Tumi Bjornsson Ph.d

Regional scale modelling of the lower River Murray wetlands A model for the assessment of nutrient retention of floodplain wetlands pre- and post-management

Dipl. Ökol. Kjartan Tumi Björnsson B.Sc.

Thesis Submitted for the Degree of

Doctor of Philosophy June 2007

School of Earth and Environmental Sciences

Regional Scale Modelling of the lower River Murray wetlands

II

Regional Scale Modelling of the lower River Murray wetlands

Table of Contents Table of Contents ....................................................................................................... I List of Figures and Tables .........................................................................................V Figures ..................................................................................................................V Tables .................................................................................................................. IX Declaration .............................................................................................................. XI Acknowledgements ................................................................................................ XII Abstract ................................................................................................................ XIV 1

Background ....................................................................................................... 1 1.1

Introduction ............................................................................................... 1

1.1.1

Wetland processes .............................................................................. 5

1.1.2

Spatial relationships of wetlands to transport processes .................... 10

1.2

Degradation of floodplain wetlands .......................................................... 12

1.2.1

Eutrophication of aquatic environments ............................................ 14

1.2.2

Alternate stable states and permanent inundation impacts on wetlands 17

1.2.3 1.3

Restoration of degraded floodplain wetlands ............................................ 21

1.3.1 1.4

Irrigation drainage and constructed wetlands .................................... 19

Management strategies for restoration .............................................. 21

Predictive modelling of wetland processes and services; current state and

potential alteration due to management ................................................................ 23 1.4.1

Complexity and feasibility of modelling ........................................... 25

1.4.2

Qualitative and quantitative assessment of model accuracy and generic

applicability ..................................................................................................... 27 1.4.3

Validation ........................................................................................ 30

1.4.4

Modelling role in environmental decision-making ............................ 31

I

Regional Scale Modelling of the lower River Murray wetlands 2

Aims and objectives ......................................................................................... 39

3

Materials and Methods..................................................................................... 41 3.1

3.1.1

Design Considerations ...................................................................... 41

3.1.2

WETMOD 1 .................................................................................... 45

3.1.3

WETMOD 2 .................................................................................... 54

3.2

“Exemplar” Wetland Sites ............................................................... 65

3.2.2

Wetland Data ................................................................................... 71

3.2.3

River Data ........................................................................................ 80

Data Handling .......................................................................................... 85

3.3.1

Model Calibration ............................................................................ 88

3.3.2

Validation Procedure ........................................................................ 88

3.4

Wetland Management .............................................................................. 89

3.4.1

Options ............................................................................................ 89

3.4.2

Management scenarios for cumulative assessment ............................ 92

Validation of the model WETMOD 2 and Discussion ...................................... 97 4.1

Fitting and Validation based on calibrated (“exemplar”) wetlands ............ 97

4.1.1

Implication for irrigation affected wetland representation ................118

4.1.2

Implication for wetland representation.............................................120

4.2

Validation based on non-calibrated wetland data .....................................125

4.3

Evaluating model performance ................................................................136

4.3.1

Generic nature and structural restrictions of model ..........................136

4.3.2

Relevance of project objectives .......................................................137

4.4 5

Data: Model Driving Variables ................................................................ 63

3.2.1

3.3

4

Model Description ................................................................................... 41

Chapter summary and Implication for the first hypothesis .......................139

Simulation results of potential management scenarios and Discussion ............140

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Regional Scale Modelling of the lower River Murray wetlands 5.1.1 5.2 6

Implications for Management ..........................................................157

Chapter summary and Implications for the second hypothesis .................161

Results of the cumulative assessment of management scenarios, visualisation and

discussion ...............................................................................................................163

7

6.1

Cumulative assessment: category 3 wetlands...........................................164

6.2

Cumulative assessment: category 4 wetlands...........................................185

6.3

Implications of cumulative impact of multiple wetland management .......192

6.4

Chapter summary and Implications for the third hypothesis ....................198

Summary, Context and Discussion ..................................................................200 7.1

Assessment methodology ........................................................................201

7.2

Current capabilities .................................................................................202

8

Conclusion & Future Work .............................................................................209

9

References ......................................................................................................216

Glossary .................................................................................................................232 Appendix A: WETMOD differential equations .......................................................234 $Macrophytes .....................................................................................................235 $Phytoplankton ..................................................................................................237 $Nutrients...........................................................................................................242 $NutrientExchange .............................................................................................246 $Wetland&RiverFlowExchange .........................................................................251 $SpatialRelevantTimeSeries ...............................................................................252 $RiverNutrients ..................................................................................................252 $WetlandsTimeseriesUpdateMeasuredValues .....................................................252 $WetlandTimeseriesUpdate ................................................................................252 $RiverTimeseries4WetlandUpdateTimeseries .....................................................252 $PotentialContributionToRiver ...........................................................................252

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Regional Scale Modelling of the lower River Murray wetlands Appendix B: Driving Variables ..............................................................................253 Appendix C: Key to wetland numbers ....................................................................263 Appendix D: Cumulative Management Scenarios ...................................................266 Appendix E: WETMOD 2 Code .............................................................................291

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Regional Scale Modelling of the lower River Murray wetlands

List of Figures and Tables Figures Figure 1: Wetland exchange modelling ...................................................................... 3 Figure 2: Cumulative assessment of wetland processes .............................................. 3 Figure 3: Study Area ................................................................................................. 5 Figure 4: Driving Variables, State Variables and Major Interactions in WETMOD 1 46 Figure 5: Macrophyte Module ................................................................................. 49 Figure 6: Plankton Module ...................................................................................... 51 Figure 7: Nutrient Module ....................................................................................... 53 Figure 8: WETMOD 2 Structure and Data Flow ...................................................... 56 Figure 9: Volume Exchange Module ....................................................................... 58 Figure 10: External Nutrient Module ....................................................................... 60 Figure 11: Outflow Module ..................................................................................... 61 Figure 12: “Exemplar” Wetlands & River Monitoring Sites .................................... 65 Figure 13: Paiwalla & Sunnyside wetlands .............................................................. 68 Figure 14: Lock 6 and Pilby Creek wetlands ............................................................ 69 Figure 15: Reedy Creek wetland .............................................................................. 70 Figure 16: Wetlands (Categories 1 to 5) Driving Variables Turbidity, Water Temperature & Solar Radiation (see also in Appendix B) ................................ 73 Figure 17: Sunnyside Irrigation Drainage PO4-P, NO3-N, Phytoplankton and Estimated Flow Volume (see also in Appendix B) ........................................... 79 Figure 18: River Murray Nutrient & Phytoplankton Time Series as well as River Flow Volume (see also in Appendix B) .................................................................... 84 Figure 19: Wetlands (Categories 1 to 5) Monitored Nutrients and Phytoplankton .... 87 Figure 20: Wetland exchange modelling .................................................................. 92 Figure 21: Cumulative assessment of wetland processes .......................................... 96 Figure 22: Percentage Deviation based estimate of flow exchange: Reedy Creek wetland ............................................................................................................ 98 Figure 23: Validation of simulation results for Paiwalla wetland of PO 4-P, and NO3-N for both conditions with and without water exchange ......................................101

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Regional Scale Modelling of the lower River Murray wetlands Figure 24: Validation of simulation results for Paiwalla wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange ...............................................................................................102 Figure 25: Validation of simulation results for Sunnyside wetland of PO 4-P, and NO3N for both conditions with and without water exchange ..................................105 Figure 26: Validation of simulation results for Sunnyside wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange ...............................................................................................106 Figure 27: Validation of simulation results for Lock 6 wetland of PO4-P, and NO3-N for both conditions with and without water exchange ......................................108 Figure 28: Validation of simulation results for Lock 6 wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange ...............................................................................................109 Figure 29: Validation of simulation results for Reedy Creek wetland of PO 4-P, and NO3-N for both conditions with and without water exchange ..........................112 Figure 30: Validation of simulation results for Reedy Creek wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange ...............................................................................................113 Figure 31: Validation of simulation results for Pilby Creek wetland of PO 4-P, and NO3-N for both conditions with and without water exchange ..........................116 Figure 32: Validation of simulation results for Pilby Creek wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange ...............................................................................................117 Figure 33: Sunnyside monitoring area ....................................................................119 Figure 34: Validation of simulation results for Lock 6 wetland PO 4-P and NO3-N, using non-calibrated wetland data ...................................................................128 Figure 35: Validation of simulation results for Lock 6 wetland Macrophyte Biomass, Zooplankton and Phytoplankton biomass, using non-calibrated wetland data ..129 Figure 36: Validation of simulation results for Reedy Creek wetland PO 4-P and NO3N, using non-calibrated wetland data ..............................................................131 Figure 37: Validation of simulation results for Reedy Creek wetland Macrophyte Biomass, Zooplankton and Phytoplankton biomass, using non-calibrated wetland data.................................................................................................................132

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Regional Scale Modelling of the lower River Murray wetlands Figure 38: Validation of simulation results for Pilby Creek wetland PO 4-P and NO3N, using non-calibrated wetland data ..............................................................134 Figure 39: Validation of simulation results for Pilby Creek wetland Macrophyte Biomass, Zooplankton and Phytoplankton biomass, using non-calibrated wetland data.................................................................................................................135 Figure 40: Lock 6 impacts on Nutrient concentration due to Turbidity reduction ....143 Figure 41: Lock 6 impacts on Macrophyte, Zooplankton & Phytoplankton due to Turbidity reduction .........................................................................................144 Figure 42: Reedy Creek wetland impacts on Nutrient concentration due to irrigation drainage reduction ..........................................................................................147 Figure 43: Reedy Creek wetland impacts on Macrophyte, Zooplankton & Phytoplankton due to irrigation drainage reduction .........................................148 Figure 44: Reedy Creek wetland impacts on Nutrient concentration due to irrigation drainage reduction and 75% turbidity reduction ..............................................152 Figure 45: Reedy Creek wetland impacts on Macrophyte, Zooplankton & Phytoplankton due to irrigation drainage reduction and 75% turbidity reduction .......................................................................................................................153 Figure 46: Reedy Creek wetland impacts on Nutrient concentration due to 95 % irrigation drainage reduction at 25, 50 and 75% turbidity reduction ................154 Figure 47: Reedy Creek wetland impacts on Macrophyte, Zooplankton & Phytoplankton due to 95% irrigation drainage reduction at 25, 50 and 75% turbidity reduction ..........................................................................................155 Figure 48: Reedy Creek wetland PO4-P % reduction in outflow .............................156 Figure 49: Reedy Creek wetland NO3-N % reduction in outflow ............................156 Figure 50: Reedy Creek wetland Phytoplankton % reduction in outflow .................157 Figure 51: Cumulative retention- category 3 wetlands ............................................165 Figure 52: PO4-P Concentration Trends ..................................................................168 Figure 53: Macrophyte Biomass Growth Trends .....................................................169 Figure 54: Phytoplankton Biomass Growth Trends .................................................170 Figure 55: Zooplankton Biomass Growth Trends ....................................................171 Figure 56: NO3-N Concentration Trends.................................................................172 Figure 57: Macrophyte Biomass (size of sphere, kg/m3) plotted against Wetland Volume and Wetland Depth ............................................................................175 Figure 58: Macrophyte Biomass vs. Wetland Depth ...............................................175 VII

Regional Scale Modelling of the lower River Murray wetlands Figure 59: Average Macrophyte Biomass (size of sphere, kg/m3) plotted against Average Wetland Volume and Wetland Depth ................................................176 Figure 60: Macrophyte Biomass vs. Wetland Volume ............................................176 Figure 61: Average Macrophyte Biomass (size of sphere) Plotted against Average Wetland Volume and Wetland Depth range 1 – 2 m........................................178 Figure 62: Average PO4-P (size of sphere) Plotted against Average Wetland Volume and Wetland Depth range 1 – 2 m ...................................................................178 Figure 63: Average PO4-P vs. Macrophyte Biomass at Wetland Depth range 1 – 2 m .......................................................................................................................179 Figure 64: Average NO3-N (size of sphere) Plotted against Average Wetland Volume and Wetland Depth range 1 – 2 m ...................................................................179 Figure 65: Average NO3-N vs. Macrophyte Biomass at Wetland Depth range 1 – 2 m .......................................................................................................................180 Figure 66: Comparison of Macrophyte, Phytoplankton and Zooplankton Biomass for each category 3 wetland (Key to wetland numbers adapted from (Jensen et al. 1996), see list in Table 18 in Appendix C) ......................................................181 Figure 67: Nutrient uptake for full year wet vs. uptake for summer wet/winter dry .184 Figure 68: Cumulative loading to category 4 wetlands ............................................186 Figure 69: Macrophyte Growth Trends ...................................................................187 Figure 70: Phytoplankton Growth Trends ...............................................................188 Figure 71: Zooplankton Growth Trends ..................................................................189 Figure 72: PO4-P Trends.........................................................................................190 Figure 73: NO3-N Trends .......................................................................................191 Figure 74: Data - Model Driving Variables; From Figure 9 in section 2.3 ...............254 Figure 75: Data - Model Driving Variables; From Figure 9 in section 2.3 ...............255 Figure 76: Data - Model Driving Variables; From Figure 9 in section 2.3 ...............256 Figure 77: Time Series Irrigation Drainage ; From Figure 10 section 2.3.1 .............257 Figure 78: Time Series Irrigation Drainage; From Figure 10 section 2.3.1 ..............258 Figure 79: Time Series Irrigation Drainage ; From Figure 10 in section 2.3.1 .........259 Figure 80: River Data; From Figure 11 in section 2.3.2 ..........................................260 Figure 81: River Data; From Figure 11 in section 2.3.2 ..........................................261 Figure 82: River Data; From Figure 11 in section 2.3.2 ..........................................262

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Regional Scale Modelling of the lower River Murray wetlands

Tables Table 1: Data Sources, Type & Monitoring Frequency ............................................ 64 Table 2: Wetland Morphology ................................................................................. 76 Table 3: Calibration of inflow data for the 5-wetland categories .............................. 99 Table 4: Non calibrated validation of inflow data for 3 wetland categories .............126 Table 5: Assessment summary of wetlands realistic simulation ..............................139 Table 6: Lock 6 wetland Percentage Outflow Reduction .........................................142 Table 7: Reedy Creek wetland Percentage Inflow reduction vs. Percentage Outflow Reduction .......................................................................................................149 Table 8: Assessment summary of wetlands management scenarios .........................162 Table 9: Impact, of category 3 wetland‟s management, on river load per annum .....192 Table 10: Impact, of category 3 wetland‟s (depth range shallow <1m) management, on river load per annum ..................................................................................194 Table 11: Impact, of category 3 wetland‟s (depth range medium 1-2m) management, on river load per annum ..................................................................................194 Table 12: Impact, of category 3 wetland‟s (depth range deep >2m) management, on river load per annum .......................................................................................194 Table 13: Impact, of Lock 6 wetland management, on river load per annum ...........195 Table 14: Impact, of Lock 6 wetland management, summer wet winter dry, on river load per annum ...............................................................................................195 Table 15: Impact, of category 4 wetland‟s management, on river load per annum ...196 Table 16: Impact, of Reedy Creek wetland management, on river load per annum ..197 Table 17: Initial values ...........................................................................................234 Table 18: Wetlands simulated as category 3 wetlands .............................................263 Table 19: Wetlands simulated as category 4 wetlands .............................................265 Table 20: Change in PO4-P wetland loading and percentage outflow due to management; category 3 wetland scenarios .....................................................267 Table 21: Change in NO3-N wetland loading and percentage outflow due to management; category 3 wetland scenarios .....................................................273 Table 22: Change in Phytoplankton wetland loading and percentage outflow due to management; category 3 wetland scenarios .....................................................279 Table 23: PO4-P comparison between Full year wet versus Summer wet Winter dry for three selected wetlands; category 3 wetland scenarios ...............................285

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Regional Scale Modelling of the lower River Murray wetlands Table 24: NO3-N comparison between Full year wet versus Summer wet Winter dry for three selected wetlands; category 3 wetland scenarios ...............................286 Table 25: Phytoplankton comparison between Full year wet versus Summer wet Winter dry for three selected wetlands; category 3 wetland scenarios ..............287 Table 26: Change in PO4-P wetland loading and percentage in and outflow due to management; category 4 wetland scenarios .....................................................288 Table 27: Change in NO3-N wetland loading and percentage in and outflow due to management; category 4 wetland scenarios .....................................................289 Table 28: Change in Phytoplankton wetland loading and percentage in and outflow due to management; category 4 wetland scenarios ..........................................290

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Regional Scale Modelling of the lower River Murray wetlands

Declaration I declare that this thesis is my own work and to the best of my knowledge and belief, contains no material used for the award of another degree, or published or written by another person(s), except where appropriately referenced in the text. To the best of my knowledge and belief, this thesis contains no material previously published or written by another person, except where due reference has been made in the text. I consent to a copy of my thesis, when deposited in the university Library, being made available for loan or photocopying.

Kjartan Tumi Bjornsson Date 2007

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Regional Scale Modelling of the lower River Murray wetlands

Acknowledgements This project used monitoring data from several sources. River flow data, which is collected at all locks, was obtained from the Murray Darling Basin Commission (MDBC). This flow data, which was included in the model, was collected at Locks 1 through to 8 (Figure 12 on page 65). The River Murray nutrient data was provided by the Department of Environment and Heritage of South Australia (DEH). This nutrient data was a collection of data originally sourced from the South Australian Environmental Protection Authority (EPA), the MDBC, and the South Australian Department of Water (SA Water). The river nutrient data monitoring points are at Lock 5, Mannum, and Murray Bridge. For simplicity in this report, all river data is referred to consistently as MDB river data. However, the contributions by the MDBC, DEH, EPA and SA water are gratefully acknowledged, as without their support this project would not have been possible. Planning SA provided GIS data covering the wetlands (the South Australian Wetlands Atlas (Jensen et al. 1996)), Locks, and the River Murray. Wetland Care Australia provided the Wetlands Management Study report 1998 ((Nichols 1998)), which was used in obtaining wetland depth information. Solar radiation was obtained from the Bureau of Meteorology (BOM). Bartsch (1997) Marsh (1997) Wen (2002a) Wielen (nd) have collected a substantial quantity of water quality data for some wetlands of the lower River Murray, as well as irrigation drainage into affected wetlands and some river data at a site close to the wetlands for the same monitoring dates. Table 1 on page 64 describes the source and frequency of data collection. I thank them for their contribution of data. To my supervisors, starting with my principal supervisor Friedrich Recknagel, I first thank for the initiative to develop this project. I would also like to thank him for his role in the supervision of the project and especially input in the thesis structure and making sure I finished. Bertram Ostendorf I thank for taking on a larger role in this project than anticipated, the input was invaluable. Megan Lewis I thank for the support and input particularly during the early stage of the project when I needed the assistance the most.

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Regional Scale Modelling of the lower River Murray wetlands Lydia Cetin I thank for the input through the Honours work. The effort invested in the WETMOD contributed greatly to my work. Mardi van der Wielen I thank for the many discussions and „lessons‟ on the lower River Murray wetland particulars. I would particularly like to thank Leslie Jackowski for his reviewing of my thesis. You outperformed your role as a good friend. I thank you for helping me through a very difficult writing stage and for giving me the encouragement I needed. I would also like to thank Bjorn Björnsson, Magnus Björnsson and Jason Bobbin for their role in reviewing sections of the thesis. I would also like to thank the research group at the University of Maryland. Particularly Thomas Maxwell and Roelof Boumans for their assistance with the SME (Spatial Modeling Environment) during the early part of my project, although the project diverted from this course I thoroughly enjoyed the learning experience. Without the finance provided by the SPIRT grant and the River Murray Catchment Water Management Board this project would never have existed. I am grateful for this financial assistance. Last and by far not least I would like to thank Georgina Tate, for showing me what true patience and support is. The importance you have played can never be measured nor expressed adequately. It is now my turn to give you the same during your speciality training.

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Regional Scale Modelling of the lower River Murray wetlands

Abstract Most of the lower River Murray and its floodplain wetlands are impacted upon by degradation caused by river regulation. Increasingly the restoration of these ecosystems and the river water quality has become a high priority for federal and state governments and associated departments and agencies. Public concern is adding to the pressures on these departments and agencies to restore these ecosystems and to sustainably maintain the river water quality. The long term monitoring of floodplain wetlands has been limited, compounding the difficulties faced by managers and decision makers on assessing the potential outcome of restoration options. The role of this project in the broad scheme of restoration/rehabilitation is to contribute to the construction of a model capable of increasing managers and decision makers understanding, and build consensus of potential outcomes of management option. This model was to use available data. The developed model, based on WETMOD developed by Cetin (2001), simulates wetland internal nutrient processes, phytoplankton, zooplankton and macrophyte biomass as well as the interaction (nutrient and phytoplankton exchange) between wetlands and the river. The model further simulates the potential impact management options have on the wetlands, and their nutrient retention capacity, and therefore their impact on the river nutrient load. Due to the limitation of data, wetlands were considered in categories for which data was available. Of these two had sufficient data to develop, calibrate and validate the model. Management scenarios for these two wetlands were developed. These scenarios included, the impact of returning a degraded wetland in a turbid state to a rehabilitated clear state, and the impact the removal of nutrient from irrigation drainage inflows has on wetland nutrient retention, and consequent input to the river. Scenarios of the cumulative impact of the management of multiple wetlands were developed based on using these two wetlands, for which adequate data was available, as “exemplar” wetlands, i.e. data from these wetlands were substituted for other similar wetlands (those identified as belonging to the same category). The model scenarios of these multiple wetlands provide some insight into the potential response

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Regional Scale Modelling of the lower River Murray wetlands management may have on individual wetlands, the cumulative impact on river nutrient load and how wetland morphology may relate to management considerations. The model is restricted by data availability and consequently the outputs. Further, some limitations identified during the development of the model need to be addressed before it can be applied for management purposes. However, the model and methods provide a guide by which monitoring efforts can assist in developing future modelling assessments and gain a greater insight not only at the monitoring site but also on a landscape scale.

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Regional Scale Modelling of the lower River Murray wetlands

1 Background 1.1 Introduction Wetlands are increasingly becoming valued and used for some of the functions or services they provide. Costanza et al. (1997) prepared a study on the value of the world‟s different ecosystem services, wetland services scoring the highest of all terrestrial and aquatic ecosystems. The services and functions offered by wetlands can be broadly divided into 3 categories (Anonymous 1995; Morris 1991; Scheffer 1998). The first of these is hydrologic or flood amelioration, where wetlands can act in aid of short-term surface water storage, long-term surface water storage, or the maintenance of a high water table. The second is the preservation of flora and fauna habitat and associated food webs, through the maintenance of characteristic plant communities and characteristic energy flow. The third is biochemical or nutrient and sediment uptake, where wetlands can be involved in the transformation or the cycling of elements, the retention or the removal of dissolved substances, and the accumulation of inorganic sediments. Not all functions of wetlands are regarded as an asset; the value of a wetland function is usually only then recognised when useful or required services have been identified. However, a wetland function that presently does not have a recognised value may obtain one in the future. For example, the value of maintaining water quality by a small wetland may not be recognised until it is acquiring a relative greater percentage of representation in the area, or if it is close to a drinking water source (Anonymous 1995). Maintenance and restoration of wetlands and associated aquatic environments should therefore have a high priority for sustainable development. Whereas the flood amelioration and preservation of habitat and biodiversity has seen ongoing recognition, the nutrient uptake and sediment uptake has started gaining a greater significance than previously was the case due to the loss of wetland function. This project focused primarily on the nutrient uptake aspect of wetland function, although the potential management interventions simulated aimed at rehabilitating degraded wetlands are also expected to contribute to wetland biodiversity and habitat availability rehabilitation.

1

Regional Scale Modelling of the lower River Murray wetlands From an anthropocentric standpoint, there are a number of reasons for improving the management of wetland function and the resources or services they provide. Freshwater habitats have a very important role in sustaining human activities (Burbridge 1994). The natural functions of wetlands produce a range of resources, which affect the economic and social welfare of a diverse range of people. With the degradation of wetlands these resources are being severely and adversely affected (Burbridge 1994). One justification for reversing the trend of degradation of wetlands is that the sum of the services provided by the functioning of wetlands, which include economic and social values, is of a greater value than can be gained from degraded or converted wetland use (Burbridge 1994; Costanza et al. 1997; Pimm 1997). Furthermore, the function of a number of small wetlands may not be recognised until their cumulative capacity is fully understood. For example, swamp reclamation or flood amelioration can also lead to wetland reduction or even destruction; with a decrease in overall wetland area, reduction in average size, total numbers, linkage and density, the cumulative function of wetlands will decline (Anonymous 1995; Johnston et al. 1990; Preston et al. 1988). Therefore, the functions of wetlands, which include the uptake and storage of nutrients and sediment retention, will have an impact on a landscape scale through the improvement of water quality. The primary driving force of nutrient exchange, being the flow of nutrient into and out of a wetland, is through the water flow between the wetland and the river. The model developed in this project used a nutrient balance simulation within a wetland to calculate this exchange rate, thereby elucidating a significant unknown for wetland management. The process, by which the model assesses the exchange rate, is simplified in Figure 1.

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Regional Scale Modelling of the lower River Murray wetlands

River flow volume and river nutrient load Fraction of river flow volume (f)

Nuteirnt load from river

Nutrient load from wetlands

Wetland process modelling

Nutrient retention becomes a factor of exchange volume, river concentration and wetland concentration calculated using the wetland process model. Impact on river calculated using this output and the river nutrient load.

Figure 1: Wetland exchange modelling

To understand the impacts that wetland functions have within a catchment and the implications of management of wetlands (or of reduction of wetlands, i.e. continued wetland loss or degradation), an evaluation of the cumulative impact of the functions of multiple wetlands is required. Landscape scale modelling of the wetland processes and associated functions, such as nutrient retention by healthy wetlands or lack thereof in degraded wetlands, would contribute to knowledge and understanding and therefore provide information for decision making. A model that can be applied generically across multiple wetlands can be used to assess the cumulative nutrient retention estimate on a landscape scale; Figure 2 represents the use of a model in such a scenario.

River Load W

+

W

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W

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=> Change in River Load W

etl

etl

etl

Wetland 1

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Wetland 2

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Wetland 3

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pr

pr

pr

pr

oc

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etl Wetland n

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Figure 2: Cumulative assessment of wetland processes

3

Regional Scale Modelling of the lower River Murray wetlands The riverine ecology system is still inadequately understood (Young et al. 2000), complicating the issue of aquatic modelling. However, even with limited understanding and data resources, it is possible to develop an aquatic model to test hypotheses of wetland function and management; and to improve general understanding. To identify the processes required within a wetland model and be aware of the interactions these processes have both within the wetland as well as externally and appreciate some of the issues affecting water quality it is necessary to examine some of the wetland characteristics in detail. The complex interactions between sedimentation, re-suspension, turbidity, eutrophication, primary producers and consumers are to varying extent considered in the model developed during this project, and are therefore briefly discussed below in reference to the study area. This project focuses on the floodplain wetlands of the lower River Murray, the South Australian section of the Murray-Darling Basin in Australia (see Figure 3). The catchment area is approximately 1 million km2 or approximately one seventh of Australia (Hills 1974; Walker 1985; Walker et al. 1994). The headwaters comprise of only 500 km of the 2560 km of the river (Mackay et al. 1990; Roberts et al. 1991; Walker 1985), which has a total floodplain area of approximately 10,000 km2 (Roberts et al. 1991). The approximately 2,000 km of river floodplain section has a very shallow gradient with a drop of mere centimetres over distances of kilometres (Mackay et al. 1990; Walker 1985). The average annual runoff is approximately 11,000 GL but can vary from 2,500 GL in a dry year to 40,000 GL in a wet year (Mackay et al. 1990; Walker 1985).

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Regional Scale Modelling of the lower River Murray wetlands

Figure 3: Study Area

1.1.1 Wetland processes Wetlands are complex ecosystems with numerous interactions which link to separate aquatic systems (river, creeks, drainage flow paths etc.), terrestrial systems such as the surrounding riparian zone and atmosphere. The complex interactions such as between primary producers, consumers, predators and their feedback loops; as well as the multiple sources and losses of nutrient and energy, can make full accounting an impossible task in wetland assessment and therefore modelling seem an impossibility. 5

Regional Scale Modelling of the lower River Murray wetlands Of the interacting facets within a wetland some can however be focused on to obtain an understanding of the function of the wetland. The ones that are seen as the major processes or facets within a wetland are discussed below. Sedimentation Any wetland processes that act to decrease waterborne sediment and nutrient concentrations are considered to benefit water quality (Johnston 1991). Sedimentation and sediment re-suspension are processes that operate continually in wetlands and can have an impact on the nutrient availability and wetland turbidity. Increasing sedimentation and decreasing sediment resuspension would, through their impact on improving water quality, be seen as part of rehabilitation. That is, wetland turbidity and consequent nutrient availability affect the state of wetlands and the primary producer (phytoplankton and macrophyte) composition. In a sequence of events, the state of primary producers, along with turbidity and nutrients, compound the impacts on water quality within wetlands, i.e. self regulating processes. Turbidity Turbidity in a wetland can effectively shade out the incoming light, thereby minimising the underwater light availability. Walker and Hillman (1982) have found that even in eutrophic waters of the River Murray high turbidity can restrict primary productivity. The high turbidity is therefore an important factor controlling plant growth in River Murray wetlands (Walker et al. 1982). The reduction of turbidity particularly within wetlands is consequently seen as a major management focus. The Secchi depth of water bodies (an indication of turbidity) is increased both through an increase in suspended matter and the high nutrient flux from the sediment, which also stimulate the algal production (Soendergaard et al. 1992). Nutrients Dissolved and particulate inorganic nutrients such as phosphorus, nitrogen and silica are a natural part of the water content in rivers. In excess, these substances become pollutants and contribute to growth of phytoplankton and other aquatic plants (Shafron et al. 1990). Laboratory studies have shown that the release of phosphorus can be increased 20-30 times in a resuspended sediment compared to that of an

6

Regional Scale Modelling of the lower River Murray wetlands undisturbed sample (Soendergaard et al. 1992). Such increased phosphorus levels can lead to eutrophication of wetland water. Phytoplankton The increased growth of phytoplankton caused by eutrophication contributes to an increase in turbidity and a decrease in water transparency of the water column, limiting

light

penetration

and

therefore

submerged

macrophyte

growth.

Phytoplankton, detritus and resuspended inorganic sediment particles thereby contribute to lake turbidity (Scheffer 1998). The algal blooms tending to increase turbidity of the water column both through their presence and because macrophytes are effectively shaded out, their contribution to sedimentation thereby is lost. Binding of sediment through compaction and/or minimisation of resuspension is therefore an important management objective. Macrophytes Macrophytes are not only a part of the primary productive activity of wetlands but also contribute to its self regulated maintenance. For example, established macrophytes have been said to function as biological engineers as they act as buffering systems in wetlands and have a large role in maintaining a clear state (SandJensen 1998; Stephen et al. 1998). Some of the ‟engineering characteristics or mechanisms‟ include the reduction in flow velocity, the stabilisation of the sediment and the provision of habitats for micro-organisms, invertebrates and fish (Carpenter et al. 1997a; Sand-Jensen 1998). Biota such as macrophytes contribute to the long-term storage of nutrients, with some residual accumulating in newly formed soils (Graneli et al. 1988; Kadlec 1997). Further, macrophytes can become permanent sinks of phosphorus through the burial of plant litter (Graneli et al. 1988). Some benefits of macrophytes include habitat provision for zooplankton which feed on phytoplankton (Baldry 2000; Stephen et al. 1998; Timms et al. 1984), uptake of nutrients (Chen et al. 1988), including luxury uptake and enriched denitrification (Meijer et al. 1994; Stephen et al. 1998). Reduced chlorophyll-a (i.e. phytoplankton) has been found to occur close to macrophyte growth. This has been associated to the presence of zooplankton which find a refuge within the macrophyte growth (Stephen et al. 1998). The role of macrophytes in

7

Regional Scale Modelling of the lower River Murray wetlands supporting zooplankton and therefore control of phytoplankton can therefore be a significant aspect in wetland management. Macrophytes also reduce water movement (turbulence) and therefore reduce resuspension and increase sedimentation; they can also shade benthic algae and phytoplankton (Mitchell 1989; Sand-Jensen et al. 1988; Stephen et al. 1998). SandJensen and Mebus (1996) showed a steep reduction in flow velocity within dense macrophyte growth. The lower energy environment above the sediment within the macrophyte patches leads to a retention of fine sediment and organic matter, carbon, nitrogen and phosphorus (Chambers et al. 1994; Sand-Jensen 1998; Sand-Jensen et al. 1992; Sand-Jensen et al. 1996). Effectively the sedimentation within macrophyte beds reduces the transportation of nitrogen, phosphorus and other particles downstream (Sand-Jensen 1998). Therefore, a healthy wetland with a large macrophyte biomass should self propel a reduction in turbidity and nutrient retention. Due to the many and diverse mechanisms provided by the macrophytes they are recognised as a key step in restoring wetlands (Meijer et al. 1994; Stephen et al. 1998). Macrophytes obtain phosphorus from the surrounding water and the substrate, with minimal release found in actively growing macrophytes (Graneli et al. 1988). However, decaying macrophytes can account for a substantial contribution of phosphorus to the open water (Graneli et al. 1988). The growth and decay of macrophytes will therefore have an impact on the phosphorus balance of an aquatic system. Macrophytes affect nutrient levels in wetlands in more ways than just uptake and sedimentation. For example, phosphorus release may also be reduced through oxidation of the sediment (Stephen et al. 1998). Macrophytes readily take up soluble nitrogen from recycling processes (Stephen et al. 1998). Macrophytes also serve as a bottom up control mechanism of nitrogen both through uptake and denitrification (Carpenter et al. 1997a; Stephen et al. 1998). Macrophytes also influence the nitrogen cycle by increasing water residence time and therefore enhancing the denitrification cycle. This can be up to 3 times otherwise expected due to the organic enrichment among rooted macrophytes (Sand-Jensen 1998). Effectively, macrophyte biomass contributes to nitrification and denitrification within shallow water bodies and therefore plays a significant role in the nitrogen budget (Caffrey et al. 1992).

8

Regional Scale Modelling of the lower River Murray wetlands River flow Seasonal changes in nutrient and turbidity levels are influenced by river flow behaviour. As a result of decreased flow and increased nutrient availability the impounding of water (e.g. instillation of the locks in South Australia) will possibly favour the growth of phytoplankton leading to algal blooms (Shiel et al. 1982; Walker 1979). However, despite the eutrophic conditions there may be some limiting of algal production due to the turbid waters of the lower River Murray (Walker 1985). The turbid conditions may however not be limiting to Anabaena as it is able to control its buoyancy thereby increasing its light harvesting potential (Baker et al. 2000). Nutrient control to manage algal blooms would in this case be a significant management achievement. Zooplankton Nutrient availability (N and P) may determine the potential algal biomass production, however zooplankton grazing can have a large role in determining the biomass balance (Stephen et al. 1998). Of the zooplankton in the lower River Murray, the most common are indicative of eutrophic conditions, some of which are influenced by temperature changes, turbidity and salinity (Shiel et al. 1982), reflecting the state of the system. The zooplankton grazing rates can be correlated positively to water temperature and have a negative impact on phytoplankton biomass, i.e. chlorophyll-a (Kobayashi et al. 1996; Schwoerbel 1993). Studies by Griffin et al. (2001) showed that zooplankton grazing had a significant impact on phytoplankton biomass. They found zooplankton biomass peaks follow that of the phytoplankton biomass peaks, which is a typical Lotka-Voltera predator-prey cycle (Griffin et al. 2001). The degree to which zooplankton impacts on phytoplankton biomass is dependent on the zooplankton species as well as the species and size of phytoplankton (Schwoerbel 1993). Zooplankton are therefore an important constituent within wetlands playing a role in stabilising phytoplankton growth. They are therefore a significant aspect to consider as part of management.

9

Regional Scale Modelling of the lower River Murray wetlands

1.1.2 Spatial relationships of wetlands to transport processes Flow and flood regulation The seasonal distribution of flow has been changed by flow regulation of the River Murray. The winter flows have decreased as surplus water is taken into storage, and the summer flows have increased as irrigation demands are met (Walker 1979). There has also been significant flood amelioration; that is, through water retention of some of the surplus water followed by controlled release, the severity and incidence of flooding has been reduced significantly (Walker 1979). Flood regulation drastically affects aquatic environments, including the reduction of interaction between all but the closest wetlands to the river (Walker 1979; Walker et al. 1993). This renders wetlands, which are not adjacent to the river, dry due to the lack of periodic flooding thereby virtually eliminating these wetlands. The wetlands closer to the river remain for the most part permanently inundated with associated consequences (e.g. lack of sediment compaction and lack of macrophyte regeneration). Flow regulation has consequently been widely recognised as a major contributor to river and floodplain wetland degradation (Arthington et al. 2003; Bunn et al. 2002; Walker 1979; Walker 1985). Nutrient retention and Exchange capacity The natural retention of nutrients in wetlands occurs by cumulative fluxes into storage compartments of the wetland ecosystem. These compartments include the soil, vegetation and plant litter (Johnston 1991). Through their retention of nutrient, wetlands act as sinks of waterborne nutrients and thereby act to improve the water quality (Johnston 1991). The impact that a wetland sink or storage compartment has on the water quality depends on both the rate of nutrient uptake and the retention time (turnover rate) (Johnston 1991; Kadlec et al. 2001). The flow of water through a wetland therefore controls the nutrient transport into the wetland as well the nutrient transport out of the system, see Figure 1. The nutrient retention of the wetland is significantly determined by the water residence time that is controlled by the flow speed, wetland size and linkage to the river. The proximity of a wetland to the river as well as the wetland shape, size, depth and volume can have a substantial impact on the effectiveness of the function of the 10

Regional Scale Modelling of the lower River Murray wetlands wetland in the landscape; this impact can be influenced by the exchange capacity as well as residence time with in the wetland. The exchange capacity can be impacted on by channel volume, shape and length or by such factors as the location of the wetland in the landscape. The location of the wetland in the landscape relating back to variables such as wind direction, which in the case of the lower River Murray plays a significant role in the flow direction and flow rate of the river (Webster et al. 1997). The depth, area and volume of the wetland itself will also impact on the exchange of water between the wetland and the river, for instance wind can push the water in a large shallow wetland away from the connection channel; or evaporative processes can be influenced by the volume and surface area of a wetland. The transport of material in and out of wetlands is primarily a function of water flow (Johnston 1991). That is, the exchange rate has an impact on the exchange of nutrients and transport of salinity between wetlands and the river. These aspects must therefore be taken into consideration for wetland management, however obtaining exchange data can be prohibitive due to cost or the complexity of environmental factors mentioned. Consequently, any method of obtaining an estimate of exchange between the wetland and the river will be a valuable tool in wetland management and a significant addition to budgeting aspects of nutrient and salinity impacts to the river. This estimation of the transport of material by river exchange has the potential of being the most significant external influence acting upon a wetland and a wetlands impact on the river, and its estimation is currently a significant data gap. Despite the size, shape and position of wetlands in the landscape having a potentially large influence on the functioning of wetlands, these parameters are infrequently measured. These wetland properties can however be radically changed by human influence (Preston et al. 1988). Therefore, an understanding of how the wetland properties influence wetland functioning on a landscape scale is relevant to restoration and management decision-making. Scenario analysis of different wetland properties may therefore assist in increasing this understanding.

11

Regional Scale Modelling of the lower River Murray wetlands

1.2 Degradation of floodplain wetlands Water quality is a key indicator of river and wetland health, and of wetland functioning. Maintenance of good water quality helps to prevent further degradation of wetland and riverine ecosystems. River and wetland water quality need to be maintained in the interest of primary industry as well as water supply for urban environments. The River Murray basin accounts for a large part of Australia's agricultural production. The demands of settlement and land use have placed considerable pressure on the river system, resulting in a decline in biodiversity and aquatic habitats and therefore altered the structure and function of river and wetland ecosystems. As a result the water quality of the lower River Murray, which covers an approximately 650 km stretch of the river in South Australia, has drastically diminished. The River Murray is often viewed as the lifeblood of South Australia, the driest state on the driest continent, and water quality is a significant issue for its inhabitants. The River Murray is a significant water source for South Australia. The city of Adelaide derives between 55% and 90% of its water from the River Murray, and other South Australian towns, including those of the ``Iron Triangle'' (Whyalla, Port Augusta and Port Pirie), receive up to 90% of their water supply from this source (Jacobs 1990). Agricultural areas along the River Murray use it as a primary water source for crop irrigation, as there is very little rainfall in these areas. Other uses of the River Murray within SA includes tourism (camping, fishing, house boats and other cruises) and commercial fishing. Wetlands perform important services and functions for river water quality, such as accumulating nutrients and trapping sediments (Anonymous 1995; Johnston 1991; Mitsch et al. 2000). Wetlands also act as habitats for a wide range of flora and fauna (Boon et al. 1997; Recknagel et al. 1997). It is therefore imperative to restore and/or maintain the structure and functions of wetlands, such as nutrient retention. Of the wetlands along the River Murray few, if any, can be considered to be pristine environments (Walker 1979). Due to the present regulation of the flow regime of the River Murray, the development of new wetlands (billabongs is reduced significantly (Walker 1979). Therefore, it is becoming increasingly important to preserve, maintain

12

Regional Scale Modelling of the lower River Murray wetlands and manage the remaining diversity, and significant areas of flood-plain habitats (Walker 1979). It has become increasingly recognised that rivers and wetlands are legitimate users of water (Arthington et al. 2003; Naiman et al. 2002), with government departments, such as the South Australian Department of Water, Land and Biodiversity Conservation (DWLBC), recognising their role in preserving and restoring ecological processes and ecosystems. Legislation for the protection of aquatic ecosystems, such as the Water Resources Act 1997 and as amended by the Natural Resources Management Act 2004, shows the progress towards the recognition of the importance of aquatic ecosystems such as wetlands. As it is, there have been biological changes to the lower River Murray and its floodplain wetlands due to the introduction of the locks (Walker 1985). Effectively the river has been replaced by a series of cascading pools, which due to their difference from the normal river flow encourage a change in biodiversity such as plant community composition towards exotic species. These fish and plant species being more accustomed to permanent inundation and slow flowing pools (Pressey 1987; Walker 1985). Along the River Murray there are more than 100 different storages (Walker 1985), the lower River Murray wetlands are therefore to a large extent now either permanently inundated, or above pool level left dryer for a longer period than before (Pressey 1987). The river regulation has affected the riparian vegetation by disrupting regeneration and affecting the mature period (Roberts et al. 1991). Due to the lack of periodic flooding black box (Eucalyptus largiflorens) communities are showing a reduction in numbers, the river red gum (Eucalyptus camaldulensis) is not regenerating in significant numbers and in many areas there is a significant dieback due to drowning (Walker 1985). Some of the problems contributing to water quality degradation in the River Murray are associated with changes in catchment condition due to land use in the Murray Darling Basin over the past 100 years. The increased nutrient load in riverine water has led to an increase in algal growth and conversely a decrease in water quality. River and wetland management therefore has an important role in preserving a very significant resource for Australia.

13

Regional Scale Modelling of the lower River Murray wetlands

1.2.1 Eutrophication of aquatic environments A full understanding of wetland eutrophication is still in its infancy (Keenan & Lowe 2001). However, research has shown two alternate stable states exist for shallow water bodies; that of the turbid phytoplankton-dominated state and the clear water macrophyte-dominated state (Blindow et al. 1993; Boon et al. 1997; Scheffer 1998; Scheffer et al. 1993; Stephen et al. 1998). A wetland in one state will tend to remain so due to a number of buffer mechanisms (Boon et al. 1997; Moss 1990; Stephen et al. 1998), but with an increase in nutrient loading to a system, a wetland may change from a clear to a turbid state (Boon et al. 1997; Scheffer 1998). A reverse change can be difficult to obtain, however changes in water level and the removal of a part of the fish stock have been used as successful restoration approaches in returning wetlands to a clear state from a turbid one (Scheffer 1998). In the River Murray catchment, agricultural development (land clearing, irrigation and pasture management) has caused substantial increases in the river sediment load (Walker 1979). There is also an increase in the organic, and nutrient load to the river brought on through agricultural practices and as a consequence of loss of buffering activity of the cleared vegetation (Lijklema 1994). This, combined with the turbidity and sediment deposition downstream, affect the water quality and habitat suitability of the river and its wetlands. Through leaching of nutrients from fertilised and irrigated surrounding farmland, some wetlands of the River Murray floodplain have become eutrophic. This eutrophication, combined with turbid waters and degraded systems (e.g. by permanent inundation, or the presence of exotic species such as carp), has turned the wetlands into a turbid, algal dominated state where phytoplankton out-competes macrophytes, leading to algal blooms (Scheffer 1998). Some of the nutrients increased in the river are phosphorus and nitrogen. Both are essential nutrients for plant growth, but in excessive amounts they can reduce water quality through eutrophication, algal blooms, decreased light penetration and loss of dissolved oxygen of the water body (Marsden 1989). Eutrophication can also contribute to the reduction of macrophytes due to the shading impact of increased phytoplankton and can force a wetland into a turbid state (Asaeda et al. 2001; Graneli et al. 1988; Scheffer 1998). Therefore, increased eutrophication can alter the species composition of a wetland (Johnston 1991) and therefore change the function of a wetland. 14

Regional Scale Modelling of the lower River Murray wetlands In most wetlands of the lower River Murray, phosphorus and nitrogen concentrations exceed the limit of what is considered critical for eutrophication, reflected in the high chlorophyll-a concentrations in the water columns (up to 256 mg/l) (Boon et al. 1997; Goonan et al. 1992)). Boon et al. (1997) concluded that nutrient enrichment poses a significant threat to the ecological integrity of wetlands throughout Australia. Management of nutrients in the landscape can therefore have an impact on a large range of ecosystems. Using wetlands or at least managing wetlands to fulfil the function of nutrient retention can thereby be a strong tool to their own preservation. The sources of nitrogen to wetlands include external inflow as well as fixation of gaseous N2 that is converted into organic nitrogen (Johnston 1991). The removal of nitrogen from wetlands however often occurs through a process called denitrification where nitrogen is released into the atmosphere (Bowden 1987; Kadlec et al. 2001; Mitsch et al. 2000; Morris 1991; Reddy et al. 1989; Scheffer 1998; Schindler 1977). Therefore, the concentration of nitrogen or NO 3 is in a continual state of flux depending on the rate of nitrification and denitrification. Unlike nitrogen, phosphorus cannot escape a wetland system. It therefore remains in the system and is recycled. Phosphorus uptake in wetlands is regulated by physical, (e.g. sedimentation), and biological mechanisms, (e.g. uptake and release by vegetation) (Kadlec et al. 2001; Schindler 1977). The measurement of phosphorus within the wetland is therefore more stable and indicative of availability. Nitrogen and phosphorus can play a significant role in the eutrophication of wetlands (Reddy et al. 1995). Phosphorus is the major limiting nutrient to nitrogen fixing algae such as Anabaena (Schindler 1977) whereas increased nitrogen concentration can contribute to a shift in species composition within wetlands (Morris 1991; Schindler 1977). They are also both the most likely nutrients to limit primary productivity within wetlands (Baker et al. 2000; Beardall et al. 2001; Hecky et al. 1988; Morris 1991; Oliver 1993; Schindler 1977; Walker 1979; Walker et al. 1982). Total phosphorus includes crystalline, occluded, adsorbed, particulate organic, soluble organic and soluble inorganic phosphorus. However, not all of this phosphorus found in a water body is biologically available. The biologically available phosphorus includes the soluble reactive phosphorus (entirely biologically available), soluble unreactive phosphorus (available through enzymatic hydrolysis) and labile

15

Regional Scale Modelling of the lower River Murray wetlands phosphorus (available through desorption) (Holtan et al. 1988). The main anthropogenic input of phosphorus is through fertilisers and detergents (Holtan et al. 1988). In a system such as the lower River Murray, the phosphorus source to a wetland can be almost exclusively through fertiliser or irrigation drainage runoff, whereas sediments can act as sinks of phosphorus (Holtan et al. 1988). The phosphorus, which is found in the sediment, is to a great extent sorbed to soil particles or as part of organic matter (Holtan et al. 1988) reducing its availability. This biologically unavailable phosphorus, found in the sediment, can be released through various mechanisms such as turbulence, animal activity (bioturbation), and plant growth (Scheffer 1998), thereby becoming biologically available. Sediment can therefore in circumstances contribute to the maintenance of eutrophication in a water body where inflow may have been reduced (Lennox 1984; Lijklema 1994; Nürnberg 1984; Nürnberg 1998; Olila et al. 1995; Recknagel et al. 1995). Recknagel et al. (1995) found through simulations of Lake Mueggelsee that the best management for the reduction of eutrophication was in fact sediment dredging. In a different approach as in the case of lower River Murray wetlands, sediment compaction (drying of wetland beds) to minimise resuspension and consequent release of bound nutrients is a management strategy currently employed. The internal phosphorus loading of the sediment is a significant factor in internal loadings once external loading has been reduced. However, van der Molen (1994) found that including this in the model of phosphorus concentration did not significantly improve the predictive capacity of their model for shallow lakes which experienced high external phosphorus loadings. In shallow lakes, where the external loading was reduced, the sediment water exchange of phosphorus became significant in estimating the variability experienced. This indicated that the external source was the significant contributor of phosphorus concentration. Their hypothesis is that in shallow lakes with significant external phosphorus loadings the sediment water interaction is held in an equilibrium (van der Molen et al. 1994). It can be assumed that this is also the case in wetlands, phosphorus simulation in eutrophic lower River Murray wetlands can therefore concentrate on the external and suspended phosphorus and disregard the impact of current sediment released phosphorus.

