OCS Study 

MMS 2000-053 

 

Coastal Marine Institute 

Wave Climate and Bottom Boundary 

Layer Dynamics with Implications for 

Offshore Sand Mining and Barrier Island 

Replenishment in South-Central Louisiana 

Cooperative Agreement

Coastal Marine Institute 

Louisiana State University

U.S. Department of the Interior

Minerals Management Service 

Gulf of Mexico OCS Region 
U.S. Department of the Interior Cooperative Agreement 

Minerals Management Service Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University 

OCS Study 

 MMS 2000-053 

 

Coastal Marine Institute 

Wave Climate and Bottom Boundary 

Layer Dynamics with Implications for 

Offshore Sand Mining and Barrier Island 

Replenishment in South-Central Louisiana 

Author 

Gregory W. Stone 

December 2001 

Prepared under MMS Contract 

14-35-0001-30660-19911b 

by 

Louisiana State University 

Dept. of Oceanography and Coastal Sciences 

Howe-Russell Geoscience Complex 

Baton Rouge, Louisiana 70803 

Published by 
ii

Disclaimer

This report was prepared under contract between the Minerals Management Service (MMS) and

Louisiana State University. This report has been technically reviewed by the MMS and

approved fro publication. Approval does not signify that the contents necessarily reflect the

views and policies of the Service nor does mention of trade names or commercial products

constitute endorsement or recommendation for use. It is, however, exempt from review and

compliance with MMS editorial standards.

Report Availability

Extra copies of the report may be obtained from Public Information Office (MS 5043) at the

following address.

U.S. Department of the Interior

Minerals Management Service

Gulf of Mexico OCS Region

Public Information Office (MS 5034)

1201 Elmwood Park Boulevard

New Orleans, Louisiana 70123-2394

Telephone Number: (504) 736-2519 or

1-800-200-GULF

Citation

Stone, G.W. 2001. Wave Climate and bottom boundary layer dynamics with implications for

offshore sand mining and barrier island replenishment in south-central Louisiana. OCS

Study MMS 2000-053. U.S. Dept. of the Interior, Minerals Mgmt. Service, Gulf of Mexico

OCS Region, New Orleans, La. 90 pp.
iv

SUMMARY

The results of a three-year field study of wave climate, wave-current interactions and bottom

boundary layer dynamics, and sediment transport on Ship Shoal, off the Isles Dernieres in southcentral Louisiana, are presented. Through the procurement and fabricating of bottom boundary

layer instrumentation systems, wave characteristics were measured simultaneously at two

geographical locations on Ship Shoal to ultimately validate a spectral wave propagation model

(STWAVE) used extensively in a previously funded MMS project which concentrated on

assessing the potential impacts of mining Ship Shoal off the Louisiana coast. In addition, direct

field measurements of temporally- and spatially-varying directional wave spectra were obtained

at two locations on the inner shelf. These field measurements were conducted under different

wave conditions (storms, fair weather, etc.) to facilitate numerical model output validation and to

develop a quantitative wave climate for the study area. A third objective involved obtaining

direct field measurements of bottom boundary layer hydrodynamic processes and suspended

sediment transport. These measurements include total bed shear stress, bed roughness, drag

coefficient and their relationship to wave directional spectral characteristics, mean current

velocity profile, bedform (e.g., ripples), and suspended sediment concentrations. It is anticipated

that the data presented in this report will significantly enhance confidence in numerical modeling

of wave conditions on the inner continental shelf . In addition the data presented here are the first

on the dynamic characteristics of the bottom boundary layer, directional suspended sediment

flux, and the morphodynamic behavior (erosion and accretion) of the bottom in the study area.
vi

TABLE OF CONTENTS

Page

1. INTRODUCTION...........................................................................................................1

Program Overview ..........................................................................................................1

Research Objectives ........................................................................................................3

Program Principal Investigator and Support Personnel ..................................................3

Publications Derived from Funded Research..................................................................4

2. CONCEPTUAL FRAMEWORK AND STUDY AREA................................................7

Introduction .....................................................................................................................7

Conceptual Basis for the Research................................................................................11

Study Area.....................................................................................................................12

Meteorology ..................................................................................................................13

Hydrodynamics and Bottom Boundary Layer Regime.................................................14

Geology/Geomorphology..............................................................................................15

Practical Concerns.........................................................................................................17

3. METHODOLOGY........................................................................................................19

Instrumentation and Field Methods...............................................................................19

Laboratory Methods ......................................................................................................22

Data Processing and Analytical Methods......................................................................23

4. METEOROLOGICAL CONDITIONS DURING THE DEPLOYMENTS.................33

Classification Systems for Meteorological Events........................................................33

Analysis of Meteorological Events During the Deployment ........................................34

Meteorological Summary of the Deployment...............................................................34

5. HYDRODYNAMICS, BOTTOM BOUNDARY LAYER PARAMETERS

 AND SEDIMENT TRANSPORT DURING THE ENTIRE DEPLOYMENT

 PERIOD: TIME- AND FREQUENCY- DOMAIN ANALYSIS AND

 OVERALL SUMMARY...............................................................................................39

 Introduction ...................................................................................................................39

 Initial Considerations: Field Observations....................................................................39

Hydrodynamics .............................................................................................................42

Bottom Boundary Layer Parameters.............................................................................49

Sediment Suspension and Transport .............................................................................50
vii

6. COMPARISON OF HYDRODYNAMICS, BOTTOM BOUNDARY

 LAYER PARAMETERS AND SEDIMENT TRANSPORT DURING

 STORMS AND FAIR WEATHER CONDITIONS .....................................................57

 Storm and Fair Weather Hydrodynamics......................................................................57

Bottom Boundary Layer Parameters.............................................................................58

Sediment Transport .......................................................................................................61

Sediment Fluxes across Ship Shoal...............................................................................64

7. VALIDATION OF NUMERICAL WAVE MODEL (STWAVE)...............................69

Introduction ...................................................................................................................69

Validation Methods.......................................................................................................69

Comparison of in situ Modeled Data ............................................................................70

8. CONCLUSIONS...........................................................................................................81

9. REFERENCES..............................................................................................................83
viii

LIST OF FIGURES

Figure Page

1.1 Map of the Ship Shoal study site off the Isles Dernieres, Louisiana ............................ 2

2.1 Results of analysis of sediment from Site 1 ................................................................ 16

2.2 Results of analysis of sediment from Site 2 ................................................................ 17

3.1 System 2A during deployment at Site 2...................................................................... 20

3.2 System 1B during deployment at Site 1...................................................................... 20

3.3 Location of instrumentation sites at Ship Shoal.......................................................... 21

4.1 Wind speed during the deployment period ................................................................. 35

4.2 Feather plot of hourly wind vectors during the deployment ....................................... 35

4.3 Power spectrum of wind speed during the deployment .............................................. 36

5.1 Bed elevation and water level as measured by System 1B during the deployment.... 40

5.2 Total water depth as measured hourly and smoothed using a 24-h moving average

window as measured by System 1B............................................................................ 41

5.3 Across-shelf current flow during the deployment at Sites 1 and 2

as measured by systems 1A and 2A............................................................................ 43

5.4 Significant wave height at Site 1 and Site 2................................................................ 44

5.5 Peak wave period at Site 1 and Site 2 ......................................................................... 44

5.6 Flow speed of mean and orbital currents at Site 1 ...................................................... 45

5.7 Flow speed of mean and orbital currents At Site 2 ..................................................... 46

5.8 Spectrum of current speed at Site 1............................................................................. 46

5.9 Vector plot of mean current direction at Site 1 during the deployment...................... 47

5.10 Cross spectrum of wind and across-shelf current at Site 1 ........................................ 48

5.11 Phase spectrum of northerly wind and northerly current at Site 1............................. 48

5.12 Current and combined wave-current shear velocity as measured at Site 1................. 49

5.13 Current and combined wave-current shear velocity as measured at Site 2................. 50

5.14 Suspended sediment concentration at Site 1 ............................................................... 50

5.15 Suspended sediment concentration at Site 2 ............................................................... 51

5.16 Across-shelf longshore sediment transport for Site 2 as predicted

using the GMR method ............................................................................................... 51

5.17 Along-shelf cross-shore sediment transport for Site 2 as predicted

using the GMR method .............................................................................................. 52

5.18 Across-shelf cross-shore sediment transport for Site 1 as predicted

using the GMR method ............................................................................................... 52

5.19 Along-shelf longshore sediment transport for Site 1 as predicted

using the GMR method .............................................................................................. 53

5.20 Longshore along-shelf bed and suspended load sediment for Site 1

as predicted using the MPM and SCC methods.......................................................... 53

5.21 Cross-shore across-shelf bed and suspended load sediment transport for Site 1

as predicted using the MPM and SCC methods.......................................................... 54

5.22 Cross-shore across-shelf bed load and suspended load transport for Site 2, as

predicted using the MPM and SCC methods .............................................................. 54

5.23 Longshore along-shelf bed load and suspended load Transport for Site 2, as

predicted using the MPM and SCC methods .............................................................. 55
ix

6.1 Current flux over Ship Shoal....................................................................................... 65

6.2 Flux of sediment across Ship Shoal as calculated using the GMR Method................ 66

6.3 Flux of suspended sediment across Ship Shoal as calculated using the steady

current/ concentration method..................................................................................... 66

6.4 Flux of bed load across Ship Shoal as calculated using the Meyer-Peter

and Muller method ...................................................................................................... 67

7.1 Location of the modeling area at Ship Shoal and instrumentation deployment sites . 70

7.2 Summary of % over prediction of Hs by STWAVE for all stations ........................... 71

7.3 Summary of r2 values for measured and modeled Hs for all stations.......................... 71

7.4 Scatter plot of significant wave heights for 1998/99 deployment for

all wind directions at Inshore station........................................................................... 72

7.5 Comparison of measured and numerically modeled wave heights

for all directions in 1998/99 deployment at Inshore station........................................ 72

7.6 Scatter plot of significant wave heights for all wind direction

at Inshore station for 2000 deployment....................................................................... 73

7.7 Comparison diagram of numerically modeled and measured

wave heights for all wind directions at Inshore station for 2000 deployment ............ 73

7.8 Scatter plot of Hs measured vs. Hs numerically modeled for 2000

deployment at Middle Station ..................................................................................... 74

7.9 Comparison diagram of numerically modeled and measured wave

heights for all wind directions at Middle station for 2000 deployment ...................... 74

7.10 Scatter plot of measured and modeled Hs for wind blowing from

southwest, south and southeast for 1998/99 deployment at Inshore station ............... 75

7.11 Comparison diagram of numerically modeled and measured wave

heights for selected southwest, south, and southeast winds at Inshore

tation for 1998/99 deployment .................................................................................... 75

7.12 Scatter plot of Hs measured vs. Hs numerically modeled

at Inshore station for southwest, south, and southeast wind

directions for 2000 deployment................................................................................... 76

7.13 Comparison diagram of numerically modeled and measured wave

heights for southwest, south and southeast wind directions at Inshore

station for 2000 deployment........................................................................................ 76

7.14 Scatter plot of Hs measured vs. numerically modeled Hs at Middle

station for southwest, south, and southeast winds for 2000 deployment .................... 77

7.15 Relationship between numerically modeled and measured significant

wave heights at Middle station for southwest, south and southeast winds ................. 77

7.16 Scatter plot of Hs measured vs, Hs numerically modeled for southwest

wind only at Inshore station for 2000 deployment...................................................... 78

7.17 Relationship between numerically modeled and measured significant

wave heights for southwest wind only at Inshore station............................................ 78

7.18 Scatter plot of Hs measured vs. Hs numerically modeled for southwest

wind only at Middle station for 2000 deployment ...................................................... 79

7.19 Relationship between numerically modeled and measured significant

wave heights for southwest wind only at Middle station............................................ 79
x

LIST OF TABLES

Table Page

3.1 Sampling schemes used in data collection ..................................................................... 21

3.2 Segment, window, and overlap lengths used in spectral analysis.................................. 23

3.3 Summary of methods used to calculate shear velocity and sediment transport............. 25

4.1 Classification of storms during the deployment on the basis of

the methods discussed .................................................................................................... 37

5.1 Summary of hydrodynamics parameters recorded by the systems throughout the

deployment..................................................................................................................... 42

6.1 Summary of storm and fair weather hydrodynamic measurements taken at Site 1

using System 1B............................................................................................................. 58

6.2 Summary of storm and fair weather hydrodynamic measurements taken at Site 2

using System 2A............................................................................................................. 58

6.3 Summary of bottom boundary layer parameters calculated based on System 1B

data for storms and fair weather conditions ................................................................... 59

6.4 Summary of bottom boundary layer parameters calculated based on data

from the offshore ADV for storms and fair conditions.................................................. 59

6.5 Summary of bottom boundary layer parameters calculated based on the

System 2A data for storms and fair weather conditions at Site 2 .................................. 60

6.6 Summary of sediment transport estimates within and above the wave

boundary layer for storms and fair weather conditions at the offshore Site .................. 61

6.7 Predicted sediment transport and bed level change for Site 1 based on data from

System 1A analyzed using several models .................................................................... 62

6.8 Predicted sediment transport and bed level change for Site 2 based on

several models................................................................................................................ 62

6.9 Co-spectral estimates of suspended transport at System 1A.......................................... 63

6.10 Co-spectral estimates of suspended sediment transport at System 2A .......................... 88

6.11 Sediment flux across Ship Shoal during storms and fair weather as

predicted from Systems 1A and 2A using spectral methods and the GMR

and MPM methods ......................................................................................................... 67

7.1 Percentage of over prediction of Hs by STWAVE when compared to in situ

measurements at two locations on Ship Shoal, based on 590 model run....................... 71
1

1. INTRODUCTION

Program Overview

Coastal erosion and wetland loss in Louisiana have been a serious threat to the coastal

ecosystem and local economy. Degradation of Louisiana's barrier shorelines is interconnected

with massive wetland loss (McBride et al., (1989); Williams et al., 1992; Stone et al., 1997;

Stone and McBride, 1998). Among the most promising mitigative techniques to temporarily

offset further deterioration of the barrier island system, thereby reducing wetland loss, involves

increasing the subaerial volume of barrier islands located primarily west of the Mississippi River.

The most economically and technically feasible source of sediment appears to be Ship Shoal, a

shore-parallel sand body with approximately 1.25 x 109

 m3 of fine sand (Suter et al., 1989)

located 15 km offshore off the Isles Dernieres (Figure 1). In 1994, MMS funded the first phase

of a multi-year project designed to numerically model the impacts of shoal removal on the wave

field (Stone and Xu, 1996). The results of that project are summarized below:

1. Removal of Ship Shoal will alter wave propagation, dissipation and wave energy distribution.

The magnitude and spatial distribution of the alteration depends on the initial wave conditions,

and initial wave direction is not an important factor in determining the wave climate change.

During severe storms (Case 1; Hs=6 m, Tp=11 sec.) and strong storms (Case 2; Hs=4 m, Tp=9

sec.), the propagating waves reach breaking conditions seaward of the west part of Ship Shoal.

Therefore, removal of Ship Shoal causes a maximum increase of the significant wave height over

the shoal complex and its lee flank. Wave breaking does not occur on the east part of Ship Shoal

because of much deeper water, and the magnitude of the wave height increase due to shoal

removal is secondary on comparison with the value on the west flank of the shoal. During weak

storms (Case 3; Hs=2 m, Tp=6 sec.) and fair weather conditions (Case 4; Hs=1 m, Tp=5 sec.),

waves never reach breaking conditions over any part of Ship Shoal. The magnitude of the

significant wave height increase due to the removal of the shoal is considerably smaller, and the

magnitudes of the wave height increase on the east part of the shoal are minimal.

2. The nearshore wave fields are largely dependent on the offshore wave conditions. Numerical

simulations indicate that under high energy conditions (Case 1 and Case 2) removal of Ship

Shoal may result in larger breaking wave heights and, therefore, displacement of the breaker

zone offshore by 0.5 - 1.0 km. Energy levels however do not show a marked increase in the

nearshore zone due to post-breaking frictional dissipation, when the shoal is removed. This is

even less apparent under the weaker energy conditions in Case 3 and Case 4.

3. Inclusion of a wind forcing function in the numerical model significantly enhances the overall

significant wave height. A 20 m/s wind (Case 1) in the wave direction causes an increase of the

significant wave height by as much as 1.0 m . A 5 m/s wind in Case 4, also in the wave

direction, can increase the wave height by 0.2 m. Consequently, the width of the surf zone is also

increased significantly during "local" winds.
2

Figure 1.1. Map of the Ship Shoal study site off the Isles Dernieres, Louisiana.

Although the results obtained from the numerical modeling phase will provide guidance in

management decision making and developing the Environmental Impact Statement pertaining to

Ship Shoal, three critical questions remain unanswered:

1. To what extent does the numerical model realistically represent conditions in the

field?

As stated explicitly in phase 1 of this study, a comprehensive field data set from which the wave

climate, among other things, can be constructed for the study area off the Isles Dernieres on the

inner shelf will be necessary to help check and validate model output. The data necessary to

accomplish this are not available at present. Although the model (STWAVE) has gained

acceptance in the scientific and engineering literature (Kraus et al., 1991; McKee et al., 1999),

comparisons with measurements obtained from in situ measurement is necessary on applying the

model locally;

2. What are the dynamic characteristics of the bottom boundary layer in the region?

How do they control the suspension and transport of bed sediment?

3. If Ship Shoal is mined, what will be the transport dynamics of sediment introduced

to the inner shelf from the shoal on dredging completion, and what changes will

occur to the bottom boundary layer? How will this ultimately affect the distribution

and fate of sediment along the nourished coast?
3

This report describes the findings of a three year study that directly addresses these questions.

The project is unique in that it is the first research effort that concentrates on the dynamic

characteristics of the bottom boundary layer, directional suspended sediment flux, and the

morphodynamic behavior (erosion and accretion) of the bottom.

Research Objectives

This report presents the data and interpretation of a three-year field study of wave

climate, wave-current interactions and bottom boundary layer dynamics, and sediment transport

in the Ship Shoal area, landward to the inner shelf adjacent to the Isles Dernieres. The primary

objectives of this research are as follows:

1. Obtain direct field measurements of bottom boundary layer hydrodynamic processes and

suspended sediment transport.

These measurements include total bed shear stress, bed roughness, drag coefficient and their

relationship to wave directional spectral characteristics, mean current velocity profile, bedform

(e.g., ripples), and suspended sediment concentrations.

2. Obtain direct field measurements of temporally- and spatially-varying directional wave

parameters at several locations on Ship Shoal.

These field measurements were obtained under different wave conditions (storms and fair

weather) to facilitate skill assessment of the numerical model output and to develop a

quantitative wave climate for the study area.

Program Principal Investigator and Support Personnel

All aspects of this program, including preparation of this report, have been carried out by the

principal investigator, Dr. Gregory W. Stone (Louisiana State University). David Pepper (CSI)

has developed much of the bottom boundary layer data into a hydrodynamic-sediment transport

model as part of a Ph.D. dissertation and has contributed to this report and provided field

support. Xiongping Zhang (CSI) has assisted in running and skill assessing the numerical wave

model (STWAVE) and has contributed to this report also. Dr. Ping Wang (CSI) has assisted in

field work and data interpretation. Field deployments were accomplished through the Coastal

Studies Institute’s Field Support Group who also fabricated the bottom boundary layer

instrumentation arrays.

