Assessment of Advanced LiDAR Based Tools for Enhanced Flood Prediction

Extreme rainfall events have become more frequent and more intense in recent times, leading to increased severe flooding. Potential impacts, especially to transportation infrastructure such as roads and bridges, are magnified due to their age. Also, transportation infrastructure experiences strain to keep up with increasing land development, which causes more flow into streams. To mitigate the resulting impacts of bridge overtopping and roadway washouts on the public, a software tool is proposed that contributes to decision making in disaster response for extreme rainfall events. To create the software tool, an accurate, high resolution surface terrain model (including channels, roadways, and infrastructure for bridges and culverts) is produced of the selected study area using QL2 LiDAR data sets. Additionally, stormwater flow models are generated for flow at stream and bridge intersections, and through culverts at various storm return periods. The resulting software tool will integrate bridge overtopping and roadway washout location identification to show the severity of impact for selected watersheds in near real time. This tool will be in the form of open source software (e.g., Python) and an interactive map to display the locations and levels of severity based on forecasted rain data input for the study area. The paper first presents the proposed method, details the data collection and processing techniques, and then discusses a case study. The results validate the first steps for data collection and processing to support further modeling and analysis.