In this paper, we investigate the determinants of private ood mitigation in France. We conducted a survey among 331 inhabitants of two ood-prone areas and collected data on several topics, including individual ood mitigation, risk perception, risk experience, and sociodemographic characteristics. We estimate discrete choice models to explain either the precautionary measures taken by the household, or the intention to undertake such measures in the future. Our results conrm that the Protection Motivation Theory is a relevant framework to describe the mechanisms of private ood mitigation in France, highlighting in particular the importance of threat appraisal and previous experience of oods. Some sociodemographic features also play a signicant role in explaining private ood mitigation. We also observed that respondents who had already taken precautionary measures have a lower perception of the risk of ooding than respondents who planned to implement such measures at the time of the survey. This result can be explained by the existence of a feedback eect of having taken precautionary measures on risk perception. If subsequent studies support this assumption, it would imply that intended measures, rather than implemented ones, should be examined to explore further the determinants of private ood mitigation.

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Abstract. Effective flood risk management requires a realistic estimation of flood losses. However, available flood damage estimates are still characterized by significant levels of uncertainty, questioning the capacity of flood damage models to depict real damages. With a joint effort of eight international research groups, the objective of this study was to compare, in a blind-validation test, the performances of different models for the assessment of the direct flood damage to the residential sector at the building level (i.e. microscale). The test consisted of a common flood case study characterized by high availability of hazard and building data but with undisclosed information on observed losses in the implementation stage of the models. The nine selected models were chosen in order to guarantee a good mastery of the models by the research teams, variety of the modelling approaches, and heterogeneity of the original calibration context in relation to both hazard and vulnerability features. By avoiding possible biases in model implementation, this blind comparison provided more objective insights on the transferability of the models and on the reliability of their estimations, especially regarding the potentials of local and multivariable models. From another perspective, the exercise allowed us to increase awareness of strengths and limits of flood damage modelling, which are summarized in the paper in the form of take-home messages from a modeller's perspective.