Publications Derived from Funded Research

The following publications have dealt specifically with the data obtained from this research:
4

Stone, G. W., J. P. Xu, and X. P. Zhang. 1995. Estimation of the wave field during Hurricane

Andrew and morphological impacts along the Louisiana coast. In: Stone, G.W. and C.W.

Finkle, eds. Impacts of Hurricane Andrew on the Coastal Zones of Florida and Louisiana:

Journal Coastal Research Special Issue 21:234-253.

Stone, G.W. and J.P. Xu. 1995. Wave Climate Modeling and Evaluation Relative to Sand

Mining on Ship Shoal, Offshore Louisiana, for Coastal and Barrier Island Restoration.

Report prepared for Minerals Management Service. Coastal Morphodynamics Laboratory

Technical Paper 95-3, 21 pp.

Stone, G.W. and J. P. Xu. 1995. Wave and nearshore transport modelling Louisiana coast.

Coastal Morphodynamics Laboratory Technical Paper 95-5, 18 pp.

Stone, G.W., J. P. Xu, and X. P. Zhang. 1995. Wave climate modeling and evaluation relative

to sand mining on Ship Shoal, offshore Louisiana, for coastal and barrier island restoration.

U.S. Minerals Management Service International Transfer Meeting, New Orleans, LA.

(invited).

Xu, J.P., G. W. Stone and X. P. Zhang. 1995. Impacts of Ship Shoal on storm wave propagation

across south-central Louisiana shelf, a simulation study. Geological Society of America

Annual Meeting, New Orleans, LA.

Stone, G.W., J. P. Xu and X. P. Zhang. 1996. Ship shoal wave climate modeling and

evaluation. Transactions, International Transfer Meeting, Minerals Management Service,

Gulf of Mexico OCS Region, New Orleans, LA.

Stone, G.W., J. P. Xu and X. P. Zhang. 1996. Physical and environmental studies within the

Ship Shoal area: wave climate study and ongoing physical field study. U.S. Minerals

Management Service International Transfer meeting, New Orleans, LA. (invited).

Xu, J.P. and G. W. Stone. 1996. Wave climate modeling and evaluation relative to sand mining

on Ship Shoal, offshore Louisiana, for coastal barrier island restoration. American

Geophysical Spring Meeting, Baltimore, MD.

Zhang, X.P. and G. W. Stone. 1997. Application of GIS to assist in wave modeling for the

central Louisiana coast. 13th. Louisiana remote Sensing and Geographical Information

Systems Conference, Lafayette, LA.

Stone, G.W. 1998. The significance of frontal boundaries, tropical storms and hurricanes on the

morphodynamics of Gulf Coast barriers, USA.” International Coastal Symposium, West

Palm Beach, FL.

Stone, G.W. 1998. Numerical wave modeling of wave energy increases in Louisiana’s bays,

USA. International Coastal Symposium, West Palm Beach, FL.
5

Stone, G.W. and R. A. McBride. 1998. Louisiana barrier islands and their importance in

wetland protection: forecasting shoreline change and subsequent response of wave climate.

Journal of Coastal Research 14, 3, 900-916.

Stone, G.W., X. P. Zhang and P. Wang. 1998. Forecasting wave energy changes along the

Louisiana coast and implications for accelerated wetland loss. International Deltas

Symposium, New Orleans, LA.

Pepper, D.A., G. W. Stone and P. Wang. 1998. A preliminary assessment of wave and sediment

interaction on the Louisiana shoreface adjacent to Ship Shoal and the Isles Dernieres. Recent

Research in Coastal Louisiana, Lafayette, LA.

Zhang, X.P. and G. W. Stone. 1998. Development of a Louisiana coastal processes geographic

information system. Recent Research in Coastal Louisiana, Lafayette, LA.

Stone, G.W. and P. Wang. 1999. The importance of cyclogenesis on the short-term evolution of

Gulf Coast barriers. Transactions, Gulf Coast Association of Geological Societies,

Lafayette, LA., XLIX:478-486.

Pepper, D.A., G. W. Stone and P. Wang. 1999. Boundary layer parameters and sediment

transport on the Louisiana inner shelf during cold front passages. Transactions, Gulf Coast

Association of Geological Societies, Lafayette, LA., XLIX: 432-438

Stone, G.W. 1999. The importance of cyclogenesis on the short-term evolution of Gulf Coast

Barriers. Association of American Geographers, 95th Annual Meeting, Honolulu, HI.

Stone, G.W. 1999. Storm impacts and the importance of subsidence on the Mississippi River

delta plain, Louisiana, USA. 52nd NGMSO Lecture Series, University of Utrecht, The

Netherlands.

Stone, G.W. and P. Wang. 1999. The importance of cyclogenesis on the short-term evolution of

Gulf Coast barriers. Gulf Coast Association of Geological Societies, Lafayette, LA.

Stone, G.W., P. Wang and D. Pepper. 1999. Importance of hurricanes, tropical storms and midlatitude cyclones on the short-term evolution of Gulf Coast barriers: the impact of Hurricane

Camille: A Storm Impacts Symposium to Mark the 30 th Anniversary, New Orleans, LA.

Stone, G.W., P. Wang and X. P. Zhang. 1999. Development of a wave-current information

system for Louisiana bay-shelf environments. Estuarine Research Federation’99, 15th

Biennial International Conference, New Orleans, LA.

Pepper, D.A., G. W. Stone and P. Wang. 1999. Bottom boundary layer parameters and

predicted sediment transport. Association of American Geographers, 95th Annual Meeting,

Honolulu, HI.
6

Pepper, D.A., G. W. Stone and P. Wang. 1999. The significance of mid latitude cyclones on the

inner continental shelf of the northern Gulf of Mexico: the impact of Hurricane Camille: A

Storm Impacts Symposium to Mark the 30 th Anniversary, New Orleans, LA.

Pepper, D.A., G. W. Stone and P. Wang. 1999. Boundary layer parameters and sediment

transport on the Louisiana inner shelf during cold front passages. Gulf Coast Association of

Geological Societies, Lafayette, LA.

Pepper, D.A., G. W. Stone P. Wang. 2000 A preliminary assessment of wave, current, and

Sediment Interaction on the Louisiana Shoreface Adjacent to the Isles Dernieres. Estuaries

(in press).
7

2. CONCEPTUAL FRAMEWORK AND STUDY AREA

Introduction

The inner shelf is the region adjacent to the coast where the entire water column is

dominated by friction with the overlying air and the underlying seabed. In spatial terms, it lies

between the shoreline and the mid-continental shelf, with the surf zone as its most landward

portion. The dominant hydrodynamic variables that operate in this environment are infragravity

and wind waves, as well as currents generated by winds and tides. These hydrodynamic

influences jointly exert stress on the water column and seabed, causing sediment to be mobilized

and transported along the bed or in suspension (Kim et al., 1997). Given the importance of wind

as a forcing mechanism, it follows that the passage of atmospheric storms often results in

hydrodynamic responses, bottom boundary layer modification, and sediment transport on inner

shelves. Not surprisingly, therefore, field research has often demonstrated that storm events can

be responsible for transporting very large quantities of sediment in comparison with fair weather

conditions.

The general model for inner shelf sediment transport that has emerged is one in which

fair weather wave asymmetry gradually moves sediment onshore, while during storms, high

wave orbital currents suspend sediment that is then transported offshore by downwelling mean

flows (Wright et al, 1991; Nittrouer and Wright, 1994). Furthermore, it is commonly assumed

that alongshelf transport of suspended sediment during both fair weather and storm conditions is

much higher than across-shelf transport, owing to stronger mean flows in the alongshore

direction. Considerable deviation from these general models results, however, from variability

in meteorological conditions, local geology, bathymetry, and physical oceanography.

Additionally, a variety of complex and poorly-understood interactions and feedback mechanisms

operate in the bottom boundary layer. For example, while it is sometimes assumed that waves

provide the shear stress (or “stirring mechanism”) that entrains sediment that is then transported

by mean currents, recent research has demonstrated that waves and currents interact in a highly

non-linear fashion, complicating sediment transport predictions (Grant and Madsen, 1979, 1986).

The Louisiana inner shelf is an example of a low-energy environment where significant

hydrodynamic activity is generated almost exclusively by local storms, including both tropical

(summer) and extratropical (winter) storms. Furthermore, the Louisiana coast is somewhat

unique as a result of its high rates of subsidence and land loss. Bearing this uniqueness in mind,

however, the following paragraphs are intended to serve as a discussion of field research

conducted on inner shelves around the world, highlighting “typical” hydrodynamic, bottom

boundary layer and sedimentary responses to meteorological forcing, and the sources of

deviation from these usual responses.

A large proportion of research dealing with continental-shelf response to meteorological

forcing emphasizes the importance of storms in generating high bed stress due to the combined

effects of waves and currents, and causing large increases in sediment transport, which varies in

direction. Nittrouer and Wright (1994) state, for example, that sediment particles can be

transported tens of kilometers seaward during storms, in contrast to fair weather conditions,
8

when sediment transport may be landward, or may not occur at all. Lyne et al. (1990a, 1990b)

estimated that 91% of sediment transport along the mid-continental shelf of the U.S. Atlantic

coast occurs during storms due to strong bed stresses resulting from wave and current

interaction. Niedoroda and Swift (1981) and Niedoroda et al. (1984) stated that winter storm

activity provides an important contribution to the long-term retreat of the Long Island coast.

They observed offshore and alongshore transport as a result of the combination of high wave

energy and strong downwelling currents at the peak of a winter storm, while during the waning

phases of the storm, when upwelling occurred, the waves were generally too low to entrain

sediment. Fair weather periods were characterized by wave asymmetry that transported

sediment landward at depths shallower than 10m. In contrast, Vincent et al. (1983) suggested

that winter storms produce a net onshore bedload sediment transport in the same region,

accompanied by a shore-parallel transport of fine suspended sediment. The researchers did note,

however, that offshore transport components were measured during one winter storm, suggesting

that there may be considerable variability in transport direction, depending on the specific wind

conditions accompanying a storm.

Despite well-documented differences in oceanographic regime, the continental shelf of

the Pacific coast of North America seems to be characterized by similar storm-driven responses.

According to Cacchione and Drake (1990), over 50% of sediment transport in a one-year period

on the northern California inner shelf occurred during that year’s 20 stormiest days. The authors

propose that, during storms, sediment transport is predominantly offshore at depths less than

50m, as a result of strong wave activity combined with downwelling, and alongshore in deeper

water. They note that transport is almost always the result of an interaction between factors,

most often mean and wave-orbital flows. Finally, they point out that transport rates and

directions are strongly dependent upon the location and intensity of the storm, the regional

pattern of wind stress, the magnitude of sea-level setup and the bottom gradient. These results

were corroborated by Cacchione et al. (1994), who calculated that offshore transport on the same

shelf reached a maximum of 0.5 g cm-1s

-1 during an early-March storm event. Cacchione et al.

(1987) concluded that the repeated occurrence of winter storms on the California coast generates

high bottom stresses due to the combined effects of waves and currents, and that this is

ultimately an important factor in controlling the spatial distribution of bottom sediment.

Lynch et al (1996) showed that sediment transport was dominated by large storms during

an eight-week winter deployment in 90 m of water off the California shelf. Transport was

predominantly along-shelf, although offshore, and occasionally, onshore components were

recorded. Interestingly, although sediment concentrations of up to 0.75 g l-1 were measured,

these did not necessarily correlate with high transport rates, since high concentrations were

sometimes accompanied by weak mean currents. According to Gross et al. (1991), suspended

sediment concentrations of 0.030 g l-1 over the California shelf are caused by high orbital

velocities generated by winter storms, and as a result, 75% of the total annual sediment flux

occurred between December and March. The researchers observed statistically-significant

logarithmic current profiles, even under strong wave-orbital flows, and calculated apparent

bottom roughness (z0c) of up to 18 cm during winter storms. This was more than 25 times the

typical non-storm value, and appears to have been the result of wave-current interaction.

Similarly, Cacchione and Drake (1982) observed large increases in shear velocity and apparent
9

bottom roughness (maximum values of 6.9 cm s-1 and 8.6 cm, respectively) at a depth of 18 m on

the continental shelf of Alaska during a storm.

Research from Canada, New Zealand and the United Kingdom has also provided

important contributions to the understanding of storm-induced bottom boundary layer and

sedimentary processes. Li et al. (1997) measured two to threefold increases in shear stress, order

of magnitude increases in apparent bed roughness, and two to three order of magnitude increases

in sediment transport on eastern Canada’s Scotian shelf during storms. Although fair weather

sediment transport in the region is determined almost exclusively by tidal flows, the researchers

found that transport direction during storms was dependent on the direction of both waves and

wind-driven currents, and showed a high degree of inter- and intra-storm variability. Amos et al.

(1999) measured sediment transport maxima of 0.027 and 0.035 g cm-1s

-1 at a 22-m deep

location on the Scotian Shelf during two storms. Amos and Judge (1991) used the sediment

transport model SEDTRANS in combination with field data to predict sediment transport at

several sites on the eastern Canadian continental shelf. They concluded that long-term sediment

transport varies over a range of temporal scales. At one site, for example, transport was

dominated by storms of the longest return interval (32 yrs.) and would thus not be well predicted

using the patterns that occur during a “typical” winter storm. On the other hand, at more easterly

sites, transport appeared to be dominated by waves and wind-driven currents generated by storms

of a one-year return interval (a “typical” strong winter storm). Certain exceptions were noted in

channels, however, where tidal currents were shown to be the dominant long-term influence.

Manighetti and Carter (1999) described a complex system in the Hauraki Gulf, New Zealand, in

which sediment may be transported offshore at times, but remains in the shelf system as a result

of rotating tidal currents, until it is ultimately lost through an adjacent channel to deep water.

The authors stress that storms are the dominant agents of sediment transport in the region,

although the specific effect of an individual storm at a particular location is highly dependent

upon local coastal geography. Green et al (1995) discussed numerous responses to the passage

of a severe winter storm from a 25-m deep site on the macrotidal British North Sea shelf. They

found that apparent bed roughness and sediment transport was two orders of magnitude higher

during the storm than fair weather conditions. High suspended sediment concentrations resulted

from incident wave and wave group activity, although transport resulting from wave orbital

flows was minimal. Instead, net transport over the course of the storm was largely a result of

sediment being suspended by waves and transported off- and alongshore by steady wind-driven

flows that distorted the tidal ellipse.

A series of papers by Wright and others describes the influence of the passage of

“Northeasters” (extratropical storms) over the inner shelf of the Mid-Atlantic Bight in terms of

distinct storm phases, or in some cases, storm types. Wright et al. (1986) measured a net

seaward flux of suspended sediment accompanied by a bed level change of 15 cm in the Middle

Atlantic Bight during a single storm. Bed level response was characterized by four distinct

stages: 1) negligible response to an initial peak in wind and current speed and suspended

sediment concentration; 2) gradual erosion of the bed following this initial peak; 3) slow bed

accretion during the second and stronger peak of the storm; 4) rapid bed accretion during the

waning phases of the storm.
10

Madsen et al. (1993) and Wright et al. (1994) reported maximum suspended sediment

concentrations of 1 g l-1 within the lowest meter of the water column during a severe Northeaster.

Suspended sediment transport during this event was highly dependent on the phase of the storm.

During the storm’s main phase, sediment flux was seaward as a result of strong downwelling in

response to onshore winds. The later swell-dominated phase of the storm was characterized by

the deployment’s highest shear velocity as well as high suspended sediment concentration,

although only low onshore fluxes occurred, owing to the presence of weak mean flows. Kim et

al. (1997) characterized a Northeaster over the Mid-Atlantic Bight in terms of four phases: 1) an

initial calm period when non-diffusive sediment transport was confined to the thin wave

boundary layer (wbl); 2) the storm’s onset, when the wbl thickened dramatically and suspended

sediment transport increased; 3) the storm’s peak, when bed stress, wbl thickness, and suspended

sediment transport were at a maximum, causing the onset of sheet flow; and 4) the post-storm

phase, when suspended sediment transport was confined to the thick wbl, owing to low current

shear. Wright et al. (1991) summarized results from three years of field deployments in 7-17 m

water depths on the Middle Atlantic Bight. They found that measurable contributions to

sediment transport were made by mean flows, infragravity oscillations and incident waves.

During storms, downwelling mean flows caused sediment to be transported offshore, while

during fair weather and moderate energy conditions, mean currents transported sediment both

on- and offshore. During all conditions, incident waves were the primary source of shear stress,

and fluxes at both incident and infragravity frequencies were just as commonly onshore as

offshore. Xu and Wright (1998) identified two significantly different storm types and their

associated currents on the North Carolina shoreface. Southerly storms caused coastal set-down

and upwelling, while northeasterly storms were associated with coastal set-up and downwelling.

It is clear from this research that considerable variability may occur during various stages of an

individual storm as well as between different storms.

In addition to the complications to bottom boundary layer response and sediment

transport introduced by local differences in geographic, geological, oceanographic factors,

important influences are exerted by negative feedback, and other non-linear mechanisms, some

of which will be introduced briefly in this paragraph. Glenn and Grant (1987) demonstrated by

means of a sophisticated mathematical model that storms may result in enhanced turbulent

mixing owing to wave-current interaction, which can, in turn, cause a reduction in shear stress

owing to the stable stratification of the water column by suspended sediment. Bed armoring

occurs when sediment in size classes with a low critical entrainment stress is winnowed from the

bed, leaving a higher bed concentration of less-easily-entrained size-fractions. Sediment

stratification and bed armoring have both been shown to reduce sediment transport on the inner

shelf during high-suspension events such as storms (Lyne et al., 1990b; Wiberg et al., 1994).

The morphology of the bed is also an important factor influencing bottom boundary layer

parameters and sediment transport. Li et al. (1996) described feedback between bed forms and

suspended sediment transport during various meteorological conditions in the Middle Atlantic

Bight. They found that during fair weather, bed ripple roughness, shear stress, and the amount of

sediment suspended by vortices were directly related. During moderate storms, bed roughness

reached a “breakoff point” where it, and hence vortex activity, began to decline with increasing

shear stress. During severe storms, ripples on the bed were completely washed out, vortex

activity was eliminated, and sheet flow prevailed. These results are supported by subsequent
11

research at a 39-m deep location on the Canadian continental shelf by Li and Amos (1999).

They observed the disappearance of large wave ripples during the strong combined flows that

accompanied storm activity, and their subsequent re-formation as sediment fell out of suspension

following the peak of the storm. Vincent and Green (1990) demonstrated that wave vortices may

have somewhat unpredictable effects on sediment transport over a rippled bed on the tidedominated inner shelf of north Norfolk, U.K. Vortices were responsible for phase differences in

sediment concentration and flow at various levels above the bed. As a result, sediment transport

was onshore near the bed, slightly offshore between five and 10 cm above the bed, and onshore

higher in the water column. Boon et al. (1996) highlighted an interesting shallow-water (11.5 m)

phenomenon in which interacting wave trains of swell and sea frequencies in an estuary caused

an enhancement of sediment transport by a factor of 2-0.5. Clearly, therefore, bottom boundary

layer responses to hydrodynamic forcing are seldom simple and linear and researchers must be

cognizant of a variety of potentially complicated interactions.

