COMMUNICATING CLIMATE CHANGE INDUCED FLOODING AND SEA LEVEL RISE RISK IN ONE OF AMERICAS MOST VULNERABLE REGIONS, CHARLESTON COUNTY, SC

Charleston South Carolina has been identified as one of Americas most vulnerable cities to the effects of climate change driven storm surge and sea level rise. The Charleston County region has a suite of environmental and natural hazards related to both climatic and geological drivers. With the low elevation, a vast coastline, and estuarine shorelines within the County, flooding is the most important hazard to the residents of the region. Sea level rise has long been communicated as a future scenario – “something to prepare for”. However, rising water levels have are already impacting the region, influencing infrastructure, property, and dollars. The increasing frequency of flooding and damage from these events has highlighted the need for better communication of the area’s most susceptible climate-induced hazards.

The physical environment of Charleston County presents multiple challenges to traditional hydrologic modeling due to its low relief, and abundant estuarine and coastal features. In addition, the high density of development in much of the county enhances runoff, often complicating and increasing flood related problems. This study adapted traditional flood modeling techniques by using high-resolution digital elevation, watershed, and impervious ground surfaces for use in hydrologic models. The models consider riverine flooding, sea level rise, and pulsed rain events within the Charleston County, SC region. A one-meter resolution digital elevation model created from LiDAR as well as one-meter resolution impervious cover created using LiDAR-Multispectral Fusion techniques were used to develop all hydrologic models. The modeling created small, connected “urban watersheds” which are used to highlight the areas risk to storm and tidal events. A regional Flood Disruption Scale is being used with the modeled data to better communicate risk to the citizens and policy makers in the region. The model presented here reflects a shift in the way data has been used in the past from reactive-based to proactive data driven decision products for response and mitigation.