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TITLE: Technical Guidance Manual for Performing Wasteload Allocations, 

Book III: Estuaries – 

Part 4: Critical Review Of Coastal Embayment And 

Estuarine Waste Load Allocation Modeling 

EPA DOCUMENT NUMBER: EPA-823-R92-005 DATE: August 1992 

ABSTRACT 

As part of ongoing efforts to keep EPA’s technical guidance readily accessible to 

water quality practitioners, selected publications on Water Quality Modeling and 

TMDL Guidance available at http://www.epa.gov/waterscience/pc/watqual.html 

have been enhanced for easier access. 

This document is part of a series of manuals that provides technical information 

related to the preparation of technically sound wasteload allocations (WLAs) that 

ensure that acceptable water quality conditions are achieved to support 

designated beneficial uses. The document provides technical information and 

policy guidance for performing WLAs in estuaries, which, because of their 

complex transport processes, cannot be treated as simple advective systems like 

many rivers. 

Book III Part 4 summarizes four estuarine case studies of where models were 

used for wasteload allocation. The studies were chosen to represent diverse 

approaches covering a range of geographic areas, estuaries, and models. 

Review of the case studies is provided by three experts who critique the relative 

merits and deficiencies of each example and provide their opinions on the proper 

approach to estuarine modeling. 

The companion volumes (Parts 1-3 of Book III) provide information on model 

processes, available models, and guidance on monitoring and calibration. 

KEYWORDS: Wasteload Allocations, Estuaries, Modeling, Water Quality Criteria 
United States

Environmental Protection

Agency

Office of Water

(WH 585)

EPA-823-R-92-005

August 1992

TechnicalGuidanceManual

forPerformingWasteLoad

Allocations

Book III:Estuaries

Part 4

Critical Review Of

Coastal Embayment And

Estuarine Waste Load

Allocation Modeling
TECHNICAL GUIDANCE MANUAL

FOR PERFORMING WASTE LOAD ALLOCATIONS

BOOK III: ESTUARIES

PART 4: Critical Review of Estuarine Waste Load Allocation Modeling

Project Officer

Hiranmay Biswas, Ph.D.

Edited By

Robert B. Ambrose, Jr. P.E.1

Prepared by

Paul L. Freedman, P.E.2

David W. Dilks, Ph.D.2

Bruce A. Monson2

1. Center for Exposure Assessment Modeling,

Environmental Research Laboratory, U.S. EPA, Athens, GA

2. LTI, Limno-Tech, Inc., Ann Arbor, MI

Prepared for

U.S. ENVIRONMENTAL PROTECTION AGENCY

401 M. Street, S.W.

Washington, D.C. 20460

Table of Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

10. Great Lakes Embayment Seasonal Phytoplankton Model of Saginaw Bay . . . . 10-1

10.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-1

10.2 Problem Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-1

10.3 Model Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-2

10.4 Post-Audit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-8

10.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-8

11. Potomac Estuary Water Quality Modeling . . . . . . . . . . . . . . . . . . . . . 11-1

11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11-1

11.2 Problem Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11-1

11.3 Dynamic Estuary Model (DEM) of Dissolved Oxygen . . . . . . . . . . . 11-2

11.4 Potomac Eutrophication Model (PEM) . . . . . . . . . . . . . . . . . . . 11-6

11.5 Finite Element Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 11-13

11.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11-18

12. Manasquan Estuary Real Time Modeling . . . . . . . . . . . . . . . . . . . . . 12-1

12.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-1

12.2 Problem Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-1

12.3 Model Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-2

12.4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-11

13. Calcasieu River Estuary Modeling . . . . . . . . . . . . . . . . . . . . . . . . 13-1

13.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-1

13.2 Problem Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-1

13.3 Model Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-2

13.4 Total Maximum Daily Loads . . . . . . . . . . . . . . . . . . . . . . . . 13-7

13.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-8

14. Expert Critique of Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . 14-1

14.1 Robert V. Thomann, Ph.D. . . . . . . . . . . . . . . . . . . . . . . . . . 14-1

14.2 Donald R.F. Harleman, Ph.D. . . . . . . . . . . . . . . . . . . . . . . . 14-10

14.3 Gerald T. Orlob, Ph.D., P.E. . . . . . . . . . . . . . . . . . . . . . . . . 14-14

iii

v 

List of Figures

Figure 10-1. Saginaw Bay site map 10-1 

Figure 10-2. Model segmentation of Saginaw Bay 10-2

Figure 10-3. Schematic diagram of principal model compartments and interaction 

pathways 10-3 

Figure 10-4. Model output and field data comparison for total phosphorus 10-7

Figure 10-5. Changes in water quality constituents between 1974 and 1980 in 

segments 2 and 4 10-9

Figure 11-1. Location map of Potomac Estuary 11-1

Figure 11-2. Potomac Estuary hydraulic calibratlon hlgh water phasing - Mean Tide11-3

Figure 11-3. Potomac Estuary chlorlde verification, time period: September-October, 

1969 11-4

Figure 11-4. DEM calibration results for ammonia, time period: August 31-September 

16, 1965 11-4

Figure 11-5. DEM calibration results for nitrate+nitrite, time period: August 31-

September 16, 1965 11-5

Figure 11-6. DEM calibration results for dissolved oxygen, time period: August 31-

September 16, 1965 11-5

Figure 11-7. PEM calibration for chlorides and chlorophyll-a, May and September, 

1966 11-9

Figure 11-8. PEM calibration for BOD5 and dissolved oxygen, May and September, 

1966 11-9

Figure 11-9. PEM calibration for total organic and total inorganic phosphorus (mg P/L), 

May and September, 1966 11-10 

Figure 11-10. PEM calibration for ammonia and nitrite+nitrate (mg N/L), May and 

September, 1966 11-10

Figure 11-11. PEM verification for chlorides (mg N/L), chlorophyll-a (mg N/L), dissolved 

inorganic phosphorus (mg P/L) and total phosphorus (mg P/L), 

July 1977 11-11

Figure 11-12. PEM verification for ammonia nitrogen (mg N/L), nitrate-nitrite nitrogen 

(mg N/L), bottle BOD5 (mg/L) and dissolved oxygen (mg/L), 

July 1977 11-11

Figure 11-13. Chlorine model finite element grid network 11-14

Figure 11-14. August 1980 dye survey calibration at stations B and C 11-16

Figure 11-15. July 11, 1984 TRC survey calibration at 12.8/day loss rate 11-17
vi 

Figure 11-16. July 11, 1984 TRC survey calibration at 6.4/day loss rate 11-17

Figure 11-17. TRC model projection for 7Q10 low flow conditions 11-19

Figure 11-18. TRC model projection for average April flow conditions 11-20

Figure 11-19. TRC model projection for lowest April flow conditions 11-21

Figure 12-1. Manasquan Estuary and Inlet 12-1

Figure 12-2. Model conceptualization of the Manasquan Estuary and the Point 

Pleasant Canal System 12-2

Figure 12-3. Time-varying salinity concentrations during August 24-28, 1980 at (a) 

Clark’s Landing and (b) Chapman’s Wharf 12-4

Figure 12-4. Temporal variation of Detritus-N, Ammonium-N, and Nitrite+Nitrate-N at 

Clark’s Landing August 25-28, 1980 12-7

Figure 12-5. Temporal variation of CBOD at Clark’s Landing, August 25-28, 1980 12-7

Figure 12-6. Temporal variation of CBOD at Chapman’s Wharf, August 25-28, 

1980 12-7

Figure 12-7. Temporal variation of NBOD at Clark’s Landing, August 25-28, 1980 12-7

Figure 12-8. Temporal variation of NBOD at Chapman’s Wharf, August 25-28, 

1980 12-8

Figure 12-9. Temporal variation of DO at Clark’s Landing, August 25-28, 1980 12-8

Figure 12-10. Temporal variation of DO at Chapman’s Wharf, August 25-28, 1980 12-8

Figure 12-11. Temporal variation of Diatom-N, Nanoplankton-N, and Dinoflagellate-N at 

Clark’s Landing August 25-28, 1980 12-8

Figure 12-12. Temporal variation of Diatom-N, Nanoplankton-N, and Dinoflagellate-N at 

Chapman’s Wharf, August 25-28, 1980 12-9 

Figure 12-13. Hydraulic verification: calculated vs. observed elevations at Clark’s 

Landing 12-9

Figure 12-14. Hydraulic verification: calculated vs. observed elevations at Chapman’s 

Wharf 12-10 

Figure 12-15. Salinity verification: calculated vs. observed salinities at Clark’s 

Landing 12-10

Figure 12-16. Salinity verification: calculated vs. observed salinities at Chapman’s 

Wharf 12-10

Figure 12-17. Temporal variation of Detritus-N, Ammonium-N, and Nitrite+Nitrate-N at 

Clark’s Landing 12-10

Figure 12-18. Temporal variation of Detritus-N, Ammonium-N, and Nitrite+Nitrate-N at 

Chapman’s Wharf 12-11

Figure 13-1. Calcasieu Estuary study area 13-1
vii 

Figure 13-2. Model segmentation diagram 13-3

Figure 13-3. Tidal stage results for August 1979 hydrodynamic calibration 

simulation 13-4 

Figure 13-4. Tidal stage results for hydrodynamic verification simulation 13-4

Figure 13-5. Selected water quality results for calibration simulation (August 1979 data 

set); salinity, dissolved oxygen, and biological oxygen demand 13-5

Figure 13-6. Selected water quality results for calibration simulation (August 1979 data 

set); phosphate, total Kjeldhal nitrogen, and ammonia 13-5

Figure 13-7. Selected water quality results for July 1978 verification simulation; salinity, 

dissolved oxygen, and biological oxygen demand 13-6

Figure 13-8. Selected water quality results for July 1978 verification simulation; 

phosphate, total Kjeldhal nitrogen, and ammonia 13-6

Figure 13-9. Selected water quality results for July 1980 and June 1984 verification 

simulation; salinity and dissolved oxygen 13-7

Figure 14-1. Illustration of relationship between model credibility and model 

complexity 14-3

Figure 14-2. Suggested strategy for determining appropriate level of model 

complexity 14-6
viii 
ix 

List of Tables

Table 10-1. Description of Model Coefficients 10-5 

Table 10-2. Summary of Selected Model Coefficients 10-6

Table 10-3. Summary of Selected Model Inputs 10-8 

Table 10-4. Statistical Comparison between Model Results and Field Data 10-8

Table 11-1. Linear Regression Statistics 11-12

Table 12.1. Effluent Quality Standards 12-2

Table 12-2. Model-Governing Rate Parameters 12-6

Table 13-1. Values for Major Coefficients 13-3
x 
Preface

The document is the third of a series of manuals

providing information and guidance for the preparation

of waste load allocations. The first documents provided general guidance for performing waste load

allocations (Book I), as well as guidance specifically

directed toward streams and rivers (Book II). This

document provides technical information and guidance for the preparation of waste load allocations in

estuaries. The document is divided into four parts:

Part 1 of this document provides technical information

and policy guidance for the preparation of estuarine

waste load allocations. It summarizes the important

water quality problems, estuarine characterisitics and

processes affecting those problems, and the simulation models available for addressing these problems.

Part 2 provides a guide to monitoring and model calibration and testing, and a case study tutorial on simulation of waste load allocation problems in simplified

estuarine systems. Part 3 summarizes initial dilution

and mixing zone processes, available models, and

their application in waste load allocation.

This part, “Part 4: Critical Review of Estuarine Waste

Load Allocation Modeling,” summarizes several historical case studies, with critical review by noted experts. The reader should refer to the preceding parts

for information on model processes, available models,

and guidance to monitoring and calibration.

The technical guidance is comprehensive and stateof-the-art. Case studies of applications serve as the

best teacher of the proper and improper use of this

technical guidance. Therefore, included in this part are

four summaries of actual estuarine studies where

models were used for waste load allocation. These

studies have been selected to provide a range of

representative geographic areas, estuaries, and models. The studies were not selected because they

were exemplary but rather because they represented

applications of diverse approaches.

Each of the studies has particular merits and deficiencies; the balance is different in each study. Perfect

examples are not always the best teachers. By examing the strengths and weaknesses of each application

the reader can appreciate how to best use the technical guidance and how to avoid misuse and common

problems.

The examples are summarized with only limited commentary. The information for each is presented with

sufficient detail to allow the reader to understand what

was done and to highlight certain noteworthy aspects.

Following the case examples, three experts critique

the relative merits and deficiencies in each case study

and provide their opinions on the proper approach to

estuarine modeling.

A draft version of this document received scientific

peer review from the following modeling experts:

Steven C. Chapra,

University of Colorado-Boulder

Donald R.F. Harleman,

Massachusetts Institute of Technology

Gerald T. Orlob,

University of California-Davis

Robert V. Thomann,

Manhattan College

Their comments have been incorporated into the final

version.

Organization: “Technical Guidance Manual for Performing Waste Load Allocations.

Book III: Estuaries”

Part Title

1 Estuaries and Waste Load Allocation Models

2 Application of Estuarine Waste Load Allocation Models

3 Use of Mixing Zone Models in Estuarine Waste Load Allocation Modeling

4 Critical Review of Estuarine Waste Load Allocation Modeling

xi

Acknowledgements

The contents of this section have been removed to

comply with current EPA practice.

xii

10. Great Lakes Embayment Seasonal Phytoplankton Model

of Saginaw Bay

10.1. Background

The Saginaw Bay phytoplankton model of Bierman

and Dolan (1986a,b) is presented here to illustrate the

application of a dynamic and kinetically complex box

model to a Great Lakes embayment. This model was

calibrated with two comprehensive data sets. Following significant reductions in loadings and changes in

the Bay’s water quality, the model projections were

tested and validated (post-audit) with another comprehensive data set. The model was developed as part of

a long-term study of eutrophication in Saginaw Bay. It

was designed as a management and research tool to

estimate phytoplankton response to various phosphorus control strategies. The model was used extensively

by the USEPA and International Joint Commission to

evaluate nutrient loading reductions for Saginaw Bay.

The authors describe the model as “a deterministic,

spatially segmented, multi-class phytoplankton

model.” The phytoplankton comprise five functional

groups: diatoms, greens, non-nitrogen-fixing bluegreens, nitrogen-fixing blue-greens, and “others.” Nutrient uptake is considered for phosphorus, nitrogen,

and silica. Herbivory, settling, and decomposition are

mechanisms of phytoplankton depletion.

10.2. Problem Setting

Located on the western shore of Lake Huron (Figure

10-1), the Saginaw Bay watershed is approximately

21,000 km2 (8108 mi2

). It is dominated by agriculture,

forest, and four urban-industrial centers: Bay City,

Flint, Midland, and Saginaw. The 1980 population for

the area was slightly over 1,200,000. The area is

drained by the Bay’s major tributary, the Saginaw

River. The River accounts for 90 percent of the tributary inflow to the Bay.

Saginaw Bay extends 90 km from the River’s mouth to

the Bay’s opening to Lake Huron. It is broad (42 km),

shallow (10 m average depth), and vertically wellmixed. The average hydraulic residence time is approximately four months.

The Bay has been characterized as behaving like a

simple estuary (Ayers et al, 1956). Like estuaries, it is

a nutrient-rich arm of a larger nutrient-poor water body,

Lake Huron (Richardson, 1974). Furthermore, water

levels and flow directions of the Bay change. Unlike an

estuary, the water level is influenced by wind patterns

rather than tides. Northern gales can create a seiche

in the Bay that raises the water level at the mouth of

the Saginaw River by more than a meter (Fish and

Wildlife Service, 1956; cited by Richardson, 1974).