Three general conclusions of the research discussed in the previous paragraphs are

evident. First, storm-induced transport is often so high that it dominates total long-term sediment

transport on a particular inner shelf, despite the fact that storm activity may account for only a

small fraction of time. Second, certain responses to storm conditions on the continental shelf are

fairly universal and are to some degree predictable. Common bottom boundary layer responses

include changes in bed form morphology and apparent bottom roughness, and increases in shear

velocity and suspended sediment concentration. Sediment transport rate during storms tends to

increase, while transport direction is largely determined by wave asymmetry, wind-driven flows,

and barotropic currents. Finally, hydrodynamic, bottom boundary layer and sedimentary

responses to storm events are extremely sensitive to the duration, intensity, track, and wind

structure of the storm as well as to the characteristics of the coastal environment itself, including

its geology, bathymetry, coastal orientation and physical oceanography. These responses are

further complicated by poorly-understood interactions between variables and complex negative

feedback mechanisms such as stratification and bed modification. Thus, the general model of

large off- and alongshore fluxes of sediment being generated by the passage of storms, while

useful, must be used with caution in the context of a specific inner-shelf site.

Conceptual Basis for the Research

It has been demonstrated in the preceding section that many issues regarding

hydrodynamic, bottom boundary layer, and sedimentary responses to meteorological forcing on

inner shelves are poorly understood. Further, it has been noted that the response of a particular

inner shelf environment is sensitive to a variety of local and regional factors. The inner shelf of

Louisiana is unique in comparison with many previously-studied oceanic shelves in that it is

exposed to a much lower mean level of hydrodynamic energy, it is dominated by higher

frequency waves, and it has a different orientation relative to mean and storm wind directions.

Furthermore, it is an important component of a system that is experiencing some of the highest

rates of land loss in the world. Finally, a submerged Holocene sand body (i.e. Ship Shoal) is a

conspicuous local bathymetric feature whose influence on hydrodynamics and sediment transport

is poorly understood. Thus, there are both theoretical and pragmatic reasons for this study.
12

Ultimately, the goal of this project is to describe and quantify hydrodynamic variables,

bottom boundary layer parameters, and directional sediment transport in the context of

meteorological forcing on the south-central Louisiana inner shelf in the vicinity of Ship Shoal.

Although many variables will be considered, particular emphasis will be placed upon wave

height and period, mean and orbital flow velocity, current and combined wave-current shear

velocity, and across-shelf (i.e. on and offshore) sediment transport. This project will address

these variables in the context of the following specific objectives:

1. To illustrate the episodic, storm-dominated, nature of the inner shelf in the region

during the winter by quantifying the differences between storms and fair weather.

2. To demonstrate the variability between individual storms with different meteorological

characteristics, and to suggest reasons for this variability.

3. To specify the differences between the seaward and landward sides of Ship Shoal,

thereby elucidating its influence on regional hydrodynamics and sediment transport.

4. To estimate the overall flux of sediment across Ship Shoal over a short time scale. This

will permit a quantitative evaluation of event-scale erosion, accretion and migration of

the shoal, and will allow forcing mechanisms to be identified and placed within the

context of the shoal’s long-term evolution.

5. To utilize the hydrodynamic measurements, primarily wave characteristics, to skill

assess the numerical wave model STWAVE.

The fulfillment of these objectives will provide a unique and useful evaluation of the influence of

both winter storms, which are arguably the most significant regional forcing mechanism, as well

as Ship Shoal, which is undoubtedly the region’s most prominent morpho-sedimentary feature.

Additionally, it is hoped that this analysis will enhance overall understanding of bottom

boundary layer, sediment transport and wave models for inner-shelf environments worldwide,

where research has been limited in both quantity and geographical coverage.

Study Area

The study area is located on the south-central Louisiana inner shelf, seaward of the Isles

Dernieres, in water depths of six to nine meters (Fig. 1.1). Two deployment sites were chosen so

as to occupy both the seaward and landward margins of Ship Shoal, the area’s most prominent

bathymetric feature. The co-ordinates of the seaward location (Site 1) are 28o 50.68’N, 91o

07.52’W, and those of the landward site (Site 2) are 28 o 55.74’ N, 91 o 01.73’W. This chapter

will discuss the specific characteristics of these study sites as well as provide a brief overview of

pertinent regional considerations.
13

Meteorology

A primary focus of this work is to investigate the influence of meteorological conditions,

and in particular, high-energy wind events (storms), on inner shelf processes in Louisiana.

Annually, average wind speed in coastal Louisiana is approximately 3 m s-1 from the southeast.

Since wind conditions vary considerably over the course of the year, however, storm climatology

is most conveniently represented by means of two “seasons”—a summer season lasting roughly

from April to November, and a winter season comprising the remainder of the year.

During the summer months, coastal Louisiana’s weather is dominated by Maritime

Tropical air masses centered over the Gulf of Mexico. This almost always results in uniformly

hot, humid, and calm weather, aside from localized convectional thunderstorm activity.

Infrequent but often very powerful tropical cyclones (tropical storms and hurricanes), do occur,

however, during this time. Tropical storms and hurricanes have made landfall on the Louisiana

coast during the past century once every 3.3 and 4.0 years, respectively, with the highest

frequency in September (Stone et al., 1997). Tropical cyclones can obviously be extremely

high-energy events; for example, sustained winds during Hurricane Camille, which struck the

Louisiana coast in 1969, were in excess of 100 m s-1 (Stone et al., 1997). The impact of such

storms on a particular section of coast, while potentially dramatic however, is highly variable,

and depends upon the intensity, duration, and track of the individual cyclone. Since no tropical

cyclones influenced the study area during the deployment period, however, no further discussion

of such events is included.

From approximately November to April, extratropical, or mid-latitude, meteorological

systems dominate coastal Louisiana’s weather. Since mid-latitude meteorology is controlled by

a complex interrelationship of air masses, cyclones, anticyclones and fronts, only a brief

overview is offered here, although more detailed references are abundant (e.g. Moran and

Morgan, 1994). Ultimately, extratropical storms are the result of Rossby waves generated by

heat transfer along the polar front, which forms the global boundary between tropical and polar

air masses (Henderson-Sellers and Robinson, 1986). Synoptic-scale storms are initiated along

this front through cyclogenesis, a regular sequence of events that commences when an area of

strong divergence in the upper atmosphere causes a drop in surface air pressure and the

formation of a low-pressure cell, or “Low” (Moran and Morgan, 1994). Clockwise, or cyclonic,

circulation develops around this Low, and the cyclone begins to migrate eastward. As this

occurs, the portion of the polar front to the east of the Low moves northward as a warm front,

while the portion to the west moves southward as a cold front. The process of cyclogenesis tends

to occur in particular geographic locations, and although there are several such source regions in

North America, the most important for coastal Louisiana are on the lee side of the Rocky

Mountains and in the western Gulf of Mexico (Chaney, 1999).

Since any portion of a mid-latitude system may impact the Louisiana coast during any

stage of development, the general term extratropical storm is used in this dissertation to include

all meteorological phenomena that originate in the mid-latitudes and generate high, sustained,

wind speeds for several hours. It should be noted, however, that other authors have used

different nomenclature to identify these events. For example, the terms cold front (Roberts, et

al., 1987, 1989; Chaney, 1999), cold air outbreak (Chuang and Wiseman, 1983), episodic
14

atmospheric forcing (Armbruster et al., 1997), Nor’easter (Wright et al., 1986), winter storm

(Drake and Cacchione, 1991) as well as mid-latitude, and extratropical cyclone, refer to

phenomena that are called extratropical storms in this dissertation.

Extratropical storms are extremely important meteorological forcing mechanisms in the

northern Gulf of Mexico. While they tend to be less intense than tropical storms, they are much

more frequent, occurring roughly 20 to 30 times per year, with a maximum frequency in January

(Roberts et al, 1987, 1989). Given their complex evolution and their spatial and temporal

variability, it is not surprising that individual extratropical storms that pass a particular location

may differ widely in terms of their meteorological characteristics. Wind speed may exceed 25 m

s

-1, as estimated for the “Storm of the Century” in 1993 (Chaney, 1999), but may be only slightly

above average for weaker events. Generally, extratropical storms are characterized by a

clockwise rotation of wind direction from the south to the north, with high wind speeds occurring

both prior to, and following the passage of the cold front (Chaney, 1999). This results in a

general shift from onshore to offshore winds along the coast of the Gulf of Mexico, unlike that

which occurs on the north-south aligned Atlantic or Pacific coasts, a factor which presumably

has implications for wave growth and propagation, current flow, and sediment transport.

Hydrodynamics and Bottom Boundary Layer Regime

The northern Gulf of Mexico is a microtidal environment characterized by low

hydrodynamic energy, except during storms (Penland et al., 1988; Wright, 1995; Jaffe et al.,

1997, Wright et al., 1997). Average significant deep-water wave height and peak period are

approximately 1 m and 5-6 s, respectively, while the dominant angle of wave approach is from

the southeast (Penland et al., 1988; Jaffe et al., 1997). Wave dissipation and refraction occur

across the shallow Louisiana shelf, however, modifying these parameters closer to shore (Stone

et al., 1995). Most notably, this causes a decrease in wave height. According to Ritchie and

Penland (1988) average wave height seaward of the Isles Dernieres (immediately landward of

the present study area) is only about 0.6 m. On the other hand, wave characteristics during

storms tend to be markedly different from those measured during fair weather. During winter

cold fronts, for example, significant wave heights of 2-3 m may occur (Dingler et al., 1993). A

typical, although variable, sequence of wave responses to these frontal passages includes the

propagation of high, long-period waves from offshore during the pre-frontal phase, followed by

the presence of sea-like conditions, with variable wave heights, periods and directions, during the

post-frontal phase (Roberts et al., 1987). Tropical storms and hurricanes generate a variety of

wave conditions depending upon their track and intensity, including waves several meters in

height and greater than ten seconds in period (Stone et al., 1997).

Tides in the study area are diurnal, with a tropic range of roughly 40 cm, resulting in only

weak tidal currents (Wright, 1995; Wright et al., 1997). On the other hand, storm surges

associated with wind-events play a significant, but highly variable, role in modulating sea level

over the shelf and in nearshore environments (Chuang and Wiseman, 1983; Biocourt et al.,

1998). For example, water level set-up along the coast may reach 0.9 m during extratropical

storms (Ritchie and Penland, 1988) and 7.0 m during hurricanes (Stone et al., 1997).
15

As would be expected from the hydrodynamic regime, only low-energy processes operate

the majority of the time in the bottom boundary layer of the Louisiana shelf (Wright, 1995;

Wright et al., 1997). Several field studies conducted on the mid- and outer shelf have indicated

that mean near-bottom flows and bed stresses are not strong enough to re-suspend sediment

during typical conditions (Adams et al, 1987; Halper and McGrail, 1988). Even on the inner

shelf, in depths of 15-20m, Wright et al. (1997) estimated a mean combined wave-current shear

velocity of less than 0.7 cm s-1, an apparent bottom roughness of 0.011-0.015 cm, and a mean

drag coefficient of 3.6 x 10-3, during fair weather conditions. They concluded that variations in

suspended particulate concentration are generally the result of the advection of sediment plumes

from nearby rivers. On the other hand, a few authors have evaluated field data with

mathematical models that suggest that bottom stress may be large enough to suspend bottom

sediment under certain conditions. For example, Crout and Hamiter (1979) analyzed pressure

transducer data from a 10-m deep location on the inner shelf of western Louisiana using the

model of Komar and Miller (1975), and estimated that summer storms, winter cold front

passages and southeasterly wind events during the spring could generate sufficient stress to

suspend bottom sediment. Jaffe et al.(1997) used the Glenn-Grant-Madsen model (Grant and

Madsen, 1979; Glenn and Grant, 1987) to predict sand resuspension on the shoreface adjacent to

the Isles Dernieres during a variety of conditions. They concluded that bottom stress would be

incapable suspending a significant amount of sediment except during storm conditions.

Specifically they emphasized that sediment transport rates on the Louisiana inner shelf during

normal fair weather conditions would be more than 103 times lower than during large storms,

such as major cold front passages, and more than 104

 times lower than during hurricanes. This

analysis indicated that extreme events are probably responsible for the vast majority of long-term

sediment transport in the region, even considering their relative infrequency. In summary,

therefore, the few studies conducted on the Louisiana shelf have indicated that its bottom

boundary layer is characterized by low hydraulic energy, except during storms, when bed

stresses may increase to a level capable of suspending and transporting bottom sediment.

Geology/Geomorphology

The geology of the Louisiana continental shelf is extremely complex, and also very well

documented. A comprehensive discussion, which would necessarily include features as diverse

as diapirs, salt domes, and any number of muddy, silty and sandy sedimentary structures, is

therefore clearly beyond the scope of this dissertation, although excellent reviews may be found

in Kolb and Van Lopik (1958), Scruton (1960), Frazier (1967) and Coleman et al. (1998).

The geology of the Louisiana inner shelf has been largely dominated during the past

several thousand years by the influence of the Mississippi River system and its associated delta

cycle (Scruton, 1960). This cycle consists of quasi-periodic delta-switching, which occurs

roughly every 1000 years, and smaller-scale switching associated with subdeltas, bayfills, and

crevasse splays, which occur with frequencies from hundreds of years to a few decades

(Coleman et al., 1998). During this cycle, coastal progradation of up to 100m yr-1 takes place

while a delta or lobe is active (regression). Following abandonment, the delta gradually becomes

submerged due to subsidence and the shoreline retreats (transgression). This cycle has created

an alternating succession of transgressive and regressive sedimentary features that dominate

Louisiana’s coastal geology. Only two areas of Louisiana’s coast, the Birdfoot and
16

Atchafalaya/Wax Lake Deltas, are presently experiencing the regression phase of this cycle,

while the majority of the coast, including the study area, is undergoing relative sea level rise at a

rate of roughly 1.0-1.1 cm yr-1 (Penland and Ramsey, 1990).

Ship Shoal is a sand body that is approximately 50 km long and 12 km wide at its western

end, where the minimum overlying water depth is 3 m. It is asymmetric in profile, with steep

landward slopes of 1:90 to 1:750 and shallower seaward slopes of 1:900 to 1:2,000 (Penland et

al., 1988). Penland et al. (1988) attempted to account for coastal features associated with deltaic

transgression in Louisiana in terms of a three-stage model that included the development of: 1.

an erosional headland with flanking barriers; 2. a transgressive barrier island arc; 3. an inner

shelf shoal. According to this classification, Ship Shoal is a typical stage 3 feature that formed

from the transgression and submergence of a former barrier shoreline, while the adjacent Isles

Dernieres chain is a transgressive barrier island arc (Penland et al., 1988). Bathymetric surveys

suggest that Ship Shoal is migrating landward across deposits from the abandoned Maringouin

Delta at a rate of between 15 m yr-1 in the west, and 7 m yr-1 in the east .

Unlike many of Louisiana’s coastal environments which are dominated by silt and mud,

bed sediment in the study area is clean quartz sand with a mean grain diameter of 0.12-0.13 mm.

Complete results of the analysis of bottom sediment from both study sites are shown in Figs.2.1

and 2.2.

Figure 2.1. Results of analysis of sediment from Site 1 (the Offshore site; see Fig. 3.3 for

location).

0

20

40

60

80

100

120

grain size (mm)

in-class %

cumulative %

 Folk& Ward Summary:

 -mean grain size: 0.12 mm

 (very fine sand)

 -very well sorted

 -coarse-skewed

 -leptokurtic
17

Figure 2.2. Results of analysis of sediment from Site 2 (the Inshore site; see Fig. 3.3 for

location).

Practical Concerns

The unique characteristics of the Louisiana coastal zone have been widely discussed in

the literature, including, but not limited to oceanographic, geological, ecological, geographical,

and policy-oriented sources. Obviously, relative sea level rise and coastal land loss are primary

concerns. One prominent proposal has been to artificially maintain the volume of eroding

offshore barrier island chains to act as a protective barrier against wave energy for the adjacent

coast. The possible means by which to do so include the implementation of hard structures, such

as breakwaters, and artificial nourishment using sediment from distant sources. Ship Shoal, with

its large quantity of clean, quartz sand, is considered a viable source for this sediment. With the

exception of an extensive numerical modeling effort of the wave field (Stone and Xu, 1996), the

shoal’s influence on waves, currents, bottom boundary layer dynamics, and sediment transport in

the region is largely unknown. Clearly, therefore, a knowledge of hydrodynamic and

sedimentary processes on the south-central Louisiana inner shelf is of great practical, as well as

theoretical concern.

0

20

40

60

80

100

120

grain size (mm)

in-class %

cumulative %

Folk & Ward Summary:

 -mean grain size: 0.13 mm

 (fine sand)

 -very well sorted

 -coarse-skewed

 -mesokurtic
19

3. METHODOLOGY

Instrumentation and Field Methods

The primary component of the field research was the deployment of instrumentation

during a period of several weeks, beginning November 24, 1998. Three bottom-mounted

instrumentation systems were used, two of which (Systems 1A and 1B) were deployed a few

meters away from each other at Site 1, while the other (System 2A) was deployed at Site 2.

System 2A was retrieved on January 12, 1999, and the others remained at Site 1 until February 2,

1999. Due to memory constraints, however, System 1A ceased logging on January 20, 1999.

During each deployment and retrieval, divers collected sediment from the bed, and water

samples from the water column, and observed and measured any visible bed forms. An

additional deployment occurred on February 9 through March 13, 2000. Data measured during

these and the previous deployments were used for model comparison and are evaluated later in

this report.

The instrumentation consisted of two types of frame-mounted system, both of which

included a self-contained data recorder module. The primary components of Systems 1A and 2A

(Fig.3.1) were SontekTM downward-looking Acoustic Doppler Velocimeters (ADV’s) that

measured seabed elevation, relative particulate concentration and 3-dimensional currents at an

elevation of 20 cm above the bed. System 1A was programmed to sample at 25 Hz, the

maximum rate achievable by the sensor, since such a high sampling rate had seldom, if ever,

been used in an inner-shelf environment (see Table 3.1, at the end of this section, for all

instrument sampling rates). Unfortunately, storage of these data necessitated that a burst

interval of only 81 seconds every three hours be used. It was thought that since System 1B was

deployed in the immediate vicinity, potential gains achieved by detecting high-frequency

turbulent fluctuations that had not previously been reported would outweigh losses incurred by

using a short burst interval. System 2A included a Paroscientific pressure sensor in addition to

the ADV, and was programmed to sample at 4Hz for 8.5 minutes every three hours. Systems 1A

and 2A included internal compasses and tilt and roll sensors to enable the rotation of directional

measurements into a planetary frame of reference.

System 1B was a unique multi-sensor package nicknamed WADMAS (Fig 3.2). It

consisted of a Paroscientific pressure sensor, a sonar altimeter, and a vertical array of three colocated Marsh-McBirney electromagnetic current meters and Seapoint optical backscatter

sensors (OBS’s). This instrumentation enabled WADMAS to measure water level, directional

wave parameters, and seabed elevation, as well as current velocity and suspended sediment

concentration at heights of 20, 60, and 100 cm above the seabed. To conserve battery power and

recorder memory, all of the sensors on WADMAS were programmed for burst-mode (i.e.

discontinuous) sampling. Specifically, the sonar altimeter collected one measurement every 15

minutes, while all other sensors sampled for 8.5 minutes per hour at a frequency of 4 Hz.
20

Figure 3.1. System 2A during deployment at Site 2. Key: (A) Acoustic Doppler Velocitmeter

(ADV); (B) Pressure Sensor; (C) Enclosed cylinder containing recorder module,

compass and power supply. System 1A was identical except that it did not include a

pressure sensor.

