1. 1 Assessment of Future Climate and its Impact on Streamflow: a Case Study of Bagmati Basin, Nepal Examination Committee: Dr. Mukand S. Babel (Chairperson) Dr. Sylvain Roger Perret Dr. Roberto S. Clemente Prof. Ashim Das Gupta Shyam Prasad Bhusal (ID: st107394) WEM/SET

2. 2 Introduction Outline  Introduction  Methodology  Study Area and Data Collection  Results and Discussion  Conclusions and Recommendations

3. 3 Introduction Rationale of the Study  Climate change is global phenomenon but its trend and extent varies spatially and temporarily(IPCC,2007) bringing uncertainties in local climate.  Changes in precipitation and temperature brings uncertainties in basin hydrology.  Uncertainties of future water availability and extreme events.  Problem in planning, design and management of water resources (Minville et al., 2008).  To address this problem climate change impact study should be conducted at local level.  GCMs provide future climatic variables but their grid size is very large and data contain bias. Bagmati river basin in Hadcm3 grid geometry Hadcm3 grid: 300 by 400 km

4. 4 Introduction Objectives To quantify the future changes in climate and its impact on streamflow in the Bagmati Basin in Nepal. Overall Objective 1. To estimate future local climate with downscaling of GCM predicted climatic variables. 2. To analyze present and future trends of extreme climate indices 3. To quantify the impact of climate change in streamflow and future water availability. 1. To estimate future local climate with downscaling of GCM predicted climatic variables. 2. To analyze present and future trends of extreme climate indices 3. To quantify the impact of climate change in streamflow and future water availability. Sub - objectives

5. 5 Introduction Scope and Limitations of the Study  Collection of required spatial, meteorological and hydrological data data  GCM selection based on the statistical analysis  Downscaling of GCM temperature and precipitation using SDSM model  Analysis of future trends temperature of precipitation changes  Analysis of extreme climate indices using WMO guideline  Simulation of future streamflow using hydrological model HEC-HMS.  Collection of required spatial, meteorological and hydrological data data  GCM selection based on the statistical analysis  Downscaling of GCM temperature and precipitation using SDSM model  Analysis of future trends temperature of precipitation changes  Analysis of extreme climate indices using WMO guideline  Simulation of future streamflow using hydrological model HEC-HMS.

6. 6 Methodology

7. 7 Research methodology Framework GCM Selection Outcome: Local level GCM precipitation and Temperature (Objective:1) Outcome: Local level GCM precipitation and Temperature (Objective:1) Extreme Climate Indices analysis Hydrological Modeling Daily precipitation at present time Downscaled GCM daily precipitation Calibrated Hydrological Model Present water availability Future water availability Future water availability Climate change impact on water availability is inferred (Objective:3) Statistical Downscaling Impact of climate change on extreme temperature and precipitation events is inferred (Objective: 2)

8. 8 Methodology (objective:1) Methodology for Downscaling Input: -Station meteorological data (Precipitation & Temperature ): Predictand -NCEP reanalyzed data: Predictors Input: -Station meteorological data (Precipitation & Temperature ): Predictand -NCEP reanalyzed data: Predictors Input: Screened predictors from GCM (Scenario A2 & B2) Input: Screened predictors from GCM (Scenario A2 & B2) Statistical Downscaling Model (SDSM) Statistical Downscaling Model (SDSM) Screening of Predictor Variables Output : Downscaled GCM Data (Precipitation and Temperature) for different periods ( A2, B2 ) Output : Downscaled GCM Data (Precipitation and Temperature) for different periods ( A2, B2 ) Calibration and Validation Predictor-Predictand Relationship -Correlation coefficient -Scatter plot Methodology for downscaling with SDSM model Daily predictor variables Code Mean Temperature Temp Mean Sea Level Pressure Mslp 500 hPa geopotential height P 500 850 hPa geopotential height P 850 Near surface relative humidity rhum Relative humidity at 500hPa height r 500 Relative humidity at 850 hPa height R 850 Near surface specific humidity Shum Geostrophic airflow velocity **_f Vorticity **_z Zonal velocity component **_u Meridonal velocity component **_v Wind direction **th Divergence **zh

9. 9 Methodology (objective:2) ID Indicator Name DefinitionsUnits Temperature Indices TXxMax Tmax Maximum of Maximum Temp οCοC TNxMax TminMaximum of minimum TempºC TXnMin Tmax Minimum of maximum Temp ºC TNnMin TminMinimum of minimum TempºC TN10pCold NightsPercentage of days when TN<10th percentileDays TX10pCold daysPercentage of days when TX<10th percentileDays TN90pWarm nightsPercentage of days when TN>90th percentileDays TX90pHot daysPercentage of days when TX>90th percentileDays Precipitation Indices Rx5dayMax 5-day precipitationMonthly maximum consecutive 5-day precipitationmm R10Heavy precipitation daysAnnual count of days when PR >= 10mmDays R20Heavy precipitation daysAnnual count of days when PR >= 20mmDays R35 Very heavy precipitation days Annual count of days when PR >= 35mmDays CDDConsecutive dry daysMaximum number of consecutive days with PRCP<1mmDays CWDConsecutive wet days Maximum number of consecutive days with PRCP>=1mm Days R95pVery wet daysAnnual total PRCP when PR>95th percentilemm Extreme Climate Indices

