated by averaging the mean maximum monthly temperature, µmxmon, and the mean minimum monthly temperature, µmnmon. The dew point temperature is the temperature at which the actual vapor pressure present in the atmosphere is equal to the saturation vapor pressure. Therefore, by substituting the dew point temperature in place of the average monthly temperature in equation 4.3.2, the actual vapor pressure may be calculated: é16.78 ⋅ µdewmon − 116.9 ù emon = exp ê ú ë µdewmon + 237.3 û
4.3.3
where emon is the actual vapor pressure at the mean month temperature (kPa), and
µdewmon is the average dew point temperature for the month (°C).
4.3.2 GENERATED DAILY VALUE The triangular distribution used to generate daily relative humidity values requires four inputs: mean monthly relative humidity, maximum relative humidity value allowed in month, minimum relative humidity value allowed in month, and a random number between 0.0 and 1.0. The maximum relative humidity value, or upper limit of the triangular distribution, is calculated from the mean monthly relative humidity with the equation: RhUmon = Rhmon + (1 − Rhmon ) ⋅ exp(Rhmon − 1)
4.3.4
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
77
where RhUmon is the largest relative humidity value that can be generated on a given day in the month, and Rhmon is the average relative humidity for the month. The minimum relative humidity value, or lower limit of the triangular distribution, is calculated from the mean monthly relative humidity with the equation: RhLmon = Rhmon ⋅ (1 − exp(− Rhmon ))
4.3.5
where RhLmon is the smallest relative humidity value that can be generated on a given day in the month, and Rhmon is the average relative humidity for the month. The triangular distribution uses one of two sets of equations to generate a æ R − RhLmon ö ÷÷ then relative humidity value for the day. If rnd1 ≤ çç hmon è RhUmon − RhLmon ø Rh = RhLmon + [rnd1 ⋅ (RhUmon − RhLmon ) ⋅ (Rhmon − RhLmon )]
0.5
4.3.6
æ R − RhLmon ö ÷÷ then If rnd1 > çç hmon è RhUmon − RhLmon ø Rh = RhUmon − (RhUmon
éR (1 − rnd1 ) − RhLmon (1 − rnd1 )ù − Rhmon ) ⋅ ê hUmon ú RhUmon − Rhmon ë û
0.5
4.3.7
where Rh is the average relative humidity calculated for the day, rnd1 is a random number generated by the model each day, Rhmon is the average relative humidity for the month, RhLmon is the smallest relative humidity value that can be generated on a given day in the month, and RhUmon is the largest relative humidity value that can be generated on a given day in the month.
4.3.3 ADJUSTMENT FOR CLEAR/OVERCAST CONDITIONS To incorporate the effect of clear and overcast weather on generated values of relative humidity, monthly average relative humidity values can be adjusted for wet or dry conditions. The continuity equation relates average relative humidity adjusted for wet or dry conditions to the average relative humidity for the month: Rhmon ⋅ daystot = RhWmon ⋅ days wet + RhDmon ⋅ daysdry
4.3.8
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SWAT USER'S MANUAL, VERSION 2000
where Rhmon is the average relative humidity for the month, daystot are the total number of days in the month, RhWmon is the average relative humidity for the month on wet days, dayswet are the number of wet days in the month, RhDmon is the average relative humidity of the month on dry days, and daysdry are the number of dry days in the month. The wet day average relative humidity is assumed to be greater than the dry day average relative humidity by some fraction: RhWmon = RhDmon + bH ⋅ (1 − RhDmon )
4.3.9
where RhWmon is the average relative humidity of the month on wet days, RhDmon is the average relative humidity of the month on dry days, and bH is a scaling factor that controls the degree of deviation in relative humidity caused by the presence or absence of precipitation. The scaling factor, bH, is set to 0.9 in SWAT. To calculate the dry day relative humidity, equations 4.3.8 and 4.3.9 are combined and solved for RhDmon: RhDmon
æ days wet = çç Rhmon − bH ⋅ daystot è
ö æ days wet ÷÷ ⋅ çç 1.0 − bH ⋅ daystot ø è
ö ÷÷ ø
−1
4.3.10
To reflect the impact of wet or dry conditions, SWAT will replace Rhmon with RhWmon on wet days or RhDmon on dry days in equations 4.3.4 through 4.3.7. Table 4-3: SWAT input variables that pertain to generation of relative humidity. Variable name Definition RHSIM Relative humidity input code: 1-measured, 2-generated TMPMN(mon) µmnmon: average minimum air temperature for month (°C) TMPMX(mon) µmxmon: average maximum air temperature for month (°C) DEWPT(mon) µdewmon: average dew point temperature for month (°C) PCPD(mon) dayswet: average number of days of precipitation in month
File Name .cod .wgn .wgn .wgn .wgn
4.4 MAXIMUM HALF-HOUR RAINFALL Maximum half-hour rainfall is required by SWAT to calculate the peak flow rate for runoff. When daily precipitation data is used by the model, the maximum half-hour rainfall is calculated from a triangular distribution using monthly maximum half-hour rainfall data. The maximum half-hour rainfall is calculated only on days where surface runoff has been generated.
CHAPTER 4: EQUATIONS—WEATHER GENERATOR
79
4.4.1 MONTHLY MAXIMUM HALF-HOUR RAIN For each month, users provide the maximum half-hour rain observed over the entire period of record. These extreme values are used to calculate representative monthly maximum half-hour rainfall fractions. Prior to calculating the representative maximum half-hour rainfall fraction for each month, the extreme half-hour rainfall values are smoothed by calculating three month average values: R0.5sm ( mon ) =
R0.5 x ( mon −1) + R0.5 x ( mon ) + R0.5 x ( mon +1)
4.4.1
3
where R0.5sm(mon) is the smoothed maximum half-hour rainfall for a given month (mm H2O) and R0.5x is the extreme maximum half-hour rainfall for the specified month (mm H2O). Once the smoothed maximum half-hour rainfall is known, the representative half-hour rainfall fraction is calculated using the equation:
α 0.5mon = adj0.5α
é æ ç ê R0.5 sm ( mon ) ç ⋅ ê1 − expç ê 0.5 ç µ mon ⋅ ln æç ê ç yrs ⋅ days ç êë wet è è
öù ÷ú ÷ú ö ÷÷ú ÷÷ ÷ú ø øúû
4.4.2
where α0.5mon is the average half-hour rainfall fraction for the month, adj0.5α is an adjustment factor, R0.5sm is the smoothed half-hour rainfall amount for the month (mm H2O), µmon is the mean daily rainfall (mm H2O) for the month, yrs is the number of years of rainfall data used to obtain values for monthly extreme halfhour rainfalls, and dayswet are the number of wet days in the month. The adjustment factor is included to allow users to modify estimations of half-hour rainfall fractions and peak flow rates for runoff.
4.4.2 GENERATED DAILY VALUE The triangular distribution used to generate the maximum half-hour rainfall fraction requires four inputs: average monthly half-hour rainfall fraction, maximum value for half-hour rainfall fraction allowed in month, minimum value for half-hour rainfall fraction allowed in month, and a random number between 0.0 and 1.0.
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SWAT USER'S MANUAL, VERSION 2000
The maximum half-hour rainfall fraction, or upper limit of the triangular distribution, is calculated from the daily amount of rainfall with the equation: æ − 125 ö ÷ α 0.5U = 1 − expçç ÷ + 5 R è day ø
4.4.3
where α0.5U is the largest half-hour fraction that can be generated on a given day, and Rday is the precipitation on a given day (mm H2O). The minimum half-hour fraction, or lower limit of the triangular distribution, α0.5L, is set at 0.02083. The triangular distribution uses one of two sets of equations to generate a æα − α 0.5 L ö ÷÷ then maximum half-hour rainfall fraction for the day. If rnd1 ≤ çç 0.5mon è α 0.5U − α 0.5 L ø
α 0.5 = α 0.5 L + [rnd1 ⋅ (α 0.5U − α 0.5 L ) ⋅ (α 0.5mon − α 0.5 L )]0.5
4.4.4
æα − α 0.5 L ö ÷÷ then If rnd1 > çç 0.5mon − α α 0.5 L ø è 0.5U
α 0.5 = α 0.5U − (α 0.5U
é α (1 − rnd 1 ) − α 0.5 L (1 − rnd 1 )ù − α 0.5mon ) ⋅ ê 0.5U ú α 0.5U − α 0.5mon ë û
0.5
4.4.5
where α0.5 is the maximum half-hour rainfall fraction for the day, α0.5mon is the average maximum half-hour rainfall fraction for the month, rnd1 is a random number generated by the model each day, α0.5L is the smallest half-hour rainfall fraction that can be generated, and α0.5U is the largest half-hour fraction that can be generated. Table 4-4: SWAT input variables that pertain to generation of maximum half-hour rainfall. Variable name Definition RAINHHMX(mon) R0.5x: extreme half-hour rainfall for month (mm H2O) APM adj0.5α: peak rate adjustment factor PCPMM(mon) average amount of precipitation falling in month (mm H2O) PCPD(mon) dayswet: average number of days of precipitation in month (µmon = PCPMM / PCPD) RAIN_YRS yrs: number of years of data used to obtain values for RAINHHMX PRECIPITATION Rday: amount of rain falling on a given day (mm H2O)
File Name .wgn .bsn .wgn .wgn .wgn .pcp
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81
4.5 WIND SPEED Wind speed is required by SWAT when the Penman-Monteith equation is used to calculate potential evapotranspiration. Mean daily wind speed is generated in SWAT using a modified exponential equation:
µ10 m = µwnd mon ⋅ (− ln(rnd1 ))0.3
4.5.1
where µ10m is the mean wind speed for the day (m s-1), µwndmon is the average wind speed for the month (m s-1), and rnd1 is a random number between 0.0 and 1.0. Table 4-5: SWAT input variables that pertain to generation of wind speed. Variable name Definition WNDSIM Wind speed input code: 1-measured, 2-generated WNDAV(mon) µwndmon: Average wind speed in month (m/s)
File Name .cod .wgn
4.6 NOMENCLATURE 3 × 3 matrix of elements defined to ensure serial and cross correlation of −1 generated temperature and radiation values A = M 1 ⋅ M 0 3 × 3 matrix of elements defined to ensure serial and cross correlation of B −1 T generated temperature and radiation values B ⋅ B T = M 0 − M 1 ⋅ M 0 ⋅ M 1 Hday Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) HMX Maximum possible solar radiation (MJ m-2 d-1) M0 3 × 3 matrix of correlation coefficients between maximum temperature, minimum temperature and solar radiation on same day M1 3 × 3 matrix of correlation coefficients between maximum temperature, minimum temperature and solar radiation on consecutive days Pi(D/D) Probability of a dry day on day i given a dry day on day i – 1 Pi(D/W) Probability of a dry day on day i given a wet day on day i – 1 Pi(W/D) Probability of a wet day on day i given a dry day on day i – 1 Pi(W/W) Probability of a wet day on day i given a wet day on day i – 1 R0.5sm Smoothed maximum half-hour rainfall for a given month (mm H2O) R0.5x Extreme maximum half-hour rainfall for the specified month (mm H2O) Rday Amount of rainfall on a given day (mm H2O) Rh Average relative humidity for the day RhDmon Average relative humidity of the month on dry days A
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SWAT USER'S MANUAL, VERSION 2000
RhLmon Smallest relative humidity value that can be generated on a given day in the month RhUmon Largest relative humidity value that can be generated on a given day in the month RhWmon Average relative humidity for the month on wet days Rhmon Average relative humidity for the month SNDday Standard normal deviate for the day Tmn Minimum air temperature for day (°C) Tmx Maximum air temperature for day (°C) adj0.5α Peak rate adjustment factor bH Scaling factor that controls the degree of deviation in relative humidity caused by the presence or absence of precipitation bR Scaling factor that controls the degree of deviation in solar radiation caused by the presence or absence of precipitation bT Scaling factor that controls the degree of deviation in temperature caused by the presence or absence of precipitation daysdry Number of dry days in the month daystot Total number of days in the month dayswet Number of wet days in the month emon Actual vapor pressure at the mean monthly temperature (kPa) o emon Saturation vapor pressure at the mean monthly temperature (kPa) gmon Skew coefficient for daily precipitation in the month rexp Exponent for exponential precipitation distribution rnd1 Random number between 0.0 and 1.0 rnd2 Random number between 0.0 and 1.0 yrs Number of years of rainfall data used to obtain values for monthly extreme halfhour rainfalls
α0.5 Maximum half-hour rainfall expressed as a fraction of daily rainfall α0.5L Smallest half-hour rainfall fraction that can be generated on a given day α0.5mon Average maximum half-hour rainfall fraction for the month α0.5U Largest half-hour rainfall fraction that can be generated on a given day εi 3 × 1 matrix of independent random components σmon Standard deviation of daily rainfall (mm H2O) for the month σmnmonStandard deviation for daily minimum temperature during the month (°C) σmxmon Standard deviation for daily maximum temperature during the month (°C) σradmon Standard deviation for daily solar radiation during the month (MJ m-2) ρ0(j,k) Correlation coefficient between variables j and k on the same day where j and k may be set to 1 (maximum temperature), 2 (minimum temperature) or 3 (solar radiation) ρ1(j,k) Correlation coefficient between variable j and k with variable k lagged one day with respect to variable j µmon Mean daily rainfall (mm H2O) for the month µDmxmon Average daily maximum temperature of the month on dry days (°C) µDradmon Average daily solar radiation of the month on dry days (MJ m-2)
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83
µWmxmonAverage daily maximum temperature of the month on wet days (°C) µWradmon Average daily solar radiation of the month on wet days (MJ m-2) µdewmon Average dew point temperature for the month (°C) µmnmon Average daily minimum temperature for the month (°C) µmxmon Average daily maximum temperature for the month (°C) µradmon Average daily solar radiation for the month (MJ m-2) µtmpmon Mean air temperature for the month (°C) µwndmon Average wind speed for the month (m s-1) µ10m Mean wind speed for the day at height of 10 meters (m s-1) χi(j) 3 × 1 matrix for day i whose elements are residuals of maximum temperature (j = 1), minimum temperature (j = 2) and solar radiation (j = 3),
4.7 REFERENCES Fiering, M.B. 1967. Streamflow synthesis. Harvard University Press, Cambridge. Matalas, N.C. 1967. Mathematical assessment of synthetic hydrology. Water Resources Res. 3(4):937-945. Nicks, A.D. 1974. Stochastic generation of the occurrence, pattern, and location of maximum amount of daily rainfall. p. 154-171. In Proc. Symp. Statistical Hydrology, Aug.-Sept. 1971, Tuscon, AZ. U.S. Department of Agriculture, Misc. Publ. No. 1275. Richardson, C.W. 1982. Dependence structure of daily temperature and solar radiation. Trans. ASAE 25(3):735-739. Richardson, C.W. 1981. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Res. 17(1):182-190. Richardson, C.W. and D.A. Wright. 1984. WGEN: a model for generating daily weather variables. U.S. Department of Agriculture, Agricultural Research Service, ARS-8. Sharpley, A.N. and J.R. Williams, eds. 1990. EPIC-Erosion Productivity Impact Calculator, 1. model documentation. U.S. Department of Agriculture, Agricultural Research Service, Tech. Bull. 1768. Williams, J.R. 1995. Chapter 25. The EPIC Model. p. 909-1000. In Computer Models of Watershed Hydrology. Water Resources Publications. Highlands Ranch, CO.
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CHAPTER 5
EQUATIONS: CLIMATE CUSTOMIZATION
SWAT is capable of simulating orographic impacts on temperature and precipitation for watersheds in mountainous regions. The model will also modify climate inputs for simulations that are looking at the impact of climatic change in a given watershed.
85
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SWAT USER'S MANUAL, VERSION 2000
5.1 ELEVATION BANDS Orographic precipitation is a significant phenomenon in certain areas of the world. To account for orographic effects on both precipitation and temperature, SWAT allows up to 10 elevation bands to be defined in each subbasin. Precipitation and maximum and minimum temperatures are calculated for each band as a function of the respective lapse rate and the difference between the gage elevation and the average elevation specified for the band. For precipitation, Rband = Rday + (ELband − ELgage ) ⋅
plaps 1000
when Rday > 0.01
5.1.1
where Rband is the precipitation falling in the elevation band (mm H2O), Rday is the precipitation recorded at the gage or generated from gage data (mm H2O), ELband is the mean elevation in the elevation band (m), ELgage is the elevation at the recording gage (m), plaps is the precipitation lapse rate (mm H2O/km), and 1000 is a factor needed to convert meters to kilometers. For temperature, Tmx ,band = Tmx + (ELband − ELgage ) ⋅
tlaps 1000
5.1.2
Tmn ,band = Tmn + (ELband − ELgage ) ⋅
tlaps 1000
5.1.3
T av ,band = T av + (ELband − ELgage ) ⋅
tlaps 1000
5.1.4
where Tmx,band is the maximum daily temperature in the elevation band (°C), Tmn,band is the minimum daily temperature in the elevation band (°C), T av ,band is the mean daily temperature in the elevation band (°C), Tmx is the maximum daily temperature recorded at the gage or generated from gage data (°C), Tmn is the minimum daily temperature recorded at the gage or generated from gage data (°C), T av is the mean daily temperature recorded at the gage or generated from gage data (°C), ELband is the mean elevation in the elevation band (m), ELgage is the elevation at the recording gage (m), tlaps is the temperature lapse rate (°C/km), and 1000 is a factor needed to convert meters to kilometers.
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87
Once the precipitation and temperature values have been calculated for each elevation band in the subbasin, new average subbasin precipitation and temperature values are calculated: Rday = Tmx = Tmn = T av =
b
åR
band
bnd =1
⋅ frbnd
b
åT
bnd =1
mx ,band
b
åT
bnd =1
mn ,band
b
åT
bnd =1
av ,band
5.1.5
⋅ frbnd
5.1.6
⋅ frbnd
5.1.7
⋅ frbnd
5.1.8
where Rday is the daily average precipitation adjusted for orographic effects (mm H2O), Tmx is the daily maximum temperature adjusted for orographic effects (°C),
Tmn is the daily minimum temperature adjusted for orographic effects (°C), T av is the daily mean temperature adjusted for orographic effects (°C), Rband is the precipitation falling in elevation band bnd (mm H2O), Tmx,band is the maximum daily temperature in elevation band bnd (°C), Tmn,band is the minimum daily temperature in elevation band bnd (°C), T av ,band is the mean daily temperature in elevation band bnd (°C), frbnd is the fraction of subbasin area within the elevation band, and b is the total number of elevation bands in the subbasin. The only processes modeled separately for each individual elevation band are the accumulation, sublimation and melting of snow. As with the initial precipitation and temperature data, after amounts of sublimation and snow melt are determined for each elevation band, subbasin average values are calculated. These average values are the values that are used in the remainder of the simulation and reported in the output files.
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Table 5-1: SWAT input variables that pertain to orographic effects. Variable Name ELEVB ELEVB_FR WELEV ELEVATION ELEVATION PLAPS TLAPS PRECIPITATION
MAX TEMP MIN TEMP
Definition ELband: Elevation at center of the elevation band (m) frbnd: Fraction of subbasin area within the elevation band. ELgage: Elevation of recording gage whose data is used to calculate values in .wgn file (m) ELgage: Elevation of precipitation recording gage (m) ELgage: Elevation of temperature recording gage (m) plaps: Precipitation lapse rate (mm H2O/km) tlaps: Temperature lapse rate (°C/km) Rday: Daily precipitation (mm H2O) Tmx: Daily maximum temperature (°C) Tmn: Daily minimum temperature (°C)
Input File .sub .sub .wgn .pcp .tmp .sub .sub .pcp .tmp .tmp
5.2 CLIMATE CHANGE The impact of global climate change on water supply is a major area of research. Climate change can be simulated with SWAT by manipulating the climatic input that is read into the model (precipitation, temperature, solar radiation, relative humidity, wind speed, potential evapotranspiration and weather generator parameters). A less time-consuming method is to set adjustment factors for the various climatic inputs. SWAT will allow users to adjust precipitation, temperature, solar radiation, relative humidity, and carbon dioxide levels in each subbasin. The alteration of precipitation, temperature, solar radiation and relative humidity are straightforward:
adj pcp ö æ ÷ Rday = Rday ⋅ çç1 + 100 ÷ø è
5.2.1
where Rday is the precipitation falling in the subbasin on a given day (mm H2O), and adjpcp is the % change in rainfall. Tmx = Tmx + adjtmp
5.2.2
where Tmx is the daily maximum temperature (°C), and adjtmp is the change in temperature (°C). Tmn = Tmn + adjtmp
5.2.3
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89
where Tmn is the daily minimum temperature (°C), and adjtmp is the change in temperature (°C). T av = T av + adjtmp
5.2.4
where T av is the daily mean temperature (°C), and adjtmp is the change in temperature (°C). H day = H day + adjrad
5.2.5
where Hday is the daily solar radiation reaching the earth’s surface (MJ m-2), and adjrad is the change in radiation (MJ m-2 d-1). Rh = Rh + adjhmd
5.2.6
where Rh is the relative humidity for the day expressed as a fraction, and adjhmd is the change in relative humidity expressed as a fraction. SWAT allows the adjustment terms to vary from month to month so that the user is able to simulate seasonal changes in climatic conditions. Changes in carbon dioxide levels impact plant growth. As carbon dioxide levels increase, plant productivity increases and plant water requirements go down. The equations used to account for the impact of carbon dioxide levels on plant water requirements are reviewed in Chapters 7 and 18. When carbon dioxide climate change effects are being simulated, the Penman-Monteith equation must be used to calculate potential evapotranspiration. This method has been modified to account for CO2 impacts on potential evapotranspiration levels. Table 5-2: SWAT input variables that pertain to climate change. Variable Name RFINC(mon) TMPINC(mon) RADINC(mon) HUMINC(mon) CO2 IPET
Definition adjpcp: % change in rainfall for month adjtmp: increase or decrease in temperature for month (°C) adjrad: increase or decrease in solar radiation reaching earth’s surface for month (MJ m-2) adjhmd: increase or decrease in relative humidity for month CO2: carbon dioxide level in subbasin (ppmv) Potential evapotranspiration method
Input File .sub .sub .sub .sub .sub .cod
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5.3 NOMENCLATURE CO2 Concentration of carbon dioxide in the atmosphere (ppmv) ELband Mean elevation in the elevation band (m) ELgage Elevation at the precipitation, temperature, or weather generator data recording gage (m) Hday Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) Rband Precipitation falling in the elevation band (mm H2O) Rday Amount of rainfall on a given day (mm H2O) Rh Average relative humidity for the day Tmn Minimum air temperature for day (°C) Tmn,band Minimum daily temperature in the elevation band (°C) Tmx Maximum air temperature for day (°C) Tmx,band Maximum daily temperature in the elevation band (°C) T av Mean air temperature for day (°C) T av ,band Mean daily temperature in the elevation band (°C) adjhmd adjpcp adjrad adjtmp frbnd plaps tlaps
Change in relative humidity expressed as a fraction % change in rainfall Change in radiation (MJ m-2 d-1) Change in temperature (°C) Fraction of subbasin area within the elevation band Precipitation lapse rate (mm H2O/km) Temperature lapse rate (°C/km)
HYDROLOGY The land phase of the hydrologic cycle is based on the water balance equation: t
SWt = SW0 + å (Rday − Q surf − E a − w seep − Q gw ) i =1
where SWt is the final soil water content (mm H2O), SW0 is the initial soil water content (mm H2O), t is the time (days), Rday is the amount of precipitation on day i (mm H2O), Qsurf is the amount of surface runoff on day i (mm H2O), Ea is the amount of evapotranspiration on day i (mm H2O), wseep is the amount of percolation and bypass flow exiting the soil profile bottom on day i (mm H2O), and Qgw is the amount of return flow on day i (mm H2O).
CHAPTER 6
EQUATIONS: SURFACE RUNOFF
Surface runoff occurs whenever the rate of water application to the ground surface exceeds the rate of infiltration. When water is initially applied to a dry soil, the application rate and infiltration rates may be similar. However, the infiltration rate will decrease as the soil becomes wetter. When the application rate is higher than the infiltration rate, surface depressions begin to fill. If the application rate continues to be higher than the infiltration rate once all surface depressions have filled, surface runoff will commence. SWAT provides two methods for estimating surface runoff: the SCS curve number procedure (SCS, 1972) and the Green & Ampt infiltration method (1911). 93
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6.1 RUNOFF VOLUME: SCS CURVE NUMBER PROCEDURE The SCS runoff equation is an empirical model that came into common use in the 1950s. It was the product of more than 20 years of studies involving rainfall-runoff relationships from small rural watersheds across the U.S. The model was developed to provide a consistent basis for estimating the amounts of runoff under varying land use and soil types (Rallison and Miller, 1981). The SCS curve number equation is (SCS, 1972): Qsurf =
(R
(R
day
− Ia )
2
day
− Ia + S )
6.1.1
where Qsurf is the accumulated runoff or rainfall excess (mm H2O), Rday is the rainfall depth for the day (mm H2O), Ia is the initial abstractions which includes surface storage, interception and infiltration prior to runoff (mm H2O), and S is the retention parameter (mm H2O). The retention parameter varies spatially due to changes in soils, land use, management and slope and temporally due to changes in soil water content. The retention parameter is defined as: æ 1000 ö S = 25.4ç − 10 ÷ CN è ø
6.1.2
where CN is the curve number for the day. The initial abstractions, Ia, is commonly approximated as 0.2S and equation 6.1.1 becomes Qsurf
(R = (R
day day
− 0.2 S )
2
+ 0.8S )
6.1.3
Runoff will only occur when Rday > Ia. A graphical solution of equation 6.1.3 for different curve number values is presented in Figure 6-1.
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95
Figure 6-1: Relationship of runoff to rainfall in SCS curve number method.
6.1.1 SCS CURVE NUMBER The SCS curve number is a function of the soil’s permeability, land use and antecedent soil water conditions. Typical curve numbers for moisture condition II are listed in tables 6-1, 6-2 and 6-3 for various land covers and soil types (SCS Engineering Division, 1986). These values are appropriate for a 5% slope. Table 6-1: Runoff curve numbers for cultivated agricultural lands Cover Hydrologic Soil Group Land Use Fallow
Treatment or practice Bare soil Crop residue cover∗
Row crops
Straight row Straight row w/ residue Contoured Contoured w/ residue Contoured & terraced Contoured & terraced w/ residue
Small grains
Straight row Straight row w/ residue
∗
Hydrologic condition ---Poor
A 77 76
B 86 85
C 91 90
D 94 93
Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor
74 72 67 71 64 70 65 69 64 66 62 65 61 65 63 64
83 81 78 80 75 79 75 78 74 74 71 73 70 76 75 75
88 88 85 87 82 84 82 83 81 80 78 79 77 84 83 83
90 91 89 90 85 88 86 87 85 82 81 81 80 88 87 86
Crop residue cover applies only if residue is on at least 5% of the surface throughout the year.
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Table 6-1, cont.: Runoff curve numbers for cultivated agricultural lands Cover Hydrologic Soil Group Land Use
Hydrologic condition Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor Good Poor Good
Treatment or practice Contoured Contoured w/ residue Contoured & terraced Contoured & terraced w/ residue
Close-seeded or broadcast legumes or rotation
Straight row Contoured Contoured & terraced
A 60 63 61 62 60 61 59 60 58 66 58 64 55 63 51
B 72 74 73 73 72 72 70 71 69 77 72 75 69 73 67
C 80 82 81 81 80 79 78 78 77 85 81 83 78 80 76
D 84 85 84 84 83 82 81 81 80 89 85 85 83 83 80
Table 6-2: Runoff curve numbers for other agricultural lands Cover Hydrologic Soil Group Hydrologic Cover Type condition Pasture, grassland, or range—continuous forage for grazing1 Poor Fair Good Meadow—continuous grass, protected from grazing and generally mowed for hay - - - Brush—brush-weed-grass mixture with brush the major element2 Poor Fair Good Woods—grass combination (orchard or tree farm) Poor Fair Good Woods3 Poor Fair Good Farmsteads—buildings, lanes, driveways, and surrounding lots. ----
A 68 49 39 30 48 35 30 57 43 32 45 36 30 59
B 79 69 61 58 67 56 48 73 65 58 66 60 55 74
C 86 79 74 71 77 70 65 82 76 72 77 73 70 82
D 89 84 80 78 83 77 73 86 82 79 83 79 77 86
Table 6-3: Runoff curve numbers for urban areas§ Cover Hydrologic Soil Group Cover Type Fully developed urban areas Open spaces (lawns, parks, golf courses, cemeteries, etc.)†
Hydrologic condition
Impervious areas: Paved parking lots, roofs, driveways, etc. (excl. right-of-way) Paved streets and roads; open ditches (incl. right-of-way) Gravel streets and roads (including right-of-way) Dirt streets and roads (including right-of way) 1
Average % impervious area
A
B
C
D
Poor Fair Good
68 49 39
79 69 61
86 79 74
89 84 80
-------------
98 83 76 72
98 89 85 82
98 92 89 87
98 93 91 89
Poor: < 50% ground cover or heavily grazed with no mulch; Fair: 50 to 75% ground cover and not heavily grazed; Good: > 75% ground cover and lightly or only occasionally grazed 2 Poor: < 50% ground cover; Fair: 50 to 75% ground cover; Good: > 75% ground cover 3 Poor: Forest litter, small trees, and brush are destroyed by heavy grazing or regular burning; Fair: Woods are grazed but not burned, and some forest litter covers the soil; Good: Woods are protected from grazing, and litter and brush adequately cover the soil. § SWAT will automatically adjust curve numbers for impervious areas when IURBAN and URBLU are defined in the .hru file. Curve numbers from Table 6-3 should not be used in this instance. † Poor: grass cover < 50%; Fair: grass cover 50 to 75%; Good: grass cover > 75%
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97
Table 6-3, continued: Runoff curve number for urban areas Cover Hydrologic Soil Group Cover Type Urban districts: Commercial and business Industrial Residential Districts by average lot size: 1/8 acre (0.05 ha) or less (town houses) 1/4 acre (0.10 ha) 1/3 acre (0.13 ha) 1/2 acre (0.20 ha) 1 acre (0.40 ha) 2 acres (0.81 ha) Developing urban areas: Newly graded areas (pervious areas only, no vegetation)
Hydrologic condition
Average % impervious area
A
B
C
D
85% 72%
89 81
92 88
94 91
95 93
65% 38% 30% 25% 20% 12%
77 61 57 54 51 46
85 75 72 70 68 65
90 83 81 80 79 77
92 87 86 85 84 82
77
86
91
94
6.1.1.1 SOIL HYDROLOGIC GROUPS The U.S. Natural Resource Conservation Service (NRCS) classifies soils into four hydrologic groups based on infiltration characteristics of the soils. NRCS Soil Survey Staff (1996) defines a hydrologic group as a group of soils having similar runoff potential under similar storm and cover conditions. Soil properties that influence runoff potential are those that impact the minimum rate of infiltration for a bare soil after prolonged wetting and when not frozen. These properties are depth to seasonally high water table, saturated hydraulic conductivity, and depth to a very slowly permeable layer. Soil may be placed in one of four groups, A, B, C, and D, or three dual classes, A/D, B/D, and C/D. Definitions of the classes are: A: (Low runoff potential). The soils have a high infiltration rate even when thoroughly wetted. They chiefly consist of deep, well drained to excessively drained sands or gravels. They have a high rate of water transmission. B: The soils have a moderate infiltration rate when thoroughly wetted. They chiefly are moderately deep to deep, moderately well-drained to well-drained soils that have moderately fine to moderately coarse textures. They have a moderate rate of water transmission. C: The soils have a slow infiltration rate when thoroughly wetted. They chiefly have a layer that impedes downward movement of water or have moderately fine to fine texture. They have a slow rate of water transmission.
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D. (High runoff potential). The soils have a very slow infiltration rate when thoroughly wetted. They chiefly consist of clay soils that have a high swelling potential, soils that have a permanent water table, soils that have a claypan or clay layer at or near the surface, and shallow soils over nearly impervious material. They have a very slow rate of water transmission. Dual hydrologic groups are given for certain wet soils that can be adequately drained. The first letter applies to the drained condition, the second to the undrained. Only soils that are rated D in their natural condition are assigned to dual classes. A summary of U.S. soils and their hydrologic group is given in Appendix D.
6.1.1.2 ANTECEDENT SOIL MOISTURE CONDITION SCS defines three antecedent moisture conditions: I—dry (wilting point), II—average moisture, and III—wet (field capacity). The moisture condition I curve number is the lowest value the daily curve number can assume in dry conditions. The curve numbers for moisture conditions I and III are calculated with the equations: CN 1 = CN 2 −
20 ⋅ (100 − CN 2 ) (100 − CN 2 + exp[2.533 − 0.0636 ⋅ (100 − CN 2 )])
CN 3 = CN 2 ⋅ exp[0.00673 ⋅ (100 − CN 2 )]
6.1.4 6.1.5
where CN1 is the moisture condition I curve number, CN2 is the moisture condition II curve number, and CN3 is the moisture condition III curve number. The retention parameter varies with soil profile water content according to the following equation:
æ ö SW ÷÷ S = S max ⋅ çç1 − è [SW + exp(w1 − w2 ⋅ SW )] ø
6.1.6
where S is the retention parameter for a given moisture content (mm), Smax is the maximum value the retention parameter can achieve on any given day (mm), SW is the soil water content of the entire profile excluding the amount of water held in the profile at wilting point (mm H2O), and w1 and
CHAPTER 6: EQUATIONS—SURFACE RUNOFF
99
w2 are shape coefficients. The maximum retention parameter value, Smax, is calculated by solving equation 6.1.2 using CN1. The shape coefficients are determined by solving equation 6.1.6 assuming that 1) the retention parameter for moisture condition I curve number corresponds to wilting point soil profile water content, 2) the retention parameter for moisture condition III curve number corresponds to field capacity soil profile water content, and 3) the soil has a curve number of 99 (S = 2.54) when completely saturated. é ù FC w1 = ln ê − FC ú + w2 ⋅ FC −1 ë1 − S 3 ⋅ S max û
6.1.7
æ é ù é ùö FC SAT ç ln ê − − − FC SAT ln ú ê ú ÷÷ −1 ç 1 − S ⋅ S −1 − ⋅ S 1 2 . 54 3 max max ë û ë ûø w2 = è 6.1.8 (SAT − FC )
where w1 is the first shape coefficient, w2 is the second shape coefficient, FC is the amount of water in the soil profile at field capacity (mm H2O), S3 is the retention parameter for the moisture condition III curve number, Smax is the retention parameter for the moisture condition I curve number, SAT is the amount of water in the soil profile when completely saturated (mm H2O), and 2.54 is the retention parameter value for a curve number of 99. When the top layer of the soil is frozen, the retention parameter is modified using the following equation: S frz = S max ⋅ [1 − exp(− 0.000862 ⋅ S )]
6.1.9
where Sfrz is the retention parameter adjusted for frozen conditions (mm), Smax is the maximum value the retention parameter can achieve on any given day (mm), and S is the retention parameter for a given moisture content calculated with equation 6.1.6 (mm).
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SWAT USER'S MANUAL, VERSION 2000
The daily curve number value adjusted for moisture content is calculated by rearranging equation 6.1.2 and inserting the retention parameter calculated for that moisture content: CN =
25400 (S + 254)
6.1.10
where CN is the curve number on a given day and S is the retention parameter calculated for the moisture content of the soil on that day.
6.1.1.3 SLOPE ADJUSTMENTS The moisture condition II curve numbers provided in the tables are assumed to be appropriate for 5% slopes. Williams (1995) developed an equation to adjust the curve number to a different slope: CN 2 s =
(CN 3 − CN 2 ) ⋅ [1 − 2 ⋅ exp(− 13.86 ⋅ slp )] + CN 3
2
6.1.11
where CN2s is the moisture condition II curve number adjusted for slope, CN3 is the moisture condition III curve number for the default 5% slope, CN2 is the moisture condition II curve number for the default 5% slope, and slp is the average percent slope of the subbasin. SWAT does not adjust curve numbers for slope. If the user wishes to adjust the curve numbers for slope effects, the adjustment must be done prior to entering the curve numbers in the management input file. Table 6-1: SWAT input variables that pertain to surface runoff calculated with the SCS curve number method. Input Variable Name Definition File IEVENT Rainfall, runoff, routing option. .cod PRECIPITATION Rday: Daily precipitation (mm H2O) .pcp CN2 CN2: Moisture condition II curve number .mgt CNOP CN2: Moisture condition II curve number .mgt
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101
6.2 RUNOFF VOLUME: GREEN & AMPT INFILTRATION METHOD The Green & Ampt equation was developed to predict infiltration assuming excess water at the surface at all times (Green & Ampt, 1911). The equation assumes that the soil profile is homogenous and antecedent moisture is uniformly distributed in the profile. As water infiltrates into the soil, the model assumes the soil above the wetting front is completely saturated and there is a sharp break in moisture content at the wetting front. Figure 6-2 graphically illustrates the difference between the moisture distribution with depth modeled by the Green & Ampt equation and what occurs in reality.
Figure 6-2: Comparison of moisture content distribution modeled by Green & Ampt and a typical observed distribution.
Mein and Larson (1973) developed a methodology for determining ponding time with infiltration using the Green & Ampt equation. The GreenAmpt Mein-Larson excess rainfall method was incorporated into SWAT to provide an alternative option for determining surface runoff. This method requires sub-daily precipitation data supplied by the user. The Green-Ampt Mein-Larson infiltration rate is defined as: æ Ψwf ⋅ ∆θ v f inf ,t = K e ⋅ ç1 + ç Finf ,t è
ö ÷ ÷ ø
6.2.1
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SWAT USER'S MANUAL, VERSION 2000
where finf is the infiltration rate at time t (mm/hr), Ke is the effective hydraulic conductivity (mm/hr), Ψwf is the wetting front matric potential (mm), ∆θv is the change in volumetric moisture content across the wetting front (mm/mm) and Finf is the cumulative infiltration at time t (mm H2O). When the rainfall intensity is less than the infiltration rate, all the rainfall will infiltrate during the time period and the cumulative infiltration for that time period is calculated: Finf ,t = Finf ,t −1 + R∆t
6.2.2
where and Finf,t is the cumulative infiltration for a given time step (mm H2O), Finf,t-1 is the cumulative infiltration for the previous time step (mm H2O), and R∆t is the amount of rain falling during the time step (mm H2O). The infiltration rate defined by equation 6.2.1 is a function of the infiltrated volume, which in turn is a function of the infiltration rates in previous time steps. To avoid numerical errors over long time steps, finf is replaced by dFinf dt in equation 6.2.1 and integrated to obtain
é Finf ,t + Ψwf ⋅ ∆θ v ù Finf ,t = Finf ,t −1 + K e ⋅ ∆t + Ψwf ⋅ ∆θ v ⋅ ln ê ú êë Finf ,t −1 + Ψwf ⋅ ∆θ v úû
6.2.3
Equation 6.2.3 must be solved iteratively for Finf,t, the cumulative infiltration at the end of the time step. A successive substitution technique is used. The Green-Ampt effective hydraulic conductivity parameter, Ke, is approximately equivalent to one-half the saturated hydraulic conductivity of the soil, Ksat (Bouwer, 1969). Nearing et al. (1996) developed an equation to calculate the effective hydraulic conductivity as a function of saturated hydraulic conductivity and curve number. This equation incorporates land cover impacts into the calculated effective hydraulic conductivity. The equation for effective hydraulic conductivity is: 56.82 ⋅ K sat −2 1 + 0.051 ⋅ exp(0.062 ⋅ CN ) 0.286
Ke =
6.2.4
where Ke is the effective hydraulic conductivity (mm/hr), Ksat is the saturated hydraulic conductivity (mm/hr), and CN is the curve number.
CHAPTER 6: EQUATIONS—SURFACE RUNOFF
103
Wetting front matric potential, Ψwf, is calculated as a function of porosity, percent sand and percent clay (Rawls and Brakensiek, 1985):
[
Ψwf = 10 ⋅ exp 6.5309 − 7.32561 ⋅ φ soil + 0.001583 ⋅ mc + 3.809479 ⋅ φ soil + 2
2
0.000344 ⋅ ms ⋅ mc − 0.049837 ⋅ ms ⋅ φ soil + 0.001608 ⋅ ms ⋅ φ soil + 2
2
0.001602 ⋅ mc ⋅ φ soil − 0.0000136 ⋅ ms ⋅ mc − 0.003479 ⋅ mc ⋅ φ soil − 2
0.000799 ⋅ ms ⋅ φ soil 2
2
2
2
]
6.2.5
where φsoil is the porosity of the soil (mm/mm), mc is the percent clay content, and ms is the percent sand content. For each time step, SWAT calculates the amount of water entering the soil. The water that does not infiltrate into the soil becomes surface runoff. Table 6-2: SWAT input variables that pertain to Green & Ampt infiltration calculations. Variable Name IEVENT IDT PRECIPITATION SOL_K CN2 CNOP SOL_BD CLAY SAND
Definition Rainfall, runoff, routing option. Length of time step (min): ∆t=IDT/60 R∆t: Precipitation during time step (mm H2O) Ksat: Saturated hydraulic conductivity of first layer (mm/hr) CN: Moisture condition II curve number CN: Moisture condition II curve number ρb: Moist bulk density (Mg/m3): φsoil=1 - ρb / 2.65 mc: % clay content ms: % sand content
Input File .cod .cod .pcp .sol .mgt .mgt .sol .sol .sol
6.3 PEAK RUNOFF RATE The peak runoff rate is the maximum runoff flow rate that occurs with a given rainfall event. The peak runoff rate is an indicator of the erosive power of a storm and is used to predict sediment loss. SWAT calculates the peak runoff rate with a modified rational method. The rational method is widely used in the design of ditches, channels and storm water control systems. The rational method is based on the assumption that if a rainfall of intensity i begins at time t = 0 and continues indefinitely, the rate of runoff will increase until the time of concentration, t = tconc, when the entire subbasin area is contributing to flow at the outlet. The rational formula is:
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SWAT USER'S MANUAL, VERSION 2000
q peak =
C ⋅ i ⋅ Area 3 .6
6.3.1
where qpeak is the peak runoff rate (m3 s-1), C is the runoff coefficient, i is the rainfall intensity (mm/hr), Area is the subbasin area (km2) and 3.6 is a unit conversion factor.
6.3.1 TIME OF CONCENTRATION The time of concentration is the amount of time from the beginning of a rainfall event until the entire subbasin area is contributing to flow at the outlet. In other words, the time of concentration is the time for a drop of water to flow from the remotest point in the subbasin to the subbasin outlet. The time of concentration is calculated by summing the overland flow time (the time it takes for flow from the remotest point in the subbasin to reach the channel) and the channel flow time (the time it takes for flow in the upstream channels to reach the outlet): t conc = tov + t ch
6.3.2
where tconc is the time of concentration for a subbasin (hr), tov is the time of concentration for overland flow (hr), and tch is the time of concentration for channel flow (hr).
6.3.1.1 OVERLAND FLOW TIME OF CONCENTRATION The overland flow time of concentration, tov, can be computed using the equation tov =
Lslp 3600 ⋅ vov
6.3.3
where Lslp is the subbasin slope length (m), vov is the overland flow velocity (m s-1) and 3600 is a unit conversion factor. The overland flow velocity can be estimated from Manning’s equation by considering a strip 1 meter wide down the sloping surface: vov =
qov
0.4
⋅ slp 0.3
n 0.6
6.3.4
CHAPTER 6: EQUATIONS—SURFACE RUNOFF
105
where qov is the average overland flow rate (m3 s-1), slp is the average slope in the subbasin (m m-1), and n is Manning’s roughness coefficient for the subbasin. Assuming an average flow rate of 6.35 mm/hr and converting units vov =
0.005 ⋅ Lslp
0.4
⋅ slp 0.3
n 0.6
6.3.5
Substituting equation 6.3.5 into equation 6.3.3 gives tov =
Lslp
0.6
⋅ n 0.6
18 ⋅ slp 0.3
6.3.6
Table 6-3: Values of Manning’s roughness coefficient, n, for overland flow (Engman, 1983). Characteristics of Land Surface Median Range Fallow, no residue 0.010 0.008-0.012 Conventional tillage, no residue 0.090 0.060-0.120 Conventional tillage, residue 0.190 0.160-0.220 Chisel plow, no residue 0.090 0.060-0.120 Chisel plow, residue 0.130 0.100-0.160 Fall disking, residue 0.400 0.300-0.500 No till, no residue 0.070 0.040-0.100 No till, 0.5-1 t/ha residue 0.120 0.070-0.170 No till, 2-9 t/ha residue 0.300 0.170-0.470 Rangeland, 20% cover 0.600 Short grass prairie 0.150 0.100-0.200 Dense grass 0.240 0.170-0.300 Bermudagrass 0.410 0.300-0.480
6.3.1.2 CHANNEL FLOW TIME OF CONCENTRATION The channel flow time of concentration, tch, can be computed using the equation: t ch =
Lc 3 .6 ⋅ v c
6.3.7
where Lc is the average flow channel length for the subbasin (km), vc is the average channel velocity (m s-1), and 3.6 is a unit conversion factor. The average channel flow length can be estimated using the equation Lc = L ⋅ Lcen
6.3.8
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where L is the channel length from the most distant point to the subbasin outlet (km), and Lcen is the distance along the channel to the subbasin centroid (km). Assuming Lcen = 0.5 ⋅ L , the average channel flow length is Lc = 0.71 ⋅ L
6.3.9
The average velocity can be estimated from Manning’s equation assuming a trapezoidal channel with 2:1 side slopes and a 10:1 bottom width-depth ratio. 0.489 ⋅ qch ⋅ slpch n 0.75 0.25
vc =
0.375
6.3.10
where vc is the average channel velocity (m s-1), qch is the average channel flow rate (m3 s-1), slpch is the channel slope (m m-1), and n is Manning’s roughness coefficient for the channel. To express the average channel flow rate in units of mm/hr, the following expression is used qch =
* qch ⋅ Area 3.6
6.3.11
* is the average channel flow rate (mm hr-1), Area is the subbasin where qch
area (km2), and 3.6 is a unit conversion factor. The average channel flow rate is related to the unit source area flow rate (unit source area = 1 ha) * qch = q0* ⋅ (100 ⋅ Area )
−0.5
6.3.12
where q0* is the unit source area flow rate (mm hr-1), Area is the subbasin area (km2), and 100 is a unit conversion factor. Assuming the unit source area flow rate is 6.35 mm/hr and substituting equations 6.3.11 and 6.3.12 into 6.3.10 gives 0.317 ⋅ Area 0.125 ⋅ slpch vc = n 0.75
0.375
6.3.13
Substituting equations 6.3.9 and 6.3.13 into 6.3.7 gives t ch =
0.62 ⋅ L ⋅ n 0.75 0.375 Area 0.125 ⋅ slpch
6.3.14
where tch is the time of concentration for channel flow (hr), L is the channel length from the most distant point to the subbasin outlet (km), n is
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Manning’s roughness coefficient for the channel, Area is the subbasin area (km2), and slpch is the channel slope (m m-1). Table 6-4: Values of Manning’s roughness coefficient, n, for channel flow (Chow, 1959).1 Characteristics of Channel Median Range Excavated or dredged Earth, straight and uniform 0.025 0.016-0.033 Earth, winding and sluggish 0.035 0.023-0.050 Not maintained, weeds and brush 0.075 0.040-0.140 Natural streams Few trees, stones or brush 0.050 0.025-0.065 Heavy timber and brush 0.100 0.050-0.150 1 Chow (1959) has a very extensive list of Manning’s roughness coefficients. These values represent only a small portion of those he lists in his book.
Although some of the assumptions used in developing equations 6.3.6 and 6.3.14 may appear liberal, the time of concentration values obtained generally give satisfactory results for homogeneous subbasins. Since equations 6.3.6 and 6.3.14 are based on hydraulic considerations, they are more reliable than purely empirical equations.
6.3.2 RUNOFF COEFFICIENT The runoff coefficient is the ratio of the inflow rate, i ⋅ Area , to the peak discharge rate, qpeak. The coefficient will vary from storm to storm and is calculated with the equation: C=
Q surf Rday
6.3.15
where Qsurf is the surface runoff (mm H2O) and Rday is the rainfall for the day (mm H2O).
6.3.3 RAINFALL INTENSITY The rainfall intensity is the average rainfall rate during the time of concentration. Based on this definition, it can be calculated with the equation: i=
Rtc t conc
6.3.16
where i is the rainfall intensity (mm/hr), Rtc is the amount of rain falling during the time of concentration (mm H2O), and tconc is the time of concentration for the subbasin (hr).
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An analysis of rainfall data collected by Hershfield (1961) for different durations and frequencies showed that the amount of rain falling during the time of concentration was proportional to the amount of rain falling during the 24-hr period. Rtc = α tc ⋅ Rday
6.3.17
where Rtc is the amount of rain falling during the time of concentration (mm H2O), αtc is the fraction of daily rainfall that occurs during the time of concentration, and Rday is the amount of rain falling during the day (mm H2O). For short duration storms, all or most of the rain will fall during the time of concentration, causing αtc to approach its upper limit of 1.0. The minimum value of αtc would be seen in storms of uniform intensity (i24 = i). This minimum value can be defined by substituting the products of time and rainfall intensity into equation 6.3.17
α tc ,min =
Rtc i ⋅ t conc t conc = = Rday i24 ⋅ 24 24
6.3.18
Thus, αtc falls in the range tconc/24 ≤ αtc ≤ 1.0. SWAT estimates the fraction of rain falling in the time of concentration as a function of the fraction of daily rain falling in the half-hour of highest intensity rainfall.
α tc = 1 − exp[2 ⋅ t conc ⋅ ln(1 − α 0.5 )]
6.3.19
where α0.5 is the fraction of daily rain falling in the half-hour highest intensity rainfall, and tconc is the time of concentration for the subbasin (hr). The determination of a value for α0.5 is discussed in Chapters 3 and 4.
6.3.4 MODIFIED RATIONAL FORMULA The modified rational formula used to estimate peak flow rate is obtained by substituting equations 6.3.15, 6.3.16, and 6.3.17 into equation 6.3.1 q peak =
α tc ⋅ Qsurf ⋅ Area 3.6 ⋅ t conc
6.3.20
where qpeak is the peak runoff rate (m3 s-1), αtc is the fraction of daily rainfall that occurs during the time of concentration, Qsurf is the surface runoff (mm H2O),
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Area is the subbasin area (km2), tconc is the time of concentration for the subbasin (hr) and 3.6 is a unit conversion factor. Table 6-5: SWAT input variables that pertain to peak rate calculations. Variable Name DA_KM HRU_FR SLSUBBSN SLOPE OV_N CH_L(1) CH_S(1) CH_N(1)
Definition Area of the watershed (km2) Fraction of total watershed area contained in HRU Lslp: Average slope length (m) slp: Average slope steepness (m/m) n: Manning’s “n” value for overland flow L: Longest tributary channel length in subbasin (km) slpch: Average slope of tributary channels (m/m) n: Manning’s “n” value for tributary channels
Input File .bsn .hru .hru .hru .hru .sub .sub .sub
6.4 SURFACE RUNOFF LAG In large subbasins with a time of concentration greater than 1 day, only a portion of the surface runoff will reach the main channel on the day it is generated. SWAT incorporates a surface runoff storage feature to lag a portion of the surface runoff release to the main channel. Once surface runoff is calculated with the curve number or Green & Ampt method, the amount of surface runoff released to the main channel is calculated: æ é − surlag ù ö ′ + Q stor ,i −1 ) ⋅ ç1 − exp ê Q surf = (Q surf ú ÷÷ ç t conc ûø ë è
6.4.1
where Qsurf is the amount of surface runoff discharged to the main channel on a ′ is the amount of surface runoff generated in the given day (mm H2O), Q surf
subbasin on a given day (mm H2O), Qstor,i-1 is the surface runoff stored or lagged from the previous day (mm H2O), surlag is the surface runoff lag coefficient, and tconc is the time of concentration for the subbasin (hrs).
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æ é − surlag ù ö The expression çç1 − exp ê ú ÷÷ in equation 6.4.1 represents the t conc ë ûø è
fraction of the total available water that will be allowed to enter the reach on any one day. Figure 6-3 plots values for this expression at different values for surlag and tconc.
Figure 6-3: Influence of surlag and tconc on fraction of surface runoff released.
Note that for a given time of concentration, as surlag decreases in value more water is held in storage. The delay in release of surface runoff will smooth the streamflow hydrograph simulated in the reach. Table 6-6: SWAT input variables that pertain to surface runoff lag calculations. Variable Name SURLAG
Definition surlag: surface runoff lag coefficient
Input File .bsn
6.5 TRANSMISSION LOSSES Many semiarid and arid watersheds have ephemeral channels that abstract large quantities of streamflow (Lane, 1982). The abstractions, or transmission losses, reduce runoff volume as the flood wave travels downstream. Chapter 19 of the SCS Hydrology Handbook (Lane, 1983) describes a procedure for estimating transmission losses for ephemeral streams which has been incorporated into SWAT. This method was developed to estimate transmission losses in the absence
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of observed inflow-outflow data and assumes no lateral inflow or out-of-bank flow contributions to runoff. The prediction equation for runoff volume after transmission losses is 0 ì volQsurf , f = í îa x + bx ⋅ volQsurf ,i
volQsurf ,i ≤ volthr volQsurf ,i > volthr
6.5.1
where volQsurf,f is the volume of runoff after transmission losses (m3), ax is the regression intercept for a channel of length L and width W (m3), bx is the regression slope for a channel of length L and width W, volQsurf,i is the volume of runoff prior to transmission losses (m3), and volthr is the threshold volume for a channel of length L and width W (m3). The threshold volume is vol thr = −
ax bx
6.5.2
The corresponding equation for peak runoff rate is q peak , f =
1 [ ( ) ] (3600 ⋅ durflw ) ⋅ a x − 1 − bx ⋅ volQsurf ,i + bx ⋅ q peak ,i
6.5.3
where qpeak,f is the peak rate after transmission losses (m3/s), durflw is the duration of flow (hr), ax is the regression intercept for a channel of length L and width W (m3), bx is the regression slope for a channel of length L and width W, volQsurf,i is the volume of runoff prior to transmission losses (m3), qpeak,i is the peak rate before accounting for transmission losses (m3/s). The duration of flow is calculated with the equation: dur flw =
Q surf ⋅ Area
3.6 ⋅ q peak
6.5.4
where durflw is the duration of runoff flow (hr), Qsurf is the surface runoff (mm H2O), Area is the area of the subbasin (km2), qpeak is the peak runoff rate (m3/s), and 3.6 is a conversion factor. In order to calculate the regression parameters for channels of differing lengths and widths, the parameters of a unit channel are needed. A unit channel is defined as a channel of length L = 1 km and width W = 1 m. The unit channel parameters are calculated with the equations:
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é K ch ⋅ dur flw ù k r = −2.22 ⋅ ln ê1 − 2.6466 ⋅ ú volQsurf ,i úû êë
6.5.5
a r = −0.2258 ⋅ K ch ⋅ durflw
6.5.6
br = exp[− 0.4905 ⋅ k r ]
6.5.7
where kr is the decay factor (m-1 km-1), ar is the unit channel regression intercept (m3), br is the unit channel regression slope, Kch is the effective hydraulic conductivity of the channel alluvium (mm/hr), durflw is the duration of runoff flow (hr), and volQsurf,i is the initial volume of runoff (m3). The regression parameters are bx = exp[− k r ⋅ L ⋅ W ]
6.5.8
ar ⋅ (1 − bx ) (1 − br )
6.5.9
ax =
where ax is the regression intercept for a channel of length L and width W (m3), bx is the regression slope for a channel of length L and width W, kr is the decay factor (m-1 km-1), L is the channel length from the most distant point to the subbasin outlet (km), W is the average width of flow, i.e. channel width (m) ar is the unit channel regression intercept (m3), and br is the unit channel regression slope. Transmission losses from surface runoff are assumed to percolate into the shallow aquifer. Table 6-7: SWAT input variables that pertain to transmission loss calculations. Variable Name DA_KM HRU_FR CH_K(1) CH_W(1) CH_L(1)
Definition Area of the watershed (km2) Fraction of total watershed area contained in HRU Kch: effective hydraulic conductivity (mm/hr) W: average width of tributary channel (m) L: Longest tributary channel length in subbasin (km)
Input File .bsn .hru .sub .sub .sub
CHAPTER 6: EQUATIONS—SURFACE RUNOFF
6.6 NOMENCLATURE
Kch Ke Ksat L Lc Lcen Lslp Qstor Qsurf R∆t Rday Rtc S S3 Sfrz Smax SAT SW W
Subbasin area (km2) Runoff coefficient in peak runoff rate calculation Curve number Moisture condition I curve number Moisture condition II curve number Moisture condition II curve number adjusted for slope Moisture condition III curve number Cumulative infiltration at time t (mm H2O) Water content of soil profile at field capacity (mm H2O) Initial abstractions which includes surface storage, interception and infiltration prior to runoff (mm H2O) Effective hydraulic conductivity of the channel alluvium (mm/hr) Effective hydraulic conductivity (mm/hr) Saturated hydraulic conductivity (mm/hr) Channel length from the most distant point to the subbasin outlet (km) Average flow channel length for the subbasin (km) Distance along the channel to the subbasin centroid (km) Subbasin slope length (m) Surface runoff stored or lagged (mm H2O) Accumulated runoff or rainfall excess (mm H2O) Amount of rain falling during the time step (mm H2O) Amount of rainfall on a given day (mm H2O) Amount of rain falling during the time of concentration (mm H2O) Retention parameter in SCS curve number equation (mm) Retention parameter for the moisture condition III curve number Retention parameter adjusted for frozen conditions (mm) Maximum value the retention parameter can achieve on any given day (mm) Amount of water in the soil profile when completely saturated (mm H2O), Amount of water in soil profile (mm H2O) Average width of flow, i.e. channel width (m)
ar ax br bx durflw finf i kr mc ms n q0*
Unit channel regression intercept (m3) Regression intercept for a channel of length L and width W (m3) Unit channel regression slope Regression slope for a channel of length L and width W Duration of flow (hr) Infiltration rate (mm/hr) Rainfall intensity (mm/hr) Decay factor (m-1 km-1) Percent clay content Percent sand content Manning’s roughness coefficient for the subbasin or channel Unit source area flow rate (mm hr-1)
Area C CN CN1 CN2 CN2s CN3 Finf FC Ia
113
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qch Average channel flow rate (m3 s-1) * qch Average channel flow rate (mm hr-1) qov Average overland flow rate (m3 s-1) qpeak Peak runoff rate (m3/s) qpeak,f Peak rate after transmission losses (m3/s) qpeak,i Peak rate before accounting for transmission losses (m3/s) slp Average slope of the subbasin (% or m/m) slpch Average channel slope (m m-1) surlag Surface runoff lag coefficient tch Time of concentration for channel flow (hr) tconc Time of concentration for a subbasin (hr) tov Time of concentration for overland flow (hr) vc Average channel velocity (m s-1) vov Overland flow velocity (m s-1) volQsurf,f Volume of runoff after transmission losses (m3) volQsurf,i Volume of runoff prior to transmission losses (m3) volthr Threshold volume for a channel of length L and width W (m3) w1 Shape coefficient in retention parameter adjustments for soil moisture content w2 Shape coefficient in retention parameter adjustments for soil moisture content
α0.5 αtc φsoil Ψwf θv
Fraction of daily rain falling in the half-hour highest intensity rainfall, Fraction of daily rainfall that occurs during the time of concentration Porosity of the soil (mm/mm) Wetting front matric potential (mm) Volumetric moisture content (mm/mm)
6.7 REFERENCES Bouwer, H. 1969. Infiltration of water into nonuniform soil. Journal Irrigation and Drainage Div., ASCE 95(IR4):451-462. Chow, V.T. 1959. Open-channel hydraulics. McGraw-Hill, New York. Engman, E.T. 1983. Roughness coefficients for routing surface runoff. Proc. Spec. Conf. Frontiers of Hydraulic Engineering. Green, W.H. and G.A. Ampt. 1911. Studies on soil physics, 1. The flow of air and water through soils. Journal of Agricultural Sciences 4:11-24. Hershfield, D.M. 1961. Rainfall frequency atlas of the United States for durations from 30 minutes to 24 hours and return periods from 1 to 100 years. U.S. Dept. Commerce Tech. Paper No. 40.
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Lane, L.J. 1983. Chapter 19: Transmission Losses. p.19-1–19-21. In Soil Conservation Service. National engineering handbook, section 4: hydrology. U.S. Government Printing Office, Washington, D.C. Lane, L.J. 1982. Distributed model for small semi-arid watersheds. J. Hydraulic Eng., ASCE, 108(HY10):1114-1131. Mein, R.G. and C.L. Larson. 1973. Modeling infiltration during a steady rain. Water Resources Research 9(2):384-394. Natural Resources Conservation Service Soil Survey Staff. 1996. National soil survey handbook, title 430-VI. U.S. Government Printing Office, Washington, D.C. Nearing, M.A., B.Y. Liu, L.M. Risse, and X. Zhang. 1996. Curve number and Green-Ampt effective hydraulic conductivities. Water Resources Bulletin 32:125-136. Rallison, R.E. and N. Miller. 1981. Past, present and future SCS runoff procedure. p. 353-364. In V.P. Singh (ed.). Rainfall runoff relationship. Water Resources Publication, Littleton, CO. Rawls, W.J. and D.L. Brakensiek. 1985. Prediction of soil water properties for hydrologic modeling. p. 293-299. In E.B. Jones and T.J. Ward (eds). Watershed management in the 80’s. ASCE, New York, N.Y. Soil Conservation Service. 1972. Section 4: Hydrology In National Engineering Handbook. SCS. Soil Conservation Service Engineering Division. 1986. Urban hydrology for small watersheds. U.S. Department of Agriculture, Technical Release 55. Williams, J.R. 1995. Chapter 25: The EPIC model. p. 909-1000. In V.P. Singh (ed). Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, CO.
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CHAPTER 7
EQUATIONS: EVAPOTRANSPIRATION
Evapotranspiration is a collective term that includes all processes by which water at the earth’s surface is converted to water vapor. It includes evaporation from the plant canopy, transpiration, sublimation and evaporation from the soil. Evapotranspiration is the primary mechanism by which water is removed from a watershed. Roughly 62% of the precipitation that falls on the continents is evapotranspired. Evapotranspiration exceeds runoff in most river basins and on all continents except Antarctica (Dingman, 1994). The difference between precipitation and evapotranspiration is the water available for human use and management. An accurate estimation of
117
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evapotranspiration is critical in the asssessment of water resources and the impact of climate and land use change on those resources.
7.1 CANOPY STORAGE The plant canopy can significantly affect infiltration, surface runoff and evapotranspiration. As rain falls, canopy interception reduces the erosive energy of droplets and traps a portion of the rainfall within the canopy. The influence the canopy exerts on these processes is a function of the density of plant cover and the morphology of the plant species. When calculating surface runoff, the SCS curve number method lumps canopy interception in the term for initial abstractions. This variable also includes surface storage and infiltration prior to runoff and is estimated as 20% of the retention parameter value for a given day (see Chapter 6). When the Green and Ampt infiltration equation is used to calculate surface runoff and infiltration, the interception of rainfall by the canopy must be calculated separately. SWAT allows the maximum amount of water that can be held in canopy storage to vary from day to day as a function of the leaf area index: can day = canmx ⋅
LAI LAI mx
7.1.1
where canday is the maximum amount of water that can be trapped in the canopy on a given day (mm H2O), canmx is the maximum amount of water that can be trapped in the canopy when the canopy is fully developed (mm H2O), LAI is the leaf area index for a given day, and LAImx is the maximum leaf area index for the plant. When precipitation falls on any given day, the canopy storage is filled before any water is allowed to reach the ground: ′ and Rday = 0 R INT ( f ) = RINT ( i ) + Rday ′ ≤ canday − R INT (i ) when Rday
7.1.2
′ − (canday − R INT (i ) ) RINT ( f ) = canday and Rday = Rday ′ > canday − R INT (i ) when Rday
7.1.3
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119
where RINT(i) is the initial amount of free water held in the canopy on a given day (mm H2O), RINT(f) is the final amount of free water held in the canopy on a given ′ is the amount of precipitation on a given day before canopy day (mm H2O), Rday
interception is removed (mm H2O), Rday is the amount of precipitation on a given day that reaches the soil surface (mm H2O), and canday is the maximum amount of water that can be trapped in the canopy on a given day (mm H2O). Table 7-1: SWAT input variables used in canopy storage calculations. Variable name Definition CANMX canmx: maximum canopy storage
File Name .hru
7.2 POTENTIAL EVAPOTRANSPIRATION Potential evapotranspiration (PET) was a concept originally introduced by Thornthwaite (1948) as part of a climate classification scheme. He defined PET is the rate at which evapotranspiration would occur from a large area uniformly covered with growing vegetation that has access to an unlimited supply of soil water and that was not exposed to advection or heat storage effects. Because the evapotranspiration rate is strongly influenced by a number of vegetative surface characteristics, Penman (1956) redefined PET as “the amount of water transpired ... by a short green crop, completely shading the ground, of uniform height and never short of water”. Penman used grass as his reference crop, but later researchers (Jensen, et al., 1990) have suggested that alfalfa at a height of 30 to 50 cm may be a more appropriate choice. Numerous methods have been developed to estimate PET. Three of these methods have been incorporated into SWAT: the Penman-Monteith method (Monteith, 1965; Allen, 1986; Allen et al., 1989), the Priestley-Taylor method (Priestley and Taylor, 1972) and the Hargreaves method (Hargreaves et al., 1985). The model will also read in daily PET values if the user prefers to apply a different potential evapotranspiration method. The three PET methods included in SWAT vary in the amount of required inputs. The Penman-Monteith method requires solar radiation, air temperature, relative humidity and wind speed. The Priestley-Taylor method requires solar
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radiation, air temperature and relative humidity. The Hargreaves method requires air temperature only.
7.2.1 PENMAN-MONTEITH METHOD The Penman-Monteith equation combines components that account for energy needed to sustain evaporation, the strength of the mechanism required to remove the water vapor and aerodynamic and surface resistance terms. The Penman-Monteith equation is:
λE =
[
]
∆ ⋅ (H net − G ) + ρ air ⋅ c p ⋅ e zo − ez ra ∆ + γ ⋅ (1 + rc ra )
7.2.1
where λE is the latent heat flux density (MJ m-2 d-1), E is the depth rate evaporation (mm d-1), ∆ is the slope of the saturation vapor pressure-temperature curve, de/dT (kPa ûC-1), Hnet is the net radiation (MJ m-2 d-1), G is the heat flux density to the ground (MJ m-2 d-1), ρair is the air density (kg m-3), cp is the specific heat at constant pressure (MJ kg-1 ûC-1), e zo is the saturation vapor pressure of air at height z (kPa), ez is the water vapor pressure of air at height z (kPa), γ is the psychrometric constant (kPa ûC-1), rc is the plant canopy resistance (s m-1), and ra is the diffusion resistance of the air layer (aerodynamic resistance) (s m-1). For well-watered plants under neutral atmospheric stability and assuming logarithmic wind profiles, the Penman-Monteith equation may be written (Jensen et al., 1990): ∆ ⋅ (H net − G ) + γ ⋅ K1 ⋅ (0.622 ⋅ λ ⋅ ρ air P ) ⋅ (e zo − ez ) ra λE t = ∆ + γ ⋅ (1 + rc ra )
7.2.2
where λ is the latent heat of vaporization (MJ kg-1), Et is the maximum transpiration rate (mm d-1), K1 is a dimension coefficient needed to ensure the two terms in the numerator have the same units (for uz in m s-1, K1 = 8.64 x 104), and P is the atmospheric pressure (kPa). The calculation of net radiation, Hnet, is reviewed in Chapter 2. The calculations for the latent heat of vaporization, λ, the slope of the saturation vapor
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pressure-temperature curve, ∆, the psychrometric constant, γ, and the saturation and actual vapor pressure, e zo and ez, are reviewed in Chapter 3. The remaining undefined terms are the soil heat flux, G, the combined term K10.622λρ/P, the aerodynamic resistance, ra, and the canopy resistance, rc.
7.2.1.1 SOIL HEAT FLUX Soil heat storage or release can be significant over a few hours, but is usually small from day to day because heat stored as the soil warms early in the day is lost when the soil cools late in the day or at night. Since the magnitude of daily soil heat flux over a 10- to 30-day period is small when the soil is under a crop cover, it can normally be ignored for most energy balance estimates. SWAT assumes the daily soil heat flux, G, is always equal to zero.
7.2.1.2 AERODYNAMIC RESISTANCE The aerodynamic resistance to sensible heat and vapor transfer, ra, is calculated: ra =
ln[(z w − d ) zom ]ln[(z p − d ) zov ] k 2uz
7.2.3
where zw is the height of the wind speed measurement (cm), zp is the height of the humidity (psychrometer) and temperature measurements (cm), d is the zero plane displacement of the wind profile (cm), zom is the roughness length for momentum transfer (cm), zov is the roughness length for vapor transfer (cm), k is the von Kármán constant, and uz is the wind speed at height zw (m s-1). The von Kármán constant is considered to be a universal constant in turbulent flow. Its value has been calculated to be near 0.4 with a range of 0.36 to 0.43 (Jensen et al., 1990). A value of 0.41 is used by SWAT for the von Kármán constant. Brutsaert (1975) determined that the surface roughness parameter, zo, is related to the mean height (hc) of the plant canopy by the relationship
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hc/zo = 3e or 8.15 where e is the natural log base. Based on this relationship, the roughness length for momentum transfer is estimated as: zom = hc 8.15 = 0.123 ⋅ hc
hc ≤ 200cm
7.2.4
zom = 0.058 ⋅ (hc )
hc > 200cm
7.2.5
1.19
where mean height of the plant canopy (hc) is reported in centimeters. The roughness length for momentum transfer includes the effects of bluff-body forces. These forces have no impact on heat and vapor transfer, and the roughness length for vapor transfer is only a fraction of that for momentum transfer. To estimate the roughness length for vapor transfer, Stricker and Brutsaert (1978) recommended using: zov = 0.1 ⋅ zom
7.2.6
The displacement height for a plant can be estimated using the following relationship (Monteith, 1981; Plate, 1971): d = 2 / 3 ⋅ hc
7.2.7
The height of the wind speed measurement, zw, and the height of the humidity (psychrometer) and temperature measurements, zp, are always assumed to be 170 cm.
7.2.1.3 CANOPY RESISTANCE Studies in canopy resistance have shown that the canopy resistance for a well-watered reference crop can be estimated by dividing the minimum surface resistance for a single leaf by one-half of the canopy leaf area index (Jensen et. al, 1990): rc = rl (0.5 ⋅ LAI )
7.2.8
where rc is the canopy resistance (s m-1), rl is the minimum effective stomatal resistance of a single leaf (s m-1), and LAI is the leaf area index of the canopy. The distribution of stomates on a plant leaf will vary between species. Typically, stomates are distributed unequally on the top and bottom of plant leaves. Plants with stomates located on only one side are
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classified as hypostomatous while plants with an equal number of stomates on both sides of the leaf are termed amphistomatous. The effective leaf stomatal resistance is determined by considering the stomatal resistance of the top (adaxial) and bottom (abaxial) sides to be connected in parallel (Rosenburg, et al., 1983). When there are unequal numbers of stomates on the top and bottom, the effective stomatal resistance is calculated: rl =
rl −ad ⋅ rl −ab rl − ab + rl −ad
7.2.9
where rl is the minimum effective stomatal resistance of a single leaf (s m-1), rl−ad is the minimum adaxial stomatal leaf resistance (s m-1), and rl−ab is the minimum abaxial stomatal leaf resistance (s m-1). For amphistomatous leaves, the effective stomatal resistance is: rl =
rl − ad rl −ab = 2 2
7.2.10
For hypostomatous leaves the effective stomatal resistance is: rl = rl−ad = rl−ab
7.2.11
Leaf conductance is defined as the inverse of the leaf resistance: gl =
1 rl
7.2.12
where g l is the maximum effective leaf conductance (m s-1). When the canopy resistance is expressed as a function of leaf conductance instead of leaf resistance, equation 7.2.8 becomes: rc = (0.5 ⋅ g l ⋅ LAI )
−1
7.2.13
where rc is the canopy resistance (s m-1), g l is the maximum conductance of a single leaf (m s-1), and LAI is the leaf area index of the canopy. For climate change simulations, the canopy resistance term can be modified to reflect the impact of change in CO2 concentration on leaf conductance. The influence of increasing CO2 concentrations on leaf
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conductance was reviewed by Morison (1987). Morison found that at CO2 concentrations between 330 and 660 ppmv, a doubling in CO2 concentration resulted in a 40% reduction in leaf conductance. Within the specified range, the reduction in conductance is linear (Morison and Gifford, 1983). Easterling et al. (1992) proposed the following modification to the leaf conductance term for simulating carbon dioxide concentration effects on evapotranspiration: g l,CO2 = g l ⋅ [1.4 − 0.4 ⋅ (CO2 330 )]
7.2.14
where g l,CO2 is the leaf conductance modified to reflect CO2 effects (m s-1) and CO2 is the concentration of carbon dioxide in the atmosphere (ppmv). Incorporating this modification into equation 7.2.8 gives CO2 öù é æ rc = rl ⋅ ê(0.5 ⋅ LAI ) ⋅ ç1.4 − 0.4 ⋅ ÷ 330 øúû è ë
−1
7.2.15
SWAT will default the value of CO2 concentration to 330 ppmv if no CO2 ö æ value is entered by the user. With this default, the term ç1.4 − 0.4 ⋅ ÷ 330 ø è
reduces to 1.0 and the canopy resistance equation becomes equation 7.2.8. When calculating actual evapotranspiration, the canopy resistance term is modified to reflect the impact of high vapor pressure deficit on leaf conductance (Stockle et al, 1992). For a plant species, a threshold vapor pressure deficit is defined at which the plant’s leaf conductance begins to drop in response to the vapor pressure deficit. The adjusted leaf conductance is calculated:
[
]
g l = g l,mx ⋅ 1 − ∆g l ,dcl (vpd − vpd thr ) if vpd > vpd thr
7.2.16
if vpd ≤ vpd thr
7.2.17
g l = g l,mx
where g l is the conductance of a single leaf (m s-1), g l,mx is the maximum conductance of a single leaf (m s-1), ∆g l,dcl is the rate of decline in leaf conductance per unit increase in vapor pressure deficit (m s-1 kPa-1), vpd is the vapor pressure deficit (kPa), and vpdthr is the threshold vapor pressure
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deficit above which a plant will exhibit reduced leaf conductance (kPa). The rate of decline in leaf conductance per unit increase in vapor pressure deficit is calculated by solving equation 7.2.16 using measured values for stomatal conductance at two different vapor pressure deficits: ∆g l ,dcl =
(1 − fr )
(vpd
g ,mx
fr
− vpd thr )
7.2.18
where ∆g l,dcl is the rate of decline in leaf conductance per unit increase in vapor pressure deficit (m s-1 kPa-1), frg,mx is the fraction of the maximum stomatal conductance, g l,mx , achieved at the vapor pressure deficit vpdfr, and vpdthr is the threshold vapor pressure deficit above which a plant will exhibit reduced leaf conductance (kPa). The threshold vapor pressure deficit is assumed to be 1.0 kPa for all plant species.
7.2.1.4 COMBINED TERM For wind speed in m s-1, Jensen et al. (1990) provided the following relationship to calculate K10.622λρ/P: K1 ⋅ 0.622 ⋅ λ ⋅ ρ P = 1710 − 6.85 ⋅ T av
7.2.19
where T av is the mean air temperature for the day (ûC).
To calculate potential evapotranspiration, the Penman-Monteith equation must be solved for a reference crop. SWAT uses alfalfa at a height of 40 cm with a minimum leaf resistance of 100 s m-1 for the reference crop. Using this canopy height, the equation for aerodynamic resistance (7.2.3) simplifies to: ra =
114. uz
7.2.20
The equation for canopy resistance requires the leaf area index. The leaf area index for the reference crop is estimated using an equation developed by Allen et al. (1989) to calculate LAI as a function of canopy height. For nonclipped grass and alfalfa greater than 3 cm in height:
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LAI = 1.5 ⋅ ln (hc ) − 1.4
7.2.21
where LAI is the leaf area index and hc is the canopy height (cm). For alfalfa with a 40 cm canopy height, the leaf area index is 4.1. Using this value, the equation for canopy resistance simplifies to: CO2 ö æ rc = 49 ç1.4 − 0.4 ⋅ ÷ 330 ø è
7.2.22
The most accurate estimates of evapotranspiration with the PenmanMonteith equation are made when evapotranspiration is calculated on an hourly basis and summed to obtain the daily values. Mean daily parameter values have been shown to provide reliable estimates of daily evapotranspiration values and this is the approach used in SWAT. However, the user should be aware that calculating evapotranspiration with the Penman-Monteith equation using mean daily values can potentially lead to significant errors. These errors result from diurnal distributions of wind speed, humidity, and net radiation that in combination create conditions which the daily averages do not replicate.
7.2.2 PRIESTLEY-TAYLOR METHOD Priestley and Taylor (1972) developed a simplified version of the combination equation for use when surface areas are wet. The aerodynamic component was removed and the energy component was multiplied by a coefficient, αpet = 1.28, when the general surroundings are wet or under humid conditions
λEo = α pet ⋅
∆ ⋅ (H net − G ) ∆ +γ
7.2.23
where λ is the latent heat of vaporization (MJ kg-1), Eo is the potential evapotranspiration (mm d-1), αpet is a coefficient, ∆ is the slope of the saturation vapor pressure-temperature curve, de/dT (kPa ûC-1), γ is the psychrometric constant (kPa ûC-1), Hnet is the net radiation (MJ m-2 d-1), and G is the heat flux density to the ground (MJ m-2 d-1).
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The Priestley-Taylor equation provides potential evapotranspiration estimates for low advective conditions. In semiarid or arid areas where the advection component of the energy balance is significant, the Priestley-Taylor equation will underestimate potential evapotranspiration.
7.2.3 HARGREAVES METHOD The Hargreaves method was originally derived from eight years of coolseason Alta fescue grass lysimeter data from Davis, California (Hargreaves, 1975). Several improvements were made to the original equation (Hargreaves and Samani, 1982 and 1985) and the form used in SWAT was published in 1985 (Hargreaves et al., 1985):
(
λEo = 0.0023 ⋅ H 0 ⋅ (Tmx − Tmn )0.5 ⋅ T av + 17.8
)
7.2.24
where λ is the latent heat of vaporization (MJ kg-1), Eo is the potential evapotranspiration (mm d-1), H0 is the extraterrestrial radiation (MJ m-2 d-1), Tmx is the maximum air temperature for a given day (°C), Tmn is the minimum air temperature for a given day (°C), and T av is the mean air temperature for a given day (°C). Table 7-2: SWAT input variables used in potential evapotranspiration calculations summarized in this section. Variable File name Definition Name IPET Potential evapotranspiration method .cod WND_SP uz: Daily wind speed (m/s) .wnd .sub CO2 CO2: Carbon dioxide concentration (ppmv) MAX TEMP .tmp Tmx: Daily maximum temperature (°C) MIN TEMP Tmn: Daily minimum temperature (°C) .tmp -1 crop.dat GSI g : maximum leaf conductance (m s ) l ,mx
FRGMAX VPDFR
frg,mx: Fraction of maximum leaf conductance achieved at the vapor pressure deficit specified by vpdfr vpdfr: Vapor pressure deficit corresponding to value given for frg,mx (kPa)
crop.dat crop.dat
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7.3 ACTUAL EVAPOTRANSPIRATION Once total potential evapotranspiration is determined, actual evaporation must be calculated. SWAT first evaporates any rainfall intercepted by the plant canopy. Next, SWAT calculates the maximum amount of transpiration and the maximum amount of sublimation/soil evaporation using an approach similar to that of Richtie (1972). The actual amount of sublimation and evaporation from the soil is then calculated. If snow is present in the HRU, sublimation will occur. Only when no snow is present will evaporation from the soil take place.
7.3.1 EVAPORATION OF INTERCEPTED RAINFALL Any free water present in the canopy is readily available for removal by evapotranspiration. The amount of actual evapotranspiration contributed by intercepted rainfall is especially significant in forests where in some instances evaporation of intercepted rainfall is greater than transpiration. SWAT removes as much water as possible from canopy storage when calculating actual evaporation. If potential evapotranspiration, Eo, is less than the amount of free water held in the canopy, RINT, then
E a = E can = Eo
7.3.1
RINT ( f ) = RINT ( i ) − E can
7.3.2
where Ea is the actual amount of evapotranspiration occurring in the watershed on a given day (mm H2O), Ecan is the amount of evaporation from free water in the canopy on a given day (mm H2O), Eo is the potential evapotranspiration on a given day (mm H2O), RINT(i) is the initial amount of free water held in the canopy on a given day (mm H2O), and RINT(f) is the final amount of free water held in the canopy on a given day (mm H2O). If potential evapotranspiration, Eo, is greater than the amount of free water held in the canopy, RINT, then E can = RINT ( i )
7.3.3
RINT ( f ) = 0
7.3.4
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129
Once any free water in the canopy has been evaporated, the remaining evaporative water demand ( Eo′ = Eo − Ecan ) is partitioned between the vegetation and snow/soil.
7.3.2 TRANSPIRATION If
the Penman-Monteith equation is selected as the
potential
evapotranspiration method, transpiration is also calculated with the equations summarized in Section 7.2.1. For the other potential evapotranspiration methods, transpiration is calculated as: Et =
Eo′ ⋅ LAI 3 .0
Et = Eo′
0 ≤ LAI ≤ 3.0
7.3.5
LAI > 3.0
7.3.6
where Et is the maximum transpiration on a given day (mm H2O), Eo′ is the potential evapotranspiration adjusted for evaporation of free water in the canopy (mm H2O), and LAI is the leaf area index. The value for transpiration calculated by equations 7.3.5 and 7.3.6 is the amount of transpiration that will occur on a given day when the plant is growing under ideal conditions. The actual amount of transpiration may be less than this due to lack of available water in the soil profile. Calculation of actual plant water uptake and transpiration is reviewed in Chapters 18 and 19.
7.3.3 SUBLIMATION AND EVAPORATION FROM THE SOIL The amount of sublimation and soil evaporation will be impacted by the degree of shading. The maximum amount of sublimation/soil evaporation on a given day is calculated as:
E s = Eo′ ⋅ cov sol
7.3.7
where Es is the maximum sublimation/soil evaporation on a given day (mm H2O),
Eo′ is the potential evapotranspiration adjusted for evaporation of free water in the canopy (mm H2O), and covsol is the soil cover index. The soil cover index is calculated cov sol = exp(− 5.0 × 10 −5 ⋅ CV )
7.3.8
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where CV is the aboveground biomass and residue (kg ha-1). If the snow water content is greater than 0.5 mm H2O, the soil cover index is set to 0.5. The maximum amount of sublimation/soil evaporation is reduced during periods of high plant water use with the relationship: é E s′ = min ê E s , ë
E s ⋅ Eo′ ù E s + Et úû
7.3.9
where E s′ is the maximum sublimation/soil evaporation adjusted for plant water use on a given day (mm H2O), Es is the maximum sublimation/soil evaporation on a given day (mm H2O), Eo′ is the potential evapotranspiration adjusted for evaporation of free water in the canopy (mm H2O), and Et is the transpiration on a given day (mm H2O). When Et is low E s′ → E s . However, as Et approaches Eo′ , E s′ →
Es . 1 + cov sol
7.3.3.1 SUBLIMATION Once the maximum amount of sublimation/soil evaporation for the day is calculated, SWAT will first remove water from the snow pack to meet the evaporative demand. If the water content of the snow pack is greater than the maximum sublimation/soil evaporation demand, then
E sub = E s′
7.3.10
SNO( f ) = SNO( i ) − E s′
7.3.11
E s′′ = 0.
7.3.12
where Esub is the amount of sublimation on a given day (mm H2O), E s′ is the maximum sublimation/soil evaporation adjusted for plant water use on a given day (mm H2O), SNO(i) is the amount of water in the snow pack on a given day prior to accounting for sublimation (mm H2O), SNO(f) is the amount of water in the snow pack on a given day after accounting for sublimation (mm H2O), and E s′′ is the maximum soil water evaporation on
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131
a given day (mm H2O). If the water content of the snow pack is less than the maximum sublimation/soil evaporation demand, then E sub = SNO( i )
7.3.13
SNO( f ) = 0.
7.3.14
E s′′ = E s′ − E sub
7.3.15
7.3.3.2 SOIL WATER EVAPORATION When an evaporation demand for soil water exists, SWAT must first partition the evaporative demand between the different layers. The depth distribution used to determine the maximum amount of water allowed to be evaporated is:
E soil , z = E s′′ ⋅
z z + exp(2.374 − 0.00713 ⋅ z )
7.3.16
where Esoil,z is the evaporative demand at depth z (mm H2O), E s′′ is the maximum soil water evaporation on a given day (mm H2O), and z is the depth below the surface. The coefficients in this equation were selected so that 50% of the evaporative demand is extracted from the top 10 mm of soil and 95% of the evaporative demand is extracted from the top 100 mm of soil. The amount of evaporative demand for a soil layer is determined by taking the difference between the evaporative demands calculated at the upper and lower boundaries of the soil layer: E soil ,ly = E soil , zl − E soil , zu
7.3.16
where Esoil,ly is the evaporative demand for layer ly (mm H2O), Esoil,zl is the evaporative demand at the lower boundary of the soil layer (mm H2O), and Esoil,zu is the evaporative demand at the upper boundary of the soil layer (mm H2O). Figure 7-1 graphs the depth distribution of the evaporative demand for a soil that has been partitioned into 1 mm layers assuming a total soil evaporation demand of 100 mm.
SWAT USER'S MANUAL, VERSION 2000 E v a p o r a tio n a llo w e d w ith d e p t h a s s u m in g 1 0 0 m m d e m a n d 9 .0 0
8 .0 0
7 .0 0 Maximum Evaporation (mm H2O)
132
6 .0 0
5 .0 0
4 .0 0
3 .0 0
2 .0 0
1 .0 0
0 .0 0 1
51
101
151
201
251
301
351
401
451
D ep th (m m )
Figure 7-1: Soil evaporative demand distribution with depth.
As mentioned previously, the depth distribution assumes 50% of the evaporative demand is met by soil water stored in the top 10 mm of the soil profile. With our example of a 100 mm total evaporative demand, 50 mm of water is 50%. This is a demand that the top layer cannot satisfy. SWAT does not allow a different layer to compensate for the inability of another layer to meet its evaporative demand. The evaporative demand not met by a soil layer results in a reduction in actual evapotranspiration for the HRU. A coefficient has been incorporated into equation 7.3.16 to allow the user to modify the depth distribution used to meet the soil evaporative demand. The modified equation is: E soil ,ly = E soil , zl − E soil , zu ⋅ esco
7.3.17
where Esoil,ly is the evaporative demand for layer ly (mm H2O), Esoil,zl is the evaporative demand at the lower boundary of the soil layer (mm H2O),
Esoil,zu is the evaporative demand at the upper boundary of the soil layer
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133
(mm H2O), and esco is the soil evaporation compensation coefficient. Solutions to this equation for different values of esco are graphed in Figure 7-2. The plot for esco = 1.0 is that shown in Figure 7-1.
E v a p o r a t io n a llo w e d w it h d e p t h a s s u m in g 1 0 0 m m d e m a n d
3 5 .0 0
Maximum Evaporation (mm H2O)
3 0 .0 0
2 5 .0 0
2 0 .0 0
1 5 .0 0
1 0 .0 0
5 .0 0
0 .0 0 1
51
101
151
201
251
301
351
401
451
D ep th (m m ) esc o = 1.0
e s c o = 0 .9
e sc o = 0 .8
esc o = 0.7
Figure 7-2: Soil evaporative demand distribution with depth
As the value for esco is reduced, the model is able to extract more of the evaporative demand from lower levels. When the water content of a soil layer is below field capacity, the evaporative demand for the layer is reduced according to the following equations: æ 2.5 ⋅ (SWly − FCly ) ö ÷ when SWly < FCly 7.3.18 ′ ,ly = E soil ,ly ⋅ expç E soil ç FC − WP ÷ ly ly è ø ′ ,ly = E soil ,ly E soil
when SWly ≥ FCly 7.3.19
′ ,ly is the evaporative demand for layer ly adjusted for water where E soil
content (mm H2O), Esoil,ly is the evaporative demand for layer ly (mm
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H2O), SWly is the soil water content of layer ly (mm H2O), FCly is the water content of layer ly at field capacity (mm H2O), and WPly is the water content of layer ly at wilting point (mm H2O). In addition to limiting the amount of water removed by evaporation in dry conditions, SWAT defines a maximum value of water that can be removed at any time. This maximum value is 80% of the plant available water on a given day where the plant available water is defined as the total water content of the soil layer minus the water content of the soil layer at wilting point (-1.5 MPa). ′′ ,ly = min(E soil ′ ,ly E soil
0.8 ⋅ ( SWly − WPly ) )
7.3.20
′′ ,ly is the amount of water removed from layer ly by evaporation where E soil ′ ,ly is the evaporative demand for layer ly adjusted for (mm H2O), E soil
water content (mm H2O), SWly is the soil water content of layer ly (mm H2O), and WPly is the water content of layer ly at wilting point (mm H2O). Table 7-3: SWAT input variables used in soil evaporation calculations. Variable name Definition ESCO esco: soil evaporation compensation coefficient
File Name .bsn, .hru
7.4 NOMENCLATURE CO2 CV E Ea Ecan Eo Eo′ Es E s′ E s′′ Esoil,ly
Concentration of carbon dioxide in the atmosphere (ppmv) Total aboveground biomass and residue present on current day (kg ha-1) Depth rate evaporation (mm d-1) Actual amount of evapotranspiration on a given day (mm H2O) Amount of evaporation from free water in the canopy on a given day (mm H2O) Potential evapotranspiration (mm d-1) Potential evapotranspiration adjusted for evaporation of free water in the canopy (mm H2O) Maximum sublimation/soil evaporation on a given day (mm H2O) Maximum sublimation/soil evaporation adjusted for plant water use on a given day (mm H2O) Maximum soil water evaporation on a given day (mm H2O) Evaporative demand for layer ly (mm H2O)
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135
′ ,ly Evaporative demand for layer ly adjusted for water content (mm H2O) E soil ′′ ,ly E soil Esoil,z Esub Et FCly G H0 Hnet K1 LAI LAImx P Rday ′ Rday
RINT SNO SWly Tmn Tmx T av WPly
Amount of water removed from layer ly by evaporation (mm H2O) Evaporative demand at depth z (mm H2O) Amount of sublimation on a given day (mm H2O) Transpiration rate (mm d-1) Water content of layer ly at field capacity (mm H2O) Heat flux density to the ground (MJ m-2 d-1) Extraterrestrial daily irradiation (MJ m-2 d-1) Net radiation on day (MJ m-2 d-1) Dimension coefficient in Penman-Monteith equation Leaf area index of the canopy Maximum leaf area index for the plant Atmospheric pressure (kPa) Amount of rainfall on a given day (mm H2O) Amount of precipitation on a given day before canopy interception is removed (mm H2O) Amount of free water held in the canopy on a given day (mm H2O) Water content of snow cover on current day (mm H2O) Soil water content of layer ly (mm H2O) Minimum air temperature for day (°C) Maximum air temperature for day (°C) Mean air temperature for day (°C) Water content of layer ly at wilting point (mm H2O).
cp Specific heat of moist air at constant pressure (1.013 × 10-3 MJ kg-1 °C-1) canday Maximum amount of water that can be trapped in the canopy on a given day (mm H2O) canmx Maximum amount of water that can be trapped in the canopy when the canopy is fully developed (mm H2O) covsol Soil cover index d Zero plane displacement of the wind profile (cm) e Actual vapor pressure on a given day (kPa) o e Saturation vapor pressure on a given day (kPa) esco Soil evaporation compensation coefficient frg,mx Fraction of the maximum stomatal conductance, g l,mx , achieved at the vapor pressure deficit, vpdfr gl Leaf conductance (m s-1) g l,mx Maximum conductance of a single leaf (m s-1) hc Canopy height (cm) k Von Kármán constant ra Diffusion resistance of the air layer (aerodynamic resistance) (s m-1) rc Plant canopy resistance (s m-1) rl Minimum effective resistance of a single leaf (s m-1)
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SWAT USER'S MANUAL, VERSION 2000
Minimum abaxial stomatal leaf resistance (s m-1) Minimum adaxial stomatal leaf resistance (s m-1) Wind speed at height zw (m s-1) Vapor pressure deficit (kPa) Vapor pressure deficit corresponding to frg,mx (kPa) Threshold vapor pressure deficit above which a plant will exhibit reduced leaf conductance (kPa) Depth below soil surface (mm) Roughness length for momentum transfer (cm) Roughness length for vapor transfer (cm) Height of the humidity (psychrometer) and temperature measurements (cm) Height of the wind speed measurement (cm)
αpet Coefficient in Priestley-Taylor equation ∆ Slope of the saturation vapor pressure curve (kPa °C-1) ∆g l,dcl Rate of decline in leaf conductance per unit increase in vapor pressure deficit (m s-1 kPa-1) ρair Air density (kg m-3) γ Psychrometric constant (kPa °C-1) λ Latent heat of vaporization (MJ kg-1)
7.5 REFERENCES Allen, R.G. 1986. A Penman for all seasons. J. Irrig. and Drain Engng., ASCE, 112(4): 348-368. Allen, R.G., M.E. Jensen, J.L. Wright, and R.D. Burman. 1989. Operational estimates of evapotranspiration. Agron. J. 81:650-662. Brutsaert, W. 1975. Comments on surface roughness parameters and the height of dense vegetation. J. Meterol. Soc. Japan 53:96-97. Dingman, S.L. 1994. Physical hydrology. Prentice-Hall, Inc., Englewood Cliffs, NJ. Easterling, W.E., N.J. Rosenburg, M.S. McKenney, C.A. Jones, P.T. Dyke, and J.R. Williams. 1992. Preparing the erosion productivity impact calculator (EPIC) model to simulate crop response to climate change and the direct effects of CO2. Agricultural and Forest Meteorology 59:17-34.
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Hargreaves, G.H. 1975. Moisture availability and crop production. Trans. ASAE 18: 980-984. Hargreaves, G.H. and Z.A. Samani. 1985. Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture 1:96-99. Hargreaves, G.H. and Z.A. Samani. 1982. Estimating potential evapotranspiration. Tech. Note, J. Irrig. and Drain. Engr. 108(3):225-230. Hargreaves, G.L., G.H. Hargreaves, and J.P. Riley. 1985. Agricultural benefits for Senegal River Basin. J. Irrig. and Drain. Engr. 111(2):113-124. Jensen, M.E., R.D. Burman, and R.G. Allen (ed). 1990. Evapotranspiration and irrigation water requirements. ASCE Manuals and Reports on Engineering Practice No. 70, ASCE, N.Y. 332 pp. Monteith, J.L. 1965. Evaporation and the environment. p. 205-234. In The state and movement of water in living organisms, XIXth Symposium. Soc. for Exp. Biol., Swansea, Cambridge University Press. Monteith, J.L. 1981. Evaporation and surface temperature. Quart. J. Roy. Meteorol. Soc. 107:1-27. Morison, J.I.L. 1987. Intercellular CO2 concentration and stomatal response to CO2. p. 229-251. In E. Zeiger, G.D. Farquhar and I.R. Cowan (ed.) Stomatal function. Standford University Press, Palo Alto, CA. Morison, J.I.L. and R.M. Gifford. 1983. Stomatal sensitivity tocarbon dioxide and humidity. Plant Physiol. 71:789-796. Penman, H.L. 1956. Evaporation: An introductory survey. Netherlands Journal of Agricultural Science 4:7-29. Plate, E.J. 1971. Aerodynamic characteristics of atmospheric boundary layers. U.S. Atomic Energy Comm., Critical Review Series, TID-25465. 190 pp. Priestley, C.H.B. and R.J. Taylor. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather. Rev. 100:81-92. Ritchie, J.T. 1972. Model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res. 8:1204-1213.
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Rosenburg, N.J., B.L. Blad, and S.B. Verma. 1983. Microclimate: the biological environment, 2nd ed. John Wiley & Sons, New York. Stockle, C.O., J.R. Williams, N.J. Rosenberg, and C.A. Jones. 1992. A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part 1—Modification of the EPIC model for climate change analysis. Agricultural Systems 38:225-238. Stricker, H. and W. Brutsaert. 1978. Actual evapotranspiration over summer period in the 'Hupsel Catchment.' J. Hydrol. 39:139-157. Thornthwaite, C.W. 1948. An approach toward a rational classification of climate. Geographical Review 38:55-94.
CHAPTER 8
EQUATIONS: SOIL WATER
Water that enters the soil may move along one of several different pathways. The water may be removed from the soil by plant uptake or evaporation. It can percolate past the bottom of the soil profile and ultimately become aquifer recharge. A final option is that water may move laterally in the profile and contribute to streamflow. Of these different pathways, plant uptake of water removes the majority of water that enters the soil profile.
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8.1 SOIL STRUCTURE Soil is comprised of three phases—solid, liquid and gas. The solid phase consists of minerals and/or organic matter that forms the matrix or skeleton of the soil. Between the solid particles, soil pores are formed that hold the liquid and gas phases. The soil solution may fill the soil pores completely (saturated) or partially (unsaturated). When the soil is unsaturated, the soil solution is found as thin films along particle surfaces, as annular wedges around contact points of particles and as isolated bodies in narrow pore passages. The soil’s bulk density defines the relative amounts of pore space and soil matrix. Bulk density is calculated:
ρb =
MS VT
8.1.1
where ρb is the bulk density (Mg m-3), MS is the mass of the solids (Mg), and VT is the total volume (m3). The total volume is defined as VT = V A + VW + VS
8.1.2
where VA is the volume of air (m3), VW is the volume of water (m3), and VS is the volume of solids (m3). The relationship between soil porosity and soil bulk density is
φ soil = 1 −
ρb ρs
8.1.3
where φsoil is the soil porosity expressed as a fraction of the total soil volume, ρb is the bulk density (Mg m-3), and ρs is the particle density (Mg m-3). The particle density, or density of the solid fraction, is a function of the mineral composition of the soil matrix. Based on research, a default value of 2.65 Mg m-3 is used for particle density. Storage, transport and availability of soil solution and soil air are not nearly as dependent on the total amount of porosity as they are on the arrangement of pore space. Soil pores vary in size and shape due to textural and structural arrangement. Based on the diameter of the pore at the narrowest point, the pores may be classified as macropores (narrowest diameter > 100 µm),
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mesopores (narrowest diameter 30-100 µm), and micropores (narrowest diameter < 30 µm) (Koorevaar et al, 1983). Macropores conduct water only during flooding or ponding rain and drainage of water from these pores is complete soon after cessation of the water supply. Macropores control aeration and drainage processes in the soil. Mesopores conduct water even after macropores have emptied, e.g. during non-ponding rain and redistribution. Micropores retain soil solution or conduct it very slowly. When comparing soils of different texture, clay soils contain a greater fraction of mesopores and micropores while sand soils contain mostly macropores. This is evident when the hydraulic conductivities of clay and sand soils are compared. The conductivity of a sand soil can be several orders of magnitude greater than that for a clay soil. The water content of a soil can range from zero when the soil is oven dried to a maximum value (φsoil) when the soil is saturated. For plant-soil interactions, two intermediate stages are recognized: field capacity and permanent wilting point. Field capacity is the water content found when a thoroughly wetted soil has drained for approximately two days. Permanent wilting point is the water content found when plants growing in the soil wilt and do not recover if their leaves are kept in a humid atmosphere overnight. To allow these two stages to be quantified more easily, they have been redefined in terms of tensions at which water is held by the soil. Field capacity is the amount of water held in the soil at a tension of 0.033 MPa and the permanent wilting point is the amount of water held in the soil at a tension of 1.5 MPa. The amount of water held in the soil between field capacity and permanent wilting point is considered to be the water available for plant extraction. Table 8-1: Water contents for various soils at different moisture conditions. Water content (fraction total soil volume) Clay Content Permanent Texture (% Solids) Saturation Field capacity wilting point sand 3% 0.40 0.06 0.02 loam 22 % 0.50 0.29 0.05 clay 47 % 0.60 0.41 0.20
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Table 8-1 lists the water content for three soils as a fraction of the total volume for different moisture conditions. Note that the total porosity, given by the water content at saturation, is lowest for the sand soil and highest for the clay soil. The sand soil drains more quickly than the loam and clay. Only 15% of the water present in the sand soil at saturation remains at field capacity. 58% of the water present at saturation in the loam remains at field capacity while 68% of the water present at saturation in the clay soil remains at field capacity. The reduction of water loss with increase in clay content is cause by two factors. As mentioned previously, clay soils contain more mesopores and micropores than sand soils. Also, unlike sand and silt particles, clay particles possess a net negative charge. Due to the polar nature of water molecules, clay particles are able to attract and retain water molecules. The higher water retention of clay soils is also seen in the fraction of water present at permanent wilting point. In the soils listed in Table 8-1, the volumetric water content of the clay is 0.20 at the wilting point while the sand and loam have a volumetric water content of 0.02 and 0.05 respectively. The plant available water, also referred to as the available water capacity, is calculated by subtracting the fraction of water present at permanent wilting point from that present at field capacity. AWC = FC − WP
8.1.4
where AWC is the plant available water content, FC is the water content at field capacity, and WP is the water content at permanent wilting point. For the three soil textures listed in Table 8-1, the sand has an available water capacity of 0.04, the loam has an available water capacity of 0.24 and the clay has an available water capacity of 0.21. Even though the clay contains a greater amount of water than the loam at all three tensions, the loam has a larger amount of water available for plant uptake than the clay. This characteristic is true in general. SWAT estimates the permanent wilting point volumetric water content for each soil layer as: WPly = 0.40 ⋅
mc ⋅ ρ b 100
8.1.5
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where WPly is the water content at wilting point expressed as a fraction of the total soil volume, mc is the percent clay content of the layer (%), and ρb is the bulk density for the soil layer (Mg m-3). Field capacity water content is estimated FCly = WPly + AWCly
8.1.6
where FCly is the water content at field capacity expressed as a fraction of the total soil volume, WPly is the water content at wilting point expressed as a fraction of the total soil volume, and AWCly is the available water capacity of the soil layer expressed as a fraction of the total soil volume. AWCly is input by the user. Water in the soil can flow under saturated or unsaturated conditions. In saturated soils, flow is driven by gravity and usually occurs in the downward direction. Unsaturated flow is caused by gradients arising due to adjacent areas of high and low water content. Unsaturated flow may occur in any direction. SWAT directly simulates saturated flow only. The model records the water contents of the different soil layers but assumes that the water is uniformly distributed within a given layer. This assumption eliminates the need to model unsaturated flow in the horizontal direction. Unsaturated flow between layers is indirectly modeled with the depth distribution of plant water uptake (equation 18.2.1) and the depth distribution of soil water evaporation (equation 7.3.16). Saturated flow occurs when the water content of a soil layer surpasses the field capacity for the layer. Water in excess of the field capacity water content is available for percolation, lateral flow or tile flow drainage unless the temperature of the soil layer is below 0°C. When the soil layer is frozen, no water movement is calculated. Table 8-1: SWAT input variables used in percolation calculations. Variable name Definition CLAY mc: Percent clay content SOL_BD ρb: Bulk density (Mg m-3) SOL_AWC AWCly: available water capacity
File Name .sol .sol .sol
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8.2 PERCOLATION Percolation is calculated for each soil layer in the profile. Water is allowed to percolate if the water content exceeds the field capacity water content for that layer. When the soil layer is frozen, no water flow out of the layer is calculated. The volume of water available for percolation in the soil layer is calculated: SWly ,excess = SWly − FC ly
if
SWly > FCly
8.2.1
SWly ,excess = 0
if
SWly ≤ FCly
8.2.2
where SWly,excess is the drainable volume of water in the soil layer on a given day (mm H2O), SWly is the water content of the soil layer on a given day (mm H2O) and FCly is the water content of the soil layer at field capacity (mm H2O). The amount of water that moves from one layer to the underlying layer is calculated using storage routing methodology. The equation used to calculate the amount of water that percolates to the next layer is: æ é − ∆t ù ö w perc ,ly = SWly ,excess ⋅ ç1 − exp ê ú ÷÷ ç TT êë perc úû ø è
8.2.3
where wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O), SWly,excess is the drainable volume of water in the soil layer on a given day (mm H2O), ∆t is the length of the time step (hrs), and TTperc is the travel time for percolation (hrs). The travel time for percolation is unique for each layer. It is calculate TT perc =
SATly − FC ly K sat
8.2.4
where TTperc is the travel time for percolation (hrs), SATly is the amount of water in the soil layer when completely saturated (mm H2O), FCly is the water content of the soil layer at field capacity (mm H2O), and Ksat is the saturated hydraulic conductivity for the layer (mm·h-1). Water that percolates out of the lowest soil layer enters the vadose zone. The vadose zone is the unsaturated zone between the bottom of the soil profile
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and the top of the aquifer. Movement of water through the vadose zone and into the aquifers is reviewed in Chapter 9. Table 8-2: SWAT input variables used in percolation calculations. Variable name Definition SOL_K Ksat: Saturated hydraulic conductivity (mm/hr)
File Name .sol
8.3 BYPASS FLOW One of the most unique soil orders is the Vertisols. These soils are characterized by a propensity to shrink when dried and swell when moistened. When the soil is dry, large cracks form at the soil surface. This behavior is a result of the type of soil material present and the climate. Vertisols contain at least 30% clay with the clay fraction dominated by smectitic mineralogy and occur in areas with cyclical wet and dry periods. Vertisols are found worldwide (Figure 8-1). They have a number of local names, some of which are listed in Table 8-3.
Figure 8-1: Soil associations of Vertisols (From Dudal and Eswaran, 1988)
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Table 8-3: Alternative names for Vertisols or soils with Vertic properties (Dudal and Eswaran, 1988). Names Countries Names that include the word “black” Barros pretos Black clays Black cotton soils Black cracking clays Black earths Black turf soils Dark clay soils Subtropical black clays Sols noirs tropicaux Terra nera Terres noires tropicales Terras negras tropicais Tierras negras de Andalucia Tropical black earths Tropical black clays
Portugal South Africa, Australia Africa, India Uganda Australia, Africa South Africa United States Africa Africa Italy Africa Mozambique Spain Angola, Ghana Africa
Names that reflect the black color Karail Melanites Teen Suda Tropical Chernozems Impact Chernozems
India Ghana Sudan Africa, India Russia
Vernacular names Adobe soils Badobes Dian Pere Gilgai soils Firki Mbuga Kahamba Makande Morogan Regur Rendzina Shachiang soils Smolnitza Smonitza Sols de paluds Tirs Vlei grond Sonsocuite
United States, Philippines Sudan French West Africa Australia Nigeria Tanzania Congo Malawi Romania India United States China Bulgaria, Romania Austria, Yugoslavia France Morocco, North Africa South Africa Nicaragua
Coined names Densinigra soils Gravinigra soils Grumusols Margalite soils Vertisols
Angola Angola United States Indonesia United States
One criteria used to classify a soil as a Vertisol is the formation of shrinkage cracks in the dry season that penetrate to a depth of more than 50 cm and are at least 1 cm wide at 50 cm depth. The cracks can be considerably wider at the surface—30 cm cracks at the surface are not unusual although 6-15 cm cracks are more typical.
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To accurately predict surface runoff and infiltration in areas dominated by soils that exhibit Vertic properties, the temporal change in soil volume must be quantified. Bouma and Loveday (1988) identified three soil moisture conditions for which infiltration needs to be defined (Figure 8-2).
Figure 8-2: Diagram showing the effect of wetting and drying on cracking in Vertisols (from Bouma and Loveday, 1988)
Traditional models of infiltration are applicable to soils in which cracks have been closed by swelling and the soil acts as a relatively homogenous porous medium (Condition 3 in Figure 8-2). Condition 1 in Figure 8-2 represents the driest state with cracks at maximum width, a condition present at the end of the dry season/beginning of the rainy season. Condition 2 in Figure 8-2 represents the crack development typical with an actively growing crop requiring multiple irrigation or rainfall events to sustain growth. Bypass flow, the vertical movement of free water along macropores through unsaturated soil horizons, will occur in conditions 1 and 2. Bypass flow (finf,2 in Figure 8-2) occurs when the rate of rainfall or irrigation exceeds the vertical infiltration rate into the soil peds (finf,1 in Figure 8-2). When bypass flow is modeled, SWAT calculates the crack volume of the soil matrix for each day of simulation by layer. On days in which precipitation events occur, infiltration and surface runoff is first calculated for the soil peds (finf,1 in Figure 8-2) using the curve number or Green & Ampt method. If any surface runoff is generated, it is allowed to enter the cracks. A volume of water
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equivalent to the total crack volume for the soil profile may enter the profile as bypass flow. Surface runoff in excess of the crack volume remains overland flow. Water that enters the cracks fills the soil layers beginning with the lowest layer of crack development. After cracks in one layer are filled, the cracks in the overlying layer are allowed to fill.
The crack volume initially estimated for a layer is calculated: crk ly ,i = crk max ,ly ⋅
coef crk ⋅ FC ly − SWly coef crk ⋅ FCly
8.3.1
where crkly,i is the initial crack volume calculated for the soil layer on a given day expressed as a depth (mm), crkmax,ly is the maximum crack volume possible for the soil layer (mm), coefcrk is an adjustment coefficient for crack flow, FCly is the water content of the soil layer at field capacity (mm H2O), and SWly is the water content of the soil layer on a given day (mm H2O). The adjustment coefficient for crack flow, coefcrk, is set to 0.10. When the moisture content of the entire profile falls below 90% of the field capacity water content for the profile during the drying stage, the crack volume for a given day is a function of the crack volume estimated with equation 8.3.1 and the crack volume of the layer on the previous day. When the soil is wetting and/or when the moisture content of the profile is above 90% of the field capacity water content, the crack volume for a given day is equal to the volume calculated with equation 8.3.1. crk ly = l crk ⋅ crk ly ,d −1 + (1.0 − l crk ) ⋅ crk ly ,i
when SW < 0.90 ⋅ FC and crk ly ,i > crk ly ,d −1
8.3.2
when SW ≥ 0.90 ⋅ FC or crk ly ,i ≤ crk ly ,d −1
8.3.3
crk ly = crk ly ,i
where crkly is the crack volume for the soil layer on a given day expressed as a depth (mm), l crk is the lag factor for crack development during drying, crkly,d-1 is the crack volume for the soil layer on the previous day (mm), crkly,i is the initial crack volume calculated for the soil layer on a given day using equation 8.3.1
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(mm), SW is the water content of the soil profile on a given day (mm H2O), and FC is the water content of the soil profile at field capacity (mm H2O). As the tension at which water is held by the soil particles increases, the rate of water diffusion slows. Because the rate of water diffusion is analogous to the coefficient of consolidation in classical consolidation theory (Mitchell, 1992), the reduction in diffusion will affect crack formation. The lag factor is introduced during the drying stage to account for the change in moisture redistribution dynamics that occurs as the soil dries. The lag factor, l crk , is set to a value of 0.99. The maximum crack volume for the layer, crkmax,ly, is calculated: crk max,ly = 0.916 ⋅ crk max ⋅ exp[− 0.0012 ⋅ z l ,ly ]⋅ depthly
8.3.4
where crkmax,ly is the maximum crack volume possible for the soil layer (mm), crkmax is the potential crack volume for the soil profile expressed as a fraction of the total volume, zl,ly is the depth from the soil surface to the bottom of the soil layer (mm), and depthly is the depth of the soil layer (mm). The potential crack volume for the soil profile, crkmax, is input by the user. Those needing information on the measurement of this parameter are referred to Bronswijk (1989; 1990). Once the crack volume for each layer is calculated, the total crack volume for the soil profile is determined. n
crk = å crk ly
8.3.5
ly =1
where crk is the total crack volume for the soil profile on a given day (mm), crkly is the crack volume for the soil layer on a given day expressed as a depth (mm), ly is the layer, and n is the number of layers in the soil profile.
After surface runoff is calculated for rainfall events using the curve number or Green & Ampt method, the amount of runoff is reduced by the volume of cracks present that day: Qsurf = Qsurf ,i − crk
if Qsurf ,i > crk
8.3.6
Qsurf = 0
if Qsurf ,i ≤ crk
8.3.7
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where Qsurf is the accumulated runoff or rainfall excess for the day (mm H2O),
Qsurf,i is the initial accumulated runoff or rainfall excess determined with the Green & Ampt or curve number method (mm H2O), and crk is the total crack volume for the soil profile on a given day (mm). The total amount of water entering the soil is then calculated: winf = Rday − Qsurf
8.3.8
where winf is the amount of water entering the soil profile on a given day (mm H2O), Rday is the rainfall depth for the day adjusted for canopy interception (mm H2O), and Qsurf is the accumulated runoff or rainfall excess for the day (mm H2O). Bypass flow past the bottom of the profile is calculated: æ crk ly = nn ö ÷ wcrk ,btm = 0.5 ⋅ crk ⋅ ç ç depth ÷ ly = nn è ø
8.3.9
where wcrk,btm is the amount of water flow past the lower boundary of the soil profile due to bypass flow (mm H2O), crk is the total crack volume for the soil profile on a given day (mm), crkly=nn is the crack volume for the deepest soil layer (ly=nn) on a given day expressed as a depth (mm), and depthly=nn is the depth of the deepest soil layer (ly=nn) (mm). After wcrk,btm is calculated, each soil layer is filled to field capacity water content beginning with the lowest layer and moving upward until the total amount of water entering the soil, winf, has been accounted for. Table 8-4: SWAT input variables used in bypass flow calculations. Variable name Definition ICRK Bypass flow code: 0-do not model bypass flow; 1-model bypass flow SOL_CRK crkmax: Potential crack volume for soil profile
File Name .cod .sol
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8.4 LATERAL FLOW Lateral flow will be significant in areas with soils having high hydraulic conductivities in surface layers and an impermeable or semipermeable layer at a shallow depth. In such a system, rainfall will percolate vertically until it encounters the impermeable layer. The water then ponds above the impermeable layer forming a saturated zone of water, i.e. a perched water table. This saturated zone is the source of water for lateral subsurface flow. SWAT incorporates a kinematic storage model for subsurface flow developed by Sloan et al. (1983) and summarized by Sloan and Moore (1984). This model simulates subsurface flow in a two-dimensional cross-section along a flow path down a steep hillslope. The kinematic approximation was used in its derivation. This model is based on the mass continuity equation, or mass water balance, with the entire hillslope segment used as the control volume. The hillslope segment has a permeable soil surface layer of depth Dperm and length Lhill with an impermeable soil layer or boundary below it as shown in Figure 8-3. The hillslope segment is oriented at an angle αhill to the horizontal.
Figure 8-3: Conceptual representation of the hillslope segment.
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The kinematic wave approximation of saturated subsurface or lateral flow assumes that the lines of flow in the saturated zone are parallel to the impermeable boundary and the hydraulic gradient equals the slope of the bed.
Figure 8-4: Behavior of the water table as assumed in the kinematic storage model.
From Figure 8-4, the drainable volume of water stored in the saturated zone of the hillslope segment per unit area, SWly,excess, is SWly ,excess =
1000 ⋅ H o ⋅ φ d ⋅ Lhill 2
8.4.1
where SWly,excess is the drainable volume of water stored in the saturated zone of the hillslope per unit area (mm H2O), Ho is the saturated thickness normal to the hillslope at the outlet expressed as a fraction of the total thickness (mm/mm), φd is the drainable porosity of the soil (mm/mm), Lhill is the hillslope length (m), and 1000 is a factor needed to convert meters to millimeters. This equation can be rearranged to solve for Ho:
Ho =
2 ⋅ SWly ,excess 1000 ⋅ φ d ⋅ Lhill
8.4.2
The drainable porosity of the soil layer is calculated:
φ d = φ soil − φ fc
8.4.3
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where φd is the drainable porosity of the soil layer (mm/mm), φsoil is the total porosity of the soil layer (mm/mm), and φfc is the porosity of the soil layer filled with water when the layer is at field capacity water content (mm/mm). A soil layer is considered to be saturated whenever the water content of the layer exceeds the layer’s field capacity water content. The drainable volume of water stored in the saturated layer is calculated: SWly ,excess = SWly − FC ly
if
SWly > FCly
8.4.4
SWly ,excess = 0
if
SWly ≤ FCly
8.4.5
where SWly is the water content of the soil layer on a given day (mm H2O) and
FCly is the water content of the soil layer at field capacity (mm H2O). The net discharge at the hillslope outlet, Qlat, is given by
Qlat = 24 ⋅ H o ⋅ vlat
8.4.6
where Qlat is the water discharged from the hillslope outlet (mm H2O/day), Ho is the saturated thickness normal to the hillslope at the outlet expressed as a fraction of the total thickness (mm/mm), vlat is the velocity of flow at the outlet (mm·h-1), and 24 is a factor to convert hours to days. Velocity of flow at the outlet is defined as
vlat = K sat ⋅ sin(α hill )
8.4.7
where Ksat is the saturated hydraulic conductivity (mm·h-1) and αhill is the slope of the hillslope segment. The slope is input to SWAT as the increase in elevation per unit distance (slp) which is equivalent to tan (α hill ) . Because tan (α hill ) ≅ sin (α hill ) , equation 8.4.3 is modified to use the value for the slope as input to the model:
vlat = K sat ⋅ tan (α hill ) = K sat ⋅ slp
8.4.8
Combining equations 8.4.2 and 8.4.8 with equation 8.4.6 yields the equation
æ 2 ⋅ SWly ,excess ⋅ K sat ⋅ slp ö ÷÷ Qlat = 0.024 ⋅ çç φ d ⋅ Lhill è ø where all terms are previously defined.
8.4.9
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8.4.1 LATERAL FLOW LAG In large subbasins with a time of concentration greater than 1 day, only a portion of the lateral flow will reach the main channel on the day it is generated. SWAT incorporates a lateral flow storage feature to lag a portion of lateral flow release to the main channel. Once lateral flow is calculated, the amount of lateral flow released to the main channel is calculated: æ é − 1 ùö ′ + Qlatstor ,i −1 ) ⋅ ç1 − exp ê Qlat = (Qlat ú ÷÷ ç TT lat ûø ë è
8.4.10
where Qlat is the amount of lateral flow discharged to the main channel on a given ′ is the amount of lateral flow generated in the subbasin on a day (mm H2O), Qlat given day (mm H2O), Qlatstor,i-1 is the lateral flow stored or lagged from the previous day (mm H2O), and TTlag is the lateral flow travel time (days). The model will calculate lateral flow travel time or utilize a user-defined travel time. In the majority of cases, the user should allow the model to calculate the travel time. If drainage tiles are present in the HRU, lateral flow travel time is calculated: TTlag =
tilelag
8.4.11
24
where TTlag is the lateral flow travel time (days) and tilelag is the drain tile lag time (hrs). In HRUs without drainage tiles, lateral flow travel time is calculated: TTlag = 10.4 ⋅
Lhill K sat ,mx
8.4.12
where TTlag is the lateral flow travel time (days), Lhill is the hillslope length (m), and Ksat,mx is the highest layer saturated hydraulic conductivity in the soil profile (mm/hr). æ é − 1 ùö The expression ç1 − exp ê ú ÷÷ in equation 8.4.10 represents the fraction ç êë TTlag úû ø è
of the total available water that will be allowed to enter the reach on any one day. Figure 8-5 plots values for this expression at different values of TTlag.
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Figure 8-5: Influence of TTlag on fraction of lateral flow released.
The delay in release of lateral flow will smooth the streamflow hydrograph simulated in the reach. Table 8-5: SWAT input variables used in lateral flow calculations. Variable name Definition SLSOIL Lhill: Hillslope length (m) SOL_K Ksat: Saturated hydraulic conductivity (mm/hr) SLOPE slp: Average slope of the subbasin (m/m) LAT_TTIME TTlag: Lateral flow travel time (days) GDRAIN tilelag: Drain tile lag time (hrs)
File Name .hru .sol .hru .hru .hru
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8.5 NOMENCLATURE AWC AWCly FC FCly Ho
Available water capacity (fraction or mm H2O) Available water capacity of soil layer (fraction or mm H2O) Water content of soil profile at field capacity (fraction or mm H2O) Water content of layer ly at field capacity (fraction or mm H2O) Saturated thickness normal to the hillslope at the outlet expressed as a fraction of the total thickness (mm/mm) Ksat Saturated hydraulic conductivity (mm/hr) Lhill Hillslope length (m) MS Mass of the solids (Mg) Qlat Lateral flow; water discharged from the hillslope outlet (mm H2O/day) Qlatstor,i-1 Lateral flow stored or lagged from the previous day (mm H2O) Qsurf Accumulated runoff or rainfall excess (mm H2O) Rday Amount of rainfall on a given day (mm H2O) SATly Amount of water in the soil layer when completely saturated (mm H2O) SW Amount of water in soil profile (mm H2O) SWly Soil water content of layer ly (mm H2O) SWly,excess Drainable volume of water stored layer (mm H2O) TTlag Lateral flow travel time (days) TTperc Travel time for percolation (hrs) VA Volume of air (m3) VS Volume of solids (m3) VT Total soil volume (m3) VW Volume of water (m3) WP Water content at wilting point (fraction or mm H2O) WPly Water content of the soil layer at wilting point (fraction or mm H2O) coefcrk Adjustment coefficient for crack flow crk Total crack volume for the soil profile on a given day (mm) crkly Crack volume for the soil layer on a given day expressed as a depth (mm) crkly,d-1 Crack volume for the soil layer on the previous day (mm) crkly,i Initial crack volume calculated for the soil layer on a given day expressed as a depth (mm) crkmax Potential crack volume for the soil profile expressed as a fraction of the total volume crkmax,ly Maximum crack volume possible for the soil layer (mm) depthly Depth of the soil layer (mm) mc Percent clay content slp Average slope of the subbasin (% or m/m) tilelag Drain tile lag time (hrs). vlat Velocity of flow at the hillslope outlet (mm·h-1) wcrk,btm Amount of water flow past the lower boundary of the soil profile due to bypass flow (mm H2O) winf Amount of water entering the soil profile on a given day (mm H2O)
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wperc,ly Amount of water percolating to the underlying soil layer on a given day (mm H2O) zl,ly Depth from the surface to the bottom of the soil layer (mm)
αhill ∆t l crk ρb ρs φd φfc φsoil
Slope of the hillslope segment (degrees) Length of the time step (hrs) Lag factor for crack development during drying Bulk density (Mg m-3) Particle density (Mg m-3) Drainable porosity of the soil (mm/mm) Porosity of the soil layer filled with water when the layer is at field capacity water content (mm/mm) Porosity of the soil (mm/mm)
8.6 REFERENCES Bouma, J. and J. Loveday. 1988. Chapter 5: Characterizing soil water regimes in swelling clay soils. p. 83-96. In L.P. Wilding and R. Puentes (ed). Vertisols: their distribution, properties, classification and management. Texas A&M University Printing Center, College Station, TX. Bronswijk, J.J.B. 1989. Prediction of actual cracking and subsidence in clay soils. Soil Science 148:87-93. Bronswijk, J.J.B. 1990. Shrinkage geometry of a heavy clay soil at various stresses. Soil Science Soc. Am. J. 54:1500-1502. Dudal, R. and H. Eswaran. 1988. Chapter 1: Distribution, properties and classification of vertisols. p. 1-22. In L.P. Wilding and R. Puentes (ed). Vertisols: their distribution, properties, classification and management. Texas A&M University Printing Center, College Station, TX. Koorevaar, P., G. Menelik, and C. Dirksen. 1983. Elements of Soil Physics. Elsevier, Amsterdam. Mitchell, A.R. 1992. Shrinkage terminology: escape from ‘normalcy’. Soil. Sci. Soc. Am. J. 56:993-994.
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Sloan, P.G. and I.D. Moore. 1984. Modeling subsurface stormflow on steeply sloping forested watersheds. Water Resources Research. 20(12): 18151822. Sloan, P.G., I.D. Morre, G.B. Coltharp, and J.D. Eigel. 1983. Modeling surface and subsurface stormflow on steeply-sloping forested watersheds. Water Resources Inst. Report 142. Univ. Kentucky, Lexington.
CHAPTER 9
EQUATIONS: GROUNDWATER
Groundwater is water in the saturated zone of earth materials under pressure greater than atmospheric, i.e. positive pressure. Water enters groundwater storage primarily by infiltration/percolation, although recharge by seepage from surface water bodies may occur. Water leaves groundwater storage primarily by discharge into rivers or lakes, but it is also possible for water to move upward from the water table into the capillary fringe.
