Integrated modeling of the Tampa Bay estuarine system

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Title:
Integrated modeling of the Tampa Bay estuarine system
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xx, 387 leaves : ill. ; 29 cm.
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English
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Yassuda, Eduardo Ayres, 1963-
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Coastal and Oceanographic Engineering thesis, Ph. D   ( lcsh )
Dissertations, Academic -- Coastal and Oceanographic Engineering -- UF   ( lcsh )
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bibliography   ( marcgt )
non-fiction   ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1996.
Bibliography:
Includes bibliographical references (leaves 369-386).
Statement of Responsibility:
by Eduardo Ayres Yassuda.
General Note:
Typescript.
General Note:
Vita.

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University of Florida
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INTEGRATED MODELING OF THE TAMPA BAY ESTUARINE SYSTEM


By

EDUARDO AYRES YASSUDA

















A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA













ACKNOWLEDGMENTS


First, I would like to express my gratitude to the CNPq Conselho Nacional de

Desenvolvimento Cientifico e Tecnol6gico (Brazilian Research Council) for the financial

support during my graduate program at the University of Florida. Several University of

Florida research projects provided the opportunities for me to gain experience in

hydrodynamics and water quality modeling and field work. These projects include the Lake

Okeechobee Phosphorus Dynamics Study funded by the South Florida Water Management

District, the Sarasota Bay Field and Modeling Study funded by the Sarasota Bay National

Estuary Program and United States Geological Survey, the Tampa Bay Circulation Modeling

Study funded by the Tampa Bay National Estuary Program, the Roberts Bay Water Quality

Modeling Study funded by the Sarasota Bay National Estuary Program, and the Indian River

Hydrodynamics and Water Quality Modeling Study funded by the St. Johns River Water

Management District.

My appreciation is extended to my advisor and chairman of the supervisory

committee, Prof. Peter Sheng, for his guidance, financial support, and patience throughout

this study; to Prof. A. Mehta, and Prof. R. Thieke from the Coastal Engineering Department

for their comments and advice; to Prof. K.R. Reddy from the Soil and Water Science

Department for helping us bridge the gap between experimentalists and modelers; and to

Prof. C. Montague from the Environmental Engineering and Science Department, for his








unconditional support and high motivation. Two former students deserve special

acknowledgment: Steve Peene and Xinjian Chen.

I also want to thank my professors at the Oceanographic Institute of the University

of Sdo Paulo, specially Prof. Joseph Harari, Prof. Luiz B. de Miranda, and Prof. Moyses

Tessler for their invaluable contributions to my career.

It would not be possible to complete this work if not for the technical guidance and

help of the following scientists and researchers: Mr. Richard Boler, Dr. Kate Bosley, Mr.

Michael DelCharco, Dr. Kent Fanning, Dr. Peggy Fong, Ms. Holly Greening, Dr. Kurt Hess,

Mr. Ronald Miller, Dr. Gerold Morrison, and Dr. David Tomasko.

Grateful thanks goes to my buddies in room 429, H.K. Lee, Yang, Justin, Liu, and

Kevin, in our quest for "bugs," and for reviewing the manuscript. I would like to express my

gratitude to Sidney Schofield, "Professor" Mark Gosselin, and "Wally" Yigong Li for bailing

me out in a great number of opportunities throughout this program. Life would not be the

same without the Coastal Lab and its staff. Acknowledgment goes to all of them. Deserving

special honors also are Subarna Malakar, Becky Hudson, Sandra Bivins, Lucy Hamm, Helen

Twedell, and John Davis.

My most sincere appreciation is extended to the DelCharco family, for adopting and

taking us as one of their own during all family occasions.

I would like to dedicate this dissertation to my parents, because only now, as Daniel's

father, do I realize how much effort they had to spend educating me.

Finally, I want to thank Monica for being there for me all the time, sharing the ups

and downs of this never-ending challenge.












TABLE OF CONTENTS

ACKNOWLEDGMENTS ............................................... ii

ABSTRACT ............ ............................................ xix

CHAPTERS

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

Background .............. ...................... .............. 1
Water Quality Modeling ............................................. 2
Integrated Modeling Approach for Estuarine Systems ...................... 4
Objectives .................. .................................6

2 TAMPA BAY CHARACTERIZATION ................................ 8

Climate ......................................................... 10
Tides ................. ............................. 11
Salinity Distribution ............... ............................ 11
Rainfall ........... .................. ....... .............. 12
W ind .................. ........................... 12
Bathymetry ................................... ............ 15
Freshwater Inflow ............................................ 15
Hillsborough River ................ .......................... 17
Alafia River ................................ .... ............. 18
Little Manatee River .............. ......................... 19
Manatee River ............................................. 20
Rocky Creek ................. ............................ 20
Lake Tarpon Canal ............................................ 21
Sweetwater Creek ............................................... 21
Non-Point Sources .......................................... 2
Nutrients Distribution and Loading ................ ................. 22
Sediment Type and Distribution ............... .................. 25

3 THE CIRCULATION AND TRANSPORT MODEL ..................... 29

Previous Work ............... ..................... ........... 29
Circulation Model ................. ............................ 31
Continuity Equation ............... .......................... 31








X-component of Momentum Equation .................. ........... 32
Y-component of Momentum Equation .............................. 32
Hydrostatic Pressure Relation ............... .................. 32
Salinity Equation .............. .................... ......... 33
Equation of State ............................................ 34
Conservative Species Equation .................................... 34
Sediment Transport Model ................ ....................... 35
Curvilinear Boundary-Fitted and Sigma Grid ............................ 36
Boundary and Initial Conditions ............... .................. 38
Vertical Boundary Conditions ............... ................. 38
Lateral Boundary Conditions ................ ................... 40

4 THE WATER QUALITY MODEL ................................ 42

Previous Work ................................... .............. 42
Development of the Numerical Model ................................. 47
Mathematical Formulation ............... ....................... 49
Nutrient Dynamics in Estuarine Systems .............................. 50
Ammonia Nitrogen ............................................ 52
Dissolved Ammonium Nitrogen ................................... 53
Nitrite+Nitrate Nitrogen ..................................... 55
Soluble Organic Nitrogen ..................................... 57
Particulate Organic Nitrogen ................ ................... 58
Particulate Inorganic Nitrogen ................ .................. 58
Algal Nitrogen .................. ............................ 59
Zooplankton Nitrogen ................ ........................ 59
Sorption and Desorption Reactions ............... ................ 60
Phytoplankton Dynamics in Estuarine Systems .......................... 61
Oxygen Balance in Estuarine Systems ................................. 63
Light Attenuation in Estuarine Systems ............................ 66
Model Coefficients ................. ........................... 70

5 THE SEAGRASS MODEL ...................................... 75

Using Seagrass as a Bioindicator of the Estuarine System .................. 75
Seagrass Ecosystems ............. ..... ............. ......... ..76
Previous Work ................................................. .79
Development of the Numerical Model ................................ 81
Mathematical Formulation ............... ................... ... .84
Light ............. ............................... 84
Temperature ......... ........... ........................... 85
Density-dependent Growth Rate ................................ 85
Growth Rate Dependence on Light ................................. 87








Growth Rate Dependence on Salinity ............................... 89
Growth Rate Dependence on Temperature ........................... 92
Growth Rate Dependence on Sediment Nutrients ...................... 94

6 APPLICATION OF THE CIRCULATION AND TRANSPORT MODEL ..... 95

Design of Tampa Bay Grid ................. ....................... 95
Forcing Mechanisms and Boundary Conditions ......................... 101
M odeling Strategy ................ ............... ............. 111
Results of the Barotropic Simulation ................................. 111
Results of the Baroclinic Simulation .................................. 115
Tides ............... ............................ 117
Currents ..................................................... 124
Salinity ................ ..................................144
Validation of the M odel ................................... .... 153
Residual Circulation ................. .......................... 164
Results of the Suspended Sediment Simulation ......................... 173

7 CALIBRATION OF THE WATER QUALITY MODEL ................. 181

Initial and Boundary Conditions of the Water Quality Model .............. 184
WaterColumn ...............................................184
Sediment Column ............................................ 197
M odeling Strategy ............................. ............ 208
Sensitivity Analysis ................... ......................... 208
Simulation of the Summer 1991 Condition ............................. 220
Dissolved Oxygen ..................................... ....... 221
Phytoplankton ............................................... 234
Nitrogen Species .................... ......... ..... ........ 246
Tidal Exchange ......................................... 257
Nutrient Budget ............ ............................... 261
Load Reduction Simulations .................................... 265
Comparison with AScI (1996) study ............... ................ 267
Comparison with Coastal Inc. (1995) study ......................... .. 270
Advantages and Limitations of this Integrated Modeling Approach .......... 271

8 CALIBRATION OF THE SEAGRASS MODEL ........................ 273

Initial Conditions ............................................276
Sensitivity Analysis ............................... ........... 278
Simulation of the Summer 1991 Condition .................. .......... 290
Load Reduction Simulation ............................ ....... .. 292








9 CONCLUSION AND RECOMMENDATIONS ........................ 299

APPENDICES

A NUMERICAL SOLUTION OF THE EQUATIONS ..................... 304

B MODELING SEDIMENT DYNAMICS ............................... 324

C DISSOLVED OXYGEN SATURATION AND REAERATION
EQUATIONS ..................................... ............334

D LIGHT MODEL EQUATIONS ..................................... 335

E RESULTS OF THE SUMMER 1991 SIMULATION .................... 337

F SENSITIVITY TESTS OF THE SEAGRASS MODEL ................... 357

REFERENCES ......... ........... ......... .. .... .............. 370

BIOGRAPHICAL SKETCH ......................................... 388












LIST OF FIGURES


Eigume page

2.1 Tampa Bay Estuarine System subdivisions as defined by
Lewis and Whitman (1985) (from Wolfe and Drew, 1990)................. 9

2.2 Monthly rainfall in Tampa Bay (Wooten, 1985). ....................... 13

2.3 Seasonal wind pattern in Florida (Echtemacht, 1975) ................... 14

2.4 Tampa Bay watershed (Wolfe and Drew, 1990). ....................... 16

2.5 Surface Sediments in Tampa Bay (Goodell and Gorsline, 1961). .......... 27

2.6 Mud zone in Hillsborough Bay (Johansson and Squires, 1989). ........... 28

5.1 Seagrass species commonly found in west Florida
(from Phillips and Meiiez, 1987). ............... ............... .. 78

5.2 Structure and components of the numerical seagrass
model used for this study. ............ .......................... 82

5.3 Epiphytic algae model flow chart. .................................. 83

5.4 Seagrass model flow chart. ................. ..................... 83

5.5 Seagrass density-dependent maximum growth rate:
Thalassia (dotted line), Halodule (solid line),
and Syringodium (dash-dotted line). ............................... 86

5.6 Seagrass growth rate dependence on light: Thalassia (dotted line),
Halodule (solid line), and Syringodium (dash-dotted line). ............... 87

5.7 Seagrass growth rate dependence on salinity: Thalassia (dotted line),
Halodule (solid line), and Syringodium (dash-dotted line). ............... 91

5.8 Seagrass growth rate dependence on temperature: Thalassia (dotted line),
Halodule (solid line), and Syringodium (dash-dotted line). .............. 93








6.1 NOAA's TOP station locations in Tampa Bay. ....................... 97

6.2 A boundary-fitted grid for the Tampa Bay Estuarine System. ............. 98

6.3 Tampa Bay bathymetric contours. .................................. 99

6.4 Bay segments (Sheng and Yassuda, 1995). ........................... 100

6.5 Tidal forcing for the 1990 simulation. .............................. 102

6.6- Tidal forcing for the 1991 simulation. ........................... 103

6.7 Initial salinity distribution (surface) for the 1990 simulation. ............ 104

6.8 Initial salinity distribution (surface) for the 1991 simulation. ............ 105

6.9 Rainfall data for the 1990 and 1991 simulations. ................... ... 107

6.10 River discharges for the 1990 and 1991 simulations ................... 108

6.11 Wind velocity for the 1990 simulation. ............................. 109

6.12 Wind velocity for the 1991 simulation. ............................. 110

6.13 Surface elevation at Egmont Key and St.Petersburg
(September 1990)................................ ............. 113

6.14 Surface elevation at Davis Island and Old Tampa Bay
(September 1990) ................ ..............................114

6.15 Spectra of water surface elevation for the 1990 simulation .............. 119

6.16 Simulated and measured bottom velocity at Egmont Channel -
September/1990............ ........ ...................... 126

6.17 Simulated and measured surface velocity at Egmont Channel -
September/1990. .................. ......................... 127

6.18 Simulated and measured bottom velocity at Skyway Bridge -
September/1990 .................... ............. ..... ...... 129

6.19 Simulated and measured mid-depth velocity at Skyway Bridge -
September/1990. .............................. ............. 130









6.20 Simulated and measured surface velocity at Skyway Bridge -
September/1990. ............ ............................... 131

6.21 Simulated and measured bottom velocity at Port of Manatee Channel -
September/1990 .............................................. 133

6.22 Simulated and measured surface velocity at Port of Manatee Channel -
September/1990....... ..................... ............... 134

6.23 Simulated and measured bottom velocity at Port of Tampa Channel -
September/1990 .................. .............. ............ 135

6.24 Simulated and measured bottom velocity at Port of Tampa Channel -
September/1990 ............................................ 137

6.25 Energy density spectra of bottom currents at Skyway Bridge -
September/1990 ............. ...... .......................... 140

6.26 Energy density spectra of surface currents at Skyway Bridge -
September/1990 .............................................141

6.27 Tidal current ellipses for the semi-diurnal components September/1990. 145

6.28 Tidal current ellipses for the diurnal components September/1990 ...... 146

6.29 Near-bottom salinity (solid line) and temperature (dashed line) at NOAA
station S-4 starting at Julian Day 150 in 1990. ........................ 147

6.30 Simulated and measured near-bottom salinity at NOAA
station C-21 September/1990 ................................ 149

6.31 Simulated and measured near-bottom salinity at C-23 -
September/1990. .............. ..................... ....... 150

6.32 Simulated and measured near-bottom salinity at C-4 -
September/1990 ............................. ...... ......... 151

6.33 Surface elevation at St.Petersburg and Davis Island -
"Marco" Storm October/1990. ............................... .152

6.34 Surface elevation at St.Petersburg and Davis Island July/1991 ........... 154








6.35 Simulated and measured bottom current at Skyway Bridge -
"Marco" Storm (October/1990). ............... ................ 157

6.36 Simulated and measured surface current at Skyway Bridge -
"Marco" Storm (October/1990). ............... ................ 158

6.37 Simulated and measured near-bottom salinity at station S-4 -
(July/1991). .............. ...................... ........... 159

6.38 Simulated and measured near-surface salinity at station S-4 -
(July/1991). .............. ..... ................. ............. 160

6.39 Relative flushing for several bay segments September/1990. ........... 163

6.40 Residual circulation after 30 days September/1990. .................. 165

6.41 Simulated velocity field representing maximum ebb currents -
September/29/1990 18:00. ................. ................. 167

6.42 Simulated velocity field representing maximum flood currents -
September/29/1990 10:00. ................. .................. 168

6.43 Velocity cross-section at Skyway Bridge looking up the Bay. Vertical
scale in meters, and horizontal scale in computational grid j-index ....... 170

6.44 Salinity cross-section at Skyway Bridge looking up the Bay. Vertical
scale in meters, and horizontal scale in computational grid j-index ....... 171

6.45 Longitudinal distribution of salinity along the navigation channel. Vertical
scale in meters, and horizontal scale in computational grid i-index ....... 172