The International Joint Commission identified Saginaw

Bay as one of forty-two Great Lakes Areas of Concern

needing remedial action. Eutrophication of the Bay had

caused taste, odor, and filter-clogging problems for

municipal water supplies. Waste discharges and runoff

have been major contributors to water quality degradation. In the late 1970’s, phosphorus reduction programs were implemented at wastewater treatment

plants and resulted in large reductions of phosphorus

loading to the Bay. From 1975 to 1980 the phosphorus

loads were reduced over 65 percent. The model was

Figure 10-1. Saginaw Bay site map [Bierman and Dolan

(1986a). Reprinted from ASCE Journal of

Environmental Engineering, Vol. 112, No. 2. p.

401. With permission].

10-1
calibrated and verified when the phosphorus loadings

were high (1974 and 1975) and tested in a post-audit

following the large reductions of phosphorus (1980).

10.3. Model Application

Although this model’s development began in a more

simple form, it is presented here in its most advanced

form as a spatially segmented, temporally dynamic

model. A more spatially simplified precursor model

(Bierman and Dolan, 1980) provided some valuable

conclusions about the biological and chemical processes in the waterbody. These findings were used to

develop the kinetic structure and calibrate the more

spatially detailed model. For example, the factors influencing phytoplankton dynamics in the model are

temperature, light, nutrients, and zooplankton grazing.

Temperature and light were generally more growthrate limiting than nutrients. However, nutrient limitation

became important for peak phytoplankton crops. In the

spring and fall, the primary source of phosphorus was

external loading, which fed the dominant diatom crops.

In mid-summer, the primary source of phosphorus was

recycling within the water column and from sediments,

which fed the summer blue-green crops.

The multi-class phytoplankton model was developed

to predict the response of the Saginaw Bay phytoplankton to various phosphorus control strategies. Of

primary concern were the nuisance, bloom-forming

blue-greens that cause taste and odor problems. The

emphasis in the model was on nutrient cycling since it

is a limiting and controllable factor in phytoplankton

growth. Several hypothetical scenarios and a postaudit are presented below following examination of the

calibration and validation of the model.

10.3.1. Model Description

The model developed for Saginaw Bay falls into a

general class of models called “box models.” The

approach involves dividing the water body into several

cells (or boxes), each of which is considered completely mixed (Figure 10-2). Transport of chemicals,

biomass, and water between cells occurs through

advective transport and dispersion.

*

The mass of pollutants, algae or other constituents in

each cell changes in response to loadings, transport,

mixing, settling, and reaction kinetics. A mass balance

equation is written for each cell and the resulting

differential equation solved simultaneously through

time for all cells by a numerical method.

The model incorporates three nutrients — nitrogen,

phosphorus, and silica — each with biologically available and unavailable components, and a biomass

component. It includes five classes of algae and two

classes of zooplankton. The interaction of the components are shown in Figure 10-3.

The model is structured in a format to simulate a

specified number of phytoplankton and zooplankton

classes. The model developers chose to use multiple

classes of phytoplankton and zooplankton to predict

the desired decline in blue-green algae. Phytoplankton

groups respond differently to zooplankton grazing and

have different nutrient requirements. Unlike the many

eutrophication models that use chlorophyll a as a

surrogate for phytoplankton, this model uses phytoplankton cell biomass.

A number of mechanisms are considered in this model,

including:

• Internal nutrient pool kinetics for phosphorus, nitrogen, and silicon.

• A reaction-diffusion mechanism for carrier-mediated uptake of phosphorus and nitrogen that includes luxury uptake of nutrients.

• Biological-chemical kinetics, included in sediment

compartments for total concentrations of phosphorus, nitrogen, and silicon.

Figure 10-2. Model segmentation of Saginaw Bay [Bierman

and Dolan (1986a). Reprinted from ASCE Journal

of Environmental Engineering, Vol. 112, No. 2. p.

402. With permission].

10-2

* Advective transport is defined as a flow based on system hydrodynamics (modeled or measured). Dispersion transports mass from

areas of high concentration to areas of low concentration with no net flow of water.
• Zooplankton grazing.

• Saturation kinetics for water column nutrient mineralization.

• Saturation kinetics for phytoplankton decomposition.

• An advective-dispersive model for transport of

chloride used to determine water exchange

among the segments.

• Wind-dependent resuspension for sediment nutrients.

The internal nutrient pool kinetics are a noteworthy

aspect of the model because they treat cell growth as

a two-step process: 1) uptake of nutrients and 2)

biomass growth. The common approach is a one step

use of the Monod (Michaelis-Menten) equation, where

cell growth is a direct function of external nutrient

concentrations. The internal pool kinetics allow for

accumulation of surplus internal nutrients when external nutrient concentration is high and use of internal

stores when external nutrient concentration is low. The

recycling of nutrients is a function of the phytoplankton

losses. This more realistic approach requires greater

model complexity and additional model coefficients.

Furthermore, it exacts a severe computational burden

because all cell history must be tracked to follow

exposure patterns.

While phytoplankton growth is a function of nutrient

kinetics, phytoplankton loss mechanisms include respiration, decomposition, sinking, and zooplankton

grazing. Respiration loss is a temperature-dependent,

first-order decay term. Microbial decomposition is a

temperature-dependent, second-order decay term

proportional to total phytoplankton concentration and

specific growth rate. Sinking loss is set at a constant

velocity for each phytoplankton class. Zooplankton

grazing loss is a temperature-dependent, two-component loss mechanism. It was included for diatoms,

greens, and “other” phytoplankton, but not for bluegreens. The zooplankton response function included

Figure 10-3. Schematic diagram of principal model compartments and interaction pathways [Bierman and Dolan (1986a).

Reprinted from ASCE Journal of Environmental Engineering, Vol. 112, No. 2. p. 403. With permission].

10-3
losses to higher-order predators. A constant “refuge

concentration” is specified for both phytoplankton and

zooplankton below which there is no grazing or predation.

10.3.2. Model Inputs

The complexity of the model required many parameters and boundary conditions. Model coefficients are

defined in Table 10-1 and values summarized in Table

10-2 (a-c) to provide the reader a sense of the model

complexity.

Each phytoplankton group was characterized by a

maximum growth rate, a temperature growth adjustment factor, and a saturation light intensity. Other

phytoplankton coefficients included respiration rate,

decomposition rate, sinking rate, and conversion rates

of nutrient forms (from unavailable to available).

Zooplankton kinetic coefficients, taken from literature

or data collected for this study, included assimilation

efficiency, maximum ingestion rate, and phytoplankton

preference factor. Growth and death rates were estimated or calibrated to field data. Coefficients were

assigned for each of the two functional groups of

zooplankton: fast ingesters and slow ingesters.

Nutrient uptake and cell growth were treated in the

model as separate processes, but have parallel sets

of equations and coefficients. Nutrient kinetics for

phosphorus and nitrogen depended on the variables

of percent dry weight and minimum cell quotas for

these nutrients. Minimum concentrations were also

assigned for external nutrients, which corresponded to

the minimum levels to which the phytoplankton could

deplete the environment. External and internal halfsaturation levels were specified for both processes.

The latter were set equal to the minimum cell quotas.

Maximum phosphorus and nitrogen uptake rates were

the same for all phytoplankton groups. Silica coefficients were specified only for diatoms.

Another important assumption of the model was the

partitioning of phosphorus into available and unavailable components. Dissolved ortho-phosphorus was

considered to be available for immediate uptake by

phytoplankton. Unavailable phosphorus was equivalent to total phosphorus minus dissolved ortho-phosphorus. For scenarios discussed below, available and

unavailable phosphorus ratios were estimated for point

and nonpoint sources. The effective ratio of available

to total phosphorus for point sources at the Saginaw

River mouth was assumed to be 34 percent. It was also

assumed that the ratio did not change with different

treatment levels.

Environmental forcing functions varied for each year

and included water temperature, incident solar radiation, pollutant and tributary loadings, boundary conditions, and water transport rates. They were determined

independently of the model and supplied as input.

Table 10-3 is a summary of selected examples of these

inputs designed to provide the reader a sense of the

range of values. These environmental factors were

supplied to the model as time series input. Water

transport rates were obtained from a separate timevariable model of a conservative tracer, chloride

[Richardson (1974)].

10.3.3. Calibration/Verification

The approach to calibration was to match general

trends of the seasonal changes in the data and obtain

model output within one standard deviation of the

mean value of the observed data for each cruise.

When this was not achieved, model coefficients were

adjusted to best approximate the peak concentrations.

A Student’s t-test was used to compare mean values

from field data and the model.

The first test of the model was to visually compare the

model calculations with the observed data for each of

the model segments. Figure 10-4 presents the phosphorus calculations. As seen here, the simulation of

trends is reasonable, but variability in the data and

model discrepancies do exist. This may be caused by

short term variation not considered in the model or

other factors. As an additional test, statistical analyses

were performed.

The results of the statistical analysis are presented in

Table 10-4 as percent of sampling cruises in which

predicted and observed means were not significantly

different at a 95 percent confidence level. Segments 1

and 3 had the lowest scores, but represent only 3.5

and 5.0 percent, respectively, of the total volume of the

Bay. Also these are shoreline segments most influenced by changes in wind and tributary loading. Because segments 1 and 3 represent a small percentage

of the total area, they were not emphasized in the

calibration.

The model did a good job at matching total phosphorus

despite large differences in total phosphorus concentrations among segments. The model was less effective in simulating the dissolved available phosphorus.

Overall, the calibration resulted in approximately 86

percent of the model output being not significantly

different than the field data for the thirteen principal

variables.

10-4
FACT phytoplankton cell size in mg dry wt/cell

f(L) phytoplankton light reduction factor

(dimensionless)

f(T) phytoplankton temperature reduction factor

(dimensionless)

Ke light extinction coefficient in meter-1

KNCELL intracellular half-saturation constant for nitrogendependent growth in moles N/cell

KPCELL intracellular half-saturation constant for phosphorusdependent growth in moles P/cell

KSCM half-saturation constant for silicon-dependent

growth of diatoms in moles Si/L

KZSATk half-saturation concentration of phytoplankton

for grazing by zooplankton k in mg/L

P, N actual moles of phosphorus (nitrogen) per phytoplankton cell

PCA, NCA intracellular available phosphorus (nitrogen)

concentrations in moles/liter cell volume

PCAMIN, minimum intracellular concentrations, correspond

NCAMIN ng to PSAMIN and NSAMMIN, respectively, for availi able phosphorus (nitrogen) in moles/liter cell volume

PCM, NCM concentrations of available nutrients (phosphorus,

SCM nitrogen, silicon) in water column in moles/L

PDETH maximum predatory death rate for zooplankton in

liter/mg-day

PHOTO photoperiod (dimensionless)

PKI, NKI affinity coefficient for phosphorus (nitrogen) uptake

mechanism in liters/mole

PO, NO minimum cell quota of phosphorus (nitrogen) per phytoplankton cell in moles/cell

PSA, NSA actual total phosphorus (nitrogen) in phytoplankton

cells in moles/mg dry wt

PSAMIN, minimum quota of phosphorus (nitrogen) in

NSAMIN phytoplankton cells in moles/mg dry wt

PSATk saturation concentration of zooplankton k above

which predatory death rate remains constant, in

mg/L

Q water circulation rate in volume/day

RIPM, RINM maximum phosphorus (nitrogen) uptake rate in

day -1

RADINC incident solar radiation in langleys/day

RADSAT saturation light intensity for phytoplankton growth in

langleys/day

RAGRZDl rate at which phytoplankton l is ingested (grazed) by

zooplankton in mg A/liter day

RAMAX phytoplankton maximum growth rate at 20 C in day-1

RLYS phytoplankton decomposition rate in liter/mg day

RRESP phytoplankton respiration rate in day-1

RTOP, rates of transformation from unavailable nutrient

RTON, forms (phosphorus, nitrogen, silicon) to availabl e

RTOS forms in day -1

RZ zooplankton specific growth rate in day-1

RZMAX zooplankton maximum ingestion rate in day-1

RZPEX, nutrient (phosphorus, nitrogen, silicon) excretion

RZNEX by zooplankton to unavailable nutrient pool in ,

RZSEX moles/mg zooplankton-day

SPGR phytoplankton specific growth rate in day -1

SSA silicon composition of diatoms in moles/mg dry wt

T temperature in C

CROP total phytoplankton concentration in mg dry wt/L

TOP, concentration of unavailable nutrient forms (phosTON, phorus, nitrogen, silicon) in moles/L

TOS

TOPSNK, sinking rates of unavailable nutrient forms (phosphoTONSNK, rus, nitrogen, silicon) in meters/day

TOSSNK

V inner bay volume in liters

WPCM, external loading rates of available nutrients (phosWNCM, phorus, nitrogen, silicon) in moles/day

WSCM

WTOP, external loading rates of unavailable nutrients

WTON, (phosphorus, nitrogen, silicon) in moles/day

WTOS

Z zooplankton concentration in mg dry wt/L

ZASSIM zooplankton assimilation efficiency

(dimensionless)

ZEFFkl ingestion efficiency of zooplankton k for phytoplankton l (dimensionless)

ZDETH specific zooplankton death rate in day-1

ZKDUM effective half-saturation concentration of total phytoplankton for grazing by zooplankton

ZSAFE refuge concentration of zooplankton below which

predatory grazing does not occur, in mg/L

NOTE: The addition of the suffix “BD” to a variable refers

to the boundary value of the variable.

Table 10-1. Description of Model Coefficients [Bierman and Dolan (1981)].

10-5
10.3.4. Projections

In a report to the International Joint Commission (Bierman and Dolan, 1980) the model was applied to seven

scenarios of phosphorus loadings. The scenarios consisted of various combinations of advanced wastewater treatment and non-point source reduction. The

results were presented as annual average total phosphorus concentration, total phytoplankton biomass,

total blue-green phytoplankton biomass, and taste and

odor in the municipal water supply. Although the Bay

was partitioned into five segments, only two contrasting segments (segments 2 and 4) were analyzed.

Segment 2 contained 73 percent of the total water

volume of the inner Bay and was the most degraded

portion of the Bay. Segment 4 had the highest water

quality in the Bay. These segments represented the

two extremes in the Bay.

In the model simulations, peak total biomass concentrations did not change significantly with reductions in

phosphorus loads; however, the blue-green phytoplankton responded in direct proportion to phosphorus

reduction in segment 2 and in a lower proportion in

segment 4. This was the first objective of nutrient

control in the Bay.* This simulation of algal species

change is a unique aspect of this multi-class phytoplankton model. The model has the ability to distinguish nutrient limitation among different types of

phytoplankton and hence allows changes in composition.

