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.

中文翻译：

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整体洪水预报：现状和未来机遇

在过去的十年中，由于整体数值天气预报和气候预报的增长，高性能计算的扩展，人们对从确定性决策转变为基于风险的决策（考虑到预测不确定性）的兴趣日益浓厚，整体洪水预报获得了巨大动力。国际水文合奏预报实验（HEPEX）等社区的努力，其重点是提高相关总体预报能力并促进其采用。随着这一转变，需要了解整体洪水预报的现状，以便对当前的能力和需要改进的领域提供见解，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，在对70篇针对全球整体洪水预报各个方面的论文进行回顾的基础上，我们对整体洪水预报的当前研究活动进行了概述，并讨论了知识差距和未来的研究机会。未来的研究方向包括改善集成洪水预报技术方面的机会，例如数据同化技术和方法以解决更多不确定性源，以及通过应用机器学习等技术来开发更多变量的集成预报，例如洪水泛滥。此外，我们得出的结论是，不仅需要改进洪水预报的技术方面，而且还需要弥合科学研究与水文气象模型开发之间的差距，