Assessing coastal vulnerability to climate change at regional scales is now mandatory in France since the adoption of recent laws to support adaptation to climate change. However, there is presently no commonly recognised method to assess accurately how sea level rise will modify coastal processes in the coming decades. Therefore, many assessments of the physical component of coastal vulnerability are presently based on a combined use of data (e.g. digital elevation models, historical shoreline and coastal geomorphology datasets), simple models and expert opinion. In this study, we assess the applicability and usefulness of a multi-criteria decision-mapping method (the analytical hierarchy process, AHP) to map physical coastal vulnerability to erosion and flooding in a structured way. We apply the method in two regions of France: the coastal zones of Languedoc-Roussillon (north-western Mediterranean, France) and the island of La Réunion (south-western Indian Ocean), notably using the regional geological maps. As expected, the results show not only the greater vulnerability of sand spits, estuaries and low-lying areas near to coastal lagoons in both regions, but also that of a thin strip of erodible cliffs exposed to waves in La Réunion. Despite gaps in knowledge and data, the method is found to provide a flexible and transportable framework to represent and aggregate existing knowledge and to support long-term coastal zone planning through the integration of such studies into existing adaptation schemes

International audienceThe severity of the impact of a natural hazard on a society depends on, among other factors, the intensity of the hazard and the exposure and resistance ability of the elements at risk (e.g., persons, buildings and infrastructures). Social conditions strongly influence the vulnerability factors for both direct and indirect impact and therefore control the possibility to transform the occurrence of a natural hazard into a natural disaster. This article presents a model to assess the relative socioeconomic vulnerability to landslides at the local to regional scale. The model applies an indicator-based approach. The indicators represent the underlying factors that influence a community's ability to prepare for, deal with, and recover from the damage and loss associated with landslides. The proposed model includes indicators that characterize the demographic, social and economic setting as well as indicators representing the degree of preparedness, effectiveness of the response and capacity to recover. Although this model focuses primarily on the indirect losses, it could easily be extended to include physical indicators accounting for the direct losses. Each indicator is individually ranked from 1 (lowest vulnerability) to 5 (highest vulnerability) and weighted, based on its overall degree of influence. The final vulnerability estimate is formulated as a weighted average of the individual indicator scores. The proposed model is applied for six case studies in Europe. The case studies demonstrate that the method gives a reasonable ranking of the vulnerability. The practical experience achieved through the case studies shows that the model is straightforward for users with knowledge on landslide locations and with access to local census data

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International audienceUsually, the vulnerability of structures is treated considering only one single hazard, for example either seismic hazard or ground instability. However, historical monuments, cultural heritage structures or old masonry structures can be affected, over the past centuries of existence, by a succession of different hazards. The objective of this paper was to quantify the changes in the seismic vulnerability of a given structure previously damaged by quasi-static differential settlements. In view of this, a soil-structure model of the masonry building is constructed using TREMURI software by putting springs at the base of the building to account for the soil behaviour. Classical empirical relationships are used for setting the mechanical properties of these foundation springs. The chosen equations are valid for both static and dynamic situations. The soil-structure model is first submitted to differential settlements with maximum magnitudes ranging from 5 to 25 cm. Then, the damaged structures are submitted to a set of time-histories. Fragility curves taking into consideration permanent differential settlements are finally obtained, using a modified regression technique accounting for the presence of residual drift due to the prior static loading