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Modeling sediment and nutrient dynamics in the Indian River Lagoon

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Title:
Modeling sediment and nutrient dynamics in the Indian River Lagoon
Series Title:
Modeling sediment and nutrient dynamics in the Indian River Lagoon
Creator:
Melanson, Joel
Place of Publication:
Gainesville, Fla.
Publisher:
Coastal & Oceanographic Engineering Dept. of Civil & Coastal Engineering, University of Florida
Language:
English

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University of Florida
Holding Location:
University of Florida
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All applicable rights reserved by the source institution and holding location.

Full Text
UFL/COEL-99/008

MODELING SEDIMENT AND NUTRIENT DYNAMICS IN THE
INDIAN RIVER LAGOON
by
Joel Melanson
Thesis

May 1999




MODELING SEDIMENT AND NUTRIENT DYNAMICS
IN THE INDIAN RIVER LAGOON
By
JOEL MELANSON

A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA

1999




ACKNOWLEDGMENTS

First, I would like to thank my advisor, Dr. Sheng, for
his guidance and financial support throughout my graduate research. In addition, I would like to thank the members of
my committee, Dr. Dean and Dr. Thieke, for reviewing this thesis.
I would like to thank the St. Johns River Water
Management District for sponsoring the Indian River Lagoon Pollutant Load Reduction Model Development Project, of which this study is a part.
I would like to thank everyone in the administrative offices, especially, Becky, Lucy, Sandra, and Joanne, who helped me through the red tape. Special thanks are sent to
everyone at the lab, Sidney, Vik, Vernon, J.J., and Chuck, for their guidance and assistance with the field work.
I am indebted to Justin, Qiu, and Sun for their advice and help with the computer modeling. In addition, I would like to thank Hugo, Kerry Anne, and especially Beth for all of their help and friendship. Finally, I would like to thank my
parents, for without their love and support, I would never be where I am today.




TABLE OF CONTENTS

ACKNOWLEDGMENTS .. ...... LIST OF TABLES ...... LIST OF FIGURES .. ...... ABSTRACT .........
INTRODUCTION .......
1.1 Background ....
1.2 Prior Studies
1.3 Scope of Study 1.4 Thesis Outline

... ii

V

. . . vi
x
8
S 10 . . . 12

SOME IRL DATA AND DATA ANALYSIS ...
2.1 Some IRL Data ........
2.1.1 WQMN Data .........
2.1.2 UFCOED Data .. ......
2.1.3 UFCOED Sediment Data 2.1.4 UFCOED Synoptic Data
2.2 Sample Locations .. ........
2.3 Data Analysis ........
2.3.1 Salinity Data .......
2.3.2 Nutrient Data .......

NUMERICAL MODELS . . . . . . . . . .
3.1 Hydrodynamic Model ..... ..............
3.2 Sediment and Water Quality Models ......
3.2.1 Modeling Sediment Transport Processes
3.2.2 Modeling Phytoplankton ........
3.2.3 Modeling The Phosphorus Cycle .......
3.2.4 Modeling The Nitrogen Cycle .. .......
3.2.5 Modeling The Oxygen Cycle .. ........
3.3 Model Review ....... .................
MODEL SIMULATIONS ........ ...................
4.1 Sediment Simulations ..... .............

iii

40 40 44 52 52 57 60
64 65
68 68




4.2 Sediment Simulation Results
4.3 Nutrient Simulations... .. ..
4.4 Nutrient Simulation Results
4.5 Model Sensitivity ......
CONCLUSION AND RECOMMENDATIONS ...
5.1 Conclusions .........
5.2 Recommendations .......
APPENDIX BOUNDARY-FITTED EQUATIONS..
LIST OF REFERENCES ..........
BIOGRAPHICAL SKETCH......... .. .. .

. . 77 . . 88 . . 91
. . 106
. . 110 . 110 . 112
. . 114
. . 117
. . 121




LIST OF TABLES

Table pagre
Table 2.1 Detailed station information for the WQMM 17
Table 2.2 Detailed station information for first set of
Synoptic stations 1-45 .................24
Table 2.3 Detailed station information for second set
of Synoptic stations 1-30. ..............26
Table 3.1 Water quality variables with the associated
model variable ....................47
Table 3.2 Definitions of the reaction coefficients for
the transformation processes in the water quality
model. ........................50
Table 4.1 The characteristics of the sediment of the
IRL. .........................73
Table 4.2 Sediment median diameter and sediment type
in various calibration boxes and segments of the
IRL. .........................76
Table 4.3 The results of the sediment RMS error
analysis. ......................83
Table 4.4 Results of the wind and modeled TSS
correlation analysis for each segment of the IRL. .87
Table 4.5 Typical values for the water quality reaction
coefficients ......................89
Table 4.6 RM~S error of simulated water quality
constituents in 8 segments of the IRL .. .......100
Table 4.7 Results of the sensitivity testing of the
water quality coefficients. .............108




LIST OF FIGURES Figure
Figure 1.1 A map of Florida and the IRL study area.

nage
2

Figure 1.2 The layout of the Indian River Lagoon. Figure 2.1 The WQMN water sampling stations. .
Figure 2.2 The UFCOED sediment sampling stations. Figure 2.3 The segments of the IRL ... .........
Figure 2.4 The UFCOED synoptic water sampling
stations 1-45 ....... ................
Figure 2.5 The UFCOED synoptic water sampling
stations 1-30 ....... ................
Figure 2.6 Temporal variations in salinity in the
northern section of the IRL ... ..........
Figure 2.7 Rainfall data for the Titusville and
Melbourne area . . . . . . . .
Figure 2.8 Temporal variations in salinity in the

southern section of the IRL

3
. . 16 . 19 . 22 . 23 . 25 . 29 . 29

. . . . 30

Figure 2.9 Total phosphorus concentrations at eight
segments in the IRL during April and May of 1997.
Figure 2.10 Total nitrogen concentrations at eight
segments in the IRL during April and May of 1997.
Figure 2.11 Phytoplankton carbon measured in segments
2, 4, and 5 of the IRL during April and May 1997.
Figure 2.12 Ammonia nitrogen measured in segments
2, 4, and 5 of the IRL during April and May 1997.

31 32 33 34




Figure 2.13 Nitrate nitrogen measured in segments
2, 4, and 5 of the IRL during April and May 1997. 34
Figure 2.14 Inorganic phosphorus measured in segments
2, 4, and 5 of the IRL during April and May 1997. 35
Figure 2.15 Total suspended sediment measured in
segments 2, 4, and 5 of the IRL during April and
May 1997 ......... ...................... .35
Figure 2.16 Phytoplankton carbon concentrations at eight
segments in the IRL during April and May of 1997. 36
Figure 2.17 Nitrate nitrogen concentrations at eight
segments in the IRL during April and May of 1997. 37
Figure 2.18 Inorganic phosphorus concentrations at eight
segments in the IRL during April and May of 1997. 37
Figure 2.19 Total suspended sediment concentrations at
eight segments in the IRL during April and May of
1997 .......... ........................ 39
Figure 2.20 Total nitrogen concentrations at eight
segments in the IRL during April and May of 1997. 39
Figure 3.1 The computational grid for the IRL ........ ..43
Figure 3.2 The computational grid with the IRL shoreline
in the northern part of the IRL ... ........... ..44
Figure 3.3 Flow chart for the nutrient cycles ........ ..45
Figure 3.4 The affect of nutrient concentration on G,,,. 56
Figure 3.5 The affect of nutrient concentration on PH3. 64
Figure 4.1 Comparison of CH2D simulated water elevation
vs. measured water elevations at FDEP data stations:
Ponce Inlet (Ponceinl), Mosquito Lagoon (Mosquito),
and Merrit Causeway West (Mcsywest) during Julian
days 98-135, 1997. ..... ................. .69
Figure 4.2 Comparison of CH2D simulated water elevation
vs. measured water elevations at FDEP data stations:
Banana River (Bananacc) Melbourne Causeway
(Melbcswy), and Sebastian Inlet (Sebasinl) during
Julian days 98-135, 1997 ..... .............. ..70

vii




Figure 4.3 Comparison of CH2D simulated water elevation
vs. measured water elevations at FDEP data stations:
Vero Bridge (Verobrid) Ft. Pierce Causeway
(Fpiercec), and Ft. Pierce Inlet (Fpiercei) during
Julian days 98-135, 1997 ...... .............. ..71
Figure 4.4 Interpolated map of the northern IRL bottom
sediment mean diameter D50. .... ............. .74
Figure 4.5 Interpolated map of the southern IRL bottom
sediment mean diameter D50. .... ............. .75
Figure 4.6 Comparison of simulated suspended sediment
concentrations by CH2D and CH3D vs. WQMN measured
data in the northern IRL ...... .............. ..79
Figure 4.7 Comparison of simulated suspended sediment
concentrations by CH2D and CH3D vs. WQMN measured
data in the southern IRL ...... .............. ..80
Figure 4.8 Comparison of simulated suspended sediment
concentrations by CH2D and CH3D vs. WQMN measured
data in the Banana River and the Mosquito Lagoon. 81
Figure 4.9 Wind speed and simulated TSS concentration in
segments 1 and 8 of the IRL during Julian days
120-135, 1997 ......... .................... .87
Figure 4.10 Wind speed and simulated TSS concentration
in segments 2 and 3 of the IRL during Julian days
120-135, 1997 ......... .................... .88
Figure 4.11 Simulated water quality constituents in
segment 1 of the IRL during Julian days 98-135,
1997 .......... ........................ ..92
Figure 4.12 Simulated water quality constituents in
segment 2 of the IRL during Julian days 98-135,
1997 .......... ........................ ..93
Figure 4.13 Simulated water quality constituents in
segment 3 of the IRL during Julian days 98-135,
1997 .......... ........................ ..94
Figure 4.14 Simulated water quality constituents in
segment 4 of the IRL during Julian days 98-135,
1997 .......... ........................ ..95

viii




Figure 4.15 Simulated water quality constituents in
segment 5 of the IRL during Julian days 98-135,
1997 .......... ........................ ..96
Figure 4.16 Simulated water quality constituents in
segment 6 of the IRL during Julian days 98-135,
1997 .......... ........................ ..97
Figure 4.17 Simulated water quality constituents in
segment 7 of the IRL during Julian days 98-135,
1997 .......... ........................ ..98
Figure 4.18 Simulated water quality constituents in
segment 8 of the IRL during Julian days 98-135,
1997 .......... ........................ ..99
Figure 4.19 Simulated and measured total phosphorus
in all segments of the IRL during the time periods
of WQMN 9704 and WQMN 9705 .... ............ .101
Figure 4.20 Simulated and measured total nitrogen in
all segments of the IRL during the time periods of
WQMN 9704 and WQMN 9705 .... .............. .101
Figure 4.21 Simulated phytoplankton carbon in all
segments of the IRL during the time periods of
WQMN 9704 and WQMN 9705 .... .............. ...102
Figure 4.22 Simulated inorganic phosphorus in all
segments of the IRL during the time periods of
WQMN 9704 and WQMN 9705 .... .............. ...103
Figure 4.23 Simulated ammonia nitrogen in all segments
of the IRL during the time periods of WQMN 9704
and WQMN 9705 ....... ................... ..103
Figure 4.24 Simulated nitrate nitrogen in all segments
of the IRL during the time periods of WQMN 9704
and WQMN 9705 ....... ................... ..104
Figure 4.25 Simulated total suspended sediment in all
segments of the IRL during the time periods of WQMN
9704 and WQMN 9705 ...... ................ 105
Figure 4.26 Simulated organic nitrogen in all segments
of the IRL during the time periods of WQMN 9704
and WQMN 9705 ....... ................... ..106




Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science
MODELING SEDIMENT AND NUTRIENT DYNAMICS
IN THE INDIAN RIVER LAGOON
By
Joel Melanson
August 1999
Chairperson: Dr. Y. Peter Sheng Major Department: Coastal and Oceanographic Engineering
The health and quality of aquatic life in coastal and estuarine waters are significantly affected by the nutrients in the water column and bottom sediments. The over-abundance of nutrients can cause the water to be overrun by algae and other autotrophs, thus inhibiting seagrass growth. Conversely, with too little nutrients, no life can exist. Specifically, in the Indian River Lagoon, the clarity of water and productivity of the seagrass beds are significantly influenced by the concentrations of the various forms of nitrogen and phosphorus in the lagoon. The spatial and
temporal distribution of the nutrient concentrations are affected by the lagoon's water circulation, suspended sediment concentrations, and various bio-geochemical reactions. As




part of a major effort by the University of Florida to develop an integrated Indian River Lagoon Pollutant Load Reduction model, this particular study focuses on a preliminary analysis of the water quality data collected from the Indian River Lagoon, and an application of a vertical ly- integrated twodimensional model for hydrodynamics, sediment transport, and water quality dynamics. The computer model has been used to
simulate the water quality dynamics of the Indian River Lagoon during the first three synoptic experiments conducted by the Coastal & Oceanographic Engineering Department in 1997. The model has been successfully used to illustrate the influence of suspended sediments and phytoplankton growth mechanics on the abundance and composition of nutrients in the water column. These results will be used to complement the development of the overall Indian River Lagoon Pollutant Load Reduction model.




CHAPTER 1
INTRODUCTION
1.1 Background
The Indian River Lagoon (IRL) is an estuary located on the east coast of Florida as shown in Figure 1.1. The IRL is a very long, narrow lagoon 195 km long with an average width
of between 2-4 kilometers and an average depth of about 2 meters. The IRL stretches from Ponce de Leon Inlet in the north to St. Lucie Inlet in the south. It includes the
Mosquito Lagoon, Banana River, the Indian River and several tributaries. In addition to its bordering inlets, the IRL includes two other connections with the Atlantic ocean: Ft. Pierce Inlet and Sebastian Inlet. Figure 1.2 illustrates the
shoreline of the IRL and the locations of its inlets. Between its narrow borders there are many islands and islets, in addition to several regions of seagrass beds. The IRL
receives most of its fresh water from 5 major canal systems
and the natural drainage basins located along its western shores.




ille
/ Study Area
4 na Beach
ltu lle
Mel ourne

Beach

* Cities SIRL Study Area State

200 0 200 400 600 Kilometers
Figure 1.1 A map of Florida and the IRL study area.




Ponce Inlet

Mosquito Lagoon ~-Banana River
--Sebastian Inlet

IRL Shoreline

Ft. Pierce Inlet

St. Lucie Inlet

10 0 10 20 Kilometers

Figure 1.2 The Layout of the Indian River Lagoon.

Indian




4
The IRL is an important natural asset to the residents and tourists who visit the area. It supports a substantial fraction of the area's economy. For example, the waters are the lifeline to many sport and commercial fisherman (Montgomery and Smith 1983). In addition, 90% of the clams harvested in Florida come from the IRL (Barile 1993).
An increase in population along the shoreline of the IRL
and its surroundings have strained its natural resources. The activities that are associated with population increase, like clearing of land, opening of inlets, and construction of causeways, significantly affect the lagoon's water circulation. In addition pesticides and fertilizers from agricultural runoff, thermal effluent from power plants, industrial chemicals, and increased fishing, have adversely
affected the water quality of the lagoon (Johnson 1983). Prior to the building of Florida's extensive canal systems,
the fresh water input to the lagoon was relatively small. The construction of the canal systems have increased the watershed by 500% (Zarillo et al. 1993) This has increased the amount of runoff and suspended sediments into the lagoon. Because of these factors, the overall health of the lagoon and its plant
and animal life has decreased in recent years (Zahorak and Swain 1995).




5
The abundance of seagrass has been identified in the IRLSWIM (Surface Water Improvement and Management) Plan as an overall indicator of the health of the lagoon (Sheng 1996). Seagrass beds are used in many ways by aquatic life, such as crabs and various types of fish, as well as manatees. The seagrass provides food, and offers protection from predators and bad weather. Further, the roots of the seagrass make the sediment more stable, making the bottom less vulnerable to erosion. The seagrass itself also removes nutrients from the water and sediment columns. Hence, healthy seagrass beds provide the foundation for a healthy aquatic system.
The IRL-SWIM plan has several objectives concerning the protection of seagrass beds (Steward et al. 1994). First, the IRL-SWIM intends to preserve the existing seagrass beds, thereby securing a foundation for future growth. Second, they plan to reestablish the previously existing beds that have been destroyed. Third, they intend to create new beds by planting new seedlings. Importantly, to accomplish these objectives there must be a good understanding of the response of seagrass beds to physical, chemical and biological stimuli.
One of the most important factors governing the growth of seagrass is the level of photosynthetically active radiation (PAR) that reaches the seagrass, which is affected by the clarity of the water. Recent studies on Tampa Bay and




6
Charlotte Bay have indicated that seagrass cannot grow below depth at which PAR is 20% of the incident light at the air-sea interface. Thus, the lower the water clarity, the less PAR will reach the bottom, and the less seagrass will grow. The water clarity is affected by many factors including total suspended solids (TSS), chlorophyll a, color and salinity. The intensity and distribution of these factors depend on such processes as tributary loadings, water circulation, and certain chemical and biological processes.
The stresses caused by the population growth over the past few decades have increased the TSS and nutrient loadings into the lagoon, which are believed to have led to poorer water quality and poorer water clarity. Specifically, higher nutrient concentrations can lead to higher phytoplankton levels, seen mostly as algal blooms, which are generally associated with higher suspended solids and color, and lowered dissolved oxygen levels. Many of these events result in increased light attenuation, i.e., lowering of the available PAR that can reach the seagrass beds, hence leading to a decline in seagrass bed production. The way to reverse this cycle is to reduce the TSS and nutrient loading, which will decrease the phytoplankton levels and the light attenuation, which ultimately leads to restored seagrass beds.




7
Lowering the nutrient concentrations in the lagoon is a
lengthy process that takes place over many years. The ef fects of reducing nutrient loadings on the lagoon now may only be seen in lowered nutrient concentrations in the lagoon several years later. The introduction of a pollutant load reduction
goal (PLRG) is a way in which the lagoon's management can ensure that the nutrient loadings on the lagoon will be reduced. A PLRG is a set limit under which all pollutants, including pesticides, nutrients, chemicals and contaminated
sediments, that are being fed into the lagoon, are to be kept.
In order to ascertain what exactly these limits should
be, resource management organizations have begun to employ the use of numerical hydrodynamic models in conjunction with sediment and water quality models (e.g., Sheng et al. 1995b,
Sheng 1997). These models consist of the inter- relat ionships of the following processes: hydrodynamic, hydrologic, sediment transport, nutrient dynamics, light attenuation and seagrass dynamics. When sufficient data have been gathered and analyzed, the model can be calibrated and validated.
There are many types of models that are used in the area of water resource management: statistical models, regression models, empirical models and process based models. Although many of these are used for understanding what has happened in
the past, the process based model, has the fundamental ability




8
of predicting future responses (Sheng 1997). That is, when the process based model can reliably reproduce past and present field conditions then it can be used to predict the study area's long term response to various hypothetical conditions. For example, the model can predict what lowering the present TSS and nutrient loadings into the lagoon will do to the future water quality and seagrass beds in the lagoon. In this way, process based models can be used to set these PLRG's.
1.2 Prior Studies
There are several shallow estuaries in Florida in which the restoration of seagrass beds has been a priority. Such areas have been studied using process based numerical models. These estuaries include Sarasota Bay (Sheng and Peene 1991a, 1992, 1993a), Roberts Bay (Sheng et al. 1995b, 1995c), Florida Bay (Sheng et al. 1995d), and Tampa Bay (Sheng et al. 1995a). In addition, freshwater lakes, such as Lake Okeechobee and Lake Apopka, have also been studied in a similar manner (Sheng et al. 1991b, 1993b, 1993c). Because of its relative
shallowness and importance of seagrass beds, the model to be used in studying the IRL is different from several utilized on deeper estuaries and lakes, such as Chesapeake Bay, San




9
Francisco Bay, and Long Island Sound (Sheng 1996). For these deeper water bodies, the seagrass beds had relatively little impact on the overall systems. Hence, the models used in these studies can not be directly applied to the IRL, although the process of determining PLRG's can be followed. However, the study of Robert's Bay used a model which included a hydrodynamic model, a nutrient model and a seagrass model (Sheng et al. 1995b), which is similar to the one that is to be developed for the IRL (Sheng 1996).
Here at the University of Florida, since 1995, we have been developing an Indian River Lagoon Pollutant Load Reduction (IRL-PLR) model for the St. John's River Water Management District (SJRWMD). The project involves the collection and analysis of field data, laboratory experiments and development of models of such processes as hydrodynamics, sediment transport, nutrient dynamics, light attenuation, and seagrass dynamics. The study, under the leadership of Dr. Y. P. Sheng, involves investigators from various departments: Coastal and Oceanographic Engineering, Environmental Engineering and Science, Fisheries and Aquatic Science, and Soil and Water Science.
This particular thesis deals with the analysis of some water quality data and the application of a 2-D hydrodynamicsediment-nutrient model to the IRL. Concurrent effort on the




10
development and application of a 3-D hydrodynamic-sedimentnutrient and a light model are being carried out in Dr. Sheng's group.
1.3 Scone of Study
The scope of this thesis includes the following:
0 Develop an enhanced synoptic water quality database for
the Indian River Lagoon.
" Analyze the collected data for spatial, temporal, and
bio-geochemical process related trends.
* Apply the vertically integrated version, CH2D, of the
three dimensional numerical model, CH3D, developed by Dr.
Y. Peter Sheng (Sheng 1986a, Sheng et al. 1995c) to the IRL. This includes the calibration and sensitivity tests
of the sediment and nutrient models.
" Analyze the model results for the trends found in the
collected field data.
In order to accomplish these objectives, first it must be ascertained what historical data is available for use. Then to obtain more insight into spatial, temporal, and biogeochemical process related water quality trends, additional data may be needed. When these data are gathered the
curvilinear-grid vertically-integrated hydrodynamic, sediment,




11
and water quality models will be used to analyze the processes dominating these trends. These models include the processes of advection, diffusion, resuspension, and the transformations involved in the oxygen, nitrogen, and phosphorus cycle. These transformation processes used in the water quality model are based on those developed by the U. S. Environmental Protection Agency in their water analysis simulation program (WASP) (Ambrose et al. 1991) with some modifications by Sheng et al. (1995b). To determine the flow field and its associated advective and diffusive fluxes, the curvilinear-grid vertically-integrated hydrodynamic model (CH2D), will be used. The CH2D sediment model will be used to determine resuspended sediment concentrations. All of these models will use the same time step and spatial grid to eliminate errors associated with having models running on different grid spacings and time steps.
After the water quality field data have been sampled and analyzed, the processes within the water quality model can be evaluated and better understood. The 2-D models will then be calibrated and applied. The findings of this thesis can then be used to complement the development of the fully integrated IRL-PLR model, which includes a 3-D curvilinear-grid hydrodynamic model (CH3D), coupled with a 3-D sediment transport model, a 3-D water quality model, a light model and




12
a seagrass model. The fully integrated model will be used as a functional management tool to set PLRG 's and perform various other types of management functions. Specifically, it will be able to address such problems as, the minimization of eutrophication by reducing nutrient loadings, controlling
freshwater release to minimize the impact on water quality and habitat, and controlling the impact of construction (bridges, causeways, marinas and inlet management) in the IRL on sediment transport and water circulation.
1.4 Thesis Outline
In Chapter 2, the IRL water quality data will be discussed, explaining, how, when and where it was collected.
Chapter 2 will also review the various analyses that have been performed on the data, and will interpret some of the trends found. In Chapter 3, the hydrodynamic, sediment and water quality models used in this study will be presented. Chapter 4 will present the various model simulations and their results. Discussions and conclusions will be presented in Chapter 5.




