Camila Cotrim

Coastal storm-induced flooding threatens millions of people and infrastructures, highlighting the need for comprehensive flood risk assessments. A key component of these assessments is the spatial characterization of total water level (TWL), the primary driver of coastal flooding. We propose a flexible methodology for developing large-scale TWL hindcasts to estimate extreme events, considering possible spatial variabilities in marine dynamics. This methodology is applied to the European coastline, integrating downscaled nearshore waves, storm surges, and tides. The resulting hourly time series of the TWL have a spatial resolution of 1 km and covers the period from 1985 to 2021. Spatial variability is considered in foreshore slopes and extreme value detection thresholds, addressing common simplifications in large-scale studies. In addition to a characterization of extreme events based on the relative contributions of TWL components, sensitivity analyses of the wave contribution, wave data resolution, foreshore slopes, and wave setup formulations are conducted. The tide-dominated Atlantic coast is where the TWL is most affected by the wave dataset. The storm surge-dominated Baltic region exhibits the lowest confidence in estimating TWL return levels, partially due to the data and methods used. The Mediterranean Sea, characterized as a mixed environment, is the most sensitive to the inclusion of wave contribution. A classification of TWL extremes revealed that no regions have extreme events dominated by wave setup when compared to the remaining components, while those dominated by tides show the highest return levels.

Coastal flooding is among the most damaging natural hazards in Europe, yet large-scale assessments have typically relied on simplified static “bathtub” models and coarse elevation data. Here, we present a novel pan-European methodology that applies a dynamic flood model at 25 m resolution, forced by location-specific total water level hydrographs. These hydrographs integrate mean sea level, tides, storm surge, and wave setup with spatially varying foreshore slopes, allowing storm type, duration, and shape to be explicitly represented. More than 51 000 coastal target points were used to reconstruct events, and the methodology was validated against 12 local-scale historical floods across diverse coastlines. The validation results confirmed the robustness of the large-scale methodology while highlighting the strong dependence of the results on the resolution and vertical accuracy of the underlying digital elevation model. At continental-scale, sensitivity analyses quantified uncertainty from model selection, hydrograph shape, and storm type. Results show that static flood models systematically overestimate inundation, with errors exceeding 25 % in low-lying coastal floodplains such as Belgium and the United Kingdom. At the continental scale, storm type variability explains 41 % of flood map uncertainty, while hydrograph shape has a smaller but measurable effect. Including coastal protection standards reduces the estimated exposed floodplain by more than half, underscoring the critical role of defenses. By bridging the gap between global static assessments and local dynamic models, this study establishes a methodological benchmark for continental-scale flood hazard mapping.

Coastal flooding is among the most damaging natural hazards in Europe, yet large-scale assessments have typically relied on simplified static "bathtub" models and coarse elevation data. Here, we present a novel pan-European methodology that applies a dynamic flood model (RFSM-EDA) at 25 m resolution, forced by location-specific total water level (TWL) hydrographs. These hydrographs integrate mean sea level, tides, storm surge, and wave setup with spatially varying foreshore slopes, allowing storm type, duration, and shape to be explicitly represented. More than 51,000 coastal target points were used to reconstruct events, and the methodology was validated against 12 historical floods across diverse coastlines. Sensitivity analyses quantified uncertainty from model selection, DEM resolution, hydrograph shape, and storm type. Results show that static flood models systematically overestimate inundation, with errors exceeding 25 % in low-lying coastal floodplains such as Belgium and the United Kingdom. At the continental scale, storm type variability explains 41 % of flood map uncertainty, while hydrograph shape has a smaller but measurable effect. Including coastal protection standards reduces the estimated exposed floodplain by more than half, underscoring the critical role of defenses. By bridging the gap between global static assessments and local dynamic models, this study establishes a methodological benchmark for continental-scale flood hazard mapping. The framework not only advances scientific understanding of large-scale coastal flooding but also provides actionable evidence to support the EU Floods Directive, adaptation planning, and climate risk management in the finance and insurance sectors.

