U.S. Geological Survey calculated percentage of time sediment is mobile for May 2010 to May 2011 at select points in the South Atlantic Bight (SAB_mobile_perc, point shapefile, Geographic, WGS84)

The WavewatchIII (WW3) numerical wave model (v3.14) was run on both a global 30' and regional North Atlantic 10' grid. The global grid is identical to the one used by the NOAA WW3 forecast system, whereas the regional grid is based on the NOAA WW3 grid but was modified slightly to remove parts of the "do not compute" mask at the outer boundaries where output was needed to pass to the nested, higher resolution grid. WW3 is a 3rd generation phase-averaged numerical wave model which conserves wave energy subject to generation, dissipation, and transformation processes and resolves spectral energy density over a range of user-specified frequencies and directions. The model was identically configured to the multi-grid system set-up used by the NOAA WW3 operational forecast (more information at http://polar.ncep.noaa.gov/waves/index2.shtml), and was rerun purely to generate full spectra boundary conditions at the boundaries of the higher resolution nested domain. Wind forcing was provided at 3-hour resolution from the NOAA North American Mesoscale (NAM) model (12 km resolution) over its domain, with the rest of the domain (outside the NAM grid) provided by the NOAA Global Forecasting System (GFS) model at 0.5 degree resolution.

The Simulating WAves Nearshore (SWAN) numerical wave model (version 40.81, modified for proper calculation of RMS bottom orbital velocity and for output of bottom wave direction) was used to create a time-series of bottom orbital velocity, bottom representative period, and bottom wave direction over the one year time period of May, 2010 - May, 2011 in each grid cell in the model domain. The wave model SWAN is a 3rd generation phase-averaged numerical wave model which conserves wave energy subject to generation, dissipation, and transformation processes and resolves spectral energy density over a range of user-specified frequencies and directions. Although stress calculations were only performed over the spatial extent of the hydrodynamic model, SWAN was run over a larger spatial scale. The model domain consists of seven overlapping regular numerical model grids that follow the eastern and Gulf of Mexico coasts of the United States at approximately 3.5 km resolution. The model was run for April 2010 using the default SWAN initial condition formulation for a non-stationary run, e.g., a JONSWAP spectrum from prescribed initial wind conditions, to develop initial conditions for the one year study period (May 2010 to May 2011). Full spectra boundary conditions at each model ocean boundary point are interpolated from the output of the regional 10' Wavewatch III model, updated every hour. Wind forcing was provided at 3-hour resolution from the NOAA North American Mesoscale (NAM) model (12 km resolution) over its domain, with forcing at the most offshore portions of the grid (outside the NAM grid) provided by the NOAA Global Forecasting System (GFS) model at 0.5 degree resolution. The SWAN directional resolution was 6 degrees (60 bins), determined via sensitivity analysis as the coarsest (and hence least computationally expensive) resolution that does not result in the "Garden-Sprinkler Effect" (GSE), wherein swell traveling over large distances inaccurately disintegrates into non-continuous wave fields as a result of frequency and directional discretization. The minimum frequency bin should be set to a value less than 0.7 times the lowest expected peak frequency and the maximum frequency bin should be set at least 2.5-3 times the highest expected peak frequency expected. In order to determine appropriate values, the peak periods from 43 NDBC buoys throughout the wave model domain were analyzed (when available) over the one year period of the study, yielding 297,533 hourly observations. The 99th and 1st percentiles of peak period were 15 s and 3 s, corresponding to frequencies of 0.07 Hz and 0.33 Hz, noting that these values may be biased by buoy limits of detection at high and low frequencies. The frequency range was therefore specified as 0.04-1 Hz. SWAN was allowed to internally determine the frequency resolution as one tenth of each frequency bin for best performance of the discrete interaction approximation (DIA) method of nonlinear 4-wave interactions, resulting in 34 frequency bins. Bottom friction calculations used the Madsen formulation with a uniform roughness length scale of 0.05 m. This value was selected for the best comparison of model output and buoy observations within the domain, and does not correspond to physical roughness values or the bottom roughness used in stress calculations. Wind generation and whitecapping parameterizations follow the modified Komen approach prescribed by Rogers et al. (2003), which reduces inaccurate attenuation of swell energy by whitecapping. Wave model outputs of bottom orbital velocity, bottom representative period, and bottom wave direction were output hourly and interpolated onto the SABGOM model grid. The same person that conducted this processing step conducted each subsequent processing step. References: Rogers, W.E., Hwang, P.A., Wang, D.W., 2003. Investigation of Wave Growth and Decay in the SWAN Model: Three Regional-Scale Applications. J. Phys. Oceanogr. 33, 366-389.