CHAPTER 2
SOME IRL DATA AND DATA ANALYSIS
2.1 Some TRL Data
The data needed to conduct the 2-D TRL hydrodynamicssediment-nutrient modeling experiments include hydrodynamic data and water quality data. A carefully designed monitoring plan for the IRL-PLR project was described in detail by Sheng
(1996). The hydrodynamic data include the offshore tide data, the wind data, and the water level data within the lagoon. The tidal forcings are taken from offshore data packages that are located directly outside of the Ponce, Sebastian, and Ft. Pierce Inlets. These packages measure pressure, temperature, and salinity. The wind data are collected from several wind stations located throughout the lagoon. These data are used
directly to represent the tide and wind forcings for the model. The water level data collected within the lagoon are
used to compare with the water level simulated by the 2-D model.
For the sediment and water quality model, several types of data have been collected. Specifically, to properly




14
calibrate and validate these models, bottom sediment characteristics, (e.g., settling velocity, erosion rate, and
critical stress) nutrient and water quality characteristics (e.g., salinity, pH, dissolved oxygen, temperature, total suspended solids, filtered, dissolved and particulate forms of phosphorus and nitrogen, chlorophyll a, b, and c, pheophytin, silica, dissolved and particulate forms of organic and inorganic nitrogen) and light data have been collected by the
University of Florida, following the monitoring plan (Sheng 1996).
2.1.1 WOMN Data
Some of the data used are provided by an ongoing sampling program called the Water Quality Monitoring Network (WQMN), which is organized by the SJRWMD for the IRL-PLR model study.
This program provides a useful source of historical and recent data. During monthly sampling events, the data that are collected include most of the aforementioned data, except for ammonia nitrogen and the filtered forms of phosphorus and nitrogen. Each of these sampling events takes place over a
three day period, during which 2-6 samples are collected from 34 stations. Although this sampling interval of 3 days is too lengthy to provide a synoptic look at the lagoon, the data are useful for providing information on temporal variations in




15
lagoon-wide water quality data. The WQMN samples water
throughout all eight segments of the IRL. The sampling
locations of the WQMN are shown in Figure 2.1. The station information is contained in Table 2.1, which contains the station name, Universal Transverse Mercator coordinates, and the model grid cell location given in I and J coordinates.
2.1.2 UFCOED Data
In addition to the WQMN data, the model needs data which require more intense and synoptic-like sampling effort
throughout segments 2, 4 and 5 in the IRL. The Coastal and Oceanographic Engineering Department of the University of Florida (UFCOED) conducted several sediment and water quality field experiments, to provide such data, according to the plan described in Sheng (1996). The scope of these experiments was to collect sediment and water quality data to provide an enhanced look at the bottom sediment characteristics and various water quality parameters, both spatially and temporally. The data will be used to calibrate and verify the CH2D and CH3D sediment transport and nutrient models, as well as DO model and light model. These data will also be used for various types of statistical analysis.




L02

102

GU

27

RJO1

20
20

WQMN Sampling Stations IRL Shoreline

VS1

10

40 Kilometers

Figure 2.1 The WQMN water sampling stations. See Table 2.1 for detailed station information.

"M 1-. 6rmw mn




Table 2.1 Detailed
information for the WQMN.

station

Station East UTM North UTM I J
B02 537561 3145297 194 39
B04 535951 3137907 211 36
B06 535979 3128676 233 32
B09 536771 3119323 253 25
CCU 539084 3105895
EGU 536285 3110995 268 9
GUS 544779 3093791 302 13
HUS 535153 3115607 258 13
102 519497 3179103 113 19
107 519739 3164087 146 14
110 522640 3152768 172 15
113 525874 3140773 203 15
116 531731 3128048 231 20
118 534482 3118824 121 537669 3111141 269 15
123 539872 3105056 281 14
127 546312 3091294 306 14
IRJO1 554311 3074834 339 15
IRJO4 560361 3063293 366 16
IRJO5 561613 3059484 374 15
IRJO7 562402 3055272 386 15
IRJ10 559783 3063906 364 14
IRJ12 562490 3054134 387 14
ML02 527556 3177735 124 32
SUS 550078 3081195 324 13
TBC 513525 3188158 __TUS 541285 3100948 288 13
V05 508820 3208927 22 30
Vil 515153 3202762 49 36
V17 515679 3194515 70 29
VMC 558967 3058525
VSC 560910 3053630




2.1.3 UFCOED Sediment Data
Among the experiments conducted, the UFCOED conducted a bottom sediment study on the IRL in November 1996. In Figure 2.2 the UFCOED sediment sampling stations are shown for the Indian River Lagoon. At each station, sediment grab samples
were collected from the top 10 cm of the bottom. The sediment samples were then analyzed for sediment size distribution and then characterized into 5 different sediment types. Each of these sediment types have their associated erosion rate, critical stress and settling velocity. Details of the bottom sediment study are described in Sheng et al. (1998).
2.1.4 UFCOED Synoptic Data
UFCOED also conducted enhanced synoptic water sampling. These experiments occurred in two periods, each with 6 field experiments conducted. The first set of experiments started on April 8, 1997 and finished on June 25, 1997. Samples were
taken on a biweekly basis during this time period. The second set of sampling trips took place from November 20, 1997 to June 28, 1998, in which monthly samples were taken. There were a total of twelve sampling trips conducted and these are
referred to as Synoptic Field Trips 1-12. These specific
dates were scheduled to offset the pre-existing WQN sampling schedule.




NWE
S
// IRL Shoreline SUFCOED Sampling Stations 20 0 20 40 Kilometers
Figure 2.2 The UFCOED sediment sampling stations. See Sheng et al. (1998) for detailed station information.




20
The water samples were collected via a modified Niskin bottle. The depth specific water was sampled, filtered, and preserved, according to UFCOED's Quality Assurance Plan, approved by the Florida Department of Environmental Protection (FDEP), as described in Melanson and Sheng (1997). The
samples were poured into bottles provided by the chemistry lab that performed the chemical analysis. The water quality
parameters evaluated include all of the necessary data that were mentioned previously in section 2.1.
Additional data were collected by several Hydrolab DataSonde-4's and LI-COR bulbs. The Hydrolabs provided
measures of conductivity, salinity, pH, dissolved oxygen, temperature and depth. Further, in order to determine the amount of light that is available at each of the sampling sites, LI-COR bulbs were used to detect the photosynthetically active radiation (PAR) immediately below the free surface, and at 50% and 80% of total depth. Sampling procedures are
described by Melanson and Sheng (1998).
2.2 Sample Locations
The sampling sites were selected throughout segments 2, 4, and 5, of the Indian River Lagoon. The segments of the IRL are shown in Figure 2.3. The sampling sites were chosen




21
according to spatial resolution and bottom sediment type, characterized from the UFCOED sediment study. For the
synoptic trips 1-6, there were 45 stations chosen with samples taken at two sampling depths; 20% of depth and 80% of depth. Figure 2.4 shows the Indian River Lagoon and those 45 original sampling stations. For the second set of sampling trips, synoptic trips 7-12, the sampling stations were slightly rearranged. Due to less available sampling time, the original 45 stations were reduced to 30. Figure 2.5 shows the 30 sampling stations for the second half of the synoptic sampling trips. Station information is contained in Tables 2.2 and
2.3.
2.3 Data Analysis
The data that have been analyzed for this study include such parameters as salinity, phosphorus, nitrogen, and phytoplankton carbon. The data came from both the UFCOED synoptic sampling trips and the WQMN trips conducted by the SJRWMD from February 1996 to June 1998. For the salinity analysis, long term temporal trends were analyzed, while for the nutrients and phytoplankton, short term spatial and biochemical trends were analyzed.




Segment 2Segment

Segment 1
Segment 3
Segment 5

Segment 6 Segment 7

Segment 8

IRL Shoreline

20 0 20 40 Kilometers

Figure 2.3 The segments of the IRL (Sheng 1996).




14
W E 1C
NI
W+ E
S
A Synoptic Stations
IRL Shoreline
20 0 20 40 Kilometers
Figure 2.4 The UFCOED synoptic water sampling stations 1-45. See Table 2.2 for detailed station information.




Table 2.2 Detailed station
information for first set of Synoptic stations 1-45.
Station East UTM North UTM I i 1 546981 3089768 309 14 2 546560 3092628 305 16 3 545566 3095209 300 17 4 544655 3097883 296 17 5 543255 3099909 292 16 6 541853 3102489 286 _16 7 540696 3105624 280 17 8 539458 3108389 274 17 9 538385 3111063 269 17 10 537068 3113643 263 17 11 535668 3116409 256 15 12 535007 3118807 251 17 13 533774 3121038 246 16 14 532621 3123786 240 16 15 531552 3126368 234 16 16 530319 3128949 228 14 17 529414 3131310 224 14 18 528427 3134133 217 16 19 527162 315-7250 211 15 20 526273 3140387 204 15 21 525402 3142915 197 15 22 524710 3145923 191 15 23 524313 3148600 184 15 24 523166 3150998 177 14 25 525041 3151463 177 18 26 522181 3154043 169 14 27 523485 3154507 168 16 28 522338 3157459 163 15 29 525272 3157926 162 20 30 521354 3160411 155 15 31 523391 3160877 156 19 32 525101 3161434 155 22 33 519637 3163363 147 14 34 521673 3164012 147 17 35 519551 3166317 139 15 36 518814 3168624 133 15 37 518565 3171393 126 15 38 518237 3173239 122 15 39 520758 3174720 121 20 40 518149 3177209 116 16 41 521569 3176199 119 21 42 517004 3180992 109 15 43 518796 3179610 112 18 44 520263 3178597 114 20 45 523031 3177864 117 24




A10
10

Synoptic Stations 1-30 IRL Shoreline

20 Kilometers

-- -- IMM M M M M M~ --- -
Figure 2.5 The UFCOED synoptic water sampling stations 1-30. See Table 2.3 for detailed station information.




