【摘 要】
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This study develops a new method to estimate spatially distributed rainfall through merging the raingauge record (RGR), the satellite observation (SLO), and the terrain digital elevation model (DEM) d
【机 构】
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State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;Dep
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This study develops a new method to estimate spatially distributed rainfall through merging the raingauge record (RGR), the satellite observation (SLO), and the terrain digital elevation model (DEM) data, including the following four steps: (1) to select a suitable SLO dataset, (2) to downscale the selected SLO dataset with the DEM data, (3) to determine the weighted differences between the RGR and the downscaled SLO dataset, and (4) to calculate the spatially distributed rainfall through merging the downscaled SLO dataset and the weighted differences.The rainstorm occurred on 21 July 2012 in Beijing, China, was considered as a case study to validate the method.Three SLO datasets (i.e., TMPA 3B41RT, 3B42RT and CMORPH) were compared with the related RGR.Using the new method, this study generated the spatially distributed rainfall data, which were further compared with the three rainfall datasets, i.e., two original rainfall datasets (the selected SLO dataset and the RGR) and one merged rainfall dataset without consideration of topographic influence.The result revealed that the spatially distributed rainfall data could represent the spatial distribution of rainfall more rational than those three other datasets.Furthermore, using the spatially distributed rainfall data and a hydrological model, the Digital Yellow River Integrated Model (DYRIM), this study simulated the streamflow process at the Dashi River basin in the southwest of Beijing.The simulation results showed that the spatially distributed rainfall data could have better performance than those three other datasets, especially for the peak flow simulation.Overall, it is concluded that the newly-developed data merging method can enhance our capability in estimating the spatial distribution of rainfall.
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