Marina Water Quality Models
Prepared for:
U.S. Environmental Protection Agency
Region IV
Atlanta, Georgia
Prepared by:
Terra Tech, Inc.
Fairfax, Virginia
EPA Contract #68-C9-0013
Work Assignment Numbers 1-62 and 2-35
February 28, 1992
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EPA cK)*/£-°IZ
Marina Water Quality Models
Prepared for:
U.S. Environmental Protection Agency
Region IV
Atlanta, Georgia
Prepared by: Java***'
Tetra Tech, Inc.
Fairfax, Virginia
EPA Contract #68-C9-0013
Work Assignment Numbers 1-62 and 2-35
February 28, 1992
-------
TABLE OF CONTEiNTS
Page
EXECUTIVE SUMMARY xiii
1. INTRODUCTION 1-1
1.1 Background 1-1
1.2 Objectives 1-3
1.3 Report Organization 1-3
2. MODEL SELECTION AND REVIEW 2-1
2.1 Model Sources 2-1
2.2 Model Attributes 2-1
2.2.1 Simple Models 2-2
2.2.2 Mid-range Models 2-10
2.2.3 Complex Models 2-12
2.3 Short-listed Models 2-18
2.3.1 Selection Criteria 2-18
2.3.2 Models Selected 2-19
3. MODEL CAPABILITIES 3-1
3.1 Model Overview 3-1
3.1.1 Data Requirements 3-2
3.1.2 Operating Costs 3-2
3.2 Model Appropriateness 3-4
3.2.1 Marina Type 3-6
3.2.2 Constituent Modeled 3-7
4. RECOMMENDED MODELS 4-1
4.1 Selection Criteria 4-1
4.2 Models Selected 4-4
4.2.1 Simple Model 4-4
4.2.2 Mid-range Models 4-5
4.2.3 Complex Model 4-6
5. DATA COLLECTION PLAN 5-1
5.1 General Considerations 5-1
5.1.1 Study Objectives 5-2
5.1.2 System Characteristics 5-2
5.1.3 Data Availability 5-2
5.1.4 Model Selection 5-2
5.1.5 Confidence 5-3
5.1.6 Resources 5-3
iii
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TABLE OF CONTENTS (cont'd.)
5.1.7 Existing Versus Proposed Marina 5-3
5.2 Types of Data 5-4
5.2.1: Reconnaissance and/or Historical Data 5-4
5.2.2 Boundary Condition Data 5-4
5.2.3 Initial Condition Data 5-5
5.2.4 Calibration/Evaluation Data 5-5
5.2.5 Post-Audit Data 5-6
5.3 Model Data Requirements 5-6
5.3.1 Bathymetry Data 5-6
5.3.2 Transport/Hydraulic Data 5-7
5.3.3 Meteorological Data 5-9
5.3.4 Water Quality Data 5-10
5.4 Sampling Guidelines for Existing Marina 5-12
5.4.1 Spatial Coverage 5-12
5.4.2 Constituents Sampled 5-15
5.4.3 Sampling Locations 5-17
5.4.4 Sampling Time and Frequency 5-17
5.5 Sampling Guidelines for Proposed Marina 5-19
5.5.1 Spatial Coverage 5-21
5.5.2 Constituents Sampled 5-21
5.5.3 Sampling Locations 5-22
5.5.4 Sampling Time and Frequency 5-22
6. MODEL APPLICATION 6-1
6.1 Marina Description 6-1
6.1.1 Indian Mills Marina 6-1
6.1.2 Beacons Reach Marina 6-3
6.1.3 Gull Harbor Marina 6-4
6.2 Marina Data Review 6-8
6.2.1 Indian Mills Marina 6-9
6.2.2 Beacons Reach Marina 6-10
6.2.3 Gull Harbor Marina 6-10
6.3 Marina Application 6-11
6.3.1 Simple Model 6-11
iv
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TABLE OF CONTENTS (cont'd.)
6.3.2 Mid-range Models 6-24
6.3.3 Complex Model 6-30
7. CONCLUSIONS 7-1
REFERENCES 8-1
APPENDICES
APPENDIX A Annotated Input Files for Recommended Models
APPENDIX B User's Guide for Recommended Models
APPENDIX C Contact List
APPENDIX D Dye Concentration Contours
APPENDIX E Case Study for a Proposed Marina
APPENDIX F Sensitivity Analysis
v
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LIST OF FIGURES
Page
FIGURE 2-1 Representative Semi-enclosed Marina Basin 2-5
FIGURE 2-2 Representative Open Marina Basin (Site A) 2-7
FIGURE 3-1 Processes Affecting Dissolved Oxygen 3-8
FIGURE 5-1 Oxygen Balance 5-13
FIGURE 5-2 Sampling Scheme for Existing Marina 5-14
FIGURE 5-3 Sampling Scheme for a Proposed Marina 5-20
FIGURE 5-4 Potential Monitoring Sites for Proposed and Existing Marinas 5-20
FIGURE 6-1 Indian Hills Marina 6-2
FIGURE 6-2 Sampling Statioins and WASP Model Application
to the Beacons Reach Marina 6-5
FIGURE 6-3 Sampling Station and WASP Model Application
to the Gull Harbor Marina 6-6
FIGURE 6-4 Graphical Method of Segmentation of a Water Body 6-26
FIGURE 6-5 Hunting Creek Showing Model Segments 6-26
FIGURE 6-6 Graphical Representation of Hunting Creek
Segmentation 6-28
FIGURE 6-7 Predicted and Observed Dye Concentration
at Indian Hills Marina 6-34
FIGURE 6-8 Predicted and Observed DO Concentration
at Indian Hills Marina (WASP Model) 6-37
FIGURE 6-9 Predicted and Observed DO Concetnration
at Indian Hills Marina (WASP Model) 6-41
FIGURE 6-9 Model Calibration Using Dye Data at
Beacons Reach Marina 6-43
vi
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LIST OF FIGURES (continued)
Page
FIGURE 6-10 EUTR04 State Variable Used in Modeling
DO at Beacons Reach Marina (WASP4 Model) 6-43
FIGURE 6-11 Observed and Predicted Dissolved Oxygen at
Beacons Reach Marina 6-44
FIGURE 6-12 Predicted and Observed Dye Concentraation
at Gull Harbor Marina 6-46
FIGURE 6-13 Observed and Predicted Dissolved Oxygen
at Gull Harbor Marina 6-47
vii
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LIST OF TABLES
Page
TABLE 2-1 List of Marina Water Quality Models 2-3
TABLE 2-2 Short-listed Marina Water Quality Models 2-19
TABLE 2-3 Capabilities of Short-listed Marina Water Quality Models 2-20
TABLE 3-1 Data Requirements 3-3
TABLE 3-2 Approximate Operating Costs 3-4
TABLE 3-3 Constituents Included in Model 3-5
TABLE 3-4 Applicability of Short-listed Models to Marina Type 3-6
TABLE 3-5 Model Capabilities: Reaeration Formulations 3-9
TABLE 4-1 Ease of Application: Sources, Support, and
Documentation 4-3
TABLE 4-2 Approximate Operating Costs for Best Qualified Models 4-4
TABLE 5-1 Geometric and Bathymetric Data Requirements for
Recommended Marina Models 5-7
TABLE 5-2 Essential Transport Data 5-8
TABLE 5-3 Transport/Hydraulic Data Requirements for
Recommended Marina Models 5-8
TABLE 5-4 Meteorological Data Requirements for WASP4
Submodels 5-10
TABLE 5-5 Water Quality Variables 5-11
TABLE 5-6 Water Quality Data Requirements for the
Recommended Marina Models 5-12
TABLE 6-1 Indian Hills Marina Water Quality Summary 6-2
viii
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LIST OF TABLES (continued)
Page
TABLE 6-2 Beacons Reach Marina Water Quality Summary 6-4
TABLE 6-3 Gull Harbor Marina Water Quality Summary 6-8
TABLE 6-4 Estimated Pollutant Contribution from Boats 6-12
TABLE 6-5 Representative Reaction Coefficients 6-15
TABLE 6-6 Input Data Used to Estimate Flushing Time (TPA) 6-16
TABLE 6-7 Observed and Predicted Flushing Time Using TPA 6-16
TABLE 6-8 Predicted and Observed Fecal Coliform Using TPA 6-18
TABLE 6-9 TPA Input Parameters Used to Estimate DO Levels 6-18
TABLE 6-10 Averaged Dye Concentration at Beacons Reach
Marina During the Study Period 6-20
TABLE 6-11 Observed and Predicted DO Levels (TPA) 6-24
TABLE 6-12 Model Paramters for Hunting Creek Model Geometry 6-28
TABLE 6-13 NCDEM Input Parameters Used to Estimate DO Levels 6-31
TABLE 6-14 Predicted DO Levels (NCDEM) 6-31
TABLE 6-15 Comparison of WASP Model Results to Observed Data
(Indian Hills Marina) 6-38
TABLE 6-16 Observed Dye Concentrations Used for Model
Calibration at Beacons Reach Marina 6-40
TABLE 6-17 Observed Dye Content Used for Model
Calibration in Gull Harbor 6-45
TABLE 7-1 Comparison of Dissolved Oxygen Results from Simple,
Mid-range, and Complex Models 7-2
ix
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ACKNOWLEDGMENTS
Funding for this study was provided through U.S. Environmental Protection Agency
(EPA) Region IV under the direction of Mr. Jim Greenfield, EPA Contract No. 68-C9-0013,
Work Assignment Numbers 1-62 and 2-35. Data sources were provided by Mr. Jay Sauber
(North Carolina Department of Environmental Health and Natural Resources) and Mr. Tom
Cavinder (U.S. EPA Region IV). This report was prepared by Dr. Mohamed Z. Moustafa and
Mr. Michael R. Morton, P.E., of Tetra Tech, Inc., Fairfax, Virginia.
xi
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Marina Water Quality Models
EXECUTIVE SUMMARY
In 1985 U.S. Environmental Protection Agency (EPA) Region IV instituted a strong
policy of protecting public uses of coastal waters from degradation by marina development. The
sighting of new marinas has been influenced in particular by the need for protection of shellfish
harvesting areas. Growth in the coastal areas of the southeastern United States during the past
two decades has been accompanied by an increasing demand for recreational facilities such as
marinas and boat maintenance facilities.
This report addresses the impacts of coastal marinas on water quality. Specifically, it
deals with the selection and use of the best available computer models for analyzing the impact
of a marina on water quality. A methodology for evaluating the water quality impacts of
proposed marina development has been formulated. This methodology balances the need for
accurate assessments of potential impacts with the limited data and resources often available
during the planning stage of a project.
Initially, all water quality models applicable to marinas were surveyed and divided into
three categories: simple, mid-range, and complex models. Simple models include desktop
screening methodologies that calculate seasonal or annual mean pollutant concentrations based
on steady-state conditions and simplified flushing time estimates. These models are designed
to examine and isolate trouble spots for more detailed analyses. They should be used to
highlight major water quality issues and important data gaps in the early stage of a study.
Mid-range models include computerized steady-state or tidally averaged quasidynamic
simulation models, which generally use a box or compartment-type network. These models use
a constant flow condition that neglects the temporal variability of tidal heights and currents.
Tidally averaged models simulate the net flow over a tidal cycle. These models cannot predict
the variability and range of dissolved oxygen (DO) and pollutants throughout each tidal cycle,
but they are capable of simulating variations in tidally averaged concentrations over time. The
mid-range models should be used to corroborate the results of the simple models when the
simple models indicate adverse water quality impacts.
Complex models include computerized one-dimensional (1-D) models, quasi-2-D models,
and a variety of 2-D intratidal models that simulate variations in tidal height and velocity
throughout each tidal cycle. The complex models are generally composed of separate but
compatible hydrodynamic and water quality models. These two models are run sequentially, and
the output of the hydrodynamic model becomes part of the input to the water quality model.
These models enable the characterization of phenomena rapidly varying within each tidal cycle,
such as pollutant spills, stormwater runoff, and batch discharges. Complex models also are
deemed appropriate for systems for which the tidal boundary impact, as a function of the
hydrodynamics and water quality, is important to the modeled system within a tidal period. In
their treatment of conventional pollutants, complex models deal mainly with biochemical
processes. Complex models considered here can simulate simple biochemical oxygen demand-
dissolved oxygen (BOD-DO) interactions. Complex models are also better predictive tools for
proposed marinas.
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Marina Water Quality Models
The best qualified model in each category was selected. The selection criteria were
appropriateness when applied to a variety of marina types (including one-segment, two-segment,
three-segment, flow-through, and open water marinas) and the usefulness of each assessment
technique for determining water quality impacts (i.e., how well it can predict DO variations).
The Tidal Prism Analysis and the NCDEM DO model were selected as the method of choice
in the simple model category. The Tidal Prism Model (TPM) is recommended for the mid-range
category, and the Water Quality Analysis Simulation Program (WASP4) is the model of choice
for the complex category. A variation of WASP4 using a full two-dimensional hydrodynamic
model is also recommended for proposed marinas.
The best qualified models were applied to two coastal marinas in the southeastern United
States. Results of model calibrations and applications are summarized herein. Comparisons
between predicted and observed data are documented for the two applications. Furthermore, a
water quality analysis of a proposed recreational boat canal is included. This example
demonstrates the application of a complex model to a proposed marina site.
The predictive models (tools) are recommended for use by regulatory agencies as well
as developers to determine and evaluate problem areas pertinent to marina development. It is
anticipated that marina developers will utilize the models to determine whether a proposed
marina will be in compliance with water quality regulatory requirements.
xiv
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Marina Water Quality Models
1. INTRODUCTION
1.1 Background
In 1983, the U.S.Environmental Protection Agency (EPA) Region IV Environmental
Assessment Branch initiated an environmental assessment of the development and operation of
coastal marinas. The study responded to existing resource-use conflicts between shellfishermen
and marina developers in Region IV coastal states and addressed the growing regulatory
concerns for balancing the development and operation of coastal marinas with the need to
conserve and protect coastal resources. The objectives of the assessment were to identify
pertinent environmental concerns and issues and to provide guidance for environmentally sound
coastal marina development and operation.
In 1985 EPA Region IV instituted a strong policy of protecting public uses of coastal
waters from degradation by marina development. The sighting of new marinas has been
influenced by the protection of shellfish harvesting areas in particular. The coastal areas of the
Southeast have undergone significant residential, industrial, and commercial growth during the
past two decades. This growth has been accompanied by an increasing demand for recreational
facilities such as marinas and boat maintenance facilities.
The Coastal Marinas Assessment Handbook (April 1985) was developed by the NEPA
Compliance Section of the Environmental Assessment Branch. This handbook provides
information on environmentally sound practices for the sighting and development of coastal
marinas. However, recent studies and new policies regarding coastal marinas have afforded new
insights and approaches for dealing with coastal marina issues.
Several states have already decided that the newly created basin connected to Class SA
waters would automatically carry the SA (shellfishing) classification, even if its intended purpose
was for use as a marina. The intent of this action was to encourage the construction of marina
basins in dug-out, high-ground areas rather than in SA waters. Initial evaluation of some
proposed sites indicated the possibility of dissolved oxygen problems.
The design, construction, and operation of coastal marinas and associated boating
activities have the potential for undesirable environmental impacts to marine and coastal
ecosystems. The potential for environmental impacts and their significance will not be the same
for all marinas. Many factors work to determine the eventual impact a marina will have on the
water quality within the immediate vicinity of the marina and areas of the adjacent waterway.
Initial marina site selection is one very important factor. Selection of a site that has favorable
hydrographic characteristics and requires the least amount of modification can do a great deal
to reduce potential water quality impacts. Because of the potential impacts marina development
can have on dissolved oxygen concentrations, many waters with average dissolved oxygen
concentrations barely at or below state standards would be found unsuitable for marina
development.
l-l
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Marina Water Quality Models
Marina-related development and operation activities are also significant factors impacting
water quality. Dredging and dredged material disposal, wastewater disposal, fueling operations,
stormwater runoff, and boat maintenance and repair are typical development and operation
activities. Discharges from marine sanitation devices and bilges can also impact water quality
in the marina waters. In inadequately flushed basins, discharges from these sources have the
potential to reduce dissolved oxygen supply and to increase turbidity, sewage bacteria
concentrations, and nutrient, metals, or hydrocarbon levels.
Perhaps the most significant factor affecting a marina's potential for water quality impacts
is basin configuration. Whether a marina has open construction (i.e., is located directly on a
river, bay, or barrier island) or semi-enclosed construction (i.e., is located in an embayment or
other protected area) affects circulation and flushing characteristics, which play important roles
in the distribution and dilution of potential contaminants. Circulation and flushing can be
influenced by the natural or dredged basin orientation regarding prevailing winds. The final
design is usually a compromise that will provide the most desirable combination of marina
capacity, services, and access, while minimizing environmental impacts, dredging, protective
structures, and other site development costs (Tetra Tech, 1988).
The construction and operation of marinas have been shown to have the potential for
adverse impacts on water quality and aquatic organisms. Increasingly, it has been found that
marina activities can adversely impact water quality. A study conducted by the North Carolina
Department of Environmental Management (NCDEM, 1990) pointed out, among other findings,
that increasing the number of segments in a marina design decreases dissolved oxygen in the
basin and in the surrounding waters. Several state regulatory agencies require that applicants
for proposed marinas provide a documented water quality assessment to show that water quality
standards will not be violated. The types of impacts on water quality resulting from marinas
include the following:
• Microbiological contamination of adjacent shellfish and swimming areas;
• Depletion of dissolved oxygen in the water or in the sediments;
• Disruption of the bottom during dredging and positioning of pilings;
• Leaching of chemicals used to protect boats and wooden dock structures from
destruction and fouling by marine organisms;
• Introduction of microbial pathogens or substances with a high biological oxygen
demand into the water during marina construction or through the deliberate or
accidental discharge of sanitary wastes from boats;
• Introduction of petroleum hydrocarbons into the water during normal boat engine
operations; and
• Shoreline erosion due to bulkheading and motorboat wake.
1-2
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Marina Water Quality Models
1.2 Objectives
To determine the effects of the above processes, a valid site-specific water quality
assessment that includes modeling should be implemented. This report addresses the coastal
marina issues, dealing with their impact on water quality. Specifically, it discusses selecting and
using the best available computer models for analyzing a marina's impact on water quality. The
purposes of this report are the following:
1. Review available information.
2. Select the best available marina water quality models.
3. Compare model capabilities.
4. Develop a comprehensive data collection plan.
5. Calibrate and evaluate the recommended models.
This report provides information on the recommended environmental assessment methods
for predicting potential impacts to water quality. Predicting the dissolved oxygen and fecal
coliform concentrations in a coastal marina is the primary focus of the impact assessment
methods presented in this report. These assessment techniques can be used by decision-makers
when evaluating marina permit applications. These predictive tools should also be used by
marina developers to show that a proposed marina will be in compliance with water quality
regulatory requirements.
1.3 Report Organization
This report is organized into seven chapters and six appendices. Chapter 2 reviews
available information concerning marina modeling and monitoring studies. The focus of the
chapter is to describe all known numerical models and methods appropriate for solving marina
water quality problems. For this study, only public domain models are considered. Models
considered are grouped into three categories: simple, mid-range, and complex models. The list
of all known marina water quality methods is reduced to a short list of those models that best
meet the needs of the regulatory agencies. Reasons for including a model on the short list are
also discussed.
Chapter 3 provides an overview of the short-listed simple, mid-range, and complex
marina water quality models. This chapter summarizes data requirements and operating costs
for the short-listed models. In addition, Chapter 3 discusses the applicability of the selected
model to marina type and to water quality constituents modeled.
Chapter 4 discusses the mathematical models that are recommended to address marina
water quality issues. A listing of the input variables required by the short-listed models is
provided. Chapter 4 discusses the reasons for selecting the best qualified models and presents
a brief description of each of the recommended models.
1-3
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Marina Water Quality Models
Chapter 5 provides a data collection plan for the best qualified models. The plan
documents the physical and water quality information necessary to properly apply the best
qualified marina models.
Chapter 6 summarizes and reviews data collected at three marinas in the Southeast,
discusses the nature of the available data, and identifies data gaps. In addition, Chapter 6
presents the results of the application of the best qualified simple, mid-range, and complex
models to the three Southeast marinas. The models are calibrated using the available data.
Model predictions are then compared to observed data taken at the two marinas. Chapter 6 also
evaluates the best qualified models according to their performance.
Chapter 7 presents a summary of the major findings of this study along with
recommendations.
Appendix A presents annotated input data files for the best qualified simple, mid-range,
and complex models. Appendix B provides a user's guide for the selected models, and
Appendix C provides a contact list of key persons who provided valuable information and
publications relevant to this study. Appendix D contains additional information that was
generated during the WASP4 model application. Appendix E describes a successful model
application to a proposed recreational boat canal on the St. Johns River, Florida. Finally,
Appendix F presents the sensitivity analysis results for the simple, mid-range, and complex
marina models.
1-4
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Marina Water Quality Models
2. MODEL SELECTION AND REVIEW
2.1 Model Sources
The identification phase of finding relevant models was multifaceted. The first approach
was a sampling of the agencies now dealing with marina water quality issues. The
environmental agencies of the states of Delaware, Florida, and North Carolina were contacted.
Those responding said they had no set procedures or models by which to evaluate water quality.
They reported that they dealt with applications or problems on a case-by-case basis or they
referred to the simplified approaches discussed later in this report. There were no procedures
in place to address complex problems requiring numerical models.
The second approach was a review of the literature. A number of estuarine and coastal
modeling monographs that survey available models and discuss model applications have them
published (Heaps, 1986; Nihoul, 1979, Nihoul and Jamart, 1987; Fischer, 1981; Johns, 1983;
Ramming and Kowalik, 1980). Additional source material was sought in the refereed literature
(Journal of Hydraulics, Marine Technology Society, Estuarine, Coastal and Shelf Science) and
reports from universities and other organizations.
The third approach was networking within the modeling community. This approach was
most fruitful, particularly networking accomplished through an American Society of Civil
Engineering (ASCE) conference held on Estuarine and Coastal Modeling in Newport, Rhode
Island, November 15-17, 1989. The emphasis of this conference was on two- and three-
dimensional numerical model development and applications for both hydrodynamic and water
quality models. A number of modelers were queried as to the status and availability of their
models.
A recommendation for full 3-D models is difficult. Full 3-D models that can predict
longitudinal, lateral, and vertical transport are the most complex and expensive models to set up
and to run. The 3-D models are newer than 2-D models and therefore have fewer applications
by which to determine their usefulness. Most 3-D models are considered research tools.
2.2 Model Attributes
The focus of this section is the description of all known numerical models appropriate
for solving marina water quality problems. Not surprisingly, these models were developed with
the much larger viewpoint of coastal and estuarine hydrodynamics and pollutant transport. In
fact, most of the models are model systems composed of a hydrodynamic model to predict
circulation patterns and a water quality model that uses those patterns along with biochemical
kinetics to predict concentrations of various water quality parameters. There is no feedback
from the water quality model to the hydrodynamics, and therefore the models can be run serially
with the hydrodynamic model calibrated and verified first.
2-1
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Marina Water Quality Models
Marina water quality models may be classified in different and somewhat arbitrary ways.
Some models may not quite fit into any category; others may fit well into several categories.
In addition, models tend to evolve with use and the exact capabilities of the individual models
described here may change; Therefore, for simplicity, water quality models applicable to
marinas are divided into three groups: (1) simple models, (2) mid-range models, and (3)
complex models. Models selected for discussion here are listed in Table 2-1. These models are
general purpose, in the public domain, and available from or supported by public agencies.
Models summarized in this section represent the typical range of capabilities currently
available. Other available computer programs can generally be grouped into one of the
following categories:
• Variants of the models discussed here;
• Proprietary models held by consulting firms; or
• Models developed for research purposes.
In this chapter, Sections 2.2.1 through 2.2.3 briefly describe the range of mathematical
models that are available to address marina water quality issues and specify available
documentation.
Section 2.3 presents a "short list" of recommended models that are capable of assessing
potential water quality impacts from a coastal marina. The short list contains between two and
five models in each of the these categories (simple, mid-range, and complex).
2.2.1 Simple Models
The methods listed here include desktop screening methodologies that calculate seasonal
or annual mean pollutant concentrations based on steady-state conditions and simplified flushing
time estimates. These models are designed to examine and isolate trouble spots for more
detailed analyses. These methods should be used to highlight major water quality issues and to
identify data gaps in the early stage of a study.
2.2.1.1 Tidal Prism Analysis
The impact assessment methods presented in Chapter 4 of the Coastal Marina Assessment
Handbook (EPA, 1985) are appropriate screening tools. Methods presented in this section,
particularly some of the mathematical descriptions, are simplifications of more sophisticated
techniques. However, these techniques as presented can provide reasonable approximations for
screening potential impact problems when site-specific data are not available.
2-2
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Marina Water Quality Models
TABLE 2-1. List of Marina Water Quality Models
Simple Models
Mid-range Models
Complex Models
Tidal Prism Analysis
North Carolina Division of
Water Quality Assessment Simulation
Environmental Management
Program (WASP4)
Flushing
(NCDEM) DO Model
Characteristics
Dynamic Estuary Model (DEM)
Diagram
Tidal Prism Model
M.I.T. Dynamic Network Model
(MITDNM)
Waterways Experiment Station Model
(CE-QUAL-W2)
H.S. Chen Two-Dimensional Water
Quality Model (WQM2-D)
M.I.T. Tidal Embayment Analysis and
Eulerian-Lagranian Transport Models
(TEA/ELA)
M.I.T. CAFE-l/DISPER-1 Models
Waterways Experiment Station Open-
Channel Flow and Sedimentation
Model (TABS-2)
Waterways Experiment Station Implicit
Flooding Model (WIFM)
Solute Transport Model for Tidal
Canal Networks (CANNET3)
Waterways Experiment Station Three-
Dimensional Models
(CH3-D/CBWQM)
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Marina Water Quality Models
Flushing Characteristics
Semi-enclosed Marina
Flushing time for a marina within a semi-enclosed area (Figure 2-1) can be estimated
using simplified dilution calculations. For semi-enclosed marinas the flushing time can be
approximated by the following equation:
T =
\TC*
LOG(D)]
'(A x L)+(b x A x R)-(I x TS
LOG
(Ax H)
(2-1)
If nontidal freshwater inflow from runoff or stream discharge into the marina basin can
be ignored and the marina has relatively vertical sides, Equation 2-1 becomes:
tf =
\TC x LOG(D)]
(A x L)+(b x A x K)
LOG
(Ax H)
and for marinas with nonvertical sides Equation 2-2 becomes:
(2-2)
T =
lF
LOG(D)]
LOG
(V,+bx VB
(2-3)
H
where:
Tf = Flushing time (hours)
Tc = Tidal cycle (hours)
A = Surface area of marina (m2)
D = Desired dilution factor
R = Range of tide (m)
b = Return flow factor (dimensionless)
I = Nontidal freshwater inflow (m3/hour)
R = Range of tide (m)
b = Return flow factor (dimensionless)
I = Nontidal freshwater inflow (m3/hour)
L = Average depth at low tide (m)
H = Average depth at high tide (m)
VL = Volume of marina at low tide (m3)
VH = Volume of marina at high tide (m3)
VP = Volume of marina tidal prism (VH - VJ
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Marina Water Quality Models
Figure 2-1. Representative Semi-enclosed Marina Basin.
2-5
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Marina Water Quality Models
Open Marinas
Marinas located directly on rivers, bays, or estuaries (Figure 2-2) and not entirely
enclosed by protective barriers would have flushing characteristics generally similar to those for
the water body. The actual flushing potential of a specific area within a large water body can
be characterized using the fraction of freshwater method (Mills et al., 1985)
' Sw-St ^
! X V,
("1
'w
/
(2-4)
where:
Tf = Flushing of total water body (hours)
Sw = Seawater salinity (ppt)
Sj = Mean salinity in the segment (ppt)
V, = Freshwater volume in segment i (m3)
I, = Freshwater inflow in segment i (m3/hour)
n = Number of segments
Dilution Methods
Semi-Enclosed Marinas
For a slug addition of pollutant in a semi-enclosed marina basin, pollutant concentrations
can be estimated by using an expression such as (EPA, 1985):
C, =
A x L +b x A x R
Ax H
N
M
F x V
rll •* vL
e'* + CA x e'b
(2-5)
where:
C, = Concentration of pollutant at time t (mg/L)
CA = Ambient concentration of pollutant prior to addition of discharge (mg/L)
M = Mass of pollutant discharged into basin (mg)
k = Decay rate for nonconservative pollutants (day1)
t = Time (days)
N = Number of tidal cycles (24t/Tc)
Fu = 1000 (converts units to mg/L)
All other parameters are as defined previously in Equations 2-1 through 2-3.
2-6
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Marina Water Quality Models
Figure 2-2. Representative Open Marina Basin (Site A).
2-7
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Marina Water Quality Models
For a continuous discharge of pollutant into a marina basin, an estimate of long-term
concentrations (steady-state conditions) may be obtained by:
C =
M.xTx F,
12
(1 ~b)xVn
+ C\
(2-6)
where:
C
Mr
12
Concentration of conservative pollutant (mg/L)
Total mass flow rate of pollutant into basin, including input by freshwater
inflow (mg/day)
4.7 x 10'5 (converts units to mg/L)
All other parameters are as defined previously in Equations 2-1 through 2-3.
Equation 2-6 estimates steady-state concentrations for conservative pollutants. For cases
where nonconservative pollutant concentrations versus time are of interest, Equation 2-7 may
be used:
c. =
VL + bxVp
-far.
x e
x Ct
-far.
*
-------
Marina Water Quality Models
impacts are complicated because the kinetics of dissolved oxygen are very complex and because
the DO concentrations vary greatly over short periods of time (Thomann and Mueller, 1987).
The best way to assess marina impacts on water quality is to design a sampling strategy
and physically measure dissolved oxygen values. During the sampling, sediment oxygen demand
and other data can be collected, which may be used to estimate future dissolved oxygen levels
using mathematical modeling procedures described in the North Carolina Coastal Marinas Water
Quality Assessment (NCDEM, 1990) and the Technical Guidance Manual for Performing
Wasteload Allocations (EPA, 1989). Prior to data collection, screening procedures such as the
equation below and those described in Thomann and Mueller (1987) and Mills et al. (1985) may
be used to identify trouble spots. Equations 2-8a and 2-8b may be used to successively estimate
dissolved oxygen concentrations at high and low tide in a semi-enclosed marina.
T,
-ka x —
DOh = (1000 x DOa x Vp + 1000 x Vpx (DOs - DOA) x (I - e u) -
-k. xh T (2-8a)
1000 x V. X CR x (I - e - B X A X — +
1 * 24
1000 x VL x DOl + DOj x I x Tc) I (1000 x VH )
~K' x B x A x Tc
1000 x DOh x VL - 1000 x VL x CB x (1 - e u) - - c
DOl =
24
(2-8 b)
1000 x V,
where:
DOH =
Approximate dissolved oxygen at high tide (mg/L)
DOA =
Ambient dissolved oxygen of water flushing into marina (mg/L)
DOL =
Dissolved oxygen level in marina at low tide (mg/L)
DO, =
Dissolved oxygen in nontidal freshwater inflow (mg/L)
K,
Oxidation coefficient (day1)
DOs =
Saturated dissolved oxygen concentration (mg/L)
K
Reaeration coefficient (day1)
B
Sediment oxygen demand (mg/m2/day)
CB =
Biochemical oxygen demand (mg/L)
Equation 2-8a may be used to estimate dissolved oxygen levels for successive high tides
by using the new value of DOH in place of DOL. Initially, the value of DOL is set equal to DOA,
which is assumed constant over the period of analysis. Reaeration due to mixing,
photosynthesis, or other sources is not considered. Loss of DO due to nitrification also is not
considered.
2-9
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Marina Water Quality Models
2.2.1.2 Flushing Characteristics Diagram
Christensen (1989) presented a simple analytical model that can be used to evaluate the
flushing characteristics of coastal marinas and residential canal systems. This method is
developed in the form of a convective one-dimensional (plug-flow) model that will provide the
flushing time of a system with a given layout. Tidal action and wind effects are considered, and
both dead-end and flow-through systems are treated.
In the case of tidal flushing without flow-through action, both accidental and continuous
inflow of pollutant are considered. Marina flushing time is calculated through use of formulas
and charts provided in Christensen's paper. Christensen (1989) also shows that unsatisfactory
flushing can be improved by introducing marsh areas and/or reducing the mean water depth. The
use of this approach is demonstrated in the second numerical example.
Wind-induced flushing is considered in flow-through and dead-end systems. Formulas for
the flushing time and the time required for the wind to accelerate the water to a useful velocity
are also provided.
The Flushing Characteristics Diagram is applicable only to conservative substances;
however, this method is applicable to all marina types (i.e., open, semi-enclosed, and flow-
through systems).
2.2.2 Mid-range Models
This section focuses on the numerical mid-range models appropriate for solving marina
water quality problems. This category includes computerized steady-state or tidally averaged
quasidynamic simulation models, which generally use a box or compartment-type network.
Steady-state models use an unvarying flow condition that neglects the temporal variability of tidal
heights and currents. Tidally averaged models simulate the net flow over a tidal cycle. These
models cannot predict the variability and range of DO and pollutants throughout each tidal cycle,
but they are capable of simulating variations in tidally averaged concentrations over time. The
mid-range models should be used to corroborate the results of the simplified methods discussed
in the previous section when the simplified methods indicate adverse water quality impacts.
2.2.2.1 North Carolina Division of Environmental Management DO Model
This model assumes that the marina to be evaluated can be approximated by two
segments. A one-segment version of the model should be used for basins without a distinctive
inlet channel.
Runoff is assumed to be equal to zero, and the volume of wastewater discharged to the
basin other than from boats is also assumed to be equal to zero. The forcing function is the
2-10
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Marina Water Quality Models
changing depth of the ambient water, which brings water into the marina during the rising tide
and takes it out during the falling tide.
An initial version of this model utilized a return flow factor (b). Dye studies conducted
by NCDEM (1990) seemed to indicate that little or none of the water that had previously been
in the marina returned on the next tide. Therefore, the return flow factor has been eliminated
from the current version of the model.
The tidal variation is assumed to follow a sinusoidal distribution. For simplicity a 12-
hour tidal cycle is used. Calculations are performed at hourly time increments. Each segment
is assumed to be completely mixed at the end of each time increment.
Changes in DO are possible from advection, reaeration, or bottom sediment oxygen
demand. Boat discharges were included in an earlier version, but they were shown to be of
minor effect and have been eliminated from the current version. The average DO in the inlet
channel or the marina basin for a given tidal cycle is the average of hourly values through that
cycle. The computer model assumes some initial values, iterates through 18 tidal cycles, and
then prints out the results of the next two tidal cycles. This allows sufficient interactions for a
steady state to be reached. The program is written in BASIC for use on an IBM-compatible
computer.
2.2.2.2 Tidal Prism Model
A water quality model has been developed for easy application to small coastal
embayments. The simulation of physical transport processes is based on Ketchum's tidal prism
theory, modified and expanded such that it becomes applicable to cases where the embayment
is treated as a branch and/or freshwater discharge is negligibly small. A model coastal
embayment is divided into segments of lengths equal to local tidal excursions. Instead of starting
the segmentation from the landward end with freshwater discharge and tidal prism as two non-
zero parameters, the modified model subdivides the water body starting from the seaward end
with the difference between tidal prism and freshwater discharge as a single parameter. The
mass balance within each segment is formulated by considering the exchange of water with its
neighboring segments due to the flushing of freshwater discharge, as well as the tidal prism on
ebb cycle, and due to the mixing of the tidal prism on flood tide. This results in an algebraic
equation that may be solved for concentration in each segment by successive substitution. For
a nonconservative substance, the biochemical reaction terms are then added to the algebraic
equation without complicating the solution scheme. The model has been applied to a number
of tidal creeks and coastal embayments in Virginia (Kuo, 1976; Kuo et al., 1988).
The nonconservative substances considered in the model include organic, nitrogen,
ammonia nitrogen, nitrate-nitrite nitrogen, organic phosphorus, inorganic phosphorus,
phytoplankton (chlorophyll-a), carbonaceous biochemical oxygen demand, dissolved oxygen, and
fecal coliform.
2-u
-------
Marina Water Quality Models
Given the initial conditions or calculated concentration fields at the slack-before-ebb
(SBE) that initiates a tidal cycle, the calculation of the concentrations at the succeeding SBE is
performed in two steps. First, the concentration fields are calculated assuming that only the
physical transport processes" are in action. Second, the calculated concentration fields are
adjusted for the relevant chemical and biological processes.
2.2.3 Complex Models
This category includes computerized one-dimensional (1-D) models, quasi-2-D models,
and a variety of 2-D intratidal models that simulate variations in tidal height and velocity
throughout each tidal cycle. The 2-D models may be further divided into two broad categories,
2-D vertically averaged (x-y) and 2-D laterally averaged (x-z).
Although many 2-D vertically averaged, finite-difference or finite-element hydrodynamic
programs exist, relatively few contain a water quality program that simulates constituents other
than salinity and/or temperature (Blumberg, 1975; Hamilton, 1975; Elliot, 1976). Examples of
finite-element models, often preferred for complex coastlines, are the CAFE1/DISPER1 and
TEA/ELA hydrodynamic and transport models and a water quality model developed by Chen
(1978). The first two models can simulate only mass transport of a nonconservative constituent,
whereas Chen's model is capable of representing most major water quality processes.
A number of 2-D, laterally averaged models (longitudinal and vertical transport
simulations) treat mass transport of salt and temperature, but very few include nonconservative
constituents or water quality routines. Models in this category simulate vertical stratification but
neglect lateral effects, including Coriolis effects. The Waterways Experiment Station (WES)
CE-QUAL-W2 model (WES, 1986) is included in this category.
The complex models are generally composed of separate but compatible hydrodynamic
and water quality models. These two models are run sequentially, and the output of the
hydrodynamic model becomes part of the input to the water quality model. These models enable
the characterization of phenomena rapidly varying within each tidal cycle, such as pollutant
spills, stormwater runoff, and batch discharges. Complex models also are deemed appropriate
for systems where the tidal boundary impact, as a function of the hydrodynamics and water
quality, is important to the modeled system within a tidal period.
In using complex models, one must decide whether a simple 1-D link-node longitudinal
system is sufficient or whether a quasi-2-D model with branching networks or
triangular/rectangular configuration is required to model the longitudinal and lateral variations
in a proposed marina. The length of model segments or links depends on the resolution
required. The length, and position of segments depend on the physical properties of the marina.
Homogeneity of physical characteristics should be the basis for defining segments. Where
bends, constrictions, or other changes occur, smaller segments are generally defined to improve
resolution.
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Marina Water Quality Models
The quasi-2-D model is applicable where there is a need to project lateral differences in
water quality. However, the quasi-2-D model, which uses 1-D equations of motion, cannot
estimate longitudinal and lateral dispersion as effectively as the true 2-D model. Although the
quasi-2-D and the true 2-D model both assume that the marina is vertically well mixed, the true
2-D model can effectively represent lateral variation in velocity and constituent concentration
for embayments with nonuniform cross-sections and branching channels. The 2-D model also
can account for the effect of Coriolis force and wind circulation.
In their treatment of conventional pollutants, complex models deal mainly with
biochemical processes. All complex models considered here can simulate simple BOD-DO
interactions. Most of these models also are formulated to simulate the reactions and interactions
of organic phosphorus and orthophosphorus; organic nitrogen, ammonia, nitrite and nitrate; algal
growth and respiration; and DO. These models also include settling rates and benthic flux rates
for several different constituents such as phosphorus, nitrogen, and sediment oxygen demand.
2.2.3.1 Water Quality Analysis Simulation Program, WASP4
The Water Quality Analysis Simulation Program (WASP4) is a dynamic compartment
modeling system that can be used to analyze a variety of water quality problems in one, two,
or three dimensions. WASP4 simulates the transport and transformation of conventional and
toxic pollutants in the water column and benthos of ponds, streams, lakes, reservoirs, rivers,
estuaries, and coastal waters. The WASP4 modeling system covers four major subjects:
hydrodynamics, conservative mass transport, eutrophication-dissolved oxygen kinetics, and toxic
chemical-sediment dynamics. The modeling system also includes a stand-alone link-node
hydrodynamic program, DYNHYD4, that simulates the movement of water.
WASP4 contains two separate kinetic submodels, EUTR04 and TOXI4, each of which
serves a distinct purpose. EUTR04 is a simplified version of the Potomac Eutrophication Model
(PEM) and is designed to simulate most conventional pollutant problems. EUTR04 can simulate
up to eight state variables: ammonia nitrogen, nitrate nitrogen, inorganic phosphorus,
phytoplankton carbon, carbonaceous BOD, dissolved oxygen, organic nitrogen, and organic
phosphorus. TOXI4 simulates organic chemicals, metals, and sediment in the water column and
underlying bed. TOXI4, EUTR04, and DYNHYD4 can be obtained from the Center for
Exposure Assessment Modeling, Athens, Georgia.
DYNHYD4 is a link-node model that may be driven by either constantly repetitive or
variable tides. Unsteady inflows may be specified, as well as wind that varies in speed and
direction. DYNHYD4 produces an output file of flows and volumes that is read by WASP4
during the water quality simulation.
Typically, the hydrodynamic submodel (DYNHYD4) is calibrated to measure tides and
velocities in the water body. However, this is not possible for a proposed marina. Instead, a
2-D hydrodynamic model (i.e., CAFE-1 and/or TEA) is applied and calibrated for the proposed
marina. Calibration of DYNHYD4 is accomplished through successive adjustment of channel
2-13
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Marina Water Quality Models
roughness and channel geometry (hydraulic radius and width) until the velocities in the
DYNHYD4 model channels match those from the 2-D model. This approach was successfully
applied to a proposed recreational boat canal in Florida. Appendix E of this report demonstrates
in further detail the appropriateness of this approach.
The WASP4 model system is supported by EPA and has been applied to many aquatic
environments (e.g., Tetra Tech, 1990). The water quality component is set up for a wide range
of pollutants; however, the hydrodynamic component is rather simplistic. The WASP4 model,
though weak in the hydrodynamic component (DYNHYD4), is a full approach to water quality
and is relatively complete. The WASP4 model can be used in 1-D, 2-D, or 3-D problems
although it is difficult to use because it requires a large number of constants. The use of
WASP4 as a water quality component with the 2-D or 3-D hydrodynamic models is also a
potential solution for complex multidimensional problems.
2.2.3.2 Dynamic Estuary Model, DEM
DEM is a quasi-2-D model that represents tidal flow in the lateral and longitudinal
directions with a branching link-node network (Genet et al., 1974). The model can be linked to
the Tidal Temperature Model (TTM) for heat budgets. Several versions of the hydrodynamic
component of DEM exist. One version is limited to steady inflows and constantly repetitive
tide. The steady inflow version cannot explicitly handle short-term stochastic transients such as
wind stress or large storm flushing and has difficulty in predicting long-term patterns such as
the 2-week spring neap tide cycle or the seasonal freshwater inflow pattern. Consequently, this
version is most reliable when predicting high and low values for diurnal or tidal cycles, or both,
averaged over a relatively steady 2-week period (Ambrose and Roesch, 1982). Real-time
simulations of water quality are possible with the steady inflow version of DEM, but with some
inaccuracies. Newer hydrodynamic versions of the model can handle variable inflows and can
thus generate a more accurate real-time prediction of water quality.
Several water quality submodels also have been used with DEM. All versions include
nutrient modeling and algal growth, photosynthesis, and respiration. The following is a brief
description of the versions of DEM currently available.
DEM, Chen-Orlob version, is the most comprehensive version of the model currently
available (Chen and Orlob, 1972). The model has the capability of representing 22 coupled
biotic and abiotic constituents including temperature, pesticides, heavy metals, carbonaceous
biochemical oxygen demand (CBOD), DO, phosphate, ammonia, nitrite, nitrate, total dissolved
solids, alkalinity, pH, carbon dioxide, phytoplankton, zooplankton, fish, benthic animals,
suspended detritus, and sediment detritus.
DEM, Potomac version, is documented as handling only steady inflows and constantly
repetitive tide, but a newer version that is capable of handling variable inflows is available
(Roesch et al., 1979). The model simulates CBOD, DO, ammonia, nitrate, phosphate, and
chlorophyll-fl.
2-14
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Marina Water Quality Models
The overall two-dimensional DEM model is composed of three separate components—a
hydrodynamic model (HYD1), a dynamic quality model (DQUAL), and a steady-state quality
model (AQUAL). The first uses the equation of motion and continuity to calculate channel
flows and nodal volume changes in response to wind and tidal boundary fluctuations. Dynamic
and/or steady-state results, averaged over a complete tidal cycle, are stored on disk files to be
used repeatedly in the calibration of the quality models. Once the physical transport mechanisms
of water flow and velocities are determined, the biological and chemical reactions can be
superimposed to calculate water quality at any location and time.
DQUAL and AQUAL can be used to simulate any combination of the following nine
constituents and have the capability to include up to four additional user-specified conservative
constituents: salinity (chloride), total nitrogen, total phosphorus, total coliform bacteria, fecal
coliform bacteria, carbonaceous BOD, TKN, dissolved oxygen, and temperature.
2.2.3.3 M.I.T. Dynamic Network Model, MITDNM
MITDNM is a one-dimensional model that uses a finite element, branching network to
simulate the flow regime of an estuary with unsteady tidal elevation and upstream flow
(Harleman et al., 1977). The model was originally developed for aerobic, nitrogen-limited
systems and includes detailed simulation of the nitrogen cycle, which includes ammonia, nitrite,
nitrate, phytoplankton-N, zooplankton-N, particulate organic-N, and dissolved organic-N, as well
as salinity, temperature, CBOD, DO, and fecal coliform. The model solves the one-dimensional
continuity and momentum equations to generate the temporal and spatial variations in the tidal
discharges and elevations. This information is used in the solution of the conservation-of-mass
equations for the water quality variables (Najarian and Harleman, 1975).
2.2.3.4 Waterways Experiment Station Model, CE-QUAL-W2
CE-QUAL-W2 is a dynamic 2-D (x-z) model developed for stratified water bodies (WES,
1986). This model is a Corps of Engineers modification of the Laterally Averaged Reservoir
Model (Edinger and Buchak, 1983; Buchak and Edinger, 1984a, 1984b). CE-QUAL-W2
consists of directly coupled hydrodynamic and water quality transport models. Hydrodynamic
computations are influenced by variable water density caused by temperature, salinity, and
dissolved and suspended solids. Developed for reservoirs and narrow, stratified estuaries, CE-
QUAL-W2 can handle a branched and/or looped system with flow and/or head boundary
conditions. With two dimensions depicted, point and nonpoint loadings can be spatially
distributed. Relative to other 2-D models, CE-QUAL-W2 is more efficient and cost-effective.
In addition to temperature, CE-QUAL-W2 simulates as many as 20 other water quality
variables. Primary physical processes included are surface heat transfer, shortwave and
longwave radiation and penetration, convective mixing, wind- and flow-induced mixing,
entrainment of ambient water by pumped-storage inflows, inflow density current placement,
selective withdrawal, and density stratification as impacted by temperature and dissolved and
2-15
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Marina Water Quality Models
suspended solids. Major chemical and biological processes in CE-QUAL-W2 include the effects
on DO of atmospheric exchange, photosynthesis, respiration, organic matter decomposition,
nitrification,and chemical oxidation of reduced substances; uptake, excretion, and regeneration
of phosphorus and nitrogen and nitrification/denitrification under aerobic and anaerobic
conditions; carbon cycling and alkalinity-pH-C02 interactions; trophic relationships for total
phytoplankton; accumulation and decomposition of detritus and organic sediment; and coliform
bacteria mortality.
2.2.3.5 H.S. Chen Water Quality Model, WQM2D
The H.S. Chen model (WQM2D) is a real-time 2-D (x-y) model that simulates
conventional pollutants (Chen, 1978). The hydrodynamic submodel considers inertial force,
convective forces, hydrostatic pressure, wind forces, Coriolis forces, bottom friction, and
internal water column forces due to eddies. The parameters simulated by the model include the
following: conservatives, salinity, coliform bacteria, chlorophyll-a, organic nitrogen, ammonia
nitrogen, nitrite-nitrate nitrogen, organic phosphorus, inorganic phosphorus, CBOD, DO, and
DO deficit. Algal growth, photosynthesis, and respiration are represented in the model, as well
as benthic oxygen demand and bottom releases of ammonia and inorganic phosphorus.
Equations are solved by a finite element technique.
2.2.3.6 M.I.T. Tidal Embayment Analysis and Eulerian-Lagrangian Transport Models,
TEA/ELA
The Tidal Embayment Analysis is a 2-D depth-averaged finite element circulation model
(Westerink et al., 1985). The model computes the spatial variation of surface elevation and
current velocity at nodal points on a finite element triangular grid representing the solution field.
The triangular elements provide a high degree of flexibility for fitting the solution grid to the
complex geometry of many tidal embayments. TEA takes advantage of the periodic nature of
the tidal phenomenon and operates in the frequency domain rather than the time domain. As
a result, TEA is more efficient in terms of computer time than time-stepping models (e.g.,
CAFE-1, Wang and Conor, 1975) for predominantly tidal flow.
ELA is a 2-D, Eulerian-Lagrangian finite element transport model (Kossik et al., 1987).
ELA is driven by hydrodynamic circulation. ELA is presently configured to accept circulation
input as provided by TEA. Other circulation input could be used with ELA with only slight
modification of the code. ELA numerically solves the depth-averaged form of the advection-
diffusion equation.
The TEA/ELA model system is based on a harmonic solution to the governing equations.
The linear version has been used in most applications to date.
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Marina Water Quality Models
2.2.3.7 M.I.T. CAFE-1 and DISPER-1 Models
CAFE-1 is a two-dimensional, depth-averaged finite element circulation model, and
DISPER-1 is a 2-D, depth-averaged finite element dispersion model (Wang and Conor, 1975).
DISPER-1 is presently configured to accept circulation input as provided by CAFE-1. This set
of models, originally developed at the Massachusetts Institute of Technology (M.I.T.), has a
substantial history of successful application. A two-layer version of the model is also available
(Christodoulou et al., 1976). The model predicts contaminant concentration at the nodal points
of a two-dimensional finite element grid representing the solution field.
2.2.3.8 Waterways Experiment Station Open-Channel Flow and Sedimentation Model,
TABS-2
TABS-2 is a generalized numerical modeling system for open-channel flows,
sedimentation, and constituent transport developed and supported by the U.S. Army Corps of
Engineers, Waterways Experiment Station, Hydraulics Laboratory (Thomas and McAnally,
1985). It consists of more than 40 computer programs to perform modeling and related tasks.
The major modeling components, RHA-2V, STUDH, and RMA-4, calculate two-dimensional,
depth-averaged flows, sedimentation, and dispersive transport, respectively. The other programs
in the system perform digitizing, mesh generation, data management, graphical display, output
analysis, and model interfacing tasks. Utilities include file management and automatic
generation of computer job control instructions. TABS-2 has been applied to a variety of
waterways, including rivers, estuaries, bays, and marshes. It is designed for use by engineers
and scientists who may not have a computer background.
Transport calculations with RMA-4 are made using a form of the convection-diffusion
equation that has general source-sink terms. Up to seven conservative substances or substances
requiring a decay term can be routed. The code uses the same grid mesh as RMA-2V.
Recently, an improved two-layer version has been developed to improve the predictive capability
for estuarine circulation (Jin and Raney, 1990).
2.2.3.9 Waterways Experiment Station Implicit Flooding Model, WIFM
W1FM-SAL is a two-dimensional, depth-averaged (x-y) finite difference model that
generates time-varying water surface elevations, velocities, and constituent fields over a space-
staggered grid (Schmalz, 1985). This model was developed by the U.S. Army Corps of
Engineers, Waterways Experiment Station. Units of measure are expressed in the English
system (slug-ft-second). Results computed' on a global grid may be employed as boundary
conditions on a more spatially limited refined grid concentrated around the area of interest. In
addition, the user may select either of two distinct transport schemes. Scheme 1 is a flux-
corrected transport scheme capable of resolving sharp front without oscillation; Scheme 2 is a
full three-time-level scheme directly compatible with the three-time-level hydrodynamics. The
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Marina Water Quality Models
telescoping grid capability, in conjunction with the user-selectable constituent transport scheme,
is a powerful concept in practical transport problem solving.
2.2.3.10 Solute Transport Model for Tidal Canal Networks, CANNET3
CANNET3 is a three-dimensional (x-y-z) time-varying model. This model was
developed by Morris et al. (1977) to investigate solute fate and transport in tidal canal networks.
Water quality variables (e.g., DO) are not considered in the current version (Christensen, 1990).
2.2.3.11 Waterways Experiment Station Three-Dimensional Models, CH3-D and
CBWQM
The most advanced complex models are the 3-D hydrodynamic and water quality models,
which allow for most physical processes to be included. Models in this category include the
CH3-D and CBWQM models of the USACOE (Cerco and Cole, 1989). These models simulate
hydrodynamics, transport of salt, temperature, and other conservative and water quality
constituents. However, fully 3-D models that can predict longitudinal, lateral, and vertical
transport are the most complex and expensive to set up and run. Because of their cost and
complexity, these models have not been widely used. A recent modeling strategy is to drive a
compartment model that has been configured in two or three dimensions with flows and volumes
from a 2-D or 3-D hydrodynamic model. This strategy attempts to combine the transport rigor
of complex models with the convenience, flexibility, and cost efficiency of compartment models.
Examples include recent studies of the Patuxent estuary with a 2-D vertically averaged model
linked to a version of WASP4 and studies of the Chesapeake Bay with the Sheng model linked
to another specially adapted version of WASP4. Plans for CH3-D and CBWQM models are to
generalize the models for any area and eventually to provide a microcomputer version.
2.3 Short-listed Models
2.3.1 Selection Criteria
This section presents the short-listed marina water quality models selected from the many
models and methods described in previous sections. The short list of models that can adequately
describe marina water conditions is given in Table 2-2 for the simple, mid-range, and complex
categories. The selection criteria for including a model on the short list are as follows:
1. Public domain model
2. Ease of application
3. Documentation
4. Constituents modeled
5. Hydrodynamic capabilities
6. Applicability to small systems (i.e., marinas)
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Marina Water Quality Models
2.3.2 Models Selected
Simple marina models are desktop screening methodologies that calculate mean pollutant
concentrations based on steady-state conditions. These models are in the public domain, are
easy to apply, and are well documented (USEPA, 1982 and 1985). The hydrodynamics of
simple models are represented as user-supplied velocity and flow data. These models can be
easily applied to marinas to calculate DO and fecal coliform concentrations.
Marina mid-range models consist of the NCDEM DO Model and the Tidal Prism Model
of Kuo and Neilson (1988). Both models are in the public domain, are easy to apply, and are
supported with good documentation. The NCDEM Model is a steady-state program that is
capable only of predicting DO concentrations. On the other hand, the Tidal Prism Model is a
steady-state model that is capable of simulating up to 10 water quality variables. The
nonconservative substances considered in the model include organic nitrogen, ammonia nitrogen,
nitrate-nitrite nitrogen, organic phosphorus, inorganic phosphorus, phytoplankton (chlorophyll-
a), carbonaceous biochemical oxygen demand, dissolved oxygen, and fecal coliform. The user's
manual is well written and includes input/output examples as well as guidance on how to
calibrate the model.
Complex marina models consist of two components—hydrodynamics and water quality.
In this category, hydrodynamics may be represented by numerical solution of the 1-D (i.e.,
WASP4, DEM) or the full 2-D (i.e., CE-QUAL-W2 and WQM2-D) equations of motion and
continuity. Water quality conservation-of-mass equations are executed using the hydrodynamic
output of water volumes and flows. The water quality component of the model calculates
pollutant dispersion and transformation or decay, giving resultant concentrations over time. All
water quality models discussed in this category are in the public domain and are supported by
public agencies. These models are very complex and require an extensive effort for a specific
application. Table 2-3 summarizes the capabilities of the short-listed models with respect to the
selection criteria.
TABLE 2-2. Short-listed Marina Water Quality Models
Simple Models
Mid-range Models
Complex Models
Tidal Prism Analysis
NCDEM DO
WASP4
Model
Flushing
DEM
Characteristics
Tidal Prism Model
Diagram
MITDNM
CE-QUAL-W2
WQM2-D
2-19
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Marina Water Quality Models
TABLE 2-3. Capabilities of Short-listed Marina Water Quality Models
Public Domain ||
Ease of Application ||
Documentation ||
Constituent
Modeled
Hydrodynamic
Capabilities
Applicability to Small Systems
Conservative
Non-Conservative
Oxygen
Nutrients
Metals
User's Supplied
Timescale
Spatial Dimension
Tidal Prism Analysis
*
s
E
A
A
A
~
~
Flushing Characteristics Diagram
*
s
G
A
~
A
NCDEM DO Model
*
M
G
A
ss
X
~
Tidal Prism Model
*
M
E
A
A
A
A
ss
X
~
WASP
*
C
E
A
A
A
A
A
D
XX
A
DEM
*
c
E
A
A
A
A
D
XX
A
MITDNM
*
c
E
A
A
A
A
A
D
X
A
CE-QUAL-W2
*
c
E
A
A
A
A
D
xz
A
WQM2D
*
c
F
A
A
A
A
D
XY
A
E
Excellent
S
Simple
SS Steady state
X
X-direction
G
Good
M
Mid-range
D Dynamic
XX
Link-node network
F
Fair
C
Complexr
XV
Horizontial direction
P
Poor
XZ
Longitudinal and vertical dimension
2-20
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Marina Water Quality Models
3. MODEL CAPABILITIES
The purpose of Chapter 3 is to compare the capabilities of the various water quality
models applicable to coastal marinas and to select the best qualified simple, mid-range, and
complex models. The best simple model is the Tidal Prism Analysis presented in Chapter 4 of
the Coastal Marina Assessment Handbook (USEPA, 1985). The best mid-range model is the
Tidal Prism Model developed at the Virginia Institute of Marine Science (Kuo, 1976). The
complex model chosen as most applicable for marina water quality assessment is the WASP4
model developed and supported by EPA's Athens Research Laboratory (Ambrose et al., 1987).
The reasons for selecting the above methodologies are presented in greater detail in the
remaining sections of this chapter.
Chapter 3 is divided into three sections. Section 3.1 presents data requirements and the
associated model application costs for applying the short-listed models selected in Section 3.2.
Section 3.2 discusses each model's capabilities and applicability to various marina types with
respect to the typical water quality constituents of interest in marina assessments. Section 3.3
discusses the criteria for selecting the best qualified models. The section includes
recommendations of models appropriate for application to coastal marinas along with a
discussion of the strengths and limitations of each model.
Computer models may be used to predict water quality through the simulation of the
physical and chemical processes of an aquatic system. In this report, the term model, following
commonly used terminology, is used to describe a computer program that simulates water quality
processes. However, strictly speaking, a computer program is not a model until the user
structures it with the geometry, hydrology, loading rates, and rate factors that are representative
of the particular system being analyzed. It is only when this is done that a computer program
can be considered a mathematical model of a particular system.
3.1 Model Overview
This section focuses on describing the input data requirements for marina water quality
models. In general, all mid-range and complex models, except the NCDEM DO model, are
written in FORTRAN 77 and most are machine independent. In addition, mid-range and
complex models can print results of the model simulation and the input data.
As a common rule, storage requirements for marina water quality models increase with
program complexity. For example, input data requirements for simple and NCDEM models are
similar and include.such parameters as tide range, marina surface area, depths, channel
geometry, freshwater inflow, and salinity. These parameters are generally available from
federal, state, or private agencies. On the other hand, complex models are composed of two
components: a hydrodynamic model to predict the circulation patterns and a water quality model
that uses those patterns along with biochemical kinetics to predict concentrations of various water
quality parameters. Since there is no feedback from the water quality model to the
3-1
-------
Marina Water Qualiry Models
hydrodynamic model, all complex models require scratch disks or tapes for storing intermediate
results. These tapes or disks are subsequently used in the water quality submodels or for storing
information to be plotted.
3.1.1 Data Requirements
In the application of most models, there are two fundamental types of data requirements.
First, there are the data needed simply to make the model function, that is, input parameters and
time series data for the model. These data typically include freshwater input, tides, and other
meteorological information. The second type of information, required for the calibration of
more complex models, is measured water quality data with which to test the model.
All marina water quality models require data for input and for calibration. It is best if
model selection is not restricted by availability of data and the decision to acquire the specific
type of data required for the model. However, if data availability is a constraint, selection of
a less sophisticated model than would be warranted on technical grounds may be appropriate.
Table 3-1 summarizes input data requirements for each of the short-listed water quality
models. Input data requirements increase with the complexity of the hydraulics and water
quality mathematical formulations of the system modeled. For example, simple models assume
steady state, which then requires specification of freshwater inflows and average depth at high
and low tide. The more complex models, such as WASP4 and CE-QUAL-W2, solve a form
of the momentum equation, which requires more detailed characterization of the system
geometry and roughness. Similarly, the data required to simulate the nonlinear nutrient-algal-
DO linkage are extensive. The water quality data required, beyond those needed to quantify
transport, will vary depending on how the variables will be used and the anticipated impacts of
the system to changes in the value of the variables. Data requirements will vary if the analysis
is intended for dissolved oxygen, eutrophication, or toxics. For example, variables critical to
an analysis of toxicity, such as pH for ammonia and metals, may not be required if the
parameter of interest is DO. If the response time of the system or the period of interest is less
than the rate of change of a variable, such as bottom demand, then measurement of that variable
may be sufficient. However, if the response time or period of interest is greater than the
variable's rate of change, then it may be necessary to model factors affecting that variable,
requiring collection of supporting data.
3.1.2 Operating Costs
For each modeling study several steps are applied in an iterative process. The first
involves data review and model identification and/or selection. The second step is initial
calibration of the model to existing data. As more data become available, the calibrated model
is tested and refined. After some effort at recalibration and testing, the modeler/analyst decides
3-2
-------
TABLE 3-1. Data Requirements
u>
I
Geometric
Meteorologic
I ly dr au lic/1 ly dr ologic
Water Quality
Tidal Prism
Analysis
surface area and depth
NA
non-tidal freshwater inflow, volume of
tidal prism, and return ratio
ambient/initial concentration of pollutant, mass
of pollutant discharged into basin, and rate
coefficients for kinetic reactions
Rushing
Characteristics
Diagram
surface area, depth, total
length of shoreline inverse
bankslope
wind speed and direction
non-tidal freshwater and groundwater
inflow, tidal amplitude, and return ratio
initial concentration of pollutant
NCDEM DO
Model
segment and channel surface
area and depth
NA
tidal amplitude, and return ratio
ambient and saturation DO concentration,
sediment oxygen demand, channel and manna
boat activities, reaeration and decay coefficient
Tidal Prism
Model
transect distance from mouth
of river, connection scheme,
and segment mean depth
NA
freshwater inflow, tidal prism volume
per segment, return ratio
inflow concentration, temperature, initial and
boundary conditions for all modeled slate
variables expressed as daily average for time
varying application, and reaction rates
WASP4
channel length, width and
direction, connection
scheme, segment surface
area, and depth
time series of solar radiation, wind
speed and direction, photoperiod,
and temperature
coefficients for velocity flow regression
(steady-state), time scries of segment
volume, flow, and bottom roughness
(Manning's n), time series of headwater
and tributary inflows and tides
inflow concentration, temperature, initial and
boundary conditions for all modeled state
variables (time series) and rate coefficients for
kinetic reactions
DEM
channel length, width and
direction, connection
scheme, segment surface
area, and depth
time varying meteorological and
climalological data, including cloud
cover, dry and wet bulb air
temperature, atmospheric pressure,
wind speed and direction,
precipitation and photoperiod
tributary and ground water inflows,
time varying tidal slage and currents,
time series of segment volume, flow,
and bottom roughness rating curves
and/or stage/routing
initial in situ water quality parameters
concentration, time-varying water quality
variables at boundaries, tributary inflows and
waste discharges, time-varying stormwater
inflow and quality characteristics, rate
coefficients for kinetic reactions
M1TDNM
side and bottom slope of
channel, length of reach,
cross-section area,
connection scheme and
bottom elevation
time varying of ambient
temperature, relative humidity,
wind speed, net solar flux, net
atmospheric flux, atmospheric
pressure, and solar radiation
time series of headwater and tributary
inflow, Chezy coefficient, and tidal
elevation
initial in situ water quality parameters
concentration, time-varying water quality
concentration at boundaries, inflow
concentration of water quality variables, and
rate coefficients for kinetic reactions
CE-QUAL-W2
segment length, width and
depth, and layer thickness
for main channel and
branches
time varying meteorological data
including wind speed, coefficient of
surface heat exchange, equilibrium
temperature, solar radiation, and
attenuation coefficient
time series of headwater and tributary
inflows. Manning's n, and tidal
elevation
inflow concentration, temperature, nonpoinl
inflows, and rate coefficients for kinetic
reactions
WQM2-D
element surface area and
depth
wind speed and direction
time series of headwater and tributary
inflows, Manning's n, and tidal
elevation
inflow concentration, temperature, initial and
boundary conditions for all modeled state
variables (lime series), and rale coefficients lor
kinetic reactions
1
2
S'
&
I
to
C
a-
G-
-------
Marina Water Quality Models
either that the model is sufficiently reliable to produce sound results or that available time and
resources do not permit continued refinement. Since these iterative processes are problem
dependent, it is difficult to estimate overall costs involved in a model application. Each
application differs in scope and complexity, and the ability to solve or avoid certain problems
is very dependent on the experience and technical background of the analysts involved.