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9.1 GROUNDWATER SYSTEMS Within the saturated zone of groundwater, regions of high conductivity and low conductivity will be found. The regions of high conductivity are made up of coarse-grained particles with a large percentage of macropores that allow water to move easily. The regions of low conductivity are made up of fine-grained particles with a large percentage of mesopores and micropores that restrict the rate of water movement. An aquifer is “a geologic unit that can store enough water and transmit it at a rate fast enough to be hydrologically significant” (Dingman, 1994). An unconfined aquifer is an aquifer whose upper boundary is the water table. The water table is defined as the depth at which the water pressure equals atmospheric pressure. A confined aquifer is an aquifer bounded above and below by geologic formations whose hydraulic conductivity is significantly lower than that of the aquifer. Figure 9-1 illustrates the two types of aquifers.
Figure 9-1: Unconfined and confined aquifers (from Dingman, 1994).
Recharge to unconfined aquifers occurs via percolation to the water table from a significant portion of the land surface. In contrast, recharge to confined aquifers by percolation from the surface occurs only at the upstream end of the
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confined aquifer, where the geologic formation containing the aquifer is exposed at the earth’s surface, flow is not confined, and a water table is present. Topography exerts an important influence on groundwater flow. The flow of groundwater in an idealized hilly upland area is depicted in Figure 9-2. The landscape can be divided into areas of recharge and areas of discharge. A recharge area is defined as a portion of a drainage basin where ground water flow is directed away from the water table. A discharge area is defined as a portion of the drainage basin where ground water flow is directed toward the water table. The water table is at or near the surface in discharge areas and surface water bodies are normally located in discharge areas.
Figure 9-2: Groundwater flow net in an idealized hilly region with homogenous permeable material resting on an impermeable base (from Hubbert, 1940)
Streams may be categorized by their relationship to the groundwater system. A stream located in a discharge area that receives groundwater flow is a gaining or effluent stream (Figure 9-3a). This type of stream is characterized by an increase in discharge downstream. A stream located in a recharge area is a losing or influent stream. This type of stream is characterized by a decrease in discharge downstream. A losing stream may be connected to (Figure 9-3b) or perched above (Figure 9-3c) the groundwater flow area. A stream that simultaneously receives and loses groundwater is a flow-through stream (Figure 9-3d).
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Figure 9-3: Stream-groundwater relationships: a) gaining stream receiving water from groundwater flow; b) losing stream connected to groundwater system; c) losing stream perched above groundwater system; and d) flow-through stream (from Dingman, 1994).
SWAT simulates two aquifers in each subbasin. The shallow aquifer is an unconfined aquifer that contributes to flow in the main channel or reach of the subbasin. The deep aquifer is a confined aquifer. Water that enters the deep aquifer is assumed to contribute to streamflow somewhere outside of the watershed (Arnold et al., 1993).
9.2 SHALLOW AQUIFER The water balance for the shallow aquifer is: aq sh ,i = aqsh ,i −1 + wrchrg − Q gw − wrevap − wdeep − w pump ,sh
9.2.1
where aqsh,i is the amount of water stored in the shallow aquifer on day i (mm H2O), aqsh,i-1 is the amount of water stored in the shallow aquifer on day i-1 (mm H2O), wrchrg is the amount of recharge entering the aquifer on day i (mm H2O), Qgw is the groundwater flow, or base flow, into the main channel on day i (mm H2O), wrevap is the amount of water moving into the soil zone in response to water deficiencies on day i (mm H2O), wdeep is the amount of water percolating from the
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shallow aquifer into the deep aquifer on day i (mm H2O), and wpump,sh is the amount of water removed from the shallow aquifer by pumping on day i (mm H2O).
9.2.1 RECHARGE Water that moves past the lowest depth of the soil profile by percolation or bypass flow enters and flows through the vadose zone before becoming shallow aquifer recharge. The lag between the time that water exits the soil profile and enters the shallow aquifer will depend on the depth to the water table and the hydraulic properties of the geologic formations in the vadose and groundwater zones. An exponential decay weighting function proposed by Venetis (1969) and used by Sangrey et al. (1984) in a precipitation/groundwater response model is utilized in SWAT to account for the time delay in aquifer recharge once the water exits the soil profile. The delay function accommodates situations where the recharge from the soil zone to the aquifer is not instantaneous, i.e. 1 day or less. The recharge to the aquifer on a given day is calculated: wrchrg ,i = (1 − exp[− 1 δ gw ]) ⋅ wseep + exp[− 1 δ gw ]⋅ wrchrg ,i −1
9.2.2
where wrchrg,i is the amount of recharge entering the aquifer on day i (mm H2O),
δgw is the delay time or drainage time of the overlying geologic formations (days), wseep is the total amount of water exiting the bottom of the soil profile on day i (mm H2O), and wrchrg,i-1 is the amount of recharge entering the aquifer on day i-1 (mm H2O). The total amount of water exiting the bottom of the soil profile on day i is calculated: wseep = w perc ,ly =n + wcrk ,btm
9.2.3
where wseep is the total amount of water exiting the bottom of the soil profile on day i (mm H2O), wperc,ly=n is the amount of water percolating out of the lowest layer, n, in the soil profile on day i (mm H2O), and wcrk,btm is the amount of water flow past the lower boundary of the soil profile due to bypass flow on day i (mm H2O).
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The delay time, δgw, cannot be directly measured. It can be estimated by simulating aquifer recharge using different values for δgw and comparing the simulated variations in water table level with observed values. Johnson (1977) developed a simple program to iteratively test and statistically evaluate different delay times for a watershed. Sangrey et al. (1984) noted that monitoring wells in the same area had similar values for δgw, so once a delay time value for a geomorphic area is defined, similar delay times can be used in adjoining watersheds within the same geomorphic province.
9.2.2 GROUNDWATER/BASE FLOW The shallow aquifer contributes base flow to the main channel or reach within the subbasin. Base flow is allowed to enter the reach only if the amount of water stored in the shallow aquifer exceeds a threshold value specified by the user, aqshthr,q. The steady-state response of groundwater flow to recharge is (Hooghoudt, 1940): Q gw =
8000 ⋅ K sat ⋅ hwtbl 2 Lgw
9.2.4
where Qgw is the groundwater flow, or base flow, into the main channel on day i (mm H2O), Ksat is the hydraulic conductivity of the aquifer (mm/day), Lgw is the distance from the ridge or subbasin divide for the groundwater system to the main channel (m), and hwtbl is the water table height (m). Water table fluctuations due to non-steady-state response of groundwater flow to periodic recharge is calculated (Smedema and Rycroft, 1983):
dhwtbl wrchrg − Q gw = 800 ⋅ µ dt where
9.2.5
dhwtbl is the change in water table height with time (mm/day), wrchrg is the dt
amount of recharge entering the aquifer on day i (mm H2O), Qgw is the groundwater flow into the main channel on day i (mm H2O), and µ is the specific yield of the shallow aquifer (m/m).
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Assuming that variation in groundwater flow is linearly related to the rate of change in water table height, equations 9.2.5 and 9.2.4 can be combined to obtain: dQ gw dt
= 10 ⋅
K sat ⋅ (wrchrg − Q gw ) = α gw ⋅ (wrchrg − Q gw ) µ ⋅ Lgw 2
9.2.6
where Qgw is the groundwater flow into the main channel on day i (mm H2O), Ksat is the hydraulic conductivity of the aquifer (mm/day), µ is the specific yield of the shallow aquifer (m/m), Lgw is the distance from the ridge or subbasin divide for the groundwater system to the main channel (m), wrchrg is the amount of recharge entering the aquifer on day i (mm H2O) and αgw is the baseflow recession constant or constant of proportionality. Integration of equation 9.2.6 and rearranging to solve for Qgw yields:
Q gw,i = Q gw,i −1 ⋅ exp[− α gw ⋅ ∆t ] + wrchrg ⋅ (1 − exp[− α gw ⋅ ∆t ])
9.2.7
where Qgw,i is the groundwater flow into the main channel on day i (mm H2O), Qgw,i-1 is the groundwater flow into the main channel on day i-1 (mm H2O), αgw is the baseflow recession constant, ∆t is the time step (1 day), and wrchrg is the amount of recharge entering the aquifer on day i (mm H2O). The baseflow recession constant, αgw, is a direct index of groundwater flow response to changes in recharge (Smedema and Rycroft, 1983). Values vary from 0.1-0.3 for land with slow response to recharge to 0.9-1.0 for land with a rapid response. Although the baseflow recession constant may be calculated, the best estimates are obtained by analyzing measured streamflow during periods of no recharge in the watershed. When the shallow aquifer receives no recharge, equation 9.2.7 simplifies to:
Q gw = Qgw,0 ⋅ exp[− α gw ⋅ t ]
9.2.8
where Qgw is the groundwater flow into the main channel at time t (mm H2O), Qgw,0 is the groundwater flow into the main channel at the beginning of the recession (time t=0) (mm H2O), αgw is the baseflow recession constant, and t is
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the time lapsed since the beginning of the recession (days). The baseflow recession constant is measured by rearranging equation 9.2.8.
α gw =
é Q gw, N ù 1 ⋅ ln ê ú N êë Q gw,0 ûú
9.2.9
where αgw is the baseflow recession constant, N is the time lapsed since the start of the recession (days), Qgw,N is the groundwater flow on day N (mm H2O), Qgw,0 is the groundwater flow at the start of the recession (mm H2O). It is common to find the baseflow days reported for a stream gage or watershed. This is the number of days for base flow recession to decline through one log cycle. When baseflow days are used, equation 9.2.9 can be further simplified:
α gw =
é Q gw , N ù 1 1 2 .3 ⋅ ln ê ⋅ ln[10] = ú= N BFD êë Q gw,0 ûú BFD
9.2.10
where αgw is the baseflow recession constant, and BFD is the number of baseflow days for the watershed.
9.2.3 REVAP Water may move from the shallow aquifer into the overlying unsaturated zone. In periods when the material overlying the aquifer is dry, water in the capillary fringe that separates the saturated and unsaturated zones will evaporate and diffuse upward. As water is removed from the capillary fringe by evaporation, it is replaced by water from the underlying aquifer. Water may also be removed from the aquifer by deep-rooted plants which are able to uptake water directly from the aquifer. SWAT models the movement of water into overlying unsaturated layers as a function of water demand for evapotranspiration. To avoid confusion with soil evaporation and transpiration, this process has been termed ‘revap’. This process is significant in watersheds where the saturated zone is not very far below the surface or where deep-rooted plants are growing. Because the type of plant cover will affect the importance of revap in the water balance, the parameters governing revap are usually varied by land use. Revap is allowed to occur only if the amount
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of water stored in the shallow aquifer exceeds a threshold value specified by the user, aqshthr,rvp. The maximum amount of water than will be removed from the aquifer via ‘revap’ on a given day is: wrevap ,mx = β rev ⋅ Eo
9.2.11
where wrevap,mx is the maximum amount of water moving into the soil zone in response to water deficiencies (mm H2O), βrev is the revap coefficient, and Eo is the potential evapotranspiration for the day (mm H2O). The actual amount of revap that will occur on a given day is calculated: wrevap = 0
if aq sh ≤ aqshthr ,rvp
wrevap = wrevap ,mx − aqshthr ,rvp
if aqshthr ,rvp < aqsh < (aqshthr ,rvp + wrevap ,mx ) 9.2.13
wrevap = wrevap ,mx
if aqsh ≥ (aqshthr ,rvp + wrevap ,mx )
9.2.12
9.2.14
where wrevap is the actual amount of water moving into the soil zone in response to water deficiencies (mm H2O), wrevap,mx is the maximum amount of water moving into the soil zone in response to water deficiencies (mm H2O), aqsh is the amount of water stored in the shallow aquifer at the beginning of day i (mm H2O) and aqshthr,rvp is the threshold water level in the shallow aquifer for revap or percolation to deep aquifer to occur (mm H2O).
9.2.4 PERCOLATION TO DEEP AQUIFER A fraction of the total daily recharge can be routed to the deep aquifer. Percolation to the deep aquifer is allowed to occur only if the amount of water stored in the shallow aquifer exceeds a threshold value specified by the user, aqshthr,rvp. The maximum amount of water than will be removed from the shallow aquifer via percolation to the deep aquifer on a given day is: wdeep ,mx = β deep ⋅ wrchrg
9.2.15
where wdeep,mx is the maximum amount of water moving into the deep aquifer on day i (mm H2O), βdeep is the aquifer percolation coefficient, and wrchrg is the amount of recharge entering the aquifer on day i (mm H2O). The actual amount of percolation to the deep aquifer that will occur on a given day is calculated:
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wdeep = 0
if aq sh ≤ aqshthr ,rvp
wdeep = wdeep ,mx − aqshthr ,rvp
if aqshthr ,rvp < aqsh < (aqshthr ,rvp + wrevap ,mx ) 9.2.13
wdeep = wdeep ,mx
if aqsh ≥ (aqshthr ,rvp + wrevap ,mx )
9.2.12
9.2.14
where wdeep is the actual amount of water moving into the deep aquifer on day i (mm H2O), wdeep,mx is the maximum amount of water moving into the deep aquifer on day i (mm H2O), aqsh is the amount of water stored in the shallow aquifer at the beginning of day i (mm H2O) and aqshthr,rvp is the threshold water level in the shallow aquifer for revap or percolation to deep aquifer to occur (mm H2O).
9.2.5 PUMPING If the shallow aquifer is specified as the source of irrigation water or water removed for use outside the watershed, the model will allow an amount of water up to the total volume of the shallow aquifer to be removed on any given day. Detailed information on water management may be found in Chapter 21.
9.2.6 GROUNDWATER HEIGHT Although SWAT does not currently print groundwater height in the output files, the water table height is updated daily by the model. Groundwater height is related to groundwater flow by equation 9.2.4. Q gw =
8000 ⋅ K sat 8000 ⋅ µ 10 ⋅ K sat ⋅ hwtbl = ⋅ ⋅ hwtbl = 800 ⋅ µ ⋅ α gw ⋅ hwtbl 2 10 Lgw µ ⋅ Lgw 2
9.2.15
where Qgw is the groundwater flow into the main channel on day i (mm H2O), Ksat is the hydraulic conductivity of the aquifer (mm/day), Lgw is the distance from the ridge or subbasin divide for the groundwater system to the main channel (m), hwtbl is the water table height (m), µ is the specific yield of the shallow aquifer (m/m), and αgw is the baseflow recession constant. Substituting this definition for Qgw into equation 9.2.7 gives hwtbl ,i = hwtbl ,i −1 ⋅ exp[− α gw ⋅ ∆t ] +
wrchrg ⋅ (1 − exp[− α gw ⋅ ∆t ]) 800 ⋅ µ ⋅ α gw
9.2.16
where hwtbl,i is the water table height on day i (m), hwtbl,i-1 is the water table height on day i-1 (m), αgw is the baseflow recession constant, ∆t is the time step (1 day),
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wrchrg is the amount of recharge entering the aquifer on day i (mm H2O), and µ is the specific yield of the shallow aquifer (m/m). Table 9-1: SWAT input variables used in shallow aquifer calculations. Variable name Definition GW_DELAY δgw: Delay time for aquifer recharge (days) GWQMN aqshthr,q: Threshold water level in shallow aquifer for base flow (mm H2O) ALPHA_BF αgw: Baseflow recession constant REVAPMN aqshthr,rvp: Threshold water level in shallow aquifer for revap or percolation to deep aquifer (mm H2O) GW_REVAP βrev: Revap coefficient RCHRG_DP βdeep: Aquifer percolation coefficient GW_SPYLD µ: Specific yield of the shallow aquifer (m/m)
File Name .gw .gw .gw .gw .gw .gw .gw
9.3 DEEP AQUIFER The water balance for the deep aquifer is: aqdp ,i = aqdp ,i −1 + wdeep − w pump ,dp
9.3.1
where aqdp,i is the amount of water stored in the deep aquifer on day i (mm H2O), aqdp,i-1 is the amount of water stored in the deep aquifer on day i-1 (mm H2O), wdeep is the amount of water percolating from the shallow aquifer into the deep aquifer on day i (mm H2O), and wpump,dp is the amount of water removed from the deep aquifer by pumping on day i (mm H2O). The amount of water percolating into the deep aquifer is calculated with the equations reviewed in section 9.2.4. If the deep aquifer is specified as the source of irrigation water or water removed for use outside the watershed, the model will allow an amount of water up to the total volume of the deep aquifer to be removed on any given day. Water entering the deep aquifer is not considered in future water budget calculations and can be considered to be lost from the system.
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9.4 NOMENCLATURE BFD Eo Ksat Lgw N Qgw Qgw,0 Qgw,N
Number of baseflow days for the watershed Potential evapotranspiration (mm d-1) Hydraulic conductivity of the aquifer (mm/day) Distance from the ridge or subbasin divide for the groundwater system to the main channel (m) Time lapsed since the start of the recession (days) Groundwater flow, or base flow, into the main channel (mm H2O) Groundwater flow at the start of the recession (mm H2O) Groundwater flow on day N (mm H2O)
aqdp Amount of water stored in the deep aquifer (mm H2O) aqsh Amount of water stored in the shallow aquifer (mm H2O) aqshthr,q Threshold water level in shallow aquifer for base flow (mm H2O) aqshthr,rvp Threshold water level in shallow aquifer for revap or percolation to deep aquifer (mm H2O) hwtbl Water table height (m) wcrk,btm Amount of water flow past the lower boundary of the soil profile due to bypass flow (mm H2O) wdeep Amount of water percolating from the shallow aquifer into the deep aquifer (mm H2O) wdeep,mx Maximum amount of water moving into the deep aquifer (mm H2O) wpump,dp Amount of water removed from the deep aquifer by pumping (mm H2O) wpump,sh Amount of water removed from the shallow aquifer by pumping (mm H2O) wrchrg Amount of water entering the aquifer via recharge (mm H2O) wrevap Amount of water moving into the soil zone in response to water deficiencies (mm H2O) wrevap,mx Maximum amount of water moving into the soil zone in response to water deficiencies on day i (mm H2O) wseep Total amount of water exiting the bottom of the soil profile (mm H2O)
αgw βdeep βrev δgw µ
Baseflow recession constant Aquifer percolation coefficient Revap coefficient Delay time or drainage time for aquifer recharge (days) Specific yield of the shallow aquifer (m/m)
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9.5 REFERENCES Arnold, J.G., P.M. Allen, and G. Bernhardt. 1993. A comprehensive surfacegroundwater flow model. Journal of Hydrology 142: 47-69. Dingman, S.L. 1994. Physical hydrology. Prentice-Hall, Inc., Englewood Cliffs, NJ. Hooghoudt, S.B. 1940. Bijdrage tot de kennis van enige natuurkundige grootheden van de grond. Versl. Landbouwkd. Onderz. 46: 515-707. Hubbert, M.K. 1940. The theory of groundwater motion. Journal of Geology 48: 785-944. Johnson, K.H. 1977. A predictive method for ground water levels. Master’s Thesis, Cornell University, Ithica, N.Y. Sangrey, D.A., K.O. Harrop-Williams, and J.A. Klaiber. 1984. Predicting groundwater response to precipitation. ASCE J. Geotech. Eng. 110(7): 957-975. Smedema, L.K. and D.W. Rycroft. 1983. Land drainage—planning and design of agricultural drainage systems, Cornell University Press, Ithica, N.Y. Venetis, C. 1969. A study of the recession of unconfined aquifers. Bull. Int. Assoc. Sci. Hydrol. 14(4): 119-125.
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NUTRIENTS/PESTICIDES The fate and transport of nutrients and pesticides in a watershed depend on the transformations the compounds undergo in the soil environment. SWAT models the complete nutrient cycle for nitrogen and phosphorus as well as the degradation of any pesticides applied in an HRU. The following three chapters review the methodology used by SWAT to simulate nutrient and pesticide processes in the soil.
CHAPTER 10
EQUATIONS: NITROGEN
The complexity of the nitrogen cycle and nitrogen’s importance in plant growth have made this element the subject of much research. The nitrogen cycle is a dynamic system that includes the water, atmosphere and soil. Plants require nitrogen more than any other essential element, excluding carbon, oxygen and hydrogen.
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10.1 NITROGEN CYCLE The three major forms of nitrogen in mineral soils are organic nitrogen associated with humus, mineral forms of nitrogen held by soil colloids, and mineral forms of nitrogen in solution. Nitrogen may be added to the soil by fertilizer, manure or residue application, fixation by symbiotic or nonsymbiotic bacteria, and rain. Nitrogen is removed from the soil by plant uptake, leaching, volatilization, denitrification and erosion. Figure 10-1 shows the major components of the nitrogen cycle.
Figure 10-1: The nitrogen cycle
Nitrogen is considered to be an extremely reactive element. The highly reactive nature of nitrogen results from its ability to exist in a number of valance states. The valence state or oxidation state describes the number of electrons orbiting the nucleus of the nitrogen atom relative to the number present in an
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electronically neutral atom. The valence state will be positive as the atom looses electrons and will be negative as the atom gains electrons. Examples of nitrogen in different valence states are: most oxidized
+5
NO -3
nitrate
NO 2 NO -2 NO N 2O N2 NH 4 OH N2H4
nitrogen dioxide nitrite nitrogen monoxide (gas) nitrous oxide (laughing gas) N2 gas or elemental N hydroxylamine hydrozine
most reduced
+4 +3 +2 +1 0 -1 -2 -3
NH3 or NH 4+ ammonia gas or ammonium
The ability of nitrogen to vary its valence state makes it a highly mobile element. Predicting the movement of nitrogen between the different pools in the soil is critical to the successful management of this element in the environment. SWAT monitors five different pools of nitrogen in the soil (Figure 10-2). Two pools are inorganic forms of nitrogen, NH4+ and NO -3 , while the other three pools are organic forms of nitrogen. Fresh organic N is associated with crop residue and microbial biomass while the active and stable organic N pools are associated with the soil humus. The organic nitrogen associated with humus is partitioned into two pools to account for the variation in availability of humic substances to mineralization.
Figure 10-2: SWAT soil nitrogen pools and processes that move nitrogen in and out of pools.
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10.1.1 INITIALIZATION OF SOIL NITROGEN LEVELS Users may define the amount of nitrate and organic nitrogen contained in humic substances for all soil layers at the beginning of the simulation. If the user does not specify initial nitrogen concentrations, SWAT will initialize levels of nitrogen in the different pools. Initial nitrate levels in the soil are varied by depth using the relationship: æ −z ö NO3conc , z = 7 ⋅ expç ÷ è 1000 ø
10.1.1
where NO3conc,z is the concentration of nitrate in the soil at depth z (mg/kg or ppm), and z is the depth from the soil surface (mm). The nitrate concentration with depth calculated from equation 10.1.1 is displayed in Figure 10-3. The nitrate concentration for a layer is calculated by solving equation 10.1.1 for the horizon’s lower boundary depth.
Figure 10-3: Nitrate concentration with depth.
Organic nitrogen levels are assigned assuming that the C:N ratio for humic materials is 14:1. The concentration of humic organic nitrogen in a soil layer is calculated: æ orgCly ö ÷÷ orgN hum ,ly = 10 4 ⋅ çç è 14 ø
10.1.2
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179
where orgNhum,ly is the concentration of humic organic nitrogen in the layer (mg/kg or ppm), and orgCly is the amount of organic carbon in the layer (%). The humic organic N is partitioned between the active and stable pools using the following equations: orgN act ,ly = orgN hum ,ly ⋅ fractN
10.1.3
orgN sta ,ly = orgN hum ,ly ⋅ (1 − fractN )
10.1.4
where orgNact,ly is the concentration of nitrogen in the active organic pool (mg/kg), orgNhum,ly is the concentration of humic organic nitrogen in the layer (mg/kg), fractN is the fraction of humic nitrogen in the active pool, and orgNsta,ly is the concentration of nitrogen in the stable organic pool (mg/kg). The fraction of humic nitrogen in the active pool, fractN, is set to 0.02. Nitrogen in the fresh organic pool is set to zero in all layers except the top 10 mm of soil. In the top 10 mm, the fresh organic nitrogen pool is set to 0.15% of the initial amount of residue on the soil surface. orgN frsh ,surf = 0.0015 ⋅ rsd surf
10.1.5
where orgNfrsh,surf is the nitrogen in the fresh organic pool in the top 10 mm (kg N/ha), and rsdsurf is material in the residue pool for the top 10 mm of soil (kg/ha). The ammonium pool for soil nitrogen, NH4ly, is initialized to 0 ppm. While SWAT allows nutrient levels to be input as concentrations, it performs all calculations on a mass basis. To convert a concentration to a mass, the concentration is multiplied by the bulk density and depth of the layer and divided by 100: conc N ⋅ ρ b ⋅ depthly 100
=
kg N ha
10.1.6
where concN is the concentration of nitrogen in a layer (mg/kg or ppm), ρb is the bulk density of the layer (Mg/m3), and depthly is the depth of the layer (mm).
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Table 10-1: SWAT input variables that pertain to nitrogen pools. Variable Name SOL_NO3 SOL_ORGN RSDIN SOL_BD SOL_CBN
Definition NO3conc,ly: Initial NO3 concentration in soil layer (mg/kg or ppm) orgNhum,ly: Initial humic organic nitrogen in soil layer (mg/kg or ppm) rsdsurf: Material in the residue pool for the top 10mm of soil (kg ha-1) ρb: Bulk density of the layer (Mg/m3) orgCly: Amount of organic carbon in the layer (%)
Input File .chm .chm .hru .sol .sol
10.2 MINERALIZATION & DECOMPOSITION / IMMOBILIZATION Decomposition is the breakdown of fresh organic residue into simpler organic components. Mineralization is the microbial conversion of organic, plantunavailable nitrogen to inorganic, plant-available nitrogen. Immobilization is the microbial conversion of plant-available inorganic soil nitrogen to plantunavailable organic nitrogen. Bacteria decompose organic material to obtain energy for growth processes. Plant residue is broken down into glucose which is then converted to energy: energy released C 6 H 12 O 6 + O 2 → 6CO 2 + 6 H 2 O
The energy released by the conversion of glucose to carbon dioxide and water is used for various cell processes, including protein synthesis. Protein synthesis requires nitrogen. If the residue from which the glucose is obtained contains enough nitrogen, the bacteria will use nitrogen from the organic material to meet the demand for protein synthesis. If the nitrogen content of the residue is too low to meet the bacterial demand for nitrogen, the bacteria will use NH4+ and NO -3 from the soil solution to meet its needs. If the nitrogen content of the residue exceeds the bacterial demand for nitrogen, the bacterial will release the excess nitrogen into soil solution as NH4+. A general relationship between C:N ratio and mineralization/immobilization is: C:N > 30:1
immobilization occurs, a net decrease in soil NH4+ and NO -3
20:1 ≤ C:N ≤ 30:1
expect no net change; immobilization mineralization processes are at equilibrium
and
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C:N < 20:1
181
mineralization occurs, a net gain in soil NH4+ and NO -3
The nitrogen mineralization algorithms in SWAT are net mineralization algorithms which incorporate immobilization into the equations. The algorithms were adapted from the PAPRAN mineralization model (Seligman and van Keulen, 1981). Two sources are considered for mineralization: the fresh organic N pool associated with crop residue and microbial biomass and the active organic N pool associated with soil humus. Mineralization and decomposition are allowed to occur only if the temperature of the soil layer is above 0°C. Mineralization and decomposition are dependent on water availability and temperature. Two factors are used in the mineralization and decomposition equations to account for the impact of temperature and water on these processes. The nutrient cycling temperature factor is calculated:
γ tmp ,ly = 0.9 ⋅
Tsoil ,ly
Tsoil ,ly + exp[9.93 − 0.312 ⋅ Tsoil ,ly ]
+ 0 .1
10.2.1
where γtmp,ly is the nutrient cycling temperature factor for layer ly, and Tsoil,ly is the temperature of layer ly (°C). The nutrient cycling temperature factor is never allowed to fall below 0.1. The nutrient cycling water factor is calculated:
γ sw,ly =
SWly FCly
10.2.2
where γsw,ly is the nutrient cycling water factor for layer ly, SWly is the water content of layer ly on a given day (mm H2O), and FCly is the water content of layer ly at field capacity (mm H2O). The nutrient cycling water factor is never allowed to fall below 0.05.
10.2.1 HUMUS MINERALIZATION Nitrogen is allowed to move between the active and stable organic pools in the humus fraction. The amount of nitrogen transferred from one pool to the other is calculated: æ 1 ö N trns ,ly = β trns ⋅ orgN act ,ly ⋅ çç − 1÷÷ − orgN sta ,ly è fractN ø
10.2.3
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Ntrns,ly is the amount of nitrogen transferred between the active and stable organic pools (kg N/ha), βtrns is the rate constant (1×10-5), orgNact,ly is the amount of nitrogen in the active organic pool (kg N/ha), fractN is the fraction of humic nitrogen in the active pool (0.02), and orgNsta,ly is the amount of nitrogen in the stable organic pool (kg N/ha). When Ntrns,ly is positive, nitrogen is moving from the active organic pool to the stable organic pool. When Ntrns,ly is negative, nitrogen is moving from the stable organic pool to the active organic pool. Mineralization from the humus active organic N pool is calculated: N mina,ly = β min ⋅ (γ tmp ,ly ⋅ γ sw,ly ) ⋅ orgN act ,ly 12
10.2.4
where Nmina,ly is the nitrogen mineralized from the humus active organic N pool (kg N/ha), βmin is the rate coefficient for mineralization of the humus active organic nutrients, γtmp,ly is the nutrient cycling temperature factor for layer ly, γsw,ly is the nutrient cycling water factor for layer ly, orgNact,ly is the amount of nitrogen in the active organic pool (kg N/ha). Nitrogen mineralized from the humus active organic pool is added to the nitrate pool in the layer.
10.2.2 RESIDUE DECOMPOSITION & MINERALIZATION Decomposition and mineralization of the fresh organic nitrogen pool is allowed only in the first soil layer. Decomposition and mineralization are controlled by a decay rate constant that is updated daily. The decay rate constant is calculated as a function of the C:N ratio and C:P ratio of the residue, temperature and soil water content. The C:N ratio of the residue is calculated:
ε C:N =
0.58 ⋅ rsd ly orgN frsh ,ly + NO3ly
10.2.5
where εC:N is the C:N ratio of the residue in the soil layer, rsdly is the residue in layer ly (kg/ha), 0.58 is the fraction of residue that is carbon, orgNfrsh,ly is the nitrogen in the fresh organic pool in layer ly (kg N/ha), and NO3ly is the amount of nitrate in layer ly (kg N/ha). The C:P ratio of the residue is calculated:
CHAPTER 10: EQUATIONS—NITROGEN
ε C :P =
0.58 ⋅ rsd ly orgPfrsh ,ly + Psolution,ly
183
10.2.6
where εC:P is the C:P ratio of the residue in the soil layer, rsdly is the residue in layer ly (kg/ha), 0.58 is the fraction of residue that is carbon, orgPfrsh,ly is the phosphorus in the fresh organic pool in layer ly (kg P/ha), and Psolution,ly is the amount of phosphorus in solution in layer ly (kg P/ha). The decay rate constant defines the fraction of residue that is decomposed. The decay rate constant is calculated:
δ ntr ,ly = β rsd ⋅ γ ntr ,ly ⋅ (γ tmp ,ly ⋅ γ sw,ly )1 2
10.2.7
where δntr,ly is the residue decay rate constant, βrsd is the rate coefficient for mineralization of the residue fresh organic nutrients, γntr,ly is the nutrient cycling residue composition factor for layer ly, γtmp,ly is the nutrient cycling temperature factor for layer ly, and γsw,ly is the nutrient cycling water factor for layer ly. The nutrient cycling residue composition factor is calculated:
γ ntr ,ly
(ε C:N − 25)ù ì é ï exp ê− 0.693 ⋅ úû 25 ë ï ï (ε − 200)ù ï é = min íexp ê− 0.693 ⋅ C:P úû 200 ï ë ï 1.0 ï ï î
10.2.8
where γntr,ly is the nutrient cycling residue composition factor for layer ly, εC:N is the C:N ratio on the residue in the soil layer, and εC:P is the C:P ratio on the residue in the soil layer. Mineralization from the residue fresh organic N pool is then calculated: N minf,ly = 0.8 ⋅ δ ntr ,ly ⋅ orgN frsh ,ly
10.2.9
where Nminf,ly is the nitrogen mineralized from the fresh organic N pool (kg N/ha),
δntr,ly is the residue decay rate constant, and orgNfrsh,ly is the nitrogen in the fresh organic pool in layer ly (kg N/ha). Nitrogen mineralized from the fresh organic pool is added to the nitrate pool in the layer.
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Decomposition from the residue fresh organic N pool is calculated: N dec,ly = 0.2 ⋅ δ ntr ,ly ⋅ orgN frsh ,ly
10.2.9
where Ndec,ly is the nitrogen decomposed from the fresh organic N pool (kg N/ha),
δntr,ly is the residue decay rate constant, and orgNfrsh,ly is the nitrogen in the fresh organic pool in layer ly (kg N/ha). Nitrogen decomposed from the fresh organic pool is added to the humus active organic pool in the layer. Table 10-2: SWAT input variables that pertain to mineralization. Variable Name CMN RSDCO RSDCO_PL
Definition βmin: Rate coefficient for mineralization of the humus active organic nutrients βrsd: Rate coefficient for mineralization of the residue fresh organic nutrients βrsd: Rate coefficient for mineralization of the residue fresh organic nutrients
Input File .bsn .bsn crop.dat
10.3 NITRIFICATION & AMMONIA VOLATILIZATION Nitrification is the two-step bacterial oxidation of NH4+ to NO -3 . −
−12 e → 2 NO -2 + 2 H 2 O + 4H + step 1: 2 NH +4 + 3O 2
(Nitrosomonas)
-4e 2NO -3 step 2: 2 NO -2 + O 2 →
(Nitrobacter)
-
Ammonia volatilization is the gaseous loss of NH3 that occurs when ammonium, NH4+, is surface applied to a calcareous soil or when urea, (NH2)2CO, is surface applied to any soil. NH4+ surface applied to a calcareous soil: →(NH 4 )2 CO 3 + CaX 2 step 1: CaCO 3 + 2 NH 4+ X ←
step 2: (NH 4 )CO 3 ← → 2NH 3 + CO 2 + H 2 O Urea surface applied to any soil: urease enzyme →(NH 4 )2 CO 3 step 1: (NH 2 )2 CO + 2 H 2 O ← step 2: (NH 4 )2 CO 3 ← → 2NH 3 + CO 2 + H 2 O
SWAT simulates nitrification and ammonia volatilization using a combination of the methods developed by Reddy et al. (1979) and Godwin et al. (1984). The total amount of nitrification and ammonia volatilization is calculated, and then partitioned between the two processes. Nitrification is a function of soil
CHAPTER 10: EQUATIONS—NITROGEN
185
temperature and soil water content while ammonia volatilization is a function of soil
temperature
and
depth.
Three
coefficients
are
used
in
the
nitrification/volatilization algorithms to account for the impact of these parameters. Nitrification/volatilization occurs only when the temperature of the soil layer exceeds 5°C. The nitrification/volatilization temperature factor is calculated:
ηtmp ,ly = 0.41 ⋅
(T
soil ,ly
− 5)
if Tsoil ,ly > 5
10
10.3.1
where ηtmp,ly is the nitrification/volatilization temperature factor, and Tsoil,ly is the temperature of layer ly (°C). The nitrification soil water factor is calculated:
η sw,ly =
SWly − WPly
0.25 ⋅ (FCly − WPly )
if SWly − WPly < 0.25 ⋅ (FC ly − WPly ) 10.3.2 if SWly − WPly ≥ 0.25 ⋅ (FC ly − WPly ) 10.3.3
η sw,ly = 1.0
where ηsw,ly is the nitrification soil water factor, SWly is the soil water content of layer ly on a given day (mm H2O), WPly is the amount of water held in the soil layer at wilting point water content (mm H2O), and FCly is the amount of water held in the soil layer at field capacity water content (mm H2O). The volatilization depth factor is calculated:
η midz ,ly = 1 −
z mid ,ly
z mid ,ly + exp[4.706 − 0.305 ⋅ z mid ,ly ]
10.3.4
where ηmidz,ly is the volatilization depth factor, and zmid,ly is the depth from the soil surface to the middle of the layer (mm). The impact of environmental factors on nitrification and ammonia volatilization in a given layer is defined by the nitrification regulator and volatilization regulator. The nitrification regulator is calculated:
η nit ,ly = η tmp ,ly ⋅ η sw,ly
10.3.5
and the volatilization regulator is calculated:
η vol ,ly = η tmp ,ly ⋅ η midz ,ly
10.3.6
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SWAT USER'S MANUAL, VERSION 2000
where ηnit,ly is the nitrification regulator, ηvol,ly is the volatilization regulator, ηtmp,ly is the nitrification/volatilization temperature factor, ηsw,ly is the nitrification soil water factor, and ηmidz,ly is the volatilization depth factor. The total amount of ammonium lost to nitrification and volatilization is calculated using a first-order kinetic rate equation (Reddy et al., 1979): N nit vol ,ly = NH4ly ⋅ (1 − exp[− η nit ,ly − η vol ,ly ])
10.3.7
where N nit vol ,ly is the amount of ammonium converted via nitrification and volatilization in layer ly (kg N/ha), NH4ly is the amount of ammonium in layer ly (kg N/ha), ηnit,ly is the nitrification regulator, and ηvol,ly is the volatilization regulator. N nit vol ,ly
To partition
between nitrification and volatilization, the
expression by which NH4ly is multiplied in equation 10.3.7, is solved using each regulator individually to obtain a fraction of ammonium removed by each process:
frnit ,ly = 1 − exp[− η nit ,ly ]
10.3.8
frvol ,ly = 1 − exp[− η vol ,ly ]
10.3.9
where frnit,ly is the estimated fraction of nitrogen lost by nitrification, frvol,ly is the estimated fraction of nitrogen lost by volatilization, ηnit,ly is the nitrification regulator, and ηvol,ly is the volatilization regulator. The amount of nitrogen removed from the ammonium pool by nitrification is then calculated: N nit ,ly =
( fr
frnit ,ly
nit ,ly
+ frvol ,ly )
⋅ N nit vol ,ly
10.3.10
and the amount of nitrogen removed from the ammonium pool by volatilization is: N vol ,ly =
( fr
frvol ,ly
nit ,ly
+ frvol ,ly )
⋅ N nit vol ,ly
10.3.11
where Nnit,ly is the amount of nitrogen converted from NH4+ to NO -3 in layer ly (kg N/ha), Nvol,ly is the amount of nitrogen converted from NH4+ to NH3 in layer ly (kg
CHAPTER 10: EQUATIONS—NITROGEN
187
N/ha), frnit,ly is the estimated fraction of nitrogen lost by nitrification, frvol,ly is the estimated fraction of nitrogen lost by volatilization, and N nit vol ,ly is the amount of ammonium converted via nitrification and volatilization in layer ly (kg N/ha)
10.4 DENITRIFICATION Denitrification is the bacterial reduction of nitrate, NO -3 , to N2 or N2O gases under anaerobic (reduced) conditions. Denitrification is a function of water content, temperature, presence of a carbon source and nitrate. In general, when the water-filled porosity is greater than 60% denitrification will be observed in a soil. As soil water content increases, anaerobic conditions develop due to the fact that oxygen diffuses through water 10,000 times slower than through air. Because the rate of oxygen diffusion through water slows as the water temperature increases, temperature will also influence denitrification. Cropping systems where water is ponded, such as rice, can lose a large fraction of fertilizer by denitrification. For a regular cropping system, an estimated 10-20% of nitrogen fertilizer may be lost to denitrification. Under a rice cropping system, 50% of nitrogen fertilizer may be lost to denitrification. In a flooded cropping system, the depth of water plays an important role because it controls the amount of water oxygen has to diffuse through to reach the soil. SWAT determines the amount of nitrate lost to denitrification with the equation:
N denit ,ly = NO3ly ⋅ (1 − exp[− 1.4 ⋅ γ tmp ,ly ⋅ orgCly ]) if γ sw,ly ≥ 0.95
10.4.1
if γ sw,ly < 0.95
10.4.2
N denit ,ly = 0.0
where Ndenit,ly is the amount of nitrogen lost to denitrification (kg N/ha), NO3ly is the amount of nitrate in layer ly (kg N/ha), γtmp,ly is the nutrient cycling temperature factor for layer ly calculated with equation 10.2.1, γsw,ly is the nutrient cycling water factor for layer ly calculated with equation 10.2.2, orgCly is the amount of organic carbon in the layer (%).
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Table 10-3: SWAT input variables that pertain to denitrification. Variable Name SOL_CBN
Input File .sol
Definition orgCly: Amount of organic carbon in the layer (%)
10.5 NITROGEN IN RAINFALL Lightning discharge converts atmospheric N2 to nitric acid which can then be transferred to the soil with precipitation. The chemical steps involved are: arc of electricity step 1: N 2 + O 2 → 2NO (monoxide)
→ NO 2 (dioxide) step 2: 2NO + O 2 → 2HNO 2 + NO (nitric acid and monoxide) step 3: 3NO 2 + H 2 O More nitrogen will be added to the soil with rainfall in areas with a high amount of lightning activity than in areas with little lightning. The amount of nitrate added to the soil in rainfall is calculated: N rain = 0.01 ⋅ RNO 3 ⋅ Rday
10.5.1
where Nrain is nitrate added by rainfall (kg N/ha), RNO3 is the concentration of nitrogen in the rain (mg N/L), and Rday is the amount of precipitation on a given day (mm H2O). The nitrogen in rainfall is added to the nitrate pool in the top 10 mm of soil. Table 10-4: SWAT input variables that pertain to nitrogen in rainfall. Variable Name RCN
Definition RNO3: Concentration of nitrogen in the rain (mg N/L)
Input File .bsn
10.6 FIXATION Legumes are able to obtain a portion of their nitrogen demand through fixation of atmospheric N2 performed by rhizobia living in association with the plant. In exchange for nitrogen, the plant supplies the bacteria with carbohydrates. SWAT simulates nitrogen fixation by legumes when the soil does not supply the plant with the amount of nitrogen needed for growth. The nitrogen obtained by fixation is incorporated directly into the plant biomass and never enters the soil (unless plant biomass is added to the soil as residue after the plant
CHAPTER 10: EQUATIONS—NITROGEN
189
is killed). The equations for nitrogen fixation by legumes are reviewed in Chapter 18.
10.7 UPWARD MOVEMENT OF NITRATE IN WATER As water evaporates from the soil surface, the water content at the surface drops, creating a gradient in the profile. Water from lower in the profile will move upward in response to the gradient, carrying dissolved nutrients with it. SWAT allows nitrate to be transported from the first soil layer defined in the .sol file to the surface top 10 mm of soil with the equation: N evap = 0.1 ⋅ NO3ly ⋅
′′ ,ly E soil SWly
where Nevap is the amount of nitrate moving from the first soil layer to the soil surface zone (kg N/ha), NO3ly is the nitrate content of the first soil layer (kg ′′ ,ly is the amount of water removed from the first soil layer as a result N/ha), E soil
of evaporation (mm H2O), and SWly is the soil water content of the first soil layer (mm H2O).
10.8 LEACHING The majority of plant-essential nutrients are cations which are attracted and sorbed to negatively-charged soil particles. As plants extract these cations from soil solution, the soil particles release bound cations into soil solution to bring the ratio of nutrients in solution and on soil particles back into equilibrium. In effect, the soil buffers the concentration of cations in solution. In contrast, nitrate is an anion and is not attracted to or sorbed by soil particles. Because retention of nitrate by soils is minimal, nitrate is very susceptible to leaching. The algorithms used by SWAT to calculated nitrate leaching simultaneously solve for loss of nitrate in surface runoff and lateral flow also. These algorithms are reviewed in Chapter 14.
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SWAT USER'S MANUAL, VERSION 2000
10.9 NOMENCLATURE ′′ ,ly E soil FCly Ndec,ly Ndenit,ly Nevap
Amount of water removed from layer ly by evaporation (mm H2O) Water content of layer ly at field capacity (mm H2O) Nitrogen decomposed from the fresh organic N pool (kg N/ha) Amount of nitrogen lost to denitrification (kg N/ha) Amount of nitrate moving from the first soil layer to the soil surface zone (kg N/ha) Nmina,ly Nitrogen mineralized from the humus active organic N pool (kg N/ha) Nminf,ly Nitrogen mineralized from the fresh organic N pool (kg N/ha) Nnit,ly Amount of nitrogen converted from NH4+ to NO -3 in layer ly (kg N/ha) N nit vol ,ly Amount of ammonium converted via nitrification and volatilization in layer ly
(kg N/ha) Nrain Nitrate added by rainfall (kg N/ha) Ntrns,ly Amount of nitrogen transferred between the active and stable organic pools (kg N/ha) Nvol,ly Amount of nitrogen converted from NH4+ to NH3 in layer ly (kg N/ha) NH4ly Ammonium content of layer ly (kg NH4-N/ha) NO3conc,z Concentration of nitrate in the soil at depth z (mg/kg or ppm) NO3ly Nitrate content of soil layer ly (kg NO3-N/ha) Psolution,ly Solution phosphorus content of soil layer ly (kg P/ha) Rday Amount of rainfall on a given day (mm H2O) RNO3 Concentration of nitrogen in the rain (mg N/L) SWly Soil water content of layer ly (mm H2O) Tsoil,ly Temperature of layer ly (°C) WPly Water content of layer ly at wilting point (mm H2O) concN Concentration of nitrogen in a layer (mg/kg or ppm) depthly Depth of the layer (mm) fractN Fraction of humic nitrogen in the active pool frnit,ly Estimated fraction of nitrogen lost by nitrification frvol,ly Estimated fraction of nitrogen lost by volatilization orgCly Amount of organic carbon in the layer (%) orgNact,ly Nitrogen in the active organic pool in layer ly (mg/kg or kg N/ha) orgNfrsh,ly Nitrogen in the fresh organic pool in layer ly (kg N/ha) orgNhum,ly Concentration of humic organic nitrogen in the layer (mg/kg or ppm) orgNsta,ly Nitrogen in the stable organic pool in layer ly (mg/kg or kg N/ha) orgPfrsh,ly Phosphorus in the fresh organic pool in layer ly (kg P/ha) rsdly Residue in layer ly (kg/ha) z Depth below soil surface (mm) zmid,ly Depth from the soil surface to the middle of the layer (mm)
βmin βrsd
Rate coefficient for mineralization of the humus active organic nutrients Rate coefficient for mineralization of the residue fresh organic nutrients
CHAPTER 10: EQUATIONS—NITROGEN
βtrns δntr,ly εC:N εC:P γntr,ly γsw,ly γtmp,ly ηmidz,ly ηnit,ly ηsw,ly ηtmp,ly ηvol,ly ρb
191
Rate constant for nitrogen transfer between active and stable organic pools (1×10-5) Residue decay rate constant Residue C:N ratio in the soil layer Residue C:P ratio in the soil layer Nutrient cycling residue composition factor for layer ly Nutrient cycling water factor for layer ly Nutrient cycling temperature factor for layer ly Volatilization depth factor Nitrification regulator Nitrification soil water factor Nitrification/volatilization temperature factor Volatilization regulator Bulk density of the layer (Mg/m3)
10.10 REFERENCES Godwin, D.C., C.A. Jones, J.T. Ritchie, P.L.G. Vlek, and L.G. Youngdahl. 1984. The water and nitrogen components of the CERES models. p. 95-100. In Proc. Intl. Symp. on Minimum Data Sets for Agrotechnology Transfer, March 1983, Patancheru, India. Intl. Crops Research Institute for the Semi-Arid Tropics. Reddy, K.R., R. Khaleel, M.R. Overcash, and P.W. Westerman. 1979. A nonpoint source model for land areas receiving animal wastes: II. Ammonia volatilization. Trans. ASAE 22:1398-1404. Seligmand, N.G. and H. van Keulen. 1981. PAPRAN: A simulation model of annual pasture production limited by rainfall and nitrogen. p. 192-221. In M.J. Frissel and J.A. van Veeds. (eds) Simulation of nitrogen behaviour of soil-plant systems, Proc. Workshop. Wageningen, Jan.-Feb. 1980.
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 11
EQUATIONS: PHOSPHORUS
Although plant phosphorus demand is considerably less than nitrogen demand, phosphorus is required for many essential functions. The most important of these is its role in energy storage and transfer. Energy obtained from photosynthesis and metabolism of carbohydrates is stored in phosphorus compounds for later use in growth and reproductive processes.
193
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SWAT USER'S MANUAL, VERSION 2000
11.1 PHOSPHORUS CYCLE The three major forms of phosphorus in mineral soils are organic phosphorus associated with humus, insoluble forms of mineral phosphorus, and plant-available phosphorus in soil solution. Phosphorus may be added to the soil by fertilizer, manure or residue application. Phosphorus is removed from the soil by plant uptake and erosion. Figure 11-1 shows the major components of the phosphorus cycle.
Figure 11-1: The phosphorus cycle
Unlike nitrogen which is highly mobile, phosphorus solubility is limited in most environments. Phosphorus combines with other ions to form a number of insoluble compounds that precipitate out of solution. These characteristics contribute to a build-up of phosphorus near the soil surface that is readily available for transport in surface runoff. Sharpley and Syers (1979) observed that
CHAPTER 11: EQUATIONS—PHOSPHORUS
195
surface runoff is the primary mechanism by which phosphorus is exported from most catchments. SWAT monitors six different pools of phosphorus in the soil (Figure 112). Three pools are inorganic forms of phosphorus while the other three pools are organic forms of phosphorus. Fresh organic P is associated with crop residue and microbial biomass while the active and stable organic P pools are associated with the soil humus. The organic phosphorus associated with humus is partitioned into two pools to account for the variation in availability of humic substances to mineralization. Soil inorganic P is divided into solution, active, and stable pools. The solution pool is in rapid equilibrium (several days or weeks) with the active pool. The active pool is in slow equilibrium with the stable pool.
Figure 11-2: SWAT soil phosphorus pools and processes that move P in and out of pools.
11.1.1 INITIALIZATION OF SOIL PHOSPHORUS LEVELS Users may define the amount of soluble P and organic phosphorus contained in humic substances for all soil layers at the beginning of the simulation. If the user does not specify initial phosphorus concentrations, SWAT will initialize levels of phosphorus in the different pools. The concentration of solution phosphorus in all layers is initially set to 5 mg/kg soil. This concentration is representative of unmanaged land under native vegetation. A concentration of 25 mg/kg soil in the plow layer is considered representative of cropland (Cope et al., 1981).
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SWAT USER'S MANUAL, VERSION 2000
The concentration of phosphorus in the active mineral pool is initialized to (Jones et al., 1984): minPact ,ly = Psolution,ly ⋅
1 − pai pai
11.1.1
where minPact,ly is the amount of phosphorus in the active mineral pool (mg/kg), Psolution,ly is the amount of phosphorus in solution (mg/kg), and pai is the phosphorus availability index. The concentration of phosphorus in the stable mineral pool is initialized to (Jones et al., 1984): minPsta ,ly = 4 ⋅ minPact ,ly
11.1.2
where minPsta,ly is the amount of phosphorus in the stable mineral pool (mg/kg), and minPact,ly is the amount of phosphorus in the active mineral pool (mg/kg). Organic phosphorus levels are assigned assuming that the N:P ratio for humic materials is 8:1. The concentration of humic organic phosphorus in a soil layer is calculated: orgPhum ,ly = 0.125 ⋅ orgN hum ,ly
11.1.3
where orgPhum,ly is the concentration of humic organic phosphorus in the layer (mg/kg) and orgNhum,ly is the concentration of humic organic nitrogen in the layer (mg/kg). Phosphorus in the fresh organic pool is set to zero in all layers except the top 10mm of soil. In the top 10 mm, the fresh organic phosphorus pool is set to 0.03% of the initial amount of residue on the soil surface. orgPfrsh ,surf = 0.0003 ⋅ rsd surf
11.1.4
where orgPfrsh,surf is the phosphorus in the fresh organic pool in the top 10mm (kg P/ha), and rsdsurf is material in the residue pool for the top 10mm of soil (kg/ha). While SWAT allows nutrient levels to be input as concentrations, it performs all calculations on a mass basis. To convert a concentration to a mass, the concentration is multiplied by the bulk density and depth of the layer and divided by 100:
CHAPTER 11: EQUATIONS—PHOSPHORUS
conc P ⋅ ρ b ⋅ depthly 100
=
kg P ha
197
11.1.5
where concP is the concentration of phosphorus in a layer (mg/kg or ppm), ρb is the bulk density of the layer (Mg/m3), and depthly is the depth of the layer (mm). Table 11-1: SWAT input variables that pertain to nitrogen pools. Variable Name SOL_SOLP SOL_ORGP PSP RSDIN SOL_BD
Definition Psolution,ly: Initial soluble P concentration in soil layer (mg/kg or ppm) orgPhum,ly: Initial humic organic phosphorus in soil layer (mg/kg or ppm) pai: Phosphorus availability index rsdsurf: Material in the residue pool for the top 10mm of soil (kg ha-1) ρb: Bulk density of the layer (Mg/m3)
Input File .chm .chm .bsn .hru .sol
11.2 MINERALIZATION & DECOMPOSITION / IMMOBILIZATION Decomposition is the breakdown of fresh organic residue into simpler organic components. Mineralization is the microbial conversion of organic, plantunavailable phosphorus to inorganic, plant-available phosphorus. Immobilization is the microbial conversion of plant-available inorganic soil phosphorus to plantunavailable organic phosphorus. The
phosphorus
mineralization
algorithms
in
SWAT
are
net
mineralization algorithms which incorporate immobilization into the equations. The phosphorus mineralization algorithms developed by Jones et al. (1984) are similar in structure to the nitrogen mineralization algorithms. Two sources are considered for mineralization: the fresh organic P pool associated with crop residue and microbial biomass and the active organic P pool associated with soil humus. Mineralization and decomposition are allowed to occur only if the temperature of the soil layer is above 0°C. Mineralization and decomposition are dependent on water availability and temperature. Two factors are used in the mineralization and decomposition equations to account for the impact of temperature and water on these processes. The nutrient cycling temperature factor is calculated:
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SWAT USER'S MANUAL, VERSION 2000
γ tmp ,ly = 0.9 ⋅
Tsoil ,ly
Tsoil ,ly + exp[9.93 − 0.312 ⋅ Tsoil ,ly ]
+ 0 .1
11.2.1
where γtmp,ly is the nutrient cycling temperature factor for layer ly, and Tsoil,ly is the temperature of layer ly (°C). The nutrient cycling temperature factor is never allowed to fall below 0.1. The nutrient cycling water factor is calculated:
γ sw,ly =
SWly
11.2.2
FCly
where γsw,ly is the nutrient cycling water factor for layer ly, SWly is the water content of layer ly on a given day (mm H2O), and FCly is the water content of layer ly at field capacity (mm H2O). ). The nutrient cycling water factor is never allowed to fall below 0.05.
11.2.1 HUMUS MINERALIZATION Phosphorus in the humus fraction is partitioned between the active and stable organic pools using the ratio of humus active organic N to stable organic N. The amount of phosphorus in the active and stable organic pools is calculated: orgPact ,ly = orgPhum ,ly ⋅ orgPsta ,ly = orgPhum ,ly ⋅
orgN act ,ly orgN act ,ly + orgN sta ,ly orgN sta ,ly orgN act ,ly + orgN sta ,ly
11.2.3
11.2.4
where orgPact,ly is the amount of phosphorus in the active organic pool (kg P/ha), orgPsta,ly is the amount of phosphorus in the stable organic pool (kg P/ha), orgPhum,ly is the concentration of humic organic phosphorus in the layer (kg P/ha), orgNact,ly is the amount of nitrogen in the active organic pool (kg N/ha), and orgNsta,ly is the amount of nitrogen in the stable organic pool (kg N/ha). Mineralization from the humus active organic P pool is calculated: Pmina,ly = 1.4 ⋅ β min ⋅ (γ tmp ,ly ⋅ γ sw,ly ) ⋅ orgPact ,ly 12
11.2.5
where Pmina,ly is the phosphorus mineralized from the humus active organic P pool (kg P/ha), βmin is the rate coefficient for mineralization of the humus active organic nutrients, γtmp,ly is the nutrient cycling temperature factor for layer ly, γsw,ly
CHAPTER 11: EQUATIONS—PHOSPHORUS
199
is the nutrient cycling water factor for layer ly, and orgPact,ly is the amount of phosphorus in the active organic pool (kg P/ha). Phosphorus mineralized from the humus active organic pool is added to the solution P pool in the layer.
11.2.2 RESIDUE DECOMPOSITION & MINERALIZATION Decomposition and mineralization of the fresh organic phosphorus pool is allowed only in the first soil layer. Decomposition and mineralization are controlled by a decay rate constant that is updated daily. The decay rate constant is calculated as a function of the C:N ratio and C:P ratio of the residue, temperature and soil water content. The C:N ratio of the residue is calculated:
ε C:N =
0.58 ⋅ rsd ly orgN frsh ,ly + NO3ly
11.2.6
where εC:N is the C:N ratio of the residue in the soil layer, rsdly is the residue in layer ly (kg/ha), 0.58 is the fraction of residue that is carbon, orgNfrsh,ly is the nitrogen in the fresh organic pool in layer ly (kg N/ha), and NO3ly is the amount of nitrate in layer ly (kg N/ha). The C:P ratio of the residue is calculated:
ε C :P =
0.58 ⋅ rsd ly orgPfrsh ,ly + Psolution,ly
11.2.7
where εC:P is the C:P ratio of the residue in the soil layer, rsdly is the residue in layer ly (kg/ha), 0.58 is the fraction of residue that is carbon, orgPfrsh,ly is the phosphorus in the fresh organic pool in layer ly (kg P/ha), and Psolution,ly is the amount of phosphorus in solution in layer ly (kg P/ha). The decay rate constant defines the fraction of residue that is decomposed. The decay rate constant is calculated:
δ ntr ,ly = β rsd ⋅ γ ntr ,ly ⋅ (γ tmp ,ly ⋅ γ sw,ly )1 2
11.2.8
where δntr,ly is the residue decay rate constant, βrsd is the rate coefficient for mineralization of the residue fresh organic nutrients, γntr,ly is the nutrient cycling
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SWAT USER'S MANUAL, VERSION 2000
residue composition factor for layer ly, γtmp,ly is the nutrient cycling temperature factor for layer ly, and γsw,ly is the nutrient cycling water factor for layer ly. The nutrient cycling residue composition factor is calculated:
γ ntr ,ly
(ε C:N − 25)ù ì é ï exp ê− 0.693 ⋅ úû 25 ë ï ï (ε − 200)ù ï é = min íexp ê− 0.693 ⋅ C:P úû 200 ï ë ï 1.0 ï ï î
11.2.9
where γntr,ly is the nutrient cycling residue composition factor for layer ly, εC:N is the C:N ratio on the residue in the soil layer, and εC:P is the C:P ratio on the residue in the soil layer. Mineralization from the residue fresh organic P pool is then calculated: Pminf,ly = 0.8 ⋅ δ ntr ,ly ⋅ orgPfrsh ,ly
11.2.10
where Pminf,ly is the phosphorus mineralized from the fresh organic P pool (kg P/ha), δntr,ly is the residue decay rate constant, and orgPfrsh,ly is the phosphorus in the fresh organic pool in layer ly (kg P/ha). Phosphorus mineralized from the fresh organic pool is added to the solution P pool in the layer. Decomposition from the residue fresh organic P pool is calculated: Pdec,ly = 0.2 ⋅ δ ntr ,ly ⋅ orgPfrsh ,ly
11.2.11
where Pdec,ly is the phosphorus decomposed from the fresh organic P pool (kg P/ha), δntr,ly is the residue decay rate constant, and orgPfrsh,ly is the phosphorus in the fresh organic pool in layer ly (kg P/ha). Phosphorus decomposed from the fresh organic pool is added to the humus organic pool in the layer. Table 11-2: SWAT input variables that pertain to mineralization. Variable Name CMN RSDCO RSDCO_PL
Definition βmin: Rate coefficient for mineralization of the humus active organic nutrients βrsd: Rate coefficient for mineralization of the residue fresh organic nutrients βrsd: Rate coefficient for mineralization of the residue fresh organic nutrients
Input File .bsn .bsn crop.dat
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11.3 SORPTION OF INORGANIC P Many studies have shown that after an application of soluble P fertilizer, solution P concentration decreases rapidly with time due to reaction with the soil. This initial “fast” reaction is followed by a much slower decrease in solution P that may continue for several years (Barrow and Shaw, 1975; Munns and Fox, 1976; Rajan and Fox, 1972; Sharpley, 1982). In order to account for the initial rapid decrease in solution P, SWAT assumes a rapid equilibrium exists between solution P and an “active” mineral pool. The subsequent slow reaction is simulated by the slow equilibrium assumed to exist between the “active” and “stable” mineral pools. The algorithms governing movement of inorganic phosphorus between these three pools are taken from Jones et al. (1984). Equilibration between the solution and active mineral pool is governed by the phosphorus availability index. This index specifies the fraction of fertilizer P which is in solution after an incubation period, i.e. after the rapid reaction period. A number of methods have been developed to measure the phosphorus availability index. Jones et al. (1984) recommends a method outlined by Sharpley et al. (1984) in which various amounts of phosphorus are added in solution to the soil as K2HPO4. The soil is wetted to field capacity and then dried slowly at 25°C. When dry, the soil is rewetted with deionized water. The soil is exposed to several wetting and drying cycles over a 6-month incubation period. At the end of the incubation period, solution phosphorus is determined by extraction with anion exchange resin. The P availability index is then calculated: pai =
Psolution, f − Psolution,i fert minP
11.3.1
where pai is the phosphorus availability index, Psolution,f is the amount of phosphorus in solution after fertilization and incubation, Psolution,i is the amount of phosphorus in solution before fertilization, and fertminP is the amount of soluble P fertilizer added to the sample.
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The movement of phosphorus between the solution and active mineral pools is governed by the equilibration equations: æ pai ö Psol act ,ly = Psolution,ly − minPact ,ly ⋅ çç ÷÷ è 1 − pai ø æ pai ö if Psolution,ly > minPact ,ly ⋅ çç ÷÷ è 1 − pai ø
11.3.2
æ æ pai ö ö Psol act ,ly = 0.1 ⋅ çç Psolution,ly − minPact ,ly ⋅ çç ÷÷ ÷÷ è 1 − pai ø ø è æ pai ö if Psolution,ly < minPact ,ly ⋅ çç ÷÷ è 1 − pai ø
11.3.3
where Psol act ,ly is the amount of phosphorus transferred between the soluble and active mineral pool (kg P/ha), Psolution,ly is the amount of phosphorus in solution (kg P/ha), minPact,ly is the amount of phosphorus in the active mineral pool (kg P/ha), and pai is the phosphorus availability index. When Psol act ,ly is positive, phosphorus is being transferred from solution to the active mineral pool. When
Psol act ,ly is negative, phosphorus is being transferred from the active mineral pool to solution. Note that the rate of flow from the active mineral pool to solution is 1/10th the rate of flow from solution to the active mineral pool. SWAT simulates slow phosphorus sorption by assuming the active mineral phosphorus pool is in slow equilibrium with the stable mineral phosphorus pool. At equilibrium, the stable mineral pool is 4 times the size of the active mineral pool. When not in equilibrium, the movement of phosphorus between the active and stable mineral pools is governed by the equations:
Pact sta ,ly = β eqP ⋅ (4 ⋅ minPact ,ly − minPsta ,ly ) if minPsta ,ly < 4 ⋅ minPact ,ly
11.3.4
Pact sta ,ly = 0.1 ⋅ β eqP ⋅ (4 ⋅ minPact ,ly − minPsta ,ly ) if minPsta ,ly > 4 ⋅ minPact ,ly
11.3.5
CHAPTER 11: EQUATIONS—PHOSPHORUS
203
where Pact sta ,ly is the amount of phosphorus transferred between the active and stable mineral pools (kg P/ha), βeqP is the slow equilibration rate constant (0.0006 d-1), minPact,ly is the amount of phosphorus in the active mineral pool (kg P/ha), and minPsta,ly is the amount of phosphorus in the stable mineral pool (kg P/ha). When Pact sta ,ly is positive, phosphorus is being transferred from the active mineral pool to the stable mineral pool. When Pact sta ,ly is negative, phosphorus is being transferred from the stable mineral pool to the active mineral pool. Note that the rate of flow from the stable mineral pool to the active mineral pool is 1/10th the rate of flow from the active mineral pool to the stable mineral pool. Table 11-3: SWAT input variables that pertain to inorganic P sorption processes. Variable Name PSP
Definition pai: Phosphorus availability index
Input File .bsn
11.4 LEACHING The primary mechanism of phosphorus movement in the soil is by diffusion. Diffusion is the migration of ions over small distances (1-2 mm) in the soil solution in response to a concentration gradient. The concentration gradient is created when plant roots remove soluble phosphorus from soil solution, depleting solution P in the root zone. Due to the low mobility of phosphorus, SWAT allows soluble P to leach only from the top 10 mm of soil into the first soil layer. The amount of solution P moving from the top 10 mm into the first soil layer is: Pperc =
Psolution,surf ⋅ w perc ,surf 10 ⋅ ρ b ⋅ depthsurf ⋅ k d , perc
11.4.1
where Pperc is the amount of phosphorus moving from the top 10 mm into the first soil layer (kg P/ha), Psolution,surf is the amount of phosphorus in solution in the top 10 mm (kg P/ha), wperc,surf is the amount of water percolating to the first soil layer from the top 10 mm on a given day (mm H2O), ρb is the bulk density of the top 10 mm (Mg/m3) (assumed to be equivalent to bulk density of first soil layer),
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depthsurf is the depth of the “surface” layer (10 mm), and kd,perc is the phosphorus percolation coefficient (10 m3/Mg). The phosphorus percolation coefficient is the ratio of the phosphorus concentration in the surface 10 mm of soil to the concentration of phosphorus in percolate. Table 11-4: SWAT input variables that pertain to phosphorus leaching. Variable Name SOL_BD PPERCO
Definition ρb: Bulk density of the layer (Mg/m3) kd,perc: Phosphorus percolation coefficient (10 m3/Mg)
Input File .sol .bsn
11.5 NOMENCLATURE FCly Water content of layer ly at field capacity (mm H2O) NO3ly Nitrate content of soil layer ly (kg NO3-N/ha) Pact sta ,ly Amount of phosphorus transferred between the active and stable mineral pools (kg P/ha) Phosphorus decomposed from the fresh organic P pool (kg P/ha) Phosphorus mineralized from the humus active organic P pool (kg P/ha) Phosphorus mineralized from the fresh organic P pool (kg P/ha), Amount of phosphorus moving from the top 10 mm into the first soil layer (kg P/ha) Psolution,ly Amount of phosphorus in solution (mg/kg) Psol act ,ly Amount of phosphorus transferred between the soluble and active mineral pool
Pdec,ly Pmina,ly Pminf,ly Pperc
SWly Tsoil,ly
(kg P/ha) Soil water content of layer ly (mm H2O) Temperature of layer ly (°C)
concP Concentration of phosphorus in a layer (mg/kg or ppm) depthly Depth of the layer (mm) kd,perc Phosphorus percolation coefficient (10 m3/Mg) minPact,ly Amount of phosphorus in the active mineral pool (mg/kg or kg P/ha) minPsta,ly Amount of phosphorus in the stable mineral pool (mg/kg or kg P/ha) orgNact,ly Nitrogen in the active organic pool in layer ly (mg/kg or kg N/ha) orgNfrsh,ly Nitrogen in the fresh organic pool in layer ly (kg N/ha) orgNhum,ly Amount of nitrogen in humic organic pool in the layer (mg/kg or kg N/ha) orgNsta,ly Nitrogen in the stable organic pool in layer ly (mg/kg or kg N/ha) orgPact,ly Amount of phosphorus in the active organic pool (kg P/ha) orgPfrsh,ly Phosphorus in the fresh organic pool in layer ly (kg P/ha) orgPhum,ly Amount of phosphorus in humic organic pool in the layer (mg/kg or kg P/ha) orgPsta,ly Amount of phosphorus in the stable organic pool (kg P/ha) pai Phosphorus availability index rsdly Residue in layer ly (kg/ha)
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wperc,ly Amount of water percolating to the underlying soil layer on a given day (mm H2O)
βeqP βmin βrsd δntr,ly εC:N εC:P γntr,ly γsw,ly γtmp,ly ρb
Slow equilibration rate constant (0.0006 d-1) Rate coefficient for mineralization of the humus active organic nutrients Rate coefficient for mineralization of the residue fresh organic nutrients Residue decay rate constant Residue C:N ratio in the soil layer Residue C:P ratio in the soil layer Nutrient cycling residue composition factor for layer ly Nutrient cycling water factor for layer ly Nutrient cycling temperature factor for layer ly Bulk density of the layer (Mg/m3)
11.6 REFERENCES Barrow, N.J. and T.C. Shaw. 1975. The slow reactions between soil and anions. 2. Effect of time and temperature on the decrease in phosphate concentration in soil solution. Soil Sci. 119:167-177. Cope, J.T., C.E. Evans, and H.C. Williams. 1981. Soil test fertility recommendations for Alabama crops. Alabama Agric. Station Circular No. 251. Jones, C.A. C.V. Cole, A.N. Sharpley, and J.R. Williams. 1984. A simplified soil and plant phosphorus model. I. Documentation. Soil Sci. Soc. Am. J. 48:800-805. Munns, D.N. and R.L. Fox. 1976. The slow reaction which continues after phosphate adsorption: Kinetics and equilibrium in some tropical soils. Soil Sci. Soc. Am. J. 40:46-51. Rajan, S.S.S. and R.L. Fox. 1972. Phosphate adsorption by soils. 1. Influence of time and ionic environment on phosphate adsorption. Commun. Soil. Sci. Plant Anal. 3:493-504. Sharpley, A.N. 1982. A prediction of the water extractable phosphorus content of soil following a phosphorus addition. J. Environ. Qual. 11:166-170.
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Sharpley, A.N., C. Gray, C.A. Jones, and C.V. Cole. 1984. A simplified soil and plant phosphorus model. II. Prediction of labile, organic, and sorbed P amounts. Soil Sci. Soc. Am. J. 48:805-809. Sharpley, A.N. and J.K. Syers. 1979. Phosphorus inputs into a stream draining an agricultural watershed: II. Amounts and relative significance of runoff types. Water, Air and Soil Pollution 11:417-428.
CHAPTER 12
EQUATIONS: PESTICIDES
One of the primary purposes of tillage and harvesting practices in early farming systems was to remove as much plant residue from the field as possible so that pests had no food source to sustain them until the next growing season. As research linked erosion to lack of soil cover, farmers began to perform fewer tillage operations and altered harvesting methods to leave more residue. As mechanical methods of pest control were minimized or eliminated, chemical methods of pest control began to assume a key role in the management of unwanted organisms. 207
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Pesticides are toxic by design, and there is a natural concern about the impact of their presence in the environment on human health and environmental quality. The fate and transport of a pesticide are governed by properties such as solubility in water, volatility and ease of degradation. The algorithms in SWAT used to model pesticide movement and fate are adapted from GLEAMS (Leonard et al., 1987). Pesticide may be aerially applied to an HRU with some fraction intercepted by plant foliage and some fraction reaching the soil. Pesticide may also be incorporated into the soil through tillage. SWAT monitors pesticide amounts on foliage and in all soil layers. Figure 12-1 shows the potential pathways and processes simulated in SWAT.
Figure 12-1: Pesticide fate and transport in SWAT.
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209
12.1 WASH-OFF A portion of the pesticide on plant foliage may be washed off during rain events. The fraction washed off is a function of plant morphology, pesticide solubility, and the timing and intensity of the rainfall event. Wash-off will occur when the amount of precipitation on a given day exceeds 2.54 mm. The amount of pesticide washing off plant foliage during a precipitation event on a given day is calculated: pst f ,wsh = frwsh ⋅ pst f
12.1.1
where pstf,wsh is the amount of pesticide on foliage that is washed off the plant and onto the soil surface on a given day (kg pst/ha), frwsh is the wash-off fraction for the pesticide, and pstf is the amount of pesticide on the foliage (kg pst/ha). The wash-off fraction represents the portion of the pesticide on the foliage that is dislodgable. Table 12-1: SWAT input variables that pertain to pesticide wash-off. Variable Name WOF
Definition frwsh: Wash-off fraction for the pesticide
Input File pest.dat
12.2 DEGRADATION Degradation is the conversion of a compound into less complex forms. A compound in the soil may degrade upon exposure to light (photo degradation), reaction with chemicals present in the soil (chemical degradation) or through use as a substrate for organisms (biodegradation). The majority of pesticides in use today are organic compounds. Because organic compounds contain carbon, which is used by microbes in biological reactions to produce energy, organic pesticides may be susceptible to microbial degradation. In contrast, pesticides that are inorganic are not susceptible to microbial degradation. Examples of pesticides that will not degrade are lead arsenate, a metallic salt commonly applied in orchards before DDT was invented, and arsenic acid, a compound formerly used to defoliate cotton.
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Pesticides vary in their susceptibility to degradation. Compounds with chain structures are easier to break apart than compounds containing aromatic rings or other complex structures. The susceptibility of a pesticide to degradation is quantified by the pesticide’s half-life. The half-life for a pesticide defines the number of days required for a given pesticide concentration to be reduced by one-half. The soil half-life entered for a pesticide is a lumped parameter that includes the net effect of volatilization, photolysis, hydrolysis, biological degradation and chemical reactions in the soil. Because pesticide on foliage degrades more rapidly than pesticide in the soil, SWAT allows a different half-life to be defined for foliar degradation. Pesticide degradation or removal in all soil layers is governed by firstorder kinetics:
pst s ,ly ,t = pst s ,ly ,o ⋅ exp[− k p ,soil ⋅ t ]
12.2.1
where psts,ly,t is the amount of pesticide in the soil layer at time t (kg pst/ha), psts,ly,o is the initial amount of pesticide in the soil layer (kg pst/ha), kp,soil is the rate constant for degradation or removal of the pesticide in soil (1/day), and t is the time elapsed since the initial pesticide amount was determined (days). The rate constant is related to the soil half-life as follows:
t1 2,s =
0.693 k p ,soil
12.2.2
where t1/2,s is the half-life of the pesticide in the soil (days). The equation governing pesticide degradation on foliage is: pst f ,t = pst f ,o ⋅ exp[− k p , foliar ⋅ t ]
12.2.3
where pstf,t is the amount of pesticide on the foliage at time t (kg pst/ha), pstf,o is the initial amount of pesticide on the foliage (kg pst/ha), kp,foliar is the rate constant for degradation or removal of the pesticide on foliage (1/day), and t is the time elapsed since the initial pesticide amount was determined (days). The rate constant is related to the foliar half-life as follows: t1 2, f =
0.693 k p , foliar
where t1/2,f is the half-life of the pesticide on foliage (days).
12.2.4
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211
Table 12-2: SWAT input variables that pertain to pesticide degradation. Variable Name HLIFE_S HLIFE_F
Definition t1/2,s: Half-life of the pesticide in the soil (days) t1/2,f: Half-life of the pesticide on foliage (days)
Input File pest.dat pest.dat
12.3 LEACHING Highly water-soluble pesticides can be transported with percolation deep into the soil profile and potentially pollute shallow groundwater systems. The algorithms used by SWAT to calculated pesticide leaching simultaneously solve for loss of pesticide in surface runoff and lateral flow also. These algorithms are reviewed in Chapter 15.
12.4 NOMENCLATURE frwsh kp,foliar kp,soil pstf pstf,wsh psts,ly t t1/2,f t1/2,s
Wash-off fraction for the pesticide Rate constant for degradation or removal of the pesticide on foliage (1/day) Rate constant for degradation or removal of the pesticide in soil (1/day) Amount of pesticide on the foliage (kg pst/ha) Amount of pesticide on foliage that is washed off the plant and onto the soil surface on a given day (kg pst/ha) Amount of pesticide in the soil (kg pst/ha) Time elapsed since the initial pesticide amount was determined (days) Half-life of the pesticide on foliage (days) Half-life of the pesticide in the soil (days)
12.5 REFERENCES Leonard, R.A., W.G. Knisel., and D.A. Still. 1987. GLEAMS: Groundwater loading effects of agricultural management systems. Trans. ASAE. 30:1403-1418.
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EROSION Transport of sediment, nutrients and pesticides from land areas to water bodies is a consequence of weathering that acts on landforms. Soil and water conservation planning requires knowledge of the relations between factors that cause loss of soil and water and those that help to reduce such losses. The following three chapters review the methodology used by SWAT to simulate erosion processes.
CHAPTER 13
EQUATIONS: SEDIMENT
Erosion caused by rainfall and runoff is computed with the Modified Universal Soil Loss Equation (MUSLE) (Williams, 1975). MUSLE is a modified version of the Universal Soil Loss Equation (USLE) developed by Wischmeier and Smith (1965, 1978). USLE predicts average annual gross erosion as a function of rainfall energy. In MUSLE, the rainfall energy factor is replaced with a runoff factor. This improves the sediment yield prediction, eliminates the need for delivery ratios, and allows the equation to be applied to individual storm events. Sediment yield 215
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prediction is improved because runoff is a function of antecedent moisture condition as well as rainfall energy. Delivery ratios (the sediment yield at any point along the channel divided by the source erosion above that point) are required by the USLE because the rainfall factor represents energy used in detachment only. Delivery ratios are not needed with MUSLE because the runoff factor represents energy used in detaching and transporting sediment.
13.1 MUSLE The modified universal soil loss equation (Williams, 1995) is: sed = 11.8 ⋅ (Qsurf ⋅ q peak ⋅ area hru )
0.56
⋅ KUSLE ⋅ CUSLE ⋅ PUSLE ⋅ LSUSLE ⋅ CFRG
13.1.1
where sed is the sediment yield on a given day (metric tons), Qsurf is the surface runoff volume (mm H2O/ha), qpeak is the peak runoff rate (m3/s), areahru is the area of the HRU (ha), KUSLE is the USLE soil erodibility factor (0.013 metric ton m2 hr/(m3-metric ton cm)), CUSLE is the USLE cover and management factor, PUSLE is the USLE support practice factor, LSUSLE is the USLE topographic factor and CFRG is the coarse fragment factor. Surface runoff and peak rate calculations are reviewed in Chapter 6. The USLE factors are discussed in the following sections.
13.1.1 SOIL ERODIBILITY FACTOR Some soils erode more easily than others even when all other factors are the same. This difference is termed soil erodibility and is caused by the properties of the soil itself. Wischmeier and Smith (1978) define the soil erodibility factor as the soil loss rate per erosion index unit for a specified soil as measured on a unit plot. A unit plot is 22.1-m (72.6-ft) long, with a uniform length-wise slope of 9percent, in continuous fallow, tilled up and down the slope. Continuous fallow is defined as land that has been tilled and kept free of vegetation for more than 2 years. The units for the USLE soil erodibility factor in MUSLE are numerically equivalent to the traditional English units of 0.01 (ton acre hr)/(acre ft-ton inch).
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Wischmeier and Smith (1978) noted that a soil type usually becomes less erodible with decrease in silt fraction, regardless of whether the corresponding increase is in the sand fraction or clay fraction. Direct measurement of the erodibility factor is time consuming and costly. Wischmeier et al. (1971) developed a general equation to calculate the soil erodibility factor when the silt and very fine sand content makes up less than 70% of the soil particle size distribution. KUSLE =
0.00021 ⋅ M 1.14 ⋅ (12 − OM ) + 3.25 ⋅ (csoilstr − 2 ) + 2.5 ⋅ (c perm − 3) 100
13.1.2
where KUSLE is the soil erodibility factor, M is the particle-size parameter, OM is the percent organic matter (%), csoilstr is the soil structure code used in soil classification, and cperm is the profile permeability class. The particle-size parameter, M, is calculated M = (msilt + mvfs ) ⋅ (100 − mc )
13.1.3
where msilt is the percent silt content (0.002-0.05 mm diameter particles), mvfs is the percent very fine sand content (0.05-0.10 mm diameter particles), and mc is the percent clay content (< 0.002 mm diameter particles). The percent organic matter content, OM, of a layer can be calculated: OM = 1.72 ⋅ orgC
13.1.4
where orgC is the percent organic carbon content of the layer (%). Soil structure refers to the aggregation of primary soil particles into compound particles which are separated from adjoining aggregates by surfaces of weakness. An individual natural soil aggregate is called a ped. Field description of soil structure notes the shape and arrangement of peds, the size of peds, and the distinctness and durability of visible peds. USDA Soil Survey terminology for structure consists of separate sets of terms defining each of these three qualities. Shape and arrangement of peds are designated as type of soil structure; size of peds as class; and degree of distinctness as grade. The soil-structure codes for equation 13.1.2 are defined by the type and class of soil structure present in the layer. There are four primary types of structure:
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-Platy, with particles arranged around a plane, generally horizontal -Prismlike, with particles arranged around a verticle line and bounded by relatively flat vertical surfaces -Blocklike or polyhedral, with particles arranged around a point and bounded by flat or rounded surfaces which are casts of the molds formed by the faces of surrounding peds -Spheroidal or polyhedral, with particles arranged around a point and bounded by curved or very irregular surfaces that are not accomodated to the adjoining aggregates Each of the last three types has two subtypes: -Prismlike Prismatic: without rounded upper ends Columnar: with rounded caps -Blocklike Angular Blocky: bounded by planes intersecting at relatively sharp angles Subangular Blocky: having mixed rounded and plane faces with vertices mostly rounded -Spheroidal Granular: relatively non-porous Crumb: very porous The size criteria for the class will vary by type of structure and are summarized in Table 13-1. The codes assigned to csoilstr are: 1 2 3 4
very fine granular fine granular medium or coarse granular blocky, platy, prismlike or massive
Table 13-1: Size classes of soil structure
Size Classes Very fine Fine Medium Coarse Very coarse
Platy < 1 mm 1-2 mm 2-5 mm 5-10 mm > 10 mm
Shape of structure Prismatic and Columnar Blocky < 10 mm < 5 mm 10-20 mm 5-10 mm 20-50 mm 10-20 mm 50-100 mm 20-50 mm > 100 mm > 50 mm
Granular < 1 mm 1-2 mm 2-5 mm 5-10 mm > 10 mm
Permeability is defined as the capacity of the soil to transmit water and air through the most restricted horizon (layer) when moist. The profile permeability classes are based on the lowest saturated hydraulic conductivity in the profile. The codes assigned to cperm are:
CHAPTER 13: EQUATIONS—SEDIMENT
1 2 3 4 5 6
219
rapid (> 150 mm/hr) moderate to rapid (50-150 mm/hr) moderate (15-50 mm/hr) slow to moderate (5-15 mm/hr) slow (1-5 mm/hr) very slow (< 1 mm/hr)
Williams (1995) proposed an alternative equation: KUSLE = f csand ⋅ f cl − si ⋅ f orgc ⋅ f hisand
13.1.5
where fcsand is a factor that gives low soil erodibility factors for soils with high coarse-sand contents and high values for soils with little sand, fcl-si is a factor that gives low soil erodibility factors for soils with high clay to silt ratios, forgc is a factor that reduces soil erodibility for soils with high organic carbon content, and fhisand is a factor that reduces soil erodibility for soils with extremely high sand contents. The factors are calculated:
æ é æ m öù ö f csand = çç 0.2 + 0.3 ⋅ exp ê − 0.256 ⋅ ms ⋅ ç1 − silt ÷ ú ÷÷ 100 ø û ø è ë è f cl − si
æ msilt ö ÷÷ = çç è mc + msilt ø
0.3
æ ö 0.25 ⋅ orgC f orgc = çç1 − ÷÷ è orgC + exp[3.72 − 2.95 ⋅ orgC ] ø
f hisand
13.1.6
æ ö m ö æ ç ÷ 0.7 ⋅ ç1 − s ÷ 100 ø ç ÷ è = ç1 − é m m ù÷ çç æç1 − s ö÷ + exp ê − 5.51 + 22.9 ⋅ æç1 − s ö÷ú ÷÷ è 100 øû ø ë è è 100 ø
13.1.7
13.1.8
13.1.9
where ms is the percent sand content (0.05-2.00 mm diameter particles), msilt is the percent silt content (0.002-0.05 mm diameter particles), mc is the percent clay content (< 0.002 mm diameter particles), and orgC is the percent organic carbon content of the layer (%).
13.1.2 COVER AND MANAGEMENT FACTOR The USLE cover and management factor, CUSLE, is defined as the ratio of soil loss from land cropped under specified conditions to the corresponding loss
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from clean-tilled, continuous fallow (Wischmeier and Smith, 1978). The plant canopy affects erosion by reducing the effective rainfall energy of intercepted raindrops. Water drops falling from the canopy may regain appreciable velocity but it will be less than the terminal velocity of free-falling raindrops. The average fall height of drops from the canopy and the density of the canopy will determine the reduction in rainfall energy expended at the soil surface. A given percentage of residue on the soil surface is more effective that the same percentage of canopy cover. Residue intercepts falling raindrops so near the surface that drops regain no fall velocity. Residue also obstructs runoff flow, reducing its velocity and transport capacity. Because plant cover varies during the growth cycle of the plant, SWAT updates CUSLE daily using the equation: CUSLE = exp([ln (0.8) − ln (CUSLE ,mn )]⋅ exp[− 0.00115 ⋅ rsd surf ] + ln[CUSLE ,mn ])
13.1.10
where CUSLE,mn is the minimum value for the cover and management factor for the land cover, and rsdsurf is the amount of residue on the soil surface (kg/ha). The minimum C factor can be estimated from a known average annual C factor using the following equation (Arnold and Williams, 1995): CUSLE ,mn = 1.463 ln[CUSLE ,aa ] + 0.1034
13.1.11
where CUSLE,mn is the minimum C factor for the land cover and CUSLE,aa is the average annual C factor for the land cover.
13.1.3 SUPPORT PRACTICE FACTOR The support practice factor, PUSLE, is defined as the ratio of soil loss with a specific support practice to the corresponding loss with up-and-down slope culture. Support practices include contour tillage, stripcropping on the contour, and terrace systems. Stabilized waterways for the disposal of excess rainfall are a necessary part of each of these practices. Contour tillage and planting provides almost complete protection against erosion from storms of low to moderate intensity, but little or no protection against occasional severe storms that cause extensive breakovers of contoured
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rows. Contouring is most effective on slopes of 3 to 8 percent. Values for PUSLE and slope-length limits for contour support practices are given in Table 13-2. Table 13-2: P factor values and slope-length limits for contouring (Wischmeier and Smith, 1978). Land slope (%) PUSLE Maximum length (m) 1 to 2 0.60 122 3 to 5 0.50 91 6 to 8 0.50 61 9 to 12 0.60 37 13 to 16 0.70 24 17 to 20 0.80 18 21 to 25 0.90 15
Stripcropping is a practice in which contoured strips of sod are alternated with equal-width strips of row crop or small grain. Recommended values for contour stripcropping are given in Table 13-3. Table 13-3: P factor values, maximum strip width and slope-length limits for contour stripcropping (Wischmeier and Smith, 1978). PUSLE values1 Land slope Strip width Maximum (%) (m) length (m) A B C 1 to 2 0.30 0.45 0.60 40 244 3 to 5 0.25 0.38 0.50 30 183 6 to 8 0.25 0.38 0.50 30 122 9 to 12 0.30 0.45 0.60 24 73 13 to 16 0.35 0.52 0.70 24 49 17 to 20 0.40 0.60 0.80 18 37 21 to 25 0.45 0.68 0.90 15 30 1
P values: A: For 4-year rotation of row crop, small grain with meadow seeding, and 2 years of meadow. A second row crop can replace the small grain if meadow is established in it. B: For 4-year rotation of 2 years row crop, winter grain with meadow seeding, and 1-year meadow. C: For alternate strips of row crop and winter grain
Terraces are a series of horizontal ridges made in a hillside. There are several types of terraces. Broadbase terraces are constructed on gently sloping land and the channel and ridge are cropped the same as the interterrace area. The steep backslope terrace, where the backslope is in sod, is most common on steeper land. Impoundment terraces are terraces with underground outlets. Terraces divide the slope of the hill into segments equal to the horizontal terrace interval. With terracing, the slope length is the terrace interval. For broadbase terraces, the horizontal terrace interval is the distance from the center of the ridge to the center of the channel for the terrace below. The horizontal terrace interval for steep backslope terraces is the distance from the point where
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cultivation begins at the base of the ridge to the base of the frontslope of the terrace below. Values for PUSLE for contour farming terraced fields are listed in Table 134. These values apply to broadbase, steep backslope and level terraces. Keep in mind that the values given in Table 13-4 do not account for all erosion control benefits of terraces. The shorter slope-length used in the calculation of the lengthslope factor will produce additional reduction. Table 13-4: P factor values for contour-farmed terraced fields1 Computing sediment yield3 Land Farm planning slope (%) Contour P Stripcrop P Graded channels Steep backslope factor2 factor sod outlets underground outlets 1 to 2 0.60 0.30 0.12 0.05 3 to 8 0.50 0.25 0.10 0.05 9 to 12 0.60 0.30 0.12 0.05 13 to 16 0.70 0.35 0.14 0.05 17 to 20 0.80 0.40 0.16 0.06 21 to 25 0.90 0.45 0.18 0.06 1
Slope length is the horizontal terrace interval. The listed values are for contour farming. No additional contouring factor is used in the computation. Use these values for control of interterrace erosion within specified soil loss tolerances. 3 These values include entrapment efficiency and are used for control of offsite sediment within limits and for estimating the field’s contribution to watershed sediment yield. 2
13.1.4 TOPOGRAPHIC FACTOR The topographic factor, LSUSLE, is the expected ratio of soil loss per unit area from a field slope to that from a 22.1-m length of uniform 9 percent slope under otherwise identical conditions. The topographic factor is calculated: m
LSUSLE
æL ö = ç hill ÷ ⋅ (65.41 ⋅ sin 2 (α hill ) + 4.56 ⋅ sin α hill + 0.065) è 22.1 ø
13.1.12
where Lhill is the slope length (m), m is the exponential term, and αhill is the angle of the slope. The exponential term, m, is calculated:
m = 0.6 ⋅ (1 − exp[− 35.835 ⋅ slp ])
13.1.13
where slp is the slope of the HRU expressed as rise over run (m/m). The relationship between αhill and slp is: slp = tan α hill
13.1.14
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223
13.1.5 COARSE FRAGMENT FACTOR The coarse fragment factor is calculated: CFRG = exp(− 0.053 ⋅ rock )
13.1.15
where rock is the percent rock in the first soil layer (%). Table 13-5: SWAT input variables that pertain to sediment yield. Variable Name USLE_K USLE_C USLE_P SLSUBBSN SLOPE ROCK
Definition KUSLE: USLE soil erodibility factor (0.013 metric ton m2 hr/(m3metric ton cm)) CUSLE,mn: Minimum value for the cover and management factor for the land cover PUSLE: USLE support practice factor Lhill: Slope length (m) slp: Average slope of the subbasin (% or m/m) rock: Percent rock in the first soil layer (%)
Input File .sol crop.dat .mgt .hru .hru .sol
13.2 USLE For comparative purposes, SWAT prints out sediment loadings calculated with USLE. These values are not used by the model, they are for comparison only. The universal soil loss equation (Williams, 1995) is:
sed = 1.292 ⋅ EI USLE ⋅ KUSLE ⋅ CUSLE ⋅ PUSLE ⋅ LSUSLE ⋅ CFRG
13.2.1
where sed is the sediment yield on a given day (metric tons/ha), EIUSLE is the rainfall erosion index (0.017 m-metric ton cm/(m2 hr)), KUSLE is the USLE soil erodibility factor (0.013 metric ton m2 hr/(m3-metric ton cm)), CUSLE is the USLE cover and management factor, PUSLE is the USLE support practice factor, LSUSLE is the USLE topographic factor and CFRG is the coarse fragment factor. The factors other than EIUSLE are discussed in the preceeding sections.
13.2.1 RAINFALL ERODIBILITY INDEX The value of EIUSLE for a given rainstorm is the product, total storm energy times the maximum 30 minute intensity. The storm energy indicates the volume of rainfall and runoff while the 30 minute intensity indicates the prolonged peak rates of detachment and runoff. EI USLE = E storm ⋅ I 30
13.2.2
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where EIUSLE is the rainfall erosion index (0.017 m-metric ton cm/(m2 hr)), Estorm is the total storm energy (0.0017 m-metric ton/m2), and I30 is the maximum 30minute intensity (mm/hr). The energy of a rainstorm is a function of the amount of rain and of all the storm’s component intensities. Because rainfall is provided to the model in daily totals, an assumption must be made about variation in rainfall intensity. The rainfall intensity variation with time is assumed to be exponentially distributed: æ t ö it = imx ⋅ expçç − ÷÷ è ki ø
13.2.3
where it is the rainfall intensity at time t (mm/hr), imx is the maximum rainfall intensity (mm/hr), t is the time (hr), and ki is the decay constant for rainfall intensity (hr). The USLE energy equation is æ é ∆Rday ù ö E storm = ∆Rday ⋅ çç12.1 + 8.9 ⋅ log10 ê ú ÷÷ t ∆ ë ûø è
13.2.4
where ∆Rday is the amount of rainfall during the time interval (mm H2O), and ∆t is the time interval (hr). This equation may be expressed analytically as: ∞
∞
0
0
E storm = 12.1ò it dt + 8.9 ò it log10 it dt
13.2.5
Combining equation 13.2.5 and 13.2.3 and integrating gives the equation for estimating daily rainfall energy: E storm =
Rday 1000
⋅ (12.1 + 8.9 ⋅ (log10 [imx ] − 0.434))
13.2.6
where Rday is the amount of precipitation falling on a given day (mm H2O), and imx is the maximum rainfall intensity (mm/hr). To compute the maximum rainfall intensity, imx, equation 13.2.3 is integrated to give Rday = imx ⋅ k i
13.2.7
æ é t ùö Rt = Rday ⋅ çç 1 − exp ê − ú ÷÷ ë ki û ø è
13.2.8
and
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225
where Rday is the amount of precipitation falling on a given day (mm H2O), imx is the maximum rainfall intensity (mm/hr), ki is the decay constant for rainfall intensity (hr), Rt is the amount of rain falling during a time interval (mm H2O), and t is the time interval (hr). The maximum half-hour rainfall for the precipitation event is known: R0.5 = α 0.5 ⋅ Rday
13.2.9
where R0.5 is the maximum half-hour rainfall (mm H2O), α0.5 is the maximum half-hour rainfall expressed as a fraction of daily rainfall, and Rday is the amount of precipitation falling on a given day (mm H2O). Calculation of α0.5 is reviewed in Chapter 4. Substituting equation 13.2.9 and 13.2.7 into 13.2.8 and solving for the maximum intensity gives: imx = −2 ⋅ Rday ⋅ ln (1 − α 0.5 )
13.2.10
where imx is the maximum rainfall intensity (mm/hr), Rday is the amount of precipitation falling on a given day (mm H2O), and α0.5 is the maximum half-hour rainfall expressed as a fraction of daily rainfall. The maximum 30 minute intensity is calculated: I 30 = 2 ⋅ α 0.5 ⋅ Rday
13.2.3
where I30 is the maximum 30-minute intensity (mm/hr), α0.5 is the maximum halfhour rainfall expressed as a fraction of daily rainfall, and Rday is the amount of precipitation falling on a given day (mm H2O). Table 13-6: SWAT input variables that pertain to USLE sediment yield. Variable Name USLE_K USLE_C USLE_P SLSUBBSN SLOPE ROCK
Definition KUSLE: USLE soil erodibility factor (0.013 metric ton m2 hr/(m3metric ton cm)) CUSLE,mn: Minimum value for the cover and management factor for the land cover PUSLE: USLE support practice factor Lhill: Slope length (m) slp: Average slope of the subbasin (% or m/m) rock: Percent rock in the first soil layer (%)
Input File .sol crop.dat .mgt .hru .hru .sol
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13.3 SNOW COVER EFFECTS The erosive power of rain and runoff will be less when snow cover is present than when there is no snow cover. During periods when snow is present in an HRU, SWAT modifies the sediment yield using the following relationship: sed =
sed ′ é 3 ⋅ SNO ù exp ê ë 25.4 úû
13.3.1
where sed is the sediment yield on a given day (metric tons), sed ′ is the sediment yield calculated with MUSLE (metric tons), and SNO is the water content of the snow cover (mm H2O).
13.4 SEDIMENT LAG IN SURFACE RUNOFF In large subbasins with a time of concentration greater than 1 day, only a portion of the surface runoff will reach the main channel on the day it is generated. SWAT incorporates a surface runoff storage feature to lag a portion of the surface runoff release to the main channel. Sediment in the surface runoff is lagged as well. Once the sediment load in surface runoff is calculated, the amount of sediment released to the main channel is calculated: æ é − surlag ù ö sed = (sed ′ + sed stor ,i −1 ) ⋅ çç 1 − exp ê ú ÷÷ t conc ûø ë è
13.4.1
where sed is the amount of sediment discharged to the main channel on a given day (metric tons), sed ′ is the amount of sediment load generated in the HRU on a given day (metric tons), sedstor,i-1 is the sediment stored or lagged from the previous day (metric tons), surlag is the surface runoff lag coefficient, and tconc is the time of concentration for the HRU (hrs).
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227
æ é − surlag ù ö The expression çç1 − exp ê ú ÷÷ in equation 13.4.1 represents the t conc ë ûø è
fraction of the total available sediment that will be allowed to enter the reach on any one day. Figure 13-1 plots values for this expression at different values for surlag and tconc.
Figure 13-1: Influence of surlag and tconc on fraction of surface runoff and sediment released.
Note that for a given time of concentration, as surlag decreases in value more sediment is held in storage. Table 13-7: SWAT input variables that pertain to sediment lag calculations. Variable Name SURLAG
Definition surlag: surface runoff lag coefficient
Input File .bsn
13.5 SEDIMENT IN LATERAL & GROUNDWATER FLOW SWAT allows the lateral and groundwater flow to contribute sediment to the main channel. The amount of sediment contributed by lateral and groundwater flow is calculated: sed lat =
(Q
lat
+ Q gw ) ⋅ area hru ⋅ conc sed 1000
13.5.1
where sedlat is the sediment loading in lateral and groundwater flow (metric tons), Qlat is the lateral flow for a given day (mm H2O), Qgw is the groundwater flow for
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a given day (mm H2O), areahru is the area of the HRU (km2), and concsed is the concentration of sediment in lateral and groundwater flow (mg/L). Table 13-8: SWAT input variables that pertain to sediment lag calculations. Variable Name LAT_SED
Definition concsed: Concentration of sediment in lateral and groundwater flow (mg/L)
Input File .hru
13.6 NOMENCLATURE CUSLE USLE cover and management factor CUSLE,aa Average annual C factor for the land cover CUSLE,mn Minimum value for the cover and management factor for the land cover CFRG Coarse fragment factor Estorm Total storm energy (0.0017 m-metric ton/m2), EIUSLE Rainfall erosion index (0.017 m-metric ton cm/(m2 hr)) I30 Maximum 30 minute intensity (mm/hr) KUSLE USLE soil erodibility factor (0.013 metric ton m2 hr/(m3-metric ton cm)) Lhill Slope length (m) LSUSLE USLE topographic factor M Particle-size parameter for estimation of USLE K factor OM Percent organic matter (%) PUSLE USLE support practice factor Qgw Groundwater flow for a given day (mm H2O) Qlat Lateral flow (mm H2O) Qsurf Surface runoff volume (mm H2O/ha) Rday Amount of rainfall on a given day (mm H2O) SNO Water content of the snow cover (mm H2O) areahru HRU area (ha or km2) cperm Profile-permeability class csoilstr Soil-structure code used in soil classification concsed Concentration of sediment in lateral and groundwater flow (mg/L) fcl-si Factor that gives low soil erodibility factors for soils with high clay to silt ratios fcsand Factor that gives low soil erodibility factors for soils with high coarse-sand contents and high values for soils with little sand fhisand Factor that reduces soil erodibility for soils with extremely high sand contents forgc Factor that reduces soil erodibility for soils with high organic carbon content imx Maximum rainfall intensity (mm/hr) it Rainfall intensity at time t (mm/hr) ki Decay constant for rainfall intensity (hr) m Exponential term in USLE LS factor calculation mc Percent clay content (< 0.002 mm diameter particles) ms Percent sand content
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msilt Percent silt content (0.002-0.05 mm diameter particles) mvfs Percent very fine sand content (0.05-0.10 mm diameter particles) orgCly Amount of organic carbon in the layer (%) qpeak Peak runoff rate (m3/s) rock Percent rock in soil layer (%) rsdsurf Amount of residue on the soil surface (kg/ha) sed Sediment yield on a given day (metric tons) sedlat Sediment loading in lateral and groundwater flow (metric tons) sedstor,i-1 Sediment stored or lagged from the previous day (metric tons) slp Average slope of the subbasin (m/m) surlag Surface runoff lag coefficient t Time (hr) tconc Time of concentration for a subbasin (hr)
α0.5 αhill
Maximum half-hour rainfall expressed as a fraction of daily rainfall Angle of the slope
13.7 REFERENCES Arnold, J.G. and J.R. Williams. 1995. SWRRB—A watershed scale model for soil and water resources management. p. 847-908. In V.P. Singh (ed) Computer models of watershed hydrology. Water Resources Publications. Williams, J.R. 1975. Sediment-yield prediction with universal equation using runoff energy factor. p. 244-252. In Present and prospective technology for predicting sediment yield and sources: Proceedings of the sedimentyield workshop, USDA Sedimentation Lab., Oxford, MS, November 2830, 1972. ARS-S-40. Williams, J.R. 1995. Chapter 25: The EPIC model. p. 909-1000. In V.P. Singh (ed.) Computer models of watershed hydrology. Water Resources Publications. Wischmeier, W.H., C.B. Johnson, and B.V. Cross. 1971. A soil erodibility nomograph for farmland and construction sites. Journal of Soil and Water Conservation 26:189-193. Wischmeier, W.H. and D.D. Smith. 1965. Predicting rainfall-erosion losses from cropland east of the Rocky Mountains. Agriculture Handbook 282. USDA-ARS
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Wischmeier, W.H. and D.D. Smith. 1978. Predicting rainfall erosion losses: a guide to conservation planning. Agriculture Handbook 282. USDA-ARS
CHAPTER 14
EQUATIONS: NUTRIENT TRANSPORT
The transport of nutrients from land areas into streams and water bodies is a normal result of soil weathering and erosion processes. However, excessive loading of nutrients into streams and water bodies will accelerate eutrophication and render the water unfit for human consumption. This chapter reviews the algorithms governing movement of mineral and organic forms of nitrogen and phosphorus from land areas to the stream network.
231
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14.1 NITRATE MOVEMENT Most soil minerals are negatively charged at normal pH and the net interaction with anions such as nitrate is a repulsion from particle surfaces. This repulsion is termed negative adsorption or anion exclusion. Anions are excluded from the area immediately adjacent to mineral surfaces due to preferential attraction of cations to these sites. This process has a direct impact on the transport of anions through the soil for it effectively excludes anions from the slowest moving portion of the soil water volume found closest to the charged particle surfaces (Jury et al, 1991). In effect, the net pathway of the anion through the soil is shorter than it would be if all the soil water had to be used (Thomas and McMahon, 1972). Nitrate may be transported with surface runoff, lateral flow or percolation. To calculate the amount of nitrate moved with the water, the concentration of nitrate in the mobile water is calculated. This concentration is then multiplied by the volume of water moving in each pathway to obtain the mass of nitrate lost from the soil layer. The concentration of nitrate in the mobile water fraction is calculated:
conc NO 3,mobile
é − wmobile ù NO3ly ⋅ exp ê ú ê (1 − θ e ) ⋅ SATly ûú ë = wmobile
14.1.2
where concNO3,mobile is the concentration of nitrate in the mobile water for a given layer (kg N/mm H2O), NO3ly is the amount of nitrate in the layer (kg N/ha), wmobile is the amount of mobile water in the layer (mm H2O), θe is the fraction of porosity from which anions are excluded, and SATly is the saturated water content of the soil layer (mm H2O). The amount of mobile water in the layer is the amount of water lost by surface runoff, lateral flow or percolation: wmobile = Qsurf + Qlat ,ly + w perc ,ly
for top 10 mm
14.1.3
wmobile = Qlat ,ly + w perc ,ly
for lower soil layers
14.1.4
where wmobile is the amount of mobile water in the layer (mm H2O), Qsurf is the surface runoff generated on a given day (mm H2O), Qlat,ly is the water discharged
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233
from the layer by lateral flow (mm H2O), and wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O). Surface runoff is allowed to interact with and transport nutrients from the top 10 mm of soil. Nitrate removed in surface runoff is calculated: NO3surf = β NO 3 ⋅ conc NO 3,mobile ⋅ Qsurf
14.1.5
where NO3surf is the nitrate removed in surface runoff (kg N/ha), βNO3 is the nitrate percolation coefficient, concNO3,mobile is the concentration of nitrate in the mobile water for the top 10 mm of soil (kg N/mm H2O), and Qsurf is the surface runoff generated on a given day (mm H2O). The nitrate percolation coefficient allows the user to set the concentration of nitrate in surface runoff to a fraction of the concentration in percolate. Nitrate removed in lateral flow is calculated: NO3lat ,ly = β NO 3 ⋅ conc NO 3,mobile ⋅ Qlat ,ly
for top 10 mm
14.1.6
NO3lat ,ly = conc NO 3,mobile ⋅ Qlat ,ly
for lower layers
14.1.7
where NO3lat,ly is the nitrate removed in lateral flow from a layer (kg N/ha), βNO3 is the nitrate percolation coefficient, concNO3,mobile is the concentration of nitrate in the mobile water for the layer (kg N/mm H2O), and Qlat,ly is the water discharged from the layer by lateral flow (mm H2O). Nitrate moved to the underlying layer by percolation is calculated: NO3 perc ,ly = conc NO 3,mobile ⋅ w perc ,ly
14.1.8
where NO3perc,ly is the nitrate moved to the underlying layer by percolation (kg N/ha), concNO3,mobile is the concentration of nitrate in the mobile water for the layer (kg N/mm H2O), and wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O). Table 14-1: SWAT input variables that pertain to nitrate transport. Variable Name ANION_EXCL NPERCO
Definition θe: Fraction of porosity from which anions are excluded βNO3: Nitrate percolation coefficient
Input File .sol .bsn
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14.2 ORGANIC N IN SURFACE RUNOFF Organic N attached to soil particles may be transported by surface runoff to the main channel. This form of nitrogen is associated with the sediment loading from the HRU and changes in sediment loading will be reflected in the organic nitrogen loading. The amount of organic nitrogen transported with sediment to the stream is calculated with a loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978). orgN surf = 0.001 ⋅ concorgN ⋅
sed ⋅ ε N :sed area hru
14.2.1
where orgNsurf is the amount of organic nitrogen transported to the main channel in surface runoff (kg N/ha), concorgN is the concentration of organic nitrogen in the top 10 mm (g N/ metric ton soil), sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and εN:sed is the nitrogen enrichment ratio. The concentration of organic nitrogen in the soil surface layer, concorgN, is calculated: concorgN = 100 ⋅
(orgN
frsh , surf
+ orgN sta ,surf + orgN act ,surf )
ρ b ⋅ depthsurf
14.2.2
where orgNfrsh,surf is nitrogen in the fresh organic pool in the top 10mm (kg N/ha), orgNsta,surf is nitrogen in the stable organic pool (kg N/ha), orgNact,surf is nitrogen in the active organic pool in the top 10 mm (kg N/ha), ρb is the bulk density of the first soil layer (Mg/m3), and depthsurf is the depth of the soil surface layer (10 mm).
14.2.1 ENRICHMENT RATIO As surface runoff flows over the soil surface, part of the water’s energy is used to pick up and transport soil particles. The smaller particles weigh less and are more easily transported than coarser particles. When the particle size distribution of the transported sediment is compared to that of the soil surface layer, the sediment load to the main channel has a greater proportion of clay sized particles. In other words, the sediment load is enriched in clay particles. Organic
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235
nitrogen in the soil is attached primarily to colloidal (clay) particles, so the sediment load will also contain a greater proportion or concentration of organic N than that found in the soil surface layer. The enrichment ratio is defined as the ratio of the concentration of organic nitrogen transported with the sediment to the concentration in the soil surface layer. SWAT will calculate an enrichment ratio for each storm event, or allow the user to define a particular enrichment ratio for organic nitrogen that is used for all storms during the simulation. To calculate the enrichment ratio, SWAT uses a relationship described by Menzel (1980) in which the enrichment ratio is logarithmically related to sediment concentration. The equation used to calculate the nitrogen enrichment ratio, εN:sed, for each storm event is:
ε N :sed = 0.78 ⋅ (conc sed ,surq )−0.2468
14.2.3
where concsed,surq is the concentration of sediment in surface runoff (Mg sed/m3 H2O). The concentration of sediment in surface runoff is calculated:
conc sed ,surq =
sed 10 ⋅ area hru ⋅ Qsurf
14.2.4
where sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and Q,surf is the amount of surface runoff on a given day (mm H2O). Table 14-2: SWAT input variables that pertain to organic N loading. Variable Name SOL_BD ERORGN
Definition
ρb: Bulk density (Mg/m3) εN:sed: Organic nitrogen enrichment ratio
Input File .sol .hru
14.3 SOLUBLE PHOSPHORUS MOVEMENT The primary mechanism of phosphorus movement in the soil is by diffusion. Diffusion is the migration of ions over small distances (1-2 mm) in the soil solution in response to a concentration gradient. Due to the low mobility of solution phosphorus, surface runoff will only partially interact with the solution P stored in the top 10 mm of soil. The amount of solution P transported in surface runoff is:
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SWAT USER'S MANUAL, VERSION 2000
Psurf =
Psolution,surf ⋅ Qsurf
14.3.1
ρ b ⋅ depthsurf ⋅ k d ,surf
where Psurf is the amount of soluble phosphorus lost in surface runoff (kg P/ha), Psolution,surf is the amount of phosphorus in solution in the top 10 mm (kg P/ha), Q,surf is the amount of surface runoff on a given day (mm H2O), ρb is the bulk density of the top 10 mm (Mg/m3) (assumed to be equivalent to bulk density of first soil layer), depthsurf is the depth of the “surface” layer (10 mm), and kd,surf is the phosphorus soil partitioning coefficient (m3/Mg). The phosphorus soil partitioning coefficient is the ratio of the soluble phosphorus concentration in the surface 10 mm of soil to the concentration of soluble phosphorus in surface runoff. Table 14-3: SWAT input variables that pertain to soluble P runoff. Variable Name SOL_BD PHOSKD
Definition
ρb: Bulk density (Mg/m3) kd,surf: Phosphorus soil partitioning coefficient (m3/Mg)
Input File .sol .bsn
14.4 ORGANIC & MINERAL P ATTACHED TO SEDIMENT IN SURFACE RUNOFF Organic and mineral P attached to soil particles may be transported by surface runoff to the main channel. This form of phosphorus is associated with the sediment loading from the HRU and changes in sediment loading will be reflected in the loading of these forms of phophorus. The amount of phosphorus transported with sediment to the stream is calculated with a loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978). sedPsurf = 0.001 ⋅ conc sedP ⋅
sed ⋅ ε P:sed area hru
14.4.1
where sedPsurf is the amount of phosphorus transported with sediment to the main channel in surface runoff (kg P/ha), concsedP is the concentration of phosphorus attached to sediment in the top 10 mm (g P/ metric ton soil), sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and εP:sed is the phosphorus enrichment ratio.
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237
The concentration of phosphorus attached to sediment in the soil surface layer, concsedP, is calculated: conc sedP = 100 ⋅
(minP
act ,surf
+ minPsta ,surf + orgPhum ,surf + orgPfrsh ,surf )
ρ b ⋅ depthsurf
14.4.2
where minPact,surf is the amount of phosphorus in the active mineral pool in the top 10 mm (kg P/ha), minPsta,surf is the amount of phosphorus in the stable mineral pool in the top 10 mm (kg P/ha), orgPhum,surf is the amount of phosphorus in humic organic pool in the top 10 mm (kg P/ha), orgPfrsh,surf is the amount of phosphorus in the fresh organic pool in the top 10 mm (kg P/ha), ρb is the bulk density of the first soil layer (Mg/m3), and depthsurf is the depth of the soil surface layer (10 mm).
14.4.1 ENRICHMENT RATIO The enrichment ratio is defined as the ratio of the concentration of phosphorus transported with the sediment to the concentration of phosphorus in the soil surface layer. SWAT will calculate an enrichment ratio for each storm event, or allow the user to define a particular enrichment ratio for phosphorus attached to sediment that is used for all storms during the simulation. To calculate the enrichment ratio, SWAT uses a relationship described by Menzel (1980) in which the enrichment ratio is logarithmically related to sediment concentration. The equation used to calculate the phosphorus enrichment ratio, εP:sed, for each storm event is:
ε P:sed = 0.78 ⋅ (concsed ,surq )−0.2468
14.4.3
where concsed,surq is the concentration of sediment in surface runoff (Mg sed/m3 H2O). The concentration of sediment in surface runoff is calculated: conc sed ,surq =
sed 10 ⋅ area hru ⋅ Qsurf
14.4.4
where sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and Qsurf is the amount of surface runoff on a given day (mm H2O).
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Table 14-4: SWAT input variables that pertain to loading of P attached to sediment. Variable Name SOL_BD ERORGP
Definition
ρb: Bulk density (Mg/m3) εP:sed: Phosphorus enrichment ratio
Input File .sol .hru
14.5 NUTRIENT LAG IN SURFACE RUNOFF AND LATERAL FLOW In large subbasins with a time of concentration greater than 1 day, only a portion of the surface runoff and lateral flow will reach the main channel on the day it is generated. SWAT incorporates a storage feature to lag a portion of the surface runoff and lateral flow release to the main channel. Nutrients in the surface runoff and lateral flow are lagged as well. Once the nutrient load in surface runoff and lateral flow is determined, the amount of nutrients released to the main channel is calculated: æ é − surlag ù ö ′ + NO3surstor ,i −1 ) ⋅ ç1 − exp ê NO3surf = (NO3surf ú ÷÷ ç ë t conc û ø è
14.5.1
æ é − 1 ùö ′ + NO3latstor ,i −1 ) ⋅ ç1 − exp ê NO3lat = (NO3lat ú ÷÷ ç TT ë lat û ø è
14.5.2
æ é − surlag ù ö ′ + orgN stor , i −1 ) ⋅ ç1 − exp ê orgN surf = (orgN surf ú ÷÷ ç t ë conc û ø è
14.5.3
æ é − surlag ù ö ′ + Pstor ,i −1 ) ⋅ ç 1 − exp ê Psurf = (Psurf ú ÷÷ ç t conc ûø ë è
14.5.4
æ é − surlag ù ö ′ + sedPstor ,i −1 ) ⋅ ç 1 − exp ê sedPsurf = (sedPsurf ú ÷÷ ç ë t conc û ø è
14.5.5
where NO3surf is the amount of nitrate discharged to the main channel in surface ′ is the amount of surface runoff nitrate runoff on a given day (kg N/ha), NO3surf
generated in the HRU on a given day (kg N/ha), NO3surstor,i-1 is the surface runoff nitrate stored or lagged from the previous day (kg N/ha), NO3lat is the amount of nitrate discharged to the main channel in lateral flow on a given day (kg N/ha),
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′ is the amount of lateral flow nitrate generated in the HRU on a given day NO3lat (kg N/ha), NO3latstor,i-1 is the lateral flow nitrate stored or lagged from the previous day (kg N/ha), orgNsurf is the amount of organic N discharged to the main channel ′ is the organic N loading in surface runoff on a given day (kg N/ha), orgN surf
generated in the HRU on a given day (kg N/ha), orgNstor,i-1 is the organic N stored or lagged from the previous day (kg N/ha), Psurf is the amount of solution P ′ is discharged to the main channel in surface runoff on a given day (kg P/ha), Psurf
the amount of solution P loading generated in the HRU on a given day (kg P/ha), Pstor,i-1 is the solution P loading stored or lagged from the previous day (kg P/ha), sedPsurf is the amount of sediment-attached P discharged to the main channel in ′ is the amount of sedimentsurface runoff on a given day (kg P/ha), sedPsurf
attached P loading generated in the HRU on a given day (kg P/ha), sedPstor,i-1 is the sediment-attached P stored or lagged from the previous day (kg P/ha), surlag is the surface runoff lag coefficient, tconc is the time of concentration for the HRU (hrs) and TTlag is the lateral flow travel time (days). Table 14-5: SWAT input variables that pertain to nutrient lag calculations. Variable Name SURLAG LAT_TTIME
Definition surlag: surface runoff lag coefficient TTlag: Lateral flow travel time (days)
Input File .bsn .hru
14.6 NOMENCLATURE NO3lat,ly Nitrate removed in lateral flow from a layer (kg N/ha) ′ Amount of lateral flow nitrate generated in HRU on a given day (kg N/ha) NO3lat NO3latstor,i-1 Lateral flow nitrate stored or lagged from the previous day (kg N/ha) NO3ly Amount of nitrate in the layer (kg N/ha) NO3perc,ly Nitrate moved to the underlying layer by percolation (kg N/ha) NO3surf Nitrate removed in surface runoff (kg N/ha) ′ Amount of surface runoff nitrate generated in HRU on a given day (kg N/ha) NO3surf NO3surstor,i-1 Surface runoff nitrate stored or lagged from the previous day (kg N/ha) Psolution,surf Amount of phosphorus in solution in the top 10 mm (kg P/ha) Pstor,i-1 Solution P loading stored or lagged from the previous day (kg P/ha) Psurf Amount of soluble phosphorus lost in surface runoff (kg P/ha) ′ Psurf Amount of solution P loading generated in HRU on a given day (kg P/ha)
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Lateral flow from soil layer (mm H2O) Accumulated runoff or rainfall excess (mm H2O) Saturated water content of the soil layer (mm H2O) Lateral flow travel time (days)
areahru HRU area (ha) concNO3,mobile Concentration of nitrate in the mobile water for a given layer (kg N/mm H2O) concorgN Concentration of organic nitrogen in the soil surface top 10 mm (g N/ metric ton soil) concsed,surq Concentration of sediment in surface runoff (Mg sed/m3 H2O) concsedP Concentration of phosphorus attached to sediment in the top 10 mm (g P/ metric ton soil) depthsurf Depth of the “surface” layer (10 mm) kd,surf Phosphorus soil partitioning coefficient (m3/Mg) minPact,ly Amount of phosphorus in the active mineral pool (kg P/ha) minPsta,ly Amount of phosphorus in the stable mineral pool (kg P/ha) orgNact,ly Nitrogen in the active organic pool (mg/kg or kg N/ha) orgNfrsh,surf Nitrogen in the fresh organic pool in the top 10mm (kg N/ha) orgNsta,ly Nitrogen in the stable organic pool (mg/kg or kg N/ha) orgNstor,i-1 Surface runoff organic N stored or lagged from the previous day (kg N/ha) orgNsurf Amount of organic nitrogen transport to the main channel in surface runoff (kg N/ha) ′ orgN surf Amount of surface runoff organic N generated in HRU on a given day (kg N/ha) orgPfrsh,ly Phosphorus in the fresh organic pool in layer ly (kg P/ha) orgPhum,ly Amount of phosphorus in humic organic pool in the layer (kg P/ha) sed Sediment yield on a given day (metric tons) sedPstor,i-1 Sediment-attached P stored or lagged from the previous day (kg P/ha) sedPsurf Amount of phosphorus transported with sediment to the main channel in surface runoff (kg P/ha) ′ Amount of sediment-attached P loading generated in HRU on a given day (kg sedPsurf P/ha) surlag Surface runoff lag coefficient tconc Time of concentration for a subbasin (hr) wmobile Amount of mobile water in the layer (mm H2O) wperc,ly Amount of water percolating to the underlying soil layer on a given day (mm H2O)
βNO3 θe εN:sed εP:sed ρb
Nitrate percolation coefficient Fraction of porosity from which anions are excluded Nitrogen enrichment ratio Phosphorus enrichment ratio Bulk density (Mg/m3)
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14.7 REFERENCES Jury, W.A., W.R. Gardner, and W.H. Gardner. 1991. Soil physics, 5th edition. John Wiley & Sons, Inc. New York, N.Y. McElroy, A.D., S.Y. Chiu, J.W. Nebgen, A. Aleti, and F.W. Bennett. 1976. Loading functions for assessment of water pollution from nonpoint sources. Environ. Prot. Tech. Serv., EPA 600/2-76-151. Menzel, R.G. 1980. Enrichment ratios for water quality modeling. p. 486-492. In W.G. Knisel (ed.) CREAMS, A field scale model for chemicals, runoff, and erosion from agricultural management systems. U.S. Dept. Agric. Conserv. Res. Rept. No. 26. Thomas, G.W. and M. McMahon. 1972. The relation between soil characteristics, water movement and nitrate concentration of ground water. Univ. of Kentucky Water Resources Institute Research Report No. 52, Lexington, KY. Williams, J.R. and R.W. Hann. 1978. Optimal operation of large agricultural watersheds with water quality constraints. Texas Water Resources Institute, Texas A&M Univ., Tech. Rept. No. 96.
