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UFL/COEL-TR/113
INTEGRATED MODELING OF THE TAMPA BAY
ESTUARINE SYSTEM
by
Eduardo Ayres Yassuda
Dissertation
1996
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
1996
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 Sao 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 ............................................... iii
ABSTRACT .................................... .................... xxi
CHAPTERS
1 INTRODUCTION .................................................. 1
B background ....................................................... 1
Water Quality Modeling ..............................................2
Integrated Modeling Approach for Estuarine Systems ...................... 4
O objectives ........................................................7
2 TAMPA BAY CHARACTERIZATION ................................9
Climate ......................................................... 11
Tides ....................................................... 12
Salinity D distribution ............................................... 12
Rainfall ......................................................... 13
W ind ............. ....................... ..................... 13
B athym etry ...................................................... 16
Freshw ater Inflow .................................................16
Hillsborough River .......................................... 18
Alafia River ................................................... 19
Little Manatee River ........................................... 20
M anatee River ................................................. 21
Rocky Creek ................................................. 21
Lake Tarpon Canal ............................................ 22
Sweetwater Creek ............................................. 22
Non-Point Sources ............................................. 22
Nutrients Distribution and Loading ................ .................. 23
Sediment Type and Distribution ..................................... 26
3 THE CIRCULATION AND TRANSPORT MODEL ..................... 31
Previous W ork ................................................ 31
Circulation Model ................................................ 33
Continuity Equation ............................................ 33
X-component of Momentum Equation .............................. 34
Y-component of Momentum Equation ............................. 34
Hydrostatic Pressure Relation .................................... 34
Salinity Equation .............................................. 35
Equation of State ......................... ...................... 36
Conservative Species Equation .................................. 36
Sediment Transport Model ......................................... 37
Curvilinear Boundary-Fitted and Sigma Grid ............................ 38
Boundary and Initial Conditions ..................................... 40
Vertical Boundary Conditions .................................... 40
Lateral Boundary Conditions ..................................... 42
4 THE WATER QUALITY MODEL ................................. 45
Previous Work ................................................... 45
Development of the Numerical Model ................................. 50
Mathematical Formulation ......................................... 52
Nutrient Dynamics in Estuarine Systems .............................. 53
Ammonia Nitrogen ............................................ 55
Dissolved Ammonium Nitrogen ................................. 56
Nitrite+Nitrate Nitrogen ........................................ 58
Soluble Organic Nitrogen ....................................... 60
Particulate Organic Nitrogen ..................................... 61
Particulate Inorganic Nitrogen .................................... 61
Algal Nitrogen ................................................ 62
Zooplankton Nitrogen .......................................... 62
Sorption and Desorption Reactions .................................. 63
Phytoplankton Dynamics in Estuarine Systems .......................... 64
Oxygen Balance in Estuarine Systems ................................ 66
Light Attenuation in Estuarine Systems ................................ 69
Model Coefficients ............................................... 73
5 THE SEAGRASS MODEL ......................................... 79
Using Seagrass as a Bioindicator of the Estuarine System .................. 79
Seagrass Ecosystems .............................................. 80
Previous Work ................................................... 83
Development of the Numerical Model ............................... 85
Mathematical Formulation ......................................... 88
Light ....................................................... 88
Temperature .................................................. 89
Density-dependent Growth Rate ............... ................. 89
Growth Rate Dependence on Light ............................... 91
Growth Rate Dependence on Salinity ............................. 93
Growth Rate Dependence on Temperature ........................... 96
Growth Rate Dependence on Sediment Nutrients ...................... 98
