Out-Performing Bias-Corrected GCM Models and CMIP6-Based Precipitation and Temperature Projections for the Bagmati Irrigation Area

Abstract

The selection of General circulation models (GCMs) and suitable bias correction methods for any particular study area in very crucial for the projection of precipitation and temperature using climate models which can be used for estimating the future crop water requirement. The results of a General Circulation Model (GCM) are being downscaled and compared to a baseline climatology for two IPCC scenarios (ssp245 and ssp585) based on Coupled Model Inter-comparison Project Phase 6 (CMIP6) climate model. We choose four GCMs models out of ten by evaluating their performance to observe historical data. Performance indicators (NSE, PBAIS, and RSR) are computed by comparing bias-corrected historical data with observed historical data. We found that GCM models EC-Earth3, NorESM2-MM, GDFL-ESM4, and IPSL-CM6A-LR showed a higher rating for maximum and minimum temperature, and GCM models EC-Earth3, NorESM2-MM, GDFLESM4, and MPI-ESM2-MM showed a higher rating for precipitation. Among the different bias correction functions power Xo transformation and Power transformed functions, Bernoulli’s Weibull showed the best performance for minimum temperature), maximum temperature, and precipitation, respectively. These models and bias correction could be used to project the climate variables of the surrounding basins.