Simulation and analysis of sediment resuspension observed in Old Tampa Bay, Florida

UFL/COEL-TR/091

SIMULATION AND ANALYSIS OF SEDIMENT
RESUSPENSION OBSERVED IN OLD TAMPA BAY,
FLORIDA

by

David H. Schoellhamer
and
Y. Peter Sheng

1993

REPORT DOCUMENTATION PAGE
1. Report No. 2. 3. Recipient's Accessioo o.

UFL/COEL-TR/091
4. Title and Subtitle 5. Report Date
Simulation and Analysis of Sediment Resuspension April 1993
observed in Old Tampa Bay, Florida 6.

7. Author(s) S. Performing Orfgniztion Report No.

David H. Schoellhamer and Y. Peter Sheng UFL/COEL-TR/091
9. Perforting Organizatio ameo and Address 10. Project/Task/Mork Unit No.
University of Florida
Obastal & Oceanographic Engineering 11 Contract or Grant o.
11. Contract or Grant No.
336 Weil Hall
Gainesville, FL 32611 Rep
13. Type of lport
12. Sponsoring Organizetion Name nd Address

14.

15. Supplementary otes

16. Abtsract

Estuarine sediments may limit light availability for photosynthesis, supply
nutrients to the water column, and affect the fate of contaminants in estuaries,
including Old Tampa Bay, Florida. These adverse impacts are enhanced when bottom
sediments are resuspended into the water column. Sediment resuspension was
intermittently monitored in Old Tampa Bay from 1988 to 1990. The data indicates that
net sediment resuspension is caused by depth-transitional wind-waves that are generated
by strong and sustained winds associated with storm systems and not tidal currents.
The bottom roughness regime in Old Tampa Bay is transitional between the smooth and
rough limits.

A vertical one-dimensional numerical model was modified and used to help
interpret the Old Tampa Bay sediment resuspension data. The model was modified to
simulate low Reynolds number flows, multiple wave frequencies, variable bottom
roughness regimes, and spatially-averaged grain shear stress. The model successfully
simulated steady flow profiles with laminar, transitional, and turbulent regions and
turbulent dissipation in the marine surface layer. Simulated shear stresses are in
agreement with observed critical conditions for sediment motion for combined wave and
current motion on the continental shelf. Wave spectra observed in Old Tampa Bay and
simulations of the sediment resuspension data indicate that formation of large

(continued on back)

17. Originator's gey Uords 18. Availability Stateent
Sediment Resuspension, Numerical Simulation,
Tampa Bay

19. U. S. Security Classif. of the Report 20. U. S. Security Classif. of This Page 21. No. of pages ;2. Price
Unclassified Unclassified 262

UFL/COEL-TR/091

SIMULATION AND ANALYSIS OF SEDIMENT RESUSPENSION
OBSERVED IN OLD TAMPA BAY, FLORIDA

by

David H. Schoellhamer

and

Y. Peter Sheng

Coastal & Oceanographic Engineering Department
University of Florida
Gainesville, FL 32611

1993

TABLE OF CONTENTS

page

ACKNOWLEDGEMENTS................................................ iii

LIST OF TABLES.................................................. vi

LIST OF FIGURES................................................. viii

KEY TO SYMBOLS.................................................. x

ABSTRACT ........................................................ xvi

CHAPTERS

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

Significance of Estuarine Sediments....................... 4
Sediment Transport Processes................................ 8
Initiation of Motion of Bed Sediments................... 8
Bed Load Transport....................................... 15
Erosion and Bottom Shear Stress......................... 16
Suspended Load Transport................................. 24
Flocculation and Aggregation .............................. 26
Settling and Deposition.................................. 27
Other Bed and Near-bed Factors........................... 31
Field Studies Related to Estuarine Sediment Transport..... 35
Numerical Models Applicable to
Estuarine Sediment Transport.............................. 43
Relation of this Research to Previous Studies.............. 49

2 SEDIMENT RESUSPENSION DATA COLLECTION METHODOLOGY......... 52

Study Area ................................................ 52
Sediment Resuspension Monitoring Sites.................... 54
Old Tampa Bay Instrumentation Platform.................... 56
Calibration and Output of Electromagnetic Current Meters.. 63
Response Threshold and Biological Interference of
OBS Sensors............................................. 66
Water Sample Collection................................... 68
Instrument Deployment Strategies........................... 69

3 SEDIMENT RESUSPENSION DATA AND ANALYSIS................... 70

March 1990 Storm......................................... 73
November 1990 Storm...................................... 84
Tropical Storm Keith...................................... 90
Tropical Storm Marco ...................................... 94

Implications for Numerical Modeling......................... 96

4 NUMERICAL MODEL........................ .................. 98

Momentum and Transport Equations.......................... 102
Turbulence Closure....................................... 106
Nondimensional Equations................................... 112
Steady State Conditions................................... 116
Wave-Induced Pressure Gradients ........................... 117
Bottom Shear Stress and Erosion............................ 119
Suspended-Sediment Stratification......................... 122

5 NUMERICAL SIMULATIONS OF THE MARINE SURFACE LAYER
AND CRITICAL SHEAR STRESSES ON CONTINENTAL SHELVES...... 123

Simulation of Turbulence in the Marine Surface Layer...... 123
Critical Shear Stresses Observed on Continental Shelves... 126

6 OLD TAMPA BAY NUMERICAL SIMULATION RESULTS................ 138

Steady Flow Simulation.................................... 138
Reproduction of Energy Spectra of Observed Currents....... 142
Simulated Shear Stresses.................................. 144
Old Tampa Bay Suspended-Sediment Simulation Procedure..... 154
Old Tampa Bay November 1990 Suspended-Sediment
Calibration Simulation.................................. 161
Old Tampa Bay March 1990 Suspended-Sediment
Validation Simulation.................................... 166
Old Tampa Bay March 1990 Suspended-Sediment
Improved Simulation..................................... 171
Old Tampa Bay November 1990 Sensitivity Simulations....... 175

7 SUMMARY AND CONCLUSIONS ................................... 181

APPENDICES

A OCM1D FINITE-DIFFERENCED EQUATIONS AND TURBULENCE
CLOSURE ALGORITHMS....................................... 188

Finite-Differenced Equations for Momentum and
Suspended Sediment...................................... 188
Turbulence Closure Algorithms............................... 192

REFERENCES...................................................... 196

BIOGRAPHICAL SKETCH............................................. 215

LIST OF TABLES

page

Table 1-1, Previous studies that are related to this research
and include an estuarine environment, wind waves,
field measurements of suspended-sediment
concentrations, or a numerical vertical sediment
transport model..................................... 50

3-1, Old Tampa Bay platform instrumentation deployments... 71

3-2, Old Tampa Bay platform data, March 8, 1990........... 75

3-3, Correlation coefficients for various wave properties
and bottom shear stresses with suspended-solids
concentrations at the Old Tampa Bay platform,
March 1990........................................... 78

3-4, Old Tampa Bay platform data, November 30 -
December 1, 1990..................................... 86

3-5, Correlation coefficients for various wave properties
and bottom shear stresses with suspended-solids
concentrations at the Old Tampa Bay platform,
November 1990....................................... 88

4-1, Modifications made to the 1986 version of OCM1D
(Sheng 1986) for this research........................ 101

4-2, Comparison of the equilibrium closure and TKE
closure methods for turbulent transport............... 113

5-1, Mean current speed, maximum wave orbital velocity,
wave period, and critical shear stresses from a
wave-current model (Drake and Cacchione 1986) and
the OCM1D model...................................... 128

5-2, Mean current speed, maximum wave orbital velocity,
wave period, angle between the mean current and
waves, and critical shear stresses from a
wave-current model (Larsen et al. 1981) and
the OCM1D model ...................................... 133

6-1, Total and grain shear stress distribution and
critical stresses, 1800 hours March 8, 1990........... 153

6-2, Old Tampa Bay suspended-sediment simulations......... 157

6-3, Values of the calibration coefficients determined by
other studies and determined for the November 1990
calibration simulation and the March 1990 improved
simulation............................................ 173

6-4, Mean percent differences of simulated suspended-solids
concentrations 70 and 183 cm above the bed caused by
20% changes in the calibration coefficients,
November 1990 sensitivity simulations................ 176

LIST OF FIGURES

page

Figure 1-1, Shields critical shear stress diagram................ 12

1-2, Roughness function B in terms of Reynolds number,
from Schlichting (1969).........; ................... 19

2-1, Old Tampa Bay study area............................ 53

2-2, Size distribution of bottom sediment at the
Old Tampa Bay platform............................... 57

2-3, Old Tampa Bay platform............................... 58

2-4, Suspended-solids concentration and median OBS
sensor output 70 cm above the bed at the Old Tampa
Bay platform, March 1990 ............................ 67

3-1, Measured and calculated quantities at the Old Tampa
Bay platform, March 1990 ............................ 74

3-2, Measured and calculated quantities at the Old Tampa
Bay platform, November 1990 ......................... 85

3-3, Mean water depth, mean current speed, and maximum
bottom orbital velocity at the Old Tampa Bay
platform during tropical storm Keith,
November 1988 ....................................... 91

3-4, Wind speed, wind vector azimuth, and
suspended-solids concentration at the Old Tampa Bay
platform during tropical storm Marco, October 1990.. 95

5-1, Measured, simulated, and theoretical turbulent
dissipation in the marine surface layer, measurements
reported by Soloviev et. al (1988).................. 124

5-2, Extended Shields diagram for continental shelf
data, shear stresses from wave-current models (WC)
and OCM1D........................................... 130

6-1, Computational grid for 45 layers, 1.15 neighboring
layer height ratio, and a 399 cm domain height...... 140

6-2, Comparison of Reichardt and simulated velocity
profiles............................................ 141

viii

6-3, Comparison of turbulence macroscale from the
dynamic equation and the integral constraints....... 143

6-4, Raw energy spectra computed from measured and
simulated velocities, 1500 hours November 30, 1990.. 145

6-5, Spatial convergence of total and
grain shear stress.................................. 147

6-6, Maximum grain shear stress calculated by OCM1D and
from Engelund's experimental results ................ 149

6-7, Simulated total and grain shear stresses,
1800 hours March 8, 1990 ....... .................... 150

6-8, Simulated grain shear stress,
1800 hours March 8, 1990 ............................ 152

6-9, Maximum total bottom shear stress from the
OCM1D model and the Grant and Madsen (1979)
wave-current model, November 1990................... 155

6-10, Simulated and measured suspended-solids
concentrations 70 cm above the bed,
November 1990 calibration simulation................ 162

6-11, Simulated and measured suspended-solids
concentrations 183 cm above the bed,
November 1990 calibration simulation................ 163

6-12, Simulated and measured suspended-solids
concentrations 24 cm above the bed,
March 1990 validation and improved simulations..... 168

6-13, Simulated and measured suspended-solids
concentrations 70 cm above the bed,
March 1990 validation and improved simulations..... 169

6-14, Simulated and measured suspended-solids
concentrations 183 cm above the bed,
March 1990 validation and improved simulations..... 170

6-15, Sensitivity of November 1990 calibration simulation
results to erosion rate exponent q ................. 177

6-16, Sensitivity of November 1990 calibration simulation
results to erosion rate coefficient a.............. 178

6-17, Sensitivity of November 1990 calibration simulation
results to settling velocity ws.................... 180

A-l, Coordinate axes definition and grid structure for
program OCM1D....................................... 189

KEY TO SYMBOLS

A invariant constant for turbulence algorithm (0.75, eqn. 4-26)

Ab orbital amplitude just above the boundary layer (eqn. 1-14)

A eddy viscosity (eqn. 1-16)
nondimensional eddy viscosity (eqn. 4-42)

a wave amplitude (eqn. 1-12)
small reference elevation above the bed (eqn. 1-18)
Fourier series coefficient (eqn. 2-3)
invariant constant for turbulence algorithm (3, eqn. 4-23)

a corrected Fourier series coefficient (eqn. 2-9)
c
B roughness function (eqn. 1-5)

b intercept of line that fits a segment of fig. 1-2 (eqn. 1-6)
Fourier series coefficient (eqn. 2-3)
invariant constant for turbulence algorithm (0.125, eqn. 4-23)

b corrected Fourier series coefficient (eqn. 2-10)

C well-mixed suspended-sediment concentration (eqn. 1-19)

C coefficient for integral constraint on A (eqn. 4-32)
q
c mean suspended-sediment concentration (eqn. 1-15)
magnitude in frequency domain (eqn. 2-4)
nondimensional suspended-sediment concentration (eqn. 4-42)

ca suspended-sediment concentration at elevation a (eqn. 1-18)

cc corrected magnitude in frequency domain (eqn. 2-7)

cm suspended-sediment mass concentration (eqn. 4-62)

c suspended-sediment concentration at upper boundary (eqn. 4-19)
nondimensional c (eqn. 4-52)

D rate of deposition (eqn. 1-19)

d particle diameter for which n percent of sediments are finer

d sediment particle diameter (eqn. 1-1)
E erosion rate (eqn. 1-3)
E erosion rate (eqn. 1-3)

nondimensional erosion rate (eqn. 4-42)

Ez Ekman number (eqn. 4-43)

F Froude number (eqn. 4-43)
neighboring layer height ratio (eqn. 5-2)

f friction factor (eqn. 1-12)
Coriolis coefficient (eqn. 4-4)

G filter gain for current meter, function of w (eqn. 2-1)

g gravitational acceleration (fig. 1-1)

H elevation of model domain (eqn. 4-42)
nondimensional elevation of model domain (eqn. 4-42)

h water depth (eqn. 1-12)

i counter and exponent (eqn. 5-2)

K pressure transfer function (eqn. 4-60)

K eddy diffusivity (eqn. 1-16)
nondimensional eddy diffusivity (eqn. 4-42)

k wave number (eqn. 1-12)
molecular diffusion (eqn. 4-26)

kb bottom roughness (eqn. 1-14)

ks height of bottom roughness elements (eqn. 1-5)

M erosion rate constant (eqn. 1-3)

N number of data points for fast Fourier transform (eqn. 2-3)
Brunt-Vaisala frequency (eqn. 4-31)
number of layers (eqn. 5-2)

n counter (eqn. 2-3)

P Prandtl number (eqn. 4-43)

p pressure (eqn. 4-2)

p pressure at the water surface (eqn. 4-6)
nondimensional pressure at the water surface (eqn. 4-42)

Q nondimensional quantity (eqn. A-8)

q turbulent fluctuating velocity (eqn. 4-23)
nondimensional turbulent fluctuating velocity (eqn. 4-42)

R roughness Reynolds number u ks/v (fig. 1-2)

Re Reynolds number (eqn. 4-43)

Ri Richardson number (eqn. A-11)

Rx horizontal Rossby number (eqn. 4-43)

R vertical Rossby number (eqn. 4-43)

R, boundary Reynolds number U ds/v (eqn. 1-1)

RC electronic filter time constant for current meter (eqn. 2-1)

s slope of line that fits a segment of fig. 1-2 (eqn. 1-6)
invariant constant for turbulence algorithm (1.8, eqn. 4-27)

T wave period (table 5-1)

t time (eqn. 1-15)
nondimensional time (eqn. 4-42)

U1/10 mean of the highest 1/10 ub (table 5-2)

U100 mean current speed 100 cm above bed (table 5-1)

U maximum wave orbital velocity 20 cm above bed (table 5-1)

U, shear velocity (eqn. 1-1)

u horizontal velocity at elevation z (eqn. 1-5)
mean velocity of sediment particles (eqn. 1-15)
mean velocity component (eqn. 4-1)
nondimensional mean velocity component (eqn. 4-42)

ub maximum bottom orbital velocity (eqn. 1-11)

u* shear velocity (eqn. 1-5)

v mean horizontal velocity component (eqn. 4-4)
nondimensional mean velocity component (eqn. 4-42)

v invariant constant for turbulence algorithm (0.3, eqn. 4-23)

w mean vertical velocity component (eqn. 4-4)

w terminal settling velocity (eqn. 1-17)
nondimensional settling velocity (eqn. 4-42)

x coordinate axis (eqn. 1-15)
nondimensional horizontal coordinate axis (eqn. 4-42)

y horizontal coordinate axis (eqn. 4-4)
nondimensional horizontal coordinate axis (eqn. 4-42)

Z Rouse number ws/(Pru,) (eqn. 1-18)

z elevation above bed (eqn. 1-5)
vertical coordinate axis (eqn. 1-17)
vertical coordinate (eqn. 4-4, fig. A-i)

z vertical coordinate of velocity measurement (eqn. 4-61)

z nondimensional quantity u.z/v (eqn. 1-10)

z elevation with zero velocity (eqn. 1-9)
o
zo nondimensional quantity u zo/v (eqn. 1-10)

a erosion rate coefficient (eqn. 1-4)
invariant constant for turbulence algorithm (0.75, eqn. 4-26)

P K/A, the inverse of the turbulent Schmidt number (eqn. 1-16)

7 specific weight of fluid (eqn. 1-2)

Ts specific weight of sediment (eqn. 1-2)

At time interval of data (eqn. 2-3)
nondimensional simulation time step (eqn. A-i)
simulation time step (eqn. 4-59)

