Statistical Downscaling for Evaluating Precipitation and Extremes for Bhima River Basin

Abstract

The hydrological implications of global climate change on regional levels are often studied by scaling down large-scale climatic variables modelled by General Circulation Models (GCMs).Hydro meteorological variables refers to the use of statistical downscaling methods (SDSM) for minimising precipitation. In this paper, we recommend a statistical downscaling model that relies on three different methods namely delta, quantile mapping, empirical quantile mapping. In order to explore statistical downscaling method, the station Chaskaman, Paragon, Shirur, Sakhar have been chosen for a study area to test the precipitation methodology. All stations are located in Bhima river basin. To find the pattern from the historical base on observation (training period) and then apply the pattern to historical and SSPs periods. The forecasted future based on climate predictions which isCMIP6 model namely CNRM-CM6-1 is used. The downscaling findings suggest that the SDSM model could be effectively accepted in terms of daily precipitation down scaling through out the calibrationas well as evaluation stages. SDGCM model predicts that overall average annual rainfall will increase at all chosen stations in the future (2021-2100) in river basins for SSP245 scenarios and also increased total average rainfall for all the selected station for SSP585 scenarios. The downscaling results reveal how the statistical downscaling model performs effectively in the downscaling of daily precipitation.