GSA Connects 2023 Meeting in Pittsburgh, Pennsylvania

Paper No. 47-6
Presentation Time: 8:00 AM-5:30 PM

MODELING THE IMPACT OF STORM SURGE FLOODING AND ASSOCIATED COSTS ON NORTH CAROLINA COASTAL REGION CORN AND SOYBEAN FIELDS VIA REMOTE SENSING


CHEN, Peggy, Green Hope High School, 2500 Carpenter Upchurch Road, Cary, NC, NC 27519

Hurricane and tropical storm-driven storm surge flooding places coastal farmlands at high risk of crop damage and soil salinization. The damage can total to millions of dollars in costs and force farmers to abandon coastal fields. Additionally, many agricultural, and often rural, areas are unable to access accurate and reliable flood-risk projection maps and analyses that can inform prevention and mitigation strategies. This study demonstrates the simplicity and accessibility with which remote sensing data can be used to assess storm surge flood risk in coastal North Carolina farmland. While similar studies have been conducted in the past, few focus on North Carolina, and most use techniques that require years of expertise and formal training to be able to execute. Soybean and corn, two crops that are critical suppliers to the state economy and have a multitude of uses, were selected to be analyzed. Soybean and corn fields in three of North Carolina’s coastal counties were evaluated under various storm surge models. Cropland maps were taken from CroplandCROS, a geospatial database maintained by the United States Department of Agriculture (USDA) for the selected counties. Storm surge models of one, three, five, and nine meters in elevation were overlaid with crop data in ArcGIS, and total farmland area submerged by the models was calculated. Models were validated with official flood risk projections and high-risk cropland areas were identified. Under the highest storm surge model, an estimated 672,226,470 m2, or 86.775% of farmland across all three counties is at risk of flooding. Economic losses were calculated under each model, resulting in damages as high as $135,148,007.30. Further research will be conducted to include more data layers in order to produce more holistic risk projections and enhance the accessibility of flood risk assessment via GIS. Potential mitigation techniques can also be modeled and evaluated.