However, machine requirements and costs associated with typical runs are usually estimated in
the program documentation. As a rule, the simpler the model, the less expensive it is to apply.
Also, it is essential that the support agency and other experienced professionals be contacted for
information or assistance.
Information presented in Table 3-2 is primarily nontechnical and is related to operational
features of the short-listed models. This information is provided to evaluate the cost associated
with and the ease of acquiring the model, getting the model running on the system, calibrating
the model, and finally applying the model. The information provided in Table 3-2 is primarily
qualitative and sufficient to determine whether a model may be suitable for a practical
application. For complete information the potential user must consult the appropriate user's
manuals and other supporting documentation. The Center for Exposure Assessment Modeling
(CEAM), EPA Environmental Research Laboratory, Athens, Georgia (Mr. Thomas O. Barnwell)
is a good source of information and technical support.
TABLE 3-2. Approximate Operating Costs
Dimensionality
Water-Quality Problem
Approximate
Level of Effort
1-D steady state
1-D, 2-D steady state
2-D, 3-D time
variable
DO,BOD, nutrient
DO, BOD, nutrient,
phytoplankton, toxics
DO, BOD, nutrient,
phytoplankton, toxics
1-2 person-months
1-4 person-months
3-12 person-months
3.2 Model Appropriateness
Table 3-3 presents the short-listed models that can adequately describe marina water
quality conditions and summarizes the water quality constituents that can be simulated by these
models. With the exception of the NCDEM Model, all mid-range and complex models address
salinity and bacteria either explicitly or by specifying the appropriate first-order decay constant
for another state variable. This table compares the short-listed models with respect to the
constituents simulated. The models vary significantly in terms of the number and constituents
for which calculations are performed. The number of constituents analyzed usually reflects the
number and complexity of biochemical processes simulated. For example, NCDEM is limited
to steady-state DO analyses, while WASP4 and other complex models can be used for dissolved
3-4
-------
TABLE 3-3. Constituents Included in Model
i
Coliform
Bactcna
DO
CBOD
or total
BOD
NBOD
SOD
Temp
Tot P
Org P
PO,
Tot N
Org N
NH,
NO,
NO,
Carbon
Algae or
Chl-a
Zoo-
plank-
ton
Salinity
Conser-
vative
pH
Alk
TDS
Tidal Prism Analysis
X
X
X
Flushing
Characteristic
Diagram
X
NCDEM DO Model
X
Tidal Prism Model
X
X
X
•
X
X
X
X
X
X
X
X
WASP4
X
X
A
•
•
X
X
X
X
X
X
X
X
•
X
DP-M
X
X
X
X
•
X
X
X
X
X
X
X
X
X
MITDNM
X
X
X
X
X
X
X
X
X
X
X
X
X
CE-QUAL W2
X
X
X
X
X
X
X
X
X
X
X
X
X
X
WQM2-D
X
X
X
X
•
X
X
X
X
specified by model users
NBOD simulated as aitnficatioD of ammonia S
5
a
|
*o
c
6
§
cr
-------
Marina Water Quality Models
oxygen analyses as well as eutrophication analyses. The later models simulate the effects of
photosynthesis, respiration, and temperature on diurnal variations of dissolved oxygen. Complex
models are truly dynamic since they simulate continuous temporal variations in systems
hydraulics and waste loadings. The Tidal Prism Model assumes that these features remain
constant but allows water quality conditions to vary (quasi-dynamic). The following sections
discuss model capabilities when applied to a variety of marina types (including one-segment,
two-segment, open-water, and flow-through marinas) and the usefulness of each model for
determining water quality impacts.
3.2.1 Marina Type
Table 3-4 presents the appropriateness of each of the short-listed models when applied
to each marina type considered in this study. Mid-range models are not applicable to flow-
through marina types. However, the Tidal Prism Model is capable of addressing all water quality
problems in open and semi-enclosed marina types.
TABLE 3-4. Applicability of Short-listed Models to Marina Type
Open
Vater
Semi-Enclosed
Conservative
Non-
conservative
One-Segment
Two-Segment
Flow-Through
Conservative
Non-
conservative
Conservative
Non-
conservative
Conservative
Non-
conservative
Tidal Prism
Analysis
X
NA
X
X
NA
NA
NA
NA
Flushing
Characteristics
Diagram
X
NA
X
NA
X
NA
X
NA
NCDEM DO
Model
NA
•
NA
•
NA
•
NA
NA
Tidal Prism
Model
X
X
X
X
X
X
NA
NA
WASP4
X
X
X
X
X
X
X
X
DEM
X
X
X
X
X
X
X
X
MITDNM
X
X
X
X
X
X
X
X
CE-QUAL-W2
X
X
X
X
X
X
X
X
WQM2-D
X
X
X
X
X
X
X
X
• DO only
X other water quality constituents in addition to DO
NA Not Applicable
3-6
-------
Marina Water Quality Models
All complex models are applicable to all marina types considered in this study (i.e., open
water, one-segment, two-segment, and flow-through marinas). Data management and meeting
data requirements, as well as the level of effort involved to set up, run, calibrate, and apply
these models, are formidable-tasks for the inexperienced analyst. Good, defensible model results
are dependent on a careful, detailed application of the model to the specific site.
3.2.2 Constituents Modeled
The purpose of this section is to evaluate the usefulness of each short-listed model for
determining water quality degradation (e.g., how well the model can predict dissolved oxygen
variations or fecal coliform bacteria).
3.2.2.1 Dissolved Oxygen
Adequate sustained DO concentrations are required for the survival of most aquatic
organisms. Seasonal or diurnal depletion of dissolved oxygen disrupts or displaces aquatic
communities. Ambient DO levels are affected by many natural processes, such as oxidation of
organic material, nitrification, reaeration, decay, settling of CBOD, and
photosynthesis/respiration. The natural balance can be disrupted by excessive wastewater loads
of organic material, ammonia, and nutrients. Other sources of nutrients, such as runoff from
agricultural, residential, and urban lands and atmospheric deposition, also can disrupt the DO
balance. Excessive heat input from power plants can aggravate existing problems. Because of
its intrinsic importance, and because it is affected by so many natural and human-influenced
processes, DO has been selected as the best indicator of water quality problems in coastal
marinas.
Dissolved oxygen dynamics depend on the interactions of several constituents and
processes. The constituents include dissolved oxygen, carbonaceous BOD, nitrogenous BOD
(ammonia and nitrite), temperature, and in some cases phytoplankton, periphyton, and aquatic
plants. The major processes include (Figure 3-1):
• Reaeration;
• BOD reactions;
• Sediment oxygen demand (SOD);
• Photosynthesis, respiration; and
• Nitrification.
Reaeration
Atmospheric reaeration is the process of mass exchange of oxygen between the
atmosphere and the surface layer of the water column. Typically, the net transfer of oxygen
is from the atmosphere into the water since the oxygen levels in the water are usually less than
the saturated concentration. The mass flux of exchange of oxygen across the air-water interface
3-7
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Marina Water Quality Models
Figure 3-1. Processes Affecting Dissolved Oxygen.
3-8
-------
Marina Water Quality Models
is modeled as a first-order kinetic process that is dependent on the oxygen mass transfer
coefficient, the local oxygen concentration in the water, and the saturation concentration of
oxygen. Table 3-5 presents model capabilities with respect to reaeration formulation. Most
marina water quality models- permit direct input of the reaeration coefficient or selection from
several commonly used correlations or methods. For example, simple and mid-range models
permit only direct input of the reaeration coefficient. On the other hand, WASP4 allows the
user to choose from five options of the reaeration coefficient.
TABLE 3-5. Model Capabilities: Reaeration Formulations
Model
Options
Tidal Prism Analysis
One option, input directly
Flushing Characteristics Diagram
NA
NCDEM DO Model
One option: input directly
Tidal Prism Model
Two options: input directly, calculated as a function of velocity and depth
(O'Connor and Dobbins, 1958)
WASP4
Five options: Churchill, el al., O'Connor and Dobbins, Owens et al.,
Covar's, and wind-dependent reaeration for lakes and estuaries
DEM
One option: input directly
MITDNM
Two options: input directly, calculated as a function of channel velocity
and geometry
CE-QUAL-W2
No reaeration coefficient: instead the model calculates interfacial exchange
rate (wind speed dependent) according to Kanwisher's (1963) or Mackay's
(1980) formulation
WQM2-D
One option
BOD Reactions
Biochemical oxygen demand (BOD) is a measure of the biodegradable material oxidized
in a stream or a sample of the stream water. Both carbonaceous organic and nitrogenous
compounds are oxidized. These forms of BOD are distinguish as CBOD and NBOD. All
nitrogenous oxygen demand is labeled NBOD. NBOD is determined from the Kjeldahl nitrogen
(organic plus ammonia nitrogen) measurements of the water.
In the past, models have simulated both NBOD and BOD (total), but these demands were
difficult to calibrate reliably and did not result in well-calibrated predictive models. Modeling
NBOD and CBOD as separate demands has been more reliable than modeling total BOD;
however, this approach is not as accurate as modeling CBOD, ammonia, nitrite, and nitrate
separately. Some of the early total BOD models ignored sediment oxygen demand (SOD) by
combining these effects with simulations of total BOD. These models could be calibrated with
some difficulty (i.e., the BOD decay rate coefficient was selected so simulations matched
measurements of BOD) when SOD was a small effect, but these calibrations lacked predictive
3-9
-------
Marina Water Quality Models
validity. Recently water quality models (e.g., MITDNM and WASP4) tend to represent
carbonaceous oxygen-demanding materials as CBOD and nitrogenous materials as the various
species of the nitrogen cycle (i.e., organic nitrogen, ammonia, nitrite, and nitrate) rather than
the traditional NBOD.
Sediment Oxygen Demand (SOD)
Chemical reduction and bacterial respiration of organic matter that occur in sediments
create a demand for oxygen from overlying waters. This sediment oxygen demand (SOD) can
strongly influence oxygen conditions in a water column; therefore, SOD is an important
component of models that predict oxygen concentrations. Decomposition of organic matter and
respiration of resident invertebrates form the major oxygen demands from the sediment.
Although these processes are distinct, they are typically modeled together since in situ forms of
measurement combine oxygen uptake and separation would result in additional model
complexity.
SOD rates are highly site-specific and are influenced by substrate composition, sediment
organic content, and environmental factors such as temperature (Hatcher, 1986). For example,
SOD rates were an essential component of the NCDEM DO model used to evaluate DO changes
due to coastal marinas (NCDEM, 1990). However, data collected during this study were
insufficient to determine whether the observed rates were typical or what the expected range of
SOD rates might be for coastal marinas. Therefore, marina water quality models should use
site-specific SOD rates as actual estimates can range over several orders of magnitude (Bowie
et al., 1985; NCDEM, 1990). Although often of critical importance, the predictive capability
of most presently available models of sediment interactions is limited. In most models
description of these impacts is often reduced to field measurements followed by use of zeroth
order rate to describe sediment interactions and their effects on other variables and processes.
For example, WASP4, WQM2-D, and DEM are the only complex models that account for SOD
rates.
Photosynthesis and Respiration
Through the biological processes of photosynthesis and respiration, phytoplankton,
periphyton, and rooted aquatic plants (macrophytes) can exert a significant influence on dissolved
oxygen levels. The mass flux source and sink of dissolved oxygen from photosynthesis and
respiration of aquatic plants in a system are dependent on plant biomass, water temperature,
saturated growth rate, the availability of light in the water column and benthos, and the
respiration rate. All complex models account for the effects of photosynthesis and respiration
on dissolved oxygen concentration in a marina.
Nitrification
In natural waters, nitrogen consists of organic constituents (dissolved and particulate) and
inorganic dissolved constituents (ammonia, nitrite, nitrate) and nitrogen gas. The species of
nitrogen undergo a sequence of chemical (hydrolysis) and bacterially mediated (nitrification and
3-10
-------
Marina Water Quality Models
denitrification) reactions that result in the sequential transformation of the various species of
nitrogen. Organic nitrogen is initially hydrolyzed to ammonia. Through the bacterially
mediated aerobic two-stage process of nitrification, ammonia is first transformed to nitrite and
nitrite is then converted to nitrate. Under anaerobic conditions in either the sediments or the
water column, the bacterially mediated process of denitrification converts nitrate to nitrogen gas.
The gaseous form of nitrogen is then lost across the air-water interface to the atmosphere. Some
short-listed marina models (i.e., DEM, CE-QUAL-W2, and WQM2-D) account for a segment
or a fraction of the nitrogen cycle, while others (i.e., WASP4 and MITDNM) are capable of
modeling all major constituents in the nitrogen cycle. The mass flux reduction of organic
nitrogen and ammonia in a marina is modeled as a first-order kinetic process that is dependent
on the organic nitrogen hydrolysis rate, the organic nitrogen settling/removal rate, nitrification,
the local organic nitrogen and ammonia concentrations in the water, and the local water
temperature.
3.2.2.2 Fecal Coliform Bacteria
The abundance of coliform bacteria has traditionally been used as an indicator of
pathogen contamination. Standards and criteria have been formulated and promulgated based
on coliform concentrations to indicate the safety of water for drinking or recreational purposes.
The discharge of sanitary waste from boats in the vicinity of marinas has the potential to
contaminate adjacent shellfish beds and to pose a serious public health risk if the shellfish are
harvested for human consumption. The potential for pathogenic contamination of shellfish-
producing water bodies can be assessed by determining the concentration of total coliform or
fecal coliform organisms in the water body. Predictions of coliform bacteria are, therefore,
important because of their impact on project purposes such as recreation and water supply.
With the exception of the Flushing Characteristics Diagram and NCDEM DO models,
all short-listed models are capable of calculating fecal coliform concentrations in a coastal
marina.
3-11
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Marina Water Quality Models
4. RECOMMENDED MODELS
4.1 Selection Criteria
Marina areas contribute pollutants from the sanitary wastes of their human occupants, as
well as from materials leaching from hulls or discharging with engine exhausts. These wastes
pose a variety of potential problems for water quality including microbiological contamination
of adjacent shellfish and swimming areas, depletion of dissolved oxygen in the water column or
sediments, and toxic effects on estuarine biological resources. The use of an area for a marina
may infringe or preclude other uses of the resources, and it is this potential conflict that must
be evaluated through the use of a water quality model.
To understand what is required of a model, it is essential to focus on the physical,
chemical, and biological processes that move water into and out of the marina area, control
mixing with adjacent waters, regulate chemical reactions in the water and sediments, and
facilitate biological growth and decay (die-off). A variable combination of winds, tides,
currents, and density differences is responsible for the physical movement of water volumes and
pollutants. Numerous references are available which describe these processes and the
relationships between the physical processes and the changes in concentrations of dissolved or
suspended particulate materials (Officer, 1976; Thomann and Mueller, 1987; Moffatt and
Nichol, 1989; and Biswas, 1981). The geometry of a site can also have a major effect on
flushing and dispersion and is an important issue in selecting the model, collecting the data, and
attaining the required water quality standards (Tetra Tech, 1988).
Biodegradation of organic material, growth and decay of bacteria and other organisms,
nutrient uptake, and chemical transformations of various kinds are typical of biochemical
processes affecting the contaminants of interest. The capability of various models to describe
these reactions, which are termed nonconservative processes, ranges from a first-order decay
term added to the basic hydrodynamic equations for the simplest models to complex kinetic
expressions for the most advanced models (Thomann and Mueller, 1987; Mills et al., 1985).
Physical, chemical, and biological processes must be combined to form a conceptual
model of the site and its consequent contaminant assimilation potential. After the site in question
has been conceptualized, the next step is to choose a model that incorporates the appropriate
physical processes and biochemistry to predict water quality. Depending on the sophistication
level at which the assessment is taking place, the model selected may be a simple screening
calculation (e.g., Tidal Prism Analysis) or a multidimensional numerical model (e.g., WASP4,
DEM, and WQM2D). This approach guides the user to the models that are most appropriate
under various situations; for example, tidal prism model where tide level changes are the
predominant mode of material exchange, a freshwater fraction model where upstream inflow and
density differences are dominant, and so on.
4-1
-------
Marina Water Quality Models
The short-listed models discussed in Chapter 3 are capable of simulating water quality
constituent variations in a coastal marina. These models have been selected for the following
reasons:
• They are in the public domain.
• They are available at a minimal cost from various public agencies.
• They are supported to a varying extent by federal and/or state agencies. The form
of support is generally telephone contact with a staff of engineers and programmers
who have experience with the model and provide guidance (usually free of charge).
• They have been used extensively for various purposes and are generally accepted by
the profession.
• Together they form a sequence of increasingly more technically complex models;
i.e., each model takes additional phenomena into account in a more detailed manner
than the preceding model.
Selection from among these models is made on the basis of the needed model capabilities.
In addition to model capabilities, the two most important factors in the selection of a
model are the adequacy of the documentation and the adequacy of the support available. The
documentation should state the theory and assumptions in adequate detail, describe the program
organization, and clearly present the input data requirements and format. A well-organized data
scheme is essential. The support provided by the support agency should include user access via
telephone to programmers and engineers familiar with the model. It may be possible that special
support (including short courses or informational or personnel exchanges) is available under
existing intra-agency or interagency agreements or otherwise could be made available to the
potential user. The support agency may also be able to provide the potential user with a list of
local users who could be contacted for information regarding their past or current experience
with the computer program. Table 4-1 presents documentation and user's support available for
the short-listed models.
In addition to having adequate documentation and user's support, the selected model must
address all marina water quality problems of concern. For example, for the Flushing
Characteristics Diagram, Christensen (1989) stated the theory and the assumptions made in his
model in adequate detail. However, the model is not capable of simulating water quality
variables such as DO or fecal coliform and therefore was excluded from further consideration.
r
The following section provides an overview of the best qualified marina water quality
model in each of the selected categories. These models are listed in Table 4-2, which provides
information related to the operational features of the models. This information is provided to
evaluate the estimated cost associated with and the ease of acquiring the model, getting the
model running on the user's system, calibrating the model, and finally applying the model.
4-2
-------
TABLE 4-1. Ease of Application: Sources, Support, and Documentation
i
Uj
Model
Sources) of Model
Nature of Support
Reference
Adequacy of Documentation
Tidal Prism Analysis
NA
NA
EPA (1985)
Mills et al. (1985)
Excellent documentation with
example application
Flushing Characteristics
Diagram
NA
NA
Chnstensen (1989)
Good illustrations with
numerical example application
NCDEM DO Model
North Carolina Dept. of
Environmental Health and
Natural Resources, Division of
Environmental Management
(919) 733-6510
Telephone contact
NCDEHNR (1990)
Good documentation with
several applications
Tidal Prism Model
Virginia Institute of Marine
Science, Gloucester Point, VA
23062 (804) 642-7212
Telephone contact
Diana et al. (1987)
Excellent documentation of
theory and assumptions;
excellent user's guide with inpul
and output information
WASP4
Center for Exposure Assessment
Modeling, U.S. Environmental
Protection Agency, Athens, GA
30613 (404) 546-3585
Software maintenance,
workshop technical assistance
through EPA channels
Ambrose el al. (1987)
Excellent documentation of
theory and assumptions,
excellent user's guide with input
and output information
DEM
None
Roesch et al. (1979)
Genet et al. (1974)
Chen and Orlob (1972)
Excellent documentation of
theory and assumptions;
excellent user's guide with input
and output information
MITDNM
None
Najarian and Harleman
(1975)
Harleman et al. (1977)
USEPA (1977)
Thatcher et al. (1975)
Excellent documentation of
theory and assumptions;
excellent user's guide with inpul
and output information
CE-QUAL-W2
U.S. Army Engineer
Waterways Experiment Station,
Environmental and Hydraulics
Laboratories, P.O. Box 631,
Vicksburg, MS 39180-0631
(601) 634-5069
Telephone contact
Waterways Experiment
Station (1986)
Excellent documentation of
theory and assumptions;
excellent user's guide with input
and output information
WQM2-D
Virginia Institute of Marine
Science, Gloucester Point, VA
23062 (804) 642-7212
None
Chen et al. (1979)
Chen et al (1978)
Good documentation of theory
and application with the user's
guide almost nonexistent
§
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Marina Water Quality Models
TABLE 4-2. Approximate Operating Costs for Best Qualified Models
Complexity
Model
Water Quality Problem
Approximate
Level of Effort
Simple
Tidal Prism Analysis
DO, fecal coliform
1-2 Days
Mid-range
Tidal Prism Model
DO, BOD, nutrient, phytoplankton,
fecal coliform
3-7 Days
Mid-range
NCDEM DO
DO
1-2 Days
Complex
WASP4
DO, BOD, nutrient, phytoplankton,
toxics, fecal coliform
3-4 Weeks
4.2 Models Selected
The most rigorous tools that can be used for assessing marina impacts on water quality
are numerical models. Models range in complexity from simple desktop calculations to full
three-dimensional models that simulate physical and chemical processes by solving equations of
motion and rate equations for chemical processes.
Model complexity will determine the degree of resolution in the results. For example,
in an early part of a study the Tidal Prism Analysis strategy is used to obtain a general
understanding of potential impacts caused by pollutant discharged from a proposed marina. It
is likely that the simplified strategy will predict substantial impacts to the environment.
Therefore, an advanced model is required to conduct further detailed analyses. The mid-range
model is used in situations where steady-state conditions may be assumed and tidal flushing is
the predominant mode of flushing. The complex model is used in dynamic environments subject
to estuarine circulation and full biochemical kinetics with sources and sinks for all dissolved
constituents and for proposed marinas.
4.2.1 Simple Model
The methods listed here include desktop screening methodologies that calculate seasonal
or annual mean pollutant concentrations based on steady-state conditions and simplified flushing
time estimates. These models are designed to examine and isolate trouble spots for more
detailed analyses. They should be used to highlight major water quality issues and important
data gaps in the early stage of a study.
The impact .assessment methods presented in Chapter 4 of the Coastal Marina
Assessment Handbook (USEPA, 1985) are appropriate screening tools. Methods presented in
this chapter, particularly some of the mathematical descriptions, are simplifications of more
sophisticated techniques. These techniques, as presented, can provide reasonable approximations
for screening potential impact problems when site-specific data are not available. Water Quality
Assessment: A Screening Procedure for Toxic and Conventional Pollutants (Mills et al., 1985)
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Marina Water Quality Models
provides additional and more detailed descriptions of screening methodologies. The Tidal Prism
Analysis was selected as the method of choice in this category. This method is capable of
addressing all marina water quality issues of concern (e.g., DO and fecal coliform) and comes
with an excellent source of documentation. The primary strengths and advantages of the EPA
screening procedures are as follows:
1. Excellent user documentation and guidance.
2. No computer requirements since the procedures can be preformed on hand
calculators.
3. Relatively simple procedures with minimal data requirements that can be satisfied
from the user's manual when site-specific data are lacking.
The Tidal Prism Analysis procedures can be easily implemented in a computer program.
This will allow the user to test model sensitivity and determine the range of potential water
quality impacts from a proposed marina quickly and efficiently.
4.2.2 Mid-range Models
The recommended marina mid-range models are the Tidal Prism Model and the NCDEM
DO Model. Both models are in the public domain, are easy to apply, and are supported with
good documentation.
4.2.2.1 Tidal Prism Model
The Tidal Prism Model is a steady-state model that is capable of simulating up to 10
water quality variables including dissolved oxygen and fecal coliform. The user's manual is well
written and includes input/output examples as well as guidance on how to calibrate and apply
the model. Based on constituents modeled, the Tidal Prism Model is recommended as the best
qualified marina mid-range model. The primary strengths and advantages of the Tidal Prism
Model are as follows:
1. Excellent user documentation and guidance.
2. Minimal computer storage requirements.
3. Relatively simple procedures with data requirements that can be satisfied from
existing data when site-specific time series data are lacking.
The Tidal Prism Model is applicable only to marinas where tidal forces are predominant
with oscillating flow (e.g., an estuary or a tidal river). Therefore, the Tidal Prism Model can
not be applied to marinas located on a sound or an open sea. Since the Tidal Prism Model is
not applicable to the majority of marina situations, the NCDEM DO model is recommended as
an alternative best qualified model for mid-range applications when the Tidal Prism Model can
not be applied.
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Marina Water Quality Models
4.2.2.2 NCDEM DO Model
The NCDEM DO model is a steady-state program that is only capable of predicting DO
concentrations. The NCDEM DO model is applicable to one-, two-, and three-segment marinas.
Model theory, assumptions, and input parameters are presented in adequate detail (NCDEM,
1990). Model documentation includes input and output examples of several applications as well
as a listing of the model code. The model code is written in BASIC.
The NCDEM DO model incrementally mixes the ambient and marina waters as a
function of the average lunar tides. The tidal variation is assumed to follow a sinusoidal
distribution. For simplicity, a 12-hour tidal cycle is used. By running this time-variable model
through a sufficient number of tidal cycles, the average marina basin DO value will approach
a steady-state value.
4.2.3 Complex Model
Complex models consist of two components: hydrodynamics and water quality. In this
model category, hydrodynamics may be represented by numerical solution of the 1-D or the full
2-D equations of motion and continuity. Water quality conservation-of-mass equations are
executed using the hydrodynamic output of water volumes and flows. The water quality
component of the model calculates pollutant dispersion and transformation or decay, giving
resultant concentrations over time. These models are very complex and require an extensive
effort for specific applications.
Complex models can be further divided into two categories according to number of
dimensions. The first group is the 1-D or quasi-2-D models, which include WASP4, DEM, and
MIT DNM. The second group, the full 2-D models, includes CE-QUAL-W2 and WQM2D.
WQM2D is a real-time 2-D model that simulates conventional pollutants such as dissolved
oxygen and fecal coliform (Chen, 1978). This model was originally developed for EPA, but the
model is not currently supported by any state or federal agencies. In addition, the existing
documentation for the model does not include a user's manual; therefore, WQM2D is excluded
from the final list of recommended models.
CE-QUAL-W2 is a dynamic 2-D model developed for stratified waterbodies (WES,
1986). This model consists of directly coupled hydrodynamic and water quality transport
models. Hydrodynamic computations are influenced by variable water density caused by
temperature, salinity, and dissolved and suspended solids. The CE-QUAL-W2 model can handle
a branched and/or looped system with flow and/or head boundary conditions. With two
dimensions depicted, point and nonpoint, loadings can be spatially distributed. In addition to
temperature, CE-QUAL-W2 simulates as many as 20 other water quality variables including
dissolved oxygen and fecal coliform. Relative to other 2-D models, CE-QUAL-W2 is efficient
and cost-effective. The existing documentation includes a user's manual, which contains an
explanation of theory and numerical procedures, data needs, data input format, and a description
of the associated software. However, user support is almost nonexistent, and therefore CE-
QUAL-W2 is excluded from the final list of recommended marina models.
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Marina Water Quality Models
DEM is a quasi-2-D model that represents tidal flow in the lateral and longitudinal
directions with a branching link-node network (Genet et al., 1974). The overall two-dimensional
DEM model is composed of three separate components: a hydrodynamic model (HYD1), a
dynamic quality model (DQUAL), and a steady-state quality model (AQUAL). The first uses
the equation of motion and continuity to calculate channel flows and nodal volume changes in
response to wind and tidal boundary fluctuations. Dynamic and/or steady-state results, averaged
over a complete tidal cycle, are stored on disk files to be used repeatedly in the calibration of
the quality models. Once the physical transport mechanisms of water flow and velocities are
determined, the biological and chemical reactions can be superimposed to calculate water quality
at any location and time. The DEM model can be used to simulate up to nine water quality
parameters including fecal coliform bacteria and dissolved oxygen in a coastal marina. The
documentation, which includes a user's manual, is an excellent source of information. On the
other hand, no user's support is currently available for the DEM model, and consequently, the
model is excluded from the final list of recommended marina models.
MITDNM is a one-dimensional model that uses a finite-element, branching network to
simulate the flow regime of an estuary with unsteady tidal elevation and upstream flow
(Harleman et al., 1977). The model was originally developed for aerobic, nitrogen-limited
systems and includes detailed simulation of the nitrogen cycle, as well as dissolved oxygen and
fecal coliform. The model solves the one-dimensional continuity and momentum equations to
generate the temporal and spatial variations in the tidal discharges and elevations. This
information is used in the solution of the conservation-of-mass equations for the water quality
variables (Najarian and Harleman, 1975). As is the case for most models considered in this
section, the MITDNM user's guide is an excellent source of information, but currently no user's
support is available. Hence, the MITDNM is also excluded from the final list of recommended
marina models.
The final model in this category is the Water Quality Analysis Simulation Program,
WASP4 (Ambrose et al., 1987). This program is a dynamic compartment modeling system that
can be used to analyze a variety of water quality problems in one, two, or three dimensions.
WASP4 simulates the transport and transformation of conventional and toxic pollutants in the
water column and benthos of ponds, streams, lakes, reservoirs, rivers, estuaries, and coastal
waters. The WASP4 modeling system covers four major subjects: hydrodynamics, conservative
mass transport, eutrophication-dissolved oxygen kinetics, and toxic chemical-sediment dynamics.
The modeling system also includes a stand-alone hydrodynamic program, DYNHYD4, that
simulates the movement of water. DYNHYD4 is a link-node model that may be driven by either
constantly repetitive or variable tides. Unsteady inflows may be specified, as well as wind that
varies in speed and direction. DYNHYD4 produces an output file of flows and volumes that
can be read by WASP4 during the water quality simulation. WASP4 contains two separate
kinetic submodels, EUTR04 and TOXI4. EUTR04 is a simplified version of the Potomac
Eutrophication Model (PEM) and is designed to simulate most conventional pollutant problems.
EUTR04 can simulate up to eight state variables including dissolved oxygen and fecal coliform.
TOXI4 simulates organic chemicals, metals, and sediment in the water column and underlying
bed.
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Marina Water Quality Models
The WASP4 model system is supported by the U.S. EPA Center for Exposure
Assessment Modeling (CEAM), Athens, Georgia, and has been applied to many aquatic
environments. The WASP4 model may be obtained over the CEAM electronic bulletin board
system, or by mailing the appropriate number of diskettes to CEAM. The water quality
component is set up for a wide range of pollutants and the model is the most versatile and most
widely applicable of all models considered in this study. For these reasons WASP4 is the model
of choice in this category. The primary strengths and advantages of the WASP4 model are as
follows:
1. Documentation: WASP4 has excellent user documentation and guidance. Theory
and assumptions are presented in adequate detail; program organization and input data
requirements and format are clearly presented.
2. Support: User access is available via telephone to programmers and engineers
familiar with the model. Occasional workshops, sponsored by the EPA Center for
Exposure Assessment Modeling, Athens, Georgia, are available. The support agency
can provide the potential user with a list of local users who could be contacted for
information regarding their past or current experience with the computer program.
3. Flexibility: Model users can add their own subroutines to model other constituents
that may be more important to the specific application with minimum or virtually no
programming effort required. WASP4 can be operated by the user at various levels
of complexity to simulate some or all of these variables and interactions.
The Center for Exposure Assessment Modeling maintains and updates software for
WASP4 and the associated programs. Continuing model development and testing within the
CEAM community will likely lead to further enhancements and developments of the WASP4
modeling system. In fact, USEPA CEAM is currently supporting the development of a 3-D
hydrodynamic model that will be linked to the WASP4 model.
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Marina Water Quality Models
5. DATA COLLECTION PLAN
The two main purposes of this section are (1) to help water quality specialists design a
monitoring plan to support modeling activities for a proposed coastal marina and (2) to assist in
the design of a monitoring plan for an existing coastal marina. The planner is guided through
the data collection process so that the models used for marina water quality analysis can be
applied to critical design conditions.
For a number of aspects of water quality sampling, significant reference material already
exists, including equipment requirements, personnel requirements, collection of water quality
samples, and laboratory analytical techniques for analyzing samples. These facets will not be
discussed further in this report.
A secondary purpose of this section is to educate field personnel on the relationship
between sampling requirements and water quality modeling needs. Field personnel may
sometimes not fully understand why historical data are not adequate to meet study objectives,
why specially designed surveys are required to generate the data, and what the rationale is for
selecting certain sampling locations and parameters. By understanding these factors, field
personnel are more likely to perform their tasks more effectively. Moreover, when unforeseen
field conditions indicate the necessity for a change in sampling strategy, they have a better basis
for deciding how to modify the sampling program design.
Planning a monitoring study should be a collaborative effort of participants involved in
field collection, analysis, data processing, quality assurance, data management, modeling, and
budgeting activities. Close collaboration ensures that the fundamental design elements are
properly stated so that the available resources are used in an efficient manner.
5.1 General Considerations
The level of sampling effort is proportionate to the complexity of the model used. For
example, the Tidal Prism Analysis calculations and the NCDEM DO model require only
summary data, while WASP4 needs detailed bathymetry and time series data for both physical
and water quality parameters.
The type and amount of data will depend largely on the following: (1) study objectives,
(2) system characteristics, (3) data presently available, (4) modeling approach selected, (5)
degree of confidence required for the modeling results, (6) project resources, and (7) whether
the marina site is an existing or a proposed site. Each of these factors should be considered in
the planning stage of the sampling effort in order to formulate fundamental questions that can
be used in the data collection design.
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Marina Water Quality Models
5.1.1 Study Objectives
The study objectives will often determine the degree of effort required for data collection.
The objectives should be clearly defined and well known prior to the planning of any monitoring
study. Obviously, the purpose of such a study will be to assess the potential impacts of a
proposed marina on coastal water quality. For an existing marina, the objective of the
monitoring program is to determine compliance with water quality standards inside and outside
a coastal marina. For example, the monitoring program must be of much higher resolution if
the main objective is to define hourly variations than if the objective is to determine the mean
or overall effect of an existing or proposed marina on the adjacent water body. Until all
objectives are defined it will be difficult to establish the basic criteria for a monitoring study.
5.1.2 System Characteristics
Each marina site is unique, and the scope of the sampling effort should be related to the
problems and characteristics of that particular system. The particular advantages of models are
that they can be used to interpolate between known events and to extrapolate to conditions for
which data are not available. The kind of data required is determined by the characteristics of
the system, the dominant processes controlling the constituent investigated, and the time and
space scales of interest. The selection of modeled processes and degree of resolution will be the
driving force in determining the sampling required. Water quality model results are limited by
the data used to calibrate the hydrodynamic submodel and the kinetic rate coefficients. If data
are collected during a major storm, then the model may provide valid results only during storm
events.
5.1.3 Data Availability
Some data must be available to make initial judgments as to the location and frequency
of sampling as well as to make decisions concerning the selection and application of the marina
model. Where data are not available for the constituents of interest, it may be necessary to use
some alternative surrogate parameters for these initial judgments, such as, possibly, suspended
solids for strongly sorbed constituents. Reconnaissance surveys may be required to provide a
sufficient data base for planning where only limited data are available.
5.1.4 Model Selection
Data collection requirements for water quality modeling depend to some extent on the
particular model or the calculation procedure selected and the detail required in the modeling
analysis. The modeling approach should be selected prior to the monitoring study based on
historical data and reconnaissance surveys. Ideally, preliminary model applications should be
conducted to assess available data, define data gaps, and provide guidance on monitoring
requirements. Critical examination of the model input data requirements and studies of the
model's sensitivity to parameters and processes should aid in the development of monitoring
strategies. Several iterative cycles of data collection and model application optimize both
monitoring and modeling efforts.
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Marina Water Quality Models
5.1.5 Confidence
To a large degree the quantity and quality of the data determine the confidence that can
be placed in the model application. Without data, it is impossible to determine the uncertainties
associated with model predictions. Uncertainties in the driving forces for the model (i.e.,
loadings, wind, tide) will be propagated in model predictions. The greater the uncertainty
(spatial, temporal, or analytical) associated with data used in model forcing functions, estimation
of model parameters, or evaluation of model predictions, the greater the resulting uncertainty
associated with those predictions. One fundamental question that may impact monitoring studies
is the acceptable degree of uncertainty in both data and model predictions.
Quantitative measurements of the error limits and confidence intervals should be made
for all collected data so that the gains or losses in model accuracy and precision can be
determined. Accuracy can be lost in a number of ways. For example, accuracy can be affected
by improper field procedures such as faulty sample labeling, insufficient instrument calibration,
instrument breakdown, and poor training of field personnel. Even if high-quality data are
collected the data must also reflect the processes being modeled in order to provide a rational
aid for making decisions governing the sampling plan. For example, if study objectives require
that boundary conditions must be sampled with 95 percent confidence, then there are established
quantitative methods available to estimate the sampling effort required (e.g., Cochran, 1977;
Whitefield, 1982).
5.1.6 Resources
The major factors usually limiting sampling programs are budget and time constraints.
Complex models such as WASP4 require large amounts of data and the corresponding
expenditure of trained personnel, instrumentation, and ship time. Such an expenditure may not
be feasible in many circumstances. In cases of limited funding and time, the Tidal Prism Model
may be more appropriate due to its less stringent data requirements. It should be noted, though,
that the quality of the model result is related to the complexity of the model employed.
5.1.7 Existing Versus Proposed Marina
The number and location of sampling stations and the level of sampling effort depend to
a large extent on the physical status of the marina site (i.e., existing versus proposed marina).
Since sampling resources are generally limited, it is important to locate the stations in places that
will provide the most information. In general, fewer stations will be available to sample for a
proposed marina since stations within the proposed marina do not exist and therefore, cannot be
sampled. This factor will affect the level of effort in the monitoring program. For a proposed
marina, it is more important to perform sensitivity analysis under wide range of conditions,
especially where water quality standards are more likely to be violated, and to establish a
concentration range for the water quality constituent of concern. Most model coefficients and
reaction rates are site-specific; therefore, sensitivity analysis must be a fundamental component
of water quality assessment at a proposed marina.
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Marina Water Quality Models
5.2 Types of Data
In general, the simpler the model, the fewer the data requirements. Tidal Prism Analysis
calculations require only bathymetry, tidal range, and estimates of pollutant discharge rates. In
addition to these data, the WASP4 model requires meteorological data, currents, salinity,
temperature, and SOD measurements, as well as other data. These data are used to calculate
rate coefficients, to determine boundary and forcing conditions, and to judge the accuracy of the
model results. The general types of data required are discussed in further detail in the following
section.
5.2.1 Reconnaissance and/or Historical Data
The amount and types of historical data available determine which level of model (simple,
mid-range, or complex) can be initially applied to a marina site. Historical data sources should
always be surveyed, but where historical data are not available it may be necessary to perform
reconnaissance surveys to obtain sufficient data for model selection. Additional reconnaissance
surveys may be required particularly in areas where the greatest uncertainties exist. The data
required at this stage includes system geometry, bathymetry, and tidal range, to evaluate the
flushing characteristics and to estimate typical dissolved oxygen and fecal coliform concentration.
Initial Tidal Prism Analysis calculations should be carried out so that any potential water quality
problems can be identified.
A sensitivity analysis is useful when applied to a preliminary calibration or an application
of a simple model using historic or estimated conditions. In this case, the sensitivity analysis
results can be used to determine which coefficients and parameters should be measured and
which can be estimated. For example, if the model is sensitive to SOD rates, then these should
be measured rather estimated. If other parameters such as the wind speed function have little
influence, then very little effort should be expended to measure their exact form.
The data needs for the WASP4 and Tidal Prism models are not likely to be fulfilled
through historical and/or reconnaissance surveys because of the extensive data requirements of
these models. However, information such as wind, tide, and geometry may be available in
sufficient amounts to calibrate the hydrodynamic submodels, and such data may not need to be
collected.
5.2.2 Boundary Condition Data
Boundary condition data are external to the model domain and are driving forces for
model simulations. For example, tides, atmospheric temperature, solar radiation, and wind
speeds are not calculated but are specified to the model as boundary conditions to drive modeled
processes such as mixing, heat transfer, algal growth, reaeration, photolysis, volatilization, etc.
Nonpoint and point source loadings as well as fresh water inflows are model boundary input for
both the Tidal Prism and the WASP4 models. Therefore, boundary condition data (i.e., water
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Marina Water Quality Models
surface elevation and flows) must be collected for the WASP4 and Tidal Prism models. Data
collection at boundary areas should be done at a higher frequency than other sampling due to
its critical nature.
Boundary conditions for either Tidal Prism Analysis or the NCDEM DO model requires
only the average dissolved oxygen concentration in the adjacent water, a single point. In
addition, the NCDEM DO model requires tidal information, which is available through NOAA's
tide tables.
5.2.3 Initial Condition Data
Generally, initial conditions are not required for internal flows or velocities. However,
for water quality constituents in both the Tidal Prism Model and the WASP4 model initial
conditions are required where the simulated period of interest is less than the required time for
these initial conditions to be "flushed out." For example, if the model is run to a steady state,
then by definition initial conditions are not required. If, however, simulations are conducted
over "short" periods of time, then proper initial conditions may be critical in order to ensure that
model calculations have reached equilibrium.
Initial conditions are generally not required for flows in the WASP4 hydrodynamic
submodel. Generally, velocity fields are set up within relatively few model time steps.
However, initial conditions are required for materials such as tracers, salinity, or temperature
used to calibrate the transport predictions. An exception is where the initial conditions are
rapidly flushed out, or the flushing period is short in comparison to the simulation period. In
this case, it is often reasonable to run the model to a steady state, using the initial boundary
conditions, and to use the results of steady-state simulations as the initial conditions for
subsequent simulations.
5.2.4 Calibration/Evaluation Data
The Tidal Prism and WASP4 models are general in that they can be applied to a variety
of sites and situations. However, the values of water quality coefficients must be selected on
a site-specific basis, within some acceptable range. The process of adjusting the model
parameters to fit site-specific information is known as model calibration, and it requires that
sufficient data be available for estimation of coefficients. Calibration applies only to existing
marinas since calibration data cannot be collected from a non-existing marina. While resources
often limit the extent of the calibration data, more than one set of data describing a range of
conditions is desirable. Ideally, the calibration data should reflect the entire range of conditions
that are normally found at the marina site.
It is always wise to calibrate either model with one or more independent data sets to
ensure that the model accurately describes the system. Evaluation conditions should be
sufficiently different from calibration conditions to test model assumptions without violating
them. For example, if the rate of sediment oxygen demand is assumed not to change (i.e., is
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Marina Water Quality Models
specified as a zero order rate), then the model obviously would not predict well under situations
where the sediment oxygen demand was drastically different because of a storm event.
The calibration of therhydrodynamic model may require an iterative effort in conjunction
with the application of the water quality models for the constituent of interest (e.g., dissolved
oxygen). However, initial calibration is usually conducted against materials such as conservative
tracers, salinity, or temperature. Salinity, temperature, and suspended solids concentrations will
impact density, which will in turn affect computed velocity distributions. The transport of at
least salinity, and possibly temperature and suspended solids, should generally be directly linked
to hydrodynamic predictions. Continuous dye discharges may be used to estimate lateral mixing
and flushing characteristics.
Dye tracer studies are one of the better means of calibration. The spread of the dye will
aid in estimation of dispersion coefficient, and the movement of its centroid can help to estimate
net flows.
5.2.5 Post-Audit Data
One type of data that is often ignored is post-audit data. Models will usually be
calibrated and applied to produce predictions for the various water quality processes under
investigation for a time period of interest. These results are then used for making regulatory
decisions that may affect the design of the marina modeled (Tetra Tech, 1988). This is often
the end of most modeling and monitoring studies. Post-audit data for the water quality
parameters must also be collected to provide a direct comparison between model predictions and
observed data. There are relatively few cases where studies are conducted after the
implementation of management decisions to determine whether the model predictions were
accurate and the decisions were appropriate. Without a follow-up investigation, the overall
success or failure of a modeling study often cannot be determined. An example where post-audit
data were used to verify model results is the Wexford Marina study (EPA, 1986).
5.3 Model Data Requirements
5.3.1 Bathymetry Data
Bathymetry data are always required to determine model morphometry. Marine
morphometry controls tidal flushing and subsequently DO and fecal coliform concentrations.
All models require essentially the same types of information to define the geometric
characteristics of the marina and the adjacent water. The basic types of data required for each
segment include:
• Segment length;
• Variation of channel width and cross-sectional area with depth;
• Bottom slope (or bed elevations);
• Variation of wetted perimeter or hydraulic radius with depth; and
• Bottom roughness coefficient (Manning's n).
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Marina Water Quality Models
Length and average slope over long distances can be determined from topographic maps,
while the other variables usually require field surveys. The first two data types, length and
cross-sectional area, are fundamental to any modeling study since they are necessary in the
transport calculations. The remaining information may or may not be required, depending on
the type of hydraulic computations used in the model. WASP4 internally computes the cross-,
sectional area as a function of depth based on idealized representations of the channel shape.
On the other hand, the Tidal Prism Analysis procedures require the average depth and the
marina surface area. Table 5-1 lists the geometric and bathymetric data requirements for the
recommended marina models. Bathymetry data are available for most estuaries and coastal areas
from U.S. Coastal and Geodetic Navigation Charts and Boat Sheets or from sounding studies
conducted by the U.S. Army Corps of Engineers. The charts tend to slightly underestimate
depths in navigation channels to allow for siltation. Bathymetric charts may be sufficient enough
to provide all information needed for the Tidal Prism Model geometry. If these charts do not
provide the required geometric data, then bathymetric survey of the existing marina should be
included in the data sampling plan.
TABLE 5-1. Geometric and Bathymetric Data Requirements for
Recommended Marina Models
Model
Parameters
Tidal Prism Analysis
Surface area
Average depth at high and low tide
NCDEM DO Model
Marina and channel
Surface area
Average depth at high and low tide
Tidal Prism Model
Distance to transect from mouth
Segment volume at low and high tide
WASP4 Model
Channel length and width
Segment depth and surface area
5.3.2 Transport/Hydraulic Data
Either direct observation or the prediction of transport is essential to assess potential
water quality impacts from coastal marinas. All marina water quality models are based on mass
balance principles, and both concentrations and flows are required to compute mass rates of
change. Essential physical data required for prediction or description of transport for the
recommended marina models are outlined in Tables 5-2 and 5-3. Table 5-2 lists essential data
types required to model transport and dispersion and Table 5-3 provides required input data for
the recommended models.
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Marina Water Quality Models
TABLE 5-2. Essential Transport Data
Data Type
Parameters
Morphometic
Segment geometry
Hydrodynamic
Water surface elevations
Current speed and direction
Point and distributed flows
Meteorological
Solar radiation
Air temperature
Precipitation
Wind speed and direction
Physical
Salinity
Water temperature
Suspended sediments
Dye studies
TABLE 5-3. Transport/Hydraulic Data Requirements for Recommended Marina Models
Model
Parameters
Tidal Prism Analysis
Freshwater inflow
Marina tidal prism volume
NCDEM DO Model
Tidal amplitude
Tidal Prism Model
Average solar radiation during simulation
period
Freshwater inflows
Tributary inflows
Tidal prism volume per segment
WASP4 Model
Precipitation rate
Wind speed and direction
Average solar radiation
Air temperature
Cloud cover
Time series of headwater
Time series of tide
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Marina Water Quality Modeb
The type of data used to quantify transport depends on the model selected and the
characteristics of the system. For example, geometry, freshwater flow, tidal range, salinity
distribution, and boundary concentrations representative of conditions being analyzed are
necessary input for applications involving the WASP4 and Tidal Prism models.
For complex marinas, time-varying flows, depths, and cross-sections will make
estimation of flows and dispersion from field data difficult. In these cases the flows have to be
measured either by dye studies or by current meters. No matter how these parameters are
determined, they must adequately reflect the flushing characteristics of the system.
An intensive data collection program that includes concurrent water surface elevation,
velocity, and dye dispersion or salinity gradient studies provides the most complete set of data
for calibration of hydrodynamics for the WASP4 and the Tidal Prism models.
Hydrodynamic boundary conditions for the WASP4 model consist of flows or heads.
Head refers to the elevation of the water surface above some datums. Generally, flow
information is provided for tributary and point sources and water surface elevations provided at
the channel entrance boundary.
Water surface elevation information is often available from tide gauge records or from
the Coast and Geodetic Survey tide tables published annually by NOAA. These tide tables do
not include the day-to-day variations in sea level caused by changes in wind or barometric
conditions, nor do they account for unusual changes in freshwater conditions. All of these
conditions will cause the tide to be higher or lower than predicted in the tables. Where possible,
water surface elevation gauges should be placed at the model boundaries as part of the
monitoring program for use with the WASP4 model application. Tide information obtained from
NOAA's tide table is sufficient for both the Tidal Prism Analysis calculations and the Tidal
Prism Model.
5.3.3 Meteorological Data
Meteorological data, including precipitation, wind speed, and direction, are required to
compute surface shear, vertical mixing, and pressure gradients. Meteorological data are often
available for nearby National Weather Service stations from the National Climatic Center in
Asheville, North Carolina.
Different meteorological parameters are required for the various models. Tidal Prism
Analysis does not utilize this type of data and thus biochemical reaction rates cannot be
extensively varied. The Tidal Prism Model requires only the average solar radiation for the time
period being modeled. The meteorological data requirements of WASP4 for each submodel are
outlined in Table 5-4. Wind speed and direction are important input data for the WASP4 model
application since wind data are included in the hydrodynamic and the dissolved oxygen
calculation (reaeration rate). Measured wind speed and direction for the WASP4 model
application are good to have for realistic predictions at a marina site.
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Marina Water Quality Models
TABLE 5-4. Meteorological Data Requirements for WASP4 Submodels
WASP4 Submodel
Parameters
Hydrodynamic
Wind speed and direction
Water Quality (Dissolved Oxygen)
Instantaneous and average solar radiation
Temperature
Wind speed
Toxic Chemical
Wind speed
Solar radiation
Cloud cover
5.3.4 Water Quality Data
Given the semi-empirical nature of water quality models, water quality data are necessary
to set up, calibrate, and verify any water quality model. Input data are needed for all parameters
that will be simulated. For models that simulate conventional pollutants, data may include water
temperature, dissolved oxygen, carbonaceous BOD, phosphorus, nitrogen (ammonia, nitrite, and
nitrate), fecal coliform bacteria, chlorophyll-a or phytoplankton dry weight biomass, and
conservative constituents such as total dissolved solids. The WASP4 model also includes
additional constituents such as total inorganic carbon, alkalinity, pH, inorganic suspended solids,
suspended organic detritus, periphyton, and zooplankton grazing rates.
Data requirements for WASP4 will vary if the application is intended for dissolved
oxygen, fecal coliform, or toxics. Variables critical for an analysis of toxicity, such as pH for
ammonia and metals, may not be required if the parameter of interest is DO. If pollutants are
not expected to impact particular variables, such as pH, then it may be sufficient to use available
data to determine their effects. If, however, data are not available for conditions of interest or
if the variable is expected to change, either directly or indirectly, in response to marina wastes,
then modeling will require collection of additional supporting data. When simulating dissolved
oxygen with the WASP4 model, diurnal DO data is very helpful for calibrating the
phytoplankton growth rate, death rate, and respriation coefficients. The diurnal DO data
collection period should span at least one complete diurnal tide cycle (i.e., at least 25 hours) to
ensure that both the daily maximum and minimum DO are measured. Ideally, sampling begins
at 6:00 am and continues to 7:00 am the following day to cover two daily minimums.
Table 5-5 provides an overview of some commonly measured water quality variables,
their problem context, and an indication of the processes they impact. The specific type of data
for a particular application will vary depending on the factors listed in Section 5.1.
Concentrations for all pertinent water quality variables should be provided at the model
boundaries, providing the driving forces for model predictions, as well as at stations within the
model system to provide a basis for estimating model parameters and evaluating model
predictions. The user's manuals for the Tidal Prism and WASP4 models should be consulted
for the exact data requirements for each application.
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Marina Water Quality Models
TABLE 5-5. Water Quality Variables
Constituent
Problem Context
Effects
Salinity
All
Transport, dissolved oxygen
Temperature
All
Transport, kinetics, dissolved
oxygen
Dissolved oxygen
All
Indicator, toxicity, sediment
release
BOD-5
DO
Dissolved oxygen
NBOD
DO
Dissolved oxygen
Bottom demand
DO
Dissolved oxygen, nutrient
release
Total phosphorus
DO
Algae
Soluble reactive phosphorus
DO
Dissolved oxygen, algae
Total Kjeldahl nitrogen
DO
Dissolved oxygen, algae
Ammonia-nitrogen
DO, Toxicity
Dissolved oxygen, toxicity,
algae
Nitrite-nitrogen
DO
Dissolved oxygen, algae
Nitrate-nitrogen
DO
Dissolved oxygen, algae
Dissolved available silica
DO
Algae
Chlorophyll-a and
phaeophyton
DO
Algal indicator
Phytoplankton (major
groups)
DO
Dissolved oxygen, nutrient
cycles, pH
Meteorologic data (wind,
temperature, etc.)
All
Gas transfer, reaction rates
Fecal coliform bacteria
Shellfishery areas
Recreational contact
Decay rates
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Manna Water Quality Models
In the WASP4 model, five state variables participate in the DO balance: phytoplankton
carbon, ammonia, nitrate, carbonaceous biochemical oxygen demand, and dissolved oxygen.
A summary is illustrated in Figure 5-1. The reduction of dissolved oxygen is a consequence of
the aerobic respiratory processes in the water column and the anaerobic processes in the
underlying sediments. Table 5-6 lists water quality data requirements for the recommended
marina models.
TABLE 5-6. Water Quality Data Requirements for the
Recommended Marina Models
MODEL
Fecal
Coli.
DO
BOD
SOD
Temp.
NHj
Algae
Zoo.
Salinity
Tidal Prism
Analysis
X
X
X
X
NCDEM DO
Model
X
X
Tidal Prism
Model
X
X
X
X
X
X
X
X
WASP4 Model
X
X
X
X
X
X
X
X
X
5.4 Sampling Guidelines for Existing Marinas
General guidance is presented to develop the framework for a site-specific water quality
sampling program suitable for an existing marina. A monitoring study at an existing marina may
be required by regulatory agencies if it is suspected that the marina is causing degradation of
water quality standards. As shown in Figure 5-2, the overall monitoring program can consist
of three phases or levels. In Level 1, preliminary screening is conducted to gather baseline
information on the marina. If historical data are available on the marina, then this level may
not be required or the quantity of data needed may be reduced. Based on the historical and/or
Level 1 data, if it is established that the marina may be causing impacts on water quality, then
Level 2 sampling which incorporates additional sampling of the receiving waters, would
commence. If evaluation of Level 2 data also indicates that the marina is impacting water
quality, then marina design changes may be recommended and eventually implemented. Level
3 sampling would be initiated to evaluate the performance of any implemented marina design
changes. Examples of potential marina design changes inclufle removal of sills, which tend to
trap water in the lower depths of a marina, and improvement of flushing by altering sharp
corners within the marina or by enlarging the marina entrance.
5.4.1 Spatial Coverage
An intensive spatial coverage of the marina and the adjacent water body for some
indicator or surrogate water quality parameter, such as salinity or turbidity, is generally needed
to estimate spatial variability and to determine the model type and the segmentation required.
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Marina Water Quality Models
Figure 5-1. Oxygen balance.
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Marina Water Quality Models
Figure 5-2. Sampling Scheme for Existing Marina.
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Marina Water Quality Models
Generally, the spatial coverage of the modeled marina should extend away from the marina site
to the extent that normal background levels for DO are encountered. At this location, model
boundary conditions (i.e., surface elevations or current velocities) can be established. In this
manner the total effect of the marina can be measured.
The preceding approach is appropriate when using the WASP4 model. Sampling stations
for the WASP4 model should be spaced throughout the model grid system with the spatial
coverage being governed by the gradients in velocities and water quality constituents. For
existing marinas, the adjacent water bodies are divided into a series of reaches for WASP4
model application, with each reach described by a specific set of channel geometry dimensions
(i.e., cross-sectional dimensions) and flow characteristics (i.e., flow rates, tidal range, velocities
and biochemical processes). The model assumes that these conditions are uniform within each
reach. Each reach is in turn divided into a series of model segments or computational elements
in order to provide spatial variation for the water quality analysis. Each segment is represented
by a grid point in the model where all water quality variables are computed. For the WASP4
model, the segment length is dependent on the degree of resolution desired and the natural
variability in the system. Enough detail should be provided to characterize anticipated spatial
variation in water quality.
The hydrodynamics of the Tidal Prism Model are based on the tidal prism volume at each
segment. Therefore, the spatial coverage of a marina, using the Tidal Prism Model, will include
the entire estuary/river where the marina is located (Kuo et al., 1989). The length of each
segment is defined by the tidal excursion, the average distance traveled by a water particle on
the flood tide, since this is the maximum length over which complete mixing can be assumed.
A sampling station for each model segment is the minimum requirement to calibrate the
returning ratios of the Tidal Prism Model. Sampling stations should generally be located along
the length of the estuary and in the main channel. The returning ratio is defined as the
percentage of tidal prism that was previously flushed from the marina on the outgoing tide.
More information concerning the Tidal Prism Model returning ratio calibration is provided in
a later section of this report.
5.4.2 Constituents Sampled
The specific constituents that must be sampled, as well as the sampling frequency, depend
to some extent on the particular modeling framework to be used in the analysis. The selected
model should include all of the processes that are significant in the area under investigation
without the unnecessary complexity of processes that are insignificant. A few preliminary
measurements may be useful to define which processes are important.
The minimum sampling requirements for all dissolved oxygen studies should include
dissolved oxygen, temperature, CBOD, and total Kjeldahl nitrogen since these parameters are
fundamental to any dissolved oxygen analysis. Biochemical oxygen demand (BOD) is typically
measured as a 5-day BOD, but a few measurements of long-term BOD are also necessary. The
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Marina Warer Quality Models
Tidal Prism Model considers only the CBOD component, and therefore the model should be
used only in situations where the nitrogenous components are known to be unimportant.
In addition to total Kjeldahl nitrogen (TKN), ammonia and nitrate (or nitrite plus nitrate)
should be measured for dissolved oxygen investigations for both the Tidal Prism and WASP4
models. Even if they are not modeled, ammonia, nitrate, and nitrite data are useful for
estimating the nitrogenous BOD decay rate or ammonia oxidation rate.
Concentrations of algal dry weight biomass or chlorophyll-a should be measured because
both WASP4 and the Tidal Prism Model simulate algae growth for dissolved oxygen analysis.
Light extinction coefficients (or Secchi depths) will also be needed for the algal growth
computations in dissolved oxygen analysis if the WASP4 model is used.
In situ sediment oxygen demand (SOD) should be measured in situations where it is
expected to be a significant component of the oxygen budget. This is most likely to occur in
shallow areas where the organic content of the sediments is high or in deep marina basins where
flushing is minimal (NCDEM, 1990). In developing a strategy for SOD measurement, it is
logical to assume that those factors important in establishing model reaches or segments are also
relevant to selecting SOD measurement sites. The more important of these factors are:
• Geometric - depth and width;
• Hydraulic - velocity, slope, flow, and bottom roughness; and
• Water quality - location of: point sources, nonpoint source runoff, and abrupt changes
in DO/SOD concentrations.
The most important factor for SOD is likely to be the location of abrupt changes in
DO/BOD concentrations such as surrounding the entrance channels of marinas and in the marina
basin proper (NCDEM, 1990). The final point to consider is that SOD may vary with season.
This observation is particularly relevant to marinas and adjacent areas dominated by algal
activity and/or oxidation of organic and inorganic nutrients by benthic microorganisms, both of
which may occur seasonally. The modeler should thus be aware of this potential concern and
structure the SOD measurement times accordingly.
In addition to sampling for the constituents to be simulated, measurements are also
necessary to help quantify the various coefficients and parameters included in the model
equations. Coefficient values can be obtained in four ways: (1) direct measurement, (2)
estimation from field data, (3) literature values, and (4) model calibration. Model calibration
is usually required regardless of the selected approach. However, coefficients that tend to be
site specific or that can take on a wide range of values should either be measured directly or
estimated from field samples. This could include the following parameters:
• Carbonaceous BOD decay rate;
• Carbonaceous BOD settling rate;
• Ammonia oxidation rate (nitrogenous BOD decay rate); and
• Sediment oxygen demand.
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Marina Water Quality Models
In addition to the above model parameters, which are determined primarily from the
results of field sampling surveys, several other rate coefficients can be measured in the field.
For example, stream reaeration rates for the WASP4 model, and returning ratios for the Tidal
Prism Model, can be measured using tracer techniques. WASP4 provides several options for
the reaeration rate equation since many of the equations are applicable only over certain ranges
of depth and velocity.
5.4.3 Sampling Locations
Water quality data should be collected at the downstream boundary of the study area for
model calibration. While a single downstream station is the minimum requirement for short
channel sections, additional sampling stations are desirable to provide more spatial data for
calibrating the model. Logical locations for additional stations are sharp corners and deadend
segments in the marina basin proper. If the marina is segmented for a WASP4 model
application, then each segment must be sampled. Additionally, adjacent waters both upstream
and downstream must also be sampled to determine background concentration of water quality
constituents. However, an NCDEM study at 11 marina sites indicated that water quality
variations are negligible at stations located upstream and downstream immediately outside those
marinas (NCDEM, 1990).
In the Tidal Prism Model, water quality is assumed to be well-mixed and uniform over
each segment of the stream. Therefore, samples taken immediately downstream of the marina
would probably not match conditions in the model unless they were taken far enough
downstream for complete cross-sectional mixing to occur. In general, increased sampling should
be allocated to those areas of the marina and the adjacent water that have the most impact (i.e.,
along the shoreline). In general, all of the major water quality parameters of interest (DO,
CBOD, TKN, NH3, N03, fecal coliform, temperature, etc.) should be measured at each station
in the sampling network.
Rate coefficients and model parameters can be estimated from literature values before
site-specific measurements are available. For important parameters such as the BOD decay rate,
sensitivity analyses can be performed to evaluate the effects of different coefficient values in
formulating DO concentrations. These analyses should provide enough information so that
sampling stations can be located in critical areas.
5.4.4 Sampling Time and Frequency
The duration and frequency of water quality sampling depend to a large extent on
whether the Tidal Prism or the WASP4 model will be used. The Tidal Prism Model computes
water quality conditions only at slack before ebb; thus, sampling at a higher rate is not
necessary. The WASP4 model has a user-specified time step, which means that sampling must
be greater for shorter time steps.
Since the Tidal Prism Model assumes that conditions remain constant with time, it is
important to conduct the sampling program during a period when this assumption is valid.
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Marina Water Quality Models
Synoptic surveys (e.g., sampling all stations over 2 to 3 days) should be conducted to the extent
possible so that water quality conditions at different locations are not affected significantly by
changes in the weather or variations in the marina discharge that are not accounted for in the
model. However, since temperature varies diurnally and temperature influences the process
rates of most biological and chemical reactions, some variability will be inevitable in the
sampling results. It should be noted that the Tidal Prism Model uses the first day of field data
as initial and boundary condition input to the model. Field data from succeeding cycles are then
used to compare the output simulations at the same cycle.
The WASP4 model computes the continuous changes that occur over time due to
variations in stream flow, temperature, nonpoint and point source loadings, meteorology, and
processes occurring within a marina and its adjacent waters. In the WASP4 model, all of the
factors that are assumed constant for a Tidal Prism analysis are free to vary continuously with
time. This allows an analysis of diurnal variations in temperature and water quality, as well as
continuous prediction of daily variations or even seasonal variations in water quality.
WASP4 generally requires a much more detailed sampling program than that required
by the Tidal Prism Model. Enough data must be collected to define the temporal variations in
water quality throughout the simulation period at the model boundary conditions. Therefore,
more frequent data collection must be conducted at the model boundary condition. The WASP4
model excels in investigating the temporal variations in dissolved oxygen and fecal coliform
bacteria. To achieve this resolution, intensive surveys should be mixed with long-term trend
monitoring. The significance of the temporal variations depends on the context of the problem.
For example, if the daily average dissolved oxygen concentration is around 5 mg/L or less, then
a diurnal variation of less than 1 mg/L could be very important with respect to meeting water
quality standards, while if the average dissolved oxygen concentration is around 10 mg/L, then
diurnal variations of 2 or 3 mg/L may not matter. If preliminary sampling indicates diurnal
variations are important, then the sampling program should include 2 or 3 days of intensive
sampling for dissolved oxygen and temperature at all of the key stations. As a minimum, these
stations would include the stations designated as the model boundary, as well as the stations
surrounding the marina and adjacent waters and stations within the marina. These locations
satisfy the minimum requirements of defining the boundary and loading conditions plus a few
calibration stations in the critical areas for DO, SOD, and fecal coliform.
Long-term dynamic simulations of seasonal variations in stream water quality may be
impractical. Where seasonal variation is of interest, the typical practice is to run the Tidal Prism
Model or the WASP4 model (with short-term simulations) several times for different sets of
conditions that represent the full spectrum of conditions expected over the period of interest.
Enough data should be collected to characterize the seasonal variations, and to provide adequate
data for calibrating and applying the model. If possible, enough data should be collected to
cover the full range of conditions of the model analysis. As a minimum, this should include
conditions during the critical season for the water quality variable of interest. For dissolved
oxygen, the critical season occurs during the hot summer months (July through September).
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Marina Water Quality Modeb
Two general types of studies can be defined—those used to identify short-term variations
in water quality (i.e., intensive surveys) and those used to estimate trends or mean values (i.e.,
trend monitoring). Intensive surveys are intended to identify intertidal variations or variations
that occur due to a particular event in order to make short-term forecasts. Intensive surveys
should encompass at least four full tidal cycles (Brown and Ecker, 1978). Intensive surveys
should usually be conducted regardless of the type of modeling study being conducted.
Wherever possible, all stations and depths should be sampled synoptically. Boundary conditions
should be measured concurrently with the monitoring of the marina basin and the adjacent water.
