Résumé
Flood risk mapping is recognized as an effective tool in flood risk management, and the elaboration of flood risk maps is now required for all major river basins in the European Union (Euro-pean directive 2007/60/EC). Flood risk maps can be based on the computation of potential damage due to different flood events, estimated for each individual stake over the study area. Mean Annual Damage indicator (MAD) is a synthetic indicator obtained by averaging over time, using the return period of each event, that can be mapped too. The issue of uncertainty associated with these flood damage maps should be carefully scrutinized, as they may be used to inform the relevant stakeholders or to design flood mitigation measures. Flood damage maps are based on the combination of hydrological, hydraulic, geographic and economic modeling efforts. Consequently, numerous sources of uncertainty propagate in their elaboration. Recent studies describe these various sources of uncertainty. Some authors propagate these uncertainties through the flood risk modeling chain and estimate confidence bounds around the resulting flood damage estimates. It is of great interest to go a step further by identifying which sources of uncertainty account for most of the uncertainty in flood damage maps. We demonstrate the use of variance-based global sensitivity analysis to rank sources of uncertainty in flood risk mapping, taking into account spatial scaling. We use a quasi-Monte-Carlo scheme to propagate input uncertainties through the process of flood damage map elaboration and to compute importance measures (Sobol' sensitivity indices) for each source of uncertainty. The variability of the damage or MAD indicator and the associated sensitivity indices are estimated at different spatial scales: individual stake, district, whole floodplain. This approach is illustrated on a case study on the Orb River, France. By mapping the uncertainty of damage and MAD indicator, we identified zones where flood damage estimates were less accurate. Then, sensitivity indices allowed ranking the sources of uncertainty at different scales. For instance, the accuracy of the digital elevation model proved to be the key source of uncertainty when estimating the MAD indicator on an individual stake (e.g. a single building), whereas return period of flood events were the most influential when examining the accuracy of total MAD over a larger zone. Finally, maps of sensitivity indices showed the spatial variability of sensitivities over the study area.