Journal-format_cabundocanignaciotesnado..docx

Impact of Climate Change on the Frequency and Severity of Floods in the Pasig Marikina River Basin Cris Edward F. Monjardin1*, Clarence L. Cabundocan1, Camille P. Ignacio1, Christian Jedd D. Tesnado1 1

School of Civil, Environmental, and Geological Engineering, Mapua University, Muralla St.,Intramuros, Manila, 1002 Philippines *Corresponding author E-mail: [email protected] Abstract This study was carried out to assess the impacts of climate change on the frequency and severity of floods in the Pasig-Marikina River basin. This study used the historical data from PAG-ASA, specifically from Science Garden weather station. The historical data are coupled with a global climate model, the Hadley Center Model version 3 (HadCM3) to account for the natural variability of the climate system in the area. The observed data and the hydroclimatic data from HadCM3 is inputted in Statistical Downscaling Model (SDSM) that results to rainfall data from 1961-2017 and change in temperature data from 2018-2048. A rainfall time series for the river basin was generated taking into account the average seasonal effects in the area. A flood frequency curve was modelled. From that, flood value for 2048 is derived to be at 3950cu.m/s. Additionally, the rapid urbanization in the area has contributed to the changes in the river system making it more vulnerable to floods. The results of this study support the claim that the Pasig-Marikina River basin will definitely be affected by the climate variability in terms of the increase in rainfall depth and average temperatures, higher flood frequency and more massive floods. Keywords: rainfall, flood, GIS, HadCM3, SDSM

1. Introduction The strategic location of the Philippines, lying beyond the western boundary of the massive Pacific Ocean, is the main cause why the country has long been subjected and exposed to extreme weather conditions. However, over the past few years, the country had experienced most of the world’s strongest and destructive typhoons. Study shows that man-made greenhouse gas emissions are the major attributions of climate change. Climate change, enhanced by global warming, induces the rising of sea surface and subsurface temperatures whilst contributes in producing stronger typhoons. These stronger typhoons carry more moisture which may also mean more precipitation. Wherein the highest recorded rainfall in Metro Manila was triggered by Typhoon Ketsana or locally known as Tropical Storm Ondoy. According to PAGASA, the rainfall produced by the typhoon itself amounted to 455 millimetres in 24 hrs. The soil can only absorb a maximum of 200 mm of rainfall, and the recordbreaking amount of rainfall produced by Ondoy caused extreme flooding in Metro Manila. Among the affected areas by the flooding caused by Tropical Storm Ondoy, Marikina City was mostly devastated. The whole city was almost submerged in flood water which went as high as 10 feet deep. The Marikina River overflowed transforming its streets to rivers. This traumatic consequence of floods has called for the growing attention because of the need to prevent or control flood damages in our society. Developing countries have identified various adaptation policies, most of which focus on direct and tangible impacts. However, climate change impacts are not limited to tangible damages: the drastic changes they bring also have enormous

influence on people’s daily lives in affected communities and economic activities in affected areas. In this context, Marikina River Basin was chosen to comprehensively simulate the impacts of future climate change and identify necessary actions. The study area is located east of Metropolitan Manila. This basin is the source of flood waters that inundates low lying areas along the Pasig-Marikina River and Mangahan Floodway. The rivers that overflowed and resulted to exceptionally high and extensive flooding during the TS Ketsana event are the rivers that drain the basin. Studying the future impacts of climate change on the severity of flooding in rainfall in this basin may prevent or lessen future damages caused by flooding in the study area.

Figure 1: Study Area

Source: Development, calibration and validation of a flood model for Marikina River Basin, Philippines and its applications for flood forecasting, reconstruction, and hazard mapping The main objective of the study is to determine the impact of climate change on the frequency, and severity of floods in the Marikina River Basin. In line with the main objective, the specific objectives are (1) to determine the rainfall pattern in Marikina River Basin, (2) to gather historical records of the water level in Marikina River Basin, (3) to assess the changes in land cover in the area of Marikina River Basin affected by the flooding, and (4) to predict future temperature and precipitation trends. The results and findings of this study can be of interest to several concerned sectors, especially to the local government unit of Marikina. Future government policies and long-term development plans of the city can be incorporated on the potential impacts of climate change. Since there are restrictions in the access to general circulation model (GCM) data, technical resources and technical expertise, climate projections are limited. This study will prove to be beneficial for future researches on the impacts of climate change in the Philippine setting. This study covers the assessment of the impacts of climate change in terms of precipitation and run-off in the Pasig-Marikina river basin. The analysis includes gathering of hydro climatological data from agencies, such as PAGASA and DRRM Marikina, collection and downscaling of GCM data through available software, identifying the climate change scenario of the river basin, and simulation of rainfallrunoff.

2. Review of Related Literature This chapter covers related literature and studies both from international and local research studies. This chapter includes the hydrologic cycle, the study of climate and how it is affected by anthropogenic forces, general circulation models, downscaling, uncertainties, hydrological model, and relating climate change to floods. These will be used by the researchers as basis in the entire course of the study. Climate Climate is described as the average condition of the atmosphere, land surfaces, bodies of water, and the ecosystems that are in them. According to a study conducted by Neelin in 2011, climate is not limited to what stated above but includes the direction and strength of the wind, average cloud cover, and the currents in the ocean that have effects on the temperature of the sea surface. According to a study conducted by Badilla in 2008, our study area, the PasigMarikina River Basin, has a type I climate. With that, the annual rainfall in the basin ranges from 1700 to 3200 millimeters per year, where 80 percent of this precipitation occurs during the wet season. Climate change According to a journal called The Climate Reality Project of 2016, the Philippines is listed as the number one country that suffers the effects of climate change. The reason for this is its strategic location lying at the western Pacific ocean, surrounded by warm waters that will most likely get even warmer as an adverse effect of climate change,

In the study of Badilla in 2008, it was mentioned that the Pasig-Marikina River basin is the source of flood waters as it overflow in the low-lying areas. Given that the Philippines is the number one country affected by climate change in historical data, floods in the areas around the basin is worried to worsend. Floods Flooding takes place in regarded floodplains when extended rainfall over numerous days, severe rainfall over a brief time frame, or an ice or debris jam causes a river or flow to overflow and flood the encompassing vicinity. Extreme thunderstorms can deliver heavy precipitation or typhoons can carry excessive rainfall over the coastal and inland states within the summer time and fall. Currently, fields or forests are being developed and transformed into paved roads, parking lots, or even establishments. The land, being covered by concrete pavements, loses its ability to absorb the runoff. Urbanization significantly caused the increase of runoff from two to six times. At times of flooding in the urban, streets are transformed into rivers, while basements and underpass becomes impassable due to the runoff. Several factors cause flooding. Significant factors include rainfall intensity and period. Intensity is the amount of rainfall over a period of time, and period is how lengthy the rain lasts. Topography, soil situations, and ground cover also are of significant consideration. According to a journal, most of the flash floods are caused by thunderstorms that are slow-shifting and those that occur over the same vicinity or heavy rains from hurricanes and tropical storms. Floods, on the other hand, may be gradual- or rapid-rising, but normally increase over duration of hours or days.

General Circulation models According to a study, 97% of domestic water being supplied in the Metro comes from the Angat-Umiray System (Jaranilla-Sanchez, et al, 2012). A study was made to critically assess the evolution of the water sources' system. In this study, spatial correlation and relative error focusing were used in selecting the six GCM models from CMIP3. In determining the effects of climate change on sources of water, the researchers of the said study compared past and future discharge simulations. Results shows that flood will definitely increase in the future, this was based from the processed 6 GCM models. However, base flow will slightly decrease in the future based on 4 analysed models. Lastly, SA drought index on discharge quantified the hydrological drought in the years 20, 50, 100, and 20 Hydrological model The hydrological model, SLURP, explains the complete hydrological cycle for each land cover within a series of sub-basins which includes all dams, reservoirs, regulators, and irrigation schemes in the basin (Kite, 2001). The advantages of using this model are that it obtains results for each day of an indefinite period, and it is utilized to simulate alternate scenarios. Another researcher applied a hydrological model to the Brosna catchment to simulate a runoff under four recommended climate scenarios for the year 2030 (Cunnane and Regan, 1994). The outcomes indicated that the magnitude of high and low flows would be marginally greater than those within the range presently

experienced therefore, the frequency of flood and drought events would increase within the catchment. Uncertainties in Climate models According to a journal written by Thomas M. Smith, today’s climate models are based on the climate history of Earth over 150 years which may also include uncertainties from the observation. In evaluating the results of the models with developments in sea floor temperature in numerous ocean basins, they estimate the uncertainty in model output came from the existing drastic variability of the climate system from the unfolding of three separate simulations of a single climate model "compelled with the identical greenhouse gases and sulfate aerosols" but initiated with unique situations. In conclusion, the variety in modelled temperature traits coming from the nonlinear dynamics of the climate system is small relative to the uncertainty in observations. However, because the version only used one method, the conclusions have overlooked the main source of the uncertainty in model simulations. 

