U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the U.S. Fish and Wildlife Service

Effects of Proposed Sediment Borrow Pits on Nearshore 

Wave Climate and Longshore Sediment Transport Rate 

Along Breton Island, Louisiana

By P. Soupy Dalyander, Rangley C. Mickey, Joseph W. Long, and James Flocks

Open-File Report 2015–1055

Version 2.0, August 2017
ii

U.S. Department of the Interior

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Acting Director

U.S. Geological Survey, Reston, Virginia

First release: 2015

Revised: August 2017 (ver. 2.0)

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secured from the copyright owner.

Suggested citation: 

Dalyander, P.S., Mickey, R.C., Long, J.W., and Flocks, James, 2017, Effects of proposed sediment borrow pits on nearshore wave climate and longshore sediment transport rate along Breton Island, Louisiana 

(ver. 2.0, August 2017): U.S. Geological Survey Open-File Report 2015–1055, 41 p., 

https://doi.org/10.3133/ofr20151055.

ISSN 2331-1258 (online)
iii

Acknowledgments

This research was chartered by the U.S. Fish and Wildlife Service. This project included funding 

from the Louisiana Outer Coast Early Restoration Project, obtained by the natural resource trustees for 

the Deepwater Horizon Oil Spill, pursuant to the Framework for Early Restoration Addressing Injuries 

Resulting from the Deepwater Horizon Oil Spill, executed April 20, 2011.
iv

Contents

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

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

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

Background .............................................................................................................................................................. 2

Methods.................................................................................................................................................................... 4

Wave Climatology ................................................................................................................................................. 4

Numerical Model................................................................................................................................................... 6

Borrow Pit Designs ............................................................................................................................................... 7

Wave Impact Analysis........................................................................................................................................... 8

Results.................................................................................................................................................................... 11

Numerical Model Assessment ............................................................................................................................ 11

Sensitivity Analysis ............................................................................................................................................ 18

Effects of Borrow Pits on Nearshore Wave Climate and Longshore Transport .................................................. 21

Conclusions............................................................................................................................................................ 24

References Cited ................................................................................................................................................... 25

Appendix 1. Scenarios............................................................................................................................................ 29

Appendix 2. Example Model Input Files ................................................................................................................. 32

Appendix 3. File Naming Conventions ................................................................................................................... 41

Appendix 4. Analysis of Borrow Pit 3...................................................................................................................... 41

Introduction ......................................................................................................................................................... 41

Results: Effects of Borrow Pits on Nearshore Wave Climate and Longshore Transport .................................... 41

Conclusions ........................................................................................................................................................ 42

Figures

 1. Maps showing the location of Breton Island in the Gulf of Mexico, the G1, G2, and G3 numerical model domains, 

and the three National Data Buoy Center buoys used in model scenario development and assessment; and 

the spatial extent of the G3 model domain, showing the G4 domain as well as the extent of two proposed 

borrow pits considered in the wave modeling study .............................................................................................2

 2. Wave bins used to establish the wave climatology at National Data Buoy Center buoy 42040 ...........................5

 3. Two proposed borrow pit designs evaluated for the impact to the wave climate around Breton Island ...............7

 4. Portion of G4 showing the 135 cross-shore transects used in calculating the alongshore flux ..........................10

 5. Depth at which depth-induced wave breaking dissipation exceeded 0.01 watt per square meter at 95 percent 

of alongshore locations for each wave scenario.................................................................................................10

 6. Comparison of the Simulating WAves Nearshore grid G1 and WavewatchIII® model output of significant wave 

height, peak wave period, and mean wave direction versus observations at National Data Buoy Center buoys 

42040, 42012, and 42007...................................................................................................................................12

 7. Comparison of Simulating WAves Nearshore and WavewatchIII® G2 model output of significant wave height, 

peak wave period, and mean wave direction versus observations at National Data Buoy Center buoys 42012 

and 42007 for each scenario ..............................................................................................................................13

 8. Time series above shows a portion of the entire probabilistic wave reconstructed time series at buoy 42012 

comparing the observations, reconstructed values, and measurement at offshore buoy 42040........................16
v

 9. Time series above shows a portion of the entire probabilistic wave reconstructed time series at buoy 42007 

comparing the observations, constructed values, and measurement at offshore buoy 42040...........................17

10. Comparison of wave time series constructed using probabilistic method and observations at National Data Buoy 

Center buoy 42012 for April 2, 2010, to December 30, 2013, and comparison of the WavewatchIII® dataset 

and the observations for the same time period...................................................................................................17

11. Comparison of wave time series constructed using probabilistic method and observations at NDBC buoy 42007 

for the time period January 19, 2005, to February 15, 2008, and comparison of the WavewatchIII® dataset 

and the observations for the same time period...................................................................................................18

12. Model sensitivity to wave diffraction in grid G4 with pit 1 present for scenario H6_D7.......................................19

13. Model sensitivity to wave triads in grid G4 with pit 1 present for scenario H6_D7 .............................................20

14. Weighted average significant wave height for the baseline, no pit bathymetry, bathymetry including borrow 

pit 1, and bathymetry including borrow pit 2 .......................................................................................................21

15. For 135 locations along Breton Island, longshore transport rate for each of the 116 wave scenarios for the no pit 

case ....................................................................................................................................................................22

16. For 135 locations along Breton Island, change in the longshore transport rate with the addition of pit 1 and 

pit 2 for each of the 116 wave scenarios ............................................................................................................22

17. For 135 locations along Breton Island, gradient of average longshore transport rate calculated by weighting 

each of the 116 individual scenario results by its frequency of occurrence in the wave climatology..................23

18. Gradient of average longshore transport rate constructed from the longshore transport calculated using wave 

conditions at 135 locations along Breton Island for each of the 116 individual scenario results by weighting 

each scenario by its frequency of occurrence in the wave climatology ..............................................................24

A4-1. Maps showing the location of Breton Island in the Gulf of Mexico, the G1, G2, and G3 numerical model domains, 

and the three National Data Buoy Center buoys used in model scenario development and assessment; and the 

elevations and spatial extent of the G3 model domain, showing the G4 domain as well as the extent of three 

proposed borrow pits considered in the wave modeling study ...........................................................................42

A4-2. Weighted average significant wave height for bathymetry including borrow pit 3, and change from the baseline 

case ....................................................................................................................................................................43

A4-3. For 135 locations along Breton Island, average longshore transport rate calculated by weighting each of the 116 

individual scenario results by its frequency of occurrence in the wave climatology ...........................................43

A4-4. Gradient of average longshore transport rate constructed from the longshore transport calculated using wave 

conditions at 135 locations along Breton Island for each of the 116 individual scenario results by weighting each 

scenario by its frequency of occurrence in the wave climatology.......................................................................44

Tables

1. Dimensions of the proposed borrow pits designed for the modeling scenarios....................................................8

2. Comparison of the Simulating WAves Nearshore (SWAN) model output from grids G1 and G2 to WavewatchIII® 

model output evaluated against observed buoy data from the representative time step of each scenario ........13

3. Reconstruction performance and error statistics using the 116 unique scenarios generated for grid G2. ........15

A4-1. Dimensions of proposed borrow pit 3 used in the modeling scenarios...............................................................42
vi

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Density

kilogram per cubic meter (kg/m3) 0.06242 pound per cubic foot (lb/ft3) 

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

Horizontal coordinate information is referenced to the World Geodetic System 1984 (WGS84)

Elevation, as used in this report, refers to distance above the vertical datum.
1

Effects of Proposed Sediment Borrow Pits on Nearshore 

Wave Climate and Longshore Sediment Transport Rate 

Along Breton Island, Louisiana

P. Soupy Dalyander, Rangley C. Mickey, Joseph W. Long, and James Flocks 

Abstract

As part of a plan to preserve bird habitat on Breton Island, the southernmost extent of the 

Chandeleur Islands and part of the Breton National Wildlife Refuge in Louisiana, the U.S. Fish 

and Wildlife Service plans to increase island elevation with sand supplied from offshore resources. 

Proposed sand extraction sites include areas offshore where the seafloor morphology suggests suitable 

quantities of sediment may be found. Two proposed locations east and south of the island, between 

5.5–9 kilometers from the island in 3–6 meters of water, have been identified. Borrow pits are perturbations to shallow-water bathymetry and thus can affect the wave field in a variety of ways, including 

alterations in sediment transport and new erosional or accretional patterns along the beach. A scenariobased numerical modeling strategy was used to assess the effects of the proposed offshore borrow pits 

on the nearshore wave field. Effects were assessed over a range of wave conditions and were gaged by 

changes in significant wave height and wave direction inshore of the borrow sites, as well as by changes 

in the calculated longshore sediment transport rate. The change in magnitude of the calculated sediment 

transport rate with the addition of the two borrow pits was an order of magnitude less than the calculated 

baseline transport rate.

Introduction

North Breton Island, located at the southern end of the Chandeleur Islands, Louisiana, and 

part of the Breton National Wildlife Refuge (BNWR), provides important habitat for nesting colonies 

of brown pelicans. Loss of subaerial island extent can affect this species through reduction of nesting 

area. Due to storm impacts, relative sea level rise, and diminished sediment supply from dredging of 

the Mississippi River Gulf Outlet (MRGO), island area has been reduced by 93 percent since the 1920s 

(Martinez and others, 2009). In an effort to preserve Breton Island (fig. 1), the southernmost extent of 

the BNWR, the U.S. Fish and Wildlife Service (FWS) plans to nourish the island by restoring island 

elevation using nearby offshore sand resources. FWS requested that the U.S. Geological Survey (USGS) 

evaluate the potential effects of mining offshore sand on the wave climate and longshore sediment transport at Breton Islands; results of that evaluation are presented in this report. Studies have shown that 

sediment deposits within BNWR suitable for shoreline nourishment are rare (Twichell and others, 2009) 

and are confined to buried distributary channels, terminal spits, and tidal deposits (Flocks and others, 

2009). Analyzing the seafloor morphology offshore of Breton Island, potential relict spit and tidal deposits have been identified in approximately 3–6 m of water. Dredging borrow pits in these deposits will 

change the seafloor morphology, which could alter the wave transformation and result in changes in the 

wave climate locally and around the island. If effects on the wave climate extend to nearshore regions 

around the island, the breaking wave characteristics (significant height and peak wave direction), which 

dictate alongshore sediment transport magnitude and direction, could be altered. Changes in sediment 
2

transport processes may alter patterns in erosion or accretion with implications for the planned nourishment of the island. To evaluate these potential effects, two proposed borrow pits within the suitable offshore deposits have been positioned in a numerical model to evaluate the impact on wave dynamics. The 

goal of the effort is to employ a scenario-based approach to quantify what effects the proposed borrow 

pit designs would have on the nearshore wave climate at Breton Island by considering the entire range 

of wave conditions in the region. 

Figure 1. Maps showing (A) the location of Breton Island in the Gulf of Mexico, the G1, G2, and G3 numerical model domains, and the three 

National Data Buoy Center (NDBC) buoys (42040, 42012, and 42007) used in model scenario development and assessment, and (B) the spatial 

extent of the G3 model domain, showing the G4 domain as well as the extent of two proposed borrow pits considered in the wave modeling study. 

The channel running northeast of Breton Island is the Mississippi River Gulf Outlet (MRGO). 

Background

The potential effects of borrow sites at any given location will depend on the local bathymetry, 

geology, wave climate, and borrow pit design. As waves enter intermediate to shallow water depths, 

that is, depths less than approximately half the wavelength of the surface gravity waves, they begin to 

interact with the seafloor and as a result undergo several transformation processes including shoaling, 

refraction, diffraction, reflection, and dissipation (Komar, 1998). Perturbations such as borrow pits can 
3

alter the wave field to varying degrees through each of these processes. In wave refraction, the direction of wave propagation rotates such that wave crests align to be more parallel with depth contours. 

