Flood plain inundation modeling with explicit description of land surface macrostructures

Abstract

Although high-resolution digital surface model (DSM) data derived from lidar surveys can describe land surface macrostructures like trees and buildings, digital terrain model (DTM) data obtained by filtering out these macrostructures are commonly used in flood inundation models. In the present study, it is shown for the first time that DSM data can be used directly in flood inundation models by employing automatically-extracted ridges as breaklines for the generation of geomorphologically-informed meshes (GIMs). Even under the simplifying assumption of impermeable macrostructures, especially when GIM refinement is applied, the use of DSM data in preference to DTM data leads to significant improvement in flood predictions. By comparing simulations and observations for a real flood inundation, it is found that the direct use of 1-m DSM data in place of the related DTM data leads to a 42% improvement in predicted flood area, a 36% improvement in predicted flood areal position, and a 25% improvement in predicted times of travel.