Figure 3.2. System 1B during deployment at Site 1. Key: (A) Stacked array of co-located

electromagnetic current meters and optical backscatter sensors; (B) Pressure Sensor;

(C) Water-tight cylinder containing recorder module, compass and power supply;

(D) Sonar altimeter.
21

 

Figure 3.3. Location of instrumentation sites at Ship Shoal; Site 1 = Offshore Station, Site 2 =

Inshore Station. An additional site (Middle Station) was established for the 2000

deployment.

Table 3.1: Sampling schemes used in data collection. * Note: Sampling scheme shown for the

meteorological station indicates GDIL1 data selected for use in this study, and not the

entire data set collected by NOAA, which was more comprehensive.

System Sensor/

Measurement

Hours

between

Bursts

Samples/

Burst

Burst

Duration

(min)

Rate (Hz)

1A (ADV) Pressure 3 2048 8.5 4

3-D Current 3 2048 8.5 4

Suspended Sediment

Concentration

3 2048 8.5 4

Bed Level 3 1 - -

2A (ADV) 3-D Current 3 2048 1.35 25

Suspended Sediment

Concentration

3 2048 1.35 25

Bed Level 3 1 - -

1B Pressure 1 2048 8.5 4

(WADMAS) Current 1 2048 8.5 4

OBS 1 2048 8.5 4

Sonar Altimeter 0.25 1 - -

GDIL1

(NOAA)

Wind 1* 1 10 -
22

Unlike many comparable instrumentation packages that have been deployed on inner

shelves, the systems used in this study are notable in that they do not employ a traditional tripod

or tetrapod-type frame design. Instead, sensors are supported by thinner, less-obtrusive metal

supports that allow them to remain separated from the heavy bottom-mounted frames. The intent

of this design was to minimize the interference of the equipment with the parameters being

measured; in particular. In particular design of System 1B allowed the sonar altimeter to measure

bed elevation at a distance of nearly 1 m from the bottom-mounted section of the frame, bed

level changes relative to it could, in certain cases, be localized effects, such as ripple migration,

that did not effect the entire instrument.

Hourly wind data for the deployment period were obtained from the National

Oceanographic and Atmospheric Administration (NOAA) station located on Grand Isle,

Louisiana at 29o27’ N, 89 o 96’ W (GDIL1). These measurements were supplemented by daily

national weather maps obtained from the National Weather Service, which were inspected

visually to verify the occurrence of cold front passages.

Laboratory Methods

Laboratory procedures for this project included two components: 1) instrument

calibration, testing and preparation, and 2) analysis of sediment and water samples from the field

site. All instrumentation was calibrated, prior to deployment, by the Coastal Studies Institute

Field Support Group in their testing facilities. Since optical backscatter sensors are more

sensitive to fine than to coarse sediment, while the reverse is true for acoustic systems,

appropriate field conversion factors were established using bottom sediment from the study sites.

This procedure consisted of exposing the sensors to a series of uniformly-stirred mixtures of

distilled water and known concentrations of field sediment. The voltage output from the sensors

was then related to the sediment concentration by using regression to fit a calibration curve to a

scatter-plot of these variables. Since the field data from the optical backscatter sensors were

ultimately found to be faulty, OBS calibration results will not be discussed. Field data from the

ADV’s appeared to be reliable, however, and as such, the electronic signal strength was

converted from the calibration curve obtained in the laboratory, which took the form:

C=7.20197 x 10-10 (10 0.043SS) (3.1)

 where C is the volumetric concentration of sediment and SS is the ADV signal strength.

Dry sieving at 0.25 φ intervals was conducted to determine the grain-size composition of the

samples of bottom sediment. The water samples, collected at the surface and at 0.5, 2 and 4 m

above the bed, were filtered through 0.7-m paper using a pump-operated filtration system, dried

in an oven at 60oC, and weighed to determine the sediment concentration.
23

Data Processing and Analytical Methods

Spectral Analysis

An initial discussion of spectral analysis is warranted since it played a prominent and

varied role in this project. Spectral plots of individual variables and cross-spectral plots of paired

variables were generated on several time-scales. In addition, plots of coherence and phase

spectra were derived from the cross-spectra of the paired variables. Generally speaking, the

purpose of spectral, or frequency-domain, representation is to identify periodicities (essentially

recurrence intervals) over which phenomena fluctuate. Power spectra indicate the frequency

ranges over which an individual variable fluctuates whereas cross-spectra do the same for the

cross-product of two variables. Coherence spectra illustrate, on a scale of 0 to 1, the correlation

between two variables at different frequencies, while phase spectra show the lead or lag of one

variable in relation to a second.

Spectral analysis generally involves the application of smoothing, segmenting, or

windowing techniques to increase the confidence level of the results. The Welch method, in

which a single data series is initially subdivided into several shorter segments with a specified

overlap length, was used in this study. A Hanning window was then applied to smooth these

series, and Fourier series expansion was used to convert these series from the frequency to the

time domain. Since spectral techniques have been applied in this project in situations where

sampling schemes and record lengths have varied widely, the details of analysis techniques are

summarized in Table 3.2.

Table 3.2: Segment, window, and overlap lengths used in spectral analysis.

System

Series

Length Samples Freqency

Segment

Length

Window

Length

Overlap

Length

1A 81 s 2048 25 Hz 256 256 128

1B & 2A 8.5 min 2048 4 Hz 256 256 128

1A 56 d 448 8 day-1 64 32 0

1B &

GDIL1 65.5 d 1574 24 day-1 256 256 128

2A 49 d 392 8 day-1 64 32 0

Directional Wave Processing

Directional wave parameters were calculated from the pressure and current-meter data by using a

spectral approach to generate the first five coefficients (a0, a1, b1, a2, and b2) of the directional

Fourier series (Earle et al., 1995). To compensate for the effect of depth attenuation, wavepressure and horizontal-velocity-amplitude correction factors (Rp and Ru, respectively) were

applied to the coefficients. These correction factors were calculated for each frequency (f) using:
24

 

Rp(f) = cosh[k(zd + d)]

cosh(kd) (3.2)

Ru(f) = cosh[k(zd + d)]

sinh(kd) (3.3)

where zd and d are the mean sensor and total water depths, and wave number (k) was calculated

iteratively using the dispersion equation:

 2 (2πf) = (gk)tanh(kd) (3.4)

The five Fourier coefficients were calculated by generating all possible combinations of

the cross-spectra (Cxy) of the pressure (p) and horizontal velocity components (uc and vc), and

using the following formulas:

 

a0(f) = Cpp(f)

Rp2 (f)p (3.5)

a1(f) = Cpuc (f)

Rp(f)Ru(f)(2pf)p (3.6) b1(f) = Cpvc (f)Rp(f)Ru(f)(2pf)p (3.7) 

a2(f) = (Cucuc (f) − Cvcvc (f))

Ru2(f)(2pf) 2 p (3.8) b2(f) = Cucvc (f)Ru2 (f)(2pf) 2 p (3.9) 

It should be noted that the correction factors Rp and Ru are frequency-dependent, and thus will

approach zero as the frequency increases. As such, a high-frequency “cut-off” value of 0.35 Hz

was selected in accordance with Long and Oltman-Shay (1991).

Mean and principal wave direction (Φ1 and Φ2) were calculated using:

Φ1 = arctan(b1/a1) (3.10)

and Φ2 = 0.5 arctan(b2/a2) (3.11)

These Cartesian directions were converted to geographical directions on the basis of the

instrument orientation measured by the compasses included on the systems.

Peak wave period (Tp) and significant wave height (Hmo) were calculated using the nondirectional wave spectrum, Czz, which is equal to the product of a0 and Β. Peak period is simply

the reciprocal of the spectral frequency at which the highest energy occurs (i.e. where Czz is the

highest). Significant wave height was computed from:

 

Hmo = 4.0 m0 (3.12)

where the zero moment of the non-directional spectrum (m0) is the summation of spectral energy

over the total number (Nb) of frequency bands of bandwidth df:

 

m0 = Czz( f )df n=1

Nb

∑ (3.13)
25

This calculation is commonly used in wave analysis, although it may yield estimates 5-10%

higher than the traditional definition of significant wave height (H1/3), calculated using the

highest one-third of the waves in the wave field (Longuet-Higgins, 1980).

Calculation of Bottom Boundary Layer Parameters and Sediment Transport

This section describes the procedures used to calculate bottom boundary layer parameters

and predict flow, sediment suspension and sediment transport. Since it is a lengthy and detailed

section, a few initial notes of explanation are warranted to clarify how each technique relates to

the overall structure of the research.

Two methods were used to calculate an initial value of shear velocity, depending on the

system used. Values from Systems 1A and 2A were obtained using the Reynolds Stress

technique (RS), while values from System 1B were calculated on the basis of the logarithmic

profile (LOG) method. Sediment transport was calculated using essentially three techniques,

called, for the purposes of this project, the Grant-Madsen-Rouse (GMR), the Meyer-Peter and

Muller (MPM), and the spectral cross-product (SCP) methods. The first two of these (GMR and

MPM) were based on the concept of shear velocity, while the SCP method was based on

instantaneous field measurements. It was assumed in this study that sediment transport could be

subdivided into bed and suspended load modes, as is very commonly done, despite the somewhat

arbitrary nature of this classification scheme (Davies and Li, 1997). Bed load is generally defined

as all sediment that maintains occasional contact with the bed, while moving horizontally at a

measurably slower rate than the flow, while suspended sediment is assumed to remain above the

bed at all times and to be transported horizontally at approximately the fluid velocity. In this

study, the MPM method was employed to calculate bed load transport, while the GMR and SCP

methods were used to calculate suspended sediment transport. Table 3.3 summarizes the methods

used to calculate shear velocity and sediment transport. Finally, although the relevant equations

in this section are presented sequentially, the reader should bear in mind that the actual physical

processes they represent are interrelated by feedback mechanisms, and therefore, calculations

were often performed iteratively.

Table 3.3: Summary of methods used to calculate shear velocity and sediment transport. *

Although all sensors were used to make these calculations, results from all sensors are

not necessarily presented.

Abbreviation Full Name System Mode Basis

Shear Velocity 

LOG Logarithmic profile 1B 

RS Reynolds stress 1A, 2A 

Sediment Transport 

GMR Grant-Madsen-Rouse All* suspended shear velocity

SCP Spectral cross product All* suspended sensor

MPM Meyer-Peter and Muller All* bed shear velocity
26

Bottom Boundary Layer (BBL Parameters

Two important parameters in bottom boundary layer modeling, particularly with respect

to sediment transport, are the apparent bottom roughness length, z0c and the shear velocity,

defined as u* = (τ/ρ)

0.5, where ∆ is the density of seawater (1.025 g cm-3), and τ is the shear

stress. Two approaches were used to calculate these parameters in this study. For System 1B

(WADMAS) data, velocity profiles were initially estimated from log-linear regression of the

burst-averaged current meter velocities (the “log-profile” method). Two conditions must be

satisfied for a profile to be considered logarithmic in a statistically significant sense: first, the

correlation coefficient (r2

) must be equal to or greater than 0.994 (Drake and Cacchione, 1992);

second, the variation in mean direction between current meters must be less than 20o. Shear

velocity and apparent bottom-roughness length were calculated for all logarithmic profiles using

the von Karman-Prandtl equation:

u(z)=u*/κ ln(z/z0c) (3.14)

where u(z) is the horizontal velocity at height z above the bed, and κ is von Karman’s constant

(0.4).

The Reynolds stress, or eddy correlation, technique was used to estimate bottom

boundary layer parameters from the ADV data (Systems 1A and 2A). The total horizontal and

vertical velocities (u and w) were represented as the sum of mean (u ), periodic ( or w up or wp),

and turbulent (u’ or w’) components:

 u =u + up + u’ (3.15)

and w = w + wp + w’ (3.16)

which is based on the assumption that turbulent and mean velocities are uncorrelated at all

frequencies. The turbulent velocity was isolated by subtracting the periodic (wave-orbital)

velocity component from the total-velocity-power spectrum (Green, 1992). To do so, wave

orbital velocity was defined as the portion of the velocity spectrum (PUU) that was coherent with

pressure:

 Puwuw (f) = γ 2

Up (f)PUU (f) (3.17)

where Puwuw is the wave-driven component of the velocity spectrum and γ

2Up is the coherence

between pressure and velocity (note that the same was done for the vertical, w, component).

Obviously, this also has the effect of removing any turbulence that is coherent with pressure,

including wave-induced secondary flows. Although such flows were not directly observed during

this study, they may have been present at certain times. However, it is assumed that their

influence can be neglected in calculating shear stress and bed roughness, since these parameters

are based on diffusive, rather than convective processes. When measurements are taken in the

constant stress layer, shear velocity is defined as:
27

u* = − u' w' (3.18)

Bottom roughness was calculated by applying these results to Equation 3.14.

The Combined Effect of Waves and Currents

Numerous field studies have demonstrated that the superimposition of waves and currents

enhances bottom shear stress and apparent bottom roughness (Wiberg and Smith, 1983;

Cacchione et al., 1987; Lyne et al., 1990a; Drake and Cacchione, 1992; Kim et al., 1997). Wavecurrent interaction is a highly non-linear and poorly-understood phenomenon, and various

approaches have been applied to model it. According to Dyer and Soulsby (1988) the following

four categories of models are commonly applied in combined wave and current situations: 1.

Prescribed mixing-length distribution; 2. Prescribed eddy viscosity distribution; 3. Momentum

deficit integral; 4. Turbulent kinetic-energy closure. These model categories differ widely not

only in their assumptions and inputs, but also in the results they may produce. Since a field

comparison of these model-types, not to mention all available models themselves, would

constitute a project unto itself, the Grant-Madsen model (1979, 1986) was used in this study,

owing to its widespread familiarity and high level of empirical verification (Cacchione et al.,

1987; Lyne et al., 1990a). According to the model, a wave boundary layer (wbl) of thickness (dw)

develops during wave activity and the velocity profile is defined separately within and above this

layer as:

where 

 

uc = u*c

k

u*c

u*cw



   



   ln z

z0

 , z ≤ dw (3.19)

uc = u*c

k

ln z

z0c

 , z ≥ dw (3.20)

u*c and u*cw are the current-, and combined wave-current-induced shear velocities. z0 is the

roughness produced by the sand grains, defined as D/30, where D is the mean grain diameter,

and z0c is the apparent bottom roughness experienced by the current above the wave boundary

layer. Wave boundary layer thickness is defined by the equation:

δw = n u*cw ω (3.21)

 where n has a value of 1-2, depending upon the reference, and ω is the wave radian frequency,

2π/Tp. Apparent bottom roughness, z0c, is used because the current experiences drag due to the

combined influences of physical elements (grain roughness and bed forms) as well as non-linear

interaction with the wave boundary and mobile bedload layers (Gross et al., 1992). Equation

3.14 was used to determine u*c and z0c, and u*cw was calculated using an iterative procedure

involving the following equations:

u*cw = u*wm [1+ 2(u*c/u*wm )

2

cosf + (u*c /u*wm )

4

]

1/4 = CRu*wm (3.22)

where u*wm is the wave shear velocity, φ is the acute angle between the waves and the current

(waves were considered to be bi-directional, thus ! φ ≤ 90 ), and CR is a coefficient initially

assumed to equal one. A wave friction factor (fw) was then defined through:
28

 

u*wm = CR fw / 2ub (3.23) 

and 1

4 fw

+ log 1

4 fw

= log CRub

z0ω

 

 

  

 

 

  −1.65 + 0.24(4 fw ) (3.24)

where ub is the maximum near-bottom orbital velocity per wave period.

The current-induced shear velocity, u*c, was assumed to act in the same direction as the

mean current, while the direction of u*cw was expected to oscillate during the course of the wave

cycle. When the wave orbital velocity was at a minimum (near zero) the direction of u*cw was the

same as that of the current; when it was at its maximum, its direction (φmax) was between the

wave and current directions, specified by (modified from Cacchione et al., 1994):

φ max = arctan

sinφ

cosφ + ub

u



 

    



 

    

 (3.25)

Obviously, the direction of u*cw has implications for sediment transport within the wave

boundary layer, which will be discussed in greater detail in a subsequent section.

Sediment Suspension, Flow Stratification, and Bed Armoring

Sediment transport occurs when the shear stress (τ) exerted by the fluid on grains of sizeclass n, exceeds the critical shear stress (τncrit) required to initiate sediment motion. In practice,

determination of the critical shear stress of seabed sediment is problematic, as a result of three

general factors outlined by Drake and Cacchione (1986). First, the grain-size distribution of shelf

sediment may be quite broad, although this is not the case for the study area. Second, the

presence of even a small fraction of clay-sized sediment may cause cohesiveness, which

increases τncrit. Finally, benthic organisms exert a significant, but poorly understood, influence on

the properties of bed sediment. Not surprisingly, various methods may be used to determine τncrit

under combined flows, including the modified Yalin method, which was used in this study

following (Li et al., 1996). The Yalin parameter (Ξ n) is defined by:

Ξ = [(ρs − ρ)gD3

/ ρν2]

0.5 (3.26)

where ρs and ρ are the densities of sediment (2.65 gcm-3) and seawater (1.025 g cm-3), D is the

grain diameter, and < is the kinematic fluid viscosity (0.013 cm2 s

-1). The Yalin parameter was

first used to calculate a critical Shield’s criterion (2crit), and then τcrit using:

logθcrit = 0.041(logΞ)

2 − 0.356logΞ − 0.977 (3.27)

and

τ crit = θcrit(ρs − ρ)gD (3.28)
29

Critical shear velocity was then simply calculated by: u*crit = (τcriτ/ρ)

1/2. An additional parameter

to be used in this study was the normalized excess shear stress (S’):

 

S ' = τ −τ crit

τ crit



 

  



 

  (3.29)

where τ is the observed shear stress.

The sediment suspension profile over a sandy bottom was shown by Lynch et al. (1997)

to be well represented by the standard Rouse equation, even under combined wave and current

flows. This profile is the result of a balance between the upward-diffusive and downward-settling

fluxes of sediment. It is represented by:

C(z) = C (za ) z

za



 

  



 

  

−α

, where α = γws

κu*

 (3.30)

C(za) is the reference concentration at height za, γ is the ratio of the eddy diffusivity of sediment

to that of momentum (~1), and ws is the sediment fall velocity. These equations are based on the

somewhat vaguely defined concept of a reference concentration of sediment near the bed. The

concentration C(za) is commonly defined by the equation from Glenn and Grant (1987):

 

C(z) = C (za ) z

za



 

  



 

  

−α

, where α = γws

κu*

 (3.31)

where Cbed is the sediment concentration in the bed (0.65 for the sum of all size classes) and γ0 is

an empirical constant with a value of approximately 1.3 x 10-4.

Under certain conditions, suspended sediment may cause the water column to become

stable-stratified, increasing the vertical velocity gradient, but inhibiting the upward diffusion of

mass and momentum (Smith and McLean, 1977; Adams and Weatherly, 1981; Glenn and Grant,

1987; Huntley et al., 1994). Some authors have suggested that this phenomenon should be

represented numerically by modifying von Karman’s constant (Adams and Weatherly, 1981;

Gust and Southard, 1983). The more common approach, however, as was used in this study, is to

apply a stratification correction to the velocity profile based on the predicted sediment

concentration. As suggested by Glenn and Grant (1987), it was applied only above the wave

boundary layer and took the form:

uz = u*c

κ



    [ln(z / z0 ) + βz/ L] (3.32)

where is an empirical constant with a suggested value of 4.7 (Glenn and Grant, 1987), and L is

the Monin-Obukhov length scale, defined by:

 

L = u*c

3

zkg((rs − r)/r)wsC (3.33).

