Abstract.A model has been developed in order to estimate insurance-related losses caused by coastal flooding in France. The deterministic part of the model aims at identifying the potentially flood-impacted sectors and the subsequent insured losses a few days after the occurrence of a storm surge event on any part of the French coast. This deterministic component is a combination of three models: a hazard model, a vulnerability model, and a damage model. The first model uses the PREVIMER system to estimate the water level resulting from the simultaneous occurrence of a high tide and a surge caused by a meteorological event along the coast. A storage-cell flood model propagates these water levels over the land and thus determines the probable inundated areas. The vulnerability model, for its part, is derived from the insurance schedules and claims database, combining information such as risk type, class of business, and insured values. The outcome of the vulnerability and hazard models are then combined with the damage model to estimate the event damage and potential insured losses. This system shows satisfactory results in the estimation of the magnitude of the known losses related to the flood caused by the Xynthia storm. However, it also appears very sensitive to the water height estimated during the flood period, conditioned by the junction between seawater levels and coastal topography, the accuracy for which is still limited by the amount of information in the system.

No abstract

CCR (Caisse Centrale de Réassurance) is a French reinsurance company playing a major role in the natural catastrophe coverage in France. Since 2003, CCR has been developing tools for the estimation of its exposure to climatic risks. These tools cover three main perils: flood, storm surge and drought. Models are used to estimate the insurance losses and are systematically used for all major climatic events. Both modelling calibration and validation are based on an important policy and claim database. It was created in 2003 and supplied every year with insurer's data. In order to evaluate the financial exposure for insurance of extreme events, a stochastic approach has been developed since 2011, for flood, storm surge and drought. The simulation of the stochastic event set allows us to estimate the mean annual losses and losses associated with different return periods. The objective of this approach is to connect the impact models for all perils with a large set of climate simulations. ARPEGE-Climate (Météo-France) is a model that is used to generate two sets of 200 years of hourly atmospheric time series: at current conditions and at year 2050 conditions according to RCP (Representative Concentration Pathways) 4.5. The main climate data used are: hourly rainfall, wind speed and atmospheric pressure and the Soil Wetness Index that is issued from a complementary surface model. The hazard and vulnerability models developed are based on the climatic data to compute continuous loss estimations. The method proposed will take into consideration development scenarios to evaluate the consequences of demographic growth and insured values evolution. The simulations show a global loss increase in 2050 which can be attributed to climatic factors such as extreme rainfall increase or sea level rise as well as, for a major part, the population and insured value growth in areas at risk.

Afin d'anticiper les dommages engendrés par les phénomènes de submersion marine et d'y faire face, CCR a développé un modèle spécifique à ce péril pour la France métropolitaine. Ce modèle présente deux volets : une modélisation déterministe permettant d'estimer le coût d'un événement quelques jours après sa survenance et une modélisation probabiliste permettant d'estimer l'exposition au risque de submersion marine. Ces deux volets sont construits sur la combinaison entre un modèle d'aléa permettant d'estimer les hauteurs d'eau affectant les polices d'assurance, un modèle de vulnérabilité caractérisant l'exposition des polices et un modèle de dommage, traduisant les sorties des deux modèles précédents en coût. Le volet déterministe est actuellement fonctionnel sur l'ensemble du littoral français et les travaux se poursuivent sur le modèle probabiliste. La chaîne de modélisation développée permet ainsi d'appréhender le risque de submersion marine actuel par le prisme des dommages assurantiels ainsi que son évolution future dans le contexte du changement climatique.

Abstract. A model has been developed in order to estimate insurance-related losses caused by coastal flooding in France. The deterministic part of the model aims at identifying the potentially flood-impacted sectors and the subsequent insured losses a few days after the occurrence of a storm surge event on any part of the French coast. This deterministic component is a combination of three models: a hazard model, a vulnerability model and a damage model. The first model uses the PREVIMER system to estimate the water level along the coast. A storage-cell flood model propagates these water levels over the land and thus determines the probable inundated areas. The vulnerability model, for its part, is derived from the insurance schedules and claims database; combining information such as risk type, class of business and insured values. The outcome of the vulnerability and hazard models are then combined with the damage model to estimate the event damage and potential insured losses. This system shows satisfactory results in the estimation of the magnitude of the known losses related to the flood caused by the Xynthia storm. However, it also appears very sensitive to the water height estimated during the flood period, conditioned by the junction between sea water levels and coastal topography for which the accuracy is still limited in the system.