A novel downscaling-integration approach of satellite rainfall estimates for accurate river flow prediction

绘制全球年和月降雨侵蚀力地图的依据是：高时间分辨率(30分钟)、长期(2001-2020年)基于卫星的降水产品；全球降水综合多卫星反演(GPM-IMERG)；以及来自全球降雨侵蚀力数据库(GloREDa)站的年平均降雨侵蚀力(n=3286)。采用一种基于残差的合并方案，通过地理加权回归将基于GPM-IMERG的降雨侵蚀力与GloREDa相结合。总体而言，GPM-IMERG-只低估了降雨侵蚀力。GPM-IMERG与GloREDa相结合的降雨侵蚀力估算精度显著提高(纳什-萨克利夫效率=0.83，百分比偏差=-2.4%，均方误差=1122MJ mm/ha/h/yr)，而仅用GPM-IMERG估算的降雨侵蚀力(纳什-萨克利夫效率=0.51，百分比偏差=27.8%，均方根误差=1730 mJ mm/ha/h/yr)显著提高。

Global annual and monthly rainfall erosivity was mapped based on a high-temporal-resolution (30-min), long-term (2001-2020) satellite-based precipitation product; the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG); and mean annual rainfall erosivity from the Global Rainfall Erosivity Database (GloREDa) stations (n = 3286). A residual-based merging scheme was applied to integrate GPM-IMERG-based rainfall erosivity with GloREDa using Geographically Weighted Regression. Overall, GPM-IMERG-only estimates underestimated rainfall erosivity. The accuracy of rainfall erosivity estimates from GPM-IMERG merged with GloREDa substantially improved (Nash-Sutcliffe efficiency = 0.83, percent bias = －2.4%, and root mean square error = 1122 MJ mm/ha/h/yr) compared to estimates by GPM-IMERG-only (Nash-Sutcliffe efficiency = 0.51, percent bias = 27.8%, and root mean square error = 1730 MJ mm/ha/h/yr).