• TABLE OF CONTENTS
HIDE
 Front Cover
 Title Page
 Acknowledgement
 Table of Contents
 List of Tables
 List of Figures
 Abstract
 Introduction
 Some IRL data and data analysi...
 Numerical models
 Model simulations
 Conclusion and recommendations
 Appendix
 Reference
 Biographical sketch














Title: 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
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Language: English
Creator: Melanson, Joel
Publisher: Coastal & Oceanographic Engineering Dept. of Civil & Coastal Engineering, University of Florida
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Table of Contents
    Front Cover
        Front Cover
    Title Page
        Page i
    Acknowledgement
        Page ii
    Table of Contents
        Page iii
        Page iv
    List of Tables
        Page v
    List of Figures
        Page vi
        Page vii
        Page viii
        Page ix
    Abstract
        Page x
        Page xi
    Introduction
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
        Page 8
        Page 9
        Page 10
        Page 11
        Page 12
    Some IRL data and data analysis
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
        Page 34
        Page 35
        Page 36
        Page 37
        Page 38
        Page 39
    Numerical models
        Page 40
        Page 41
        Page 42
        Page 43
        Page 44
        Page 45
        Page 46
        Page 47
        Page 48
        Page 49
        Page 50
        Page 51
        Page 52
        Page 53
        Page 54
        Page 55
        Page 56
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
        Page 62
        Page 63
        Page 64
        Page 65
        Page 66
        Page 67
    Model simulations
        Page 68
        Page 69
        Page 70
        Page 71
        Page 72
        Page 73
        Page 74
        Page 75
        Page 76
        Page 77
        Page 78
        Page 79
        Page 80
        Page 81
        Page 82
        Page 83
        Page 84
        Page 85
        Page 86
        Page 87
        Page 88
        Page 89
        Page 90
        Page 91
        Page 92
        Page 93
        Page 94
        Page 95
        Page 96
        Page 97
        Page 98
        Page 99
        Page 100
        Page 101
        Page 102
        Page 103
        Page 104
        Page 105
        Page 106
        Page 107
        Page 108
        Page 109
    Conclusion and recommendations
        Page 110
        Page 111
        Page 112
        Page 113
    Appendix
        Page 114
        Page 115
        Page 116
    Reference
        Page 117
        Page 118
        Page 119
        Page 120
    Biographical sketch
        Page 121
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 .


.II1


Sv


. . . vi

x




8
. . . 10
. . . 1
. . . 8
. . . 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


S40
S40
S44
52
S52
S57
S60
S64
S65

S68
S68









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 . . . . .


S . . . 77
. . . . 88
. . . . 91
. . . 106

. . . 110
. . . 110
. . . 112

. . . 114

. . . 117

. . . 121














LIST OF TABLES


Table page

Table 2.1 Detailed station information for the WQMN. .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 RMS 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.


page
. 2
.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
. . 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 PN3. 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 D0. . . . . . . 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 vertically-integrated two-

dimensional 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.
























*Tallahassee
," --_ ,,


'Jacksonville


'Gainesville


/'?;Tamp
i'


0i,


Study Area


"Daytona Beach

ptusville
Orlando
"Melbourne
a


S Fort Pierce


West Pa m Beach

*Naples
L.._.


'Miami


-7~-


00 0 200 400 600 Kilometers
I =MEI I


A map of Florida and the IRL study area.


*I
|


Cities
IRL Study Area
State


------------_ '.


-I
y-~b~b
-_ _~
II.~
r


Figure 1.1









Ponce Inlet


Mosquito Lagoon






-- Banana River








-- Sebastian Inlet


r

S





IRL Shoreline


Ft. Pierce Inlet


St. Lucie Inlet


10 0 10 20 Kilometers
II


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 IRL-

SWIM (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 effects

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-relationships

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 hydrodynamic-

sediment-nutrient model to the IRL. Concurrent effort on the








10

development and application of a 3-D hydrodynamic-sediment-

nutrient and a light model are being carried out in Dr.

Sheng's group.



1.3 Scope of Study



The scope of this thesis includes the following:

* 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 bio-

geochemical 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 IRL Data



The data needed to conduct the 2-D IRL hydrodynamics-

sediment-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 forcing 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 forcing 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


VS<


10


40 Kilometers


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


"M 1 w














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
IRJ04 560361 3063293 366 16
IRJ05 561613 3059484 374 15
IRJ07 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
V11 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 WQMN sampling

schedule.




