In general, the model showed phytoplankton growth to

be nitrogen-limited, but for a two month period (May

and June) diatoms were silica-limited. This agreed with

actual observations of nutrient depletion. In mid-August, the nitrogen-fixing blue-greens capitalized on the

depletion of nitrogen and proliferated. Their growth

was then restricted by phosphorus limitation.

a. Summary of Phytoplankton Coefficients

Blue-Greens Blue-Greens

Coef. Units Diatoms Greens Others (non-N2) (N2-Fixing)

R1PM day-1 0.500 0.500 0.500 0.500 0.500

PK1 liters/mole 0.518x106 0.167x107 0.158x 106 0.200x 107 0.518x106

PO mole P/cell 0.724x10-13 0.312x1014 0.148x10-13 0.566x1014 0.488x1014

CONCP 0.250x106 0.250s106 0.250x106 0.356x106 0.356x106

KPCELL mole P/cell 0.724x10-13 0.312x10-14 0.148x10-13 0.566x10-14 0.488x10-14

RINM day-1 0.125 0.125 0.125 0.125 0.125

NKI liters/mole 0.100x107 0.100x107 0.100x107 0.100x107 0.100x107

NO mole N/Cell 0801x10-11 0.345x10-12 0.163x10-11 0.438x10-12 0.377x10-12

CONCN 0.208x107 0.208x107 0.208x107 0.208x107 0.208x107

KNCELL mole N/cell 0.801x10-11 0.345x10-12 0.163x10-11 0.438x10-12 0.377x10-12

SSA mole Si/mg 0.334x10-5

KSCM mole Si/liter 0.357x10-5

RAMAX day-1 1.6 1.4 1.2 1.0 0.70

ASINK meter/day 0.05 0.05 0.05 0.05 0.05

RLYS liters/mg/day 0.004 0.004 0.004 0.012 0.012

FACT mg/cell 0.450x10-5 0.194x10-6 0.918x10-6 0.246.10-6 0.212x10-6

RADSAT langleys/day 100 100 100 50 50

RRESP day-1 0.05 0.05 0.05 0.05 0.05

b. Summary of Zooplankton Coefficients c. Summary of Coefficients for Unavailable Nutrients

Coef. Unit Faster Slow Coefficient Units Value

Ingester Ingester

RZMAX day-1 0.70 0.10 RTOP, RTON, day-1 0.005

ZASSIM 0.60 0.60 RTOS, TOPSNK, meters/day 0.05

KZSATk mg/liter 1.0 1.0 TONSNK

AZMIN mg/liter 0.20 0.20

BDETH day-1 0.05 0.01

PDETH day-1 0.50 0.10

ZSAFE mg/liter 0.01 0.01

PSATk mg/liter 1.0 1.0

Table 10-2. Summary of Selected Model Coefficients [Bierman and Dolan (1981)].

10-6

* Later application of the model to 1980 data in a post-audit shows the blue-greens actually responded in a much greater proportion

to phosphorus reduction.
Figure 10-4. Model output and field data comparison for total phosphorus (solid line is model output; data are sampling

cruise means and three standard deviations) [Bierman and Dolan (1986a). Reprinted from ASCE Journal of

Environmental Engineering, Vol. 112, No. 2. p. 409. With permission].

10-7
10.4. Post-Audit

In 1980, a survey was conducted and used in a postaudit of the model. A post-audit provides a test of the

model for use in projections by comparing forecasts to

actual observations. Environmental conditions

changed substantially in five years. From 1975 to

1980, total annual load of total phosphorus decreased

66 percent and available phosphorus decreased 78

percent. It was estimated that 44 percent of the drop

in phosphorus load was because of decreases in

tributary flow. The other 56 percent was attributed to

point source controls and a detergent phosphorus ban

for the State of Michigan initiated in 1977. Total phytoplankton biomass also decreased substantially, with

the nitrogen-fixing blue-greens being nearly eliminated.

The predictive capability of the model was tested using

the 1980 data. The model was rerun using the 1974

and 1975 model coefficients but loading and environmental conditions for 1980. The results are presented

as a comparison of predicted and observed percent

reductions between the 1974-75 calibration years and

the 1980 resurvey year (Figure 10-5). In general, the

model overestimated the percent reduction in total

phosphorus, and underestimated reductions in diatoms and blue-green algae.

Underestimation of phosphorus concentrations was a

characteristic of model results during the calibration

years and in the post-audit survey. This discrepancy

was attributed to the underestimation of wind-driven

resuspension of sediments. Nevertheless, the model’s

prediction of elimination of threshold odor violations at

the water treatment plant agreed with the data. This

was the primary management need for the model.

Blue-green phytoplankton biomass in segment 4 was

correlated with threshold odor in the drinking water

intake. The model predictions for threshold odor violations in the drinking water intake agreed with observations because both were below the blue-green

biomass threshold.

Overall, the model predictions did not match observed

concentrations closely, but were consistent with observed trends. The model correctly predicted that if

phosphorus loadings were reduced to 400-500 metric

ton/year, blue-green algae would decrease more than

other species and threshold odor would be eliminated.

The response of the blue-greens exceeded the prediction of the model in absolute values.

10.5. References

Ayers., J.C., Anderson, D.V., Chandler, D.C., and G.H.

Lauff, 1956. Currents and water masses of Lake

Huron, (1954) Synoptic Surveys. Ontario Dept. Lands

and Forests, Division of Research and University of

Michigan, Great Lakes Res. Inst. (Referenced in

Richardson, 1974).

Bierman, V.J. and D.M. Dolan. 1980. Responses of

Saginaw Bay, Lake Huron, to reductions in phosphorus loadings from Saginaw River. Report prepared for

the International Joint Commission.

Bierman, V.J. and D.M. Dolan. 1981. Modeling of

phytoplankton- nutrient dynamics in Saginaw Bay,

Lake Huron. J. Great Lakes Res. 7(4): 409-439.

Parameter Sample Year

1974 1975 1980

Saginaw River Loadings (Metric Tons):

Total Phosphorus 1266 1470 493

Total Nitrogen 14,100 15,290 11,030

Total Silicon 23,000 31,000 12,250

Environmental Forcing Factors:

Number of Days Where Wind Speed Exceeded

Threshold for Resuspension (Annual)

29 40 35

Annual Average Water Temperature (o

C)

Segment 2 12.0 13.9 14.9

Segment 4 9.8 11.1 12.8

Table 10-3. Summary of Selected Model Inputs [Bierman and Dolan (1986b). Reprinted from ASCE Journal of Environmental

Engineering, Vol. 112, No. 2. p. 422. With permission].

Year

Cell*

12345

1974 72 85 65 88 87

1975 64 80 72 86 87

1980 57 64 52 85 86

* Percent of sampling cruises for which computed mean values

not significantly different from observed mean values at 95%

confidence level; average of 13 variables.

Table 10-4. Statistical Comparison between Model Results

and Field Data [Bierman and Dolan (1986b)].

10-8
Bierman, V.J. and D.M. Dolan. 1986. Modeling of

phytoplankton in Saginaw Bay: I. Calibration Phase. J.

Env. Eng., ASCE 112(2): 400-414.

Bierman, V.J. and D.M. Dolan. 1986. Modeling of

phytoplankton in Saginaw Bay: II. Post-Audit Phase.

J. Env. Eng., ASCE 112(2): 415-429.

Bierman, V.J., Dolan, D.M., Stoermer, E.F., Gannon,

J.E., and V.E. Smith. 1980. The development and

calibration of a spatially simplified multi-class phytoplankton model for Saginaw Bay, Lake Huron. Great

Lakes Environmental Planning Study (GLEPS) Contribution No. 33.

Fish and Wildlife Service, U.S. Department of Interior.

1956. Surface current studies of Saginaw Bay and

Lake Huron. (Referenced in Richardson, 1974).

Richardson, W.L. 1974. Modeling Chloride Distribution

in Saginaw Bay. Proc. 17th Conf. Great Lakes Res.

1974: 462-470. Internat. Assoc. Great Lakes Res.

Figure 10-5. Changes in water quality constituents between 1974 and 1980 in segments 2 and 4 [Bierman and Dolan (1986b).

Reprinted from ASCE Journal of Environmental Engineering, Vol. 112, No. 2. p. 409. With permission].

10-9

11. Potomac Estuary Water Quality Modeling

11.1. Background

The studies discussed here include application of three

different waste load allocation-related models for the

Potomac Estuary near Washington, D.C. The three

models, although covering basically the same location,

have markedly different structures to address three

different water quality issues. The water quality concerns consisted of:

• Dissolved Oxygen Depression

• Nutrient Enrichment and Algal Proliferation

• Total Residual Chlorine

These three water quality concerns each had unique

spatial and temporal considerations, such that all concerns could not be properly addressed by a single

model. In this regard, three separate (but inter-related)

models were developed to specifically address each

issue. The Dynamic Estuary Model (DEM), a one-dimensional spatially detailed and real-time dissolved

oxygen model was applied to determine effluent limitations for oxygen-demanding materials. The Potomac

Eutrophication Model (PEM), a tidally-averaged eutrophication model, was applied to determine the impact

of nutrient control strategies on regional algal concentrations. Neleus, a real-time two-dimensional finite element model, was applied to determine very localized

total residual chlorine impacts and the potential for

forming a barrier to fish passage.

11.2. Problem Setting

The Potomac Estuary drains an 11,560 square-mile

area, comprising portions of Maryland, Virginia, West

Virginia, and Pennsylvania. It is used for a wide variety

of activities, ranging from industrial water supply (primarily cooling water supplies), to navigation, boating

and commercial and sport fishing.

The Potomac Estuary extends 114 miles from the fall

line at Chain Bridge in Washington, D.C. to its junction

with the Chesapeake Bay (see Figure 11-1.) The esFigure 11-1. Location map of Potomac Estuary [USGS (1985)]

11-1
tuary can be divided into three zones: the freshwater

or tidal river zone, the transition zone, and the saline

zone. The upper reach, although tidal, contains only

freshwater, and extends from Chain Bridge to just

above Quantico. The middle zone is characterized by

a transition from fresh to brackish water and extends

from Quantico to the Highway 301 bridge. The lower

reach is highly saline, vertically stratified, and often

anoxic near the bottom. The modeling and waste load

allocation discussed herein focuses on the freshwater

zone.

The major source of pollutants in the upper Potomac

Estuary is the District of Columbia and its suburbs.

Population in the Washington, D.C. area increased

from 2.1 million in 1960 to 3.2 million in 1980. At least

14 wastewater treatment plants with a combined flow

well over 500 MGD discharged into the Potomac Estuary in 1980. This discharge is a significant increase

over the 325 MGD wastewater flow in 1966. While

effluent flow has increased, the load of phosphorus

and BOD5 from these point sources has decreased

approximately seventy-five and fifty per cent respectively during this period because of substantial improvements in wastewater treatment.

The most significant point source discharge to the

estuary is the Blue Plains wastewater treatment plant

in Washington, D.C., which has an average annual

flow of 227 MGD. Other sources of nutrients and

oxygen demanding material to the Potomac Estuary

include nonpoint source discharges from upper basin

drainage and downstream tributaries, combined sewer

overflows, and atmospheric pollutants.

The upper portion of the Potomac Estuary has been

plagued with occurrences of low levels of dissolved

oxygen, floating algal mats, and high concentrations of

chlorophyll a, indicating a relatively advanced state of

eutrophication. In recent years these problems have

dramatically declined because of increased wastewater treatment.

11.3. Dynamic Estuary Model (DEM) of

Dissolved Oxygen

The Potomac Estuary was regularly depleted of dissolved oxygen during the 1960s and early 1970s in

response to point sources of pollution and combined

sewer overflows in the Washington, D.C. area. U.S.

EPA Region III, in their <169>Potomac Strategy<170>, highlighted the need to develop and validate water quality models for the Potomac that could

be used for wasteload allocation purposes (Clark,

1982). The Potomac Strategy State/EPA Technical

Committee subsequently recommended DEM as the

appropriate model to use to assess dissolved oxygen

impacts in the Upper Potomac. Their decision was, in

large part, based on the capability of the model to

provide good spatial resolution and diurnal calculations.

DEM represents the Potomac using a series of interconnected channels and junctions. These channels

and junctions can be arranged to simulate simple

two-dimensional features of the estuary, but are primarily one-dimensional (e.g. no lateral variation) with

branching. DEM as configured for the Potomac extends from Chain Bridge (River Mile 0.0) as an upstream boundary to Piney Point (River Mile 96.2) as a

downstream boundary. This configuration consists of

133 junctions and 139 channels, but the focus of the

water quality modeling was in the upper 20 miles. DEM

simulates in <169>real time<170>, meaning that the

model predicts conditions as they vary through diurnal

and tidal variations.

DEM consists of two separate but closely related models. The first, a hydrodynamic model, simulates both

the tidal and net advective movement of water. This

model provides predictions of water depth, velocity,

and direction of flow based upon input information on

geometry, roughness, tributary inflows and tidal variations in depth at Piney Point. The results of the

hydrodynamic model are input to the second model,

which simulates water quality.

The water quality model predicts the transport and

transformation of pollutants in the Potomac Estuary.

The model, as applied for Potomac dissolved oxygen,

simulates three variables: dissolved oxygen, carbonaceous biochemical oxygen demand (CBOD), and ammonia.

Dissolved oxygen concentrations are increased by

atmospheric reaeration and algal photosynthesis, and

are decreased by oxidation of CBOD, nitrification of

ammonia, sediment oxygen demand, and algal respiration. The model does not predict algal photosynthesis or respiration. Instead, these values must be input

by the modeler based upon observed data or calculations performed external to the model. CBOD concentrations are increased by point and nonpoint loadings,

and are decreased by settling and deoxygenation of

CBOD. Ammonia concentrations are increased by

point and nonpoint loadings, and are decreased by a

first-order loss term defined in DEM as nitrification.

Water quality data for model calibration and verification

consisted of both wet and dry weather surveys conducted in 1965, 1966, 1967, 1968, 1970, 1977, 1978,

1979, and 1980. The Blue Plains wastewater treatment plant, the primary point source of pollutants to the

river, implemented secondary treatment in 1977.

11-2
11.3.1. Model Calibration/Verification

Calibration of DEM to the Potomac required separate

calibration of both the hydrodynamic and water quality

submodels. Hydrodynamic calibration focused on the

channel roughness coefficient to best describe the

magnitude and phasing of predicted tides. The model

was calibrated using mean upstream freshwater flow

(11,000 cfs) and elevation data published in the National Oceanographic and Atmospheric Administration

(NOAA) Tide Tables. Sample model calibration results

are shown in Figure 11-2. The hydrodynamic submodel was then verified to observed data from the

periods January 11-13, 1971 and July 22-28, 1981.

The calibrated roughness coefficients accurately reproduced tidal range and phasing for all data sets.

The water quality submodel calibration was divided

into two separate tasks: 1) calibration of dispersion

(using conservative tracers) and 2) calibration of reaction rate coefficients (using water quality concentrations). The dispersive transport coefficient was

calibrated to chloride data collected during the period

August 1 to September 8, 1977, and verified to chloride

data from the period September 15 to November 12,

19611. The model predicted the majority of data quite

well, but was unable to simulate the steepest portion

of the chloride gradient due to numerical dispersion

(Figure 11-3). The dispersion rates determined

through calibration and verification of the chloride data

were also tested against a 1978 dye survey. The model

was able to simulate observed far-field data quite well,

with discrepancies in near-field embayments.

Water quality data for model calibration of reaction

kinetics consisted of surveys conducted in 1965, 1966,

1967, 1968, 1970, 1977, 1978, and 19711. The objective of the calibration procedure was to simulate as

many data sets as possible and to provide a test of the

model’s ability to duplicate a wide range of conditions.

