The South Carolina Coastal Counties Pond Inventory 2013 was developed to serve as a baseline dataset for the spatial distribution of ponds and stormwater ponds in the (8) coastal counties of South Carolina. The goals of the project were to capture all ponds that were present in the year 2013. The ponds were heads-up digitized manually from the USDA NAIP 2013 1-meter true color aerial imagery. Ponds were digitized at a minimum scale of 1:3000. The ponds were visually classified into (7) categories based on the digitizers' estimation of land-use adjacent to the pond. The (7) classes were as follows: 1) rural –personal ponds in rural backyards presumably created for fishing or other uses plus ponds associated with agricultural practices; 2) forest –ponds in the middle of woods with nothing nearby, swamps were not considered to be ponds; 3) mining –ponds located adjacent to borrow pits or sand mining operations; 4) residential –ponds located in residential neighborhoods and not adjacent to golf course land; 5) golf –ponds located on golf courses with no adjacent houses; 6) commercial –ponds located in or adjacent to shopping areas, office complexes, downtowns, or school properties; and 7) mixed –ponds adjacent to a golf course and residential or commercial area. Water bodies that were intentionally excluded from the pond inventory were those visually interpreted as any of the following: 1) marsh or riverine impoundments, including former rice fields; 2) ponds or lagoons associated with wastewater treatment plants; 3) any pond or lagoon associated with industrial facilities or power plants; 4) open water within swamp land or other ephemeral pools; and 5) small water features created for fountains and visual aesthetics, such as those associated with miniature golf sites or similar. A visual QA/QC was conducted using a variety of other datasets, including hydrobreak lines where available and the National Hydrographic Data (NHD) in other areas. Due to time constraints, scale, and the subjective nature of visual classifications, there may be omissions and other discrepancies in the dataset. The dataset was thoroughly checked for topological and geometric errors. This project was conducted by Erik Smith (PI) from the University of South Carolina, Denise Sanger, Andrew Tweel, and Erin Koch from the SC Department of Natural Resources with funding from the South Carolina Sea Grant Consortium as part of the Stormwater Ponds Research and Management Collaborative through a National Oceanic and Atmospheric Administration Award No. NA10OAR4170073.