Depending on the configuration of the borrow pit and its orientation relative to the bathymetry contours, 

waves will refract around the edges of the pit in various directions resulting in a divergence or convergence of wave energy (Komar, 1998; Bender and Dean, 2003). Diffraction transfers energy along 

the wave crest from high wave height to low wave height, resulting in complex patterns in the lee of 

alongshore pertubations to the wave field such as breakwaters or borrow pits (Komar, 1998). Diffraction 

typically results in relatively minor impact to the wave field compared to refraction (Demir and others, 

2004); however, excluding the effects of diffraction may result in overprediction of shoreline changes 

as a result of wave field modification (Benedet and List, 2008). The reflection of wave energy off the 

borrow pit perturbation results in a reduction of wave energy transmission onshore of the borrow pit 

(Demir and others, 2004) and is a larger factor during storm wave conditions than under calmer wave 

conditions (Michelsen and others, 2008). Refraction, diffraction, and reflection are all processes of wave 

scattering; in contrast, wave dissipation is the process by which wave energy is lost through breaking or 

frictional interaction with the seafloor. In the case of borrow pits, the depth increase reduces the wave 

energy bottom dissipation at that location, increasing the wave energy (and wave heights) leeward of the 

pit (Komar, 1998).

Borrow pits placed in shallow, nearshore areas may directly alter the sediment budget along 

coastlines by trapping sediment that would otherwise be transported across that location. These effects 

are assumed to be minimal if the pits are located beyond the site-specific closure depth, an engineering criterion beyond which sediment mobility is considered limited (Bender and Dean, 2003); however, 

depth of closure may not adequately capture the disproportionate impact of infrequent storm events 

(Kennedy and others, 2009; Gonçalves and others, 2014). The aforementioned changes to the wave environment can alter sediment transport patterns and morphology along the coastline of areas in the lee of 

borrow pits particularly as a result of changes in wave-induced alongshore currents. The complexity of 

wave transformation processes and the resultant convergences and divergences of wave energy can create a site-specific shoreline response if the wave variations extend to the surf zone where waves break 

because of limited water depth and wave momentum is transferred into cross-shore and alongshore currents. In some cases, the shoreline in the lee of the pit accretes, resulting in a salient behind the pit with 

adjacent areas of erosion (Combe and Soileau, 1987; Bender and Dean, 2003). In other cases, erosional 

“hot spots” develop directly shoreward or offset to one side of the pit (depending on predominant wave 

direction), with the eroded sand transported alongshore (Wang and Dean, 2001; Bender and Dean, 2003; 

Benedet and List, 2008) or possibly cross-shore (Kraus and Galgano, 2001). Alternately, if the borrow 

pit is far enough offshore and in deep enough water, effects to the coastline may be minimal (Maa and 

Hobbs, 1998; Byrnes and others, 2004; Zarillo and others, 2009).

The impact a borrow pit will have on the wave field and adjacent shoreline depends on the seafloor geology, wave climate, and design characteristics of the pit such as distance offshore, depth of cut, 

orientation, and cross- and alongshore extent (Benedet and others, 2013). Because the seafloor geology 

and wave climate are relatively fixed for any given site, sensitivity analysis has typically focused on 

design characteristics. The farther from shore a borrow pit is located and the deeper the water depth, the 

smaller the impact will be to the adjacent shoreline; however, the increased cost of sediment extraction 

farther from shore may prohibit extraction beyond a certain depth (Benedet and others, 2013). Borrow 

pits designed as narrow alongshore trenches, thus minimizing the cross-shore length of wave effects, 

may have a limited impact on wave climate compared to pits with a more square profile (Benedet and 

others, 2013).
4

A synopsis of the various approaches that have been used to assess the effects of borrow pits on 

waves and adjacent shorelines can be found in Bender and Dean (2003) and is only briefly reviewed 

here. These approaches include field and laboratory experiments, as well as the use of analytical 1-D 

and 2-D models. In addition, parabolic and elliptic numerical models (RCPWAVE, REF/DEF, MIKE 21 

EMS Module) and wave-action balance equation models (SWAN, STWAVE) have been used (Bender 

and Dean, 2003). In the 10 years since the Bender and Dean (2003) study, increases in computer 

power have resulted in more applications of wave spectra-resolving numerical models, such as SWAN 

or CMS-WAVE and high-fidelity, and 2-D or 3-D models with sediment transport modules, such as 

Delft 3-D, to the assessment of the effects of borrow pits on the wave climate, alongshore currents, and 

nearshore sediment transport (Benedet and others, 2007; Benedet and List, 2008; Hartog and others, 

2008; Benedet and others, 2013). 

Methods

Wave conditions in the northern Gulf of Mexico around Breton Island were quantified using an 

approach developed by Long and others (2014). In this methodology, the wave climatology at any given 

location is characterized into a discrete set of scenarios, which are subsequently numerically modeled 

to provide the spatial variability over the domain of interest in wave conditions represented by that 

scenario. The methodology and its application here are described in more detail in the section “Wave 

Climatology,” and the numerical model used to construct the scenarios is described in “Numerical 

Model.” This approach was used to investigate the effects of two possible configurations of offshore 

sand extraction, described in “Borrow Pit Designs,” with the resultant effects on the wave climate quantified in an approach described in “Wave Impact Analysis.” 

Wave Climatology

In the application of the wave-scenario approach, waves at National Data Buoy Center (NDBC) 

buoy 42040 over the time period December 1995 through December 2013, the time range of consistent 

buoy operation, were used to build a wave climatology for the northern Gulf of Mexico by weighted 

averaging a set of representative scenarios. Hourly wave observations were divided into a discrete 

set of bins by significant wave height and mean wave direction. Eight significant wave height bins of 

width ranging from 0.5 to 1 meter (m) and 16 wave direction bins of width 22.5 degrees (°) were used 

(fig. 2). For each bin (appendix 1), a single representative time was chosen when the wave height and 

direction at buoy 42040 most closely matched the wave height and direction averaged over all observational times assigned to that bin. Because forcing conditions were provided by the National Oceanic 

and Atmospheric Administration (NOAA) operational WAVEWATCH III® model (http://polar.ncep.

noaa.gov/waves/index2.shtml), an additional constraint was imposed that at the selected representative 

time step for each bin the error in significant wave height and wave direction for WAVEWATCH III® at 

buoy 42040 could be no more than 20 percent of the observed value at the buoy. The wave model was 

then run for each of these conditions to simulate the spatial variability of waves within the study area. 

Wave period, wind speed, and wind direction were not explicitly matched using this approach; however, 

Bayesian analysis that identifies the best matched times using significant wave height, peak wave period, peak wave direction, and wind speed showed only minor variation in the time periods chosen. Using 

the two methods to hindcast complete wave time series showed only minor improvement in predicting 

significant wave height and peak period and a slight decrease in the prediction of wave direction (Plant 

and others, 2013; Long and others, 2014). Analysis of the overall accuracy of the model in this study is 
5

presented with the results. The primary benefit of scenario-based modeling for this application is that 

the use of deterministic modeling to capture wave conditions over multiannual time scales in terms of 

computational expense would be prohibitive, while modeling over a shorter time scale might not capture 

truly representative conditions. A complete description of this wave-scenario approach and its application can be found in Long and others (2014); the current study increased the number of bins from 80 to 

116 to better resolve the variability of larger wave heights (waves >2 m, fig. 2). 

Figure 2. Wave bins used to establish the wave climatology at National Data Buoy Center (NDBC) buoy 42040 (fig. 1). A total of 16 wave 

direction bins (D1–D16) and 8 wave height bins (H1–H8) were used to discretize the observations. Of the 128 direction and height bins, 12 

contained no observations, indicated in gray, leaving 116 model scenarios. For the other wave bins, colors indicate the percentage of observations 

falling within that bin for the time period December 1995 through December 2013, with values ranging between 0.001–6.3 percent. [m, meters] 

Waves at buoy 42040 were most often out of the southeast with wave height less than 1.5 m 

(fig. 2). The largest storm wave conditions, with wave heights greater than 4 m, were also out of the 

south and southeast and commonly were associated with tropical storm events. A total of 12 bins out of 

the 128 wave height and direction combinations, all representing northerly waves of greater than 4 m, did 

not contain observations; therefore, 116 scenarios were used to capture the wave climatology at this site.

The scenario-based wave modeling approach allowed for the spatial variability in wave conditions (and longshore flux) to be estimated for a given offshore wave height and direction. By using the 

percentage occurrences computed for each wave condition (fig. 2), a weighted average of the scenario 

results for a variable of interest was created. This weighted averaging technique, applied to significant 

wave height and longshore transport rates, was used to quantify changes in wave and sediment transport 

conditions around Breton Island for the two borrow pits.
6

Numerical Model

The Simulating WAves Nearshore (SWAN) numerical wave model (version 41.01; Booij and 

others, 1999; Ris and others, 1999; The SWAN Team, 2014) was used to characterize wave conditions 

for each scenario. A system of four one-way nested grids was constructed (fig. 1): G1 covered the northern Gulf of Mexico from Louisiana to western Florida at 1,500 m resolution; G2 covered the western 

half of the G1 domain from Louisiana to Alabama at 300 m resolution; G3 covered Breton Island and 

the surrounding offshore at 60 m resolution; and G4 focused on Breton Island itself and the proposed 

offshore borrow pits at 20 m resolution with an increased cross-shore resolution of 10 m in the nearshore region and over the island. Bathymetry for grids G1, G2, and G3 were provided by the NOAA 

National Geophysical Data Center (NGDC) Northern Gulf Coast digital elevation model (DEM) (Love 

and others, 2012). Bathymetric and topographic elevations for grid G4 were derived from the NGDC 

Northern Gulf Coast DEM, regional bathymetry collected in 2006–07 described by Miner and others 

(2009), and a 2014 topographic/bathymetric light detection and ranging (lidar) survey contracted by the 

U.S. Geological Survey. These elevations were merged and interpolated to the G4 grid using the scalecontrolled interpolation method of Plant and others (2002). Wave boundary conditions for the G1 model 

at the open boundaries are prescribed from archived operational output of the NOAA WavewatchIII® 

4-minute (~7.5 kilometer) resolution U.S. east coast multigrid (Tolman, 2008) using wave height, 

peak period, and peak direction with an assumed Joint North Sea Wave Project (JONSWAP) spectrum 

(Hasselmann and others, 1973). Wind forcing for all of the model grids was also provided by archived 

model output from the same WavewatchIII® grid. 

For each scenario, SWAN was run in stationary mode with a frequency range of 0.04–1.0 hertz 

(Hz), with frequency spacing of 0.1 times the frequency. Directional resolution was 6° over the full 

360°. Minimum depth values were set at 0.2 m for G1 and G2, and 0.05 m for G3 and G4. Third generation Komen physics (Komen and others, 1984), including wind generation and whitecapping, were 

used for all simulations with parameterizations following Rogers and others (2003) to reduce inaccurate 

attenuation of swell energy by whitecapping. A default parameterization of JONSWAP (Hasselmann and 

others, 1973) bottom friction with 0.067 m2s-3 roughness was used, consistent with other applications of 

the SWAN or Delft3-D application to assess the effects of borrow pits on waves and alongshore transport (Benedet and others, 2007; Benedet and List, 2008; Hartog and others, 2008). The backward space 

backward time (BSBT) finite difference scheme was employed. Quadruplet computations were included 

using the default discrete interaction approximation (DIA) method for nonlinear 4-wave interactions 

(The SWAN Team, 2014). Depth-induced breaking following the Battjes and Janssen (1978) parameterization was included with default values for alpha (1) and gamma (0.73). Sensitivity testing was conducted to determine if triad wave interaction and wave diffraction needed to be activated in the model. 

Triad interactions are non-linear wave transformations in shallow water that result in transfer of energy 

across different frequencies, changing the shape of the wave spectra (Madsen and Sørensen, 1993; 

Booij and others, 1999). A more complete description of this analysis is found in the section “Results.” 

Diffraction did not significantly influence the wave model output, although triad wave interactions were 

found to be important and were activated in grids G3 and G4, using the default SWAN values of 0.1 

for the proportionality constant, a maximum frequency cutoff of 5, a critical Ursell number of 0.2, and 

a lower threshold Ursell number of 0.01. For grids G1–G3, the SWAN NESTOUT command was used 

to generate the boundary condition files for the next higher resolution grid (grids G2–G4). Examples of 

input files for each of the four grids are provided in appendix 2.
7

Borrow Pit Designs

Two possible borrow pit designs, targeting potential sediment resources suitable for shoreline nourishment, were considered for their potential effects to the wave climate around Breton Island 

(fig. 3). Historical nautical charts of the area reveal geomorphic features that may contain sandy material: ebb-tide shoals associated with Breton Island Pass prior to the excavation of the MRGO and possible 

terminal spit deposits that may be related to former southward progradation of the northern Chandeleur 

barrier platform during the transgressive evolution of the islands seaward of the Breton Island platform. 