Model calibration (coefficient adjustment) was conducted on data sets through 1977, with the later data

sets used for verification (without coefficient adjustment). The data sets from 1965 to 1970 were collected

during periods of relatively constant environmental

conditions and used for steady-state model comparisons. The 1977 data set was collected over a two

month period characterized by a massive algal bloom

(100-300 ug/l chlorophyll a) and die-off, and used a

real-time model to characterize the significant transient

processes. Example model calibration to data are

shown in Figures 11-4 to 11-6 for the parameters

ammonia, nitrate+nitrite, and dissolved oxygen. Comparisons of BOD were not provided because algae

complicated its measurement and comparisons. The

model generally reproduced trends in observed data

quite well and was also very successful in matching

1978 and 1979 data during model validation.

Figure 11-2. Potomac Estuary hydraulic calibratlon hlgh water phasing - Mean Tide [Adapted from Clark (1982)].

11-3
Flgure 11-3. Potomac Estuary chlorlde verification, time period: September-October, 1969 [Adapted from Clark (1982)].

Figure 11-4. DEM calibration results for ammonia, time period: August 31-September 16, 1965 [Adapted from Clark (1982)]

11-4
Flgure 11-5. DEM calibration results for nitrate+nitrite, time period: August 31-September 16, 1965

[Adapted from Clark (1982)]

Figure 11-6. DEM calibration results for dissolved oxygen, time period: August 31-September 16, 1965

[Adapted from Clark (1982)]

11-5
11.3.2 . Model Application

Application of DEM was conducted over the course of

several years and modeling efforts. Initial wasteload

allocation projections were made by U.S. EPA (Clark,

1982). A revised and updated examination was performed in 1984 but recommendations from this effort

were deferred when data from the mid-1980’s appeared inconsistent with model predictions (MWCOG,

1987). The model was then revalidated in 1987 to more

recent water quality data, and new wasteload allocation projections performed.

DEM was applied by Greeley and Hansen (1984) as

part of the Washington D.C. Blue Plains Feasibility

Study, to determine regional capacity treatment needs

and establish plant allowable effluent loads for dischargers to the Upper Estuary. Numerous alternatives

were examined for water quality compliance and other

factors. Seven final regional wastewater treatment

scenarios were evaluated for their ability to lead to

compliance with water quality standards for dissolved

oxygen. Model projections were made at critical environmental conditions consisting of drought (7Q10)

freshwater flow and a water temperature of 28oC, and

the upper 90th percentile temperature at summer low

flow. Model coefficients were based on the average of

post-1977 simulations. Algal productivity and respiration inputs were derived from drought flow simulations

using the Potomac Eutrophication Model (see later

discussion). Sediment oxygen demand (SOD) was

proportionately reduced with loadings toward background values due to the expected decrease in pollutant loading. Boundary concentrations were

representative of the period 1977-19711.

DEM model results for both daily averaged and daily

minimum dissolved oxygen indicated that all final alternatives evaluated would lead to compliance with dissolved oxygen standards for the critical conditions

scenario. Water quality differences between scenarios

were viewed as small in comparison to the substantial

differences in cost. The recommended treatment scenario was subsequently based upon cost, engineering

and other considerations.

11.3.3. DEM Post Audit

State and Federal regulators originally rejected the

DEM-based wasteload allocation recommendations,

due primarily to a review of 1982-1985 dissolved oxygen data from the Upper Potomac. These data indicated that dissolved oxygen standards violations were

still occurring, even though treatment plants were performing at recommended levels. Given that DEM predicted that additional nitrification treatment at two area

POTWs would improve minimum dissolved oxygen

concentrations by 0.8 mg/l, they recommended nitrification treatment at these plants.

Local governments expressed considerable reservation regarding the need for improved treatment, and

conducted a study to revisit the DEM modeling analysis and examine regulatory agency concerns

(MWCOG, 1987). Extensive water quality surveys

were conducted in the Upper Potomac in 1986 to

validate (or refute) the predictive capability of DEM. In

addition, special studies were conducted investigating

current pollutant decay rates, sediment oxygen demand, and occurrence and cause of water quality

standard violations. Limno-Tech (1987) applied DEM

to simulate 1985 and 1986 conditions. This analysis

determined that DEM calculations of dissolved oxygen

were very sensitive (<F128M>’<F255D>3 mg/l) to algal-productivity related parameters which were not

directly measured. Given judicious selection of inputs,

DEM could simulate recent dissolved oxygen data.

Since neither observed (nor eutrophication model predicted) algal productivity information was available,

DEM predictions could not be explicitly confirmed or

refuted. An important outcome of this analysis was that

transient changes in algal productivity could be responsible for dissolved oxygen standards violations,

irrespective of point source impacts. Furthermore, detailed examination of DEM indicated that it over-calculated the benefits from additional nitrification treatment

because it simplistically assumed all ammonia lost was

due to nitrification. The ammonia mass balance is a

net combination of nitrification, algal uptake of ammonia, sediment ammonia release, and hydrolysis of

organic nitrogen. Re-evaluation indicated a reduced

nitrification rate and a benefit due to additional nitrification treatment of 0.2 to 0.5 mg/l.

As a result of these findings, the dominance of net algal

productivity and the small benefits from additional

nitrification treatment, further nitrification treatment requirements were deferred.

11.4. Potomac Eutrophication Model (PEM)

The Potomac Estuary began exhibiting signs of eutrophication (algal blooms, floating mats of vegetation) in

the late 1940s and continued through the 1960s. In an

effort to control these problems, point source discharges of total phosphorus to the estuary were reduced by seventy-five percent over the period 1968 to

19711. However, algal bloom conditions persisted into

the late 1970’s, causing concern as to whether the

decrease in point source phosphorus was controlling

eutrophication. The Potomac Eutrophication Model

(PEM) was developed to determine the impact of

historical pollution controls on Potomac Estuary eutrophication, and to guide regulators in setting future

effluent limitations.

The PEM model was developed because the existing

DEM model focused more on spatial resolution than

11-6
on the kinetic complexities of eutrophication which

were necessary to forecast the benefits of nutrient

controls. In addition, the tidally averaged and large

segment approach of PEM is more consistent with the

regional and seasonal focus of eutrophication. PEM is

a version of the EPA supported Water Quality Analysis

Simulation Program WASP, but developed specifically

for the Potomac (Hydroqual, 1982). Compartment or

box modeling techniques are used to represent the

estuary as a series of water column and sediment

segments. There is no hydrodynamic submodel included in PEM. Average flows, velocities, and dispersion coefficients are not computed by the model; they

are specified as model inputs. The hydrodynamic inputs are tidal averaged and reflect seasonal changes,

not daily or hourly changes. The kinetic equations

employed in PEM link phytoplankton growth and death

to non-linear nutrient interactions and recycle mechanisms, directly couple phyto-plankton to dissolved oxygen concentrations, and internally compute sediment

nutrient release and oxygen demand. The following

state variables are included in PEM:

• Chlorides

• Phytoplankton carbon

• Total organic nitrogen

• Ammonia nitrogen

• Nitrite-nitrate nitrogen

• Dissolved and particulate organic phosphorus

• Dissolved and particulate inorganic phosphorus

• Carbonaceous biochemical oxygen demand

• Dissolved oxygen

PEM computes water column concentrations on a

daily basis, but in calibrating the model, the focus was

on matching monthly and annual trends over a regional

scale of 75-100 miles. Such spatial and temporal

scales represent the global responses of the estuary

to seasonally transient nonpoint source inputs from the

upper Potomac Basin and tributaries, and point

sources from wastewater treatment plants.

The PEM network consists of 23 main channel segments and 15 tidal embayment segments, each with a

sediment layer segment below. These segments

range in length from one to two miles in the upper tidal

freshwater portion of the estuary, to 10-15 miles in the

lower, saline portion of the estuary. The focus of the

modeling was on the freshwater segments.

11.4.1. Model Calibration/Verification

Historical data from several sources were used for

both the calibration and verification of PEM. Data sets

were selected that provided spatial coverage of at least

the upper 50 miles of the estuary on a biweekly or

monthly basis for the crucial summer period, and that

included simultaneous measurements of chlorophyll a,

nutrients, and dissolved oxygen. Data from different

sources were often combined to produce a more robust characterization of the estuary. The data sets

generally had biweekly sampling during the warm

weather season at stations 1 to 2 miles apart in the

freshwater portions of the Upper Estuary. Data collected during 1966, and 1968 through 1970 were used

in the calibration, and are representative of water

quality conditions prior to the implementation of phosphorus removal at the major sewage treatment plants

along the estuary.

USGS data from the years 1977 through 1979 were

used to verify PEM. These years were selected because they offered the chance to study the changes in

the estuary after institution of phosphorus removal at

Blue Plains. Thus, the verification period provided an

opportunity to further test the model’s ability to simulate

the eutrophication process in the Potomac Estuary.

The verification data set involved short, intensive

week-long surveys in 1977 and 1978. The entire length

of the estuary was usually sampled twice during the

1977 and 1978 surveys, with vertical samples collected at a number of stations. In 1979, the spatial and

temporal coverage was reduced, and sampling was

limited to twice a week at five major stations.

11.4.2. Environmental Inputs

The PEM application for 1966 to 1979 required extensive inputs for environmental conditions including

flows, loads, and boundary conditions which are summarized below.

PEM does not include a hydrodynamic submodel, so

flows must be input for each completely mixed segment of the model. To simplify model input during the

calibration period, only the two major and dominant

sources of freshwater flow were included, the Potomac

River at Little Falls (the upstream boundary) and the

Blue Plains sewage treatment plant effluent. Downstream tributary flows and other treatment plant discharges were deemed minimal. Both upstream

freshwater flows and Blue Plains effluent flows were

input to the model using piece-wise linear approximations of seasonal flow patterns, not actual day-to-day

fluctuation. For model verifications, the model also

included flows for the Anacostia River and Occoquan

Reservoir. These flows were insignificant during the

extreme drought of the calibration period, but were of

sufficient magnitude during verification that they had

to be considered.

11-7
Pollutant loads to the Potomac were divided into three

categories: 1) Point Sources, 2) Combined Sewer

Overflows, and 3) Non-Point Sources. Point source

inputs of pollutants were defined by monitoring data

and daily operating reports from the area’s municipal

wastewater treatment plants. The Blue Plains treatment facility accounted for the large majority of these

inputs.

In addition to permitted outfalls, an unregulated

<169>gap<170> in a major sewer line contributed

approximately 6 MGD of raw sewage until closed in

July 1972. Estimates of monthly averaged combined

sewer overflow pollutant loadings for Washington D.C.

were generated with a SWMM model simulation of the

D.C. sewer network. Combined sewer overflows for

Alexandria were estimated based on calculated stormwater runoff and the average CSO concentrations

measured in the D.C. sewer system.

Nonpoint source loads to the estuary were estimated

for all tributaries to the main stem of the Upper Potomac Estuary. The nonpoint source flow for each tributary was based on data from USGS gaging stations.

Estimates of flow for ungaged tributaries were based

on the gaged discharge in neighboring tributaries.

Seasonal flow trends were defined for each year by

smoothing out many of the small peak flows using

linear approximations. Water quality concentrations

associated with nonpoint runoff were based on predictions of the Nonpoint Source (NPS) model. Simulated

daily flows and pollutant loadings from 1977 to 1979

were analyzed, and a mean concentration for each of

three flow ranges were determined and used in the

model inputs. Slight reductions in concentrations were

used for the 1960’s simulations to reflect the less

developed land use.

11.4.3. Boundary Conditions

Model inputs for upstream boundary conditions were

based on data but required considerable extrapolation

and interpolation to simulate the several years of conditions. Available data were statistically analyzed and

correlated to flow. Where applicable, relationships

were used between pollutant concentration (e.g. nitrate) and flow; otherwise, average concentrations

were matched to observed USGS flow. All inputs were

smoothed to characterize seasonal trends, not day to

day transients.

11.4.4. Calibration

The model calibration included reaction rates for phytoplankton growth, nitrogen and phosphorus cycling,

and the distribution of CBOD and dissolved oxygen.

Calibration was accomplished by varying rate coefficients until a satisfactory fit was obtained between the

predicted and observed water quality data. Model

coefficients were identical for all calibration surveys.

External inputs such as flow, temperature, solar radiation, and light extinction coefficients were as measured

during the surveys.

Figures 11-7 to 11-10 show predicted and observed

water quality data. These figures present calibration

results for chlorides, chlorophyll a, DO, BOD5, total

organic phosphorus, total inorganic phosphorus, ammonia nitrogen, and nitrite-nitrate nitrogen during May

and September of 1966, the year with the lowest

recorded flow. The model predicted the overall variation in the data well. Of particular note is the chloride

calibration, which validated water transport. Other calibration runs were similar.

11.4.5. Verification

Initial verification used 1977-1979 environmental conditions and the model coefficients derived during calibration. Some of the calibrated coefficients had to be

modified for the verification period to reflect improved

treatment and the altered settling characteristics of

inorganic phosphorus. These changes included the

relocation of the Blue Plains outfall and the use of ferric

chloride to precipitate phosphorus. To account for the

altered settling characteristics, a spatial settling function was developed that was unique to the verification.

The instream nitrification rate and the oxidation rate for

carbonaceous BOD were also changed to reflect improved treatment levels.

Predicted and observed water quality are compared in

Figures 11-11 and 11-12, which illustrate the July 1977

PEM verification for chlorides, chlorophyll a, dissolved

inorganic phosphorus, total phosphorus, ammonia nitrogen, nitrite-nitrate nitrogen, BOD5 and DO. Similar

results were attained for other surveys.

11.4.6. Statistical Assessment of Validation

In addition to the graphical comparisons, statistical

measurements of goodness-of-fit tested the adequacy

of PEM for future predictions. The three statistical

procedures used in the PEM study are:

• Regression analyses

• Relative error

• Comparison of means.

In regression analyses, the calculated values from the

model are compared to the observed values, and a

number of standard statistics computed, including the

correlation coefficient and the standard error of the

estimate. Table 11-1 shows that 73 to 83 percent of

the variability in the observed chlorophyll a data and

60 to 93 percent of the variability in the observed

dissolved oxygen are explained by the model.

11-8
Figure 11-7. PEM calibration for chlorides and chlorophyll-a, May and September, 1966 [Hydroqual (1982)]

Figure 11-8. PEM calibration for BOD5 and dissolved oxygen, May and September, 1966 [Hydroqual (1982)]

11-9
Figure 11-9. PEM calibration for total organic and total inorganic phosphorus (mg P/L), May and September, 1966

[Hydroqual (1982)]

Figure 11-10. PEM calibration for ammonia and nitrite+nitrate (mg N/L), May and September, 1966 [Hydroqual (1982)]

11-10
Figure 11-11. PEM verification for chlorides (mg N/L), chlorophyll-a (mg N/L), dissolved inorganic phosphorus (mg P/L)

and total phosphorus (mg P/L), July 1977 [Hydroqual (1982)].

Figure 11-12. PEM verification for ammonia nitrogen (mg N/L), nitrate-nitrite nitrogen (mg N/L), bottle BOD5 (mg/L) and

dissolved oxygen (mg/L), July 1977 [Hydroqual (1982)].

11-11
The relative errors of the summer average means of

the principal state variables were also calculated in the

PEM study. These values indicate a large degree of

variation among variables for any one year, as well as

across years for any one variable. The median relative

errors, ranged from 10 to 30 percent for chlorophyll a,

5 to 10 percent for DO, and 15 to 25 percent across all

variables.

