A catchment-scale screening tool for the assessment of bridge overtopping using GIS and LiDAR-derived digital elevation models

Bridges are critical infrastructures of the transport network given their high construction costs and limited alternative routes. Flood events are the most frequent cause of damage to transport infrastructure compared to any other natural hazard. Bridge overtopping is a phenomenon with serious safety consequences for drivers and leads to cascading effects such as traffic disruption and reduced efficiency of evacuation and emergency plans. Whereby, proactive management is essential to enhance bridge resilience and ensure user safety.
This work introduces a catchment-scale screening method using GIS and remotely sensed data to assess the propensity of riverine bridges to overtopping. The application of the method is based on the use of elements such as road network (OSM), hydrographic network, and LiDAR-derived Digital Elevation Models of the bare terrain (DTM) and of the surface (DSM). The propensity of bridges to overtopping is evaluated considering the geometric and morphological characteristics of river-roads intersections, independent of hydrological forcing. The method assumes that bridges with intersection heights (H_i), i.e. the difference between the road level (DSM) and river thalweg (DTM), lower than the corresponding cross-section heights (H_s), are more prone to overtopping during floods.
Intersections between roads and the hydrographic network were identified, and H_i values were calculated by extracting elevation differences within a defined buffer. To minimize noise from vegetation and other elements in the DSM, the topographic ruggedness index was employed as a filter, assuming that roads have smooth surfaces compared to the high roughness of vegetation. Field measurements of H_i were performed to validate the remotely sensed H_i values. Riverbanks and their corresponding H_s values were identified using the Iso Cluster Unsupervised Classification approach, testing various morphometric derivatives of the DTM. A combination of profile curvature and maximum difference from mean elevation provided the clusters of landforms corresponding to riverbanks.
The method was applied to the Magra River basin in Italy (970 km²), an area frequently impacted by flood events.
Results indicate that for roads intersecting streams with Strahler order (S) <4 the median height error (∆_he) between remotely sensed and measured H_i is significant (2 m, i.e. 40%). In contrast, the method proved effective for S>3 (∆_he= 0.4 m, i.e. 12%). The mean cross-section width for such streams is 35 m (excluding the main river), which is two orders of magnitude larger than the planimetric accuracy of the DTM (0.3 m). A total of 231 bridges were identified, and approximately 30% exhibited H_i<H_s, indicating a high propensity for overtopping. This approach enables large-scale screening to identify road-river intersections with geometric and morphological predispositions to overtopping. It provides a valuable tool for prioritizing bridges for further hydrologic-hydraulic and traffic disruption modeling, supporting infrastructure resilience, and flood risk management.

Acknowledgments
This study was founded by the European Union - Next Generation EU through the PRIN 2022 call powered by MUR, within the project “FLOOD@ROAD” (Prot. 202257JJSJ).