Abstract
Abstract: Evaluating the effects of climate change is crucial in regions grappling with water scarcity. The Ujjani dam, a significant structure in Maharashtra, India, erected on the Bhima river in 1980, serves as a vital water source for the agricultural lands downstream in Solapur and Pune districts. Assessing climate change's impact involves analysing data from General Circulation Models (GCMs), which project climate parameters under various emission scenarios but often at a broader scale. Since hydrological models necessitate finer-scale climate data, a downscaling technique is employed to derive localized variables from GCM outputs. This study employs Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques for downscaling. By utilizing projected temperature and rainfall data, three distinct models are developed to predict reservoir inflow: multiple linear regression, artificial neural network, and wavelet neural network. The findings indicate significant alterations in rainfall distribution patterns, with a decline during the monsoon season and an increase post-monsoon
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