In addition, geophysical investigations indicate that distributary channels of the former St. Bernard delta 

are preserved in the subsurface in this area and may contain suitable sand deposits (Twichell and others, 

2009). Because these deposits are located 5–10 km offshore of the Breton Island platform, with intervening deeper water, they are unlikely to contribute to the natural sediment budget of the island. Use of 

these deposits would contribute beneficially to the island’s sediment budget if the sediments were placed 

on the island or within the litorral system. Finally, the proposed borrow pits were positioned between the 

island and the prevailing wave direction to provide a scenario where affected wave action would have 

the most impact on the island platform.

Figure 3. Two proposed borrow pit designs evaluated for the impact to the wave climate around Breton Island. [MRGO, Mississippi River 

Gulf Outlet]

The thickness and extent of the offshore deposits are not known. Because the borrow pit design is intended for evaluating wave scenarios, the borrow design was adapted from the closest fully 

designed, evaluated, and implemented borrow pit in the area. In 2011 a borrow pit was excavated at 

the northern Chandeleur Islands to construct an emergency sand berm intended to mitigate island oiling from the Deepwater Horizon oil spill (Lavoie and others, 2010; Plant and others, 2014). Because 

the environment is similar, the same pit dimensions were used in the current study; results are only 

applicable for the specific potential designs evaluated with the numerical model. The proposed Breton 

Island restoration design is intended to mimic the island shoreline, dune platform, and back-barrier 
8

marsh dimensions prior to Hurricane Katrina, which caused severe erosion. To accomplish this design 

it is estimated that a minimum of 2.3 x 106 m3 of sand will be required to replenish the shoreline and 

dune platform to pre-Katrina conditions. Some additional material may be necessary to support construction of the back-barrier marsh, which is estimated to require 550,000 m3

 of muddy sediments. To 

fully accommodate sand requirements, the size of each borrow pit was designed to provide more than 

4.0 x 106 m3 of sediment, assuming a variable sand content of 70–100 percent and that a loss of material 

during excavation may occur. It is assumed that sufficient sand resources are present at each site to accommodate these excavation sand volumes. Dimensions for the borrow pits generated for the modeling 

scenarios are provided in table 1. For G4, the wave model was run for each of the wave scenarios for the 

baseline, no pit case, as well as for bathymetry grids including pit 1 and pit 2 (fig. 3).

Table 1. Dimensions of the proposed borrow pits designed for the modeling scenarios.

[m, meters]

Water depth

(m)

Pit depth1

(m)

Area

(m2 x 106)

Volume

(m3 x 106)

Volume per

unit area

Pit 1 3.5 – 5.5 3.0 1.83 4.45 2.43

Pit 2 3.0 – 4.0 3.0 2.08 5.13 2.47

1

Maximum pit depth in meters below the seafloor.

Wave Impact Analysis

The primary concern if borrow pits impact patterns in wave propagation is the potential implications those changes might have on longshore sediment transport rates in the littoral system around 

Breton Island. In the current study, the methodology described by Adams and others (2011) was followed in which an empirical formulation was used to estimate the volumetric longshore sediment transport rate based on wave conditions. This methodology was originally proposed by Komar and Inman 

(1970) and later modified and named the CERC formula (Komar, 1998; Rosati and others, 2002). Even 

though the absolute transport values have uncertainty due to a lack of site specific calibration factors, 

the method has previously been used to determine relative sediment transport rates and gradients in the 

northern Chandeleur Islands (Georgiou and Schindler, 2009; Martinez and others, 2009). The crossshore integrated volumetric sediment transport rate for sand-sized sediment is estimated as 

(1)

where Ql is volumetric sediment transport rate;

Il is immersed weight transport rate;

ρs is density of sediment (2,650 kilograms per cubic meter;

ρw is density of water (1,024 kilograms per cubic meter);

g is gravity coefficient (9.8 meters per square second); and

n is in-place sediment porosity (taken as 0.4). 
9

The immersed weight transport rate is calculated by scaling the longshore component of the 

wave energy flux

(2)

where Il is immersed weight transport rate;

K is scaling parameter (taken as 0.8); and

Pl is longshore component of wave energy flux.

The longshore wave energy flux, as it is commonly referred to despite some objections to the 

terminology (Longuet-Higgins, 1972), is calculated as

(3)

where Pl is longshore component of wave energy flux;

E is wave energy density;

C

g is group wave celerity;

Hb is breaking wave height; 

κ	 is breaker index (take as 1⁄2); and

αb is incident breaking wave angle relative to the shoreline. 

The SWAN model outputs significant wave height (HS), whereas Hb in the equation above is 

root-mean-square wave height (Hrms); therefore, output values of HS were converted to Hrms by dividing 

by 1.4 (Longuet-Higgins, 1952). Alongshore flux was calculated at a set of 135 shore-normal transects 

at 20 m spacing along the island (fig. 4). For each scenario and transect, the breaking wave height and 

direction were indepently found at the offshore location where the energy dissipation due to depth-induced breaking first exceeded 0.01 watt per square meter (W/m2

) as waves traveled onshore. Alongshore 

transport was then calculated (eqs. 1–3) at each transect. The depth and cross-shore location of incipient 

wave breaking was not sensitive to the choice of threshold (fig. 5). For larger energy wave cases (offshore wave height 4+ m), the shallow slope of the inner shelf around Breton Island resulted in energy 

dissipation due to depth-induced breaking exceeding the 0.01 W/m2 threshold at locations off the island 

platform, defined as the 4 m contour (figs. 4, 5). In these cases, the wave height and direction at 4 m 

water depth were used. Alongshore flux was smoothed with a 200 m filter to remove small-scale variation not well resolved at the spatial resolution of the model. Resulting values were compared between 

the baseline “no pit” case and the two considered borrow pit configurations.
10

Figure 4. Portion of G4 showing the 135 cross-shore transects (in black) used in calculating the alongshore flux. Alongshore flux was calculated 

using wave height and direction at the most offshore point along each transect where the depth-induced wave breaking dissipation exceeded 

0.01 watt per square meter. If this threshold was exceeded offshore of the island platform (defined as the 4 m contour, shown in purple), values at 

the 4 m contour were used instead. [m, meters]

Figure 5. Depth at which depth-induced wave breaking dissipation (output from SWAN) exceeded 0.01 watt per square meter (W/m2)(dark blue 

line) at 95 percent of alongshore locations for each wave scenario. Also shown are the depth at which breaking dissipation exceeded 0.001 W/m2

and 0.1 W/m2 (light blue band) and the percentage occurrence (black line) for each scenario. The island platform was defined as the 4 m contour 

(in pink; fig. 4), and in cases where the threshold of breaking dissipation was exceeded at deeper than 4 m, values from this depth were used in 

calculating alongshore flux. 
11

The focus of the work was to examine changes in wave propagation and wave-breaking induced 

longshore sand-sized sediment transport near the island. Hence, no account was made for sediment input 

from the adjacent Mississippi River plume, which brings fine-grained sediment to the inner shelf and beyond. The effect of tidal currents around the island was also not considered due to the microtidal range 

of 30 centimeters (cm) (Boyd and Penland, 1988). These processes have likely contributed to the shape 

of the underlying island platform, but are expected to have less influence on shoreline evolution than the 

wave-induced transport of sand-sized sediment estimated by equations 1–3. Model results can only be 

used to infer the potential effects of the specific borrow pit designs considered on longshore transport. 

Analysis did not include assessments of cross-shore sediment transport or island overwash and inundation processes that also affect the short- and long-term topology and morphology of the island and island 

platform but are not expected to be affected by the dredging of borrow pits.

Results

An accurate assessment of the effects of proposed borrow pits on inshore wave climatology relies on the skill of the scenario-based wave modeling approach in predicting the nearshore wave field. A 

comparison of wave model results to observations at buoys within the model domains for grids G1 and 

G2 are described in the section “Numerical Model Assessment;” grids G3 and G4 did not encompass 

buoys to be used for comparison. Included are (1) a comparison of the shallower wave buoy observations to numerical model results, evaluating the ability of the model to represent the spatial transformation of waves, and (2) the accuracy of a scenario-based wave reconstruction for all available buoy 

times, indicating the ability of the scenario-based approach to capture wave climate at any given time. 

The results of tests to determine the need to include triad interactions and wave diffraction in the model 

are described in the section “Sensitivity Analysis.” Finally, analysis of the effects of the proposed wave 

borrow pit designs on the wave climate around Breton Island is presented in “Effects of Borrow Pits on 

Nearshore Wave Climate and Longshore Transport.”

Numerical Model Assessment

Data from three NDBC directional wave buoys (42040, 42012, and 42007; fig. 1) were used 

to assess model output (significant wave height, wave period, and wave direction) for each of the 116 

different scenarios for G1 and G2. For G1 (fig. 6), all three buoys were used to compare model outputs 

for each of the 116 scenarios, whereas for G2 (fig. 7), only buoys 42012 and 42007 were within the 

bounds of the grid domain. The comparison of wave characteristics to observations at the buoys for the 

representative times for each scenario assesses the ability of the wave model to propagate waves inshore 

(for buoys 42012 and 42007) and assesses the accuracy of boundary conditions for buoy 42040. The 

ability of the G1 and G2 models to predict observations is comparable to the ability of the operational 

WavewatchIII® model (table 2).

The ability of WavewatchIII® to simulate wave conditions at buoy 42040, which lies just inside the boundary of G1, is acceptable for wave height, period, and direction, illustrating that the 

WavewatchIII® model resolves wave height conditions sufficiently to provide boundary conditions for 

SWAN G1 (table 2; fig. 6). The accuracy and precision of WavewatchIII®, however, decreases moving 

into shallower water, exhibiting higher magnitude bias, much lower values of squared-correlation coefficient (R2

), and higher root-mean square error (RMSE). In contrast, the G1 (fig. 6) and G2 (fig. 7) grids 

retain lower magnitude bias and RMSE and higher values of R2 moving into shallower water. The improved predictions are possibly a result of the finer spatial resolution of G1 and G2 (1,500 m and 300 m, 
12

respectively, compared to a spatial resolution for WavewatchIII® of ~7.5 km) or better ability of SWAN 

to resolve shallow water and nearshore wave transformation processes than WavewatchIII®. There is 

little difference in the RMSE and R2 values for buoys 42012 and 42007 in G2 compared to G1 (table 2).

Figure 6. Comparison of the Simulating WAves Nearshore (SWAN) grid G1 and WavewatchIII® model output of significant wave height 

(A,D,G), peak wave period (B,E,H), and mean wave direction (C,F,I) versus observations at National Data Buoy Center (NDBC) buoys 42040, 

42012, and 42007. Operational WavewatchIII® does not archive mean direction, so this variable cannot be assessed. G1 has a spatial resolution 

of approximately 500 m and encompasses the northern Gulf of Mexico. The spatial extent of the grid and location of the buoys used for model 

assessment are shown in figure 1. [m, meters; s, seconds; deg, degrees]
13

Figure 7. Comparison of Simulating WAves Nearshore (SWAN) and WavewatchIII® G2 model output of significant wave height (A,D), peak 

wave period (B,E), and mean wave direction (C,F) versus observations at National Data Buoy Center (NDBC) buoys 42012 and 42007 for 

each scenario. Operational WavewatchIII® does not archive mean direction, so this variable cannot be assessed. G2 has a spatial resolution of 

approximately 300 m and encompasses the western half of the northern Gulf of Mexico. The spatial extent of the grid and location of the buoys 

used for model assessment are shown in figure 1. [m, meters; deg, degrees]

Table 2. Comparison of the Simulating WAves Nearshore (SWAN) model output from grids G1 and G2 to WavewatchIII® model output evaluated against observed buoy data from the representative time step of each scenario. Operational WavewatchIII® does not archive mean direction, 

so this variable cannot be assessed.

