The value of ultra-detailed survey data for an improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0

Abstract

Abstract. Accurate flood damage modelling is essential to estimate the potential impact of floods and to develop effective mitigation strategies. However, flood damage models rely on diverse sources of hazard, exposure and vulnerability data, which are often incomplete, inconsistent, or totally missing. These issues with data quality or availability introduce uncertainties in the modelling process and affect the final risk estimations. In this study, we present INSYDE 2.0, a flood damage modelling tool that integrates ultra-detailed survey and desk-based data for an enhanced reliability and informativeness of flood damage predictions, including an explicit representation of the effect of uncertainties arising from an incomplete knowledge on the variables characterizing the system under investigation.