Az1 height of bottom layer (eqn. 5-2)

Aak nondimensional layer elevation (eqn. A-l)

AC-k nondimensional distance between grid points k and k-I (eqn. A-i)

Aa+k nondimensional distance between grid points k and k-i (eqn. A-i)

AW angular frequency increment 2r/(NAt) (eqn. 2-3)

6 Kronecker delta (eqn. 4-23)

6 distance for integral constraint on A (eqn. 4-32)
q
S phase (eqn. 2-4)
alternating tensor (eqn. 4-2)
energy dissipation rate (eqn. 5-1)

Sc corrected phase (eqn. 2-8)

S nondimensional quantity (eqn. 1-13)

S erosion rate exponent (eqn. 1-4)

e production term (eqn. 4-3)
nondimensional production term (eqn. 4-42)

X von Karman's constant (eqn. 1-5)

A turbulence macroscale (eqn. 4-23)

xiii

nondimensional turbulence macroscale (eqn. 4-42)

A Taylor microscale (eqn. 4-28)

v kinematic viscosity (eqn. 1-1)
nondimensional kinematic viscosity (eqn. 4-42)

S surface displacement from the mean water depth (eqn. 4-6)
nondimensional surface displacement (eqn. 4-42)

p fluid density (eqn. 1-1)
nondimensional fluid density (eqn. 4-42)

po reference fluid density (eqn. 4-2)

Ps sediment density (eqn. 4-62)

Pw water density (eqn. 4-62)

a nondimensional vertical coordinate (eqn. 4-42)

7 bottom shear stress (eqn. 1-3)

rb total bottom shear stress (table 5-1)

7 critical shear stress for erosion (eqn. 1-3)
c
r critical shear stress for deposition (eqn. 1-19)

min minimum r that maintains sediment in suspension (eqn. 1-19)

o bottom shear stress (eqn. 1-2)

Ss grain shear stress (table 5-1)

w maximum bottom shear stress in oscillatory flow (eqn. 1-11)

7 x component of wind shear stress at free surface (eqn. 4-14)
nondimensional x component of wind shear stress (eqn. 4-47)

7 y component of wind shear stress at free surface (eqn. 4-15)
WY nondimensional y component of wind shear stress (eqn. 4-48)

7 x component of total bottom shear stress (eqn. 4-20)
nondimensional x component of total shear stress (eqn. 4-53)

7 y component of total bottom shear stress (eqn. 4-21)
Y nondimensional y component of total shear stress (eqn. 4-54)

r, dimensionless shear stress (Shields parameter, eqn 1-2)

filter phase delay for current meter, a function of w (eqn. 2-2)
angle between the mean current and wave direction (table 5-2)

n angular velocity of the Earth (eqn. 4-2)

xiv

w angular wave frequency (eqn. 1-12)
angular frequency of periodic input signal to filter (eqn. 2-1)

Subscripts:

k layer number (eqn. A-l)

r reference value (eqn. 4-42)

Superscripts:

n time step (eqn. A-l)

* nondimensional value (eqn. 4-42)

S turbulent fluctuation

ABSTRACT

Estuarine sediments may limit light availability for

photosynthesis, supply nutrients to the water column, and affect the

fate of contaminants in estuaries, including Old Tampa Bay, Florida.

These adverse impacts are enhanced when bottom sediments are resuspended

into the water column. Sediment resuspension was intermittently

monitored in Old Tampa Bay from 1988 to 1990. The data indicates that

net sediment resuspension is caused by depth-transitional wind-waves

that are generated by strong and sustained winds associated with storm

systems and not tidal currents. The bottom roughness regime in Old

Tampa Bay is transitional between the smooth and rough limits.

A vertical one-dimensional numerical model was modified and used

to help interpret the Old Tampa Bay sediment resuspension data. The

model was modified to simulate low Reynolds number flows, multiple wave

frequencies, variable bottom roughness regimes, and spatially-averaged

grain shear stress. The model successfully simulated steady flow

profiles with laminar, transitional, and turbulent regions and turbulent

dissipation in the marine surface layer. Simulated shear stresses are

in agreement with observed critical conditions for sediment motion for

combined wave and current motion on the continental shelf. Wave spectra

observed in Old Tampa Bay and simulations of the sediment resuspension

data indicate that formation of large aggregates is an important process

that controls settling in Old Tampa Bay. A simple aggregation algorithm

xvi

was added to the model and the calibrated coefficients are reasonable

compared to values from other studies. Only the finer particles are

probably intermittently transported as bed load. Simulation results

indicate that the bottom sediments were more erodible in March 1990 than

November 1990. Reduced biological binding of the fine bed sediments and

increased storm activity may increase bottom sediment erodibility in

March 1990.

xvii

CHAPTER 1
INTRODUCTION

Estuaries are transition zones between riverine and marine

environments. Potential sources of sediment particles for an estuary

include rivers, net sediment flux from the marine environment, overland

runoff, and anthropogenic point sources. Sediment particles are

commonly trapped and deposited in the deeper parts of an estuary. The

bed sediment affects the overall health of an estuary in several ways.

Bed sediment, especially fine sediment, can be resuspended up into the

water column where it may reduce the amount of light penetrating the

water column, may act as a source for constituents adsorbed onto the

sediment, and may be transported to undesirable locations. The

reduction of light in the water column may adversely affect biological

communities. Adsorbed constituents that can be released to the water

column during suspension and possibly while on the bed include

nutrients which may contribute to eutrophication of the estuary, heavy

metals, pesticides, and organic carbons that may decrease the

productivity of the estuary. Resuspended sediment may be transported

throughout the estuary and spread the adverse effects and possibly

become deposited in undesirable locations, such as shipping channels,

turning basins, and marinas. Thus, the overall health of an estuarine

environment is partially dependent upon the resuspension, transport,

and deposition of sediment.

Numerical models can be used to study and predict sediment

dynamics in an estuary. An accurate numerical model must include

algorithms that represent significant hydrodynamic and sediment

transport processes which may be identified from comprehensive field

data. Governmental regulators could use a sediment model to help

predict the effect of proposed anthropogenic alterations to an estuary

on light attenuation, transport and fate of toxic substances, and

sedimentation. Potentially adverse alterations include increased

wastewater discharge, increased stormwater runoff, dredging, dredge

material disposal, and wetland destruction. Because sediment particles

are negatively buoyant and settle, accurate sediment models must

consider vertical sediment dynamics. Two obstacles, however, limit

application of sediment models. Field data are needed to calibrate and

validate estuarine sediment models, and the bottom boundary conditions

for the sediment must be specified.

Improved understanding and simulation of estuarine sediment

processes are dependent upon reliable field data. The importance of

the processes that potentially control sediment dynamics must be

understood and included in a realistic model. For example, if wind-

waves are an important sediment resuspension mechanism, then a model

that ignores water motions at wind-wave frequencies can not

realistically simulate estuarine sediment transport. Laboratory

studies can be used to improve understanding of sediment transport

process, but field conditions are often difficult to recreate in the

laboratory. Reconstruction of realistic sediment beds in a laboratory

is difficult, especially when the natural sediments are biologically

active. A field data collection program has far less control than

found in a laboratory, but the data and insights gained in the field

are directly applicable to the estuary being studied. Calibration is

the selection of model parameters that permit the model to accurately

simulate field data. Once selected, validation may be performed on an

independent data set to prove that the model algorithms and model

parameters can be used to make reliable predictions. Field data is

therefore required to develop a predictive model in which managers,

scientists, and the public can have confidence.

Sediment transport processes at the interface of the water column

and bottom sediments must be accurately simulated in a numerical model

because the bed is an omnipresent potential source and sink of

suspended sediment. A particle resting on the bed will move (erode)

when the lift force generated by the hydrodynamics is sufficiently

large. If a particle moves by rolling, sliding, or saltating

(jumping), then it contributes to the bed load. A mobilized particle

may also become suspended (or resuspended) in the water column.

Interaction of suspended particles may be caused by salt flocculation

of clay minerals and formation of large aggregates of organically bound

inorganic particles. Deposition is the process that returns particles

to the bed. The sedimentary processes of initiation of particle

motion, bed load, and deposition all occur at or near the interface of

the sediment bed and the water column. Other near-bed factors which

may affect hydrodynamics and sediment transport include bed forms,

armoring, porous beds, suspended-sediment stratification, and

biological activity.

The purpose of this research was to improve the understanding and

numerical modeling of sediment resuspension and the vertical transport

of resuspended sediment in the shallow estuarine environment. Field

data and a numerical model were used to accomplish this task. From

1988 to 1990, the author collected sediment resuspension data during

potential periods of resuspension in Old Tampa Bay, a shallow estuary

on the west-central coast of Florida. These field data were analyzed

by the author to determine the sediment resuspension mechanisms in Old

Tampa Bay. The author modified and used a vertical one-dimensional

model to simulate the Old Tampa Bay data and other data. The numerical

model was used as a tool to help analyze the Old Tampa Bay data and to

help determine significant sediment transport processes in Old Tampa

Bay.

The remainder of this chapter discusses existing literature on the

significance of estuarine sediments, sediment transport processes,

field studies related to estuarine sediment transport, numerical models

applicable to estuarine sediment transport, and the relation between

previous studies and this research. The data collection methodology

is discussed in chapter 2, and the data are presented and analyzed in

chapter 3. The numerical model is described in chapter 4. Numerical

simulations of the marine surface layer and critical shear stresses on

continental shelves were conducted to test modifications made to the

model during this research, and these simulation results are presented

and discussed in chapter 5. Simulations of suspended-solids

concentrations in Old Tampa Bay are presented and discussed in chapter

6. Conclusions of this study are summarized in chapter 7.

Significance of Estuarine Sediments

Bottom sediments are an omnipresent factor that affect the water

quality and biological productivity of an estuary. Potential sources

of sediment particles for an estuary include rivers, net sediment flux

from the marine environment, overland runoff, and anthropogenic point

sources. Sediment particles are commonly trapped and deposited in the

deeper or vegetated parts of the estuary. Bed sediments provide the

substrate for benthic organisms, seagrasses, and marshes, and chemical

exchange processes occur between the bed sediments and the water

column. Bed sediments, especially fine sediments, can be resuspended

up into the water column where they may 1) increase nutrient

concentrations, 2) transport trace metals, 3) influence the mortality,

life cycle, food supply, and photosynthesis of estuarine species, and

4) may move and settle in ports or marshes.

Bed sediments chemically interact with the overlying water column

and benthic biological communities. Diffusive fluxes between the bed

sediments and water column commonly remove dissolved oxygen from the

water column (Hinton and Whittemore 1991, Svensson and Rahm 1991) and

recycle nutrients to the water column (Callender and Hammond 1982,

Hammond et al. 1985, Simon 1988, Ullman and Aller 1989). In addition,

sediment geochemistry in seagrass beds determines the limiting nutrient

for seagrass growth (Short 1987).

The release of nutrients from sediments to the water column is

enhanced during resuspension events. Increased nutrient concentrations

in the water column that were caused by resuspension events have been

observed by Gabrielson and Lukatelich (1985) during wind related

sediment resuspension events in the Peel-Harvey estuarine system in

Australia, by Fanning et al. (1982) during storms on the continental

shelf of the Gulf of Mexico, and by Schwing et al. (1990) after

destabilization of bottom sediments by a seiche in Monterey Bay,

California. Grant and Bathmann (1987) found that bacterial mats

deposit sulfur on surficial bottom sediments and that resuspension is

an important mechanism for returning sulfur to the water column.

Phytoplankton and organic detritus resting on the bottom can also be

resuspended and impact estuarine productivity (Roman and Tenore 1978,

Gabrielson and Lukatelich 1985). The supply of Radon 222, a tracer, to

the water column from resuspension flux and diffusion flux is about the

same in the Hudson River estuary (Hammond et al. 1977). Simon (1989)

estimates that one resuspension event in the Potomac River that lasts

minutes can add as much ammonium to the water column as the diffusive

flux can in 5 to 1000 days, depending upon the site. Laboratory

experiments show that typical water column concentrations of

particulate nutrients would double in a few hours during a resuspension

event (Wainright 1990) and that biological growth is increased when

resuspended material is added to microcosms (Wainright 1987).

In addition to nutrients, trace metals and other contaminants may

be adsorbed to sediment particles and these contaminants are

detrimental to the biological health of many estuaries. For example,

metals are partitioned between adsorbed and dissolved phases, so the

transport of metals is related to sediment transport (Dolan and Bierman

1982, Li et al. 1984, Horowitz 1985). In South San Francisco Bay,

availability of trace metals may be a factor that limits growth of some

phytoplankton species, sorption processes influence dissolved

concentrations of metals, and sorption processes vary among specific

metals (Kuwabara et al. 1989). Sediment concentrations of trace

metals, PCBs, pesticides, or polynuclear aromatic hydrocarbons exceed

the median concentration associated with biological effects in

estuaries in Alaska, California, Connecticut, Florida, Hawaii,

Maryland, Massachusetts, New Jersey, New York, Oregon, Texas, and

Washington (Long and Morgan 1990).

The mortality, food supply, and life cycle of some estuarine

species may be affected by suspended sediments and sediment transport

processes. For species restricted to the benthos, mortality may be

increased by resuspension or burial and sediment transport may regulate

the food supply to both suspension and deposit feeders (Nowell et al.

1987). Eggs, cysts, and spores of many zooplankton and phytoplankton

species reside in bottom sediments and erosion may inject them into the

water column where they may hatch. Circumstantial evidence indicates

that this process may contribute to red tide outbreaks (Nowell et al.

1987). Sellner et al. (1987) found that increased suspended-sediment

concentrations reduced the survival rate of newborn larval copepods in

Chesapeake Bay. In addition, development and reproduction of survivors

was inhibited.

Suspended sediments reduce the sunlight available for

photosynthesis. Smaller particles are more efficient light attenuators

(Baker and Lavelle 1984, Campbell and Spinard 1987). McPherson and

Miller (1987) found that non-chlorophyll suspended material is the most

important component of light attenuation in Charlotte Harbor, Florida.

Inorganic suspended material is the dominant cause of light attenuation

in several New Zealand estuaries (Vant 1990) and sediment resuspension

by tidal currents and wind waves is an important cause of attenuation

(Vant 1991). In the coastal waters of northwest Africa, light

attenuation is greatest in nearshore waters where sediment

concentrations are greatest, compared to offshore waters, and nearshore

light attenuation reduces phytoplankton growth (Smith 1982). The

reduction of light in the water column reduces seagrass photosynthesis

and the maximum depth at which seagrasses can grow (Dennison 1987).

Resuspended sediments may move throughout the estuary, depending

upon the circulation, and, in addition to possibly spreading the

adverse effects already mentioned, may deposit in ports or marshes.

Large man-made basins that serve as ports and marinas are commonly

depositional environments that require costly maintenance dredging

(Granat 1987, Kobayashi 1987, Headland 1991). Estuarine sediments may

also deposit in marshes where vegetation and benthic algae impede water

motion and resuspension and enhance deposition, formation, and

maintenance of an important habitat (Ward et al. 1984, Krone 1985, Huh

et al. 1991).

Sediment Transport Processes

Sediment transport processes differ somewhat depending on whether

the sediment is noncohesive or cohesive. Noncohesive sediment

particles do not interact electro-chemically with other particles, and

cohesive sediment particles interact electro-chemically with other

cohesive particles. Sediments with a diameter larger than 20 pm are

generally noncohesive. Thus, gravel, sand, and coarse silts are

noncohesive. Cohesion increases as particle size decreases below 20 pm

(Migniot 1968), and clay minerals are generally cohesive. Transport of

noncohesive sediments is controlled by the processes of initiation of

particle motion, bed load transport, suspended load transport, and

deposition. Cohesive sediments differ in that they are not transported

as bed load, and interparticle electro-chemical forces may cause

flocculation in brackish estuarine waters. Both cohesive and

noncohesive suspended inorganic particles may adhere to large

organically bound aggregates that can deposit rapidly in an estuary.

Bed and near-bed factors that may affect hydrodynamics and sediment

transport include bed forms, bed armoring, suspended-sediment

stratification, fluid-mud, porous beds, and biological activity.

Initiation of Motion of Bed Sediments

The horizontal transport of noncohesive sediment as bed load and

suspended bed-material load is dependent upon the initiation of motion

of stationary particles in the sediment bed. Particles that roll,

slide, or saltate along or near the bed are part of the bed load, and

particles that are lifted into suspension are part of the suspended

load. Initiation of particle motion has been reviewed by the Task

Committee on Preparation of Sedimentation Manual (1966), Vanoni (1975),

Miller et al. (1977), Simons and Senturk (1977), and Lavelle and

Mofjeld (1987a, 1987b).

A noncohesive particle lying on a sediment bed, for which fluid is

flowing above, will be acted upon by the hydrodynamic forces of lift

and drag in addition to gravity and normal forces from adjacent

touching particles. Vanoni (1975, pp. 92-93), Simons and Senturk

(1977, pp. 400-407), Yalin (1977), and Wiberg and Smith (1987) describe

the forces acting on a bed particle in detail. The formulation of the

hydrodynamic forces will differ depending on whether the flow over the

particle is laminar or turbulent and whether the bed is composed of

uniform or heterogeneous particles. If the hydrodynamic forces exceed

a threshold or critical value, then the particle will move. Particle

motion may also be initiated by organisms that disturb the bottom

sediments and by trawling in coastal waters (Churchill 1989).