In comparing the means, a Student’s “t” test was used

to determine the difference between the observed

mean and the computed mean. If there was no significant statistical difference between the means, the

model was assumed to be verified. This statistic indicates that there was no statistical difference between

observed and computed summer means for 77 percent of the variable-segment pairs for which a comparison could be made.

11.4.8 . Post-Audit

Despite the continued reduction in point source phosphorus loading and gradual improvement in water

quality, a massive and unexpected bloom of bluegreen algae occurred in the Upper Potomac during the

summer of 1983. By August, the bloom had exceeded

200 <F128M>m<F255>g/l of chlorophyll a. The bloom

continued into the months of September and October.

The occurrence of the 1983 algal bloom offered a

unique opportunity to evaluate the predictive capability

of PEM. A post-audit PEM simulation was performed

to test the ability of the model to predict the observed

bloom conditions (Hydroqual, 1989).

The PEM post-audit was conducted in conjunction with

an Expert Panel convened to investigate the cause of

the bloom. Their conclusions (Thomann et al, 1985)

can be summarized as follows:

• PEM was able to successfully predict chlorophyll

concentrations in the portions of the estuary upstream of the bloom, and was able to predict the

onset of the bloom to nuisance levels through the

end of July.

• PEM was not able to predict the intensification of

the bloom, neither in magnitude nor spatial or

temporal extent.

• Model comparison to data indicated that there was

a significant source of phosphorus to the bloom

area that was not being considered by PEM.

The Expert Panel subsequently recommended that

investigations be undertaken to define the source of

increased nutrients. These investigations were to include evaluation of pH effects on sediment nutrient

release, and evaluation of the factors controlling alkalinity and pH in the Potomac. The Expert Panel also

Chlorophyll a

Year r

2 Standard Error

(ug/L)

Slope Intercept (ug/L) Hypothesis

1968 0.81 10.8 0.79 7.7 R

1969 0.75 10.8 0.76 2.5 R

1977 0.73 17.6 0.84 12.99 A

1978 0.88 4.9 0.70 12.26 R

1979 0.78 2.3 0.35 20.00 A

Dissolved Oxygen

Year r

2 Standard Error

(mg/L)

Slope Intercept (mg/L) Hypothesis

1968 0.60 0.74 0.83 1.55 A

1969 0.93 0.38 1.16 -0.76 A

1970 0.74 1.41 0.58 2.45 R

1977 0.73 0.93 1.21 -1.22 A

1978 0.75 0.59 0.68 2.77 R

1979 0.68 0.56 1.08 -0.29 A

Table 11-1. Linear Regression Statistics

11-12
recommended that PEM be updated to include newly

identified factors.

The first revision of PEM incorporated the results of

bloom-related experiments that indicated that increases in water column pH could significantly increase the magnitude of sediment nutrient flux. This

resulted in the addition of two components to PEM: 1)

simulation of pH, and 2) inclusion of a pH-mediated

sediment flux. The simulation of pH required the addition of a separate submodel to simulate the equilibria

between the multiple forms of inorganic carbon. This

pH-driven equilibrium is also affected by algal photosynthesis, which increases water column pH. The

second submodel added to PEM related to pH-mediated sediment release. The original version of PEM

simulated sediment quality and the flux of nutrients

across the sediment water interface. The updated

PEM removed these sediment computations and replaced the nutrient flux as a pH driven boundary condition.

This "first revision" of PEM provided improved prediction of 1983 conditions over the original version, but

was still unsatisfactory for the relationship between

phytoplankton, dissolved oxygen, nutrients, and the

carbonate system. PEM was then further updated to

include a second algal species representative of the

blue-green alga Microcystis, which was the primary

component of the observed bloom. Re-calibration of

the model provided an improved description of the

observed data. This revised PEM is now available for

use in evaluating the impact of various future nutrient

control strategies in the Upper Potomac Estuary.

11.5. Finite Element Model

Chlorine has been used extensively as a wastewater

disinfectant and as an agent to prevent biofouling in

cooling waters. Concerns have been raised that the

discharge of chlorine in wastewater to the Upper Potomac Estuary might pose ecological health risks. In

particular, discharges from opposing shorelines might

result in a cross channel barrier that could prevent fish

movement and migration. This study was conducted

to determine the occurrence and fate of residual chlorine in the Potomac and to evaluate the likelihood of

the formation of a toxic cross channel barrier.

A comprehensive study was conducted involving field

surveys of discharge and Potomac Estuary total residual chlorine (TRC) to document the current spatial

extent of TRC; to develop and calibrate a two dimensional TRC model for testing various environmental

scenarios; and to conduct model analysis of the various scenarios to establish the risk of a chlorine barrier.

The study area of the Potomac Estuary is freshwater

but hydraulically influenced by ocean tides. The confluence with the Anacostia River, numerous embayments, and highly variable channel physiography

make this section of the Potomac Estuary hydrodynamically complex. The data available to support a

TRC model were limited to grab samples in only the

longitudinal and lateral dimensions. Modeling was

therefore constrained to two dimensions. This was,

however, consistent with the purpose of the modeling

- to define the lateral and longitudinal extent of effluent

residual chlorine plumes as a potential barrier to fish

migration.

The complex physiography of the upper Potomac Estuary did not allow use of simple analytical models.

One-dimensional water quality models were of little

use for evaluating the chloride discharges because the

lateral extent of contamination could not be simulated.

Branching one-dimensional estuary models, such as

the Dynamic Estuary Model (DEM) may be configured

to run as pseudo-two-dimensional models but have

unrealistically high dispersion for localized calculations

and poor characterization of two dimensional transport. For these reasons, a true two dimensional hydrodynamic and water quality model was required.

The Neleus chlorine model selected for this study

consists of a hydrodynamic model linked to a water

quality model. The hydrodynamic model solves the

complete non-linear, two-dimensional, partial differential equations of fluid motion (Katopodes, 1987; LTI,

1987). The equations are integrated over time using a

modified Petrov-Galerkin finite element model numerical technique yielding surface elevation and velocity at

each of the model grid nodes. The results are input as

mass transport terms to the water quality model.

The water quality model uses the same grid framework

as the hydrodynamic model and is represented by a

two-dimensional, vertically averaged, partial differential equation of mass transport. The equation includes

terms for advective and diffusive mass transfer, mass

sources and/or sinks, and first-order decay. The numerical solution is obtained in the same manner as with

the flow equations except that an iterative solution is

not required since the mass transport equation becomes linear with the assumption of zero diffusive flux

at the model boundaries.

11.5.1. Model Inputs

The Neleus model required a finite element grid comprising 1171 quadrilateral elements with 1408 nodes

(element intersections) as shown in Figure 11-13. This

fine detail was required because of complex

bathymetry. In addition, grid resolution had to be high

near pollutant sources to maintain numerical stability

11-13
during computation and to provide accurate model

predictions within fairly short distances of discharge

locations.

After setting the model grid, model inputs for boundary

conditions and loadings were determined. These included tidal elevation and flows. The NOAA Tide Tables provided minimum and maximum tidal elevations

and a sinusoidal interpolation scheme was used to

provide tidal elevations for each hydrodynamic model

time step. Some actual recorded tidal elevation data

were available for use in modeling the residual chlorine

surveys. Minimum and maximum elevations and time

(NOAA, 1984) were abstracted from the continuous

record. Advective freshwater discharges were specified as nodal velocities at the upstream ends of the

model for each simulation. These were determined

using information from USGS flow records for both the

Potomac (at Chain Bridge) and Anacostia channels.

Figure 11-13. Chlorine model finite element grid network [LTI (1987)]

11-14
Daily variations in discharge were incorporated in

simulations when appropriate.

In terms of pollutant inputs, four chlorine discharge

locations were identified in the study area as:

• Blue Plains WWTP • Alexandria WWTP

• Arlington WWTP • PEPCO Power Plant

The Blue Plains wastewater plant was the only source

for which information was known about outfall configuration and precise location. As a result, the other

chlorine sources were treated as mass pollutant loadings with no momentum effects. The impact of this

simplification on main channel model results was minimal since Arlington and Alexandria discharge to embayments and PEPCO discharges chlorine

intermittently at very low levels.

11.5.2 Available Data

Four surveys conducted prior to the modeling effort

were available for model calibration. First, the USGS

conducted a dye survey over a six day period in

August, 1980 (Hearn, 1984). Dye was injected for one

tidal day (24.8 hours) from the Blue Plains outfall and

subsequently measured throughout the study area.

Three surveys conducted by the District of Columbia

Department of Consumer and Regulatory Affairs provided effluent and ambient TRC concentrations

throughout the tidal cycle.

11.5.3. Model Calibration/Verification

The Neleus model involved validation for both hydrodynamic and water quality models. The hydrodynamic

model has one calibration parameter <197> Manning’s

n, which reflects the hydrodynamic effects of bottom

roughness. The lack of hydrodynamic field data limited

the calibration of the hydrodynamic model. However,

previous work by Katopodes (1987) resulted in a limited calibration of the model hydrodynamics through

comparison with DEM hydrodynamic predictions. A

constant Manning coefficient of 0.026 was used by

Katopodes (1987) and was chosen for use in the

chlorine study. The water quality model has three

parameters that require calibration: longitudinal and

lateral dispersion coefficient, and the first-order chlorine decay rate. The dispersion terms were adjusted

through simulation of the August 1980 USGS dye

study, while the chlorine decay rate was selected

through simulation of two of the 1984 chlorine field

studies.

The 1980 USGS dye study was used to calibrate the

lateral and longitudinal dispersion coefficients. The

model simulation began on the 10th of August with the

dye release simulation starting on the 11th. Discharge

from the Blue Plains outfall was constant with a flow of

517 cfs (334 MGD) and dye concentration was

0.03446 mg/l over the release period of 24.8 hours.

Longitudinal and lateral dispersion coefficients were

first estimated from literature information (Fischer et

al., 1979 and McDowell and O’Connor, 1977) but

refined to values of 120 ft2/sec for longitudinal dispersion and 10 ft2/sec for lateral dispersion. Figure 11-14

presents the model dye predictions compared to

measured dye concentrations for two survey stations.

These simulations assumed no decay of dye.

The model predictions follow the trends in the dye data

for all stations. Evidence of dye loss is seen for stations

B and C beginning on approximately August 13th. The

inclusion of dye decay would improve fit of model to

dye data, but would not affect the calibration of the

dispersion terms. Since dye decay was not important

to the modeling of TRC, no further model refinement

for simulation of dye was performed.

The July, 1984 survey was selected for initial chlorine

modeling because the sampling covered a longer time

period than the other surveys. Data were collected

during both day and night. The effects of daytime

photolysis on chlorine decay could then be analyzed

by comparing day versus night results.

Loading during the survey included a total residual

chlorine concentration in the Blue Plains effluent of

0.333 mg/l at 330 MGD, in the Arlington WWTP effluent of 1.9 mg/l at 26 MGD, and the Alexandria wastewater treatment facility produced a total residual

chlorine level of 1.9 mg/l at a discharge rate of 43 MGD.

The PEPCO discharge was 401 MGD with intermittent

effluent chlorine levels. The exact times during which

chlorination occurred were not known, but the levels of

chlorine applied to the cooling water were low. A

constant residual chlorine concentration of 0.02 mg/l

was used to represent the likely level of discharge from

PEPCO.

For initial simulations a chlorine decay rate of 12.8 per

day was determined experimentally. A more conservative decay rate of 6.4 per day was also tested. The

comparison of model versus data is shown in Figures

11-15 and 11-16 for averaged field data and model

predictions. Averaging was used to simplify the presentation of results and because the field data were not

sufficient to justify detailed comparisons. Contour lines

of constant concentration are used to depict model

output whereas field data are shown as singular numeric values. In general, measured chlorine levels at

most field stations were too near detection limits to be

considered accurate except as order of magnitude

estimates. Therefore, the averaging represents the

plume character well.

11-15
The comparisons of model to TRC data were considered reasonable for both loss rates. The differences in

the simulations were not dramatic and and indicated

that physical transport was dominant. The model characterized the dissipation of TRC especially when considering the data can only be best relied on as an order

of magnitude indicator. The value of 96 μg/l just to the

north of Blue Plains represents only one observation,

and appears to be an anomaly. Further sensitivity

analyses suggest that the lower decay rate of 6.4/day

might be more representative of night time conditions,

while the higher rate of 12.8/day may be appropriate

for daytime.

Figure 11-14. August 1980 dye survey calibration at stations B and C [LTI (1987)]

11-16
Figure 11-15. July 11, 1984 TRC survey calibration at 12.8/day loss rate [LTI (1987)].

Figure 11-16. July 11, 1984 TRC survey calibration at 6.4/day loss rate [LTI (1987)].

11-17
The October, 1984 survey was chosen for model validation because it had the greatest spatial quantity of

chlorine data. The dispersion and chlorine decay rates

adopted for the July 1984 calibration runs were used

for modeling of this survey, but inputs for actual observed loadings and ambient environmental conditions

were used.

The chlorine model predictions for this survey did not

compare well at certain stations but the predicted

plume front and general decrease in chlorine levels

moving away from the Blue Plains outfall compared

well with data. As a result the model was considered

sufficiently validated to evaluate the potential for a

cross channel barrier. Refined model calibration of the

chlorine loss rate was not possible because of data

limitations and variability. The model was still deemed

well suited to assess the presence or absence of a

cross channel barrier. The more conservative loss rate

was used for this purpose. The modeling effort was not

considered to be well suited for highly precise predictions or for waste load allocations.

11.5.4. Model Application

The potential existence of a chlorine concentration

barrier was examined by model simulation over a

range of conditions. These included variations in effluent loads, river flow, and tidal conditions. All other

aspects of the model were identical to those used in

the calibration procedure. Effluent loads to the Upper

Potomac Estuary included the Blue Plains wastewater

facility (370 MGD) on the east shoreline, and the

Alexandria (54 MGD) and Arlington (30 MGD) wastewater facilities plus the PEPCO cooling water discharge (350 MGD) on the Western shoreline. The

discharge values for the wastewater treatment facilities represent estimated capacity needs for the period

2005-2010 (MWWRPB, 1986). The Arlington and Alexandria discharges were examined at a 1.0 mg/l total

residual chlorine (TRC) concentration. The PEPCO

chlorine discharge level used was 0.02 mg/l. Blue

Plains, the largest wastewater plant was examined

under two TRC scenarios, 0.02 mg/l and 0.40 mg/l

TRC. This represented conditions with and without

dechlorination.

Three Potomac river flow conditions were examined.

The critical seven day, ten year drought low flow

(7Q10) of 470 cfs, and two April flow conditions to

characterize a period of likely fish migration. The long

term average April flow (19,900 cfs) was simulated as

well as the lowest recorded mean monthly April flow

(7,573 cfs). For the three conditions the actual corresponding Anacostia River flows were 8, 165, and 345

cfs, respectively.

Model results for the various simulations are summarized in Figures 11-17 to 11-111. For each simulation,

model results were examined for all phases of the tide:

ebb, flood, and slack. For these purposes, the model

output has been displayed for the most critical condition where the chlorine residual extends the furthest

distance across the Potomac. Model results for other

periods in the tidal cycle were less critical and are not

shown. A 10 μg/l criterion for TRC was used to characterize the plume boundary because this is the District of Columbia water quality standard. The figures

display the boundaries of the 10 and 2 μg/l TRC

concentration contours. Higher concentrations were

only apparent in the immediate vicinity of the discharge

pipes and dissipated quickly. These very near zone

discriminations were not a model objective, and cannot

be examined accurately by this model. A jet plume

model that incorporates the hydraulic characteristics

of the the discharge itself would be required to evaluate

water quality impacts in the immediate vicinity of the

discharge.

