A methodological framework for flood hazard assessment for land transport infrastructures. (1st February 2023) - Samela, Caterina; Carisi, Francesca; Domeneghetti, Alessio; Petruccelli, Natasha; Castellarin, Attilio; Iacobini, Franco; Rinaldi, Alessandro; Zammuto, Alessandra; Brath, Armando
Abstract: The complexity of land transport infrastructures, their length and the heterogeneity of potential hazardous scenarios across large areas make flood hazard assessment a challenging task. We propose a parsimonious, rapid and operational procedure that enables large scale applications and, at the same time, a detailed evaluation of individual segments of linear transport infrastructures. In particular, we focus on extensive land transport infrastructure networks (i.e., railways) and propose a methodological framework for: (1) identifying segments of the network that are prone to fluvial flooding, and (2) grouping the identified elements into different hazard classes depending on the mechanism and severity of the expected flooding scenario (i.e., damage due to potential embankment overtopping, embankment instability due to infiltration, erosion or washout, train or vehicles collision against fluvial deposits or debris, etc.). Network sections at risk, as well as main hazardous flood indices (e.g., water depth), are estimated referring to official inundation maps, when available, or by means of Digital Elevation Model (DEM)-based algorithms and geomorphic indices (e.g., Geomorphic Flood Index-GFI). In addition, a DEM-based approach is proposed to identify potential debris flow sources threatening the infrastructure. The procedure has been developed and tested for the Italian Railway Network (focusing on seven river basins), but it can be adapted to different cases and different land transport infrastructures. A first comparison with historical flood events proves the potential of the procedure, which can support stakeholders in planning measures for managing and mitigating the flood risk associated with the transport networks across large spatial scales.