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CHAPTER 15
EQUATIONS: PESTICIDE TRANSPORT
The transport of pesticide from land areas into streams and water bodies is a result of soil weathering and erosion processes. Excessive loading of pesticides in streams and water bodies can produce toxic conditions that harm aquatic life and render the water unfit for human consumption. This chapter reviews the algorithms governing movement of soluble and sorbed forms of pesticide from land areas to the stream network. Pesticide transport algorithms in SWAT were taken from EPIC (Williams, 1995). 243
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15.1 PHASE DISTRIBUTION OF PESTICIDE Pesticide in the soil environment can be transported in solution or attached to sediment. The partitioning of a pesticide between the solution and soil phases is defined by the soil adsorption coefficient for the pesticide. The soil adsorption coefficient is the ratio of the pesticide concentration in the soil or solid phase to the pesticide concentration in the solution or liquid phase: Kp =
C solidphase C solution
15.1.1
where Kp is the soil adsorption coefficient ((mg/kg)/(mg/L) or m3/ton), Csolidphase is the concentration of the pesticide sorbed to the solid phase (mg chemical/kg solid material or g/ton), and Csolution is the concentration of the pesticide in solution (mg chemical/L solution or g/ton). The definition of the soil adsorption coefficient in equation 15.1.1 assumes that the pesticide sorption process is linear with concentration and instantaneously reversible. Because the partitioning of pesticide is dependent upon the amount of organic material in the soil, the soil adsorption coefficient input to the model is normalized for soil organic carbon content. The relationship between the soil adsorption coefficient and the soil adsorption coefficient normalized for soil organic carbon content is: K p = K oc ⋅
orgC 100
15.1.2
where Kp is the soil adsorption coefficient ((mg/kg)/(mg/L)), Koc is the soil adsorption coefficient normalized for soil organic carbon content ((mg/kg)/(mg/L) or m3/ton), and orgC is the percent organic carbon present in the soil. Table 15-1: SWAT input variables that pertain to pesticide phase partitioning. Variable Name SOL_CBN SKOC
Definition orgCly: Amount of organic carbon in the layer (%) Koc: Soil adsorption coefficient normalized for soil organic carbon content (ml/g or (mg/kg)/(mg/L) or L/kg)
Input File .sol pest.dat
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245
15.2 MOVEMENT OF SOLUBLE PESTICIDE Pesticide in the soluble phase may be transported with surface runoff, lateral flow or percolation. The change in the amount of pesticide contained in a soil layer due to transport in solution with flow is a function of time, concentration and amount of flow: dpst s ,ly dt
= 0.01 ⋅ C solution ⋅ wmobile
15.2.1
where psts,ly is the amount of pesticide in the soil layer (kg pst/ha), Csolution is the pesticide concentration in solution (mg/L or g/ton), and wmobile is the amount of mobile water on a given day (mm H2O). The amount of mobile water in the layer is the amount of water lost by surface runoff, lateral flow or percolation: wmobile = Qsurf + Qlat ,surf + w perc ,surf
for top 10 mm
15.2.2
wmobile = Qlat ,ly + w perc ,ly
for lower soil layers
15.2.3
where wmobile is the amount of mobile water in the layer (mm H2O), Qsurf is the surface runoff generated on a given day (mm H2O), Qlat,ly is the water discharged from the layer by lateral flow (mm H2O), and wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O). The total amount of pesticide in the soil layer is the sum of the adsorbed and dissolved phases: pst s ,ly = 0.01 ⋅ (C solution ⋅ SATly + C solidphase ⋅ ρ b ⋅ depthly )
15.2.4
where psts,ly is the amount of pesticide in the soil layer (kg pst/ha), Csolution is the pesticide concentration in solution (mg/L or g/ton), SATly is the amount of water in the soil layer at saturation (mm H2O), Csolidphase is the concentration of the pesticide sorbed to the solid phase (mg/kg or g/ton), ρb is the bulk density of the soil layer (Mg/m3), and depthly is the depth of the soil layer (mm). Rearranging equation 15.1.1 to solve for Csolidphase and substituting into equation 15.2.4 yields: pst s ,ly = 0.01 ⋅ (C solution ⋅ SATly + C solution ⋅ K p ⋅ ρ b ⋅ depthly )
which rearranges to
15.2.5
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C solution =
pst s ,ly
0.01 ⋅ (SATly + K p ⋅ ρ b ⋅ depthly )
15.2.6
Combining equation 15.2.6 with equation 15.2.1 yields dpst s ,ly dt
=
(SAT
ly
pst s ,ly ⋅ wmobile
+ K p ⋅ ρ b ⋅ depthly )
15.2.7
Integration of equation 15.2.7 gives é ù − wmobile pst s ,ly ,t = pst s ,ly ,o ⋅ exp ê ú êë (SATly + K p ⋅ ρ b ⋅ depthly )úû
15.2.8
where psts,ly,t is the amount of pesticide in the soil layer at time t (kg pst/ha), psts,ly,o is the initial amount of pesticide in the soil layer (kg pst/ha), wmobile is the amount of mobile water in the layer (mm H2O), SATly is the amount of water in the soil layer at saturation (mm H2O), Kp is the soil adsorption coefficient ((mg/kg)/(mg/L)), ρb is the bulk density of the soil layer (Mg/m3), and depthly is the depth of the soil layer (mm). To obtain the amount of pesticide removed in solution with the flow, the final amount of pesticide is subtracted from the initial amount of pesticide: æ é ùö − wmobile pst flow = pst s ,ly ,o ⋅ ç1 − exp ê ú ÷÷ ç ( ) + ⋅ ⋅ SAT K ρ depth ê úû ø ly p b ly ë è
15.2.9
where pstflow is the amount of pesticide removed in the flow (kg pst/ha) and all other terms were previously defined. The pesticide concentration in the mobile water is calculated:
conc pst , flow
ì pst flow / wmobile ï = min í ï pst / 100. sol î
15.2.10
where concpst,flow is the concentration of pesticide in the mobile water (kg pst/hamm H2O), pstflow is the amount of pesticide removed in the flow (kg pst/ha), wmobile is the amount of mobile water in the layer (mm H2O), and pstsol is the solubility of the pesticide in water (mg/L). Pesticide moved to the underlying layer by percolation is calculated: pst perc ,ly = conc pst , flow ⋅ w perc ,ly
15.2.11
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247
where pstperc,ly is the pesticide moved to the underlying layer by percolation (kg pst/ha), concpst,flow is the concentration of pesticide in the mobile water for the layer (kg pst/mm H2O), and wperc,ly is the amount of water percolating to the underlying soil layer on a given day (mm H2O). Pesticide removed in lateral flow is calculated: pstlat ,surf = β pst ⋅ conc pst , flow ⋅ Qlat ,surf
for top 10 mm
15.2.12
pstlat ,ly = conc pst , flow ⋅ Qlat ,ly
for lower layers
15.2.13
where pstlat,ly is the pesticide removed in lateral flow from a layer (kg pst/ha), βpst is the pesticide percolation coefficient, concpst,flow is the concentration of pesticide in the mobile water for the layer (kg pst/mm H2O), and Qlat,ly is the water discharged from the layer by lateral flow (mm H2O). The pesticide percolation coefficient allows the user to set the concentration of pesticide in runoff and lateral flow from the top 10 mm to a fraction of the concentration in percolate. Pesticide removed in surface runoff is calculated: pst surf = β pst ⋅ conc pst , flow ⋅ Qsurf
15.2.14
where pstsurf is the pesticide removed in surface runoff (kg pst/ha), βpst is the pesticide percolation coefficient, concpst,flow is the concentration of pesticide in the mobile water for the top 10 mm of soil (kg pst/mm H2O), and Qsurf is the surface runoff generated on a given day (mm H2O). Table 15-2: SWAT input variables that pertain to pesticide transport in solution. Variable Name SOL_BD WSOL PERCOP
Definition ρb: Soil bulk density (Mg m-3) pstsol: Solubility of the pesticide in water (mg/L) βpst: Pesticide percolation coefficient
Input File .sol pest.dat .bsn
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15.3 TRANSPORT OF SORBED PESTICIDE Pesticide attached to soil particles may be transported by surface runoff to the main channel. This phase of pesticide is associated with the sediment loading from the HRU and changes in sediment loading will be reflected in the loading of sorbed pesticide. The amount of pesticide transported with sediment to the stream is calculated with a loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978). pst sed = 0.001 ⋅ C solidphase ⋅
sed ⋅ ε pst:sed area hru
15.3.1
where pstsed is the amount of sorbed pesticide transported to the main channel in surface runoff (kg pst/ha), Csolidphase is the concentration of pesticide on sediment in the top 10 mm (g pst/ metric ton soil), sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and εpst:sed is the pesticide enrichment ratio. The total amount of pesticide in the soil layer is the sum of the adsorbed and dissolved phases: pst s ,ly = 0.01 ⋅ (C solution ⋅ SATly + C solidphase ⋅ ρ b ⋅ depthly )
15.3.2
where psts,ly is the amount of pesticide in the soil layer (kg pst/ha), Csolution is the pesticide concentration in solution (mg/L or g/ton), SATly is the amount of water in the soil layer at saturation (mm H2O), Csolidphase is the concentration of the pesticide sorbed to the solid phase (mg/kg or g/ton), ρb is the bulk density of the soil layer (Mg/m3), and depthly is the depth of the soil layer (mm). Rearranging equation 15.1.1 to solve for Csolution and substituting into equation 15.3.2 yields: æ C solidphase ö pst s ,ly = 0.01 ⋅ ç ⋅ SATly + C solidphase ⋅ ρ b ⋅ depthly ÷ ç K ÷ p è ø
15.3.3
which rearranges to C solidphase =
100 ⋅ K p ⋅ pst s ,ly
(SAT
ly
+ K p ⋅ ρ b ⋅ depthly )
15.3.4
where Csolidphase is the concentration of the pesticide sorbed to the solid phase (mg/kg or g/ton), Kp is the soil adsorption coefficient ((mg/kg)/(mg/L) or m3/ton)
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249
psts,ly is the amount of pesticide in the soil layer (kg pst/ha), SATly is the amount of water in the soil layer at saturation (mm H2O), , ρb is the bulk density of the soil layer (Mg/m3), and depthly is the depth of the soil layer (mm).
15.3.1 ENRICHMENT RATIO As surface runoff flows over the soil surface, part of the water’s energy is used to pick up and transport soil particles. The smaller particles weigh less and are more easily transported than coarser particles. When the particle size distribution of the transported sediment is compared to that of the soil surface layer, the sediment load to the main channel has a greater proportion of clay sized particles. In other words, the sediment load is enriched in clay particles. The sorbed phase of pesticide in the soil is attached primarily to colloidal (clay) particles, so the sediment load will also contain a greater proportion or concentration of pesticide than that found in the soil surface layer. The enrichment ratio is defined as the ratio of the concentration of sorbed pesticide transported with the sediment to the concentration in the soil surface layer. SWAT will calculate an enrichment ratio for each storm event, or allow the user to define a particular enrichment ratio for sorbed pesticide that is used for all storms during the simulation. To calculate the enrichment ratio, SWAT uses a relationship described by Menzel (1980) in which the enrichment ratio is logarithmically related to sediment concentration. The equation used to calculate the pesticide enrichment ratio, εpst:sed, for each storm event is:
ε pst:sed = 0.78 ⋅ (concsed ,surq )−0.2468
15.3.5
where concsed,surq is the concentration of sediment in surface runoff (Mg sed/m3 H2O). The concentration of sediment in surface runoff is calculated:
conc sed ,surq =
sed 10 ⋅ area hru ⋅ Qsurf
15.3.6
where sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and Qsurf is the amount of surface runoff on a given day (mm H2O).
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Table 15-3: SWAT input variables that pertain to sorbed pesticide loading. Variable Name SOL_BD PSTENR
Definition
ρb: Bulk density (Mg/m3) εpst:sed: Pesticide enrichment ratio
Input File .sol .chm
15.4 PESTICIDE LAG IN SURFACE RUNOFF AND LATERAL FLOW In large subbasins with a time of concentration greater than 1 day, only a portion of the surface runoff and lateral flow will reach the main channel on the day it is generated. SWAT incorporates a storage feature to lag a portion of the surface runoff and lateral flow release to the main channel. Pesticides in the surface runoff and lateral flow are lagged as well. Once the pesticide load in surface runoff and lateral flow is determined, the amount of pesticide released to the main channel is calculated: æ é − surlag ù ö ′ + pst surstor,i −1 ) ⋅ ç1 − exp ê pst surf = ( pst surf ú ÷÷ ç ë tconc û ø è
15.4.1
æ é −1 ùö ′ + pstlatstor ,i −1 ) ⋅ ç1 − exp ê pstlat = ( pstlat ú ÷÷ ç TT ë lat û ø è
15.4.2
æ é − surlag ù ö ′ + pst sedstor ,i −1 ) ⋅ ç1 − exp ê pst sed = ( pst sed ú ÷÷ ç t ë conc û ø è
15.4.3
where pstsurf is the amount of soluble pesticide discharged to the main channel in ′ is the amount of surface runoff surface runoff on a given day (kg pst/ha), pst surf
soluble pesticide generated in HRU on a given day (kg pst/ha), pstsurstor,i-1 is the surface runoff soluble pesticide stored or lagged from the previous day (kg pst/ha), pstlat is the amount of soluble pesticide discharged to the main channel in ′ is the amount of lateral flow soluble lateral flow on a given day (kg pst/ha), pstlat pesticide generated in HRU on a given day (kg pst/ha), pstlatstor,i-1 is the lateral flow pesticide stored or lagged from the previous day (kg pst/ha), pstsed is the amount of sorbed pesticide discharged to the main channel in surface runoff on a
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251
′ is the sorbed pesticide loading generated in HRU on given day (kg pst/ha), pst sed a given day (kg pst/ha), pstsedstor,i-1 is the sorbed pesticide stored or lagged from the previous day (kg pst/ha), surlag is the surface runoff lag coefficient, tconc is the time of concentration for the HRU (hrs) and TTlag is the lateral flow travel time (days). Table 15-4: SWAT input variables that pertain to pesticide lag calculations. Variable Name SURLAG LAT_TTIME
Definition surlag: surface runoff lag coefficient TTlag: Lateral flow travel time (days)
Input File .bsn .hru
15.5 NOMENCLATURE Csolidphase Concentration of the pesticide sorbed to the solid phase (mg/kg or g/ton) Csolution Concentration of the pesticide in solution (mg/L or g/ton) Soil adsorption coefficient normalized for soil organic carbon content (ml/g or Koc (mg/kg)/(mg/L) or L/kg) Soil adsorption coefficient ((mg/kg)/(mg/L)) Kp Lateral flow from soil layer (mm H2O) Qlat Qsurf Accumulated runoff or rainfall excess (mm H2O) SATly Soil water content of layer ly at saturation (mm H2O) TTlag Lateral flow travel time (days) areahru HRU area (ha) concpst,flow Concentration of pesticide in the mobile water (kg pst/ha-mm H2O) concsed,surq Concentration of sediment in surface runoff (Mg sed/m3 H2O) depthly Depth of the soil layer (mm) orgCly Amount of organic carbon in the layer (%) pstflow Amount of pesticide removed in the flow (kg pst/ha) pstlat,ly Pesticide removed in lateral flow from a layer (kg pst/ha) ′ Amount of lateral flow soluble pesticide generated in HRU on a given day (kg pstlat pst/ha) pstlatstor,i-1 Lateral flow pesticide stored or lagged from the previous day (kg pst/ha) pstperc,ly Pesticide moved to the underlying layer by percolation (kg pst/ha) psts,ly Amount of pesticide in the soil (kg pst/ha) pstsed Amount of sorbed pesticide transported to the main channel in surface runoff (kg pst/ha) ′ Sorbed pesticide loading generated in HRU on a given day (kg pst/ha) pst sed pstsedstor,i-1 Sorbed pesticide stored or lagged from the previous day (kg pst/ha) pstsol Solubility of the pesticide in water (mg/L) pstsurf Pesticide removed in surface runoff (kg pst/ha)
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′ Amount of surface runoff soluble pesticide generated in HRU on a given day (kg pst surf pst/ha) pstsurstor,i-1 Surface runoff soluble pesticide stored or lagged from the previous day (kg pst/ha) sed Sediment yield on a given day (metric tons) surlag Surface runoff lag coefficient tconc Time of concentration for a subbasin (hr) wmobile Amount of mobile water in the layer (mm H2O) wperc,ly Amount of water percolating to the underlying soil layer on a given day (mm H2O)
βpst εpst:sed ρb ρw
Pesticide percolation coefficient Pesticide enrichment ratio Soil bulk density (Mg m-3) Density of water (1 Mg m-3)
15.6 REFERENCES McElroy, A.D., S.Y. Chiu, J.W. Nebgen, A. Aleti, and F.W. Bennett. 1976. Loading functions for assessment of water pollution from nonpoint sources. Environ. Prot. Tech. Serv., EPA 600/2-76-151. Menzel, R.G. 1980. Enrichment ratios for water quality modeling. p. 486-492. In W.G. Knisel (ed.) CREAMS, A field scale model for chemicals, runoff, and erosion from agricultural management systems. U.S. Dept. Agric. Conserv. Res. Rept. No. 26. Williams, J.R. 1995. Chapter 25: The EPIC model. p. 909-1000. In V.P. Singh (ed.). Computer models of watershed hydrology. Water Resources Publications. Williams, J.R. and R.W. Hann. 1978. Optimal operation of large agricultural watersheds with water quality constraints. Texas Water Resources Institute, Texas A&M Univ., Tech. Rept. No. 96.
CHAPTER 16
EQUATIONS: WATER QUALITY PARAMETERS
In addition to sediment, nutrients and pesticides, SWAT calculates the amount of algae, dissolved oxygen and carbonaceous biological oxygen demand (CBOD) entering the main channel with surface runoff. Loadings of these three parameters are required to monitor the quality of stream water. This chapter reviews the algorithms governing movement of algae, dissolved oxygen and CBOD from land areas to the stream network.
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16.1 ALGAE Suspended algal biomass is assumed to be directly proportional to chlorophyll a. Therefore, the algal biomass loading to the stream can be estimated as the chlorophyll a loading from the land area. Cluis et al. (1988) developed a relationships between the nutrient enrichment index (total N: total P), chlorophyll a, and algal growth potential in the North Yamaska River, Canada.
( AGP + chla ) ⋅ v surf
æ TN ö = f ⋅ç ÷ è TP ø
g
16.1.1
where AGP is the algal growth potential (mg/L), chla is the chlorophyll a concentration in the surface runoff (µg/L), vsurf is the surface runoff flow rate (m3/s), TN is the total Kjeldahl nitrogen load (kmoles), TP is the total phosphorus load (kmoles), f is a coefficient and g is an exponent. The chlorophyll a concentration in surface runoff is calculated in SWAT using a simplified version of Cluis et al.’s exponential function (1988): chla = 0
if ( v surf < 10 −5 m 3 /s ) or ( TP and TN < 10 −6 )
16.1.2
chla =
0.5 ⋅ 10 2.7 v surf
if v surf > 10 −5 m 3 /s , and ( TP and TN > 10 −6 )
16.1.3
chla =
0.5 ⋅ 10 0.5 v surf
if v surf > 10 −5 m 3 /s , TP < 10 −6 and TN > 10 −6
16.1.4
16.2 CARBONACEOUS BIOLOGICAL OXYGEN DEMAND Carbonaceous biological oxygen demand (CBOD) defines the amount of oxygen required to decompose the organic matter transported in surface runoff. The SWAT loading function for the ultimate CBOD is based on a relationship given by Thomann and Mueller (1987): cbod surq =
2.7 ⋅ orgC surq Qsurf ⋅ area hru
16.2.1
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255
where cbodsurq is the CBOD concentration in surface runoff (mg CBOD/L), orgCsurq is the organic carbon in surface runoff (kg orgC), Qsurf is the surface runoff on a given day (mm H2O), and areahru is the area of the HRU (km2). The amount of organic carbon in surface runoff is calculated: orgC surq = 1000 ⋅
orgC surf 100
⋅ sed ⋅ ε C:sed
16.2.2
where orgCsurq is the organic carbon in surface runoff (kg orgC), orgCsurf is the percent organic carbon in the top 10 mm of soil (%), sed is the sediment loading from the HRU (metric tons), and εC:sed is the carbon enrichment ratio.
16.2.1 ENRICHMENT RATIO As surface runoff flows over the soil surface, part of the water’s energy is used to pick up and transport soil particles. The smaller particles weigh less and are more easily transported than coarser particles. When the particle size distribution of the transported sediment is compared to that of the soil surface layer, the sediment load to the main channel has a greater proportion of clay sized particles. In other words, the sediment load is enriched in clay particles. Organic carbon in the soil is attached primarily to colloidal (clay) particles, so the sediment load will also contain a greater proportion or concentration of organic carbon than that found in the soil surface layer. The enrichment ratio is defined as the ratio of the concentration of organic carbon transported with the sediment to the concentration in the soil surface layer. SWAT will calculate an enrichment ratio for each storm event. To calculate the enrichment ratio, SWAT uses a relationship described by Menzel (1980) in which the enrichment ratio is logarithmically related to sediment concentration. The equation used to calculate the carbon enrichment ratio, εC:sed, for each storm event is:
ε C:sed = 0.78 ⋅ (concsed ,surq )−0.2468
16.2.3
where concsed,surq is the concentration of sediment in surface runoff (Mg sed/m3 H2O). The concentration of sediment in surface runoff is calculated:
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conc sed ,surq =
sed 10 ⋅ area hru ⋅ Qsurf
16.2.4
where sed is the sediment yield on a given day (metric tons), areahru is the HRU area (ha), and Qsurf is the amount of surface runoff on a given day (mm H2O). Table 16-1: SWAT input variables that pertain to CBOD in surface runoff. Variable Name SOL_CBN
Definition orgCly: Percent organic carbon in the top 10 mm of soil (%)
Input File .sol
16.3 DISSOLVED OXYGEN Rainfall is assumed to be saturated with oxygen. To determine the dissolved oxygen concentration of surface runoff, the oxygen uptake by the oxygen demanding substance in runoff is subtracted from the saturation oxygen concentration. Ox surf = Ox sat − κ 1 ⋅ cbod surq ⋅
tov 24
16.3.1
where Oxsurf is the dissolved oxygen concentration in surface runoff (mg O2/L), Oxsat is the saturation oxygen concentration (mg O2/L), κ1 is the CBOD deoxygenation rate (day-1), cbodsurq is the CBOD concentration in surface runoff (mg CBOD/L), and tov is the time of concentration for overland flow (hr). For loadings from HRUs, SWAT assumes κ1 = 1.047 day-1.
16.3.1 OXYGEN SATURATION CONCENTRATION The amount of oxygen that can be dissolved in water is a function of temperature, concentration of dissolved solids, and atmospheric pressure. An equation developed by APHA (1985) is used to calculate the saturation concentration of dissolved oxygen: é 1.575701 × 105 6.642308 × 10 7 Ox sat = exp ê − 139.34410 + − T (Twat ,K )2 wat K , ë
+
1.243800 × 1010 8.621949 × 1011 ù − (Twat ,K )3 (Twat ,K )4 úúû
16.3.2
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257
where Oxsat is the equilibrium saturation oxygen concentration at 1.00 atm (mg O2/L), and Twat,K is the water temperature in Kelvin (273.15+°C).
16.4 NOMENCLATURE AGP Oxsat Oxsurf Qsurf Twat,K TN TP
Algal growth potential (mg/L) Saturation oxygen concentration (mg O2/L) Dissolved oxygen concentration in surface runoff (mg O2/L) Surface runoff on a given day (mm H2O) Water temperature in Kelvin (273.15+°C) Total Kjeldahl nitrogen load (moles) Total phosphorus load (moles)
areahru Area of the HRU (km2) cbodsurq CBOD concentration in surface runoff (mg CBOD/L) chla Chlorophyll a concentration in the surface runoff (µg/L) concsed,surq Concentration of sediment in surface runoff (Mg sed/m3 H2O) f Coefficient g Exponent orgCsurf Percent organic carbon in the top 10 mm of soil (%) orgCsurq Organic carbon in surface runoff (kg orgC), sed Sediment loading from the HRU (metric tons) Time of concentration for overland flow (hr) tov Surface runoff flow rate (m3/s) vsurf
εC:sed Carbon enrichment ratio κ1 CBOD deoxygenation rate (day-1)
16.5 REFERENCES American Public Health Association. 1985. Standard methods for the examination of water and wastewater, 16th edition. American Public Health Association, Inc. Cluis, D., P. Couture, R. Bégin, and S.A. Visser. 1988. Potential eutrophication assessment in rivers; relationship between produced and exported loads. Schweiz. Z. Hydrol. 50:166-181. Menzel, R.G. 1980. Enrichment ratios for water quality modeling. p. 486-492. In W.G. Knisel (ed.) CREAMS, A field scale model for chemicals, runoff,
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and erosion from agricultural management systems. U.S. Dept. Agric. Conserv. Res. Rept. No. 26. Thomann, R.V. and J.A. Mueller. 1987. Principles of surface water quality modeling and control. Harper & Row Publishers, New York.
LAND COVER/PLANT The plant growth component of SWAT is a simplified version of the EPIC plant growth model. As in EPIC, phenological plant development is based on daily accumulated heat units, potential biomass is based on a method developed by Monteith, a harvest index is used to calculate yield, and plant growth can be inhibited by temperature, water, nitrogen or phosphorus stress. Portions of the EPIC plant growth model that were not incorporated into SWAT include detailed root growth, micronutrient cycling and toxicity responses, and the simultaneous growth of multiple plant species in the same HRU.
CHAPTER 17
EQUATIONS: GROWTH CYCLE
The growth cycle of a plant is controlled by plant attributes summarized in the plant growth database and by the timing of operations listed in the management file. This chapter reviews the heat unit theory used to regulate the growth cycle of plants. Chapter 20 focuses on the impact of user inputs in management operations on the growth and development of plants.
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17.1 HEAT UNITS Temperature is one of the most important factors governing plant growth. Each plant has its own temperature range, i.e. its minimum, optimum, and maximum for growth. For any plant, a minimum or base temperature must be reached before any growth will take place. Above the base temperature, the higher the temperature the more rapid the growth rate of the plant. Once the optimum temperature is exceeded the growth rate will begin to slow until a maximum temperature is reached at which growth ceases. In the 1920s and 1930s, canning factories were searching for ways to time the planting of sweet peas so that there would be a steady flow of peas at the peak of perfection to the factory. Crops planted at weekly intervals in the early spring would sometimes come to maturity with only a 1- or 2-day differential while at other times there was a 6- to 8-day differential (Boswell, 1926; 1929). A heat unit theory was suggested (Boswell, 1926; Magoon and Culpepper, 1932) that was revised and successfully applied (Barnard, 1948; Phillips, 1950) by canning companies to determine when plantings should be made to ensure a steady harvest of peas with no “bunching” or “breaks”. The heat unit theory postulates that plants have heat requirements that can be quantified and linked to time to maturity. Because a plant will not grow when the mean temperature falls below its base temperature, the only portion of the mean daily temperature that contributes towards the plant’s development is the amount that exceeds the base temperature. To measure the total heat requirements of a plant, the accumulation of daily mean air temperatures above the plant’s base temperature is recorded over the period of the plant’s growth and expressed in terms of heat units. For example, assume sweet peas are growing with a base temperature of 5°C. If the mean temperature on a given day is 20°C, the heat units accumulated on that day are 20 – 5 = 15 heat units. Knowing the planting date, maturity date, base temperature and mean daily temperatures, the total number of heat units required to bring a crop to maturity can be calculated.
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The heat index used by SWAT is a direct summation index. Each degree of the daily mean temperature above the base temperature is one heat unit. This method assumes that the rate of growth is directly proportional to the increase in temperature. It is important to keep in mind that the heat unit theory without a high temperature cutoff does not account for the impact of harmful high temperatures. SWAT assumes that all heat above the base temperature accelerates crop growth and development. 30
Temperature (deg C)
20
10
0
-10 Mean daily temperature
Base temperature, corn
1/ 1/ 9 1/ 2 15 /9 1/ 2 29 /9 2/ 2 12 /9 2/ 2 26 /9 3/ 2 11 /9 3/ 2 25 /9 2 4/ 8/ 9 4/ 2 22 /9 2 5/ 6/ 92 5/ 20 /9 2 6/ 3/ 9 6/ 2 17 /9 2 7/ 1/ 92 7/ 15 /9 7/ 2 29 /9 8/ 2 12 /9 8/ 2 26 /9 2 9/ 9/ 92 9/ 23 /9 10 2 /7 / 10 92 /2 1/ 9 11 2 /4 /9 11 2 /1 8/ 9 12 2 /2 / 12 92 /1 6/ 12 92 /3 0/ 92
-20
Date
Figure 17-1: Mean daily temperature recorded for Greenfield, Indiana
The mean daily temperature during 1992 for Greenfield, Indiana is plotted in Figure 17-1 along with the base temperature for corn (8°C). Crop growth will only occur on those days where the mean daily temperature exceeds the base temperature. The heat unit accumulation for a given day is calculated with the equation:
HU = T av − Tbase
when T av > Tbase
17.1.1
where HU is the number of heat units accumulated on a given day (heat units),
T av is the mean daily temperature (°C), and Tbase is the plant’s base or minimum
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temperature for growth (°C). The total number of heat units required for a plant to reach maturity is calculated: m
PHU = å HU
17.1.2
d =1
where PHU is the total heat units required for plant maturity (heat units), HU is the number of heat units accumulated on day d where d = 1 on the day of planting and m is the number of days required for a plant to reach maturity. PHU is also referred to as potential heat units. When calculating the potential heat units for a plant, the number of days to reach maturity must be known. For most crops, these numbers have been quantified and are easily accessible. For other plants, such as forest or range, the time that the plants begin to develop buds should be used as the beginning of the growing season and the time that the plant seeds reach maturation is the end of the growing season. For the Greenfield Indiana example, a 120 day corn hybrid was planted on May 15. Summing daily heat unit values, the total heat units required to bring the corn to maturity was 1456.
17.1.1 HEAT UNIT SCHEDULING As the heat unit theory was proven to be a reliable predictor of harvest dates for all types of crops, it was adapted by researchers for prediction of the timing of other plant development stages such as flowering (Cross and Zuber, 1972). The successful adaptation of heat units to predict the timing of plant stages has subsequently led to the use of heat units to schedule management operations. SWAT allows management operations to be scheduled by day or by fraction of potential heat units. For each operation the model checks to see if a month and day has been specified for timing of the operation. If this information is provided, SWAT will perform the operation on that month and day. If the month and day are not specified, the model requires a fraction of potential heat units to be specified. As a general rule, if exact dates are available for scheduling operations, these dates should be used. Scheduling by heat units allows the model to time operations as a function of temperature. This method of timing is useful for several situations. When very
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large watersheds are being simulated where the climate in one portion of the watershed is different enough from the climate in another section of the watershed to affect timing of operations, heat unit scheduling may be beneficial. By using heat unit scheduling, only one generic management file has to be made for a given land use. This generic set of operations can then be used wherever the land use is found in the watershed. Also, in areas where the climate can vary greatly from year to year, heat unit scheduling will allow the model to adjust the timing of operations to the weather conditions for each year. To schedule by heat units, the timing of the operations are expressed as fractions of the potential heat units for the plant or fraction of maturity. Let us use the following example for corn in Indiana. Date
Operation
April 24 April 30 May 7 May 15 June 3 June 17 October 15 October 29 November 5
Tandem disk Tandem disk Field cultivator Plant corn (PHU = 1456) Row cultivator Row cultivator Harvest & Kill Tandem disk Chisel
Heat Units Accumulated
Fraction of PHU
0 165 343 1686
.00 .11 .24 1.16
The number of heat units accumulated for the different operation timings is calculated by summing the heat units for every day starting with the planting date (May 15) and ending with the day the operation takes place. To calculate the fraction of PHU at which the operation takes place, the heat units accumulated is divided by the PHU for the crop (1456). Note that the fraction of PHU for the harvest operation is 1.16. The fraction is greater than 1.0 because corn is allowed to dry down prior to harvesting. The model will simulate plant growth until the crop reaches maturity (where maturity is defined as PHU = 1456). From that point on, plants will not transpire or take up nutrients and water. They will stand in the HRU until converted to residue or harvested. While the operations after planting have been scheduled by fraction of
PHU, operations—including planting—which occur during periods when no crop
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is growing must still be scheduled. To schedule these operations, SWAT keeps track of a second heat index where heat units are summed over the entire year using Tbase = 0°C. This heat index is solely a function of the climate and is termed the base zero heat index. For the base zero index, the heat units accumulated on a given day are:
HU 0 = T av
when T av > 0°C
17.1.3
where HU0 is the number of base zero heat units accumulated on a given day (heat units), and T av is the mean daily temperature (°C). The total number of heat units for the year is calculated: 365
PHU 0 = å HU 0
17.1.4
d =1
where PHU0 is the total base zero heat units (heat units), HU0 is the number of base zero heat units accumulated on day d where d = 1 on January 1 and 365 on December 31. Unlike the plant PHU which must be provided by the user, PHU0 is the average calculated by SWAT using long-term weather data provided in the .wgn file. For the example watershed in Indiana, PHU0 = 4050. The heat unit fractions for the remaining operations are calculated using this value for potential heat units. Date
Operation
Base Zero Heat Units Accumulated
April 24 April 30 May 7 May 15 June 3 June 17 October 15 October 29 November 5
Tandem disk Tandem disk Field cultivator Plant corn (PHU = 1456) Row cultivator Row cultivator Harvest & Kill Tandem disk Chisel
564 607 696 826 1136 1217 3728 3860 3920
Plant Heat Units Accumulated
0 165 343 1686
Fraction of PHU0 (PHU0 = 4050)
Fraction of PHU (PHU = 1456)
.14 .15 .17 .20 .11 .24 1.16 .95 .97
As stated previously, SWAT always keeps track of base zero heat units. The base zero heat unit scheduling is used any time there are no plants growing in the HRU (before and including the plant operation and after the kill operation). Once plant growth is initiated, the model switches to plant heat unit scheduling until the plant is killed.
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The following heat unit fractions have been found to provide reasonable timings for the specified operations: 0.15 1.0 1.2 0.6
planting harvest/kill for crops with no dry-down harvest/kill for crops with dry-down hay cutting operation
fraction of PHU0 fraction of PHU fraction of PHU fraction of PHU
Table 17-1: SWAT input variables that pertain to heat units. Variable Name Definition PHU PHU: potential heat units for plant that is growing at the beginning of the simulation in an HRU HEAT UNITS PHU: potential heat units for plant whose growth is initiated with a planting operation. HUSC Fraction of potential heat units at which operation takes place. T_BASE Tbase: Minimum temperature for plant growth (°C)
Input File .mgt .mgt .mgt crop.dat
17.2 DORMANCY SWAT assumes trees, perennials and cool season annuals can go dormant as the daylength nears the shortest or minimum daylength for the year. During dormancy, plants do not grow. The beginning and end of dormancy are defined by a threshold daylength. The threshold daylength is calculated: TDL,thr = TDL ,mn + t dorm
17.2.1
where TDL,thr is the threshold daylength to initiate dormancy (hrs), TDL,mn is the minimum daylength for the watershed during the year (hrs), and tdorm is the dormancy threshold (hrs). When the daylength becomes shorter than TDL,thr in the fall, plants other than warm season annuals that are growing in the watershed will enter dormancy. The plants come out of dormancy once the daylength exceeds
TDL,thr in the spring. The dormancy threshold, tdorm, varies with latitude.
t dorm = 1.0 t dorm =
φ − 20 20
t dorm = 0.0
if φ > 40 º N or S
17.2.2
if 20 º N or S ≤ φ ≤ 40 º N or S
17.2.3
if φ < 20 º N or S
17.2.4
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where tdorm is the dormancy threshold used to compare actual daylength to minimum daylength (hrs) and φ is the latitude expressed as a positive value (degrees). At the beginning of the dormant period for trees, leaf biomass is converted to residue and the leaf area index for the tree species is set to the minimum value allowed (defined in the plant growth database). At the beginning of the dormant period for perennials, 95% of the biomass is converted to residue and the leaf area index for the species is set to the minimum value allowed. For cool season annuals, none of the biomass is converted to residue.
17.3 PLANT TYPES SWAT categorizes plants into seven different types: warm season annual legume, cold season annual legume, perennial legume, warm season annual, cold season annual, perennial and trees. The differences between the different plant types, as modeled by SWAT, are as follows: 1
2
3
4 5
warm season annual legume: • simulate nitrogen fixation • root depth varies during growing season due to root growth cold season annual legume: • simulate nitrogen fixation • root depth varies during growing season due to root growth • fall-planted land covers will go dormant when daylength is less than the threshold daylength perennial legume: • simulate nitrogen fixation • root depth always equal to the maximum allowed for the plant species and soil • plant goes dormant when daylength is less than the threshold daylength warm season annual: • root depth varies during growing season due to root growth cold season annual: • root depth varies during growing season due to root growth • fall-planted land covers will go dormant when daylength is less than the threshold daylength
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6
perennial: • root depth always equal to the maximum allowed for the plant species and soil • plant goes dormant when daylength is less than the threshold daylength 7 trees: • root depth always equal to the maximum allowed for the plant species and soil • partitions new growth between leaves/needles (30%) and woody growth (70%). At the end of each growing season, biomass in the leaf fraction is converted to residue
17.4 NOMENCLATURE HU
Number of heat units accumulated on a given day where base temperature is dependant on the plant species (heat units) HU0 Number of base zero heat units accumulated on a given day (heat units) PHU Potential heat units or total heat units required for plant maturity where base temperature is dependant on the plant species (heat units) PHU0 Total base zero heat units or potential base zero heat units (heat units) Tbase Plant’s base or minimum temperature for growth (°C) TDL,mn Minimum daylength for the watershed during the year (hrs) TDL,thr Threshold daylength to initiate dormancy (hrs) T av Mean air temperature for day (°C)
tdorm
Dormancy threshold (hrs)
φ
Latitude expressed as a positive value (degrees)
17.5 REFERENCES Barnard, J.D. 1948. Heat units as a measure of canning crop maturity. The Canner 106:28. Boswell, V.G. 1929. Factors influencing yield and quality of peas—Biophysical and biochemical studies. Maryland Agr. Exp. Sta. Bul. 306. Boswell, V.G. 1926. The influence of temperature upon the growth and yield of garden peas. Proc. Amer. Soc. Hort. Sci. 23:162-168.
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Cross, H.Z. and M.S. Zuber. 1972. Prediction of flowering dates in maize based on different methods of estimating thermal units. Agronomy Journal 64:351-355. Magoon, C.A. and C.W. Culpepper. 1932. Response of sweet corn to varying temperatures from time of planting to canning maturity. U.S.D.A. tech. Bull. 312. Phillips, E.E. 1950. Heat summation theory as applied to canning crops. The Canner 27:13-15.
CHAPTER 18
EQUATIONS: OPTIMAL GROWTH
For each day of simulation, potential plant growth, i.e. plant growth under ideal growing conditions, is calculated. Ideal growing conditions consist of adequate water and nutrient supply and a favorable climate. Differences in growth between plant species are defined by the parameters contained in the plant growth database.
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18.1 POTENTIAL GROWTH Plant growth is modeled by simulating leaf area development, light interception and conversion of intercepted light into biomass assuming a plant species-specific radiation-use efficiency.
18.1.1 BIOMASS PRODUCTION The amount of daily solar radiation intercepted by the leaf area of the plant is calculated using Beer’s law (Monsi and Saeki, 1953): H phosyn = 0.5 ⋅ H day ⋅ (1 − exp(k l ⋅ LAI ))
18.1.1
where Hphosyn is the amount of intercepted photosynthetically active radiation on a given day (MJ m-2), Hday is the incident total solar (MJ m-2), 0.5 ⋅ H day is the incident photosynthetically active radiation (MJ m-2), k l is the light extinction coefficient, and LAI is the leaf area index. In SWAT, the light extinct coefficient is –0.65 for all plants. Photosynthetically active radiation is radiation with a wavelength between 400 and 700 mm (McCree, 1972). Direct solar beam radiation contains roughly 45% photosynthetically active radiation while diffuse radiation contains around 60% photosynthetically active radiation (Monteith, 1972; Ross, 1975). The fraction of photosynthetically active radiation will vary from day to day with variation in overcast conditions but studies in Europe and Israel indicate that 50% is a representative mean value (Monteith, 1972; Szeicz, 1974; Stanhill and Fuchs, 1977). Radiation-use efficiency is the amount of dry biomass produced per unit intercepted solar radiation. The radiation-use efficiency is defined in the plant growth database and is assumed to be independent of the plant’s growth stage. The maximum increase in biomass on a given day that will result from the intercepted photosynthetically active radiation is estimated (Monteith, 1977): ∆bio = RUE ⋅ H phosyn
18.1.2
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273
where ∆bio is the potential increase in total plant biomass on a given day (kg/ha), RUE is the radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), and Hphosyn is the amount of intercepted photosynthetically active radiation on a given day (MJ m-2). Equation 18.1.2 assumes that the photosynthetic rate of a canopy is a linear function of radiant energy. The total biomass on a given day, d, is calculated as: d
bio = å ∆bioi
18.1.3
i =1
where bio is the total plant biomass on a given day (kg ha-1), and ∆bioi is the increase in total plant biomass on day i (kg/ha).
18.1.1.1 IMPACT OF CLIMATE ON RADIATION-USE EFFICIENCY Radiation-use efficiency is sensitive to variations in atmospheric CO2 concentrations and equations have been incorporated into SWAT to modify the default radiation-use efficiency values in the plant database for climate change studies. The relationship used to adjust the radiation-use efficiency for effects of elevated CO2 is (Stockle et al., 1992): RUE =
100 ⋅ CO2 CO2 + exp(r1 − r2 ⋅ CO2 )
18.1.4
where RUE is the radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), CO2 is the concentration of carbon dioxide in the atmosphere (ppmv), and r1 and r2 are shape coefficients. The shape coefficients are calculated by solving equation 18.1.4 using two known points (RUEamb, CO2amb) and (RUEhi, CO2hi):
é ù CO2 amb r1 = ln ê − CO2 amb ú + r2 ⋅ CO2 amb ë (0.01 ⋅ RUE amb ) û
18.1.5
æ é ù é ùö CO2 amb CO2 hi ç ln ê ÷ ç (0.01 ⋅ RUE ) − CO2 amb ú − ln ê (0.01 ⋅ RUE ) − CO2 hi ú ÷ amb hi ë û ë ûø è r2 = 18.1.6 CO2 hi − CO2 amb
where r1 is the first shape coefficient, r2 is the second shape coefficient, CO2amb is the ambient atmospheric CO2 concentration (ppmv), RUEamb is
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the radiation-use efficiency of the plant at ambient atmospheric CO2 concentration (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), CO2hi is an elevated atmospheric CO2 concentration (ppmv), RUEhi is the radiation-use efficiency of the plant at the elevated atmospheric CO2 concentration, CO2hi, (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ). Equation 18.1.4 was developed when the ambient atmospheric CO2 concentration was 330 ppmv and is valid for carbon dioxide concentrations in the range 330-660 ppmv. Even though the ambient atmospheric concentration of carbon dioxide is now higher than 330 ppmv, this value is still used in the calculation. If the CO2 concentration used in the simulation is less than 330 ppmv, the model defines RUE = RUEamb. Stockle and Kiniry (1990) have shown that a plant’s radiation-use efficiency is affected by vapor pressure deficit. For a plant, a threshold vapor pressure deficit is defined at which the plant’s radiation-use efficiency begins to drop in response to the vapor pressure deficit. The adjusted radiation-use efficiency is calculated: RUE = RUE vpd =1 − ∆ruedcl ⋅ (vpd − vpd thr )
if vpd > vpd thr
18.1.7
RUE = RUE vpd =1
if vpd ≤ vpd thr
18.1.8
where RUE is the radiation-use efficiency adjusted for vapor pressure deficit (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), RUEvpd=1 is the radiation-use efficiency for the plant at a vapor pressure deficit of 1 kPa (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), ∆ruedcl is the rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit (kg/ha⋅(MJ/m2)-1⋅kPa-1 or (10-1 g/MJ)⋅kPa-1), vpd is the vapor pressure deficit (kPa), and vpdthr is the threshold vapor pressure deficit above which a plant will exhibit reduced radiation-use efficiency (kPa). The radiation-use efficiency value reported for the plant in the plant growth database, RUEamb, or adjusted for elevated carbon dioxide levels (equation 18.1.4) is the value used for RUEvpd=1. The threshold vapor pressure deficit for reduced radiation-use efficiency is assumed to be 1.0 kPa for all plants ( vpd thr = 1.0 ).
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The radiation-use efficiency is never allowed to fall below 27% of RUEamb. This minimum value was based on field observations (Kiniry, personal communication, 2001).
18.1.2 CANOPY COVER AND HEIGHT The change in canopy height and leaf area through the growing season as modeled by SWAT is illustrated using parameters for Alamo Switchgrass in Figures 18-1 and 18-2.
Figure 18-1: Seasonal change in plant canopy height during growing season.
Figure 18-2: Seasonal change in plant leaf area index during growing season.
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In the initial period of plant growth, canopy height and leaf area development are controlled by the optimal leaf area development curve: frLAImx =
frPHU
frPHU + exp(l 1 − l 2 ⋅ frPHU )
18.1.9
where frLAImx is the fraction of the plant’s maximum leaf area index corresponding to a given fraction of potential heat units for the plant, frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and l 1 and l 2 are shape coefficients. The fraction of potential heat units accumulated by a given date is calculated: d
frPHU =
å HU i =1
i
PHU
18.1.10
where frPHU is the fraction of potential heat units accumulated for the plant on day d in the growing season, HU is the heat units accumulated on day i (heat units), and PHU is the total potential heat units for the plant (heat units). The shape coefficients are calculated by solving equation 18.1.9 using two known points (frLAI,1,frPHU,1) and (frLAI,2,frPHU,2): é fr ù l 1 = ln ê PHU ,1 − frPHU ,1 ú + l 2 ⋅ frPHU ,1 ë frLAI ,1 û
æ é frPHU ,1 ù é fr ùö ç ln ê − frPHU ,1 ú − ln ê PHU , 2 − frPHU , 2 ú ÷ ç ÷ ë frLAI ,1 û ë frLAI , 2 ûø l2 = è frPHU , 2 − frPHU ,1
18.1.11
18.1.12
where l 1 is the first shape coefficient, l 2 is the second shape coefficient, frPHU,1 is the fraction of the growing season (i.e. fraction of total potential heat units) corresponding to the 1st point on the optimal leaf area development curve, frLAI,1 is the fraction of the maximum plant leaf area index (i.e. fraction of LAImx) corresponding to the 1st point on the optimal leaf area development curve, frPHU,2 is the fraction of the growing season corresponding to the 2nd point on the optimal leaf area development curve, and frLAI,2 is the fraction of the maximum plant leaf area index corresponding to the 2nd point on the optimal leaf area development curve.
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The canopy height on a given day is calculated: hc = hc ,mx ⋅
frLAImx
18.1.13
where hc is the canopy height for a given day (m), hc,mx is the plant’s maximum canopy height (m), and frLAImx is the fraction of the plant’s maximum leaf area index corresponding to a given fraction of potential heat units for the plant. As can be seen from Figure 18-1, once the maximum canopy height is reached, hc will remain constant until the plant is killed. The amount of canopy cover is expressed as the leaf area index. The leaf area added on day i is calculated: ∆LAI i = ( frLAImx ,i − frLA Im x ,i −1 ) ⋅ LAI mx ⋅ (1 − exp(5 ⋅ (LAI i −1 − LAI mx ))) 18.1.14
And the total leaf area index is calculated: LAI i = LAI i −1 + ∆LAI i
18.1.15
where ∆LAIi is the leaf area added on day i, LAIi and LAIi-1 are the leaf area indices for day i and i-1 respectiviely, frLAImx,i and frLAImx,i-1 are the fraction of the plant’s maximum leaf area index calculated with equation 18.1.9 for day i and i-1, and LAImx is the maximum leaf area index for the plant. Leaf area index is defined as the area of green leaf per unit area of land (Watson, 1947). As shown in Figure 18-2, once the maximum leaf area index is reached, LAI will remain constant until leaf senescence begins to exceed leaf growth. Once leaf senescence becomes the dominant growth process, the leaf area index is calculated:
LAI = 16 ⋅ LAI mx ⋅ (1 − frPHU )
2
frPHU > frPHU ,sen
18.1.16
where LAI is the leaf area index for a given day, LAImx is the maximum leaf area index, frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and frPHU,sen is the fraction of growing season (PHU) at which senescence becomes the dominant growth process.
18.1.3 ROOT DEVELOPMENT The amount of total plant biomass partitioned to the root system is 30-50% in seedlings and decreases to 5-20% in mature plants (Jones, 1985). SWAT varies
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the fraction of total biomass in roots from 0.40 at emergence to 0.20 at maturity. The daily root biomass fraction is calculated with the equation: frroot = 0.40 − 0.20 ⋅ frPHU
18.1.17
where frroot is the fraction of total biomass partitioned to roots on a given day in the growing season, and frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season. Calculation of root depth varies according to plant type. SWAT assumes perennials and trees have roots down to the maximum rooting depth defined for the soil throughout the growing season: z root = z root ,mx
18.1.18
where zroot is the depth of root development in the soil on a given day (mm), and zroot,mx is the maximum depth for root development in the soil (mm). The simulated root depth for annuals varies linearly from 0.0 mm at the beginning of the growing season to the maximum rooting depth at frPHU = 0.40 using the equation: z root = 2.5 ⋅ frPHU ⋅ z root ,mx
if frPHU ≤ 0.40
18.1.19
z root = z root ,mx
if frPHU > 0.40
18.1.20
where zroot is the depth of root development in the soil on a given day (mm), frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and zroot,mx is the maximum depth for root development in the soil (mm). The maximum rooting depth is defined by comparing the maximum potential rooting depth for the plant from the plant growth database (RDMX in crop.dat), and the maximum potential rooting depth for the soil from the soil input file (SOL_ZMX in .sol—if no value is provided for this variable the model will set it to the deepest depth specified for the soil profile). The shallower of these two depths is the value used for zroot,mx.
18.1.4 MATURITY Plant maturity is reached when the fraction of potential heat units accumulated, frPHU, is equal to 1.00. Once maturity is reached, the plant ceases to
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transpire and take up water and nutrients. Simulated plant biomass remains stable until the plant is harvested or killed via a management operation. Table 18-1: SWAT input variables that pertain to optimal plant growth. Variable Name Definition BIO_E RUEamb: Radiation use efficiency in ambient CO2((kg/ha)/(MJ/m2)) CO2HI CO2hi: Elevated CO2 atmospheric concentration (ppmv) BIOEHI RUEhi: Radiation use efficiency at elevated CO2 atmospheric concentration value for CO2HI ((kg/ha)/(MJ/m2)) WAVP ∆ruedcl: Rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit (kg/ha⋅(MJ/m2)-1⋅kPa-1 or (10-1 g/MJ)⋅kPa-1) PHU PHU: potential heat units for plant growing at beginning of simulation (heat units) HEAT UNITS PHU: potential heat units for plant whose growth is initiated in a planting operation (heat units) FRGRW1 frPHU,1: Fraction of the growing season corresponding to the 1st point on the optimal leaf area development curve LAIMX1 frLAI,1: Fraction of the maximum plant leaf area index corresponding to the 1st point on the optimal leaf area development curve FRGRW2 frPHU,2: Fraction of the growing season corresponding to the 2nd point on the optimal leaf area development curve LAIMX2 frLAI,2: Fraction of the maximum plant leaf area index corresponding to the 2nd point on the optimal leaf area development curve CHTMX hc,mx: Plant’s potential maximum canopy height (m) BLAI LAImx: Potential maximum leaf area index for the plant DLAI frPHU,sen: Fraction of growing season at which senescence becomes the dominant growth process SOL_ZMX zroot,mx: Maximum rooting depth in soil (mm) RDMX zroot,mx: Maximum rooting depth for plant (mm)
Input File crop.dat crop.dat crop.dat crop.dat .mgt .mgt crop.dat crop.dat crop.dat crop.dat crop.dat crop.dat crop.dat .sol crop.dat
18.2 WATER UPTAKE BY PLANTS The potential water uptake from the soil surface to any depth in the root zone is estimated with the function: wup , z =
é æ Et z ⋅ ê1 − expçç − β w ⋅ [1 − exp(− β w )] ë z root è
öù ÷÷ú øû
18.2.1
where wup,z is the potential water uptake from the soil surface to a specified depth, z, on a given day (mm H2O), Et is the maximum plant transpiration on a given day (mm H2O), βw is the water-use distribution parameter, z is the depth from the soil surface (mm), and zroot is the depth of root development in the soil (mm). The potential water uptake from any soil layer can be calculated by solving equation
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18.2.1 for the depth at the top and bottom of the soil layer and taking the difference. wup ,ly = wup , zl − wup , zu
18.2.2
where wup,ly is the potential water uptake for layer ly (mm H2O), wup,zl is the potential water uptake for the profile to the lower boundary of the soil layer (mm H2O), and wup,zu is the potential water uptake for the profile to the upper boundary of the soil layer (mm H2O). Since root density is greatest near the soil surface and decreases with depth, the water uptake from the upper layers is assumed to be much greater than that in the lower layers. The water-use distribution parameter, βw, is set to 10 in SWAT. With this value, 50% of the water uptake will occur in the upper 6% of the root zone. Figure 18-3 graphically displays the uptake of water at different depths in the root zone.
Figure 18-3: Depth distribution of water uptake
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The amount of water uptake that occurs on a given day is a function of the amount of water required by the plant for transpiration, Et, and the amount of water available in the soil, SW. Equations 18.2.1 and 18.2.2 calculate potential water uptake solely as a function of water demand for transpiration and the depth distribution defined in equation 18.2.1. SWAT modifies the initial potential water uptake from a given soil layer to reflect soil water availability in the following ways. If upper layers in the soil profile do not contain enough water to meet the potential water uptake calculated with equation 18.2.2, users may allow lower layers to compensate. The equation used to calculate the adjusted potential water uptake is: ′ ,ly = wup ,ly + wdemand ⋅ epco wup
18.2.3
′ ,ly is the adjusted potential water uptake for layer ly (mm H2O), wup,ly is where wup
the potential water uptake for layer ly calculated with equation 18.2.2 (mm H2O), wdemand is the water uptake demand not met by overlying soil layers (mm H2O), and epco is the plant uptake compensation factor. The plant uptake compensation factor can range from 0.01 to 1.00 and is set by the user. As epco approaches 1.0, the model allows more of the water uptake demand to be met by lower layers in the soil. As epco approaches 0.0, the model allows less variation from the depth distribution described by equation 18.2.1 to take place. As the water content of the soil decreases, the water in the soil is held more and more tightly by the soil particles and it becomes increasingly difficult for the plant to extract water from the soil. To reflect the decrease in the efficiency of the plant in extracting water from dryer soils, the potential water uptake is modified using the following equations: é æ öù SWly ′′ ,ly = wup ′ ,ly ⋅ exp ê5 ⋅ ç wup − 1÷ú when SWly < (.25 ⋅ AWCly ) ç ÷ êë è (.25 ⋅ AWCly ) øúû
18.2.4
′′ ,ly = wup ′ ,ly wup
18.2.5
when SWly ≥ (.25 ⋅ AWCly )
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′′ ,ly is the potential water uptake adjusted for initial soil water content where wup ′ ,ly is the adjusted potential water uptake for layer ly (mm H2O), (mm H2O), wup
SWly is the amount of water in the soil layer on a given day (mm H2O), and AWCly is the available water capacity for layer ly (mm H2O). The available water capacity is calculated: AWCly = FCly − WPly
18.2.6
where AWCly is the available water capacity for layer ly (mm H2O), FCly is the water content of layer ly at field capacity (mm H2O), and WPly is the water content of layer ly at wilting point (mm H2O). Once the potential water uptake has been modified for soil water conditions, the actual amount of water uptake from the soil layer is calculated: ′′ ,ly , (SWly − WPly )] wactualup ,ly = min[wup
18.2.7
where wactualup,ly is the actual water uptake for layer ly (mm H2O), SWly is the amount of water in the soil layer on a given day (mm H2O), and WPly is the water content of layer ly at wilting point (mm H2O). The total water uptake for the day is calculated: n
wactualup = å wactualup ,ly
18.2.8
ly =1
where wactualup is the total plant water uptake for the day (mm H2O), wactualup,ly is the actual water uptake for layer ly (mm H2O), and n is the number of layers in the soil profile. The total plant water uptake for the day calculated with equation 18.2.8 is also the actual amount of transpiration that occurs on the day. Et ,act = wactualup
18.2.9
where Et,act is the actual amount of transpiration on a given day (mm H2O) and wactualup is the total plant water uptake for the day (mm H2O). Table 18-2: SWAT input variables that pertain to plant water uptake. Variable Name Definition EPCO epco: Plant uptake compensation factor
Input File .bsn, .hru
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18.3 NUTRIENT UPTAKE BY PLANTS SWAT monitors plant uptake of nitrogen and phosphorus.
18.3.1 NITROGEN UPTAKE Plant nitrogen uptake is controlled by the plant nitrogen equation. The plant nitrogen equation calculates the fraction of nitrogen in the plant biomass as a function of growth stage given optimal growing conditions. é ù frPHU frN = ( frN ,1 − frN ,3 ) ⋅ ê1 − + frN ,3 frPHU + exp(n1 − n2 ⋅ frPHU )úû ë
18.3.1
where frN is the fraction of nitrogen in the plant biomass on a given day, frN,1 is the normal fraction of nitrogen in the plant biomass at emergence, frN,3 is the normal fraction of nitrogen in the plant biomass at maturity, frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and n1 and n2 are shape coefficients. The shape coefficients are calculated by solving equation 18.3.1 using two known points (frN,2, frPHU,50%) and (frN,3, frPHU,100%): ù é ú ê frPHU ,50% ê n1 = ln ê − frPHU ,50% úú + n2 ⋅ frPHU ,50% æ ( fr − frN ,3 ) ö÷ ú ê ç1 − N ,2 ç ( frN ,1 − frN ,3 ) ÷ø úû êë è
18.3.2
æ é ù é ùö ç ê ú ê ú÷ ç ê ÷ frPHU ,50% fr PHU ,100% − frPHU ,50% úú − ln êê − frPHU ,100% úú ÷ ç ln ê æ ( frN ,2 − frN ,3 ) ö÷ ( fr − frN ,3 ) ö÷ ç ê æç ú ê ç 1 − N ,~ 3 ú÷ ç ê ç 1 − ( fr − fr ) ÷ ç ÷ ( frN ,1 − frN ,3 ) ø úû êë è úû ÷ø N ,1 N ,3 ø ëè è n2 = 18.3.3 frPHU ,100% − frPHU ,50%
where n1 is the first shape coefficient, n2 is the second shape coefficient, frN,1 is the normal fraction of nitrogen in the plant biomass at emergence, frN,2 is the normal fraction of nitrogen in the plant biomass at 50% maturity, frN,3 is the normal fraction of nitrogen in the plant biomass at maturity, frN,~3 is the normal fraction of nitrogen in the plant biomass near maturity, frPHU,50% is the fraction of
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potential heat units accumulated for the plant at 50% maturity (frPHU,50%=0.5), and frPHU,100% is the fraction of potential heat units accumulated for the plant at maturity (frPHU,100%=1.0). The normal fraction of nitrogen in the plant biomass near maturity (frN,~3) is used in equation 18.3.3 to ensure that the denominator term
æ ( fr − frN ,3 ) ö÷ ç 1 − N ,~ 3 ç ( frN ,1 − frN ,3 ) ÷ø è
( fr
− frN ,3 ) = 0.00001 .
N ,~ 3
does
not
equal
1.
The
model
assumes
To determine the mass of nitrogen that should be stored in the plant biomass on a given day, the nitrogen fraction is multiplied by the total plant biomass: bio N ,opt = frN ⋅ bio
18.3.4
where bioN,opt is the optimal mass of nitrogen stored in plant material for the current growth stage (kg N/ha), frN is the optimal fraction of nitrogen in the plant biomass for the current growth stage, and bio is the total plant biomass on a given day (kg ha-1). The plant nitrogen demand for a given day is determined by taking the difference between the nitrogen content of the plant biomass expected for the plant’s growth stage and the actual nitrogen content: N up = bio N ,opt − bio N
18.3.5
where Nup is the potential nitrogen uptake (kg N/ha), bioN,opt is the optimal mass of nitrogen stored in plant material for the current growth stage (kg N/ha), and bioN is the actual mass of nitrogen stored in plant material (kg N/ha). The depth distribution of nitrogen uptake is calculated with the function: N up , z =
é æ z öù ÷÷ú ⋅ ê1 − expçç − β n ⋅ [1 − exp(− β n )] ë z root ø û è
N up
18.3.6
where Nup,z is the potential nitrogen uptake from the soil surface to depth z (kg N/ha), Nup is the potential nitrogen uptake (kg N/ha), βn is the nitrogen uptake distribution parameter, z is the depth from the soil surface (mm), and zroot is the depth of root development in the soil (mm). Note that equation 18.3.6 is similar in form to the depth distribution for water uptake described by equation 18.2.1. The
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285
potential nitrogen uptake for a soil layer is calculated by solving equation 18.3.6 for the depth at the upper and lower boundaries of the soil layer and taking the difference. N up ,ly = N up , zl − N up , zu
18.3.7
where Nup,ly is the potential nitrogen uptake for layer ly (kg N/ha), Nup,zl is the potential nitrogen uptake from the soil surface to the lower boundary of the soil layer (kg N/ha), and Nup,zu is the potential nitrogen uptake from the soil surface to the upper boundary of the soil layer (kg N/ha). Root density is greatest near the surface, and nitrogen uptake in the upper portion of the soil will be greater than in the lower portion. The depth distribution of nitrogen uptake is controlled by βn, the nitrogen uptake distribution parameter, a variable users are allowed to adjust. Figure 18-4 illustrates nitrogen uptake as a function of depth for four different uptake distribution parameter values.
Figure 18-4: Depth distribution of nitrogen uptake
Nitrogen removed from the soil by plants is taken from the nitrate pool. The importance of the nitrogen uptake distribution parameter lies in its control
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over the maximum amount of nitrate removed from the upper layers. Because the top 10 mm of the soil profile interacts with surface runoff, the nitrogen uptake distribution parameter will influence the amount of nitrate available for transport in surface runoff. The model allows lower layers in the root zone to fully compensate for lack of nitrate in the upper layers, so there should not be significant changes in nitrogen stress with variation in the value used for βn. The actual amount if nitrogen removed from a soil layer is calculated: N actualup ,ly = min[N up ,ly + N demand , NO3ly ]
18.3.8
where Nactualup,ly is the actual nitrogen uptake for layer ly (kg N/ha), Nup,ly is the potential nitrogen uptake for layer ly (kg N/ha), Ndemand is the nitrogen uptake demand not met by overlying soil layers (kg N/ha), and NO3ly is the nitrate content of soil layer ly (kg NO3-N/ha).
18.3.1.1 NITROGEN FIXATION If nitrate levels in the root zone are insufficient to meet the demand of a legume, SWAT allows the plant to obtain additional nitrogen through nitrogen fixation. Nitrogen fixation is calculated as a function of soil water, soil nitrate content and growth stage of the plant. N fix = N demand ⋅ f gr ⋅ min( f sw ,
f no 3 , 1)
18.3.9
where Nfix is the amount of nitrogen added to the plant biomass by fixation (kg N/ha), Ndemand is the plant nitrogen demand not met by uptake from the soil (kg N/ha), fgr is the growth stage factor (0.0-1.0), fsw is the soil water factor (0.0-1.0), and fno3 is the soil nitrate factor (0.0-1.0). The maximum amount of nitrogen that can be fixed by the plant on a given day is Ndemand. Growth stage exerts the greatest impact on the ability of the plant to fix nitrogen. The growth stage factor is calculated: f gr = 0
when frPHU ≤ 0.15
18.3.10
f gr = 6.67 ⋅ frPHU − 1
when 0.15 < frPHU ≤ 0.30
18.3.11
f gr = 1
when 0.30 < frPHU ≤ 0.55
18.3.12
f gr = 3.75 − 5 ⋅ frPHU
when 0.55 < frPHU ≤ 0.75
18.3.13
CHAPTER 18: EQUATIONS—OPTIMAL GROWTH
f gr = 0
when frPHU > 0.75
287
18.3.14
where fgr is the growth stage factor and frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season. The growth stage factor is designed to reflect the buildup and decline of nitrogen fixing bacteria in the plant roots during the growing season. The soil nitrate factor inhibits nitrogen fixation as the presence of nitrate in the soil goes up. The soil nitrate factor is calculated: f no 3 = 1
when NO 3 ≤ 100
18.3.15
f no 3 = 1.5 − 0.0005 ⋅ NO 3
when 100 < NO 3 ≤ 300
18.3.16
f no 3 = 0
when NO 3 > 300
18.3.17
where fno3 is the soil nitrate factor and NO3 is the nitrate content of the soil profile (kg NO3-N/ha). The soil water factor inhibits nitrogen fixation as the soil dries out. The soil water factor is calculated: f sw =
SW .85 ⋅ FC
18.3.18
where fsw is the soil water factor, SW is the amount of water in soil profile (mm H2O), and FC is the water content of soil profile at field capacity (mm H2O).
18.3.2 PHOSPHORUS UPTAKE Plant phosphorus uptake is controlled by the plant phosphorus equation. The plant phosphorus equation calculates the fraction of phosphorus in the plant biomass as a function of growth stage given optimal growing conditions.
é ù frPHU frP = ( frP ,1 − frP ,3 ) ⋅ ê1 − + frP ,3 frPHU + exp( p1 − p2 ⋅ frPHU )úû ë
18.3.19
where frP is the fraction of phosphorus in the plant biomass on a given day, frP,1 is the normal fraction of phosphorus in the plant biomass at emergence, frP,3 is the normal fraction of phosphorus in the plant biomass at maturity, frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season, and p1 and p2 are shape coefficients.
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The shape coefficients are calculated by solving equation 18.3.19 using two known points (frP,2, frPHU,50%) and (frP,3, frPHU,100%): ù é ú ê frPHU ,50% ê p1 = ln ê − frPHU ,50% úú + p2 ⋅ frPHU ,50% æ ( fr − frP ,3 ) ö ú ê ç1 − P , 2 ÷ ( frP,1 − frP,3 ) ÷ø úû êë çè
18.3.20
æ é ù ùö é ç ê ú ú÷ ê ÷ ç ê frPHU ,50% fr PHU ,100% − frPHU ,50% úú − ln êê − frPHU ,100% úú ÷ ç ln ê æ ( fr − frP ,3 ) ö ç ê æç ( frP , 2 − frP ,3 ) ö÷ ÷ ú ú÷ ê ç 1 − P ,~ 3 1 − ç êç ç ÷ ÷ ( frP,1 − frP,3 ) ø ( frP,1 − frP,3 ) ø úû úû ÷ø êë è ëè è p2 = frPHU ,100% − frPHU ,50%
18.3.21
where p1 is the first shape coefficient, p2 is the second shape coefficient, frP,1 is the normal fraction of phosphorus in the plant biomass at emergence, frP,2 is the normal fraction of phosphorus in the plant biomass at 50% maturity, frP,3 is the normal fraction of phosphorus in the plant biomass at maturity, frP,~3 is the normal fraction of phosphorus in the plant biomass near maturity, frPHU,50% is the fraction of potential heat units accumulated for the plant at 50% maturity (frPHU,50%=0.5), and frPHU,100% is the fraction of potential heat units accumulated for the plant at maturity (frPHU,100%=1.0). The normal fraction of phosphorus in the plant biomass near maturity (frN,~3) is used in equation 18.3.21 to ensure that the denominator term
( fr
P , ~3
æ ( frP ,~3 − frP ,3 ) ö ÷ ç1 − ÷ ç ( ) fr − fr P ,1 P ,3 ø è
does
not
equal
1.
The
model
assumes
− frP ,3 ) = 0.00001 .
To determine the mass of phosphorus that should be stored in the plant biomass for the growth stage, the phosphorus fraction is multiplied by the total plant biomass: bioP ,opt = frP ⋅ bio
18.3.22
where bioP,opt is the optimal mass of phosphorus stored in plant material for the current growth stage (kg P/ha), frP is the optimal fraction of phophorus in the
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plant biomass for the current growth stage, and bio is the total plant biomass on a given day (kg ha-1). The plant phosphorus demand for a given day is a function of the difference between the phosphorus content of the plant biomass expected for the plant’s growth stage and the actual phosphorus content: Pup = 1.5 ⋅ (bio P ,opt − bioP )
18.3.23
where Pup is the potential phosphorus uptake (kg P/ha), bioP,opt is the optimal mass of phosphorus stored in plant material for the current growth stage (kg P/ha), and bioP is the actual mass of phosphorus stored in plant material (kg P/ha). The difference between the phosphorus content of the plant biomass expected for the plant’s growth stage and the actual phosphorus content is multiplied by 1.5 to simulate luxury phosphorus uptake. The depth distribution of phosphorus uptake is calculated with the function: Pup , z =
é æ z öù ÷ú ⋅ ê1 − expçç − β p ⋅ [1 − exp(− β p )] ë z root ÷ø û è Pup
18.3.24
where Pup,z is the potential phosphorus uptake from the soil surface to depth z (kg P/ha), Pup is the potential phosphorus uptake (kg P/ha), βP is the phosphorus uptake distribution parameter, z is the depth from the soil surface (mm), and zroot is the depth of root development in the soil (mm). The potential phosphorus uptake for a soil layer is calculated by solving equation 18.3.24 for the depth at the upper and lower boundaries of the soil layer and taking the difference. Pup ,ly = Pup ,zl − Pup , zu
18.3.25
where Pup,ly is the potential phosphorus uptake for layer ly (kg P/ha), Pup,zl is the potential phosphorus uptake from the soil surface to the lower boundary of the soil layer (kg P/ha), and Pup,zu is the potential phosphorus uptake from the soil surface to the upper boundary of the soil layer (kg P/ha). Root density is greatest near the surface, and phosphorus uptake in the upper portion of the soil will be greater than in the lower portion. The depth distribution of phosphorus uptake is controlled by βp, the phosphorus uptake
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distribution parameter, a variable users are allowed to adjust. The illustration of nitrogen uptake as a function of depth for four different uptake distribution parameter values in Figure 18-4 is valid for phosphorus uptake as well. Phosphorus removed from the soil by plants is taken from the solution phosphorus pool. The importance of the phosphorus uptake distribution parameter lies in its control over the maximum amount of solution P removed from the upper layers. Because the top 10 mm of the soil profile interacts with surface runoff, the phosphorus uptake distribution parameter will influence the amount of labile phosphorus available for transport in surface runoff. The model allows lower layers in the root zone to fully compensate for lack of solution P in the upper layers, so there should not be significant changes in phosphorus stress with variation in the value used for βp. The actual amount if phosphorus removed from a soil layer is calculated: Pactualup ,ly = min[Pup ,ly + Pdemand , Psolution ,ly ]
18.3.26
where Pactualup,ly is the actual phosphorus uptake for layer ly (kg P/ha), Pup,ly is the potential phosphorus uptake for layer ly (kg P/ha), Pdemand is the phosphorus uptake demand not met by overlying soil layers (kg P/ha), and Psolution,ly is the phosphorus content of the soil solution in layer ly (kg P/ha). Table 18-3: SWAT input variables that pertain to plant nutrient uptake. Variable Name Definition BN(1) frN,1: Normal fraction of N in the plant biomass at emergence BN(2) frN,2: Normal fraction of N in the plant biomass at 50% maturity BN(3) frN,3: Normal fraction of N in the plant biomass at maturity UBN βn: Nitrogen uptake distribution parameter BP(1) frP,1: Normal fraction of P in the plant biomass at emergence BP(2) frP,2: Normal fraction of P in the plant biomass at 50% maturity BP(3) frP,3: Normal fraction of P in the plant biomass at maturity UBP βp: Phosphorus uptake distribution parameter
Input File crop.dat crop.dat crop.dat .bsn crop.dat crop.dat crop.dat .bsn
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18.4 CROP YIELD When a harvest or harvest/kill operation is performed, a portion of the plant biomass is removed from the HRU as yield. The nutrients and plant material contained in the yield are assumed to be lost from the system (i.e. the watershed) and will not be added to residue and organic nutrient pools in the soil with the remainder of the plant material. In contrast, a kill operation converts all biomass to residue. The fraction of the above-ground plant dry biomass removed as dry economic yield is called the harvest index. For the majority of crops, the harvest index will be between 0.0 and 1.0. However, plants whose roots are harvested, such as sweet potatoes, may have a harvest index greater than 1.0. The economic yield of most commercial crops is the reproductive portion of the plant. Decades of crop breeding have lead to cultivars and hybrids having maximized harvest indices. Often, the harvest index is relatively stable across a range of environmental conditions. SWAT calculates harvest index each day of the plant’s growing season using the relationship: HI = HI opt ⋅
(100 ⋅ frPHU
100 ⋅ frPHU + exp[11.1 − 10 ⋅ frPHU ])
18.4.1
where HI is the potential harvest index for a given day, HIopt is the potential harvest index for the plant at maturity given ideal growing conditions, and frPHU is the fraction of potential heat units accumulated for the plant on a given day in the growing season. The variation of the optimal harvest index during the growing season is illustrated in Figure 18-5. The crop yield is calculated as: yld = bioag ⋅ HI
when HI ≤ 1.00
18.4.2
æ 1 ö yld = bio ⋅ çç1 − ÷÷ è (1 + HI ) ø
when HI > 1.00
18.4.3
where yld is the crop yield (kg/ha), bioag is the aboveground biomass on the day of harvest (kg ha-1), HI is the harvest index on the day of harvest, and bio is the
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total plant biomass on the day of harvest (kg ha-1). The aboveground biomass is calculated: bio ag = (1 − frroot ) ⋅ bio
18.4.4
where frroot is the fraction of total biomass in the roots the day of harvest, and bio is the total plant biomass on the day of harvest (kg ha-1).
Figure 18-5: Variation in optimal harvest index (HIi/HIopt) with fraction of growing season (frPHU)
The amount of nutrients removed in the yield are calculated: yld N = frN , yld ⋅ yld
18.4.5
yld P = frP , yld ⋅ yld
18.4.6
where yldN is the amount of nitrogen removed in the yield (kg N/ha), yldP is the amount of phosphorus removed in the yield (kg P/ha), frN,yld is the fraction of nitrogen in the yield, frP,yld is the fraction of phosphorus in the yield, and yld is the crop yield (kg/ha). If the harvest index override is used in the harvest only operation, the model assumes that a significant portion of the plant biomass is being removed in addition to the seed. Therefore, instead of using the nitrogen and phosphorus yield fractions from the plant growth database, the model uses the total biomass nitrogen and phosphorus fractions to determine the amount of nitrogen and phosphorus removed:
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yld N = frN ⋅ yld
18.4.7
yld P = frP ⋅ yld
18.4.8
where yldN is the amount of nitrogen removed in the yield (kg N/ha), yldP is the amount of phosphorus removed in the yield (kg P/ha), frN is the fraction of nitrogen in the plant biomass calculated with equation 18.3.1, frP is the fraction of phosphorus in the plant biomass calculated with equation 18.3.19, and yld is the crop yield (kg/ha). Table 18-4: SWAT input variables that pertain to crop yield. Variable Name Definition HVSTI HIopt: Potential harvest index for the plant at maturity given ideal growing conditions CNYLD frN,yld: Fraction of nitrogen in the yield CPYLD frP,yld: Fraction of phosphorus in the yield
Input File crop.dat crop.dat crop.dat
18.5 NOMENCLATURE AWCly Available water capacity for layer ly (mm H2O) CO2 Concentration of carbon dioxide in the atmosphere (ppmv) CO2amb Ambient atmospheric CO2 concentration (330 ppmv) CO2hi Elevated atmospheric CO2 concentration (ppmv) Et Maximum transpiration rate (mm d-1) Et,act Actual amount of transpiration on a given day (mm H2O) FC Water content of soil profile at field capacity (mm H2O) FCly Water content of layer ly at field capacity (mm H2O) Hday Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) Hphosyn Intercepted photosynthetically active radiation on a given day (MJ m-2) HI Potential harvest index for a given day HIopt Potential harvest index for the plant at maturity given ideal growing conditions HU Number of heat units accumulated on a given day (heat units) LAI Leaf area index of the canopy LAImx Maximum leaf area index for the plant Nactualup,ly Actual nitrogen uptake for layer ly (kg N/ha) Ndemand Nitrogen uptake demand not met by overlying soil layers (kg N/ha) Nfix Amount of nitrogen added to the plant biomass by fixation (kg N/ha) Nup Potential nitrogen uptake (kg N/ha) Nup,ly Potential nitrogen uptake for layer ly (kg N/ha) Nup,z Potential nitrogen uptake from the soil surface to depth z (kg N/ha) Nup,zl Potential nitrogen uptake from the soil surface to the lower boundary of the soil layer (kg N/ha)
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Nup,zu Potential nitrogen uptake from the soil surface to the upper boundary of the soil layer (kg N/ha) NO3 Nitrate content of the soil profile (kg NO3-N/ha) NO3ly Nitrate content of soil layer ly (kg NO3-N/ha) Pactualup,ly Actual phosphorus uptake for layer ly (kg P/ha) Pdemand Phosphorus uptake demand not met by overlying soil layers (kg P/ha) Pup Potential phosphorus uptake (kg P/ha) Pup,ly Potential phosphorus uptake for layer ly (kg P/ha) Pup,z Potential phosphorus uptake from the soil surface to depth z (kg P/ha) Pup,zl Potential phosphorus uptake from the soil surface to the lower boundary of the soil layer (kg P/ha) Pup,zu Potential phosphorus uptake from the soil surface to the upper boundary of the soil layer (kg P/ha) PHU Potential heat units or total heat units required for plant maturity (heat units) Psolution,ly Phosphorus content of soil solution in layer ly (kg P/ha) RUE Radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) RUEamb Radiation-use efficiency of the plant at ambient atmospheric CO2 concentration (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) RUEhi Radiation-use efficiency of the plant at the elevated atmospheric CO2 concentration, CO2hi, (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) RUEvpd=1 Radiation-use efficiency for the plant at a vapor pressure deficit of 1 kPa (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) SW Amount of water in soil profile (mm H2O) SWly Soil water content of layer ly (mm H2O) WPly Water content of layer ly at wilting point (mm H2O). bio Total plant biomass on a given day (kg/ha) bioag Aboveground biomass on the day of harvest (kg ha-1) bioN Actual mass of nitrogen stored in plant material (kg N/ha) bioN,opt Optimal mass of nitrogen stored in plant material for the growth stage (kg N/ha) bioP Actual mass of phosphorus stored in plant material (kg P/ha) bioP,opt Optimal mass of phosphorus stored in plant material for the current growth stage (kg P/ha) epco Plant uptake compensation factor fgr Growth stage factor in nitrogen fixation equation fno3 Soil nitrate factor in nitrogen fixation equation fsw Soil water factor in nitrogen fixation equation frLAI,1 Fraction of the maximum plant leaf area index corresponding to the 1st point on the optimal leaf area development curve frLAI,2 Fraction of the maximum plant leaf area index corresponding to the 2nd point on the optimal leaf area development curve frLAImx Fraction of the plant’s maximum leaf area index corresponding to a given fraction of potential heat units for the plant frN Optimal fraction of nitrogen in the plant biomass for current growth stage frN,1 Normal fraction of nitrogen in the plant biomass at emergence frN,2 Normal fraction of nitrogen in the plant biomass at 50% maturity
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frN,3 frN,~3 frN,yld frP frP,1 frP,2 frP,3 frP,~3 frP,yld frPHU
Normal fraction of nitrogen in the plant biomass at maturity Normal fraction of nitrogen in the plant biomass near maturity Fraction of nitrogen in the yield Fraction of phosphorus in the plant biomass Normal fraction of phosphorus in the plant biomass at emergence Normal fraction of phosphorus in the plant biomass at 50% maturity Normal fraction of phosphorus in the plant biomass at maturity Normal fraction of phosphorus in the plant biomass near maturity Fraction of phosphorus in the yield Fraction of potential heat units accumulated for the plant on a given day in the growing season frPHU,1 Fraction of the growing season corresponding to the 1st point on the optimal leaf area development curve frPHU,2 Fraction of the growing season corresponding to the 2nd point on the optimal leaf area development curve frPHU,50% Fraction of potential heat units accumulated for the plant at 50% maturity (frPHU,50%=0.5) frPHU,100% Fraction of potential heat units accumulated for the plant at maturity (frPHU,100%=1.0) frPHU,sen Fraction of growing season at which senescence becomes the dominant growth process frroot Fraction of total biomass in the roots on a given day in the growing season hc Canopy height (cm) hc,mx Plant’s maximum canopy height (m) kl Light extinction coefficient n1 First shape coefficient in plant nitrogen equation n2 Second shape coefficient in plant nitrogen equation p1 First shape coefficient in plant phosphorus equation p2 Second shape coefficient in plant phosphorus equation r1 First shape coefficient for radiation-use efficiency curve r2 Second shape coefficient for radiation-use efficiency curve vpd Vapor pressure deficit (kPa) vpdthr Threshold vapor pressure deficit above which a plant will exhibit reduced radiation-use efficiency (kPa) wactualup Total plant water uptake for the day (mm H2O) wactualup,ly Actual water uptake for layer ly (mm H2O) wdemand Water uptake demand not met by overlying soil layers (mm H2O) wup,ly Potential water uptake for layer ly (mm H2O) ′ ,ly Adjusted potential water uptake for layer ly (mm H2O) wup ′′ ,ly Potential water uptake when the soil water content is less than 25% of plant wup available water (mm H2O) wup,z Potential water uptake from the soil surface to a specified depth, z, on a given day (mm H2O) wup,zl is the potential water uptake for the profile to the lower boundary of the soil layer (mm H2O)
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wup,zu is the potential water uptake for the profile to the upper boundary of the soil layer (mm H2O) yld Crop yield (kg/ha) yldN Amount of nitrogen removed in the yield (kg N/ha) yldP Amount of phosphorus removed in the yield (kg P/ha) z Depth below soil surface (mm) zroot Depth of root development in the soil (mm) zroot,mx Maximum depth for root development in the soil (mm)
βn Nitrogen uptake distribution parameter βp Phosphorus uptake distribution parameter βw Water-use distribution parameter ∆LAIi Leaf area added on day i ∆bio Potential increase in total plant biomass on a given day (kg/ha) ∆ruedcl Rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit (kg/ha⋅(MJ/m2)-1⋅kPa-1 or (10-1 g/MJ)⋅kPa-1) l1 First shape coefficient for optimal leaf area development curve l2 Second shape coefficient for optimal leaf area development curve
18.6 REFERENCES Jones, C.A. 1985. C-4 grasses and cereals. John Wiley & Sons, Inc., New York. 419 pp. McCree, K.J. 1972. The action spectrum, absorption and quantum yield of photosynthesis in crop plants. Agric. Met. 9:191-216. Monsi, M. and T. Saeki. 1953. Uber den Lictfaktor in den Pflanzengesellschaften und sein Bedeutung fur die Stoffproduktion. Japan J. Bot. 14:22-52. Monteith, J.L. 1972. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9:747-766. Monteith, J.L. 1977. Climate and the efficiency of crop production in Britian. Phil. Trans. Res. Soc. London Ser. B 281:277-329. Ross, J. 1975. Radiative transfer in plant communities. p. 13-55 In J.L. Monteith (ed.) Vegetation and the atmosphere, Vol. 1. Academic Press, London. Stanhill, G. and M. Fuchs. 1977. The relative flux density of photosynthetically active radiation. J. Appl. Ecol. 14:317-322.
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Stockle, C.O. and J.R. Kiniry. 1990. Variability in crop radiation-use efficiency associated with vapor-pressure deficit. Field Crops Res. 25:171-181. Stockle, C.O., J.R. Williams, N.J. Rosenburg, and C.A. Jones. 1992. A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part 1—Modification of the EPIC model for climate change analysis. Agricultural Systems 38:225-238. Szeicz, G. 1974. Solar radiation for plant growth. J. Appl. Ecol. 11:617-636. Watson, D.J. 1947. Comparative physiological studies on the growth of field crops. 1. Variation in net assimilation rate and leaf area index between species and varieties and within and between years. Ann. Bot. N.S. 11:4176. Williams, J. R., C.A. Jones, and P.T. Dyke. 1984. A modeling approach to determining the relationship between erosion and soil productivity. Trans. ASAE 27:129-144.
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CHAPTER 19
EQUATIONS: ACTUAL GROWTH
Actual growth varies from potential growth due to extreme temperatures, water deficiencies and nutrient deficiencies. This chapter reviews growth constraints as well as overrides that the user may implement to ignore growth constraints.
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19.1 GROWTH CONSTRAINTS Plant growth may be reduced due to extreme temperatures, and insufficient water, nitrogen or phosphorus. The amount of stress for each of these four parameters is calculated on a daily basis using the equations summarized in the following sections.
19.1.1 WATER STRESS Water stress is simulated by comparing actual and potential plant transpiration: wstrs = 1 −
w Et ,act = 1 − actualup Et Et
19.1.1
where wstrs is the water stress for a given day, Et is the maximum plant transpiration on a given day (mm H2O), Et,act is the actual amount of transpiration on a given day (mm H2O) and wactualup is the total plant water uptake for the day (mm H2O). The calculation of maximum transpiration is reviewed in Chapter 7 and the determination of actual plant water uptake/transpiration is reviewed in Chapter 18.
19.1.2 TEMPERATURE STRESS Temperature stress is a function of the daily average air temperature and the optimal temperature for plant growth. Near the optimal temperature the plant will not experience temperature stress. However as the air temperature diverges from the optimal the plant will begin to experience stress. The equations used to determine temperature stress are: tstrs = 1
when Tav ≤ Tbase
é − 0.1054 ⋅ (Topt − Tav )2 ù tstrs = 1 − exp ê ú when Tbase < Tav ≤ Topt (Tav − Tbase )2 úû êë
19.1.2
19.1.3
é − 0.1054 ⋅ (Topt − Tav )2 ù tstrs = 1 − exp ê when Topt < Tav ≤ 2 ⋅ Topt − Tbase 19.1.4 2 ú êë (2 ⋅ Topt − Tav − Tbase ) úû tstrs = 1
when Tav > 2 ⋅ Topt − Tbase
19.1.5
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where tstrs is the temperature stress for a given day expressed as a fraction of optimal plant growth, T av is the mean air temperature for day (°C), Tbase is the plant’s base or minimum temperature for growth (°C), and Topt is the plant’s optimal temperature for growth (°C). Figure 19-1 illustrates the impact of mean daily air temperature on plant growth for a plant with a base temperature of 0°C and an optimal temperature of 15°C.
Figure 19-1: Impact of mean air temperature on plant growth for a plant with Tbase= 0°C and Topt=15°C
19.1.3 NITROGEN STRESS Nitrogen stress is calculated only for non-legumes. SWAT never allows legumes to experience nitrogen stress. Nitrogen stress is quantified by comparing actual and optimal plant nitrogen levels. Nitrogen stress varies non-linearly between 0.0 at optimal nitrogen content and 1.0 when the nitrogen content of the plant is 50% or less of the optimal value. Nitrogen stress is computed with the equation: nstrs = 1 −
ϕn ϕ n + exp[3.535 − 0.02597 ⋅ ϕ n ]
19.1.6
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where nstrs is the nitrogen stress for a given day, and ϕn is a scaling factor for nitrogen stress. The scaling factor is calculated: æ bio N ö − 0 .5 ÷ ϕ n = 200 ⋅ çç ÷ è bio N ,opt ø
19.1.7
where bioN,opt is the optimal mass of nitrogen stored in plant material for the current growth stage (kg N/ha) and bioN is the actual mass of nitrogen stored in plant material (kg N/ha).
19.1.4 PHOSPHORUS STRESS As with nitrogen, phosphorus stress is quantified by comparing actual and optimal plant phosphorus levels. Phosphorus stress varies non-linearly between 0.0 at optimal phosphorus content and 1.0 when the phosphorus content of the plant is 50% or less of the optimal value. Phosphorus stress is computed with the equation: pstrs = 1 −
ϕp ϕ p + exp[3.535 − 0.02597 ⋅ ϕ p ]
19.1.8
where pstrs is the phosphorus stress for a given day, and ϕp is a scaling factor for phosphorus stress. The scaling factor is calculated: æ bio P ö ϕ p = 200 ⋅ çç − 0 .5 ÷ ÷ è bioP ,opt ø
19.1.9
where bioP,opt is the optimal mass of phosphorus stored in plant material for the current growth stage (kg N/ha) and bioP is the actual mass of phosphorus stored in plant material (kg N/ha). Table 19-1: SWAT input variables that pertain to stress on plant growth. Variable Name Definition T_BASE Tbase: Base temperature for plant growth (°C) T_OPT Topt: Optimal temperature for plant growth (°C)
Input File crop.dat crop.dat
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19.2 ACTUAL GROWTH The plant growth factor quantifies the fraction of potential growth achieved on a given day and is calculated:
γ reg = 1 − max (wstrs, tstrs, nstrs,
pstrs )
19.2.3
where γreg is the plant growth factor (0.0-1.0), wstrs is the water stress for a given day, tstrs is the temperature stress for a given day expressed as a fraction of optimal plant growth, nstrs is the nitrogen stress for a given day, and pstrs is the phosphorus stress for a given day. The potential biomass predicted with equation 18.1.2 is adjusted daily if one of the four plant stress factors is greater than 0.0 using the equation: ∆bioact = ∆bio ⋅ γ reg
19.2.1
where ∆bioact is the actual increase in total plant biomass on a given day (kg/ha), ∆bio is the potential increase in total plant biomass on a given day (kg/ha), and
γreg is the plant growth factor (0.0-1.0). The potential leaf area added on a given day is also adjusted daily for plant stress: ∆LAI act ,i = ∆LAI i ⋅ γ reg
19.2.2
where ∆LAIact,i is the actual leaf area added on day i, ∆LAIi is the potential leaf area added on day i that is calculated with equation 18.1.14, and γreg is the plant growth factor (0.0-1.0).
19.2.1 BIOMASS OVERRIDE The model allows the user to specify a total biomass that the plant will produce each year. When the biomass override is set in the plant operation (.mgt), the impact of variation in growing conditions from year to year is ignored, i.e. γreg is always set to 1.00 when biomass override is activated in an HRU. When a value is defined for the biomass override, the change in biomass is calculated: ∆bioact = ∆bioi ⋅
(bio
trg
− bioi −1 )
biotrg
19.2.4
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where ∆bioact is the actual increase in total plant biomass on day i (kg/ha), ∆bioi is the potential increase in total plant biomass on day i calculated with equation 18.1.2 (kg/ha), biotrg is the target biomass specified by the user (kg/ha), and bioi-1 is the total plant biomass accumulated on day i-1 (kg/ha). Table 19-2: SWAT input variables that pertain to actual plant growth. Variable Name Definition BIO_TARG biotrg/1000: Biomass target (metric tons/ha)
Input File .mgt
19.3 ACTUAL YIELD The harvest index predicted with equation 18.4.1 is affected by water deficit using the relationship: HI act = (HI − HI min ) ⋅
γ wu
γ wu + HI min + exp[6.13 − 0.883 ⋅ γ wu ]
19.3.1
where HIact is the actual harvest index, HI is the potential harvest index on the day of harvest calculated with equation 18.4.1, HImin is the harvest index for the plant in drought conditions and represents the minimum harvest index allowed for the plant, and γwu is the water deficiency factor. The water deficiency factor is calculated: m
γ wu = 100 ⋅
åE i =1 m
åE
a
19.3.2 o
i =1
where Ea is the actual evapotranspiration on a given day, Eo is the potential evapotranspiration on a given day, i is a day in the plant growing season, and m is the day of harvest if the plant is harvested before it reaches maturity or the last day of the growing season if the plant is harvested after it reaches maturity.
19.3.1 HARVEST INDEX OVERRIDE In the plant and harvest only operations (.mgt), the model allows the user to specify a target harvest index. The target harvest index set in a plant operation is used when the yield is removed using a harvest/kill operation. The target
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harvest index set in a harvest only operation is used only when that particular harvest only operation is executed. When a harvest index override is defined, the override value is used in place of the harvest index calculated by the model in the yield calculations. Adjustments for growth stage and water deficiency are not made. HI act = HI trg
19.3.3
where HIact is the actual harvest index and HItrg is the target harvest index.
19.3.2 HARVEST EFFICIENCY In the harvest only operation (.mgt), the model allows the user to specify a harvest efficiency. The harvest efficiency defines the fraction of yield biomass removed by the harvesting equipment. The remainder of the yield biomass is converted to residue and added to the residue pool in the top 10 mm of soil. If the harvest efficiency is not set or a 0.00 is entered, the model assumes the user wants to ignore harvest efficiency and sets the fraction to 1.00 so that the entire yield is removed from the HRU. yld act = yld ⋅ harv eff
19.3.4
where yldact is the actual yield (kg ha-1), yld is the crop yield calculated with equation 18.4.2 or 18.4.3 (kg ha-1), and harveff is the efficiency of the harvest operation (0.01-1.00). The remainder of the yield biomass is converted to residue: ∆rsd = yld ⋅ (1 − harv eff )
19.3.5
rsd surf ,i = rsd surf ,i −1 + ∆rsd
19.3.6
where ∆rsd is the biomass added to the residue pool on a given day (kg ha-1), yld is the crop yield calculated with equation 18.4.2 or 18.4.3 (kg ha-1) and harveff is the efficiency of the harvest operation (0.01-1.00) rsdsurf,i is the material in the residue pool for the top 10 mm of soil on day i (kg ha-1), and rsdsurf,i-1 is the material in the residue pool for the top 10 mm of soil on day i-1 (kg ha-1).
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Table 19-3: SWAT input variables that pertain to actual plant yield. Variable Name Definition WSYF HImin: Harvest index for the plant in drought conditions, the minimum harvest index allowed for the plant HITAR HItrg: Harvest index target HIOVR HItrg: Harvest index target HARVEFF harveff: Efficiency of the harvest operation
Input File crop.dat .mgt .mgt .mgt
19.4 NOMENCLATURE Ea Eo Et Et,act HI HIact HImin HItrg Tbase Topt T av
Actual amount of evapotranspiration on a given day (mm H2O) Potential evapotranspiration (mm d-1) Maximum transpiration rate (mm d-1) Actual amount of transpiration on a given day (mm H2O) Potential harvest index for a given day Actual harvest index Harvest index for the plant in drought conditions and represents the minimum harvest index allowed for the plant Target harvest index Plant’s base or minimum temperature for growth (°C) Plant’s optimal temperature for growth (°C) Mean air temperature for day (°C)
bioN Actual mass of nitrogen stored in plant material (kg N/ha) bioN,opt Optimal mass of nitrogen stored in plant material for the growth stage (kg N/ha) bioP Actual mass of phosphorus stored in plant material (kg P/ha) bioP,opt Optimal mass of phosphorus stored in plant material for the current growth stage (kg P/ha) biotrg Target biomass specified by the user (kg/ha) harveff Efficiency of the harvest operation nstrs Nitrogen stress for a given day pstrs Phosphorus stress for a given day rsdsurf,i Material in the residue pool for the top 10mm of soil on day i (kg ha-1) tstrs Temperature stress for a given day expressed as a fraction of optimal plant growth wactualup Total plant water uptake for the day (mm H2O) wstrs Water stress for a given day yldact Actual yield (kg ha-1) ∆LAIi Leaf area added on day i (potential) ∆LAIact,i Actual leaf area added on day i ∆bio Potential increase in total plant biomass on a given day (kg/ha) ∆bioact Actual increase in total plant biomass on a given day (kg/ha) ∆rsd Biomass added to the residue pool on a given day (kg ha-1) γreg Plant growth factor (0.0-1.0)
CHAPTER 19: EQUATIONS—ACTUAL GROWTH
γwu ϕn ϕp
Water deficiency factor Scaling factor for nitrogen stress equation Scaling factor for phosphorus stress equation
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MANAGEMENT PRACTICES Quantifying the impact of land management and land use on water supply and quality is a primary focus of environmental modeling. SWAT allows very detailed management information to be incorporated into a simulation. The following three chapters review the methodology used by SWAT to simulate water management, tillage and urban processes.
CHAPTER 20
EQUATIONS: GENERAL MANAGEMENT
Management operations that control the plant growth cycle, the timing of fertilizer and pesticide and the removal of plant biomass are explained in this chapter. Water management and the simulation of urban areas are summarized in subsequent chapters.
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20.1 PLANTING/BEGINNING OF GROWING SEASON The plant operation initiates plant growth. This operation can be used to designate the time of planting for agricultural crops or the initiation of plant growth in the spring for a land cover that requires several years to reach maturity (forests, orchards, etc.). The plant operation will be performed by SWAT only when no land cover is growing in an HRU. Before planting a new land cover, the previous land cover must be removed with a kill operation or a harvest and kill operation. If two plant operations are placed in the management file and the first land cover is not killed prior to the second plant operation, the second plant operation is ignored by the model. Information required in the plant operation includes the timing of the operation (month and day or fraction of base zero potential heat units), the total number of heat units required for the land cover to reach maturity, and the specific land cover to be simulated in the HRU. If the land cover is being transplanted, the leaf area index and biomass for the land cover at the time of transplanting must be provided. Also, for transplanted land covers, the total number of heat units for the land cover to reach maturity should be from the period the land cover is transplanted to maturity (not from seed generation). Heat units are reviewed in Chapter 17. The user has the option of varying the curve number in the HRU throughout the year. New curve number values may be entered in a plant operation, tillage operation and harvest and kill operation. The curve number entered for these operations are for moisture condition II. SWAT adjusts the entered value daily to reflect change in water content. For simulations where a certain amount of crop yield and biomass is required, the user can force the model to meet this amount by setting a harvest index target and a biomass target. These targets are effective only if a harvest and kill operation is used to harvest the crop.
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Table 20-1: SWAT input variables that pertain to planting. Variable Name
Definition
Variables in plant operation line: MONTH/DAY or HUSC Timing of planting operation.
MGT_OP HEAT UNITS NCR Optional inputs: HITAR BIO_TARG ALAINIT BIOINIT CNOP
Operation code. MGT_OP = 1 for plant operation PHU : Total heat units required for plant maturity (heat units) Plant/land cover code from crop.dat HItrg: Target harvest index biotrg: Target biomass specified by the user (kg/ha) LAI: Leaf area index of the canopy for transplanted species bio: Total plant biomass on a given day (kg/ha) CN2: Moisture condition II curve number
Variables in second line of .mgt file IGRO Land cover status code Inputs for plants growing at the beginning of the simulation NCRP Plant/land cover code from crop.dat ALAI LAI: Leaf area index of the canopy BIO_MS biotrg: Target biomass specified by the user (kg/ha) PHU PHU : Total heat units required for plant maturity (heat
Input File .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt .mgt
units)
20.2 HARVEST OPERATION The harvest operation will remove plant biomass without killing the plant. This operation is most commonly used to cut hay or grass. The only information required by the harvest operation is the date. However, a harvest index override and a harvest efficiency can be set. When no harvest index override is specified, SWAT uses the plant harvest index from the plant growth database to set the fraction of biomass removed. The plant harvest index in the plant growth database is set to the fraction of the plant biomass partitioned into seed for agricultural crops and a typical fraction of biomass removed in a cutting for hay. If the user prefers a different fraction of biomass to be removed, the harvest index override should be set to the desired value. A harvest efficiency may also be defined for the operation. This value specifies the fraction of harvested plant biomass removed from the HRU. The remaining fraction is converted to residue on the soil surface. If the harvest efficiency is left blank or set to zero, the model assumes this feature is not being
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used and removes 100% of the harvested biomass (no biomass is converted to residue). After biomass is removed in a harvest operation, the plant’s leaf area index and accumulated heat units are set back by the fraction of biomass removed. Reducing the number of accumulated heat units shifts the plant’s development to an earlier period in which growth is usually occurring at a faster rate. Table 20-2: SWAT input variables that pertain to harvest. Variable Name
Definition
Variables in harvest operation line: MONTH/DAY or HUSC Timing of harvest operation.
Input File
MGT_OP
Operation code. MGT_OP = 7 for harvest operation
.mgt .mgt
Optional inputs: HIOVR HARVEFF
HItrg: Harvest index override or target harvest index harveff: Efficiency of the harvest operation
.mgt .mgt
20.3 GRAZING OPERATION The grazing operation simulates plant biomass removal and manure deposition over a specified period of time. This operation is used to simulate pasture or range grazed by animals. Information required in the grazing operation includes the time during the year at which grazing begins (month and day or fraction of plant potential heat units), the length of the grazing period, the amount of biomass removed daily, the amount of manure deposited daily, and the type of manure deposited. The amount of biomass trampled is an optional input. Biomass removal in the grazing operation is similar to that in the harvest operation. However, instead of a fraction of biomass being specified, an absolute amount to be removed every day is given. In some conditions, this can result in a reduction of the plant biomass to a very low level that will result in increased erosion in the HRU. To prevent this, a minimum plant biomass for grazing may be specified (BIO_MIN in the second line of the management file). When the plant biomass falls below the amount specified for BIO_MIN, the model will not graze, trample, or apply manure in the HRU on that day.
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If the user specifies an amount of biomass to be removed daily by trampling, this biomass is converted to residue. Nutrient fractions of the manure applied during grazing must be stored in the fertilizer database. The manure nutrient loadings are added to the topmost 10 mm of soil. This is the portion of the soil with which surface runoff interacts. After biomass is removed by grazing and/or trampling, the plant’s leaf area index and accumulated heat units are set back by the fraction of biomass removed. Table 20-3: SWAT input variables that pertain to grazing. Variable Name
Definition
Variables in grazing operation line: MONTH/DAY or HUSC Time grazing operation is initiated (1st day of grazing)
MGT_OP NDGRAZ BMEAT IGFTYP WMANURE Optional inputs: BMTRMP
Input File
Operation code. MGT_OP = 9 for grazing operation Number of days of grazing. bio: Total plant biomass consumed daily (kg/ha) Manure code from fert.dat fert: Amount of manure applied—dry weight (kg/ha)
.mgt .mgt .mgt .mgt .mgt .mgt
bio: Total plant biomass trampled daily (kg/ha)
.mgt
Variables in second line of .mgt file BIO_MIN bio: Minimum plant biomass for grazing to occur (kg/ha)
.mgt
20.4 HARVEST & KILL OPERATION The harvest and kill operation stops plant growth in the HRU. The fraction of biomass specified in the land cover’s harvest index (in the plant growth database) is removed from the HRU as yield. The remaining fraction of plant biomass is converted to residue on the soil surface. The only information required by the harvest and kill operation is the timing of the operation (month and day or fraction of plant potential heat units). The user also has the option of updating the moisture condition II curve number in this operation. Table 20-4: SWAT input variables that pertain to harvest & kill. Variable Name
Definition
Variables in harvest & kill operation line: MONTH/DAY or HUSC Timing of harvest and kill operation.
Input File
MGT_OP
Operation code. MGT_OP = 5 for harvest/kill operation
.mgt .mgt
Optional inputs: CNOP
CN2: Moisture condition II curve number
.mgt
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20.5 KILL/END OF GROWING SEASON The kill operation stops plant growth in the HRU. All plant biomass is converted to residue. The only information required by the kill operation is the timing of the operation (month and day or fraction of plant potential heat units). Table 20-5: SWAT input variables that pertain to kill. Variable Name
Definition
Variables in kill operation line: MONTH/DAY or HUSC Timing of kill operation.
MGT_OP
Operation code. MGT_OP = 8 for kill operation
Input File .mgt .mgt
20.6 TILLAGE The tillage operation redistributes residue, nutrients, pesticides and bacteria in the soil profile. Information required in the tillage operation includes the timing of the operation (month and day or fraction of base zero potential heat units), and the type of tillage operation. The user has the option of varying the curve number in the HRU throughout the year. New curve number values may be entered in a plant operation, tillage operation and harvest and kill operation. The curve number entered for these operations are for moisture condition II. SWAT adjusts the entered value daily to reflect change in water content. The mixing efficiency of the tillage implement defines the fraction of a residue/nutrient/pesticide/bacteria pool in each soil layer that is redistributed through the depth of soil that is mixed by the implement. To illustrate the redistribution of constituents in the soil, assume a soil profile has the following distribution of nitrate. Layer # surface layer 1 2 3 4
Depth of Layer 0-10 mm 10-100 mm 100-400 mm 400-1050 mm 1050-2000 mm
NO3 Content 50 kg/ha 25 kg/ha 20 kg/ha 10 kg/ha 10 kg/ha
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If this soil is tilled with a field cultivator, the soil will be mixed to a depth of 100 mm with 30% efficiency. The change in the distribution of nitrate in the soil is:
Layer # surface layer 1 2 3 4
Mixed NO3 (30%)
Redistribution of Mixed NO3
Final NO3
35 kg/ha 17.5 kg/ha 20 kg/ha 10 kg/ha 10 kg/ha
15 kg/ha 7.5 kg/ha
22.5×10mm/100mm = 2.25 kg/ha 22.5×90mm/100mm = 20.25 kg/ha
37.25 kg/ha 37.75 kg/ha 20 kg/ha 10 kg/ha 10 kg/ha
Total mixed:
22.5 kg/ha
Depth of Layer
Initial NO3
Unmixed NO3 (70%)
0-10 mm 10-100 mm 100-400 mm 400-1050 mm 1050-2000 mm
50 kg/ha 25 kg/ha 20 kg/ha 10 kg/ha 10 kg/ha
Because the soil is mixed to a depth of 100 mm by the implement, only the nitrate in the surface layer and layer 1 is available for redistribution. To calculated redistribution, the depth of the layer is divided by the tillage mixing depth and multiplied by the total amount of nitrate mixed. To calculate the final nitrate content, the redistributed nitrate is added to the unmixed nitrate for the layer. All nutrient/pesticide/bacteria/residue pools are treated in the same manner as the nitrate example above. Bacteria mixed into layers below the surface layer is assumed to die.
20.6.1 BIOLOGICAL MIXING Biological mixing is the redistribution of soil constituents as a result of the activity of biota in the soil (e.g. earthworms, etc.). Studies have shown that biological mixing can be significant in systems where the soil is only infrequently disturbed. In general, as a management system shifts from conventional tillage to conservation tillage to no-till there will be an increase in biological mixing. SWAT allows biological mixing to occur to a depth of 300 mm (or the bottom of the soil profile if it is shallower than 300 mm). The efficiency of biological mixing is defined by the user. The redistribution of nutrients by biological mixing is calculated using the same methodology as that used for a tillage operation.
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Table 20-6: SWAT input variables that pertain to tillage. Variable Name
Definition
Variables in tillage operation line: MONTH/DAY or HUSC Timing of planting operation.
MGT_OP TILLAGE_ID Optional inputs: CNOP
Input File
Operation code. MGT_OP = 6 for tillage operation Tillage implement code from till.dat
.mgt .mgt .mgt
CN2: Moisture condition II curve number
.mgt
Variables in second line of .mgt file BIOMIX Biological mixing efficiency Variable in tillage database: EFFMIX Mixing efficiency of tillage operation. DEPTIL Depth of mixing by tillage operation.
.mgt till.dat till.dat
20.7 FERTILIZER APPLICATION The fertilizer operation applies fertilizer or manure to the soil. Information required in the fertilizer operation includes the timing of the operation (month and day or fraction of plant potential heat units), the type of fertilizer/manure applied, the amount of fertilizer/manure applied, and the depth distribution of fertilizer application. SWAT assumes surface runoff interacts with the top 10 mm of soil. Nutrients contained in this surface layer are available for transport to the main channel in surface runoff. The fertilizer operation allows the user to specify the fraction of fertilizer that is applied to the top 10 mm. The remainder of the fertilizer is added to the first soil layer defined in the HRU .sol file. In the fertilizer database, the weight fraction of different types of nutrients and bacteria are defined for the fertilizer. The amount of nutrient added to the different pools in the soil are calculated: NO3 fert = fert minN ⋅ (1 − fert NH 4 ) ⋅ fert
20.7.1
NH4 fert = fert minN ⋅ fert NH 4 ⋅ fert
20.7.2
orgN frsh , fert = 0.5 ⋅ fertorgN ⋅ fert
20.7.3
orgN act , fert = 0.5 ⋅ fertorgN ⋅ fert
20.7.4
Psolution , fert = fert minP ⋅ fert
20.7.5
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orgPfrsh , fert = 0.5 ⋅ fertorgP ⋅ fert
20.7.6
orgPhum , fert = 0.5 ⋅ fertorgP ⋅ fert
20.7.7
bact lpsol , fert = fert lpbact ⋅ k bact ⋅ fert
20.7.8
bact lpsorb, fert = fert lpbact ⋅ (1 − k bact ) ⋅ fert
20.7.9
bact psol , fert = fert pbact ⋅ k bact ⋅ fert
20.7.10
bact psorb, fert = fert pbact ⋅ (1 − k bact ) ⋅ fert
20.7.11
where NO3fert is the amount of nitrate added to the soil in the fertilizer (kg N/ha), NH4fert is the amount of ammonium added to the soil in the fertilizer (kg N/ha), orgNfrsh,fert is the amount of nitrogen in the fresh organic pool added to the soil in the fertilizer (kg N/ha), orgNact,fert is the amount of nitrogen in the active organic pool added to the soil in the fertilizer (kg N/ha), Psolution,fert is the amount of phosphorus in the solution pool added to the soil in the fertilizer (kg P/ha), orgPfrsh,fert is the amount of phosphorus in the fresh organic pool added to the soil in the fertilizer (kg P/ha), orgPhum,fert is the amount of phosphorus in the humus organic pool added to the soil in the fertilizer (kg P/ha), bactlpsol,fert is the amount of less persistent bacteria in the solution pool added to the soil in the fertilizer (# bact/ha), bactlpsorb,fert is the amount of less persistent bacteria in the sorbed pool added to the soil in fertilizer (# bact/ha), bactpsol,fert is the amount of persistent bacteria in the solution pool added to the soil in the fertilizer (# bact/ha), bactpsorb,fert is the amount of persistent bacteria in the sorbed pool added to the soil in fertilizer (# bact/ha), fertminN is the fraction of mineral N in the fertilizer, fertNH4 is the fraction of mineral N in the fertilizer that is ammonium, fertorgN is the fraction of organic N in the fertilizer, fertminP is the fraction of mineral P in the fertilizer, fertorgP is the fraction of organic P in the fertilizer, fertlpbact is the concentration of less persistent bacteria in the fertilizer (# bact/kg fert), fertpbact is the concentration of persistent bacteria in the fertilizer (# bact/kg fert), kbact is the bacterial partition coefficient, and fert is the amount of fertilizer applied to the soil (kg/ha).
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Table 20-7: SWAT input variables that pertain to fertilizer application. Variable Name
Definition
Variables in fertilizer operation line: MONTH/DAY or HUSC Timing of fertilizer operation.
MGT_OP FERT_ID FRT_KG FRT_LY1
Operation code. MGT_OP = 3 for fertilizer operation Type of fertilizer/manure applied (code from fert.dat). fert: Amount of fertilizer/manure applied (kg/ha) Fraction of fertilizer applied to top 10 mm
Variables in fertilizer database: FMINN fertminN: Fraction of mineral nitrogen in the fertilizer FMINP fertminP: Fraction of mineral P in the fertilizer FORGN fertorgN: Fraction of organic N in the fertilizer FORGP fertorgP: Fraction of organic P in the fertilizer FNH3N fertNH4: Fraction of mineral N in the fertilizer that is BACTPDB BACTLPDB BACTKDDB
ammonium fertpbact: Concentration of persistent bacteria in manure (# bact/kg) fertlpbact: Concentration of less-persistent bacteria in manure (# bact/kg) kbact: Bacterial partition coefficient
Input File .mgt .mgt .mgt .mgt .mgt fert.dat fert.dat fert.dat fert.dat fert.dat fert.dat fert.dat fert.dat
20.8 AUTO-APPLICATION OF FERTILIZER Fertilization in an HRU may be scheduled by the user or automatically applied by SWAT. When the user selects auto-application of fertilizer in an HRU, a nitrogen stress threshold must be specified. The nitrogen stress threshold is a fraction of potential plant growth. Anytime actual plant growth falls below this threshold fraction due to nitrogen stress, the model will automatically apply fertilizer to the HRU. The user specifies the type of fertilizer, the fraction of total fertilizer applied to the soil surface, the maximum amount of fertilizer that can be applied during the year, the maximum amount of fertilizer that can be applied in any one application, and the application efficiency. To determine the amount of fertilizer applied, an estimate of the amount of nitrogen that will be removed in the yield is needed. For the first year of simulation, the model has no information about the amount of nitrogen removed from the soil by the plant. The nitrogen yield estimate is initially assigned a value using the following equations: yld est , N = 350 ⋅ frN , yld ⋅ RUE
if HI opt < 1.0
20.8.1
yld est , N = 1000 ⋅ frN , yld ⋅ RUE
if HI opt ≥ 1.0
20.8.2
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where yldest,N is the nitrogen yield estimate (kg N/ha), frN,yld is the fraction of nitrogen in the yield, RUE is the radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ), and HIopt is the potential harvest index for the plant at maturity given ideal growing conditions. The nitrogen yield estimate is updated at the end of every simulation year using the equation: yld est , N =
yld est , Nprev ⋅ yrsim + yld yr , N yrsim + 1
20.8.3
where yldest,N is the nitrogen yield estimate update for the current year (kg N/ha), yldest,Nprev is the nitrogen yield estimate from the previous year (kg N/ha), yrsim is the year of simulation, yldyr,N is the nitrogen yield target for the current year (kg N/ha). The nitrogen yield target for the current year is calculated at the time of harvest using the equation: yld yr , N = bioag ⋅ frN ⋅ fert eff
20.8.4
where yldyr,N is the nitrogen yield target for the current year (kg N/ha), bioag is the aboveground biomass on the day of harvest (kg ha-1), frN is the fraction of nitrogen in the plant biomass calculated with equation 18.3.1, and ferteff is the fertilizer application efficiency assigned by the user. The fertilizer application efficiency allows the user to modify the amount of fertilizer applied as a function of plant demand. If the user would like to apply additional fertilizer to adjust for loss in runoff, ferteff will be set to a value greater than 1. If the user would like to apply just enough fertilizer to meet the expected demand, ferteff will be set to 1. If the user would like to apply only a fraction of the demand, ferteff will be set to a value less than 1. The optimal amount of mineral nitrogen to be applied is calculated: minN app = yld est , N − (NO3 + NH4 ) − bio N
20.8.5
where minNapp is the amount of mineral nitrogen applied (kg N/ha), yldest,N is the nitrogen yield estimate (kg N/ha), NO3 is the nitrate content of the soil profile (kg NO3-N/ha), NH4 is the ammonium content of the soil profile (kg NH4-N/ha), and bioN is the actual mass of nitrogen stored in plant material (kg N/ha). If the amount of mineral nitrogen calculated with equation 20.8.5 exceeds the maximum
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amount allowed for any one application, minNapp is reset to the maximum value ( minN app = minN app ,mx ). The total amount of nitrogen applied during the year is also compared to the maximum amount allowed for the year. Once the amount applied reaches the maximum amount allowed for the year (minNapp,mxyr), SWAT will not apply any additional fertilizer regardless of nitrogen stress. Once the amount of mineral nitrogen applied is determined, the total amount of fertilizer applied is calculated by dividing the mass of mineral nitrogen applied by the fraction of mineral nitrogen in the fertilizer: fert =
minN app
20.8.6
fertminN
where fert is the amount of fertilizer applied (kg/ha), minNapp is the amount of mineral nitrogen applied (kg N/ha), and fertminN is the fraction of mineral nitrogen in the fertilizer. The type of fertilizer applied in the HRU is specified by the user. In addition to mineral nitrogen, organic nitrogen and phosphorus and mineral phosphorus are applied to the HRU. The amount of each type of nutrient is calculated from the amount of fertilizer and fraction of the various nutrient types in the fertilizer as summarized in Section 20.7. While the model does not allow fertilizer to be applied as a function of phosphorus stress, the model does monitor phosphorus stress in the autofertilization subroutine. If phosphorus stress causes plant growth to fall below 75% of potential growth, the model ignores the fraction of mineral phosphorus in 1 the fertilizer and applies an amount of mineral phosphorus equal to ( ⋅ minN app ). 7 Table 20-8: SWAT input variables that pertain to auto-fertilization. Variable Name
Definition
Variables in auto-fertilizer operation line: MONTH/DAY or HUSC Timing of fertilizer operation.
MGT_OP FERT_ID AFRT_LY1 AUTO_NSTR AUTO_EFF AUTO_NMXS
Operation code. MGT_OP = 11 for auto-fertilizer operation Type of fertilizer/manure applied (code from fert.dat). Fraction of fertilizer applied to top 10 mm nstrs: Nitrogen stress that triggers fertilizer application ferteff: Application efficiency minNapp,mx: Maximum amount of mineral N allowed to be applied on any one day (kg N/ha)
Input File .mgt .mgt .mgt .mgt .mgt .mgt .mgt
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Table 20-8, cont.: SWAT input variables that pertain to auto-fertilization. Variable Name AUTO_NMXA
Definition minNapp,mxyr: Maximum amount of mineral N allowed to be applied during a year (kg N/yr)
Input File .mgt
Other variables: CNYLD
frN,yld: Fraction of nitrogen in the yield RUE: Radiation use efficiency ((kg/ha)/(MJ/m2)) HIopt: Potential harvest index for the plant at maturity given ideal growing conditions fertminN: fraction of mineral N in the fertilizer
BIO_E HVSTI FMINN
crop.dat crop.dat crop.dat fert.dat
20.9 PESTICIDE APPLICATION The pesticide operation applies pesticide to the HRU. Information required in the pesticide operation includes the timing of the operation (month and day or fraction of plant potential heat units), the type of pesticide applied, and the amount of pesticide applied. Field studies have shown that even on days with little or no wind, a portion of pesticide applied to the field is lost. The fraction of pesticide that reaches the foliage or soil surface is defined by the pesticide’s application efficiency. The amount of pesticide that reaches the foliage or ground is: pest ′ = ap ef ⋅ pest
20.9.1
where pest ′ is the effective amount of pesticide applied (kg pst/ha), apef is the pesticide application efficiency, and pest is the actual amount of pesticide applied (kg pst/ha). The amount of pesticide reaching the ground surface and the amount of pesticide added to the plant foliage is calculated as a function of ground cover. The ground cover provided by plants is: gc =
1.99532 − erfc[1.333 ⋅ LAI − 2] 2 .1
20.9.2
where gc is the fraction of the ground surface covered by plants, erfc is the complementary error function, and LAI is the leaf area index.
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The complementary error function frequently occurs in solutions to advective-dispersive equations. Values for erfc(β) and erf(β) (erf is the error function for β), where β is the argument of the function, are graphed in Figure 201. The figure shows that erf(β) ranges from –1 to +1 while erfc(β) ranges from 0 to +2. The complementary error function takes on a value greater than 1 only for negative values of the argument.
Figure 20-1: erf(β) and erfc(β) plotted versus β (from Domenico and Schwartz, 1990)
Once the fraction of ground covered by plants is known, the amount of pesticide applied to the foliage is calculated: pest fol = gc ⋅ pest ′
20.9.3
and the amount of pesticide applied to the soil surface is pest surf = (1 − gc ) ⋅ pest ′
20.9.4
where pestfol is the amount of pesticide applied to foliage (kg pst/ha), pestsurf is the amount of pesticide applied to the soil surface (kg pst/ha), gc is the fraction of the ground surface covered by plants, and pest ′ is the effective amount of pesticide applied (kg pst/ha). Table 20-9: SWAT input variables that pertain to pesticide application. Variable Name
Definition
Variables in pesticide operation line: MONTH/DAY or HUSC Timing of pesticide operation.
MGT_OP PEST_ID PST_KG
Operation code. MGT_OP = 4 for pesticide operation Type of pesticide applied (code from pest.dat). pest: Amount of pesticide applied (kg/ha)
Variables in pesticide database: AP_EF apef: Pesticide application efficiency
Input File .mgt .mgt .mgt .mgt pest.dat
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20.10 FILTER STRIPS Edge-of field filter strips may be defined in an HRU. Sediment, nutrient, pesticide and bacteria loads in surface runoff are reduced as the surface runoff passes through the filter strip. The filter strip trapping efficiency for bacteria is calculated: trapef ,bact = 1 −
(12 + 4.5 ⋅ width
filtstrip
)
100
20.10.1
where trapef,bact is the fraction of the bacteria loading trapped by the filter strip, and widthfiltstrip is the width of the filter strip (m). The filter strip trapping efficiency for sediment, nutrients and pesticides is calculated: trapef = 0.367 ⋅ (width filtstrip )
0.2967
20.10.2
where trapef is the fraction of the constituent loading trapped by the filter strip, and widthfiltstrip is the width of the filter strip (m). Table 20-10: SWAT input variables that pertain to filter strips. Variable Name FILTERW
Definition widthfiltstrip: Width of filter strip (m)
Input File .hru
20.11 NOMENCLATURE CN2 Moisture condition II curve number HIopt Potential harvest index for the plant at maturity given ideal growing conditions HItrg Target harvest index LAI Leaf area index of the canopy NH4 Ammonium content of the soil profile (kg NH4-N/ha) NH4fert Amount of ammonium added to the soil in the fertilizer (kg N/ha) NO3 Nitrate content of the soil profile (kg NO3-N/ha) NO3fert Amount of nitrate added to the soil in the fertilizer (kg N/ha) Psolution,fert Amount of phosphorus in the solution pool added to the soil in the fertilizer (kg P/ha) PHU Potential heat units or total heat units required for plant maturity where base temperature is dependant on the plant species (heat units) RUE Radiation-use efficiency of the plant (kg/ha⋅(MJ/m2)-1 or 10-1 g/MJ) apef
Pesticide application efficiency
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bactlpsol,fert Amount of less persistent bacteria in the solution pool added to the soil in the fertilizer (# bact/ha) bactlpsorb,fert Amount of less persistent bacteria in the sorbed pool added to the soil in fertilizer (# bact/ha) bactpsol,fert Amount of persistent bacteria in the solution pool added to the soil in the fertilizer (# bact/ha) bactpsorb,fert Amount of persistent bacteria in the sorbed pool added to the soil in fertilizer (# bact/ha) bio Total plant biomass on a given day (kg/ha) bioag Aboveground biomass on the day of harvest (kg ha-1) bioN Actual mass of nitrogen stored in plant material (kg N/ha) biotrg Target biomass specified by the user (kg/ha) fert Amount of fertilizer applied (kg/ha) ferteff Fertilizer application efficiency assigned by the user fertlpbact Concentration of less persistent bacteria in the fertilizer (# bact/kg fert) fertminN Fraction of mineral nitrogen in the fertilizer fertminP Fraction of mineral P in the fertilizer fertNH4 Fraction of mineral N in the fertilizer that is ammonium fertorgN Fraction of organic N in the fertilizer fertorgP Fraction of organic P in the fertilizer fertpbact Concentration of persistent bacteria in the fertilizer (# bact/kg fert) frN Optimal fraction of nitrogen in the plant biomass for current growth stage frN,yld Fraction of nitrogen in the yield gc Fraction of the ground surface covered by plants harveff Efficiency of the harvest operation kbact Bacterial partition coefficient minNapp Amount of mineral nitrogen applied (kg N/ha) minNapp,mx Maximum amount of mineral N allowed to be applied on any one day (kg N/ha) minNapp,mxyr Maximum amount of mineral N allowed to be applied during a year (kg N/ha) nstrs Nitrogen stress for a given day orgNact,fert Amount of nitrogen in the active organic pool added to the soil in the fertilizer (kg N/ha) orgNfrsh,fert Amount of nitrogen in the fresh organic pool added to the soil in the fertilizer (kg N/ha) orgPfrsh,fert Amount of phosphorus in the fresh organic pool added to the soil in the fertilizer (kg P/ha) orgPhum,fert Amount of phosphorus in the humus organic pool added to the soil in the fertilizer (kg P/ha) pest Actual amount of pesticide applied (kg pst/ha) pest ′ Effective amount of pesticide applied (kg pst/ha) pestfol Amount of pesticide applied to foliage (kg pst/ha) pestsurf Amount of pesticide applied to the soil surface (kg pst/ha) trapef Fraction of the constituent loading trapped by the filter strip trapef,bact Fraction of the bacteria loading trapped by the filter strip
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widthfiltstrip Width of filter strip (m) yldest,N Nitrogen yield estimate (kg N/ha) yldest,Nprev Nitrogen yield estimate from the previous year (kg N/ha) yldyr,N Nitrogen yield target for the current year (kg N/ha) yrsim Year of simulation
20.12 REFERENCES Domenico, P.A. and F.W. Schwartz. 1990. Physical and chemical hydrology. John Wiley & Sons, New York, NY.
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CHAPTER 21
EQUATIONS: WATER MANAGEMENT
Accurately reproducing water management practices can be one of the most complicated portions of data input for the model. Because water management affects the hydrologic balance, it is critical that the model is able to accommodate a variety of management practices. Water management options modeled by SWAT include irrigation, tile drainage, impounded/depressional areas, water transfer, consumptive water use, and loadings from point sources.
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21.1 IRRIGATION Irrigation in an HRU may be scheduled by the user or automatically applied by SWAT. In addition to specifying the timing and application amount, the user must specify the source of irrigation water. Water applied to an HRU is obtained from one of five types of water sources: a reach, a reservoir, a shallow aquifer, a deep aquifer, or a source outside the watershed. In addition to the type of water source, the model must know the location of the water source (unless the source is outside the watershed). For the reach, shallow aquifer or deep aquifer, SWAT needs to know the subbasin number in which the source is located. If a reservoir is used to supply water, SWAT must know the reservoir number. If the source of the irrigation water is a reach, SWAT allows additional input parameters to be set. These parameters are used to prevent flow in the reach from being reduced to zero as a result of irrigation water removal. Users may define a minimum in-stream flow, a maximum irrigation water removal amount that cannot be exceeded on any given day, and/or a fraction of total flow in the reach that is available for removal on a given day. For a given irrigation event, SWAT determines the amount of water available in the source. The amount of water available is compared to the amount of water specified in the irrigation operation. If the amount available is less than the amount specified, SWAT will only apply the available water. Water applied to an HRU is used to fill the soil layers up to field capacity beginning with the soil surface layer and working downward until all the water applied is used up or the bottom of the profile is reached. If the amount of water specified in an irrigation operation exceeds the amount needed to fill the soil layers up to field capacity water content, the excess water is returned to the source. For HRUs that are defined as potholes or depressional areas, the irrigation water is added to the ponded water overlying the soil surface.
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21.1.1 AUTO-APPLICATION OF IRRIGATION When the user selects auto-application of irrigation water in an HRU, a water stress threshold must be specified. The water stress threshold is a fraction of potential plant growth. Anytime actual plant growth falls below this threshold fraction due to water stress the model will automatically apply water to the HRU. If enough water is available from the irrigation source, the model will add water to the soil until it is at field capacity. The water stress threshold is usually set somewhere between 0.90 and 0.95. Table 21-1: SWAT input variables that pertain to irrigation. Variable Name
Definition
Variables in irrigation operation line: MONTH/DAY or HUSC Timing of irrigation operation.
