Summary
This narrative review of 70 peer-reviewed studies examines the evolution of ensemble flood forecasting from deterministic to probabilistic risk-based approaches, driven by advances in ensemble weather prediction and computing capacity. The authors identify technical opportunities including improved data assimilation, comprehensive uncertainty quantification, and machine learning applications for flood inundation forecasting. A critical finding is the persistent gap between scientific research development and real-world operational implementation, which the authors argue requires improved communication and integration of probabilistic forecasts into flood management decision-making.
UK applicability
The findings are directly applicable to UK flood forecasting and management, where the Met Office and Environment Agency operate ensemble prediction systems. The identified need for better communication between research and operational practice aligns with UK efforts to enhance probabilistic flood risk communication and decision-support tools for water authorities and emergency responders.
Key measures
Current ensemble flood forecasting methods, data assimilation techniques, uncertainty quantification approaches, machine learning applications, and adoption barriers in operational flood management
Outcomes reported
The narrative review synthesised current research across 70 papers on ensemble flood forecasting, identifying technical capabilities, knowledge gaps, and opportunities for improving probabilistic flood risk prediction and operational adoption.
Topic tags
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