A record of all point source waste loads, located near the marina site, during the week prior to
the survey is recommended. Variables that should be sampled during the intensive surveys
include tide, current velocity, salinity, DO, fecal coliform, nitrogen, and phosphorus measured
hourly.
Trend monitoring is conducted to establish seasonal and long-term trends in water quality.
Trend sampling may take place on a biweekly or monthly basis for a year at a time. Stations
should be sampled at a consistent phase of the tide and time of day to minimize tidal and diurnal
influences on water quality variations (Ambrose, 1983). Some stations may be selected for more
detailed evaluation during the intensive survey. Long-term trend monitoring should also be
considered as a way to track changes in water quality between the intensive surveys (Brown and
Ecker, 1978).
Most states have water quality standards for the 24-hour average concentration and the
instantaneous minimum concentration of dissolved oxygen. Therefore, it is important to collect
dissolved oxygen data throughout a complete cycle, i.e., from the high value, which normally
occurs at mid-afternoon, to the low value, which usually occurs at dawn. This will allow the
DO range in the model to be calibrated to specific field conditions. If the water body is
stratified, then samples should be collected at the surface, mid-depth, and bottom. In general,
it is necessary to collect samples at a 2-hour frequency over a 24-hour period in order to
adequately define the daily average and the minimum DO concentrations.
5.5 Sampling Guidelines for Proposed Marina
This subsection presents general guidance to develop a framework for a site-specific
water quality sampling program suitable for a proposed marina. Regulatory agencies require
that it should be demonstrated (i.e., modeled) that a proposed marina will not adversely impact
water quality. The sampling guidelines outlined here provide the procedure for collecting
information necessary to develop a water quality model of the proposed marina. The overall
monitoring program consists of three levels (Figure 5-3). Initially, a preliminary investigation
is conducted to determine the site characteristics necessary to properly design a monitoring
program. Required information includes the locations of any nearby point source discharges
(including flow rates and concentrations of effluent constituents) that could potentially be
transported inside the marina. Any past studies of the area can also provide useful information
and should be collected during the preliminary investigation. If adequate historical information
is not available, then Level 1 sampling should be conducted to gather the initial screening data
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Marina Water Quality Mod eh
Figure 5-3. Sampling Scheme for a Proposed Marina.
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Marina Water Quality Models
necessary to develop a simple model (i.e., the Tidal Prism Analysis method or NCDEM DO
Model). If application of the simple model to the proposed marina indicates that water quality
is marginal or will be degraded, then Level 2 sampling is initiated to collect field data necessary
to apply the more complex Tidal Prism Model or WASP4 model. After the marina has been
permitted and constructed, Level 3 sampling may be required to determine compliance with
water quality standards.
5.5.1 Spatial Coverage
An intensive spatial coverage of the proposed marina site and the adjacent water body,
for some indicator or surrogate water quality parameter, such as salinity or turbidity, is generally
needed in order to estimate spatial variability. As discussed earlier in this chapter, the
hydrodynamics of the Tidal Prism Model are based on the tidal prism volume at each segment.
Therefore, the spatial coverage of a proposed marina, using the Tidal Prism Model, will include
the entire estuary/river where the marina is located (Kuo et al., 1989). The proposed marina
is treated as a single-segment tributary with no freshwater input. A sampling station for each
model segment is the minimum requirement to calibrate the returning ratios of the Tidal Prism
Model. Sampling stations should be located along the length of the estuary and in the main
channel. Model segmentation is explained in further detail in Chapter 6.
Sampling stations, for the WASP4 model, should be spaced throughout the model grid.
A sampling station for each model segment is the minimum requirement to calibrate the WASP4
model. However, for a proposed marina the model cannot be calibrated or verified based on
field data. Instead, the link-node hydrodynamic model may be calibrated against hydrodynamic
results obtained from a two-dimensional model application to the proposed marina site (see
Appendix E for more detail).
5.5.2 Constituents Sampled
The specific constituents that must be sampled, as well as the sampling frequency, depend
to some extent on the particular modeling framework that will be used in the analysis. The
selected model should include all of the processes that are significant at the proposed site. Since
the system does not yet exist, neither WASP4 nor the Tidal Prism Model can be calibrated.
Instead, a base condition consisting of average typical values of the various rate coefficients for
the kinetic reaction terms and source/sink terms should be defined. A model sensitivity analysis
is then performed by varying the rate coefficients about their typical ranges, and the effect on
water quality constituents is documented. Water quality impacts of the proposed system can then
be estimated from the sensitivity analysis.
At the proposed marina site, concentrations of algal dry weight biomass or chlorophyll-a
should be measured because both WASP4 and the Tidal Prism Model simulate algae growth for
dissolved oxygen analysis. Light extinction coefficients are also needed for the algal growth
computations in dissolved oxygen analysis if the WASP4 model is used. In addition, in situ
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Marina Water Quality Models
sediment oxygen demand (SOD) and other water quality variables (e.g., BOD) should be
measured.
5.5.3 Sampling Locations
Water quality data should be collected at or near the proposed marina site. While a
single downstream station is the minimum requirement for short channel sections with no major
tributaries, additional sampling stations are desirable to provide more spatial data for calibrating
the model. Additional stations should logically be located at biologically sensitive areas (e.g.,
shellfish harvesting areas). Water quality, in the Tidal Prism Model, is assumed to be well-
mixed and uniform over each segment of the stream, requiring a sampling station in each
segment along the length of the estuary in the main channel.
In general all of the major water quality parameters of interest (DO, CBOD, TKN, NH3,
N03, fecal coliform, temperature, etc.) should be measured at each station in the sampling
network. Sampling locations for a proposed marina are limited since the proposed marina does
not yet exist. Sampling stations are located in the adjacent waterways to determine existing
water quality conditions and model boundary conditions. Figure 5-4 illustrates potential
monitoring sites for proposed and existing open marinas and various types of existing enclosed
marinas. For proposed enclosed marinas, monitoring stations should be positioned at the
ambient water locations shown in Figure 5-4. If a nearby marina exists, then water quality
parameters can be measured at the existing marina and extrapolated to the proposed marina.
When analyzing dissolved oxygen problems at a proposed marina site with several nearby
marinas, more of the sampling effort should be allocated to areas where water quality standards
are most likely to be violated. Areas where large water quality gradients exist should be
sampled more thoroughly.
The Tidal Prism Model treats the proposed marina as a single-segment tributary,
therefore, complicated marina design cannot be simulated. The WASP4 model, however, is
capable of handling any complicated design as long as the model is calibrated with a two-
dimensional hydrodynamic application to the proposed marina site.
5.5.4 Sampling Time and Frequency
The duration and frequency of water quality sampling depend to a large extent on the
selected model. For example, since the Tidal Prism Model computes water quality conditions
only at slack before ebb, sampling at a higher rate is not necessary. An intensive survey of the
entire estuary (2 or 3 days) is required for the calibration of the Tidal Prism Model.
Sampling time and frequency are not affected by whether the marina exists or not and
therefore issues discussed under Section 5.4.4 for an existing marina are also applicable to a
proposed marina.
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Marina Water Quality Models
Figure 5-4. Potential Monitoring Sites for Proposed and Existing Marinas.
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Marina Water Qualiry Modeb
6. MODEL APPLICATION AND EVALUATION
Chapter 6 presents, in summary form, the findings of three modeling studies conducted
at the Indian Hills canal/marina development in Boca Raton, Florida; Beacons Reach marina,
in Carteret County, North Carolina; and Gull Harbor marina in Carteret County, North
Carolina. Field studies and data collection for all these marinas were performed by the staffs
of the U.S. EPA Region IV Environmental Services Division (ESD) and the Florida Department
of Environmental Regulation (FL-DER).
In this chapter, data collected during the intensive surveys are used to calibrate and to
apply the best qualified simple, mid-range, and complex marina models. Descriptions of
existing water quality conditions during the intensive surveys are presented as well as model
applications to the selected marinas.
6.1 Marina Description
6.1.1 Indian Hills Marina
The Indian Hills Yacht Club in Boca Raton, Florida, features a flow-through waterway
system that is cross-connected by a canal near its midpoint and has a non-navigable cross-
connecting circulation channel at its landward extreme. East of the cross-connecting midpoint
canal, a mangrove forest remains intact and borders the Intracoastal Waterway. East to west the
system spans approximately 1300 feet; the north-to-south dimension is approximately 900 feet
(Figure 6-1).
The Indian Hills system was characterized chemically, hydrographically, and biologically
during July 1984 and January 1985. Hydrographic investigations consisted of depth profiles and
centerline traces, tidal dynamics, and current velocities. In conjunction with this data collection,
circulation studies were accomplished through the use of dye-tracing techniques.
Water quality sampling occurred over a 27-hour period with the primary focus on
dissolved oxygen, temperature, and salinity measurements, as well as slack tide water sample
collection for chemical analysis. The laboratory analysis included ammonia, nitrite-nitrate, and
total Kjeldahl nitrogen, as well as phosphorus, total organic carbon (TOC), and biochemical
oxygen demand (BOD). Water quality data are summarized in Table 6-1.
Water level instruments were placed at the north entrance and near the circulation channel
at Indian Hills. No notable differences were observed between these records in both water level
heights and phases. Mean high water during the July study period was 1.28 feet above mean
tidal level (MTL). During the winter study, water level recorders were placed within the Indian
Hills marina near the circulation channel and also in a mosquito ditch within the mangrove area.
6-1
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Marina Water Quality Models
Figure 6-1. Indian Hills Marina.
TABLE 6-1. Indian Hills Marina Water Quality Summary
Parameter
Mean of Data Observed on July 12-13, 1984
Ambient
Sta 1-4
Sta 1-1
Sta 1-2
. Sta 1-3
Sta 1-5
Sta 1-6
DO (mg/L)
4 94
4 59
4.86
4.74
4.69
4.34
Salinity (ppt)
27 56
26 17
26.45
26.76
26.38
25.97
Temperature (deg C)
31.00
31 06
31 04
30.91
31.03
30.98
CBOD 20-day (mg/L)
2.4
3.3
3.4
3.0
3 1
3.6
Ammonia (mg/L)
0.07
0.07
0.06
0.06
0.06
0.07
Total Kieldahl N (mg/L).
<0.10
0.53
0.44
0 35
0.16
0.19
Nitrate + nitrite (mg/L)
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
Total phosphorus (mg/L)
0.12
0.13
0.14
0.11
0.11
0.13
6-2
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Marina Water Quality Models
Circulation studies were used to measure mixing within the system as well as
determinations of water exchange (flushing) rates. During phase I, a dye tracer (Rhodamine
WT) was introduced into the basin in a slug fashion. The dye cloud configuration was then
monitored by means of a bOat-mounted fluorometer and a pump fashioned in a flow-through
mode. One liter of dye was introduced into the Indian Hills Canal at station 13 (Figure 6-1) at
1715 hours on July- 12, 1984.
Water quality sampling was accomplished in conjunction with diel monitoring of
dissolved oxygen, salinity, and temperature (DST) during both study phases. During the course
of the diel monitoring, and corresponding as closely as possible to low and high slack tide, mid-
depth 500-ml water samples were collected for chemical analysis of NH3 N02-N03, TKN, T-P,
and TOC in the ESD laboratory. In addition, BOD samples were also collected.
6.1.2 Beacons Reach Marina
Beacons Reach is located on the south side of Bogue Sound near Pine Knoll Shores,
Carteret County, North Carolina. Also called Westport Marina, it is part of a larger
development owned by the Beacons Reach Master Association. The marina is densely
surrounded by condominiums that are most highly occupied during the summer.
The boat basin, constructed around 1978, consists of approximately 58 slips. The surface
area of the marina basin is 2.3 acres. There are no pumpout facilities. A boat ramp is provided
for residents. The number of boats observed during sampling ranged from 11 to 35, with both
powerboats and sailboats present. Average depths measured in the basin ranged from 1.0 to 2.0
meters. Adjacent ambient waters were of about the same depth.
Water quality data are summarized in Table 6-2. Statistical analysis revealed that the
average summer DO was significantly higher at the ambient stations than at either the basin or
channel stations. SOD rates were approximately equal in ambient and basin waters, but greater
residence time may allow SOD to exert a stronger effect on overlying oxygen concentrations in
the basin compared to ambient waters, resulting in lower basin DO (NCDEM, 1990). Sampling
stations for the Beacons Reach marina are shown in Figure 6-2.
NCDEM (1990) reported that both basin and ambient DO values were less than 5.0 mg/L
on July 21, 1988. On that date, the Bogue Sound marinas were sampled during an unusually
low tide, and DO values were low throughout the sound. Aside from this one episode, wind and
tidal stage had no observable effect on DO. Dissolved oxygen in Beacons Reach never fell
below 1.5 mg/L at any depth. Throughout the summer, the water column was well mixed.
Although DO profiles were not stratified, values were depressed immediately above the sediment
during the two August sampling events. Four of the 10 fecal coliform samples collected in the
marina basin were greater than 14 fecal coliform/100 mL. A maximum value of 50/100 mL was
found on May 26 at BR-6. Although some individual observations exceeded the North Carolina
State criterion of 70/100 mL for shellfish, the median value was 10/100 mL for the basin.
6-3
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Marina Water Quality Models
TABLE 6-2. Beacons Reach Marina Water Quality Summary
Parameter
Summer Mean
Ambient
Basin
Column DO (mg/L)
6.3
5.0
Surface DO (mg/L)
6.3
5.3
Photic zone DO (mg/L)
6.3
5.1
Fecal coliform/100 mL
< 10*
10a
Turbidity (NTU)
8.3
7.0
Chlorophyll-a (^g/L)
12
15
Ammonia (mg/L)
0.04
0.07
Total Kieldahl N (mg/L)
0.4
0.4
Nitrate-nitrite (mg/L)
<.01
0.02
Total phosphorus (mg/L)
0.05
0.07
a Median
Therefore, these waters did not violate the state standard for class SA waters (14/100 mL median
value). All eight of the fecal coliform samples collected in ambient waters were below the
laboratory detection limit of 10/100 mL. A station at the mouth of Beacons Reach Marina was
sampled once during the study period by the Division of Health Services Shellfish Sanitation
Branch. Collected on May 26, this sample contained 79 fecal coliform/100 mL.
Turbidity and chlorophyll-a values were all within state standards. Nutrients were
slightly higher in the basin than in ambient waters. One long-term BOD sample was collected
at station BR-4 on September 8. The 5-day BOD value for this sample was 2.47 mg/L. After
81 days, 7.23 mg/L of oxygen had been consumed (NCDEM, 1990).
6.1.3 Gull Harbor Marina
Gull Harbor Marina is located on the northern shore of Bogue Sound in Carteret County,
North Carolina, about 6.5 miles west of Morehead City. Gull Harbor Marina basin contains
approximately 32 slips, and the surface area is 1.4 acres. The average depth measured in the
basin ranged from 1.0 to 1.8 meters. Both sailboats and powerboats were docked in the marina
during the dye study, and the number of boats observed ranged from 16 to 28. Sampling
stations are indicted on the marina map (Figure 6-3). Gull Harbor shared ambient stations with
Brandy wine Bay Marina (BB-1UPS and DS). These ambient stations were located in the
Intracoastal Waterway on either side of the two marinas.
6-4
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Marina Water Quality Models
BR-1DS
BOGUE SOUND
BR-1 UPS
m PHYSICAL AND CHEMICAL
MEASUREMENTS
• PHYSICAL MEASUREMENTS
ONLY
X DYE STATIONS
MARINA SURFACE AREA - 2.3
ACRES
60 SLIPS
METERS
I
0 50 100
Figure 6-2. Sampling Stations and WASP Model Application to the Beacons Reach Marina.
6-5
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Marina Water Quality Models
Figure 6-3. Sampling Station and WASP Model Application to the Gull Harbor Marina
6-6
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Marina Water Quality Models
The marina is surrounded by a residential development of large single-family houses.
Stormwater runoff from adjacent roads enters the basin via the boat ramp and a roadside culvert.
In addition, a small drainpipe discharges water from a well-water, once-through heat pump into
the marina. Drainage also enters the marina though a small marsh, which borders on the eastern
edge of the marina channel (adjacent to station GH-2).
Gull Harbor is one of three marinas where intensive work was done to determine
sediment oxygen demand (SOD) and marina flushing characteristics. Gull Harbor is a semi-
enclosed marina with distinct basin and channel sections (i.e., two-segment marina). The
channel, however, is relatively wide compared to the basin. Adjacent areas outside the marina
are similar in depth to the basin. However, a well-defined boat channel has been dredged
between the Waterway and the marina inlet. Measurements indicate that while the basin is
slightly deeper than the inlet channel, both are much shallower than the Intracoastal Waterway.
The dye tracer study performed at this marina confirmed that this design, coupled with the tidal
amplitude of the area, results in rapid flushing of Gull Harbor Marina.
Water quality data are summarized in Table 6-3. The marina basin was never anoxic,
but oxygen profiles were weakly stratified during half of the sampling events. Minimum basin
dissolved oxygen values of 2.6 mg/L were measured just above the bottom sediments during the
August sampling events.
Statistical analysis revealed that the average summer DO was significantly higher at the
ambient stations than at either the basin stations or the channel stations (NCDEM, 1990). Both
basin and ambient DO values were less than 5.0 mg/L on July 21, 1988. On that date, the
Bogue Sound marinas were sampled during an unusually low tide, and DO values were low
throughout the sound. Aside from this one episode, wind and tidal variations had no observable
effect on DO.
All metals values were below laboratory reporting levels. Turbidity levels were low as
well, with no values exceeding the state standard (25 NTU). One chlorophyll-a value of 10 in
the marina basin was above the state standard of 40 /xg/L (50 /ig/L on August 3 at GH-5).
Nitrogen fractions were similar in ambient and basin waters, but total phosphorus was slightly
higher in the basin. One long-term BOD sample was collected at station GH-5 on September
8, 1988. The 5-day BOD value for this sample was 2.15 mg/L. After 81 days, 6.01 mg/L of
oxygen had been consumed.
Station GH-5 was sampled on August 18 for pesticides and organics analyses. Pesticide
extractions and analyses detected one identified peak by the gas chromatograph/electron capture
sector method. Acid herbicide extraction and analyses detected three unidentified peaks by the
gas chromatograph/electron capture method (NCDEM, 1990). None of these results represent
violation of state pesticide or organics standards.
6-7
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Marina Water Quality Models
TABLE 6-3. Gull Harbor Marina Water Quality Summary
Parameter
Summer Mean
Ambient
Basin
Column DO (mg/L)
6.0
4.0
Surface DO (mg/L)
6.1
4.6
Photic zone DO (mg/L)
6.0
4.2
Fecal coliform/100 mL
< 101
15'
Turbidity (NTU)
6.3
7.4
Chlorophyll-a (/xg/L)
8.3
19
Ammonia (mg/L)
0.04
0.06
Total Kjeldahl N (mg/L)
0.3
0.4
Nitrate-nitrite (mg/L)
<.01
<.01
Total phosphorus (mg/L)
0.04
0.06
" Median.
6.2 Marina Data Review
The purpose of this section is to summarize the marina data received from the U.S. EPA
and other sources for use in calibrating the marina water quality models chosen for this study.
Data were received from a number of sources including the EPA-Athens Laboratory, the North
Carolina Department of Environmental Management (NCDEM), and the Delaware Department
of Natural Resources and Environmental Control (DNREC). EPA-Athens Laboratory provided
a data set on the Indian Hills Yacht Club canal in Boca Raton, Florida. NCDEM provided the
North Carolina Coastal Marinas: Water Quality Assessment (1990) report, which provided data
collected at 11 coastal marinas in that state. The Delaware DNREC recently conducted flushing
time characteristics studies at two coastal marinas; however, the final reports of those studies
are not yet available.
Based on the three levels of water quality models chosen for application to coastal
marinas, the following marina data sets were selected:
Indian Hills Yacht Club, Boca Raton, Florida
Beacons Reach Marina, Bogue Sound, Pine Knoll Shores, North Carolina
Gull Harbor Marina, Bogue Sound, North Carolina
6-8
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Marina Water Quality Models
Descriptions of the available data for the above marinas and data gaps related to model
application are provided in the following subsections.
6.2.1 Indian Hills Marina'
Data for this marina were provided by Mr. Tom Cavinder at USEPA Athens Laboratory.
The Indian Hills facility is a flow-through canal rather than a typical marina design; from a data
standpoint, however, this facility was one of the best choices because of the amount of water
quality information available on the system. Data were collected in the canal system during
1984 and 1985 and included the following parameters:
Tide range
Current velocity
Dye tracer data
Sediment oxygen demand
Sediment core characterizations
Water temperature
Salinity
Dissolved oxygen
Light transmissivity
Ammonia (NH3)
Nitrate+nitrite (N02 + N03)
Total Kjeldahl nitrogen (TKN)
Total phosphorus
Total organic carbon
Carbonaceous Biochemical Oxygen Demand (CBOD)
Data Gaps
The following information was not available for the Indian Hills system:
Meteorological data (air temperature, wind speed, wind direction)
Coliform bacteria data
Number of boats and boat slips
Lack of meteorological data may bias the reaeration used in the WASP4 model since
wind speed is used in the calculation of hydrodynamic current velocities and in the reaeration
formulas themselves. It is not anticipated that the lack of wind data will have a large impact on
calibration of the WASP4 model at the Indian Hills canal.
Since no coliform data are available, it is not possible to calibrate any of the models to
this parameter. Also, the number of boats and number of boat slips in this facility were not
documented. Information on the number of boats in a marina can be used to estimate potential
coliform loading (USEPA, 1985).
6-9
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Marina Water Quality Models
6.2.2 Beacons Reach Marina
Data for this coastal marina were received from NCDEM. The marina is located on
Bogue Sound near Pine Knoll Shores, North Carolina, and is an example of the classic two-
segment coastal marina design showing distinct basin and channel segments. This marina was
chosen because of its classical design configuration and because the data included a dye study.
Data were collected during the summer of 1988 and included the following parameters:
Tide range
Dye tracer data
Sediment oxygen demand
Water temperature
Salinity
Dissolved oxygen
Turbidity (NTU)
Chlorophyll-a
Ammonia (NH3)
Nitrate+nitrite (N02 + N03)
Total Kjeldahl nitrogen (TKN)
Total phosphorus
Fecal coliform
BOD
Metals (chromium, arsenic)
Pesticides and organics
Number of boats and boat slips
Data Gaps
The following information was not available for the Beace s Reach Marina:
Current velocities
Meteorological data (air temperature, wind speed, and wind direction)
6.2.3 Gull Harbor Marina
Data for this coastal marina were received from NCDEM. The marina is located on the
northern shore of Bogue Sound in Carteret County, North Carolina, and is a two-segment coastal
marina having a main basin segment and a channel entrance segment. This marina was chosen
because of its classical design and because the data included a dye study. Data at the Gull
Harbor Marina were collected in 1988 and include the following parameters:
Tide range
Sediment oxygen demand
Dissolved oxygen
Turbidity (NTU)
6-10
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Marina Water Quality Models
Chlorophyll-a
Ammonia (NH3)
Nitrate + nitrite (N02 + N03)
Total Kjeldahl nitrogen (TKN)
Total phosphorus
Fecal coliform
BOD
Metals (chromium, arsenic)
Pesticides and organics
Number of boats and boat slips
Data Gaps
The following information was not available for the Gull Harbor Marina:
Water temperature and salinity
Current velocities
Meteorological data (air temperature, wind speed, and wind direction)
6.3 Model Application
6.3.1 Simple Model
The Tidal Prism Analysis was selected as the method of choice in the simple model
category. This technique, as presented, can provide reasonable approximations for screening
potential impact problems when site-specific data are not available. This method is capable of
addressing all marina water quality issues of concern (e.g., DO and fecal coliform) and comes
with excellent documentation.
To assess the water quality impacts of marina-derived pollutants on the environment using
the methods discussed in this section, certain pollutant loading values must be available for use.
If actual values for various loadings are not available, estimations can be made using Table 6-4.
The loadings shown in Table 6-4 are based on the following assumptions (Carstea et al., 1975):
• Average number of persons per boat is three.
• Average per capita discharges of coliform bacteria and BOD are 2 billion MPN and
75.6 g, respectively.
• Half of the people on board contribute fecal material in 24 hours.
• Coliform bacteria populations do not increase.
• A boat in use spends 1 hour in the marina.
• 25 to 40 percent of boats present are in use and evenly distributed.
• An average boat has a 2-cycle 30-hp outboard motor, consumes 4.458 liters of
gasoline per hour, and operates at 1000 rpm; the fuel has a gasoline-to-oil ratio of
50:1.
6-11
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Marina Water Quality Models
TABLE 6-4. Estimated Pollutant Contribution from Boats
Boats
Total
Boats
in Use
BOD
(g/tar)
Coliform
Bacteria
(billions/hr)
Nonvolatile
Oil (g/hr)
Volatile
Oil (g/hr)
Phenol
(g/hr)
Lead
(g/hr)
1
1
4.54
0.13
66.7
37.8
0.8
0.4
5
2
9.08
0.25
133.5
75.6
1.6
0.8
10
3
13.62
0.38
200.1
113.4
2.4
1.2
20
5
22.70
0.63
333.5
189.0
4.0
2.0
Density of waste fuels is 0.7 g/ml.
Source: Carstea et al., 1975.
Fecal coliform bacteria loadings for all marinas were estimated using the values reported
in Table 6-4, as will be shown later in this section.
Flushing Time
Flushing time for Beacons Reach, Indian Hills, and Gull Harbor is estimated using
Equation 2-3 (USEPA, 1985). This equation represents a simplified approach to estimate
flushing time assuming that:
¦ • The majority of flushing is due to tidal flow.
• The tidal prism volume completely mixes with basin waters.
• The pollutant concentration decreases with each tidal dilution but will never
completely flush.
• The influx of pollutant by nontidal fresh water is small in comparison to the mass of
pollutant in the low tide water and return tidewater volumes.
• The concentration of the pollutant in ambient waters outside the marina is very small.
The parameter b in Equation 2-3 represents the returning ratio (the percentage of the tidal
prism that was previously flushed from the marina on the outgoing tide) and is expressed as a
decimal fraction. For example, if a river had a relatively low flow rate, water discharged from
the marina at the completion of one tidal cycle may still exist in proximity to the marina inlet
and portions may flow back into the marina on the incoming tide.
In using Equation 2-3, the return flow factor (b) may be the most difficult parameter to
determine. This value may be estimated based on the circulation characteristics of the affected
water bodies. Without definitive field data, subjective estimations would have to be made. The
return flow factor (b) is assumed to be zero in calculating flushing time at both marinas.
In using Equation 2-3, the value for D should be chosen depending on the amount of
flushing desired. If complete flushing is desired, a very low value of D can be selected, such
6-12
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Manna Water Quality Models
as 0.01. Since the remaining pollutant concentration will be diluted by each tidal cycle,
complete flushing will be approached asymptotically, so a reasonable cutoff value for dilution
must be chosen (USEPA, 1985). For most cases it is satisfactory to achieve the desired dilution
(D) within a 2- to 4-day flushing time. Longer flushing times may not be acceptable (Boozer,
1979).
Fecal Coliform Bacteria
Marina sites in the vicinity of harvestable shellfish beds represent potential sources for
bacterial contamination of the shellfish. Therefore, issues related to the potential for
contravention of state water quality standards in waters classified as suitable for shellfish
propagation and harvesting may arise. The following methods available for predicting impacts
from boat wastes may not be conclusive because coliform counts vary with temperature,
turbidity, boat densities, tides, day of the week, season of the year, and the number of persons
aboard each boat. The contribution of boats to fecal coliform pollution of the water can be
estimated by methods used by FWPCA (1967), USFDA (1972), Faust (1982), Furfari (1968),
and SCDHHS (1982). Many of these studies were directed toward estimating the number of
boats allowable in a shellfishing area.
For a continuous discharge of a nonconservative pollutant into a marina basin, an
estimate of long-term concentrations may be obtained by Equation 2-7 (USEPA, 1985). In
Equation 2-7, it is assumed that the pollutants remain in solution and loss by sedimentation is
minimal. The result of these assumptions is that concentrations obtained would probably exceed
actual concentrations measured (USEPA, 1985). It is also assumed that the pollutant decay is
first order. Values for the reaction rate K are critical for accurate estimates. Therefore, the K
values in Table 6-5 should be used only if actual values cannot be obtained for the site under
consideration. These values are generally determined empirically and are specific to a
temperature of 20°C and the set of physical conditions existing at the time of measurement.
Additional K. values for bacteria may be obtained from Thomann and Mueller (1987). Another
reference for K values, including procedures to estimate K, is USEPA (1985). The first order
decay coefficient is temperature dependent according to the following equation:
K = K 0(r-2O) (6-1)
(7) (20)
where:
K = first order rate coefficient (0.5 - 1.0 day'1)
T = water temperature in °C
9 = 1.02 for coliform decay coefficient
Dissolved Oxygen
Low dissolved oxygen levels are being recognized as a serious water quality impact that
may result from poorly designed and maintained marinas. The assessment of DO levels is
complicated because the kinetics of dissolved oxygen are very complex. Dissolved oxygen
6-13
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Marina Water Quality Models
concentrations can vary greatly over short periods of time as a function of teh following
(Thomann and Mueller, 1987):
• Reaeration from the atmosphere;
• Photosynthetic oxygen production;
• Dissolved oxygen inputs by tidal flow and tributaries;
• Sediment oxygen demands;
• Biochemical oxygen demands; and
• Oxygen use by aquatic organisms.
The best way to assess marina impacts on water quality is to design a sampling strategy
and physically measure dissolved oxygen values. During the sampling, sediment oxygen demand
and other data can be collected. These data may be used to estimate future dissolved oxygen
levels using mathematical modeling procedures described in the North Carolina Coastal Marinas
Water Quality Assessment (NCDEM, 1990) and the Technical Guidance Manual for Performing
Wasteload Allocations (USEPA, 1989). Prior to data collection, screening procedures such as
Equation 2-8 should be used to identify trouble spots. Equations 2-8a and 2-8b should be used
to successively estimate dissolved oxygen concentrations at high and low tide in a semi-enclosed
marina. Equations 2-8a and 2-8b are based on a mass balance of the following dissolved oxygen
sources and sinks:
• Tidal inflow;
• Reaeration;
• Biochemical oxygen demand;
• Sediment oxygen demand;
• Low tide dissolved oxygen; and
• Freshwater inflow.
It is assumed that there is no increase in oxygen levels within the basin due to biological
activity, that dissolved oxygen carried in by tidal flushing is retained within the basin, and that
benthic oxygen demand is uniform throughout the basin.
Values for Ka should be determined for the specific site considered. Thomann and
Mueller (1987) and USEPA (1985) describe procedures to estimate K,. If site-specific values
are not available, an estimate can be obtained using the K values shown in Table 6-5.
Dissolved oxygen concentrations at saturation can be obtained from solubility tables
(APHA, 1985), which provide DOs values for water-saturated air at standard pressure given
various chloride concentrations. For dissolved oxygen calculations, chloride concentrations are
typically set equal to.chlorinity, the chlorine equivalent of the total halide concentration in the
seawater. The following equation relates chlorinity and salinity (USEPA, 1985):
Salinity, ppt = 0.03 + (0.001805) (chlorinity, mg/L) (6-2)
6-14
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Marina Water Quality Models
TABLE 6-5. Representative Reaction Coefficients
Pollutant
Typical K
(day1)
Typical Range
(day1)
K,, Oxidation Rate
(1/day)
0.25
0.10 - 0.30
SOD, Sediment Oxygen
Demand Rate
(g/m2/day)
0.10
0.00 - 5.00
K„, Reaeration Rate
(1/day)
0.12
0.05 - 3.00
Kx, Fecal Coliform
(1/day)
1.20
0.30 - 1.20
Source: USEPA, 1978.
6.3.1.1 TPA Application to Indian Hills Marina
Flushing Time
A dye study was conducted at the Indian Hills marina during phase I. The tracer was
quickly mixed within the Indian Hills waterways and within 42 hours, 90 percent of the dye had
been flushed from this system. Accordingly, for a 0.10 dilution factor the observed flushing
time at the Indian Hills marina is 42 hours (3.36 tidal cycles).
Input parameters used in Equation 2-2 to estimate the flushing time for Indian Hills
marina are listed in Table 6-6. The observed tidal range at Indian Hills marina is 0.8 meter.
Depths at the Indian Hills marina are relatively uniform, averaging 2.0 meters at Mean Tide
Level. Predicted and observed flushing times of the Indian Hills marina are listed in Table 6-7.
Fecal Coliform Bacteria
Estimates of fecal coliform contribution from boats in Indian Hills were calculated based
on Carstea et al., (1975). Available data for Indian Hills marina did not include the number of
boat slips available at this site. It was decided that representative data from Table 6-4 would
be used (Cavinder, 1-991). Table 6-4 assumes that for every 20 boats available 5 boats are in
use. Loading rates of fecal coliform, for both marinas, are estimated as follow:
Mr = ((20/20) x 0.25) x 0.63x10' x 24 hours = 0.38x10'° organisms/day
6-15
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Marina Water Quality Models
TABLE 6-6. Input Data Used to Estimate Flushing Time (TPA)
Parameter
units
Beacons
Reach
Indian
Hills
Gull
Harbor
Tc
Tidal cycle
hours
12.5
12.5
12.5
A
Surface area of marina
m2
9448
34355
5360
D
Desired dilution factor
dimensionless
0.1
0.1
0.1
R
Range of tide
m
0.6
0.8
0.6
b
Return flow factor
dimensionless
0.0
0.0
0.0
I
Non-tidal freshwater inflow
m3/hour
0.0
0.0
0.0
L
Average depth at low tide
m
1.8
1.6
1.2
H
Average depth at high tide
m
2.4
2.4
1.8
vL
Volume of marina at low tide
m3
17006
54968
8040
V„
Volume of marina at high tide
m3
22675
82452
11256
Vp
Volume of marina tidal prism
(V„ - V[)
m3
5669
27484
3216
TABLE 6-7. Observed and Predicted Flushing Time Using TPA
Marina Site
Dilution
Factor (D)
(dimensionless)
Predicted
Flushing Time
Equation 2-3
(tidal cycles)
Observed Flushing
Time
Dye Study
(tidal cycles)
Beacons Reach
0.25
0.10
OO
O oo
3.8
7.2*
Indian Hills
0.10
5.7
3.4
Gull Harbor
0.10
5.7
1.5
* Extrapolated from observed data.
6-16
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Marina Water Quality Models
Since the number of boat slips at the Indian Hill marina during the study was not
reported, it was assumed that 20 boats were present during the study period. The assumed 20-
boat value is used thereafter to estimate fecal coliform loadings at the Indian Hills marina. Fecal
coliform loading for the Indian Hills marina is estimated at 0.38 E10 organisms/day. Predicted
and observed fecal coliform concentrations for the Indian Hills marina, over the observed
flushing time of 42 hours, are also listed in Table 6-8. Fecal coliform predictions are calculated
for illustrative purposes, and no further conclusions should be made with regard to model
predictions at this marina site.
Dissolved Oxygen
Equation 2-8 was used to estimate DO levels at Indian Hills marina. For the Indian Hills
marina the averaged CBOD was 3.15 mg/L. The oxidation coefficient for BOD, ku will vary
depending on sedimentation rates, waste characteristics, water depth, temperature, and other
values. Table 6-9 lists default values for K, used at the Indian Hill marina. An oxidation rate
of 0.33 was used in Equation 2-8 for Indian Hills marina. The value for k, used in Equation
2-8 is 0.70 per day for Indian Hills marina (0.70 was obtained from the Indian Hills WASP4
model run).
Chemical reduction and bacterial respiration of organic matter that occur in sediments
create a demand for oxygen from overlying waters. This sediment oxygen demand (SOD) can
strongly influence oxygen conditions in a water column; therefore, SOD is an important
component of models that predict oxygen concentrations. SOD rates are highly site-specific and
are influenced by substrate composition, sediment organic content, and environmental factors
such as temperature (Hatcher, 1986). SOD rates were measured once at Indian Hills marina.
After correcting for water column respiration, results from replicate chambers were averaged
to determine one SOD value. The Indian Hills marina basin SOD rate is 3.99 g/m2/day.
Observed DO values at station 1-4, Figure 6-1, were used to calculate DOA in Equation
2-8. The ambient DOA used in the calibration runs was based on whole-column averages of all
DOa values sampled adjacent to each marina during the study. The DOs saturation value was
based on similar averages of temperature and salinity. The DOs value used in Equation 2-8 for
Indian Hills marina is 4.94 mg/L. Results obtained from Equation 2-8 were compared to similar
whole-column averages of all inlet channel and marina basin DO values sampled at the marina
during the study. It was assumed that these values were approximately equivalent to average
summer conditions.
6-17
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Marina Water Quality Models
TABLE 6-8. Predicted and Observed Fecal Coliform Using TPA
Summer
Temperature
K,
Predicted
Observed
Marina Site
(°C)
day1
per/lOOmL
per/100 mL
Beacons Reach
20
0.5
45
> 50
Average Summer Conditions
20
1.0
27
-
29
0.6
41
-
29
1.2
22
< 10
Beacons Reach May 26
20.5
0.5
45
50
20.5
1.0
27
79"
Indian Hills'
27.5
0.6
<10
Average Summer Conditions
27.5
1.16
<10
-
Gull Harbor
20
0.5
51
15 - 140
Average summer conditions
20
1.0
32
15 - 140
29
0.6
46
15 - 140
29
1.2
27
15 - 140
* Fecal coliform loadings are based on estimates of number of slips at this site.
TABLE 6-9. TPA Input Parameters Used to Estimate DO Levels
Marina Site
DOA
mg/L
DOs
mg/L
DO,
mg/L
Ka
day1
K,
day1
B
g/m2/day
cb
mg/L
Beacons Reach
6.30
6.90
6.30
0.30
0.10
2.68
7.23
Indian Hills
4.94
6.40
4.94
0.70
0.10
3.99
3.15
Gull Harbor
6.00
7.00
6.00
0.30
0.10
3.00
6.01
6-18
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Marina Water Quality Models
6.3.1.2 TPA Application to Beacons Reach Marina
Flushing Time
A dye study was conducted at the Beacons Reach marina site. The purpose of the dye
study was to provide data on the flushing characteristics of the marina by tracing Rhodamine WT
dye through consecutive tidal cycles until 90 percent of the average dye concentration had
flushed out from the marina (D=0.1). Table 6-10 lists average dye concentration at Beacons
Reach during the study period. A reduction of 74.3 percent of the initial dye concentration was
achieved at the end of 48 hours (D=0.25). Therefore, the observed flushing time at Beacons
Reach marina is 48 hours (3.84 tidal cycles) for a 0.25 dilution factor.
Equation 2-3 was used to estimate the flushing time at Beacons Reach marina. Input
parameters used in Equation 2-3 to estimate the flushing time for Beacons Reach marina are
listed in Table 6-8. The observed tidal range at Beacons Reach marina was 0.6 meter. Depths
at Beacons Reach marina are relatively uniform averaging 2.1 meters at Mean Tide Level.
Predicted and observed flushing times at Beacons Reach marina are listed in Table 6-7.
Fecal Coliform Bacteria
Results of whole column averaging of all temperature values sampled adjacent to each
marina during the study were used to estimate the k values used in Equation 2-7. It was
assumed that these values were representative of typical conditions at these sites. Adjacent
temperatures at Beacons Reach marina ranged from 20°C to 29°C during the study period.
High and low ambient temperatures were used in Equation 2-7 to estimate decay rate coefficient.
The k
-------
Marina Water Quality Models
TABLE 6-10. Averaged Dye Concentration at Beacons Reach Marina
During the Study Period
Hour after Dye Release
Average Dye Cone, (ppb)
6
95.3
12
78.6
18
57.5
24
35.6
30
29.9
36
28.4
42
26.2
48
24.5
Dissolved Oxygen
Equation 2-8 was used to estimate DO levels at Beacons Reach marina. Initially, the
value of DOl was set equal to DOA, which is assumed constant over the period of analysis. The
DOa value used in Equation 2-8 for Beacons Reach marina was 6.3 mg/L. Values for K„ should
be determined for the specific site considered. Since site-specific values were not available, a
Ka value of 0.30 per day was used in Equation 2-8 for Beacons Reach marina.
Values of CB will decrease as biochemical oxygen demand (BOD) is consumed. For
screening purposes a steady state BOD concentration may be calculated for CB by using several
techniques provided in USEPA (1985). One long-term BOD sample was collected at Beacons
Reach on September 8, 1988. The 5-day value for this sample was 2.47 mg/L. After 81 days,
7.23 mg/L of oxygen had been consumed. The long-term BOD value of 7.23 mg/L is used in
the analysis. SOD rates were measured once at Beacons Reach marina. After correcting for
water column respiration, results from replicate chambers were averaged to determine one SOD
value. The Beacons Reach marina basin SOD rate is 2.60 g/m2/day.
Observed DO values at station BR-1 (Figure 6-2) were used to calculate DOA in Equation
2-8. The ambient DOA used in the calibration runs was based on whole-column averages of all
DOa values sampled adjacent to Beacons Reach marina during the study. The DOs saturation
value was based on similar averages of temperature and salinity. The DOs value used in
Equation 2-8 for Beacons Reach is 6.9 mg/L. Results obtained from Equation 2-8 were
compared to similar whole-column averages of all inlet channel and marina basin DO values
sampled at the marina: during the study. It was assumed that these values were approximately
equivalent to average summer conditions. Whole-column averages were used because the
equation assumes a completely mixed inlet channel and a completely mixed basin.
6-20
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Marina Water Quality Models
6.3.1.3 TPA Application to Gull Harbor Marina
Flushing Time
A dye study was conducted in the Gull Harbor Marina near Morehead City on October
24-26, 1988. The purpose of the study was to provide data on the flushing characteristics of the
marina by tracing Rhodamine WT dye through consecutive tidal cycles until 90 percent of the
average dye concentration had flushed from the marina.
The study consisted of pouring, in a south-to-north pattern, 4 liters of Rhodamine WT
dye from a boat into the surface waters of the marina and then mixing the dye by running the
boat through the dye cloud several times. Dye samples were then collected by hand from piers
and by boat at 10 selected sites (stations A through J in Figure 6-3). Samples were taken with
a depth-integrating sampler and represent an average of the water column.
The dye was placed in the marina 30 minutes after low tide, which occurred at
approximately 1630 October 24. The dye sampling was done at high tide and low tide for 3 1/2
tidal cycles staring at the high tide after the dye dose.
The average initial dye concentration at the first high tide sampling run was 75.8 ppb,
and on the next low tide sampling run it was 9.23 ppb, an 88 percent reduction. The second
high tide run showed an average dye concentration of 3.42 ppb, a 95.5 percent reduction, and
the remaining sampling runs had concentrations of 5.0 ppb (low tide) and 3.1 ppb (high tide).
This marina almost attained the 90 percent reduction goal on the first tidal period, which is a
quicker flushing time than those of the other marinas studied. It is also of interest that the dye
concentration in this marina seemed to reach a certain concentration (3-5 ppb) and then stay at
that concentration, almost as if it had reached equilibrium.
Equation 2-3 was used to estimate the flushing time at Gull Harbor marina. Input
parameters used in Equation 2-3 to calculate flushing time for Gull Harbor marina are listed in
Table 6-6. Observed tidal range was assumed 0.6 meter (similar to Beacons Reach marina) and
depths are relatively uniform, averaging 1.5 meters at Mean Tide Level. Predicted and observed
flushing times at Gull Harbor marina are listed in Table 6-7.
Fecal Colifbrm Bacteria
Water temperatures adjacent to Gull Harbor marina were not available during the study
period. Therefore, high and low ambient temperatures at Beacons Reach were used in Equation
2-7 to estimate the decay rate coefficient for the Gull Harbor marina. Both marinas are located
on the Bogue Sound, and minimum and maximum temperatures should be very similar. The K(x)
values used were 0.6 and 1.2 for the temperature range at Gull Harbor marina. Equation 2-7
was then used to calculate fecal coliform concentrations.
6-21
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Marina Water Quality Models
At the Gull Harbor marina, 32 slips are available; therefore, loading rates of fecal
coliform are estimated as follows:
Mr = ((32/20) x 0.25) x 0.63xl09 x 24 hours = 0.60x10'° organisms/day
According to the NCDEM monitoring study, five of ten fecal coliform samples collected
in the marina basin were greater than 14/100 mL. A maximum value of 140/100 mL was found
on September 8 at GH-3. The median of the individual basin observations was 15/100 mL,
which exceeds that state standard of 14/100 mL. One high value (less than 600/100 mL) was
found in ambient waters, but the median (less than 10/100 mL) was below the state standard.
Gull Harbor Marina was sampled twice during the study period by the Division of Health
Services, Shellfish Sanitation Branch. On both sampling dates, values of 49/100 mL were found
at the mouth of the marina. A station in the Intracoastal Waterway just outside of the marina
was sampled four times. One value (17/100 mL) above the standard was found on June 20.
Dissolved Oxygen
Equation 2-8 was used to estimate DO levels at Gull Harbor marina. Initially, the value
of DOl was set equal to DOA, which is assumed constant over the period of analysis. DOA
value used in Equation (2-8) for Gull Harbor marina was 6.0 mg/L. Values for k, should be
determined for the specific site considered. Since site-specific values were not available, a k,
value of 0.30 per day was used in Equation 2-8.
One long-term BOD sample was collected at station GH-5 on September 8. The 5-day
value for this sample was 2.15 mg/L. After 81 days, 6.01 mg/L of oxygen had been consumed.
The long-term BOD value of 6.01 mg/L was used in the analysis.
The widest variation in SOD values was obtained at the Gull Harbor marina. Two
relatively low values were obtained out of a total of five; however, these values occurred on
substrate that was not representative of the average bottom condition (NCDEM, 1990). The
Gull Harbor marina basin SOD rate used was 3.0 g/m2/day as suggested in NCDEM (1990).
Observed summer DO values at ambient stations were used to calculate DOA in Equation
2-8. The ambient DOA used in the calibration runs was based on whole column averages of all
DOa values sampled adjacent to Gull Harbor marina during the study. The DOs saturation value
was based on similar averages of temperature and salinity. The DOs value used in Equation 2-8
for Gull Harbor was 7.0 mg/L. Results obtained from Equation 2-8 were compared to similar
whole-column averages of all inlet channel and marina basin DO values sampled at the marina
during the study. It was assumed that these values were approximately equivalent to average
summer conditions. _ Whole-column averages were used because the equation assumes a
completely mixed inlet channel and a completely mixed basin.
6-22
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Marina Water Quality Models
6.3.1.4 Summary of Simple-Methods Model Results
Flushing Time
Equation 2-3 over predicted flushing times at all marina sites. The smallest discrepancy
between observed and predicted flushing time is found at the Beacons Reach marina
(approximately 20 percent deviation from observed flushing time), while the highest discrepancy
is found at the Gull Harbor marina (approximately four times the observed flushing time). An
average deviation of approximately 68 percent from observed flushing time is found at the Indian
Hills marina. It should be noted that the Indian Hills marina is a flow-through type marina with
a network of canals. These channels increase water exchanges between the water inside the
marina basin and the water outside the marina, resulting in a much shorter flushing time.
Equation 2-3 does not take into account the added water exchange, through the existence of
several openings/canals, with the adjacent water. In addition, Equation 2-3 is a simple tool that
calculates flushing time based on tidal prism volume at a site. Tidal prism, in turn, depends on
the surface area of a site and the averaged depth for high and low waters.
The Gull Harbor marina is a two-segment marina (not a flow-through type); however, as
illustrated in Figure 6-3, the marina basin is connected to two canals. The first canal is directly
connected to the main water body outside the marina, while the second canal is connected to a
marsh area bordering the eastern side of the marina. As stated earlier, Equation 2-3 does not
account for added water exchanges through existing openings (exchange canals) and therefore
a discrepancy arises in predicting flushing time at Gull Harbor marina.
Fecal Coliform Bacteria
Fecal coliform values predicted using Equation 2-7 are in good agreement with observed
data (Table 6-8). In general, predicted values fell within the range of fecal coliform levels found
during the summer. According to both observed and predicted fecal coliform counts, conditions
at the Gull Harbor marina are consistently above the standard of 14mL/100mL.
Dissolved Oxygen
TPA model results are in good agreement with observed DO levels at the three marina
considered for this study. TPA procedure over predicted DO at Indian Hills and Gull Harbor
marinas and under predicted DO at Beacons Reach marina. The comparison between predicted
and actual values of DO is summarized in Table 6-11. The highest deviation between predicted
and observed DO levels is found at the Gull Harbor marina (approximately 38 percent deviation
from observed value), and the lowest discrepancy is found at the Beacons Reach marina
(approximately 5 percent deviation from observed value). It should be noted that according to
TPA model results at all three marinas are consistently above 5 mg/L (i.e. all three marinas are
in compliance with State water quality standard for dissolved oxygen). However, observed DO
levels at Indian Hills and Gull Harbor marinas indicate otherwise.
6-23
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Marina Water Quality Models
TABLE 6-11. Observed and Predicted DO levels (TPA)
Marina Site
Predicted
mg/L
Observed
mg/L
Beacons Reach
5.34
5.60
Indian Hills
5.06
4.64
Gull Harbor
5.54
4.0*
" Summer Mean.
6.3.2 Mid-range Model
The recommended marina mid-range models are the Tidal Prism Model and the faCDEM
DO model. Both models are in the public domain, are easy to apply, and are supported by good
documentation.
6.3.2.1 Tidal Prism Model (TPM)
The Tidal Prism Model is a steady-state model that is capable of simulating up to 10
water quality variables including dissolved oxygen and fecal coliform. The Tidal Prism Model
should be used to corroborate the results of the simplified methods in the previous section when
the simplified methods indicate adverse water quality impacts.
Based on constituents modeled, the Tidal Prism Model was selected as the best qualified
marina mid-range model. The Tidal Prism Model predicts the longitudinal distribution of
conservative and nonconservative dissolved constituents at high slackwater (slack-before-ebb).
The rise and fall of the tide at the mouth of a marina causes an exchange of water masses
through the entrance. This results in the temporary storage of large amounts of bay or river
water in the marina during flood tide and the drainage of this water during ebb tide. This
volume of water is known as the tidal prism. Since water brought into the marina on flood tides
mixes with the creek water, a portion of the pollutant mass in the marina is flushed out on ebb
tides. This flushing mechanism due to the rise and fall of the tide is called tidal flushing.
The model is based on the division of the adjacent water body into segments, each of
which is considered to be completely mixed at high tide. The length of each segment is defined
by the local tidal excursion, the average distance traveled by a water particle on the flood tide,
since this is the maximum length over which complete mixing can be assumed. Instead of
starting the segmentation from the landward end with freshwater discharge and tidal prism as
two non-zero parameters, the modified model subdivides the water body starting from the
seaward end with the difference between tidal prism and freshwater discharge as a single
parameter. The mass balance within each segment is formulated by considering the exchange
of water with its neighboring segments due to the flushing of freshwater discharge, as well as
6-24
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Marina Water Quality Models
the tidal prism on ebb cycle, and to the mixing of the tidal prism on flood tide. This results in
an algebraic equation that may be solved for concentration in each segment by successive
substitution. For a nonconservative substance, the biochemical reaction terms are then added
to the algebraic equation without complicating the solution scheme. The model has been
successfully applied to a number of tidal creeks and coastal embayments (Kuo, 1989).
Given the initial conditions or calculated concentration fields at the slack-before-ebb
(SBE) that initiates a tidal cycle, the calculation of the concentrations at the succeeding SBE is
performed in two steps. First, the concentration fields are calculated assuming that only the
physical transport processes are in action. Second, the calculated concentration fields are
adjusted for the relevant chemical and biological processes.
To properly apply the mid-range Tidal Prism Model, salinity data from the mouth of
adjacent water to the head of tide are needed. In lieu of salinity data, dye tracer data from the
mouth to the head of tide can be used to calibrate the Tidal Prism Model. The freshwater inflow
at the upstream end of the adjacent river/creek is also required for the Tidal Prism Model.
Since not all data were available for proper application of this mid-range model, a hypothetical
case study will be used.
TPM Model Application
The first step in applying the model is segmentation of the water body. Geometrical data
are needed regarding river/creek and marina geometry, volume, and tidal prism. Figure 6-4
shows for a hypothetical tidal creek the accumulated low tide volume, Vol(x), and the difference
between the tidal prism and the river flow upstream of a point, [P(x) - R(x)], plotted as a
function of x, the distance from the mouth. Vol(x) is defined as the accumulated low tide
volume of the mainstem from the mouth to any distance x. P(x) is defined as the intertidal
volume, including the volumes of the tributaries and marinas, upstream of a transect located at
x. R(x) is defined as the freshwater input, summed over a half tidal cycle, which enters the
creek upstream of a transect located at x. The volume P( 1) is the intertidal volume of the entire
creek. R(l) is the total freshwater input to the creek: river flow, waste flows (point sources),
and lateral inputs (surface runoff). The volume V(l) is defined as a dummy volume located
outside the creek mouth. The first volume within the creek is defined as V(2). For the
assumption of complete mixing within each segment to be valid, segment lengths must be less
than or equal to the local tidal excursions. Therefore, the low tide volume of the first segment
within the river should equal the intertidal volume, minus the river flow, upstream of the
landward boundary of the segment. In a segment where a tributary and/or marina comes in,
segment 4 in Figure 6-4, the tidal prism of the tributary/marina should be included in
determining the segment volume. Therefore the curve Pr(x) - Ri(x) needs to be extrapolated
from the tributary junction to the landward transect of the segment.
As an example of application, consider a freshwater creek, shown in Figure 6-5, located
along the border of Fairfax County and Alexandria, Virginia. Hunting Creek drains a largely
urban area (approximately 44 mi2) and consists of a creek-like reach with upland and tidal
sections, which join to a small embayment of the Potomac.
6-25
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Marina Water Quality Models
Figure 6-4. Graphical Method of Segmentation of a Water Body.
Figure 6-5. Hunting Creek Showing Model Segments.
6-26
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Marina Water Quality Models
The hydrodynamic regime in Hunting Creek is dominated by tidal transport. During a
typical 12.4-hour tidal cycle, 29 million cubic feet of water is exchanged between the creek and
the Potomac River as the result of tidal flushing. During the same period, only about 1 million
cubic feet of fresh water enters the system. Thus a model based on substance transport by tidal
mixing is appropriate. The creek has no tributaries and is divided into 11 segments. It has two
point sources of pollution as well as upstream freshwater runoff.
If, during a flood tide, a particle at A can move only to B, and B only to C, then
everything between A and B will be contained at high tide within B and C (Figure 6-6). This
must be the value of the tidal prism above D, not counting mass inputs above B (runoff, etc.);
that is,
P(x) - R(x) = V(x) at B
For segment 2, the position B must be found such that between it and the mouth the
volume equals P(B)-R(B) (i.e., P(T2)-R(T2)). Volumes from the mouth to X will be less than
P(x)-R(x) until a distance has been reached equal to the local particle excursion since the prism
at any point in between includes particles entering from below (seaward of) the mouth.
Point B is the position of transect #2 (transect if I is at the mouth) and V2 is contained
between the curves of P(x)-R(x) and accumulated volume versus cross section at B. For the
third segment, move from B upriver until the total volume less V(2) equals P(x)-R(x). This is
the position of transect #3. For the fourth segment, move from transect #3 upriver until the total
volume less V(2) and V(3) equals P(x)-R(x); this is transect #4. Segmentation continues in this
manner until the cut-off guideline is approached (i.e., when the width of a segment exceeds its
length. Each of the tributaries/marinas may be segmented in the same way as that of the
mainstem. For segments landward of the transect at which P(n) = R(n), the creek behaves as
a fluvial stream and no water is transported landward during flood tide. The total volume of
water flowing through a transect during a tidal cycle is 2R(n).
In Figure 6-6, the accumulated low-tide volume vs. tidal prism (less runoff) is shown for
Hunting Creek. Accumulated (summed from the mouth to points upstream) low-tide volume is
used for graphical segment determination. The prism is summed from the head to points
downstream. Volumes are with regard to the bathymetric transects at this point since the model
segments have not yet been determined.
Table 6-12 shows the local (not accumulated) high-tide volume of the model segments,
which is the "volume" input to the model, and the prism above each transect, which is the
"prism" input to the model for the Hunting Creek example.
6-27
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Marina Water Quality Models
30
cu
20
u
c
o
E
^ 10
0)
E
_2
o
>
T1 T2 T3 T* T5 T6 T7 T8 T9 ' HO T1 1
0 5000 10000 15000
Distance (feet from mouth)
Figure 6-6. Graphical Representation of Hunting Creek Segmentation.
TABLE 6-12. Model Parameters for Hunting Creek Model Geometry
Transect
Segment
Distance
from
Mouth (mi)
Local High
Tide Volume
(1000 cf)
Tidal Prism
above Transect
(1000 cf)
Average
Depth
(ft)
Return
Ratio
1
1
0.00
0.00
28.97
3.1
0.0
2
2
0.33
27.61
11.59
4.5
0.0
3
3
0.50
10.68
4 96
7.1
0.6
4
4
0.76
3.20
2.98
3.2
0.0
5
5
0.95
0 90
2.44
2.9
0 0
6
6
1.14
0.81
1.91
2 4
0.0
7
7
1.33
0.69
1.40
2.0
0 0
8
8
1 52
0 56
0.94
1.7
0.0
9
9
1 70
0.49
0.54
1.4
0.0
10
10
1.89
0.32
0.24
1.2
00
11
11
2.75
1.95
0 00
0.5
0.0
Upstream and point source inputs, August 1 Flow (cfs)
Segment 11 Upstream
1.7 cfs
Segment 6
Alexandria STP
36 5 cfs
Segment 2
Westgate STP
13.2 cfs
6-28
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Marina Water Quality Models
TPM Model Calibration
The first step in model calibration is to simulate conservative substances, such as salt,
since the distribution of these substances is solely the result of physical processes. That is, the
variations in salinity in the estuary are the result of bay-derived salty water being transported
and mixed with land-derived fresh water. The calibration process is accomplished through the
calibration of the returning ratios against a dye study or salinity measurements. Low returning
ratios result in vigorous mixing, while a returning ratio of one has the physical meaning that
absolutely no mixing has occurred. For example, transects in areas where the depth suddenly
increases may be found to have much larger returning ratios, because mixing throughout the
larger volume takes longer and more of the old, unmixed water is left to return.
It is assumed that all substances will be transported and dispersed in a similar manner,
but that nonconservative substances will experience biochemical transformations during the
process. Therefore, the second stage of calibration is to simulate the concentration field of
nonconservative substances. Normally the fecal coliform submodel would be calibrated next
since it is simple, having essentially no interactions with other components.
Calibration of the nutrient cycle is complicated and difficult since numerous elements and
rate constants are involved. Rate constants that are not directly measured in the field may be
determined by successive trials using literature values as guides. The first stop in this trial-and-
error process is to reproduce the observed chlorophyll-a levels. This process is found to be
efficient in the sense that most model components are close to calibration by the time
chlorophyll-o levels are properly adjusted. Then there remains only some fine tuning of rate
constants, which have a minor influence on chlorophyll-a levels.
The dissolved oxygen component is the last to be adjusted since the phytoplankton have
an effect on DO levels. Changes in the decay rate of oxygen-demanding material tend to affect
BOD levels more than DO levels since reaeration plays a dominant role in the DO cycle. The
model predicts concentrations at high water slack, and it is against these observations that the
predictions should be compared. Perfect fits of all constituents and total verification of all data
sets are undoubtedly impossible. Fine tuning is probably best done after a gross calibration,
sensitivity, and verification sequence has been carried out and the main features of the physical
system are seen to be correctly reproduced.
6.3.2.2 NCDEM Model
The NCDEM DO model is selected as an alternative method in the mid-range model
category. The NCDEM DO model is a steady-state program that is only capable of predicting
DO concentrations. This model is applicable to one-, two-, and three-segment marinas. The
NCDEM DO model incrementally mixes the ambient and marina waters as a function of the
average lunar tides. The NCDEM Model version used for this study assumes that the marina
to be evaluated can be approximated by two segments: an inlet channel and the marina basin.
6-29
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Marina Water Quality Models
Runoff is assumed to be equal to zero, and the volume of wastewater discharged to the
basin other than from boats is also assumed to be equal to zero. The net flow out of the basin
is therefore zero. The forcing function is the changing depth of the ambient water, primarily
due to tidal forces, which brings water into the marina during the rising tide and takes water out
of the marina during the falling tide.
The tidal variations are assumed to follow a sinusoidal distribution. For simplicity, a 12-
hour tidal cycle is used. Calculations are performed at hourly time increments. Each segment
is assumed to be completely mixed at the end of each time increment.
Changes in dissolved oxygen are possible from advection, reaeration, or bottom sediment
oxygen demand. Boat discharges are not included since they have been shown to have a minor
effect or no effect on DO concentrations. The NCDEM model assumes some initial values,
iterates through 18 tidal cycles, and then prints out the results of the next two tidal cycles. This
allows sufficient interactions for a steady state to be reached, which is verified by comparing the
results of the last two tidal cycles.
NCDEM Model Application
The NCDEM DO model was applied to the Beacons Reach and Gull Harbor marinas.
Since the Indian Hills marina is a flow-through type marina, the NCDEM model version used
in this study, which is a two-segment marina model, could not be applied to this marina. Input
parameters used for NCDEM model applications at the two marinas are listed in Table 6-13.
Predicted dissolved oxygen levels at Beacons Reach and Gull Harbor marinas using the NCDEM
model are listed in Table 6-14.
6.3.2.3 Summary of Mid-range Model Results
The NCDEM model underpredicted DO levels at both marinas (Table 6-14). Deviations
from observed DO values ranged from 6 percent at the Gull Harbor marina to 14 percent at the
Beacons Reach marina. Considering the limitations and variability of the parameters measured
in those studies, the NCDEM Model appears to reasonably predict actual conditions.
6.3.3 Complex Model
The Water Quality Analysis Simulation Program (WASP4, Ambrose et al., 1987) is
selected as the method of choice in the complex model category. This program is a dynamic
compartment modeling system that can be used to analyze a variety of water quality problems
in one, two, or three dimensions. WASP4 simulates the transport and transformation of
conventional and toxic pollutants in the water column and benthos of ponds, streams, lakes,
reservoirs, rivers, estuaries, and coastal waters. The WASP4 model system is supported by the
U.S. EPA Center for Exposure Assessment Modeling (CEAM), Athens, Georgia, and has been
applied to many aquatic environments. The primary strengths and advantages of the WASP4
model are discussed in Chapter 4.
6-30
-------
Marina Water Quality Models
TABLE 6-13. NCDEM Input Parameters Used to Estimate DO Levels
Parameter
Description (units)
Beacons
Reach
Gull Harbor
HM
Average marina depth (ft)
6.0
4.5
HC
Average channel depth (ft)
7.0
5.0
AC
Channel surface area (ft2)
8700
11700
AM
Marina surface area (ft2)
93000
46000
TA
Tidal amplitude; half the tidal range (ft)
1
1
SOD
Sediment oxygen demand (g/m2/day)
2.68
3.00
DOa
Ambient DO (mg/L)
6.3
6.0
DOs
Saturation DO mg/L)
6.9
7.0
Kd
Decay coefficient (per day)
1.0
1.0
Kr
Reaeration rate (per day)
0.3
0.3
NBC
Channel boat activity (boat-hr/day)
0
0
NBM
Marina boat activity (boat-hr/day)
0
0
TABLE 6-14. Predicted DO levels (NCDEM)
Marina Site
Predicted DO (mg/L)
Observed DO (mg/L)
Beacons Reach
4.82
5.60
Gull Harbor
3.74
4.00
6-31
-------
Marina Water Quality Models
Cautionary Note:
Typically, the DYNHYD4 hydrodynamic submodel of WASP4 is calibrated to measured
tides and velocities in the water body. Since this is not possible for a proposed marina, a
different approach is recommended. The DYNHYD4 submodel can be calibrated in two steps.
First, a full two-dimensional hydrodynamic model is configured and calibrated to reproduce tidal
heights and velocity at the proposed marina site. Second, DYNHYD4 is calibrated through
successive adjustment of channel roughness and channel geometry (hydraulic radius and width)
until the velocities in the DYNHYD4 submodel match those from the 2-D hydrodynamic model.
In the first step, any two-dimensional hydrodynamic model can be used to calibrate the
DYNHYD4 submodel. The Tidal Embayment Analysis (TEA) program and/or the CAFE1
model are recommended to establish the hydrodynamics at a proposed marina site (see Sections
2.2.3.6 and 2.2.3.7). An example application is provided in Appendix E to illustrate this
approach.
6.3.3.1 WASP4 Model Application to Indian Hills Marina
The goal of applying WASP4 to the Indian Hills Marina is to determine how well the
model provides site-specific predictions of water quality parameters such as dissolved oxygen.
This is the most stringent type of modeling task. A good set of monitoring data is needed to
provide credible predictions. Monitoring data in the canal provide the necessary rate
coefficients, constants, and other parameters required by WASP4.
The WASP4 model applied to the Indian Hills Marina consisted of 12 model segments
and 12 channels as shown in Figure 6-1. The segments were chosen so that monitoring stations
were appropriately located near segment centers. Initially, the hydrodynamic submodel,
DYNHYD4, was applied to compute the tidal elevations and current flows in the canal. Next,
WASP4 was applied using only system variable #12 as the dye tracer variable. The dispersion
coefficients were then adjusted through several iterations until a good match between observed
and model dye concentration was obtained throughout the canal. Finally, dissolved oxygen was
simulated using WASP4 by including the intermediate eutrophication kinetics option.
