Peat Thickness Measurements from Grand Bay, Mississippi and Alabama

Location and elevation data were collected along with peat auger cores during six U.S. Geological Survey (USGS) field activities from 2013-2018 in and around Grand Bay, Mississippi (MS) and Alabama (AL) and used by Smith and others (2024). Peat auger cores were described, photographed, and the thickness of the peat unit was measured with a measuring tape. Following collection, the distance from the core location to various geomorphic boundaries (e.g., upland, marsh shoreline, water edge, etc.) and other metrics (angle and width of marsh) were determined using geoprocessing tools from the Data Management and Analysis Toolboxes in ArcGIS Pro (version 3.1) using 2015 shorelines from Terrano and others (2018). This dataset includes all the aforementioned metrics along with the angle from the shoreline and upland boundary to the core locations. The cores used in this data release were collected during the following six field activities identified by the following USGS assigned field activity numbers (FANs): 13BIM01, 2014-323-FA (alternate FAN 14CCT01), 2016-331-FA (16CCT03), 2016-358-FA (16CCT07), 2017-315-FA (17CCT02), and 2018-332-FA (18CCT01). Additional marsh and estuarine data from these six field activities (sediment characteristics, loss-on-ignition (LOI), foraminiferal census data, radiochemistry, and site location information) are available in previously published data releases, which are cross-referenced in the metadata of this data release. Downloadable data are available as a comma-separated values text file (.csv) and Federal Geographic Data Committee (FGDC) metadata (.txt and .xml).

Terrano, J.F., Smith, K.E.L., Pitchford, J., McIlwain, J., and Archer, M., 2018, Shoreline change analysis for the Grand Bay National Estuarine Research Reserve, Mississippi Alabama—1848 to 2017 (ver. 2.0, February 2019): U.S. Geological Survey data release, https://doi.org/10.5066/P9JMA8WK.

Smith, C.G., Bernier, J., Ellis, A.M., Smith, K.E.L., 2024, Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets.: Applied Computing and Geosciences, v. 100215, https://doi.org/10.1016/j.acags.2024.100215.