Development of a Process-Based Littoral Sediment 

Transport Model for Dauphin Island, Alabama 

By Robert L. Jenkins III, Joseph W. Long, P. Soupy Dalyander, David M. Thompson, and Rangley C. Mickey 

Open–File Report 2020–1011 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

DAVID BERNHARDT, Secretary 

U.S. Geological Survey 

James F. Reilly II, Director 

U.S. Geological Survey, Reston, Virginia: 2020 

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Although this information product, for the most part, is in the public domain, it also may 

contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items 

must be secured from the copyright owner. 

Suggested citation: 

Jenkins, R.L., III, Long, J.W., Dalyander, P.S., Thompson, D.M., and Mickey, R.C., 2020, Development of 

a process-based littoral sediment transport model for Dauphin Island, Alabama: U.S. Geological Survey 

Open-File Report 2020–1011, 43 p., https://doi.org/10.3133/ofr20201011. 

ISSN 2331-1258 (online) 
Acknowledgments 

The authors would like to give special thanks to our partners in U.S. Army Corps of Engineers for providing 

the observational data for comparison to model output. Thanks also goes to the entire Delft-3D team at 

Deltares, many of whom provided vital input on morphology parameterization. The authors specifically 

thank Timothy Nelson and Alfredo Aretxabaleta for reviewing this report. 

iii 
Contents 

Acknowledgments ..........................................................................................................................................iii 

Abstract .......................................................................................................................................................... 1 

Introduction..................................................................................................................................................... 1 

Model Setup .................................................................................................................................................. 2 

Numerical Models ....................................................................................................................................... 2 

Hydrodynamic and Sediment Transport Model....................................................................................... 2 

Model Setup and Coupling...................................................................................................................... 3 

Topobathymetric Data and Digital Elevation Models............................................................................... 6 

Deterministic Application......................................................................................................................... 7 

Climatological Application ....................................................................................................................... 9 

Observations..............................................................................................................................................11 

Hydrodynamic Data................................................................................................................................11 

Shoreline Change Data......................................................................................................................... 12 

Results.......................................................................................................................................................... 13 

Comparison to Observations—Deterministic Simulations ........................................................................ 13 

Acoustic-Doppler Current Profile Comparison ...................................................................................... 13 

Wave-Gage Comparison....................................................................................................................... 17 

Tide-Gage Validation ............................................................................................................................ 24 

Comparison to Observations: Mormerge Simulations .............................................................................. 27 

Lidar Shoreline Validation ..................................................................................................................... 27 

Conclusions.................................................................................................................................................. 31 

References Cited ......................................................................................................................................... 32 

Appendix 1.................................................................................................................................................... 34 

Figures 

1. Flow chart in which S1, S2, ...Sn represent hydrodynamic forcing conditions at various simulation 

timesteps ........................................................................................................................................... 5 

2. Map showing the study area and digital elevation model for this report............................................ 6 

3. Colored map of the digital elevation model for this model showing observation locations within the 

study area.......................................................................................................................................... 8 

4. Graphs showing water-level observations and Hybrid Coordinated Ocean Model data at Dauphin 

Island, Alabama for a 6-month period ............................................................................................... 9 

5. Scatter plots of A, trip 1 and trip 2 modeled versus observed through-channel velocity in Mobile 

Bay Inlet. B, trip 1 and trip 2 modeled versus observed V-velocity in Mobile Bay Inlet ................... 17 

iv 
6. Graphs showing comparisons between observed depth and model digital elevation model depths 

on all acoustic Doppler current profiler transects during trip 1 and trip 2 ........................................ 18 

7. Time series graphs showing observed and modeled wave variables at the Aquadopp 

wave gage....................................................................................................................................... 22 

8. Time series graphs showing observed and modeled wave variables at the acoustic wave and 

current sensor wave gage............................................................................................................... 23 

9. Graphs showing a comparison between WaveWatch III wind and wave height forcing and 

observed winds and wave heights at National Data Buoy Center 42012........................................ 24 

10. Graphs showing modeled and and observed water levels.............................................................. 27 

11. Graph and map showing observed and modeled shoreline linear regression rates for the period of 

2005–2007 ...................................................................................................................................... 30 

Tables 

1. Characteristics and percent occurrence for each wave condition of the wave climatology............. 10 

2. Amplitudes and phases of morphological tide constituents............................................................. 10 

3. Mean values, coefficient of determination, normalized root mean squared error, mean bias, mean

normalized bias, slope, and intercept of linear fit between modeled and observed stream-wise

velocity components and volumetric flow rates during ebb and flood tide ...................................... 15 

4. Coefficient of determination, mean bias, mean normalized bias, root mean squared error, and

normalized RMSE statistics between observed and modeled significant wave height, peak period, 

average period, direction, and average direction at two nearshore wave-gage stations ................ 20 

5. Results of a sensitivity test to wind forcing...................................................................................... 21 

6. Results of a classical tidal harmonic analysis of the applied water-level forcing and the observed 

water levels at station 8735180....................................................................................................... 26 

7. Coefficient of determination, mean bias, mean normalized bias, root mean square error and

normalized RMSE statistics between observed and modeled shoreline linear regression rates by 

island segment ................................................................................................................................ 29 

Appendix 1 

1.1. Model parameter and settings for the FLOW portion of the deterministic applications as set in 

model master definition file.............................................................................................................. 34 

1.2. Model parameters and settings for the WAVE portion of the deterministic applications as set in 

model master definition wave file .................................................................................................... 37 

1.3. Model parameter and settings for the morphology portion of each deterministic application as set in 

model morphology file ..................................................................................................................... 40 

1.4. Model parameter and settings for the FLOW portion of the mormerge application as set in model 

master definition file ........................................................................................................................ 41 

1.5. Model parameter and settings for the WAVE portion of the mormerge application as set in model 

master definition wave file............................................................................................................... 41 

1.6. Model parameter and settings for the morphology portion of the mormerge application as set in 

model morphology file ..................................................................................................................... 43 

v 
Conversion Factors 

International System of Units to U.S. customary units 

Multiply By To obtain 

Length 

centimeter (cm) 0.3937 inch (in.) 

millimeter (mm) 0.03937 inch (in.) 

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi) 

kilometer (km) 0.5400 mile, nautical (nmi) 

meter (m) 1.094 yard (yd) 

Volume 

cubic meter (m3

) 264.2 gallon (gal) 

cubic meter (m3

) 0.0002642 million gallons (Mgal) 

cubic meter (m3

) 35.31 cubic foot (ft3) 

cubic meter (m3

) 1.308 cubic yard (yd3) 

cubic meter (m3

) 0.0008107 acre-foot (acre-ft) 

Flow rate 

cubic meter per second (m3

/s) 70.07 acre-foot per day (acre-ft/d) 

meter per second (m/s) 3.281 foot per second (ft/s) 

cubic meter per second (m3

/s) 22.83 million gallons per day (Mgal/d) 

Datum 

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD 88). 

Abbreviations 

1/2dt half time-step 

2D Two-dimensional 

3D Three-dimensional 

ADCP acoustic Doppler current profiler

AWAC acoustic wave and current sensor 

cm centimeter 

CONED Coastal National Elevation Database 

cy cubic yard 

D rotation angle 

D3D Delft-3D 

Da average direction 

Dp peak direction 

deg degree 

dL transect length 

DEM Digital elevation model 

ECMWF European Center for Medium-Range Weather Forecast 

EDRC Engineering Research and Development Center 

vi 
EFM Energy flux method

ERA ECMWF re-analysis 

GFS Global Forecasting System 

Hm0 zeroth-moment wave height 

Hs significant wave height

HYCOM Hybrid Coordinated Ocean Model 

Hz Hertz 

JONSWAP Joint North Sea Wave Project 

lidar Light detection and ranging 

LRR Linear regression rate 

m meter 

.mdf model master definition file

.mdw model master definition wave file

MHW mean high water 

mm millimeter 

.mor model morphology file

N number of observations in a record 

NAVD 88 North American Vertical Datum of 1988 

NDBC National Data Buoy Center 

NGDC National Geophysical Data Center 

NOAA National Oceanographic and Atmospheric Administration 

Q volumetric flow rate

R2 coefficient of determination

RMSE root mean square error 

s second 

Ta average period 

Tp peak period 

USACE U.S. Army Corps of Engineers 

USGS U.S. Geological Survey 

U x-direction component of velocity 

V y-direction component of velocity 

Uavg x-direction component of the average velocity vector 

Vavg y-direction component of the average velocity vector 

U through-channel velocity stream 

V cross-channel velocity stream 

Wn weight value 

WW3 WaveWatch III 

Z depth 

Zn bin-specific bed level data

vii 
Development of a Process-Based Littoral Sediment 

Transport Model for Dauphin Island, Alabama 

By Robert L. Jenkins III, 1 Joseph W. Long, 2 P. Soupy Dalyander, 3 David M. Thompson, 4 and 

Rangley C. Mickey 4 

Abstract 

Dauphin Island, Alabama, located in the Northern Gulf of Mexico just outside of Mobile 

Bay, is Alabama’s only barrier island and provides an array of historical, natural, and economic 

resources. The dynamic island shoreline of Dauphin Island evolved across time scales while 

constantly acted upon by waves and currents during both storms and calm periods. Reductions 

in the vulnerability and enhancements to the resiliency of Dauphin Island—through offshore 

sand placement, breach closure, berm construction, and other means—have been used to protect 

the island and its vital resources. Planning for a resilient Dauphin Island requires predicting the 

long-term evolution of the barrier island system and the dominant, temporally varying processes 

that influence it, including littoral alongshore sediment transport under typical wave conditions, 

beach and dune erosion, the island overwash and breaching that occur rapidly during storm 

events, and the recovery of primary sand dunes through Aeolian transport over decadal time 

scales. Littoral sediment transport within the Dauphin Island decadal-scale framework was simulated using the Delft-3D modeling software suite. The influences of wind, waves, water levels, 

and sediment transport are incorporated into the model. Model skill in the prediction of waves, 

water levels, currents, volumetric flow rates through inlets, and shoreline position was assessed 

by using a set of deterministic and statistical hindcast simulations. The Delft-3D modeling application described here can be coupled with validated models of storm-response and dune recovery 

to predict the evolution of Dauphin Island on decadal time scales. 