[m, meters; R2, coefficient of determination; RMSE, root-mean square error; s, seconds; --, indicates no error calculated for buoy observations ]

Buoy Depth (m) Significant wave height (m) Peak wave period (s) Mean wave direction (degree)

Mean Bias RMSE R2 Mean Bias RMSE R2 Mean Bias RMSE R2

Observed

42040 164.6 2.46 -- -- -- 6.92 -- -- -- 134.4 -- -- --

42012 27.7 1.1 -- -- -- 5.77 -- -- -- 189.5 -- -- --

42007 14.9 1.46 -- -- -- 5.88 -- -- -- 117.6 -- -- --

WaveWatch III

42040 164.6 2.36 -0.1 0.42 0.96 6.15 -0.77 0.85 0.87 -- -- -- --

42012 27.7 0.77 -0.33 0.44 0.05 5.1 -0.67 1.51 0.02 -- -- -- --

42007 14.9 0.48 -0.98 0.31 0.01 4.12 -1.77 1.46 0.05 -- -- -- --
14

Buoy Depth (m) Significant wave height (m) Peak wave period (s) Mean wave direction (degree)

Mean Bias RMSE R2 Mean Bias RMSE R2 Mean Bias RMSE R2

SWAN G1

42040 164.6 2.36 -0.11 0.52 0.95 6.25 -0.67 0.98 0.83 119.8 5 27.43 0.93

42012 27.7 0.97 -0.13 0.25 0.87 5.42 -0.35 1.15 0.64 216.3 11.87 44.85 0.72

42007 14.9 1.21 -0.25 0.24 0.93 5.37 -0.52 0.83 0.88 84.49 16.41 38.66 0.87

SWAN G2

42012 27.7 0.95 -0.15 0.25 0.87 5.37 -0.4 1.15 0.64 216.4 11.37 45.14 0.72

42007 14.9 1.18 -0.29 0.25 0.92 5.28 -0.6 0.87 0.87 86.32 15.99 38.08 0.87

Continuous time series of wave characteristics (height, period, and direction) were constructed at 

NDBC buoys 42012 (fig. 8) and 42007 (fig. 9) on the basis of probabilistic methods from Long and others (2014). The probabilistic method used offshore buoy observations at NDBC buoy 42040 to identify 

which of the 116 scenarios was the best match to each time step in the observed record. Time series of 

wave characteristics were then reconstructed for comparison to observations by extracting G2 model 

results at the locations of buoys 42012 and 42007 and ordering them on the basis of the sequence of best 

match scenarios. The accuracy of the time series reconstructions evaluated the ability of the scenariobased approach to capture the full local wave climatology and variability, in addition to the ability of the 

model to capture spatially variant wave transformation processes within the domain (previously evaluated in the assessment of model accuracy for the representative time steps, as discussed previously). 

Model assessment included bias, linear regression slope, RMSE, R2, and the model skill (S) defined 

(Gallagher and others, 1998; Reniers and others, 2004) as

(4)

where S is model skill score;

N is number of observations;

αo is observed values; and

αm is predicted values.

A summary of these statistical results is presented in table 3. A value of S = 1 indicates perfect 

model-data agreement. Model skill is dependent on the ratio between the standard deviation in modeldata mismatch and the standard deviation in the data; skill increases when the standard deviation of the 

data is large compared to the mismatch.

The bias for significant wave height at buoys 42012 and 42007 was low at 3 cm and 5 cm, with 

an R2 of 0.70 and 0.37, respectively (table 3). Model skill, S, was 0.70 and 0.5 for 42012 and 42007, 

respectively. Bias for peak wave period for both buoys was low (~0.2 s), R2 values were lower for peak 

Table 2. Comparison of the Simulating WAves Nearshore (SWAN) model output from grids G1 and G2 to WavewatchIII® model output evaluated against observed buoy data from the representative time step of each scenario. Operational WavewatchIII® does not archive mean direction, 

so this variable cannot be assessed.—Continued

[m, meters; R2, coefficient of determination; RMSE, root-mean square error; s, seconds; --, indicates no error calculated for buoy observations ]
15

wave period (~0.2) than for wave height, and the skill score improved (0.73 and 0.67). For mean wave 

direction, the bias was 3.31 degrees for 42012 and double that (7.67) for 42007, with model skill values 

similar to peak wave period and somewhat higher R2 values (0.58 and 0.55, respectively). 

The transformation of waves from the offshore buoy (42040) to the inshore buoys (42012 and 

42007) was effectively captured by the reconstruction (figs. 8 and 9). In addition, when winds were 

blowing from the north, the reconstruction predicted the smaller waves observed at the inshore buoys as 

a result of short fetch distance between the coastline and the location of observations despite larger measured offshore wave heights. The majority of wave height predictions (height, period, and direction) fall 

along a 1:1 line with observations (figs. 10 and 11), and accuracy in predicting larger wave heights was 

improved relative to a previous application of the scenario-based approach by increasing the number of 

bins for wave heights greater than 2 m (see Long and others, 2014). 

The primary concern with the reconstruction application was whether wave characteristics were 

modeled accurately enough to create robust predictions of alongshore transport. Alternate approaches 

to predicting nearshore wave conditions are (1) deploying an array of instruments and (2) running a 

deterministic, time-variant model. Both strategies have the inherent problem of typically capturing a 

shorter record of time (due to cost or computational expense) than is needed to fully capture the wave 

climatology; however, comparing the accuracy of the scenario-based model against a deterministic approach is useful for benchmarking it against a commonly used strategy for predicting waves. The model 

assessment indicates that the probabilistic reconstruction method was able to resolve wave characteristics at least as effectively as the deterministic model Wavewatch III®, while eliminating a bias observed 

in WavewatchIII® at higher wave periods at buoy 42012. Comparing tables 2 and 3 indicates that the 

error in wave predictions is similar between the direct simulations (table 2) and the probabilistic reconstruction (table 3). These results indicate that the probabilistic reconstruction used for the assessment is 

capable of providing results with accuracy similar to assessments that use deterministic model simulations. A previous assessment of the wave climatology approach (Long and others, 2014) also found this 

approach as accurate as both a Bayesian approach to transforming waves between two locations with 

historical observations to train the model and a deterministic model run on the same numerical model 

domain, the latter approach of which is often employed in modeling borrow pit effects on wave climatology (for example, Benedet and List, 2008; Adams and others, 2011). 

Table 3. Reconstruction performance and error statistics using the 116 unique scenarios generated for grid G2. 

[m, meters; Hs, significant wave height; Tp, peak wave period; Θ, mean wave direction; R2, coefficient of determination; RMSE, root-mean square error]

Buoy 42012 (30 m) Buoy 42007 (15 m)

Observed mean

Hs[m] 0.76 0.68

Tp[s] 5.29 5.05

Θ[deg] 156.9 122.4

Reconstructed 

mean

Hs[m] 0.81 0.64

Tp[s] 5.12 4.82

Θ[deg] 156.5 131.1

RBias

Hs[m] -0.03 -0.05

Tp[s] -0.28 -0.23

Θ[deg] -3.31 7.67
16

Buoy 42012 (30 m) Buoy 42007 (15 m)

RMSE

Hs[m] 0.26 0.42

Tp[s] 1.50 1.75

Θ[deg] 44.7 56.7

Slope

Hs 0.88 0.85

Tp 0.48 0.54

Θ 0.87 0.92

R2

Hs 0.70 0.37

Tp 0.22 0.28

Θ 0.58 0.55

Skill

Hs 0.70 0.50

Tp 0.73 0.67

Θ 0.75 0.65

Table 3. Reconstruction performance and error statistics using the 116 unique scenarios generated for grid G2.—Continued 

[m, meters; Hs, significant wave height; Tp, peak wave period; Θ, mean wave direction; R2, coefficient of determination; RMSE, root-mean square error]

Figure 8. Time series above shows a portion (January 1, 2011, through March 31, 2011) of the entire probabilistic wave reconstructed time 

series (August 24, 2000, to January 31, 2013) at buoy 42012 comparing the observations (black), reconstructed values (red; Long and others, 

2014), and measurement at offshore buoy 42040 (blue). [m, meters; s, seconds; deg, degrees]
17

Figure 9. Time series above shows a portion (January 1, 2003, through March 31, 2003) of the entire probabilistic wave reconstructed time 

series (December 4, 1995, to December 30, 2013) at buoy 42007 comparing the observations (black), constructed values (red; Long and others, 

2014), and measurement at offshore buoy 42040 (blue). [m, meters; s, seconds; deg, degrees]

Figure 10. Comparison of wave time 

series constructed using probabilistic 

method and observations at National 

Data Buoy Center (NDBC) buoy 42012 

for April 2, 2010, to December 30, 

2013, (left column) and comparison 

of the WavewatchIII® dataset and the 

observations for the same time period 

(right column). Comparisons of (A, B) 

significant wave height, (C, D) peak wave 

period, and (E) mean wave direction. 

WavewatchIII® output is every 3 hours, 

whereas Simulating WAves Nearshore 

(SWAN) results are hourly, resulting in a 

larger number of points. [m, meters; 

s, seconds; deg, degrees]
18

Sensitivity Analysis 

Sensitivity tests were perfomed on grids G3 and G4 to determine the effect of triad wave-wave 

interactions and wave diffraction on the wave model predictions when a borrow pit was present (pit 1 

used for comparisons). Comparison of model scenarios with diffraction turned on versus off in the 

SWAN model showed minimal change (less than 1 cm) in significant wave height (fig. 12), and thus 

diffraction was not included in runs analyzing borrow pit impact. Triad wave interactions are relevant 

to shallow water wave transformation, and sensitivity testing was undertaken to determine triad effects 

on the G3 and G4 grids. Triads did prove to alter the wave model output enough (~10 cm) (fig. 13) to 

prompt use of these wave interactions in all of the G3 and G4 modeled scenarios. In particular, wave 

heights near Breton Island were reduced with the inclusion of triads, possibly as a result of greater dissipation due to bottom friction of the higher frequency (longer period) waves that were generated. Note, 

one other possible explanation would be that SWAN integration of energy to calculate wave height excluded the higher frequencies generated through triad interactions; however, the version of SWAN used 

(41.01) integrates over the entire range of user-defined frequencies, which at 0.04–1 Hertz includes the 

range of frequencies expected to be generated through triads (Herbers and others, 2000). 

Figure 11. Comparison of wave time 

series constructed using probabilistic 

method and observations at NDBC 

buoy 42007 for the time period 

January 19, 2005, to February 15, 

2008, (left column) and comparison 

of the WavewatchIII® dataset and 

the observations for the same time 

period (right column). Comparisons 

of (A, B) significant wave height, (C, 

D) peak wave period, and (E) mean 

wave direction. WavewatchIII® output 

is every 3 hours, whereas Simulating 

WAves Nearshore (SWAN) results are 

hourly, resulting in a larger number of 

points. [m, meters; s, seconds; 

deg, degrees]
19

Figure 12. Model sensitivity to wave diffraction in grid G4 with pit 1 present for scenario H6_D7. (A) Significant wave height (in meters) results 

with diffraction on, (B) Significant wave height results with diffraction off, (On minus Off), difference in significant wave height from both results. 

[deg, degrees; m, meters]
20

Figure 13. Model sensitivity to wave triads in grid G4 with pit 1 present for scenario H6_D7. Significant wave height (in meters) results 

with triads on (A), significant wave height results with triads off (B), difference in significant wave height from both results (On minus Off). 

[deg, degrees; m, meters]
21

Effects of Borrow Pits on Nearshore Wave Climate and Longshore Transport

The effect of the proposed borrow pits on the wave climate around Breton Island was first assessed by calculating a weighted average wave height over the model domain for the baseline (no pit), 

pit 1, and pit 2 configurations. To compute this composite wave height distribution, the wave height 

results for each scenario were weighted by the frequency of occurrence from the wave climatology 

(fig. 2). Comparing the weighted average wave height for the three configurations showed that the wave 

climate was only noticeably modified in the immediate vicinity of the borrow pit, resulting in a slight 

shadowing effect originating from the western corners of each pit (fig. 14). The magnitude of the change 

in weighted average wave height was less than 10 cm, or 5 percent of the baseline wave height, with 

no discernable change in wave height extending to the nearshore contours around Breton Island. The 

maximum change in significant wave height for a single scenario was an increase of 74 cm, occurring 

on the northeast side of borrow pit 1 (fig. 3) for scenario H8_D1 (fig. 2) when the wave conditions in the 

baseline case at that location were 1.6 m. 

