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The characterisation of past coastal flood events is crucial for risk prevention. However, it is limited by the partial character of historical information on flood events and the lack or limited quality of past hydro-meteorological data. In addition coastal flood processes are complex, driven by many hydro-meteorological processes, making mechanisms and probability analysis challenging. These issues are tackled by joining historical, statistical and modelling approaches. We focus on a macrotidal site (Gâvres, France) subject to overtopping and investigate the 1900-2010 period. A continuous hydro-meteorological database is built and a damage event database is set up based on archives, newspapers, maps and aerial photographies. Using together historic information, hindcasts and hydrodynamic models, we identified 9 flood events, among which 5 significant flood events (4 with high confidence: 1924, 1978, 2001, 2008; 1 with a lower confidence: 1904). These flood events are driven by the combination of sea-level rise, tide, atmospheric surge, offshore wave conditions and local wind. The critical conditions leading to flood are further analysed, including the effect of coastal defences, showing that the present coastal defences would not have allowed to face the hydro-meteorological conditions of 09/02/1924 for instance, whose bi-variate return periods of exceedance Tr (still water level relative to the mean sea level and significant wave height) is larger than 1000 y. In addition, Tr is expected to significantly decrease with the sea-level rise, reaching values smaller than 1 y, for 8 of the 9 historical events, for a sea-level rise of 0.63 m, which is equal to the median amount of sea-level rise projected by the 5th Assessment Report of the IPCC in this region for RCP8.5 in 2100.

The characterisation of past coastal flood events is crucial for risk prevention. However, it is limited by the partial nature of historical information on flood events and the lack or limited quality of past hydro-meteorological data. In addition, coastal flood processes are complex, driven by many hydro-meteorological processes, making mechanisms and probability analysis challenging. Here, we tackle these issues by joining historical, statistical and modelling approaches. We focus on a macrotidal site (Gâvres, France) subject to overtopping and investigate the 1900–2010 period. We build a continuous hydro-meteorological database and a damage event database using archives, newspapers, maps and aerial photographs. Using together these historical information, hindcasts and hydrodynamic models, we identify nine flood events, among which five are significant flood events (four with high confidence: 1924, 1978, 2001, 2008; one with a lower confidence: 1904). These flood events are driven by the combination of sea-level rise, tide, atmospheric surge, offshore wave conditions and local wind. We further analyse the critical conditions leading to flood, including the effect of coastal defences, showing, for instance, that the present coastal defences would not have allowed to face the hydro-meteorological conditions of 09/02/1924, whose bi-variate return periods of exceedance $$T_\mathrm{R}$$ (still water level relative to the mean sea level and significant wave height) are larger than 1000 year. In the coming decades, $$T_\mathrm{R}$$ is expected to significantly decrease with sea-level rise, reaching values smaller than 1 year, for eight of the nine historical events, for a sea-level rise of 0.63 m, which is equal to the median sea-level rise projected by the 5th Assessment Report of the IPCC in this region for RCP8.5 in 2100.

Rohmer, J.; Louisor, J.; Le Cozannet, G.; Naveau, P.; Thao, S., and Bertin, X., 2020. Attribution of extreme wave height records along the North Atlantic coasts using hindcast data: Feasibility and limitations. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1268-1272. Coconut Creek (Florida), ISSN 0749-0208.Extreme Event Attribution (EEA) aims at answering questions about how much climate change influenced the probability or intensity of a specific type of extreme meteo-oceanic event. Here, we focus on wave record breaking i.e. the occurrence of an extreme significant wave height (hs) value that exceeds all past observations. The objective is to evaluate the probability changes related to climate change by estimating the fraction of attributable risk far=1–p0/p1, where p0 and p1 are probabilities of the wave record event in two different worlds: a counterfactual world without anthropogenic forcings i.e. a “world that might have been”, and the factual world, i.e. “world that is.” To define these different worlds, we rely on a wave hindcast database, which provides very long time series (1900-2008) of hs over the whole North Atlantic Ocean Basin (NAOB). We assume that the counterfactual world corresponds to the series of annual hs maxima over the period 1900-1930, and that the factual world corresponds to the annual hs maxima over the period 1978-2008. The extreme event attribution approach dedicated to record breakings was applied over NAOB, and we show large far values (>0.5) along the northern Canadian, Scottish and southwestern Norwegian coasts (over the latitudes 50°-65°N). These results are, however, carefully discussed with respect to different uncertainty sources, namely the validity of the assumptions underlying the EEA, the statistical uncertainties, the use of hindcast data instead of global climate model's results, and the limitations in the hindcast database setup.

Idier, D.; Aurouet, A.; Bachoc, F.; Baills, A.; Betancourt, J.; Durand, J.; Mouche, R.; Rohmer, J.; Gamboa, F.; Klein T.; Lambert, J.; Le Cozannet, G.; Le Roy, S.; Louisor, J.; Pedreros, R., and Veron, A.L., 2020. Toward a user-based, robust and fast running method for coastal flooding forecast, early warning, and risk prevention. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1111–1116. Coconut Creek (Florida), ISSN 0749-0208.Scientific progresses now allow properly modelling coastal flooding events. Such models are nevertheless very expensive in terms of computation time (>hours) which prevents any use for forecast and warning or even for estimating the coastal flood hazard return period together with uncertainties. In addition, there is a gap between model outputs and information actually needed by the decision makers. Within the RISCOPE project, we aim at developing a user-based method contributing to forecast, early-warning and prevention of coastal flooding risks. The method should be robust, fast and integrate the complexity of coastal flood processes. To build such coastal flooding models, the solution explored relies on meta-models, i.e. mathematical functions which estimate, with good precision and at a negligible computational cost (

Sandy shorelines are constantly evolving, threatening frequently human assets such as buildings or transport infrastructure. In these environments, sea-level rise will exacerbate coastal erosion to an amount which remains uncertain. Sandy shoreline change projections inherit the uncertainties of future mean sea-level changes, of vertical ground motions, and of other natural and anthropogenic processes affecting shoreline change variability and trends. Furthermore, the erosive impact of sea-level rise itself can be quantified using two fundamentally different models. Here, we show that this latter source of uncertainty, which has been little quantified so far, can account for 20 to 40% of the variance of shoreline projections by 2100 and beyond. This is demonstrated for four contrasting sandy beaches that are relatively unaffected by human interventions in southwestern France, where a variance-based global sensitivity analysis of shoreline projection uncertainties can be performed owing to previous observations of beach profile and shoreline changes. This means that sustained coastal observations and efforts to develop sea-level rise impact models are needed to understand and eventually reduce uncertainties of shoreline change projections, in order to ultimately support coastal land-use planning and adaptation.

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Identifying the offshore forcing and breaching conditions that lead to marine inundation is of high importance for risk management. This task cannot be conducted by using a numerical hydrodynamic model due to its high computation time cost (of several minutes or even hours). In the present study, we show how the random forest (RF) classification technique can approximate the numerical model to explore these critical conditions. We focus on the Boucholeurs site, which is located on the French Atlantic coast and exposed to overflow processes. An iterative strategy is developed for selecting the numerical simulations (a total of 200) to train the RF model. The sensitivity to the input parameters is studied using permutation-based importance measures and extended versions of the partial dependence plots. The results highlight the key interplay among the high-tide level, the surge peak and the phase difference, and the complex role of the breaching location.