Ensemble flood forecasting: Current status and future opportunities
Ensemble flood forecasting: Current status and future opportunities
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DOI：
10.1002/wat2.1432
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发表时间：
2020-03-29
影响因子：
8.2
通讯作者：
Robertson, David E.
中科院分区：
文献类型：
--
作者：
Wu, Wenyan;Emerton, Rebecca;Robertson, David E.
由于集合数值天气和气候预测的增长，高性能计算的扩展，对从确定性决策转向基于风险的决策（解释预测不确定性）的兴趣日益增加，以及国际水文Englance预测实验（HEPEX）等社区的努力，Englance洪水预报在过去十年中获得了显著的发展势头。其重点是提高相关集合预报能力并促进其采用。随着这一转变，需要了解集合洪水预报的现状，以便深入了解当前的能力和改进领域，从而确定未来的研究机会，以便更好地分配研究资源。在这篇文章中，我们提供了一个概述，目前的研究活动在集合洪水预报和讨论的知识差距和未来的研究机会，70篇论文的审查的基础上，集中在各个方面的集合洪水预报在地球仪。未来的研究方向包括有机会改进集合洪水预报的技术方面，如数据同化技术和方法，以考虑更多的不确定性来源，并通过应用机器学习等技术，开发更多变量的集合预报，如洪水泛滥。此外，我们得出结论，不仅需要改进洪水预报的技术方面，还需要弥合科学研究和水文气象模型开发之间的差距，以及使用概率集合预报的现实世界洪水管理，特别是通过有效的沟通。这篇文章分类下：工程水利>方法水科学>水极端
Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk-based decision-making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this article, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focusing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example, flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real-world flood management using probabilistic ensemble forecasts, especially through effective communication.This article is categorized under:Engineering Water > MethodsScience of Water > Water Extremes