Table 2.3 Detailed station
information for second set of Synoptic stations 1-30.
Station East UTM North UTM I i 1 546981 3089768 309 14 2 545566 3095209 300 17 3 541853 3102489 286 16 4 538385 3111063 269 17 5 535668 3116409 256 15 6 532621 3123786 240 16 7 531552 3126368 234 16 8 533774 3121038 246 16 9 539458 3108389 274 17 10 546560 3092628 305 16 11 530319 3128949 228 14 12 527162 3137250 211 15 13 524710 3145923 191 15 14 523166 3150998 177 14 15 522338 3157459 163 15 16 523391 3160877 156 19 17 521354 3160411 155 15 18 525272 3157926 162 20 19 525402 3142915 197 15 20 529414 3131310 224 14 21 523031 3177864 117 24 22 518796 3179610 112 18 23 521569 3176199 119 21 24 520758 3174720 121 20 25 518565 3171393 126 15 26 519551 3166317 139 15 27 519637 3163363 147 14 28 518814 3168624 133 15 29 518237 3173239 122 15 30 517004 3180992 109 1 15




2.3.1 Salinity Data
The salinity analysis extended temporally over the entire period (2/96-6/98), and spatially over the entire lagoon. The analysis showed a gradual seasonal variation of salinity in the northern part of Indian River and more erratic but less seasonal fluctuations of salinity in the southern IRL. This
is due to the relatively low influence of the tide on the northern waters. In the north the waters are relatively shallow compared to the south and are more constricted for tidal flushing. This combined with low water circulation, compound the effect of watershed inflow and evaporation on salinity fluctuations, which dominate the observed trends.
Figure 2.6 shows the salinity data for five stations from the WQMN, 102, 107, 110, 113, and 116, located in the northern part of the Indian River. Ref er to Figure 2. 1 f or their relative locations in the IRL. The stations are in order from north to south, with station 102 located farthest north and station 116 located farthest south. As shown in the figures, the higher the latitude, the more profound the temporal
salinity fluctuations become. This, as illustrated in Figure 2.7, is most likely attributed to the unusually low rainfall that was experienced during that period. The rainfall
collected at two cities located in the northern part of the lagoon, Melbourne and Titusville, show a period of unusually




28
low rainfall during the months of June 1996 through June 1997. This dry period correlates with the elevated salinity readings found in the northern IRL from April 1996 to December 1997. Further, there were no great changes experienced in the
evaporation levels in this area during this time, so the elevated salinity readings can be attributed to low fresh water renewal from the watershed.
Figure 2.8 shows the salinity data at the WQMN sampling
stations in the southern part of the lagoon. The f igure
illustrates that in the south the temporal variations in salinity are more erratic (with periodicity of 2-4 months) with less distinct seasonal trend than the north. The range of temporal salinity fluctuations appears to be the same in the north and south. This is probably due to the high tidal influence from the three inlets on the southern waters.
During a typical tidal cycle, or any other 12 hour period, the salinity at the inlets can vary from 29 to 36 part per thousand. Hence, depending upon the sampling time during the day the salinity readings can vary greatly. This is one of the reasons for the rather highly variable salinity readings in this area.




35
30
25
20
-Ustation 107 10 -a-Station 107
5. X* Station 113
---Station 116
0
F-96 A-96 J-96 A-96 0-96 D-96 F-97 A-97 J-97 A-97 0-97 D-97 F-98 A-98 J-98
Date

Figure 2.6 Temporal variations in salinity section of the IRL.

in the northern

0-1!I
A-95 J-95 A-95 0-95 D-95 F-96 A-96 J-96 A-96 0-96 0-96 F-97 A-97 J-97 A-97 0-97 D-97 F-98 A-98
Date
Figure 2.7 Rainfall Data for the Titusville and Melbourne
area.




4 0 .......... ..... ...... ....... ......... ................................ ... . .... .................... ........... .................................. ... ....... ... .... ................. .... ..... ................ .... ............... ..................................... ..... ........................ .............
40
39 30 25
' 20
15
10 Station 123
---Station 127
---Station IRJ01
5 *Station IRJO4
--M-- Statio n IRJ12
0
Feb-96 Apr-96 Jun-96 Aug-96 Oct-96 Dec-96 Feb-97 Apr-97 Jun-97 Aug-97 Oct-97 Dec-97 Feb-98 Apr-98 Jun-98
Date
Figure 2.8 Temporal variations in salinity in the southern
section of the IRL.
2.3.2 Nutrient Data
For analysis of the spatial trend in nutrient
concentrations, the data used include those collected during the WQMN trip 9704, from April 14-24, 1997, and the WQMN trip
9705, from May 12-14, 1997. These data were used due to fact that they encompass the entire eight segments of the IRL.
A spatial trend was found in the phosphorus data:
concentrations of total phosphorus were found to be high in the southern waters and gradually decrease in magnitude
towards the north. This trend, illustrated in Figure 2.9 is




31
probably due to the long term effect of the external phosphorus loadings.
The nitrogen levels exhibited a similar spatial trend, except that it is reversed, i.e., concentrations were lower in the southern waters, but higher in the north. This trend, as shown in Figure 2.10, is believed to be due to the long term effect of the external loadings.
Some other water quality variables that were analyzed for temporal trends include the phytoplankton carbon (PP), nitrate nitrogen (NN) ammonia nitrogen (AN), and inorganic phosphorus
(IP). The data used were those measured during the UFCOED synoptic trips conducted on April 14, 1997 and May 6, 1997 and
0.120
0.100
0.080 o
0.060 --0.040
0.020
0.000I
1 2 3 4 5 6 7 8
Segment
Figure 2.9 Total phosphorus concentrations at eight segments in the IRL during April and May of 1997.




1.200 1 000
z
z 0.800
0.600
0.400 0.200 0.000
1 2 3 4 5 6 7
Segment
Figure 2.10 Total nitrogen concentrations at eight segments in the IRL during April and May of 1997.
the WQMN trips 9704 (April 1997) and 9705 (May 1997). The synoptic data cover the segments 2, 4, and 5 of the IRL and as before, the WQMN data cover all of the segments of the lagoon. The synoptic data were used because they contained more comprehensive information of the nutrients.
During phytoplankton growth, NN, AN, and IP are used up as fuels. One general trend that can be looked for in the data, is an increase in phytoplankton carbon over a certain time period, with a concurrent decrease in NN, AN and IP. As illustrated in Figure 2.11, in segments 4 and 5, there was an overall decrease in phytoplankton carbon over the sampling period. For segments 4 and 5, the associated increases in AN,




33
NN and IP were generally found, as shown in Figs, 2.12 2.14. The phytoplankton carbon in segment 2 had a slight decrease. There was an associated increase in AN and NN, but the IP in segment 2 decreased. This discrepancy could be due to the fact that IP in resuspended sediment can be a source for IP in the water column. As illustrated in Figure 2.15, there is a significant reduction in TSS in segment 2 over the sampling period. This reduction in TSS reduces a significant source of IP in the water column, coming from the adsorbed IP on the bottom sediment. The reduction in TSS correlates with the observed reduction of IP in the water column for segment 2. Therefore, the expected increase in IP due to phytoplankton growth would not be present.
0 .7 ....... ................................. ............. ...... ............. ....... ...... ....... ......
,Ar-97
0.6 06/
0.4 0.3 0.2 0.1
2 4 6
Segment
Figure 2.11 Phytoplankton carbon measured in segments 2, 4, and 5 of the IRL during April and May 1997.




0.160
0.140 0.120 0.100
z
0.060
0.040 0.020 0.000

mApr-97 EM ay-97

2 4 5
Segment
Figure 2.12 Ammonia nitrogen measured in segments 2, 4, and
5 of the IRL during April and May 1997.

0.090 0.080 0.070
0060 0.050
E
z
z 0.040 0,030
0.020 0.010
0.000

CApr-97 N May-97

Segment
Figure 2.13 Nitrate nitrogen measured in segments 2, 4, and
5 of the IRL during April and May 1997.

F-




0.050 0.045 0.040 0.035 0.030 E 0.025 0.020 0.015 0.010 0.005 0.000

2 4 5
Segment
Figure 2.14 Inorganic Phosphorus measured in segments and 5 of the IRL during April and May 1997.

25.000 ,- ---

20.000

15.000

10.000

5.000 -

2, 4,

OApr-97
EMay-97

0.000 I I I i
2 4 5
Segment
Figure 2.15 Total suspended sediment measured in segments 2, 4, and 5 of the IRL during April and May 1997.

i POApr-97 tW y-97

I




36
This relationship between PP and NN, AN, and IP was further examined using the WQMN data. Since the WQMN does not collect AN, only NNi and IP were analyzed. The WQMN data, illustrated in Figures 2.16 2.18, show that both the NN and IP more or less follow this relationship. As shown in Figure 2.17, the NN concentrations in segments 2, 3, 4, 5, 7, and 8, seem to follow this inverse relationship, but not in segments 1 and 6. The IP concentrations in segments 1, 5, 7, and 8 also seem to follow this relationship, but not in segments 2, 3, 4, and 6, as shown in Figure 2.18. These discrepancies indicate that the collected data over this sampling period do not entirely validate these inverse relationships.
1,000 0.900
NWQMN -9704
0.800 nWQMN -9705
0.700 0.600
0.500
0.400
0.200 0.100 0 000
1 2 3 4 5 6 7 8
Segment
Figure 2.16 Phytoplankton carbon concentrations at eight segments in the IRL during April and May of 1997.




0 040 ---0.035
IWOMN -9704 OWOMN -9705
0.030
0 025
S0 020
z
0.015
0010
0 005
0.000
1 2 3 4 5 6
Segment
Figure 2.17 Nitrate nitrogen concentrations at
in the IRL during April and May of 1997.
0.060
0.050 WOMN -9704
OWQMN -9705
0 040
E 0.030 0.020
0010 ...
0.000 -

rr

7

eight segments

Segment
Figure 2.18 Inorganic phosphorus concentrations at eight segments in the IRL during April and May of 1997.




38
In addition to this trend, it was found that the total nitrogen in the system is influenced greatly by the suspended bottom sediments (TSS). According to the UFCOED Synoptic and WQMN sampling data, the total nitrogen in the IRL is comprised mostly (89% 95%) of organic nitrogen. A significant source of organic nitrogen in the water column is the organic nitrogen contained in the suspended bottom sediments. As illustrated in Figures 2.19 and 2.20, as the TSS changes over time, the total nitrogen seems to follow the same trend. In segments 3, 4, 5, 7, and 8 where decreases in TSS where found, decreases in total nitrogen were also found. In segment 1 there was an increase in TSS and total nitrogen. In segments 2 and 6, there was a decrease in TSS but the total nitrogen for this segment increased slightly. This increase could be due to other possible nitrogen sources, such as phytoplankton death, or diffusion between the bottom sediment and the water column.
These spatial, temporal, phytoplankton related, and suspended sediment related trends will also be investigated in the model simulations conducted for this same period of time.