Introduction 

Dauphin Island, Alabama, located in the northern Gulf of Mexico, just outside of Mobile 

Bay, is Alabama’s only barrier island. The island supports sensitive populations of shorebirds, sea 

turtles, and other wildlife, as well as the town of Dauphin Island, which is situated on the island’s 

east end. Historical, natural, and economic resources make this an area of importance. As a result, numerous activities have been used to enhance the resiliency of the island (for example, offshore sand placement, berm construction, and breach closure). Waves and currents during storms 

and calm periods shape both the natural and restored areas of the island across varied time scales. 

Understanding and predicting the long-term evolution of Dauphin Island and the processes that 

1

Cherokee Nation Technologies 

2

U.S. Army Corp of Engineers 

3

University of North Carolina at Wilmington 

4

U.S. Geological Survey 
2 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

shape it are vital to planning management actions that can ensure a resilient barrier island system 

capable of supporting its critical resources. 

Various processes, across multiple temporal scales, must be considered when modeling 

barrier island evolution. These processes include littoral alongshore sediment transport under 

quiescent wave conditions that can cause the elongation, narrowing, or widening of the barrier on 

annual time scales; the beach and dune erosion, overwash, and breaching that occur in a matter 

of hours during storm events; and the recovery of primary sand dunes through aeolian transport 

over decadal time scales. Because these processes occur across different spatial and temporal 

scales and the physics associated with them vary, different modeling approaches are necessary. 

As part of a larger project to develop and integrate models for each of these processes 

(https://coastal.er.usgs.gov/alabama-barrier-island-restoration-study/), a modeling system was 

developed to characterize and efficiently simulate the annual to decadal-scale littoral sediment 

transport and resultant morphological response along Dauphin Island, Alabama. The modeling 

system uses the Delft-3D (D3D) modeling software suite (Deltares, 2018a), which integrates the 

influences of wind, waves, water levels, and sediment transport. Skill in the model’s prediction of 

waves, water levels, currents, volumetric flow rates associated with ebb and flood tides through 

inlets, and shoreline position was quantified through a set of deterministic and statistical hindcast 

simulations. The sensitivity of the model to wind forcing was also assessed. 

The objectives of this report are to: 

• Describe the modeling system methods and observations used in validation. 

• Demonstrate model skill at predicting hydrodynamics, including water levels, 

water velocities, volumetric flow rates, and wave characteristics.

• Demonstrate model ability to predict annual to decadal morphology evolution using 

a statistical wave binning approach. 

Model Setup 

Numerical Models 

Hydrodynamic and Sediment Transport Model 

Delft-3D is an integrated numerical modeling system and software suite composed of 

modules for simulating hydromorphodynamics. D3D can be used to model a variety of phenomena 

including tides, wind, density- and wave-driven flows, sediment transport, and morphologic evolution in coastal and riverine environments. D3D FLOW solves either the depth averaged (twodimensional [2D] applications) or full, nonlinear, shallow-water equations (three-dimensional [3D] 

applications) derived from the 3D Navier-Stokes equation for incompressible free-surface flow

(Lesser and others, 2004; Deltares, 2018a). Depending on the configuration, D3D can account for

barotropic and baroclinic effects, turbulence, tidal forcing, space and time varying wind and bottom 

shear-stresses, space and time varying atmospheric pressure, and intermittent drying and flooding.

When coupled with D3D WAVE, the model accounts for wave radiation stress gradients that drive 

nearshore circulation. By solving the spectral action density equation, D3D WAVE accounts for the 

propagation, shoaling, refraction, and dissipation of wind-driven surface gravity waves in coastal 

waters. Dissipation as a result of bottom friction, white-capping, and depth-induced breaking are 
Model Setup 3

included (Booji and others, 1999; Deltares, 2018b). When WAVE and FLOW are coupled, wavecurrent interactions, including current-induced frequency shifting, are simulated. 

The sediment transport module of D3D solves the equation for suspended load transport 

while also accounting for sediment concentration, 3D flow velocities, eddy diffusivity, and sediment settling velocity. Breaking-wave induced shear stresses, surface mass flux, and bed shear 

stress under nonbreaking waves are also accounted for in both the mobilization and transport of 

bedload and suspended sediments. Variable and depth-dependent bed friction coefficients may be 

applied in the calculations. Bedload transport of noncohesive sediments accounts for bed-slope, 

bed composition, and the concentration of available sediment. When fluxes of sediment are allowed between bed and suspended sediment loads, the net fluxes result in bathymetric changes. 

This morphological updating of the bed affects flow and wave calculations at subsequent time 

steps. Finally, a morphological time-scale factor (“morfac”) may be applied, which accelerates 

the rate of morphological change and compensates for the longer time scale of morphological 

change relative to the time scale of hydrodynamic flows. Applying a morphological time-scale 

factor increases the computational efficiency.

The prediction of morphology using fully deterministic simulations is not computationally feasible for time scales on the order of months to years. Instead, a schematization of wave 

conditions can be used to simulate coastal evolution over these temporal scales (as in Walstra and 

others, 2013). “Mormerge” is a specific configuration of D3D that allows a computationally efficient prediction of morphological change by simultaneously simulating the wave, flow, and sediment transport associated with multiple wave conditions. The approach periodically combines 

the morphological change results from each simulation to produce a cumulative morphological 

change that combines the effects of all wave conditions over a given period of time. Figure 1 

illustrates the mormerge algorithm process. Multiple D3D model scenarios are initialized simultaneously using the same initial digital elevation model (DEM) and run in parallel. Each scenario can have unique wind, wave, and water-level forcing and a percent-occurrence, or weight, 

associated with how often that forcing occurs. Each scenario evolves the initial DEM at user-set 

intervals and the mormerge software combines the evolved topography and bathymetry from 

each scenario using a weighted average, where the weight of each scenario is equal to a percent 

occurrence of that wave condition. The resulting weighted-average bathymetry is applied to all 

scenarios for the next time interval. The set of unique forcing scenarios simulate again, using an 

identical bathymetry, and evolve independently until the next merging interval. The product of 

mormerge is a change in morphology that accounts for the effects of all the individual scenarios, 

and therefore the effects of the full range of regional wave conditions (Deltares, 2018a). 

Model Setup and Coupling 

A coupled D3D FLOW–WAVE depth-averaged 2D application was developed for the 

Dauphin Island barrier island system and the adjacent coastal region. The study area encompasses 

the entirety of Mobile Bay to the north, parts of Gulf Shores, Ala., to the east, the coastal shelf 

region to approximately the 30 meter (m) depth contour to the south, and the eastern end of Petit 

Bois Island, Miss., to the west (fig. 2). This extent was chosen to include Dauphin Island, all the

inlets that immediately surround it, and elements of the offshore coastal bathymetry that affect 

wave propagation to the nearshore. The extent was also chosen to maximize numerical stability, 

minimize artificial boundary effects near Dauphin Island, and allow for computational efficiency.
4 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

The modeling system uses three computational grids. The WAVE module uses two grids 

to simulate wave propagation and eventual dissipation near the island. The first grid is a coarsely 

resolved curvilinear grid consisting of 245 (alongshore) x 449 (cross-shore) grid points that covers the extent of the study area and serves as the outer wave domain. The outer coarse wave grid 

has a variable alongshore resolution ranging from 250 m to 325 m and a variable cross-shore 

resolution ranging from 15 m closer to the island and Mobile Bay Inlet to 300-m grid spacing 

offshore. The inner fine wave grid is nested within the outer grid and designed to resolve the surf 

zone along Dauphin Island to capture the depth-induced wave breaking that drives alongshore 

currents and sediment transport in the nearshore. 

The fine wave grid consists of 1,367 (alongshore) x 458 (cross-shore) grid points with 

variable cross-shore resolution ranging from an approximate 5-m grid spacing close to the island 

to over 250-m to the south and north. The alongshore resolution of the fine wave grid ranges 

from a 40-m grid spacing along the island and across the span of the Petit Bois and Mobile Bay 

inlets to a 100-m grid spacing at points east of Mobile Bay. The fine wave grid is limited longitudinally so that the east and west lateral boundaries extend from Fort Morgan Peninsula in Alabama and the eastern tip of Petit Bois Island, respectively. The latitudinal limits of the fine wave 

grid extend from the nearshore of Dauphin Island to north of Little Dauphin Island, excluding 

much of Mobile Bay (fig. 2). 

The FLOW module uses a single curvilinear grid that consists of 1,368 (alongshore) x 

657 (cross-shore) grid points with variable alongshore and cross-shore resolution that covers the 

extent of the study area. Consistent with the fine wave grid, cross-shore resolution of the flow 

grid ranges from a less than 5-m grid spacing close to the island to an over 300-m grid spacing in 

the northernmost reaches of Mobile Bay. The alongshore resolution of the flow grid ranges from 

a 40-m grid spacing along the island and across the span of Petit Bois and Mobile Bay inlets 

to a 100-m grid spacing east of Mobile Bay. Coefficients of bottom friction are spatially varied 

throughout the domain and are depth dependent, as described in Passeri and others (2018b). 

Model parameter settings for sediments were based on the observation of Ellis and others (2017). 