There are 13 state variables included in the version of WASP4 applied to the Indian Hills
canal: two size-fractionated functional groups of phytoplankton (#1 nanoplankton chlorophytes
and #2 netplankton diatoms); inorganic nitrogen (nitrate, nitrite and ammonia); organic nitrogen;
dissolved silica; inorganic phosphorous; organic phosphorus; dissolved oxygen; CBOD; salinity;
and total coliform bacteria. However, not all of these state variables were used to simulate the
Indian Hills canal system. The state variables not used were phytoplankton #2, dissolved silica,
and total coliform bacteria. All phytoplankton activity was combined into the phytoplankton #1
state variable as total chlorophyll.
6-32
-------
Marina Water Quality Models
Calibration
For model calibration, boundary conditions for DYNHYD5 included tidal heights
recorded during the period Ally 12-15, 1984 (see data file listing in Appendix A). To ensure
stability in the hydrodynamic solution, a time step of 8 seconds was required based on the
following equation:
t <> (6-2)
+ U
where g is the gravitational acceleration constant (9.81 m2/sec), the shortest channel length (L)
is 44 m, the water depth (y) is about 2.3 m, and an assumed maximum velocity (U) is 0.5
m/sec. A time step of 6 seconds was actually used in the DYNHYD5 model run.
Hydrodynamic results were stored in an output file at a time interval of 15 minutes for use by
the WASP4 model.
One liter of dye tracer (20 percent Rhodamine WT) was introduced in a slug fashion in
model segment 11 (dye station 13) at 1715 hours on July 12, 1984. In an iterative fashion, the
dispersion coefficients were adjusted and a value of 1.0 m2/sec was determined to provide a
good match between observed and model dye concentrations. Results of model calibration to
the dye tracer data are shown in Figure 6-7. In segments 6 through 12, the model matches the
observed dye concentrations quite well. In Segments 1 through 5, however, the high initial peak
in dye concentration is not reproduced in the model. Since no meteorological information was
available with the data set, no wind data were input into the model. This could be one possible
cause for the failure of the model to predict the early peaks in Segments 1 through 5. Another
possible reason may be boat traffic in the canal, which is not accounted for in the model.
Overall, the model advection and dispersion appear to be well calibrated based on the results of
the dye comparisons.
Water quality data were collected in the canal on July 12 and 13, 1984. Next, the water
quality monitoring data were used to provide initial conditions and boundary conditions for the
WASP4 model. Since not all data required by WASP4 were available in the monitoring data
set, certain assumptions were made to fill in missing data gaps. Orthophosphate was estimated
as 60 percent of total phosphorus, and organic phosphorus was estimated to be 40 percent of
total phosphorus. Chlorophyll-a was estimated from total organic carbon (TOC). It was
assumed that particulate organic carbon (POC) was 20 percent of TOC and that the POC-to-
chlorophyll ratio was 0.080 mg C/fxg chlorophyll.
Temporal forcing functions constant in space but varying in time included incident solar
radiation, photoperiod, wind velocity, zooplankton biomass, and temperature-dependent sediment
flux terms for sediment oxygen demand, benthic ammonia, silica and phosphorous regeneration,
and benthic nitrate flux. In the version of WASP4 used for this study, temporal forcing
functions can be specified for up to five clusters of segments to represent subregions of a water
system. However, since the Indian Hills canal is such a small system, only one of the temporal
forcing function groups was used to represent the entire waterbody.
6-33
-------
Marina Water Quality Models
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-------
Marina Water Quality Models
Time invariant spatial forcing functions included sediment oxygen demand; benthic
nutrient fluxes of ammonia, nitrate, phosphate and silica; groundwater concentrations of
ammonia and nitrate; fraction of groundwater flow into a segment; and lateral dispersion. Non-
point source loading included spatially and temporally constant estimates of atmospheric
deposition. Groundwater inflow into the Indian Hills canal was assumed to be zero since no
information was available to indicate otherwise.
Temporal variation of the nonphytoplankton (background) extinction coefficient was
estimated using observed light transmission data at several stations in the Indian Hills canal.
The total extinction coefficient (KE) was determined to be 1.5/m. Riley (1956) estimated the
total extinction coefficient (in units of m'1) as a function of chlorophyll-a as:
KE = KE0 = 0.0088 P * 0.054 P2/3 (6-3)
where KEq is the background non-chlorophyll-related extinction coefficient and P is the
phytoplankton biomass expressed as ng Chl-a/L. Given KE = 1.5/m and P = 16 /xg/L, the
background extinction coefficient of KE0 = 1.02/m can be computed. This is the value used
in the Indian Hills WASP4 model.
Sediment oxygen demand was measured at two locations (Station 1-1 and Station 1-3) in
the canal during July 1984 and again in January 1985. The temperature dependence coefficient
(9) was computed at the two stations as 1.015 and 1.072, respectively. The higher value was
used in the WASP4 model because it yielded dissolved oxygen concentrations that better matched
observed values. The equation for computing temperature-dependent SOD is:
SODt = SODw e7'20 (6-4)
where:
SODt = Sediment oxygen demand at temperature T
SOD20 = Measured SOD at 20°C
0 = Temperature dependence coefficient (1.072)
Benthic diagenesis of organic nitrogen is accounted for with empirical temperature-
dependent forcing functions for ammonia regeneration and nitrification and denitrification for
nitrate. Nutrient sediment flux data were not available for the Indian Hills canal. However,
sediment flux rates of ammonia and phosphate were assumed to be stoichiometrically related to
the sediment oxygen demand rate via the classical ratios for 0:C:N:P (109:41:7.2:1) by weight
(Redfield, 1963). The Redfield relationships for ammonia flux 0 NH4) and phosphorus flux
(j P04) as a function of SOD (gC/m2/day) are noted below:
6-35
-------
Marina Water Quality Models
j NH4 = SOD I (109/7.2) = SOD / 15.14 (6-5)
j P04 = j NH4 I (7.2/1) = j NH4 / 7.2 (6-6)
Kinetic processes do not affect the distribution of salinity because salt is a conservative
substance. Salinity is included in the model as a tracer to verify the transport submodel and to
calculate density and the saturation value of dissolved oxygen.
The dissolved oxygen results of the WASP4 model are given in Figure 6-8. Observed
values of DO were recorded at a number of stations in the canal at approximately 2-hour
intervals over the course of a 27-hour time period (from about 1100 hours on July 12, 1984 to
about 1300 hours on July 13, 1984). The WASP4 model does a reasonable job of predicting
the mean DO concentration at most of the stations; however, it cannot reproduce the daily range
in DO observed in the canal. This stems from the algorithm used in WASP4 to compute the
dissolved oxygen and phytoplankton dynamics. The model computes daily average dissolved
oxygen and the expected diurnal range that would be attributed to algal primary production. In
its present form, the model cannot reproduce the hour-to-hour DO changes observed in the real
world. The maximum and minimum DO curves computed by the model (see Figure 6-8) tend
to underestimate the observed daily DO range evident in the monitoring data. A tabulation of
the WASP model results and observed daily average and daily minimum dissolved oxygen data
are also presented in Table 6-15.
6.3.3.2 WASP4 Model Application to Beacons Reach Marina
The WASP4 model was configured to represent the Beacons Reach marina by a grid
network of nodes linked together by channels (Figure 6-2). The following are underlying
assumptions of the WASP4 model:
• Beacon Reach marina is well mixed vertically.
• The law of conservation of mass is obeyed for water quality constituents.
• Chemical reaction rates may be estimated using first-order kinetics characterized by
reaction-specific rate coefficients.
The area modeled by the WASP4 model includes the main channel and the entire basin.
The WASP4 model node and channel geometry was selected to give approximately uniform
representation for the Beacons Reach marina. The model consisted of four channels and five
segments as shown in Figure 6-2. The first segment encompasses the marina channel, and
segment 5 represents the dead end of the marina. The center of each segment was carefully
selected to coincide with station locations where water quality observations were taken in the
marina basin. Channel length ranged from 28 to 59 meters. To ensure numerical stability for
all DYNHYD5 model runs, a time step of 6 seconds was used. All input parameters used in
the DYNHYD5 and WASP4 models are listed in Appendix A.
6-36
-------
Marina Water Quality Models
8
Station 13 / Segment 01' Indian Hill
s Canal
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Marina Water Quality Models
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-------
Marina Water Quality Models
The dye study survey data were used for calibration of the WASP4 model. During this
survey samples were collected over a 48-hour period at the Beacons Reach marina (stations A
through O). Dye sampling stations did not coincide with stations sampled for physical and water
quality parameters (stations BR-2 through BR-6). At each sampling time, contour plots of the
measured dye data were made using data collected at stations A through O. Representative
values of the observed dye at stations BR-2 through BR-6, where water quality parameters were
sampled, were calculated, and the WASP4 model was calibrated against these values. The dye
contour plots are shown in Appendix D. These observed values are used for model comparisons
and are listed in Table 6-16.
Calibration
The purpose of model calibration is to supply reliable values for empirically based
coefficients for bottom friction, boundary tidal exchange rates, and water quality reaction rates
such as CBOD decay, reaeration rates, etc. The model is run using input conditions (tides,
inflows, meteorological conditions, waste discharges, boundary conditions, etc.) that characterize
one or more periods for which semisynoptic survey data are available. The model results are
compared to in situ data, and system coefficients are adjusted until reasonable agreement
between model and prototype is achieved.
Observed tidal information was measured at the channel entrance. Tidal observations of
time and corresponding high and low water stages at the channel entrance were used as the basis
for the seaward boundary tide for the calibration period October 10 to October 13, 1988. The
boundary (tide at model segment 1 was taken as the range of the observed tide at the entrance.
Calibration of the Beacons Reach WASP4 model was accomplished in two steps. First,
DYNHYD5 was run with a set of bottom friction coefficients (Manning's n). Second, the
hydrodynamics created by DYNHYD5, i.e., flows and volumes, were used to run the WASP4
model. Calibration of the WASP4 model was accomplished through successive adjustment of
dispersion coefficients until the predicted dye concentrations matched those observed in the field.
The results of WASP4 model calibration for the October 10 through October 13, 1988, period
are shown in Figure 6-9. Reasonable agreement was achieved in all segments as indicated in
Figure 6-9. Excellent agreement between model results and observed dye data is evident in both
segments 4 and 5. The calibrated WASP4 model, based on the dye study, was used as the base
for dissolved oxygen simulation.
Tide information was not provided for the summer period where observed water quality
data are available. Tide predictions from a NOAA tide station at Bogue Inlet, North Carolina,
were used as the boundary conditions for the DYNHYD5 submodel during the dissolved oxygen
simulation period (May 20, to May 27, 1988). Hydrodynamics of this period were provided to
the WASP4 model to simulate the dissolved oxygen concentration within the Beacons Reach
marina.
6-39
-------
Marina Water Quality Models
TABLE 6-16. Observed Dye Concentration Used for Model Calibration
at Beacons Reach iMarina
Julian
Dye
Julian
Dye
Day
Concentration
Day
Concentration
1988
(ppb)
1988
(ppb)
BR-2
BR-5
285.4847
20.0
285.4847
73.0
285.7431
19.0
285.7431
66.0
286.0166
19.0
286.0166
46.0
286.2674
5.0
286.2674
32.0
286.5014
4.0
286.5014
22.0
286.7500
4.0
286.7500
21.0
287.0069
4.0
287.0069
20.0
287.2708
2.0
287.2708
19.0
BR-3
BR-6
285.4847
29.0
285.4847
74.0
285.7431
31.0
285.7431
60.0
286.0166
24.0
286.0166
40.0
286.2674
24.0
286.2674
26.0
286.5014
20.0
286.5014
20.0
286.7500
23.0
286.7500
18.0
287.0069
18.0
287.0069
18.0
287.2708
18.0
287.2708
16.0
BR-4
Date
Julian Day
285.4847
46.0
10/11/88
285
285.7431
48.0
10/12/88
286
286.0166
34.0
10/13/88
287
286.2674
27.0
286.5014
20.0
286.7500
21.0
287.0069
18.0
287.2708
19.0
6-40
-------
Marina Water Quality Models
& so
a
s
~ 60
o
c
V
c 40
o
o
«
& 20
Beocons Reach Marina - Segment \
Beocons Reoch Morino - Segment 4
a.
8 to
. \
284.0 254 5 265 0 283.5 2M 0 2V 3 287 0 287 S
Julian Day (1988)
2
-------
Marina Water Quality Models
For dissolved oxygen simulation, the Beacons Reach WASP4 model was configured for
an Intermediate Eutrophication Kinetics complexity level (Figure 6-10). Systems 1 through 8
are included in the dissolved oxygen balance. The Intermediate Eutrophication Kinetics add
several nonlinear terms and functions to the simple eutrophication kinetics. For example, a
variable carbon-to-chlorophyll ratio is used instead of a fixed ratio in dissolved oxygen
calculations. Additional features are also included in this option (see WASP4 User's Manual
for additional information).
Initial and boundary conditions for the Beacons Reach WASP4 model are based on
average summer ambient and basin water quality conditions and are summarized in Table 6-2.
Both sediment and biochmeical oxygen demand were assumed constant at all locations inside the
Beacons Reach marina. The input data file for dissolved oxygen simulation is included in
Appendix A.
Results
Model predictions are compared against dissolved oxygen data collected at stations BR-2,
BR-4, and BR-6 on May 26, 1988. The comparison between predicted and observed dissolved
oxygen in Beacons Reach marina is illustrated in Figure 6-11. This figure shows dissolved
oxygen concentration as a function of distance from the marina entrance. WASP4 model
predictions and observed dissolved oxygen are in excellent agreement. Agreement is also
achieved at the dead end segment of Beacons Reach marina (model segment 5, 170 meters from
the mouth). It is also clear that water inside the Beacons Reach marina is above the State of
North Carolina Water Quality Standard of 5.0 mg/L dissolved oxygen during this time period.
6.3.3.3 VVASP4 Model Application to Gull Harbor Marina
The WASP4 model was configured to represent the Gull Harbor marina by a grid
network of nodes linked together by channels (Figure 6-3). The area modeled by the WASP4
model includes the main channel and the entire basin. The WASP4 model node and channel
geometry was selected to give approximately uniform representation for the Gull Harbor marina.
The model consisted of two channels and three segments as shown in Figure 6-3. The first
segment encompasses the marina channel, and segment 3 represents the dead end of the marina.
The center of each segment was carefully selected to coincide with station locations where water
quality observations were taken in the marina basin. Channel length ranged from 32 to 39
meters. In all DYNHYD5 model runs, a time step of 6 seconds was used. All input parameters
used in the DYNHYD5 and WASP4 models are listed in Appendix A.
The dye study survey data were used for calibration of the WASP4 model. During this
survey samples were collected over a 44-hour period at the Gull Harbor marina (stations A
through J). Dye sampling stations did not coincide with stations sampled for physical and water
quality parameters (stations GH-3 and GH-5). At each sampling time, contour plots of the
measured dye data were made using data collected at stations A through J. Representative
values of the observed dye at stations GH-3 through GH-5, where water quality parameters were
sampled, were calculated, and the WASP4 model was calibrated against these values. The dye
6-42
-------
Manna Water Quality Models
Figure 6-10. EUTR04 State Variable Used in Modeling DO at
Beacons Reach Marina (WASP4 Model).
6-43
-------
Marina Water Quality Models
CP
c
QJ
CJi
X
O
"O
(D
J>
O
CO
Q
6 5
6.0
5.5
5.0
4.5
Beacons Reach Marina (May 26, 1988)
£ Observ'
Model
id DO (m
Results
2an and
;tandard c
eviation)
~
~T^-
25 50 75 100 125 150
Distance from Marina Entrance (meters)
1 75
Figure 6-11. Observed and Predicted Dissolved Oxygen at Beacons Reach Marina
contour plots are shown in appendix D. These observed values are used for model comparisons
and are listed in Table 6-17.
Calibration
Observed tidal information was measured at the channel entrance. Tidal observations
of time and corresponding high and low water stages at the channel entrance were used as the
basis for the seaward boundary tide for the calibration period October 24 to October 26, 1988.
The boundary tide at model segment 1 was taken as the range of the observed tide at the
entrance.
Calibration of the Gull Harbor WASP4 model was accomplished in two steps. First,
DYNHYD5 was run with a set of bottom friction coefficients (Manning's n). Second, the
hydrodynamics created by DYNHYD5, i.e., flows and volumes, were used to run the WASP4
model. Calibration of the WASP4 model was accomplished through successive adjustment of
dispersion coefficients until the predicted dye concentrations matched those observed in the field.
The results of WASP4 model calibration for the October 23 through October 26, 1988, period
are shown in Figure 6-12. Reasonable agreement was achieved in all segments as indicated in
Figure 6-12. The calibrated WASP4 model, based on the dye study, was used as the base for
dissolved oxygen simulation.
6-44
-------
Marina Water Quality Models
TABLE 6-17. Observed Dye Content Used for
Model Calibraton in Gull Harbor
Julian Day
1988
Dye
Concentration
(ppb)
GH-3
298.9326
60.0
299.1458
10.0
299.4111
3.6
299.7194
5.5
300.2410
3.2
GH-5
298.9326
70.0
299.1458
7.5
299.4111
3.4
299.7194
5.9
300.2410
3.0
Tide information was not provided for the summer period where observed water quality
data are available. Tide predictions from NOAA's tide station at Bogue Inlet, North Carolina,
were used as the boundary conditions for the DYNHYD5 submodel during the dissolved oxygen
simulation period (May 20 to May 27, 1988). Hydrodynamics obtained by DYNHYD5 for this
period were used to run the WASP4 model to simulate dissolved oxygen concentration within
the Gull Harbor marina. WASP4 model was configured for an intermediate eutrophication
kinetics complexity level for dissolved oxygen simulation at the Gull Harbor marina.
Initial and boundary conditions for the Gull Harbor WASP4 model are based on average
summer ambient and basin water quality conditions and are summarized in Table 6-3. It was
assumed that sediment oxygen demand and biochemical oxygen demand were equal at all
locations inside the Gull Harbor marina. Input data files used with DYNHYD5 and WASP4
model for dissolved oxygen simulation are included in Appendix A.
6-45
-------
Marina Water Quality Models
200
160
.o
a
a
^ 120
c
4>
O
C
o
o
4>
>s
o
80
40
0
298.0
Gull Harbor Marina (Segment 2)
A Observec
— Model Ri
iSultS
H
\
r
A
A
298.5 299.0 299.5 300.0
Julian Day (1988)
300.5
301.0
200
160
¦O
a.
a
c
.2 120
o
c
V
o
c
o
o
N
o
80
40
0
298.0
Gull Harbor Marina (Segment 3)
\
\
¦
A
A
A
298.5 299.0 299.5 300.0
Julian Day (1988)
300.5
301.0
Figure 6-12. Observed and Predicted Dye Concentration at Gull Harbor Marina.
6-46
-------
Marina Water Quality Models
a
Gull Harbor Marina
CT
7
-
i i i i i i i
Model results
a Observed data (May 26, 1938)
i i
cn
6
-
-
c
3
-
-
O
cn
U)
2
-
-
O
1
0
-
1 1 I i 1 1 1
i i
0
10 20 30 40 50 60 70
80 90 100
Distance from Marina Entrance
^meters)
Figure 6-13. Observed and Predicted Dissolved Oxygen at Gull Harbor Marina.
Results
Gull Harbor WASP4 DO model results are shown in Figure 6-13. Observed DO at
station GH-3 and GH-5 during May 26, 1988 are also shown in Figure 6-13. DO values are
plotted as a function of distance from the marina entrance. Good agreement is obtained at both
stations GH-3 and GH-5. DO levels within Gull Harbor marina basin decrease as distance
increases from the marina entrance. DO at GH-3 is above the State water quality standard
(WQS) of 5 mg/L, however, DO levels at GH-5 is approximately 3.5 mg/L (less than the WQS).
6.3.3.4 Summary of Advanced Model Results
Several conclusions can be drawn from the results of the modeling effort. The most
important point is the major influence of SOD on DO. This highlights the importance of
obtaining accurate values of SOD to estimate the DO of a proposed marina. While the return
flow factor was assumed to be zero for the three marinas, the results were not conclusive and
further study is needed on similar and less flushed marinas. The discharge of sewage from boats
had a negligible impact on DO for the situations evaluated during this study. Therefore, except
for situations with numerous slips in a poorly flushed marina, the number of boats should not
be a critical factor with respect to DO. Sediment oxygen demand and flushing characteristics
are far more important. Finally, marina shape was shown to have a significant impact on DO.
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7. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
Summary
The purpose of applying a model to a proposed marina is to make the best possible
scientific estimate of the impacts that the marina may have on water quality (i.e., dissolved
oxygen levels, fecal coliform counts, and/or toxic contaminant concentrations). It is important
to remember that waters inside a marina are subject to the same water quality criteria and
standards as the ambient waters outside the marina. Thus, it is not sufficient to simply
demonstrate that a marina will have no impact on ambient waters; it must be shown that waters
inside the marina will also meet appropriate water quality standards.
State and Federal regulators often receive marina permit applications accompanied by
water quality assessments using a variety of models and approaches. To enable regulators to
review marina applications from a common baseline, it would be helpful to provide guidance
to permit applicants that directs them toward a consistent modeling and monitoring approach
depending on the required complexity of analysis.
The focus of this study was to select the most appropriate simple, mid-range, and
complex models available for predicting water quality phenomena in coastal marinas. After
reviewing a number of models and methods, the following were selected as most appropriate for
the coastal marina environment:
Simple Method: Tidal Prism Analysis
Mid-Range Method: Tidal Prism Model and NCDEM DO Model
Complex Method: WASP4 Model
Because of certain limitations, the mid-range Tidal Prism Model was applied only to a
hypothetical test case. Simple, mid-range (NCDEM DO), and complex models were applied to
the Beacons Reach Marina (North Carolina), the Gull Harbor Marina (North Carolina), and the
Indian Hills Canal/Marina (Florida). Given the limitations of the available data for the marinas,
the complex WASP4 model was able to adequately match dye tracer data both spatially and
temporally at the three marinas. WASP4 also adequately reproduced the observed daily average
dissolved oxygen levels at the Beacons Reach, Gull Harbor, and Indian Hills marinas.
A comparison of the results of the simple method (Tidal Prism Analysis), the mid-range
method (NCDEM DO), and the complex method (WASP4) is presented in Table 7-1 for Indian
Hills Canal/Marina, Beacons Reach Marina, and Gull Harbor Marina. The Tidal Prism Analysis
computes only a single value of dissolved oxygen for the entire marina. Using the Tidal Prism
Analysis (TPA), the dissolved oxygen computed for Indian Hills was 5.06 mg/L; for Beacons
Reach, 5.34 mg/L; and for Gull Harbor, 5.54 mg/L. The WASP4 model produced results that
were generally closer to the observed dissolved oxygen levels than those produced by the Tidal
Prism Analysis for the same set of coefficients. The Tidal Prism Analysis method
underestimated the observed dissolved oxygen levels at the Beacons Reach marina and
overestimated the observed dissolved oxygen levels at both the Gull Harbor and Indian Hills
marinas. The NCDEM DO model consistently underestimated dissolved oxygen levels at both
the Beacons Reach and Gull Harbor marinas. For the Beacons Reach Marina, the TPA method
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TABLE 7-1.
Comparison of Dissolved Oxygen Results from Simple,
Mid-range, and Complex Models
WASP4
Monitor
Observed*
TPA
NCDEM DO
WASP4
Segment
Station
mg/L
mg/L
mg/L
mg/L
Indian Hills Marina
1
1-3
4.74
5.45
NA
4.78
2
1-5
4.69
5.45
NA
4.78
3
5.45
NA
4.76
4
5.45
NA
4.82
5
1-2
4.86
5.45
NA
4.77
6
5.45
NA
4.79
7
5.45
NA
4.78
8
5.45
NA
4.84
9
1-6
4.34
5.45
NA
4.85
10
5.45
NA
4.87
11
5.45
NA
4.82
12
1-1
4.59
5.45
NA
4.96
Ambient
1-4
4.94
Beacons Reach Marina
1
BR-2
5.60
5.06
4.82
5.12
2
BR-3
5.35
5.06
4.82
5.38
3
BR-4
5.15
5.06
4.82
5.30
4
BR-5
5.08
5.06
4.82
5.15
5
BR-6
5.10
5.06
4.82
5.12
Ambient
BR-1
6.70
Gull Harbor Marina
2
GH-3
5.30
5.54
3.74
5.13
3
GH-5
3.90
5.54
3.74
4.08
Ambient
BB-1
6.60
For Indian Hills Marina observed DO is the average over 1100 hours 07/12/84 to 1300 hours 07/13/84.
For Beacons Reach Marina observed DO was measured on May 26, 1988.
For Gull Harbor Marina observed DO was measured on May 26, 1988.
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and WASP4 provided similar results; however, results of the WASP4 model matched the
observed data better than the results of the simple method.
Conclusions and Recommendations
For all practical purposes, the Tidal Prism Analysis is selected as the method of choice
in the simple model category. The NCDEM DO (NCDEMDO) model is recommended for the
mid-range category, and the Water Quality Analysis Simulation Program (WASP4) is the model
of choice for the complex category. A variation of WASP4 using a full two-dimensional
hydrodynamic model is also recommended for proposed marinas (i.e., where the marina basin
or waterway does not yet exist). These predictive models (tools) are recommended for use by
regulatory agencies as well as developers to determine and evaluate problem areas pertinent to
marina development. It is anticipated that marina developers will utilize the models to determine
whether a proposed marina will be in compliance with water quality regulatory requirements.
In the early stages of this study, the model chosen as the best available method to apply
to a coastal marina was the mid-range Tidal Prism Model (Diana et al., 1987) because of its
simplicity and its ability to simulate up to ten constituents, including dissolved oxygen and fecal
coliform. When it came time to apply the model to the Beacons Reach, Gull Harbor, and Indian
Hills Marinas, however, it became apparent that this model was not the most appropriate for
application to marinas. Instead, it is intended more for a small coastal embayment that has tidal
forcing at its mouth and freshwater input at its head and can be divided into segments based on
the tidal excursion length. A typical marina will not have any significant freshwater input, and
the tidal excursion length is likely to be greater than the largest dimension of the marina itself.
The Tidal Prism Model can be applied to a marina that lies on a coastal embayment having a
freshwater input by treating the marina as a tributary of the embayment. However, the Tidal
Prism Model is not applicable to a marina constructed directly on the coast or on a sound (e.g.,
Beacon Reach Marina, and Gull Harbor Marina). In light of this, the Tidal Prism Model has
limited applicability to coastal marinas.
In this study, WASP4 was applied to the Indian Hills, Beacons Reach, and Gull Harbor
marinas using complexity level 5 (intermediate eutrophication kinetics), which is one of the more
complicated means of applying the model. This report is a guidance document that steers the
user through the application of WASP4 to a coastal marina. The guidance in this report directs
the user through all the steps involved in a proper water quality assessment of a proposed
marina, including data monitoring prior to, during, and after construction. Good planning and
design practices will ensure that both appropriate and adequate environmental precautions will
be incorporated into a prospective marina project. The guidance in this report has been written
to help prospective developers and marina operators plan their projects with a vision toward
protecting the aquatic ecosystem. A key part of this report was the compilation of the various
modeling coefficients and parameters along with their typical values and site-specific data from
field measurements in marinas.
An accurate application of the WASP4 model to a coastal marina requires more data than
either the simple or mid-range methods. For the hydrodynamic submodel of WASP4, the
following data are necessary for either existing or proposed marinas: bathymetry data, tidal stage
data, wind speed and direction, and point or nonpoint source inflow data. Other useful data
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Marina Water Quality Models
(spatially and temporally varying) for calibrating the hydrodynamic submodel for an existing
marina include: current speed and direction, salinity, and dye concentrations. Assuming WASP4
is applied as a full eutrophication model, the following data are needed: meteorological data
(solar radiation, air temperature, wind speed), physical data (salinity, water temperature),
nutrients (ammonia, nitrite+nitrate, organic nitrogen, ortho-phosphate, organic nitrogen), water
quality data (BOD, dissolved oxygen, chlorophyll-a), sediment sources and sinks (SOD,
ammonia flux, phosphorus flux), as well as a number of kinetic constants. A more detailed
description of the data requirements for WASP4 can be found in Section 5.3 of this document
and in Ambrose et al. (1988a).
WASP4 is a complex model. Depending on the application, WASP4 set-up requires a
substantial number of parameters and a significant amount of data. The novice modeler may
have difficultly applying WASP4; however, the intermediate or advanced modeler should have
little trouble. The USEPA Center for Exposure Assessment Modeling, has recently developed
a pre-processing system for WASP4 that provides guidance, including typical values for model
parameters as well as maximum and minimum limits. This pre-processor system helps to
simplify the use of the WASP4 model.
Given the fact that WASP4 requires a large amount of data and is more difficult to apply
than the simple Tidal Prism Analysis method or the mid-range NCDEM DO model, one might
question why WASP4 should be used, especially when the complex and simple methods yielded
similar values for computed dissolved oxygen for the three marinas in this study. The reasons
for using a complex model such as WASP4 for marina water quality analysis include the
following:
• The simple method provides information only on the average conditions in the marina.
The simple method will not give an indication of spatial or time-varying conditions in
different portions of the marina. For instance, in a marina of complex design (such as
the Indian Hills Canal), the simple method will not be as sensitive to spatial dissolved
oxygen differences as would a complex model such as WASP4. Thus, an analysis made
with the simple method and/or the NCDEM DO model may not represent existing water
quality conditions in the marina. Portions of the marina may exhibit poor flushing and
poor water quality, which the simple and the mid-range methods may miss. The
complex method, on the other hand, will identify those areas as locations where water
quality standards may be contravened.
• Some states, (e.g., Florida and South Carolina) require post-construction water quality
monitoring of new marinas to determine whether they will meet applicable water quality
standards. If post-construction monitoring shows violations of standards, then the owner
of the marina may be forced to either close the marina or modify the design to comply
with standards. Thus, considering the long-term costs, it is often to the owner's
advantage to invest a little more up-front for the application of a complex model to
determine the design alternative that provides optimal flushing and water quality as
insurance against potential costly modifications later.
For example, Tetra Tech (1988) applied the Dynamic Estuary Model, which is the
predecessor to the selected complex model (WASP4), to determine the optimal flushing design
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Marina Water Quality Models
and critical dissolved oxygen for a proposed marina/canal in Jacksonville, Florida. This case
study, presented in Appendix E, clearly shows the advantages of applying complex models as
cost-effective tools to determine water quality conditions at a proposed marina site. In addition,
the Tetra Tech Interim report, Environmental Assessment for Siting and Design of Marinas
(1992), presented the cost summary associated with applying numerical models. Based on
literature surveys, the Tetra Tech report estimated that costs associated with applying the
complex models ranged from 0.2 to 2.0 percent of the total project cost. The high end of this
cost range (i.e., 2.0 percent) was only realized when a full environmental assessment was
required (e.g., surveys of critical habitat areas, littoral transport studies, geotechnical studies,
and physical modeling). Therefore, in most situations it appears reasonable from a cost
standpoint for both the developers and the permitting agency to require the use of a complex
model to perform water quality assessment for a proposed marina.
• The simple TPA method cannot be used to determine optimal design geometries for a
marina since the only geometric properties included in this simple method are tide heights
at high and low water and total marina surface area. The TPA method produced the
same DO results for the Indian Hills Marina even when the total surface area was
doubled to 68,000 m2. If a long dead-end channel were added to the landward side of
the Indian Hills Marina, one would expect the DO at the end of this channel to be very
low due to extremely poor flushing. Unlike WASP4, however, the TPA method is
simply not able to make this prediction.
• The NCDEM DO model is only capable of predicting steady-state dissolved oxygen
conditions for a coastal marina. The NCDEM DO model predicts overall DO levels in
the marina channel and marina basin, however, the model will not flag areas within the
marina basin where water quality standards may be violated. The NCDEM DO model
cannot be used to determine optimal design geometries, as discussed in the previous
paragraph. The NCDEM DO model cannot simulate other pollutants such as coliform
bacteria and toxic contaminants.
• The WASP4 model can be readily expanded to simulate other pollutants such as coliform
bacteria and toxic contaminants.
• The combined effects of benthic fluxes, nitrification, and phytoplankton kinetics on
dissolved oxygen are not included in the simple method.
Although WASP4 has been selected as the most appropriate model for coastal marina
water quality analysis, it is not without limitations. The hydrodynamic submodel, DYNHYD5,
is a pseudo two-dimensional model that relies on the modeler to select the pathways (i.e.,
channels) for water movement by advection. This type of "link-node" hydrodynamic model is
more appropriate for channelized systems, but it may not simulate advection in systems where
gyres are present. Linking a full two-dimensional finite-element or finite-difference
hydrodynamic model to WASP4 would provide a much more rigorous advective solution. This
is especially important for a proposed marina because the hydrodynamics cannot be calibrated
to observed data. When applied using the proper physical dimensions, a full two-dimensional
model requires little calibration, unlike the link-node type model.
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Marina Water Quality Models
In most States, a dissolved oxygen water quality standard exists for the 24-hour average
concentration and an instantaneous minimum concentration. Since instantaneous DO at a
proposed marina site must be addressed in an application for a marina permit, and since only
the WASP4 model is capable of addressing time related DO concentrations, a supplementary
method is suggested for the simple and mid-range methods. As a supplement to any of the
models recommended in this report, the analytical solution calculates the diurnal minimum and
maximum dissolved oxygen. This analytical solution takes into account benthic nutrient fluxes,
light intensity, water depth, water velocity, water column nutrients, and phytoplankton kinetics.
Complete documentation of the derivation of the diurnal oxygen analytical method is provided
in Thomann and Mueller (1987).
7-6
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Marina Water Quality Models
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Westerink, J.J., J.J. Connor, K.D. Stolzenbach, E.E. Adams, A.M. Baptista, 1984. TEA: A
linear frequency domain finite element model for tidal embayment analysis. Report MIT-EL-84-
012. MIT Energy Laboratory, Cambridge, MA.
Westerink, J.J., K.D. Stolzenbach and J.J. Connor, 1985. A frequency domain finite element
model for tidal circulation. Report No. 85-006. MIT Energy Laboratory, Cambridge, MA.
8-6
-------
APPENDIX A: Annotated Input Files
Tidal Prism Model (TPM)
1.1 STEADY. INP
1.2 VARIABLE.INP
Water Quality Analysis Simulation Program (WASP)
2.1 Indian Hills Marina
2.1.1 DYNHYD5 Hydrodynamic File for Indian Hills Marina
2.1.2 WASP4 DO Water Quality File for Indian Hills Marina
2.2 Beacons Reach Marina
2.2.1 Dye Hydrodynamic File for Beacons Reach Marina
2.2.2 DO Hydrodynamic File for Beacons Reach Marina
2.2.3 Dye Water Quality File for Beacons Reach Marina
2.2.4 DO Water Quality File for Beacons Reach Marina
2.3 Gull Harbor Marina
2.3.1 Dye Hydrodynamic File for Gull Harbor Marina
2.3.2 DO Hydrodynamic File for Gull Harbor Marina
2.3.3 Dye Water Quality File for Gull Harbor Marina
2.3.4 DO Water Quality File for Gull Harbor Marina
-------
1.
Tidal Prism Model (TPM)
1.1 STEADY.INP
5 3 10 6
1 3 5
HUNTING CREEK GEOMETRICAL DATA August 1
11
0
1
11 main
channel
0.00
0.33
0.50
0.76
0.95
1.14
1.33
1.52
1.70
1.89
2.75
0.00
27.61
10.68
3.20
0.90
0.81
0.69
0.56
0.49
0.32
1.95
28.97
11.59
4.96
2.98
2.44
1.91
1.40
0.94
0.52
0.24
0.00
0.00
0.00
0.60
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
3.10
4.50
7.10
3.20
2.90
2.40
2.00
1.70
1.40
1.20
0.50
99
HUNTING CREEK
PHYSICAL
DATA GROUPS
1 THRU 8
August 1
1 main channel
1 1 WATER TEMPERATURE
28 8
2 11 INITIAL CONCENTRATIONS
0.0 0.0 0.0 14. 12. 8.5 7.0 4.0 2.0 0.0 0.0
-------
1.2 VARIABLE. EVP
3 2 August 1,2,3 POINTSOURCE WASTEWATER
2 13.2
6 36.5
4 1 August 1 NONPOINTSOURCE FRESHWATER
11 1.7
8 1 BOUNDARY CONCENTRATIONS
00.0
99
999
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0 1.0 1.0
1.0
August 2nd FRESHWATER INPUT (THIRD CYCLE)
1 MAIN CHANNEL
4 1
11 90.0
99
999
August 3rd FRESHWATER INPUT (FIFTH CYCLE)
1 MAIN CHANNEL
4 1
11 55.9
99
999
A-3
-------
2. Water Quality Analysis Simulation Program (WASP)
2.1 Indian Hills Marina
2.1.1 DYNHYD5 Hydrodynamic File for Indian Hills Marina
-------
Filename: HIH_03 INP
---- + 1 + 2-
DYNHYD5 Input File for Indian Hills Marina
3 + 4 + 5 + 6 + 7 + 0
Data Group
0.001
0.001
0 001
0 001
0 001
0.001
0.001
0.001
0 001
0.001
0.001
0.001
*******************************
DYNHYD5 - Indian Hills Yacht Club Loop Canal (TC 5162-01)
HIH_03.INP - Hydrodynamics simulation for Jul 10, 1984 to Jul 15. 1984
***** Data Group A. PROGRAM CONTROL DATA
12 12 0000 6.00000 5 1984/07/08 00-00 1984/07/16 00.00
***** Data Group B: OUTPUT CONTROL DATA *************************
1984/07/08 01 00 1.0-1 12 0
1
1 2 3 4 5 6 7 8 9 10 11 12
***** Data Group C: SUMMARY CONTROL DATA ***********************
1 1984/07/08 00.00 12.5000 150
HIH03,HYD
JUNCTION DATA
3734 -2.3 421
3569 -2.3 433
4197 -2.3 278
4313 -2.3 294
5490 -2 3 290
3577 -2.3 147
3261 -2.3 171
3519 -2.3 89
9 0 001 3065 -2.3 92
10 0.001 420 -2 3 114 182 10
11 0.001 1146 -2.3 106 225 9
12 0.001 1956 -2.3 151 212 12
350
138
348
127
243
352
117
280
126
182
225
212
3
4
4
7
8
9
10
11
11
1
2
3
4
5
6
7
8
9
10
11
12
Data Group E: CHANNEL DATA
********************************
144
139
107
114
131
123
115
82
64
65
44
46
Data Group F.1
23
26
26
28
31
34
31
34
14
33
10
28
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
0.080
0.020
0.020
0.020
0.020
0.020
0.020
0.020
0.020
0.015
0.020
0.020
.001
.001
.001
.001
.001
.001
001
.001
.001
.001
.001
1
2
3
4
3
4
6
7
8
9
10
3
4
5
5
6
7
8
9
11
10
11
12
CONSTANT INFLOWS (m/sec)
Data Group F.2: VARIABLE INFLOWS (m/sec) - Daily Flows
.001 11 12
************************
**************
Data Group G: SEAWARD BOUNDARY DATA
2
- Variable Tide
3 1 38 15
0.0
0.0000 0.0
0.3048
1984/07/07
15:15
-1.41
1984/07/07 21:27
1.74
1984/07/08
03:40
-1.11
1984/07/08 09:25
1.13
1984/07/08
15:15
-1.41
1984/07/08 21:27
1.74
1984/07/09
03-40
-1.11
1984/07/09 09:25
1.13
1984/07/09
15:15
-1.41
1984/07/09 21:27
1.74
1984/07/10
03:40
-1.11
1984/07/10 09:25
1.13
1984/07/10
15:15
-1.41
1984/07/10 21:27
1.74
1984/07/11
03:40
-1.11
1984/07/11 09:25
1 13
1984/07/11
15:15
-1.41
1984/07/11 21:27
1.74
1984/07/12
03:40
-1.11
1984/07/12 09.25
1.13
1984/07/12
15:55
-1.41
1984/07/12 22:27
1.74
1984/07/13
04:20
-1.11
1984/07/13 10:23
1.13
1984/07/13
16:35
-1.59
1984/07/13 23:27
1.48
1984/07/14
05:00
-1.15
1984/07/14 11:32
0.99
1984/07/14
17:25
-1.46
1984/07/15 00:26
1.56
1984/07/15
05:59
- -1.06
1984/07/15 12:51
1.08
1984/07/15
18.14
-1 33
1984/07/16 00:56
1.48
1984/07/16
06:10
-1.06
1984/07/16 13:10
1.08
1984/07/16
18:30
-1.33
1984/07/17 01:10
1.48
3 2 38 15
0.0
0.0000 0.0
0.3048
1984/07/07
15:15
-1.41
1984/07/07 21:27
1.74
1984/07/08
03:40
-1.11
1984/07/08 09:25
1.13
1984/07/08
15:15
-1.41
1984/07/08 21:27
1.74
1984/07/09
03:40
-1.11
1984/07/09 0?:25
1.13
1984/07/09
15:15
-1.41
1984/07/09 21:27
1.74
0.0000
0.0000
-------
Filename- HIH_03.INP DYNHY05 Input File for Indian Hills Marina
+ 1 + 2 + 3 + 4---- + 5 + 6 + 7 + 8
984/07/10
03-40
-1
11
1984/07/10
09 25
1.13
984/07/10
15 15
-1
41
1984/07/10
21 27
1.74
984/07/11
03.40
-1
11
1984/07/11
09.25
1 13
984/07/11
15 15
-1
41
1984/07/11
21-27
1.74
984/07/12
03:40
-1
11
1984/07/12
09:25
1.13
984/07/12
15:55
-1
41
1984/07/12
22:27
1.74
984/07/13
04:20
-1
11
1984/07/13
10-23
1 13
984/07/13
16:35
-1
59
1984/07/13
23:27
1.48
984/07/14
05:00
-1
15
1984/07/14
11.32
0.99
984/07/14
17-25
-1
46
1984/07/15
00:26
1 56
984/07/15
05.59
-1
06
1984/07/15
12-51
1 08
984/07/15
18.14
-1
33
1984/07/16
00:56
1.48
984/07/16
06:10
-1
06
1984/07/16
13:10
1.08
984/07/16
18:30
-1
33
1984/07/17
01.10
1.48
** Data Group H: WIND DATA (m/sec) mean monthly winds at Beacons Reach **
0
I + 2 + 3 + 4 5 + 6 + 7 + 8
-------
2.1.2 WASP4 DO Water Quality File for Indian Hills Marina
-------
Filename. WIH_ 11 INP WASP4 00 Water Quality Model for Indian Hills Manna
+ 1 + 2 * 3 + 4 + 5 + 6 + 7 + 8
WIH_11 INP - Indian Hills Canal WASP4 Model Water 07/08/84 to 07/15/84
Dissolved Oxygen Calibration Run
KSIM NSE5
N S Y S
ICFL
MFLQ
IDMP
0 13
13
1
1
1
12
1 2
3
4
5
6
1
900.
196.00
3
1800.
365.
86400
0 0
0
0
0
0
1 +
+
*
+
*
4 1
.000
1.00
2
52.9
79
0
1
59.8
72
0
2
2
0 10E+00
0.
0.10E+00
9
52.9
144
1
3
59.8
139
2
4
59.8
107
3
5
64.4
114
4
5
71.3
131
3
6
78.2
123
4
7
71.3
115
6
8
78.2
82
7
9
32.2
64
8
11
2
0.10E+01
0.
0.10E+01
2
75.9
65
9
10
23.0
44
10
11
2
0.10E+01
0.
0.10E+01
1
64 4
46
11
12
2
0 10E+01
0.
0.10E+01
0 0
0
0
0
0
2 0
366.0
+
*
1.0000
1
1
.0000
13
1
2
13
1
3
13
1
4
13
1
5
13
1
6
13
1
7
8
13
13
1
1
9
13
1
10
13
1
11
13
1
12
13
1
13
0
3
2 5
SUMRY2
.OUT
(HI H
2 1
2
1 2
3
4
5
6
0
1.0
1.0
1
1
1.0
1.157E-05
1
12
1
3734
1
13
3369
5490
5
13
3577
3065
9
13
420
2
1
0.50
0.0
a
1.50
1
1.0
1 157E-05
1
0 0 0 0 1984/07/08 0000
7 8 9 10 11 12
A MODEL OPTIONS
1 2 11 0
366.
0 0
366.
366.
366.
86400.
1 1
640.
0 0
(surface water)
0
B EXCHANGES
366.
0 0
1 1
0
0
0
+ *
+ *
+
*
c-
VOLUMES
8588
0.0
1.0
1 0
1.0
8209
0.0
1.0
1.0
1.0
9653
0.0
1.0
1.0
1.0
9920
0.0
1.0
1.0
1.0
12627
0.0
1.0
1.0
1.0
8227
0.0
1.0
1.0
1.0
7500
0.0
1.0
1.0
1.0
8094
0.0
1 0
1 0
1.0
7050
0.0
1 0
1 0
1.0
966
0.0
1 0
1.0
1.0
2636
0.0
1.0
1.0
1.0
4499
0.0
1.0
1.0
1.0
87968
0.0
1.0
1.0
1.0
. HYD)
* +
*
+
*
D: FLOWS
12
F#2
F#3
F#3
F#3
F#4
F#4
3734
13
3369
(Data Block
7 8 9 10
—NINQ(2) SCALQ=
—NINQ(3) SCALQ=
—N0QS(3.ni) -->
2 13 4197
6 13 3261
10 13 1146
--NBRKQ(4,ni)
366.0
— NINQ(3) SCALQ=
—NOQS{3,nl) -->
2 13 4197
D.2 Field One Flows)
11 12
1 C0NVQ= 1
1 C0NVQ= m/day * 1/86400
number of segment pairs
3 13 4313 4
7 13 3519 8
11 13 1956 12
number of time breaks
13
13
13
1 C0NVQ= m/day * 1/86400
number of segment pairs
3 13 4313 4 13
-------
Filename VIH_11.INP WASP4 DO Water Quality Model for Indian Hills Marina
+ ! + 2 + 3 + 4 + 5 + 6 + 7 + 8
5490
3065
0 02
1
12
0
1.0
3734
5490
3065
0.20
0
2
1 0
1 14
0 070
0.070
0.070
0 070
2 14
0.070
0.070
0.070
0 070
2
1 0
1 14
0.050
0.050
0 050
0.050
2 14
0.050
0.050
0 050
0.050
2
1.0
1 14
0.102
0 102
0.066
0.054
2 14
0.102
0.102
0.066
0.054
2
1 0
1 14
16.00
16.00
16.00
16.00
2 14
16.00
16.00
16.00
16.00
2
1.0
1 14
2.30
2.30
2.60
2.60
2 14
2.30
13
13
0.0
1.157E-05
13
13
13
0 0
0 0
3577 6 13 3261
420 10 13 1146
F#4—NBRKQ(5,ni) --
0 02 366 0
F#5 — NINQ(3) SCALQ=1
7 13 3519 8
11 13 1956 12
number of time breaks
C0NVQ= m/day * 1/86400
1.0
OCN :
1.0000
194.8069
195 3229
250 0000
OCN :
1.0000
194.8069
195.3229
250.0000
1.0
OCN
1.0000
194.8069
195.3229
250.0000
OCN .
1.0000
194.8069
195.3229
250.0000
1 0
OCN :
1.0000
194.8069
195.3229
250.0000
OCN :
1.0000
194.8069
195.3229
250.0000
1.0
OCN :
1.0000
194.8069
195.3229
250.0000
OCN :
1.0000
194.8069
195 3229
250.0000
1.0
OCN :
1.0000
194 8069
195.3229
250 0000
OCN :
1.0000
3369 2 13 4197 3 13 4313 4
3577 6 13 3261 7 13 3519 8
420 10 13 1146 11 13 1956 12
F#5 — NBRKQ(5,m) --> number of time breaks
0.20 366 0
0 0 0 1 1 0 0 0
Sys#l NH3
13
13
13
13
13
(Data Group E Boundary Conditions)
0.070
0.050
0.070
0.070
0 070
0.050
0.070
0.070
Sys#2
0.050
0.050
0 050
0.050
0.050
0.050
0.050
0.050
OCNBC001
194.4674
194.9583
195.5243
366.0000
QCN8C002
194.4674
194.9583
195.5243
366.0000
N03
0CNBC001
194.4674
194.9583
195.5243
366.0000
0CNBC002
194.4674
194.9583
195.5243
366.0000
Sys#3 0P04
0.102
0 054
0.054
0 054
0.102
0.054
054
.054
Sys#4
16.00
16.00
16.00
16.00
16.00
16.00
16.00
-16.00
0CNBC001
194.4674
194.9583
195.5243
366 0000
0CNBC002
194 4674
194.9583
195.5243
366.0000
Phyt#l
OCNBCOOl
194.4674
194.9583
195.5243
366.0000
0CNBC002
194.4674
194.9583
195.5243
366 0000
Sys#5 CBOD
2.30
2.30
2.60
2.60
2.30
Sys# 1 mg/l
0.070 194 5833
0.050 195.0972
0.070 195 5799
Sys# 1 mg/l
0.070 194 5833
0 050 195 0972
0.070 195.5799
0.070 194.6993
0.080 195.2000
0.070 200.0000
0.070 194.6993
0.080 195.2000
0.070 200.0000
(Data Group E Boundary Conditions)
Sys# 2
050 194.5833
050 195.0972
050 195.5799
Sys# 2
050 194.5833
050 195.0972
050 195.5799
050 194 6993
050 195.2000
050 200.0000
050 194.6993
050 195 2000
050 200.0000
(Data Group E Boundary Conditions)
Sys# 3
0.102 194.5833
0 054 195 0972
0.054 195.5799
0.102
0.054
0.054
Sys# 3
194.5833
195.0972
195.5799
0.102 194.6993
0 066 195.2000
0 054 200.0000
0.102 194 6993
0.066 195.2000
0.054 200.0000
(Data Group E Boundary Conditions)
Sys# 4 ug chl/L
16.00 194.5833 16.00 194 6993
16 00 195.0972 16 00 195.2000
16.00 195.5799 16.00 200 0000
Sys# 4 ug chl/L
16.00 194.5833 16.00 194 6993
16.00 195.0972 16.00 195.2000
16.00 195.5799 16.00 200.0000
(Data Group E Boundary Conditions)
OCNBCOOl
194.4674
194.9583
195.5243
366.0000
OCNBC002
194.4674
2.30
2.30
2.60
Sys# 5 mg/L (CB0D20)
2.30
194.5833
195.0972
195.5799
Sys# 5
194 5833
2.30 194.6993
2.60 195.2000
2.60 200.0000
2 30 194.6993
-------
F1lename
+ 1
WIH_11 INP
---+ 2-
UASP4 DO Uater Quality Model for Indian Hills Manna
-3 + 4 + 5 + 5 + 7 + g
2.30
2.60
2.60
2
1.0
1 14
4.61
5.27
4.74
5.21
2 14
4.61
5.27
4.74
5.21
2
1.0
1 14
0.030
0 030
0.020
0.030
2 14
0.030
0.030
0 020
0.030
2
1.0
1 14
0.068
0.036
0.044
0 036
2 14
0.068
0.036
0.044
0.036
2
1.0
1 14
0 000
0 000
0.000
0.000
2 14
0.000
0.000
0.000
0.000
2
1.0
1 14
0.000
0.000
0.000
0.000
2 14
0.000
0.000
0 000
0 000
2
1.0
14
0.000
0.000
0.000
194 8069
195 3229
250 0000
1.0
OCN
1.0000
194.8069
195.3229
250.0000
OCN :
1.0000
194.8069
195.3229
250 0000
1.0
OCN :
1.0000
194.8069
195.3229
250.0000
OCN :
1 0000
194 8069
195.3229
250 0000
1.0
OCN
1.0000
194.8069
195.3229
250.0000
OCN :
1.0000
194.8069
195.3229
250 0000
1.0
OCN :
1.0000
194.8069
195.3229
250.0000
OCN :
1 0000
194 8069
195 3229
250.0000
1.0
OCN :
1.0000
194.8069
195.3229
250.0000
OCN :
1.0000
194.8069
195.3229
250.0000
1.0
OCN :
1.0000
194.8069
195 3229
2 30 194 9583
2.60 195 5243
2.60 366 0000
Sys#6 00
2.30
2.60
195 0972
195 5799
2 60
2 60
195 2000
200 0000
61
73
90
21
4.61
5.73
4.90
5.21
Sys#7 OrgN
0.030
0.050
0 030
0.030
0 030
0.050
0.030
0.030
Sys#8 OrgP
0.068
0.036
0.036
0.036
0 068
0.036
0.036
0.036
Sys#9
0 000
0.000
0.000
0 000
000
000
000
0.000
Sys#10 Phyt#2
0.000
0.000
0.000
0.000
¦ 0 000
0 000
0.000
0.000
Sys#11 Si04
OCN8C001
0.000 194.4674
0.000 194 9583
0.000 195.5243
(Data Group E Boundary Conditions)
- 0CNBC001
194.4674
194.9583
195.5243
366 0000
OCN8C002
194.4674
194.9583
195.5243
366.0000
Sys# 6 mg/L
4.34 194.5833
4.94 195.0972
5.21 195.5799
Sys# 6
4.34 194.5833
4.94 195.0972
5.21 195.5799
4.58 194 6993
4.58 195.2000
5.21 200.0000
4 58 194.6993
4 58 195 2000
5 21 200.0000
(Data Group E Boundary Conditions)
0CNBC001
194.4674
194.9583
195.5243
366.0000
OCNBC002
194.4674
194.9583
195.5243
366 0000
0.030
0 050
0 030
0 030
0.050
0.030
Sys# 7 mg/L
194.5833
195.0972
195.5799
Sys# 7
194 5833
195 0972
195.5799
030 194.6993
020 195.2000
030 200.0000
030 194.6993
020 195.2000
030 200.0000
(Data Group E Boundary Conditions)
OCNBC001
194.4674
194.9583
195.5243
366.0000
0CNBC002
194.4674
194.9583
195.5243
366.0000
Phyt#2
OCNBCOOl
194 4674
194.9583
195.5243
366.0000
OCNBC002
194.4674
194.9583
195.5243
366 0000
Sys# 8 mg/L
0.068 194.5833
0.036 195.0972
0.036 195.5799
Sys# 8 mg/L
0 068 194 5833
0.036 195.0972
0.036 195.5799
0.068 194.6993
0.044 195.2000
0.036 200.0000
0.068 194.6993
0.044 195.2000
0.036 200.0000
{Data Group E Boundary Conditions)
Sys# 9 ug/L
0.000 194.5833
0.000 195.0972
0 000 195.5799
Sys# 9 ug/L
0.000 194.5833
0.000 195.0972
0.000 195 5799
0.000 194.6993
0.000 195.2000
0.000 200.0000
0.000 194.6993
0.000 195.2000
0.000 200.0000
OCNBCOOl
194.4674
194.9583
195.5243
366.0000
OCNBC002
194.4674
194.9583
195.5243
366.0000
(Data Group E Boundary Conditions)
Sys# 10 ug/L
0.000 194.5833 0 000 194.6993
0.000 195.0972 0 000 195.2000
0.000 195.5799 0.000 200.0000
Sys# 10 ug/L
0.000 194.5833 0.000 194.6993
0.000 195.0972 0 000 195.2000
0.000 195.5799 0.000 200.0000
(Data Group E Boundary Conditions)
Sys# 11 mg/L
0.000 194.5833 0.000 194.6993
0000 195.0972 0.000 195.2000
0.000 195.5799 0.000 200.0000
-------
Filename W[H_11.INP WASP4 DO Water Quality Model for Indian Hills Marina
+ 1 + 2 + 3 + 4 + 5 + 6 + 7 * 8
2
1
000
14
0 000
0 000
0.000
0 000
2
1.0
14
27 43
27 33
28.20
28 00
2 14
27.43
27.33
28.20
28.00
2
1.0
1 14
0.000
0.000
0 000
0.000
2 14
0.000
0.000
0.000
0 000
1
0.100E+01
11 6
0.000
0.000
1
0 100E+01
11 6
0 000
0.000
1
0 100E+01
11 6
0 000
0 000
1
0 100E+01
11 6
0 000
0.000
1
0.100E+01
11 6
0 000
0.000
1
0.100E+01
11 6
0.000
0.000
1
0 100E+01
11 6
0.000
0.000
1
0.100E+01
11 6
0.000
250.0000
OCN
1.0000
194.8069
195 3229
250.0000
1.0
OCN :
1.0000
194.8069
195.3229
250.0000
OCN •
1.0000
194.8069
195.3229
250.0000
0.000
0.000
0.000
0.000
0 000
366 0000
0CNBC002
194.4674
194 9583
195.5243
3S6.0000
Sys# 11
0 000 194 5833
0 000 195 0972
0.000 195 5799
0.000 194.6993
0 000 195 2000
0 000 200 0000
Sys#12 Salinity (Data Group E Boundary Conditions)
27 43
27.76
28 75
28.00
27.43
27.76
28.75
28.00
0CNBC001
194 4674
194 9583
195 5243
366.0000
0CNBC002
194.4674
194.9583
195.5243
366.0000
26 76
27.40
28.00
26.76
27.40
28.00
Sys# 12
194 5833
195 0972
195 5799
Sys# 12
194.5833
195.0972
195.5799
26 69
27.24
28 00
26.69
27 24
28.00
194.6993
195 2000
200.0000
194.6993
195 2000
200.0000
1.0
OCN :
1.0000 0.000
194.8069 0.000
195.3229 0.000
250.0000 0.000
OCN :
1.0000 0.000
194.8069 0.000
195.3229 0.000
250.0000 0.000
PS(t) Sys#l
0.100E+01 PS(t)
Seg Oil Sys# 1
194.000 0.000
250.000 000.000
PS(t) Sys#2
0.100E+01 PS(t)
Seg Oil Sys# 2
194.000 0.000
250.000 000.000
PS(t) Sys#3
0.100E+01 PS(t)
Seg Oil Sys# 3
194 000 0.000
250.000 000.000
PS(t) Sys#4
0 100E+01 PS(t)
Seg Oil Sys# 4
194.000 0.000
250.000 000.000
PS(t) Sys#5
0.100E+01 PS(t)
Seg Oil Sys# 5
194.000 0.000
250.000 000.000
PS(t) Sys#6
0.100E+01 PS(t)
Seg Oil Sys# 6
194.000 0.000
250.000 000.000
PS(t) Sys#7
0 100E+01 PS(t)
Seg Oil Sys# 7
194.000 0.000
250.000 000.000
PS(t) Sys#8
0.100E+01 PS(t)
Seg Oil Sys# 8
194.000 0.000
Sys#13 Coliforms (Data Group E Boundary Conditions)
0CNBC001 Sys# 13
194 4674 0.000 194 5833 0.000 194.6993
194.9583 0 000 195 0972 0.000 195.2000
195.5243 0.000 195.5799 0.000 200.0000
366.0000
0CNBC002 Sys# 13
194.4674 0 000 194.5833 0 000 194 6993
194.9583 0 000 195.0972 0.000 195.2000
195.5243 0 000 195.5799 0.000 200.0000
366.0000
(Data Block F 1 Waste Loads for Point Source)
¦System # 1 NH3 Kg/Day Scale/conv fct
194.7083 0.00 194.71875 0.000 194.7292
366.000
(Data Block F.l Waste Loads for Point Source)
:System # 2 N03
Kg/Day
194 7083
366.000
(Data Block F
:System # 3 0P04
0 00 194.71875
Scale/conv fct
0.000 194.7292
Waste Loads for Point Source)
Kg/Day Scale/conv fct
0.000 194 7292
194 7083 0.00 194.71875
366.000
(Data Block F.l Waste Loads for Point Source)
:System # 4 Phytl Kg/Day Scale/conv fct
194.7083 0.00 194.71875 0.000 194.7292
366.000
(Data Block F.l Waste Loads for Point Source)
:System # 5 CBOD Kg/Day Scale/conv fct
0 00 194.71875
0.000 194.7292
194.7083
366.000
(Data Block F.l Waste Loads for Point Source)
:System # 6 DO Kg/Day Scale/conv fct
0.00 194.71875
0.000 194.7292
194.7083
366 000
(Data Block F.l Waste Loads for Point Source)
:System # 7 OrgN Kg/Day Scale/conv fct
194 7083 0.00 194.71875 0 000 194 7292
366.000
(Data Block F.l Waste Loads for Point Source)
:System # 8 OrgP Kg/Day Scale/conv fct
194.7083
0.00 194.71875
0.000 194.7292
-3 + 4 5 + 6 +-
-7 + 8
A-ll
-------
Filename. WIH_11 INP WASP4 00 Water Quality Model for Indian Hills Marina
+ 1 + 2 3 ^ 4 + 5 + 6 + 7 8
0.000 250.000 000.000
1 PS(t) Sys#9
0 100E+01 0 100E+01 PS(t)
11 6 Seg Oil Sys# 9
0.000 194 000 0 000
0.000 250.000 000.000
1 PS(t) Sys#10
0.100E+01 0.100E+01 PS(t)
11 6 Seg Oil Sys# 10
0.000 194 000 " 0 000
0.000 250 000 000.000
1 PS(t) Sys#11
0.100E+01 0.100E+01 PS c t)
11 6 Seg Oil Sys# 11
0.000 194.000 0.000
0.000 250.000 000.000
3 PS(t) Sys#12
0.100E+01 0.100E+01 PS(t)
8 6 Seg Oil Sys# 12
0.000 194.000 0.000
0 000 250.000 000.000
9 6 Seg Oil Sys# 12
0.000 194.000 0.000
0.000 250.000 000.000
11 6 Seg Oil Sys# 12
0.000 194.000 0.000
0 000 250.000 000.000
1 PS(t) Sys#13
0.100E+01 0.100E+01 PS(t)
11 6 Seg Oil Sys# 13
0.
.000
194.000 0.
.000
0.
.000
250.000 000.
.000
0
0
17
6
ZOO IX
1
I.FSI04
2
KESG
5
l.KEFN
6
SOD1D
9
1.MACRO
12
SHL3
15
1.GWC1
21
FN03
24
1.
1
of
13
ZOO IX
1
1.000FS104
2
KESG
5
1.OOOKEFN
6
SOD1D
9
2.400MACR0
12
SHL3
15
0.OOOGWC1
21
FN03
24
4 570
2
of
13
Z001X
1
1.000FSI04
2
KESG
5
1.OOOKEFN
6
SOD1D
9
2.400MACRO
12
SHL3
15
0.000GWC1
21
FN03
24
4.570
3
of
13
ZOOIX
1
1.000FS104
2
KESG
5
1.OOOKEFN
6
S0D10
9
2.400MACRO
12
SHL3
15
0.000GWC1
21
FN03
24
4.570
4
of
13
Z001X
1
1.OOOFS104
2
KESG
5
1. OOOKEFN.
6
S0D1D
9
2.400MACRO
12
SHL3
15
0.000GWC1
21
FN03
24
4.570
5
of
13
Z001X
1
1.OOOFS104
2
KESG
5
1.OOOKEFN
6
S0D1D
9
2.400MACR0
12
SHL3
15
0.000GWC1
21
FN03
24
4.570
366.000
(Data Block F.l Waste Loads for Point Source)
System # 9 Phyt2 Kg/Day Scale/conv fct
194.7083 0 00 194.71875 0 000 194.7292
.366.000
(Data Block F 1 Waste Loads for Point Source)
:System # 10 Phyt3 Kg/Day Scale/conv fct
194.7083 0 00 194.71875 0.000 194.7292
366.000
(Data Block F.l Waste Loads for Point Source)
:System # 11 Si04 Kg/Day Scale/conv fct
194.7083 0.00 194.71875 0.000 194.7292
366.000
(Data 8lock F 1 Waste Loads for Point Source)
:System # 12 Dye Kg/Day Scale/conv fct
194 7083 0.00 194.71875 0 000 194.7292
366 000
194.7083 0.00 194.71875 0.000 194.7292
366.000
194.7083 0.00 194.71875 0.000 194.7292
366.000
(Data Block F.l Waste Loads for Point Source)
:System # 13 Coli Kg/Day Scale/conv fct
194 7083 0.00 194.71875 0 000 194.7292
366.000
(Data Group F.2 ..NPS Loads)
FF(xyz)- G wasp4g01.dat 05/12/90 21:20:28
l.TMPSG 3 l.TMPFN 4 1.
1.FNH4 7 1.FP04 8 1.
1.SHL1 13 1.SHL2 14 1.
1.GWC2 22 l.GWSW 23 1.