20.000 .
18 000
*WOMN -9704 16000 --WQMN -9705
14 000
12.000
10.000 8,000
6 000
4000 2000
2000
1 2 3 4 5 6 7 8
Segment
Figure 2.19 Total suspended sediment concentrations at eight segments in the IRL during April and May of 1997.
1.800
1.600 W WQMN -9704
OW QMN -9705
1.400 1.200
1.000
S0.800 0.600
0.400 0.200
0.000
1 2 3 4 5 6 7 B

Figure 2.20 Total nitrogen in the IRL during April and

Segment
concentrations at eight May of 1997.

segments




CHAPTER 3
NUMERICAL MODELS
Nutrients in bodies of water come from various sources, such as rivers, oceans, commercial and residential land runoff, bottom sediments, ground water, and the atmosphere. These nutrients experience various forms of transformation processes. In addition, they are subjected to the advective and diffusive fluxes that are produced by the hydrodynamics of the body of water. These processes are constantly changing the concentrations of the specific nutrients at various locations. The first step in modeling sediment and nutrient dynamics is to quantify the advective and diffusive fluxes produced by the flow field. This is done using the CH2D curvilinear-grid vertically-integrated hydrodynamic model (Sheng et al. 1995b, Sheng et al. 1996).
3.1 Hydrodynamic Model
The equations that govern the hydrodynamic model are the two dimensional time dependent Navier-Stokes equations for an incompressible fluid. The basic assumptions of the 2-D model are the Boussinesq approximations, the hydrostatic pressure




distribution, and the eddy-viscosity concept. The equations of motion for this study are the vertically-integrated equations presented in the Cartesian coordinate system, as follows:

dt dxu
d+g;

dU t+

dV
-=0
dy

d (U)U (UV S )

+To
-Al+ x

(3.1)

1bx
Po

(3.2)

+d A + d A +T{AH dJX (AH j
H Pa gH2 dp Ho-Px gH d x p, 0 x x 2 p x

dV d UV d V dt dx H )dy H)

"f + y Tby =fU +--Po Po

(3.3)

d ( V d V
+ -( dx ) d ( AH
H dP'a 8 gH2dp
pgo y gHy 2 o y

oT 8 8 q dT+ (UT)+ d(VT dt dx dy po
dS + d (US)+ d(VS) dt dx dy pA

qb
A
Ps

S(KHI + (KH xdx T" dx y H

eb d dS a dS
-P+ S K + K p, dx x dy Hy

(3.4) (3.5)




42
where U and V, are the vertically-integrated velocities in the x and y directions, and are defined as f4-hudz and f?_hvdz, respectively, is the free surface elevation, H= +h and is the total depth, (T., qs, es) represent the surface fluxes of (momentum, heat, salinity), (Tb, q, eb) represent the bottom fluxes, f is the Coriolis parameter, p is the density, p is the pressure, T is the temperature, S is the salinity, where p, S, and T are the vertically-integrated quantities, and AH andKH are the horizontal turbulent eddy coefficients. For these simulations in this study the temperature and the salinity equations were not solved. Both the temperature and the salinity were kept constant (T=250C, S=29ppt).
Due to the complex geometry of the IRL, a boundary-fitted curvilinear grid will be used. This type of grid allows better resolution of the water circulation in the lagoon as compared to the Cartesian grid. Thompson (1983) developed a process using elliptic equations to generate a 2-D boundary fitted grid in complex domain. Sheng (1996) used Thompson's method to generate a boundary-fitted grid for the IRL. The IRL computational grid, as shown in Figure 3.1, has a total of 20988 (477 x 44) grid cells. Further, Figure 3.2, more
clearly illustrates how the curvilinear grid is fitted to the existing IRL shoreline. When using the curvilinear grid, the governing equations must be transformed to conform to the new




43
coordinate system (Sheng 1996). These transformed equations are then non-dimensionalized. Finally, the boundary and initial conditions are then applied (Sheng 1996). The boundary fitted equations are shown in the Appendix.

Figure 3.1 The computational grid for the IRL.




IRL Shoreline
IGrid
10 0 10 20 Kilometers
Figure 3.2 The computational grid with
the IRL shoreline in the northern part
of the IRL.
3.2 Sediment And Water Quality Models
Nutrients are essential to a productive body of water. They are the fuel for primary production of organic matter, such as algae and various other autotrophs. The nutrients that are important in the life cycles of these autotrophs, are nitrogen, phosphorus and carbon, of which nitrogen and phosphorus are usually the limiting growth factors.




45
To quantify the temporal and spatial values of nutrient concentrations in water bodies, water quality models can be used. Water quality models solve the conservation equations for various forms of the nutrients involved in the phosphorus, nitrogen, and oxygen cycles. A flow chart for these nutrient cycles based on the modified WASP model are shown in Figure
3.3 (Sheng et al. 1995b, Sheng et al. 1996).

Figure 3.3 Flow chart for the nutrient cycles.

As can be seen in the diagram, phosphorus, in its dissolved form, and nitrogen in its ammonia and nitrate form, are used by phytoplankton for growth. Both phosphorus and nitrogen are returned from the phytoplankton biomass through




46
endogenous respiration and non-predatory mortality. Other transformation processes include the conversion of dissolved organic phosphorus to dissolved inorganic phosphorus (DOP mineralization), organic nitrogen to ammonia (ON mineralization), ammonia nitrogen to nitrate nitrogen (nitrification), and nitrate nitrogen to nitrogen gas (denitrification) For the oxygen cycle, sources of oxygen include rearation and growth of phytoplankton. Sinks include respiration, oxidation of carbonaceous material, and nitrification. In addition, all three cycles are affected by the process of diffusion, resuspension and settling of nutrients between the water column and the bottom sediment layer. The original WASP model (Ambrose et al. 1991) does not include the resuspension of nutrients.
The differential equations describing the conservation of various water quality constituents in the water column in a Cartesian coordinate system are as follows:
dtv +d + =H. + 2 +R. (3.6)
d x dy )X d2
where C1 is the concentration of the i-th water quality variable being modeled, and Ri is the source/sink term associated with the transformation processes for the i-th variable. The curvilinear grid version of the above equation




47
is shown in the Appendix. Table 3.1 lists each of the water quality variables modeled. The Re's describing the
transformation processes for each water quality variable are as follows (the subscript j indicates sediment column) (Sheng 1996):

Table 3.1 Water Quality variables with the associated model variable. Variable Water Quality Variable Number
C1 Ammonia Nitrogen
C2 Nitrate Nitrogen
C3 Inorganic Phosphorus
C4 Phytoplankton Carbon
Cs Dissolved Oxygen
C6 Carbonaceous Oxygen
Demand
C7 Organic Nitrogen
C8 Organic Phosphorus

Water Column:

R,= Drd P sas, Nl/4 +K,38(r20 K me +C4 C,-ean~aC
death ON mineralization growth
-Ki22r20)rKC+ CC, E- I f -C~ 1212 ctK NI+Ca HHtic diffu nitrification vertical diffusion

(3.7)




R = K120T-2) K, + C C, Ga(i PH3)C4 K 62D2 K, C 20 lKN+C4}
nitrification grow th denitrification (3. 8)
E
- (Cf, -C2 f2 )
vertical diffusion
R3= D Pa 1.(I- f )C + 20) m C 4 CS G la.C4
death DOP mineralization growth (39)
(3.9)
Esed C3j
+ H Credresuspension
V
R4 = GP1C4 -DpIC4 V, (3.10)
H-f-'C (3 .10)
growth death settling
R5 = accK C4 KO r-20) C6 C -,(I f5C
D D D K +C6) H
death oxidation
532 K (r20) KNO3 C
4 14 0,KNo +C6 (3.11)
denitrification
+-- (I fDS)Cs H-(C5fS Cs, f,)
resuspension diffusion
R6 = K2(Cs C6)- K,) 0r-2o) C 64 K12 1( T-20) Cl
Keon +C6 ) 14 K r +C )
rearation oxidation nitrification (312
(3.12)
Edif TC_. '(-20) (32 48 14: _32K Mo(_2o)c
(C, C )r20) +G, + 1 PsN3 C4 Kg -20)C4
HHjsediment demand phytoplankton growth respiration sediment demand phytoplankton growth respiration




C V, (1 fD7)
R7 = Dpla,,foC4 + K710(7r-2) C7 C7
1 K.,C +C4 H
death ON mineralization settling (313
(3.13)
+ Ed ( C7
HH Cred)
resuspension
R8 = DilacfopC4 + K,3,)(-20) CC4 C8, ( f8 C (K=.PC + C4 H
death DOP mineralization settling (314)
(3.14)
Esed (C8i
HH, CSd
resuspension
Sediment Column:
=k" T a-20)t (1 0(T-2 0) C Edi2f C j)
RI; = KPZDPZD 2)(1- f)C4 + KONDO 2)C + Cf C f1 (3.15)
algal decomposition mineralization vertical diffusion
R2 -20C, + 'KCDf -C1f 1) (3.16
denitrification vertical diffusion
= ) (T_20) T-2 E.,d ( Cl,
R, = KPZzo 2o)aPc(l f,)C4, +KoPDO20) fDSCS j C2
Hi (C..a (3.17)
algal decomposition mineralization resuspension
R5, = a K O(T-20)C K 0(T-20)C, K832 -20)c DZD 4 DS DS 4 2D 2D 2j
decomposition oxidation denitrification
vi C +(3.18) SV,1 (- sVs E ffso
settling resuspension diffusion




R =-K s 20)C5j E d (C6 C6j ) ST-2o)
1

oxidation

diffusion

(T-20). I" (T-2) Eed ( C7 i V,30 (1- fD7 )7
R,7 = K PZD o20) a nconC .- K OND OONDC7 2 3 7)
algal decomposition mineralization resuspension settling
:. T-20) ..-20) C _.._0 C8 j V, / 1 fDS
R8 = KpZD PZD oaPC fopC4j KOPDOPD fD8j Sj -H2 + V 3 C8
algal decomposition mineralization resuspension settling
All the variables in the above equations are defined

(3.20)

(3.21)

in Table

3.2. These transformation processes are described in more detail in sections 3.2.3-3.2.5.

Table 3.2 Definitions of the reaction coefficients for the transformation processes in the water quality model.

WQ
WQ Definition and Units
Coefficient
E), Temperature coefficient for nitrification
84 Temperature coefficient for endogenous respiration
67 Temp coef for denitrification
e,_ Temperature coefficient for dissolved organic phosphorus mineralization O8 Temperature coefficient for Dissolved organic phosphorus mineralization
Op Temp Coef for organic carbon oxidation in the water column
ODS Temperature coefficient for organic carbon oxidation in the sediment column
CoN Temperature coefficient for organic nitrogen decomposition
60,p Temperature coefficient for Organic Phosphorus decomposition
6PZp Temperature coefficient decomposition rate for Phytoplankton decomposition
)s Temperature coefficient for DO diffusive exchange
PNH3 Ammonia preference factor
Solari Maximum daily light intensity (Ig/day)
V3 Organic matter resuspension velocity (m/day)
V,_ Organic matter settling velocity (m/day) V. Phytoplankton settling velocity (m/day)

(3.19)