The longshore transport rate (LSTR) around Breton Island varied by wave scenario, with the 

largest transport rates associated with larger offshore wave heights (fig. 15; no pit case). Transport magnitude and direction also varied alongshore with incident wave direction. 

Figure 14. Weighted average significant wave height for the (A) baseline, no pit bathymetry, (B) bathymetry including borrow pit 1 (change 

from baseline shown in D), and (C) bathymetry including borrow pit 2 (change from baseline shown in E). Borrow pits are outlined in white. The 

average is created from the output of individual scenarios, weighted by their percentage occurrence over the time period December 1995 through 

December 2013 (fig. 2). The pits create a shadowing effect originating from the corners. 
22

The LSTR with the addition of the borrow pits (fig. 16) was similar to the LSTR without the pits, 

with a change of less than 0.004 cubic meters per second (m3/s) (approximately 10 percent of the magnitude of the baseline condition without borrow pits).

Figure 16. For 135 locations (A, black line) along Breton Island (0 m contour shown in green), change in the longshore transport rate (eq. 1) 

with the addition of pit 1 (B) and pit 2 (C) for each of the 116 wave scenarios (fig. 2). Red colors indicate a positive change in transport (more 

northerly or less southerly) and blue colors indicate negative change transport (more southerly or less northerly). 

Figure 15. For 135 locations (A, black line) along Breton Island (0 m contour shown in green), longshore transport rate (B; eq. 1) for each of the 

116 wave scenarios (fig. 2) for the no pit case. Red colors indicate northerly transport and blue colors indicate southerly transport. Moving from 

left to right in (B), the magnitude of transport increases for larger wave scenarios. Because the dominant wave direction for storms is from the 

south and southeast (fig. 2), there are few large wave scenarios with predominantly southerly directed transport. [m, meters]
23

Changes in the transport were also assessed by calculating the alongshore variant average LSTR 

(fig. 17) weighted by the frequency of occurrence of the 116 wave climatology scenarios (fig. 2). The 

shape of the alongshore variant LSTR curve did not change with the addition of the borrow pits, and the 

change in transport magnitude was an order of magnitude less than the baseline case.

Also considered were changes in the gradient of alongshore variant average LSTR (fig. 18). In 

all cases (pits and no pit), the model predicted accretion at the northern and southern ends and erosion 

in the center of the island arc. Changes in the gradient of transport were an order of magnitude less 

than the gradient for the no pit case. The overall shape of the gradient in LSTR did not change with the 

addition of the pits, and no new convergences or divergences were identified that would potentially be 

associated with the formation of accretional or erosional “hot spots.” In the case of pit 2 (fig. 1), the 

magnitude of erosion would be expected to decrease slightly in a region toward the southern end of the 

island (fig. 17), with slightly more erosion to the north and slightly less accretion to the south of that 

stretch of island.

Figure 17. For 135 locations (A, black line) along Breton Island (0 m contour shown in green), average longshore transport rate (B, LSTR) 

calculated by weighting each of the 116 individual scenario results by its frequency of occurrence in the wave climatology (fig. 2). The change 

in magnitude of the weighted average longshore transport rate (C) with the addition of the pits is an order of magnitude less than the baseline 

values, and the overall pattern in transport is the same (B).
24

Conclusions

A numerical wave model was developed to study the potential impact of proposed sediment borrow pits on the wave characteristics and longshore sediment transport rate (LSTR) near Breton Island, 

Louisiana. The model consisted of four nested domains that were used to simulate a set of 116 scenarios 

derived from a wave climatology for a baseline (no pit) condition and two proposed borrow pit configurations. Surf zone wave characteristics for each scenario provided the input for an empirical model for 

the LSTR. Model results for bathymetries including the proposed pits were compared to the baseline 

case to determine the impact the particular borrow pit designs considered would have on wave climatology and alongshore transport at Breton Island.

Comparisons between the wave height predicted for the baseline case and the two borrow pit 

configurations showed that changes in significant wave height were less than 10 centimeters and largest in the immediate vicinity of the borrow pit. The LSTR direction and magnitude depended on the 

individual wave scenario. The change in magnitude of the calculated LSTR with the addition of the two 

borrow pits was an order of magnitude less than the LSTR calculated for the baseline case. The gradient 

of LSTR changed somewhat with the addition of the borrow pits, particularly in a region of slightly less 

erosion toward the southern end of the island, with slightly more erosion to the north and slightly less accretion to the south of that location; however, changes were less than an order of magnitude of the baseline gradient with no pits, and no new convergences or divergences of sediment transport were identified. 

Figure 18. Gradient of average longshore transport rate (B, LSTR) constructed from the longshore transport calculated using wave conditions at 

135 locations (A, black line) along Breton Island (0 m contour shown in green) for each of the 116 individual scenario results (e.g., fig. 15 for the no 

pit case) by weighting each scenario by its frequency of occurrence in the wave climatology (fig. 2). The change in magnitude of the gradient of the 

LSTR (C) with the addition of the pits is an order of magnitude less than the baseline values, and the overall pattern in transport is the same (B). 

Positive values in (C) indicate more accretion or less erosion, negative values indicate less accretion or more erosion.
25

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29

Appendix 1. Scenarios

This file contains information on 128 individual wave condition

scenarios that were modeled in order to provide guidance on impacts

of potential proposed sediment borrow pits on Breton Iisland wave climate 

The columns in the table are:

Scenario: scenario name, assigned by USGS

Wave Height Bin (m): the range of wave heights this scenario represents

Wave Dir. Bin (meteorological convention, degs.): the wave 

directions this scenario represents (in degrees clockwise from north,

direction waves are coming from)

% of Obs.: the percentage of observations between May, 1995, and 

December, 2013 that this scenario represents

Representative Time: specific time which that was modeled for the scenario

Scenario Wave Height Bin 

Range (m)

Wave Direction Bin Range (degs. 

from N, waves coming from)

Percentage of Observations Representative Time

H1_D1 0-0.5 0.00-22.50 0.73 10/21/2010 21:00

H1_D2 0-0.5 22.50-45.00 0.53 10/9/2005 21:00

H1_D3 0-0.5 45.00-67.50 0.86 10/1/2009 0:00

H1_D4 0-0.5 67.50-90.00 1.1 7/18/2008 6:00

H1_D5 0-0.5 90.00-112.50 1.45 8/26/2011 3:00

H1_D6 0-0.5 112.50-135.00 1.81 9/3/2010 21:00

H1_D7 0-0.5 135.00-157.50 3.73 3/16/2012 12:00

H1_D8 0-0.5 157.50-180.00 4.67 6/8/2010 18:00

H1_D9 0-0.5 180.00-202.50 2.23 6/13/2010 3:00

H1_D10 0-0.5 202.50-225.00 1.39 8/7/2008 0:00

H1_D11 0-0.5 225.00-247.50 1.02 7/24/2009 12:00

H1_D12 0-0.5 247.50-270.00 0.46 3/26/2012 12:00

H1_D13 0-0.5 270.00-292.50 0.48 8/15/2011 9:00

H1_D14 0-0.5 292.50-315.00 0.49 7/15/2010 0:00

H1_D15 0-0.5 315.00-337.50 0.47 7/31/2010 3:00

H1_D16 0-0.5 337.50-360.00 0.44 1/14/2012 18:00

H2_D1 0.5-1 0.00-22.50 1.06 9/30/2009 6:00

H2_D2 0.5-1 22.50-45.00 1.34 12/30/2008 6:00

H2_D3 0.5-1 45.00-67.50 2.41 12/8/2011 21:00

H2_D4 0.5-1 67.50-90.00 2.48 7/24/2007 9:00

H2_D5 0.5-1 90.00-112.50 3.4 1/20/2013 9:00

H2_D6 0.5-1 112.50-135.00 3.51 3/13/2007 3:00

H2_D7 0.5-1 135.00-157.50 6.3 7/20/2010 12:00

H2_D8 0.5-1 157.50-180.00 5.18 5/9/2009 3:00

H2_D9 0.5-1 180.00-202.50 2.5 7/10/2007 9:00

H2_D10 0.5-1 202.50-225.00 1.6 6/1/2012 6:00
30

Scenario Wave Height Bin 

Range (m)

Wave Direction Bin Range (degs. 

from N, waves coming from)

Percentage of Observations Representative Time

H2_D11 0.5-1 225.00-247.50 1.1 12/21/2008 12:00

H2_D12 0.5-1 247.50-270.00 0.5 8/2/2008 12:00

H2_D13 0.5-1 270.00-292.50 0.44 8/16/2010 0:00

H2_D14 0.5-1 292.50-315.00 0.42 10/31/2012 18:00

H2_D15 0.5-1 315.00-337.50 0.53 3/5/2012 0:00

H2_D16 0.5-1 337.50-360.00 0.88 11/11/2005 3:00

H3_D1 1-1.5 0.00-22.50 1.16 9/25/2006 15:00

H3_D2 1-1.5 22.50-45.00 1.28 1/19/2007 18:00

H3_D3 1-1.5 45.00-67.50 1.66 12/24/2010 6:00

H3_D4 1-1.5 67.50-90.00 1.41 8/11/2010 0:00

H3_D5 1-1.5 90.00-112.50 2.85 1/18/2007 12:00

H3_D6 1-1.5 112.50-135.00 2.6 10/26/2006 21:00

H3_D7 1-1.5 135.00-157.50 4.14 4/29/2009 9:00

H3_D8 1-1.5 157.50-180.00 2.78 6/29/2010 0:00

H3_D9 1-1.5 180.00-202.50 1.37 6/4/2010 3:00

H3_D10 1-1.5 202.50-225.00 0.68 1/9/2006 9:00

H3_D11 1-1.5 225.00-247.50 0.38 10/28/2010 0:00

H3_D12 1-1.5 247.50-270.00 0.17 1/19/2009 3:00

H3_D13 1-1.5 270.00-292.50 0.13 1/19/2009 6:00

H3_D14 1-1.5 292.50-315.00 0.24 3/1/2005 6:00

H3_D15 1-1.5 315.00-337.50 0.54 11/30/2005 6:00

H3_D16 1-1.5 337.50-360.00 1.03 2/8/2011 12:00

H4_D1 1.5-2 0.00-22.50 0.71 4/5/2011 18:00

H4_D2 1.5-2 22.50-45.00 0.72 1/16/2009 9:00

H4_D3 1.5-2 45.00-67.50 0.76 10/30/2011 6:00

H4_D4 1.5-2 67.50-90.00 0.71 10/12/2008 15:00

H4_D5 1.5-2 90.00-112.50 1.65 10/8/2007 15:00

H4_D6 1.5-2 112.50-135.00 1.5 3/24/2009 12:00

H4_D7 1.5-2 135.00-157.50 2.19 12/9/2008 18:00

H4_D8 1.5-2 157.50-180.00 1.15 2/12/2009 9:00

H4_D9 1.5-2 180.00-202.50 0.69 2/14/2012 21:00

H4_D10 1.5-2 202.50-225.00 0.2 12/24/2012 15:00

H4_D11 1.5-2 225.00-247.50 0.16 1/12/2012 18:00

H4_D12 1.5-2 247.50-270.00 0.04 11/29/2011 18:00

H4_D13 1.5-2 270.00-292.50 0.13 12/1/2008 18:00

H4_D14 1.5-2 292.50-315.00 0.19 2/5/2006 0:00

H4_D15 1.5-2 315.00-337.50 0.53 2/2/2011 12:00

H4_D16 1.5-2 337.50-360.00 0.8 12/4/2006 3:00

H5_D1 2.0-3.0 0.00-22.50 0.51 11/21/2008 15:00

H5_D2 2.0-3.0 22.50-45.00 0.37 12/4/2006 15:00

H5_D3 2.0-3.0 45.00-67.50 0.29 10/28/2007 12:00
31

Scenario Wave Height Bin 

Range (m)

Wave Direction Bin Range (degs. 

from N, waves coming from)

