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首页> 外文期刊>Hydrology and Earth System Sciences >Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)
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Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)

机译：从中期天气预报（10天）到降雨径流模型到欧洲洪水预报系统（EFFS）中的洪水淹没预报的级联模型不确定性

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The political pressure on the scientific community to provide medium to longterm flood forecasts has increased in the light of recent flooding events inEurope. Such demands can be met by a system consisting of three differentmodel components (weather forecast, rainfall-runoff forecast and floodinundation forecast) which are all liable to considerable uncertainty in theinput, output and model parameters. Thus, an understanding of cascadeduncertainties is a necessary requirement to provide robust predictions. Inthis paper, 10-day ahead rainfall forecasts, consisting of one deterministic,one control and 50 ensemble forecasts, are fed into a rainfall-runoff model(LisFlood) for which parameter uncertainty is represented by six differentparameter sets identified through a Generalised Likelihood UncertaintyEstimation (GLUE) analysis and functional hydrograph classification. Therunoff of these 52 * 6 realisations form the input to a flood inundationmodel (LisFlood-FP) which acknowledges uncertainty by utilising ten differentsets of roughness coefficients identified using the same GLUE methodology.Likelihood measures for each parameter set computed on historical data areused to give uncertain predictions of flow hydrographs as well as spatialinundation extent. This analysis demonstrates that a full uncertaintyanalysis of such an integrated system is limited mainly by computer power aswell as by how well the rainfall predictions represent potential futureconditions. However, these restrictions may be overcome or lessened in thefuture and this paper establishes a computationally feasible methodologicalapproach to the uncertainty cascade problem.
机译：鉴于欧洲最近发生的洪水事件，科学界提供中长期洪水预报的政治压力增加了。可以通过由三个不同的模型组件（天气预报，降雨径流预报和洪水泛滥预报）组成的系统来满足这些需求，这些组件在输入，输出和模型参数方面都存在很大的不确定性。因此，对级联不确定性的理解是提供可靠预测的必要条件。本文将由一种确定性，一种控制性和50种总体预报组成的10天前降雨预报输入降雨径流模型（LisFlood），该模型的参数不确定性由通过广义似然不确定性估计（ GLUE）分析和功能水线分类。这52 * 6个实现的径流形成了洪水泛滥模型（LisFlood-FP）的输入，该模型通过使用十种不同的粗糙度系数集（使用相同的GLUE方法识别）来确认不确定性。流水线图的预测以及空间淹没程度。该分析表明，这种集成系统的完全不确定性分析主要受到计算机功能以及降雨预测代表未来潜在条件的能力的限制。然而，这些限制可能会在未来被克服或减轻，因此本文针对不确定性级联问题建立了一种在计算上可行的方法。

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