6 APPLICATION OF THE CIRCULATION AND TRANSPORT MODEL ..... 99
Design ofTampa Bay Grid ......................................... 99
Forcing Mechanisms and Boundary Conditions ......................... 105
Modeling Strategy ............................................... 115
Results of the Barotropic Simulation ................................ 115
Results of the Baroclinic Simulation ..................................... 119
Tides ................................................... 121
Currents ................................................. 128
Salinity ... ................................................. 148
Validation of the Model ................ ......................... 156
Residual Circulation ............................................. 168
Results of the Suspended Sediment Simulation ......................... 177
7 CALIBRATION OF THE WATER QUALITY MODEL ................. 185
Initial and Boundary Conditions of the Water Quality Model .............. 188
Water Column ............................................... 188
Sediment Column ............................................ 201
Modeling Strategy ............................................ 212
Sensitivity Analysis ............................................ 212
Simulation of the Summer 1991 Condition ............................. 224
Dissolved Oxygen ............................................ 225
Phytoplankton ............................................. 238
Nitrogen Species ............................................. 250
Tidal Exchange ...............................................261
Nutrient Budget .............................................. 265
Load Reduction Simulations ......................................... 269
Comparison with AScI (1996) study ................ ................ 271
Comparison with Coastal Inc. (1995) study ............................ 274
Advantages and Limitations of this Integrated Modeling Approach .......... 275
8 CALIBRATION OF THE SEAGRASS MODEL ...................... 277
Initial Conditions ................................................ 280
Sensitivity Analysis .............................................. 282
Simulation of the Summer 1991 Condition ........................... 294
Load Reduction Simulation ......................................... 296
9 CONCLUSION AND RECOMMENDATIONS ........................ 303
APPENDICES
A NUMERICAL SOLUTION OF THE EQUATIONS .................... 309
B MODELING SEDIMENT DYNAMICS .................... .......... 329
C DISSOLVED OXYGEN SATURATION AND REAERATION
EQUATIONS .................................................. 339
D LIGHT MODEL EQUATIONS ................................... 341
E RESULTS OF THE SUMMER 1991 SIMULATION .................... 343
F SENSITIVITY TESTS OF THE SEAGRASS MODEL ................... 363
REFERENCES ..................................................... 377
BIOGRAPHICAL SKETCH ............................................ 395
LIST OF FIGURES
Figure page
2.1 Tampa Bay Estuarine System subdivisions as defined by
Lewis and Whitman (1985) (from Wolfe and Drew, 1990)................ 10
2.2 Monthly rainfall in Tampa Bay (Wooten, 1985). ........................ 14
2.3 Seasonal wind pattern in Florida (Echternacht, 1975) .................... 15
2.4 Tampa Bay watershed (Wolfe and Drew, 1990). ....................... 17
2.5 Surface Sediments in Tampa Bay (Goodell and Gorsline, 1961). .......... 28
2.6 Mud zone in Hillsborough Bay (Johansson and Squires, 1989). ........... 29
5.1 Seagrass species commonly found in west Florida
(from Phillips and Mefiez, 1987) .................................. 82
5.2 Structure and components of the numerical seagrass
model used for this study. ....................................... 86
5.3 Epiphytic algae model flow chart. ................................. 87
5.4 Seagrass model flow chart. ....................................... 87
5.5 Seagrass density-dependent maximum growth rate:
Thalassia (dotted line), Halodule (solid line),
and Syringodium (dash-dotted line). .............................. 90
5.6 Seagrass growth rate dependence on light: Thalassia (dotted line),
Halodule (solid line), and Syringodium (dash-dotted line). ............... 92
5.7 Seagrass growth rate dependence on salinity: Thalassia (dotted line),
Halodule (solid line), and Syringodium (dash-dotted line). ............... 95
5.8 Seagrass growth rate dependence on temperature: Thalassia (dotted line),
Halodule (solid line), and Syringodium (dash-dotted line). ............... 97
6.1 NOAA's TOP station locations in Tampa Bay. ....................... 101
6.2 A boundary-fitted grid for the Tampa Bay Estuarine System. ............. 102
6.3 Tampa Bay bathymetric contours. ................. .............. 103
6.4 Bay segments (Sheng and Yassuda, 1995)............................. 104
6.5 Tidal forcing for the 1990 simulation .......................... .. .106
6.6 Tidal forcing for the 1991 simulation. .............................. 107
6.7 Initial salinity distribution (surface) for the 1990 simulation. ............. 108
6.8 Initial salinity distribution (surface) for the 1991 simulation. ............ 109
6.9 Rainfall data for the 1990 and 1991 simulations ....................... 111
6.10 River discharges for the 1990 and 1991 simulations ................... 112