143.750TMPSG
1.000FNH4
0.000SHL1
0.201GWC2
3
7
13
22
1.000TMPFN
15.140FP04
0.000SHL2
1.262GWSW
4
8
14
23
1.000
2.100
0.000
0 000
143.750TMPSG
1.O00FNH4
0.000SHL1
0.000GWC2
3
7
13
22
1.OOOTMPFN
15.140FP04
0 OOOSHL2
0.000GWSW
14
23
1.000
2.100
0.000
0.000
143.750TMPSG
1.000FNH4
O.OOOSHLl
0.803GUC2
3
7
13
22
1.OOOTMPFN
15.140FP04
0.OOOSHL2
5.047GWSW
4
8
14
23
1.000
2.100
0.000
0.000
143.750TMPSG
1.000FNH4
O.OOOSHLl
0.201GWC2
3
7
13
22
1.OOOTMPFN
15.140FP04
0.000SHL2
1.262GWSW
4
8
14
23
1.000
2.100
0.000
0.000
143.750TMPSG
1.000FNH4
O.OOOSHLl
0.134GWC2
3
7
13
22
1.OOOTMPFN
15.140FP04
0.OOOSHL2
0.841GWSW
4
8
14
23
1.000
2.100
0.000
0.000
-------
Filename WIH_11.INP WASP4 00 Water Quality Model for Indian Hills Marina
+. 1 + 2 + 3----+ 4 + 5 + 6 + 7 + 3
6
of
13
Z001X
1
1.OOOFS104
2
143 750TMPSG
3
1 OOOTMPFN
4
1 000
KESG
5
1 OOOKEFN
6
1.OOOFNH4
7
15 140FP04
8
2 100
S001D
9
2.200MACRO
12
0 000SHL1
13
0 000SHL2
14
0 000
SHL3
15
0 000GWC1
21
0 201GWC2
22
1.262GWSU
23
0.000
FN03
24
4.570
¦_
7
of
13
Z001X
1
1.OOOFS104
2
143.750TMPSG
3
1 OOOTMPFN
4
1 000
KESG
5
1.OOOKEFN
6
1.OOOFNH4
7
15 140FP04
8
2.100
S0D10
9
2 200MACR0
12
0.OOOSHL1
13
0.OOOSHL2
14
0.000
5HL3
15
0.000GWC1
21
0 000GWC2
22
0.OOOGWSW
23
0.000
FN03
24
4.570
8
of
13
Z001X
1
1.OOOFS104
2
143.750TMPSG
3
1.OOOTMPFN
4
1.000
KESG
5
1.OOOKEFN
6
1.OOOFNH4
7
15.140FP04
8
2.100
S0D10
9
2.OOOMACRO
12
0.OOOSHL1
13
0.000SHL2
14
0.000
SHL3
15
0.000GWC1
21
0 803GWC2
22
5.047GWSW
23
0.000
FN03
24
4 570
9
of
13
Z001X
1
1 OOOFS104
2
143.750TMPSG
3
1 .OOOTMPFN
4
1 000
KESG
5
1.OOOKEFN
6
1.OOOFNH4
7
15.140FP04
8
2.100
S0D10
9
2.OOOMACRO
12
0 000SHL1
13
0.000SHL2
14
0.000
SHL3
15
0 000GWC1
21
0 201GWC2
22
1 262GWSW
23
0.000
FN03
24
4.570
10
of
13
ZOO IX
1
1.OOOFS104
2
143.750TMPSG
3
1 OOOTMPFN
4
1.000
KESG
5
1 OOOKEFN
6
1.000FNH4
7
15.140FP04
8
2.100
S001D
9
1.800MACR0
12
0.000SHL1
13
0.000SHL2
14
0.000
SHL3
15
0.000GWC1
21
0.134GWC2
22
0.841GWSW
23
0 000
FN03
24
4.570
11
of
13
Z001X
1
1.OOOFS104
2
143.750TMPSG
3
1.OOOTMPFN
4
1.000
KESG
5
1.OOOKEFN
6
1 OOOFNH4
7
15.140FP04
8
2 100
S0D1D
9
1.SOOMACRO
12
0.OOOSHL1
13
0.000SHL2
14
0.000
SHL3
15
0.000GWC1
21
0.201GWC2
22
1.262GWSW
23
0.000
FN03
24
4.570
12
of
13
Z001X
1
1.OOOFS104
2
143.750TMPSG
3
1 OOOTMPFN
4
1.000
KESG
5
1 OOOKEFN
6
1.000FNH4
7
15.140FP04
8
2.100
S0D1D
9
0.700MACR0
12
0 000SHL1
13
0 OOOSHL2
14
0 000
SHL3
15
0.000GWC1
21
0.134GWC2
22
0.841GWSW
23
0.000
FN03
24
4.570
13
of
13
(Duirmy 8enthos Segment)
Z001X
1
1.OOOFS104
2
0.OOOTMPSG
3
1.OOOTMPFN
4
1.000
KESG
5
0.OOOKEFN
6
1.000FNH4
7
0.OOOFP04
8
0.000
S001D
9
0.OOOMACRO
12
0.000SHL1
13
0.OOOSHL2
14
0 000
SHL3
15
0.000GWC1
21
0.000GWC2
22
0.OOOGWSW
23
0.000
FN03
24
0.000
Nosys= 14
Const-H
GLOBAL
0
NH3-N
1
group
#1-1
10
K12C
11 0.
250
K12T
12
1.080
KNIT
13 2.
000
ATMNH3
14
1.400
GWFLOW
15 0.000000
GWNH3
16
0.488
Z1CRB
17 2.
500
Z1CDW
18
0.450
NCRBZ1
19 0.
167
NCRBM
39
0.082
N03+N02-N
1
group
#1-2
5
K20C
21 - 0.
090
K20T
22
1.045
KN03
23 0.
100
ATMN03
24
1.865
GWN03
25 3.
065
0-P04
1
all param
4
ATMDIP
' 31 0.
083
GWOIP
32
0.005
PCRBZ1
33 0.
013
PCRBM
40
0.004
Phyt#l
2
group#l-4
15
WS1
28 0
100
K1C
41
3.000
+_
• —1-
+ 2 +--
—3-
—+ 4 +-
— 5--
--+ 6 +-
— 7-
---+ 8
-------
Filename' WIH_11 INP WASP4 DO Water Quality Model for Indian Hills Marina
+ 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8
KIT
42
1 066
LGHTS
43
2.000
PHIMX
44
720.000
XKC
45
0 017
CCHL1
46
80.000
IS1
47
300.000
KMNG1
48
0 007
KMPG1
49
0.001
K1RC
50
0 150
K1RT
51
1.080
TUL1
112
40 000
- T0PT1
113
24.000
TLL1
114
0.000
group#2-4
10
KID
W1
KPZDT
NCRB1
SICRBl
CBOD
group#l-5
KDC
KDSC
KBOD
OCRB
Diss 02
group #1-6
K4C
K2
KM1RC
Org-N
group #1-7
K71C
KONDC
FON
GW_0N
Org-P
group #1-8
K83C
KOPDC
FOP
GW_0P
Phyt#2
group #1-9
WS2
IS2
K2D
K2RC
K2T
T0PT2
group#2-9
KMNG2
NCRB2
SICRB2
Phyt#3
group #1-1
CLLCR83
CCHL3
K3C
W3
K3RT
group#2-10
KMNG3
NCRB3
SICRB3
S n 04
group #1-1
GUSI
Sal inity
group #1-1
GWSALT
Co 11forms
21
TEMP 1
30.83
FF(t)
14
52
54
56
58
89
1
7
71
73
75
81
1
6
26
82
109
1
7
91
93
95
97
1
7
100
102
104
106
2
12
29
35
37
60
62
116
6
63
65
90
2
10
20
67
69
77
79
6
84
86
98
1
1
88
1
1
99
0
WASP Data Group
FT#01-Temp #1
.00 30.83
0 500
1.000
1.000
0.155
0.300
0.200
0.000
0.500
2.670
0 000
0.000
0.000
0.075
0.000
0.700
0.091
0.220
0.000
0.600
0.005
0.200
75.000
0.020
0 150
1.066
15.000
0.007
0.155
0.462
0.000
1.000
0.000
0.000
1.000
0.000
0.000
0.000
20 000
0.000
K1G
KPZDC
PCRB1
KMPHYT
KMSG1
KDT
KDST
GWBOD
K4T
GWOXY
KM1RT
K71T
KONDT
ATM ON
K83T
KOPDT
ATM OP
CCHL2
K2C
W2
K2RT
TUL2
TLL2
KMPG2
PCRB2
KMSG2
WS3
IS3
K3D
K3RC
K3T
KMPG3
PCRB3
KMSG3
53
55
57
59
111
72
74
76
27
83
110
92
94
96
101
103
105
34
36
38
61
115
117
64
66
107
30
68
70
78
80
85
87
108
0 800
0.000
0.030
1 000
0.0140
1.047
1.000
1.800
1 000
4.000
1.000
1.080
1.000
2.801
1.080
1.000
0.247
65.000
1.750
1.000
1 045
37.000
0.000
0.0015
0.030
0.028
0.000
0.000
0.000
0.000
1.000
0.000
0 000
0.000
I FF Time
(deg C)
15.00
- I
30.83
45.00
30.83
75.00
A-14
-------
Filename: WIH_ 11 INP WASP4 DO Water Quality Model for Indian Hills Manna
1 + 2 t 3 + 4-- 5 + 7 + 8
30.83
31 67
30.51
TEMP 2
30.83
30.83
31.67
30 51
TEMP 3
30 83
30 83
31.67
30.51
TEMP 4
30 83
30.83
31.67
30 51
SUN 5
365
606
608
357
PHOTO 6
0.440
0.534
0.546
0.436
WIND 7
0.000
0.000
0 000
0.000
KE#01 8
1.020
1 020
1.020
1.020
KE#02 9
1.020
1 020
1.020
1.020
KE#03 10
1.020
1.020
1.020
1.020
KE#04 11
1.020
1.020
1.020
1.020
KE#05 12
1.020
1 020
1.020
1 020
TFNH4 13
2.301
2.301
2.455
2 245
TFP04 14
2.301
2 301
2.455
2.245
MACRO 15
106
228.
350.
14
1
106
228.
350
14
1
106
228
350.
14
1.
106
228.
350.
14
1
106.
228
350
14
1.
106
228.
350.
14
1.
106.
228.
350.
14
1.
106.
228.
350.
14
1
106.
228.
350.
14
1.
106
228
350.
14
1.
106
228.
350.
14
1.
106.
228.
350.
14
1.
106.
228.
350.
14
1.
106.
228
350.
14
00
00
00
F T #02 -
00
00
00
00
FT#03-
00
00
00
00
FT#04
00
00
00
00
FT #05
00
00
00
00
FT#06'
00
00
00
00
FT#07
00
00
00
00
FT#08
00
00
00
00
FT#09
00
00
00
00
FT# 10
00
00
00
00
FT# 11
00
00
00
00
FT# 12
00
00
00
00
FT #13
00
00
00
00
FT# 14
00
00
00
00
FT# 15
30 83 136 00 31 27
30 51 259.00 3<
30.83 366 00
¦Temp #2 (deg C)
30 83 15.00 3(
30.83 ¦- 136.00 3:
30.51 259.00 3(
30.83 366 00
¦Temp #3 (deg C)
30.83 15.00 3(
30.83 136.00 3!
30.51 259 00 3(
30.83 366.00
¦Temp #4 (deg C)
30.83 15.00 3<
30.83 136.00 3:
30.51 259.00 3(
30.83 366.00
¦solar radiation (lang
380 15.00
648 136.00
562 259.00
365 366 00
¦photoperiod (fraction
0.444 15.00 0
0.562 136.00 0
0.512 259 00 0
0.440 366.00
-wind velocity (m/sec)
167 00
31.67
197 00
0.000
0.000
0.000
0.000
-Ke #1
1.020
1.020
1.020
1.020
¦Ke #2
1.020
1.020
1 020
1.020
¦Ke #3
• 1.020
1 020
1.020
1.020
¦Ke #4
1 020
1.020
1.020
1.020
•Ke #5
1.020
1.020
1.020
1.020
¦NH4 flux
- 2.301
2.301
2.245
2.301
¦P04 flux
2.301
2.301
2.245
2.301
¦Macrophy
15.00
136.00
259 00
366 00
(1/meter)
15.00
136.00
259.00
366.00
(1/meter)
15.00
136.00
259.00
366.00
(1/meter)
15.00
136.00
259.00
366.00
(1/meter)
15.00
136.00
259.00
366.00
(1/meter)
15.00
136.00
259.00
366.00
(theta =
15.00
136.00
259.00
366.00
(theta =
15.00
136.00
259.00
366 00
1.51
289 00
30
51
320.00
1.83
45 00
30
83
75.00
..27
167 00
31
67
197 00
1.51
289.00
30
51
320.00
) 83
45.00
30
.83
75.00
. 27
167 00
31
67
197.00
). 51
289.00
30
51
320.00
1.83
45.00
30
.83
75.00
.27
167 00
31
.67
197.00
1.51
289.00
30
51
320.00
leys/day) assumes
25%
c loud
cover
454
45.00
535
75.00
661
167 00
644
197 00
485
289.00
403
320.00
of day which is
sunny)
456
45.00
0.
497
75.00
573
167.00
0.
568
197.00
478
289.00
0
452
320.00
000
45.00
0.
000
75.00
000
167.00
0.
000
197.00
000
289.00
0
000
320.00
020
45.00
1.
020
75.00
.020
.020
1.020
1.020
1.020
1.020
1.020
1.020
1.020
1.020
1 020
167 00
289.00
45.00
167.00
289.00
45.00
167.00
289.00
45.00
167 00
289.00
1.020
1 020
1.020
1.020
1.020
1 020
1.020
1.020
1.020
1.020
1.020
197.00
320.00
75.00
197.00
320.00
75.00
197.00
320.00
75.00
197.00
320.00
1.020
45.00
1.020
75.00
1.020
157.00
1.020
197.00
1.020
289.00
1.020
320.00
08)
2.301
45.00
2.301
75.00
2.381
167.00
2.455
197.00
2.245
289.00
2.245
320.00
08)
2.301
45.00
2.301
75.00
2.381
167.00
2 455
197.00
2.245
289.00
2.245
320.00
A-15
-------
Filename: WIH_11 INP WASP4 00 Water Quality Model for Indian Hills Marina
+ 1 + 2 + 3 + 4 + 5-1--+ 6 + 7 +. g
1.000 I
1 000 106
1.000 228
1.000 350
SHL#1 16 14
1.000 1
1.000 106
1.000 228
1.000 350
TFK1G 17 14
1.000 1
1 000 106
1 000 228
1.000 350
TFSOD 18 14
2.123
2.123
2.251
2 077
Z00PL 19 14
20000 1
20000 106
40000 228
20000 350
TFSI0 20 14
2.301 1
2.301 106
2.455 228
2.245 350
TFN03 21 14
2.301 1
2 301 106
2.455 228
2.245 350
NH3_N (mg/L)
SG01 0.0600
SG04 0.0500
SG07 0 0550
SG10 0.0550
SG13 0.0000
N02_N (mg/L)
SG01 0.0500
5G04 0.0500
SG07 0.0500
SG10 0.0500
SG13 0.0000
0P04 (mg/L)
SG01 0.0660
SG04 0.0700
SG07 0.0740
SG10 0.0800
5G13 0.0000
Phytl (mg/L)
SG01 17.0000
SG04 18.5000
SG07 18.0000
SG10 20.0000
SG13 0.0000
CBOD (mg/L)
SG01 3 1000
SG04 3.2000
SG07 3.3000
SG10 3 3000
SG13 0.0000
DO (mg/L)
SG01 4.5100
SG04 4.2000
SG07 4.0000
SG10 4.1000
00
00
00
00
1 000
1.000
1 000
1.000
15 00
136 00
259 00
366 00
1 000 45.00
1.000 167.00
1 000 289.00
1.000
1 000
1 000
75 00
197 00
320 00
FT# 16-She11 #1
.00 1.000 - 15 00
.00 1.000 136.00
.00 1.000 259.00
.00 . 1.000 366.00
FT#17-time dependent grazing rate multiplier (theta=1.00)
1 000
1 000
1.000
45 00
167.00
289.00
1 000
1.000
1.000
75.00
197.00
320.00
.00
.00
.00
00
1.000
1.000
1.000
1.000
15 00
136 00
259.00
366 00
1 000 45.00
1.000 167.00
1.000 289.00
FTiCia-SOD flux (theta = 1
1
106
228
350
2.123
2 123
2.077
2.123
15
136
259
366
072)
2.123
2.189
2 077
45
167
289
1.000
1.000
1.000
2.123
2.251
2.077
75.00
197.00
320.00
75
197
320
FT#19-Zoopl... modified 73
.00
.00
.00
.00
20000
20000
20000
20000
FT#20-S104 flux
.00 2.301
.00 2.301
.00 2.245
.00 2.301
15.00
136.00
259.00
366.00
(theta
15.00
136.00
259.00
366.00
micron net size
20000 45.00
40000 167.00
20000 289.00
1 08)
2.301
2.381
2.245
45.00
167.00
289.00
FT#21-N03 flux (theta = 1
.00
.00
.00
.00
2.301
2.301
2 245
15.00
136.00
259.00
08)
2.301 45.00
2.381 167.00
2.245 289.00
20000 75.00
80000 197.00
20000 320.00
2.301 75.00
2.455 197.00
2.245 320.00
2.301 75.00
2.455 197.00
2.245 320 00
2.301
366.00
1.000
SG02
0.0500
1.000
SG05
0.0500
1.000
SG08
0 0600
1.000
SG11
0.0500
1.000
1.000
SG02
0.0500
1.000
SG05
0.0500
1.000
SG08
0 0500
1.000
SG11
0.0500
1.000
0.600
SG02
0.0660
0.600
SG05
0.0780
0.600
SG08
0.0800
0.600
SG11
0.0820
0.600
0.000
SG02
18.7500
0.000
SG05
18.0000
0.000
SG08
19.0000
0.000
SGI]
20.0000
0.000
0.500
SG02
3.1000
0.500
SG05
3.4000
0.500
SG08
3.1000
0.500
SG11
3.1000
0.500
1.000
SG02
3.9800
1.000
SG05
4.4100
1.000
SG08
4.3000
1.000
SG11
4.1000
5 1. 9999. J:INITIAL CONC.
1.000 SG03 0.0600 1.000
1.000 SG06 0.0500 1.000
1.000 SG09 0.0600 1 000
1 000 SG12 0.0500 1.000
5 1. 9999. J:INITIAL CONC.
1.000 SG03 0.0500 1.000
1.000 SG06 0.0500 1.000
1.000 SG09 0.0500 1.000
1 000 SG12 0.0500 1.000
5 1. 9999. J:INITIAL CONC.
0.600 SG03 0.0700 0,600
0.600 SG06 0.0750 0.600
0.600 SG09 0.0780 0.600
0.600 SG12 0.0840 0.600
4 1. 9999. J.INITIAL CONC.
0.000 SG03 17.5000 0.000
0.000 SG06 18.0000 0.000
0.000 SG09 18.2500 0.000
0.000 SG12 21.5000 0.000
4 1.
0.500 SG03
0.500 SG06
0.500 SG09
0.500 SG12
9999. J:INITIAL CONC.
3.2C00 0.500
3.1000 0.500
3.4000 0.500
3.1000 0.500
5 1. 9999. J:INITIAL CONC.
1.000 SG03 4.4600 1.000
1.000 SG06 4.3000 1 000
1.000 SG09 3.9500 1.000
1.000 SG12 4.2100 1.000
A-16
-------
Filename: WIH_11.INP UASP4 00 Water Quality Model for Indian Hills Marina
* 1 + 2 + 3 + 4 + 5 + s + 7 + 3
SG13
0.0000
1 000
OrgN
(mg/L)
SG01
0 3400
0.500
SG02
0 1000
SG04
0.2000
0.500
SG05
0 4500
SG07
0.1000
0.500
SG08
0.3600
SG10
0 2000
0.500
SGI 1
¦_ 0.3500
SG13
0.0000
0.500
OrgP
{mg/L)
SG01
0.0440
0.700
SG02
0.0440
SG04
0.0480
0.700
SG05
0 0520
SG07
0.0500
0.700
SG08
0 0520
SG10
0 0540
0 700
SGI 1
0.0540
SG13
0.0000
0.700
Phy 12
(mg/L)
SG01
0.0000
0.000
SG02
0 0000
SG04
0.0000
0.000
SG05
0.0000
SG07
0.0000
0.000
SG08
0.0000
SG10
0.0000
0.000
SGI 1
0.0000
SG13
0.0000
0.000
Phyt3
(mg/L)
SG01
0 0000
0.000
SG02
0.0000
SG04
0.0000
0 000
SG05
0.0000
SG07
0 0000
0.000
SG08
0 0000
SG10
0 0000
0.000
SGI 1
0.0000
5G13
0.0000
0.000
S104
(mg/L)
SG01
0 0000
0.300
SG02
0.0000
SG04
0.0000
0.300
SG05
0.0000
SG07
0.0000
0.300
SG08
0.0000
SG10
0.0000
0.300
SG11
0.0000
SG13
0.0000
0.300
Salinity (mg/L)
SG01
26860
1.000
SG02
25710
5G04
26200
1.000
SG05
26410
SG07
26300
1 000
SG08
26500
SG10
26300
1.000
SGI 1
26200
SG13
0.0000
1.000
Coliforms (MPN/lOOml)
SG01
0.0000
1.000
SG02
0.0000
SG04
0.0000
1.000
SG05
0.0000
SG07
0.0000
1.000
SG08
0.0000
SG10
0.0000
1.000
SGI 1
0.0000
SG13
0 0000
1.000
5 i. 9999. J.INITIAL CONC
0.500 SG03 0.4000 0 500
0 500 SG06 0 3700 0.500
0 500 SG09 0.0900 0 500
0.500 SG12 0.3500 0.500
4 1. 9999. J INITIAL CONC.
0.700 SG03 0 0480 0 700
0 700 SG06 0 0520 0.700
0 700 SG09 0 0520 0 700
0.700 SG12 0.0560 0.700
4 1. 9999 J INITIAL CONC.
0 000 SG03 0 0000 0.000
0.000 SG06 0 0000 0.000
0.000 SG09 0.0000 0 000
0.000 SG12 0.0000 0.000
4 1. 9999. J:INITIAL CONC.
0 000 SG03 0.0000 0.000
0 000 SG06 0.0000 0.000
0 000 SG09 0.0000 0.000
0 000 SG12 0.0000 0.000
3 1. 9999. J-INITIAL CONC
0.300 SG03 0.0000 0.300
0 300 SG06 0.0000 0.300
0.300 SG09 0.0000 0.300
0.300 SG12 0.0000 0 300
5 1. 9.99E+06 J:INITIAL CONC.
1.000 SG03 26600 1 000
1.000 SG06 26400 1 000
1.000 SG09 26300 1.000
1.000 SG12 26260 1 000
5 1. 9.99E+14 J:INITIAL CONC.
1.000 SG03 0.0000 1 000
1.000 SG06 0.0000 1.000
1.000 SG09 0.0000 1.000
1 000 SG12 0.0000 1.000
A-17
-------
2.1 Beacons Reach Marina
2.2.1 DYNHYD5 Dye Hydrodynamic File for Beacons Reach Marina
-------
Filename. BEACONS INP DVNHYD5 File for Beacons Reach Manna Dye Calibration
+ ! +. 2 3 + 4 + 5 + 6 + 7 + 8
0YNHYD5 - Beacons
BEACONS. INP - Hyd
***** Data Group
5 4 0000
***** Data Group
1988/10/10 01
1
1 2 3
***** Data Group
1 1988/10/
BEACONS.HYD
***** Data Group
Reach Marina (TC 5162-01)
rodynamics for dye simulation Oct 11
A. PROGRAM CONTROL DATA ************
6.00000 5 1988/10/10 00 00
B- OUTPUT CONTROL DATA ********
:00 1.0-1 5 0
1988 to Oct 13, 1988
r ***********
1988/10/14 00.00
****************
4 5
C: SUMMARY CONTROL DATA
10 00.00 25.0000 150
D: JUNCTION DATA
***********
****************
1
0.001
1303
-2 1
109
56
1
2
0 001
3165
-2.1
133
110
1
2
3
0.001
2032
-2.1
101
117
2
3
4
0.001
2172
-2.1
72
126
3
4
5
0.001
2377
-2.1
44
134
4
1
2
3
4
*****
0
*****
Data Group E. CHANNEL DATA ********************************
59 39 2.1 0.030
31 81 2.1 0.030
32 76 2.1 0.030
28 73 2.1 0.030
Data Group
F.l:
0.001
0.001
0.001
0 001
*********************
1 2
2 3
3 4
4 5
*******
CONSTANT INFLOWS (m/sec)
Data Group F.2: VARIABLE INFLOWS (m/sec) - Daily Flows **************
Variable Tide
0.0000
Data Group G: SEAWARD BOUNDARY DATA (m)
1
3 1 18 15
1988/10/09 23-00
1988/10/10 11:30
1988/10/10 23.00
1988/10/11 11:30
1988/10/11 23 00
1988/10/12 12.00
1988/10/12 23:45
1988/10/13 13:15
1988/10/14 01.30
Data Group H: WIND DATA (m/sec) mean monthly winds at Beacons Reach
0
0.0
0.300
0.300
0.300
0.300
0.300
0.300
0 300
0.300
0 300
0.0000 0.0
1988/10/10 06:00
1988/10/10 18:00
1988/10/11 06:00
1988/10/11 18:00
1988/10/12 05:30
1988/10/12 19:00
1988/10/13 06-45
1988/10/13 19:45
1988/10/14 07-45
1.0000
-0.300
-0 300
-0.300
-0.300
-0.300
-0.300
-0.300
-0 300
-0.300
-------
2.2.2 DYNHYD5 DO Hydrodynamic File for Beacons Reach Marina
-------
Filename- HBR_11 INP 0YNHYD5 File for Beacons Reach Marina 00 Calibration
+ ! + 2 + 3 + 4 5 + 6 + 7 + 8
0YNHYD5 - Beacons Reach Marina (TC 5162-01)
HBR_11 INP - Hydrodynamics for water quality May 20-27, 1988
***** Data Group A: PROGRAM CONTROL DATA ************************
5 4 0000 6 00000 5 1988/05/20 00 00 1988/05/28 00.00
***** Data Group B: OUTPUT CONTROL DATA *************************
1988/05/20 01:00 10-1 5 0
1
1 2 3 4 5
***** Data Group C: SUMMARY CONTROL DATA ************************
1 1988/05/20 00:00 25.0000 150
H8R_11 HYO
***** Data Group D: JUNCTION DATA *******************************
1
0.001
1303
-2.1
109
56
1
2
0.001
3165
-2.1
133
110
1
2
3
0.001
2032
-2.1
101
117
2
3
4
0 001
2172
-2.1
CSJ
f—.
126
3
4
5
0 001
2377
-2.1
44
134
4
***** Data Group E: CHANNEL DATA ********************************
1
59
39
2.1
0.
030
0.001
1
2
2
31
81
2.1
0
030
0.001
2
3
3
32
76
2.1
0
030
0.001
3
4
4
28
73
2 1
0
030 .
0.001
4
5
***** Data Group F.l: CONSTANT INFLOWS (m/sec) ****************************
0
***** Data Group F.2: VARIABLE INFLOWS (m/sec) - Daily Flows **************
0
** Data Group G: SEAWARD BOUNDARY DATA (m) - Variable Tide
3 1 34
15
0.0
1988/05/19
22:24
0.715
1988/05/20
10:56
0.539
1988/05/20
23 09
0.662
1988/05/21
11:47
0.522
1988/05/21
23.57
0.613
1988/05/22
12:39
0.515
1988/05/23
00-47
0.569
1988/05/23
13:32
0.518
1988/05/24
01:39
0.534
1988/05/24
14:24
0.533
1988/05/25
02:32
0.507
1988/05/25
15:13
0.556
1988/05/26
03.24
0.489
1988/05/26
15:59
0.588
1988/05/27
04:12
0.479
1988/05/27
16:42
0.625
1988/05/28
04:59
0.478
Data Group H: WIND
DATA (m/sec
0
0 0000 0.0 1.0000 0 0000
1988/05/20 04:53 0 058
1988/05/20 16:43 0 109
1988/05/21 05:39 0 081
1988/05/21 17 33 0.139
1988/05/22 06:25 0 094
1988/05/22 18:29 0.154
1988/05/23 07-12 0.096
1988/05/23 19-29 0.152
1988/05/24 07 58 0.086
1988/05/24 20:28 0 132
1988/05/24 08:44 0.066
1988/05/24 21:25 0.098
1988/05/24 09:27 0 038
1988/05/24 22:17 0.057
1988/05/24 10:10 0.008
1988/05/24 23:05 0 016
1988/05/24 10:52 -0.022
mean monthly winds at Beacons Reach **
-------
2.2.3 WASP4 Dye Calibration File for Beacons Reach Marina
-------
Filename: BEACONWQ.INP UASP4 File for Beacons Reach Marina DO Calibration
....+ 1 + 1 + 3 + 4 1- 5 t 6 j- / + 8
BEACONWQ INP - Beacons Reach WASP4 Model Water 10/10/88 10/13/88
Dye Results are used for model Calibration
KSIM NSE6 NSYS ICFL MFLG IDMP N5LN INTY ADFC zyr/irm/dd hhrrm A MODEL OPTIONS
0 6
5
1 2
1
900.
3
30.
1 1 1
1 + +
5 1.000
1
75.0
2
0.50E+00
1
82.0
2
0.40E+00
1
170.0
2
0.30E+00
1
160.0
2
0.10E+00
1
153.0
2
0.30E+00
1 1 1
2
13 1
3 4
287.50
1
0 0 0 1988/10/10 0000
1
1
0.00
365
1303
2377
0.0
1303
2377
0.0
1
*
59
0.
59
0.
31
0
32
0.
28
0.
86400.
1 1
+
1.00
0 1
0.50E+00
1 2
0.40E+00
2 3
0.30E+00
3 4
0.10E+00
4 5
0.30E+00
1 1
366.
1 1
86400
1 1
640.
1 0
1
B:EXCHANGES
(surface water)
640.
640.
640.
640.
640.
0
720.0
+
*
+ *
+ *
+ *
c- VOLUMES
0000
1 0000
1
6
1
1303
0 0
1.0
1.0
1.0
2
6
1
3165
0.0
1.0
1.0
1 0
3
6
1
2032
0.0
1.0
1.0
1 0
4
6
1
2172
0 0
1.0
1.0
1.0
5
6
1
2377
0 0
1.0
1.0
1 0
6
0
3
11049
0.0
1.0
1.0
1.0
SUMRY2.0UT (BEACONS.HYD)
3 4 5
1.0 1.0
1.0 1.157E-05
+ * + * 0: FLOWS
(Data Block 0.2 Field One Flows)
F#2 --NINQ(2) SCALQ=1 CONVQ= 1
F#3 —NINQ(3) SCALQ=1 CONVQ= m/day * 1/86400
F#3 —NOQS(3,ni) number of segment pairs
3165 2 6 2032 3 6 2172 4
1 6
5 6
F#3 --NBRKQ(4.nl) --> number of time breaks
0.0 0.0 640.0
1.0 1.157E-05 F#4 --NINQ(3) SCALQ=1 C0NVQ= m/day * 1/86400
F#4 — NOQS{3,ni) --> number of segment pairs
1 6 3165 2 6 2032 3 6 2172 4
5 6
F#4—NBRKQ(5,ni} --> number of time breaks
0.0 . 0.0 640.0
1.0 1.157E-05 F#5 —NINQ(3) SCALQ=1 C0NVQ= m/day * 1/86400
1303
2377
0.0
1 1
1
1.0
1 6
0.000
1
6
6
0 0
1
3165
2032
2172
1.0
0CN ¦
284 000
F#5—NBRKQ(5,ni) --> number of time breaks
0.0 640.0
11111110 1
Sys#l NH3 (Data Group E Boundary Conditions)
0.000
0CNBC001
285.354 000.00
Sys# 1
285.355
0.000 285.396
— 1-
.+ 2 + 3 + 4 —
--+ 6 +-
-7-
-+ 8
A-23
-------
Filename- BEACONWQ INP WASP4 File for Beacons Reach Marina DO Calibration
+ 1 + 2 + 3 * 4 + 5 + 6 + 7 „ 8
0 000 285 397 000.000 365 000
1 Sys#2 N03 (Data Group E Boundary Conditions)
1.0 1.0
1 6 0CN . OCNBC001 Sys* 2
0 000 284.000 0.000 285.354 000.00 285.355 0.000 285 396
0.000 285 397 000 000 --365.000
1 Sys#3 0P04 (Data Group E Boundary Conditions)
10 10
1 6 0CN : „ OCNBCOO1 Sys# 3
0 000 284.000 0 000 285.354 000.00 285.355 0.000 285 396
0 000 285 397 000.000 365.000
1 Sys#4 Phyt#l (Data Group E Boundary Conditions)
1.0 1.0
1 6 0CN : OCNBCOOl Sys# 4
0.000 284 000 0.000 285 354 000.00 285.355 0.000 285.396
0 000 285.397 000.000 355.000
1 Sys#5 CB0D (Data Group E Boundary Conditions)
1.0 1.0
1 6 0CN OCNBCOOl Sys# 5
0.000 284.000 0.000 285 354 000.00 285.355 0.000 285.396
0.000 285.397 000.000 365.000
1 Sys#6 00 (Data Group E Boundary Conditions)
1.0 1.0
1 6 0CN : OCNBCOOl Sys# 6
0.000 284.000 0 000 285.354 000.00 285.355 0.000 285.396
0 000 285.397 000.000 365.000
1 Sys#7 OrgN (Data Group E Boundary Conditions)
1.0 1.0
1 6 0CN : OCNBCOOl Sys# 7
0 000 284.000 0.000 285.354 000.00 285.355 0.000 285.396
0.000 285.397 000.000 365.000
1 Sys#8 OrgP (Data Group E Boundary Conditions)
1.0 1.0
1 6 0CN : OCNBCOOl Sys# 8
0.000 284.000 0.000 285.354 000.00 285.355 0.000 285.396
0.000 285.397 000.000 365.000
1 Sys#9 Phyt#2 (Data Group E Boundary Conditions)
1.0 1.0
1 6 OCN : OCNBCOOl Sys# 9
0.000 284.000 0.000 285.354 000 00 285.355 0.000 285 396
0.000 285 397 000.000 365 000
1 Sys#10 Phyt#2 (Data Group E Boundary Conditions)
1.0 1.0
1 6 OCN : OCNBCOOl Sys# 10
0 000 284.000 0.000 285.354 000.00 285 355 0 000 285.396
0.000 285.397 000.000 365.000
1 Sys#11 S104 (Data Group E Boundary Conditions)
1.0 1 0
1 6 OCN . OCNBCOOl Sys# 11
0 000 284 000 0.000 285.354 000.00 285 355 0.000 285.396
0 000 285 397 000.000 365.000
1 Sys#12 Salinity (Data Group E Boundary Conditions)
1.0 1.0
1 6 OCN : OCNBCOOl Sys# 12
0.000 284.000 0.000 285.354 0.000 285.355 0.000 285.396
0.000 285.397 0.000 365.000
1 Sys#13 Coliforms (Data Group E Boundary Conditions)
10 1.0
1 6 OCN : OCNBCOOl Sys# 13
0.000 284.000 -0.000 285.354 000.00 285.355 0.000 285.396
0.000 285.397 000.000 365.000
4 PS(t) Sys#l (Data Block F 1 Waste Loads for Point Source)
0.100E+01 0.100E+01 PS(t) :System # 1 Dye Kg/Day Scale/conv fct
2 6 Seg 002 Oye Input OCNBCOOl INE 5 17 Sys# 1 0CN88RAT INE
0.000 284.000 0.000 285 354 10.800 285.355 10.800 285.396
0.000 285.397 000.000 365.000
3 6 Seg 003 Oye Input OCNBCOOl INE 5 17 Sys# 1 0CN88RAT.INE
0 000 284.000 0.000 285.354 10.800 285.355 10.800 285.396
0.000 285.397 000.000 365 000
A-24
-------
Filename BEACONWQ.INP WASP4 File for Beacons Reach Marina DO Calibration
+ 1 + 2 + 3 + 4 + s + 5 + 7 + 8
4 6
0.000
0.000
5 6
0 000
0 000
4
0 100E+01
2 6
0 000
0.000
3 6
0 000
0 000
4 6
0.000
0.000
' 5 6
0.000
0.000
4
0.100E+01
2 6
0 000
0 000
3 6
0.000
0.000
4 6
0.000
0.000
5 6
0.000
0.000
4
0.100E+01
2 6
0.000
0.000
3 6
0 000
0 000
4 6
0.000
0.000
5 6
0 000
0.000
4
0.100E+01
2 6
0.000
0.000
3 6
0.000
0.000
4 6
0.000
0.000
5 6
0.000
0.000
4
0.100E+01
2 6
0 000
0.000
3 6
0 000
Seg 004
284.000
285.397
Seg 005
284 000
285.397
Dye Input
0 000
000 000
Oye Input
0 000
0 000
PS(t) Sys#2
0.100E+01
Seg 002
284.000
285 397
Seg 003
284.000
285.397
Seg 004
284.000
285.397
Seg 005
284.000
285.397
PS(t) Sys#3
0 100E+01 PS(t)
PS(t)
Oye Input
0 000
000.000
Dye Input
0.000
000 000
Oye Input
0.000
000.000
Dye Input
0.000
0.000
Seg 002
284 000
285.397
Seg 003
284.000
285.397
Seg 004
284.000
285.397
Seg 005
284.000
285.397
PS(t) Sys#4
0.100E+01
Seg 002
284.000
285.397
Seg 003
284.000
285.397
Seg 004
284.000
285.397
Seg 005
284.000
285.397
PS(t) Sys#5
0 100E+01 PS(t)
Seg 002
284.000
285.397
Seg 003
284.000
285.397
Seg 004
284.000
285.397
Seg 005
284.000
285.397
PS(t) Sys#6
0.100E+01
Seg 002
284.000
285.397
Seg 003
284.000
285 354
355 000
OCNBCOOl INE 5 17 Sys# 1 0CN88RAT.INE
10 800 285 355 10 800 285.396
5 17 Sys# 1 0CN88RA1 INE
10.800 285 355 10.800 285 396
285.354
-365.000
(Data Block F.l Waste Loads for Point Source
:System # 2 N03 Kg/Day Scale/conv fct
OCNBCOOl INE 5 17 Sys# 1 0CN88RAT.INE
285.354
365 000
285.354
365.000
285.354
365.000
0.00 285.355
0.000 285.396
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT INE
0 00 285.355 0.000 285 396
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 0.000 285.396
5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 0.000 285.396
Dye Input
0.000
000.000
Dye Input
0.000
000.000
Oye Input
0.000
000.000
Dye Input
0.000
0.000
285.354
365.000
(Data Block F.l Waste Loads for Point Source)
System # 3 0P04 Kg/Day Scale/conv fct
OCNBCOOl INE 5 17 Sys# 1 0CN88RAT.INE
285.354
365.000
285.354
365 000
285.354
365.000
0.00 285.355
1 000 285.396
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT INE
0.00 285.355 1.000 285.396
OCNBCOOl INE 5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 1.000 285.396
5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 1.000 285.396
PS(t)
Oye Input
0.000
000.000
Dye Input
0 000
000 000
Dye Input
0.000
000.000
Dye Input
0.000
0.000
Dye Input
0.000
000.000
Dye Input
0.000
000.000
Dye Input
0.000
000.000
Dye Input
0.000
0.000
285.354
365.000
(Data Block F.l Waste Loads for Point Source)
System # 4 Phytl Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354
365.000
285.354
365.000
285.354
365.000
0 00 285.355
1.000 285.396
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 1.000 285 396
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
0 00 285.355 1 000 285 396
5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 1.000 285.396
285.354
365 000
(Data Block F.l Waste Loads for Point Source)
:System # 5 CBOD Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354
365.000
285.354
365.000
285.354
365.000
0.00 285.355
1.000 285.396
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 1.000 285.396
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
0 00 285.355 1.000 285.396
5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 1.000 285.396
PS(t)
Dye Input
0.000
000 000
Dye Input
0 000
285.354
365.000
(Data Block F.l Waste Loads for Point Source)
System # 6 DO Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT INE
285.354 0 00 285.355 1.000 285 396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285.396
-! + 2-
--3 + 4 + 5 + 6 + 7-
A-25
-------
Filename: BEACONWQ.INP WASP4 File for Beacons Reach Marina 00 Calibration
+ ! + 2 + 3 + 4 + 5 + 5 + 7 + 8
0 000
285.397
000.000
4 6
Seg 004
Dye Input
0.000
284 000
0 000
0 000
285.397
000.000
5 6
Seg 005
Dye Input
0 000
284.000
0.000
0.000
285.397
0.000
4
PS(t) Sys#7
0
100E+01
0.100E+01
PS(t)
2 6
Seg 002
Dye Input
0.000
284.000
0.000
0.000
285.397
000 000
3 6
Seg 003
Dye Input
0 000
284.000
0.000
0.000
285 397
000 000
4 6
Seg 004
Dye Input
0.000
284.000
0.000
0 000
285.397
000.000
5 6
Seg 005
Dye Input
0.000
284.000
0.000
0.000
285.397
0.000
4
PS(t) Sys#8
0.
100E+01
0.100E+01
PS(t)
2 6
Seg 002
Dye Input
0.000
284.000
0.000
0.000
285.397
000.000
3 6
Seg 003
Dye Input
0.000
284 000
0.000
0.000
285.397
000.000
4 6
Seg 004
Oye Input
0.000
284.000
0.000
0.000
285.397
000.000
5 6
Seg 005
Dye Input
0.000
284.000
0.000
0.000
285.397
0.000
4
PS(t) Sys#9
0
100E+01
0.100E+01
PS(t)
2 6
Seg 002
Dye Input
0.000
284.000
0.000
0.000
285.397
000.000
3 6
Seg 003
Dye Input
0.000
284.000
0.000
0.000
285.397
000.000
4 6
Seg 004
Dye Input
0.000
284 000
0.000
0.000
285.397
000.000
5 6
Seg 005
Dye Input
0.000
284.000
0.000
0 000
285.397
0.000
4
PS(t) Sys#10
0.
100E+01
0.100E+01
PS(t)
2 6
Seg 002
Dye Input
0.000
284.000
0.000
0 000
285.397
000.000
3 6
Seg 003
Dye Input
0.000
284.000
0.000
0 000
285.397
000.000
4 6
Seg 004
Dye Input
0.000
284.0D0
0 000
0.000
285 397
000.000
5 6
Seg 005
Dye Input
0.000
284.000
0.000
0.000
285.397
0.000
4
PS(t) Sys#11
0.
.100E+01
0.100E+01
PS(t)
2 6
Seg 002
Dye Input
0.000
284.000
0.000
0.000
285.397
000.000
3 6
Seg 003
Dye Input
365 000
0CNBC001 INE 5 17 Sys# 1 0CN88RAT INE
285.354 0 00 285 355 1 000 285 396
365 000
5 17 Sys# 1 0CN88RAT.INE
-285.354 0.00 285.355 1 000 285.396
365.000
(Data Block F.l Waste Loads for Point Source)
System # 7 OrgN Kg/Day Scale/conv fct
OCNBCOOl INE 5 17 Sys# 1 0CN88RAT INE
285 354 0.00 285.355 1.000 285 396
365 000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285 354 0 00 285.355 1.000 285.396
365 000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285 354 0.00 285.355 1.000 285.396
365.000
5 17 Sys# 1 0CN88RAT INE
285.354 0.00 285.355 1.000 285.396
365.000
(Data Block F 1 Waste Loads for Point Source)
•System # 8 OrgP Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285 355 1.000 285 396
365 000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0 00 285.355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285.396
365.000
5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1 000 285 396
365.000
(Data Block F.l Waste Loads for Point Source)
:System # 9 Phyt2 Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT INE
285.354 0.00 285.355 1.000 285 396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285.396
365.000
5 17 Sys# 1 0CN88RAT.INE
285.354 0 00 285 355 1 000 285.396
365.000
(Data Block F.l Waste Loads for Point Source)
:System # 10 Phyt3 Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285 355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285.396
365.000
5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285 396
365 000
(Data Block F.l Waste Loads for Point Source)
:System # 11 Sl04 Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
1
2
.3 + 4 + 5 + 6 + 7 + 8
A-26
-------
Filename: BEACONWQ. INP WASP4 File for Beacons Reach Manna DO Calibration
+ 1 + 2 + 3 * 4 * 5 + 6 + 7 + 8
0 000
0 000
4 6
0 000
0.000
5 6
0.000
0.000
4
0.100E+01
2 6
0.000
0 000
3 6
0 000
0 000
4 6
0.000
0 000
5 6
0.000
0.000
4
0 100E+01
2 6
0 000
0.000
3 6
0.000
0.000
4 6
0 000
0.000
5 6
0.000
0 000
0.000
000.000
Dye Input
0.000
000.000
Dye Input
0.000
0 000
284.000
285 397
Seg 004
284.000
285.397
Seg 005
284 000
285 397
PS(t) Sys#12
0 iOOE+Ol PS(t)
Seg 002
284.000
300.000
Seg 002
284.000
300.000
Seg 003
284.000
300.000
Seg 004
284 000
300.000
PS(t) Sys#13
0.100E+01
Seg 002
284.000
285.397
Seg 003
284.000
285 397
Seg 004
284.000
285.397
Seg 005
284.000
285.397
0
Z001X
KESG
S001D
SHL3
FN03
Z001X
KESG
S001D
SHL3
FN03
Z001X
KESG
S0D10
SHL3
FN03
Z001X
KESG
SOD1D
SHL3
FN03
Z001X
KESG
S001D
SHL3
FN03
Z001X
0
17
1
5
9
15
24
1
1
5
9
15
24
2
1
5
9
15
24
3
1
5
9
15
24
4
1
5
9
15
24
5
1
6
1.FSI04
l.KEFN
1.MACRO
1.GWC1
1.
6
1.000FSIO4
1.OOOKEFN
0.600MACR0
0.000GWC1
4.570
6
1.OOOFSI04
1.OOOKEFN
0.600MACR0
O.OOOGWCl
4.570
6
1.000FSI04
1.OOOKEFN
0 600MACRO
0 OOOGWCr
4.570
6
1 000FSI04
1.OOOKEFN
0.600MACRO
O.OOOGWCl
4.570
6
1.000FSI04
2
6
12
21
2
6
12
21
2
6
12
21
2
6
12
21
2
6
12
21
285 354
365 000
285.354
365 000
0 00
285 355
1 000 285 396
0CN8C001.INE 5 17 Sys# 1 0CN88RAT INE
0 00 285 355 1 000 285 396
5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 1.000 285.396
Dye Input
0 000
0.000
Dye Input
0.000
000 000
Dye Input
0 000
000.000
Dye Input
0.000
000 000
PS(t)
Dye Input
0.000
000 000
Dye Input
0.000
000.000
Dye Input
0.000
000.000
Dye Input
0 000
0.000
285.354
365.000
(Data Block F.l Waste Loads for Point Source)
•System # 12 Sal in Kg/Day
Sys# 12
285.354 021 600 285.375
365.000
Sys# 12
285 354 021.600 285.375
365.000
Sys# 12
285.354 021.600 285.375
365.000
Sys# 12
285.354 021.600 285 375
365.000
(Data Block F.l Waste Loads for Point Source)
System # 13 Coll Kg/Day Scale/conv fct
0CNBC001. INE 5 17 Sys# 1 0CN88RAT.INE
Scale/conv fct
00.000 285.396
00 000 285.396
00 000 285.396
00.000 285 396
285 354
365.000
285 354
365.000
285 354
365.000
285.354
365.000
0 00 285.355
1.000 285.396
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
0 00 285.355 1.000 285.396
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 1.000 285.396
5 17 Sys# 1 0CN88RAT.INE
0.00 285.355 1.000 285.396
(Data Group F 2 ..NPS Loads)
FF(xyz)- G wasp4g01 dat 05/12/90 21-20.28
1 TMPSG
1.FNH4
1 SHL1
1 GWC2
143.750TMPSG
5.000FNH4
0 000SHL1
0 201GWC2
143.750TMPSG
5.000FNH4
0.000SHL1
0 000GWC2
143.75QTMPSG
5.000FNH4
0 000SHL1
0.803GWC2
143.750TMPSG
5 000FNH4
0.000SHL1
0.201GWC2
3
7
13
22
3
7
13
22
3
7
13
22
3
7
13
22
3
7
13
22
143.750TMPSG 3
l.TMPFN
1.FP04
1 SHL2
1 GWSW
1.OOOTMPFN
8.400FP04
0.000SHL2
1.262GWSW
1.OOOTMPFN
8.400FP04
0.000SHL2
0.000GWSW
1.OOOTMPFN
8.400FP04
0.000SHL2
5.047GWSW
1.OOOTMPFN
8.400FP04
0.000SHL2
1.262GWSW
1.OOOTMPFN
14
23
4
8
14
23
4
8
14
23
4
8
14
23
4
8
14
23
1.
1
1.
1.
4.000
1.560
0.000
0.020
4.000
1.560
0.000
0.000
4.000
1 560
0.000
0.020
4.000
1.560
0.000
0.020
4.000
-------
Filename. BEACONWQ INP UASP4 File for Beacons Reach Manna 00 Calibration
1 [ 1 2 1 3 - 1 4 1 5 1 6 7 A 8
KESG
5
1
OOOKEFN
6
S001D
9
0
600MACRO
12
SHL3
15
0
OOOGWC1
21
FN03
24
4
.570
6
of
6
(t
ZOO IX
1
1.
OOOFSI04
2
KESG
5
0
OOOKEFN
6
S001D
9
0
OOOMACRO
12
SHL3
15
0
000GWC1
21
FN03
24
0.
.000
5 000FNH4
0 000SHL1
0 134GWC2
7
13
22
(Dummy Benthos Segment)
0 000TMP5G
1 000FNH4
0.000SHL1
0 000GWC2
3
7
13
22
Nosys= 14
Const-H
8.400FP04
0 000SHL2
0 841GWSW
1 000TMPFN
0.000FPO4
0.000SHL2
0.OOOGWSW
GLOBAL
0
NH3-N
1
group #1-1
10
K12C
11
0 250
K12T
12
1.080
KNIT
13
2.000
ATMNH3
14
0.000
GWFLOW
15
0.000000
GWNH3
16
0 000
Z1CRB
17
2.500
Z1CDW
18
0.450
NCRBZ1
19
0.167
NCRBM
39
0.082
N03+N02-N
1
group #1-2
5
K20C
21
0.045
K20T
22
1.045
KN03
23
0.100
ATMN03
24
2.359
GWN03
25
3.065
0-P04
1
all param
4
ATMDIP
31
0.053
GWDIP
32
0.005
PCRBZ1
33
0.013
PCRBM
40
0.004
Phyt#l
2
group#l-4
12
WS1
28
0.100
K1C
41
2.500
KIT
42
1.066
LGHTS
43
2.000
PHI MX
44
720.000
XKC
45
0.017
CCHL1
46
80.000
IS1
47
300.000
KMNG1
48
0.014
KMPG1
49
0.001
K1RC
50
0.100
K1RT
51
1.080
group#2-4
10
KID
52
0.040
K1G
53
0.800
W1
54
1.000
KPZDC
55
0.000
KPZDT
56
1.000
PCRB1
57
0 030
NCRB1
58
0 150
KMPHYT
59
1.000
SICRB1
89
0.177
KMSG1
111
0.0014
CBOO
1
group#l-5
7
KOC
71
0.200
KDT
72
1.047
KDSC
73
0.000
KDST
74
1.000
KBOD
75
0.500
GWBOD
76
1 800
OCRB
81
2.670
Diss 02
1
group #1-6
6
K4C
26
0.000
K4T
27
1 000
K2
82
0.000
GWOXY
83
4.000
KM1RC
109
0.000
KM1RT
110
1 000
Org-N
1
group #1-7
7
K71C
91
0.075
K71T
92
1.080
KONDC
93
0.000
KONDT
94
1.000
FON
95
0.750
ATM_ON
96
2.801
GW_ON
97
0.091
Org-P
1
group #1-8
7
K83C
100
0 220
K83T
101
1.080
KOPOC
102
0.000
KOPOT
103
1.000
FOP
104
0.600
ATM_OP
105
0.247
GW OP
106
0.005
Phyt#2
2
group #1-9
9
WS2
29
0.200
CCHL2
34
50.000
IS2
35
150.000
K2C
36
2.100
14
23
14
23
1.560
0 000
0.000
1 000
0.000
0.000
0.000
-------
Filename BEACQNWQ.INP WASP4 File for Beacons Reach Manna DO Calibration
1 1 i 2 H 3 1 4 i 5 1 6 ¦* 7 1 8
K2D
37
0.040
W2
38
1 000
K.2RC
60
0.100
K2RT
61
1.045
K2T
62
1.066
group#2-9
6
KMNG2
63
0.021
KMPG2
64
0 0015
NCRB2
65
0 154 •
- PCRB2
66
0 030
SICRB2
90
0.462
K.MS62
107
0 042
Phyt#3
2
group #1-1
10
CLLCRB3
20
0.000
WS3
30
0 000
CCHL3
67
1 000
IS3
68
0.000
K3C
69
0 000
K3D
70
0.000
W3
77
0.000
K3RC
78
0.000
K3RT
79
1.000
K3T
80
1.000
group#2-10
6
KMNG3
84
0 000
KMPG3
85
0.000
NCRB3
86
0.000
PCR83
87
0.000
SICRB3
98
0 000
KMSG3
108
0.000
S i 04
1
group #1-1
1
GWSI
88
20 000
Sal mity
1
group #1-1
1
GWSALT
99
0.000
Collforms
0
21 FF(t) WASP Data Group I FF Time- I wasp4i01.dat 10/16/90
TEMP 1 14 FT#01-Temp #1 PR/TRB tempxxxx.ini 5 8 FF time # 1 Temp#l
0.138E+02 0 267E+03 0.138E+02 0.289E+03 0.122E+02 0 320E+03 0.449E+01 0.350E+03
0.393E+01 0 381E+03 0 338E+01 0.411E+03 0.601E+01 0 440E+03 0.117E+02 0.471E+03
0.186E+02 0.501E+03 0.226E+02 0.532E+03 0.262E+02 0 562E+03 0.246E+02 0.593E+03
0.209E+02 0.624E+03 0 209E+02 0 639E+03
TEMP 2 14 FT#02-Temp #2 FB tempxxxx mi 6 8 FF time # 2 Temp#2
0.131E+02 0.267E+03 0.131E+02 0.289E+03 0.868E+01 0.320E+03 0.500E+01 0.350E+03
0.128E+01 0.381E+03 0 265E+01 0 411E+03 0.480E+01 0.440E+03 0.105E+02 0.471E+03
0.164E+02 0 501E+03 0.216E+02 0.532E+03 0.240E+02 0.562E+03 0.245E+02 0.593E+03
0.211E+02 0. 624E+03 0.2UE+02 0 639E+03
TEMP 3 14 FT#03-Temp #3 GLPB/SIS tempxxxx.ini 7 8 FF time # 3 Temp#3
0.139E+02 0 267E+03 0 139E+02 0.289E+03 0 964E+01 0.320E+03 0.275E+01 0 350E+03
0.840E+00 0.381E+03 0.205E+01 0.411E+03 0 375E+01 0.440E+03 0.914E+01 0 471E+03
0.149E+02 0.501E+03 0.213E+02 0.532E+03 0.237E+02 0.562E+03 0.244E+02 0.593E+03
0.218E+02 0.624E+03 0 218E+02 0.639E+03
TEMP 4 14 FT#04-Temp #4 GB tempxxxx.ini 8 8 FF time # 4 Temp#4
0.161E+02 0.267E+03 0 161E+02 0.289E+03 0.105E+02 0.320E+03 0.300E+01 0.350E+03
0.185E+Q1 0 381E+03 0.490E+01 0.4UE+03 0.819E+01 0.440E+03 0.143E+02 0.471E+03
0 200E+02 0.501E+03 0.232E+02 0.532E+03 0 232E+02 0.562E+03 0.214E+02 0.593E+03
0 218E+02 0 624E+03 0.218E+02 0.639E+03
SUN 5 14 FT#05-solar rad solarbnl.ini 5 6 FF time # 5 Solar
0.233E+03 0.267E+03 0.233E+03 0.289E+03 0.156E+03 0.320E+03 0.121E+03 0.350E+03
0.137E+03 0.381E+03 0.198E+03 0.411E+03 0.286E+03 0.440E+03 0.384E+03 0.471E+03
0 462E+03 0.501E+03 0.499E+03 0.532E+03 0.485E+03 0 562E+03 0.422E+Q3 0 593E+03
0.330E+03 0 624E+03 0.330E+03 0.639E+03
PHOTO 6 14 FT#06-photopenod solarbnl mi 6 6 FF time # 6 Photo
0 460E+00 0.267E+03 0.460E+00 0.289E+03 0.406E+00 0.32QE+Q3 0.381E+00 0.350E+03
0.392E+00 0.381E+03 0.435E+00 0.411E+03 0 498E+00 0.440E+03 0.568E+00 0.471E+03
0.623E+00 0.501E+03 0.649E+00 0.532E+03 0.639E+00 0.562E+03 0.595E+00 0.593E+03
0.530E+00 0.624E+03 0 530E+00 0.639E+03
WIND 7 14 FT#07-wind vel wind-nyb ini 5 6 FF time # 7 Wind v
0.412E+01 0.267E+03 0.412E+01 0.289E+03 0.412E+01 0.320E+03 0.412E+01 0.350E+03
0.514E+01 0.381E+03 0.514E+01 0.411E+03 0.514E+01 0.440E+03 0.514E+01 0.471E+03
0.412E+O1 0 501E+03 0.412E+01 0.532E+03 0.412E+01 0.S62E+03 0.412E+01 0.593E+03
0.412E+01 0.624E+03 0 412E+01 0.639E+03
KE#0l 8 14 FT#08-Ke #1 PR/TRB extcoeff.ini 5 9 FF time # 8 Ke #1
0.208E+01 0 267E+03 0.208E+01 0.289E+03 0.257E+01 0.320E+03 0.136E+01 0.350E+03
0.996E+00 0.381E+03 0.123E+01 0.411E+03 0.116E+01 0.440E+03 0.109E+01 0.471E+03
0.101E+01 0.501E+03 0.941E+00 0.532E+03 0.868E+00 0.562E+03 0.795E+00 0.593E+03
0.723E+00 0.624E+03 0.723E+00 0.639E+03
KE#02 9 14 FT#09-Ke #2 FB extcoeff.ini 6 9 FF time # 9 Ke #2
0.132E+01 0 267E+03 0.132E+01 0 289E+03 0.163E+01 0 320E+03 0.862E+00 0 350E+03
0.634E+00 0.381E+03 0.781E+00 0 411E+03 0.736E+00 0 440E+03 0.931E+00 0.471E+03
-------
Filename. BEACONWQ.INP WASP4 File for Beacons Reach Marina DO Calibration
+ J + 1 + 3 + 4 + 5 + g + 7 + 8
0.907E+00 0.501E-03 0.632E+00 0 532E+03 0.709E+00 0.562E+03 0 604E+00 0 593E+03
0 522E+00 0.624E+03 0 522E+00 0.639E+03
KE#03 10 14 FT#10-Ke #3 QPB extcoeff.ini 7 9 FF time #10 Ke #3
0 157E+01 0.267E+03 0.157E+01 0.289E+03 0 146E+01 0.320E+03 0 933E+00 0.350E+03
0 568E+00 0.381E+03 0.801E+00 0.411E+03 0.610E+00 0.440E+03 0 958E+00 0.471E+03
0.571E+00 0.501E+03 0 453E+00 0.532E+03 0 681E+00 0.562E+03 0 482E+00 0 593E+03
0 634E+00 0.624E+03 0.634E+00 0 639E+03
KE#04 11 14 FT#11-Ke #4 LPB extcoeff mi 8 9 FF time #11
-------
Filename. BEACONWQ.INP WASP4 File for Beacons Reach Manna DO Calibration
....+ ----2--- + 3 +. 4 + 5 + s + 7---- + ----8
SG01 0 0000 1 000 SG02 0 0000
SG04 0 0000 L 000 SG0S 0 0001
Phytl (mg/L)
SG01 0.0000 1.000 SG02 0 0000
SG04 0 0000 1.000 SG05 0.0001
CBOO (mg/L)
SG01 0.0000 1.000 SG02 0 0000
SG04 0 0000 1.000 SG05 0.0001
00 (mg/L)
SG01 0.0000 1.000 SG02 0 0000
5G04 0.0000 1 000 SG05 0 0001
OrgN (mg/L)
SG01 0 0000 1 000 SG02 0 0000
SG04 0.0000 1.000 SG05 0 0001
OrgP (mg/L)
SG01 0.0000 1.000 SG02 0 0000
SG04 0 0000 1.000 SG05 0 0001
Phyt2 (mg/L)
SG01 0.0000 1.000 SG02 0 0000
SG04 0 0000 1 000 SG05 0 0001
Phyt3 (mg/L)
SG01 0 0000 1.000 SG02 0.0000
SG04 0.0000 1.000 SG05 0.0001
Si04 (mg/L)
SG01 0 0000 1.000 SG02 0 0000
SG04 0.0000 1.000 SG05 0.0001
Salinity (mg/L)
SG01 0.0000 1 000 SG02 0 0000
SG04 0.0000 1.000 SG05 0.0000
Coliforms (MPN/lOOml)
SG01 0.0000 1.000 SG02 0.0000
SG04 0.0000 1.000 SG05 0.0001
1.000 SG03 0 0000 1 000
1 000 SG06 0 0000 0 000
4 1. 9999 J.INITIAL CONC
1.000 SG03 0 0000 1.000
1 000 SG06 0.0000 0.000
4 1. 9999. J.INITIAL CONC.
1 000 SG03 0.0000 1 000
I.000 SG06 0.0000 0.000
5 1 9999 J.INITIAL CONC
1.000 SG03 0.0000 1.000
1.000 SG06 0.0000 0 000
5 1. 9999. J-INITIAL CONC
1.000 SG03 0 0000 1 000
1.000 SG06 0 0000 0 000
4 1 9999. J INITIAL CONC
1.000 SG03 0.0000 1 000
1.000 SG06 0.0000 0 000
4 1. 9999. J:INITIAL CONC.
1.000 SG03 0.0000 1.000
1.000 5G06 0 0000 0 000
4 1. 9999. J¦INITIAL CONC.
1 000 SG03 0 0000 1.000
1 000 SG06 0.0000 0.000
3 1. 9999 J - INITIAL CONC.
1.000 SG03 0.0000 1 000
1 000 SG06 0 0000 0 000
5 1. 9.99E+03 J:INITIAL CONC.
1 000 SG03 0 0000 1.000
1.000 SG06 0.0000 0.000
5 1 9.99E+03 J: INITIAL CONC.
1.000 SG03 0 0000 1.000
1.000 SG06 0.0000 0.000
-------
2.2.4 WASP4 DO Water Quality File for Beacons Reach Marina
-------
Filename: WBR_11 INP WASP4 File for Beacons Reach Marina DO Calibration
+ 1 T 2 + 3 4 + 5 + 6 * 7 8
WBR_11 INP - Beacons Reach WASP4 Model Water 05/20/88 05/28/88
Dissolved Oxygen Calibration Run
KSIM NSEG NSYS ICFL MFLG IDMP NSLN INTY ADFC zyr/nm/dd hhrrm
0
5
1 2
1
900.
3
1800
0 0 0
1 + +
5 1.000
1
75 0
2
0 50E+00
1
82.0
2
0.40E+00
1
170.0
2
0 30E+00
1
160.0
2
0 10E+00
1
153.0
2
0.30E+00
0 0 0
2 0
1.0000 1
1
2
3
4
5
6
5
1
2
13 1
3 4
148 00
1 0 0 0.0 1988/05/20 0000
A MODEL OPTIONS
13 5 0
365.
86400.
0 0
366
0 0
86400.
1 1
640.
0 0
1.00
(surface water)
B EXCHANGES
59
0. 0.50E+00
59
0. 0.40E+00
31
0.
32
0.
28
0.
0
720 0
.0000
6
6
6
6
6
0
2 3
0.30E+00
3 4
0.10E+00
4 5
0.30E+00
0 0
640.
640.
640.
640.
3
640.
0
1 1
0 0
0
*
+ *
+ *
C:
VOLUMES
1303
0.0
1.0
1.0
1.0
3165
0.0
1.0
1.0
1.0
2032
0.0
1.0
1.0
1.0
2172
0.0
1.0
1.0
1.0
2377
0.0
1.0
1.0
1.0
11049
0.0
1.0
1.0
1.0
SUMRY2.OUT (BEACONS.HYD)
3 4 5
1.0 1.0
1.0 1.157E-05
+ * + * D: FLOWS
(Data Block D.2 Field One Flows)
F#2 —NINQ(2) SCALQ=1 C0NVQ= 1
F#3 — NINQ(3) SCALQ=1 C0NVQ= m/day * 1/86400
F#3 — NOQS(3,nl) --> number of segment pairs
3165 2 6 2032 3 6 2172 4
1303 1 6
2377 5 6
2 F#3 —NBRKQ(4,ni) --> number of time breaks
0.0 0.0 0.0 640 0
1 1.0 1.157E-05 F#4 —NINQ(3) SCALQ=1 C0NVQ= m/day * 1/86400
5 F#4 --NOQS{3.m) --> number of segment pairs
1303 1 6 3165 2 6 2032 3 6 2172 4
2377 5 6
2 F#4—NBRKQ(5,ni) number of time breaks
0.0 0.0 0.0 640.0
1 1 0 1.157E-05 F#5 —NINQ(3) SCALQ=1 C0NVq= m/day * 1/86400
1303
1
6
2377
5
6
0.0
0 0
0
0
0
1
1 0
1.0
3
0CN
0.040
140
.000
3165
2032
2172
F#5—NBRKQ(5,ni) --> number of time breaks
0.0 640.0
0 0 0 0 1 1 0 0 0
Sys#l NH3 (Data Group E Boundary Conditions)
0.040
0CNBC001
150.000
Sys# I
0.040 365.000
0 000 366 000
-------
Filename' WBR_11 INP WASP4 File for Beacons Reach Manna 00 Calibration
+ 1 + 2 + 3 4 + 5 + 6 + 7 + 8
1
Sys#2
N03
(Data Group E Boundary Conditions)
1.0
1 0
1
3
0CN
OCNBCOOl
Sys# 2
0
010
140 000
0 010
150.000
0 010 365 000 0.000 366 000
1
Sys#3
0P04
(Data Group E Boundary Conditions)
1 0
1.0
1
3
0CN
OCNBCOOl
Sys# 3
0.