6.46 Location of the USGS station in Old Tampa Bay
(Schoellhammer, 1993) ......... ..... ................... 176

6.47 Wind speed and direction, and suspended sediment concentration at
USGS station during tropical storm "Marco" (Schoellhammer, 1993). .... 177

6.48 Simulated significant wave height and period during tropical storm
"Marco" (October/1990). ..................................... 178

6.49 Simulated wave-induced bottom shear stress and suspended sediment
concentration at the USGS station for October 10 and 11, 1990. ......... 179








6.50 Simulated suspended sediment concentration at 6:00am -
October 11, 1990 ............................................180

7.1 Water quality monitoring stations of the Hillsborough County
Environmental Protection Commission (EPC) (Boler, 1992). ............ 185

7.2 Measured near-bottom dissolved oxygen concentration (mg/L) in
Tampa Bay (June 1991). .................. ...................... 186

7.3 Measured near-surface dissolved oxygen concentration (mg/L) in
Tampa Bay (June 1991)......................................... 187

7.4 Measured organic nitrogen concentration (mg/L) in
Tampa Bay (June 1991)............... ...................... 188

7.5 Measured dissolved ammonium-nitrogen concentration (mg/L) in
Tampa Bay (June 1991) ............ ....... .................... 189

7.6 Measured nitrite+nitrate concentration (mg/L) in
Tampa Bay (June 1991) ............................ ........... 190

7.7 Measured chlorophyll-a concentration (pg/L) in
Tampa Bay (June 1991). ................... .................. 191

7.8 Measured color (Pt-Co) in Tampa Bay (June 1991). ................... 192

7.9 Measured turbidity (NTU) in Tampa Bay (June 1991). ................. 193

7.10 Water quality zones in Tampa Bay used in the model simulations of the
summer of 1991 conditions. ................. ................... 196

7.11 Total organic nitrogen (dry weight %) in the surface sediments of
Tampa Bay during 1963 (Taylor and Saloman, 1969) .................. 200

7.12 Total Kjeldahl nitrogen (dry weight %) in Tampa Bay sediments,
1982-86 (Brooks and Doyle, 1992). ............................. ... 201

7.13 Sedimentary nitrogen (dry weight %) in Hillsborough Bay
in 1986 (COT, 1988). ................ ................... 202

7.14 Location of the NOAA sediment sampling stations in 1991 (phase 1) and
1992 (phase 2) (NOAA, 1994)..................................... 203








7.15 Total sediment nitrogen (dry weight %) obtained from NOAA
(1994) data ...............................................204

7.16 Dry density profile for water quality zone 1 in Tampa Bay
(Sheng et al., 1993)............................................. 205

7.17 Water quality parameters after 30 days for a simulation using the lower
limit of the mineralization constant rate. ............................ 217

7.18 Water quality parameters after 30 days for a simulation using the higher
limit of the mineralization constant rate. ............................ 220

7.19 Near-bottom dissolved oxygen levels after 30 days for the mineralization
constant rate tests. ................. ........................... 221

7.20 Near-bottom dissolved oxygen concentration in Tampa Bay for
June 26, after 30 days of simulation. ............................ 222

7.21 Near-bottom dissolved oxygen concentration in Tampa Bay for
July 26, after 60 days of simulation. ............................ 223

7.22 Near-bottom dissolved oxygen concentration in Tampa Bay for
August 25, after 90 days of simulation. ............................. 224

7.23 Near-bottom dissolved oxygen concentration in Tampa Bay for
September 24, after 120 days of simulation. ........................ 225

7.24 Model results for segment-averaged near-bottom DO (solid line),
segment maximum and minimum (dashed line), and the EPC
data inside Hillsborough Bay. ................ ................... 227

7.25 Model results for segment-averaged near-bottom DO (solid line),
segment maximum and minimum (dashed line), and the EPC
data inside Old Tampa Bay. ................... ................. 228

7.26 Model results and measured data for near-bottom DO at
EPC stations 70 and 8........................ ............. 229

7.27 Model results and measured data for near-bottom DO at
EPC stations 73 and 80. .................................... 230








7.28 Model results for segment-averaged near-bottom DO (solid line),
segment maximum and minimum (dashed line), and the EPC
data inside Middle Tampa Bay. .................. .......... .232

7.29 Model results for segment-averaged near-bottom DO (solid line),
segment maximum and minimum (dashed line), and the EPC
data inside Lower Tampa Bay. ................ ................... 233

7.30 Near-surface chlorophyll-a concentration in Tampa Bay for
June 26, after 30 days .......................................... 236

7.31 Near-surface chlorophyll-a concentration in Tampa Bay for
July 26, after 60 days ......................... ... .......... 237

7.32 Near-surface chlorophyll-a concentration in Tampa Bay for
August 25, after 90 day ....................................... 238

7.33 Near-surface chlorophyll-a concentration in Tampa Bay for
September 24, after 120 .......................... ...... ...... 239

7.34 Model results for segment-averaged near-surface chlorophyll-a (solid line)
and the EPC data inside Hillsborough Bay. ................... ....... 241

7.35 Model results for segment-averaged near-surface chlorophyll-a (solid line)
and the EPC data inside Old Tampa Bay. ........................ .. 242

7.36 Model results for segment-averaged near-surface chlorophyll-a (solid line)
and the EPC data inside Middle Tampa Bay ......................... 244

7.37 Model results for segment-averaged near-surface chlorophyll-a (solid line)
and the EPC data inside Lower Tampa Bay .......................... 245

7.38 Near-surface Kjeldahl nitrogen concentration in Tampa Bay for
June 26, after 30 days of simulation ............................ 247

7.39 Near-surface Kjeldahl nitrogen concentration in Tampa Bay for
July 26, after 60 days of simulation ................................. 248

7.40 Near-surface Kjeldahl nitrogen concentration in Tampa Bay for
August 25, after 90 days of simulation ........................... 249

7.41 Near-surface Kjeldahl nitrogen concentration in Tampa Bay for
September 24, after 120 days of simulation .......................... 250









7.42 Model results for near-bottom segment-averaged Kjeldahl nitrogen
(solid line) and the EPC data inside Hillsborough Bay.................. 252

7.43 Model results for near-bottom segment-averaged Kjeldahl nitrogen
(solid line) and the EPC data inside Old Tampa Bay. .................. 253

7.44 Model results for near-bottom segment-averaged Kjeldahl nitrogen
(solid line) and the EPC data inside Middle Tampa Bay. ............... 254

7.45 Model results for near-bottom segment-averaged Kjeldahl nitrogen
(solid line) and the EPC data inside Lower Tampa Bay ................ 256

7.46 Measured and simulated transport across the mouth of Hillsborough Bay,
along with the Kjeldahl nitrogen concentration (mean and standard
deviation) presented by Rines (1991). ........................... 259

7.47 Measured and simulated transport across the entrance of Tampa Bay,
along with the Kjeldahl nitrogen concentration (mean and standard
deviation) presented by Rines (1991). ............... ............ 260

7.48 Simulated nitrogen cycle for the summer of 1991 conditions:
(a) Loading, (b) biogeochemical processes in the water column,
(c) biogeochemical processes in the sediment column. ................. 264

7.49 Near-bottom dissolved oxygen concentration in Tampa Bay, after
60 days of the load reduction simulation. ........................ 268

7.50 Near-surface chlorophyll-a concentration in Tampa Bay, after
60 days of the load reduction simulation. .......................... 269

8.1 Extent of seagrass meadows in Tampa Bay. (a) corresponding to 1943,
and (b) to 1983 (Lewis etal., 1985). ............................ 274

8.2 Initial seagrass distribution in the computational grid. Dark areas indicate
seagrass meadows (100 gdw/m2) ................. ............. 277

8.3 Simulated seagrass biomass in Tampa Bay. ....................... .. 279

8.4 Growth rate dependence on temperature. ..................... ....... 281

8.5 Growth rate dependence on light. ................... ....... 282








8.6 Growth rate dependence on salinity. .............................. 283

8.7 Growth rate dependence on sediment nutrient concentration. ............ 284

8.8 Simulated seasonal distribution of Thalassia. ........................ 286

8.9 Simulated Thalassia biomass in Tampa Bay for July 26,
after 60 days of simulation .................... ......... .. 293

8.10 Simulated Halodule biomass in Tampa Bay for July 26,
after 60 days of simulation. ......... .. .................. 294

8.11 Simulated Syringodium biomass in Tampa Bay for July 26,
after 60 days of simulation. ............................ .... 295

8.12 Near-bottom light levels in Tampa Bay for July 26,
after 60 days of simulation. ................................... .. 296

8.13 Comparison between simulated light levels for the Present Condition
simulation (solid line) and the 100% Load Reduction (dashed line). ....... 297











LIST OF TABLES


Table page

2.1 Area of the subdivisions in Tampa Bay (Lewis and Whitman, 1985)......... 10

2.2 Surface water discharges to Tampa Bay (Lewis and Estevez, 1985). ........ 17

2.3 1991 annual average water quality of eight point sources discharging
into Tampa Bay (Boler, 1992) and (USGS, 1991) (mg/L). ................ 22

2.4 Mean annual total nitrogen loading into each segment of Tampa Bay
(Coastal, 1994) ................................... ............. 25

4.1 Description of the coefficients used in the water quality model. ............. 70

4.2 Literature ranges and values of the coefficients used in the water
quality model.............. .................................. 72

6.1 The rms error (Erms) between measured and simulated
water surface elevation September/90. ............... ....... 117

6.2 The distribution of tidal energy for water surface elevation -
September 1990........................... ................ 120

6.3 Major tidal constituents in Tampa Bay September/1990. ............... 123

6.4 The rms error between measured and simulated bottom (b) and
surface (s) currents September/1990. ....................... .. .138

6.5 The distribution of tidal energy for bottom (b) and surface (s) currents -
September 1990. ............................................. 142

6.6 The rms error between measured and simulated salinity -
September 1990. .................... ................... 153

6.7 The rms error between measured and simulated water surface elevation
October/1990 and July/91. ........... .................... 155








6.8 The rms error between measured and simulated bottom (b) and
surface (s) currents "Marco" Storm. ............................ 156

6.9 The rms error between measured and simulated salinity July/1991........ 161

7.1 Estimated total suspended solids concentration (TSS), and calculated
water column partition coefficients for particulate organic nitrogen
(pcon) and adsorbed ammonium (pcan). ............................ 197

7.2 Estimated dry density for the sandy zones of Tampa Bay ................ 206

7.3 Initial nitrogen concentration in the sediment (Ae) aerobic layer, and
(An) anaerobic layer for each water quality zone. (SON) soluble organic
nitrogen, (NH4) dissolved ammonium nitrogen, (N03) nitrite+nitrate. ..... 207

7.4 Model coefficients in the (W) water column, (Ae) aerobic layer, and
(An) anaerobic layer for each water quality zone. ................... ... 207

7.5 Parameters, baseline values, and range used in the sensitivity analysis. ..... 210

7.6 Sensitivity tests description. .................................. .. 211

7.7 Sensitivity analysis results. ............................ .. 213

7.8- Nitrogen budget between July 1 and August 31, 1991. .................. 263

8.1 Sensitivity tests description. ........................ ........ 287

8.2 Simulated and reported seagrass biomass in the Tampa Bay area. ......... 290













Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

INTEGRATED MODELING OF THE TAMPA BAY ESTUARINE SYSTEM

By

Eduardo Ayres Yassuda

December 1996

Chairperson: Dr. Y. Peter Sheng
Major Department: Coastal and Oceanographic Engineering

Integrated modeling of the Tampa Bay Estuarine System is conducted in an attempt

to further the understanding of estuaries as integrated systems, and to provide quantitative

assessment of various management practices. The primary objective is to use models and

field data to produce a detailed characterization of the hydrodynamics and water quality

dynamics within the system. To test the hypothesis that seagrass is a bioindicator of the

overall health state of the estuarine system, a conceptual seagrass model is coupled to the

hydrodynamics and water quality models. The integrated model is then used to study the

effect of anthropogenic inputs to the estuarine system.

This study combines the enhanced versions of a 3-D hydrodynamics model (Sheng,

1989), a 3-D water quality model (Chen and Sheng, 1994), and a seagrass model (Fong and

Harwell, 1994) to simulate the circulation, transport, water quality, and seagrass dynamics

in Tampa Bay. The hydrodynamics component of this integrated model has been

successfully calibrated and verified using Tampa Bay data provided by the National Oceanic








and Atmospheric Administration (NOAA) and the United States Geological Survey (USGS).

The effects of hydrodynamics have been incorporated into the water quality model by using

the same grid spacing and time step, hence eliminating the need for ad-hoc tuning of

advective fluxes and dispersion coefficients. The water quality component has been tested

using monthly water quality data provided by the Hillsborough County Environmental

Protection Commission (EPC), although a more comprehensive data set is needed to fully

validate the water quality model. Results of previous statistical and mass-balance models

were used to determine the relevant biogeochemical processes, and to test causal

relationships among state variables. These simple models also proved to be useful tools for

calibration of the water quality model coefficients in the absence of process-specific data

(e.g., remineralization, nitrification, denitrification). Incident-light data provided by USGS

allowed the calibration of a light model of the MacPherson and Miller (1994) type. The

seagrass model has been used to investigate the ecological relationships between nutrient

loading, water quality dynamics, and the response of seagrass.

Once validated, this integrated model can be used to determine nutrient loading

reduction targets required to maintain and expand seagrass meadows in Tampa Bay.

Simulated load reduction scenarios indicate that water quality can respond quickly (within

2 months), while seagrass responds more slowly (more than 6 months) to load reduction.

Nevertheless, the results indicate that integrated modeling is a viable approach to provide

quantitative assessment of various management practices for restoring estuarine systems.













CHAPTER 1
INTRODUCTION



Background



Historically, an estuary has been defined as "a semi-enclosed coastal body of water

which has a free connection with the open sea and within which the sea water is measurably

diluted with fresh water derived from continental drainage" (Cameron and Pritchard, 1963 -

p. 306). In order to assess environmental problems along the entire coastal zone, the classical

definition of estuary was revised by the National Research Council (1977) to include not only

the estuary from the classical definition, but also all coastal environments characterized as

transitional zones. Following this new definition, an estuarine system comprises bays, coastal

lagoons, inlets, deltas, and salt marshes; all affected by different tidal regimes and freshwater

discharges.

In an estuarine system, the region characterized by accentuated gradients of some

specific properties is defined as the mixing zone (Harleman, 1971). It is usually located

between two stable zones, the freshwater and the oceanic ecosystems, wherein these

properties are treated as "reservoirs," with relative slower temporal variations.

Salinity is the primary physical property that presents a markedly longitudinal gradient.

Upstream from the tidal portion of the river, salinity is usually constant and nearly equal to








2

zero. In the coastal zone beyond the region of freshwater influence, salinity is equal to the

"oceanic reservoir" condition.

In a cross-section, the mixing zone reveals important vertical gradients. The most

evident is the intertidal zone, which is periodically flooded and exposed. In the intertidal

zone, there may be salt marshes, mangroves, beaches, and oyster banks. To overcome the

stresses originated by flood and dry conditions, organisms living in this zone have developed

special adaptations. Human presence is visible through structures like seawalls, piers, and

harbors.

According to Day et al. (1989), a second important vertical gradient is light

attenuation, going from a lighted, euphotic zone to a light depleted, aphotic zone. Where the

euphotic zone reaches the bottom, submerged aquatic vegetation like seagrasses is able to

thrive. Usually, water clarity also increases towards the ocean side. Another extremely

important gradient for biogeochemical processes is the redox potential in the sediment layer,

ranging from oxidized to reduced conditions. In a healthy estuarine system, the water column

is usually aerobic, but the bottom sediments become anaerobic in a very short distance (order

of few centimeters) from the water-sediment interface.