The FLOW and WAVE modules are coupled in a two-way configuration in which timedependent bathymetry, water velocities, water levels, and winds are passed from FLOW to 

WAVE and radiation stress gradients created by wave breaking are passed back from WAVE to 

FLOW. For a complete list of model inputs and parameters, see appendix 1. 
Model Setup 5

Figure 1. Flow chart in which S1, S2, ...Sn (with inset thumbnail maps of the study area) represent 

hydrodynamic forcing conditions at various simulation timesteps (t). Each scenario is a variation on the 

same simulation and has an associated weight value (wn) based on the percent occurrence in the historical 

record. Each scenario must have the same initial bathymetry (z) and morphological parameterization but 

may have varying boundary conditions, wind, and wave forcing. All scenarios initialize and run simultaneously and exchange bed level data (zn

) at each half time-step (1⁄2dt). A weighted sum of all bed levels (z̄) is 

calculated, and that weighted sum is distributed to each scenario and used as the bed level for the next half 

time-step. This process is repeated throughout the mormerge simulation such that the final result is a bed 

level that represents the cumulative effect of all the scenarios. 
6 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Figure 2. Map showing the study area and digital elevation model (DEM) for this report. Depth contours 

indicate 0-, 5-, 10-, 15-, 20-, 25-, and 30-meter (m) isobaths (black lines). The domain pictured shows the 

full extent of the flow grid and coarse wave grid. The extent of the fine wave grid is shown with a yellow 

dash-dotted line. 

Topobathymetric Data and Digital Elevation Models 

Two DEMs were developed for the deterministic and climatological applications, and 

both used available light detection and ranging (lidar) data to characterize the initial shape of the 

subaerial island and the respective shoreline position at the start of each run. The first DEM corresponded to Dauphin Island in 2015 and the second to Dauphin Island after Hurricane Katrina 

in late 2005. Each DEM shared Coastal National Elevation Data (CONED) (Danielson and 

others, 2013) and National Geophysical Data Center (NGDC) topographic and bathymetric data, 

which supplied elevation information for broad swaths of the study area, including the upper bay, 
Model Setup 7

offshore, Petit Bois, and Gulf Shores areas not covered by lidar data during the time periods of 

interest (National Oceanic and Atmospheric Administration, 2009). Portions of the 2015 DEM, 

including the island shoreline position and topography, were also updated with bathymetry data 

from a 2015 survey that covered only the nearshore regions immediately surrounding Dauphin 

Island (Flocks and others, 2015; Dewitt and others, 2017). 

For the 2005 post-Katrina DEM, the island shoreline position and topography were 

provided by USGS lidar survey data (Kranenburg and others 2016; Thompson and others, 2017) 

collected immediately after Hurricane Katrina, dated 2005/09/04. The updated bathymetric 

coverage of Mobile Bay was provided by the U.S. Army Corps of Engineers (USACE) Mobile 

District and incorporated into the 2015 DEM, as outlined by Passeri and others (2018a). For the 

2005 post-Katrina DEM, no updated bathymetric data were available for the nearshore region. In 

this case, the nearshore bathymetry was taken as the final bottom depth from an independently 

validated simulation of Dauphin Island using an XBeach (Roelvink and others, 2009) simulation 

of Hurricanes Ivan and Katrina (Passeri and others, 2018b) to characterize the movement of sand 

that occurred from the island to the nearshore and estuarine areas during Hurricane Katrina. 

Deterministic Application 

A deterministic application of the model was used to compare model outputs to in situ 

observations of waves and currents. Deterministic applications of the model to the Dauphin Island region were forced with time-dependent waves, wind, and water levels derived from largerscale models. The model was applied during time periods when nearshore observations of waves, 

water levels, and currents were available for model validation, from 2015/08/27–2015/08/29 and 

2015/12/09–2015/12/11 for ADCP observations, and 2015/06/19–2015/11/20 for wave observations. Observation locations are shown in figure 3. Waves were forced at the offshore boundary 

with JONSWAP (Joint North Sea Wave Project) spectra (Hasselmann and others, 1973) created 

using a 3-hourly output from the National Oceanic and Atmospheric Administration (NOAA) 

operational WaveWatch III (WW3) (Tolman, 1989) model, which included significant wave 

height (Hs), peak period (T

p

), and peak direction (D

p

). Using these large-scale wave predictions 

rather than spatially discrete wave-buoy observations allows for alongshore non-uniformity 

in wave conditions at the offshore boundary. Long and others (2014) showed that WW3 modeled wave information compared well with observed waves at the National Data Buoy Center 

(NDBC) buoy offshore of Orange Beach, Alabama. 

Along both lateral boundaries, D3D WAVE was used to compute the cross-shore variation of the incident spectra by performing standalone wave simulations over straight and parallel 

contours that mimic the cross-shore bathymetry at the individual boundaries. Wind forcing was 

applied to both the FLOW and WAVE modules at 3-hour intervals using WaveWatchIII associated Global Forecast System (GFS) 10-meter wind speed and directions (Tolman, 1989). This 

wind forcing was chosen over locally observed wind speeds at the NDBC offshore buoy because 

it provides spatially varying wind characteristics over the entire domain and is the same source of 

wind values used to generate the WW3 wave inputs. Water levels, including tides, were derived 

from HYCOM and applied at the southern offshore open boundary at hourly intervals (Bleck, 

2002). An initial analysis found a 21-centimeter (cm) offset between HYCOM water levels and 

observed water levels at NOAA Dauphin Island tide gage 8735180 (fig. 4) (National Oceanic and 

Atmospheric Administration, 2017). A 21-cm offset was therefore added to the HYCOM water 

levels before they were applied to the model. Water levels were only applied to the southern open 
8 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

boundary to avoid numerical instabilities caused by fixing water levels too close to shore along the eastern and western open boundaries. The 

deterministic simulations were initialized at rest and allowed to spin-up for a period of 6 hours prior to the beginning of observations. 

Figure 3. Colored map of the digital elevation model (DEM) for this model showing observation locations within the study area. Depth contours indicate 

5-, 10-, 15-meter (m) isobaths (black lines). Acoustic Doppler current profile (ADCP) transect observations locations in Mobile Bay Inlet, Pass Aux Herons, 

and Petit Bois Pass are shown with white dotted lines. Deployed wave-gage locations offshore of Katrina Cut and on the ebb tidal shoal are shown with red 

circles. National Oceanic and Atmospheric Administration (NOAA) tide gage location at Dauphin Island (station 8735180) is shown with a yellow circle. 
9 Model Setup

Figure 4. Graphs showing water-level observations and Hybrid Coordinated Ocean Model (HYCOM) data at Dauphin Island, Alabama for a 6-month 

period. (A) Scatter plot of HYCOM water levels versus observed water levels at Dauphin Island tide gage (blue dots) and scatter plot of HYCOM water levels 

plus 21 centimeters (cm) versus observed water levels at Dauphin Island tide gage (orange dots). Line of y=x is shown in blue. The line of best fit is shown 

for each scatter with black dotted and dashed lines, respectively. The equation of fit for each scatter plot is also shown. (B) 6-month time series of water-level 

observations and HYCOM model data at Dauphin Island. Tide gage illustrates offset between unmodified HYCOM water levels. NOAA measured water levels 

at Dauphin Island tide gage (8735180) are shown with a black line. Unmodified HYCOM water levels are shown by a blue line. A time series of HYCOM 

water levels plus 21 cm is shown is orange. NOAA, National Oceanic and Atmospheric Administration. 

Climatological Application 

For the application of mormerge at Dauphin Island, regional wave conditions were derived from the European Centre for MediumRange Weather Forecast (ECMWF) Reanalysis (ERA)-Interim model (Dee and others 2011) and adjusted by a correction factor attained 

through comparison to wave height data from NDBC buoy 42040 (National Data Buoy Center, 2017a; Passeri and others, 2018a). This time 

series provided the longest record of wave data (1979–present) relative to the shorter WW3 (2005–present) or observational NDBC (1983–1984 

and 2009–present) records. Hs

, T

p

, and D

p

, at a model grid point located offshore of Dauphin Island (lat 30.000° N, long −88.125° W) were used

to define the regional wave conditions. Time periods with waves not directed toward shore between 110° and 250° (nautical convention) were

excluded, given that they would not propagate into the model domain and drive longshore transport. Time periods containing tropical events 

were also excluded from the wave binning, as this D3D model only simulates decadal-scale littoral transport for quiescent periods. 

The binning of the wave conditions was defined such that all bins contained equal amounts of wave-energy flux following the energy

flux method (EFM) of Benedet and others (2016). The wave conditions were divided into nine wave bins delimited by three directions and

three heights. Nine scenarios were developed using the binned information. The wave period (Ta ) of each scenario is the mean period of all 

waves within each bin, wave direction (Da) is the mean direction of the wave conditions in each bin, and wave height (Hs), assuming linear 
10 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

wave theory, is calculated from the mean wave-energy flux of all wave conditions in each bin.

The percent occurrence is calculated as the number of ERA wave data points contained in a bin 

divided by the total number of ERA wave data points in the record. Hs

, Ta, Da, and the percent 

occurrence of each of the nine scenarios are given in table 1. Nine wave scenarios were used for 

computational efficiency while also exceeding the noted lower limitation of the six wave scenarios

outlined by Benedet and others (2016). Along both lateral boundaries, D3D WAVE was used to 

compute the cross-shore variation of the incident spectra for each scenario by performing standalone wave simulations over straight and parallel contours that mimic the cross-shore bathymetry 

at the individual boundaries, similar to the deterministic application. 

Water-level forcing is applied to the model at the southern open boundary as a morphological tide derived using the method outlined by Lesser (2009). This method uses observed 

amplitudes and phases obtained at the Dauphin Island NOAA tide gage (8735180) to generate 

constituent amplitudes and phases representative of the observed water-level conditions without 

being date-range specific. The tide constituents were applied to each of the mormerge scenarios 

with a consistent and progressive phase shift that was added to improve model stability. Constituent amplitudes and phases of the derived morphological tide are given in table 2. Neumann 

boundary conditions are applied to the east and west lateral boundaries. The mormerge application is initialized at rest, and a morphological start time of 6 hours is applied. This allows time 

for the mean alongshore total transport to spin-up to a quasi-steady state before the morphology 

is allowed to evolve. A morfac of 365 is used in all cases, such that 1 day in the model simulation represents 365 days of morphologic evolution. Local wind forcing is not included in the 

mormerge application given that the predominant driver of waves seaward of Dauphin Island 

is expected to be generated outside of the model domain; resultant wave-driven currents, along 

with tides, are expected to be the dominant driver of nearshore circulation on the Gulf-side of the 

island. Overall model sensitivity to wind forcing, in terms of reproducing waves, was assessed. 