Because the exact geometry, size, and shape of every sediment

particle in a bed can not be determined, practical analysis of the

problem of initiation of particle motion requires assuming that the bed

is a continuum of particles instead of a large quantity of discrete

particles. Noncohesive sediment particles are usually assumed to be

spherical. The particle sizes (measured by sediment particle diameter

ds) that are present in an actual bed will vary vertically and

horizontally, but it is commonly represented as one or more sizes for

which a certain percentage N of the bed material is finer (denoted as

dN). The physical properties of the sediment particles at a fixed

location also will vary with time as particles are transported.

In addition to sediment particle properties, the flow field will

vary spatially and temporally. The shear force exerted by the flow on

the bed is commonly used to indicate the magnitude of the hydrodynamic

force on the bed. Because most natural flows are turbulent and

turbulent flows fluctuate in space and time, the bottom shear stress at

a fixed point will vary about the mean bottom shear stress. The bottom

shear stress will also vary spatially, especially if bed forms are

present. For example, for three-dimensional ripples, Ikeda and Asaeda

(1983) found that sediment is eroded from the side slopes of

longitudinally trailing ridges by lee side eddies and that sediment

entrainment is correlated with intermittent bursts of the lee side

eddy.

If the bed is assumed to be a continuum of particles, the

inception of particle motion is a stochastic rather than a

deterministic process because the particle sizes and bottom shear

stress vary spatially and temporally (He and Han 1982). Usually, the

existence of particle motion is determined by assuming a threshold

shear stress or threshold velocity. Definitions of threshold have been

categorized by Lavelle and Mofjeld (1987a) as those based on sediment

flux in a flume, visual flume observations (Kramer 1935, White 1970,

Mantz 1977), erosion rate experiments for cohesive sediments

(Partheniades 1965, Ariathurai and Arulanandan 1978, Sheng and Lick

1979, Kelly and Gularte 1981, Parchure and Mehta 1985), and field

measurements in marine environments (Sternberg 1971, Wimbush and Lesht

1979, Lesht et al. 1980, Larsen et al. 1981). Because of the

stochastic nature of the inception of particle motion problem, however,

some particle motion will still occur below the threshold values

(Einstein 1941, 1966, Taylor and Vanoni 1972, Vanoni 1975, Christensen

1981, Lavelle and Mofjeld 1987a). The concept of threshold is useful

for practical problems (Simons and Senturk 1977 pp. 417-487, Blaisdell

et al. 1981, Blaisdell 1988) and when ability to observe particle

motion is limited in the field (Sternberg 1971, Wimbush and Lesht 1979,

Lesht et al. 1980). Threshold criteria should only be applied with the

knowledge that initiation of particle motion is a stochastic process.

The most common threshold criterion is probably that presented in

the Shields diagram as modified by Rouse (fig. 1-1) (Vanoni 1975 p. 96,

Simons and Senturk 1977 p. 410). The Shields threshold criterion was

determined by extrapolating measured transport rates of laboratory

experiments to the point of zero transport for fully developed

turbulent flows, noncohesive sediments, and flat beds. The abscissa is

the boundary Reynolds number

R* U, ds / v (1-1)

in which U is the shear velocity for which U. (r /p) where r is

the bottom shear stress and p is the fluid density, and v is the

kinematic fluid viscosity. The curve for R. less than two was

extrapolated by Shields and is not based upon data. The ordinate is a

dimensionless shear stress or Shields parameter

0
o
r* (7-) d (1-2)
(7 s -7) d

in which ys is the specific weight of the sediment particle and 7 is

the specific weight of the fluid. The line on figure 1-1 indicates the

critical condition for sediment motion. If (R*,r*) is below the line,

then there is no sediment motion, and if (R*,r*) is above the line,

then there is sediment motion.

1.u - -
0.8 : : : :- -

0.5
0.4 -- --- -- --- -
0.3- -

0.2 -
ALUI O tO ( l)gd,
0.1 1. 0 .00
0.08
0.08
0.05-
0.04
0.03

0.02

0.2

0.4 0.6 1.0

! 4

6 810 20 40 80100 200

500 1.000

BOUNDARY REYNOLDS NUMBER, R.-,

Figure 1-1, Shields critical shear stress diagram, from Vanoni (1975).

Several modifications to the Shields diagram have been proposed

because the bottom shear stress is included on both axes of the

diagram, so an iterative procedure must be used to determine the

critical shear stress for a given particle. The Task Committee on

Preparation of Sedimentation Manual (1966) added the quantity

d s 0.5
d Vs
[ 0.1( 1) g d ]
v s
to the diagram in which g is the acceleration of gravity. Yalin

(1977), Bonnefille (see Vollmers 1987) and Gessler (1971) regrouped the

dimensionless variables to make the diagram easier to use.

The Shields diagram also has been modified to account for bed

forms and small boundary Reynolds numbers. Gessler (1971) adjusted

Shields diagram because some of Shields' flume experiments formed

ripples and small dunes that increased the critical shear values by 10

percent. Inman (1963) shows a second curve for rippled beds. Shields

diagram was extended for values of R, less than 2 by Miller et al.

(1977) (mostly with data from White (1970)) with an envelope

encompassing the data scatter and by Mantz (1977) with a power function

that lies inside the envelope (Larsen et al. 1981). The extended

Shields diagrams are applicable to noncohesive fine sands and silts.

Although the Shields diagram was developed for unidirectional

flows, it also may be applicable to the estuarine and marine

environments. Field observations in oscillatory flow environments have

been in good agreement with the Shields criterion (Davies 1985).

Madsen and Grant (1977) stated that Shields diagram is applicable to

locations with both waves and currents and good agreement with data in

such environments was observed by Drake and Cacchione (1986). Field

measurements in Puget Sound by Sternberg (1971) were in good agreement

with the Shields diagram modified to account for ripples presented by

Inman (1963). Larsen et al. (1981) found that the extended Shields

diagram was in good agreement with observed threshold grain motion for

oscillatory flows on a continental shelf.

Other threshold relations have been developed for noncohesive

sediments. Lane (1955) used field data to develop curves of critical

shear stress vs. mean particle diameter, which give higher critical

shear stresses than Shields' diagram. Sundborg (1956) developed a

threshold criterion based on particle size and mean velocity 1 meter

above the bed, and Inman (1949, 1963) developed a criterion that is

dependent on particle size and shear velocity, both of which agree with

Puget Sound data (Sternberg 1971). Wiberg and Smith (1987) derived a

critical shear stress equation for beds with uniform particle size that

corresponded closely to Shields' diagram. In addition, they also

derived a critical shear stress equation for heterogeneous beds that

was in good agreement with experimental data.

For cohesive sediment beds, the consolidation of the bed is an

important factor that helps determine whether and how the bed sediment

will move. When initially deposited, cohesive sediment beds are

unconsolidated high concentration suspensions (fluid-mud) and have

little shear strength. Consolidation is a time dependent function of

the overbearing pressure, particle size, and of the clay mineralogy

that dewaters, compresses, and strengthens the bed (Meade 1966,

Terzaghi and Peck 1967 p. 84, Hayter 1986). Therefore, there is no

general threshold of motion criterion for cohesive sediments such as

the Shields diagram for noncohesive sediments. Critical shear stresses

for various cohesive sediments and consolidation states have been

determined in the laboratory (Partheniades 1965, Ariathurai and

Arulanandan 1978, Sheng and Lick 1979, Thorn and Parsons 1980, Kelly

and Gularte 1981, Mehta et al. 1982) and the field (Gust and Morris

1989). Three modes of initial motion of cohesive sediments are re-

entrainment of unconsolidated high concentration suspensions, surface

erosion of individual particles and flocs, and bulk erosion (also

called mass erosion) which is the sudden failure of the upper part of

the bed (Krone 1986, Mehta et al. 1989a).

Bed Load Transport

If a particle moves by rolling, sliding, or saltating (jumping),

then it contributes to the bed load. Determining whether a particle

that is not in contact with the bed is contributing to the bed load or

suspended load can be difficult (Einstein 1950, Bagnold 1966, Murphy

and Aguirre 1985). Bed load usually is composed of sand-sized and

coarser particles. Fine sediments (silts and clays) are usually

immediately suspended upon initiation of motion and are not transported

as bed load.

Calculation and measurement of bed load are difficult. Reviews of

equations for calculating bed load have been presented by Vanoni (1975,

pp. 168-172), Simons and Senturk (1977, pp. 508-543), Yalin (1977),

Gomez and Church (1989) (for gravel beds only), Ludwick (1989), and

Stevens and Yang (1989). Bed load transport equations were compared by

van Rijn (1984a), who concluded that predicted bed load transport rates

are accurate only within a factor of 2. Gomez and Church (1989)

assessed bed load equations for gravel beds and determined that none of

the reviewed equations consistently performed well. Carson (1987)

evaluated several factors used to estimate bed load in alluvial

channels and determined that the grain component of the bed shear

stress (shear stress corrected for sidewalls and bed forms) is a better

predictor of bed load than mean velocity, stream power, and unit stream

power. The bed load transport rate is related to the grain component

of the bed shear stress so this quantity should be used in bed load

formulas when bed forms are present (Wiberg and Smith 1989). Field

measurements of bed load are discussed by Emmett (1980), Edwards and

Glysson (1988), and Ludwick (1989), but bed load is difficult to

measure because any device placed near the bed may disturb the flow and

the rate of bed load transport.

Erosion and Bottom Shear Stress

The rate at which particle motion is initiated is an important

quantity for studies and numerical models of sediment transport. van

Rijn (1984b) developed a sediment pick-up function (mass per unit area

per unit time) by utilizing a mechanical device (a sediment lift) at

the bottom of a flume to supply erodible noncohesive particles and

compared the developed sediment pick-up function to the sediment pick-

up functions by Einstein (1950), Yalin (1977), Nagakawa and Tsujimoto

(1980), de Ruiter (1982), and Fernandez-Luque (see van Rijn 1984b).

Bed load and suspended load transport rates calculated with the

developed sediment pick-up function compared well with field and

laboratory data (van Rijn 1986b). Noncohesive particles that are

picked up are initially saltating and will either contribute to the bed

load or suspended load (Murphy and Aguirre 1985).

Quantification of the erosion of cohesive sediments is difficult

because of the lack of understanding of erosion mechanisms and the

numerous factors involved. Thus, the erosion relationships that have

been developed are simple relationships that contain coefficients that

must be determined in the laboratory or field. Assuming that the rate

of surface erosion is proportional to the nondimensional excess shear

gives (Ariathurai and Arulanandan 1978)

E M 1 (1-3)

in which E is the surface erosion rate in mass per unit area per unit

time, M is an erosion rate constant defined as 'the increase in the

rate of erosion for an increase in the interface fluid shear by an

amount equal to the critical shear stress of that soil' (Ariathurai and

-2 -1
Arulanandan 1978) that has a range of values from 0.003 g cm min to
-2 -1
0.03 g cm min r is the bottom shear stress, and r is the critical

shear stress for erosion. For shear stresses less than the critical

value, no surface erosion occurs. Equation 1-3 was developed using

data from placed beds with uniform shear strength which is not

representative of cohesive beds in nature (Mehta et al. 1982). Erosion

functions by Mehta et al. (1982) and Parchure and Mehta (1985) were

determined using more realistic laboratory sediment bed conditions.

Equation 1-3 has been applied in numerical models of cohesive

sedimentation (Ariathurai and Krone 1976, Thomas and McAnally 1985,

Sheng et al. 1990b, Uncles and Stephens 1989). Because equation 1-3 is

an empirical erosion formula, it is not limited to cohesive sediments,

and it has been used to simulate transport of noncohesive suspended

sediments in the lower Mississippi River (Schoellhamer and Curwick

1986).

A general erosion equation is determined by setting the erosion

rate equal to a power of the excess shear stress

E a Ir7r (1-4)

in which a and r are constants that are determined by calibration.

Values of a have been found to range from to 3.7x-6, for
Values of a have been found to range from 1.9xlO to 3.7xi0 for r

in dynes/cm2, and n has been found to range from 0.23 to 10 (Lavelle et

al. 1984). Lavelle et al. (1984) used equation 1-4 to simulate erosion

as a stochastic process (Lavelle and Mofjeld 1987a).

The erosion rate is dependent upon the shear stress applied to the

bed by the flowing water and the bottom shear stress from the near-bed

velocity. Schlichting (1969) gave velocity profile equations for

turbulent flow in the near-wall region for hydraulically smooth, rough

and transitional cases. Given a measured or simulated velocity

profile, these equations can be used to calculate the bottom shear

stress. In general, the velocity distribution is

u 1 In(z/k ) + B (1-5)
u1 Ki s

in which u. is the shear velocity for which =- p u, the velocity u is

at an elevation z above the bed, x is von Karman's constant (0.4), k
s

is the height of the bottom roughness elements, and B is a roughness

function that has the form
u. k u, k
B = s log- + b = In + b (1-6)
v 2.3 v
in which u*ks/v is a roughness Reynolds number (R), and s and b are the

slope and intercept of a line that fits a segment of the data presented

in figure 1-2 (Schlichting 1969 fig. 20.21), which is a plot of B vs.

log R developed from laboratory data. The boundary is smooth for R <

5, rough for R > 70, and transitional for intermediate values of R.

For a smooth wall, s 5.75 and b 5.5, and equations 1-5 and 1-6 can

be written as

u_ 1 In (9.03 z u* / v) (1-7)
u*

For a rough wall s 0.0 and b 8.5 and equations 1-5 and 1-6 can be

written as

u_- In (30 z / ks) (1-8)
u s

19

11 I I--- -- ; -- ;------ i -

a 10' I
i .. ... .-. __

1 i i I
7 o, *

smoIt77 l i- transition completely rough

02 04 a6 as ZO UZ 1 .6 LB Z O 22 Z# 2.8 Z2. 30 32

Figure 1-2, Roughness function B in terms of Reynolds number, from
Schlichting (1969).

for which the elevation with zero velocity is z =k /30. Equations 1-5

and 1-6 can be combined to derive a general expression for the

elevation with zero velocity

z k R-s/2.3 e-b (1-9)
o s
For transitional flow regimes, figure 1-2 and equations 1-5 and 1-6

must be applied in an iterative manner.

If the velocity used to calculate the bottom shear stress is from

the near-bed region of a hydraulically smooth (R < 5) or transitional

flow (5 < R < 70), the velocity profile may not be logarithmic near the

bed and a different velocity profile equation is applicable. Reichardt

(see Wiberg and Smith 1987) developed a velocity distribution equation

that gives a linear profile for elevations much smaller than the top of

the viscous sublayer, a logarithmic profile for elevations much larger

than the top of the viscous sublayer, and a smooth and accurate

transition between the two regions. This velocity distribution is

u = [ In(l+nz )

+ + +33z
(ln(z ) + ln(K)) (1 e-z /11.6 e-0.33
11.6e )
(1-10)
+ + UZ/V
in which the nondimensional quantities z = uz/v and z = uz /v.

The bottom roughness is dependent upon grain size in the bed and

bed forms. The bottom roughness element that controls the resistance

in the region of the water column adjacent to the bed is the grain

roughness and above this region the roughness is controlled by larger

roughness elements such as ripples, dunes, or surface irregularities

caused by bioturbation (Smith and McLean 1977). The grain roughness is

normally related to the bed sediment size distribution.

Two types of shear stresses, the total shear stress and the grain

shear stress, can be considered. The total shear stress is the shear

stress exerted on the flow by all of the bottom roughness elements.

The grain shear stress is the spatially-averaged (over a bed form

wavelength) shear stress exerted on the sediment particles in the bed

by the flow. The grain shear stress is less than the total shear

stress. Sediment motion is dependent upon the grain shear stress

(Vanoni 1977, McLean 1991).

If the height and wavelength of the bed forms are constant, then

the matching elevation at which the velocities from the two regions are

equal can be calculated based on the bed form geometry (Smith and

McLean 1977). Smith and McLean (1977) measured velocity profiles at

several positions on dunes in the Columbia River and showed that the

spatially-averaged (or "zero-order") velocity profile could be

constructed from equation 1-5. The zero-order velocity profile

averages near-bed velocity variations caused by the bed forms and the

resulting total shear stress and grain shear stress are spatially-

averaged values. Expressions for the bottom roughness due to bed forms

have been developed by van Rijn (1984c) for steady flow in alluvial

channels and by Grant and Madsen (1982) for oscillatory flow.