Input File
IRR_AMT
Operation code. MGT_OP = 2 for irrigation operation Depth of irrigation water applied on HRU (mm)
.mgt .mgt .mgt
Variables in .hru file IRR IRRNO FLOWMIN DIVMAX FLOWFR
Type of water body from which irrigation water is obtained Source location Minimum in-stream flow (m3/s) Maximum daily irrigation diversion (mm or 104 m3) Fraction of available flow allowed to be used for irrigation
.hru .hru .hru .hru .hru
MGT_OP
Variables in auto-irrigation operation line: MONTH/DAY or HUSC Initialization of auto-irrigation
MGT_OP AUTO_WSTR
Operation code. MGT_OP = 10 for auto-irrigation Water stress that triggers irrigation
.mgt .mgt .mgt
21.2 TILE DRAINAGE To simulate tile drainage in an HRU, the user must specify the depth from the soil surface to the drains, the amount of time required to drain the soil to field capacity, and the amount of lag between the time water enters the tile till it exits the tile and enters the main channel. Tile drainage occurs when the soil water content exceeds field capacity. In the soil layer where the tile drains are installed, the amount of water entering the drain on a given day is calculated:
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æ é − 24 ù ö tilewtr = (SWly − FCly ) ⋅ çç 1 − exp ê ú ÷÷ t drain ë ûø è
if SWly > FCly
21.2.1
where tilewtr is the amount of water removed from the layer on a given day by tile drainage (mm H2O), SWly is the water content of the layer on a given day (mm H2O), FCly is the field capacity water content of the layer (mm H2O), and tdrain is the time required to drain the soil to field capacity (hrs). Water entering tiles is treated like lateral flow. The flow is lagged using equations reviewed in Chapter 8. Table 21-2: SWAT input variables that pertain to tile drainage. Variable Name DDRAIN TDRAIN GDRAIN
Definition Depth to subsurface drain (mm). tdrain: Time to drain soil to field capacity (hrs) tilelag: Drain tile lag time (hrs)
Input File .hru .hru .hru
21.3 IMPOUNDED/DEPRESSIONAL AREAS Impounded/depressional areas are simulated as a water body overlying a soil profile in an HRU. This type of ponded system is needed to simulate the growth of rice, cranberries or any other plant that grows in a waterlogged system. The simulation and management operations pertaining to impounded/depressional areas is reviewed in Chapter 27.
21.4 WATER TRANSFER While water is most typically removed from a water body for irrigation purposes, SWAT also allows water to be transferred from one water body to another. This is performed with a transfer command in the watershed configuration file. The transfer command can be used to move water from any reservoir or reach in the watershed to any other reservoir or reach in the watershed. The user must input the type of water source, the location of the source, the type of water body receiving the transfer, the location of the receiving water body, and the amount of water transferred.
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Three options are provided to specify the amount of water transferred: a fraction of the volume of water in the source; a volume of water left in the source; or the volume of water transferred. The transfer is performed every day of the simulation. The transfer of water from one water body to another can be accomplished using other methods. For example, water could be removed from one water body via consumptive water use and added to another water body using point source files. Table 21-3: SWAT input variables that pertain to water transfer. Variable Name DEP_TYPE DEP_NUM DEST_TYPE DEST_NUM TRANS_AMT TRANS_CODE
Definition Water source type Water source location Destination type Destination location Amount of water transferred Rule code governing water transfer.
Input File .fig .fig .fig .fig .fig .fig
21.5 CONSUMPTIVE WATER USE Consumptive water use is a management tool that removes water from the basin. Water removed for consumptive use is considered to be lost from the system. SWAT allows water to be removed from the shallow aquifer, the deep aquifer, the reach or the pond within any subbasin in the watershed. Water also may be removed from reservoirs for consumptive use. Consumptive water use is allowed to vary from month to month. For each month in the year, an average daily volume of water removed from the source is specified. For reservoirs, the user may also specify a fraction of the water removed that is lost during removal. The water lost in the removal process becomes outflow from the reservoir.
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Table 21-4: SWAT input variables that pertain to consumptive water use. Variable Name WUPND(1-12) WURCH(1-12) WUSHAL(1-12) WUDEEP(1-12) WURESN(1-12) WURTNF
Definition Average daily water removal from pond in subbasin (104 m3) Average daily water removal from reach in subbasin (104 m3) Average daily water removal from shallow aquifer in subbasin (104 m3) Average daily water removal from deep aquifer in subbasin (104 m3) Average daily water removal from reservoir (104 m3) Fraction of water removal lost in transfer and returned as reservoir outflow.
Input File .wus .wus .wus .wus .res .res
21.6 POINT SOURCE LOADINGS SWAT directly simulates the loading of water, sediment and other constituents off of land areas in the watershed. To simulate the loading of water and pollutants from sources not associated with a land area (e.g. sewage treatment plants), SWAT allows point source information to be read in at any point along the channel network. The point source loadings may be summarized on a daily, monthly, yearly, or average annual basis. Files containing the point source loads are created by the user. The loads are read into the model and routed through the channel network using recday, recmon, recyear, or reccnst commands in the watershed configuration file. SWAT will read in water, sediment, organic N, organic P, nitrate, soluble P, ammonium, nitrite, metal, and bacteria data from the point source files. Chapter 31 reviews the format of the command lines in the watershed configuration file while Chapter 43 reviews the format of the point source files.
21.7 NOMENCLATURE SWly FCly
Water content of the layer on a given day (mm H2O) Field capacity water content of the layer (mm H2O)
tdrain Time required to drain the soil to field capacity (hrs) tilewtr Amount of water removed from the layer on a given day by tile drainage (mm H2O)
CHAPTER 22
EQUATIONS: URBAN AREAS
Most large watersheds and river basins contain areas of urban land use. Estimates of the quantity and quality of runoff in urban areas are required for comprehensive management analysis. SWAT calculates runoff from urban areas with the SCS curve number method or the Green & Ampt equation. Loadings of sediment and nutrients are determined using one of two options. The first is a set of linear regression equations developed by the USGS (Driver and Tasker, 1988) for estimating storm runoff volumes and constituent loads. The other option is to simulate the buildup and washoff mechanisms, similar to SWMM - Storm Water Management Model (Huber and Dickinson, 1988).
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22.1 CHARACTERISTICS OF URBAN AREAS Urban areas differ from rural areas in the fraction of total area that is impervious. Construction of buildings, parking lots and paved roads increases the impervious cover in a watershed and reduces infiltration. With development, the spatial flow pattern of water is altered and the hydraulic efficiency of flow is increased through artificial channels, curbing, and storm drainage and collection systems. The net effect of these changes is an increase in the volume and velocity of runoff and larger peak flood discharges. Impervious areas can be differentiated into two groups: the area that is hydraulically connected to the drainage system and the area that is not directly connected. As an example, assume there is a house surrounded by a yard where runoff from the roof flows into the yard and is able to infiltrate into the soil. The rooftop is impervious but it is not hydraulically connected to the drainage system. In contrast, a parking lot whose runoff enters a storm water drain is hydraulically connected. Table 22-1 lists typical values for impervious and directly connected impervious fractions in different urban land types. Table 22-1: Range and average impervious fractions for different urban land types. Average directly Range directly connected connected Average total Range total impervious impervious Urban Land Type impervious impervious Residential-High Density (> 8 unit/acre or unit/2.5 ha) .60 .44 - .82 .44 .32 - .60 Residential-Medium Density (1-4 unit/acre or unit/2.5 ha) .38 .23 - .46 .30 .18 - .36 Residential-Med/Low Density (> 0.5-1 unit/acre or unit/2.5 ha) .20 .14 - .26 .17 .12 - .22 Residential-Low Density (< 0.5 unit/acre or unit/2.5 ha) .12 .07 - .18 .10 .06 - .14 Commercial .67 .48 - .99 .62 .44 - .92 Industrial .84 .63 - .99 .79 .59 - .93 Transportation .98 .88 - 1.00 .95 .85 – 1.00 Institutional .51 .33 - .84 .47 .30 - .77
During dry periods, dust, dirt and other pollutants build up on the impervious areas. When precipitation events occur and runoff from the impervious areas is generated, the runoff will carry the pollutants as it moves through the drainage system and enters the channel network of the watershed.
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337
22.2 SURFACE RUNOFF FROM URBAN AREAS In urban areas, surface runoff is calculated separately for the directly connected impervious area and the disconnected impervious/pervious area. For directly connected impervious areas, a curve number of 98 is always used. For disconnected impervious/pervious areas, a composite curve number is calculated and used in the surface runoff calculations. The equations used to calculate the composite curve number for disconnected impervious/pervious areas are (Soil Conservation Service Engineering Division, 1986): impdcon ö æ æ impdcon ö CN p ⋅ ç1 − imptot + ÷ + 98 ⋅ ç ÷ 2 ø 2 ø è è if imptot ≤ 0.30 CN c = 1 − impcon
CN c =
CN p ⋅ (1 − imptot ) + 98 ⋅ impdcon 1 − impcon
if imptot > 0.30
22.2.1
22.2.2
where CNc is the composite moisture condition II curve number, CNp is the pervious moisture condition II curve number, imptot is the fraction of the HRU area that is impervious (both directly connected and disconnected), impcon is the fraction of the HRU area that is impervious and hydraulically connected to the drainage system, impdcon is the fraction of the HRU area that is impervious but not hydraulically connected to the drainage system. Table 22-2: SWAT input variables that pertain to surface runoff calculations in urban areas. Input Variable Name Definition File CN2 CNp: SCS moisture condition II curve number for pervious areas .mgt CNOP CNp: SCS moisture condition II curve number for pervious areas .mgt specified in plant, harvest/kill and tillage operation urban.dat FIMP imptot: fraction of urban land type area that is impervious FCIMP impcon: fraction of urban land type area that is connected urban.dat impervious
22.3 USGS REGRESSION EQUATIONS The linear regression models incorporated into SWAT are those described by Driver and Tasker (1988). The regression models were developed from a national urban water quality database that related storm runoff loads to urban
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physical, land use, and climatic characteristics. USGS developed these equations to predict loadings in ungaged urban watersheds. The regression models calculate loadings as a function of total storm rainfall, drainage area and impervious area. The general equation is
β 0 ⋅ (Rday 25.4 )β ⋅ (DA 2.59 )β ⋅ (imptot ⋅ 100 + 1)β ⋅ β 4 Y = 2.205 1
2
3
22.3.1
where Y is the total constituent load (kg), Rday is precipitation on a given day (mm H2O), DA is the HRU drainage area (km2), imptot is the fraction of the total area that is impervious, and the β variables are regression coefficients. The regression equations were developed in English units, so conversion factors were incorporated to adapt the equations to metric units: 25.4 mm/inch, 2.59 km2/mi2, and 2.205 lb/kg. USGS derived three different sets of regression coefficients that are based on annual precipitation. Category I coefficients are used in watersheds with less than 508 mm of annual precipitation. Category II coefficients are used in watersheds with annual precipitation between 508 and 1016 mm. Category III coefficients are used in watersheds with annual precipitation greater than 1016 mm. SWAT determines the annual precipitation category for each subbasin by summing the monthly precipitation totals provided in the weather generator input file. Regression coefficients were derived to estimate suspended solid load, total nitrogen load, total phosphorus load and carbonaceous oxygen demand (COD). SWAT calculates suspended solid, total nitrogen, and total phosphorus loadings (the carbonaceous oxygen demand is not currently calculated). Regression coefficients for these constituents are listed in Table 22-3. Once total nitrogen and phosphorus loads are calculated, they are partitioned into organic and mineral forms using the following relationships from Northern Virginia Planning District Commission (1979). Total nitrogen loads consist of 70 percent organic nitrogen and 30 percent mineral (nitrate). Total phosphorus loads are divided into 75 percent organic phosphorus and 25 percent orthophosphate.
CHAPTER 22: EQUATIONS—URBAN AREAS Table 22-3: Urban regression coefficients (from Driver and Tasker, 1988). Precipitation Loading Category β0 β1 β2 β3 suspended solids I 1778.0 0.867 0.728 0.157 II 812.0 1.236 0.436 0.202 III 97.7 1.002 1.009 0.837
β4 2.367 1.938 2.818
total nitrogen
I II III
20.20 4.04 1.66
0.825 0.936 0.703
1.070 0.937 0.465
0.479 0.692 0.521
1.258 1.373 1.845
total phosphorus
I II III
1.725 0.697 1.618
0.884 1.008 0.954
0.826 0.628 0.789
0.467 0.469 0.289
2.130 1.790 2.247
I 407.0 0.626 II 151.0 0.823 III 102.0 0.851 I = annual precipitation < 508 mm II = 508 mm < annual precipitation < 1,016 mm III = annual precipitation > 1,016 mm
0.710 0.726 0.601
0.379 0.564 0.528
1.518 1.451 1.978
COD
339
Table 22-4: SWAT input variables that pertain to urban modeling with regression equations. Input Variable Name Definition File IURBAN Urban simulation code .hru URBLU Urban land type identification number from urban database .hru FIMP urban.dat Fraction of HRU that is impervious. imptot = FIMP ⋅ 100 PRECIPITATION Rday: Precipitation on a given day (mm H2O) .pcp HRU_FR Fraction of total watershed area in HRU .hru DA_KM Area of watershed (km2) .bsn PCPMM(mon) Average amount of precipitation falling in month (mm H2O) .wgn
22.4 BUILD UP/WASH OFF In an impervious area, dust, dirt and other constituents are built up on street surfaces in periods of dry weather preceding a storm. Build up may be a function of time, traffic flow, dry fallout and street sweeping. During a storm runoff event, the material is then washed off into the drainage system. Although the build up/wash off option is conceptually appealing, the reliability and credibility of the simulation may be difficult to establish without local data for calibration and validation (Huber and Dickinson, 1988). When the build up/wash off option is used in SWAT, the urban hydrologic response unit (HRU) is divided into pervious and impervious areas. Management operations other than sweep operations are performed in the pervious portion of
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the HRU. Sweep operations impact build up of solids in the impervious portion of the HRU. For the pervious portion of the HRU, sediment and nutrient loadings are calculated using the methodology summarized in Chapters 13 and 14. The impervious portion of the HRU uses the build up/wash off algorithm to determine sediment and nutrient loadings. The build up/wash off algorithm calculates the build up and wash off of solids. The solids are assumed to possess a constant concentration of organic and mineral nitrogen and phosphorus where the concentrations are a function of the urban land type. Build up of solids is simulated on dry days with a Michaelis-Menton equation: SED =
SEDmx ⋅ td (thalf + td )
22.4.1
where SED is the solid build up (kg/curb km) td days after the last occurrence of SED = 0 kg/curb km, SEDmx is the maximum accumulation of solids possible for the urban land type (kg/curb km), and thalf is the length of time needed for solid build up to increase from 0 kg/curb km to ½ SEDmx (days). A dry day is defined as a day with surface runoff less than 0.1 mm. An example build-up curve is shown in Figure 22-1. As can be seen from the plot, the Michaelis-Menton function will initially rise steeply and then approach the asymptote slowly. 100 90 80 70 60 50 40 30 20 10 0
Figure 22-1: Build-up function for solids in urban areas.
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341
The two parameters that determine the shape of this curve are SEDmx and thalf. These parameters are a function of the urban land type. Wash off is the process of erosion or solution of constituents from an impervious surface during a runoff event. An exponential relationship is used to simulate the wash off process (Huber and Dickinson, 1988): Ysed = SED0 ⋅ (1 − e − kk ⋅t ) 22.4.2 where Ysed is the cumulative amount of solids washed off at time t (kg/curb km),
SED0 is the amount of solids built up on the impervious area at the beginning of the precipitation event (kg/curb km), and kk is a coefficient. The coefficient, kk, may be estimated by assuming it is proportional to the peak runoff rate: kk = urbcoef ⋅ q peak
22.4.3
where urbcoef is the wash off coefficient (mm-1) and qpeak is the peak runoff rate (mm/hr). The original default value for urbcoef was calculated as 0.18 mm-1 by assuming that 13 mm of total runoff in one hour would wash off 90% of the initial surface load. Later estimates of urbcoef gave values ranging from 0.002-0.26 mm-1. Huber and Dickinson (1988) noted that values between 0.039 and 0.390 mm-1 for urbcoef give sediment concentrations in the range of most observed values. They also recommended using this variable to calibrate the model to observed data. To convert the sediment loading from units of kg/curb km to kg/ha, the amount of sediment removed by wash off is multiplied by the curb length density. The curb length density is a function of the urban land type. Nitrogen and phosphorus loadings from the impervious portion of the urban land area are calculated by multiplying the concentration of nutrient by the sediment loading.
22.4.1 STREET CLEANING Street cleaning is performed in urban areas to control buildup of solids and trash. While it has long been thought that street cleaning has a beneficial effect on the quality of urban runoff, studies by EPA have found that street sweeping has
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little impact on runoff quality unless it is performed every day (U.S. Environmental Protection Agency, 1983). SWAT performs street sweeping operations only when the build up/wash off algorithm is specified for urban loading calculations. Street sweeping is performed only on dry days, where a dry day is defined as a day with less than 0.1 mm of surface runoff. The sweeping removal equation (Huber and Dickinson, 1988) is: SED = SED0 ⋅ (1 − frav ⋅ reff )
22.4.4
where SED is amount of solids remaining after sweeping (kg/curb km), SED0 is the amount of solids present prior to sweeping (kg/curb km), frav is the fraction of the curb length available for sweeping (the availability factor), and reff is the removal efficiency of the sweeping equipment. The availability factor and removal efficiency are specified by the user. Table 22-5: Removal efficiencies (fraction removed) from street cleaner path (from Pitt, 1979) COD KN PO4 Pesticides Street Cleaning Program and Total BOD5 Street Surface Loading Conditions Solids Vacuum Street Cleaner (5.5-55 kg/curb km)
1 pass 2 passes 3 passes
.31 .45 .53
.24 .35 .41
.16 .22 .27
.26 .37 .45
.08 .12 .14
.33 .50 .59
1 pass 2 passes 3 passes
.37 .51 .58
.29 .42 .47
.21 .29 .35
.31 .46 .51
.12 .17 .20
.40 .59 .67
1 pass 2 passes 3 passes
.48 .60 .63
.38 .50 .52
.33 .42 .44
.43 .54 .57
.20 .25 .26
.57 .72 .75
1 pass 2 passes 3 passes
.54 .75 .85
.40 .58 .69
.31 .48 .59
.40 .58 .69
.20 .35 .46
.40 .60 .72
Flusher
.30
a
a
a
a
a
Mechanical Street Cleaner followed by a Flusher
.80
b
b
b
b
b
Vacuum Street Cleaner (55-280 kg/curb km)
Vacuum Street Cleaner (280-2820 kg/curb km)
Mechanical Street Cleaner (50-500 kg/curb km)
a: efficiency fraction estimated .15 to .40 b: efficiency fraction estimated .35 to 1.00
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343
The availability factor, frav, is the fraction of the curb length that is sweepable. The entire curb length is often not available for sweeping due to the presence of cars and other obstacles. The removal efficiency of street sweeping is a function of the type of sweeper, whether flushing is a part of the street cleaning process, the quantity of total solids, the frequency of rainfall events and the constituents considered. Removal efficiency can vary depending on the constituent being considered, with efficiencies being greater for particulate constituents. The removal efficiencies for nitrogen and phosphorus are typically less than the solid removal efficiency (Pitt, 1979). Because SWAT assumes a set concentration of nutrient constituents in the solids, the same removal efficiency is in effect used for all constituents. Table 225 provides removal efficiencies for various street cleaning programs. Table 22-6: SWAT input variables that pertain to build up/wash off. Variable Name IURBAN URBLU DIRTMX THALF URBCOEF CURBDEN TNCONC TPCONC TNO3CONC SWEEPEFF AVWSP
Definition Urban simulation code Urban land type identification number from urban database SEDmx: maximum amount of solids allowed to build up on impervious areas (kg/curb km) thalf: number of days for amount of solids on impervious area to build up from 0 kg/curb km to ½ SEDmx urbcoef: wash off coefficient (mm-1) curb length density in urban land type (km/ha) concentration of total nitrogen in suspended solid load (mg N/kg) concentration of total phosphorus in suspended solid load (mg N/kg) concentration of nitrate in suspended solid load (mg N/kg) reff: removal efficiency of the sweeping equipment frav: fraction of the curb length that is sweepable.
Input File .hru .hru urban.dat urban.dat urban.dat urban.dat urban.dat urban.dat urban.dat .mgt .mgt
22.5 NOMENCLATURE CN DA Rday SED SEDmx Y Ysed
Curve number HRU drainage area (km2) Amount of rainfall on a given day (mm H2O) Solid build up (kg/curb km) Maximum accumulation of solids possible for the urban land type (kg/curb km) Total constituent load (kg) Cumulative amount of solids washed off at time t (kg/curb km)
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frav Fraction of the curb length available for sweeping (the availability factor) impcon Fraction of the HRU area that is impervious and hydraulically connected to the drainage system impdcon Fraction of the HRU area that is impervious but not hydraulically connected to the drainage system imptot Fraction of the HRU area that is impervious (both connected and disconnected) kk Coefficient in urban wash off equation qpeak Peak runoff rate (mm/hr) reff Removal efficiency of the sweeping equipment Length of time needed for solid build up to increase from 0 kg/curb km to ½ thalf SEDmx (days) urbcoef Wash off coefficient (mm-1)
β0 β1 β2 β3 β4
Coefficient for USGS regression equations for urban loadings Coefficient for USGS regression equations for urban loadings Coefficient for USGS regression equations for urban loadings Coefficient for USGS regression equations for urban loadings Coefficient for USGS regression equations for urban loadings
22.6 REFERENCES Driver, N.E. and G.D. Tasker. 1988. Techniques for estimation of storm-runoff loads, volumes, and selected constituent concentrations in urban watersheds in the United States. U.S. Dept. of the Interior, U.S. Geological Survey: Books and Open-File Reports Section 88-191. Huber, W.C. and R.E. Dickinson. 1988. Storm water management model, version 4: user’s manual. U.S. Environmental Protection Agency, Athens, GA. Northern Virginia Planning District Commission. 1979. Guidebook for screening urban nonpoint pollution management strategies: a final report prepared for Metropolitan Washington Council of Governments. Northern Virginia Planning District Commission, Falls Church, VA. Pitt, R. 1979. Demonstration of non-point pollution abatement through improved street cleaning practices. EPA-600/2-79-161 (NTIS PB80-108988), U.S. Environmental Protection Agency, Cincinnati, OH. Soil Conservation Service Engineering Division. 1986. Urban hydrology for small watersheds. U.S. Department of Agriculture, Technical Release 55.
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U.S. Environmental Protection Agency. 1983. Results of the nationwide urban runoff program; Volume 1 final report. NTIS PB84-185552, U.S. Environmental Protection Agency, Washington, D.C.
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MAIN CHANNEL PROCESSES Flow in a watershed is classified as overland or channelized. The primary difference between the two flow processes is that water storage and its influence on flow rates is considered in channelized flow. Main channel processes modeled by SWAT include the movement of water, sediment and other constituents (e.g. nutrients, pesticides) in the stream network, in-stream nutrient cycling, and in-stream pesticide transformations. Optional processes include the change in channel dimensions with time due to downcutting and widening.
CHAPTER 23
EQUATIONS: WATER ROUTING
Open channel flow is defined as channel flow with a free surface, such as flow in a river or partially full pipe. SWAT uses Manning’s equation to define the rate and velocity of flow. Water is routed through the channel network using the variable storage routing method or the Muskingum river routing method. Both the variable storage and Muskingum routing methods are variations of the kinematic wave model. A detailed discussion of the kinematic wave flood routing model can be found in Chow et al. (1988).
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23.1 CHANNEL CHARACTERISTICS SWAT assumes the main channels, or reaches, have a trapezoidal shape (Figure 23-1).
Figure 23-1: Trapezoidal channel dimensions
Users are required to define the width and depth of the channel when filled to the top of the bank as well as the channel length, slope along the channel length and Manning’s “n” value. SWAT assumes the channel sides have a 2:1 run to rise ratio (zch = 2). The slope of the channel sides is then ½ or 0.5. The bottom width is calculated from the bankfull width and depth with the equation: Wbtm = Wbnkfull − 2 ⋅ z ch ⋅ depthbnkfull
23.1.1
where Wbtm is the bottom width of the channel (m), Wbnkfull is the top width of the channel when filled with water (m), zch is the inverse of the channel side slope, and depthbnkfull is the depth of water in the channel when filled to the top of the bank (m). Because of the assumption that zch = 2, it is possible for the bottom width calculated with equation 23.1.1 to be less than or equal to zero. If this occurs, the model sets Wbtm = 0.5 ⋅ Wbnkfull and calculates a new value for the channel side slope run by solving equation 23.1.1 for zch: z ch =
(W
bnkfull
− Wbtm )
2 ⋅ depthbnkfull
23.1.2
For a given depth of water in the channel, the width of the channel at water level is:
CHAPTER 23: EQUATIONS—WATER ROUTING
W = Wbtm + 2 ⋅ z ch ⋅ depth
351
23.1.3
where W is the width of the channel at water level (m), Wbtm is the bottom width of the channel (m), zch is the inverse of the channel slope, and depth is the depth of water in the channel (m). The cross-sectional area of flow is calculated: Ach = (Wbtm + z ch ⋅ depth ) ⋅ depth
23.1.4
where Ach is the cross-sectional area of flow in the channel (m2), Wbtm is the bottom width of the channel (m), zch is the inverse of the channel slope, and depth is the depth of water in the channel (m). The wetted perimeter of the channel is defined as Pch = Wbtm + 2 ⋅ depth ⋅ 1 + z ch
2
23.1.5
where Pch is the wetted perimeter for a given depth of flow (m). The hydraulic radius of the channel is calculated Rch =
Ach Pch
23.1.6
where Rch is the hydraulic radius for a given depth of flow (m), Ach is the crosssectional area of flow in the channel (m2), and Pch is the wetted perimeter for a given depth of flow (m). The volume of water held in the channel is Vch = 1000 ⋅ Lch ⋅ Ach
23.1.7
where Vch is the volume of water stored in the channel (m3), Lch is the channel length (km), and Ach is the cross-sectional area of flow in the channel for a given depth of water (m2). When the volume of water in the reach exceeds the maximum amount that can be held by the channel, the excess water spreads across the flood plain. The flood plain dimensions used by SWAT are shown in Figure 23-2.
Figure 23-2: Illustration of flood plain dimensions.
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The bottom width of the floodplain, Wbtm,fld, is Wbtm , fld = 5 ⋅ Wbnkfull . SWAT assumes the flood plain side slopes have a 4:1 run to rise ratio (zfld = 4). The slope of the flood plain sides is then ¼ or 0.25. When flow is present in the flood plain, the calculation of the flow depth, cross-sectional flow area and wetting perimeter is a sum of the channel and floodplain components: depth = depthbnkfull + depth fld
23.1.8
Ach = (Wbtm + z ch ⋅ depthbnkfull ) ⋅ depthbnkfull + (Wbtm , fld + z fld ⋅ depth fld ) ⋅ depth fld 23.1.9 Pch = Wbtm + 2 ⋅ depthbnkfull ⋅ 1 + z ch + 4 ⋅ Wbnkfull + 2 ⋅ depth fld ⋅ 1 + z fld 2
2
23.1.10
where depth is the total depth of water (m), depthbnkfull is the depth of water in the channel when filled to the top of the bank (m), depthfld is the depth of water in the flood plain (m), Ach is the cross-sectional area of flow for a given depth of water (m2), Wbtm is the bottom width of the channel (m), zch is the inverse of the channel side slope, Wbtm,fld is the bottom width of the flood plain (m), zfld is the inverse of the flood plain side slope, Pch is the wetted perimeter for a given depth of flow (m), and Wbnkfull is the top width of the channel when filled with water (m). Table 23-1: SWAT input variables that pertain to channel dimension calculations. Variable name Definition CH_W(2) Wbnkfull: Width of channel at top of bank (m) CH_D depthbnkfull: Depth of water in channel when filled to bank (m) CH_L(2) Lch: Length of main channel (km)
File Name .rte .rte .rte
23.2 FLOW RATE AND VELOCITY Manning’s equation for uniform flow in a channel is used to calculate the rate and velocity of flow in a reach segment for a given time step: qch = vc =
Ach ⋅ Rch
23
⋅ slpch
n Rch
23
⋅ slpch n
12
23.2.1
12
23.2.2
where qch is the rate of flow in the channel (m3/s), Ach is the cross-sectional area of flow in the channel (m2), Rch is the hydraulic radius for a given depth of flow (m),
CHAPTER 23: EQUATIONS—WATER ROUTING
353
slpch is the slope along the channel length (m/m), n is Manning’s “n” coefficient for the channel, and vc is the flow velocity (m/s). SWAT routes water as a volume. The daily value for cross-sectional area of flow, Ach, is calculated by rearranging equation 23.1.7 to solve for the area: Ach =
Vch 1000 ⋅ Lch
23.2.3
where Ach is the cross-sectional area of flow in the channel for a given depth of water (m2), Vch is the volume of water stored in the channel (m3), and Lch is the channel length (km). Equation 23.1.4 is rearranged to calculate the depth of flow for a given time step: 2
depth =
Ach æ Wbtm ö W ÷÷ − btm + çç 2 ⋅ z ch z ch è 2 ⋅ z ch ø
23.2.4
where depth is the depth of flow (m), Ach is the cross-sectional area of flow in the channel for a given depth of water (m2), Wbtm is the bottom width of the channel (m), and zch is the inverse of the channel side slope. Equation 23.2.4 is valid only when all water is contained in the channel. If the volume of water in the reach segment has filled the channel and is in the flood plain, the depth is calculated: depth = depthbnkfull +
(A
ch
− Ach ,bnkfull ) æ Wbtm , fld +ç ç 2⋅z z fld fld è
2
ö W ÷ − btm , fld ÷ 2 ⋅ z fld ø
23.2.5
where depth is the depth of flow (m), depthbnkfull is the depth of water in the channel when filled to the top of the bank (m), Ach is the cross-sectional area of flow in the channel for a given depth of water (m2), Ach,bnkfull is the cross-sectional area of flow in the channel when filled to the top of the bank (m2), Wbtm,fld is the bottom width of the flood plain (m), and zfld is the inverse of the flood plain side slope. Once the depth is known, the wetting perimeter and hydraulic radius are calculated using equations 23.1.5 (or 23.1.10) and 23.1.6. At this point, all values required to calculate the flow rate and velocity are known and equations 23.2.1 and 23.2.2 can be solved.
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Table 23-2: SWAT input variables that pertain to channel flow calculations. Variable name Definition CH_S(2) slpch: Average channel slope along channel length (m m-1) CH_N(2) n: Manning’s “n” value for the main channel CH_L(2) Lch: Length of main channel (km)
File Name .rte .rte .rte
23.3 VARIABLE STORAGE ROUTING METHOD The variable storage routing method was developed by Williams (1969) and used in the HYMO (Williams and Hann, 1973) and ROTO (Arnold et al., 1995) models. For a given reach segment, storage routing is based on the continuity equation: Vin − Vout = ∆Vstored
23.3.1
where Vin is the volume of inflow during the time step (m3 H2O), Vout is the volume of outflow during the time step (m3 H2O), and ∆Vstored is the change in volume of storage during the time step (m3 H2O). This equation can be written as æ q + qin , 2 ö æ q + qout , 2 ö ∆t ⋅ çç in ,1 ÷÷ − ∆t ⋅ çç out ,1 ÷÷ = Vstored ,2 − Vstored ,1 2 2 è ø è ø
23.3.2
where ∆t is the length of the time step (s), qin,1 is the inflow rate at the beginning of the time step (m3/s), qin,2 is the inflow rate at the end of the time step (m3/s), qout,1 is the outflow rate at the beginning of the time step (m3/s), qout,2 is the outflow rate at the end of the time step (m3/s), Vstored,1 is the storage volume at the beginning of the time step (m3 H2O), and Vstored,2 is the storage volume at the end of the time step (m3 H2O). Rearranging equation 23.3.2 so that all known variables are on the left side of the equation, qin ,ave +
Vstored ,1 ∆t
−
qout ,1 2
=
Vstored , 2 ∆t
+
qout , 2
23.3.3
2
where qin,ave is the average inflow rate during the time step: qin ,ave =
qin ,1 + qin , 2 2
.
Travel time is computed by dividing the volume of water in the channel by the flow rate.
CHAPTER 23: EQUATIONS—WATER ROUTING
TT =
Vstored Vstored ,1 Vstored , 2 = = qout qout ,1 qout , 2
355
23.3.4
where TT is the travel time (s), Vstored is the storage volume (m3 H2O), and qout is the discharge rate (m3/s). To obtain a relationship between travel time and the storage coefficient, equation 23.3.4 is substituted into equation 23.3.3: qin ,ave +
Vstored ,1 æ ∆t ö æç Vstored ,1 ö÷ ç ÷⋅ è TT ø çè qout ,1 ÷ø
−
qout ,1 Vstored , 2 q = + out , 2 2 2 æ ∆t ö æç Vstored , 2 ö÷ ç ÷⋅ç è TT ø è qout , 2 ÷ø
23.3.5
which simplifies to 2 ⋅ ∆t ö æ 2 ⋅ ∆t ö æ qout , 2 = ç ÷ ⋅ qin ,ave + ç 1 − ÷ ⋅ qout ,1 2 ⋅ TT + ∆t ø è 2 ⋅ TT + ∆t ø è
23.3.6
This equation is similar to the coefficient method equation qout , 2 = SC ⋅ qin ,ave + (1 − SC ) ⋅ qout ,1
23.3.7
where SC is the storage coefficient. Equation 23.3.7 is the basis for the SCS convex routing method (SCS, 1964) and the Muskingum method (Brakensiek, 1967; Overton, 1966). From equation 23.3.6, the storage coefficient in equation 23.3.7 is defined as SC =
2 ⋅ ∆t 2 ⋅ TT + ∆t
23.3.8
It can be shown that
(1 − SC ) ⋅ qout
= SC ⋅
Vstored ∆t
23.3.9
Substituting this into equation 23.3.7 gives V æ ö qout , 2 = SC ⋅ çç qin ,ave + stored ,1 ÷÷ ∆t ø è
23.3.10
To express all values in units of volume, both sides of the equation are multiplied by the time step Vout ,2 = SC ⋅ (Vin + Vstored ,1 )
23.3.11
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23.4 MUSKINGUM ROUTING METHOD The Muskingum routing method models the storage volume in a channel length as a combination of wedge and prism storages (Figure 23-3).
Figure 23-3: Prism and wedge storages in a reach segment (from Chow et al., 1988)
When a flood wave advances into a reach segment, inflow exceeds outflow and a wedge of storage is produced. As the flood wave recedes, outflow exceeds inflow in the reach segment and a negative wedge is produced. In addition to the wedge storage, the reach segment contains a prism of storage formed by a volume of constant cross-section along the reach length. As defined by Manning’s equation (equation 23.2.1), the cross-sectional area of flow is assumed to be directly proportional to the discharge for a given reach segment. Using this assumption, the volume of prism storage can be expressed as a function of the discharge, K ⋅ qout , where K is the ratio of storage to discharge and has the dimension of time. In a similar manner, the volume of wedge storage can be expressed as K ⋅ X ⋅ (qin − qout ) , where X is a weighting factor that controls the relative importance of inflow and outflow in determining the storage in a reach. Summing these terms gives a value for total storage Vstored = K ⋅ qout + K ⋅ X ⋅ (qin − qout )
23.4.1
CHAPTER 23: EQUATIONS—WATER ROUTING
357
where Vstored is the storage volume (m3 H2O), qin is the inflow rate (m3/s), qout is the discharge rate (m3/s), K is the storage time constant for the reach (s), and X is the weighting factor. This equation can be rearranged to the form Vstored = K ⋅ ( X ⋅ qin + (1 − X ) ⋅ qout )
23.4.2
This format is similar to equation 23.3.7. The weighting factor, X, has a lower limit of 0.0 and an upper limit of 0.5. This factor is a function of the wedge storage. For reservoir-type storage, there is no wedge and X = 0.0. For a full-wedge, X = 0.5. For rivers, X will fall between 0.0 and 0.3 with a mean value near 0.2. The definition for storage volume in equation 23.4.2 can be incorporated into the continuity equation (equation 23.3.2) and simplified to qout , 2 = C1 ⋅ qin , 2 + C 2 ⋅ qin ,1 + C3 ⋅ qout ,1
23.4.3
where qin,1 is the inflow rate at the beginning of the time step (m3/s), qin,2 is the inflow rate at the end of the time step (m3/s), qout,1 is the outflow rate at the beginning of the time step (m3/s), qout,2 is the outflow rate at the end of the time step (m3/s), and C1 =
∆t − 2 ⋅ K ⋅ X 2 ⋅ K ⋅ (1 − X ) + ∆t
23.4.4
C2 =
∆t + 2 ⋅ K ⋅ X 2 ⋅ K ⋅ (1 − X ) + ∆t
23.4.5
C3 =
2 ⋅ K ⋅ (1 − X ) − ∆t 2 ⋅ K ⋅ (1 − X ) + ∆t
23.4.6
where C1 + C2 + C3 = 1 . To express all values in units of volume, both sides of equation 23.4.3 are multiplied by the time step Vout , 2 = C1 ⋅ Vin , 2 + C2 ⋅ Vin ,1 + C3 ⋅ Vout ,1
23.4.7
To maintain numerical stability and avoid the computation of negative outflows, the following condition must be met:
2 ⋅ K ⋅ X < ∆t < 2 ⋅ K ⋅ (1 − X )
23.4.8
The value for the weighting factor, X, is input by the user. The value for the storage time constant is estimated as:
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K = coef 1 ⋅ K bnkfull + coef 2 ⋅ K 0.1bnkfull
23.4.9
where K is the storage time constant for the reach segment (s), coef1 and coef2 are weighting coefficients input by the user, Kbnkfull is the storage time constant calculated for the reach segment with bankfull flows (s), and K0.1bnkfull is the storage time constant calculated for the reach segment with one-tenth of the bankfull flows (s). To calculate Kbnkfull and K0.1bnkfull, an equation developed by Cunge (1969) is used: K=
1000 ⋅ Lch ck
23.4.10
where K is the storage time constant (s), Lch is the channel length (km), and ck is the celerity corresponding to the flow for a specified depth (m/s). Celerity is the velocity with which a variation in flow rate travels along the channel. It is defined as ck =
d (qch ) dAch
23.4.11
where the flow rate, qch, is defined by Manning’s equation. Differentiating equation 23.2.1 with respect to the cross-sectional area gives ck =
23 12 5 æ Rch ⋅ slpch ö 5 ÷ = ⋅ vc ⋅ çç ÷ 3 3 è n ø
23.4.12
where ck is the celerity (m/s), Rch is the hydraulic radius for a given depth of flow (m), slpch is the slope along the channel length (m/m), n is Manning’s “n” coefficient for the channel, and vc is the flow velocity (m/s). Table 23-3: SWAT input variables that pertain to Muskingum routing. Variable name Definition MSK_X X: weighting factor MSK_CO1 coef1: weighting factor for influence of normal flow on storage time constant value MSK_CO2 coef2: weighting factor for influence of low flow on storage time constant
File Name .bsn .bsn .bsn
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359
23.5 TRANSMISSION LOSSES The classification of a stream as ephemeral, intermittent or perennial is a function of the amount of groundwater contribution received by the stream. Ephemeral streams contain water during and immediately after a storm event and are dry the rest of the year. Intermittent streams are dry part of the year, but contain flow when the groundwater is high enough as well as during and after a storm event. Perennial streams receive continuous groundwater contributions and flow throughout the year. During periods when a stream receives no groundwater contributions, it is possible for water to be lost from the channel via transmission through the side and bottom of the channel. Transmission losses are estimated with the equation tloss = K ch ⋅ TT ⋅ Pch ⋅ Lch
23.5.1
where tloss are the channel transmission losses (m3 H2O), Kch is the effective hydraulic conductivity of the channel alluvium (mm/hr), TT is the flow travel time (hr), Pch is the wetted perimeter (m), and Lch is the channel length (km). Transmission losses from the main channel are assumed to enter bank storage or the deep aquifer. Typical values for Kch for various alluvium materials are given in Table 23-4. For perennial streams with continuous groundwater contribution, the effective conductivity will be zero. Table 23-4: Example hydraulic conductivity values for various bed materials (from Lane, 1983). Bed material Bed material characteristics Hydraulic group conductivity 1 Very high loss rate 2 High loss rate 3 Moderately high loss rate 4 Moderate loss rate 5 Insignificant to low loss rate
Very clean gravel and large sand
> 127 mm/hr
Clean sand and gravel, field conditions
51-127 mm/hr
Sand and gravel mixture with low silt-clay content
25-76 mm/hr
Sand and gravel mixture with high silt-clay content
6-25 mm/hr
Consolidated bed material; high silt-clay content
0.025-2.5 mm/hr
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Table 23-5: SWAT input variables that pertain to transmission losses. Variable name Definition CH_K(2) Kch: Effective hydraulic conductivity of channel (mm/hr) CH_L(2) Lch: Length of main channel (km)
File Name .rte .rte
23.6 EVAPORATION LOSSES Evaporation losses from the reach are calculated: E ch = coef ev ⋅ Eo ⋅ Lch ⋅ W ⋅ fr∆t
23.6.1
where Ech is the evaporation from the reach for the day (m3 H2O), coefev is an evaporation coefficient, Eo is potential evaporation (mm H2O), Lch is the channel length (km), W is the channel width at water level (m), and fr∆t is the fraction of the time step in which water is flowing in the channel. The evaporation coefficient is a calibration parameter for the user and is allowed to vary between 0.0 and 1.0. The fraction of the time step in which water is flowing in the channel is calculated by dividing the travel time by the length of the time step. Table 23-6: SWAT input variables that pertain to evaporation losses. Variable name Definition EVRCH coefev: Reach evaporation adjustment factor CH_L(2) Lch: Length of main channel (km)
File Name .bsn .rte
23.7 BANK STORAGE The amount of water entering bank storage on a given day is calculated: bnk in = tloss ⋅ (1 − frtrns )
23.7.1
where bnkin is the amount of water entering bank storage (m3 H2O), tloss are the channel transmission losses (m3 H2O), and frtrns is the fraction of transmission losses partitioned to the deep aquifer. Bank storage contributes flow to the main channel or reach within the subbasin. Bank flow is simulated with a recession curve similar to that used for groundwater. The volume of water entering the reach from bank storage is calculated:
CHAPTER 23: EQUATIONS—WATER ROUTING
Vbnk = bnk ⋅ (1 − α bnk )
361
23.7.2
where Vbnk is the volume of water added to the reach via return flow from bank storage (m3 H2O), bnk is the total amount of water in bank storage (m3 H2O), and
αbnk is the bank flow recession constant or constant of proportionality. Water may move from bank storage into an adjacent unsaturated zone. SWAT models the movement of water into adjacent unsaturated areas as a function of water demand for evapotranspiration. To avoid confusion with soil evaporation and transpiration, this process has been termed ‘revap’. This process is significant in watersheds where the saturated zone is not very far below the surface or where deep-rooted plants are growing. ‘Revap’ from bank storage is governed by the groundwater revap coefficient defined for the last HRU in the subbasin. The maximum amount of water than will be removed from bank storage via ‘revap’ on a given day is: bnk revap ,mx = β rev ⋅ Eo ⋅ Lch ⋅ W
23.7.3
where bnkrevap,mx is the maximum amount of water moving into the unsaturated zone in response to water deficiencies (m3 H2O), βrev is the revap coefficient, Eo is the potential evapotranspiration for the day (mm H2O), Lch is the channel length (km), and W is the width of the channel at water level (m). The actual amount of revap that will occur on a given day is calculated: bnk revap = bnk
if bnk < bnk revap ,mx
23.7.4
bnk revap = bnk revap ,mx
if bnk ≥ bnk revap ,mx
23.7.5
where bnkrevap is the actual amount of water moving into the unsaturated zone in response to water deficiencies (m3 H2O), bnkrevap,mx is the maximum amount of water moving into the unsaturated zone in response to water deficiencies (m3 H2O), and bnk is the amount of water in bank storage at the beginning of day i (m3 H2O).
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Table 23-7: SWAT input variables that pertain to bank storage. Variable name Definition TRNSRCH frtrns: Fraction of transmission losses partitioned to the deep aquifer ALPHA_BNK αbnk: Bank flow recession constant or constant of proportionality GW_REVAP βrev: Revap coefficient
File Name .bsn .rte .gw
23.8 CHANNEL WATER BALANCE Water storage in the reach at the end of the time step is calculated: Vstored , 2 = Vstored ,1 + Vin − Vout − tloss − E ch + div + Vbnk
23.8.1
where Vstored,2 is the volume of water in the reach at the end of the time step (m3 H2O), Vstored,1 is the volume of water in the reach at the beginning of the time step (m3 H2O), Vin is the volume of water flowing into the reach during the time step (m3 H2O), Vout is the volume of water flowing out of the reach during the time step (m3 H2O), tloss is the volume of water lost from the reach via transmission through the bed (m3 H2O), Ech is the evaporation from the reach for the day (m3 H2O), div is the volume of water added or removed from the reach for the day through diversions (m3 H2O), and Vbnk is the volume of water added to the reach via return flow from bank storage (m3 H2O). SWAT treats the volume of outflow calculated with equation 23.3.11 or 23.4.7 as the net amount of water removed from the reach. As transmission losses, evaporation and other water losses for the reach segment are calculated, the amount of outflow to the next reach segment is reduced by the amount of the loss. When outflow and all losses are summed, the total amount will equal the value obtained from 23.3.11 or 23.4.7.
CHAPTER 23: EQUATIONS—WATER ROUTING
363
23.9 NOMENCLATURE Ach Cross-sectional area of flow in the channel (m2) Ach,bnkfull Cross-sectional area of flow in the channel when filled to the top of the bank (m2) C1 Coefficient in Muskingum flood routing equation C2 Coefficient in Muskingum flood routing equation C3 Coefficient in Muskingum flood routing equation Ech Evaporation from the reach for the day (m3 H2O) Eo Potential evapotranspiration (mm d-1) K Storage time constant for the reach (s) K0.1bnkfull Storage time constant calculated for the reach segment with one-tenth of the bankfull flows (s) Kbnkfull Storage time constant calculated for the reach segment with bankfull flows (s) Kch Effective hydraulic conductivity of the channel alluvium (mm/hr) Lch Length of main channel (km) Pch Wetted perimeter for a given depth of flow (m) Rch Hydraulic radius for a given depth of flow (m) SC Storage coefficient for variable storage flow routing TT Travel time (s) Vbnk Volume of water added to the reach via return flow from bank storage (m3 H2O) Vch Volume of water stored in the channel (m3) Vin Volume of inflow during the time step (m3 H2O) Vout Volume of outflow during the time step (m3 H2O) Vstored Volume of water stored in water body or channel (m3 H2O) W Width of channel at water level (m) Wbnkfull Top width of the channel when filled with water (m) Wbtm Bottom width of the channel (m) Wbtm,fld Bottom width of the flood plain (m) X Weighting factor in Muskingum routing bnk Total amount of water in bank storage (m3 H2O) bnkin Amount of water entering bank storage (m3 H2O) bnkrevap,mx Maximum amount of water moving into the unsaturated zone in response to water deficiencies (m3 H2O) ck Celerity corresponding to the flow for a specified depth (m/s) coef1 Weighting coefficient for storage time constant calculation coef2 Weighting coefficient for storage time constant calculation coefev Evaporation coefficient depth Depth of water in the channel (m) depthbnkfull Depth of water in the channel when filled to the top of the bank (m) depthfld Depth of water in the flood plain (m) div Volume of water added or removed from the reach for the day through diversions (m3 H2O) frtrns Fraction of transmission losses partitioned to the deep aquifer
364
fr∆t n qch qin qout slpch tloss vc
SWAT USER'S MANUAL, VERSION 2000
Zch Zfld
Fraction of the time step in which water is flowing in the channel Manning’s roughness coefficient for the subbasin or channel Average channel flow rate (m3 s-1) Inflow rate (m3/s) Outflow rate (m3/s) Average channel slope along channel length (m m-1) Channel transmission losses (m3 H2O) Average channel velocity (m s-1) Inverse of the channel side slope Inverse of the flood plain side slope
αbnk βrev ∆t
Bank flow recession constant or constant of proportionality Revap coefficient Length of the time step (s)
23.10 REFERENCES Arnold, J.G., J.R. Williams, and D.R. Maidment. 1995. Continuous-time water and sediment routing model for large basins. Journal of Hydraulic Engineering 121(2): 171-183. Brakensiek, D.L. 1967. Kinematic flood routing. Transactions of the ASAE 10(3):340-343. Chow, V.T., D.R. Maidment, and L.W. Mays. 1988. Applied hydrology. McGraw-Hill, Inc., New York, NY. Cunge, J.A. 1969. On the subject of a flood propagation method (Muskingum method). J. Hydraulics Research 7(2):205-230. Lane, L.J. 1983. Chapter 19: Transmission Losses. p.19-1–19-21. In Soil Conservation Service. National engineering handbook, section 4: hydrology. U.S. Government Printing Office, Washington, D.C. Overton, D.E. 1966. Muskingum flood routing of upland streamflow. Journal of Hydrology 4:185-200. Soil Conservation Service. 1964. Chapter 17: Flood routing, Section 4, Hydrology, National engineering handbook. U.S. Department of Agriculture. U.S. Gov’t Printing Office, Washington, D.C.
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365
Williams, J.R. 1969. Flood routing with variable travel time or variable storage coefficients. Transactions of the ASAE 12(1):100-103. Williams, J.R. and R.W. Hann. 1973. HYMO: Problem-oriented language for hydrologic modeling—User’s manual. USDA, ARS-S-9.
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CHAPTER 24
EQUATIONS: SEDIMENT ROUTING
Sediment transport in the channel network is a function of two processes, deposition and degradation, operating simultaneously in the reach. SWAT will compute deposition and degradation using the same channel dimensions for the entire simulation. Alternatively, SWAT will simulate downcutting and widening of the stream channel and update channel dimensions throughout the simulation.
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24.1 SEDIMENT CHANNEL ROUTING Previous versions of SWAT used stream power to predict degradation and fall velocity to estimate deposition in the channels (Arnold et al, 1995). Williams (1980) used Bagnold’s (1977) definition of stream power to develop a method for determining degradation as a function of channel slope and velocity. In this version, the equations have been simplified and the maximum amount of sediment that can be transported from a reach segment is a function of the peak channel velocity. The peak channel velocity, vch,pk, is calculated: v ch , pk =
qch , pk
24.1.1
Ach
where qch,pk is the peak flow rate (m3/s) and Ach is the cross-sectional area of flow in the channel (m2). The peak flow rate is defined as: qch , pk = prf ⋅ qch
24.1.2
where prf is the peak rate adjustment factor, and qch is the average rate of flow (m3/s). Calculation of the average rate of flow, qch, and the cross-sectional area of flow, Ach, is reviewed in Chapter 23. The maximum amount of sediment that can be transported from a reach segment is calculated: conc sed ,ch ,mx = c sp ⋅ vch , pk
spexp
24.1.3
where concsed,ch,mx is the maximum concentration of sediment that can be transported by the water (ton/m3 or kg/L), csp is a coefficient defined by the user, vch,pk is the peak channel velocity (m/s), and spexp is an exponent defined by the user. The exponent, spexp, normally varies between 1.0 and 2.0 and was set at 1.5 in the original Bagnold stream power equation (Arnold et al., 1995). The maximum concentration of sediment calculated with equation 24.1.3 is compared to the concentration of sediment in the reach at the beginning of the time step, concsed,ch,i. If conc sed ,ch ,i > conc sed ,ch ,mx , deposition is the dominant process in the reach segment and the net amount of sediment deposited is calculated:
CHAPTER 24: EQUATIONS—SEDIMENT ROUTING
sed dep = (conc sed ,ch ,i − conc sed ,ch ,mx ) ⋅ Vch
369
24.1.4
where seddep is the amount of sediment deposited in the reach segment (metric tons), concsed,ch,i is the initial sediment concentration in the reach (kg/L or ton/m3), concsed,ch,mx is the maximum concentration of sediment that can be transported by the water (kg/L or ton/m3), and Vch is the volume of water in the reach segment (m3 H2O). If conc sed ,ch ,i < conc sed ,ch ,mx , degradation is the dominant process in the reach segment and the net amount of sediment reentrained is calculated: sed deg = (conc sed ,ch ,mx − conc sed ,ch ,i ) ⋅ Vch ⋅ K CH ⋅ CCH
24.1.5
where seddeg is the amount of sediment reentrained in the reach segment (metric tons), concsed,ch,mx is the maximum concentration of sediment that can be transported by the water (kg/L or ton/m3), concsed,ch,i is the initial sediment concentration in the reach (kg/L or ton/m3), Vch is the volume of water in the reach segment (m3 H2O), KCH is the channel erodibility factor (cm/hr/Pa), and CCH is the channel cover factor. Once the amount of deposition and degradation has been calculated, the final amount of sediment in the reach is determined: sed ch = sed ch ,i − sed dep + sed deg
24.1.6
where sedch is the amount of suspended sediment in the reach (metric tons), sedch,i is the amount of suspended sediment in the reach at the beginning of the time period (metric tons), seddep is the amount of sediment deposited in the reach segment (metric tons), and seddeg is the amount of sediment reentrained in the reach segment (metric tons). The amount of sediment transported out of the reach is calculated: sed out = sed ch ⋅
Vout Vch
24.1.7
where sedout is the amount of sediment transported out of the reach (metric tons), sedch is the amount of suspended sediment in the reach (metric tons), Vout is the volume of outflow during the time step (m3 H2O), and Vch is the volume of water in the reach segment (m3 H2O).
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24.1.1 CHANNEL ERODIBILITY FACTOR The channel erodibility factor is conceptually similar to the soil erodibility factor used in the USLE equation. Channel erodibility is a function of properties of the bed or bank materials. Channel erodibility can be measured with a submerged vertical jet device. The basic premise of the test is that erosion of a vegetated or bare channel and local scour beneath an impinging jet are the result of hydraulic stresses, boundary geometry, and the properties of the material being eroded. Hanson (1990) developed a method for determining the erodibility coefficient of channels in situ with the submerged vertical jet. Allen et al. (1999) utilized this method to determine channel erodibility factors for thirty sites in Texas. A submerged, vertical jet of water directed perpendicularly at the channel bed causes erosion of the bed material in the vicinity of the jet impact area (Figure 24-1). Important variables in the erosion process are: the volume of material removed during a jetting event, elevation of the jet above the ground surface, diameter of the jet nozzle, jet velocity, time, mass density of the fluid and coefficient of erodibility.
Figure 24-1: Simplified cross-section of submerged jet test.
CHAPTER 24: EQUATIONS—SEDIMENT ROUTING
371
Hanson (1991) defined a jet index, Ji, to relate erodibility to scour created by the submerged jet. The jet index is a function of the depth of scour beneath the jet per unit time and the jet velocity. The jet index is determined by a least squares fit following the procedures outlined in ASTM standard D 5852-95. Once the jet index is determined, the channel erodibility coefficient is calculated: K CH = 0.003 ⋅ exp[385 ⋅ J i ]
24.1.8
where KCH is the channel erodibility coefficient (cm/h/Pa) and Ji is the jet index. In general, values for channel erodibility are an order of magnitude smaller than values for soil erodibility.
24.1.2 CHANNEL COVER FACTOR The channel cover factor, CCH, is defined as the ratio of degradation from a channel with a specified vegetative cover to the corresponding degradation from a channel with no vegetative cover. The vegetation affects degradation by reducing the stream velocity, and consequently its erosive power, near the bed surface. Table 24-1: SWAT input variables that pertain to sediment routing. Variable Name PRF SPCON SPEXP CH_COV CH_EROD
Definition prf: Peak rate adjustment factor csp: Coefficient in sediment transport equation spexp: Exponent in sediment transport equation CCH: Channel cover factor KCH: Channel erodibility factor (cm/hr/Pa)
Input File .bsn .bsn .bsn .rte .rte
24.2 CHANNEL DOWNCUTTING AND WIDENING While sediment transport calculations have traditionally been made with the same channel dimensions throughout a simulation, SWAT will model channel downcutting and widening. When channel downcutting and widening is simulated, channel dimensions are allowed to change during the simulation period. Three channel dimensions are allowed to vary in channel downcutting and widening simulations: bankfull depth, depthbnkfull, channel width, Wbnkfull, and
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channel slope, slpch. Channel dimensions are updated using the following equations when the volume of water in the reach exceeds 1.4 × 106 m3. The amount of downcutting is calculated (Allen et al., 1999): depthdcut = 358 ⋅ depth ⋅ slpch ⋅ K CH
24.2.1
where depthdcut is the amount of downcutting (m), depth is the depth of water in channel (m), slpch is the channel slope (m/m), and KCH is the channel erodibility coefficient (cm/h/Pa). The new bankfull depth is calculated: depthbnkfull = depthbnkfull ,i + depthdcut
24.2.2
where depthbnkfull is the new bankfull depth (m), depthbnkfull,i is the previous bankfull depth, and depthdcut is the amount of downcutting (m). The new bank width is calculated: Wbnkfull = ratioWD ⋅ depthbnkfull
24.2.3
where Wbnkfull is the new width of the channel at the top of the bank (m), ratioWD is the channel width to depth ratio, and depthbnkfull is the new bankfull depth (m). The new channel slope is calculated: slpch = slpch ,i −
depthdcut 1000 ⋅ Lch
24.2.4
where slpch is the new channel slope (m/m), slpch,i is the previous channel slope (m/m), depthbnkfull is the new bankfull depth (m), and Lch is the channel length (km). Table 24-2: SWAT input variables that pertain to channel downcutting and widening. Variable Name IDEG CH_WDR
Definition Channel degradation code ratioWD: Channel width to depth ratio
Input File .cod .rte
CHAPTER 24: EQUATIONS—SEDIMENT ROUTING
24.3 NOMENCLATURE Ach CCH Ji KCH Lch Vch Vout Wbnkfull
Cross-sectional area of flow in the channel (m2) Channel cover factor Jet index used to calculate channel erodibility Channel erodibility factor (cm/hr/Pa) Channel length (km) Volume of water in the reach segment (m3 H2O) Volume of outflow during the time step (m3 H2O) Top width of the channel when filled with water (m)
Coefficient in sediment transport equation csp concsed,ch,i Initial sediment concentration in the reach (kg/L or ton/m3) concsed,ch,mx Maximum concentration of sediment that can be transported by the water (kg/L or ton/m3) depth Depth of water in channel (m) depthbnkfull Depth of water in the channel when filled to the top of the bank (m) depthdcut Amount of downcutting (m) prf Peak rate adjustment factor Average rate of flow in the channel (m3/s) qch qch,pk Peak flow rate (m3/s) ratioWD Channel width to depth ratio sedch Amount of suspended sediment in the reach (metric tons) seddeg Amount of sediment reentrained in the reach segment (metric tons) seddep Amount of sediment deposited in the reach segment (metric tons) sedout Amount of sediment transported out of the reach (metric tons) slpch Average channel slope along channel length (m m-1) spexp Exponent in sediment transport equation vch,pk Peak channel velocity (m/s)
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24.4 REFERENCES Allen, P.M., J. Arnold, E. Jakubowski. 1999. Prediction of stream channel erosion potential. Environmental and Engineering Geoscience 5:339-351. American Society for Testing and Materials. 1995. Annual book of ASTM standards, Vol. 04.08, Designation: D 5852-5. American Society for Testing and Materials, Philidelphia, PA. p. 653-657. Arnold, J.G., J.R. Williams, and D.R. Maidment. 1995. Continuous-time water and sediment-routing model for large basins. Journal of Hydraulic Engineering. 121:171-183. Bagnold, R.A. 1977. Bedload transport in natural rivers. Water Resour. Res. 13:303-312. Hanson, G.J. 1990. Surface erodibility of earthen channels at high stresses. Part II-Developing an in situ testing device. Trans. ASAE 33:132-137. Hanson, G.J. 1991. Development of a jet index method to characterize erosion resistance of soils in earthen spillways. Trans. ASAE 34:2015-2020. Williams, J.R. 1980. SPNM, a model for predicting sediment, phosphorus, and nitrogen yields from agricultural basins. Water Resour. Bull. 16:843-848.
CHAPTER 25
QUATIONS IN-STREAM NUTRIENT PROCESSES
Parameters which affect water quality and can be considered pollution indicators include nutrients, total solids, biological oxygen demand, nitrates, and microorganisms (Loehr, 1970; Paine, 1973). Parameters of secondary importance include odor, taste, and turbidity (Azevedo and Stout, 1974). The SWAT in-stream water quality algorithms incorporate constituent interactions and relationships used in the QUAL2E model (Brown and Barnwell, 1987). The documentation provided in this chapter has been taken from Brown 375
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and Barnwell (1987). The modeling of in-stream nutrient transformations has been made an optional feature of SWAT. To route nutrient loadings downstream without simulating transformations, the variable IWQ in the input control code (.cod) file should be set to 0. To activate the simulation of in-stream nutrient transformations, this variable should be set to 1.
25.1 ALGAE During the day, algae increase the stream’s dissolved oxygen concentration via photosynthesis. At night, algae reduce the concentration via respiration. As algae grow and die, they form part of the in-stream nutrient cycle. This section summarizes the equations used to simulate algal growth in the stream.
25.1.1 CHLOROPHYLL a Chlorophyll a is assumed to be directly proportional to the concentration of phytoplanktonic algal biomass. chla = α 0 ⋅ algae
25.1.1
where chla is the chlorophyll a concentration (µg chla/L), α0 is the ratio of chlorophyll a to algal biomass (µg chla/mg alg), and algae is the algal biomass concentration (mg alg/L).
25.1.2 ALGAL GROWTH Growth and decay of algae/chlorophyll a is calculated as a function of the growth rate, the respiration rate, the settling rate and the amount of algae present in the stream. The change in algal biomass for a given day is:
æ æ σ öö ∆algae = çç (µ a ⋅ algae ) − (ρ a ⋅ algae ) − çç 1 ⋅ algae ÷÷ ÷÷ ⋅ TT è depth øø è
25.1.2
where ∆algae is the change in algal biomass concentration (mg alg/L), µa is the local specific growth rate of algae (day-1), ρa is the local respiration or death rate of algae (day-1), σ1 is the local settling rate for algae (m/day), depth is the depth of water in the channel (m), algae is the algal biomass concentration at the beginning
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
377
of the day (mg alg/L), and TT is the flow travel time in the reach segment (day). The calculation of depth and travel time are reviewed in Chapter 23.
25.1.2.1 LOCAL SPECIFIC GROWTH RATE OF ALGAE The local specific growth rate of algae is a function of the availability of required nutrients, light and temperature. SWAT first calculates the growth rate at 20°C and then adjusts the growth rate for water temperature. The user has three options for calculating the impact of nutrients and light on growth: multiplicative, limiting nutrient, and harmonic mean. The multiplicative option multiplies the growth factors for light, nitrogen and phosphorus together to determine their net effect on the local algal growth rate. This option has its biological basis in the mutiplicative effects of enzymatic processes involved in photosynthesis:
µ a , 20 = µ max ⋅ FL ⋅ FN ⋅ FP
25.1.3
where µa,20 is the local specific algal growth rate at 20°C (day-1), µmax is the maximum specific algal growth rate (day-1), FL is the algal growth attenuation factor for light, FN is the algal growth limitation factor for nitrogen, and FP is the algal growth limitation factor for phosphorus. The maximum specific algal growth rate is specified by the user. The limiting nutrient option calculates the local algal growth rate as limited by light and either nitrogen or phosphorus. The nutrient/light effects are multiplicative, but the nutrient/nutrient effects are alternate. The algal growth rate is controlled by the nutrient with the smaller growth limitation factor. This approach mimics Liebig’s law of the minimum:
µ a , 20 = µ max ⋅ FL ⋅ min(FN , FP )
25.1.4
where µa,20 is the local specific algal growth rate at 20°C (day-1), µmax is the maximum specific algal growth rate (day-1), FL is the algal growth attenuation factor for light, FN is the algal growth limitation factor for nitrogen, and FP is the algal growth limitation factor for phosphorus. The maximum specific algal growth rate is specified by the user.
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The harmonic mean is mathematically analogous to the total resistance of two resistors in parallel and can be considered a compromise between equations 25.1.3 and 25.1.4. The algal growth rate is controlled by a multiplicative relation between light and nutrients, while the nutrient/nutrient interactions are represented by a harmonic mean.
µ a , 20 = µ max ⋅ FL ⋅
2
25.1.5
1 ö æ 1 + ç ÷ è FN FP ø
where µa,20 is the local specific algal growth rate at 20°C (day-1), µmax is the maximum specific algal growth rate (day-1), FL is the algal growth attenuation factor for light, FN is the algal growth limitation factor for nitrogen, and FP is the algal growth limitation factor for phosphorus. The maximum specific algal growth rate is specified by the user. Calculation of the growth limiting factors for light, nitrogen and phosphorus are reviewed in the following sections. ALGAL GROWTH LIMITING FACTOR FOR LIGHT. A
number
of
mathematical
relationships
between
photosynthesis and light have been developed. All relationships show an increase in photosynthetic rate with increasing light intensity up to a maximum or saturation value. The algal growth limiting factor for light is calculated using a Monod half-saturation method. In this option, the algal growth limitation factor for light is defined by a Monod expression: FLz =
I phosyn , z K L + I phosyn , z
25.1.6
where FLz is the algal growth attenuation factor for light at depth z,
Iphosyn,z is the photosynthetically-active light intensity at a depth z below the water surface (MJ/m2-hr), and KL is the half-saturation coefficient for light (MJ/m2-hr). Photosynthetically-active light is radiation with a wavelength between 400 and 700 nm. The halfsaturation coefficient for light is defined as the light intensity at
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379
which the algal growth rate is 50% of the maximum growth rate. The half-saturation coefficient for light is defined by the user. Photosynthesis is assumed to occur throughout the depth of the water column. The variation in light intensity with depth is defined by Beer’s law: I phosyn , z = I phosyn ,hr exp(− k l ⋅ z )
25.1.7
where Iphosyn,z is the photosynthetically-active light intensity at a depth z below the water surface (MJ/m2-hr), Iphosyn,hr is the photosynthetically-active solar radiation reaching the ground/water surface during a specific hour on a given day (MJ/m2-hr), k l is the light extinction coefficient (m-1), and z is the depth from the water surface (m). Substituting equation 25.1.7 into equation 25.1.6 and integrating over the depth of flow gives: ù K L + I phosyn ,hr ö é æ 1 ÷÷ ⋅ ln ê FL = çç ú è k l ⋅ depth ø ëê K L + I phosyn ,hr exp(− k l ⋅ depth )ûú
25.1.8
where FL is the algal growth attenuation factor for light for the water column, KL is the half-saturation coefficient for light (MJ/m2-hr), Iphosyn,hr is the photosynthetically-active solar radiation reaching the ground/water surface during a specific hour on a given day (MJ/m2-hr), k l is the light extinction coefficient (m-1), and depth is the depth of water in the channel (m). The photosynthetically-active solar radiation is calculated: I phosyn ,hr = I hr ⋅ frphosyn
25.1.9
where Ihr is the solar radiation reaching the ground during a specific hour on current day of simulation (MJ m-2 h-1), and frphosyn is the fraction of solar radiation that is photosynthetically active. The calculation of Ihr is reviewed in Chapter 2. The fraction of solar radiation that is photosynthetically active is user defined. For daily simulations, an average value of the algal growth attenuation factor for light calculated over the diurnal cycle must
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be used. This is calculated using a modified form of equation 25.1.8: ù K L + I phosyn ,hr ö é æ 1 ÷÷ ⋅ ln ê FL = 0.92 ⋅ frDL ⋅ çç ú 25.1.10 è k l ⋅ depth ø ëê K L + I phosyn ,hr exp(− k l ⋅ depth )ûú
where frDL is the fraction of daylight hours, I phosyn ,hr is the daylight average photosynthetically-active light intensity (MJ/m2-hr) and all other variables are defined previously. The fraction of daylight hours is calculated: frDL =
TDL 24
25.1.11
where TDL is the daylength (hr). I phosyn ,hr is calculated:
I phosyn ,hr =
frphosyn ⋅ H day
25.1.12
TDL
where frphosyn is the fraction of solar radiation that is photosynthetically active, Hday is the solar radiation reaching the water surface in a given day (MJ/m2), and TDL is the daylength (hr). Calculation of Hday and TDL are reviewed in Chapter 2. The light extinction coefficient, k l , is calculated as a function of the algal density using the nonlinear equation:
k l = k l,0 + k l,1 ⋅ α 0 ⋅ algae + k l, 2 ⋅ (α 0 ⋅ algae )
23
25.1.13
where k l,0 is the non-algal portion of the light extinction coefficient (m-1), k l,1 is the linear algal self shading coefficient (m1
(µg-chla/L)-1), k l, 2 is the nonlinear algal self shading coefficient
(m-1 (µg-chla/L)-2/3), α0 is the ratio of chlorophyll a to algal biomass (µg chla/mg alg), and algae is the algal biomass concentration (mg alg/L). Equation 25.1.13 allows a variety of algal, self-shading, light extinction relationships to be modeled. When k l ,1 = k l, 2 = 0 , no
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381
algal self-shading is simulated. When k l,1 ≠ 0 and k l, 2 = 0 , linear algal self-shading is modeled. When k l,1 and k l, 2 are set to a value other than 0, non-linear algal self-shading is modeled. The Riley equation (Bowie et al., 1985) defines k l,1 = 0.0088 m -1 (µg - chla/L )
−1
and k l, 2 = 0.054 m -1 (µg - chla/L )
−2 3
.
ALGAL GROWTH LIMITING FACTOR FOR NUTRIENTS. The algal growth limiting factor for nitrogen is defined by a Monod expression. Algae are assumed to use both ammonia and nitrate as a source of inorganic nitrogen. FN =
(C NO 3 + C NH 4 ) (C NO 3 + C NH 4 ) + K N
25.1.14
where FN is the algal growth limitation factor for nitrogen, CNO3 is the concentration of nitrate in the reach (mg N/L), CNH4 is the concentration of ammonium in the reach (mg N/L), and KN is the Michaelis-Menton half-saturation constant for nitrogen (mg N/L). The algal growth limiting factor for phosphorus is also defined by a Monod expression. FP =
C solP C solP + K P
25.1.15
where FP is the algal growth limitation factor for phosphorus, CsolP is the concentration of phosphorus in solution in the reach (mg P/L), and KP is the Michaelis-Menton half-saturation constant for phosphorus (mg P/L). The Michaelis-Menton half-saturation constant for nitrogen and phosphorus define the concentration of N or P at which algal growth is limited to 50% of the maximum growth rate. Users are allowed to set these values. Typical values for KN range from 0.01 to 0.30 mg N/L while KP will range from 0.001 to 0.05 mg P/L.
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Once the algal growth rate at 20°C is calculated, the rate coefficient is adjusted for temperature effects using a Streeter-Phelps type formulation:
µ a = µ a , 20 ⋅ 1.047 (T
water − 20
)
25.1.16
where µa is the local specific growth rate of algae (day-1), µa,20 is the local specific algal growth rate at 20°C (day-1), and Twater is the average water temperature for the day (°C).
25.1.2.2 LOCAL RESPIRATION RATE OF ALGAE The local respiration or death rate of algae represents the net effect of three processes: the endogenous respiration of algae, the conversion of algal phosphorus to organic phosphorus, and the conversion of algal nitrogen to organic nitrogen. The user defines the local respiration rate of algae at 20°C. The respiration rate is adjusted to the local water temperature using the relationship:
ρ a = ρ a , 20 ⋅ 1.047 (T
water − 20
)
25.1.17
where ρa is the local respiration rate of algae (day-1), ρa,20 is the local algal respiration rate at 20°C (day-1), and Twater is the average water temperature for the day (°C).
25.1.2.3 LOCAL SETTLING RATE OF ALGAE The local settling rate of algae represents the net removal of algae due to settling. The user defines the local settling rate of algae at 20°C. The settling rate is adjusted to the local water temperature using the relationship:
σ 1 = σ 1, 20 ⋅ 1.024 (T
water − 20
)
25.1.18
where σ1 is the local settling rate of algae (m/day), σ1,20 is the local algal settling rate at 20°C (m/day), and Twater is the average water temperature for the day (°C).
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES Table 25-1: SWAT input variables used in algae calculations. Variable name Definition AI0 α0: Ratio of chlorophyll a to algal biomass (µg chla/mg alg) MUMAX µmax: Maximum specific algal growth rate (day-1) K_L KL: Half-saturation coefficient for light (MJ/m2-hr) TFACT frphosyn: Fraction of solar radiation that is photosynthetically active LAMBDA0 k l,0 : Non-algal portion of the light extinction coefficient (m-1)
383 File Name .wwq .wwq .wwq .wwq .wwq
LAMBDA1
k l,1 : Linear algal self shading coefficient (m-1 (µg-chla/L)-1)
.wwq
LAMBDA2
k l, 2 : Nonlinear algal self shading coefficient (m-1 (µg-chla/L)-2/3)
.wwq
K_N
KN: Michaelis-Menton half-saturation constant for nitrogen (mg N/L) KP: Michaelis-Menton half-saturation constant for phosphorus (mg P/L) ρa,20: Local algal respiration rate at 20°C (day-1) σ1,20: Local algal settling rate at 20°C (m/day)
.wwq .wwq .wwq .swq
K_P RHOQ RS1
25.2 NITROGEN CYCLE In aerobic water, there is a stepwise transformation from organic nitrogen to ammonia, to nitrite, and finally to nitrate. Organic nitrogen may also be removed from the stream by settling. This section summarizes the equations used to simulate the nitrogen cycle in the stream.
25.2.1 ORGANIC NITROGEN The amount of organic nitrogen in the stream may be increased by the conversion of algal biomass nitrogen to organic nitrogen. Organic nitrogen concentration in the stream may be decreased by the conversion of organic nitrogen to NH4+ or the settling of organic nitrogen with sediment. The change in organic nitrogen for a given day is: ∆orgN str = (α1 ⋅ ρ a ⋅ algae − β N ,3 ⋅ orgN str − σ 4 ⋅ orgN str ) ⋅ TT
25.2.1
where ∆orgNstr is the change in organic nitrogen concentration (mg N/L), α1 is the fraction of algal biomass that is nitrogen (mg N/mg alg biomass), ρa is the local respiration or death rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), βN,3 is the rate constant for hydrolysis of organic nitrogen to ammonia nitrogen (day-1), orgNstr is the organic nitrogen concentration at the beginning of the day (mg N/L), σ4 is the rate coefficient for organic nitrogen settling (day-1), and TT is the flow travel time in the reach
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SWAT USER'S MANUAL, VERSION 2000
segment (day). The fraction of algal biomass that is nitrogen is user-defined. Equation 25.1.17 describes the calculation of the local respiration rate of algae. The calculation of travel time is reviewed in Chapter 23. The user defines the local rate constant for hydrolysis of organic nitrogen to NH4+ at 20°C. The organic nitrogen hydrolysis rate is adjusted to the local water temperature using the relationship:
β N ,3 = β N , 3, 20 ⋅ 1.047 (T
water − 20
)
25.2.2
where βN,3 is the local rate constant for hydrolysis of organic nitrogen to NH4+ (day-1), βN,3,20 is the local rate constant for hydrolysis of organic nitrogen to NH4+ at 20°C (day-1), and Twater is the average water temperature for the day (°C). The user defines the rate coefficient for organic nitrogen settling at 20°C. The organic nitrogen settling rate is adjusted to the local water temperature using the relationship:
σ 4 = σ 4, 20 ⋅ 1.024 (T
water − 20
)
25.2.3
where σ4 is the local settling rate for organic nitrogen (day-1), σ4,20 is the local settling rate for organic nitrogen at 20°C (day-1), and Twater is the average water temperature for the day (°C).
25.2.2 AMMONIUM The amount of ammonium (NH4+) in the stream may be increased by the mineralization of organic nitrogen and diffusion of ammonium from the streambed sediments. The ammonium concentration in the stream may be decreased by the conversion of NH4+ to NO -2 or the uptake of NH4+ by algae. The change in ammonium for a given day is:
σ3 æ ö ∆NH4str = çç β N ,3 ⋅ orgN str − β N ,1 ⋅ NH4str + − frNH 4 ⋅ α1 ⋅ µ a ⋅ algae ÷÷ ⋅ TT 25.2.4 (1000 ⋅ depth ) è ø where ∆NH4str is the change in ammonium concentration (mg N/L), βN,3 is the rate constant for hydrolysis of organic nitrogen to ammonia nitrogen (day-1), orgNstr is the organic nitrogen concentration at the beginning of the day (mg N/L),
βN,1 is the rate constant for biological oxidation of ammonia nitrogen (day-1),
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
385
NH4str is the ammonium concentration at the beginning of the day (mg N/L), σ3 is the benthos (sediment) source rate for ammonium (mg N/m2-day), depth is the depth of water in the channel (m), frNH4 is the fraction of algal nitrogen uptake from ammonium pool, α1 is the fraction of algal biomass that is nitrogen (mg N/mg alg biomass), µa is the local growth rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), and TT is the flow travel time in the reach segment (day). The local rate constant for hydrolysis of organic nitrogen to NH4+ is calculated with equation 25.2.2. Section 25.1.2.1 describes the calculation of the local growth rate of algae. The calculation of depth and travel time is reviewed in Chapter 23. The rate constant for biological oxidation of ammonia nitrogen will vary as a function of in-stream oxygen concentration and temperature. The rate constant is calculated:
β N ,1 = β N ,1,20 ⋅ (1 − exp[− 0.6 ⋅ Ox str ]) ⋅ 1.083(T
water − 20
)
25.2.5
where βN,1 is the rate constant for biological oxidation of ammonia nitrogen (day-1),
βN,1,20 is the rate constant for biological oxidation of ammonia nitrogen at 20°C (day-1), Oxstr is the dissolved oxygen concentration in the stream (mg O2/L), and Twater is the average water temperature for the day (°C). The second term on the right side of equation 25.2.5, (1 − exp[− 0.6 ⋅ Ox str ]) , is a nitrification inhibition correction factor. This factor inhibits nitrification at low dissolved oxygen concentrations. The user defines the benthos source rate for ammonium at 20°C. The benthos source rate for ammonium nitrogen is adjusted to the local water temperature using the relationship:
σ 3 = σ 3, 20 ⋅ 1.074 (T
water − 20
)
25.2.6
where σ3 is the benthos (sediment) source rate for ammonium (mg N/m2-day),
σ3,20 is the benthos (sediment) source rate for ammonium nitrogen at 20°C (mg N/m2-day), and Twater is the average water temperature for the day (°C). The fraction of algal nitrogen uptake from ammonium pool is calculated:
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SWAT USER'S MANUAL, VERSION 2000
frNH 4 =
f NH 4 ⋅ NH4str ( f NH 4 ⋅ NH4str + (1 − f NH 4 ) ⋅ NO3str )
25.2.7
where frNH4 is the fraction of algal nitrogen uptake from ammonium pool, fNH4 is the preference factor for ammonia nitrogen, NH4str is the ammonium concentration in the stream (mg N/L), and NO3str is the nitrate concentration in the stream (mg N/L).
25.2.3 NITRITE The amount of nitrite ( NO -2 ) in the stream will be increased by the conversion of NH4+ to NO -2 and decreased by the conversion of NO -2 to NO -3 . The conversion of NO -2 to NO -3 occurs more rapidly than the conversion of NH4+ to NO -2 , so the amount of nitrite present in the stream is usually very small. The change in nitrite for a given day is: ∆NO2str = (β N ,1 ⋅ NH4str − β N , 2 ⋅ NO2str ) ⋅ TT
25.2.8
where ∆NO2str is the change in nitrite concentration (mg N/L), βN,1 is the rate constant for biological oxidation of ammonia nitrogen (day-1), NH4str is the ammonium concentration at the beginning of the day (mg N/L), βN,2 is the rate constant for biological oxidation of nitrite to nitrate (day-1), NO2str is the nitrite concentration at the beginning of the day (mg N/L), and TT is the flow travel time in the reach segment (day). The local rate constant for biological oxidation of ammonia nitrogen is calculated with equation 25.2.5. The calculation of travel time is reviewed in Chapter 23. The rate constant for biological oxidation of nitrite to nitrate will vary as a function of in-stream oxygen concentration and temperature. The rate constant is calculated:
β N ,2 = β N , 2, 20 ⋅ (1 − exp[− 0.6 ⋅ Ox str ]) ⋅ 1.047 (T
water − 20
)
25.2.9
where βN,2 is the rate constant for biological oxidation of nitrite to nitrate (day-1),
βN,2,20 is the rate constant for biological oxidation of nitrite to nitrate at 20°C (day-1), Oxstr is the dissolved oxygen concentration in the stream (mg O2/L), and Twater is the average water temperature for the day (°C). The second term on the
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
387
right side of equation 25.2.9, (1 − exp[− 0.6 ⋅ Ox str ]) , is a nitrification inhibition correction factor. This factor inhibits nitrification at low dissolved oxygen concentrations.
25.2.4 NITRATE The amount of nitrate ( NO -3 ) in the stream may be increased by the oxidation of NO -2 . The nitrate concentration in the stream may be decreased by the uptake of NO -3 by algae. The change in nitrate for a given day is: ∆NO3str = (β N , 2 ⋅ NO2str − (1 − frNH 4 ) ⋅ α1 ⋅ µ a ⋅ algae ) ⋅ TT
25.2.10
where ∆NO3str is the change in nitrate concentration (mg N/L), βN,2 is the rate constant for biological oxidation of nitrite to nitrate (day-1), NO2str is the nitrite concentration at the beginning of the day (mg N/L), frNH4 is the fraction of algal nitrogen uptake from ammonium pool, α1 is the fraction of algal biomass that is nitrogen (mg N/mg alg biomass), µa is the local growth rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), and TT is the flow travel time in the reach segment (day). The local rate constant for biological oxidation of nitrite to nitrate is calculated with equation 25.2.9 while the fraction of algal nitrogen uptake from ammonium pool is calculated with equation 25.2.7. Section 25.1.2.1 describes the calculation of the local growth rate of algae. The calculation of travel time is reviewed in Chapter 23.
Table 25-2: SWAT input variables used in in-stream nitrogen calculations. Variable name Definition AI1 α1: Fraction of algal biomass that is nitrogen (mg N/mg alg biomass) RHOQ ρa,20: Local algal respiration rate at 20°C (day-1) BC3 βN,3,20: Local rate constant for hydrolysis of organic nitrogen to NH4+ at 20°C (day-1 or hr-1) RS4 σ4,20: Local settling rate for organic nitrogen at 20°C (day-1) BC1 βN,1,20: Rate constant for biological oxidation of ammonia nitrogen at 20°C (day-1) RS3 σ3,20: Benthos (sediment) source rate for ammonium nitrogen at 20°C (mg N/m2-day or mg N/m2-hr) P_N fNH4: Preference factor for ammonia nitrogen BC2 βN,2,20: Rate constant for biological oxidation of nitrite to nitrate at 20°C (day-1 or hr-1)
File Name .wwq .wwq .swq .swq .swq .swq .wwq .swq
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SWAT USER'S MANUAL, VERSION 2000
25.3 PHOSPHORUS CYCLE The phosphorus cycle is similar to the nitrogen cycle. The death of algae transforms algal phosphorus into organic phosphorus. Organic phosphorus is mineralized to soluble phosphorus which is available for uptake by algae. Organic phosphorus may also be removed from the stream by settling. This section summarizes the equations used to simulate the phosphorus cycle in the stream.
25.3.1 ORGANIC PHOSPHORUS The amount of organic phosphorus in the stream may be increased by the conversion of algal biomass phosphorus to organic phosphorus. Organic phosphorus concentration in the stream may be decreased by the conversion of organic phosphorus to soluble inorganic phosphorus or the settling of organic phosphorus with sediment. The change in organic phosphorus for a given day is: ∆orgPstr = (α 2 ⋅ ρ a ⋅ algae − β P , 4 ⋅ orgPstr − σ 5 ⋅ orgPstr ) ⋅ TT
25.3.1
where ∆orgPstr is the change in organic phosphorus concentration (mg P/L), α2 is the fraction of algal biomass that is phosphorus (mg P/mg alg biomass), ρa is the local respiration or death rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), βP,4 is the rate constant for mineralization of organic phosphorus (day-1), orgPstr is the organic phosphorus concentration at the beginning of the day (mg P/L), σ5 is the rate coefficient for organic phosphorus settling (day-1), and TT is the flow travel time in the reach segment (day). The fraction of algal biomass that is phosphorus is user-defined. Equation 25.1.17 describes the calculation of the local respiration rate of algae. The calculation of travel time is reviewed in Chapter 23. The user defines the local rate constant for mineralization of organic phosphorus at 20°C. The organic phosphorus mineralization rate is adjusted to the local water temperature using the relationship:
β P , 4 = β P , 4, 20 ⋅ 1.047 (T
water − 20
)
25.3.2
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
389
where βP,4 is the local rate constant for organic phosphorus mineralization (day-1),
βP,4,20 is the local rate constant for organic phosphorus mineralization at 20°C (day-1), and Twater is the average water temperature for the day (°C). The user defines the rate coefficient for organic phosphorus settling at 20°C. The organic phosphorus settling rate is adjusted to the local water temperature using the relationship:
σ 5 = σ 5, 20 ⋅ 1.024 (T
water − 20
)
25.3.3
where σ5 is the local settling rate for organic phosphorus (day-1), σ5,20 is the local settling rate for organic phosphorus at 20°C (day-1), and Twater is the average water temperature for the day (°C).
25.3.2 INORGANIC/SOLUBLE PHOSPHORUS The amount of soluble, inorganic phosphorus in the stream may be increased by the mineralization of organic phosphorus and diffusion of inorganic phosphorus from the streambed sediments. The soluble phosphorus concentration in the stream may be decreased by the uptake of inorganic P by algae. The change in soluble phosphorus for a given day is: æ ö σ2 ∆solPstr = çç β P , 4 ⋅ orgPstr + − α 2 ⋅ µ a ⋅ algae ÷÷ ⋅ TT (1000 ⋅ depth ) è ø
25.3.4
where ∆solPstr is the change in solution phosphorus concentration (mg P/L), βP,4 is the rate constant for mineralization of organic phosphorus (day-1), orgPstr is the organic phosphorus concentration at the beginning of the day (mg P/L), σ2 is the benthos (sediment) source rate for soluble P (mg P/m2-day), depth is the depth of water in the channel (m), α2 is the fraction of algal biomass that is phosphorus (mg P/mg alg biomass), µa is the local growth rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), and TT is the flow travel time in the reach segment (day). The local rate constant for mineralization of organic phosphorus is calculated with equation 25.3.2. Section 25.1.2.1 describes the calculation of the local growth rate of algae. The calculation of depth and travel time is reviewed in Chapter 23.
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SWAT USER'S MANUAL, VERSION 2000
The user defines the benthos source rate for soluble P at 20°C. The benthos source rate for soluble phosphorus is adjusted to the local water temperature using the relationship:
σ 2 = σ 2, 20 ⋅ 1.074 (T
water − 20
)
25.3.5
where σ2 is the benthos (sediment) source rate for soluble P (mg P/m2-day), σ2,20 is the benthos (sediment) source rate for soluble phosphorus at 20°C (mg P/m2day), and Twater is the average water temperature for the day (°C). Table 25-3: SWAT input variables used in in-stream phosphorus calculations. Variable name Definition AI2 α2: Fraction of algal biomass that is phosphorus (mg P/mg alg biomass) RHOQ ρa,20: Local algal respiration rate at 20°C (day-1) BC4 βP,4,20: Local rate constant for organic phosphorus mineralization at 20°C (day-1) RS5 σ5,20: Local settling rate for organic phosphorus at 20°C (day-1) RS2 σ2,20: Benthos (sediment) source rate for soluble phosphorus at 20°C (mg P/m2-day)
File Name .wwq .wwq .swq .swq .swq
25.4 CARBONACEOUS BIOLOGICAL OXYGEN DEMAND The carbonaceous oxygen demand (CBOD) of the water is the amount of oxygen required to decompose the organic material in the water. CBOD is added to the stream with loadings from surface runoff or point sources. Within the stream, two processes are modeled that impact CBOD levels, both of which serve to reduce the carbonaceous biological oxygen demand as the water moves downstream. The change in CBOD within the stream on a given day is calculated: ∆cbod = −(κ 1 ⋅ cbod + κ 3 ⋅ cbod ) ⋅ TT
25.4.1
where ∆cbod is the change in carbonaceous biological oxygen demand concentration (mg CBOD/L), κ1 is the CBOD deoxygenation rate (day-1), cbod is the carbonaceous biological oxygen demand concentration (mg CBOD/L), κ3 is the settling loss rate of CBOD (day-1), and TT is the flow travel time in the reach segment (day). The calculation of travel time is reviewed in Chapter 23.
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The user defines the carbonaceous deoxygenation rate at 20°C. The CBOD deoxygenation rate is adjusted to the local water temperature using the relationship:
κ 1 = κ 1, 20 ⋅ 1.047 (T
water − 20
)
25.4.2
where κ1 is the CBOD deoxygenation rate (day-1), κ1,20 is the CBOD deoxygenation rate at 20°C (day-1), and Twater is the average water temperature for the day (°C). The user defines the settling loss rate of CBOD at 20°C. The settling loss rate is adjusted to the local water temperature using the relationship:
κ 3 = κ 3, 20 ⋅ 1.024 (T
water − 20
)
25.4.3
where κ3 is the settling loss rate of CBOD (day-1), κ3,20 is the settling loss rate of CBOD at 20°C (day-1), and Twater is the average water temperature for the day (°C). Table 25-4: SWAT input variables used in in-stream CBOD calculations. Variable name RK1 RK3
Definition κ1,20: CBOD deoxygenation rate at 20°C (day-1) κ3,20: Settling loss rate of CBOD at 20°C (day-1)
File Name .swq .swq
25.5 OXYGEN An adequate dissolved oxygen concentration is a basic requirement for a healthy aquatic ecosystem. Dissolved oxygen concentrations in streams are a function of atmospheric reareation, photosynthesis, plant and animal respiration, benthic (sediment) demand, biochemical oxygen demand, nitrification, salinity, and temperature. The change in dissolved oxygen concentration on a given day is calculated: ∆Ox str = (κ 2 ⋅ (Ox sat − Ox str ) + (α 3 ⋅ µ a − α 4 ⋅ ρ a ) ⋅ algae − κ 1 ⋅ cbod − − α 5 ⋅ β N ,1 ⋅ NH4str − α 6 ⋅ β N , 2 ⋅ NO2str ) ⋅ TT
κ4 1000 ⋅ depth 25.5.1
where ∆Oxstr is the change in dissolved oxygen concentration (mg O2/L), κ2 is the reaeration rate for Fickian diffusion (day-1), Oxsat is the saturation oxygen
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concentration (mg O2/L), Oxstr is the dissolved oxygen concentration in the stream (mg O2/L), α3 is the rate of oxygen production per unit of algal photosynthesis (mg O2/mg alg), µa is the local specific growth rate of algae (day-1), α4 is the rate of oxygen uptake per unit of algae respired (mg O2/mg alg), ρa is the local respiration or death rate of algae (day-1), algae is the algal biomass concentration at the beginning of the day (mg alg/L), κ1 is the CBOD deoxygenation rate (day-1), cbod is the carbonaceous biological oxygen demand concentration (mg CBOD/L), κ4 is the sediment oxygen demand rate (mg O2/(m2⋅day)), depth is the depth of water in the channel (m), α5 is the rate of oxygen uptake per unit NH4+ oxidation (mg O2/mg N), βN,1 is the rate constant for biological oxidation of ammonia nitrogen (day-1), NH4str is the ammonium concentration at the beginning of the day (mg N/L), α6 is the rate of oxygen uptake per unit NO -2 oxidation (mg O2/mg N), βN,2 is the rate constant for biological oxidation of nitrite to nitrate (day-1), NO2str is the nitrite concentration at the beginning of the day (mg N/L) and TT is the flow travel time in the reach segment (day). The user defines the rate of oxygen production per unit algal photosynthesis, the rate of oxygen uptake per unit algal respiration, the rate of oxygen uptake per unit NH4+ oxidation and rate of oxygen uptake per unit NO -2 oxidation. Section 25.1.2.1 describes the calculation of the local growth rate of algae while equation 25.1.17 describes the calculation of the local respiration rate of algae. The rate constant for biological oxidation of NH4+ is calculated with equation 25.2.5 while the rate constant for NO -2 oxidation is determined with equation 25.2.9. The CBOD deoxygenation rate is calculated using equation 25.4.2. The calculation of depth and travel time are reviewed in Chapter 23. The user defines the sediment oxygen demand rate at 20°C. The sediment oxygen demand rate is adjusted to the local water temperature using the relationship:
κ 4 = κ 4, 20 ⋅ 1.060 (T
water − 20
)
25.5.2
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393
where κ4 is the sediment oxygen demand rate (mg O2/(m2⋅day)), κ4,20 is the sediment oxygen demand rate at 20°C (mg O2/(m2⋅day)), and Twater is the average water temperature for the day (°C).
25.5.1 OXYGEN SATURATION CONCENTRATION The amount of oxygen that can be dissolved in water is a function of temperature, concentration of dissolved solids, and atmospheric pressure. An equation developed by APHA (1985) is used to calculate the saturation concentration of dissolved oxygen: Ox sat
é 1.575701 × 105 6.642308 × 10 7 − = exp ê − 139.34410 + 2 Twat ,K Twat ,K ë +
1.243800 × 1010 8.621949 × 1011 ù − ú 3 4 Twat Twat ,K ,K û
25.5.3
where Oxsat is the equilibrium saturation oxygen concentration at 1.00 atm (mg O2/L), and Twat,K is the water temperature in Kelvin (273.15+°C).
25.5.2 REAERATION Reaeration occurs by diffusion of oxygen from the atmosphere into the stream and by the mixing of water and air that occurs during turbulent flow.
25.5.2.1 REAERATION BY FICKIAN DIFFUSION The user defines the reaeration rate at 20°C. The reaeration rate is adjusted to the local water temperature using the relationship:
κ 2 = κ 2, 20 ⋅ 1.024 (T
water − 20
)
25.5.4
where κ2 is the reaeration rate (day-1), κ2,20 is the reaeration rate at 20°C (day-1), and Twater is the average water temperature for the day (°C). Numerous methods have been developed to calculate the reaeration rate at 20°C, κ2,20. A few of the methods are listed below. Brown and Barnwell (1987) provide additional methods. Using field measurements, Churchill, Elmore and Buckingham (1962) derived the relationship:
κ 2, 20 = 5.03 ⋅ vc 0.969 ⋅ depth −1.673
25.5.5
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where κ2,20 is the reaeration rate at 20°C (day-1), v c is the average stream velocity (m/s), and depth is the average stream depth (m). O’Connor and Dobbins (1958) incorporated stream turbulence characteristics into the equations they developed. For streams with low velocities and isotropic conditions,
κ 2, 20 = 294 ⋅
(Dm ⋅ vc )0.5 depth1.5
25.5.6
where κ2,20 is the reaeration rate at 20°C (day-1), Dm is the molecular diffusion coefficient (m2/day), v c is the average stream velocity (m/s), and depth is the average stream depth (m). For streams with high velocities and nonisotropic conditions,
κ 2, 20
D ⋅ slp 0.25 = 2703 ⋅ m depth1.25 0.5
25.5.7
where κ2,20 is the reaeration rate at 20°C (day-1), Dm is the molecular diffusion coefficient (m2/day), slp is the slope of the streambed (m/m), and depth is the average stream depth (m). The molecular diffusion coefficient is calculated Dm = 177 ⋅ 1.037Twater −20
25.5.8
where Dm is the molecular diffusion coefficient (m2/day), and Twater is the average water temperature (°C). Owens et al. (1964) developed an equation to determine the reaeration rate for shallow, fast moving streams where the stream depth is 0.1 to 3.4 m and the velocity is 0.03 to 1.5 m/s.
κ 2, 20
0.67
v = 5.34 ⋅ c 1.85 depth
25.5.9
where κ2,20 is the reaeration rate at 20°C (day-1), v c is the average stream velocity (m/s), and depth is the average stream depth (m).
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25.5.2.2 REAERATION BY TURBULENT FLOW OVER A DAM Reareation will occur when water falls over a dam, weir, or other structure in the stream. To simulate this form of reaeration, a “structure” command line is added in the watershed configuration file (.fig) at every point along the stream where flow over a structure occurs. The amount of reaeration that occurs is a function of the oxygen deficit above the structure and a reaeration coefficient: 1 ö æ ∆Ox str = Da − Db = Da ç 1 − ÷ rea ø è
25.5.10
where ∆Oxstr is the change in dissolved oxygen concentration (mg O2/L), Da is the oxygen deficit above the structure (mg O2/L), Db is the oxygen deficit below the structure (mg O2/L), and rea is the reaeration coefficient. The oxygen deficit above the structure, Da, is calculated: D a = Ox sat − Ox str
25.5.11
where Oxsat is the equilibrium saturation oxygen concentration (mg O2/L), and Oxstr is the dissolved oxygen concentration in the stream (mg O2/L). Butts and Evans (1983) documents the following relationship that can be used to estimate the reaeration coefficient: rea = 1 + 0.38 ⋅ coef a ⋅ coef b ⋅ h fall ⋅ (1 − 0.11 ⋅ h fall ) ⋅ (1 + 0.046 ⋅ Twater ) 25.5.12 where rea is the reaeration coefficient, coefa is an empirical water quality factor, coefb is an empirical dam aeration coefficient, hfall is the height through which water falls (m), and Twater is the average water temperature (°C). The empirical water quality factor is assigned a value based on the condition of the stream: coefa = 1.80 in clean water coefa = 1.60 in slightly polluted water coefa = 1.00 in moderately polluted water coefa = 1.00 in moderately polluted water coefa = 0.65 in grossly polluted water
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The empirical dam aeration coefficient is assigned a value based on the type of structure: coefb = 0.70 to 0.90 for flat broad crested weir coefb = 1.05 for sharp crested weir with straight slope face coefb = 0.80 for sharp crested weir with vertical face coefb = 0.05 for sluice gates with submerged discharge Table 25-5: SWAT input variables used in in-stream oxygen calculations. Variable name RK2 AI3 AI4 RHOQ RK1 RK4 AI5 AI6 AERATION_COEF
Definition κ2,20: Reaeration rate at 20°C (day-1) α3: Rate of oxygen production per unit algal photosynthesis (mg O2/mg alg) α4: Rate of oxygen uptake per unit algal respiration (mg O2/mg alg) ρa,20: Local algal respiration rate at 20°C (day-1) κ1,20: CBOD deoxygenation rate at 20°C (day-1) κ4,20: Sediment oxygen demand rate at 20°C (mg O2/(m2⋅day)) α5: Rate of oxygen uptake per unit NH4+ oxidation (mg O2/mg N) α6: Rate of oxygen uptake per unit NO2 oxidation (mg O2/mg N) rea: Reaeration coefficient
File Name .swq .wwq .wwq .wwq .swq .swq .wwq .wwq .fig
25.6 NOMENCLATURE CNH4 CNO3 CsolP Da Db Dm FL FLz FN FP Hday Ihr
Concentration of ammonium in the reach (mg N/L) Concentration of nitrate in the reach (mg N/L) Concentration of phosphorus in solution in the reach (mg P/L) Oxygen deficit above the structure (mg O2/L) Oxygen deficit below the structure (mg O2/L) Molecular diffusion coefficient for oxygen (m2/day) Algal growth attenuation factor for light for the water column Algal growth attenuation factor for light at depth z Algal growth limitation factor for nitrogen Algal growth limitation factor for phosphorus Solar radiation reaching ground on current day of simulation (MJ m-2 d-1) Solar radiation reaching ground during specific hour on current day of simulation (MJ m-2 h-1) Iphosyn,hrPhotosynthetically-active solar radiation reaching ground during specific hour on current day of simulation (MJ m-2 h-1) Iphosyn,z Photosynthetically-active light intensity at a depth z below the water surface (MJ/m2-hr) I phosyn ,hr Daylight average photosynthetically-active light intensity (MJ/m2-hr) Half-saturation coefficient for light (MJ/m2-hr) KL KN Michaelis-Menton half-saturation constant for nitrogen (mg N/L) KP Michaelis-Menton half-saturation constant for phosphorus (mg P/L)
CHAPTER 25: EQUATIONS—IN-STREAM NUTRIENT PROCESSES
NH4str NO2str NO3str Oxsat Oxstr TDL Twater Twat,K Twater TT
Ammonium concentration in the stream (mg N/L) Nitrite concentration in the stream (mg N/L) Nitrate concentration in the stream (mg N/L) Saturation oxygen concentration (mg O2/L) Dissolved oxygen concentration in the stream (mg O2/L) Daylength (h) Average daily water temperature (°C) Water temperature in Kelvin (273.15+°C) Average water temperature (°C) Travel time (day)
algae cbod chla coefa coefb depth fNH4 frDL frNH4 frphosyn hfall kl
Algal biomass concentration (mg alg/L) Carbonaceous biological oxygen demand concentration (mg CBOD/L) Chlorophyll a concentration (µg chla/L) Empirical water quality factor Empirical dam aeration coefficient Depth of water in the channel (m) Preference factor for ammonia nitrogen Fraction of daylight hours Fraction of algal nitrogen uptake from ammonium pool, Fraction of solar radiation that is photosynthetically active Height through which water falls (m) Light extinction coefficient (m-1)
k l,0
Non-algal portion of the light extinction coefficient (m-1)
k l,1
Linear algal self shading coefficient (m-1 (µg-chla/L)-1)
397
k l,2 Nonlinear algal self shading coefficient (m-1 (µg-chla/L)-2/3) orgNstr Organic nitrogen concentration in the stream (mg N/L) orgPstr Organic phosphorus concentration in the stream (mg P/L) rea Reaeration coefficient slp Slope of the streambed (m/m) vc Average stream velocity (m/s) z Depth from the water surface (m)
α0 α1 α2 α3 α4 α5 α6 βN,1 βN,1,20 βN,2
Ratio of chlorophyll a to algal biomass (µg chla/mg alg) Fraction of algal biomass that is nitrogen (mg N/mg alg biomass), Fraction of algal biomass that is phosphorus (mg P/mg alg biomass) Rate of oxygen production per unit algal photosynthesis (mg O2/mg alg) Rate of oxygen uptake per unit algal respiration (mg O2/mg alg) Rate of oxygen uptake per unit NH4+ oxidation (mg O2/mg N) Rate of oxygen uptake per unit NO2 oxidation (mg O2/mg N) Rate constant for biological oxidation of ammonia nitrogen (day-1 or hr-1) Rate constant for biological oxidation of ammonia nitrogen at 20°C (day-1 or hr-1) Rate constant for biological oxidation of nitrite to nitrate (day-1 or hr-1)
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βN,2,20 Rate constant for biological oxidation of nitrite to nitrate at 20°C (day-1 or hr-1) βN,3 Rate constant for hydrolysis of organic nitrogen to ammonia nitrogen (day-1 or hr-1) βN,3,20 Local rate constant for hydrolysis of organic nitrogen to NH4+ at 20°C (day-1 or hr-1) βP,4 Rate constant for mineralization of organic phosphorus (day-1 or hr-1) βP,4,20 Local rate constant for organic phosphorus mineralization at 20°C (day-1 or hr-1) ∆algae Change in algal biomass concentration (mg alg/L) ∆NH4str Change in ammonium concentration (mg N/L) ∆NO2str Change in nitrite concentration (mg N/L) ∆orgNstr Change in organic nitrogen concentration (mg N/L) ∆orgPstr Change in organic phosphorus concentration (mg P/L) ∆Oxstr Change in dissolved oxygen concentration (mg O2/L) ∆solPstr Change in solution phosphorus concentration (mg P/L) κ1 CBOD deoxygenation rate (day-1 or hr-1) κ1,20 CBOD deoxygenation rate at 20°C (day-1 or hr-1) κ2 Reaeration rate for Fickian diffusion (day-1 or hr-1) κ2,20 Reaeration rate at 20°C (day-1 or hr-1) κ3 Settling loss rate of CBOD (day-1 or hr-1) κ3,20 Settling loss rate of CBOD at 20°C (day-1 or hr-1) κ4 Sediment oxygen demand rate (mg O2/(m2⋅day)) κ4,20 Sediment oxygen demand rate at 20°C (mg O2/(m2⋅day) or mg O2/(m2⋅hr)) ρa Local respiration rate of algae (day-1 or hr-1) ρa,20 Local algal respiration rate at 20°C (day-1 or hr-1) σ1 Local settling rate for algae (m/day or m/hr) σ1,20 Local algal settling rate at 20°C (m/day or m/hr) σ2 Benthos (sediment) source rate for soluble P (mg P/m2-day or mg P/m2-hr) σ2,20 Benthos (sediment) source rate for soluble phosphorus at 20°C (mg P/m2-day or mg P/m2-hr) σ3 Benthos (sediment) source rate for ammonium (mg N/m2-day or mg N/m2-hr) σ3,20 Benthos (sediment) source rate for ammonium nitrogen at 20°C (mg N/m2-day or mg N/m2-hr) σ4 Rate coefficient of organic nitrogen settling (day-1 or hr-1) σ4,20 Local settling rate for organic nitrogen at 20°C (day-1 or hr-1) σ5 Rate coefficient for organic phosphorus settling (day-1 or hr-1) σ5,20 Local settling rate for organic phosphorus at 20°C (day-1 or hr-1) µa Local specific growth rate of algae (day-1 or hr-1) µa,20 Local specific algal growth rate at 20°C (day-1 or hr-1) µmax Maximum specific algal growth rate (day-1 or hr-1)
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25.7 REFERENCES American Public Health Association. 1985. Standard methods for the examination of water and wastewater, 16th edition. American Public Health Association, Inc. Azevedo, J., and P.R. Stout. 1974. Farm animal manures: An overview of their role in the agricultural environment (Service Manual no. 44). University of California, Agricultural Experiment Station Extension. Bowie, G.L. W.B. Mills, D.B. Porcella, C.L. Campbell, J.R. Pagenkopt, G.L. Rupp, K.M. Johnson, P.W.H. Chan, and S.A. Gherini. 1985. Rates, constants, and kinetic formulations in surface water quality modeling, 2nd ed. EPA/600/3-85/040, U.S. Environmental Protection Agency, Athens, GA. Brown, L.C. and T.O. Barnwell, Jr. 1987. The enhanced water quality models QUAL2E and QUAL2E-UNCAS documentation and user manual. EPA document EPA/600/3-87/007. USEPA, Athens, GA. Churchill, M.A., H.L. Elmore, and R.A. Buckingham. 1962. The prediction of stream reaeration rates. International Journal of Air and Water Pollution. 6: 467-504. Loehr, R.C. 1970. Drainage and pollution from beef cattle feedlots (Proceedings paper No. 7726). Journal of the Sanitary Engineers Division, American Society of Civil Engineers, 96 (SA6): 1295-1309. O’Connor, D.J. and W.E. Dobbins. 1958. Mechanism of reaeration in natural streams. Trans. ASCE. 123:641-684. Owens, M. R.W. Edwards, and J.W. Gibbs. 1964. Some reaeration studies in streams. International Journal of Air and Water Pollution 8:469-486. Paine, M.D. 1973. Confined animals and public environment (Great Plains Beef Cattle Feeding Handbook, GPE-7000/EC-72-246). Lincoln: University of Nebraska, College of Agriculture, Cooperative Extension Service.
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CHAPTER 26
EQUATIONS: IN-STREAM PESTICIDE TRANSFORMATIONS
SWAT incorporates a simple mass balance developed by Chapra (1997) to model the transformation and transport of pesticides in streams. The model assumes a well-mixed layer of water overlying a sediment layer. Only one pesticide can be routed through the stream network. The pesticide to be routed is defined by the variable IRTPEST in the .bsn file.
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26.1 PESTICIDE IN THE WATER Pesticide in a reach segment is increased through addition of mass in inflow as well as resuspension and diffusion of pesticide from the sediment layer. The amount of pesticide in a reach segment is reduced through removal in outflow as well as degradation, volatilization, settling and diffusion into the underlying sediment.
26.1.1 SOLID-LIQUID PARTITIONING Pesticides will partition into particulate and dissolved forms. The fraction of pesticide in each phase is a function of the pesticide’s partition coefficient and the reach segment’s suspended solid concentration: Fd =
1 1 + K d ⋅ conc sed
26.1.1
Fp =
K d ⋅ conc sed = 1 − Fd 1 + K d ⋅ conc sed
26.1.2
where Fd is the fraction of total pesticide in the dissolved phase, Fp is the fraction of total pesticide in the particulate phase, Kd is the pesticide partition coefficient (m3/g), and concsed is the concentration of suspended solids in the water (g/m3). The pesticide partition coefficient can be estimated from the octanol-water partition coefficient (Chapra, 1997): K d = 3.085 × 10 −8 ⋅ K ow
26.1.3
where Kd is the pesticide partition coefficient (m3/g) and Kow is the pesticide’s −3 3 (mg m −water ) ). Values for the octanol-water partition coefficient ( mg m octanol −1
octanol-water partition coefficient have been published for many chemicals. If a published value cannot be found, it can be estimated from solubility (Chapra, 1997): ′ ) log(K ow ) = 5.00 − 0.670 ⋅ log( pst sol
26.1.4
′ is the pesticide solubility (µmoles/L). The solubility in these units is where pst sol calculated:
CHAPTER 26: EQUATIONS—IN-STREAM PESTICIDE TRANSFORMATIONS
′ = pst sol
pst sol ⋅ 10 3 MW
403
26.1.5
′ is the pesticide solubility (µmoles/L), pstsol is the pesticide solubility where pst sol (mg/L) and MW is the molecular weight (g/mole).
26.1.2 DEGRADATION Pesticides in both the particulate and dissolved forms are subject to degradation. The amount of pesticide that is removed from the water via degradation is: pstdeg,wtr = k p ,aq ⋅ pst rchwtr ⋅ TT
26.1.6
where pstdeg,wtr is the amount of pesticide removed from the water via degradation (mg pst), kp,aq is the rate constant for degradation or removal of pesticide in the water (1/day), pstrchwtr is the amount of pesticide in the water at the beginning of the day (mg pst), and TT is the flow travel time (days). The rate constant is related to the aqueous half-life:
k p ,aq =
0.693 t1 / 2,aq
26.1.7
where kp,aq is the rate constant for degradation or removal of pesticide in the water (1/day), and t1/2,aq is the aqueous half-life for the pesticide (days).
26.1.3 VOLATILIZATION Pesticide in the dissolved phase is available for volatilization. The amount of pesticide removed from the water via volatilization is: pstvol ,wtr =
vv ⋅ Fd ⋅ pst rchwtr ⋅ TT depth
26.1.8
where pstvol,wtr is the amount of pesticide removed via volatilization (mg pst), vv is the volatilization mass-transfer coefficient (m/day), depth is the flow depth (m), Fd is the fraction of total pesticide in the dissolved phase, pstrchwtr is the amount of pesticide in the water (mg pst), and TT is the flow travel time (days). The volatilization mass-transfer coefficient can be calculated based on Whitman’s two-film or two-resistance theory (Whitman, 1923; Lewis and Whitman, 1924 as described in Chapra, 1997). While the main body of the gas and liquid phases are assumed to be well-mixed and homogenous, the two-film
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theory assumes that a substance moving between the two phases encounters maximum resistance in two laminar boundary layers where transfer is a function of molecular diffusion. In this type of system the transfer coefficient or velocity is: vv = K l ⋅
He H e + R ⋅ TK ⋅ (K l K g )
26.1.9
where vv is the volatilization mass-transfer coefficient (m/day), Kl is the masstransfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), He is Henry’s constant (atm m3 mole-1), R is the universal gas constant (8.206 × 10-5 atm m3 (K mole)-1), and TK is the temperature (K). For rivers where liquid flow is turbulent, the transfer coefficients are estimated using the surface renewal theory (Higbie, 1935; Danckwerts, 1951; as described by Chapra, 1997). The surface renewal model visualizes the system as consisting of parcels of water that are brought to the surface for a period of time. The fluid elements are assumed to reach and leave the air/water interface randomly, i.e. the exposure of the fluid elements to air is described by a statistical distribution. The transfer velocities for the liquid and gaseous phases are calculated: K l = rl ⋅ Dl
K g = rg ⋅ Dg
26.1.10
where Kl is the mass-transfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), Dl is the liquid molecular diffusion coefficient (m2/day), Dg is the gas molecular diffusion coefficient (m2/day), rl is the liquid surface renewal rate (1/day), and rg is the gaseous surface renewal rate (1/day). O’Connor and Dobbins (1956) defined the surface renewal rate as the ratio of the average stream velocity to depth. rl =
86400 ⋅ vc depth
26.1.11
where rl is the liquid surface renewal rate (1/day), vc is the average stream velocity (m/s) and depth is the depth of flow (m).
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405
26.1.4 SETTLING Pesticide in the particulate phase may be removed from the water layer by settling. Settling transfers pesticide from the water to the sediment layer. The amount of pesticide that is removed from the water via settling is: pst stl ,wtr =
vs ⋅ Fp ⋅ pst rchwtr ⋅ TT depth
26.1.12
where pststl,wtr is the amount of pesticide removed from the water due to settling (mg pst), vs is the settling velocity (m/day), depth is the flow depth (m), Fp is the fraction of total pesticide in the particulate phase, pstrchwtr is the amount of pesticide in the water (mg pst), and TT is the flow travel time (days).
26.1.5 OUTFLOW Pesticide is removed from the reach segment in outflow. The amount of dissolved and particulate pesticide removed from the reach segment in outflow is: pst sol ,o = Q ⋅
Fd ⋅ pst rchwtr V
pst sorb,o = Q ⋅
Fp ⋅ pst rchwtr V
26.1.13 26.1.14
where pstsol,o is the amount of dissolved pesticide removed via outflow (mg pst), pstsorb,o is the amount of particulate pesticide removed via outflow (mg pst), Q is the rate of outflow from the reach segment (m3 H2O/day), Fd is the fraction of total pesticide in the dissolved phase, Fp is the fraction of total pesticide in the particulate phase, pstrchwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the reach segment (m3 H2O). Table 26-1: SWAT input variables that pesticide partitioning. Variable Name Definition CHPST_KOC Kd: Pesticide partition coefficient (m3/g) CHPST_REA kp,aq: Rate constant for degradation or removal of pesticide in the water (1/day) CHPST_VOL vv: Volatilization mass-transfer coefficient (m/day) CHPST_STL vs: Pesticide settling velocity (m/day)
Input File .swq .swq .swq .swq
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26.2 PESTICIDE IN THE SEDIMENT Pesticide in the sediment layer underlying a reach segment is increased through addition of mass by settling and diffusion from the water. The amount of pesticide in the sediment layer is reduced through removal by degradation, resuspension, diffusion into the overlying water, and burial.
26.2.1 SOLID-LIQUID PARTITIONING As in the water layer, pesticides in the sediment layer will partition into particulate and dissolved forms. Calculation of the solid-liquid partitioning in the sediment layer requires a suspended solid concentration. The “concentration” of solid particles in the sediment layer is defined as: * conc sed =
M sed Vtot
26.2.1
* where conc sed is the “concentration” of solid particles in the sediment layer
(g/m3), Msed is the mass of solid particles in the sediment layer (g) and Vtot is the total volume of the sediment layer (m3). Mass and volume are also used to define the porosity and density of the sediment layer. In the sediment layer, porosity is the fraction of the total volume in the liquid phase:
φ=
Vwtr Vtot
26.2.2
where φ is the porosity, Vwtr is the volume of water in the sediment layer (m3) and Vtot is the total volume of the sediment layer (m3). The fraction of the volume in the solid phase can then be defined as: 1−φ =
Vsed Vtot
26.2.3
where φ is the porosity, Vsed is the volume of solids in the sediment layer (m3) and Vtot is the total volume of the sediment layer (m3). The density of sediment particles is defined as:
ρs =
M sed Vsed
26.2.4
CHAPTER 26: EQUATIONS—IN-STREAM PESTICIDE TRANSFORMATIONS
407
where ρs is the particle density (g/m3), Msed is the mass of solid particles in the sediment layer (g), and Vsed is the volume of solids in the sediment layer (m3). Solving equation 26.2.3 for Vtot and equation 26.2.4 for Msed and substituting into equation 26.2.1 yields: * conc sed = (1 − φ ) ⋅ ρ s
26.2.5
* is the “concentration” of solid particles in the sediment layer where conc sed
(g/m3), φ is the porosity, and ρs is the particle density (g/m3). Assuming φ = 0.5 and ρs = 2.6 × 106 g/m3, the “concentration” of solid particles in the sediment layer is 1.3 × 106 g/m3. The fraction of pesticide in each phase is then calculated: Fd ,sed =
1 φ + (1 − φ ) ⋅ ρ s ⋅ K d
Fp ,sed = 1 − Fd ,sed
26.2.6 26.2.7
where Fd,sed is the fraction of total sediment pesticide in the dissolved phase, Fp,sed is the fraction of total sediment pesticide in the particulate phase, φ is the porosity,
ρs is the particle density (g/m3), and Kd is the pesticide partition coefficient (m3/g). The pesticide partition coefficient used for the water layer is also used for the sediment layer.
26.2.2 DEGRADATION Pesticides in both the particulate and dissolved forms are subject to degradation. The amount of pesticide that is removed from the sediment via degradation is: pstdeg,sed = k p ,sed ⋅ pst rchsed
26.2.8
where pstdeg,sed is the amount of pesticide removed from the sediment via degradation (mg pst), kp,sed is the rate constant for degradation or removal of pesticide in the sediment (1/day), and pstrchsed is the amount of pesticide in the sediment (mg pst). The rate constant is related to the sediment half-life: k p ,sed =
0.693 t1 / 2,sed
26.2.9
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where kp,sed is the rate constant for degradation or removal of pesticide in the sediment (1/day), and t1/2,sed is the sediment half-life for the pesticide (days).
26.2.3 RESUSPENSION Pesticide in the sediment layer is available for resuspension. The amount of pesticide that is removed from the sediment via resuspension is: pst rsp ,wtr =
vr ⋅ pst rchsed ⋅ TT depth
26.2.10
where pstrsp,wtr is the amount of pesticide removed via resuspension (mg pst), vr is the resuspension velocity (m/day), depth is the flow depth (m), pstrchsed is the amount of pesticide in the sediment (mg pst), and TT is the flow travel time (days). Pesticide removed from the sediment layer by resuspension is added to the water layer.
26.2.4 DIFFUSION Pesticide in the dissolved phase is available for diffusion. Diffusion transfers pesticide between the water and sediment layers. The direction of movement is controlled by the pesticide concentration. Pesticide will move from areas of high concentration to areas of low concentration. The amount of pesticide that is transferred between the water and sediment by diffusion is: pstdif =
vd ⋅ (Fd ,sed ⋅ pst rchsed − Fd ⋅ pst rchwtr ) ⋅ TT depth
26.2.11
where pstdif is the amount of pesticide transferred between the water and sediment by diffusion (mg pst), vd is the rate of diffusion or mixing velocity (m/day), depth is the flow depth (m), Fd,sed is the fraction of total sediment pesticide in the dissolved phase, pstrchsed is the amount of pesticide in the sediment (mg pst), Fd is the fraction of total water layer pesticide in the dissolved phase, pstrchwtr is the amount of pesticide in the water (mg pst), and TT is the flow duration (days). If Fd ,sed ⋅ pst rchsed > Fd ⋅ pst rchwtr , pstdif is transferred from the sediment to the water
layer. If, Fd ,sed ⋅ pst rchsed < Fd ⋅ pst rchwtr , pstdif is transferred from the water to the sediment layer.
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The diffusive mixing velocity, vd, can be estimated from the empirically derived formula (Chapra, 1997): vd =
69.35 ⋅ φ ⋅ MW − 2 / 3 365
26.2.12
where vd is the rate of diffusion or mixing velocity (m/day), φ is the sediment porosity, and MW is the molecular weight of the pesticide compound.
26.2.5 BURIAL Pesticide in the sediment layer may be lost by burial. The amount of pesticide that is removed from the sediment via burial is: pstbur =
vb ⋅ pst rchsed Dsed
26.2.13
where pstbur is the amount of pesticide removed via burial (mg pst), vb is the burial velocity (m/day), Dsed is the depth of the active sediment layer (m), and pstrchsed is the amount of pesticide in the sediment (mg pst). Table 26-2: SWAT input variables related to pesticide in the sediment. Variable Name Definition CHPST_KOC Kd: Pesticide partition coefficient (m3/g) SEDPST_REA kp,sed: Rate constant for degradation or removal of pesticide in the sediment (1/day) CHPST_RSP vr: Resuspension velocity (m/day) SEDPST_ACT Dsed: Depth of the active sediment layer (m) CHPST_MIX vd: Rate of diffusion or mixing velocity (m/day) SEDPST_BRY vb: Pesticide burial velocity (m/day)
Input File .swq .swq .swq .swq .swq .swq
26.3 MASS BALANCE The processes described above can be combined into mass balance equations for the well-mixed reach segment and the well-mixed sediment layer: ∆pst rchwtr = pstin − ( pst sol ,o + pst sorb,o ) − pstdeg ,wtr − pstvol ,wtr − pst stl ,wtr + pst rsp ,wtr ± pst dif 26.3.1 ∆pst rchsed = − pstdeg,sed + pst stl ,wtr − pst rsp ,wtr − pstbur ± pstdif
26.3.2
where ∆pstrchwtr is the change in pesticide mass in the water layer (mg pst), ∆pstrchsed is the change in pesticide mass in the sediment layer (mg pst), pstin is the
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pesticide added to the reach segment via inflow (mg pst), pstsol,o is the amount of dissolved pesticide removed via outflow (mg pst), pstsorb,o is the amount of particulate pesticide removed via outflow (mg pst), pstdeg,wtr is the amount of pesticide removed from the water via degradation (mg pst), pstvol,wtr is the amount of pesticide removed via volatilization (mg pst), pststl,wtr is the amount of pesticide removed from the water due to settling (mg pst), pstrsp,wtr is the amount of pesticide removed via resuspension (mg pst), pstdif is the amount of pesticide transferred between the water and sediment by diffusion (mg pst), pstdeg,sed is the amount of pesticide removed from the sediment via degradation (mg pst), pstbur is the amount of pesticide removed via burial (mg pst)
26.4 NOMENCLATURE Dg Dl Dsed Fd Fd,sed Fp Fp,sed He Kd Kg Kl Msed MW Q R Vsed Vtot Vwtr
Gas molecular diffusion coefficient (m2/day) Liquid molecular diffusion coefficient (m2/day) Depth of the active sediment layer (m) Fraction of total pesticide in the dissolved phase Fraction of total sediment pesticide in the dissolved phase Fraction of total pesticide in the particulate phase Fraction of total sediment pesticide in the particulate phase Henry’s constant (atm m3 mole-1) Pesticide partition coefficient (m3/g) Mass-transfer velocity in the gaseous laminar layer (m/day) Mass-transfer velocity in the liquid laminar layer (m/day) Mass of solid phase in the sediment layer (g) Molecular weight of the pesticide compound Rate of outflow from the reach segment (m3 H2O/day) Universal gas constant (8.206 × 10-5 atm m3 (K mole)-1) Volume of solids in the sediment layer (m3) Total volume of the sediment layer (m3) Volume of water in the sediment layer (m3)
concsed Concentration of suspended solids in the water (g/m3) * conc sed “Concentration” of solid particles in the sediment layer (g/m3) depth Depth of flow (m) kp,aq Rate constant for degradation or removal of pesticide in the water (1/day) kp,sed Rate constant for degradation or removal of pesticide in the sediment (1/day) pstbur Amount of pesticide removed via burial (mg pst) pstdeg,sed Amount of pesticide removed from the sediment via degradation (mg pst)
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pstdeg,wtr Amount of pesticide removed from the water via degradation (mg pst) pstdif Amount of pesticide transferred between the water and sediment by diffusion (mg pst) pstrchsed Amount of pesticide in the sediment (mg pst) pstrchwtr Amount of pesticide in the water (mg pst) pstrsp,wtr Amount of pesticide removed from sediment via resuspension (mg pst) pstsol,o Amount of dissolved pesticide removed via outflow (mg pst) pstsorb,o Amount of particulate pesticide removed via outflow (mg pst) pststl,wtr Amount of pesticide removed from the water due to settling (mg pst) pstvol,wtr Amount of pesticide removed via volatilization (mg pst) Gaseous surface renewal rate (1/day) rg Liquid surface renewal rate (1/day) rl t1/2,aq Aqueous half-life for the pesticide (days) t1/2,sed Sediment half-life for the pesticide (days) Pesticide burial velocity (m/day) vb Average stream velocity (m/s) vc Rate of diffusion or mixing velocity (m/day) vd Resuspension velocity (m/day) vr Settling velocity (m/day) vs Volatilization mass-transfer coefficient (m/day) vv ∆pstrchwtr Change in pesticide mass in the water layer (mg pst) ∆pstrchsed Change in pesticide mass in the sediment layer (mg pst) φ Porosity ρs Particle density (g/m3)
26.5 REFERENCES Chapra, S.C. 1997. Surface water-quality modeling. WCB/McGraw-Hill, Boston, MA. Danckwerts, P.V. 1951. Significance of liquid-film coefficients in gas absorption. Ind. Eng. Chem. 43:1460-1467. Higbie, R. 1935. The rate of adsorption of a pure gas into a still liquid during short periods of exposure. Trans. Amer. Inst. Chem. Engin. 31:365-389. Lewis, W.K. and W.G. Whitman. 1924. Principles of gas absorption. Ind. Eng. Chem. 16:1215-1220. Whitman, W.G. 1923. The two-film theory of gas adsorption. Chem. Metallurg. Eng. 29:146-148.
