Projected Seafloor Elevation Change and Relative Sea Level Rise Near St. Croix, U.S. Virgin Islands 25, 50, 75, and 100 Years from 2014

Step 1: Seafloor elevation change analyses were performed using methods described in Yates and others (2017b). The STC_ElevationChange.csv file was downloaded from Yates and others (2017a) data release and was imported into ArcMap 10.7 to create a point shapefile named STC_SeafloorElevationChangePoints.shp. Original column headers in the .csv data were too long for shapefile attribute names, so attributes were either renamed or deleted if not required. The historical sounding elevation data corrected for SLR, 2014 lidar elevation data, longitude, latitude, and hydrographic sheet information attributes were renamed to HistE_SLRc, 2014_elev, POINT_X, POINT_Y, and Survey, respectively. A new attribute was added to this shapefile and named 'Rate' and values were populated in the field by using the Field Calculator and the following expression: Rate = [Diff_m]/(2014-[Year]), where [Diff_m] is the elevation difference between the historical hydrographic data and modern lidar data and [Year] is the attribute value containing the hydrographic survey collection year.

Step 2: The habitat shapefile used in this analysis was downloaded from NOAA's National Centers for Coastal Ocean Science Data Collection site (NOAA, 2001). The original shapefile, stc_fin.shp, was renamed to STC_Habitat.shp. The elevation-change dataset extended beyond the bounds of the available habitat data. An additional polygon was incorporated into the shapefile during an edit session to account for this discrepancy. The additional polygon was given the 'Unclassified' habitat class in the 'TYPE' attribute field. The 'TYPE' attribute was used in this analysis, which had the most similar definition to other US coral reef habitat maps. The habitat shapefile was then exported as STC_Habitat_UnclassifiedAdded.shp. Further information on the attributes and their definitions can be found in the DataDictionary_HabitatTypes_STC.pdf file in the STC_HabitatShapefile.zip download file

Step 3: Additional fields were added to the attribute table to represent projected elevations (N_e) based on elevation change rates, projected elevation change (N_e_diff) based on N_e elevations, projected water depth (N_WDepth) after accounting for RSLR and projected elevation change, and relative sea level rise (N_RSLR) based on NOAA SLR rates and projected elevation change rates, where N represents the number of years projected from the collection date of the most recent bathymetry used in the analysis: 25_e, 50_e, 75_e, 100_e, 25_e_diff, 50_e_diff, 75_e_diff, 100_e_diff, 25_WDepth, 50_WDepth, 75_WDepth, 100_WDepth, 25_RSLR, 50_RSLR, 75_RSLR, and 100_RSLR. The Field Calculator was used to populate values for each of these attributes using the following equations: N_e = [2014_elev]+(N*[Rate]), N_e_diff = [N_e]-[2014_elev], N_WDepth = Abs([2014_elev]+(N*[Rate]))+(N*0.00229), and N_RSLR = (N*0.00229)–[N_e_diff]. The shapefile was then exported as STC_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2014.shp. Further information on the attributes and their definitions can be found in the DataDictionary_Shapefile_STC.pdf file in the STC_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2014.zip download file.

Step 4: A visual inspection of the projected elevation change data and comparison to existing aerial imagery was conducted and erroneous elevation change points were removed using methods of Yates et al., 2017b. Additional points were identified for visual inspection and potential removal using the select features tool and a demarcating Rate value of +/- 0.20 m per year in ArcMap 10.7. Any point that was subaerial at the 100-year water depth projection (100_WDepth) was also selected and removed. These points were assumed to be due to substantial sediment transport events such as landslides and slumps that occur on these dynamic shorelines. Satellite imagery and topographic maps from various time periods were used in determining likely landslide and slump activity at these points.

Step 5: Elevation change statistics were determined for each habitat type using the XYZ points from the STC_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2014.shp shapefile. The "Select Layer by Location (Data Management)" tool was used to extract points within or on the boundary of a specific habitat type by using the following parameters: Input Feature Layer: STC_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2014.shp shapefile; Relationship: INTERSECT; Selecting Features: STC_Habitat_UnclassifiedAdded.shp shapefile; Search Distance: left blank; and Selection type: NEW_SELECTION. The Seafloor Elevation Change Analysis Tool (SECAT) was created to automate the process, since these steps had to be repeated for 12 habitat types under 5 different scenarios: one between historical hydrographic data and contemporary lidar data (diff_m), and one for each N_e_diff attribute. Elevation change statistics were compiled by habitat type into the following comma-separated values (CSV) files: STC_Historical.csv, STC_25e.csv, STC_50e.csv, STC_75e.csv, and STC_100e.csv. Entries that do not have a large enough sample size to compute statistics display "N/A" in the associated cell. These statistics can be found in STC_ProjectedSECAT.zip. For more information on SECAT and its capabilities see Zieg and Zawada (2021). Further information on the attributes and their definitions can be found in the DataDictionary_HabitatTypes_STC.pdf and DataDictionary_ElevationChangeAnalysis_STC.pdf files in the STC_ProjectedSECAT.zip download file.

Step 6: Maps were rendered for each projected change attribute calculated in Step 3. Maps appended in ‘N_e_diff' depict projected elevation change at each point N years into the future. Projected RSLR maps that consider projected elevation change analyses were developed using separate symbology palettes and were appended ‘N_RSLR,’ respectively. All maps are available within the STC_ProjectionMaps.zip download file.