Water Quality Modeling



The primary requirement in any estuarine water quality modeling is a thorough

understanding of the circulation and transport processes. Differently from freshwater systems,

where uni-directional flow and steady-state conditions may be applied in a variety of cases,










estuaries are complex systems where the circulation dynamics are driven by tides, wind, river

discharges, waves, Coriolis force, and density gradients, which give the estuarine circulation

an unsteady, multi-dimensional character. The baroclinic effect in estuarine circulation has

been studied by various investigators, including the classical works of Pritchard (1956),

Cameron and Pritchard (1963), and Hansen and Rattray (1965).

Weisberg and Williams (1991) demonstrated that horizontal salinity gradients in

Tampa Bay are capable of creating a density-driven circulation, through the generation of a

baroclinic forcing. Galperin et al. (1991) refuted the barotropic residual circulation pattern

obtained by Goodwin (1987) and Ross et al. (1984) in Tampa Bay, showing that when

baroclinic effects are included, the residual circulation changed substantially.

Models with a limited resolution (spatial and time scales) are useful tools to depict a

general trend in the overall circulation pattern or to study the response of the system to a

specific forcing mechanism. However, estuarine processes are not in steady state, and they

often present a three-dimensional distribution. Biogeochemical and ecological processes

occurring inside an estuarine system are primarily driven by physical factors with an unsteady,

multi-dimensional character. To implement an integrated hydrodynamics, water quality and

ecological model, it is essential to fully understand the coupling among the hydrodynamics,

water quality and ecological processes.








4

Integrated Modeling Approach for Estuarine Systems



Competitive demands for natural resources in estuarine systems can lead to a serious

deterioration of the environment. Solutions to environmental problems have been attempted

by resources management agencies to support a holistic approach to environmental

management. For example, the Florida Department of Environmental Protection has been

emphasizing that ecosystem management is an integrated, flexible approach to manage

Florida's biological and physical environment.

An efficient strategy to prevent or reverse the degradation of important estuarine

systems makes use of numerical models in conjunction with monitoring programs. Through

monitoring, not only the present state of the system can be obtained, but it is also possible to

evaluate the effectiveness of past management efforts.

Numerical models can be used to study management options and the corresponding

response of the system. In estuarine systems, numerical models can be applied to study the

hydrodynamics, sediment dynamics, water quality dynamics and system ecology.

Hydrodynamics and sediment dynamics models have been significantly advanced

during the past decade (e.g., Sheng, 1994). The developments in numerical techniques and

computer technology have been fully capitalized. Also, advancement in instrumentation and

basic understanding has led to the development of process-based models rather than empirical

lumped-process models.

On the other hand, applications of traditional water quality models (e.g., Ambrose et

at, 1994) are often based on coupling the hydrodynamics and water quality dynamics on an










intertidal basis (i.e., tidally averaged). This simplification was supported by high

computational cost of robust multi-dimensional models and the large time scale of the kinetics

in water quality models. But, by doing so, several hydrodynamic processes (e.g., wave

actions) and sediment dynamics (e.g., resuspension, deposition) which can significantly affect

the water quality dynamics are not accurately represented. Chen and Sheng (1994) developed

an integrated hydrodynamics-sediment-water quality model applied it to Lake Okeechobee.

A coupled hydrodynamics-water quality model has been used to study the response of

Chesapeake Bay to various loading scenarios (Chesapeake Bay Program, 1994).

A useful application of a reliable water quality model is the development of a budget

for the specific pollutant of interest. In the case of Tampa Bay, where eutrophication is one

of the issues of greatest concern, nutrient loading levels have to be defined. To develop a

nutrient budget it is essential to quantify the sources of (Sheng et al., 1993): (i) external

nutrient loading from tributaries and non-point sources, (ii) nutrient fluxes into and from the

connecting ocean, and (iii) benthic nutrient fluxes. The most difficult source to quantify is the

benthic flux, due to measurement techniques, and the competing influences of molecular

diffusion, resuspension, and groundwater seepage. Consequently, it is common to find

nutrient budgets that consider the net benthic flux to be simply the difference between the

external loading and flux to the ocean. However, the oceanic flux, induced primarily by tidal

forcing is also difficult to estimate; hence, subtracting tidally-averaged oceanic flux from the

external loading may not give the correct benthic flux. Results of McClelland (1984) nutrient

box model of Tampa Bay shows that nitrogen benthic flux can be as much as twice the

external loading of point and non-point sources. The author suggested that the supply of










nitrogen through sediment resuspension and biogeochemical reactions in the water column

are likely important. Results of Johansson and Squires (1989) nutrient budget for Tampa Bay

suggest that the internal loading of nitrogen associated with sediment resuspension events can

be quite significant.

Ecological models are the primary tool in developing an overall picture of the system.

Using energy flow and Emergy concept (Odum, 1994), it is possible to identify the main

forcing functions that drive the system, and the causal relationships between state variables.

The conflicts between the "apparently" adversarial uses of an estuarine system can be better

mediated when they are evaluated on a common measure. System ecology models can be used

to connect environmental products with human use. The Emergy of the system measures both

the work of nature and that of humans in generating products and services. By selecting

choices that maximize Emergy production and use, policies and judgments can favor those

environmental alternatives that maximize real wealth, the whole economy, and the public

benefit (Odum, 1971).

In estuarine systems where seagrass has declined due to anthropogenic effects,

restoration of seagrass beds can be linked exclusively to environmental quality. In this sense,

seagrass provides a more direct assessment of the restoration processes (Dennison et al.,

1993). Seagrasses serve as habitat for fish and benthic invertebrates. Seagrass leaves provide

substrate for many epiphytic organisms. Herbivores such as manatees, fishes, sea turtles, and

sea urchins graze directly on seagrass blades. Dead leaves can constitute the majority of the

detritus pool. Seagrass also interacts with the physical components of the estuary by slowing

down the currents and enhancing the deposition of organic and inorganic material from the

water column. Their presence also inhibit the resuspension of sediments, which also affects

the nutrient cycles. Therefore, seagrass is a crucial indicator of the state of the estuary.
















Recognizing the important relationships among the various ecological components

(including hydrodynamics, sediment dynamics, water quality, aquatic vegetation, etc.), it is

now appropriate to take advantage of the advancement in computer resources and scientific

understanding to integrate models with multiple dimensions, more robust and coupled

processes. The purpose of this effort would be to further the understanding of estuaries as an

integrated system, and to provide a quantitative evaluation of various management practices.

The goals of this study are to conduct an integrated modeling of the Tampa Bay

Estuarine System, and to produce a detailed characterization of the hydrodynamics and water

quality dynamics within the system. In order to test the hypothesis that seagrass is a

bioindicator of the overall health state of the Tampa Bay Estuarine System, a conceptual

seagrass model is coupled to the hydrodynamics and water quality models. This integrated

model will then be used to provide mechanisms for relating anthropogenic inputs to the

overall health of the estuary. The following questions have to be addressed in order to

accomplish these goals:

I) How important are the three-dimensional characteristics of the estuarine circulation

in the overall dynamics of the system?

2) What are the most important environmental parameters and biogeochemical

processes in the water quality dynamics of the estuarine system?

3) Can the integrated modeling provide a quantitative assessment of various

management practices for restoring the estuarine system?














CHAPTER 2
TAMPA BAY CHARACTERIZATION


Tampa Bay, classified as a subtropical estuarine system (Lewis and Estevez, 1988),

is the largest coastal plain estuary in the state of Florida. It is located on the west central part

of the Florida peninsula, between coordinates 27" 30'and 28" 02'N, and 82" 20'and


82" 50'W. The Y-shaped bay is approximately 60 km long, 15 km wide, covering

approximately 1,000 km2, and having a shoreline 1450 km long (Lewis and Whitman, 1985).

It is a highly complex system composed of numerous basins and subdivisions (Figure 2.1).

Some of them (e.g. Hillsborough Bay) are bordered by highly industrialized and urbanized

areas and others are bordered by mangroves, bayous, and seagrasses (e.g. Boca Ciega Bay).

Table 2.1 shows the morphometric features of each subdivision (Lewis and Whitman, 1985).

Major anthropogenic modifications that have altered the natural evolution of the system are

the four causeways (Sunshine Skyway Bridge, Courtney-Campbell Parkway, W. Howard

Frankland Bridge, and Gandy Bridge), an extensive network of dredged channels, turning

basins, and spoil islands.











HImMOUgh t~tm


-(
- 21P o0











,.
o


Figure 2.1 Tampa Bay Estuarine System subdivisions as defined by Lewis and Whitman
(1985) (from Wolfe and Drew, 1990).








10

Table 2.1 Area of the subdivisions in Tampa Bay (Lewis and Whitman, 1985).
Subdivision Area (km2)
Old Tampa Bay 201
Hillsborough Bay 105
Middle Tampa Bay 310
Lower Tampa Bay 247
Boca Ciega Bay 93
TerraCeiaBay 21
Manatee River 55

Total 1032




Climate



The Tampa Bay Estuarine System is located in a zone of transition between a

temperate continental climate and a tropical Caribbean one (Lewis and Estevez, 1988). The

climate of the Tampa Bay area generally consists of a warm humid summer and a relatively

dry cool winter. Lewis and Estevez (1988) suggested three weather regimes for the Bay: the

warm, dry period between late April to mid-June, the warm, wet period during summer and

early fall, and the dry cold period between November to April. Based on four decades of

records, the mean annually averaged temperature in Tampa Bay is 22.3 C, with a low mean

of 16.0Cin January and a high mean of 27.8 "C in August (Lewis and Estevez, 1988).

Annual variation in water temperature ranges from 16 to 30 C, with a vertical stratification

of no more than 2 C(Boler, 1992).








11

Tides



Tides and currents in the Gulf of Mexico are classified as mixed type, with K,, 01,

and P, the major diurnal and M2 and S2 the major semi-diurnal components. The strong

diurnal components are attributed to the interaction between co-oscillating tides propagating

from the Florida Straits and Yucatan Channel and the natural frequency of the Gulf of Mexico

basin. The average tidal range is 0.67 m, while typical current speeds range from 1.2 to 1.8

m/s at the entrance (Egmont Channel), to much smaller values in the upper reaches of the Bay

(NOAA, 1993). The tidal wave takes approximately three hours to travel from the mouth to

the upper reaches of Hillsborough Bay, and approximately 4 hours to upper Old Tampa Bay.

Harmonic analysis of year-long tidal records at St. Petersburg (NOAA, 1993) yielded

amplitudes of 37 constituents, which indicated that the shallow water components are

relatively small, and overtides are not significant in the overall circulation pattern of the Bay.


Salinity Distribution



As in any other typical estuarine system, Tampa Bay generally exhibits significant

horizontal gradients in salinity. The higher salinity values in the adjacent Gulf of Mexico

fluctuate around 36 ppt, whereas the lowest salinity levels occur near the mouth of creeks and

rivers. Generally, the salinity distribution follows the annual precipitation pattern (Boler,

1992). Higher salinity tends to extend further up into the Bay during the dry winter and

spring, and the entire Bay becomes less saline, specially in the upper parts, during the wet










summer and fall. Vertically, salinity generally shows a homogeneous profile, with vertical

difference rarely exceeding 2 ppt.


Rainfall



Mean annual precipitation is approximately 140 cm (Heath and Connover, 1981),

which on an annual basis balances with evapotranspiration (Palmer, 1978). Dry season rains

vary from 5 to 6.5 cm per month. Wet season rainfall is much more variable, both temporally

and spatially, ranging from 13 to over 20 cm (Palmer, 1978). Figure 2.2 illustrates the

monthly rainfall pattern for Tampa Bay.



Wind



The annual average wind speed is 3.9 m/s from the east. The four seasonal wind-field

patterns are shown in Figure 2.3. In the winter months, the easterly trade winds dominate the

region south of latitude 270 N, while the westerlies dominate the area north of latitude 290 N.

Spring and Summer generally exhibit more southerly winds, and Fall is characterized by

easterly or northeasterly winds. Wind speed can exceed 10 m/s during the passage of winter

storms or during summer squalls, hurricanes and tornadoes (Wolfe and Drew, 1990).









13










Historical Monthly Rainfall in Tampa Bay


-....-.- Minimun
S -U-- Average
\ Maximum


I \

I


Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec


Figure 2.2 Monthly rainfall in Tampa Bay (Wooten, 1985).


-- w-- 48














































Figure 2.3 Seasonal wind pattern in Florida (Echtemacht, 1975).












Bathymetry



Tampa Bay is a relatively wide and shallow estuarine system, with an average depth

of 3.7 m (Goodwin, 1987). Depth generally does not exceed 10 m, except along the 96-km-

long navigational channel, which has been dredged to about 15 m. The navigational channel

extends parallel to the shorelines from the mouth to the upper reaches of Middle Tampa Bay,

where it splits into two branches, one connecting to the Port of Tampa in Old Tampa Bay,

and the other one entering Hillsborough Bay.



Freshwater Inflow

Unlike other well-studied estuarine systems in the U.S. (e.g. Chesapeake Bay,

Delaware Bay, etc.), Tampa Bay is not associated with any large river. All tributaries flowing

into Tampa Bay originate in the Florida peninsula, and therefore are relatively small (Figure

2.4). The Bay receives drainage from a watershed that covers approximately 5700 km2,

which delivers an average annual discharge of about 63 m'/s (Lewis and Estevez, 1988).

The analysis of existing and historical freshwater inflows to Tampa Bay (Coastal, 1994)

demonstrated that inflows have not changed significantly in the past fifty years.

Table 2.2 shows the historical average discharge values for seven rivers or streams

flowing into Tampa Bay. The primary source of freshwater are the four major tributaries

(Hillsborough River, Alafia River, Little Manatee River, and Manatee River) which supply

about 70% of the total discharge. It has been estimated that Hillsborough Bay receives 63

to 77% of the total freshwater inflow to Tampa Bay (Lewis and Estevez, 1988).
















C, O
o
006

i^


Figure 2.4 Tampa Bay watershed (Wolfe and Drew, 1990).










Table 2.2 Surface water discharges to Tampa Bay (Lewis and Estevez, 1985).
Period of Record Average annual
River
(years) discharge (m'/s)
Hillsborough River 39 17.0
Alafia River 45 14.0
Little Manatee River 38 6.0
Manatee River 11 11.0
Rocky Creek 24 1.3
Lake Tarpon Canal 3 0.8
Sweetwater Creek 26 0.6
Others 12.3
Total 63.0






Hillsborough River


Draining a highly urbanized area, the Hillsborough River watershed collects the

discharge from most of Tampa, Temple Terrace, the eastern Interbay Peninsula, and Davis

Island. A dam constructed in 1945 (approximately 16 km from the mouth of the river in

Hillsborough Bay) separates two distinct water quality and hydrological environments:

upstream of the dam, the river is a freshwater reservoir, which provides freshwater to the City

of Tampa; and downstream of the river proper, which is tidal and brackish. Freshwater

discharges are controlled by the dam and range from 3.5 m3/s in the dry season to 48 m'/s

during the wet season, averaging 17 m3/s (Dooris and Dooris, 1985). Tidal action can be

found at 16 km upstream the mouth (Wolfe and Drew, 1990). The salt wedge can penetrate










as far as 13 km into the river during low-flow conditions (< 3 m3/s), or it can be flushed

downstream to near 4 km from the mouth when the flow exceeds 25 m3/s (Wolfe and Drew,

1990). Low flow rates and upstream salt wedge location seem to be well correlated with low

dissolved oxygen (DO) value and high nutrient concentrations inside the Hillsborough River.