Among all examined scenarios, no conditions were

simulated where the 10 μg/l concentration boundary

extended across the entire Potomac and presented a

potential TRC barrier. At the 0.4 mg/l level of TRC in

Blue Plains effluent, the boundary extends approximately one third of the river width. Discharges from

Alexandria and Arlington were largely dissipated in

their respective embayments. The PEPCO discharge

only minimally impacted the main channel. For 0.02

mg/l TRC (dechlorination) at Blue Plains, the plume is

barely observable in the main channel. All predicted

main channel concentrations were less than 10 μg/l.

In the model calibration section of this report, the

deficiencies in the calibration data sets were noted, as

was their significance to model uncertainty. Nonetheless, the uncertainty in model rates would not be

sufficient to alter the basic findings. Reasonable

changes in dispersion rates had small effects on the

plume width. In addition, for forecast purposes a conservatively low chlorine loss rate was used. This maximized the predicted plume persistence.

11.6. References

Clark, L. J. 1982. A Modeling Study of the Upper

Potomac Estuary: Application of the Dynamic Estuary

Model. Draft Report. U.S. EPA Annapolis Field Office,

Annapolis, Maryland.

Fischer, H.B., List, J.E., Koh, R.C.Y., Imberger, J., and

N.H. Brooks, 19711. Mixing in Inland and Coastal

Waters. Academic Press, New York.

Greeley and Hansen Engineers. 1984. Blue Plains

Feasibility Study Final Report. D.C. Department of

11-18
Figure 11-17. TRC model projection for 7Q10 low flow conditions [LTI (1987)].

11-19
Figure 11-18. TRC model projection for average April flow conditions [LTI (1987)].

11-20
Figure 11-19. TRC model projection for lowest April flow conditions [LTI (1987)].

11-21
Public Works, Water and Sewer Administration,

Washington, D.C.

Hearn, Paul P. Jr., 1984. Controls on Phosphorus

Mobility in the Potomac River Near the Blue Plains

Wastewater Treatment Plant, USGS Water Supply

Paper 2231.

Hydroqual, 1982. Calibration and Verification of

Mathematical Model of Eutrophication of the Potomac

Estuary. District of Columbia Department of Environmental Services, Washington, D.C.

Hydroqual, 19811. Re-Calibration of the Potomac Eutrophication Model to the 1983 Algal Bloom. Metropolitan Washington Council of Governments, Washington,

D.C.

Katapodes, N. D., 1987. Finite Element Model for

Hydrodynamics and Mass Transport in the Upper Potomac Estuary, in <169>Dissolved Oxygen Study of

the Upper Potomac Estuary,<170> Volume 1, Technical Appendix A-7, Metropolitan Washington Council of

Governments, Washington, D.C.

Limno-Tech and Metropolitan Washington Council of

Governments, 1987. Summary Report: Potomac River

Residual Chlorine Study. Department of Consumer

and Regulatory Affairs, Washington, D.C.

LTI, Limno-Tech, Inc. 1987. Validation of DEM to 1985

and 1986 Upper Potomac Estuary Data. Metropolitan

Washington Council of Governments, Washington,

D.C.

McDowell, D.M., and B.A. O’Connor, 1977. Hydraulic

Behaviour of Estuaries. Halstead Press, John Wiley

and Sons, New York.

Metropolitan Washington Council of Goverments,

1987. A Dissolved Oxygen Study of the Upper Potomac Estuary, Final Report. Washington, D.C.

NOAA, 1984. Observed Hourly Potomac River Tidal

Heights for for 1984 at Washington, D.C., provided by

the National Ocean Survey.

Thomann, R.V., N.J. Jaworski, S. W. Nixon, H. W.

Paerl and Jay Taft, 1985. The 1983 Algal Bloom in the

Potomac Estuary. Prepared for the Potomac Strategy

State/EPA Management Committee.

11-22
12. Manasquan Estuary Real Time Modeling

12.1. Background

This study of the MIT-Dynamic Network Model (MITDNM) demonstrates the successful calibration and

verification of a real-time estuary model. Unlike tidallyaveraged or steady-state models, real time models

simulate changes in flow and water quality constituents on an hour to hour basis. MIT-DNM was selected

by the Manasquan River Regional Sewerage Authority

to predict the effect that the discharge from a proposed

wastewater treatment plant would have on the water

quality and ecology of the Manasquan Estuary (Najarian et al., 1981). The Authority was primarily concerned with nutrient enrichment and primary

productivity in the estuary. A real time model was

selected to predict photosynthesis effects on diurnal

DO concentrations and investigate the transient impacts of nonpoint source pollution and salt water intrusion.

The hydrodynamic submodel of MIT-DNM uses a finite

element approach to solve the one-dimensional continuity and momentum equations for unsteady flow in a

variable area channel. Dispersion is defined by the

degree of stratification and the non-dimensional longitudinal salinity gradient using the relationship formulated by Thatcher and Harleman (1972, 1981). The

flows and velocities calculated by this submodel are

used in another submodel in which a sequence of

conservation of mass equations calculates the temporal and spatial variation in the water quality parameters.

The following state variables are included in this version of MIT-DNM:

• Diatoms • Nitrite and nitrate

• Nanoplankton • Carbonaceous BOD

• Dinoflagellates • Dissolved Oxygen

• Organic detritus N • Chlorides

• Ammonium - N • Fecal coliform

• Herbivorous zooplankton

The model assumes that the dominant activity in the

estuary is aerobic and that nitrogen is the only nutrient

that limits the growth of algae. Water quality processes

represented in the model include phytoplankton

growth, mortality, and sinking; zooplankton grazing,

mortality, and excretion; nitrogen cycling and fluxes at

the sediment/water interface.

12.2. Problem Setting

The Manasquan Estuary is approximately 7.6 miles

long, extending from the Atlantic Ocean to Brick Township in east central New Jersey. The estuary receives

inflow from the Atlantic Ocean, the Manasquan River,

and Barnegat Bay, which is connected to the estuary

by Point Pleasant Canal. The landward reaches of the

estuary are very shallow, with large embayment and

marsh areas. Figure 12-1 shows the study area with

sampling stations.

Flow records for this area came from USGS gage data

at Squankum on the Manasquan River. The freshwater

low flow was 17.0 cfs, which included 6.2 cfs from

wastewater treatment plants discharging upstream of

the gage. At the time of the study, no other major point

sources discharged into the river or the estuary. The

Manasquan River Regional Sewerage Authority, however, proposed the construction of a regional advanced

wastewater treatment facility that would discharge 9.4

cfs of effluent at the head of tide of the estuary. The

plant would obviously be a major contributor to the

freshwater flow into the estuary under low flow conditions.

Figure 12-1. Manasquan Estuary and Inlet [Najarian et al.

(1981)].

12-1
Effluent standards to be met by the proposed plant

were established by the Authority and are shown in

Table 12-1.

The flow and salinity dynamics in the Manasquan

Estuary system are forced by two tidal boundaries at

Barnegat Bay and the Atlantic Ocean and by the

freshwater inflow from the Manasquan River. Differences intidal amplitudes and phases between the

Table 12.1. Effluent Quality Standards

Parameter Standard

BOD5 95% removal

NH3-N 2 mg/l

DO 6 mg/l

pH 5.5-7.5

NO3-N 7 mg/l

Cl2 None detectable by EPAapproved methods of analysis.

Others Such that New Jersey Surface

Water Quality Standards for

FW-2 Trout Maintenance

Streams will be met.

ocean and the bay cause a complex flow regime in the

estuary. The tidal boundaries also differ in water quality.

While the constituent concentrations at the ocean

boundary are relatively constant, the concentrations at

the bay boundary are much more variable due to the

mixing of bay waters with Manasquan water.

Figure 12-2 shows a schematic of the MIT-DNM reach

system established for the estuary. The first reach

extends 2.26 miles landward from Osborn Island. The

second reach extends from Osborn Island to the Atlantic Ocean, and is 5.32 miles long. The third reach, 1.78

miles long, represents Point Pleasant Canal. Each

reach is represented by geometrically irregular crosssections, with embayment volumes specified for Lake

Stockton and Sawmill Creek. Tidal boundaries are

specified for nodes 1 and 3, and an inflow boundary is

specified at node 4.

12.3. Model Calibration

Figure 12-1 showed the location of stations for model

calibration sampling performed in July and August,

1980, by Elson T. Killam Associates, Inc. Two sampling

events were conducted over a four-day period — July

21-24 and August 25-28, 1980. Salinity and nutrient

concentrations at each station were measured during

daylight hours at frequencies of 3-4 observations per

tidal cycle.

Figure 12-2. Model conceptualization of the Manasquan Estuary and the Point Pleasant Canal System [Najarian et al. (1981)].

12-2
The measured water quality parameters were as follows:

• Temperature • Nitrite

• Dissolved Oxygen • Nitrate

• Salinity • Ortho-phosphate

• Secchi Depth • Silicate

• Dissolved Org.-N • Chlorophylla

• Particulate Org.-N • BOD

• Ammonia

The zooplankton and phytoplankton species present

during each sampling event were identified. In addition, synoptic data on the tides and the freshwater

inflow at the boundaries and at three instream stations

in the Manasquan Estuary were also collected. Freshwater inflows at the head of the estuary in July and

August persisted at about 30 to 40 cfs without any

dramatic increases between the two sampling events.

The August data set was selected for model calibration

because all three algal species represented in the

model (diatoms, nanoplankters, and dinoflagellates)

were present during this month. The July data set was

used for the purpose of model verification.

12.3.1. Hydrodynamic Submodel Calibration

Calibration of model hydrodynamics must precede

water quality calibration. In the Manasquan study, it

was imperative to start the model with realistic initial

hydraulic and salinity conditions since field observations only covered a 4 day or 9 tidal cycle period of

time. To establish realistic initial conditions for the

August 24-28 sampling event, the model was run for

an antecedent period of three tidal cycles that damped

out transients resulting from unrealistic initial conditions. Repeating tides of 12.42 hour periodicity were

imposed at the inlet and at the Bay Head Harbor

boundaries. These tides were extracted from the tides

observed during the first day of sampling. The necessary adjustment to tidal records at the Bay Head

Harbor boundary was made to reflect the differences

in the MSL elevation between the inlet and the head of

Barnegat Bay. The surface elevations and velocities

computed during the last time step of repeating tide

simulation were then taken to be the initial hydraulic

conditions for 12:30 p.m. on August 24, 1980.

Three sets of hydraulic boundary conditions were

specified for each day of the simulation using observed

data at the head of tide, the inlet, and Bay Head

Harbor. The hydraulic calibration of the model was

accomplished by matching the observed tidal ranges

and the phases measured at Clark’s Landing and Chapman’s Wharf by calibrating Manning’s friction coefficient. The model accurately simulated the observed

hydraulics. The maximum difference in observed and

computed data was approximately 7% of the tidal elevation range.

Salinity was calibrated next. Salinity observations did

not begin until August 25, 1980. As done for tidal

elevations, initial conditions for salinity were calculated

by running the model for an antecedent period. The

observed salinities at sampling stations on August 25

were averaged for that day and assumed as concentrations at these stations. Initial salinity concentrations

at computational points between stations were generated by linear interpolation. At the two tidal boundary

stations, the extreme observed salinities were assigned

during the end of flooding flows and the model computed salinity concentrations during the ebbing flows.

The model requires specification of the time it takes for

the boundary salinities to reach the extreme observed

salinities after the flood flows begin.

The freshwater inflow boundary condition is assumed

to have a background salinity of 0.09 percent. To calibrate the mass transport of chlorides in the estuary,

dispersion must be represented adequately. This requires calibration of the stratification parameter K and

Taylor’s dispersion multiplier m, both of which are used

in the following dispersion equation:

E(x,t) = KdS

dX + mET

where,

E(x,t) = Temporally and spatially varying dispersion

coefficient (ft2

/sec)

S = s/so, where s(x,t) is the spatial and temporal

distribution of salinity (dimensionless)

so = Ocean salinity (ppm)

X = x/L (dimensionless)

L = Length of estuary to head of tide (ft)

ET = Taylor’s dispersion coefficient (ft2

/sec)

= 77 unRh

5/6

u = u(x,t) = tidal velocity (ft/sec)

n = Manning’s friction coefficent

Rh = Hydraulic radius (ft)

12-3
K = Estuary dispersion parameter (ft2

/sec)

= uoL/1000

uo = Maximum ocean velocity at ocean entrance (ft/s)

m = Multiplying factor for bends and channel irregularities

In this case, values of K = 58.5 ft2

/sec and m = 25 were

used. More information regarding the development of

this equation can be found in Thatcher and Harleman

(1972, 1981).

The results of the simulation of salinity concentrations

at Clark’s Landing and Chapman’s Wharf are shown in

Figure 12-3. The observed and computed salinities

matched well at Clark’s Landing, except for two unusually high observed ocean salinity concentrations. Because these observed values exceeded the observed

ocean salinity concentrations on those days, the modelers concluded that the data points were unrealistic.

Based on the plots of observed vs. simulated salinity

concentration, the calculated dispersion coefficients

were considered adequate.

Figure 12-3. Time-varying salinity concentrations during August 24-28, 1980 at (a) Clark’s Landing and (b) Chapman’s Wharf

[Najarian et al. (1981)].

12-4
12.3.2. Water Quality Submodel Calibration

Once the hydraulics and mass transport within the

estuary were adequately defined, the model was calibrated for water quality parameters. Like the tidal

elevation and salinity calibrations, this calibration required initial and boundary conditions, and also involved the evaluation of transformation rate constants

based on plots of simulated versus observed data.

The system again requires the establishment of two

ocean boundaries and a time-varying boundary at the

head of tide, as well as initial conditions throughout the

system. The ocean boundaries were handled as in the

salinity calibration, where ocean concentrations were

specified at the end of flood flows and water quality

values were computed internally during ebb flows.

Observed conditions at the Squankum USGS gage

were used to define the time-varying water quality

conditions at the Manasquan head of tide. Because the

phytoplankton and zooplankton concentrations were

sampled only once during the sampling period, timeinvariant concentrations were specified at the three

boundaries. Initial conditions were estimated using

sampling data and linearly interpolated to establish

values between sampling stations.

Unlike the hydraulics and salinity calibrations, where a

combined total of three constants were calibrated on

the basis of observed versus simulated data, the water

quality calibration requires the determination of many

constants. Table 12-2 showed the values that were

established through model calibration. These are the

values that best represent the site-specific kinetic

processes in the Manasquan Estuary while still falling

within the range of values found in the technical literature.

Examples of simulated versus observed plots for the

various water quality parameters are illustrated in Figures 12-4 to 12-10. The individual symbols indicate

observed data points, while the straight line shows the

continuous simulation model output. These plots represent the best simulation of observed data using

reasonable rate constants and coefficients. Model

goodness-of-fit was determined only through visual

observation of the plots; no statistical tests were performed.