Results of this sensitivity study are presented in the Results section of the report. 

Table 1. Characteristics and percent occurrence for each wave condition of the wave climatology. 

[m, meters; % percent; s, seconds] 

Condition name H (m) s T (s) a D (degrees) a Occurrence (%) 

Bin 1 0.5275 6.2007 129.4898 26.054 

Bin 2 0.57721 6.3974 154.7268 24.523 

Bin 3 0.57327 5.7028 200.7705 28.165 

Bin 4 1.1401 7.1784 128.7983 5.684 

Bin 5 1.1299 7.4228 155.1817 5.5534 

Bin 6 1.1841 7.0636 197.2398 5.3663 

Bin 7 2.1797 8.4778 127.9215 1.3379 

Bin 8 1.9525 8.4175 155.7543 1.7199 

Bin 9 2.0213 8.3957 199.2142 1.5971 

Table 2. Amplitudes and phases of morphological tide constituents. 

[deg, degrees; Hz, hertz; m, meters] 

Measured element 15 Hz 30 Hz 

Amplitude (m) 0.19727139 0.0015 

Phase (deg) 321.194 310.5783 
Model Setup 11

Observations 

Output from the deterministic model was compared with observations of waves, water levels, and tidal currents to assess the skill of the hydromorphological modeling system for Mobile 

Bay and Dauphin Island, Alabama. Due to limited direct observations of sediment transport and 

morphology change in the nearshore, observations of shoreline positions and shoreline linear regression rates were used as a proxy for littoral sediment transport to compare to the model. 

Hydrodynamic Data 

As part of the Alabama Barrier Island Restoration Study, two ship-board acousticDoppler current profile (ADCP) surveys were carried out during spring tide conditions by the 

USACE Engineering Research and Development Center (ERDC), Field Data Collection Branch. 

At the time of publication, data are not publicly available from USACE. Each ADCP survey 

traversed the three major inlets surrounding Dauphin Island: Mobile Pass, Pass Aux Herons, and 

Petit Bois Pass (fig. 3). Data were captured along transects at an approximate 3.5-m horizontal 

resolution. Each survey captured approximately half of a tidal cycle (approximately 12 hours). 

The first survey, which collected data during the period of 2015/08/27, 12:01:00 through 

2015/08/28, 00:35:00, captured primarily ebb tide conditions. The second survey, which collected data during the period of 2015/12/09, 13:02:00 through 2015/12/09, 23:59:00, primarily 

captured the characteristics of a flood tide. However, the first survey also captured several passes 

during flood and slack tide conditions, while the second survey captured passes during slack tide 

conditions. Data were collected at a frequency of 0.4 Hertz (Hz). 

The ADCP captured horizontal position, water depth, and horizontal and vertical components of flow velocities from which stream-wise velocity components and volumetric flow rates 

were derived. Stream-wise velocity components were calculated from water velocities by first 

finding the average velocity vector, defined by the average velocity component vectors, u

avg

 and 

v

avg in the original coordinate system. The rotation angle (equation 1) from the original coordinate 

system to the stream-wise coordinate system in degrees (D), is defined as:

(1) 

So that the stream-wise velocity components can be calculated as: 

(2) 

(3) 

where 

D the rotation angle from the original coordinate system to the stream-wise coordinate system in degrees; 

v

avg the y-direction component of the average velocity vector; 

u

avg the x-direction component of the average velocity vector; 

u the through-channel velocity, regardless of the geographic orientation of the stream 

inlet channel to the original coordinate system; 
12 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

v the cross-channel velocity, regardless of the geographic orientation of the inlet stream 

channel to the original coordinate system; 

u the x-direction component of velocity; and 

v the y-direction component of velocity. 

Volumetric flow rate (equation 4), or flux, through each transect through time was calculated using the stream-wise u velocity, as 

(4) 

where 

Q the volumetric flow rate;

dL the length of each transect; and 

z the depth. 

Two directional wave gage and current profilers were also deployed in the nearshore 

areas of Dauphin Island by the USACE. A Nortek acoustic wave and current (AWAC) sensor was 

deployed near Pelican Island in the area of the Mobile Pass ebb tidal shoal at lat 30.242248° N., 

long 88.2125° W (fig. 3). The sensor sampled pressure and velocity at a rate of 2 Hz and returned frequency-direction wave spectra, hourly bulk-wave statistics, pressure, and water velocities at 5-minute intervals for the period of 2015/08/31–2015/11/20. A bottom-mounted Nortek 

Aquadopp wave sensor was deployed in the surf zone near Katrina Cut at lat 30.200543° N., 

long 88.0927778° W. The sensor sampled pressure and velocity at a rate of 2 Hz and returned 

frequency-directional wave spectra, hourly wave statistics, pressure, and water velocities at 

5-minute intervals for the period of 2015/06/20–2015/08/23. At the time of publication, data are 

not publicly available from the USACE. Hourly historical wave data were also downloaded from 

the NDBC at station 42012 near Orange Beach, Ala. (National Data Buoy Center, 2017b), for 

time periods overlapping both the AWAC and Aquadopp observation periods to evaluate the accuracy of wave inputs derived from WW3. 

Water-level data were downloaded from the NOAA Dauphin Island tide gage 8735180 

(National Oceanic and Atmospheric Administration, 2017) for periods that coincided with the 

other hydrodynamic observation periods. Measured and predicted tides at 6-minute intervals were 

obtained from the Dauphin Island tide gage for the periods of 2015/08/27 through 2015/08/28, and 

2015/12/09 through 2015/12/09. Measured and predicted tides of 6 minutes were also collected 

for the periods of 2015/08/31–2015/11/20 and 2015/06/20–2015/08/23. 

Shoreline Change Data 

Repeat observations of bathymetric change surrounding Dauphin Island were not available for validating the littoral sediment transport model. As a proxy, the model skill in capturing patterns of convergent and divergent alongshore sediment transport was determined using 

historical shoreline positions and shoreline change rates. Dauphin Island shorelines, defined as 

the mean high water (MHW) line (24 cm, NAVD 88) and historical rates of shoreline change 

were derived from historical lidar data from thirteen airborne lidar surveys that took place between 1998 and 2014 (Henderson and others, 2017). 
Results 13 

Results 

Comparison to Observations—Deterministic Simulations 

Acoustic-Doppler Current Profile Comparison

A deterministic application of the model for each of the ADCP observation periods was 

developed to simulate flow and wave conditions through each of the adjacent inlets. The model 

setup for each simulation used the 2015 DEM as the initial topobathymetric input. Each of 

the two simulation periods was forced with a wind, wave, and water-level time series for each 

representative time period: the first observation and simulation period extends from 2015/08/27 

through 2015/08/29 and the second period extends form 2015/12/09 through 2015/12/11. Each 

simulation was set to begin at least 12 hours prior to the beginning of the observation record to 

allow for the spin-up of water levels and velocities in the model. Sediment transport was computed, but the morphology remained constant throughout each simulation. 

From both simulation periods, modeled water velocities were interpolated from the model 

grid onto the observation transects in each of the inlets. Modeled and observed water velocities 

were then rotated to the stream-wise convention to attain through-channel velocity (u ) and stream 

cross-channel velocity (v ) components. Observed and modeled through-channel u velocities stream 

were used to calculate observed and modeled volumetric flow rate (Q) through each observation 

transect, as described above. Additionally, the u , v , and Q of both surveys were parsed stream stream 

and collated so that a dataset for the ebb and flood phases of the tide cycle could be assessed 

separately. The Q value was used to characterize survey passes as either ebb, flood, or slack tide; 

survey passes at slack tide were defined as having Q near zero, or roughly 1 order of magnitude 

smaller than the mean value, while ebb and flood tide were determined by flow direction (into or 

out of the channel). For both the ebb and flood phases, at each observation transect, linear fits between modeled and observed values were calculated for the following variables: u , v , and stream stream 

Q. In table 3, the coefficient of determination (R2), normalized RMSE, mean bias, mean normalized bias, slope, and intercept values associated with each linear fit are presented.

Figure 5A illustrates the u analysis in Mobile Bay Inlet for both tide phases. A scatter stream 

plot of modeled versus observed u for the ebb tide (blue dots) is given along with the line of stream 

best fit (solid black line) and shows a strong agreement between the model output and observations, with an R2

 value of 0.84. A comparison of the modeled and observed u for the second stream 

observation period (yellow dots) is also given along with the line of best fit (dashed black line). 

There is more scatter over the flood period than for the ebb period, which is reflected in a lower 

R2

 value of 0.53. Overall, figure 5A demonstrates that the model reasonably predicts u in stream 

Mobile Bay Inlet during the ebb portion of the tidal cycle and is relatively less skilled in predicting u in Mobile Bay Inlet during the flood tide. However, the modeled and observed u stream stream 

were of a similar magnitude. The mean bias between the observed and through-channel ebbflow velocities has a magnitude of −0.19 meters per second (m/s) and a mean normalized bias of 

−35.1 percent, which suggests underprediction of through-channel velocities. Similarly, for the

flood tide, observed and modeled u are of a similar magnitude, as shown by the mean bias of stream 

−0.03 m/s, while the mean normalized bias is −8.14 percent.

