GIS BASED HYDROLOGICAL MODELLING FOR CLIMATE CHANGE IMPACT ASSESSMENT Dr. Amardeep Singh, MoWR Prof. A. K. Gosain, IIT Delhi
Model Description SWAT (Soil and Water Assessment Tool) Conceptual, distributed, continuous time model Developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in large, watersheds Version used SWAT 2000 with an Arc View (GIS) interface.
Description of the study area (Cauvery River Basin) Area of the basin = 81,155 sq km Length of the river = 802 km Riparian States = Karnataka, Tamil Nadu, Kerala, Pondicherry Earlier Agreements = 1892 and 1924 Cauvery Water Dispute Tribunal = 1990 Interim Order = 1991 Cauvery River Authority = 1998 Final Award= 2007
Data Used for Hydrological Modelling Survey of India Contour Maps on a scale of 1:250,000 Land Use Maps on a scale of 1:250,000 Soil Series Maps on a scale of 1:250,000 Weather data pertaining to the precipitation, temperature, solar radiation, wind speed, relative humidity Reservoir and irrigation data
Steps involved in Modelling Digitisation of contours DEM generation Watershed delineation Hydrologic Response Units (HRU) generation through overlay of land use and soil maps Input of climatic data Input of reservoir and irrigation data SWAT run for the simulation period (1970 to 1989)
Simulation of Cauvery basin Area modelled = 67,000 sq km Number of: Subbasins= 42 HRU s =231 Soil classes=6 Land use classes=8 (Threshold 5/5%)
Cauvery river basin Hemavathy Hemavathy $ Harangi $ $ $ Marconahalli Krishnaraj Sagar Kabini $ Suvarnavathy $ Mettur $ Bhavani $ Amravathy $ $ Reservoirs Streams Watershed N W E S
Watershed 34 $ 35 4 36 2 $ 1 3 $ $ 39 9 38 10 19 20 $ 5 $ 37 $ 32 8 6 11 7 12 40 26 33 21 13 16 15 17 41 $ 25 29 22 14 $ 42 18 23 27 24 28 31 30 $ Reservoirs Subbasins Watershed N 100 0 100 200 Miles W S E
Watershed 38 34 35 4 36 2 1 3 10 39 9 19 20 5 37 32 8 6 11 7 12 40 26 21 13 16 15 17 41 25 29 22 14 18 23 27 24 28 31 30 Subbasins Dem 0-288.754 288.754-577.509 577.509-866.263 866.263-1155.017 1155.017-1443.772 1443.772-1732.526 1732.526-2021.281 2021.281-2310.035 2310.035-2598.789 33 42 N W E 100 0 100 200 Miles S
Cauvery river basin SwatLandUseClass AGRC FRSD FRSE FRST ORCD PAST UIDU URHD WATR WETL WETN WPAS N W E S
Watershed SoilClass COIMBTORE GUTTAPAL PALATHURAI Sample TYAYAMAGNDALU VIJYAPURA N 100 0 100 200 Miles W S E
Model Calibration
Land Use Scenarios
Variation of Average Flows
Variation of Average Flows
Variation of 75% Dependable Flows
Variation of 75% Dependable Flows
Future Climate Change Scenarios Data generated by Hadley Centre UK HadRM2 Series Daily values of precipitation, max temp, min temp, wind speed,relative humidity and solar radiation taken Period taken 2041 to 2060 Precipitation taken as the variable of interest and its mean monthly as well as annual values compared over a 20 year period.
Comparison between present and future climate change flows
Some Inferences Even though the above table suggests that there is a very significant increase in flows (for example for S 11 case, the increase in flow is more than 50 percent for the futuristic climate change scenario), the same may not be taken directly. This is because, as shown earlier, there was a significant difference in the RM2 control data and the present actual data. For example, consider the values of annual precipitation. If we compare RM2 control data with the RM2 futuristic data, increase in precipitation is less than 2%. This agrees with the average increase in precipitation between the RM2 control and futuristic scenario, calculated as a part of NATCOM Project (India s National Communication to the UNFCCC) for the Cauvery basin (2.7%). (Gosain and Rao, 2003). However if we now compare the present actual value (1000 mm) and RM2 control values (1253.5), the difference is of the order of 27%.
Some Inferences Hence, it can be stated that the results obtained using the simulated climatic series like RM2 should be used only after verifying them for the area under consideration, which can be done by comparing the control values with the actually observed data.
μ μ+σ μ-σ μ+2σ μ-2σ Precipitation 30 25 20 15 10 5 0-5 -10-15 Variability of January precipitations for Present data
μ μ+σ μ-σ μ+2σ μ-2σ Precipitation 100 80 60 40 20 0-20 -40 Variability of January precipitations for RM2 Future data
Variability of monthly precipitations of present and futuristic climate change scenarios Month Present RM2 Future µ ± σ µ ± 2σ µ ± σ µ ± 2σ January 80 90 80 90 February 90 90 80 95 March 90 95 75 90 April 65 100 74 100 May 55 95 90 100 June 70 95 80 95 July 64 100 68 95 August 68 100 69 95 September 58 100 63 100 October 80 95 80 95 November 75 90 85 95 December 75 95 95 95
Though RM2 future monthly precipitation series shows more variance than present series, there is no significant difference in the variance of two series as determined using F two sample test for variance. However, on an annual scale, RM2 future annual precipitation series shows more variance than present series and the difference is found to be significant using F two sample test for variances. In other words, intensification of the hydrological cycle can be seen in the future climate change scenario, and it appears to be significant on an annual basis.
Conclusions Simulation modelling can play a very significant role in conflict resolution by generating a series of scenarios or options for the stakeholders, so as to enable them to take sound rational decisions. Also, implications of climate change on the availability of water in the shared watercourse and consequently, share of each riparian state, can be analysed using modelling techniques.
Thank You