Assessing applicability of two gridded precipitation datasets for hydrological simulation in a poorly gauged river basin towards supporting sustainable water resources management

Environ Res. 2023 Nov 15;237(Pt 1):116956. doi: 10.1016/j.envres.2023.116956. Epub 2023 Aug 22.

Abstract

Reliable and accurate precipitation estimates are important for hydrological studies and sustainable water resource management. However, networks of rain gauges are often sparsely and unevenly distributed in many large river basins in the world including the Red River basin (RRB). Thus this study aimed to comprehensively evaluate the applicability of two widely used gridded precipitation products, gauge-based APHRODITE and gauge satellite-based GSMaP-Gauge, over the RRB using both statistical and hydrological assessment approaches. The accuracy assessment of the gridded precipitation datasets was performed by comparing with the reference precipitation dataset derived from the local weather stations. The hydrological performance of both gridded products was evaluated through the Variable Infiltration Capacity (VIC) hydrological modelling scheme for simulation of daily streamflow at the hydrological stations in the RRB. The results demonstrated that both gridded products could generally capture the spatiotemporal variation of the reference precipitation over the RRB during the period of 2005-2014, although both underestimated the reference precipitation. Results of statistical analysis showed that the APHRODITE data outperformed the GSMaP-Gauge data in precipitation estimation. The performance of the VIC model driven by the gridded precipitation products in streamflow simulation was satisfactory, although simulations forced with APHRODITE data displayed the better performance. Generally, the APHRODITE product showed its encouraging potential for hydrological studies over the RRB.

Keywords: Complex topography; Gridded precipitation products; Red River basin; Statistical evaluation; Streamflow simulation; Variable Infiltration Capacity model.