Flood Risk Vulnerability Assessment: What are the Main Factors? Hierarchization of The Main Factors at a Regional Scale

Tuesday, 15 December 2015: 14:10
302 (Moscone South)
Zehir Kolli1, Gwenael Jouannic1, Tiffany Legendre1, Mario Marchetti1, Philippe Gastaud1, Julien Gargani2, Romain Lermet1, Clement Augeard3, Didier Felts3 and Fabrice Arki1, (1)Center for expertise and engineering on risks, urban and country planning, environment and mobility - Cerema, Laboratoire RĂ©gional de Nancy, TOMBLAINE, France, (2)Univ. Paris-Sud, Orsay, France, (3)Cerema, Laboratoire RĂ©gional de Bordeaux, 33073, Bordeaux, France
Recent studies have shown that the national flood risk exposure is high in France, with one fourth of the total population and a third of jobs located in risk areas. In this context, a global vulnerability assessment methodology is currently being developed in France to bring adequate tools for local territories to manage flood risk. This study addresses the question of the quantification, the qualification and the choice of these vulnerability indicators for a given territory. This work aims to propose a classification of nearly 40 of these indicators in terms of their relative impacts on the risk level estimated on two territories:
  • Chalon-sur-Saône (Saône river)
  • Garonne estuary (Garonne and Dordogne rivers, and Atlantic ocean)

Through these cases study, 3 different spatial scales have been compared: the Prés-Saint-Jean district inside Chalon (0.6 km²), the city of Ambès (28.8 km²) and Chalon with its suburbs (72.2 km²).

A principal component analysis (PCA) was applied and indicated a threshold in terms of urban impacts between the different flood scenarios. On Chalon, the PCA discriminates 2 groups of flood and highlighted a threshold between T20 and T50. A partial least-square regression (PLS) was computed to make predictions on vulnerability indicators values modelled on new flood scenarios. Their results were is useful to identify the most relevant vulnerability indicators as a function of their flood exposure. These statistical analysis aims to highlight the relationship between a variable of exposure level (hydrologic impact: water levels and flow velocity) with spatialized vulnerability indicators in a 100 m grid (e.g., population, job, etc.). Finally, to get a hierarchy of variables depending on their impact on the risk level, an ANOVA was computed. The selection of variables was performed with a stepwise selection to assess contributions of each dependant variable on the F-statistic as they are added to or removed from the model.