Use observed surficial sediment texture data to estimate the critical shear stress at those points where sediment texture data are available. Calculations are performed in Mathworks MATLAB (v2011A). The texture data includes the distribution of sediment over grain size classes ranging from -5 to 11 phi, ranging from gravel through sand and silt to clay. Texture observations are first classified as cohesive or non-cohesive based on the fraction of clay: if the clay fraction exceeds 7.5%, the sample is deemed cohesive, if less than or equal to 7.5% the sample is non-cohesive. Critical stress thresholds for non-cohesive sediment mixtures are calculated from the median grain size following Soulsby (1997). Because a variety of unavailable parameters influence the critical shear stress for cohesive sediments, a value of 0.1 Pa is used for all samples identified as cohesive. Although the density of sediment in the South Atlantic Bight can vary, analysis determined that the resultant variability in critical stress is small compared to the influence of particle size, and an assumed density value of 2650 kg/m3 (quartz) was used to allow inclusion of samples lacking composition information. Critical stress values, median grain sizes, and classifications as cohesive or non-cohesive at each location are saved in MATLAB .mat format. Additional information may be found in Dalyander et al. (2012). References: Dalyander, P.S., Butman, B., Sherwood, C.R., and Signell, R.P. (2012). Documentation of the U.S. Geological Survey Seafloor Stress and Sediment Mobility Database. USGS OFR 2012-1137. Soulsby, R., 1997. Dynamics of Marine Sands, a Manual for Practical Applications. Thomas Telford Publications, London.

Use the wave model and current model results to calculate the time series of bottom shear stress at each point for which sediment texture data are available using Mathworks MATLAB software (v2011A). Bottom shear stress estimates are made following Grant-Madsen (GM) (Madsen, 1994), from the estimated bottom orbital velocities and bottom wave periods generated with SWAN, and near-bed current estimates from the ESPreSSO hydrodynamic model. The GM approach relies on an eddy viscosity turbulence closure model and formulates the wave stress, current stress, and combined wave-current bottom stress as functions of a representative bottom wave orbital velocity, representative bottom wave period, current flow at some reference height, the angle between wave and current propagation, and bottom roughness. Full details of the GM formulation may be found elsewhere (Glenn, 1983; Glenn and Grant, 1987; Grant and Madsen, 1979, 1982, 1986; Madsen, 1994; Madsen et al., 1988). Wave direction, bottom orbital velocities, and bottom periods are calculated internally by the wave model. Near-bed current magnitude and direction are taken from the hydrodynamic model, with the reference height taken as the distance from the cell vertical midpoint to the seabed. GM requires that the current velocity be taken above the wave boundary layer (WBL) but within the log-profile current velocity layer. If the thickness of the WBL calculated using GM exceeds of one or more of the deepest grid cells, the current estimate and associated reference height are used from the deepest grid cell at each location where the reference height exceeds the width of the WBL. An estimate must be used for the maximum reference height where the log-profile velocity layer assumption is valid. As discussed in Grant and Madsen (1986), the thickness of the log-profile layer based on laboratory experiments is approximately 10% of the current boundary layer thickness (Clauser, 1956). Because tidal currents, storm currents, and mean flow have a boundary layer thickness on the order of magnitude 10's of meters (Goud, 1987), a maximum value for reference height is set as 5 m. The GM bottom boundary layer model also requires a value for bottom roughness. For the mobility estimates, it is the skin friction acting on the particles, and not the total bottom shear stress, which is the relevant parameter. For that reason, observed sediment texture data from the USGS East Coast Sediment Texture Database (v2.2) are used to calculate the bottom roughness at each point for which they are available. For non-cohesive samples (see definition in Process Step 2), the median grain size is used as the roughness. For cohesive samples a roughness of 62.5 micrometers, which has a critical stress based on Soulsby (1997) of 0.1 Pa, is used. References: Clauser, F.H., 1956. The turbulent boundary layer. Adv. Appl. Mech. 4, 1-51. Madsen, O.S., 1994. Spectral wave-current bottom boundary layer flows, Proceedings 24th Conf. Coastal Eng., pp. 384-398. Glenn, S.M., 1983. A Continental Shelf Bottom Boundary Layer Model: The Effects of Waves, Currents, and a Moveable Bed. Dissertation, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, Cambridge, MA, 237 pp. Glenn, S.M., Grant, W.D., 1987. A suspended sediment stratification correction for combined wave and current flows. J. Geophys. Res. 92, 8244-8264. Goud, M.R., 1987. Prediction of Continental Shelf Sediment Transport Using a Theoretical Model of the Wave-Current Boundary Layer. Dissertation, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, Cambridge, MA, 211 pp. Grant, W.D., Madsen, O.S., 1986. The continental-shelf bottom boundary-layer. Annu. Rev. Fluid Mech. 18, 265-305. Grant, W.D., Madsen, O.S., 1982. Movable bed roughness in unsteady oscillatory flow. J. Geophys. Res. 87, 469-481. Grant, W.D., Madsen, O.S., 1979. Combined wave and current interaction with a rough bottom J. Geophys. Res. 84, 1797-1808. Madsen, O.S., 1994. Spectral wave-current bottom boundary layer flows, Proceedings 24th Conf. Coastal Eng., pp. 384-398. Madsen, O.S., Poon, Y., Graber, H.C., 1988. Spectral wave attenuation by bottom friction: theory, Proceedings 21st Int. Conf. Coast. Eng., pp. 492-504. Soulsby, R., 1997. Dynamics of Marine Sands, a Manual for Practical Applications. Thomas Telford Publications, London.