N


W4E

\ it
S





SIRL Shoreline
A UFCOED 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.









SSegment 1


Segment 2----,


S/Segment 3
Segment -- ',

Segment 5

\ Segment 6

Segment 7

W m E

s Segment 8

'\

IRL Shoreline

20 0 20 40 Kilometers
Figure 2.3 The segments of the IRL (Sheng 1996).






























N14

g 1 A


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 J
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 3137250 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













































A
1/


10


Synoptic Stations 1-30
IRL Shoreline


20 Kilometers


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 J
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 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. Refer to Figure 2.1 for 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 figure

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

C 15__________ ---Station 102
S-- Station 107
10 -- Station 107
10 --- Station 110
S-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


A-95 J-95 A-95 0-95 D-95 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
Date


Figure 2.7 Rainfall Data for the Titusville and Melbourne
area.

















40 - ,


39







20




10 ~-- Station 123
--- Station 127
-- Station IRJ01
Station IRJ04
----Station 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


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.























1.000

' 0.800

0 600

0.400

0.200

0.000


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 -______

OApr-97
0.6 M -97


0.5 -


0.4 -


0.3


0.2 -


0.1 -


0
2 4 5
Segment

Figure 2.11 Phytoplankton carbon measured in segments 2, 4,
and 5 of the IRL during April and May 1997.












OApr-97
*May-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.


- Apr-97


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


0.160

0.140

0.120

0.100

S0.080
z
0.060

0.040

0.020

0.000


0.090
0.080
0070
0.060
0.050
0.040
0.030
0.020
0.010
0.000


i


I


I


I



























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 2, 4,

and 5 of the IRL during April and May 1997.


25 000


20.000


15.000


10.000


5 000 -


OApr-97

EMay-97


Segment

Figure 2.15 Total suspended sediment measured in segments 2,

4, and 5 of the IRL during April and May 1997.


0.000 ---










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 NN 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
10900 *-------------------------------------------------------------------------
0 900
*WQMN -9704
0.800 -UWQMN -9705

0700

0 600

0.500

0.400

0.300

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


0 020
z
z


0.000 ----
1 2 3 4 5 6 7 8
Segment

Figure 2.17 Nitrate nitrogen concentrations at eight segments

in the IRL during April and May of 1997.







0060




0050 -WQOMN -9704
DWQMN -9705


0.040



o0 030 -





0.020




o.oo010




0 000
1 2 3 4 5 6 7 8
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

MWWMN -9704
1 8000 OWQMN -9705


14 000


12 000


10.000


8.000


6000


4000


2.000



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


1WOMN -9704
OWQMN -9705
1.400


1.200


1.000

z
S0.800


0.600


0.400


0.200


0.000
1 2 3 4 5 6 7 8


Figure 2.20 Total nitrogen

in the IRL during April and


Segment

concentrations

May of 1997.


at eight 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 Hvdrodvnamic 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:


do du
+- +
dt dx


dU
+t


dv
--=0
dy


d (UU a (UV
dx H ) y H


lo


(3.1)


1bx
P0


(3.2)


+ +d AH d
d^ fx dy dy[ Hy
H dPa gd gH2 dp
p x g dxgH
po 0 x dx 2p, 9x


dv a UVd 9VV
dt dx H )dy H


f sy Tby
=fU +--
Po Po


(3.3)


d( 9V 9 ( 9V
+(A7 dx A ) d+- AH

H dP'a 0 gH2dp
po Hy dy 2 p 9y


dT 8 d q
+ d(UT)+ (VT)=-
dt dx dy po



9S 9 9 e
d + (US)+ (VS)=
dt dx dy pA


qb
P-
Ps


- K+I + (Ku
dx H dx) +yd H


--eb+ K + K
p, dx ax dy Hay


(3.4)




(3.5)








42

where U and V, are the vertically-integrated velocities in

the x and y directions, and are defined as Jf-udz and fS_,vdz,

respectively, ( is the free surface elevation, H=(+h and is

the total depth, (Tr, qs, es) represent the surface fluxes of

(momentum, heat, salinity), (Tb, qc, 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 andK,

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.













