| Summary: | Study region: This study focuses on three data-limited inland river basins in the arid Hexi Corridor, Northwest China. Study focus: The lack of ground-based observation data for estimating potential evapotranspiration (PET) usually limits the streamflow simulation. This study explored the feasibility of using three gridded PET datasets (GLDAS, GLEAM, and ERA5) to force the hydrologic model. The hydrologic performance of these PET datasets was investigated using the HBV-light model. New hydrological insights for the region: The results show that GLEAM performs best in estimating PET on both a daily and monthly scale. GLDAS generally captures the seasonal variations and patterns well but shows a higher estimation in magnitude. For the hydrological simulation, all three PET datasets can be used as forcing data to drive the HBV-light model, as the Nash-Sutcliffe efficiency (NSE) values are greater than 0.64 during the validation period for the three PET inputs in the three basins. The magnitude and temporal signal of PET forcing data do not significantly affect streamflow simulations of the HBV-light. The model mainly adjusts the water content of the soil box (WCS) to eliminate the efficiency loss caused by the PET magnitude difference to achieve the best simulation results.
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