6.11 Wind velocity for the 1990 simulation. ............................. 113
6.12 Wind velocity for the 1991 simulation. ............................ 114
6.13 Surface elevation at Egmont Key and St.Petersburg
(September 1990)............................................... 117
6.14 Surface elevation at Davis Island and Old Tampa Bay
(September 1990)............................................... 118
6.15 Spectra of water surface elevation for the 1990 simulation ............. 123
6.16 Simulated and measured bottom velocity at Egmont Channel -
September/1990. .............................................. 130
6.17 Simulated and measured surface velocity at Egmont Channel -
September/1990. .............................................. 131
6.18 Simulated and measured bottom velocity at Skyway Bridge -
September/1990. ................. ............................ 133
6.19 Simulated and measured mid-depth velocity at Skyway Bridge -
September/1990. .............................................. 134
6.20 Simulated and measured surface velocity at Skyway Bridge -
September/1990 ............................................135
6.21 Simulated and measured bottom velocity at Port of Manatee Channel -
September/1990. .............................................. 137
6.22 Simulated and measured surface velocity at Port of Manatee Channel -
September/1990. .............................................. 138
6.23 Simulated and measured bottom velocity at Port of Tampa Channel -
September/1990 ............................................ 139
6.24 Simulated and measured bottom velocity at Port of Tampa Channel -
September/1990. ........................................... .141
6.25 Energy density spectra of bottom currents at Skyway Bridge -
September/1990. ............................................... 144
6.26 Energy density spectra of surface currents at Skyway Bridge -
September/1990. .............................................. 145
6.27 Tidal current ellipses for the semi-diurnal components September/1990. 149
6.28 Tidal current ellipses for the diurnal components September/1990. ...... 150
6.29 Near-bottom salinity (solid line) and temperature (dashed line) at NOAA
station S-4 starting at Julian Day 150 in 1990. ...................... 151
6.30 Simulated and measured near-bottom salinity at NOAA
station C-21 September/1990 .................................153
6.31 Simulated and measured near-bottom salinity at C-23 -
September/1990 ............... ............................ 154
6.32 Simulated and measured near-bottom salinity at C-4 -
September/1990. .............................................. 155
6.33 Surface elevation at St.Petersburg and Davis Island -
"Marco" Storm October/1990. .................................157
6.34 Surface elevation at St.Petersburg and Davis Island July/1991........... 158
6.35 Simulated and measured bottom current at Skyway Bridge -
"Marco" Storm (October/1990) ....................................161
6.36 Simulated and measured surface current at Skyway Bridge -
"Marco" Storm (October/1990) ................................... 162
6.37 Simulated and measured near-bottom salinity at station S-4 -
(July/1991). .................................................. 163
6.38 Simulated and measured near-surface salinity at station S-4 -
(July/1991). ...................................................164
6.39 Relative flushing for several bay segments September/1990 ........... 167
6.40 Residual circulation after 30 days September/1990 .................. 169
6.41 Simulated velocity field representing maximum ebb currents -
September/29/1990 18:00. ..................................... 171
6.42 Simulated velocity field representing maximum flood currents -
September/29/1990 10:00. ..................................... 172
6.43 Velocity cross-section at Skyway Bridge looking up the Bay. Vertical
scale in meters, and horizontal scale in computational grid j-index ....... 174
6.44 Salinity cross-section at Skyway Bridge looking up the Bay. Vertical
scale in meters, and horizontal scale in computational grid j-index ........ 175
6.45 Longitudinal distribution of salinity along the navigation channel. Vertical
scale in meters, and horizontal scale in computational grid i-index. ....... 176
6.46 Location of the USGS station in Old Tampa Bay
(Schoellhammer, 1993). ........................................ 180
6.47 Wind speed and direction, and suspended sediment concentration at
USGS station during tropical storm "Marco" (Schoellhammer, 1993). ..... 181
6.48 Simulated significant wave height and period during tropical storm
"Marco" (October/1990). .......................................182
6.49 Simulated wave-induced bottom shear stress and suspended sediment
concentration at the USGS station for October 10 and 11, 1990. ......... 183
6.50 Simulated suspended sediment concentration at 6:00am -
October 11, 1990............................................... 184
7.1 Water quality monitoring stations of the Hillsborough County
Environmental Protection Commission (EPC) (Boler, 1992). ............ 189
7.2 Measured near-bottom dissolved oxygen concentration (mg/L) in
Tampa Bay (June 1991) ......................................... 190
