Abstract or Description

This project aims to examine the effect of varying levels of sea level rise on the residents of South Carolina, particularly within the coastal communities, and provide rough estimations for the impact of sea level rise for use of classroom analysis. Regions along the coast continue to be popular destinations and living areas, despite the increasing prominence of rising sea levels, flooding events, and strong weather events triggered by climate change. This poses a great risk to those living near or on the coast, as almost 40% of the U.S.’s population live in densely populated areas near the coast despite the associated risks. While there are many different predictions for the severity of sea level rise for the next century and beyond (e.g. NASA 2017 and EPA 2016), it is clear that coastal communities are in danger. Therefore, this project investigates methods of assessing the degree to which South Carolina’s residents may be impacted by rising sea levels, given different estimates of global sea level rise for the next century.

About the Author

The author is a student at Colby College, and produced this project as part of the Environmental Studies Program's Introduction to GIS and Remote Sensing class of the spring of 2017. The author created this project for use within the classroom ONLY, as these are simple models that make many assumptions about real world variables. The author and those associated with Colby College are not responsible for any actions taken as a result of the information presented in this project.

Source Data Note

A digital elevation model for the state of South Carolina was imported into an ArcMap document from the South Carolina Department of Natural Resources. This raster file was transformed from a GSC_Clarke_1866 projection to the NAD 1983 Zone 19N projection; this projection was used for all layers. To develop a population density model based on proximity to roads, statewide roads and highways of South Carolina were downloaded from the South Carolina Department of Transportation, merged, and dissolved in the map document. A raster line density map was then created, and a weighted linear combination was computed to create standardized index values for population density on a scale of 1-10, with higher values representing greater population densities.

Calculations representing a rise in sea level of 0.5 feet, 1, 1.5, 2, 3, 4, 5, 8, 10, and 12 feet using the digital elevation model for South Carolina from the South Carolina Department of Natural Resources were made, with each value reclassified on a scale from 1-10 representing hazard; a rise of 0.5 feet was classified as 10, signifying the highest risk of impact from sea level rise, and a rise of 12 was classified as 1, signifying the lowest risk relative to the other values. All 10 projections for sea level rise were merged into a single raster layer. Next, the sum of the population density model and the sea level rise projections map rasters was computed, creating a population density-dependent hazard map for projected sea level rise for coastal South Carolina.

Analysis of populations for affected cities along the coast of South Carolina was obtained from the Social Explorer database using the American Community Survey form 2011-2015.

  • American Community Survey 2011-2015: Population of principal cities in South Carolina. Social Explorer, web. http://www.socialexplorer.com/
  • SC DNR, 2015, Statewide DEM for SC; DLG County Boundary. South Carolina Department of Natural Resources, web. http://www.dnr.sc.gov/GIS/gisdnrdata.html
  • SCDOT, 2017, Shape Files: Statewide Highways; Statewide Other Roads; Municipal Areas. South Carolina Department of Transportation, web. http://info.scdot.org/sites/GIS/SitePages/GISFiles.aspx?MapType=Shape


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