For an oscillatory flow, a simple expression for the maximum

bottom shear stress is (Jonnson 1967)

7w ub Ub (1-11)

in which f is a friction factor and
2ak
gak (1-12)
Ub w cosh(kh) (1-12)
is the maximum bottom orbital velocity from linear wave theory, where a

is the wave amplitude, k is the wave number, w is the angular wave

frequency, and h is the water depth. Shallow-water waves are assumed

to be present for kh < w/10 and deep-water waves are present for kh > r

(Dean and Dalrymple 1984). Equations and diagrams for the friction

factor f as a function of the wave properties and bottom roughness have

been determined empirically (Jonsson 1967, Kamphius 1975). For waves

in the absence of a mean current and for rough turbulent flow, Grant

and Madsen (1979, 1982) determined that the friction factor is given by

f 0.08 / [Ker2(2T) + Kei2(2Tf)] (1-13)

in which Ker and Kei are Kelvin functions of zero order and
kb 2
= 30f (1-14)
30 x ub Tf

in which kb is the bottom roughness. The friction factor appears on

both sides of equation 1-13, so the equation must be solved

iteratively. Equation 1-13 is valid for rough turbulent flow for which

Ab/kb > 1 where Ab=ub/w is the orbital amplitude just above the

boundary layer. Bottom stress in oscillatory boundary layers (e.g.

Jonsson and Carlsen 1976) has also been computed with turbulent

boundary layer models (Sheng 1984, Sheng and Villaret 1989).

A potentially important resuspension mechanism in estuaries is the

nonlinear interaction of a wave field and a mean current that can

increase the shear stress on the bed to a value greater than the sum of

the wave only and current only shear stresses. Grant and Madsen (1979)

developed a model to estimate the bed shear stress when waves and

current are present. The model is based on the assumption of rough

turbulent flow that is wave dominated. They also developed a model

that includes ripple formation and the effect of ripples on the bed

shear stress (Grant and Madsen 1982). Weaknesses of the Grant and

Madsen models include the introduction of a fictitious reference

velocity at an unknown level, a rather arbitrarily estimated thickness

of the wave boundary layer, and the model being valid only for wave

dominant cases (Christoffersen and Jonsson 1985). The Grant and Madsen

models also assume that 1) the thickness of the logarithmic layer is

constant, which is not correct when waves are present (Sheng 1984), 2)

the wave field can be represented by a single wave period and wave

height, and 3) the eddy viscosity is linear and time invariant.

Cacchione et al. (1987) found that the shear stress and bottom

roughness estimated by the moveable bed model were in good agreement

with estimates from measured velocity profiles, but no bottom

photographs were available to check the estimated bed form geometry.

Drake et al. (1992) took bottom photographs and found that the moveable

bed model overestimated the size of bottom ripples. Larsen et al.

(1981) present a simpler solution for the model formulation by Grant

and Madsen (1979) that is applicable to smooth, transitional, and rough

bottom roughness regimes and the shear stress at the observed threshold

of motion of noncohesive sediments observed in the field was in good

agreement with Shields diagram. The results of the Larsen et al.

(1981) model and the Grant and Madsen (1979) model are virtually

identical for rough bottoms. Simpler wave-current models that compare

well with laboratory data have also been developed by Christoffersen

and Jonsson (1985) for wave and current dominated cases and by Sleath

(1991) for wave-dominated cases and rough beds. All of the above

models consider wave-averaged bottom stresses by invoking some sort of

a priori parameterization of wave-current interaction.

Wave-current interaction in bottom boundary layers has also been

studied without such a priori parameterization. Sheng (1984) used a

Reynolds stress turbulence model to simulate the detailed dynamics of

the boundary layer over the wave cycle by using a small time step

(1/100 of the wave cycle) and specifying the mean and orbital currents

at the outer edge of the boundary layer. The results were then

averaged over the wave cycle to produce wave-averaged stresses. Sheng

(1984) found that the model of Grant and Madsen (1979) generally

overestimated the wave-averaged stress and apparent roughness height.

Suspended Load Transport

Suspended sediment is transported by the flow in the water column.

The Reynolds time-averaged equation for three-dimensional sediment

transport, written in tensor notation, is (Vanoni 1975)
ac ac au'c'
at + uj x x (1-15)
Jt ax

in which c is the mean suspended-sediment concentration, u is the mean

velocity of the sediment particles, c' is the turbulent fluctuation of

sediment concentration about the mean, u' is the turbulent fluctuating

velocity of sediment particles, t is time, and x is the coordinate

axis. The first term in equation 1-15 represents the time rate of

change of sediment concentration, the second term represents the

advection and settling of particles, and the term on the right hand

side represents the turbulent dispersion of particles. The second

order correlation of velocity fluctuation and concentration fluctuation

is often represented as the product of the mean concentration gradient

and an eddy diffusivity K such that (Vanoni 1975)

uc' K ac (1-16)
1 v. 8x.
1 1
The eddy diffusivity is often assumed to be proportional to the eddy

viscosity A (Kv -A ) which is often assumed to be related to mean flow

variables (Fischer et al. 1979) or to have a particular distribution

(Vanoni 1975, Fischer et al. 1979). Eddy diffusivity also can be

calculated with an advanced turbulence closure algorithm (Sheng 1986a,

Celik and Rodi 1988, Sheng and Villaret 1989).

An analytic expression for the vertical distribution of suspended

sediment in an open channel can be derived from equations 1-15 and 1-

16. Assuming that lateral and longitudinal variations are small and

that the mean vertical water velocity is small, equation 1-15 reduces

to the vertical conservation of mass equation for sediment
ac a
at az (WsC w'c') (1-17)
in which w is the terminal settling velocity of sediment particles and

z is the vertical coordinate axis (Vanoni 1975). The first term within

the parenthesis is the settling flux of sediment and the second term is

the vertical flux of sediment (usually upward) caused by turbulence.

For steady flow and a parabolic distribution of the eddy diffusivity,

equations 1-16 and 1-17 can be used to derive the Rouse equation

( h-z a )Z (1-18)
-a z h-a
in which z is the elevation above the bed, ca is the suspended-sediment

concentration at a usually small reference elevation a above the bed,

and the exponent Z is the Rouse number equal to ws/(siu*) (Vanoni

1975). Equation 1-18 gives a suspended sediment distribution that is

greatest near the bed, as is expected due to the negative buoyancy of

sediment particles. In addition, the vertical gradient of suspended-

sediment concentration is greatest near the bed. The water velocity is

small near the bed compared to the vertically-averaged velocity, so the

dissimilar distributions of suspended sediment and velocity must be

considered when calculating the suspended load in a river (Schoellhamer

1986, McLean 1991).

Flocculation and Aggregation

Salt flocculation of clay minerals and formation of large

organically bound aggregates of inorganic particles may occur in the

water column, especially in estuaries. Clay minerals transported to

estuaries by rivers encounter high cation concentrations that reduce

repulsive forces that prevent flocculation in freshwater. Particle

cohesion begins at salinities of 0.6 to 2.4 ppt, depending on the clay

mineralogy, and increases with salinity up to about 10 ppt, although

the rate of increase is small for salinities greater than 3 ppt (Krone

1962, Mehta 1986). Seawater has salinity of about 34 ppt.

Flocculation has been observed for clay concentrations as low as 50

mg/L (Ozturgut and Lavelle 1986). As a floc grows, the density,

settling velocity, and shear strength of the floc decrease (Krone

1986). Turbulence may break up relatively weak flocs (Krank 1984).

Meade (1972) states that salt flocculation has been overemphasized in

the literature while biological agglomeration by filter feeding

organisms has not received enough attention.

Suspended material in estuaries and oceans is commonly found in

the form of large aggregates (length scale 0.5 mm or larger), often

called marine snow (Wells and Shanks 1987). In the ocean, large

aggregates are primarily biogenic material (Fowler and Knauer 1986),

but estuarine aggregates are primarily inorganic particles attached to

organic material (Eisma 1986). The primary collision mechanism that

forms large aggregates is differential settling, not Brownian motion or

fluid shear (Hawley 1982, Eisma 1986). Aggregation by organisms may

also be important in estuaries (Meade 1972, Krank 1984, Eisma 1986).

Large aggregates have been observed to remain intact in currents as

much as 50 cm/s in estuaries (Wells 1989), but large aggregates tend to

break apart when sampled (Shanks and Trent 1980, Krank 1984, Eisma

1986, Fowler and Knauer 1986). Sampling procedures are limited to in

situ settling columns (Shanks and Trent 1980, Gibbs 1985, Fowler and

Knauer 1986) and photography (Krank 1984, Eisma 1986, Wells and Shanks

1987, Wells 1989). Eisma (1986) and Dyer (1989) state that the size of

the aggregates is limited by the turbulence microscale, which is the

size of the smallest turbulent eddies. The density of large aggregates

decreases with increasing size and the settling velocity and porosity

increases with increasing size (McCave 1975, Hawley 1982, Gibbs 1985).

Settling and Deposition

Particles that are part of the bed or suspended load may settle

through the water column and deposit on the bed. A noncohesive

particle falling in quiescent fluid is affected by the forces of

gravity, buoyancy, and drag. The terminal settling velocity for a

sphere in quiescent fluid is given by Stokes law and Rubey's equation,

which are presented by Vanoni (1975) and Simons and Senturk (1977). A

nonspherical particle, which is common in nature, may have a different

terminal settling velocity. A shape factor defined by Alger and Simons

(1968) can be used to predict settling velocity and settling behavior

of nonspherical particles and shells (Mehta et al. 1980). Terminal

settling velocity equations are for a single particle, but high

concentrations of noncohesive particles, which are most likely to occur

near the bed, may hinder settling and decrease the terminal settling

velocity (Vanoni 1975, Simons and Senturk 1977). The size of suspended

particles tends to decrease with elevation above the bed, so assuming a

single suspended particle size with a single settling velocity may not

be appropriate (McLean 1991).

Deposition of suspended sediments in natural hydrologic systems is

complicated by turbulence. The effective settling velocity of a

particle settling in a vertically oscillating flow will be less than

the terminal settling velocity for the particle in quiescent fluid

because of nonlinear modification of the drag force (Hwang 1990) or

vortex trapping (Nielson 1984). As a particle settles toward the bed,

the turbulence intensity generally increases and, thus, the probability

increases that a turbulent eddy will carry the particle either upward

or downward. The turbulence intensity will decrease very near the bed

and vanish in the viscous sublayer adjacent to the bed. If a particle

passes through the high turbulence zone near the bed, it can then

deposit on the bed. Li and Shen (1975) and Bechteler and Farber (1985)

presented random walk models that stochastically simulate particle

settling. Turbulence, however, is not an independent variable.

Density stratification by suspended sediments can dampen turbulence

(Sheng and Villaret 1989) and reduce the transport capacity of the flow

(McLean 1991).

Because turbulent intensity and the vertical gradient of sediment

concentration are greatest near the bed, near-bed hydrodynamic

processes determine deposition rates. Sheng (1986c) derived a

deposition velocity formula by considering the resistances that

sediment particles experience in various near-bed layers including the

logarithmic layer, vegetation canopy, laminar sublayer, and biochemical

effects. Numerical suspended-sediment transport models that solve

equation 1-15 generally have finer vertical grid resolution closer to

the bed and represent the bottom boundary condition either by setting

the net upward sediment flux equal to an erosion rate minus a

deposition rate (Sheng and Lick 1979, Schoellhamer 1988, Sheng et al.

1990a), by estimating a near-bed sediment concentration (van Rijn

1986a, Celik and Rodi 1988, Schoellhamer 1988), or by setting a net

erosion or deposition flux which is dependent on the bottom shear

stress (Thomas and McAnally, 1985).

Estimates of sediment deposition rates in estuaries have been made

with sediment traps and acoustic devices. Interpretation of sediment

trap data is difficult, but sediment traps have been used to estimate

deposition rates (Oviatt and Nixon 1975, Gabrielson and Lukatelich

1985). Bedford et al. (1987) used an acoustic transceiver to measure

near-bed suspended-sediment concentration profiles and calculated

deposition and erosion fluxes over a 3.5 hour period and a tidal cycle

at one site in Long Island Sound.

Large aggregates control settling and are an important mechanism

for transporting material to the bottom of oceans (McCave 1975, Shanks

and Trent 1980, Hawley 1982, Fowler and Knauer 1986) and estuaries

(Wells and Shanks 1987, Dyer 1989). Fowler and Knauer (1986) give a

minimum range of large aggregate settling velocities of 1 to 1000

m/day. Shanks and Trent (1980) measured settling velocities of large

aggregates in Monterey Bay, California, and the northeastern Atlantic

Ocean and found that the range of settling velocities was 43 to 95

m/day.

The settling velocity of depositing cohesive sediments is

dependent upon the sediment concentration. Krone (1962) measured

settling velocities of San Francisco Bay sediment in still water and

determined that the median settling velocity was proportional to the

concentration to the 4/3 power. This is a typical result for settling

column experiments with cohesive sediments (Mehta 1986), probably

because differential settling is the dominant collision mechanism

(Farley and Morel 1986). In estuaries, however, collisions caused by

velocity gradients are most important because they form the strongest

aggregates (Krone 1986). Ross (1988, also in Mehta 1989) conducted a

settling column experiment and found that the settling velocity of

Tampa Bay mud increased as the concentration increased up to about 1

g/L, the settling velocity was about constant (about 0.32 mm/s or 27

m/day) for concentrations from about 1 to 10 g/L, and the settling

velocity decreased as the concentration increased above 10 g/L due to

hindered settling.

Deposition of flocs of cohesive sediments may be prevented by the

turbulent boundary layer above the bed in which flocs may be broken

apart and lifted up into the water column. Krone (1962) conducted

deposition experiments in a recirculating flume from which he

determined that the rate of deposition of cohesive sediment from a

vertically mixed flow is

D C s (1 7/c), r < cr (1-19)
s cr cr
in which C is the vertically well-mixed suspended-sediment

concentration and r is the critical shear stress for deposition that
cr
must be determined by analyzing time series of concentration and shear

stress measured in the laboratory or field. Laboratory experiments

indicate that r is less than r the critical shear stress for
cr c
erosion. If r > rcr, no deposition occurs, and if 7 < rcr, equation 1-

19 will eventually deposit all of the sediment in suspension.

Laboratory experiments by Mehta and Partheniades (1975), however,

showed that a constant fraction of the original suspension will be

maintained in suspension indefinitely for 7cr > r > min where r min is

a minimum shear stress below which all of the suspended sediment will

deposit. Equation 1-19 is an empirical formula for deposition that

does not include the near-bed hydrodynamic processes that determine

deposition rates. Equation 1-19 has been used to simulate noncohesive

suspended-sediment transport in the lower Mississippi River

(Schoellhamer and Curwick 1986) in addition to cohesive sediment

transport (Ariathurai and Krone 1976, Thomas and McAnally 1985, Uncles

and Stephens 1989).

Other Bed and Near-bed Factors

Many bed and near-bed factors may affect hydrodynamics and

sediment transport. These factors include bed forms, bed armoring,

suspended-sediment stratification, fluid-mud, and porous beds. Some of

these factors (bed forms, bed armoring, suspended-sediment

stratification, and fluid-mud) are the result of sediment transport and

affect the hydrodynamics, which, in turn, affect the sediment

transport. Biological activity also affects sediment transport.

One consequence of noncohesive sediment transport can be the

formation of bed forms in riverine (Kennedy 1969, Vanoni 1975, Simons

and Senturk 1977, Yalin 1977) and coastal (Boothroyd 1985)

environments. Bed forms such as ripples and dunes do not occur in

cohesive sediment beds. Bed forms increase the bottom roughness of

open channels compared to flat bed conditions in which the only

roughness elements are the bed sediment particles. Because some of the

total bottom shear stress is caused by the bed forms, the shear stress

applied to the particles will decrease, so the total (grain and form)

critical shear stress for initiation of particle motion will increase

(Vanoni 1975, McLean 1991). On the upstream face of a dune, the grain

shear increases from zero at the reattachment point to a maximum at the

crest (Shen et al. 1990). van Rijn (1984c) used flume and field data

to develop and verify a methodology to predict bed form dimensions and

effective hydraulic roughness for steady flow in alluvial channels.

For oscillating flows, Grant and Madsen (1982) present empirical

relations for bed ripple geometry and an expression for the bottom

roughness. Drake et al. (1992), however, found that the Grant and

Madsen empirical relations overestimated ripple height on the Northern

California continental shelf.

An armored or paved bed has finer particles removed from the bed

surface so that only coarser noncohesive particles remain at the

surface thus preventing erosion of finer particles below (Simons and

Senturk 1977, Sutherland 1987). For equilibrium conditions, Jain

(1990) defines an armored bed as having a coarse surface layer that is

inactive (or immobile) and a paved bed as having a coarse surface layer

that is actively eroding and depositing (or mobile). For

nonequilibrium or degrading conditions, the particle-size distribution

of the surficial bottom sediments coarsens and the erosion rate

decreases with time. Therefore, near-bed sediment transport may be

dependent upon the armoring process, especially in alluvial channels.

Dawdy and Vanoni (1986) review several bed armoring and pavement

studies and predictive algorithms. More recently, bed armoring

algorithms have been presented by Karim and Holly (1986) and Park and

Jain (1987).

