Downscaling of surface temperature for lake catchment in an arid region in India using linear multiple regression and neural networks

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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 maximum and minimum temperatures (Tmax and Tmin) to lake‐basin scale in an arid region in India. The effectiveness of these techniques is demonstrated through application to downscale the predictands 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: (i) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000; and (ii) 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 and to study the predictor–predictand relationships. The performance of the linear multiple regression and ANN models was evaluated based on several statistical performance indicators. The ANN‐based models are found to be superior to LMR‐based models and subsequently, the ANN‐based model is applied to obtain future climate projections of the predictands. An increasing trend is observed for Tmax and Tmin for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario by using predictors. Copyright © 2011 Royal Meteorological Society

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