Flood risk assessment through large-scale modeling under uncertainty

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 investment, 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 in 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 Central Apennines District Authority (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.