Effect of Climate Change on the Rainfall Trends: A case study of Pune, India

Shalaka Shah, Shreenivas N. Londhe

Research Square · 16 November 2021

In one sentence. Artificial neural network downscales five GCMs' rainfall over Pune, India, giving a higher correlation than distribution-based scaling for the best models.

Abstract

Abstract It is the need of the hour to predict the impact of climate change, especially rainfall on the future environmental conditions on local as well as global scales. The present work aims at studying the impact of climate change on the rainfall occurring over Pune, the eighth largest city in India. The General Circulation Models (GCMs) are predominantly used to obtain the climate data all over the globe, at various grid points, for past and future years. Rainfall values obtained from these grid points need to be downscaled to make them location specific. This study proposes a soft computing tool, Artificial Neural Network (ANN) for the purpose of downscaling. The rainfall data at 4 grid points surrounding Pune, was extracted from 5 different GCMs and given as input to ANN with observed rainfall as output, thus forming 5 models. For comparison, a pre-existing downscaling technique, Distribution based scaling (DBS) was used. The coefficient of correlation (r) showed that ANN was working better than DBS. The value of r for ANN was 0.73 for its least accurate model whereas DBS managed to reach 0.73 for its most accurate model. The future rainfall estimated with the help of the trained ANN models show an increase in mean rainfall over the Pune region by ∼2 – 15% and decrease in maximum rainfall by ∼40 – 65%. Peak prediction of rainfall simulated by ANN was not very accurate and hence there is still an opportunity for improvement which is the future scope of this study.

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