16

Regional Scale Modelling of the lower River Murray wetlands

1.2.2 Alternate stable states and permanent inundation impacts on wetlands To maintain natural ecological integrity rivers, floodplains and their wetlands need their natural flow regime in its full spatial and temporal variability (Arthington et al. 2003; Bunn et al. 2002; Poff et al. 1997). The wetlands, as part of their ecological function, provide resilience mechanisms by which extreme events are buffered. Some of these have been discussed above, such as phosphorus and nitrogen loads, the role of macrophytes, plankton (phytoplankton and zooplankton) and flow regime. All of these complex interactions, many of which have not been described, act to provide the wetlands with a certain resilience mechanism. However, with the destruction of these resilience mechanisms new resilience mechanisms develop to adapt to the new state of the ecosystem (Carpenter et al. 1997a; Ludwig et al. 1997; Scheffer et al. 1993). The change of wetlands from one buffered state to another is due to the resilience being overcome by an extreme event. Such a change will transform an aquatic ecosystem, such as a wetland, from one stable state to another (Carpenter et al. 1997a; Carpenter et al. 1997b). The change can be driven by a complex interaction of eutrophication, loss of macrophytes, water regime and turbidity, or by extreme events for any of these (Carpenter et al. 1997a; Carpenter et al. 1997b; Scheffer et al. 1993). The two states can be seen as alternate stable states of clear and turbid (Scheffer et al. 1993). Through river regulation many of the lower River Murray wetlands have degraded to the turbid state reducing the function of the wetland in the landscape. Returning a wetland to a clear state, once it has switched to a turbid one, can be more complex than reversing the cause (Scheffer et al. 1993). For example, eutrophication contributes to changing a shallow aquatic ecosystem such as a wetland from a clear stable state to a turbid one. Reducing the nutrients may however not bring the wetland back to a clear state due to the resilience of the alternate turbid stable state, which acts through the buffering release of nutrients from the sediment or resuspension as winds are not reduced by the otherwise present macrophytes (Scheffer et al. 1993). Management of these wetlands could lie in the forceful change from one state to another, such as the reduction of turbidity. This would induce macrophyte growth through increased light availability that then reinvigorate the resilience of the clear stable state (Scheffer et al. 1993). Scheffer et. al. (1993) suggest the reduction of

17

Regional Scale Modelling of the lower River Murray wetlands turbidity through either the management of fish stock such as carp or the management of water levels to induce macrophyte growth. Australian wetlands do not need constant inundation, and in fact their constant inundation is detrimental. Drying and refilling of wetlands are natural processes in Australian wetlands to which the flora and fauna are adapted and dependent (Pressey 1990). Permanent inundation reduces the growth and regeneration potential of ephemeral vegetation common to River Murray floodplains (Nielsen et al. 1997), and the lack of periodic flooding, due to river regulation, may contribute to the lack of regeneration of terrestrial vegetation. This permanent inundation of wetlands resulting from the regulation of river flow has favoured invasion by the common carp (Cyprinus carpio). The feeding habits of carp are thought to be a potential contributor to wetland turbidity further limiting macrophyte growth. Carp together with the lack of drying cycles in the floodplain wetlands are therefore believed to have contributed to the demise of wetland macrophytes (Blindow et al. 1993; Pressey; van der Wielen 2001; Walker et al. 1993). Further macrophyte loss is due to a lack of dry periods in wetlands. The lack of drying cycles reduces sediment compaction leading to easier re-suspension and increased wetland turbidity (McComb et al. 1997). Through increased turbidity macrophytes can be shaded out causing their dieback. Their regeneration cycle, which is dependent on dry spells, is also interrupted through the permanent inundation. The lack of competition for underwater light due to the loss of macrophytes, as well as the loss of nutrient buffering actions of macrophytes, stimulates phytoplankton growth and increases the potential for future algal blooms (Carpenter et al. 1997a; Recknagel et al. nd). Although the introduction of drying cycles is partly expected to reduce wetland turbidity other couses for water turbidity also exists for the River Murray and have an impact on the potential reduction of turbdidty possible in a wetland. Darling River water for example, which is known to be turbid and sodic soils, widespread in Australia (Rengasany et al. 1991), contribute to maintaining tubidity within wetlands. In 2000 the Department of Water, Land and Biodiversity Conservation introduced a flood event to a section of the lower River Murray floodplain (DWLBC 2004; Siebentritt et al. 2004). A study on the impacts of flooding on riparian plants of the

18

Regional Scale Modelling of the lower River Murray wetlands River Murray showed an increase in flood dependent species, the reduction in flood intolerant species but no change in aquatic species (associated with an impoverished seed bank) (Siebentritt et al. 2004). The recommendation of this study was future repeat flooding to increase the aquatic species seed bank and enhance their regeneration. This flooding in 2000 also seemed to be effective in reducing exotic species numbers. This study confirms the hypothesis of the impact river regulation has had on riparian and aquatic species, with the reduction in aquatic species seed bank of the lower River Murray wetlands. The study also shows one method of influencing and improving species regeneration, i.e. flooding. Nielsen and Chick (1997) conducted a study on sixteen artificial billabongs on the River Murray floodplain. Their findings were that the longer a billabong remained flooded the less diverse the plant communities became. The permanent flooding in their study did not allow ephemeral or terrestrial species to grow, whereas in billabongs where extended periods of drying followed by spring flooding was introduced more diverse plant growth including terrestrial taxa were seen as a consequence. This shows that should wetlands in the lower River Murray floodplain have a natural water regime a more diverse plant community should become evident. As a wetland management strategy the alteration of wetland inundation through the introduction of dry periods and consequent re-flooding should stimulate responses to species regeneration in turn returning a wetland to a stable clear state. In the lower River Murray this management response may however be reduced when the main water source is from the more turbid Darling River.

1.2.3 Irrigation drainage and constructed wetlands Both constructed wetlands and natural wetlands can be used to improve water quality (Keenan et al. 2001). Braskerud (2002) found that constructed wetlands placed at first order streams removed between 21% and 44% of the phosphorus inflow. Constructed wetlands, in a study by Burgoon (2001), were found to remove from 50% to 99% of the nitrate inflow load. In a study of constructed wetlands in Flanders Belgium, the nutrient removal efficiencies ranged from 31% to 65% for nitrogen and 26% to 70% for phosphorus (Rousseau et al. 2004). Whereas Schulz et al. (2004) found constructed wetlands for the treatment of aquaculture runoff were able to remove 41% to 53% of phosphorus and 19% to 30% of nitrogen. Lüderitz et al. (2002), who

19

Regional Scale Modelling of the lower River Murray wetlands studied the effectiveness of constructed wetlands on sewage treatment, found removal rates of phosphorus to be between 27% and 97% depending on the constructed wetland design and management. Stormwater treatment, in Australia, with constructed wetlands were found to remove up to approximately 80% of phosphorus (Bavor et al. 2001). This shows that the reason for using constructed wetlands in the removal of the dissolved nutrients phosphorus and nitrogen can be diverse, the effectiveness of the constructed wetlands also varying significantly. The optimal design parameters of the constructed wetlands and the retention time required for nutrient removal depend on the nutrients being removed (Bavor et al. 2001; Hunter et al. 2001; Rousseau et al. 2004). However, all studies showed that wetlands can be used for nutrient removal. One of the major contributors of nutrients to wetlands is irrigation drainage from agriculture. Constructed and natural wetlands are capable of absorbing phosphorus and can be used for phosphorus load reduction (Kadlec 1997). How they impact on a river system (i.e. capacity of wetlands to remove nutrients from the system) depends on their location in the landscape (Crumpton 2001). In identifying the best landscape position of restored wetlands Crumpton (2001) found that where wetlands are placed to intercept a higher load of nutrients there is an increased retention capacity. Studies by Wen and Recknagel (2002) and Wen (2002a) at a wetland in the lower River Murray show that constructed wetlands can reduce wetland nutrient inflow from irrigation drainage by up to 90% (Wen 2002a; Wen et al. 2002). Therefore, in cases where the main wetland degradation impact comes from „reclaimed swamp‟ or dairy pasture irrigation drainage outflow, the eutrophication source can be reduced substantially. Consequently, where possible the interception of irrigation drainage and treatment prior to its flow into wetlands could contribute considerably to the reduction of nutrient inflow loads into wetlands.

20

Regional Scale Modelling of the lower River Murray wetlands

1.3 Restoration of degraded floodplain wetlands 1.3.1 Management strategies for restoration Following wetland restoration, through the re-introduction of drying cycles and carp restriction during re-wetting of wetland degraded by permanent inundation, Recknagel et al. (nd) observed the recovery of wetland habitats and the improvement of water quality. By introducing drying periods or partial draw down, the germination and growth of macrophytes are stimulated allowing for a return of macrophytes in a reflooded wetland. Although initial conditions following re-wetting show increased nutrient availability and therefore algal growth in the wetlands, macrophytes once established out compete the algal community for nutrients (Recknagel et al. nd). Where possible, such as in constructed wetlands, the harvesting of macrophytes can partially remove the nutrients from the system (Hunter et al. 2001). The main benefit of the drying of a wetland is the consolidation of the wetland sediments, which reduces re-suspension, minimising turbidity and release of nutrients from the sediment (McComb et al. 1997; Recknagel et al. 2000; van der Wielen 2001). Therefore, re-introducing a dry period to a wetland can have the impact of switching a wetland from a turbid stable state to a clear stable state (Scheffer 1998; Scheffer et al. 1993) as discussed above. Consequently, the reintroduction of dry phases has been recommended as a management strategy to improve or restore wetlands of the Murray-Darling Basing (Scholz et al. 2002). Equipping wetland inlets with grills will prevent large carp from entering the reflooded wetland. It is assumed that this will protect macrophytes from being uprooted by carp, as well as reducing the re-suspension of sediment expected as a consequence of their feeding behaviour. As a summary of the above discussed issues of wetland degradation, the potential management strategies therefore available to improve water quality of degraded wetlands, which have been considered in this project, are as follows: 1. The reintroduction of drying and wetting cycle‟s thereby reducing turbidity of wetlands. Through this measure, the function provided by emergent and submerged macrophytes can be reinstated revitalising a degraded wetland (Recknagel et al.; Recknagel et al. 1997; Recknagel et al. 2000; Scholz et al. 21

Regional Scale Modelling of the lower River Murray wetlands 2002; van der Wielen 2001). Drying consolidates the sediment and therefore reduces the quantity of suspended solids in the water column. The re-emerging macrophytes act to improve water quality by nutrient uptake, reduce flow speed increasing sedimentation (Sand-Jensen 1998) and by out competing phytoplankton for nutrient (Recknagel et al. nd). Experiments have shown that water quality in wetlands managed in this manner can improve (Recknagel et al.; Recknagel et al. 1997; Recknagel et al. 2000; van der Wielen 2001). There are two possible mechanisms for introducing dry periods; these are through the construction of regulators at individual wetlands or to implement it at a broader scale through the change in water retention and therefore river height at individual locks. 2. Irrigation drainage nutrient reduction through constructed wetlands. In wetlands where external point nutrient sources such as irrigation runoff contribute to the wetland nutrient load, there is an opportunity for the construction of artificial wetlands where macrophyte harvesting can be used to reduce nutrient loads. An example of this management strategy, in the lower River Murray, was a research project situated between the Reedy Creek wetland and the Basby farm near Murray Bridge (Wen 2002a). Here an experimental pond system was constructed to eliminate inorganic phosphorous from agricultural drainage by native water plants (Wen 2002a). This research may lead to the design of constructed wetlands for the treatment of agricultural drainage water before it enters floodplain wetlands.

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Regional Scale Modelling of the lower River Murray wetlands

1.4 Predictive modelling of wetland processes and services; current state and potential alteration due to management Real environmental systems are complex and it is therefore extremely difficult to measure the parameters with accuracy (Parsons et al. 1995). The predictive ability of water quality models is seriously limited by the difficulty in identifying complex environmental processes and defining these within parameters (McIntosh 2003; McIntyre et al. 2003; McIntyre et al.; Reckhow 1994). One method to overcome this is to invest in monitoring and field based research. However, this quantitative understanding and data are difficult and expensive to obtain. Ecologists and resource/land managers cannot always employ traditional quantitative simulation because of financial and temporal constraints (McIntosh et al. 2003), and therefore need to use alternative approaches such as modelling. Clearly substantial and complex data are required in order to assess and understand the processes within a wetland, the interactions of these processes within the wetland, and processes having influences upon wetlands, let alone assessing the implication of potential management strategies. Assessing such a substantial and complex data set is therefore outside the capacity of an individual. To facilitate the understanding of processes operating on such a large scale computer models can be created to assist in evaluating the wetland processes. This enables an assessment of management scenarios as well as the testing of hypotheses of wetland function (Caswell 1988; Goodall 1972; McIntosh 2003; McIntosh et al. 2003; Oreskes et al. 1994; Rykiel 1996; Wallach et al. 1998). As a consequence of the complexity of assessing such a vast and complex data, there has been an increasing use of simulation models in the study of aquatic and other ecological systems over the past couple of decades (Elliott et al. 2000; Oreskes et al. 1994; Wallach et al. 1998). There are two strategies for the management of degraded wetlands considered for this modelling work, the choice being dependent on the reason underlying the degradation. For wetlands where the main degradation is the inflow of nutrients constructed wetlands would be considered. These constructed wetlands would eliminate nutrients by absorption to nutrient poor sediments and nutrient uptake by macrophytes. Simulation of the management of these wetlands would help determine the impact of successful nutrient removal on the wetland and its exchange rate of 23

Regional Scale Modelling of the lower River Murray wetlands nutrients with the river. Where permanent inundation is the primary cause of wetland degradation, a model could help determine the impact of the introduction of drying and wetting cycle, on internal nutrient dynamics and wetland nutrient uptake. Both of the simulations would provide assistance in decision support by providing an estimate of: overall wetland recovery and restoration wetland specific responses to restoration management, and degree of response required from either nutrient removal (constructed wetlands) or from turbidity reduction (sediment compaction) Modelling can be useful in understanding ecosystem processes and predicting intervention outcomes. There is an inherent value in the analysis of past and present in setting goals and objectives for the future (Thomann 1998). That is, modelling can be used as a tool in predicting the ecological consequences of restoration plans or management scenarios (Costanza et al. 1998; DeAngelis et al. 1998), which are vital factors in environmental decision-making. Recognition of model validity, and transparency to stakeholders, increases understanding and contributes to informed dialogue, thereby enhancing decision making by consensus (Thomann 1998). A model can also be helpful in a situation where non-linear mechanisms cause unexpected patterns that cannot be grasped intuitively (Scheffer et al. 2000), or where systems are too complex or cumbersome for human interpretation alone. For example, computer models can be used to predict wetland response to environmental change (Sklar et al. 1993). A model is not expected to achieve exact predictions of ecosystem function, but its development provides a tool for an approximation of outcomes. After all, modelling often involves stressed systems with a view to return them to a natural state (Beck 1997). Not all potential impacts can be modelled successfully following intervention, as there is always some lack of knowledge. However, modelling can help minimise (but not eliminate) the variability of potential outcomes (Beck 1997). Simulation models have become widespread and are playing an increasingly important role to assist in the decision making process (Griffin et al. 2001; McIntyre et al.; Sullivan 1997). With the fragmentation and lack of wetland specific data and knowledge in the lower River Murray region, it is difficult for managers and other 24

Regional Scale Modelling of the lower River Murray wetlands stakeholders to make decisions in the management of wetland restoration. This is particularly true in assessing the highest return of investment (cost-benefit analysis). Managers ideally desire models capable of quantitative predictions of restoration scenarios, taking into account hydrology and ecological processes of aquatic environments (Arthington et al. 2003). However such models are not yet available for Australian conditions as their development is prohibitively expensive, particularly models that take into account catchment or regional scale (Arthington et al. 2003). A simplified model capable of regional scale scenarios may however be able to answer some of the questions posed by managers.

1.4.1 Complexity and feasibility of modelling There are two important factors which will dictate the complexity of any model of an ecological system. The first is the purpose of the model, dependent on the aims of the potential user, e.g. flexibility may be an important issue. The second factor is the feasibility of a model. This can be dependent on the understanding or knowledge available for a system. That is, to what extent can the system be explained within a modelling framework based on the current knowledge (McIntosh et al. 2003; Reckhow 1994; Young et al. 2000). Furthermore, the incorporation of too many factors into a model can obscure the action of some processes and render the model mathematically inflexible (Caswell 1988). Caswell (1988) even suggested omitting important factors to avoid obscuring the focus of the model. De Wit and Pebesma (2001) compare four models of increasing complexity to assess the value of complex models versus simple models. Their conclusions are that the complexity of models may not improve the modelled results if the data quality is restrictive. A model does not have to be extremely complex as good data for model development may be all that is required to produce a simulation that will answer questions (Gibbs et al. 1994). Taking this further, the simplest possible model, which can accurately predict an observed phenomenon, provides a valuable contribution to ecological knowledge (Caswell 1988). It provides a starting point on which there is a possibility to build on observations and develop new theories (Caswell 1988). Whereas unnecessarily complex models may lack the flexibility that may be required and may contain inherent flaws (Wood 2001).

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Regional Scale Modelling of the lower River Murray wetlands The choice between simple and complex models is affected by knowledge and data availability. Young et al. (2000) found aquatic ecology to be complex and dynamic with a multitude of interactions. However limited data, such as for the lower River Murray wetlands, and limited detailed knowledge and understanding, of aquatic ecology (Keenan et al. 2001; Young et al. 2000) argues for a simple model structure (de Wit et al. 2001; Li et al. 2002; Li et al. 2003; Reckhow 1994). Reckhow (1994) claims that limited data and knowledge are incompatible with high detail and large models. The lack of detailed understanding of each process required to develop a holistic quantitative model of an aquatic ecosystem restricted the modelling by Young et al. (2000) to a few parameters. Young et al. (2000) therefore adopted a simplistic modelling approach. The degree of knowledge is therefore an important determinant of the level of complexity allowable within the model to achieve a meaningful and accurate scenario (Wood 2001). Wetlands are variable ecological systems and can be complex to model. This is due to their morphology, susceptibility to sporadic external influences such as wind, temperature, river flow (directional change is a possibility in the case of lower River Murray wetlands (Webster et al. 1997)) and a multitude of complex dynamics and interactions that cannot be monitored and studied without disturbing (and therefore influencing) the system. Modelling wetlands can therefore become a complex venture often hampered by the lack of detailed monitored data as well as rapid and sporadic change in condition such as water availability, weather etc. Despite the lack of knowledge, many complex descriptions of wetland behaviour and nutrient cycling have been developed for modelling purposes. However, the complexity complicates and often defies calibration and validation. Through this complexity, the ecosystem can behave counter-intuitively despite individual components being well understood. Therefore, wetland models are often kept simple, with well-understood parameters and processes assigned a defined value (Kadlec et al. 2001). Additionally, simple models are easier for non-modellers such as water quality managers and the general public to understand (Murray 2001), which contributes to consensus building. Young et al. (2000) began with a simple model, intending to extend model complexity in the future. The key to their model development was keeping the degree of complexity consistent with the current level of understanding. Therefore, the model 26

Regional Scale Modelling of the lower River Murray wetlands can be developed as the understanding develops. This premise was also used in WETMOD development (Cetin and Recknagel, pers. Com). Therefore, WETMOD can be further developed with increasing understanding and data availability, and is therefore a basis from which to conduct further research for lower River Murray wetlands. Another example of simple wetland nutrient retention models, simple due to due to limited data and knowledge, are described by Li, Xiao et al. (2003) and Li et al. (2002) and discussed in relation WETMOD below. Different ways how the accuracy of models that simulate complex systems can be assessed are examined below.

1.4.2 Qualitative and quantitative assessment of model accuracy and generic applicability As the output requirements for models can vary depending on their intended application and purpose; further differentiated by data availability by which to run scenarios, many opinions on the need for quantitative vs. qualitative modelling output have developed. Different methods of assessment of model performance have therefore been developed. Judgmental terms such as excellent, good, fair, and poor are useful because they can invite, rather than discourage, contextual definition (Oreskes et al.). It is not uncommon for water quality models to have a small amount of data available for model development, leaving even less for model evaluation and testing. In this situation, rigorous testing and assessment of model predictions is rare and has little meaning (Reckhow 1994). Water quality model calibration should compensate to some degree for errors arising from model limitations (spatial averaging, model structure errors and numerical dispersion) (McIntyre et al.). Data restriction to modelling Due to the limit in data availability “exemplar” data have been used to develop predictive models. The model output along with continued monitoring can then be used for adaptive management relating model outcomes with real occurrences (Young et al. 2000). It must however be understood by both the model developer and future users that the level of assumptions regarding the use of “exemplar” data will affect the modelling accuracy in a quantitative way (Beck 1997; Wood 2001). In using assumptions within models some otherwise unsolvable process given the current data availability or knowledge can be resolved. 27

Regional Scale Modelling of the lower River Murray wetlands McIntosh (2003) states that there is no reason why relationships between abiotic quantities such as soil and nitrogen, or between biotic and abiotic quantities such as vegetation biomass and soil or water, cannot be modelled imprecisely if such an approach is required by the level of available knowledge/data or the model purpose. The model output in such a case should however not be expected to be quantitatively accurate. However, despite a lack of quantitative accuracy, qualitative results can be used as a guide in future monitoring, research needs and further model development. The argument may be that qualitative models outputs have an intrinsic uncertainty due to the imprecision of the outcomes. In fact stakeholders and managers are often aware of model uncertainty, however they do not see this as detrimental to the value of models in decision support (Andersson 2004). That is, the role of the model may be such that the only output possible is a qualitative one due to data limitations and therefore inherent assumptions. However, such a qualitative output can be informative and therefore assist in decision-making, even if this decision is of the necessity of further research. A qualitative model therefore fulfils its function where inadequate data is available for quantitative predictions. Assessing accuracy Many studies have discussed imprecise and qualitative modelling techniques (Dambacher et al. 2003; Guerrin et al. 2001; McIntosh 2003; McIntosh et al. 2003; Parsons et al. 1995; Wood 2001). An understanding of, and rigorous comparison to monitored data, can be used to assess a models qualitative and quantitative accuracy. Wetland management decision support models are not necessarily dependent on optimal statistical accuracy, and may fulfil their role when assessed as qualitative models (McIntosh et al. 2003), despite their quantitative output and need therefore not be validated as rigorously as purpose built quantitative models. Some models such as WETMOD (Cetin 2001) are developed as quantitative models that provide qualitative outcomes. The comparison of model output, to establish modelling accuracy, can be performed in a qualitative or a quantitative manner, the decision of the methodology being dependent on circumstances (Wood 2001). The qualitative approach assumes that if the composition and structure of the ecosystem is known, it can be encompassed in models qualitatively (Dambacher et al. 2003). Users of such a model must however

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Regional Scale Modelling of the lower River Murray wetlands understand that qualitative predictions are only a relative benchmark of expected system behaviour (Dambacher et al. 2003). In fact, Andersson (2004) found that stakeholders and decision makers were more interested in relative changes over long periods than prediction of exact time-series. Andersson (2004) found that the use of a simple qualitative model to assess future environmental conditions depending on management strategies was effective in stimulating a three-way communication between model developers, stakeholders and decision makers, the stakeholder however were cautious about regional information. The modelled results were found to provide a common base for understanding the impact of management. Andersson (2004) also found that qualitative information based on a generic environment was an effective model output for stakeholders and that quantitative information could be seen as confusing. A descriptive analysis of ecological model output compared to monitored data is a valid method of assessment (Wood 2001). The assumptions required in the development of ecological models also cause a mismatch between model output and monitored data, therefore reducing the potential of statistical accuracy (Wood 2001). Dambacher et al. (2003) suggest that an over-emphasis on precision in ecological research is neither necessary nor essential for mathematical rigor. They argue that an emphasis on statistics and precision may detract from a valuable qualitative understanding of the system. Another view is that the pursuit of optimal quantitative mathematical programs is not necessarily the primary concern of modellers. Beck (1997) argues that rather than asking what numerical optimisation can do for us, we should be asking how we can use our understanding of a system to successively improve numerical optimisation. That is to say, an obsessive pursuit of optimal numerical precision is not necessarily the role of a modeller. The successive development of models or model parameters should be seen as a step forward in the modelling process. The identification of good candidate models or equations may assist in directing research, leading to the discovery of better future potential models (Beck 1997). Generic applicability McIntosh et al. (2003) present the view that flexible and cost effective models are more beneficial than one-off models, which perform very well for one ecosystem

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Regional Scale Modelling of the lower River Murray wetlands only. In an extension of this concept, the applicability of a model as a testing tool in wetlands where minimal or virtually no data exist, can expand the potential of management understanding and thus advance the decision making process as well as guide future research needs. Goodall (1972) and Rykiel (1996) make a distinction between testing the adequacy of a model‟s predictions for a particular ecosystem, and generalisation of its applicability to a range of ecosystems. A model might be applicable and accurate for one particular ecosystem, however it may not be generic and therefore applicable to other ecosystems (Rykiel 1996). This is clearly important in determining whether predictions from a given model can be generally applied to decision-making and management strategies in other ecosystems. WETMOD (Cetin 2001) sacrifices some quantitative accuracy for a few wetlands, in favour of flexibility, applicability and cost. The point should be taken, as stated by Rykiel (1996), that a model may accurately simulate the qualitative behaviour of the system without the quantitative accuracy. In the development of WETMOD it was decided that the qualitative assessment was the more appropriate assessment approach, so as to maintain the generic applicability required in the region (Cetin 2001).

1.4.3 Validation The necessity of validation is an issue that is the subject of considerable controversy in the literature. To increase the understanding of the approach used for a given project, and to potentially minimise conflict, a modeller should clearly state what the validation criteria are in the context of the model. Modellers should also state any restrictions and limitations of the application of the models. For the modeller to fulfil this obligation, the purpose of the model, the criteria the model must meet to be declared acceptable for use, and the context in which the model is intended to operate, must be specified (Rykiel 1996). Rykiel (1996) discusses that models should be judged on usefulness rather than validity. However, model validation is required regardless of whether a model is expected to produce quantitative or qualitative outputs. Model validation is also important for end-user acceptance in the decision-making processes (Power 1993; Rykiel 1996). Mayer and Butler (1993) relate validation to the potential application and users of the model, where the validation is a comparison of model prediction to real world monitoring, to determine whether the model is suitable for its intended

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Regional Scale Modelling of the lower River Murray wetlands purpose (Mayer et al. 1993; Rykiel 1996). Rykiel (1996) states that a valid model is one whose scientific or conceptual content is acceptable for its purpose. According to Goodall (1972), validation is testing to determine the degree of agreement between a model and the real system, that is, how good is the prediction, not whether it should be accepted or rejected (Goodall 1972; Rykiel 1996). Caswell (1988) argues against the case of validation being the decisive part of a successful model. His view is of the role of a model in expanding understanding and contributing to knowledge in a similar vein to experiments contributing to empirical problems. Validation procedures range from general qualitative tests to highly restrictive quantitative tests (Rykiel 1996). Rykiel (1996), Oreskes et al. (1994) and Tsang (1991) all examine and discuss different validation concepts. Although a detailed discussion of this topic is beyond the scope of this thesis, it is necessary to state how the term “validation” is understood in the context of this project. WETMOD is assessed on the basis of Rykiel‟s (1996) description of validation, where validation is a test to confirm that the model is acceptable for its intended use and meets its specified performance requirement (Rykiel 1996). For pragmatic purposes “a model only needs to be good enough to accomplish the goals of the task to which it is applied” (Rykiel 1996)

1.4.4 Modelling role in environmental decision-making Scale The study of ecological function and the management of natural resources have often been at a local scale, even though the ecological processes within wetlands, streams, and rivers occur at a larger (catchment) scale. One of the reasons for this local scale approach has been an inability to manage and analyse large and complex data sets. However, there has been a gradual recognition that management must be handled at large spatial scales to obtain meaningful results (Crumpton 2001; Fitz et al. 1996; Johnson et al. 1997). Fortunately, technology, spatial data, and software tools have advanced to such an extent that landscape-scale studies are now feasible (Johnson et al. 1997). As discussed in the previous chapter, to fully understand management implications and evaluate options the full impacts of restoration of wetland functions will need to be assessed on a landscape scale rather than at an ecosystem scale.

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Regional Scale Modelling of the lower River Murray wetlands Individual wetlands, through the food web, provision of habitat and flood mitigation, have an impact on surrounding wetlands, on the surrounding ecosystems and local land use (Bedford et al. 1988). Without consideration of wetland processes at watershed, landscape, and ecosystem scales, the most effective management strategies cannot be assessed (Lemly 1997). That is, the spatial modelling of ecosystems is necessary to develop a description of past behaviour, or to predict impacts caused by alternative management strategies (Mitasova et al. 1998; Sklar et al. 1993), and their impacts beyond their boundaries. The benefit of landscape models is the ability to use them for the prediction of management impacts on wetlands, without actual alteration or potential destruction (Sklar et al. 1993). Spatial variation is important in assessing the response of a system to excessive nutrient loads and the impact on the system (Murray 2001). Specifically, landscape models can be used to study ecological principles, evaluate cumulative impacts, mitigate environmental alterations, and prevent large-scale anthropogenic mistakes from degrading wetland functions (Sklar et al. 1993). Models can also be used to predict “missing” data that can further be used in management decisions (such as flow exchange). Part of the strength of landscape models is the integration of disciplines due to their ability to handle large amounts of data and information, and provide output that is simple to convey (Boumans et al. 2001). Perhaps the major advantage of landscape models in catchment management is their comprehensive and systematic integration of knowledge and data for a specified region (Voinov et al. 1999a). Thereby a model user can be forced to view and interpret data normally not considered. Cumulative impacts As mentioned above, environmental impacts have often been assessed in the past on the local scale, and have not considered the broader scale impacts (Bedford et al. 1988). However, in a cumulative approach, the different external activities that impact upon a study area are considered. Therefore, on a landscape scale cumulative impacts from processes or activities external to the project area may become apparent that otherwise were not apparent using a local scale approach. The cumulative impacts can result from individually minor but collectively significant actions taking place over a period of time (Preston et al. 1988). When assessing 32

Regional Scale Modelling of the lower River Murray wetlands cumulative impact, the impacts caused by external activities and projects set the assessment boundaries i.e. the landscape scale (Bedford et al. 1988; Preston et al. 1988). Therefore, the area considered in cumulative assessment can expand from the wetland scale to catchment or regions. Only by allowing all external activities and processes that affect a wetland to determine the project boundary, can cumulative impacts be monitored or measured (Bedford et al. 1988). The ultimate aim of a cumulative impact assessment is to evaluate the impacts that may result from change. These impacts include the physical, chemical, and biological changes to an environment (Abbruzzese et al. 1997). The cumulative impact of nutrient uptake due to management, whether improved or degraded, falls within the scope of impact assessment of potential landscape scale wetland management application. Accordingly, the cumulative impact observed due to the simulation of multiple wetland management scenarios can be viewed as a cumulative impact assessment of the proposed management strategies. Models role in estimation of nutrient retention Simulating nutrient flux within a river environment using models taking into account pollution sources through to river outlets should be able to assist managers to target intervention options for nutrient load reduction (de Wit 2001). There are models of various complexity which attempt to provide this capability such as PolFlow by de Wit (2001), which is based on physical laws and is embedded in a GIS (geographical information system). As well as a model by Crumpton (2001) who attempt to identify the position in the landscape of wetland restoration sites for optimal nutrient (Nitrogen) removal. Peijl et al.(1999) developed a model that was able to describe the carbon, nitrogen and phosphorus dynamics and interactions in riverine wetlands, and Muhammetoglu et al. (1997) developed a dynamic three dimensional water quality model for macrophyte dominated shallow lakes. An example of a simple spatial wetland model which simulates nutrient retention of wetlands is described by Li et al. (2002) and Li et al. (2003). These models all try to simulate the nutrient retention capacity of wetlands and relate this back to the landscape scale, e.g. downstream nutrient load. The model by Crumpton (2001) attempts to direct management for an optimal return on investment,

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Regional Scale Modelling of the lower River Murray wetlands i.e. by addressing the question of where in the landscape investment in a wetland would deliver the greatest return as far as nutrient retention is concerned. The PolFlow model was designed as a complex model. In following investigations de Wit and Pebesma (2001) found that under given circumstances, where the available data quality is the limiting factor in model development, simple models may in fact provide as accurate model results as complex models. Crumpton (2001), who study wetland restoration on a catchment scale, found through the application of their model that the location of restored wetlands was decisive in the ability of the wetland to effectively remove Nitrogen loads of the system. They showed that the interception of nutrients by the wetland should be a focus by managers in deciding on wetland restoration sites. Wetland managers lack the information to make any such decisions for lower River Murray wetlands. Landscape scale assessment through modelling can be used in a similar manner to Crumpton (2001), with due consideration given to the availability of data for the lower River Murray catchment. Peijl et al. (2000b), who investigated the importance of landscape geochemical flows using a dynamic model, that simulated carbon, nitrogen and phosphorus cycling in riverine wetlands, show an example of a wetland model that did not manage to predict the field experiment. However this model did contribute to their understanding of the system (Peijl et al. 2000a). This shows that a model can be counted as successful simply based on the improvement of knowledge or understanding. Muhammetoglu et al. (1997) developed a dynamic three-dimensional water quality model for macrophyte dominated shallow lakes. The model simulates the interactions between macrophytes and water quality parameters. The parameters, which are considered, include dissolved oxygen (DO), organic nitrogen, ammonia, nitrate, organic phosphorus, orthophosphate, biochemical oxygen demand, phytoplankton and the sediment. The model has been successful in prediction compared to measured values and can be used as a eutrophication management tool (Muhammetoglu et al. 1997). The model developed and described by Li et al. (2002) and Li et al. (2003) is an example of a simple wetland model which simulates nutrient retention of wetlands and relates this to a landscape scale. Their stance is similar to that of research needs identified for the lower River Murray wetlands model; in that the data availability and

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Regional Scale Modelling of the lower River Murray wetlands knowledge of the system being modelled was limited, the model was therefore impacted upon by a number of assumptions. As a consequence they opted for a simple model. Their model outcomes are in some instances contrary to those anticipated. But they point out that the trends displayed by the model are useful in guiding land use planning (Li et al. 2002). Due to the simple structure of their model Li et al. (2002) and Li et al. (2003) claim that it is applicable to other areas and therefore not location specific. The model output, from which management recommendations are made are only indicative of a trend (Li et al. 2003). This shows that in circumstances where limited data is available, model scenarios of wetland nutrient retention can be used for land use and other environmental management decision-making. Sediment compaction Sediment resuspension accounts for a large part of wetland turbidity influenced by climatic factors. To study the impact within one wetland a model could be made to account for wind direction and speed, and macrophytes role in sediment resuspension. An example of a project which accounts for these influences is a study performed by Hamilton and Mitchell (1996). The objective of the study performed by Hamilton and Mitchell (1996) in shallow New Zealand lakes was to derive relationships between suspended sediment concentrations and the physical forces caused by wave action, and to quantify the factors responsible for the differences between a number of lakes. They were successful in obtaining statistical evidence of the stabilisation mechanism of sediments and the inhibition of resuspension posed by macrophytes. One of the major causes degradation of the wetlands of the lower River Murray is their turbidity as discussed previously. Climatic data on a regional scale could be an option to be included in future monitoring studies of select wetlands thereby providing a representative case for the region. Wetland specific issues such as vegetation cover would also have to be included, complicating a landscape model. In the meantime a case exists for the development of a model capable of simulating the impact turbidity has on wetland ecosystem process and thereby assist managers in evaluating wetland rehabilitation needed to achieve a set turbidity reduction target. River Murray models The Murray-Darling Basin Commission (MDBC) has been using computer models for more than thirty years for water resources planning, development of operating rules, 35

Regional Scale Modelling of the lower River Murray wetlands development of salinity and drainage strategies and forecasting of flow and salinity (Close 1996). The history of mathematical modelling for the MDBC to evaluate management options dates back to 1902 (Close 1986). From 1965 a computer water supply model was being used. Since then flow and salinity models have been created and their interactions improved (Close 1986). In 1996 a model called BigMod was taken up by the MDBC, which replaced the older models and had the role of salt routing prediction in planning studies, short term flow and salinity forecasts, calculating solute loads based on historical data and modelling daily flow variations (Close 1996). Its role is to estimate electrical conductivity (EC) and track parcels of water throughout the system so that salinity load can be defined anywhere in a reach (Close 1996). The use of models by the MDBC has been a successful venture. Due to the complexity of the Murray Darling Basin, and therefore the difficulty to qualify the impacts of changes to the system and the impossibility of quantification without modelling, the developed models have been extremely useful to the MDBC to aid in management decisions (Close 1986). The Flood Inundation Model (FIM) is based on historical flood inundation extent extracted from satellite imagery, known flow at the border, flood levels and lock levels (Overton 2000; Overton 2005). The FIM takes into consideration backwater curves. It provides managers of lower River Murray assets, such as wetlands and floodplains, with a tool to simulate potential inundation areas by changing lock levels at given flows across the SA border (Overton 2000; Overton 2005). The model output is for example used for assisting wetland management by simulating their inundation at given flow levels and relating this back to a potential hydrological regime. The FIM however identifies neither the flow paths connecting the wetlands and the river nor the turnover rate (water volume exchange) within wetlands. A salinity model was developed for The Department of Water, Land and Biodiversity Conservation (DWLBC) to account for salinity impacts of wetlands on the lower River Murray, i.e. salinity accounting (Murdoch et al. 2004; RMCWMB 2002). The use of the model was intended provide a generic daily salt water balance as a consequence of wetland hydrology regimes .This model Salinity Impacts of Wetland Manipulation (SIWM) is a generic model relying on “exemplar” data and qualitative outcomes for generating quantitative assessments. The hydrology estimations within SIWM were taken from BigMod which propagates inaccuracies based on BigMod 36

Regional Scale Modelling of the lower River Murray wetlands assumptions. Consequently SIWM output quality is degraded (Murdoch et al. 2004). Although a novel approach the accuracy of the model output was not adequate for the purposes intended, i.e. salinity accounting for individual wetlands. It was therefore withdrawn from use by DWLBC. A replacement model is currently being developed by DWLBC (Croucher 2005). Further models for the lower River Murray are a number of groundwater models. These models simulate groundwater sources and impact on floodplain and river salinity and have been combined to make up a Floodplain Risk Methodology (FRM). The FRM is a collection of models used to assess the impact of groundwater on floodplain vegetation and the impacts periodic flooding and weir manipulation would have (Holland et al. 2005). As discussed, to fully understand management implications and evaluate options the full impacts of restoration strategies on wetland functions will need to be assessed on a landscape scale rather than at an ecosystem scale. This having been identified as a research need for the wetland processes of the lower River Murray, a wetland ecosystem model called WETMOD 1, initially developed by Cetin (2001), was identified as a first step from which landscape scale could be built on. The aim of WETMOD 1 was to simulate macrophytes, phytoplankton, zooplankton and nutrients in the open water of wetlands. Cetin (2001) based the structure of WETMOD 1 on the Patuxent Landscape Model (PLM) (see Maxwell et al. 1997; Voinov et al. 1999a; Voinov et al. 1999b), and the lake ecosystem model SALMO (Simulation by means of an Analytical Lake Model) (see Benndorf et al. 1982; Recknagel et al. 1982). The PLM is a complex landscape scale model of aquatic ecosystems including wetlands where ecosystem processes are simulated. This model allows simulations of a catchment using detailed morphological data (digital elevation models DEM‟s) of the catchment and wetlands as well as time series of point source nutrient inflow. The model simulates an entire catchment using raster GIS data as the main driving variables where the model is run for each cell at each time step, propagating the results to the next cell for the next time step (Boumans 2001; Voinov et al. 1999b). The complexity of the PLM particularly the requirement of a detailed DEM prevents it from being applied in the lower River Murray system. An added complication for the lower River Murray system for a linear model is the non linear nature of flows in the lower River Murray. This can be through the reversing of flows “upstream” by wind and the bypassing of the main channel through rapidly flowing anabranches 37

Regional Scale Modelling of the lower River Murray wetlands such as those of the Chowilla floodplain. Therefore, cell to cell modelling such as the PLM methodology would not be easy to adapt or implement. Part of the PLM could however be adapted to the lower River Murray system. Particularly when adapted with equations such as from time series dependent models such as SALMO. SALMO was designed for the management of lake ecosystems, based on state variables phytoplankton, zooplankton and orthophosphate time series data. The SALMO model allowed for management simulations of nutrient cycles within lakes and the consequences of different management strategies for the control of eutrophication in lake and reservoir ecosystems (Benndorf et al. 1982). Using select equations from both as well as further literature Cetin (2001) was able to develop a generic model (WETMOD 1) for simulation of internal nutrient dynamics. The WETMOD 2 model described in the remaining chapters, built on WETMOD 1, is a contribution to the simulation of the lower River Murray system to aid informed decision making research and management of the lower River Murray wetlands.

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Regional Scale Modelling of the lower River Murray wetlands

2 Aims and objectives The main aim of this work is to develop a model, which facilitates the analysis of management options for informed selection of wetlands requiring restoration, with the aim of re-establishing wetland landscape function through optimal means. To fulfil this aim the following objectives must be met: I. Adapt a generic wetland process model for the lower River Murray floodplain wetlands and improve the resolution of the spatial influences acting upon a wetland II. Evaluate these spatially relevant impacts on wetland nutrient uptake III. Appraise the potential river nutrient-load buffer capacity of wetlands both preand post-management, on a landscape scale. It is generally expected that restoring wetlands will reinstate their river-nutrient buffering capacity, consequently improving water quality in rivers by reducing nutrients otherwise available to support algal growth. The model is expected to deliver an estimate of the potential nutrient reduction in the lower River Murray following management intervention. This project focuses on the lower River Murray wetlands and relies on previous work done in that area. Some of the research in the Lower Murray area has focused data collection and survey work, and has been summarised in the Wetlands Atlas of the South Australian Murray Valley by Jensen et al. (1996). Other projects in the Murray Darling Basin have been compiled and catalogued by the Murray Darling Basin Commission (Kirk 1998). However, the work this project mostly depends on are projects in the lower River Murray that have had objectives of producing solutions for particular problems. These past projects include for example the creation of weirs at individual wetlands for the introduction of drying cycles, and the construction of wetlands for nutrient removal from agricultural drainage water (prior to being released into the system). Recent baseline surveys (SKM 2004; SKM 2006) have added to the information available on the condition of individual wetlands for the purpose of wetland management. This data provides a simplified snapshot of the current condition of a few wetlands. However, a key lack of data, which impacts on a

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Regional Scale Modelling of the lower River Murray wetlands number of research projects (such as fish habitat) and management decisions, is the exchange of water (turnover rate) between wetlands and the river. Hypotheses: I. A simplified generic wetland model can be used to realistically simulate multiple and different wetlands qualitatively. It is the premise of this project that a simple model will, with the available data, produce results that are sufficiently accurate so as to aid in decision-making (see 1.4.1). II. A simplified generic wetland model can be used to answer “what if” questions for landscape scale scenarios, and III. A simplified generic wetland model can be used to assess the cumulative impact of managing multiple wetlands. This project adopts a generic wetland process model WETMOD 1 to account for wetland local external influences. These influences include improvement of the resolution of spatial influences such as river nutrient content, river flow volume and where appropriate external irrigation drainage inflow, which act upon a wetland. The model will evaluate the impact these influences have on wetland uptake of PO4-P, NO3-N, and production of phytoplankton, as well as how uptake can change at different locations. To be able to apply the model at different locations, despite restricted data availability, a wetland classification system incorporating the use of monitored data from intensely studied wetlands as regional-scale exemplars will be adopted. Therefore, the model will be applicable on a regional (landscape) scale providing qualitative understanding of the cumulative impact of wetland management.

40

Regional Scale Modelling of the lower River Murray wetlands

3 Materials and Methods 3.1 Model Description For application of the model in decision-making, managers and stakeholders need to understand the models purpose as understood by the developer, any assumptions made during its development and the model structure (Bart 1995). The following chapter describes the assumptions of wetland behaviour as well as the model structure. The model used for the basis of extended development is WETMOD 1 developed by Cetin (2001), which was based mainly on literature data.

3.1.1 Design Considerations Problems current for wetland management is the acute lack of awareness of impact of management on a regional scale. Given that wetlands will have a varying nutrient retention capacity depending on the turnover rate, i.e. longer turnover rate will allow for more nutrients to be absorbed, finding an optimum turnover rate to maximise the nutrient retention capacity of wetlands could be a management aim for river nutrient reduction. To assess the impact of wetland management for nutrient reduction in a river it is necessary to assess the capacity of a wetland to retain nutrients individually and cumulatively at the landscape scale. Therefore, multiple variables come into play to assess the capacity of wetlands to retain nutrients on a landscape scale. The first step of assessing individual wetland nutrient retention was addressed in part by WETMOD 1 (Cetin 2001). The limiting factors are, as is often the case, the acute lack of sufficient data when the model is to be applied to a landscape scale. The wetland model WETMOD 1 has the ability to simulate the general internal dynamics of a wetland with minimal monitored driving variables, therefore allowing the model to be applicable at sites with minimal data. With site-specific data on water exchange, nutrient through flow and wetland morphology, introduced during the development of WETMOD 2, the modelling of wetland dynamics becomes more specific for an individual wetland although using landscape scale available data. None the less, with the limited resources invested in the monitoring of wetlands, only very few can reliably be simulated. To overcome the restriction hindering the testing of

41

Regional Scale Modelling of the lower River Murray wetlands management strategies for wetlands and assessing the potential cumulative impact two options remained: 1. The substantial investment of resources in monitoring of wetlands, prior to the extensive development of a model. Such a model would potentially be capable of the simulation of each of the monitored wetlands in detail, thereby providing managers with a robust and comprehensive decision making tool. This option has the drawback of the investment of substantial resources, loss of valuable time in monitoring and model development. The largest drawback being that the model would still be restricted to the monitored wetlands. 2. The development of a modelling tool capable of assisting in the development of understanding of potential management decisions, which would be based on current available data and knowledge. The restriction on the complexity of such a model would be ensuring its applicability to all wetlands within the catchment area based on the current data availability. Therefore, as such a model would need to rely on available data some of the accuracy of model results would be dependent on the range of data quality and quantity. Going with the second option, a developed generic model which allows the assessment on a landscape scale of wetland function and cumulative impact, the simplification of wetland into wetland classes becomes necessary, to such an extent that no wetland is seen as unique nor all wetlands as equal (Bedford et al. 1988). If this simplification is not introduced, the data required for a landscape scale assessment becomes insurmountable. There are multitudes of ways to classify wetlands. The system that is chosen is dependent on the purpose of the classification, the time available, the data and the knowledge available, as well as the preconception of the classifier, which will affect any wetland classification. In a general sense, there are 2 approaches, one through geomorphology and the other through the hydrological relationship of the wetland to the river (Bedford et al. 1988; Pressey 1990). As an example of a classification procedure Strager et al. (2000) used a landscape based approach to classify wetlands and riparian areas based on habitat requirements of amphibians and reptiles. This classification also included forested and non-forested groupings (Strager et al. 2000). 42

Regional Scale Modelling of the lower River Murray wetlands The classification used in this project is partially driven by the limited data availability for both geomorphology and hydrological relationship between the wetland and river. The approach was therefore a very simplified hydrological connectivity classification, which will be discussed in more detail below. The description of the model is broken down into two segments, WETMOD 1 and WETMOD 2. The first description, WETMOD 1, relates to the model sections developed by Cetin (2001) that relate to internal nutrient dynamics. The second section, WETMOD 2, relates to the redesign of the model to account for external influences acting upon a wetland. The methodology for the application of WETMOD 2 to assess cumulative impact of wetland management is discussed at the end of this chapter. The macrophyte biomass module is described in the macrophyte sector below. The phytoplankton and zooplankton biomass module are described together as part of the plankton sector, and the PO4-P and NO3-N module is described as part of the nutrients sector. The “Fitted River exchange and Irrigation Drainage Inflow” was, due to its complexity, split into separate modules within WETMOD 2, which are described as Flow Exchange Sector and External Nutrient Source Sector. Both of these relate to the significant addition to the model where internal nutrient dynamics are related to external and therefore landscape scale impacts such as river nutrient load. The output of both of these sectors contributes significantly to management considerations on a landscape scale. The sources of differential equations are described in Appendix A. The descriptions of the macrophyte, plankton and nutrient sectors have been adapted from Cetin (2001). Units of input data (conversions are performed within the model, descriptions of which can be found in section 3.3); MDBC river data; o Nitrate + Nitrite as N: mg/L o Filterable Reactive Phosphorus as P: mg/L Reedy Creek river data; o Nitrate as NO3-N: mg/L o Soluble Reactive Phosphorous as PO4-P: mg/L 43

Regional Scale Modelling of the lower River Murray wetlands Miscellaneous; o Turbidity: NTU o Temperature: C o Secchi depth: metres o Chlorophyll-a: μg/L o Solar Radiation: MJm2 o Wetland Volume: cubic metres (m3) Units of output data; o Nutrients (PO4-P and NO3-N): g/L o Phytoplankton biomass: cm3/m3 o Zooplankton biomass: cm3/m3 o Macrophyte biomass: kg/m3 Principal Model Assumptions (for simplification of model design) The following assumptions were made at the commencement of the project to compensate for a general lack of data, and data quality for the lower River Murray. These were needed as part of the simple model design strategy employed. 1. As the considered wetlands are permanently inundated wetlands, it is assumed that as a result of lock management, where locks are maintained at a constant level, all wetlands included in potential management scenarios have a constant volume as well as a permanent connection with the lower River Murray. Consequently, there is a bi-directional and permanent exchange of water and nutrient with the river, the exchange volumes (in- and out-flow) being equal. 2. There were no data on exchange flow or channel morphology; therefore it was assumed that the exchange volume was solely dependent on the river flow volume. 3. It was assumed the wetland is homogeneously mixed for each modelling time step. Simulated wetland nutrient data would therefore represent the concentration throughout the wetland.

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Regional Scale Modelling of the lower River Murray wetlands 4. South Australia is a dry state, and there generally are no significant catchment areas for individual lower River Murray wetlands. It was therefore assumed, that there would be only low or insignificant nutrient inflow though precipitation runoff for most wetlands. The exception is Reedy Creek wetland, and therefore by extrapolation, all category 4 wetlands (wetland classification is described below). 5. For management simulation purposes, it was assumed that the introduction of dry periods to wetlands would compact the sediment and reduce the turbidity within the wetland during the next wetting event. The inherent assumption is that the turbidity is caused by suspended sediment and is not significantly contributed to by phytoplankton. However, phytoplankton will increase the turbidity in proportion to its own growth. A future user of the model must therefore take this into account when assessing model output. The management scenarios also assume that the turbidity is independent of the potential inflow of suspended sediment from the river as the river turbidity fluctuates dependent on the water source of upper River Murray versus the Darling River. A modeller must therefore take into account the realistic potential reduction in turbidity for a given wetland dependent on external sources as well as its internal dynamics, i.e. resuspension and sedimentation. 6. For management simulation purposes it was assumed that all same category wetlands resemble each other in exchange volume. In an operational application local knowledge of the exchange volume for simulated wetlands would assist in improving potential modelling output.

3.1.2 WETMOD 1 The WETMOD 1 model (Cetin 2001; Cetin et al. 2001) is a generic wetland ecosystem model. WETMOD 1 simulates internal wetland nutrient dynamics, i.e. the growth of macrophytes, phytoplankton and zooplankton through mass balance equations (Figure 4). WETMOD 1 simulates internal wetland nutrient processes using water temperature, turbidity, Secchi depth and solar radiation as driving variables (model time-series input). Phosphorus as PO4-P, nitrogen as NO3-N, macrophytes, phytoplankton and zooplankton are state variables (model output). This section, represented as WETMOD 1, was rigorously adapted into the WETMOD 2 45

Regional Scale Modelling of the lower River Murray wetlands environment to account for advancement in data and addressing model limitations. The description in this thesis is therefore of the model sections, WETMOD 1, as they are found in the WETMOD 2 model. Any generic wetland ecosystem model such as WETMOD 1 could be adapted in a similar fashion to account for the river, wetland interaction and cumulative impact on a landscape scale. The Model WETMOD 1 was developed and implemented by means of the modelling developmental software STELLA (2000). STELLA provides an intuitive user interface for domain experts with little modelling experience. Models developed within STELLA are, due to its rigid structure, transparent. IN P U T S W E T L A N D D A T A T urbidity

M acrophy tes P O 4 -P

W ater T em perature S olar R adiation

N O 3 -N

S ecchi D epth W etland V olum e

Z ooplankton

N utrient Inflow (F rom L iterature) S edim entation/O utflow

P hy toplankton

D riving V ariables M odel Interactions

Figure 4: Driving Variables, State Variables and Major Interactions in WETMOD 1

Nutrient contribution to the wetland occurs through sediment release, surface runoff, irrigation drainage and river inflow. The data used being either sourced from literature or approximate values obtained from expert recommendation. Nutrient loss occurs through uptake by macrophytes and phytoplankton as well as sedimentation and wetland outflow, this data being mainly calibrated or based on expert recommendation. Zooplankton increase is through growth, and reduction is through mortality. Macrophyte and phytoplankton increase is through growth (phytoplankton inflow being introduced in WETMOD 2). Macrophyte and phytoplankton biomass loss is through respiration and mortality, phytoplankton additionally through sedimentation, zooplankton grazing and outflow.

46

Regional Scale Modelling of the lower River Murray wetlands The model is divided into individual modules where related process equations are grouped together. The descriptions below are of the individual modules as they appear in WETMOD 2. Macrophyte Sector The macrophytes are simulated within WETMOD 1, and represented by their photosynthetic biomass in the open water (Cetin 2001). To obtain a simple model structure, all submerged macrophyte species found within the wetland were aggregated and represented as macrophyte biomass. Emergent macrophytes play an important role in the lower River Murray ecosystem, not only through their nutrient retention but also as habitat and sediment traps. WETMOD 1 however did not consider emergent macrophytes in the model and consequently neither will WETMOD 2. The increase in macrophyte biomass within the model is controlled by the productivity of the photosynthetic biomass („Mac Gross PP‟ Figure 5). The loss of macrophyte biomass is through mortality and respiration of photosynthetic biomass („Mac mortality‟ and „Mac respiration‟). The growth of macrophyte (photosynthetic) biomass is influenced by the growth rate, turbidity and nutrients, underwater light and water temperature. In Australian waters turbidity can be a controlling factor in macrophyte growth (Roberts et al. 1986), with the growth rate reaching a maximum when the turbidity is below 70 NTU (Shiel et al. 1982). Therefore, the reduction in turbidity is seen as a major aim of wetland management and is consequently the main focus of management scenarios of the model. Productivity of the photosynthetic biomass („Mac Gross PP‟), i.e. macrophyte biomass growth, is contributed to by the macrophyte production coefficient („mac prod cf‟), Gross Primary Production rate for the total plant biomass („Mac GPP‟) and can be limited by turbidity if it surpasses the 70 NTU threshold. The production coefficient („mac prod cf‟) is calculated from the availability of nutrients, underwater light and water temperature (see Appendix A for equations). The underwater light coefficient calculation is based on the Beer-Lambert Law for light attenuation, where the data required is Secchi depth and solar radiation. Solar radiation input is MJm2/day. The equation used in WETMOD 1, which was obtained 47

Regional Scale Modelling of the lower River Murray wetlands from literature, demands units in Jcm2/day, therefore, in WETMOD 2 MJm2/day is multiplied by 100 to convert to Jcm2/day. Where Secchi depth data are missing or of very poor quality, the Secchi depth is calculated based on the turbidity within the wetland. The equation for calculating the Secchi depth from the wetland turbidity is discussed in detail in section 3.2.1. Therefore, the Secchi depth data source can be either monitored, calculated from the turbidity or fixed manually. The water temperature is one of the driving variables of WETMOD (1 and 2). The macrophyte temperature coefficient („mac temp cf‟) is based on the optimum water temperature for macrophyte growth (Boumans 2001). The macrophyte nutrient coefficient („mac nut cf‟) is based on the Michaelis-Menten expression, where the nutrient uptake is dependent on the concentration of the nutrient in the water and the nutrient half saturation constant („mac Ks N‟ and „mac Ks P‟, see Appendix A) for uptake.

