Bias Correction of Satellite Precipitation Products for Hydrologic Modeling in Western Ghats Region, India

Abstract

A comprehensive examination of regional errors in Satellite Precipitation Products (SPPs) is crucial for accurate hydrometeorological modelling. In this study, a multiplicative error-based approach was used for correcting systematic bias in the SPPs at Western Ghats (WG) region of India. Most of the SPPs available so far underestimate the monsoon rainfall in WG. Quality controlled gridded rain gauge data from the Indian Meteorological Department (IMD) was used as the ground data for bias correction. Bias correction of three multi-satellite precipitation products, namely, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 Version 7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center (CPC) MORPHed (CMORPH) precipitation, were performed in this study. The results show that bias correction remarkably reduced the bias between the SPPs and IMD rainfall measurements. The efficacy of bias-corrected SPPs in hydrologic modelling was investigated with the help of two conceptual rainfall runoff models, GR4J and HYMOD. The bias-corrected SPPs were able to provide improved streamflow simulations with daily Nash-Sutcliffe efficiency (NSE) and correlation coefficients greater than 0.4 and 0.7, respectively. It was also found that the performance of the model HYMOD was marginally better than that of GR4J in predicting streamflow in terms of NSE, linear correlation coefficient, and p-factor for all five validation catchments in the WG region. This study contributes to the ongoing research on error characterization of SPPs for improved global hydrometeorological modelling.