Development and evaluation of pre and post integration techniques for enhancing drought predictions over India

Abstract

AbstractAccurate estimation and prediction of drought events are highly essential for implementing effective planning and management strategies to handle this complex natural phenomenon. Application of machine learning algorithms (MLAs) for integrating satellite precipitation products (SPPs), unlike gauge observations, can furnish precise drought estimations. In this study, we have proposed and tested two approaches (pre and post‐integration of SPPs) that deal with the prediction of drought employing 13 MLAs. Three SPPs are integrated under four combinations (involves two and three datasets integration) employing pre and post integration approaches to predict Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index at various temporal scales (1, 3, 6 and 12‐month). From the overall results, Approach‐2 that involves estimation of drought before integration using MLAs proved effective than Approach‐1 (prediction of drought post‐integration). Neural Networks based Bayesian Regularization (NBR) under three dataset integration outperformed at all temporal scales and climatic zones of India when compared to the other 12 MLAs and two dataset integration combinations. The blended product (NBR) manifested enhancements in statistical results at all temporal scales and climatic zones. European Centre for Medium‐Range Weather Forecasts ReAnalysis (ERA‐5) dataset performed better in predicting drought events in more climatic zones compared to Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR) and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) when compared against Indian Meteorological Department (IMD) dataset. In contrast, PERSIANN‐CDR proved effective in predicting drought at the country scale. ERA‐5 could be suitable for real‐time drought monitoring and prediction, whereas PERSIANN‐CDR can be used for retrospective drought analysis. The proposed approach and the best‐performed algorithm (NBR) can be extended and applied in any climatic region for enhancing the drought predictions where remotely sensed information are accessible even in regions with finite ground data availability.