Percentage of Observations Representative Time

H5_D4 2.0-3.0 67.50-90.00 0.38 12/17/2005 18:00

H5_D5 2.0-3.0 90.00-112.50 1.15 10/4/2005 0:00

H5_D6 2.0-3.0 112.50-135.00 1.04 11/6/2006 9:00

H5_D7 2.0-3.0 135.00-157.50 1.23 3/6/2011 3:00

H5_D8 2.0-3.0 157.50-180.00 0.64 10/18/2006 6:00

H5_D9 2.0-3.0 180.00-202.50 0.47 2/14/2012 12:00

H5_D10 2.0-3.0 202.50-225.00 0.2 1/7/2009 9:00

H5_D11 2.0-3.0 225.00-247.50 0.15 1/11/2012 15:00

H5_D12 2.0-3.0 247.50-270.00 0.05 3/28/2005 6:00

H5_D13 2.0-3.0 270.00-292.50 0.12 12/1/2008 6:00

H5_D14 2.0-3.0 292.50-315.00 0.3 2/18/2007 6:00

H5_D15 2.0-3.0 315.00-337.50 0.6 12/21/2012 3:00

H5_D16 2.0-3.0 337.50-360.00 0.63 3/24/2006 9:00

H6_D1 3.0-4.0 0.00-22.50 0.06 11/17/2005 9:00

H6_D2 3.0-4.0 22.50-45.00 0.03 12/8/2006 9:00

H6_D3 3.0-4.0 45.00-67.50 0.02 1/19/2008 21:00

H6_D4 3.0-4.0 67.50-90.00 0.07 6/25/2012 6:00

H6_D5 3.0-4.0 90.00-112.50 0.23 12/8/2005 12:00

H6_D6 3.0-4.0 112.50-135.00 0.19 4/30/2006 3:00

H6_D7 3.0-4.0 135.00-157.50 0.18 4/30/2006 21:00

H6_D8 3.0-4.0 157.50-180.00 0.11 5/3/2010 6:00

H6_D9 3.0-4.0 180.00-202.50 0.14 3/10/2006 3:00

H6_D10 3.0-4.0 202.50-225.00 0.07 12/26/2012 6:00

H6_D11 3.0-4.0 225.00-247.50 0.05 12/26/2012 6:00

H6_D12 3.0-4.0 247.50-270.00 0.02 12/12/2008 0:00

H6_D13 3.0-4.0 270.00-292.50 0.02 12/12/2008 0:00

H6_D14 3.0-4.0 292.50-315.00 0.05 12/12/2008 0:00

H6_D15 3.0-4.0 315.00-337.50 0.06 12/26/2010 0:00

H6_D16 3.0-4.0 337.50-360.00 0.04 12/26/2010 0:00

H7_D1 4.0-5.0 0.00-22.50 0.01 10/24/2005 12:00

H7_D2 4.0-5.0 22.50-45.00 0.01 12/8/2006 9:00

H7_D3 4.0-5.0 45.00-67.50 0

H7_D4 4.0-5.0 67.50-90.00 0.01 12/8/2005 15:00

H7_D5 4.0-5.0 90.00-112.50 0.05 12/8/2005 15:00

H7_D6 4.0-5.0 112.50-135.00 0.03 9/1/2008 21:00

H7_D7 4.0-5.0 135.00-157.50 0.04 9/22/2005 15:00

H7_D8 4.0-5.0 157.50-180.00 0.04 9/3/2011 9:00

H7_D9 4.0-5.0 180.00-202.50 0.03 9/5/2011 9:00

H7_D10 4.0-5.0 202.50-225.00 0.004 8/30/2005 3:00

H7_D11 4.0-5.0 225.00-247.50 0.01 8/30/2005 3:00

H7_D12 4.0-5.0 247.50-270.00 0.004 8/30/2005 3:00
32

Scenario Wave Height Bin 

Range (m)

Wave Direction Bin Range (degs. 

from N, waves coming from)

Percentage of Observations Representative Time

H7_D13 4.0-5.0 270.00-292.50 0

H7_D14 4.0-5.0 292.50-315.00 0

H7_D15 4.0-5.0 315.00-337.50 0

H7_D16 4.0-5.0 337.50-360.00 0

H8_D1 5.0-6.0 0.00-22.50 0.001 8/29/2005 12:00

H8_D2 5.0-6.0 22.50-45.00 0

H8_D3 5.0-6.0 45.00-67.50 0

H8_D4 5.0-6.0 67.50-90.00 0

H8_D5 5.0-6.0 90.00-112.50 0.04 9/1/2008 12:00

H8_D6 5.0-6.0 112.50-135.00 0.04 9/1/2008 15:00

H8_D7 5.0-6.0 135.00-157.50 0.02 8/28/2005 21:00

H8_D8 5.0-6.0 157.50-180.00 0.02 9/12/2008 12:00

H8_D9 5.0-6.0 180.00-202.50 0.003 8/29/2005 15:00

H8_D10 5.0-6.0 202.50-225.00 0.01 8/29/2005 18:00

H8_D11 5.0-6.0 225.00-247.50 0.01 8/29/2005 18:00

H8_D12 5.0-6.0 247.50-270.00 0.003 8/29/2005 12:00

H8_D13 5.0-6.0 270.00-292.50 0

H8_D14 5.0-6.0 292.50-315.00 0

H8_D15 5.0-6.0 315.00-337.50 0

H8_D16 5.0-6.0 337.50-360.00 0

Appendix 2. Example Model Input Files

Example SWAN input files for each of the four grids (G1–G4) are provided in this appendix. The 

example provided is for scenarios H1_D1 (wave height bin 1, direction bin 1). Other scenarios would 

vary in the forcing input wind file used. For grid 4, the example given is for the baseline case of no borrow pit; cases with borrow pits use a different bathymetry file incorporating the proposed pit design.

Grid G1:

PROJECT ‘Breton’ ‘ ‘

‘G1’

‘H1_D1’

‘21-Oct-2010 21:00:00’

MODE STATIONARY TWODIMENSIONAL

SET DEPMIN 0.20 INRHOG 1 NAUTICAL PWTAIL 5

COORDINATES CARTESIAN

&& KEYWORDS TO CREATE AND READ COMPUTATIONAL GRID &&

CGRID CURVILINEAR 324 103 EXC 9.999000e+003 &

 CIRCLE 60 0.04 1.0

READGRID COORDINATES 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\grids\

NGMexG1v2.grd’ 4 0 0 FREE
33

&& KEYWORDS TO CREATE AND READ BATHYMETRY GRID &&

INPGRID BOTTOM CURVILINEAR 0 0 324 103 EXC 9.999000e+003

READINP BOTTOM 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\grids\NGMexG1v2.bot’ 4 0 FREE

&& KEYWORD TO CREATE CURRENT GRID &&

$INPGRID CURRENT CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& KEYWORD TO CREATE WATER LEVEL GRID &&

$INPGRID WLEV CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& KEYWORD TO CREATE BOTTOM FRICTION GRID &&

$INPGRID FRIC CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& Wind forcing &&