7.3 Measured near-surface dissolved oxygen concentration (mg/L) in
Tampa Bay (June 1991). ........................................ 191
7.4 Measured organic nitrogen concentration (mg/L) in
TampaBay (June 1991) ........................................ 192
7.5 Measured dissolved ammonium-nitrogen concentration (mg/L) in
Tampa Bay (June 1991) ........................................ 193
7.6 Measured nitrite+nitrate concentration (mg/L) in
Tampa Bay (June 1991). ........................................ 194
7.7 Measured chlorophyll-a concentration (pg/L) in
Tampa Bay (June 1991). ......................................... 195
7.8 Measured color (Pt-Co) in Tampa Bay (June 1991). .................. 196
7.9 Measured turbidity (NTU) in Tampa Bay (June 1991). ................. 197
7.10 Water quality zones in Tampa Bay used in the model simulations of the
summer of 1991 conditions ..................................... 200
7.11 Total organic nitrogen (dry weight %) in the surface sediments of
Tampa Bay during 1963 (Taylor and Saloman, 1969) .................. 204
7.12 Total Kjeldahl nitrogen (dry weight %) in Tampa Bay sediments,
1982-86 (Brooks and Doyle, 1992) ................................ 205
7.13 Sedimentary nitrogen (dry weight %) in Hillsborough Bay
in 1986 (COT, 1988). .......................................... 206
7.14 Location of the NOAA sediment sampling stations in 1991 (phase 1) and
1992 (phase 2) (NOAA, 1994)..................................... 207
7.15 Total sediment nitrogen (dry weight %) obtained from NOAA
(1994) data. .................. ................................208
7.16 Dry density profile for water quality zone 1 in Tampa Bay
(Sheng et al., 1993). ................ ......................... 209
7.17 Water quality parameters after 30 days for a simulation using the lower
limit of the mineralization constant rate. ............................ 221
7.18 Water quality parameters after 30 days for a simulation using the higher
limit of the mineralization constant rate. ............................ 222
7.19 Near-bottom dissolved oxygen levels after 30 days for the mineralization
constant rate tests. ............................................. 223
7.20 Near-bottom dissolved oxygen concentration in Tampa Bay for
June 26, after 30 days of simulation. ............................. 226
7.21 Near-bottom dissolved oxygen concentration in Tampa Bay for
July 26, after 60 days of simulation. ............................. 227
7.22 Near-bottom dissolved oxygen concentration in Tampa Bay for
August 25, after 90 days of simulation. ............................ 228
7.23 Near-bottom dissolved oxygen concentration in Tampa Bay for
September 24, after 120 days of simulation ........................... 229
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. ................................... 231
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 .................................... 232
7.26 Model results and measured data for near-bottom DO at
EPC stations 70 and 8. ......................................... 233
7.27 Model results and measured data for near-bottom DO at
EPC stations 73 and 80. ............... ........................ 234
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. .................................. 236
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 .................................... 237
7.30 Near-surface chlorophyll-a concentration in Tampa Bay for
June 26, after 30 days .......................................... 240
7.31 Near-surface chlorophyll-a concentration in Tampa Bay for
July 26, after 60 days .... .............. ........................ 241
7.32 Near-surface chlorophyll-a concentration in Tampa Bay for
August 25, after 90 day ......................................... 242
7.33 Near-surface chlorophyll-a concentration in Tampa Bay for
September 24, after 120 ...................................... 243
7.34 Model results for segment-averaged near-surface chlorophyll-a (solid line)
and the EPC data inside Hillsborough Bay. .......................... 245
7.35 Model results for segment-averaged near-surface chlorophyll-a (solid line)
and the EPC data inside Old Tampa Bay ........................... 246
7.36 Model results for segment-averaged near-surface chlorophyll-a (solid line)
and the EPC data inside Middle Tampa Bay ......................... 248
7.37 Model results for segment-averaged near-surface chlorophyll-a (solid line)
and the EPC data inside Lower Tampa Bay.......................... 249
7.38 Near-surface Kjeldahl nitrogen concentration in Tampa Bay for
June 26, after 30 days of simulation ............................... 251
7.39 Near-surface Kjeldahl nitrogen concentration in Tampa Bay for
July 26, after 60 days of simulation .............................. 252
7.40 Near-surface Kjeldahl nitrogen concentration in Tampa Bay for
August 25, after 90 days of simulation ............................ 253
7.41 Near-surface Kjeldahl nitrogen concentration in Tampa Bay for
September 24, after 120 days of simulation .......................... 254
7.42 Model results for near-bottom segment-averaged Kjeldahl nitrogen
(solid line) and the EPC data inside Hillsborough Bay.................. 256
7.43 Model results for near-bottom segment-averaged Kjeldahl nitrogen
