Sensitivity of lumped and semi-distributed hydrological models to 20 gridded precipitation products in a transboundary basin

This study proposes a dual approach to assess 20 Gridded Precipitation Products (GPPs) within a transboundary region with complex topography. GPPs were first compared with observed precipitation to assess their spatio-temporal accuracy. They were then integrated into a lumped (GR4J) and a semi-distributed (MGB-IPH) hydrological model to evaluate their impact on streamflow simulations for three basins. Even if most GPPs effectively captured the dominant north–south precipitation gradient shaped by the Andean topography, the results show significant variations in GPP effectiveness across the considered basins. The most reliable GPPs for streamflow simulation across Katari, Ilave, and Ramis basins are MSWEP, CHIRPS, and MSWEP when considering the lumped GR4J model and SM2Rain_CCI, IMERG_FR, and SM2Rain_CCI when considering the semi-distributed MGB model. This discrepancy among the models shows that GPPs’ reliability assessment is sensitive to the model structure and that different conclusions could be made according to the selected model. Our findings show that the GR4j lumped model is barely influenced by precipitation bias due to its buffering capacity. In contrast, the semi-distributed MGB-IPH model is sensitive to precipitation bias in space and time and therefore is more suitable to reveal GPP inconsistencies. Overall, this study not only provides GPP reliability feedback but also new insights on the respective limits and advantages of different assessment methods (i.e., gauges, lumped and semi-distributed models). These findings support the development of a practical framework for GPP selection according to the forecast use.