Module 3 - Coastal Flooding

 Coastal flooding is one of the most destructive hazards associated with hurricanes, making accurate flood modeling an important component of emergency planning and disaster response. In this lab, I used ArcGIS Pro and LiDAR-derived elevation data to evaluate the potential impacts of Hurricane Sandy and model storm surge flooding in coastal communities.

The project began by comparing pre- and post-Hurricane Sandy LiDAR data to identify changes in elevation caused by erosion and deposition. Elevation models were created from the LiDAR point clouds and compared to produce an elevation change raster. This provided a visual representation of how the coastline changed as a result of the storm and demonstrated the value of high-resolution elevation data for identifying subtle landscape changes.

The next phase focused on modeling a potential 2-meter storm surge. Using raster reclassification and polygon conversion tools, I identified low-lying areas that could potentially be inundated during a coastal storm. The results were clipped to Cape May County and analyzed to estimate the percentage of the county that could be affected by flooding. During this process, I also compared calculated areas with published county statistics, reinforcing the importance of understanding dataset sources, metadata, and how different boundary definitions can influence GIS results.

The final analysis compared building impacts using a LiDAR-derived DEM and a lower-resolution USGS DEM. The comparison clearly demonstrated that elevation data resolution has a significant effect on flood modeling results. The LiDAR model produced a more realistic estimate of impacted structures, while the lower-resolution DEM substantially overestimated flooding in several building categories.

Overall, this exercise demonstrated that GIS is much more than producing maps. Reliable hazard analysis depends on selecting appropriate datasets, understanding the assumptions built into analytical models, validating results, and recognizing the limitations of spatial data. These skills are essential for supporting informed decision-making before, during, and after coastal disasters.