Quantifying Land Subsidence Along the South Texas Coast using Radar Interferometric Techniques

Abstract:

Land deformation is a well-known problem affecting the coastlines worldwide. In South Texas, large amounts of hydrocarbon and groundwater extraction put this community at a higher risk of land deformation, especially compounded with sea level rise. Much of the area is comprised of rural countryside and small towns, so conventional methods of tracking land deformation such as GPS stations are not viable. Using radar interferometric techniques is one of the solutions to quantifying land deformation on a regional scale. In this study, we are integrating two interferometric techniques to quantify land deformation in the Texas Coastal Bend. Specifically, we applied the Small Baseline Subset (SBAS) and Persistent Scatterers (PS) techniques on the Sentinel-1 data (October 2016 – July 2019) to quantify spatiotemporal variability in land deformation rates. The radar-derived land deformation rates data have been validated against those extracted from Continuously Operating Reference Station (CORS) stations. We have found subsidence in the port of Corpus Christi area (> -3 mm/yr), Kingsville (> -2 mm/yr), Victoria (> -1 mm/yr), and Rockport (> -3 mm/yr). Conversely, we are seeing uplift in the greater Corpus Christi area (> +2 mm/yr). We have compared our generated deformation rates and locations spatially and temporally with surface and subsurface lithology, geologic structures (e.g., fault locations), and fluid (groundwater and hydrocarbon) extraction rates and locations. We are seeing a significant positive correlation between increased subsidence rates and increased hydrocarbon extraction in Rockport area. Additionally, a significant positive correlation between uplift and decreased extraction rates was observed in Corpus Christi area. The results of this research will help the scientific community better understand land deformation rates, factors affecting these rates, as well as provide a useful dataset for hazard prediction studies in the Texas Coastal Bend area.