The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0

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 into the modelling process and affect the final risk estimations. In this study, we present INSYDE 2.0, a flood damage modelling tool that integrates detailed survey and desk-based data for enhanced reliability and informativeness of flood damage predictions, including an explicit representation of the effect of uncertainties arising from incomplete knowledge of the variables characterising the system under investigation.