(solid line) and the EPC data inside Old Tampa Bay. ................. 257
7.44 Model results for near-bottom segment-averaged Kjeldahl nitrogen
(solid line) and the EPC data inside Middle Tampa Bay. ............... 258
7.45 Model results for near-bottom segment-averaged Kjeldahl nitrogen
(solid line) and the EPC data inside Lower Tampa Bay ................ 259
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). ........................... 263
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). ......................... . 264
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. ................. 268
7.49 Near-bottom dissolved oxygen concentration in Tampa Bay, after
60 days of the load reduction simulation. ........................... 272
7.50 Near-surface chlorophyll-a concentration in Tampa Bay, after
60 days of the load reduction simulation. .......................... 273
8.1 Extent of seagrass meadows in Tampa Bay. (a) corresponding to 1943,
and (b) to 1983 (Lewis etal., 1985). .............................. 278
8.2 Initial seagrass distribution in the computational grid. Dark areas indicate
seagrass meadows (100 gdw/m2). ................................ 281
8.3 Simulated seagrass biomass in Tampa Bay. .......................... 283
8.4 Growth rate dependence on temperature ........................... 285
8.5 Growth rate dependence on light. ................................. 286
8.6 Growth rate dependence on salinity................................ 287
8.7 Growth rate dependence on sediment nutrient concentration. ............. 288
8.8 Simulated seasonal distribution of Thalassia. ........................ 290
8.9 Simulated Thalassia biomass in Tampa Bay for July 26,
after 60 days of simulation....................................... 297
8.10 Simulated Halodule biomass in Tampa Bay for July 26,
after 60 days of simulation....................................... 298
8.11 Simulated Syringodium biomass in Tampa Bay for July 26,
after 60 days of simulation...................... ................. 299
8.12 Near-bottom light levels in Tampa Bay for July 26,
after 60 days of simulation........................................ 300
8.13 Comparison between simulated light levels for the Present Condition
simulation (solid line) and the 100% Load Reduction (dashed line) ....... 301
xvii
LIST OF TABLES
Table page
2.1 Area of the subdivisions in Tampa Bay (Lewis and Whitman, 1985).......... 11
2.2 Surface water discharges to Tampa Bay (Lewis and Estevez, 1985). ........ 18
2.3 1991 annual average water quality of eight point sources discharging
into Tampa Bay (Boler, 1992) and (USGS, 1991) (mg/L). ............... 23
2.4 Mean annual total nitrogen loading into each segment of Tampa Bay
(Coastal, 1994) ................................................26
4.1 Description of the coefficients used in the water quality model ............ 74
4.2 Literature ranges and values of the coefficients used in the water
quality model. ................................................... 75
6.1 The rms error (Erms) between measured and simulated
water surface elevation September/90 ............................. 121
6.2 The distribution of tidal energy for water surface elevation -
September 1990. ............................................... 124
6.3 Major tidal constituents in Tampa Bay September/1990. ............... 127
6.4 The rms error between measured and simulated bottom (b) and
surface (s) currents September/1990 .............................. 142
6.5 The distribution of tidal energy for bottom (b) and surface (s) currents -
September 1990................................................. 146
6.6 The rms error between measured and simulated salinity -
September 1990................................................. 156
6.7 The rms error between measured and simulated water surface elevation
October/1990 and July/91. ........................................ 159
xviii
6.8 The rms error between measured and simulated bottom (b) and
surface (s) currents "Marco" Storm. .............................. 160
6.9 The rms error between measured and simulated salinity July/1991. ....... 165
7.1 Estimated total suspended solids concentration (TSS), and calculated
water column partition coefficients for particulate organic nitrogen
(pcon) and adsorbed ammonium (pcan). ............................ 201
7.2 Estimated dry density for the sandy zones of Tampa Bay ................ 210
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. ..... 211
7.4 Model coefficients in the (W) water column, (Ae) aerobic layer, and
(An) anaerobic layer for each water quality zone. ...................... 211
7.5 Parameters, baseline values, and range used in the sensitivity analysis. ..... 214
7.6 Sensitivity tests description. ............ ........................... 215
7.7 Sensitivity analysis results............. ...... ..................... 217
7.8 Nitrogen budget between July 1 and August 31, 1991. .................. 267
8.1 Sensitivity tests description....................................... 291
8.2 Simulated and reported seagrass biomass in the Tampa Bay area. ......... 294
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
xxi
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.