Table 3.2-continued
WQ
WQ Definition and Units
Coefficient
An Phytoplankton nitrogen-carbon ratio (mg N/mg C)
Aoc Oxygen to carbon Ratio (mg O/mg C)
Apc Phosphorus to carbon ratio (mg P/mg C)
Cias Fraction of inorganic phosphorus in the sediment layer (kg/mg)
C7iS Fraction of organic nitrogen in the sediment layer (kg/mg)
C8ins Fraction of organic phosphorus in the sediment layer (kg/mg)
D. Phytoplankton death rate (day")
Ef Diffusive exchange coefficient (m/day)
f, Fraction of dissolved ammonium nitrogen in the water column
fli Fraction of dissolved ammonium nitrogen in the sediment column f, Fraction of dissolved (nitrate-nitrite) nitrogen in the water column
f& Fraction of dissolved (nitrate-nitrite) nitrogen in the sediment column
fdS Fraction of dissolved CBOD in the water column
fei Fraction of dissolved CBOD in the sediment layer
fd7 Fraction of dissolved organic nitrogen in the water column
fdTi Fraction of dissolved organic nitrogen in the sediment column fd8 Fraction of dissolved organic phosphorus in the water column
fdi8 Fraction of dissolved organic phosphorus in the sediment column
fon Fraction of dead and respired Phytoplankton recycled to the nitrogen pool
fon Fraction of dead and respired Phytoplankton recycled to the phosphorus pool
Gpl Phytoplankton growth rate (day')
Hi Thickness of active sediment layer (cm)
K12 Nitrification rate in the water column (day')
K12i Nitrification rate in the sediment column (day1)
KIc Phytoplankton maximum growth rate (day')
k p Phytoplankton death ratio (day')
Km Phytoplankton endogenous respiration (day')
K, Re-aeration rate at 20uC (day")
K2p Denitrification rate (day')
K71 Organic nitrogen mineralization rate at 20uC (day-)
K3 Dissolved organic phosphorus mineralization rate at 20uC (day-)
K[on Half-saturation constant for oxidation of CBOD (mg 02/ L)
K, Light attenuation coefficient due to phytoplankton (m O2/mg Chl-a)
K, De-oxygenation rate at 20uC (day1)
Kns Organic nitrogen (as CBOD) decomposition rate (day-')
KMN Half-saturation constant for inorganic nitrogen uptake by phytoplankton (pg N/L)
Kmp Half-saturation constant for inorganic phosphorus uptake by phytoplankton (pg P/L,
KMp Half-saturation constant for mineralization of phytoplankton (mg C/L)
KNIT Half-saturation constant for DO limitation in the nitrification process (mg O /L)
KNo3 Half-saturation constant for DO limitation in the denitrification process (mg N/L)
KoNp Organic nitrogen decomposition rate (day-)
Kopp Organic Phosphorus decomposition rate (day')
Kpyn Phvtoplankton decomposition rate (day"')




52
3.2.1 Modeling Sediment Transport Processes
Resuspended sediments from the bottom of a shallow estuary can be a major source of nutrients in the water column. Because of its effect on the nutrient cycle, the suspended sediment concentration is being explicitly modeled in this study (Sheng 1996).
The rate of resuspension of sediment, indicated as, Eed, is modeled as follows (Sheng 1986b), Esd = (,-'0, (3.22)
where Eo is the erosion rate coefficient, -rb is the bottom shear stress, and, r, is the critical shear stress. The bottom shear stress is a combination of the stress created by the current and that which is induced by waves. The resuspended sediment is then subjected to the same advective and diffusive fluxes produced by the flow field. The suspended sediment settles with a site specific settling velocity, Vse,, which is based on the sediment characteristics from the UFCOED sediment study.
3.2.2 Modeling PhvtoPlankton
The growth of phytoplankton is a very complicated process. Each of the many species of phytoplankton have a unique growth rate and react differently to temperature, light




53
and nutrient variations. In order to simplify the analysis of phytoplankton, a growth function that characterizes the phytoplankton population as a whole, will be used. In a
stable environment, the growth rate of phytoplankton is exponential, and is proportional to the number of cells at any one given time (Chen and Sheng 1995):

(3.23)

dt aM,

where M is the number of cells present and Pa is the growth rate constant. The growth rate constant is dependent upon the available light, temperature and nutrient concentration. The site specific growth rate of phytoplankton is a function of the maximum growth rate, and several other functions that describe the limiting affect of temperature, light and nutrient dynamics as depicted in the following equation (Ambrose et al. 1991):

GPI = Glmax(20)f (G RTGRI' GRN ),

(3.24)

where G1,(20) is the maximum optimal growth rate, GRT is a temperature adjustment function, GRI is the light attenuation function, and GRN is a nutrient limiting function. GRI is dependent upon temperature, incident light, water column depth, and a light attenuation constant. GRN is dependent upon the available phosphorus and nitrogen. All of these




54
functions can vary from 0 to 1. If the function is 1, then there is no limiting effect on the growth rate, whereas if the function is 0, then all growth is inhibited. Further, the operation that combines all of these functions can be an average, weighted average, minimum or maximum. In the model used here, these limiting functions are combined as an average of the sum.
When an initial maximum growth rate is decided upon from known phytoplankton dynamics, this value can be temperature varied by the following (Ambrose et al. 1991):
Glmax(t) = Glmax(20)0, (3.25)
where 0, is the temperature coefficient. The temperature corrected growth rate, can then be corrected for the available light.
The availability of light is one of the most important factors limiting phytoplankton growth (Sheng 1996). In a natural environment, all of the light that is present at the surface of the water is not available to be used by phytoplankton for growth. Available light can be inhibited at the air sea interface and attenuated through the water column due to natural and nutrient induced turbidity. The light limiting factor, GRI is modeled as follows (Ambrose et al. 1991):




e
GRI= f exp (3.26)
KD Is ( L;) 1
where f is the fraction of day light during the day, Ke is the light attenuation, based on the phytoplankton population, I, is incident light intensity just below the surface, I, is the saturating light intensity of phytoplankton.
The phosphorus and nitrogen concentrations of the local environment can also have a limiting effect on the growth rate. Monod (1949) suggests that in a phosphorus limiting environment, the growth rate limiting factor due to nutrient concentrations, GRN becomes,
(DIP
GRN DIP (3.27)
G~ K +DIP}'
where DIP is the dissolved inorganic phosphorus, and Kmp is the half saturation constant of the dissolved inorganic phosphorus for growth. In a nitrogen limiting environment GRN becomes,
DRN = (3.28)
G ( =KmN + DIN )'
where Km is the half saturation concentration of nitrogen for growth and DIN is the dissolved inorganic nitrogen. To




56
determine the growth rate limiting factor including both nutrients, the smallest of the two is selected, thereby suggesting which of the two nutrients has the greatest limiting effect on the growth rate.
The following graph shows the effect of these limiting functions on the growth rate. For example, with K, set to 25 pg/l, and Kmp is set to 1 vg/l, Figure 3.4 shows that the area in which the growth rate is most greatly inhibited by this limiter is when the dissolved inorganic phosphorus concentration drops below 0.2 mg/l and when the dissolved inorganic nitrogen concentration drops below 0.008 mg/l.
0.9/ 0.8
0.7 0.6
z 0.5
0.4 0.3
0.2
0.1
DIN 0 8 16 24 32
DIP 0 200 400 600 800
Nutrient Concentration (ug/)
Figure 3.4 The affect of nutrient concentration on
G. (Ambrose et al. 1991).




3.2.3 Modeling The Phosphorus Cycle
Phosphorus can enter estuaries or other bodies of water from many sources. An important source is rain runoff that carries with it the weatherings from rocks, soil particles, and fertilizers. In addition, out-falls from waste water treatment plants can also be a significant source. As
discussed earlier, another major source would come from the resuspension of existing phosphorus from the bottom of an estuary. Additional sources of phosphorus include ground water seepage and atmospheric deposition. However, since
little data are available, ground water seepage and atmospheric deposition of phosphorus are not considered in this study.
There are three forms of phosphorus that are to be modeled: phytoplankton phosphorus, organic phosphorus, and inorganic phosphorus (also known as orthophosphate). Organic phosphorus and inorganic phosphorus are divided into their dissolved and particulate forms by spatially varying fractions. Part of the phosphorus released during death or respiration of phytoplankton is soluble reactive phosphorus (SRP), which can be used directly by algae. The other
fraction of the phytoplankton phosphorus must undergo a process know as mineralization before it is able to be used by




58
the phytoplankton. The dissolved form of phosphorus is also affected by the adsorption-desorption process.
Mineralization is a biological decomposition process, mediated by bacteria, which transfers the dissolved organic phosphorus to SRP. The mineralization process, will be modeled using a saturating recycle mechanism, which is a combination of a first and second order recycling, where the recycling rate is proportional to the phytoplankton biomass present (Chen and Sheng 1995). The DOP mineralization rate, is shown in Equations 3.9 and 3.14, where K83 is the dissolved organic phosphorus mineralization rate, 083 is the temperature coefficient for DOP mineralization, and Kmpc is the half-saturation constant for recycling. The C4/(Kmpc+C4) term allows first-order recycling when the phytoplankton concentration (C4) greatly exceeds the half-saturation constant, and second order recycling at low phytoplankton concentration. What this mechanism basically accomplishes is to slow down the recycling rate at low phytoplankton concentration, while not allowing the rate to increase continuously as the phytoplankton concentration increases.
The adsorption-desorption processes represent interactions between the dissolved phosphorus and the particulate phosphorus adsorbed onto suspended sediment particle in the water column. Concentrations of particulate




59
phosphorus adsorbed onto the bottom sediments can be two to three orders of magnitude that of the concentration of total phosphorus in the water column. Desorption of adsorbed
phosphorus on resuspended sediments can be a significant source of phosphorus in the water column. This process has a
reaction rate that depends on such environmental parameters as dissolved oxygen, pH, and concentrations of iron, calcium, and aluminum (Sheng et al. 1998) The reaction time scale can vary from minutes to hours. For faster reactions, the
adsorption/ desorption process are modeled as instantaneous. This means that the various forms of phosphorus react
instantaneously with any outside source of phosphorus and redistributes this new phosphorus into its equilibrium particulate and dissolved forms (Ambrose et al. 1991).
The particulate forms of phosphorus are subject to
settling and resuspension. Particulate organic phosphorus settles at a settling velocity that is the same for all organic matter, V,3 (Ambrose et al. 1991). The particulate organic phosphorus settling rate is given in Equations 3.14
and 3.21, where C8 is the particulate organic phosphorus, fD8 is the dissolved fraction of organic phosphorus, and H is the depth of water column. The resuspension of organic phosphorus, is given in Equations 3.14 and 3.21, where C8 is organic phosphorus, Eed is the erosion rate of bottom




60
sediments, Hj is the sediment column depth, CS/C se, given in mg/Kg and determined from IRL sediment samples, is the fraction of the resuspended sediment that is organic phosphorus. Further, the resuspension of inorganic phosphorus is also modeled in this way as shown in Equations 3.9 and 3.17, where C3 is inorganic phosphorus and C3/Csed, is the fraction of the resuspended sediment that is inorganic phosphorus.
3.2.4 Modeling The Nitrogen Cycle
Nitrogen enters estuaries from point and non-point sources on the land. The atmosphere is comprised of 78% of elemental nitrogen which can be a source of nitrogen through atmospheric diffusion. Other sources would include
biological fixation, the resuspension of nitrogen from bottom sediment, and ground water seepage.
There are four components of the nitrogen cycle that will be modeled: phytoplankton nitrogen, organic nitrogen, ammonia nitrogen, and nitrate nitrogen. As with phosphorus, a
fraction of the nitrogen from algal death and respiration enters the inorganic pool in the form of ammonia nitrogen. The other fraction goes into the organic pool. Dissolved organic nitrogen undergoes a bacterial decomposition, similar to the mineralization process of organic phosphorus, of which




61
the by product is ammonia nitrogen. Ammonia nitrogen is
converted into nitrate nitrogen by a process called nitrification. Nitrate nitrogen may undergo the process of
denitrif ication, which converts nitrate nitrogen into nitrogen gas. In addition, the particulate fraction of organic nitrogen can settle out of the water column, be resuspended into the water column and diffused between the water column and the sediment column, similar to that of particulate organic and inorganic phosphorus.
DON mineralization is the biological process that transforms dissolved organic nitrogen into ammonia nitrogen.
The process of mineralization will be modeled as a temperature dependent first order reaction rate, which can be spatially variable, as given in Equations 3.7 and 3.13, where K7, is the dissolved organic nitrogen mineralization rate, 67 is the temperature correction coefficient, C7 is the concentration of dissolved organic nitrogen, C4 is the concentration of phytoplankton, and Kmp, is half saturation constant for the mineralization of phytoplankton.
Nitrification, in which ammonia nitrogen is oxidized to nitrate nitrogen, requires the presence of oxygen as well as certain bacteria. This process is complex and dependent upon temperature and oxygen levels. The process of nitrification
will also be modeled as a first-order reaction as shown in