Figure 5B shows the same analysis applied to the smaller magnitude v in Mobile Bay stream 

Inlet for both ADCP observation periods. A scatter plot of modeled versus observed v for stream 
14 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

the first observation period (blue dots) is shown along with the line of best fit (solid black line), 

and it shows agreement between the model output and observations, with an R2

 value of 0.62. A 

comparison of the modeled and observed v for the second observation period (yellow dots) is stream 

shown along with the line of best fit (dashed black line). The associated R2 value of this linear fit 

is 0.45. In both the ebb and flood tide, the mean bias statistics for v show that the magnitudes stream 

of observed and modeled velocities are similar, with near-zero mean biases. Mean normalized 

bias statistics for ebb and flood tide periods are 40.6 percent and −73.0 percent, respectively. As 

was the case for the u in Mobile Bay Inlet, these comparisons show that the model reason- stream 

ably predicts v in Mobile Bay Inlet during the ebb portion of the tidal cycle and is relatively stream 

less skilled in predicting v in Mobile Bay Inlet during the flood tide. stream 

A comparison of the model output and the observed values (table 3) highlights several 

aspects of model skill. First, the model skill is best in Mobile Bay Inlet for each variable examined (u , v , and Q) compared with other inlets for both the ebb and flood phases; the stream stream 

model skill is similar overall in Pass Aux Herons and in Petit Bois Inlet for each variable. Second, model skill is generally higher for the dominant u than for the smaller magnitude v , stream stream 

which is the case except in Pass Aux Herons during the ebb tide, where the R2 of u and v stream stream 

are comparable, and in Petit Bois Inlet during the flood tide period, where the skill is higher for 

the v component. Third, the model shows skill in reproducing Q for the ebb-tide phase everywhere, with R2

 values comparable to, or exceeding, those associated with the u-velocity component. The ability to accurately reproduce a volumetric flow rate suggests that the bathymetry 

used in the model may not properly capture the depth variations across the inlets at the time the 

observations were collected (fig. 6), which would negatively impact the velocity predictions.

The lower model skill in Pass Aux Herons than in Mobile Bay Inlet is, in part, attributed 

to lower grid resolution across the Pass Aux Herons inlet, where grid resolution becomes coarser 

north of the island. The lower model skill in Petit Bois Inlet is attributed to an inaccurate initial 

bathymetry, as there was no up-to-date bathymetric data for this inlet. Figures 6C and 6F show 

a significant and consistent discrepancy of at least 1 meter between the depth on the observation 

transect and the initial model depth interpolated onto the observation transect. The lower model 

skill in producing v velocities may result from smaller magnitudes when compared with stream 

u velocities. Small deviations in modeled water-level velocities may have led to lower model stream 

skill due to the small magnitude of the overall flow. Each of these factors is likely to contribute to 

model error in reproducing Q. 

Overall, the comparison to ADCP observations for each of these periods shows that the 

model is skilled at reproducing water velocities and volumetric flow rates in the most significant 

of the three inlets, Mobile Bay inlet, which has the highest through-channel velocities. Model 

skill is also greatest during the dominant ebb tide. The lower skill in Petit Bois Inlet can, in part, 

be attributed to an inaccurate initial bathymetry. The lower overall skill of the cross-channel 

components is accounted for by smaller overall magnitude velocities, which are more sensitive to 

model variability. 
Results 15

The bias statistic presented throughout this report is calculated as 

(5) 

where 

N the number of observations in a record; 

Modeled any modeled quantity; and 

Observed any observed quantity. 

Additionally, the mean normalized bias discussed throughout this report is calculated 

generally as: 

(6) 

Table 3. Mean values, coefficient of determination (R2

), normalized root mean squared error (NRMSE), 

mean bias, mean normalized bias (NBIAS), slope, and intercept of linear fit between modeled and observed stream-wise velocity components (x-direction component of velocity [u] and y-direction component 

of velocity [v]) and volumetric flow rates (Q) during ebb and flood tide. NRMSE is normalized by the range 

of the data. 

[m3/s, cubic meters per second; m/s, meters per second; %, percent] 

Flow type Mobile Bay Inlet Pass Aux Herons Petit Bois Inlet 

Mean observed value 

u, (m/s) ebb 0.58 0.41 0.28 

v, (m/s) ebb 3.13e-15 9.31e-17 −6.90e-15

Q, (m3

/s) ebb 1.30e+07 1.3998e+06 9.7578e+06 

u, (m/s) flood 0.32 0.31 0.24 

v, (m/s) flood 5.24e-15 −7.84e-15 1.68e-15 

Q, (m3

/s) flood 9.82e+06 1.51e+06 9.44e+06 

R2 

u, (m/s) ebb 0.84 0.25 0.45 

v, (m/s) ebb 0.62 0.36 0.28 

Q, (m3

/s) ebb 0.96 0.57 0.50 

u, (m/s) flood 0.53 0.16 0.002 

v, (m/s) flood 0.45 0.07 0.46 

Q, (m3

/s) flood 0.44 0.08 0.02 

Normalized RMSE 

u, (m/s) ebb 0.08 0.10 0.13 

v, (m/s) ebb 0.07 0.16 0.09 

Q, (m3

/s) ebb 0.09 0.25 0.29 

u, (m/s) flood 0.10 0.14 0.14 

v, (m/s) flood 0.09 0.13 0.05 

Q, (m3

/s) flood 0.24 0.28 0.39 
16 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Table 3. Mean values, coefficient of determination (R2), normalized root mean squared error (NRMSE), 

mean bias, mean normalized bias (NBIAS), slope, and intercept of linear fit between modeled and observed stream-wise velocity components (x-direction component of velocity [u] and y-direction component 

of velocity [v]) and volumetric flow rates (Q) during ebb and flood tide. NRMSE is normalized by the range 

of the data.—Continued 

[m3/s, cubic meters per second; m/s, meters per second; %, percent] 

Flow type Mobile Bay Inlet Pass Aux Herons Petit Bois Inlet 

Mean bias 

u, (m/s) ebb −0.19 −0.20 −0.15

v, (m/s) ebb −7.81e-16 −2.28e-16 5.81e-16 

Q, (m3

/s) ebb −1.91e+06 −3.10e+05 −4.53e+06

u, (m/s) flood −0.03 −0.15 −0.08

v, (m/s) flood 7.24e-16 7.22e-16 −1.20e-16

Q, (m3

/s) flood 9.98e+04 −6.59e+05 −2.46e+06

Mean normalized bias 

u, (m/s) ebb −35.1 −34.9 −111.7

v, (m/s) ebb 40.6 −89.0 −84.9

Q, (m3

/s) ebb −17.7 10.8 −61.2

u, (m/s) flood 8.14 −73.8 −14.9

v, (m/s) flood −73.0 −179.9 −73.2

Q, (m3

/s) ebb 2.08 −30.5 −26.0

Slope 

u, (m/s) ebb 0.99 1.69 0.69 

v, (m/s) ebb 1.04 2.57 0.75 

Q, (m3

/s) ebb 0.81 1.58 0.52 

u, (m/s) flood 0.92 1.52 −0.05

v, (m/s) flood 0.95 0.79 1.43 

Q, (m3

/s) flood 0.55 1.14 0.04 

Intercept 

u, (m/s) ebb 0.19 0.08 0.19 

v, (m/s) ebb 8.10e-16 −7.54e-16 −6.46e-16

Q, (m3

/s) ebb 4.04e+06 5.39e+05 7.03e+06 

u, (m/s) flood 0.05 0.04 0.25 

v, (m/s) flood −6.33e-16 5.57e-16 3.30e-17 

Q, (m3/s) flood 4.39e+06 −3.22e+05 9.19e+06 
17 Model Setup

Figure 5. Scatter plots of (A) trip 1 (blue dots) and trip 2 (yellow dots) modeled versus observed through-channel velocity in Mobile Bay Inlet. A line of y=x 

(orange) is provided along with the lines of best fit for trip 1 (solid black) and trip 2 (dashed black). The R2

 value associated with each linear fit is also given. 

(B) Trip 1 (blue dots) and trip 2 (yellow dots) modeled versus observed V-velocity in Mobile Bay Inlet. A line of y=x (orange) is provided along with the lines of 

best fit for trip 1 (solid black) and trip 2 (dashed black). The R2

 value associated with each linear fit is also given.

Wave-Gage Comparison 

A single deterministic simulation was set up to span both wave-gage observation periods (2015/06/19 through 2015/11/20). The observation periods were consecutive and not overlapping. The 2015 DEM was used as the initial topobathymetric input. Sediment transport was 

computed, but the associated morphologic change was not. The simulation was set up to begin 6 hours before the start of observations. 

The observed significant wave height, peak period, and direction associated with the peak period were compared with the model output on the fine wave grid at the grid points nearest the Aquadopp observation location (lat 30.242248° N., long 88.2125° W) near Katrina Cut 

and to the AWAC observation location (30.200543° N., long 88.0927778° W) near Pelican Island on the ebb tidal shoal. The linear fit and associated R2

 value between the observed and fine-grid model predictions were calculated for the bulk wave characteristics of Hs, T

p

, and D

p

 for 

data from both observation periods (table 4). The time series of observed and modeled Hs, T

p

, and D

p at the Aquadopp and AWAC wave gages 

are shown in figures 7 and 8, respectively.
18 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Figure 6. Graphs showing comparisons between observed depth and model digital elevation model 

(DEM) depths on all acoustic Doppler current profiler (ADCP) transects during trip 1 and trip 2. Panels A, B, 

and C respectively show depth along the Mobile Bay Inlet, Pass Aux Herons, and Petit Bois Inlet observation transects, during trip 1. Panels D, E, and F respectively show depth along the Mobile Bay Inlet, Pass 

Aux Herons, and Petit Bois Inlet observation transects, during trip 2. The mean observed depth is given 

by the solid black line while the standard deviation of those observed depths is shown by the grey shaded 

region. The model DEM depth interpolated to the observation transect is shown in yellow. 
Results 19

R2 values show that the model is skilled in reproducing significant wave heights at both 

the Aquadopp and AWAC location and that the model skill is greater for the AWAC observation 

period. This observation is also shown for Hs by the mean bias and normalized mean bias, which 

shows that the model underpredicts wave heights by roughly 8 cm and 11 cm at the AWAC and 

Aquadopp stations, respectively, or by about 3–8 percent—relative to the observed mean—as 

shown by the mean normalized bias. Overall, the model is relatively less skilled at reproducing 

peak periods and the direction of peak periods than it is at reproducing significant wave heights. 