A report from the Hillsborough County Environmental Protection Commission (EPC) (Boler,

1992) shows DO levels in the bottom saltier layer inside Hillsborough River below 4.0 mg/L,

with lowest values (< 2.0 mg/L) in May and June when flow is minimal. Surface values of

DO are generally above saturation (7.9 mg/L). BOD is reported to be less than 2.0 mg/L

throughout the year.

Ammonia and nitrate concentrations generally show a well mixed vertical distribution

upstream the salt wedge location, where the saltier water contains higher concentrations,

specially ammonia. Combined ammonia and nitrate values in the river range from 0 to 0.7

mg/L. In the dry season ammonia concentrations vary from 0.05 mg/L close to the dam to

0.2-0.3 mg/L towards the Bay. Nitrate concentrations vary from zero to 0.4 mg/L, and

organic nitrogen ranges from 0.38 to 5.60 mg/L (Wolfe and Drew, 1990).

Alafia River


The Alafia River watershed drains about 105 km2, south of the Hillsborough River

watershed. Flow in the Alafia River averages 14 m'/s and ranges from 5.4 m3/s in the dry

season to 28.3 m'/s during the wet season (Dooris and Dooris, 1985). Tidal action is

present up to 18 km upstream from the mouth. Johansson and Squires (1989) found that the

Alafia River, a major source of dissolved material to the Bay, can supply 51% of the Bay's








19

phosphate uptake for phytoplankton growth, and sediment flux rates are sufficient to meet

140% of the uptake. The authors attributed the high phosphate concentrations to leaching

of Florida's phosphate beds, fertilizer drainage from agricultural lands, and industrial and

sewage inputs. Phosphate concentration ranges from 4.8 mg/L in the upper reaches of the

river, decreasing to 1.2 mg/L near the Bay (Wolfe and Drew, 1990).

The salt wedge penetration depends on the river discharge and tidal regime, ranging

from 16 km during high tide and low flow condition to 4 km during low tide high flow

situation (Giovanelli, 1981).

Dissolved oxygen exhibits a vertical stratification near the mouth, ranging from below

4.0 mg/L at middle and bottom layers to saturation levels (7.9 mg/L) at the surface (Boler,

1992), where chlorophyll-a averages 24.1 pg/L.

The Alafia River tributaries exhibit high levels of nutrients. The poor water quality

in the North Prong is due to mining activities, and a greater number of phosphate and

chemical dischargers (Wolf and Drew, 1990). High levels of ammonia can reach as much as

85-120 mg/L, and nitrate values as high as 3.0 mg/L. Dissolved oxygen remains below 5

mg/L 50% of the time.

Little Manatee River


The Little Manatee watershed is the least urbanized of the four major rivers, and it

generally exhibits the best water quality conditions (Flannery, 1989). Flow averages 6 m'/s

and ranges from 1.7 m3/s in the dry season to 17 m3/s during the wet season (Dooris and

Dooris, 1985). Tidal action is found up to 25 km upstream the mouth (Wolfe and Drew,










1990). Salinity close to the mouth averages 9.0 to 12.0 ppt (EPC, 1984). Flannery (1989)

presented some water quality data for a station 25 km upstream from the mouth, which

showed the following average concentrations: 0.63 mg/L of nitrate+nitrite, 0.09 mg/L of

ammonia, 1.3 mg/L of BOD, 0.60 mg/L of organic nitrogen, and 7.0 mg/L of DO.

Manatee River


The Manatee River is impounded 38 km upstream from the Bay. Downstream of the

dam, the Manatee River and its major tributary, the Braden River, collect drainage from the

cities of Palmetto and Bradenton before discharge into the Bay. Flow averages 11 m3/s and

ranges from 1 m3/s in the dry season to 25 m3/s during the wet season (Dooris and Dooris,

1985). Tidal action is present up to 31 km upstream from the mouth. Nutrient levels are high

and generally decrease from the dam to the river mouth (Wolfe and Drew, 1990). Salinity

ranges from 14 to 26 ppt in the dry season to 2 to 19 ppt during the wet season (Heyl, 1982).

Close to the mouth, dissolved oxygen levels are low during summer months, ranging from 2.0

to 4.0 mg/L. Concentrations of total nitrogen, mostly in the organic nitrogen form, varies

between 0.1 to 4.4 mg/L (Heyl, 1982).

Rocky Creek


The Rocky Creek drainage area is approximately 115 km2. The discharge 9.5 km

upstream from the river mouth averages 1.3m3/s, ranging from 6.9 (wet season) to

0.05m3/s (dry season) (USGS, 1991). In its upper reaches, water quality is generally good

with pockets of high concentrations of ammonia and total phosphorus (Wolfe and Drew,

1990). Dissolved oxygen is usually below saturation.










Lake Tarpon Canal


The Lake Tarpon Canal is a man-made canal which was completed in 1971 to control

flooding. A saltwater-barrier/flood-control structure is located midway between Lake Tarpon

and Old Tampa Bay. Discharges from the canal average 0.8m's3, ranging from 22m3/s in

wet season to no flow in dry season (USGS, 1991). DO levels are usually high (7.0 to 8.0

mg/L) in the canal, pH is neutral (7.0), and nutrient concentrations are low (Dooris and

Dooris, 1985).

Sweetwater Creek


Sweetwater Creek is 17 km long and drains about 65 km2 of a primarily urban region.

The discharge at 6 km upstream from the river mouth averages 0.6m3/s, with a range

between 4.4 (wet season) to 0.03m3/s (dry season) (USGS, 1991). In the tidal portion of

the creek, DO (3.7 mg/L), BOD (6.0 mg/L), and nutrient concentrations (Ammonia

concentration ranging from 0.1 to 0.4 mg/L) indicate poor water quality (Wolfe and Drew,

1990).

Non-Point Sources


Coastal, Inc. (1994) developed a statistical model for the Tampa Bay National Estuary

Program (TBNEP) to support the preparation of the "Pollutant Load Reduction Goals"

(PLRG) for total nitrogen, total phosphorus, and total suspended solids for Tampa Bay. They

used measured data to develop regression relationships to describe the response of the

watershed to flow and loadings from non-point surface water sources, given a set of rainfall










and land use conditions. Results of that study indicate that non-point sources can have a

significant contribution to the total nutrient loading. Moreover, the study showed that

groundwater and nutrient inflow to Tampa Bay represent a smaller fraction of the total

loading. Table 2.3 summarizes the water quality of the seven point sources discharging into

Tampa Bay.



Table 2.3 1991 annual average water quality of seven point sources discharging into Tampa
Bay (Boler, 1992) and (USGS, 1991) (mg/L).
Near-Bottom Soluble Nitrate
Ammonium
River Dissolved Organic Noni +
Oxygen Nitrogen Nitrogen Nitrite
Hillsborough River 5.0 0.66 0.07 0.04
Alafia River 4.3 0.68 0.06 0.36
Little Manatee River 5.3 0.75 0.05 0.11
Manatee River 7.1 0.52 0.01 0.08
Rocky Creek 3.0 0.93 0.07 0.02
Lake Tarpon Canal 4.4 0.89 0.10 0.04
Sweetwater Creek 4.7 0.85 0.14 0.35




Nutrients Distribution and Loading



Tampa Bay has high phosphate levels in both water column and sediment layer,

especially in Hillsborough Bay. Tiffany and Wilkinson (1989) reported that 20% of world's

phosphate production and 80% of all United States phosphate output take place in the area.

Approximately 50% of all tonnage leaving Tampa Bay is composed of phosphate related










products. The mean annual water column phosphate concentration in Hillsborough Bay can

be as high as 1.28 mg/L (Fanning and Bell, 1985).

Nitrogen, however, is probably the single most important macro nutrient that limits

primary production in Tampa Bay. Assuming that phytoplankton assimilates N and P in

proportion to the Redfield C:N:P atomic ratios of 106:16:1, if N:P is higher than 16, the

system is primarily phosphorus limited. Otherwise, the system is considered to be nitrogen

limited. Fanning and Bell (1985) reported that the N:P ratio in Tampa Bay ranged from 0.3

to 1.3 in 1981, and concluded that phytoplankton have been historically nitrogen limited.

According to Simon (1974), municipal sewage treatment plants are the primary source

of nitrogen to Tampa Bay. The Alafia River provides the highest annual loading of nitrate

to Tampa Bay (about 3.9 x 105 kg/yr), followed by the Manatee and Hillsborough Rivers

(each about 9.0xl04 kg/yr). In terms of organic nitrogen, the Manatee and Alafia Rivers have

the highest loadings ( 2.5 x 105 kg/yr), followed by the Hillsborough River with 2.0 x 105

kg/yr (Dooris and Dooris, 1985). High levels of organic nitrogen in the Manatee River have

been related to the Bradenton sewage treatment plant and pulp effluent from citrus processing

plant (DeGrove, 1984). McClelland (1984) reported municipal sewage treatment plants

elsewhere around the Bay as significant nitrogen sources. Goetz and Goodwin (1980),

summarizing data collected between 1972 to 1976, obtained a mean organic nitrogen

concentration ranging from 0.5-1.0 mg/L in Old Tampa Bay, around 0.5 mg/L in upper

Tampa Bay, and the same level or below in the Lower Bay. In all three areas, seasonal and

year-to-year variation was low. On the other hand, mean organic nitrogen concentration in

Hillsborough Bay ranged from 0.75 to 1.25 mg/L, and temporal variation was greater. Nitrite








24

and nitrate concentrations were similarly low and steady everywhere in the Bay, except in

Hillsborough Bay. Ammonia levels were variable in all zones. Seasonal minima were less

than 0.1 mg/L in most places but more than 0.1 mg/L in Hillsborough Bay. Fanning and Bell

(1985) reported a mean ratio of ammonia to inorganic nitrogen of 0.84 (ranging from 0.54

to 0.99) in Hillsborough Bay. Seasonality was evident for total inorganic nitrogen, which

decreases substantially after rainy seasons, without an apparent reason (Lewis and Estevez,

1988).

In a preliminary nitrogen budget for Tampa Bay, Ross et al. (1984) suggested a

nitrogen storage of 3.87 x 107kg, an input from rainfall and anthropogenic sources of 21,470

kg/day, and a benthic release of 55,750 kg/day. Exports would occur in tidal exchange

(16,100 kg/day), biological losses (8,140 kg/day), and benthic uptake (53,000 kg/day).

Fanning and Bell (1985) estimated a turnover rate for nitrate and nitrite of 42 days, due to

runoff, and that benthic releases of ammonia could replace the overlying ammonia in 14 days.

Coastal, Inc. (1994) developed estimates of total nitrogen, total phosphorus, and total

suspended solids loading, as well as total freshwater inflow, to Tampa Bay. Two scenarios

(existing and "benchmark" conditions) were presented for the major seven segments of the

Bay. In order to account for ungaged areas, the Coastal, Inc. study used a statistical model

that related watershed characteristics to streamflow. Table 2.4 summarizes the mean annual

total nitrogen loading into each segment of Tampa Bay.










Table 2.4 Mean annual total nitrogen loading into each segment of Tampa Bay (Coastal,
1994).
Bay Segment Loading (tons/year)
Old Tampa Bay 600

Hillsborough Bay 2100

Middle Tampa Bay 1100

Lower Tampa Bay 500

Boca Ciega Bay 300

Terra Ceia Bay 80

Manatee River 600





Sediment Type and Distribution



Goodell and Gorsline (1961) studied the surface sediments composition and

distribution from all major areas of the Bay. They reported that Tampa Bay sediments are a

mixture of eroded quartz sands from Pleistocene terrace deposits and carbonates from shell

fragments produced within the system. The present sediment distribution is related to tide

generated currents, while sedimentary types correspond with bathymetric features. In sand

and grass flats less than 2 m deep, mean grain size was determined as 0.132 mm and sediment

was 2.7% carbonate. In deeper natural channels (> 6 m), mean grain size was 0.241 mm and

sediment was 25.2% carbonate, whereas mangrove areas contained no carbonate. Mean grain

size decreased from 0.218 mm at the mouth to 0.109 mm at the upper reaches of the Bay.

Mean carbonate content decreased from 16% to 2% over the same distance. Figure 2.5








26

shows the surface sediment distribution for Tampa Bay. According to Johansson and Squires

(1989), the descriptive work of Goodell and Gorsline (1961), conducted thirty five years ago,

did not intend to map fine grained sediments specifically, so the area coverage of these

sediments was not well defined as the mud zone delineated in later studies. Figure 2.6 shows

the mud zones in Hillsborough Bay delineated by the Bay Study Group of the City of Tampa

Sanitary Sewer Department in 1986.











IIf



r28. city Of











City of
St. Petersburg








O0









- 27 30



Very coarse-coarse sand Fine sand Silt
Medium sand Very fine sand
Figure 2.5 Surface Sediments in Tampa Bay (Goodell and Gorsline, 1961).













































Figure 2.6 Mud zone in Hillsborough Bay (Johansson and Squires,














CHAPTER 3
THE CIRCULATION AND TRANSPORT MODEL



Previous Work



Circulation and sediment transport models for estuaries have been significantly

advanced during the past 30 years. Sheng (1994) provided a comprehensive review on

circulation models for shallow waters. Sheng (1986) and Sheng et al. (1991) presented

comprehensive reviews of sediment transport models for estuaries and lakes.

Circulation in Tampa Bay has previously been modeled by Ross et al. (1984),

Goodwin(1987), Galperin etal. (1991), Sheng and Peene (1992), Peene etal. (1992), and

Hess (1994). The Ross etal. (1984) modeling system consisted of an integrated set of a 2-D

vertically-averaged circulation model, and box models for water quality and particulate

transport. Goodwin (1987) used a 2-D vertically-averaged model to study the effects of the

dredged navigation channel and dredged disposal sites on the circulation of Tampa Bay. The

residual circulation obtained from both studies (Ross et al., 1984 and Goodwin, 1987)

showed a complex pattern of numerous gyres, that were assumed to be responsible for poor

flushing conditions. Galperin et al. (1991) demonstrated that the barotropic residual

circulation pattern, obtained by Ross et al. (1984) and Goodwin (1987) in Tampa Bay, can

be completely overwhelmed by baroclinic effects. The baroclinic residual circulation obtained










by Galperin et al. (1991) exhibited a classical two-layer flow, with the surface layer flowing

out of the Bay and the saltier bottom layer flowing into the Bay. Sheng and Peene (1992),

studying the circulation and transport in Sarasota Bay, used a coarse grid (grid spacing on the

order of 1 to 2 km) to describe the circulation in Tampa Bay. Tampa Bay was added to the

Sarasota Bay grid of that study to evaluate the importance of Manatee River discharge on the

residual circulation of Anna Maria Sound. Peene et al. (1992) simulated the tide- and wind-

driven circulation in the Sarasota and Tampa Bay system during the passage of Tropical

Storm Marco in October 1990 using an earlier version of the three-dimensional boundary-

fitted grid model used in this study. Hess (1994) developed a three-dimensional orthogonal

curvilinear model with seven sigma grid layers for Tampa Bay, based on the Princeton

University ocean model (Blumberg and Mellor, 1987). The main goals of Hess (1994) were

to update the NOAA tidal current atlas for Tampa Bay, and to synthesize the extensive

observational data set obtained during the survey performed by NOAA in 1990-91.

The three-dimensional hydrodynamics model CH3D (Sheng, 1989) forms the basis

of the numerical simulations in this study. The model framework has been improved and

modified from earlier versions (e.g., Sheng, 1989; Sheng et al., 1991; Sheng and Peene, 1992)

in order to develop an integrated model that couples hydrodynamics, sediment and water

quality dynamics. The application (model setup, calibration, and validation) of the circulation

and transport model to produce a detailed characterization of the hydrodynamics within

system constituted the most important step in the development of the integrated model of the

Tampa Bay Estuarine System. Within the scope of this dissertation, the complete details of








31

model equations in the curvilinear boundary-fitted and sigma coordinates are of secondary

interest, and are therefore presented in Appendix A.