N'


' '
N


S, IRL Shoreline
A Grid


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:


9HC (dUCt 9VC, (d2HC, dHC,
dHCi + C vc. -D 'HC+ +R. (3.6)
9t dx dy x) d2 2


where Ci 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 Ri'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

Cg Organic Phosphorus


Water Column:


R, = Da(l-P )c, N 4 +K7(&-20) KK C4C -G ,C,

death ON mineralization growth
r-2K ) K"C C6 C Ic f -C~
1212 nitrification vertical diffusion
nitrification vertical diffusion


(3.7)











R, = K -2K + C, Gai PNH3 )C -K K 2-20) K NO, C4

nitrification growth denitrification (3. 8
E d :
-H (C f, -C2 f2 )

vertical diffusion



R = DaPl pc fo )C + 3 K ,, 4 C, GlaC4

death DOP mineralization growth
(3.9)
Esed C3j
+ H H Cd--
HH,[C )
resuspension



V
R4 = GIC4 aDp ,C 3 .10)
growth death settling



R5 = a cKDC4 K,8D -20) Co fD 5
Ko D +C6) H
death oxidation
5 32 K O(T20) KNO3 C
414 KO +C6 (3.11)
denitrification

+- (i fDS;)Cs '- (C5fS Cs, f,5

resuspension diffusion




R6 = K(Cs -C6)-K,0r-2o). C 64 K112T-20) Cl
Ko o + C6 14 KNr +CO6
rearation oxidation nitrification
(3.12)
(C, C )20) +Gp + 1 PN3) C4 K,, -20 )C,
sediment demand phytoplan32 kto48 14growth 32respiration
HH .j ( 6 "6 i" -P12 14412 12 4
sediment demand phytoplankton growth respiration












( V, (1 f 7)
R7 = Dpa,,afoC4 + K 71r-20) O m C7 C7
71V7 \. \+C,) H 1
(K.Pc + C4 H
death ON mineralization settling
(3.13)
+ ed C71
HH CedJ
resuspension



rC V, ( f },
R8 = DplapcfopC4 + K,3(T-2o K) C4 C8 v C8
KPC + C4 H
death DOP mineralization settling
(3.14)
+ E ,, (C ,8i
HH, j CCd)
resuspension



Sediment Column:

= f(rT-2o)/_ (1 r(r-2o)C Edi2 Cl( {_r t )
RI = KPZD PZD \2)(- fn)C4j + KONDOO 7C + 7 2 C1 2fJ (3.15)
algal decomposition mineralization vertical diffusion




R2=-K -20C + K(Cf, -Cf, ) (3.16)
denitrification vertical diffusion




RK, = KPoD Co)a(l f, )C, + KOPDOP20fD DSCSj \
Hi (C .ad (3.17)
algal decomposition mineralization resuspension



R, = a, aKO(T-20)Cr K (T-20)C, 32 T (-20)rC
ZD oc' Pz ZD 4j DS DS 4 14'" 2D 2D 0 2j
decomposition oxidation denitrification
v( Cf +(3.18)
+-V, (1- f2 5 ) SV,z E2 D-CjDi)
s+ e C -r espi dffso
settli resuspens ion diffusion
settling resuspension diffusion











R6 =-K DSDS C20)5 -- Hi(c C
R 4 = K s" -2 )Cr j 6 6j) /" ( -2 o)
1


oxidation


diffusion


r(T-20). T y (-" C7 (T-20) E d, 1 gt3 -- fD7 C7
R,7 = K PZ)PZD a ,nc io- K OND, OO N-Ci 2 C7

