Direct 3D extreme rainfall risk mapping from urban aerial LiDAR point clouds

Abstract

Flooding is one of the most frequent and damaging natural hazards, increasingly intensified by rapid urbanization and extreme precipitation events. Accurate modelling of water accumulation and runoff in dense urban environments remains challenging due to complex geometries and the limitations of conventional surface-based representations. Most approaches require extensive LiDAR preprocessing before hydrological analysis. This study introduces a methodology for direct flood risk analysis on Aerial Laser Scanning (ALS) point clouds, avoiding intermediate surface reconstruction steps. The proposed approach enriches points with indicators of water accumulation and surface runoff potential. Water accumulation is estimated through elevation differences within a 100-m local neighbourhood to detect topographic depressions, while runoff potential is derived from three geometric descriptors (verticality, planarity, and scattering) that describe the capacity of urban structures to channel or obstruct water flow. The method was evaluated using open ALS datasets from three cities with contrasting urban morphologies: Vigo (Spain), Rotterdam (The Netherlands), and Hong Kong (China). Results demonstrate the capacity of the enriched point clouds to identify flood-prone urban features such as low-lying harbour areas, semi-tunnels, and urban canyons, as well as runoff pathways including sloped roofs, stairways, ramps, and road surfaces. The resulting point-level risk representation shows strong agreement with historical flood events and traditional multi-criteria assessments. By using LiDAR point clouds as an analytical medium, the framework enables intuitive 3D flood risk mapping while preserving their geometric richness.