Statistical downscaling of rainfall under climate change in Krishna River sub-basin of Andhra Pradesh, India using artificial neural network (ANN)

Abstract

Due to the very coarse spatial resolution of the different global circulation model (GCM), we cannot use them in their natural form to study the various impacts of climate change. For matching this spatial inequality between the GCMs output (predictor) and historical precipitation data (predictands), we need to establish a relation between them which is known as downscaling. In the present study, we tried to examine the efficiency of the Artificial Neural Network (ANN) with Principal Component Analysis (PCA) for downscaling the rainfall for 3 districts of Andhra Pradesh of India. Firstly, for all the regions, the downscaling was performed by using ANN. Then seasonal and annual analysis was performed based on the R2 and RMSE. The results show that the ANN worked adequately based on the statistical parameters. The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, re-analysis from the National Centre for Environmental Prediction (NCEP) as GCM model, and observed rainfall data as the observed rainfall. The analysis was performed for the three RCPs scenario, RCP 2.6, 4.5 and 8.5. Finally, the ANN model is applied to downscale the precipitation.