Characterization of elements at risk in the multirisk coastal context and at different spatial scales: Multi-database integration (normandy, France). (October 2019) - Graff, K.
Abstract: In risk analyses, two components are taken into account: (1) hazard analysis, including susceptibility and temporal occurrence, and (2) consequence analysis, including characterization of elements at risk (EaRs) and their vulnerability. This study focused on characterization of EaRs, in which items are spatially displayed and impacted by natural events. Several methods can assess these EaRs through expert or engineering approaches. Among the expert approaches, the multicriteria method is more flexible and allows integration of a wide range of information in order to characterize and discretize different EaRs. Traditionally, the mapping and criteria accuracy of EaRs is the same at all spatial analysis scales, while the hazard accuracy changes according to the spatial scales. Therefore, we propose an approach based on the selection of different information/criteria among several private or open access multiple geographical databases to adapt the mapping and criteria accuracy of each EaR according to the hazard analysis spatial scale. After harmonizing the different databases and merging them under GIS, a single database per work scale is created through a specific procedure with interoperability of results between scales. Thus, the number of criteria used to describe these EaRs will depend on the scale of work and the spatial scale of the analysis. To develop and test the transposability of this method, three experimental coastal study sites subject to several hazards (multirisk) have been selected in Normandy (France) with error estimations ranging between 10% and 20%. Subsequently, these data can be integrated into risk and multirisk analyses. Highlights: We propose a methodology to characterize the Elements at Risk in multirisk coastal context with multi-criteria approach. Three spatial scale analyses are used in this method: at medium scale, secondly at large scale and at local scale. Two integration models are proposed to merge multiple databases in two final data warehouses. The uncertainties about accuracy of these models are less than 10 % for medium scale and around 15 % at large scale.