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WATER BODIES Impoundment structures modify the movement of water in the channel network by lowering the peak flow and volume of flood discharges. Because impoundments slow down the flow of water, sediment will fall from suspension, removing nutrient and chemicals adsorbed to the soil particles.
CHAPTER 27
EQUATIONS: IMPOUNDMENT WATER ROUTING
Impoundments play an important role in water supply and flood control. SWAT models four types of water bodies: ponds, wetlands, depressions/potholes, and reservoirs. Ponds, wetlands, and depressions/potholes are located within a subbasin off the main channel. Water flowing into these water bodies must originate from the subbasin in which the water body is located. Reservoirs are located on the main channel network. They receive water from all subbasins upstream of the water body.
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27.1 RESERVOIRS A reservoir is an impoundment located on the main channel network of a watershed. No distinction is made between naturally-occurring and man-made structures. The features of an impoundment are shown in Figure 27.1.
Figure 27.1: Components of a reservoir with flood water detention features.
The water balance for a reservoir is: V = Vstored + V flowin − V flowout + V pcp − Vevap − Vseep
27.1.1
where V is the volume of water in the impoundment at the end of the day (m3 H2O), Vstored is the volume of water stored in the water body at the beginning of the day (m3 H2O), Vflowin is the volume of water entering the water body during the day (m3 H2O), Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), Vpcp is the volume of precipitation falling on the water body during the day (m3 H2O), Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), and Vseep is the volume of water lost from the water body by seepage (m3 H2O).
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27.1.1 SURFACE AREA The surface area of the reservoir is needed to calculate the amount of precipitation falling on the water body as well as the amount of evaporation and seepage. Surface area varies with change in the volume of water stored in the reservoir. The surface area is updated daily using the equation: SA = β sa ⋅ V expsa
27.1.2
where SA is the surface area of the water body (ha), βsa is a coefficient, V is the volume of water in the impoundment (m3 H2O), and expsa is an exponent. The coefficient, βsa, and exponent, expsa, are calculated by solving equation 27.1.2 using two known points. The two known points are surface area and volume information provided for the principal and emergency spillways. expsa =
β sa
log10 (SAem ) − log10 (SApr ) log10 (Vem ) − log10 (V pr )
æ SA ö = çç em ÷÷ è Vem ø
27.1.3
expsa
27.1.4
where SAem is the surface area of the reservoir when filled to the emergency spillway (ha), SApr is the surface area of the reservoir when filled to the principal spillway (ha), Vem is the volume of water held in the reservoir when filled to the emergency spillway (m3 H2O), and Vpr is the volume of water held in the reservoir when filled to the principal spillway (m3 H2O).
27.1.2 PRECIPITATION The volume of precipitation falling on the reservoir during a given day is calculated: V pcp = 10 ⋅ Rday ⋅ SA
27.1.5
where Vpcp is the volume of water added to the water body by precipitation during the day (m3 H2O), Rday is the amount of precipitation falling on a given day (mm H2O), and SA is the surface area of the water body (ha).
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27.1.3 EVAPORATION The volume of water lost to evaporation on a given day is calculated: Vevap = 10 ⋅ η ⋅ Eo ⋅ SA
27.1.6
where Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), η is an evaporation coefficient (0.6), Eo is the potential evapotranspiration for a given day (mm H2O), and SA is the surface area of the water body (ha).
27.1.4 SEEPAGE The volume of water lost by seepage through the bottom of the reservoir on a given day is calculated: Vseep = 240 ⋅ K sat ⋅ SA
27.1.7
where Vseep is the volume of water lost from the water body by seepage (m3 H2O), Ksat is the effective saturated hydraulic conductivity of the reservoir bottom (mm/hr), and SA is the surface area of the water body (ha).
27.1.5 OUTFLOW The volume of outflow may be calculated using one of four different methods: measured daily outflow, measured monthly outflow, average annual release rate for uncontrolled reservoir, controlled outflow with target release.
27.1.5.1 MEASURED DAILY OUTFLOW When measured daily outflow (IRESCO = 3) is chosen as the method to calculate reservoir outflow, the user must provide a file with the outflow rate for every day the reservoir is simulated in the watershed. The volume of outflow from the reservoir is then calculated: V flowout = 86400 ⋅ qout
27.1.8
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), and qout is the outflow rate (m3/s).
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419
27.1.5.2 MEASURED MONTHLY OUTFLOW When measured monthly outflow (IRESCO = 1) is chosen as the method to calculate reservoir outflow, the user must provide a file with the average daily outflow rate for every month the reservoir is simulated in the watershed. The volume of outflow from the reservoir is then calculated using equation 27.1.8.
27.1.5.3 AVERAGE ANNUAL RELEASE RATE FOR UNCONTROLLED RESERVOIR
When the average annual release rate (IRESCO = 0) is chosen as the method to calculate reservoir outflow, the reservoir releases water whenever the reservoir volume exceeds the principal spillway volume, Vpr. If the reservoir volume is greater than the principal spillway volume but less than the emergency spillway volume, the amount of reservoir outflow is calculated: V flowout = V − V pr
if V − V pr < qrel ⋅ 86400
27.1.9
V flowout = qrel ⋅ 86400
if V − V pr > qrel ⋅ 86400
27.1.10
If the reservoir volume exceeds the emergency spillway volume, the amount of outflow is calculated: V flowout = (V − Vem ) + (Vem − V pr )
if Vem − V pr < qrel ⋅ 86400
27.1.11
V flowout = (V − Vem ) + qrel ⋅ 86400
if Vem − V pr > qrel ⋅ 86400
27.1.12
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the reservoir (m3 H2O), Vpr is the volume of water held in the reservoir when filled to the principal spillway (m3 H2O), Vem is the volume of water held in the reservoir when filled to the emergency spillway (m3 H2O), and qrel is the average daily principal spillway release rate (m3/s).
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27.1.5.4 TARGET RELEASE FOR CONTROLLED RESERVOIR When target release (IRESCO = 2) is chosen as the method to calculate reservoir outflow, the reservoir releases water as a function of the desired target storage. The target release approach tries to mimic general release rules that may be used by reservoir operators. Although the method is simplistic and cannot account for all decision criteria, it can realistically simulate major outflow and low flow periods. For the target release approach, the principal spillway volume corresponds to maximum flood control reservation while the emergency spillway volume corresponds to no flood control reservation. The model requires the beginning and ending month of the flood season. In the nonflood season, no flood control reservation is required, and the target storage is set at the emergency spillway volume. During the flood season, the flood control reservation is a function of soil water content. The flood control reservation for wet ground conditions is set at the maximum. For dry ground conditions, the flood control reservation is set at 50% of the maximum. The target storage may be specified by the user on a monthly basis or it can be calculated as a function of flood season and soil water content. If the target storage is specified: Vtarg = starg
27.1.13
where Vtarg is the target reservoir volume for a given day (m3 H2O), and starg is the target reservoir volume specified for a given month (m3 H2O). If the target storage is not specified, the target reservoir volume is calculated: Vtarg = Vem
Vtarg
if mon fld ,beg < mon < mon fld ,end
æ é SW ùö , 1ú ÷ ç1 − min ê ë FC ûø = V pr + è ⋅ (Vem − V pr ) 2
27.1.14
CHAPTER 27: EQUATIONS—IMPOUNDMENT WATER ROUTING
if mon ≤ mon fld ,beg or mon ≥ mon fld ,end
421
27.1.15
where Vtarg is the target reservoir volume for a given day (m3 H2O), Vem is the volume of water held in the reservoir when filled to the emergency spillway (m3 H2O), Vpr is the volume of water held in the reservoir when filled to the principal spillway (m3 H2O), SW is the average soil water content in the subbasin (mm H2O), FC is the water content of the subbasin soil at field capacity (mm H2O), mon is the month of the year, monfld,beg is the beginning month of the flood season, and monfld,end is the ending month of the flood season. Once the target storage is defined, the outflow is calculated: V flowout =
V − Vtarg
27.1.16
NDtarg
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the reservoir (m3 H2O), Vtarg is the target reservoir volume for a given day (m3 H2O), and NDtarg is the number of days required for the reservoir to reach target storage. Once outflow is determined using one of the preceding four methods, the user may specify maximum and minimum amounts of discharge that the initial outflow estimate is checked against. If the outflow doesn’t meet the minimum discharge or exceeds the maximum specified discharge, the amount of outflow is altered to meet the defined criteria. ′ V flowout = V flowout
′ if qrel ,mn ⋅ 86400 ≤ V flowout ≤ qrel ,mx ⋅ 86400
27.1.17
V flowout = qrel ,mn ⋅ 86400
′ if V flowout < qrel ,mn ⋅ 86400
27.1.18
V flowout = qrel ,mx ⋅ 86400
′ if V flowout > qrel ,mx ⋅ 86400
27.1.19
where Vflowout is the volume of water flowing out of the water body during the day ′ (m3 H2O), V flowout is the initial estimate of the volume of water flowing out of the
water body during the day (m3 H2O), qrel,mn is the minimum average daily outflow for the month (m3/s), and qrel,mx is the maximum average daily outflow for the month (m3/s).
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Table 27-1: SWAT input variables that pertain to reservoirs. Variable name Definition RES_ESA SAem: Surface area of the reservoir when filled to the emergency spillway (ha) RES_PSA SApr: Surface area of the reservoir when filled to the principal spillway (ha) RES_EVOL Vem: Volume of water held in the reservoir when filled to the emergency spillway (104 m3 H2O) RES_PVOL Vpr: Volume of water held in the reservoir when filled to the principal spillway (104 m3 H2O) RES_K Ksat: Effective saturated hydraulic conductivity of the reservoir bottom (mm/hr) IRESCO Outflow method RES_OUTFLOW qout: Outflow rate (m3/s) RESOUT qout: Outflow rate (m3/s) RES_RR qrel: Average daily principal spillway release rate (m3/s) STARG(mon) starg: Target reservoir volume specified for a given month (m3 H2O) IFLOD1R monfld,beg: Beginning month of the flood season IFLOD2R monfld,end: Ending month of the flood season NDTARGR NDtarg: Number of days required for the reservoir to reach target storage OFLOWMN(mon) qrel,mn: Minimum average daily outflow for the month (m3/s) OFLOWMX(mon) qrel,mx: Maximum average daily outflow for the month (m3/s)
File Name .res .res .res .res .res .res resdayo.dat resmono.dat .res .res .res .res .res .res .res
27.2 PONDS/WETLANDS Ponds and wetlands are water bodies located within subbasins that received inflow from a fraction of the subbasin area. The algorithms used to model these two types of water bodies differ only in the options allowed for outflow calculation. The water balance for a pond or wetland is: V = Vstored + V flowin − V flowout + V pcp − Vevap − Vseep
27.2.1
where V is the volume of water in the impoundment at the end of the day (m3 H2O), Vstored is the volume of water stored in the water body at the beginning of the day (m3 H2O), Vflowin is the volume of water entering the water body during the day (m3 H2O), Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), Vpcp is the volume of precipitation falling on the water body during the day (m3 H2O), Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), and Vseep is the volume of water lost from the water body by seepage (m3 H2O).
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27.2.1 SURFACE AREA The surface area of the pond or wetland is needed to calculate the amount of precipitation falling on the water body as well as the amount of evaporation and seepage. Surface area varies with change in the volume of water stored in the impoundment. The surface area is updated daily using the equation: SA = β sa ⋅ V expsa
27.2.2
where SA is the surface area of the water body (ha), βsa is a coefficient, V is the volume of water in the impoundment (m3 H2O), and expsa is an exponent. The coefficient, βsa, and exponent, expsa, are calculated by solving equation 27.1.2 using two known points. For ponds, the two known points are surface area and volume information provided for the principal and emergency spillways. expsa =
β sa
log10 (SAem ) − log10 (SApr ) log10 (Vem ) − log10 (V pr )
æ SA ö = çç em ÷÷ è Vem ø
27.2.3
expsa
27.2.4
where SAem is the surface area of the pond when filled to the emergency spillway (ha), SApr is the surface area of the pond when filled to the principal spillway (ha), Vem is the volume of water held in the pond when filled to the emergency spillway (m3 H2O), and Vpr is the volume of water held in the pond when filled to the principal spillway (m3 H2O). For wetlands, the two known points are surface area and volume information provided for the maximum and normal water levels. expsa =
log10 (SAmx ) − log10 (SAnor ) log10 (Vmx ) − log10 (Vnor )
æ SA ö β sa = çç mx ÷÷ è Vmx ø
27.2.5
expsa
27.2.6
where SAmx is the surface area of the wetland when filled to the maximum water level (ha), SAnor is the surface area of the wetland when filled to the normal water level (ha), Vmx is the volume of water held in the wetland when filled to the
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maximum water level (m3 H2O), and Vnor is the volume of water held in the wetland when filled to the normal water level (m3 H2O).
27.2.2 PRECIPITATION The volume of precipitation falling on the pond or wetland during a given day is calculated: V pcp = 10 ⋅ Rday ⋅ SA
27.2.7
where Vpcp is the volume of water added to the water body by precipitation during the day (m3 H2O), Rday is the amount of precipitation falling on a given day (mm H2O), and SA is the surface area of the water body (ha).
27.2.3 INFLOW The volume of water entering the pond or wetland on a given day is calculated: V flowin = frimp ⋅ 10 ⋅ (Qsurf + Q gw + Qlat ) ⋅ ( Area − SA)
27.2.8
where Vflowin is the volume of water flowing into the water body on a given day (m3 H2O), frimp is the fraction of the subbasin area draining into the impoundment, Qsurf is the surface runoff from the subbasin on a given day (mm H2O), Qgw is the groundwater flow generated in a subbasin on a given day (mm H2O), Qlat is the lateral flow generated in a subbasin on a given day (mm H2O), Area is the subbasin area (ha), and SA is the surface area of the water body (ha). The volume of water entering the pond or wetland is subtracted from the surface runoff, lateral flow and groundwater loadings to the main channel.
27.2.4 EVAPORATION The volume of water lost to evaporation on a given day is calculated: Vevap = 10 ⋅ η ⋅ Eo ⋅ SA
27.2.9
where Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), η is an evaporation coefficient (0.6), Eo is the potential evapotranspiration for a given day (mm H2O), and SA is the surface area of the water body (ha).
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27.2.5 SEEPAGE The volume of water lost by seepage through the bottom of the pond or wetland on a given day is calculated: Vseep = 240 ⋅ K sat ⋅ SA
27.2.10
where Vseep is the volume of water lost from the water body by seepage (m3 H2O), Ksat is the effective saturated hydraulic conductivity of the pond or wetland bottom (mm/hr), and SA is the surface area of the water body (ha).
27.2.6 OUTFLOW The primary difference between ponds and wetlands is the method in which the outflow is calculated.
27.2.6.1 POND OUTFLOW Pond outflow is calculated as a function of target storage. The target storage varies based on flood season and soil water content. The target pond volume is calculated: Vtarg = Vem
Vtarg
if mon fld ,beg < mon < mon fld ,end
27.2.11
æ é SW ùö , 1ú ÷ ç1 − min ê ë FC ûø = V pr + è ⋅ (Vem − V pr ) 2
if mon ≤ mon fld ,beg or mon ≥ mon fld ,end
27.2.12
where Vtarg is the target pond volume for a given day (m3 H2O), Vem is the volume of water held in the pond when filled to the emergency spillway (m3 H2O), Vpr is the volume of water held in the pond when filled to the principal spillway (m3 H2O), SW is the average soil water content in the subbasin (mm H2O), FC is the water content of the subbasin soil at field capacity (mm H2O), mon is the month of the year, monfld,beg is the beginning month of the flood season, and monfld,end is the ending month of the flood season. Once the target storage is defined, the outflow is calculated:
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V flowout =
V − Vtarg
27.2.13
NDtarg
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the pond (m3 H2O), Vtarg is the target pond volume for a given day (m3 H2O), and NDtarg is the number of days required for the pond to reach target storage.
27.2.6.2 WETLAND OUTFLOW The wetland releases water whenever the water volume exceeds the normal storage volume, Vnor. Wetland outflow is calculated: V flowout = 0 V flowout =
V − Vnor 10
V flowout = V − Vmx
if V < Vnor
27.2.14
if Vnor ≤ V ≤ Vmx
27.2.15
if V > Vmx
27.2.16
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the wetland (m3 H2O), Vmx is the volume of water held in the wetland when filled to the maximum water level (m3 H2O), and Vnor is the volume of water held in the wetland when filled to the normal water level (m3 H2O). Table 27-2: SWAT input variables that pertain to ponds and wetlands. Variable name Definition PND_ESA SAem: Surface area of the pond when filled to the emergency spillway (ha) PND_PSA SApr: Surface area of the pond when filled to the principal spillway (ha) PND_EVOL Vem: Volume of water held in the pond when filled to the emergency spillway (104 m3 H2O) PND_PVOL Vpr: Volume of water held in the pond when filled to the principal spillway (104 m3 H2O) WET_MXSA SAmx: Surface area of the wetland when filled to the maximum water level (ha) WET_NSA SAnor: Surface area of the wetland when filled to the normal water level (ha) WET_MXVOL Vmx: Volume of water held in the wetland when filled to the maximum water level (m3 H2O) WET_NVOL Vnor: Volume of water held in the wetland when filled to the normal water level (m3 H2O) PND_FR frimp: Fraction of the subbasin area draining into the pond WET_FR frimp: Fraction of the subbasin area draining into the wetland
File Name .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd
CHAPTER 27: EQUATIONS—IMPOUNDMENT WATER ROUTING Table 27-2, cont.: SWAT input variables that pertain to ponds and wetlands Variable name Definition PND_K Ksat: Effective saturated hydraulic conductivity of the pond bottom (mm/hr) WET_K Ksat: Effective saturated hydraulic conductivity of the wetland bottom (mm/hr) IFLOD1 monfld,beg: Beginning month of the flood season IFLOD2 monfld,end: Ending month of the flood season NDTARG NDtarg: Number of days required for the reservoir to reach target storage
427
File Name .pnd .pnd .pnd .pnd .pnd
27.3 DEPRESSIONS/POTHOLES In areas of low relief and/or young geologic development, the drainage network may be poorly developed. Watersheds in these areas may have many closed depressional areas, referred to as potholes. Runoff generated within these areas flows to the lowest portion of the pothole rather than contributing to flow in the main channel. Other systems that are hydrologically similar to potholes include playa lakes and fields that are artifically impounded for rice production. The algorithms reviewed in this section are used to model these types of systems. To define an HRU as a pothole, the user must set IPOT (.hru) to the HRU number. To initiate water impoundment, a release/impound operation must be placed in the .mgt file. The water balance for a pothole is: V = Vstored + V flowin − V flowout + V pcp − Vevap − Vseep
27.3.1
where V is the volume of water in the impoundment at the end of the day (m3 H2O), Vstored is the volume of water stored in the water body at the beginning of the day (m3 H2O), Vflowin is the volume of water entering the water body during the day (m3 H2O), Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), Vpcp is the volume of precipitation falling on the water body during the day (m3 H2O), Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), and Vseep is the volume of water lost from the water body by seepage (m3 H2O).
27.3.1 SURFACE AREA The surface area of the pothole is needed to calculate the amount of precipitation falling on the water body as well as the amount of evaporation and
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seepage. Surface area varies with change in the volume of water stored in the impoundment. For surface area calculations, the pothole is assumed to be coneshaped. The surface area is updated daily using the equation:
π SA = 4 10
æ 3 ⋅V ö ⋅ çç ÷÷ è π ⋅ slp ø
2/3
27.3.2
where SA is the surface area of the water body (ha), V is the volume of water in the impoundment (m3 H2O), and slp is the slope of the HRU (m/m).
27.3.2 PRECIPITATION The volume of precipitation falling on the pothole during a given day is calculated: V pcp = 10 ⋅ Rday ⋅ SA
27.3.3
where Vpcp is the volume of water added to the water body by precipitation during the day (m3 H2O), Rday is the amount of precipitation falling on a given day (mm H2O), and SA is the surface area of the water body (ha).
27.3.3 INFLOW Water entering the pothole on a given day may be contributed from any HRU in the subbasin. To route a portion of the flow from an HRU into a pothole, the variable IPOT (.hru) is set to the number of the HRU containing the pothole and POT_FR (.hru) is set to the fraction of the HRU area that drains into the pothole. This must be done for each HRU contributing flow to the pothole. Water routing from other HRUs is performed only during the period that water impoundment has been activated (release/impound operation in .mgt). Water may also be added to the pothole with an irrigation operation in the management file (.mgt). Chapter 21 reviews the irrigation operation. The inflow to the pothole is calculated: V flowin = irr +
å [ fr n
hru =1
pot ,hru
⋅ 10 ⋅ (Qsurf ,hru + Q gw ,hru + Qlat ,hru ) ⋅ area hru ]
27.3.4
where Vflowin is the volume of water flowing into the pothole on a given day (m3 H2O), irr is the amount of water added through an irrigation operation on a given day (m3 H2O), n is the number of HRUs contributing water to the pothole, frpot,hru
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429
is the fraction of the HRU area draining into the pothole, Qsurf,hru is the surface runoff from the HRU on a given day (mm H2O), Qgw,hru is the groundwater flow generated in the HRU on a given day (mm H2O), Qlat,hru is the lateral flow generated in the HRU on a given day (mm H2O), and areahru is the HRU area (ha).
27.3.4 EVAPORATION The volume of water lost to evaporation on a given day is calculated: æ LAI ö÷ Vevap = 10 ⋅ ç1 − ⋅ Eo ⋅ SA ç LAI evap ÷ø è
if LAI < LAI evap
27.3.5
Vevap = 0
if LAI ≥ LAI evap
27.3.6
where Vevap is the volume of water removed from the water body by evaporation during the day (m3 H2O), LAI is the leaf area index of the plants growing in the pothole, LAIevap is the leaf area index at which no evaporation occurs from the water surface, Eo is the potential evapotranspiration for a given day (mm H2O), and SA is the surface area of the water body (ha).
27.3.5 SEEPAGE The volume of water lost by seepage through the bottom of the pothole on a given day is calculated as a function of the water content of the soil profile beneath the pothole. Vseep = 240 ⋅ K sat ⋅ SA
if SW < 0.5 ⋅ FC
27.3.7
SW ö æ Vseep = 240 ⋅ ç1 − ÷ ⋅ K sat ⋅ SA FC ø è
if 0.5 ⋅ FC ≤ SW < FC
27.3.8
Vseep = 0
if SW ≥ FC
27.3.9
where Vseep is the volume of water lost from the water body by seepage (m3 H2O), Ksat is the effective saturated hydraulic conductivity of the 1st soil layer in the profile (mm/hr), SA is the surface area of the water body (ha), SW is the soil water content of the profile on a given day (mm H2O), and FC is the field capacity soil water content (mm H2O). Water lost from the pothole by seepage is added to the soil profile.
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27.3.6 OUTFLOW Water may be removed from the pothole in three different types of outflow. When the volume of water in the pothole exceeds the maximum storage, the excess water is assumed to overflow and enter the main channel in the subbasin. When the retaining wall or berm is removed (this is done with a release/impound operation in the management file), all water stored in the pothole enters the main channel. The third type of flow from the pothole is via drainage tiles installed in the pothole.
27.3.6.1 OVERFLOW Pothole outflow caused by overflow is calculated: V flowout = V − V pot ,mx
if V > V pot . mx
27.3.10
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), V is the volume of water stored in the pothole (m3 H2O), and Vpot,mx is the maximum amount of water that can be stored in the pothole (m3 H2O).
27.3.6.2 RELEASE OPERATION When a release operation is scheduled, all water in the pothole becomes outflow: V flowout = V
27.3.11
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), and V is the volume of water stored in the pothole (m3 H2O).
27.3.6.3 TILE FLOW When drainage tiles are installed in a pothole, the pothole will contribute water to the main channel through tile flow. The pothole outflow originating from tile drainage is: V flowout = qtile ⋅ 86400
if V > qtile ⋅ 86400
27.3.12
V flowout = V
if V ≤ qtile ⋅ 86400
27.3.13
CHAPTER 27: EQUATIONS—IMPOUNDMENT WATER ROUTING
431
where Vflowout is the volume of water flowing out of the water body during the day (m3 H2O), qtile is the average daily tile flow rate (m3/s), and V is the volume of water stored in the pothole (m3 H2O). Table 27-3: SWAT input variables that pertain to potholes. Variable name Definition IPOT Number of HRU that is impounding water (that contains the pothole) Variables in release/impound operation line: MONTH/DAY or HUSC Timing of release/impound operation.
MGT_OP IREL_IMP
SLOPE POT_FR EVLAI POT_VOLX POT_TILE
Operation code. MGT_OP = 13 for release/impound operation Release/impound action code: 0: impound, 1: release slp: Slope of the HRU (m/m) frpot: Fraction of the HRU area draining into the pothole LAIevap: Leaf area index at which no evaporation occurs from the water surface Vpot,mx: Maximum amount of water that can be stored in the pothole (m3 H2O) qtile: Average daily tile flow rate (m3/s)
File Name .hru .mgt .mgt .mgt .hru .hru .bsn .hru .hru
27.4 NOMENCLATURE Area Subbasin area (ha) Potential evapotranspiration for a given day (mm H2O) Eo FC Water content of the soil at field capacity (mm H2O) Ksat Effective saturated hydraulic conductivity of the reservoir bottom (mm/hr) LAI Leaf area index of the plants growing in the pothole LAIevap Leaf area index at which no evaporation occurs from the water surface NDtarg Number of days required for the reservoir to reach target storage Qgw Groundwater flow generated in a subbasin on a given day (mm H2O) Lateral flow generated in a subbasin on a given day (mm H2O) Qlat Qsurf Surface runoff from the subbasin on a given day (mm H2O) Rday Amount of precipitation falling on a given day (mm H2O) SA Surface area of the water body (ha) SAem Surface area of the reservoir or pond when filled to the emergency spillway (ha) SAmx Surface area of the wetland when filled to the maximum water level (ha) SAnor Surface area of the wetland when filled to the normal water level (ha) SApr Surface area of the reservoir when filled to the principal spillway (ha) SW Average soil water content (mm H2O) V Volume of water in the impoundment at the end of the day (m3 H2O) Volume of water held in the reservoir when filled to the emergency spillway (m3 Vem H2O) Vevap Volume of water removed from the water body by evaporation during the day (m3 H2O) Vflowin Volume of water entering the water body during the day (m3 H2O)
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Vflowout Volume of water flowing out of the water body during the day (m3 H2O) ′ V flowout Initial estimate of the volume of water flowing out of the water body during the day (m3 H2O) Vmx Volume of water held in the wetland when filled to the maximum water level (m3 H2O) Vnor Volume of water held in the wetland when filled to the normal water level (m3 H2O) Vpcp Volume of precipitation falling on the water body during the day (m3 H2O) Vpot,mx Maximum amount of water that can be stored in the pothole (m3 H2O) Vpr Volume of water held in the reservoir when filled to the principal spillway (m3 H2O) Vseep Volume of water lost from the water body by seepage (m3 H2O) Vstored Volume of water stored in the water body at the beginning of the day (m3 H2O) Vtarg Target reservoir volume for a given day (m3 H2O) areahru HRU area (ha) expsa Exponent for impoundment surface area calculation frimp Fraction of the subbasin area draining into the impoundment frpot Fraction of the HRU area draining into the pothole irr Amount of irrigation water added on a given day (m3 H2O) mon Month of the year monfld,beg Beginning month of the flood season monfld,end Ending month of the flood season Outflow rate (m3/s) qout qrel Average daily principal spillway release rate (m3/s) qrel,mn Minimum average daily outflow for the month (m3/s) qrel,mx Maximum average daily outflow for the month (m3/s) qtile Average daily tile flow rate (m3/s) slp Slope of the HRU (m/m) starg Target reservoir volume specified for a given month (m3 H2O)
βsa η
Coefficient for impoundment surface area equation Evaporation coefficient (0.6)
CHAPTER 28
EQUATIONS: SEDIMENT IN WATER BODIES
SWAT incorporates a simple mass balance model to simulate the transport of sediment into and out of water bodies. SWAT defines four different types of water bodies: ponds, wetlands, reservoirs and potholes. Sediment processes modeled in ponds, wetlands, reservoirs, and potholes are identical. When calculating sediment movement through a water body, SWAT assumes the system is completely mixed. In a completely mixed system, as sediment enters the water body it is instantaneously distributed throughout the volume. 433
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28.1 MASS BALANCE The mass balance equation for sediment in a water body is: sed wb = sed wb,i + sed flowin − sed stl − sed flowout
28.1.1
where sedwb is the amount of sediment in the water body at the end of the day (metric tons), sedwb,i is the amount of sediment in the water body at the beginning of the day (metric tons), sedflowin is the amount of sediment added to the water body with inflow (metric tons), sedstl is the amount of sediment removed from the water by settling (metric tons), sedflowout is the amount of sediment transported out of the water body with outflow (metric tons).
28.2 SETTLING The amount of suspended solid settling that occurs in the water body on a given day is calculated as a function of concentration. The initial suspended solid concentration is: conc sed ,i =
(sed (V
wb ,i
stored
+ sed flowin ) + V flowin )
28.2.1
where concsed,i is the initial concentration of suspended solids in the water (Mg/m3), sedwb,i is the amount of sediment in the water body at the beginning of the day (metric tons), sedflowin is the amount of sediment added to the water body with inflow (metric tons), Vstored is the volume of water stored in water body or channel at the beginning of the day (m3 H2O), and Vflowin is the volume of water entering water body on given day (m3 H2O). Settling occurs only when the sediment concentration in the water body exceeds the equilibrium sediment concentration specified by the user, concsed,eq. The concentration of sediment in the water body at the end of the day is calculated: conc sed , f = (conc sed ,i − conc sed ,eq ) ⋅ exp[− k s ⋅ t ⋅ d 50 ] + conc sed ,eq
conc sed , f = conc sed ,i
if conc sed ,i > conc sed ,eq
28.2.2
if conc sed ,i ≤ conc sed ,eq
28.2.3
CHAPTER 28: EQUATIONS—SEDIMENT IN WATER BODIES
435
where concsed,f is the final sediment concentration in the water body (Mg/m3), concsed,i is the initial concentration of suspended solids in the water body (Mg/m3), concsed,eq is the equilibrium concentration of suspended solids in the water body (Mg/m3), ks is the decay constant (1/day), t is the length of the time step (1 day), and d50 is the median particle size of the inflow sediment (µm). Assuming 99% of the 1 µm size particles settle out of solution within 25 days, ks is equal to 0.184. The median particle size of the inflow sediment is calculated: m m m ö æ d 50 = expç 0.41 ⋅ c + 2.71 ⋅ silt + 5.7 ⋅ s ÷ 100 100 100 ø è
28.2.4
where d50 is the median particle size of the inflow sediment (µm), mc is percent clay in the surface soil layer in the subbasin, msilt is the percent silt in the surface soil layer in the subbasin, ms is the percent sand in the surface soil layer in the subbasin. The amount of sediment settling out of solution on a given day is then calculated: sed stl = (conc sed ,i − conc sed , f ) ⋅ V
28.2.5
where sedstl is the amount of sediment removed from the water by settling (metric tons), concsed,i is the initial concentration of suspended solids in the water body (Mg/m3), concsed,f is the final sediment concentration in the water body (Mg/m3), and V is the volume of water in the impoundment (m3 H2O). Table 28-1: SWAT input variables that pertain to sediment settling. Variable Name Definition RES_NSED concsed,eq: Equilibrium sediment concentration in water body (mg/L) PND_NSED concsed,eq: Equilibrium sediment concentration in water body (mg/L) WET_NSED concsed,eq: Equilibrium sediment concentration in water body (mg/L) POT_NSED concsed,eq: Equilibrium sediment concentration in water body (mg/L) CLAY mc: Percent clay in the surface soil layer in the subbasin SILT msilt: Percent silt in the surface soil layer in the subbasin SAND ms: Percent sand in the surface soil layer in the subbasin
Input File .res .pnd .pnd .hru .sol .sol .sol
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28.3 SEDIMENT OUTFLOW The amount of sediment transported out of the water body on a given day is calculated as a function of the final concentration. The initial suspended solid concentration is: sed flowout = conc sed , f ⋅ V flowout
28.3.1
where sedflowout is the amount of sediment transported out of the water body with outflow (metric tons), concsed,f is the final sediment concentration in the water body (Mg/m3), and Vflowout is the volume of outflow from the impoundment (m3 H2O).
28.4 NOMENCLATURE V Vflowin Vflowout Vstored
Volume of water in the impoundment (m3 H2O) Volume of water entering water body on given day (m3 H2O) Volume of outflow from the impoundment (m3 H2O) Volume of water stored in water body or channel (m3 H2O)
concsed Concentration of suspended solids in the water (Mg/m3) concsed,eq Equilibrium concentration of suspended solids in the water body (Mg/m3) d50 Median particle size of the inflow sediment (µm) ks Decay constant (1/day) t Length of the time step (1 day) mc Percent clay in the surface soil layer in the subbasin Percent sand in the surface soil layer in the subbasin ms msilt Percent silt in the surface soil layer in the subbasin sedflowin Amount of sediment added to the water body with inflow (metric tons) sedflowout Amount of sediment transported out of the water body (metric tons) sedstl Amount of sediment removed from the water by settling (metric tons) sedwb Sediment in the water body (metric tons)
CHAPTER 29
EQUATIONS: NUTRIENTS IN WATER BODIES
SWAT incorporates a simple empirical model to predict the trophic status of water bodies. For studies that require detailed modeling of lake water quality, SWAT has been linked to distributed lake water quality models such as WASP. SWAT defines four different types of water bodies: ponds, wetlands, reservoirs and depressional/impounded areas (potholes). Nutrient processes modeled in ponds, wetlands and reservoirs are identical. Nutrient processes are not yet modeled in potholes.
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29.1 NUTRIENT TRANSFORMATIONS When calculating nutrient transformations in a water body, SWAT assumes the system is completely mixed. In a completely mixed system, as nutrients enter the water body they are instantaneously distributed throughout the volume. The assumption of a completely mixed system ignores lake stratification and intensification of phytoplankton in the epilimnion. The initial amount of nitrogen and phosphorus in the water body on the given day is calculated by summing the mass of nutrient entering the water body on that day with the mass of nutrient already present in the water body. M initial = M stored + M flowin
29.1.1
where Minitial is the initial mass of nutrient in the water body for the given day (kg), Mstored is the mass of nutrient in the water body at the end of the previous day (kg), and Mflowin is the mass of nutrient added to the water body on the given day (kg). In a similar manner, the initial volume of water in the water body is calculated by summing the volume of water entering the water body on that day with the volume already present in the water body. Vinitial = Vstored + V flowin
29.1.2
where Vinitial is the initial volume of water in the water body for a given day (m3 H2O), Vstored is the volume of water in the water body at the end of the previous day (m3 H2O), and Vflowin is the volume of water entering the water body on the given day (m3 H2O). The initial concentration of nutrients in the water body is calculated by dividing the initial mass of nutrient by the initial volume of water. Nutrient transformations simulated in ponds, wetlands and reservoirs are limited to the removal of nutrients by settling. Transformations between nutrient pools (e.g. NO3 ⇔ NO2 ⇔ NH4) are ignored.
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439
Settling losses in the water body can be expressed as a flux of mass across the surface area of the sediment-water interface (Figure 29-1) (Chapra, 1997).
Figure 29-1: Settling losses calculated as flux of mass across the sediment-water interface.
The mass of nutrient lost via settling is calculated by multiplying the flux by the area of the sediment-water interface. M settling = ν ⋅ c ⋅ As ⋅ dt
29.1.3
where Msettling is the mass of nutrient lost via settling on a day (kg), ν is the apparent settling velocity (m/day), As is the area of the sediment-water interface (m2), c is the initial concentration of nutrient in the water (kg/m3 H2O), and dt is the length of the time step (1 day). The settling velocity is labeled as “apparent” because it represents the net effect of the different processes that deliver nutrients to the water body’s sediments. The water body is assumed to have a uniform depth of water and the area of the sediment-water interface is equivalent to the surface area of the water body. The apparent settling velocity is most commonly reported in units of m/year and this is how the values are input to the model. For natural lakes, measured phosphorus settling velocities most frequently fall in the range of 5 to 20 m/year although values less than 1 m/year to over 200 m/year have been reported (Chapra, 1997). Panuska and Robertson (1999) noted that the range in apparent settling velocity values for man-made reservoirs tends to be significantly greater than for natural lakes. Higgins and Kim (1981) reported phosphorus
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apparent settling velocity values from –90 to 269 m/year for 18 reservoirs in Tennessee with a median value of 42.2 m/year. For 27 Midwestern reservoirs, Walker and Kiihner (1978) reported phosphorus apparent settling velocities ranging from –1 to 125 m/year with an average value of 12.7 m/year. A negative settling rate indicates that the reservoir sediments are a source of N or P; a positive settling rate indicates that the reservoir sediments are a sink for N or P. A number of inflow and impoundment properties affect the apparent settling velocity for a water body. Factors of particular importance include the form of phosphorus in the inflow (dissolved or particulate) and the settling velocity of the particulate fraction. Within the impoundment, the mean depth, potential for sediment resuspension and phosphorus release from the sediment will affect the apparent settling velocity (Panuska and Robertson, 1999). Water bodies with high internal phosphorus release tend to possess lower phosphorus retention and lower phosphorus apparent settling velocities than water bodies with low internal phosphorus release (Nürnberg, 1984). Table 29-1 summarizes typical ranges in phosphorus settling velocity for different systems. Table 29-1: Recommended apparent settling velocity values for phosphorus (Panuska and Robertson, 1999) Range in settling velocity Nutrient Dynamics values (m/year) Shallow water bodies with high net internal phosphorus flux ν≤0 Water bodies with moderate net internal phosphorus flux 1<ν<5 Water bodies with minimal net internal phosphorus flux 5 < ν < 16 Water bodies with high net internal phosphorus removal ν > 16
SWAT input variables that pertain to nutrient settling in ponds, wetlands and reservoirs are listed in Table 29-2. The model allows the user to define two settling rates for each nutrient and the time of the year during which each settling rate is used. A variation in settling rates is allowed so that impact of temperature and other seasonal factors may be accounted for in the modeling of nutrient settling. To use only one settling rate for the entire year, both variables for the nutrient may be set to the same value. Setting all variables to zero will cause the model to ignore settling of nutrients in the water body.
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441
After nutrient losses in the water body are determined, the final concentration of nutrients in the water body is calculated by dividing the final mass of nutrient by the initial volume of water. The concentration of nutrients in outflow from the water body is equivalent to the final concentration of the nutrients in the water body for the day. The mass of nutrient in the outflow is calculated by multiplying the concentration of nutrient in the outflow by the volume of water leaving the water body on that day. Table 29-2: SWAT input variables that control settling in ponds, wetlands and reservoirs. Variable Name Definition IPND1 Beginning month of mid-year nutrient settling period for pond and wetland modeled in subbasin IPND2 Ending month of mid-year nutrient settling period for pond and wetland modeled in subbasin PSETL1 Phosphorus settling rate in pond during mid-year nutrient settling period (IPND1 ≤ month ≤ IPND2) (m/year) PSETL2 Phosphorus settling rate in pond during time outside mid-year nutrient settling period ( month < IPND1 or month > IPND2) (m/year) NSETL1 Nitrogen settling rate in pond during mid-year nutrient settling period (IPND1 ≤ month ≤ IPND2) (m/year) NSETL2 Nitrogen settling rate in pond during time outside mid-year nutrient settling period ( month < IPND1 or month > IPND2) (m/year) PSETLW1 Phosphorus settling rate in wetland during mid-year nutrient settling period (IPND1 ≤ month ≤ IPND2) (m/year) PSETLW2 Phosphorus settling rate in wetland during time outside mid-year nutrient settling period ( month < IPND1 or month > IPND2) (m/year) NSETLW1 Nitrogen settling rate in wetland during mid-year nutrient settling period (IPND1 ≤ month ≤ IPND2) (m/year) NSETLW2 Nitrogen settling rate in wetland during time outside mid-year nutrient settling period ( month < IPND1 or month > IPND2) (m/year) IRES1 Beginning month of mid-year nutrient settling period for reservoir IRES2 Ending month of mid-year nutrient settling period for reservoir PSETLR1 Phosphorus settling rate in reservoir during mid-year nutrient settling period (IRES1 ≤ month ≤ IRES2) (m/year) PSETLR2 Phosphorus settling rate in reservoir during time outside mid-year nutrient settling period ( month < IRES1 or month > IRES2) (m/year) NSETLR1 Nitrogen settling rate in reservoir during mid-year nutrient settling period (IRES1 ≤ month ≤ IRES2) (m/year) NSETLR2 Nitrogen settling rate in reservoir during time outside mid-year nutrient settling period ( month < IRES1 or month > IRES2) (m/year)
Input File .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .pnd .lwq .lwq .lwq .lwq .lwq .lwq
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29.2 TOTAL BALANCE Assuming that the volume of the water body remains constant over time, the processes described above (inflow, settling, outflow) can be combined into the following mass balance equation for a well-mixed water body: V⋅
dc = W (t ) − Q ⋅ c − ν ⋅ c ⋅ As dt
29.2.1
where V is the volume of the system (m3 H2O), c is the concentration of nutrient in the system (kg/m3 H2O), dt is the length of the time step (1 day), W(t) is the amount of nutrient entering the water body during the day (kg/day), Q is the rate of water flow exiting the water body (m3 H2O/day), ν is the apparent settling velocity (m/day), and As is the area of the sediment-water interface (m2).
29.3 EUTROPHICATION Under favorable conditions of light and temperature, excess amounts of nutrients in water can increase the growth of algae and other plants. The result of this growth is an increase in the rate of eutrophication, which is a natural ecological process of change from a nutrient-poor to a nutrient-rich environment. Eutrophication is defined as the process by which a body of water becomes enriched in dissolved nutrients (as phosphates) that stimulate the growth of aquatic plant life, usually resulting in the depletion of dissolved oxygen (Merriam-Webster, Inc., 1996). Nutrient enrichment of moving waters and lakes is a normal result of soil weathering and erosion processes. The gradual evolution of Ice Age lakes into marshes and, eventually, organic soils is a result of eutrophication. However, this process can be accelerated by the discharge of wastes containing high levels of nutrients into lakes or rivers. One example of this is Lake Erie, which is estimated to have aged the equivalent of 150 natural years in a 15-year span of accelerated eutrophication. Excessive plant growth caused by accelerated eutrophication can lead to stagnation of the water. The stagnation is caused by an increased biological
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oxygen demand created by decaying plant remains. The result of this increased oxygen demand is a tendency toward anaerobic conditions and the inability of the water body to support fish and other aerobic organisms. Nitrogen, carbon and phosphorus are essential to the growth of aquatic biota. Due to the difficulty of controlling the exchange of nitrogen and carbon between the atmosphere and water and fixation of atmospheric nitrogen by some blue-green algae, attempts to mitigate eutrophication have focused on phosphorus inputs. In fresh-water systems, phosphorus is often the limiting element. By controlling phosphorus loading, accelerated eutrophication of lake waters can be reduced. In systems where phosphorus is the primary, controllable limiting nutrient of water body eutrophication, the amount of phosphorus present in the water body can be used to estimate the amount of eutrophication present in the water body.
29.3.1 PHOSPHORUS/CHLOROPHYLL a CORRELATIONS A number of empirically derived equations have been developed to calculate chlorophyll a level as a function of total phosphorus concentration. SWAT uses an equation developed by Rast and Lee (1978) to calculate the chlorophyll a concentration in the water body. Chla = 0.551 ⋅ p 0.76
29.3.1
where Chla is the chlorophyll a concentration (µg/L) and p is the total phosphorus concentration (µg/L). The equation has been modified to include a user-defined coefficient: Chla = Chlaco ⋅ 0.551 ⋅ p 0.76
29.3.2
The user-defined coefficient, Chlaco, is included to allow the user to adjust the predicted chlorophyll a concentration for limitations of nutrients other than phosphorus. When Chlaco is set to 1.00, equation 29.3.2 is equivalent to equation 29.3.1. For most water bodies, the original equation will be adequate..
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29.3.2 CHLOROPHYLL a/SECCHI-DISK DEPTH CORRELATION The secchi-disk depth is another measure of the trophic status of a water body. The secchi-disk depth quantifies the clarity of the water, an attribute easily perceived by the general public. The secchi-disk depth can be calculated from chlorophyll levels using the equation (Chapra, 1997): SD = 6.35 ⋅ Chla −0.473
29.3.3
where SD is the secchi-disk depth (m) and Chla is the chlorophyll a concentration (µg/L). For incorporation into SWAT, equation 29.3.3 was modified to include a user-defined coefficient: SD = SDco ⋅ 6.35 ⋅ Chla −0.473
29.3.4
The user-defined coefficient, SDco, is included to allow the user to adjust the predicted secchi-disk depth for impacts of suspended sediment and other particulate matter on water clarity that are ignored by the original equation. When SDco is set to 1.00, equation 29.3.4 is equivalent to equation 29.3.3. For most water bodies, the original equation will be adequate. While evaluation of water quality by secchi-disk depth measurements is subjective, some general correlations between secchi-disk depth and public perception of water quality have been made. One such correlation made for Annebessacook Lake in Maine (EPA, 1980) is given in Table 29-3. Table 29-3: Relationship between secchi-disk depth and public perception of water quality. Secchi-disk depth (m) Public perception of water quality 0.0 – 0.9 gross pollution; water body totally unsuitable for recreation 1.0 – 1.9 algae blooms still evident; quality unacceptable for most uses 2.0 – 2.9 some complaints of declining water quality; some impairment of water use 3.0 – 3.9 satisfactory quality; no impairment of water use 4.0 – 4.9 excellent water quality; a positive factor encouraging use of lake 5.0 + exceptional quality
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Table 29-4: SWAT input variables that impact eutrophication calculations in ponds, wetlands and reservoirs. Variable Input Name Definition File CHLA Chlaco variable for calculation of chlorophyll a concentration in a pond .pnd CHLAW Chlaco variable for calculation of chlorophyll a concentration in a .pnd wetland CHLAR Chlaco variable for calculation of chlorophyll a concentration in a .lwq reservoir .pnd SECCI SDco variable for calculation of secchi-disk depth in a pond SECCIW SDco variable for calculation of secchi-disk depth in a wetland .pnd SECCIR SDco variable for calculation of secchi-disk depth in a reservoir .lwq
29.4 NOMENCLATURE As Area of sediment-water interface (m2) Chla Chlorophyll a concentration (µg/L) Chlaco User-defined coefficient to adjust predicted chlorophyll a concentration Mflowin Mass of nutrient entering water body on the given day (kg) Minitial Initial mass of nutrient in water body for the given day (kg) Msettling Mass of nutrient lost via settling on a given day (kg) Mstored Mass of nutrient in water body at end of previous day (kg) Q Volumetric flow rate for water exiting water body (m3 H2O/day) SD Secchi-disk depth (m) SDco User-defined coefficient to adjust predicted secchi-disk depth V Volume of water in water body (assumed constant) (m3 H2O) Vflowin Volume of water entering water body on given day (m3 H2O) Vinitial Initial volume of water in water body on given day (m3 H2O) Vstored Volume of water in water body at end of previous day (m3 H2O) W(t) Rate of nutrient loading (kg/day) c dt p
Concentration of nutrient in the water (kg/m3 H2O) Length of time step (1 day) Total phosphorus concentration (µg P/L)
ν
Apparent settling velocity (m/day)
29.5 REFERENCES Chapra, S.C. 1997. Surface water-quality modeling. WCB/McGraw-Hill, Boston, MA. Higgins, J.M. and B.R. Kim. 1981. Phosphorus retention models for the Tennessee Valley Authority reservoirs. Wat. Resour. Res. 17:571-576.
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Merriam-Webster, Inc. 1996. Merriam-Webster’s collegiate dictionary, 10th edition. Merriam-Webster, Inc. Springfield, MA. Nürnberg, G.K. 1984. The prediction of internal phosphorus load in lakes with anoxic hypolimnia. Limnol. Oceanogr. 29:111-124. Panuska, J.C. and D.M. Robertson. 1999. Estimating phosphorus concentration following alum treatment using apparent settling velocity. Lake and Reserv. Manage. 15:28-38. Rast, W. and Lee, G.F. 1978. Summary analysis of the North American project (US portion) OECD eutrophication project: nutrient loading-lake response relationships and trophic state indices. USEPA Corvallis Environmental Research Laboratory, Corvallis, OR. EPA-600/3-78-008. USEPA. 1980. Lake restoration in Cobbossee watershed. Capsule Rept. Office of Water Planning and Standards Div., ORD, Cincinnati, OH. EPA-624/280-027. Walker, W.W. and J. Kiihner. 1978. An empirical analysis of factors controlling eutrophication in midwestern impoundments. Paper presented at the International Symposium on the Environmental Effects of Hydraulic Engineering Works, Univ. of Tenn., Knoxville.
CHAPTER 30
EQUATIONS: PESTICIDES IN WATER BODIES
SWAT incorporates a simple mass balance developed by Chapra (1997) to model the transformation and transport of pesticides in water bodies. The model assumes a well-mixed layer of water overlying a sediment layer. Figure 30-1 illustrates the mechanisms affecting the pesticide mass balance in water bodies. SWAT defines four different types of water bodies: ponds, wetlands, reservoirs and depressional/impounded areas (potholes). Pesticide processes are modeled only in reservoirs.
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Figure 30-1: Pesticide mass balance for well-mixed water body with sediment layer.
30.1 PESTICIDE IN THE WATER Pesticide in a well-mixed water body is increased through addition of mass in inflow, resuspension and diffusion from the sediment layer. The amount of pesticide in a well-mixed water body is reduced through removal in outflow, degradation, volatilization, settling and diffusion into the underlying sediment.
30.1.1 SOLID-LIQUID PARTITIONING Pesticides will partition into particulate and dissolved forms. The fraction of pesticide in each phase is a function of the pesticide’s partition coefficient and the water body’s suspended solid concentration: Fd =
1 1 + K d ⋅ conc sed
30.1.1
Fp =
K d ⋅ conc sed = 1 − Fd 1 + K d ⋅ conc sed
30.1.2
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where Fd is the fraction of total pesticide in the dissolved phase, Fp is the fraction of total pesticide in the particulate phase, Kd is the pesticide partition coefficient (m3/g), and concsed is the concentration of suspended solids in the water (g/m3). The pesticide partition coefficient can be estimated from the octanol-water partition coefficient (Chapra, 1997): K d = 3.085 × 10 −8 ⋅ K ow
30.1.3
where Kd is the pesticide partition coefficient (m3/g) and Kow is the pesticide’s −3 3 (mg m −water ) ). Values for the octanol-water partition coefficient ( mg m octanol −1
octanol-water partition coefficient have been published for many chemicals. If a published value cannot be found, it can be estimated from solubility (Chapra, 1997): ′ ) log(K ow ) = 5.00 − 0.670 ⋅ log( pst sol
30.1.4
′ is the pesticide solubility (µmoles/L). The solubility in these units is where pst sol calculated: ′ = pst sol
pst sol ⋅ 10 3 MW
30.1.5
′ is the pesticide solubility (µmoles/L), pstsol is the pesticide solubility where pst sol (mg/L) and MW is the molecular weight (g/mole).
30.1.2 DEGRADATION Pesticides in both the particulate and dissolved forms are subject to degradation. The amount of pesticide that is removed from the water via degradation is: pstdeg,wtr = k p ,aq ⋅ pstlkwtr
30.1.6
where pstdeg,wtr is the amount of pesticide removed from the water via degradation (mg pst), kp,aq is the rate constant for degradation or removal of pesticide in the water (1/day), and pstlkwtr is the amount of pesticide in the water at the beginning of the day (mg pst). The rate constant is related to the aqueous half-life:
k p ,aq =
0.693 t1 / 2,aq
30.1.7
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where kp,aq is the rate constant for degradation or removal of pesticide in the water (1/day), and t1/2,aq is the aqueous half-life for the pesticide (days).
30.1.3 VOLATILIZATION Pesticide in the dissolved phase is available for volatilization. The amount of pesticide removed from the water via volatilization is: pstvol ,wtr = vv ⋅ SA ⋅
Fd ⋅ pstlkwtr V
30.1.8
where pstvol,wtr is the amount of pesticide removed via volatilization (mg pst), vv is the volatilization mass-transfer coefficient (m/day), SA is the surface area of the water body (m2), Fd is the fraction of total pesticide in the dissolved phase, pstlkwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the water body (m3 H2O). The volatilization mass-transfer coefficient can be calculated based on Whitman’s two-film or two-resistance theory (Whitman, 1923; Lewis and Whitman, 1924 as described in Chapra, 1997). While the main body of the gas and liquid phases are assumed to be well-mixed and homogenous, the two-film theory assumes that a substance moving between the two phases encounters maximum resistance in two laminar boundary layers where transfer is a function of molecular diffusion. In this type of system the transfer coefficient or velocity is: vv = K l ⋅
He H e + R ⋅ TK ⋅ (K l K g )
30.1.9
where vv is the volatilization mass-transfer coefficient (m/day), Kl is the masstransfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), He is Henry’s constant (atm m3 mole-1), R is the universal gas constant (8.206 × 10-5 atm m3 (K mole)-1), and TK is the temperature (K). For lakes, the transfer coefficients are estimated using a stagnant film approach: Kl =
Dl zl
Kg =
Dg zg
30.1.10
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where Kl is the mass-transfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), Dl is the liquid molecular diffusion coefficient (m2/day), Dg is the gas molecular diffusion coefficient (m2/day), zl is the thickness of the liquid film (m), and zg is the thickness of the gas film (m). Alternatively, the transfer coefficients can be estimated with the equations: æ 32 ö K l = K l ,O 2 ⋅ ç ÷ è MW ø
0.25
30.1.11
æ 18 ö K g = 168 ⋅ µ w ⋅ ç ÷ è MW ø
0.25
30.1.12
where Kl is the mass-transfer velocity in the liquid laminar layer (m/day), Kg is the mass-transfer velocity in the gaseous laminar layer (m/day), K l ,O2 is the oxygen transfer coefficient (m/day), MW is the molecular weight of the compound, and
µw is the wind speed (m/s). Chapra (1997) lists several different equations that can be used to calculate K l ,O2 .
30.1.4 SETTLING Pesticide in the particulate phase may be removed from the water layer by settling. Settling transfers pesticide from the water to the sediment layer. The amount of pesticide that is removed from the water via settling is: pst
= v ⋅ SA ⋅ stl , wtr s
F ⋅ pst p lkwtr V
30.1.13
where pststl,wtr is the amount of pesticide removed from the water due to settling (mg pst), vs is the settling velocity (m/day), SA is the surface area of the water body (m2), Fp is the fraction of total pesticide in the particulate phase, pstlkwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the water body (m3 H2O).
30.1.5 OUTFLOW Pesticide is removed from the water body in outflow. The amount of dissolved and particulate pesticide removed from the water body in outflow is:
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pst sol ,o = Q ⋅
Fd ⋅ pstlkwtr V
pst sorb,o = Q ⋅
Fp ⋅ pstlkwtr V
30.1.14 30.1.15
where pstsol,o is the amount of dissolved pesticide removed via outflow (mg pst), pstsorb,o is the amount of particulate pesticide removed via outflow (mg pst), Q is the rate of outflow from the water body (m3 H2O/day), Fd is the fraction of total pesticide in the dissolved phase, Fp is the fraction of total pesticide in the particulate phase, pstlkwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the water body (m3 H2O). Table 30-1: SWAT input variables that pesticide partitioning. Variable Name Definition LKPST_KOC Kd: Pesticide partition coefficient (m3/g) LKPST_REA kp,aq: Rate constant for degradation or removal of pesticide in the water (1/day) LKPST_VOL vv: Volatilization mass-transfer coefficient (m/day) LKPST_STL vs: Pesticide settling velocity (m/day)
Input File .lwq .lwq .lwq .lwq
30.2 PESTICIDE IN THE SEDIMENT Pesticide in the sediment layer underlying a water body is increased through addition of mass by settling and diffusion from the water. The amount of pesticide in the sediment layer is reduced through removal by degradation, resuspension, diffusion into the overlying water, and burial.
30.2.1 SOLID-LIQUID PARTITIONING As in the water layer, pesticides in the sediment layer will partition into particulate and dissolved forms. Calculation of the solid-liquid partitioning in the sediment layer requires a suspended solid concentration. The “concentration” of solid particles in the sediment layer is defined as: * conc sed =
M sed Vtot
30.2.1
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* where conc sed is the “concentration” of solid particles in the sediment layer
(g/m3), Msed is the mass of solid particles in the sediment layer (g) and Vtot is the total volume of the sediment layer (m3). Mass and volume are also used to define the porosity and density of the sediment layer. In the sediment layer, porosity is the fraction of the total volume in the liquid phase:
φ=
Vwtr Vtot
30.2.2
where φ is the porosity, Vwtr is the volume of water in the sediment layer (m3) and Vtot is the total volume of the sediment layer (m3). The fraction of the volume in the solid phase can then be defined as: 1−φ =
Vsed Vtot
30.2.3
where φ is the porosity, Vsed is the volume of solids in the sediment layer (m3) and Vtot is the total volume of the sediment layer (m3). The density of sediment particles is defined as:
ρs =
M sed Vsed
30.2.4
where ρs is the particle density (g/m3), Msed is the mass of solid particles in the sediment layer (g), and Vsed is the volume of solids in the sediment layer (m3). Solving equation 30.2.3 for Vtot and equation 30.2.4 for Msed and substituting into equation 30.2.1 yields: * conc sed = (1 − φ ) ⋅ ρ s
30.2.5
* where conc sed is the “concentration” of solid particles in the sediment layer
(g/m3), φ is the porosity, and ρs is the particle density (g/m3). Typical values of porosity and particle density for fine-grained sediments are φ = 0.8-0.95 and ρs = 2.4-2.7 × 106 g/m3 (Chapra, 1997). Assuming φ = 0.8 and ρs = 2.6 × 106 g/m3, the “concentration” of solid particles in the sediment layer is 5.2 × 105 g/m3.
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The fraction of pesticide in each phase is then calculated: Fd ,sed =
1 φ + (1 − φ ) ⋅ ρ s ⋅ K d
Fp ,sed = 1 − Fd ,sed
30.2.6 30.2.7
where Fd,sed is the fraction of total sediment pesticide in the dissolved phase, Fp,sed is the fraction of total sediment pesticide in the particulate phase, φ is the porosity,
ρs is the particle density (g/m3), and Kd is the pesticide partition coefficient (m3/g). The pesticide partition coefficient used for the water layer is also used for the sediment layer.
30.2.2 DEGRADATION Pesticides in both the particulate and dissolved forms are subject to degradation. The amount of pesticide that is removed from the sediment via degradation is: pstdeg,sed = k p ,sed ⋅ pstlksed
30.2.8
where pstdeg,sed is the amount of pesticide removed from the sediment via degradation (mg pst), kp,sed is the rate constant for degradation or removal of pesticide in the sediment (1/day), and pstlksed is the amount of pesticide in the sediment (mg pst). The rate constant is related to the sediment half-life: k p ,sed =
0.693 t1 / 2,sed
30.2.9
where kp,sed is the rate constant for degradation or removal of pesticide in the sediment (1/day), and t1/2,sed is the sediment half-life for the pesticide (days).
30.2.3 RESUSPENSION Pesticide in the sediment layer is available for resuspension. The amount of pesticide that is removed from the sediment via resuspension is: pst rsp ,wtr = v r ⋅ SA ⋅
pstlksed Vtot
30.2.10
where pstrsp,wtr is the amount of pesticide removed via resuspension (mg pst), vr is the resuspension velocity (m/day), SA is the surface area of the water body (m2),
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pstlksed is the amount of pesticide in the sediment (mg pst), and Vtot is the volume of the sediment layer (m3). The volume of the sediment layer is calculated: Vtot = SA ⋅ Dsed
30.2.11
where Vtot is the volume of the sediment layer (m3), SA is the surface area of the water body (m2), Dsed is the depth of the active sediment layer (m). Pesticide removed from the sediment layer by resuspension is added to the water layer.
30.2.4 DIFFUSION Pesticide in the dissolved phase is available for diffusion. Diffusion transfers pesticide between the water and sediment layers. The direction of movement is controlled by the pesticide concentration. Pesticide will move from areas of high concentration to areas of low concentration. The amount of pesticide that is transferred between the water and sediment by diffusion is: ⋅ pstlksed Fd ⋅ pstlkwtr ö æF ÷÷ pstdif = vd ⋅ SA ⋅ çç d ,sed − Vtot V ø è
30.2.12
where pstdif is the amount of pesticide transferred between the water and sediment by diffusion (mg pst), vd is the rate of diffusion or mixing velocity (m/day), SA is the surface area of the water body (m2), Fd,sed is the fraction of total sediment pesticide in the dissolved phase, pstlksed is the amount of pesticide in the sediment (mg pst), Vtot is the volume of the sediment layer (m3), Fd is the fraction of total water layer pesticide in the dissolved phase, pstlkwtr is the amount of pesticide in the water (mg pst), and V is the volume of water in the water body (m3 H2O). If Fd ,sed ⋅ pstlksed Vtot layer. If
>
Fd ⋅ pstlkwtr , pstdif is transferred from the sediment to the water V
Fd ,sed ⋅ pstlksed Vtot
<
Fd ⋅ pstlkwtr , pstdif is transferred from the water to the V
sediment layer. The diffusive mixing velocity, vd, can be estimated from the empirically derived formula (Chapra, 1997): vd =
69.35 ⋅ φ ⋅ MW − 2 / 3 365
30.2.13
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where vd is the rate of diffusion or mixing velocity (m/day), φ is the sediment porosity, and MW is the molecular weight of the pesticide compound.
30.2.5 BURIAL Pesticide in the sediment layer may be lost by burial. The amount of pesticide that is removed from the sediment via burial is: pstbur = vb ⋅ SA ⋅
pstlksed Vtot
30.2.14
where pstbur is the amount of pesticide removed via burial (mg pst), vb is the burial velocity (m/day), SA is the surface area of the water body (m2), pstlksed is the amount of pesticide in the sediment (mg pst), and Vtot is the volume of the sediment layer (m3). Table 30-2: SWAT input variables related to pesticide in the sediment. Variable Name Definition LKPST_KOC Kd: Pesticide partition coefficient (m3/g) LKSPST_REA kp,sed: Rate constant for degradation or removal of pesticide in the sediment (1/day) LKPST_RSP vr: Resuspension velocity (m/day) LKSPST_ACT Dsed: Depth of the active sediment layer (m) LKPST_MIX vd: Rate of diffusion or mixing velocity (m/day) LKSPST_BRY vb: Pesticide burial velocity (m/day)
Input File .lwq .lwq .lwq .lwq .lwq .lwq
30.3 MASS BALANCE The processes described above can be combined into mass balance equations for the well-mixed water body and the well-mixed sediment layer: ∆pstlkwtr = pstin − ( pst sol ,o + pst sorb,o ) − pstdeg ,wtr − pstvol ,wtr − pst stl ,wtr + pst rsp ,wtr ± pst dif 30.3.1 ∆pstlksed = pstdeg,sed + pst stl ,wtr − pst rsp ,wtr − pstbur ± pstdif
30.3.2
where ∆pstlkwtr is the change in pesticide mass in the water layer (mg pst), ∆pstlksed is the change in pesticide mass in the sediment layer (mg pst), pstin is the pesticide added to the water body via inflow (mg pst), pstsol,o is the amount of dissolved pesticide removed via outflow (mg pst), pstsorb,o is the amount of particulate pesticide removed via outflow (mg pst), pstdeg,wtr is the amount of
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pesticide removed from the water via degradation (mg pst), pstvol,wtr is the amount of pesticide removed via volatilization (mg pst), pststl,wtr is the amount of pesticide removed from the water due to settling (mg pst), pstrsp,wtr is the amount of pesticide removed via resuspension (mg pst), pstdif is the amount of pesticide transferred between the water and sediment by diffusion (mg pst), pstdeg,sed is the amount of pesticide removed from the sediment via degradation (mg pst), pstbur is the amount of pesticide removed via burial (mg pst)
30.4 NOMENCLATURE Dg Dl Dsed Fd Fd,sed Fp Fp,sed He Kd Kg Kl K l ,O 2 Msed MW Q R SA V Vsed Vtot Vwtr
Gas molecular diffusion coefficient (m2/day) Liquid molecular diffusion coefficient (m2/day) Depth of the active sediment layer (m) Fraction of total pesticide in the dissolved phase Fraction of total sediment pesticide in the dissolved phase Fraction of total pesticide in the particulate phase Fraction of total sediment pesticide in the particulate phase Henry’s constant (atm m3 mole-1) Pesticide partition coefficient (m3/g) Mass-transfer velocity in the gaseous laminar layer (m/day) Mass-transfer velocity in the liquid laminar layer (m/day) Oxygen transfer coefficient (m/day) Mass of solid phase in the sediment layer (g) Molecular weight of the pesticide compound Rate of outflow from the water body (m3 H2O/day) Universal gas constant (8.206 × 10-5 atm m3 (K mole)-1) Surface area of the water body (m2) Volume of water in the water body (m3 H2O) Volume of solids in the sediment layer (m3) Total volume of the sediment layer (m3) Volume of water in the sediment layer (m3)
concsed Concentration of suspended solids in the water (g/m3) * conc sed “Concentration” of solid particles in the sediment layer (g/m3) kp,aq Rate constant for degradation or removal of pesticide in the water (1/day) kp,sed Rate constant for degradation or removal of pesticide in the sediment (1/day) pstbur Amount of pesticide removed via burial (mg pst) pstdeg,sed Amount of pesticide removed from the sediment via degradation (mg pst) pstdeg,wtr Amount of pesticide removed from the water via degradation (mg pst) pstdif Amount of pesticide transferred between the water and sediment by diffusion (mg pst)
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pstin Pesticide added to the water body via inflow (mg pst) pstlksed Amount of pesticide in the sediment (mg pst) pstlkwtr Amount of pesticide in the water (mg pst) pstrsp,wtr Amount of pesticide removed from sediment via resuspension (mg pst) pstsol,o Amount of dissolved pesticide removed via outflow (mg pst) pstsorb,o Amount of particulate pesticide removed via outflow (mg pst) pststl,wtr Amount of pesticide removed from the water due to settling (mg pst) pstvol,wtr Amount of pesticide removed via volatilization (mg pst) t1/2,aq Aqueous half-life for the pesticide (days) t1/2,sed Sediment half-life for the pesticide (days) vb Pesticide burial velocity (m/day) vd Rate of diffusion or mixing velocity (m/day) vr Resuspension velocity (m/day) vs Settling velocity (m/day) vv Volatilization mass-transfer coefficient (m/day) zg Thickness of the gas film (m) zl Thickness of the liquid film (m)
φ Porosity ∆pstlkwtr Change in pesticide mass in the water layer (mg pst) ∆pstlksed Change in pesticide mass in the sediment layer (mg pst) ρs Particle density (g/m3) µw Wind speed (m/s)
30.5 REFERENCES Chapra, S.C. 1997. Surface water-quality modeling. WCB/McGraw-Hill, Boston, MA. Lewis, W.K. and W.G. Whitman. 1924. Principles of gas absorption. Ind. Eng. Chem. 16:1215-1220. Whitman, W.G. 1923. The two-film theory of gas adsorption. Chem. Metallurg. Eng. 29:146-148.
PART 2
MODEL OPERATION
CHAPTER 31
SWAT INPUT DATA: WATERSHED CONFIGURATION
The first step in setting up a watershed simulation is to define the relative arrangement of the parts or elements, i.e. the configuration, of the watershed. If the watershed has only one primary channel and there is very little variation in topography and climate across the watershed, there may not be a need to partition the watershed into smaller units. However, the majority of watersheds will exhibit enough complexity in the stream network, topography or climate to warrant subdivision for modeling purposes. There are several techniques used to discretize a watershed. In the past, models could only apply one type of discretization scheme to a watershed. This
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SWAT USER’S MANUAL, VERSION 2000
resulted in the development of several models that differ only in the watershed discretization scheme used.
31.1 DISCRETIZATION SCHEMES The three most common techniques used to discretize a watershed are: ♦ Grid cell. This configuration allows the user to incorporate significant spatial detail into a simulation. Models which use this technique include AGNPS (Young et al., 1987), ANSWERS (Beasley et al., 1980) and the WEPP grid version (Foster, 1987). ♦ Representative hillslope. This configuration is useful for modeling hillslope processes. This technique is used in APEX (Williams, et al., 1998) and the WEPP hillslope version (Lane and Nearing, 1989). ♦ Subwatershed.
This configuration preserves the natural channels
and flow paths of the watershed. This technique is used in the WEPP watershed version (Foster, 1987), HYMO (Williams and Hann, 1973) and SWRRB (Arnold et al., 1990).
All of these schemes have strengths and weaknesses and applications for which they are most appropriate. SWAT uses the subwatershed configuration as the primary discretization scheme for a watershed. However, because of the routing command language utilized in SWAT, it is possible to use any of these three, alone or in combination, to model a watershed.
31.2 WATERSHED CONFIGURATION FILE (.FIG) The watershed configuration file contains information used by SWAT to simulate processes occurring within the HRU/subbasin and to route the stream
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
31
loadings through the channel network of the watershed. A reach routing command structure, similar to that developed for HYMO (Williams and Hann, 1973), is utilized to route and add flows through the watershed. The following sections review the different features of the watershed configuration file.
31.2.1 INCORPORATION OF COMMENTS To assist the user in interpreting the watershed configuration file, an unlimited number of comment lines are allowed. These comments can be used to isolate the routing commands for different reaches, etc. To included comments in the watershed configuration file, a line must have an asterisk (*) in the 1st space on the line. When SWAT reads the asterisk, it will skip to the next line.
31.2.2 COMMAND LINES Thirteen different commands may be used in the watershed configuration file. The commands, along with their numeric codes, are: finish subbasin route routres transfer add recmon recyear save recday reccnst structur saveconc
0 1 2 3 4 5 7 8 9 10 11 12 14
The most commonly used commands are: subbasin, route, add, and finish. In brief, these commands simulated the land phase of the hydrologic cycle and determine the loadings to the reach (subbasin), model the movement and transformations occurring in the reach (route), allow the output from different
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SWAT USER’S MANUAL, VERSION 2000
subbasins to be summed together (add), and identify the end of the routing command sequence (finish). The remaining commands are utilized to model more unique configurations. This set of commands can be divided several subgroups: routing of water through a reservoir (routres), humanly contrived movement of water (transfer), aeration of water resulting from flow through structures along the channel (structur), incorporation of point source data (recday, recmon, recyear, reccnst), formatting of watershed outflow for input into a different SWAT simulation (save), and formatting of water quality simulation results at specified points in the reach network (saveconc).
31.2.2.1 SUBBASIN COMMAND (1) The subbasin command simulates all processes involved in the land phase of the hydrologic cycle and computes runoff, sediment, and chemical loadings from each HRU within the subbasin. Variables required on the subbasin command line are: Variable name
Definition
COMMAND
The command code = 1 for the subbasin command.
HYD_STOR
The hydrograph storage location number. After a command is executed, the results are stored in an array at the position defined by this number. It is crucial that all hydrograph storage location numbers are unique. If the same number is used twice, output from one command line will be overwritten by that from another and simulation results will be incorrect.
SUB_NUM
Subbasin number. This number is assigned in file.cio. Every subbasin in the watershed has a different number.
GIS_CODE
GIS code printed to output files (optional)
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
33
The format of the subbasin command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_STOR
space 17-22
6-digit integer
i6
SUB_NUM
space 23-28
6-digit integer
i6
GIS_CODE
space 47-55
9-digit integer
i9
31.2.2.2 ROUTE COMMAND (2) The route command routes the water, sediment, and chemical loadings through a main channel or reach. Variables required on the route command line are: Variable name
Definition
COMMAND
The command code = 2 for the route command.
HYD_STOR
The hydrograph storage location number. After a command is executed, the results are stored in an array at the position defined by this number. It is crucial that all hydrograph storage location numbers are unique. If the same number is used twice, output from one command line will be overwritten by that from another and simulation results will be incorrect.
RCH_NUM
Reach number. The reach number is the same as the subbasin number.
HYD_NUM
Inflow hydrograph storage location number. The data that is to be routed through the reach.
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SWAT USER’S MANUAL, VERSION 2000
Variable name
Definition
FLOW_OVN
Fraction of overland flow (0.000 to 1.000). If flow leaving a subbasin is completely channelized, FLOW_OVN = 0.000. In cases where a hillslope is being simulated, overland flow from one subbasin to another occurs and the value of FLOW_OVN can be increased to account for the amount of non-channelized overland flow taking place between the subbasins. The overland flow to the next subbasin is added to the rainfall of the receiving subbasin and allowed to infiltrate or run off. The sediment and chemical loadings associated with the overland flow are assumed to be deposited on the upper soil layer of the receiving subbasin. The fraction of the flow in the channel is routed directly to the reach of the receiving subbasin.
The format of the route command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_STOR
space 17-22
6-digit integer
i6
RCH_NUM
space 23-28
6-digit integer
i6
HYD_NUM
space 29-34
6-digit integer
i6
FLOW_OVN
space 41-46
decimal (xx.xxx)
f6.3
31.2.2.3 ADD COMMAND (5) The add command is used to sum the water, sediment, and chemical loadings of any two flows. Variables required on the add command line are: Variable name
Definition
COMMAND
The command code = 5 for the add command.
HYD_STOR
The hydrograph storage location number for the results.
HYD_NUM1
The hydrograph storage location number of the 1st set of data to be added.
HYD_NUM2
The hydrograph storage location number of the 2nd set of data to be added.
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
35
The format of the add command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_STOR
space 17-22
6-digit integer
i6
HYD_NUM1
space 23-28
6-digit integer
i6
HYD_NUM2
space 29-34
6-digit integer
i6
31.2.2.4 FINISH COMMAND (0) The last command line in the .fig file must be a finish command line. The finish command notifies the model that the end of the command lines in the watershed configuration file has been reached. Variables required on the finish command line are: Variable name
Definition
COMMAND
The command code = 0 for the finish command
The format of the finish command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
31.2.2.5 ROUTRES COMMAND (3) The routres command routes water, sediment, and chemical loadings through a reservoir. The routres command requires two lines. Variables required on the routres command lines are: Variable name
Definition
COMMAND
The command code = 3 for the routres command.
HYD_STOR
The hydrograph storage location number for results.
RES_NUM
Reservoir number. Each reservoir modeled in the watershed must be assigned a unique consecutive number beginning at 1.
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SWAT USER’S MANUAL, VERSION 2000
Variable name
Definition
HYD_NUM
Inflow hydrograph storage location number. The data that is to be routed through the reservoir.
RESFILE
Name of reservoir input file (.res)
LWQFILE
Name of reservoir water quality input file (optional) (.lwq)
The format of the routres command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
RES_NUM
1
space 23-28
6-digit integer
i6
HYD_NUM
1
space 29-34
6-digit integer
i6
RESFILE
2
space 11-23
character
a13
LWQFILE
2
space 24-36
character
a13
31.2.2.6 TRANSFER COMMAND (4) Originally, the transfer command was the only method available to irrigate an HRU. While the irrigation scenarios are now handled primarily in the management files, the transfer command was retained for flexibility. The transfer command moves water from any reach or reservoir to any other reach or reservoir. Variables required on the transfer command line are: Variable name
Definition
COMMAND
The command code = 4 for the transfer command.
DEP_TYPE
Water source type: 1 reach 2 reservoir
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
Variable name
Definition
DEP_NUM
Water source number. The number of the reach or reservoir from which the flow will be diverted.
DEST_TYPE
Destination type. Defines the receiving body. 1 reach 2 reservoir Destination number. Number of reach or reservoir receiving the water.
DEST_NUM
37
TRANS_AMT
The flow amount transferred. (defined by TRANS_CODE)
TRANS_CODE
The rule code governing the transfer of water: 1 A fraction of the flow or volume to be transferred out of the reach or reservoir is specified 2 A minimum flow (reach) or volume (reservoir) to leave in the reach or reservoir is specified (m3/day) 3 An exact amount of water to be transferred is specified (m3/day)
The format of the transfer command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
DEP_TYPE
space 17-22
6-digit integer
i6
DEP_NUM
space 23-28
6-digit integer
i6
DEST_TYPE
space 29-34
6-digit integer
i6
DEST_NUM
space 35-40
6-digit integer
i6
TRANS_AMT
space 41-46
decimal (xx.xxx)
f6.3
TRANS_CODE
space 47-55
9-digit integer
i9
31.2.2.7 STRUCTURE COMMAND (12) The structure command simulates aeration caused by the tumbling of water as it moves over weirs or other structures along the stream network. In highly polluted streams, the aeration of the stream by this method is a significant source of oxygen. The structure command alters the dissolved oxygen content based on the aeration coefficient input by the user. Variables required on the structure command line are:
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SWAT USER’S MANUAL, VERSION 2000
Variable name
Definition
COMMAND
The command code = 12 for the structur command.
HYD_STOR
The hydrograph storage location number for data.
HYD_NUM
Inflow hydrograph storage location number. The data that is to be adjusted to reflect aeration. (Dissolved oxygen content is the only value that is altered with this command.)
AERATION_COEF
Aeration coefficient.
The format of the structure command is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_STOR
space 17-22
6-digit integer
i6
HYD_NUM
space 23-28
6-digit integer
i6
AERATION_COEF
space 41-46
decimal (xx.xxx)
f6.3
31.2.2.8 RECMON COMMAND (7) The recmon command is one of four routing commands that reads in flow, sediment and chemical loading records from a file and routes the input through the watershed. The recmon command is used to read in data summarized by month. The recmon command requires two lines. Variables required on the recmon command lines are: Variable name
Definition
COMMAND
The command code = 7 for the recmon command.
HYD_STOR
The hydrograph storage location number for data.
FILEMON_NUM
The file number. Unique file numbers should be used for each recmon command.
DRAINAGE_AREA Drainage area associated with records (km2) (optional) FILE_MON
Name of the file containing the monthly records
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
39
The format of the recmon command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
FILEMON_NUM
1
space 23-28
6-digit integer
i6
DRAINAGE_AREA
1
space 41-46
decimal (xx.xxx)
f6.3
FILE_MON
2
space 11-23
character
a13
31.2.2.9 RECYEAR COMMAND (8) The recyear command is one of four routing commands that reads in flow, sediment and chemical loading records from a file and routes the input through the watershed. The recyear command is used to read in annual output. The recyear command requires two lines. Variables required on the recyear command lines are: Variable name
Definition
COMMAND
The command code = 8 for the recyear command.
HYD_STOR
The hydrograph storage location number for data.
FILEYR_NUM
The file number. Unique file numbers should be used for each recyear command.
DRAINAGE_AREA Drainage area associated with records (km2) (optional) FILE_YR
Name of file containing annual output.
The format of the recyear command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
FILEYR_NUM
1
space 23-28
6-digit integer
i6
DRAINAGE_AREA
1
space 41-46
decimal(xx.xxx)
f6.3
FILE_YR
2
space 11-23
character
a13
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SWAT USER’S MANUAL, VERSION 2000
31.2.2.10 RECDAY COMMAND (10) The recday command is one of four routing commands that reads in flow, sediment and chemical loading records from a file and routes the input through the watershed. This command is useful for reading in point source data or data from simulations of upstream areas. The recday command is used to read in data summarized on a daily basis. The recday command requires two lines. Variables required on the recday command lines are: Variable name
Definition
COMMAND
The command code = 10 for the recday command.
HYD_STOR
The hydrograph storage location number for data.
FILEDAY_NUM
The file number. Unique file numbers should be used for each recday command.
DRAINAGE_AREA Drainage area associated with records (km2) (optional) FILE_DAY
Name of file containing daily records.
The format of the recday command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
FILEDAY_NUM
1
space 23-28
6-digit integer
i6
DRAINAGE_AREA
1
space 41-46
decimal (xx.xxx)
f6.3
FILE_DAY
2
space 11-23
character
a13
31.2.2.11 RECCNST COMMAND (11) The reccnst command is one of four routing commands that reads in flow, sediment and chemical loading records from a file and routes the input through the watershed. This command is useful for reading in point source data. The reccnst command is used to read in average annual data. The reccnst command requires two lines. Variables required on the reccnst command lines are:
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
Variable name
Definition
COMMAND
The command code = 11 for the reccnst command.
HYD_STOR
The hydrograph storage location number for data.
FILECNST_NUM
The file number. Unique file numbers should be used for each reccnst command.
41
DRAINAGE_AREA Drainage area associated with records (km2) (optional) FILE_CNST
Name of file containing average annual records.
The format of the reccnst command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_STOR
1
space 17-22
6-digit integer
i6
FILECNST_NUM
1
space 23-28
6-digit integer
i6
DRAINAGE_AREA
1
space 41-46
decimal(xx.xxx)
f6.3
FILE_CNST
2
space 11-23
character
a13
31.2.2.12 SAVE COMMAND (9) The save command allows the user to print daily SWAT output to the event output file specified in file.cio. This output file can then be read into another SWAT run using the recday command. Variables required on the save command line are: Variable name
Definition
COMMAND
The command code = 9 for save command.
HYD_NUM
The hydrograph storage location number of the data to be printed to file.
The format of the save command line is: Variable name
Position
Format
F90 Format
COMMAND
space 11-16
6-digit integer
i6
HYD_NUM
space 17-22
6-digit integer
i6
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SWAT USER’S MANUAL, VERSION 2000
31.2.2.13 SAVECONC COMMAND (14) The saveconc command saves flow, sediment and water quality indicator information from a specified point on the reach network to a file. The water quality information is reported as concentrations. This command is useful for isolating reach information at a particular point on the channel network. Up to 50 saveconc commands can be specified in the watershed configuration file. The saveconc command requires two lines. Variables required on the saveconc command lines are: Variable name
Definition
COMMAND
The command code = 14 for the saveconc command.
HYD_NUM
The hydrograph storage location number of the data to be printed to file.
FILECONC_NUM
The file number. Unique file numbers should be used for each saveconc command.
PRINT_FREQ
Printing frequency. For simulations using a sub-daily time step, water quality information may be summarized and printed for every hour or every day. Simulations using a daily time step will always print daily average values. 0 1
report daily averages report hourly averages (currently not operational)
If no printing frequency is specified, the model will print daily averages. FILE_CONC
Name of file to which the water quality information is written.
The format of the saveconc command lines is: Variable name
Line #
Position
Format
F90 Format
COMMAND
1
space 11-16
6-digit integer
i6
HYD_NUM
1
space 17-22
6-digit integer
i6
FILECONC_NUM
1
space 23-28
6-digit integer
i6
PRINT_FREQ
1
space 29-34
6-digit integer
i6
FILE_CONC
2
space 11-23
character
a13
CHAPTER 31: SWAT INPUT—WATERSHED CONFIGURATION
43
31.3 REFERENCES Arnold, J.G., J.R. Williams, A.D. Nicks, and N.B. Sammons. 1990. SWRRB, a basin scale simulation model for soil and water resources management. Texas A&M University Press, College Station, TX. Beasley, D.B., L.F. Huggins, and E.J. Monke. 1980. ANSWERS: A model for watershed planning. Trans. of the ASAE 23(4): 938-944. Foster, G.R. 1987. User requirements: USDA-Water erosion prediction project. Lane, L.J. and M.A. Nearing (ed.). 1989. USDA-Water erosion prediction project: hillslope profile model documentation. NSERL Report No. 2. National Soil Erosion Research Laboratory. USDA-Agricultural Research Service. W. Lafayette, IN. Williams, J.R. J.G. Arnold, R. Srinivasan, and T.S. Ramanarayanan. 1998. Chapter 33. APEX: a new tool for predicting the effects of climate and CO2 changes on erosion and water quality. p. 441-449. In J. Boardman and D. Favis-Mortlock (ed.) Modeling soil erosion by water. Springer-Verlag, Berlin. Williams, J.R. and R.W. Hann. 1973. HYMO: Problem oriented computer language for hydrologic modeling. USDA ARS-S-9. 76 pp. Young, R.A. et al. 1987. AGNPS, Agricultural non-point source pollution model: a watershed analysis tool. USDA Agricultural Research Service.
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SWAT USER’S MANUAL, VERSION 2000
CHAPTER 32
SWAT INPUT DATA: SIMULATION MANAGEMENT
Two different files contain information used by SWAT to govern the processing of input data and the formatting and type of output data produced by the simulation. These files also include parameters that identify any specialized processes to be simulated. They are the control input/output file (file.cio) and the input control code file (.cod).
45
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SWAT USER'S MANUAL, VERSION 2000
32.1 CONTROL INPUT/OUTPUT FILE (FILE.CIO) File management is performed with the control input/output file (file.cio). The control input/output file contains the name of files associated with the subbasins, database files, climate files and watershed-level input files accessed by SWAT during a simulation as well as the name of the files in which output will be stored. While the user may adopt any file naming scheme, we recommend that the file extensions listed in the manual are used to facilitate identification of the different file types. Files required by the model that are not listed in the control input/output file include HRU files listed in the subbasin general input (.sub) file, reservoir and point source input files listed in the watershed configuration (.fig) file and "unique" files which contain input for uncommon or specialized processes not typically simulated. The control input/output file can be divided into a number of different sections. Figure 32.1 illustrates the different groupings of files within file.cio.
Figure 32.1: General organization of control input/output files
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
47
Following is a brief description of the variables in the control input/output file. They are listed in the order they appear within the file.
32.1.1 TITLE SECTION (FILE.CIO) Variable name
Definition
TITLE
The first three lines of ‘file.cio’ are reserved for a description of the simulation run. The description may take up to 80 spaces per line. The title given in file.cio is printed to every output file. (optional)
32.1.2 GENERAL INPUT/OUTPUT SECTION (FILE.CIO)
Variable name
Definition
BIGSUB
Name of subbasin output file (.bsb). Loadings to the reach (water, sediment and nutrients) are summarized for each subbasin.
SBSOUT
Name of hydrologic response unit (HRU) output file (.sbs). Loadings to the reach (water, sediment and nutrients) and crop growth are summarized for each HRU.
RCHOUT
Name of main channel, or reach, output file (.rch). Summarizes the amount of water, sediment and pollutants entering and leaving the reach and provides data on in-steam processes (e.g. sediment deposition, evaporation occurring in the channel)
RSVOUT
Name of reservoir output file (.rsv). Summarizes the amount of water, sediment and pollutants entering and leaving the reservoir and quantifies processes occurring in the reservoir (e.g. sediment deposition).
WTROUT
Name of HRU impoundment output file (.wtr). Summarizes the amount of water, sediment and pollutants entering and leaving ponds, wetlands and depressional areas (potholes) and quantifies processes occurring in the different impoundments.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PESTOUT
Name of pesticide output file (.pso). The movement and transformation of the different pesticides used in the simulation are summarized for each HRU.
EVENT
Name of event output file (.eve). When very large basins are being simulated, it is often easier to split them into several SWAT runs. This file writes daily output which can be read into a different SWAT run.
ROUTIN
Name of watershed configuration file (.fig). Contains the commands to add and route flows through the watershed.
CODEDAT
Name of input control code file (.cod). The input control code file summarizes information that affects the operation of SWAT (e.g. print codes, weather generator codes, number of years being simulated, number of subbasins being simulated)
BASNDAT
Name of basin input file (.bsn). Contains inputs for processes modeled at the watershed level.
WATQAL
Name of watershed water quality input file (.wwq)
CROPDB
Name of land cover/plant growth database file (crop.dat). This file contains growth parameters for the different land covers.
TILLDAT
Name of tillage database file (till.dat). This file contains mixing efficiencies for different tillage implements.
PESTIDAT
Name of pesticide database file (pest.dat). This file contains parameters governing movement and degradation of pesticides.
FERTDAT
Name of fertilizer/manure database file (fert.dat). This file contains nutrient content data for fertilizers.
URBDAT
Name of urban land type database file (urban.dat). This file contains data required to model build-up/wash-off in urban areas.
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
49
32.1.3 CLIMATE INPUT SECTION (FILE.CIO) Variable name
Definition
NRGAGE
Number of precipitation gage (.pcp) files used in the simulation. Up to 18 files may be used.
NTGAGE
Number of temperature gage (.tmp) files used in the simulation. Up to 18 files may be used.
NRTOT
Total number of precipitation gage records used in the simulation. If each .pcp file contains only one precipitation gage record, NRTOT = NRGAGE. Otherwise, NRTOT > NRGAGE. A maximum of 5400 precipitation gage records may be used in a simulation.
NTTOT
Total number of temperature gage records used in the simulation. If each .tmp file contains only one temperature gage record, NTTOT = NTGAGE. Otherwise, NTTOT > NTGAGE. A maximum of 2700 temperature gage records may be used in a simulation.
NRGFIL
Number of precipitation gage records within each .pcp file. A maximum of 300 precipitation gage records may be placed in each .pcp file.
NTGFIL
Number of temperature gage records within each .tmp file. a maximum of 150 temperature gage records may be placed in each .tmp file
NSTOT
Number of solar radiation records within the .slr file. A maximum of 300 solar radiation records may be placed in the .slr file.
NHTOT
Number of relative humidity records within the .hmd file. A maximum of 300 relative humidity records may be placed in the .hmd file.
NWTOT
Number of wind speed records within the .wnd file. A maximum of 300 wind speed records may be placed in the .wnd file.
RFILE(1)
Name of measured precipitation input file #1 (.pcp).
RFILE(2)
Name of measured precipitation input file #2 (.pcp).
RFILE(3)
Name of measured precipitation input file #3 (.pcp).
RFILE(4)
Name of measured precipitation input file #4 (.pcp).
RFILE(5)
Name of measured precipitation input file #5 (.pcp).
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
RFILE(6)
Name of measured precipitation input file #6 (.pcp).
RFILE(7)
Name of measured precipitation input file #7 (.pcp).
RFILE(8)
Name of measured precipitation input file #8 (.pcp).
RFILE(9)
Name of measured precipitation input file #9 (.pcp).
RFILE(10)
Name of measured precipitation input file #10 (.pcp).
RFILE(11)
Name of measured precipitation input file #11 (.pcp).
RFILE(12)
Name of measured precipitation input file #12 (.pcp).
RFILE(13)
Name of measured precipitation input file #13 (.pcp).
RFILE(14)
Name of measured precipitation input file #14 (.pcp).
RFILE(15)
Name of measured precipitation input file #15 (.pcp).
RFILE(16)
Name of measured precipitation input file #16 (.pcp).
RFILE(17)
Name of measured precipitation input file #17 (.pcp).
RFILE(18)
Name of measured precipitation input file #18 (.pcp).
TFILE(1)
Name of measured temperature input file #1 (.tmp).
TFILE(2)
Name of measured temperature input file #2 (.tmp).
TFILE(3)
Name of measured temperature input file #3 (.tmp).
TFILE(4)
Name of measured temperature input file #4 (.tmp).
TFILE(5)
Name of measured temperature input file #5 (.tmp).
TFILE(6)
Name of measured temperature input file #6 (.tmp).
TFILE(7)
Name of measured temperature input file #7 (.tmp).
TFILE(8)
Name of measured temperature input file #8 (.tmp).
TFILE(9)
Name of measured temperature input file #9 (.tmp).
TFILE(10)
Name of measured temperature input file #10 (.tmp).
TFILE(11)
Name of measured temperature input file #11 (.tmp).
TFILE(12)
Name of measured temperature input file #12 (.tmp).
TFILE(13)
Name of measured temperature input file #13 (.tmp).
TFILE(14)
Name of measured temperature input file #14 (.tmp).
TFILE(15)
Name of measured temperature input file #15 (.tmp).
TFILE(16)
Name of measured temperature input file #16 (.tmp).
TFILE(17)
Name of measured temperature input file #17 (.tmp).
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
Definition
TFILE(18)
Name of measured temperature input file #18 (.tmp).
SLRFILE
Name of measured solar radiation input file (.slr).
RHFILE
Name of measured relative humidity input file (.hmd).
WNDFILE
Name of measured wind speed input file (.wnd).
PETFILE
Name of potential evapotranspiration input file (.pet).
51
32.1.4 SUBBASIN INPUT SECTION (FILE.CIO) Variable name
Definition
ISB
Subbasin number. This number is present to assist the user— the model ignores this number. Input files for the different subbasins must be listed consecutively in the subbasin input section.
SUBDAT
Name of subbasin general input data file (.sub).
RTEDAT
Name of subbasin routing input data file (.rte). This file contains parameters for the main channel.
PNDDAT
Name of subbasin pond input data file (.pnd).
WUSDAT
Name of subbasin water use management data file (.wus).
WGNDAT
Name of subbasin weather generator data file (.wgn).
SWQDAT
Name of subbasin stream water quality data file (.swq).
IRGAGE
Number of the measured precipitation record used within subbasin. Optional.
ITGAGE
Number of the measured temperature record used within the subbasin. Optional.
ISGAGE
Number of the solar radiation record used within the subbasin. Optional.
IHGAGE
Number of the relative humidity record used within the subbasin. Optional.
IWGAGE
Number of the wind speed record used within the subbasin. Optional.
52
SWAT USER'S MANUAL, VERSION 2000
32.1.5 FILE FORMAT (FILE.CIO) Variable name
Position
Format
F90 Format
1-3
space 1-80
character
a80
BIGSUB
4
space 1-13
character
a13
SBSOUT
4
space 14-26
character
a13
RCHOUT
4
space 27-39
character
a13
RSVOUT
4
space 40-52
character
a13
empty location
4
space 53-65
character
a13
WTROUT
4
space 66-78
character
a13
PESTOUT
5
space 1-13
character
a13
EVENT
5
space 14-26
character
a13
ROUTIN
5
space 27-39
character
a13
CODEDAT
5
space 40-52
character
a13
BASNDAT
5
space 53-65
character
a13
WATQAL
5
space 66-78
character
a13
CROPDB
6
space 1-13
character
a13
TILLDAT
6
space 14-26
character
a13
PESTIDAT
6
space 27-39
character
a13
FERTDAT
6
space 40-52
character
a13
URBDAT
6
space 53-65
character
a13
NRGAGE
7
space 1-4
integer
i4
NTGAGE
7
space 5-8
integer
i4
NRTOT
7
space 9-12
integer
i4
NTTOT
7
space 13-16
integer
i4
NRGFIL
7
space 17-20
integer
i4
NTGFIL
7
space 21-24
integer
i4
NSTOT
7
space 25-28
integer
i4
NHTOT
7
space 29-32
integer
i4
NWTOT
7
space 33-36
integer
i4
RFILE(1)
8
space 1-13
character
a13
RFILE(2)
8
space 14-26
character
a13
RFILE(3)
8
space 27-39
character
a13
RFILE(4)
8
space 40-52
character
a13
RFILE(5)
8
space 53-65
character
a13
TITLE
Line #
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT Variable name
Line #
Position
Format
F90 Format
RFILE(6)
8
space 66-78
character
a13
RFILE(7)
9
space 1-13
character
a13
RFILE(8)
9
space 14-26
character
a13
RFILE(9)
9
space 27-39
character
a13
RFILE(10)
9
space 40-52
character
a13
RFILE(11)
9
space 53-65
character
a13
RFILE(12)
9
space 66-78
character
a13
RFILE(13)
10
space 1-13
character
a13
RFILE(14)
10
space 14-26
character
a13
RFILE(15)
10
space 27-39
character
a13
RFILE(16)
10
space 40-52
character
a13
RFILE(17)
10
space 53-65
character
a13
RFILE(18)
10
space 66-78
character
a13
TFILE(1)
11
space 1-13
character
a13
TFILE(2)
11
space 14-26
character
a13
TFILE(3)
11
space 27-39
character
a13
TFILE(4)
11
space 40-52
character
a13
TFILE(5)
11
space 53-65
character
a13
TFILE(6)
11
space 66-78
character
a13
TFILE(7)
12
space 1-13
character
a13
TFILE(8)
12
space 14-26
character
a13
TFILE(9)
12
space 27-39
character
a13
TFILE(10)
12
space 40-52
character
a13
TFILE(11)
12
space 53-65
character
a13
TFILE(12)
12
space 66-78
character
a13
TFILE(13)
13
space 1-13
character
a13
TFILE(14)
13
space 14-26
character
a13
TFILE(15)
13
space 27-39
character
a13
TFILE(16)
13
space 40-52
character
a13
TFILE(17)
13
space 53-65
character
a13
TFILE(18)
13
space 66-78
character
a13
SLRFILE
14
space 1-13
character
a13
RHFILE
14
space 14-26
character
a13
WNDFILE
14
space 27-39
character
a13
PETFILE
14
space 40-52
character
a13
53
54
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
The remaining lines provide input data for all of the subbasins. Two lines are devoted to each subbasin's input data. In the equation for the line number, i is the subbasin number. ISB
13 + 2i
space 1-5
integer
i5
SUBDAT
13 + 2i
space 7-19
character
a13
RTEDAT
13 + 2i
space 21-33
character
a13
PNDDAT
13 + 2i
space 35-47
character
a13
WUSDAT
13 + 2i
space 49-61
character
a13
WGNDAT
14 + 2i
space 7-19
character
a13
SWQDAT
14 + 2i
space 21-33
character
a13
IRGAGE
14 + 2i
space 49-52
integer
i4
ITGAGE
14 + 2i
space 53-56
integer
i4
ISGAGE
14 + 2i
space 57-60
integer
i4
IHGAGE
14 + 2i
space 61-64
integer
i4
IWGAGE
14 + 2i
space 65-68
integer
i4
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
55
32.2 INPUT CONTROL CODE FILE (.COD) The input control code file regulates the general operation of SWAT. In addition to setting output file formats, the input control code file defines which processes are/are not modeled in the SWAT simulation. Following is a brief description of the variables in the input control file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .cod file is reserved for user comments. The comments may take up to 80 spaces. (optional)
NBYR
Number of calendar years simulated. The number of years simulated in a SWAT run can vary from 1 to 9,999 years. If a simulation is begun on August 1st of the year 1995 and ends July 30th of the year 1997, the model will be simulating 3 calendar years (1995, 1996 and 1997).
IYR
Beginning year of simulation (for example, 1980). The value entered for this variable is not important unless measured data (e.g. weather) is used in the run. When measured data is used, the model uses this variable to locate the beginning year within the data file.
IDAF
Beginning julian day of simulation. With this variable, SWAT is able to begin a simulation at any time of the year. If the variable is left blank or set to zero, the model starts the simulation on January 1st.
IDAL
Ending julian day of simulation. With this variable, SWAT will end the simulation on the date specified. If the variable is left blank or set to zero, the model ends the simulation on December 31st.
56
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
IPD
Print code. This variable governs the frequency that model results are printed to output files. There are three options: 0 1 2
monthly daily annually
If you choose to print results on a daily basis, the number of years simulated should be limited and/or the variables printed to the output file should be restricted. If these precautions are not taken, the output files will be too large to view. NYSKIP
Number of years to not print output. The options are 0 print output for all years of the simulation 1 print output after the first year of simulation 2 print output after the second year of simulation $ nbyr no output will be printed If initial conditions are not well known, a warm-up or equilibration period may be needed to get the values of soil water, residue, etc. to representative amounts. NYSKIP defines the duration of the equilibration period. In addition to not writing data to the output files, annual averages are not computed for the skipped years. Averages for the entire simulation period will also exclude data from the skipped years. The default value for NYSKIP is 0.
IPRN
Print code for input.std file. There are two options: 0 1
ILOG
entire input.std file is printed condensed version of input.std file is printed
Streamflow print code. This variable allows the user to take the log10 of the flow prior to printing streamflow values to the .rch file. There are two options: 0 1
print streamflow in .rch file print log of streamflow in .rch file
In large basins (for example, the Mississippi River basin), streamflow values printed to the .rch file may exceed the range allowed by the file format statements. This variable will eliminate print errors caused by very large values.
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
Definition
IPRP
Print code for .pso file. There are two options: 0 1
IGN
57
do not print pesticide output (.pso file will be empty) print pesticide output
Random generator seed code. A set of random numbers is needed by SWAT to generate weather data. SWAT has a set of default random numbers embedded in the code. To use the default random numbers, the user should set IGN = 0. This is the default value for IGN. In some situations, a user may wish to vary the weather sequence between runs. This is done by setting IGN to a different number every time the model is run. This code will activate a random number generator, which will replace the default set of random numbers with a new set. The value to which IGN is set determines the number of times the random number generator is cycled before the simulation begins. The seeds produced by the random number generator are then utilized by the weather generator instead of the default values. Measured weather data read into the model is not affected by this variable. However, if the measured data contains missing values, the weather generator is activated to produce data to replace the missing values. The data produced to replace missing values will be affected by this variable.
PCPSIM
Rainfall input code. This variable identifies the method the model will use to process rainfall data. There are two options: 1 measured data read for each subbasin 2 rainfall generated for each subbasin
IDT
Time step used to report measured rainfall data (minutes). Required if IEVENT = 2 or 3. One of the following should be chosen: 1 min, 2 min, 3 min, 4 min, 5 min, 6 min, 10 min, 12 min, 15 min, 20 min, 30 min.
IDIST
Rainfall distribution code. There are two options: 0 1
skewed distribution mixed exponential distribution
58
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
REXP
Value of exponent for mixed exponential rainfall distribution. A value for REXP must be entered if IDIST = 1. The model will set REXP = 1.3 if no value is entered.
TMPSIM
Temperature input code. This variable identifies the method the model will use to process temperature data. There are two options: 1 2
SLRSIM
Solar radiation input code. This variable identifies the method the model will use to process solar radiation data. There are two options: 1 2
RHSIM
measured data read for each subbasin relative humidity generated for each subbasin
Wind speed input code. This variable identifies the method the model will use to process wind speed data. There are two options: 1 2
IPET
measured data read for each subbasin solar radiation generated for each subbasin
Relative humidity input code. This variable identifies the method the model will use to process relative humidity data. There are two options: 1 2
WNDSIM
measured date read for each subbasin daily max/min generated for each subbasin
measured data read for each subbasin wind speed generated for each subbasin
Potential evapotranspiration method. There are four options for potential ET calculations: 0 1 2 3
Priestley-Taylor method Penman/Monteith method Hargreaves method read in potential ET values
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
Definition
IEVENT
Rainfall/runoff/routing option:
59
0 1
daily rainfall/curve number runoff/daily routing daily rainfall/Green & Ampt runoff/daily routing (sub-hourly rainfall required for Green & Ampt is generated from daily) this option not yet operational 2 sub-hourly rainfall/Green & Ampt runoff/daily routing 3 sub-hourly rainfall/Green & Ampt runoff/hourly routing Option 0 was the only active option in prior versions of the model and is the default. ICRK
Crack flow code. There are two options: 0 1
do not model crack flow in soil model crack flow in soil
The default option is ICRK=0. IRTE
Channel water routing method: 0 1
variable storage method Muskingum method
The default option is IRTE=0. The Muskingum method is a new option available with SWAT2000. The user must be careful to define MSK_CO1, MSK_CO2 and MSK_X (in .bsn) when the Muskingum method is chosen. IDEG
Channel degradation code. There are two options: 0
1
channel dimensions are not updated as a result of degradation (the dimensions remain constant for the entire simulation) channel dimensions are updated as a result of degradation
Channel degradation refers to the downcutting and widening of the channel with time and is always calculated. The default option is IDEG=0 (the channel width and depth remain constant).
60
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
IRESQ
Lake water quality code. The variable identifies whether or not lake water quality is simulated in the reservoirs. There are two options: 0 1
do not model lake water quality model lake water quality
The default option is IRESQ=0. IWQ
In-stream water quality code. The variable identifies whether in-stream transformation of nutrients is allowed to occur. 0 1
do not model in-stream nutrient transformations model in-stream nutrient transformations
The default option is IWQ=0. ISPROJ
Special project flag. SWAT includes sections of code specific to particular projects. This variable flags the code used in the particular simulation. There are three options: 0 1 2
not a special project HUMUS project Missouri River climate change project
A user will set this variable to something other than zero only if the SWAT programmers have told him to do so.
For long runs, the output files can get so large that the user may have difficulty in opening the files to look at output. The user has the option of customizing the output printed to the output files. Lines of the .cod file are used to specify the variables to be printed to the reach output file (.rch), the subbasin output file (.bsb), and the HRU output file (.sbs). If these lines contain only zeros, the model will print all the output variables to the file.
Variable name IPDVAR(:)
Definition Output variables printed to the .rch file. (up to 20 variables may be chosen in customized output.) The codes for the output variables are: 1 2
FLOW_IN: Average daily streamflow into reach (m3/s) FLOW_OUT: Average daily streamflow out of reach (m3/s)
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
61
Definition continued from previous page: 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
EVAP: Average daily loss of water from reach by evaporation (m3/s) TLOSS: Average daily loss of water from reach by transmission (m3/s) SED_IN: Sediment transported with water into reach (metric tons) SED_OUT: Sediment transported with water out of reach (metric tons) SEDCONC: Concentration of sediment in reach (mg/L) ORGN_IN: Organic nitrogen transported with water into reach (kg N) ORGN_OUT: Organic nitrogen transported with water out of reach (kg N) ORGP_IN: Organic phosphorus transported with water into reach (kg P) ORGP_OUT: Organic phosphorus transported with water out of reach (kg P) NO3_IN: Nitrate transported with water into reach (kg N) NO3_OUT: Nitrate transported with water out of reach (kg N) NH4_IN: Ammonium transported with water into reach (kg N) NH4_OUT: Ammonium transported with water out of reach (kg N) NO2_IN: Nitrite transported with water into reach (kg N) NO2_OUT: Nitrite transported with water out of reach (kg N) MINP_IN: Mineral phosphorus transported with water into reach (kg P) MINP_OUT: Mineral phosphorus transported with water out of reach (kg P) CHLA_IN: Chlorophyll-a transported with water into reach (kg) CHLA_OUT: Chlorophyll-a transported with water out of reach (kg) CBOD_IN: Carbonaceous biochemical oxygen demand transported into reach (kg O2) CBOD_OUT: Carbonaceous biochemical oxygen demand transported out of reach (kg O2) DISOX_IN: Dissolved oxygen transported into reach (kg O2) DISOX_OUT: Dissolved oxygen transported out of reach (kg O2) SOLPST_IN: Soluble pesticide transported with water into reach (mg a.i.) SOLPST_OUT: Soluble pesticide transported with water out of reach (mg a.i.) SORPST_IN: Pesticide sorbed to sediment transported with water into reach (mg a.i.) SORPST_OUT: Pesticide sorbed to sediment transported with water out of reach (mg a.i.) REACTPST: Loss of pesticide from water by reaction (mg a.i.) VOLPST: Loss of pesticide from water by volatilization (mg a.i.) SETTLPST: Transfer of pesticide from water to river bed sediment by settling (mg a.i.) RESUSP_PST: Transfer of pesticide from river bed sediment to water by resuspension (mg a.i.) DIFFUSEPST: Transfer of pesticide from water to river bed sediment by diffusion (mg a.i.)
62
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition continued from previous page: 35 REACBEDPST: Loss of pesticide from river bed sediment by reaction (mg a.i.) 36 BURYPST: Loss of pesticide from river bed sediment by burial (mg a.i.) 37 BED_PST: Pesticide in river bed sediment (mg a.i.) 38 BACTP_OUT: Number of persistent bacteria transported out of reach 39 BACTLP_OUT: Number of less persistent bacteria transported out of reach 40 CMETAL#1: Conservative metal #1 transported out of reach (kg) 41 CMETAL#2: Conservative metal #2 transported out of reach (kg) 42 CMETAL#3: Conservative metal #3 transported out of reach (kg)
IPDVAB(:)
Output variables printed to the .bsb file (up to 15 variables may be chosen in customized output.) The codes for the output variables are: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
IPDVAS(:)
PRECIP: Average total precipitation on subbasin (mm H2O) SNOMELT: Snow melt (mm H2O) PET: Potential evapotranspiration (mm H2O) ET: Actual evapotranspiration (mm H2O) SW: Soil water content (mm H2O) PERC: Amount of water percolating out of root zone (mm H2O) SURQ: Surface runoff (mm H2O) GW_Q: Groundwater discharge into reach (mm H2O) WYLD: Net water yield to reach (mm H2O) SYLD: Sediment yield (metric tons/ha) ORGN: Organic N released into reach (kg/ha) ORGP: Organic P released into reach (kg/ha) NSURQ: Nitrate released into reach (kg/ha) SOLP: Soluble P released into reach (kg/ha) SEDP: Mineral P attached to sediment released into reach (kg/ha)
Output variables printed to the .sbs file (up to 20 variables may be chosen in customized output.) The codes for the output variables are: 1 2
PRECIP: Total precipitation on HRU (mm H2O) SNOFALL: Precipitation falling as snow, sleet, or freezing rain (mm H2O) 3 SNOMELT: Amount of snow or ice melting (mm H2O) 4 IRR: Amount of irrigation water applied to HRU (mm H2O) 5 PET: Potential evapotranspiration (mm H2O) 6 ET: Amount of water removed by evapotranspiration (mm H2O) 7 SW: Soil water content at end of time period (mm H2O) 8 PERC: Amount of water percolating out of the root zone (mm H2O) 9 GW_RCHG: Amount of water entering both aquifers (mm H2O) 10 DA_RCHG: Amount of water entering deep aquifer from root zone (mm H2O) 11 REVAP: Water in shallow aquifer returning to root zone (mm H2O)
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
Variable name
63
Definition continued from previous page: 12 SA_IRR: Amount of water removed from shallow aquifer for irrigation (mm H2O) 13 DA_IRR: Amount of water removed from deep aquifer for irrigation (mm H2O) 14 SA_ST: Amount of water in shallow groundwater storage at end of time period (mm H2O) 15 DA_ST: Amount of water in deep groundwater storage at end of time period (mm H2O) 16 SURQ: Surface runoff contribution to reach (mm H2O) 17 TLOSS: Amount of water removed from tributary channels by transmission (mm H2O) 18 LATQ: Lateral flow contribution to reach (mm H2O) 19 GW_Q: Groundwater discharge into reach (mm H2O) 20 WYLD: Net amount of water contributed by the HRU to the reach (mm H2O) 21 SYLD: Amount of sediment contributed by the HRU to the reach (metric tons/ha) 22 USLE: USLE soil loss (metric tons/ha) 23 N_APP: Amount of N fertilizer applied (kg N/ha) 24 P_APP: Amount of P fertilizer applied (kg P/ha) 25 NAUTO: Amount of N fertilizer applied automatically (kg N/ha) 26 PAUTO: Amount of P fertilizer applied automatically (kg P/ha) 27 NGRZ: Nitrogen applied to HRU in grazing operation during time step (kg N/ha) 28 PGRZ: Phosphorus applied to HRU in grazing operation during time step (kg P/ha) 29 NRAIN: Nitrate added in rainfall (kg N/ha) 30 NFIX: Amount of N fixed by legumes (kg N/ha) 31 F-MN: Transformation of N from fresh organic to mineral pool (kg N/ha) 32 A-MN: Transformation of N from active organic to mineral pool (kg N/ha) 33 A-SN: Transformation of N from active organic to stable organic pool (kg N/ha) 34 F-MP: Transformation of P from fresh organic to mineral pool (kg P/ha) 35 AO-LP: Transformation of P from organic to labile pool (kg P/ha) 36 L-AP: Transformation of P from labile to active mineral pool (kg P/ha) 37 A-SP: Transformation of P from active mineral to stable mineral pool (kg P/ha) 38 DNIT: Amount of N removed from soil by denitrification (kg N/ha) 39 NUP: Nitrogen uptake by plants (kg N/ha) 40 PUP: Phosphorus uptake by plants (kg P/ha) 41 ORGN: Organic N contributed by HRU to reach (kg N/ha) 42 ORGP: Organic P contributed by HRU to reach (kg P/ha) 43 SEDP: Mineral P attached to sediment contributed by HRU to reach (kg P/ha) 44 NSURQ: NO3 contributed by HRU in surface runoff to reach (kg N/ha)
64
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition continued from previous page: 45 NLATQ: NO3 contributed by HRU in lateral flow to reach (kg N/ha) 46 NO3L: NO3 leached below the soil profile (kg N/ha) 47 NO3GW: NO3 contributed by HRU in groundwater flow to reach (kg N/ha) 48 SOLP: Soluble phosphorus contributed by HRU in surface runoff to reach (kg P/ha) 49 P_GW: Soluble phosphorus contributed by HRU in groundwater flow to reach (kg P/ha) 50 W_STRS: Number of water stress days. 51 TMP_STRS: Number of temperature stress days 52 N_STRS: Number of nitrogen stress days. 53 P_STRS: Number of phosphorus stress days. 54 BIOM: Total plant biomass (metric tons/ha) 55 LAI: Leaf area index 56 YLD: Harvested yield (metric tons/ha) 57 BACTP: Number of persistent bacteria in surface runoff (count) 58 BACTLP: Number of less persistent bacteria in surface runoff (count)
The input control code file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
NBYR
2
integer
free
IYR
3
integer
free
IDAF
4
integer
free
IDAL
5
integer
free
IPD
6
integer
free
NYSKIP
7
integer
free
IPRN
8
integer
free
ILOG
9
integer
free
IPRP
10
integer
free
IGN
11
integer
free
PCPSIM
12
integer
free
IDT
13
integer
free
IDIST
14
integer
free
REXP
15
real
free
TMPSIM
16
integer
free
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT Variable name
Line #
Format
F90 Format
SLRSIM
17
integer
free
RHSIM
18
integer
free
WNDSIM
19
integer
free
IPET
20
integer
free
IEVENT
21
integer
free
ICRK
22
integer
free
IRTE
23
integer
free
IDEG
24
integer
free
IRESQ
25
integer
free
IWQ
26
integer
free
ISPROJ
27
integer
free
COMMENT LINE
28
character
a80
IPDVAR(1)
29
integer
free
IPDVAR(2)
29
integer
free
IPDVAR(3)
29
integer
free
IPDVAR(4)
29
integer
free
IPDVAR(5)
29
integer
free
IPDVAR(6)
29
integer
free
IPDVAR(7)
29
integer
free
IPDVAR(8)
29
integer
free
IPDVAR(9)
29
integer
free
IPDVAR(10)
29
integer
free
IPDVAR(11)
29
integer
free
IPDVAR(12)
29
integer
free
IPDVAR(13)
29
integer
free
IPDVAR(14)
29
integer
free
IPDVAR(15)
29
integer
free
IPDVAR(16)
29
integer
free
IPDVAR(17)
29
integer
free
IPDVAR(18)
29
integer
free
IPDVAR(19)
29
integer
free
IPDVAR(20)
29
integer
free
COMMENT LINE
30
character
a80
IPDVAB(1)
31
integer
free
IPDVAB(2)
31
integer
free
65
66
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
IPDVAB(3)
31
integer
free
IPDVAB(4)
31
integer
free
IPDVAB(5)
31
integer
free
IPDVAB(6)
31
integer
free
IPDVAB(7)
31
integer
free
IPDVAB(8)
31
integer
free
IPDVAB(9)
31
integer
free
IPDVAB(10)
31
integer
free
IPDVAB(11)
31
integer
free
IPDVAB(12)
31
integer
free
IPDVAB(13)
31
integer
free
IPDVAB(14)
31
integer
free
IPDVAB(15)
31
integer
free
COMMENT LINE
32
character
a80
IPDVAS(1)
33
integer
free
IPDVAS(2)
33
integer
free
IPDVAS(3)
33
integer
free
IPDVAS(4)
33
integer
free
IPDVAS(5)
33
integer
free
IPDVAS(6)
33
integer
free
IPDVAS(7)
33
integer
free
IPDVAS(8)
33
integer
free
IPDVAS(9)
33
integer
free
IPDVAS(10)
33
integer
free
IPDVAS(11)
33
integer
free
IPDVAS(12)
33
integer
free
IPDVAS(13)
33
integer
free
IPDVAS(14)
33
integer
free
IPDVAS(15)
33
integer
free
IPDVAS(16)
33
integer
free
IPDVAS(17)
33
integer
free
IPDVAS(18)
33
integer
free
IPDVAS(19)
33
integer
free
IPDVAS(20)
33
integer
free
CHAPTER 32:SWAT INPUT—SIMULATION MANAGEMENT
67
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 33
SWAT INPUT DATA: GENERAL WATERSHED ATTRIBUTES
General watershed attributes are defined in the basin input file. These attributes control physical processes at the watershed level. Other than the variable DA_KM, the area of the watershed, the variables in this file are calibration variables or variables normally modified only for advanced or research applications.
69
70
SWAT USER'S MANUAL, VERSION 2000
33.1 BASIN INPUT FILE (.BSN) Following is a brief description of the variables in the basin input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line is reserved for a description. The description may take up to 80 spaces. Optional.
DA_KM
Area of the watershed (km2)
DT
Variable not currently used.
SFTMP
Snowfall temperature (ºC). Mean air temperature at which precipitation is equally likely to be rain as snow/freezing rain. The snowfall temperature should be between –5 ºC and 5 ºC. A default recommended for this variable is SFTMP = 1.0. Optional.
SMTMP
Snow melt base temperature (ºC). Snow pack temperature above which snow melt will occur. The snow melt base temperature should be between –5 ºC and 5 ºC. A default recommended for this variable is SMTMP = 0.50. Optional.
SMFMX
Melt factor for snow on June 21 (mm H2O/ºC-day). If the watershed is in the Northern Hemisphere, SMFMX will be the maximum melt factor. If the watershed is in the Southern Hemisphere, SMFMX will be the minimum melt factor. SMFMX and SMFMN allow the rate of snow melt to vary through the year. The variables account for the impact of snow pack density on snow melt. If no value for SMFMX is entered, the model will set SMFMX = 4.5. Optional.
SMFMN
Melt factor for snow on December 21 (mm H2O/ºC-day). If the watershed is in the Northern Hemisphere, SMFMN will be the minimum melt factor. If the watershed is in the Southern Hemisphere, SMFMN will be the maximum melt factor. SMFMX and SMFMN allow the rate of snow melt to vary through the year. The variables account for the impact of snow pack density on snow melt. If no value for SMFMN is entered, the model will set SMFMN = 4.5. Optional.
CHAPTER 33: SWAT INPUT—GENERAL WATERSHED ATTRIBUTES
71
Variable name
Definition
TIMP
Snow pack temperature lag factor. This parameter controls the impact of the current day's air temperature on the snow pack temperature. TIMP can vary between 0.01 and 1.0. When TIMP=1.0 there is no lag, i.e. the snow pack temperature is the same as the current day's air temperature. As TIMP goes to zero, the snow pack's temperature will be less influenced by the current day's air temperature. If no value for TIMP is entered, the model will set TIMP = 1.0. Optional.
SNOCOVMX
Minimum snow water content that corresponds to 100% snow cover (mm H2O). If the snow water content is less than SNOCOVMX, then a certain percentage of ground cover will be bare. If no value for SNOCOVMX is entered, the model will set SNOCOVMX = 1.00. Optional.
SNO50COV
Fraction of snow volume represented by SNOCOVMX that corresponds to 50% snow cover. SWAT assumes a nonlinear relationship between snow water and snow cover. SNO50COV can vary between 0.01 and 0.99. If no value for SNO50COV is entered, the model will set SNO50COV = 0.50, i.e. 50% of SNOCOVMX. Optional.
RCN
Concentration of nitrogen in rainfall (mg N/L). If no value for RCN is entered, the model will set RCN = 1.0. Optional.
SURLAG
Surface runoff lag coefficient. This parameter is needed in subbasins where the time of concentration is greater than 1 day. SURLAG is used to create a "storage" for surface runoff to allow runoff to take longer than one day to reach the subbasin outlet. If no value for SURLAG is entered, the model will set SURLAG = 4.0. Optional.
APM
Peak rate adjustment factor for sediment routing in the subbasin (tributary channels). Sediment routing is a function of peak flow rate and mean daily flow. Because SWAT can not directly calculate the sub-daily hydrograph due to the use of precipitation summarized on a daily basis, this variable was incorporated to allow adjustment for the effect of the peak flow rate on sediment routing. This factor is used in the MUSLE equation and impacts the amount of erosion generated in the HRUs. If no value for APM is entered, the model will set APM=1.0. Optional.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PRF
Peak rate adjustment factor for sediment routing in the main channel. Sediment routing is a function of peak flow rate and mean daily flow. Because SWAT can not directly calculate the sub-daily hydrograph, this variable was incorporated to allow adjustment for the effect of the peak flow rate on sediment routing. This variable impacts channel degradation. If no value for PRF is entered, the model will set PRF = 1.0. Optional. Linear parameter for calculating the maximum amount of sediment that can be reentrained during channel sediment routing ( DEGr=SPCON × (Vc)SPEXP where DEGr is the amount of sediment reentrained and Vc is stream velocity in the channel). SPCON should be between 0.0001 and 0.01. If no value for SPCON is entered, the model will set SPCON = 0.0001. Optional.
SPCON
SPEXP
Exponent parameter for calculating sediment reentrained in channel sediment routing ( DEGr=SPCON × (Vc)SPEXP where DEGr is the amount of sediment reentrained and Vc is stream velocity in the channel). SPEXP should be between 1.0 and 1.5. If no value for SPEXP is entered, the model will set SPEXP = 1.0. Optional.
EVRCH
Reach evaporation adjustment factor. If no value for EVRCH is entered, the model will set EVRCH = 1.00. Optional
EVLAI
Leaf area index at which no evaporation occurs from water surface. EVLAI is used in HRUs where a plant is growing in a ponded environment (e.g. rice). Evaporation from the water surface is allowed until the leaf area of the plant reaches the value specified for EVLAI. EVLAI should be between 0.0 and 10.0. If no value for EVLAI is entered, the model will set EVLAI = 3.0. Optional.
FFCB
Initial soil water storage expressed as a fraction of field capacity water content. All soils in the watershed will be initialized to the same fraction. If FFCB is not set to a value, the model will calculate it as a function of average annual precipitation. FFCB should be between 0.0 and 1.0. The default method is to allow the model to calculate FFCB (set FFCB = 0.0). Optional.
CMN
Rate factor for humus mineralization of active organic nutrients (N and P). If no value for CMN is specified, the model will set CMN = 0.0003. Optional.
CHAPTER 33: SWAT INPUT—GENERAL WATERSHED ATTRIBUTES
73
Variable name
Definition
UBN
Nitrogen uptake distribution parameter. This parameter controls the amount of nitrogen removed from the different soil layers by the plant. In particular, this parameter controls the amount of nitrogen removed from the surface layer via plant uptake. While the relationship between UBN and nitrogen removed from the surface layer is affected by the depth of the soil profile, in general as UBN increases the amount of N removed from the surface layer relative to the amount removed from the entire profile increases. If no value for UBN is entered, the model will set UBN = 20.0. Optional.
UBP
Phosphorus uptake distribution parameter. This parameter controls plant uptake of phosphorus from the different soil horizons in the same way that UBN controls nitrogen uptake. If no value for UBP is entered, the model will set UBP = 20.0. Optional.
NPERCO
Nitrate percolation coefficient. NPERCO controls the amount of nitrate removed from the surface layer in runoff relative to the amount removed via percolation. The value of NPERCO can range from 0.01 to 1.0. As NPERCO → 0.0, the concentration of nitrate in the runoff approaches 0. As NPERCO → 1.0, surface runoff has the same concentration of nitrate as the percolate. If no value for NPERCO is entered, the model will set NPERCO = 0.20. Optional.
PPERCO
Phosphorus percolation coefficient (10 m3/Mg). The phosphorus percolation coefficient is the ratio of the solution phosphorus concentration in the surface 10 mm of soil to the concentration of phosphorus in percolate. The value of PPERCO can range from 10.0 to 17.5 If no value for PPERCO is entered, the model will set PPERCO = 10.0. Optional.
PHOSKD
Phosphorus soil partitioning coefficient (m3/Mg). The phosphorus soil partitioning coefficient is the ratio of the soluble phosphorus concentration in the surface 10 mm of soil to the concentration of soluble phosphorus in surface runoff. If no value for PHOSKD is entered, the model will set PHOSKD = 175.0. Optional.
PSP
Phosphorus availability index. The fraction of mineral phosphorus remaining in solution after initial rapid sorption to soil. If no value for PSP is entered, the model will set PSP = 0.40. Optional.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
RSDCO
Residue decomposition coefficient. The fraction of residue which will decompose in a day assuming optimal moisture, temperature, C:N ratio and C:P ratio. If no value for RSDCO is entered, the model will set RSDCO = 0.05. Optional.
PERCOP
Pesticide percolation coefficient. PERCOP controls the amount of pesticide removed from the surface layer in runoff relative to the amount removed via percolation. The value of PERCOP can range from 0.01 to 1.0. As PERCOP → 0.0, the concentration of pesticide in the runoff approaches 0. As PERCOP → 1.0, surface runoff has the same concentration of pesticide as the percolate. If no value for PERCOP is entered, the model will set PERCOP = 0.50. Optional.
