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A method for probabilistic flash flood forecasting

机译:概率性山洪预报方法

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Flash flooding is one of the most costly and deadly natural hazards in the United States and across the globe. This study advances the use of high-resolution quantitative precipitation forecasts (QPFs) for flash flood forecasting. The QPFs are derived from a stormscale ensemble prediction system, and used within a distributed hydrological model framework to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Before creating the PFFFs, it is important to characterize QPF uncertainty, particularly in terms of location which is the most problematic for hydrological use of QPFs. The SAL methodology (Wernli et al., 2008), which stands for structure, amplitude, and location, is used for this error quantification, with a focus on location. Finally, the PFFF methodology is proposed that produces probabilistic hydrological forecasts. The main advantages of this method are: (1) identifying specific basin scales that are forecast to be impacted by flash flooding; (2) yielding probabilistic information about the forecast hydrologic response that accounts for the locational uncertainties of the QPFs; (3) improving lead time by using stormscale NWP ensemble forecasts; and (4) not requiring multiple simulations, which are computationally demanding. Published by Elsevier B.V.
机译:洪水泛滥是美国乃至全球最昂贵,最致命的自然灾害之一。这项研究促进了将高分辨率定量降水预报(QPF)用于山洪预报。 QPF源自暴风雨系综预报系统,并在分布式水文模型框架内用于得出流域特定的概率性山洪预报(PFFF)。在创建PFFF之前,重要的是表征QPF的不确定性,尤其是在位置上,这对于QPF的水文使用最成问题。 SAL方法(Wernli等人,2008)代表结构,幅度和位置,用于误差量化,重点是位置。最后,提出了可产生概率水文预报的PFFF方法。这种方法的主要优点是:(1)确定预计会受到山洪泛滥影响的特定流域规模; (2)得出有关预测水文响应的概率信息,这些信息说明了QPF的位置不确定性; (3)通过使用暴风雨NWP集合预报来缩短交货时间; (4)不需要在计算方面要求很高的多个仿真。由Elsevier B.V.发布

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