INPGRID WIND REGULAR 155000.0 3200000.0 0 92 32 6350.0 7400.0 EXC 

9.999000e+003

READINP WIND 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\swan_

wind\H1_D1_wind.dat’ 4 0 FREE

&& BOUNDARY FORCING &&

BOUND SHAPE JONSWAP 3.3 PEAK DSPR DEGREES

BOUNDSPEC SEGMENT IJ 0 0 2 0 CONSTANT PAR 0.1 5.1 127.7 20.0

BOUNDSPEC SEGMENT IJ 2 0 5 0 CONSTANT PAR 0.1 5.1 130.2 20.0

BOUNDSPEC SEGMENT IJ 5 0 9 0 CONSTANT PAR 0.1 5.2 132.4 20.0

BOUNDSPEC SEGMENT IJ 9 0 13 0 CONSTANT PAR 0.0 5.2 139.7 20.0

BOUNDSPEC SEGMENT IJ 13 0 17 0 CONSTANT PAR 0.0 5.2 139.7 20.0

BOUNDSPEC SEGMENT IJ 17 0 21 0 CONSTANT PAR 0.0 5.2 157.8 20.0

BOUNDSPEC SEGMENT IJ 21 0 25 0 CONSTANT PAR 0.0 5.2 193.2 20.0

BOUNDSPEC SEGMENT IJ 25 0 29 0 CONSTANT PAR 0.0 5.1 219.6 20.0

BOUNDSPEC SEGMENT IJ 29 0 33 0 CONSTANT PAR 0.0 5.1 228.8 20.0

BOUNDSPEC SEGMENT IJ 33 0 37 0 CONSTANT PAR 0.0 4.8 232.9 20.0

BOUNDSPEC SEGMENT IJ 37 0 41 0 CONSTANT PAR 0.1 4.3 232.9 20.0

BOUNDSPEC SEGMENT IJ 41 0 45 0 CONSTANT PAR 0.1 3.4 260.0 20.0

BOUNDSPEC SEGMENT IJ 45 0 49 0 CONSTANT PAR 0.1 3.9 170.3 20.0

BOUNDSPEC SEGMENT IJ 49 0 53 0 CONSTANT PAR 0.1 4.2 170.3 20.0

BOUNDSPEC SEGMENT IJ 53 0 57 0 CONSTANT PAR 0.1 4.1 52.9 20.0

BOUNDSPEC SEGMENT IJ 57 0 61 0 CONSTANT PAR 0.3 2.7 45.1 20.0

BOUNDSPEC SEGMENT IJ 61 0 65 0 CONSTANT PAR 0.2 2.9 39.2 20.0

BOUNDSPEC SEGMENT IJ 65 0 69 0 CONSTANT PAR 0.2 3.2 34.2 20.0

BOUNDSPEC SEGMENT IJ 69 0 73 0 CONSTANT PAR 0.3 3.2 34.2 20.0

BOUNDSPEC SEGMENT IJ 73 0 77 0 CONSTANT PAR 0.3 3.2 31.4 20.0

BOUNDSPEC SEGMENT IJ 77 0 81 0 CONSTANT PAR 0.3 3.2 29.3 20.0

BOUNDSPEC SEGMENT IJ 81 0 85 0 CONSTANT PAR 0.3 3.2 27.4 20.0

BOUNDSPEC SEGMENT IJ 85 0 89 0 CONSTANT PAR 0.3 3.2 25.6 20.0

BOUNDSPEC SEGMENT IJ 89 0 93 0 CONSTANT PAR 0.3 3.2 23.7 20.0
34

BOUNDSPEC SEGMENT IJ 93 0 97 0 CONSTANT PAR 0.3 3.2 21.5 20.0

BOUNDSPEC SEGMENT IJ 97 0 101 0 CONSTANT PAR 0.3 3.1 19.1 20.0

BOUNDSPEC SEGMENT IJ 101 0 105 0 CONSTANT PAR 0.3 3.1 16.5 20.0

BOUNDSPEC SEGMENT IJ 105 0 109 0 CONSTANT PAR 0.3 3.1 13.7 20.0

BOUNDSPEC SEGMENT IJ 109 0 113 0 CONSTANT PAR 0.3 3.1 10.8 20.0

BOUNDSPEC SEGMENT IJ 113 0 117 0 CONSTANT PAR 0.3 3.1 8.2 20.0

BOUNDSPEC SEGMENT IJ 117 0 121 0 CONSTANT PAR 0.3 3.2 8.2 20.0

BOUNDSPEC SEGMENT IJ 121 0 125 0 CONSTANT PAR 0.3 3.2 6.4 20.0

BOUNDSPEC SEGMENT IJ 125 0 129 0 CONSTANT PAR 0.3 3.3 5.7 20.0

BOUNDSPEC SEGMENT IJ 129 0 133 0 CONSTANT PAR 0.3 3.3 6.1 20.0

BOUNDSPEC SEGMENT IJ 133 0 137 0 CONSTANT PAR 0.4 3.4 15.1 20.0

BOUNDSPEC SEGMENT IJ 137 0 141 0 CONSTANT PAR 0.4 3.4 14.3 20.0

BOUNDSPEC SEGMENT IJ 141 0 145 0 CONSTANT PAR 0.4 3.4 13.6 20.0

BOUNDSPEC SEGMENT IJ 145 0 149 0 CONSTANT PAR 0.4 3.4 13.1 20.0

BOUNDSPEC SEGMENT IJ 149 0 153 0 CONSTANT PAR 0.4 3.4 13.3 20.0

BOUNDSPEC SEGMENT IJ 153 0 157 0 CONSTANT PAR 0.4 3.4 14.6 20.0

BOUNDSPEC SEGMENT IJ 157 0 161 0 CONSTANT PAR 0.4 3.4 16.4 20.0

BOUNDSPEC SEGMENT IJ 161 0 165 0 CONSTANT PAR 0.4 3.3 13.1 20.0

BOUNDSPEC SEGMENT IJ 165 0 169 0 CONSTANT PAR 0.4 3.3 12.0 20.0

BOUNDSPEC SEGMENT IJ 169 0 173 0 CONSTANT PAR 0.4 3.3 10.7 20.0

BOUNDSPEC SEGMENT IJ 173 0 177 0 CONSTANT PAR 0.4 3.3 10.7 20.0

BOUNDSPEC SEGMENT IJ 177 0 181 0 CONSTANT PAR 0.4 3.3 9.6 20.0

BOUNDSPEC SEGMENT IJ 181 0 185 0 CONSTANT PAR 0.4 3.3 8.4 20.0

BOUNDSPEC SEGMENT IJ 185 0 189 0 CONSTANT PAR 0.4 3.3 7.4 20.0

BOUNDSPEC SEGMENT IJ 189 0 193 0 CONSTANT PAR 0.4 3.3 6.2 20.0

BOUNDSPEC SEGMENT IJ 193 0 197 0 CONSTANT PAR 0.4 3.3 4.9 20.0

BOUNDSPEC SEGMENT IJ 197 0 201 0 CONSTANT PAR 0.4 3.3 3.4 20.0

BOUNDSPEC SEGMENT IJ 201 0 205 0 CONSTANT PAR 0.4 3.3 1.6 20.0

BOUNDSPEC SEGMENT IJ 205 0 209 0 CONSTANT PAR 0.4 3.3 359.6 20.0

BOUNDSPEC SEGMENT IJ 209 0 213 0 CONSTANT PAR 0.4 3.3 357.5 20.0

BOUNDSPEC SEGMENT IJ 213 0 217 0 CONSTANT PAR 0.4 3.3 355.5 20.0

BOUNDSPEC SEGMENT IJ 217 0 221 0 CONSTANT PAR 0.4 3.3 353.6 20.0

BOUNDSPEC SEGMENT IJ 221 0 225 0 CONSTANT PAR 0.4 3.3 351.7 20.0

BOUNDSPEC SEGMENT IJ 225 0 229 0 CONSTANT PAR 0.4 3.3 351.7 20.0

BOUNDSPEC SEGMENT IJ 229 0 233 0 CONSTANT PAR 0.4 3.3 349.6 20.0

BOUNDSPEC SEGMENT IJ 233 0 237 0 CONSTANT PAR 0.4 3.3 347.3 20.0

BOUNDSPEC SEGMENT IJ 237 0 241 0 CONSTANT PAR 0.4 3.3 344.7 20.0

BOUNDSPEC SEGMENT IJ 241 0 245 0 CONSTANT PAR 0.3 3.3 342.0 20.0

BOUNDSPEC SEGMENT IJ 245 0 249 0 CONSTANT PAR 0.3 3.3 339.1 20.0

BOUNDSPEC SEGMENT IJ 249 0 253 0 CONSTANT PAR 0.3 3.3 336.3 20.0

BOUNDSPEC SEGMENT IJ 253 0 257 0 CONSTANT PAR 0.3 3.3 333.6 20.0

BOUNDSPEC SEGMENT IJ 257 0 261 0 CONSTANT PAR 0.3 3.3 331.1 20.0

BOUNDSPEC SEGMENT IJ 261 0 265 0 CONSTANT PAR 0.3 3.4 319.8 20.0

BOUNDSPEC SEGMENT IJ 265 0 269 0 CONSTANT PAR 0.3 3.4 317.3 20.0

BOUNDSPEC SEGMENT IJ 269 0 273 0 CONSTANT PAR 0.3 3.5 315.0 20.0

BOUNDSPEC SEGMENT IJ 273 0 277 0 CONSTANT PAR 0.3 3.5 312.9 20.0

BOUNDSPEC SEGMENT IJ 277 0 281 0 CONSTANT PAR 0.3 3.6 310.7 20.0

BOUNDSPEC SEGMENT IJ 281 0 285 0 CONSTANT PAR 0.3 3.6 310.7 20.0

BOUNDSPEC SEGMENT IJ 285 0 289 0 CONSTANT PAR 0.3 3.7 308.6 20.0
35

BOUNDSPEC SEGMENT IJ 289 0 293 0 CONSTANT PAR 0.3 3.7 296.7 20.0

BOUNDSPEC SEGMENT IJ 293 0 297 0 CONSTANT PAR 0.3 3.7 294.8 20.0

BOUNDSPEC SEGMENT IJ 297 0 301 0 CONSTANT PAR 0.3 3.8 293.2 20.0

BOUNDSPEC SEGMENT IJ 301 0 305 0 CONSTANT PAR 0.3 3.8 291.9 20.0

BOUNDSPEC SEGMENT IJ 305 0 309 0 CONSTANT PAR 0.3 3.9 290.8 20.0

BOUNDSPEC SEGMENT IJ 309 0 313 0 CONSTANT PAR 0.3 3.9 289.9 20.0

BOUNDSPEC SEGMENT IJ 313 0 317 0 CONSTANT PAR 0.3 3.9 289.0 20.0

BOUNDSPEC SEGMENT IJ 317 0 322 0 CONSTANT PAR 0.3 4.0 288.2 20.0

BOUNDSPEC SEGMENT IJ 322 0 324 0 CONSTANT PAR 0.3 4.1 278.9 20.0

BOUNDSPEC SEGMENT IJ 324 0 324 2 CONSTANT PAR 0.3 4.1 278.9 20.0

BOUNDSPEC SEGMENT IJ 324 2 324 5 CONSTANT PAR 0.3 4.1 278.9 20.0

BOUNDSPEC SEGMENT IJ 324 5 324 9 CONSTANT PAR 0.3 4.0 276.9 20.0

BOUNDSPEC SEGMENT IJ 324 9 324 13 CONSTANT PAR 0.3 4.0 282.9 20.0

BOUNDSPEC SEGMENT IJ 324 13 324 17 CONSTANT PAR 0.2 4.0 280.3 20.0

BOUNDSPEC SEGMENT IJ 324 17 324 21 CONSTANT PAR 0.2 4.0 277.0 20.0

BOUNDSPEC SEGMENT IJ 324 21 324 25 CONSTANT PAR 0.2 4.1 277.0 20.0

BOUNDSPEC SEGMENT IJ 324 25 324 29 CONSTANT PAR 0.1 4.2 265.0 20.0

BOUNDSPEC SEGMENT IJ 324 29 324 33 CONSTANT PAR 0.0 4.4 255.8 20.0

BOUNDSPEC SEGMENT IJ 324 33 324 37 CONSTANT PAR 0.0 4.3 255.8 20.0

BOUNDSPEC SEGMENT IJ 324 37 324 41 CONSTANT PAR 0.0 4.3 255.8 20.0

BOUNDSPEC SEGMENT IJ 324 41 324 45 CONSTANT PAR 0.0 4.3 255.8 20.0

BOUNDSPEC SEGMENT IJ 324 45 324 49 CONSTANT PAR 0.0 3.5 255.8 20.0

BOUNDSPEC SEGMENT IJ 324 49 324 53 CONSTANT PAR 0.0 3.2 255.8 20.0

BOUNDSPEC SEGMENT IJ 324 53 324 57 CONSTANT PAR 0.0 3.1 231.0 20.0

BOUNDSPEC SEGMENT IJ 324 57 324 59 CONSTANT PAR 0.0 4.5 231.0 20.0

& Restart name **********************************

&INIT HOTSTART ‘swan_restart.dat’

& PHYSICS **************************************

GEN3 KOMEN

PROP BSBT

WCAP KOM 2.36E-5 3.02E-3 2.0 1.0 1.0

FRICTION JONSWAP 0.067

POINTS ‘NDBC’ FILE &

‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\out_locs\buoys.loc’

NGRID ‘NGMEXG2’ 208000. 3246100. 0. 252000. 138900. 840 463

SPEC ‘NDBC’ SPEC2D ABSOLUTE ‘ndbc_buoy.spc’

TABLE ‘NDBC’ HEAD ‘ndbc_buoy.tab’ &

 TIME XP YP DEPTH HSIGN RTP PDIR TM02 DIR WIND

NESTOUT ‘NGMEXG2’ &

 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\nst_G2\G2_H1_

D1.nst’

BLOCK ‘COMPGRID’ NOHEADER ‘hsig.mat’ LAY 4 HSIGN 1.
36

BLOCK ‘COMPGRID’ NOHEADER ‘rtp.mat’ LAY 4 RTP 1.

BLOCK ‘COMPGRID’ NOHEADER ‘tm02.mat’ LAY 4 TM02 1.

BLOCK ‘COMPGRID’ NOHEADER ‘pwdir.mat’ LAY 4 PDIR 1.

BLOCK ‘COMPGRID’ NOHEADER ‘mwdir.mat’ LAY 4 DIR 1.

BLOCK ‘COMPGRID’ NOHEADER ‘xp.mat’ LAY 4 XP 1.

BLOCK ‘COMPGRID’ NOHEADER ‘yp.mat’ LAY 4 YP 1.

COMPUTE STATIONARY

$HOTFILE ‘.swanP1208_restart.dat01’

STOP

Grid G2:

PROJECT ‘Breton’ ‘ ‘

‘G2’

‘H1_D1’

‘21-Oct-2010 21:00:00’

MODE STATIONARY TWODIMENSIONAL

SET DEPMIN 0.20 INRHOG 1 NAUTICAL PWTAIL 5

COORDINATES CARTESIAN

&& KEYWORDS TO CREATE AND READ COMPUTATIONAL GRID &&

CGRID CURVILINEAR 840 463 EXC 9.999000e+003 &

 CIRCLE 60 0.04 1.0

READGRID COORDINATES 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\grids\

NGMexG2.grd’ 4 0 0 FREE

&& KEYWORDS TO CREATE AND READ BATHYMETRY GRID &&

INPGRID BOTTOM CURVILINEAR 0 0 840 463 EXC 9.999000e+003

READINP BOTTOM 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\grids\NGMexG2.bot’ 4 0 FREE

&& KEYWORD TO CREATE CURRENT GRID &&

$INPGRID CURRENT CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& KEYWORD TO CREATE WATER LEVEL GRID &&

$INPGRID WLEV CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& KEYWORD TO CREATE BOTTOM FRICTION GRID &&

$INPGRID FRIC CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& Wind forcing &&

INPGRID WIND REGULAR 155000.0 3200000.0 0 92 32 6350.0 7400.0 EXC 

9.999000e+003
37

READINP WIND 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\swan_

wind\H1_D1_wind.dat’ 4 0 FREE

&& BOUNDARY FORCING &&

BOUN NEST &

‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\nst_G2\G2_H1_D1.nst’ 

CLOSED

& Restart name **********************************

&INIT HOTSTART ‘swan_restart.dat’

& PHYSICS **************************************

GEN3 KOMEN

PROP BSBT

WCAP KOM 2.36E-5 3.02E-3 2.0 1.0 1.0

FRICTION JONSWAP 0.067

POINTS ‘NDBC’ FILE &

‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\out_locs\buoys.loc’

NGRID ‘NGMEXG3’ 277400. 3251750. 0. 32700. 19440. 545 324

SPEC ‘NDBC’ SPEC2D ABSOLUTE ‘ndbc_buoy.spc’

TABLE ‘NDBC’ HEAD ‘ndbc_buoy.tab’ &

 TIME XP YP DEPTH HSIGN RTP PDIR TM02 DIR WIND

NESTOUT ‘NGMEXG3’ &

 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\nst_G3\G3_H1_

D1.nst’

BLOCK ‘COMPGRID’ NOHEADER ‘hsig.mat’ LAY 4 HSIGN 1.

BLOCK ‘COMPGRID’ NOHEADER ‘rtp.mat’ LAY 4 RTP 1.

BLOCK ‘COMPGRID’ NOHEADER ‘tm02.mat’ LAY 4 TM02 1.

BLOCK ‘COMPGRID’ NOHEADER ‘pwdir.mat’ LAY 4 PDIR 1.