Near-bed suspended-sediment stratification will affect the

hydrodynamics and, therefore, affect sediment transport processes at

the bed. As previously mentioned, high near-bed sediment

concentrations may hinder settling of particles. Several authors have

found that sediment-laden flows reduced near-bed turbulence intensity

or bottom shear stress in steady and uniform open-channel flows (Vanoni

and Nomicos 1960, Itakura and Kishi 1980, Lau 1983, Julien and Lan

1988, Xingkui and Ning 1989). West and Oduyemi (1989) measured

turbulence and near-bed density stratification in an estuary and found

that density stratification damped turbulence. Flume experiments by

Vanoni and Nomicos (1960), however, indicated that the effect of bed

forms on the total bottom shear stress is much greater than the effect

of suspended sediment. Parker and Coleman (1986) found that suspended

sediments may either increase or decrease near-bed turbulence intensity

and bottom shear stress (discussed by Julien and Lan (1988)). Near-bed

stratification in sediment-laden flows may reduce near-bed turbulent

shear stresses and, therefore, reduce erosion rates (Sheng and Villaret

1989) and the sediment-transport capacity of the flow (McLean 1991).

Costa and Mehta (1990) collected hydrodynamic and suspended sediment

data in a high energy coastal environment and found a hysteresis in the

relationship between suspended-sediment concentration and the flow

velocity, with higher concentrations for accelerating flows, due to

near-bed density stratification.

An extreme case of near-bed suspended-sediment stratification is

the formation of an unconsolidated fluid-mud by cohesive sediments

above a consolidated bed in estuarine and near-shore environments. In

this situation, the water column is composed of two layers--a thin

dense non-Newtonian fluid underlying a Newtonian fluid. The fluid-mud

may interact with the overlying fluid. Surface waves cause mud waves

to form in the fluid-mud and the fluid-mud acts to attenuate (dampen)

surface waves (Suhayda 1986, Wells and Kemp 1986, Mehta 1991, Jiang and

Mehta 1992). Suhayda, Mehta, and Jiang and Mehta developed models to

simulate the interaction of the two fluids. Wells and Kemp also point

out that the formation of mud waves can produce significant

reentrainment of the fluid-mud.

The bed is commonly assumed to be an impermeable boundary but

water exchange between the bed and water column may advect sediment and

ripples may enhance water exchange at the bed/water column interface.

In an alluvial river water may exchange between the river and pore

space of the alluvium (Bencala et al. 1984) and this water exchange may

transport fine sediments by advection to and from the relatively coarse

alluvium (Jobson and Carey 1989). Thus, advection may be an important

transport process at the bed/water interface for fine, noncohesive

sediments in an alluvial channel. For oscillatory flows over porous

beds in coastal environments, the total water exchange across the

bed/water column interface averaged over the wave period is

significantly greater for rippled beds than for flat beds (Shum 1992).

Benthic biological communities may influence sediment-transport

processes, especially in biologically abundant estuaries. Erosion

rates may be decreased by microbial films and benthic algae that

stabilize sandy sediments (Gabrielson and Lukatelich 1985, Grant et al.

1986, Meadows et al. 1990) and cohesive sediments (Montague 1986).

Tracks made by motile bivalves, however, reduced the critical shear

velocity of fine sandy sediments in a laboratory flume by 20 percent

(Nowell et al. 1981). Fecal mounds from polychaetes and a deposit-

feeding bivalve in a laboratory flume had a higher critical shear

velocity than ambient cohesionless sediments and were transported as

bed load (Nowell et al. 1981). Sediments that have been bound by

secretions from benthic invertebrates may settle more slowly than

unbound particles when resuspended (Meadows et al. 1990). Seagrasses

reduce bottom orbital velocities of wind-waves and thus reduce erosion

and increase deposition (Ward et al. 1984). Biological effects

frequently are ignored in sediment-transport studies because biological

activity is difficult to quantify in the field, difficult to reproduce

in a laboratory, and difficult to incorporate into a numerical model.

Field Studies Related to Estuarine Sediment Transport

Field studies of estuarine sediment transport are often conducted

to determine the mechanisms that control sediment transport processes,

such as resuspension and horizontal transport. Data is collected by in

situ instrumentation, water sample collection and analysis, and/or

bottom sediment sampling and observation. Statistical analysis, time

series analysis, and numerical process models are used to analyze the

data. Field studies provide less control than laboratory studies, but

they are more applicable because of the difficulty recreating natural

conditions in the laboratory. For numerical studies, an initial or

concurrent field study may indicate the processes that must be included

in the numerical model in order to accurately simulate sediment

transport and a field study will provide data for calibration and

validation of the numerical model. Sediment resuspension mechanisms

are often the focus of estuarine field studies because the bottom of

the estuary is an omnipresent potential source of suspended sediment

and many potential sediment resuspension mechanisms are present in the

estuarine environment. The potential sediment resuspension mechanisms

include tidal currents, wind waves, wave/current interactions, seiches,

trawling, and vessel traffic. Many of these potential mechanisms are

also present on the continental shelf and in large lakes, so some

relevant field studies in these environments will also be mentioned in

this section.

The mean current in estuaries and on the continental shelf may

resuspend bottom sediments. Wimbush and Lesht (1979) deployed a tripod

with current meters and a bottom camera at a site with a bottom

sediment of medium sands in the deep (710 meters) Florida Straits and

estimated the critical velocity for ripple formation. Lavelle et al.

(1984) deployed a current meter and transmissometer 5 meters above a

fine sediment bottom in 200 meters of water in Puget Sound. They

observed that tidal currents resuspended bottom sediments and that the

erosion rate could be described with the shear stress power law given

by equation 1-4. Both of these studies were conducted in deep water

where wave motion was not observed. Bohlen (1987) deployed an

instrument array that included an electromagnetic current meter and

transmissometers for monitoring suspended-solids concentrations in 12

meters of water in Chesapeake Bay. Spectral analysis of the suspended-

solids concentration showed variation at tidal frequencies, including a

spring/neap variation. Roman and Tenore (1978) collected and analyzed

water samples over several tidal cycles in Buzzards Bay, Massachusetts,

and observed resuspension of organic carbon and chlorophyll-a at a site

with a muddy bottom in 13 meters of water by tidal currents that was

significant enough to potentially affect estuarine productivity. They

did not address resuspension by wind waves, possibly because the data

may contain a fair weather bias due to the manual collection of water

samples and the lack of in situ instrumentation. Costa and Mehta

(1990) observed resuspension by tidal currents in Hangshou Bay, China,

a high energy tidal environment.

Wind waves may resuspend bottom sediments in relatively shallow

water. Anderson (1972) collected water samples in a tidal lagoon with

fine sediments and found that wave heights of only a few centimeters

resuspended bottom sediments in water depths of 40 to 150 cm. Water

samples and sediment traps were used by Ward et al. (1984) to determine

that shallow water sites (less than 2 meters) in Chesapeake Bay with

seagrasses attenuate wave motion and reduce resuspension compared to

sites without seagrasses. Gabrielson and Lukatelich (1985) found that

sedimentation rates calculated from sediment traps deployed in the

Peel-Harvey estuarine system were temporally correlated with the

strength and duration of wind events and spatially correlated with wind

direction and fetch. Kenney (1985) deployed sediment traps in Lake

Manitoba at a depth of 4.2 meters and found that resuspension of bottom

sediments during wind events stratified the water column. Lavelle et

al. (1978) deployed a current meter and turbidimeter 1 meter above the

medium sand bed of Long Island Sound at an average depth of 10.5

meters. Wind waves during a storm resuspended bottom sediments and

tidal currents did not cause resuspension. An empirical relationship

between suspended sediment concentration and wave orbital velocity in

Long Island Sound was developed by Lesht et al. (1980). In addition to

resuspension by tidal currents in Chesapeake Bay discussed previously,

root-mean-squared velocity fluctuations and suspended-solids

concentrations increased when the wind was blowing up the longitudinal

axis of Chesapeake Bay (Bohlen 1987). Davies (1985) deployed a

tetrahedra containing electromagnetic current meters and a video camera

in 4 to 10 meters of water in Start Bay, England, and found that the

observed threshold of sediment motion in oscillatory flow was in good

agreement with Shields diagram (fig. 1-1). Sheng et al. (1990b)

collected synoptic suspended sediment data and deployed anemometers,

pressure transducers, electromagnetic current meters, and optical

backscatterance suspended-solids sensors from several platforms and

piles over two one-month periods in Lake Okeechobee, Florida. It was

found that wind waves associated with the diurnal lake breeze

resuspended fine sediments and this field data were used to calibrate

and validate three-dimensional and vertical one-dimensional numerical

sediment transport models (Sheng et al. 1990b, Sheng et al. 1992).

Mehta (1991) also collected data and applied a vertical one-dimensional

model to study fluid-mud and sediment resuspension by wind waves in

Lake Okeechobee. Perjup (1986) deployed an instrument tower in about 2

meters of water in the Ho Bugt estuary, Denmark, from which water

velocity, suspended-solids concentration, salinity, depth, wind speed,

and wind direction were measured. Perjup found that the suspended-

solids concentration did not correlate with mean water velocity but was

correlated to parameters containing wind speed and onshore wind

direction and inversely correlated with salinity, which was well-

correlated with water depth. These results indicate that resuspension

was caused by onshore wind waves, especially at low tide when the near-

bed orbital wave motion can be greatest.

As mentioned previously, the nonlinear interaction of waves and

current can significantly increase the bottom shear stress and thus it

may be an important sediment resuspension mechanism. Drake and

Cacchione (1986) used data from Norton Sound, Alaska, and the northern

California continental shelf to calculate the grain shear stress with a

modified version of the fixed bed wave/current interaction model of

Grant and Madsen (1979). The observed resuspension threshold was in

good agreement with Shield's diagram (fig. 1-1). Cacchione et al.

(1987) and Drake et al. (1992) compared field data collected on the

northern California continental shelf (water depth 85 meters) and

results of the moveable bed wave/current interaction model of Grant and

Madsen (1982) and found good agreement for bottom shear stress but not

bed form geometry. The high bottom stresses caused by wave/current

interaction during winter storms were a major factor controlling the

distribution of surficial sediment on the northern California

continental shelf. Measurements were made with a tripod containing

electromagnetic current meters, a pressure transducer, a nephelometer

(for measuring light scattering which can be calibrated to suspended

solids), and a camera (Cacchione and Drake 1979).

Estuaries contain abundant fisheries and they are convenient sites

for ports, so trawling and commercial vessels are potential sediment

resuspension mechanisms. Churchill (1989) reviewed several sets of

hydrodynamic and sediment data from the middle Atlantic Bight and

determined that sediment resuspension by trawling is a significant

source of suspended sediments. Schoellhamer (1991b) found that during

the departure and arrival of a cruise ship at the Port of St.

Petersburg the suspended-solids concentration increased almost an order

of magnitude due to the maneuvering of the vessel. The resuspended

sediments settled within 1 to 2 hours once the cruise ship either

docked or departed.

In partially-mixed and well-mixed estuaries, the maximum

suspended-solids concentration is often present in brackish waters and

is greater than concentrations found elsewhere in the estuary (Uncles

and Stephens 1989). This feature is called a turbidity maximum and

field data collection programs and numerical models have been used to

determine the controlling mechanisms. Uncles and Stephens (1989)

collected water samples and data from an optical suspended-solids

sensor to measure salinity and suspended-solids concentrations during

twice monthly sampling trips that proceeded up the Tamar estuary,

England. These data were used with a longitudinal one-dimensional

model to determine that the location of the turbidity maximum is

associated with the location of the freshwater/saltwater interface and

that local resuspension, and perhaps gravitational circulation and

stratification, determined suspended-solids concentrations in the

turbidity maximum. West and Oduyemi (1989) deployed electromagnetic

current meters and suspended-solids sensors on a bed frame in the Tamar

estuary and observed that near-bed stratification by suspended sediment

reduced the turbulence intensity. Hamblin (1989) collected vertical

profiles of velocity, salinity, and suspended-solids concentrations

from a vessel anchored at several stations near the turbidity maximum

in the upper Saint Lawrence estuary. These data showed that local

resuspension by the mean current controlled the suspended-solids

concentrations at the measurement sites and that a landward flux of

sediment near the bed is maintained by ebb-flood asymmetry and a

reduction in vertical mixing during flood tide caused by the salt

wedge. A vertical one-dimensional sediment transport model was

developed with these data. Grabemann and Krause (1989) analyzed time

series of long-term salinity, current meter, and optical transmittance

sensor data collected in the Weser River estuary to determine that

tidally controlled resuspension and deposition is the dominant process

in the turbidity maximum and that gravitational circulation is a long-

term source and sink of particles. An intensive two-week experiment on

the Weser River estuary included the deployment of instrumentation to

measure velocity, salinity, and suspended-solids concentration at

several locations and similar measurements were taken from vessels

(Lang et al. 1989). These data showed that there was a close

relationship between suspended-sediment concentration and near-bed

velocity gradient and stratification and the data was used to develop a

three-dimensional numerical model.

Most of the field studies discussed so far have focused on tidal

transport processes and vertical sediment transport processes, but

field studies have also been conducted to quantify subtidal horizontal

sediment transport. Powell et al. (1989) collected water samples in

South San Francisco Bay and observed that during spring runoff fresh

water and suspended sediment from the Sacramento River entered South

San Francisco Bay. Wells and Kim (1991) used vibracores, surficial

sediment data, and vertical profiles of velocity, salinity, and

suspended sediment concentration collected during monthly sampling

trips from several sites in the Neuse River estuary, North Carolina, to

describe long-term sedimentation patterns. In Puget Sound, Baker

(1984) collected suspended-solids concentration data with a

transmissometer at several sites during several sampling trips to help

determine that gravitational circulation and surface and bottom sources

of particles control the distribution and transport of suspended

solids.

Sample collection for the preceding studies was generally

performed by collecting water samples and/or collecting pressure,

velocity, and suspended solids concentration data from conventional

sensors. Alternative methods of data collection have been utilized to

collect data on estuarine sediment transport processes. Rubin and

McCulloch (1979) used side scan sonar to determine the shape of

bedforms in central San Francisco Bay, which indicate near-bed

circulation patterns and sediment transport directions. Bedford et al.

(1987) used an acoustic transceiver to sample the sediment

concentration in 1 cm bins in the bottom 70 cm of the water column over

a 3.5 hour period and a tidal cycle at a site in Long Island Sound.

This device provides much better resolution of the near bed sediment

concentration profile than is available from optical instrumentation,

but calibration is difficult due to sensitivity to the particle size

distribution. Resuspension fluxes were calculated and were found to be

best correlated with the squared velocity fluctuation (horizontal and

vertical) due to wind waves and turbulence about the mean velocity.

Portable flumes that are deployed on the bottom of the estuary have

been used to study the erosion of in situ sediments under controlled

conditions. For example, Young and Southard (1978) deployed a sea

floor flume in Buzzards Bay, Massachusetts, and observed that the in

situ critical shear velocity was one-half the value found in laboratory

experiments due to bioturbation. Laboratory and sea flume values for

the critical shear velocity also differed in Puget Sound (Gust and

Morris 1989). To estimate the residence time of fine particles

introduced at the water surface in Puget Sound (11-16 days), Lavelle et

al. (1991) used vertical profiles of radioactive isotope activities and

a sorption model.

Due to practical limitations, it is very difficult to collect

synoptic samples throughout a large water body by conventional means,

but remote sensing from aircraft and satellites can be used to

synoptically determine suspended-solids concentrations near the water

surface in large water bodies. The advantage of remote sensing is that

a measurement of an entire estuary can be made instantly but the

disadvantages are that remote measurements must be calibrated with

suspended-solids concentrations, sampling times and locations are

limited by the satellite orbit, clouds and other weather may degrade or

prevent satellite observation, and resolution may be limited. Sheng

and Lick (1979) used remote-sensing data and field data to produce the

near-surface suspended-sediment concentrations in the western basin of

Lake Erie, which were then used to provide initial conditions and

validation for a numerical sediment transport model of Lake Erie. Huh

et al. (1991) used remote sensing to help determine that storms 1)

produce landward sediment transport along coastal Louisiana and 2) help

build marshes. In Mobile Bay, remote sensing has shown rapid changes

in sediment concentrations due to high river inflow and wind-induced

sediment resuspension (Stumpf 1991).

Numerical Models Applicable to Estuarine Sediment Transport

Numerical models can be used to simulate or predict estuarine

sediment transport, resuspension events, sedimentation rates, adsorbed

constituent transport, and light availability. Transport processes are

dependent upon hydrodynamics, so sediment transport models require an

accurate hydrodynamic model. Deposition, erosion, and density

stratification caused by suspended sediments may affect the

hydrodynamics, so a coupled hydrodynamic and sediment transport model

may be required. Many numerical models of suspended sediment transport

have been developed for steady riverine flows (for example van Rijn

1986a, Celik and Rodi 1988, Schoellhamer 1988), but they are not

applicable to estuaries because of the unsteady motions of tidal waves,

seiches, and wind waves. Some riverine models are stochastic or random

walk models that use Lagrangian particles to represent suspended

sediment (Alonso 1981, Bechteler and Farber 1985). The random walk

approach may be applicable to estuarine sediment transport. Mehta et

al. (1989b) reviewed estuarine applications of primarily cohesive

sediment transport models, and they discussed simulation of the bed and

zero-, one-, two-, and three-dimensional models. A similar outline

will be followed herein. Some estuarine sediment transport processes

are also present on the continental shelf and in large lakes, so some

relevant numerical models for these environments will also be discussed

in this section.

