10 Jul 2024
 | 10 Jul 2024
Status: this preprint is currently under review for the journal NHESS.

Flood risk assessment through large-scale modeling under uncertainty

Luciano Pavesi, Elena Volpi, and Aldo Fiori

Abstract. The complexity of flood risk models is intrinsically linked to a variety of sources of uncertainty (hydrology, hydraulics, exposed assets, vulnerability, coping capacity, etc.) that affect the accuracy and reliability of the analyses. Estimating the uncertainties associated with the different components allows us to be more confident in the risk values on the ground, thus providing a more reliable assessment for investors, insurance and flood risk management purposes. In this study, we investigate the flood risk of the entire Central Apennines District (CAD) in Central Italy using the laRgE SCale inUndation modEl – Flood Risk, RESCUE-FR, focusing on the interaction between the uncertainty of the hydraulic Manning parameter and the risk variability. We assess the coherence between the quantile flood risk maps generated by our model and the official risk maps provided by the CAD authority and focusing on three specific zones within the CAD region. Thus, RESCUE-FR is used to estimate the Expected Annual Damage (EAD) and the Expected Annual Population Affected (EAPA) across the CAD region and to conduct a comprehensive uncertainty analysis. The latter provides a range of confidence of risk estimation that is essential for identifying vulnerable areas and guiding effective mitigation strategies.

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Luciano Pavesi, Elena Volpi, and Aldo Fiori

Status: open (until 21 Aug 2024)

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Luciano Pavesi, Elena Volpi, and Aldo Fiori
Luciano Pavesi, Elena Volpi, and Aldo Fiori


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Short summary
Several sources of uncertainty affect flood risk estimation for reliable assessment for investors, insurance and risk management. Here, we consider the uncertainty of large-scale flood hazard modeling, providing a range of risk values that show significant variability depending on geomorphic factors and land use types. This allows to identify the critical points where single value estimates may underestimate the risk, and the areas of vulnerability to prioritize risk reduction efforts.