The model error at the Aquadopp location may be partially due to the complicated and dynamic 

bathymetry near Katrina Cut, which would result in a poor comparison of model output using 

what could be rapidly outdated bathymetry to a single observation point. Offshore wave-forcing 

from WW3 compared well with observed wave heights at NDBC buoy 42012 with an R2 value 

of 0.81, as shown in figure 9. The winds used from WW3 also compared well with the observed 

winds at the buoy. However, the 6-hourly WW3 winds exclude the daily seabreeze and landbreeze cycles that could have influenced wave heights nearshore during the Aquadopp observation period of mid-June through late August, which would negatively impact model predictions. 

A sensitivity test was carried out for this same simulation period to evaluate the sensitivity of the model to wind forcing. Model conditions and time periods were held constant with one 

modification: wind forcing was not applied to either the flow or wave domains, thereby replicating the wind conditions used in the mormerge applications. Statistics for this sensitivity study 

are given in table 5. A comparison between the case with forced winds and the case without wind 

forcing reveals reduced model skill in reproducing Hs at both wave gage locations when winds 

are not applied. This difference is reflected by slightly lower R2 values and higher mean bias, 

mean normalized bias, and normalized RMSE values. Comparing the statistics of R2, mean bias, 

mean normalized bias, and normalized RMSE for T

p show that the model skill in reproducing 

peak wave periods increases when winds are removed. The reduced model skill in reproducing Hs

 when winds are removed suggests that the presence of winds improves the ability of the 

model to reproduce locally generated wind waves and bulk wave energy. Improved model skill 

in reproducing T

p

 when wind forcing is removed suggests that the wave environment is primarily 

forced remotely (peak energy in higher period waves) and that the generation of wind-waves in 

the model can degrade the prediction of T

p during bimodal wave-spectra time periods, when the 

model erroneously predicts more energy in the wind-wave band. 
20 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Table 4. Coefficient of determination (R2), mean bias, mean normalized bias, root mean squared error 

(RMSE), and normalized RMSE statistics between observed and modeled significant wave height (Hs

), 

peak period (T

p

), average period (Ta), direction (Dir), and average direction (Dira) at two nearshore wavegage stations. 

[deg, degree; m, meter; %, percent; s, second] 

Data field AWAC Aquadopp 

R2 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

0.777 

0.460 

0.403 

0.203 

0.334 

−0.081

0.593 

0.001 

0.021 

0.177 

0.370 

−0.118

Mean Bias 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

−0.081

−0.352

−1.022

−18.19

−14.03

−0.081

−0.118

−1.024

−0.932

18.88 

19.95 

−0.118

Mean normalized bias 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

−3.010

−4.671

−27.63

12.27 

151.3 

−3.010

−8.457

−13.88

−35.90

88.64 

201.31 

−8.457

RMSE 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

0.196 

1.164 

0.559 

51.66 

44.73 

0.196 

0.174 

1.682 

0.552 

26.40 

18.28 

0.174 

Normalized RMSE 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

0.316 

0.235 

0.149 

0.316 

0.281 

0.316 

0.373 

0.354 

0.171 

0.153 

0.110 

0.373 
Results 21

Table 5. Results of a sensitivity test to wind forcing. R2, mean bias, mean normalized bias, root mean 

squared error (RMSE) and normalized RMSE statistics for linear fits of observed and modeled significant 

wave height (Hs), peak period (T

p

), average period (Ta), direction (Dir), and average direction (Dira) at two 

nearshore wave-gage stations for simulations in which wind forcing was not applied. 

[deg, degree; m, meter; %, percent; s, second] 

Data field AWAC Aquadopp 

R2 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

0.708 

0.490 

0.525 

0.163 

0.247 

0.708 

0.567 

0.024 

0.108 

0.432 

0.235 

0.567 

Mean Bias 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

−0.217

−0.084

−0.212

0.656 

4.683 

−0.217

−0.206

−0.849

−0.275

12.27 

13.33 

−0.206

Mean normalized bias 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

−34.21

2.156 

−6.253

53.01 

145.9 

−34.21

−32.27

−10.44

−17.92

81.26 

184.9 

−32.27

RMSE 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

0.224 

1.132 

0.499 

52.83 

47.56 

0.224 

0.179 

1.663 

0.527 

25.45 

17.36 

0.179 

Normalized RMSE 

H (m) s 

T (s) 

p

T (s) 

a

Dir (deg) 

Dir (deg) 

a

H (m) s 

0.361 

0.228 

0.133 

0.324 

0.299 

0.361 

0.384 

0.350 

0.163 

0.147 

0.101 

0.384 
22 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Figure 7. Time series graphs showing observed and modeled wave variables at the Aquadopp wave gage. (A) Modeled significant wave height (Hs) 

(orange line) and observed zeroth-moment wave height (Hm0) (blue line). The comparison is presented with an associated coefficient of determination (R2), 

in which R2=0.593. (B) Observed and modeled peak period (T

p

) (blue line and orange line, respectively). The comparison is presented with an associated 

R2=0.001. (C) Observed and modeled direction of the peak waves (θ peak) (blue line and orange line, respectively). The comparison is presented with an associated R2

=0.177. 
Results 23

Figure 8. Time series graphs showing observed and modeled wave variables at the acoustic wave and current sensor (AWAC) wave gage. (A) Modeled 

significant wave height (Hs

) (orange line) and observed zeroth-moment wave height (Hm0) (blue line). The comparison is presented with an associated 

coefficient of determination (R2), in which R2=0.777. (B) Observed and modeled peak period (T

p

) (blue line and orange line, respectively). The comparison is 

presented with an associated R2

=0.460. (C) Observed and modeled direction of the peak waves (θpeak) (blue line and orange line, respectively). The comparison is presented with an associated R2

=0.203. 
24 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Figure 9. Graphs showing a comparison between WaveWatch III (WW3) wind and wave height forcing and observed winds and wave heights at National 

Data Buoy Center (NDBC) 42012. (A) Scatter plot of wind speeds (black dots) with a line of best fit (blue) and a line of y=x (red). The comparison is presented with an associated coefficient of determination (R2

) of 0.71. (B) time series of WW3 and observed significant wave height (Hs) at NDBC 42012 for the 

Aquadopp observation period and is presented with an associated R2

= 0.81. (C) Time series of WW3 and observed Hs at NDBC 42012 for the acoustic wave 

and current sensor (AWAC) observation period and is presented with an associated R2

= 0.91. 

Tide-Gage Validation 

The wave-gage validation simulation period of 2015/06/19 through 2015/11/20 discussed in the previous section also provided a 6-month 

time series of simulated water levels. Modeled water levels were taken from the flow grid output at the grid point near the Dauphin Island tide

gage 8735180 (lat 30.250° N., long 88.075° W.) and compared with observed water levels at this station. Figure 10 illustrates the agreement 

(R2=0.68) between the modeled and observed water levels at the Dauphin Island tide gage. The bias-corrected HYCOM water-level forcing applied at the offshore boundary was also compared with observations at the tide gage (fig. 4) and produced an R2

 value of 0.87. The degraded D3D 

model error in reproducing water levels may be due in part to inaccuracies in the water-level boundary forcing, particularly inaccuracies in the 

phase of certain harmonic constituents. 

A classical tidal-harmonic analysis was applied to both the observed water levels and the water-level forcing at the Dauphin Island tide gage, 

using the methods described in Pawlowicz and others (2002). The results of this analysis are given in table 6, as “significance,” “amplitude,” and

“phase” for each harmonic constituent. A constituent is considered “significant” if the ratio of the squared root of the amplitude to error is less than 1,
Results 25

which serves to separate tidal signals from noise. The results reveal discrepancies in the phase of 80° 

to 200° between the water-level forcing and observed water levels for certain nonlinear and shallow 

water harmonic tide-constituents. The individual amplitudes of the applied MK3, SK3, M4, MN4, 

MS4, M6 and 2SM6 constituents are small, with none exceeding 1 millimeter (mm); however, the sum 

of these amplitudes is equal to 16 percent of the fourth largest applied amplitude (Q1) and 4 percent 

of the largest applied amplitude (O1). Modeled water levels also do not reach the same peak during 

a storm which occurred during this period, consistent with an underprediction of a storm peak water 

level in the HYCOM water-level boundary forcing (fig. 4B). Underprediction of modeled water levels 

is quantified by a mean bias of −1.72e-04 m and a mean normalized bias of −11.78 percent. Some

error in reproducing water levels may be due to the absence of time-dependent water-level forcing at 

the lateral boundaries, where only a Neumann boundary condition is applied. Finally, a portion of the 

error may be due to an imperfect location match between the in situ tide gage, which is on a tower 

near to land at the end of a pier and the observation location, by constraints of the model grid not 

being so close to shore, may mean there is some difference in the water depth at the tide gage and the 

water depth of the model point used. 
26 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Table 6. Results of a classical tidal harmonic analysis of the applied water-level forcing (HYCOM) and the observed water levels at station 8735180. Results of the modeled output near the water-level station are also included. Only those harmonic constituents that were found to be significant for at least one 

of the tide datasets (applied, observed, or modeled) are presented. Tidal harmonic amplitudes and phases are given for each significant harmonic constituent. In this analysis, a constituent is said to be significant if the square ratio of its amplitude to the error in amplitude is less than 1. 