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Regional Scale Modelling of the lower River Murray wetlands

Figure 5: Macrophyte Module

Plankton Sector The plankton sector, seen in Figure 6, (labelled as phytoplankton module) comprises both the phytoplankton and zooplankton equations within the model. Both phytoplankton and zooplankton have, for the sake of model simplicity, been aggregated into their respective state variable, i.e. phytoplankton biomass and zooplankton biomass. The phytoplankton biomass can have two sources of input. One is the wetland growth of phytoplankton expressed as the phytoplankton gross primary productivity („pht 49

Regional Scale Modelling of the lower River Murray wetlands Gross PP‟), which is dependent on the phytoplankton production coefficient („pht prod cf‟), and limited by the maximum biomass of phytoplankton („pht max‟), i.e. the carrying capacity which is calibrated for each wetland. The phytoplankton production coefficient („pht prod cf‟) is obtained in a similar manner to the macrophyte sector. The second input source of phytoplankton is from external sources such as the river or irrigation drainage inflow („Phytoplankton In‟), the load is fitted with the exchange estimate; see External Nutrient Source sector description below. The phytoplankton biomass reduction is through five sources, mortality („pht mortality‟), respiration („pht respiration‟), sedimentation („pht sedimentation‟) outflow („Phytoplankton Out‟) and zooplankton uptake („Pht grazing‟). The phytoplankton respiration is dependent on water temperature and the temperature limitation coefficient („pht temp cf‟). The phytoplankton temperature coefficient equation is adapted from Hamilton and Schadlow (1997), which relates the growth to a constant multiplier of the water temperature. Within wetlands there is a net increase in effective sedimentation as a consequence of increasing turbidity. That is, due to the increase in suspended particles available there is a net increase in sedimentation as when compared with a clear wetland. The sedimentation is controlled through a calibrated sedimentation rate, which is altered by the turbidity of the wetland. In WETMOD the change in sedimentation rate is controlled through a calibrated turbidity threshold at 95 NTU. Mortality of phytoplankton is controlled through a set mortality rate and the respiration through a respiration rate. The outflow is dependent on the fitted (estimated) exchange rate of the wetland, described in Flow Exchange and External Nutrient Source Sectors. The zooplankton uptake is dependent on the zooplankton growth rate controlled through the grazing rate of phytoplankton by zooplankton („Pht grazing‟). The zooplankton equations are adapted exclusively from SALMO (Simulation by means of an Analytical Lake Model) (Recknagel et al. 1982). The zooplankton outflow is simplified with the zooplankton mortality, controlled through a calibrated mortality rate for each wetland, accounting for all sources of zooplankton biomass reduction. The phytoplankton biomass growth is controlled through the phytoplankton grazing equation („Pht grazing‟), which is the grazing of phytoplankton by zooplankton. Affecting the grazing, and therefore zooplankton growth, is the zooplankton respiration rate and the zooplankton growth rate. The zooplankton 50

Regional Scale Modelling of the lower River Murray wetlands growth rate is a function of both the macrophyte biomass and the phytoplankton grazing rate. The phytoplankton grazing rate is a function of the day length and the water temperature. The macrophyte biomass has an influence on the zooplankton growth rate due the assumption that it provides a shelter for zooplankton (Asaeda et al. 1997). Therefore, if the macrophyte biomass is low, the zooplankton biomass will reduce. The zooplankton respiration rate is controlled by the phytoplankton grazing rate and the water temperature.

Figure 6: Plankton Module

Nutrients Sector Both of the nutrient equations consist of similar inflows and outflows, Figure 7. As discussed in section 1.1.2 and 1.2 the main contributors of nutrient inflow to wetlands 51

Regional Scale Modelling of the lower River Murray wetlands are external sources such as the river or irrigation drainage inflow. As with phytoplankton, the inflow rate is determined by the fitted rate for the particular wetland, which is described in Flow Exchange and External Nutrient Source Sectors. Other inflows include „P loading‟ and „N loading‟ respectively, as well as „P sediment‟ and „N sediment‟ release. Nitrate flux is also potentially affected by nitrification and denitrification. However, due to insufficiencies in data, the sediment dynamics could not be modelled within WETMOD. The nutrient dynamics of the wetland are for the open water only, with sedimentation rates calibrated to adjust for missing complexity. This has simplified the model, but future research may need to invest in expanding this section of the model despite increasing complexity, as the present simplification does account for some model limitation. The outflows include „P soil coprecipitation‟, or the sedimentation of PO4-P and NO3N „N soil coprecipitation‟, P or N uptake and nutrient outflow as per the fitted exchange estimate, described in Flow Exchange and External Nutrient Source Sectors. The sedimentation rate accounts for the coprecipitation of nutrients, (which is the sorption of nutrients to suspended soil particles that then precipitate to the wetland floor). The coprecipitation is more pronounced at high turbidity due to the high availability of suspended soil particles, and can account for significant nutrient uptake by wetlands. The model assumes a calibrated sedimentation rate (calibrated for wetland categories) for both PO4-P and NO3-N of 50% at turbidity levels 70 NTU or above (the 70 NTU being a calibrated estimate that acts as a threshold), and 10% below 70 NTU (for Lock 6 wetland) or 50% vs. 20% (for Reedy Creek wetland), wetland classification is discussed below. The uptake of nutrients by macrophytes and phytoplankton was adapted from the PLM (Patuxent Landscape model) (Boumans 2001). The uptake is dependent on nutrient to carbon ratio and the net primary productivity of both macrophyte and phytoplankton biomass.

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Regional Scale Modelling of the lower River Murray wetlands

Figure 7: Nutrient Module

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Regional Scale Modelling of the lower River Murray wetlands

3.1.3 WETMOD 2 External influences were not well constructed, or represented, in WETMOD 1, therefore to study the impact of respective external influences for differing wetlands these need to be added to the model. WETMOD 2 is a modification of the original WETMOD 1 model to suit the requirements of this project. As accounting for external influences is essential in regional scale scenarios, a modified version of WETMOD had to be developed, i.e. WETMOD 2. The capacity of the model to simulate potential impacts of restoration scenarios for the different wetlands would, through improved local relevant data, also be enhanced. The first two aims in the modifications of WETMOD 2 playing a part in fulfilling the third. These aims are listed below. I. Overcome shortcomings in knowledge due to limited data and incomplete system understanding. II. Address processes requiring further development, which were identified at the beginning of the study. These included river and wetland water exchange, nutrient exchange, and irrigation drainage data influence, and III. Adapt and test the application of the model on a regional scale; i.e. develop a cumulative assessment of potential management impacts of multiple wetlands on the river nutrient load. One challenge in modifying WETMOD to simulate landscape scale scenarios was to account for external influences acting on wetland water quality. This involved the further development of estimates of the inflow and outflow of nutrient to the wetlands. To accomplish this, the following sources were included in the model: I. Irrigation (Reedy Creek wetland and Sunnyside wetland) II. River Exchange modelling (Lower Murray River flow dependent) During these key WETMOD modifications the following two principles were maintained: I. Model transferability (Model Generic Nature) II. Model Expectations (Reliable prediction of system dynamics, i.e. trends) The model overview, WETMOD 2, and the data flow between modules are presented in Figure 8. The sections initially sourced from WETMOD 1 are represented as the 54

Regional Scale Modelling of the lower River Murray wetlands three green modules where internal wetland processes are simulated. The newer modules, which encompass the major modifications of WETMOD 2, include wetland data updates (yellow, rigorous reconstruction and update of the data base), new wetland specific morphology data (white) and wetland external nutrient sources (blue).

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Regional Scale Modelling of the lower River Murray wetlands

W E T M O D S tru c tu re an d D ata F lo w P la n k to n S e c to r W e tla n d B io m a ss L o a d Z o op la n kton P h ytop la n kton

M a c rop h yte B io m a ss

P O 4 -P & N O 3 -N

P O 4 -P & N O 3 -N

M a c r o p h y te S e c to r

P h ytop la n kton In flo w (in c lu d in g Irriga tion D ra in a ge )

W e tla n d B io m a ss L o a d M a c rop h yte s

T u rb id ity, S e c c h i & T e m p e ra tu re

S ola r R a d ia tion

D a ta

W e tla n d T im e s-S e r ie s

S ola r R a d ia tion

P h ytop la n kton O u tflo w

W e tla n d T im e S e rie s T u rb id ity, S e c c h i & T e m p e ra tu re

D a ta F lo w F e e d b a c k on V olu m e (F itte d for e a c h w e tla n d )

W e tla n d In te rn a l D yn a m ic s W ETM OD 1

W e tla n d S p e c ific D a ta W ETM OD 1

E x te rn a l S ou rc e s W ETM OD 2

W e tla n d S p e c ific D a ta W ETM OD 2

W e tla n d V olu m e

N u tr ie n ts S e c to r W e tla n d C o n c e n tra tio n P O 4 -P & N O 3 -N

P O 4 -P & N O 3 -N O u tflo w

M o r p h o lo g ic a l D a ta W e tla n d A re a W e tla n d D e p th

P O 4 -P & N O 3 -N In flo w (in c lu d in g Irriga tion D ra in a ge )

F itte d R iv e r exchange and Ir r ig a tio n D r a in a g e In flow D ra in a g e In flo w R iv e r N u trie n t E x c h a n g e : P O 4 -P , N O 3 -N , P h ytop la n kton In flo w

R iv e r T im e -S e r ie s P O 4 -P , N O 3 -N , P h ytop la n kton & F lo w V olu m e

R iv e r T im e S e rie s a t L o c k 5 , M a n n u m a n d M u rra y B rid g e P O 4 -P , N O 3 -N & P h ytop la n kton L o c k s 1 -8 F lo w V olu m e

P O 4 -P , N O 3 -N , P h ytop la n kton & D ra in a ge V olu m e

Ir r ig a tio n D r a in a g e T im e -S e r ie s D ra in a g e In flo w P O 4 -P , N O 3 -N P h ytop la n kton D ra in a ge V olu m e

Figure 8: WETMOD 2 Structure and Data Flow

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Regional Scale Modelling of the lower River Murray wetlands Flow Exchange Sector As discussed in section 1.1.2 the transport of material in and out of wetlands is primarily a function of water flow (Johnston 1991). Therefore, the transport of material through water flow has the potential of being the most significant external influence acting upon a wetland. The process of fitting the exchange volume to a particular wetland based on Equation 6 which is discussed in detail in section 3.3. Essentially Equation 6 relates the exchange volume to the wetland nutrient concentration, river nutrient concentration and the river flow rate. This sector shows the adjustment of exchange volume estimation (Percentage of River Flow regarded as exchange) based on the river flow ML per day (converted to appropriate units as required within the model) (Figure 9). As the wetland is assumed to maintain a constant volume, any irrigation drainage inflow into the wetland is included in the respective wetland outflow volume.

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Regional Scale Modelling of the lower River Murray wetlands

Figure 9: Volume Exchange Module

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Regional Scale Modelling of the lower River Murray wetlands External Nutrient Source Sector This sector encompasses the Nutrient Exchange module, as seen in Figure 10, where both the river exchange and the irrigation drainage inflow are introduced. That is, the inflow load of the nutrients and phytoplankton can be from two sources. The first is the river and the second, when applicable, the irrigation drainage inflow. The calculation of the individual loads is discussed in section 3.4 and calculated as per Equation 9 (irrigation drainage load to the wetland) and Equation 10 (nutrient load to the wetland from the river). Both are described in section 3.4.1. The sum of both loads is fed into the relevant modules described in the Plankton Sector and Nutrients Sector. Within the nutrient exchange sector the irrigation drainage concentration and volume are selected and adjusted based on the wetland being simulated. Time-series for both irrigation affected wetlands Sunnyside and Reedy Creek wetland (described in section 3.2.1) are selected if either is being simulated; the option of testing for irrigation drainage affecting Paiwalla wetland was included in the model as it was also potentially impacted by irrigation drainage. The irrigation flow volume is manually set for Sunnyside as accurate volume data were not available, see section 3.2.1. Reedy Creek wetland irrigation flow was fixed at a set volume. The calculated irrigation drainage load („PDrainLoad‟, „NDrainLoad‟ and „Chla DrainLoad (Reedy or Sunnyside)‟, see Figure 10) is distributed for each of the wetlands („P Drain Water Inflow‟, „N Drain Water Inflow‟ or „(Reedy or Sunnyside) Chla divided into wetland‟) as per the seasonal flow pattern („Seasonal Flow Pattern SunnyORReedy‟) described in section 3.2.1. The methodology of conversion of Chlorophyll-a to phytoplankton biomass is discussed in section 3.3, and performed within the model in „Phytoplankton Inflow cm3m3‟. The outflow module, Figure 11, is where the outflow of PO4-P, NO3-N and phytoplankton are calculated based on the fitted exchange volumes from the Flow Exchange Sector, expressed in terms of Equation 11 or Equation 12 (both equations for estimating the nutrient load from a wetland to the river, Equation 12 taking into account irrigation drainage, see section 3.4.1). These outflow concentrations are then fed back to the relevant modules described in Plankton and Nutrient Sectors.

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Regional Scale Modelling of the lower River Murray wetlands

Figure 10: External Nutrient Module

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Regional Scale Modelling of the lower River Murray wetlands

Figure 11: Outflow Module

Miscellaneous Sectors Other modules (Sectors) within the model contain data handling such as data source selection (including driving variables based on wetland category), wetland volume calculation, and appropriate solar radiation and river data selection. In certain circumstances data conversions between units are handled within these modules e.g. river data conversion. Model accuracy is tested using MS Excel based on the evaluation criterion D described in section 3.3.2 (statistical estimation of the accuracy of model output in comparison to monitored data). Excel was also used to calculate the retention capacity of the wetland, the potential impact that this retention capacity has on the river nutrient load, the potential impact of management scenarios (Equation 13, Equation 14, Equation 16 and Equation 17), and the cumulative impact of multiple wetland management. Equation 13 and Equation 14 both relate to the change in nutrient 61

Regional Scale Modelling of the lower River Murray wetlands outflow from a wetland to the river. They are both described in section 3.4.1. Equation 16 and Equation 17 calculate the change in river nutrient load following wetland management and its percentage change in river nutrient load respectively. Both Equation 16 and Equation 17 are used to calculate the impact the management of a wetland or multiple wetlands has on the river nutrient load (they are both described in section 3.4.2).

62

Regional Scale Modelling of the lower River Murray wetlands

3.2 Data: Model Driving Variables WETMOD 1 was developed to study the impacts of internal wetland nutrient dynamics. At the time of its development, most of the external nutrient inflow data had not yet been collated, nor was all the wetland data available. Literature and other “exemplar” data were used to supplement the datasets used as the driving variables of the model. A working model was therefore developed which could be improved through the introduction of appropriate real data. As this (WETMOD 2) project was to focus on regional scale applications of wetland models and to develop scenarios to study the potential impact of management, WETMOD 1 was deemed to be an acceptable basis from which to continue development. By using WETMOD 1, the time otherwise invested in redeveloping an internal wetland nutrient dynamic model was saved. However, rigorous data preprocessing was required to adapt WETMOD 1 to both the regional data set requirements and the updated data set. This describes the data set used and it‟s preprocessing for WETMOD 2. This project used several different monitoring databases as summarised in Table 1. Processes and conditions within wetlands have been monitored in several studies focusing on select wetlands. These studies occurred between 1997 and 2001 for periods ranging from 9 months to 2 years. Data were collected approximately every two weeks (Bartsch 1997; Marsh 1997; van der Wielen nd; Wen 2002a; Wen 2002b). These case studies are the source for all type-specific wetland properties as well as most abiotic and biotic time-series that are used as the main driving variables in the model, i.e. “exemplar” data. The monitored wetland locations, used in the modelling, were from open water sampling in the centre of the wetland. Error Bars in the data graphs displayed below were calculated from all monitoring sites within a particular wetland. The exchange of nutrients between the river and wetlands depends on the river flow and river nutrient load. River flow data and water quality data (nutrient load) are collected at all locks and were obtained from the Murray Darling Basin Commission (MDBC) and the South Australian Department of Environment and Heritage (DEH). The flow data included in the model were collected at Locks 1 through to 8 (Figure 12), therefore for the model the most appropriate river data can be chosen for a given 63

Regional Scale Modelling of the lower River Murray wetlands scenario. The main climatic driving variable is solar radiation data obtained from BOM (Bureau of Meteorology). To apply the model to all wetlands along the river, location specific data have been incorporated. These include wetland size, depth, influence of irrigation drainage and connection to the river; and were obtained from Planning SA and Wetland Care Australia. From this morphological data the wetland could be assigned to categories, depending on hydrology and irrigation drainage influence. Table 1: Data Sources, Type & Monitoring Frequency Monitoring Frequency

Data Included

Wetland, Drainage Inflow & River (water quality)

Fortnightly

NO3, PO4, University of Turbidity, Adelaide Temperature, Chl-a & Secchi depth

River Monitoring

Weekly

Temperature & Turbidity

DEH

Fortnightly

Chl-a,

DEH

Monthly

PO4 & NO3

DEH

River Flow Volume

Daily

Water Flow

MDBC

Solar Radiation

Daily

Solar Radiation

BOM

Wetland Depth

Wetland Care Australia

Data Type

Wetlands Management Study N/a Report

Source

64

Regional Scale Modelling of the lower River Murray wetlands

3.2.1 “Exemplar” Wetland Sites The study area, which contains the modelled wetlands, covers a length of the River Murray of just over 600 km from the South Australian and Victorian border to the entry of the river into Lake Alexandrina (Figure 12).

Figure 12: “Exemplar” Wetlands & River Monitoring Sites

The overall purpose of the model is to simulate as many wetlands as possible along the lower River Murray in order to identify management strategies that may potentially improve wetland state and function. A range of different wetlands have 65

Regional Scale Modelling of the lower River Murray wetlands been studied in the past. Of these, selected wetlands that best represent the range of wetlands, based on hydrological connections, act as “exemplars” of driving variable data time-series used in management simulations. The assumption is that if physically similar wetlands respond in the way “exemplar” wetlands do it will be possible to expand the model application and simulate the cumulative impact of multiple management intervention. The wetlands for which data was available and serve as “exemplar” data sources are Paiwalla, Sunnyside, Lock 6, Reedy Creek and Pilby Creek wetlands. Their locations within the lower River Murray catchment are shown in Figure 12. The classification of wetlands is based on the hydrological relationship of the wetlands to the river. This has been divided into 2 basic types, 1. through-flow wetlands and 2. dead-end river connections. Through-flow wetlands occur where river water can flow through the wetland, either as the wetland has one complete side open to the river or the wetland has two distinct flow channels acting as water inflow and outflow channels. Dead-end river connected wetlands occur where the river water flows in and out at the only available flow channel in the wetland (i.e. one channel only connects the wetland to the river). Both of these have furthermore been divided into the following two categories, permanent inundation (with carp presence) and permanent inundation (with-out carp presence) as well as being influenced by agricultural drainage. In the fifth category are the managed wetlands, which are controlled through drying and wetting cycles. These managed wetlands could be made of either through flow or dead end wetlands, in both cases carp restriction would be built into the wetland control barriers to restrict large carp from entering during reflooding of the wetland and potentially disturbing the sediment. This study addressed five categories of wetlands as follows: Category 1, Through flow, permanent inundation (Paiwalla wetland) Category 2, Through flow, permanent inundation & irrigation drainage (Sunnyside wetland) Category 3, Dead end, permanent inundation (Lock 6 wetland) Category 4, Dead end, permanent inundation & irrigation drainage (Reedy Creek wetland)

66

Regional Scale Modelling of the lower River Murray wetlands Category 5, Managed - Dry periods & carp restriction (Pilby Creek wetland, in this case a dead end wetland) Category 1: Through flow wetlands permanent inundation and no irrigation drainage (Paiwalla wetland) Paiwalla and Sunnyside wetlands are situated approximately 14 km North of Murray Bridge in the lower River Murray region. Prior to swamp reclamation, the two wetlands were a part of the same riparian wetland. Sellicks swamp was reclaimed in 1967 (Bartsch 1997) and was until recently used as irrigated dairy pasture. Due to the nature of the reclamation, the retired pasture area was situated lower than the average pool height of the River Murray (Philcox 1997). Both water seepage from the river and irrigation, necessitated the construction of drainage channels within the reclaimed area to remove excess water and prevent water logging. The collected irrigation drainage water was pumped into the Sunnyside wetland. Paiwalla wetland is situated directly north or upstream of Sellicks swamp (Figure 13). For the purpose of this study, as in Bartsch (1997), it was assumed that Paiwalla wetland was not influenced by irrigation drainage discharge. This assumption was justified by Paiwalla being upstream of Sellicks swamp and did not receive direct irrigation drainage through active pumping. Paiwalla acts as an “exemplar” of category 1 wetlands; permanently inundated through flow wetlands with no irrigation drainage. Category 2: Through flow wetlands with irrigation drainage (Sunnyside wetland) Sunnyside is south of and downstream from Sellicks swamp (Figure 13). Like Paiwalla wetland, Sunnyside was considered to be a through flow wetland, the main difference between the two wetlands being the influence of Sellicks swamp irrigation drainage outlet that flowed directly into the northeast corner of Sunnyside. Sunnyside was used in the study as an “exemplar” for category 2 wetlands; through flow permanently inundated wetlands with irrigation drainage.

67

Regional Scale Modelling of the lower River Murray wetlands

Figure 13: Paiwalla & Sunnyside wetlands

Category 3: Dead end wetlands with no irrigation drainage (Lock 6 wetland) Lock 6 wetland (Figure 14) is a dead end wetland situated immediately upstream of Lock 6 in the Riverland region of the River Murray. Due to the controlled and constantly maintained volume of Lock 6, the wetland is permanently inundated. As with all unmanaged wetlands directly connected with the lower River Murray, there is carp presence potentially contributing to resuspension of sediment and therefore wetland turbidity. There is no irrigation drainage directly affecting this wetland. Permanent inundation and high turbidity levels have led to a reduction in macrophyte growth and therefore nutrient uptake. Lock 6 wetland is therefore, considered to be in a degraded state, with an increased possibility of blue green algae growth (Blindow et al. 1993; Boon et al. 1997; Scheffer 1998; Scheffer et al. 1993; Stephen et al. 1998), see section 1.2. Lock 6 wetland was used in the modelling project as an “exemplar” for category 3 wetlands; dead end wetlands with no irrigation drainage. It served, in the modelling of potential management strategies (described in section 3.4), as a prime example of a

68

Regional Scale Modelling of the lower River Murray wetlands wetland that has the potential of being improved through management. The management considered in modelling scenarios was the construction of a wetland weir, as found in neighbouring Pilby Creek wetland, for the introduction of dry periods.

Figure 14: Lock 6 and Pilby Creek wetlands

Category 4: Dead end wetlands with irrigation drainage (Reedy Creek wetland) Reedy Creek wetland (Figure 15) is permanently inundated and situated approximately 6 km south of Mannum in the lower River Murray region. It is influenced by irrigation drainage runoff from surrounding agricultural areas. Intensive monitoring of this wetland over a 2-year period provided data for wetland internal nutrient dynamics, river nutrient load and irrigation drainage from Basby farm. It was used in the project as an “exemplar” for category 4 wetlands; dead end permanently inundated wetlands with irrigation drainage. The management strategies employed in simulations for this wetland (described in section 3.4) are based on nutrient reduction of irrigation drainage using constructed wetlands.

69

Regional Scale Modelling of the lower River Murray wetlands

Figure 15: Reedy Creek wetland

Category 5: Dead end wetlands, managed through implementation of dry periods followed by large carp exclusion and no irrigation drainage (Pilby Creek wetland) Pilby Creek wetland is found directly north of Lock 6 wetland (Figure 14). A minor through flow creek “Pilby creek” feeds into the wetland at the northern end. As this creek feeds in and out at one point of the wetland only, Pilby Creek wetland is considered to be a dead end wetland with no through flow (any wetland managed wetland is considered to fall within this category for the purpose of this project although it is recognised that through flow wetlands can also be managed). There is no irrigation drainage considered to influence Pilby Creek wetland. The introduction of a control structure and the consequent management with dry periods has dried and compacted the sediment and returned the wetland to a clear stable state (discussed in section 1.2.2). A further advantage of the management has been the exclusion of large carp though screening off of the inflow channel. The potential re-suspension of sediment by bottom feeding carp has therefore been reduced.

70

Regional Scale Modelling of the lower River Murray wetlands Pilby Creek wetland was used in the model to simulate an ideal target condition wetland, which is considered to be in a natural, clear, non-degraded, stable state. Pilby creek was used as an “exemplar” for category 5 wetlands; dead end wetlands managed through implementation of dry periods with carp restriction and no irrigation drainage.

3.2.2 Wetland Data The data presented in this chapter were used in developing the model as well as serving as data “exemplars” for each wetland category. The main driving variables of the model are turbidity, water temperature, solar radiation, Secchi depth and the morphological data; wetland volume and surface area. Spatially relevant driving variables include external sources of the nutrients Nitrate (as NO3-N), Soluble Reactive Phosphorous (as PO4-P) and phytoplankton, the external sources being river exchange; and if applicable irrigation drainage. Additional monitoring time-series of wetlands, not used in WETMOD 2 development, were used for validation and confirmation. The validation data were prepared in the same manner as the driving variable data as described below. Time Series of Wetland Physical Condition One of the key driving variables is wetland turbidity, which affects PO 4-P and NO3-N sedimentation and re-suspension, as well as macrophyte and phytoplankton growth. The turbidity time-series are provided in Figure 16A, D and G. Most of the wetland data was monitored in 1997 however, Reedy Creek wetland (category 4) was monitored between 20/10/1999 and 16/09/2001, and represents the most complete and reliable study in the database. Wetland water temperature data can be seen in Figure 16B, E and H. This driving variable affects zooplankton and phytoplankton growth, grazing and mortality, and macrophyte growth. The Secchi depth is another driving variable required for the modelling of macrophyte growth. Secchi depth was not monitored constantly for either category 1 & 2 (Paiwalla & Sunnyside) wetlands, but assumed to be constant at 0.7 metres due to the wetland depth. In Reedy Creek wetland, a turbid wetland, the Secchi depth was assumed to be constant at 0.2 metres. The Secchi depth for Pilby Creek wetland, 71

Regional Scale Modelling of the lower River Murray wetlands being in a stable clear state where the bottom could be observed, was assumed to be at a constant 1.8 metres. The Secchi depth for Loch 6 was considered to be variable and was therefore calculated from turbidity data. Equation 1 was used to calculate Secchi depth from turbidity data and was derived from the power regression of Secchi data versus turbidity data from van der Wielen‟s time-series (van der Wielen nd), where the only reliable monitoring of both had been undertaken. The R 2 of the power regression was 0.7748. Equation 1:

Secchi

2 . 4355 Turbidity ^ -0.5675

72

P a iw a lla W e tla nd 1 9 9 7 S unnysid e W e tla nd 1 9 9 7 L o c k 6 w e tla nd 1 9 9 7 P ilb y C re e k W e tla nd 1 9 9 7

S o lar R ad iatio n P ilb y C re e k & L o ck 6 We tlan d s

I

35

30

20

15

10

5

0 20

15

10

5

0

Fe b -0 1

M a y -0 1

25

M a y -0 1

30

A p r-0 1

35

A p r-0 1

40 M a r-0 1

S o lar R ad iatio n R e e d y C re e k We tlan d

M a r-0 1

Fe b -0 1

0 J a n -0 1

5

J a n -0 1

15

D e c -0 0

30

M a y -0 1

A p r-0 1

M a r-0 1

Fe b -0 1

J a n -0 1

D e c -0 0

N o v -0 0

G

D e c -0 0

H

N o v -0 0

35

N o v -0 0

Wate r T e mp e ratu re

-5 0 O c t-0 0

50

O c t-0 0

0 S e p -0 0

50

S e p -0 0

100

A u g -0 0

40 100

A u g -0 0

200

J u l-0 0

200

J u n -0 0

N TU 250

J u l-0 0

20

d eg C

S e p -9 7

A u g -9 7

150

J u n -0 0

S e p -9 7

A u g -9 7

J u l-9 7

300

250

O c t-0 0

0 300

S e p -0 0

5 J u l-9 7

120 350

A u g -0 0

10

T u rb id ity

J u l-0 0

15 25

M J p er sq u are m eter

25 J u n -9 7

140 350

J u n -0 0

30

S e p -9 8

F

A u g -9 8

S o lar R ad iatio n P aiw alla & S u n n ysid e We tlan d s

J u l-9 8

0 J u n -9 7

10 M a y -9 7

D

J u n -9 8

15

M a y -9 7

E

M a y -9 8

25 A p r-9 7

0

A p r-9 7

20

A p r-9 8

100

M a r-9 7

N TU

160

M a r-9 7

Wate r T e mp e ratu re Fe b -9 7

80

Fe b -9 7

d eg C

A u g -9 7

J u l-9 7

J u n -9 7

T u rb id ity

M a r-9 8

20

M J p e r s q u a re m e te r

A u g -9 7

J u l-9 7

J u n -9 7

M a y -9 7

A p r-9 7

M a r-9 7

Fe b -9 7

N TU 180

Fe b -9 8

A u g -9 8

J u l-9 8

J u n -9 8

deg C

B

M a y -9 7

A p r-9 7

M a r-9 7

Fe b -9 7

A

M a y -9 8

M J p e r s q u a re m e te r

C

A p r-9 8

M a r-9 8

Fe b -9 8

Regional Scale Modelling of the lower River Murray wetlands

T u rb id ity

60 150

0

30

Wate r T e mp e ratu re

20 25

25

20

15

5 10 10

5

0

R e e d y C re e k W e tla nd 2 0 0 0 -2 0 0 1

Figure 16: Wetlands (Categories 1 to 5) Driving Variables Turbidity, Water Temperature & Solar Radiation (see also in Appendix B)

73

Regional Scale Modelling of the lower River Murray wetlands Climatic Time Series Solar Radiation The solar radiation data used in WETMOD 1 were obtained from literature (Bowles et al. 1979; Cetin 2001). This literature data were adequate in the early development of the model. However, the source area of the radiation is somewhat remote from the lower River Murray and did not provide the model with reliable daily values. A CD containing solar radiation data was obtained from the Bureau of Meteorology (Forgan 2001). Solar radiation time-series were obtained from BOM solar data, as derived from the processing of Japanese Geostationary Meteorological Satellite (GMS) imagery. The data is essentially exposure data from Meteorological Satellite Imagery collected daily. Data for any given location is obtained for the pixel encompassing the given area and is not interpolated. The resolution of each pixel is between 6x6 to 24x24 km (Forgan 2001). The BOM model calculated the surface insolation (solar radiation) from the measured upward solar radiation measured by the Visible and Infrared Spin Scan Radiometer (VISSR) taking into account atmospheric influences such as the absorption by water vapour and ozone, cloud reflection and absorption. Effectively the solar radiation is modelled for hourly images from which a daily total is derived. For a detailed account of the model used to calculate the solar radiation refer to (Weymouth et al. 1994). Figure 16C, F and I show the solar radiation used as driving variables in the model. Solar radiation is used in the model to calculate macrophyte and phytoplankton productivity. Unfortunately, no data were available for the period between February 1994 and July 1997, which is the period that Paiwalla, Sunnyside, Pilby and Lock 6 wetlands were monitored. However, South Australia is a very dry State with minimal cloud cover; therefore the seasonal pattern of the solar radiation for 1998 is similar to what would be expected for 1997. The intensity of the solar radiation, which impacts on macrophyte and phytoplankton biomass growth, follows such a seasonal pattern. It was found during simulation test runs of WETMOD 2 that slight variation in the solar radiation time-series does not have a noticeable impact on the simulation output. As the use of 1998 solar radiation pattern is assumed to have minimal impact on the modelling accuracy, the Solar Radiation for 1998 is used in WETMOD 2 for the simulation of Paiwalla, Sunnyside, Pilby and Lock 6 wetlands. The solar radiation 74

Regional Scale Modelling of the lower River Murray wetlands data were available for the period where Reedy Creek wetland was monitored, and was used accordingly. The solar radiation at two locations, one at either end of the study area, was adopted into the model. Simulation of either solar radiation positions did not alter the modelled output significantly. Solar radiation from the northern end of the study area was therefore used for all modelling scenarios, as this contained the least amount of missing daily values and therefore represented the most complete seasonal range of solar radiation. Wetland Morphology - Spatial Data In WETMOD 1, subjective estimated wetland volume was used in each wetland simulation. One modification for WETMOD 2 was to use a more correct wetland volume during wetland scenario modelling. The wetland volumes for all wetlands that can be potentially simulated by WETMOD 2 were obtained or estimated using the wetland surface area multiplied by the wetland depth. The GIS data covering the wetlands and the lower River Murray and Locks were used for a number of data extractions. These GIS data reflect the wetlands as shown in the “Wetlands Atlas of the South Australian Murray Valley” (Jensen et al. 1996). The data extracted, related to wetland morphology (surface area, depth and river connection), as well as the geographical position of the wetland in relation to the river. The wetlands data sets, “Locks”, and “lower River Murray”, were also used in determination of regional scale scenarios. Wetland volume was used in the model to calculate nutrient concentration as well as the nutrient and water exchange capacity of the wetland. Therefore, relatively accurate wetland volume estimation was required. As no DEM‟s were available the surface area in conjunction with the wetland depth provided the necessary wetland volume estimation. The surface areas of the wetlands were obtained from the digitised version of the SA Wetland Atlas. The “Wetlands Management Study report” (Nichols 1998) surveyed many of the lower River Murray wetlands, and contains some data relating to average wetland depth. Many wetlands in the lower River Murray are regular in depth (Recknagel nd; van der Wielen nd), it therefore seemed justified, given the lack of better data, to assume each wetland to be a basin of uniform depth and the “Wetlands Management Study report” (Nichols 1998) depth data used in the model. 75

Regional Scale Modelling of the lower River Murray wetlands Although many of the wetlands described in the “Wetlands Management Study report” (Nichols 1998) had an estimate of their average depth, some did not have quantitative data for depth, and were simply referred to as shallow, deep, or unknown. Given that the model needs quantitative data for depth, assumptions had to be made regarding descriptive terms. For all the wetlands described as: shallow

a depth of 0.3 metres was used in the model,

deep

a depth of 2 metres was used, and

“unknown”

an average value of 0.92 metres was used.

This last figure was calculated from the actual wetland depths presented in the “Wetlands Management Study report” (Nichols 1998). The wetland volume was calculated using Equation 2, the results, for “exemplar” category wetlands only, are presented in Table 2. The wetland volume was used in the nutrient sector of the model (section 3.1.2) and the nutrient exchange sector (section 3.1.2). Equation 2:

Wetland _ Volume

Wetland _ Surface _ Area

Wetland _ Depth

Table 2: Wetland Morphology Wetland Wetland Name Category

Area Depth m Volume Hectares m3

1

Paiwalla Wetland

49.009

0.7

343061

2

Sunnyside Wetland

27.309

0.7

191160

3

Lock 6 Wetland

17.92

0.92

164860

4

Reedy Creek Wetland 98.633

0.8

789064

5

Pilby Creek Wetland

1.8

215843

11.991

Time Series Irrigation Drainage A number of the wetlands under consideration are influenced by irrigation drainage runoff to varying degrees. As irrigation drainage can be a source of high nutrient loads, this may have a significant impact on wetland nutrient content and must therefore be taken into consideration in wetland modelling. In WETMOD 1, a constant nutrient load contribution from irrigation drainage flow was assumed for all irrigation affected wetlands. The extended model includes time-series data for irrigation drainage nutrient contribution, and therefore nutrient and flow variations within the irrigation drainage.

76

Regional Scale Modelling of the lower River Murray wetlands “Wetlands Management Study report” (Nichols 1998) was again used to identify features of wetlands. In this instance where Nichols (1998) identified wetlands subject to 10% irrigation inflow or more (where 10% of inflow into a wetland can be assumed to be from irrigation areas) were considered as irrigation impacted wetlands during modelling scenarios. Two of the wetlands (Sunnyside and Reedy Creek wetlands) considered in the development of the wetland category structure, have irrigation drainage inflow. For both of these wetlands, monitoring of the drainage inflow was included during the wetland-monitoring project. These data were used in simulation modelling of these wetlands and their respective categories. The pump supplying the irrigation drainage to Sunnyside wetland was not observed at every monitoring date. The pumping of irrigation into Sunnyside wetland may have occurred either intermittently or daily. In either case, the volume pumped will have varied with requirements. In a situation of intermittent pumping, it is not possible to retrospectively estimate when pumping occurred, nor the nutrient concentration of the drainage water. In the absence of better data constant daily pumping was assumed based on the agricultural need to prevent water logging of reclaimed dairy pasture and the raising of water tables that can cause damage to pasture growth (Harrison 1994). The data shown in Figure 17 provides the model with additional input of NO3-N & PO4-P and phytoplankton to Sunnyside wetland, received as irrigation drainage. The inflow amount into the wetland can be set at a constant volume, the units being in litres per day. The supply of irrigation drainage to Reedy Creek wetland was monitored at one inlet. The flow volume at this inlet was not monitored and an annual rate of 600 ML was estimated for this inlet into Reedy creek wetland (Wen 2002b). The inflow amount is controlled by an estimate where the volume distribution pattern is based on the relative average monthly precipitation, the distribution pattern having a mean of one over a one-year period. Therefore, the monthly drainage pattern resembles that of the average precipitation pattern. The irrigation drainage flow pattern for Reedy Creek wetland was adopted to account for the estimated load of 600 ML per annum. The irrigation and drainage multiplication factor chosen during modelling, in the case of Reedy Creek wetland, is therefore a direct multiplication of estimated nutrient inflow loads. The Reedy Creek wetland base irrigation rate of 600 ML per annum is included 77

Regional Scale Modelling of the lower River Murray wetlands once irrigation drainage flow simulation is selected for the wetland category. Figure 17D shows the irrigation and drainage inflow pattern developed and Figure 17E, F and G the additional input of NO3-N & PO4-P and phytoplankton loads supplied as part of the irrigation and precipitation drainage. Surface Runoff Data As the lower River Murray flows through a predominantly arid landscape water contribution through precipitation does not account for a significant nutrient or water source for most of the wetlands, the exception being Reedy Creek wetland. Therefore, to maintain the generic nature of this model site-specific surface flow would unnecessarily complicate the model with no significant advantage to modelling scenarios. Precipitation and consequent surface flows were ignored for most wetlands in this generic model, with the exception of Reedy Creek wetland that had a separate contributing minor catchment. Annual average rainfall in the east Adelaide hills was used to provide the seasonal precipitation pattern. This was believed to be the most appropriate source of a rainfall pattern as the east Adelaide hills is the source of surface runoff flowing into Reedy Creek sub-catchment, see Figure 17D.

78

A u g -9 7

J u l-9 7

0 .4 5

G

0 .3 5 0 .4

80

60

40

0 .0 5 20

-2 0

0

S unnys id e W e tla nd J a n -0 1

D e c -0 0

M a y -0 1

100

M a y -0 1

120

A p r-0 1

0 .3

A p r-0 1

140

M a r-0 1

D rain ag e P h yto p lan kto n

M a r-0 1

160

Fe b -0 1

-0 .4

Fe b -0 1

0 .1

J a n -0 1

-0 .2

N o v -0 0

F

D e c -0 0

0

O c t-0 0

0 .4

S e p -0 0

1 .2

A u g -0 0

-1

M a y-0 1

Ap r-0 1

M a r-0 1

Fe b -0 1

Ja n -0 1

D e c-0 0

N o v-0 0

Oct-0 0

Se p -0 0

Au g -0 0

Ju l-0 0

0 .0 0

Ju n -0 0

0 .2 0

J u l-0 0

M a y -0 1

A p r-0 1

M a r-0 1

Fe b -0 1

m g/L -

E

N o v -0 0

0 0 .4 0

O c t-0 0

0 .1 5 0 .6 0

S e p -0 0

0 .2 0 .8 0

A u g -0 0

D r a in a g e P h yto p la n k to n

S e a s o n a l D r a in a g e P a tte r n R e e d y C r e e k S u b c a tc h m e n t

J u l-0 0

0 .6

m g /L -

D rain ag e N O 3-N J a n -0 1

0

D e c -0 0

0 .5

N o v -0 0

1

O c t-0 0

1 .5

S e p -0 0

2

A u g -0 0

--

3

J u l-0 0

2 .5

R e la tiv e R a te P e r M o n th

3 .5

J u n -0 0

A u g -9 7

J u l-9 7

J u n -9 7

D

J u n -0 0

A u g -9 7

J u l-9 7

J u n -9 7

M a y -9 7

A p r-9 7

M a r-9 7

Fe b -9 7

m g /L -

D rain ag e P O 4-P

J u n -0 0

0 .2 5

c m 3 /m 3 --

C

J u n -9 7

B

M a y -9 7

A p r-9 7

M a r-9 7

Fe b -9 7

m g /L -

A

M a y -9 7

A p r-9 7

M a r-9 7

Fe b -9 7

c m 3 /m 3 -

Regional Scale Modelling of the lower River Murray wetlands

1 .8 0

Drain ag e PO 4-P

1 .6 0

8

1 .4 0

7

1 .2 0

6

1 .0 0

5

4

3

2

1

0

1 .4

D rain ag e N O 3-N

1 1 .2

0 .8 0 .8

1

0 .6

0 .4

0 .2 0 .2

0

R e e d y C re e k W e tla nd

Figure 17: Sunnyside Irrigation Drainage PO4-P, NO3-N, Phytoplankton and Estimated Flow Volume (see also in Appendix B)

79

Regional Scale Modelling of the lower River Murray wetlands

3.2.3 River Data External sources, such as river exchange, precipitation, and irrigation drainage, impact upon wetlands. The most important of these for most of the considered wetlands is river exchange. Although the river flow data are limited to the Lock locations, using this data for relatively long stretches of the river is more appropriate than using models of river flow and inundation. Around Mildura the river fall is less than 5cm per kilometre and near the sea is as little as 1.6 cm per kilometre (Mackay et al. 1990). Therefore, due to the shallow gradient of the river as it flows in its course through South Australia (Walker 1985) with alternating flow direction based on wind direction, the development of a rudimentary flow model becomes difficult and would add a complexity and inaccuracy that would compound in the generic ecosystem model WETMOD 2. In the past, series of aerial photographs and satellite images have been used to develop a flood inundation model (FIM) (Overton 2000; Overton 2005). The data required which is water exchange between a wetland and the river, is dependent on river flow and could not be extracted from FIM for individual wetlands. The development of an estimation of the exchange volume between the wetlands and the river was achieved using WETMOD 2 in combination with river flow and nutrient load. The methodology of estimating the exchange volume, between the river and individual wetlands, is described below and is a major output of the model. River Flow Volume As all the wetlands considered in this model are permanently inundated and have a constant connection with the river, it was assumed that the controlling factor for nutrient exchange between the river and the wetlands is river flow volume. The river flow is monitored at each river lock and this data is presented in Table 1. The flow volume of the River Murray has been monitored daily since the construction of the Locks in the late 1920‟s (Lock 6 being completed in 1930), the relevant time-series for this project was obtained from the Murray Darling Basin Commission (MDBC). This provides an important source of information that can be related to the connection between wetlands and the river and consequently exchange of nutrients.

80

Regional Scale Modelling of the lower River Murray wetlands On occasional days where river flow data were unavailable, a linear interpolation between the monitored dates was performed. However, for a fortnight in December 2000, a number of the locks failed to monitor the flow volume due to particularly high flow levels during a flood. Fortunately, the locks at the beginning and end of the river stretch under consideration recorded the flow through their location. Regression equations, based on the correlation of flow during simultaneously monitored dates in the weeks preceding the flood, were used to estimate the missing flow data based on the nearest lock with monitored flow volume. The R2 values for these regression equations ranged from 0.95 to 0.99. To corroborate these estimated flow volumes, the data were compared to the flow levels monitored at an independent lock. Through this methodology, it was possible to reconstruct a probable flow volume pattern during the fortnight of high flow event through all the relevant locks. Figure 18 shows the river flow pattern and volume used in the modelling. River Water Quality The River Murray is the major nutrient source or deposit area for wetlands within the study area. The river nutrient data, monitored simultaneously with wetland data, provides a more accurate representation of the modelled situation and also a more accurate comparison of wetland vs. river nutrient load than lock monitored data. This is due to river nutrient data monitored simultaneously with wetland data was a direct indication of the river nutrient load at that time and not at a location further from the site such as at locks. In those wetlands where the river and wetland were monitored concurrently (Reedy Creek, Sunnyside and Paiwalla wetlands) only the Reedy Creek river nutrient data were comprehensive enough to be considered for the wetland modelling. Consequently, for Reedy Creek wetland it is possible to simulate the wetland with either the MDBC data or the concurrent monitored river data. Other sources of river data were required for the remaining wetlands. River data from the same time period as wetland monitoring that could be included in the model were acquired from the sources listed in Table 1. The river data quality, obtained from DEH and MDBC see Table 1, were suitable for use in the model, see Figure 18. As with the wetland data, all the river data were extrapolated linearly to obtain daily values.

81

Regional Scale Modelling of the lower River Murray wetlands River concentrations of both NO3-N and PO4-P were generally higher than the concentration within the wetlands (Figure 18 and Figure 19), exceptions occurred where wetlands had a high-modelled river exchange volume. This suggests that where there is an inflow of nutrient from the river to the wetland, the river will act as a source of both NO3-N and PO4-P to the wetland. If the wetland processes manage to take up the nutrients in macrophyte and phytoplankton growth, and these are retained within the wetland, the water outflow from the wetland into the river would contain lower nutrient concentrations. The wetland would therefore act as a nutrient sink. For wetlands with higher concentrations of nutrients than the river, the wetlands may act as point sources of nutrients to the river. Figure 18A and E contain the MDBC river time-series of PO4-P and Figure 18I contains the Reedy Creek river time-series of PO4-P. River Filterable Reactive Phosphorus as PO4-P monitoring was discontinued early in the wetlands study time period, whereas Filterable Reactive Phosphorus as P was continued. As WETMOD requires phosphorus as PO4-P, a linear regression was calculated between Filterable Reactive Phosphorus as P and Filterable Reactive Phosphorus as PO 4-P, from a time when both were monitored. Equation 3 was used to convert the monitored Filterable Reactive Phosphorus as P to PO4-P. The R2 for Equation 3 was 0.9988. Equation 3:

PO

4

P

3 . 0575

P

0 . 0004

The Reedy Creek river monitored Filterable Reactive Phosphorus time-series (PO4-P) could be used in the model without any conversion. Figure 18B and F contain the MDBC and Figure 18J the Reedy Creek time-series of river Nitrate as NO3-N. As with PO4-P, river Nitrate as NO3-N monitoring was discontinued early in the wetlands study time period. As the model input required is Nitrate as NO3-N, a linear regression to obtain estimated NO3-N was calculated using Equation 4 to convert from Nitrate as N to Nitrate as NO3-N. The R2 for the linear regression was 0.9998. Equation 4:

NO

3

N

4 . 412

N

0 . 0044

The Reedy Creek river monitored Nitrate time-series (NO3-N) could be used in the model without any conversion.

82

Regional Scale Modelling of the lower River Murray wetlands Chlorophyll-a is used as a surrogate for phytoplankton. The conversion between the two is given in Equation 5 below where C is Chlorophyll-a in μg/L and P is phytoplankton in cm3/m3 (Recknagel nd). Equation 5:

P

C 2 .5

The MDBC could not supply river Chlorophyll-a, Figure 18C and G for the entire study area, as monitoring ceased in early 1998 for some locations, therefore Chlorophyll-a was not available at all monitoring locations. Only the Reedy Creek project monitored river Chlorophyll-a concurrently and comprehensively for the entire study period, see Figure 18K. However, as phytoplankton exchange plays an important role in the wetland modelling it was opted to use data from further downstream rather than none at all. Therefore, for all other wetlands the MDBC river Chlorophyll-a time-series monitored at Murray Bridge was used in the model for all river to wetland inflow. The remoteness of Murray Bridge from Pilby and Lock 6 wetlands must be taken into consideration when assessing the model simulation performance for these wetlands. The modelling of Category 4 wetlands used the Chlorophyll-a time-series obtained from the Reedy Creek wetland data. This approach was far from optimal. However, as the data was not central in the development of the model and it could be ignored during validation it was deemed acceptable during this stage of the modelling process. Through future river Chlorophyll-a monitoring this discrepancy could be remedied.

83

P a iw a lla & S unnys id e W e tla nd s

4000 5

4

3

2

1

0

H River Flow

L

12000

10000

8000

6000

4000

River Flow

40000

35000

30000

25000

20000

2000 2000 15000 5000

0 0 10000 0

L o c k 6 & P ilb y C re e k W e tla nd s

May-01

14

7

May-01

8

7

Apr-01

8

Apr-01

16

Mar-01

K

Mar-01

River Phytoplankton

Feb-01

9

May-01

Apr-01

Mar-01

Feb-01

0.1

Feb-01

0 Jan-01

0.2

0.2

Jan-01

0.4

Jan-01

0.7

Dec-00

1

Dec-00

1.2

Nov-00

J

Nov-00

0.8

Oct-00

1.4

Oct-00

River NO3-N

Sep-00

May-01

Apr-01

Mar-01

Feb-01

Jan-01

Dec-00

Nov-00

Oct-00

Sep-00

0

Sep-00

0 Aug-00

0.2

0

Aug-00

0.1 Jul-00

0.05

Aug-00

0.6

Jun-00

0.2

Jul-00

0.8

mg/L -

mg/L -

0.3

Jun-00

Sep-97

0.1

Jul-00

6

cm3/m3 -

Sep-97

Aug-97

Jul-97

0.4

Jun-00

Sep-97

Aug-97

Jul-97

Jun-97

0.5

Dec-00

6000

I

Nov-00

River Flow Jun-97

0.25

Oct-00

8000

May-97

1.2

0.6

Sep-00

10000

Apr-97

1.4

0.7

Aug-00

14000

Mar-97

0.8

0.3

mg/L -

0.4

0.35

Jul-00

16000

ML/day -

D River PO4-P

Jun-00

0

Sep-97

1

Aug-97

2

Aug-97

3

Jul-97

4

May-97

F

Jul-97

G

Jun-97

5

Feb-97

E

Jun-97

6

May-97

River Phytoplankton

May-97

0

Apr-97

0.5

Apr-97

2

Apr-97

2.5

Mar-97

1

mg/L -

1.5

Feb-97

River NO3-N

Mar-97

9

cm3/m3 -

Aug-97

Jul-97

Jun-97

May-97

Apr-97

Mar-97

Feb-97

mg/L 0.15

Feb-97

Aug-97

Jul-97

mg/L 0.2

Mar-97

12000

ML/day -

Aug-97

Jul-97

Jun-97

May-97

Apr-97

Mar-97

Feb-97

River PO4-P

Feb-97

Aug-97

Jul-97

Jun-97

C

Jun-97

cm3/m3 -

B

May-97

Apr-97

Mar-97

Feb-97

A

May-97

Apr-97

Mar-97

Feb-97

ML/day -

Regional Scale Modelling of the lower River Murray wetlands

River PO4-P

0.8

1

0.6

0.4

River NO3-N

0.6

0.5

0.4

0.3

0

River Phytoplankton

12

10 8

6

4

2

0

R e e d y C re e k W e tla nd

Figure 18: River Murray Nutrient & Phytoplankton Time Series as well as River Flow Volume (see also in Appendix B)

84

Regional Scale Modelling of the lower River Murray wetlands

3.3 Data Handling Calculating River and Wetland Exchange One of the major attributes of WETMOD 2 is its ability to calculate exchange rate (turnover) of water and nutrients between the wetlands and the river. The nutrient exchange between the river and the wetland is calculated for each time-step in the model. The net outflow of nutrient from the wetland is subtracted from the net inflow of nutrient. The equation for the bi-directional exchange between the wetland and the river

NR t

[mg/day] (Nutrient Retention) can be expressed as per Equation 6 with CR

and CW denoting concentrations of nutrients in the river and wetland respectively, and ƒ being a fraction of river flow rate R [L/day], see Figure 1. Equation 6:

NR t

(C R

CW ) f

R

The factor f quantifies in a simple way, how the wetland is connected to the river. It summarises the complex morphology of linkage of wetlands and the river through channels, topographic conditions and distance. The factor f is varied for each modelling scenario, and the model performance with respect to PO4-P and NO3-N is tested. The best performing scenario is chosen to represent the optimum exchange volume for a given wetland. An example of the exchange volume estimation is provided in section 4.1. The methodology for the assessment of model performance is discussed in section 3.3.1. Based on the modelled exchange volume it is possible to estimate the wetland water turnover rate where the turnover rate (τ [1/day]) relates to the factor f, R and Vw as per Equation 7, Vw being the wetland volume. Equation 7:

f

R

VW

The turnover rate gives a secondary method to assess the potential accuracy of the rate of exchange expected for a given wetland, see section 3.4.2. As mentioned in section 1.1.2 the potential nutrient uptake of wetlands is related to the turnover rate, i.e. the retention time.