Circulation Model



The governing equations that describe the velocity and surface elevation fields in

shallow water are derived from the Navier-Stokes equations. In general, four simplifying

approximations are applied. First, it is assumed that the water is incompressible, which results

in a simplified continuity equation. Second, based on the fact that the characteristic vertical

length scale is much smaller than the horizontal counterpart, i.e., H/L << 1, the vertical

velocity is small and the vertical acceleration may be neglected. Hence, the vertical

momentum equation is reduced to the hydrostatic pressure relation. Third, with the

Boussinesq approximation, an average density can be used in the equations except in the

buoyancy term. Finally, the eddy-viscosity concept, which assumes that the turbulent

Reynolds stresses are the product of mean velocity gradients and "eddy viscosities", is

employed. In the transport equation, this concept means that the turbulent mass fluxes are

the product of mean concentration gradients and "eddy diffusivities".

With the above assumptions, the basic equations of motion in a right-handed Cartesian

coordinate system (x, y, z) are as follows:

Continuity Equation



au 8v aw
a + =0 (3.1)
ax ay az










X-component of Momentum Equation


au auu auv auw 1 ap a au
+ -- + +-- = fv p+ A-
at 9x ay Z pl, 8x ax ax
(3.2)
+ A au + (a au
+ -AH +- A
ayy ay z e z


Y-component of Momentum Equation


v auv avv avw= -fu- L p + A a
at ax ay dz po y ax x
(3.3)
+ a A av- + a av
ay y az az

Hydrostatic Pressure Relation


9p
pg (3.4)
az

where (u, v, w) are mean fluid velocities in the (x, y, z) directions, p is pressure, g is the

Earth's gravitational acceleration, p, is a reference fluid density, p(x,y,z) is the fluid density,

and f is the Coriolis parameter. A and A, are the horizontal and vertical turbulent eddy

viscosity coefficients, respectively.







Salinity Equation


In Cartesian coordinates, the conservation of salt can be written as:

as +a(uS) a(vS) + a(wS)
at ax Oy az
(3.5)
a as) a as a a as
-x x y D, y a v az)


where S is the salinity, DH and Dv are the horizontal and vertical turbulent eddy diffusivity

coefficients, respectively.

Since the length scales of horizontal motion in estuarine systems are much greater than

those of vertical motion, it is common to treat the vertical turbulence and horizontal

turbulence separately. It has been shown (e.g. Sheng et al, 1995) that in shallow estuaries,

the effect of the horizontal eddy viscosities on circulation is much smaller than the effect of

the vertical one. In the model, the horizontal turbulent mixing, which describes the effect of

sub-grid scale motion, is represented by a constant diffusion coefficient.

Vertical turbulent mixing is an important process which can significantly affect the

circulation and transport in an estuary. Since turbulence is a property of the flow instead of

the fluid, it is essential to use a robust turbulence model to parameterize the vertical turbulent

mixing. In this study, the vertical eddy coefficients (Av and D, ) are computed from a

simplified second-order closure model developed by Sheng and Chiu (1986) and Sheng and

Villaret (1989).













p = p(T,S) (3.6)

where p is density, T is temperature.

Various forms of the equation of state can be used. In the present model, the equation

given by Eckert (1958) is used:

p = P/(a + 0.698P)

P = 5890 + 38T 0.375T2 + 3S (3.7)

a = 1779.5 + 11.25T 0.0745T2 (3.8 + 0.10T)S

where Tis in C, S is in ppt and p is in g/cm3.

Conservative Species Equation


Flushing and residence time studies in an estuarine system can be carried out by

solving the conservation equation for a conservative species, c,:

ac, +auc I vc awc
-, \ i (vc,-1+ a (wc,)
at 8x ay az
(3.8)
DH--S' + D. a cy a D aZ
ax -ax ay a y C, z z

First, the estuarine system needs to be divided into segments with similar circulation

characteristics. To study the tidal flushing, a uniform concentration is released into all the

cells of a specific segment, while the concentration in the other ones are given zero values.

As the simulation proceeds, the remaining mass of the conservative species in each segment








35

is calculated as a fraction of the original mass. The flushing capacity of each segment is then

defined in terms of the reduction in the relative mass (Sheng et a., 1996).


Sediment Transport Model



An integrated model of the Tampa Bay Estuarine System must contain a sediment

transport model that can be used to address environmental problems related to dredging

operations in the navigation channels, and especially, the ecological problems related to the

adsorptive capacity of fine sediments to carry particulate forms of nutrients, heavy metals,

PCB's, and other organic pollutants.

The suspended sediment model includes the advection-diffusion processes, which are

computed by the hydrodynamics model, as well as such processes as erosion, deposition,

flocculation, settling, consolidation, and entrainment (Sheng, 1986; Mehta, 1986).

The governing equation that represents the transport of suspended sediments is given

by:


dc auc avc (w-w)c
--+--+ +
at ax ay at
(3.9)
+ D ( ac 8 [ v ZD c
+-D +-D +- D


where c is the suspended sediment concentration, w, is the settling velocity of suspended

sediment particles (positive downward), D is the horizontal turbulent eddy diffusivity, and D,

is the vertical turbulent eddy diffusivity.








36

Three simplifying approximations are implied in Equation (3.9). First, the concept of

eddy diffusivity is valid for the turbulent mixing of suspended sediments. Second, the

suspended sediment dynamics are represented by the concentration of a single particle size

group, assuming a homogeneous distribution of sediment particles size. Third, the suspended

sediment concentration is sufficiently low (5 1000 mg/L) such that non-Newtonian behavior

can be neglected.

In this study, the determination of settling, flocculation, deposition, erosion,

fluidization, and consolidation processes is based on the previous work of Sheng and Lick

(1979), Sheng(1986), Hwang and Mehta (1989), Sheng et al. (1991), and Chen and

Sheng(1994), and is described in Appendix B.


Model Equations in Curvilinear Boundary-Fitted and Sigma Grid



In three-dimensional modeling, complex bottom topographies can be better

represented with the application of a-stretching (Sheng, 1983), since it is possible to obtain

the same vertical resolution for the shallow coastal areas and the deeper navigation channels.

The vertical coordinate z is transformed into a new coordinate a by (Phillips, 1957):

Z (x,y,t)
a = (3.10)
h(x,y) + (x,y,t) (3

where Cis the surface elevation, and h is the mean water depth.

With this transformation, the numerical grid in the computational plane becomes

constant in space and time. However, in the physical plane, since the water surface is

constantly changing in time due to dynamic forcing conditions, the sigma grid is time








37

dependent. A o-grid formulation is suitable for simulating flow and salinity transport in

regions of gradual bathymetric variations and gives a more accurate estimation of bottom

stress than a z-grid model, which resolves the depth with "stair-step" grids. Nevertheless,

recent studies (Sheng et al., 1989a; Haney, 1991) showed that a o-grid model is accurate only

when there are sufficient grid points across regions of sharp bathymetric gradients. In the

case of insufficient grid points, Sheng et al. (1989a) suggested a direct evaluation of the

horizontal density gradient terms along constant z-plane, and avoiding higher-order advective

schemes along the sharp bathymetric variation, to reduce numerical error.

Using non-orthogonal boundary-fitted horizontal grid, it is possible to better represent

the circulation and transport processes in estuarine systems with complex geometries.

Thompson (1983) developed a method to generate 2-D boundary-fitted grids in complex

domain by solving a set of elliptic equations. These equations relate the generally non-

orthogonal curvilinear coordinates in the physical plane x and y with the uniformly-spaced

coordinates in the transformed plane, and T1.

The spatial coordinates in the physical plane, (x, y, z), have dimensions of length, while

the coordinate system in the computational plane, ( f r, a), is dimensionless. In this new

coordinate system ( ,r,a), the velocity vector are expressed in terms of contravariant

components, with dimension of [t'] (Sheng, 1989). The equations of motion in the ( r, o)

coordinates are shown in Appendix A.








38

Boundary and Initial Conditions



In order to numerically solve the set of equations presented, boundary conditions are

required for the dependent variables.

Vertical Boundary Conditions


3The boundary conditions for Equations (3.1), (3.2), (3.3), (3.5), and (3.9) at the free

surface ( o = 0) are:

au av


0 (3.11)
ao

-(w+w)c + D = 0



At the free surface, wind velocity is converted to stress by:


(tx, ,) = p, C (u v( ) (u+, v,) (3.12)


where Ts, and :r are the components of the wind stress, p. is the air density

(0.0012 g/cm3), uw and v are the components of wind speed measured at some height

above the sea level. C the drag coefficient, is given as a function of the wind speed

measured at 10 meters above the water surface by (Garrat, 1977):

Cd = (0.075 + 0.067 W,) 0.001 (3.13)


where W, is the wind speed magnitude in m/s.










The vertical velocity is obtained from the kinematic boundary condition imposed at

the surface:

ac ac ac
d + u + -+ (3.14)
at ax ay


The boundary conditions for Equations (3.1), (3.2), (3.3), (3.5), and (3.9) at the

bottom

(o=-1 )are:





(^= Cd 2 112)12 (U' I)
as (3.15)
= 0
ao

-(w+w)c + D, c = dc E
dz

where v, is the deposition velocity, E is the rate of erosion, Ay, and Dv are vertical turbulent

eddy coefficients. Cdb is the bottom friction coefficient, and u, v, represent the velocity

components at the first grid point above the bottom. Taking z, as half of the bottom layer

thickness (which starts at the bottom roughness height, z,,), C,, for a hydraulic rough flow,

is given by (Sheng, 1983):


Cdb [( 1-2 (3.16)


where Kis the von Karman constant.










Lateral Boundary Conditions


Along the shoreline where river inflow or outflow may occur, the conditions are

generally:


u= u(x,y,z,t)


v= v(x,y,z,t) (3.17)


w= 0


Along a solid boundary, the normal velocity component is zero. In addition, the

normal derivatives of salinity and suspended sediment concentration are assumed to be zero.

Along an open boundary, the surface elevation, c, is given by either a time series of

measured data or specified through harmonic constituents using the following equation:



S= C(x,y,t) = E Ancos 2 (3.18)



where An, T, and )n are the amplitude, period, and phase angle of the astronomical tidal


constituents.

When open boundary conditions are given in terms of C, the normal velocity

component is assumed to be of zero slope while the tangential velocity component may be

either zero, of zero slope, or computed from the momentum equations.








41

The salinity and suspended sediment concentration along an open boundary or river

entrance is computed from a I-D advection equation during the outflow. During the inflow,

the concentration takes on a prescribed value.

Contravariant velocity components provide lateral boundary conditions similar to

those in the (x, y) system. Along solid boundaries, the normal velocity is zero. When flow

is specified at the open boundary, the normal velocity component is prescribed.

To initiate a simulation, the initial spatial distributions of C, u, v, S and c need to be

specified. When initial data are unknown, the simulation starts with zero initial fields. When

initial data are known at a limited number of locations, an initial field can be interpolated. For

salinity simulations, the "spin-up" time is longer and sufficient time should be allowed in

model simulations.














CHAPTER 4
THE WATER QUALITY MODEL



Previous Work



Considerable effort has been expended in the past 20 years to develop water quality

models for freshwater and marine systems. In freshwater systems, Streeter and Phelps (1925)

were the first researchers to introduce a set of equations for predicting the biochemical

oxygen demand (BOD) and dissolved oxygen (DO) concentrations. Since then, simple zero-

and first-order exponential decay, dilution and sedimentation terms have been added to

predict other conservative and non-conservative species. Sheng (1994) provided a

comprehensive review on water quality models for shallow waters. Jorgensen et al. (1996)

provided the most recent review of environmental models developed in the last two decades.

Water quality models can be classified in terms of the approach undertaken for

solution and analysis. Steady-state models are usually simpler and require less computational

effort than dynamic models. On the other hand, multi-dimension, robust models can provide

more detailed and comprehensive information on the water quality. Stochastic models require

more data for calibration and validation than deterministic models. Water quality parameters

simulated by deterministic models are expressed in terms of expected values, while

simulations performed by stochastic models explicitly take into account the uncertainty of










physical and biogeochemical processes. Validation of stochastic models is particularly

difficult due to the quantity of observational data required to compare probability distributions

of variables rather than just their expected or mean values (Loucks, 1981). Moreover, it is

more meaningful, in terms of interpretation, to estimate biogeochemical parameters like

growth and nitrification rates than empirical parameters like autoregressive and moving

average coefficients (Solow, 1995).

Until recently, water quality models, originally developed for rivers and stream flows,

were indiscriminately used in estuarine systems. The assumption of steady or quasi-steady

state of the hydrodynamics processes justified the use of coarse grids, and models were either

uncoupled or loosely coupled with hydrodynamics models. However, even in freshwater

systems, there is increasing evidence that hydrodynamics processes have very significant

effects on water quality and ecological processes. Chen and Sheng (1994) found that the

internal loading of nutrients from bottom sediments in Lake Okeechobee could not be

accurately calculated by a water quality model using a large time step of 6 hours. During one

time step, the internal loading of nutrients from bottom sediments calculated by a

conventional water quality model may be zero, because of the zero average net flux from the

bottom sediments in this 6-hour period. In reality, the resuspension and deposition processes

can significantly affect internal loading through sorption/desorption processes. Model

simulations which include such effects produced results that agree well with field data (Sheng

et al., 1993). The water quality model developed by Chen and Sheng (1994) forms the basis

of the water quality model of the present study, although their model did not include the

dissolved oxygen balance and was limited to rectangular grid system.








44

In estuarine systems, the necessity to accurately represent hydrodynamics and

biogeochemical processes is even more relevant. Estuarine systems are physically dominated

ecosystems, where the action of the sun, tides, wind, atmospheric disturbances, river

discharges, and complex geomorphometric features interact. It is the balance of these

physical forces acting as subsidies and stresses that will dictate the water quality dynamics of

each estuarine system.

In traditional water quality box models like WASP (Ambrose et al., 1994), salinity

data is used to obtain the so-called dispersivee coefficients" during model calibration. This

salinity calibration consists of first averaging the flows over the calibration period and then

estimating tidal dispersion coefficients, assuming steady-state conditions (AScI, 1996). In

an estuarine environment, this approach is questionable since salinity is an active species. Its

concentration and gradients affect the temporal and spatial distribution of the density field,

driving baroclinic forces that completely change the hydrodynamic characteristics of the flow.



AScI (1996) has applied WASP4 in Tampa Bay aiming at the development of a

"broad-based, management-oriented model". The primary objective of the AScI study is to

provide the Southwest Florida Water Management District (SWFWMD) with a modeling tool

to define eutrophication management strategies. The rationale of this approach was that the

hydrodynamic and water quality data gathered in Tampa Bay, from 1985 to 1991, was

sufficient for the determination of the dispersive and the other "ad-hoc" coefficients of the

model. In addition to the 28 model coefficients, sediment oxygen demand and benthic fluxes

were also determined for specific Bay segments during the calibration process. Another










model from capturing episodic events. Schoellhammer (1993) showed that resuspension in

Old Tampa Bay is closely related to storm systems and local wind-generated waves. Sheng

et al. (1993) showed that the contribution of resuspension flux to the internal loading during

episodic events can be orders of magnitude greater than the normal diffusive benthic flux.