IRTPEST
Number of pesticide to be routed through the watershed channel network. This is the pesticide ID number from the pesticide database. While more than one type of pesticide may be applied to the HRUs, the model will monitor the movement of only one pesticide through the channel network. Optional.
WDPQ
Die-off factor for persistent bacteria in soil solution. (1/day) Optional.
WGPQ
Growth factor for persistent bacteria in soil solution. (1/day) Optional.
WDLPQ
Die-off factor for less persistent bacteria in soil solution. (1/day) Optional.
WGLPQ
Growth factor for less persistent bacteria in soil solution. (1/day) Optional.
WDPS
Die-off factor for persistent bacteria adsorbed to soil particles. (1/day) Optional.
WGPS
Growth factor for persistent bacteria adsorbed to soil particles. (1/day) Optional.
WDLPS
Die-off factor for less persistent bacteria adsorbed to soil particles. (1/day) Optional.
WGLPS
Growth factor for less persistent bacteria adsorbed to soil particles. (1/day) Optional.
BACTKDQ
Bacteria partition coefficient. Partition coefficient for bacteria between soluble and sorbed phase in surface runoff. If no value for BACTKDQ is entered, the model will set BACTKDQ = 175.0. Optional.
CHAPTER 33: SWAT INPUT—GENERAL WATERSHED ATTRIBUTES
75
Variable name
Definition
THBACT
Temperature adjustment factor for bacteria die-off/growth. If no value for THBACT is entered, the model will set THBACT = 1.07. Optional.
MSK_CO1
Calibration coefficient used to control impact of the storage time constant (Km) for normal flow (where normal flow is when river is at bankfull depth) upon the Km value calculated for the reach. Required only if IRTE = 1 in .cod file.
MSK_CO2
Calibration coefficient used to control impact of the storage time constant (Km) for low flow (where low flow is when river is at 0.1 bankfull depth) upon the Km value calculated for the reach. Required only if IRTE = 1 in .cod file.
MSK_X
Weighting factor controlling relative importance of inflow rate and outflow rate in determining water storage in reach segment. The values for MSK_X can range from 0.01 - 0.30. If no value for MSK_X is entered, the model will set MSK_X = 0.2. Required only if IRTE = 1 in .cod file.
ESCO
Soil evaporation compensation factor. This factor adjusts the depth distribution for evaporation from the soil to account for the effect of capillary action, crusting and cracks. ESCO must be between 0.01 and 1.0. If no value for ESCO is entered, the model will set ESCO = 0.95. The value for ESCO may be set at the watershed or HRU level (ESCO in .hru).
EPCO
Plant uptake compensation factor. This factor adjusts the depth distribution for plant uptake of water from the soil to account for the variation in root density with depth. EPCO must be between 0.01 and 1.0. If no value for EPCO is entered, the model will set EPCO = 1.0. The value for EPCO may be set at the watershed or HRU level (EPCO in .hru).
The basin input file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
DA_KM
2
real
free
DT
3
real
free
SFTMP
4
real
free
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
SMTMP
5
real
free
SMFMX
6
real
free
SMFMN
7
real
free
TIMP
8
real
free
SNOCOVMX
9
real
free
SNO50COV
10
real
free
RCN
11
real
free
SURLAG
12
real
free
APM
13
real
free
PRF
14
real
free
SPCON
15
real
free
SPEXP
16
real
free
BLANK LINE
17
real
free
BLANK LINE
18
real
free
BLANK LINE
19
real
free
BLANK LINE
20
real
free
BLANK LINE
21
real
free
EVRCH
22
real
free
EVLAI
23
real
free
FFCB
24
real
free
CMN
25
real
free
UBN
26
real
free
UBP
27
real
free
NPERCO
28
real
free
PPERCO
29
real
free
PHOSKD
30
real
free
PSP
31
real
free
RSDCO
32
real
free
PERCOP
33
real
free
IRTPEST
34
integer
free
WDPQ
35
real
free
WGPQ
36
real
free
WDLPQ
37
real
free
WGLPQ
38
real
free
WDPS
39
real
free
CHAPTER 33: SWAT INPUT—GENERAL WATERSHED ATTRIBUTES Variable name
Line #
Format
F90 Format
WGPS
40
real
free
WDLPS
41
real
free
WGLPS
42
real
free
BACTKDQ
43
real
free
THBACT
44
real
free
MSK_CO1
45
real
free
MSK_CO2
46
real
free
MSK_X
47
real
free
ESCO
48
real
free
EPCO
49
real
free
77
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 34
SWAT INPUT DATA: CLIMATE SWAT requires daily precipitation, maximum/minimum air temperature, solar radiation, wind speed and relative humidity. Values for all these parameters may be read from records of observed data or they may be generated. Seven types of files store the climatic data required by SWAT—the weather generator input file (.wgn), the measured precipitation file (.pcp), the measured temperature file (.tmp), the solar radiation input file (.slr), the wind speed input file (.wnd), the relative humidity input file (.hmd), and the potential evapotranspiration input file (.pet). Up to 18 precipitation files and 18 temperature files may be utilized in a simulation. The precipitation and temperature files are able to hold records for more than one gage, so there is not a limitation on the number of gages that can be used in a simulation. One solar radiation file, wind 77
78
SWAT USER'S MANUAL, VERSION 2000
speed file and relative humidity input file may be used in a simulation. These files are able to hold records for more than one gage, so there is not a limitation on the number of gages that can be used in a simulation. The potential evapotranspiration file holds only one record that is used for the entire watershed.
34.1 WEATHER GENERATOR INPUT FILE (.WGN) The weather generator input file contains the statistical data needed to generate representative daily climate data for the subbasins. Climatic data will be generated in two instances: when the user specifies that simulated weather will be used or when measured data is missing. Following is a brief description of the variables in the weather generator input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .wgn file is reserved for user comments. The comments may take up to 80 spaces. (optional)
WLATITUDE
Latitude of weather station used to create statistical parameters (degrees). The latitude is expressed as a real number with minutes and seconds converted to fractions of a degree. Optional.
WLONGITUDE
Longitude of weather station (degrees). This variable is not used by the model. Optional.
WELEV
Elevation of weather station (m). Optional.
RAIN_YRS
The number of years of maximum monthly 0.5 h rainfall data used to define values for RAIN_HHMX(1,:) RAIN_HHMX(12,:).
TMPMX(mon)
Average daily maximum air temperature for month (ºC).
TMPMN(mon)
Average daily minimum air temperature for month (ºC).
TMPSTDMX(mon)
Standard deviation for daily maximum air temperature in month (ºC).
CHAPTER 34: SWAT INPUT—CLIMATE
79
Variable name
Definition
TMPSTDMN(mon)
Standard deviation for daily minimum air temperature in month (ºC).
PCPMM(mon)
Average amount of precipitation falling in month (mm H2O).
PCPSTD(mon)
Standard deviation for daily precipitation in month (mm H2O/day ).
PCPSKW(mon)
Skew coefficient for daily precipitation in month.
PR_W(1,mon)
Probability of a wet day following a dry day in the month.
PR_W(2,mon)
Probability of a wet day following a wet day in the month.
PCPD(mon)
Average number of days of precipitation in month.
RAINHHMX(mon)
Maximum 0.5 hour rainfall in entire period of record for month (mm).
SOLARAV(mon)
Average daily solar radiation for month (MJ/m2/day).
DEWPT(mon)
Average daily dew point temperature in month (ºC).
WNDAV(mon)
Average daily wind speed in month (m/s).
The format of the weather generator input file is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
space 1-80
character
a80
WLATITUDE
2
space 13-19
decimal(xxxx.xx)
f7.2
WLONGITUDE
2
space 32-38
decimal(xxxx.xx)
f7.2
WELEV
3
space 13-19
decimal(xxxx.xx)
f7.2
RAIN_YRS
4
space 13-19
decimal(xxxx.xx)
f7.2
TMPMX(1)
5
space 1-6
decimal(xxx.xx)
f6.2
TMPMX(2)
5
space 7-12
decimal(xxx.xx)
f6.2
TMPMX(3)
5
space 13-18
decimal(xxx.xx)
f6.2
TMPMX(4)
5
space 19-24
decimal(xxx.xx)
f6.2
TMPMX(5)
5
space 25-30
decimal(xxx.xx)
f6.2
TMPMX(6)
5
space 31-36
decimal(xxx.xx)
f6.2
TMPMX(7)
5
space 37-42
decimal(xxx.xx)
f6.2
TMPMX(8)
5
space 43-48
decimal(xxx.xx)
f6.2
TMPMX(9)
5
space 49-54
decimal(xxx.xx)
f6.2
TMPMX(10)
5
space 55-60
decimal(xxx.xx)
f6.2
80
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
TMPMX(11)
5
space 61-66
decimal(xxx.xx)
f6.2
TMPMX(12)
5
space 67-72
decimal(xxx.xx)
f6.2
TMPMN(1)
6
space 1-6
decimal(xxx.xx)
f6.2
TMPMN(2)
6
space 7-12
decimal(xxx.xx)
f6.2
TMPMN(3)
6
space 13-18
decimal(xxx.xx)
f6.2
TMPMN(4)
6
space 19-24
decimal(xxx.xx)
f6.2
TMPMN(5)
6
space 25-30
decimal(xxx.xx)
f6.2
TMPMN(6)
6
space 31-36
decimal(xxx.xx)
f6.2
TMPMN(7)
6
space 37-42
decimal(xxx.xx)
f6.2
TMPMN(8)
6
space 43-48
decimal(xxx.xx)
f6.2
TMPMN(9)
6
space 49-54
decimal(xxx.xx)
f6.2
TMPMN(10)
6
space 55-60
decimal(xxx.xx)
f6.2
TMPMN(11)
6
space 61-66
decimal(xxx.xx)
f6.2
TMPMN(12)
6
space 67-72
decimal(xxx.xx)
f6.2
TMPSTDMX(1)
7
space 1-6
decimal(xxx.xx)
f6.2
TMPSTDMX(2)
7
space 7-12
decimal(xxx.xx)
f6.2
TMPSTDMX(3)
7
space 13-18
decimal(xxx.xx)
f6.2
TMPSTDMX(4)
7
space 19-24
decimal(xxx.xx)
f6.2
TMPSTDMX(5)
7
space 25-30
decimal(xxx.xx)
f6.2
TMPSTDMX(6)
7
space 31-36
decimal(xxx.xx)
f6.2
TMPSTDMX(7)
7
space 37-42
decimal(xxx.xx)
f6.2
TMPSTDMX(8)
7
space 43-48
decimal(xxx.xx)
f6.2
TMPSTDMX(9)
7
space 49-54
decimal(xxx.xx)
f6.2
TMPSTDMX(10)
7
space 55-60
decimal(xxx.xx)
f6.2
TMPSTDMX(11)
7
space 61-66
decimal(xxx.xx)
f6.2
TMPSTDMX(12)
7
space 67-72
decimal(xxx.xx)
f6.2
TMPSTDMN(1)
8
space 1-6
decimal(xxx.xx)
f6.2
TMPSTDMN(2)
8
space 7-12
decimal(xxx.xx)
f6.2
TMPSTDMN(3)
8
space 13-18
decimal(xxx.xx)
f6.2
TMPSTDMN(4)
8
space 19-24
decimal(xxx.xx)
f6.2
TMPSTDMN(5)
8
space 25-30
decimal(xxx.xx)
f6.2
TMPSTDMN(6)
8
space 31-36
decimal(xxx.xx)
f6.2
TMPSTDMN(7)
8
space 37-42
decimal(xxx.xx)
f6.2
TMPSTDMN(8)
8
space 43-48
decimal(xxx.xx)
f6.2
TMPSTDMN(9)
8
space 49-54
decimal(xxx.xx)
f6.2
CHAPTER 34: SWAT INPUT—CLIMATE Variable name
Line #
Position
Format
F90 Format
TMPSTDMN(10)
8
space 55-60
decimal(xxx.xx)
f6.2
TMPSTDMN(11)
8
space 61-66
decimal(xxx.xx)
f6.2
TMPSTDMN(12)
8
space 67-72
decimal(xxx.xx)
f6.2
PCPMM(1)
9
space 1-6
decimal(xxx.xx)
f6.2
PCPMM(2)
9
space 7-12
decimal(xxx.xx)
f6.2
PCPMM(3)
9
space 13-18
decimal(xxx.xx)
f6.2
PCPMM(4)
9
space 19-24
decimal(xxx.xx)
f6.2
PCPMM(5)
9
space 25-30
decimal(xxx.xx)
f6.2
PCPMM(6)
9
space 31-36
decimal(xxx.xx)
f6.2
PCPMM(7)
9
space 37-42
decimal(xxx.xx)
f6.2
PCPMM(8)
9
space 43-48
decimal(xxx.xx)
f6.2
PCPMM(9)
9
space 49-54
decimal(xxx.xx)
f6.2
PCPMM(10)
9
space 55-60
decimal(xxx.xx)
f6.2
PCPMM(11)
9
space 61-66
decimal(xxx.xx)
f6.2
PCPMM(12)
9
space 67-72
decimal(xxx.xx)
f6.2
PCPSTD(1)
10
space 1-6
decimal(xxx.xx)
f6.2
PCPSTD(2)
10
space 7-12
decimal(xxx.xx)
f6.2
PCPSTD(3)
10
space 13-18
decimal(xxx.xx)
f6.2
PCPSTD(4)
10
space 19-24
decimal(xxx.xx)
f6.2
PCPSTD(5)
10
space 25-30
decimal(xxx.xx)
f6.2
PCPSTD(6)
10
space 31-36
decimal(xxx.xx)
f6.2
PCPSTD(7)
10
space 37-42
decimal(xxx.xx)
f6.2
PCPSTD(8)
10
space 43-48
decimal(xxx.xx)
f6.2
PCPSTD(9)
10
space 49-54
decimal(xxx.xx)
f6.2
PCPSTD(10)
10
space 55-60
decimal(xxx.xx)
f6.2
PCPSTD(11)
10
space 61-66
decimal(xxx.xx)
f6.2
PCPSTD(12)
10
space 67-72
decimal(xxx.xx)
f6.2
PCPSKW(1)
11
space 1-6
decimal(xxx.xx)
f6.2
PCPSKW(2)
11
space 7-12
decimal(xxx.xx)
f6.2
PCPSKW(3)
11
space 13-18
decimal(xxx.xx)
f6.2
PCPSKW(4)
11
space 19-24
decimal(xxx.xx)
f6.2
PCPSKW(5)
11
space 25-30
decimal(xxx.xx)
f6.2
PCPSKW(6)
11
space 31-36
decimal(xxx.xx)
f6.2
PCPSKW(7)
11
space 37-42
decimal(xxx.xx)
f6.2
PCPSKW(8)
11
space 43-48
decimal(xxx.xx)
f6.2
81
82
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
PCPSKW(9)
11
space 49-54
decimal(xxx.xx)
f6.2
PCPSKW(10)
11
space 55-60
decimal(xxx.xx)
f6.2
PCPSKW(11)
11
space 61-66
decimal(xxx.xx)
f6.2
PCPSKW(12)
11
space 67-72
decimal(xxx.xx)
f6.2
PR_W(1,1)
12
space 1-6
decimal(xxx.xx)
f6.2
PR_W(1,2)
12
space 7-12
decimal(xxx.xx)
f6.2
PR_W(1,3)
12
space 13-18
decimal(xxx.xx)
f6.2
PR_W(1,4)
12
space 19-24
decimal(xxx.xx)
f6.2
PR_W(1,5)
12
space 25-30
decimal(xxx.xx)
f6.2
PR_W(1,6)
12
space 31-36
decimal(xxx.xx)
f6.2
PR_W(1,7)
12
space 37-42
decimal(xxx.xx)
f6.2
PR_W(1,8)
12
space 43-48
decimal(xxx.xx)
f6.2
PR_W(1,9)
12
space 49-54
decimal(xxx.xx)
f6.2
PR_W(1,10)
12
space 55-60
decimal(xxx.xx)
f6.2
PR_W(1,11)
12
space 61-66
decimal(xxx.xx)
f6.2
PR_W(1,12)
12
space 67-72
decimal(xxx.xx)
f6.2
PR_W(2,1)
13
space 1-6
decimal(xxx.xx)
f6.2
PR_W(2,2)
13
space 7-12
decimal(xxx.xx)
f6.2
PR_W(2,3)
13
space 13-18
decimal(xxx.xx)
f6.2
PR_W(2,4)
13
space 19-24
decimal(xxx.xx)
f6.2
PR_W(2,5)
13
space 25-30
decimal(xxx.xx)
f6.2
PR_W(2,6)
13
space 31-36
decimal(xxx.xx)
f6.2
PR_W(2,7)
13
space 37-42
decimal(xxx.xx)
f6.2
PR_W(2,8)
13
space 43-48
decimal(xxx.xx)
f6.2
PR_W(2,9)
13
space 49-54
decimal(xxx.xx)
f6.2
PR_W(2,10)
13
space 55-60
decimal(xxx.xx)
f6.2
PR_W(2,11)
13
space 61-66
decimal(xxx.xx)
f6.2
PR_W(2,12)
13
space 67-72
decimal(xxx.xx)
f6.2
PCPD(1)
14
space 1-6
decimal(xxx.xx)
f6.2
PCPD(2)
14
space 7-12
decimal(xxx.xx)
f6.2
PCPD(3)
14
space 13-18
decimal(xxx.xx)
f6.2
PCPD(4)
14
space 19-24
decimal(xxx.xx)
f6.2
PCPD(5)
14
space 25-30
decimal(xxx.xx)
f6.2
PCPD(6)
14
space 31-36
decimal(xxx.xx)
f6.2
PCPD(7)
14
space 37-42
decimal(xxx.xx)
f6.2
CHAPTER 34: SWAT INPUT—CLIMATE Variable name
Line #
Position
Format
F90 Format
PCPD(8)
14
space 43-48
decimal(xxx.xx)
f6.2
PCPD(9)
14
space 49-54
decimal(xxx.xx)
f6.2
PCPD(10)
14
space 55-60
decimal(xxx.xx)
f6.2
PCPD(11)
14
space 61-66
decimal(xxx.xx)
f6.2
PCPD(12)
14
space 67-72
decimal(xxx.xx)
f6.2
RAINHHMX(1)
15
space 1-6
decimal(xxx.xx)
f6.2
RAINHHMX(2)
15
space 7-12
decimal(xxx.xx)
f6.2
RAINHHMX(3)
15
space 13-18
decimal(xxx.xx)
f6.2
RAINHHMX(4)
15
space 19-24
decimal(xxx.xx)
f6.2
RAINHHMX(5)
15
space 25-30
decimal(xxx.xx)
f6.2
RAINHHMX(6)
15
space 31-36
decimal(xxx.xx)
f6.2
RAINHHMX(7)
15
space 37-42
decimal(xxx.xx)
f6.2
RAINHHMX(8)
15
space 43-48
decimal(xxx.xx)
f6.2
RAINHHMX(9)
15
space 49-54
decimal(xxx.xx)
f6.2
RAINHHMX(10)
15
space 55-60
decimal(xxx.xx)
f6.2
RAINHHMX(11)
15
space 61-66
decimal(xxx.xx)
f6.2
RAINHHMX(12)
15
space 67-72
decimal(xxx.xx)
f6.2
SOLARAV(1)
16
space 1-6
decimal(xxx.xx)
f6.2
SOLARAV(2)
16
space 7-12
decimal(xxx.xx)
f6.2
SOLARAV(3)
16
space 13-18
decimal(xxx.xx)
f6.2
SOLARAV(4)
16
space 19-24
decimal(xxx.xx)
f6.2
SOLARAV(5)
16
space 25-30
decimal(xxx.xx)
f6.2
SOLARAV(6)
16
space 31-36
decimal(xxx.xx)
f6.2
SOLARAV(7)
16
space 37-42
decimal(xxx.xx)
f6.2
SOLARAV(8)
16
space 43-48
decimal(xxx.xx)
f6.2
SOLARAV(9)
16
space 49-54
decimal(xxx.xx)
f6.2
SOLARAV(10)
16
space 55-60
decimal(xxx.xx)
f6.2
SOLARAV(11)
16
space 61-66
decimal(xxx.xx)
f6.2
SOLARAV(12)
16
space 67-72
decimal(xxx.xx)
f6.2
DEWPT(1)
17
space 1-6
decimal(xxx.xx)
f6.2
DEWPT(2)
17
space 7-12
decimal(xxx.xx)
f6.2
DEWPT(3)
17
space 13-18
decimal(xxx.xx)
f6.2
DEWPT(4)
17
space 19-24
decimal(xxx.xx)
f6.2
DEWPT(5)
17
space 25-30
decimal(xxx.xx)
f6.2
DEWPT(6)
17
space 31-36
decimal(xxx.xx)
f6.2
83
84
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
DEWPT(7)
17
space 37-42
decimal(xxx.xx)
f6.2
DEWPT(8)
17
space 43-48
decimal(xxx.xx)
f6.2
DEWPT(9)
17
space 49-54
decimal(xxx.xx)
f6.2
DEWPT(10)
17
space 55-60
decimal(xxx.xx)
f6.2
DEWPT(11)
17
space 61-66
decimal(xxx.xx)
f6.2
DEWPT(12)
17
space 67-72
decimal(xxx.xx)
f6.2
WNDAV(1)
18
space 1-6
decimal(xxx.xx)
f6.2
WNDAV(2)
18
space 7-12
decimal(xxx.xx)
f6.2
WNDAV(3)
18
space 13-18
decimal(xxx.xx)
f6.2
WNDAV(4)
18
space 19-24
decimal(xxx.xx)
f6.2
WNDAV(5)
18
space 25-30
decimal(xxx.xx)
f6.2
WNDAV(6)
18
space 31-36
decimal(xxx.xx)
f6.2
WNDAV(7)
18
space 37-42
decimal(xxx.xx)
f6.2
WNDAV(8)
18
space 43-48
decimal(xxx.xx)
f6.2
WNDAV(9)
18
space 49-54
decimal(xxx.xx)
f6.2
WNDAV(10)
18
space 55-60
decimal(xxx.xx)
f6.2
WNDAV(11)
18
space 61-66
decimal(xxx.xx)
f6.2
WNDAV(12)
18
space 67-72
decimal(xxx.xx)
f6.2
34.2 PRECIPITATION INPUT FILE (.PCP) Measured precipitation data is read into the model from the .pcp file. The precipitation data may be read into the model in daily or sub-daily time increments. The following sections describe the format for a daily and a subdaily precipitation file.
34.2.1 DAILY PRECIPITATION DATA Daily precipitation data is used when the SCS curve number method is chosen to model surface runoff (Set by IEVENT in the .cod file). While the input file must contain data for the entire period of simulation, the record does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the file, saving editing time on the user's part. Once SWAT locates the record for the beginning day of simulation, it no longer
CHAPTER 34: SWAT INPUT—CLIMATE
85
processes the year and date. Because it does not check the subsequent dates, it is very important that the data for the remaining days in the simulation are listed sequentially. (If no year and date are entered for any of the records, the model assumes the first line data corresponds to the first day of simulation.) A negative 99.0 (-99.0) should be inserted for missing data. This value tells SWAT to generate precipitation for that day. Following is a brief description of the variables in the precipitation input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the precipitation file is reserved for comments.
LATITUDE
Latitude of precipitation recording gage location. This value is not used by the model.
LONGITUDE
Longitude of precipitation recording gage location. This value is not used by the model.
ELEVATION
Elevation of precipitation recording gage station (m). Precipitation values are adjusted for elevation in subbasins where elevation bands are defined.
YEAR
Year (4-digit)
DATE
Julian date
PRECIPITATION
Amount of precipitation falling in the time period (mm)
The format of the daily precipitation file with one record is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
unrestricted
character
unrestricted
LATITUDE
2
space 8-12
free
unrestricted
LONGITUDE
3
space 8-12
free
unrestricted
ELEVATION
4
space 8-12
integer
i5
YEAR
5-END
space 1-4
integer
i4
DATE
5-END
space 5-7
integer
i3
PRECIPITATION
5-END
space 8-12
decimal(xxx.x)
f5.1
86
SWAT USER'S MANUAL, VERSION 2000
To place more than one data record within the .pcp file, repeat the original formatting for the recorded data to the right of the existing data. Simulations have been run with 200 records placed in the precipitation files. For example, assume there are records for six different rain gages stored in the daily .pcp. The formatting of the .pcp file is Gage
Variable name
ALL
TITLE
1
Line #
Position
Format
F90 Format
1
unrestricted
character
unrestricted
LATITUDE
2
space 8-12
free
unrestricted
2
LATITUDE
2
space 13-17
free
unrestricted
3
LATITUDE
2
space 18-22
free
unrestricted
4
LATITUDE
2
space 23-27
free
unrestricted
5
LATITUDE
2
space 28-32
free
unrestricted
6
LATITUDE
2
space 33-37
free
unrestricted
1
LONGITUDE
3
space 8-12
free
unrestricted
2
LONGITUDE
3
space 13-17
free
unrestricted
3
LONGITUDE
3
space 18-22
free
unrestricted
4
LONGITUDE
3
space 23-27
free
unrestricted
5
LONGITUDE
3
space 28-32
free
unrestricted
6
LONGITUDE
3
space 33-37
free
unrestricted
1
ELEVATION
4
space 8-12
integer
i5
2
ELEVATION
4
space 13-17
integer
i5
3
ELEVATION
4
space 18-22
integer
i5
4
ELEVATION
4
space 23-27
integer
i5
5
ELEVATION
4
space 28-32
integer
i5
6
ELEVATION
4
space 33-37
integer
i5
ALL
YEAR
5-END
space 1-4
4-digit integer
i4
ALL
DATE
5-END
space 5-7
3-digit integer
i3
1
PRECIPITATION
5-END
space 8-12
decimal(xxx.x)
f5.1
2
PRECIPITATION
5-END
space 13-17
decimal(xxx.x)
f5.1
3
PRECIPITATION
5-END
space 18-22
decimal(xxx.x)
f5.1
4
PRECIPITATION
5-END
space 23-27
decimal(xxx.x)
f5.1
5
PRECIPITATION
5-END
space 28-32
decimal(xxx.x)
f5.1
6
PRECIPITATION
5-END
space 33-37
decimal(xxx.x)
f5.1
CHAPTER 34: SWAT INPUT—CLIMATE
87
34.2.2 SUB-DAILY PRECIPITATION Sub-daily precipitation data is required if the Green & Ampt infiltration method is being used (Set by IEVENT in the .cod file). When the Green & Ampt infiltration method is used to calculate surface runoff, SWAT is unable to generate precipitation data. Because of this, PCPSIM in the .cod file must be set to 1 and negative 99.0 (-99.0) should not be inserted for missing data. An independent weather generator or extrapolation from adjacent weather stations should be used to fill in missing data. While the input file must contain data for the entire period of simulation, the record does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the file, saving editing time on the user’s part. Unlike the daily precipitation data, SWAT verifies that the date is correct on all lines. If the model reads in an incorrect date, it will print an error message to the input.std file stating the day and year in the precipitation record where the inconsistency is located and the program will stop. The number of lines of precipitation data per day is governed by the time step used (IDT in the .cod file). To save space, only one line is required for days with no rain at all. When SWAT reads a blank for the delimiter (see variable list below), it knows that all time steps on the day have no precipitation and that there are no more lines of precipitation data for that day. Following is a brief description of the variables in the sub-daily precipitation input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the precipitation file is reserved for comments.
LATITUDE
Latitude of precipitation recording gage location. This value is not used by the model.
LONGITUDE
Longitude of precipitation recording gage location. This value is not used by the model.
88
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
ELEVATION
Elevation of precipitation recording gage station (m). Precipitation values are adjusted for elevation in subbasins where elevation bands are defined.
YEAR
Year (4-digit)
DATE
Julian date
HOUR
Hour of day (0-23). The hour and minute are at the end of the time step.
DELIMITER
Space is allowed on the line for a colon to separate the hour and minute readings. The delimiter is used by the model to identify days where there is no rain and only one line is present for the day in the .pcp file. If a blank space is inserted instead of the colon, the model will assign zero precipitation to all time steps on the day.
MINUTE
Minute of hour (0-59). The hour and minute are at the end of the time step.
PRECIPITATION
Amount of precipitation falling in the time period (mm)
The format of the sub-daily precipitation file with one record is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
unrestricted
character
unrestricted
LATITUDE
2
space 13-17
free
unrestricted
LONGITUDE
3
space 13-17
free
unrestricted
ELEVATION
4
space 13-17
integer
i5
YEAR
5-END
space 1-4
integer
i4
DATE
5-END
space 5-7
integer
i3
HOUR
5-END
space 8-9
integer
i2
DELIMITER
5-END
space 10
character
a1
MINUTE
5-END
space 11-12
integer
i2
PRECIPITATION
5-END
space 13-17
decimal(xxx.x)
f5.1
To place more than one data record within the .pcp file, repeat the original formatting for the recorded data to the right of the existing data. Simulations have been run with 200 records placed in the precipitation files. For example, assume there are records for six different rain gages stored in the sub-daily .pcp. The formatting of the .pcp file is
CHAPTER 34: SWAT INPUT—CLIMATE Gage
Variable name
ALL
TITLE
1
Line #
89
Position
Format
F90 Format
1
unrestricted
character
unrestricted
LATITUDE
2
space 13-17
free
unrestricted
2
LATITUDE
2
space 18-22
free
unrestricted
3
LATITUDE
2
space 23-27
free
unrestricted
4
LATITUDE
2
space 28-32
free
unrestricted
5
LATITUDE
2
space 33-37
free
unrestricted
6
LATITUDE
2
space 38-42
free
unrestricted
1
LONGITUDE
3
space 13-17
free
unrestricted
2
LONGITUDE
3
space 18-22
free
unrestricted
3
LONGITUDE
3
space 23-27
free
unrestricted
4
LONGITUDE
3
space 28-32
free
unrestricted
5
LONGITUDE
3
space 33-37
free
unrestricted
6
LONGITUDE
3
space 38-42
free
unrestricted
1
ELEVATION
4
space 13-17
integer
i5
2
ELEVATION
4
space 18-22
integer
i5
3
ELEVATION
4
space 23-27
integer
i5
4
ELEVATION
4
space 28-32
integer
i5
5
ELEVATION
4
space 33-37
integer
i5
6
ELEVATION
4
space 38-42
integer
i5
ALL
YEAR
5-END
space 1-4
4-digit integer
i4
ALL
DATE
5-END
space 5-7
3-digit integer
i3
ALL
HOUR
5-END
space 8-9
integer
i2
ALL
DELIMITER
5-END
space 10
character
a1
ALL
MINUTE
5-END
space 11-12
integer
i2
1
PRECIPITATION
5-END
space 13-17
decimal(xxx.x)
f5.1
2
PRECIPITATION
5-END
space 18-22
decimal(xxx.x)
f5.1
3
PRECIPITATION
5-END
space 23-27
decimal(xxx.x)
f5.1
4
PRECIPITATION
5-END
space 28-32
decimal(xxx.x)
f5.1
5
PRECIPITATION
5-END
space 33-37
decimal(xxx.x)
f5.1
6
PRECIPITATION
5-END
space 38-42
decimal(xxx.x)
f5.1
90
SWAT USER'S MANUAL, VERSION 2000
34.3 TEMPERATURE INPUT FILE (.TMP) Measured temperature data is read into the model from the .tmp file. A negative 99.0 (-99.0) should be inserted for missing maximum or minimum temperatures. This value tells SWAT to generate the missing value(s). As with the precipitation file, the record in the temperature input file does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the temperature file and all the comments made for this feature in the discussion of the precipitation file pertain to the temperature file as well. Following is a brief description of the variables in the temperature input file. They are listed in the order they appear within the file.
Variable name
Definition
TITLE
The first line of the temperature file is reserved for comments.
LATITUDE
Latitude of temperature recording gage location. This value is not used by the model.
LONGITUDE
Longitude of temperature recording gage location. This value is not used by the model.
ELEVATION
Elevation of temperature recording gage station (m). Temperature values are adjusted for elevation in subbasins where elevation bands are defined.
YEAR
Year (4-digit)
DATE
Julian date
MAX TEMP
Daily maximum temperature (ºC).
MIN TEMP
Daily minimum temperature (ºC).
CHAPTER 34: SWAT INPUT—CLIMATE
91
The format of the temperature file with one record is: Variable name
Line #
Position
Format
F90 Format unrestricted
TITLE
1
unrestricted
character
LATITUDE
2
space 8-17
free
LONGITUDE
3
space 8-17
free
ELEVATION
4
space 8-17
integer
i10
YEAR
5-END
space 1-4
4-digit integer
i4
DATE
5-END
space 5-7
3-digit integer
i3
MAX TEMP
5-END
space 8-12
decimal(xxx.x)
f5.1
MIN TEMP
5-END
space 13-17
decimal(xxx.x)
f5.1
To place more than one data record within the .tmp file, repeat the original formatting for the recorded data to the right of the existing data. Simulations have been run with 150 records placed in the temperature files. For example, assume there are records for three different temperature gages stored in the .tmp. The formatting of the .tmp file is Gage
Variable name
ALL
TITLE
1
Line #
Position
Format
F90 Format
1
unrestricted
character
unrestricted
LATITUDE
2
space 8-17
free
unrestricted
2
LATITUDE
2
space 18-27
free
unrestricted
3
LATITUDE
2
space 28-37
free
unrestricted
1
LONGITUDE
3
space 8-17
free
unrestricted
2
LONGITUDE
3
space 18-27
free
unrestricted
3
LONGITUDE
3
space 28-37
free
unrestricted
1
ELEVATION
4
space 8-17
integer
i10
2
ELEVATION
4
space 18-27
integer
i10
3
ELEVATION
4
space 28-37
integer
i10
ALL
YEAR
5-END
space 1-4
4-digit integer
i4
ALL
DATE
5-END
space 5-7
3-digit integer
i3
1
MAX TEMP
5-END
space 8-12
decimal(xxx.x)
f5.1
1
MIN TEMP
5-END
space 13-17
decimal(xxx.x)
f5.1
2
MAX TEMP
5-END
space 18-22
decimal(xxx.x)
f5.1
2
MIN TEMP
5-END
space 23-27
decimal(xxx.x)
f5.1
3
MAX TEMP
5-END
space 28-32
decimal(xxx.x)
f5.1
3
MIN TEMP
5-END
space 33-37
decimal(xxx.x)
f5.1
92
SWAT USER'S MANUAL, VERSION 2000
34.4 SOLAR RADIATION INPUT FILE (.SLR) Measured solar radiation data is read into the model from the .slr file. A negative 99.0 (-99.0) should be inserted for missing radiation values. This value tells SWAT to generate the missing value(s). As with the precipitation file, the record in the solar radiation input file does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the solar radiation file and all the comments made for this feature in the discussion of the precipitation file pertain to the solar radiation file as well. Following is a brief description of the variables in the solar radiation input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the solar radiation file is reserved for comments.
YEAR
Year (4-digit)
DATE
Julian date
SOL_RAD
Daily total solar radiation (MJ/m2).
The format of the solar radiation input file with one record is: Variable name
Line #
Position
Format
F90 Format
unrestricted
character
unrestricted
TITLE
1
YEAR
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
SOL_RAD
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
To place more than one data record within the .slr file, repeat the original formatting for the recorded data to the right of the existing data. For example, assume there are records for ten different solar radiation gages stored in the .slr. The formatting of the .slr file is
CHAPTER 34: SWAT INPUT—CLIMATE
Gage
Variable name
Line #
ALL
TITLE
1
ALL
YEAR
ALL
93
Position
Format
F90 Format
unrestricted
character
unrestricted
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
1
SOL_RAD
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
2
SOL_RAD
2-END
space 16-23
decimal(xxxx.xxx)
f8.3
3
SOL_RAD
2-END
space 24-31
decimal(xxxx.xxx)
f8.3
4
SOL_RAD
2-END
space 32-39
decimal(xxxx.xxx)
f8.3
5
SOL_RAD
2-END
space 40-47
decimal(xxxx.xxx)
f8.3
6
SOL_RAD
2-END
space 48-55
decimal(xxxx.xxx)
f8.3
7
SOL_RAD
2-END
space 56-63
decimal(xxxx.xxx)
f8.3
8
SOL_RAD
2-END
space 64-71
decimal(xxxx.xxx)
f8.3
9
SOL_RAD
2-END
space 72-79
decimal(xxxx.xxx)
f8.3
10
SOL_RAD
2-END
space 80-87
decimal(xxxx.xxx)
f8.3
34.5 WIND SPEED INPUT FILE (.WND) Measured wind speed data is read into the model from the .wnd file. A negative 99.0 (-99.0) should be inserted for missing wind speed values. This value tells SWAT to generate the missing value(s). As with the precipitation file, the record in the wind speed input file does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the wind speed file and all the comments made for this feature in the discussion of the precipitation file pertain to the wind speed file as well. Following is a brief description of the variables in the wind speed input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the wind speed radiation file is reserved for comments.
YEAR
Year (4-digit)
DATE
Julian date
WND_SP
Daily average wind speed (m/s).
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SWAT USER'S MANUAL, VERSION 2000
The format of the wind speed input file with one record is: Variable name
Line #
Position
Format
F90 Format
unrestricted
character
unrestricted
TITLE
1
YEAR
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
WND_SP
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
To place more than one data record within the .wnd file, repeat the original formatting for the recorded data to the right of the existing data. For example, assume there are records for ten different wind speed gages stored in the .wnd. The formatting of the .wnd file is Gage
Variable name
Line #
Position
Format
F90 Format
ALL
TITLE
1
unrestricted
character
unrestricted
ALL
YEAR
2-END
space 1-4
4-digit integer
i4
ALL
DATE
2-END
space 5-7
3-digit integer
i3
1
WND_SP
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
2
WND_SP
2-END
space 16-23
decimal(xxxx.xxx)
f8.3
3
WND_SP
2-END
space 24-31
decimal(xxxx.xxx)
f8.3
4
WND_SP
2-END
space 32-39
decimal(xxxx.xxx)
f8.3
5
WND_SP
2-END
space 40-47
decimal(xxxx.xxx)
f8.3
6
WND_SP
2-END
space 48-55
decimal(xxxx.xxx)
f8.3
7
WND_SP
2-END
space 56-63
decimal(xxxx.xxx)
f8.3
8
WND_SP
2-END
space 64-71
decimal(xxxx.xxx)
f8.3
9
WND_SP
2-END
space 72-79
decimal(xxxx.xxx)
f8.3
10
WND_SP
2-END
space 80-87
decimal(xxxx.xxx)
f8.3
34.6 RELATIVE HUMIDITY INPUT FILE (.HMD) Measured relative humidity data is read into the model from the .hmd file. A negative 99.0 (-99.0) should be inserted for missing relative humidity values. This value tells SWAT to generate the missing value(s). As with the precipitation file, the record in the relative humidity input file does not have to begin with the first day of simulation. SWAT is able to search
CHAPTER 34: SWAT INPUT—CLIMATE
95
for the beginning date in the relative humidity file and all the comments made for this feature in the discussion of the precipitation file pertain to the relative humidity file as well. Following is a brief description of the variables in the relative humidity input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the wind speed radiation file is reserved for comments.
YEAR
Year (4-digit)
DATE
Julian date
RHD
Daily average relative humidity expressed as a fraction.
The format of the relative humidity input file with one record is: Variable name
Line #
Position
Format
F90 Format
unrestricted
character
unrestricted
TITLE
1
YEAR
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
RHD
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
To place more than one data record within the .hmd file, repeat the original formatting for the recorded data to the right of the existing data. For example, assume there are records for seven different relative humidity gages stored in the .hmd. The formatting of the .hmd file is Gage
Variable name
Line #
Position
Format
F90 Format
ALL
TITLE
1
unrestricted
character
unrestricted
ALL
YEAR
2-END
space 1-4
4-digit integer
i4
ALL
DATE
2-END
space 5-7
3-digit integer
i3
1
RHD
2-END
space 8-15
decimal(xxxx.xxx)
f8.3
2
RHD
2-END
space 16-23
decimal(xxxx.xxx)
f8.3
3
RHD
2-END
space 24-31
decimal(xxxx.xxx)
f8.3
4
RHD
2-END
space 32-39
decimal(xxxx.xxx)
f8.3
5
RHD
2-END
space 40-47
decimal(xxxx.xxx)
f8.3
6
RHD
2-END
space 48-55
decimal(xxxx.xxx)
f8.3
7
RHD
2-END
space 56-63
decimal(xxxx.xxx)
f8.3
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SWAT USER'S MANUAL, VERSION 2000
34.7 POTENTIAL EVAPOTRANSPIRATION INPUT FILE (.PET) Daily potential evapotranspiration data is read into the model from the .pet file. As with the precipitation file, the record in the potential evapotranspiration input file does not have to begin with the first day of simulation. SWAT is able to search for the beginning date in the potential evapotranspiration input file and all the comments made for this feature in the discussion of the precipitation file pertain to the potential evapotranspiration file as well. Following is a brief description of the variables in the potential evapotranspiration input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the wind speed radiation file is reserved for comments.
YEAR
Year (4-digit)
DATE
Julian date
PETMEAS
Daily potential evapotranspiration for watershed (mm H2O).
The format of the potential evapotranspiration input file is: Variable name
Line #
Position
Format
F90 Format
unrestricted
character
unrestricted
TITLE
1
YEAR
2-END
space 1-4
4-digit integer
i4
DATE
2-END
space 5-7
3-digit integer
i3
PETMEAS
2-END
space 8-12
decimal(xxx.x)
f5.1
CHAPTER 34: SWAT INPUT—CLIMATE
97
34.8 MULTIPLE RECORDS IN CLIMATE FILES Multiple records may be placed in all measured climatic data files except the potential evapotranspiration file. To assign the different gages to the subbasins in file.cio, a gage number is specified on the subbasin input lines for each type of measured climate data file used in the simulation. Unique gage numbers are assigned to the individual records in the following manner: Using the precipitation files as an example, numbering is begun in the file assigned to RFILE(1). If ten gages are present in this file, they are numbered from left to right: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Moving to the next precipitation file which also contains 10 files, RFILE(2), the gages are numbered from left to right beginning where the previous file left off: 11, 12, 13, 14, 15, 16, 17, 18, 19, 20. In the same manner, the gages in RFILE(3), RFILE(4), RFILE(5), RFILE(6), etc. will be processed.
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 35
SWAT INPUT DATA: GENERAL ATTRIBUTES
The subbasin and HRU general input files contain information related to a diversity of features within the HRU and its subbasin. Data contained in the subbasin input file can be grouped into the following categories: properties of tributary channels within the subbasin, the amount of topographic relief within the subbasin and its impact on the climate, variables related to climate change, the number of HRUs in the subbasin and the names of HRU input files. Data contained in the HRU input file can be grouped into the following categories: area contained in HRU, parameters affecting surface and subsurface water flow, parameters affecting erosion and management inputs related to the simulation of urban areas, irrigation, tile drains and potholes. 89
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SWAT USER'S MANUAL, VERSION 2000
35.1 SUBBASIN GENERAL INPUT FILE (.SUB) Following is a brief description of the variables in the subbasin general input file. They are listed in the order they appear within the file.
Variable name
Definition
TITLE
The first line of the .sub file is reserved for user comments. The comments may take up to 80 spaces. Optional.
HRUTOT
Total number of HRUs modeled in the subbasin.
LATITUDE
Latitude of subbasin (degrees). The latitude is expressed as a real number with minutes and seconds converted to fractions of a degree.
ELEV
Elevation of subbasin (m).
ELEVB(band)
Elevation at the center of the elevation band (m). Up to 10 zones may be specified. Optional.
ELEVB_FR(band)
Fraction of subbasin area within the elevation band. Values for ELEVB_FR should be between 0.0 and 1.0. Optional.
SNOEB(band)
Initial snow water content in elevation band (mm H2O). Optional.
PLAPS
Precipitation lapse rate (mm H2O/km). A positive value denotes an increase in precipitation with an increase in elevation while a negative value denotes a decrease in precipitation with an increase in elevation. The lapse rate is used to adjust precipitation for elevation bands in the subbasin. If no elevation bands are defined, the precipitation generated or read in from the .pcp file is used for the subbasin with no adjustment. To adjust the precipitation, the elevation of the recording station or the weather station is compared to the elevation specified for the elevation band. Optional.
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
91
Variable name
Definition
TLAPS
Temperature lapse rate (ºC/km). A positive value denotes an increase in temperature with an increase in elevation while a negative value denotes a decrease in temperature with an increase in elevation. If no value is entered for TLAPS, the model sets TLAPS = -6 ºC/km. The lapse rate is used to adjust temperature for elevation bands in the subbasin. If no elevation bands are defined, the temperature generated or read in from the .tmp file is used for the subbasin with no adjustment. To adjust the temperature, the elevation of the recording station or the weather station is compared to the elevation specified for the elevation band. Optional.
SNO_SUB
Initial snow water content (mm H2O). This value is not needed if the subbasin is divided into elevation bands (see variables ELEVB, ELEVB_FR and SNOEB in this file). Optional.
CH_L(1)
Longest tributary channel length in subbasin (km). The channel length is the distance along the channel from the subbasin outlet to the most distant point in the subbasin.
CH_S(1)
Average slope of tributary channels (m/m). The average channel slope is computed by taking the difference in elevation between the subbasin outlet and the most distant point in the subbasin and dividing by CH_L.
CH_W(1)
Average width of tributary channels (m).
CH_K(1)
Effective hydraulic conductivity in tributary channel alluvium (mm/hr).
CH_N(1)
Manning's "n" value for the tributary channels.
CO2
Carbon dioxide concentration (ppmv). If no value for CO2 is entered the model will set CO2 = 330 ppmv (ambient CO2 concentration). (Optional—used in climate change studies only)
RFINC(mon)
Rainfall adjustment (% change). (Optional—used in climate change studies only). Daily rainfall within the month is adjusted by the specified percentage. For example, setting RFINC = 10 will make rainfall equal to 110% of the original value.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
TMPINC(mon)
Temperature adjustment (ºC). (Optional—used in climate change studies only). Daily maximum and minimum temperatures within the month are raised or lowered by the specified amount.
RADINC(mon)
Radiation adjustment (MJ/m2-day). (Optional—used in climate change studies only). Daily radiation within the month is raised or lowered by the specified amount.
HUMINC(mon)
Humidity adjustment. (Optional—used in climate change studies only). Daily values for relative humidity within the month are raised or lowered by the specified amount. The relative humidity in SWAT is reported as a fraction.
HRUDAT
Name of HRU general input data file (.hru).
MGTDAT
Name of HRU land use management data file (.mgt).
SOILDAT
Name of HRU soil data file (.sol).
CHEMDAT
Name of HRU soil chemical data file (.chm).
GWDAT
Name of HRU groundwater data file (.gw).
The subbasin general input file is partially free format and partially fixed format. The variables that are free format will have free listed in the F90Format column and will not have a position defined. The variables that are fixed format will have a FORTRAN format and position specified. The free format variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The fixed format variables must be entered using the specified format and positioning on the line in order for the model to read them properly.
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
The format for the subbasin general input file is: Variable name
Line #
Position
Format
F90 Format
space 1-80
character
a80
TITLE
1
HRUTOT
2
integer
free
LATITUDE
3
real
free
ELEV
4
real
free
COMMENT LINE
5
space 1-80
character
a80
ELEVB(1)
6
space 1-8
decimal (xxxx.xxx)
f8.3
ELEVB(2)
6
space 9-16
decimal (xxxx.xxx)
f8.3
ELEVB(3)
6
space 17-24
decimal (xxxx.xxx)
f8.3
ELEVB(4)
6
space 25-32
decimal (xxxx.xxx)
f8.3
ELEVB(5)
6
space 33-40
decimal (xxxx.xxx)
f8.3
ELEVB(6)
6
space 41-48
decimal (xxxx.xxx)
f8.3
ELEVB(7)
6
space 49-56
decimal (xxxx.xxx)
f8.3
ELEVB(8)
6
space 57-64
decimal (xxxx.xxx)
f8.3
ELEVB(9)
6
space 65-72
decimal (xxxx.xxx)
f8.3
ELEVB(10)
6
space 73-80
decimal (xxxx.xxx)
f8.3
COMMENT LINE
7
space 1-80
character
a80
ELEVB_FR(1)
8
space 1-8
decimal (xxxx.xxx)
f8.3
ELEVB_FR(2)
8
space 9-16
decimal (xxxx.xxx)
f8.3
ELEVB_FR(3)
8
space 17-24
decimal (xxxx.xxx)
f8.3
ELEVB_FR(4)
8
space 25-32
decimal (xxxx.xxx)
f8.3
ELEVB_FR(5)
8
space 33-40
decimal (xxxx.xxx)
f8.3
ELEVB_FR(6)
8
space 41-48
decimal (xxxx.xxx)
f8.3
ELEVB_FR(7)
8
space 49-56
decimal (xxxx.xxx)
f8.3
ELEVB_FR(8)
8
space 57-64
decimal (xxxx.xxx)
f8.3
ELEVB_FR(9)
8
space 65-72
decimal (xxxx.xxx)
f8.3
ELEVB_FR(10)
8
space 73-80
decimal (xxxx.xxx)
f8.3
COMMENT LINE
9
space 1-80
character
a80
SNOEB(1)
10
space 1-8
decimal (xxxx.xxx)
f8.3
SNOEB(2)
10
space 9-16
decimal (xxxx.xxx)
f8.3
SNOEB(3)
10
space 17-24
decimal (xxxx.xxx)
f8.3
SNOEB(4)
10
space 25-32
decimal (xxxx.xxx)
f8.3
SNOEB(5)
10
space 33-40
decimal (xxxx.xxx)
f8.3
93
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
SNOEB(6)
10
space 41-48
decimal (xxxx.xxx)
f8.3
SNOEB(7)
10
space 49-56
decimal (xxxx.xxx)
f8.3
SNOEB(8)
10
space 57-64
decimal (xxxx.xxx)
f8.3
SNOEB(9)
10
space 65-72
decimal (xxxx.xxx)
f8.3
SNOEB(10)
10
space 73-80
decimal (xxxx.xxx)
f8.3
PLAPS
11
real
free
TLAPS
12
real
free
SNO_SUB
13
real
free
CH_L(1)
14
real
free
CH_S(1)
15
real
free
CH_W(1)
16
real
free
CH_K(1)
17
real
free
CH_N(1)
18
real
free
CO2
19
real
free
COMMENT LINE
20
space 1-80
character
a80
RFINC(1)
21
space 1-8
decimal (xxxx.xxx)
f8.3
RFINC(2)
21
space 9-16
decimal (xxxx.xxx)
f8.3
RFINC(3)
21
space 17-24
decimal (xxxx.xxx)
f8.3
RFINC(4)
21
space 25-32
decimal (xxxx.xxx)
f8.3
RFINC(5)
21
space 33-40
decimal (xxxx.xxx)
f8.3
RFINC(6)
21
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
22
space 1-80
character
a80
RFINC(7)
23
space 1-8
decimal (xxxx.xxx)
f8.3
RFINC(8)
23
space 9-16
decimal (xxxx.xxx)
f8.3
RFINC(9)
23
space 17-24
decimal (xxxx.xxx)
f8.3
RFINC(10)
23
space 25-32
decimal (xxxx.xxx)
f8.3
RFINC(11)
23
space 33-40
decimal (xxxx.xxx)
f8.3
RFINC(12)
23
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
24
space 1-80
character
a80
TMPINC(1)
25
space 1-8
decimal (xxxx.xxx)
f8.3
TMPINC(2)
25
space 9-16
decimal (xxxx.xxx)
f8.3
TMPINC(3)
25
space 17-24
decimal (xxxx.xxx)
f8.3
TMPINC(4)
25
space 25-32
decimal (xxxx.xxx)
f8.3
TMPINC(5)
25
space 33-40
decimal (xxxx.xxx)
f8.3
TMPINC(6)
25
space 41-48
decimal (xxxx.xxx)
f8.3
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES Variable name
Line #
Position
Format
F90 Format
COMMENT LINE
26
space 1-80
character
a80
TMPINC(7)
27
space 1-8
decimal (xxxx.xxx)
f8.3
TMPINC(8)
27
space 9-16
decimal (xxxx.xxx)
f8.3
TMPINC(9)
27
space 17-24
decimal (xxxx.xxx)
f8.3
TMPINC(10)
27
space 25-32
decimal (xxxx.xxx)
f8.3
TMPINC(11)
27
space 33-40
decimal (xxxx.xxx)
f8.3
TMPINC(12)
27
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
28
space 1-80
character
a80
RADINC(1)
29
space 1-8
decimal (xxxx.xxx)
f8.3
RADINC(2)
29
space 9-16
decimal (xxxx.xxx)
f8.3
RADINC(3)
29
space 17-24
decimal (xxxx.xxx)
f8.3
RADINC(4)
29
space 25-32
decimal (xxxx.xxx)
f8.3
RADINC(5)
29
space 33-40
decimal (xxxx.xxx)
f8.3
RADINC(6)
29
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
30
space 1-80
character
a80
RADINC(7)
31
space 1-8
decimal (xxxx.xxx)
f8.3
RADINC(8)
31
space 9-16
decimal (xxxx.xxx)
f8.3
RADINC(9)
31
space 17-24
decimal (xxxx.xxx)
f8.3
RADINC(10)
31
space 25-32
decimal (xxxx.xxx)
f8.3
RADINC(11)
31
space 33-40
decimal (xxxx.xxx)
f8.3
RADINC(12)
31
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
32
space 1-80
character
a80
HUMINC(1)
33
space 1-8
decimal (xxxx.xxx)
f8.3
HUMINC(2)
33
space 9-16
decimal (xxxx.xxx)
f8.3
HUMINC(3)
33
space 17-24
decimal (xxxx.xxx)
f8.3
HUMINC(4)
33
space 25-32
decimal (xxxx.xxx)
f8.3
HUMINC(5)
33
space 33-40
decimal (xxxx.xxx)
f8.3
HUMINC(6)
33
space 41-48
decimal (xxxx.xxx)
f8.3
COMMENT LINE
34
space 1-80
character
a80
HUMINC(7)
35
space 1-8
decimal (xxxx.xxx)
f8.3
HUMINC(8)
35
space 9-16
decimal (xxxx.xxx)
f8.3
HUMINC(9)
35
space 17-24
decimal (xxxx.xxx)
f8.3
HUMINC(10)
35
space 25-32
decimal (xxxx.xxx)
f8.3
HUMINC(11)
35
space 33-40
decimal (xxxx.xxx)
f8.3
HUMINC(12)
35
space 41-48
decimal (xxxx.xxx)
f8.3
95
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Position
Format
F90 Format
36
space 1-80
character
a80
HRUDAT
37-END
space 1-13
character
a13
MGTDAT
37-END
space 14-26
character
a13
SOILDAT
37-END
space 27-39
character
a13
CHEMDAT
37-END
space 40-52
character
a13
GWDAT
37-END
space 53-65
character
a13
COMMENT LINE
Line #
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
97
35.2 HRU GENERAL INPUT FILE (.HRU) Following is a brief description of the variables in the HRU general input file. They are listed in the order they appear within the file.
Variable name
Definition
TITLE
The first line of the .hru file is reserved for user comments. The comments may take up to 80 spaces. (optional)
HRU_FR
Fraction of total watershed area contained in HRU (km2/km2). If no value for HRU_FR is entered, the model will set HRU_FR = 0.0000001.
SLSUBBSN
Average slope length (m). If no value for SLSUBBSN is entered, the model will set SLSUBBSN = 50. The GIS interfaces will assign the same value to this variable for all HRUs within a subbasin. However, some users like to vary this value by soil type and land cover.
SLOPE
Average slope steepness (m/m). The GIS interfaces will assign the same value to this variable for all HRUs within a subbasin. However, some users like to vary this value by soil type and land cover.
OV_N
Manning's "n" value for overland flow.
LAT_TTIME
Lateral flow travel time (days). Setting LAT_TTIME = 0.0 will allow the model to calculate the travel time based on soil hydraulic properties. This variable should be set to a specific value only by hydrologists familiar with the base flow characteristics of the watershed.
LAT_SED
Sediment concentration in lateral and groundwater flow (mg/L). Sediment concentration in lateral and groundwater flow is usually very low and does not contribute significantly to total sediment yields unless return flow is very high.
SLSOIL
Slope length for lateral subsurface flow (m). If no value is entered for SLSOIL, the model sets SLSOIL = SLSUBBSN. The GIS interfaces will assign the same value to this variable for all HRUs within a subbasin. However, some users like to vary this value by soil type and land cover.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
CANMX
Maximum canopy storage (mm H2O). (Optional)
ESCO
Soil evaporation compensation factor. This factor adjusts the depth distribution for evaporation from the soil to account for the effect of capillary action, crusting and cracks. ESCO must be between 0.0 and 1.0. If no value for ESCO is entered, the model will set ESCO = 0.95. The value for ESCO may be set at the watershed or HRU level (ESCO in .bsn).
EPCO
Plant uptake compensation factor. This factor adjusts the depth distribution for plant uptake of water from the soil to account for the variation in root density with depth. EPCO must be between 0.0 and 1.0. If no value for EPCO is entered, the model will set EPCO = 1.0. The value for EPCO may be set at the watershed or HRU level (EPCO in .bsn).
RSDIN
Initial residue cover (kg/ha). (Optional)
ERORGN
Organic N enrichment ratio. If the value for ERORGN is set to zero, the model will calculate an enrichment ratio for every storm event. The default option is to allow the model to calculate the enrichment ratio.
ERORGP
Organic P enrichment ratio. If the value for ERORGP is set to zero, the model will calculate an enrichment ratio for every storm event. The default option is to allow the model to calculate the enrichment ratio.
FILTERW
Width of edge-of-field filter strip.
IURBAN
Urban simulation code: 0 no urban sections in HRU 1 urban sections in HRU, simulate using USGS regression equations 2 urban sections in HRU, simulate using build up/wash off algorithm.
URBLU
Urban land type identification number from urban.dat.
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
99
Variable name
Definition
IRR
Irrigation code. This variable, along with IRRNO, specifies the source of irrigation water applied in the HRU. Irrigation water may be diverted from anywhere in the watershed or outside the watershed. IRR tells the model what type of water body the irrigation water is being diverted from. The options are: 0 no irrigation 1 divert water from reach 2 divert water from reservoir 3 divert water from shallow aquifer 4 divert water from deep aquifer 5 divert water from unlimited source outside watershed
IRRNO
Irrigation source location. The definition of this variable depends on the setting of IRR. If IRR = 1, IRRNO is the number of the reach that water is removed from. If IRR = 2, IRRNO is the number of the reservoir that water is removed from. If IRR = 3 or 4, IRRNO is the number of the subbasin that water is removed from. If IRR = 0 or 5, this variable is not used.
FLOWMIN
Minimum in-stream flow for irrigation diversions (m3/s). If irrigation water being applied in the HRU is from a reach (IRR = 1), a threshhold level of streamflow can be specified. Irrigation water will be diverted from the reach only if flow in the reach is at or above FLOWMIN.
DIVMAX
Maximum daily irrigation diversion from the reach (if value entered for DIVMAX is positive the units are mm, if the value entered for DIVMAX is negative the units are 104 m3). If irrigation water being applied in the HRU is from a reach (IRR = 1), a value can be defined which specifies the maximum amount of water that can be removed from the reach and applied to the HRU on any one day.
FLOWFR
Fraction of available flow (total flow in reach – FLOWMIN) that is allowed to be applied to the HRU. If FLOWMIN is left at zero, the fraction of available flow becomes the fraction of total flow in reach that is allowed to be applied to the reach. The value for FLOWFR should be between 0.0 and 1.0. The model will default FLOWFR = 1.0 if no value is entered. Used only if IRR = 1.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
DDRAIN
Depth to subsurface drain (mm). If drainage tiles are installed in the HRU, the depth to the tiles is needed (optional).
TDRAIN
Time to drain soil to field capacity (hours). If tile drainage is installed in the HRU, the time required to drain the soil from saturation to field capacity is needed (optional).
GDRAIN
Drain tile lag time (hours). The amount of time between the transfer of water from the soil to the drain tile and the release of the water from the drain tile to the reach (optional).
IPOT
Number of HRU (must be located in subbasin) that is ponding water, i.e. the number of the HRU into which surface runoff from the current HRU drains. This variable identifies closed depressional areas or impounded areas used to grow plants in water (e.g. rice paddies). These areas are commonly referred to as potholes. Optional.
POT_FR
Fraction of HRU area that drains into the pothole. Required if IPOT is set to a number other than zero.
POT_TILE
Average daily outflow to main channel from tile flow if drainage tiles are installed in the pothole (m3/s). Required only for the HRU that is ponding water (IPOT = current HRU number).
POT_VOLX
Maximum volume of water stored in the pothole (104 m3 H2O). Required only for the HRU that is ponding water (IPOT = current HRU number).
POT_VOL
Initial volume of water stored in the pothole (104 m3 H2O). Required only for the HRU that is ponding water (IPOT = current HRU number).
POT_NSED
Normal sediment concentration in pothole (mg/L). Required only for the HRU that is ponding water (IPOT = current HRU number).
POT_NO3L
Not currently active. Nitrate decay rate in pothole (1/day). Required only for the HRU that is ponding water (IPOT = current HRU number).
CHAPTER 35: SWAT INPUT—GENERAL HRU ATTRIBUTES
101
The HRU general input file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format for the HRU general input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
HRU_FR
2
real
free
SLSUBBSN
3
real
free
SLOPE
4
real
free
OV_N
5
real
free
LAT_TTIME
6
real
free
LAT_SED
7
real
free
SLSOIL
8
real
free
CANMX
9
real
free
ESCO
10
real
free
EPCO
11
real
free
RSDIN
12
real
free
ERORGN
13
real
free
ERORGP
14
real
free
FILTERW
15
real
free
IURBAN
16
integer
free
URBLU
17
integer
free
IRR
18
integer
free
IRRNO
19
integer
free
FLOWMIN
20
real
free
DIVMAX
21
real
free
FLOWFR
22
real
free
DDRAIN
23
real
free
TDRAIN
24
real
free
GDRAIN
25
real
free
NO VARIABLE
26
free
free
IPOT
27
integer
free
POT_FR
28
real
free
POT_TILE
29
real
free
102
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
POT_VOLX
30
real
free
POT_VOL
31
real
free
POT_NSED
32
real
free
POT_NO3L
33
real
free
CHAPTER 36
SWAT INPUT DATA: SOIL
The soils data used by SWAT can be divided into two groups, physical characteristics and chemical characteristics. The physical properties of the soil govern the movement of water and air through the profile and have a major impact on the cycling of water within the HRU. Inputs for chemical characteristics are used to set initial levels of the different chemicals in the soil. While the physical properties are required, information on chemical properties is optional. Two files, the soil input file (.sol) and the soil chemical input file (.chm), contain the soil properties used by SWAT. The soil input file defines the physical properties and initializes chemical quantities for all layers in the soil. The soil chemical input file initializes additional chemical quantities for the first soil layer. 97
98
SWAT USER'S MANUAL, VERSION 2000
36.1 SOIL INPUT FILE (.SOL) Following is a brief description of the variables in the soil input file. They are listed in the order they appear within the file. The soil input file will hold data for up to 10 layers. Variable name
Definition
TITLE/TEXT
The first line of the .sol file is reserved for user comments. The comments may take up to 80 spaces. (optional)
SNAM
Soil name. If given, the soil name is used in data summarization.
HYDGRP
Soil hydrologic group (A, B, C, or D (required by the SWAT ArcView interface) The criteria for placement in hydrologic groups are: A
Minimum saturated hydraulic conductivity in the uppermost 0.5 m is > 110 mm/hr and internal free water occurrence is below 1.5 m.
B
Minimum saturated hydraulic conductivity in the uppermost 0.5 m is between 11 and 110 mm/hr and internal free water occurrence is below 1.0 m.
C
Minimum saturated hydraulic conductivity in the uppermost 0.5 m is between 1.1 and 11 mm/hr and internal free water occurrence is below 0.25 m.
D
Minimum saturated hydraulic conductivity in the uppermost 0.5 m is below 1.1 mm/hr and internal free water occurrence may be at any depth.
SOL_ZMX
Maximum rooting depth of soil profile (mm). If no depth is specified, the model assumes the roots can develop throughout the entire depth of the soil profile.
ANION_EXCL
Fraction of porosity (void space) from which anions are excluded. This parameter is currently used only in nitrate transport. If no value for ANION_EXCL is entered, the model will set ANION_EXCL = 0.50
CHAPTER 36: SWAT INPUT—SOIL
99
Variable name
Definition
SOL_CRK
Crack volume potential of soil (optional)
TEXTURE
Texture of soil layer (optional). This data is not used by the model.
SOL_Z(layer #)
Depth from soil surface to bottom of layer (mm).
SOL_BD(layer #)
Moist bulk density (Mg/m3 or g/cm3). The soil bulk density expresses the ratio of the mass of solid particles to the total volume of the soil, ρb = MS /VT. In moist bulk density determinations, the mass of the soil is the oven dry weight and the total volume of the soil is determined when the soil is at or near field capacity. Bulk density values should fall between 1.1 and 1.9 Mg/m3.
SOL_AWC(layer #)
Available water capacity of the soil layer (mm H2O/mm soil). This is the volume of water that should be available to plants if the soil, inclusive of rock fragments, was at field capacity. Available water capacity is estimated by determining the amount of water released between in situ field capacity (the soil water content at soil matric potential of -0.033 MPa) and the permanent wilting point (the soil water content at soil matric potential of -1.5 MPa).
SOL_K(layer #)
Saturated hydraulic conductivity (mm/hr). The saturated hydraulic conductivity, Ksat, relates soil water flow rate (flux density) to the hydraulic gradient and is a measure of the ease of water movement through the soil. Ksat is the reciprocal of the resistance of the soil matrix to water flow.
SOL_CBN(layer #)
Organic carbon content (% soil weight). When defining by soil weight, the soil is the portion of the sample that passes through a 2 mm sieve.
CLAY(layer #)
Clay content (% soil weight). The percent of soil particles which are < 0.002 mm in equivalent diameter.
SILT(layer #)
Silt content (% soil weight). The percentage of soil particles which have an equivalent diameter between 0.05 and 0.002 mm.
SAND(layer #)
Sand content (% soil weight). The percentage of soil particles which have a diameter between 2.0 and 0.05 mm.
100
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
ROCK(layer #)
Rock fragment content (% total weight). The percent of the sample which has a particle diameter > 2 mm, i.e. the percent of the sample which does not pass through a 2 mm sieve.
SOL_ALB(layer #)
Moist soil albedo. The ratio of the amount of solar radiation reflected by a body to the amount incident upon it, expressed as a fraction. The value for albedo should be reported when the soil is at or near field capacity.
USLE_K(layer #)
USLE equation soil erodibility (K) factor (units: 0.013 (metric ton m2 hr)/(m3-metric ton cm)). The units given are numerically equivalent to the traditional English units (0.01 (ton acre hr)/(acre ft-ton inch)). The values for the metric units will be exactly the same as those for the English units.
SOL_EC(layer #)
Not currently active Electrical conductivity (dS/m).
The format of the soil input file is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
space 1-80
character
a80
SNAM
2
space 13-28
character
a16
HYDGRP
3
space 25
character
a1
SOL_ZMX
4
space 29-35
decimal(xxxxxxxxx.xx)
f12.2
ANION_EXCL
5
space 52-56
decimal(x.xxx)
f5.3
SOL_CRK
6
space 34-38
decimal(x.xxx)
f5.3
COMMENT LINE
7
space 1-147
character
a80
SOL_Z(1)
8
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(2)
8
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(3)
8
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(4)
8
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(5)
8
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(6)
8
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(7)
8
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(8)
8
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(9)
8
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_Z(10)
8
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(1)
9
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
CHAPTER 36: SWAT INPUT—SOIL Variable name
Line #
101
Position
Format
F90 Format
SOL_BD(2)
9
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(3)
9
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(4)
9
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(5)
9
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(6)
9
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(7)
9
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(8)
9
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(9)
9
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_BD(10)
9
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(1)
10
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(2)
10
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(3)
10
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(4)
10
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(5)
10
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(6)
10
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(7)
10
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(8)
10
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(9)
10
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_AWC(10)
10
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(1)
11
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(2)
11
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(3)
11
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(4)
11
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(5)
11
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(6)
11
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(7)
11
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(8)
11
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(9)
11
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_K(10)
11
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(1)
12
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(2)
12
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(3)
12
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(4)
12
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(5)
12
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(6)
12
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
102
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
SOL_CBN(7)
12
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(8)
12
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(9)
12
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_CBN(10)
12
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
CLAY(1)
13
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
CLAY(2)
13
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
CLAY(3)
13
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
CLAY(4)
13
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
CLAY(5)
13
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
CLAY(6)
13
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
CLAY(7)
13
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
CLAY(8)
13
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
CLAY(9)
13
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
CLAY(10)
13
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SILT(1)
14
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SILT(2)
14
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SILT(3)
14
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SILT(4)
14
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SILT(5)
14
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SILT(6)
14
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SILT(7)
14
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SILT(8)
14
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SILT(9)
14
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SILT(10)
14
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SAND(1)
15
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SAND(2)
15
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SAND(3)
15
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SAND(4)
15
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SAND(5)
15
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SAND(6)
15
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SAND(7)
15
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SAND(8)
15
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SAND(9)
15
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SAND(10)
15
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
ROCK(1)
16
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
CHAPTER 36: SWAT INPUT—SOIL Variable name
Line #
103
Position
Format
F90 Format
ROCK(2)
16
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
ROCK(3)
16
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
ROCK(4)
16
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
ROCK(5)
16
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
ROCK(6)
16
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
ROCK(7)
16
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
ROCK(8)
16
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
ROCK(9)
16
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
ROCK(10)
16
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(1)
17
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(2)
17
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(3)
17
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(4)
17
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(5)
17
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(6)
17
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(7)
17
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(8)
17
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(9)
17
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_ALB(10)
17
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(1)
18
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(2)
18
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(3)
18
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(4)
18
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(5)
18
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(6)
18
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(7)
18
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(8)
18
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(9)
18
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
USLE_K(10)
18
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(1)
19
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(2)
19
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(3)
19
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(4)
19
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(5)
19
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(6)
19
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
SOL_EC(7)
19
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(8)
19
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(9)
19
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_EC(10)
19
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
CHAPTER 36: SWAT INPUT—SOIL
105
36.2 SOIL CHEMICAL INPUT FILE (.CHM) Following is a brief description of the variables in the soil chemical input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .chm file is reserved for user comments. The comments may take up to 80 spaces. Optional.
NUTRIENT TITLE
The second line of the .chm file is reserved for the nutrient data title. This is not used by the model. Optional.
SOIL LAYER
Number of soil layer. This line in the .chm file is not used by the model and may be left blank.
SOL_NO3(layer #)
Initial NO3 concentration (mg/kg) in the soil layer Optional.
SOL_ORGN(layer #)
Initial organic N concentration in the soil layer (mg/kg). Optional.
SOL_SOLP(layer #)
Initial soluble P concentration in soil layer (mg/kg). Optional.
SOL_ORGP(layer #) Initial organic P concentration in soil layer (mg/kg). Optional. PESTICIDE TITLE
Lines 8-11 are reserved for pesticide data titles. The data on these lines are not used by the model.
PESTNUM
Number of pesticide from pesticide database (pest.dat) Required if pesticide amounts are given.
PLTPST
Initial pesticide amount on foliage (kg/ha). Optional.
SOLPST
Initial pesticide amount in soil (mg/kg). The pesticide is assumed to be found at this concentration in all soil layers. Optional.
PSTENR
Enrichment ratio for pesticide in the soil. This is the ratio of the pesticide concentration on the sediment transported in surface runoff to the pesticide concentration in the soil. Optional.
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SWAT USER'S MANUAL, VERSION 2000
The format of the soil chemical input file is: Variable name
Line #
Position
Format
F90 Format
TITLE
1
space 1-80
character
a80
NUTRIENT TITLE
2
space 1-80
character
a80
SOIL LAYERS
3
space 1-80
character
a80
SOL_NO3(1)
4
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(2)
4
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(3)
4
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(4)
4
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(5)
4
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(6)
4
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(7)
4
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(8)
4
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(9)
4
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_NO3(10)
4
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(1)
5
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(2)
5
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(3)
5
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(4)
5
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(5)
5
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(6)
5
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(7)
5
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(8)
5
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(9)
5
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGN(10)
5
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(1)
6
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(2)
6
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(3)
6
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(4)
6
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(5)
6
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(6)
6
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(7)
6
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(8)
6
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(9)
6
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_SOLP(10)
6
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
CHAPTER 36: SWAT INPUT—SOIL Variable name
Line #
SOL_ORGP(1)
107
Position
Format
F90 Format
7
space 28-39
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(2)
7
space 40-51
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(3)
7
space 52-63
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(4)
7
space 64-75
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(5)
7
space 76-87
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(6)
7
space 88-99
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(7)
7
space 100-111
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(8)
7
space 112-123
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(9)
7
space 124-135
decimal(xxxxxxxxx.xx)
f12.2
SOL_ORGP(10)
7
space 136-147
decimal(xxxxxxxxx.xx)
f12.2
space 1-80
character
a80
PESTICIDE TITLE
8-11
PSTNUM
12-END
integer
free
PLTPST
12-END
real
free
SOLPST
12-END
real
free
PSTENR
12-END
real
free
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 37
SWAT INPUT DATA: LAND/WATER MANAGEMENT
A primary goal of environmental modeling is to assess the impact of anthropogenic activities on a given system. Central to this assessment is the itemization of the land and water management practices taking place within the system. SWAT utilizes two files to summarize these practices within the HRUs, the HRU management file (.mgt) and the subbasin water use file (.wus).
105
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SWAT USER'S MANUAL, VERSION 2000
37.1 MANAGEMENT INPUT FILE (.MGT) This file contains input data for planting, harvest, irrigation applications, nutrient applications, pesticide applications, and tillage operations. Operations may be scheduled by month and day or by heat units. If both month/day and heat units are given for a scheduled operation, the model will implement the operation on whichever occurs first. For example, assume a fertilizer operation is scheduled for April 1 or a heat unit index of 0.3. In year one, the heat unit index of the crop reaches 0.3 on April 14 while in year two the heat unit index of the crop reaches 0.3 on March 25. SWAT will apply the fertilizer on April 1 the first year and March 25 the second year. SWAT calculates two different heat unit indices throughout a year. The first is a base zero, or annual, heat unit index for the HRU. The total heat units that can be accumulated annually is equal to the sum of degrees C above 0˚C in the daily mean temperature for every day of the year. Throughout each year of simulation, an index is calculated which tells the fraction of the total heat units accumulated. The base zero, or annual, heat unit index is reset to zero on January 1st. This heat unit index is used to schedule operations when no plant is growing. The second heat unit index calculated by SWAT is the plant heat unit index. The base temperature for the plant heat unit index is the minimum temperature required by the plant for growth. The total number of heat units required to bring the plant to maturity, referred to as the potential heat units, is equal to the sum of degrees C above the plant base temperature in the daily mean temperature for every day of the plant growing season. The plant heat unit index is calculated only when something is growing in the HRU.
37.1.1 GENERAL MANAGEMENT VARIABLES The first two lines of the management input file contain general management variables. The remaining lines in the file list the sequence of management operations which occur throughout a year of simulation.
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
107
The general management variables are: Variable name
Definition
TITLE
The first line of the .mgt file is reserved for user comments. The comments may take up to 80 spaces. (optional)
IGRO
Land cover status code. This code informs the model whether or not a land cover is growing at the beginning of the simulation. 0 no land cover growing 1 land cover growing
NROT
Number of years of rotation. This code identifies the number of years of management practices given in the .mgt file. (A blank line should be inserted between each different year of management.) If the management doesn't change from year to year, the management operations for only one year are needed. Two land covers/crops may not be grown simultaneously, but they may be grown in the same year. For two or more crops grown in the same year, NROT is equal to 1 for 1 year of management practices listed. NROT has nothing to do with the number of different crops grown.
NMGT
Management code. Used by SWAT/GRASS (GIS) interface. The model doesn't use this variable.
NCRP
Land cover identification number. If a land cover is growing at the beginning of the simulation (IGRO = 1), this variable defines the type of land cover. The identification number is the numeric code for the land cover given in the crop.dat file.
ALAI
Initial leaf area index. If a land cover is growing at the beginning of the simulation (IGRO = 1), the leaf area index of the land cover must be defined.
BIO_MS
Initial dry weight biomass (kg/ha). If a land cover is growing at the beginning of the simulation (IGRO = 1), the initial biomass must be defined.
PHU
Total number of heat units or growing degree days needed to bring plant to maturity. This value is needed only if a land cover is growing at the beginning of the simulation (IGRO = 1).
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
BIO_MIN
Minimum plant biomass for grazing (kg/ha). Grazing will not be simulated unless the biomass is at or above BIO_MIN.
BIOMIX
Biological mixing efficiency. Mixing of the soil due to activity of earthworms and other soil biota. Mixing is performed at the end of every calendar year. If no value for BIOMIX is entered, the model will set BIOMIX = 0.20
CN2
Initial SCS runoff curve number for moisture condition II. The curve number may be updated in plant, tillage, and harvest/ kill operations. If CNOP is never defined for these operations, the value set for CN2 will be used throughout the simulation. If CNOP is defined for an operation, the value for CN2 is used until the time of the operation containing the first CNOP value. From that point on, the model only uses operation CNOP values to define the curve number for moisture condition II. Values for CN2 and CNOP should be entered for pervious conditions. In HRUs with urban areas, the model will adjust the curve number to reflect the impact of the impervious areas.
USLE_P
USLE equation support practice (P) factor. The ratio of soil loss with a support practice like contouring, stripcropping, or terracing to that with straight-row farming up and down the slope.
The format of the first two lines in the management file are: Variable name
Line #
Position
Format
F90 Format
TITLE
1
space 1-80
character
a80
IGRO
2
space 1
1-digit integer
i1
NROT
2
space 2-4
3-digit integer
i3
NMGT
2
space 5-8
4-digit integer
i4
NCRP
2
space 9-12
4-digit integer
i4
ALAI
2
space 13-20
decimal (xxxxx.xx)
f8.2
BIO_MS
2
space 21-28
decimal (xxxxx.xx)
f8.2
PHU
2
space 29-36
decimal (xxxxx.xx)
f8.2
BIO_MIN
2
space 37-44
decimal (xxxxx.xx)
f8.2
BIOMIX
2
space 45-52
decimal (xxxxx.xx)
f8.2
CN2
2
space 53-60
decimal (xxxxx.xx)
f8.2
USLE_P
2
space 61-68
decimal (xxxxx.xx)
f8.2
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
109
37.1.2 SCHEDULED MANAGEMENT OPERATIONS SWAT will simulate 14 different types of management operations. The first four variables on all management lines are identical while the remaining ten are operation specific. The variables for the different operations will be defined in separate sections. The type of operation simulated is identified by the code given for the variable MGT_OP. The different codes for MGT_OP are: 1 2 3 4 5
6 7
8
9 10
11
12
13 0
planting/beginning of growing season: this operation initializes the growth of a specific land cover/plant type in the HRU irrigation operation: this operation applies water to the HRU fertilizer application: this operation adds nutrients to the soil in the HRU pesticide application: this operation applies a pesticide to the plant and/or soil in the HRU harvest and kill operation: this operation harvests the portion of the plant designated as yield, removes the yield from the HRU and converts the remaining plant biomass to residue on the soil surface. It also sets IGRO = 0 which allows the next crop to be planted. tillage operation: this operation mixes the upper soil layers and redistributes the nutrients/chemicals/etc. within those layers harvest only operation: this operation harvests the portion of the plant designated as yield and removes the yield from the HRU, but allows the plant to continue growing. (this operation is used for hay cuttings) kill/end of growing season: this operation stops all plant growth and converts all plant biomass to residue. It also sets IGRO = 0 which allows the next crop to be planted. grazing operation: this operation removes plant biomass at a specified rate and allows simultaneous application of manure. auto irrigation initialization: this operation initializes auto irrigation within the HRU. Auto irrigation applies water whenever the plant experiences a user-specified level of water stress. auto fertilization initialization: this operation initializes auto fertilization within the HRU. Auto fertilization applies nutrients whenever the plant experiences a user-specified level of nitrogen stress. street sweeping operation: this operation removes sediment and nutrient build-up on impervious areas in the HRU. This operation can only be used when the urban build up/wash off routines are activated for the HRU. release/impound: this operation releases/impounds water in HRUs growing rice or other plants end of year rotation flag: this operation identifies the end of the operation scheduling for the year.
The operations must be listed in chronological order starting in January.
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.1 PLANTING/BEGINNING OF GROWING SEASON The variables which may be entered on the planting line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total base zero heat units at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 1 for planting/beginning of growing season
HEAT UNITS
Total heat units for cover/plant to reach maturity.
NCR
Land cover/plant identification number from crop.dat.
HITAR
Harvest index target ((kg/ha)/(kg/ha)) (optional). This variable along with BIO_TARG allow the user to specify the harvest index and biomass produced by the plant every year. The model will then simulate plant growth to meet these specified values. If you are studying the effect of management practices on yields or you want the biomass to vary in response to different weather conditions, you would not want to use HITAR or BIO_TARG.
BIO_TARG
Biomass (dry weight) target (metric tons/ha) (optional). This variable along with HITAR allow the user to specify the harvest index and biomass produced by the plant every year. The model will then simulate plant growth to meet these specified values. If you are studying the effect of management practices on yields or you want the biomass to vary in response to different weather conditions, you would not want to use HITAR or BIO_TARG.
ALAINIT
Initial leaf area index (optional). This variable would be used only for covers/plants which are transplanted rather than established from seeds.
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
111
Variable name
Definition
BIO_INIT
Initial dry weight biomass (kg/ha) (optional). This variable would be used only for covers/plants that are transplanted rather than established from seeds.
CNOP
SCS runoff curve number for moisture condition II (optional). The initial curve number for the HRU is input in the second line of the .mgt file. If you wish to use one moisture condition II curve number for the entire year, place that value in the second line of the .mgt file and do not enter values for CNOP in the operation lines. If you want the moisture condition II value to vary through the year, management operations 1, 5, and 6 allow new runoff curve numbers to be entered. The curve number value for CNOP should be for pervious ground. In HRUs with urban areas, the model will adjust the curve number to reflect the impact of the impervious areas.
The format of the planting operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
HEAT UNITS
space 21-28
decimal (xxxx.xxx)
f8.3
NCR
space 29-32
4-digit integer
i4
HITAR
space 33-40
decimal (xxxx.xxx)
f8.3
BIO_TARG
space 41-48
decimal (xxxx.xxx)
f8.3
ALAINIT
space 49-56
decimal (xxxx.xxx)
f8.3
BIO_INIT
space 61-66
decimal (xx.xxx)
f6.3
CNOP
space 67-72
decimal (xx.xxx)
f6.3
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.2 IRRIGATION OPERATION The variables which may be entered on the irrigation line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 2 for irrigation operation
IRR_AMT
Depth of irrigation water applied on HRU (mm).
IRR_SALT
Concentration of salt in irrigation water (mg/L) (optional). not currently active
The format of the irrigation operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
IRR_AMT
space 33-40
decimal (xxxx.xxx)
f8.3
IRR_SALT
space 41-48
decimal (xxxx.xxx)
f8.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
113
37.1.2.3 FERTILIZER APPLICATION The variables which may be entered on the fertilization line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 3 for fertilizer application
FRT_LY1
Fraction of fertilizer applied to top 10mm of soil. The remaining fraction is applied to the 1st soil layer below 10 mm. If FRT_LY1 is set to 0, the model applies 20% of the fertilizer to the top 10mm and the remainder to the 1st soil layer below 10mm.
FERT_ID
Fertilizer identification number. This corresponds to the line number of the fertilizer in fert.dat. If no identification number is provided, the model assumes 28-10-10 is being applied.
FRT_KG
Amount of fertilizer applied to HRU (kg/ha).
The format of the fertilizer application line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
FRY_LY1
space 21-28
decimal (xxxx.xxx)
f8.3
FERT_ID
space 29-32
4-digit integer
i4
FRT_KG
space 33-40
decimal (xxxx.xxx)
f8.3
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.4 PESTICIDE APPLICATION The variables which may be entered on the pesticide application line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 4 for pesticide application
PEST_ID
Pesticide identification code from pesticide database (pest.dat).
PST_KG
Amount of pesticide applied to HRU (kg/ha).
The format of the pesticide application line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
PEST_ID
space 57-60
4-digit integer
i4
PST_KG
space 61-66
decimal (xx.xxx)
f6.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
115
37.1.2.5 HARVEST AND KILL OPERATION The variables which may be entered on the harvest and kill line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 5 for harvest and kill operation
CNOP
SCS runoff curve number for moisture condition II (optional). The initial curve number for the HRU is input in the second line of the .mgt file. If you wish to use one moisture condition II curve number for the entire year, place that value in the second line of the .mgt file and do not enter values for CNOP in the operation lines. If you want the moisture condition II value to vary through the year, management operations 1, 5, and 6 allow new runoff curve numbers to be entered. The curve number value for CNOP should be for pervious ground. In HRUs with urban areas, the model will adjust the curve number to reflect the impact of the impervious areas.
The format of the harvest and kill line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
CNOP
space 67-72
decimal (xx.xxx)
f6.3
116
SWAT USER'S MANUAL, VERSION 2000
37.1.2.6 TILLAGE OPERATION The variables which may be entered on the tillage line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 6 for tillage operation
TILLAGE_ID
Tillage implement code from till.dat
CNOP
SCS runoff curve number for moisture condition II (optional). The initial curve number for the HRU is input in the second line of the .mgt file. If you wish to use one moisture condition II curve number for the entire year, place that value in the second line of the .mgt file and do not enter values for CNOP in the operation lines. If you want the moisture condition II value to vary through the year, management operations 1, 5, and 6 allow new runoff curve numbers to be entered. The curve number value for CNOP should be for pervious ground. In HRUs with urban areas, the model will adjust the curve number to reflect the impact of the impervious areas.
The format of the tillage operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
TILLAGE_ID
space 29-32
4-digit integer
i4
CNOP
space 67-72
decimal (xx.xxx)
f6.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
117
37.1.2.7 HARVEST OPERATION The variables which may be entered on the harvest line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 7 for the harvest only operation
HIOVR
Harvest index override ((kg/ha)/(kg/ha)) Optional. This variable will force the ratio of yield to total aboveground biomass to the specified value. The harvest index in the plant growth database (crop.dat) assumes only the seed is being harvested. If biomass is cut and removed (for example, in hay cuttings), HIOVR must be used to specify the amount of biomass removed.
HARVEFF
Harvest efficiency. Optional. This variable defines the efficiency of the harvest operation and is set to a value between 0.0 and 1.0. If the efficiency is set to a value less than 1.0 the fraction of biomass or yield removed is the fraction defined by the harvest efficiency. The remainder is converted to residue. If no value is defined for HARVEFF, the model assumes all of the biomass/yield is removed.
The format of the harvest operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
HIOVR
space 21-28
decimal (xxxx.xxx)
f8.3
HARVEFF
space 33-40
decimal (xxxx.xxx)
f8.3
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.8 KILL OPERATION The variables which may be entered on the kill line are listed and described below Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units for the year at which operation takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 8 for kill operation
The format of the kill line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
119
37.1.2.9 GRAZING OPERATION The variables which may be entered on the grazing line are listed and described below Variable name
Definition
MONTH
Month grazing begins.
DAY
Day grazing begins.
HUSC
Fraction of total heat units for the year at which grazing begins. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 9 for grazing operation
BMEAT
Dry weight of biomass consumed daily ((kg/ha)/day).
NDGRAZ
Number of consecutive days grazing takes place in the HRU.
BMTRMP
Dry weight of biomass trampled daily ((kg/ha)/day) (optional). Trampling becomes significant as the number of animals grazed per hectare increases. This is a very subjective value which is typically set equal to BMEAT, i.e. the animals trample as much as they eat.
WMANURE
Dry weight of manure deposited daily ((kg/ha)/day).
IGFTYP
Manure identification code from fert.dat.
The format of the grazing operation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
BMEAT
space 21-28
decimal (xxxx.xxx)
f8.3
NDGRAZ
space 29-32
4-digit integer
i4
BMTRMP
space 33-40
decimal (xxxx.xxx)
f8.3
WMANURE
space 49-56
decimal (xxxx.xxx)
f8.3
IGFTYP
space 57-60
4-digit integer
i4
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.10 AUTO IRRIGATION INITIALIZATION The variables which may be entered on the auto irrigation line are listed and described below Variable name
Definition
MONTH
Month auto irrigation is initialized.
DAY
Day auto irrigation is initialized.
HUSC
Fraction of total heat units for the year at which auto irrigation is initialized. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 10 for auto irrigation initialization
AUTO_WSTR
Water stress factor of cover/plant that triggers irrigation. The water stress factor is calculated by dividing the growth of the plant undergoing water stress by the growth of the plant if there was no water stress. This factor ranges from 0.0 to 1.0 where 0.0 indicates there is no growth of the plant due to water stress and 1.0 indicates there is no reduction of plant growth due to water stress.
The format of the auto irrigation line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
AUTO_WSTR
space 33-40
decimal (xxxx.xxx)
f8.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
121
37.1.2.11 AUTO FERTILIZATION INITIALIZATION Auto fertilization needs to be initialized only once in the management file. The variables which may be entered on the auto fertilization line are listed and described below. Variable name
Definition
MONTH
Month initialization takes place.
DAY
Day initialization takes place.
HUSC
Fraction of total heat units for the year at which initialization takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 11 for auto fertilization initialization
AUTO_NSTR
Nitrogen stress factor of cover/plant that triggers fertilization. The nitrogen stress factor is calculated by dividing the growth of the plant undergoing nitrogen stress by the growth of the plant if there was no nitrogen stress. This factor ranges from 0.0 to 1.0 where 0.0 indicates there is no growth of the plant due to nitrogen stress and 1.0 indicates there is no reduction of plant growth due to nitrogen stress.
FERT_ID
Fertilizer identification number. This corresponds to the line number of the fertilizer in fert.dat. If this variable is left blank or set to zero, the model will apply the commercial fertilizer 28-10-10.
AUTO_NMXS
Maximum amount of mineral N allowed in any one application (kg N/ha). If this variable is left blank, the model will set AUTO_NMXS = 200.
AUTO_NMXA
Maximum amount of mineral N allowed to be applied in any one year (kg N/ha). If this variable is left blank, the model will set AUTO_NMXA = 300.
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Variable name
Definition
AUTO_EFF
Application efficiency. The amount of fertilizer applied in auto fertilization is based on the amount of nitrogen removed at harvest. If you set AUTO_EFF = 1.0, the model will apply enough fertilizer to replace the amount of nitrogen removed at harvest. If AUTO_EFF > 1.0, the model will apply fertilizer to meet harvest removal plus an extra amount to make up for nitrogen losses due to surface runoff/leaching. If AUTO_EFF < 1.0, the model will apply fertilizer at the specified fraction below the amount removed at harvest. If this variable is left blank, the model will set AUTO_EFF = 1.3.
AFRT_LY1
Fraction of fertilizer applied to top 10mm of soil. The remaining fraction is applied to the 1st soil layer below 10mm. If this variable is left blank, the model will set AFRT_LY1 = 0.2.
The format of the auto fertilization line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
AUTO_NSTR
space 21-28
decimal (xxxx.xxx)
f8.3
FERT_ID
space 29-32
4-digit integer
i4
AUTO_NMXS
space 33-40
decimal (xxxx.xxx)
f8.3
AUTO_NMXA
space 41-48
decimal (xxxx.xxx)
f8.3
AUTO_EFF
space 61-66
decimal (xx.xxx)
f6.3
AFRT_LY1
space 67-72
decimal (xx.xxx)
f6.3
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
123
37.1.2.12 STREET SWEEPING OPERATION The street sweeping operation can be used only if the urban build up/wash off routines have been selected for the HRU. The variables which may be entered on the street sweeping line are listed and described below. Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of heat units at which street sweeping takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 12 for street sweeping
SWEEPEFF
Removal efficiency of sweeping operation. SWEEPEFF is a fraction that ranges between 0.0 and 1.0. A value of 0.0 indicates that none of the built-up sediments are removed while a value of 1.0 indicates that all of the built-up sediments are removed.
AVWSP
Fraction of curb length available for sweeping. The amount of curb length available for sweeping may be less than the total length due to the presence of parked cars and other obstructions. AVWSP can range from 0.01 to 1.00. If no value is entered for AVWSP (AVWSP left blank or set to 0.0, the model will assume 100% of the curb length is available for sweeping.
The format of the street sweeping line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
SWEEPEFF
space 21-28
decimal (xxxx.xxx)
f8.3
AVWSP
space 33-40
decimal (xxxx.xxx)
f8.3
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SWAT USER'S MANUAL, VERSION 2000
37.1.2.13 RELEASE/IMPOUND OPERATION The release/impound operation can be used only in the HRU designated as a depressional/impounded area in the subbasin (IPOT in .hru). The variables which may be entered on the release/impound line are listed and described below. Variable name
Definition
MONTH
Month operation takes place.
DAY
Day operation takes place.
HUSC
Fraction of total heat units at which release/impounding takes place. (If MONTH and DAY are not provided, HUSC must be set to a value)
MGT_OP
Management operation number. MGT_OP = 13 for release/impoundment of water
IREL_IMP
Release/impound action code: 0 initiate water impoundment 1 initiate water release
The format of the release/impound line is Variable name
Position
Format
F90 Format
MONTH
space 1-4
4-digit integer
i4
DAY
space 5-8
4-digit integer
i4
HUSC
space 9-16
decimal (xxxx.xxx)
f8.3
MGT_OP
space 17-20
4-digit integer
i4
IREL_IMP
space 29-32
4-digit integer
i4
37.1.2.14 END OF YEAR OPERATION SWAT requires a blank line to be inserted after all operations for a single year are listed. The blank line lets the model know that there will be no more operations in the year. If a rotation is being simulated in which the land is left fallow for one of the years with no operations occurring, a blank line should be entered for the fallow year.
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT
125
37.2 WATER USE INPUT FILE (.WUS) The water use file quantifies consumptive water use in the watershed. The water removed is considered to be lost from the system. This file is used to simulate removal of water for irrigation outside the watershed or removal of water for urban/industrial use. Following is a brief description of the variables in the water use input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first three lines of the .wus file are reserved for user comments. The comments may take up to 80 spaces on each line. (optional)
WUPND(mon)
Average daily water removal from the pond for the month (104 m3/day). (optional)
WURCH(mon)
Average daily water removal from the reach for the month (104 m3/day). (optional)
WUSHAL(mon)
Average daily water removal from the shallow aquifer for the month (104 m3/day). (optional)
WUDEEP(mon)
Average daily water removal from the deep aquifer for the month (104 m3/day). (optional)
The format of the water use file is: Variable name
Position
Format
F90 Format
1-3
space 1-80
character
a80
WUPND(1)
4
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUPND(2)
4
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUPND(3)
4
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUPND(4:)
4
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUPND(5)
4
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUPND(6)
4
space 51-60
decimal (xxxxxxxx.x)
f10.1
TITLE
Line #
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
WUPND(7)
5
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUPND(8)
5
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUPND(9)
5
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUPND(10)
5
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUPND(11)
5
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUPND(12)
5
space 51-60
decimal (xxxxxxxx.x)
f10.1
WURCH(1)
6
space 1-10
decimal (xxxxxxxx.x)
f10.1
WURCH(2)
6
space 11-20
decimal (xxxxxxxx.x)
f10.1
WURCH(3)
6
space 21-30
decimal (xxxxxxxx.x)
f10.1
WURCH(4)
6
space 31-40
decimal (xxxxxxxx.x)
f10.1
WURCH(5)
6
space 41-50
decimal (xxxxxxxx.x)
f10.1
WURCH(6)
6
space 51-60
decimal (xxxxxxxx.x)
f10.1
WURCH(7)
7
space 1-10
decimal (xxxxxxxx.x)
f10.1
WURCH(8)
7
space 11-20
decimal (xxxxxxxx.x)
f10.1
WURCH(9)
7
space 21-30
decimal (xxxxxxxx.x)
f10.1
WURCH(10)
7
space 31-40
decimal (xxxxxxxx.x)
f10.1
WURCH(11)
7
space 41-50
decimal (xxxxxxxx.x)
f10.1
WURCH(12)
7
space 51-60
decimal (xxxxxxxx.x)
f10.1
WUSHAL(1)
8
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUSHAL(2)
8
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUSHAL(3)
8
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUSHAL(4)
8
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUSHAL(5)
8
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUSHAL(6)
8
space 51-60
decimal (xxxxxxxx.x)
f10.1
WUSHAL(7)
9
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUSHAL(8)
9
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUSHAL(9)
9
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUSHAL(10)
9
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUSHAL(11)
9
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUSHAL(12)
9
space 51-60
decimal (xxxxxxxx.x)
f10.1
WUDEEP(1)
10
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUDEEP(2)
10
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUDEEP(3)
10
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUDEEP(4)
10
space 31-40
decimal (xxxxxxxx.x)
f10.1
CHAPTER 37: SWAT INPUT—LAND/WATER MANAGEMENT Variable name
Line #
127
Position
Format
F90 Format
WUDEEP(5)
10
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUDEEP(6)
10
space 51-60
decimal (xxxxxxxx.x)
f10.1
WUDEEP(7)
11
space 1-10
decimal (xxxxxxxx.x)
f10.1
WUDEEP(8)
11
space 11-20
decimal (xxxxxxxx.x)
f10.1
WUDEEP(9)
11
space 21-30
decimal (xxxxxxxx.x)
f10.1
WUDEEP(10)
11
space 31-40
decimal (xxxxxxxx.x)
f10.1
WUDEEP(11)
11
space 41-50
decimal (xxxxxxxx.x)
f10.1
WUDEEP(12)
11
space 51-60
decimal (xxxxxxxx.x)
f10.1
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 38
SWAT INPUT DATA: GROUNDWATER
SWAT partitions groundwater into two aquifer systems: a shallow, unconfined aquifer which contributes return flow to streams within the watershed and a deep, confined aquifer which contributes return flow to streams outside the watershed. The properties governing water movement into and out of the aquifers are initialized in the groundwater input file.
127
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38.1 GROUNDWATER INPUT FILE (.GW) Following is a brief description of the variables in the groundwater input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .gw file is reserved for user comments. The comments may take up to 80 spaces. (optional)
SHALLST
Initial depth of water in the shallow aquifer (mm H2O).
DEEPST
Initial depth of water in the deep aquifer (mm H2O). If no value for DEEPST is entered, the model sets DEEPST = 1000.0.
GW_DELAY
Groundwater delay (days). The time required for water leaving the bottom of the root zone to reach the shallow aquifer.
ALPHA_BF
Baseflow alpha factor (days). The baseflow alpha factor, or recession constant, characterizes the groundwater recession curve. This constant will be some number less than 1.0, and will be large (approach one) for flat recessions and small (approach zero) for steep recessions.
GWQMN
Threshold depth of water in the shallow aquifer required for return flow to occur (mm H2O). Groundwater flow to the reach is allowed only if the depth of water in the shallow aquifer is equal to or greater than GWQMN.
GW_REVAP
Groundwater "revap" coefficient. This variable controls the amount of water which will move from the shallow aquifer to the root zone as a result of soil moisture depletion and the amount of direct water uptake from deep rooted trees and shrubs. As GW_REVAP approaches 0, movement of water from the shallow aquifer to the root zone is restricted. As GW_REVAP approaches 1, the rate of transfer from the shallow aquifer to the root zone approaches the rate of potential evapotranspiration. The value for GW_REVAP should be between 0.02 and 0.20.
CHAPTER 38: SWAT INPUT—GROUNDWATER
129
Variable name
Definition
REVAPMN
Threshold depth of water in the shallow aquifer for "revap" or percolation to the deep aquifer to occur (mm H2O). Movement of water from the shallow aquifer to the unsaturated zone or to the deep aquifer is allowed only if the volume of water in the shallow aquifer is equal to or greater than REVAPMN.
RCHRG_DP
Deep aquifer percolation fraction. The fraction of percolation from the root zone which recharges the deep aquifer. The value for RCHRG_DP should be between 0.0 and 1.0.
GWHT
Initial groundwater height (m). Optional.
GW_SPYLD
Specific yield of the shallow aquifer (m3/m3). Specific yield is defined as the ratio of the volume of water that drains by gravity to the total volume of rock. Optional.
GWNO3
Concentration of nitrate in groundwater contribution to streamflow from subbasin (mg N/L). Optional.
GWSOLP
Concentration of soluble phosphorus in groundwater contribution to streamflow from subbasin (mg P/L). Optional.
The groundwater file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
SHALLST
2
real
free
DEEPST
3
real
free
GW_DELAY
4
real
free
ALPHA_BF
5
real
free
GWQMN
6
real
free
GW_REVAP
7
real
free
REVAPMN
8
real
free
RCHRG_DP
9
real
free
GWHT
10
real
free
GW_SPYLD
11
real
free
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
GWNO3
12
real
free
GWSOLP
13
real
free
CHAPTER 39
SWAT INPUT DATA: MAIN CHANNEL
In order to simulate the physical processes affecting the flow of water and transport of sediment in the channel network of the watershed, SWAT requires information on the physical characteristics of the main channel within each subbasin. The main channel input file (.rte) summarizes the physical characteristics of the channel which affect water flow and sediment and pesticide transport.
131
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SWAT USER'S MANUAL, VERSION 2000
39.1 MAIN CHANNEL INPUT FILE (.RTE) Following is a brief description of the variables in the main channel input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the .rte file is reserved for user comments. The comments may take up to 80 spaces. (optional)
CH_W(2)
Average width of main channel at top of bank (m).
CH_D
Depth of main channel from top of bank to bottom (m).
CH_S(2)
Average slope of main channel along the channel length (m/m).
CH_L(2)
Length of main channel (km). If no value for CH_L is entered, the model will set CH_L = 0.001.
CH_N(2)
Manning's "n" value for the main channel.
CH_K(2)
Effective hydraulic conductivity in main channel alluvium (mm/hr).
CH_EROD
Channel erodibility factor. CH_EROD is set to a value between 0.0 and 1.0. A value of 0.0 indicates a nonerosive channel while a value of 1.0 indicates no resistance to erosion.
CH_COV
Channel cover factor. CH_COV is set to a value between 0.0 and 1.0. A value of 0.0 indicates that the channel is completely protected from degradation by cover while a value of 1.0 indicates there is no vegetative cover on the channel.
CH_WDR
Channel width-depth ratio (m/m). Required only if channel degradation is being modeled (IDEG = 1 in .cod).
ALPHA_BNK
Baseflow alpha factor for bank storage (days). The baseflow alpha factor, or recession constant, characterizes the bank storage recession curve. This constant will be some number less than 1.0, and will be large (approach one) for flat recessions and small (approach zero) for steep recessions. If no value is entered for ALPHA_BNK, the variable will be set to the same value as ALPHA_BF from the groundwater (.gw) file.
CHAPTER 39: SWAT INPUT—MAIN CHANNEL
133
The main channel file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the main channel input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
CH_W(2)
2
real
free
CH_D
3
real
free
CH_S(2)
4
real
free
CH_L(2)
5
real
free
CH_N(2)
6
real
free
CH_K(2)
7
real
free
CH_EROD
8
real
free
CH_COV
9
real
free
CH_WDR
10
real
free
ALPHA_BNK
11
real
free
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 40
SWAT INPUT DATA: RESERVOIRS/PONDS
Impoundment structures modify the movement of water in the channel network by lowering the peak flow and volume of flood discharges. Because impoundments slow down the flow of water, sediment will fall from suspension, removing nutrient and chemicals adsorbed to the soil particles. SWAT is able to model three types of impoundments. The first type is a small structure with one spillway. Releases occur only when the storage volume of the structure is exceeded and the excess volume is released within one day. The second type of impoundment is a small, uncontrolled reservoir with a principal and emergency spillway. Water is released at a specified rate when the volume of the reservoir exceeds the principal spillway volume. Volume exceeding the
135
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SWAT USER'S MANUAL, VERSION 2000
emergency spillway storage is released within one day. The third type of impoundment is a managed reservoir. Water may be released from the managed reservoir based on measured outflow or target reservoir volumes. The features of an impoundment are shown in Figure 40.1.
Figure 40.1: Components of a reservoir with flood water detention features.
SWAT uses two files to store information on impoundments, the reservoir input file (.res) and the pond input file (.pnd). An unlimited number of reservoirs may be modeled in a watershed, while a pond may be modeling in each subbasin. Originally, the pond input file contained data for simple impoundment structures with only one spillway while data for the more complex impoundments was stored in the reservoir input file. Due to user needs, the options allowed for impoundments directly linked to subbasins have been increased over the years and the distinction between "reservoirs" and "ponds" as simulated by SWAT has become somewhat blurred. The key difference between the two structures is that a reservoir is located on the main channel network of the watershed while a pond is located off of the main channel within a subbasin. Consequently, water contribution from a pond is limited to the water loading generated within a subbasin. The following table has been prepared to assist the user in identifying the input file and required variables to be used for different situations. The structures are identified on the basis of how water is released.
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
137
Structure to be modeled: impoundment with one spillway where water is released when the storage capacity is exceeded. There is no control on the rate of water release from the impoundment. File: .pnd Additional comments: values are entered for the principal spillway only—no data should be entered for the emergency spillway (PND_ESA, PND_EVOL) or target storage (IFLOD1, IFLOD2, NDTARG). Structure to be modeled: small, uncontrolled reservoir with a principal and emergency spillway where water release is passive (e.g. gravity driven). Water is not released until the level stored in the reservoir is above the height of the principal spillway. If the water level is above the principal spillway, water is released at a known rate. If the level of the reservoir rises above the emergency spillway, the volume of water exceeding the storage capacity of the emergency spillway is released within a day. File: .res Additional comments: the variable IRESCO should be set to 0 and the data required for this option entered. Structure to be modeled: large, managed reservoir where the daily or monthly values for outflow are known. File: .res Additional comments: the variable IRESCO should be set to 1 or 3 depending on the outflow data available. Structure to be modeled: a reservoir which is managed to maintain a certain volume of water (the target storage) at all times File: .pnd or .res Additional comments: In the .pnd file, the target storage is calculated by the model. The target storage varies between the principal and emergency storage as a function of the time of year—the target storage approaches the principal spillway storage in the flood season while during the non-flood season it approaches the emergency spillway storage. In the .res file, the user specifies the target storage for each month of the year. To use target storage in the .pnd file, values must be entered for PND_ESA, PND_EVOL, IFLOD1, IFLOD2 and NDTARG. To use target storage in the .res file, IRESCO is set to 2 and the data required for this option is entered.
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SWAT USER'S MANUAL, VERSION 2000
40.1 RESERVOIR INPUT FILE (.RES) Following is a brief description of the variables in the reservoir input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the file is reserved for user comments. The comments may take up to 80 spaces. (optional)
RES_SUB
Number of the subbasin the reservoir is in (weather for subbasin is used for the reservoir). If no subbasin number is assigned to RES_SUB, the model uses weather data from subbasin 1 to model climatic processes on the reservoir.
MORES
Month the reservoir became operational (1-12). If 0 is input for MORES and IYRES, the model assumes the reservoir is in operation at the beginning of the simulation.
IYRES
Year of the simulation the reservoir became operational (eg 1980). If 0 is input for MORES and IYRES, the model assumes the reservoir is in operation at the beginning of the simulation.
RES_ESA
Reservoir surface area when the reservoir is filled to the emergency spillway (ha)
RES_EVOL
Volume of water needed to fill the reservoir to the emergency spillway (104 m3).
RES_PSA
Reservoir surface area when the reservoir is filled to the principal spillway (ha).
RES_PVOL
Volume of water needed to fill the reservoir to the principal spillway (104 m3).
RES_VOL
Initial reservoir volume. If the reservoir is in existence at the beginning of the simulation period, the initial reservoir volume is the volume on the first day of simulation. If the reservoir begins operation in the midst of a SWAT simulation, the initial reservoir volume is the volume of the reservoir the day the reservoir becomes operational (104 m3).
RES_SED
Initial sediment concentration in the reservoir (mg/L).
RES_NSED
Normal sediment concentration in the reservoir (mg/L).
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
Variable name
Definition
RES_K
Hydraulic conductivity of the reservoir bottom (mm/hr).
IRESCO
Outflow simulation code: 0
1 2
3
139
compute outflow for uncontrolled reservoir with average annual release rate (if IRESCO=0, need RES_RR) measured monthly outflow (if IRESCO=1, need RESOUT) simulated controlled outflowtarget release (if IRESCO=2, need STARG, IFLOD1R, IFLOD2D, and NDTARGR measured daily outflow (if IRESCO=3, need RESDAYO)
OFLOWMX(mon)
Maximum daily outflow for the month (m3/s). Set all months to zero if you do not want to trigger this requirement.
OFLOWMN(mon)
Minimum daily outflow for the month (m3/s). Set all months to zero if you do not want to trigger this requirement.
RES_RR
Average daily principal spillway release rate (m3/s). Needed if IRESCO = 0.
RESMONO
Name of monthly reservoir outflow file. Required if IRESCO = 1.
IFLOD1R
Beginning month of non-flood season. Needed if IRESCO = 2.
IFLOD2R
Ending month of non-flood season. Needed if IRESCO = 2.
NDTARGR
Number of days to reach target storage from current reservoir storage. Needed if IRESCO = 2.
STARG(mon)
Monthly target reservoir storage (104 m3). Needed if IRESCO = 2.
RESDAYO
Name of daily reservoir outflow file. Required if IRESCO = 3.
WURESN(mon,:)
Average amount of water withdrawn from reservoir each day in the month for consumptive use (104 m3). This variable allows water to be removed from the reservoir for use outside the watershed. (optional)
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
WURTNF(:)
Fraction of water removed from the reservoir via WURESN that is returned and becomes flow out of reservoir (m3/m3). (optional)
The reservoir file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the reservoir input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
RES_SUB
2
integer
free
MORES
3
integer
free
IYRES
4
integer
free
RES_ESA
5
real
free
RES_EVOL
6
real
free
RES_PSA
7
real
free
RES_PVOL
8
real
free
RES_VOL
9
real
free
RES_SED
10
real
free
RES_NSED
11
real
free
RES_K
12
real
free
IRESCO
13
integer
free
COMMENT LINE
14
character
a80
OFLOWMX(1)
15
real
free
OFLOWMX(2)
15
real
free
OFLOWMX(3)
15
real
free
OFLOWMX(4)
15
real
free
OFLOWMX(5)
15
real
free
OFLOWMX(6)
15
real
free
COMMENT LINE
16
character
a80
OFLOWMX(7)
17
real
free
OFLOWMX(8)
17
real
free
OFLOWMX(9)
17
real
free
OFLOWMX(10)
17
real
free
OFLOWMX(11)
17
real
free
CHAPTER 40: SWAT INPUT—RESERVOIR/POND Variable name
Line #
Format
F90 Format
OFLOWMX(12)
17
real
free
COMMENT LINE
18
character
a80
OFLOWMN(1)
19
real
free
OFLOWMN(2)
19
real
free
OFLOWMN(3)
19
real
free
OFLOWMN(4)
19
real
free
OFLOWMN(5)
19
real
free
OFLOWMN(6)
19
real
free
COMMENT LINE
20
character
a80
OFLOWMN(7)
21
real
free
OFLOWMN(8)
21
real
free
OFLOWMN(9)
21
real
free
OFLOWMN(10)
21
real
free
OFLOWMN(11)
21
real
free
OFLOWMN(12)
21
real
free
RES_RR
22
real
free
RESMONO
23
character (len=13)
a13
IFLOD1R
24
integer
free
IFLOD2R
25
integer
free
NDTARGR
26
integer
free
COMMENT LINE
27
character
a80
STARG(1)
28
real
free
STARG(2)
28
real
free
STARG(3)
28
real
free
STARG(4)
28
real
free
STARG(5)
28
real
free
STARG(6)
28
real
free
COMMENT LINE
29
character
a80
STARG(7)
30
real
free
STARG(8)
30
real
free
STARG(9)
30
real
free
STARG(10)
30
real
free
STARG(11)
30
real
free
STARG(12)
30
real
free
RESDAYO
31
character (len=13)
a13
141
142
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
COMMENT LINE
32
character
a80
WURESN(1)
33
real
free
WURESN(2)
33
real
free
WURESN(3)
33
real
free
WURESN(4)
33
real
free
WURESN(5)
33
real
free
WURESN(6)
33
real
free
COMMENT LINE
34
character
a80
WURESN(7)
35
real
free
WURESN(8)
35
real
free
WURESN(9)
35
real
free
WURESN(10)
35
real
free
WURESN(11)
35
real
free
WURESN(12)
35
real
free
WURTNF
36
real
free
40.2 DAILY RESERVOIR OUTFLOW FILE When measured daily outflow is used for a reservoir, the name of the file containing the data is assigned to the variable RESDAYO. The daily outflow file contains the flow rate for every day of operation of the reservoir, beginning with the first day of operation in the simulation. The daily outflow file contains one variable: Variable name
Definition
TITLE
The first line of the file is reserved for a description. The description may take up to 80 spaces. (optional)
RES_OUTFLOW
The water release rate for the day (m3/sec).
The format of the daily reservoir outflow file is: Variable name TITLE RES_OUTFLOW
Line #
Position
Format
F90 Format
1
space 1-80
character
a80
2-END
space 1-8
decimal(xxxxx.xx)
f8.2
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
143
40.3 MONTHLY RESERVOIR OUTFLOW FILE When outflow data average over a month is used for a reservoir, the name of the file containing the data is assigned to the variable RESMONO. The monthly outflow file contains the average daily flow rate for every month of operation of the reservoir, beginning with the first month of operation in the simulation. The monthly outflow file contains the following variables: Variable name
Definition
TITLE
The first line of the file is reserved for a description. The description may take up to 80 spaces. (optional)
RESOUT(mon,yr)
Measured average daily outflow from the reservoir for the month (m3/s). Needed when IRESCO = 1. There must be a line of input for every year of simulation.
Variable name TITLE
Line #
Format
F90 Format
1
character
a80
If IRESCO = 1, the model will read the input data for RESOUT. There should be one line for data for RESOUT for each year of simulation beginning with the 1st year of simulation. RESOUT(1,yr)
2-END
real
free
RESOUT(2,yr)
2-END
real
free
RESOUT(3,yr)
2-END
real
free
RESOUT(4,yr)
2-END
real
free
RESOUT(5,yr)
2-END
real
free
RESOUT(6,yr)
2-END
real
free
RESOUT(7,yr)
2-END
real
free
RESOUT(8,yr)
2-END
real
free
RESOUT(9,yr)
2-END
real
free
RESOUT(10,yr)
2-END
real
free
RESOUT(11,yr)
2-END
real
free
RESOUT(12,yr)
2-END
real
free
144
SWAT USER'S MANUAL, VERSION 2000
40.4 POND INPUT FILE (.PND) Following is a brief description of the variables in the subbasin pond input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line of the file is reserved for user comments. The comments may take up to 80 spaces. Optional.
POND SECTION TITLE
The second line of the file is reserved for a section title for the pond data. The model does not process this line. The title may take up to 80 spaces. Optional.
PND_FR
Fraction of subbasin area that drains into ponds. The value for PND_FR should be between 0.0 and 1.0.
PND_PSA
Surface area of ponds when filled to principal spillway (ha).
PND_PVOL
Volume of water stored in ponds when filled to the principal spillway (104 m3 H2O).
PND_ESA
Surface area of ponds when filled to emergency spillway (ha). Optional.
PND_EVOL
Volume of water stored in ponds when filled to the emergency spillway (104 m3 H2O). Optional.
PND_VOL
Initial volume of water in ponds (104 m3 H2O).
PND_SED
Initial sediment concentration in pond water (mg/L).
PND_NSED
Normal sediment concentration in pond water (mg/L).
PND_K
Hydraulic conductivity through bottom of ponds (mm/hr).
IFLOD1
Beginning month of non-flood season. Optional.
IFLOD2
Ending month of non-flood season. Optional.
NDTARG
Number of days needed to reach target storage from current pond storage. The default value for NDTARG is 15 days. Optional.
PSETL1
Phosphorus settling rate in pond for months IPND1 through IPND2 (m/year). Optional.
PSETL2
Phosphorus settling rate in pond for months other than IPND1-IPND2 (m/year). Optional.
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
145
Variable name
Definition
NSETL1
Nitrogen settling rate in pond for months IPND1 through IPND2 (m/year). Optional.
NSETL2
Nitrogen settling rate in pond for months other than IPND1-IPND2 (m/year). Optional.
CHLA
Chlorophyll a production coefficient for ponds. Chlorophyll a concentration in the pond is calculated from the total phosphorus concentration. The equation assumes the system is phosphorus limited. The chlorophyll a coefficient was added to the equation to allow the user to adjust results to account for other factors ignored by the basic equation such as nitrogen limitations. The default value for CHLA is 1.00, which uses the original equation. Optional.
SECCI
Water clarity coefficient for ponds. The clarity of the pond is expressed by the secci-disk depth (m) which is calculated as a function of chlorophyll a. Because suspended sediment also can affect water clarity, the water clarity coefficient has been added to the equation to allow users to adjust for the impact of factors other than chlorophyll a on water clarity. The default value for SECCI is 1.00, which uses the original equation. Optional.
PND_NO3
Initial concentration of NO3-N in pond (mg N/L). Optional.
PND_SOLP
Initial concentration of soluble P in pond (mg P/L). Optional.
PND_ORGN
Initial concentration of organic N in pond (mg N/L). Optional.
PND_ORGP
Initial concentration of organic P in pond (mg P/L). Optional.
COMMON VARIABLES SECTION TITLE
The 25th line of the file is reserved for a section title for data used for ponds and wetlands. The model does not process this line. The title may take up to 80 spaces. Optional.
IPND1
Beginning month of mid-year nutrient settling “season”. Optional.
IPND2
Ending month of mid-year nutrient settling “season”. Optional.
146
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
WETLAND SECTION TITLE
The 28th line of the file is reserved for a section title for the wetland data. The model does not process this line. The title may take up to 80 spaces. Optional.
WET_FR
Fraction of subbasin area that drains into wetlands.
WET_NSA
Surface area of wetlands at normal water level (ha).
WET_NVOL
Volume of water stored in wetlands when filled to normal water level (104 m3 H2O).
WET_MXSA
Surface area of wetlands at maximum water level (ha).
WET_MXVOL
Volume of water stored in wetlands when filled to maximum water level (104 m3 H2O).
WET_VOL
Initial volume of water in wetlands (104 m3 H2O).
WET_SED
Initial sediment concentration in wetland water (mg/L).
WET_NSED
Normal sediment concentration in wetland water (mg/L).
WET_K
Hydraulic conductivity of bottom of wetlands (mm/hr).
PSETLW1
Phosphorus settling rate in wetland for months IPND1 through IPND2 (m/year). Optional.
PSETLW2
Phosphorus settling rate in wetlands for months other than IPND1-IPND2 (m/year). Optional.
NSETLW1
Nitrogen settling rate in wetlands for months IPND1 through IPND2 (m/year). Optional.
NSETLW2
Nitrogen settling rate in wetlands for months other than IPND1-IPND2 (m/year). Optional.
CHLAW
Chlorophyll a production coefficient for wetlands. Chlorophyll a concentration in the wetland is calculated from the total phosphorus concentration. The equation assumes the system is phosphorus limited. The chlorophyll a coefficient was added to the equation to allow the user to adjust results to account for other factors ignored by the basic equation such as nitrogen limitations. The default value for CHLA is 1.00, which uses the original equation. Optional.
CHAPTER 40: SWAT INPUT—RESERVOIR/POND
147
Variable name
Definition
SECCIW
Water clarity coefficient for wetlands. The clarity of the wetland is expressed by the secci-disk depth (m) which is calculated as a function of chlorophyll a. Because suspended sediment also can affect water clarity, the water clarity coefficient has been added to the equation to allow users to adjust for the impact of factors other than chlorophyll a on water clarity. The default value for SECCI is 1.00, which uses the original equation. Optional.
WET_NO3
Initial concentration of NO3-N in wetland (mg N/L). Optional.
WET_SOLP
Initial concentration of soluble P in wetland (mg P/L). Optional.
WET_ORGN
Initial concentration of organic N in wetland (mg N/L). Optional.
WET_ORGP
Initial concentration of organic P in wetland (mg P/L). Optional.
The pond input file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the pond input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
POND SECT. TITLE
2
character
a80
PND_FR
3
real
free
PND_PSA
4
real
free
PND_PVOL
5
real
free
PND_ESA
6
real
free
PND_EVOL
7
real
free
PND_VOL
8
real
free
PND_SED
9
real
free
PND_NSED
10
real
free
PND_K
11
real
free
IFLOD1
12
integer
free
IFLOD2
13
integer
free
148
SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
NDTARG
14
integer
free
PSETL1
15
real
free
PSETL2
16
real
free
NSETL1
17
real
free
NSETL2
18
real
free
CHLA
19
real
free
SECCI
20
real
free
PND_NO3
21
real
free
PND_SOLP
22
real
free
PND_ORGN
23
real
free
PND_ORGP
24
real
free
POND/WETLAND SECT. TITLE
25
character
a80
IPND1
26
integer
free
IPND2
27
integer
free
WETLAND SECT. TITLE
28
character
a80
WET_FR
29
real
free
WET_NSA
30
real
free
WET_NVOL
31
real
free
WET_MXSA
32
real
free
WET_MXVOL
33
real
free
WET_VOL
34
real
free
WET_SED
35
real
free
WET_NSED
36
real
free
WET_K
37
real
free
PSETLW1
38
real
free
PSETLW2
39
real
free
NSETLW1
40
real
free
NSETLW2
41
real
free
CHLAW
42
real
free
SECCIW
43
real
free
WET_NO3
44
real
free
WET_SOLP
45
real
free
WET_ORGN
46
real
free
WET_ORGP
47
real
free
CHAPTER 41
SWAT INPUT DATA: WATER QUALITY
While water quality is a broad subject, the primary areas of concern are nutrients, organic chemicals—both agricultural (pesticide) and industrial, heavy metals, bacteria and sediment levels in streams and large water bodies. SWAT is able to model processes affecting nutrient, pesticide and sediment levels in the main channels and reservoirs. The data used by SWAT for water quality is primarily contained within three files: the stream water quality input file (.swq), the general water quality input file (.wwq), and the lake water quality input file (.lwq). The stream water quality input file and the general water quality input file contain input data used in the QUAL2E subroutines in the model.
147
148
SWAT USER'S MANUAL, VERSION 2000
41.1 GENERAL WATER QUALITY INPUT FILE (.WWQ) The general water quality input file contains information used by SWAT to initialize stream water quality variables that apply to the entire watershed. Following is a brief description of the variables in the general water quality input file. The variables are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line is reserved for user comments. This line is not processed by the model.
LAO
Qual2E light averaging option. Qual2E defines four light averaging options. 1
Depth-averaged algal growth attenuation factor for light (FL) is computed from one daylight average solar radiation value calculated in the steady state temperature heat balance.
2
FL is computed from one daylight average solar radiation value supplied by the user.
3
FL is obtained by averaging the hourly daylight values of FL computed from the hourly daylight values of solar radiation calculated in the steady state temperature heat balance.
4
FL is obtained by averaging the hourly daylight values of FL computed from the hourly daylight values of solar radiation calculated from a single value of total daily, photosynthetically active, solar radiation and an assumed cosine function.
The only option currently active in SWAT is 2. IGROPT
Qual2E algal specific growth rate option. Qual2E provides three different options for computing the algal growth rate. 1
Multiplicative: the effects of nitrogen, phosphorus and light are multiplied together to calculate the net effect on the local algal growth rate
CHAPTER 41: SWAT INPUT—WATER QUALITY
Variable name
Definition
IGROPT, cont.
2
3
149
Limiting nutrient: the local algal growth rate is limited by light and one of the nutrients (nitrogen or phosphorus) Harmonic mean: the local algal growth rate is limited by light and the harmonic mean of the nutrient interactions
The default option is the limiting nutrient option (2). AI0
Ratio of chlorophyll-a to algal biomass (µg-chla/mg algae). Values for AI0 should fall in the range 10-100. If no value for AI0 is entered, the model will set AI0 = 50.0.
AI1
Fraction of algal biomass that is nitrogen (mg N/mg alg). Values for AI1 should fall in the range 0.07-0.09. If no value for AI1 is entered, the model will set AI1 = 0.08.
AI2
Fraction of algal biomass that is phosphorus (mg P/mg alg). Values for AI2 should fall in the range 0.01-0.02. If no value for AI2 is entered, the model will set AI2 = 0.015.
AI3
The rate of oxygen production per unit of algal photosynthesis (mg O2/mg alg). Values for AI3 should fall in the range 1.4-1.8. If no value for AI3 is entered, the model will set AI3 = 1.6.
AI4
The rate of oxygen uptake per unit of algal respiration (mg O2/mg alg). Values for AI4 should fall in the range 1.6-2.3. If no value for AI4 is entered, the model will set AI4 = 2.0.
