Projected Seafloor Elevation Change and Relative Sea Level Rise Surrounding Maui, Hawaii 25, 50, 75, and 100 Years from 1999

Step 1: Seafloor elevation and volume change analyses were performed using methods described in Yates and others (2017b). Historical elevation change data were downloaded from the Yates and others (2017a) data release. The Maui_ElevationChange.csv file was downloaded from the data release and imported into ArcMap 10.7 to create a point shapefile named Maui_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, 1999 lidar elevation data, longitude, latitude, and hydrographic sheet information attributes were renamed to HistE_SLRc, 1999_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]/(1999-[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 NCCOS Data Collection site (NOAA, 2007). The original shapefile, Maui.shp, was renamed to Maui_Habitat.shp. The habitat type attribute used in this analysis was the [D_STRUCT] attribute, which had the most similar definitions to other US coral reef habitat maps. Further information on the attributes and their definitions can be found in the DataDictionary_HabitatTypes_Maui.pdf file in the Maui_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 relative sea level rise (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 = [1999_elev]+(N*[Rate]), N_e_diff = [N_e]-[1999_elev], N_WDepth = Abs([1999_elev]+(N*[Rate]))+(N*0.00202), and N_RSLR = (N*0.00202)–[N_e_diff]. The shapefile was then exported as Maui_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_1999.shp. Further information on the attributes and their definitions can be found in the DataDictionary_Shapefile_Maui.pdf file in the Maui_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_1999.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 Maui_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_1999.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: Maui_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_1999.shp shapefile; Relationship: INTERSECT; Selecting Features: Maui_Habitat.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: Maui_Historical.csv, Maui_25e.csv, Maui_50e.csv, Maui_75e.csv, and Maui_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 Maui_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_Maui.pdf and DataDictionary_ElevationChangeAnalysis_Maui.pdf files in the Maui_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’. All maps are available within the Maui_ProjectionMaps.zip download file.