Assessing road-watercourse crossing overtopping potential using GIS and remote sensing: a large-scale screening approach

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.