030
140.000
0.030
150.000
0.030 365.000 0.000 366.000
1
Sys#4 Phyt#l
(Data Group E Boundary Conditions)
1.0
1.0 "
1
3
0CN •
OCNBCOOl
Sys# 4
12
00
140.000
12 00
150.000
12 00 365.000 0.000 366.000
1
Sys#5 CBOD
(Data Group E Boundary Conditions)
1 0
1.0
1
3
OCN ¦
OCNBCOOl
Sys# 5
2
470
140.000
2.470
150 000
2.470 365 000 0 000 366.000
1
Sys#6 DO
(Data Group E Boundary Conditions)
1.0
1 0
1
3
OCN :
OCNBCOOl
Sys# 6
6
150
140.000
6.150
150 000
6.150 365 000 6 150 366.000
1
Sys#7 OrgN
(Data Group E Boundary Conditions)
1.0
1.0
1
3
OCN :
OCNBCOOl
Sys# 7
0
360
140 000
0.360
150.000
0.360 365.000 0.000 366.000
1
Sys#8 OrgP
(Data Group E Boundary Conditions)
1.0
1 0
1
3
OCN .
OCNBCOOl
Sys# 8
0
020
140.000
0.020
150 000
0 020 365.000 0.000 366.000
1
Sys#9 Phyt#2
(Data Group E Boundary Conditions)
1.0
1.0
1
3
OCN :
OCNBCOOl
Sys# 9
0
000
140.000
0.000
150 000
0.000 365 000 0.000 366.000
1
Sys#10 Phyt#2
(Data Group E Boundary Conditions)
1.0
1.0
1
3
OCN
OCNBCOOl
Sys# 10
0
000
140 000
0.000
150 000
0.000 365 000 0.000 366.000
1
Sys# 11
S104
(Data Group E Boundary Conditions)
1.0
1.0
1
3
OCN •
OCNBCOOl
Sys# 11
0
000
140.000
0.000
150 000
0.000 365 000 0 000 366.000
1
Sys#12
Sal in ity
(Data Group E Boundary Conditions)
1 0
1.0
1
3
OCN :
OCNBCOOl
Sys# 12
0
000
140.000
30 80
150.000
30.80 365.000 0.000 366.000
1
Sys#13 Coliforms
(Data Group E Boundary Conditions)
1.0
1.0
1
3
OCN :
OCNBCOOl
Sys# 13
0
000
140.000
0.000
150.000
0.000 365.000 0.000 366.000
0
PS(t) Sys#l
(Data 6
lock F.l Waste Loads for Point Source)
0
PS(t) Sys#2
0
PS(t) Sys#3
0
PS(t) Sys#4
0
PS(t) Sys#5
0
PS(t) Sys#6
0
PS(t) Sys#7
0
PS(t) Sys#8
0
PS(t) Sys#9
0
PS(t) Sys#10
0
PS(t) Sys#11
0
PS(t) Sys#12
0
PS(t) Sys#13
0
0
(Data Group F.2 ..NPS Loads)
17
6
FF(xyz)
- G wasp4g01.dat 05/12/90 21.20:28
ZOO IX
1
1.FSI04 2
l.TMPSG 3 l.TMPFN 4 1.
KESG
5
l.KEFN
6
1.FNH4 7 3.35FP04 8 3.35
S001D
9
3.35MACR0 12
1.SHL1 13 1.SHL2 14 1.
SHL3
15
1.GWC1
21
1.GWC2 22 l.GWSW 23 1.
FN03
24
1.
1
of 6
1 \ 1 2 1 3 * 4 1 5 1 6 1 7 1 &
A-34
-------
Filename: WBR 11 INP WASP4 File for Beacons Reach Marina DO Calibration
Z001X
1
1
OOOFS104
2
143 750TMPSG
3
1 OOOTMPFN
4
1 000
KESG
5
1.
OOOKEFN
6
1.000FNH4
7
15 140FP04
8
2 100
SOD 1D
9
1
OOOMACRO
12
0 000SHL1
13
0 OOOSHL2
14
0 000
SHL3
15
0
000GWC1
21
0.201GWC2
22
1 262GWSW
23
0 000
FN03
24
4.
570
2
of
6
Z001X
1
1
OOOFS104
2
143 750TMPSG
3
1 OOOTMPFN
4
1.000
KESG
5
1
OOOKEFN
6
1.000FNH4
7
15 140FP04
8
2.100
S0D10
9
1
OOOMACRO
12
0 000SHL1
13
0.000SHL2
14
0 000
SHL3
15
0
000GWC1
21
0 000GWC2
22
0.OOOGWSW
23
0.000
FN03
24
4
570
3
of
6
Z001X
1
1
OOOFS104
2
143 750TMPSG
3
1 OOOTMPFN
4
1 000
KESG
5
1
OOOKEFN
6
1 000FNH4
7
15 140FP04
8
2 100
S001D
9
1
OOOMACRO
12
0 000SHL1
13
0.000SHL2
14
0.000
SHL3
15
0
000GWC1
21
0 803GUC2
22
5.047GWSW
23
0.000
FN03
24
4
.570
4
of
6
ZOOIX
1
1
.OOOFS104
2
143.750TMPSG
3
1 OOOTMPFN
4
1.000
KESG
5
1
.OOOKEFN
6
1.000FNH4
7
15 140FP04
8
2.100
S0D1D
9
1
OOOMACRO
12
0 000SHL1
13
0.000SHL2
14
0.000
SHL3
15
0
000GWC1
21
0.201GWC2
22
1.262GWSW
23
0 000
FN03
24
4
570
5
of
6
ZOOIX
1
1
OOOFS104
2
143.750TMPSG
3
1.OOOTMPFN
4
1 000
KESG
5
1
.OOOKEFN
6
1.000FNH4
7
15 140FP04
8
2. 100
S0D10
9
1
.OOOMACRO
12
0.OOOSHL1
13
0.000SHL2
14
0.000
SHL3
15
0
.000GWC1
21
0.134GWC2
22
0.841GWSW
23
0.000
FN03
24
4
.570
6
of
6
ZOOIX
1
1
.OOOFS104
2
0 OOOTMPSG
3
1.OOOTMPFN
4
1.000
KESG
5
1
.OOOKEFN
6
1 000FNH4
7
0.000FP04
8
0.000
SOD1D
9
0
.OOOMACRO
12
0.000SHL1
13
0.000SHL2
14
0.000
SHL3
15
0
.000GUC1
21
0.000GWC2
22
0 OOOGWSW
23
0.000
FN03
24
0
.000
Nosys= 14
Const-H
GLOBAL
0
NH3-N
1
group
#1-1
10
K12C
11 0
250
K12T
12
1.080
KNIT
13 2.
000
ATMNH3
14
1.400
GWFLOW
15 0.000000
GWNH3
15
0 488
Z1CRB
17 2
500
Z1CDW
18
0.450
NCRBZ1
19 0.
167
NCRBM
39
0.082
N03+N02-N
1
group
#1-2
5
K20C
21 0
090
K20T
22
1.045
KN03
23 0.
100
ATMN03
24
1 865
GWN03
25 3
065
0-P04
1
all param
4
ATMDIP
31 0.
083
GWDIP
32
0.005
PCRBZ1
33 ¦ 0.
013
PCRBM
40
0.004
Phyt#1
2
group#l-4
15
WS1
28 0.
100
K1C
41
1 000
KIT
42 1.
066
LGHTS
43
2.000
PHIMX
44 720.
000
XKC
45
0.017
CCHL1
46 80.
000
IS1
47
300.000
KMNG1
48 " 0.
007
KMPG1
49
0.001
K1RC
50 0.
300
K1RT
51
1.080
TUL1
112 40.
000
T0PT1
113
24.000
TLL1
114 0.
.000
group#2-4
10
KID
52 1
.000
K1G
53
0.800
U1
54 1
000
KPZDC
55
0.000
KPZDT
56 1
000
PCRB1
57
0.030
NCRB1
58 0
.155
KMPHYT
59
1.000
SICRB1
89 0
.300
KMSG1
111
0.0140
-------
Filename: WBR_11. INP WASP4 File for Beacons Reach Manna DO Calibration
+ ! + 2 + 3 + 4 + 5 + 5 + 7 + g
CBOO
1
group#l-5
7
KDC
71
0 300
KDT
72
1.047
KDSC
73
0 000
KDST
74
1.000
KBOD
75
0.500
GWB0D
76
1.800
OCRB
81
2 670
Diss 02
1
group #1-6
6
K4C
26
0.000
K4T
27
1.000
K2
82
0 000
GWOXY
83
4.000
KM IRC
109
0 000
KM1RT
110
1.000
Org-N
1
group #1-7
7
K71C
91
0.075
K71T
92
1 080
KONDC
93
0 000
KONDT
94
1.000
FON
95
0.700
ATM_0N
96
2.801
GW_ON
97
0.091
Org-P
1
group #1-8
7
K83C
100
0.220
K83T
101
1.080
KOPDC
102
0.000
KOPDT
103
1.000
FOP
104
0.600
ATM_0P
105
0.247
GW OP
106
0.005
Phyt#2
2
group #1-9
12
WS2
29
0.200
CCHL2
34
65.000
IS 2
35
75.000
K2C
36
1.750
K2D
37
0.020
W2
38
1.000
<2RC
60
0.150
K2RT
61
1.045
K2T
62
1.066
TUL2
115
37.000
T0PT2
116
3.000
TLL2
117
0.000
group#2-9
6
KMNG2
63
0.007
KMPG2
64
0.0015
NCRB2
65
0.155
PCRB2
66
0.030
SICRB2
90
0.462
KMSG2
107
0.028
Phyt#3
2
group #1-1
10
CLLCRB3
20
0.000
WS3
30
0.000
CCHL3
67
1.000
I S3
68
0.000
K3C
69
0.000
K30
70
0.000
W3
77
0 000
K3RC
78
0.000
K3RT
79
1 000
K3T
80
1.000
group#2-10
6
KMNG3
84
0.000
KMPG3
85
0 000
NCRB3
86
0.000
PCRB3
87
0.000
SICRB3
98
0.000
KMSG3
108
0.000
S104
1
group #1-1
1
GWSI
88
20.000
Sa 11nlty
1
group #1-1
1
GWSALT
99
0.000
Co 11forms
0
21
FF(t) WASP Data Group
I FF Time-
- I wasp4i01.dat 10/15/90
1
14 FT#01
-Temp #1
PR/TRB
tempxxxx.
ini 5 8
FF time #
1 Temp#l
20 70
1.00
20.70
15.00
20.70
45.00
20.70
75.00
20.70
106.00
20.70
136.00
20.70
167.00
20.70
197.00
20.70
228.00
20.70
259.00
20.70
289.00
20.70
320.00
20.70
350 00
20.70
366.00
2
14 FT#02
-Temp #2
(deg C)
20.70
1.00
20.70
15 00
20.70
45.00
20.70
75.00
20.70
106.00
20.70
136.00
20.70
167.00
20.70
197.00
20.70
228.00
20.70
259.00
20.70
289.00
20.70
320.00
20.70
350.00
20.70
366.00
3
14 FT#03-Temp #2
(deg C)
20.70
1.00
20.70
15 00
20.70
45.00
20.70
75.00
20.70
106.00
20.70
136.00
20.70
167.00
20.70
197.00
20.70
228.00
20.70
259.00
20.70
289.00
20.70
320.00
20 70
350.00
20.70
366.00
+ ! + 2 + 3 + 4 —
-5 6 * 7 8
A-36
-------
Filename' WBR_11 1NP WASP4 File for Beacons Reach Manna DO Calibration
„ [ + 2 + 3 + 4 + 5 + 6 * 7 +-
TEMP 4
14 FT#04-Temp #2
(deg C)
20.70
1.00
20 70
15 00
20 70
45.00
20.70
75.00
20 70
106 00
20.70
136 00
20.70
167 00
20 70
197.00
20.70
223.00
20 70
259 00
20.70
289 00
20.70
320.00
20.70
350.00
20.70
366.00
SUN 5
14 FT #05-
-solar radiation (
langleys/day) assumes
25% cloud
cover
365
1.00
380
15.00
454
45.00
535
75 00
606
106 00
479
136 00
479
167.00
644
197 00
608
228 00
562
259.00
485
289.00
403
320 00
357
350 00
365
366 00
PHOTO 6
14 FT#06-photoperiod (fraction of day which is
sunny)
0.440
1.00
0.444
15 00
0.456
45 00
0 497
75.00
0.534
106 00
0.438
136.00
0.438
167.00
0.568
197.00
0 546
228.00
0 512
259.00
0.478
289.00
0.452
320.00
0 436
350 00
0.440
366.00
WIND 7
14 FT# 0 7
-wind vel
ocity (m/sec)
0.000
1.00
0.000
15.00
0.000
45.00
0 000
75.00
0 000
106.00
0.000
136.00
0.000
167.00
0.000
197.00
0.000
228 00
0 000
259.00
0.000
289.00
0.000
320 00
0.000
350 00
0.000
366.00
KE#01 8
14 FT#08'
-Ke #1
(1/meter)
2 430
1.00
2 430
15 00
2.430
45.00
2 430
75.00
2.430
106.00
2 430
136 00
2 430
167.00
2 430
197.00
2.430
228 00
2.430
259.00
2.430
289.00
2 430
320 00
2.430
350.00
2.430
366.00
KE#02 9
14 FT#09'
-Ke #2
(1/meter)
2.430
1.00
2.430
15.00
2 430
45.00
2.430
75.00
2.430
106.00
2.430
136.00
2 430
167 00
2 430
197.00
2 430
228 00
2.430
259.00
2 430
289.00
2 430
320.00
2 430
350 00
2.430
366.00
KE#03 10
14 FT# 10-
-Ke #3
(1/meter)
2.430
1.00
2 430
15.00
2.430
45.00
2.430
75.00
2.430
106.00
2.430
136.00
2 430
167.00
2.430
197.00
2.430
228.00
2.430
259.00
2.430
289.00
2 430
320.00
2 430
350 00
2.430
366.00
o
UJ
14 FT#11
-Ke #4
(1/meter)
2 430
1.00
2.430
15.00
2 430
45.00
2 430
75.00
2 430
106.00
2.430
136.00
2.430
167.00
2.430
197.00
2.430
228.00
2.430
259.00
2.430
289.00
2.430
320.00
2.430
350.00
2.430
366.00
KE#05 12
14 FT#12
-Ke #5
(1/meter)
2.430
1.00
2.430
15 00
2 430
45.00
2.430
75.00
2.430
106.00
2.430
136.00
2.430
167 00
2.430
197.00
2.430
228 00
2.430
259.00
2 430
289 00
2.430
320.00
2.430
350 00
2.430
366 00
TFNH4 13
14 FT#13
-NH4 flux
(theta =
1.
08)
2.301
1.00
2 301
15.00
2.301
45.00
2.301
75.00
2 301
106.00
2.301
136.00
2.381
167.00
2.455
197.00
2.455
228.00
2.245
259.00
2.245
289.00
2.245
320.00
2 245
350.00
2.301
366.00
TFP04 14
14 FT#14•
-P04 flux
(theta =
1.
08)
2 301
1.00
2.301
15.00
2.301
45.00
2.301
75 00
2 301
106.00
2.301
136.00
2.381
167 00
2.455
197.00
2 455
228.00
2.245
259.00
2.245
289 00
2.245
320.00
2.245
350.00
2.301
366.00
MACRO 15
14 F T#15
-Macrophy
1.000
1.00
1.000
15.00
1.000
45.00
1 000
75.00
1.000
106.00
1.000
136.00
1.000
167.00
1.000
197 00
1.000
228.00
1.000
259.00
1 000
289.00
1.000
320.00
1 000
350.00
- 1 000
366.00
SHl_#l 16
14 FT# 16-
-Shell #1
1.000
1.00
1.000
15.00
1.000
45.00
1.000
75.00
1.000
106.00
1.000
136.00
1.000
167.00
1.000
197.00
1.000
228.00
1.000
259.00
1.000
289 00
1.000
320.00
1.000
350.00
1.000
366.00
TFK1G 17
14 FT#17
-time dependent grazing rate
multipl ier
(theta=l.00)
1.000
1 00
1.000
15 00
1.000
45.00
1.000
75.00
1.000
106 00
1.000
136.00
1 000
167.00
1.000
197.00
1.000
228 00
1.000
259.00
1 000
289.00
1 000
320.00
A-37
-------
Filename. WBR_11 INP WASP4 File for Beacons Reach Marina DO Calibration
+ ! + 2 + 3 + 4 + 5 + 6 + 7 * 8
1.000
350
00
1.000
366 00
18
14
FT# 18-
-SOD flux
(theta =
1.047)
1 644
1
00
1 644
15.00
1.644
45.00
1.644
75
1 644
106.
00
1 644
136.00
1.678
167 00
1 709
197
1 709
228.
00
1.620
259.00
1.620
289.00
1.620
320
1 620
350
00
1.620
- 366.00
19
14
FT#19-Zoop 1.
modified
73 micron
net size
20000
1.
.00
20000
15.00
20000
45.00
20000
75
20000
106.
.00
20000
136.00
40000
167.00
80000
197
20000
228
00
20000
259 00
20000
289.00
20000
320
20000
350.
.00
20000
366.00
20
14
FT #20-
- S 104 flux (theta
- 1 08)
2.301
1.
.00
2.301
15.00
2.301
45.00
2.301
75
2.301
106.
.00
2 301
136.00
2.381
167.00
2 455
197
2.455
228.
.00
2.245
259.00
2.245
289.00
2.245
320
2 245
350
.00
2 301
366.00
21
14
FT #21-
-N03 flux
(theta =
1.08)
2.301
1
00
2.301
15.00
2.301
45.00
2 301
75
2 301
106
.00
2 301
136.00
2.381
167 00
2.455
197
2.455
228
00
2.245
259 00
2.245
289.00
2 245
320
2 245
350
00
2.301
366 00
TFSOO
ZOOPL
TFSIO
TFN03
NH3_N (mg/L) 5 1. 9.99E+03 J:INITIAL CONC.
SG01 0.0700 1.000 S602 0.0700 1.000 SG03 0.0700 1.000
SG04 0.0700 1.000 SG05 0 0700 1.000 SG06 0.0000 0.000
N02_N (mg/L) 5 1. 9999. J INITIAL CONC.
SG01 0.0200 1 000 SG02 0.0200 1.000 SG03 0.0200 1.000
5G04 0.0200 1 000 SG05 0.0200 1.000 SG06 0.0000 0.000
0P04 (mg/L) 5 1. 9999. J:INITIAL CONC.
SGOl 0.0420 0.600 SG02 0 0420 0.600 SG03 0.0420 0.600
SG04 0.0420 0.600 SG05 0.0420 0.600 SG06 0.0000 0 000
Phytl (mg/L) 4 I. 9999. J:INITIAL CONC.
SGOl 14 000 0.000 SG02 14.000 0.000 SG03 14 000 0.000
SG04 14.000 0.000 SG05 14.000 0.000 SG06 0 0000 0.000
CBOD (mg/L) 4 1. 9999. J:INITIAL CONC.
SGOl 2.4700 0.500 SG02 2.4700 0.500 SG03 2 4700 0.500
SG04 2.4700 0.500 SG05 2.4700 0.500 SG06 0.0000 0.000
DO (mg/L) 5 1. 9999 J:INITIAL CONC.
SGOl
5 0000
1.000
SG02
5.0000
1
000
SG03 5.0000 1.000
SG04
5.0000
1.000
SG05
5.0000
1
000
SG06 0 0000 0.000
OrgN
(mg/L)
5
I.
9999. J: INITIAL CONC
SGOl
0.0420
0.500
SG02
0 0420
0
500
SG03 0.0420 0.500
SG04
0 0420
0.500
SG05
0.0420
0.
500
SG06 0.0000 0.000
OrgP
(mg/L)
4
1.
9999. J.INITIAL CONC
SGOl
0.0280
0.700
SG02
0.0280
0
700
SG03 0.0280 0 700
SG04
0.0280
0.700
SG05
0.0280
0.
700
SG06 0.0000 0.000
P hy 12
(mg/L)
4
1.
9999. J: INITIAL CONC
SGOl
0.0000
0.000
SG02
0.0000
0.
000
SG03 0.0000 0.000
SG04
0.0000
0.000
SG05
0.0001
0
000
SG06 0.0000 0.000
Phyt3
(mg/L)
4
1.
9999. J.INITIAL CONC
SGOl
0 0000
0.000
SG02
0.0000
0.
000
SG03 0.0000 0.000
SG04
0.0000
0.000
SG05
0.0001
0.
000
SG06 0 0000 0.000
S i 04
(mg/L)
3
1.
9.99E+06 J:INITIAL CONC
SGOl
0.0000
0.300
SG02
0 0000
0.
.300
SG03 0.0000 0.300
SG04
0.0000
0.300
SG05
0.0001
0
.300
SG06 0.0000 0.000
Salinity (mg/L)
5
1.
9 99E+03 J:INITIAL CONC
SGOl
30.8000
1.000
SG02
30.8000
1.
.000
SG03 30.8000 1.000
SG04
30.8000
1.000
SG05
30.8000
1.
,000
SG06 0.0000 0 000
Coliforms (MPN/lOOml)
5
1.
9.99E+10 J:INITIAL CONC
SGOl
0.0000
1.000
SG02
0.0000
1.
.000
SG03 0.0000 1.000
SG04
0.0000
1.000
SG05
0.0001
1
.000
SG06 0.0000 0.000
-------
2.3 Guil Harbor Marina
2.3.1 DYNHYD5 Dye Hydrodynamic File for Guil Harbor Marina
-------
Filename' GULL.INP DYNHYD5 File for Sull Harbor Marina Oye Calibration
+ 1 + 2 3 + 4 + 5 + 6 + 7 * 8
DYNHYD5 -Gull Harbor Marina
GULL INP - Hydrodynamics for dye simulation Oct 24, 1988 to Oct 26, 1S88
***** Data Group A. PROGRAM CONTROL DATA ************************
3 2 0000 6.00000 5 1988/10/23 00.00 1988/10/27 00 00
***** Data Group B- OUTPUT CONTROL DATA *************************
1988/10/23 01 00 1.0 - 1 3 0
1
1 2 3
***** Data Group C. SUMMARY CONTROL DATA ************************
1 1988/10/23 00.00 25.0000 150
Gull HYD
***** Data Group D: JUNCTION DATA *******************************
1
0 001
698
-1 5
44
33
1
2
0 001
1023
-1 5
35
71
1 2
3
0.001
1245
-1 5
36
103
2
** + *¦*
Data Group E:
CHANNEL
DATA ******
***************************
. 1
39
18
1.5
0 030
0.001
1 2
2
32
33
1.5
0.030
0.001
2 3
***** Data Group F 1: CONSTANT INFLOWS (m/sec) ****************************
0
***** Data Group F 2: VARIA8LE INFLOWS (m/sec) - Daily Flows **************
0
** Data Group G: SEAWARD BOUNDARY DATA (m) - Variable Tide
1
3 1 22
: 15
0.0
0.0000
0.0
1.0000
1988/10/22
16 30
0 300
1988/10/22
21.30
-0.300
1988/10/23
04.45
0.300
1988/10/23
10.45
-0 300
1988/10/23
18:30
0.300
1988/10/24
01:30
-0.300
1988/10/24
06:30
0.300
1988/10/24
11:30
-0 300
1988/10/24
16:30
0.300
1988/10/24
21-30
-0.300
1988/10/25
04 45
0.300
1988/10/25
10:45
-0.300
1988/10/25
17-30
0.300
1988/10/25 22:45
-0.300
1988/10/26
05-30
0.300
1988/10/26
11:30
-0.300
1988/10/26
16:30
0.300
1988/10/26 21:30
-0.300
1988/10/27
04:45
0.300
1988/10/27
10:45
-0.300
1988/10/27
17:30
0.300
1988/10/14
22:45
-0.300
** Data Group H: WIND DATA (m/sec) mean monthly winds at Gull Harbor **
0
-------
2.3.2 DYNHYD5 DO Hydrodynamic File for Gull Harbor Marina
-------
Filename HGH_ll INP DYNHYD5 File for Gull Harbor Marina 00 Calibration
+. 1 + 2 + 3 + 4 + 5 + 6 + 7 * 8
QYNHYD5 - Gull Harbor Marina
HGH_11.INP - Hydrodynamics for water quality simulation May 20,to May 27, 1988
***** Data Group A PROGRAM CONTROL DATA ************************
3 2 0000 6.00000 5 1988/05/20 00 00 1988/05/28 00 00
***** Data Group B- OUTPUT CONTROL DATA *************************
1988/05/20 01:00 1.0 *1 3 0
1
1 2 3
***** Data Group C. SUMMARY CONTROL DATA ************************
1 1988/05/20 00:00 25.0000 150
HGH_11.HYD
***** Data Group D: JUNCTION DATA *******************************
1
0.001
698
-1.5
44
33
1
2
0.001
1023
-1.5
35
71
1 2
3
0.001
1245
-1.5
36
103
2
*****
Data Group E:
CHANNEL
DATA ********************************
1
39
18
1 5
0.030
0.001
1 2
2
32
33
1.5
0.030
0.001
2 3
***** Data Group F.l: CONSTANT INFLOWS (m/sec) ****************************
0
***** Data Group F.2: VARIABLE INFLOWS (m/sec) - Daily Flows **************
0
Data Group G
1
: SEAWARD
BOUNDARY
DATA (m) -
Variable
T ide
1
3 1 34
15
0.0
0 0000
0.0
1.0000
1988/05/19
22:24
0.715
1988/05/20
04:53
0.580
1988/05/20
10.56
0.539
1988/05/20
16:43
0 109
1988/05/20
23.09
0.662
1988/05/21
05:39
0.081
1988/05/21
11:47
0.522
1988/05/21
17:33
0.139
1988/05/21
23:57
0.613
1988/05/22
06:25
0.094
1988/05/22
12 39
0.515
1988/05/22
18.29
0.154
1988/05/23
00:47
0.569
1988/05/23
07.12
0.096
1988/05/23
13-32
0.518
1988/05/23
19:29
0.152
1988/05/24
01:39
0.534
1988/05/24
07:58
0.086
1988/05/24
14:24
0.533
1988/05/24
20:28
0.132
1988/05/25
02:32
0.507
1988/05/25
08:44
0.066
1988/05/25
15:13
0.556
1988/05/25
21:25
0.098
1988/05/25
03 24
0.489
1988/05/26
09:27
0.038
1988/05/26
15:59
0.588
1988/05/26
22:17
0.057
1988/05/26
04:12
0.479
1988/05/27
10:10
0.008
1988/05/27
16:42
0.625
1988/05/27
23:05
0.016
1988/05/27
04:59
0.478
1988/05/28
10:52
-0.022
** Data Group H: WIND DATA (m/sec) mean monthly winds at Gull Harbor **
0
-------
2.3.3 WASP4 Dye Water Quality File for Gull Harbor Marina
-------
Filename: GULLDYE INP WASP4 File for Gull Harbor Marina Dye Calibration
* 1 + 2 * 3 + 4 + 5 + 6 + / * 8
GULLDYE.INP - Gull Harbor WASP4 Model Dye Release Simulation 10/23/88 10/27/88
Oye Results are used for model Calibration
I INTY AOFC zyr/rrm/dd hhirm A MODEL OPTIONS
I 0 0 0 1988/10/24 1200 113 0.
KS IM
NSEG
N5YS
ICFL
0
4
13
1
3
1
2
3
1
180.
301 00
3
30.
365.
1
1
1
1
1
+
+
*
3
1
000
1
27.0
40
2
0
.90E+00
0.
1
50.0
39
2
0.
. 90E+00
0.
1
50.0
32
2
0
. 30E+00
0.
1
1
1
1
2
0
720 0
1
.0000
1
.0000
1
1
0.0
86400
1 1
+ *
1.00
0 1
O.OOE+OO
1 2
0 OOE+OO
2 3
366
1 1
640
1 0
86400
1 1
+ * +
(surface water)
EXCHANGES
640.
640.
1
1
1
4 1
4 1
0 3
SUMRY2.OUT (GULL.HYD)
640.
1
1 1
1 0
1
*
+ *
+ *
C: VOLUMES
1047
0.0
1.0
1.0
1.0
1535
0.0
1.0
1.0
1.0
1868
0.0
1.0
1.0
1.0
6622
0.0
1.0
1.0
1.0
*
+ *
+ *
0: FLOWS
(Data Block 0.2 Field One Flows)
3
1.0 1.0
1.0 1.157E-05
1047
1047
0 0
1047
0.0
1 1
1
1.0
1 6
0 000
0.000
1
1.0
1 6
0.000
0.000
1
1.0
1 6
0.000
0.000
1
1.0
F#2 — NINQ(2) SCALQ=1 C0NVQ= 1
F#3 —NINQ(3) SCALQ=1 C0NVQ= m/day * 1/86400
F#3 — N0QS(3,ni) --> number of segment pairs
1 4 1535 2 4 1858 3 4
F#3 —NBRKQ(4,ni) number of time breaks
0 0 0.0 540.0
1.0 1.157E-05 F#4 --NINQ(3) SCALQ=1 C0NVQ= in/day * 1/86400
F#4 —NOQS(3,ni) --> number of segment pairs
1 4 1535 2 4 1858 3 4
F#4—NBRKQ(5,ni) number of time breaks
0.0 0.0 540.0
1.0 1.157E-05 F#S —N1 NQ{3) SCALQ=1 C0NVQ= in/day * 1/86400
F#5 --N0QS(5,ni) --> number of segment pairs
1 4 1535 2 4 1858 3 4
F#5—NBRKQ{5,ni) --> number of time breaks
0.0 0.0 640.0
1111111110 1
Sys#l NH3 (Data Group E Boundary Conditions)
1.0
0CN :
284.000
285.397
1.0
0CN :
284.000
285.397
1.0
0CN ¦
284.000
285.397
1 0
OCNBCOOl Sys# 1
0.000 285.354 000.00 285.355 0.000 285.396
000.000 365.000
Sys#2 N03 (Data Group E Boundary Conditions)
OCNBCOOl Sys# 2
0.000 285.354 000.00 285.355 0.000 285 396
000.000 365.000
Sys#3 0P04 (Data Group E Boundary Conditions)
OCNBCOOl Sys# 3
0.000 285.354 000.00 285 355 0.000 285 396
000.000 365 000
Sys#4 Phyt#l (Data Group E Boundary Conditions)
-------
Filename GULLDYE 1NP WASP4 File for Gull Harbor Marina Dye Calibration
+ ! + 2 + 3 * 4 + 5 + 6 + 7 + 8
1
1
1
1
1 6
0 000
0.000
1
1.0
6
0.000
0.000
1
1.0
6
.000
.000
1
1.0
6
0.000
0.000
1
1.0
6
0.000
0.000
1
1.0
1 6
0.000
0 000
1
1 0
1 6
0.000
0.000
1
1.0
1 6
0.000
0.000
. 1
1.0
1 6
0.000
0.000
1
1.0
1 6
0.000
0.000
2
0 100E+01
2 6
0.000
0 000
3 6
0.000
0.000
2
0.100E+01
2 6
0.000
0.000
3 6
0.000
0.000
2
0.100E+01
2 6
0.000
0 000
OCN
284 000
285 397
1 0
OCN .
284.000
285 397
1 0
OCN :
284.000
285.397
1 0
OCN :
284.000
285.397
1.0
OCN :
284 000
285.397
1 0
OCN .
284.000
285.397
1 0
OCN :
284 000
285.397
1 0
OCN :
284 000
285.397
1.0
OCN :
284.000
285 397
OCNBCOOl Sys# 4
0 000 285 354 000 00 285 355 0.000 285 396
000.000 365 000
Sys#5 C800 (Data Group E Boundary Conditions)
- 0CNBC001 Sys# 5
0.000 285 354 000.00 285.355 0 000 285.396
000.000 365 000
Sys#6 DO (Data Group E Boundary Conditions)
OCNBCOOl Sys# 6
0 000 285.354 000 00 285.355 0 000 285.396
000 000 365 000
Sys#7 OrgN (Data Group E Boundary Conditions)
OCNBCOOl Sys# 7
0.000 285 354 000 00 285.355 0.000 285.396
000 000 365.000
Sys#8 OrgP (Data Group E Boundary Conditions)
OCNBCOOl Sys# 8
0 000 285 354 000.00 285.355 0.000 285 396
000 000 365.000
Sys#9 Phyt#2 (Data Group E Boundary Conditions)
OCNBCOOl Sys# 9
0.000 285.354 000.00 285 355 0.000 285 396
000.000 365.000
Sys#10 Phyt#2 (Data Group E Boundary Conditions)
OCNBCOOl Sys# 10
0.000 285.354 000.00 285 355 0.000 285.396
000 000 365.000
Sys#11 Si04 (Data Group E Boundary Conditions)
OCNBCOOl Sys# 11
0.000 285.354 000.00 285.355 0.000 285.396
000.000 365 000
Sys#12 Salinity (Data Group E Boundary Conditions)
OCNBCOOl Sys# 12
0 000 285.354 0 000 285.355 0 000 285.396
0.000 365.000
Sys#13 Coliforms (Data Group E Boundary Conditions)
1.0
OCN :
284.000 0.000
295 720 000.000
PS(t) Sys#l
0.100E+01 PS(t)
Seg 002 Dye Input
284.000 0.000
295.720 000 000
Seg 003 Dye Input
284.000 0.000
295.720 000.000
PS(t) Sys#2
0.100E+01 PS(t)
Seg 002 Dye Input
284.000 - 0.000
285.397 000 000
Seg 003 Dye Input
284.000 0 000
285.397' 000.000
PS(t) Sys#3
0 100E+01 PS(t)
Seg 002 Dye Input
284.000 0.000
285.397 000.000
Sys# 13
00 000 298.698
00.000 298.719
OCNBCOOl
298.697
365.000
(Data Block F.l Waste Loads for Point Source)
:System # 1 Dye Kg/Day Scal^/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
298.677 19.200 298.698 00.000 298.719
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
298.677 19.200 298.698 00.000 298.719
365.000
(Data Block F.l Waste Loads for Point Source)
:System # 2 N03 Kg/Day Scale/conv fct
OCNBCOOl INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285 355 0.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 0.000 285.396
365.000
(Data Block F.l Waste Loads for Point Source)
¦System # 3 0P04 Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285 354 0.00 285.355 1.000 285.396
365.000
-------
Filename' GULLDYE.INP WASP4 File for Gull Harbor Marina Dye Calibration
„ ! + 2 + 3 + 4 + 5 + 6 + 7 + 8
3 6
Seg 003
Dye Input
0 000
284.000
0.000
0 000
285 397
000.000
2
PS(t) Sys#4
0.
100E+01
0.100E+01
PS(t)
2 6
Seg 002
Dye Input
0 000
284.000
0 000
0 000
285.397
000.000
3 6
Seg 003
Dye Input
0 000
284.000
" 0.000
0 000
285.397
000.000
2
PS(t) Sys#5
0
100E+01
O
+
UJ
o
o
o
PS(t)
2 6
Seg 002
Dye Input
0 000
284 000
0.000
0.000
285.397
000.000
3 6
Seg 003
Dye Input
0.000
284.000
0.000
0.000
285.397
000.000
2
PS(t) Sys#6
0.
100E+01
0.100E+01
PS(t)
2 6
Seg 002
Dye Input
0 000
284.000
0.000
0.000
285.397
000 000
3 6
Seg 003
Dye Input
0.000
284 000
0.000
0 000
285.397
000.000
2
PS(t) Sys#7
0.
100E+01
0.100E+01
PS(t)
2 6
Seg 002
Dye Input
0 000
284.000
0.000
0.000
285.397
000.000
3 6
Seg 003
Dye Input
0.000
284.000
0.000
0.000
285.397
000.000
2
PS(t) Sys#8
0.
100E+01
0 100E+01
PS(t)
2 6
Seg 002
Dye Input
0.000
284.000
0.000
0.000
285.397
000 000
3 6
Seg 003
Dye Input
0.000
284.000
0.000
0 000
285.397
000.000
2
PS(t) Sys#9
0
100E+01
0 100E+01
PS(t)
2 6
Seg 002
Oye Input
0.000
284.000
0 000
0.000
285.397
000.000
3 6
Seg 003
Dye Input
0.000
284.000
0.000
0.000
285.397
000.000
2
PS(t) Sys#10
0.
. 100E+01
0.100E+01
PS(t)
2 6
Seg 002
Dye Input
0.000
284.000
0.000
0.000
285.397
000.000
3 6
Seg 003
Dye Input
0 000
284.000
0.000
0.000
285.397
000.000
2
PS(t) Sys#ll
0
100E+01
0 100E+01
PS(t)
2 6
Seg 002
Dye Input
0.000
284.000
0.000
0 000
285.397
000.000
3 6
Seg 003
Dye Input
0 000
284.000
0.000
0.000
285.397
000.000
2
PS(t) Sys#12
0
. 100E+01
0.100E+01
PS(t)
0CNBC001 INE 5 17 Sys# 1 0CN88RAT INE
285 354 0.00 285 355 1.000 285 396
365 000
(Data Block F.l Waste Loads for Point Source)
System # 4 Phytl Kg/Day Scale/conv fct
0CNBC001.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285 355 1.000 285.396
365 000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT INE
285 354 0.00 285.355 1 000 285 396
365.000
(Data Block F 1 Waste Loads for Point Source)
System # 5 CBOO Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 OCN88RAT INE
285 354 0.00 285.355 1 000 285 396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1 000 235.396
365 000
(Data Block F.l Waste Loads for Point Source)
System # 6 DO Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285 354 0.00 285.355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 OCN88RAT.INE
285.354 0 00 285.355 1.000 285.396
365 000
(Data Block F.l Waste Loads for Point Source)
System # 7 OrgN Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0 00 285 355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285 354 0 00 285.355 1.000 285.396
365.000
(Data Block F.l Waste Loads for Point Source)
System # 8 OrgP Kg/Oay Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT INE
285 354 0.00 285.355 1.000 285.396
365.000
OCNBCOOl. INE 5 17 Sys# 1 0CN88RAT. INE
285.354 0.00 285.355 1.000 285.396
365.000
(Data Block F 1 Waste Loads for Point Source)
System # 9 Phyt2 Kg/Day Scale/conv fct
OCNBCOOl,INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285 354 0 00 285.355 1.000 285.396
365.000
(Data Block F.l Waste Loads for Point Source)
System # 10 Phyt3 Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT INE
285.354 0.00 285.355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT. INE
285.354 0.00 285.355 1.000 285.396
365.000
(Data Block F.l Waste Loads for Point Source)
System # 11 Sl04 Kg/Day Scale/conv fct
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285.396
365.000
OCNBCOOl.INE 5 17 Sys# 1 0CN88RAT.INE
285.354 0.00 285.355 1.000 285 396
365.000
(Data Block F.l Waste Loads for Point Source)
:System # 12 Salin Kg/Day Scale/conv fct
-------
Filename GULLDYE INP WASP4 File for Gull Harbor Marina Dye Calibration
+ 1 + 2 + 3 + 4 ~ 5 + 6 * 7 + 8
2 6
0 000
0 000
3 6
0 000
0.000
2
0.100E+01
2 6
0.000
0.000
3 6
0.000
0.000
0 0
17
1
5
9
15
24
1
1
5
9
15
24
2
1
5
9
15
24
3
1
5
9
15
24
4
1
5
9
15
24
Z001X
KESG
S0D1D
SHL3
FN03
ZOO IX
KESG
S0D10
SHL3
FN03
Z001X
KESG
S0010
SHL3
FN03
Z001X
KESG
S0D10
SHL3
FN03
Z001X
KESG
S0D10
SHL3
FN03
Seg 002 Oye Input
284.000 0.000
300.000 0.000
Seg 002 Dye Input
284 000 0.000
300.000 000.000
PS(t) Sys#13
0 100E+01 PS(t)
Seg 002
284.000
285.397
Seg 003
284.000
285.397
of
of
of
GLOBAL
NH3-N
group #1-1
K12C
KNIT
GWFLOW
Z1CR8
NCRBZ1
N03+N02-N
group #1-2
K20C
KN03
GWN03
0-P04
all param
ATMDIP
PCR8Z1
Phyt#l
group#l-4
WS1
KIT
' PHIMX
CCHL1
1 FS104
1 KEFN
1 MACRO
1 GWC1
1.
2
6
12
21
of
1.000FS104
1.000KEFN
0.600MACR0
0 000GWC1
4.570
4
1.OOOFSI04
1.000KEFN
0.600MACR0
0.OOOGWCl
4.570
4
1.OOOFS104
1.OOOKEFN
0.600MACR0
0.OOOGWCl
4.570
4
1.OOOFS104
1.OOOKEFN
0.600MACR0
0.OOOGWCl
4.570
Nosys= 14
0
2
6
12
21
2
6
12
21
2
6
12
21
2
6
12
21
1
10
11
13
15
17
19
1
5
21
23
25
1
4
31
33
2
12
28
42
44
46
0.250
2.000
0.000000
2.500
0.167
045
100
065
Sys# 12
298 630 019 200 298 704
365 000
Sys# 12
298 680 019 200 298.704
-365.000
(Data Block F.l Waste Loads for Point Source)
Kg/Day
Dye Input
0 000
000.000
Dye Input
0.000
000.000
:System # 13 Co 11
DC NBC 001.1NE
285 354 0.00
365 000
0CNBC001 INE
00 000 298.728
00 000 295 728
Scale/conv fct
5 17 Sys# 1 OCN88RAT.INE
285.355
1.000 285.396
5 17 Sys# 1 0CN88RAT.INE
0 00
285.355
1.000 285.396
285 354
365 000
(Data Group F.2 ..NPS Loads)
FF(xyz)- G wasp4g01.dat 05/12/90 21:20:28
0.053
0.013
0.100
1.066
720.000
80.000
1 TMPSG
1 FNH4
1 SHL1
1 GWC2
143 750TMPSG
5 000FNH4
0 000SHL1
0.201GWC2
143.750TMPSG
5.000FNH4
O.OOOSHLl
0.000GWC2
143 750TMPSG
5 OOOFNH4
O.OOOSHLl
0.803GWC2
143.750TMPSG
5.000FNH4
0 000SHL1
0.201GWC2
Const-H ¦
K12T
ATMNH3
GWNH3
Z1C0W
NCRBM
K20T
ATMN03
GWDIP
PCRBM
K1C
LGHTS
XKC
IS1
3
7
13
22
3
7
13
22
3
7
13
22
3
7
13
22
3
7
13
22
12
14
16
18
39
22
24
32
40
41
43
45
47
l.TMPFN
1.FP04
1.SHL2
1 GWSW
1 .OOOTMPFN
8.400FP04
0.000SHL2
1.262GWSW
1 OOOTMPFN
8 400FP04
0.OOOSHL2
0 OOOGWSW
1.OOOTMPFN
8 400FP04
0.000SHL2
5.047GWSW
1 OOOTMPFN
8.400FP04
0.000SHL2
1.262GWSW
1.080
0.000
0.000
0.450
0.082
1.045
2.359
0.005
0.004
2.500
2.000
0.017
300.000
14
23
4
8
14
23
14
23
14
23
4
8
14
23
1.
1.
1.
1.
4 000
1 560
0 000
0.020
4.000
1.560
0.000
0.000
4.000
560
000
020
4 000
1.560
0.000
0 020
-------
Filename. GULLDYE INP WASP4 File for Gull HarDor Manna Dye Calibration
[ + 2 + 3 + 4 + 5 + g + ; + 8
KMNG1
48
0 014
KMPG1
49
0 001
K1RC
50
0.100
K1RT
51
1.080
group#2-4
10
KID
52
0.040
K. 1G
53
0 800
W1
54
1.000
KPZDC
55
0 000
KPZDT
56
1.000
PCRB1
57
0.030
NCR81
58
0 150
KMPHYT
59
1 000
SICRB1
89
0.177
KMSG1
111
0.0014
CBOD
1
group#l-5
7
-------
Filename GULLDYE INP WASP4 File for Gull Harbor Marina Dye Calibration
+ ! + 2 1- 3.----+ 4 + 5 + 6 + 7 + 8
0 164E+02 0 501E+03 0 216E+02 0.532E+03
0 211E+02 0 624E+03 0 2HE+02 0 639E+03
TEMP 3 14 FT#03-Temp #3 GLPB/SIS
0 139E+02 0 267E+03 0 139E-02 0.289E+03
0.840E+00 0.381E+03 0.205E+01 0.411E+03
0. 149E+02 0 501E+-03 0.213E+02 0-532E+03
0.218E+02 0 624E+03 0 218E+02 0 639E+03
TEMP 4 14 FT#04-Temp #4 GB
0 161E+02 0.267E+03 0.161E+02 0 289E+03
0.185E+01 0 381E+03 0 490E+01 0.411E+03
0 200E+02 0.501E+03 0 232E+02 0.532E+03
0.218E+02 0.624E+03 0.218E+02 0.639E+03
SUN 5 14 FT#05-solar rad
0.233E+03 0.267E+03 0.233E+03 0.289E+03
0 137E+03 0.381E+03 0.198E+03 0.4UE+03
0.462E+03 0.501E+03 0.499E+03 0.S32E+03
0.330E+03 0 624E+03 0.330E+03 0 639E+03
PHOTO 6 14 FT#06-photoperiod
0.460E+00 0 267E+03 0.460E+00 0 289E+03
0.392E+00 0.381E+03 0.435E+00 0 411E+03
0.623E+00 0.501E+03 0.649E+00 0 532E+03
0.530E+00 0.624E+03 0.530E+00 0.639E+03
WIND 7 14 FT#07-wind vel
0.412E+01 0.267E+03 0.412E+01 0 289E+03
0 514E+01 0 381E+03 0.514E+01 0.411E+03
0.412E+01 0 501E+03 0 412E+01 0.532E+03
0.412E+01 0.624E+03 0.412E+01 0.639E+03
KE#01 8 14 FT#08-Ke #1 PR/TRB
0 208E+01 0 267E+03 0.208E+01 0.289E+03
0.996E+00 0.381E+03 0.123E+01 0.411E+03
0.101E+01 0.501E+03 0.941E+00 0.532E+03
0.723E+00 0.624E+03 0.723E+00 0.639E+03
KE#02 9 14 FT#09-Ke #2 FB
0.132E+01 0.267E+03 0.132E+01 0.289E+03
0.634E+00 0 381E+03 0.781E+00 0.4UE+03
0.907E+00 0 501E+03 0.632E+00 0.532E+03
0.522E+00 0.624E+03 0.522E+00 0.639E+03
KE#03 10 14 FT#10-Ke #3 GPB
0 157E+01 0.267E+03 0 1S7E+01 0.289E+03
0.568E+00 0.381E+03 0.801E+00 0.411E+03
0 571E+00 0 501E+03 0.453E+00 0.532E+03
0.634E+00 0.624E+03 0 634E+00 0.639E+03
KE#04 11 14 FT#11-Ke #4 LPB
0.144E+01 0 267E+03 0 144E+01 0.289E+03
0.520E+00 0.381E+03 0 606E+00 0.411E+03
0.698E+00 0.501E+03 0 727E+00 0.532E+03
0.590E+00 0.624E+03 0 590E+00 0.639E+03
KE#05 12 14 FT#12-Ke #5 SIS/GB
0 122E+01 0.267E+03 0 122E+01 0.289E+03
0 332E+00 0.381E+03 0 512E+00 0.411E+03
0.438E+00 0.501E+03 0.473E+00 0.532E+03
0.453E+00 0.624E+03 0.453E+00 0.639E+03
TFNH4 13 14 FT#13-NH4 flux
0 268E+00 0.267E+03 0.268E+00 0.289E+03
0.148E-01 0.381E+03 0.218E-01 0.411E+03
0.468E+00 0.501E+03 0.152E+01 0.532E+03
0.141E+01 0 624E+03 0.141E+01 0.639E+03
TFP04 14 14 FT#14-P04 flux
0 396E+00 0.267E+03 0.396E+00 0.289E+03
0 517E-01 0.381E+03 0.677E-01 0.411E+03
0.586E+00 0.501E+03 0 134E+01 0.532E+03
0.128E+01 0.624E+03 0.128E+0X 0.639E+03
MACRO 15 14 FT#15-Macrophy
0 670E+00 0 267E+03 0.670E+00 0.289E+03
0.400E+00 0.381E+03 0.450E+00 0 4UE+03
0 930E+00 0.501E+03 0.114E+01 0 532E+03
0.450E+00 0.624E+03 0 450E+00 0 639E+03
SHL#1 16 14 FT#16-She 11 #1
0 100E+01 0 267E+03 0 100E+01 0 289E+03
0 240E+02 0.562E+03 0 245Ef02 0.593E+03
tempxxxx ini 7 8 FF time # 3 Temp#3
0.964E+01 0 320Ef03 0.275E^01 0 350E+03
0.375E+01 0 440E+03 0.914E+01 0.471E+03
0.237E+02 0.562E+03 0 244E+02 0 593E+03
tempxxxx.ini 8 8 FF time # 4 Temp#4
0.105E+02 0.320E+03 0 300E+01 0.350E+03
0 819E+01 0.440E+03 0.143E+02 0.471E+03
0.232E+02 0.562E+03 0.214E+02 0.593E+03
solarbnl ini 5 6 FF time # 5 Solar
0.156E+03 0.320E+03 0.121E+03 0 350E+03
0.286E+03 0.440E+03 0 384E+03 0 471E+03
0 485E+03 0.562E-t-03 0 422E+03 0 593E+03
solarbnl.ini 6 6 FF time # 6 Photo
0 406E+00 0 320E+03 0.381E+00 0.3S0E+03
0.498E+00 0 440E+03 0.568E+00 0.471E+03
0.639E+00 0.562E+03 0.595E+00 0.593E+03
wind-nyb.ini 5 6 FF time # 7 Wind v
0.412E+01 0.320E+03 0.412E+01 0.350E+03
0.514E+01 0 440E+03 0.514E+01 0.471E+03
0.412E+01 0.562E+03 0.412E+01 0 593E+03
extcoeff ini 5 9 FF time # 8 Ke #1
0.257E+01 0.320E+03 0.136E+01 0.350E+03
0 116E+01 0.440E+03 0.109E+01 0.471E+03
0.868E+00 0 562E+03 0.795E+00 0.593E+03
extcoeff.ini 6 9 FF time # 9 Ke #2
0.163E+01 0.320E+03 0 862E+00 0.350E+03
0.736E+00 0.440E+03 0 931E+00 0.471E+03
0.709E+00 0.562E+03 0.604E+00 0.593E+03
extcoeff.ini 7 9 FF time #10 Ke #3
0.146E+01 0.320E+03 0.933E+00 0.350E+03
0.610E+00 0 440E+03 0.958E+00 0 471E+03
0.681E+00 0.562E+03 0.482E+00 0.593E+03
extcoeff ini 8 9 FF time #11 Ke #4
0.134E+01 0.320E+03 0 855E+00 0.350E+03
0.619E+00 0.440E+03 0.720E+00 0.471E+03
0.707E+00 0.562E+03 0 562E+00 0.593E+03
extcoeff.ini 9 9 FF time #12 Ke #5
0.113E+01 0 320E+03 0.722E+00 0.350E+03
0.645E+00 0 440E+03 0.513E+00 0 471E+03
0.513E+00 0.562E+03 0.474E+00 0.593E+03
sedfluxx.ini 5 9 FF time #13 NH4 fl
0.109E+00 0.320E+03 0.287E-01 0.350E+03
0.355E-01 0.440E+03 0.124E+00 0.471E+03
0.250E+01 0.562E+03 0.241E+01 0.593E+03
sedfluxx.ini 6 9 FF time #14 P04 fl
0 210E+00 0.320E+03 0.821E-01 0.350E+03
0 955E-01 0.440E+03 0.230E+00 0.471E+03
0 190E+01 0.562E+03 0.186E+01 0 593E+03
benthbio.ini 5 8 FF time #15 Macrop
0.730E+00 0 320E+03 0.780E+00 0.350E+03
0.360E+00 0.440E+03 0.520E+00 0.471E+03
0 120E+01 0.562E+03 0.100E+01 0.593E+03
benthbio ini 6 8 FF time #16 She 11 f
0.100E+01 0 320E+03 0.100E+01 0.350E+03
-------
Filename' GULLDYE INP WASP4 File for Gull Harbor Marina Dye Calibration
4. 1 4. 2 ^ 3 + 4 5 + 6 + 7 + 8
0 100E+01 0 381E+03 0.100E+01 0 411E+03 0.100E+01 0 440E+03 0.100E+01 0.471E+03
0 IOOE+OI 0 501E-03 0 lOOE^Ol 0 532E+03 0 100E+01 0 562E+03 0.100E+01 0.593E+03
0.100E+01 0.624E+03 0.100E+01 0 639E+03
TFK1G 17 14 FT#17-time dependent grazing rate multiplier (theta=l 08)
0 635
267 0 635
289
0 465 320
0 294
350
0 234
381 0.267
411
0 316 440
0.487
471
0 770
501 1.150
532
1 370 552
1 350
593
1.130
624 1.130
639
TFSOD 18
14 FT#18-S0D flux
sedfluxx. in 1 7 9
1 FF time #18 SOD fl
0.729E+00
0
267E+03 0:729E+00
0.289E+03
0 587E+00 0.320E+03
0.426E+00
0.350E+03
0.364E+00
0
381E+03 0.399E+00
0.411E+03
0 449E+00 0 440E+03
0.606E+00
0.471E+03
0 833E+00
0
501E+03 0.111E+01
0.532E+03
0.125E+01 0 562E+03
0.124E+01
0.593E+03
0 109E+01
0
.624E+03 0.109E+01
0.639E+03
ZOOPL 19
14 FT#19-Zoop1.
modified 73 micron net size
9000
267 9000
289
12000 320
22000
350
40000
381 80000
411
50000 440
40000
471
40000
501 40000
532
50000 562
35000
593
' 15000
624 15000
639
TFSIO 20
14 FT#20-Si04 f
lux
sedf luxx. 1 n1 4 9
FF time #20 Si04
0.709E+00
0
.267E+03 0.709E+00
0.289E+03
0.560E+00 0.320E+03
0.396E+00
0 350E+03
0 333E+00
0.
,381E+03 0.368E+00
0.411E+03
0.418E+00 0.440E+03
0.580E+00
0 471E+03
0.820E+00
0.
.501E+03 0.112E+01
0.532E+03
0 127E+01 0.562E+03
0.126E+01
0 593E+03
0.109E+01
0
.624E+03 0.109E+01
0.639E+03
TFN03 21
14 FT#21-N03 flux
sedf luxx. mi 8 9
1 FF time #21 N03
0 793E+00
0
.267E+03 0.793E+00
0.289E+03
0.677E+00 0.320E+03
0.536E+00
0.350E+03
0 477E+00
0
381E+03 0.510E+00
0.411E+03
0.556E+00 0.440E+03
0.693E+00
0.471E+03
0 875E+00
0
501E+03 0.108E+01
0.532E+03
0.117E+01 0.562E+03
0.117E+01
0.593E+03
0.106E+01
0
624E+03 0.106E+01
0.639E+03
NH3 N
(mg/L)
5 1
9.99E+03 J:INITIAL CONC
SG01
0 0000
1.000
SG02
0.0000
1.000
SG03 0.0000 1 000
5G04
0 0000
1.000
SG05
0.0001
1.000
SG06 0.0000 0.000
N02 N
(mg/L)
5 1.
9999. J:INITIAL CONC
SG01
0.0000
1.000
SG02
0 0000
1.000
SG03 0.0000 1.000
SG04
0.0000
1.000
SG05
0.0001
1.000
SG06 0.0000 0.000
0P04
(mg/L)
5 1.
9999. J¦INITIAL CONC
SG01
0 0000
1.000
SG02
0.0000
1.000
SG03 0.0000 1.000
SG04
0.0000
1.000
SG05
0 0001
1.000
SG06 O.OOOO 0.000
Phytl
(mg/L)
4 1.
9999. J:INITIAL CONC
SG01
0 0000
1.000
SG02
0.0000
1.000
SG03 0.0000 1.000
SG04
0 0000
1.000
SG05
0 0001
1.000
SG06 0.0000 0 000
CB0D
(mg/L)
4 1.
9999 J:INITIAL CONC
SG01
0 0000
1.000
SG02
0 0000
1.000
SG03 0 0000 1.000
SG04
0 0000
1.000
SG05
0 0001
1 000
SG06 0.0000 0 000
00
(mg/L)
5 1.
9999. J:INITIAL CONC
SG01
0 0000
1.000
SG02
0.0000
1.000
SG03 0.0000 1.000
SG04
0.0000
1.000
SG05
0.0001
1.000
SG06 0.0000 0.000
OrgN
(mg/L)
5 1.
9999. J:INITIAL CONC
SG01
0.0000
1.000
SG02
0.0000
1.000
SG03 0.0000 1 000
SG04
0.0000
1.000
SG05
0.0001
1 000
SG06 0.0000 0 000
OrgP
(mg/L)
4 1.
9999. J:INLTIAL CONC
SG01
0.0000
1.000
SG02
0.0000
1.000 SG03 0.0000 1.000
SG04
0.0000
1.000
SG05
0.0001
1.000
SG06 0.0000 0.000
Phyt2
(mg/L)
4 1.
9999. J.INITIAL CONC
SG01
0.0000
1.000
SG02
0.0000
1.000
SG03 0.0000 1.000
SG04
0.0000
1.000
SG05
0.0001
1.000
SG06 0.0000 0 000
Phyt3
(mg/L)
4 1.
9999. J:INITIAL CONC
SG01
0.0000
1.000
SG02
0.0000
1.000
SG03 0.0000 1.000
SG04
0 0000
1.000
SG05
0.0001
1.000
SG06 0.0000 0 000
S104
(mg/L)
3 1.
9999. J:INITIAL CONC
SG01
0 0000
1.000
SG02
0.0000
1.000
SG03 0.0000 1.000
SG04
0.0000
1.000
SG05
0.0001
1.000
SG06 0.0000 0.000
Sa1 in¦
ity (mg/L)
5 1.
9.99E+03 J:INITIAL CONC
SG01
0.0000
1.000
SG02
0 0000
1.000
SG03 0.0000 1.000
SG04
0 0000
1.000
SG05
0 0000
1 000
SG06 0.0000 0.000
Coliforms (MPN/lOOml)
5 1.
9 99E+03 J:INITIAL CONC
SG01
0.0000
1 000
SG02
0.0000
1.000
SG03 0.0000 1 000
5G04
0.0000
1.000
SG05
0.0001
1.000
SG06 0.0000 0.000
-------
2.3.4 WASP4 DO Water Quality File for Gull Harbor Marina
-------
Filename. WGH_U.INP WASP4 File for Gull Harbor 00 Dye Calibration
+ 1 + 2 + 3 + 4 + 5 + 6 7
8
WGH_11 INP - Gull Harbor WASP4 Model Water 05/20/88 05/28/88
Dissolved Oxygen Calibration Run
KSIM NSEG NSYS ICFl MFLG IDMP NSLN INTY ADFC zyr/im/dd hhrnn
13
3
0
3
1 2
1
900.
3
1800.
0 0
1 + +
3 1.000
1
27.0
2
0 20E+00
1
50 0
2
0.15E+00
1
50 0
2
1
1
1
0 0 0 1988/05/20 0000
A.MODEL OPTIONS
113 0
148.00
365.
0 0
86400.
0 0
366
0 0
86400.
1 1
640
0 0
1.00
40 0 1
0. 0.20E+00
39 1 2
0. 0.15E+00
32 2 3
(surface water)
EXCHANGES
640.
640.
10E+00
0.
0.10E+00
640.
0 0
0 0
0
0
0 0
1 1
0 0
0
2 0
720.0
+
~
+ *
+ *
+ *
C: VOLUMES
1 0000
1.0000
1
4
1
1047
0.0
1.0
1.0
1.0
2
4
1
1535
0.0
1.0
1.0
1.0
3
4
1
1868
0.0
1.0
1.0
1.0
4
0
3
6622
0.0
1.0
1.0
1.0
SUMRY2.OUT (GULLHYD HYO)
3
1.0 1 0
1.0 1.157E-05
+ * + * D: FLOWS
(Data Block D.2 Field One Flows)
F#2 — NINQ(2) SCALQ=1 C0NVQ= 1
F#3 — NINQ(3) SCALQ=1 C0NVQ= m/day * 1/86400
F#3 --NDQS(3,ni) number of segment pairs
1303
1
4
3165 2 4
2032 3 4
F#3 —NBRKQ(4,
ni) --> number of time breaks
0 5
0.5
0.0 640.0
1.0 1
. 15 7 E -
-05 F#4 —NI NQ f 3)
SCALQ=1 C0NVQ= m/day * 1/86400
F#4 —NOQS(3.nl) number of segment pairs
1303
1
4
3165 2 4
2032 3 4
F#4—NBRKQ(5,
ni) --> number of time breaks
0 3
0.3
0.0 640.0
1.0 1
.157E-
-05 F#5 —NINQ{3)
SCALQ=1 C0NVQ= m/day * 1/86400
1303
1
4
3165 2 4
2032 3 4
F#5—NBRKQ (5,
ni) --> number of time breaks
0.2
0 2
0.0 640.0
0
0
0
0 0 0 0
110 0 0
1
Sys#l NH3
(Data Group E Boundary Conditions)
1.0
1.0
3
OCN
0CNBC001
Sys# 1
0.040
140.
000
0.040 150.000
0.040 365.000 0.000 366.000
1
Sys#2 N03
(Data Group E Boundary Conditions)
1.0
1.0
3
OCN
OCNBCOOl
Sys# 2
0.010
140.
000
-0 010 150.000
0.010 365.000 0 000 366.000
1
Sys#3 0P04
(Data Group E Boundary Conditions)
1.0
1.0
3
OCN
OCNBCOOl
Sys# 3
0.030
140.
000
0.030 150.000
0.030 365.000 0.000 366.000
1
Sys#4 Phyt#l
(Data Group E Boundary Conditions)
1.0
1.0
3
OCN
OCNBCOOl
Sys# 4
12.00
140.
000
12.00 150.000
12.00 365.000 0.000 366.000
1
Sys#5 CBOD
(Data Group E Boundary Conditions)
-------
F ilename.
WGH_11.INP WASP4 Fi
le for Gil 11
Harbor DO Dye Calibration
--1-
---+ 2 + -
—3 —
-+ 4
f 5 + 5 + 7 + 3
1 0
1 0
1
3
OCN
0CNBC001
Sys# 5
2
470
140.000 2
470
150 000
2.470 365 000 0.000 366 000
1
Sys#6 00
(Data Group E Boundary Conditions)
1 0
1.0
1
3
OCN :
. 0CNBC001
Sys# 6
6
150
140 000 6
.150
150.000
6 150 365.000 6.150 366.000
1
Sys#7 OrgN
(Data Group E Boundary Conditions)
1.0
1 0
1
3
OCN :
0CNBC001
Sys# 7
0.
360
140.000 0
360
150.000
0 350 365.000 0.000 366.000
1
Sys#8 OrgP
(Data Group E Boundary Conditions)
1.0
1.0
1
3
OCN :
0CNBC001
Sys# 8
0
020
140.000 0
.020
150.000
0.020 365.000 0 000 366.000
1
Sys#9 Phyt#2
(Data Group E Boundary Conditions)
1.0
1.0
1
3
OCN .
0CNBC001
Sys# 9
0.
000
140.000 0
.000
150.000
0.000 365.000 0.000 366.000
1
Sys#10
Phyt#2
(Data Group E Boundary Conditions)
1.0
1 0
1
3
OCN :
0CNBC001
Sys# 10
0
.000
140.000 0
.000
150.000
0 000 365.000 0 000 366.000
1
Sys#11
S i04
(Data Group E Boundary Conditions)
1.0
1.0
1
3
OCN :
OCNBCOOl
Sys# 11
0
.000
140.000 0
.000
150 000
0 000 365.000 0 000 366.000
1
Sys#12
Sal mity
(Data Group E Boundary Conditions)
1.0
1.0
1
3
OCN :
OCNBCOOl
Sys# 12
0
.000
140.000 30.80
150 000
30.80 365.000 0.000 366.000
1
Sys#13 Coliforms
(Oata Group E Boundary Conditions)
1.0
1.0
1
3
OCN :
OCNBCOOl
Sys# 13
0
.000
140.000 0
i 000
150.000
0.000 365 000 0.000 366 000
0
PS(t) Sys#l
(Data Block F.l Waste Loads for Point Source)
0
PS(t) Sys#2
0
PS(t) Sys#3
0
PS(t) Sys#4
0
PS(t) Sys#5
0
PS(t) Sys#6
0
PS(t) Sys#7
0
PS(t) Sys#8
0
PS(t) Sys#9
0
PS(t) Sys#10
0
PS(t) Sys#11
0
PS(t) Sys#12
0
PS(t) Sys#13
0
0
(Oata Group F.2 ..NPS Loads)
17
4
FF(xyz)-
G wasp4g01.dat 05/12/90 21.20 28
Z001X
1
1.FSI04
2
l.TMPSG 3 l.TMPFN 4 1.
KESG
5
1 KEFN
6
1.FNH4
7 1.FP04 8 1.
S001D
9
1 MACRO
12
1.SHL1
13 1.SHL2 14 1.