Calculate the percentage of time sediment is mobile by year and season in Mathworks MATLAB software (v2011A) by comparing the critical stress value at each point location where sediment texture data are available to the time series of combined wave-current stress at that location. The percentage of time sediment is mobile is calculated as the number of hours the critical shear stress is exceeded divided by the total number of hours in the time period of interest (year or season), converted from a fraction to a percentage by multiplying by 100. These values are saved in MATLAB .mat format.

Export the point values from MATLAB format into an ArcGIS shapefile using the Mathworks MATLAB Mapping Toolbox (v2011A). In some cases, data may exist during parts of the year and not others; in this case, the statistic is calculated and included for the season where model output exist, and a missing data value of -9999 is used for seasons where no valid statistic can be calculated. A geographic data structure is created in MATLAB with the following fields: Geometry ('Point'), Lon (the longitude at the point), Lat (the latitude at the point), Winter (the statistic calculated for December, January, and February), Spring (the statistic calculated for March, April, and May), Summer (the statistic calculated for June, July, and August), and Fall (the statistic calculated for September, October, and November). The shapefile is then written with the "shapewrite" command. Because MATLAB does not assign a projection, the projection corresponding to the projection associated with the bathymetry used in the numerical models is added in ArcCatalog 9.3. The file was then quality checked in ArcMap to insure values were properly exported to the shapefile from MATLAB.

Edits to the metadata were made to fix any errors that MP v 2.9.32 flagged. This is necessary to enable the metadata to be successfully harvested for various data catalogs. In some cases, this meant adding text "Information unavailable" or "Information unavailable from original metadata" for those required fields that were left blank. Other minor edits were probably performed (title, publisher, publication place, etc.). The metadata date (but not the metadata creator) was edited to reflect the date of these changes. The metadata available from a harvester may supersede metadata bundled within a download file. Compare the metadata dates to determine which metadata file is most recent.

Keywords section of metadata optimized for discovery in USGS Coastal and Marine Geology Data Catalog.

Added keywords from Coastal and Marine Ecological Classification Standard (CMECS) to metadata.

The data release was retroactively assigned a DOI number, and that information was added to the metadata. Additionally, the location of the data release changed, and the metadata links updated accordingly. Other small edits, such as the program name, were also modified.

Added keywords section with USGS persistent identifier as theme keyword (20200908). Fixed one of the USGS Thesaurus terms.