Statistical Downscaling of GCM Output and Simulation of Rainfall Scenarios for Brahmani Basin

Abstract

The change in climate threats the abundance of usable water across the globe. Most of the river basins are unable to cope up with the impact of climate change. Hence, assessing the future scenario has become the need of today. General Circulation Model(GCM) provide information at a course grid resolution. Downscaling can help in getting the information at a local scale level from GCM data which will help the researchers to work on a regional level. Statistical downscaling method is preferred over dynamic downscaling method due to its less complex calculations. Statistical downscaling model (SDSM) is widely used in prediction of future climate scenarios. Here Brahmani-Baitarani river basin is selected as a case study for the downscaling of precipitation in the monthly time scale. SDSM version 4.2 is used as the model and precipitation is taken as the predictand parameter. Predictors are chosen from the NCEP global variables like air temperature, geopotential height, specific humidity, zonal and meridional wind velocities, precipitable water and surface pressure data. The outcome of the study shows that the mean rainfall will increase in this river basin in the 2040s. Increase in dry spell and decrease in wet spell also observed in the results.