[m, meter] 

Constituent Significant Amplitude (m) Phase (degrees) 

HYCOM Observed Modeled HYCOM Observed Modeled HYCOM Observed Modeled 

Q1 Large lunar elliptic 

diurnal Yes Yes Yes 0.0218 0.0257 0.0189 21.20 30.43 53.82 

O1 Lunar diurnal Yes Yes Yes 0.0942 0.1049 0.0984 37.60 35.80 68.76 

K1 Lunar diurnal Yes Yes Yes 0.0893 0.1106 0.0975 43.07 45.85 72.40 

N2 Larger lunar elliptic 

semidiurnal Yes -- -- 0.0051 0.0032 0.0024 136.92 154.91 231.91 

M2 Principal lunar 

semidiurnal Yes Yes Yes 0.0224 0.0178 0.0186 129.42 139.73 208.99 

S2 Principal solar 

semidiurnal Yes Yes Yes 0.0127 0.0085 0.0088 113.28 123.80 216.80 

MO3 ----------------- Yes Yes Yes 0.0008 0.0021 0.0020 264.71 149.30 313.36 

MK3 Shallow water 

terdiurnal Yes -- Yes 0.0010 0.0008 0.0019 278.32 142.76 297.88 

SK3 Compound tide Yes -- Yes 0.0007 0.0011 0.0014 294.98 213.84 333.38 

M4 

Shallow water 

overtides of 

principal lunar 

-- Yes -- 0.0003 0.0037 0.0001 76.86 352.78 327.36 

MN4 Shallow water 

quarter diurnal -- Yes -- 0.0003 0.0019 0.0002 111.78 308.99 10.06 

MS4 Shallow water 

quarter diurnal Yes -- Yes 0.0007 0.0007 0.0008 120.78 39.42 151.27 

M6 

Shallow water 

overtides of 

principal lunar 

Yes -- -- 0.0003 0.0006 0.0001 269.81 67.11 191.22 

2SM6 ----------------- -- -- Yes 0.0001 0.0005 0.0002 245.68 42.60 197.91 
Results 27

Figure 10. Graphs showing modeled and and observed water levels. (A) Scatter plot of modeled and observed water levels at station 8735180 (blue dots). 

The line of best fit is given (black line) as well as a line of y=x (dashed orange line). The coefficient of determination (R2) of the linear fit is also provided and 

is equal to 0.68. (B) Time series comparison between modeled and observed water levels at the Dauphin Island tide gage for the same period. The mean 

bias between observed and modeled water levels is equal to −1.72e-04 meters (m), while the mean normalized bias is equal to −11.78 percent. 

Comparison to Observations: Mormerge Simulations 

Lidar Shoreline Validation 

A mormerge simulation was set up in D3D to simulate littoral sediment transport and island morphological conditions over longer 

temporal scales. The model setup used the post-storm 2005 DEM as the topobathymetric input. Water-level forcing was supplied by harmonic morphological tides at the southern open boundary. The wave parameters of each mormerge scenario (table 1) were used to generate a 

JONSWAP spectra applied at the offshore boundary. The simulation period, September 2005 through June 2007, captured a quiescent period 

with no tropical storms affecting Dauphin Island that would require the use of a storm model (for example, XBeach in Roelvink and others, 

2009). The simulation began 6 hours prior to the start of the morphological change to allow for the spin-up of model sediment transport to a 

quasi-steady state driven by water levels, water velocities, and waves. 

Observed shorelines are defined by the 24-cm MHW position. For comparison between modeled and observed lidar shoreline-change 

rates, the zero-meter contour of the modeled depth was extracted. Modeled depths at each output time were first interpolated onto an array of 

cross-shore transects spaced 100 m apart along Dauphin Island (Hendersen and others, 2017). The zero crossing of modeled depth from subaerial to subaqueous was then located on each transect for each output time to create a time series of modeled shoreline positions, from which 
28 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

a shoreline linear regression rate (LRR,) in meters per year, was calculated for each transect. 

Similarly, observed LRRs were calculated from the observed MHW shoreline position on each 

transect. Separate bulk R2 values were calculated between modeled and observed LRRs for all 

east-end (east of Katrina Cut) and west-end transects (west of Katrina Cut), as well as for the five 

sub-segments (A, B, C, D, and E) delineated in figure 11, along with the additional error statistics 

(table 7). Lidar data showed that some isolated breaches in the island were not captured well in 

the initial model DEM due to the alongshore grid-spacing. No comparison was made at locations 

where there were discrepancies between the initial observed-shoreline model DEM. 

The overall R2

 values show that the model is more skilled at reproducing littoral sediment transport on the west end of the island (R2

=0.12) than on the island’s east end (R2=0.001). 

Region A on figure 11 compares observed and modeled LRRs and shows qualitative agreement 

between observed and modeled LRRs on the west end, lending confidence to model skill at 

reproducing convergent and divergent transport patterns over this region. Regions of B and D 

in figure 11 also highlight model skill in reproducing general LRR trends for those transects 

in highly variable and rapidly evolving areas near Katrina Cut and Pelican Island, although at 

relatively less skill than in region A. Both observed and modeled LRR show little evolution on 

transects in region E of figure 11. Observed shorelines accreted much faster over a number of 

east-end transects that were affected by anthropogenic influences. Specifically, in region C of figure 11, was the placement of a 0.382 million cubic meters (m3

) Federal Emergency Management 

Agency sand berm constructed between January and June 2007 that was not accounted for in the 

model. The footprint of this sand berm design was such that its shoreline was farther offshore 

than the initial shoreline used in the model. Additional model error in predicting LRRs are likely 

due to a mismatch between real bathymetric conditions at the beginning of the simulation period 

and the model DEM used. 
Results 29

Table 7. Coefficient of determination (R2), mean bias, mean normalized bias, root mean square error 

(RMSE) and normalized RMSE statistics between observed and modeled shoreline linear regression rates 

by island segment. Segments (A, B, C, D, and E) are delineated in figure 11 and do not necessarily contain 

the same number of transects. 

[m/yr, meters per year; %, percent] 

Map segment 

R2 

A 0.102 

B 0.198 

C 0.287 

D 0.006 

E 0.005 

Mean bias (m/yr) 

A 4.452 

B 7.679 

C −12.63

D 17.54 

E −0.080

Mean normalized bias 

A −40.16

B −57.48

C −84.25

D −133.2

E −176.3

RMSE (m/yr) 

A 10.92 

B 15.468 

C 5.539 

D 8.543 

E 7.904 

Normalized RMSE 

A 0.483 

B 0.463 

C 0.420 

D 4.815 

E 2.558 
30 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Figure 11. Graph and map showing observed and modeled shoreline linear regression rates for the period of 2005–2007 (A–E) shown alongside (F) Dauphin Island digital elevation model (DEM). Vertical red lines indicate bounds of regions A, B, C, D, and E. Regions A, B, D, and E show that overall shoreline 

regression rates are fairly well-modeled during this period. Region E shows agreement with little change East of Pelican Island, while region D shows that 

the model captures impacts of Pelican Island. Region B illustrates that the evolution of Katrina Cut is also represented, while region A also shows that the 

general linear regression rate (LRR) trends are captured by the model. Region C shows a significant and consistent offset between modeled and observed 

LRR. In region C, a sand berm (yellow region on F) was built in 2007 that was not included in the model, resulting in this offset. 
Conclusions 31

Conclusions 

A modeling system was developed to simulate the littoral sediment transport along 

Dauphin Island, Alabama, using the Delft3D (D3D) FLOW and WAVE and “mormerge” configurations. A deterministic application of the modeling system was implemented, and three 

simulations were run to coincide with two periods of observations of circulation (ebb and flood 

tide during summer and winter, respectively) and two periods of wave observation (back-to-back 

summer period). The comparison between modeled and observed water velocities and volumetric 

flow rates through inlets showed that the model skill was greatest in Mobile Bay Inlet during the 

dominant ebb tide and less skilled in Pass Aux Herons and Petit Bois Inlet. Additionally, it was 

shown that model skill was greater for predicting the dominant through-channel velocity than for 

the weaker cross-channel velocity. Lesser model skill in Pass Aux Herons and Petit Bois Inlet 

is likely due to the lower north-south grid resolution across this inlet and inaccurate, outdated 

initial bathymetry. The expectation, however, is that Mobile Bay Inlet has the largest influence on 

the evolution of Dauphin Island. 

A comparison of the model output to the wave-gage data indicated that the model predicted wave propagation to the island and was generally limited by the skill of the WW3 wave 

data applied at the boundaries (fig. 9). The model reproduced wave characteristics better on the 

ebb tidal shoal than in front of the rock wall constructed after a previous breach in the island, 

likely due to the complex and potentially dynamic bathymetry at the latter location. Further reduction in model skill may result from the seabreeze and land-breeze cycles observed during the 

observation period that were absent in the applied wind forcing. Comparisons between modeled 

and observed water levels for a 6-month period show strong model skill. 

A statistical mormerge application of the modeling system was implemented, for a single 

simulation, to model alongshore littoral sediment transport. Comparisons between the observed 

and modeled shoreline position linear-regression rates showed that the model was skilled in capturing overall trends in shoreline linear-regression rates along Dauphin Island, which served as a 

proxy for alongshore littoral sediment transport. The region for which the model had the lowest 

predictive skill corresponded to an area of sand berm construction that was not incorporated into 

the model. These comparisons to both hydrodynamic and morphological observations suggest 

that the modeling system can characterize the littoral sediment transport along Dauphin Island, 

Alabama. 
32 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

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Appendix 1. 

In this appendix, the model inputs for the Delft 3D (D3D) model used in this study are 

defined. The model parameters and settings for the FLOW module for the deterministic applications are described in table 1.1, while table 1.2 lists the settings for the WAVE module. The 

model parameters and settings for the morphology for the deterministic applications are given 

in table 1.3. Tables 1.4, 1.5, and 1.6 provide the model parameters and settings for the FLOW, 

WAVE, and morphology, respectively, of the mormerge application. Note that in tables 1.4, 1.5, 

and 1.6, the duplicate parameters from 1.1, 1.2, and 1.3 are not given, and any parameter not presented in the latter three tables should be assumed identical to the former three. All parameters 

provided are relevant to D3D version number 4.00.01. 

Table 1.1. Model parameter and settings for the FLOW portion of the deterministic applications as set 

in model master definition file (.mdf). Specific input file names for grid, initial dates, runtimes, enclosure, 

depth, friction, dry points, winds, boundary conditions, sediment, morphology, and observations are excluded. 