Bed armoring occurs when sediment in size classes with a low critical entrainment stress is

winnowed from the bed, leaving a higher bed concentration of less-easily-entrained size-
30

fractions. This phenomenon, which serves as a negative feedback mechanism for sediment

transport, has been observed on the inner shelf during high-suspension events such as storms

(Lyne et al., 1990b; Wiberg et al., 1994). Its possible effect was included in the analysis by

incorporating the mixing-depth limitation ( mix) suggested by Green et al. (1990):

δmix =2.5 S’/(ρs-ρ)g (3.34)

Sediment Transport

Suspended sediment transport is represented mathematically by time- and depthintegrating the product of the horizontal velocity and suspended sediment concentration. As

simple as this may seem, it is a very complex problem in combined-flow regimes, owing to

phase differences in velocity and concentration, and the possible occurrence of secondary flows

including ejected vortices (Agrawal and Aubrey, 1992; Osborne and Greenwood, 1993; Davies,

1995). As a result, the time-scale chosen for this integration procedure is of great importance. In

fact, Osborne and Vincent (1996) indicated that not only may the magnitude of transport vary on

the basis of averaging period, but in some cases the direction may be completely reversed. On

the other hand, the use of instantaneous measurements is problematic, since the time scales of

velocity- and suspended-sediment-profile development are different (Davidson et al., 1993).

Lesht (1979) and Shauer (1987), for example, recommend scales of several minutes for the

establishment of logarithmic velocity profiles. As such, two approaches were employed in this

study, the first based on time-averaged values and the second on instantaneous field

measurements.

The first technique, which was earlier labeled the GMR approach, was to multiply the

burst-averaged velocity and concentration profiles as calculated on the basis of the shear

velocity. This approach has often been employed in wave-dominated environments (e.g. Vincent

et al., 1983; Kim et al., 1996) despite the fact that it assumes temporally-uniform values, a

condition that may not be satisfied during unsteady oscillatory flow. The profiles were integrated

both within and above the wave boundary layer using:

 

Qsn = 1

t

uCn dzdt

0

t

∫ z= dw

z =h

∫ for z > δ w (3.35) 

Qsn = 1

t

uCn dzdt

0

t

∫ z = z0

z= dw

∫ for z < δ w (3.36)

where η is the sea surface elevation.

The cross-product of instantaneous values (i.e. every 0.04 s or 0.25s) of velocity and

concentration from Systems 1 and 2A were also used to calculate suspended sediment transport.

This had the advantage of accounting for time-varying effects of waves on the sediment

suspension and velocity profiles as well as allowing transport to be analyzed according to

frequency components. However, quantitative assessments were made less reliable since it was

necessary to assume (very simplistically) that the mean sediment concentration and flow velocity

throughout the water column were equal to the burst-averaged values measured at the sensor.
31

Bed load transport rate (Qbl) was calculated by using the combined wave-current shear stress as

an input the empirical formula of Meyer-Peter and Muller (1948) as adapted by Wiberg et al.

(1994):

 

Qbl = 8

(t − t

crit )

3/2

(rs − r)g (3.37)

The direction of bedload transport under the combined flow of waves and currents is as

yet an inadequately resolved issue. Cacchione et al. (1994) assumed that bedload transport would

occur in same direction as that of the maximum shear stress ( max) within the wbl. Although this

seems to be a somewhat simplistic assumption since the direction of stress may vary up to 180o

over the course of a wave cycle, these workers were able to reasonably represent observed trends

of bed form migration. As such, this method was adopted for this study.

A wide variety of methods have been presented in this section, many of which involve

important assumptions that have not necessarily been well tested in the field. All have a solid

grounding in the literature, however, and as will be apparent in later sections, the trends they

produce are similar in most instances. Nonetheless, the choice of the most reliable method must,

to some degree, be left to the discretion of the reader.
33

4. METEOROLOGICAL CONDITIONS DURING THE DEPLOYMENTS

Classification Systems for Meteorological Events

One objective of this project was to differentiate between various meteorological

conditions that occurred during the study period and to associate these with hydrodynamic,

bottom boundary layer and sedimentary responses. It is useful, therefore, to establish a

classification system by which to characterize atmospheric conditions, specifically those related

to winter extratropical storms and fair weather in the northern Gulf of Mexico. Numerous

classification schemes have been proposed to categorize atmospheric conditions in a variety of

environments—however, since meteorological processes are inherently complicated, these are of

necessity based on criteria that suit a particular purpose. Depending on the requirements of a

specific study, for example, a classification scheme may be based on local atmospheric

measurements, on synoptic or global-scale atmospheric circulation, or on the effect of

atmospheric forcing on some aspect of the physical or human environment. The system

employed in this project was ultimately designed to differentiate between: (1) fair weather and

storm conditions; (2) different phases of extratropical storms; (3) extratropical storms of different

intensities and synoptic types. As such, it draws upon several classification systems suggested in

the literature, as well as criteria specific to the research, and employs both hourly wind velocity

data and daily national weather maps.

Storm magnitude scales, such as the Saffir-Simpson scale for hurricanes and the Fujita

scale for tornadoes, are a fairly simple and familiar type of meteorological classification system

based largely on wind speed and barometric pressure. Although magnitude scales for

extratropical storms are somewhat less familiar, several have been proposed. One example is the

Northeast storm scale of Halsey (1986), who ranked storms in the Atlantic qualitatively, on the

basis of their effect (damage potential) on coastal beaches. More recently, Dolan and Davis

(1992a, 1992b) suggested a scale for Atlantic coast Northeast storms (Nor’easters) that was also

based on coastal damage potential, but included, in addition, a quantitative index of storm power

calculated using the square of the significant wave height times the duration of the storm. Hsu

(1993) proposed a classification system for extratropical cyclones in the Gulf of Mexico. This

scale is based on the minimum central pressure of a Gulf cyclone and the predicted maximum

wind speed, and is thus more fundamental than the scales proposed for Atlantic storms. Chaney

(1999) used a simple measure of magnitude for Gulf Coast storms known as the V square value,

which is based on the sum of the squares of the hourly wind velocity during a storm event, thus

incorporating the influence of both storm wind duration and speed.

Synoptic-scale classification systems have also been applied to the meteorology of the

northern Gulf of Mexico. Notably, Muller (1977) subdivided New Orleans weather into eight

synoptic types that included both storms and fair weather. Roberts et al. (1987) identified two

end member types of extratropical storms in coastal Louisiana: the migrating cyclone,

characterized by the passage of a cold front aligned oblique to the coast, and the arctic surge, in

which a front is aligned parallel to the coast. Chaney (1999) subdivided characteristic synoptic

weather patterns responsible for extratropical storms over the northern Gulf of Mexico into seven

categories: 1) Primary Front (P) 2) Secondary Front (S) 3) Secondary Gulf Front (SG) 4)
34

Secondary Gulf Low (SL) 5) Gulf Front (GF) 6) Gulf Low (GL) and 7) Primary Low (PL). The

first two of these were found to account for approximately 90% of storm activity along the

northern Gulf of Mexico.

The “cold front cycle” has commonly been used to characterize the sequence of events

that accompanies a “typical” extratropical storm passage (e.g., Roberts et al., 1987; Roberts et

al., 1989; Armbruster et al., 1997; Chaney 1999). Initially a pre-frontal phase occurs during

which, strong, warm, moist winds blow from the southerly quadrant. The ensuing frontal phase

is characterized by a sudden drop in air pressure, erratic winds, and short-lived, but occasionally

intense, squalls. Finally, a post-frontal phase occurs, during which temperature and humidity

drop, air pressure rises, and winds are strong and northwesterly to northeasterly. It should be

noted, however, that this sequence, although considered typical, exhibits considerable variability.

This will become apparent in the discussion of the data from this study.

Analysis of Meteorological Events During the Deployment

Aspects from several of the sources discussed above were used to characterize

extratropical storms during the study period. Since wind velocity is a critical meteorological

variable in coastal systems, the onset of storm conditions was considered to occur when a

threshold wind speed was exceeded. The value assigned to this threshold was 7.4 m s-1, which

was equal to one standard deviation above the mean speed for the study period. The end of the

storm was identified as the hour that wind speeds fell, and subsequently remained, below the

threshold for six hours or more. Wind direction was also analyzed to identify phases of

extratropical storm passages that corresponded to the cold front cycle described in the previous

paragraph. Pre-frontal storm winds were defined as those that blew from a direction between 90

and 270o

 and appeared, from weather maps, to occur prior to a cold front passage. The postfrontal phase included the period subsequent to the frontal passage when storm winds blew from

a direction between 270 and 90o. All other wind conditions were considered fair weather.

Furthermore, storms were classified on the basis of intensity and synoptic characteristics

according to several of the classification systems discussed earlier.

Meteorological Summary of the Deployment

Wind speed during the deployment averaged 4.8 m s-1 and had a mean direction toward

the Southwest (228o). It is important to note that the oceanographic and not the meteorological

convention is used for wind direction in this project; thus, the stated direction indicates the

direction toward which the wind was blowing. Hourly wind speed and direction for the

deployment period are shown in Figures 4.1 and 4.2. These figures clearly demonstrate the

increases in wind speed characteristic of extratropical storms, as well as the clockwise rotation of

wind direction during their passage.

Spectral analysis of the wind speed over the 61-day deployment period shows a

statistically significant peak in energy at a frequency of roughly every five days, or

approximately the same as that of extratropical storm passages (Fig.4.3). This suggests that

extratropical storms were responsible for most of the variability in wind speed during this time, a
35

result consistent with other published research for the northern Gulf of Mexico (e.g. Chuang and

Wiseman, 1983).

Figure 4.1. Wind speed during the deployment period. The time of the cold front passages

associated with extratropical storms is indicated by black arrows.

 

Figure 4.2. Feather plot of hourly wind vectors during the deployment.

0

2

4

6

8

10

12

14

16
According to the quantitative definition outlined previously, nine storms occurred during

the 61-day deployment, a frequency of one every 6.8 days. Mean wind speed and direction were

8.1 m s-1 and 174o during storms and 3.8 m s-1 and 293o during fair weather. On the whole,

therefore, storms during the period were characterized by strong winds blowing toward the

south, while the mean wind direction during fair weather was westerly.

Figure 4.3. Power spectrum of wind speed during the deployment. C.I. represents the 90%

confidence interval.

Classification of the storms that occurred during this study, using the described

previously, is shown in Table 4.1. Several results are evident. First, analysis of the synoptic

types associated with storms indicates that the majority of cold fronts affecting the coast were

aligned oblique to it (i.e. the migrating cyclone of Roberts, et al., 1989). Six of the nine storms

were classified as the Primary front type described by Chaney (1999), while an additional two

were of the Secondary Front type. Despite the sequence of atmospheric events that “typically”

accompany cold front passages, strong, winds did not often blow toward the north during this

study, and as such, only two storms were considered to have a notable pre-frontal phase at all.

On the other hand, all storms had a marked post-frontal phase during which strong winds blew

from north to south.

Clearly, there was considerable variation in the intensity of storm events, with maximum

wind speeds varying by as much as a factor of two, Power V varying by nearly an order of

magnitude and Dolan and Davis values ranging by more than two orders of magnitude. Storms 3

and 5 were particularly weak, while Storms 2, 4, 7, and 9, and especially, Storm 6, were

energetic. This is an important factor to bear in mind, since it will be demonstrated in later

sections that the relative strength of storms is a key element in determining their influence on the

marine environment.

0

20

40

60

80

100

120

140

frequency (cycles/day)

period (day)

wind

C.I. Peak Period = 5.3 days
37

Table 4.1. Classification of storms during the deployment on the basis of the methods

discussed. In all cases, rank is based on a five-point scale.

Storm

Number

Month/

Dayhour Orientation

Maximum

Velocity

(south)

Maximum

Velocity

(north)

Type

(Chaney

1999)

V2 (h ms-1)

[Rank]

Hsu

Rank

Dolan &

Davis

[Rank] Stages

1 12/8-18 Oblique 5.3 11.0 P 2402[2] 2 18[1] Post

2

12/12-

17 Perp. 5.8 10.5 SG 4106[3] 1 31[1] Post

3

12/17-

13 Oblique 6.1 9.1 P 774[1] 1 1[1] Post

4

12/22-

14 Oblique 8.2 13.8 P 4779[3] 2 40[1] Post

5

12/29-

12 Parallel 7.5 8.5 S 1224[1] 1 3[1] Post

6 1/2-22 Perp. 11.5 14.5 S 5712[4] 2 52[1] All

7 1/9-12 Oblique 5.8 15.1 P 3392[3] 3 27[1] Post

8 1/14-20 Oblique 6.3 9.7 P 852[1] 1 8[1] Post

9 1/23-13 Perp. 10.0 10.7 P 3616[3] 1 178[3] All

All - - 7.4 11.4 - 2984 - 40 -

According to both the Dolan and Davis and Hsu scales, storms that occurred during this

study tended to be weak with only one Rank 3 event taking place during the deployment in each

case (Table 4.1). There are several reasons for this. Magnitude of the Dolan and Davis scale was

based on measured wave height in the Atlantic, which would presumably be much greater than in

the Gulf of Mexico as a result of regional oceanographic considerations. The Hsu scale was

based on the maximum wind speed calculated from the lowest central pressure of a Low in the

Gulf of Mexico, whereas this study employs the maximum wind speed at a particular location.

Clearly, site-specific wind measurements would be lower unless the Low passed directly over the

study area.

The Power V rating (Chaney, 1999) appears to have been the most useful representation

of storm intensity for present purposes. Unlike the system used in this study, however, where a

value of one standard deviation above the mean was used to define storms, Chaney included all

winds that exceeded the mean. According to this classification, three storms were weak (Rank 1),

five were moderate to significant (Rank 2-3), while only Storm 6 was severe (Rank 4). These

results should be noted by the reader, since Power V classifications will be often referenced

during later sections of this project to differentiate between the storms that occurred during the

deployment.
39

5. HYDRODYNAMICS, BOTTOM BOUNDARY LAYER PARAMETERS

AND SEDIMENT TRANSPORT DURING

THE ENTIRE DEPLOYMENT PERIOD:

TIME- AND FREQUENCY-DOMAIN ANALYSIS

AND OVERALL SUMMARY

Introduction

Long-term measurements in the bottom boundary layer of inner shelves are fairly rare,

and published results are often confined to a single storm. Furthermore, as discussed previously,

the only research conducted in coastal Louisiana that employed a similar methodology to the

present study consisted of two summer deployments devoid of appreciable storm activity

(Wright et al., 1997). Thus, an important objective of this research is to summarize prevailing

winter hydrodynamic, bottom boundary layer, and sediment transport patterns in the region, thus

helping to establish a “climate” from which regularities may be drawn in the future. This section

will therefore focus on the results of the entire deployment by means of general summaries, as

well as time-series and spectral (frequency domain) representations. Although the connection

between atmospheric forcing mechanisms and marine and sedimentary processes will become

evident, more detailed representations of these linkages are reserved for later sections.

Initial Considerations: Field Observations

Divers characterized the bed at the field sites as being largely free of bed forms during

both the emplacement and retrieval stages of the deployment. While they did report bed

irregularities with an estimated height of 1 cm during the emplacement phase, these were

apparently localized, non-periodic, and were thus not likely the result of organized wave or

current activity. Unfortunately, it was unrealistic for divers to monitor the bed throughout the

duration of the deployment, owing to obvious logistical, financial, and, most importantly,

environmental limitations. Video camera surveillance was also impossible as a result of

extremely poor visibility. Therefore, the assumption adopted during this project is that the bed at

the study sites was essentially flat (i.e. free of bed forms), unless data from bed level sensors

suggested otherwise.

The initial trip to the field sites to retrieve all instrumentation occurred on January 12,

1999. Diver recognizance revealed that all systems, which had initially rested on the bed, were

submerged beneath at least 20 cm of sediment, impeding their safe return to the research vessel.

Only System 2A, located at the nearshore site and submerged to a lesser extent than the two

offshore systems, was retrieved that day. Several subsequent attempts were made to recover the

systems at Site 1, and eventually, on February 2, 1999, both were successfully retrieved. The

sedimentary material overlying the instrumentation upon recovery was fine sand, similar to

typical bed sediment in the study area. Although the cause of the burial of the systems was

unclear at the time, two hypotheses were considered for further investigation: (1) overlying

deposition of sediment (i.e., bed level increase) or (2) scouring or sinkage of the instruments into

the bed (i.e. sensor level decrease).
40

Figure 5.1. Bed elevation and water level (smoothed using a 24-h moving average window), as

measured by System 1B during the deployment. Storms are indicated with black

arrows, as will be the case in subsequent figures.

Recorded data from all systems were used to investigate these hypotheses. Since results

from all systems were similar, data from only System 1B, specifically, bed level (relative to the

sonar altimeter) and water depth (to the pressure sensor) will be considered in this section. Time

series of these data are shown in Fig. 5.1. One important, but probably safe, assumption that

should be noted was that the instrumentation system moved as a contiguous unit (i.e. it did not

warp or bend), and thus the relative location of the sensors was constant. Although large short

term-fluctuations (which will be discussed later) are evident in the time series of bed level,

overall, it corroborates the field observations, indicating a total increase of approximately 20 cm

during the deployment. Unfortunately, this trend is not particularly enlightening by itself since it

could be a result of either hypothesized mechanism. Specifically, deposition of sediment would

cause the bed to move closer to the (fixed) sensor, whereas downward motion of the entire

instrument through sinkage or scour would cause the sensor to move closer to the (fixed) bed.

However, the pressure gauge also enabled the distance from the system to the sea surface

to be quantified. There is no reason to believe that the water level at the site increased over the

course of the deployment, beyond obvious short-term fluctuations due to tides and wind forcing.

This is supported by NOAA data from Grand Isle (GDIL1), which indicated little change in

water level between the beginning and end of the deployment period for the research. The time

series of 24-h moving average water level at System 1B, however, did indicate a 20-cm increase

during the period, and was strikingly similar to the time-series of bed level. Therefore, when the

sum of the depth to the sensor and the distance from the sensor to the bed (i.e. the total water

depth) was considered, no appreciable long-term trend over the course of the deployment was

evident (Fig.5.2). Thus, it would appear that there was probably no appreciable long-term

8.4

8.45

8.5

8.55

8.6

8.65

8.7

8.75

8.8

8.85

8.9

8.95

9

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

storm

depth

bed level
41

change in bed level at the sites, but instead, a downward displacement of the instruments relative

to it. All calculations of water level or total depth were therefore corrected for the influence of

deployment-length instrument level change.

Figure 5.2. Total water depth (to the bed) measured hourly and smoothed using a 24-h moving

average window as measured by System 1B.

Two possible causes for the downward displacement of the instruments were suggested

previously: in-place sinkage; and scouring and immediate re-deposition of sediment around the

instruments’ bases, likely as a result of energetic wave-orbital currents. The second of these

possibilities is far more likely, for two reasons. First, sinkage appears somewhat implausible,

since the frames of the systems were wide and stable and the seabed in the study area was flat

and sandy. Second, the vertical motion of the instruments was highly episodic, suggesting the

importance of forcing mechanisms that vary considerably over time, such as hydrodynamic

processes. Sinkage, driven essentially by the constant force of gravity, would be expected to be

relatively steady over time. It appears, therefore, that scour was an important factor around the

bases of the instruments. However, it is important to point out that flow modification and scour

does not appear (with a few exceptions to be noted) to have influenced the sensors themselves,

which were separated by tens of centimeters from the heaviest, most-intrustive, parts of the

instrument frames.