The properties of the sediment bed may vary with time and depth

below the interface with the water column, especially for cohesive

sediments. Bed properties such as density and shear strength may vary

with distance below the top of the sediment bed, the elevation of which

may vary during a tidal cycle. In order for a numerical sediment

transport model to account for these temporal and spatial variations,

the bed could be divided into layers with different properties, and

new layers could be added during deposition and existing layers could

be removed by erosion. Properties such as layer density, thickness,

and shear strength can vary temporally and spatially in the simulated

estuarine bed (Thomas and McAnally 1985, Hayter 1986, Sheng 1991).

If the spatial variation of suspended-sediment concentration can

be ignored, then only the temporal variation of suspended-sediment

concentrations needs to be considered. This type of modeling is

referred to as zero-dimensional and is equivalent to assuming that the

study area is well-mixed. Krone (1985) used a zero-dimensional model

to simulate and predict deposition in a marsh. Amos and Tee (1989)

simulated the Cumberland Basin in the Bay of Fundy as a well-mixed

water body in order to calculate sediment fluxes at the mouth of the

Basin. Because the distribution of sediment sources in an estuary is

likely to be nonuniform and because the settling property of sediment

increases concentrations deeper in the water column, the assumption

that the spatial variation of suspended sediment is negligible is

generally poor.

The longitudinal variation of suspended sediment in an estuary can

be simulated with horizontal one-dimensional models. Cross sectional

variations are averaged transversely and vertically, so if the

suspended-sediment concentration varies significantly in the cross

section, one-dimensional model may not be applicable. Uncles and

Stephens (1989) used a longitudinal one-dimensional model to describe

the magnitude and location of the turbidity maximum in the Tamar

estuary. Equations 1-3 and 1-19 were used to simulate deposition and

erosion and the coefficients in the equations were selected by

calibration with measured suspended-sediment concentrations. Hayter et

al. (1985) predicted shoaling rates in the Hooghly River estuary,

India, with a longitudinal one-dimensional model that was calibrated

with measured channel dredged material volumes.

The vertical profile of suspended sediment at a given location in

an estuary can be simulated with a vertical one-dimensional model.

These models are applicable when horizontal gradients of suspended-

sediment concentration can be neglected. Weisman et al. (1987)

simulated a depositional tidal lagoon with a series of vertical layers

for which vertical dispersion was neglected and the simulated shoaling

rate was reasonable compared to shoaling rates estimated with

radioactive isotopes. Teeter (1986) developed a vertical transport

model that uses a Richardson number dependent parabolic eddy

diffusivity to include the effect of density stratification. Hamblin

(1989) used this model to simulate vertical mixing of suspended

sediment at a site in the upper St. Lawrence estuary were suspended-

sediment concentrations were observed to depend upon local

resuspension. Field data were used to determine the erosion function

and particle settling velocity. Costa and Mehta (1990) also applied a

Richardson number dependent model to simulate vertical sediment

transport in Hangzhou Bay, China. Steady state vertical profiles of

near-bed suspended sediment in the Florida Straits were estimated with

a model by Adams and Weatherly (1981) that used three sediment size

classes. Velocity data but no suspended-sediment concentration data

were collected to calibrate the model. Sheng and Villaret (1989) used a

vertical one-dimensional model (OCMID, which is presented in detail and

utilized later) with a simplified second-order turbulence closure model

to determine vertical profiles of velocity and suspended-sediment

concentration and the erosion rate of bottom sediments for laboratory

experiments. They found that near-bed stratification by high

suspended-sediment concentrations reduces turbulence intensity and

erosion rates. The same basic model was used to determine erosion

rates of sediments from measured suspended-sediment concentrations from

Lake Okeechobee and was successfully applied to simulate the dynamics

of the vertical structure of suspended-sediment concentration over

several 3-day and 1-week periods (Sheng et al. 1990b, Sheng 1991, Sheng

et al. 1992).

Vertical hydrodynamic models have also been developed for

estuarine applications. OCM1D has been used to simulate storm-induced

currents in Grand Bank (Sheng 1986b) and in the Atlantic Ocean during

the passage of hurricane Josephine (Sheng and Chiu 1986). Davies et

al. (1988) used a one-dimensional vertical hydrodynamic model to

simulate the interaction of waves and a mean current. This model

achieves turbulence closure with a dynamic equation for turbulence

energy and turbulence scaling relations for the mixing length and eddy

viscosity, but results were not compared to data. A vertical

hydrodynamic model with complete second order turbulence closure model

(i.e., Reynolds stress model) was used by Sheng (1984) to simulate the

development and evolution of the logarithmic wave boundary layer for

laboratory and field data.

The horizontal transport of sediment in an estuary has been

simulated with depth-averaged two-dimensional models. Ariathurai and

Krone (1976) developed a two-dimensional finite element model for

simulating cohesive sediment transport in estuaries. Erosion and

deposition were simulated with equations 1-3 and 1-19. This model is

included in the U.S. Army Corps of Engineers TABS-2 modeling system for

estuarine hydrodynamics and sedimentation (Thomas and McAnally 1985)

which has been applied to several estuaries (Heltzel 1985, Granat 1987,

Hauck 1991). Heltzel (1985) and Granat (1987) used physical model

results for the simulated hydrodynamic boundary conditions and the

validation of simulated hydrodynamics and measured shoaling rates

(instead of measured suspended-sediment concentrations) were used to

calibrate sediment simulations. For San Francisco Bay (Hauck 1991),

hydrodynamic simulations were in agreement with measured data but

sediment simulations did not successfully reproduce field data,

possibly because simulated settling velocities for cohesive sediment

were not dependent upon the concentration, poor simulation of wave

action and related shear stress, and only one grain size for cohesive

sediments was simulated.

Depth-averaging may not be appropriate for estuarine hydrodynamic

and sediment transport models. Density stratification and

gravitational circulation are three-dimensional flow features in

estuaries that can not be simulated by a depth-averaged model.

Sediment resuspension may be dependent upon the bottom shear stress but

depth-averaged models do not calculate the vertical velocity profile

and therefore empirical relations must be used to calculate the bottom

shear stress instead of equation 1-5. Because of the settling property

of sediment, sediment concentrations are usually much higher near the

bed than up in the water column, so depth-averaging may not be

appropriate. For example, in an open channel, the near-bed region

contains the largest suspended-sediment concentrations but the smallest

velocities, so the depth-averaged longitudinal velocity of suspended

sediment is less than the depth-averaged water velocity (Schoellhamer

1986). Downing et al. (1985) combined an analytic vertical one-

dimensional sediment model with linear eddy viscosity and diffusivity,

the Grant and Madsen (1979) wave-current model, and a horizontal two-

dimensional sediment transport model and obtained reasonable simulation

results based on a comparison with field data from the Sagavonirktok

River Delta in Alaska.

Three-dimensional models permit vertical discretization for

simulation of vertical suspended sediment profiles and density

stratification in addition to vertically variable horizontal transport

by tidal currents, wind-induced circulation, and gravitational

circulation. Sheng and Lick (1979) used vertical two- and three-

dimensional circulation and sediment transport models and a wave-

hindcasting model, in addition to remote-sensing data, to simulate

wind-wave sediment resuspension and transport in Lake Erie. Laboratory

experiments were used to determine settling velocity, critical shear

stress, and erosion rates which were a bilinear function of the excess

shear stress. Wang et al. (1987) used a three-dimensional hydrodynamic

and sediment transport model to simulate sediment transport in Kachemak

Bay, Alaska, but field data were not included in the study. A three-

dimensional hydrodynamic and sediment transport model of the turbidity

maximum in the Weser estuary, Germany, by Lang et al. (1989) was able

to reproduce some of the features present in measured suspended-

sediment concentrations. Hayter and Pakala (1989) applied a three-

dimensional model of estuarine hydrodynamics, sediment transport, and

contaminant transport to the Sampit River in South Carolina but field

data was not available to validate the model. Sheng (1991) and Sheng

et al. (1992) collected field data and successfully applied a three-

dimensional model of hydrodynamics, fine sediment transport, and

contaminant transport to study phosphorus dynamics in Lake Okeechobee,

Florida. A preliminary simulation of three-dimensional sediment

transport in Tampa Bay was performed by Sheng et al. (1992).

Relation of this Research to Previous Studies

The purpose of this research was to improve the understanding and

numerical modeling of sediment resuspension and the vertical transport

of resuspended sediment in the shallow estuarine environment and this

was accomplished by combining three important elements of previous

studies that have not been previously combined. These three elements

are: 1) resuspension by wind-waves in a shallow estuary, 2) field

measurements of suspended-sediment concentrations, and 3) a vertical

sediment transport model. Estuarine sediments and their transport are

important because they may reduce the amount of light penetrating the

water column, may act as a source for adsorbed constituents, and may be

transported to undesirable locations. Estuaries are also biologically

active, and this activity may affect sediment transport. Wind waves

are an important resuspension mechanism in many estuaries (table 1-1),

and they are shown to be an important resuspension mechanism in Old

Tampa Bay in chapter 3. Field measurements of sediment concentrations

are needed to understand estuarine sediment transport processes and to

develop accurate numerical models. Resuspension from the bed and

Table 1-1, Previous studies that are related to this research and
include an estuarine environment, wind waves, field measurements of
suspended-sediment concentrations, or a numerical vertical sediment
transport model.

Estuarine Wind Field Vertical
Environment waves cone. sed. model

Adams and Weatherly (1981) N N N Y
Amos and Tee (1989) Y N Y N
Anderson (1972) Y Y Y N
Baker (1984) Y N Y N
Bedford et al. (1987) Y N Y N
Bohlen (1987) Y N Y N
Cacchione et al. (1987) N Y Y N
Churchill (1989) N N Y N
Costa and Mehta (1990) Y N Y Y
Davies (1985) Y Y N N
Davies et al. (1988) Y Y N N
Downing et al. (1985) Y Y Y N
Drake and Cacchione (1986) N Y Y N
Drake et al. (1992) N Y Y N
Gabrielson and Lukatelich (1985) Y Y N N
Hamblin (1989) Y N Y Y
Hauck (1991) Y Y Y N
Hayter and Pakala (1989) Y N N Y
Kenney (1985) N Y N N
Lang et al. (1989) Y N Y Y
Lavelle et al. (1978) Y Y Y N
Lavelle et al. (1984) Y N Y N
Lavelle et al. (1991) Y N Y Y
Lesht et al. (1980) Y Y Y N
Mehta (1991) N Y Y Y
Perjup (1986) Y Y Y N
Powell et al. (1989) Y N Y N
Roman and Tenore (1978) Y N Y N
Schoellhamer (1991b) Y N Y N
Sheng (1991) N Y Y Y
Sheng and Lick (1979) N Y Y Y
Sheng and Villaret (1989) N N N Y
Sheng et al. (1990b) N Y Y Y
Sheng et al. (1992) N Y Y Y
Stumpf (1991) Y Y N N
Teeter (1986) N N N Y
Uncles and Stephens (1989) Y N Y N
Wang et al. (1987) Y N N Y
Ward et al. (1984) Y Y Y N
Weisman et al. (1987) Y N N Y
Wells and Kim (1991) Y N Y N
West and Oduyemi (1989) Y N Y N

settling of suspended sediment exemplify the importance of simulating

the vertical axis in a numerical sediment transport model.

None of the field and numerical model studies described previously

include estuarine wind-wave resuspension, field measurements of

suspended-sediment concentration, and a vertical sediment transport

model (table 1-1). Studies that include almost all of the elements

include those by Hamblin (1989), Costa and Mehta (1990), Sheng et al.

(1990b), Mehta (1991), and Sheng et al. (1992). Hamblin studied

resuspension by tidal currents near a turbidity maximum, Costa and

Mehta studied resuspension by tidal currents in a high tidal energy

environment, and Mehta and Sheng and his colleagues studied sediment

transport in a large shallow lake. This research used field

measurements of suspended-sediment concentration and a vertical

sediment transport model to study sediment resuspension by wind waves

in an estuary. The numerical model was used as a tool to help analyze

the Old Tampa Bay data and to help determine significant sediment

transport processes in Old Tampa Bay.

CHAPTER 2
SEDIMENT RESUSPENSION DATA COLLECTION METHODOLOGY

In 1987, the U.S. Geological Survey began a study to determine the

effect of fine sediment resuspension on light attenuation in Tampa Bay

and to determine the mechanisms that cause resuspension of fine

sediments. Light attenuation in the waters of Tampa Bay may adversely

affect benthic organisms, seagrasses, and fish and other marine

communities that are dependent upon the seagrasses. Resuspension of

sediment on the bottom of the bay may contribute to light attenuation,

and the mechanisms that cause sediment resuspension in Tampa Bay had

not previously been studied. The author was the project chief for the

study and his duties included project administration, project planning,

data collection, data analysis, presentation of project results at

meetings, and report preparation.

During the U.S. Geological Survey study, pressure, water velocity,

and suspended-solids concentration data were collected in Old Tampa

Bay, a subembayment of Tampa Bay, in order to observe sediment

resuspension events and to determine the hydrodynamic mechanisms that

cause sediment resuspension. In this research, sediment resuspension

data collected from Old Tampa Bay were simulated with a numerical model

of vertical one-dimensional hydrodynamics and sediment transport, and

the model results were analyzed.

Study Area

Tampa Bay is located on the west-central coast of Florida as shown

in figure 2-1. The estuary is Y-shaped, really large (about 1000

Figure 2-1, Old Tampa Bay study area.

km2), shallow (average depth 3.6 meters), vertically well-mixed,

microtidal (spring tide range about 1 meter), and warm (temperature

range from about 14 to 31 "C in 1988 and 1989) (Goodwin 1987, Boler

1990). The northwest subembayment is called Old Tampa Bay and the

northeast subembayment is called Hillsborough Bay. These subembayments

are of most concern ecologically because seagrass loss is more common

and water-quality is probably more impacted by point and non-point

nutrient loading and by reduced tidal flushing (Lewis et al. 1985,

Goodwin 1987). The cities of Tampa, St. Petersburg, and Clearwater are

adjacent to the bay. The subtropical weather includes almost daily

thunderstorms during the summer, occasional storms from winter cold

fronts, and the possibility of tropical storms primarily during the

fall. The river inflow is small compared to the volume of the bay, and

the riverine flushing time, the bay volume divided by the combined

river discharge, is about 2 years (Goodwin 1987). Bottom sediments in

Old Tampa Bay are generally silty very fine sands in the deeper water

(4 meters) and fine sands in shallow water (less than 2 meters) near

the shoreline. Goodell and Gorsline (1961) found clay minerals only in

isolated portions of Old Tampa Bay, and the sedimentology has not

changed significantly since their study (Schoellhamer 1991a).

Sediment Resuspension Monitoring Sites

State variables in an estuary, such as water velocity, salinity,

and suspended-solids concentration, vary spatially and temporally.

Unfortunately, it is neither technically or economically feasible to

obtain complete spatial and temporal coverage when measuring these

variables, so a limited data collection network must be designed that

is representative of a large portion of the estuary. Therefore,

representative sites for resuspension monitoring were selected.

The most important site selection criterion was that a potential

site be at the center of a large area of homogeneous sediment. A

potential problem with monitoring sediment resuspension at one site is

the possibility that horizontal advection may transport suspended

sediment to the site and the resulting increase in suspended-solids

concentration may be mistaken for local resuspension. Selection of a

site in the center of a large homogeneous area of bed sediments reduces

the possibility that more erodible sediments will be transported to the

site and insures that the site is representative of a large fraction of

the bay bottom. Careful data analysis is required to identify the

source of an increase in suspended-solids concentration, and specific

data are discussed in chapter 3. Other criteria were 1) nearly uniform

residual currents over the homogeneous sediment bed, based upon a

depth-averaged two-dimensional barotropic hydrodynamic model by Goodwin

(1987), 2) site location far from ship channels for safety and

homogeneity of bottom sediments and currents, and 3) a secluded

location to reduce vandalism.

The size classification of the bottom sediments in upper Tampa Bay

(north of a line that extends approximately east from St. Petersburg)

were determined with a fathometer and grab samples in 1987 and 1988

(Schoellhamer 1991a). Finer sediments, which are more easily

suspended, are generally found in the deeper parts of the bay. The

fine inorganic bed sediments are commonly in the form of fecal pellets

and organically bound aggregates (Ross 1975). Coarser sediments found

closer to shore in shallower water may experience more wave activity,

however, and also are likely to be resuspended. Thus, selection of a

deeper site with finer sediments and a shallow site with coarser

sediments in Old Tampa Bay was desired.

In Old Tampa Bay, typical deep- and shallow- water sediment

resuspension monitoring sites were selected. A deep-water site

(average depth about 4 meters) was located in the approximate center of

a large area of silty-fine sand at latitude 2757'01" N and longitude

82*37'55" W. The particle size distribution at this site is shown in

figure 2-2, and it has changed little from the 1950's to the 1980's

(Goodell and Gorsline 1961, Taylor and Saloman 1969, Schoellhamer

1991a). The mean particle diameter is 127 pm, and 16% of the material

is fine material (particle diameter less than 63 um). The density of

the bottom sediments is 2.68 g/cm3 and 2.7%, by weight, of the bottom

sediments are organic. A shallow-water site (average depth about 2

meters) was selected 3.0 km south-southwest of the deep-water site in

an area of fine sands on the estuarine shoal at latitude 27o55'30" N

and longitude 8238'33" W. The mean particle diameter is 152 pm, and

the material is 1.5% fine sediment. A submersible instrument package

was used to collect hydrodynamic and sediment resuspension data at the

shallow-water site (Schoellhamer 1990), but only a single point

velocity was measured so the bottom roughness could not be determined.