Coastal flooding is among the most damaging natural hazards in Europe, yet large-scale assessments have typically relied on simplified static "bathtub" models and coarse elevation data. Here, we present a novel pan-European methodology that applies a dynamic flood model (RFSM-EDA) at 25 m resolution, forced by location-specific total water level (TWL) hydrographs. These hydrographs integrate mean sea level, tides, storm surge, and wave setup with spatially varying foreshore slopes, allowing storm type, duration, and shape to be explicitly represented. More than 51,000 coastal target points were used to reconstruct events, and the methodology was validated against 12 historical floods across diverse coastlines. Sensitivity analyses quantified uncertainty from model selection, DEM resolution, hydrograph shape, and storm type. Results show that static flood models systematically overestimate inundation, with errors exceeding 25 % in low-lying coastal floodplains such as Belgium and the United Kingdom. At the continental scale, storm type variability explains 41 % of flood map uncertainty, while hydrograph shape has a smaller but measurable effect. Including coastal protection standards reduces the estimated exposed floodplain by more than half, underscoring the critical role of defenses. By bridging the gap between global static assessments and local dynamic models, this study establishes a methodological benchmark for continental-scale flood hazard mapping. The framework not only advances scientific understanding of large-scale coastal flooding but also provides actionable evidence to support the EU Floods Directive, adaptation planning, and climate risk management in the finance and insurance sectors.

Coastal flooding is among the most damaging natural hazards in Europe, yet large-scale assessments have typically relied on simplified static "bathtub" models and coarse elevation data. Here, we present a novel pan-European methodology that applies a dynamic flood model (RFSM-EDA) at 25 m resolution, forced by location-specific total water level (TWL) hydrographs. These hydrographs integrate mean sea level, tides, storm surge, and wave setup with spatially varying foreshore slopes, allowing storm type, duration, and shape to be explicitly represented. More than 51,000 coastal target points were used to reconstruct events, and the methodology was validated against 12 historical floods across diverse coastlines. Sensitivity analyses quantified uncertainty from model selection, DEM resolution, hydrograph shape, and storm type. Results show that static flood models systematically overestimate inundation, with errors exceeding 25 % in low-lying coastal floodplains such as Belgium and the United Kingdom. At the continental scale, storm type variability explains 41 % of flood map uncertainty, while hydrograph shape has a smaller but measurable effect. Including coastal protection standards reduces the estimated exposed floodplain by more than half, underscoring the critical role of defenses. By bridging the gap between global static assessments and local dynamic models, this study establishes a methodological benchmark for continental-scale flood hazard mapping. The framework not only advances scientific understanding of large-scale coastal flooding but also provides actionable evidence to support the EU Floods Directive, adaptation planning, and climate risk management in the finance and insurance sectors.

Coastal storm-induced flooding threatens millions of people and infrastructures, highlighting the need for comprehensive flood risk assessments. A key component of these assessments is the spatial characterization of total water level (TWL), the primary driver of coastal impacts. We propose a homogeneous methodology for developing large-scale TWL hindcasts to estimate extreme events, considering possible spatial variabilities in marine dynamics. This methodology is applied to the European coastline, integrating downscaled nearshore waves, storm surges, and tides. The resulting hourly time series of the TWL have a spatial resolution of 1 km and covers the period from 1985 to 2021. Spatial variability is considered in foreshore slopes and extreme value detection thresholds, addressing common simplifications in large-scale studies. In addition to a characterization of extreme events based on the relative contributions of TWL components, sensitivity analyses of the wave contribution, wave data resolution, foreshore slopes, and wave setup formulations are conducted. The tide-dominated Atlantic coast is most affected by the wave dataset. The storm surge-dominated Baltic region exhibits the lowest confidence in estimating TWL return levels, partially due to the data and methods used. The Mediterranean Sea, characterized by a mixed environment, is the most sensitive to the inclusion of wave contribution. A classification of TWL extremes revealed that no regions have extreme events dominated by wave setup, while those dominated by tides show the highest return levels.