10. Quantification of Climate Change Impact on Water Resources 10 Methodology (objective:3) River Flow Hydrograph (Water availability) River Flow Hydrograph (Water availability) HEC-GeoHMS (Arc View GIS 3.2) HEC-GeoHMS (Arc View GIS 3.2) Hydrological Model : HEC- HMS Observed precipitation Input : -DEM & Soil Data -Land Use data Gauged Discharge Outcome: 1.Watershed Delineation 2.Stream Network Development 3.Stream Characteristics 4.Watershed Characteristics Outcome: 1.Watershed Delineation 2.Stream Network Development 3.Stream Characteristics 4.Watershed Characteristics Model Calibration/Validation Model Calibration/Validation Daily precipitation for Simulation period

11. Study Area: Bagmati River Basin 11 Study area & Data collection CHINA INDIA Bagmati river basin Nepal boundary and districts Location Map of the Bagmati river basin Location: Lat 26 ο 45’- 27 ο 49’ N and Long 85 ο 02’- 85 ο 57’ E Basin area: 3759 km 2 Altitude: 75 m – 2900 msl Climate: Sub-tropical to Cold temperate Upper part: Kathmandu, Lalitpur and Bhaktapur districts Area = 662 km 2 69% of basin population inhabit Water stress Middle & lower part: experiencing flood problem Location: Lat 26 ο 45’- 27 ο 49’ N and Long 85 ο 02’- 85 ο 57’ E Basin area: 3759 km 2 Altitude: 75 m – 2900 msl Climate: Sub-tropical to Cold temperate Upper part: Kathmandu, Lalitpur and Bhaktapur districts Area = 662 km 2 69% of basin population inhabit Water stress Middle & lower part: experiencing flood problem INDIA CHINA

12. Data Collection 12 Study area & Data collection Climatic data Station Name Elevation (msl) Latitude ( o E) Longitude ( o N) Daily Rainfall Daily Min/Max Temp. Source Ktm Airport133627 o 42'85 o 22' 1971-20051968-2005 DHM Daman231427 o 63'85 o 05' 1971-20051981-2005 DHM Budhanilkanttha135027 o 47'85 o 22' 1971-20051978-2005 DHM Sankhu144927 o 45'85 o 29' 1971-2005- DHM Godavary140027 o 34'85 o 24' 1971-20051981-2000 DHM Khopasi151727 o 35'85 o 31' 1971-2005- DHM Hariharpurgadhi25027 o 20'85 o 30' 1971-2005- DHM Sindhuligadhi146327 o 17'85 o 58' 1971-20051978-2000 DHM Ramolibariya15227 o 01'85 o 23' 1971-2005- DHM Pattharkot27527 o 05'85 o 40' 1971-2005- DHM  Land use: 35% agriculture, 57 % forest  Soil Map : loamy soil is dominant  DEM of 90 m resolution from CGIAR website Daily discharge for period (1990-2006 ) Station Name Latitude ( o E) Longitude ( o N) Catchment Area (Km 2 ) Source Pandheradovan27 o 06'85 o 28'30'2789.3 DHM, Nepal Spatial data GCM data from IPCC-DDC NCEP predictors from SDSM website GCM data from IPCC-DDC NCEP predictors from SDSM website Data downloaded from website

13. 13 Results and Discussions

14. 14 Result & Discussion (objective:1) Downscaling GCM Temperature Validation of model result for downscaling maximum temperature (1990-2000)

15. 15 Result & Discussion (objective:1) Downscaling of GCM Precipitation Comparison of statistics of observed and simulated Precipitation for period 1990-2000

16. 16 Result & Discussion (objective:1) Downscaling Performance Statistical performance of downscaled result Climate Variable Max. TempPrecipitation GCM DataSDR2R2 R2R2 Observed3.63 -4.89 - Raw A2 5.460.6114.170.40 Downscaled A2 3.670.894.600.87 Statistical performance of downscaled result Climate Variable Max. TempPrecipitation GCM DataSDR2R2 R2R2 Observed3.63 -4.89 - RawB25.580.542.910.52 Downscaled B23.910.874.630.81