85

Regional Scale Modelling of the lower River Murray wetlands Model Expected Simulation Output (monitored data) As mentioned in section 3.1 the model simulates the PO4-P and NO3-N concentration in a wetland and the phytoplankton, macrophyte and zooplankton biomasses. The wetlands used as “exemplars” were monitored for the outputs PO4-P, NO3-N and phytoplankton. These output data were used to test, develop, and calibrate the model, and to adjust the exchange volume and nutrient inflow to achieve a best fit. Neither zooplankton nor macrophyte biomass were used in the first instance as these data were unavailable for comparison with model outputs and thus could not be used to assess the model. Any discussion and conclusions made based on macrophyte and zooplankton modelled biomass is limited by this lack of data and may not necessarily reflect what may occur in a natural setting. Validation of the model continued as discussed in section 4.2. The monitored data for the different wetlands representing the categories, i.e. providing the “exemplar” data, are presented in Figure 19, which an ideal model would simulate accurately. For Paiwalla and Sunnyside wetlands Figure 19A, B and C represents the monitored PO4-P, NO3-N concentration and phytoplankton biomass respectively. Figure 19D, E and F represent the Lock 6 and Pilby Creek wetlands monitored PO4-P, NO3-N concentration and phytoplankton biomass respectively. Figure 19G, H and I the Reedy Creek wetland monitored PO4-P, NO3-N concentration and phytoplankton biomass respectively. At least three monitoring sites were used for each of the wetlands, usually one close to the inlet (or the river), one in the littoral zone of the wetland, and one in the open water of the wetland. The model however uses the driving variables from the open water monitoring site of the wetland. The monitored data used to test and validate the model were also derived from the open water location. To represent the variability of the wetlands and therefore the potential variability of the modelling outcome, the Standard Error was calculated for each sampling date and is displayed along with monitored concentrations in Figure 19. Only data from one sampling location in Reedy Creek was obtained (i.e. only one measurement per monitoring date), the Standard Error for the entire monitoring period had been calculated based on all sampling dates Figure 19G, H and I.

86

Regional Scale Modelling of the lower River Murray wetlands

A

D

P O 4-P

G

PO4-P

4

PO4-P

1.2

0.2 3 .5

1 3

0.15 0.8

2 .5

0.1

mg/L

0.6

mg/L

m g /L

2

1 .5

0.05

0.4

1

0.2 0 .5

Feb-01

Mar-01

Apr-01

May-01

Feb-01

Mar-01

Apr-01

May-01

Fe b -0 1

M a r-0 1

A p r-0 1

M a y -0 1

Dec-00

Nov-00

Oct-00

Sep-00

Aug-00

Jul-00

Jun-00

Sep-97

Aug-97

Jul-97

Jan-01

E

0.7

Jun-97

May-97

-0.2

NO3-N

Jan-01

B

-0.05

0

J a n -0 1

NO3-N

Apr-97

A u g -9 7

J u l-9 7

J u n -9 7

M a y -9 7

A p r-9 7

-0 .5

M a r-9 7

Fe b -9 7

0

Mar-97

Feb-97

0

NO3-N

H 0.8

0.7

0.6

0.7

0.6 0.5

0.6

0.5

0.4

0.5

0.2

Phytoplankton

C

II

12

12

10

10

8

8

cm3/m3

cm 3/m 3

1.2

0.6

6

Dec-00

Nov-00

Oct-00

Sep-00

Aug-00

P h yto p lan kto n

1.4

0.8

Jul-00

Sep-97

Aug-97

Jul-97

Jun-97

May-97

Apr-97

Phytoplankton

F

-0.1

F14

1

cm3/m3

0

Mar-97

-0.1

-0.3

C

0.1

0

-0.2

0.3 0.2

0.1

Aug-97

Jul-97

Jun-97

May-97

Apr-97

Mar-97

-0.1

Feb-97

0

0.4

Jun-00

mg/L

0.1

0.3

Feb-97

mg/L

0.2

mg/L

0.4

0.3

14

6

0.4

4

4

0.2

2

P a iw a lla W e tla nd 1 9 9 7

S unnysid e W e tla nd 1 9 9 7

2

L o c k 6 w e tla nd 1 9 9 7

P ilb y C re e k W e tla nd 1 9 9 7

D e c -0 0

N o v -0 0

O c t-0 0

S e p -0 0

A u g -0 0

J u l-0 0

J u n -0 0

Sep-97

Aug-97

Jul-97

0

Jun-97

May-97

Apr-97

Mar-97

0 Feb-97

-0.4

Aug-97

Jul-97

Jun-97

May-97

Apr-97

Mar-97

Feb-97

0 -0.2

R e e d y C re e k W e tla nd 2 0 0 0 -2 0 0 1

Figure 19: Wetlands (Categories 1 to 5) Monitored Nutrients and Phytoplankton

87

Regional Scale Modelling of the lower River Murray wetlands

3.3.1 Model Calibration As WETMOD 1 was substantially adapted and the driving variable database rebuilt with updated data for WETMOD 2, the model calibrations needed re-evaluation. The model was run based on its original calibrations and the optimal exchange rate established. Once the optimal exchange rate had been estimated the model output was assessed to identifying discrepancies such as unexpected trends. The model parameters identified to be adversely affecting the model output were recalibrated to account for the new data set. Many parameters calibrated in the original WETMOD 1 model were unaltered with only the following parameters being recalibrated. Turbidity sedimentation threshold for phytoplankton was recalibrated from 70 NTU to 95 NTU. The ones for PO4-P and NO3-N were unaltered at 70 NTU. The sedimentation rate for phytoplankton (pht sed) was recalibrated Zooplankton mortality rate (ZooMortRate) was recalibrated The maximum phytoplankton growth rate (Phyt max) was recalibrated Once the model had been recalibrated the exchange rate between the wetlands and the river was reconfirmed and readjusted as appropriate.

3.3.2 Validation Procedure It was found during the initial validation procedure that squared error estimates overrepresented errors at peaks in the model output. This was seen as an inaccurate representation of a generic model where short term peak fluctuations can not be modelled. Therefore, an evaluation criterion where the average linear deviation from the measured values as a fraction of the average observed values was used and is referred to as D (Equation 8). The index D is derived as per Equation 8 with M being the modelled and E monitored PO4-P, NO3-N concentrations or phytoplankton biomass at the monitoring dates. Equation 8:

D

ABS M

E

E

Any reduction in D was considered to be an improvement in performance of a model scenario, however some improvements had a greater impact than others and should be emphasised. The following descriptive grades of improvement were adopted to better 88

Regional Scale Modelling of the lower River Murray wetlands convey the importance of each improvement. Improvements of 10% or greater were regarded as noteworthy, improvements of 20% or greater to be considerable and 30% or above to be a significant improvement to the modelling performance. When assessing modelling performance the PO4-P D was valued prior to NO3-N D as PO4-P does not escape to or return from a gaseous phase, like NO3-N does in a wetland environment, and is therefore more constant in the system, see section 1.2. In scenarios where PO4-P D optimum performance could not be achieved due to data or modelling peculiarities, NO3-N optimum D performance was strived for. As mentioned in section 3.2.3 the Chlorophyll-a data, used to calculate phytoplankton biomass, were sourced from Murray Bridge. Due to this limit of location specific concentrations of phytoplankton, and the methodology for calculating the phytoplankton from Chlorophyll-a concentration, phytoplankton was never used to assess the model performance.

3.4 Wetland Management 3.4.1 Options In the application of the model there were two management strategies simulated by WETMOD for the wetlands of the lower River Murray, turbidity reduction and irrigation drainage reduction. Scenarios were developed for potential turbidity reduction management for both Lock 6 wetland and Reedy Creek wetland. Scenarios of the second management strategy were developed for only the Reedy Creek wetland; however, it was applied both with and without the management strategy of turbidity reduction. The management strategies have two different approaches to nutrient reduction within a wetland, therefore potentially reducing both nutrient and phytoplankton outflow from the wetland. Strategy; Turbidity reduction - Construction of wetland flow control structures and grids for introduction of wetland dry periods and consequent carp restriction. The presumed wetland response expected is sediment compaction as a consequence of a wetland drying, see section 1.3. Through grids being constructed at the wetland flow inlet large carp re-colonisation would be avoided minimising any bioturbation impact i.e. sediment re-suspension and therefore turbidity. Management simulations were 89

Regional Scale Modelling of the lower River Murray wetlands performed for 0% reduction in turbidity, 25%, 50% and 75% (100% reduction in turbidity regarded as unattainable). Secchi depth increases with the reduction of turbidity; therefore the Secchi depth was altered appropriately for each assumed turbidity reduction scenario. In Lock 6 wetland management scenarios, where turbidity was reduced by: o 25% the Secchi depth was set at 0.3 metres, o 50% the Secchi depth was set at 0.6 metres, and o 75% the Secchi depth was set at the wetland depth of 0.9 metres. Strategy; Irrigation drainage nutrient reduction - Constructed wetlands for nutrient removal. Nutrient normally entering the wetland through irrigation drainage would be diverted into constructed wetlands, where macrophytes would assist in nutrient uptake. Theoretically the harvesting of the macrophytes would remove the nutrients permanently from the system. The effective removal of nutrients can be variable, as discussed in the introduction, see section 1.3. Therefore, variable nutrient removal successes were modelled with scenarios representing 0% nutrient reduction, 25%, 50% and 75%. An example of “fully” restored wetlands with 85%, 90% and 95% nutrient reduction was also simulated. To examine the impact of a two pronged management strategy a combination of both management interventions was simulated for Reedy Creek wetland. It was assumed that in the period prior to simulation the wetland had been dry and therefore resulted in turbidity reduction. The scenarios of the Reedy Creek twin management strategies were simulated for twelve months with no allowance made for a second dry period. Simulations were made for 25, 50, 75, 85, 90 and 95% irrigation drainage load reduction (nutrient reduction scenarios). High irrigation nutrient reductions were performed to display the hypothetical impacts of a nearly fully restored wetland. For each of the nutrient reduction scenarios, scenarios of 25, 50 and 75% turbidity reduction were also simulated. In Reedy Creek wetland simulations, the wetland Secchi depth during the turbidity reduction scenarios were adjusted to 0.2, 0.3, 0.6 metres and the maximum wetland depth of 0.8 metres used in the 75% turbidity reduction scenario.

90

Regional Scale Modelling of the lower River Murray wetlands Assessment of Management Scenario Impact To assess the management scenario impact on nutrient retention capacity of a wetland a comparison between the change in inflow and outflow was made, see Figure 20. The percentage Reduction of Inflow (%RI) is calculated as per Equation 13 where ID is the total Irrigation Drainage load (calculated using Equation 9). C I denotes the concentration of irrigation drainage nutrient and I the Irrigation Drainage flow in litres/day. ∆ID is the change in total Irrigation Drainage load after management and RF the total River Inflow load, RF is calculated as per Equation 10. Equation 14 calculates the percentage Reduction in Outflow (%RO), where OF is total Outflow load (calculated as per Equation 11), and ∆OF is the change in total Outflow load post management. The OF for Reedy Creek wetland and category 4 wetlands is calculated by Equation 12 to account for the additional irrigation flow volume exiting the wetland. As in Equation 6, CR and CW denote concentrations of nutrients in the river and wetland respectively and ƒ represents a fraction of the river flow rate R. Equation 9:

ID

CI I

Equation 10:

RF

CR

f

R

Equation 11:

OF

CW

f

R

Equation 12:

OF

C W (( f

Equation 13:

% RI

Equation 14:

% RO

100 100

R)

I)

ID

RF / ID

OF

OF

RF

100

100

The %RO Equation 14 above is therefore, the change in outflow due to management when compared to the status quo (no management). With a positive %RO there is a net improvement of the nutrient or phytoplankton retention of the wetland due to management. The %RI Equation 13 only applies to Reedy Creek and category 4 wetlands and represents the effective change in wetland nutrient inflow due to nutrient reduction scenario as compared with the status quo. The impact of water loss through other means, specifically evaporation, has not been included in the mass balance equations. The current method of evaporation estimation is itself inaccurate and would have added further complications, to model calibration and validation, than is acceptable at such an early stage of the model development. This is an aspect that can in future be included in the model when full monitoring (of

91

Regional Scale Modelling of the lower River Murray wetlands at least one wetland) including all water sources, sinks (including evaporation) and nutrient balance becomes available to effectively calibrate and validate the model.

River Flow volume (R) (River Nutrient load (LR) = R X CR) Fraction of river flow volume (f)

Nutrient concentration in

Nutrient concentration in

river (CR) X exchange

wetland

volume (f)

(CW)

X

(exchange volume (f) + Wetland process modelling

irrigation

flow

volume

(I)) Nutrient

concentration

from Irrigation runnoff (CI) X exchange volume (I) Nutrient retention (

NR t

) becomes a factor of exchange volume (f & R), river concentration (CR) and wetland

concentration (CW) calculated using the wetland process model. Irrigation inflow is considered where appropriate using CI and I. Change in (

NR t

) due to management is assessed for different scenarios (influenced by the

change in (CW)).

Figure 20: Wetland exchange modelling

3.4.2 Management scenarios for cumulative assessment Wetland candidates for simulations There are more than a thousand individual wetlands in the lower River Murray, ranging from small, temporary wetlands to large and more permanent examples. However, of this multitude of wetlands, only 250 individual wetlands or groups of closely related wetlands (complexes) are identified in the „Wetlands Atlas of the South Australian Murray Valley‟ (Jensen et al. 1996). For the purposes of this project, the 250 identified wetlands were perused with the intent of consideration for management. In the cumulative assessment of management scenarios two wetland categories were considered, these being category 3 (wetlands resembling Lock 6 wetland) and category 4 (wetlands resembling Reedy Creek wetland). Identified wetlands were assigned to a particular category, depending on their similarities to the

92

Regional Scale Modelling of the lower River Murray wetlands Lock 6 and Reedy Creek ”exemplars”, with each category having a defined management strategy. In the lower River Murray 54 of 250 wetlands (wetland groups) were identified as being similar to Lock 6 wetland and therefore classified as category 3 wetlands. Including Lock 6 wetland, 35 were found to be over 0.6 metres depth, the minimum depth of wetlands found to be effectively simulated by WETMOD 2. These 35 wetlands and wetland groups, make up a total of 57 individual lagoons that can be simulated within WETMOD 2 (a list of these wetlands is provided in Table 18 in Appendix C). The method for Secchi depth adjustment in cumulative wetland management scenarios was handled in the same way as for Lock 6 wetland simulations discussed in section 3.4.1. Due to the nature of Reedy Creek wetland, more stringent restrictions had to be placed on the wetlands that could be regarded as potential category 4 wetlands. If wetlands less than half the volume of Reedy Creek were simulated using the exchange volume found for Reedy Creek then the average volume exchanged per day would exceed the total wetland volume. When the exchange volume exceeds the wetland volume the nutrient retention time within the wetland is reduced below that of the model timestep. WETMOD 2 has not been developed nor calibrated for such a continual high exchange volume. WETMOD 2 was therefore restricted to simulation of wetlands where the average exchange volume is below that of the wetland volume. Consequently, due to the high river exchange volume estimated for Reedy Creek wetland, category 4 modelled wetlands are restricted to those with a volume greater than half the volume of Reedy Creek wetland. A further restriction, for wetlands to be considered for management scenarios of category 4 wetlands, was based on the irrigation flow volume. Reedy Creek wetland was estimated to receive a high volume of irrigation drainage flow. Therefore, wetlands that were deemed to receive only a low volume of irrigation drainage flow were also excluded from management consideration. Therefore, 7 of the 250 wetlands (wetland groups), including Reedy Creek wetland, were identified as being category 4 wetlands for which WETMOD 2 had the potential capacity to reliably simulate (a list of these wetland is provided in Table 19 in Appendix C). This did not include the potential irrigation drainage nutrient concentration that these wetlands may receive, as

93

Regional Scale Modelling of the lower River Murray wetlands this information was unavailable, Reedy Creek irrigation data was therefore used as the driving variables. Exchange volume During the simulation of wetland management scenarios using “exemplar”-driving variables, the wetland volume is changed to reflect the wetland that is being simulated. However, the exchange volume between the wetlands and the river was maintained at the same percentage of river volume as was estimated for the “exemplar” wetland (i.e. the wetland which provided the driving variable data). For category 3 wetlands the river exchange was maintained at 0.1% of the river flow volume per day, this being the volume fitted for Lock 6 wetland. For category 4 wetlands the fitted exchange rate for Reedy Creek wetland of 3.5% of the river flow volume per day was used. This fitted volume for each of the two categories was maintained based on the assumption that all category wetlands resemble each other unless specific data is available. Consequently future improvement of the model could be achieved with a proper estimate of individual wetland water exchange with the river, thereby providing improved wetland scenario accuracy. For each category wetland scenario the driving variables for the river data are sourced from the nearest upstream monitoring location, the exception being Reedy Creek wetland which has its own monitored nutrient river data set. Therefore, the flow volume was adjusted below each successive lock and the river nutrient data was adjusted to each individual nutrient monitoring locations. The behaviour of wetlands of a particular category was expected to be similar, particularly where the only major difference between the wetlands is the morphology. Implication of the change in nutrient retention capacity on river nutrient load Through the management of both category 3 and category 4 wetlands, a cumulative impact on the river nutrient load would become evident. Although the modelling accuracy of category wetlands allows only a qualitative understanding of the trends expected due to wetland management and not quantitative accuracy, the model results will, for this section, be assumed to be quantitatively accurate. The rationale is two fold.

94

Regional Scale Modelling of the lower River Murray wetlands First, although the results are not quantitatively accurate the assessment of the quantitative output helps to develop a qualitative trend analysis of the cumulative impact of management. Second, although this model, due to the poor data quality, is of low quantitative accuracy the methodology of assessing the cumulative impact could be applied in the same manner should the model quantitative performance improve through future data improvement. However, this assumption is made in order to understand and discuss the potential cumulative impact on nutrient loads within the river, and should only be seen as a trend analysis. To understand the cumulative impact that the management of multiple wetlands would have on the river nutrient load, the change in wetland nutrient retention capacity was compared to the river load. To this purpose the initial river nutrient load ( L R ) was required see Equation 15, see Figure 21. Equation 15:

LR

CR R

The initial river load is calculated from the first available monitoring locations post inflow into South Australia, i.e. the flow volume data is obtained from Lock 6 whereas the river nutrient concentration is obtained from Lock 5. The calculation of the river nutrient load based on the earliest available monitoring locations was chosen so that the river data would not reflect the status quo impacts of the wetland that are simulated, i.e. wetland impacts would otherwise be counted both status quo and as per management scenario. The wetland nutrient retention calculation is similar to Equation 6 (see Box) where the retention in the wetland is calculated per day. Equation 16 needs to calculate the sum over the modelled period for each of the management scenarios. The status quo (i.e. no wetland management) subtracted from the nutrient retention in the wetland as per a management scenario, gives the change in nutrient retention ( N R ) due to management. Where,

NR

is the change in wetland retention due to management and

is calculated as per Equation 16 where quo scenario and

NR t

ms

NR t

sq

is the nutrient retention at the status

the nutrient retention at the respective management

scenario. 95

Regional Scale Modelling of the lower River Murray wetlands

NR

Equation 6:

t

Equation 16

The

NR

NR

(C R

CW ) f

NR t

ms

R

NR t

sq

was used to calculate the change in river load where the % River Load

removed due to the wetland management (%RL) is calculated as per Equation 17, see Figure 21. Equation 17:

% RL

NR LR

Equation 16 and Equation 17 are used to calculate the impact of a single wetland on the river nutrient load as well as the cumulative impact the management of multiple wetlands would have on the river nutrient load, see Figure 21.

L

=> %RL

R

W

L

R

(

W

W

W

etl

etl

etl

Wetland 1

an

Wetland 2

an

Wetland 3

d

d

d

NR pr

1

+

NR pr

2

etl Wetland n

an

+

NR pr

3

an

+

Nd R pr

oc

oc

oc

oc

es

es

es

es

s

s

s

s

m

m

m

m

od

od

od

od

Figure 21: Cumulative assessment of ell ell wetland processesell

ell

in

in

in

in

g

g

g

g

n

L

R

)/

L

R

96

Regional Scale Modelling of the lower River Murray wetlands

4 Validation of the model WETMOD 2 and Discussion During the development of WETMOD 1, neither river flow nor nutrient load data were available. To varying extents the wetlands are reliant upon the exchange of water and nutrient with the river. The addition of river flow and nutrient load as well as the exchange volume between the wetlands and the river is therefore a significant improvement of the WETMOD 2 model. This chapter will test the first hypothesis of whether “a simplified generic wetland model can be used to realistically simulate multiple and different wetlands qualitatively”.

4.1 Fitting and Validation based on calibrated (“exemplar”) wetlands The results presented in this chapter show the validation steps used for WETMOD 2 using data from the five different wetlands. The validation of WETMOD 2 is based on D (Percentage Deviation of modelled time-series from monitored time-series) for PO4-P, NO3-N and phytoplankton, and is represented in Table 3. River water quality is influenced by adjacent wetlands. The water exchange estimate is a step in the process of developing a model capable of simulating management strategies for wetlands of the lower River Murray and their impact on nutrient load in the river. WETMOD 2 was used to find the water exchange between wetlands, where there is a lack of channel morphology data and no measured wetland water turnover. The added spatial driving variables for WETMOD 2 are used to account for local variations and inflow into a wetland, particularly to reflect bi-directional water and nutrient exchange between the River Murray and the wetlands (see section 3.3). This was based on a combination of the river flow volume and the wetland specific budget of PO4-P or NO3-N simulated by WETMOD 2. Through this methodology it is possible to obtain the turnover volume of water in a wetland using nutrient modelling output (Bjornsson et al. 2003). The optimal river exchange estimate was determined by WETMOD 2 based on the best percentage deviation (D) (see box). Given the availability of accurate daily river flow data as well as fortnightly nutrient data, it was possible to estimate the flow of nutrients carried by the lower River Murray. This provided accurate data for the estimation of the most significant external nutrient source, i.e. the river. Combined 97

Regional Scale Modelling of the lower River Murray wetlands with successive calculations of wetland internal nutrient load by WETMOD 2, the wetland simulation results improved until the optimum exchange was attained. Once the external load was increased past the optimum the wetland simulations degraded, see Figure 22.

The lower the D the closer the fit of modelled data to monitored data. As discussed in section 3.3.1, PO4-P was in most cases used as the primary indicator of model D as PO4-P is the most reliably modelled and monitored nutrient within the system (once PO4-P enters a wetland it is not diminished through a gaseous state). The flow exchange between a wetland and the River Murray was mostly estimated based on the model percentage deviation (D) calculation of PO4-P, with NO3-N only used for Lock 6 wetland. Figure 22 presents an example of the selection of D for Reedy Creek wetland. In this example the PO4-P shows the best fit at a river exchange of 3.5% of the daily river flow volume. E s tim a tio n o f R iv e r a n d W e tla n d E x c h a n g e V o lu m e s : R e e d y C re e k w e tla n d

100 90 80

% D e v ia tio n (%D )

70 60 50 40 30 20 10 0 1

1 .5

2

2 .5

3

3 .5

4

4 .5

5

5 .5

% o f R iv e r F lo w E x c h a g e d p e r D a y % D e via tio n P O 4 -P

% D e via tio n N O 3 -N

% D e via tio n P h yto p la n kto n

A ve ra g e W e tla n d V o lu m e % E xc h a n g e d p e r D a y

Figure 22: Percentage Deviation based estimate of flow exchange: Reedy Creek wetland

98

Regional Scale Modelling of the lower River Murray wetlands Table 3: Calibration of inflow data for the 5-wetland categories Wetland Wetland Category Name

1

2

3

4

5

Paiwalla Wetland

Modelled

Modelled

Modelled

PO4-P

NO3-N

Phytoplankton

D

D

D

NO River Exchange & NO Irrigation Drainage

92

71

74

0.7% River Flow/day Exchange NO Irrigation

74

73

28

71

69

75

NO River Exchange & 500L Irrigation Drainage

71

69

75

0.06% River Flow/day Exchange NO Irrigation

70

58

64

0.06% River Flow/day Exchange 500L Irrigation Drainage

70

58

64

NO River Exchange

55

54

44

0.1% River Flow/day Exchange

81

34

49

NO River Exchange & NO Irrigation Drainage

94

101

67

NO River Exchange & 3500 X Irrigation Drainage

87

100

58

3.5% River Flow/day Exchange & NO Irrigation Drainage

61

72

49

3.5% River Flow/day Exchange & 3500 X Irrigation Drainage

56

71

40

NO River Exchange

77

74

67

0.32% River Flow/day Exchange

53

67

65

Wetland External Input Variables

Sunnyside NO River Exchange & Wetland NO Irrigation Drainage

Lock 6 Wetland Reedy Creek Wetland

Pilby Creek Wetland

Wetland modelled results are presented and discussed in the sections below. Each wetland is assessed independently, and some comparisons are made. Category 1: Through flow wetlands with carp presence and no irrigation drainage (Paiwalla wetland) Paiwalla wetland is situated upstream of Sunnyside wetland (see section 3.2.1), with an area of reclaimed „swamp‟ situated between them, which was used as dairy pasture prior to 1997 (refer to map in chapter 2). The runoff from this pasture was pumped into Sunnyside wetland and thereby transported nutrients from the irrigation drainage into Sunnyside wetland. In contrast there was no direct input of nutrient from the dairy pasture into Paiwalla wetland (Bartsch 1997). Paiwalla wetland was therefore

99

Regional Scale Modelling of the lower River Murray wetlands chosen to represent through flow wetlands with possible carp presence and no irrigation. The comparison between modelled and monitored concentrations of PO 4-P is seen in Figure 23A and Figure 23B for NO3-N; macrophytes, zooplankton and phytoplankton are represented in Figure 24A, B and C respectively. Each graph of Figure 23 and Figure 24 includes results for the scenarios “no flow exchange” and “optimum flow exchange”. For monitored data in Figure 23 and Figure 24, error bars represent the standard error for measurements made on that date. As seen in Table 3 Paiwalla modelling results for PO4-P and phytoplankton improved due to the consideration of river exchange, phytoplankton result being significant. Figure 23A reflects this improvement. The NO3-N D shown in Table 3 does not show an improvement, however the graph in Figure 23B indicates a distinctive change in the model output due to river exchange. The NO3-N variability, range and seasonality are realistically reflected by the river exchange scenario. It is therefore concluded that the model validation improved with regard to qualitative trends even though the quantitative accuracy is not optimal. There is a major improvement in the modelling results for phytoplankton following the introduction of river exchange. The modelled D for phytoplankton (Table 3) is the best result of all output from modelled wetlands and scenarios; this modelling performance is also being displayed in Figure 24C where the modelled phytoplankton corresponds well with the trends of the monitored phytoplankton. There is some early macrophyte biomass growth in Paiwalla wetland however; there is a rapid decline due to increasing turbidity, see Figure 24A. Phytoplankton growth, as seen in Figure 24C, increases as expected following diminished macrophyte competition. As the solar radiation and wetland water temperature increase in spring, the growth of phytoplankton increases accordingly (Figure 24C). Zooplankton biomass increases in response to the growth of phytoplankton. This is due to the phytoplankton serving the zooplankton as a food source (Figure 24B) following the typical Lotka-Voltera predator-prey cycle as discussed in the introduction. Through flow wetlands are highly variable due to the close link to the river and are therefore difficult to model with a simplistic model such as WETMOD. Although the modelling results for this category of wetlands were not as good as expected there was an improvement in the model output for Paiwalla wetland due to the introduction of 100

Regional Scale Modelling of the lower River Murray wetlands river exchange. It shows the potential of simplistic models to assess the exchange volume of water and nutrients between riparian wetlands and the river.

A

P O 4 -P

0.80

0.70

0.60

m g /L -

0.50

0.40

0.30

0.20

0.10

Jul-97

A ug-97 A u g -9 7

Jun-97

Ju l-9 7

B

May-97

A pr-97

Mar-97

Feb-97

0.00

NO 3 -N

0 .7 0

0 .6 0

m g /L -

0 .5 0

0 .4 0

0 .3 0

0 .2 0

0 .1 0

M o d e lle d C o nc e ntratio n No R ive r E xc hang e No I rrig atio n

Ju n -9 7

M a y-9 7

A p r-9 7

M a r-9 7

F e b -9 7

0 .0 0

M o d e lle d C o nc e ntratio n 0 .7 % R ive r E xc hang e

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

Figure 23: Validation of simulation results for Paiwalla wetland of PO4-P, and NO3-N for both conditions with and without water exchange

101

Regional Scale Modelling of the lower River Murray wetlands

A

M a c r o p h y te B io m a s s

6

5

k g /m 3 -

4

3

2

1

B

A u g -9 7

Ju l-9 7

Ju n -9 7

M a y-9 7

A p r-9 7

F e b -9 7

M a r-9 7

0

Zo o p la n k to n

1 .4

1 .2

c m 3 /m 3

--

1

0 .8

0 .6

0 .4

0 .2

Ju n -9 7

Ju l-9 7

A u g -9 7

Ju n -9 7

Ju l-9 7

A u g -9 7

C

M a y-9 7

A p r-9 7

M a r-9 7

F e b -9 7

0

P h y to p la n k to n

10

9

8

c m 3 /m 3 -

7

6

5

4

3

2

1

M o d e lle d C o nc e ntratio n No R ive r E xc hang e No I rrig atio n

M a y-9 7

A p r-9 7

M a r-9 7

F e b -9 7

0

M o d e lle d C o nc e ntratio n 0 .7 % R ive r E xc hang e

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

Figure 24: Validation of simulation results for Paiwalla wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange

102

Regional Scale Modelling of the lower River Murray wetlands Category 2: Through flow wetlands with carp presence and irrigation drainage (Sunnyside wetland) Figure 25A portrays the PO4-P and Figure 25B the NO3-N monitored and modelled concentrations for Sunnyside wetland. Figure 26A, B and C depicts macrophyte, zooplankton and phytoplankton monitored and modelled concentrations respectively. The monitored concentrations of PO4-P, NO3-N and phytoplankton in the wetland, and of PO4-P and NO3-N concentrations in the irrigation drainage, are represented in Figure 25A and B and Figure 26C. For each monitored concentration, error bars represent the standard error for measurements. Each graph includes results of scenarios where no river flow exchange and no irrigation drainage were considered. Another trendline in each of the graphs includes river flow exchange estimated at a modelled best-fit D (Table 3), according to monitored wetland nutrient concentration. This scenario was re-run with irrigation drainage included. To estimate the impact of irrigation drainage on the wetland simulation Sunnyside wetland was also simulated with only irrigation drainage influencing the scenario results and no river exchange. The response of the scenario where irrigation drainage inflow was the only outside nutrient source was minimal and effectively covers the simulation where no outside nutrient source was considered (Figure 25 and Figure 26). Sunnyside wetland is an “exemplar” for the category 2 wetlands considered in the modelling project, which are wetlands having river water through flow and are directly affected by irrigation drainage. Simulation results demonstrated that an improvement in D of only 0.01 was evident when a realistic volume of 500L of irrigation drainage per day was included in a scenario. In order to clarify the reason for this result, we must look at both assumptions made at the start of the modelling project as well as the monitoring design; this is discussed in section 4.1.1. A better scenario of a wetland with irrigation drainage inflow in this wetland category is not possible due to the limited data available. However, the small response of the model to scenarios with drainage nutrient and the success of modelling Reedy Creek wetland with its irrigation drainage (described below in category 4 wetlands), indicate the possibility of a more successful modelling scenario when adequate data for this wetland category become available.

103

Regional Scale Modelling of the lower River Murray wetlands The modelling of PO4-P (Figure 25A) does not pick up the early high wetland concentration monitored, neither with nor without the river exchange and irrigation drainage. However, with the introduction of river exchange there is a slight improvement in the trend modelled, as can be seen in the results between the months of May to June in Figure 25A. The improvement in the modelling trend of NO3-N due to the introduction of river exchange can similarly be seen in Figure 25B. The D for PO4-P, NO3-N and phytoplankton (Table 3) does improve with the introduction of river exchange, with a noteworthy improvement for NO 3-N and phytoplankton, however this improvement is not great. As mentioned, better data is required to successfully model this wetland. There was a longer growth period of macrophytes in Sunnyside wetland than in Paiwalla wetland simulations (Figure 26A and Figure 24A respectively). Again, this can be attributed to the turbidity of the wetlands. The delayed increase in turbidity in Sunnyside wetland extended the growth period for the macrophytes. The growth in zooplankton and its high concentration (Figure 26B) is probably due to the shelter provided by macrophytes (Figure 26A), the first zooplankton growth phase followed by the increased food source phytoplankton in the second growth phase (Figure 26C). The combination of simulated nutrient competition by macrophytes and grazing by zooplankton restrict the initial growth of the phytoplankton (Figure 26). The major growth phase of phytoplankton simulated occurs from May to July corresponding well with the monitored trend (Figure 26C).

104

Regional Scale Modelling of the lower River Murray wetlands

A

P O 4 -P

4.00

3.50

3.00

m g /L -

2.50

2.00

1.50

1.00

0.50

Jul-97

A ug-97

Ju l-9 7

A u g -9 7

Jun-97

May-97

A pr-97

Mar-97

-0.50

Feb-97

0.00

NO 3 -N

B

2 .5 0

2 .0 0

m g /L -

1 .5 0

1 .0 0

0 .5 0

Ju n -9 7

M a y-9 7

A p r-9 7

M a r-9 7

F e b -9 7

0 .0 0

M o d e lle d C o nc e ntratio n N o R ive r E xc hang e

M o d e lle d C o nc e ntratio n 0 % R ive r E xc hang e 5 0 0 L I rrig atio n

M o d e lle d C o nc e ntratio n 0 .0 6 % R ive r E xc hang e

M o d e lle d C o nc e ntratio n 0 .0 6 % R ive r 5 0 0 L I rrig atio n

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

M o nito re d D ate s O nly C o nc e ntratio n in I rrig atio n D rain

Figure 25: Validation of simulation results for Sunnyside wetland of PO4-P, and NO3-N for both conditions with and without water exchange For both Figure 25 and Figure 26 the grey line (modelled concentration (PO4-P or NO3-N) with 0.06% river exchange and no irrigation) falls behind the green line (modelled concentration (PO4-P or NO3-N) with 0.06% river exchange and 500L irrigation drainage inflow). The blue line (modelled concentration (PO4-P or NO3-N) with no river exchange and no irrigation) falls behind the pink line (modelled concentration (PO4-P or NO3-N) with no river exchange but with 500L irrigation drainage).

105

Regional Scale Modelling of the lower River Murray wetlands

A

M a c ro p h y te B io m a s s

25

20

k g /m 3

15

10

5

B

A u g -9 7

Ju l-9 7

Ju n - 9 7

M a y- 9 7

A p r-9 7

F e b -9 7

M a r -9 7

0

Zo o p la n kto n

0.9

0.8

0.7

c m3 /m 3 --

0.6

0.5

0.4

0.3

0.2

0.1

Jul-97

A ug-97 Aug-97

Jun-97

Jul-97

C

M ay-97

A pr-97

Mar-97

F eb-97

0

P h yto p la n k to n

9

8

7

cm 3 /m3 --

6

5

4

3

2

1

Jun-97

May-97

Ap r-97

Mar-97

F eb-97

0

M o d e lle d C o nc e ntra tio n No R ive r E xc hang e

M o d e lle d C o nc e ntra tio n 0 % R ive r E xc hang e 5 0 0 L I rrig a tio n

M o d e lle d C o nc e ntratio n 0 .0 6 % R ive r 5 0 0 L I rrig atio n

M o n ito re d D ate s O nly C o nc e ntratio n in W e tland

M o d e lle d C o nc e ntratio n 0 .0 6 % R ive r E xc hang e

Figure 26: Validation of simulation results for Sunnyside wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange

106

Regional Scale Modelling of the lower River Murray wetlands Category 3: Dead end wetlands with carp presence and no irrigation drainage (Lock 6 wetland) Figure 27 and Figure 28 depict the modelled output of Lock 6 wetland for PO4-P, NO3-N, macrophytes, zooplankton and phytoplankton respectively. The error bars represent the standard error for the monitoring at that particular date based on three separate measurements. Lock 6 wetland is a permanently inundated wetland situated adjacent to Lock 6 of the River Murray. It is a wetland classified as a “dead end” wetland. This wetland‟s hypothetical management strategy was drying and compacting the sediment. Therefore, it was assumed for the modelling that following a re-flooding event the sediment re-suspension and wetland turbidity would be reduced (see section 3.4). As there is no irrigation drainage flowing directly into Lock 6 wetland, only the river exchange volume was considered as an external influence upon this wetland. It was expected that all output parameters would have an improved response. It is possible that the high PO4-P level modelled in the wetland was overestimated due to relatively high river concentrations. However, the trend was clearly modelled correctly when compared to monitored concentrations (Figure 27A) despite the D indicating a worse fit (Table 3). This discrepancy is also reflected in the modelling result of the phytoplankton (Figure 28C). This shows that although the D is a good method of finding the best-fit scenario during modelling, it is by no means a perfect method and model results should be analysed with an understanding of the expected trends. The modelling performance of NO3-N was improved considerably by the introduction of river exchange, as seen in Table 3 and Figure 27B, and is the best modelling response of NO3-N for all wetlands and scenarios. Due to the high turbidity levels of Lock 6 wetland, the modelled macrophyte growth is inhibited showing that the original estimate of the potential macrophyte biomass used in the modelling scenario was probably overestimated (Figure 28A). It can be assumed that the high turbidity levels limited underwater light for macrophyte growth. However, due to the high nutrient levels within the wetland (Figure 27), and the lack of competition provided by the macrophytes, the phytoplankton were able to grow effectively (Figure 28C), reaching a peak biomass prior to the onset of winter. The lack of the spring growth phase can be attributed to the large volume of Lock 6 107

Regional Scale Modelling of the lower River Murray wetlands wetland effectively buffering early rise in water temperatures. It must be remembered that the river chlorophyll-a used in calculating the river phytoplankton, which is consequently used in representing the exchange rate inflow into the wetland, was not available for this part of the river. As phytoplankton has a significant role in this model, its part in the wetland simulations could not be ignored. The zooplankton growth (Figure 28B) in Lock 6 wetland follows the phytoplankton growth as expected, and declines during the winter months.

A

P O 4 -P

0 .20 0 .18 0 .16 0 .14

m g /L -

0 .12 0 .10 0 .08 0 .06 0 .04

0 .02

B

S e p -9 7

A ug -9 7

J u l-9 7

J u n -9 7

M a y-9 7

A p r-9 7

M a r-9 7

F e b -9 7

0 .00

NO 3 -N

0 .45

0 .40

0 .35

m g /L -

0 .30

0 .25

0 .20

0 .15

0 .10

0 .05

Mo d e lle d C o n c e n tratio n 0 R ive r

M o d e lle d C o nc e ntratio n 0 .1 % R ive r

S e p -9 7

A u g -9 7

J u l-97

Ju n -9 7

M a y-9 7

A pr-9 7

M a r-9 7

F e b -97

0 .00

Mo nito re d D ate s O nly C o nc e ntratio n in W e tland

Figure 27: Validation of simulation results for Lock 6 wetland of PO 4-P, and NO3-N for both conditions with and without water exchange

108

Regional Scale Modelling of the lower River Murray wetlands

A

M ac ro p h yte B io m a s s

6

5

k g /m 3 -

4

3

2

1

B

Sep-97

Aug-97

Jul-97

Jun-97

M ay-97

A pr-97

Feb-97

Mar-97

0

Zo o p la n kto n

1.6

1.4

c m 3 /m 3 -=

1.2

1

0.8

0.6

0.4

0.2

Aug-97

S ep-97 S ep-97

Jul-97

A ug-9 7

C

Jun-97

M ay-97

Apr-97

Mar-97

Feb-97

0

P h yto p la n k to n

2 0.00 1 8.00 1 6.00

c m 3 /m 3 --

1 4.00 1 2.00 1 0.00 8.00 6.00 4.00 2.00

Mo d e lle d C o n c e n tratio n 0 R ive r

M o d e lle d C o nc e ntratio n 0 .1 % R ive r

Jul-97

Jun-97

M ay-97

A pr-97

M ar-97

Feb-97

0.00

Mo nito re d D ate s O nly C o nc e ntratio n in W e tland

Figure 28: Validation of simulation results for Lock 6 wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange

109

Regional Scale Modelling of the lower River Murray wetlands Category 4: Dead end wetlands with carp presence and irrigation drainage (Reedy Creek wetland) The Reedy creek wetland data set monitored by Wen (2002a) includes time-series for the water quality of the wetland, the River Murray and the irrigation drainage originating from the adjacent Basby farm. A period of 12 months with high internal wetland nutrient variability (1 st Jun 2000 to 31st May 2001) was chosen from the data set, to represent the condition of Reedy Creek wetland. Figure 29A & B and Figure 30A, B & C contain the simulated results for PO4-P, NO3-N, macrophytes, zooplankton and phytoplankton respectively for Reedy Creek wetland. The monitored concentrations for PO4-P, NO3-N, and phytoplankton are displayed in Figure 29A, B and Figure 30C; the error bars represent the mean error for the entire monitoring period of 20th October 1999 to 16th September 2001. A limitation of the drainage inflow time-series is that it was obtained from one source, that being a small drainage inflow from Basby farm. The catchment area of Reedy creek is 315 km2, whereas Basby farm covers an area of 85ha (0.85 km2) (Wen 2002a). The Reedy Creek catchment area results in significant natural flows and nutrient loadings to Reedy Creek wetland in response to precipitation. Unfortunately, no monitoring data existed of the nutrient inflow from Reedy Creek, as this was not required for the project responsible for the monitoring. Its contribution was therefore approximated by higher surface runoff and irrigation drainage into Reedy Creek wetland than was monitored at the one source; inflow from Reedy Creek catchment is known to grow to a substantial amount following rains in the region (Frears 2006). Accordingly it was assumed that the expected seasonal precipitation (described in section 2.3.1) would have reflected the relative seasonal flow pattern over the modelling timeframe. The monitored drainage source would have reflected the average concentration of nutrients per unit volume expected from surrounding farms contributing to the Reedy Creek. In order to determine the most appropriate flow, multiple scenarios were run each with an increasing multiplication of the irrigation volume entering the wetland. The best fit was chosen depending on the deviation of modelled values from the monitored values D (Table 3). As with previous wetlands the best values D for the river exchange was separately modelled.

110

Regional Scale Modelling of the lower River Murray wetlands As seen in Figure 29A & B and Figure 30C there was a significant improvement in the modelling results of both PO4-P and NO3-N, and a considerable improvement on the modelling of phytoplankton. The PO4-P results in Reedy Creek wetland improved clearly through the introduction of the irrigation drainage inflow; however the D (Table 3) shows the river exchange flow to have the greater impact. As can be seen in Figure 29A this result is skewed by a particularly good fit for a short period from March to the end of May. The combination of both river exchange flow and irrigation drainage not only produced the best D for both PO4-P and NO3-N, but also showed a better fit when the trend is observed as seen in Figure 29A. The Reedy Creek PO4-P modelling shows the most significant improvement of PO 4-P simulation when compared with the other modelled wetlands. NO 3-N is influenced by both the river flow exchange and the irrigation drainage inflow to produce a significant improvement in model fit D (Table 3). The phytoplankton modelling of Reedy Creek wetland shows a considerable improvement in D through the introduction of river exchange and drainage. Some of the extreme events in PO4-P and NO3-N concentrations from October to December (Figure 29A) were not realistically simulated by the model, although the trend is clearly visible. A limitation of the generic nature of the model WETMOD2 may be that short lived and extreme events cannot be successfully simulated. Reedy Creek wetland is in a turbid state with minor macrophyte growth (section 3.2.1). The macrophyte growth curve shown in Figure 30A is a result of the high turbidity, which limits underwater light for growth. The zooplankton, lacking the shelter assumed to be provided by macrophytes, are reliant on the phytoplankton as their food source. The zooplankton growth, seen in Figure 30B, closely follows the phytoplankton growth seen in Figure 30C. As seen in Figure 30C, a combination of both river exchange and irrigation drainage inflow was required for phytoplankton to resemble the monitored and therefore expected concentrations. This further strengthens the validation of the model, showing that one external influence such as the river exchange is not enough to drive the simulation for a wetland such as Reedy Creek wetland. But rather the combinations of external influences such as the river flow exchange and irrigation drainage are

required to successfully and

comprehensively simulate the Reedy Creek wetland.

111

Regional Scale Modelling of the lower River Murray wetlands

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M o nito re d D ate s O nly C o nc e ntratio n in W e tland

M o nito re d D a te s O n ly C o n c e ntratio n in I rrig atio n D rain

Figure 29: Validation of simulation results for Reedy Creek wetland of PO 4-P, and NO3-N for both conditions with and without water exchange

112

Regional Scale Modelling of the lower River Murray wetlands

A

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cm3/m3 --

80 8 60 6 40 4

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100 10

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120

12

M o d e lle d C o nc e ntratio n No R ive r E xc hang e No I rrig atio n

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M o nito re d D ate s O nly C o nc e ntratio n in W e tland

M o nito re d D a te s O n ly C o n c e ntratio n in I rrig atio n D rain

Figure 30: Validation of simulation results for Reedy Creek wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange

113

Regional Scale Modelling of the lower River Murray wetlands Category 5: Dead end wetlands managed through implementation of dry periods with carp restriction and no irrigation drainage (Pilby Creek wetland) Figure 31 represents simulation results for PO4-P and NO3-N concentrations in Pilby Creek wetland and Figure 32 the simulation results for macrophyte, zooplankton, and phytoplankton biomass within the wetland. The error bars represent the standard error, of three separate measurements, of the monitored concentration for each monitoring date. Pilby Creek wetland is a dead end wetland adjacent to Lock 6 wetland (Category 3). Pilby Creek wetland is managed by artificial drying and wetting cycles resulting in sediment compaction. Restriction on the presence of large bottom-feeding fish such as carp, which are believed to stir up wetland sediment, is also believed to have contributed to reduced turbidity. The case study for Pilby Creek wetland was included in the modelling project to test the model validity for a restored wetland. Although Pilby Creek wetland is not directly connected to the river, as well as being a dead end wetland, an exchange of water and nutrient with the river was assumed. The justification for this assumption is the possibility of an exchange through Pilby creek, which flows through at one end of the wetland (see Figure 14). The possible nutrient load change during the exchange through an intermediary creek should be taken into consideration when assessing the modelling success of this wetland. The model results support the assumption of water exchange through Pilby creek, as the model scenario D improves with the introduction of river flow exchange (Table 3). The D shows a considerable improvement for the PO4-P modelling (Table 3). The peak concentration of PO4-P simulated by the river exchange scenario (Figure 31A) was due to both a high peak in river flow and high river PO4-P concentration (see section 3.2.3). This nutrient peak did not reach the wetland during the monitoring period as indicated by the internal wetland nutrient monitoring (Figure 31A), which may be due to the lag time of nutrient flow to Pilby Creek wetland from the River Murray. The NO3-N curve is lower than expected during late February until April. However, with the exception of an extreme event at the end of April the curve does show a similar trend to that of monitored concentrations (Figure 31B), which is not as apparent in the simulation without the river exchange. The improvement in NO 3-N simulation is also reflected by the D value (Table 3).

114

Regional Scale Modelling of the lower River Murray wetlands Following a drying period of two months in 1997, of Pilby Creek wetland, that was long enough to compact the sediments the high macrophyte growth seen in Figure 32A was a result of low turbidity as expected in a managed wetland within a short time after re-flooding. The macrophyte biomass decreased over the winter months with low water temperatures but increased during spring. Monitoring ceased at the beginning of October. The observed phytoplankton growth in Figure 32C showed a rapid growth phase prior to the macrophyte growth, directly following wetland re-flooding. In this instance, the phytoplankton took advantage of the lack of competition as well as the high nutrient availability. Once competition set in with the growth of macrophytes, there was a reduction in the phytoplankton biomass. The phytoplankton biomass growth was thereby restricted until the decreasing macrophyte biomass in winter when phytoplankton again took advantage of less nutrient competition and increased its biomass. The phytoplankton had a faster response time in growth than macrophytes at the onset of the warmer period of spring. As with Lock 6 wetland, the river phytoplankton was derived from river chlorophyll-a levels monitored further downstream. The zooplankton growth can be linked to the provision of a nourishment source, the phytoplankton growth, and possibly to a lesser extent the assumed provision of a shelter from predators by macrophytes (Figure 32). The lowest number of zooplankton occurred when there was a combination of both low phytoplankton and low macrophyte biomass. The lack of phytoplankton as a food source explains the reduction in zooplankton observed despite the potential supply of shelter provided by the macrophytes. The secondary growth phase of zooplankton corresponded to the secondary growth phase of the phytoplankton. During the spring growth phase of phytoplankton the zooplankton follows suit, again possibly as a consequence of shelter provided by the increase in macrophyte growth. The modelled growth behaviour of the macrophytes, phytoplankton and zooplankton described follows expectations of a wetland in the Pilby Creek wetland category (category 5). It must however be remembered that no data was available to validate model output for zooplankton and macrophyte biomass. It is interesting to note that the growth of phytoplankton in a category 5 wetland (Pilby creek) was less than in a category 3 wetland (Lock 6). This can be attributed to 115

Regional Scale Modelling of the lower River Murray wetlands the competition between the macrophytes and the phytoplankton in Pilby Creek wetland, which is virtually absent in Lock 6 wetland. However, Pilby Creek wetland shows a relatively greater zooplankton growth than Lock 6 wetland when compared to the phytoplankton availability in each of the wetlands. The cause of the relatively larger zooplankton growth in Pilby Creek wetland may be as a consequence of added shelter opportunity within Pilby Creek wetland assumed to be provided by the macrophytes. The only discrepancy in the modelling of Pilby Creek wetland is the very late spike in PO4-P levels described earlier, attributed to river flow and river nutrient concentration.