algal decomposition mineralization resuspension settling



f(T-20). (T.. _. (T-0{ E,. C cj V" (1- fD)
R,8 = KZD PZD oaPC opC4 KOPDOOPD JD8j Sj -H2 + V3 C8
", \C.d) ,
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
Q Definition and Units
Coefficient
E, Temperature coefficient for nitrification
0, Temperature coefficient for endogenous respiration
p Temp coef for denitrification
67, Temperature coefficient for dissolved organic phosphorus mineralization
OE Temperature coefficient for Dissolved organic phosphorus mineralization
On Temp Coef for organic carbon oxidation in the water column
6Ds Temperature coefficient for organic carbon oxidation in the sediment column
ONo Temperature coefficient for organic nitrogen decomposition
eOpr Temperature coefficient for Organic Phosphorus decomposition
pzp_ Temperature coefficient decomposition rate for Phytoplankton decomposition
)s Temperature coefficient for DO diffusive exchange
PNH Ammonia preference factor
Solari Maximum daily light intensity (Ig/day)
Vr 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
CWQ Definition and Units
Coefficient
An, Phytoplankton nitrogen-carbon ratio (mg N/mg C)
Aoc Oxygen to carbon Ratio (mg O/mg C)
Ape Phosphorus to carbon ratio (mg P/mg C)
Cass Fraction of inorganic phosphorus in the sediment layer (kg/mg)
C7iS Fraction of organic nitrogen in the sediment layer (kg/mg)
Caics Fraction of organic phosphorus in the sediment layer (kg/mg)
D. Phytoplankton death rate (day")
Ef,, Diffusive exchange coefficient (m7/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
fS Fraction of dissolved CBOD in the water column
fi Fraction of dissolved CBOD in the sediment layer
fd Fraction of dissolved organic nitrogen in the water column
f7i, Fraction of dissolved organic nitrogen in the sediment column
fd8 Fraction of dissolved organic phosphorus in the water column
f8ia Fraction of dissolved organic phosphorus in the sediment column
fon Fraction of dead and respired Phytoplankton recycled to the nitrogen pool
foQ Fraction of dead and respired Phytoplankton recycled to the phosphorus pool
Gpl Phytoplankton growth rate (day')
Hi Thickness of active sediment layer (cm)
K Nitrification rate in the water column (day')
K12i Nitrification rate in the sediment column (day')
K c Phytoplankton maximum growth rate (day')
k p Phytoplankton death ratio (day')
K, Phytoplankton endogenous respiration (day')
K, Re-aeration rate at 20uC (day-)
K2p Denitrification rate (day')
Ky, Organic nitrogen mineralization rate at 20uC (day')
K83 Dissolved organic phosphorus mineralization rate at 20uC (day1)
KB[o Half-saturation constant for oxidation of CBOD (mg 0/ L)
K, Light attenuation coefficient due to phytoplankton (m O2/mg Chl-a)
K, De-oxygenation rate at 20uC (day1)
KDs 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
KMpC 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)
KoND Organic nitrogen decomposition rate (day')
KopD Organic Phosphorus decomposition rate (day')
Kp7n 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, Esed,

is modeled as follows (Sheng 1986b),

Esed = Eo(r- ), (3.22)


where Eo is the erosion rate coefficient, T, 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


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):


GP= Glmax(20)f(GRT,, G G,, G),


(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. G,, 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),(, (3.25)

where 08 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):










GRI = f exp -exp(eD exp (3.26)





where f is the fraction of day light during the day, Ke is the

light attenuation, based on the phytoplankton population, Io

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, GR becomes,

DIP
G = +DIP (3.27)
R KmP+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,


GN DIN (3.28)
SKmN + 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 Km set to 25

pg/l, and Kmp is set to 1 ug/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.




1i
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
Gm (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 Kmp is the

half-saturation constant for recycling. The C4/(Kc+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, Vs3 (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, Cg/Csed, 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

denitrification, 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 K71 is the

dissolved organic nitrogen mineralization rate, 6i 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 K,2 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),










S.NO3
P NHB( N)3
3 = NH3 (KmN + NH3)(KmN + NO3)
(3.29)
Km
+NH(NO3 +NH3)(KN + NO3)


where KmN is the half-saturation constant for inorganic

nitrogen uptake by phytoplankton, NH3 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 Gl 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 C, is

the concentration of ammonia nitrogen.















0.9 1 NH3 200 u

0.8
0.8 NH3 =100 ug/1

& 0.7
S1 \ NH3 =50 ug/
S0.6

0.5 NH3= 25 ug/

a 0.4

0. NH3-= 10 ugA

0.2

0.1


0 20 40 60 80 100 120 140 160 180 200
Nitrate Concentration (ug/)

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, K2 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 effort (Qiu and Sheng

1999).

The component models hydrodynamicc, 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

S4 1 00 1 0 110 11 120 12 13

0
0
8 -0.2

-0.4

-0.68 t ; i

1 I I I I It

100 105 110 115 120 125 130 135
Julian Day



0.8

0.6

0.4



F 4 Comparison o C simated wat elevationvs.
0.2
t2 0
0

-0.2

2 -0.4

-0.6 i i

-0.8
-1
100 105 110 115 120 125 130 135
Julian Day




0





-0.1





D .



