This work presents the approach used to estimate coastal flood impact, developed within the EU H2020 European Coastal Flood Awareness System (ECFAS) project, for assessing flood direct impacts on population, buildings, and roads along European coasts. The methodology integrates object-based and probabilistic evaluations to provide uncertainty estimates for damage assessment. The approach underwent a user-driven co-evaluation process. It was applied to 16 test cases across Europe and validated against reported impact data in three major reference cases: Xynthia in La Faute-sur-Mer (France) in 2010, Xaver in Norfolk (UK) in 2013, and Emma in Cádiz (Spain) in 2018. A comparison with grid-based damage evaluation methods was also conducted. The findings demonstrate that the ECFAS impact approach offers valuable estimates for affected populations, reliable damage assessments for buildings and roads, and improved accuracy compared to traditional gridbased approaches. The methodology also provides information for prevention and preparedness activities, and it facilitates further evaluations of risk scenarios and cost–benefit analysis of disaster risk reduction strategies. The approach is a tool suitable for large-scale coastal flood impact assessments, offering improved accuracy and operational capability for coastal flood forecasts. It represents a potential advancement of the existing European-scale impact method used by the European Flood Awareness System (EFAS) for riverine flood warnings. The integration of object-based and probabilistic evaluations, along with uncertainty estimation, enhances the understanding and probabilistic evaluations, along with uncertainty estimation, enhances the understanding and management of flood impacts along European coasts.

Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines

Enrico Duo
Writing – Original Draft Preparation
;
Juan Montes
Writing – Original Draft Preparation
;
Marine Le Gal
Investigation
;
Paolo Ciavola
Supervision
;
2025

Abstract

This work presents the approach used to estimate coastal flood impact, developed within the EU H2020 European Coastal Flood Awareness System (ECFAS) project, for assessing flood direct impacts on population, buildings, and roads along European coasts. The methodology integrates object-based and probabilistic evaluations to provide uncertainty estimates for damage assessment. The approach underwent a user-driven co-evaluation process. It was applied to 16 test cases across Europe and validated against reported impact data in three major reference cases: Xynthia in La Faute-sur-Mer (France) in 2010, Xaver in Norfolk (UK) in 2013, and Emma in Cádiz (Spain) in 2018. A comparison with grid-based damage evaluation methods was also conducted. The findings demonstrate that the ECFAS impact approach offers valuable estimates for affected populations, reliable damage assessments for buildings and roads, and improved accuracy compared to traditional gridbased approaches. The methodology also provides information for prevention and preparedness activities, and it facilitates further evaluations of risk scenarios and cost–benefit analysis of disaster risk reduction strategies. The approach is a tool suitable for large-scale coastal flood impact assessments, offering improved accuracy and operational capability for coastal flood forecasts. It represents a potential advancement of the existing European-scale impact method used by the European Flood Awareness System (EFAS) for riverine flood warnings. The integration of object-based and probabilistic evaluations, along with uncertainty estimation, enhances the understanding and probabilistic evaluations, along with uncertainty estimation, enhances the understanding and management of flood impacts along European coasts.
2025
Duo, Enrico; Montes, Juan; Le Gal, Marine Helene Jeanne; Fernández-Montblanc, Tomás; Ciavola, Paolo; Armaroli, Clara
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2585610
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