Coastal, Inc. (1995) developed a statistical model to investigate the relationships

among nutrient loading, water quality parameters (chlorophyll-a), and light attenuation

coefficients. Like the AScI (1996) study, the rationale supporting this simple approach was

the large amount of data gathered between 1985 and 1991. It was thought that if the

available water quality data were sufficient to calibrate and validate this empirically-based

model, it would serve as a management tool to determine external nitrogen loadings

consistent with seagrass light requirements. Using regression analysis, Coastal, Inc.

determined the relationships between total nitrogen loading, chlorophyll-a, turbidity and light

attenuation coefficients in the four major Bay segments (Old Tampa Bay, Hillsborough Bay,

Middle Tampa Bay, and Lower Tampa Bay). The conclusions of the Coastal, Inc. study was

that no reduction in annual average nitrogen loading and chlorophyll a concentration would

be required for the 20% near-bottom light level target for the four major Bay segments.

However, a substantial reduction in nitrogen loads would be required in order to achieve the

25% light level target.

The limitations of the Coastal, Inc. study are related to the fact that the simplifying

assumptions applied (linear correlation between cause and effect) proved deficient to explain

any correlation between external loading and nitrogen and chlorophyll-a concentrations

inside the Bay (Coastal, 1995). This limitation suggests that the internal loading has a








46

significant role in the nutrient budget, and consequently should be considered in the strategies

to control the eutrophication process. Furthermore, another uncertainty of the Coastal

analysis was originated from their conclusion that a three month cumulative lag period for

nitrogen loads could explain the variation in the chlorophyll-a data. Johansson (1991), using

a similar statistical approach presented evidences for a three-year lag between external

nitrogen loading and chlorophyll-a response. Again, different rates of internal nitrogen

loading may explained the lag difference between Coastal, Inc. (1995) and Johansson (1991)

studies.

These previous modeling efforts on Tampa Bay provided useful foundation for this

more comprehensive modeling study. Despite their simplified approach, these studies were

able to isolate relevant processes and determine some specific model coefficients. At the end

of this chapter, Table 4.2 presents the model coefficients used in this study, the range of each

coefficient found in the literature, and the values used by AScI (1996). In order to account

for both point and non-point sources of nitrogen loading into the Bay, the water quality

species concentration along model boundaries were determined from the nutrient loadings

presented by Coastal, Inc. (1994). Total nitrogen loadings were converted to concentrations

and used along with river discharges.

The water quality component of this integrated model for Tampa Bay focuses on the

interactions between oxygen balance, nutrient dynamics, light attenuation, phytoplankton and

zooplankton dynamics. To develop the water quality model, the mass conservation principle

can be applied to each water quality parameter related to the phytoplankton and zooplankton

dynamics, phosphorus cycle, nitrogen cycle, and oxygen balance. With regard to nutrients,








47

the nitrogen cycle is more important than the phosphorus cycle since nitrogen has been the

macro-nutrient limiting phytoplankton growth in Tampa Bay (FWCA,1969; Lewis and

Estevez, 1988; Johansson, 1991; Coastal, 1995; AScI, 1996). Hence phosphorus cycle is not

included as part of the water quality model for Tampa Bay.


Development of the Numerical Model



The nitrogen cycle in Tampa Bay is modeled through a series of first-order kinetics,

which start with the biogeochemical process controlling nitrogen fixation. Phytoplankton

growth controls ammonia and nitrate uptake. The uptake rate for each species is proportional

to its concentration relative to the total inorganic nitrogen content, and a preferential factor

for ammonia uptake. Nitrogen returns from the planktonic biomass pool as dissolved and

particulate organic nitrogen and as dissolved inorganic nitrogen through endogenous

respiration and non-predatory mortality. Organic nitrogen is converted to ammonia

mineralizationn) at a temperature-dependent rate, and ammonia is then converted to nitrate

(nitrification) in a temperature and oxygen-dependent rate. The stability of the dissolved form

of ammonium in water is pH dependent. It can exist in its ionic form, ammonium (NH4*) or

as ammonia (NH3), with the latter being lost from the system through volatilization. Low

levels of dissolved oxygen may induce a bacterial-mediated transformation of nitrate into

nitrogen gas (denitrification) at a temperature-dependent rate.

The oxygen balance couples dissolved oxygen to the other state variables. Reaeration

through the atmosphere-water interface, and phytoplankton production during photosynthesis










are the main sources for oxygen. Oxidation of organic matter and carbonaceous material,

respiration by zoo and phytoplankton, and oxygen consumption during the nitrification

process are collectively grouped into the CBOD (carbonaceous-biochemical oxygen demand)

variable, which is a sink for dissolved oxygen (Ambrose et al., 1994).

The light penetration inside water can be determined through measurements of

turbidity, color, and light penetration (Kirk, 1994). In the integrated model for Tampa Bay,

the primary concern is the availability of photosynthetically active radiation (PAR), which is

influenced by the intensity of incident solar radiation, solar elevation angle, weather

conditions, water depth, tidal range, concentrations of sediments, detritus and phytoplankton

(Miller and McPherson, 1995).

Phytoplankton kinetics is the central part of this water quality model, since the primary

water quality issue in the Tampa Bay Estuarine System is eutrophication (Boler et al., 1991).

Phytoplankton population is a complex variable to obtain in the field. For single species, a

direct measurement of the population size is the number of cells per unit of volume.

However, in natural multi-species environment, it is difficult to distinguish viable and non-

viable cells and, for species that tend to colonize, counting requires an extra effort to separate

individual cells because the size of the colonies are quite variable (Ambrose et al., 1994). An

alternate solution is to measure phytoplankton population through chlorophyll analysis,

although this is not an absolute indicator of planktonic biomass. Some species do not contain

chlorophyll and when chloroplasts (chlorophyll-containing structures found in algal and green

plant cells) are present, they vary in number, size and pigment content per cell (Boler et al.,










1991). The conversion to phytoplankton dry weight or carbon involves further species-

dependent constants that depend on nutrient and light levels.

The rationale behind this water quality modeling effort is that planktonic organisms

have a fast response to environmental conditions. In other words, by combining chlorophyll

with nutrient levels, dissolved oxygen balance, and light attenuation, it is possible to evaluate

and quantify short and long term water quality processes such as hypoxia and eutrophication.


Mathematical Formulation



In this study, the water quality equations are derived from an Eulerian approach,

using a control volume formulation. In this method, the time rate of change of the

concentration of any substance within this control volume is the net result of (i) concentration

fluxes through the sides of the control volume, and (ii) production and sink inside the control

volume. The conservation equation for each of the water quality parameters is given by:


a + V-((a) = V.[DV()a)] + Q
t (4.1)

(i) (ii) (iii) (iv)


where (i) is the evolution term (rate of change of concentration in the control volume), (ii)

is the advection term (fluxes into/out of the control volume due to advection of the flow

field), (iii) is the dispersion term (fluxes into/out of the control volume due to turbulent

diffusion of the flow field), and (iv) is the sink/source term, representing the kinetics and

transformations due to sorption/desorption, oxidation, excretion, decay, growth,









biodegradation, etc. The water quality equations in the curvilinear non-orthogonal boundary-

fitted system(S7, T ) are given by:

1 aH, a D a9
H Bt H2 8o aoo
Ha a
g J, (F,"Hu) Hat, g H ao (4.2)

+ D. 1g" + 2g 2 2 2.~ Q



where ) represents any water quality parameter, (g) is the Jacobian of horizontal

transformation, (g ,, g 12, 22) are the metric coefficients of coordinate transformation, and

Q represents the biogeochemical processes.

In the following sections, the biogeochemical processes controlling the sink/source

term of Equation (4.2) will be discussed in detail for the nutrient dynamics, zooplankton and

phytoplankton dynamics, and oxygen balance in estuarine systems.


Nutrient Dynamics in Estuarine Systems



As explained earlier, the nutrient dynamics will be centered in the nitrogen cycle,

assuming it is the macro-nutrient that limits phytoplankton growth. For the present study, the

basic transformation processes for the nitrogen cycle are similar to those described in Chen

and Sheng (1994).

Nitrogen comprises 78% of the atmosphere, mostly molecular N2 This form is

biologically unavailable except for fixation by procaryotic organisms containing the enzyme










nitrogenase. Considering the kinetic pathway organic nitrogen -ammonia nitrate -*N2,

fixed forms of nitrogen such as nitrate, ammonium, and organic nitrogen would gradually be

depleted from the biosphere if not for nitrogen fixation.

Nitrogen inputs to estuarine systems are related to point and non-point sources from

land, atmospheric deposition, and fixation. Additionally, internal loadings such as from

resuspended sediments containing inorganic and organic forms are also important. The

specification and quantification of each of these contributions is the first step towards the

determination of nitrogen budget in an estuarine system.

As shown in Equation (4.2), the nitrogen cycle is highly dependent on the

hydrodynamics and sediment dynamics of the estuarine system. Resuspension events,

combined with desorption processes can significantly change the input and budget of nitrogen

in the system. On the other hand, deposition and sorption may contribute to major losses of

nitrogen from the water column. The hydrodynamics not only drive the sediment processes,

but also affect the sorption/desorption reactions, through turbulent mixing.

The processes simulated in this study include:

a) Mineralization of organic nitrogen

b) Nitrification of ammonium

c) Volatilization of ammonia

d) Denitrification of nitrate

e) Uptake of ammonia and nitrate by phytoplankton

f) Conversion of algal-nitrogen into zooplankton-nitrogen through grazing

g) Excretion by algae and zooplankton








52

For the purpose of studying its cycle, the nitrogen species are first divided into

dissolved and particulate groups. This division is usually established in the laboratory using

filtering techniques. In the dissolved group, this study will consider nitrogen as ammonia

nitrogen, represented by the state variable NH3; dissolved ammonium nitrogen, represented

by the state variable NH4; nitrate+nitrite nitrogen, represented by the state variable N03; and

dissolved or soluble organic nitrogen (SON). Particulate nitrogen includes: particulate

inorganic nitrogen (PIN), and particulate organic nitrogen (PON). Zooplankton nitrogen

(ZOON), and algal nitrogen (ALGN) relate biomass to nitrogen concentration through fixed

stoichiometric ratios: zooplankton nitrogen to carbon ratio (zc ), and algal nitrogen to

carbon ratio (a N).

In order to couple the water quality model with hydrodynamics and sediment

dynamics, Equation (4.2) needs to be modified for the particulate forms of nitrogen, so that

it includes a settling velocity. For the inorganic species, it is reasonable to assume the same

settling velocity of the suspended sediment particles. For phytoplankton, literature values of

algae settling velocity, which accounts for the limited vertical motion of these organisms will

be used.

Ammonia Nitrogen


Ammonia volatilization is a physico-chemical process where ammonium N is in

equilibrium between its gaseous and hydroxyl form:


NH3(aq) + H20 NH4 + OH








53

As stated, the process is pH dependent, with an alkaline environment driving the

reaction to the left, i.e. favoring the aqueous form. Since the concentration of ammonia in

the atmosphere is very low, the partial pressure difference may produce a sink for nitrogen

in the system, according to Henry's law. The kinetic pathway for ammonia nitrogen (state

variable NH3) is represented in the sink term of Equation (4.2) as:


Q = K,,- pH NH4 K [h,-NH3 (NH3) ] (4.4)



where K, is the ammonia conversion rate constant, and Ha is the half-saturation constant

for ammonia conversion. KvoListhe volatilization rate constant, h, is Henry's constant, and (NH3 ),,

is the ammonia concentration in the air.

Dissolved Ammonium Nitrogen


Nitrogen fixation is a biogeochemical process mediated by a variety of autotrophic and

heterotrophic bacteria, by which nitrogen gas is reduced to ammonium:


N2(g) + 8H + 6e-- 2NH4* (4.5)


In aquatic systems, this reaction is only possible in very reduced environments

(Snoeyink and Jenkins, 1980). Such an environment exists inside photosynthetic cells of

blue-green algae, and in the symbiotic association in root nodules between bacteria of the

genus Rhizobium and certain plants. It has been reported that cyanobacteria are responsible

for most planktonic fixation in aquatic environments, with a high correlation between fixation

rates and cyanobacteria biomass (Howarth et al., 1988). In most estuaries, the biomass of










these nitrogen-fixing species of cyanobacteria usually makes up a very small percentage of the

phytoplanktonic biomass (< 1%), suggesting insignificant amount of nitrogen fixation

(Howarth et al., 1988). Johansson et al. (1985) showed that, prior to 1984, planktonic

filamentous blue-green algae (Schizothrix calcicola sensu Drouet) dominated the

phytoplankton population in Tampa Bay from early summer to early winter. However,

Johansson (1991) stated that there was no information to support that this blue-green algae

is responsible for nitrogen fixation in Tampa Bay. Actually, it has been estimated that nitrogen

fixation should account for no more than 5% of the total nitrogen budget in the Bay

(Johansson, personal communication). These evidences support the hypothesis generally

accepted that many estuaries are nitrogen limited in part due to the low rates of nitrogen

fixation. Hence, nitrogen fixation was not considered in this study.

The biogeochemical transformation of organic nitrogen to ammonium is defined as

ammonification. Another source for dissolved ammonium is the release of NH4 during

mortality and excretion of algae and zooplankton, and the sorption/desorption reaction with

sediment particles. The kinetic pathway of ammonium nitrogen (state variable NH4) is

represented in the source term of Equation (4.2) by a first-order reaction (Rao et al., 1984),

and a partitioning between particulate and dissolved form regulated by the

sorption/desorption kinetics (Chen and Sheng, 1994):











DO
Q = Ko SON K, NH4
HOit + DO

+ d (PIN p,,, cNH4)
(4.6)
P. I.ALGN + K-,ALGN + K-ZOON

K pH NH4
SH + pH



whereKoNm the rate of organic nitrogen mineralization is a function of water temperature,

pH, and the C/N ratio of the residue (Reddy and Patrick, 1984). KN is the nitrification rate

constant, DO is the dissolved oxygen concentration, H,,i is the half saturation constant for

oxygen limitation, dn is the desorption rate of NH4 from sediment particles, p.a is the

partition coefficient between NH4 and PIN, and c is the suspend sediment concentration. Pn


is the ammonium preference factor for algae uptake, pa is the algae growth rate,

K, and K are the algae and zooplankton excretion rate constants, respectively. All

coefficients related to zoo and phytoplankton dynamics will be discussed later in this Chapter.

Nitrite+Nitrate Nitrogen


In an aerobic environment, the mineralization of organic nitrogen proceeds with a

bacterial-mediated transformation of ammonium into nitrate. The nitrification process is a

two step process, in which the chemoautotrophic bacteria of the genera Nitrosomonas

mediate the formation of nitrite, and bacteria of the genera Nitrobacter the formation of

nitrate:











NH4' + 1.5 02 NO2- + 2H' + H20
(4.7)
NO2- + 0.5 02 NO3






Nitrification is a strictly aerobic process, occurring only in the water column and in

the aerobic layer of the sediment column. Equation (4.7) shows that the nitrification process

is a sink for dissolved oxygen in the system.

Denitrification is defined as the biogeochemical transformation of nitrate N to gaseous

end products such as molecular nitrogen or nitrous oxide (Reddy and Patrick, 1984). Like

volatilization, denitrification represents a sink for nitrogen in the system. Under anaerobic

conditions and in the presence of available organic substrate, denitrifying bacteria (e.g.

Pseudomonas denitrificans) can use nitrate as an electron acceptor during anaerobic

respiration. As an example, the oxidation of a carbohydrate substrate to CO2 and H20 using

nitrate instead of oxygen can be given as:


5(CH20) + 4NO, + 4H' 5 CO2 + 2N2 + 7H20 (4.8)



This irreversible reaction is actually a two-step process in which nitrate is reduced to

nitrous oxide before being converted into molecular nitrogen. Nitrous oxide has been related

to the Earth's "greenhouse" effect because N20 reacts and breaks down atmospheric ozone

(McElroy et al., 1978). Reddy et al. (1978) showed that under carbon-limiting conditions,










the denitrification process described in Equation (4.8) can be represented by a first-order

reaction.