,-I0, . I i i II I

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




-0.2




-0.3




100 105 110 115 120 125 130 135
Julian Day




0




-0 1




-0.2




-0.3 -



-04 ______ I ,_,__ ,_ I ,_ I I I I t I i
100 105 110 115 120 125 130 135
Julian Day




03
0.2
0.1
0
-0.1
Julian ,

-0.2
-0.3
-0.4
-05
U -0.6 : i
-0.7( (
-0.8 in during Julian days 98-135, 1997.
-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 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

After 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 rate, EED, which is modeled as shown in Equation

3.8. The critical shear stress, rz, 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
(10-6s/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

4--
3.17E+06 -

-
3.16E+06 -

-
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 (D o<0.125)
2= fine (0.125 3= medium (0.25 4= coarse (Do5>0.5)






D50(mm)
0.5
0.375
0.25
0.125


520000 540000
UTM (East-West)


560000


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


IRL bottom


580000

















Indian River Lagoon Sediment Map 2:
Classification

1= silts (D o<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, z~, 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):

n" 2

RMS imeas (4.1)
Fn i=1

where Ximes is the measured data, Xisi is the model simulation

results, and n is the number of data points compared.















































90 121
80
70
e 60
E 50
0 40
1- 30
20
10
n


100 105 110 115 120 125 130 135
Julian Day


Julian Day Jullan Day

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


. Ll- Lfe^






























IRJO4 IRJO5


50 I50
8 40 40
30 30
20 20
10 10
0 100 105 110 115 120 125 130 135 100 105 110 115 120 125 130 135
Julian Day Julian Day


90 IRJO7 90 IRJ12
80 80-
70 70
60 60
50 50
CO 40 (40
30 30
20 20
10 10

100 105 110 115 120 125 130 135 100 105 110 115 120 125 130 135
Julian Day Julian Day


90 GUS 90 HUS
80 80
70 70
60 60
S50 W50
M 40 '40
30 30
20 20
10 10
0 100 105 110 115 120 125 130 135 0 100 105 110 115 120 125 130 135
Julian Day Julian Day

Figure 4.7 Comparison of simulated suspended sediment
concentrations by CH2D and CH3D vs. WQMN measured data (O
indicates upper water column, O indicates lower water column
data) in the southern IRL.













V11


-- Ch2d
-- Ch3d,
Ch3d.
0 WQMNb


V05


I


, 60 WQMN,
? 50
O 40
30 -
20
10
100 105 110 115 120 125 130 135
Julian Day


V17


M L02


Julian Day


90
80
70
60
50
040
-30
20
10


SB04


S/
-

-
r %f~Jt }o.. ^ L. .0


0 01 105 1 10 1 15 120 125 130 13
Jullan Day


90o B09
80
70
60 -
50-
U)40
30
20
10
10010511011512012513013510010511011512012513013


100 105 110 115 120 125 130 135 100 105 110 115 120 125 130 135
Julian Day Julian Day
Figure 4.8 Comparison of simulated suspended sediment
concentrations by CH2D and CH3D vs. WQMN measured data (O
indicates upper water column, 0 indicates lower water column
data) in the Banana River and the Mosquito Lagoon.


L S


"~s~"l~~'`- "-`~"'~


rl= f r








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 Ximeas and the CH2D results are used as the Xsi. For the

second analysis, the CH2D model results are compared to the

WQMN measured data. Here the CH2D model results are the Xisim

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, IRJ05, IRJ07, 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 errr error
(Ch2d-data)
(Ch2d-Ch3d) (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%
121 96% 5.24 69%
123 85% 13.00 88%
127 102% 4.25 40%
IRJ01 101% 3.04 31%
IRJ10 104% 4.85 32%
IRJ04 102% 4.80 37%
IRJ05 106% 19.55 184%
IRJ07 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%
ML02 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 IRJ05, 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.








85

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,

Cov(X,Y)
Cor = C(,) (4.2)
x,y =
Ox ay


where,

-1K Cor,, i1 (4.3)

and,

1 (4.4)
Cov(X,Y)- (x- x )(Y-y, 4.4)
in =1



where Cov(X,Y) is the covariance, aq, and o, are the standard

deviations, and Px, and p, are the mean values for the data

sets being correlated. If the data sets are perfectly








86

correlated, then Cor 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.











Table 4.4 Results of the wind and
modeled TSS correlation analysis for
each segment of the IRL.


Correlation
Segment Number Coe
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


0 1- I --ol .--i.- -I .-- 0
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.













60 6
-X-TSS for Segment 2 Cor = 0.364
--- TSS for Segment 3 Cor = 0.454
50 -Wind Speed 5


40 4


30 31










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.




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