AI5
The rate of oxygen uptake per unit of NH3-N oxidation (mg O2/mg NH3-N). Values for AI5 should fall in the range 3.0-4.0. If no value for AI5 is entered, the model will set AI5 = 3.5.
AI6
The rate of oxygen uptake per unit of NO2-N oxidation (mg O2/mg NO2-N). Values for AI6 should fall in the range 1.00-1.14. If no value for AI6 is entered, the model will set AI6 = 1.07.
MUMAX
Maximum specific algal growth rate at 20º C (day-1). Values for MUMAX should fall in the range 1.0-3.0. If no value for MUMAX is entered, the model will set MUMAX = 2.0.
RHOQ
Algal respiration rate at 20º C (day-1). Values for RHOQ should fall in the range 0.05-0.50. If no value for RHOQ is entered, the model will set RHOQ = 0.30.
150
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
TFACT
Fraction of solar radiation computed in the temperature heat balance that is photosynthetically active. Values for TFACT should fall in the range 0.01-1.0. If no value for TFACT is entered, the model will set TFACT = 0.3.
K_L
Half-saturation coefficient for light (kJ/(m2·min)). Values for K_L should fall in the range 0.2227-1.135. If no value for K_L is entered, the model will set K_L = 0.75.
K_N
Michaelis-Menton half-saturation constant for nitrogen (mg N/L). Values for K_N should fall in the range 0.010.30. If no value for K_N is entered, the model will set K_N = 0.02.
K_P
Michaelis-Menton half-saturation constant for phosphorus (mg P/L). Values for K_P should fall in the range 0.0010.050. If no value for K_P is entered, the model will set K_P = 0.025.
LAMBDA0
Non-algal portion of the light extinction coefficient (m-1). If no value for LAMBDA0 is entered, the model will set LAMBDA0 = 1.0.
LAMBDA1
Linear algal self-shading coefficient (m-1·(µg chla/L)-1). Values for LAMBDA1 should fall in the range 0.00650.065. If no value for LAMBDA1 is entered, the model will set LAMBDA1 = 0.03.
LAMBDA2
Nonlinear algal self-shading coefficient (m-1·(µg chla/L)-2/3). The recommended value for LAMBDA2 is 0.0541. If no value for LAMBDA2 is entered, the model will set LAMBDA2 = 0.054.
P_N
Algal preference factor for ammonia. Values for P_N should fall in the range 0.01-1.0. If no value for P_N is entered, the model will set P_N = 0.5.
The watershed water quality file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the general water quality input file is:
CHAPTER 41: SWAT INPUT—WATER QUALITY
Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
LAO
2
integer
free
IGROPT
3
integer
free
AI0
4
real
free
AI1
5
real
free
AI2
6
real
free
AI3
7
real
free
AI4
8
real
free
AI5
9
real
free
AI6
10
real
free
MUMAX
11
real
free
RHOQ
12
real
free
TFACT
13
real
free
K_L
14
real
free
K_N
15
real
free
K_P
16
real
free
LAMBDA0
17
real
free
LAMBDA1
18
real
free
LAMBDA2
19
real
free
P_N
20
real
free
151
152
SWAT USER'S MANUAL, VERSION 2000
41.2 STREAM WATER QUALITY INPUT FILE (.SWQ) The stream water quality input file contains information used by SWAT to set main channel water quality attributes in the subbasins. Following is a brief description of the variables in the stream water quality input file. The variables are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line is reserved for user comments. This line is not processed by the model.
NUTRIENT TITLE
The second line is reserved for the nutrient section title. This line is not processed by the model.
DISOX
Initial dissolved oxygen concentration in the reach (mg O2/L). If no value for DISOX is entered, the model sets DISOX = 0.0.
BOD
Initial carbonaceous biochemical oxygen demand in the reach (mg O2/L). If no value for BOD is entered, the model sets BOD = 0.0.
ALGAE
Initial chlorophyll-a concentration in the reach (mg chla/L). If no value for ALGAE is entered, the model sets ALGAE = 0.0.
ORGANICN
Initial organic nitrogen concentration in the reach (mg org N-N/L). If no value for ORGANICN is entered, the model sets ORGANICN = 0.0.
AMMONIAN
Initial ammonia concentration in the reach (mg NH3-N/L). If no value for AMMONIAN is entered, the model sets AMMONIAN = 0.0.
NITRITEN
Initial nitrite concentration in the reach (mg NO2-N/L). If no value for NITRITEN is entered, the model sets NITRITEN = 0.0.
NITRATEN
Initial nitrate concentration in the reach (mg NO3-N/L). If no value for NITRATEN is entered, the model sets NITRATEN = 0.0.
CHAPTER 41: SWAT INPUT—WATER QUALITY
153
Variable name
Definition
ORGANICP
Initial organic phosphorus concentration in the reach (mg org P-P/L). If no value for ORGANICP is entered, the model sets ORGANICP = 0.0.
DISOLVP
Initial dissolved phosphorus concentration in the reach (mg sol P-P/L). If no value for DISOLVP is entered, the model sets DISOLVP = 0.0.
RS1
Local algal settling rate in the reach at 20º C (m/day). Values for RS1 should fall in the range 0.15 to 1.82. If no value for RS1 is entered, the model sets RS1 = 1.0.
RS2
Benthic (sediment) source rate for dissolved phosphorus in the reach at 20º C (mg dissolved P/(m2·day)). If no value for RS2 is entered, the model sets RS2 = 0.05.
RS3
Benthic source rate for NH4-N in the reach at 20º C (mg NH4-N/(m2·day)). If no value for RS3 is entered, the model sets RS3 = 0.5.
RS4
Rate coefficient for organic N settling in the reach at 20º C (day-1). Values for RS4 should fall in the range 0.001 to 0.10. If no value for RS4 is entered, the model sets RS4 = 0.05.
RS5
Organic phosphorus settling rate in the reach at 20º C (day-1). Values for RS5 should fall in the range 0.001 to 0.1. If no value for RS5 is entered, the model sets RS5 = 0.05.
RS6
Rate coefficient for settling of arbitrary non-conservative constituent in the reach at 20º C (day-1). If no value for RS6 is entered, the model sets RS6 = 2.5. (not currently used by the model)
RS7
Benthic source rate for arbitrary non-conservative constituent in the reach at 20º C (mg ANC/(m2·day)). If no value for RS7 is entered, the model sets RS7 = 2.5. (not currently used by the model)
RK1
Carbonaceous biological oxygen demand deoxygenation rate coefficient in the reach at 20º C (day-1). Values for RK1 should fall in the range 0.02 to 3.4. If no value for RK1 is entered, the model sets RK1 = 1.71.
RK2
Oxygen reaeration rate in accordance with Fickian diffusion in the reach at 20º C (day-1). Values for RK2 should fall in the range 0.01 to 100.0. If no value for RK2 is entered, the model sets RK2 = 50.0.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
RK3
Rate of loss of carbonaceous biological oxygen demand due to settling in the reach at 20º C (day-1). Values for RK3 should fall in the range -0.36 to 0.36. The recommended default for RK3 is 0.36 (not set by model).
RK4
Benthic oxygen demand rate in the reach at 20º C (mg O2/(m2·day)). If no value for RK4 is entered, the model sets RK4 = 2.0.
RK5
Coliform die-off rate in the reach at 20º C (day-1). Values for RK5 should fall in the range 0.05 to 4.0. If no value for RK5 is entered, the model sets RK5 = 2.0.
RK6
Decay rate for arbitrary non-conservative constituent in the reach at 20º C (day-1). If no value for RK6 is entered, the model sets RK6 = 1.71. (not currently used by the model)
BC1
Rate constant for biological oxidation of NH4 to NO2 in the reach at 20º C (day-1). Values for BC1 should fall in the range 0.1 to 1.0. If no value for BC1 is entered, the model sets BC1 = 0.55.
BC2
Rate constant for biological oxidation of NO2 to NO3 in the reach at 20º C (day-1). Values for BC2 should fall in the range 0.2 to 2.0. If no value for BC2 is entered, the model sets BC2 = 1.1.
BC3
Rate constant for hydrolysis of organic N to NH4 in the reach at 20º C (day-1). Values for BC3 should fall in the range 0.2 to 0.4. If no value for BC3 is entered, the model sets BC3 = 0.21.
BC4
Rate constant for mineralization of organic P to dissolved P in the reach at 20º C (day-1). Values for BC4 should fall in the range 0.01 to 0.70. If no value for BC4 is entered, the model sets BC4 = 0.35.
PESTICIDE TITLE
This line is reserved for the pesticide section title. This line is not processed by the model.
CHPST_CONC
Initial pesticide concentration in reach (mg/m3 H2O). (Optional)
CHPST_REA
Pesticide reaction coefficient in reach (day-1). If no value for CHPST_REA is entered, the model will set CHPST_REA = 0.007. (Optional)
CHAPTER 41: SWAT INPUT—WATER QUALITY
155
Variable name
Definition
CHPST_VOL
Pesticide volatilization coefficient in reach (m/day). If no value for CHPST_VOL is entered, the model will set CHPST_VOL = 0.01. (Optional)
CHPST_KOC
Pesticide partition coefficient between water and air in reach (m3/day). If no value for CHPST_KOC is entered, the model will set CHPST_KOC = 0. (Optional)
CHPST_STL
Settling velocity for pesticide sorbed to sediment (m/day). If no value for CHPST_STL is entered, the model will set CHPST_STL = 1.0. (Optional)
CHPST_RSP
Resuspension velocity for pesticide sorbed to sediment (m/day). If no value for CHPST_RSP is entered, the model will set CHPST_RSP = 0.002. (Optional)
CHPST_MIX
Mixing velocity (diffusion/dispersion) for pesticide in reach (m/day). If no value for CHPST_MIX is entered, the model will set CHPST_MIX = 0.001. (Optional)
SEDPST_CONC
Initial pesticide concentration in reach bed sediment (mg/m3 sediment). (Optional)
SEDPST_REA
Pesticide reaction coefficient in reach bed sediment (day-1). If no value for SEDPST_REA is entered, the model will set SEDPST_REA = 0.05. (Optional)
SEDPST_BRY
Pesticide burial velocity in reach bed sediment (m/day). If no value for SEDPST_BRY is entered, the model will set SEDPST_BRY = 0.002. (Optional)
SEDPST_ACT
Depth of active sediment layer for pesticide (m). If no value for SEDPST_ACT is entered, the model will set SEDPST_ACT = 0.03. (Optional)
The stream water quality file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the stream water quality input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
NUTRIENT TITLE
2
character
a80
DISOX
3
real
free
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Variable name
Line #
Format
F90 Format
BOD
4
real
free
ALGAE
5
real
free
ORGANICN
6
real
free
AMMONIAN
7
real
free
NITRITEN
8
real
free
NITRATEN
9
real
free
ORGANICP
10
real
free
DISOLVP
11
real
free
RS1
12
real
free
RS2
13
real
free
RS3
14
real
free
RS4
15
real
free
RS5
16
real
free
RS6
17
real
free
RS7
18
real
free
RK1
19
real
free
RK2
20
real
free
RK3
21
real
free
RK4
22
real
free
RK5
23
real
free
RK6
24
real
free
BC1
25
real
free
BC2
26
real
free
BC3
27
real
free
BC4
28
real
free
PESTICIDE TITLE
29
character
a80
CHPST_CONC
30
real
free
CHPST_REA
31
real
free
CHPST_VOL
32
real
free
CHPST_KOC
33
real
free
CHPST_STL
34
real
free
CHPST_RSP
35
real
free
CHPST_MIX
36
real
free
SEDPST_CONC
37
real
free
SEDPST_REA
38
real
free
CHAPTER 41: SWAT INPUT—WATER QUALITY Variable name
Line #
Format
F90 Format
SEDPST_BRY
39
real
free
SEDPST_ACT
40
real
free
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41.3 LAKE WATER QUALITY INPUT FILE (.LWQ) The lake water quality input file contains information used by SWAT to model nutrient and pesticide water quality in reservoirs. Following is a brief description of the variables in the lake water quality input file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first line is reserved for user comments. This line is not processed by the model.
NUTRIENT TITLE
The second line is reserved for the nutrient section title. This line is not processed by the model.
IRES1
Beginning month of mid-year nutrient settling period. Optional.
IRES2
Ending month of mid-year nutrient settling period. Optional.
PSETLR1
Phosphorus settling rate in reservoir for months IRES1 through IRES2 (m/year). Optional.
PSETLR2
Phosphorus settling rate in reservoir for months other than IRES1-IRES2 (m/year). Optional.
NSETLR1
Nitrogen settling rate in reservoir for months IRES1 through IRES2 (m/year). Optional.
NSETLR2
Nitrogen settling rate in reservoir for months other than IRES1-IRES2 (m/year). Optional.
CHLAR
Chlorophyll a production coefficient for reservoir. Chlorophyll a concentration in the reservoir is calculated from the total phosphorus concentration. The equation assumes the system is phosphorus limited. The chlorophyll a coefficient was added to the equation to allow the user to adjust results to account for other factors not taken into account by the basic equation such as nitrogen limitations. The default value for CHLA is 1.00, which uses the original equation. Optional.
CHAPTER 41: SWAT INPUT—WATER QUALITY
159
Variable name
Definition
SECCIR
Water clarity coefficient for the reservoir. The clarity of the reservoir is expressed by the secci-disk depth (m) which is calculated as a function of chlorophyll a. Because suspended sediment also can affect water clarity, the water clarity coefficient has been added to the equation to allow users to adjust for the impact of factors other than chlorophyll a on water clarity. The default value for SECCI is 1.00, which uses the original equation. Optional.
RES_ORGP
Initial concentration of organic P in reservoir (mg P/L). Optional.
RES_SOLP
Initial concentration of soluble P in reservoir (mg P/L). Optional.
RES_ORGN
Initial concentration of organic N in reservoir (mg N/L). Optional.
RES_NO3
Initial concentration of NO3-N in reservoir (mg N/L). Optional.
RES_NH3
Initial concentration of NH3-N in reservoir (mg N/L). Optional.
RES_NO2
Initial concentration of NO2-N in reservoir (mg N/L). Optional.
PESTICIDE TITLE
This line is reserved for the pesticide section title. This line is not processed by the model.
LKPST_CONC
Initial pesticide concentration in the reservoir water for the pesticide defined by IRTPEST (.bsn). While up to ten pesticides may be applied in a SWAT simulation, only one pesticide (IRTPEST) is routed through the channel network. (mg/m3)
LKPST_REA
Reaction coefficient of the pesticide in reservoir water (day-1)
LKPST_VOL
Volatilization coefficient of the pesticide from the reservoir water (m/day)
LKPST_KOC
Partition coefficient (m3/day)
LKPST_STL
Settling velocity of pesticide sorbed to sediment (m/day)
LKPST_RSP
Resuspension velocity of pesticide sorbed to sediment (m/day).
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Variable name
Definition
LKPST_MIX
Mixing velocity (m/day)
LKSPST_CONC
Initial pesticide concentration in the reservoir bottom sediments. (mg/m3)
LKSPST_REA
Reaction coefficient of pesticide in reservoir bottom sediment (day-1)
LKSPST_BRY
Burial velocity of pesticide in reservoir bottom sediment (m/day)
LKSPST_ACT
Depth of active sediment layer in reservoir (m)
The lake water quality file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the lake water quality input file is: Variable name
Line #
Format
F90 Format
TITLE
1
character
a80
NUTRIENT TITLE
2
character
a80
IRES1
3
integer
free
IRES2
4
integer
free
PSETLR1
5
real
free
PSETLR2
6
real
free
NSETLR1
7
real
free
NSETLR2
8
real
free
CHLAR
9
real
free
SECCIR
10
real
free
RES_ORGP
11
real
free
RES_SOLP
12
real
free
RES_ORGN
13
real
free
RES_NO3
14
real
free
RES_NH3
15
real
free
RES_NO2
16
real
free
CHAPTER 41: SWAT INPUT—WATER QUALITY
Variable name
Line #
Format
F90 Format
PESTICIDE TITLE
17
character
a80
LKPST_CONC
18
real
free
LKPST_REA
19
real
free
LKPST_VOL
20
real
free
LKPST_KOC
21
real
free
LKPST_STL
22
real
free
LKPST_RSP
23
real
free
LKPST_MIX
24
real
free
LKSPST_CONC
25
real
free
LKSPST_REA
26
real
free
LKSPST_BRY
27
real
free
LKSPST_ACT
28
real
free
161
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CHAPTER 42
SWAT INPUT DATA: DATABASES
SWAT uses five databases to store information required for plant growth, urban land characteristics, tillage, fertilizer components and pesticide properties. These databases are supplied with the model. The following sections summarize the required information needed within the five databases.
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42.1 LAND COVER/PLANT GROWTH DATABASE FILE (CROP.DAT) Following is a brief description of the variables in the land cover/plant growth database file. They are listed in the order they appear within the file. Variable name
Definition
ICNUM
Land cover/plant code. The different plants listed in crop.dat must have consecutive values for ICNUM. ICNUM is the numeric code used in the management file to identify the land cover to be modeled.
CPNM
A four character code to represent the land cover/plant name. This code is printed to the output files.
IDC
Land cover/plant classification: 1 2 3 4 5 6 7
warm season annual legume cold season annual legume perennial legume warm season annual cold season annual perennial trees
DESCRIPTION
Full land cover/plant name. This description is not used by the model and is present to assist the user in differentiating between plant species.
BIO_E
Radiation-use efficiency or biomass-energy ratio ((kg/ha)/(MJ/m2)). This is the potential (unstressed) growth rate (including roots) per unit of intercepted photosynthetically active radiation. This parameter can greatly change the rate of growth, incidence of stress during the season and the resultant yield. This parameter should be one of the last to be adjusted. Adjustments should be based on research results. Care should be taken to make adjustments based only on data with no drought, nutrient or temperature stress.
HVSTI
Harvest index. This is the plant yield divided by the total aboveground biomass ((kg/ha)/(kg/ha)). This plant parameter should be based on experimental data where crop stresses have been minimized to allow the crop to attain its potential. SWAT will adjust the harvest index if water stress occurs near flowering.
CHAPTER 42: SWAT INPUT—DATABASES
159
Variable name
Definition
BLAI
Maximum potential leaf area index. The values for BLAI in the plant growth database are based on average plant densities in dryland (rainfed) agriculture. BLAI may need to be adjusted for drought-prone regions where planting densities are much smaller or irrigated conditions where densities are much greater.
FRGRW1
Fraction of the plant growing season or fraction of total potential heat units corresponding to the 1st point on the optimal leaf area development curve. The total potential heat units are the number of heat units required to bring the plant to maturity.
LAIMX1
Fraction of the maximum leaf area index corresponding to the 1st point on the optimal leaf area development curve.
FRGRW2
Fraction of the plant growing season or fraction of total potential heat units corresponding to the 2nd point on the optimal leaf area development curve. The total potential heat units are the number of heat units required to bring the plant to maturity.
LAIMX2
Fraction of the maximum leaf area index corresponding to the 2nd point on the optimal leaf area development curve.
DLAI
Fraction of growing season when leaf area declines (heat units/heat units).
CHTMX
Maximum canopy height (m).
RDMX
Maximum root depth (m).
T_OPT
Optimal temperature for plant growth (ºC). Both optimal and base temperatures are very stable for cultivars within a species.
T_BASE
Minimum (base) temperature for plant growth (ºC).
CNYLD
Normal fraction of nitrogen in yield (kg N/kg yield). This value is estimated on a dry weight basis.
CPYLD
Normal fraction of phosphorus in yield (kg P/kg yield). This value is estimated on a dry weight basis.
BN(1)
Nitrogen uptake parameter #1: normal fraction of nitrogen in plant biomass at emergence (kg N/kg biomass)
BN(2)
Nitrogen uptake parameter #2: normal fraction of nitrogen in plant biomass at 50% maturity (kg N/kg biomass)
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
BN(3)
Nitrogen uptake parameter #3: normal fraction of nitrogen in plant biomass at maturity (kg N/kg biomass)
BP(1)
Phosphorus uptake parameter #1: normal fraction of phosphorus in plant biomass at emergence (kg P/kg biomass)
BP(2)
Phosphorus uptake parameter #2: normal fraction of phosphorus in plant biomass at 50% maturity (kg P/kg biomass)
BP(3)
Phosphorus uptake parameter #3: normal fraction of phosphorus in plant biomass at maturity (kg P/kg biomass)
WSYF
Lower limit of harvest index ((kg/ha)/(kg/ha)). The value between 0.0 and HVSTI which represents the lowest harvest index expected due to water stress.
USLE_C
Minimum value of USLE C factor for water erosion applicable to the land cover/plant.
GSI
Maximum stomatal conductance at high solar radiation and low vapor pressure deficit (m·s-1). Used in PenmanMonteith evapotranspiration calculations.
VPDFR
Vapor pressure deficit (kPa) corresponding to the second point on the stomatal conductance curve. (The first point on the stomatal conductance curve is comprised of a vapor pressure deficit of 1 kPa and the fraction of maximum stomatal conductance equal to 1.00.)
FRGMAX
Fraction of maximum stomatal conductance corresponding to the second point on the stomatal conductance curve. (The first point on the stomatal conductance curve is comprised of a vapor pressure deficit of 1 kPa and the fraction of maximum stomatal conductance equal to 1.00.)
WAVP
Rate of decline in radiation use efficiency per unit increase in vapor pressure deficit. The value of WAVP varies among species, but a value of 6 to 8 is suggested as an approximation for most plants.
CO2HI
Elevated CO2 atmospheric concentration (µL CO2/L air) corresponding the 2nd point on the radiation use efficiency curve. (The 1st point on the radiation use efficiency curve is comprised of the ambient CO2 concentration, 330 µL CO2/L air, and the biomass-energy ratio reported for BIO_E)
CHAPTER 42: SWAT INPUT—DATABASES
161
Variable name
Definition
BIOEHI
Biomass-energy ratio corresponding to the 2nd point on the radiation use efficiency curve. (The 1st point on the radiation use efficiency curve is comprised of the ambient CO2 concentration, 330 µL CO2/L air, and the biomassenergy ratio reported for BIO_E.)
RSDCO_PL
Plant residue decomposition coefficient. The fraction of residue that will decompose in a day assuming optimal moisture, temperature, C:N ratio, and C:P ratio. Optional. If no value is entered for RSDCO_PL, it is set equal to the value given for RSDCO in the watershed general attribute (.bsn) file.
42.1.1 FILE FORMAT (CROP.DAT) Four lines are required to store the plant growth parameters for a land cover/plant in the database (crop.dat) file. The plant growth database file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. Variable name
Line #
Format
F90 Format
ICNUM
1
integer
free
CPNM
1
character
a4
IDC
1
integer
free
BIO_E
2
real
free
HVSTI
2
real
free
BLAI
2
real
free
FRGRW1
2
real
free
LAIMX1
2
real
free
FRGRW2
2
real
free
LAIMX2
2
real
free
DLAI
2
real
free
CHTMX
2
real
free
RDMX
2
real
free
T_OPT
3
real
free
T_BASE
3
real
free
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Variable name
Line #
Format
F90 Format
CNYLD
3
real
free
CPYLD
3
real
free
BN(1)
3
real
free
BN(2)
3
real
free
BN(3)
3
real
free
BP(1)
3
real
free
BP(2)
3
real
free
BP(3)
3
real
free
WSYF
4
real
free
USLE_C
4
real
free
GSI
4
real
free
VPDFR
4
real
free
FRGMAX
4
real
free
WAVP
4
real
free
CO2HI
4
real
free
BIOEHI
4
real
free
RSDCO_PL
4
real
free
CHAPTER 42: SWAT INPUT—DATABASES
163
42.2 TILLAGE DATABASE FILE (TILL.DAT) Following is a brief description of the variables in the tillage database file. They are listed in the order they appear within the file. Variable name
Definition
ITNUM
Tillage number. The different tillage operations in till.dat must have consecutive values for ITNUM. ITNUM is the numeric code used in the management file to identify the tillage practice to be modeled.
TILLNM
An eight character code representing the tillage operation name.
EFFMIX
Mixing efficiency of tillage operation. The mixing efficiency specifies the fraction of materials (residue and nutrients) on the soil surface which are mixed uniformly throughout the soil depth mixed by the implement. The remaining fraction of residue and nutrients is left in the original location (soil surface or layer).
DEPTIL
Depth of mixing caused by the tillage operation (mm).
The format of the tillage database file is: Variable name
Line #
Position
Format
F90 Format
ITNUM
ALL
space 1-4
4-digit integer
i4
TILLNM
ALL
space 9-16
character
a8
EFFMIX
ALL
space 25-32
decimal(xxxx.xxx)
f8.3
DEPTIL
ALL
space 41-48
decimal(xxxx.xxx)
f8.3
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42.3 PESTICIDE/TOXIN DATABASE FILE (PEST.DAT) Following is a brief description of the variables in the pesticide/toxin database file. They are listed in the order they appear within the file. Variable name
Definition
IPNUM
Pesticide/toxin number. The different toxins in pest.dat must have consecutive values for IPNUM. IPNUM is the numeric code used in the management file to identify the pesticide/toxin to be applied.
PNAME
Name of pesticide/toxin. (up to 17 characters allowed)
SKOC
Soil adsorption coefficient normalized for soil organic carbon content (mg/kg)/(mg/L). Koc is equal to the soil adsorption coefficient, Kp, divided by the fraction of organic carbon in the soil. Kp is calculated by dividing Csolid phase by Csolution where Csolid phase is the concentration of the chemical sorbed to the solid phase (expressed as mg chemical/kg solid material) and Csolution is the concentration of the chemical in the solution (expressed as mg chemical/L solution). Koc and Kp have the same units.
WOF
Wash-off fraction. Fraction of pesticide on foliage available for wash off by rainfall (0.0-1.0). The pesticide is deposited on the soil surface.
HLIFE_F
Degradation half-life of the chemical on the foliage (days).
HLIFE_S
Degradation half-life of the chemical in the soil (days).
AP_EF
Application efficiency. The fraction of pesticide applied which is deposited on the foliage and soil surface (0.11.0). The remainder is lost.
WSOL
Solubility of the chemical in water (mg/L or ppm)
CHAPTER 42: SWAT INPUT—DATABASES
165
Variable name
Definition
HENRY
Henry's Law Constant (KH) for the chemical (unitless). Henry's Law Constant characterizes the partitioning of the chemical between the air and water. Values for Henry's Law are reported in several different units and care must be taken when obtaining values to make sure they are in the correct units. For calculations in SWAT, KH is defined as Cgas/Csolution where Cgas is the concentration of the chemical in the gas phase (mg/L) and Csolution is defined as the concentration of the chemical in solution. (not currently operational)
The format of the pesticide/toxin database file is: Variable name
Line #
Position
Format
F90 Format
IPNUM
ALL
space 1-3
integer
i3
PNAME
ALL
space 4-20
character
a17
SKOC
ALL
space 21-30
decimal(xxxxxxxx.x)
f10.1
WOF
ALL
space 31-35
decimal(xx.xx)
f5.2
HLIFE_F
ALL
space 36-43
decimal(xxxxxx.x)
f8.1
HLIFE_S
ALL
space 44-51
decimal(xxxxxx.x)
f8.1
AP_EF
ALL
space 52-56
decimal(xx.xx)
f5.2
WSOL
ALL
space 57-67
decimal(xxxxxxx.xxx)
f11.3
HENRY
ALL
space 68-79
exponential(x.xxxxExxx)
e12.4
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42.4 FERTILIZER DATABASE FILE (FERT.DAT) Following is a brief description of the variables in the fertilizer database file. They are listed in the order they appear within the file. Variable name
Definition
IFNUM
Number of fertilizer in database. This number should be equivalent to the line number and is the reference number used in the management file to identify the fertilizer type being applied.
FERTNM
Name of fertilizer/manure (up to 8 characters allowed).
FMINN
Fraction of mineral N (NO3 and NH4) in fertilizer (kg min-N/kg fertilizer). Value should be between 0.0 and 1.0.
FMINP
Fraction of mineral P in fertilizer (kg min-P/kg fertilizer). Value should be between 0.0 and 1.0.
FORGN
Fraction of organic N in fertilizer (kg org-N/kg fertilizer). Value should be between 0.0 and 1.0.
FORGP
Fraction of organic P in fertilizer (kg org-P/kg fertilizer). Value should be between 0.0 and 1.0.
FNH3N
Fraction of mineral N in fertilizer applied as ammonia (kg NH3-N/kg min-N). Value should be between 0.0 and 1.0.
BACTPDB
Concentration of persistent bacteria in manure/fertilizer (# bacteria/kg manure). Optional.
BACTLPDB
Concentration of less-persistent bacteria in manure/fertilizer (# bacteria/kg manure). Optional.
BACTKDDB
Bacteria partition coefficient. Value should be between 0.0 and 1.0. When the bacteria partition coefficient is 0.0, all bacteria are sorbed to soil particles. When the bacteria partition coefficient is 1.0 all bacteria is in solution. Optional.
CHAPTER 42: SWAT INPUT—DATABASES
The format of the fertilizer database file is: Variable name
Line #
Position
Format
F90 Format
IFNUM
ALL
space 1-4
integer
i4
FERTNM
ALL
space 6-13
character
a8
FMINN
ALL
space 14-21
decimal(xxxx.xxx)
f8.3
FMINP
ALL
space 22-29
decimal(xxxx.xxx)
f8.3
FORGN
ALL
space 30-37
decimal(xxxx.xxx)
f8.3
FORGP
ALL
space 38-45
decimal(xxxx.xxx)
f8.3
FNH3N
ALL
space 46-53
decimal(xxxx.xxx)
f8.3
BACTPDB
ALL
space 54-61
decimal(xxxx.xxx)
f8.3
BACTLPDB
ALL
space 62-69
decimal(xxxx.xxx)
f8.3
BACTKDDB
ALL
space 70-77
decimal(xxxx.xxx)
f8.3
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42.5 URBAN DATABASE FILE (URBAN.DAT) Following is a brief description of the variables in the urban database file. They are listed in the order they appear within the file. Variable name
Definition
IUNUM
Number of urban land type. This value should be equivalent to the line number.
URBNAME
4-character code for urban land type.
URBFLNM
Full description for urban land type—may take up to 54 characters. (not used by SWAT)
FIMP
Fraction total impervious area in urban land type. This includes directly and indirectly connected impervious areas.
FCIMP
Fraction directly connected impervious area in urban land type.
CURBDEN
Curb length density in urban land type (km/ha).
URBCOEF
Wash-off coefficient for removal of constituents from impervious area (mm-1)
DIRTMX
Maximum amount of solids allowed to build up on impervious areas (kg/curb km).
THALF
Number of days for amount of solids on impervious areas to build up from 0 kg/curb km to half the maximum allowed, i.e. 1/2 DIRTMX (days).
TNCONC
Concentration of total nitrogen in suspended solid load from impervious areas (mg N/kg sed).
TPCONC
Concentration of total phosphorus in suspended solid load from impervious areas (mg P/kg sed).
TNO3CONC
Concentration of nitrate in suspended solid load from impervious areas (mg NO3-N/kg sed).
CHAPTER 42: SWAT INPUT—DATABASES
169
Every urban land type uses two lines in the urban.dat file to store input values. The format of every set of two lines is described below. Variable name
Line #
Position
Format
F90 Format
IUNUM
1
space 1-3
integer
i3
URBNAME
1
space 5-8
character
a4
URBFLNM
1
space 10-64
character
a54
FIMP
1
space 65-72
decimal(xxxx.xxx)
f8.3
FCIMP
1
space 73-80
decimal(xxxx.xxx)
f8.3
CURBDEN
2
space 5-12
decimal(xxxx.xxx)
f8.3
URBCOEF
2
space 13-20
decimal(xxxx.xxx)
f8.3
DIRTMX
2
space 21-28
decimal(xxxx.xxx)
f8.3
THALF
2
space 29-36
decimal(xxxx.xxx)
f8.3
TNCONC
2
space 37-44
decimal(xxxx.xxx)
f8.3
TPCONC
2
space 45-52
decimal(xxxx.xxx)
f8.3
TNO3CONC
2
space 53-60
decimal(xxxx.xxx)
f8.3
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CHAPTER 43
SWAT INPUT DATA: MEASURED
Four different file formats may be used to store stream loading data that is incorporated directly into the watershed routing. This stream loading data may come from a point source, such as a town's sewage treatment discharge, or it may be output from simulation of an upstream area. The four different file formats allow the user to summarize the data in one of four ways: daily, monthly, yearly, or average annual.
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43.1 DAILY RECORDS (RECDAY .DAT FILE) The recday command in the watershed configuration (.fig) file requires a file containing SWAT input data summarized on a daily time step. An unlimited∗ number of files with daily flow data are allowed in the simulation. The file numbers assigned to the recday files in the watershed configuration file (.fig) must be ≥ 1 and numbered sequentially. Following is a brief description of the variables in the recday input file. They are listed in the order they appear within the file.
∗
Variable name
Definition
TITLE
The first six lines of the file are reserved for user comments. The comments may take up to 80 spaces per line.
DAY
Julian date for record (optional). If the julian date and year are provided for the records, SWAT will search for the beginning day of simulation in the record. If the julian date and year are left blank, SWAT assumes that the first line of record corresponds to the first day of simulation. (SWAT uses the date and year to locate the record corresponding to the first day of simulation. From that point on, the day and year are ignored.)
YEAR
Four-digit year for record (optional). See description of DAY for more information.
FLODAY
Contribution to streamflow for the day (m3)
SEDDAY
Sediment loading to reach for the day (metric tons)
ORGNDAY
Organic N loading to reach for the day (kg N)
ORGPDAY
Organic P loading to reach for the day (kg P)
NO3DAY
NO3 loading to reach for the day (kg N)
MINPDAY
Mineral P loading to reach for the day (kg P)
NH3DAY
NH3 loading to reach for the day (kg N)
NO2DAY
NO2 loading to reach for the day (kg N)
CMTL1DAY
Loading of conservative metal #1 to reach for the day (kg)
Please keep in mind that FORTRAN limits the total number of files that can be open at one time to something in the neighborhood of 250. The input files containing daily data (.pcp, .tmp, and recday) remain open throughout the simulation.
CHAPTER 43: SWAT INPUT—MEASURED
Variable name
Definition
CMTL2DAY
Loading of conservative metal #2 to reach for the day (kg)
CMTL3DAY
Loading of conservative metal #3 to reach for the day (kg)
BACTPDAY
Loading of persistent bacteria to reach for the day (# bact)
BACTLPDAY
Loading of less persistent bacteria to reach for the day (# bact)
169
One line of data is required for every day of the simulation period. The recday data file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the recday data file is: Variable name TITLE
Line #
Format
F90 Format
1-6
character
a80
DAY
7-END
integer
free
YEAR
7-END
integer
free
FLODAY
7-END
real or exponential
free
SEDDAY
7-END
real or exponential
free
ORGNDAY
7-END
real or exponential
free
ORGPDAY
7-END
real or exponential
free
NO3DAY
7-END
real or exponential
free
MINPDAY
7-END
real or exponential
free
NH3DAY
7-END
real or exponential
free
NO2DAY
7-END
real or exponential
free
CMTL1DAY
7-END
real or exponential
free
CMTL2DAY
7-END
real or exponential
free
CMTL3DAY
7-END
real or exponential
free
BACTPDAY
7-END
real or exponential
free
BACTLPDAY
7-END
real or exponential
free
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SWAT USER'S MANUAL, VERSION 2000
43.2 MONTHLY RECORDS (RECMON .DAT FILE) The recmon command in the watershed configuration (.fig) file requires a file containing input data summarized on a monthly time step. SWAT will accept an unlimited number of data files with monthly flow data. The file numbers assigned to the files in the watershed configuration file (.fig) must be numbered sequentially and begin at 1. Following is a brief description of the variables in the recmon data file. They are listed in the order they appear within the file.
Variable name
Definition
TITLE
The first 6 lines of the data file is reserved for user comments. The comments may take up to 80 spaces.
MONTH
Month of measured data. This variable is provided for the user—it is ignored by SWAT. The model assumes the first line of measured data in the file contains data for January of the first year of simulation. The monthly data file must contain a line of data for every month of simulation in consecutive order.
YEAR
4-digit year of measured data. This variable is provided for the user—it is ignored by SWAT. The model assumes the first line of measured data in the file contains data for January of the first year of simulation. The monthly data file must contain a line of data for every month of simulation in consecutive order.
FLOMON
Average daily water loading for month (m3/day).
SEDMON
Average daily sediment loading for month (metric tons/day).
ORGNMON
Average daily organic nitrogen loading for month (kg N/day).
ORGPMON
Average daily organic phosphorus loading for month (kg P/day).
NO3MON
Average daily nitrate loading for month (kg N/day).
MINPMON
Average daily mineral (soluble) P loading for month (kg P/day).
CHAPTER 43: SWAT INPUT—MEASURED
171
Variable name
Definition
NH3MON
Average daily ammonia loading for month (kg N/day).
NO2MON
Average daily nitrite loading for month (kg N/day).
CMTL1MON
Average daily loading of conservative metal #1 for month (kg/day).
CMTL2MON
Average daily loading of conservative metal #2 for month (kg/day).
CMTL3MON
Average daily loading of conservative metal #3 for month (kg /day).
BACTPMON
Average daily loading of persistent bacteria for month (# bact/day).
BACTLPMON
Average daily loading of less persistent bacteria for month (# bact/day).
The file must contain one line of data for every month of simulation (Even if the simulation begins in a month other than January, the file must contain lines for every month of the first year.) The recmon data file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line. The format of the recmon data file is: Variable name TITLE
Line #
Format
F90 Format
1-6
character
a80
MONTH
7 - END
integer
free
YEAR
7 - END
integer
free
FLOMON
7 - END
real or exponential
free
SEDMON
7 - END
real or exponential
free
ORGNMON
7 - END
real or exponential
free
ORGPMON
7 - END
real or exponential
free
NO3MON
7 - END
real or exponential
free
MINPMON
7 - END
real or exponential
free
NH3MON
7 - END
real or exponential
free
NO2MON
7 - END
real or exponential
free
CMTL1MON
7 - END
real or exponential
free
CMTL2MON
7 - END
real or exponential
free
CMTL3MON
7 - END
real or exponential
free
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
BACTPMON
7 - END
real or exponential
free
BACTLPMON
7 - END
real or exponential
free
43.3 YEARLY RECORDS (RECYEAR .DAT FILE) The recyear command in the watershed configuration (.fig) file requires a file containing SWAT input data summarized on an annual time step. SWAT will accept an unlimited number of data files with yearly flow data. The file numbers assigned to the recyear files in the watershed configuration file (.fig) must be numbered sequentially and begin at 1. Following is a brief description of the variables in the recyear data file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first six lines of the data file are reserved for user comments. The comments may take up to 80 spaces per line.
YEAR
4-digit year of measured data. This variable is provided for the user—it is ignored by SWAT. The model assumes the first line of measured data in the file contains data for the first year of simulation. The yearly data file must contain a line of data for every year of simulation in consecutive order.
FLOYR
Average daily water loading for year (m3/day).
SEDYR
Average daily sediment loading for year (metric tons/day).
ORGNYR
Average daily organic nitrogen loading for year (kg N/day).
ORGPYR
Average daily organic phosphorus loading for year (kg P/day).
NO3YR
Average daily nitrate loading for year (kg N/day).
CHAPTER 43: SWAT INPUT—MEASURED
173
Variable name
Definition
MINPYR
Average daily mineral (soluble) P loading for year (kg P/day).
NH3YR
Average daily ammonia loading for year (kg N/day).
NO2YR
Average daily nitrite loading for year (kg N/day).
CMTL1YR
Average daily loading of conservative metal #1 for year (kg/day).
CMTL2YR
Average daily loading of conservative metal #2 for year (kg/day).
CMTL3YR
Average daily loading of conservative metal #3 for year (kg/day).
BACTPYR
Average daily loading of persistent bacteria for year (# bact/day).
BACTLPYR
Average daily loading of less persistent bacteria for year (# bact/day).
The recyear data file is a free format file. The variables may be placed in any position the user wishes on the line. Values for variables classified as integers should not include a decimal while values for variables classified as reals must contain a decimal. A blank space denotes the end of an input value and the beginning of the next value if there is another on the line.The format of the recyear data file is: Variable name
Line #
Format
F90 Format
TITLE
1-6
character
a80
YEAR
7 - END
integer
free
FLOYR
7 - END
real or exponential
free
SEDYR
7 - END
real or exponential
free
ORGNYR
7 - END
real or exponential
free
ORGPYR
7 - END
real or exponential
free
NO3YR
7 - END
real or exponential
free
MINPYR
7 - END
real or exponential
free
NH3YR
7 - END
real or exponential
free
NO2YR
7 - END
real or exponential
free
CMTL1YR
7 - END
real or exponential
free
CMTL2YR
7 - END
real or exponential
free
CMTL3YR
7 - END
real or exponential
free
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Format
F90 Format
BACTPYR
7 - END
real or exponential
free
BACTLPYR
7 - END
real or exponential
free
43.4 AVERAGE ANNUAL RECORDS (RECCNST .DAT FILE) The reccnst command in the watershed configuration (.fig) file requires a file containing average annual SWAT input data. SWAT will accept an unlimited number of data files with average annual flow data. The file numbers assigned to the reccnst files in the watershed configuration file (.fig) must be numbered sequentially and begin at 1. Following is a brief description of the variables in the reccnst data file. They are listed in the order they appear within the file. Variable name
Definition
TITLE
The first six lines of the data file are reserved for user comments. The comments may take up to 80 spaces on each line.
FLOCNST
Average daily water loading (m3/day)
SEDCNST
Average daily sediment loading (metric tons/day)
ORGNCNST
Average daily organic N loading (kg N/day)
ORGPCNST
Average daily organic P loading (kg P/day)
NO3CNST
Average daily NO3 loading (kg N/day)
MINPCNST
Average daily mineral P loading (kg P/day)
NH3CNST
Average daily NH3 loading (kg N/day)
NO2CNST
Average daily NO2 loading (kg N/day)
CMTL1CNST
Average daily loading of conservative metal #1 (kg/day)
CMTL2CNST
Average daily loading of conservative metal #2 (kg/day)
CMTL3CNST
Average daily loading of conservative metal #3 (kg/day)
BACTPCNST
Average daily loading of persistent bacteria (# bact/day)
BACTLPCNST
Average daily loading of less persistent bacteria (# bact/day)
CHAPTER 43: SWAT INPUT—MEASURED
The format of the reccnst data file is: Variable name TITLE
Line #
Format
F90 Format
1-6
character
a80
FLOCNST
7
real or exponential
free
SEDCNST
7
real or exponential
free
ORGNCNST
7
real or exponential
free
ORGPCNST
7
real or exponential
free
NO3CNST
7
real or exponential
free
MINPCNST
7
real or exponential
free
NH3CNST
7
real or exponential
free
NO2CNST
7
real or exponential
free
CMTL1CNST
7
real or exponential
free
CMTL2CNST
7
real or exponential
free
CMTL3CNST
7
real or exponential
free
BACTPCNST
7
real or exponential
free
BACTLPCNST
7
real or exponential
free
175
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SWAT USER'S MANUAL, VERSION 2000
CHAPTER 44
SWAT OUTPUT DATA: PRIMARY OUTPUT FILES
A number of output files are generated in every SWAT simulation. These files are: the summary output file (output.std), the HRU output file (.sbs), the subbasin output file (.bsb), and the main channel or reach output file (.rch). The detail of the data printed out in each file is controlled by the print codes in the input control code (.cod) file. Average daily values are always printed in the HRU, subbasin and reach files, but the time period they are summarized over will vary. Depending on the print code selected, the output files may include all daily values, daily amounts averaged over the month, daily amounts averaged over the year, or daily amounts averaged over the entire simulation period.
173
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SWAT USER'S MANUAL, VERSION 2000
44.1 INPUT SUMMARY FILE (INPUT.STD) The input summary file prints summary tables of important input values. This file provides the user with a mechanism to spot-check input values. All model inputs are not printed, but the file does contain some of the most important.
44.2 OUTPUT SUMMARY FILE (OUTPUT.STD) The output summary file provides watershed average loadings from the HRUs to the streams. Tables are also included that present average annual HRU and subbasin values for a few parameters.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
175
44.3 HRU OUTPUT FILE (.SBS) The HRU output file contains summary information for each of the hydrologic response units in the watershed. The file is written in spreadsheet format. Following is a brief description of the output variables in the HRU output file. Variable name
Definition
LULC
Four letter character code for the cover/plant on the HRU. (code from crop.dat file)
HRU
Hydrologic response unit number
GIS
GIS code reprinted from watershed configuration file (.fig). See explanation of subbasin command.
SUB
Topographically-defined subbasin to which the HRU belongs.
MGT
Management number. This is pulled from the management (.mgt) file. Used by the SWAT/GRASS interface to allow development of output maps by landuse/management type.
MON
Daily time step: the julian date Monthly time step: the month (1-12) Annual time step: four-digit year Average annual summary lines: total number of years averaged together
AREA
Drainage area of the HRU (km2).
PRECIP
Total amount of precipitation falling on the HRU during time step (mm H2O).
SNOFALL
Amount of precipitation falling as snow, sleet or freezing rain during time step (water-equivalent mm H2O).
SNOMELT
Amount of snow or ice melting during time step (waterequivalent mm H2O).
IRR
Irrigation (mm H2O). Amount of irrigation water applied to HRU during the time step.
PET
Potential evapotranspiration (mm H2O). Potential evapotranspiration from the HRU during the time step.
ET
Actual evapotranspiration (soil evaporation and plant transpiration) from the HRU during the time step (mm H2O).
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
SW
Soil water content (mm H2O). Amount of water in the soil profile at the end of the time period.
PERC
Water that percolates past the root zone during the time step (mm H2O). There is usually a lag between the time the water leaves the bottom of the root zone and reaches the shallow aquifer. Over a long period of time, this variable should equal groundwater recharge (PERC = GW_RCHG as time → ∞).
GW_RCHG
Recharge entering aquifers during time step (total amount of water entering shallow and deep aquifers during time step) (mm H2O).
DA_RCHG
Deep aquifer recharge (mm H2O). The amount of water from the root zone that recharges the deep aquifer during the time step. (shallow aquifer recharge = GW_RCHG - DA_RCHG)
REVAP
Water in the shallow aquifer returning to the root zone in response to a moisture deficit during the time step (mm H2O). The variable also includes water uptake directly from the shallow aquifer by deep tree and shrub roots.
SA_IRR
Irrigation from shallow aquifer (mm H2O). Amount of water removed from the shallow aquifer for irrigation during the time step.
DA_IRR
Irrigation from deep aquifer (mm H2O). Amount of water removed from the deep aquifer for irrigation during the time step.
SA_ST
Shallow aquifer storage (mm H2O). Amount of water in the shallow aquifer at the end of the time period.
DA_ST
Deep aquifer storage (mm H2O). Amount of water in the deep aquifer at the end of the time period.
SURQ
Surface runoff contribution to streamflow in the main channel during time step (mm H2O).
TLOSS
Transmission losses (mm H2O). Water lost from tributary channels in the HRU via transmission through the bed. This water becomes recharge for the shallow aquifer during the time step. Net surface runoff contribution to the main channel streamflow is calculated by subtracting TLOSS from SURQ.
LATQ
Lateral flow contribution to streamflow (mm H2O). Water flowing laterally within the soil profile that enters the main channel during time step.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
177
Variable name
Definition
GW_Q
Groundwater contribution to streamflow (mm H2O). Water from the shallow aquifer that enters the main channel during the time step. Groundwater flow is also referred to as baseflow.
WYLD
Water yield (mm H2O). Total amount of water leaving the HRU and entering main channel during the time step. (WYLD = SURQ + LATQ + GWQ – TLOSS – pond abstractions)
SYLD
Sediment yield (metric tons/ha). Sediment from the HRU that is transported into the main channel during the time step.
USLE
Soil loss during the time step calculated with the USLE equation (metric tons/ha). This value is reported for comparison purposes only.
N_APP
Nitrogen fertilizer applied (kg N/ha). Total amount of nitrogen (mineral and organic) applied in fertilizer during the time step.
P_APP
Phosphorus fertilizer applied (kg P/ha). Total amount of phosphorus (mineral and organic) applied in fertilizer during the time step.
NAUTO
Nitrogen fertilizer auto-applied (kg N/ha). Total amount of nitrogen (mineral and organic) auto-applied during the time step.
PAUTO
Phosphorus fertilizer auto-applied (kg P/ha). Total amount of phosphorus (mineral and organic) auto-applied during the time step.
NGRZ
Nitrogen applied during grazing operation (kg N/ha). Total amount of nitrogen (mineral and organic) added to soil by grazing operation during the time step.
PGRZ
Phosphorus applied during grazing operation (kg P/ha). Total amount of phosphorus (mineral and organic) added to soil by grazing operation during the time step.
NRAIN
Nitrate added to soil profile by rain (kg N/ha).
NFIX
Nitrogen fixation (kg N/ha). Amount of nitrogen fixed by legumes during the time step.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
F-MN
Fresh organic to mineral N (kg N/ha). Mineralization of nitrogen from the fresh residue pool to the nitrate (80%) pool and active organic nitrogen (20%) pool during the time step. A positive value denotes a net gain in the nitrate and active organic pools from the fresh organic pool while a negative value denotes a net gain in the fresh organic pool from the nitrate and active organic pools.
A-MN
Active organic to mineral N (kg N/ha). Movement of nitrogen from the active organic pool to the nitrate pool during the time step.
A-SN
Active organic to stable organic N (kg N/ha). Movement of nitrogen from the active organic pool to the stable organic pool during the time step.
F-MP
Fresh organic to mineral P (kg P/ha). Mineralization of phosphorus from the fresh residue pool to the "active" mineral (80%) pool (P sorbed to soil surface) and the active organic (20%) pool. A positive value denotes a net gain in the active mineral and active organic pools from the fresh organic pool while a negative value denotes a net gain in the fresh organic pool from the active mineral and active organic pools.
AO-LP
Organic to labile mineral P (kg P/ha). Movement of phosphorus between the organic pool and the labile mineral pool during the time step. A positive value denotes a net gain in the labile pool from the organic pool while a negative value denotes a net gain in the organic pool from the labile pool.
L-AP
Labile to active mineral P (kg P/ha). Movement or transformation of phosphorus between the "labile" mineral pool (P in solution) and the "active" mineral pool (P sorbed to the surface of soil particles) during the time step. A positive value denotes a net gain in the active pool from the labile pool while a negative value denotes a net gain in the labile pool from the active pool.
A-SP
Active to stable P (kg P/ha). Movement or transformation of phosphorus between the "active" mineral pool (P sorbed to the surface of soil particles) and the "stable" mineral pool (P fixed in soil) during the time step. A positive value denotes a net gain in the stable pool from the active pool while a negative value denotes a net gain in the active pool from the stable pool.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
179
Variable name
Definition
DNIT
Denitrification (kg N/ha). Transformation of nitrate to gaseous compounds during the time step.
NUP
Plant uptake of nitrogen (kg N/ha). Nitrogen removed from soil by plants during the time step.
PUP
Plant uptake of phosphorus (kg P/ha). Phosphorus removed from soil by plants during the time step.
ORGN
Organic N yield (kg N/ha). Organic nitrogen transported out of the HRU and into the reach during the time step.
ORGP
Organic P yield (kg P/ha). Organic phosphorus transported with sediment into the reach during the time step.
SEDP
Sediment P yield (kg P/ha). Mineral phosphorus sorbed to sediment transported into the reach during the time step.
NSURQ
NO3 in surface runoff (kg N/ha). Nitrate transported with surface runoff into the reach during the time step.
NLATQ
NO3 in lateral flow (kg N/ha). Nitrate transported by lateral flow into the reach during the time step.
NO3L
NO3 leached from the soil profile (kg N/ha). Nitrate that leaches past the bottom of the soil profile during the time step. The nitrate is not tracked through the shallow aquifer.
NO3GW
NO3 transported into main channel in the groundwater loading from the HRU (kg N/ha).
SOLP
Soluble P yield (kg P/ha). Soluble mineral forms of phosphorus transported by surface runoff into the reach during the time step.
P_GW
Soluble phosphorus transported by groundwater flow into main channel during the time step (kg P/ha).
W_STRS
Water stress days during the time step (days).
TMP_STRS
Temperature stress days during the time step (days).
N_STRS
Nitrogen stress days during the time step (days).
P_STRS
Phosphorus stress days during the time step (days).
BIOM
Biomass (metric tons/ha). Total biomass, i.e. aboveground and roots at the end of the time period reported as dry weight.
LAI
Leaf area index at the end of the time period.
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
YLD
Harvested yield (metric tons/ha). The model partitions yield from the total biomass on a daily basis (and reports it). However, the actual yield is not known until it is harvested. The harvested yield is reported as dry weight.
BACTP
Number of persistent bacteria in surface runoff entering reach (count).
BACTLP
Number of less persistent bacteria in surface runoff entering reach (count).
The file format for the HRU output file (.sbs) is: Variable name
Line #
Position
Format
F90 Format
LULC
All
space 1-4
character
a4
HRU
All
space 5-8
4-digit integer
i4
GIS
All
space 10-17
8-digit integer
i8
SUB
All
space 19-22
4-digit integer
i4
MGT
All
space 24-27
4-digit integer
i4
MON
All
space 29-32
4-digit integer
i4
AREA
All
space 33-42
decimal(xxxxxx.xxx)
f10.3
PRECIP
All
space 43-52
decimal(xxxxxx.xxx)
f10.3
SNOFALL
All
space 53-62
decimal(xxxxxx.xxx)
f10.3
SNOMELT
All
space 63-72
decimal(xxxxxx.xxx)
f10.3
IRR
All
space 73-82
decimal(xxxxxx.xxx)
f10.3
PET
All
space 83-92
decimal(xxxxxx.xxx)
f10.3
ET
All
space 93-102
decimal(xxxxxx.xxx)
f10.3
SW
All
space 103-112
decimal(xxxxxx.xxx)
f10.3
PERC
All
space 113-122
decimal(xxxxxx.xxx)
f10.3
GW_RCHG
All
space 123-132
decimal(xxxxxx.xxx)
f10.3
DA_RCHG
All
space 133-142
decimal(xxxxxx.xxx)
f10.3
REVAP
All
space 143-152
decimal(xxxxxx.xxx)
f10.3
SA_IRR
All
space 153-162
decimal(xxxxxx.xxx)
f10.3
DA_IRR
All
space 163-172
decimal(xxxxxx.xxx)
f10.3
SA_ST
All
space 173-182
decimal(xxxxxx.xxx)
f10.3
DA_ST
All
space 183-192
decimal(xxxxxx.xxx)
f10.3
SURQ
All
space 193-202
decimal(xxxxxx.xxx)
f10.3
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES Variable name
Line #
Position
Format
F90 Format
TLOSS
All
space 203-212
decimal(xxxxxx.xxx)
f10.3
LATQ
All
space 213-222
decimal(xxxxxx.xxx)
f10.3
GW_Q
All
space 223-232
decimal(xxxxxx.xxx)
f10.3
WYLD
All
space 233-242
decimal(xxxxxx.xxx)
f10.3
SYLD
All
space 243-252
decimal(xxxxxx.xxx)
f10.3
USLE
All
space 253-262
decimal(xxxxxx.xxx)
f10.3
N_APP
All
space 263-272
decimal(xxxxxx.xxx)
f10.3
P_APP
All
space 273-282
decimal(xxxxxx.xxx)
f10.3
NAUTO
All
space 283-292
decimal(xxxxxx.xxx)
f10.3
PAUTO
All
space 293-302
decimal(xxxxxx.xxx)
f10.3
NGRZ
All
space 303-312
decimal(xxxxxx.xxx)
f10.3
PGRZ
All
space 313-322
decimal(xxxxxx.xxx)
f10.3
NRAIN
All
space 323-332
decimal(xxxxxx.xxx)
f10.3
NFIX
All
space 333-342
decimal(xxxxxx.xxx)
f10.3
F-MN
All
space 343-352
decimal(xxxxxx.xxx)
f10.3
A-MN
All
space 353-362
decimal(xxxxxx.xxx)
f10.3
A-SN
All
space 363-372
decimal(xxxxxx.xxx)
f10.3
F-MP
All
space 373-382
decimal(xxxxxx.xxx)
f10.3
AO-LP
All
space 383-392
decimal(xxxxxx.xxx)
f10.3
L-AP
All
space 393-402
decimal(xxxxxx.xxx)
f10.3
A-SP
All
space 403-412
decimal(xxxxxx.xxx)
f10.3
DNIT
All
space 413-422
decimal(xxxxxx.xxx)
f10.3
NUP
All
space 423-432
decimal(xxxxxx.xxx)
f10.3
PUP
All
space 433-442
decimal(xxxxxx.xxx)
f10.3
ORGN
All
space 443-452
decimal(xxxxxx.xxx)
f10.3
ORGP
All
space 453-462
decimal(xxxxxx.xxx)
f10.3
SEDP
All
space 463-472
decimal(xxxxxx.xxx)
f10.3
NSURQ
All
space 473-482
decimal(xxxxxx.xxx)
f10.3
NLATQ
All
space 483-492
decimal(xxxxxx.xxx)
f10.3
NO3L
All
space 493-502
decimal(xxxxxx.xxx)
f10.3
NO3GW
All
space 503-512
decimal(xxxxxx.xxx)
f10.3
SOLP
All
space 513-522
decimal(xxxxxx.xxx)
f10.3
P_GW
All
space 523-532
decimal(xxxxxx.xxx)
f10.3
W_STRS
All
space 533-542
decimal(xxxxxx.xxx)
f10.3
TMP_STRS
All
space 543-552
decimal(xxxxxx.xxx)
f10.3
181
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
N_STRS
All
space 553-562
decimal(xxxxxx.xxx)
f10.3
P_STRS
All
space 563-572
decimal(xxxxxx.xxx)
f10.3
BIOM
All
space 573-582
decimal(xxxxxx.xxx)
f10.3
LAI
All
space 583-592
decimal(xxxxxx.xxx)
f10.3
YLD
All
space 593-602
decimal(xxxxxx.xxx)
f10.3
BACTP
All
space 603-612
decimal(xxxxxx.xxx)
f10.3
BACTLP
All
space 613-622
decimal(xxxxxx.xxx)
f10.3
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
183
44.4 SUBBASIN OUTPUT FILE (.BSB) The subbasin output file contains summary information for each of the subbasins in the watershed. The reported values for the different variables are the total amount or weighted average of all HRUs within the subbasin. The subbasin output file is written in spreadsheet format. Following is a brief description of the output variables in the subbasin output file.
Variable name
Definition
SUB
Subbasin number.
GIS
GIS code reprinted from watershed configuration file (.fig). See explanation of subbasin command.
MON
Daily time step: julian date Monthly time step: the month (1-12) Annual time step: four-digit year Average annual summary lines: total number of years averaged together
AREA
Area of the subbasin (km2).
PRECIP
Total amount of precipitation falling on the subbasin during time step (mm H2O).
SNOMELT
Amount of snow or ice melting during time step (waterequivalent mm H2O).
PET
Potential evapotranspiration from the subbasin during the time step (mm H2O).
ET
Actual evapotranspiration from the subbasin during the time step (mm).
SW
Soil water content (mm). Amount of water in the soil profile at the end of the time period.
184
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PERC
Water that percolates past the root zone during the time step (mm). There is potentially a lag between the time the water leaves the bottom of the root zone and reaches the shallow aquifer. Over a long period of time, this variable should equal groundwater percolation.
SURQ
Surface runoff contribution to streamflow during time step (mm H2O).
GW_Q
Groundwater contribution to streamflow (mm). Water from the shallow aquifer that returns to the reach during the time step.
WYLD
Water yield (mm H2O). The net amount of water that leaves the subbasin and contributes to streamflow in the reach during the time step. (WYLD = SURQ + LATQ + GWQ – TLOSS – pond abstractions)
SYLD
Sediment yield (metric tons/ha). Sediment from the subbasin that is transported into the reach during the time step.
ORGN
Organic N yield (kg N/ha). Organic nitrogen transported out of the subbasin and into the reach during the time step.
ORGP
Organic P yield (kg P/ha). Organic phosphorus transported with sediment into the reach during the time step.
NSURQ
NO3 in surface runoff (kg N/ha). Nitrate transported by the surface runoff into the reach during the time step.
SOLP
Soluble P yield (kg P/ha). Phosphorus that is transported by surface runoff into the reach during the time step.
SEDP
Mineral P yield (kg P/ha). Mineral phosphorus attached to sediment that is transported by surface runoff into the reach during the time step.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
The format of the subbasin output file (.bsb) is: Variable name
Line #
Position
Format
F90 Format
SUB
All
space 7-10
4-digit integer
i4
GIS
All
space 12-19
8-digit integer
i8
MON
All
space 21-24
4-digit integer
i4
AREA
All
space 25-34
decimal(xxxxxx.xxx)
f10.3
PRECIP
All
space 35-44
decimal(xxxxxx.xxx)
f10.3
SNOMELT
All
space 45-54
decimal(xxxxxx.xxx)
f10.3
PET
All
space 55-64
decimal(xxxxxx.xxx)
f10.3
ET
All
space 65-74
decimal(xxxxxx.xxx)
f10.3
SW
All
space 75-84
decimal(xxxxxx.xxx)
f10.3
PERC
All
space 85-94
decimal(xxxxxx.xxx)
f10.3
SURQ
All
space 95-104
decimal(xxxxxx.xxx)
f10.3
GW_Q
All
space 105-114
decimal(xxxxxx.xxx)
f10.3
WYLD
All
space 115-124
decimal(xxxxxx.xxx)
f10.3
SYLD
All
space 125-134
decimal(xxxxxx.xxx)
f10.3
ORGN
All
space 135-144
decimal(xxxxxx.xxx)
f10.3
ORGP
All
space 145-154
decimal(xxxxxx.xxx)
f10.3
NSURQ
All
space 155-164
decimal(xxxxxx.xxx)
f10.3
SOLP
All
space 165-174
decimal(xxxxxx.xxx)
f10.3
SEDP
All
space 175-184
decimal(xxxxxx.xxx)
f10.3
185
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SWAT USER'S MANUAL, VERSION 2000
44.5 MAIN CHANNEL OUTPUT FILE (.RCH) The main channel output file contains summary information for each routing reach in the watershed. The file is written in spreadsheet format. Following is a brief description of the output variables in the .rch file. Variable name
Definition
RCH
Reach number. The reach number is also the hydrograph number of the subbasin as defined in the .fig file.
GIS
GIS number reprinted from watershed configuration (.fig) file. See explanation of subbasin command.
MON
Daily time step: the julian date Monthly time step: the month (1-12) Annual time step: 4-digit year Average annual summary lines: number of years averaged together
AREA
Area drained by reach (km2).
FLOW_IN
Average daily streamflow into reach during time step (m3/s).
FLOW_OUT
Average daily streamflow out of reach during time step (m3/s).
EVAP
Average daily rate of water loss from reach by evaporation during time step (m3/s).
TLOSS
Average daily rate of water loss from reach by transmission through the streambed during time step (m3/s).
SED_IN
Sediment transported with water into reach during time step (metric tons).
SED_OUT
Sediment transported with water out of reach during time step (metric tons).
SEDCONC
Concentration of sediment in reach during time step (mg/L).
ORGN_IN
Organic nitrogen transported with water into reach during time step (kg N).
ORGN_OUT
Organic nitrogen transported with water out of reach during time step (kg N).
ORGP_IN
Organic phosphorus transported with water into reach during time step (kg P).
ORGP_OUT
Organic phosphorus transported with water out of reach during time step (kg P).
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
187
Variable name
Definition
NO3_IN
Nitrate transported with water into reach during time step (kg N).
NO3_OUT
Nitrate transported with water out of reach during time step (kg N).
NH4_IN
Ammonium transported with water into reach during time step (kg N).
NH4_OUT
Ammonium transported with water out of reach during time step (kg N).
NO2_IN
Nitrite transported with water into reach during time step (kg N).
NO2_OUT
Nitrite transported with water out of reach during time step (kg N).
MINP_IN
Mineral phosphorus transported with water into reach during time step (kg P).
MINP_OUT
Mineral phosphorus transported with water out of reach during time step (kg P).
ALGAE_IN
Algal biomass transported with water into reach during time step (kg).
ALGAE_OUT
Algal biomass transported with water out of reach during time step (kg).
CBOD_IN
Carbonaceous biochemical oxygen demand of material transported into reach during time step (kg O2).
CBOD_OUT
Carbonaceous biochemical oxygen demand of material transported out of reach during time step (kg O2).
DISOX_IN
Amount of dissolved oxygen transported into reach during time step (kg O2).
DISOX_OUT
Amount of dissolved oxygen transported out of reach during time step (kg O2).
While more than one pesticide may be applied to the HRUs, due to the complexity of the pesticide equations only the pesticide listed in .bsn is routed through the stream network. SOLPST_IN
Soluble pesticide transported with water into reach during time step (mg active ingredient)
SOLPST_OUT
Soluble pesticide transported with water out of reach during time step (mg active ingredient).
188
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
SORPST_IN
Pesticide sorbed to sediment transported with water into reach during time step (mg active ingredient).
SORPST_OUT
Pesticide sorbed to sediment transported with water out of reach during time step (mg active ingredient).
REACTPST
Loss of pesticide from water by reaction during time step (mg active ingredient).
VOLPST
Loss of pesticide from water by volatilization during time step (mg active ingredient).
SETTLPST
Transfer of pesticide from water to river bed sediment by settling during time step (mg active ingredient).
RESUSP_PST
Transfer of pesticide from river bed sediment to water by resuspension during time step (mg active ingredient).
DIFFUSEPST
Transfer of pesticide from water to river bed sediment by diffusion during time step (mg active ingredient).
REACBEDPST
Loss of pesticide from river bed sediment by reaction during time step (mg active ingredient).
BURYPST
Loss of pesticide from river bed sediment by burial during time step (mg active ingredient).
BED_PST
Pesticide in river bed sediment during time step (mg active ingredient).
BACTP_OUT
Number of persistent bacteria transported out of reach during time step.
BACTLP_OUT
Number of less persistent bacteria transported out of reach during time step.
CMETAL#1
Conservative metal #1 transported out of reach (kg).
CMETAL#2
Conservative metal #2 transported out of reach (kg).
CMETAL#3
Conservative metal #3 transported out of reach (kg).
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
The format of the main channel output file (.rch) is: Variable name
Line #
Position
Format
F90 Format
RCH
All
space 7-10
4-digit integer
i4
GIS
All
space 12-19
8-digit integer
i8
MON
All
space 21-25
5-digit integer
i5
AREA
All
space 26-37
exponential
e12.4
FLOW_IN
All
space 38-49
exponential
e12.4
FLOW_OUT
All
space 50-61
exponential
e12.4
EVAP
All
space 62-73
exponential
e12.4
TLOSS
All
space 74-85
exponential
e12.4
SED_IN
All
space 86-97
exponential
e12.4
SED_OUT
All
space 98-109
exponential
e12.4
SEDCONC
All
space 110-121
exponential
e12.4
ORGN_IN
All
space 122-133
exponential
e12.4
ORGN_OUT
All
space 134-145
exponential
e12.4
ORGP_IN
All
space 146-157
exponential
e12.4
ORGP_OUT
All
space 158-169
exponential
e12.4
NO3_IN
All
space 170-181
exponential
e12.4
NO3_OUT
All
space 182-193
exponential
e12.4
NH4_IN
All
space 194-205
exponential
e12.4
NH4_OUT
All
space 206-217
exponential
e12.4
NO2_IN
All
space 218-229
exponential
e12.4
NO2_OUT
All
space 230-241
exponential
e12.4
MINP_IN
All
space 242-253
exponential
e12.4
MINP_OUT
All
space 254-265
exponential
e12.4
CHLA_IN
All
space 266-277
exponential
e12.4
CHLA_OUT
All
space 278-289
exponential
e12.4
CBOD_IN
All
space 290-301
exponential
e12.4
CBOD_OUT
All
space 302-313
exponential
e12.4
DISOX_IN
All
space 314-325
exponential
e12.4
DISOX_OUT
All
space 326-337
exponential
e12.4
SOLPST_IN
All
space 338-349
exponential
e12.4
SOLPST_OUT
All
space 350-361
exponential
e12.4
SORPST_IN
All
space 362-373
exponential
e12.4
SORPST_OUT
All
space 374-385
exponential
e12.4
REACTPST
All
space 386-397
exponential
e12.4
189
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SWAT USER'S MANUAL, VERSION 2000
Variable name
Line #
Position
Format
F90 Format
VOLPST
All
space 398-409
exponential
e12.4
SETTLPST
All
space 410-421
exponential
e12.4
RESUSP_PST
All
space 422-433
exponential
e12.4
DIFFUSEPST
All
space 434-445
exponential
e12.4
REACBEDPST
All
space 446-457
exponential
e12.4
BURYPST
All
space 458-469
exponential
e12.4
BED_PST
All
space 470-481
exponential
e12.4
BACTP_OUT
All
space 482-493
exponential
e12.4
BACTLP_OUT
All
space 494-505
exponential
e12.4
CMETAL#1
All
space 506-517
exponential
e12.4
CMETAL#2
All
space 518-529
exponential
e12.4
CMETAL#3
All
space 530-541
exponential
e12.4
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
191
44.6 HRU IMPOUNDMENT OUTPUT FILE (.WTR) The HRU impoundment output file contains summary information for ponds, wetlands and depressional/impounded areas in the HRUs. The file is written in spreadsheet format. Following is a brief description of the output variables in the HRU impoundment output file. Variable name
Definition
LULC
Four letter character code for the cover/plant on the HRU. (code from crop.dat file)
HRU
Hydrologic response unit number
GIS
GIS code reprinted from watershed configuration file (.fig). See explanation of subbasin command.
SUB
Topographically-defined subbasin to which the HRU belongs.
MGT
Management number. This is pulled from the management (.mgt) file. Used by the SWAT/GRASS interface to allow development of output maps by landuse/management type.
MON
Daily time step: the julian date Monthly time step: the month (1-12) Annual time step: year Average annual summary lines: total number of years averaged together
AREA
Drainage area of the HRU (km2).
PNDPCP
Precipitation falling directly on the pond during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
PND_IN
Pond inflow (mm H2O). Surface runoff entering the pond during the time step. The depth of water is the volume divided by the area of the HRU.
PSED_I
Pond sediment inflow (metric tons/ha). Sediment transported into the pond during the time step. The loading is the mass divided by the area of the HRU.
PNDEVP
Evaporation from the pond surface during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
192
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PNDSEP
Water that seeps through the bottom of the pond and recharges the shallow aquifer during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
PND_OUT
Pond outflow (mm H2O). Water leaving the pond and entering the reach during the time step. The depth of water is the volume divided by the area of the HRU.
PSED_O
Pond sediment outflow (metric tons/ha). Sediment transported out of the pond and entering the reach during the time step. . The loading is the mass divided by the area of the HRU.
PNDVOL
Volume of water in pond at end of time step (m3 H2O).
PNDORGN
Concentration of organic N in pond at end of time step (mg N/L or ppm).
PNDNO3
Concentration of nitrate in pond at end of time step (mg N/L or ppm).
PNDORGP
Concentration of organic P in pond at end of time step (mg P/L or ppm).
PNDMINP
Concentration of mineral P in pond at end of time step (mg P/L or ppm).
PNDCHLA
Concentration of chlorophyll-a in pond at end of time step (mg chl-a/L or ppm).
PNDSECI
Secchi-disk depth of pond at end of time step (m).
WETPCP
Precipitation falling directly on the wetland during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
WET_IN
Wetland inflow (mm H2O). Surface runoff entering the wetland during the time step. The depth of water is the volume divided by the area of the HRU.
WSED_I
Wetland sediment inflow (metric tons/ha). Sediment transported into the wetland during the time step. The loading is the mass divided by the area of the HRU.
WETEVP
Evaporation from the wetland during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
WETSEP
Water that seeps through the bottom of the wetland and recharges the shallow aquifer during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
193
Variable name
Definition
WET_OUT
Wetland outflow (mm H2O). Water leaving the wetland and entering the reach during the time step. The depth of water is the volume divided by the area of the HRU.
WSED_O
Wetland sediment outflow (metric tons/ha). Sediment transported out of the wetland and entering the reach during the time step. . The loading is the mass divided by the area of the HRU.
WET_VOL
Volume of water in wetland at end of time step (m3 H2O).
WETORGN
Concentration of organic N in wetland at end of time step (mg N/L or ppm).
WETNO3
Concentration of nitrate in wetland at end of time step (mg N/L or ppm).
WETORGP
Concentration of organic P in wetland at end of time step (mg P/L or ppm).
WETMINP
Concentration of mineral P in wetland at end of time step (mg P/L or ppm).
WETCHLA
Concentration of chlorophyll-a in wetland at end of time step (mg chl-a/L or ppm).
WETSECI
Secchi-disk depth of wetland at end of time step (m).
POTPCP
Precipitation falling directly on the pothole during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
POT_IN
Pothole inflow (mm H2O). Surface runoff entering the pothole during the time step. The depth of water is the volume divided by the area of the HRU.
OSED_I
Pothole sediment inflow (metric tons/ha). Sediment transported into the pothole during the time step. The loading is the mass divided by the area of the HRU.
POTEVP
Evaporation from the pothole during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
POTSEP
Water that seeps through the bottom of the pothole and enters the underlying soil during the time step (mm H2O). The depth of water is the volume divided by the area of the HRU.
POT_OUT
Pothole outflow (mm H2O). Water leaving the pothole and entering the reach during the time step. The depth of water is the volume divided by the area of the HRU.
194
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
OSED_O
Pothole sediment outflow (metric tons/ha). Sediment transported out of the pothole and entering the reach during the time step. . The loading is the mass divided by the area of the HRU.
POTVOL
Volume of water in pothole at end of time step (m3 H2O).
POT_SA
Surface area of pothole at end of time step (ha).
HRU_SURQ
Surface runoff contribution to streamflow in the main channel from entire HRU during the time step (mm H2O).
PLANT_ET
Amount of water removed by transpiration from plants during the time step (mm H2O).
SOIL_ET
Amount of water removed by evaporation from the soil during the time step (mm H2O).
The format of the HRU impoundment output file (.wtr) is: Variable name
Line #
Position
Format
F90 Format
LULC
All
space 1-4
character
a4
HRU
All
space 5-8
4-digit integer
i4
GIS
All
space 10-17
8-digit integer
i8
SUB
All
space 19-22
4-digit integer
i4
MGT
All
space 24-27
4-digit integer
i4
MON
All
space 29-32
4-digit integer
i4
AREA
All
space 33-42
decimal(xxxxxx.xxx)
f10.3
PNDPCP
All
space 43-52
decimal(xxxxxx.xxx)
f10.3
PND_IN
All
space 53-62
decimal(xxxxxx.xxx)
f10.3
PSED_I
All
space 63-72
decimal(xxxxxx.xxx)
f10.3
PNDEVP
All
space 73-82
decimal(xxxxxx.xxx)
f10.3
PNDSEP
All
space 83-92
decimal(xxxxxx.xxx)
f10.3
PND_OUT
All
space 93-102
decimal(xxxxxx.xxx)
f10.3
PSED_O
All
space 103-112
decimal(xxxxxx.xxx)
f10.3
PNDVOL
All
space 113-122
exponential
e10.4
PNDORGN
All
space 123-132
decimal(xxxxxx.xxx)
f10.3
PNDNO3
All
space 133-142
decimal(xxxxxx.xxx)
f10.3
PNDORGP
All
space 143-152
decimal(xxxxxx.xxx)
f10.3
PNDMINP
All
space 153-162
decimal(xxxxxx.xxx)
f10.3
PNDCHLA
All
space 163-172
decimal(xxxxxx.xxx)
f10.3
PNDSECI
All
space 173-182
decimal(xxxxxx.xxx)
f10.3
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES Variable name
Line #
Position
Format
F90 Format
WETPCP
All
space 183-192
decimal(xxxxxx.xxx)
f10.3
WET_IN
All
space 193-202
decimal(xxxxxx.xxx)
f10.3
WSED_I
All
space 203-212
decimal(xxxxxx.xxx)
f10.3
WETEVP
All
space 213-222
decimal(xxxxxx.xxx)
f10.3
WETSEP
All
space 223-232
decimal(xxxxxx.xxx)
f10.3
WET_OUT
All
space 233-242
decimal(xxxxxx.xxx)
f10.3
WSED_O
All
space 243-252
decimal(xxxxxx.xxx)
f10.3
WET_VOL
All
space 253-262
exponential
e10.4
WETORGN
All
space 263-272
decimal(xxxxxx.xxx)
f10.3
WETNO3
All
space 273-282
decimal(xxxxxx.xxx)
f10.3
WETORGP
All
space 283-292
decimal(xxxxxx.xxx)
f10.3
WETMINP
All
space 293-302
decimal(xxxxxx.xxx)
f10.3
WETCHLA
All
space 303-312
decimal(xxxxxx.xxx)
f10.3
WETSECI
All
space 313-322
decimal(xxxxxx.xxx)
f10.3
POTPCP
All
space 323-332
decimal(xxxxxx.xxx)
f10.3
POT_IN
All
space 333-342
decimal(xxxxxx.xxx)
f10.3
OSED_I
All
space 343-352
decimal(xxxxxx.xxx)
f10.3
POTEVP
All
space 353-362
decimal(xxxxxx.xxx)
f10.3
POTSEP
All
space 363-372
decimal(xxxxxx.xxx)
f10.3
POT_OUT
All
space 373-382
decimal(xxxxxx.xxx)
f10.3
OSED_O
All
space 383-392
decimal(xxxxxx.xxx)
f10.3
POTVOL
All
space 393-402
exponential
e10.4
POT_SA
All
space 403-412
decimal(xxxxxx.xxx)
f10.3
HRU_SURQ
All
space 413-422
decimal(xxxxxx.xxx)
f10.3
PLANT_ET
All
space 423-432
decimal(xxxxxx.xxx)
f10.3
SOIL_ET
All
space 433-442
decimal(xxxxxx.xxx)
f10.3
195
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SWAT USER'S MANUAL, VERSION 2000
44.7 RESERVOIR OUTPUT FILE (.RSV) The reservoir output file contains summary information for reservoirs in the watershed. The file is written in spreadsheet format. Following is a brief description of the output variables in the reservoir output file. Variable name
Definition
RES
Reservoir number (assigned in .fig file)
MON
Daily time step: the julian date Monthly time step: the month (1-12) Annual time step: four-digit year
VOLUME
Volume of water in reservoir at end of time step (m3 H2O).
FLOW_IN
Average flow into reservoir during time step (m3/s H2O).
FLOW_OUT
Average flow out of reservoir during time step (m3/s H2O).
PRECIP
Precipitation falling directly on the reservoir during the time step (m3 H2O).
EVAP
Evaporation from the reservoir during the time step (m3 H2O).
SEEPAGE
Water that seeps through the bottom of the reservoir and enters the shallow aquifer during the time step (m3 H2O).
SED_IN
Reservoir sediment inflow (metric tons). Sediment transported into the reservoir during the time step.
SED_OUT
Reservoir sediment outflow (metric tons). Sediment transported out of the reservoir during the time step.
ORGN_IN
Amount of organic nitrogen transported into reservoir during the time step (kg N).
ORGN_OUT
Amount of organic nitrogen transported out of reservoir during the time step (kg N).
ORGP_IN
Amount of organic phosphorus transported into reservoir during the time step (kg P).
ORGP_OUT
Amount of organic phosphorus transported out of reservoir during the time step (kg P).
NO3_IN
Amount of nitrate transported into reservoir during the time step (kg N).
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES
197
Variable name
Definition
NO3_OUT
Amount of nitrate transported out of reservoir during the time step (kg N).
NO2_IN
Amount of nitrite transported into reservoir during the time step (kg N).
NO2_OUT
Amount of nitrite transported out of reservoir during the time step (kg N).
NH3_IN
Amount of ammonia transported into reservoir during the time step (kg N).
NH3_OUT
Amount of ammonia transported out of reservoir during the time step (kg N).
MINP_IN
Amount of mineral phosphorus transported into reservoir during the time step (kg P).
MINP_OUT
Amount of mineral phosphorus transported out of reservoir during the time step (kg P).
CHLA_IN
Amount of chlorophyll a transported into reservoir during the time step (kg chla).
CHLA_OUT
Amount of chlorophyll a transported out of reservoir during the time step (kg chla).
SECCHIDEPTH
Secchi-disk depth of reservoir at end of time step (m).
PEST_IN
Amount of pesticide transported into reservoir during the time step (mg pesticide active ingredient).
REACTPST
Loss of pesticide from water by reaction during time step (mg active ingredient).
VOLPST
Loss of pesticide from water by volatilization during time step (mg active ingredient).
SETTLPST
Transfer of pesticide from water to reservoir bed sediment by settling during time step (mg active ingredient).
RESUSP_PST
Transfer of pesticide from reservoir bed sediment to water by resuspension during time step (mg active ingredient).
DIFFUSEPST
Transfer of pesticide from water to reservoir bed sediment by diffusion during time step (mg active ingredient).
REACBEDPST
Loss of pesticide from reservoir bed sediment by reaction during time step (mg active ingredient).
BURYPST
Loss of pesticide from reservoir sediment by burial during time step (mg active ingredient).
198
SWAT USER'S MANUAL, VERSION 2000
Variable name
Definition
PEST_OUT
Amount of pesticide transported out of reservoir during the time step (mg pesticide active ingredient).
PSTCNCW
Average concentration of pesticide in reservoir water during time step (mg active ingredient/m3 H2O or ppb).
PSTCNCB
Average concentration of pesticide in reservoir bed sediment during time step (mg active ingredient/m3 H2O or ppb).
The format of the reservoir output file (.rsv) is: Variable name
Line #
Position
Format
F90 Format
RES
All
space 7-14
integer
i8
MON
All
space 16-19
integer
i4
VOLUME
All
space 20-31
exponential
e12.4
FLOW_IN
All
space 32-43
exponential
e12.4
FLOW_OUT
All
space 44-55
exponential
e12.4
PRECIP
All
space 56-67
exponential
e12.4
EVAP
All
space 68-79
exponential
e12.4
SEEPAGE
All
space 80-91
exponential
e12.4
SED_IN
All
space 92-103
exponential
e12.4
SED_OUT
All
space 104-115
exponential
e12.4
ORGN_IN
All
space 116-127
exponential
e12.4
ORGN_OUT
All
space 128-139
exponential
e12.4
ORGP_IN
All
space 140-151
exponential
e12.4
ORGP_OUT
All
space 152-163
exponential
e12.4
NO3_IN
All
space 164-175
exponential
e12.4
NO3_OUT
All
space 176-187
exponential
e12.4
NO2_IN
All
space 188-199
exponential
e12.4
NO2_OUT
All
space 200-211
exponential
e12.4
NH3_IN
All
space 212-223
exponential
e12.4
NH3_OUT
All
space 224-235
exponential
e12.4
MINP_IN
All
space 236-247
exponential
e12.4
MINP_OUT
All
space 248-259
exponential
e12.4
CHLA_IN
All
space 260-271
exponential
e12.4
CHLA_OUT
All
space 272-283
exponential
e12.4
SECCHIDEPTH
All
space 284-295
exponential
e12.4
PEST_IN
All
space 296-307
exponential
e12.4
CHAPTER 44: SWAT OUTPUT—PRIMARY FILES Variable name
Line #
Position
Format
F90 Format
REACTPST
All
space 308-319
exponential
e12.4
VOLPST
All
space 320-331
exponential
e12.4
SETTLPST
All
space 332-343
exponential
e12.4
RESUSP_PST
All
space 344-355
exponential
e12.4
DIFFUSEPST
All
space 356-367
exponential
e12.4
REACBEDPST
All
space 368-379
exponential
e12.4
BURYPST
All
space 380-391
exponential
e12.4
PEST_OUT
All
space 392-403
exponential
e12.4
PSTCNCW
All
space 404-415
exponential
e12.4
PSTCNCB
All
space 416-427
exponential
e12.4
199
200
SWAT USER'S MANUAL, VERSION 2000
SWAT MODEL CALIBRATION Calibration of a model run can be divided into several steps: ♦ water balance and stream flow ♦ sediment ♦ nutrients ♦ pesticides
WATER BALANCE AND STREAM FLOW To calibrate the water balance and stream flow you need to have some understanding of the actual conditions occurring in the watershed. Ideally, you have data from a stream gage located within or at the outlet of your watershed. The U.S. Geological Survey maintains a website (http://water.usgs.gov/) with daily records for all stream gages in the U.S. available for downloading.
Calibration for water balance and stream flow is first done for average annual conditions. Once the run is calibrated for average annual conditions, the user can shift to monthly or daily records to fine-tune the calibration. The average annual observed and simulated results should be summarized in a manner similar to the following table:
Actual SWAT
Total Water Yield 200 mm 300 mm
Baseflow 80 mm 20 mm
Surface Flow 120 mm 280 mm
(When calibrating, we usually summarize data as depth of water in millimeters over the drainage area. Feel free to use whatever units you prefer.) If you are calibrating at the watershed outlet, the SWAT values for the table are provided in the .std file. These values are listed in the table titled "Ave Annual Basin Values" located near the end of the file. If you are calibrating a gage located within the watershed, the total water yield can be calculated from the FLOW_OUT variable in the reach (.rch) file. The values for Baseflow and Surface Flow have to be estimated from the HRU output (.sbs) file or the subbasin output file (.bsb). To estimate the contributions by baseflow and streamflow, the average annual values for GWQ, SURQ and WYLD need to be averaged so that an areally weighted value for the drainage area of interest is obtained. The surface flow and baseflow then need to be converted to fractions by dividing by the total water yield (WYLD). These fractions are then multiplied by the total water yield obtained from the reach output file. The values for GWQ and SURQ cannot be used directly because in-stream precipitation, evaporation, transmission losses, etc. will alter the net water yield from that predicted by the WYLD variable in the HRU or Subbasin Output files.
There are a number of methods available for partitioning observed stream flow into fractions contributed by baseflow and surface runoff. If daily steam flow is available, a baseflow filter program can be run which performs this analysis.
I. BASIC WATER BALANCE & TOTAL FLOW CALIBRATION CALIBRATE SURFACE RUNOFF: Step 1: Adjust the curve number (CN2 in .sub or .mgt) until surface runoff is acceptable. Appendix A contains tables of curve number values for a wide variety of land covers. Appendix B contains tables summarizing ranges for the general categories of land cover and lists the land cover category for all plants in the SWAT Land Cover/Plant database. If surface runoff values are still not reasonable after adjusting curve numbers, adjust: -soil available water capacity (±0.04) (SOL_AWC in .sol) and/or -soil evaporation compensation factor (ESCO in .sub). CALIBRATE SUBSURFACE FLOW: Step 2: Once surface runoff is calibrated, compare measured and simulated values of baseflow. If simulated baseflow is too high: -increase the groundwater "revap" coefficient (GW_REVAP in .gw)—the maximum value that GW_REVAP should be set at is 0.20. -decrease the threshold depth of water in the shallow aquifer for "revap" to occur (REVAPMN in .gw)—the minimum value that REVAPMN should be set at is 0.0. -increase the threshold depth of water in the shallow aquifer required for base flow to occur (GWQMN in .gw)—the maximum value that GWQMN should be set at is left to user discretion. If simulated baseflow is too low, check the movement of water into the aquifer. If groundwater recharge (GWQ in .sbs or .bsb) is greater than or equal to the desired baseflow: -decrease the groundwater "revap" coefficient (GW_REVAP in .gw)—the minimum value that GW_REVAP should be set at is 0.02. -increase the threshold depth of water in the shallow aquifer for "revap" to occur (REVAPMN in .gw). -decrease the threshold depth of water in the shallow aquifer required for base flow to occur (GWQMN in .gw)—the minimum value that GWQMN should be set at is 0.0. Step 3: Repeat steps 1 and 2 until values are acceptable. It may take several reiterations to get the surface runoff and baseflow correct. II. TEMPORAL FLOW CALIBRATION
Flow
Once average annual and annual surface runoff and baseflow are realistic, the temporal flow should look reasonable as well. A few problems that may still be present include:
Observed Simulated
Time (days)
1) Peaks are reasonable, but the recessions "bottom out": Check the transmission losses/values for channel hydraulic conductivity (CH_K in .rte). The value for channel hydraulic conductivity is an effective hydraulic conductivity for movement of water out of the stream bed. For perennial streams receiving groundwater contribution to flow, the groundwater enters the stream through the sides and bottom of the stream bed, making the effective hydraulic conductivity of the channel beds to water losses equal to zero. The only time the channel hydraulic conductivity would be greater than zero is for ephemeral and transient streams that do not receive continuous groundwater contributions to streamflow.
Flow
A second variable that will affect the shape of the hydrograph is the baseflow alpha factor (ALPHA_BF in .gw).
Observed Simulated
Time (days)
2) In snow melt months, the peaks are too high and recessions are too low: Check the values for maximum and minimum melt rates for snow (SMFMX and SMFMN in .bsn). These values may need to be lowered. Another variable that will impact snow melt is the temperature lapse rate (TLAPS in .sub). These values may need to be increased. Finally, the baseflow alpha factor may need to be modified (ALPHA_BF in .gw).
III. SPATIAL FLOW CALIBRATION If you are calibrating a watershed with multiple stream gages, calibrate streamflow for the gage furthest upstream. Once that gage is calibrated, move downstream to the next gage and calibrate for that area. It is important that, as you calibrate downstream gages, you do not change parameters within the files associated with the drainage area of the upstream gages already calibrated.
SEDIMENT There are two sources of sediment in the SWAT simulation: loadings from HRUs/subbasins and channel degradation/deposition. Once the ratio of surface runoff to baseflow contribution to streamflow is being simulated correctly, the sediment contribution (loadings from HRUs/subbasins) should be close to measured values. In most situations, the user will probably have little information about channel degradation/deposition. For those unable to go out and assess the channel, we suggest that you adjust the loadings from the subbasins until they look reasonable and then assume that the remaining difference between actual and observed is due to channel degradation/deposition. The average annual observed and simulated results should be summarized in a manner similar to the following table. A more detailed table which contains the loadings by land use on a given soil type may be used also.
Loadings from mixed forest Loadings from bermuda pasture Loadings from range Loading from HRUs/subbasins Amount of sediment leaving reach
Sediment 187 metric tons/yr 354 metric tons/yr 1459 metric tons/yr 2,000 metric tons/yr 2,873 metric tons/yr
Actual
2,321 metric tons/yr
0.14 metric tons/ha/yr 0.23 metric tons/ha/yr 0.35 metric tons/ha/yr 0.28 metric tons/ha/yr
Sediment loadings from the HRUs/subbasins can be calculated by summing values for SYLD in either the .sbs or .bsb file. The amount of sediment leaving the reach can be obtained from values reported for SED_OUT in the .rch file.
CHECK RESERVOIR/POND SIMULATION:
Reservoirs and ponds have a big impact on sediment loadings. If the amount of sediment being simulated in the watershed is off, first verify that you are accounting for all the ponds and reservoirs in the watershed and that they are being simulated properly. CALIBRATE SUBBASIN LOADINGS: While surface runoff is the primary factor controlling sediment loadings to the stream, there are a few other variables that affect sediment movement into the stream. 1) Tillage has a great impact on sediment transport. With tillage, plant residue is removed from the surface causing erosion to increase. Verify that the tillage practices are being accurately simulated. 2) USLE equation support practices (P) factor (USLE_P in the .sub file): Verify that you have accurately accounted for contouring and terracing in agricultural areas. In general, agricultural land with a slope greater that 5% will be terraced. 3) USLE equation slope length factor (SLSUBBSN in .sub file): There is usually a large amount of uncertainty in slope length measurements. The slope length will also be affected by support practices used in the HRU. 4) Slope in the HRUs (SLOPE in .sub file): Verify that the slopes given for the subbasin are correct. 5) USLE equation cropping practices (C) factor (USLE_C in crop.dat): In some cases, the minimum C value reported for the plant cover may not be accurate for your area. CALIBRATE CHANNEL DEGRADATION/DEPOSITION: Channel degradation will be significant during extreme storm events and in unstable subbasins. Unstable subbasins are those undergoing a significant change in land use patterns such as urbanization. Variables that affect channel degradation/deposition include: 1) The linear and exponential parameters used in the equation to calculate sediment reentrained in channel sediment routing (SPCON and SPEXP in .bsn file). These variables affect sediment routing in the entire watershed. 2) The channel erodibility factor (CH_EROD in .rte) 3) The channel cover factor (CH_COV in .rte)
NUTRIENTS The nutrients of concern in SWAT are nitrate, soluble phosphorus, organic nitrogen and organic phosphorus. When calibrating for a nutrient, keep in mind that changes made will have an effect all the nutrient levels. Nutrient calibration can be divided into two steps: calibration of nutrient loadings and calibration of in-stream water quality processes.
CALIBRATE NUTRIENT LOADINGS (ALL NUTRIENTS): 1) Check that the initial concentrations of the nutrients in the soil are correct. These are set in the soil chemical input file (.chm) and in the soil input file (.sol): nitrate (SOL_NO3 in .sol) soluble P (SOL_MINP in .chm) organic N (SOL_ORGN in .chm) organic P (SOL_ORGP in .chm) 2) Verify that fertilizer applications are correct. Check amounts and the soil layer that the fertilizer is applied to. The fertilizer may be applied to the top 10mm of soil or incorporated in the first soil layer. The variable FRT_LY1 identifies the fraction of fertilizer applied to the top 10mm of soil. (If this variable is left at zero, the model will set FRT_LY1 = 0.20). 3) Verify that tillage operations are correct. Tillage redistributes nutrients in the soil and will alter the amount available for interaction or transport by surface runoff. 4) Alter the biological mixing efficiency (BIOMIX in .bsn file). Biological mixing acts the same as a tillage operation in that it incorporates residue and nutrients into the soil. This variable controls mixing due to biological activity in the entire watershed. CALIBRATE NUTRIENT LOADINGS (NITRATE): In addition to the variables mentioned above, 1) Modify the nitrogen percolation coefficient (NPERCO in .bsn file) CALIBRATE NUTRIENT LOADINGS (SOLUBLE P): In addition to the variables mentioned in the section for all nutrients, 1) Modify the phosphorus percolation coefficient (PPERCO in .bsn file) 2) Modify the phosphorus soil partitioning coefficient (PHOSKD in .bsn file). CALIBRATE NUTRIENT LOADINGS (ORGANIC N & P): Organics are transported to the stream attached to sediment, so the movement of sediment will greatly impact the movement of organics.
CALIBRATE IN-STREAM NUTRIENT PROCESSES: SWAT includes in-stream nutrient cycling processes as described in the QUAL2E documentation. Variables in the watershed water quality (.wwq) and stream water quality (.swq) files control these processes.
Tables of Runoff Curve Number Values‡ Table 1: Runoff curve numbers for cultivated agricultural lands Cover Hydrologic Soil Group Land Use
Treatment or practice
Fallow
Bare soil Crop residue cover
Row crops
∗
Straight row Straight row w/ residue Contoured Contoured w/ residue Contoured & terraced Contoured & terraced w/ residue
Small grains
Straight row Straight row w/ residue Contoured Contoured w/ residue Contoured & terraced Contoured & terraced w/ residue
Close-seeded or broadcast legumes or rotation
Straight row Contoured Contoured & terraced
‡
Hydrologic condition
A
B
C
D
---Poor
77 76
86 85
91 90
94 93
Good
74
83
88
90
Poor
72
81
88
91
Good
67
78
85
89
Poor
71
80
87
90
Good
64
75
82
85
Poor
70
79
84
88
Good
65
75
82
86
Poor
69
78
83
87
Good
64
74
81
85
Poor
66
74
80
82
Good
62
71
78
81
Poor
65
73
79
81
Good
61
70
77
80
Poor
65
76
84
88
Good
63
75
83
87
Poor
64
75
83
86
Good
60
72
80
84
Poor
63
74
82
85
Good
61
73
81
84
Poor
62
73
81
84
Good
60
72
80
83
Poor
61
72
79
82
Good
59
70
78
81
Poor
60
71
78
81
Good
58
69
77
80
Poor
66
77
85
89
Good
58
72
81
85
Poor
64
75
83
85
Good
55
69
78
83
Poor
63
73
80
83
Good
51
67
76
80
These tables are reproduced from Urban Hydrology for Small Watersheds, USDA Soil Conservation Service Engineering Division, Technical Release 55, June 1986. ∗ Crop residue cover applies only if residue is on at least 5% of the surface throughout the year.
Table 2: Runoff curve numbers for other agricultural lands Cover Hydrologic Soil Group Cover Type Pasture, grassland, or range—continuous forage for grazing
1
Hydrologic condition
A
B
C
D
Poor Fair
68 49
79 69
86 79
89 84
Good
39
61
74
80
Meadow—continuous grass, protected from grazing and generally mowed for hay.
----
30
58
71
78
Brush—brush-weed-grass mixture with brush the major element2
Poor
48
67
77
83
Fair
35
56
70
77
Good
30
48
65
73
Poor
57
73
82
86
Fair
43
65
76
82
Good
32
58
72
79
Poor
45
66
77
83
Woods—grass combination (orchard or tree farm)
3
Woods
Farmsteads—buildings, lanes, driveways, and surrounding lots.
1
Fair
36
60
73
79
Good ----
30 59
55 74
70 82
77 86
Poor: < 50% ground cover or heavily grazed with no mulch Fair: 50 to 75% ground cover and not heavily grazed Good: > 75% ground cover and lightly or only occasionally grazed 2 Poor: < 50% ground cover Fair: 50 to 75% ground cover Good: > 75% ground cover 3 Poor: Forest litter, small trees, and brush are destroyed by heavy grazing or regular burning Fair: Woods are grazed but not burned, and some forest litter covers the soil. Good: Woods are protected from grazing, and litter and brush adequately cover the soil.
Table 3: Runoff curve numbers for urban areas Cover Hydrologic Soil Group
Cover Type
Hydrologic condition
Fully developed urban areas Open spaces (lawns, parks, golf courses, cemeteries, etc.)4
Average % impervious area
A
B
C
D
Poor
68
79
86
89
Fair
49
69
79
84
Good
39
61
74
80
Paved parking lots, roofs, driveways, etc. (excluding right-of-way)
----
98
98
98
98
Paved streets and roads; curbs and storm sewers (excluding right-of-way)
----
98
98
98
98
Paved streets and roads; open ditches (including right-of-way)
----
83
89
92
93
Gravel streets and roads (including right-ofway)
----
76
85
89
91
Dirt streets and roads (including right-of way)
----
72
82
87
89
Impervious areas:
Urban districts: Commercial and business
85%
89
92
94
95
Industrial
72%
81
88
91
93
Residential Districts by average lot size: 1/8 acre (0.05 ha) or less (town houses)
65%
77
85
90
92
1/4 acre (0.10 ha)
38%
61
75
83
87
1/3 acre (0.13 ha)
30%
57
72
81
86
1/2 acre (0.20 ha)
25%
54
70
80
85
1 acre (0.40 ha)
20%
51
68
79
84
2 acres (0.81 ha)
12%
46
65
77
82
77
86
91
94
Developing urban areas: Newly graded areas (pervious areas only, no vegetation)
4
Poor: grass cover < 50% Fair: grass cover 50 to 75% Good: grass cover > 75%
Table 4: Runoff curve numbers for arid and semiarid rangelands Cover Hydrologic Soil Group Cover Type
Hydrologic condition5
Herbaceous—mixture of grass, weeds, and low-growing brush, with brush the minor element.
Poor
Oak-aspen—mountain brush mixture of oak brush, aspen, mountain mahogany, bitter brush, maple, and other brush.
Pinyon-juniper—pinyon, juniper, or both: grass understory.
Sagebrush with grass understory.
Desert shrub—major plants include saltbrush, greasewood, creosotebush, blackbrush, bursage, palo verde, mesquite, and cactus.
5
A
B
C
D
80
87
93
Fair
71
81
89
Good
62
74
85
Poor
66
74
79
Fair
48
57
63
Good
30
41
48
Poor
75
85
89
Fair
58
73
80
Good
41
61
71
Poor
67
80
85
Fair
51
63
70
Good
35
47
55
Poor
63
77
85
88
Fair
55
72
81
86
Good
49
68
79
84
Poor: < 30% ground cover (litter, grass, and brush overstory) Fair: 30 to 70% ground cover Good: > 70% ground cover
Curve Number Calibration Table 1: Guideline runoff curve number ranges Land Cover Category Row crop Small grain/close grown crop Perennial grasses Annual grasses (close-seeded legumes) Range Semiarid/arid range Brush Woods Orchard/tree farm Urban
A 61-72 58-65 30-68 51-66 39-68 39-74 30-48 25-45 32-57 46-89
Hydrologic Soil Group B C 70-81 77-88 69-76 77-84 58-79 71-86 67-77 76-85 61-79 74-86 62-80 74-87 48-67 65-77 55-66 70-77 58-73 72-82 65-92 77-94
D 80-91 80-88 78-89 80-89 80-89 85-93 73-83 77-83 79-86 82-95
APPENDIX A
DATABASES
The following sections describe the source of input for databases included with the model and any assumptions used in compilation of the database. Also, a methodology for appending additional information to the various databases is summarized.
29
30
SWAT USER’S MANUAL, VERSION 2000
A.1 LAND COVER/PLANT GROWTH DATABASE The land cover/plant growth database contains information needed by SWAT to simulate the growth of a particular land cover. The growth parameters in the plant growth database define plant growth under ideal conditions and quantify the impact of some stresses on plant growth. Table A-1 lists all the default plant species and Table A-2 lists all the generic land covers included in the database. When adding a new plant/land cover to the database, a review of existing literature should provide most of the parameter values needed to simulate plant growth. For users that plan to collect the data directly, the following sections briefly describe the methods used to obtain the plant growth parameters needed by SWAT. Table A-1: Plants included in plant growth database. Plant Common Name Code Taxonomic Name Corn Zea mays L. CORN Corn silage Zea mays L. CSIL Zea mays L. saccharata Sweet corn SCRN Tripsacum dactyloides (L.) L. Eastern gamagrass EGAM Sorghum bicolor L. (Moench) Grain sorghum GRSG
Plant type warm season annual warm season annual warm season annual perennial warm season annual
Sorghum hay Johnsongrass Sugarcane Spring wheat Winter wheat
SGHY JHGR SUGC SWHT WWHT
Sorghum bicolor L. (Moench) Sorghum halepense (L.) Pers. Saccharum officinarum L. Triticum aestivum L. Triticum aestivum L.
warm season annual perennial perennial cool season annual cool season annual
Durum wheat Rye Spring barley Oats Rice
DWHT RYE BARL OATS RICE
Triticum durum Desf. Secale cereale L. Hordeum vulgare L. Avena sativa L. Oryza sativa L.
cool season annual cool season annual cool season annual cool season annual warm season annual
Pearl millet Timothy Smooth bromegrass Meadow bromegrass Tall fescue
PMIL TIMO BROS BROM FESC
Pennisetum glaucum L. Phleum pratense L. Bromus inermis Leysser Bromus biebersteinii Roemer & Schultes Festuca arundinacea
warm season annual perennial perennial perennial perennial
Kentucky bluegrass Bermudagrass Crested wheatgrass Western wheatgrass Slender wheatgrass
BLUG BERM CWGR WWGR SWGR
Poa pratensis Cynodon dactylon Agropyron cristatum (L.) Gaertner Agropyron smithii (Rydb.) Gould Agropyron trachycaulum Malte
perennial perennial perennial perennial perennial
APPENDIX A: DATABASES
Italian (annual) ryegrass Russian wildrye Altai wildrye Sideoats grama Big bluestem
Plant Code RYEG RYER RYEA SIDE BBLS
Little bluestem Alamo switchgrass Indiangrass Alfalfa Sweetclover
Common Name
31
Taxonomic Name
Plant type
Lolium multiflorum Lam. Psathyrostachys juncea (Fisch.) Nevski Leymus angustus (Trin.) Pilger Bouteloua curtipendula (Michaux) Torrey Andropogon gerardii Vitman
cool season annual perennial perennial perennial perennial
LBLS SWCH INDN ALFA CLVS
Schizachyrium scoparium (Michaux) Nash Panicum virgatum L. Sorghastrum nutans (L.) Nash Medicago sativa L. Melilotus alba Med.
perennial perennial perennial perennial legume perennial legume
Red clover Alsike clover Soybean Cowpeas Mung bean
CLVR CLVA SOYB CWPS MUNG
Trifolium pratense L. Trifolium hybridum L. Glycine max L., Merr. Vigna sinensis Phaseolus aureus Roxb.
cool season annual legume perennial legume warm season annual legume warm season annual legume warm season annual legume
Lima beans Lentils Peanut Field peas Garden or canning peas
LIMA LENT PNUT FPEA PEAS
Phaseolus lunatus L. Lens esculenta Moench J. Arachis hypogaea L. Pisum arvense L. Pisum sativum L. ssp. sativum
warm season annual legume warm season annual legume warm season annual legume cool season annual legume cool season annual legume
Sesbania Flax Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco
SESB FLAX COTS
Sesbania macrocarpa Muhl [exaltata] Linum usitatissum L. Gossypium hirsutum L.
warm season annual legume cool season annual warm season annual
COTP
Gossypium hirsutum L.
warm season annual
TOBC
Nicotiana tabacum L.
warm season annual
Sugarbeet Potato Sweetpotato Carrot Onion
SGBT POTA SPOT CRRT ONIO
Beta vulgaris (saccharifera) L. Solanum tuberosum L. Ipomoea batatas Lam. Daucus carota L. subsp. sativus (Hoffm.) Arcang. Allium cepa L. var cepa
warm season annual cool season annual warm season annual cool season annual cool season annual
Sunflower Spring canola-Polish Spring canola-Argentine Asparagus Broccoli
SUNF CANP CANA ASPR BROC
Helianthus annuus L. Brassica campestris Brassica napus Asparagus officinalis L. Brassica oleracea L. var italica Plenck.
warm season annual cool season annual cool season annual perennial cool season annual
Cabbage Cauliflower Celery Head lettuce Spinach
CABG CAUF CELR LETT SPIN
Brassica oleracea L. var capitata L. Brassica oleracea L. var botrytis L. Apium graveolens L. var dulce (Mill.) Pers. Lactuca sativa L. var capitata L. Spinacia oleracea L.
perennial cool season annual perennial cool season annual cool season annual
Green beans Cucumber
GRBN CUCM
Phaseolus vulgaris Cucumis sativus L.
warm season annual legume warm season annual
32
SWAT USER’S MANUAL, VERSION 2000
Eggplant Cantaloupe Honeydew melon Watermelon Bell pepper
Plant Code EGGP CANT HMEL WMEL PEPR
Strawberry Tomato Apple Pine Oak Poplar Honey mesquite
Common Name
Taxonomic Name
Plant Type
Solanum melongena L. Cucumis melo L. Cantaloupensis group Cucumis melo L. Inodorus group Citrullus lanatus (Thunb.) Matsum and Nakai Capsicum annuum L. Grossum group
warm season annual warm season annual warm season annual warm season annual warm season annual
STRW TOMA APPL PINE OAK
Fragaria X Ananassa Duchesne. Lycopersicon esculentum Mill. Malus domestica Borkh. Pinus Quercus
perennial warm season annual trees trees trees
POPL MESQ
Populus Prosopis glandulosa Torr. var. glandulosa
trees trees
Table A-2: Generic Land Covers included in database. Plant Name Code Origin of Plant Growth Values Agricultural Land-Generic AGRL use values for Grain Sorghum Agricultural Land-Row Crops AGRR use values for Corn Agricultual Land-Close-grown AGRC use values for Winter Wheat Orchard ORCD use values for Apples use values for Bermudagrass Hay‡ HAY use values for Oak Forest-mixed FRST use values for Oak Forest-deciduous FRSD use values for Pine Forest-evergreen FRSE Wetlands WETL use values for Alamo Switchgrass Wetlands-forested WETF use values for Oak Wetlands-nonforested WETN use values for Alamo Switchgrass use values for Bermudagrass Pasture‡ PAST use values for Bermudagrass Summer pasture SPAS Winter pasture WPAS use values for Fescue Range-grasses RNGE use values for Little Bluestem (LAImax=2.5) Range-brush RNGB use values for Little Bluestem (LAImax=2.0) Range-southwestern US SWRN use values for Little Bluestem (LAImax=1.5) Water∗ WATR
‡
Plant Type warm season annual warm season annual cool season annual trees perennial trees trees trees perennial trees perennial perennial perennial perennial perennial perennial perennial not applicable
The Bermudagrass parameters input for Hay and Pasture are valid only in latitudes less than 35 to 37°. At higher latitudes, Fescue parameters should be used to model generic Hay and Pasture. ∗ Water was included in the plant growth database in order to process USGS map layers in the HUMUS project. This land cover should not be used as a land cover in an HRU. To model water bodies, create ponds, wetlands or reservoirs.
APPENDIX A: DATABASES
33
A.1.1 LAND COVER/PLANT TYPES IN DATABASE When compiling the list of plants in the default database, we attempted to include the most economically important plants as well as those that are widely distributed in the landscape. This list is by no means exhaustive and users may need to add plants to the list. A number of generic land cover types were also compiled to facilitate linkage of land use/land cover maps to SWAT plant categories. Because of the broad nature of the some of the categories, a number of assumptions had to be made when compiling the plant growth parameter values. The user is strongly recommended to use parameters for a specific plant rather than those of the generic land covers any time information about plant types is available for the region being modeled. Plant code (CPNM): The 4-letter codes in the plant growth and urban databases are used by the GIS interfaces to link land use/land cover maps to SWAT plant types. When adding a new plant species or land cover category, the four letter code for the new plant must be unique. Land cover/plant classification (IDC): SWAT groups plants into seven categories: warm season annual legume, cold season annual legume, perennial legume, warm season annual, cold season annual, perennial and trees. (Biannual plants are classified as perennials.) The differences between the categories as modeled by SWAT are summarized in Chapter 17. Plant classifications can be easily found in horticulture books that summarize characteristics for different species. The classifications assigned to the plants in Table A-1 were obtained from Martin et al. (1976) and Bailey (1935).
A.1.2 TEMPERATURE RESPONSES SWAT uses the base temperature (T_BASE) to calculate the number of heat units accrued every day. The minimum or base temperature for plant growth varies with growth stage of the plant. However, this variation is ignored by the model—SWAT uses the same base temperature throughout the growing season. The optimal temperature (T_OPT) is used to calculate temperature stress for the plant during the growing season (temperature stress is the only calculation
34
SWAT USER’S MANUAL, VERSION 2000
in which optimal temperature is used). Chapter 19 reviews the influence of optimal temperature on plant growth. Base temperature is measured by growing plants in growth chambers at several different temperatures. The rate of leaf tip appearance as a function of temperature is plotted. Extrapolating the line to the leaf tip appearance rate of 0.0 leaves/day gives the base or minimum temperature for plant growth. Figure A-1 plots data for corn. (Note that the line intersects the x-axis at 8°C.)
Figure A-1: Rate of leaf tip appearance as a function of temperature for corn.
Optimal temperature for plant growth is difficult to measure directly. Looking at Figure A-1, one might be tempted to select the temperature corresponding to the peak of the plot as the optimal temperature. This would not be correct. The peak of the plot defines the optimal temperature for leaf development—not for plant growth. If an optimal temperature cannot be obtained through a review of literature, use the optimal temperature listed for a plant already in the database with similar growth habits. Review of temperatures for many different plants have provided generic values for base and optimal temperatures as a function of growing season. In situations, where temperature information is unavailable, these values may be
APPENDIX A: DATABASES
35
used. For warm season plants, the generic base temperature is ~8ºC and the generic optimal temperature is ~25ºC. For cool season plants, the generic base temperature is ~0ºC and the generic optimal temperature is ~13ºC. Base and optimal temperatures for the plants included in the database are listed in Table A-3. Table A-3: Temperature parameters for plants included in plant growth database. Plant Common Name Code Tbase Topt Corn 8 25 CORN Corn silage 8 25 CSIL 12 24 Sweet corn SCRN 12 25 Eastern gamagrass EGAM 11 30 Grain sorghum GRSG
Reference (Kiniry et al, 1995) (Kiniry et al, 1995) (Hackett and Carolane, 1982) (Kiniry, personal comm., 2001) (Kiniry et al, 1992a)
Sorghum hay Johnsongrass Sugarcane Spring wheat Winter wheat
SGHY JHGR SUGC SWHT WWHT
11 11 11 0 0
30 30 25 18 18
Durum wheat Rye Spring barley Oats Rice
DWHT RYE BARL OATS RICE
0 0 0 0 10
15 12.5 25 15 25
Pearl millet Timothy Smooth bromegrass Meadow bromegrass Tall fescue
PMIL TIMO BROS BROM FESC
10 8 8 6 0
30 25 25 25 15
(Kiniry et al, 1991) estimated estimated (Kiniry et al, 1995) estimated
Kentucky bluegrass Bermudagrass Crested wheatgrass Western wheatgrass Slender wheatgrass
BLUG BERM CWGR WWGR SWGR
12 12 6 6 8
25 25 25 25 25
(Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry et al, 1995) (Kiniry et al, 1995) estimated
Italian (annual) ryegrass Russian wildrye Altai wildrye Sideoats grama Big bluestem
RYEG RYER RYEA SIDE BBLS
0 0 0 12 12
18 15 15 25 25
estimated (Kiniry et al, 1995) (Kiniry et al, 1995) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001)
Little bluestem Alamo switchgrass Indiangrass Alfalfa Sweetclover
LBLS SWCH INDN ALFA CLVS
12 12 12 4 1
25 25 25 20 15
(Kiniry, personal comm., 2001) (Kiniry et al, 1996) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) estimated
(Kiniry et al, 1992a) (Kiniry et al, 1992a) (Kiniry and Williams, 1994) (Kiniry et al, 1995) (Kiniry et al, 1995) estimated estimated (Kiniry et al, 1995) (Kiniry, personal comm., 2001) (Martin et al, 1976)
36
SWAT USER’S MANUAL, VERSION 2000
Red clover Alsike clover Soybean Cowpeas
Plant Code CLVR CLVA SOYB CWPS
Mung bean
Common Name
Tbase
Topt
1 1 10 14
15 15 25 28
MUNG
15
30
estimated estimated (Kiniry et al, 1992a) (Kiniry et al, 1991; Hackett and Carolane, 1982) (Hackett and Carolane, 1982)
Lima beans Lentils Peanut Field peas Garden or canning peas
LIMA LENT PNUT FPEA PEAS
18 3 14 1 5
26 20 27 15 14
(Hackett and Carolane, 1982) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982) estimated (Hackett and Carolane, 1982)
Sesbania Flax Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco
SESB FLAX COTS
10 5 15
25 22.5 30
estimated estimated (Martin et al, 1976)
COTP
15
30
(Martin et al, 1976)
TOBC
10
25
(Martin et al, 1976)
Sugarbeet Potato Sweetpotato
SGBT POTA SPOT
4 7 14
18 22 24
Carrot Onion
CRRT ONIO
7 7
24 19
(Kiniry and Williams, 1994) (Hackett and Carolane, 1982) (estimated; Hackett and Carolane, 1982) (Kiniry and Williams, 1994) (Hackett and Carolane, 1982; Kiniry and Williams, 1994)
Sunflower
SUNF
6
25
Spring canola-Polish Spring canola-Argentine Asparagus Broccoli
CANP CANA ASPR BROC
5 5 10 4
21 21 24 18
(Kiniry et al, 1992b; Kiniry, personal communication, 2001) (Kiniry et al, 1995) (Kiniry et al, 1995) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982)
Cabbage Cauliflower Celery Head lettuce Spinach
CABG CAUF CELR LETT SPIN
1 5 4 7 4
18 18 22 18 24
(Hackett and Carolane, 1982) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982) (Kiniry and Williams, 1994)
Green beans Cucumber Eggplant Cantaloupe
GRBN CUCM EGGP CANT
10 16 15 15
19 32 26 35
Honeydew melon
HMEL
16
36
(Hackett and Carolane, 1982) (Kiniry and Williams, 1994) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982; Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Watermelon Bell pepper Strawberry Tomato Apple
WMEL PEPR STRW TOMA APPL
18 18 10 10 7
35 27 32 22 20
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Hackett and Carolane, 1982) (Hackett and Carolane, 1982)
Reference
APPENDIX A: DATABASES
Plant Code PINE OAK POPL MESQ
Common Name Pine Oak Poplar Honey mesquite
Tbase
Topt
0 10 10 10
30 30 30 30
37
Reference (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001)
A.1.3 LEAF AREA DEVELOPMENT Leaf area development is a function of the plant’s growing season. Plant growth database variables used to quantify leaf area development are: BLAI, FRGRW1, LAIMX1, FRGRW2, LAIMX2, and DLAI. Figure A-2 illustrates the relationship of the database parameters to the leaf area development modeled by SWAT.
Figure A-2: Leaf area index as a function of fraction of growing season for Alamo switchgrass.
To identify the leaf area development parameters, record the leaf area index and number of accumulated heat units for the plant species throughout the growing season and then plot the results. For best results, several years worth of field data should be collected. At the very minimum, data for two years is recommended. It is important that the plants undergo no water or nutrient stress during the years in which data is collected.
38
SWAT USER’S MANUAL, VERSION 2000
The leaf area index incorporates information about the plant density, so field experiments should either be set up to reproduce actual plant densities or the maximum LAI value for the plant determined from field experiments should be adjusted to reflect plant densities desired in the simulation. Maximum LAI values in the default database correspond to plant densities associated with rainfed agriculture. The leaf area index is calculated by dividing the green leaf area by the land area. Because the entire plant must be harvested to determine the leaf area, the field experiment needs to be designed to include enough plants to accommodate all leaf area measurements made during the year. Although measuring leaf area can be laborious for large samples, there is no intrinsic difficulty in the process. The most common method is to obtain an electronic scanner and feed the harvested green leaves and stems into the scanner. Older methods for estimating leaf area include tracing of the leaves (or weighed subsamples) onto paper, the use of planimeters, the punch disk method of Watson (1958) and the linear dimension method of Duncan and Hesketh (1968). Chapter 17 reviews the methodology used to calculate accumulated heat units for a plant at different times of the year as well as determination of the fraction of total, or potential, heat units that is required for the plant database. Leaf area development parameter values for the plants included in the database are listed in Table A-4 (LAImx = BLAI; frPHU,1 = FRGRW1; frLAI,1 = LAIMX1; frPHU,2 = FRGRW2; frLAI,2 = LAIMX2; frPHU,sen = DLAI). Table A-4: Leaf area development parameters for plants included in plant growth database. Plant Common Name Code LAImx frPHU,1 frLAI,1 frPHU,2 frLAI,2 frPHU,sen Corn 3 0.15 0.05 0.50 0.95 0.70 CORN Corn silage
CSIL
4
0.15
0.05
0.50
0.95
0.70
Sweet corn
SCRN
2.5
0.15
0.05
0.50
0.95
0.50
Eastern gamagrass Grain sorghum
EGAM GRSG
2.5 3
0.05 0.15
0.18 0.05
0.25 0.50
0.90 0.95
0.40 0.64
Sorghum hay
SGHY
4
0.15
0.05
0.50
0.95
0.64
Reference (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; Kiniry and Williams, 1994) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; Kiniry and Bockholt, 1998) (Kiniry, personal comm., 2001; Kiniry and Bockholt, 1998)
APPENDIX A: DATABASES
39
LAImx
frPHU,1
frLAI,1
frPHU,2
frLAI,2
frPHU,sen
Reference
Johnsongrass
Plant Code JHGR
2.5
0.15
0.05
0.57
0.95
0.50
Sugarcane Spring wheat
SUGC SWHT
6 4
0.15 0.15
0.01 0.05
0.50 0.50
0.95 0.95
0.75 0.60
Winter wheat
WWHT
4
0.05
0.05
0.45
0.95
0.50
Durum wheat
DWHT
4
0.15
0.01
0.50
0.95
0.80
(Kiniry, personal comm., 2001; Kiniry et al, 1992a) (Kiniry and Williams, 1994) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal communication, 2001; estimated)
Rye
RYE
4
0.15
0.01
0.50
0.95
0.80
Spring barley
BARL
4
0.15
0.01
0.45
0.95
0.60
Oats Rice
OATS RICE
4 5
0.15 0.30
0.02 0.01
0.50 0.70
0.95 0.95
0.80 0.80
Pearl millet
PMIL
2.5
0.15
0.01
0.50
0.95
0.85
Timothy
TIMO
4
0.15
0.01
0.50
0.95
0.85
Smooth bromegrass
BROS
5
0.15
0.01
0.50
0.95
0.85
Meadow bromegrass
BROM
3
0.45
0.02
0.80
0.95
0.85
Tall fescue
FESC
4
0.15
0.01
0.50
0.95
0.80
Kentucky bluegrass
BLUG
2
0.05
0.05
0.30
0.70
0.35
Bermudagrass Crested wheatgrass
BERM CWGR
4 4
0.05 0.35
0.05 0.02
0.49 0.62
0.95 0.95
0.99 0.85
Western wheatgrass
WWGR
4
0.50
0.02
0.89
0.95
0.85
Slender wheatgrass
SWGR
4
0.15
0.01
0.50
0.95
0.85
Italian (annual) ryegrass
RYEG
4
0.20
0.32
0.45
0.95
0.50
Russian wildrye Altai wildrye Sideoats grama Big bluestem Little bluestem
RYER RYEA SIDE BBLS LBLS
3 3 1.7 3 2.5
0.35 0.35 0.05 0.05 0.05
0.02 0.02 0.05 0.10 0.10
0.62 0.62 0.30 0.25 0.25
0.95 0.95 0.70 0.70 0.70
0.80 0.80 0.35 0.35 0.35
(Kiniry et al, 1995) (Kiniry et al, 1995) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001)
Alamo switchgrass
SWCH
6
0.10
0.20
0.20
0.95
0.70
Indiangrass Alfalfa Sweetclover
INDN ALFA CLVS
3 4 4
0.05 0.15 0.15
0.10 0.01 0.01
0.25 0.50 0.50
0.70 0.95 0.95
0.35 0.90 0.75
Red clover
CLVR
4
0.15
0.01
0.50
0.95
0.75
(Kiniry, personal comm., 2001; Kiniry et al, 1996) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated)
Alsike clover
CLVA
4
0.15
0.01
0.50
0.95
0.75
Common Name
(Kiniry, personal communication, 2001; estimated) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal comm, 2001; estimated) (Kiniry, personal comm., 2001) (Kiniry, personal comm, 2001) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry et al, 1995; Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated)
(Kiniry, personal comm., 2001; estimated)
40
SWAT USER’S MANUAL, VERSION 2000
LAImx
frPHU,1
frLAI,1
frPHU,2
frLAI,2
frPHU,sen
Soybean
Plant Code SOYB
3
0.15
0.05
0.50
0.95
0.60
Cowpeas
CWPS
4
0.15
0.01
0.50
0.95
0.80
Mung bean
MUNG
4
0.15
0.01
0.50
0.95
0.90
Lima beans Lentils
LIMA LENT
2.5 4
0.10 0.15
0.05 0.02
0.80 0.50
0.95 0.95
0.90 0.90
Peanut
PNUT
4
0.15
0.01
0.50
0.95
0.75
Field peas
FPEA
4
0.15
0.01
0.50
0.95
0.75
Garden or canning peas Sesbania
PEAS SESB
2.5 5
0.10 0.15
0.05 0.01
0.80 0.50
0.95 0.95
0.60 0.90
Flax
FLAX
2.5
0.15
0.02
0.50
0.95
0.90
Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco Sugarbeet Potato
COTS
4
0.15
0.01
0.50
0.95
0.95
COTP
4
0.15
0.01
0.50
0.95
0.95
TOBC SGBT POTA
4.5 5 4
0.15 0.05 0.15
0.05 0.05 0.01
0.50 0.50 0.50
0.95 0.95 0.95
0.70 0.60 0.60
Sweetpotato
SPOT
4
0.15
0.01
0.50
0.95
0.60
Carrot Onion Sunflower
CRRT ONIO SUNF
3.5 1.5 3
0.15 0.15 0.15
0.01 0.01 0.01
0.50 0.50 0.50
0.95 0.95 0.95
0.60 0.60 0.62
Spring canola-Polish
CANP
3.5
0.15
0.02
0.45
0.95
0.50
(Kiniry, personal comm., 2001; estimated) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; Kiniry et al, 1992b) (Kiniry et al, 1995)
Spring canola-Argentine Asparagus Broccoli Cabbage Cauliflower
CANA ASPR BROC CABG CAUF
4.5 4.2 4.2 3 2.5
0.15 0.25 0.25 0.25 0.25
0.02 0.23 0.23 0.23 0.23
0.45 0.40 0.40 0.40 0.40
0.95 0.86 0.86 0.86 0.86
0.50 1.00 1.00 1.00 1.00
(Kiniry et al, 1995) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Celery Head lettuce Spinach Green beans Cucumber
CELR LETT SPIN GRBN CUCM
2.5 4.2 4.2 1.5 1.5
0.25 0.25 0.10 0.10 0.15
0.23 0.23 0.05 0.05 0.05
0.40 0.40 0.90 0.80 0.50
0.86 0.86 0.95 0.95 0.95
1.00 1.00 0.95 0.90 0.60
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Eggplant Cantaloupe Honeydew melon Watermelon Bell pepper
EGGP CANT HMEL WMEL PEPR
3 3 4 1.5 5
0.15 0.15 0.15 0.15 0.15
0.05 0.05 0.05 0.05 0.05
0.50 0.50 0.50 0.50 0.50
0.95 0.95 0.95 0.95 0.95
0.60 0.60 0.60 0.60 0.60
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Common Name
Reference (Kiniry, personal comm., 2001; Kiniry et al, 1992a) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001; estimated) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; Kiniry and Williams, 1994)
APPENDIX A: DATABASES
Strawberry Tomato Apple
Plant Code STRW TOMA APPL
Pine Oak
PINE OAK
Poplar Honey mesquite
POPL MESQ
Common Name
LAImx
frPHU,1
frLAI,1
frPHU,2
frLAI,2
frPHU,sen
3 3 4
0.15 0.15 0.10
0.05 0.05 0.15
0.50 0.50 0.50
0.95 0.95 0.75
0.60 0.95 0.99
5 5
0.15 0.05
0.70 0.05
0.25 0.40
0.99 0.95
0.99 0.99
5 1.25
0.05 0.05
0.05 0.05
0.40 0.40
0.95 0.95
0.99 0.99
41
Reference (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry, personal comm., 2001; estimated) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, personal comm., 2001) (Kiniry, 1998; Kiniry, personal communication, 2001)
A.1.4 ENERGY-BIOMASS CONVERSION Radiation-use efficiency (RUE) quantifies the efficiency of a plant in converting light energy into biomass. Four variables in the plant growth database are used to define the RUE in ideal growing conditions (BIO_E), the impact of reduced vapor pressure on RUE (WAVP), and the impact of elevated CO2 concentration on RUE (CO2HI, BIOEHI). Determination of RUE is commonly performed and a literature review will provide those setting up experiments with numerous examples. The following overview of the methodology used to measure RUE was summarized from Kiniry et al (1998) and Kiniry et al (1999). To calculate RUE, the amount of photosynthetically active radiation (PAR) intercepted and the mass of aboveground biomass is measured several times throughout a plant’s growing season. The frequency of the measurements taken will vary but in general 4 to 7 measurements per growing season are considered to be adequate. As with leaf area determinations, the measurements should be performed on non-stressed plants. Intercepted radiation is measured with a light meter. Whole spectrum and PAR sensors are available and calculations of RUE will be performed differently depending on the sensor used. A brief discussion of the difference between whole spectrum and PAR sensors and the difference in calculations is given in Kiniry (1999). The use of a PAR sensor in RUE studies is strongly encouraged.