In this study, the kinetic pathway of nitrite+nitrate (state variable N03) is represented

in the source term of Equation (4.2) as a sequence of first-order reactions, limited by the

dissolved oxygen concentration:


Q = K,- DO *NH4-K. H3 N03 -P (I- .ALGN (4.9)
SHit+DO HN H +DO


where K, is the denitrification rate constant, and HN, is the half saturation constant for

denitrification, which can be calibrated to only allow the denitrification process to occur under

low dissolved oxygen conditions (Ambrose et al., 1994).

Soluble Organic Nitrogen


Besides Nz the largest pool of nitrogen in estuarine systems are dissolved and

particulate organic nitrogen. The kinetic pathway of dissolved or soluble organic nitrogen is

the convention of SON to NH4 during ammonification, and the sorption/desorption reaction

with sediment particles. For soluble organic nitrogen (state variable SON), the source term

of Equation (4.2) can be represented by:

Q = KoM SON + don *(PON p_,- c SON) (4.10)


where d,, is the desorption rate of SON from the sediment particles, and pn is the partition

coefficient between SON and PON.










Particulate Organic Nitrogen


The kinetic pathway of particulate organic nitrogen is the release of PON during

mortality and excretion of algae and zooplankton, and the sorption/desorption reaction with

sediment particles. In estuarine systems with organic-rich sediments, benthic mineralization

of detritus can be a major recycling pathway, and account for a significant fraction of the

nutrient requirements of primary producers in overlying water column (Klump and Martens,

1981). For particulate organic nitrogen (state variable PON), the source term of Equation

(4.2) can be represented as:

Q = K ALGN + K, ZOON d,, (PON po c* SON) (4.11)

where K is the mortality rate of zooplankton.

Particulate Inorganic Nitrogen


Sources of PIN are related to nitrogen contained in the suspended particulate matter

derived from landward and seaward origin (Keefe, 1994). There have been several studies,

at various spatial and temporal scales, of particulate nitrogen distribution in estuarine systems

(Sharp et al., 1982; Edmond et al., 1985; Wafar et al, 1989). Nevertheless, little insight is

available concerning the partitioning between the inorganic and organic fraction of these

materials (Froelich, 1988). The kinetic pathway of particulate inorganic nitrogen (state

variable PIN) is related to the sorption/desorption reaction with sediment particles, and the

source term of Equation (4.2) can be written as:

Q = d (PIN pn c NH4) (4.12)











Algal Nitrogen


Through uptake of inorganic nitrogen, algae assimilates nitrogen in proportion to its

growth rate. The particulate nitrogen recycles to the inorganic pool by means of excretion

and non-predatory mortality. Inasmuch as there is no data on excretion of zoo and

phytoplankton under field conditions, most of water quality models consider constant

excretion and mortality rates proportional to the biomass (J0rgensen, 1983; Najarian et al.,

1984; Ambrose et al., 1994; Chen and Sheng, 1994). Another sink for ALGN is due to

grazing by zooplankton, at a rate proportional to the zooplankton growth rate. Growth rates

for phytoplankton and zooplankton in estuarine systems are complex functions of the species

present, and they will be discussed later in this Chapter.

The algal nitrogen (state variable ALGN) is represented in this model by a fixed

stoichiometric ratio relating algal biomass and nitrogen concentration as:

ALGN = aNc (Algal Biomass) (4.13)


where aNc is the algal nitrogen to carbon constant ratio.

Zooplankton Nitrogen


Similar to ALGN, the kinetic pathway of particulate zooplankton nitrogen depends on

growth, excretion, and mortality rates. In this case, the zooplankton nitrogen (state variable

ZOON) is given by:

ZOON = ZN, ( Zooplankton Biomass)


where ZNC is the zooplankton nitrogen to carbon constant ratio.











Sorption and Desorption Reactions



In the nitrogen cycle, sorption processes refer to the conversion from soluble to solid

phase of inorganic and organic species, while desorption reactions describe the inverse

process. Sorption/desorption processes, combined with resuspension events can significantly

alter the nitrogen cycle in the system.

The kinetics of sorption/desorption reactions are dependent on each nitrogen species

characteristics, sediment properties, pH, temperature, and dissolved oxygen concentration

(Simon, 1989). Some studies have shown that sorption/desorption processes can be more

important in marine environment than in freshwater. The primary reason has been attributed

to a six times higher adsorptive capacity of clays for organic matter in seawater than in

freshwater, due to salinity effects (Pocklington, 1977; Martinova, 1993).

The most commonly used mathematical representation of sorption/desorption

processes is the linear, reversible, isotherm (Berkheiser et al., 1980; Reddy et al., 1988):


aNa
-N D, Nd + Sr N (4.15)



where D, is the desorption rate constant, S, is the sorption rate constant, Nd is the adsorbed

nitrogen concentration, and N, is the dissolved nitrogen concentration.

Equation (4.15) can be reformulated, considering that at equilibrium, the ratio

between the desorption and sorption rates gives the partition coefficient between dissolved

and particulate forms:











N -d -D,(Nd -pN) (4.16)
at



where p, is the partition coefficient.


Phytoplankton Dynamics in Estuarine Systems



The overall water quality in the Tampa Bay Estuarine System is markedly influenced

by the dynamics of the zoo and phytoplankton communities (Lewis and Estevez, 1988; Boler

et al., 1991; AScI, 1996). In a review of the phytoplankton in Tampa Bay, Steidinger and

Gardiner (1985) reported the dominance of nannoplankton (less than 20 pm), with a head to

mouth gradient, following the salinity distribution. The authors also reported that Tampa Bay

presents more than 250 species of phytoplankton, with diatoms making up the bulk of the

distribution. However, the lack of data on each specific species prevented a more detailed

characterization, and the entire phytoplanktonic community is represented in this study by a

single state variable.

In this study, a quantitative model of phytoplankton population dynamics also uses

the conservation of mass principle, in which hydrodynamics transport plays a major role.

Phytoplankton growth is represented by a temperature-dependent maximum growth rate that

is limited by nutrient availability and light. Light limitation is formulated according to the

equation first proposed by Steele (1965). The nutrient limitation is represented by a modified

version of the Michaelis-Menton formulation. Some researchers (e.g. Jorgensen, 1976)

suggest that the nutrient-limited growth rate of phytoplankton is a function of the internal










nutrient content. According to this approach, external nutrients are taken up by

phytoplankton and stored. Ensuing growth would then be related to this internal nutrient

content. Assuming a dynamic state of equilibrium between the external concentration and

internal content (Di Toro, 1980), it is possible to represent the nutrient limitation according

to the formulation suggested by Riley and Stefan (1988).

In this study, the phytoplankton growth rate is represented by:

SOr-2o I ( I NH4+NO3
p=a) 0 exp( 1 (4.17)
(L *T- I H + NH4 + NO3


where (p.) is the algae maximum growth rate, 0 is the temperature correction factor, I is

the light intensity, 1 is the optimum light intensity for algal growth, H/ is the half saturation

constant for algal growth.

The phytoplankton kinetics are represented by growth, respiration, non-predatory

mortality, grazing by zooplankton, and a settling term which accounts for the limited vertical

motion. The source term of Equation (4.2) can be written as:


Q = (wsage PHY) + ( a. -K K,)PHY pl *ZO0 (4.18)


where wsgue is the phytoplankton settling velocity. Chen and Sheng (1994) reviewed algal

settling rate measured in eutrophic water bodies, and showed that it is not only species

dependent, but it also a function of flocculation and senescence.

Zooplankton are the lower-trophic level consumers that constitute the primary

herbivorous component of an estuarine ecosystem (Kennish, 1990). In this study,










phytoplankton are the object of concern, therefore, no attempt is made to investigate the

details of the zooplankton dynamics. Zooplankton is only considered as the predators of

phytoplankton, utilizing their available biomass as food supply.

Zooplankton growth is represented by a temperature-dependent maximum growth

rate, that is limited by phytoplankton availability:

S( 0T-20. PHY
S )m 2 Hpy +PHY (4.19)



where (iz)x is the maximum growth rate for zooplankton, 0 is the temperature limiting

function for zooplankton, and Hphy is the half saturation constant for phytoplankton uptake.

The zooplankton kinetics, influenced by growth, respiration, and mortality, is

represented in the source term of Equation (4.2) by:


Q = ( K,- K ZOO (4.20)



Oxygen Balance in Estuarine Systems



Dissolved oxygen dynamics in aquatic systems have been extensively studied (Streeter

and Phelps, 1925; O'Connor and Thomann, 1972; Orlob, 1983; Ambrose et al., 1994).

Dissolved oxygen evolution depends on the balance between photosynthetic production, total

respiration, and exchanges with the atmosphere. Oxygen, as a byproduct of photosynthesis,

increases as a result of autotrophs' growth. Dissolved oxygen saturation in seawater is

determined as function of temperature and salinity (APHA, 1985). For dissolved oxygen








64

levels below saturation, DO diffuses into surface waters. When the water is super-saturated,

mainly as a result of primary production, oxygen will be diffused out to the atmosphere. As

any other water quality parameter, dissolved oxygen is also subject to advective transport in

the estuarine system.

In this study, the formulation of the oxygen balance is based on the WASP5 model,

with some modifications. The rate of dissolved oxygen production is assumed to be

proportional to the growth rate of the phytoplankton in a fixed stoichiometry reaction. For

each milligram of phytoplankton carbon produced by growth using nitrate, a fixed amount of

phytoplankton nitrogen (ALGN) is reduced, and (48/14) aN (phytoplankton nitrogen/carbon

ratio) mg of 02 is produced. The dissolved oxygen fluxes on the air-water interface are

determined as a product of a reaeration coefficient multiplied by the difference between

dissolved oxygen saturation and the dissolved oxygen concentration at the surface layer. The

reaeration coefficient is assumed to be proportional to the water velocity, depth, and wind

speed (Thomann and Fitzpatrick, 1982). Details of the dissolved oxygen saturation and

reaeration coefficient calculations are presented in Appendix C.

In this model, there are two options for the kinetic pathway of DO. The first one,

describing the oxygen balance through a full non-linear equation is represented in the source

term of Equation (4.2) as:










32 48 14
Q = KA(DO, DO) + 32 + 14( ) ALG

32 DO
--2 K a, PHY K,- DO CBOD (4
12 ", + DO (4.21)

64 DO
KNN *NH4
14 Hnit + DO




where DO, is the saturation value for dissolved oxygen concentration, KAE is the reaeration

coefficient, andao, is the constant oxygen to carbon ratio for phytoplankton respiration (gO2

/ gC).

In order to minimize "spin-up" time due to the non-linear character of Equation

(4.21), a second option describes the oxygen balance through a linear equation where the

source term of Equation (4.2) is given as (Ambrose et al. 1994):


Q = K,(DO, DO) + [p-(K ,+Kx .PHY

(4.22)
-K, CBOD 6 K NH4
14


The use of carbonaceous oxygen demand (CBOD) as a measure of the oxygen-

demanding processes simplifies modeling efforts by aggregating their potential effects

(Ambrose etal. 1994). Oxidation organic matter, nitrification, non-predatory mortality and

respiration by zoo and phytoplankton are nitrogenous-carbonaceous-oxygen-demand,

collectively combined into the state variable CBOD.










The kinetic pathway of CBOD is represented in the source term of Equation (4.2) as:


= [ScBoo (1- fdctoD)- CBOD]


K CBOD
H + DO
(4.23)
5 32 .K K N03
4 14 H3 + DO

+ K ALGN + K ZOON


where fdcoo corresponds to the fraction of the dissolved CBOD, and wsCBOD is the

settling velocity for the particulate fraction of CBOD. fdcBOD and WSCBOD are empirically-

based coefficients that represent the fact that under quiescent flow conditions, the particulate

fraction of CBOD can settle through the water column, and eventually deposit on the bottom

(Ambrose et at 1994). The determination of both coefficients should proceed in terms of the

best fit between measured and modeled data (J0rgensen and Gromiec, 1989).


Light Attenuation in Estuarine Systems



The solar radiation that reaches the ocean's surface includes the ultraviolet range

(290-380 nm), the visible range (380-760 nm), and the infra-red (760-3000 nm). As to

primary production in estuarine systems, ecologists are normally concerned with light in the

range of wavelengths from 400-700 nm. Defined as "Photosynthetically Active Radiation"

(PAR), this range of irradiance provides the predominant source of energy for autotrophic

organisms (Day et al., 1989). Moreover, instead of measuring PAR in terms of energy,








67

commercially available quantum meters record the number of quanta (or photons, in the

visible range) received per unit area per unit time. The unit of this photon flux density is

micro Einstein per squared meter per second ( Em -2 s I).

The incident light can be reflected, absorbed, and refracted by dissolved and

suspended substances in the water and by the water itself. The Beer-Lambert law can be used

to describe the light distribution with depth (Day et at., 1989):


I = e- K (4.24)

where ,, is light intensity at the water surface, IZ is light intensity at depth z, z is the depth

in meters, and Ko is the vertical light attenuation coefficient in m'.

For long term simulations, the seasonal variation of surface irradiance in Tampa Bay

can be represented by a sine curve:


1,,= 1800 + (400 sin( + 2t (4.25)


corresponding to an average solar radiation of 1800 pE/m2 s. Monthly variations range from

1600 in January to 2200 pE m -2 s in mid July.

Some studies reported higher values in middle to late spring rather than summer

because of increased precipitation and cloud cover associated with the rainy season (Wolfe

and Drew, 1990). For this study, real data of surface irradiance, obtained by USGS (Tampa

Bay) between June 1990 and September 1991, was used.

McPherson and Miller (1994) and Miller and McPherson (1995) developed a model

by partially adjusting the attenuation coefficient in Tampa Bay for changing solar elevation,










and used multiple regression analysis to partition the coefficient into the relative contribution

of seawater, water color, chlorophyll and non-chlorophyll suspended matter.

A simplified geometric description of the incident direct solar beam and diffuse

skylight is used to describe the effects of solar elevation angle and cloudiness on the amount

of PAR that passes through the air-sea interface. Since so far there is no process-based model

that relates optical characteristics of the water to mass (or concentration) of constituents

inside the water column, a large data set which covers a wide range of conditions is required

to determine statistical correlations. In their work, Miller and McPherson (1995) used 16083

observations (255 days between 6/02/90 and 9/29/91) of scalar PAR, measured in air and at

two depths in the water column, to evaluate irradiance that entered the water and subsequent

attenuation.

The attenuation coefficient is obtained as the product of the partitioned coefficient and

a correction term that accounts for the geometry of the incident irradiance:


K, = P wd Ka (4.26)

where lid is the correction factor (weighted average cosine), andKdj is the partitioned

attenuation coefficient. The formulation for the correction factor, pd, developed in Miller

and McPherson (1995) is presented in Appendix D.

Lorenzen (1970) showed that the vertical attenuation of PAR can be linearly

partitioned into a set of partial attenuation coefficients:


K = K + K + Kd + K(


(4.27)








69

where K, is the attenuation coefficient due to water, K, is due to the presence of chlorophyll

a, Kd is due to dissolved substances, and Kp is due to non-algal particulate matter.

Nevertheless, other researchers have shown that the partitioning of the components of light

attenuation into an empirical model using standard water quality parameters is not precise

(e.g. Mote Marine Lab, 1995).

According to Kirk (1994), the inherent properties of the water can be determined by

linear superposition of the partial contributions (e.g. color, chlorophyll, etc.). However, the

vertical attenuation coefficient is an "apparent" optical property of the water (Kirk, 1994),

and it is not only a function of the inherent properties of absorption and scatter, but also the

angular and spectral distribution of the incident light.