xxii
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
2
equal to 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 Ouality 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
2
equal to 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 Ouality 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
3
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
al., 1994) are often based on coupling the hydrodynamics and water quality dynamics on an
5
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 a coupled hydrodynamics-sediment-water quality model and 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).
The primary prerequisite in the implementation 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
6
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
7
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.
Objectives
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 develop a comprehensive model 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:
1) 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?
7
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.
Objectives
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 develop a comprehensive model 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:
1) 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 270 30'and 280 02'N, and 820 20'and
820 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.
Mobbly
- 2D00'
City of
St. Petersburg
P9 ^?S LII Maote Rver
Cockroach &OaLI ~l --
Bay Subdivisions of Tampa Bay
( ---- Demarcation Line)
1. Old Tampa Bay
as 2. Hillsborough Bay
r3. Middle Tampa Bay
4. Lower Tampa Bay
5. Boca Clega Bay
6. Terra Cela Bay
S77. Manatee River
8. Anna Maria Sound
SManatee Re
Bradenton
- Tampa Bay Estuarine System subdivisions as defined by Lewis and Whitman
(1985) (from Wolfe and Drew, 1990).
S30
UZ 0U
Hillborough Rver
%0
a
- 2f 30'
Figure 2.1
11
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
Terra Ceia Bay 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 16.0 Cin 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 oC, with a vertical stratification
of no more than 2 C(Boler, 1992).
11
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
Terra Ceia Bay 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 16.0 Cin 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 oC, with a vertical stratification
of no more than 2 C(Boler, 1992).
12
Tides
Tides and currents in the Gulf of Mexico are classified as mixed type, with K,, O1,
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
12
Tides
Tides and currents in the Gulf of Mexico are classified as mixed type, with K,, O1,
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
13
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 29
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
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 29
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).
Historical Monthly Rainfall in Tampa Bay
30
20 --
I
I\ I
I\ /
I \i
\I
/
--A-.--.. Minimun
- Average
---'-- Maximum
<^C-.
01 A A A A I I I I ,, A
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 2.2 Monthly rainfall in Tampa Bay (Wooten, 1985).
E
r
Figure 2.3 Seasonal wind pattern in Florida (Echternacht, 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 3/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).
0
SO
Figure 2.4 Tampa Bay watershed (Wolfe and Drew, 1990).
18
Table 2.2 Surface water discharges to Tampa Bay (Lewis and Estevez, 1985).
.r Period of Record Average annual
(years) discharge (m3/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 m 3/s in the dry season to 48 m /s
during the wet season, averaging 17 m 3/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 m 3/s (Wolfe and Drew,
19
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 3/s and ranges from 5.4 m 3/s in the dry
season to 28.3 m3/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
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
19
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 3/s and ranges from 5.4 m 3/s in the dry
season to 28.3 m3/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
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
20
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 m3/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
20
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 m3/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
21
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 m 3/s and
ranges from 1 m 3/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
0.05 m 3/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.
21
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 m 3/s and
ranges from 1 m 3/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
0.05 m 3/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 0.8 m 3/s, 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
23
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 rogen +
Nitrogen
Oxygen 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
23
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 rogen +
Nitrogen
Oxygen 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
24
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 9.0x104 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
25
was greater. Nitrite 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.
26
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.
27
Figure 2.5 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 areal
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.
- 28oo00 City of
Tampa
Pinellas inter- NaR Rier
Peninsula Bay
Peninsula
City of
St. Petersburg
O
0 0
Maatge Rier
[ Very coarse-coarse sand Fine sand Silt
Medium sand Very fire sand
Figure 2.5 Surface Sediments in Tampa Bay (Goodell and Gorsline, 1961).