Therefore, the data collected at the shallow-water site is not well-

suited for numerical modeling and only data collected at the platform

were utilized in this research.

Old Tampa Bay Instrumentation Platform

In June 1988, a platform was constructed at the Old Tampa Bay

deep-water site in order to support sediment resuspension monitoring

instrumentation (fig. 2-3). The platform consists of three vertical

pilings that are the apexes of a 3.7 m equilateral triangle

(Schoellhamer 1990, Levesque and Schoellhamer in press). The pilings

support a triangular galvanized expanded-steel deck approximately 2

I'J i i i i i i i i i iki -i -

80 -

60

40

20

. | a | I I a a I I I I a a a

1000

GRAIN DIAMETER, IN MICRONS

Figure 2-2, Size distribution of bottom sediment at the Old Tampa Bay
platform.

.

Figure 2-3, Old Tampa Bay platform (Levesque and Schoellhamer,
manuscript in review).

meters (average) above the water surface. Water column instrument

sensors were mounted on movable horizontal aluminum arms that were 61-

cm-long and extended perpendicular to a vertical 610-cm-long, 10-cm-

diameter aluminum pipe that was fixed to the center of the steel deck.

The entire pipe structure resembled an inverted tree.

Several types of sensors were deployed from the platform. A

biaxial electromagnetic current meter was mounted at the end of each

horizontal arm, and an optical backscatterance (OBS) suspended-solids

sensor was mounted at the midpoint of each arm. In addition to a

current meter and an OBS sensor, one of the horizontal arms also

supported a pressure transducer for measuring water depth and wave

activity. Wind velocity was measured with a cup anemometer and a wind

vane that were mounted at the top of one platform pile, located about 3

meters above the steel deck. The sensor electronics, data recorder,

and associated power supplies were housed in an aluminum shelter

mounted at one corner of the steel deck. An underwater camera for

taking bottom photographs was not deployed because visibility was

usually insufficient, especially during resuspension events.

Resuspension monitoring instrumentation consisted of Marsh

McBirney Model 512 biaxial electromagnetic current meters, Downing and

Associates Instruments Model OBS-1P backscatterance sensors, and two

types of pressure transducers. The biaxial current meters have a 5.1-

cm-diameter sphere attached very near the end of a 20.3-cm-long metal

rod. The biaxial electromagnetic current meters measure water velocity

using the Faraday principle of electromagnetic induction, where a

conductor (water) moving in a magnetic field (induced by the current

sensor) produces a voltage that is proportional to the water velocity.

The OBS sensors are thumb-size, and they have an optical window at the

relative position of the thumbnail (Downing et al. 1981, Downing 1983).

The optical window is used to transmit an infrared pulse of light that

scatters or reflects off particles in the water up to a distance of

about 10 to 20 cm at angles up to 140* in front of the window. Some of

this scattered or reflected light returns to the optical window where a

receiver converts the backscattered light to an output voltage. For

well-sorted suspended material, the output voltage is proportional to

the suspended-solids concentration and turbidity in the water column.

The calibration of the OBS output to suspended-solids concentration

varies depending on the size and optical properties of the suspended

solids, so the OBS sensors must be calibrated either in the field or in

a laboratory with the same suspended material as is found in the field.

A laboratory evaluation by Ludwig and Hanes (1990) concluded that

instrument response to suspended mud was linear up to a concentration

of 3,000 to 4,000 mg/L, and they recommended that OBS sensors not be

used for the measurement of suspended sand in areas that concurrently

experience suspended mud. Originally, a Geokon vibrating-wire pressure

transducer was used at the platform-site and was eventually replaced

with a Druck strain-gage transducer for improved reliability and

increased sensitivity. Data acquisition, data storage, and sensor

timing were controlled by a Campbell Scientific CR10 data logger. A 5-

minute burst sample of the current meter outputs, OBS sensor outputs,

and the pressure transducer output was collected every hour during

deployments at the platform-site. The burst sample consisted of 1-

second data of all sensor outputs for the duration of the 5-minute

sampling interval. The burst sample was temporarily stored in the data

logger and following the end of the burst sample collection the data

was sent to an external data storage module.

The instruments were submerged continuously when initially

deployed in August 1988. Data was transmitted from the platform in Old

Tampa Bay to the U.S. Geological Survey office in Tampa by a modem and

a cellular phone. Every night, the platform data logger would turn on

a cellular telephone. At the same time, a shore-based personal

computer would automatically call the cellular phone through a modem,

establish communications with the data logger through a modem on the

platform, and issue commands for the data logger to transmit data that

were then stored by the shore-based personal computer. Approximately 2

hours were required to transmit data from 24 burst samples.

Transmission time and power requirements of the cellular phone required

the connection of a deep-cycle 12 volt battery to the solar panel on

the platform, which was accomplished in mid-October 1988. If the

weather conditions were poor during data transmission, the transmission

would fail and cause a loss of data. Both the platform and shore-based

modems used an error checking protocol to help insure accurate data

transmission.

In October 1988, analyses of the available data indicated that the

OBS sensors had fouled, and when the sensors were cleaned by SCUBA

equipped divers, large amounts of marine growth were observed on all of

the sensors. The output from the OBS sensors began to increase as the

sensors fouled, usually about 24 to 48 hours after cleaning, and the

current meters fouled in about seven days. The OBS sensors were coated

with an antifoulant for optical surfaces (Spinard 1987) that only

prevented barnacle growth on the optical surface. The cause of the

fouling was probably an algal slime that would grow on the face of the

sensors and affect their optical properties. In late 1988, when the

instruments were submerged continuously, cleaning dives were conducted

about every two weeks. Therefore, the OBS sensors were fouled most of

the time, and only data collected within 24 to 48 hours of cleaning

were reliable.

The sensor fouling caused by the continuously submerged system

proved to be impractical, and a modification was required, so the

vertical pipe that supported the instruments was attached to an A-frame

and pulley system in December 1988. The vertical pipe was suspended

from an A-frame steel-pipe structure secured to the steel deck that

allowed the vertical pipe to be raised above the water surface for

sensor cleaning and storage. Daily servicing visits to clean the

sensors were usually made when the sensors were deployed, so the

cellular phone and modem were removed from the platform and the data

storage module was exchanged daily during the servicing visits.

Flow around the platform pilings is a potential cause of sediment

resuspension that could affect suspended-solids concentrations at the

platform, so several steps were taken to reduce this possibility and to

determine that any local scour caused by the platform did not

significantly affect concentrations at the platform. Barnacles were

removed from the platform pilings on February 22, 1990, to reduce their

effect on the flow. Scour holes were not observed by divers at the

bases of the pilings, possibly due to bioturbation. The bottom of the

aluminum pipe that supported the instruments was about 20 cm above the

bed to reduce the possibility that it would cause local scour. Four

sets of water samples collected at the platform and 750 and 1500 meters

south-southwest of the platform on November 30, 1989, July 13, 1990,

October 12, 1990, and November 30, 1990, indicate that the average

concentration of suspended-solids at the platform was 6.9 mg/L greater

than the other sites. In March 1990, however, two water samples were

collected at the platform at the same time on 8 different occasions,

and the average concentration difference between concurrently collected

sample pairs was 7.1 mg/L. Thus, the higher observed platform

concentration is equivalent to the sample concentration variation.

Although only a few data points are available, these results indicate

that any local scour caused by the platform did not significantly

affect suspended-solids concentrations at the platform.

Calibration and Output of Electromagnetic Current Meters

The relationship between the output voltages of the

electromagnetic current meters and the water velocity must be known.

The electromagnetic current meters have two separate output voltages,

one for each velocity component, that are linearly related to the water

velocity components. Linear calibration equations are used to convert

output voltages from the meters to water velocities. After

construction and following any repairs, the manufacturer calibrated the

meters by adjusting the output voltages to match specifications. About

annually, the current meter calibrations were checked by the U.S.

Geological Survey hydraulics laboratory at the Stennis Space Center in

Mississippi. The calibration check generally agreed within 10% of the

manufacturers stated calibration values. All calibrations were for

steady flows. If a U.S. Geological Survey calibration was available,

then it was used; otherwise, the manufacturer's calibration was used.

A potential limitation of the electromagnetic current meters used

in this study is a reduction in the meter's output response to short

period water waves, such as wind-waves with 2 to 4 second periods

(frequencies 0.25 to 0.5 Hz). The current meter output response to

short period water waves is reduced by an electronic filter network

that is used to suppress a 60 Hertz carrier signal that is inherent in

the current meter design. At wind-wave frequencies of interest to this

study, the gain (output voltage) of the meters is reduced by the

electronic filter so that actual velocities are greater than the

recorded values.

The recorded velocities can be corrected for the electronic

filtering (Guza 1988). The output filter is an active RC

(resistor/capacitor) 2-pole filter, 6dB per octave rolloff, with a time

constant RC = 0.94 seconds for the meters used in this study. The

filter reduces the magnitude of output signal and the reduction

increases as the frequency of the input signal increases. For a

periodic input signal with angular frequency w, the gain of the filter

is

G(w) [1 + (wRC)2]'2 (2-1)

The filter also causes a phase delay of the output signal. The phase

delay, a negative number in radians, is

4(w) = tan-1 [l/(wRC)] n/2 (2-2)

Equations 2-1 and 2-2 can be applied to correct the recorded data

in the frequency domain. The time series is converted to the frequency

domain via the fast Fourier transform (FFT), resulting in the series
N/2+1
a(l) + 2 Z a(n) cos[(n-l) Aw t] + b(n) sin[(n-l) Aw t] =
n=2
N/2+1
2 a(n) cos[(n-l) Aw t] + b(n) sin[(n-l) Aw t] +
n-l
N
2 a(n) cos[(n-N-l) Aw t] + b(n) sin[(n-N-l) Aw t] (2-3)
n=N/2+2
for which N is the number of data points, a power of 2, the angular

frequency increment Aw-2r/(NAt), At is the time interval of the data,

and the Fourier series coefficients a(2)-a(N), b(2)=-b(N), a(3)-a(N-l),

b(3)--b(N-l), ., a(n)=a(N-n+2), b(n)--b(N-n+2), .

a(N/2)=a(N/2+2), b(N/2)=-b(N/2+2). The left hand side of the equality

is how the Fourier series is commonly presented and the right hand side

is how the FFT algorithm represents the Fourier series, which is

visually more complicated but computationally more efficient. Each

discrete frequency w is represented by a sine and cosine term that can

also be written as

a(n) cos(wt) + b(n) sin(wt) = c(n) cos(wt+e(n)) (2-4)

in which the magnitude is

c(n) ( a(n)2 + b(n)2 ) (2-5)

and the phase is

e(n) tan1 (b(n)/a(n)) (2-6)

The corrected magnitude at the frequency w is

c (n) = c(n)/G(w) (2-7)

and the corrected phase is

e (n) = e(n) + 4(w) (2-8)

The corrected Fourier series coefficients are

ac(n) cc(n) cos[ c(n)] (2-9)

bc(n) cc(n) sin[ c(n)] (2-10)

The inverse FFT is then applied to the corrected Fourier coefficients

to determine the corrected velocity time series. Guza and Thornton

(1980) found that the significant wave height from pressure and

velocity sensors agreed within 20%, and similar agreement occurred

between pressure and corrected velocity data from platform sensors at

the same elevation in November 1990. Raw wave spectra from pressure

and corrected velocity data were also in good agreement, so the

corrected velocities seem to be reasonable.

Response Threshold and Biological Interference of OBS Sensors

Ambient suspended-solids concentrations were often below the

response threshold of the OBS sensors, and biological interference with

the sensors was a potential problem (Schoellhamer, manuscript to be

published in Marine Geology). The sediment load of the rivers that

flow into Tampa Bay is small and Tampa Bay is microtidal, so the

ambient suspended-solids concentrations are also small, about 10 to 50

mg/L. Due to the response threshold of OBS sensors, accurate

interpretation of OBS data may be difficult except during episodic

events that resuspend bottom sediments. Backscatterance from

phytoplankton may be detected when suspended-solids are at ambient

concentrations. The OBS sensor electronics were factory adjusted in

mid-1989 to improve their sensitivity, which diminished but did not

eliminate these problems. Laboratory calibrations of the OBS sensors

with bottom sediments from the platform site did not agree with the

suspended-solids concentrations of collected water samples, so the

sensors were calibrated with water sample data. The standard error of

an OBS sensor in Old Tampa Bay is 5.8 mg/L, based upon a set of 21 OBS

measurements and water samples collected from an elevation 70.1 cm

above the bed by an automatic water sampler during a storm in March

1990 (fig. 2-4). Shallow depths, high water temperatures, and

eutrophic conditions encouraged biological growth on the OBS sensors

that sometimes increased their output voltages and invalidated the

data, so daily cleaning were desirable.

Fish would sometimes interfere with the OBS sensors. During dives

and instrument cleaning, it was noted that fish would sometimes be

congregated around the instrumentation and occasionally would swim past

the OBS sensors. The infrared light pulse would reflect off the fish

02
C-

O

12Z
0-
en Zj
60
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60

40 -

20 -

0-
30

0 0

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00 O
0 o

35 40 45 50 55 50 65

MEDIAN OBS OUTPUT, IN MILLIVOLTS

Figure 2-4, Suspended-solids concentration and median OBS sensor output
70 cm above the bed at the Old Tampa Bay platform, March
1990 (Schoellhamer, manuscript to be published in Marine
Geology).

and produce a high spike (short-duration increase in output voltage)

during the burst sample collection. Usually no more than a few spikes

would occur during a burst, but they were large enough to significantly

affect the resulting mean value for the burst, so the median value

proved to be a simple and more appropriate measure of the OBS burst

average than the mean value. Regular sensor maintenance and careful

data analyses to identify spikes and fouling were used to minimize

abnormalities in OBS data.

Water Sample Collection

Water samples were collected manually and automatically at the

platform. The water samples were usually analyzed by the methods of

Fishman and Friedman (1989) to determine specific conductance,

turbidity, and concentrations of suspended-solids, volatile suspended-

solids, and dissolved chloride. At the Old Tampa Bay platform, point

water samples used for the calibration of the OBS sensors were

collected each day from each OBS sensor depth using a peristaltic pump

connected to tygon tubing that was attached at the end of a long pole

that was lowered to the desired depth.

A continuous water sample collection technique was required for

accurate suspended solids monitoring during storm events and nighttime.

An automatic water sampler was secured in one corner of the steel deck

of the Old Tampa Bay platform, beginning with deployments in March

1990. A SIGMAMOTOR Model 6601 automatic water sampler, connected to

nylon-reinforced teflon-tubing, collected an OBS calibration point

sample every hour at one OBS depth for the duration of most instrument

deployments. The water sampler was set to sample during the sensor on-

time.

Instrument Deployment Strategies

Sediment resuspension monitoring instrumentation was deployed

intermittently in Old Tampa Bay from 1988 to 1990. As mentioned

previously, instrumentation was continuously deployed from the Old

Tampa Bay platform in fall 1988. Analyses of the data indicated that

bottom sediment resuspension did not occur with normal or spring tidal

currents. Waves generated by strong winds were determined to be the

most likely sediment resuspension mechanism in Old Tampa Bay. In

Florida, the typical sources for strong winds are winter storm systems,

tropical storms, and summer thunderstorms. Therefore, in late 1989 and

1990, instrumentation was deployed in Old Tampa Bay before the

anticipated arrival of selected meteorological events. The automatic

water sampler was set up at the platform, the instruments were tested,

and the vertical pipe that supported the instruments was lowered into

the water and secured to the steel deck at the beginning of each

deployment. The submersible instrument package was also deployed at

the shallow-site shortly after the platform instrumentation was

deployed. Weather permitting, daily servicing trips were made to clean

sensors, retrieve data, and collect water samples. The vertical pipe

was secured out of-the water, and the submersible instrument package

was recovered several days after being deployed.

CHAPTER 3
SEDIMENT RESUSPENSION DATA AND ANALYSIS

Data collected intermittently from 1988 to 1990 in Old Tampa Bay

shows that sediment resuspension coincided with wind-waves generated by

strong sustained winds associated with storm systems (Schoellhamer

1990, Schoellhamer and Levesque 1991, Schoellhamer manuscript in

review). Tidal currents were too weak to resuspend measurable

quantities of sediment at the Old Tampa Bay platform, but some bottom

sediment motion probably did occur because of the stochastic nature of

the process (Lavelle and Mofjeld 1987a). Suspended-solids

concentrations returned to ambient values within several (4 to 8) hours

as wave activity diminished.

Sediment resuspension data collected at the Old Tampa Bay platform

during storms in March 1990 and November 1990 were suitable for

numerical simulation, and these data and the analysis of these data by

Schoellhamer (manuscript in review) are presented in this chapter.