Coastal storm-induced flooding threatens millions of people and infrastructures, highlighting the need for comprehensive flood risk assessments. A key component of these assessments is the spatial characterization of total water level (TWL), the primary driver of coastal impacts. We propose a homogeneous methodology for developing large-scale TWL hindcasts to estimate extreme events, considering possible spatial variabilities in marine dynamics. This methodology is applied to the European coastline, integrating downscaled nearshore waves, storm surges, and tides. The resulting hourly time series of the TWL have a spatial resolution of 1 km and covers the period from 1985 to 2021. Spatial variability is considered in foreshore slopes and extreme value detection thresholds, addressing common simplifications in large-scale studies. In addition to a characterization of extreme events based on the relative contributions of TWL components, sensitivity analyses of the wave contribution, wave data resolution, foreshore slopes, and wave setup formulations are conducted. The tide-dominated Atlantic coast is most affected by the wave dataset. The storm surge-dominated Baltic region exhibits the lowest confidence in estimating TWL return levels, partially due to the data and methods used. The Mediterranean Sea, characterized by a mixed environment, is the most sensitive to the inclusion of wave contribution. A classification of TWL extremes revealed that no regions have extreme events dominated by wave setup, while those dominated by tides show the highest return levels.

Coastal flooding is among the most damaging natural hazards in Europe, yet large-scale assessments have typically relied on simplified static "bathtub" models and coarse elevation data. Here, we present a novel pan-European methodology that applies a dynamic flood model (RFSM-EDA) at 25 m resolution, forced by location-specific total water level (TWL) hydrographs. These hydrographs integrate mean sea level, tides, storm surge, and wave setup with spatially varying foreshore slopes, allowing storm type, duration, and shape to be explicitly represented. More than 51,000 coastal target points were used to reconstruct events, and the methodology was validated against 12 historical floods across diverse coastlines. Sensitivity analyses quantified uncertainty from model selection, DEM resolution, hydrograph shape, and storm type. Results show that static flood models systematically overestimate inundation, with errors exceeding 25 % in low-lying coastal floodplains such as Belgium and the United Kingdom. At the continental scale, storm type variability explains 41 % of flood map uncertainty, while hydrograph shape has a smaller but measurable effect. Including coastal protection standards reduces the estimated exposed floodplain by more than half, underscoring the critical role of defenses. By bridging the gap between global static assessments and local dynamic models, this study establishes a methodological benchmark for continental-scale flood hazard mapping. The framework not only advances scientific understanding of large-scale coastal flooding but also provides actionable evidence to support the EU Floods Directive, adaptation planning, and climate risk management in the finance and insurance sectors.

Coastal storm-induced flooding threatens millions of people and infrastructures, highlighting the need for comprehensive flood risk assessments. A key component of these assessments is the spatial characterization of total water level (TWL), the primary driver of coastal impacts. We propose a homogeneous methodology for developing large-scale TWL hindcasts to estimate extreme events, considering possible spatial variabilities in marine dynamics. This methodology is applied to the European coastline, integrating downscaled nearshore waves, storm surges, and tides. The resulting hourly time series of the TWL have a spatial resolution of 1 km and covers the period from 1985 to 2021. Spatial variability is considered in foreshore slopes and extreme value detection thresholds, addressing common simplifications in large-scale studies. In addition to a characterization of extreme events based on the relative contributions of TWL components, sensitivity analyses of the wave contribution, wave data resolution, foreshore slopes, and wave setup formulations are conducted. The tide-dominated Atlantic coast is most affected by the wave dataset. The storm surge-dominated Baltic region exhibits the lowest confidence in estimating TWL return levels, partially due to the data and methods used. The Mediterranean Sea, characterized by a mixed environment, is the most sensitive to the inclusion of wave contribution. A classification of TWL extremes revealed that no regions have extreme events dominated by wave setup, while those dominated by tides show the highest return levels.