Figure 12-4 shows the simulation of detritus-N, ammonium-N and nitrite+nitrate—N at Clark’s Landing and

Chapman’s Wharf. At both sites, the ammonium-N

concentrations predicted by the calibrated model were

reasonably close to observed values. The simulation

of detritus-N and nitrite+nitrate-N was less accurate,

particularly at Chapman’s Wharf. The computed nitrite+nitrate-N concentrations were sometimes an order of magnitude lower than the observed

concentrations at the station. The modelers could find

no explanation for this problem. To adequately simulate

detritus-N at Chapman’s Wharf, a source of 240 lb/day

was introduced as a distributed load. Because the

estuary is very shallow, the modelers justified this input

to the model by speculation that tidal disturbances

could have resuspended some of the settled detritus.

The calibration of CBOD, NBOD, and DO at Clark’s

Landing and Chapman’s Wharf are shown in Figures

12-6 to 12-10. Like the detritus-N simulation, an adequate CBOD simulation at Chapman’s Wharf was not

possible without the introduction of a distributed CBOD

load. Even after assuming a load of 3,500 lb/day, the

observed and simulated data did not match well. The

DO simulation results proved to be confusing at both

stations. Although low concentrations of NBOD and

CBOD were observed at Clark’s Landing, the observed

DO levels at this station are lower than the concentrations predicted by the model. Conversely, the observed

DO levels at Chapman’s Wharf climbed much higher

than the simulated DO concentrations, even though

large CBOD and detrital nitrogen concentrations were

observed there.

Because no sampling was conducted to measure phytoplankton concentrations over time, the model could

not actually be calibrated for these water quality parameters. The relative proportion of each algal species

was input to the model based on the observed data

gathered from the single sampling event and species

identification. Figures 12-11 and 12-12 show the simulated concentrations of phytoplankton nitrogen at the

two stations. The plots clearly indicate a strong tidal

effect upon phytoplankton concentrations.

12.3.3. Model Verification

The purpose of model calibration is to establish the

values of coefficients, such as Manning’s “n” or decay

rates, which accurately represent the physical and biochemical nature of the system. Once these values are

established, they must be verified. Using the same

values to represent the estuary, the model must be

applied to a different time period for which sampling

data are available. If the simulated concentrations accurately predict observed concentrations the model can

be considered verified.

The verification data were obtained during a sampling

event in July 1980. This event, like the August 1980

sampling that provided the calibration data, was four

days long, with salinity and nutrient concentrations

measured during daylight hours at frequencies of 3-4

observations per tidal cycle. As in calibration, the observed data were used to establish initial and boundary

12-5
Transformation Rate Unit

K1: Ammonification of detritus-N 0.08 day-1

K2: Diatom uptake of ammonium-N

K2

opt 0.415 day-1

Y2 (half saturation constant) 0.0122 mg-N/l

Is (Optimum intensity of solar radiation) 144 ly/day

Topt (Optimum reaction temperature) 10-15 0

C

Tmax (Maximum temperature beyond 30 0

C

which denaturation of cell protein occurs)

K3: Nanoplankton uptake to ammonium-N

K3

opt 0.553 day-1

Y3 0.004 mg-N/l

Is 64.8 ly/day

Topt 27.5 0

C

Tmax 30 0

C

K4: Diatom mortality rate 0.05 day-1

K5: Nanoplankton mortality rate 0.05 day-1

K6: Copepod uptake of diatom-N

K6

opt 0.50 day-1

Y5 0.10 mg-N/A

Topt 24-28 0

C

Tmax 35 0

C

K7: Copepod mortality rate 0.20 day-1

K8: Copepod excretion rate 0.08 day-1

K9: Nitrification rate 0.15 day-1

K10:Diatom uptake of (NO2+NO3)-N

K10opt 0.376 day-1

Y4 0.0063 mg-N/l

Is 144 ly/day

Topt 10-15 0

C

Tmax 30 0

C

K11:Nanoplankton uptake of (NO2+NO3)-N

K11opt 0.501 day-1

Y6 0.015 mg-N/l

Is 64.8 ly/day

Topt 24-28 0

C

Tmax 30 0

C

Transformation Rate Unit

K12:Copepod uptake of detritus-N

K12opt 0.34 day-1

Y1 0.250 mg-N/l

Topt 24-28 0

C

Tmax 35 0

C

K13:Copepod uptake of nanoplankton-N

K13opt 0.34 day-1

Y7 0.22 mg-N/l

Topt 24-28 0

C

Tmax 35 0

C

K14:Dinoflagellate uptake of ammonium-N

K14opt 2.075 day

Y8 0.021 mg-N/l

Is 288 ly/day

Topt 20-25 0

C

Tmax 32 0

C

K15:Dinoflagellate uptake of (NO2 + NO3)-N

K15opt 1.880 day

Y9 0.042 mg-N/l

Is 288 ly/day

Topt 20-25 0

C

Tmax 32 0

C

K16:Copepod uptake of dinoflagellate-N

K16opt 0.5 day-1

Y10 0.1 mg-N/lTopt 24-28 0

C

Tmax 35 0

C

K17:Dinoflagellate mortality rate

K17 0.05 day-1

K18:Sedimentation of detritus

K18 0.1 day-1

K19:Sedimentation of (NO2+NO3)

K19 100 ug-at

N/m2

/hr

K20:Sediment release of ammonium-N

K20 10 ug-at

N/m2

/hr

Table 12-2. Model-Governing Rate Parameters [Najarian et al. (1981)]

12-6
Figure 12-4. Temporal variation of Detritus-N, Ammonium-N,

and Nitrite+Nitrate-N at Clark’s Landing, August

25-28, 1980 [Najarian et al. (1981)].

Figure 12-5. Temporal variation of CBOD at Clark’s Landing,

August 25-28, 1980 [Najarian et al. (1981)].

Figure 12-6. Temporal variation of CBOD at Chapman’s

Wharf, August 25-28, 1980 [Najarian et al. (1981)].

Figure 12-7. Temporal variation of NBOD at Clark’s Landing,

August 25-28, 1980 [Najarian et al. (1981)].

12-7
Figure 12-8. Temporal variation of NBOD at Chapman’s

Wharf, August 25-28, 1980 [Najarian et al. (1981)].

Figure 12-9. Temporal variation of DO at Clark’s Landing,

August 25-28, 1980 [Najarian et al. (1981)].

Figure 12-10. Temporal variation of DO at Chapman’s Wharf,

August 25-28, 1980 [Najarian et al. (1981)].

Figure 12-11. Temporal variation of Diatom-N,

Nanoplankton-N, and Dinoflagellate-N at Clark’s

Landing, August 25-28, 1980

[Najarian et al. (1981)].

12-8
conditions, and to evaluate the adequacy of the simulation.

The simulation of tidal elevations was investigated first.

The initial water surface elevations, the time-varying

water surface elevations at the tidal boundaries, and

the time-varying freshwater inflow at the head of tide

were established from observed data. The Manning’s

“n” values, 0.018 downstream and 0.022 upstream of

Chapman’s Wharf, were used without change from the

calibration study.

The simulation of tidal elevations at Clark’s Landing

and Chapman’s Wharf is shown in Figures 12-13 and

12-14. The largest difference in predicted and calculated water surface elevation is approximately 0.2 feet

at Clark’s Landing.

The simulation of salinity was the next step in the

verification procedure. The initial and boundary conditions were established in a manner consistent with the

calibration study, using observed salinity concentrations at the sampling stations. The stratification parameter K and the calibration multiplier m were set

equal to the values used in the calibration study.

Analysis of Figures 12-15 and 12-16 show again that

observed and simulated values were more similar for

the verification than the calibration. The modelers were

particularly pleased that the furthest inland sampling

station in the estuary, Chapman’s Wharf, gave the best

comparison between predicted and observed values.

Based on these results, they concluded that the advective and dispersive processes throughout the estuary were well represented in the model.

Finally, the model was verified for water quality processes. Again, initial and boundary conditions were

established using observed data and an antecedent

period simulation, and all the constants evaluated in

the calibration study were used without modification in

the verification study. Because of project constraints,

the simulation of CBOD, NBOD, and DO was not

performed. The results of the water quality comparisons are shown in Figures 12-17 and 12-18. As in the

calibration study, the detritus-N concentration at Chapman’s Wharf could not be accurately simulated without

the specification of a distributed detrital load. The

modelers found that a load of 360 lbs/day resulted in

an adequate simulation; however, this load was set at

240 lbs/day in the calibration study. Because the detritus-N concentrations calculated in the calibration

study were lower than the observed concentrations,

the modelers concluded that the 360 lbs/day load

would be valid for the calibration as well as the verification period. Other than the ammonium-N concentrations predicted at Chapman’s Wharf and Osborn

Island, (a station that is not included in the modeling

Figure 12-12. Temporal variation of Diatom-N,

Nanoplankton-N, and Dinoflagellate-N at

Chapman’s Wharf, August 25-28, 1980 [Najarian

et al. (1981)].

Figure 12-13. Hydraulic verification: calculated vs. observed

elevations at Clark’s Landing [Najarian et al.

(1981)].

12-9
Figure 12-14. Hydraulic verification: calculated vs. observed

elevations at Chapman’s Wharf [Najarian et al.

(1981)].

Figure 12-15. Salinity verification: calculated vs. observed

salinities at Clark’s Landing [Najarian et al.

(1981)].

Figure 12-16. Salinity verification: calculated vs. observed

salinities at Chapman’s Wharf [Najarian et al.

(1981)].

Figure 12-17. Temporal variation of Detritus-N,

Ammonium-N, and Nitrite+Nitrate-N at Clark’s

Landing [Najarian et al. (1981)].

12-10
report), the water quality simulation was considered

satisfactory.

12.3.4. Model Projections

The original goal of the modeling effort was to determine the impact that a proposed wastewater treatment

plant effluent would have upon water quality in the

Manasquan Estuary. However, the plans for the new

wastewater treatment plant were abandoned before

the calibration and verification studies were completed. Consequently, no production runs of the model

were conducted to assess discharge quality alternatives for the proposed plant.

Though the developed model was not used to achieve

the original goal of the study, several important recommendations were made regarding future model use.

These were:

1) External sources and sinks of nutrients should be

better defined,

2) Additional phytoplankton sampling should be done

to verify the model, and

3) Once these two steps are completed, the model

should be applied to the Manasquan Estuary.

The problems in calibrating detrital-N and CBOD at

Chapman’s Wharf illustrated the need to better define

external sources and sinks of nutrients. Potential

sources and sinks would include non-point source

discharges, sediment-water exchanges, and marshestuary exchanges. This last potential source/sink

could have been significant in the upper portion of the

estuary, where the estuary is shallow and the tidal

portions include marshlands. The other major observation made by the modelers was that a more complete set of data would increase confidence in the

model . With additional phytoplankton sampling, model

simulation of the algal species could be verified, and

the model’s simulation of nighttime estuary activity

could be evaluated with round-the-clock sampling

data. Once the additional data were obtained, the

recommendations were made that the model be used

to:

(1) Determine the existing and potential impact of

nonpoint source pollution within the Manasquan River

Basin and

(2) Evaluate the potential impacts of proposed reservoir development within the basin on the downstream

Manasquan Estuary.

12.4 References

Najarian, T.O., Kenata, P.J. and Thatcher, M.L. December, 1981. Manasquan Estuary Study.

Manasquan River Regional Sewerage Authority.

Thatcher, M.L. and Harleman, D.R.F. February 1972.

“Mathematical Model for the Prediction of Unsteady

Salinity Intrusion in Estuaries,” Technical Report No.

144, R.M. Parsons Laboratory for Water Resources

and Hydrodynamics, Department of Civil Engineering,

M.I.T., Cambridge, MA.

Thatcher, M.L., and Harleman, D.R.F., February 1981.

“Long-Term Salinity Calculation in Delaware Estuary,”

Journal of the Environment Engineering Division,

ASCE, Volume 107, No. EE1, Proc. Paper 16011, , pp.

11-27.

Figure 12-18. Temporal variation of Detritus-N,

Ammonium-N, and Nitrite+Nitrate-N at

Chapman’s Wharf [Najarian et al. (1981)].

12-11

13. Calcasieu River Estuary Modeling

13.1. Background

The Calcasieu River Estuary modeling study is presented here to illustrate a time-variable waste load

allocation model applied to a complex Gulf of Mexico

estuary. The general model framework of RECEIV-II

(Raytheon, 1974) was used to model simulate a fortymile stretch of river from the salt water barrier near

Lake Charles, Louisiana, extending downstream to the

Intracoastal Waterway (shaded area in Figure 13-1).

The primary water quality problems were the result of

point source discharges. There were 64 wastewater

dischargers to the Calcasieu River below the salt water

barrier. In the forty-mile study area, there is a seven

mile reach (between river miles 24 and 31) characterized by depressed dissolved oxygen concentrations, elevated temperatures and elevated ammonia

concentrations. The water above the salt water barrier

also suffers from low dissolved oxygen.

The poor water quality and the complexity of the system has led to a series of water quality modeling

studies on the Calcasieu. Prior to the development of

this model, four other water quality modeling studies

had been completed on the Calcasieu. The first study

was reported in January 1974 by Roy F. Weston,

covering the entire Calacasieu River basin. It used a

nomographic (graphical) technique for preliminary

waste load allocation. A 1980 study was conducted by

Hydroscience as part of a state-wide water quality

planning effort. This second model was an improvement over the first, but it lacked a hydrodynamic module and relied on the modeler to specify flow conditions.

Hydrodynamic data were very limited. In 1981,

AWARE Inc. completed a third water quality model of

the Calcasieu River estuary for the section below the

salt water barrier using a two-dimensional application

of the RECEIV-II model. The model was later used by

Roy F. Weston for waste load allocation analysis. The

focus of the study described herein is a more recent

use of the RECEIV-II model for the Calcasieu River

basin (Duke, 1985). Duke built on the work of AWARE

and Weston by improving the calibration procedure

and using new estuary cross-section information.

13.2. Problem Setting

13.2.1. Site Description

The Calcasieu River estuary is a complex system of

natural and artificial channels. From its headwaters

near Slagle, the Calcasieu River flows southward for

160 miles to the Gulf of Mexico. The study area for this

model application was the lower 40 miles of river,

below the salt water barrier (Figure 13-1).

The Army Corps of Engineers constructed the barrier

and maintains dredged a ship channel to a depth of 40

feet and bottom width of 400 feet in most of the estuary.

Stretches of the natural channel not dredged for the

ship channel are referred to as “loops” or “lakes.” The

system is a tidal estuary with extensive side channel

and reservoir-like storage. Side channel and tributary

hydraulics are complicated by man-made channels and

the main channel flow is complicated by the presence

of large lakes.

High flows in the Calcasieu occur in the winter and low

flows occur in the summer. There are no permanent

stream flow measuring stations in the study area, although six tide gages measure water levels. A seven

day, ten year drought flow (7Q10) was calculated using

relative drainage area sizes and the drought flow of the

nearest upstream gage station (Kinder, LA). The drainage area above the salt water barrier is 3,100 square

miles. The nearest upsteam station has a long

Figure 13-1. Calcasieu Estuary study area [NOAA (1985)].

13-1
term mean flow of 2,600 cfs and a 7Q10 of 202 cfs.

The 7Q10 below the salt water barrier was estimated

to be 375 cfs.

