Enhancing Flood Inundation Mapping Accuracy: Assessing the Impact of Culvert Data on Hydro-Conditioned DEMs

Keywords: Flooding, FIM, Flood Risk Communication
Abstract Type: Poster Abstract

Abstract

Flood forecasting presents significant challenges in hydrology, often addressed through flood inundation mapping (FIM), which identifies areas likely to be inundated by floods of specific magnitudes. Additionally, effective flood risk communication needs accurate information and is crucial for informed public decision-making. Current models rely on hydro-conditioned digital elevation models (DEMs) to generate a filled DEM from which flow direction can be derived. However, urbanization has significantly altered many landscapes due to the construction of culverts designed to mitigate flooding. These culverts directly influence flow direction and should be incorporated during the data preprocessing phase. This study utilizes a dataset of over 100,000 culvert locations across Alabama to assess the impact on flow direction and improve FIM accuracy. A hydro-conditioned DEM without culvert data is compared to one that includes culvert locations to evaluate differences before modeling and assess the impact on FIM accuracy after modeling.