Figure 2.6 Mud zone in Hillsborough Bay (Johansson and Squires, 1989).
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 et al. (1991), Sheng and Peene (1992), Peene et al. (1992), and
Hess (1994). The Ross et al. (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
32
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
33
details of 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 av aw
+- + = (3.1)
ax ay az
X-component of Momentum Equation
au + uu uv + uw 1 ap A u
-+- + -+ -- = fv +- A
at ax ay az p, ax ax ax
(3.2)
+ AH + A u
ay ( ay z az)
Y-component of Momentum Equation
av + uv + avv vw f 1 p +a AH v
-+- + -+-= -fu +- AH
at ax 9y az p, ay ax ax
(3.3)
a av ( av
+ ( AH (+ A
ay ay 9z 9z
Hydrostatic Pressure Relation
9p
ap 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, po is a reference fluid density, p(x,y,z) is the fluid density,
and f is the Coriolis parameter. AH 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 ay az
(3.5)
a as a as a v as) (3
ax i ax) ay ay) az( Va
where S is the salinity, DH and D, 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 Dv ) are computed from a
simplified second-order closure model developed by Sheng and Chiu (1986) and Sheng and
Villaret (1989).
Equation of State
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 T is 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, cs:
ac + (uc,) (vc ) a (wc,)
at ax ay az
(3.8)
ax ax ay' Hay) az[ az )
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
37
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 al., 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:
c uc+ avc + a(w-w)c
at ax ay az
(3.9)
a a Dac a H ac
ax ax) ay9 ay) az z )
where c is the suspended sediment concentration, w, is the settling velocity of suspended
sediment particles (positive downward), DH is the horizontal turbulent eddy diffusivity, and Dv
is the vertical turbulent eddy diffusivity.
38
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 ( 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)
where is 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
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 al., 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:
c uc+ avc + a(w-w)c
at ax ay az
(3.9)
a a Dac a H ac
ax ax) ay9 ay) az z )
where c is the suspended sediment concentration, w, is the settling velocity of suspended
sediment particles (positive downward), DH is the horizontal turbulent eddy diffusivity, and Dv
is the vertical turbulent eddy diffusivity.
38
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 ( 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)
where is 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
39
dependent. A a-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 a-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 il.
The spatial coordinates in the physical plane, (x, y, z), have dimensions of length,
while the coordinate system in the computational plane, ( Cr,, a), is dimensionless. In this
new coordinate system ( et, a), the velocity vector are expressed in terms of contravariant
components, with dimension of [t'] (Sheng, 1989). The equations of motion in the ( 77, a)
coordinates are shown in Appendix A.
40
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
The boundary conditions for Equations (3.1), (3.2), (3.3), (3.5), and (3.9) at the free
surface ( a = 0) are:
P, Av u v' \
SS
0 (3.11)
+=0
a c
az
At the free surface, wind velocity is converted to stress by:
(s s) Pa Cds. + (u+ V)2 V) (3.12)
where sx, and cy are the components of the wind stress, p. is the air density
(0.0012 g/cm3), uw and vw 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):
Cds = (0.075 + 0.067 W) 0.001 (3.13)
where Ws is the wind speed magnitude in m/s.
41
The vertical velocity is obtained from the kinematic boundary condition imposed at
the surface:
w + 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
(a = -1) are:
p. Av a aV = (vbx 'by
az az ) 'by
= Cdb 2 V2 1/2 (U1,V
as (3.15)
a S
-0
ao
S(w + wc + D, = Vd c E
where vd is the deposition velocity, E is the rate of erosion, Av, 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,), Cdb, for a hydraulic rough flow,
is given by (Sheng, 1983):
2iI ZI 1-2
Cdb = K2 In (3.16)
zo
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, ;, is given by either a time series of
measured data or specified through harmonic constituents using the following equation:
"max 2 r t
C = (x,y,t) = Ancos + d)n (3.18)
n=1 Tn
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 ;, 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.
The salinity and suspended sediment concentration along an open boundary or river
entrance is computed from a 1-D advection equation during the outflow. During the inflow,
the concentration takes on a prescribed value.