As noted previously, short-term fluctuations of the bed level, both up and down, appear

in the deployment record. Unlike episodic deposition of sediment, which can be interpreted from

the data record as either bed or instrument displacement, decreases in bed elevation are less

ambiguous to interpret since sediment cannot plausibly accumulate under the bases of

instrumentation systems. Low rates of episodic bed erosion must have occurred locally beneath

the bed sensors. It appears, therefore, that in addition to the movement of the systems

themselves, short-term bed fluctuations in bed level caused by erosion and accretion occurred

8.2

8.4

8.6

8.8

9

9.2

9.4

9.6
42

during the deployment, suggesting that sedimentary processes during the winter are quite

dynamic at these sites.

Hydrodynamics

An overall summary of hydrodynamic parameters for the entire deployment is shown in

Table 5.1. Important points to note include the total depth, which was 1.5-2 m deeper offshore

(Site 1) than nearshore (Site 2), and the depth range, which was slightly more than 1 m at both

sites. Significant wave height and wave orbital velocity were higher at Site 1 than at Site 2, by

36 and 18 %, respectively, which is consistent with the expectation that waves crossing Ship

Shoal are attenuated as a result of depth-limited energy dissipation. Wave period was also higher

at the offshore site, which likely reflects the reduced importance of northward-propagating longperiod swell waves, also due to attenuation, relative to locally generated sea.

Table 5.1. Summary of hydrodynamic parameters recorded by the systems throughout the

deployment. It should be noted (as discussed previously) that the final recording dates

of the instruments were different and that the sensors on System 1A were buried for

several hours during the deployment.

Location 

Site 1

(Offshore)

Site 2

(Nearshore)

System Statistic 1A (ADV) 1B (WADMAS) 2A (ADV)

Total Depth (m) Mean 8.8 9.0 7.3

Minimum 8.2 8.4 6.7

Maximum 9.2 9.5 7.8

Hs (m) Mean n/a 0.61 0.45

Minimum n/a 0.07 0.10

Maximum n/a 2.80 1.53

Tp (s) Mean n/a 5.3 5.0

Minimum n/a 3.6 3.6

Maximum n/a 9.1 9.1

Orbital Velocity Mean 11.7 10.6 9.9

(cm s-1) Minimum 2.6 0.8 0.0

Maximum 35.9 53.1 36.5

Current Speed Mean 5.8 4.6 6.3

(cm s-1) Minimum 0.1 0.1 0.0

(~0.3m above bed) Maximum 44.8 34.2 47.6

Current Speed Mean 12.4 8.0 13.9

(cm s-1) Minimum 0.1 0.1 0.0

(~1m above bed) Maximum 72.4 53.2 62.3

Current Direction Mean 245 240 292

In contrast to the somewhat predictable differences in wave parameters between sites,

current velocity differences, while equally evident, were less expected and in some senses, less

explicable. Interestingly, for example, the inter-site comparison in wave energy described above
43

was reversed in the case of current energy, with mean current speed being approximately 10%

higher at Site 2 (nearshore) than Site 1 (offshore). Current direction had a strong westerly

component at both sites, which is consistent with general trends suggested in previous research.

More notably, however, the across shelf component was seaward at the offshore site and

landward at the nearshore site (Fig 5.3). Since the two sites are separated by only a few

kilometers and are thus influenced by nearly equivalent atmospheric and tidal forcing

mechanisms, this was apparently the result of flow modulation by the bathymetry associated with

Ship Shoal. The reasons for this are not entirely clear, although one likely possibility is that

westward flowing currents are steered downslope by gravity when they encounter the shallow

shoal, thus resulting in an onshore flow to the north and an offshore flow to the south.

Unfortunately, it is difficult to verify the cause of the observed behavior from the available data

set, although preliminary results from a more recent deployment that included an instrument

located in the center of the shoal suggest that this interpretation is correct. Nonetheless, it is

clear that Ship Shoal exerts a measurable influence on mean current flow that requires further

quantification.

Figure 5.3. Across-shelf current flow during the deployment at Sites 1 and 2 (at ~20 cm above

the bed) as measured by Systems 1A and 2A.

Obviously, Ship Shoal has an important effect on regional hydrodynamics, an influence

that is presumably also significant on any inner shelf that includes submerged sand bodies or

other prominent bathymetric features. Furthermore, this has important implications for bottom

boundary layer dynamics and sediment transport on the south-central Louisiana inner shelf, a

point that will be discussed further in subsequent sections of this project. Time-series plots

clearly illustrate the importance of storms in generating episodic increases in hydrodynamic

energy, as well as the differences in hydrodynamic response between the study sites. Figures 5.4

and 5.5 show wave parameters at the offshore and nearshore sites, highlighting not only the

differences between storms and fair weather, but also the changes in wave characteristics caused

by Ship Shoal.

-50

-40

-30

-20

-10

0

10

20

30

40

Onshore Site1 Site2 storm

Offshore
44

Figure 5.4. Significant wave height (Hs) at Site 1 and Site 2.

Figure 5.5. Peak wave period (Tp) at Site 1 and Site 2.

Figures 5.6 and 5.7 illustrate mean current and wave orbital speed at Sites 1 and 2,

respectively, revealing several regularities. First, dramatic increases in both mean and wavedriven flow tended to accompany storms, particularly during Storm 6. Second, although mean

and orbital current speeds were similar overall, each attained a relatively higher level at Three

peaks in wave height are particularly noteworthy, two associated with Storms 6 and 9,

respectively, and the other occurring during the fairly brief interval between Storms 3 and 4 (it

0

0.5

1

1.5

2

2.5

3

Site 1

Site 2

storm

0

1

2

3

4

5

6

7

8

9

10

Site 1 Site 2 storm
45

should be noted that later sections will demonstrate that Storm 4 was responsible for the majority

of the observed response, and as such, this interval will be referred to as Storm 4 for the

remainder of this section). Significant wave height during these storms was several times the

mean fair weather value and was clearly higher at Site 1 (offshore) than at Site 2 (nearshore),

supporting the conclusion that Ship Shoal is responsible for measurable wave energy attenuation.

Trends in peak wave period are not especially clear from the time series, although it appears to

have fluctuated in a temporally similar manner at the two sites. As such, it will be considered

further in later sections. different times during the deployment, apparently as a result of

meteorological forcing mechanisms. For example, while wave orbital flows were dominant at

both sites during Storm 4, comparatively stronger mean currents accompanied Storm 6. The

situation therefore contrasts both with surf zones, where orbital flows are nearly always

dominant, and outer continental shelves, where mean currents are expected to be much more

important than orbital flows. This highlights the uncertainty inherent in the study of sediment

transport on the inner continental shelf, since either of these hydrodynamic forcing mechanisms

may dominate depending on a complex interaction of a variety of geographical and

oceanographic factors. The near parity between these hydrodynamic mechanisms also has clear

implications for sediment suspension, which is thought to be closely related to wave orbital flow,

and suspended sediment transport, which, requires the presence of a mean current (in addition,

of course, to the presence of suspended sediment).

Figure 5.6. Flow speed of mean (Ua) and orbital (Ub) currents at Site 1.

0

10

20

30

40

50

60

Ua

Ub

storm
46

Figure 5.7. Flow speed of mean (Ua) and orbital (Ub) currents at Site 2.

Frequency-domain analysis shows the important time-scales over which across-shelf

mean currents fluctuated. Figure 5.8 is a spectral plot of current speed during the deployment.

Several statistically significant peaks are evident. The highest (i.e. most energetic) peak is at a

period of 5.3-10.7 days, which reflects the importance of quasi-periodic extratropical storm

passages in generating currents in the area. The next-highest peak occurs at a period of

approximately 24 hours, illustrating the influence of diurnal tides, and possibly inertial currents,

a phenomenon that will be discussed in more detail in subsequent sections. A minor peak is also

evident at 12 hours, equivalent to that of the lunar tide, which is known to be much less

important than the diurnal tidal signal, given the diurnal tidal regime in the area.

frequency (cycles/day)

Figure 5.8. Spectrum of current speed at Site 1.

0

5

10

15

20

25

30

35

40

45

50

Ua

Ub

storm

0

100

200

300

400

500

600

700

period (day)

current

C.I. Peak Period = 10.7 days
47

Figure 5.9 is a vector plot of near-bed current velocity at Site 1. Although the figures

clearly indicate that currents rotated during the deployment, the expected time-scales of 5-10

days, reflecting the influence of extratropical storms, and 24 hours, indicating the presence of

tidal currents, are difficult to visualize. On the other hand, detailed inspection of the figures

suggests that wind and near-bottom current generally moved in the same direction, presumably

as a result of direct wind stress on the water column. This is supported by cross-spectral

analysis. Figure 5.10 shows that a statistically-significant, positive, peak between across-shelf

winds and currents was present at periods of 5-10 days (the extratropical storm band) while the

phase spectrum indicates that there was little or no phase difference between these variables (Fig.

5.11). In other words, northerly winds were coincident with northerly currents, and southerly

winds were coincident with southerly currents, with extratropical storms providing the major

energy input. The same relationship appears to be true of along-shelf winds and along-shelf

currents, although the cross-spectrum was not statistically significant over most frequencies.

Cross-spectra of winds and currents at 90o

 to each other did suggest possible Ekman effects at

storm frequencies farther out on the shelf, however these results were not statistically significant.

 

Figure 5.9. Vector plot of mean current direction at Site 1 during the deployment.

These results are somewhat puzzling since most research, as discussed previously,

indicates that onshore storm winds normally generate coastal set-up which causes downwelling

(offshore) mean flows near the bottom, while the reverse is true for offshore winds. Clearly, on

the basis of mass conservation and an impenetrable coastal boundary, either return bottom flow

or spatially-variable along-shelf flow are necessary if across-shelf currents are to flow in the

same direction for an extended period of time. Inertial currents, which result when a wind

blowing steadily in one direction ceases (Pond and Pickard, 1983), are a possible explanation for

the observed behavior. Such currents continue to flow despite the removal of a forcing

mechanism, with their direction and intensity modified by Coriolis force and friction. Daddio
48

(1977) stated that his study site in south-central Louisiana was sufficiently far from the coast (25

km) for the effect of sea surface slope (i.e. set up) to be negligible. Instead, Coriolis-driven

inertial currents, which rotated clockwise with a period of approximately 24 h, accompanied

frontal passages. This effect was enhanced where sudden removal of onshore wind forcing

released sea surface set-up. It is possible that the near-bottom currents measured in the present

study were at least partially the result of this effect, and not exclusively a product of direct wind

forcing. Unfortunately, the lack of on-site wind data preclude a more detailed analysis of causal

mechanisms. Despite this, the sequence of mean flow patterns that accompanied extratropical

storm passages was distinctive, and has clear implications for inner-shelf sediment transport.

Figure 5.10. Cross spectrum of wind and across-shelf current at Site 1.

Figure 5.11. Phase spectrum of northerly wind and northerly current at Site 1.

-4

-3

-2

-1

0

1

2

3

4

frequency (cycles/day)

period (day)

 Phase at Max. C^2 = 0 h

-100

0

100

200

300

400

500

frequency (cycles/day)

period (day)

current

C.I.

 Peak Period = 5.3 days
49

Bottom Boundary Layer Parameters

As outlined previously, several methods were used to calculate bottom boundary layer

parameters, depending at least partially on the instrumentation used. In this case, results from the

Reynolds Stress (RS) method are shown. Although the values computed using this method are

probably higher than those derived using other means, magnitudes during storm and fair weather

conditions and between sites are useful for comparative purposes.

Not surprisingly, episodic increases in current- and wave-current shear velocity were

associated with storm activity (Figs. 5.12 and 5.13). Shear velocity was particularly high during

the period of strong wave-orbital flow between Storms 3 and 4, as well as during Storm 6, when

mean flows were particularly strong. The interval of very high shear velocity that accompanied

Storm 8 is somewhat difficult to explain, however, given that neither mean nor orbital currents

were especially strong. As discussed previously, however, shear velocity is a complex parameter

that is related not only to the flow, but also to non-linear wave and current interaction, physical

bottom roughness and sediment transport. It is notable, in light of these considerations, that

Storm 8 was, in fact, characterized by a particularly high apparent bottom roughness value,

which could account for the high shear velocity values. Trends in other bottom boundary layer

parameters, such as bottom roughness, drag coefficient and wave friction factor were

unfortunately not particularly clear from time series representations. As such, their discussion is

reserved for later sections.

Figure 5.12. Current and combined wave-current shear velocity as measured at Site 1.

0

2

4

6

8

10

12

14

u*c 

u*cw

storm

N.D.
50

Figure 5.13. Current and combined wave-current shear velocity as measured at Site 2.

Sediment Suspension and Transport

Suspended sediment concentration at each site is shown in Figs. 5.14 and 5.15. It is clear

that sediment suspension was episodic, and increased dramatically as a result of extratropical

storm influences. At Site 1, Storm 4 and, to a lesser degree, Storm 6, had the highest measured

concentrations, while at Site 2, the maximum concentration clearly occurred during Storm 6.

Maximum concentrations were slightly higher at the offshore than the nearshore location,

possibly as a result of the higher waves that occurred there during the majority of the

deployment.

Figure 5.14. Suspended sediment concentration at Site 1 (System 1A).

0

2

4

6

8

10

12

14

Date

u*c

u*cw

storm

0

0.5

1

1.5

2

2.5
51

Figure 5.15. Suspended sediment concentration at Site 2 (System 2A).

Sediment transport was episodic and storm-driven at both locations and in both the

across-shore and along-shore directions (Figs. 5.16-5.19). Enhancement due to storms was much

more dramatic than for hydrodynamic parameters or shear velocity, for two reasons: first,

sediment suspension is subject to a threshold value, below which transport is zero; and second,

sediment transport, depending on how it is calculated, is subject to a power law, such that

increases in flow velocity lead to exponential increases in transport.

Figure 5.16. Across-shelf longshore sediment transport for Site 2 as predicted using the

GMR method.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

-15

-10

-5

0

5

10

15

20

Onshore

Offshore
52

Figure 5.17. Along-shelf cross-shore sediment transport for Site 2 as predicted using the

GMR method.

Figure 5.18. Across-shelf cross-shore sediment transport for Site 1 (System 1A) as predicted

using the GMR method.

-20

-15

-10

-5

0

5

10

15

20

25

30

East

West

-15

-10

-5

0

5

10

Offshore

Onshore

N/D
53

Figure 5.19. Along-shelf longshore sediment transport for Site 1 (System 1A) as predicted using

the GMR method.

Figure 5.20. Longshore long-shelf bed and suspended load sediment transport for Site 1 (System

1A) as predicted using the MPM and SCC methods (respectively).

-15

-10

-5

0

5

10

15

East

West

N/D

-40

-30

-20

-10

0

10

20

30

40

50

60

70

suspended

bed

storm

West

East
54

Figure 5.21. Cross-shore across-shelf bed and suspended load sediment transport for Site 1

(System 1A) as predicted using the MPM and SCC methods (respectively).

Figure 5.22. Cross-shore across-shelf bed load and suspended load transport for Site 2, as

predicted using the MPM and SCC methods (respectively).

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

suspended

bed

storm

Offshore

Onshore

-60

-50

-40

-30

-20

-10

0

10

20

suspended 

bed

storm

Onshore

Offshore
55

Figure 5.23. Longshore along-shelf bed load and suspended load transport for Site 2, as

predicted using the MPM and SCC methods (respectively).

Four high sediment transport events are notable from Figs. 5.16-5.23, which show

transport as predicted using the Grant-Madsen-Rouse (GMR), Meyer-Peter and Muller (MPM),

and steady current concentration (SCC) methods. Results predicted using other methods were

similar, and are thus not presented. High rates of sediment transport were generally associated

with storms, specifically Storms 2, 4, 6 and 7. Sediment transport direction varied considerably

between storms as well as during individual storms. Two of the most significant storms (4 and

6), were characterized by opposing trends in sediment transport direction—while onshore and

eastward (i.e., NE) transport dominated during Storm 4, offshore and westward (i.e., SW)

transport dominated during Storm 6. Within these storms, transport direction fluctuated by 180o

on a very short time scale (i.e. several times per storm). This may have been related to diurnal

fluctuations resulting from either tidal or inertial current flow, or to other variations in relative

wave and current energy and direction. This question clearly requires further investigation.

This section has demonstrated several basic ideas. First, and most fundamentally, winter

hydrodynamic, bottom boundary layer and sedimentary responses on the inner shelf of Louisiana

are episodic, and are closely associated with extratropical storm passages. Second, these

responses are highly dependent upon the characteristics of a particular storm. Finally, responses

are variable over the course of individual storms, although the causes of this are not known.

Clearly, these are complex issues that must be addressed through further research.

-20

-15

-10

-5

0

5

10

15

20

25

Date

suspended

bed

East storm

West
57

6. COMPARISON OF HYDRODYNAMICS, BOTTOM BOUNDARY

LAYER PARAMETERS AND SEDIMENT TRANSPORT DURING

STORMS AND FAIR WEATHER CONDITIONS

As noted in the introduction, coastal scientists have often used the distinction between

storms and fair weather as an informative and convenient means by which to categorize

hydrodynamic, bottom boundary layer and sediment transport regimes in a variety of

environments. Although this approach is limited by the fact that it neglects both the various

phases of individual storms and to some degree, the differences between storms, it can provide a

basis by which to evaluate the long-term impact of atmospheric forcing, particularly if a long

data record is available. Since several storms, with a variety of characteristics, occurred during

this deployment, it appears to have been representative of a wide range of typical winter

conditions in coastal Louisiana. This section is therefore devoted to quantifying the magnitude

and variability associated with storms and fair weather conditions on the Louisiana coast.

Storm and Fair Weather Hydrodynamics

Hydrodynamic variables that exert direct influences on the bottom boundary layer and

ultimately, on inner shelf sediment transport, include wave height and period, near-bed orbital

velocity, and mean current velocity. These are summarized for Sites 1 and 2 in Tables 6.1 and

6.2, respectively. At both sites, hydrodynamic conditions during an average storm clearly

differed from those that occurred during fair weather. As expected, wave height and current

speed (both mean and oscillatory) generally increased during storms, while peak wave period

decreased, presumably as a result of sea-like conditions that were generated by sudden increases

in wind speed. Mean current direction at both sites was southwesterly during storms, and thus

had an offshore component, although this is much more pronounced at Site 1 Fair- weather

current direction was very close to westerly at the offshore site, while it was north-northwesterly

at the nearshore site, indicating a strong onshore component. Some storms were clearly very

energetic, and were characterized by hydrodynamic indices many times in excess of average fair

weather conditions. Notably, however, there was considerable variability between storms. In the

case of meteorologically weak events, such as Storm 3, and to some extent, Storm 5, waves and

currents were actually less energetic than during typical fair weather conditions. Another notable

point is that, even during powerful storms, wave and current hydrodynamic characteristics were

not necessarily proportionately high—in other words, high waves and strong mean flows were

not necessarily concurrent. For example, while waves at the offshore site during Storm 9 were

more than twice as high as they were during Storm 1, mean current speed was measurably

weaker. It is clear therefore, that while storms were usually responsible for generating

comparatively high-energy hydrodynamic conditions, there was considerable variation between

storms.
58

Table 6.1. Summary of storm and fair weather hydrodynamic measurements taken at Site 1

using System 1B (WADMAS). Hs is significant wave height, Tp is peak wave

period, and Ub is orbital velocity, while Top, Mid, and Bot refer to the current

meter velocity at heights of 100, 60 and 20 cm, respectively.