Software implementation using a map viewer linked to a spatial database allowing the flexible selection of areas for generation of series.

River Discharge Projection River discharge projection is necessary to manage water-related disasters caused by climate change, such as floods, droughts, and water scarcity[ CITATION HUN12 \l 1033 ]. Hydrologic and flow routing models are used in transferring the climate model outputs into river discharge. With these river discharge information, it can assess future changes in water resources, flood discharge, droughts, and possible future hotspots on water-related disasters can be identified.

The results of the river discharge projections are the following: (1) clear changes in hourly flood peak discharge, daily drought discharge, and monthly discharge were detected; (2) for every discharge, the degree of change differed by location; (3) the changes appeared in the neared future climate experiment and became clearer in the future climate experiment; and (4) a significant decrease in discharge detected.

Hadley Center Coupled Model version 3 (HadCM3) HadCM3 has a good simulation of present climate without using varying adjustments which is a major advance compared to other models[ CITATION Rei08 \l 1033 ]. It is capable to take the time-dependent characteristic of historical climate change to help natural and anthropogenic forcing, which is useful in studies concerning the detection and attribution of past climate changes [ CITATION Sto00 \l 1033 ]. Statistical Downscaling Model (SDSM) Statistical downscaling model (SDSM) is utilized to make high-resolution climate data from coarse-resolution climate model (GCM) simulations. It also features weather generator methods to make various understanding of synthetic weather series. [ CITATION Gov17 \l 1033 ] ArcGIS ArcGIS, a geographic information system (GIS), is used to (1) create and use maps, (2) organize geographic data, (3) studying mapped information and geographic

information, and (4) handling geographic information in a database. [ CITATION ESR \l 1033 ] 3. Methodology 3.1 Research Design The researchers will adopt a quantitative method to analyze the data of rainfall and runoff. Simulation approach will be implemented to understand and visualize the impact of climate change in the area of study. 3.2 Data Collection The researchers will gather the necessary historical data of rainfall, water level from PAG-ASA, DRRM Council, and the Marikina Local Government Unit. This will be the initial step for this study. 3.3 Basin delineation A 90-m spatial resolution Shuttle Radar Topography Mission – Digital Elevation Model (SRTM-DEM), available online at www.philgis.org, was analysed through ArcGIS 10.6.1 to delineate the basin and determine the study area. ArcGIS’s ArcHydro tools were the primary instruments used to process the DEM. The process includes Terrain pre-processing which then includes, DEM manipulation, filling sinks, flow direction, flow accumulation, stream definition, stream segmentation, catchment grid delineation, catchment polygon processing, drainage line processing, drainage point processing, batch point generation, and after that, the final process called watershed processing then generates a sub watershed through the command batch sub watershed delineation.

Figure 4: Study Area: Marikina River Basin Generated By ArcGIS 10.6.1 The process includes manual selection of a point of interest which is based on the outlet generated by the application. The point was also based on a real map containing the study area. The nearest point or outlet selected in this process was the Manggahan Floodway which is also the lower boundary of the said basin. 3.4 Observed Data Collection To better understand how climate may change in the future for the area of concern, long-term observational records must be utilized. In the acquisition of data for rainfall and temperature, the researchers used the data from PAG-ASA-DOST. This agency collects date in respect of the atmospheric differences of the area on dayto-day basis. Precipitation, maximum temperature, and minimum temperature data for the years 1961-2017 were collected. These data were obtained from Science Garden in Quezon City since it is the closest weather station to the area of study. Since, the climate in the Philippines is diverse due to differences in land and sea level temperatures; these temperatures must be recorded on long term basis at various climatic zones of the country.

3.5 GCM Data Collection The GCM used was the UK Meteorological Office, HadCM3 forced by combined CO2 and albedo changes. For this study, the model run starts in 1961 and is forced with an estimate of historical forcing to 2017 and a projected future forcing scenario over 2018 – 2048 (30 years). This forcing is only an estimation of the ‘real’ forcing. For the climate variability to be considered in this study, the researchers used a tool, named Statistical Downscaling Model (SDSM 5.2). HadCM3 data were also coupled in the downscaling technique. In the figure below, the global map has been divided into seven smaller windows, with each window surrounding a main land area, with the land-sea boundaries defined according to the HadCM3 land-sea mask. The predictor variables are supplied on a grid box by grid box basis. The researchers chose the region closes to the area of study. Three directories were assembled.

Figure 5: The global window where the researchers collected the HadCM3 data. • “NCEP_1961-2001: This directory contains 41 years of daily observed predictor data, derived from the NCEP reanalyzes, normalized over the complete 1961-1990 period. These data were interpolated to the same grid as HadCM3 (2.5 latitude x 3.75 longitude) before the normalization was implemented.” • “H3A2a_1961-2099: This directory contains 139 years of daily GCM predictor data, derived from the HadCM3 A2(a) experiment, normalized over the 1961-1990 period.”

• “H3B2a_1961-2099: This directory contains 139 years of daily GCM predictor data, derived from the HadCM3 B2(a) experiment, normalized over the 1961-1990 period.” The predictor utilized was under SRES scenarios. A2 climate change scenario was adopted for this study since it is the only available option and closely resembles that of the real scenario. The A2 scenario corresponds to a very heterogeneous world with high population growth, and less concern for rapid economic development. [CITATION Int \l 1033 ] 3.6 Statistical Downscaling Model (SDSM) SDSM is used to calculate statistical relationships based on multiple linear regression techniques between the predictors and the predictand. Using the observed atmospheric data, these relationships are developed. [CITATION Wil \l 1033 ]. Prior to data analysis, quality control for the data has been done to check whether there are missing values and to show the maximum and minimum values and other several information. Likewise, screening of variables is completed to check and filter the data that the researchers will be using for analysis. The historical rainfall data is chosen as the predictand for this stage. The start date and end date are 01/01/1961 and 12/31/2001, using the NCEP as basis. There are 26 predictor variables from the directory of NCEP 1961-2001 but due to some restriction in SDSM, only 12 variables at a time can be run. As shown in the figure below, the highest value of correlation for every variable is shown in red. These data show the effect of each variable on the data for each month.