43
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
46
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.
47
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
I
48
limitation of the AScI study is the time scale (time step of one month), which prevents the
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
I
49
inside the Bay (Coastal, 1995). This limitation suggests that the internal loading has a
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
I
50
dynamics, phosphorus cycle, nitrogen cycle, and oxygen balance. With regard to nutrients,
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
(NH1,) 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.
51
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
52
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:
+ V-((ii) = V-[D V(() )] + Q
a 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
53
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 (T, rl, o) is given by:
1 aHd 1 a (Dado
H -t H2 ao v" 9a
1 [_/(r-Hudo)\+9 I (-oHV,)] 1 9 Hodo
ao (FHu + Hdo H (4.2)
H- g, aE an
+ DH [g11 a2 +2g 12 2 + g222 ]
Wa2 2~rl ar|2
where d represents any water quality parameter, (g,) is the Jacobian of horizontal
transformation, (g g 12, g 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).
I
52
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:
+ V-((ii) = V-[D V(() )] + Q
a 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
53
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 (T, rl, o) is given by:
1 aHd 1 a (Dado
H -t H2 ao v" 9a
1 [_/(r-Hudo)\+9 I (-oHV,)] 1 9 Hodo
ao (FHu + Hdo H (4.2)
H- g, aE an
+ DH [g11 a2 +2g 12 2 + g222 ]
Wa2 2~rl ar|2
where d represents any water quality parameter, (g,) is the Jacobian of horizontal
transformation, (g g 12, g 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).
I
54
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 -4 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
55
f) Conversion of algal-nitrogen into zooplankton-nitrogen through grazing
g) Excretion by algae and zooplankton
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 (zNc ), and algal nitrogen to
carbon ratio (ad).
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:
I I
NH3(aq) + H2O NH4' + OH- (4.3)
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 = KA pH *NH4 K l[hv'NH3 (NH3),] (4.4)
Hi + pH
where KAI is the ammonia conversion rate constant, and H, is the half-saturation constant
for ammonia conversion. KvoLis the volatilization rate constant, hV is Henry's constant, and (NH3)a.
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
I
57
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 = K SON K O NH4
NM SON NN Hnit + DO
+ da(PIN pn-c-NH4)
(4.6)
Pnpa-ALGN + KC-ALGN + KZ-ZOON
K pH -NH4
SH. + pH
whereKoNu 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, Hkit is the half saturation constant for
oxygen limitation. d, is the desorption rate of NH4 from sediment particles, p, is the
partition coefficient between NH4 and PIN, and c is the suspend sediment concentration. P,
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 HO using
nitrate instead of oxygen can be given as:
5(CH20) + 4NO3- + 4H' 5CO2 + 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.
60
In this study, the kinetic pathway of nitrite+nitrate (state variable NO3) is
represented in the source term of Equation (4.2) as a sequence of first-order reactions, limited
by the dissolved oxygen concentration:
S Hit+DO Hno3+DO
where KD is the denitrification rate constant, and HN3 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 N2 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 SONtoNH4 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 = KON SON + d,, (PON Pn, c SON) (4.10)
where dn 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 do (PON P 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 Pan 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 (Jorgensen, 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 = aN (Algal Biomass) (4.13)
where a 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 = ZNC (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):
N -Dr N, + Sr N (4.15)
at
where Dr is the desorption rate constant, Sr is the sorption rate constant, N. 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:
aN
S-Dr(Nd p N) (4.16)
where Pc 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
aN
S-Dr(Nd p N) (4.16)
where Pc 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
65
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:
S oT-20 I I NH4 + N03
o = a exp 1 ( 4.17)
Ia = (a)m 2ax I e p( H+ NH4 + N03 (4.17)
where (a)max is the algae maximum growth rate, 0 is the temperature correction factor, I is
the light intensity, I is the optimum light intensity for algal growth, Hn 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 = (WSge -PHY) + (p,,- K) PHY z,' ZOO (4.18)
where wSigae 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,
66
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:
z = )mx T-20. PHY
Hphy + PHY (4.19)
where (z)max is the maximum growth rate for zooplankton, 6z 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 = (Fz Kzx-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
67
dissolved oxygen 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 that in the WASPS
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) a, (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:
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