Flood Plain Inundation Modeling With Explicit Description of Land Surface Macrostructures

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 plain inundation models. In the present study, it is shown for the first time that DSM data can be used directly in HEC-RAS 2D flood inundation models by employing automatically extracted ridges as breaklines for the generation of geomorphologically-informed meshes (GIMs). GIMs are used in preference to geomorphologically-uninformed meshes where mesh elements do not necessarily adapt to ridges observed in high-resolution topographic data. Ridges are extracted from DSM data in a fully automated manner by using the LANDMARK algorithm (Moretti and Orlandini, 2023, Journal of Geophysical Research: Earth Surface, 128, e2022JF006943). Breaklines are lines used in HEC-RAS 2D to connect data representing distinct land surface features. When a breakline is used to describe a ridge, the HEC-RAS 2D mesh generator seeks a Voronoi tessellation that follows the breakline by placing polygon edges coincident with the breakline segments. Ridges and breaklines can in principle be identified and incorporated in HEC-RAS 2D independently from LANDMARK, but in practice this process becomes a prohibitive task without LANDMARK as the level of mesh refinement increases. The impact of the proposed modeling strategy is shown in the figure below, where results obtained at the single-house scale from DSM and DTM data are compared. Ridges extracted from DSM data and the related flood plain inundation are shown in figures (a) and (b), respectively. Ridges extracted from DTM data and the related flood plain inundation are shown in figures (c) and (d), respectively. 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 plain 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.