Meanwhile, the Correlation matrix shows the correlation of the variables with the observed data. The partial-r and P-value are used for choosing a parameter for this study. The researchers will have to choose a parameter that has the highest correlation. The next step is to create a scatter plot for each parameter. The scatter plot will help the researchers to inspect each predictor visually based on their inter-variable behavior for specified season. For this research, the researchers select relative humidity at 500 hPa (ncepr500as.dat), relative humidity at 850 hPa (ncepr850as.dat), and mean temperature at 2m (nceptempas.dat) as the predictor variables. For the model calibration, the three predictors were utilized. There are twelve rows correspond to the twelve months in a year from January to December. The first column is the intercepts while the second to fourth columns are comprised of the three parameters used and the last two columns are for the standard error and R-squared statistics. The next stage is to create a weather generator. The weather generator produces groups of artificial weather series daily given the observed climatic data/variables and the results of the model calibration. Table 1: Summary of Downscaled Annual Average Rainfall from 1961-2001 Year 1961 1962 1963 1964 1965 1966 1967 1968

Rainfa ll 6.8 5.8 6.3 6.2 5.8 6.3 6.4 5.8

Year 1969 1970 1971 1972 1973 1974 1975 1976

Rainfa ll 6.5 6.3 5.9 7.3 6.3 6.3 6.9 6.6

Year 1977 1978 1979 1980 1981 1982 1983 1984

Rainfa ll 6.3 7.1 6.7 6.6 7.1 6.7 7.6 6.6

Year 1985 1986 1987 1988 1989 1990 1991 1992

Rainfa ll 6.6 6.7 6.8 6.9 6.3 6.8 6.9 6.5

Year 1993 1994 1995 1996 1997 1998 1999 2000 2001

Rainfa ll 6.6 7.1 6.8 6.5 6.7 7.2 7.1 7.2 7.2

3.7 Change in Temperature data Raw dataset for temperature from 1961-2017 were collected from the available sources such as PAG-ASA. Meanwhile, projected change in temperature from 1961-2099 were quantified through the SDSM 5.2 tool. Table 2: Projected change in Temperature from Downscaled data 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987

0.845 0.674 0.658 0.689 0.665 0.562 0.58 0.487 0.619 0.408 0.501 0.5 0.524 0.458 0.382 0.255 0.279 0.169 0.194 0.298 0.27 0.075 0.3 0.3 0.207 0.195 0.27

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

0.275 0.224 0.138 0.234 0.212 0.239 0.24 0.196 0.172 0.251 0.285 0.266 0.252 0.405 0.282 0.222 0.509 0.34 0.483 0.557 0.476 0.555 0.576 0.57 0.5 0.502 0.533 0.56

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043

0.634 0.623 0.753 0.769 0.719 0.723 0.707 0.765 0.66 0.72 0.767 0.81 0.803 0.772 0.761 0.811 0.761 0.65 0.777 0.739 0.77 0.666 0.603 0.598 0.606 0.575 0.603 0.47

4. Results and Discussions 4.1 Rainfall Pattern based on Observed data

2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071

0.539 0.482 0.523 0.31 0.285 0.419 0.415 0.22 0.489 0.6 0.261 0.187 0.218 0.294 0.357 0.238 0.38 0.475 0.295 0.32 0.328 0.427 0.558 0.393 0.546 0.541 0.548 0.498

2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099

0.524 0.865 0.6 0.643 0.764 0.776 0.688 0.699 0.782 0.944 0.877 0.666 0.808 0.895 0.954 0.813 0.946 1.036 0.946 1.011 1.049 0.938 1.031 1.05 1.052 1.104 1.163 1.034

Graph 1: Observed Average Rainfall vs. Year from 1968-2017 As seen from the graph above, the observed average rainfall is understood to have increased from the year 1961 to year 2017. The best fit trend line is y = 0.0399x – 72.188. At the year 2012, a 12.1mm rainfall was noted in the Science Garden; this is regarded as the highest average rainfall recorded from the time frame of 1961 to 2017. On the other hand, the lowest average rainfall recorded was on 1969. It was during this year when a 4.4mm rainfall is observed. An increase in rainfall of 0.0399mm per year likely. Based on the historical records gathered from PAG-ASA, the months of May, August, and September incurred the highest increase in amount of rainfall. Since these three months fall under the dry season in the study area; it can be inferred that the dry season is much more affected by climate variability as compared to the wet season from November to April. The month of September has seen the most drastic increase in rainfall with 0.1175mm increase per year.

4.2 Rainfall Pattern from Downscaled data

Object 3

Graph 5: Downscaled Average Rainfall vs. Year from 1961-2001

The graph above depicts the rainfall pattern gathered from the SDSM 5.2, a downscaling tool for climate models. The best fit trend line is y = 0.021x – 34.951. The highest average rainfall was tallied during the year 1983, while it was during 1962, 1965, and 1968 when the lowest mean rainfall is seen for the time frame of 1961-2001. The downscaled precipitation data show that July, September and October have experienced the greatest increase in rainfall from 1961 to 2001. September has the highest incremental increase of 0.0744mm per year. From these information, it can be said that the results of downscaling support the historical records in saying that the dry season will be much affected by the impacts of climate change in terms of precipitation increase. 4.3 Maximum Water level and Discharge in the Pasig-Marikina River The flood area of Sto. Niño is the most significant because it has an area of 517 sq. km which is located between mountain area and alluvial plain. It has an average estimated lag time of 5.5 hours which is influenced by the slope of the channel of about 1/1500 and its length of 36.5 km. The JICA Preparatory Study

3

projected the release of 3211

m s

at Sto. Niño for the year 2009. Manning’s

roughness coefficient of 0.033 is used to come up with the data. Table 3: Historical Data for Water level and discharge

1958 1959 1960 1961 1962 1963 1964

Water Level (m) 14.78 N/A 18.06 16.82 17.1 16.19 17.45

1965

15.48

702

1966

19.4

2036

1967

18.2

1609

1968 1969 1970

16.68 17.45 20.48

1107 1367 2464

Year

Discharg e (cu.m/s) 507 2072 1562 1161 1261 931 1367

Water Level (m) 14.5 18.05 13.95 13.98 13.7 16.9 19.44

Year 1971 1972 1973 1974 1975 1976 1977 1978 1985 1986 1987 1993 1994 1995 1996

Discharg e (cu.m/s)

Year

439 1559 318 324 269 1192 2051

1997 1998 1999 2000 2001 2002 2003

Water Level (m) 17.16 18.41 18.3 19.02 16.31 17.94 17.76

N/A

N/A

2004

19.08

1917

20.92

2650

2005

16.03

876

N/A

N/A

2006

16.37

993

16.33 18.4 16.08

980 1676 893

2007 2008 2009

16.9 16.74 22.16

1192 1130 3211

4.4 Rainfall vs. Temperature

Object 7

Graph 11: Rainfall vs. Temperature

Discharge (cu.m/s) 1279 1680 1642 1895 972 1523 1464

The graph above shows the climate graph of the Pasig-Marikina River Basin. It consists of monthly temperature and precipitation on the area. With these parameters, the climate in the area can be predicted. Wet season is evident on a month where the average precipitation is 2.4 in (60 mm). According to the graph above, the wet season starts from May to December and dry from January to April, where August is the wettest month with an average precipitation of 17.24 inches and February as the driest with an average precipitation of 0.59 inches. The temperature is high with very little variation. The lowest maximum temperature recorded is 30.31˚C in December and highest maximum temperature of 34.66˚C during the month of May. 4.5 Temperature Change Projection

Using the statistical downscaling tool, the annual change in temperature was derived for the area of concern. An increasing trendline can be seen in the graph given by the equation of y = 0.0033x – 6.0491, and a coefficient of determination of 0.1616. There is a fairly convincing relationship between the variables as seen in the graph. It can be interpreted based on the trend that an increase of 0.0033⁰C is expected each year.

Object 9

Graph 9: Projected change in Annual Temperatures from Downscaled data 4.6 Flood Frequency Curve

Object 11

Graph 10: Flood Frequency Curve

The researchers estimated the probable frequency of floods based on annual maximum discharges at Sto. Niño as shown in the graph above. The linear trend is observed to be at y= 729.22ln(x)+663.75, where y is the maximum discharge expressed in cu.m/s and x is the recurrence interval in years. For the year 2048, the estimated maximum discharge follows the trend and is said to be at 3945 cu.m/s. 4.7 Rainfall Time Series

Object 13

Graph 11: Rainfall Time Series

The graph above shows the time series for rainfall in the Pasig-Marikina River basin using the historical records on precipitation. The researchers arrived at this graph with y=0.0249x+5.8707 trend line. Taking the mean of the individual seasonal effects gives the average seasonal effects of the area. The average seasonal effects are considered to smooth out the seasonal variation. The area of study has two distinct seasons according to the Modified Coronas Classification; wet from November to April and dry for the rest of the year. For the projection of future rainfall based on this time series, on the year 2048 an amount of 15.2 mm average rainfall during the dry season while 4.6 mm average rainfall are to be expected. 4.8 Land Cover Data

(1984)

(2016)

Figure 1. Land Cover maps of the Marikina River Basin captured from Google Earth Pro: 1984 and 2016 The maps of the Marikina River Basin shown for 1984 and 2016 (Figure 1) illustrate a significant change in the land cover of the study area. Based on the visual interpretation of the maps captured from Google Earth Pro, a large reduction of brushland area is shown in the east portion of the basin. The results revealed the greatly increase of built-up (establishments) also in the east portion and south portion of the basin. The results from the map in 1984 showed that the brushland area was dominant on Marikina River Basin. The built-up area was also present in the east portion of the basin and a little portion in the south. The forest area covers the northeast portion of the basin. However, the results from the map in 2016, the brushland areas noticeably reduced and the built-up area remarkably increased. The built-up areas largely increased in the east and south portion of the basin. The forest area has also reduced its area in the northeast portion of the basin.

