03 Nov 2023
 | 03 Nov 2023
Status: this preprint is currently under review for the journal NHESS.

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

Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini

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.

Mario Di Bacco et al.

Status: open (until 20 Dec 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-179', Julius Schlumberger, 21 Nov 2023 reply

Mario Di Bacco et al.

Mario Di Bacco et al.


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Short summary
INSYDE 2.0, a tool for flood damage modelling  to residential buildings. By incorporating ultra-detailed survey and desk-based data, it improves the reliability and informativeness of damage assessments while addressing input data uncertainties.