A

P O 4 -P

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M o d e lle d C o nc e ntratio n 0 .3 3 % R ive r E xc hang e

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

Figure 31: Validation of simulation results for Pilby Creek wetland of PO 4-P, and NO3-N for both conditions with and without water exchange

116

Regional Scale Modelling of the lower River Murray wetlands

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Jun-97

May-97

A pr-97

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0.00

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

Figure 32: Validation of simulation results for Pilby Creek wetland of Macrophyte Biomass, Zooplankton and Phytoplankton for both conditions with and without water exchange

117

Regional Scale Modelling of the lower River Murray wetlands

4.1.1 Implication for irrigation affected wetland representation Considering the generic nature of the model and its structural restrictions and how this interacts with potential quantitative modelling performance, the qualitative modelling performance, the time and data available for model development and most importantly the project goals, the model displays the potential of a developed tool with purpose designed monitoring scenarios. The following discussion aims to represent the performance of the model in a dispassionate approach, focusing on where it has succeeded in fulfilling its objective and is at a stage where it can be applied to answer wetland specific management questions and therefore fulfilling the project aims. Category 4: Dead end wetlands with carp presence and irrigation drainage (Reedy Creek wetland) The modelling results from Reedy Creek wetland are an example of a successful simulation of a wetland that is affected by irrigation drainage. Both the quality of the trend as well as the statistical comparison improved with the introduction of irrigation drainage. The methodology of estimating the inflow volumes from the Reedy Creek catchment can at this stage not be confirmed as no monitoring of the sub-catchment inflow was performed concurrent with the wetland-monitoring project. However, although the inflow volume used in the model may be debateable, the methodology of the model derived optimum level gives future modellers the option to adjust the scenarios as this data becomes available. Any consequent monitoring could potentially refute or confirm the range of estimated nutrient and volume inflow. It is therefore not regarded as a high priority at this stage to invest expense and time in the improvement of the Reedy Creek wetland modelling scenarios. The validation of the macrophyte and zooplankton modelling output may however increase the confidence in the model. Future monitoring could assist in this regard by providing adequate data for model validation. Category 2: Through flow wetlands with carp presence and irrigation drainage (Sunnyside wetland) Modelling scenarios of Sunnyside wetland improved with the introduction of river exchange. However, the inflow of monitored irrigation drainage did little to improve

118

Regional Scale Modelling of the lower River Murray wetlands the scenario performance. The monitoring of Sunnyside wetland was not designed with this project in mind. Bartsch (1997) designed her monitoring project with the sole intention of comparing the two wetlands Paiwalla and Sunnyside; and therefore study the impacts irrigation drainage has had on Sunnyside wetland. Minor effort was therefore made to assess internal wetland dynamics by that project. Due to Bartsch‟s (1997) project aims, and particularly the need to assess the impact of irrigation drainage into Sunnyside wetlands, most of the monitoring sites were located at one end of the wetland and close to the drainage outlet. The monitoring of nutrients was made mainly in, what can be recognised in aerial photos as, a channel through the macrophyte growth leading from the irrigation drainage outlet to the river (see Figure 33).

Figure 33: Sunnyside monitoring area

One of the central assumptions made in this modelling project is that all wetlands are homogeneously mixed from one time step to the next. However, partly due to the sporadic point source inflow of nutrients (irrigation drainage) the concentrations of nutrients are highly variable within the wetland. Further, the nature of Sunnyside wetland, macrophyte growth within the wetland and the channel through the 119

Regional Scale Modelling of the lower River Murray wetlands macrophytes from the irrigation drainage source to the river (see Figure 33), hampers the mixing of water and nutrients within the wetland. Sunnyside wetland, due to its highly variable nature, can therefore not be considered as homogeneously mixed. Monitoring within and close to the channel will therefore represent the concentration of nutrients entering the wetland. If this concentration is then assumed to encompass the entire wetland, the true concentration and particularly the inflow of irrigation drainage will be over represented. The monitoring of irrigation flow did not include volume. It is therefore not possible to estimate the true impact the irrigation drainage has on the concentration of nutrient within the wetland. Another issue impacting on the use of the model to estimate a realistic irrigation inflow scenario may be due to the drainage inflow being sporadic (infrequent and short-lived), despite our assumptions of daily pumping. However, the methodology available to estimate the best-fit scenario shows a slight change in model fit as the drainage inflow only affects a minimum number of monitored dates. That does not mean the model proves there to be an insignificant detrimental impact on the wetland due to drainage, but rather that the infrequent nature and the unknown exact drainage volume for each particular pumping date complicates the modelling estimate. The cumulative impact of the drainage inflow on the wetland would however still persist as suggested by the slight change in model fit.

4.1.2 Implication for wetland representation Comparison of wetlands Paiwalla and Sunnyside For both Paiwalla wetland and Sunnyside wetland, being similar wetlands and in close proximity, the modelling scenarios performed well enough to allow a comparison. Sunnyside wetland scenario underestimated PO4-P considerably. The D (Table 3) is due to the low variability of PO4-P concentration in the wetland. Further, from early April to the end of the simulation period the PO4-P trend was simulated very well (Figure 25A). The Paiwalla wetland scenario PO4-P D (Table 3) showed a somewhat worse fit than the Sunnyside wetland scenario, although the concentrations within Paiwalla wetland scenario are for the most part closer to the monitored (Figure 23A). In the Paiwalla wetland scenario the model fails to mimic the PO 4-P trend as well as it does in the Sunnyside wetland simulation (Figure 23A and Figure 25A). The most

120

Regional Scale Modelling of the lower River Murray wetlands likely reason for under prediction in Sunnyside wetland was discussed in section 4.1.1. The Paiwalla wetland scenario PO4-P prediction, although with a worse fit than anticipated, did improve considerably with the introduction of river exchange. The Sunnyside wetland NO3-N simulation performance was good with a low D (Table 3) and with a trend, displayed in the time-series, showing a very good fit (Figure 25B). The Paiwalla wetland D (Table 3) of NO3-N simulation, although not poor, is an indication to the potential failings of D, if used alone, in assessing a comparative modelling output. This statement is made as, although this is not evidenced in the D, the trend or rather the time-series fit (Figure 23B) shows a great improvement with the introduction of river exchange. The Paiwalla wetland D (Table 3) for phytoplankton improves dramatically and can also be seen in the time-series display (Figure 24C) and provides a strong argument for the validity of WETMOD 2 phytoplankton simulation capacity. The Sunnyside wetland phytoplankton simulation also improves with the introduction of river exchange both as represented by the D (Table 3) and by the visual trend assessment (Figure 26C). The macrophyte biomass increase within the Paiwalla wetland scenarios is low, with a rapid decline following the initial growth phase (Figure 24A). The cause of the decline is related to the turbidity level within the wetland limiting underwater light penetration. This monitored wetland turbidity does not increase in the Sunnyside wetland scenario until two weeks later, therefore allowing for a longer macrophyte growth phase. The later increase in turbidity in Sunnyside wetland is assumed to be as a result of the higher macrophyte levels within Sunnyside wetland that act both to settle out the turbidity and to reduce sediment re-suspension. The lower exchange volume of Sunnyside wetland (Table 3) is also assumed to be as a result of the macrophyte growth, whereby the water flow through Sunnyside wetland in comparison to Paiwalla wetland is reduced. The significance of the difference in the macrophyte growth phase between the two wetlands is reflected in the phytoplankton time-series. Where, as a consequence of competition for nutrients and light the Sunnyside wetland scenario shows a very small summer phytoplankton growth phase compared with Paiwalla (Figure 26C vs. Figure 24C). Another consequence of the higher macrophyte biomass content within the Sunnyside wetland scenario is the habitat availability assumed to be provided to zooplankton represented by the higher 121

Regional Scale Modelling of the lower River Murray wetlands summer zooplankton biomass compared with the winter biomass (Figure 26B). In contrast in Paiwalla wetland, with low macrophyte biomass, the zooplankton growth (Figure 24B) mimics the phytoplankton growth (Figure 24C) more closely. The macrophyte and zooplankton model output assessments are limited however by the lack of validation data. Comparison of wetlands Lock 6 and Pilby Lock 6 wetland and Pilby Creek wetland are located geographically close. Prior to the management of Pilby Creek wetland they were both in a similar degraded state. Unfortunately no monitoring of Pilby Creek wetland was undertaken prior to management so no direct comparison can be made at this time of simulations of a particular wetland in a degraded and in a restored state. For Lock 6 wetland, both the PO4-P and phytoplankton D (Table 3) increased once river exchange was introduced. However, a visual assessment of the time-series trend (Figure 27A and Figure 28C) showed a marginal improvement in both cases. The improvement in the Lock 6 NO3-N simulation performance was exceptionally good both visually (Figure 27B) and according to D (Table 3 reducing by a full 20%), supporting the claim that the simulation of Lock 6 was successful. This discrepancy in PO4-P and phytoplankton results was not seen in the Pilby Creek wetland scenarios, where there was an improvement in both D (Table 3) and the visual assessment (Figure 31A and Figure 32C). Both wetlands showed the assumed expected macrophyte biomass growth trends (Figure 28A and Figure 32A). In the Lock 6 wetland scenario there was a rapid decline from initial macrophyte biomass and in Pilby Creek wetland there was a substantial macrophyte biomass increase post rewetting followed by an expected winter reduction. The phytoplankton biomass growth, in both wetlands (Figure 28C and Figure 32C), responded appropriately to the level of competition expected in respect to the macrophyte biomass present (Figure 28A and Figure 32A). In the Lock 6 wetland scenario low macrophyte competition caused phytoplankton biomass to reach high levels, only matched by Reedy Creek wetland, which can be viewed as another wetland with high nutrients concentrations (Figure 29) and low macrophyte competition (Figure 30A). The phytoplankton biomass in the Pilby Creek wetland scenario matched the time-series trend expected, with a growth phase both prior to and directly following the macrophyte growth phase

122

Regional Scale Modelling of the lower River Murray wetlands (Figure 32A and C). Zooplankton in the Pilby Creek wetland scenario responded to the shelter availability assumed to be afforded by macrophytes. However, the zooplankton in Pilby Creek wetland (Figure 32B), despite being relatively more abundant when compared to the phytoplankton availability in both wetlands (Figure 32 and Figure 28), were restricted by the low food source of phytoplankton (Figure 32C). Whereas in the Lock 6 wetland scenario there was a large zooplankton biomass increase (Figure 28B) due to the ample nutrient source the phytoplankton biomass (Figure 28C). The ample phytoplankton biomass therefore minimised the otherwise negative impact of the lack of habitat normally provided by macrophytes (Figure 28A). The good scenario trend results provided by the model in the case of Lock 6 wetland and Pilby Creek wetland confirms the applicability of WETMOD 2 to wetlands in both extreme stable states (turbid and clear). The model can therefore be applied with confidence to category wetlands belonging to either Lock 6 wetland or Pilby Creek wetland (category 3 and 5). This confidence being both placed in the representation of the realistic trend of wetland nutrient concentration as well as in the impact respective external nutrient sources have upon the wetlands. However, as stated for Paiwalla and Sunnyside wetlands the macrophyte and zooplankton model output assessments are limited by the lack of validation data. Comparison of wetlands Sunnyside and Reedy Creek The main difference between the two wetlands is the data quality and quantity. Reedy Creek wetland has more comprehensive data so is more suitable for modelling purposes. The Reedy Creek wetland simulation succeeds where the Sunnyside simulation struggles. Results from Reedy Creek wetland simulations provide the strongest argument for the validity of WETMOD 2. For the Reedy Creek wetland scenarios, as can be seen by the D in Table 3 and the wetland time-series data in Figure 29 and Figure 30, there are obvious improvements in the model output both with the introduction of river exchange, as well as the introduction of irrigation drainage nutrient inflow. There were significant improvements in the overall modelling performance at Reedy Creek wetland for NO 3N and PO4-P modelling (Figure 29) as well as considerable improvement in phytoplankton modelling performance (Figure 30C). Visual assessment of Figure 29 123

Regional Scale Modelling of the lower River Murray wetlands and Figure 30 shows the model to simulate the Reedy Creek nutrient and phytoplankton time-series trend satisfactorily. The Reedy Creek wetland macrophyte simulated biomass is low due to the high turbidity and low Secchi depth, the zooplankton therefore mimicking only the growth of its food source the phytoplankton. WETMOD 2 shows great success with the notable performance in simulating Reedy Creek wetland. The results of Reedy Creek wetland simulations support the argument that the model is capable of simulating wetlands with both river and irrigation drainage as external nutrient sources. Therefore, the reasoning that the poor data quality for Sunnyside wetland affects its simulation performance is justified based on the successful Reedy Creek wetland scenarios.

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Regional Scale Modelling of the lower River Murray wetlands

4.2 Validation based on non-calibrated wetland data WETMOD 2 has a generic nature; through the use of wetland categories and its simplicity it is applicable to wetlands and timescales other that where it was developed. In order to verify the model applicability at different timescales and wetlands, the model must show itself to be accurate outside of the data range where it was developed. Therefore, to rigorously test the model, it should be fitted to one set of data, while checking for agreement with independent data (Goodall 1972; Tsang 1991; Wood 2001). Extra validation therefore, not only serves the validation of the model for the monitored wetlands, but also supports the argument of the models generic applicability. If the model is capable of accurately simulating a separate set of data than used in the calibration, the acceptance of the qualitative simulations for category wetlands where no time-series are available should be strengthened. For the purpose of rigorous validation, some of the data for Reedy Creek wetland (category 4 wetland) were withheld during the model calibration stage. This extra data stems from the same source project and covers the seven months prior to the data used in the model calibration stage (the data used in the model calibration stage spanned one year, see Box). The time period chosen, for Reedy Creek wetland data, in the model development stage was due to a two significant factors; 1. It was a highly variable year therefore providing the model with complex data and dynamics. 2. It was from the winter period of low growth to the next winter period (so it encompassed an entire growth cycle) Following the monitoring project that provided data for the modelling of Lock 6 and Pilby Creek wetlands, another project monitored the same wetlands. The data from this second monitoring study, performed by van der Wielen (nd), was kept separate from the data used in the model development. It is therefore also possible to validate the developed WETMOD 2 on the data not used in 3 of the 5 category wetlands. The method used by van der Wielen in assessing the NO3-N concentration was a colorimetric method (Cadmium Reduction Method) (van der Wielen nd). Colorimetric methods require an optically clear sample as the turbidity of a sample can conflict with the colorimetric measurement (APHA et al. 1992). After discussions with van 125

Regional Scale Modelling of the lower River Murray wetlands der Wielen (nd), it was considered likely that the very turbid waters of the River Murray wetlands compromised the monitored NO3-N values. As a consequence the NO3-N measurements in the Pilby Creek and Lock 6 data set cannot be relied upon. In this case the modelled PO4-P compared to the monitored PO4-P gives the best estimation of model validity. The D for the modelled results of the validation data is presented in Table 4 below. The individual results are discussed below. Table 4: Non calibrated validation of inflow data for 3 wetland categories Wetland Wetland Category Name 3

4

5

Lock 6 Wetland

Reedy Creek Wetland

Pilby Creek Wetland

Wetland External Input Variables

Modelled Modelled Modelled PO4-P NO3-N Phytoplankton D D D

NO River Exchange

52

312

151

0.05% River Flow/day Exchange

43

361

161

0.1% River Flow/day Exchange

58

406

169

NO River Exchange & NO Irrigation Drainage

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Category 3: Dead end wetlands with carp presence and no irrigation drainage (Lock 6 wetland) The simulated time-series for the non calibrated data validation of WETMOD 2 for category 3 wetlands are presented in Figure 34 and Figure 35. The standard error, at each monitoring date, is represented for PO4-P, NO3-N and phytoplankton biomass. The wetland scenario did not initially perform as well as was expected. The D actually degraded with the introduction of exchange (Table 4). The time-series graph in Figure 34 however, does show an improvement in the modelling trend after the introduction of the river exchange. For this scenario the default exchange volume for Lock 6 wetland was kept at the same level as used during the model development stage. 126

Regional Scale Modelling of the lower River Murray wetlands However, during simulations using monitored data from three different locations within Lock 6 (available in van der Wielen‟s data), it was discovered that the impact of the river exchange diminishes as the distance of the monitoring location from the river channel increases. It was therefore assumed to be reasonable to examine a different exchange volume as the monitoring site locations within the wetland differed. A reduced exchange rate at 0.05% of the river daily flow volume showed an improved D (Table 4) and a well fitting time-series as can be seen in Figure 34A. The Lock 6 D improvement for PO4-P is noteworthy despite the model not being calibrated for this data. Therefore, the model was considered valid for the PO4-P scenario within Lock 6 wetland. However, the NO3-N and phytoplankton D results were poor. As discussed previously the NO3-N monitored data was to be considered with scepticism and cannot be relied upon. Looking at the result in Figure 34B one can however see a slight improvement in NO3-N estimation during October 1998. Based on this scenario and due to the unreliable nature of the monitored NO 3-N the model can, for NO3-N wetland concentration simulation, neither be considered valid nor invalid. In Figure 35C the phytoplankton shows an improvement, despite the D results, during the October 1998 to January 1999 modelled period. For this scenario the phytoplankton modelling results show a significant overestimation for the modelled period. However, due to the performance of the model with regard to phytoplankton, both during model development and in the following validation scenarios at other wetlands described below, the model should not yet be considered invalid. Future model development should focus on addressing the phytoplankton discrepancy, which may be as simple as the monitoring methodology, the conversion of chlorophyll-a to phytoplankton or addressing the sediment impact on wetland water nutrient load. In the mean time phytoplankton volume estimation from modelling scenarios should be reviewed carefully before management decisions are made based on the model results.

127

Regional Scale Modelling of the lower River Murray wetlands

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Figure 34: Validation of simulation results for Lock 6 wetland PO4-P and NO3-N, using noncalibrated wetland data

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Regional Scale Modelling of the lower River Murray wetlands M acro p h y te B io m ass

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Figure 35: Validation of simulation results for Lock 6 wetland Macrophyte Biomass, Zooplankton and Phytoplankton biomass, using non-calibrated wetland data

129

Regional Scale Modelling of the lower River Murray wetlands Category 4: Dead end wetlands with carp presence and irrigation drainage (Reedy Creek wetland) Reedy Creek wetland data provided the best data for model development. The data from Reedy Creek wetland withheld during model development also provided the most comprehensive and reliable data for extensive model validation based on noncalibrated driving variables. Figure 36 and Figure 37 display the simulated output for the non-calibrated data validation of category 4 wetlands. The standard error at each monitoring data based on three separate measurements is included where appropriate. Evaluating these scenario results for PO4-P simulation there is a significant improvement in the D (Table 4) where both river exchange and irrigation drainage are considered. Although the model was not calibrated for this time-series the results show a satisfactory resemblance to the monitored PO4-P as seen in Figure 36A. The NO3-N D (Table 4) during this time-series actually shows a better fit to the monitored data than the original calibrated data time-series. This can be attributed to the high variability in the development data series, which were partly chosen as a consequence of this variability. The time-series seasonality and fit can be seen in Figure 36B. The NO3-N modelling result is the only NO3-N data available with which to verify the model outside of the data used in model development. The notable improvement in the improvement of the D and the good fit shown in Figure 36B provide a strong case for the validity of WETMOD 2 with regard to NO3-N simulation. The phytoplankton D shows a significant improvement (Table 4), although the seasonality is somewhat exaggerated as seen in Figure 37C. The performance of WETMOD 2 for Reedy Creek wetland, with both data sets calibrated and non-calibrated, demonstrates the performance that can be obtained when adequate data is available. The model performance for Reedy Creek wetland is the strongest argument in the favour of model validity. Therefore, the shortcoming of the model in previous instances can to a large degree be attributed to data quality. The Reedy Creek wetland results show that the availability of adequate quality data improves the performance of the model. However, it is a generic modelling tool where simple data sets can be used giving reasonable trends, thereby assisting potential management decisions. The lack of quality data should in this case not necessarily hinder scenario analysis however; the decision maker must understand that the quality 130

Regional Scale Modelling of the lower River Murray wetlands of the modelling output is very dependent on the quality of the data used as driving variables.

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M o n ito re d D ate s O nly C o nc e n tra tio n in W e tland

M o nito re d D ate s O n ly C o nc e ntratio n in I rrig a tio n D rain

Figure 36: Validation of simulation results for Reedy Creek wetland PO 4-P and NO3-N, using non-calibrated wetland data

131

Regional Scale Modelling of the lower River Murray wetlands

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Figure 37: Validation of simulation results for Reedy Creek wetland Macrophyte Biomass, Zooplankton and Phytoplankton biomass, using non-calibrated wetland data

132

Regional Scale Modelling of the lower River Murray wetlands Category 5: Dead end wetlands managed through implementation of dry periods with carp restriction and no irrigation drainage (Pilby Creek wetland) The non-calibrated driving variable validation of WETMOD 2 based on category 5 wetlands is displayed in Figure 38 and Figure 39. The standard error of the monitored data set is included where available. Pilby Creek wetland PO4-P in this case has a noteworthy improvement in D. Nevertheless, the improvement is best judged from the time-series in Figure 38A where the concentrations in the early modelling period were very close to the monitored concentrations. The September to February performance is exaggerated but is at least showing a similar trend to the monitored data. The main discrepancy in the PO4-P modelling is the lack of the late November early December peak. Pilby Creek wetland validation data stems from the same source as the Lock 6 validation data. The NO3-N monitoring results are therefore, as in the case of Lock 6 wetland, to be considered suspect and therefore no model validation will be made based on NO3-N model output for this data. The phytoplankton biomass growth is greater than expected (see Figure 39C) particularly the initial peak growth phase, which is due to the lack of macrophyte competition. However, the model scenario does retain a low phytoplankton biomass load as is expected of Pilby Creek wetland given the simulated macrophyte biomass. The macrophyte biomass growth does show an increase; followed by a winter decrease (see Figure 39A). The zooplankton biomass pattern as can be seen in Figure 39B follows both its food source pattern, i.e. phytoplankton, and assumed shelter availability afforded by the macrophytes. The zooplankton does in this instance have a more complex growth pattern than the phytoplankton due to the high shelter availability provided by the macrophytes. From the modelling results in this case as well as the two above, the model has shown itself capable of simulating wetlands for which it has been calibrated, but with noncalibrated data sets. Each of these wetlands is either in a different stable state, i.e. clear vs. turbid, or has added external influences (Reedy Creek wetland irrigation drainage inflow). This supports the argument that the model is generically applicable to similar wetlands. Where data for these similar wetlands is non existent, the accuracy WETMOD 2 trend development allows the use of “exemplar” data obtained 133

Regional Scale Modelling of the lower River Murray wetlands from the calibration wetlands, and consequently the development of qualitative scenarios and hypothetical quantitative outcomes. The application of WETMOD 2 to category wetlands in such a manner is explored in chapter 6.

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Figure 38: Validation of simulation results for Pilby Creek wetland PO4-P and NO3-N, using noncalibrated wetland data

134

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Figure 39: Validation of simulation results for Pilby Creek wetland Macrophyte Biomass, Zooplankton and Phytoplankton biomass, using non-calibrated wetland data

135

Regional Scale Modelling of the lower River Murray wetlands

4.3 Evaluating model performance 4.3.1 Generic nature and structural restrictions of model When wetland scenario results are evaluated and compared, WETMOD 2 performs satisfactorily and as expected, even for wetlands with extreme conditions of turbid and clear. The quantitative results may not reflect the accuracy expected of a dedicated wetland model. However, as discussed in the introduction, the limiting model structure, the lack of data availability and the models generic nature does not allow for WETMOD 2 to be fitted to one wetland in particular. This allows the model to be applied to a larger range of wetlands, even where verification may not be possible, with confidence in the simulation results qualitative trend. Therefore, in developing WETMOD 2, a compromise on quantitative accuracy was made in order to be able to compare the relative conditions of wetlands, including impacts external influences may have on the wetlands and/or wetlands with minimal or no time-series data. The data quality available for a given wetland has a direct impact on the accuracy of WETMOD 2 to simulate internal nutrient dynamics, as seen for Sunnyside and Reedy Creek wetlands. The potential to simulate management scenarios is directly linked with model performance. Consequently, due to the lack of data quantity and particularly quality for Sunnyside wetland management simulations for Sunnyside wetland and therefore category 2 wetlands were not feasible. However, for both Lock 6 wetland and Reedy Creek wetland, scenarios of potential management strategies were possible and are described and discussed in chapter 5. Using both Lock 6 and Reedy Creek wetland data “exemplar” category wetlands could therefore also be simulated, this is described and discussed in chapter 6. In WETMOD 2 macrophyte growth is controlled to a large extent by light availability, where the growth of macrophytes increases with a decrease in turbidity and therefore increase in Secchi depth. This relates back to Secchi depth representation of underwater light availability. The equation in the model assumes that at increased Secchi depth there will be an increase in underwater light availability and therefore in the macrophyte growth. A limitation discovered during model validation pertains to the equation used. The equation shows a logarithmic growth curve with increasing Secchi depth, which in itself is not regarded as inaccurate. However, the limitation of 136

Regional Scale Modelling of the lower River Murray wetlands this equation is its lack of consideration of the maximal depth of the wetland, i.e. there is no correlation of the equation to the water depth and therefore the maximal light penetration possible. Therefore, a shallow wetland with a depth less than 0.6m is not simulated as having substantial macrophyte growth despite the underwater light being fully available to macrophyte growth, represented by the Secchi depth effectively penetrating to the wetland bottom. The wetlands, which were monitored and provide the wetland time-series driving variables, are all of a depth where this restriction is not of significant concern; and where appropriate the macrophyte growth is calibrated to expected trends. This limitation impacts on the application of the model to very shallow wetlands. As there currently is no model calibration data available or sufficient data available as driving variables for very shallow wetlands this limitation is currently not an issue. Future development of WETMOD should however take this limitation into account and replace the current Secchi depth equation with a more appropriate one.

4.3.2 Relevance of project objectives The principal objective calls for the improvement of the resolution of spatial influences acting upon wetlands. That is, to develop or adopt a generic wetland process model to local external influences acting on a wetland. The purpose of the objective is to improve the understanding of the respective spatial influences acting upon a wetland, such as morphology and external nutrient sources, and how management can impact on the nutrient retention capacity of wetlands at each spatial location. The spatial differences considered in WETMOD 2 are any significant external sources acting upon wetlands, including: river nutrient load, the presence or absence of agricultural drainage (irrigation drainage) with its associated nutrient load contribution, and in isolated cases the impact of precipitation on irrigation drainage nutrient contribution. (South Australia is a very dry state with minimal precipitation. Most wetlands in the South Australian stretch of the River Murray do not have independent catchment areas. Precipitation is therefore in most cases not relevant).

137

Regional Scale Modelling of the lower River Murray wetlands As the principal focus of WETMOD 2 development was the spatial context of the wetlands, i.e. the individual external influences, it is important to discuss whether the model behaves logically based on the anticipated impact of external influences as well as the comparative differences of two wetlands. The validation of the model and the comparison of wetlands, discussed above, have shown the successful improvement of the model simulation output following the introduction of external influences. The simulation outputs therefore enable the study of the local and assumed external impact on wetland fulfilling this principal project objective.

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Regional Scale Modelling of the lower River Murray wetlands

4.4 Chapter summary and Implication for the first hypothesis Based on the validation results presented above WETMOD 2 is considered capable of simulating wetland seasonal nutrient flux for individual wetlands that are affected by varying external influences. Further, WETMOD 2 is considered valid, based on the wetlands that were used in model development. To improve this confidence further model validation using separate wetland data was performed and is described in section 4.2. From this the model can be applied to category wetlands with reasonable confidence placed in the output trend. The first hypothesis is that “a simplified generic wetland model can be used to realistically simulate multiple and different wetlands qualitatively”. To address this hypothesis the results of the different wetlands scenarios, developed as part of the model calibration and model validation, were reviewed as to their realistic representation of expected wetland nutrient and biomass growth trends. These wetlands are listed in Table 5. Table 5: Assessment summary of wetlands realistic simulation

Category

Wetland

Simulated realistically

Category 1 wetland

Paiwalla

YES

Category 2 wetland

Sunnyside

Limited

Category 3 wetland

Lock 6

YES

Category 4 wetland

Reedy Creek

YES

Category 5 wetland

Pilby Creek

YES

Category 3 wetland (non Lock 6 calibrated data)

YES

Category 4 wetland (non Reedy Creek calibrated data)

YES

Category 5 wetland (non Pilby Creek calibrated data)

YES

This shows that the model is capable of simulating different wetlands, for which adequate data is available, realistically although not to the accuracy of individually tailored wetland models. This argument is strengthened by the results of the model validations based on non-calibrated wetland data.

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Regional Scale Modelling of the lower River Murray wetlands

5 Simulation

results

of

potential

management

scenarios and Discussion This chapter attempts to address the second hypothesis of whether a simplified generic wetland model can be used to answer “what if” questions. The two degraded wetlands for which there is adequate data to simulate effectively, Lock 6 and Reedy Creek wetlands, are used to test simulation effectiveness of potential management strategies. In the management simulations of Lock 6 and Reedy Creek wetlands, WETMOD 2 was used to explore potential management strategies. Both wetlands are considered permanently inundated and one was additionally severely degraded due to excessive nutrient inflow from irrigation drainage. Lock 6 wetland Table 6 displays the potential percentage reduction in the outflow of PO 4-P, NO3-N and phytoplankton biomass as a consequence of three different management scenarios. Figure 40 and Figure 41 represent the impact, on nutrient concentration and macrophyte, zooplankton and phytoplankton biomass respectively, due to potential management of turbidity reduction through the introduction of wetland dry periods in Lock 6 wetland. To illustrate the status quo, the monitored concentrations and biomass of PO4-P, NO3-N and phytoplankton, and the standard error, are also displayed. The scenarios during the months of February, March and April reflect the anticipated wetland response to turbidity management (see section 3.4) during the macrophyte growth period. The sedimentation rate of PO4-P, NO3-N and phytoplankton during the months of March and April was constant for all scenarios (see Box). During this period, the reduction in both PO4-P and NO3-N wetland concentrations is therefore a direct result of the reduction in turbidity and improved uptake of nutrients by the wetland, Figure 40A and B. This is reflected in the increase in macrophyte and zooplankton growth seen in Figure 41A and B. The nutrient reduction success during this period improves with each increment of turbidity reduction management, as can clearly be seen in Figure 40A and B. The improvement in wetland condition can also be seen in the dramatic increase in macrophyte growth, first at the 50% turbidity reduction management scenario, and then at the 75% turbidity reduction scenario, as 140

Regional Scale Modelling of the lower River Murray wetlands shown in Figure 41A. The 75% reduction in turbidity (Figure 41A) demonstrates a healthy growth phase of macrophytes, which reduces in the cooler months. The zooplankton population growth seen during March and April for the 75% turbidity scenario is a consequence of the assumed improved habitat conditions provided by the macrophytes. The reduction in phytoplankton during this time is a consequence of the competition with macrophytes for underwater light. The initial growth spurt of phytoplankton for the 75% turbidity reduction scenario during February, is caused by the improved underwater light conditions, and reduced competition due to, an expected, lag in macrophyte growth. The zooplankton growth during February also shows a slight increase as a consequence of the improved nutrient source (phytoplankton), followed by a slight reduction in its population during the transition from phytoplankton to macrophyte dominant phase. The 50% reduction in turbidity (Figure 41A) signifies the first real improvement in macrophyte growth, with a corresponding wetland nutrient load reduction. However, as expected, the macrophyte growth is not as pronounced as that of the 75% turbidity reduction scenario. The 50% reduction in turbidity scenario also shows some of the February increase in phytoplankton growth prior to macrophyte competition, as well as a slight improvement in the zooplankton. The 25% turbidity reduction scenario shows minimal improvement in macrophyte growth, reflected mainly in the slight improvement in the uptake of nutrients (PO4-P and NO3-N) during March and April. When the turbidity is below that of the sedimentation threshold of 70 NTU, there is a reduction in sedimentation of both PO4-P and NO3-N. This is apparent during a short, but clear, high turbidity event in February for the 0% turbidity reduction scenario where the wetland concentration of both PO4-P and NO3-N show a sudden and substantial reduction, as seen in Figure 40. This trough in PO4-P and NO3-N concentration is due to a rise in turbidity above the sedimentation threshold of 70 NTU that, in the unmanaged scenario, causes a sudden increase in nutrient sedimentation. More significantly, there is an early drop in nutrient concentration for the 0% scenario at the beginning of May (Figure 40), which continues for the reminder of the simulation period. The 25% simulation, where the turbidity was reduced by 25%, has a similar but more drastic drop in nutrient concentration at the end of May, followed by the 50% scenario at the end of June. The 75% scenario has only a small drop in nutrient concentration for a relatively short period of time as in 141

Regional Scale Modelling of the lower River Murray wetlands this scenario the turbidity only surpasses the 70 NTU sedimentation threshold for a short period of 7 days (28th August 1997 to 3rd September 1997). In summary, due to the management simulation of turbidity reduction, the period of time where the turbidity remained below that of the calibrated sedimentation threshold steadily increased for each improved management scenario, i.e. each increase in turbidity reduction. Accordingly, the sedimentation rate reduced for each increasing turbidity reduction scenario until, at the 75% turbidity reduction scenario, only 7 days remained where turbidity within the wetland exceeded 70 NTU. Consequently, Lock 6 wetland progressively lost modelled sink (adsorption) capacity for both PO4-P and NO3-N with each increase in turbidity reduction. This does however not accurately account for any resuspension of nutrient highlighting one discrepancy in a generic model. Phytoplankton was also affected by the sedimentation change, the phytoplankton threshold being calibrated to 95 NTU. The phytoplankton maintained a longer growth period both due to the reduction in turbidity with its inherent increased light availability and a low sedimentation rate (Figure 41C, June onwards). This growth period was extended with each improved management scenario, until the phytoplankton sedimentation is absent in the 75% turbidity reduction scenario (Figure 41C). The augmented phytoplankton availability resulted in an increased phytoplankton outflow from the wetland as can be seen in Table 6, with the 50% turbidity reduction scenario showing the highest amount of phytoplankton in the outflow. The increase in macrophytes and zooplankton in the 75% turbidity simulation, during March and April, reduced the phytoplankton growth that can be seen in the lower phytoplankton outflow in Table 6 as well as in Figure 41C. The zooplankton growth increased as a consequence of, and proportionally to, the extended phytoplankton growth, as can be seen in Figure 41B. Table 6: Lock 6 wetland Percentage Outflow Reduction 25% Turbidity Reduction

50% Turbidity Reduction

75% Turbidity Reduction

PO4-P

% Reduction in -17.1 Outflow

-34.1

-47.7

NO3-N

% Reduction in -17.2 Outflow

-18.0

9.1

142

Regional Scale Modelling of the lower River Murray wetlands Phytoplankton% Reduction in -3.0 Outflow

A

-11.1

-5.6

P O 4 -P

0 .2 0 0 .1 8 0 .1 6

-

0 .1 2

m g /L

0 .1 4

0 .1 0 0 .0 8 0 .0 6 0 .0 4 0 .0 2

B

S e p -9 7

A u g -9 7

J u l-9 7

J u n -9 7

M a y -9 7

A p r-9 7

M a r-9 7

F e b -9 7

0 .0 0

N O 3 -N 0.45

0.40

0.35

m g /L -

0.30

0.25

0.20

0.15

0.10

0.05

T urb id ity R e d uc e d b y 0 %

T urb id ity R e d uc e d b y 2 5 %

T urb id ity R e d uc e d b y 7 5 %

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

S ep-97

A ug-97

J ul-97

J un-97

M ay -97

A pr-97

M ar-97

F eb-97

0.00

T urb id ity R e d uc e d b y 5 0 %

Figure 40: Lock 6 impacts on Nutrient concentration due to Turbidity reduction

143

Regional Scale Modelling of the lower River Murray wetlands

A

M a c r o p h y te B io m a s s

1 6 .0 0

1 4 .0 0

1 2 .0 0

k g /m 3 -

1 0 .0 0

8 .0 0

6 .0 0

4 .0 0

2 .0 0

A u g -9 7

S e p -9 7

A u g -9 7

S e p -9 7 S e p -9 7

J u l -9 7

J u n -9 7

M a y -9 7

A p r-9 7

A u g -9 7

B

M a r-9 7

F e b -9 7

0 .0 0

Z o o p la n k to n 2 .5 0

-

2 .0 0

c m 3 /m 3

1 .5 0

1 .0 0

0 .5 0

C

J u l -9 7

J u n -9 7

M a y -9 7

A p r-9 7

M a r-9 7

F e b -9 7

0 .0 0

P h y to p la n k to n 2 5 .0 0

-

2 0 .0 0

c m 3 /m 3

1 5 .0 0

1 0 .0 0

5 .0 0

T urb id ity R e d uc e d b y 0 %

T urb id ity R e d uc e d b y 2 5 %

T urb id ity R e d uc e d b y 7 5 %

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

J u l -9 7

J u n -9 7

M a y -9 7

A p r-9 7

M a r-9 7

F e b -9 7

0 .0 0

T urb id ity R e d uc e d b y 5 0 %

Figure 41: Lock 6 impacts on Macrophyte, Zooplankton & Phytoplankton due to Turbidity reduction

144

Regional Scale Modelling of the lower River Murray wetlands Reedy Creek wetland Figure 42A and B and Figure 43C show the management scenarios for Reedy Creek wetland and the impact on wetland biomass and nutrient concentrations as a result of the management scenarios of successful reduction of irrigation drainage. Table 7 shows the percentage reduction of the total inflow (irrigation drainage and river concentrations) versus the percentage outflow reduction due to different management scenarios for Reedy Creek wetland. The Reedy Creek wetland is adjacent to dairy farms whose pasture areas are situated on reclaimed swamps. The irrigation runoff from the dairy pastures is pumped from the adjacent farms into the wetland. This irrigation drainage has heavily influenced Reedy Creek wetland and caused substantial degradation. One potential management strategy that can be applied to Reedy Creek wetland is the nutrient reduction of irrigation drainage load through the use of constructed wetlands. Wen (2002a; 2002b), who contributed his data to this project, conducted preliminary trials of constructed wetlands on Basby farm, which is a dairy farm immediately adjacent to Reedy Creek wetland. His findings were that PO4-P could potentially be reduced by 50% to 90%. Based on his findings, three scenarios of management were performed. The management scenarios represented increasing reductions of 25%, 50% and 75% of PO4-P, NO3-N and phytoplankton irrigation drainage loads, the time-series of which can be seen in Figure 42A and B and Figure 43C. The percentage reduction in the wetland outflow concentration compared with the reduction in inflow concentration can be seen in Table 7. In Table 7 the effective percentage of reduction of the total nutrient inflow is labelled as %RI and is displayed for each of the irrigation drainage reduction scenarios. The ensuing percentage reduction in outflow is labelled %RO (see section 3.4.1). The management scenarios of increasing reductions in nutrient inflow to the wetland are controlled through the irrigation nutrient reduction option of the model. Each of the PO4-P simulations, for increased nutrient removal capacity, shows the same trend, and for a large time period a virtually identical wetland concentration. However, in October and again in February, as seen in Figure 42A, the simulated wetland PO4-P concentration shows a reduction as a result of the management. The NO3-N reduction can also be seen in Table 7. The high phytoplankton biomass is due to a high phytoplankton inflow load from the irrigation drainage. There is a major 145

Regional Scale Modelling of the lower River Murray wetlands reduction of phytoplankton during the January, February and March periods. The phytoplankton reduction was successful, as clearly seen in Figure 43C, particularly during the months of January, February and March. The reduction in percentage inflow versus reduction in percentage outflow is most extreme for phytoplankton, as seen in Table 7. This indicates that through a minor reduction in irrigation nutrient inflow, there can be a substantial impact on the outflow concentration of nutrients and phytoplankton from the wetland. Drop in phytoplankton growth phase 19 th to 27th March is due to a spike in turbidity. The zooplankton growth follows the phytoplankton concentration, with a similar reduction due to management. The high turbidity of the wetland, which restricts the Secchi depth to an estimated depth of 0.2 m, severely limits the macrophyte growth within the wetland. The degradation of the wetland macrophyte concentration from the initial starting level adopted for the model is a consequence of this macrophyte growth restriction. Therefore, despite the positive impact that simulated management (irrigation nutrient reduction) has on outflow nutrient reduction, the lack of macrophyte growth hampers an increase in the nutrient retention capacity of the wetland. The impact that the reduction of turbidity, as a second management strategy, may have on macrophyte growth and therefore nutrient retention is examined below.

146

Regional Scale Modelling of the lower River Murray wetlands

A

PO4-P

1.20

8 7

1.00 6 5

mg/L

4 0.60 3 0.40

2

mg/L Drainage only

0.80

1 0.20 0

B

May-01

Apr-01

Mar-01

Feb-01

Jan-01

Dec-00

Nov-00

Oct-00

Sep-00

Aug-00

Jul-00

-1 Jun-00

0.00

NO3-N

0.80

1.2

0.70

1

0.60

mg/L

0.6 0.40 0.4 0.30

mg/L Drainage only

0.8

0.50

0.2 0.20 0

0.10

May-01

Apr-01

Mar-01

Feb-01

Jan-01

Dec-00

Nov-00

Oct-00

Sep-00

Aug-00

Jul-00

-0.2 Jun-00

0.00

I rrig atio n D rain ag e C o nc e ntra tio n R e d uc e d b y 0 %

I rrig a tio n D ra in ag e C o nc e ntratio n R e duce d b y 2 5%

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 5 0 %

I rrig atio n D rain ag e C o nc e ntra tio n R e d uc e d b y 7 5 %

M o nito re d D ate s O nly C o nc e n tra tio n in W e tla nd

M o nito re d D ate s O nly C o n c e ntratio n in I rrig atio n D rain

Figure 42: Reedy Creek wetland impacts on Nutrient concentration due to irrigation drainage reduction

147

Regional Scale Modelling of the lower River Murray wetlands

A

M acrophyte Biomass

0.12

0.1

kg/m3

0.08

0.06

0.04

0.02

F eb-01

Mar-01

A pr-01

F eb-01

Mar-01

A pr-01

May-01

Jan-01 Jan-01

B

D ec-00

N ov-00

Oct-00

S ep-00

A ug-00

Jul-00

Jun-00

0

Zo o p la n kto n

1.8 1.6 1.4

c m 3/m 3

1.2

1

0.8 0.6

0.4 0.2

C

M ay-01

D ec-00

N ov-00

Oct-00

Sep-00

Aug-00

Jul-00

Jun-00

0

Phytoplankton

14

160 140

12

120

cm3/m3

100 8

80 60

6

40 4

cm3/m3 Drainage only

10

20 2

0

May-01

Apr-01

Mar-01

Feb-01

Jan-01

Dec-00

Nov-00

Oct-00

Sep-00

Aug-00

Jul-00

-20 Jun-00

0

I rrig atio n D ra ina g e C o nc e n tra tio n R e d uc e d b y 0 %

I rrig a tio n D rainag e C o nc e ntratio n R e duce d b y 2 5 %

I rrig atio n D ra ina g e C o nc e ntratio n R e d uc e d b y 5 0 %

I rrig atio n D ra ina g e C o nc e n tra tio n R e d uc e d b y 7 5 %

M o nito re d D ate s O n ly C o nc e ntratio n in W e tla nd

M o n ito re d D ate s O nly C o nc e n tra tio n in I rrig a tio n D rain

Figure 43: Reedy Creek wetland impacts on Macrophyte, Zooplankton & Phytoplankton due to irrigation drainage reduction

148

Regional Scale Modelling of the lower River Murray wetlands Table 7: Reedy Creek wetland Percentage Inflow reduction vs. Percentage Outflow Reduction

PO4-P

NO3-N

0% Nutrient Reduction

25% Nutrient Reduction

50% Nutrient Reduction

75% Nutrient Reduction

%RI

0

0

0

0

%RO (Irrigation Reduction Only)

0

1.2

2.8

4

%RI

0

0

0

0

%RO (Irrigation Reduction Only)

0

0.7

1.4

2.1

0

0

0

0

0

4.1

8.2

12.2

Phytoplankton %RI %RO (Irrigation Reduction Only)

Reedy Creek wetland twin management strategies For Reedy Creek wetland a combination of both management strategies was simulated. This was in an attempt to assess the cumulative impact of intensive management of one large and severely degraded wetland. The high irrigation drainage reduction scenarios of 85, 90 and 95% were used to assess the impact of potential full restoration of the wetland. Figure 44 and Figure 45 represent the concentrations in the open water of Reedy Creek wetland when the turbidity is modelled at 75% reduction and the nutrients are reduced by 25%, 50% and 75% successively. Whereas, Figure 46 and Figure 47 represent the concentrations in the open water of Reedy Creek wetland when the irrigation drainage nutrient reduction scenario is maintained at 95% and the turbidity reduction scenarios are at 25, 50 and 75% respectively. Figure 44, Figure 45 and Figure 46, Figure 47 are plotted separately to distinguish between the impacts of various turbidity reduction scenarios at the best possible nutrient reduction scenario, and the impact of the nutrient reduction scenario at the best turbidity reduction scenario. Note, in Figure 44, Figure 45, Figure 46 and Figure 47 the monitored irrigation drainage concentration and the monitored concentration in the wetland are those monitored for Reedy Creek wetland. Figure 48, Figure 49 and Figure 50 show the percentage reduction in outflow load from Reedy Creek wetland as a consequence of the double management strategies. In these figures the results from all simulated combinations are presented.

149

Regional Scale Modelling of the lower River Murray wetlands The reduction irrigation drainage inflow has also reduced the wetland outflow of nutrients and phytoplankton. This can be seen in the gradual increase in “% Reduction in wetland nutrient outflow compared to status quo” in Figure 48 for PO4-P, Figure 49 for NO3-N and Figure 50 for phytoplankton. In Figure 48 to Figure 50 the percentage reduction in wetland nutrient outflow is related back to the status quo, i.e. without management scenarios. With the exception of NO3-N each increment of reduction in turbidity results in a “drop in the percentage reduction” in wetland PO4-P outflow, and phytoplankton outflow, see Figure 49, Figure 48 and Figure 50 respectively, i.e. PO4-P and phytoplankton outflow increase. The reason for the comparatively increased nutrient outflow, despite the management strategy of “reduction in irrigation” remaining the same at 95%, is related back to the decrease in the sedimentation rate, i.e. the turbidity simulated at below 70 NTU for nutrients, and 95 NTU for phytoplankton. The NO3-N wetland retention, seen in Figure 49, as a general trend improves, however the NO3-N retention reduces again for the 75% turbidity reduction as the turbidity passes below the sedimentation threshold (discussed for Lock 6 wetland above). That is, the 75% turbidity reduction scenario has a higher NO 3-N outflow due to the loss of sedimentation of nutrients (Figure 44B). This can be seen as an increase in NO3-N wetland concentration, i.e. decreased nutrient retention, and is visible during September in Figure 46. The scenarios with minimal turbidity reduction display a higher NO3-N retention, attributed to higher sedimentation of NO 3-N in more turbid wetlands. This again raises the question whether the model needs an improvement to account for sedimentation resuspension. An improvement in PO4-P retention is observable in the irrigation nutrient scenarios; however the turbidity reduction scenarios cause a steady drop in the PO4-P retention (Figure 48). The turbidity reduction scenarios reduce the PO4-P sedimentation as they do the NO3-N sedimentation. The difference between NO3-N and PO4-P is that the PO4-P concentration is very low during the period that has such a great influence on NO3-N, i.e. September see Figure 44A. Therefore, the variability in wetland concentration for PO4-P during September becomes negligible. There is an early low macrophyte biomass for turbidity reduction scenarios which is not as apparent in nutrient reduction and status quo scenarios, which can be seen in Figure 45A and Figure 47A for the periods July 2000 to January 2001. This fast drop 150

Regional Scale Modelling of the lower River Murray wetlands in macrophyte biomass for the turbidity reduction scenarios is a model artefact with negligible repercussions. It is caused by the minimum fixed macrophyte gross primary productivity being slightly higher than the calculated, when the turbidity is below the 70 NTU threshold and the macrophyte growth is restricted due to other causes. The trend of macrophyte growth and its peak is due to the underwater light availability as well as nutrient availability during the simulation period. This can be seen in Figure 45A when compared to Figure 44 where initially the underwater light for macrophyte growth is limited. The macrophyte growth is again restricted, this time by NO3-N limitation in late May 2001, see Figure 44B, which causes the rapid macrophyte dieback seen in Figure 45A. The same limitations caused by underwater light and NO3-N can be seen in Figure 47A for the scenario of 75% turbidity reduction and 95% nutrient reduction when compared to Figure 46A and B. However, in this instance the macrophyte biomass is at its lowest for a 75% turbidity reduction scenario, compare macrophytes at Figure 45A on page 153 and Figure 47A. Effectively the higher macrophyte biomass growth is seen in the high turbidity reduction scenario (75%) with an incremental increase in biomass with each successive nutrient reduction scenario, seen in Figure 45A. Phytoplankton retention improves with each irrigation drainage reduction (Figure 50). However, with the decrease in turbidity (Figure 50) and the late start of the macrophyte growth season discussed above, the phytoplankton has ample opportunity to increase its biomass (Figure 50, Figure 45C and Figure 47C). Therefore, the turbidity reduction scenarios actually contribute to the phytoplankton growth for Reedy Creek twin management scenarios. As seen previously the zooplankton growth trend, Figure 45B and Figure 47B, follows that of its food source the phytoplankton seen in Figure 45C and Figure 47C.

