Kinematic Hierarchical Filling-and-Spilling Algorithm for Advanced DEM-based Modelling of Pluvial Flooding in Urban Areas

Pluvial flooding on roadways poses a significant threat to drivers. In addition, frequency of such pluvial floods is increasing due to climate change, urbanization, impervious surfaces, and aging stormwater infrastructure. While physics-based hydrodynamic models provide detailed results, their computational demand and reliance on often-uncertain drainage layouts make them impractical for rapid, real-time urban risk assessment.
SaferPlaces (saferplaces.co) addresses this with a web-platform that integrates high-resolution geospatial and climate data from sources like GEE, OSM, and Copernicus to automate the construction of digital-twins for flood risk modelling in urban areas. As part of the SaferPlaces platform, Safer_RAIN, a fast-processing DEM based algorithm that combines a pixel-based Green-Ampt infiltration model with a Hierarchical Filling-and-Spilling Algorithm (HFSA) approach to enable building-level risk assessments. By leveraging cloud-based infrastructure, the platform delivers high-resolution, real-time results for urban planning. However, when compared to 2D hydrodynamic models, Safer_RAIN showed some limitations, including the underestimation of flooded extents due to the absence of hydraulic backwater effects and single flow-path constraints. Therefore, this study introduces an enhancement to Safer_RAIN, termed Kinematic Safer_RAIN, by incorporating a kinematic approach to simulate flooding beyond depressions and integrating a flow-path inundation extension feature. This enhancement utilizes the Height Above Nearest Drainage (HAND) approach, coupled with Manning’s equation, to represent flood inundation along flow paths (e.g. essential for assessing risks to urban road networks).
Kinematic Safer_RAIN, featuring flow-path flood extension, was benchmarked against HEC-RAS 2D Rain-on-Grid hydrodynamic simulations using 1m-resolution LiDAR DEMs in two case studies. In the Cottonwood Lake Study Area (USA), Kinematic Safer_RAIN produced maximum flooding extents and water depth distributions that closely match HEC-RAS results. The model was further validated in Pamplona (Spain), using the extreme storm event of July 2010. Kinematic Safer_RAIN successfully identified flood-prone depressions and flooding along flow paths (primarily main roads and lanes), yielding high True Positive Rates and aligning with flood evidence from local newspaper images. This research provides a robust, low-cost, and rapid alternative for authorities to accurately predict roadway flooding risks, bridging the gap between topographic simplicity and hydrodynamic complexity to enhance flood mitigation strategies.

Keywords: Flow path flood extension, Hierarchical Filling-and-Spilling Algorithm (HFSA), Height Above Nearest Drainage (HAND), Pluvial Flooding, Safer_RAIN, Kinematic_SaferRAIN, SaferPlaces