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Validation of Extreme Precipitation Reconstructed by Dynamical Downscaling for the Upper Feather, Yuba, and American Watersheds

机译:动态降尺度重建的上层羽毛,浴霸和美国流域重建的极端降水的验证

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Fine grid resolution and temporal scales are crucial to analyze the hydrologic impacts from extreme floods for a watershed. A regional scale atmospheric model such as MM5 is useful to achieve this purpose because ground observation stations are rarely located within watersheds. However, the achieved data set by a model should be validated against observation data before it is used. In this study, historical global reanalysis atmospheric data (NCAR/NCEP) was dynamically downscaled to a 3km resolution over the Upper Feather, Yuba, and American River watersheds using the Fifth-Generation NCAR / Penn State Mesoscale Model (MM5). To validate the reconstructed precipitation, historical data from California Data Exchange Center (CDEC) stations were first used. However, the data that can be obtained from them is coarse throughout the watersheds so historical data from PRISM (parameter-elevation regressions on independent slopes model) were also used. The hourly increment precipitation data found at the CDEC stations in each watershed are used to verify the temporal accuracy of the model simulation, and the PRISM simulations are used to verify the monthly basin-average precipitation and its spatial distribution over each watershed.
机译:细网格分辨率和时间尺度至关重要,以分析流域的极端洪水的水文影响至关重要。诸如MM5的区域规模大气模型可用于实现此目的,因为地面观察站很少位于流域内。但是,应在使用之前验证由模型设置的实现数据。在这项研究中,历史全球再分析大气数据(NCAR / NCEP)使用第五代NCAR / PENN状态Mescle Model(MM5)动态地折叠到上羽毛,yuba和美国河流流域的3公里分辨率。为了验证重建的降水,首先使用来自加利福尼亚数据交换中心(CDEC)站的历史数据。但是,可以从它们获得的数据在整个流域中粗糙,因此也使用来自棱镜的历史数据(独立斜坡模型上的参数升高回归)。每个流域的CDEC站发现的每小时增量降水数据用于验证模型仿真的时间准确性,棱镜模拟用于验证每次流域的月平均降水及其空间分布。

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