Reconstructing Flow Connectivity and Channel Conveyance in LiDAR-Derived Terrain for National-Scale High-Resolution Flood Modelling

LiDAR-derived digital elevation models (DEMs) are increasingly adopted in hydrodynamic flood modelling; however, their direct use, particularly in complex urban environments, remains problematic. Although LiDAR provides high-resolution surface information and supports the generation of bare-earth digital terrain models (DTMs), unresolved flow-permeable structures such as bridges, culverts, and elevated transport infrastructure, together with micro-scale urban features including narrow river channels, pathways, kerbs, and missing submerged channel bathymetry, systematically distort flow connectivity and channel conveyance. These deficiencies introduce structural biases into flood simulations, yet existing studies typically address individual features in isolation, limiting transferability and large-scale applicability.

This study reframes LiDAR DEM preprocessing as a process-based investigation into how unresolved terrain features bias flood hydraulics and introduces an automated, physically consistent terrain reconstruction framework that explicitly targets these bias mechanisms. The framework is implemented at the national scale using the 2 m LiDAR-derived DTM for England.

Three dominant sources of hydrodynamic bias are addressed. First, flow-permeable structures, including bridges, culverts, and elevated transport infrastructure, are systematically identified using observed water surface information and river network data, and the terrain beneath these structures is reconstructed using interpolation-based techniques to restore hydraulic connectivity. Second, impermeable urban features, such as buildings and kerbs, are selectively elevated while preserving longitudinal connectivity along roads and pathways, ensuring realistic overland flow routing. Third, submerged river bathymetry is reconstructed using empirical relationships between river width and water depth to recover channel conveyance absent from bare-earth DTMs.

The resulting terrain dataset is directly applicable to hydrodynamic flood modelling without manual intervention. Sensitivity analyses across multiple historical flood events demonstrate that restoring flow connectivity and reconstructing channel bathymetry exert distinct and flow-regime-dependent controls on simulated flood extent, water levels, and discharge. In particular, unresolved flow-permeable structures predominantly govern urban inundation patterns, whereas missing bathymetry represents the primary source of error in channel hydraulics.

By systematically isolating and correcting key terrain-induced bias mechanisms, this study provides generalisable insights into the process sensitivity of catchment and urban flood models to DEM representation and offers a scalable pathway for improving large-scale flood simulations using LiDAR data.