4.9 Rating Curve

Object 15

Graph 15: Water level vs. Discharge of Marikina River in Sto. Niño station The graph above shows the rating curve for Marikina River taking the data from Sto. Niño station. As seen from the trend, there is a positive relationship between the water level of the river and the discharge over the years. The relationship is expressed by the power function of y = 4.748 x 0.1818

with y representing the water

level and x for the discharge. For the year 2048, when the discharge reaches 3950 cu.m/s, the average water level is expected to have an annual average of 21.34m which is considered a 3rd alarm according to Marikina River’s water level monitoring. 4.10 Flood Hazard Map from ArcGIS

Figure 13. 2018 Flood Hazard map of Marikina City captured from ArcGIS Figure 13 is the 2018 flood hazard map of Marikina City based on different rainfall scenarios using ArcGIS. The flood hazard map presents three colors according to the severity of the flood hazard. Yellow represents low flood hazard, orange denotes medium flood hazard, and red signifies high flood hazard. From figure 13, medium flood hazard is observed to be dominant in the flood hazard map for the next five years. It also shows high flood hazard in the west portion of Marikina City, including the river system of Marikina River and the areas near the river.

Figure 14. 2048 Flood Hazard map of Marikina City captured from ArcGIS Figure 14 is the 2048 flood hazard map of Marikina City, also created using ArcGIS. The figure shows a flood hazard map in accordance to the rainfall return rate per region and detailed flood reports from citizens. From figure 14, high flood hazard is remarked to be dominant in the flood hazard map for the next twenty-five years.

Most areas of Marikina City will expect high risk of flooding. Also seen in the flood hazard map are the medium flood hazards near the areas with high flood hazard.

4.11 Comparison of Results to Similar Studies In a study by Tebakari, et. Al on Chao Phraya River basin in Thailand, they used MRI-AGCM3.1 and 3.2 as their global climate model for the analysis. Their results show that the average annual increase in air temperature in their area was found to be 2.8⁰C for the period of 2075-2099. They have used two methods: HSBC and CLM for data bias correction. The area has seen a gradual increase in rainfall according to the historical records and likewise with the results of their simulation. The months of July and August have incurred the highest increase. Likewise, the discharge flow of the river in the Nakhon Sawan Station is affected by the increase in the amount of rain, as the flow rate increases. Based on their findings, considering the effects of climate change, severe flooding and high-flow discharge are likely to happen toward the end of 21st century. Meanwhile, in a research conducted in Indonesia’s Kapuas River Basin by Henny Herawati et al., the change in the annual and monthly precipitation for the area has seen a declining trend for the period of 1968-2013. However, the amount of rainfall has been increasing for the past 30 years, although the intensity is decreasing. In comparison, to the researchers’ study on the impacts of climate change in the Pasig-Marikina River Basin; the same conclusions were formulated. For the past 30 years, the precipitation values has seen an increasing trend and that trend is assumed to continue for the next 30 years.

5 Conclusion This study focused on the determination of the impact of climate change on the frequency and severity of floods in the Pasig-Marikina River basin. The atmospheric data, referring to rainfall and temperature, from PAG-ASA was coupled with the climate model, HadCM3 to account for the natural variability of the climatic system in the area of study. The observed atmospheric data from PAG-ASA covers the year 1961-2017. Through a series of multiple linear regressions and statistical analysis from the Statistical Downscaling Model (SDSM), the amount of precipitation for the year 1961-2001 and change in temperature were generated for the time span of 20182048. Projections on the amount of precipitation for the year 2018-2048 were obtained through the generation of the rainfall time series while taking into consideration, the average seasonal effects in the area. Meanwhile, a flood frequency curve is generated based on the available data on water level and water discharge in a stretch of the river basin which is in Sto. Niño. The results show that climate change will affect the precipitation in the PasigMarikina River basin considering the climate scenario used. The amount of precipitation in the area will observe an increasing trend for the next 30 years (20182048). Based on the outputs, both the wet and the dry seasons will incur an increase in precipitation but the dry season of May to October will acquire a greater increase in amount of rainfall. It was projected that the annual rainfall depth will increase by 0.0399 mm per year. Considering the average seasonal effects of ±5.3, rainfall depth will increase by 0.0249 mm per year. Likewise, the average temperature in the area will increase for the next 30 years with an incremental increase of 0.0033⁰C each year starting from the baseline year of 1961. The researchers were able to estimate for the

future discharge in the river basin. The projection for the year 2048 is about 3,950 cu.m/s. In addition, changes in land use impact the pattern of floods in the area. About half of the river basin is protected areas under the authority of DENR. The remaining areas are mostly owned by private sectors. Rapid population growth and urban sprawl contributed to increase in flooding due to impermeable ground surface. Most floods occur in the lower catchments of the basin due to indiscriminate discharge of wastes from the inhabitants nearby the river. Thus, the river basin will overflow resulting to the inundation of the areas it traverses. The outcomes of this study suggest that the Pasig-Marikina River basin will be affected by the climate variability in terms of the increase in rainfall depth and average temperatures, higher flood frequency and more massive floods. 6 Acknowledgement The researchers would like to express their sincere gratitude to the following people who unselfishly gave their invaluable assistance towards the understandings of this humble piece of work.

Engr. Cris Edward F. Monjardin, their thesis adviser,

for his dedication and keen interest above all his overwhelming attitude to help his students had been solely and mainly responsible for completing their paper. Engr. Fibor J. Tan, their course coordinator, for providing the materials and working links, for his guidance in leading the members of the group in finishing this paper. Freedom of Information (FOI) Receiving Officer, for providing the necessary data used by the researchers in preparation for the results of this paper. Their parents and friends, for their constant encouragement throughout their work, for their kind help and providing them necessary references during the work duration. Above all, to our Lord God, who

made all things possible, for all the blessings He showers them, for His every day guidance, for providing physical, mental, emotional, and spiritual strengths that resulted to the success of this work.

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Badilla, R. A. (2008). Flood Modelling on Pasig-Marikina River Basin.

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Hamed, K., & Rao, A. R. (1999). Flood Frequency Analysis.

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HUNUKUMBURA, P. B., & TACHIKAWA, Y. (2012). River Discharge Projection under Climate Change in the Chao Phraya River Basin, Thailand, Using the MRI-GCM3.1S Dataset. Journal of the Meteorological Society of Japan. Ser. II, 90, 137-150.

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Jojene R. Santillan, E. C. (2012). NEAR-REAL TIME FLOOD EXTENT MONITORING IN MARIKINA RIVER PHILIPPINES: MODEL

PARAMETERISATION USING REMOTELY-SENSED DATA AND FIELD MEASUREMENTS. [14]

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Wilby, R. (2002). SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental Modelling Software, 145-157.