 Waves Currents

Meterology Hs (m) Tp (s) Ub(cms-1) Top (cms-1) Mid (cms-1) Bot (cms-1) Direction

Storm 1 0.73 4.40 13.5 17.0 15.5 11.1 237

Storm 2 0.69 4.89 12.4 11.7 10.0 5.4 132

Storm 3 0.29 3.77 5.3 4.8 4.2 3.3 346

Storm 4 0.76 3.98 11.7 16.8 15.9 8.8 231

Storm 5 0.33 3.87 6.0 12.1 11.1 7.6 219

Storm 6 0.84 4.81 14.0 13.8 13.1 8.5 167

Storm 7 0.98 4.07 14.3 15.4 15.0 10.0 214

Storm 8 0.67 5.22 10.6 7.5 7.0 4.5 205

Storm 9 1.81 8.31 34.1 14.1 11.9 6.6 49

All Storms 0.87 4.99 15.1 13.7 12.5 7.7 210

Fair Weather 0.52 5.46 9.1 6.2 5.3 3.6 260

Table 6.2: Summary of storm and fair weather hydrodynamic measurements taken at Site 2 using

System 2A. The U100 measurement is the current speed at 100 cm estimated using the

Logarithmic profile method.

 Waves Currents

Meteorology Hs (m) Tp (s) Ub(cms-1) U(cms-1) U100(cms-1) Direction

Storm 1 0.53 3.85 12.3 9.9 16.0 288

Storm 2 0.73 5.35 15.8 9.4 21.6 146

Storm 3 0.24 3.56 5.6 7.4 11.7 301

Storm 4 0.59 3.87 12.2 11.2 20.1 274

Storm 5 0.23 3.95 6.3 8.7 12.8 191

Storm 6 0.62 4.70 13.2 10.6 22.0 173

Storm 7 0.73 3.75 14.0 12.8 24.4 262

All Storms 0.57 4.27 12.3 10.3 19.5 250

Fair

Weather 0.42 5.25 9.2 5.1 12.1 335

Bottom Boundary Layer Parameters

Hydrodynamic differences between storms and fair weather obviously lead to differences

in the bottom boundary layer regime, and these were evident at the deployment sites, as shown in

Tables 6.3-6.5. Overall, current- and wave-current shear velocity were appreciably higher during

storms than during fair weather, indicating an increased potential for sediment entrainment and

transport during high-energy events. However, there was considerable variation between

individual storms, largely as a result of their meteorological and hydrodynamic intensity. Not
59

surprisingly, the more powerful storms, such as Storms 2, 4, 6 and 7, were characterized by high

shear velocity values.

Table 6.3. Summary of bottom boundary layer parameters (current, and wave-current, shear

velocity, apparent bottom roughness, R-squared, wave friction factor, 100-cm drag

coefficient, and wave boundary layer thickness) calculated based on the System 1B

(WADMAS) data for storms and fair weather conditions. 

 u*c(cms-1) u*cw(cms-1) Zoc(cm) r

2 fw CD100 WBL(cm)

Storm 1 1.50 2.63 2.21 0.9716 0.0374 0.0114 1.41

Storm 2 1.61 2.42 5.73 0.9539 0.0404 0.0143 1.62

Storm 3 0.41 0.67 1.25 0.9676 0.0222 0.0157 0.31

Storm 4 1.54 2.62 1.36 0.9394 0.0455 0.0063 1.34

Storm 5 1.02 1.62 2.02 0.9301 0.0558 0.0135 0.81

Storm 6 1.36 2.31 2.89 0.8473 0.0328 0.0139 1.55

Storm 7 1.42 2.27 1.69 0.9102 0.0309 0.0072 1.15

Storm 8 0.85 1.38 3.72 0.7956 0.0230 0.0170 0.86

Storm 9 1.64 3.06 3.84 0.9714 0.0161 0.0106 3.25

All Storms 1.41 2.37 3.00 0.9233 0.0352 0.0116 1.60

Fair Weather 0.64 1.09 3.23 0.8485 0.0276 0.0217 0.76

Table 6.4. Summary of bottom boundary layer parameters (current, and wave-current, shear

velocity, apparent bottom roughness, wave friction factor, 100-cm drag coefficient,

and wave boundary layer thickness) calculated based on data from the offshore ADV

(System 1A) for storms and fair weather conditions.

 u*c (cms-1) u*cw (cms-1) Zoc(cm) fw CD100 WBL(cm)

Storm 1 1.20 2.18 2.19 0.0326 0.0094 1.18

Storm 2 1.89 3.08 3.11 0.0298 0.0094 1.96

Storm 3 0.45 0.73 5.91 0.0350 0.0463 0.36

Storm 4 1.06 1.54 0.69 0.0261 0.0048 0.70

Storm 5 0.89 1.56 0.97 0.0338 0.0074 0.77

Storm 6 2.03 3.21 1.52 0.0284 0.0052 2.00

Storm 7 2.43 3.66 1.05 0.0378 0.0031 1.83

Storm 8 2.28 3.61 3.25 0.0489 0.0061 2.54

All Storms 1.66 2.67 2.11 0.0326 0.0090 1.58

Fair Weather 1.12 1.87 3.22 0.0311 0.0182 1.33

Current shear velocity was in excess of 1.5 cm s-1, and combined wave-current shear velocity

exceeded 3.0 cm s-1, during these events as calculated using the Reynolds Stress (RS) method.

On the other hand, the shear velocity during Storm 3 and Storm 5 were weaker than during fair

weather at two of the systems. Coefficient of determination (r2

) estimates, obtained by applying

log-linear regression to the stacked current meter data from System 1B, were used to evaluate the

degree to which flows were characterized by a well-organized logarithmic structure. Values
60

were generally higher during storms than during fair weather as has been reported previously for

extratropical storm passages (Pepper et al., 1999). An increase in the statistical significance of

logarithmic flow profiles did not always accompany strong currents, however, as illustrated by

Storm 6, which was characterized by r2 values similar to those during fair weather (~0.85). This

was caused by a few extremely low r2 values that occurred during the waning phases of the

storm, when apparent bottom roughness (z0c) was very high (10-15cm). However, the reason for

these large z0c values during the final hours of the storm is unknown.

Table 6.5. Summary of bottom boundary layer parameters (current, and wave-current, shear

velocity, apparent bottom roughness, wave friction factor, 100-cm drag coefficient,

and wave boundary layer thickness) calculated based on the System 2A (ADV) data

for storms and fair weather conditions at Site 2.

 u*c (cm s-1) u*cw (cm s-1) Z0c (cm) fw CD100 WBL(cm)

Storm 1 1.58 2.70 3.42 0.0462 0.0079 1.33

Storm 2 3.03 4.40 6.75 0.0383 0.0085 3.15

Storm 3 1.04 1.45 2.15 0.0540 0.0116 0.66

Storm 4 1.97 3.12 2.23 0.0557 0.0057 1.55

Storm 5 0.86 1.38 0.72 0.0457 0.0063 0.71

Storm 6 2.46 3.88 3.15 0.0472 0.0065 2.80

Storm 7 2.34 3.46 1.56 0.0490 0.0041 1.66

All Storms 2.08 3.22 3.10 0.0481 0.0068 1.95

Fair Weather 1.58 2.50 5.76 0.0447 0.0168 1.76

Apparent bottom roughness (z0c) decreased during storm activity, in most cases, when

values were generally less than 3.0 cm, as compared with mean fair weather values of 3.0-6.0

cm. Increased values were also observed, however, during some high-energy events, such as

Storm 2. Drag coefficients at 100 cm above the bed (CD100) decreased during storms, when mean

values were near 0.01, roughly half the mean fair weather value, likely as a result of the

decreased bottom roughness. The response of these factors to storm activity is thought to be a

function of bed form changes during the deployment, as described previously by several authors

(e.g., Amos et al, 1999). It is possible that during prolonged fair weather periods, wave ripples

eventually formed, increasing the physical roughness of the bed, while high energy conditions

caused bed forms to be washed out. Unfortunately, the limited observations made of the bed

during this study neither confirm nor disprove this, and as such, further investigation of this

question is necessary.

Wave friction factor (fw), was higher during storms than during fair weather, although

interestingly, it was high during one of the weakest events (Storm 3) and low during one of the

strongest (Storm 9). It is somewhat unclear why this was the case, although it should be noted

that wave friction factor was calculated numerically, based on a very complex set of interactions

between bottom boundary layer variables, and thus generalizations based on meteorological

conditions may not be entirely appropriate. Wave boundary layer (WBL) thickness, on the other

hand, is strongly a function, as shown in Equation. 3.21, of combined wave-current shear

velocity (u*cw), and thus responded much more predictably, occasionally reaching values during

strong storms that were twice that of mean fair weather conditions. As was the case with nearly

all bottom boundary layer parameters, however, deviations from general patterns were
61

sometimes apparent. Not surprisingly, this variabilty was also apparent for sediment transport,

as will be discussed in the next section.

Sediment Transport

Tables 6.6 through 6.9 show sediment transport predicted using a variety of models as

well as bed level change for storms and fair weather at the two deployment sites. As noted in

earlier sections, the absolute values of sediment transport predictions varied widely, and as such,

they should be used chiefly as relative indices for the purposes of comparison. Generally

speaking, the differences between storm and fair-weather conditions that were evident in

hydrodynamic and bottom boundary layer parameters are also observable in the sediment

transport data. According to nearly all indices, the predicted rate of sediment transport was

higher during storms than during fair weather, with mean storm values calculated using certain

methods exceeding fair-weather values by nearly an order of magnitude. Sediment transport

values varied widely between storms as well. Storm 3 was characterized by little or no sediment

transport, while strong storms, most notably Storm 6, caused sediment transport rates well over

an order of magnitude in excess of fair-weather rates. It is apparent, therefore, that overall

sediment transport was dominated by larger storms. It is also interesting that the mean sediment

transport rate during fair weather was not zero as calculated by any of the techniques, indicating

that sediment transport may occur at this location during mean winter fair weather conditions;

previously, fair weather resuspension and transport of bottom sediment has often been

considered unlikely for much of the Louisiana inner shelf (e.g. Adams et al., 1987; Wright et al.,

1997).

Table 6.6. Summary of sediment transport estimates within and above the wave boundary layer

for storms and fair weather conditions at the offshore site. These calculations are

based on WADMAS data analyzed using the Grant-Madsen Model combined with

Rouse Profiles (the GMR method).

 z<wbl z>wbl Total

Meteorology Q (mg cm-1 s

-1) Direction Q (mg cm-1

 s

-1) Direction Q (mg cm-1

 s

-1) Direction

Storm 1 0.330 238 0.608 248 0.934 245

Storm 2 0.235 125 0.591 112 0.822 116

Storm 3 0.000 9 0 - 0 9

Storm 4 0.207 247 0.398 231 0.600 237

Storm 5 0.053 253 0.040 257 0.092 255

Storm 6 0.433 152 2.237 135 2.655 138

Storm 7 0.160 190 0.083 202 0.241 194

Storm 8 0.028 161 0.001 179 0.029 162

Storm 9 0.055 37 0.399 9 0.448 12

All Storms 0.135 175 0.465 141 0.581 148

Fair Weather 0.072 305 0.113 258 0.170 276
62

Table 6.7. Predicted sediment transport and bed level change for Site 1 based on data from

System 1A analyzed using several models.

GMR MPM

 Q (mgcm-1s

-1) Direction Q (mgcm-1

s

-1) Direction Bed Change (cm)

Storm 1 25.8 251 56.6 245 -6.3

Storm 2 213.3 119 82.1 113 1.1

Storm 3 0.0 343 0.3 343 0.2

Storm 4 0.7 227 5.5 203 -8.6

Storm 5 0.6 208 8.1 176 -0.3

Storm 6 1425.1 120 159.0 120 2.2

Storm 7 1157.8 185 355.2 165 -2.1

Storm 8 284.1 269 115.9 260 -0.2

All Storms 549.1 139 97.8 145 -14.0

Fair Weather 138.7 298 85.6 253 40.5

Table 6.8: Predicted sediment transport and bed level change for Site 2 based on several models.

GMR MPM

 Q (mgcm-1s

-1) Direction Q (mgcm-1

s

-1) Direction Bed Change (cm)

Storm 1 20.2 295 101.2 296 -0.5

Storm 2 887.8 129 451.4 66 8.3

Storm 3 4.8 276 20.8 309 -0.1

Storm 4 112.5 281 175.1 261 -2.7

Storm 5 0.7 309 12.0 324 0.1

Storm 6 2277.4 199 975.7 230 -1.6

Storm 7 544.8 268 267.7 237 -1.6

All Storms 803.0 180 402.3 238 1.9

Fair Weather 579.0 54 325.6 351 11.1

The direction of sediment transport also varied between storm and fair weather

conditions, as well as between sites. The first point to note is that mean sediment transport during

storms at both sites had a strong seaward (offshore) component as predicted by all methods. This

was apparently the case both within and above the wave boundary layer, as indicated by columns

3 and 5 of Table 6.6. It was also true for both suspended and bed load transport, as shown by

Tables 6.7 –6.9. Strong seaward components were most pronounced during more energetic

storms, which, as noted previously, generally dominated overall transport. Landward transport

was sometimes evident, however, during weaker events such as Storm 3. This was particularly

notable at the nearshore site, where roughly half of the storms transported sediment onshore,

although at generally lower rates than the seaward transport that occurred during the other of the

storms. One exception to this was Storm 4, which was fairly energetic, but appeared to have a

slight landward component (at Site 2), owing to the presence of mean west-north-westerly

flowing currents. Across-shelf transport during fair weather, in contrast to energetic storm
63

conditions, had a landward component at all sites, according to the majority of prediction

methods used.

Table 6.9. Cospectral estimates of suspended sediment transport (mg cm-1s

-1) at System 1A

(~20cm above the bed). Periods are Infragravity:=>10.25s;Wind-Wave: 2.15s10.24s.

Mean Transport

Infragravity

Transport

Wind-Wave

Transport Total Transport

 mgcm

-1

s

-1 Direction mgcm-1

s

-1 Direction mgcm-1

s

-1 Direction mgcm-1

s

-1 Direction

Storm 1 14.1 252 163.0 73 107.4 73 250.5 73

Storm 2 8.6 146 95.8 349 394.9 8 504.6 3

Storm 3 0.0 57 4.3 174 2.5 232 6.1 189

Storm 4 15.9 253 199.3 29 607.2 177 467.9 158

Storm 5 1.1 249 13.3 59 35.7 31 48.6 38

Storm 6 47.8 141 336.6 338 905.6 351 1536.5 346

Storm 7 2.8 171 91.1 157 496.6 237 496.2 227

Storm 8 0.2 273 11.8 39 8.5 75 18.6 50

All Storms 11.4 165 119.8 4 139.2 345 322.7 353

Fair Weather 2.2 274 100.6 316 429.9 312 532.2 312

Table 6.10. Cospectral estimates of suspended sediment transport (mg cm-1s

-1) at System 2A

(~20 cm above the bed). Periods are Infragravity:=>10.25s;Wind-Wave: 2.15s10.24s.

Mean Transport

Infragravity

Transport

Wind-Wave

Transport Total Transport

 mgcm

-1

s

-1 Direction mgcm-1

s

-1 Direction mgcm-1

s

-1 Direction mgcm-1

s

-1 Direction

Storm 1 21.6 294 84.3 116 96.3 167 145.0 147

Storm 2 30.6 141 106.3 321 77.3 359 180.4 334

Storm 3 0.8 279 3.2 103 1.7 103 4.2 102

Storm 4 31.7 287 110.2 94 71.7 106 165.3 97

Storm 5 0.1 353 0.3 167 0.4 179 0.7 175

Storm 6 151.1 140 152.5 314 791.4 71 757.2 69

Storm 7 49.2 265 150.5 79 154.9 107 283.6 93

All Storms 32.9 155 40.1 7 214.5 74 232.7 70

Fair Weather 2.7 334 16.9 257 102.6 358 100.5 345

Along-shelf transport varied somewhat according to the techniques used, and

generalizations are difficult to make. Westerly transport predictions were more prevalent during

storms than easterly predictions, although there was considerable variability between storms that

did not seem to be related to intensity. During fair weather, easterly sediment transport

predictions were somewhat more common than westerly predictions. In both cases, the causes of
64

this variability are unknown. It is possible that since east-west shifts in wind direction

accompanying extratropical storms were not generally as regular or dramatic as north-south

shifts, alongshore changes in hydrodynamic and sediment transport parameters were not as clear

they were in the across-shelf. This suggests that, unlike many coastlines where along-shelf

fluxes dominate, notably the Atlantic and Pacific coasts of North America, the northern coast of

the Gulf of Mexico may be most strongly influenced by meteorological, hydrodynamic and

sedimentary variations in the across-shelf direction.

Sediment Fluxes across Ship Shoal

It is apparent that Ship Shoal exerts a significant influence on regional hydrodynamic and

sediment transport patterns, and as such, convergences and divergences (i.e. fluxes) of sediment

are expected to occur during certain conditions. Calculating these fluxes is important, of course,

in providing a clearer representation of the short-term modulating effect of the shoal on sediment

transport patterns. However, since convergences and divergences indicate potential accretion

and erosion of the shoal, calculation of flux is perhaps even more crucial in describing the longterm evolution of Ship Shoal, and ultimately, predicting its fate.

The issues discussed above are important for both theoretical and practical reasons. First,

as noted earlier, Ship Shoal is a conspicuous and influential bathymetric feature on the Louisiana

inner shelf that reduces wave energy and modulates current velocity. Changes to its morphology

are therefore closely linked with regional changes in hydrodynamics and sediment transport.

Furthermore, its sandy sedimentary composition is somewhat anomalous in the regional context

of the otherwise muddy Louisiana coast, and it may therefore serve as an important source of

sandy sediment to adjacent barrier islands and wetlands, either through natural processes or by

means of human nourishment projects. Globally, the shoal is somewhat distinctive in terms of

inner-shelf geology, since it formed recently as a result of exceptionally rapid rates of coastal

transgression and barrier island submergence. In a sense, therefore, Ship Shoal may serve as a

“laboratory” in which transgressive responses over short time scales reflect long-term barrier

island responses to relative sea level rise on more “typical” coasts. In light of these regional, and

global considerations, this section is therefore devoted to discussing the sedimentary fluxes

across Ship Shoal associated with meteorological forcing.

There was considerable variability in flux during the deployment, as shown in Figures

6.1-6.4. This is not surprising, given the short-term variability in currents and sediment transport

that occurred at each site individually. Figure 6.1 represents the current flux throughout the

deployment, which appears to have been predominantly divergent, aside from a few convergent

peaks, such as those accompanying Storms 2 and 6. The mean tendency toward divergence was

presumably the result of the persistent seaward current component at Site 1 and landward current

component at Site 2. The current convergence during Storms 2 and 6, on the other hand,

occurred when flows were seaward at both sites, but were comparatively stronger at Site 2.
65

Figure 6.1. Current flux over Ship Shoal. The occurrence of storms is indicated with black

arrows and N.D. represents a time for which no data are available, owing to sensor

burial.

Figures 6.2-6.4 show the flux of sediment across the shoal as calculated using various

methods. The pattern is similar in all cases—fairly low mean values were punctuated by high

levels of episodic convergence or divergence. High-volume events often occurred in response to

atmospheric storms, although this was not always the case. Such events were sometimes

characterized by alternating periods of convergence and divergence, and, as will be demonstrated

subsequently, net storm flux is therefore much lower in volume than would be expected.