BLOCK ‘COMPGRID’ NOHEADER ‘mwdir.mat’ LAY 4 DIR 1.

BLOCK ‘COMPGRID’ NOHEADER ‘xp.mat’ LAY 4 XP 1.

BLOCK ‘COMPGRID’ NOHEADER ‘yp.mat’ LAY 4 YP 1.

COMPUTE STATIONARY

$HOTFILE ‘.swanP1208_restart.dat01’

STOP
38

Grid G3:

PROJECT ‘Breton’ ‘ ‘

‘G3’

‘H1_D1’

‘21-Oct-2010 21:00:00’

MODE STATIONARY TWODIMENSIONAL

SET DEPMIN 0.05 INRHOG 1 NAUTICAL PWTAIL 5

COORDINATES CARTESIAN

&& KEYWORDS TO CREATE AND READ COMPUTATIONAL GRID &&

CGRID CURVILINEAR 545 324 EXC 9.999000e+003 &

 CIRCLE 60 0.04 1.0

READGRID COORDINATES 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\grids\

NGMexG3.grd’ 4 0 0 FREE

&& KEYWORDS TO CREATE AND READ BATHYMETRY GRID &&

INPGRID BOTTOM CURVILINEAR 0 0 545 324 EXC 9.999000e+003

READINP BOTTOM 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\grids\NGMexG3.bot’ 4 0 FREE

&& KEYWORD TO CREATE CURRENT GRID &&

$INPGRID CURRENT CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& KEYWORD TO CREATE WATER LEVEL GRID &&

$INPGRID WLEV CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& KEYWORD TO CREATE BOTTOM FRICTION GRID &&

$INPGRID FRIC CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& Wind forcing &&

INPGRID WIND REGULAR 155000.0 3200000.0 0 92 32 6350.0 7400.0 EXC 

9.999000e+003

READINP WIND 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\swan_

wind\H1_D1_wind.dat’ 4 0 FREE

&& BOUNDARY FORCING &&

BOUN NEST &

‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\nst_G3\G3_H1_D1.nst’ 

CLOSED

& Restart name **********************************

&INIT HOTSTART ‘swan_restart.dat’

& PHYSICS **************************************

GEN3 KOMEN
39

PROP BSBT

WCAP KOM 2.36E-5 3.02E-3 2.0 1.0 1.0

FRICTION JONSWAP 0.067

TRIAD

POINTS ‘NDBC’ FILE &

‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\out_locs\buoys.loc’

NGRID ‘NGMEXG4’ 283660. 3256780. 0. 18220. 10820. 1024 541

SPEC ‘NDBC’ SPEC2D ABSOLUTE ‘ndbc_buoy.spc’

TABLE ‘NDBC’ HEAD ‘ndbc_buoy.tab’ &

 TIME XP YP DEPTH HSIGN RTP PDIR TM02 DIR WIND

NESTOUT ‘NGMEXG4’ &

 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\nst_G4\G4_H1_

D1.nst’

BLOCK ‘COMPGRID’ NOHEADER ‘hsig.mat’ LAY 4 HSIGN 1.

BLOCK ‘COMPGRID’ NOHEADER ‘rtp.mat’ LAY 4 RTP 1.

BLOCK ‘COMPGRID’ NOHEADER ‘tm02.mat’ LAY 4 TM02 1.

BLOCK ‘COMPGRID’ NOHEADER ‘pwdir.mat’ LAY 4 PDIR 1.

BLOCK ‘COMPGRID’ NOHEADER ‘mwdir.mat’ LAY 4 DIR 1.

BLOCK ‘COMPGRID’ NOHEADER ‘xp.mat’ LAY 4 XP 1.

BLOCK ‘COMPGRID’ NOHEADER ‘yp.mat’ LAY 4 YP 1.

COMPUTE STATIONARY

$HOTFILE ‘.swanP1208_restart.dat01’

STOP

Grid G4:

PROJECT ‘Breton’ ‘ ‘

‘G4’

‘H1_D1’

‘21-Oct-2010 21:00:00’

MODE STATIONARY TWODIMENSIONAL

SET DEPMIN 0.05 INRHOG 1 NAUTICAL PWTAIL 5

COORDINATES CARTESIAN

&& KEYWORDS TO CREATE AND READ COMPUTATIONAL GRID &&

CGRID CURVILINEAR 1024 541 EXC 9.999000e+003 &

 CIRCLE 60 0.04 1.0

READGRID COORDINATES 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\grids\

NGMexG4.grd’ 4 0 0 FREE

&& KEYWORDS TO CREATE AND READ BATHYMETRY GRID &&
40

INPGRID BOTTOM CURVILINEAR 0 0 1024 541 EXC 9.999000e+003

READINP BOTTOM 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\grids\NGMexG4.bot’ 4 0 FREE

&& KEYWORD TO CREATE CURRENT GRID &&

$INPGRID CURRENT CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& KEYWORD TO CREATE WATER LEVEL GRID &&

$INPGRID WLEV CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& KEYWORD TO CREATE BOTTOM FRICTION GRID &&

$INPGRID FRIC CURVILINEAR 0 0 500 135 EXC 9.999000e+003 &

$ NONSTAT 20100801.000000 1 DAY 20100901.000000

&& Wind forcing &&

INPGRID WIND REGULAR 155000.0 3200000.0 0 92 32 6350.0 7400.0 EXC 

9.999000e+003

READINP WIND 1 ‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\swan_

wind\H1_D1_wind.dat’ 4 0 FREE

&& BOUNDARY FORCING &&

BOUN NEST &

‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\forcings\nst_G4\G4_H1_D1.nst’ 

CLOSED

& Restart name **********************************

&INIT HOTSTART ‘swan_restart.dat’

& PHYSICS **************************************

GEN3 KOMEN

PROP BSBT

WCAP KOM 2.36E-5 3.02E-3 2.0 1.0 1.0

FRICTION JONSWAP 0.067

TRIAD

POINTS ‘NDBC’ FILE &

‘C:\Users\rmickey\Documents\SWAN_ngomex\breton\out_locs\buoys.loc’

SPEC ‘NDBC’ SPEC2D ABSOLUTE ‘ndbc_buoy.spc’

TABLE ‘NDBC’ HEAD ‘ndbc_buoy.tab’ &

 TIME XP YP DEPTH HSIGN RTP PDIR TM02 DIR WIND

BLOCK ‘COMPGRID’ NOHEADER ‘hsig.mat’ LAY 4 HSIGN 1.

BLOCK ‘COMPGRID’ NOHEADER ‘rtp.mat’ LAY 4 RTP 1.

BLOCK ‘COMPGRID’ NOHEADER ‘tm02.mat’ LAY 4 TM02 1.

BLOCK ‘COMPGRID’ NOHEADER ‘pwdir.mat’ LAY 4 PDIR 1.
41

BLOCK ‘COMPGRID’ NOHEADER ‘mwdir.mat’ LAY 4 DIR 1.

BLOCK ‘COMPGRID’ NOHEADER ‘xp.mat’ LAY 4 XP 1.

BLOCK ‘COMPGRID’ NOHEADER ‘yp.mat’ LAY 4 YP 1.

COMPUTE STATIONARY

$HOTFILE ‘.swanP1208_restart.dat01’

STOP

Appendix 3. File Naming Conventions

Model results from all four grids are available in geographic information systems (GIS) polygon 

shapefile format, with all files for a given scenario having a prefix of Gg_Hh_Dd, where g denotes the 

grid number (1, 2, 3, or 4), and h and d denote the scenario wave height (H) and direction (D) bin number, respectively. So, for example, a prefix of “G1_H1_D1” corresponds to output from grid G1 for a 

wave height in the lowest bin (0.0 to 0.5 meter) and wave direction in the first bin (0 to 22.5 degrees). A 

table of scenario characteristics is provided in appendix 1 and is also included in a text file (.txt) zipped 

with each GIS shapefile. In the case of grid G4, for cases where a borrow pit is included, there is an 

additional prefix “Pp” where p denotes which proposed borrow pit was considered (1 or 2; fig. 3). The 

final portion of the filename denotes the output type. For example, G1_H1_D1_wave_height.xxx would 

contain significant wave height for scenario H1_D1 for grid G1, with various file extensions (.xxx) associated with the GIS shapefile.

Appendix 4. Analysis of Borrow Pit 3

Introduction

After the analysis of the two borrow pits described in this report was completed, a third borrow 

site was identified as a potential location to obtain sediment for the Breton Island restoration project. 

The same techniques (see Methods) were applied to analyze the impacts of this third site (hereafter referred to as borrow pit 3), and the results are presented here. The model output from borrow pit 3 can be 

found in Mickey and others (2017). The location of borrow pit 3 is shown in figure A4-1, and its characteristics are described in table A4-1.

Results: Effects of Borrow Pits on Nearshore Wave Climate and Longshore Transport

The change in weighted average wave height for borrow pit 3 was less than 10 cm at the location 

of maximum difference (fig. A4-2), or 8 percent of the baseline case. 

Scenario-weighted longshore transport rate (LSTR) with the addition of borrow pit 3 was similar 

to the baseline LSTR, with a maximum difference of 0.08 x 105 m3/year (0.0002 m3/s). This magnitude 

of change is less than 35 percent of the baseline LSTR (fig. A4-3). The maximum change in the gradient of LSTR with the addition of borrow pit 3 was 0.04 x 103

 m3/year, approximately 35 percent of the 

baseline (fig. A4-4).
42

Conclusions

An analysis of the proposed borrow pit 3 was conducted following the same methodology used 

for the previous two borrow pits described in the main body of this report. The change in weightedaverage wave height with the addition of borrow pit 3 was less than 10 cm and greatest near the proposed site (fig. A4-1). Changes to the LSTR and its gradient were less than 35 percent of the baseline 

case, with the largest changes toward the northern end of Breton Island (figs. A4-3, A4-4). 

Table A4-1. Dimensions of proposed borrow pit 3 used in the modeling scenarios. 

[m, meters]

Water depth (m) Pit depth1

(m)

Area 

(m2 x 106)

Volume 

(m3 x 106)

Volume per unit 

area

Borrow pit 3 7.00-8.50 6.30 1.01 4.70 4.65

1

Maximum pit depth in meters below the seafloor.

Figure A4-1. Maps showing (A) the location of Breton Island in the Gulf of Mexico, the G1, G2, and G3 numerical model domains, and the 

three National Data Buoy Center (NDBC) buoys (42040, 42012, and 42007) used in model scenario development and assessment, and (B) the 

elevations and spatial extent of the G3 model domain, showing the G4 domain as well as the extent of three proposed borrow pits considered in 

the wave modeling study. The channel running northeast of Breton Island is the Mississippi River Gulf Outlet (MRGO).
43

Figure A4-2. Weighted average significant wave height for (A) bathymetry including borrow pit 3 (outlined in white) and (B) change from the 

baseline case. The average is created from the output of individual scenarios, weighted by their percentage occurrence over the time period 

December 1995 through December 2013 (fig. 2). Borrow pit 3 creates a shadowing effect originating from the corners.

Figure A4-3. For 135 locations (A, black line) along Breton Island (0 m contour shown in green), average longshore transport rate (B, LSTR) 

calculated by weighting each of the 116 individual scenario results by its frequency of occurrence in the wave climatology (fig. 2). The change in 

magnitude of the weighted average longshore transport rate (C) with the addition of the pits is less than 30 percent of the baseline values, and the 

overall pattern in transport is the same (B).
44

Figure A4-4. Gradient of average longshore transport rate (B, LSTR) constructed from the longshore transport calculated using wave 

conditions at 135 locations (A, black line) along Breton Island (0 m contour shown in green) for each of the 116 individual scenario results (e.g., 

fig. 15 for the no pit case) by weighting each scenario by its frequency of occurrence in the wave climatology (fig. 2). The change in magnitude 

of the gradient of the LSTR (C) with the addition of the pits is an order of magnitude less than the baseline values, and the overall pattern in 

transport is the same (B). Positive values in (C) indicate more accretion or less erosion, negative values indicate less accretion or more erosion.
Dalyander and others—Effects of Proposed Sediment Borrow Pits on Nearshore Wave Climate and Longshore Sediment Transport Rate Along Breton Island, Louisiana—

Open-File Report 2015–1055, ver. 2.0

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20151055