GPM卫星降水产品在长江流域的空间降尺度研究

Spatial Downscaling of GPM Satellite Precipitation Products in the Yangtze River Basin,China

高精度以及高时空分辨率的卫星降水产品对于区域或流域尺度的水文气象研究至关重要。以长江流域为研究区,以IMERG和GSMaP卫星降水产品为研究对象,在充分评价两种产品的适用性后,对表现更好的产品进行空间降尺度分析。考虑到降水与地理特征(DEM)、植被指数(NDVI)、地表温度(LST)等可能存在的相关性,基于随机森林回归分别建立了DEM、DEM+NDVI、DEM+NDVI+LST等多种统计降尺度模型,并以研究区内133个地面站点的实测降水数据对各模型的降尺度效果进行检验。研究表明:IMERG和GSMaP两种产品在长江流域均具有较高的观测精度,且以GSMaP表现更优;在多年平均、年等时间尺度上,DEM+NDVI+LST统计降尺度模型始终表现最佳,在提高降水产品空间分辨率的同时(从0.1°到0.05°),对于产品的观测精度也有一定程度提升,且在月尺度上依旧表现理想。该研究可为卫星降水产品的空间降尺度提供一种新的思路。

Satellite precipitation products with a high spatio-temporal resolution are essential for hydrometeoro⁃logical research on the regional or watershed scale.By comparing the accuracy of IMERG and GSMaP satellite precipitation products in the Yangtze River basin during 2015~2020,the outperformed product is then used for spatial downscaling.Considering the relationship between precipitation and geographic features(DEM),vegeta⁃tion index(NDVI),and Land Surface Temperature(LST),various statistical downscaling models based on random forest regression are developed,such as DEM model,DEM+NDVI model and DEM+NDVI+LST model.Then,the performance of the three models is further evaluated against observations from 133 stations in the study area.The results indicate GSMaP outperforms IMERG in the Yangtze River basin.Among the three downscaling models,DEM+NDVI+LST model performs best on the multi-year mean scale and annual scale,and the performance of the model remains stable on the monthly scale.This study can provide a new way for spatial downscaling of satellite precipitation products.