Abstract
The emergence of various high-resolution satellite precipitation products (SPPs) solves the problem of precipitation data sources for areas with a lack of precipitation data and is recognized as a reliable supplement to rain gauge observations in hydrometeorological applications. However, there still exists a shortcoming of coarse spatial resolution when applying these products to small and microscale river basins. In this study, a typical karst watershed in Southwest China—the Pingtang River Basin (PTRB)—was selected, and based on the relationship between precipitation and normalized difference vegetation index (NDVI), aspect, slope, and elevation, we used the geographically weighted regression (GWR) to downscale three SPPs, namely, global precipitation measurement (GPM), global satellite mapping of precipitation (GSMAP), and multisource weighted-ensemble precipitation (MSWEP), to 1 km × 1 km, respectively. Combined with rain gauge stations, the geographical differential analysis (GDA) was used to carry out error corrections to obtain three downscaling correction satellite precipitation products (DC-SPPs) with a 1 km spatial resolution, including DC-GPM, DC -GSMAP, and DC-MSWEP. Several statistical indices were used to perform error evaluation and precipitation capture ability analysis on SPPs and DC-SPPs, and the Grid-Xin’anjiang (the Grid-XAJ) model was used to compare their hydrological utility. The results show the following: (1) The downscaling correction method is effective. GWR can effectively improve the spatial resolution of SPPs, while GDA can reduce errors and further improve the accuracy of precipitation estimation. In addition, (2) the precipitation event characterization capabilities of GPM and GSMAP have been improved after downscaling correction, while the ability to capture precipitation events before and after the MSWEP correction is poor, showing a high hit rate and a high false alarm rate, which is unreliable to monitor precipitation events in the PTRB. Finally, (3) compared with SPPs, the hydrological performances of the three kinds of DC-SPPs have been significantly improved, and the NSE are all above 0.75 with low error. In general, the overall performance of DC-GSMAP is satisfactory. The accuracy of different SPPs after downscaling correction is different, but the applicability has been improved to different degrees.
Highlights
Academic Editor: Tomeu Rigo e emergence of various high-resolution satellite precipitation products (SPPs) solves the problem of precipitation data sources for areas with a lack of precipitation data and is recognized as a reliable supplement to rain gauge observations in hydrometeorological applications
Downscaled and Corrected DC-GPM1km/DC-GSMAP1km/DC-MSWEP1km products and daily scale rainfall station observations. e CCs of the three SPPs are all higher than 0.65, the global precipitation measurement (GPM) has the best consistency with rainfall at the rainfall station (CC is 0.69), and the Relative bias (RB) ranges from − 8.29% to − 12.09%, all of which show an underestimation of the daily precipitation in the basin. e maximum Mean absolute error (MAE) of multisource weighted-ensemble precipitation (MSWEP) is 4.06 mm, and the maximum root mean square error (RMSE) of global satellite mapping of precipitation (GSMAP) is 10.51 mm. e error performance of GSMAP is slightly better than that of GPM and MSWEP. e three kinds of DC-SPPS are better than the corresponding SPPs in terms of consistency and reduction of various errors
Research shows that MAE can better reflect the error level of SPPs. e RBs of the three types of DC-SPPS are all less than 0 (− 7.12%, − 4.72%, and − 4.14%, respectively), indicating that the precipitation products still underestimate the daily precipitation in the basin after downscaling correction
Summary
Academic Editor: Tomeu Rigo e emergence of various high-resolution satellite precipitation products (SPPs) solves the problem of precipitation data sources for areas with a lack of precipitation data and is recognized as a reliable supplement to rain gauge observations in hydrometeorological applications. A typical karst watershed in Southwest China—the Pingtang River Basin (PTRB)— was selected, and based on the relationship between precipitation and normalized difference vegetation index (NDVI), aspect, slope, and elevation, we used the geographically weighted regression (GWR) to downscale three SPPs, namely, global precipitation measurement (GPM), global satellite mapping of precipitation (GSMAP), and multisource weighted-ensemble precipitation (MSWEP), to 1 km × 1 km, respectively. Combined with rain gauge stations, the geographical differential analysis (GDA) was used to carry out error corrections to obtain three downscaling correction satellite precipitation products (DC-SPPs) with a 1 km spatial resolution, including DC-GPM, DC -GSMAP, and DC-MSWEP. Previous studies have selected one or more environmental factors to spatially downscale satellite precipitation products. According to the hydrological and climatic conditions of the watershed, four environmental factors (NDVI, DEM, slope, and aspect) were selected to downscale the three SPPs
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