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Ensemble flood forecasting: Current status and future opportunities

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Abstract 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 > Methods Science of Water > Water Extremes
Study focus of papers selected
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Origins of selected studies
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Number of publications in each year
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Ensemble‐member numbers considered
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Ensemble evaluation methods used
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Ensemble generation methods used
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Flood driving processes considered
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(Surrogate) Flood variables predicted
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(Surrogate) Flood models used
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Forecast lead time considered
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Temporal resolution of floods investigated
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Spatial scale of floods investigated
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Types of floods investigated (Note: A flood event could be double‐counted, as an event could be a flash flood event occurred in an urban catchment. Therefore, the total number in the figure exceeds the total number of papers reviewed)
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Locations of case studies investigated. The whole numbers represent the number of case study systems investigated in each region during each time period. The percentages in parenthesis represent the percentage of cases study systems in each region over the total number of case study systems considered during each time period
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Science of Water > Water Extremes
Engineering Water > Methods

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