SHL3
15
1.GWC1
21
1.GWC2
22 l.GWSW 23 1.
FN03
24
1.
1
of 4
Z001X
1
1.OOOFS104
2
143.750TMPSG 3 1.000TMPFN 4 4.000
KESG
5
1.OOOKEFN
6
5.000FNH4
7 30.OOOFP04 8 6.000
S0D1D
9
2.680MACR0
12
0.000SHL1
13 0.000SHL2 14 0.000
SHL3
15
0.000GWC1-
21
0.201GWC2
22 1.262GWSW 23 0.020
FN03
24
4.570
2
of 4
Z001X
1
1 OOOFS104
2
143.750TMPSG 3 1.000TMPFN 4 4 000
KESG
5
1.OOOKEFN
6
5 000FNH4
7 30.000FP04 8 6.000
S0D1D
9
4.400MACR0
12
0 000SHL1
13 0.000SHL2 14 0.000
SHL3
15
0.OOOGWC1
21
0.000GWC2
22 0.000GWSW 23 0.000
FN03
24
4.570
3
of 4
ZOO IX
1
1.000FSI04
2
143 750TMPSG 3 l.OOOTMPFN 4 4.000
-------
Filename. WGH 11 INP WASP4 File for Gull Harbor DO Dye Calibration
KESG 5
1
OOOKEFN
6
5.000FNH4
7
30.000FP04
8
6 000
S0D1D 9
5
500MACRO 12
0 OOOSHLl
13
0 000SHL2
14
0 000
SHL3 15
0
OOOGWC1
21
0.803GWC2
22
5 047GUSW
23
0 020
FN03 24
4
570
4
of
4
Z001X 1
1.
.000FS104 2
0.OOOTMPSG
3
1.000TMPFN
4
1.000
KESG 5
t
OOOKEFN
6
1 OOOFNH4
7
0.000FP04
8
0.000
SOD 1D 9
0.
000MACR0 12
0 OOOSHLl
13
0 OOOSHL2
14
0 000
SHL3 15
0
000GWC1
21
0.000GWC2
22
0.000GWSW
23
0.000
FN03 24
0
000
Nosys=
14
Const-H
GLOBAL
0
NH3-N
1
group #1-1
10
K12C
11
0 250
K12T
12
1.080
KNIT
13
2.000
ATMNH3
14
1.400
GWFLOW
15 0.
000000
GWNH3
16
0.488
Z1CRB
17
2.500
Z1CDW
18
0.450
NCR BZ1
19
0.167
NCR8M
39
0.082
N03+N02-N
1
group #1-2
5
K20C
21
0.090
K20T
22
1 045
KN03
23
0.100
ATMN03
24
1.865
GUN03
25
3.065
0-P04
1
all param
4
ATMDIP
31
0.083
GWDIP
32
0.005
PCRBZ1
33
0.013
PCRBM
40
0.004
-
Phyt#l
2
group#l-4
15
WS1
28
0.100
K1C
41
1.000
KIT
42
1.066
LGHTS
43
2.000
PHI MX
44 720 000
XKC
45
0.017
CCHL1
46
80.000
IS1
47
300.000
KMNG1
48
0.007
KMPG1
49
0.001
K1RC
50
0 300
K1RT
51
1.080
TUL1
112
40 000
TOPTl
113
24.000
TLL1
114
0.000
grouD#2-4
10
KID
52
1.000
K1G
53
0.800
W1
54
1.000
KPZDC
55
0 000
KPZDT
56
1 000
PCRB1
57
0 030
NCRB1
58
0.155
KMPHYT
59
1 000
SICRB1
89
0.300
KMSG1
111
0.0140
CBOD
1
group#l-5
7
KDC
71
0.300
KDT
72
1.047
KDSC
73
0.000
KDST
74
1.000
KBOD
75
0 500
GWBOD
76
1.800
OCRB
81
2.670
Diss 02
1
group #1-6
6
K4C
26
0 000
K4T
27
1.000
K2
82
0.200
GWOXY
83
4 000
KM1RC
109
0.000
KM1RT
110
1.000
Org-N
1
group #1-7
7
K71C
91
-0.075
K71T
92
1.080
KONDC
93
0.000
KONDT
94
1.000
FON
95
¦0.700
ATM_ON
96
2.801
GW ON
97
0.091
Org-P .
1
group #1-8
7
K83C
100
0.220
K83T
101
1 080
KOPDC
102
0 000
KOPDT
103
1.000
FOP
104
0.600
ATM OP
105
0 247
GW OP
106
0 005
Phyt#2 2
group #1-9 12
-------
Filename' WGH_11.INP WASP4 File for Gull Harbor 00 Dye Calibration
+ [ 4. 2 + 3 + 4 + 5 + 6 + ^ + 8
WS2
29
0 200
CCHL2
34
65 000
IS2
35
75 000
K2C
36
1.750
K2D
37
0 020
W2
38
1 000
K2RC
60
0 150
K2RT
61
1 045
K2T
62
1.066
TUL2
115
37.000
T0PT2
116
3.000
TLL2
117
0 000
group#2-9
6
KMNG2
63
0.007
KMPG2
64
0.0015
NCRB2
65 _
0 155
PCRB2
66
0.030
SICRB2
90
0 462
KMSG2
107
0.028
Phyt#3
2
group #1-1
10
CLLCRB3
20
0 000
WS3
30
0.000
CCHL3
67
1.000
IS3
68
0.000
K3C
69
0.000
K3D
70
0 000
W3
77
0.000
K3RC
78
0.000
K3RT
79
1.000
K3T
80
1 000
group#2-10
6
KMNG3
84
0.000
KMPG3
85
0.000
NCRB3
86
0 000
PCRB3
87
0 000
SICRB3
98
0.000
KMSG3
108
0.000
S i 04
1
group #1-1
1
GWSI
88
20.000
Sa1 inity
1
group #1-1
1
GWSALT
99
0.000
Collforms
0
21 FF
TEMP 1
20.70
TEMP 2
20.70
TEMP 3
20.70
TEMP 4
20.70
SUN 5
630
PHOTO 6
0.530
WIND 7
0.000
KE#01 8
2.430
KE#02 9
2.430
KE#03 10
2.430
KE#04 11
2.430
KErfOS 12
2.430
TFNH4 13
1.055
TFP04 14
1.055
MACRO 15
1.000
SHL#1 16
1.000
TFK1G 17
1
TFSOD
1.032
ZOOPL 19
40000
TFSIO 20
1.055
[t) WASP Data Group I FF Time-
2 FT#01-Temp #1 PR/TRB
1.00 20.70 366.00
(deg C)
1 00 20.70 366.00
(deg C)
1.00 20.70 366.00
(deg C)
366.00
1
1
1
wasp4i01.dat 10/16/90
tempxxxx.ini 5 8 FF time # 1 Temp#l
00 20.70
FT#02-Temp #2
00 20.70
FT#03-Temp #2
00 20.70
FT#04-Temp #2
00 20.70
FT#05-solar radiation
00 630 336.00
FT#06-photoperiod (fraction of day which is sunny)
00 0.530 366 00
FT#07-wind velocity (m/sec)
00 0.000 366.00
FT#08-Ke #1 (1/meter)
[ langleys/day) assumes 25% cloud cover
.000
18
.430
n
.430
#3
430
#4
1.00 2.
FT#09-Ke
1.00 2.
FT#10-Ke
1 00 2
FT#11-Ke
1.00 2.430
FT#12-Ke #5 (
1.00 2.430
FT#13-NH4 flux
1 00 1.055
FT#14-P04 flux
1.00 1.055
FT#15-Macrophy
1.00 1.000
FT# 16-"S he 11 #1
1.00 1.000
366.00
1/meter)
366.00
1/meter)
366.00
1/meter)
366.00
1/meter)
366.00
(theta =
366.00
(theta = 1.08)
366.00
366.00
366.00
1.08)
FT#17-time dependent grazing rate multiplier (theta=1.00)
1.00 1 000 366.00
FT#18-SOD flux (theta = 1.047)
1.00 1.032 366.00
FT#19-Zoopl... modified 73 micron net size
1 00 40000 366.00
FT#20-Si04 flux (theta = 1.08)
1.00 1.055 366.00
+ 1 + 2-
__4 + 5 +
-7 + 8
A-55
-------
Filename WGH_11 INP WASP4 File for Gull Harbor 00 Dye Calibration
....+ 1 + 2 + 3 J--- -4 + 5 * 6 + 7
8
TFN03 21
FT«*21-N03 flux (theta = 1 08)
1 00
1 055
356.00
1.000 SG02
1.000
1 000 SG02
1.000
0.600 SG02
0.600
0 000 SG02
0.000
0.0700
0.0200
0 0420
14.000
1 055
NH3_N (mg/L)
SG01 0.0700
SG04 0 0700
N02_N (mg/L)
SG01 0 0200
SG04 0 0200
0P04 (mg/L)
SG01 0 0420
SG04 0 0420
Phytl (mg/L)
SG01 14.000
SG04 14 000
CBOD (mg/L)
SG01 2.4700
SG04 2.4700
DO (mg/L)
SG01 5.0000
SG04 5 0000
OrgN (mg/L)
SG01 0.0420
SG04 0.0420
OrgP (mg/L)
SG01 0 0280
SG04 0.0280
Phyt2 (mg/L)
SG01 0.0000
SG04 0.0000
Phyt3 (mg/L)
SG01 0.0000
SG04 0.0000
Si04 (mg/L)
SG01 0 0000
SG04 0.0000
Salinity (mg/L)
SG01 30.8000 1.000 SG02 30.8000
SG04 30.8000 1.000
Coliforms (MPN/lOOml)
SG01 0.0000 1.000 SG02 0.0000
SG04 0.0000 1.000
0.500 SG02 2 4700
0.500
1 000 SG02 5.0000
1 000
0 500 SG02 0 0420
0.500
0.700 SG02 0.0280
0.700
0 000 SG02 0.0000
0.000
0.000 SG02 0.0000
0.000
0 300 SG02 0.0000
0 300
5 1.9 99E+03 J'INITIAL CONC.
1 000 SG03 0.0700 1.000
5 1. 9999. J-INITIAL CONC.
1.000 5G03 0 0200 1 000
5 1. 9999. J.INITIAL CONC
0 600 SG03 0 0420 0.600
4 1. 9999. J:INITIAL CONC.
0 000 SG03 14.000 0 000
4 1. 9999. J¦INITIAL CONC.
0.500 SG03 2.4700 0.500
5 1. 9999 J- INITIAL CONC.
1.000 SG03 5 0000 1.000
5 1. 9999. J:INITIAL CONC.
0.500 SG03 0 0420 0.500
4 1. 9999. J:INITIAL CONC.
0.700 SG03 0.0280 0 700
4 1. 9999. J:INITIAL CONC.
0.000 SG03 0 0000 0.000
4 1. 9999. J:INITIAL CONC.
0.000 SG03 0.0000 0.000
3 1 9.99E+06 J:INITIAL CONC.
0.300 SG03 0.0000 0.300
5 1. 9.99E+03 J-INITIAL CONC.
1.000 SG03 30.8000 1.000
5 1. 9 99E+10 J:INITIAL CONC.
1 000 SG03 0.0000 1 000
-------
APPENDIX B: User's Guides
for
Tidal Prism Model (TPM)
and
WASP Model
-------
1.0 Tidal Prism iModel (TPM)
This is a line-by-line, entry-by-entry Guide to creating your own input
files for the model. You may create a valid input file which will work with the
model simply by starting at the beginning of the Guide and following through to
the end, entering all the inputs as described.
If no variable data is available, only the steady data file is necessary.
Time-constant data are generally things like geometry, returning ratio, initial
concentration, and boundary conditions. When you have both types of data, you
will simply open two files, and put time-constant data into one, and time-varying
data into the other. Time-variable data are most often non-point sources,
involving daily measurements of runoff over several weeks or months. The
variable file contains data which vary in time from the SECOND day of simulation
onward. Its format and units are the same as for the steady file except that
generally only data groups 3 or 4 will be specified, along with a title and date
for each entry.
Two types of data are required, geometric and physical data. Geometric
data defines the system being simulated and specifies the printing option and the
length of simulation. In addition, geometry, returning ratio, initial
concentration, and boundary conditions are also specified under the geometric
data type.
Physical data includes: Water temperature, reaction rates, point and
nonpoint sources, and initial as well as boundary conditions for water quality
parameters modeled. This section summarizes the input parameters that must be
specified in order to run the model.
A. GEOMETRIC DATA
Miscellaneous introductory data:
Record 1 (515):
NTMAX = Total number of tidal cycles the model is to be run.
NTN = The total number of tidal cycles at which you want output
concentrations if you request it to print out after 1, 3, and
5 cycles, enter ' 3' here.
TIMDEP = Flag for indicating presence of variable data file.
= 1 tells it to look for a variable file.
= 0 tells it not to look for a variable file.
MPREF = Flag to indicate algal nitrogen preference.
REF = 0 indicates ammonia-nitrogen preference.
~REF = 1 indicates nitrate-nitrogen preference.
ID = This input has actually become extinct- leave it at 6'. When
we still used punched card inputs, '5' would specify the
punched cards, and '6' was for disk file.
B-l
-------
Record 2 (1415):
NTOUT(I) = List the number of each tidal cycle at which to print output
concentration, If you want it to print out after 1, 3, and 5
cycles, list '13 5' here.
I = 1 ,NTN
Segment and tributarv/marina numbering:
Record 1 (lx.35A2):
TITLE = Alphanumeric characters to describe the data. FORMAT(lx,35A2)
Record 2 : Number of Segments and Tributaries/Marinas (215)
NS = is the number of segments in main channel
NUT = is the number of tributaries. Note: The water outside the
mouth of the main channel is numbered as main channel
segment(l).
In the example, there are no tributaries (NUT=0). If there are
tributaries/marinas, include the following data about them here.
NSTR(I) = (One card for each tributary/marina.) NSTR(I) is the number
of segments in the Ith tributary/marina.
NMT(I) = is the number of the main channel segment into which the Ith
tributary/marina empties (this main channel segment is the
tributary's segment 1.)
I 1 , NUT
Geometry and returning ratio :
Record 1 (2I5.30A2):
NCH = channel number, Main Channel NCH=1, First Tributary/Marina
NCH=2, Second Tributary/Marina NCH=3, Last Tributary/marina
NCH-NUT+1.
NS = number of segments in channel
TITLE = description of channel.
Record 2 f7F10.0):
DIST(I) = distance of transect from mouth, in statute miles.
Record 3 (7F10.0):
B-2
-------
VH(I) = volume of the Ith segment at high tide, in millions of cubic
ft.
Record 4 (7F10.Q):
P(I) = tidal prism upstream of the Ith transect, in millions of cubic
ft.
Record 5 (7F10.0):
AL(I) = returning ratio at the Ith transect.
Record 2 (7F10.0):
HA(I) = average depth of the Ith segment, in feet.
I = 1 , NS
If there are tributaries/marinas, now enter this data for the first tributary
exactly as for the main channel, beginning with the first card above, with NCH=2.
Do the same for all the tributaries/marinas.
************* OF DATA CARD *****************
At the end of the data for the LAST TRIBUTARY,
insert one card with '99' in first five columns.
¦Ik***********************************************
B. PHYSICAL DATA
This section describes the additional information required to run the Tidal Prism
Model. To arrange the input into a logical format, the data are divided into 8
groups, 1 through 8.
Data Group 1:
Data Group 2:
Data Group 3
Data Group 4
Data Group 5
Data Group 6;
Data Group 7:
Provide the correct water temperature.
Add the initial concentrations of each component and salinity,
unless freshwater
Include volume/mass flow rate of point sources/ constituents.
Include volume/mass flow rate of runoff/constituents.
Sediment oxygen demand field data are entered hare, and an
exponential temperature base generally taken from the
literature. (See 'BEN').
Turbidity field data are entered here.
Decay coefficient of CBOD may need to be calibrated for, the
temperature coefficient is generally taken from the literature
B-3
-------
(See 'CKC').
Data Group 8: Include initial concentrations at the mouth for all
components.
Data Group 1 :
Record 1 HX.35A2):
TITLE = Alphanumeric characters to identify the data group.
Record 2 fI3.2X.30A2):
NCH = channel number (=1 main channel).
TITLE = channel identification (main/tributary/marina).
Record 3 (2I5.30A2):
NDG = number of points in group A.
TITLE = parameter name 'WATER TEMPERATURE.'
Record 3 (7F10.0):
TEMP = Water temperature in degrees centigrade.
You will now enter 8 different sets of physical data, identified by a data group
name (A through H), listed below, beginning with those for the main channel.
Data Group Data Group Number of points
Description Number-NDG in group-a
Water Temperature 1 1
Initial Concentrations 2 of segments
Point Sources 3 of segments with point sources
Non-point Sources 4 of segments with non-pt sources
Benthic demand 5 of segments
Turbidity 6 of segments
[CLOD decay 7 0 of segments
Boundary Conditions 8 1
The first group simply contains the water temperature. The rest of the groups
follow the first-and are illustrated on the following pages. When you have
finished entering all the data groups for the main channel, begin again and
repeat the same process for the 1st tributary/marina, beginning with the second
card above, with NCH=2. Repeat for all tributaries/marina.
B-4
-------
~*~~~~~~~~~~~ FNH OF DATA PARD^
At the end of the data for EACH CHANNEL,insert
one card with '99' in first 5 columns. After
'99' card for the last tributary,insert one
card with '999'in first 3 columns.
~****¦****~~**~
Data Group 2 Initial concentrations:
Record 1 (2I5.30A2):
NP = number of data group (2).
NBG = number of segments.
HEADER = alphanumeric characters to identify data group 'INITIAL
CONCENTRATIONS'
Record 2 (14F5.0):
S(I) = initial salinity, in part per thousand.
Record 3 (14F5.0):
N1(I) = initial organic nitrogen concentration, in milligrams per
1 iter.
Record 4 (14F5.0):
N2(I) = The initial ammonia nitrogen concentration, in milligrams per
1i ter.
Record 5 (14F5.Q):
N3(I) = initial nitrate nitrogen concentration, in parts per thousand.
Record 6 (14F5.0):
PI(I) = initial organic phosphorus concentration, in milligrams per
1 iter.
Record 7 (14F5.0):
P2(I) = initial inorganic phosphorus concentrations, in milligrams
per 1 iter.
Record 8 (14F5.0):
CH(I) = initial chlorophyll concentration, in micrograms per liter.
B-5
-------
CBOD(I)
=
initial
CBOD concentration, in milligrams
per liter.
Record 10
(14F5.0):
~0(1)
initial
1 iter.
dissolved oxygen concentration,
in milligrams
Record 11
(14F5.0):
BAC(I) = initial coliform bacteria concentration, in MPN per 100 ml.
Note: This data group need not be specified by the user.
Default values are as follows.
S(I) -
0.10
P2(D
= 0.02
N1 (I)
= 0.10
CH( I)
= 10.0
N2(D
= 0.10
CBOD(I)
= 1.50
N3( I)
= 0.10
DO (I)
= 7.00
PI (I)
= 0.02
BAC(I)
= 5.00
I = 1 , NP
Data Group 3 Point Sources:
Record 1 (2I5.30A2):
NDG = number of data group (3).
NP = number of segments into which sources of wastewater are
introduced.
HEADER = alphanumeric characters to identify data group
'P0INTS0URCE WASTEWATER.'
Record 3 (I5.5X.5F10.0):
K = Reach Number.
QWAST(K) = Flow rate of wastewater in cubic feet per second.
WS(K) = Concentration of salinity in wastewater in parts per
thousand.
WN1(K) = Flow rate of HI in wastewater, in pounds every two tidal
cycles.
WN2(K) = Flow rate of H2 in wastewater, in pounds every two tidal
cycles.
B-6
-------
WN3(K)
Flow rate of N3 in wastewater, in pounds every two tidal
cycles.
Record 4 (IPX. 5F10.0):
WP1(K) = -
WP2(K)
WBOD(K)
DOWAST(K)
WBAC(K)
Flow rate of PI in wastewater, in pounds every two tidal
cycles.
Flow rate of P2 in wastewater, in pounds every two tidal
cycles.
Flow rate of CBOD is wastewaer, in pounds every two tidal
cycles.
Concentration of DO in wastewater, in milligrams per
1 iter.
Concentration of coliform bacteria in wastewater, in MPH
per 100 milliliter.
Note: This data group need not be specified by the user. Default values are zero
for each variable. However, if you have data to enter on the first card, but not
for the second, do not omit a blank second card.
Data Group 4 Non-point Sources:
Record 1 (2I5.30A2):
NDG
NP
HEADER
number of data group (4).
number of segments into which non-point sources are
introduced.
alphanumeric characters to identify data group 'NON-POINT
SOURCE WASTEWATER.'
Record 2 (13. IX. F6.0. 7F10.0):
K
RINC(K)
WNINP(K)
WH2NP(K)
WH3NP(K)
Reach number.
Flow rate of freshwater input including all types of
runoff in cfs.
Flow rate of Nl, in pounds every two tidal cycles.
Flow rate of N2, in pounds every two tidal cycles.
Flow rate of H3, in pounds every two tidal cycles.
B-7
-------
WPINP(K) = Flow rate of PI; in pounds every two tidal cycles.
WP2NP(K) = Flow rate of P2, in pounds every two tidal cycles.
WBODHP(K) = Flow rate of CBOD, in pounds every two tidal cycles.
WBACNP(K) = Flow rate of coliform bacteria, in billions every two
tidal cycles.
Note: This data group need not be specified by the user. Default values are zero
for each variable. To reset the non-point loadings to zero after storm event,
set NP to > 100 for the main channel and each of the branches.
Data Group 5: Benthic Demand
Record 1 (2I5.30A21:
NDG = number of data group (5).
NP = number of segments in main channel.
HEADER = alphanumeric characters to identify data group 'Benthic
Demand.'
Record 2 (F10.0):
TCBEN = Exponential base for the temperature dependence of benthic demand.
Record 3 (14F5.0k
BEM(I) = Benthic oxygen demand at 20 deg. C, in gm per square meter
per day.
Note: This data group need not be specified by the user. Default values are
zero.
Data Group 6: Turbidity
Record 1 (2I5.30A2):
NDG = number of data group (6).
NP = number of segments in main channel.
HEADER = alphanumeric characters to identify data group
'Turbidity.'
Record 2 (14F5.01:
TURB(I) = Turbidity of water in 1/meter.
B-8
-------
NOTE: This data group need not be specified by the user. Default values are 1.0.
Data Group 7: CBOD Reaction Rate
Record 1 (2I5.30A2):
NDG
NP
HEADER
Record 2 (F10.0):
CKC
number of data group (6).
number of segments in main channel, (or setting NP=1
establishes uniform values throughout)
alphanumeric characters to identify data group 'CBOD
decay'
Temperature coefficient for CBOD decay rate.
Record 3 (14F5.0):
CK1(I) = Decay coefficient of CBOD at 20 deg. C, in 1/day.
Data Group 8 Boundary Conditions:
Record 1 (2I5.30A2):
NDG
NP
HEADER
Record 2 (F10.0):
S(l)
Record 3 (3F10.0):
N1 (1)
N2(l)
N3(l)
Record 4 (2F10.0):
Pl(l)
P2(l)
Record 5 (F10.0):
number of data group (8).
1, segment 1. Specifies the down stream boundary at the
mouth of estuary.
alphanumeric characters to identify data group 'INITIAL
CONCENTRATION AT MOUTH'
Salinity in parts per thousand.
Organic nitrogen, in mg/L.
ammonia nitrogen, in mg/L.
nitrate- nitrite nitrogen, in mg/L.
Organic phosphorus, in mg/L.
inorganic phosphorus, in mg/L.
B-9
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CH(1) = Chlorophyll 'a', in ug/L.
Record 6 (2F10.0):
CB0D(1) = dissolved oxygen, in mg/L.
DO(1) = Carbonaceous biochemical oxygen demand, in mg/L.
Record 7 (F10.0):
BAC(l) = Coliform bacteria in MPN/100 mL.
Biological parameters:
Record 1 (4F10.0):
KN11
KN12
KN23
KN33
Record 2 (3F10.0):
KP11 = Settling rate of organic phosphorus, in 1/day.
KP12 = Rate of conversion of organic phosphorus to inorganic
phosphorus, in 1/day/degree centigrade.
KP22 = Settling rate of inorganic phosphorus, in 1/day.
Record 3 (2F10.0):
KBODS = Settling rate of carbonaceous oxygen demand, in 1/day.
REAR = Reaeration coefficient
Record 4 (7F10.0):
AC
AN
AP
KMN
KMP
KCC
RIS
Record 6 (5F10.0):
RIA = Average light level over photo period in units of
Settling rate of organic nitrogen, in l('one')/day.
Rate of conversion of organic nitrogen to ammonia in
1/day/degree centigrade.
Rate of oxidation of ammonia to nitrate, in 1/day/ degree
centigrade.
Denitrification rate, in 1/day.
Carbon to chlorophyll ratio .in mg/ug.
Nitrogen to chlorophyll ratio in mg/ug.
Phosphorus to chlorophyll ratio in mg/ug.
Half-saturation concentration of nitrogen for
phytoplankton growth rate, in mg/liter.
Half-saturation concentration of phosphorus for
phytoplankton growth rate, in mg/liter.
Saturation growth rate, in 1/day/degree centigrade.
Saturation light level, in units of power/unit area.
B-10
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power/unit area.
RESP = Phytoplankton endogenous respiration rate, in 1/day/degree
centigrade.
RCS = Phytoplankton settling rate, in 1/day.
PQ = Photosynthesis quotient, or the ratio of oxygen produced
to carbon fixed, in moles per mole.
RQ = Respiration quotient, or the ratio of carbon dioxide
liberated to oxygen consumed, in moles per mole.
Record 7 (F10.0):
FRAL = Fraction of day with sunlight.
Record 8 (F10.0):
KGRAZ = Zooplankton grazing rate, in 1/day.
Record 9 (F10.0):
KBAC = Net die-off rate of coliform, in 1/day.
B-ll
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2.0 Water Quality Analysis Simulation Program (WASP)
USER'S GUIDE FOR WASP5
Tetra Tech Version
February 20, 1991
This is a modified version of WASP4 which was developed for the Peconic
Bay Brown Tide Comprehensive Assessment and Management Plan for the Department
of Health Services, Suffolk County, New York. The model includes five additional
state variables not found in the EPA version: three phytoplankton groups
(netplankton, nanoplankton, and picoplankton), silica, salinity, and coliform
bacteria.
THE BASIC WATER QUALITY MODEL
Introduction
This section describes the input required to run the WASP water quality
program. To arrange the input into a logical format, the data are divided into
10 groups, A through J.
A
- Model Identification and Simulation Control
B
- Exchange Coefficients
C
- Volumes
D
- Flows
E
- Boundary Concentrations
F
- Waste Loads
G
- Environmental Parameters
H
- Chemical Constants
I
- Time Functions
J
- Initial Conditions
The following is a brief explanation of each data group:
Data Group A is generally for model identification and contains simulation
control options. The user must specify the number of segments and the number of
systems. The user must also specify time steps and print intervals here.
DATA GROUP B contains dispersive exchange information. Dispersion occurs
B-12
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between segments and along a characteristic length.
DATA GROUP C supplies initial segment volume information.
DATA GROUP D supplies flow and sediment transport information between
segments. Flows may be constant or variable.
DATA GROUP E supplies concentrations for each system at the boundaries.
All system concentrations must be supplied for each boundary.
DATA GROUP F defines the waste loads and segments that receive the waste
loads for both point and diffuse sources.
DATA GROUP G contains appropriate environmental characteristics of the
water body. These parameters are spatially variable.
DATA GROUP H contains appropriate chemical characteristics or constants.
DATA GROUP I contains appropriate environmental or kinetic time functions.
DATA GROUP J contains initial concentrations for each segment and each
system.
WASP4 Data Group Descriptions
DATA GROUP A: Model Identification and Simulation Control
VARIABLES
Record 1--Title of Simulation (A80)
TITLE1 = descriptive title of simulation (A80).
Record 2--Description of Simulation (A80)
TITLE2 = description of simulation (A80).
Record 3--Record 4 Names (A80)
HEADER = names of Record 4 variables, positioned properly;
for user convenience only (A80).
Record 4--Simulation Control Parameters (8I5,2F5.0,F3.0,F2.0.3I5.F10.0)
KSIM = simulation type: 0 - dynamic, 1 - steady state.
(15)
NOSEG- = number of segments in model network. (15)
NOSYS = number of model systems (state variables). (15)
ICFL = flag controlling use of restart file; 0 = neither
read from nor write to restart file (initial
conditions located in input file); 1 = write
final simulation results to restart file (initial
B-13
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MFLAG
conditions located in input file); 2 = read
initial conditions from restart file created by
earlier simulation, and write final simulation
results to new restart file. (15)
flag controlling messages printed on screen
during simulation; 0 = all messages printed; 1 =
simulation time only printed; 2 = all messages
are suppressed. (15)
IDMP
NEGSLN
INTYP
ADFAC
ZYR
system number for which mass balance analysis
will be performed. (15)
negative solution option; 0 = prevents negative
solutions; 1 = allows negative solutions. (15)
time step option; 0 = user inputs time step
history; 1 = model calculates time step. (15)
advection factor; 0 = backward difference; 0.5 =
central difference; 0-0.4 recommended. (F5.0)
year at beginning of simulation (15)
ZMON = month at beginning of simulation (IX,12)
ZDAY = day at beginning of simulation (IX,12)
ZHR = hour at the beginning of simulation. (F3.0)
MIN = minute at the beginning of simulation. (F2.0)
IDSY = system for which concentrations will be displayed
on screen throughout the simulation. (15)
IDSG1 = segments for which system "IDSY" concentrations
will be IDSG2 displayed on screen throughout the
simulation. (215)
TADJ = factor by which input kinetic rates will be
adjusted; 0 or 1.0 will cause no adjustment; 24.0
will adjust input rates in hours"1 to days'1;
86400. will adjust input rates in seconds"1 to
days"1. (F10.0)
Record 5--Number of Time Steps (15)
N0BRK" = number of different model time steps (15)
Record 6--Time Steps (4(F10.0. F10.0)
DTS(I)
T( I)
time step to be used until time T(I), seconds.
(F10.0)
time up to when time step DTS(I) will be used,
B-14
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Julian Day. (F10.0)
Record 7--Number of Print Intervals (15)
NPRINT = number of print intervals. NOTE: The maximum
number of printouts must be equal to or less than
the FORTRAN parameter MP that was used when
compiling the program. (15)
Record 8--Print Intervals (4(F10.0, F10.0))
PRINT(I) = print interval to be used until time TPRINT(I),
seconds. (F10.0)
TPRINT(I) = time up to when print interval PR INT(I) will be
used, Julian Day. (F10.0)
Record 9--Svstem Bypass Options (1615)
SYSBY(ISYS) = bypass option for system ISYS; 0 = system will be
simulated; 1 = system will be bypassed. (15)
DATA GROUP B: Exchange Coefficients
Exchange coefficients are computed from input dispersion coefficients,
cross-sectional areas, and characteristic lengths. Dispersion coefficients may
vary in time according to piecewise-1inear time functions, with groups of segment
pairs having the same dispersion time function. Exchange data are read for each
exchange field. Field one contains dispersion coefficients for water column
exchanges. Field two contains exchange data for pore water exchange. Fields
three, four and five contain sediment exchange data, with a separate field
available for each solid type.
VARIABLES
Record 1--Number of Exchange Fields (15. 75X)
NRFLD = number of exchange fields. NRFLD will generally
equal 2 for water column and pore water
exchanges. (15)
TITLE = name of data group. (75X)
If no exchange rates are to be read, set NRFLD to zero and continue with
Data Group C.
Record 2--Exchanae Time Functions for Each Field (15. 2F10.0)
NTEX(NF) = number of exchange time functions for field NF.
(15)
SCALR = scale factor for exchange coefficients. All
exchange coefficients for field NF will be
B-15
-------
multiplied by this factor. (F10.0)
CONVR = conversion factor for exchanges in field NF.
(F10.0)
NF = 1, NRFLD
To skip exchange field NF, set NTEX(NF) to zero and continue with the next
exchange field.
Record 3--Exchanqe Data (15)
NORS(NF,NT) = number of exchanges for field NF, time function
NT. (15)
NT = 1, NTEX(NF)
Record 4--Areas. Characteristic Lengths (2F10.0, 215)
A(K) = area in square meters for exchange pair K.
(F10.0)
EL(K) = characteristic length in meters for exchange pair
K. (F10.0)
IR(K),JR(K) = segments between which exchange occurs. The
order of the segments is unimportant. (215)
K = 1, NORS(NF,NT)
Record 5--Number of Breaks
NBRKR(NF,NT)= number of values and times used to describe
dispersion coefficient piecewise-1inear time
function. (15)
Record 6--Piece Linear Dispersion Time Function (4(F10.0, F10.0))
RT(K) = value of dispersion coefficient in m2/sec at time
TR(K). (F10.0)
TR(K) = time in days. (F10.0)
K = .1, NBRKR(NF,NT)
Record 7--Exchanqe Bypass Options (1615)
RBY(K) = 0, exchange occurs in system K. (15)
1, bypass exchange for system K.
K = 1, NOSYS
ORGANIZATION OF RECORDS
Record 1 is entered once for Data Group B. Records 2 through 6 are
repeated for each exchange field, and Records 3, 4, 5, and 6 are repeated for
B-16
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each time function in a given exchange field. Record 4 uses as many lines as
necessary to input NORS sets of A(K), EL(K), IR(K), and JR(K), with 1 set on each
line. Record 6 uses as many lines as needed to input NBRKR pairs of RT(K) and
TR(K), with 4 pairs occupying each line. After data for all exchange fields are
entered, Record 7 is input on the following line with NOSYS entries.
DATA GROUP C: Volumes
Record 1--Preliminary Data (215. F10.0. 60X)
IVOPT = 1, constant water column volumes.(15)
= 2, 3, volumes adjusted to maintain flow continuity.
(15)
IBEDV = 0, constant bed volumes. (15)
= 1, bed volumes change in response to sediment
transport. (15)
TDINTS = time step in days for porosity computations, IBEDV = 0.
(F10.0)
= time step in days for sediment bed
IBEDV = 1. (F10.0)
TITLE = name of data group. (60X)
compaction,
Record 2--Scale FactoVs (2fl0.0)
SCALV
CONVV
scale factor for volumes. All volumes will be
multiplied by this factor. (F10.0)
conversion factor for volumes. (F10.0)
Record 3--Seqment Types and Volumes (3110. 5F10.0)
ISEG
IBOTSG
ITYPE(ISEG)
segment number.
segment immediately below ISEG. Enter zero if no
segment is below ISEG. (110)
segment types;
1 = surface water segment,
2 = subsurface water segment,
3 = upper bed segment,
4 = lower bed segment. (110)
B-17
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BVOL(IS EG)
VMULT(ISEG)
VEXP(ISEG)
DMULT(ISEG)
volume of segment ISEG in cubic meters. (F10.0)
hydraulic coefficient "a" for velocity in ISEG as
a function of flow:
v = a Qb
If b = 0, VMULT is a constant velocity in m/sec.
(F10.0)
hydraulic exponent "b" for velocity in ISEG as a
function of flow (0-1). A value of 0.4
represents rectangular channels. (F10.0)
hydraulic coefficient "c" for depth of ISEG as a
function of flow:
c Qc
If d = 0, DMULT is a constant depth in m. (F10.0)
DXP(ISEG) = hydraulic exponent "d" for depth of ISEG as a
function of flow (0-1). A value of 0.6
represents rectangular channels. (F10.0)
ISEG = 1, NOSEG
ORGANIZATION OF RECORDS
Records 1 and 2 are entered once for Data Group C. Record 3 is repeated
NOSEG times. If ICFL = 2 in Data Group A, volumes are read from the restart file
( *.RST, where * is the input data set name), and Records 2 and 3 should not be
included in the input data set.
DATA GROUP D: Flows
Data Group D consists of the flows that are used in the model. Flows may
be input for several fields. Field one consists of advective flows in the water
column, and may be input by one of three options. Field two consists of pore
water flows, while Fields three, four, and five consist of sediment transport
velocities and cross-sectional areas. A separate sediment transport field is
specified for each solid type. Field six is for evaporation and precipitation
velocities and cross-sectional areas. All flows may vary in time according to
piecewise linear time functions.
Record 1 is read first. If IQOPT = 1, Data Block D1 is read next; if
IQOPT = 2 or 3, Data Block D2 is read. Data Blocks D3, D4, D4, D4, and D5 follow
in order for NFIELD = 2, 3, 4, 5, and 6, respectively. Following all specified
Data Blocks, Record 7 is read.
VARIABLES
Record I--Data Input Options: Number of Flow Fields (215)
B-18
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IQOPT = 1, Field one (advective) flows are specified directly
by user.
= 2, Field one flows are read from an unformatted file
(SUMRV2.OUT) created by DYNHYD4.
= "3, flows are read from a formatted file created by
DYNHYD4. (15)
NFIELD = number of flow fields. The first two fields are
advective and pore water flows. An additional field
(3, 4, or 5) is used for each type of solid modeled.
Field 6 is used for evaporation and precipitation. If
no flows are used, set NFIELD to zero and continue with
Data Group E. (15)
FNAME = DYNHYD5 hydrodymanic file name (e.g., SUMRY2.0UT)
DATA BLOCK D.l: Direct Input of Field One Flows (IQOPT = 1)
VARIABLES
Record 2--Number of Flow Time Functions (15. 2F10.0)
NINQ(l) = number of time functions for Field One. If no flows
are used in field one, set NINQ to zero and skip to
next field. (15)
SCALQ = scaling factor. All flows in Field one are multiplied
by SCALQ. (F10.0)
CONVQ = units conversion factor. (F10.0)
Record 3--Number of Flows (15)
N0QS(1,NI)= number of unit flow responses in field one, time
function NI; each unit flow is defined for a single
segment pair. (15)
Record 4--Flow Routing for Field One (4(F10.0. 2115))
BQ(1,NI,K)= portion of flow for field one, time function NI that
flows between segment pair K. (F10.0)
JQ(1,NI,K)= upstream segment. (15)
IQ( 1 ,-NI,K) = downstream segment. (15)
K = 1, NOQS(1,NI)
Record 5--Number of Breaks in Advective Time Functions (15)
NBRKQ(1,NI)= the number of flows and times used to describe
B-19
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piecewise linear time function NI. (15)
Record 6--Piecewise Linear Advective Time Function (4(2F10.0))
QT(1,NI,K)= advective flow in m3/s. (F10.0)
TQ(1,NI,K)=_ time in days. (F10.0)
K = 1, NBRKQ(l.NI)
ORGANIZATION OF RECORDS
Records 1 and 2 are input once for Data Block D.l. Records 3, 4, 5, and
6 are input once for each flow time function. Record 4 uses as many lines as
needed to input NOQS sets of BQ, JQ, and IQ, with four sets per line. Record 6
uses as many lines as necessary to input NBRKQ sets of QT and TQ, with four sets
on each 1 ine.
DATA BLOCK D.2: DYNHYD4 Field One Flows (IQOPT = 2 or 3)
VARIABLES
Record 3--Seaward Boundaries (15)
NSEA = number of downstream (seaward) boundary segments
(same as in hydrodynamic simulation). (15)
JSEA(I) = segment numbers for downstream boundary junction.
(15)
1=1, NSEA
Record 4--Junction-Segment Map (1615)
JUNSEG(I) = segment number corresponding to hydrodynamic
junction I. (15)
I = 1, NJ
ORGANIZATION OF RECORDS
Records 2 and 3 are read in once for Data Block D.2. Record 4 will be
repeated until NJ entries have been input.
DATA BLOCK D.3: Field Two (Pore Water) Flows
VARIABLES
Record 2--Number of Pore Water Time Functions (15, 2F10.0)
B-20
-------
NINQ(2) = number of pore water time functions. If no flows
are used in Field Two, set NINQ to zero and skip
to sediment transport fields. (15)
SCALQ = scaling factor for pore water flows. (F10.0)
CONVQ = units conversion factor. (F10.0)
Record 3--Number of Flows (15)
NOQS(2,NI) = number of segment pair flows in Field 2, time
function NI. (15)
NI = 1, NI NQ(2)
Record 4--Flow Routing for Field Two f4fF10.0, 215))
BQ(2,NI,K) = portion of pore water flow for time function NI
that flows between segment pair K. (F10.0) .
JQ(2,NI,K) = upstream segment. (15)
IQ(2,NI,K) = downstream segment. (15)
Record 5--Number of Breaks in Pore Water Time Function (15)
NBRKQ(2,NI) = number of pore water flows and times used to
describe piecewise linear time function NI. (15)
Record 6--Piecewise Linear Velocity Time Function (4(2F10.0))
QT(2,NI,K) = pore water flow in m3/s. (F10.0)
TQ(2,NI,K) = time in days. (F10.0)
K = 1, NBRKQ(2,NI)
ORGANIZATION OF RECORDS
Record 2 is input once for Data Group D.3. Records 3, 4, 5 and 6 are
input once for each pore water time function. Record 4 uses as many lines as
necessary to input NOQS sets of BQ, JQ, and IQ, with four sets on each line.
Record 6 uses as many lines as necessary to input NBRKQ sets of QT and TQ, with
four sets on each line.
DATA BLOCK D.4: Sediment Transport Fields
Sediment transport flow data are input as velocities and areas.
Velocities may vary in time, and represent settling, sedimentation, deposition,
and scour. Only solids and sorbed chemical are transported by these fields. A
separate field is specified for each sediment size fraction. If no solids are
B-21
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modeled, skip directly to Record 7 (Flow Bypass Options).
VARIABLES
Record 2--Number of Velocity Time Functions (15. 2F10.0)
NINQ(NF) = number of velocity time functions for Field NF.
(15)
SCALQ = scaling factor for velocities. (F10.0)
CONVQ = units conversion factor. (F10.0)
NF = 3, 5
Record 3--Number of Segment Pairs (15)
NOQS(NF,NI) = number of segment pairs involved in sediment
transport. (15)
NI = 1, NINQ(NF)
Record 4--Areas for Evaporation, Precipitation (4(F10.0, 215))
BQ(NF,NI,K) = area in square meters between segment pair K.
(F10.0)
JQ(NF,NI,K) = segment sediment is transported from. (15)
IQ(NF,NI,K) = segment sediment is transported to. (15)
K = 1, NOQS(NF,NI)
Record 5--Number of Breaks in Velocity Time Function (15)
NBRKQ(NF,NI)= number of velocities and times used to describe
piecewise linear time function NI. (15)
QT(NF,NI,K) = sediment transport velocity in m/s. (F10.0)
TQ(NF,NI,K) = time in days. (F10.0)
K = 1, NBRKQ(NF,NI)
ORGANIZATION OF RECORDS
Records 2 through 6 are read for each solid transport field. Records 3,
4, 5 and 6 are input for each time function in each field. Record 4 uses as many
lines as needed to input NOQS sets of BQ, JQ, and IQ, with four sets on one line.
Record 5 uses as many lines as needed to input NBRKQ sets of QT and TQ, with four
sets per line.
B-22
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DATA BLOCK D.5: Evaporation and Precipitation Field
Evaporation and precipitation flow data are input as velocities and
areas. Velocities may vary in time to represent rainfall events or seasonal
evaporation. No chemical is transported with evaporation, but volumes are
adjusted to maintain continuity. If this field is not modeled, skip directly to
Record 7 (Flow Bypass Options). After all transport field data is entered,
Record 7 is input with NOSYS entries. If no evaporation or precipitation fields
are specified, Record 7 follows Data Group D.4 (solids transport).
VARIABLES
Record 2--Number of Velocity Time Functions (15, 2F10.0))
NINQ(NF) = number of velocity time functions for Field 6.
(15)
SCALQ = scaling factor for velocities. (F10.0)
CONVQ = units conversion factor. (F10.0)
NF = 6
Record 3--Number of Segments (15)
N0QS(NF,NI) = number of segments involved in evaporation and
precipitation transport. (15)
Record 4--Areas for Mater Transport (4(F10.0, 215))
BQ(NF,NI,K) = area in square meters of segment K. (F10.0)
JQ(NF,NI,K) = segment water is transported from; if = 0, this
is precipitation. (15)
IQ(NF,NI,K) = segment water is transported to; if = 0, this is
evaporation. (15)
K = 1, NOQS(NF,NI)
Record 5--Number of Breaks in Velocity Time Function (15)
NBRKQ(NF,NI)= number of velocities and times used to describe
piecewise linear time function NI. (15)
Record 6--Piecewise Linear Velocity Time Function (4(2F10.0))
QT(NF,NI,K) = water transport velocity in m/s; if more
traditional units of cm/day or cm/year are
desired, then specify CONVQ = 1.1574E"7 or
3.169E"10, respectively. (F10.0)
TQ(NF,NI,K) = time in days. (F10.0)
K = 1, NBRKQ(NF,NI)
B-23
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-END OF DATA BLOCKS FOR D-
Card 7--Flow Bypass Options (1615)
QBY(ISYS) -= 0, flow transport occurs in system ISYS.
1, bypass flow transport for system K. (15)
K =1, NOSYS
The flow bypass option allows flow transport to be set to zero in one or
more systems. The bypass option applies to all transport fields.
DATA GROUP E: Boundary Concentrations
Data Group E is repeated, in its entirety, NOSYS times.
VARIABLES
Record 1--Data Input Option—Number of Boundary Conditions (IIP, 70X)
NOBC(K) = number of boundary conditions used for system K.
(110)
TITLE = name of data group. (70X)
K = 1, NOSYS
If no boundary conditions are to be input, set NOBC(K) equal to zero and
either continue with the next system or go to the next card group.
Record 2--Scale Factor for Boundary Conditions (2F10.0)
SCALB = scale factor for boundary conditions. All
boundary conditions will be multiplied by this
factor. (F10.0)
CONVB = unit conversion factor for boundary conditions.
Boundary conditions are expected to be in
milligrams per liter (mg/L). If boundary
conditions are given in SI units (grams per cubic
meter), CONVB will be 1.0. (F10.0)
Record 3--Boundarv Conditions (215)
IBC(K) = boundary segment number. (15)
NOBRK(K) = number of values and times used to describe the
broken line approximation. The number of breaks
must be equal for all boundary conditions within
a system. (15)
B-24
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K = 1, NOBC
Record 4--Boundary Concentrations (4(2F10.Q))
BCT(K) = value of the boundary concentration at time T(K)
in mg/L. (F10.0)
T(K) = time in days. If the length of the simulation
exceeds T(NOBRK), the broken line approximation
is repeated, starting at T(l), i.e., the
approximation is assumed to be periodic, with
period equation to T(NOBRK). All break times
must agree for all segments, i.e., T(l) must be
the same for all boundaries, T(2) must be the
same for all boundaries, etc. (F10.0)
K = 1, NOBRK
ORGANIZATION OF RECORDS
Records 1 and 2 are entered once. Records 3 and 4 are a set and are
repeated NOBC times. Within each NOBC set, Record 3 is entered once and Record
4 is repeated until NOBRK entries are input. Four entries (four BCT(K)-T(K)
pairs) will fit on each 80-space line. The whole group is repeated NOSYS times,
once for each model system.
DATA GROUP F.l: Waste Loads
Data Group F.l contains the point source waste loads used in the model.
Data Group F.l is repeated NOSYS times for point source loads. Following
complete specification of point source loads, nonpoint source loads will be read
from Data Group F.2.
VARIABLES
Record 1--Data Input Option: No. of Forcing Functions (IIP, 70X)
NOWK(ISYS) = number of forcing functions used for system ISYS.
Forcing functions may also be considered as
sources (loads) or sinks of a water quality
constituent. If no forcing functions are to be
input, set NOWK(ISYS) to zero, and continue with
next system or go to next data group.(110)
name of data group. (70X)
scale factor for forcing functions. All forcing
functions will be multiplied by this factor.
(F10.0)
unit conversion factor for forcing functions.
TITLE
SCALW
CONVW
B-25
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Forcing functions are expected to be in kilograms
per day. If forcing functions are given in
English units (pounds per day), this factor will
be 0.4535. (F10.0)
Record 3--Number of Point Sources (215)
IWK(K) = segment number that has forcing function BWK(K). (15)
NOBRK(K) = number of breaks used to describe the forcing function
approximation. The number of breaks must be equal for
all forcing functions within a system. (15)
K = 1, NOWK
Record 4--Point Source Time Function (4(2F10.0))
WKT(K) = value of the forcing function at time T(K), in kg/day.
(F10.0)
T(K) = time in days. If the length of the simulation exceeds
T(NOBRK), the approximation is repeated, starting at
T(l), i.e., the approximation is assumed to be periodic
with period equal to T(NOBRK). All break times must
agree for all segments; i.e., T(l) must be the same for
all loads, T(2) must be the same for all loads, etc.
(F10.0)
K = 1, NOBRK
ORGANIZATION OF RECORDS
Records 1 and 2 are input once. Records 3 and 4 are a set and are
repeated (as a set) NOWK times. Within each set, Record 3 is entered once and
Record 4 is repeated until all NOBRK entries are entered. Four entries
(WKT(K)-T(K) pairs) will fit on each 80-space line. The entire group is repeated
NOSYS times, once for each system.
DATA GROUP F.2, Nonpoint Source Waste Loads
VARIABLES
Record 1--Number of Runoff Loads. Initial Dav (215)
This record must be included in the data file. If there are no segments
receiving runoff loads, enter zero for NOWKS.
NOWKS = number of segments receiving runoff loads. (15)
NPSDAY = the time in the runoff file corresponding to the
B-26
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initial simulation time, in days. (15)
If NOWKS = 0, skip to Data Group G. If NOWKS >0, read records 2, 3, and 4.
Record 2--Scale Factor for Runoff Loads (2F10.0)
SCALN = _ scale factor for runoff loads. All runoff loads will
be multiplied by this factor. (F10.0)
CONVN = unit conversion factor for runoff loads. Runoff loads
are expected in kilograms per day. If runoff loads are
given in English units (pounds per day), this factor
will be 0.4535. (F10.0)
Record 3--Runoff Segments (1615)
INPS(J) = segment number to which runoff load J is applied. (15)
J = 1, NOWKS
Record 4--Print Specifications (1615)
KT1 = initial day for which nonzero runoff loads from file
NPS.DAT will be printed. (15)
KT2 = final day for which nonzero runoff loads from file
NPS.DAT will be printed. (15)
KPRT(I) = indicator specifying whether nonzero runoff loads will
be printed for each system. If KPRT(I) is greater than
zero, then runoff loads will be printed for system I.
(15)
I = l.NOSYS
ORGANIZATION OF RECORDS
Records 1 and 2 are entered once in Data Group F2. Record 3 has NOWKS
entries and uses as many 80-space lines as needed to enter all NOWKS segment
numbers. Sixteen entries will fit on one line. Record 4 is entered once.
B-27
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DATA GROUP G: Parameters
The definition of the parameters will vary, depending upon the structure
and kinetics of the systems comprising each model. The input format, however,
is constant.
VARIABLES
Record 1-^Number of Parameters (IIP, 70X)
NOPAM = number of parameters reguired by the model. If
no parameters are to be input, set NOPAM to zero
and go to Data Group H. (110)
TITLE = name of data group. (70X)
Record 2--Scale Factors for Parameters (4(A5. 15. F10.0)
ANAME(ISC) = descriptive name for parameter ISC.
ISC = parameter number identifying
parameter. (15)
PSCAL(ISC) = scale factor for parameter ISC.
K = 1, NOPAM
Record 3--Seqment Number (IIP)
ISG = segment number for the following parameter
values. (IIP)
Record 4--Seqment Parameters f4fA5. 15. F10.0))
PNAME(ISC) = an optional one to five alphanumeric character
descriptive name for parameter PARAM(ISG,ISC).
(A5)
(A5)
(F10.0)
ISC = parameter number identifying
parameter. (15)
PARAM(ISEG,K)= the value of parameter ISC in segment ISG.
(F10.0)
K = 1, NOPAM
ISEG = 1, NOSEG
ORGANIZATION OF RECORDS
Record 1 is input once in Data Group G, occupying one line. Record 2 has
NOPAM entries. Four entries will fit on one line; thus, Record 2 uses as many
80-space lines as needed to enter all NOPAM entries. Records 3 and 4 are entered
NOSEG times, once for each segment. For each segment, Record 4 uses as many
lines as needed to enter all NOPAM entries.
B-28
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DATA GROUP H: Constants-
The definition of the constants will vary, depending upon the structure
and kinetics of the systems comprising each model . This data group is subdivided
into global constants and constants for each system (thus NOSYS+1 groups are
read). Each of these groups can be subdivided into any number of fields
containing similar kinds of data.
VARIABLE
Record 1--Header (80X)
TITLE = name of data group. (80X)
Record 2--Data Fields in Group H fA10. IIP)
CHNAME(K)= a ten-character descriptive name for System (K). (A10)
NFLD = number of fields of constants for this group;
0 = no constants for this group; the user may subdivide
the constants into any number of arbitrary fields.
(HO)
If no constants are to be input for this group, set NFLD equal to zero and
continue with next group.
Record 3--Number of Constants in Field (A10, IIP)
FLDNAME = ten-character name identifying field of constants.
(A10)
NCONS = number of constants to be entered in this field; 0 =
no constants for this field (skip to next field). (IIP)
Record 4--Constants (2(A10, IIP. F10.0))
TNAME(ISC)= name identifying constant ISC. (A1P)
ISC = number identifying constant; these numbers are set by
model developer. (110)
CONST(ISC)= value of constant ISC. (F1P.P)
ORGANIZATION OF RECORDS
Record 1 is entered once in Data Group H. Records 2 through 4 are
entered as NOSYS +1 groups. For each group, Records 3 and 4 are entered NFLD
times. For each field, Record 4 uses as many lines as needed for NCONS entries
(2 per 1ine).
B-29
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DATA GROUP I: Kinetic Time Functions-
The definition of the kinetic time function will vary depending upon the
structure and the kinetics of the systems comprising each model. The input
format, however, is constant.
VARIABLES
Record 1--Number of Time Functions (IIP. 70X)
NFUNC = number of time functions required by the model. If no
time functions are to be input, set NFUNC equal to zero
and go to Card Group J. (110)
TITLE = name of data group. (70X)
Record 2--Time Function Descriptions (A5, 215)
ANAME(ISC)= an optional one to five alphanumeric character
descriptive name for the time function K. (A5)
ISC = number of breaks used to describe the time function K.
(15)
NOBRK(ISC)= number identifying the time function; these numbers are
set by the model developer. (15)
1=1, NFUNC
Record 3--Time Functions (4(2F10.0))
VALT(K) = value of time function ISC at time T(K). (F10.0)
T(K) = time in days. If the length of the simulation exceeds
T(NOBRK), the time function will repeat itself,
starting at T(l), i.e., the approximation is assumed to
be periodic, with period equal to T(NOBRK). (F10.0)
K = 1, NOBRK
ORGANIZATION OF RECORDS
Record 1 in entered once in Data Group I. Records 2 and 3, as a set, are
repeated NFUNC times. Within each NFUNC set, Record 2 is input once and Record
3 uses as many 80-space lines as needed to input NOBRK entries. Four entries
(four VALK(K)-T(IC) pairs) will fit on each 80-space line.
DATA GROUP J: Initial Concentrations--
The initial conditions are the segment concentrations and densities for
the state variables at time zero (or the start of the simulation).
B-30
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VARIABLES
Record 1--Svstem Information (A40. 15. F5.0, F10.0, 20X)
CHEML = chemical or system name (A40).
IFI ELD = solids field (3, 4, or 5) that transports this system
in its pure or sorbed form (15).
DSED = density of system; 0.0 for chemical, 0.5-2.5 for
solids, kg/L. (F5.0).
CMAX = maximum concentration, mg/L. (F10.0)
TITLE = name of data group. (20X)
Record 2--Initial Conditions (3(A5. 2F10.0)
ANAME(K) = an optional one to five alphanumeric character
descriptive name or number identifying segment K. (A5)
C(ISYS,K)= initial concentration in segment K of system ISYS in
the appropriate units, mg/L. (F10.0)
DISSF = dissolved fraction of chemical in segment K. (F10.0)
K = 1, NOSEG
ISYS = 1, NOSYS
ORGANIZATION OF RECORDS
Records 1 and 2 are a set and will be repeated NOSYS times. Within each
NOSYS set Record 2 will use as many 80-space lines as needed to input NOSEG
entries. Three entries (ANAME-C-DISSF) will fit on one line. After NOSEG
entries have been entered in a NOSYS set, begin the next NOSYS set on the
following line. If ICFL = 2 in Data Group A, initial conditions are read from
the restart file (*.RST, where * is the input data set name), and Data Group J
should not be included in the input data set.
B-31
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TABLE 2.2.3 CROSS REFERENCES FOR WASP4 INPUT VARIABLES
Name
Data
Record
Name
Data
Record
Name
Data
Record
A
B 4
ADFAC
A 4
ANAME
G 2, I
2
BCT
E 4
BQ
D 4
BVOL
C 3
C
J 2
CHEML
J 1
CHKNAME
H 2
CMAX
I 1
CONST
H 4
CONVB
E 2
CONVN
F2 2
CONVR
B 2
CONVQ
C 2
CONVV
C 2
CONVW
F1 1
DISSF
J 2
DMULT
C 3
SDED
I 1
DTS
A 6
DXP
C 3
IL
B 4
FLDNAME
H 3
IBEDV
C 1
IBC
E 3
IBOTSG
C 3
ICFL
A 4
IDMP
A 4
IFI ELD
0 1
INPS
F2 3
INTYP
A 4
IQ
D 4
EQOPT
D 1
IR
B 4
ISC
G 2, G <
4, I 2
ISEG
G 3
ISG
G 3
IVOPT
C 1
IWK
F1 1
JQ
D 4
JR
B 4
JSEA
D2 3
JMASS
A4
IDSY
A4
IDSG1
A4
IDSG2
A4
JUNSEG
D2 4
KPRT
F2 4
KSIM
A 4
KT1
F2 4
KT2
F2 4
MFLAG
A 4
NBRKQ
D 5
NBRKR
B 5
NCONS
H 3
NEGSLN
A 4
NFIELD
D 1
NFLD
H 2
NFUNC
I 1
NINQ
D 2
NOBC
E 1
NOBRK
A 5, E :
3, I 2
NOPAM
G 1
NOQS
D 3
NORS
B 3
NOSEG
A 4
NOSYS
A 4
NOWK
F1 1
NOWKS
F2 1
NPRINT
A 7
NPSDAY
F2 1
NRFLD
B 1
NSEA
D2 3
NTEX
B 2
PARAM
G 4
PNAME
G 4
PRINT
A 8
PSCAL
G 2
QBY
D 7
QT
D 6
RBY
B 7
RT
B 6
SCALB
E 2
SCALN
F2 2
SCALQ
D 2
SCALR
B 2
SCALV
C 2
SCALW
F1 1
T
A 6, I :
4, F1 4
B-32
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TABLE 2.2.3 CROSS REFERENCES FOR WASP4 INPUT VARIABLES
Name
Data
Record
Name
Data
Record
Name
Data
Record
SYSBY
A 9
TADJ
A 4
TDINTS
C 1
TNAME
H 4
TPRINT
A 8
TQ
D 6
TR
B 6
VALT
I 3
VEXP
C 3
VMULT
C 3
WKT
F1 4
ZDAY
A 4
ZHR
A 4
ZMIN
A 4
WASP4 Output
WASP4 simulations produce several files that may be examined by the user.
These files use the file name of the input data set with a unique extension. The
most important of these is the DMP file, which contains all kinetic display
variables for each segment at each print interval throughout the simulation.
These display variables include concentrations, certain calculated variables, and
some rates. Available display variables for EUTR04 and T0XI4 are summarized in
the eutrophication and toxics user manual sections.
The W4DSPLY program is provided to help the user interactively examine the
display variables contained in the DMP file. To use this program, simply type
in the VAX (VMS) command "RUN W4DSPLY" or the PC (DOS) command "W4DSPLY." The
program will prompt the user for information, as explained in Section 2.1.
Other files created by a WASP simulation include *.0UT, *.TRN, *.MSB, and
*.RST (where * is the name of the input data set). The OUT file contains a
record of the input data plus any simulation error messages that may have been
generated. The TRN file contains a set of transport associated variables for
each segment at each print interval throughout the simulation. These variables
include the time step (day), calculated maximum time steps (day), segment volumes
(m3), segment flows (m3/sec), flow changes (m3/sec), time constants for segment
flow (day1), segment exchange flows (m3/sec), the time constant for segment
exchanges (day1), the segment dispersion coefficient (m3/sec), and the numerical
dispersion coefficient (m2/sec). The MSB file contains a mass balance record for
one designated system in the model network as a whole (in kg). For each print
interval, this file records the accumulated mass in from advection, dispersion,
and loading; the accumulated mass out through advection, dispersion, burial (or
volatilization, and kinetic transformation; the total resident mass; and the
residual (unaccounted for) mass.
The RST file contains a snapshot of volumes and concentrations of each
system in each segment at the conclusion of the simulation. This file can be
read by WASP4 to continue a series of simulations.
2.4 THE EUTROPHICATION MODEL
Introduction
EUTR04 requires the same input format as the basic WASP4 model. This
format is explained in detail in Section 2.3. This section describes variables
B-33
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needed specifically for EUTR04. Elaborations on WASP4 occur only in Data Groups
G, H, and I. Records or variables within a record that are not mentioned here
remain the same as described in Section 2.3.
As described in Section 1.4, the 13 systems for eutrophication modeling
are: ammonia nitrogen, nitrate nitrogen, ortho-phosphate phosphorus, three
phytoplankton carbon groups, carbonaceous BOD, dissolved oxygen, organic
nitrogen, organic phosphorus, silica, salinity, and coliform bacteria. Table
2.4.1 summarizes these systems and their use in six levels of complexity.
EUTR04 Data Descriptions
DATA GROUP A: Model Identification and System Bypass Option--
Record 4--Model Identification
NOSYS = 13
SYSBY(K) = 0 for those variables checked in the relevant
complexity level in Table 2.4.1.
= 1 for those variables not checked in the relevant
complexity level in Table 2.4.1.
TABLE 2.4.1 EURT04 SYSTEMS AND COMPLEXITY LEVELS
System
Number
Symbol
Name
Use in
Level
1 2
Complexity
3 4 5
6
1
NH3
Ammonia nitrogen
X
X
X
X
X
2
N03
Nitrate nitrogen
X
X
X
X
3
P04
Inorganic phosphorus
X
X
X
4
PHY1
Phytoplanktonl carbon
X
X
X
5
CBOD
Carbonaceous BOD
X
X
X
X
X
X
6
DO
Dissolved oxygen
X
X
X
X
X
X
7
ON
Organic nitrogen
X
X
X
X
8
OP
Organic phosphorus
X
X
X
9
PHY2
Phytoplankton2 Carbon
X
X
X
10
PHY3
Phytoplankton3 Carbon
X
X
X
11
SI 04
Silica
X
X
X
12
SAL
Salinity
X
X
X
X
X
X
13
BAC
Coliform Bacteria
X
X
X
X
X
X
B-34
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TABLE 2.4.1 EURT04 SYSTEMS AND COMPLEXITY LEVELS
Complexity
Level Expl anation
1 "Streeter-Phelps" BOD-DO with SOD
2 "Modified Streeter-Phelps" with NBOD
3 Linear DO balance with nitrification
4 Simple eutrophication
5 Intermediate eutrophication
6 Intermediate eutrophication with benthos
DATA GROUP B: Exchange Coefficients--
No changes.
DATA GROUP C: Volumes--
No changes.
DATA GROUP D: Flows--
No changes.
DATA GROUP E: Boundary Concentrations--
No changes. Input is repeated 13 times, once for each system. No
boundary concentrations need be specified for those systems being bypassed.
DATA GROUP F: Waste Loads--
No changes. Input is repeated 13 times, once for each system. No loads
need be specified for those systems being bypassed.
DATA GROUP G: Environmental Parameters--
Listed below are the 9 parameters required for eutrophication. For Level
1 and 2 analyses, only TMPSG, TMPFN, and S0D1D (3, 4, and 9) need be specified.
For Level 3 analysis, VELFN and FNH4 (1 and 7) may be added (DEPTH and VELFN are
used to compute reaeration; if rate constant K2 is specified (Constant 82), then
VELFN can be omitted). For analyses at Level 4 and above, all parameters should
be specified.
ISC PARAM (ISEG.ISC1 Definitions and Units
1 VELFN(ISEG) Pointer to the time-variable velocity function to
be used for ISEG. The four velocity functions are
defined by the user in data group I.
B-35
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2 SAL(ISEG)
Average salinity of ISEG, in g/L; used in calculation
of DO saturation.
3 TMPSG (ISEG)
4 TMPFN (ISEG)
5 KESG (ISEG)
6 KEFN (ISEG)
7 FNH4 (ISEG)
8 FP04 (ISEG)
9 SOD1D (ISEG)
Segment temperature multiplier (CC). TMPSG varies
overspace and can be either actual temperature or a
normalized function, depending on the definition of
TEMP. TMPSG(ISEG) * TEMP(TMPFN(ISEG)) = STP, the
temperature of segment ISEG.
Flag designating the time-variable temperature function
to be used for ISEG. The four temperature functions
are defined by the user in data group I.
Segment extinction coefficient multiplier (m'1). KESG
varies over space and can be either an actual
extinction coefficient or a normalized function,
depending on the definition of KE. KESG(ISEG) *
KE(KEFN(ISEG)) = Ke, the extinction coefficient for
segment ISEG.