17. 17 Result & Discussion (objective:1) Future scenario of downscaled temperature Future scenario of downscaled temperature Basin average future changes in maximum temperature ( o C) relative to base period Year WinterSummer Scenario A2Scenario B2Scenario A2Scenario B2 2010-2039 (2020s)0.4 0.60.5 2040-2069 (2050s)1.10.91.20.9 (2070-2099) 2080s1.81.42.31.6  Winter: Dec-Feb, Summer: March-June, Base period: 1970-1999  Summer has higher increased rate  Spatial variation

18. 18 Result & Discussion (objective:1) Temporal variation in basin average precipitation relative to base period Basin average changes (%) in annual total precipitation relative to base period PeriodScenario A2Scenario B2 2020s 7.5 8.3 2050s 12 14.8 2080s 14.7 20.5

19. 19 Result & Discussion (objective:1) Spatial variation in temporal trend of annual precipitation relative to base period Upper par of basin

20. 20 Result & Discussion (objective:2) Basin average trends of extreme temperature indices TXx = Summer maximum temp extreme, TNx = Summer minimum temp extreme TXn = Winter maximum temp extreme, TNn = Winter minimum temp extreme

21. 21 Result & Discussion (objective:2) Basin average trends of extreme precipitation indices  CDD = consecutive dry days  CWD = Consecutive wet days  R10 = No of days with precipitation events > 10mm  Rx5day = Maximum 5 day prcp.

22. 22 Result & Discussion (objective:3) Hydrological Model Calibration and Validation Statistical performance during model calibration & Validation Statistical ParametersCalibrationValidation Coefficient of determination (R 2 ) 0.710.66 Nash-Sutcliffe Efficiency (NSE) 0.690.66 Volume Error (%) 1.2-5.78 Calibration (1999-2001)Validation (2002)

23. 23 Result & Discussion (objective:3) Scenario Generated to Assess Climate Change Impact Scenario runs developed to assess climate change impact on river flow and water availability Simulation runs with downscaled precipitation SRESS A2SRESS B2 RunsSimulation PeriodRunsSimulation Period A2-2000 (base period) 1995-2005B2_20001995-2005 A2_20202015-2025B2_20202015-2025 A2_20502045-2055B2_20502045-2055 A2_20802075-2085B2_20802075-2085

24. 24 Result & Discussion (objective:3) Climate Change Impact on River Flow Hydrograph Comparison of future flow hydrographs with base period flow hydrograph as predicted by scenario B2 Comparison of future flow hydrographs with base period hydrograph as predicted by scenario A2

25. 25 Result & Discussion (objective:3) Seasonal Variation of Water Availability on Spatial and Temporal Scale Scenario A2 (Whole Basin) Season Future Changes (%) in water availability relative to base period 2020s2050s2080s Pre- monsoon 3.52-8.90-12.72 Monsoon7.779.8318.23 Post- monsoon 0.9612.059.39 Annual6.387.3912.76 Seasonal changes (%) in water availability relative to base period according to Scenario B2, whole basin Season Future Changes (%) in water availability relative to base period (2000) 2020s2050s2080s Pre-monsoon 7.9012.4717.45 Monsoon 4.017.6614.41 Post-monsoon 22.279.7419.17 Annual 6.568.5115.33 Scenario A2 (Upper part of basin ) Season Future Changes (%) in water availability relative to base period 2020s2050s2080s Pre-monsoon-8.85-9.88-16.94 Monsoon1.671.306.61 Post-monsoon-3.14-1.34-12.47 Annual-0.73-0.960.28 Monsoon: June-Sep Pre-monsoon: Jan – May Post monsoon: Oct-Dec

26. 26 Conclusions and Recommendations

27. 27 Conclusions Downscaling technique has well estimated the mean and extreme values of temp whereas it could not estimate the extreme precipitation events well. Wide variation of future changes in temperature and precipitation within the basin suggests that the impact studies should be conducted in smaller areas. Increased heavy precipitation events (R20, R35, Rx5day and R95p) may lead to increased frequency and intensity of floods. The increase in CDD and decrease in CWD indicates the occurrence of more intense droughts in the future. Summer may have more severe impact of warming than the winter Both scenarios A2 and B2 show higher increase in water availability during monsoon indicating increased flood problems in lower part of basin. The spatial analysis of Climate change within the basin indicates that the upper part of the basin is expected to be drier than lower part

28. 28 Recommendations Recommendations based on Conclusions  Citing the increased water stress in the upper part of the basin during dry period, it is recommended to the water management authorities in the basin to make necessary plans to cope with possible degrading situation of water stress.  The extreme indices analysis shows increase in frequency and intensity of droughts and floods. To mitigate the dire consequences of such extreme events, adaptation strategies should be designed. Recommendations for Further Studies  Similar study using more GCM and downscaling techniques  Further studies on vulnerability and adaptation using the result of this study.  Climate change impact studies on water quality considering the result of this study.