Weekly Wind Speed and Frequency for a Wave Exposure Model of Grand Bay, Mississippi

Bathymetric data covering Grand Bay and Mississippi Sound were compiled and rasterized for wave model input. Data from hydrographic charts were downloaded from the NOAA Bathymetry Data Viewer. The following hydrographic surveys (with collection year) covered the study area: F00588 (2010), H11620 (2007), F11621 (2006), H09118 (1970), H08647 (1962), and H08648 (1962). In addition, the USGS single-beam bathymetry data collected in 2015 was included. The data were converted to a consistent tidal datum (Mean Lower Low Water (MLLW)) using Vdatum software (https://vdatum.noaa.gov/). Using latitude and longitude coordinates from each file, data were imported into ArcGIS (version 10.6) as shapefiles and merged into one file. Data were weighted based on date of survey, with recent surveys given a weight of 10 (2015 and 2010), recent past surveys a weight of 5 (2007 and 2006) and historical surveys a weight of 1 (1970 and 1962). Using the Inverse Distance Weighted tool in ArcGIS, a raster was created using the vertical elevation as the Z value input, a standard search radius of 50 to 100 nearest neighbors, and the weight field. The Focal Statistics tool was used to smooth the dataset using a circle radius of 20 cells. A raster mask was created by buffering the 2012 shoreline and enclosing the Grand Bay estuary and Mississippi Sound, out to the landward side of Petit Bois and Dauphin islands.

Wind data for 2016 and 2017 were downloaded for the Crooked Bayou meteorological station from the National Estuarine Research Reserve's Centralized Data Management Office (CDMO). Using Excel (version 1803), the data for both years were appended. Wind speed was corrected for gauge height using the Power Law Method (described here: https://www.ndbc.noaa.gov/adjust_wind.shtml) and processed for model input using the WEMo Wind Analysis tool. WEMo calculates the wind speed and corresponding frequency for every 6.43 degrees (56 rays) angular resolution and is resampled or linearly interpolated for any other angular resolution. Wind frequency for a direction is defined as the ratio of the number of hours the wind blows from that direction and the total number of hours of wind data. Wind data for each week (beginning with October 24th, 2016 and ending on January 24th, 2017) were exported as representative wind energy (RWE) wind files for model input. Files were named "GB_wind_<start date>_<end date>_corr.Wind_RWE". RWE files are formatted specifically for WEMo input but can be viewed in any text editing software. For more information, see WEMo documentation (Malhotra and Fonseca, 2007).

Mean wave height was calculated for the same location at two USGS observational wave stations in Grand Bay. A shapefile of the station locations was created in ArcGIS by obtaining latitude and longitude coordinates from the KML file located on the data publication (Nowacki and others, 2018). Stations used in this study include 1078 and 1076. Several stations were excluded because they were either located within a river mouth and lack bathymetry data or adjacent to the shoreline and may be impacted by refractive waves. Wave height is estimated by the model through a series of wave propagation and dissipation steps, using the wind speed and direction data (RWE wind), along with depth (bathy grid) and shoreline (2012 shoreline). Wave generation equations are modified to account for limited fetch and wave dissipation factors that account for wave friction, shoaling and breaking due to shallow water. For details on model parameterization and calculations, see Malhotra and Fonseca (2007). Mean wave height for each weekly model run was populated in an Excel file.

Mean wave height was calculated from the observational wave data for two wave sensors. Netcdf datasets containing wave height data was downloaded from the data publication (Nowacki et al. 2018). Only data for the RBR virtuoso D|wave sensor at sites 1076 and 1078 was used (data file '10763Bdws-a.nc' and '10781Baqd-a.nc') because the sensor provides the most accurate wave height data, bathymetry data was available for those locations, and the sensor is located far enough from the shoreline to reduce impact of refractive waves. The datasets were imported into R (version 3.5.1) and weekly mean wave height was calculated from observational measurements. Data was entered into the Excel spreadsheet containing modeled mean wave height. The Excel spreadsheet was exported into tab-delimited text file for publication purposes.

Added keywords section with USGS persistent identifier as theme keyword.