62
Equations 3.7, 3.8, and 3.12, where K12 is the nitrification rate, C, is the concentration of ammonia nitrogen, C6 is the concentration of oxygen, and KNIT is the half saturation constant for the dissolved oxygen limitation in the nitrification process.
Denitrification refers to the reduction of nitrate nitrogen to the gaseous form of elemental nitrogen. In waters with normal dissolved oxygen levels, above 4 mg/l, anaerobes use oxygen to oxidize organic material. Under anaerobic
conditions, nitrate nitrogen replaces oxygen in this process (Snoeyink and Jenkins 1980). This process occurs all of the time in the sediment layer, but only occurs at low oxygen levels in the water column. This is modeled by a first-order reaction rate as shown in Equations 3.8, 3.11, 3.18, and 3.16, where K2D is the denitrification rate, C2 is the concentration of nitrate nitrogen, and KNO3 is the half saturation constant for the dissolved oxygen limitation in the denitrification process.
In the growth of phytoplankton, both ammonia nitrogen and nitrate nitrogen are used during photosynthesis. The
preferred form of nitrogen is ammonia nitrogen. To model this, the parameter, PNH3, is used to distinguish this preference, as follows (Ambrose et al. 1991),




PN3= H NO3
NH3 (KmN + NH3 )(KmN + NO3)
KmNN(3.29) +NHj(NO3 + NH3)(KmN + NO3)
where KmN is the half-saturation constant for inorganic nitrogen uptake by phytoplankton, NH is ammonia nitrogen and NO3 is nitrate nitrogen. Figure 3.5 illustrates how the ammonia nitrogen and nitrate nitrogen concentrations affect the ammonia preference factor. Here the KmN value is set to 25 micro g/l. It can be seen that the ammonia preference is most sensitive at low levels of ammonia nitrogen or nitrate nitrogen. This preference factor is used in modeling the affect of phytoplankton growth on the concentrations of ammonia nitrogen and nitrate nitrogen. This is shown for nitrate nitrogen in Equations 3.8, where GPl is the growth rate of phytoplankton, C4 is the concentration of phytoplankton carbon, C2 is the concentration of nitrate nitrogen in the water column, and PNH3 is the ammonia preference factor. For ammonia nitrogen, this is given in Equation 3.7, where C1 is the concentration of ammonia nitrogen.




0.9
0.8-- NH3=100uo
0.7
zNH-3 =50 ugA
0 0.6
c0.5- NH3 =25 ugA
a 04
0 E
0.3
0.2 0.1
0 20 40 60 80 100 120 140 160 180 200
Nitrate Concentration (ugn)
Figure 3.5 The affect of nutrient concentration on
PN3 (Ambrose et al. 1991).
3.2.5 Modeling The Oxygen Cycle
There are five variables that are involved in the oxygen cycle. These include: phytoplankton carbon, ammonia nitrogen, nitrate nitrogen, carbonaceous oxygen demand, and dissolved oxygen.
The most obvious source of dissolved oxygen in the water column is through the process of diffusion in which oxygen gas is diffused from the atmosphere into the water column. This is modeled as shown in Equation 3.12, where C6 is the concentration of dissolved oxygen in the water column, 2 is the re-aeration rate, and C, is the dissolved oxygen




65
saturation value, which is a function of salinity, temperature and atmospheric pressure. The other major source for oxygen is the oxygen given off during the growth of phytoplankton.
Oxygen in the water column has many sinks, including, the oxidation of organic material, phytoplankton respiration, nitrification of ammonia nitrogen and diffusion. The
oxidation of the organic material in the water column is modeled as given in Equation 3.11, where K is the de-oxygenation rate, KBOD is the half-saturation constant for oxidation of CBOD, and C5 is the carbonaceous biochemical oxygen demand. Organic matter found in the water column can come from man-made products such as oil, grease, and pesticides, but also includes phytoplankton carbon, from algal death, and byproducts of denitrification. The process of respiration is an ongoing process, common to all plants and animals. Respiration is a first order temperature dependent reaction rate, modeled as in Equation 3.12, where KIR is the de-oxygenation rate.
3.3 Model Review
There are a few differences between the CH2D and CH3D nutrient models. The CH3D is a 3-D model, which calculates the vertical distribution of nutrients in the water column,




66
whereas the CH2D nutrient model just calculates the vertically averaged water column concentration. In addition to this, the CH3D has a much more detailed description of the phosphorus, nitrogen, and dissolved oxygen cycles (Sheng 1996). The
phosphorus cycle in CH3D includes such species as soluble reactive phosphorus, dissolved organic phosphorus, phytoplankton particulate phosphorus, zooplankton particulate phosphorus, particulate organic phosphorus, and particulate inorganic phosphorus. The nitrogen cycle in CH3D includes such species as soluble organic nitrogen, soluble ammonium nitrogen, nitrate nitrogen, ammonia nitrogen, particulate ammonium nitrogen, particulate organic nitrogen, phytoplankton particulate nitrogen, and zooplankton particulate nitrogen. The CH3D nutrient model models zooplankton, which grazes on phytoplankton. Zooplankton does not enter into the CH2D nutrient model, but the zooplankton grazing rate is accounted for in the CH2D phytoplankton death rate. In addition, the CH3D nutrient code models two separate layers in the bottom sediments, the aerobic and anaerobic layers, whereas the CH2D combines both layers into one.
With these advantages come an extended calibration and computational time. For this study the CH2D nutrient code will be used, due to its simplistic treatment of the nutrient cycles, relatively short calibration time and computational




67
time. The results from this study will be used to help with
the more intricate CH3D calibration ef fort (Qiu and Sheng 1999).
The component models (hydrodynamic, sediment and nutrient) included in the CH2D model contain the interactive processes as presented in the previous discussions. Changes in the hydrodynamics affect sediment transport and nutrients
distribution. Similarly, sediment transport affects nutrients distribution. Further, within the nutrient model, changes in the phosphorus and nitrogen cycles affect the phytoplankton growth mechanics, which in return affect the oxygen,
phosphorus and nitrogen concentrations. These relationships, compounded with complications associated with the numerics of
the models, make the calibration and application somewhat time consuming. But with the advective and diffusive fluxes of the flow field quantified and having a sound understanding of the
transformation processes involved in the sediment, nutrient and oxygen cycles, the model is ready to be calibrated and applied.




CHAPTER 4
MODEL SIMULATIONS
The time period of model simulations for this study spans from April 8 to May 15 (Julian Day 98.5 to 136.5), 1997. This period corresponds to synoptic trips 1 to 3 and WQMN trips 9704 and 9705. For calibration purposes, the CH2D computational grid was divided into 14 boxes. These boxes
were used to simplify the specification of initial conditions and transformation coefficients, as well as the model calibration coefficients, for the sediment and nutrient
models. These boxes correlate to the 8 segments of the Indian River Lagoon as shown in Sheng 1996. Synoptic and WQMN data were used for comparison with the model results.
4.1 Sediment Simulations
The hydrodynamic model was calibrated using water
elevations collected from Florida Department of Environmental Protection (FDEP) data stations, located throughout the IRL (Davis and Sheng, 1999). Figures 4.1-4.3 show the measured and simulated water elevations at selected FDEP stations.




69
0.8 Measured Elevation (m)
.............. Simulated Elevation (m)
0.6 0.4
0
C.G
8-0.2
0- 0
C
S -0.2
C
-0.6
-0. 8
1". I I I I I
100 105 110 115 120 125 130 135
Julian Day
0.8
0.6
0.4 0.2
t2 0
0
-0.2
0
2 -0.4
-0.6
-0.8
-1
100 105 110 115 120 125 130 135
Julian Day
0
-0.1
-0.2
,-0 ., i I. II
S 100 105 110 115 120 125 130 135
Jullan Day
Figure 4.1 Comparison of CH2D simulated water elevation vs.
measured water elevations at FDEP data stations: Ponce Inlet (Ponceinl), Mosquito Lagoon (Mosquito), and Merrit Causeway
West (Mcsywest) during Julian days 98-135, 1997.




___ Measured Elevation (m) .............. Simulated Elevation (m)

-0.1
o Aj
-0.2
-0.3
4 1I II I I
100 105 110 115 120 125 130 135
Julian Day
0
-0 1
-0.2
-0.3
-04 ____________ I , I I I I I I t I i
100 105 110 115 120 125 130 135
Julian Day
03 02 0.1 0
-0.1
S -0.2
-0.3
, -0.4 -05
0 -0.6 i
-0.7
-0.8
-0.9
100 105 110 115 120 125 130 135
Julian Day
Figure 4.2 Comparison of CH2D simulated water elevation vs.
measured water elevations at FDEP data stations: Banana River (Bananacc) Melbourne Causeway (Melbcswy) and Sebastian
Inlet (Sebasinl) during Julian days 98-135, 1997.




0.2 F Measured Elevation (m)
[ ......--- Simulated Elevation (m)

Julian Day

Figure 4.3 Comparison of CH2D simulated water elevation vs. measured water elevations at FDEP data stations: Vero Bridge (Verobrid) Ft. Pierce Causeway (Fpiercec), and Ft. Pierce Inlet (Fpiercei) during Julian days 98-135, 1997.




72
Af ter the hydrodynamic model was calibrated, the next step in the modeling process was the simulation and
calibration of the sediment transport model, since resuspended sediments could be a major source of nutrients in the water column.
Temporal fluctuations in suspended sediment
concentrations in the shallow water column are dominated by the erosion rae EsED, which is modeled as shown in Equation 3. 8. The critical shear srs, r~, the erosion rate, E0, and the settling velocity, Vset are values that must be specified for each of the segments in the model. These values were
determined from the bottom sediment characteristics as measured by the University of Florida Coastal and
oceanographic Engineering Department (Sheng et al. 1998). Each of these sediment types has its associated erosion rate, critical stress and settling velocity, as shown in Table 4.1.
Figures 4.4 and 4.5 are interpolated maps of the sediment characteristics of the Indian River Lagoon generated from the data collected from the UFCOED sediment sampling stations. Each of the 14 calibration boxes were given an average sediment type, based on the interpolated sediment map. Table 4.2 shows, for each of the 14 calibration boxes, the associated segment, the average sediment size, and the sediment type.




Table 4.1 The characteristics of the sediment of the IRL (Sheng et al. 1998).
Sediment Diameter Erosion Critical Settling
Type (mm) Rate Stress Velocity
(10-'s/m) (dyne/cm2) (cm/s) 1 D50<0.125 0.40 0.20 0.0017
2 0.125 3 0.25 4 D50>0.5 0.24 0.50 0.0045




3.22E+06
3.21 E+06 3.2E+06 3.19E+06 3.18E+06

3.17E+06 o0
-2:
-,, 0
3.16E+06 z


3.15E+06
3.14E+06 3.13E+06 3.12E+06 3.11E+06
500000

Indian River Lagoon Sediment Map 1:
Classification
1= silts (D50<0.125)
2= fine (0.125<050<0.25)
3= medium (0.25 4= coarse (Dso>0.5)
D50(mm)
0.5
0.375 0.25
0.125

520000 540000
UTM (East-West)

560000

580000

Figure 4.4 Interpolated map of the northern IRL bottom sediment median diameter D50 (Sheng et al. 1998).




