Flood Modeling Using a Synthesis of Multi-Platform LiDAR Data

TLDR

The study area’s urban and mountainous terrain, combined with the dense high‑resolution ground‑based LiDAR data, posed both opportunities and challenges for flood modeling. The study examined whether supplementing high‑resolution ground‑based LiDAR with coarser airborne LiDAR improves flood inundation analysis. The authors combined multi‑platform LiDAR into a composite triangulated irregular network and compared it to an airborne‑only TIN, then used both in a one‑dimensional steady‑flow hydraulic model to assess inundation. The composite LiDAR dataset increased maximum flood height by 35 % and produced statistically distinct water surface extents, while the airborne‑only data underestimated flooding extent, volume, and peak height by up to 1.677 m.

Abstract

This study examined the utility of a high resolution ground-based (mobile and terrestrial) Light Detection and Ranging (LiDAR) dataset (0.2 m point-spacing) supplemented with a coarser resolution airborne LiDAR dataset (5 m point-spacing) for use in a flood inundation analysis. The techniques for combining multi-platform LiDAR data into a composite dataset in the form of a triangulated irregular network (TIN) are described, and quantitative comparisons were made to a TIN generated solely from the airborne LiDAR dataset. For example, a maximum land surface elevation difference of 1.677 m and a mean difference of 0.178 m were calculated between the datasets based on sample points. Utilizing the composite and airborne LiDAR-derived TINs, a flood inundation comparison was completed using a one-dimensional steady flow hydraulic modeling analysis. Quantitative comparisons of the water surface profiles and depth grids indicated an underestimation of flooding extent, volume, and maximum flood height using the airborne LiDAR data alone. A 35% increase in maximum flood height was observed using the composite LiDAR dataset. In addition, the extents of the water surface profiles generated from the two datasets were found to be statistically significantly different. The urban and mountainous characteristics of the study area as well as the density (file size) of the high resolution ground based LiDAR data presented both opportunities and challenges for flood modeling analyses.