All data are taken from existing internet sources and combined to create the index and its applications. This dataset is associated with the following publication: Summers, K., L. Harwell, A. Lamper, C. McMillion, K. Buck, and L. Smith. Gulf of Mexico Coastal County Resilience to Natural Hazards. Gulf and Caribbean Research. University of Southern Mississippi, Ocean Springs, MI, USA, 32(1): 67-78, (2021).

Cumulative Resilience Screening Tool data. This dataset is associated with the following publication: Summers, J., A. Lamper, C. Mcmillion, and L. Harwell. Observational Verification of the Cumulative Resilience Screening Index (CRSI) Using Hurricanes, Inland Floods, and Wildfires From 2016 to 2019. GeoHealth. American Geophysical Union, Washington, DC, USA, 6(10): e2022GH000660, (2022).

The goal of this project is to quantify, at the National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where physical changes are likely to occur due to sea-level rise.

The goal of this project is to quantify, at the National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where physical changes are likely to occur due to sea-level rise.

The study area included the coasts of all five U.S. states along the northern Gulf of Mexico (i.e., Florida, Alabama, Mississippi, Louisiana, and Texas). We contacted federal, state, and university-affiliated scientists working with SET-MH data within this area to obtain the geographic coordinates and the installation year for each SET-MH station. Please note that while our inventory is extensive and includes most SET-MH stations in the region, our inventory is not fully exhaustive; in other words, it is possible that some stations in the region are not contained within this inventory. The SET-MH stations in our dataset include original SET, deep rod SET (RSET), and shallow RSET benchmarks.

This USGS Data Release represents geospatial and tabular data for the Gulf Coast Vulnerability Assessment Project. The data release was produced in compliance with the new 'open data' requirements as way to make the scientific products associated with USGS research efforts and publications available to the public. The dataset consists of 2 separate items: 1. Vulnerability assessment data for habitat and species based on expert opinion (Tabular datasets) 2. Vulnerability assessment values for species across subregions in study area (Vector GIS dataset)

This USGS Data Release represents geospatial and tabular data for the Gulf Coast Vulnerability Assessment Project. The data release was produced in compliance with the new 'open data' requirements as way to make the scientific products associated with USGS research efforts and publications available to the public. The dataset consists of 2 separate items: 1. Vulnerability assessment data for habitat and species based on expert opinion (Tabular datasets) 2. Vulnerability assessment values for species across subregions in study area (Vector GIS dataset)

Datasets provided include CRSI scores and regional ranks for original national assessment, and coastal regional rescale for coastal and coastal shoreline (ENOW) counties (Figure 3; Table 1). LQ values are provided for all GOM ENOW counties for total ocean economy employment and for each sector (Table 2). The percentile ranked values for the GOM counties with total ocean economy LQ values >1 are included for CRSI and LQ (Figure 4). Links to the publicly available data used to calculate LQ values is included in the manuscript. This dataset is associated with the following publication: Smith, L., L. Harwell, K. Summers, J. Bousquin, K. Buck, J. Harvey, and M. McLaughlin. Using Re-scaled Resilience Screening Index Results and Location Quotients for Socio-Ecological Characterizations in U.S. Coastal Regions. Frontiers in Environmental Science. Frontiers, Lausanne, SWITZERLAND, 7: 96, (2019).

Datasets provided include CRSI scores and regional ranks for original national assessment, and coastal regional rescale for coastal and coastal shoreline (ENOW) counties (Figure 3; Table 1). LQ values are provided for all GOM ENOW counties for total ocean economy employment and for each sector (Table 2). The percentile ranked values for the GOM counties with total ocean economy LQ values >1 are included for CRSI and LQ (Figure 4). Links to the publicly available data used to calculate LQ values is included in the manuscript. This dataset is associated with the following publication: Smith, L., L. Harwell, K. Summers, J. Bousquin, K. Buck, J. Harvey, and M. McLaughlin. Using Re-scaled Resilience Screening Index Results and Location Quotients for Socio-Ecological Characterizations in U.S. Coastal Regions. Frontiers in Environmental Science. Frontiers, Lausanne, SWITZERLAND, 7: 96, (2019).

The Gulf Coast Vulnerability Assessment utilized expert opinion that was gathered through the Standardized Index of Vulnerability and Value (SIVVA) tool, which is an Excel-based vulnerability and prioritization tool that enables assessors to provide input in a relatively short time and allows for relatively seamless compilation of results. The vulnerability of each ecosystem and associated species was conducted by subregion, excluding those subregions where the species did not occur in significant numbers. Assessors were asked to evaluate species based on the habitats they use in a particular subregion. Because vulnerability can vary with life-stage for many species, assessors were asked to consider the most vulnerable life-stage of the species for each criterion scored.