APPENDICES Appendix A: Rainfall Pattern from Historical records

Object 19

Graph 2: Observed Average Rainfall vs. Year from 1968-2017

Object 21

Graph 3: Observed Average Rainfall vs. Year for the months of January

Object 24

Graph 4: Observed Average Rainfall vs. Year for the months of February

Object 26

Graph 5: Observed Average Rainfall vs. Year for the months of March

Object 29

Graph 6: Observed Average Rainfall vs. Year for the months of April

Object 32

Graph 7: Observed Average Rainfall vs. Year for the months of May

Object 34

Graph 8: Observed Average Rainfall vs. Year for the months of June

Object 36

Graph 9: Observed Average Rainfall vs. Year for the months of July

Object 38

Graph 10: Observed Average Rainfall vs. Year for the months of August

Object 40

Graph 11: Observed Average Rainfall vs. Year for the months of September

Object 42

Graph 12: Observed Average Rainfall vs. Year for the months of October

Object 44

Graph 13: Observed Average Rainfall vs. Year for the months of November

Object 46

Graph 14: Observed Average Rainfall vs. Year for the months of December

Appendix B: Rainfall Pattern from Downscaled data

Object 48

Graph 15:Downscaled Average Rainfall vs. Year from 1961-2001

Object 50

Graph 16: Downscaled Average Rainfall vs. Year for the months of January

Object 52

Graph 17: Downscaled Average Rainfall vs. Year for the months of February

Object 54

Graph 18: Downscaled Average Rainfall vs. Year for the months of March

Object 56

Graph 19: Downscaled Average Rainfall vs. Year for the months of April

Object 58

Graph 20: Downscaled Average Rainfall vs. Year for the months of May

Object 60

Graph 21: Downscaled Average Rainfall vs. Year for the months of June

Object 62

Graph 22: Downscaled Average Rainfall vs. Year for the months of July

Object 64

Graph 23: Downscaled Average Rainfall vs. Year for the months of August

Object 66

Graph 24: Downscaled Average Rainfall vs. Year for the months of September

Object 68

Graph 25: Downscaled Average Rainfall vs. Year for the months of October

Object 70

Graph 26: Downscaled Average Rainfall vs. Year for the months of November

Object 73

Graph 27: Downscaled Average Rainfall vs. Year for the months of December

Appendix C: Comparison between rainfall patterns of the observed and downscaled data

Object 75

Graph 28: Comparison between downscaled data and observed data from 1961-2001

Object 77

Graph 29: Comparison between downscaled data and observed data for the months of January

Object 79

Graph 30: Comparison between downscaled data and observed data for the months of February

Object 82

Graph 31: Comparison between downscaled data and observed data for the months of March

Object 84

Graph 32: Comparison between downscaled data and observed data for the months of April

Object 86

Graph 33: Comparison between downscaled data and observed data for the months of May

Object 88

Graph 34: Comparison between downscaled data and observed data for the months of June

Object 90

Graph 35: Comparison between downscaled data and observed data for the months of July

Object 93

Graph 36: Comparison between downscaled data and observed data for the months of August

Object 96

Graph 37: Comparison between downscaled data and observed data for the months of September

Object 98

Graph 38: Comparison between downscaled data and observed data for the months of October

Object 100

Graph 39: Comparison between downscaled data and observed data for the months of November

Object 103

Graph 40: Comparison between downscaled data and observed data for the months of December

Appendix D: Annual Change in Temperature from the base year 1961

Object 106

Graph 41: Projected change in Annual Temperatures from Downscaled data

Appendix E: Observed Maximum Temperature in Pasig-Marikina River Basin

Object 108

Object 110

Object 112

Object 114

Object 117

Object 120

Object 122

Object 124

Object 127

Object 130

Object 132

Object 134

Appendix F: Observed Minimum Temperature in Pasig-Marikina River Basin

Object 137

Object 140

Object 142

Object 144

Object 147

Object 150

Object 152

Object 154

Object 157

Object 160

Object 162

Object 164

Appendix G: Summary of gathered data in tables Table 1: Summary of Observed Annual Average Rainfall from 1968-2017 Year 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971

Rainfal l 7.2 6.5 6.1 7.4 6 7 7.6 5.5 4.4 6.9 5.6

Year 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982

Rainfal l 10.1 5.4 7.9 5.7 6.2 6 7.3 5.4 5.8 6.2 5.4

Year 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

Rainfal l 4.5 6.5 7.2 9.9 4.5 7.1 6.7 8.5 6.1 6.2 7.1

Year 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Rainfal l 7.1 8.8 5.6 6.1 7.4 9.2 11.1 6.3 8.2 6 6.1 6.5

Year

Rainfall

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

7.3 6.3 6.5 9.6 7.3 10 12.1 9.5 7.3 8.4 7.6 8.4

Table 2: Summary of Observed Monthly Average Rainfall data from 1968-2017 YEA R 1961 1962 1963

JAN 0 1 0.1

FE B 0.3 0.1 0

MA R 2 0.3 0.3

AP R 1.3 2.8 0

MA Y 4 3 2.8

JU N 19.4 6.3 17.8

JUL 13.5 31.9 11.2

AU G 19.2 11.1 14.5

SEP 12.9 16.2 16.5

OC T 8.8 1.6 4.9

NO V 3.6 3.2 1.7

DEC 0.4 0.4 3.1

1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1.1 0.8 0.5 1.1 0.2 0 0.3 0 1.3 0.2 0 0.5 2 2.8 0.1 0.1 0 0 0 1.1 0.5 0 0 0.1 2.8 0.5 0.2 0.4 0.2 0 1.6 0.6 0.6 0.4 0.6 1.3 0.6 0.7 0.3 0.1 0.2 0.4 1.8 0.1 1.5 1.4

0.1 1.25 1 0.3 0 0 0 0 0.1 0.1 1.5 0 0.1 0.1 0.3 0 0 0.3 0 0.1 0.1 0.4 0.1 0 1.3 0.5 0.1 0.3 0 0 0.9 0.3 0 1.2 0 0.1 1.9 3.8 0.5 0.3 1.3 0.4 0.2 0.9 0.8 0.4

0.5 0 0.2 0.2 0 0 0.2 0 1.3 0 0.1 0 0.6 0.8 0 0 3 0 0.9 0 0.7 2 0 0 0 2.3 0.1 0.2 0 0.5 1.4 0.1 0.3 0 0.2 2.7 3.3 0.4 0.7 0.3 0 0.5 2.5 0.7 1.3 3

0.7 0.5 0.3 0.1 0 0.4 1.6 1.2 1.4 0 1.2 1 0.2 0.2 1 2.4 0 1 1.6 0 2 4.8 0.8 0.4 0.8 2.3 0.6 0.8 0 0.5 1 0.2 2.7 1 0 4.6 1.3 1 0.7 0.7 2.9 1.2 0 0.9 1.2 4.6

5.7 7.9 21.6 3.3 4.3 1.4 1.2 6.4 5.7 0.7 2.9 2.3 7.6 2.2 6.3 6.9 5.1 2.6 2.4 0.9 4.3 0.9 7.8 3 5 7.1 6.3 1.6 2.8 0 6 9.8 3.7 15.6 6 6.5 19.1 8.4 1.6 15 6.7 4.4 5.6 8.3 8.4 8.7

17.1 13 0.9 31.3 6.3 5 4.9 8.4 16.4 12.9 15.2 9.7 7.3 5.3 5.8 5.1 5.7 19.6 6.4 4.4 16.6 29.3 5.4 7.7 16.1 9.9 15.6 8.7 4.9 10.9 14.1 13.1 4.7 5.6 9.6 8.2 5.9 8.3 6.1 7.7 7.2 17.6 11.1 3.3 12 14.6

12.2 17.1 10 16.4 13.3 6.9 14.4 14.1 60.8 9.2 9 4.7 10.5 12.9 9.3 16.7 13.1 13 19.7 9.9 7.5 10 25.7 5.3 15.9 15.8 18.8 17.1 16.8 16 25.8 11.6 12.9 18.2 6.5 22.8 32.4 16.7 42.3 8.7 10.8 7.1 21.4 6.6 7.3 21.3

23.8 10.9 9.1 15.9 22.9 14.9 16.6 7.7 18.8 13 36.4 17.8 8.7 17.4 23 17.7 8.3 10.8 13.7 18.6 19.8 8 25.7 10.6 5.1 22.8 19.5 23.4 24.8 15.3 11.1 20.9 11 18 8.8 27.9 16.8 17.7 14.3 12.3 22.1 11 10 21.6 13.9 13.2

8.4 12.3 22.1 12.6 12.7 13.9 29.1 3.7 8.6 8.7 2 8.1 26.8 16.8 18.7 9.3 15 8.4 10.7 8.6 8.4 15.8 20.6 13.5 12.4 9.1 19.4 12.4 12.8 17.3 14.2 25 16.2 7.8 20.6 14.5 20.1 6.1 14.2 16.6 10.3 13.8 22.3 14.6 14.9 37.5