Instrumentation deployments at the Old Tampa Bay platform are

summarized in table 3-1. Platform data collected in March 1990 and

November 1990 were suitable for simulation because net sediment

resuspension occurred and both hydrodynamic and suspended-solids

concentration data were collected successfully. Several data sets were

collected during which the OBS sensors could not be calibrated,

sediment resuspension was not observed, or instruments malfunctioned.

Data collected during two tropical storms unfortunately were not

suitable for numerical simulation, but these data and the analysis of

Table 3-1.--Old Tampa Bay platform instrumentation deployments.

Instrumentation
deployment
dates

Fall 1988

Sept 20-21, 1989

Nov 28-30, 1989

March 8-10, 1990

July 11-13, 1990

Oct 9-12, 1990

Nov 28 -
Dec 3, 1990

Hydrodynamic
data
available

Suspended-solids
concentration
data available

Net sediment
resuspension
observed

Y Y

these data by Schoellhamer (manuscript in review) are presented and

compared to the March and November 1990 data. Hydrodynamic data, but

no suspended-solids concentration data, were collected successfully

during tropical storm Keith November 21-24, 1988. No hydrodynamic data

were collected successfully during tropical storm Marco in October

1990. The limited data collected during tropical storms Keith and

Marco indicate that tropical storms can resuspend more sediment than

winter storms can resuspend.

The bottom roughness at the Old Tampa Bay platform was determined

with equation 1-5 and velocity profiles collected during several

instrumentation deployments. Velocity profiles that were measured

during periods of small wave motion and during relatively strong flood

and ebb tides were used. Velocity data collected 183 cm above the bed

were not used because the values usually were not logarithmic compared

to velocities closer to the bed. Data collected before and immediately

after sediment resuspension events did not indicate that bottom

roughness changed significantly. Bottom photographs taken by divers

were not useful due to poor visibility. Divers observed that the bed

was nearly flat with some undulations, possibly from bioturbation, and

that there were no regular bed forms. Thus, bed load transport at the

platform probably was not significant, except possibly during major

sediment resuspension events. The bottom sediment included 16% fine

material, and it is possible that this was the material that was

observed in suspension and that the sandy material either did not move

or did not create ripples that significantly affected the data. The

analysis of the velocity data produced an optimal bottom roughness

equal to 0.3 cm, and the bottom roughness regime was usually

transitional between the rough and smooth limits. The total bottom

roughness is composed of contributions from form drag and grain

roughness, but only the roughness associated with the particles

determines particle motion (Vanoni 1975, McLean 1991). Spatially-

averaged grain shear stress can be calculated by collecting velocity

data within the flow layer adjacent to the bed that is influenced by

grain roughness or by applying empirical relationships based on ripple

geometry (Smith and McLean 1977). For this study, however, velocity

data could not be collected close enough to the bed to recognize

different bottom roughness scales, and no regular ripples were

observed. Therefore, in this chapter, the total shear stress

calculated with the total bottom roughness was used to determine the

sediment resuspension mechanisms at the platform. The spatially-

averaged grain shear stress was estimated by the numerical model that

is presented in chapter 4. For a given particle diameter, a calculated

total shear stress that is greater than a critical shear stress (such

as Shields critical shear stress, fig. 1-1) may not indicate that

motion will occur because not all of the total shear stress is acting

on the grains and biological activity may increase the critical shear

stress.

March 1990 Storm

Data were collected at the Old Tampa Bay platform after a cold

front moved through the Tampa Bay area on March 8, 1990. The high

pressure system behind the front generated 8- to 9-meter-per-second

sustained northeasterly winds from 1100 to 2100 hours on March 8 (fig.

3-1, table 3-2). Wave activity increased as a result of the sustained

northeasterly winds, providing favorable conditions for sediment

resuspension. Operational equipment at the platform consisted of

electromagnetic current meters at elevations of 70 and 183 cm above the

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MARCH
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MARCH
1990 9

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1200 1800
MARCH
8

2400 0600
MARCH
loon 9

Figure 3-1, Measured and calculated quantities at the Old Tampa
Bay platform, March 1990

S70 CM ABOVE BED
T 183 CM ABOVE BED

/\
v \

\v1 V. .

1200

Table 3-2.--Old Tampa Bay platform data, March 8, 1990.

Hour Water
depth
___ (cm)

1400 402.4

1500 410.2

1600 409.8

1700 405.7

1800 399.1

1900 395.4

2000 398.1

2100 405.4

2200 413.9

2300 420.3

Mean current
(cm/s) at elev
70 cm 183 cm

3.8 4.7

1.4 1.3

3.4 4.8

8.3 9.6

8.9 9.5

2.6 2.7

0.6 4.2

8.7 10.0

7.8 8.7

5.7 7.0

Wind
speed
(m/s)

9.2

8.1

8.4

8.6

8.5

8.6

8.1

8.3

6.0

5.8

Wave
amp.
(cm)

38.2

30.8

30.0

26.4

27.7

30.8

21.6

26.2

24.8

14.0

Wave
period
(sec)

2.46/2.43

2.78/2.53

2.64/2.50

2.69/2.52

2.46/2.52

2.75/2.54

2.15/2.41

2.31/2.42

2.27/2.35

2.19/2.58

Suspended-solids
conc. (mg/L)
24 cm 70 cm 183 cm

54.0 42.0 44.0

66.0 47.9 44.0

58.0 47.9 49.1

74.0 51.5 47.8

98.0 68.2 59.3

86.0 67.0 63.2

71.9 62.2 52.9

53.9 45.6 44.0

44.0 43.2 40.1

42.0 42.0 36.3

Wave periods are maximum energy of the surface amplitude spectrum/zero
upcrossing period of the squared bottom orbital velocity spectrum.

bed and OBS sensors at 24, 70, and 183 cm above the bed. Water samples

were collected from 1500 March 8 to 1100 March 9 at a position 70 cm

above the bed using the automatic water sampler. In addition, discrete

water samples were manually collected during instrument on-time once

each day throughout the instrument deployment (March 8 10) at the

elevations of the OBS sensors and analyzed for suspended-solids

concentration. The discrete samples were used to calibrate the output

of the OBS sensors to suspended-solids concentration, and the automatic

samples validated the calibration of the OBS sensor 70 cm above the

bed. The suspended-solids concentrations (fig. 3-1) peaked at 1800

hours March 8, then decreased rapidly. No data was successfully

recorded before 1400 hours on March 8 because of a power supply

problem, but the suspended-solids concentrations for the first platform

measurement at 1400 hours were slightly greater than the upper limit of

the observed ambient concentrations (20 to 40 mg/L), which indicates

that only the initial resuspension was missed.

The temporal variation in the bottom shear stresses calculated

from the measured mean current with equation 1-5 did not correspond to

the temporal variation of the measured suspended-solids concentrations.

The mean current speeds measured 70 and 183 cm and the bottom shear

stresses calculated from the mean current speed 70 cm above the bed are

shown in figure 3-1. A bottom roughness of 0.3 cm was used. A small

ebb tide from 1600 hours to 1900 hours March 8 and a small flood time

from 1900 hours March 8 to 0100 hours March 9 had maximum speeds of

about 9 cm/s and increased the mean current bottom shear stress to

about 0.15 dynes/cm2, but these increases are not correlated with the

observed suspended-solids concentrations. A strong ebb tide at the

platform during a period of relatively little wave activity from 0200

to 0900 hours March 9 had maximum speeds of 12 to 16 cm/s and increased

the mean current bottom shear stress to 0.28 dynes/cm2, but apparently

did not resuspend bottom sediments. This total (grain and form) shear

stress is smaller than the Shields critical shear stress for the

platform sediment. The mean current during the morning of March 9 was

relatively large for the platform site, and the lack of a corresponding

increase in suspended-solids concentration indicates that the tidal

currents did not generate enough shear stress to resuspend bottom

sediments at the platform site. The suspended-solids concentrations

did not correlate with mean current bottom shear stress (r=-0.4, table

3-3).

Horizontal advection is not a likely cause of the observed

increase in suspended-solids concentration because the tidal excursion

was within the large area of homogeneous sediments that surrounded the

platform and sediments further upcurrent were probably not resuspended.

The tidal excursion of the small ebb tide from 1600 to 1900 hours

(about 750 meters) was within the large area of silty very fine sands

that surrounded the platform. The ebb tidal flow was from the

northwest to the southeast at the platform, so the most likely source

of resuspended sediment for transport to the platform during an ebb

tide was resuspension in relatively shallow water 1500 meters northwest

of the platform and within 500 meters of the southern side of the

Courtney Campbell Causeway. The wind was from the northeast, however,

so wind waves were not approaching the southern side of the causeway,

and resuspension was unlikely.

Wave properties were calculated using spectral analysis. Usually,

buoy acceleration or pressure data are used to calculate the energy

spectrum, but the pressure transducer was only partially responding to

Table 3-3.--Correlation coefficients for various wave properties and
bottom shear stresses with suspended-solids concentrations at the Old
Tampa Bay platform, March 1990.

Shear stress: mean current only

Shear stress: wave only (Kamphius 1975)

Shear stress: wave only
(Grant and Madsen 1979 and 1982)

Shear stress wave-current
(Grant and Madsen 1979)

Wave amplitude

Bottom orbital velocity

Square of bottom orbital velocity

Elevation of measured
suspended-solids concentration

24 cm 70 cm 183 cm

-0.29 -0.41 -0.52

0.74 0.61 0.70

0.73

0.73

0.73

0.74

0.69

0.59

0.59

0.66

0.61

0.58

0.68

0.67

0.78

0.70

0.66

the changes in pressure from wave activity, so the velocity component

pairs measured by the lowest current meter were used to calculate the

energy spectra after correcting the raw data for the electronic output

filter of the electromagnetic current meters as described in chapter 2.

The wave energy was located almost exclusively at wave periods from 2

to 3 seconds, and the maximum energy period of the surface amplitude

spectrum was selected to represent the wave period. The zero-

upcrossing period of the squared bottom orbital velocity spectrum

(square root of the second moment divided by the zero moment), however,

is probably more indicative of wave periods that affect the bottom

shear stress. Table 3-2 indicates that these periods are similar, and

the interpretations that result from this analysis are not affected by

this difference. For narrow banded spectra such as these, the

significant wave amplitude is twice the square root of the area under

the wave spectrum (Ochi 1990). Significant wave amplitudes calculated

with data from the current meter 70 cm above the bed (fig. 3-1)

corresponded with the wind speed and decreased after 2200 hours March

8. The wave amplitude was somewhat correlated with the suspended-

solids concentrations (r=0.7, table 3-3). The waves during the storm

(1400 to 2100 hours) were transitional between the deep-water and

shallow-water limits, but as the wind diminished the waves became deep-

water waves. The maximum orbital particle velocities calculated from

linear wave theory based upon the significant wave amplitude were close

to measured values, so the calculated wave properties appeared to be

reasonable. The bottom orbital velocity (fig. 3-1) decreased in

conjunction with the wind speed. Note that only a single wave

amplitude and period are considered, whereas realistically, there are

many periods and amplitudes present in the wave field. Thus, the

results of the spectral analysis were used to provide approximate wave

data in a consistent manner.

The bottom orbital velocities corresponded with the observed

suspended-solids concentrations. The bottom orbital velocities were

9.8 to 16 cm/s until 1900 hours March 8 and during this time the

suspended-solids concentrations increased from slightly greater than

ambient values to the maximum values measured during the deployment.

After 1900 hours March 8, the waves were deep-water waves, the bottom

orbital velocities were less than 7 cm/s, and the suspended-solids

concentrations decreased to ambient values in the early morning of

March 9. Thus, resuspension seems to have occurred during the period

of greatest wave activity, and the resuspended sediments settled as the

wave action diminished. The bottom orbital velocity and squared bottom

orbital velocity were somewhat correlated with suspended-solids

concentrations (r=0.7, table 3-3).

The calculated bottom orbital velocity and estimated maximum

bottom shear stress were more dependent on the water depth and wave

period than the wave amplitude. The bottom orbital velocity increases

with increasing wave amplitude, increasing wave period, and decreasing

mean water depth. A sensitivity analysis was performed to investigate

the relative importance of wave amplitude, wave period, and mean water

depth on the bottom orbital velocity calculation for this data set.

Typical storm values for this data set are a significant wave amplitude

of 30 cm, a maximum energy period of 2.6 seconds, and a water depth of

400 cm. A 10% increase in wave amplitude, a 10% increase in wave

period, and a 10% decrease in water depth, increase the bottom orbital

velocity 10, 29, and 25%, respectively. The bottom orbital velocity

during resuspension at the Old Tampa Bay platform was more sensitive to

wave period and mean water depth than wave amplitude because the waves

are depth transitional. The maximum bottom shear stress is

proportional to the square of the maximum bottom orbital velocity (eqn.

1-11), so an error in the estimated bottom orbital velocity may

severely degrade the estimated bottom shear stress. For example,

assuming that the wave friction factor is unchanged, if a 10%

overestimate of the wave period produces a 29% overestimate of the

maximum bottom orbital velocity, then the maximum bottom shear stress

will be overestimated by 66%.

The maximum bottom shear stresses estimated for wave motion only

are much greater than the bottom shear stresses estimated for the mean

current only and correspond to the suspended-solids concentrations.

Equation 1-11 and friction coefficients determined empirically

(Kamphius 1975) and theoretically (eqn. 1-13, Grant and Madsen 1979,

1982) were used to estimate the maximum bottom shear stress for the

observed bottom roughness (fig. 3-1). The estimated bottom shear

stress considering wave motion only is much greater than the estimated

bottom shear stress considering the mean current only. The greatest

wave shear stress occurred during the period of sediment resuspension

on March 8 and the wave shear stress is somewhat correlated with

suspended-solids concentration (r=0.7, table 3-3).

Poor knowledge of the behavior of the wave friction factor for

regime transitional waves may account for the differences between the

results of the two methods for calculating the wave only bottom shear

stress (fig. 3-1). The waves were transitional between the laminar

(smooth bottom) and fully turbulent (rough bottom) flow regimes.

Kamphius (1975) states that the data used to determine the friction

factors for regime transitional waves are poorly ordered and that the

resulting values should be used with caution. Grant and Madsen (1979)

state that their approach is applicable to regime transitional waves

but fully rough turbulent flow has previously been assumed (Drake and

Cacchione 1986, Drake et al. 1992, Cacchione et al. 1987, Grant and

Madsen 1979 and 1982, Signell et al. 1990).

The maximum wave-current bottom shear stresses were estimated with

the Grant and Madsen (1979) model (fig. 3-1). Estimated maximum bottom

shear stresses were greatest from 1400 to 1900 hours during which time

the suspended-solids concentration increased. This qualitative

behavior and the correlation coefficient with suspended-solids

concentration for the wave-current bottom shear stress (r=0.7, table 3-

3) are virtually identical to that for the maximum bottom shear

stresses estimated considering wave motion only.

Compared to the bottom shear stress estimated by the Grant and

Madsen model for wave motion only (eqns. 1-11 and 1-13) and the sum of

this wave shear stress and the mean current shear stress (eqn. 1-5),

consideration of wave-current interaction slightly increases the

estimated bottom shear stress, but this increase is smaller than the

uncertainty associated with the wave friction factor. During the

period of greatest wave activity from 1400 to 1900 hours, the maximum

bottom shear stresses estimated with the wave-current model were 9%

greater than those estimated considering waves only (eqns. 1-11 and 1-

13) and 6% greater than the sum of the mean current (eqn. 1-5) and wave

only shear stresses. The maximum wave bottom shear stress during this

period calculated with the friction factor diagram by Kamphius (1975)

is 44% greater than the shear stress calculated with the friction

factor of equation 1-13. Therefore, for this data set, the estimated

maximum bottom shear stress seems to be more sensitive to the selected

estimation procedure than the possible effect of wave-current

interaction.

Because the waves at the platform are depth transitional, the

bottom orbital velocity and estimated wave bottom shear stress are also

sensitive to the wave period and water depth and errors in these

quantities can produce large errors in the wave bottom shear stress, as

discussed previously. A similar sensitivity analysis of the wave-

current model using a water depth of 400 cm, wave period of 2.6

seconds, wave amplitude of 30 cm, an angle between the wave and current

of 150 degrees, a 0.3 cm bottom roughness, and a mean velocity of 10

cm/s at an elevation 70 cm above the bed indicates that a 10% increase

in wave amplitude, a 10% increase in wave period, and a 10% decrease in

water depth, increases the maximum wave-current bottom shear stress 14,

39, and 37%, respectively. The maximum wave-current bottom shear

stress was relatively insensitive to 10% changes in mean velocity (4%),

angle (0.4%), and bottom roughness (4%). Selection of a representative

wave period from a measured wave spectrum is probably the most likely

source of inaccuracy.

The sediment resuspension observed on March 8, 1990, at the Old

Tampa Bay platform was caused by increased wave motion associated with

strong and sustained northeasterly wind. The bottom shear stresses

estimated by considering the mean current only were much less than the

maximum bottom shear stresses estimated by considering wave motion

only. Wave-current interaction may have contributed to the bottom

shear stress, but this difference is not as significant as the

differences associated with the selected wave period and the selected

procedure used to calculate the wave friction factor. The period of

the largest estimated wave and wave-current shear stresses corresponds

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