期刊
REMOTE SENSING
卷 13, 期 16, 页码 -出版社
MDPI
DOI: 10.3390/rs13163061
关键词
satellite precipitation estimates; hydrological simulation; scale effects; PERSIANN; GPM; DTVGM; Yalong River basin
类别
资金
- National Key Research and Development Program of China [2018YFE0196000]
- U.S. Department of Energy (DOE) [DE-IA0000018]
- National Natural Science Foundation of China [51879009]
- Second Tibetan Plateau Scientific Expedition and Research Program [2019QZKK0405]
- China Scholarship Council (CSC) [202106040071]
This study evaluated the simulated discharge from eight quasi-global SPEs at different spatial scales and found a scale effect in their application in discharge simulation. When the catchment area is larger than 20,000 km(2), the overall performance of discharge simulation improves, while below 20,000 km(2), the discharge simulation capability is more randomized and relies heavily on local precipitation accuracy. The study highlights the need for more advanced retrieval algorithms, data sources, and bias correction methods to improve the overall quality of SPEs for hydrological simulations.
Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km(2). The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km(2), the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km(2), the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.
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