7.2 1.9 3.5 4 5 5.7 14 12.5 2.1 11.3 9.7 16.4 6.4 1.7 18.6 4 11.2 9.4 3.5 9.4 14 10.5 21 5.2 20.3 7.8 8.1 4.7 6.7 11.3 5 11.3 8.9 3.1 15.7 11.1 17.3 6.2 7.8 6 2.8 15.7 6.8 7.1 8 8.8

8.5 4.8 11.9 5.7 0.4 2.1 0 7.8 3.4 6.6 12.4 2.3 2.5 11.2 2.6 1 6.7 6 3.6 0.9 3.4 3.4 8.1 4.3 5.5 1.9 10.1 3.5 4.8 7.5 0.3 6.2 5.4 1.5 6 5.6 8.5 2.4 7.8 3.9 6.8 2.6 3 9.5 6.3 1.8

3.8 1.2 3.1 0.3 0 1.6 0 5.2 0.6 2.3 3.5 5.4 1.5 0 1.5 0.7 0.8 2.8 1.6 0 0.1 1.4 2.4 3.3 0.1 0 3.2 0.1 0.2 5.3 3.6 6.1 0.4 0.3 14.3 4.4 6.1 3.1 1.2 0.2 1.9 2.8 2.5 1.7 2.3 0.2

2010 2011 2012 2013 2014 2015 2016 2017

0.2 5 1.3 1.5 0 0.9 0.6 1.8

0 0 4.4 3.2 0 0.1 1.3 2.9

0.1 1.1 6.3 4.3 0.7 0.4 0.2 0.3

0.7 0.1 0.1 1.6 0.8 2.2 3 5.1

2 10.1 10.6 5.5 3.5 2.9 6.5 10.5

11.9 24.2 10.4 18.4 8.1 18.8 6.3 12

13.2 17.7 28.6 7.2 15.4 22.9 8.7 19.9

20.3 20.5 44.7 31.4 13.3 16.2 25.4 14.7

12.9 14.9 24.7 22.8 24.2 16.2 19.8 14

13.3 9.2 10.9 11.5 13 8.1 10.3 9.5

8.5 9 0.9 3 3.2 0.7 4.1 5.6

4.4 7.1 1.5 3.7 4.9 10.2 5.3 3.7

Table 3: Summary of Downscaled Annual Average Rainfall from 1961-2001 Year 1961 1962 1963 1964 1965 1966 1967 1968

Rainfal l 6.8 5.8 6.3 6.2 5.8 6.3 6.4 5.8

Year 1969 1970 1971 1972 1973 1974 1975 1976

Rainfal l 6.5 6.3 5.9 7.3 6.3 6.3 6.9 6.6

Year 1977 1978 1979 1980 1981 1982 1983 1984

Rainfal l 6.3 7.1 6.7 6.6 7.1 6.7 7.6 6.6

Year 1985 1986 1987 1988 1989 1990 1991 1992

Rainfal l 6.6 6.7 6.8 6.9 6.3 6.8 6.9 6.5

Year 1993 1994 1995 1996 1997 1998 1999 2000 2001

Table 4: Summary of Downscaled Monthly Average Rainfall from 1961-2001

196 1 196 2 196 3 196 4 196 5 196 6 196 7 196 8 196 9 197 0

JA N 0.5

FE B 0.4

MA R 0.8

AP R 1.4

MA Y 6.8

0.5

0.4

0.8

1.7

4.6

0.3

0.2

0.5

1.1

2.8

0.4

0.9

0.4

0.1

6.2

JU N 13. 4 10. 3 16. 6 9.4

0.3

0

0.7

0.8

4.7

13

0.5

0.2

0.7

0.6

8.8

9.4

0.6

0.1

0.4

0.9

3.4

9

0.6

0.3

0.3

0.4

4.5

7.3

0.4

0.5

0.8

0.7

4.3

0.5

0.3

0.4

0.8

4.1

10. 4 6.6

JU L 16. 4 16. 2 14. 5 14. 1 11. 9 12. 1 15. 3 12

AU G 15.9

OC T 6.6

NO V 3.9

DE C 2.4

10.5

SE P 12. 3 9.9

6.7

4.9

2.5

13.4

12

6.1

5

2.5

16.2

11. 7 11. 5 14. 2 14. 3 14

10

4

1.2

7.5

5.7

1.9

7.3

5.6

2.3

8

5

2

7

4.3

1.2

18. 2 15. 3

12.4

13. 7 12. 8

9.2

4.7

2.4

10.7

5.1

3.1

11.7 14 16.8 17.7

15.6

Rainfal l 6.6 7.1 6.8 6.5 6.7 7.2 7.1 7.2 7.2

197 1 197 2 197 3 197 4 197 5 197 6 197 7 197 8 197 9 198 0 198 1 198 2 198 3 198 4 198 5 198 6 198 7 198 8 198 9 199 0 199 1 199 2 199 3 199 4 199

0.7

0.3

0.8

0.6

4.3

8.7

0.6

0.2

1

1.6

5.4

0.5

0.4

0.8

0.8

3

12. 2 7.7

0.5

0.4

0.6

1.1

3.2

0.7

0.3

0.3

0.7

4.1

0.7

0.3

0.6

1.1

6.7

10. 5 13. 4 8.1

0.7

0.3

0.3

1

3.8

7.1

0.5

0.4

0.7

1

5.6

0.7

0.2

0.6

1.3

5.3

0.7

0.7

0.6

1.2

5.4

10. 4 13. 8 11.9

0.4

0.4

0.6

1.6

5.7

11.3

0.5

0.3

0.5

2.2

4

11.1

0.5

0.3

0.5

0.6

6.5

0.6

0.4

0.5

1.4

5.8

0.7

0.4

0.7

1.9

4.1

0.6

0.4

0.5

0.8

6.8

0.7

0.2

0.7

1

4.8

0.6

0.4

0.6

1.5

4.8

0.3

0.2

1

0.7

4.5

10. 1 10. 5 12. 3 10. 1 10. 8 12. 4 8.5

0.7

0.3

0.9

1

5.5

0.4

0.6

0.7

1

3.5

12. 5 11.7

0.7

0.3

0.7

0.9

5.4

9.5

0.3

0.4

0.7

1

4

9.1

0.5

0.4

0.7

0.8

7.7

8.5

0.6

0.2

0.8

0

6.1

9.2

15. 1 20. 8 15. 8 12. 5 13. 5 15. 6 17. 6 17. 7 14

10.9

14. 8 15. 6 16. 6 16. 1 13. 3 13. 3 17. 6 17. 2 15

17.2

14. 2 15. 8 16. 8 13. 2 15. 4 18. 6 16.

17.1

20 16.7 19.2 18.7 16.5 16.4 19.7 18.2

19 15.5 18.7 19.4 20 16 13 14.4

15.3 19.3 16.3 17.7 17.2 18.1

14. 9 11

7.4

4.9

2

7

5.2

2.3

13. 9 10. 8 12. 6 14. 6 13. 5 12. 2 13. 1 11. 5 12. 9 13. 7 17. 4 11. 6 10. 9 11. 8 15. 3 15. 1 15. 1 13. 3 12. 3 16. 9 14. 2 14. 1 13

7.8

5.3

2.3

8.4

5.2

2.8

9.8

5

2.8

8.4

4.2

2.5

7.8

4.5

2.5

10.5

3.2

2.2

5.9

4.6

2.2

8.8

4.1

1.6

10.3

5.2

2.2

9

5.5

1.4

12.7

5.1

2.5

8.2

4.8

2

6.8

5.6

2

8.7

4.6

2.5

9.7

5.1

3

11.5

4.2

2.6

17.6

4.7

1.5

10.2

3.9

1.8

8.4

5.5

2

7.3

4.7

2

8.5

4.8

3.1

9

5

2.5

9.6

4.6

2.4

5 199 6 199 7 199 8 199 9 200 0 200 1

0.7

0.5

0.4

1.5

5.8

6.8

0.7

0.6

0.3

1

4.5

11.9

0.5

0.6

0.7

1.2

6.7

8.8

0.9

0.6

0.6

1.5

5.4

0.7

0.7

0.4

1.4

7.3

10. 4 10

0.5

0.6

1

1.1

6.4

9.7

2 14. 2 15. 9 14. 5 17. 8 18. 8 17

14.4

17

8.8

5.3

2.3

15.6

14. 7 17. 6 15. 9 11. 7 15. 3

8.1

5.3

1.7

12.3

5.4

3.7

9.7

5.2

3.3

10.1

5.7

3.1

9

6

2.6

13.5 13.9 16.2 16.2

Table 3: Projected change in Temperature from Downscaled data 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987

