Impact of Satellite Precipitation Estimation Methods on the Hydrological Response: Case studies Wadi Nu’man Basin, Saudi Arabia

Abstract

Abstract Accurately estimating precipitation is essential for managing water resources and assessing hydrological systems, particularly in small basins. The main goal of the current study is to determine how different methods for estimating precipitation affect the hydrological response, or runoff, in the Wadi Nu'man basin in Saudi Arabia. Rainfall data was collected from ground observations and satellite rainfall products for a period of thirteen years (2006–2018). The satellite precipitation data was collected from the Tropical Rainfall Measuring Mission (TRMM-3B42), TRMM-Realtime (TRMM-3B42RT), and Climate Hazards Group InfraRed Precipitation (CHRIPS) and compared with station data for reliability analysis. Additionally, a linear scaling bias correction method was employed between satellite and ground-based station data. The HEC-HMS model was used to simulate the runoff under all available bias-corrected precipitation datasets. Different statistical matrices assessed the performance of the satellite dataset based on observed rainfall and simulated runoff. In rainfall data assessment, a strong regression (R2 = 0.90) for CHIRPS, the lowest for TRMM-3B42RT (R2 = 0.20), and a moderate regression (R2 = 0.60) for TRMM-3B42 were found as compared to observed rainfall. As for runoff modeling, the HEC-HMS model exhibited a very high regression (R2) value of 0.99 between peak discharges generated by gauges and satellite (TRMM and CHRIPS) rainfall. Overall, this research emphasizes the utilization of bias-corrected TRMM and CHRIPS precipitation datasets for runoff and hydrological water balance estimation in small wadies like Wadi Nu'man. Future studies focusing on implementing complex bias correction techniques, testing diverse satellite datasets, and integrating advanced modeling tools like machine learning offer the potential to significantly refine and expand the accuracy of hydrological predictions, vital for decision support systems in water resource management.