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首页> 外文期刊>Journal of hydrologic engineering >Assessing 32-Day Hydrological Ensemble Forecasts in the Lake Champlain-Richelieu River Watershed
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Assessing 32-Day Hydrological Ensemble Forecasts in the Lake Champlain-Richelieu River Watershed

机译:评估山寨湖 - 里德利河流域32天水文集合预测

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This paper explored various configurations of the ensemble Kalman filter, the GR4J hydrological model, and the Global Environmental Multiscale (GEM) atmospheric model in order to maximize the skill of ensemble hydrological forecasts for the Lake Champlain-Richelieu River watershed. In open-loop mode, the hydrological model represented very well the observed streamflow (Nash-Sutcliffe value above 90%). It sufficed to assimilate hydrological data to obtain a reliable and skillful analysis of streamflow; assimilation of snow water equivalent (SWE) information did not bring additional benefits. In forecast mode, the opposite was true: hydrological assimilation alone did not improve forecast performance, but assimilating SWE data improved reliability and skill of forecasts with lead times of 15 days to 1 month. The impact of SWE assimilation also depended on the quality of the precipitation analysis. It therefore is recommended to use SWE assimilation for monthly forecasting, especially if the precipitation data used to drive the hydrological model are biased.
机译:本文探讨了集合乐队滤波器,GR4J水文模型和全球环境多尺度(GEM)大气模型的各种配置,以最大限度地提高山坡山地山地河水流域的集合水文预报的技能。在开环模式下,水文模型表示,观察到的流出(NASH-SUTCLIFFE值高于90%)。它足以吸收水文数据,以获得对流流的可靠和熟练的分析;雪水等同物的同化(SWE)信息没有带来额外的福利。在预测模式下,相反的是真实的:单独的水文同化并未提高预测性能,而是同化SWE数据的可靠性和技能,以15天至1个月的交货时间提高预测。 SWE同化的影响也取决于降水分析的质量。因此,建议使用SWE同化每月预测,特别是如果用于驱动水文模型的降水数据偏置。

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