0.845 0.674 0.658 0.689 0.665 0.562 0.58 0.487 0.619 0.408 0.501 0.5 0.524 0.458 0.382 0.255 0.279 0.169 0.194 0.298 0.27 0.075 0.3 0.3 0.207 0.195 0.27

JA

FE

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 MA

0.275 0.224 0.138 0.234 0.212 0.239 0.24 0.196 0.172 0.251 0.285 0.266 0.252 0.405 0.282 0.222 0.509 0.34 0.483 0.557 0.476 0.555 0.576 0.57 0.5 0.502 0.533 0.56 AP

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 MA

0.634 0.623 0.753 0.769 0.719 0.723 0.707 0.765 0.66 0.72 0.767 0.81 0.803 0.772 0.761 0.811 0.761 0.65 0.777 0.739 0.77 0.666 0.603 0.598 0.606 0.575 0.603 0.47 JU

JU

2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 AU

0.539 0.482 0.523 0.31 0.285 0.419 0.415 0.22 0.489 0.6 0.261 0.187 0.218 0.294 0.357 0.238 0.38 0.475 0.295 0.32 0.328 0.427 0.558 0.393 0.546 0.541 0.548 0.498 SE

2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 OC

NO

0.524 0.865 0.6 0.643 0.764 0.776 0.688 0.699 0.782 0.944 0.877 0.666 0.808 0.895 0.954 0.813 0.946 1.036 0.946 1.011 1.049 0.938 1.031 1.05 1.052 1.104 1.163 1.034 DEC

196 1 196 2 196 3 196 4 196 5 196 6 196 7 196 8 196 9 197 0 197 1 197 2 197 3 197 4 197 5 197 6 197 7 197 8 197 9 198 0 198 1 198 2 198 3 198 4

N 29. 6 29. 7 27. 9 30. 5 28. 8 40. 5 28. 9 29. 8 31 31 30. 5 29. 3 31. 1 29. 6 29. 9 28. 8 26. 4 30. 6 30. 6 30. 6 29. 1 29. 7 31. 1 30. 4

B 32. 2 30. 7 29. 7 30. 8 30. 8 32

R 33.4

30. 4 30. 5 31. 2 30. 4 30

32

31. 7 31. 7 31. 2 31. 2 30. 2 25. 9 30. 3 30. 7 31. 6 31. 4 31. 6 32. 5 32. 3

31.0

32.6 32.2 32.4 31.8 33.6

32.5 32.8 33.7 32.6

33.2 32.4 33.1 32.2 26.8 32.2 34.0 32.6 33.7 33.9 34.7 34.1

R 33. 9 33. 3 33. 7 34. 5 34. 6 35. 7 34. 2 34

Y 33.9

34. 7 34

36

33. 3 33. 8 35. 7 34. 8 34. 1 34. 1 28. 7 34. 4 34. 7 34. 2 35. 6 34. 8 36. 1 35. 8

32.2

34.2 35.1 35.3 32.8 41.1 34.9 35.1

35.7

33.6 36.3 33.7 34.9 35 29.4 34.4 33.9 34.5 35.6 35.2 37.1 33.9

N 31. 5 32. 6 30. 2 31. 7 31

L 29. 9 29. 3 30. 3 31. 4 31

G 29.6

32. 3 31

31. 2 30. 7 31. 7 32

31

30

30.1

30. 7 29. 3 31. 7 31. 6 31. 7 33

30.5

27. 9 31. 7 31. 3 31. 3 32. 3 30. 7 33. 2 32. 7

27.9

34. 2 32. 6 32. 7 31. 1 32. 1 33. 7 31. 6 32. 4 31. 9 29. 1 33. 6 31. 4 32. 6 32. 2 33. 9 34. 9 32. 1

30.7 30 29.8 29.2

29.2 30.1 29.9

30.1 31.4 29.6 30.2 31.4

31.6 29.6 31.8 31.6 31.2 32.2 29.6

P 30. 2 29. 6 29. 1 30. 2 30. 1 29. 6 30. 2 30. 8 29. 8 29. 3 31. 5 31. 6 31. 4 32. 1 31. 6 32. 5 27. 2 30. 4 30. 3 30. 6 32. 9 31. 1 32. 5 32. 4

T 30. 3 31. 7 30. 5 30. 8 31. 1 30. 7 31

V 30.5

31.3

30.7

30.2

31.2

29.9

29.4

28.8

30.5

30.1

30.5

29.9

29.4

30.4

30. 1 30. 7 30. 8 29

29.9

30.5

31.2

29.7

30

29.8

30.6

30.2

31. 9 30. 6 30. 3 31. 4 31

31.5

30.8

30.7

29.1

29.8

30.1

30.7

29.2

30.3

29.5

27. 4 31. 9 29. 9 31. 2 31. 7 32. 2 31. 6 30. 8

26.2

26.1

30.1

29.2

30.7

30.8

30.7

29.7

30.8

30.2

31.9

30.9

31.3

30.4

31.6

30.7

198 5 198 6 198 7 198 8 198 9 199 0 199 1 199 2 199 3 199 4 199 5 199 6 199 7 199 8 199 9 200 0 200 1 200 2 200 3 200 4 200 5 200 6 200 7 200 8 200

30. 5 30. 3 30. 7 31. 8 31. 6 31. 1 30. 4 30. 3 30. 9 30. 6 30. 4 30. 1 29. 8 32. 3 30. 4 30. 8 30. 9 31. 1 31. 5 30. 8 30. 3 30. 4 30. 7 31. 2 29.

33. 9 31. 1 31. 3 32. 7 31. 1 32. 8 30. 9 32. 1 31. 1 32. 1 31. 4 30. 6 31. 1 33. 5 30. 8 31. 3 31. 6 30. 8 31. 0 31. 5 32. 8 31. 3 31. 3 30. 7 32.

34.7 33.7 32.2 34.2 32.7 33.1 33.0 34.4 32.8 32.7 33.3 33.2 32.4 34.6 32.5 32.2 31.9 33.1 32.9 33.8 32.6 32.3 33.2 32.7 33.2

34. 2 35. 4 35. 9 35. 8 34. 7 36. 3 34. 6 36. 3 34. 9 34. 6 35. 6 33. 0 34. 3 36. 3 33. 3 34. 8 35. 0 34. 9 35. 7 35. 0 34. 4 34. 7 34. 6 34. 5 33.

34.9 34.3 36.9 35.7 34.6 34.9 35.4 35.6 36.1 33.9 34.2 33.7 34.2 35.4 33.6 32.4 34.8 34.8 35.0 34.2 35.6 33.3 34.5 32.9 33.5

31. 7 33. 4 33. 5 32. 6 32. 8 31. 7 33. 3 34. 1 34. 3 33. 0 33. 7 33. 7 33. 2 34. 0 31. 9 32. 9 32. 9 33. 9 32. 6 31. 6 31. 1 33. 2 33. 9 32. 7 31.

32. 2 31. 8 32. 6 32. 2 32. 2 31. 4 31. 8 32. 2 32. 0 29. 9 32. 1 32. 1 31. 4 33. 7 30. 9 30. 1 31. 4 30. 4 30. 6 30. 8 32. 0 30. 3 32. 8 32. 0 31.

31.4 30.9 32.6 32.9 30.9 30.8 29.6 30.9 31.1 31.5 31.6 31.8 31.1 32.9 30.6 31.9 31.8 31.8 32.0 32.5 31.3 30.4 30.6 31.0 31.5

31. 8 31. 1 32. 2 33. 1 31. 5 31. 4 31. 1 31. 4 31. 1 31. 3 30. 7 31. 1 31. 8 31. 3 30. 7 30. 9 30. 6 31. 6 31. 1 32. 0 32. 6 32. 6 31. 5 31. 2 30.

