Exploring the potential of satellite precipitation after bias correction in streamflow simulation in a semi-arid watershed in northeastern China

Abstract

Study RegionThe Upper Taoer River Watershed (UTRW), Northeastern China Study FocusThis study presents a comprehensive hydrometeorological evaluation of six satellite precipitation products (SPPs) over a sparsely gauged semi-arid watershed, including the adjusted and unadjusted versions of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM products (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERISIANN) series. The effectiveness of monthly bias correction in improving SPPs estimation accuracy and in enhancing their feasibility in hydrological applications was also analyzed. New hydrological insights for the regionThe SPPs, especially bias-corrected SPPs, adequately provide complementary precipitation information for UTRW where ground measurement is insufficient. The adjusted SPPs after bias correction obtained a higher Nash-Sutcliffe Efficiency (NSE) than gauge data when driving streamflow simulation. The unadjusted SPPs hydrological applicability was significantly enhanced after bias correction, with NSE< 0 (except for IMERG product) and NSE> 0.34 before and after correction, respectively. Performance of the TMPA series improved the most through bias correction, making it the preferred choice for daily and monthly simulations. Additionally, SPPs were more applicable in wet and normal years and require improvement in dry years. This study provides valuable references for identifying better alternative precipitation sources for local water resource management and exploring an effective SPP utilization approach in practical applications.