151

Regional Scale Modelling of the lower River Murray wetlands

A

P O 4 -P

1 .2 0

8 7

1 .0 0

m g/L

0 .8 0

5 4

0 .6 0 3 0 .4 0

2

m g/L D r ainage only

6

1 0 .2 0 0

B

M a y-0 1

A p r-0 1

M a r-0 1

F e b -0 1

Ja n -0 1

D e c-0 0

N o v-0 0

O ct-0 0

S e p -0 0

A u g -0 0

Ju l-0 0

-1

Ju n -0 0

0 .0 0

N O 3 -N

0 .8 0

1 .2

0 .7 0

1

0 .6 0

m g /L

0 .6 0 .4 0 0 .4 0 .3 0 0 .2

m g /L D ra in a g e o n ly

0 .8 0 .5 0

0 .2 0 0

0 .1 0

M a y -0 1

A p r-0 1

M a r-0 1

F e b -0 1

J a n -0 1

D e c -0 0

N o v -0 0

O c t-0 0

S e p -0 0

A u g -0 0

J u l-0 0

-0 .2

J u n -0 0

0 .0 0

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 0 %

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 2 5 %

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 5 0 %

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 7 5 %

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

M o nito re d D ate s O nly C o nc e ntratio n in I rrig atio n D rain

S tatus Q uo (N o M anag e m e nt)

Figure 44: Reedy Creek wetland impacts on Nutrient concentration due to irrigation drainage reduction and 75% turbidity reduction

152

Regional Scale Modelling of the lower River Murray wetlands

A

M a c ro p h y te B io m a s s

0.7

0.6

0.5

k g /m 3

0.4

0.3

0.2

0.1

F eb-01

M ar-01

A pr-01

M ay -01

M ar-01

A pr-01

M ay -01

J an-01

F eb-01

B

D ec -00

N ov -00

O c t-00

S ep-00

A ug-00

J ul-00

J un-00

0

Zo o p la n k to n

1.8

1.6

1.4

c m 3 /m 3

1.2

1

0.8

0.6

0.4

0.2

C

J an-01

D ec -00

N ov -00

O c t-00

S ep-00

A ug-00

J ul-00

J un-00

0

P h y to p la n k to n

14

160

140

12

c m 3 /m 3

100 8

80

60

6

40 4

c m 3 /m 3 D ra in a g e o n ly

120 10

20 2

0

M a y -0 1

A p r-0 1

M a r-0 1

F e b -0 1

J a n -0 1

D e c -0 0

N o v -0 0

O c t-0 0

S e p -0 0

A u g -0 0

J u l-0 0

-2 0

J u n -0 0

0

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 0 %

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 2 5 %

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 5 0 %

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 7 5 %

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

M o nito re d D ate s O nly C o nc e ntratio n in I rrig atio n D rain

S tatus Q uo (No M anag e m e nt)

Figure 45: Reedy Creek wetland impacts on Macrophyte, Zooplankton & Phytoplankton due to irrigation drainage reduction and 75% turbidity reduction

153

Regional Scale Modelling of the lower River Murray wetlands

A

P O 4 -P

1 .2 0

8 7

1 .0 0

m g/L

0 .8 0

5 4

0 .6 0 3 0 .4 0

2

m g/L D r ainage only

6

1 0 .2 0 0

B

M a y-0 1

A p r-0 1

M a r-0 1

F e b -0 1

Ja n -0 1

D e c-0 0

N o v-0 0

O ct-0 0

S e p -0 0

A u g -0 0

Ju l-0 0

-1

Ju n -0 0

0 .0 0

N O 3 -N

0 .8 0

1 .2

0 .7 0

1

0 .6 0

m g /L

0 .6 0 .4 0 0 .4 0 .3 0 0 .2

m g /L D ra in a g e o n ly

0 .8 0 .5 0

0 .2 0 0

0 .1 0

M a y -0 1

A p r-0 1

M a r-0 1

F e b -0 1

J a n -0 1

D e c -0 0

N o v -0 0

O c t-0 0

S e p -0 0

A u g -0 0

J u l-0 0

-0 .2

J u n -0 0

0 .0 0

S tatus Q uo (N o M anag e m e nt)

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 9 5 % & T urb id ity R e d uc e d b y 2 5 %

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 9 5 % & T urb id ity R e d uc e d b y 5 0 %

I rrig atio n D rainag e C o nc e ntratio n R e d uc e d b y 9 5 % & T urb id ity R e d uc e d b y 7 5 %

M o nito re d D ate s O nly C o nc e ntratio n in W e tland

M o nito re d D ate s O nly C o nc e ntratio n in I rrig atio n D rain

Figure 46: Reedy Creek wetland impacts on Nutrient concentration due to 95 % irrigation drainage reduction at 25, 50 and 75% turbidity reduction

154

Regional Scale Modelling of the lower River Murray wetlands

A

M a c ro p h y te B io m a s s

0.2 0.18 0.16 0.14

k g /m 3

0.12 0.1 0.08 0.06 0.04 0.02

B

M ay -01

A pr-01

M ar-01

F eb-01

J an-01

D ec -00

N ov -00

O c t-00

S ep-00

A ug-00

J un-00

J ul-00

0

Zo o p la n k to n

1.8

1.6

1.4

c m 3 /m 3

1.2

1

0.8

0.6

0.4

0.2

C

M ay -01

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Figure 47: Reedy Creek wetland impacts on Macrophyte, Zooplankton & Phytoplankton due to 95% irrigation drainage reduction at 25, 50 and 75% turbidity reduction

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Regional Scale Modelling of the lower River Murray wetlands PO4-P Outflow at Reedy Creek

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Figure 48: Reedy Creek wetland PO4-P % reduction in outflow

% Reduction in wetland nutrient outflow compared to status quo

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Figure 49: Reedy Creek wetland NO3-N % reduction in outflow

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Regional Scale Modelling of the lower River Murray wetlands Phytoplankton Outflow at Reedy Creek

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Figure 50: Reedy Creek wetland Phytoplankton % reduction in outflow

5.1.1 Implications for Management Lock 6 wetland The improvement in nutrient uptake during the macrophyte growth period, March and April, shows that management scenarios, particularly the 75% turbidity reduction scenario, are extremely successful in nutrient reduction. Scenarios of increasing management success, represented by increased percentage of reduced turbidity, demonstrate gradual improvement in nutrient retention, with 75% turbidity reduction showing a drop in almost a third of wetland nutrient load. During the winter period, where the poorest performance of managed wetlands can be seen, nutrient sedimentation rate exceeds the nutrient uptake of macrophytes, phytoplankton and zooplankton. As a result, the mass balance seen in Table 6 shows the turbid state to be a more effective nutrient and phytoplankton sink. Although the macrophyte growth of March and April indicated an improvement due to turbidity reduction the main concern to wetland management was the dramatic reduction in the sedimentation of PO4-P and NO3-N. This reduction of sedimentation of PO4-P and NO3-N was as a direct consequence of the reduced turbidity, which is mainly apparent during the periods of May through to late September. This does however not adequately consider any potential resuspension of nutrient, which could be a future model enhancement. The excess nutrient availability and lack of macrophyte competition in the cooler 157

Regional Scale Modelling of the lower River Murray wetlands months led to an increased phytoplankton growth, and therefore a possible resultant degradation of water quality for the river. However, when the 50% turbidity management scenario is studied in detail, it responds to macrophyte growth and has the lowest nutrient load during most of the winter period. This, along with the healthy macrophyte growth of the 75% turbidity reduction scenario, indicates that the optimal wetland state will be found in a balance between maintaining as high a sedimentation rate as possible with some suspended sediment inflow and therefore slightly turbid waters (effectively a sedimenting wetland). Comparing the nutrient mass balance of the different management strategies shows that the increasing macrophyte growth could not compete with the loss in nutrient sedimentation in the management scenarios, the exception being the Lock 6 wetland NO3-N retention in the 75% turbidity reduction simulation. This shows that the model output may improve with some increased complexity, although this would need to be weighed up against the loss in model applicability on a landscape scale. The main reason for the PO4-P mass balance failing to show an improvement in the mass balance, despite there being a very clear and significant PO 4-P uptake during the macrophyte growth phase, was a short-term high nutrient load in the inflow water from the river. This inflow occurred in late September. During this month there were high river PO4-P loads, which caused a large inflow load. The high turbidity of the 0% and 25% scenarios contained the increased load through a high sedimentation rate, as the turbidity levels were above the 70 NTU sedimentation threshold. Due to the turbidity controlled sedimentation threshold, the 50% and 75% turbidity reduction scenarios were unable to buffer this excess load, which is reflected in the increase in phytoplankton growth during the final simulation week, seen in Figure 40. The 50% and 75% turbidity reduction scenarios, having low turbidity and a low nutrient sedimentation rate, have a seemingly greater wetland nutrient load, and hence there is a higher outflow load of nutrient and phytoplankton during this period. This increased nutrient load has an adverse impact on the nutrient mass balance, showing the 50% and 75% turbidity reduction management scenarios to be ineffective in improving wetland nutrient retention. However, the scenarios show that during the period with increased macrophyte growth, see Figure 41, as predominantly seen with the 75% turbidity reduction, the phytoplankton and particularly NO3-N outflow was reduced (Table 6). Therefore, assessing the results for a season where the model assumes low 158

Regional Scale Modelling of the lower River Murray wetlands sedimentation of nutrients for all scenarios (i.e. all scenarios having the same turbidity sedimentation) there is an obvious visual decrease in wetland nutrient load with each reduced turbidity simulation. Increasing the complexity of the model through introducing sediment resuspension and nutrient release may therefore not be necessary. The model in this case (Lock 6 wetland) can be used to assess the minimum turbidity improvement required for the wetland to have a positive response to nutrient retention. With this information, wetland managers can more confidently judge the potential success rate of wetland restoration based on their expectation of turbidity improvement. Another management option based on the Lock 6 wetland management scenarios may lead wetland managers to inundate the wetland during the macrophyte growth period only, and introduce wetland dry periods during the cooler winter months where the nutrient removal may not be as successful or when macrophyte health starts to deteriorate. This would then maximise the macrophyte driven nutrient uptake of the wetland. In this case the model would have been used in optimising the choice of wetland dry periods. This is examined in section 6.1. Reedy Creek wetland The management option of irrigation reduction through constructed wetlands shows an improvement of wetland nutrient and phytoplankton retention. This model simulation suggests a positive result on wetland nutrient and phytoplankton load, and therefore outflow as a consequence of reducing irrigation drainage inflow into the wetland. The outflow reduction was in each instance higher in percentage than the percentage reduction of inflow, suggesting that a small change in irrigation drainage inflow can have a substantial impact on the total exchange of nutrients between the wetland and river. The impact this nutrient reduction has on river nutrient load is discussed in section 6.3. The model shows that Reedy Creek wetland itself, as a consequence of its high turbidity and lack of macrophyte growth, is presently not capable of improving its nutrient retention. Due to a lack of data, the effective turbidity reduction as a consequence of a reduction in phytoplankton is not taken into account in the model. Decision makers must therefore keep in mind the possibility that phytoplankton reduction may also reduce turbidity and increase Secchi depth. This increase in Secchi 159

Regional Scale Modelling of the lower River Murray wetlands depth would allow macrophyte growth, which may further reduce wetland nutrient load. Reedy Creek wetland twin management strategies Simulating twin management actions provides the opportunity to assess the compounding impact one management strategy may have on the other. With an effective net phytoplankton production, the management strategy of turbidity reduction proved counter productive. Along with the ample nutrient availability in the wetland, the primary cause of net phytoplankton production increase was the added underwater light availability that was enough for phytoplankton growth but still restricted macrophyte growth. This increased phytoplankton growth, became evident during the high nutrient reduction scenario Figure 47C and Figure 46. Further, the simulated loss of sedimentation of PO4-P and NO3-N resulted in the loss of nutrient retention. Therefore, for a net increase in wetland nutrient and phytoplankton retention, the nutrient reduction scenarios through constructed wetlands and without the added management scenarios of turbidity reduction proved to be the more effective management strategies. Although this conclusion can be drawn at this stage from the twin management strategies scenarios, Beck (1997) discusses the problems faced by modellers when models are calibrated for stressed systems and may therefore have some difficulty in simulating the system when returned to a natural state. WETMOD 2 was calibrated for optimal wetland response for category 4 wetlands, i.e. for a degraded system, largely influenced by irrigation drainage with no significant macrophyte growth. Therefore, allowance must be made to question the accuracy of simulated macrophyte biomass growth particularly as the model is compounding the potential errors of assumptions for two management strategies, as in the case of twin management. The confidence in the model output must rely on the assessment of expected trends for the wetland as a consequence of twin management. Therefore, before deciding on refraining from twin management of a wetland such as Reedy Creek wetland, the question must be raised as to whether the simulated volume of macrophyte biomass was realistic enough to truthfully represent the impact of macrophyte uptake of nutrients. Although the conclusion drawn at this stage indicates that twin management may be counterproductive, the results elicited help formulate new questions and 160

Regional Scale Modelling of the lower River Murray wetlands therefore focus further potential research. For example, further research could be directed at discovering the true potential response of macrophyte growth trend in such an instance, as well as to discover at what stage of nutrient reduction (through a constructed wetland) would the introduction of wetland dry periods assist in promoting macrophyte growth. As a start for example, monitoring would be required to validate model macrophyte simulations.

5.2 Chapter summary and Implications for the second hypothesis The simulations of wetland management, based on the two wetlands presented, show that WETMOD 2 can be applied to assess and better understand the impacts of wetland management. The model was effectively applied in the management of wetlands facing different degradation pressures. Both wetlands were degraded as a consequence of permanent inundation, and one was additionally degraded due to irrigation drainage inflow. WETMOD 2 could, as it is a generically applicable model, be applied to other wetlands within these categories. The model developed management scenarios that were successfully used to assess the impact of management, see Table 8. Table 8 shows that “a simplified generic wetland model can be used to answer what if questions”.

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Regional Scale Modelling of the lower River Murray wetlands Table 8: Assessment summary of wetlands management scenarios

Category

Wetland

Management question

Answer

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3 Lock 6

Can the model identify the turbidity reduction that is required for a positive response if a turbid wetland is managed?

YES

Category wetland

4 Reedy Creek

Can the model simulate the implications of the reduction of irrigation drainage nutrient on a wetland impacted on by irrigation drainage?

YES

Category wetland

4 Reedy Creek

Can the model indicate the YES impact of introducing two (although management strategies to a limited) wetland such as Reedy Creek wetland?

The use of deterministic differential models provides a platform with which some of the complexity of wetland management can be organised and examined. Thereby the model user is able to gain a better understanding of the impact of intervention options such as different wetland management strategies or intensities, e.g. minimum turbidity reduction required, and therefore answer “what if” questions. A modeller can experiment with the model to study the impacts of minor alterations within a wetland and therefore gain a larger understanding of the complexity of the ecosystem. By using the model, decision makers can agree on which scenarios are to be run, assess the output, and if necessary trace back the trigger variable to either gain a better understanding or increase consensus. Whether modellers are also able to gain some insight into the potential outcomes of multiple wetland management and therefore the cumulative impact on the river nutrient load is discussed in the next chapter. This would assist managers and decision makers in estimating what intensity of management may be required for a desired regional response.

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Regional Scale Modelling of the lower River Murray wetlands

6 Results

of

management

the

cumulative

scenarios,

assessment

visualisation

of and

discussion As the model is generic, and therefore shown to be applicable to category wetlands for which “exemplar”-driving variables are available, an assessment of the cumulative impact of multiple managed wetlands was therefore possible. The cumulative impact assessment allows the discovery of the potentially optimal management strategy, not only for one wetland but multiple wetlands, and therefore the optimal strategy for regional scale wetland management. For a cumulative assessment of the impact wetland management would have within regional scale management, scenarios were developed with WETMOD 2 for those wetlands identified as belonging to category 3 and category 4 wetlands (“exemplar” driving variables from Lock 6 wetland and Reedy Creek wetland respectively). The management of 57 category 3, and 7 category 4 wetlands were simulated (see methodology in section 3.4.2). The application of the model to category wetlands tests the hypothesis of whether “a simplified generic wetland model can be used to assess the cumulative impact of managing multiple same category wetlands”. This would expand the applicability of the model to wetlands where limited data is available and therefore the assessment of potential multiple wetland management on a regional scale. The category simulation output represents the estimation of the nutrient, plankton and macrophyte trends within a wetland as a result of the differences between the wetlands. These wetland differences are wetland volume, depth and location along the river. The location along the river dictates river flow volume and river nutrient concentration. However, there are important differences between wetlands, which could not be considered in “category wetland” simulations. Future wetland simulation modelling has the potential to upgrade category simulations with improved data for the following, without substantial model alteration; Specific exchange volume estimate for each simulated wetland (based on future monitoring, digital elevation models and/or expert input)

163

Regional Scale Modelling of the lower River Murray wetlands Substrate composition, i.e. will the wetland sediment compact? (soils surveys may have to determine the sediment compaction potential of individual wetlands) Specific irrigation volume, category 4 wetlands only (assumed to be equal to Reedy Creek due to lack of data, results used to show feasibility of simulation only, not result accuracy) These limitations to scenario modelling were anticipated and, as this project did not include on-site data collection, these limitations were deemed not to be a priority concern. This is discussed further in the conclusions, in section 7.2.

6.1 Cumulative assessment: category 3 wetlands The cumulative assessment of management of multiple wetlands (a list of all wetlands selected for category 3 management simulations can be seen in Appendix C) shows a trend towards the improvement in NO3-N retention, which can be seen in Figure 51. As seen in Figure 51 the PO4-P retention does not show an improvement. However, this could be due to the spike seen in the final week of simulation (Figure 52), which relates back to the river load at that time where the river load inflow causes a spike in modelling output, see section 3.2.3. The increase in phytoplankton outflow, seen in Figure 51, is due reduced turbidity leading to the increased availability of underwater light. Phytoplankton responds earlier to increased light availability than macrophytes, see Figure 54. Consequently, there is a trend towards an increased phytoplankton growth, particularly in the 50% turbidity reduction scenario, see Figure 51. The 75% turbidity reduction scenario conversely shows a trend toward reducing phytoplankton growth, see Figure 51. This reduction in phytoplankton, when compared to the 50% turbidity reduction scenario, could be associated with increased macrophyte growth seen in the 75% turbidity reduction scenario leading to competition with phytoplankton for underwater light, see Figure 53. For detailed output for category 3 wetlands cumulative assessment refer to Appendix D Table 20 toTable 22 (PO4-P, NO3-N and phytoplankton biomass respectively). A detailed change in retention for each wetland and each management scenario, as well as the percentage change in the outflow concentration is shown. At the end of each table there is a summary of the cumulative retention, as shown in Figure 51.

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Figure 51: Cumulative retention- category 3 wetlands

Results of category 3 wetland management at the 50% (A) and 75% (B) turbidity reduction scenarios, as compared with the average of the status quo for all 57 wetlands are shown in Figure 52, to Figure 56. As the cumulative impact and particularly trends are of concern, not individual wetland responses, the results of individual wetlands are shown in grey only. The average response is shown in green and the median in red. The 50% turbidity reduction scenario was the first to show a response to the management scenario. The 75% turbidity reduction scenario shows the best simulated response to turbidity reduction, with healthy macrophyte growth. Thus only the 50% and 75% turbidity reduction scenarios are shown. For the 50% turbidity reduction scenario, the wetlands macrophyte growth vary from no growth to healthy summer growth Figure 53A. A 50% reduction of turbidity therefore leads to a response in the form of macrophyte growth. In the 75% turbidity reduction scenario there is also a range in the successful growth of macrophytes of the different wetlands (Figure 53B). For most wetlands the median shows a clear trend towards summer growth (i.e. late summer immediately following inundation) slowing down with temperature and light reduction in winter. In the 75% turbidity reduction scenario some wetlands showed only minor macrophyte growth, such as wetland 165

Regional Scale Modelling of the lower River Murray wetlands numbers S0115 (367), S0229 (978) and S0230 (47) (wetland numbers are as per South Australian Wetlands Atlas (Jensen et al. 1996)). Other wetlands showed an exceptional macrophyte growth such as wetland numbers S0174 (1036), S0203 (471) and S0229 (84) (Figure 53B). These differences in macrophyte growth are related to individual wetland morphology. The clear trend towards summer growth phase with a winter dieback supports the argument for managed winter dry periods with the aim of re-introducing sediment compaction. Reflooding would lead to macrophyte germination and the summer wet would maximise macrophyte growth and therefore nutrient retention. The phytoplankton growth phase occurs in response to improved underwater light and the lack of competition due to macrophyte dieback in winter. With the winter dry period this would be minimised (Figure 54B). The net impact on a cumulative scale would be nutrient retention by the wetlands. The winter dry/summer wet management strategy is explored more below, with an example of three wetlands that are assumed to be dried following the onset of macrophyte dieback (i.e. with the onset of winter and therefore reduced modelled macrophyte growth). Going back to the cumulative assessment of the 57 wetlands, some wetlands show a trend towards a better macrophyte growth phase than others, such as wetland number S0219 (996) that has a very short winter macrophyte dieback period. This wetland shows a trend towards a long macrophyte growth period, a minimal phytoplankton growth phase and positive nutrient retention. The main difference between these wetlands is wetland morphology. The trends within wetlands based on wetland depth and volume are discussed below. The sudden reduction in phytoplankton biomass in the 50% turbidity reduction scenario in mid July (Figure 54), stems from the increase in sedimentation due to turbidity increasing past the sedimentation threshold as discussed previously. The zooplankton biomass trend (Figure 55), follows suit due to its reduced food source. As turbidity (NTU) never exceeds the sediment threshold in the 75% turbidity reduction scenario there is no change in the rate of phytoplankton biomass sedimentation. Consequently, phytoplankton biomass in the 75% turbidity reduction scenario (Figure 54) remains high through the winter period. However, the trend for phytoplankton biomass outflow in the 75% turbidity reduction scenario is less than that of the 50%

166

Regional Scale Modelling of the lower River Murray wetlands turbidity reduction scenario, (Figure 51). The greater macrophyte growth in the 75% turbidity reduction scenario accounts for this variation (as discussed above). Based mainly on “category wetlands” morphological differences and the different exchange and nutrient loads at the respective river locations, WETMOD 2 was capable of simulating differences of biomass growth and nutrient retention within these wetlands. The implication of multiple wetland management and the potential cumulative impact on river nutrient load, through alteration of nutrient and phytoplankton retention, is discussed below in section 6.3.

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Figure 52: PO4-P Concentration Trends

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Regional Scale Modelling of the lower River Murray wetlands

Figure 53: Macrophyte Biomass Growth Trends

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Regional Scale Modelling of the lower River Murray wetlands

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Figure 56: NO3-N Concentration Trends

172

Regional Scale Modelling of the lower River Murray wetlands Wetland size, volume and location Category 3 wetlands scenarios include differences in wetland volume, and differences in the monitoring location of river flow data and river nutrient data. The 75% turbidity reduction scenario also includes wetland depth differences due to improved Secchi depth (underwater light penetration was limited by wetland depth). Since in the 75% turbidity reduction scenarios Secchi depth equals the actual wetland depth, these scenarios would be best to compare with each other to understand the impact depth has on wetland response to management. When macrophyte biomass volume is compared against wetland size and wetland depth (Figure 57), a trend towards greater macrophyte growth with an increasing wetland depth is apparent. However, with a corresponding increase in wetland volume macrophyte biomass reduces. This is however limited by the lack of validation data for macrophyte biomass. These size assessments therefore are subject to this significant model limitation. The assessments are however made to indicate the potential use of the model once adequate validation has been undertaken. First, there is a trend towards an increase in wetland depth leading to an increase in macrophyte biomass (Figure 58). This goes back to the issue discussed earlier in the model validation, section 4.3, where the underwater light availability, and therefore macrophyte growth, is dependent on the Secchi depth (i.e. logarithmic increase in macrophyte growth with increasing depth). This calculation is not taking into account the maximal wetland depth and the amount of underwater light actually reaching the wetland substrate nor the maximum growth depth of macrophytes (not currently an acute issue). Second, a wetland with the same depth but smaller surface area and therefore volume seems to have more macrophyte growth. This would relate back to the amount of nutrient entering the wetland, i.e. the model assumes the same fraction of river flow volume is the exchange volume for both the larger and smaller wetland. A small wetland therefore effectively has a greater turnover rate. A more accurate wetland exchange volume for the wetland would improve the results in such an instance, again highlighting the need for improved data on potential exchange volumes. With the current modelling capacity, WETMOD 2 however poses the question whether wetlands with a small volume would be more apt at nutrient uptake (retention) due to 173

Regional Scale Modelling of the lower River Murray wetlands the greater macrophyte growth within these wetlands compared to wetlands with a larger volume? In an attempt to address this question the results of cumulative wetland assessments were investigated further. Figure 57 shows the relationship between macrophyte biomass, wetland volume and depth. Indicating that greater macrophyte biomass is related more to wetland depth than it is to wetland volume (Figure 57). This is supported by Figure 58, which shows a slight increase in macrophyte biomass with increasing wetland depth. The 57 category 3 wetlands were divided into three wetland depth ranges shallow (<1 m), medium (1 to <2 m) and deep (>2 m). The average depth of the shallow wetlands was 0.9 m. The average depth of the medium wetlands was 1.3 m, and for the deep wetlands 2.1 m. Figure 59 shows the average macrophyte biomass and average wetland volume for the wetland depth ranges. Figure 59 indicates that medium sized wetlands favour optimal macrophyte growth. Figure 60 shows that for medium depth range wetlands, which have the largest average macrophyte biomass, there is an exponential decline in macrophyte growth with increasing wetland volume. These wetlands have a similar depth and the same turbidity (same “exemplar” data source), therefore they have the same macrophyte growth potential according to the modelled underwater light. Consequently, the major difference between wetlands is volume. The cause of lower macrophyte growth in greater volume wetlands can be correlated back to nutrient availability. That is, the larger wetlands have a greater dilution of the inflow nutrient load within the water body. It must however be remembered that the wetland has not been validated agains macrophyte growth. These results are therefore only indicative based on the current model capabilities.

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Regional Scale Modelling of the lower River Murray wetlands Average Cumulative Macrophyte Biomass vs. Average Wetland Volume and Depth 2000000 1800000 Wetland Volume m3

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2.1

2.3

Wetland Depth m

Figure 59: Average Macrophyte Biomass (size of sphere, kg/m3) plotted against Average Wetland Volume and Wetland Depth S ame We tlan d D e p th R an g e (1m - < 2m), In cre asin g We tlan d Vo lu me v 's M acro p h yte B io mass

M acro p h yte B io m ass kg /m 3

30000.0

25000.0 y = 2E + 08x

-0 . 8 5 2 6

2

R = 0.9113

20000.0

15000.0

10000.0

5000.0

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500000

1000000

1500000

2000000

2500000

W e tla n d V o lu m e m 3

Figure 60: Macrophyte Biomass vs. Wetland Volume

176

Regional Scale Modelling of the lower River Murray wetlands As discussed above, for maximum macrophyte biomass growth within wetlands, WETMOD 2 indicates an optimal wetland volume and depth range. Figure 61 to Figure 65 help to demonstrate the relationship between wetlands volume, macrophyte biomass and nutrient dilution. To establish which wetlands were producing the greatest biomass within the medium wetland depth range (1 to 2 metre), macrophyte biomass was plotted against wetland volume and depth (Figure 61). With the increase in volume macrophyte biomass reduces significantly. If this is compared to Figure 62, where the average concentration of PO4-P within the wetland for the simulation period is plotted instead of macrophyte biomass, a similar pattern is produced. The pattern in Figure 62 indicates a lower average PO4-P load for the wetlands where the macrophyte biomass is low. This suggests that PO4-P may be the limiting nutrient to macrophyte growth. This is supported by Figure 63, which shows the macrophyte biomass vs. average PO4-P load within these wetlands. No such dependency of macrophyte biomass on NO3-N was seen in Figure 64 and Figure 65. Therefore, the optimal wetland volume and depth discovered within WETMOD 2 simulations can within the confines of the present model capability be related back to the PO4-P availability (volume relating to dilution) and underwater light availability controlled by the wetland and Secchi depth.

177

Regional Scale Modelling of the lower River Murray wetlands Macrophyte Biomass cm3/m3 vs. Wetland Volume and Wetland Depth 2500000

Wetland Volume m3

2000000 1500000 1000000 500000 0 1

1.1

1.2

1.3

1.4

1.5

1.6

-500000 Wetland Depth m

Figure 61: Average Macrophyte Biomass (size of sphere) Plotted against Average Wetland Volume and Wetland Depth range 1 – 2 m

Average PO4-P mg/L vs. Wetland Volume and Wetland Depth 2500000

Wetland Volume m3

2000000

1500000

1000000

500000

0 1

1.1

1.2

1.3

1.4

1.5

1.6

-500000 Wetland Depth m

Figure 62: Average PO4-P (size of sphere) Plotted against Average Wetland Volume and Wetland Depth range 1 – 2 m

178

Regional Scale Modelling of the lower River Murray wetlands Average PO4-P Concentration vs. Macrophyte Biomass 0.07 0.06

y = -2E-10x2 + 6E-06x + 0.008 2

R = 0.921

PO4-P mg/L

0.05 0.04

y = 2E-06x + 0.018 R2 = 0.6904

0.03 0.02 0.01 0 0.0

5000.0

10000.0

15000.0

20000.0

25000.0

Macrophyte Biomass kg/m3

Figure 63: Average PO4-P vs. Macrophyte Biomass at Wetland Depth range 1 – 2 m

Average NO3-N mg/L vs. Wetland Volume and Wetland Depth 2500000

Wetland Volume m3

2000000

1500000

1000000

500000

0 1

1.1

1.2

1.3

1.4

1.5

1.6

-500000 Wetland Depth m

Figure 64: Average NO3-N (size of sphere) Plotted against Average Wetland Volume and Wetland Depth range 1 – 2 m

179

Regional Scale Modelling of the lower River Murray wetlands Average NO3-N Concentration vs. Macrophyte Biomass 0.07 0.06

y = -1E-10x2 + 2E-06x + 0.0547 2

R = 0.7452

NO3-N mg/L

0.05 0.04

y = -1E-06x + 0.0613 2 R = 0.5466

0.03 0.02 0.01 0 0.0

5000.0

10000.0

15000.0

20000.0

25000.0

Macrophyte Biomass kg/m3

Figure 65: Average NO3-N vs. Macrophyte Biomass at Wetland Depth range 1 – 2 m

180

Regional Scale Modelling of the lower River Murray wetlands As can be seen in Figure 66, zooplankton biomass trend follows macrophyte biomass trend (the data has been ranked by macrophyte biomass (kg/m3)). This would indicate that more so than the food source phytoplankton biomass, the assumed shelter provided by macrophytes is very important for zooplankton (the wetland names corresponding to the numbers used in Figure 66 can be found in Appendix D). Nevertheless, despite a general increase in zooplankton biomass trend following the macrophyte increase, zooplankton exhibits dependence on its food source phytoplankton. This can be observed in wetland S0106 (645) where the phytoplankton is relatively low, which is consequently reflected in the zooplankton.

Figure 66: Comparison of Macrophyte, Phytoplankton and Zooplankton Biomass for each category 3 wetland (Key to wetland numbers adapted from (Jensen et al. 1996), see list in Table 18 in Appendix C)

181

Regional Scale Modelling of the lower River Murray wetlands Summer wet winter dry Siebentritt (2003) describes a number of different water regimes in the restoration options via flooding and draw down of water for the wetlands of the lower River Murray. Each of these regimes is intended to illicit a diversity of vegetation and habitat types. One of these is the use of the natural flow regime as suggested by Poff et al. (1997), and which has been applied experimentally by the Department of Water, Land and Biodiversity Conservation (DWLBC 2004; Siebentritt et al. 2004). Another recommendation by Siebentritt (2003) is the implementation of restoration water regimes to enhance a mosaic of vegetation structures within the lower River Murray wetlands. Most current wetland management practices attempt to mimic the natural flow regime and enhance macrophyte biomass. The model scenario discussed here however, focuses on the minimisation of phytoplankton growth and suggests a return to a more natural flow regime. The natural (historical) flow pattern of the River Murray is minimal flow in March, which increases slightly in April and May. In the upper reaches of the River Murray catchment, where the majority of the water is sourced, the flow reduces in early winter as freezing sets in, binding the precipitation in snow and ice. The major annual flow occurs in spring due to snowmelt and continues into mid December due to westerly influenced precipitation. The flow therefore achieves its height in spring and slowly declines until it reaches a minimum in March (Burton 1974; Walker 1979; Walker 1985). Due to the slow transport of water along the river the flow can be delayed for 4 to 6 weeks until it reaches the lower River Murray wetlands (Mackay et al. 1990). Three wetlands were randomly selected to assess the impact, on wetland nutrient and phytoplankton retention, of restricting the wet period to the macrophyte summer growth period. Assuming that the wetlands are wet for the period of major macrophyte growth only, a change in trend may be observed (Figure 67, phytoplankton on secondary axis). Figure 67A shows a full year wet where the retention is calculated as the average per day for the simulated time period. Figure 67B shows the results of summer wet/winter dry scenario; here the retention is calculated from the average per day for the summer growth period of 88 days. The PO4-P retention per day does not show a large improvement; however, there is a slight 182

Regional Scale Modelling of the lower River Murray wetlands improvement when comparing the status quo and the 75% turbidity reduction scenario (detailed results can be seen in Appendix D Table 23 to Table 25). With summer wet winter dry wetland management, there should be a large reduction in turbidity and therefore increased macrophyte growth for this period. With macrophyte growth for the entire wet period, PO4-P retention should be mainly through macrophytes rather than phytoplankton. The scenarios in Figure 67 show the NO3-N retention per day to improve, both when comparing the management scenarios “full year” and “summer wet winter dry”, and as a response to increased turbidity reduction within each of the different management scenarios. The reduction in phytoplankton growth is as a direct consequence of the loss of its growth period, which would normally have occurred as the macrophyte biomass reduced during the winter period. Therefore, the management strategy of summer wet would assist in minimising phytoplankton growth. The nutrient retention during the winter months would otherwise have been utilised for phytoplankton growth that has now been limited. The cumulative trend shows that if the aim of management was to minimise phytoplankton inflow into the river with a maximum potential of nutrient retention through macrophyte growth then, the 75% turbidity reduction scenario with summer wet winter dry management would be the optimum management scenario as it produces less phytoplankton. This scenario is limited by the monitoring period available. The modelled scenarios are run for the time frame for which there is data available, which is in late summer. The scenarios show that if the wetlands were to be flooded, i.e. the turbidity reduced, at the time of year in which data was available, the macrophytes would be limited to the available timeframe when water temperature is appropriate, and underwater light and nutrients are available. However, the height of the natural flow regime of the lower River Murray when wetlands would naturally have been inundated is considerably earlier, i.e. during spring to early summer (Burton 1974). The results provided here, although shifted in season, do show the impact of managing the flow regime of the wetlands to optimise the use of macrophytes in nutrient removal and reduction in phytoplankton. With full season (one year) data, scenarios could be produced to obtain a more accurate assessment of the impact of mimicking the natural hydrological regime in wetland management. In the mean time the scenarios presented here give an indication of the impact the control of a wetland hydrological regime may have on nutrient retention.

183

Regional Scale Modelling of the lower River Murray wetlands

A

Full year

6

0

5

-0.01 -0.02

4

-0.03 3 -0.04 2

-0.05

1

-0.06

0

-0.07 Status Quo

25% Turbidity Reduction 50% Turbidity Reduction 75% Turbidity Reduction

Turbidity PO4-P Net Retention kg/annum

NO3-N Net Retention kg/annum

Phytoplankton Net Retention m3/annum

S u m m e r W e t W in te r Dry

B 12

0 - 0.01

10

- 0.02

8

- 0.03 6 - 0.04 4

- 0.05

2

- 0.06

0

- 0.07 Status Quo

25% Tur bidity Reduc tion 50% Tur bidity Reduc tion 75% Tur bidity Reduc tion

Tur bidity PO4-P Net Retention kg/annum

NO3-N Net Retention kg/annum

Phy toplankton Net Retention m3/annum

Figure 67: Nutrient uptake for full year wet vs. uptake for summer wet/winter dry

184

Regional Scale Modelling of the lower River Murray wetlands

6.2 Cumulative assessment: category 4 wetlands This section presents the results of category 4 wetland scenarios where 7 wetlands were simulated and compared to status quo (a list of wetlands simulated can be seen in Appendix C Table 19). Figure 68 shows the influence of the cumulative loading to category 4 wetlands, where there is a steady increase in the PO 4-P and phytoplankton retention. NO3-N retention however is more variable. Due to the high turbidity of the wetlands there is virtually no macrophyte growth (as discussed in section 5.1.1). The phytoplankton shows some growth during the spring and summer months and the zooplankton growth trend follows that of the phytoplankton (Figure 69 to Figure 71). The concentrations PO4-P and NO3-N reduce slightly as evidenced by the slight decrease in the wetland average (Figure 72 and Figure 73). Of the five wetlands used in model development only Reedy Creek has adequate river data, for its location, that is monitored on the same day as the wetland data, see (Wen 2002a; Wen 2002b). However, although Reedy Creek wetland data is used as an “exemplar” for other wetlands of the same category, the river flow and nutrient load for appropriate wetland locations must also be used (see Box) as in category 3 wetlands described above.

The Reedy Creek monitored river nutrient data was compared to the available river data (from river lock monitoring points) otherwise used in the model. The scenarios that were based on the river data responded with relatively good results. This is despite the model not being calibrated to this river data. Therefore, the use of river data from the respective monitoring locations close to the simulated wetlands was considered to improve the potential spatial accuracy of WETMOD. There is no significant role played by wetland internal nutrient dynamics. This is due to the lack of macrophyte growth and therefore there being no change in the nutrient uptake. The main impact of category 4 wetlands is therefore produced by the reduction of irrigation drainage concentration. The results in Figure 68 to Figure 73 reflect the change of concentration within the open water of the various wetlands. Detailed results for Figure 68 can be seen in Appendix D (Table 26 to Table 28). The potential cumulative impact the management of the category 4 wetlands have on river nutrient load is discussed in section 6.3. 185

Regional Scale Modelling of the lower River Murray wetlands

Cumulative retention by Category 4 Wetlands 80000

900

70000

800

60000

700 600

50000

500

40000

400

30000

300

20000

200

10000

100

0

0 Status Quo 25% Irrigation 50% Irrigation 75% Irrigation Irrigation Drainage Drainage Reduction Drainage Reduction Drainage Reduction PO4-P Net Retention kg/annum

NO3-N Net Retention kg/annum

Phytoplankton Net Retention m3/annum

Figure 68: Cumulative loading to category 4 wetlands

186

Regional Scale Modelling of the lower River Murray wetlands

A

M a c r o p h y t e Bio m a s s in C a t e g o r y 4 w e t la n d s a t 5 0 % Ir r ig a t io n Dr a in a g e Nu t r ie n t Re d u c t io n Sc e n a r io 0 .1 2

0 .1

kg /m 3

0 .0 8

0 .0 6

0 .0 4

0 .0 2

B

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

N ov - 00

O c t- 00

Sep- 00

Aug- 00

J ul- 00

J un- 00

0

M acr o p h yt e Bio m as s in C at e g o r y 4 w e t lan d s at 75% Ir r ig at io n Dr ain ag e Nu t r ie n t Re d u ct io n Sce n ar io 0.12

0.1

kg /m 3

0.08

0.06

0.04

0.02

I nd ivid ual W e tland s

A ve rag e

M e d ian

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

N ov - 00

O c t- 00

Sep- 00

Aug- 00

J ul- 00

J un- 00

0

S tatus Q uo A ve rag e (no m anag e m e nt)

Figure 69: Macrophyte Growth Trends All fall below the red line showing that the irrigation drainage inflow has no impact on the macrophyte growth trends.

187

Regional Scale Modelling of the lower River Murray wetlands

A

Ph y t o p la n k t o n Bio m a s s in C a t e g o r y 4 w e t la n d s a t 5 0 % Ir r ig a t io n Dr a in a g e Nu t r ie n t Re d u c t io n Sc e n a r io 12

10

cm 3/m 3

8

6

4

2

B

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

N ov - 00

O c t- 00

Sep- 00

Aug- 00

J ul- 00

J un- 00

0

Ph yt o p lan k t o n Bio m as s in C at e g o r y 4 w e t lan d s at 75% Ir r ig at io n Dr ain ag e Nu t r ie n t Re d u ct io n Sce n ar io 12

10

cm 3/m 3

8

6

4

2

I nd ivid ual W e tland s

A ve rag e

M e d ian

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

N ov - 00

O c t- 00

Sep- 00

Aug- 00

J ul- 00

J un- 00

0

S tatus Q uo A ve rag e (no m anag e m e nt)

Figure 70: Phytoplankton Growth Trends

188

Regional Scale Modelling of the lower River Murray wetlands

A

Z o o p la n k t o n Bio m a s s in C a t e g o r y 4 w e t la n d s a t 5 0 % Ir r ig a t io n Dr a in a g e Nu t r ie n t Re d u c t io n Sc e n a r io 1 .8 1 .6 1 .4

cm 3/m 3

1 .2 1 0 .8 0 .6 0 .4 0 .2

B

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

N ov - 00

O c t- 00

Sep- 00

Aug- 00

J ul- 00

J un- 00

0

Z o o p lan k t o n Bio m as s in C at e g o r y 4 w e t lan d s at 75% Ir r ig at io n Dr ain ag e Nu t r ie n t Re d u ct io n Sce n ar io 1.8 1.6 1.4

cm 3/m 3

1.2 1 0.8 0.6 0.4 0.2

I nd ivid ual W e tland s

A ve rag e

M e d ian

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

N ov - 00

O c t- 00

Sep- 00

Aug- 00

J ul- 00

J un- 00

0

S tatus Q uo A ve rag e (no m anag e m e nt)

Figure 71: Zooplankton Growth Trends

189

Regional Scale Modelling of the lower River Murray wetlands

A

PO4 - P C o n c e n t r a t io n in C a t e g o r y 4 w e t la n d s a t 5 0 % Ir r ig a t io n Dr a in a g e Nu t r ie n t Re d u c t io n Sc e n a r io

0 .8 0 .7 0 .6

m g /L

0 .5 0 .4 0 .3 0 .2 0 .1

B

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

N ov - 00

O c t- 00

Sep- 00

Aug- 00

J ul- 00

- 0 .1

J un- 00

0

PO4- P C o n ce n t r at io n in C at e g o r y 4 w e t lan d s at 75% Ir r ig at io n Dr ain ag e Nu t r ie n t Re d u ct io n Sce n ar io

0.8 0.7 0.6

m g /L

0.5 0.4 0.3 0.2 0.1

I nd ivid ual W e tland s

A ve rag e

M e d ian

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

N ov - 00

O c t- 00

Sep- 00

Aug- 00

J ul- 00

- 0.1

J un- 00

0

S tatus Q uo A ve rag e (no m anag e m e nt)

Figure 72: PO4-P Trends

190

Regional Scale Modelling of the lower River Murray wetlands

A

NO3 - N C o n c e n t r a t io n in C a t e g o r y 4 w e t la n d s a t 5 0 % Ir r ig a t io n Dr a in a g e Nu t r ie n t Re d u c t io n Sc e n a r io

1 .4

1 .2

1

m g /L

0 .8

0 .6

0 .4

0 .2

B

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

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Sep- 00

Aug- 00

J ul- 00

- 0 .2

J un- 00

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NO3- N C o n ce n t r at io n in C at e g o r y 4 w e t lan d s at 75% Ir r ig at io n Dr ain ag e Nu t r ie n t Re d u ct io n Sce n ar io

1.4

1.2

1

m g /L

0.8

0.6

0.4

0.2

I nd ivid ual W e tland s

A ve rag e

M e d ian

M ay - 01

Apr - 01

M ar - 01

F eb- 01

J an- 01

D ec - 00

N ov - 00

O c t- 00

Sep- 00

Aug- 00

J ul- 00

- 0.2

J un- 00

0

S tatus Q uo A ve rag e (no m anag e m e nt)

Figure 73: NO3-N Trends

191

Regional Scale Modelling of the lower River Murray wetlands

6.3 Implications of cumulative impact of multiple wetland management For the purposes discussed in the methodology, in this section the assumption is made that the model is quantitatively accurate. Category 3: Dead end wetlands with carp presence and no irrigation drainage To assess and discuss the potential cumulative impact that the management of all category 3 wetlands may have upon the river nutrient load the model quantitative output is assumed to be relatively accurate. Therefore, evaluating these results, the cumulative impact shows that there would be a net retention of NO3-N (Table 9). However, the PO4-P inflow into the river may increase due to the loss of retention through wetland sedimentation (the model does not fully take into account sediment resuspension) and the phytoplankton load may also increase due to the increased underwater light availability (Table 9). Table 9: Impact, of category 3 wetland’s management, on river load per annum PO4-P NO3-N Phytoplankton kg/annum kg/annum m3/annum Load in River

245604

364372

3880

Change from status quo at 75% Turbidity Reduction

-803

6223

-61

% of River load removed through 75% Turbidity Reduction management

-0.33

1.71

-1.58

The simulations of introducing dry periods as a management strategy for wetlands need to be scrutinised further. WETMOD 2 uses a simplistic sedimentation and resuspension equation for PO4-P, NO3-N and phytoplankton. The wetland internal nutrient concentrations are more dynamic than portrayed in the model. This is one of the most significant limitations (i.e. the abrupt sedimentation threshold) of the model. Although the model can be applied to more extensive wetlands due to its simplistic construction and data prerequisites, it is acknowledged that for management purposes a more accurate estimation of nutrient and phytoplankton retention by the wetlands would be favourable. However, the model does provide a framework for expansion of research to assist in the assessment of the cumulative impact of wetland management on a regional scale.

192

Regional Scale Modelling of the lower River Murray wetlands Presently the model provides the opportunity of simulating the trends within a wetland due to potential management strategies. These wetland simulations would become more accurate with the present model (WETMOD 2) as more data, and particularly comprehensive data, becomes available. As discussed above (chapter 6), model accuracy could be improved if more local knowledge of particular wetlands were applied in cumulative assessments (i.e. better turnover estimate) “exemplar” driving variables could however still be used for category wetlands. Future work on the extension of WETMOD 2 should focus on the inclusion of detailed water and sediment interaction, particularly nutrient uptake, and the potential change that may occur due to sediment compaction. As discovered in section 6.1 (Figure 59 and Figure 60) there seems to be optimum wetland morphology for macrophyte growth and therefore maximal nutrient and phytoplankton retention. Wetlands were split into the depth categories shallow, medium and deep (Table 10 to Table 12). The cumulative impact scenarios made by WETMOD 2 for the shallow range of wetlands (58% of wetlands), shows this range to be least effective at nutrient retention (Table 10). In this shallow range of simulations, for the 75% turbidity reduction, there is a net increase of 0.39% in the PO4-P river load and a full 1% of the phytoplankton load. However, there is a decrease of NO3-N of 0.75%. In contrast, the medium and deep wetlands (each 21% of wetlands) show retention for both PO4-P and NO3-N. Of these two depth ranges, deep wetlands have a minimal impact on phytoplankton river load with only a 0.06% increase. From these simulations the conclusion that can be drawn is that the medium and deep wetlands on the whole have a greater impact on nutrient retention than the shallow wetlands. Consequently, if only a small number of wetlands were to be managed the medium and deep wetlands would potentially provide the greatest cost benefit return. Model application limitations

Prior to WETMOD 2 being used to make management decisions, beyond the theoretical examination presented here, some restrictive issues must be addressed. The reliability of the macrophyte growth representation in very shallow wetlands is questionable. This issue was discussed in section 4.3. As the validation of Lock 6 wetland, which is within the range of shallow wetlands, confirmed the macrophyte

193

Regional Scale Modelling of the lower River Murray wetlands growth trend within this range the issue must be raised as to the accuracy of the macrophyte growth trend of the deep wetland (for which the model was not specifically calibrated). If the Secchi depth influence on macrophyte growth equation were to be modified (i.e. to take into account maximum wetland depth) to better reflect the situation in lower River Murray wetlands this result may change considerably. Validation with monitored macrophyte data would however still be required. Table 10: Impact, of category 3 wetland’s (depth range shallow <1m) management, on river load per annum PO4-P NO3-N Phytoplankton 3 kg/annum kg/annum m /annum Load in River

245604

364372

3880

Change from status quo at 75% Turbidity Reduction

-961

2741

-39

% of River load removed through 75% Turbidity Reduction management

-0.39

0.75

-1.01

Table 11: Impact, of category 3 wetland’s (depth range medium 1-2m) management, on river load per annum PO4-P NO3-N Phytoplankton kg/annum kg/annum m3/annum Load in River

245604

364372

3880

Change from status quo at 75% Turbidity Reduction

91

1981

-20

% of River load removed through 75% Turbidity Reduction management

0.04

0.54

-0.51

Table 12: Impact, of category 3 wetland’s (depth range deep >2m) management, on river load per annum PO4-P NO3-N Phytoplankton kg/annum kg/annum m3/annum Load in River

245604

364372

3880

Change from status quo at 75% Turbidity Reduction

68

1501

-2.43

% of River load removed through 75% Turbidity Reduction management

0.03

0.41

-0.06

Looking at the impact of the management of Lock 6 wetland only, which is within the shallow depth range, there is still a positive impact on the reduction of river nutrient load, Table 13. There is a very small PO4-P uptake, which suggests that the retention capacity of the wetland is improved through the turbidity reduction management, although this is virtually negligible. Comparing Lock 6 wetland results in Table 13

194

Regional Scale Modelling of the lower River Murray wetlands with those produced when Lock 6 wetland is considered for summer wet winter dry cycles in Table 14, Lock 6 wetland shows a slightly more promising retention capacity. In this scenario Lock 6 wetland has a slightly greater effective PO4-P retention and less phytoplankton contribution to the river. Despite this very small improvement, an assessment of the simulation output of multiple wetland management of category 3 wetlands can be used to gain insight into the cumulative impact that might be obtained on the lower River Murray nutrient and phytoplankton load. Some indication as to the wetlands that may be the most effective at nutrient retention can also be deduced. Where qualitative scenario results can assist in assessing a wetland in the lower River Murray, WETMOD 2 is a functional tool. That is, WETMOD 2 can simulate category 3 wetlands for which limited data is available. The modelling output reliability for these wetlands can be improved with local knowledge of exchange volume, macrophyte growth trend and sediment compaction potential. However, to reliably apply the WETMOD model and decide on potential management scenarios to be applied, the model should still be developed further, as discussed above. Table 13: Impact, of Lock 6 wetland management, on river load per annum PO4-P NO3-N Phytoplankton kg/annum kg/annum m3/annum Load in River

245604

364372

3880

Change from status quo at 75% Turbidity Reduction

0.44

24

-1.89

% of River load removed through 75% Turbidity Reduction management

0.00

0.01

-0.05

Table 14: Impact, of Lock 6 wetland management, summer wet winter dry, on river load per annum PO4-P NO3-N Phytoplankton 3 kg/annum kg/annum m /annum Load in River

245604

364372

3880

Change from status quo at 75% Turbidity Reduction

1.79

17

-0.21

% of River load removed through 75% Turbidity Reduction management

0.00

0.01

-0.01

Category 4: Dead end wetlands with carp presence and irrigation drainage As in category 3 wetlands, to assess the cumulative impact on river nutrient load, the scenarios for category 4 wetlands are assumed to be quantitatively accurate. In 195

Regional Scale Modelling of the lower River Murray wetlands category 4 wetlands the nutrient and phytoplankton retention calculated includes the irrigation drainage inflow reduction. Therefore, the improvement in category 4 wetland retention and its impact on river load includes the PO4-P, NO3-N and phytoplankton assumed to be removed through constructed wetlands that would otherwise have been flowing into the wetland as part of the irrigation drainage. Table 15 shows the potential nutrient retention capacity of category 4 wetlands and the impact on the river nutrient load. It must be remembered that due to the limited data available on wetlands of the lower River Murray, which are affected by irrigation drainage, the data available from Reedy Creek wetland was applied to wetlands within this category as an “exemplar” data source. Despite these wetlands being within the same category as Reedy Creek wetland, the irrigation drainage inflow would vary more than is accounted for in these scenarios. However, although the irrigation concentration and volumes would differ, some floodplain wetlands of the lower River Murray are directly impacted by irrigation drainage, having very high nutrient loads. Category 4 wetland cumulative assessment is hypothetical scenario testing intended to examine the cumulative impact of management, and to assess the capacity of the model to simulate category 4 wetlands. Through the introduction of constructed wetlands, to reduce irrigation drainage nutrients entering a wetland, a net retention of nutrients normally flowing into the river is achieved. Table 15 shows the hypothetical cumulative retention if all category 4 wetlands are successfully managed. The model indicate that these 7 wetlands would, in case of 75% irrigation drainage nutrient reduction, contribute a 2.68% reduction of the river phytoplankton load, as well as a small reduction of PO4-P and NO3-N river load. Table 15: Impact, of category 4 wetland’s management, on river load per annum PO4-P NO3-N Phytoplankton kg/annum kg/annum m3/annum Load in River

1376872

338228

12231

Change from status quo at 75% Irrigation Drainage Nutrient Reduction

5850

1205

328

% of River load removed through 75% Irrigation Drainage Nutrient Reduction management

0.42

0.36

2.68

The wetland retention portrayed in Table 16 is that of Reedy Creek wetland, whose data quality and therefore modelling accuracy, both qualitatively and quantitatively, 196

Regional Scale Modelling of the lower River Murray wetlands was most comprehensive and accurate. Table 16 is therefore bound to be the most accurate reflection qualitatively and quantitatively of the impact of wetland management on wetland nutrient retention capacity. Table 16: Impact, of Reedy Creek wetland management, on river load per annum PO4-P NO3-N Phytoplankton kg/annum kg/annum m3/annum Load in River

1376872 338228

12231

Change from status quo at 75% Irrigation Drainage Nutrient Reduction

1052

163

48

% of River load removed through 75% Irrigation Drainage Nutrient Reduction management

0.08

0.05

0.40

Through the management of Reedy Creek wetlands, with the assumption of 75% nutrient reduction capacity, the model indicates a small reduction of PO4-P, NO3-N and phytoplankton to the river. Therefore, the model suggests that the management of even one category 4 wetland may slightly reduce river nutrient load.

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Regional Scale Modelling of the lower River Murray wetlands

6.4 Chapter

summary

and

Implications

for

the

third

hypothesis Although there is a limited availability of data for wetlands of the lower River Murray modelling does allow for scenario development of multiple wetlands. The generic nature of WETMOD 2 has therefore allowed its application to multiple wetlands where only rudimentary morphological data is available. The model has thereby been applied on a landscape scale. The modelling limitations have been described and include the important point that the quantitative results can only be qualitatively indicative of potential management outcomes. Reviewing the data produced during cumulative assessment of multiple wetland management the third hypotheses “A simplified generic wetland model can be used to assess the cumulative impact of managing multiple same category wetlands” can be addressed. The simulations above show that this is possible. The main outcomes from the cumulative simulations are to find the optimum wetland morphology (for the best return on investment), the hydrology season for optimum nutrient uptake, and the impact of effective constructed wetlands in removing irrigation drainage load. However, this ability is presently restricted as per the following; The output is qualitative and not quantitative due to the nature of simplified models and the use of “exemplar” data. Due to the limitation in the simulation of turbidity reduction, i.e. turbidity controlled sedimentation threshold of nutrients and phytoplankton biomass, the management scenarios estimate comparisons of nutrient removal efficiency may become biased towards a turbid wetland. This limitation is solvable through further model development; therefore the methodology used and described above remains applicable particularly when this limitation has been addressed. Thus, the output of the cumulative assessments of management of multiple category 3 wetlands is preliminary. However, the potential of using generic models for cumulative assessments is substantiated by the methodology used as shown by its application to category 4 wetlands and the application to the preliminary management scenarios of the category 3 wetlands. 198

Regional Scale Modelling of the lower River Murray wetlands The nature of differential equations allows the conservative use of available mass. Theoretically therefore the assessment of the mass balance should be possible; for the wetlands where monitoring has taken place some indication of mass balance is available, particularly for Reedy Creek wetland. However, due to the limitation of data availability the current modelling effort should only be viewed as being capable of estimating potential mass balance. That is, the qualitative information obtained through the landscape scale scenarios allow for a simplistic understanding of the cumulative impact of management of multiple same category wetlands on river nutrient load.

