Comparative Assessment of Remote Sensing Precipitation Products for Runoff Estimation in Indian Watersheds

Abstract Remote sensing precipitation products (RSPPs) are indispensable for hydrological modeling in regions with sparse and uneven rain gauge coverage, particularly in complex terrains. Their ability to deliver spatially continuous, high-resolution rainfall estimates enables improved runoff simulations, flood forecasting, and water resource assessments across heterogeneous hydroclimatic regimes. This study quantifies the manifestation of variability in using different RSPPs (viz., IMERG, MSWEP, GPCP, and PERSIANN) to simulate streamflow using rainfall-runoff model (HYSIM). The study encompasses diverse Indian watersheds—including Halia (Krishna), Lowara (Saurashtra), Mannot (Narmada), Muri (Subernarekha), and Saklespur (Cauvery)—to capture broad spatial, climatic, and topographic variability for a comprehensive assessment of RSPPs. Results demonstrate superior performance by the IMERG Final Run product over orographically influenced watersheds (e.g., Halia and Saklespur) to estimate high-intensity, short-duration rainfall regimes. In contrast, MSWEP exhibited higher skill in semiarid and large river watersheds (e.g., Krishna, Narmada, Saurashtra) with more homogeneous rainfall distributions, benefitting from its multisource integration of satellite retrievals, in situ gauge records, and reanalysis products. Both IMERG and MSWEP achieved satisfactory model performance ( N S E > 0.5 , R 2 > 0.6 ) in Halia, Lowara, Mannot, and Muri watersheds, while in the high-relief, high-rainfall Sakleshpur watershed, only IMERG maintained acceptable accuracy. The results underscore the necessity of precipitation product selection based on watershed-specific physiographic and climatic characteristics, and reaffirm that RSPPs can serve as a robust alternative to gauge-based rainfall data for operational hydrology, enabling enhanced predictive capability in flood forecasting, water resources planning, and watershed management.