Application and assessment of a downscaled GPM dataset in the simulation of snowmelt runoff in alpine mountainous areas

Abstract

Study region: the Jingou River basin (located in central Xinjiang, China). Study focus: Estimating snowmelt runoff in alpine areas lacking monitoring stations is essential for water resource management. The newly released global precipitation measurement (GPM) data provide a high-precision precipitation input data source for snowmelt runoff simulations in such areas. However, the resolution (10 km × 10 km) is still insufficient for small and medium watersheds, which reduces the application efficiency of the GPM dataset in runoff simulations. Therefore, the geographic weighted regression model (GWR) is used to downscale the GPM dataset and drive the snowmelt runoff model (SRM) in this study. New hydrological insights for the region: The results show that the average R2 values of daily runoff simulated by observed precipitation data and the downscaled GPM dataset (1 km × 1 km) are 0.869 and 0.847, respectively, and the average Nash-Sutcliffe efficiency (NSE) values are 0.795 and 0.715, respectively. The accuracy of the runoff data simulated by the downscaled GPM dataset is 2.5% lower than that simulated based on the observed data, and good results are obtained. This study shows that the downscaled GPM dataset can be regarded as an effective alternative product in areas lacking observed data. The GPM dataset has good application prospects as substitute data in mountainous areas, and the GPM dataset resolves the precipitation data scarcity in high-elevation mountainous areas without stations and provides a rare data source for modeling.

Keywords

• Snowmelt runoff model (SRM)
• Snow cover fraction (SCF)
• GPM
• Geographically weighted regression (GWR)
• Spatial downscaling