Clearly, therefore, sediment flux, like sediment transport at a particular point, is highly episodic.

Table 6.11 shows the flux of sediment across Ship Shoal for the deployment, in the context of

meteorological conditions. As expected, regularities in sediment flux over the shoal mirror those

in sediment transport at the individual sites. Therefore, there was considerable variation in flux

depending upon both the individual storm and the computational method used. Despite these

sources of variability, however, the data clearly indicate that storms were most often associated

with convergence of sediment over the shoal (accretion), while fair weather conditions were

related to divergence (erosion). In particular, strong flux convergence occurred during Storms 2

and 6, apparently as a result of differences in sediment transport rate between the two sites.

Specifically, although seaward transport occurred at both locations, the rate was much higher at

the nearshore site. On the other hand, flux divergence occurred during Storm 7 for just the

opposite reason—a higher rate of seaward transport at Site 1 than at Site 2. Fair weather

conditions were characterized by flux divergence over the shoal, caused by high rates of

landward transport at the nearshore location, accompanied by lower, and predominantly

westward, sediment transport at the seaward site.

-40

-30

-20

-10

0

10

20

30

40

Convergence

Divergence

N/D
66

Figure 6.2. Flux of sediment across Ship Shoal as calculated using the GMR Method.

Figure 6.3. Flux of suspended sediment across Ship Shoal as calculated using the steady

current/concentration (SCC) method.

-10

-5

0

5

10

15

Convergence

Divergence

N/D

-40

-30

-20

-10

0

10

20

30

40

Convergence

Divergence

N/D
67

Figure 6.4: Flux of bed load across Ship Shoal as calculated using the Meyer-Peter and Muller

(MPM) method.

Table 6.11. Sediment flux (in mg cm-1 s

-1) across Ship Shoal during storms and fair weather as

predicted from Systems1A and 2A using spectral methods and the GMR and MPM

models (outlined previously). Negative values indicate a divergence of sediment

from the shoal while positive values indicate a convergence.

 Spectral Time-Averaged

Mean Infragravity Wind-wave Sum GMR MPM

Storm 1 -53.31 713.54 528.94 1160.25 -17.29 -41.62

Storm 2 -1.76 158.14 1093.63 1286.80 220.72 -177.46

Storm 3 0.68 -10.33 -12.86 -21.11 1.87 -2.54

Storm 4 -52.63 715.41 -1333.94 -405.05 4.43 11.99

Storm 5 -5.22 63.70 147.63 214.22 -0.55 -9.18

Storm 6 12.81 230.85 672.64 1207.16 1307.85 444.65

Storm 7 40.10 -191.00 -2477.24 -2548.22 -583.88 -66.03

All Storms -11.82 313.47 -32.02 408.22 281.34 57.75

Fair Weather -11.08 -157.16 -933.56 -1132.63 -62.25 -30.28

In summary, therefore, sediment flux on Ship Shoal tends to be divergence (potentially,

erosion of the shoal) during fair weather, due largely to high rates of onshore transport on its

landward side, and convergence (potentially, shoal accretion) during storms, due to strong

offshore transport on the seaward side. The situation is somewhat analogous to the wellestablished model of surf zone storm sediment transport in which seaward transport during

storms creates an offshore bar that is then steadily reworked landward during fair weather. This

-6

-4

-2

0

2

4

6

8

10

Convergence

Divergence

N/D
68

comparison should not be carried too far, however, since the forcing mechanisms operating on

Ship Shoal are poorly understood, and may be completely unrelated to those that operate in the

nearshore. Furthermore, the sediment flux initiated by individual storms was highly variable,

suggesting that a single “typical” pattern of flux due to storms may not be a realistic paradigm

for of Ship Shoal. Nonetheless, it appears that the natural evolution of Ship Shoal is the result of

balances between erosive fair weather influences, and aggradational winter-storm influences.
69

7. VALIDATION OF NUMERICAL WAVE MODEL (STWAVE)

Introduction

Phase 1 of this program concentrated on quantifying the impacts of sand removal

at Ship Shoal on the wave field (Stone and Xu, 1996). In order to accomplish this goal, a

number of state-of-the-art numerical wave models were evaluated (STWAVE v3,

REF/DIF, REFDIFS, RCPWAVE). These models were compared against the following

criteria: representation (scale), efficiency, accuracy, spectral capability, computational

grid size requirement, breaking criteria, and wind-wave generating. STWAVE was given

the highest composite score because of its spectral capability, inclusion of a wind-forcing

function, high accuracy, and high efficiency.

STWAVE is a finite-difference model for near-coast time-independent spectral

wave energy propagation simulations (Cialone et al., 1992; McKee et al., 1999). It is

based on a simplified spectral balance equation

where

E(f,θ) =spectral energy density

f =frequency of spectral component

θ =propagation direction of spectral component

Si =source terms (shoaling, refraction, wind forcing, wave-wave

nonlinear interactions, bottom interaction, etc.) (see McKee et al.,

1999 for a detailed description.)

STWAVE simulation requires a wave energy spectrum specified for the input boundary

of the computational grid. It transforms the spectrum across the grid, including refraction

and shoaling effects. The spectrum is modified to include the effects of bottom

diffraction and the convergence/divergence of energy influenced by the local bathymetry.

Wind-wave generation, nonlinear energy transfer, wave field and wave-bottom

dissipation and wave breaking are considered. The model is computationally efficient

because of its assumption that only wave energy directed into the computational grid is

significant, i.e., wave energy not directed into the grid is neglected.

Validation Methods

The output from STWAVE version 3 was tested for two CSI field deployments in

1998/1999 and 2000. Two stations were established for the first deployment (offshore

and inshore on Figure 7.1) and a third station mid-way between the former during the

2000 deployment. For both deployments, wave information measured at the offshore

station was selected as the input boundary condition for the model. The wind conditions

for the 1998/99 deployment were obtained from Grand Isle, Louisiana, and a Terrebonne

∂

∂x

( ) C C E(f,q) g + 

∂

∂y

( ) C C E(f, g θ) + 

i =1

N

∑ S = 0 i
70

Bay site for the 2000 deployment. The input wave spectra (JONSWAP) were calculated

by STWAVE from measured significant wave heights, peak wave period, and wave

direction and corresponding wind information. A range of 15 frequencies was applied

over 35 approach angles. Peak, low, and high cut off frequencies were dependant on the

individual measured wave parameters at the boundary station. Because STWAVE is a

half-plane model (i.e., wave energy can only propagate from offshore to onshore or +/-

87.5 degrees from the grid x axis), wind generated waves from the north are neglected.

The bathymetric grid at Ship Shoal had the dimensions 16.6 km by 27.1 km. As shown

on Figure 7.1, the offshore station was located on the south side boundary of modeling

area, and the mid and inshore stations to the north. The bathymetric grid was generated

from surveys conducted in the 1980’s by the United States Geological Survey.

Bathymetry for the west and north west part of the study site was obtained from the

National Ocean Service. The grid size is 166 by 271 with 100 meters spacing. Measured

wave and wind data were input to the model for both time series every 3 hours for the

1998/99 time series and 4 hours for the 2000 time series. A total of 590 models runs

were conducted and the data are presented in Figures 7.2-7.17 as scatter plots of

measured and numerically derived Hs along with the respective coefficient of

determination (r2), and time series plots of measured and computed Hs . Plots of all wind

directions for both deployments are presented in addition to wind directions from the SW,

S and SE.

 Figure 7.1. Location of the modeling area at Ship Shoal and instrumentation

deployment sites.

Comparison of In Situ and Modeled Data

High r2 values of 0.85 and 0.89 were obtained for all comparisons in both

deployments at the Inshore station indicating that STWAVE has performed well in

predicting Hs (Table 7.1 and Figures 7.2 and 7.3). As shown in Figures 7.4 -7.7, the
71

measured and predicted values are in good agreement throughout the entire range of

wave heights measured, 0.1 to 1.6m. At both stations for each deployment, the model

over predicts wave height by between 23 and 24% (Table 7.1). At the Middle station for

the 2000 deployment, the r2

 value is 0.76 (Figures 7.8 and 7.9) for all wind directions and

the percentage over prediction is 13% (Table 7.1) for Hs values ranging between 0.1 to

1.2 m.

Table 7.1. Percentage of over prediction of Hs by STWAVE when compared to in situ

measurements at two locations on Ship Shoal, based on 590 model runs.

Wind

Direction

1998/1999

Deployment (Inshore)

2000 Deployment

(Inshore)

2000 Deployment

(Middle)

Percentage r2 Percentage r2 Percentage r2

From:

SW, S, SE 14.1 0.90 23.4 0.81 7.6 0.56

From:

SW

No waves from this

direction 19.4 0.79 6.4 0.79

All Data 24.2 0.85 23.4 0.89 13.1 0.76

 

0

5

10

15

20

25

30

All SW,S,SE SW

Wind Direction

Percentage Over 

Prediction 

by STWAVE of Hs 

Inshore: 1998/1999 Inshore: 2000 Middle: 2000

Figure 7.2: Summary of % over prediction of Hs by STWAVE for all stations.

0

0.2

0.4

0.6

0.8

1

Coefficient of Determination

All SW,S,SE SW

(r squared)

Inshore: 1998/1999 Inshore: 2000 Middle: 2000

Figure 7.3: Summary of r2 values for measured and modeled Hs for all stations.
72

 

Inshore Station (98/99 Deployment, All)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Hs Numerically Modeled (m)

Hs Measured (m)

Figure 7.4. Scatter plot of significant wave heights for 1998/99 deployment for all wind

directions at Inshore station.

 

Inshore Station (98/99 Deployment, All)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1 51 101 151 201 251 301 351

Number of Measurements

Wave Heights(m)

Hs Measured

Hs Numerically Modeled

Figure 7.5. Comparison of measured and numerically modeled wave heights for all wind

directions in 1998/99 deployment at Inshore station.

r

2

=0.85
73

 

Inshore Station (2000 Deployment, All) 

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

Hs Numerically Modeled (m)

Hs Measured (m)

Figure 7.6. Scatter plot of significant wave heights for all wind direction at Inshore

station for 2000 deployment.

 

Inshore Station (2000 Deployment, All) 

0

0.2

0.4

0.6

0.8

1

1.2

1 21 41 61 81 101 121 141 161 181

Number of measurements

Hs (m)

Hs Measured

Hs Numerically modeled

Figure 7.7. Comparison diagram of numerically modeled and measured wave heights for

all wind directions at Inshore station for 2000 deployment.

r

2

=0.89
74

 

Middle Station (2000 Deployment, All) 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Hs Numerically Modeled (m)

Hs Measured (m)

Figure 7.8. Scatter plot of Hs measured vs. Hs numerically modeled for 2000

deployment at Middle station.

 

Middle Station (2000 Deployment, All) 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 21 41 61 81 101 121 141 161 181

Number of mesurements

Hs (m)

Hs Measured

Hs Numerically modeled

Figure 7.9. Comparison diagram of numerically modeled and measured wave heights for

all wind directions at Middle station for 2000 deployment.

r

2

=0.76
75

Given that STWAVE does not account for waves generated and propagated from

the north, input wave parameters of waves approaching from the southwest, south and

southeast were extracted from the measured data sets and input to the model. For the

1998/99 deployment at the Inshore station, the r2

 value increased to 0.9 and the

percentage over prediction of Hs decreased to 14.1% when compared to all data (i.e.,

winds from all four quadrants) (Figures 7.10 and 7.11).. For the 2000 deployment,

however, the rr

 value decreased slightly to 0.81 and the percentage over prediction

remained the same (23.4%) (Figures 7.12 and 7.13). Data obtained from the Middle

station showed a marked decrease in over prediction from 13.1% down to 7.6% and a

decrease in the r2

 value from 0.76 to 0.56 (Figures 7.14 and 7.15).

 

Inshore Station (98/99 Deployment, SW-S-SE Wind)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Hs Numerically Modeled (m)

Hs Measured (m)

Figure 7.10. Scatter plot of measured and modeled Hs for wind blowing from southwest,

south and southeast for 1998/99 deployment at Inshore station.

 

Inshore Station (98/99 Deployment, SW-S-SE Wind)

-0.2

0.3

0.8

1.3

1.8

1 6 11 16 21 26

Number of Measurements

Hs (m)

Hs Measured

Hs Numerically Modeled

Figure 7.11. Comparison diagram of numerically modeled and measured wave heights

for selected southwest, south and southeast winds at Inshore station for

1998/99 deployment.

r

2

=0.90
76

 

Inshore Station (2000 Deployment, SW-S-SE wind) 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.2 0.4 0.6 0.8 1

Hs Numerically Modeled (m)

Hs Measured (m)

Figure 7.12. Scatter plot of Hs measured vs. Hs numerically modeled at Inshore station

for southwest, south and southeast wind directions for 2000 deployment.

 

Inshore Station (2000 Deployment, SW-S-SE wind) 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 11 21 31 41 51

Number of measurements

Hs (m)

Hs Measured

Hs Numerically modeled

Figure 7.13: Comparison diagram of numerically modeled and measured wave heights

for southwest, south and southeast wind directions at Inshore station for

2000 deployment.

r

2

=0.81
77

 

Middle Station (2000 Deployment, SW-S-SE Wind) 

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1

Hs Numerically Modeled (m)

Hs Measured (m)

Figure 7.14. Scatter plot of Hs measured vs. numerically modeled Hs at Middle station

for southwest, south and southeast winds foe 2000 deployment.

 Middle Station (2000 Deployment, SW-S-SE wind) 

0

0.2

0.4

0.6

0.8

1

1.2

1 11 21 31 41 51

Number of measurements

Hs (m)

Hs Measured

Hs Numerically modeled

Figure 7.15. Relationship between numerically modeled and measured significant wave

heights at Middle station for southwest, south and southeast winds.

To test the model further, waves approaching from the southwest were extracted

from the time series and used as input. This was done to test if the orientation of the

instrumentation array (slightly southwest to northeast) and wave refraction effects across

the seaward flank of Ship Shoal were of significance in the comparisons of data sets.

During the 1998/1999 deployment waves did not approach from the southwest, a

common phenomenon during winter months off the Louisiana coast. For the 2000

deployment at the Inshore station, the r2

 value decreased slightly when compared to SW,

r

2

=0.56
78

S and SE approaches from 0.81 to 0.79 (Figures 7.16 and 7.17). The percent over

prediction in Hs decreased by 4% to 19.4%. At the Middle station, the r2

 value increased

from 0.56 to 0.79, and the percent over prediction of Hs decreased by 1.2% to 6.4%

(Figures 7.18 and 7.19).

 

Inshore Station (2000 Deployment, SW wind) 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.2 0.4 0.6 0.8 1

Hs Numerically Modeled (m)

Hs Measured (m)

Figure 7.16. Scatter plot of Hs measured vs. Hs numerically modeled for southwest wind

only at Inshore station for 2000 deployment.

 

Inshore Station (2000 Deployment, SW wind) 

0

0.2

0.4

0.6

0.8

1

1.2

1 6 11 16 21 26 31 36 41

Number of Measurements

Hs (m)

Hs measured

Hs numerically modeled

Figure 7.17. Relationship between numerically modeled and measured significant wave

heights for south-west wind only at Inshore station.

r

2

=0.79
79

 

Middle Station (2000 Deployment, SW Wind) 

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1

Hs Numerically Modeled (m)

Hs Measured (m)

Figure 7.18. Scatter plot of Hs measured vs. Hs numerically modeled for southwest wind

only at Middle station for 2000 deployment.

 

Middle Station (2000 Deployment, SW wind) 

0

0.2

0.4

0.6

0.8

1

1.2

1 6 11 16 21 26 31 36 41

Number of Measurements

Hs (m)

Hs measured

Hs numerically modeled

Figure 7.19. Relationship between numerically modeled and measured significant wave

heights for south-west wind only at Middle station.

As summarized in Table 7.1 and Figures 7.2 and 7.3, the data presented indicate

that STWAVE over predicts Hs by between 6 and 24%. Over prediction shows a general

decrease when winds from the northern two quadrants are removed from the time series.

Modeling waves propagating from the southwest to incorporate possible refraction effects

across the shoal does not significantly alter either the over prediction percentage or r2

r

2

=0.79
80

value when compared to wave approaches from both southern quadrants. Overall, the

model has predicted Hs very well over a substantial spectrum of wave conditions for the

northern Gulf of Mexico.
81

8. CONCLUSIONS

Based on the data presented, the following conclusions are made:

1. Hydrodynamic, bottom boundary layer, and sedimentary variability on the Louisiana inner

shelf during the winter is episodic, and is largely the result of recurring extratropical storm

passages.

2. Considerable variability between storms, as well as during storms themselves, is reflected in

hydrodynamic, bottom boundary layer, and sedimentary parameters. Some indices are

several orders of magnitude greater during strong storms than during fair weather, while in

the case of weak storms, the same parameters may actually be weaker.

3. Despite this considerable variability, storms are generally characterized by increases in: wave

height, near-bed orbital, and mean current speed, shear velocity, suspended sediment

concentration, and sediment transport. Decreases in wave period and apparent bottom

roughness are also apparent.

4. Sediment transport during the winter is dominated by the strongest storms, when net sediment

flux tends to be seaward.

5. Differences between the seaward and landward flanks of Ship Shoal are apparent. Waves

tend to be higher and longer in period on the seaward side, while mean currents are generally

higher landward, where they are directed onshore, in comparison with the seaward currents

that predominate at the offshore site. It is apparent, therefore, that Ship Shoal exerts a

significant influence on regional hydrodynamics, reducing wave energy and modulating

current velocity.

6. The long-term evolution of Ship Shoal appears to be the result of a balance between fair

weather influences, which cause erosion, and winter storm influences, which cause accretion.

Superficially, this closely follows the commonly-held notions of nearshore storm/fair weather

sediment transport on barred, but direct parallels are avoided for the moment since the details

of process and response require further investigation.

7. The numerical wave model STWAVE version 3, appears to represent the wave field across

Ship Shoal very well and on considering the complexity of wave-wave/current interactions at

the site, the tendency for over prediction is relatively minor.

8. There is a considerable amount of additional experimentation that should be conducted at the

site, particularly to answer questions pertaining to large-scale sedimentation patterns and

event-scale morphodynamics.
83

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The Department of the Interior Mission

As the Nation's principal conservation agency, the Department of the Interior has responsibility 

for most of our nationally owned public lands and natural resources. This includes fostering 

sound use of our land and water resources; protecting our fish, wildlife, and biological diversity; 

preserving the environmental and cultural values of our national parks and historical places; 

and providing for the enjoyment of life through outdoor recreation. The Department assesses 

our energy and mineral resources and works to ensure that their development is in the best 

interests of all our people by encouraging stewardship and citizen participation in their care. 

The Department also has a major responsibility for American Indian reservation communities 

and for people who live in island territories under U.S. administration. 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) 

primary responsibilities are to manage the mineral resources located on the Nation's Outer 

Continental Shelf (OCS), collect revenue from the Federal OCS and onshore Federal and Indian 

lands, and distribute those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program 

administers the OCS competitive leasing program and oversees the safe and environmentally 

sound exploration and production of our Nation's offshore natural gas, oil and other mineral 

resources. The MMS Minerals Revenue Management meets its responsibilities by ensuring the 

efficient, timely and accurate collection and disbursement of revenue from mineral leasing and 

production due to Indian tribes and allottees, States and the U.S. Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially 

affected parties and (2) carrying out its programs with an emphasis on working to enhance the 

quality of life for all Americans by lending MMS assistance and expertise to economic 

development and environmental protection.