Evaluating the performance of bias-corrected IMERG satellite rainfall estimates for hydrological simulation over the Upper Bhima River basin, India

Abstract

In this study, the performance and hydrological utility of IMERG rainfall estimates over the Upper Bhima River basin, India, are comprehensively evaluated using a VIC-RAPID hydrologic model. Moreover, a bias-correction scheme based on the long short-term memory (LSTM) neural network method is proposed, and the results are compared with two commonly used bias-correction techniques. Results indicated that the spatial distribution of observed rainfall is well captured by IMERG; however, it showed a general tendency of overestimation, especially on a daily timescale. The LSTM-based approach showed notable improvements against the other bias-correction techniques substantiating its utility for accurately estimating rainfall amount and skillful detection of rainfall events in the study area. The VIC-RAPID model simulations for hydrological variables revealed a significant improvement in the performance of the bias-corrected IMERG product. In addition, the effect of the hydrological model recalibration with the different rainfall input datasets has also been elucidated.