31. 7 31. 3 32. 8 30. 8 31. 9 31. 1 31. 6 31. 9 31. 2 31. 2 31. 3 32. 1 32. 4 31. 2 31. 7 31. 0 32. 7 32. 3 32. 8 32. 2 31. 3 31. 9 30. 9 30. 9 31.

31.9

30.7

31.9

30.8

32.1

31.2

30.9

30.2

31.2

29.8

31.2

30.1

30.4

30.2

30.4

30.7

31.4

30.1

32.2

31.4

31.2

29.1

30.3

29.7

32.3

31.9

31.9

30.2

30.8

30.2

31.3

30.5

32.0

30.0

31.1

32.1

32.2

30.4

31.6

30.6

31.2

30.8

32.1

30.7

30.8

30.8

31.7

30.2

31.9

31.2

9 201 0 201 1 201 2 201 3 201 4 201 5 201 6 201 7

196 1 196 2 196 3 196 4 196 5 196 6 196 7 196 8 196 9 197 0 197 1 197 2 197 3 197 4

8 31. 2 30. 1 31. 1 30. 3 29. 4 29. 4 31. 6 30. 4

1 33. 3 31. 3 31. 4 32. 0 30. 7 30. 9 31. 2 30. 4

JA N 18. 4 30. 5 18. 9 20. 2 19. 5 25

FE B 20. 1 19

MA R 22.4

18. 4 19. 9 19. 1 20. 1 19. 4 19. 6 20. 8 19. 3 20

19.8

20. 3 20. 3 21. 2

20.3

19. 6 19. 6 21 21. 3 21. 1 20. 3 20. 4 19. 6

34.1 31.8 32.0 32.8 32.6 32.3 33.2 32.6

27.3

20.9 20.5 20.6 20.5 20.8 21.5 21.6 22.8

20.1 20.9

2 35. 6 32. 9 34. 7 35. 1 34. 2 34. 5 35. 5 34. 0 AP R 22. 4 22. 7 21. 5 21. 8 22. 2 22. 6 22. 3 21. 5 23. 1 22. 5 21. 8 22. 3 22. 9 23. 2

36.2 33.6 33.8 34.4 35.3 34.6 34.8 34.5

MA Y 23.5 23 22.7 24.8 23.1 23.2 23.6 23.5 24.5 23.1 23.8 23.7 24.9 23.8

6 33. 7 31. 8 32. 0 33. 3 32. 9 34. 3 33. 7 33. 9

4 32. 9 30. 9 31. 6 32. 4 31. 4 31. 8 32. 7 32. 3

JU N 22. 6 23. 9 23. 3 23

JU L 22. 2 23. 2 22. 9 22. 5 23. 5 23. 3 23. 5 23. 6 24. 2 23. 3 23. 4 24. 0 24. 2 23. 6

23. 6 23. 7 23. 2 23. 9 24. 2 23. 5 23. 8 23. 9 24. 5 23. 9

7 33. 0 31. 4 31. 3 30. 9 31. 7 32. 7 31. 6 32. 4

3 31. 4 31. 7 31. 6 31. 1 31. 9 32. 1 31. 9 31. 2

22.3

SE P 22. 7 22. 9 22. 8 23

22.9 23.5

32.4 30.8 30.2 30.8 31.6 32.2 30.9 32.4

AU G 23.1 22.9 22.9

23.5 23.7 23 23 23.2 23.4 23.6 23.8

31.2

30.3

32.0

30.7

32.7

31.8

31.6

31.2

31.8

30.6

33.4

31.2

31.8

31.2

31.8

30.3

NO V 20.8

DEC

21.2

19.7

21.2

20.7

21.8

20.5

23

OC T 22. 2 22. 4 21. 9 22. 6 22

22

20.8

22. 6 23. 3 24. 4 23. 1 22. 6 23. 6 23. 4 23. 4 23. 6

22. 5 22. 6 22. 2 22. 9 22. 7 22. 8 22. 7 22. 9 23. 8

22.3

21.6

21.2

19.3

20.5

20.2

21.3

22.3

23.4

20.2

21.5

21.3

22.2

21.5

22.6

21.7

22.7

21.7

20.7

197 5 197 6 197 7 197 8 197 9 198 0 198 1 198 2 198 3 198 4 198 5 198 6 198 7 198 8 198 9 199 0 199 1 199 2 199 3 199 4 199 5 199 6 199 7 199 8 199

21. 3 20. 3 22. 2 22. 2 19. 9 20. 8 19. 3 18. 7 20. 4 19. 7 18. 6 20. 0 18. 9 21. 3 21. 5 20. 7 20. 3 20. 1 20. 4 21. 3 21. 1 20. 8 19. 9 21. 2 22.

21. 0 19. 9 21. 6 21. 6 20. 5 20. 9 20. 2 19. 2 18. 8 19. 6 20. 8 20. 0 18. 4 21. 2 21. 1 21. 1 21. 4 20. 6 19. 9 21. 4 20. 3 20. 3 21. 1 22. 2 21.

22.3 21.1 21.4 21.4 22.8 21.4 20.7 21.2 21.6 22.2 21.1 21.2 21.0 21.4 22.4 21.9 21.0 22.1 21.4 22.4 21.5 22.8 20.9 22.5 23.4

23. 7 22. 9 22. 9 22. 9 23. 1 22. 4 23. 2 23. 4 22. 1 23. 2 23. 1 22. 7 23. 3 23. 8 23. 2 24. 4 23. 4 24. 0 23. 0 23. 7 23. 2 23. 8 23. 8 24. 5 24.

24.4 25 24.4 24.4 24.5 24.0 24.6 24.1 23.7 23.3 23.6 24.3 24.6 24.7 24.3 24.8 24.9 24.8 24.4 25.2 24.8 24.9 24.4 25.9 24.4

24. 2 23. 9 24. 6 24. 6 24. 2 23. 2 23. 9 24. 4 24. 3 23. 3 23. 6 24. 1 24. 6 24. 0 24. 1 24. 6 24. 9 24. 8 25. 2 24. 4 24. 9 24. 6 24. 4 25. 4 23.

23. 7 24. 1 24. 1 24. 1 24. 1 23. 1 23. 7 23. 8 23. 5 23. 2 22. 8 23. 2 24. 2 24. 2 24. 2 23. 7 24. 1 23. 7 24. 3 24. 5 24. 1 24. 3 24. 1 25. 1 24.

24.1 25.3 24.3 24.3 23.8 23.1 24.3 23.6 23.5 23.2 24.1 23.8 23.6 23.9 23.9 24.4 23.8 23.8 23.8 24.3 24.1 23.9 24.6 24.5 24.2

23. 5 23. 5 23. 9 23. 9 23. 4 23. 4 23. 3 23. 4 23. 2 22. 3 23. 1 22. 7 23. 8 23. 9 24. 3 23. 6 24. 2 24. 1 23. 9 24. 3 24. 1 24. 2 24. 2 24. 4 24.

23. 7 21. 6 22. 9 22. 9 23. 4 23. 1 22. 7 22. 2 22. 8 22. 3 22. 7 22. 7 23. 1 23. 9 23. 8 22. 9 22. 9 23. 6 23. 2 23. 4 23. 8 23. 8 23. 4 24. 6 23.

21.7

21.8

24

20.8

22.2

22.1

22.2

22.7

21.9

21.7

22.4

21.3

22.1

20.5

21.4

21.5

21.2

19.9

21.7

19.8

21.9

20.3

22.3

20.1

22.3

21.7

22.6

20.3

22.1

20.3

22.8

21.8

21.8

20.6

21.5

21.0

23.1

22.7

22.0

21.4

23.6

22.2

23.2

20.8

22.3

21.0

24.0

23.2

22.9

22.7

9 200 0

4 21. 3

2 22. 3

22.9

1 24. 2

24.3

9 24. 5

1 24. 0

24.5

4 23. 9

7 23. 7

Appendix H: Land cover data from Google Earth Pro (1984 – 2016) 1984

23.4

22.9

1992

2000

2008

2016