Abstract:
The coarse resolution future climatic data need to be downscaled for regional and basin scale application. The Statistical Downscaling Model (SDSM) has been used to predict future scenarios and variation during three future periods FP-1(2020-2035), FP-2 (2046-2064) and FP-3 (2081-2100) under SRES A1B and A2 scenarios for maximum temperature at Raipur, located in upper Mahandi basin of Chhattisgarh state (India). Five different combinations of predictors have been tried in calibration (1971-1990) and validations (1991-2003) processes using site specific data and residual error, Nash-Sutcliff efficiency and root mean square error have been used to adjudge the best-fit combination of predictors for future projections. The combination consisting mean sea level pressure, surface vorticity, 500 hPa geopotential height (ncepp500gl), surface and 850hPa specific humidity were found the best fit combination which gave efficiency of 86.38% in calibration while 90.78% in validation. The number of hot days with temperature more than 400C may increase in March to May under SRES A1B scenario and March & April under SRES A2 climate forcing condition.