42
SWAT USER’S MANUAL, VERSION 2000
When measuring radiation, three to five sets of measurements are taken rapidly for each plant plot. A set of measurements consists of 10 measurements above the leaf canopy, 10 below, and 10 more above. The light measurements should be taken between 10:00 am and 2:00 pm local time. The measurements above and below the leaf canopy are averaged and the fraction of intercepted PAR is calculated for the day from the two values. Daily estimates of the fraction of intercepted PAR are determined by linearly interpolating the measured values. The fraction of intercepted PAR is converted to an amount of intercepted PAR using daily values of incident total solar radiation measured with a standard weather station. To convert total incident radiation to total incident PAR, the daily solar radiation values are multiplied by the percent of total radiation that has a wavelength between 400 and 700 mm. This percent usually falls in the range 45 to 55% and is a function of cloud cover. 50% is considered to be a default value. Once daily intercepted PAR values are determined, the total amount of PAR intercepted by the plant is calculated for each date on which biomass was harvested. This is calculated by summing daily intercepted PAR values from the date of seedling emergence to the date of biomass harvest. To determine biomass production, aboveground biomass is harvested from a known area of land within the plot. The plant material should be dried at least 2 days at 65°C and then weighed. RUE is determined by fitting a linear regression for aboveground biomass as a function of intercepted PAR. The slope of the line is the RUE. Figure A-3 shows
the
plots
of
aboveground
biomass
and
summed
intercepted
photosynthetically active radiation for Eastern gamagrass. (Note that the units for RUE values in the graph, as well as values typically reported in literature, are different from those used by SWAT. To obtain the value used in SWAT, multiply by 10.)
APPENDIX A: DATABASES
43
Figure A-3: Aboveground biomass and summed intercepted photosynthetically active radiation for Eastern gamagrass (from Kiniry et al.,1999).
Stockle and Kiniry (1990) first noticed a relationship between RUE and vapor pressure deficit and were able to explain a large portion of within-species variability in RUE values for sorghum and corn by plotting RUE values as a function of average daily vapor pressure deficit values. Since this first article, a number of other studies have been conducted that support the dependence of RUE on vapor pressure deficit. However, there is still some debate in the scientific community on the validity of this relationship. If the user does not wish to simulate a change in RUE with vapor pressure deficit, the variable WAVP can be set to 0.0 for the plant. To define the impact of vapor pressure deficit on RUE, vapor pressure deficit values must be recorded during the growing seasons that RUE determinations are being made. It is important that the plants are exposed to no other stress than vapor pressure deficit, i.e. plant growth should not be limited by lack of soil water and nutrients. Vapor pressure deficits can be calculated from relative humidity (see Chapter 3) or from daily maximum and minimum temperatures using the technique of Diaz and Campbell (1988) as described by Stockle and Kiniry (1990). The change in RUE with vapor pressure deficit is determined by fitting a
44
SWAT USER’S MANUAL, VERSION 2000
linear regression for RUE as a function of vapor pressure deficit. Figure A-4 shows a plot of RUE as a function of vapor pressure deficit for grain sorghum.
Figure A-4: Response of radiation-use efficiency to mean daily vapor pressure deficit for grain sorghum.
From Figure A-4, the rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit, ∆ruedcl, for sorghum is 8.4×10-1 g⋅MJ-1⋅kPa-1. When RUE is adjusted for vapor pressure deficit, the model assumes the RUE value reported for BIO_E is the radiation-use efficiency at a vapor pressure deficit of 1 kPa. In order to assess the impact of climate change on agricultural productivity, SWAT incorporates equations that adjust RUE for elevated atmospheric CO2 concentrations. Values must be entered for CO2HI and BIOEHI in the plant database whether or not the user plans to simulate climate change. For simulations in which elevated CO2 levels are not modeled, CO2HI should be set to some number greater than 330 ppmv and BIOEHI should be set to some number greater than BIO_E. To obtain radiation-use efficiency values at elevated CO2 levels for plant species not currently in the database, plants should be established in growth chambers set up in the field or laboratory where CO2 levels can be controlled. RUE values are determined using the same methodology described previously.
APPENDIX A: DATABASES
45
Radiation-use efficiency parameter values for the plants included in the database are listed in Table A-5 (RUE = BIO_E; ∆ruedcl = WAVP; RUEhi = BIOEHI; CO2hi = CO2HI).
Table A-5: Biomass production parameters for plants included in plant growth database. Plant Common Name Code RUE ∆ruedcl RUEhi CO2hi Reference Corn 39 7.2 45 660 (Kiniry et al, 1998; Kiniry et al, 1997; Kiniry, CORN personal communication, 2001) (Kiniry et al, 1998; Kiniry et al, 1997; Kiniry, personal communication, 2001) (Kiniry and Williams, 1994; Kiniry et al, 1997; Kiniry, personal communication, 2001) (Kiniry et al, 1999; Kiniry, personal communication, 2001) (Kiniry et al, 1998; Kiniry, personal communication, 2001)
Corn silage
CSIL
39
7.2
45
660
Sweet corn
SCRN
39
7.2
45
660
Eastern gamagrass
EGAM
21
10
58
660
Grain sorghum
GRSG
33.5
8.5
36
660
Sorghum hay
SGHY
33.5
8.5
36
660
Johnsongrass
JHGR
35
8.5
36
660
Sugarcane
SUGC
25
10
33
660
Spring wheat
SWHT
35
8
46
660
Winter wheat
WWHT
30
6
39
660
(Kiniry et al, 1998; Kiniry, personal communication, 2001) (Kiniry et al, 1992a; Kiniry, personal communication, 2001) (Kiniry and Williams, 1994; Kiniry, personal communication, 2001) (Kiniry et al, 1992a; Kiniry, personal communication, 2001; estimated) (Kiniry et al, 1995; estimated)
Durum wheat Rye Spring barley Oats Rice
DWHT RYE BARL OATS RICE
30 35 35 35 22
7 7 7 10 5
45 45 45 45 31
660 660 660 660 660
(estimated) (estimated) (Kiniry et al, 1995; estimated) (Kiniry, personal communication, 2001) (Kiniry et al, 1989; estimated)
Pearl millet Timothy Smooth bromegrass Meadow bromegrass Tall fescue
PMIL TIMO BROS BROM FESC
35 35 35 35 30
8 8 8 8 8
40 45 45 45 39
660 660 660 660 660
(estimated) (estimated) (estimated) (Kiniry et al, 1995; estimated) (estimated)
Kentucky bluegrass Bermudagrass Crested wheatgrass
BLUG BERM CWGR
18 35 35
10 10 8
31 36 38
660 660 660
Western wheatgrass Slender wheatgrass
WWGR SWGR
35 35
8 8
45 45
660 660
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al, 1995; Kiniry, personal communication, 2001) (Kiniry et al, 1995; estimated) (estimated)
Italian (annual) ryegrass Russian wildrye Altai wildrye
RYEG RYER RYEA
30 30 30
6 8 8
39 39 46
660 660 660
Sideoats grama
SIDE
11
10
21
660
(estimated) (Kiniry et al, 1995; estimated) (Kiniry et al, 1995; Kiniry, personal communication, 2001) (Kiniry et al, 1999; Kiniry, personal communication, 2001)
46
SWAT USER’S MANUAL, VERSION 2000 RUE
∆ruedcl
RUEhi
CO2hi
Big bluestem
Plant Code BBLS
14
10
39
660
Little bluestem Alamo switchgrass
LBLS SWCH
34 47
10 8.5
39 54
660 660
Indiangrass Alfalfa
INDN ALFA
34 20
10 10
39 35
660 660
Sweetclover Red clover Alsike clover Soybean
CLVS CLVR CLVA SOYB
25 25 25 25
10 10 10 8
30 30 30 34
660 660 660 660
Cowpeas
CWPS
35
8
39
660
(estimated) (estimated) (estimated) (Kiniry et al, 1992a; Kiniry, personal communication, 2001) (estimated)
Mung bean Lima beans Lentils Peanut Field peas
MUNG LIMA LENT PNUT FPEA
25 25 20 20 25
10 5 10 4 10
33 34 33 25 30
660 660 660 660 660
(estimated) (Kiniry and Williams, 1994; estimated) (estimated) (estimated) (estimated)
Garden or canning peas Sesbania Flax Upland cotton (harvested with stripper) Upland cotton (harvested with picker)
PEAS SESB FLAX COTS
25 50 25 15
5 10 10 3
34 60 33 19
660 660 660 660
(Kiniry and Williams, 1994; estimated) (estimated) (estimated) (estimated)
COTP
15
3
19
660
(estimated)
Tobacco Sugarbeet Potato Sweetpotato Carrot
TOBC SGBT POTA SPOT CRRT
39 30 25 15 30
8 10 14.8 3 10
44 35 30 19 35
660 660 660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Manrique et al, 1991; estimated) (estimated) (Kiniry and Williams, 1994; estimated)
Onion Sunflower
ONIO SUNF
30 46
10 32.3
35 59
660 660
Spring canola-Polish Spring canola-Argentine Asparagus
CANP CANA ASPR
34 34 90
10 10 5
39 40 95
660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry et al, 1992b; Kiniry, personal communication, 2001) (Kiniry et al, 1995; estimated) (Kiniry et al, 1995; estimated) (Kiniry and Williams, 1994; estimated)
Broccoli Cabbage Cauliflower Celery Head lettuce
BROC CABG CAUF CELR LETT
26 19 21 27 23
5 5 5 5 8
30 25 25 30 25
660 660 660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated)
Spinach Green beans Cucumber Eggplant Cantaloupe
SPIN GRBN CUCM EGGP CANT
30 25 30 30 30
5 5 8 8 3
35 34 39 39 39
660 660 660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated)
Common Name
Reference (Kiniry et al, 1999; Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al, 1996; Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
APPENDIX A: DATABASES
Honeydew melon Watermelon Bell pepper Strawberry Tomato
Plant Code HMEL WMEL PEPR STRW TOMA
Apple Pine Oak Poplar Honey mesquite
APPL PINE OAK POPL MESQ
Common Name
47
RUE
∆ruedcl
RUEhi
CO2hi
30 30 30 30 30
3 3 8 8 8
39 39 39 39 39
660 660 660 660 660
(Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated) (Kiniry and Williams, 1994; estimated)
15 15 15 30 16.1
3 8 8 8 8
20 16 16 31 18
660 660 660 660 660
(estimated) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, 1998; Kiniry, personal comm., 2001)
Reference
A.1.5 STOMATAL CONDUCTANCE Stomatal conductance of water vapor is used in the Penman-Monteith calculations of maximum plant evapotranspiration. The plant database contains three variables pertaining to stomatal conductance that are required only if the Penman-Monteith equations are chosen to model evapotranspiration: maximum stomatal conductance (GSI), and two variables that define the impact of vapor pressure deficit on stomatal conductance (FRGMAX, VPDFR). Körner et al (1979) defines maximum leaf diffusive conductance as the largest value of conductance observed in fully developed leaves of well-watered plants under optimal climatic conditions, natural outdoor CO2 concentrations and sufficient nutrient supply. Leaf diffusive conductance of water vapor cannot be measured directly but can be calculated from measurements of transpiration under known climatic conditions. A number of different methods are used to determine diffusive conductance: transpiration measurements in photosynthesis cuvettes, energy balance measurements or weighing experiments, ventilated diffusion porometers and non-ventilated porometers. Körner (1977) measured diffusive conductance using a ventilated diffusion porometer. To obtain maximum leaf conductance values, leaf conductance is determined between sunrise and late morning until a clear decline or no further increase is observed. Depending on phenology, measurements are taken on at least three bright days in late spring and summer, preferably just after a rainy period.
48
SWAT USER’S MANUAL, VERSION 2000
The means of maximum leaf conductance of 5 to 10 samples each day are averaged, yielding the maximum diffusive conductance for the species. Due to the variation of the location of stomata on plant leaves for different plant species, conductance values should be calculated for the total leaf surface area. Körner et al (1979) compiled maximum leaf diffusive conductance data for 246 plant species. The data for each individual species was presented as well as summarized by 13 morphologically and/or ecologically comparable plant groups. All maximum stomatal conductance values in the plant growth database were based on the data included in Körner et al (1979) (see Table A-6). As with radiation-use efficiency, stomatal conductance is sensitive to vapor pressure deficit. Stockle et al (1992) compiled a short list of stomatal conductance response to vapor pressure deficit for a few plant species. Due to the paucity of data, default values for the second point on the stomatal conductance vs. vapor pressure deficit curve are used for all plant species in the database. The fraction of maximum stomatal conductance (FRGMAX) is set to 0.75 and the vapor pressure deficit corresponding to the fraction given by FRGMAX (VPDFR) is set to 4.00 kPa. If the user has actual data, they should use those values, otherwise the default values are adequate.
A.1.6 CANOPY HEIGHT/ROOT DEPTH Maximum canopy height (CHTMX) is a straightforward measurement. The canopy height of non-stressed plants should be recorded at intervals throughout the growing season. The maximum value recorded is used in the database. To determine maximum rooting depth (RDMX), plant samples need to be grown on soils without an impermeable layer. Once the plants have reached maturity, soil cores are taken for the entire depth of the soil. Each 0.25 m increment is washed and the live plant material collected. Live roots can be differentiated from dead roots by the fact that live roots are whiter and more elastic and have an intact cortex. The deepest increment of the soil core in which live roots are found defines the maximum rooting depth. Table A-6 lists the
APPENDIX A: DATABASES
49
maximum canopy height and maximum rooting depths for plants in the default database. Table A-6: Maximum stomatal conductance ( g l ,mx ), maximum canopy height (hc,mx), maximum root depth (zroot,mx), minimum USLE C factor for land cover (CUSLE,mn). Plant g l,mx Common Name Code hc,mx Corn .0071 2.5 CORN
zroot,mx
CUSLE,mn
Reference
2.0
.20
(Körner et al, 1979; Martin et al, 1976; Kiniry et al, 1995; Kiniry, personal comm., 2001) (Körner et al, 1979; Martin et al, 1976; Kiniry et al, 1995; Kiniry, personal comm., 2001) (Körner et al, 1979, Kiniry and Williams, 1994; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001)
Corn silage
CSIL
.0071
2.5
2.0
.20
Sweet corn
SCRN
.0071
2.5
2.0
.20
Eastern gamagrass
EGAM
.0055
1.7
2.0
.003
Grain sorghum
GRSG
.0050
1.0
2.0
.20
Sorghum hay
SGHY
.0050
1.5
2.0
.20
Johnsongrass Sugarcane
JHGR SUGC
.0048 .0055
1.0 3.0
2.0 2.0
.20 .001
Spring wheat
SWHT
.0056
0.9
2.0
.03
Winter wheat
WWHT
.0056
0.9
1.3
.03
Durum wheat
DWHT
.0056
1.0
2.0
.03
Rye
RYE
.0100
1.0
1.8
.03
Spring barley
BARL
.0083
1.2
1.3
.01
Oats
OATS
.0055
1.5
2.0
.03
Rice
RICE
.0078
0.8
0.9
.03
Pearl millet
PMIL
.0143
3.0
2.0
.20
Timothy Smooth bromegrass
TIMO BROS
.0055 .0025
0.8 1.2
2.0 2.0
.003 .003
Meadow bromegrass
BROM
.0055
0.8
1.3
.003
Tall fescue
FESC
.0055
1.5
2.0
.03
Kentucky bluegrass
BLUG
.0055
0.2
1.4
.003
Bermudagrass
BERM
.0055
0.5
2.0
.003
Crested wheatgrass
CWGR
.0055
0.9
1.3
.003
Western wheatgrass
WWGR
.0083
0.6
1.3
.003
Slender wheatgrass
SWGR
.0055
0.7
2.0
.003
(Körner et al, 1979; Martin et al, 1976; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry et al, 1992a) (Körner et al, 1979; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001; Kiniry et al, 1995) (Körner et al, 1979; estimated; Kiniry, personal comm., 2001) (Körner et al, 1979; estimated; Martin et al, 1976; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry and Williams, 1994; Kiniry et al, 1995) (Körner et al, 1979; Martin et al, 1976; Kiniry, personal comm., 2001) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; Kiniry, personal comm., 2001; estimated) (Körner et al, 1979; estimated) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; estimated; Kiniry et al, 1995) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Martin et al, 1976; Kiniry et al, 1995) (Körner et al, 1979; Martin et al, 1976; Kiniry et al, 1995; estimated) (Körner et al, 1979; estimated)
50
SWAT USER’S MANUAL, VERSION 2000
g l,mx
hc,mx
zroot,mx
CUSLE,mn
Italian (annual) ryegrass Russian wildrye
Plant Code RYEG RYER
.0055 .0065
0.8 1.0
1.3 1.3
.03 .03
Altai wildrye
RYEA
.0055
1.1
1.3
.03
Sideoats grama
SIDE
.0055
0.4
1.4
.003
Big bluestem
BBLS
.0055
1.0
2.0
.003
Little bluestem
LBLS
.0055
1.0
2.0
.003
Alamo switchgrass
SWCH
.0055
2.5
2.2
.003
Indiangrass
INDN
.0055
1.0
2.0
.003
Alfalfa
ALFA
.0100
0.9
3.0
.01
Sweetclover
CLVS
.0055
1.5
2.4
.003
Red clover
CLVR
.0065
0.75
1.5
.003
Alsike clover
CLVA
.0055
0.9
2.0
.003
Soybean Cowpeas Mung bean
SOYB CWPS MUNG
.0071 .0055 .0055
0.8 1.2 1.5
1.7 2.0 2.0
.20 .03 .20
Lima beans
LIMA
.0055
0.6
2.0
.20
Lentils
LENT
.0055
0.55
1.2
.20
Peanut Field peas
PNUT FPEA
.0063 .0055
0.5 1.2
2.0 1.2
.20 .01
Garden or canning peas
PEAS
.0055
0.6
1.2
.20
Sesbania
SESB
.0055
2.0
2.0
.20
Flax
FLAX
.0055
1.2
1.5
.20
Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco
COTS
.0091
1.0
2.5
.20
COTP
.0091
1.0
2.5
.20
TOBC
.0048
1.8
2.0
.20
Sugarbeet
SGBT
.0071
1.2
2.0
.20
Potato
POTA
.0050
0.6
0.6
.20
Common Name
Reference (Körner et al, 1979; estimated) (Körner et al, 1979; estimated; Kiniry et al, 1995) (Körner et al, 1979; Kiniry, personal comm., 2001; Kiniry et al, 1995) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001; Kiniry et al, 1996) (Körner et al, 1979; Kiniry, personal comm., 2001) (Jensen et al, 1990; Martin et al, 1976; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001; Martin et al, 1976; estimated) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; Martin et al, 1976; estimated) (Körner et al, 1979; Kiniry et al, 1992a) (Körner et al, 1979; estimated) (Körner et al, 1979; estimated) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Martin et al, 1976; Maynard and Hochmuth, 1997) (Körner et al, 1979; estimated) (Körner et al, 1979; Martin et al, 1976; Maynard and Hochmuth, 1997; estimated) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001; estimated) (Körner et al, 1979; Martin et al, 1976; Jensen et al, 1990; estimated) (Monteith, 1965; Kiniry, personal comm., 2001; Martin et al, 1976) (Monteith, 1965; Kiniry, personal comm., 2001; Martin et al, 1976) (Körner et al, 1979; Martin et al, 1976; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry and Williams, 1994) (Körner et al, 1979; Martin et al, 1976; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994)
APPENDIX A: DATABASES
g l,mx
hc,mx
zroot,mx
CUSLE,mn
Sweetpotato
Plant Code SPOT
.0065
0.8
2.0
.05
Carrot
CRRT
.0065
0.3
1.2
.20
Onion
ONIO
.0065
0.5
0.6
.20
Sunflower
SUNF
.0077
2.5
2.0
.20
Spring canola-Polish
CANP
.0065
0.9
0.9
.20
Spring canola-Argentine
CANA
.0065
1.3
1.4
.20
Asparagus
ASPR
.0065
0.5
2.0
.20
Broccoli
BROC
.0065
0.5
0.6
.20
Cabbage
CABG
.0065
0.5
0.6
.20
Cauliflower
CAUF
.0065
0.5
0.6
.20
Celery
CELR
.0065
0.5
0.6
.20
Head lettuce
LETT
.0025
0.2
0.6
.01
Spinach
SPIN
.0065
0.5
0.6
.20
Green beans
GRBN
.0077
0.6
1.2
.20
Cucumber
CUCM
.0033
0.5
1.2
.03
Eggplant
EGGP
.0065
0.5
1.2
.03
Cantaloupe
CANT
.0065
0.5
1.2
.03
Honeydew melon
HMEL
.0065
0.5
1.2
.03
Watermelon
WMEL
.0065
0.5
2.0
.03
Bell pepper
PEPR
.0053
0.5
1.2
.03
Strawberry
STRW
.0065
0.5
0.6
.03
Common Name
51
Reference (Körner et al, 1979; estimated; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; estimated; Kiniry et al, 1995) (Körner et al, 1979; estimated; Kiniry et al, 1995) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry and Williams, 1994; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994)
52
SWAT USER’S MANUAL, VERSION 2000
g l,mx
hc,mx
zroot,mx
CUSLE,mn
Tomato
Plant Code TOMA
.0077
0.5
2.0
.03
Apple
APPL
.0071
3.5
2.0
.001
Pine
PINE
.0019
10.0
3.5
.001
Oak
OAK
.0020
6.0
3.5
.001
Poplar
POPL
.0036
7.5
3.5
.001
Honey mesquite
MESQ
.0036
6.0
3.5
.001
Common Name
Reference (Körner et al, 1979; Kiniry, personal comm., 2001; Maynard and Hochmuth, 1997; Kiniry and Williams, 1994) (Körner et al, 1979; estimated; Jensen et al, 1990) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001) (Körner et al, 1979; Kiniry, personal comm., 2001)
A.1.7 PLANT NUTRIENT CONTENT In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: BN(1), BN(2), BN(3), BP(1), BP(2), and BP(3). Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. The plant samples can be sent to testing laboratories to obtain the fraction of nitrogen and phosphorus in the biomass. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured. The fractions of nitrogen and phosphorus for the plants included in the default database are listed in Table A-7.
Table A-7: Nutrient parameters for plants included in plant growth database. Plant Common Name Code frN,1 frN,2 frN,3 frP,1 frP,2 Corn .0470 .0177 .0138 .0048 .0018 CORN Corn silage .0470 .0177 .0138 .0048 .0018 CSIL .0470 .0177 .0138 .0048 .0018 Sweet corn SCRN .0200 .0100 .0070 .0014 .0010 Eastern gamagrass EGAM
frP,3 .0014 .0014 .0014 .0007
Reference (Kiniry et al., 1995) (Kiniry et al., 1995) (Kiniry and Williams, 1994) (Kiniry, personal communication, 2001)
APPENDIX A: DATABASES
53
frN,1
frN,2
frN,3
frP,1
frP,2
frP,3
Grain sorghum Sorghum hay Johnsongrass Sugarcane Spring wheat
Plant Code GRSG SGHY JHGR SUGC SWHT
.0440 .0440 .0440 .0100 .0600
.0164 .0164 .0164 .0040 .0231
.0128 .0128 .0128 .0025 .0134
.0060 .0060 .0060 .0075 .0084
.0022 .0022 .0022 .0030 .0032
.0018 .0018 .0018 .0019 .0019
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al., 1992a) (Kiniry and Williams, 1994) (Kiniry et al., 1992a)
Winter wheat Durum wheat Rye Spring barley Oats
WWHT DWHT RYE BARL OATS
.0663 .0600 .0600 .0590 .0600
.0255 .0231 .0231 .0226 .0231
.0148 .0130 .0130 .0131 .0134
.0053 .0084 .0084 .0057 .0084
.0020 .0032 .0032 .0022 .0032
.0012 .0019 .0019 .0013 .0019
(Kiniry et al., 1995) estimated estimated (Kiniry et al., 1995) (Kiniry, personal communication, 2001)
Rice Pearl millet Timothy Smooth bromegrass Meadow bromegrass
RICE PMIL TIMO BROS BROM
.0500 .0440 .0314 .0400 .0400
.0200 .0300 .0137 .0240 .0240
.0100 .0100 .0103 .0160 .0160
.0060 .0060 .0038 .0028 .0028
.0030 .0022 .0025 .0017 .0017
.0018 .0012 .0019 .0011 .0011
estimated estimated estimated (Kiniry et al., 1995) (Kiniry et al., 1995)
Tall fescue Kentucky bluegrass Bermudagrass Crested wheatgrass Western wheatgrass
FESC BLUG BERM CWGR WWGR
.0560 .0200 .0600 .0300 .0300
.0210 .0100 .0231 .0200 .0200
.0120 .0060 .0134 .0120 .0120
.0099 .0014 .0084 .0020 .0020
.0022 .0010 .0032 .0015 .0015
.0019 .0007 .0019 .0013 .0013
estimated (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al., 1995) (Kiniry et al., 1995)
Slender wheatgrass Italian (annual) ryegrass Russian wildrye Altai wildrye Sideoats grama
SWGR RYEG RYER RYEA SIDE
.0300 .0660 .0226 .0226 .0200
.0200 .0254 .0180 .0180 .0100
.0120 .0147 .0140 .0140 .0060
.0020 .0105 .0040 .0040 .0014
.0015 .0040 .0040 .0040 .0010
.0013 .0024 .0024 .0024 .0007
estimated estimated (Kiniry et al., 1995) (Kiniry et al., 1995) (Kiniry, personal communication, 2001)
Big bluestem Little bluestem Alamo switchgrass Indiangrass Alfalfa
BBLS LBLS SWCH INDN ALFA
.0200 .0200 .0350 .0200 .0417
.0120 .0120 .0150 .0120 .0290
.0050 .0050 .0038 .0050 .0200
.0014 .0014 .0014 .0014 .0035
.0010 .0010 .0010 .0010 .0028
.0007 .0007 .0007 .0007 .0020
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry et al., 1996) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
Sweetclover Red clover Alsike clover Soybean Cowpeas
CLVS CLVR CLVA SOYB CWPS
.0650 .0650 .0600 .0524 .0600
.0280 .0280 .0280 .0265 .0231
.0243 .0243 .0240 .0258 .0134
.0060 .0060 .0060 .0074 .0049
.0024 .0024 .0025 .0037 .0019
.0024 .0024 .0025 .0035 .0011
estimated estimated estimated (Kiniry et al., 1992a) estimated
Mung bean Lima beans Lentils Peanut Field peas
MUNG LIMA LENT PNUT FPEA
.0524 .0040 .0440 .0524 .0515
.0265 .0030 .0164 .0265 .0335
.0258 .0015 .0128 .0258 .0296
.0074 .0035 .0074 .0074 .0033
.0037 .0030 .0037 .0037 .0019
.0035 .0015 .0023 .0035 .0014
estimated (Kiniry and Williams, 1994) estimated estimated estimated
Garden or canning peas Sesbania Flax
PEAS SESB FLAX
.0040 .0500 .0482
.0030 .0200 .0294
.0015 .0150 .0263
.0030 .0074 .0049
.0020 .0037 .0024
.0015 .0035 .0023
(Kiniry and Williams, 1994) estimated estimated
Common Name
Reference
54
SWAT USER’S MANUAL, VERSION 2000 Plant Code COTS
frN,1
frN,2
frN,3
frP,1
frP,2
frP,3
.0580
.0192
.0177
.0081
.0027
.0025
estimated
COTP
.0580
.0192
.0177
.0081
.0027
.0025
estimated
TOBC SGBT POTA
.0470 .0550 .0550
.0177 .0200 .0200
.0138 .0120 .0120
.0048 .0060 .0060
.0018 .0025 .0025
.0014 .0019 .0019
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Sweetpotato Carrot Onion Sunflower Spring canola-Polish
SPOT CRRT ONIO SUNF CANP
.0450 .0550 .0400 .0500 .0440
.0160 .0075 .0300 .0230 .0164
.0090 .0012 .0020 .0146 .0128
.0045 .0060 .0021 .0063 .0074
.0019 .0030 .0020 .0029 .0037
.0015 .0020 .0019 .0023 .0023
estimated (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry, personal communication, 2001) (Kiniry et al., 1995)
Spring canola-Argentine Asparagus Broccoli Cabbage Cauliflower
CANA ASPR BROC CABG CAUF
.0440 .0620 .0620 .0620 .0620
.0164 .0500 .0090 .0070 .0070
.0128 .0400 .0070 .0040 .0040
.0074 .0050 .0050 .0050 .0050
.0037 .0040 .0040 .0035 .0035
.0023 .0020 .0030 .0020 .0020
(Kiniry et al., 1995) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Celery Head lettuce Spinach Green beans Cucumber
CELR LETT SPIN GRBN CUCM
.0620 .0360 .0620 .0040 .0663
.0150 .0250 .0400 .0030 .0075
.0100 .0210 .0300 .0015 .0048
.0060 .0084 .0050 .0040 .0053
.0050 .0032 .0040 .0035 .0025
.0030 .0019 .0035 .0015 .0012
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Eggplant Cantaloupe Honeydew melon Watermelon Bell pepper
EGGP CANT HMEL WMEL PEPR
.0663 .0663 .0070 .0663 .0600
.0255 .0255 .0040 .0075 .0350
.0075 .0148 .0020 .0048 .0250
.0053 .0053 .0026 .0053 .0053
.0020 .0020 .0020 .0025 .0020
.0015 .0012 .0017 .0012 .0012
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994)
Strawberry Tomato Apple Pine Oak
STRW TOMA APPL PINE OAK
.0663 .0663 .0060 .0060 .0060
.0255 .0300 .0020 .0020 .0020
.0148 .0250 .0015 .0015 .0015
.0053 .0053 .0007 .0007 .0007
.0020 .0035 .0004 .0004 .0004
.0012 .0025 .0003 .0003 .0003
(Kiniry and Williams, 1994) (Kiniry and Williams, 1994) estimated (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
Poplar Honey mesquite
POPL MESQ
.0060 .0200
.0020 .0100
.0015 .0080
.0007 .0007
.0004 .0004
.0003 .0003
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
Common Name Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco Sugarbeet Potato
Reference
A.1.8 HARVEST Harvest operations are performed on agricultural crops where the yield is sold for a profit. Four variables in the database provide information used by the model to harvest a crop: HVSTI, WSYF, CNYLD, and CPYLD. The harvest index defines the fraction of the aboveground biomass that is removed in a harvest operation. This value defines the fraction of plant biomass
APPENDIX A: DATABASES
55
that is “lost” from the system and unavailable for conversion to residue and subsequent decomposition. For crops where the harvested portion of the plant is aboveground, the harvest index is always a fraction less than 1. For crops where the harvested portion is belowground, the harvest index may be greater than 1. Two harvest indices are provided in the database, the harvest index for optimal growing conditions (HVSTI) and the harvest index under highly stressed growing conditions (WSYF). To determine the harvest index, the plant biomass removed during the harvest operation is dried at least 2 days at 65°C and weighed. The total aboveground plant biomass in the field should also be dried and weighed. The harvest index is then calculated by dividing the weight of the harvested portion of the plant biomass by the weight of the total aboveground plant biomass. Plants will need to be grown in two different plots where optimal climatic conditions and stressed conditions are produced to obtain values for both harvest indices. In addition to the amount of plant biomass removed in the yield, SWAT needs to know the amount of nitrogen and phosphorus removed in the yield. The harvested portion of the plant biomass is sent to a testing laboratory to determine the fraction of nitrogen and phosphorus in the biomass. Table A-8 lists values for the optimal harvest index (HIopt), the minimum harvest index (HImin), the fraction of nitrogen in the harvested portion of biomass (frN,yld), and the fraction of phosphorus in the harvested portion of biomass (frP,yld).
Table A-8: Harvest parameters for plants included in the plant growth database. Plant Common Name Code HIopt HImin frN,yld frP,yld Reference Corn 0.50 0.30 .0140 .0016 (Kiniry, personal communication, 2001; CORN Corn silage
CSIL
0.90
0.90
.0140
.0016
Sweet corn
SCRN
0.50
0.30
.0214
.0037
Eastern gamagrass Grain sorghum
EGAM GRSG
0.90 0.45
0.90 0.25
.0160 .0199
.0022 .0032
Kiniry et al, 1995) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984a) (Kiniry, personal communication, 2001) (Kiniry and Bockholt, 1998; Nutrition Monitoring Division, 1984b)
56
SWAT USER’S MANUAL, VERSION 2000 HIopt
HImin
frN,yld
frP,yld
Sorghum hay
Plant Code SGHY
0.90
0.90
.0199
.0032
Johnsongrass
JHGR
0.90
0.90
.0200
.0028
Sugarcane Spring wheat Winter wheat
SUGC SWHT WWHT
0.50 0.42 0.40
0.01 0.20 0.20
.0000 .0234 .0250
.0000 .0033 .0022
Durum wheat
DWHT
0.40
0.20
.0263
.0057
Rye
RYE
0.40
0.20
.0284
.0042
Spring barley Oats
BARL OATS
0.54 0.42
0.20 0.175
.0210 .0316
.0017 .0057
Rice
RICE
0.50
0.25
.0136
.0013
Pearl millet
PMIL
0.25
0.10
.0200
.0028
Timothy
TIMO
0.90
0.90
.0234
.0033
Smooth bromegrass
BROS
0.90
0.90
.0234
.0033
Meadow bromegrass
BROM
0.90
0.90
.0234
.0033
Tall fescue
FESC
0.90
0.90
.0234
.0033
Kentucky bluegrass Bermudagrass Crested wheatgrass
BLUG BERM CWGR
0.90 0.90 0.90
0.90 0.90 0.90
.0160 .0234 .0500
.0022 .0033 .0040
Western wheatgrass
WWGR
0.90
0.90
.0500
.0040
Slender wheatgrass
SWGR
0.90
0.90
.0500
.0040
Italian (annual) ryegrass
RYEG
0.90
0.90
.0220
.0028
Russian wildrye
RYER
0.90
0.90
.0230
.0037
Altai wildrye
RYEA
0.90
0.90
.0230
.0037
Sideoats grama Big bluestem
SIDE BBLS
0.90 0.90
0.90 0.90
.0160 .0160
.0022 .0022
Little bluestem Alamo switchgrass Indiangrass Alfalfa Sweetclover
LBLS SWCH INDN ALFA CLVS
0.90 0.90 0.90 0.90 0.90
0.90 0.90 0.90 0.90 0.90
.0160 .0160 .0160 .0250 .0650
.0022 .0022 .0022 .0035 .0040
(Kiniry, personal communication, 2001) (Kiniry et al, 1996) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001; estimated)
Red clover
CLVR
0.90
0.90
.0650
.0040
(Kiniry, personal communication, 2001; estimated)
Common Name
Reference (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry, personal communication, 2001; Kiniry et al, 1992a) (Kiniry and Williams, 1994) (Kinry et al, 1995; Kiniry et al, 1992a) (Kiniry et al, 1995) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry et al, 1995) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry, personal communication, 2001; Nutrition Monitoring Division, 1984b) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001; Kiniry et al, 1995) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
APPENDIX A: DATABASES
HIopt
HImin
frN,yld
frP,yld
Alsike clover
Plant Code CLVA
0.90
0.90
.0600
.0040
Soybean Cowpeas
SOYB CWPS
0.31 0.42
0.01 0.05
.0650 .0427
.0091 .0048
Mung bean
MUNG
0.31
0.01
.0420
.0040
Lima beans
LIMA
0.30
0.22
.0368
.0046
Lentils
LENT
0.61
001
.0506
.0051
Peanut
PNUT
0.40
0.30
.0505
.0040
Field peas Garden or canning peas
FPEA PEAS
0.45 0.30
0.10 0.22
.0370 .0410
.0021 .0051
Sesbania
SESB
0.31
0.01
.0650
.0091
Flax Upland cotton (harvested with stripper) Upland cotton (harvested with picker) Tobacco Sugarbeet
FLAX COTS
0.54 0.50
0.40 0.40
.0400 .0140
.0033 .0020
COTP
0.40
0.30
.0190
.0029
TOBC SGBT
0.55 2.00
0.55 1.10
.0140 .0130
.0016 .0020
Potato
POTA
0.95
0.95
.0246
.0023
Sweetpotato
SPOT
0.60
0.40
.0097
.0010
Carrot
CRRT
1.12
0.90
.0135
.0036
Onion
ONIO
1.25
0.95
.0206
.0032
Sunflower
SUNF
0.30
0.18
.0454
.0074
Spring canola-Polish Spring canola-Argentine Asparagus
CANP CANA ASPR
0.23 0.30 0.80
0.01 0.01 0.95
.0380 .0380 .0630
.0079 .0079 .0067
Broccoli
BROC
0.80
0.95
.0512
.0071
Cabbage
CABG
0.80
0.95
.0259
.0031
Cauliflower
CAUF
0.80
0.95
.0411
.0059
Celery
CELR
0.80
0.95
.0199
.0049
Head lettuce
LETT
0.80
0.01
.0393
.0049
Spinach
SPIN
0.95
0.95
.0543
.0058
Common Name
Reference (Kiniry, personal communication, 2001; estimated) (Kiniry et al, 1992a) (estimated; Nutrition Monitoring Division, 1984c) (estimated; Nutrition Monitoring Division, 1984c) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (estimated; Nutrition Monitoring Division, 1984c) (estimated; Nutrition Monitoring Division, 1984c) estimated (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) estimated estimated (Kiniry, personal communication, 2001; estimated) (Kiniry, personal communication, 2001; estimated) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (estimated; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry et al, 1992b; Nutrition Monitoring Division, 1984d) (Kiniry et al, 1995) (Kiniry et al, 1995) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a)
57
58
SWAT USER’S MANUAL, VERSION 2000 HIopt
HImin
frN,yld
frP,yld
Green beans
Plant Code GRBN
0.10
0.10
.0299
.0039
Cucumber
CUCM
0.27
0.25
.0219
.0043
Eggplant
EGGP
0.59
0.25
.0218
.0041
Cantaloupe
CANT
0.50
0.25
.0138
.0017
Honeydew melon
HMEL
0.55
0.25
.0071
.0010
Watermelon
WMEL
0.50
0.25
.0117
.0011
Bell pepper
PEPR
0.60
0.25
.0188
.0030
Strawberry
STRW
0.45
0.25
.0116
.0023
Tomato
TOMA
0.33
0.15
.0235
.0048
Apple
APPL
0.10
0.05
.0019
.0004
(Kiniry and Williams, 1994; Consumer Nutrition Center, 1982) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Consumer Nutrition Center, 1982) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (estimated; Consumer Nutrition Center, 1982)
Pine Oak Poplar Honey mesquite
PINE OAK POPL MESQ
0.76 0.76 0.76 0.05
0.60 0.01 0.01 0.01
.0015 .0015 .0015 .0015
.0003 .0003 .0003 .0003
(Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001) (Kiniry, personal communication, 2001)
Common Name
Reference (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Nutrition Monitoring Division, 1984a) (Kiniry and Williams, 1994; Consumer Nutrition Center, 1982) (Kiniry and Williams, 1994; Consumer Nutrition Center, 1982)
A.1.9 USLE C FACTOR The USLE C factor is the ratio of soil loss from land cropped under specified conditions to the corresponding loss from clean-tilled, continuous fallow. This factor measures the combined effect of all the interrelated cover and management variables. SWAT calculates the actual C factor based on the amount of soil cover and the minimum C factor defined for the plant/land cover. The minimum C factor quantifies the maximum decrease in erosion possible for the plant/land cover. Because the USLE C factor is influenced by management, this variable may be adjusted by the user to reflect management conditions in the watershed of interest. The minimum C factor can be estimated from a known average annual C factor using the following equation (Arnold and Williams, 1995): CUSLE ,mn = 1.463 ln[CUSLE ,aa ] + 0.1034
APPENDIX A: DATABASES
59
where CUSLE,mn is the minimum C factor for the land cover and CUSLE,aa is the average annual C factor for the land cover. The minimum C factor for plants in the database are listed in Table A-6.
A.1.10 RESIDUE DECOMPOSITION The plant residue decomposition coefficient is the fraction of residue that will decompose in a day assuming optimal moisture, temperature, C:N ratio, and C:P ratio. This variable was originally in the basin input file (.bsn), but was added to the crop database so that users could vary decomposition by land cover. A default value of 0.05 is used for all plant species in the database.
60
SWAT USER’S MANUAL, VERSION 2000
A.2 TILLAGE DATABASE The tillage database contains information needed by SWAT to simulate the redistribution of nutrients and pesticide that occurs in a tillage operation. Table A-9 lists all the default tillage implements. This list was summarized from a farm machinery database maintained by the USDA Economic Research Service. Depth of tillage for each implement was also obtained from the USDA Economic Research Service. The fraction of residue mixed into the soil was estimated for each implement from a ‘Residue Scorecard’ provided by NACD’s (National Association of Conservation Districts) Conservation Technology Information Center. Table A-9: Implements included in the tillage database. Implement Tillage Code Duckfoot Cultivator DUCKFTC Field Cultivator FLDCULT Furrow-out Cultivator FUROWOUT Marker (Cultivator) MARKER Rolling Cultivator ROLLCULT Row Cultivator ROWCULT Discovator DISCOVAT Leveler LEVELER Harrow (Tines) HARROW Culti-mulch Roller CULMULCH Culti-packer Pulverizer CULPKPUL Land Plane-Leveler LANDLEVL Landall, Do-All LANDALL Laser Planer LASRPLAN Levee-Plow-Disc LEVPLDIS Float FLOAT Field Conditioner (Scratcher) FLDCDSCR Lister (Middle-Buster) LISTRMID Roller Groover ROLLGROV Roller Packer Attachment ROLPKRAT Roller Packer Flat Roller ROLPKRFT Sand-Fighter SANDFIGT Seedbed Roller SEEDROLL Crust Buster CRUSTBST Roller Harrow ROLLHRRW Triple K TRIPLE K Finishing Harrow FINHARRW Flex-Tine Harrow CL FLEXHARW Powered Spike Tooth Harrow SPIKETTH Spike Tooth Harrow SPIKTOTH Springtooth Harrow SPRGTOTH Soil Finisher SOILFINS
Mixing Depth
Mixing Efficiency
100 mm 100 mm 25 mm 100 mm 25 mm 25 mm 25 mm 25 mm 25 mm 25 mm 40 mm 75 mm 150 mm 150 mm 25 mm 60 mm 60 mm 40 mm 60 mm 40 mm 40 mm 100 mm 100 mm 60 mm 60 mm 100 mm 100 mm 25 mm 75 mm 25 mm 25 mm 75 mm
0.55 0.30 0.75 0.45 0.50 0.25 0.50 0.50 0.20 0.25 0.35 0.50 0.30 0.30 0.75 0.10 0.10 0.15 0.25 0.05 0.35 0.70 0.70 0.10 0.40 0.40 0.55 0.20 0.40 0.25 0.35 0.55
APPENDIX A: DATABASES
Implement Rotary Hoe Roterra Roto-Tiller Rotovator-Bedder Rowbuck Ripper Middle Buster Rod Weeder Rubber-Wheel Weed Puller Multi-Weeder Moldboard Plow Reg Chisel Plow Coulter-Chisel Disk Plow Stubble-mulch Plow Subsoil Chisel Plow Row Conditioner Hipper Rice Roller Paraplow Subsoiler-Bedder Hip-Rip Deep Ripper-Subsoiler V-Ripper Bed Roller Bedder (Disk) Bedder Disk-Hipper Bedder Disk-Row Bedder Shaper Disk Border Maker Disk Chisel (Mulch Tiller) Offset Disk-Heavy Duty Offset Disk-Light Duty One-Way (Disk Tiller) Tandem Disk Plow Tandem Disk Reg Single Disk Power Mulcher Blade 10 ft Furrow Diker Beet Cultivator Cultiweeder Packer
Tillage Code ROTHOE ROTERRA ROTOTILL ROTBEDDR ROWBUCK RIPPER MIDBST1R RODWEEDR RUBWHWPL MULTIWDR MLDBOARD CHISPLOW CCHPLOW DISKPLOW STUBMLCH SUBCHPLW ROWCOND HIPPER RICEROLL PARAPLOW SBEDHIPR RIPRSUBS VRIPPER BEDROLLR BEDDER D BEDDHIPR BEDDKROW BEDDER S DSKBRMKR DKCHMTIL OFFSETHV OFFSETLT ONE-WAYT TANDEMPL TANDEMRG SINGLDIS PWRMULCH BLADE 10 FURWDIKE BEETCULT CLTIWEED PACKER
61
Mixing Depth
Mixing Efficiency
5 mm 5 mm 5 mm 100 mm 100 mm 350 mm 100 mm 25 mm 5 mm 25 mm 150 mm 150 mm 150 mm 100 mm 75 mm 350 mm 25 mm 100 mm 50 mm 350 mm 350 mm 350 mm 350 mm 50 mm 150 mm 150 mm 100 mm 150 mm 150 mm 150 mm 100 mm 100 mm 100 mm 75 mm 75 mm 100 mm 50 mm 75 mm 100 mm 25 mm 100 mm 40 mm
0.10 0.80 0.80 0.80 0.70 0.25 0.70 0.30 0.35 0.30 0.95 0.30 0.50 0.85 0.15 0.45 0.50 0.50 0.10 0.15 0.70 0.25 0.25 0.25 0.55 0.65 0.85 0.55 0.55 0.55 0.70 0.55 0.60 0.55 0.60 0.45 0.70 0.25 0.70 0.25 0.30 0.35
In addition to information about specific implements, the tillage database includes default information for the different crop residue management categories. Table A-10 summarizes the information in the database on the different residue management categories.
62
SWAT USER’S MANUAL, VERSION 2000
Table A-10: Generic management scenarios included in the tillage database. Implement Tillage Code Mixing Depth Generic Fall Plowing Operation 150 mm FALLPLOW Generic Spring Plowing Operation 125 mm SPRGPLOW 100 mm Generic Conservation Tillage CONSTILL 25 mm Generic No-Till Mixing ZEROTILL
Mixing Efficiency 0.95 0.50 0.25 0.05
ASAE (1998b) categorizes tillage implements into five different categories—primary tillage, secondary tillage, cultivating tillage, combination primary tillage, and combination secondary tillage. The definitions for the categories are (ASAE, 1998b): Primary tillage: the implements displace and shatter soil to reduce soil strength and bury or mix plant materials, pesticides, and fertilizers in the tillage layer. This type of tillage is more aggressive, deeper, and leaves a rougher soil surface relative to secondary tillage. Examples include plows—moldboard, chisel, disk, bedder; moldboard listers; disk bedders; subsoilers; disk harrows—offset disk, heavy tandem disk; and powered rotary tillers. Secondary tillage: the implements till the soil to a shallower depth than primary tillage implements, provide additional pulverization, mix pesticides and fertilizers into the soil, level and firm the soil, close air pockets, and eradicate weeds. Seedbed preparation is the final secondary tillage operation. Examples include harrows—disk, spring, spike, coil, tine-tooth, knife, packer, ridger, leveler, rotary ground driven; field or field conditioner cultivators; rod weeders; rollers; powered rotary tillers; bed shapers; and rotary hoes. Cultivating tillage: the implements perform shallow post-plant tillage to aid the crop by loosening the soil and/or by mechanical eradication of undesired vegetation. Examples include row crop cultivators—rotary ground-driven, spring tooth, shank tooth; rotary hoes; and rotary tillers. Combination primary tillage: the implements perform primary tillage functions and utilize two or more dissimilar tillage components as integral parts of the implement. Combination secondary tillage: the implements perform secondary tillage functions and utilize two or more dissimilar tillage components as integral parts of the implement. ASAE (1998b) provides detailed descriptions and illustrations for the major implements. These are very helpful for those who are not familiar with farm implements.
APPENDIX A: DATABASES
63
A.3 PESTICIDE DATABASE The pesticide database file (pest.dat) summarizes pesticide attribute information for various pesticides. The pesticide data included in the database was originally compiled for the GLEAMS model in the early nineties (Knisel, 1993). The following table lists the pesticides included in the pesticide database. Table A-11: SWAT Pesticide Database Washoff Frac.
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
Trade Name
Common Name
Koc (ml/g)
2,4,5-TP 2 Plus 2 Aatrex Abate Acaraben
Silvex Mecoprop Amine Atrazine Temephos Chlorobenzilate
2600 20 100 100000 2000
0.40 0.95 0.45 0.65 0.05
5.0 10.0 5.0 5.0 10.0
20.0 21.0 60.0 30.0 20.0
2.5 660000 33 0.001 13
Accelerate Acclaim Alanap Alar Aldrin
Endothall Salt Fenoxaprop-Ethyl Naptalam Sodium Salt Daminozide Aldrin
20 9490 20 10 300
0.90 0.20 0.95 0.95 0.05
7.0 5.0 7.0 4.0 2.0
7.0 9.0 14.0 7.0 28.0
100000 0.8 231000 100000 0.1
Aliette Ally Amiben Amid-Thin W Amitrol T
Fosetyl-Aluminum Metsulfuron-Methyl Chloramben Salts NAA Amide Amitrole
20 35 15 100 100
0.95 0.80 0.95 0.60 0.95
0.1 30.0 7.0 5.0 5.0
0.1 120.0 14.0 10.0 14.0
120000 9500 900000 100 360000
Ammo Antor A-Rest Arsenal Arsonate
Cypermethrin Diethatyl-Ethyl Ancymidol Imazapyr Acid MSMA
100000 1400 120 100 10000
0.40 0.40 0.50 0.90 0.95
5.0 10.0 30.0 30.0 30.0
30.0 21.0 120.0 90.0 100.0
0.004 105 650 11000 1000000
Asana Assert (m) Assert (p) Assure Asulox
Esfenvalerate Imazamethabenz-m Imazamethabenz-p Quizalofop-Ethyl Asulam Sodium Salt
5300 66 35 510 40
0.40 0.65 0.65 0.20 0.95
8.0 18.0 18.0 15.0 3.0
35.0 35.0 35.0 60.0 7.0
0.002 1370 875 0.31 550000
Avenge Azodrin Balan Banol Banvel
Difenzoquat Monocrotophos Benefin Propamocarb Dicamba
54500 1 9000 1000000 2
0.95 0.95 0.20 0.95 0.65
30.0 2.0 10.0 15.0 9.0
100.0 30.0 30.0 30.0 14.0
817000 1000000 0.1 1000000 400000
Basagran Basta Bayleton
Bentazon Glufosinate Ammonia Triadimefon
34 100 300
0.60 0.95 0.30
2.0 4.0 8.0
20.0 7.0 26.0
2300000 1370000 71.5
64
SWAT USER’S MANUAL, VERSION 2000
Washoff Frac.
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
Trade Name
Common Name
Koc (ml/g)
Baytex Baythroid Benlate Benzex Betamix
Fenthion Cyfluthrin Benomyl BHC Phenmedipham
1500 100000 1900 55000 2400
0.65 0.40 0.25 0.05 0.70
2.0 5.0 6.0 3.0 5.0
34.0 30.0 240.0 600.0 30.0
4.2 0.002 2 0.1 4.7
Betanex Bidrin Bladex Bolero Bolstar
Desmedipham Dicrotophos Cyanazine Thiobencarb Sulprofos
1500 75 190 900 12000
0.70 0.70 0.60 0.70 0.55
5.0 20.0 5.0 7.0 0.5
30.0 28.0 14.0 21.0 140.0
8 1000000 170 28 0.31
Bordermaster Botran Bravo Buctril Butyrac Ester
MCPA Ester DCNA (Dicloran) Chlorothalonil Bromoxynil Octan. Ester 2,4-DB Ester
1000 1000 1380 10000 500
0.50 0.50 0.50 0.20 0.45
8.0 4.0 5.0 3.0 7.0
25.0 10.0 30.0 7.0 7.0
5 7 0.6 0.08 8
Caparol Carbamate Carsoron Carzol Cerone
Prometryn Ferbam Dichlobenil Formetanate Hydrochlor Ethephon
400 300 400 1000000 100000
0.50 0.90 0.45 0.95 0.95
10.0 3.0 5.0 30.0 5.0
60.0 17.0 60.0 100.0 10.0
33 120 21.2 500000 1239000
Chem-Hoe Chlordane Chopper Classic Cobra
Propham (IPC) Chlordane Imazapyr Amine Chlorimuron-ethyl Lactofen
200 100000 100 110 100000
0.50 0.05 0.80 0.90 0.20
2.0 2.5 30.0 15.0 2.0
10.0 100.0 90.0 40.0 3.0
250 0.1 500000 1200 0.1
Comite Command Cotoran Counter Crossbow
Propargite Clomazone Fluometuron Terbufos Triclopyr Amine
4000 300 100 500 20
0.20 0.80 0.50 0.60 0.95
5.0 3.0 30.0 2.5 15.0
56.0 24.0 85.0 5.0 46.0
0.5 1000 110 5 2100000
Curacron Cygon Cyprex Cythion Dacamine
Profenofos Dimethoate Dodine Acetate Malathion 2,4-D Acid
2000 20 100000 1800 20
0.90 0.95 0.50 0.90 0.45
3.0 3.0 10.0 1.0 5.0
8.0 7.0 20.0 1.0 10.0
28 39800 700 130 890
Dacthal Dalapon Dasanit DDT Dedweed
DCPA Dalapon Sodium Salt Fensulfothion DDT MCPA Amine
5000 1 10000 240000 20
0.30 0.95 0.90 0.05 0.95
10.0 37.0 4.0 10.0 7.0
100.0 30.0 24.0 120.0 25.0
0.5 900000 0.01 0.1 866000
DEF Dessicant L-10 Devrinol
Tribufos Arsenic Acid Napropamide
5000 100000 400
0.25 0.95 0.60
7.0 10000.0 15.0
30.0 10000.0 70.0
2.3 17000 74
APPENDIX A: DATABASES
Trade Name
Common Name
Di-Syston Dibrom Dieldrin Dimilin Dinitro
Disulfoton Naled Dieldrin Diflubenzuron Dinoseb Phenol
Diquat Dithane Dowpon Dropp DSMA
Diquat Dibromide Mancozeb Dalapon Thidiazuron Methanearsonic Acid Na
Du-ter Dual Dyfonate Dylox Dymid
Koc (ml/g)
Washoff Frac.
65
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
600 180 50000 10000 500
0.50 0.90 0.05 0.05 0.60
3.0 5.0 5.0 27.0 3.0
30.0 1.0 1400.0 10.0 20.0
25 2000 0.1 0.08 50
1000000 2000 4 110 100000
0.95 0.25 0.95 0.40 0.95
30.0 10.0 37.0 3.0 30.0
1000.0 70.0 30.0 10.0 1000.0
718000 6 1000 20 1400000
Triphenyltin Hydroxide Metolachlor Fonofos Trichlorfon Diphenamid
23000 200 870 10 210
0.40 0.60 0.60 0.95 0.80
18.0 5.0 2.5 3.0 5.0
75.0 90.0 40.0 10.0 30.0
1 530 16.9 120000 260
Dyrene Elgetol EPN Eradicane Ethanox
Anilazine DNOC Sodium Salt EPN EPTC Ethion
3000 20 13000 200 10000
0.50 0.95 0.60 0.75 0.65
5.0 8.0 5.0 3.0 7.0
1.0 20.0 5.0 6.0 150.0
8 100000 0.5 344 1.1
Evik Evital Far-Go Fenatrol Fenitox
Ametryn Norflurazon Triallate Fenac Fenitrothion
300 600 2400 20 2000
0.65 0.50 0.40 0.95 0.90
5.0 15.0 15.0 30.0 3.0
60.0 90.0 82.0 180.0 8.0
185 28 4 500000 30
Fruitone CPA Fundal Funginex Furadan Fusilade
3-CPA Sodium Salt Chlordimeform Hydroclo. Triforine Carbofuran Fluazifop-P-Butyl
20 100000 540 22 5700
0.95 0.90 0.80 0.55 0.40
3.0 1.0 5.0 2.0 4.0
10.0 60.0 21.0 50.0 15.0
200000 500000 30 351 2
Glean Goal Guthion Harmony Harvade
Chlorsulfuron Oxyfluorfen Azinphos-Methyl Thifensulfuron-Methyl Dimethipin
40 100000 1000 45 10
0.75 0.40 0.65 0.80 0.80
30.0 8.0 2.0 3.0 3.0
160.0 35.0 10.0 12.0 10.0
7000 0.1 29 2400 3000
Hoelon Hyvar Imidan Isotox Karate
Diclofop-Methyl Bromacil Phosmet Lindane Lambda-Cyhalothrin
16000 32 820 1100 180000
0.45 0.75 0.90 0.05 0.40
8.0 20.0 3.0 2.5 5.0
37.0 60.0 19.0 400.0 30.0
0.8 700 20 7.3 0.005
Karathane Karmex Kelthane
Dinocap Diuron Dicofol
550 480 180000
0.30 0.45 0.05
8.0 30.0 4.0
20.0 90.0 60.0
4 42 1
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SWAT USER’S MANUAL, VERSION 2000
Trade Name
Common Name
Kerb Krenite Lannate Larvadex Larvin
Pronamide Fosamine Ammon. Salt Methomyl Cyromazine Thiodicarb
Lasso Limit Lontrel Lorox Lorsban
Alachlor Amidochlor Clopyralid Linuron Clorpyrifos
Manzate Marlate Matacil Mavrik Metasystox
Maneb Methoxychlor Aminocarb Fluvalinate Oxydemeton-Methyl
Milogard Miral Mitac Modown Monitor
Propazine Isazofos Amitraz Bifenox Methamidophos
Morestan Nemacur Nemacur Sulfone Nemacur Sulfoxide Norton
Oxythioquinox Fenamiphos Fenamiphos Sulfone Fenamiphos Sulfoxide Ethofumesate
Octave Oftanol Orthene Orthocide Oust
Prochloraz Isofenphos Acephate Captan Sulfometuron-Methyl
Pay-Off Penncap-M Phenatox Phosdrin Phoskil
Flucythrinate Methyl Parathion Toxaphene Mevinphos Parathion (Ethyl)
Pipron Pix Plantvax Poast Polyram
Piperalin Mepiquat Chlor. Salt Oxycarboxin Sethoxydim Metiram
Pounce Pramitol Prefar
Permethrin Prometon Bensulide
Koc (ml/g)
Washoff Frac.
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
200 150 72 200 350
0.30 0.95 0.55 0.95 0.70
20.0 4.0 0.5 30.0 4.0
60.0 8.0 30.0 150.0 7.0
15 1790000 58000 136000 19.1
170 1000 6 400 6070
0.40 0.70 0.95 0.60 0.65
3.0 8.0 2.0 15.0 3.3
15.0 20.0 30.0 60.0 30.0
240 10 300000 75 0.4
1000 80000 100 1000000 10
0.65 0.05 0.90 0.40 0.95
3.0 6.0 4.0 7.0 3.0
12.0 120.0 6.0 30.0 10.0
6 0.1 915 0.005 1000000
154 100 1000 10000 5
0.45 0.65 0.45 0.40 0.95
5.0 5.0 1.0 3.0 4.0
135.0 34.0 2.0 7.0 6.0
8.6 69 1 0.4 1000000
2300 240 45 40 340
0.50 0.70 0.70 0.70 0.65
10.0 5.0 18.0 42.0 10.0
30.0 5.0 18.0 42.0 30.0
1 400 400 400 50
500 600 2 200 78
0.50 0.65 0.70 0.65 0.65
30.0 30.0 2.5 9.0 10.0
120.0 150.0 3.0 2.5 20.0
34 24 818000 5.1 70
100000 5100 100000 44 5000
0.40 0.90 0.05 0.95 0.70
5.0 3.0 2.0 0.6 4.0
21.0 5.0 9.0 3.0 14.0
0.06 60 3 600000 24
5000 1000000 95 100 500000
0.60 0.95 0.70 0.70 0.40
10.0 30.0 10.0 3.0 7.0
30.0 1000.0 20.0 5.0 20.0
20 1000000 1000 4390 0.1
100000 150 1000
0.30 0.75 0.40
8.0 30.0 30.0
30.0 500.0 120.0
0.006 720 5.6
APPENDIX A: DATABASES
Trade Name
Common Name
Prelude Prime Princep Probe Prowl
Paraquat Flumetralin Simazine Methazole Pendimethalin
Pursuit Pydrin Pyramin Ramrod Reflex
AC 263,499 Fenvalerate Pyrazon Propaclor Fomesafen Salt
Rescue Ridomil Ro-Neet Ronstar Roundup
2,4-DB Sodium Amine Metalaxyl Cycloate Oxadiazon Glyphosate Amine
Rovral Royal Slo-Gro Rubigan Sancap Savey
Iprodione Maleic Hydrazide Fenarimol Dipropetryn Hexythiazox
Scepter Sencor Sevin Sinbar Slug-Geta
Imazaquin Ammonium Metribuzin Carbaryl Terbacil Methiocarb
Sonalan Spectracide Spike Sprout Nip Stam
Koc (ml/g)
Washoff Frac.
67
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
1000000 10000 130 3000 5000
0.60 0.40 0.40 0.40 0.40
30.0 7.0 5.0 5.0 30.0
1000.0 20.0 60.0 14.0 90.0
620000 0.1 6.2 1.5 0.275
10 5300 120 80 60
0.90 0.25 0.85 0.40 0.95
20.0 10.0 5.0 3.0 30.0
90.0 35.0 21.0 6.0 100.0
200000 0.002 400 613 700000
20 50 430 3200 24000
0.45 0.70 0.50 0.50 0.60
9.0 30.0 2.0 20.0 2.5
10.0 70.0 30.0 60.0 47.0
709000 8400 95 0.7 900000
700 20 600 900 6200
0.40 0.95 0.40 0.40 0.40
5.0 10.0 30.0 5.0 5.0
14.0 30.0 360.0 30.0 30.0
13.9 400000 14 16 0.5
20 60 300 55 300
0.95 0.80 0.55 0.70 0.70
20.0 5.0 7.0 30.0 10.0
60.0 40.0 10.0 120.0 30.0
160000 1220 120 710 24
Ethalfluralin Diazinon Tebuthiuron Chlorpropham Propanil
4000 1000 80 400 149
0.40 0.90 0.90 0.90 0.70
4.0 4.0 30.0 8.0 1.0
60.0 40.0 360.0 30.0 1.0
0.3 60 2500 89 200
Supracide Surflan Sutan Swat Tackle
Methidathion Oryzalin Butylate Phosphamidon Acifluorfen
400 600 400 7 113
0.90 0.40 0.30 0.95 0.95
3.0 5.0 1.0 5.0 5.0
7.0 20.0 13.0 17.0 14.0
220 2.5 44 1000000 250000
Talstar Tandem Tanone Tattoo TBZ
Bifenthrin Tridiphane Phenthoate Bendiocarb Thiabendazole
240000 5600 250 570 2500
0.40 0.40 0.65 0.85 0.60
7.0 8.0 2.0 3.0 30.0
26.0 28.0 40.0 5.0 403.0
0.1 1.8 200 40 50
Temik Temik Sulfone Temik Sulfoxide
Aldicarb Aldicarb Sulfone Aldicarb Sulfoxide
40 10 30
0.70 0.70 0.70
7.0 20.0 30.0
7.0 20.0 30.0
6000 6000 6000
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SWAT USER’S MANUAL, VERSION 2000
Trade Name
Common Name
Tenoran Terbutrex Terrachlor Terraneb Terrazole
Chloroxuron Terbutryn PCNB Chloroneb Etridiazole
Thimet Thiodan Thiram Thistrol Tillam
Koc (ml/g)
Washoff Frac.
Half-Life Foliar Soil (days)
Water Solubility (mg/L)
3000 2000 5000 1650 1000
0.40 0.50 0.40 0.50 0.60
15.0 5.0 4.0 30.0 3.0
60.0 42.0 21.0 130.0 20.0
2.5 22 0.44 8 50
Phorate Endosulfan Thiram MCPB Sodium Salt Pebulate
1000 12400 670 20 430
0.60 0.05 0.50 0.95 0.70
2.0 3.0 8.0 7.0 4.0
60.0 50.0 15.0 14.0 14.0
22 0.32 30 200000 100
Tilt Tolban Topsin Tordon Tralomethrin
Propiconazole Profluralin Thiophanate-Methyl Picloram Tralomethrin
1000 2240 1830 16 100000
0.70 0.35 0.40 0.60 0.40
30.0 1.0 5.0 8.0 1.0
110.0 140.0 10.0 90.0 27.0
110 0.1 3.5 200000 0.001
Treflan Tre-Hold Tupersan Turflon Velpar
Trifluralin NAA Ethyl Ester Siduron Triclopyr Ester Hexazinone
8000 300 420 780 54
0.40 0.40 0.70 0.70 0.90
3.0 5.0 30.0 15.0 30.0
60.0 10.0 90.0 46.0 90.0
0.3 105 18 23 3300
Vendex Vernam Volck oils Vydate Weedar
Fenbutatin Oxide Vernolate Petroleum oil Oxamyl 2,4-D amine
2300 260 1000 25 20
0.20 0.80 0.50 0.95 0.45
30.0 2.0 2.0 4.0 9.0
90.0 12.0 10.0 4.0 10.0
0.013 108 100 282000 796000
Weed-B-Gon Wedone Zolone
2,4,5-T Amine Dichlorprop Ester Phosalone
80 1000 1800
0.45 0.45 0.65
10.0 9.0 8.0
24.0 10.0 21.0
500000 50 3
Knisel (1993) cites Wauchope et al. (1992) as the source for water solubility, soil half-life and Koc values. Wash-off fraction and foliar half-life were obtained from Willis et al. (1980) and Willis and McDowell (1987).
A.3.1 WATER SOLUBILITY The water solubility value defines the highest concentration of pesticide that can be reached in the runoff and soil pore water. While this is an important characteristic, researchers have found that the soil adsorption coefficient, Koc, tends to limit the amount of pesticide entering solution so that the maximum
APPENDIX A: DATABASES
69
possible concentration of pesticide in solution is seldom reached (Leonard and Knisel, 1988). Reported solubility values are determined under laboratory conditions at a constant temperature, typically between 20°C and 30°C.
A.3.2 SOIL ADSORPTION COEFFICIENT The pesticide adsorption coefficient reported in the pesticide database can usually be obtained from a search through existing literature on the pesticide.
A.3.3 SOIL HALF-LIFE The half-life for a pesticide defines the number of days required for a given pesticide concentration to be reduced by one-half. The soil half-life entered for a pesticide is a lumped parameter that includes the net effect of volatilization, photolysis, hydrolysis, biological degradation and chemical reactions. The pesticide half-life for a chemical will vary with a change in soil environment (e.g. change in soil temperature, water content, etc.). Soil half-life values provided in the database are “average” or representative values. Half-life values reported for a chemical commonly vary by a factor of 2 to 3 and sometimes by as much as a factor of 10. For example, the soil half-life for atrazine can range from 120 to 12 days when comparing values reported in cool, dry regions to those from warm, humid areas. Another significant factor is soil treatment history. Repeated soil treatment by the same or a chemically similar pesticide commonly results in a reduction in half-life for the pesticide. This reduction is attributed to the preferential build-up of microbial populations adapted to degrading the compound. Users are encouraged to replace the default soil half-life value with a site-specific or region-specific value whenever the information is available.
A.3.4 FOLIAR HALF-LIFE As with the soil half-life, the foliar half-life entered for a pesticide is a lumped parameter describing the loss rate of pesticides on the plant canopy. For most pesticides, the foliar half-life is much less than the soil half-life due to enhanced volatilization and photodecomposition. While values for foliar half-life
70
SWAT USER’S MANUAL, VERSION 2000
were available for some pesticides in the database, the majority of foliar half-life values were calculated using the following rules: 1) Foliar half-life was assumed to be less than the soil half-life by a factor of 0.5 to 0.25, depending on vapor pressure and sensitivity to photodegradation. 2) Foliar half-life was adjusted downward for pesticides with vapor pressures less than 10-5 mm Hg. 3) The maximum foliar half-life assigned was 30 days.
A.3.5 WASH-OFF FRACTION The wash-off fraction quantifies the fraction of pesticide on the plant canopy that may be dislodged. The wash-off fraction is a function of the nature of the leaf surface, plant morphology, pesticide solubility, polarity of the pesticide molecule, formulation of the commercial product and timing and volume of the rainfall event. Some wash-off fraction values were obtained from Willis et al. (1980). For the remaining pesticides, solubility was used as a guide for estimating the wash-off fraction.
A.3.6 APPLICATION EFFICIENCY The application efficiency for all pesticides listed in the database is defaulted to 0.75. This variable is a calibration parameter.
APPENDIX A: DATABASES
71
A.4 FERTILIZER DATABASE The fertilizer database file (fert.dat) summarizes nutrient fractions for various fertilizers and types of manure. The following table lists the fertilizers and types of manure in the fertilizer database. Table A-12: SWAT Fertilizer Database Name Name Code
Min-N
Min-P
Org-N
Org-P
NH3-N/ Min N
Elemental Nitrogen Elemental Phosphorous Anhydrous Ammonia Urea 46-00-00
Elem-N Elem-P ANH-NH3 UREA 46-00-00
1.000 0.000 0.820 0.460 0.460
0.000 1.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 1.000 1.000 0.000
33-00-00 31-13-00 30-80-00 30-15-00 28-10-10
33-00-00 31-13-00 30-80-00 30-15-00 28-10-10
0.330 0.310 0.300 0.300 0.280
0.000 0.057 0.352 0.066 0.044
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
28-03-00 26-13-00 25-05-00 25-03-00 24-06-00
28-03-00 26-13-00 25-05-00 25-03-00 24-06-00
0.280 0.260 0.250 0.250 0.240
0.013 0.057 0.022 0.013 0.026
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
22-14-00 20-20-00 18-46-00 18-04-00 06-24-24
22-14-00 20-20-00 18-46-00 18-04-00 16-24-24
0.220 0.200 0.180 0.180 0.060
0.062 0.088 0.202 0.018 0.106
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
16-20-20 15-15-15 15-15-00 13-13-13 12-20-00
16-20-20 15-15-15 15-15-00 13-13-13 12-20-00
0.160 0.150 0.150 0.130 0.120
0.088 0.066 0.066 0.057 0.088
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
11-52-00 11-15-00 10-34-00 10-28-00 10-10-10
11-52-00 11-15-00 10-34-00 10-28-00 10-10-10
0.110 0.110 0.100 0.100 0.100
0.229 0.066 0.150 0.123 0.044
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
08-15-00 08-08-00 07-07-00 07-00-00 05-10-15
08-15-00 08-08-00 07-07-00 07-00-00 05-10-15
0.080 0.080 0.070 0.070 0.050
0.066 0.035 0.031 0.000 0.044
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
05-10-10
05-10-10
0.050
0.044
0.000
0.000
0.000
72
SWAT USER’S MANUAL, VERSION 2000
Name
Name Code
Min-N
Min-P
Org-N
Org-P
NH3-N/ Min N
05-10-05 04-08-00 03-06-00 02-09-00 10-20-20
05-10-05 04-08-00 03-06-00 02-09-00 00-20-20
0.050 0.040 0.030 0.020 0.100
0.044 0.035 0.026 0.040 0.088
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000
00-15-00 00-06-00 Dairy-Fresh Manure Beef-Fresh Manure Veal-Fresh Manure
00-15-00 00-06-00 DAIRY-FR BEEF-FR VEAL-FR
0.000 0.000 0.007 0.010 0.023
0.066 0.026 0.005 0.004 0.006
0.000 0.000 0.031 0.030 0.029
0.000 0.000 0.003 0.007 0.007
0.000 0.000 0.990 0.990 0.990
Swine-Fresh Manure Sheep-Fresh Manure Goat-Fresh Manure Horse-Fresh Manure Layer-Fresh Manure
SWINE-FR SHEEP-FR GOAT-FR HORSE-FR LAYER-FR
0.026 0.014 0.013 0.006 0.013
0.011 0.003 0.003 0.001 0.006
0.021 0.024 0.022 0.014 0.040
0.005 0.005 0.005 0.003 0.013
0.990 0.990 0.990 0.990 0.990
Broiler-Fresh Manure BROIL-FR 0.010 0.004 Turkey-Fresh Manure TRKEY-FR 0.007 0.003 Duck-Fresh Manure DUCK-FR 0.008 0.023 Values in bold italics are estimated (see section A.4.2)
0.040 0.045 0.025
0.010 0.016 0.009
0.990 0.990 0.990
A.4.1 COMMERCIAL FERTILIZERS In compiling the list of commercial fertilizers in the database, we tried to identify and include commonly used fertilizers. This list is not comprehensive, so users may need to append the database with information for other fertilizers used in their watersheds. When calculating the fractions of N and P for the database, it is important to remember that the percentages reported for a fertilizer are %N-%P2O5-%K2O. The fraction of mineral N in the fertilizer is equal to %N divided by 100. To calculate the fraction of mineral P in the fertilizer, the fraction of P in P2O5 must be known. The atomic weight of phosphorus is 31 and the atomic weight of oxygen is 16, making the molecular weight of P2O5 equal to 142. The fraction of P in P2O5 is 62/142 = 0.44 and the fraction of mineral P in the fertilizer is equal to 0.44 (%P2O5 / 100).
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A.4.2 MANURE The values in the database for manure types were derived from manure production and characteristics compiled by the ASAE (1998a). Table A-13 summarizes the levels of nitrogen and phosphorus in manure reported by the ASAE. The data summarized by ASAE is combined from a wide range of published and unpublished information. The mean values for each parameter are determined by an arithmetic average consisting of one data point per reference source per year and represent fresh (as voided) feces and urine. Table A-13: Fresh manure production and characteristics per 1000 kg live animal mass per day (from ASAE, 1998a) Parameter Total Manure
kg†
Total Solids
kg
mean std dev mean std dev mean std dev mean std dev mean std dev mean std dev
Dairy 86 17 12 2.7 0.45 0.096 0.079 0.083 0.094 0.024 0.061 0.0058
Beef 58 17 8.5 2.6 0.34 0.073 0.086 0.052 0.092 0.027 0.030 **
Veal 62 24 5.2 2.1 0.27 0.045 0.12 0.016 0.066 0.011 ** **
Swine 84 24 11 6.3 0.52 0.21 0.29 0.10 0.18 0.10 0.12 **
Animal Type‡ Sheep Goat Horse 40 41 51 11 8.6 7.2 11 13 15 3.5 1.0 4.4 0.42 0.45 0.30 0.11 0.12 0.063 ** ** ** ** ** ** 0.087 0.11 0.071 0.030 0.016 0.026 0.032 ** 0.019 0.014 ** 0.0071
Layer 64 19 16 4.3 0.84 0.22 0.21 0.18 0.30 0.081 0.092 0.016
Broiler 85 13 22 1.4 1.1 0.24 ** ** 0.30 0.053 ** **
Turkey 47 13 12 3.4 0.62 0.13 0.080 0.018 0.23 0.093 ** **
Total Kjeldahl kg nitrogen║ Ammonia kg nitrogen Total kg phosphorus Orthokg phosphorus ** Data not found. † All values wet basis. ‡ Typical live animal masses for which manure values represent are: dairy, 640 kg; beef, 360 kg; veal, 91 kg; swine, 61 kg; sheep, 27 kg; goat, 64 kg; horse, 450 kg; layer, 1.8 kg; broiler, 0.9 kg; turkey, 6.8 kg; and duck, 1.4 kg. ║ All nutrient values are given in elemental form.
The fractions of the nutrient pools were calculated on a Total Solids basis, i.e. the water content of the manure was ignored. Assumptions used in the calculations are: 1) the mineral nitrogen pool is assumed to be entirely composed of NH3/NH4+, 2) the organic nitrogen pool is equal to total Kjeldahl nitrogen minus ammonia nitrogen, 3) the mineral phosphorus pool is equal to the value given for orthophosphorus, and 4) the organic phosphorus pool is equal to total phosphorus minus orthophosphorus. Total amounts of nitrogen and phosphorus were available for all manure types. For manure types with either the ammonia nitrogen or orthophosphorus value missing, the ratio of organic to mineral forms of the provided element were used to partition the total amount of the other element. For example, in Table A-
Duck 110 ** 31 15 1.5 0.54 ** ** 0.54 0.21 0.25 **
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SWAT USER’S MANUAL, VERSION 2000
13 amounts of total Kjeldahl N, ammonia N, and total P are provided for veal but data for orthophosphorus is missing. To partition the total P into organic and mineral pools, the ratio of organic to mineral N for veal was used. If both ammonia nitrogen and orthophosphorus data are missing, the ratio of the organic to mineral pool for a similar animal were used to partition the total amounts of element into different fractions. This was required for goat and broiler manure calculations. The ratio of organic to mineral pools for sheep was used to partition the goat manure nutrient pools while layer manure nutrient ratios were used to partition the broiler manure nutrient pools. As can be seen from the standard deviations in Table A-13, values for nutrients in manure can vary widely. If site specific data are available for the region or watershed of interest, those values should be used in lieu of the default fractions provided in the database.
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75
A.5 URBAN DATABASE The urban database file (urban.dat) summarizes urban landscape attributes needed to model urban areas. These attributes tend to vary greatly from region to region and the user is recommended to use values specific to the area being modeled. The following tables list the urban land types and attributes that are provided in the urban database. Numerous urban land type classifications exist. For the default urban land types included in the database, an urban land use classification system created by Palmstrom and Walker (1990) was simplified slightly. Table A-14 lists the land type classifications used by Palmstrom and Walker and those provided in the database. Table A-14: Urban land type classification systems Palmstrom and Walker (1990) SWAT Urban Database Residential-High Density Residential-High Density Residential-Med/High Density Residential-Medium Density Residential-Med/Low Density Residential-Med/Low Density Residential-Low Density Residential-Low Density Residential-Rural Density Commercial Commercial Industrial Industrial-Heavy Transportation Industrial-Medium Institutional Transportation Institutional
The urban database includes the following information for each urban land type: 1) fraction of urban land area that is impervious (total and directly connected); 2) curb length density; 3) wash-off coefficient; 4) maximum accumulated solids; 5) number of days for solid load to build from 0 kg/curb km to half of the maximum possible load; 6) concentration of total N in solid loading; 7) concentration of total P in solid loading; and 8) concentration of total NO3-N in solid loading. The fraction of total and directly connected impervious areas is needed for urban surface runoff calculations. The remaining information is used only when the urban build up/wash off algorithm is chosen to model sediment and nutrient loading from the urban impervious area.
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SWAT USER’S MANUAL, VERSION 2000
A.5.1 DRAINAGE SYSTEM CONNECTEDNESS When modeling urban areas the connectedness of the drainage system must be quantified. The best methods for determining the fraction total and directly connected impervious areas is to conduct a field survey or analyze aerial photographs. However these methods are not always feasible. An alternative approach is to use data from other inventoried watersheds with similar land types. Table A-15 contains ranges and average values calculated from a number of different individual surveys (the average values from Table A-15 are the values included in the database). Table A-16 contains data collected from the cities of Madison and Milwaukee, Wisconsin and Marquett, Michigan. Table A-15: Range and average impervious fractions for different urban land types. Range Average Range Average total total connected connected Urban Land Type impervious impervious impervious impervious Residential-High Density .60 .44 - .82 .44 .32 - .60 (> 8 unit/acre or unit/2.5 ha) Residential-Medium Density .38 .23 - .46 .30 .18 - .36 (1-4 unit/acre or unit/2.5 ha) Residential-Med/Low Density .20 .14 - .26 .17 .12 - .22 (> 0.5-1 unit/acre or unit/2.5 ha) Residential-Low Density .12 .07 - .18 .10 .06 - .14 (< 0.5 unit/acre or unit/2.5 ha) Commercial .67 .48 - .99 .62 .44 - .92 Industrial .84 .63 - .99 .79 .59 - .93 Transportation .98 .88 - 1.00 .95 .85 – 1.00 Institutional .51 .33 - .84 .47 .30 - .77 Table A-16: Impervious fractions for different urban land types in Madison and Milwaukee, WI and Marquett, MI. Directly Indirectly connected connected Urban Land Type impervious impervious Pervious Residential-High Density .51 .00 .49 Residential-Medium Density .24 .13 .63 Residential-Low Density .06 .10 .84 Regional Mall .86 .00 .14 Strip Mall .75 .00 .25 Industrial-Heavy .80 .02 .18 Industrial-Light .69 .00 .31 Airport .09 .25 .66 Institutional .41 .00 .59 Park .08 .06 .86
APPENDIX A: DATABASES
77
A.5.2 CURB LENGTH DENSITY Curb length may be measured directly by scaling the total length of streets off of maps and multiplying by two. To calculate the density the curb length is divided by the area represented by the map. The curb length densities assigned to the different land uses in the database were calculated by averaging measured curb length densities reported in studies by Heaney et al. (1977) and Sullivan et al. (1978). Table A-17 lists the reported values and the averages used in the database. Table A-17: Measured curb length density for various land types Location: Tulsa, 10 Ontario Average of OK Cities two values Land type km/ha km/ha km/ha Residential 0.30 0.17 0.24 Commercial 0.32 0.23 0.28 Industrial 0.17 0.099 0.14 Park 0.17 -0.17 Open 0.063 0.059 0.06 Institutional -0.12 0.12
SWAT database categories using average value: All Residential Commercial Industrial Transportation, Institutional
A.5.3 WASH-OFF COEFFICENT The database assigns the original default value, 0.18 mm-1, to the wash-off coefficient for all land types in the database (Huber and Heaney, 1982). This value was calculated assuming that 13 mm of total runoff in one hour would wash off 90% of the initial surface load. Using sediment transport theory, Sonnen (1980) estimated values for the wash-off coefficient ranging from 0.002-0.26 mm-1. Huber and Dickinson (1988) noted that values between 0.039 and 0.390 mm-1 for the wash-off coefficient give sediment concentrations in the range of most observed values. This variable is used to calibrate the model to observed data.
A.5.4 MAXIMUM SOLID ACCUMULATION AND RATE OF ACCUMULATION The shape of the solid build-up equation is defined by two variables: the maximum solid accumulation for the land type and the amount of time it takes to build up from 0 kg/curb km to one-half the maximum value. The values assigned
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SWAT USER’S MANUAL, VERSION 2000
to the default land types in the database were extrapolated from a study performed by Sartor and Boyd (1972) in ten U.S. cities. They summarized the build-up of solids over time for residential, commercial, and industrial land types as well as providing results for all land types combined (Figure A-5).
Figure A-5: Solid loading as a function of time (Sartor and Boyd, 1972)
The lines plotted in Figure A-5 were adapted for use in the database. Table A-18 lists maximum load values and time to accumulate half the maximum load that were derived from the graph. The assignment of values to the different land types is provided in the table also. Table A-18: Maximum solid load and accumulation time (from Sartor and Boyd, 1972). Maximum time to accumulate SWAT database categories loading ½ maximum load Land type kg/curb km days using value: Residential 225 0.75 All Residential Commercial 200 1.60 Commercial Industrial 400 2.35 Industrial All land types 340 3.90 Transportation/Institutional
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79
A.5.5 NUTRIENT CONCENTRATION IN SOLIDS For the default land types in the database, nutrient concentrations in the solids were extrapolated from a nationwide study by Manning et al. (1977). The data published by Manning is summarized in Table A-19. Three concentration values are required: total nitrogen (mg N/kg), nitrate nitrogen (mg NO3-N/kg), and total phosphorus (mg P/kg). Manning provided total nitrogen values for all of his land use categories, nitrate values for one land use category and mineral phosphorus values for all the land use categories. To obtain nitrate concentrations for the other land use categories, the ratio of NO3-N to total N for commercial areas was assumed to be representative for all the categories. The nitrate to total N ratio for commercial land was multiplied by the total N concentrations for the other categories to obtain a nitrate concentration. The total phosphorus concentration was estimated by using the ratio of organic phosphorus to orthophosphate provided by the Northern Virginia Planning District Commission (1979). Total phosphorus loads from impervious areas are assumed to be 75 percent organic and 25 percent mineral. Table A-20 summarizes the assignment of values to the default land types in the urban database. Table A-19: Nationwide dust and dirt build-up rates and pollutant fractions (Manning et al., 1977) Pollutant Land Use Category Dust & Dirt Accumulation (kg/curb km/day)
mean range # obs.
Single Family Residential 17 1-268 74
Mult. Family Residential 32 2-217 101
Commercial 47 1-103 158
Industrial 90 1-423 67
All Data 45 1-423 400
Total N-N (mg/kg)
mean range # obs.
460 325-525 59
550 356-961 93
420 323-480 80
430 410-431 38
480 323-480 270
NO3 (mg/kg)
mean range # obs.
----
----
24 10-35 21
----
24 10-35 21
PO4-P (mg/kg)
mean range # obs.
49 20-109 59
58 20-73 93
60 0-142 101
26 14-30 38
53 0-142 291
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SWAT USER’S MANUAL, VERSION 2000
Table A-20: Nutrient concentration assignments for default land types Manning et al (1977) Modifications: Final Value:
SWAT database categories using value:
Total Nitrogen-N Single Fam Res. 460 ppm -460 ppm Residential: Med/Low & Low Mult. Fam. Res. 550 ppm -550 ppm Residential: Med. & High Commercial 420 ppm -420 ppm Commercial Industrial 430 ppm -430 ppm Industrial All Data 480 ppm -480 ppm Transportation/Institutional Nitrate-N: multiply reported value by fraction of weight that is nitrogen to get NO3-N Single Fam Res. (5.5/420) x 460 6.0 ppm Residential: Med/Low & Low Mult. Fam. Res. (5.5/420) x 550 7.2 ppm Residential: Med. & High Commercial 5.5 ppm -5.5 ppm Commercial Industrial (5.5/420) x 430 5.6 ppm Industrial All Data (5.5/420) x 480 6.3 ppm Transportation/Institutional Total Phosphorus-P: assume PO4-P is 25% of total P Single Fam Res. 49/(.25) 196 ppm Residential: Med/Low & Low 49 ppm PO4-P 58/(.25) 232 ppm Residential: Med. & High Mult. Fam. Res. 58 ppm PO4-P 60/(.25) 240 ppm Commercial Commercial 60 ppm PO4-P 26/(.25) 104 ppm Industrial Industrial 26 ppm PO4-P 53/(.25) 212 ppm Transportation/Institutional All Data 53 ppm PO4-P
A.6 REFERENCES American Society of Agricultural Engineers, 1998a. Manure production and characteristics, p. 646-648. In ASAE Standards 1998, 45th edition, Section D384.1. ASAE, St. Joseph. American Society of Agricultural Engineers, 1998b. Terminology and definitions for agricultural tillage implements, p. 261-272. In ASAE Standards 1998, 45th edition, Section S414.1. ASAE, St. Joseph. Arnold, J.G. and J.R. Williams. 1995. SWRRB—A watershed scale model for soil and water resources management. p. 847-908. In V.P. Singh (ed) Computer models of watershed hydrology. Water Resources Publications. Bailey, L.H. 1935. The Standard cyclopedia of horticulture. The Macmillan Publishing Co., New York, N.Y. Consumer Nutrition Center. 1982. Composition of foods: Fruit and fruit juices. USDA Human Nutrition Information Service. Agricultural Handbook 8-9. Diaz, R.A. and G.S. Campbell. 1988. Assessment of vapor density deficit from available air temperature information. ASA Annual Meetings, Anaheim, CA, Agron. Abstr., 1988, 16.
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Duncan, W.G. and Hesketh, J.D. 1968. Net photosynthesis rates, relative leaf growth rates and leaf numbers of 22 races of maize grown at eight temperatures. Crop Sci. 8:670-674. Hackett, C. and J. Carolane. 1982. Edible horticultural crops, a compendium of information on fruit, vegetable, spice and nut species, Part II: Attribute data. Division of Land Use Research, CSIRO, Canberra. Heaney, J.P., W.C. Huber, M.A. Medina, Jr., M.P. Murphy, S.J. Nix, and S.M. Haasan. 1977. Nationwide evaluation of combined sewer overflows and urban stormwater discharges—Vol. II: Cost assessment and impacts. EPA600/2-77-064b (NTIS PB-266005), U.S. Environmental Protection Agency, Cincinnati, OH. Huber, W.C. and R.E. Dickinson. 1988. Storm water management model, version 4: user’s manual. U.S. Environmental Protection Agency, Athens, GA. Huber, W.C. and J.P. Heaney. 1982. Chapter 3: Analyzing residual discharge and generation from urban and non-urban land surfaces. p. 121-243. In D.J. Basta and B.T. Bower (eds). Analyzing natural systems, analysis for regional residuals—environmental quality management. John Hopkins University Press, Baltimore, MD. Jensen, M.E., R.D. Burman, and R.G. Allen. 1990. Evapotranspiration and Irrigation Water Requirements. ASCE Manuals and Reports on Engineering Practice No. 70. ASCE, New York, N.Y. Kiniry, J.R. 1998. Biomass accumulation and radiation use efficiency of honey mesquite and eastern red cedar. Biomass and Bioenergy 15:467-473. Kiniry, J.R. 1999. Response to questions raised by Sinclair and Muchow. Field Crops Research 62:245-247. Kiniry, J.R., R. Blanchet, J.R. Williams, V. Texier, C.A. Jones, and M. Cabelguenne. 1992b. Sunflower simulation using EPIC and ALMANAC models. Field Crops Res., 30:403-423. Kiniry, J.R. and A.J. Bockholt. 1998. Maize and sorghum simulation in diverse Texas environments. Agron. J. 90:682-687.
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Kiniry, J.R. C.A. Jones, J.C. O'Toole, R. Blanchet, M. Cabelguenne and D.A. Spanel. 1989. Radiation-use efficiency in biomass accumulationprior to grain-filling for five grain-crop species. Field Crops Research 20:51-64. Kiniry, J.R., J.A. Landivar, M. Witt, T.J. Gerik, J. Cavero, L.J. Wade. 1998. Radiation-use efficiency response to vapor pressure deficit for maize and sorghum. Field Crops Research 56:265-270. Kiniry, J.R., D.J. Major, R.C. Izaurralde, J.R. Williams, P.W. Gassman, M. Morrison, R. Bergentine, and R.P. Zentner. 1995. EPIC model parameters for cereal, oilseed, and forage crops in the northern Great Plains region. Can. J. Plant Sci. 75: 679-688. Kiniry, J.R., W.D. Rosenthal, B.S. Jackson, and G. Hoogenboom. 1991. Chapter 5: Predicting leaf development of crop plants. p. 30-42. In Hodges (ed.) Predicted crop phenology. CRC Press, Boca Raton, FL. Kiniry, J.R., M.A. Sanderson, J.R. Williams, C.R. Tischler, M.A. Hussey, W.R. Ocumpaugh, J.C. Read, G.V. Esbroeck, and R.L. Reed. 1996. Simulating Alamo switchgrass with the Almanac model. Agron. J. 88:602-606. Kiniry, J.R., C.R. Tischler and G.A. Van Esbroeck. 1999. Radiation use efficiency and leaf CO2 exchange for diverse C4 grasses. Biomass and Bioenergy 17:95-112. Kiniry, J.R. and J.R. Williams. 1994. EPIC Crop Parameters for Vegetables for the Nitrogen and Phosphorus Portions of the RCA Analysis. Memorandum. Kiniry, J.R., J.R. Williams, P.W. Gassman, P. Debaeke. 1992a. A general, process-oriented model for two competing plant species. Transactions of the ASAE 35:801-810. Kiniry, J.R., J.R. Williams, R.L. Vanderlip, J.D. Atwood, D.C. Reicosky, J. Mulliken, W.J. Cox, H.J. Mascagni, Jr., S.E. Hollinger and W.J. Wiebold. 1997. Evaluation of two maize models for nine U.S. locations. Agron. J. 89:421-426.
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Knisel, W.G. (ed). 1993. GLEAMS: Groundwater loading effects of agricultural management systems, Version 2.10. UGA-CPES-BAED Publication No. 5. University of Georgia, Tifton, GA. Körner, Ch. 1977. Blattdiffusionswiderstände verschiedener Pflanzen in der zentralalpinen Grasheide der Hohen Tauren. p. 69-81. In Cernusca, A. (ed.) Alpine Grasheide Hohe Tauern. Ergebnisse der Ökosystemstudie 1976. Veröff. Österr. MaB-Hochgebirgsprogr. ,,Hohe Tauern“. Vol 1. Universitätsverlag Wagner, Innsbruck. Körner, Ch., J.A. Scheel and H. Bauer. 1979. Maximum leaf diffusive conductance in vascular plants. Photosynthetica 13:45-82. Leonard, R.A. and W.G. Knisel. 1988. Evaluating groundwater contamination potential from herbicide use. Weed Tech. 2:207-216. Manning, M.J., R.H. Sullivan, and T.M. Kipp. 1977. Nationwide evaluation of combined sewer overflows and urban stormwater discharges—Vol. III: Characterization of discharges. EPA-600/2-77-064c (NTIS PB-272107) U.S. Environmental Protection Agency, Cincinnati, OH. Manrique, L.A., J.R. Kiniry, T. Hodges, and D.S. Axness. 1991. Dry matter production and radiation interception of potato. Crop Sci. 31: 1044-1049. Martin, J.H., W.H. Leonard and D.L. Stamp. 1976. Principles of field crop production, 3rd edition. Macmillan Publishing Co., Inc., New York. Maynard, D.N. and Hochmuth. 1997. Knott's handbook for vegetable growers, 4th edition. John Wiley & Sons, Inc., New York. Monteith, J.L. 1965. Evaporation and the environment. p. 205-234. In The state and movement of water in living organisms, XIXth Symposium. Soc. for Exp. Biol., Swansea. Cambridge University Press. Northern Virginia Planning District Commission. 1979. Guidebook for screening urban nonpoint pollution management strategies: a final report prepared for Metropolitan Washington Council of Governments. Northern Virginia Planning District Commission, Falls Church, VA.
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Nutrition Monitoring Division. 1984b. Composition of food: Cereal grains and pasta. USDA Human Nutrition Information Service. Agricultural Handbook 8-20. Nutrition Monitoring Division. 1984c. Composition of food: Legumes and legume products.
USDA
Human
Nutrition
Information
Service.
AgriculturalHandbook 8-16. Nutrition Monitoring Division. 1984d. Composition of food: Nut and seed products.
USDA
Human
Nutrition
Information
Service.
AgriculturalHandbook 8-12. Nutrition Monitoring Division. 1984a. Composition of food: Vegetables and vegetable products. USDA Human Nutrition Information Service. Agricultural Handbook 8-11. Palmstrom, N. and W.W. Walker, Jr. 1990. P8 Urban Catchment Model: User’s guide, program documentation, and evaluation of existing models, design concepts and Hunt-Potowomut data inventory. The Narragansett Bay Project Report No. NBP-90-50. Sartor, J.D. and G.B. Boyd. 1972. Water pollution aspects of street surface contaminants. EPA-R2-72-081 (NTIS PB-214408) U.S. Environmental Protection Agency, Washington, DC. Sonnen, M.B. 1980. Urban runoff quality: information needs. ASCE Journal of the Technical Councils 106(TC1): 29-40. Stockle, C.O. and J.R. Kiniry. 1990. Variability in crop radiation-use efficiency associated with vapor pressure deficit. Field Crops Research 25:171-181. Stockle, C.O., J.R. Williams, N.J. Rosenberg, and C.A. Jones. 1992. A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part 1—Modification of the EPIC model for climate change analysis. Agricultural Systems 38:225-238. Sullivan, R.H., W.D. Hurst, T.M. Kipp, J.P. Heaney, W.C. Huber, and S.J. Nix. 1978. Evaluation of the magnitude and significance of pollution from urban storm water runoff in Ontario. Research Report No. 81, Canada-
APPENDIX A: DATABASES
Ontario
Research
Program,
Environmental
Protection
85
Service,
Environment Canada, Ottawa, Ontario. Watson, D.J. 1958. The dependence of net assimilation rate on leaf area index. Ann. Bot. N.S. 22:37-54. Wauchope, R.D., T.M. Buttler, A.G. Hornsby, P.W.M. Augustijn-Beckers, and J.P. Burt. 1992. The SCS/ARS/CES pesticide properties database for environmental decision-making. Environ. Contam. Toxicol. Reviews 123:1-164. Willis, G.H. and L.L. McDowell. 1987. Pesticide persistence on foliage. Environ. Contam. Toxicol. Reviews 100:23-73. Willis, G.H., W.F. Spencer, and L.L. McDowell. 1980. Chapter 18: The interception of applied pesticides by foliage and their persistence and washoff potential. p. 595-606. In W.G. Knisel (ed). CREAMS: A field scale model for chemicals, runoff, and erosion from agricultural management systems, Vol. 3. U.S. Dept. of Agri., Sci., and Education Adm., Conservation Research Report No. 26. U.S. Government Printing Office, Washington, D.C.
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APPENDIX B
EXAMPLE WATERSHED CONFIGURATIONS
The watershed configuration file defines the spatial relationship of objects within the watershed. The three techniques used to subdivide a watershed are the subwatershed discretization, the hillslope discretization, and the grid cell discretization. The following sections describe how to set up the watershed configuration file for each of the different discretization techniques.
B.1 SUBWATERSHED DISCRETIZATION The subwatershed discretization divides the watershed into subbasins based on topographic features of the watershed. This technique preserves the natural flow paths, boundaries, and channels required for realistic routing of water, sediment and chemicals. All of the GIS interfaces developed for SWAT use the subwatershed discretization to divide a watershed. The number of subbasins chosen to model the watershed depends on the size of the watershed, the spatial detail of available input data and the amount of detail required to meet the goals of the project. When subdividing the watershed, keep in mind that topographic attributes (slope, slope length, channel length, channel width, etc.) are calculated or summarized at the subbasin level. The subbasin delineation should be detailed enough to capture significant topographic variability within the watershed. Once the subbasin delineation has been completed, the user has the option of modeling a single soil/land use/management scheme for each subbasin or partitioning the subbasins into multiple hydrologic response units (HRUs). Hydrologic response units are unique soil/land use/management combinations within the subbasin which are modeled without regard to spatial positioning. When multiple HRUs are modeled within a subbasin, the land phase of the hydrologic cycle is modeled for each HRU and then the loadings from all HRUs within the subbasin are summed. The net loadings for the subbasin are then routed through the watershed channel network. HRUs are set up in the subbasin general attribute file (.sub). The following sections demonstrate how to create a SWAT watershed configuration file using the subwatershed discretization.
B.1.1 SUBWATERSHED DISCRETIZATION: 3 SUBBASINS Assume we have a watershed with 3 subbasins as illustrated in Figure B-1.
Figure B-1: Subwatershed delineation
Step 1: Write the subbasin command for each subbasin. (This command simulates the land phase of the hydrologic cycle.) column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1
1 2 3
1 2 3
Writing subbasin in space 1-10 is optional. The model identifies the configuration command by the code in column 1. The option of writing the command in space 1-10 is provided to assist the user in interpreting the configuration file. Column 2 is the hydrograph storage location number (array location) where data for the loadings (water, sediment, chemicals) from the subbasin are stored. Column 3 is the subbasin number. The subbasin number tells SWAT which input files listed in file.cio contain the data used to model the subbasin. Subbasin numbers are assigned sequentially in file.cio to each pair of lines after line 14. The files listed on lines 15 & 16 of file.cio are used to model subbasin 1, the files listed on lines 17 & 18 of file.cio are used to model subbasin 2, the files listed on lines 19 & 20 of file.cio are used to model subbasin 3, and so on.
Step 2a: Route the stream loadings through the reach network. Begin by routing the headwater subbasin loadings through the main channel of the respective subbasin. (Headwater subbasins are those with no subbasins upstream.) Referring to Figure B-1, assume that subbasins 1 and 2 are upstream of subbasin 3. This would make subbasins 1 and 2 headwater subbasins. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 2
1 2 3 4 5
1 2 3 1 2
1 2
0.000 0.000
As mentioned in the last step, column 1 is used to identify the command. Column 2 is the hydrograph storage location number identifying the location where results from the route simulation are placed. Column 3 provides the number of the reach, or main channel, the inputs are routed through. The number of the reach in a particular subbasin is the same as the number of the subbasin. Column 4 lists the number of the hydrograph storage location containing the data to be routed through the reach. The loadings from subbasin 1 are stored in hydrograph storage #1 and the loadings from subbasin 2 are stored in hydrograph storage #2. Column 6 lists the fraction of overland flow. For the subwatershed discretization, this value will always be zeroflow is always considered to be channelized before entering the next subbasin. Step 2b: Route the stream loadings through the reach network. Use the add and route commands to continue routing through the watershed. For this example, the water, sediment and chemicals flowing out of subbasins 1 and 2 and the loadings from subbasin 3 must be added together and routed through the main channel of subbasin 3. The loadings from the outlet of subbasin 1 are stored in hydrograph location #4; the loadings from the outlet of subbasin 2 are stored in hydrograph location #5; and the loadings from subbasin 3 are stored in hydrograph location #3.
column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route add add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 2 5 5 2
1 2 3 4 5 6 7 8
1 2 3 1 2 4 6 3
1 2 5 3 7
0.000 0.000
0.000
The add command is specified in column 1 by the number 5. The hydrograph storage location numbers of the 2 data sets to be added are listed in columns 3 and 4. The summation results are stored in the hydrograph location number given in column 2. Step 3: Once the stream loadings have been routed to the watershed outlet, append a finish command line to signify the end of the watershed routing file. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 2 5 5 2 0
1 2 3 4 5 6 7 8
1 2 3 1 2 4 6 3
1 2 5 3 7
0.000 0.000
0.000
B.1.2 SUBWATERSHED DISCRETIZATION: SAVING SIMULATION RESULTS FOR DOWNSTREAM RUNS If the watershed of interest is split up into subwatersheds that are modeled with separate SWAT runs, the outflow from the upstream subwatersheds must be saved in a file using the save command. This data will then be input into the SWAT simulation of the downstream portion of the watershed using a recday command. In example B.1.1, the outflow from the watershed is stored in hydrograph location #8, so this is the data we need to store in a daily file for use in another SWAT simulation. The watershed configuration modified to store outflow data is: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route add add route save finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 2 5 5 2 9 0
1 2 3 4 5 6 7 8 8
1 2 3 1 2 4 6 3
1 2 5 3 7
0.000 0.000
0.000
The save command is specified in column 1 by the number 9. Column 2 lists the hydrograph storage location of the data to be saved in the event output file. The name of the event output file is listed in file.cio and usually possesses the .eve file extension. Only one save command is allowed in a simulation. The event file output is described in Chapter 44.
B.1.3 SUBWATERSHED DISCRETIZATION: INCORPORATING POINT SOURCE/UPSTREAM SIMULATION DATA Point source and upstream simulation data may be incorporated into a run using one of four record commands: recday, recmon, recyear, and reccnst. The recday command reads data from a file containing loadings of different constituents for each day of simulation. The recmon command reads data from a file containing average daily loadings for each month. The recyear command reads data from a file containing average daily loadings for each year. The reccnst command reads in average annual daily loadings. The record command chosen to read in the data is a function of the detail of data available. To read in upstream simulation data, the recday command is always used. Assuming the subbasin delineation in Figure B-1 is used with one point source (sewage treatment plants) per subbasin, the watershed configuration file is: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin recday add route recday add route add recday add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 1 3 10 4 sub1.pnt 5 5 2 6 10 7 sub2.pnt 5 8 2 9 5 10 10 11 sub3.pnt 5 12 5 13 2 14 0
1 2 3 1 4 1 2
1 5
7 2 6 3
2 8 9
11 12 3
10 3 13
0.000
0.000
0.000
All of the record commands require 2 lines. On the first line, column 1 contains the command code for the specific record command, column 2 contains the hydrologic storage location where the data from the file is stored, and column 3 contains the file number. A different file number must be used for each point source of a specific type (e.g., all recday commands must have unique file numbers). The second line lists the name of the file containing the input data. A description of the four types of record files is given in Chapter 43.
B.1.4 SUBWATERSHED DISCRETIZATION: INCORPORATING RESERVOIRS Water bodies located along the main channel are modeled using reservoirs. To incorporate a reservoir into a simulation, a routres command is used. There is no limitation on the number of reservoirs modeled. Assuming the subbasin delineation in Figure B-1 is used with one reservoir located at the outlet, the watershed configuration file is: space 1-10
subbasin subbasin subbasin route route add add route routres finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 1 3 2 4 2 5 5 6 5 7 2 8 3 9 lakefork.res 0
1 2 3 1 1 2 2 4 5 6 3 3 7 1 8 lakefork.lwq
0.000 0.000
0.000 3
The routres command requires 2 lines. On the first line, the routres command is identified with the number 3 in column 1. Column 2 gives the hydrograph storage location where outflow data from the reservoir is stored. Column 3 lists the reservoir number. Column 4 gives the hydrograph storage location of the data to be routed through the reservoir. Column 5 lists the subbasin with which the reservoir is associated. A different reservoir number must be assigned to each reservoir and the numbers should be sequential beginning with 1. The second line lists two file names, the reservoir input file (.res) and the reservoir water quality file (.lwq).
B.1.5 SUBWATERSHED DISCRETIZATION: SAVING SIMULATION RESULTS FOR ONE LOCATION Users often need to compare streamflow, sediment, nutrient and/or pesticide levels predicted by the model with levels measured in the stream. To save daily or hourly model output data for a particular location on the stream, the saveconc command is used. Assume there is a stream gage at the outlet of the watershed shown in Figure B-1 and that we want to compare simulated and measured streamflow for this location. Hydrograph storage location #8 stores the flow data for this location in the watershed, so this is the data we need to process to create the saveconc output file. The watershed configuration modified to process data for this location is: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin route route add add route saveconc finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 2 1 3 2 4 2 5 5 6 5 7 2 8 14 8 strgage.out 0
1 2 3 1 2 4 6 3 1
1 2 5 3 7 0
0.000 0.000
0.000
The saveconc command requires 2 lines. On the first line, the saveconc command is identified with the number 14 in column 1. Column 2 gives the hydrograph storage location of the data to be processed for the saveconc output file. Column 3 lists the file number. Column 4 gives the print frequency (daily or hourly). More than one saveconc command may be used in a simulation. A different file number must be assigned to each saveconc output file and the file numbers should be sequential beginning with 1. The second line lists the name of the saveconc output file. The saveconc command differs from the save command in that it converts the mass amounts of water, sediment, and chemicals to units that are commonly used to report measured values. Output files produced by the saveconc command cannot be read into another SWAT run—the save command must be used to produce input for another simulation.
B.2 HILLSLOPE DISCRETIZATION The hillslope discretization allows overland flow from one subbasin to flow onto the land area of another subbasin. As the name implies, this discretization allows SWAT to model hillslope processes. The hillslope discretization incorporates more detail into the watershed configuration file than the subwatershed discretization. The number of subbasins chosen to model the watershed will depend on the size of the watershed, the spatial relationship of different land uses to one another, the spatial detail of available input data and the amount of detail required to meet the goals of the project. Because this discretization scheme places more emphasis on land use, the subbasins are delineated so that there is one land use and soil per subbasin. The hillslope discretization can be combined with the subwatershed discretization to provide detailed modeling of particular land use areas while modeling the remaining land use areas with the more generalized approach. Useful applications of this discretization include: watersheds with concentrated animal feeding operations, watersheds where detailed modeling of filter strips is desired, and microwatersheds where the scale of the simulation allows detail about relative land use positions to be incorporated.
B.2.1 HILLSLOPE DISCRETIZATION: MODELING A DAIRY OPERATION Assume a microwatershed containing a concentrated animal feeding operation with several different areas of land use and management is being modeled. Milking cows are confined in stalls. All waste produced by the milking cows is collected and applied over manure application fields also located in the microwatershed. The dry cows are kept in pastures. The pastured cows keep the areas adjacent to the farm buildings denuded of grass. Runoff from the denuded areas flows onto the pasture. Runoff from the pasture flows into a filter strip or buffer zone. Runoff exiting the filter strip enters the stream. The manure application fields are isolated from the rest of the dairy operation. Runoff from the application fields flows into a filter strip, and then enters the steam. Figure B-2 illustrates the relationship of land areas in the dairy operation. Areas of the microwatershed outside of the daily operation are forested.
Figure B-2: Spatial positioning of land areas in dairy operation.
This microwatershed will be divided into 6 subbasins: Subbasin 1: loafing area Subbasin 2: pasture Subbasin 3: filter strip associated with pasture Subbasin 4: waste application area Subbasin 5: filter strip associated with waste application area Subbasin 6: completely channelized stream and forest in microwatershed
Step 1: Write the subbasin command for each subbasin. (This command simulates the land phase of the hydrologic cycle.) column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
subbasin subbasin subbasin subbasin subbasin subbasin
1 1 1 1 1 1
1 2 3 4 5 6
1 2 3 4 5 6
Writing subbasin in space 1-10 is optional. The model identifies the configuration command by the code in column 1. The option of writing the command in space 1-10 is provided to assist the user in interpreting the configuration file. Column 2 is the hydrograph storage location number (array location) where data for the loadings (water, sediment, chemicals) from the subbasin are stored. Column 3 is the subbasin number. The subbasin number tells SWAT which input files listed in file.cio contain the data used to model the subbasin. Subbasin numbers are assigned sequentially in file.cio to each pair of lines after line 14. The files listed on lines 15 & 16 of file.cio are used to model subbasin 1, the files listed on lines 17 & 18 of file.cio are used to model subbasin 2, the files listed on lines 19 & 20 of file.cio are used to model subbasin 3, and so on. Step 2: Route the stream loadings. The hillslope discretization differs from the subwatershed discretization primarily in the method used to route loadings through the watershed. Loadings from subbasins are not routed through the subbasin if the flow leaving the subbasin is not completely channelized. For our example, subbasin 6 is the only subbasin completely channelized. Assume that runoff from the denuded areas (subbasin 1) is sheet flow, i.e. there are no rills, gullies or any other evidence of channelized flow in the denuded area. Runoff from the denuded area will be routed to the pasture (subbasin 2) using the route command: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
2
7
2
1
1.000
As mentioned in the last step, column 1 is used to identify the command. Column 2 is the hydrograph storage location number identifying the location where results from the channelized portion of the route simulation are placed. In this instance, because there is no channelized flow, this storage location will contain no data.
Column 3 provides the number of the reach or subbasin the inputs are routed through. (The number of the reach in a particular subbasin is the same as the number of the subbasin.) The fraction of the loadings classified as overland flow are applied to the subbasin land area while the fraction of the loadings classified as channelized flow are routed through the main channel of the subbasin and are exposed to in-stream processes. Channelized flow has no interaction with the land area in the subbasin. Column 4 lists the number of the hydrograph storage location containing the data to be routed through the reach. The loadings from subbasin 1 are stored in hydrograph storage #1. Column 6 lists the fraction of overland flow. For completely channelized flow this fraction is zero. For 100% overland flow, this fraction is 1.00. The entire watershed configuration to this point looks like: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2
1 2 3 4 5 6 7
1 2 3 4 5 6 2
1
1.000
Assume that runoff from the pasture is slightly channelized (10% channels). Flow from the pasture is routed to the filter strip (subbasin 3) using the next route command: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2
1 2 3 4 5 6 7 8
1 2 3 4 5 6 2 3
1 2
1.000 0.900
As mentioned previously, hydrograph storage location #7 contains no data because none of the runoff entering subbasin 2 is channelized. Consequently, when routing runoff leaving subbasin 2, this hydrograph storage location can be ignored. For subbasin 3, however, there will be data in hydrograph storage location #8 from the 10% of flow that is channelized in that subbasin. Loadings from subbasin 3 will enter the main stream in subbasin 6. The total loadings from the denuded area/pasture/filter strip section of the microwatershed are determined
by adding the runoff generated from subbasin 3 and the channelized flow routing results. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 2 3 3
1 2 8
1.000 0.900
The loadings from simulation of the land phase of the hydrologic cycle in subbasin 3 are stored in hydrograph storage location #3 and the loadings from simulation of the channelized flow in subbasin 3 are stored in hydrograph location #8. The add command is specified in column 1 by the number 5. The hydrograph storage location numbers of the 2 data sets to be added are listed in columns 3 and 4. The summation results are stored in the hydrograph location number given in column 2. Net loadings from the denuded area/pasture/filter strip is stored in hydrograph location #9. Assume that the manure application area (subbasin 4) is well managed and all runoff from this area is overland flow (no channelized flow). To route flow from the application area to the associated filter strip (subbasin 5) a route command will be appended to the end of the configuration: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 2 3 3 5
1 2 8 4
1.000 0.900 1.000
Hydrograph storage location #10 contains no data because none of the runoff entering subbasin 5 is channelized. Consequently, when routing runoff leaving subbasin 5, this hydrograph storage location can be ignored. Net loadings from the waste application area/filter strip section of the watershed is stored in hydrograph location #5.
Flow through subbasin 6, which contains the stream, is completely channelized. All of the loadings for the stream must be summed together and then routed through the stream. There are 3 sources of loading to the stream: the denuded area/pasture/filter strip (hydrograph location #9), the waste application area/filter strip (hydrograph location #10), and the forest land area (hydrograph location #6). Add commands are used to sum the loadings. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route add add
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2 5 5
1 2 3 4 5 6 7 8 9 10 11 12
1 2 3 4 5 6 2 3 3 5 9 6
1 2 8 4 5 11
1.000 0.900 1.000
Flow is routed through the stream using a route command: column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route add add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2 5 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13
1 2 3 4 5 6 2 3 3 5 9 6 6
1 2 8 4 5 11 12
1.000 0.900 1.000
0.000
Step 3: Once the stream loadings have been routed to the watershed outlet, append a finish command line to signify the end of the watershed routing file. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2 5 5 2 0
1 2 3 4 5 6 7 8 9 10 11 12 13
1 2 3 4 5 6 2 3 3 5 9 6 6
1 2 8 4 5 11 12
1.000 0.900 1.000
0.000
B.2.2 HILLSLOPE DISCRETIZATION: COMBINING WITH SUBWATERSHED DISCRETIZATION The hillslope discretization is a very detailed discretization scheme and is suited to small watersheds. However, it can be used in combination with the subwatershed discretization to provide detailed simulation of certain land uses in a large watershed whose spatial relationships are important to the study. As an example, assume that the dairy operation described in Section B.2.1 is located in a headwater region of the watershed example used in Section B.1. Figure B-3 illustrates the location of the dairy in the larger watershed. (Assume the microwatershed modeled in Section B.2.1 is subbasin B in Figure B-3.)
Figure B-3: Watershed with dairy operation
There are two options that may be used to combine the detailed modeling of the dairy with the less detailed modeling of the other land uses in the watershed. The first option is to model the dairy in a separate simulation and save the loadings from the microwatershed using the save command. These daily loadings will then be read into the simulation of the larger watershed using a recday command. The second option is to merge the watershed configuration given in Section B.2.1 with the watershed configuration given Section B.1.1
Option 1: Two separate runs. The watershed configuration file for simulation of the microwatershed with the dairy will be modified to save the outflow data to an event file. The name of the event file is specified as “dairy.eve” in the file.cio for the microwatershed simulation. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin route route add route add add route save finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 2 2 5 2 5 5 2 9 0
1 2 3 4 5 6 7 8 9 10 11 12 13 13
1 2 3 4 5 6 2 3 3 5 9 6 6
1 2 8 4 5 11 12
1.000 0.900 1.000
0.000
Because the area in subbasin B is modeled in the microwatershed simulation, the area will not be directly modeled in the large watershed simulation. Instead, the data in the file dairy.eve will be read in and routed. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin route recday add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 2 10 dairy.eve 5 5 2 0
1 2 3 4
1 2 1 1
5 6 7
3 5 2
1
0.000
4 2 6
0.000
In the above configuration, subbasin A is subbasin 1, subbasin C is subbasin 2 and outflow from subbasin B is read in with the recday command.
Option 2: A combined simulation. In this simulation, the routing for the entire watershed is contained in one configuration file. We will include comment lines in this watershed configuration to identify the different portions of the watershed being simulated. Subbasin B will be divided into 6 separate subbasins numbered 1-6 with the same land use assignments listed in section B.2.1. Subbasin A is subbasin 7 in this simulation while subbasin C is subbasin 8. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
* land phase for subbasin B subbasin 1 1 1 subbasin 1 2 2 subbasin 1 3 3 subbasin 1 4 4 subbasin 1 5 5 subbasin 1 6 6 * land phase for subbasin A subbasin 1 7 7 * land phase for subbasin C subbasin 1 8 8 * route flow through subbasin A route 2 9 7 7 0.000 * route flow through subbasin B route 2 10 2 1 1.000 route 2 11 3 2 0.900 add 5 12 3 11 route 2 13 5 4 1.000 add 5 14 12 5 add 5 15 6 14 route 2 16 6 15 0.000 * add outflow from subbasin A and B to loadings from subbasin C add 5 17 9 16 add 5 18 8 17 * route flow through subbasin C route 2 19 8 18 0.000 finish 0
Comment lines are denoted by an asterisk in the first space. When SWAT reads an asterisk in this location it knows the line is a comment line and does not process the line.
B.3 GRID CELL DISCRETIZATION The grid cell discretization allows a user to capture a high level of spatial heterogeneity or variability in the simulation. The grid cells should be small enough to obtain homogenous land use, soil, and topographic characteristics for the area in each cell but large enough to keep the amount of data required for the run at a reasonable level. The routing methodology for the grid cell discretization is the same as that for the subwatershed discretization. The difference between the two discretization schemes lies in the average size of the subbasin and the method used to define subbasin boundaries. The GIS interfaces are currently not able to delineate a watershed using a grid cell discretization. However, there are plans to create a GIS tool capable of generating a grid cell discretization.
B.3.1 GRID CELL DISCRETIZATION: 9 CELLS To illustrate the grid cell discretization, a simple nine-cell example will be used.
Figure B-4: Grid cell delineation with flow paths shown.
Step 1: Write the subbasin command for each cell. (This command simulates the land phase of the hydrologic cycle.) column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
Writing subbasin in space 1-10 is optional. The model identifies the configuration command by the code in column 1. The option of writing the command in space 1-10 is provided to assist the user in interpreting the configuration file. Column 2 is the hydrograph storage location number (array location) where data for the loadings (water, sediment, chemicals) from the subbasin are stored. Column 3 is the subbasin number. The subbasin number tells SWAT which input files listed in file.cio contain the data used to model the subbasin. Subbasin numbers are assigned sequentially in file.cio to each pair of lines after line 14.
The files listed on lines 15 & 16 of file.cio are used to model subbasin 1, the files listed on lines 17 & 18 of file.cio are used to model subbasin 2, the files listed on lines 19 & 20 of file.cio are used to model subbasin 3, and so on. Step 2a: Route the stream loadings through the flow path network. Begin by routing the headwater subbasin loadings through the main channel of the respective subbasin. (Headwater subbasins are those with no subbasins upstream.) Referring to Figure B-4, subbasins 1, 3, 6 and 7 are headwater subbasins. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2
1 2 3 4 5 6 7 8 9 10 11 12 13
1 2 3 4 5 6 7 8 9 1 3 6 7
1 3 6 7
0.000 0.000 0.000 0.000
As mentioned in the last step, column 1 is used to identify the command. Column 2 is the hydrograph storage location number identifying the location where results from the route simulation are placed. Column 3 provides the number of the reach, or main channel, the inputs are routed through. The number of the reach in a particular subbasin is the same as the number of the subbasin. Column 4 lists the number of the hydrograph storage location containing the data to be routed through the reach. Column 6 lists the fraction of overland flow. For the grid cell discretization, this value will always be zero. Step 2b: Route the stream loadings through the reach network. Use the add and route commands to continue routing through the watershed. First, add the outflow from subbasin 1 to the loadings from subbasin 4 and route the total through the channel in subbasin 4.
column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4
1 3 6 7 4 14
0.000 0.000 0.000 0.000 0.000
The loadings from the outlet of subbasin 1 are stored in hydrograph location #10; the loadings from subbasin 4 are stored in hydrograph location #4. The add command is specified in column 1 by the number 5. The hydrograph storage location numbers of the 2 data sets to be added are listed in columns 3 and 4. The summation results are stored in the hydrograph location number given in column 2. Next, add the outflow from subbasin 3 to the loadings from subbasin 2 and route the total through the channel in subbasin 2. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4 11 2
1 3 6 7 4 14 2 16
0.000 0.000 0.000 0.000 0.000 0.000
Next, add the outflow from subbasin 2 and 4 to the loadings from subbasin 5 and route the total through the channel in subbasin 5. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route add route add add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2 5 2 5 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4 11 2 15 18 5
1 3 6 7 4 14 2 16 17 5 19
0.000 0.000 0.000 0.000 0.000 0.000
0.000
Next, add the outflow from subbasin 5 and 7 to the loadings from subbasin 8 and route the total through the channel in subbasin 8. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route add route add add route add add route
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2 5 2 5 5 2 5 5 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4 11 2 15 18 5 20 21 8
1 3 6 7 4 14 2 16 17 5 19 13 8 22
0.000 0.000 0.000 0.000 0.000 0.000
0.000
0.000
Next, add the outflow from subbasin 8 and 6 to the loadings from subbasin 9, route the total through the channel in subbasin 9, and append a finish command line to signify the end of the watershed routing file. column 1 column 2 column 3 column 4 column 5 column 6 column 7 space 1-10
subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin subbasin route route route route add route add route add add route add add route add add route finish
space 11-16 space 17-22 space 23-28 space 29-34 space 35-40 space 41-46 space 47-55
1 1 1 1 1 1 1 1 1 2 2 2 2 5 2 5 2 5 5 2 5 5 2 5 5 2 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
1 2 3 4 5 6 7 8 9 1 3 6 7 10 4 11 2 15 18 5 20 21 8 23 24 9
1 3 6 7 4 14 2 16 17 5 19 13 8 22 12 9 25
0.000 0.000 0.000 0.000 0.000 0.000
0.000
0.000
0.000
As illustrated in section B.2.2 for the hillslope discretization, it is possible to combine the grid cell discretization with the subwatershed discretization to provide detailed modeling of portions of a large watershed while treating less significant areas in the more generalized approach used in the subwatershed discretization.
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