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7 Summary, Context and Discussion The use of differential equations allows a deterministic approach where simulations or scenario analysis are possible. The predictive modelling of wetlands contributes to informed decisions on management strategies based on the data available. The uncertainty or lack of knowledge and data does affect the quality of model predictions (Wallach et al. 1998). However, this does not prevent management and decisionmaking and is a part of ecological simulation modelling (Reckhow 1994; Wallach et al. 1998). As long as the decision makers understand the limitations, they can still use a model to assess scenarios within these model limitations. The model may, in fact, provide decision makers with the only tool to experiment and increase understanding, without which they could be limited in assessing potential management impacts. This enhanced knowledge enables a better prediction of outcomes and therefore aids decision making in regard to management. Further, with enhanced knowledge and transparent assessment, consensus between stakeholders involved in the decisionmaking is more readily achieved (Thomann 1998). Management decisions for ecosystems may be made by many stakeholders, not all of whom would fully understand the ecological implications of different intervention options. Furthermore, experts in the field can hold opposing views on subjective topics. Modelling can be seen as a structure to assist in regulating knowledge, data and assumptions used for decision-making. Other experts can participate and comment on the model as it is defined. Most decision support models have inherent uncertainties of an acceptable magnitude (Reckhow 1994). It must however be made clear where there is a lack of knowledge and/or other uncertainties, and how this has been dealt with within the model. This information will reflect on how the model can and should be used, and how much reliance can be placed on the modelling predictions. Which detail is required and the appropriateness of assumptions is dependent on the purpose of the model (Caswell 1988). For the model developed in this project to be applicable on a regional scale, data obtained from monitored wetlands was assumed to be appropriate for internal wetland behaviour and relationships of similar wetlands. This assumption was used to overcome the lack of knowledge and data for the lower River Murray floodplain wetlands and simulate regional scale scenarios; and thereby obtain a cumulative 200

Regional Scale Modelling of the lower River Murray wetlands impact assessment of the management of multiple wetlands on river nutrient load. Therefore, implicitly the understanding should be that the model output is of trend behaviour and potential impacts on the river, both prior to and post management scenarios.

7.1 Assessment methodology The application of WETMOD 2 was designed for wetlands where minimal data, such as morphology and spatial location, has been sourced. For these wetlands the driving variables are borrowed from their associated “exemplar” wetland. Quantitative data from parameters measured in wetlands were used in WETMOD 2 to act as “exemplars” to provide qualitative outcomes in other wetland systems based on wetland categories. Due to the assumptions made and described in section 3.1, as well as the intended purpose or aims of the model, WETMOD 2 is maintained in a generic form to be qualitatively applicable to wetlands where only basic morphological data are available. Through this methodology a model was developed that is based not only on scant data but is also applicable to wetlands with no time series data (the modelling predictions of WETMOD 2 were therefore not assessed strictly in a quantitative manner). It is not possible to statistically assess the model outputs for these wetlands, as no data with which to compare the output exist. There must therefore be general confidence in the simulated time-series seasonal trend and approximate magnitude produced by WETMOD 2, for the wetlands used in validation of the model. Otherwise, no confidence will be placed in the scenarios produced for category wetlands, i.e. those using “exemplar” driving variables. It can be said that the qualitative assessment of such model scenarios may be a more significant assessment of the model performance, than an improvement in statistical accuracy of individual wetlands (i.e. optimisation of quantitative performance of the model). Modelling effort was therefore directed at the development and improvement of spatial contributions to wetland modelling, rather than focusing on the improvement of the individual wetland process modelling. This approach is an extension of the view presented by McIntosh, et al. (2003) that flexible and cost effective models are more beneficial than one off models that perform very well for one ecosystem only.

201

Regional Scale Modelling of the lower River Murray wetlands To represent qualitative model performance the score D was used and served the model development well and is used extensively in the model validation. Other statistical options are discussed in (Mayer et al. 1993), however the statistical accuracy of the model would not solely or adequately improve the confidence of users when WETMOD 2 is used as a landscape decision support tool. When assessing the performance of WETMOD 2, by comparing the modelled output with its monitored counterpart, the model behaves qualitatively correctly and logically for each wetland considered. This is reflected in the similarity of seasonality of the modelled response and monitored concentrations. The seasonal response of the non-monitored wetland parameters macrophyte and zooplankton in model scenarios was logical, supporting model validity. As discussed in the introduction (section 1.4.2), the qualitative difference in the comparison of different wetlands is a legitimate model assessment methodology. The purpose of the model determines its required precision. In the case of WETMOD 2, the qualitative assessment of model results can in fact be the most appropriate methodology when the model is applied outside its development envelope. Evidence of this is in the discrepancy between visual assessment of validation results and the D for some of the wetlands, see section 4.1. This would in fact particularly be the case where WETMOD 2 is applied to category wetlands where data from “exemplar” wetlands is used. Nevertheless, the statistical evaluation of the modelling accuracy is a significant validation step required to assess the model performance. This can however, only be undertaken for scenarios where the model is simulating actual monitored data using monitored driving variables. Values of D are presented in Table 3 and Table 4 and discussed during the validation of the model.

7.2 Current capabilities The results, described and discussed in chapters 4, 5 and 6, have shown the applicability of the model at the present stage of development, its limitations and identified areas requiring further research and model improvement. A summary of the present capabilities of the model, in providing information which was previously not available, include:

202

Regional Scale Modelling of the lower River Murray wetlands finding the exchange volume of water and therefore nutrient and phytoplankton load between wetlands and the river (For wetlands with nutrient date time series) calculating the status quo nutrient retention of wetlands developing estimates of the potential impact of management on the nutrient retention of wetlands estimating the impact wetlands may have on the river nutrient load due to improved management producing an estimate, based on qualitative output, of the cumulative impact of multiple wetlands management on the river nutrient load, and developing comparative studies of wetlands based on their morphological differences, using the same driving variable time-series. Further advantages of the model include: presenting a framework from which to expand the model capabilities through an improvement in the workings of the model (some of the model expansion would not require a dramatic increase in model complexity) presenting a framework from which to expand the model capabilities as data availability increases focusing future monitoring for improved assessment, and enhanced modelling capabilities which may aid management decisions, and posing questions where model limitations are encountered due to a lack of data or knowledge Currently the central problem for modelling wetlands of the lower River Murray is data quality and quantity. Now that the model has been developed, future monitoring can take its data prerequisites into account to alleviate this restriction, thereby the model serves the purpose of focusing future research needs. Model limitations are discussed below.

203

Regional Scale Modelling of the lower River Murray wetlands External influences and Landscape Scale WETMOD 2 is capable of estimating the exchange volume between a wetland and the river where wetland nutrient time series are available. Using the exchange volume the model can further account for external influences acting upon, and therefore improve the modelling of, wetland internal dynamics. Together with the exchange estimate and the internal nutrient dynamics the probable outflow load, of nutrients and phytoplankton biomass, can be estimated. Thereby, the model can be used to assess the impact the wetland has on river nutrient load, and how this can be altered through potential management strategies. A call for such a model for Australian wetlands was made by McComb and Qiu (1997). Due to the models simple structure and low driving variable demand it is generically applicable to other wetlands within the region, which were not used in model development. Thereby, the model can be used to estimate the status quo or the potential impacts management may have on wetland nutrient and phytoplankton retention and consequent river load, even if minimal data for the wetland is available. WETMOD 2 simulates the qualitative behaviour without the quantitative accuracy. In this case the qualitative behaviour of multiple floodplain wetlands (where morphological data only is available), reflecting model potential as proposed by Rykiel (1996). Specifically, although the data simulated for each category of wetlands may not be quantitatively accurate, the trends are plausible. In a cumulative assessment the simulated impact of multiple wetland management, is indicative of potential results. As discussed in chapter 6, cumulative assessment assists in focusing management oriented research. The model allows the user to determine the implications of assumptions made, i.e. whether they are valid or otherwise. The role of the modelling tool is therefore (in part) to confront users with the implications of beliefs that they may hold (Bart 1995). Therefore, the potential outcomes of modelling on a regional scale, where minimal data are available, may assist managers in directing future monitoring studies and thereby aid in eventual decision making. For example, modelling outcomes of optimal wetland morphology are related to exchange rate for nutrient retention.

204

Regional Scale Modelling of the lower River Murray wetlands Although the results presented can be used to the degree discussed in chapters 4, 5 and 6, it is stressed that the model is still in early development. Model improvements and validation with specifically monitored data should be performed. Further research is suggested in chapter 8. The application of the model is at this stage still restricted to wetlands of category 1, 3, 4 and 5, as the data available for wetlands of category 2 were insufficient for proper validation. Management strategies are available only for category 3 and 4 wetlands however; model applicability can be enhanced as data becomes available. Presently the model can be used to assess, qualitatively, the potential cumulative impact of multiple wetland management. For example, the comparison of two wetlands, for which there is limited data availability, is possible by developing scenarios based on wetland categories and the morphological data available for these wetlands. Future feedback when comparing model predictions with actual outcomes will aid in identifying incorrect hypothesis, model inaccuracies and therefore contribute to future improvement of the model and enhancing its performance and applicability. Limitations There are four significant limitations to the model at this stage (in order of significance), with the second and third being related. The first is the abrupt sedimentation threshold (70 NTU for PO4-P and NO3-N and 95 NTU for phytoplankton), which makes distinguishing change in nutrient retention due to varying management scenarios difficult. More data on sedimentation rate and resuspension would be helpful. The second limitation is the inapplicability of the model to very shallow wetlands. Although the wetlands, where data was available, were not shallower than the 0.6 m, some wetlands of the lower River Murray are. An update of the equation that considers macrophyte growth in relation to Secchi and maximum depth of the wetland should improve this model aspect. Currently the data available for wetland depth is only used to calculate wetland volume within model simulation. The addition of wetland depth to

205

Regional Scale Modelling of the lower River Murray wetlands factor in the impact on macrophyte growth would improve the generic modelling applicability and allow the simulation of shallow wetlands. The third limitation relates to model output. The shallowness of modelled wetlands (below 1 metre depth) may still be the best wetlands for management, despite the model results. Asaeda et al. (2001) in fact found in their modelling studies that, despite shallow wetlands having a higher concentration of phytoplankton, macrophyte growth did increase due to more favourable light conditions. In shallow wetlands macrophyte growth may be expected throughout the wetland, causing increased sedimentation, increased nutrient uptake and shading out of phytoplankton. The increased macrophyte growth would also provide more shelter for zooplankton, which feed on phytoplankton further reducing their numbers. Therefore, the equation that was discussed in model validation section 4.3, and which shows a logarithmic growth pattern with increasing wetland depth needs to be reviewed. As stated in the methodology, zooplankton and macrophyte biomass data were not available for model development, validation and calibration. The model output and conclusions made are therefore limited by this lack of data and do not necessarily accurately reflect what could occur in a real environment. Despite these limitations to the methods applied, the WETMOD 2 simulation results and assessment of potential cumulative impact of wetland management remain applicable. WETMOD 2 is a work in progress, and this project mainly contributes to the spatial factors of lower River Murray wetland modelling. The present assessment of the model‟s capabilities has helped to identify future research requirements such as the model structure (equation improvement/replacement), model expansion (sediment water interaction), and data acquisition (wetland monitoring). The use of river Chlorophyll-a levels from Murray Bridge as the driving variable for all phytoplankton exchange (as discussed in section 3.2.3) led to Pilby Creek and Lock 6 being the only wetlands that showed virtually no improvement in model performance with regard to phytoplankton simulation (as is shown in section 4.1). With additional monitoring of river Chlorophyll-a levels the accuracy of model phytoplankton simulation should be improved. Other avenues to improve model performance include: 206

Regional Scale Modelling of the lower River Murray wetlands measurement/establishment of exchange volume estimates for simulated wetland (based on future monitoring and digital elevation models) determining the sediment compaction potential of individual wetlands measurement of irrigation volume (category 4 wetlands only), and determination of evaporation impact on wetland nutrient retention balance calculations. In its present state the model can be used for some restricted management assessment. This management focus would be on potential: nutrient retention of wetlands exchange volume and nutrient load twin management (limited) comparative studies of wetlands based on morphology (limited) impacts on river nutrient loads (indication only), and cumulative impact of multiple wetlands management. Revisiting the Project Aims Now the model capabilities have been assessed it is necessary to revisit the aims of the project to assess whether the model extension has fulfilled the intended purposes. Model extension aimed to: I. overcome shortcomings in knowledge due to limited data and incomplete system understanding II. address processes requiring further development, which were identified at the beginning of the study. These included river and wetland water exchange, nutrient exchange, and irrigation drainage data influence, and III. adapt and test the application of the model on a regional scale; i.e. develop a cumulative assessment of potential management impacts of multiple wetlands on the river nutrient load. To fulfil the first aim the model first fulfilled the second, which is the extension of the models capabilities. The model is now able to estimate water exchange, therefore

207

Regional Scale Modelling of the lower River Murray wetlands developing data for a previously unknown quantity for those wetlands where data is available. This has led to the ability to estimate the nutrient retention capacity of monitored wetlands and simulate potential change due to management. From this the bi-directional nutrient exchange has been modelled. Based on a similar methodology the irrigation drainage influence has also been accounted for, where relevant. The third aim was fulfilled with the use of the different “wetland categories”, i.e. using “exemplar” driving variable data. Thereby, qualitative estimates of the cumulative impact of multiple wetlands on the river nutrient load could be developed, as well as an assessment of the impact of management of these wetlands.

208

Regional Scale Modelling of the lower River Murray wetlands

8 Conclusion & Future Work This project set out to develop a model capable of simulating nutrient retention capacity of the lower River Murray wetlands. The model was to be applied on a regional scale encompassing wetlands for which limited data is available. In applying the model, it was to assess the change in nutrient retention capacity of multiple wetlands and the cumulative impact on the river following hypothetical management interventions of these wetlands. The application of the developed regional model WETMOD 2 is constrained by the availability of comprehensive data of adequate quality and frequency in the lower River Murray. However, the study does serve the purpose; demonstrate the provision of a tool for examining the impact of management interventions on the broader scale. The model also helps to purpose of focus future research, including purpose driven monitoring and model improvement. Hypotheses The modelling has fulfilled most of the objectives and aims of the project, with the assessment of model output and its limitations discussed in the respective results and discussion chapters and summarised in section 7.2. These hypotheses were: I. A simplified generic wetland model can be used to realistically simulate multiple and different wetlands qualitatively. 

Given adequate driving variables the model can simulate different wetlands realistically, e.g. Lock 6 and Reedy creek using noncalibration data see section 4.4.

II. A simplified generic wetland model can be used to answer “what if” questions, and 

Management simulations for selected degraded wetlands have been successfully run, see section 5.2.

III. A simplified generic wetland model can be used to assess the cumulative impact of managing multiple same category wetlands.

209

Regional Scale Modelling of the lower River Murray wetlands 

Limited qualitative assessments are possible. For category 3 wetlands this assessment is preliminary due to (solvable) model limitations (see section 6.4). For category 4 wetlands these same limitations restrict the adequate modelling of two concurrent management strategies.

With the scenarios developed of the different wetlands, general understanding of the system can be enhanced and the hypotheses tested with regard to alternate management options and their required response. The differential equation based deterministic model WETMOD 2 does provides a tool for hypothesis testing of management effectiveness for wetland regeneration. WETMOD 2 is a tool that can be used in the facilitation of understanding of the required management effort for successful wetland restoration, i.e. percentage of turbidity reduction required for macrophyte growth response and therefore wetland regeneration. Understanding of the cumulative response of multiple wetland management is enhanced by the model scenario output. Although the model output is qualitative it does provide some assessment of cumulative impact. Further development of the model would enhance this feature. Some understanding can also be obtained of the general differences between wetlands (smaller versus larger, shallow versus deeper etc.), although minimal data is available. While the model outcomes cannot be viewed as quantitatively accurate (particularly in individual category wetland comparison), the model outcomes do provide a point of reference from which further research can be made. The model outcomes, in such a comparative use, are for general understanding as well as an aid in facilitating consensus on the potential impact of restoration options, assuming there is confidence in the model. Future development of WETMOD During the development, calibration, validation and application of the model, certain limitations were discovered, as well as potential improvement identified for which there was inadequate time to address. The following recommendations for future model improvements are made (this list is not exhaustive as other improvements could be made). Model improvements need to take into account the lack of data in the region. Underwater light and Secchi depth need to be fixed for very shallow wetlands.

210

Regional Scale Modelling of the lower River Murray wetlands This projects purpose was to use the previously developed wetland ecosystem process model WETMOD 1 and extend this beyond theoretical wetland dynamics to include spatially relevant data. The project therefore was not primarily concerned with improving internal modelling dynamics. The prerequisite for this omission being that limitations did not affect model verification, and that consequent management simulation restrictions were identified. Where limitations were identified, future improvements are suggested. This model restriction was therefore an issue that was not only outside of the scope of this project, but also one for which there was not sufficient data to address the problem. For future application of WETMOD 2 this limitation must be taken into account, as very shallow wetlands will, with the present model structure, not be simulated accurately. Therefore, this limitation is of a high priority for future development of WETMOD 2. River turbidity & temperature are not used in the model and are only included as potentially relevant data for the future. Both the river turbidity and temperature will impact on wetland ecosystems and should therefore in an ideal model be included. Depending on the distance of the wetlands from the river the full impact of river turbidity and temperature on wetlands may be variable. Therefore, their inclusion in a model may add to its complexity. As discussed previously the relative simplicity of WETMOD 2 should be maintained. Given the implications added complexity has on the model generic applicability it must therefore contribute substantially to model output. Testing of relative improvement in model performance following increased complexity will need to be a deciding factor as to its merit and ultimate acceptance (i.e. a sensitivity analysis). Rather than relying on estimates of expected efficiencies of constructed wetlands a separate module for which artificial wetlands can be individually modelled should be added to WETMOD 2. Although this would add complexities to the model this module would only need to be operational in circumstances where the availability of data allows. Such a module could be turned on in circumstances such as done for the external nutrient inflow (irrigation drainage) in the Reedy Creek wetland example. Include wetland soil substrate and therefore sediment re-suspension (turbidity) potential in status quo (in permanently inundated wetlands) and as an 211

Regional Scale Modelling of the lower River Murray wetlands assessment of the potential success of management through the introduction of dry periods. Include sediment nutrient dynamics to more accurately account for sediment nutrient source and sink. Sedimentation of suspended particulate matter improves water quality by reducing turbidity and suspended solids concentration. Any nutrients and contaminants adhered to particulate matter are also deposited during sedimentation effectively removing them from the water column thereby further improving the water quality (Johnston 1991; Oliver 1993; Walker et al. 1982). Sediment retention and reduction of turbidity within multiple individual wetlands can have an important cumulative impact on water quality at a catchment scale (Johnston 1991; Johnston et al. 1990). Despite some sediment re-suspension, sedimentation is a long term and relatively irreversible sink (Johnston 1991). This could therefore be included in sedimentation expansion of the model to account for the nutrient impact of sedimentation and sediment compaction/binding. However, some sediment nutrient source is still a possibility. Modelling of sediment as a nutrient source is therefore necessary to accurately assess the impact of sediment and water nutrient balance. Again a balance of model complexity and generic applicability will need to be found. The model is still in its infancy. When more spatial patterns are introduced more complexities will develop within the model, making it more discriminate to individual wetland characteristics. This can to some degree still be done whilst maintaining the simplistic model structure. An example where this was accomplished is the inclusion of spatial dependent wetland characteristics, wetland depth and volume (section 6.1 (Wetland size, volume and location)). One of the next development stages could be to include soil substrate data. Sediment properties are the deciding factor to changes due to drying and reflooding (McComb et al. 1997), therefore the wetland substrate plays an important role in the effectiveness of the reintroduction of wetland dry periods. The fieldwork would only need to be conducted once, as the results would be conclusive and therefore not constitute an ongoing expense. This would deliver a strong spatial criterion in modelling of scenarios, so much so that a potential wetland may be found to be entirely unsuitable for management through the introduction of dry periods.

212

Regional Scale Modelling of the lower River Murray wetlands Improve twin management simulations Currently the twin management scenarios are effective in formulating further research questions such as “is sedimentation (e.g. using clay to adsorb nutrients followed by sedimentation) the best management strategy in a highly eutrophic system or will constructed wetlands allow sufficient nutrient removal to facilitate wetland rehabilitation?” Developing this capacity within the model may provide some direction for further field based research. Adoption of WETMOD 2 into Spatial Modelling Environment (SME) modelling software The initial attempt at using GIS (geographical information systems), with SME as a platform, as a data source to the model was deemed as inappropriate in the case of the development of a wetlands model for the lower River Murray. The sole reason for this was the lack of GIS data, particularly a DEM (digital elevation model). The model however was designed in a manner of keeping this option open should adequate GIS data become available. The major advantages would be the simultaneous simulation of all wetlands, thereby making cumulative assessments and/or comparisons between wetlands that much easier. The recent baseline surveys of select River Murray wetlands (SKM 2004; SKM 2006) have included relatively accurate DEM developments, the accuracy of the DEM being between 0.25 and 0.5 meters. Modifying WETMOD 2 for these wetlands may be possible in the future although this would restrict the model to the monitored wetlands. Development of an evaporation module Evaporation as a water loss can be added using an evaporation spreadsheet complied by DWLBC (Simpson 2003), the “Water Loss Calculator”. This was avoided early in model development due to its own inherent inaccuracy that would have complicated model development. The water exchange volume for a wetland was based on the inflow estimation required to reach an optimal nutrient dynamic simulation. The evaporation loss would reduce the simulated outflow from a wetland which was previously assumed to be equal to the inflow. Therefore, a wetland could actually be retaining higher loads of nutrient than so far simulated. Consequently the full development of an evaporation module for WETMOD 2 may improve the assessment of nutrient retention. As “Water Loss Calculator” is currently used by state 213

Regional Scale Modelling of the lower River Murray wetlands government agencies and wetland managers to calculate wetland evaporative water loss, building this into the model would work in with current practice (despite its inherent inaccuracy). The “Water Loss Calculator” is as generically applicable as WETMOD 2 and would therefore not add to the model complexity. Monitoring needs Progress in model development to enhance results requires the availability of validation data or improvement of and/or inclusion of new driving variable data (de Wit et al. 2001). Although some of these would increase model complexity, the relative improvement in model output may warrant their inclusion. Many would therefore need to be considered. These new data could include: all driving variables within a wetland; o temperature o turbidity o Secchi depth o PO4-P o NO3-N o phytoplankton results of monitoring within wetlands for; o dissolved oxygen o zooplankton o macrophyte biomass o substrate (soil composition and compaction potential) o ground truthing of through flow information from monitoring external nutrient sources concurrent with the wetland monitoring including concentration and volume, such as; o irrigation drainage o river o groundwater 214

Regional Scale Modelling of the lower River Murray wetlands external climatic factors besides solar radiation, such as wind direction and speed, shelter by surrounding vegetation (could contribute to resuspension modelling and flow direction of water exchange). All of these factors could impact on the division of the wetland categories. As an example of a classification procedure Strager et al. (2000) used a landscape based approach to classify wetlands and riparian areas based on habitat requirements of amphibians and reptiles. This classification also included forested and non-forested groupings as this had an impact on the wind reaching the wetlands (Strager et al. 2000). Borrowing this approach, forest cover mapping or obtaining a cover representation from satellite imagery, might be used to differentiate classifications in the Murray wetlands model in future work, particularly if wind and therefore sediment resuspending equations are developed in the model. The model developed by Muhammetoglu et al. (1997) is too complex to apply to the lower River Murray wetlands given the lack of data, but it shows the work presently underway to develop models of nutrient retention by wetlands. As such WETMOD 2 contributes to this research by providing an example of a simple generic model applicable on a regional scale where very limited data are available. In the modelling of complex environmental ecosystems, particularly where scant data is available, simple models provide a basis with which to advance or focus management and future research. The desire to increase complexity therefore needs to be carefully balanced between improved model performance and applicability of the model on a landscape scale.

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Glossary Terminology Wetland categories

The division of wetlands into very simplified hydrological connectivity classification, i.e. wetlands of similar type

“Exemplar”

The monitored data of a wetland of a given category

Category wetlands

wetland with no driving variable data within a give wetland category for which “exemplar” data will be used as driving variables, i.e. wetlands of a particular category

GIS

Geographical Information System

DEM

Digital Elevation Model

SME

Spatial Modelling Environment – A GIS based modelling environment

Simulation

Running the model based on a management scenario

Scenario

Hypothetical management situation which is modelled by WETMOD 2 at a simulation run. One run of the model

Development

Construction of the model including adapting WETMOD 1, spatial data, wetland monitored data and river data followed by calibration and validation of the model.

Calibration

Fitting the model output to monitored data and adjusting parameters such as thresholds

Validation

Testing the model with data not used during the model development to determine the degree of agreement between a model and the real system.

State variables

Model output (Phosphorus as PO4-P, nitrogen as NO3-N, macrophytes, phytoplankton and zooplankton)

Driving variables

Model time-series input (water temperature, turbidity, Secchi depth and solar radiation)

Calibration

Set parameters adjusted within the model to fit the model to monitored data (e.g. turbidity sedimentation threshold, zooplankton mortality rate, maximum phytoplankton growth rate)

Retention

Nutrient retain within a wetland

Uptake

The reduction of nutrient load in a wetland through phytoplankton and macrophyte growth ≈ Retention

Load

Amount of suspended nutrient in the wetland, irrigation drainage or river (resulting in inflow load to the wetland). Directly related to the concentration simulated.

NTU

Nephelometric Turbidity Units

232

Regional Scale Modelling of the lower River Murray wetlands Organisations MDBC

Murray Darling Basin Commission

BOM

Bureau of Meteorology

DWLBC

South Australian Department of Water, Land and Biodiversity Conservation

DEH

South Australian Department for Environment and Heritage

Equations NR t

[mg/day]

Nutrient Retention

τ [1/day]

Turnover rate

D

Average linear deviation from the measured values as a fraction of the average observed values

ID

Total Irrigation Drainage load

CI

Concentration of irrigation drainage nutrient

I

Irrigation Drainage flow in litres/day

∆ID

Change in total Irrigation Drainage load after management

RF

Total River Inflow load

%RO

Percentage Reduction in Outflow

OF

Total Outflow load

∆OF

Change in total Outflow load post management.

CR and CW

Concentrations of nutrients in the river

CR and CW

Concentrations of nutrients in the wetland

R

River flow rate

ƒ

Represents a fraction of the river flow rate R.

%RO:

Change in outflow due to management when compared to the status quo (no management).

%RI:

Effective change in wetland nutrient inflow due to nutrient reduction scenario as compared with the status quo.

LR

Initial river nutrient load

NR

%RL

Change in wetland retention due to management Percentage River Load removed due to the wetland management

233

Appendix

Appendix A: WETMOD differential equations The initial concentrations for each wetland category are fixed as in Table 17. Table 17: Initial values

Category

Macrophyte

Phytoplankton, Zooplankton, PO4-P,

NO3-N

(MAC_BIOMASS), 1

5

0.0001

1.2

0.00011

0.0003

2

15

0.0001

0.001,

0.00275

0.0004

3

5

0.0001

1.2

0.000133

0.00011

4

0.1

7.04

1.2

0.00026

0

5

0.1

2.51

1.2

0.000109 5

0.00026

6

15

Look at Data

1.2

Look at Data

Look at Data

The descriptions of the Macrophyte, Phytoplankton and Nutrient sectors were adapted from (Cetin 2001).

234

Appendix

$Macrophytes Equations

Source

MAC_BIOMASS(t) = MAC_BIOMASS(t - dt) + (Mac_Gross_PP - Mac_mortality - Mac_respiration) * dt INFLOWS: Mac_Gross_PP = if Turbidity
(Boumans 2001)

OUTFLOWS: Mac_mortality = Mac_mort_rate*MAC_BIOMASS

(Asaeda et al. 1997)

Mac_respiration = Mac_resp_rate*MAC_BIOMASS

(Asaeda et al. 1997)

mac_net_prod = Mac_Gross_PP-Mac_respiration mac_nut_cf = (NO3N/(NO3N+mac_Ks_N))*(PO4P/(PO4P+mac_Ks_P))

Jorgensen 1986

mac_prod_cf = underwater_light_cf*mac_temp_cf*mac_nut_cf

(Boumans 2001)

mac_temp_cf = EXP(0.2*(water_temp-mac_temp_opt))*((40water_temp)/(40-mac_temp_opt))^(0.2*(40-mac_temp_opt))

(Boumans 2001)

reflection = 0.9*(SolarRadiationInCalculation*100)

(Recknagel et al. 1982)

surface_light = 0.5*reflection

(Recknagel et al. 1982)

Turbidity2Secchi = IF (2.4355*(Turbidity)^-0.5675) =0 Then 0.000001 Else (2.4355*(Turbidity)^-0.5675) underwater_light_cf = surface_light*EXP((4.6/Zeu_Calculated)*1)

(Recknagel et al. 1982)

Zeu_Calculated = IF(Manual_Secchi_Overide=0) THEN(1.7*(Manual_Secchi_Overide+0.001)) ELSE(1.7*Manual_Secchi_Overide)

(Recknagel et al. 1982)

Parameters

Units

Source

Mac_GPP = 0.005

kg/m3/d

(Boumans 2001)

mac_Ks_N = 0.0001

kg/m3

Calibrated

mac_Ks_P = 0.00005

kg/m3

Calibrated

Mac_mort_rate = 0.01

kg/m3/d

(Asaeda et al. 1997)

Mac_resp_rate = 0.018

cm3/m3/d

(Asaeda et al. 1997)

TurbGrowthLimiting = 70

NTU

Calibrated 235

Appendix Model terms

Definition

MAC BIOMASS

The biomass of the photosynthetic portion of the macrophytes.

Mac GPP

The gross primary production rate for the total plant biomass.

Mac Gross PP

The gross primary productivity of the photosynthetic biomass.

Mac Ks N

The half-saturation constant for the uptake of nitrates by macrophytes.

Mac Ks P

The half-saturation constant for the uptake of phosphate by macrophytes.

Mac mort rate

Mortality rate for the photosynthetic biomass

Mac mortality

The mortality of the photosynthetic biomass.

Mac net prod

The net primary productivity for total macrophyte biomass.

Mac nut cf

The macrophyte nutrient coefficient.

Mac prod cf

The macrophyte production coefficient.

Mac resp rate

Respiration rate of photosynthetic biomass

Mac respiration

The respiration of photosynthetic biomass.

Mac temp cf

Macrophyte temperature coefficient

Mac temp opt

The optimum temperature for macrophyte growth

Manual Secchi override

Switch between sources of Secchi depth.

Manual vs Monitored Secchi

Switch between sources of Secchi depth.

Reflection

Determines the proportion incoming solar radiation reflected from the water surface.

Secchi

Selection of calculated or measured Secchi depth

Site assumed Secchi Manual

Manual input of Secchi depth (fixed)

Surface Light

Defines the proportion of light entering the surface water.

Turbidity2Secchi

The calculation of the Secchi depth based on turbidity (see Methodology)

Underwater light cf

The underwater light coefficient.

Zeu Calculated

Defines euphotic zone at 1 metre depth.

236

Appendix

$Phytoplankton Equations

Source

PHYTOPLANKTON(t) = PHYTOPLANKTON(t - dt) + (pht_Gross_PP + Phytoplankton_In - Pht_grazing pht_respiration - pht_mortality - pht_sedimentation Phytoplankton_Out) * dt INFLOWS: pht_Gross_PP = if PHYTOPLANKTON>pht_max or Turbidity>TurbGrowthLimiting then pht_max else pht_prod_cf*pht_GPP*PHYTOPLANKTON

(Boumans 2001)

Phytoplankton_In = PhytoplanktonInflow_cm3m3 OUTFLOWS: Pht_grazing = PHYTOPLANKTON*(zoo_growth_rateZoo_resp_rate)

(Recknagel et al. 1982)

pht_respiration = pht_resp_rate*pht_temp_cf*PHYTOPLANKTON pht_mortality = pht_mort_rate*PHYTOPLANKTON

(Asaeda et al. 1997)

pht_sedimentation = pht_sed*PHYTOPLANKTON

(Recknagel et al. 1982)

Phytoplankton_Out = PhytoplanktonOutflow_cm3m3 pht_max = IF Cat_Cal_Used=6 THEN (pht_max_6) ELSE IF Cat_Cal_Used = 5 THEN (pht_max_5) ELSE ((IF(Cat_Cal_Used = 1) THEN(pht_max_1) ELSE ((IF(Cat_Cal_Used = 2 ) THEN (pht_max_2) ELSE ((IF(Cat_Cal_Used =3) THEN (pht_max_3) ELSE ((IF(Cat_Cal_Used = 4) THEN (pht_max_4) ELSE 2))))))))

(Recknagel et al. 1982)

pht_net_prod = pht_Gross_PP-pht_respiration pht_nut_cf = (NO3N/(NO3N+pht_Ks_N))*(PO4P/(PO4P+pht_Ks_P))

Jorgensen 1986

pht_prod_cf = underwater_light_cf*pht_temp_cf*pht_nut_cf

(Boumans 2001)

pht_sed = IF Cat_Cal_Used=6 THEN (pht_sed_6) ELSE IF Cat_Cal_Used = 5 THEN (pht_sed_5) ELSE ((IF(Cat_Cal_Used = 1) THEN(pht_sed_1) ELSE ((IF(Cat_Cal_Used = 2 ) THEN (pht_sed_2) ELSE ((IF(Cat_Cal_Used =3) THEN (pht_sed_3) ELSE ((IF(Cat_Cal_Used = 4) THEN (pht_sed_4) ELSE 0.2))))))))

(Recknagel et al. 1982)

pht_temp_cf = 1.08^(water_temp-20)

Hamilton and Schladow 1997

237

Appendix Equations

Source

ZOOPLANKTON(t) = ZOOPLANKTON(t - dt) + (Pht_grazing - Zoo_mortality) * dt INFLOWS: Pht_grazing = PHYTOPLANKTON*(zoo_growth_rateZoo_resp_rate)

(Recknagel et al. 1982)

OUTFLOWS: Zoo_mortality = ZOOPLANKTON*zoo_mort_rate*(1.05^(water_temp-20))

(Recknagel et al. 1982)

dark_grazing = grazing_temp_cf*zoo_grazing_cf

(Recknagel et al. 1982)

day_length = 12-7*COS(Time_period)

(Recknagel et al. 1982)

grazing_temp_cf = IF(water_temp=0) THEN(1.05*EXP(2*ABS(LOGN((water_temp+0.001)/20))+0.26)) ELSE(1.05*EXP(-2*ABS(LOGN(water_temp/20))+0.26))

(Recknagel et al. 1982)

pht_grazing_rate = dark_grazing*(24day_length)/24+0.8*dark_grazing*day_length/24

(Recknagel et al. 1982)

pht_Ks_grazing = If PHYTOPLANKTON>0 THEN 4*0.4*PHYTOPLANKTON^1.5 Else 4*0.4*(PHYTOPLANKTON+0.00001)^1.5

(Recknagel et al. 1982)

zoo_grazing_cf = if ZOOPLANKTON>0 then PHYTOPLANKTON *pht_pref/ZOOPLANKTON/(5/pht_Ks_grazing +PHYTOPLANKTON*pht_pref/pht_Ks_grazing +5/ZOOPLANKTON+PHYTOPLANKTON *pht_pref/ZOOPLANKTON) else 0.001

(Recknagel et al. 1982)

zoo_growth_rate = if MAC_BIOMASS>10 then ((0.80.4/1.3)*pht_grazing_rate) else 0.05

(Recknagel et al. 1982)

zoo_mort_rate = IF Cat_Cal_Used=6 THEN (ZooMortRate_6) ELSE IF Cat_Cal_Used = 5 THEN (ZooMortRate_5) ELSE ((IF(Cat_Cal_Used = 1) THEN(ZooMortRate_1) ELSE ((IF(Cat_Cal_Used = 2 ) THEN (ZooMortRate_2) ELSE ((IF(Cat_Cal_Used =3) THEN (ZooMortRate_3) ELSE ((IF(Cat_Cal_Used = 4) THEN (ZooMortRate_4) ELSE 0.3))))))))

(Recknagel et al. 1982)

Zoo_resp_rate = (((0.22-0.08/1.3)*pht_grazing_rate)*0.36) *(0.17*(water_temp/20)^2+0.05)

(Recknagel et al. 1982)

238

Appendix

Parameters

Units

Source

pht_GPP = 1.8

cm3/m3/d

(Boumans 2001)

pht_Ks_N = 0.00001

3

kg/m

Hamilton and Schladow 1997

pht_Ks_P = 0.00001

kg/m3

Hamilton and Schladow 1997

pht_max_1 = 0.1

Calibrated

pht_max_2 = 0.1 pht_max_3 = 1 pht_max_4 = 1 pht_max_5 = 0.5 pht_max_6 = 2 pht_mort_rate = 0.019

cm3/m3/d

(Asaeda et al. 1997)

pht_pref = 2.5

dimless

(Recknagel et al. 1982)

pht_resp_rate = 0.047

cm3/m3/d

(Asaeda et al. 1997)

pht_sed_1 = if Turbidity >TurbSed_pht then 0.1 else 0.01

Fraction of biomass (Where 1 is 100%)

Calibrated

TurbSed_pht = 95

NTU

Calibrated

ZooMortRate_1 = 0.2

cm3/m3/d

Calibrated

pht_sed_2 = if Turbidity >TurbSed_pht then 0.05 else 0.01 pht_sed_3 = if Turbidity >TurbSed_pht then 0.05 else 0.01 pht_sed_4 = if Turbidity >TurbSed_pht then 0.5 else 0.2 pht_sed_5 = if Turbidity >TurbSed_pht then 0.5 else 0.2 pht_sed_6 = if Turbidity >TurbSed_pht then 0.5 else 0.2

ZooMortRate_2 = 0.2 ZooMortRate_3 = 0.5 ZooMortRate_4 = 0.2 ZooMortRate_5 = 0.6 ZooMortRate_6 = 0.3

239

Appendix Model terms

Definition

PHYTOPLANKTON

The biomass of phytoplankton (defines Chl-a concentration in terms of biomass).

Dark grazing

Defies the grazing rate of zooplankton during night-time feeding on phytoplankton.

Day length

Defines the length of the day.

Grazing temp cf

Temperature coefficient for grazing.

Pht GPP

The phytoplankton gross primary production rate.

Pht grazing

The grazing of phytoplankton by zooplankton.

Pht grazing rate

Determines the grazing rate dependent on the time of day.

Pht Gross PP

The phytoplankton gross primary productivity.

Pht Ks grazing

The half-saturation constant for zooplankton grazing on phytoplankton.

Pht Ks N

The half-saturation constant for the uptake of nitrates by phytoplankton.

Pht Ks P

The half-saturation constant for the uptake of phosphate by phytoplankton.

Pht max

The maximum possible biomass of phytoplankton, i.e. the carrying capacity.

Pht mort rate

The phytoplankton mortality rate.

Pht mortality

The phytoplankton mortality.

Pht net prod

The phytoplankton net primary productivity.

Pht nut cf

The phytoplankton nutrient coefficient.

Pht pref

The zooplankton preference factor for phytoplankton grazing.

Pht prod cf

The phytoplankton production coefficient.

Pht resp rate

The phytoplankton respiration rate.

Pht respiration

The phytoplankton respiration.

Pht sed

The sedimentation rate of phytoplankton, which is dependent on turbidity.

Pht sedimentation

The sedimentation of phytoplankton.

Pht temp cf

The phytoplankton temperature coefficient.

Phytoplankton in

The inflow of phytoplankton into the wetland.

Phytoplankton out

The inflow of phytoplankton into the river.

PhytoplanktonInflow cm3m3

The phytoplankton inflow concentration in cm3/m3

PhytoplanktonOutflow cm3m3

The phytoplankton outflow concentration in cm3/m3

240

Appendix ZOOPLANKTON

The biomass of zooplankton.

Zoo grazing cf

The grazing coefficient of zooplankton, which changes with the phytoplankton biomass.

Zoo growth rate

The growth rate of zooplankton.

Zoo mort rate

The mortality rate for zooplankton.

Zoo mortality

The zooplankton mortality.

Zoo resp rate

The respiration rate of zooplankton.

241

Appendix

$Nutrients Equations

Source

PO4P(t) = PO4P(t - dt) + (P_loading + P_sed_release + P_IN_gL - P_uptake - P_soil_coprecip - P_OUT) * dt INFLOWS: P_loading = (P_from_land+P_loading_rate)/Wetlandvolume_Liters

Jorgensen 1986

P_sed_release = Turbidity/900*P_from_land

(Recknagel et al. 1982)

P_IN_gL = PInflowAmount_mgL/1000 OUTFLOWS: P_uptake = PO4P*((pht_net_prod*pht_PC)+(mac_net_prod*Mac_PC))

(Boumans 2001)

P_soil_coprecip = P_sed*PO4P

(Recknagel et al. 1982)

P_OUT = POutflow_Amount_gL P_sed = IF Cat_Cal_Used=6 THEN (P_sed_6) ELSE IF Cat_Cal_Used = 5 THEN (P_sed_5) ELSE ((IF(Cat_Cal_Used = 1) THEN(P_sed_1) ELSE ((IF(Cat_Cal_Used = 2 ) THEN (P_sed_2) ELSE ((IF(Cat_Cal_Used =3) THEN (P_sed_3) ELSE ((IF(Cat_Cal_Used = 4) THEN (P_sed_4) ELSE 0.05))))))))

(Recknagel et al. 1982)

pht_PC = IF Cat_Cal_Used=6 THEN (pht_PC_6) ELSE IF Cat_Cal_Used = 5 THEN (pht_PC_5) ELSE ((IF(Cat_Cal_Used = 1) THEN(pht_PC_1) ELSE ((IF(Cat_Cal_Used = 2 ) THEN (pht_PC_2) ELSE ((IF(Cat_Cal_Used =3) THEN (pht_PC_3) ELSE ((IF(Cat_Cal_Used = 4) THEN (pht_PC_4) ELSE 0.05))))))))

(Boumans 2001)

Equations

Source

NO3N(t) = NO3N(t - dt) + (N_loading + N_sed_release + N_IN_gL - N_uptake - N_soil_coprecip - N_OUT Denitrification) * dt INFLOWS: N_loading = (N_from_land+N_loading_rate)/Wetlandvolume_Liters

Jorgensen 1986

N_sed_release = Turbidity/2500*N_from_land

(Recknagel et al. 1982)

N_IN_gL = NInflowAmount_mgL/1000 242

Appendix OUTFLOWS: N_uptake = NO3N*((pht_net_prod*pht_NC)+(mac_net_prod*Mac_NC))

(Boumans 2001)

N_soil_coprecip = N_sed*NO3N

(Recknagel et al. 1982)

N_OUT = NOutflow_Amount_gL N_sed = IF Cat_Cal_Used=6 THEN (N_sed_6) ELSE IF Cat_Cal_Used = 5 THEN (N_sed_5) ELSE ((IF(Cat_Cal_Used = 1) THEN(N_sed_1) ELSE ((IF(Cat_Cal_Used = 2 ) THEN (N_sed_2) ELSE ((IF(Cat_Cal_Used =3) THEN (N_sed_3) ELSE ((IF(Cat_Cal_Used = 4) THEN (N_sed_4) ELSE 0.1))))))))

(Recknagel et al. 1982)

pht_NC = IF Cat_Cal_Used=6 THEN (pht_NC_6) ELSE IF Cat_Cal_Used = 5 THEN (pht_NC_5) ELSE ((IF(Cat_Cal_Used = 1) THEN(pht_NC_1) ELSE ((IF(Cat_Cal_Used = 2 ) THEN (pht_NC_2) ELSE ((IF(Cat_Cal_Used =3) THEN (pht_NC_3) ELSE ((IF(Cat_Cal_Used = 4) THEN (pht_NC_4) ELSE 0.05))))))))

(Boumans 2001)

Parameters

Units

Source

P_loading_rate = 0.0005

g/L

Walker and Hillman

N_loading_rate = 0.005

g/L

Walker and Hillman

Mac_NC = 0.5

Ratio

(Boumans 2001)

Mac_PC = 0.1

Ratio

(Boumans 2001)

N_from_land = 0.0005

g/m2

Young et al 1996

P_from_land = 0.00003

g/m2

Young et al 1996

N_sed_1 = if Turbidity>TurbSedN Ratio then 0.32 else 0.22

Calibrated

N_sed_2 = if Turbidity>TurbSedN then 0.15 else 0.12 N_sed_3 = if Turbidity>TurbSedN then 0.5 else 0.1 N_sed_4 = if Turbidity>TurbSedN then 0.5 else 0.2 N_sed_5 = if Turbidity>TurbSedN then 0.2 else 0.1 N_sed_6 = if Turbidity>70 then 0.2 else 0.1 P_sed_1 = if Turbidity>TurbSedP then Ratio 0.32 else 0.22

Calibrated

243

Appendix P_sed_2 = if Turbidity>TurbSedP then 0.15 else 0.12 P_sed_3 = if Turbidity>TurbSedP then 0.5 else 0.1 P_sed_4 = if Turbidity>TurbSedP then 0.5 else 0.2 P_sed_5 = if Turbidity>TurbSedP then 0.2 else 0.1 P_sed_6 = if Turbidity>70 then 0.2 else 0.1 pht_NC_1 = 0.05

Ratio

(Boumans 2001)

Ratio

(Boumans 2001)

TurbSedN = 70

NTU

Calibrated

TurbSedP = 70

NTU

Calibrated

pht_NC_2 = 0.05 pht_NC_3 = 0.05 pht_NC_4 = 0.05 pht_NC_5 = 0.05 pht_NC_6 = 0.05 pht_PC_1 = 0.05 pht_PC_2 = 0.05 pht_PC_3 = 0.1 pht_PC_4 = 0.5 pht_PC_5 = 0.05 pht_PC_6 = 0.05

Model terms

Definition

NO3N

Nitrate as NO3-N

PO4P

Orthophosphate as PO4-P

N sed

Coprecipitation rate for NO3-N dependent on turbidity

P sed

Coprecipitation rate for PO4-P dependent on turbidity

Mac NC

N:C ratio required by macrophytes

Pht NC

N:C ratio required by phytoplankton

N loading

Non point source of NO3-N

N loading rate

Non point source of NO3-N (minimal)

N from land

Non point source of NO3-N (minimal) 244

Appendix P loading

Non point source of PO4-P

P loading rate

Non point source of PO4-P (minimal)

P from land

Non point source of PO4-P (minimal)

Mac PC

P:C ratio required by macrophytes

Pht PC

P:C ratio required by phytoplankton

N soil coprecip

The coprecipitation rate for NO3-N O

P soil coprecip

The coprecipitation rate for PO4-P

N in gL

The inflow of NO3-N into the wetland.

P in gL

The inflow of PO4-P into the wetland.

Ninflow Amount gL

The NO3-N inflow concentration in g/L

Noutflow Amount gL

The NO3-N outflow concentration in g/L

N sed release

The NO3-N released from sediments.

N out

The outflow of NO3-N to the river

P out

The outflow of PO4-P to the river

Pinflow Amount gL

The PO4-P inflow concentration in g/L

Poutflow Amount gL

The PO4-P outflow concentration in g/L

P sed release

The PO4-P released from sediments.

N uptake

The uptake of NO3-N associated with macrophyte and algae production.

P uptake

The uptake of PO4-P associated with macrophyte and algae production.

245

Appendix

$NutrientExchange Equations/Rules

Description/definition

DrainFlow_SunnyORPaiw = IF(Category_Time_Series_Used=1)THEN(PDrainFlo w_Paiwalla) ELSE(IF(Category_Time_Series_Used=2) THEN(PDrainFlow_Sunnyside) ELSE(0))

Gives the modeller the option to simulate irrigation inflow into Paiwalla wetland. Intended to test whether the hypothesis that no irrigation drainage was affecting Paiwalla wetland.

DrainFlow_PreMultiplication_Factor = IF(IrrigationDrainage=1) THEN(DrainFlow_SunnyORPaiw) ELSE(IF(IrrigationDrainage=2) THEN(PDrainFlow_REEDY) ELSE(0))

Selects the appropriate drainage flow depending to the wetland being simulated.

DrainFlow_L = IF (Drainage_Channel_multiplication_Factor=0) THEN (DrainFlow_PreMultiplication_Factor) ELSE ((DrainFlow_PreMultiplication_Factor *(Drainage_Channel_multiplication_Factor *Seasonal_Flow_Pattern_SunnyORReedy)))

Calculates the drain flow volume given the average flow volume per day and the seasonal flow pattern. Therefore the average flow can be increased and the seasonal flow pattern maintained.

Seasonal_Flow_Pattern_SunnyORReedy = IF(Category_Time_Series_Used = 2) THEN(Seasonal_Flow_Pattern_Sunnyside)ELSE(IF( Category_Time_Series_Used = 4) THEN(Seasonal_Flow_Pattern_Reedy) ELSE(1)) PDrainFlow_Paiwalla = IF((Paiwalla_P_Drain_mg_perL+Paiwalla_N_Drain_ mg_perL)>0) THEN(DrainFlowVolume_Liters_perDay_Sunnyside) ELSE(0)

Selects the drain flow volume from the appropriate wetland data.

PDrainFlow_REEDY = IF((Reedy_DrainPConc_mg_perL+REEDY_DrainNC onc_mg_perL)>0) THEN(DrainFlowVolume_Liters_perDay_REEDY) ELSE(0) PDrainFlow_Sunnyside = IF((Sunnyside_P_Drain_mg_perL+Sunnyside_N_Drai n_mg_perL)>0) THEN(DrainFlowVolume_Liters_perDay_Sunnyside) ELSE(0)

246

Appendix Equations/Rules

Description/definition

Chla%_Removed_from_Drainage_Load = 0

Manual control to reduce the Chl-a inflow.

Chla_DrainLoad_REEDY = IF(REEDY_Chla_Drain_ugL>0) THEN(REEDY_Chla_Drain_ugL *DrainFlowVolume_Liters_perDay_REEDY) ELSE(0)

Calculates inflow load from the concentration and flow volume.

Chla_DrainLoad_Sunnyside = IF(Sunnyside_Chla_ugL>0) THEN(Sunnyside_Chla_ugL*DrainFlowVolume_Lite rs_perDay_Sunnyside) ELSE(0) Chla_Drain_Load_Reedy2 = (IF(IrrigationDrainage=1) THEN(0) ELSE(IF(IrrigationDrainage=2) THEN(Chla_DrainLoad_REEDY)/100 ELSE(0)))*(IF (Chla%_Removed_from_Drainage_Load >0) THEN (100-Chla%_Removed_from_Drainage_Load) Else 100)

Calculates the actual load used in the simulation. This is where the load is reduced as per potential management strategy.

Chla_DrainLoad_Sunnyside2 = (IF(IrrigationDrainage=1) THEN(Chla_DrainLoad_Sunnyside)/100 ELSE(IF(IrrigationDrainage=2) THEN(0) ELSE(0)))*(IF (Chla%_Removed_from_Drainage_Load >0) THEN (100-Chla%_Removed_from_Drainage_Load) Else 100)

REEDY_Chla_Drainage_divided_into_wetland = IF(Drainage_Channel_multiplication_Factor=0) THEN(Chla_Drain_Load_Reedy2/Wetlandvolume_Li ters) ELSE((Chla_Drain_Load_Reedy2/Wetlandvolume_Li ters)*(Drainage_Channel_multiplication_Factor*Seas onal_Flow_Pattern_SunnyORReedy))

Calculates the dispersal of inflow load into the wetland, i.e. to obtain concentration. Fits the concentration to the seasonal flow pattern.