Originally, McPherson and Miller (1994) partitioned the attenuation coefficient into

a set of partial attenuation coefficients:

Kj = k. + E2C2+E3 C3 +E4*C4 (4.28)

where k, is the PAR-waveband average attenuation coefficient of seawater, 0.0384 mn'

(Lorenzen, 1972); E2 is the attenuation coefficient of dissolved matter, in (m Pt-Co units)'';

C, is the water color, in Pt-Co units; E, is the attenuation coefficient of chlorophyll and other

matter associated with chlorophyll a, in m2 mg '; C3 is the concentration of chlorophyll a, in

mg m'3; E4 is the attenuation coefficient of nonchlorophyll suspended matter (NSM), which

includes inorganic and organic particulate not directly associated with color or chlorophyll

a, in m2 mg"'; and C4 is the concentration of NSM, in mg m3.








70

The lack of good measurements of NSM (in terms of total suspended solids and

turbidity) constrained the determination of the "E4- C," term, and the equation was modified

to (McPherson and Miller, 1994):

Kd = 0.014 C2 + 0.062- (turbidity) + 0.049 C3 + 0.30 (4.29)

with the coefficients E2 and E, determined from Tampa Bay and Charlotte Harbor data. In

this study, the adjusted attenuation coefficient is determined from Equation (4.29), with C.

representing the average water color, in Pt-Co units; C, is the chlorophyll a concentration

in mg/m 3; and turbidity is given in NTU. Data of water color and turbidity for each segment

of the Bay was obtained from the EPC reports (Boler, 1992). Chlorophyll-a concentration

is determined from the water quality portion of the model.

After determining the correction factor (ptd ), and the partitioned attenuation

coefficient (K ), the attenuation coefficient (K ) is obtained from Equation (4.26).


Model Coefficients



The model parameters required to simulate the water quality dynamics in the Tampa

Bay Estuarine System, are described in Table 4.1. During the implementation phase, model

coefficients were determined by isolating specific processes in a test grid with a similar spatial

scale as Tampa Bay, and initial values were obtained from literature and previous modeling

studies (e.g. AScI, 1996). The value of the coefficients used in this study are presented in

Table 4.2.










Table 4.1 Description of the coefficients used in the water quality model.
Coefficient Description Units

(0A D )T2 temperature coefficient for NH4 desorption
(0)T-20 temperature coefficient for algae growth

(A)T 20 temperature coefficient for ammonium instability

(OBOD )T20 temperature coefficient for CBOD oxidation
(8, N)"20 temperature coefficient for denitrification

(ON N)T20 temperature coefficient for nitrification
(0o o)T20 temperature coefficient for SON desorption
(0o N M)T2O temperature coefficient for mineralization

(ORES)T20 temperature coefficient for algae respiration
(0)T-20 temperature coefficient for zooplankton growth

(Pa)m algae maximum growth rate I/day

(l), zooplankton maximum growth rate I/day
(NH,), ammonia concentration in the air pg/L

ac h i algal carbon-chlorophyll-a ratio mg C / mg Chl-a
a.c algal nitrogen-carbon ratio mg N / mg C

a, algal oxygen-carbon ratio mg 02 / mg C
da, desorption rate of adsorbed ammonium nitrogen I/day
do, desorption rate of adsorbed organic nitrogen I/day

dm,, molecular diffusion coefficient for dissolved species cm2/s
E2 light attenuation coefficient due to dissolved matter l/(m Pt-Co)
E, light attenuation coefficient due to chlorophyll a l/(m pg/L)
E4 light attenuation coefficient due to NSM l/(m mg/L)

fdCBOD fraction of dissolved CBOD
H, half-saturation constant for ammonia conversion (pH unit)

Hbd half-saturation constant for CBOD oxidation mg 02











tinued.


Table 4.1 cont
Coefficient

H,

H,
Hni
H.o3

h,

I,

K..

Ka,
KAE

KAI

KDN

KD

KNN

KONM

KVOL

K,

K,

Pan

Po.

WSCBOD
wsa
WS lgwy


Description

half-saturation constant for algae uptake

half-saturation constant for nitrification

half-saturation constant for denitrification

Henry's constant

optimum light intensity for algal growth

excretion rate by algae

mortality rate of algae

reaeration rate constant

ammonia conversion rate constant

denitrification rate constant

organic carbon (as CBOD) decomposition rate

nitrification rate constant

organic nitrogen mineralization rate constant

volatilization rate constant

excretion rate by zooplankton

mortality rate of zooplankton

partition coefficient for ammonia nitrogen

partition coefficient for organic nitrogen

settling velocity for the particulate fraction of CBOD

algal settling velocity


--


Units

mg/L

mg 02

mg 02

mg/L-atm

pE / m2 / s

I/day

I/day

I/day

I/day

I/day

I/day

I/day

I/day

I/day

I/day

I/day

I/mg

I/mg

m/day

m/day










Table 4.2 Literature ranges and values of the coefficients used in the water quality model.
Coefficient Literature Range Tampa Bay Source

(OA D)T-20 1.08 1.08 Assumption
(0a)T20 1.01-1.2 1.08 Di Toro & Connoly(1980);AScI(1996)
(OAt)T20 1.08 1.08 Assumption
(OBOD)T-20 1.02-1.15 1.08 Bowie et al. (1980); AScI (1996)
(ODN)T-20 1.02-1.09 1.08 Baca & Amett (1976); AScI (1996)
(0NN)T-20 1.02-1.08 1.08 Bowie et al. (1980); AScI (1996)
(0o D)X20 1.08 1.08 Assumption
(0 NM)T'20 1.02-1.09 1.08 Baca & Arnett (1976); AScI (1996)
(OREP)'T-20 1.045 1.05 Ambrose et al. (1994); AScI (1996)
(0)T-20 1.01-1.2 1.08 Di Toro & Connoly (1980)
(Pa)m- 0.2-8. 1.47 Baca & Arnett (1976); AScI (1996)
(az), 0.15-0.5 0.5 Jorgensen (1976)
(NH3), 0.1 0.1 Freney etal. (1981)

,,hl 10- 112 112 Jorgensen (1976);AScI (1996)
auc 0.05-0.43 0.15 Jorgensen (1976); AScI (1996)
aoc 2.67 2.67 Ambrose et al. (1994); AScI (1996)
d., 4.0 Simon (1989)
do. 4.0 Assumption
dmo, 4.E-6-1.E-5 I.E-5 Rao et a.(1984);Krom & Berner (1980)
E2 0.014 Miller & McPherson (1995)
E3 0.062 Miller & McPherson (1995)
E4 0.30 Miller & McPherson (1995)
fdCBOD 0.7 Assumption
H., 9.0 9.0 Freney et al. (1981)
Hbod 0.02-5.6 0.18 AScI (1996)
H. 0.0015-0.4 0.05 AScI (1996)










Table 4.2 continued.
Coefficient Literature Range Tampa Bay Source
H. 0.1-2.0 2.0 Ambrose et al. (1994); AScI (1996)
Hoo3 0.1 0.1 Ambrose et al. (1994); AScI (1996)
h, 43.8 43.8 Sawyer & McCarty (1978)

I, 300-350 200. Di Toro & Connoly(1980);AScI(1996)
K, 0.05-0.2 0.15 J0rgensen (1976); AScI (1996)

K, 0.01-0.1 0.08 J0rgensen (1976); AScI (1996)
K see Appendix C

KA 0.003-0.008 0.003 Reddy et al.(1990)

Ko 0.02-0.6 0.15 Bowie et al. (1980); AScI (1996)

KDN 0.02-1.0 0.90 Baca & Arnett (1976); Assumption
KNN 0.001-0.6 0.08 AScI (1996); Reddy et al.(1990)

KONM 0.01-0.4 0.1 Di Toro & Connoly(1980);AScI(1996)
KVOL 3.5-9.0 7.0 Fillery and DeDatta (1986)
K, 0.05-0.3 0.05 Baca & Arnett (1976)

Kx 0.03-0.075 0.05 Jorgensen (1976)

Pa, 0.5E-7-1.0E-5 1.0E-5 Simon (1989)
po 1.0E-5 1.0E-5 Simon (1989)
WSCBOD 5.0 Assumption
wsage 0.0-30. 5.0 Jorgensen (1976)














CHAPTER 5
THE SEAGRASS MODEL



Using Seagrass as a Bioindicator of the Estuarine System



Seagrasses have an important role in the ecology of estuarine systems where they are

present (e.g. Culter, 1992; Tomasko et al., 1996; Phillips and Mefiez, 1988; Short, 1980).

They serve as habitat for fish and benthic invertebrates. Seagrass leaves provide substrate for

many epiphytic organisms. Herbivores such as manatees, fishes, sea turtles, and sea urchins

graze directly on seagrass blades. Dead leaves constitute the majority of the detritus pool in

seagrass beds. Seagrass also interacts with the physical components of the estuary by slowing

down the currents (Fonseca et al., 1982). Their presence also inhibits the resuspension of

sediments, which also affects nutrient cycles by reinforcing the deposition of organic and

inorganic material from the water column (Bartleson, 1988).

Since each species of seagrass has its own particular response to physical and

oceanographical factors, one species is usually dominant in any given area (Dawes et al.,

1985; Williams, 1990; Short et al., 1989). Some studies even suggest that succession or

replacement can be attributed to the water quality and trophic state of the system (Tomasko

et al., 1996). According to the authors, Thalassia can be characterized as a truly

oligotrophic species, which cannot prevail in areas of elevated nutrient loading.










Therefore, in estuarine systems where seagrass has declined due to anthropogenic

effects, restoration of seagrass beds can be linked exclusively to environmental quality. In this

sense, seagrass provides a direct assessment of the success of the restoration processes

(Dennison et a., 1993).


Seagrass Ecosystems



Seagrasses are unique for the marine environment as they are the only flowering plants

that have totally returned to the sea (Zieman, 1982). Florida enjoys one the largest seagrass

resources on Earth. Of the 10,000 km2 of seagrass bed in the Gulf of Mexico, over 8,500 km2

are in Florida waters, primarily in the southern end of the peninsula. Physical and

oceanographical factors drastically reduce the amount of seagrass bed north of Florida Bay,

on both coasts. Along the Atlantic coast, a wave dominated environment with a relatively

unstable substrate, seagrass beds are confined to inlets and lagoons. On the Gulf of Mexico

coast, seagrass beds diminish due to the high-turbidity waters and reduced salinity coming

from land drainage. North of this area, several bays, including Tampa Bay and Boca Ciega

Bay, formerly possessed extensive seagrass coverage, but anthropogenic perturbations have

greatly reduced the extent of these beds (Zieman, 1982).

According to den Hartog (1970), marine aquatic angiosperms referred to as

seagrasses include approximately 49 species in 12 genera. The three dominant species of the

west coast of Florida are Thalassia testudinum, Halodule wrightii, and Syringodiumfiliforme

(Zieman and Zieman, 1989). A detailed description of physiology and production ecology

of seagrasses can be found in McRoy and Helfferich (1977), Phillips and McRoy (1980),








77

Zieman (1982), and Phillips and Mefiez (1987). The following is a brief description of each

species reproduced from Zieman (1982). Figure 5.1 shows a schematic picture of the three

species.

Thalassia testudinum (turtle grass) is the largest and most vigorous of the southwest

Florida seagrasses. Leaves are ribbon-like, typically 4 to 12 mm wide with rounded tips and

are 10 to 35 cm in length. There are commonly two to five leaves per turion. Rhizomes are

typically 3 to 5 mm wide and may be found as deep as 25 cm in the sediment. Turtle grass

forms extensive meadows throughout most of its range.

Halodule wrightii (shoal grass) is an early colonizer of disturbed areas. It is found

primarily in disturbed areas where Thalassia or Syringodium are excluded because of

prevailing conditions such as repeated exposure to air. Leaves are flat, typically 1 to 3 mm

wide and 10 to 20 cm long, and arise from erect shoots. The tips of the leaves are not

rounded, but have two or three points, an important recognition character. Shoal grass has

been reported to be the most tolerant of all seagrasses to variations in salinity, and exposure

to air (e.g., McMillan, 1974). However, recent low salinity and seagrass distributions in

Florida Bay may suggest that Thalassia could be more tolerant to prolonged lower salinity

levels (Montague, person, comm.).

Syringodium filiforme (manatee grass) is distinctive in having cylindrical leaves.

There are commonly two to four leaves per turion, and these are 1.0 to 1.5 mm in diameter.

Length is highly variable, but can exceed 50 cm. The rhizome is less robust than that of

Thalassia and more surficially rooted. Manatee grass is commonly mixed with the other

seagrasses, or in small, dense, monospecific patches.






























(A) Halodule wrihtll (B) Svrinoodlum fliforme




















(C) Thalasl teatudlnum



Figure 5.1 Seagrass species commonly found in west Florida (from Phillips and
Mefiez, 1987).











Previous Work



The development of a numerical model of the seagrass community may provide a

mechanism for synthesizing all the dynamic functions (hydrodynamics, water quality, primary

production, etc.) of an estuarine ecosystem. Short (1980), using energy flow diagrams

(Odum, 1971), developed a seagrass community model to investigate the mechanisms of

temperate seagrass (eelgrass) production. Results of that study predicted an average

production rate for Charlestown Pond (Rhode Island) that was comparable to observed rates.

Bach (1993) extended a general eutrophication model for the Denmark coastal area

to include the seasonal variations in growth of seagrass. Coupling between the eutrophication

and seagrass models was performed by seagrass nutrient uptake and detritus. Hydrodynamics

effects were coupled to the model simulation through a hydrodynamics box model.

Fears (1993) used a numerical model developed by Dr. C. Montague (unpublished

manuscript) to study the salinity fluctuations effects on seagrass distribution, abundance, and

species composition. The salinity fluctuations were represented in the model by step

functions, single short-lived pulses, or sinusoidal functions. For each seagrass species,

literature and experimental values were assigned for maximum growth rate, optimal salinity,

range of salinity tolerance, and death rate. Model sensitivity analysis showed maximum

growth rate and minimum death rate as the crucial parameters of the model. Optimal salinity

coefficient and tolerance range were key parameters in determining the cause/effect

relationship between salinity fluctuations and seagrass response.








80

Fong and Harwell (1994) developed a STELLA model to predict changes in the

biomass of five components (three species of tropical seagrass, epiphytic algae, and

macroalgae) of the seagrass community of Florida Bay. Environmental parameters (light,

temperature, salinity, sediment nutrients, water column nutrients) were represented by

literature values or sinusoidal functions, with no coupling with hydrodynamics. The most

important model parameters were productivity/biomass relationships, differential tolerances

to extreme salinity, and P/I curves. An important characteristic of Fong and Harwell's model

is the feedback loop between the abundance of epiphytes and the amount of light reaching the

seagrass blades, leading to a competition type of relationship.

Erftemeijer and Middelburg (1995) studied the nutrient cycling in tropical seagrass

using a simple mass balance model. Model simulations used measured data on nutrient

availability, seagrass primary production, community oxygen metabolism, seagrass tissue

nutrient contents, and sediment-water nutrient exchange rates from South Sulawesi

(Indonesia). Results of that study, showed that the ratio between leaf vs. root nutrient uptake

depend on internal nutrient recycling (translocation from old plant parts to new growing

parts), with higher internal recycling increasing the importance of leaf uptake. Throughout

the internal recycling range (from 0 to 50%), the model showed that nutrient is predominantly

uptaken by the root-system.

Sheng et al. (1995) developed a coupled 2-D hydrodynamics-water quality-light-

seagrass model to quantify the impact of reduced nutrient loading on the water quality and

light/seagrass dynamics in Roberts Bay, Florida. The results of that study showed that

reduced nutrient loading led to increased DO and light, and reduced phytoplankton, CBOD,




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