Road-watercourse crossings (RWCs), such as bridges, are critical components of transport networks. Flooding poses a major threat to these structures, with overtopping leading to safety hazards and traffic disruptions. This study presents a large-scale screening method using GIS techniques and remotely sensed data to assess the overtopping potential of RWCs. The approach integrates road and hydrographic networks with high-resolution LiDAR-derived DEMs of bare terrain (DTM) and surface (DSM). RWCs create a constriction of the river runoff cross-section, acting as bottlenecks. The method assumes that RWCs with a height difference between the road level (DSM) and river thalweg (DTM) lower than the corresponding cross-section height are more prone to overtopping. RWCs were identified, and their remotely sensed height was calculated by extracting elevation differences (DSM-DTM). Field measurements were conducted to validate the remotely sensed values. A terrain ruggedness index was used to filter noise in the DSM, assuming roads are the smoothest surfaces. Riverbanks were identified using the raster-based Unsupervised approach, and their height from the thalweg was assessed. The method is applied to the Magra River Basin in Italy (970 km²), a flood-prone area. Results showed that for watercourses with Strahler order < 4, the median error between remotely sensed and field measurements height was high (1.2 m, 35%), while for those with a higher order the error was significantly lower (0.5 m, 8%). Among 230 identified bridges, ~ 25% exhibited a high overtopping potential. This approach enables the prioritization of bridges for further hydrologic-hydraulic and traffic disruption modeling, enhancing infrastructure resilience and flood risk management.
Amaddii et al. (Sun,) studied this question.
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