Downscaling of Precipitation for Lake Catchment in Arid Region in India using Linear Multiple Regression and Neural Networks

Abstract

In this paper, downscaling models are developed using a Linear Multiple Regression (LMR) and Artificial Neural Networks (ANNs) for obtaining projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of these techniques is demonstrated through application to downscale the predictand (precipita- tion) for the Pichola lake region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1948-2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001-2100. The scatter plots and cross- correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3. The perform- ance of the linear multiple regression and ANN models was evaluated based on several statistical performance indicators. The ANN based models is found to be superior to LMR based models and subsequently, the ANN based model is applied to obtain future climate projections of the predictand (i.e precipitation). The precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors. In the COMMIT scenario, where the emissions are held the same as in the year 2000.