Indian River Lagoon Sediment Map 2:
Classification
1= silts (Dso<0.125)
2= fine (0.125 3= medium (0.25 4= coarse (D50o>0.5

3.11E+06 3.1 E+06 3.09E+06 3.08E+06 3.07E+06 3.06E+06 3.05E+06 3.04E+06 3.03E+06 3.02E+06 3.01 E+06
3E+06

I I I I I

D50(mm)
0.5
0.375 0.25 0.125

I I I I I

540000

560000
UTM

580000
(East-West)

600000

Figure 4.5 Interpolated map of the southern sediment median diameter D50 (Sheng et al.1998).

IRL bottom

I I I I I

I I I




Table 4.2 Sediment median diameter and sediment type in various calibration boxes and segments of the IRL.
Average
Calibration Associated Sediment Sediment
Box Number Segment Diameter Type
(mm)
1 1 0.1535 2
2 1 0.2053 2
3 2 0.1949 2
4 2 0.1430 2
5 2 0.1437 2
6 3 0.1746 2
7 3 0.2508 3
8 3 0.2298 2
9 4 0.1777 2
10 4 0.2541 3
11 5 0.2952 3
12 6 0.1760 2
13 7 0.2932 3
14 8 0.2866 3




4.2 Sediment Simulation Results
The CH2D model is similar to the CH3D model in that they both use Equation 3.22 as bottom boundary condition to model sediment transport. There are, however, several differences between the models. In order to solve for the bottom shear stress, rb, which is a combination of the stress created from the current and that created by the waves, the CH3D sediment model uses the CH3D hydrodynamic model, whereas the CH2D sediment model uses the CH2D hydrodynamic model. Both models use SMB model to calculate the wave-induced bottom stress and follow Sheng and Lick (1979) to calculate the combined current-wave bottom stress. CH3D simulation uses much more comprehensive bottom boundary conditions to specify such parameters as sediment type, critical stress, Tc, erosion rate, Eo, and settling velocity, V,,, for each and every grid cell. The present CH2D sediment simulation, however, specifies the bottom boundary conditions according to the 14 calibration boxes. In addition, CH2D is a vertically integrated model, which means that the sediment model gives an average value for suspended sediment in the water column for each grid cell. CH3D gives a vertical distribution of suspended solids in the water column. For these reasons, the results from the CH2D and CH3D models should differ, with the




78
CH3D sediment model results being more accurate and detailed than the CH2D results. Even though the CH2D model may have less resolution than CH3D, its advantage is that it takes much less time (5 to 10 times less) than CH3D to simulate the same time period. With this advantage, CH2D is suitable for much longer term simulations than CH3D.
The results of the sediment simulation are shown in terms of the time series of TSS concentrations for each of the WQMN stations, as shown in Figures 4.6-4.8. The CH2D results are shown with the preliminary CH3D results obtained by Sun and Sheng (1999) and the data collected from the WQMN over the same period of time. The measured data from the WQMN trips 9704 and 9705 are included in the figures to show correlation with the measured data.
The CH2D model output compares well with the preliminary CH3D model output and WQMN measured data. To quantify the difference between model results and measured data, one can use the root mean square error (RMS):
RM =imeas (4.1)
Fi=1
where Ximeas is the measured data, Xis, is the model simulation results, and n is the number of data points compared.




90 121 80
70 ,- 60 E 50
0 40 U)
1- 30 20 10

100 105 110 115 120 125 130 135
Julian Day

Julian Day Julian Day
Figure 4.6 Comparison of simulated suspended sediment concentrations by CH2D and CH3D vs. WQMN measured data (0 indicates upper water column, indicates lower water column data) in the northern IRL.

. Ll- L




80
90 IRJO1 g- R Ch2d 80 Ch3d.
70 Ch3d.
O WQMN.
_-60 WOMN,
E 50
40
-30
20
10
0 100 105 110 115 120 125 130 135
Julian Day
IRJO4 90
80
70 60
E 50 0)40 I U)
30 20
10
0 100 105 110 115 120 125 130 135
Julian Day
90 IRJO7 80
70
60
E 50 u) 40
30
20 ,,
100 105 110 115 120 125 130 135
Julian Day

IRJ10

IRJO5

IRJ12

Julian Day

HUS

100 105 110 115 120 125 130 135 u 100 105 110 115 120 125 130 135
Julian Day Julian Day
Figure 4.7 Comparison of simulated suspended sediment concentrations by CH2D and CH3D vs. WQMN measured data (E indicates upper water column, O indicates lower water column data) in the southern IRL.




Vil

-- Ch2d
- Ch3db Ch3d, O WQMNb

60 0 WOMN
E 50
W40I '- 30 ll
100
20 -1 A'044 4,
0 100 105 110 115 120 125 130 135
Julian Day

j

VO5

ML02

Julian Day

Julian Day Julian Day
Figure 4.8 Comparison of simulated suspended sediment concentrations by CH2D and CH3D vs. WQMN measured data (E indicates upper water column, O indicates lower water column data) in the Banana River and the Mosquito Lagoon.




82
For the first analysis, the CH2D sediment model results are compared to the preliminary CH3D sediment model results. In this comparison the preliminary CH3D model results are used as the Xmeas and the CH2D results are used as the Xsim;. For the second analysis, the CH2D model results are compared to the WQMN measured data. Here the CH2D model results are the Xsim and the measured data are the Ximeas. In Table 4.3 the RMS error is shown for each of the analyses performed for each of the WQMN stations. The percent RMS error is the total RMS error normalized by the average measured data.
As shown in the table, the CH2D sediment model results agree better with the collected data than they do with the preliminary CH3D sediment model results. The difference
between the CH2D and the CH3D results may be due to the fact that the CH3D sediment model is still in the preliminary calibration stages. The CH2D model produces results with relatively low RMS error vs. data, except for stations, 107, 123, IRJO5, IRJO7, GUS, Vll, B02, and B04.
For stations 123, B02, and B04, as illustrated in Figures 4.6 and 4.7, the measured data indicate that a resuspension event occurred around Julian day 113, while the CH2D model results show higher suspended sediment concentration occurring on or around Julian day 110. A possible cause for this
discrepancy is that the sediment settling velocity in the




Relative RMS
Percent RMS Relative RMS Percent RMS
. error
Station error error error
(Ch2d-data)
(Ch2d-Ch3d) (Ch2d-data) (Ch2d-data) (mg/1)
102 64% 6.84 37%
107 97% 6.79 126%
110 103% 3.43 47%
113 104% 1.43 31%
116 113% 4.26 75%
I21 96% 5.24 69%
I23 85% 13.00 88%
I27 102% 4.25 40%
IRJ01 101% 3.04 31%
IRJ10 104% 4.85 32%
IRJ04 102% 4.80 37%
IRJO5 106% 19.55 184%
IRJO7 105% 10.78 97%
IRJ12 103% 8.25 96%
GUS 106% 6.26 224%
HUS 110% 2.28 64%
V05 142% 5.68 31%
ViI 112% 15.68 101%
V17 87% 9.61 61%
MLO2 117% 6.03 70%
B02 125% 4.48 102%
B04 123% 3.35 109%
B06 107% 4.36 90%
B09 124% 5.96 68%

Table 4.3 The

results of the sediment RMS

error analysis.




84
model is too high for these stations and that the suspended sediment concentration measured on Julian day 113 consists of sediments still resuspended from the event that occurred on Julian day 110. Further, at these stations, the measured data points that do not correspond well to the model are those taken from the lower-level sampling station, at which higher suspended solid concentrations are usually measured. Since the CH2D is a vertically-integrated model, the vertical distribution of suspended sediment concentrations can not be simulated.
At stations V11, and IRJ07, shown in Figures 4.7 and 4.8, the CH2D model underestimates the measured data and the CH3D results, while at station IRJO5, as shown in Figure 4.7, the CH2D model overestimates the measured data and the CH3D model results. These errors can be attributed to differences between how CH3D and CH2D assign bottom boundary conditions (sediment characteristics). CH3D assigns an individual bottom boundary condition specific to each and every cell in the grid, while CH2D assigns an average bottom boundary condition to each of the 14 calibration boxes. Because of this, there are going to be areas in the computational grid in which the CH2D model results do not correlate well with the measured data or the CH3D model results.




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The percent RMS errors shown for stations 107 and GUS are large, even though the model results appear to correlate very well with the measured data and the CH3D model results. Since the measured data at these stations are very low, the percent RMS error is high, because the small RMS errors calculated are being normalized by an even lower measured data.
In addition to the RMS error analysis, a correlation analysis was performed on the sediment results to illustrate the influence, or the lack there of, of the wind speed on the total suspended solids concentrations. The correlation
function is as follows,
Cor Cov(X,Y) (4.2)
ax "y
where,
-1 Cor,y :!1 (4.3)
and,
C0V(X, Y)=Il(xi P)Y i-my ,(4.4) n =1
where Cov(XY) is the covariance, qx, and ay are the standard deviations, and yx, and py are the mean values for the data sets being correlated. If the data sets are perfectly




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correlated, then Cory is equal to 1. If the data sets are perfectly uncorrelated then Cor., is equal to -1.
For this analysis the average concentration of total suspended solids for each segment of the IRL were correlated to the average wind speed data. This analysis was performed on the data collected over the time period being simulated. The results of this correlation analysis are shown in Table 4.4. Because the correlation is also a function of time, these results could be stronger during another time period in these segments. It is shown in Table 4.4 that in segments 1, 4, 5, 6, and 8 where there is high tidal influence, the TSS does not correlate well with the wind speed. In these
segments the erosion rate is dominated by the tidal current induced shear stress. As illustrated in Figure 4.9, the TSS in segments 1 and 8 have diurnal fluctuations, indicating the high tidal influence. Also demonstrated, is the TSS's
relative low correlation with the wind speed. These results
indicate the importance of accurately modeled tidal surface elevation on the sediment results in these segments.
For segments 2 and 3, where there is relatively low tidal influence, the TSS correlates well with the wind speed. As illustrated in Figure 4.10, there are no diurnal fluctuations
in the TSS. These results indicate the importance of accurate wind speed data on the sediment results in these segments.




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Table 4.4 Results of the wind and modeled TSS correlation analysis for each segment of the IRL.
Correlation
Segment Number Coein
Coefficient
1 0.075
2 0.364
3 0.454
4 0.204
5 0.015
6 0.187
7 0.314
8 0.076

--TSS for Segment 1 Cor= 0.075 STSS for Segment 8 Cor 0.076
- Wind Speed
- ," 4

.

0 I0 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
Julian Day
Figure 4.9 Wind speed and simulated TSS concentration in segments 1 and 8 of the IRL during Julian days 120-135, 1997.

80 70 60 50 40
3 30




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-----TSS for Segment 2 Cor = 0.364
TSS for Segment 3 Cor = 0.454
50 _-Wind Speed 5
40 4
30 3
I- C
20 2
10 1
0. 0
120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 Julian Day
Figure 4.10 Wind speed and simulated TSS concentration in segments 2 and 3 of the IRL during Julian days 120-135, 1997.
4.3 Nutrient Simulations
After the sediment model was calibrated, the nutrient model was run. The simulation period was the same as in the sediment model simulations. Table 4.5 show the water quality reaction coefficients that were involved in the nutrient model. The tables show the coefficients recommended by the US Environmental Protection Agency's WASP model (Ambrose et al. 1991), those used in the Robert's Bay WASP model application (Sheng et al. 1995b), the IRL's CH3D nutrient model application, and the CH2D nutrient model used in this study.