Pointer designating the time variable extinction
coefficient (KE) to be used for segment ISEG. The five
extinction coefficients available are defined in data
group I.
Average ammonium
(mg/m -day).
flux multiplier for segment
Average phosphate
(mg/m -day).
flux multiplier for segment
Sediment oxygen demand for segment (g/m2-day).
DATA GROUP H: Constants-
Listed below are the 42 constants available for a full eutrophication
simulation. Figures 2.4.1 through 2.4.6 list the constants required for each
level of complexity.
ISC CONST(ISC)
11 K1320C
12 K1320T
13 KNIT
21 K140C
22 K140T
ANAME(ISC)
K12C
K12T
KNIT
K20C
K20T
Definition and Units
Nitrification rate at 20"C, per
day.
Temperature coefficient for
K1320C.
Half-saturation constant for
nitrification-oxygen limitation, mg
Oj/L.
Denitrification rate at 20"C, per
day.
Temperature coefficient for K140C.
B-36
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23
41
42
43
44
45
46
47
48
49
50
51
52
KN03
K1C
KIT
LGHTSW
PHIMX
XKC
CCHL
IS1
KMNG1
KMPG1
K1RC
K1RT
KID
KN03
K1C
KIT
LGHTS
PHIMX
XKC
CCHL
IS1
KMNG1
KMPG1
K1RC
K1RT
KID
Half-saturation constant for
denitrification oxygen limitation,
mg02/L.
Saturated growth
phytoplankton (day'1).
rate
of
Temperature coefficient.
Light formulation switch:
Dick Smith's
1, use
formulation
= 2, use Di
formulation
Toro et al
(USGS)
(1971)
Maximum quantum yield constant.
Used only when LIGHTSW = 1, mg
C/mole photons
Chlorophyll extinction coefficient.
Used only when LGHTSW = 1, (mg
chl a/m3)'1/m.
Carbon-to-chlorophyll ratio. Used
only when LGHTSW = 2 (mg carbon/mg
chla). Default = 30.
Saturation light intensity for
phytoplankton. Used only when
LGHTSW = 2 (Ly/day).
Nitrogen half-saturation constant
for nitrogen for phytoplankton
growth, which also affects ammonia
preference, mg-N/L. NOTE: This
affects ammonia preference:
= 0, PNH3G1 = 1.0
= Large, PNH3G1 = NH^NHa + N03)
NOTE: For standard model
application, use a large KMNG1.
Phosphorous half-saturation
constant for phytoplankton
growth, mg P04-P/L.
Endogenous respiration rate of
phytoplankton at 20°C, day"1
Temperature coefficient for
phytoplankton respiration.
Non-predatory phytoplankton death
rate, day"1.
B-37
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53
54
55
56
57
58
59
71
72
73
74
75
K1G
NUTLIM
KPZDC
KPZDT
PCRB
NCRB
KMPHYT
KDC
KDSC
KDST
KBOD
K1G
NUTLIM
KPZDC
KPZDT
PCRB
NCRB
KMPHY
KDC
KDSC
KDST
KBOD
Grazing rate on phytoplankton per
unit zooplankton population,
L/cel1-day.
Nutrient limitation option (default =
0).
0 = minimum
1 = multip!icati ve
Decomposition rate constant for
phytoplankton in the sediment at
20°C, per day.
Temperature coefficient for
decomposition of phytoplankton in
sediment.
Phosphorus-to-carbon ratio
phytoplankton, mg P04-P/mg C.
Nitrogen-to-carbon ratio
phytoplankton, mg N/mg C.
i n
i n
Half-saturation constant for
phytoplankton, mg carbon/L. NOTE:
As phytoplankton increases,
mineralization of organic nitrogen
and organic phosphorus increases.
KMPHYT = small; little phytoplankton
effect on mineralization
= large; large concentration of
phytoplankton needed to drive
mineralization
For standard model application, use
KMPHYT = 0.
BOD deoxygenation rate at 20°C, per
day.
Decomposition rate of carbonaceous
BOD in the sediment at 200C, per
day.
Temperature coefficient for
carbonaceous deoxygenation in the
sediment.
Half saturation constant for
carbonaceous deoxygenation oxygen
1 imitation.
KDT
KDT
Temperature coefficient for
B-38
-------
carbonaceous deoxygenation in
water column.
81 OCRB
82 K2
91
92
93
94
95
OCRB
K2_
K1013C
K1013T
KONDC
KONDT
FON
K71C
K71T
KONDC
KONDT
FON
100 K58C
101 K58T
102 KOPDC
103 KOPDT
104 FOP
K83C
K83T
KOPDC
KOPDT
FOP
Oxygen to carbon ratio in
phytoplankton, mg 02/mg C.
Reaeration rate constant at 20°C for
entire water body, day'1. NOTE: If
K2 is not entered, the reaeration
rate will be calculated from water
velocity, depth, and wind velocity.
Mineralization rate of dissolved
organic nitrogen, per day.
Temperature
K1013C.
coefficient for
Decomposition rate constant for
organic nitrogen in the sediment
at 20°C, per day.
Temperature coefficient for
decomposition of organic nitrogen
in the sediment.
Fraction of dead and respired
phytoplankton nitrogen recycled to
organic nitrogen.
Default = 1.0.
Mineralization rate of dissolved
organic phosphorus, per day.
Temperature coefficient for K58C.
Decomposition rate
phosphorus in the
20°C, per day.
of organic
sediment at
Temperature coefficient for
decomposition of organic
phosphorus in the sediment.
Fraction of dead and respired
phytoplankton phosphorus recycled to
organic phosphorus.
DATA GROUP I:
Default = 1.0.
Miscellaneous Time Functions-
Listed below are the 18 time functions available for eutrophication. Only
TEMP(l) is required for Level 1 and 2 analyses. For Level 3 analyses, TFNH4,
VELN(l), and WIND may be added (WIND is needed only for calculating reaeration
in non-flowing water bodies such as lakes). For analyses at Level 4 and above,
ITOT, F, KE, and TFP04 should be used. For resolution of spatial variability in
B-39
-------
temperature, light
five KE functions,
NOTE:
ISC ANAMEf ISO
1 TEMP(1)
2
3
4
5
6
7
8
9
10
11
12
13
14
15
TEMP(2)
TEMP(3)
TEMP(4)
I TOT
F
WIND
KE(1)
KE (2)
KE (3)
KE (4)
KE (5)
TFNH4
TFP04
VELN(l)
16 VELN(2)
17 VELN(3)
extinction, and water velocity the four TEMP functions, the
and the four VELN functions may be used.
Functions 1-4 are the four temperature-function options
available for TMPFN in Data Group G. Functions 8-12 are
the five extinction coefficient options for KEFN in Data
Group G. Functions 15-18 are the four water velocity
options for VELFN in Data Group G.
VALT fISC)
= Time-variable temperature function 1. TEMP(K) can be
either a normalized function or an actual temperature
in °C, depending upon the definition of the parameter
multiplier TMPSG(ISEG).
= Time-variable temperature function 2, unitless or °C.
= Time-variable temperature function 3, unitless or °C.
= Time-variable temperature function 4, unitless or °C.
= Total daily solar radiation, langleys.
= Fraction of daylight, days.
= Wind velocity, m/sec.
= Time-variable extinction coefficient function 1. This
can be either a normalized function or an actual
extinction coefficient in itv'1, depending upon the
definition of the parameter multiplier KESG(ISEG).
Time-variable extinction coefficient
unitless or m'1.
function 2,
Time-variable extinction coefficient function 3,
unitless or m"1.
= Time-variable extinction coefficient function 4,
function 5,
unitless or m" .
= Time-variable extinction coefficient
unitless or m"1.
= Normalized ammonium flux from bed, unitless.
= Normalized phosphate flux from bed, unitless.
= Time variable velocity function 1, m/sec. This
velocity is added to the net velocity VELOCG(ISEG)
computed from the segment flow and the hydraulic
parameters read in Data Group C.
= Time variable velocity function 2, m/sec.
= Time variable velocity function 3, m/sec.
B-40
-------
18 VELN(4) = Time variable velocity function 4, m/sec.
19 ZOO = Herbivorous zooplankton population, mgC/L.
DATA GROUP J: Initial Concentrations--
No changes. Input is repeated 8 times, once for each system. Solids transport
fields must be specified for the particulate fraction of each system (Solids
Field 3 here is"particulate organic matter; Solids Field 4 is phytoplankton;
Solids Field 5 is inorganic sediment). The dissolved fraction of each system in
each segment must also be specified.
Record l--Solids Transport Fields
IFIELD(l) = 3
IFIELD(2) = 5
IFI ELD(3) = 5
IFIELD(4) = 4 (make sure DISSF = 0.0)
IFI ELD(5) = 3
IFIELD(6) = 5 (make sure DISSF = 1.0)
IFIELD(7) = 3
IFI ELD(8) = 3
TABLE 2.4.2 CROSS REFERENCES FOR EUTR04 INPUT VARIABLES
Data
Data
Data
Name
Record
Name
Record
Name
Record
CCHL
H
46
DEPTH
G
1
F
I 6
FNH4
G
7
FP04
G
8
IS1
H 47
I TOT
I
5
K12C
H
11
FON
H 95
K1C
H
41
KID
H
52
K1G
H 53
K12T
H
12
FOP
H
104
KIT
H 42
K2
H
82
K1RC
H
50
K1RT
H 51
K71C
H
91
K71T
H
92
K20C
H 21
K20T
H
22
KBOD
H
75
KDC
H 71
K83C
H
100
K83T
H
101
KDT
H 72
KDSC
H
73
KDST
H
74
KE(1)
I 8
KE(2)
I
9
KE(3)
I
10
KE(4)
I 11
KE(5)
I
12
KEFN
G
6
KMNG1
H 48
KMPHYT
H
9
KESG
G
5
KN03
H 23
B-41
-------
TABLE 2.4.2 CROSS REFERENCES FOR EUTR04 INPUT VARIABLES
Data
Data
Data
Name
Record
Name
Record
Name
Record
KONDC
H
93
KMPG1
H
49
KNIT
H 13
KOPDT
H
103
KPZDC
H
55
KONDT
H 94
KOPDC
H
102
KCRB
H
58
NUTLIM
H 54
KPZDT
H
56
LGHTSW
H
43
PHIMX
H 44
SOD ID
G
9
OCRB
H
81
PCRB
H 57
TEMP(1)
I
1
TEMP(2)
I
2
TEMP(3)
I 3
TEMP(4)
I
4
TMPFN
G
4
TMPSG
G 3
TFNH4
I
13
TFP04
I
14
VELFN
G 1
VELN(l)
I
15
VELN(2)
I
16
VELN(3)
I 17
VELN(4)
I
18
WIND
I
7
XKC
H 45
ZOO
I
19
EUTR04 Output
The standard WASP4 output files were summarized in Section 2.3. EUTR04
stores in the DMP file 36 kinetic display variables. These variables are defined
in Table 2.5.2. To examine these variables in tabular form, the user may run
W4DSPLY as explained in Section 2.3.
TABLE 2.4.3 EUTR04 KINETIC DISPLAY VARIABLES
Number Variable Definition
1 SEG. DEPTH Depth in segment (m).
2 WATER VEL. Water velocity within segment (m/sec).
3 ITOT Incoming solar radiation (Langleys/day).
4 SEG. TEMP Temperature within segment (°C).
5 SEG. TYPE Segment type (1, 2, 3 or 4)
6 PHYT Phytoplankton biomass as carbon (mg/L).
7 RESP Phytoplankton respiration rate constant (day1).
8 DEATH Phytoplankton death rate constant
(day1).
9 LIMIT Nutrient limitation indicator ("+" = nitrogen,
" = phosphorus).
10 TCHLAX Phytoplankton chlorophyll a concentration U/g/L).
11 XEMP1 Nitrogen limitation factor for phytoplankton.
12 XEMP2 Phosphorus limitation factor for phytoplankton.
B-42
-------
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
TABLE 2.4.3 EUTR04 KINETIC DISPLAY VARIABLES
Vari abl e Definition
GP1 Light and nutrient limited phytoplankton growth
rate constant (day'1).
RLIGHT Light limitation factor for phytoplankton growth.
RNUTR Nutrient limitation factor for phytoplankton.
PHN3G1 Preference factor for ammonia over nitrate.
NH3 Segment ammonia concentration (mg/L).
N03 Segment nitrate plus nitrite concentration
(mg/L).
ON Segment organic nitrogen concentration (mg/L).
TIN Total inorganic nitrogen concentration (mg/L).
TOT. N Total nitrogen concentration (mg/L).
TON Total organic nitrogen concentration (mg/L).
CN Total inorganic nitrogen (mg/L).
OP Segment organic phosphorus concentration (mg/L).
0P04 Segment orthophosphate concentration (mg/L).
TIP Total inorganic phosphorus concentration (mg/L).
TOP Total organic phosphorus concentration (mg/L).
RATIO Inorganic nitrogen to phosphorus ratio (mg/mg).
DO Dissolved oxygen concentration (mg/L).
CBOD Carbonaceous biochemical oxygen demand (mg/L).
B0D5 5-Day biochemical oxygen demand (mg/L).
UBOD Ultimate 30-day BOD (mg/L).
DOMIN Minimum diurnal dissolved oxygen (mg/L).
DOMAX Maximum diurnal dissolved oxygen (mg/L).
CS Dissolved oxygen saturation concentration (mg/L).
KDC Dissolved carbonaceous BOD deoxygenation rate at
20°C (day'1).
DEL02 Diurnal dissolved oxygen variation (mg/L).
B-43
-------
APPENDIX C: Contact List
-------
Tom Cavinder
U.S. EPA Athens Lab
(404) 546-2294
Jay Sauber
North Carolina Deptartment of Environmental
Health & Natural Resources
(919) 733-6510
John Davis
Delaware Deptartment of Natural Resources
and Environmental Control
(302) 739-4590
Laura Hen-
Delaware Deptartment of Natural Resources
and Environmental Control
(302) 739-5731
Ken Echternacht, Ph.D., P.E.
Florida Dept. of Environmental Regulation
(904) 488-0130
John Hamrick, Ph.D.
Virginia Institute of Marine Science
College of William and Mary
(804) 642-7210
Albert Kuo, Ph.D.
Virginia Institute of Marine Science
College of William and Mary
(804) 642-7212
Fred Morris, Ph.D., P.E.
St. Johns River Water
Management District
(904) 329-4329
B. Christensen
University of Florida
(904) 392-0952
C-l
-------
APPENDIX D: Dye Concentration Contours
-------
Dye Sampling Run § 2 >/50 i 0 / ' i / 3 S
Dye Sampling Run# 3 0024- 10/12/88
D-l
-------
Ove Sampling Run# 5 1202 "10/12/33
Dye Sampling Run# 7 0010 10/13/88
Dye Sampling Run# 8 0630 10/13/33
D-2
-------
APPENDIX E: Case Study for a Proposed Marina
-------
TC-3668-04
RIVE ST. JOHNS PHASE II CANAL SYSTEM
WATER QUALITY MODEL STUDY
September 1988
prepared for
Dostie Builders, Inc.
2960 Hartley Road
Jacksonville, Florida 32217
-------
TC-3668-04
RIVE ST. JOHNS PHASE II CANAL SYSTEM
WATER QUALITY MODEL STUDY
prepared for
Dostie Builders, Inc.
2960 Hartley Road
Jacksonville, Florida 32217
September 1988
prepared by
M.R. Morton, P.E.
G.E. Hayes
Tetra Tech, Inc.
7825 Baymeadows Way
Jacksonville, Florida 32216
-------
TABLE OF CONTENTS
Page
1.0 INTRODUCTION 1
1.1 Purpose 1
1.2 Project Location 1
2.0 DEM MODEL DESCRIPTION 3
2.1 Model Selection 3
2.2 DEM Model Description 3
3.0 HYDRODYNAMIC CALIBRATION 10
3.1 DEM Model Layout 10
3.2 DEM Tide Calibration 10
4.0 WATER QUALITY MODEL (DQUAL) 14
4.1 Boundary Conditions 14
4.2 Water Quality Analyses 15
5.0 SUMMARY AND CONCLUSIONS 26.
6.0 REFERENCES 26
APPENDIX A
-------
1.0 INTRODUCTION
1.1 Purpose
This report documents a water quality analysis of a proposed recreational
boat canal for the Rive St. Johns Phase II development on the St. Johns River,
Jacksonville, Florida. This study was initiated as a result of the Environmental
Protection Agency Region IV review of the Rive St. Johns permit application
submitted to the Corps of Engineers by Dostie Builders (Public Notice No. 87IPF-
21164). Upon review of a previous study of flushing characteristics of the
proposed canal (Tetra Tech, 1988), EPA expressed concern that water quality in
the canal could possibly depress dissolved oxygen levels below the 4.0 mg/L
standard set by the State of Florida for marine waters.
In a letter from Robert F. McGhee, Chief of Water Quality Management Branch
at EPA Region IV, to Mr. Charles Ashton of the Regulatory Division of the Corps-
of Engineers (see Appendix A) it was suggested that the applicant use the Dynamic
Estuary Model (DEM) to perform the water quality calculations necessary tcr
predict dissolved oxygen levels in the canal. This report documents the
application of DEM to the proposed canal system.
1.2 Pro.iect Location
The Rive St. Johns Phase II development site is located in Jacksonville,
Duval County, Florida, on the east bank of the St. Johns River about one-half
mile south of Reddie Point (see Figure 1.1). The study area is located directly
across the river from the U.S. Coast Guard Station at the mouth of the Trout
River. The flow in the St. Johns River in the study area is affected by tides
which are semidiurnal in nature, that is, there are two high and two low waters
in a tidal day with comparatively little diurnal inequality. Boundary conditions
for the DEM model were taken from the State of Florida long-term monitoring
station #4 "Off Talleyrand Avenue" on the St. Johns River within one mile of the
study area (see -Figure 1.1). The Main Street Bridge is the approximate
geographical divisor of predominantly fresh and predominantly marine waters in
Duval County. The study area is about 7 miles downriver from the Main Street
Bridge and the waters are classified as marine.
-------
Approx. location of
Montioring Sta. #4
"Off Talleyrand Ave." ^ | j? ,
Figure 1.1 Location of Study Area for Rive St. Johns Phase II Development.
-------
2.0 DEM MODEL DESCRIPTION
2.1 Model Selection
The Dynamic Estuary Model (DEM) was originally developed for the U.S.
Environmental Protection Agency and has been suggested by EPA Region IV as the
model of choice for this study. In addition, DEM has been selected for the
following reasons:
o DEM is "network" type model which is ideally suited for simulation of
channelized streams, estuaries, and waterways, such as the Rive St. Johns
proposed canal system.
o DEM is widely used by government and private industry, and is an approved
EPA model (developed under EPA funding and supported by the EPA research
lab at Athens, Georgia).
o DEM has been tested and successfully verified based on extensive field:
studies in numerous different water systems.
2.2 DEM Model Description
The DEM model represents the estuarine system as a network of "nodes" and
"channels". Nodes are discrete volume units of a waterbody, characterized by
surface area, depth, side slope and volume (see Figure 2-1). The nodes are
interconnected by channels, each having associated length, width, cross-sectional
area, hydraulic radius, side slope and friction factor. Water is constrained
to flow from one node to another through these channels, advecting and diffusing
water quality constituents between nodes.
The overall two-dimensional DEM model is composed of three separate
components, a hydrodynamic model (HYD1), a dynamic quality model (DQUAL), and
a steady-state quality model (AQUAL). The first uses the equations of motion
and continuity to calculate channel flows and nodal volume changes in response
to wind and tidal boundary fluctuations. Dynamic and/or steady-state results
(averaged over a complete tidal cycle) are stored on disk files to be used
3
-------
V\<^e
1-\-
¦f. ^
es
V
•t\0(
d®
\v»o'
,fV-
-------
repeatedly in the calibration of the quality models. Once the physical transport
mechanisms of water flow and velocities are determined, the biological and
chemical reactions can be superimposed to calculate water quality at any location
and time.
The dynamic and steady-state water quality models used in this evaluation
assume that the estuarine-system is well mixed vertically, that the law of
conservation of mass is ' obeyed for water quality constituents, and that
biochemical reaction rates may be estimated using first order kinetics
characterized by reaction-specific rate coefficients. DQUAL and AQUAL can be
used to simulate any combination of the following 9 constituents and have the
capability to include up to four additional user specified conservative
constituents.
1.
Salinity (chloride)
2.
Total Nitrogen (TKN + N02 + N03)
3.
Total Phosphorus (P0A - P)
4.
Total Coliform Bacteria
5.
Fecal Coliform Bacteria
6.
Carbonaceous BOD (5-day) (model converts to ultimate BOD)
7.
TKN (model converts to nitrogenous BOD)
8.
Dissolved Oxygen
9.
Temperature
Inputs to the dynamic estuary model are as follows:
o Physical and geometric characteristics of the estuary, including slopes,
channel widths, friction factors, node depths and areas;
o Time-varying meteorological and cl imatological data, including cloud cover,
dry and wet bulb air temperature, atmospheric pressure, wind speed and
direction, and precipitation;
o Initial in-situ water quality parameter concentrations;
o Time-varying tidal stages and currents at the seaward boundaries;
o Time-varying water quality at boundaries;
o Tributary inflows and waste discharges;
o Groundwater.inflow;
o Inflow quality (including groundwater);
o Time-varying stormwater inflow;
o Stormwater quality characteristics, and
o Rate coefficients for the kinetic reactions.
5
-------
Outputs from the dynamic estuary model (DQUAL) include detailed time
profiles of tidal stages and water quality constituent concentrations at the
nodal locations within the estuary system.
Inputs and outputs for the steady-state estuary model (AQUAL) are
essentially the same as for the dynamic model, except inputs and outputs
represent long "term, averaged conditions.
The dissolved oxygen concentration of a natural water body is a function
of a variety of physical, chemical, and biological processes. The diagram shown
in Figure 2-2 is a schematic representation of the processes which are considered
to be the major components affecting dissolved oxygen. These processes have been
incorporated in the model which links the oxygen sinks (such as CBOD, NBOD,
phytoplankton respiration and benthic demand) with oxygen sources (such as
photosynthesis and reaeration) to calculate the dynamic dissolved oxygen
concentration.
The remaining water quality constituents are not interrelated in the model
Each is considered to act independently of the others. Since salinity (or
chloride) is considered a conservative constituent, it can be used to test the
ability of the model to simulate the hydrodynamics and transport of the system.
The phenomenon of dispersive transport in the model results from a combination
of "numerical" dispersion and dispersion coefficients which can be adjusted to
simulate the turbulent mixing properties of the water body.
All other constituents are assumed to be advected and diffused in a manner
analogous to salinity (chloride), though they may be non-conservative through
one or more of the following natural processes:
o Decay
o Macrophytic Growth
o Grazing
o Excretion
o Mortality
o Settling
o Upwelling
6
-------
=?fF
REAERATION DEOXYGENATION
NITROGENOUS BOD
V UtCAT /
h'lTir
2k.
DISSOLVED OXYGEN
PIOTOSYtmCTIC ACTIVITY-
CARBONACEOUS BOD
->
ALGAL RESPIRATION
BtNTHIC DEM©
Figure 2-2. Flow diagram of modeled processes related to dissolved oxygen.
-------
These processes are represented by the first order decay terms and rate
coefficients which add to the complexity of the mass balance equation which
describes the prototype system.
Constituents which decay in the marine environment such as BOD and TKN are
described mathematically by first order decay terms, Kd, Kn, in addition to the
terms for advection, inflow and withdrawal. The amount of constituent which
decays in a given time step is a function of the concentration of the constituent
at the beginning of the time step. The first order decay coefficients are
temperature dependent according to the following formula.
Km = K(20) 9 (T"20) (2-1)
where
K = first order rate coefficient
T = water temperature in °C
9 = 1.047 for oxygen demand and 1.02 for coliform
decay coefficients and user specified nonconservative
constituents.
Due to this temperature dependency, it is important that the water quality
model give reasonably accurate simulation of water temperature variations over
both space and time.
Since nutrients such as nitrogen and phosphorus can be affected by a
complex combination of biological processes, source/sink terms are also included
in the equations which describe these constituents in mathematical terms. These
source/sink terms are used to represent the net effect of all the above processes
acting in concert. Since it is not possible to measure the individual
contribution of each process, it is necessary to determine the collective
source/sink rate by "calibrating" the model so that it recreates what is measured
in the field. Once this is done, the resulting model is tested or verified using
one or more additional sets of data to ensure that the source/sink term which
was selected proves correct for more than one set of conditions.
However, for this study the proposed canal does not yet exist so the DEM
model cannot be calibrated and verified based on field data. Instead, a base
8
-------
condition consisting of average typical values of the various rate coefficients
for the kinetic reaction terms and source/sink terms was defined. A model
sensitivity analysis was then performed by varying the rate coefficients about
their typical ranges and the effect on dissolved oxygen was documented. Water
quality impacts of the'proposed canal can then be implied from the sensitivity
analysi s.
9
-------
3.0 HYDRODYNAMIC CALIBRATION
3.1 DEM Model Layout
The Dynamic Estuary Model was configured to represent the proposed canal
system by a grid network of nodes linked together by channels. The following
are underlying assumptions of the Dynamic Estuary Model:
o The estuarine system is well mixed vertically
o The law of conservation of mass is obeyed for water quality constituents
o Chemical reaction rates may be estimated using first order kinetics
characterized by reaction-specific rate coefficients.
The area modeled by DEM includes the main entrance channel and the entire
loop canal system. The DEM model node and channel geometry was selected to give
approximately uniform representation along the entire canal system. A node,
spacing of 289 ft to 721 ft was necessary to ensure stability for the given
2.5 ft tide range. The model consisted of 9 nodes and 9 channels as shown iiv
Figure 3-1. The node and channel geometry is given in Table 3-1. The width,
hydraulic radius, and Manning n values shown for each channel were the adjusted
numbers necessary to calibrate the HYDRO velocities with the previous CAFE1
velocities.
3.2 DEM Tide/Velocity Calibration
Typically the hydrodynamic sub-model (HYDRO) of DEM is calibrated to
measured tides and velocities in the water body. Of course, this was not
possible for the proposed canal. Instead, a different approach was undertaken
in which the hydrodynamic results from a previous study (Tetra Tech, 1988) based
on a detailed finite-element model (CAFE1) of the canal system were used to
calibrate HYDRO. Calibration of HYDRO was accomplished through successive
adjustment of channel roughness and channel geometry (hydraulic radius and width)
until the velocities in the DEM model channels matched those from the CAFE1
model. Reasonable agreement between HYDRO and CAFE1 velocities was achieved as
shown in Figures 3-2(a) through 3-2(i).
10
-------
Table 3-1
Node Geometry for DEM Model of Rive St. Johns Canal
Surface Area
Depth
Length
Width
Hyd. Rad.
Manning
Junction
(sq. feet)
(ft)
Channel
(ft)
(ft)
(ft)
n
1
39,696
5.2
1
721
100
4.7
0.017
2
71,220
5.2
2
541
60
4.7
0.017
3
43,011
5.2
3
365
40
4.7
0.018
4
28,763
5.2
4
290
35
4.5
0.019
5
33,252
5.2
5
296
60
4.5
0.020
6
34,130
5.2
6
335
150
4.7
0.025
7
37,571
5.2
7
362
260
4.7
0.035
8
43,468
5.2
8
361
220
4.7
0.045
9
31,820
5.2
9
289
130
4.7
0.065
11
-------
Figure 3-2(a). HYDRO channel 1
Figure 3-2(c). HYDRO channel 3
^ -O OS .
9
-0 10 .
-0 13 I I I I I I I
0 6 12 U 2* 30 38 *1 4|
TIW
Figure 3-2(e). HYDRO channel 5
0 0 12 II 24 30 » *3 «•
TIW
I1*» (nou-i)
Figure 3-2(b). HYDRO channel 2
nem ij ¦¦¦ - >*ro c-ei*»i «
0 10
? a as
1
I '
•
...
i
t
-9 19
J 6 12 11 *4 30 3» «l
TIM C
:igure 3-2(d). HYDRO channel 4
CAM r«a» 11
• "rCPO cf*nr*T S
0 10
? 0 09
1
| .
i
f
• 0 (0
3 0 '2
11 24 X 3B <2 » C«WI)
Figure 3-2(g). HYDRO channel 7
12 Figure 3-2(h). HYDRO channel 8
-------
Figure 3-2(i). HYDRO channel 9
13
-------
4.0 WATER QUALITY MODEL (DQUAL)
4.1 Boundary Conditions
The boundary conditions for the water quality model DQUAL were based on
measured values at the closest long-term monitoring station, Sta. #4 Talleyrand
Avenue, which is within one mile of the canal entrance (Figure 1.1). Constituent
concentrations were taken from monitoring data sheets for summer months over the
period 1983-1986 and averaged to form a data set of typical summer conditions.
The parameters and associated concentrations used to derive the boundary
conditions are given in Table 4-1. Typical ranges for the system rate
coefficients used in the DQUAL model are summarized below:
System Rate Coefficient Typical Range
Kd, Carbonaceous BOD decay rate (1/day) 0.1 - 0.3
!(„, Nitrification rate (1/day) 0.05 - 0.15
P, Algal photosynthetic oxygen production (g/m2-day) 0.0 - 15.0
R, Algal oxygen consumption due to respiration (g/m2-day) 0.0 - 7.5
SOD, Sediment oxygen demand rate (g/m2-day) 0.0 - 5.0
Ka, Reaeration rate (1/day) 0.25 - 3.0
Table 4-1
Summer Conditions in St. Johns River at Sta. #4 (Talleyrand Avenue)
Parameter
(units)
7/8/86
9/8/86
6/11/85
6/26/84
8/14/84
Average
Chiorides
(mg/L)
7200
7600
11200
7500
2700
7240
Conductivity
(mmho/cm)
21.2
23.1
29.4
23.0
9.0
22.4
Total N
(mg/L)
0.841
0.307
0.101
0.098
0.193
0.31
Total P
(mg/L)
0.202
0.069
0.104
0.124
0.069
0.11
D.O.
(mg/L)
6.3
6.2
6.9
6.5
4.7
6.3
Temp.
(•C).
29.5
28.0
29.4
28.5
29.7
29.0
TKN
(mg/L)
-
-
-
0.59*
0.60**
0.60
* 3/14/84 ** 12/14/83
14
-------
The meteorological conditions used in the DQUAL model were obtained from
NOAA and are average conditions for the month of August based on 10 years of
continuous data from 1965-1974 at,the Jacksonville Airport (NOAA, 1978).
4.2 Water Quality Analyses
The DQUAL model was initially set-up to simulate a "typical" summertime
condition referred to as the base condition or base case (Run 00). Next, 13
additional model runs were made in which each of the six system rate coefficients
was varied within the typical range of values to determine the effect on
dissolved oxygen levels in the canal system. Finally, a worst case scenario
based on the results of model runs 00 through 13 was simulated to determine the
effect on dissolved oxygen. The system rate coefficients used for the various
DQUAL model simulations are given in Table 4-2.
Figures 4-1(a) and 4-1(b) show the effect of changes in sediment oxygen,
demand (SOD) on daily average dissolved oxygen and daily minimum dissolved
oxygen, respectively. The base condition (Run 00) which represents a typical-
summertime condition shows that dissolved oxygen (D.O.) levels throughout the
canal system will remain nearly the same as those in the St. Johns River. As
SOD is increased, the D.O. concentrations are depressed in the vicinity of nodes
5, 6, and 7. However, at all times the D.O. levels remain above the Florida
state water quality standard of 4.0 mg/L for marine waters.
The effect of different reaeration rates (Ka) on dissolved oxygen is shown
in Figures 4-2(a) and 4-2(b). It is evident that dissolved oxygen is sensitive
to the value of Ka in the model. The reaeration rate of 0.50/day used in the
base case (Run 00) is thought to be conservatively low, and 0.30/day (Run 03)
was considered unrealistically low but was included for comparison purposes.
The DQUAL model calculated a reaeration rate of 0.89/day based on the
hydrodynamic velocities and meteorological conditions of the system.
Algal oxygen consumption due to respiration (R) impacts on dissolved oxygen
are shown in Figures 4-3(a) and 4-3(b). Also, the effects of changes in algal
oxygen production (P) on dissolved oxygen are shown in Figure 4-4(a) and 4-4(b).
Generally, algal consumption values are one-half algal oxygen production rates.
15
-------
Table 4-2
Summary of System Rate Coefficients used in DQUAL Model Simulations
Run SOD Ka R P 1^ Kd Figure
Number (g/m2-day) (1/day) (g/m2-day) (g/m2-day) (1/day) (1/day) Number
Run 00 1.5 0.50 2.5 4.0 0.10 0.25
Run 01 2.0 0.50 2.5 4.0 0.10 0.25 4-1
Run 02 2.5 0.50 2.5 4.0 0.10 0.25
Run 03 1.5 0.30 2.5 4.0 0.10 0.25
Run 04 1.5 0.70 2.5 4.0 0.10 0.25 4-2
Run 05 1.5 0.90 2.5 4.0 0.10 0.25
Run 06 1.5 0.50 2.0 4.0 0.10 0.25 4-3
Run 07 1.5 0.50 3.0 4.0 0.10 0.25
Run 08 1.5 0.50 2.5 3.0 0.10 0.25 4-4
Run 09 1.5 0.50 2.5 5.0 0.10 0.25
Run 10 1.5 0.50 2.5 4.0 0.05 0.25 4-5
Run 11 1.5 0.50 2.5 4.0 0.15 0.25
Run 12 1.5 0.50 2.5 4.0 0.10 0.15 4-6
Run 13 1.5 0.50 2.5 4.0 0.10 0.30
Run 14 2.5 0.50 2.0 4.0 0.15 0.30 4-7
Notes:
SOD = Sediment Oxygen Demand
Ka = Reaeration rate
R = Algal oxygen comsumption due to Respiration
P = Algal photosynthetic oxygen production
Kn = Nitrification rate
Kd = CB0D decay rate
16
-------
Ol
6
T3
OJ
Rive St Johns Water Quality Study
Dynamic Estuary Model
Effect of SOQ an daily Avg Q.Q.
Run 00 SOO
Hun 01 SOD
Hun 02 SOD
1 5 g/sq m-aay
2.0 g/sq m-day
2 5 g/sq m-day
10
9
B
7
6
0 0 Saturation
Florida State Standard
3 4 5 6
Model Node NumDer
Figure 4-1(a). Effects of sediment oxygen demand (SOD) on daily average
dissolved oxygen.
CD
£
CD
O)
O
tn
CO
Rive St Johns Water Quality Study
Dynamic Estuary Model
Effect of SOD on Daily Min. D.O.
Run 00 SOD
Hun 01 SOD
Run 02 SOO
1.5 g/sq.m-day
2.0 g/sq.m-day
2 .5 g/sq.m-day
10
9
B
7
6
5
4
3
2
1
D 0. Saturation
Florida State Standard
3 4 5 6
Model Node NumDer
Figure 4-1(b). Effects of sediment oxygen demand (SOD) on daily minimum
dissolved oxygen.
17
-------
Rive St Johns Water Quality Study
Dynamic Estuary Model
Effect of Reaeration Hate an Avg Q 0. —
10
Run 00 Ka • 0 50/day
Run 03 Ka = 0 30/day
— Run 04 Ka = 0 70/day
Run 05 Ka • 0 90/day
V
Ol
u
~1
¦a
tu
D 0 Saturation
9
8
7
6
5
4
Florida State Standard
3 4 5 6
Model Node Numder
Figure 4-2(a). Effects of reaeration rate (KJ on daily average dissolved
oxygen.
a>
e
c
o
~I
Rive St Jonns water Quality Study
Dynamic Estuary Model
Effect of Reaeration Rate an Min. O.O.
— Run 00 Ka - 0.30/day
— Run 03 Ka = 0.50/day
— Run 04 Ka =¦ 0.70/day
— - Run 05 Ka - 0.90/day
10
9
a
7
6
5
4
3
2
1
~ 0. Saturation
Florida State Standard
3 4 5 6
Model Node Numtier
Figure 4-2(b). Effects of reaeration rate (KJ on daily minimum dissolved
oxygen.
18
-------
Pun 00 R - 2.5 g/sq m-aay
Run 06 fl = 2 0 g/sa m-aay
Hive St Jonns Water Quality Study
Dynamic Estuary Moael
Run 07 fl = 3 0 g/sq.m-Gay
Effect of Algal 02 Consumption due to Respiration on Avg. 0.0.
o
T3
10
9
8
7
6
5
4
3
2
1
0
0 0 Saturation
Florida State Standard
3 4 5 6
Model Node Number
Figure 4-3(b). Effects of algal oxygen consumption due to respiration (R)
on daily average dissolved oxygen.
Rive St Johns Hater Quality Study Run 00 fl - 2.5 g/sq.m-day
Oynamic Estuary Model Run 06 R = 2.0 g/sq.m-day
Run 07 R = 3 0 g/sq m-day
Effect of Algal 02 Consumption due to Respiration on Min. 0.0.
10
g
_l
a
O)
E
7
C
CD
cn
6
>
X
o
5
¦O
0)
>
4
o
3
a
2
1
0
D 0 Saturation
Florida State Standard
3 4 5 6
Model Node Number
Figure 4-3(b). Effects of algal oxygen consumption due to respiration (R)
on daily minimum dissolved oxygen.
19
-------
Hive St. Johns water Quality Stuay Run 00 P - 4,0 g/sq m-day
Dynamic Estuary Model Run 08 P = 3 0 g/sq m-day
Run 09 P = 5 0 g/sq.m-day
Effect of Algal Photosynthetic 02 Production on Avg. D.O.
10
0 0 -Saturation
Florida State Standard
3 4 5 6
Model Node Number
Figure 4-4(a). Effects of algal photosynthetic oxygen production (P) on
daily average dissolved oxygen.
c
0)
Ol
Rive St. Johns Water Quality Study
Oynamic Estuary Model
Effect of Algal 02 Production on Mm. D.O.
Hun 00 P - 4.0 g/sq.m-day
Run 00 P - 3.0 g/sq.m-day
Pun 09 P » 5.0 g/sq m-day
10
9
B
7
0 0. Saturation
Florida State Standard
3 4 5 6
Model Node Number
Figure 4-4(b). Effects of algal photosynthetic oxygen production on daily
minimum dissolved oxygen.
20
-------
The effects of changing nitrification rates (KJ on dissolved oxygen in
the canal system is given in Figures 4-5(a) and 4-5(b). Varying K,, through its
typical range of values had little effect on dissolved oxygen values. Similarly,
changing the value of Kd, the CBOD decay rate, had a very small effect on
dissolved oxygen as shown in Figures 4-6(a) and 4-6(b).
Results for the worst case scenario (Run 14) are given in Figures 4-7(a)
and 4-7(b), the daily average dissolved oxygen and daily minimum dissolved
oxygen, respectively. The system rate coefficients for the worst case scenario
are given in Table 4-2. The algal oxygen production rate (P) was set to twice
the algal oxygen consumption rate (R) which is a typical ratio for these two
coefficients. Even under these extreme conditions, the Florida state water
quality standard for dissolved oxygen was not contravened at any time nor at
any point in the canal system. The typical range of SOD used in this analysis
(1.5 - 2.5 g/m2-day) was obtained via personal conservation with Mr. Tom
Cavinder, EPA Region IV, by M.R. Morton, Tetra Tech, Inc.
The DEM model was run for 10 days for each of the simulation runs to allow-
the dissolved oxygen concentrations at each node to reach a steady-state
condition. A time history of dissolved oxygen concentration for nodes 4, 5, and
6 for Run 00 is shown in Figure 4-8. It is evident that the model reaches steady
conditions at about 10 to 12 days.
21
-------
o>
E
c
01
at
>*
x
o
Rive St Johns Water Quality Study
Dynamic Estuary Model
Effect of Nitrification Rate on Avg. 0 0
Run 00 Kn » 0 10/day
Run 10 Kn = 0.05/day
Run 11 Kn = 0 15/day
10
0 0. Saturation
Florida State Standard
3 4 5 6
Model Node Number
Figure 4-5(a). Effects of nitrification rate (KJ on daily average
dissolved oxygen.
Rive St. Johns water Quality Study
Dynamic Estuary Model
Effect of Nitrification Rate on Mm. D.O.
Run 00 Kn - 0 .10/day
Run 10 Kn * 0.05/day
Run 11 Kn = 0.15/day
10
9
5
7
6
5
4
3
2
1
0
D.O Saturation
Florida State Standard
3 4 5 6
Model Node Number
Figure 4-5(b).
Effects of nitrification rate (KJ on daily minimum
dissolved oxygen.
22
-------
_l
\
o
¦o
o
II)
cn
Hive St Johns water Quality Study
Dynamic Estuary Model
Effect of CBQD Decay Rate on Avg. 0 0.
— Run 00 Kd = 0 25/aay
• Run 12 Kd = 0 .15/day
— Run 13 Kd = 0 30/oay
10
9
8
7
6
5
4
3
2
1
0
3 4 5 6
Model Node Numoer
0 0 Saturation
-
Florida State Standard
i i i i i
till
Figure 4-6(a).
Effects of carbonaceous BOD decay rate (Kd) on daily average
dissolved oxygen.
Rive St. Johns Water Quality Study
Dynamic Estuary Model
Effect of CBOD Decay Rate on Min. D.O.
Run 00 Kd - 0.25/day
Run 12 Kd = 0 .15/day
Run 13 Kd = 0.30/day
10
9
8
7
6
5
4
3
2
1
0
0 0. Saturation
-
Florida State Standard
i i i i i
i i i i
3 4 5 6
Model Node Numoer
Figure 4-6(b).
Effects of carbonaceous BOO decay rate (Kd) on daily minimum
dissolved oxygen.
23
-------
O)
£
0)
O)
o
TZ
o
V)
Hive St Johns Water Quality Stuay
Dynamic Estuary Model
Worst Case Scenario - Daily Avg 0 0
Run 00 Base Case
Run 14 worst Case
.
0 Q Saturation
-
Florida State Standard
i i i i i
i i i i
3 4 5 6
Model Noae NumDer
Figure 4-7(a).
Predicted daily average dissolved oxygen concentrations
under the worst case scenario.
Rive St. Johns Water Quality Study
Dynamic Estuary Model
Worst Case Scenario - Daily Min D.O.
Run 00 Base Case
Run 14 Worst Case
10
9
B
7
6
5
4
3
2
1
0
D.O. Saturation
-
^ -
Florida State Standard
-
i |
1 i 1
1 i 1 i
3 4 5 6
Model Node NumQer
Figure 4-7(b).
Predicted daily minimum dissolved oxygen concentrations
under the worst case scenario.
24
-------
~ays of Simulation
Figure 4-8. Time required for DQllAL to reach steady-state conditions.
25
-------
5.0 SUMMARY AND CONCLUSIONS
The Dynamic Estuary Model (DEM) was applied to the proposed waterway canal
at the Rive St. Johns Phase II development site on the St. Johns River,
Jacksonville, Florida. The purpose of applying DEM was to address the concerns
of Region IV Environmental Protection Agency regarding possible water quality
contraventions in the canal. The DEM model application takes into account
reaeration, sediment oxygen demand, and the water quality of receiving waters.
The results of the DEM model (see Figures 4-1 through 4-7) indicate that
the proposed canal will not cause degredation of water quality below the 4.0 mg/L
dissolved oxygen standard set by the State of Florida for marine waters. Even
under an assumed worst case condition in which the reaeration rate was set to
a conservatively low value (0.50/day) and the sediment oxygen demand was set at
a conservatively high value (2.5 g/m2-day) the daily minimum dissolved oxygen
concentrations remained at or above 4.0 mg/L (Figure 4-7).
The original canal design was significantly improved to provide for better:
flushing as documented in a previously study (Tetra Tech 1988). Based on the
results of the present water quality study, the improved canal design also
provides for a suitable aquatic habitat in terms of dissolved oxygen levels.
6.0 REFERENCES
Tetra Tech. 1988. Rive St. Johns Phase II Canal: Littoral Transport and
Flushing Analysis. Tetra Tech Report TC-3668. Prepared for Dostie Builders,
Inc. Jacksonville, Florida.
NOAA. 1978. Airport CIimatological Summary, Jacksonville, Florida,
International Airport. CIimatography of the United States No. 90 (1965-1974).
National Oceanic and Atmospheric Administration. Environmental Data Service.
National Climatic Center. Asheville, NC.
26
-------
Appendix A
-------
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
REGION IV
343 COURTLANO STREET
ATLANTA, GEORGIA 303«3
JUL 2 2 1988
REF: 4WM/VJQMB/BK
Mr. Charles Ashton
Regulatory Division
U.S. Army Corps of Engineers
P.O. Box 4970
Jacksonville, Florida 32232
SUBJECT: Dostie Builders
(Public Notice No. 87IPF-21164)
Dear Mr. Ashton:
We have completed our review of the hydrographic report prepared by
TetraTech and the wetland mitigation proposal prepared by Allen W. Potter,
Inc. for the above referenced project. In addition, Dr. Bill Kruczynski
of iry staff inspected the site of the proposed activity on June 8, 1988.
The wetland areas proposed to be filled are isolated from the St. Johns
River and are vegetated predominantly by transitional wetland species.
It is our opinion that the proposed wetland creation mitigation is adequate
and we withdraw our objections to the filling of these wetland areas.
However, it remains our opinion, as we expressed in our letter dated
March 18, 1988, that there are practicable, less environmentally damaging
alternatives to excavation of the proposed canal system. A properly
designed marina constructed along the shoreline of the St. Johns River at
this site is environmentally preferable to a canal system since it is
probable that water quality within the canals will not meet State water
quality standards. Also, an excavated central marina basin with a short
entrance channel would flush better than the canal designs analyzed in
the TetraTech study. Thus, although the applicant has made significant
improvements to the originally proposed canal system, we continue to
reccmmend that a permit for their construction be denied.
The hydrographic report concludes that of the canal designs analyzed, the
currently proposed design is the optinum configuration since it will
flush within two or three tidal cycles. The study does not address water
quality in the proposed canal system. In order to determine whether
water quality within the canal will meet State of Florida standards the
system must be modeled.
-------
-2-
If the applicant wishes to continue the permitting process for an interior
canal system/ we require that a water quality model of the proposed
system be performed. We suggest that the applicant use the Dynamic Estuary
Model which takes into account reaeration, sediment oxygen derand, and
quality of the receiving waters. We can provide estimates of the sediment
oxygen demand frcm our data file for similar systems. We suggest that
the applicant utilize water quality data from the closest long term
monitoring station for parameters required in the model. Mean dissolved
oxygen concentration for the three wannest months of the year will provide
an estimate of worst case conditions.
Thank you for the opportunity to review the mitigation plan and the
hydrographic report. If you have any questions concerning our comments,
please contact Dr. Bill Kruczynski directly at telephone (904) 438-6891.
Sincerely yours,
Robert F. McGhee, Chief
Water Quality Management Branch
Water Management Division
-------
APPENDIX F: MODEL SENSITIVITY ANALYSES
-------
F.l INTRODUCTION
Data available for a "water quality model study are likely to be both incomplete and
imprecise. Some of the model inputs will have to be estimated, while others may be taken from
measurements with unavoidable errors. Therefore, much caution is needed to insure that
estimated quantities do not dominate the output to the extent that minor alterations of an input
parameter will completely change the nature of the result. However, there are instances when
past modelling experience indicates that the model should be sensitive to a particular parameter.
There are also fundamental limiting cases to which the model should adhere, such as the
cessation of phytoplankton growth resulting from the depletion of inorganic phosphorus. Thus,
many modelers will want to examine the sensitivity of the model to various input parameters.
The sensitivity analysis, presented in this appendix, is designed to assist the ultimate
users of this report in making decisions related to the selection, application, and accuracy of the
various modeling techniques (simple, mid-range, and complex). The sensitivity analysis is
designed to help inexperienced users identify the modeling parameters which have the most
impact on dissolved oxygen results. The sensitivity analysis is designed to also help identify
those parameters for which typical data are scarce or missing, thereby helping to determine the
types of data which should be collected in future studies. For example, the sensitivity study may
show that SOD is the most sensitive model parameter—thus, if adequate observed SOD
information on coastal marinas is not available in the literature, then future monitoring studies
at existing marinas can be designed to fill that data gap. In addition, the sensitivity analysis is
useful for determining whether field data needs to be collected for a certain model parameter or
whether a typical literature value is sufficient. For example, if the sensitivity analysis shows
that computed dissolved oxygen is not responsive to changes in the phytoplankton death rate
coefficient, then the use of literature values may be appropriate.
Sensitivity analyses are performed on the simple method (Tidal Prism Analysis), the
alternative mid-range model (NCDEM DO Model), and the complex model (WASP4) to
determine the dissolved oxygen response to typical ranges of various model parameters and other
input data. Because Beacons Reach marina represent a typical two-segement marina type with
only one entrance channel, it was selected as a test site to conduct model sensitivity analysis.
For both the simple and mid-range models, the following parameters are included:
Biochemical Oxygen Demand (BOD)
Sediment Oxygen Demand (SOD)
Oxidation Coefficient (k,)
Reaeration Coefficient (kj
Tidal Range (R)
Marina Surface Area (A)
F-l
-------
For the WASP4 Model, the following parameters are included in the sensitivity analysis:
SOD, Ammonia, and Phosphorus Sediment Flux Rates
Impact of wind speed on DO concentration
Reaeration coefficient (K2)
Deoxygenation Rate (KDC)
Phytoplankton Death Rate (KID)
Phytoplankton Growth Rate Constant (K1C)
Phytoplankton Respiration Rate Constant (K1RC)
Carbon to Chlorophyll Ratio (CCHL)
F.2 Sensitivity Analysis - Simple Model (TPA)
The procedure for sensitivity tests is simply to rerun the calibrated model with a single
input altered. Generally, it is efficient and most satisfactory to run sensitivity tests in pairs, with
the given parameter shifted both up and down in separate tests.
Testing model sensitivity provides information about which processes are most influential
and/or interactive in a given system. It can also lead to improved calibration parameters and
is an excellent way to check for any gross calibration errors. Tables F-l and F-2 list the results
of the simple TPA model sensitivity analysis for Beacons Reach marina, which the user may
reproduce using the input parameters listed in these tables.
Shown are the sensitivity of dissolved oxygen levels versus the oxidation coefficient (Base
Run and Run 1), reaeration coefficient (Run 2 and 3), sediment oxygen demand (Run 4 and 5),
biochemical oxygen demand (Run 6 and 7), tidal range (Run 8 and 9), and marina surface area
(Run 10 and 11). The oxidation rate, tidal range, and sediment oxygen demand shows the
obvious result that when these parameters are halved or doubled, the dissolved oxygen levels
should decrease or increase. Such fundamental relationships between the input parameters and
outputs are easily verified in this way.
Note the small sensitivity of dissolved oxygen (Tables F-l) to changes in the marina
surface area (Run 10 and 11) and reaeration coefficient (Run 2 and 3), compared to its large
sensitivity to oxidation rate (Base Run and Run 1), sediment oxygen demand (Run 4 and 5),
biochemical oxygen demand (Run 6 and 7), and tidal range (Run 8 and 9). Apparently, tidal
range is an important source and sediment oxyegn demand is an important sink of dissolved
oxygen in the marina system.
From Table F-l, one can conclude that the most important parameters affecting dissolved
oxygen levels in the TPA model are: oxidation rate, tidal range, and sediment oxygen demand.
Therefore, these parameters should be measured at a proposed marina site if site specific data
are not available. Sensitivity analysis also indicated that the least important parameters affecting
dissolved oxyegn levels are: marina surface area and reaeration coefficient. Therefore, these
parameters can be estimated from existing data bases and/or literature.
F-2
-------
TABLE F-l. TPA Sensitivity Analysis Results
Input
Parameter
Calibrated
Run
Beacons
Reach
Run
1
Run
2
Run
3
Run
4
Run
5
Run
6
Run
7
Run
8
Run
9
Run
10
Run
11
DOp
(mg/L)
3 30
1.42
3 20
3 57
5.45
1 44
4 32
2.28
1.29
4.79
3 30
3.30
k, (day1)
OJ
a 3
0.1
0.1
0 1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
k. (day1)
0 7
0.7
0.2S
3.00
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
B (g/L)
2.680
2.68
2.68
2.68
0.0
S.O
2.68
2.68
2.68
2.68
2.68
2.68
Cb (mg/L)
7.23
7.23
7.23
7.23
7.23
7 23
0.0
14.S
7.23
7.23
7.23
7.23
R (ft)
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
OJ
1.2
0.6
0.6
A (ft2)
4724
ism
DOp = Predicted DO at high tide
k, = deoxygenation rate
k, = reaeration rate
B = Sediment Oxygen Demand (SOD)
Cb = Biochemical Oxygen Demand (BOD)
R = Tidal range
A = Marina surface area
F.3 Sensitivity Analysis - Mid-range Model (NCDEM)
The NCDEM model was initially set-up to simulate a "typical" summertime condition
at the Beacons Reach marina, referred to as the base condition. Next, eight additional model
runs were made in which each of the four parameters was varied within the typical range of
values to determine the effect on dissolved oxygen levels in the marina system. The system rate
coefficients used for the various NCDEM DO model simulations are given in Table F-2.
The effect of different reaeration rates (KJ on dissolved oxygen is listed in Table F-2.
It is evident that dissolved oxygen is sensitive to the value of K, in the model (Run 1 and 2).
The reaeration rate of 0.30/day used in the base case was reported in North Carolina Coastal
Marina Assessment (NCDEM, 1990). Similarly, the NCDEM DO model is sensitive to SOD
values used in the model. If SOD is eliminated in model application (Run 3), then an increase
of predicted dissolved oxygen is evident. However, an increase in SOD (Run 4) will cause a
F-3
-------
decrease in the predicted dissolved oxygen levels. Therefore, these parameters should be
evaluated at the proposed marina site when site specific data are not available.
The effects of changing the marina surface area on dissolved oxygen in the marina
system is minimum. Doubling and halving the marina surface area (Run 5 and 6) had no effect
on dissolved oxygen values. However, changing the tidal range (Run 7 and 8) had a noticeable
effect on dissolved oxygen.
TABLE F-2. NCDEM DO Model Sensitivity Analysis Results
Input
Parameter
Calibrated
Run
Beacons
Reach
Run
1
Run
2
Run
3
Run
4
Run
5
Run
6
Run
7
Run
8
DOp
(mg/L)
4.82
3.75
6.46
6.55
3 23
4.82
4.82
4.02
5.48
K.
(day1)
0.3
0,0$
. 3.00
0.3
0.3
0.3
0.3
0.3
0.3
SOD
(g/L)
2.6
2.6
2.6
0.0
: s.o
2.6
2.6
2.6
2.6
A (ft2)
101700
101700
101700
101700
101700
508S0
203400
101700
101700
R (ft)
2
2
2
2
2
2
2
i
4
DOp = Predicted DO at high tide
K, = reaeration rate
SOD = sediment oxygen demand
R - tidal range
A = marina surface area
F-4
-------
F.4 Sensivitity Analysis for Complex Model (WASP)
Dissolved oxygen processes can be modeled using WASP4/EUTR04 based on six
different complexity levels.- The number of reaction rate coefficients and environmental
parameters required by the WASP input file varies according to the complexity level specified
by the user. The least complex level (level 1) requires the least number of coefficients. The
most complex level (i.e., level 6 which was chosen for the marina applications of WASP4 in this
report) requires the most coefficients. The following eight WASP4 reaction rates and constants
associated with dissolved oxygen dynamics were tested for their sensitivity on dissolved oxygen
concentrations:
• sediment oxygen demand and nutrient benthic flux rates
• wind speed effects
• reaeration rate
• CBOD deoxygenation rate
• phytoplankton death rate
• phytoplankton growth rate
• phytoplankton respiration rate
• carbon-to-chlorophyll ratio
The above parameters were chosen for the sensitivity analysis partly because they are
among the eight most important constants needed to model dissolved oxygen dynamics, and
partly because existing site specific field data is often not available to estimate their values.
Often a model must be calibrated using only literature values or past modeling experience for
many of these parameters. Thus, a certain amount of uncertainty may be associated with each
of the above parameters and the sensitivity tests are a means of quantifying the impact of each
parameter's uncertainty on overall model results. The results of the sensitivity tests can also be
useful in designing field sampling programs. For example, if dissolved oxygen is sensitive to
variations in sediment oxygen demand and benthic nutrient flux rates, then a field sampling
program should be designed to include these benthic flux measurements.
For this sensitivity analysis, WASP4 was applied to the Beacons Reach Marina using the
observed data on May 26, 1988. The base run (RUNOO) was the calibration run described in
Section 6 of this report. A series of sensitivity runs was then made in which the magnitudes of
the eight sensitivity parameters were varied within their typical ranges and the model dissolved
oxygen results were plotted graphically in Figures F-l to F-8. The observed data for May 26,
1988, are also presented in the plots for reference. The parameter values used in the various
WASP4 sensitivity runs are summarized in Table F-3.
Sensitivity of DO to SOD and benthic flux rates. For the base run (RUNOO) the sediment
oxygen demand rate was specified as 3.35 g02/m2-day at 20°C. In a study conducted by North
Carolina Department of Environmental Management (NCDEM), SOD measurements (corrected
to 20°C) made in Beacons Reach Marina, in nearby Gull Harbor Marina, and in Tangle Oaks
Marina ranged from 1.89 to 6.15 g02/m2-day (NCDEM 1990). Thus, the calibrated value of
F-5
-------
3.35 g02/nr-day is within the appropriate range. No field data were available to estimate
benthic flux rates for ammonia or phosphate. Therefore, the benthic flux rates for ammonia and
phosphate were assumed to be stoichiometrically related to the sediment oxygen demand rate
based on the classical Redfieki ratios for 0:C:N:P (i.e., 109:41:7.2:1). The results of doubling
(RUN01) and halving (RUN02) the SOD and benthic flux rates is shown in Figure F-l. Clearly,
dissolved oxygen is. quite sensitive to changes in these flux rate parameters.
Sensitivity of DO to wind speed. In the base run, wind speed was set to zero. There
may be instances where wind speed may be needed to calibrate a model to data collected from
an existing marina where wind was not zero during the sampling period. The WASP4 model
can be run in two modes with regard to the reaeration coefficient: (1) the reaeration coefficient
can be computed internally by the model based on water velocity speed and wind friction effects,
or (2) the reaeration coefficient can be specified. For these two sensitivity runs, the first mode
was used in which the reaeration rate was computed by the model. In RUN03 the wind speed
was set to 7.5 m/sec and in RUN04 to 15 m/sec. Figure F-2 indicates that DO is very sensitive
to these increased wind speeds.
Sensitivity of DO to reaeration rate (K2). Figure F-3 demonstrates the effect of varying
the reaeration rate on dissolved oxygen concentrations in Beacons Reach Marina. In RUN05
the reaeration rate was set to 0.3/day, in RUN06 it was 1.0/day, and in RUN07 it was 2.0/day.
For the base run (RUN00) the reaeration rate was computed internally by the model based on
the hydrodynamic water velocities and the value was approximately 0.7/day. The results show
that dissolved oxygen is sensitive to changes in reaeration rate. This is to be expected given the
small size and shallow nature of the Beacons Reach Marina system.
Sensitivity of DO to CBOD deoxygenation rate (ICDC). The CBOD deoxygenation rate
coefficient (KDC) is commonly in the range 0.01 to 5.6/day with a typical value being 0.2/day
(at 20°C). KDC was set to 0.2/day in the base run (RUN00), 0.1/day in RUN08, and 0.4/day
in RUN09. The dissolved oxygen results shown in Figure F-4 indicate that the model is not
highly sensitive to variations in the CBOD deoxygenation rate coefficient. However, halving
the KDC coefficient produced greater variations in DO from the base run than did doubling it.
Sensitivity of DO to phvtoplankton death rate (KID). The phytoplankton death rate
coefficient (KID) in the base run (RUN00) was 0.5/day. In RUNIO this coefficient was set to
0.1/day and in RUN11 it was set to 1.5/day. The model sensitivity results indicate that changes
in KID have little impact on dissolved oxygen in Beacons Reach Marina.
Sensitivity of DO to phytoplankton growth rate (K1C). The phytoplankton growth rate
(K1C) describes the maximum, or saturated, growth rate that is obtained under non-limiting
nutrient and light conditions at a reference temperature of 20°C. KIC was set to 1.0/day in the
base run (RUN00), 0.5/day in RUN12, and 1.5/day in RUN13. Figure F-6 shows that dissolved
oxygen is not very sensitive to changes in the phytoplankton growth rate for the Beacons Reach
Marina.
F-6
-------
Sensitivity of DO to phvtoplankton respiration constant (KIRC). For the base run, the
endogenous respiration rate of phytoplankton (at 20°C) was set to 0.3/day. For sensitivity runs
RUN14 and RUN15, KIRC was set to 0.15/day and 0.50/day, respectively. The results shown
in Figure F-7 indicate dissolved oxygen is not highly sensitive to variations in this coefficient.
Sensitivity of DO to carbon-to-chlorophvll ratio CCCHL). The carbon-to-chlorophyll ratio
commonly falls in the range 20 to 200 mg C/mg Chla. For the base run CCHL was set to 80,
and for sensitivity runs RUN16 and RUN17 it was set to 40 and 120, respectively. The results
shown in Figure F-8 show that dissolved oxygen is somewhat sensitive to variationsin this
parameter.
TABLE F-3. Parameter Values Used for WASP4 Model Sensitivity Analysis
RUN##
Input Parameter
SOD
gO,/nr-day
Wind
(m/sec)
K2
(day ')
KDC
(day1)
KID
(day1)
K1C
(day1)
KIRC
(day1)
CCHL
(day1)
RUN00
3 35
0.0
computed
0.2
0.5
1.0
0.30
80
RUN01
1.69
0.0
computed
0.2
0.5
1.0
0.30
80
RUN02
6.70
0.0
computed
0.2
0.5
1.0
0.30
80
RUN03
3.35
Z5
computed
0.2
0.5
1 0
0.30
80
RUN04
3.35
15.0
computed
0.2
0.5
1.0
0.30
80
RUN05
3.35
0.0
OS
0.2
0.5
1.0
0 30
80
RUN06
3.35
0 0
1.0
0.2
0.5
1 0
0.30
80
RUN07
3.35
0.0
, 2.0
0.2
0.5
1.0
0.30
80
RUN08
3.35
0.0
computed
O.I
0.5
1.0
0.30
80
RUN09
3.35
0.0
computed
0.4
0.5
1.0
0 30
80
RUN10
3.35
0.0
computed
0.2
0.1
1.0
0.30
80
RUN 11
3.35
0 0
computed
0.2
1.5
1 0
0.30
80
RUN 12
3.35
0.0.
computed
0.2
0.5
o.s
0.30
80
RUN13
3.35
0.0
computed
0.2
0.5
L5
0.30
80
RUN14
3.35
0.0
computed
0.2
0.5
1.0
O.IS
80
RUN15
3.35
0.0
computed
0.2
0.5
1.0
0J0
80
RUN16
3 35
0.0
computed
0.2
0.5
1 0
0 30
40
RUN17
3 35
0 0
computed
0.2
0.5
1.0
0 30
120
F-7
-------
Beacons Reach Marina (Observed data are for May 26, 1983)
3 1 1 1 1 1 1
7 -
6 -
5 -
4 -
3 -
RUN01 (benthic flux reduced 50%)
25 50 75 100 125 150 17
Distance from Marina Entrance (meters)
Figure F-l. Sensitivity of DO to SOD and benthic flux rates.
8 -
7 -
6
5
4 -
3
2
Beacons Reach Marina (Observed data are for May 26, 1988)
" T ' ! J I
RUN04 (wind speed = 15 m/sec)
RUN03 (wind speed = 75 m/sec)
4-
RUN00 (wind speed = 0 m/sec)
25 50 75 100 125 150
Distance from Marina Entrance (meters)
Figure F-2. Sensitivity of DO to wind speed.
F-8
17
-------
Beacons Reach Manna (Observed data are for May 26, 1988)
9 1 1 1 1 1 1
C7>
E
qj 6
C7>
>s
"O
0)
>
O .
u) 4
RUN07 (K2 = 2 0/day)
RUN06 (K2 = 1 0/day)
RUN00 (K2 function of velocity)
~ a _Jl_
RUN05 (K2 = 0.3/day)
25 50 75 100 125 150 175
Distance from Marina Entrance (meters)
Figure F-3. Sensitivity of DO to reaeration rate (K2).
Beacons Reach Marina (Observed data are for May 26, 1988)
7 -
cr>
E 6
c
d)
(J»
>\
x
O
u
RUN08 (KDC = 0 10/day)
RUN09 (KDC = 0.40/day)
4 - RUN00 (KDC = 0.20/day)
25 50 75 100 125 150
Distance from Marina Entrance (meters)
Figure F-4. Sensitivity of DO to deoxygenation rate (KDC).
F-9
175
-------
Beacons Reach Marina (Observed data are for May 26, 1988)
£ 6
c
a;
a>
5
v. -U
TD
s 5
x u
0
"O
1 4
i/)
cn
b
RUN 13 (K1C = 1.5/day)
RUN 1 2 (K1C = 0.5/day)
RUN00 (K1C = 1 0/day)
0 25 50 75 100 125 150
Distance from Marina Entrance (meters)
Figure F-6. Sensitivity of DO to phytoplankton growth rate (K1C)
F-10
75
-------
Beacons Reach Manna (Observed data are for May 26, 1988)
8 i 1 1 1 1 1 1
7 -
E 6
c
0)
>s 5
X °
0
"O
s 5
x J
0
"O
------- |