Master definition file input

Input name Value 

Anglat 3.0200000e+001 

Grdang 0.0000000e+000 

MNKmax 1369 658 1 

Thick 1.0000000e+002 

Tunit #M# 

Tstart 0.0000000e+000 

Dt 0.375 

Tzone 0 

Sub1 # W # 

Sub2 # CW# 

Wnsvwp #Y# 

Wndint #Y# 

Zeta0 0.0000000e+000 
Appendix 1 35

Table 1.1. Model parameter and settings for the FLOW portion of the deterministic applications as set 

in model master definition file (.mdf). Specific input file names for grid, initial dates, runtimes, enclosure, 

depth, friction, dry points, winds, boundary conditions, sediment, morphology, and observations are excluded.—Continued 

Master definition file input

Input name Value 

C01 0.0000000e+000 

Rettis 0.0000000e+000 

Rettib 0.0000000e+000 

Ag 9.8100000e+000 

Rhow 1.0000000e+003 

Tempw 1.5000000e+001 

Salw 3.1000000e+001 

Rouwav #FR84# 

Wstres 6.3000000e-004 

0.0000000e+000 

7.2300000e-003 

3.0000000e+001 

7.2300000e-003 

3.0000000e+001 

Rhoa 1.0000000e+000 

Betac 5.0000000e-001 

Equili #N# 

Ktemp 0 

Fclou 0.0000000e+000 

Sarea 0.0000000e+000 

Temint #Y# 

Roumet #C# 

Xlo 0.0000000e+000 

Vicouv 1.0000000e+000 

Dicouv 1.0000000e+000 

Htur2d #N# 

Irov 0 

Iter 2 

Dryflp #YES# 

Dpsopt #DP# 
36 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Table 1.1. Model parameter and settings for the FLOW portion of the deterministic applications as set 

in model master definition file (.mdf). Specific input file names for grid, initial dates, runtimes, enclosure, 

depth, friction, dry points, winds, boundary conditions, sediment, morphology, and observations are excluded.—Continued 

Master definition file input

Input name Value 

Dpuopt #MOR# 

Dryflc 1.0000000e-001 

Dco -9.9900000e+002

Tlfsmo 6.0000000e+001 

ThetQH 0.0000000e+000 

Forfuv #N# 

Forfww #N# 

Sigcor #N# 

Trasol #Cyclic-method# 

Momsol #Cyclic# 

SMhydr #YYYYY# 

SMderv #YYYYYY# 

SMproc #YYYYYYYYYY# 

PMhydr #YYYYYY# 

PMderv #YYY# 

PMproc #YYYYYYYYYY# 

SHhydr #YYYY# 

SHderv #YYYYY# 

SHproc #YYYYYYYYYY# 

SHflux #YYYY# 

PHhydr #YYYYYY# 

PHderv #YYY# 

PHproc #YYYYYYYYYY# 

PHflux #YYYY# 

Online #N# 

Wagmod #N# 

WaveOL #Y# 
Appendix 1 37

Table 1.2. Model parameters and settings for the WAVE portion of the deterministic applications as set 

in model master definition wave file (.mdw). Specific input file names for grid, initial dates, runtimes, enclosure, depth, friction, dry points, winds, boundary conditions, sediment, morphology, and observations are 

excluded. 

Master definition wave input

Input name Value 

SimMode stationary 

DirConvention nautical 

ObstacleFile obstacle.obt 

WindSpeed 0.0000000e+000 

WindDir 0.0000000e+000 

WaterLevelCorrection 0.0000000e+000 

Gravity 9.8100004e+000 

WaterDensity 1.0250000e+003 

NorthDir 9.0000000e+001 

MinimumDepth 5.0000001e-002 

GenModePhys 3 

Breaking true 

BreakAlpha 1.0000000e+000 

BreakGamma 7.3000002e-001 

Triads false 

TriadsAlpha 1.0000000e-001 

TriadsBeta 2.2000000e+000 

BedFriction jonswap 

BedFricCoef 6.7000002e-002 

Diffraction false 

DiffracCoef 2.0000000e-001 

DiffracSteps 5 

DiffracProp true 

WindGrowth true 

WhiteCapping Komen 

Quadruplets true 

Refraction true 

FreqShiftt true 
38 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Table 1.2. Model parameters and settings for the WAVE portion of the deterministic applications as set 

in model master definition wave file (.mdw). Specific input file names for grid, initial dates, runtimes, enclosure, depth, friction, dry points, winds, boundary conditions, sediment, morphology, and observations are 

excluded.—Continued 

Master definition wave input

Input name Value 

WaveForces radiation stresses 

DirSpaceCDD 5.0000000e-001 

FreqSpaceCSS 5.0000000e-001 

RChHsTm01 2.0000000e-002 

RChMeanHs 2.0000000e-002 

RChMeanTm01 2.0000000e-002 

PercWet 9.8000000e+001 

MaxIter 15 

TestOutputLevel 0 

TraceCalls false 

UseHotFile false 

MapWriteInterval 180 

WriteCOM true 

COMWriteInterval 180 

WriteTable true 

WriteSpec1D false 

WriteSpec2D true 

Sample boundary settings 

Name Bound1 

Definition grid-coordinates 

StartCoordM 0 

EndCoordM 0 

StartCoordN 0 

EndCoordN 39 

SpectrumSpec from file

Spectrum TPAR1.bnd 
Appendix 1 39

Table 1.2. Model parameters and settings for the WAVE portion of the deterministic applications as set 

in model master definition wave file (.mdw). Specific input file names for grid, initial dates, runtimes, enclosure, depth, friction, dry points, winds, boundary conditions, sediment, morphology, and observations are 

excluded.—Continued 

Master definition wave input

Input name Value 

Outer and inner nested wave domain settings 

FlowBedLevel 1 

FlowWaterLevel 1 

FlowVelocity 1 

FlowWind 1 

DirSpace circle 

NDir 72 

StartDir 0.0000000e+000 

EndDir 0.0000000e+000 

FreqMin 5.0000001e-002 

FreqMax 1.0000000e+000 

NFreq 24 

Output true 

Inner nested wave domain settings 

NestedInDomain 1 
40 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Table 1.3. Model parameter and settings for the morphology portion of each deterministic application as 

set in model morphology file (.mor). 

Master Definition File Input

Input Name Value 

EpsPar False 

IopKCW 1 

RDC 0.01 

RDW 0.02 

MorFac 0 

MorStt 3.6000000e+002 

Thresh 5.0000001e-002 

MorUpd false 

EqmBc true 

SensIn false 

AksFac 1.0000000e+000 

RWave 2.0000000e+000 

AlfaBs 1.0000000e+000 

AlfaBn 1.5000000e+000 

Sus 1.0000000e+000 

Bed 1.0000000e+000 

SusW 0.0200000e+000 

BedW 0.0200000e+000 

SedThr 1.0000000e-001 

ThetSD 0.2000000e+000 

HMaxTH 1.5000000e+000 

FWfac 1.0000000e+000 

Multi --

NeuBcSand true 

AverageAtEachOutputTime true 

BedTranspDueToCurrentsAtZeta true 

BedTranspDueToWavesAtZeta true 

SuspTranspDueToWavesAtZeta true 
Appendix 1 41

Table 1.3. Model parameter and settings for the morphology portion of each deterministic application as 

set in model morphology file (.mor).—Continued 

Master Definition File Input

Input Name Value 

HidExp true 

Dm true 

Percentiles 10 50 60 90 

Table 1.4. Model parameter and settings for the FLOW portion of the mormerge application as set in 

model master definition file (.mdf). Only parameters and settings unique to the mormerge application are 

given. Values not given are duplicated in table 1.1, above. Specific input file names for grids, initial dates, 

runtimes, enclosure, depth, friction, dry points, winds, boundary conditions, sediment, morphology, and 

observations were excluded. 

Master definition file input

Input name Value 

Sub1 # # 

Wnsvwp #N# 

Wndint #Y# 

Table 1.5. Model parameter and settings for the WAVE portion of the mormerge application as set in 

model master definition wave file (.mdw). Only parameters and settings unique to the mormerge application are given. Values not given are duplicated in table 1.2, above. Specific input file names for grid, initial 

dates, runtimes, enclosure, depth, friction, dry points, winds, boundary conditions, sediment, morphology, 

and observations were excluded. 

Master definition wave input

Input name Value 

WindGrowth false 

Quadruplets false 

Offshore boundary settings 

Name Bound1 

Definition grid-coordinates 

StartCoordM 0 

EndCoordM 253 

StartCoordN 0 

EndCoordN 0 

SpectrumSpec parametric 
42 Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama

Table 1.5. Model parameter and settings for the WAVE portion of the mormerge application as set in 

model master definition wave file (.mdw). Only parameters and settings unique to the mormerge application are given. Values not given are duplicated in table 1.2, above. Specific input file names for grid, initial 

dates, runtimes, enclosure, depth, friction, dry points, winds, boundary conditions, sediment, morphology, 

and observations were excluded.—Continued 

Master definition wave input

Input name Value 

Spectrum jonswap 

PeriodType peak 

DirSpreadType power 

PeakEnhancedFac 3.3000000e+000 

GaussSpread 9.9999998e-003 

WaveHeight 0.57275 

Period 6.2007 

Direction 129.4898 

DirSpreading 4.0000000e+000 

Example lateral boundary settings 

Name Bound2 

Definition grid-coordinates 

StartCoordM 253 

EndCoordM 253 

StartCoordN 1 

EndCoordN 2 

SpectrumSpec from file

Spectrum p1.spc 

Outer and inner nested wave domain settings 

FlowWind 0 

DirSpace sector 

NDir 18 

StartDir 9.0000000e+001 

EndDir 2.7000000e+002 

Inner nested wave domain settings 

NestedInDomain 1 
Appendix 1 43

Table 1.6. Model parameter and settings for the morphology portion of the mormerge application as set in 

model morphology file (.mor). Only parameters and settings unique to the mormerge application are given. 

Values not given are duplicated in table 1.3. 

Master definition file input

Input name Value 

MorFac 365 

MorUpd true 

Multi true 
Jenkins and others—Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama —Open-File Report 2020–1011 

ISSN 2331-1258 (online) 

https://doi.org/10.3133/ofr20201011