Sunnyside_Chla_divided_into_wetland = IF(Drainage_Channel_multiplication_Factor=0) THEN(Chla_DrainLoad_Sunnyside2/Wetlandvolume _Liters) ELSE((Chla_DrainLoad_Sunnyside2/Wetlandvolume _Liters)*(Drainage_Channel_multiplication_Factor*S easonal_Flow_Pattern_SunnyORReedy)) Chla_Accross_Wetland =

Selects wether Reedy 247

Appendix IF(Category_Time_Series_Used=2) THEN(Sunnyside_Chla_divided_into_wetland) ELSE(REEDY_Chla_Drainage_divided_into_wetland )

Creek or Sunnyside wetland data is to be used depending on wetland being simulated.

PhytoplanktonInflow_cm3m3 = (((ChlaRiver_ugL/2.5)*Hypothetical_Inflow_m3)/(We tlandvolume_Liters/1000))+(Chla_Accross_Wetland* 2.5)

Calculates the total Phytoplankton inflow into the wetland. Merges Irrigation drainage Chla-a inflow and River Chl-a inflow. Converts Chl-a into phytoplankton.

PhytoplanktonOutflow_cm3m3 = Calculates the Hypothetical_Outflow_m3*PHYTOPLANKTON/(We concentration of outflow tlandvolume_Liters/1000) depending on the outflow volume and the concentration within the wetland.

248

Appendix

Equations/Rules

Description/definition

N%_Removed_from_Drain_Load = 0

Manual control to reduce the NO3-N inflow

NDrainLoad_REEDY = IF(REEDY_DrainNConc_mg_perL>0) THEN(REEDY_DrainNConc_mg_perL*DrainFlowV olume_Liters_perDay_REEDY) ELSE(0)

Calculates inflow load from the concentration and flow volume

NDrainLoad_Sunnyside = IF(Sunnyside_N_Drain_mg_perL>0) THEN(Sunnyside_N_Drain_mg_perL*DrainFlowVol ume_Liters_perDay_Sunnyside) ELSE(0) NDrainLoad_Paiwalla = IF(Paiwalla_N_Drain_mg_perL>0) THEN(Paiwalla_N_Drain_mg_perL*DrainFlowVolu me_Liters_perDay_Sunnyside) ELSE(0) NDrainLoad_SunnyORPaiw = IF(Category_Time_Series_Used=1)THEN(NDrainLoa d_Paiwalla) ELSE(IF(Category_Time_Series_Used=2) THEN(NDrainLoad_Sunnyside) ELSE(0))

Select the appropriate drain load for either Sunnyside or Paiwalla wetlands.

NDrainLoad = (IF(IrrigationDrainage=1) THEN(NDrainLoad_SunnyORPaiw)/100 ELSE(IF(IrrigationDrainage=2) THEN(NDrainLoad_REEDY)/100 ELSE(0)))*(IF (N%_Removed_from_Drain_Load >0) THEN (100N%_Removed_from_Drain_Load) Else 100)

Calculate the actual load used in the simulation. This is where the load is reduced as per potential management strategy.

N_Drain_Water_Inflow = IF(Drainage_Channel_multiplication_Factor=0) THEN(NDrainLoad/Wetlandvolume_Liters) ELSE((NDrainLoad/Wetlandvolume_Liters)*(Drainag e_Channel_multiplication_Factor*Seasonal_Flow_Pat tern_SunnyORReedy))

Calculates the dispersal of inflow load into the wetland, i.e. to obtain concentration.

NInflowAmount_mgL = ((Hypothetical_Inflow_Liters*NRiver_mgL)/Wetland volume_Liters)+N_Drain_Water_Inflow

Calculates the inflow concentration as a function of the wetland volume of NO3-N into the wetland.

NOutflow_Amount_gL = (NO3N*Hypothetical_Outflow_Liters)/(Wetlandvolu me_Liters)

Calculates the outflow concentration as a function of the wetland volume of NO3-N from the wetland.

Fits the concentration to the seasonal flow pattern.

249

Appendix Equations/Rules

Description/definition

P%_Removed_from_Drain_Load = 0

Manual control to reduce the PO4-P inflow

PDrainLoad_REEDY = Calculates inflow load IF(Reedy_DrainPConc_mg_perL>0) from the concentration THEN(Reedy_DrainPConc_mg_perL*DrainFlowVolu and flow volume me_Liters_perDay_REEDY) ELSE(0) PDrainLoad_Sunnyside = IF(Sunnyside_P_Drain_mg_perL>0) THEN(Sunnyside_P_Drain_mg_perL*DrainFlowVolu me_Liters_perDay_Sunnyside) ELSE(0) PDrainLoad_Paiwalla = IF(Paiwalla_P_Drain_mg_perL>0) THEN(Paiwalla_P_Drain_mg_perL*DrainFlowVolu me_Liters_perDay_Sunnyside) ELSE(0) PDrainLoad_SunnyORPaiw = IF(Category_Time_Series_Used=1)THEN(PDrainLoa d_Paiwalla) ELSE(IF(Category_Time_Series_Used=2) THEN(PDrainLoad_Sunnyside) ELSE(0))

Select the appropriate drain load for either Sunnyside or Paiwalla wetlands.

PDrainLoad = ((IF(IrrigationDrainage=1) THEN(PDrainLoad_SunnyORPaiw)/100 ELSE(IF(IrrigationDrainage=2) THEN(PDrainLoad_REEDY)/100 ELSE(0)))*(IF (P%_Removed_from_Drain_Load >0) THEN (100P%_Removed_from_Drain_Load) Else 100))

Calculate the actual load used in the simulation. This is where the load is reduced as per potential management strategy.

P_Drain_Water_Inflow = IF(Drainage_Channel_multiplication_Factor=0) THEN(PDrainLoad/Wetlandvolume_Liters) ELSE((PDrainLoad/Wetlandvolume_Liters)*(Drainag e_Channel_multiplication_Factor*Seasonal_Flow_Pat tern_SunnyORReedy))

Calculates the dispersal of inflow load into the wetland, i.e. to obtain concentration. Fits the concentration to the seasonal flow pattern.

PInflowAmount_mgL = Calculates the inflow ((Hypothetical_Inflow_Liters*PRiver_mgL)/Wetlandv concentration as a olume_Liters)+P_Drain_Water_Inflow function of the wetland volume of PO4-P into the wetland. POutflow_Amount_gL = (PO4P*Hypothetical_Outflow_Liters)/(Wetlandvolum e_Liters)

Calculates the outflow concentration as a function of the wetland volume of PO4-P from the wetland

250

Appendix

$Wetland&RiverFlowExchange Equations/Rules

Description/definition

Percentage_of_River_Flow_regarded_as_exchange = 1

Manual control of the exchange volume as percentage of the wetland.

River_Exchange_Below_1% = 1

To reduce the exchange volume below 1% of river flow

FlowExchange%ofRiverFlow = ((FlowRiver_m3_per_Day/100)*Percentage_of_River _Flow_regarded_as_exchange)/River_Exchange_Belo w_1%

Calculates the volume exchanged.

Hypothetical_Inflow_m3 = IF(Flow_In_No1_ManualInput2_Wetland3_River4 = 2) THEN(ManualControlFlowIn_m3) ELSE(IF(Flow_In_No1_ManualInput2_Wetland3_Ri ver4 = 3) THEN(FlowExchangeInVolumeDependent) ELSE(IF(Flow_In_No1_ManualInput2_Wetland3_Ri ver4 = 4)THEN(FlowExchangeInRiverDependent) ELSE(0)))

Selects the source of the control for volume exchange. Possible to manually set exchange volume.

Hypothetical_Outflow_m3 = IF(Flow_Out_No1_ManualInput2_Wetland3_River4 = 2) THEN(ManualControlFlowOut_m3) ELSE(IF(Flow_Out_No1_ManualInput2_Wetland3_R iver4 = 3) THEN(FlowExchangeOutVolumeDependent) ELSE(IF(Flow_Out_No1_ManualInput2_Wetland3_R iver4 = 4)THEN(FlowExchangeOutRiverDependent+(DrainFl ow_L/1000)) ELSE(0)))

Selects the source of the control for volume exchange. Possible to manually set exchange volume. Adds the irrigation drain inflow volume to the outflow volume.

251

Appendix

$SpatialRelevantTimeSeries Solar Radiation see Methodology

$RiverNutrients See Methodology

$WetlandsTimeseriesUpdateMeasuredValues Extra wetland data and future wetland data.

$WetlandTimeseriesUpdate Extra wetland data and future wetland data.

$RiverTimeseries4WetlandUpdateTimeseries Same as $RiverNutrients but for extra wetland data and future wetland data.

$PotentialContributionToRiver See Methodology

252

Appendix

Appendix B: Driving Variables

253

Appendix A

T u rb id ity 180 160 140

N TU

120 100 80 60 40 20

B

A u g -9 7

Ju l-9 7

Ju n -9 7

M a y-9 7

A p r-9 7

M a r-9 7

Fe b -9 7

0

Wate r T e mp e ratu re

25

20

deg C

15

10

5

Ju l-9 7

A u g -9 7 A u g -9 8

Ju n -9 7

M a y-9 7

Ju l-9 8

C

A p r-9 7

M a r-9 7

Fe b -9 7

0

S o lar R ad iatio n P aiwalla & S u n n ysid e We tlan d s 30

M J p e r s q u a re m e te r

25

20

15

10

5

P a iw a lla W e tla nd 1 9 9 7

Ju n -9 8

M a y-9 8

A p r-9 8

M a r-9 8

Fe b -9 8

0

S unnys id e W e tla nd 1 9 9 7

Figure 74: Data - Model Driving Variables; From Figure 9 in section 2.3

254

Appendix D

T u rb id ity 350

300

250

N TU

200

150

100

50

E

S e p -9 7

A u g -9 7

J u l-9 7

J u n -9 7

M a y -9 7

A p r-9 7

Fe b -9 7

M a r-9 7

0

Wate r T e mp e ratu re 35

30

d eg C

25

20

15

10

5

A u g -9 7

S e p -9 7 S e p -9 8

J u l-9 7

J u n -9 7

M a y -9 7

A u g -9 8

F

A p r-9 7

M a r-9 7

Fe b -9 7

0

S o lar R ad iatio n P ilb y C re e k & L o ck 6 We tlan d s 35

M J p e r s q u a re m e te r

30

25

20

15

10

5

L o c k 6 w e tla nd 1 9 9 7

J u l-9 8

J u n -9 8

M a y -9 8

A p r-9 8

M a r-9 8

Fe b -9 8

0

P ilb y C re e k W e tla nd 1 9 9 7

Figure 75: Data - Model Driving Variables; From Figure 9 in section 2.3

255

20

15

10

5

0 Fe b -0 1

Ja n -0 1

D e c-0 0

M a y-0 1

25

M a y-0 1

30 A p r-0 1

35

A p r-0 1

40 M a r-0 1

S o lar R ad iatio n R e e d y C re e k We tlan d

M a r-0 1

Fe b -0 1

Ja n -0 1

D e c-0 0

H

N o v-0 0

O ct-0 0

S e p -0 0

A u g -0 0

M a y -0 1

A p r-0 1

M a r-0 1

Fe b -0 1

J a n -0 1

D e c -0 0

N o v -0 0

O c t-0 0

S e p -0 0

A u g -0 0

J u l-0 0

J u n -0 0

N TU

G

N o v-0 0

I

O ct-0 0

S e p -0 0

A u g -0 0

Ju l-0 0

Ju n -0 0

d eg C -5 0

Ju l-0 0

Ju n -0 0

M J p er sq u are m eter

Appendix T u rb id ity

350

300

250

200

150

100

50

0

Wate r T e mp e ratu re

30

25

20

15

10

5

0

R e e d y C re e k W e tla nd 2 0 0 0 -2 0 0 1

Figure 76: Data - Model Driving Variables; From Figure 9 in section 2.3

256

Appendix A

D rain ag e P O 4-P

3 .5

3

2 .5

m g /L

2

1 .5

1

0 .5

Ju l-9 7 Ju l-9 7

A u g -9 7

Ju l-9 7

A u g -9 7

A u g -9 7

Ju n -9 7 Ju n -9 7 Ju n -9 7

B

M a y-9 7

A p r-9 7

M a r-9 7

Fe b -9 7

0

D rain ag e N O 3-N 1 .2

1

m g /L

0 .8

0 .6

0 .4

0 .2

C

M a y-9 7

A p r-9 7

M a r-9 7

Fe b -9 7

0

D r a in a g e P h yto p la n k to n 0 .4 5 0 .4 0 .3 5

c m 3 /m 3

0 .3 0 .2 5 0 .2 0 .1 5 0 .1 0 .0 5

M a y-9 7

A p r-9 7

M a r-9 7

Fe b -9 7

0

S unnys id e W e tla nd

Figure 77: Time Series Irrigation Drainage ; From Figure 10 section 2.3.1

257

Appendix D

Se a s o n a l D r a in a g e Pa tte r n R e e d y C r e e k Su b c a tc h m e n t

--

1 .8 0 1 .6 0

R e la tiv e R a te Pe r M o n th

1 .4 0 1 .2 0 1 .0 0 0 .8 0 0 .6 0 0 .4 0 0 .2 0

M a y-0 1

A p r-0 1

M a r-0 1

Fe b -0 1

Ja n -0 1

D e c-0 0

N o v-0 0

O ct-0 0

S e p -0 0

A u g -0 0

Ju l-0 0

Ju n -0 0

0 .0 0

Figure 78: Time Series Irrigation Drainage; From Figure 10 section 2.3.1

258

-2 0

60

40

20

0 J a n -0 1

D e c -0 0

M a y -0 1

80

M a y-0 1

100 A p r-0 1

120

A p r-0 1

140 M a r-0 1

160

M a r-0 1

D rain ag e P h yto p lan kto n Fe b -0 1

-0 .4

Fe b -0 1

Ja n -0 1

D e c-0 0

F

N o v -0 0

O c t-0 0

S e p -0 0

A u g -0 0

J u l-0 0

M a y-0 1

Ap r-0 1

M a r-0 1

Fe b -0 1

Ja n -0 1

D e c-0 0

N o v-0 0

Oct-0 0

Se p -0 0

Au g -0 0

Ju l-0 0

Ju n -0 0

m g/L

E

N o v-0 0

G

O ct-0 0

S e p -0 0

A u g -0 0

Ju l-0 0

-0 .2

J u n -0 0

m g /L -1

Ju n -0 0

c m 3 /m 3

Appendix

Drain ag e PO 4-P

8

7

6

5

4

3

2

1

0

D rain ag e N O 3-N

1 .4

1 .2

1

0 .8

0 .6

0 .4

0 .2

0

R e e d y C re e k W e tla nd

Figure 79: Time Series Irrigation Drainage ; From Figure 10 in section 2.3.1

259

Appendix A

PO4-P 6

5

4

mg/L

3

2

1

Aug-97

Jul-97

Jun-97

May-97

Apr-97

-1

Mar-97

Feb-97

0

NO3-N

B 0.7 0.6 0.5 0.4

mg/L

0.3 0.2 0.1

Jul-97

Aug-97

Jul-97

Aug-97

Jun-97

May-97

Apr-97

Mar-97

-0.1

Feb-97

0

-0.2 -0.3

Phytoplankton

C 1.4 1.2 1

cm3/m3

0.8 0.6 0.4 0.2

Jun-97

May-97

Apr-97

Mar-97

-0.2

Feb-97

0

-0.4

P a iw a lla W e tla nd

S unnys id e W e tla nd

Figure 80: River Data; From Figure 11 in section 2.3.2

260

Appendix D

PO4-P

0.2

0.15

mg/L

0.1

0.05

Jul-97

Aug-97

Sep-97

Jul-97

Aug-97

Sep-97

Aug-97

Jun-97

Jul-97

-0.05

May-97

Apr-97

Mar-97

Feb-97

0

NO3-N

E 0.7 0.6 0.5

mg/L

0.4 0.3 0.2 0.1

Jun-97

May-97

Apr-97

Mar-97

-0.1

Feb-97

0

Phytoplankton

F 14 12

cm3/m3

10 8

6 4

2

L o c k 6 w e tla nd 1 9 9 7

Sep-97

Jun-97

May-97

Apr-97

Mar-97

Feb-97

0

P ilb y C re e k W e tla nd 1 9 9 7

Figure 81: River Data; From Figure 11 in section 2.3.2

261

H

12

10

8

6

4

2

0

Jan-01

Dec-00

Nov-00

Oct-00

Sep-00

Aug-00

Jul-00

May-01

14

May-01

P h yto p lan kto n

M a y-0 1

I Apr-01

0

Apr-01

0.1

A p r-0 1

0.2 Mar-01

0.3

Mar-01

0.4

M a r-0 1

0.5 Feb-01

0.6

Feb-01

0.7

Fe b -0 1

0.8

Jan-01

NO3-N

Ja n -0 1

Dec-00

Nov-00

Oct-00

Sep-00

Aug-00

Jul-00

Jun-00

mg/L

G

D e c-0 0

N o v-0 0

O ct-0 0

S e p -0 0

A u g -0 0

-0.1 Jun-00

mg/L -0.2

Ju l-0 0

Ju n -0 0

cm 3/m 3

Appendix PO4-P

1.2

1

0.8

0.6

0.4

0.2

0

R e e d y C re e k W e tla nd

Figure 82: River Data; From Figure 11 in section 2.3.2

262

Appendix

Appendix C: Key to wetland numbers Table 18: Wetlands simulated as category 3 wetlands Australian Wetlands Wetland

Wetland

Used Volume Category

ID

Name

depth m3

Number

managed

703 S0070

CAURNAMONT

1.5 1353858

3

690 S0075

WALKER FLAT SOUTH LAGOON

0.8 710419

3

LAKE BYWATERS

0.8 310292

3

0.92 493457

3

1107 S0076 685 S0082

DEVON DOWNS SOUTH

1102 S0093

YARRAMUNDI

2 195388

3

1101 S0093

YARRAMUNDI

2 617098

3

663 S0094

YARRAMUNDI NORTH

2 704688

3

651 S0103

ARLUNGA

0.9 1497057

3

646 S0104

ROONKA

0.9 147172

3

644 S0105

REEDY ISLAND FLAT

1.2 266973

3

645 S0106

McBEAN POUND SOUTH

0.65

42489

3

642 S0107

McBEAN POUND NORTH

0.65 121855

3

641 S0108

SINCLAIR FLAT

0.92

20053

3

640 S0108

SINCLAIR FLAT

0.92 513745

3

DONALD FLAT LAGOON

1.25 1760260

3

2 881564

3

1044 S0109 391 S0110

IRWIN FLAT

383 S0111

MURBPOOK LAGOON COMPLEX

0.92

32620

3

381 S0111

MURBPOOK LAGOON COMPLEX

0.92 946764

3

380 S0111

MURBPOOK LAGOON COMPLEX

0.92

65777

3

379 S0112

MURBKO SOUTH

0.9 1147222

3

375 S0113

MURBKO FLAT COMPLEX

0.7

75477

3

374 S0113

MURBKO FLAT COMPLEX

0.7 1135665

3

371 S0113

MURBKO FLAT COMPLEX

0.7

3

65887

263

Appendix Australian Wetlands Wetland

Wetland

Used Volume Category

ID

Name

depth m3

Number

managed

367 S0115

WOMBAT REST BACKWATER

0.7 264111

3

294 S0142

BOGGY FLAT

1.5

89373

3

324 S0149

BIG TOOLUNKA FLAT

2.3 848443

3

262 S0160

YARRA COMPLEX

2 1717745

3

1036 S0174

LOCH LUNA and NOCKBURRA CREEK

2 127894

3

190 S0174

LOCH LUNA and NOCKBURRA CREEK

2 6146303

3

631 S0189

PYAP LAGOON

2 904144

3

492 S0201

AJAX ACHILLES LAKE

1.2

22764

3

486 S0201

AJAX ACHILLES LAKE

1.2 262527

3

471 S0203

SALT CREEK AND GURRA GURRA LAKES

1.5

78987

3

0.92

17437

3

1048 S0207

LYRUP CAUSEWAY WEST

1039 S0214

RUMPAGUNYAH CREEK

2 230689

3

1031 S0214

RUMPAGUNYAH CREEK

2 371340

3

1007 S0218

GOAT ISLAND AND PARINGA PADDOCK

0.92 227636

3

1006 S0218

GOAT ISLAND AND PARINGA PADDOCK

0.92 235651

3

997 S0219

PARINGA ISLAND

0.92

12402

3

996 S0219

PARINGA ISLAND

0.92

39096

3

995 S0219

PARINGA ISLAND

0.92 111272

3

93 S0219

PARINGA ISLAND

0.92 227075

3

92 S0219

PARINGA ISLAND

0.92

25097

3

91 S0219

PARINGA ISLAND

0.92

72376

3

90 S0219

PARINGA ISLAND

0.92

17157

3

89 S0219

PARINGA ISLAND

0.92

10223

3

2 6785374

3

HORSESHOE SWAMP

1.2 327432

3

WOOLENOOK BEND COMPLEX

1.2 2111925

3

956 S0220 69 S0227 978 S0229

RAL RAL CREEK AND RAL RAL WIDEWATERS

264

Appendix Australian Wetlands Wetland

Wetland

Used Volume Category

ID

Name

depth m3

Number

managed

84 S0229

WOOLENOOK BEND COMPLEX

1.2

29590

3

82 S0229

WOOLENOOK BEND COMPLEX

1.2

41520

3

67 S0230

MURTHO PARK COMPLEX

0.92

31733

3

61 S0230

MURTHO PARK COMPLEX

0.92

24337

3

60 S0230

MURTHO PARK COMPLEX

0.92

50151

3

47 S0230

MURTHO PARK COMPLEX

0.92 250315

3

32 S0242

SLANEY OXBOW

1.25

90869

3

Lock 6 Wetland

0.92 164860

3

1134 XR001

Table 19: Wetlands simulated as category 4 wetlands Australian Wetlands Wetland

Wetland

Used Volume Category

ID

Name

depth m3

Number

managed

766 S0035

TAILEM BEND

0.8 765545

4

1110 S0052

REEDY CREEK

0.8 591799

4

310 S0148

LITTLE TOOLUNKA FLAT

1.4 739622

4

329 S0151

RAMCO LAGOON

0.3 279446

4

209 S0179

KINGSTON COMMON

0.92 340410

4

1029 S0180

WACHTELS LAGOON

0.92 6259251

4

0.5 1729378

4

583 S0185

YATCO LAGOON

265

Appendix

Appendix D: Cumulative Management Scenarios

266

Appendix

Table 20: Change in PO4-P wetland loading and percentage outflow due to management; category 3 wetland scenarios PO4-P Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

S0070 CAURNAMONT

703

1.5

1353858

3

541

535

540

542

-13

-2

3

S0075 WALKER FLAT

690

0.8

710419

3

533

527

527

526

-12

-12

-14

S0076 LAKE BYWATERS

1107

0.8

310292

3

512

504

498

489

-11

-21

-34

S0082 DEVON DOWNS

685

0.92

493457

3

525

519

517

513

-11

-16

-23

1102

2

195388

3

493

483

471

496

-11

-25

4

1101

2

617098

3

530

524

523

531

-12

-14

2

663

2

704688

3

532

527

527

533

-12

-12

1

S0103 ARLUNGA

651

0.9

1497057

3

512

505

511

514

-19

-4

5

S0104 ROONKA

646

0.9

147172

3

452

438

424

419

-15

-29

-34

S0105 REEDY ISLAND FLAT

644

1.2

266973

3

480

469

464

465

-16

-23

-20

SOUTH LAGOON

SOUTH S0093 YARRAMUNDI

S0094 YARRAMUNDI NORTH

267

Appendix

PO4-P Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0106 McBEAN POUND

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

645

0.65

42489

3

350

328

297

269

-11

-27

-41

642

0.65

121855

3

441

425

409

392

-14

-29

-44

641

0.92

20053

3

264

241

210

234

-8

-19

-10

640

0.92

513745

3

498

490

489

488

-16

-18

-20

1044

1.25 1760260

3

514

506

513

516

-20

-2

6

S0110 IRWIN FLAT

391

2

881564

3

507

500

502

509

-18

-12

4

S0111 MURBPOOK LAGOON

383

0.92

32620

3

321

298

266

278

-10

-24

-19

381

0.92

946764

3

508

501

503

506

-18

-12

-5

380

0.92

65777

3

393

373

348

346

-13

-29

-30

379

0.9

1147222

3

510

503

506

510

-19

-10

-1

SOUTH S0107 McBEAN POUND NORTH S0108 SINCLAIR FLAT

S0109 DONALD FLAT LAGOON

COMPLEX

S0112 MURBKO SOUTH

268

Appendix

PO4-P Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0113 MURBKO FLAT

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

375

0.7

75477

3

405

386

362

344

-13

-30

-42

374

0.7

1135665

3

510

503

506

508

-19

-10

-6

371

0.7

65887

3

393

373

348

329

-13

-29

-41

367

0.7

264111

3

479

468

463

455

-16

-23

-35

S0142 BOGGY FLAT

294

1.5

89373

3

438

423

396

443

-11

-31

3

S0149 BIG TOOLUNKA FLAT

324

2.3

848443

3

532

526

526

533

-13

-14

2

S0160 YARRA COMPLEX

262

2

1717745

3

539

534

538

542

-15

-2

6

S0174 LOCH LUNA and

1036

2

127894

3

492

475

456

534

-12

-27

32

190

2

6146303

3

589

581

593

597

-25

11

24

631

2

904144

3

574

567

568

577

-15

-12

5

COMPLEX

S0115 WOMBAT REST BACKWATER

NOCKBURRA CREEK S0174 LOCH LUNA and NOCKBURRA CREEK S0189 PYAP LAGOON

269

Appendix

PO4-P Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0201 AJAX ACHILLES LAKE

S0203 SALT CREEK AND

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

492

1.2

22764

3

283

259

225

419

-8

-20

48

486

1.2

262527

3

496

484

477

478

-16

-26

-25

471

1.5

78987

3

420

401

375

444

-13

-30

16

1048

0.92

17437

3

250

227

195

228

-7

-17

-7

1039

2

230689

3

490

478

469

493

-15

-27

3

1031

2

371340

3

508

498

493

507

-16

-24

-1

1007

0.92

227636

3

490

478

468

461

-15

-27

-36

1006

0.92

235651

3

491

479

471

463

-15

-27

-36

997

0.92

12402

3

169

154

129

145

-7

-18

-11

996

0.92

39096

3

263

248

224

219

-12

-32

-37

995

0.92

111272

3

321

311

298

290

-16

-36

-49

GURRA GURRA LAKES S0207 LYRUP CAUSEWAY WEST S0214 RUMPAGUNYAH CREEK

S0218 GOAT ISLAND AND PARINGA PADDOCK

S0219 PARINGA ISLAND

270

Appendix

PO4-P Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0219 PARINGA ISLAND

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

93

0.92

227075

3

343

336

329

324

-18

-33

-44

92

0.92

25097

3

229

213

186

188

-10

-28

-27

91

0.92

72376

3

301

289

271

263

-14

-36

-46

90

0.92

17157

3

197

181

155

161

-9

-23

-19

89

0.92

10223

3

153

138

115

137

-6

-16

-7

956

2

6785374

3

367

360

367

369

-37

4

14

S0227 HORSESHOE SWAMP

69

1.2

327432

3

350

343

340

339

-19

-30

-32

S0229 WOOLENOOK

978

1.2

2111925

3

365

359

364

366

-29

-3

7

84

1.2

29590

3

242

226

200

244

-11

-30

1

82

1.2

41520

3

267

253

229

253

-13

-33

-12

S0220 RAL RAL CREEK AND RAL RAL WIDEWATERS

BEND COMPLEX

271

Appendix

PO4-P Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0230 MURTHO PARK

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

67

0.92

31733

3

248

232

206

204

-11

-31

-32

61

0.92

24337

3

226

210

183

185

-10

-27

-26

60

0.92

50151

3

280

266

244

237

-13

-35

-41

47

0.92

250315

3

345

338

332

328

-18

-32

-44

32

1.25

90869

3

313

302

286

291

-15

-37

-30

1134

0.92

164860

3

364

358

363

364

-28

-6

2

Min

153

138

115

137

Max

589

581

593

597

Average 406

394

382

392

Median

438

423

396

419

Total

23140

22451

21795

22338

COMPLEX

S0242 SLANEY OXBOW XR001 Lock 6 Wetland

272

Appendix

Table 21: Change in NO3-N wetland loading and percentage outflow due to management; category 3 wetland scenarios NO3-N Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

S0070 CAURNAMONT

703

1.5

1353858 3

682

684

703

738

1

12

30

S0075 WALKER FLAT

690

0.8

710419

3

665

662

675

700

-2

5

17

S0076 LAKE BYWATERS

1107

0.8

310292

3

626

609

616

650

-7

-4

10

S0082 DEVON DOWNS

685

0.92

493457

3

651

643

654

687

-4

1

16

1102

2

195388

3

590

563

567

795

-10

-8

73

1101

2

617098

3

660

655

668

759

-3

3

47

663

2

704688

3

665

661

675

759

-2

5

46

S0103 ARLUNGA

651

0.9

1497057

3

709

705

728

753

-2

10

25

S0104 ROONKA

646

0.9

147172

3

583

537

547

640

-15

-12

19

S0105 REEDY ISLAND FLAT

644

1.2

266973

3

638

608

619

726

-12

-8

35

SOUTH LAGOON

SOUTH S0093 YARRAMUNDI

S0094 YARRAMUNDI NORTH

273

Appendix

NO3-N Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0106 McBEAN POUND

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

645

0.65

42489

3

405

333

342

349

-15

-13

-12

642

0.65

121855

3

561

510

520

531

-16

-12

-9

641

0.92

20053

3

282

215

227

617

-11

-9

56

640

0.92

513745

3

678

661

676

713

-8

-1

17

1044

1.25 1760260

3

712

709

732

762

-2

12

29

S0110 IRWIN FLAT

391

2

881564

3

697

688

706

779

-5

5

43

S0111 MURBPOOK LAGOON

383

0.92

32620

3

361

289

299

610

-14

-12

48

381

0.92

946764

3

699

691

709

740

-4

5

22

380

0.92

65777

3

475

409

423

619

-16

-13

35

379

0.9

1147222

3

704

698

717

746

-3

7

23

SOUTH S0107 McBEAN POUND NORTH S0108 SINCLAIR FLAT

S0109 DONALD FLAT LAGOON

COMPLEX

S0112 MURBKO SOUTH

274

Appendix

NO3-N Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0113 MURBKO FLAT

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

375

0.7

75477

3

496

433

446

495

-16

-13

0

374

0.7

1135665

3

704

697

716

739

-4

7

19

371

0.7

65887

3

475

410

423

477

-16

-13

0

367

0.7

264111

3

637

607

618

646

-12

-8

3

S0142 BOGGY FLAT

294

1.5

89373

3

516

466

462

803

-14

-15

79

S0149 BIG TOOLUNKA FLAT

324

2.3

848443

3

686

683

691

780

-2

2

49

S0160 YARRA COMPLEX

262

2

1717745

3

701

703

718

766

1

10

36

S0174 LOCH LUNA and

1036

2

127894

3

611

560

569

947

-13

-11

88

190

2

6146303

3

811

815

842

881

2

17

39

631

2

904144

3

777

770

790

880

-3

6

48

COMPLEX

S0115 WOMBAT REST BACKWATER

NOCKBURRA CREEK S0174 LOCH LUNA and NOCKBURRA CREEK S0189 PYAP LAGOON

275

Appendix

NO3-N Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0201 AJAX ACHILLES LAKE

S0203 SALT CREEK AND

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

492

1.2

22764

3

304

234

242

805

-12

-11

85

486

1.2

262527

3

645

614

619

734

-13

-10

36

471

1.5

78987

3

508

445

451

831

-16

-15

84

1048

0.92

17437

3

263

196

205

650

-11

-9

62

1039

2

230689

3

634

600

604

820

-13

-12

72

1031

2

371340

3

669

646

654

804

-10

-7

61

1007

0.92

227636

3

633

599

603

668

-13

-12

14

1006

0.92

235651

3

636

602

607

671

-13

-11

14

997

0.92

12402

3

183

136

140

402

-12

-11

57

996

0.92

39096

3

308

258

259

394

-19

-18

33

995

0.92

111272

3

399

366

364

419

-19

-20

12

GURRA GURRA LAKES S0207 LYRUP CAUSEWAY WEST S0214 RUMPAGUNYAH CREEK

S0218 GOAT ISLAND AND PARINGA PADDOCK

S0219 PARINGA ISLAND

276

Appendix

NO3-N Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0219 PARINGA ISLAND

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

93

0.92

227075

3

436

416

416

449

-15

-15

9

92

0.92

25097

3

259

208

207

390

-17

-17

42

91

0.92

72376

3

366

326

324

405

-20

-21

19

90

0.92

17157

3

217

167

169

385

-14

-14

48

89

0.92

10223

3

164

121

124

430

-11

-10

65

956

2

6785374

3

480

477

489

514

-3

9

38

S0227 HORSESHOE SWAMP

69

1.2

327432

3

449

433

436

477

-13

-11

23

S0229 WOOLENOOK

978

1.2

2111925

3

476

472

483

504

-5

8

30

84

1.2

29590

3

278

226

225

519

-17

-18

82

82

1.2

41520

3

314

265

266

506

-19

-19

75

S0220 RAL RAL CREEK AND RAL RAL WIDEWATERS

BEND COMPLEX

277

Appendix

NO3-N Net Loading to wetland kg/annum Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0230 MURTHO PARK

50%

75%

% Reduction in Outflow 25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

67

0.92

31733

3

285

234

233

392

-18

-18

38

61

0.92

24337

3

256

204

204

389

-16

-16

42

60

0.92

50151

3

333

286

286

397

-19

-20

27

47

0.92

250315

3

440

421

422

453

-15

-14

10

32

1.25

90869

3

384

348

345

481

-19

-21

52

1134

0.92

164860

3

475

470

481

499

-5

7

25

Min

164

121

124

349

Max

811

815

842

947

Average 513

481

490

622

Median

516

477

489

650

Total

29254

27445

27935

35477

COMPLEX

S0242 SLANEY OXBOW XR001 Lock 6 Wetland

278

Appendix

Table 22: Change in Phytoplankton wetland loading and percentage outflow due to management; category 3 wetland scenarios % Reduction in Outflow

Phytoplankton Loading to wetland m3/annum

Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

50%

75%

25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

S0070 CAURNAMONT

703

1.5

1353858 3

-8

-9

-12

-10

-2

-19

-10

S0075 WALKER FLAT

690

0.8

710419

3

-8

-9

-10

-12

-3

-11

-20

S0076 LAKE BYWATERS

1107

0.8

310292

3

-7

-8

-9

-11

-3

-11

-21

S0082 DEVON DOWNS

685

0.92

493457

3

-8

-8

-10

-12

-3

-11

-22

1102

2

195388

3

-7

-7

-8

-8

-3

-11

-11

1101

2

617098

3

-8

-8

-10

-3

-3

-11

27

663

2

704688

3

-8

-9

-10

-6

-3

-11

13

S0103 ARLUNGA

651

0.9

1497057

3

-8

-8

-11

-11

-1

-20

-20

S0104 ROONKA

646

0.9

147172

3

-6

-6

-8

-6

-3

-12

-3

S0105 REEDY ISLAND FLAT

644

1.2

266973

3

-7

-7

-8

-7

-3

-11

-1

SOUTH LAGOON

SOUTH S0093 YARRAMUNDI

S0094 YARRAMUNDI NORTH

279

Appendix

% Reduction in Outflow

Phytoplankton Loading to wetland m3/annum

Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0106 McBEAN POUND

50%

75%

25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

645

0.65

42489

3

-4

-4

-4

-5

-2

-4

-8

642

0.65

121855

3

-5

-6

-7

-8

-3

-12

-18

641

0.92

20053

3

-2

-2

-3

0

-1

-3

17

640

0.92

513745

3

-7

-8

-9

-11

-3

-11

-23

1044

1.25 1760260

3

-8

-8

-11

-12

-1

-19

-25

S0110 IRWIN FLAT

391

2

881564

3

-8

-8

-9

-6

-2

-11

7

S0111 MURBPOOK LAGOON

383

0.92

32620

3

-3

-3

-4

-1

-1

-4

17

381

0.92

946764

3

-8

-8

-9

-11

-2

-11

-21

380

0.92

65777

3

-4

-5

-6

-7

-2

-14

-18

379

0.9

1147222

3

-8

-8

-10

-11

-2

-11

-21

SOUTH S0107 McBEAN POUND NORTH S0108 SINCLAIR FLAT

S0109 DONALD FLAT LAGOON

COMPLEX

S0112 MURBKO SOUTH

280

Appendix

% Reduction in Outflow

Phytoplankton Loading to wetland m3/annum

Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0113 MURBKO FLAT

50%

75%

25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

375

0.7

75477

3

-5

-5

-7

-7

-2

-13

-16

374

0.7

1135665

3

-8

-8

-9

-11

-2

-11

-18

371

0.7

65887

3

-4

-5

-6

-6

-2

-14

-13

367

0.7

264111

3

-7

-7

-8

-10

-3

-11

-20

S0142 BOGGY FLAT

294

1.5

89373

3

-5

-5

-7

-10

-3

-12

-35

S0149 BIG TOOLUNKA FLAT

324

2.3

848443

3

-8

-8

-10

-8

-3

-11

-3

S0160 YARRA COMPLEX

262

2

1717745

3

-8

-9

-12

-12

-2

-18

-18

S0174 LOCH LUNA and

1036

2

127894

3

-6

-7

-8

-10

-2

-10

-26

190

2

6146303

3

-9

-9

-12

-12

3

-12

-13

631

2

904144

3

-9

-9

-11

-6

-2

-10

15

COMPLEX

S0115 WOMBAT REST BACKWATER

NOCKBURRA CREEK S0174 LOCH LUNA and NOCKBURRA CREEK S0189 PYAP LAGOON

281

Appendix

% Reduction in Outflow

Phytoplankton Loading to wetland m3/annum

Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0201 AJAX ACHILLES LAKE

S0203 SALT CREEK AND

50%

75%

25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

492

1.2

22764

3

-2

-2

-3

0

-1

-3

18

486

1.2

262527

3

-6

-7

-8

-6

-3

-11

3

471

1.5

78987

3

-5

-5

-6

-11

-3

-12

-42

1048

0.92

17437

3

-2

-2

-2

0

-1

-2

16

1039

2

230689

3

-6

-7

-8

-7

-3

-11

-3

1031

2

371340

3

-7

-7

-9

-4

-3

-11

16

1007

0.92

227636

3

-6

-7

-8

-7

-3

-11

-3

1006

0.92

235651

3

-6

-7

-8

-7

-3

-11

-3

997

0.92

12402

3

-1

-1

-1

0

-1

-3

17

996

0.92

39096

3

-2

-2

-3

-4

-2

-14

-19

995

0.92

111272

3

-3

-4

-4

-3

-3

-12

-1

GURRA GURRA LAKES S0207 LYRUP CAUSEWAY WEST S0214 RUMPAGUNYAH CREEK

S0218 GOAT ISLAND AND PARINGA PADDOCK

S0219 PARINGA ISLAND

282

Appendix

% Reduction in Outflow

Phytoplankton Loading to wetland m3/annum

Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0219 PARINGA ISLAND

50%

75%

25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

93

0.92

227075

3

-4

-4

-5

-5

-3

-11

-15

92

0.92

25097

3

-2

-2

-2

-1

-2

-4

16

91

0.92

72376

3

-3

-3

-4

-3

-3

-12

-4

90

0.92

17157

3

-1

-2

-2

0

-1

-3

16

89

0.92

10223

3

-1

-1

-1

0

-1

-2

15

956

2

6785374

3

-4

-4

-6

-8

6

-13

-33

S0227 HORSESHOE SWAMP

69

1.2

327432

3

-4

-4

-5

-4

-3

-11

-4

S0229 WOOLENOOK

978

1.2

2111925

3

-4

-4

-6

-7

0

-15

-22

84

1.2

29590

3

-2

-2

-2

0

-2

-4

26

82

1.2

41520

3

-2

-2

-3

-6

-2

-14

-39

S0220 RAL RAL CREEK AND RAL RAL WIDEWATERS

BEND COMPLEX

283

Appendix

% Reduction in Outflow

Phytoplankton Loading to wetland m3/annum

Aus

Wetland

wetland name

Wetlands Used Volume Category Status Quo 25% id

depth m3

#

managed Turbidity

m

S0230 MURTHO PARK

50%

75%

25%

Turbidity Turbidity Turbidity Turbidity

50%

75%

Turbidity

Turbidity

Reduction Reduction Reduction Reduction Reduction Reduction

67

0.92

31733

3

-2

-2

-2

-1

-2

-5

17

61

0.92

24337

3

-2

-2

-2

-1

-2

-4

16

60

0.92

50151

3

-3

-3

-4

-3

-2

-13

-11

47

0.92

250315

3

-4

-4

-5

-6

-3

-11

-23

32

1.25

90869

3

-3

-3

-4

-4

-3

-12

-8

1134

0.92

164860

3

-4

-4

-6

-6

0

-17

-18

Min

-9

-9

-12

-12

Max

-1

-1

-1

0

Average -5

-5

-7

-6

Median

-5

-5

-7

-6

Total

-293

-309

-380

-354

COMPLEX

S0242 SLANEY OXBOW XR001 Lock 6 Wetland

284

Appendix

Table 23: PO4-P comparison between Full year wet versus Summer wet Winter dry for three selected wetlands; category 3 wetland scenarios Net Loading to wetland kg/annum Aus Wetland wetland name

Wetlands Used id depth

Volume 3 m

% Reduction in Outflow

Status 25% 50% 75% 25% 50% 75% Quo Turbidity Turbidity Turbidity Turbidity Turbidity Turbidity Turbidity Reduction Reduction Reduction Reduction Reduction Reduction

# Full Year Wet S0174

LOCH LUNA and NOCKBURRA CREEK

190

2

6146303

589

581

593

597

-25

11

24

S0219

PARINGA ISLAND

93

0.92

227075

343

336

329

324

-18

-33

-44

XR001

Lock 6 Wetland

1134

0.92

164860

364

358

363

364

-28

-6

2

1296

1274

1285

1286

Sum Full Year Wet

Summer Wet; Winter Dry S0174

LOCH LUNA and NOCKBURRA CREEK

190

2

6146303

149

143

151

154

-48

15

36

S0219

PARINGA ISLAND

93

0.92

227075

71

68

70

72

-26

-5

8

XR001

Lock 6 Wetland

1134

0.92

164860

77

73

77

79

-48

4

24

Summer Wet Only

297

284

298

304

Less Loading to wetland if Summer Wet Only

999

991

986

982

The load to the wetland, for the full year wet scenario, is calculated from the average retention in the scenario time period multiplied by 365. The load to the wetland, for the summer wet winter dry management scenario, is calculated as a sum from the 88 days simulated in the model to be the peak macrophyte growth period.

285

Appendix

Table 24: NO3-N comparison between Full year wet versus Summer wet Winter dry for three selected wetlands; category 3 wetland scenarios Net Loading to wetland kg/annum Aus Wetland wetland name

Wetlands Used id depth

Volume 3 m

% Reduction in Outflow

Status 25% 50% 75% 25% 50% 75% Quo Turbidity Turbidity Turbidity Turbidity Turbidity Turbidity Turbidity Reduction Reduction Reduction Reduction Reduction Reduction

# Full Year Wet S0174

LOCH LUNA and NOCKBURRA CREEK

190

2

6146303

811

815

842

881

2

17

39

S0219

PARINGA ISLAND

93

0.92

227075

436

416

416

449

-15

-15

9

XR001

Lock 6 Wetland

1134

0.92

164860

475

470

481

499

-5

7

25

1722

1700

1740

1830

Sum Full Year Wet

Summer Wet; Winter Dry S0174

LOCH LUNA and NOCKBURRA CREEK

190

2

6146303

374

375

393

417

1

30

71

S0219

PARINGA ISLAND

93

0.92

227075

182

173

186

206

-16

8

49

XR001

Lock 6 Wetland

1134

0.92

164860

200

198

208

217

-7

26

54

Summer Wet Only

756

746

787

841

Less Loading to wetland if Summer Wet Only

966

954

953

989

The load to the wetland, for the full year wet scenario, is calculated from the average retention in the scenario time period multiplied by 365. The load to the wetland, for the summer wet winter dry management scenario, is calculated as a sum from the 88 days simulated in the model to be the peak macrophyte growth period.

286

Appendix

Table 25: Phytoplankton comparison between Full year wet versus Summer wet Winter dry for three selected wetlands; category 3 wetland scenarios Net Loading to wetland kg/annum Aus Wetland wetland name

Wetlands Used id depth

Volume 3 m

% Reduction in Outflow

Status 25% 50% 75% 25% 50% 75% Quo Turbidity Turbidity Turbidity Turbidity Turbidity Turbidity Turbidity Reduction Reduction Reduction Reduction Reduction Reduction

# Full Year Wet S0174

LOCH LUNA and NOCKBURRA CREEK

190

2

6146303

-9.23

-8.57

-11.63

-11.81

3

-12

-13

S0219

PARINGA ISLAND

93

0.92

227075

-3.79

-4.10

-4.87

-5.30

-3

-11

-15

XR001

Lock 6 Wetland

1134

0.92

164860

-4.40

-4.41

-6.13

-6.29

0

-17

-18

-17

-17

-23

-23

Sum Full Year Wet

Summer Wet; Winter Dry S0174

LOCH LUNA and NOCKBURRA CREEK

190

2

6146303

-2.34

-1.48

-2.71

-1.99

17

-7

7

S0219

PARINGA ISLAND

93

0.92

227075

-0.87

-0.85

-1.04

-1.01

1

-7

-6

XR001

Lock 6 Wetland

1134

0.92

164860

-0.95

-0.69

-1.43

-1.16

10

-20

-9

Summer Wet Only

-4

-3

-5

-4

Less Loading to wetland if Summer Wet Only

13

14

17

19

The load to the wetland, for the full year wet scenario, is calculated from the average retention in the scenario time period multiplied by 365. The load to the wetland, for the summer wet winter dry management scenario, is calculated as a sum from the 88 days simulated in the model to be the peak macrophyte growth period.

287

Appendix

Table 26: Change in PO4-P wetland loading and percentage in and outflow due to management; category 4 wetland scenarios PO4-P Net Loading to Wetland kg/annum Aus Wetland #

Wetland name

Used depth m

Volume 3 m

S0035

TAILEM BEND

0.8

765545

S0052

REEDY CREEK

0.8

S0148

LITTLE TOOLUNKA FLAT

S0151

Wetland Category

% Reduction in Inflow

% Reduction in Outflow

Status Quo Irrigation Drainage Nutrient

25% Irrigation Drainage Nutrient Reduction

50% Irrigation Drainage Nutrient Reduction

75% Irrigation Drainage Nutrient Reduction

25% Irrigation Drainage Nutrient Reduction

50% Irrigation Drainage Nutrient Reduction

75% Irrigation Drainage Nutrient Reduction

25% Irrigation Drainage Nutrient Reduction

50% Irrigation Drainage Nutrient Reduction

75% Irrigation Drainage Nutrient Reduction

4

7171

7487

7798

8104

0.0070

0.0140

0.0210

6.00

11.90

17.69

591799

4

21778

22101

22518

22829

0.0014

0.0027

0.0041

1.23

2.81

3.99

1.4

739622

4

5088

5454

5727

6218

0.0089

0.0177

0.0266

7.67

13.42

23.70

RAMCO LAGOON

0.3

279446

4

4974

5015

5557

5861

0.0089

0.0177

0.0266

0.86

11.95

18.18

S0179

KINGSTON COMMON

0.92

340410

4

5126

5665

5723

6273

0.0082

0.0165

0.0247

9.88

10.96

21.03

S0180

WACHTELS LAGOON

0.92

6259251

4

9140

9200

9259

9317

0.0082

0.0165

0.0247

4.13

8.26

12.37

S0185

YATCO LAGOON

0.5

1729378

4

7585

7798

7973

8110

0.0082

0.0165

0.0247

7.12

12.97

17.54

Min

4974

5015

5557

5861

Max

21778

22101

22518

22829

Average

8694

8960

9222

9530

Median

7171

7487

7798

8104

Total

60861

62720

64555

66712

288

Appendix

Table 27: Change in NO3-N wetland loading and percentage in and outflow due to management; category 4 wetland scenarios NO3-N Net Loading to Wetland kg/annum Aus Wetland #

Wetland name

Used depth m

Volume 3 m

S0035

TAILEM BEND

0.8

765545

S0052

REEDY CREEK

0.8

S0148

LITTLE TOOLUNKA FLAT

S0151

Wetland Category

% Reduction in Inflow

% Reduction in Outflow

Status Quo Irrigation Drainage Nutrient

25% Irrigation Drainage Nutrient Reduction

50% Irrigation Drainage Nutrient Reduction

75% Irrigation Drainage Nutrient Reduction

25% Irrigation Drainage Nutrient Reduction

50% Irrigation Drainage Nutrient Reduction

75% Irrigation Drainage Nutrient Reduction

25% Irrigation Drainage Nutrient Reduction

50% Irrigation Drainage Nutrient Reduction

75% Irrigation Drainage Nutrient Reduction

4

19038

19102

19166

19230

0.0003

0.0006

0.0009

0.42

0.84

1.26

591799

4

3965

4019

4074

4128

0.0007

0.0014

0.0021

0.69

1.38

2.07

1.4

739622

4

1782

1837

1891

1945

0.0009

0.0017

0.0026

0.51

1.02

1.52

RAMCO LAGOON

0.3

279446

4

8078

8180

8178

8125

0.0009

0.0017

0.0026

2.31

2.28

1.06

S0179

KINGSTON COMMON

0.92

340410

4

7484

8196

6421

7977

0.0008

0.0016

0.0024

12.28

S0180

WACHTELS LAGOON

0.92

6259251

4

5493

5507

5521

5536

0.0008

0.0016

0.0024

0.19

0.37

0.56

S0185

YATCO LAGOON

0.5

1729378

4

3160

3195

3230

3265

0.0008

0.0016

0.0024

0.35

0.70

1.04

Min

1782

1837

1891

1945

Max

19038

19102

19166

19230

Average

7000

7148

6926

7172

Median

5493

5507

5521

5536

Total

49000

50036

48481

50204

-18.31

8.51

289

Appendix

Table 28: Change in Phytoplankton wetland loading and percentage in and outflow due to management; category 4 wetland scenarios Phytoplankton Net Loading to Wetland m3/annum Aus Wetland #

Wetland name

Used depth m

Volume 3 m

S0035

TAILEM BEND

0.8

765545

S0052

REEDY CREEK

0.8

S0148

LITTLE TOOLUNKA FLAT

S0151

Wetland Category

% Reduction in Inflow

% Reduction in Outflow

Status Quo Irrigation Drainage Nutrient

25% Irrigation Drainage Nutrient Reduction

50% Irrigation Drainage Nutrient Reduction

75% Irrigation Drainage Nutrient Reduction

25% Irrigation Drainage Nutrient Reduction

50% Irrigation Drainage Nutrient Reduction

75% Irrigation Drainage Nutrient Reduction

25% Irrigation Drainage Nutrient Reduction

50% Irrigation Drainage Nutrient Reduction

75% Irrigation Drainage Nutrient Reduction

4

31

47

63

80

0.0014

0.0029

0.0043

4.13

8.24

12.37

591799

4

33

49

65

81

0.0011

0.0022

0.0032

4.08

8.16

12.24

1.4

739622

4

33

50

67

83

0.0014

0.0028

0.0042

4.10

8.20

12.28

RAMCO LAGOON

0.3

279446

4

87

115

139

159

0.0014

0.0028

0.0042

7.92

14.61

20.12

S0179

KINGSTON COMMON

0.92

340410

4

77

100

123

146

0.0014

0.0027

0.0041

6.04

12.10

18.14

S0180

WACHTELS LAGOON

0.92

6259251

4

136

140

143

146

0.0014

0.0027

0.0041

1.05

2.10

3.16

S0185

YATCO LAGOON

0.5

1729378

4

91

101

110

120

0.0014

0.0027

0.0041

2.63

5.26

7.91

Min

31

47

63

80

Max

136

140

143

159

Average

70

86

101

116

Median

77

100

110

120

Total

487

601

710

815

290

Appendix

Appendix E: WETMOD 2 Code See attached CD.

291