THE USE OF AN L-THIA-BASED MODIFIED CURVE NUMBER RUNOFF MODEL FOR FLOOD HAZARD MAPPING IN CHARLESTON, SOUTH CAROLINA
CONRAD, Casey, Masters of Environmental Studies, College of Charleston, 202 Calhoun Street, Charleston, SC 29401 and LEVINE, Norman S., Department of Geology and Environmental Geosciences, College of Charleston, 66 George Street, Charleston, SC 29424
Since 2015 the Charleston region has had high intensity rainfall events that have occurred each year instigating severe flooding problems across the region. These meteorological events included the unprecedented 2015 “rain bomb”, hurricane Matthew in 2016 and multiple storms with greater than 4 inches of precipitation within an hour. Under the current climate change scenarios it is widely expected that the region will see an increase in both the frequency and scale of future flooding. This is further complicated in the region due to rapid urbanization due to unprecedented increases in population. The South Carolina coastal region is one of the fastest growing regions in the United States. Increasing urbanization converts agricultural lands, forests, and wetlands into urban land uses increasing impervious surface. Urbanization adversely alters watershed hydrology, contributing to the deterioration of water resources and water quality. Understanding how the amount of precipitation affects flooding in both urbanized and urbanizing landscapes is of highest importance to watershed managers. The ability to identify and map areas that are susceptible to runoff based flooding is the goal of this study.
This this study employs a GIS-based implementation of the L-THIA modified curve number runoff model. Using LIDAR derived digital elevation models (DEMs), soil types, and high resolution impervious surface models runoff potential inundation maps are being created to better understand the location, causes, and flood extent in Downtown Charleston, South Carolina. Additionally the (L-Thia) CN-based runoff models will be used to develop parcel level flood risk assessments in the region. The city of Charleston will be able to use the datasets and resources created by the project to protect their community’s population and infrastructure and develop strategies to mitigate future flooding events.