13.2.2. Water Quality Monitoring

The State of Louisiana conducted six water quality

surveys at 31 stations during the following periods:

• July 1978 • August 1979

• October 1978 • July 1980

• July 1979 • June 1984

At each station, the following ten constituents were

measured and simulated in the model:

1) Water temperature 6) Nitrites

2) Salinity 7) Nitrates

3) Dissolved Oxygen 8) Ammonia

4) BOD 9) Total Kjeldahl Nitrogen

5) Phosphorus 10) Chlorophyll a

Vertical profiles were measured for salinity, temperature, dissolved oxygen, pH, and conductivity. The June

1984 study was the most comprehensive. It was done

in conjunction with six other studies that included a

nonpoint source survey, a nitrogen transformation

study, a sediment oxygen demand study, a use-attainability study, a series of mini-surveys for in situ water

quality parameters, and additional hydrodynamic studies. All studies had municipal waste load data, although only the 1984 study included a full set of waste

load data from all industrial discharges.

The water quality studies showed that the ship channel

below the salt water barrier was stratified with respect

to salinity and dissolved oxygen. The channel had

once been thermally stratified, but this had been reduced because of the removal of cooling water discharges.

The estuary water quality was characteristic of water

receiving wastewater effluent — high nitrite/nitrate,

phosphorus, and BOD, and low dissolved oxygen. In

the upper half of the estuary (below the saltwater

barrier) dissolved oxygen was below the State’s 4.0

mg/l standard. Phosphorus and nitrogen concentrations were characterisitic of eutrophic conditions.

Phosphate ranged from 0.1 to 0.3 mg/l. Ammonia

concentrations ranged from 0 to 0.6 mg/l. Much of the

degraded water quality was from loading upstream of

the saltwater barrier.

13.3. Model Application

13.3.1. Model Framework

The model selected for the Calcasieu was RECEIV-II.

It is a time-variable model developed from the receiving

water component of U.S. EPA’s SWMM model. It was

modified by Raytheon (1974) for use on 28 New England rivers and harbors. The 13 subroutines that form

the model remain compatible with SWMM, but can be

run independently. The model has the following general

characteristics:

• Time variable water quality and hydraulics

• Eleven water quality variables (conservative and

nonconservative)

• Link-node approach (vertically homogenous)

• Multiple tidal forcing points

The model has both a hydraulic and water quality

component. For hydraulics, the model uses a link-node

approach. Each node or junction is connected via links

or channels. The equation written for each link incorporates fluid resistance and wind stress using the Manning and Ekman equations. Both components use a

finite difference solution. The hydraulic component requires considerably more computer time than the water

quality component because computations are performed for the entire system for time steps of five

minutes or less, whereas the water quality component

uses a one-hour time step.

For the Calcasieu, the RECEIV-II model framework

was used without major changes from that documented

by Raytheon. Certain changes to the FORTRAN

source code were required to tailor the hydrodynamic

module for site-specific characteristics.

13.3.2. Procedures

The model was calibrated with the data set from August

1979. It was verified using July 1978, July 1980, and

June 1984 data sets. The model was recalibrated by

revising the selection of model coefficients and extending the modeled area farther upstream at each tributary

to improve the representative network of water storage

in the system (Figure 13-2). The model has 67 load

sources, 162 links, and 114 nodes. The number of

cycles of the simulation were increased because the

short simulations of earlier modelers had not achieved

steady-state.

Model simulations were conducted at steady state for

several reasons. First, insufficient data were available

to calibrate or verify dynamic conditions. Second,

model projections were to be run at 7Q10 steady state

conditions. This assumption of steady state critical conditions is consistent with regulatory policy to use

13-2
conservative assumptions protective of the environment when dealing with model uncertainty.

Before proceeding with model calibration, the model

was tested to determine if the thirteen day simulation

used in earlier studies was a sufficient amount of time

to achieve steady-state conditions. The initial salinity

concentration was set to zero and a salinity wave was

propagated upstream from the Gulf, downstream from

the barrier, and from tributary inflows. After 13 days,

salinity was still simulated near zero, indicating a 13

day cycle was not sufficient to achieve steady state.

To ensure steady-state conditions, Duke ran the simulations for more than 900 days. This required approximately 4 hours of CPU time on the Louisiana DEP

Digital VAX 11/780 computer.

Model rate coefficients were first adjusted to best

simulate the August 1979 calibration data set. When

model output matched observations within acceptable

limits, model verification simulations were tested. In

model verification, rate coefficients were identical to

the calibration, but environmental conditions and loadings were adjusted to reflect the specific verification

surveys. These changes included:

• Tributary flows and loads

• Upstream flows and loads

• Waste discharge flows and loads

• Ambient temperatures

The model was run for each verification survey and

compared with the field data. Whenever a model parameter was changed during the verification, all data

sets were run again to ensure the change did not

significantly change the simulation of any data set.

The major coefficients are summarized in Table 13-1.

These values were changed spatially within each survey but not changed from survey to survey. Model

inputs for forcing conditions (e.g. tides, temperatures,

flow, etc.) and loading were as measured for each

survey.

13.3.3. Calibration/Verification

The results of the model calibration/verification are

summarized in a few representative plots. The calibration/verification was described as good for hydrodynamics and fair for water quality. Obvious

discrepancies between the data and model were seen

for both selected hydrodynamics and water quality

simulations, but not viewed as a serious problem. Problems with poorly defined loads was one complicating

factor. In addition, model predictions were for steady

state conditions, while observed data reflected dynamic

conditions.

Comparison of the results from the calibration and

verification simulations were divided into ship channel

simulations and other stations. The other stations included the lake and loop areas. The results of the other

simulations were not presented by the author since they

were described as similar to the main ship channel.

Also, tidal water quality calculations were performed but

only tidally averaged results were compared to data.

Figure 13-2. Model segmentation diagram [Duke (1985)].

Coefficient Units Range

Manning’s n none 0.018-0.035

Ammonia Oxidation per day 0.002-0.020

Nitrite Oxidation per day 1.00

BOD Oxidation per day 0.001-0.050

Benthic Oxygen Demand gm/sq.m/day 0.75-1.50

Reaeration per day 0.003-2.000

Table 13-1. Values for Major Coefficients

13-3
Hydrodynamics:

The model calibration results for August 1979 hydrodynamics are summarized in Figure 13-3 for five stations below the salt water barrier. Model performance

was measured using water elevations. The model was

considered a satisfactory match to data since the

trends and timing were well matched. The elevation

differences were considered insignificant. The model

verification comparisons were similar for July 1980 and

June 1984 in that the model matched the trends well

but was inconsistent in matching the magnitude. However, for the July 1978 data set (see Figure 13-4) the

cycles and magnitudes were poorly matched. Overall,

the hydrodynamic calibration/verification was described in the final report as good.

Water Quality:

The water quality calibration/verification simulated the

ten parameters described above (Figures 13-5

through 13-9). Figures 13-5 and 13-6 are selected

model comparisons for a few parameters from the

August 1979 model calibration. Figures 13-7, 13-8,

and 13-9 are selected results for the three verification

simulations. The water quality calibration/verification

match was characterized as fair, with many discrepancies attributed to poor information on loading conditions

and dynamics.

13.3.4. Model Sensitivity

An important modeling activity is sensitivity analysis.

This procedure tests the sensitivity of model calculations to changes in selected inputs. Results can be used

to:

• Refine coefficient selection

• Identify the most important processes and loads

• Identify areas in need of better data to improve

modeling

• Define model uncertainty

The model was tested for an elimination and tripling of

BOD and ammonia deoxygenation rates and elimination of algae. The results indicated that algae had the

largest effect on the water quality calculations. This

finding is common to estuaries where algal abundance

often is the major factor in controlling water quality. As

a result, success or failure in model validation to data

Figure 13-4. Tidal stage results for hydrodynamic verification

simulation (July 1978 data set) [Duke (1985)].

Figure 13-3. Tidal stage results for August 1979

hydrodynamic calibration simulation

[Duke (1985)]

13-4
FLOW

Figure 13-5. Selected water quality results for calibration simulation (August 1979 data set); salinity, dissolved oxygen, and

biological oxygen demand [Duke (1985)].

Figure 13-6. Selected water quality results for calibration simulation (August 1979 data set); phosphate, total Kjeldhal nitrogen,

and ammonia [Duke (1985)].

13-5
FLOW

Figure 13-7. Selected water quality results for July 1978 verification simulation; salinity, dissolved oxygen, and biological

oxygen demand [Duke (1985)].

Figure 13-8. Selected water quality results for July 1978 verification simulation; phosphate, total Kjeldhal nitrogen, and ammonia

[Duke (1985)].

13-6
can depend on proper characterization and simulation

of algal dynamics.

13.4. Total Maximum Daily Loads

The purpose of all modeling efforts on the Calcasieu

was to develop total maximum daily loads (TMDL) and

wasteload allocations. In the earliest study by Weston

(1974), the TMDL for the Calcasieu River was calculated to be 31,190 pounds ultimate oxygen demand

per day (lbs UOD/day). Fourteen municipal and industrial dischargers were then allocated waste loads for

BOD and NH3-N.

In 1980, Hydroscience produced general recommendations on waste load allocation rather than determine

specific TMDL. They emphasized the need to regulate

the area with respect to dissolved oxygen. The study

concluded that background loads were so high that

even at zero discharge below the salt water barrier, a

DO standard of 4 mg/l would not be met. Despite the

lack of a TMDL from this modeling study, the 1980

Water Quality Management Plan for the State of Louisiana listed a TMDL for the Calcasieu River of 52,760

lbs UOD/day based on a dissolved oxygen standard

of 4 mg/l. The second Weston study that followed the

AWARE 1981 modeling agreed with the Hydroscience

report, computing a zero TMDL because of a violation

of the standard at zero discharge. A use attainability

study (Thompson and Fitzhugh, 1986) demonstrated

that waters above the salt water barrier are naturally

dystrophic. The Duke study, in concurrence with the

State and EPA, developed the TMDL to protect against

the oxygen sag which occurs near river mile 26. Using

the 4 mg/l DO standard and 1979 loading pattern, the

Duke study produced an estimated TMDL of 83,130

lbs UOD/day.

Figure 13-9. Selected water quality results for July 1980 and June 1984 verification simulation; salinity and dissolved oxygen

[Duke (1985)].

13-7
13.5. References

Duke, James H., Jr., “Calcasieu River Basin, Louisiana, Modeling Study”, report prepared by James H.

Duke, Jr., Ph.D., P.E., Consulting Water Engineer,

Austin, Texas, for the Department of Environmental

Quality, State of Louisiana, 14 August 1985.

Raytheon Company, “New England River Basins Modeling Project, Documentation Report, Volume 1", draft

report submitted to the U.S. Environmental Protection

Agency, Office of Water Programs under Contract No.

68-01-1890, Program Element 2BH149, December

1974.

Thompson, B.A., and G.R. Fitzhugh, “A Use Attainability Study: An Evaluation of Fish and Macroinvertebrate

Assemblages of the Lower Calcasieu River, Louisiana,” prepared for Lousiana DEQ Office of Water

Resources, 1986.

13-8
14. Expert Critique of Case Studies

Estuarine modeling is a complex and evolving science.

As such, there is not total agreement among experts

in the field regarding the “proper” approach to estuarine waste load allocation modeling. This chapter

presents the opinions of three nationally recognized

experts in estuarine modeling. These experts were

asked to provide their thoughts on the proper approach

to estuarine WLA modeling in general and to the case

studies provided in this guidance manual in particular.It

should be noted that the case study critiques are based

primarily upon the studies as described in this document. They do not necessarily consider potentially

important factors such as resources or time available

to perform the modeling.

The reader is encouraged to examine these reviews

and to compare and contrast the expert opinions.

While all three experts are in agreement with the basic

guidance provided in Books I through III of this manual

(each having served as a technical reviewer), their

specific approach to estuarine WLA is seen to differ.

Readers should therefore be aware that while this

manual provides a general background to estuarine

modeling, the exact approach to be taken for any given

site still requires some subjective assumptions.

14.1. Robert V. Thomann, Ph.D.

Professor, Environmental Engineering and Science

Manhattan College

Riverdale, New York 10471

14.1.1. Introduction

My overall opinion on the appropriate level of estuarine

water quality model complexity can be summarized by

the observation that:

THE BEST MODELS ARE OFTEN THE SIMPLEST.

The review therefore will continually display a bias

towards doing estuarine water quality modeling in as

simple a fashion as possible and only after all simplicity

has been exhausted, should increasing complexity be

introduced and then only after careful consideration is

given to the improvements in the model that might be

realized. The reasons for this bias are: (a) most analysts have only limited experience, time and resources

available, and (b) unnecessarily complex models

sometimes tend to obscure uncertainty behind a facade of “reality.”

The choice of the appropriate level of model complexity

is determined in large measure by the nature of the

problem under investigation. The context for my opinion on an appropriate level of model complexity is the

establishment of a defensible analysis framework for

a Waste Load Allocation (WLA). The opinion is not

directed toward model development in a research context. This is not to say that one need not pay any

attention at all to the scientific correctness of the model.

Rather, modeling for WLA purposes imposes a separate, but related set of constraints on the model construction and development.

The assignment of a WLA to a particular discharger or

regional group of dischargers involves a determination

of the level of treatment over and above secondary

treatment and/or Best Practical Treatment (BPT) and

Best Available Treatment (BAT) coupled with a specification of the allowable mass loading and/or effluent

concentration. Nonpoint and transient sources may also

be a part of the WLA. The primary thrust of modeling

then for WLA purposes is from a control engineering

point of view. The modeling is not necessarily conducted

for a detailed understanding of the various interactive

processes that may be operative (e.g., the dynamic

behavior of nitrifying bacteria), but rather an engineering-scientific approximation to the real estuary which will

provide a firm basis for the WLA. Therein lies the

difficulty.

The analyst must make a delicate determination between the degree of complexity necessary for a defensible WLA, the time frame and budget available for

completion of the WLA and the natural urge to continue

to explore various components of the problem. Because

of the skill needed to make this determination and the

limited resources that are usually available, I would

generally lean in the direction of more simple models

rather than more complex models.

A. The DIfference Between a Site-Specific Model and a

Generic Model

One of the more troublesome aspects of contemporary

estuarine modeling is the confusion that exists between

(1) a mathematical model of a particular estuary with its

unique setting and (2) a generic non-site-specific model

embodied in a computer code that incorporates the

principal components of water quality theory but in a

completely general way. For purposes of this opinion, a

model is defined as the application of accepted principles of water quality fate, transport and transformation

theory, together with appropriate determination of sitespecific parameters to predict water quality under some

future conditions for the given estuary. A generic model

is considered to be a general programming framework

which also incorporates the

14-1
basic theoretical components, but has no utility in a

WLA until applied to a specific problem setting. The

computer code of a generic model is transportable, a

model of a given estuary is not.

Thus, it does not make much sense to refer to models

of Boston Harbor and Appalachicola Bay as “WASP 4”

models. The WASP 4 computing framework may have

been used in both cases, but any other suitable computer program (with similar fate and transport processes) could have been used as well. The structuring

of a water quality model for Boston Harbor requires

much more than a simple choice of computer code.

This opinion on model complexity is not directed therefore to issues associated with how to choose an appropriate computer code. Instead, my opinion is

focused on the issues associated with determining the

level of complexity for modeling a specific estuary or

coastal water body always in the context of a WLA.

B. Analytical and Numerical Models

There are fundamentally two types of water quality

models: analytical models where the solutions to a

differential equation or set of differential equations are

available, and numerical models where approximations are made to the derivatives of the operative

differential equations. Analytical models are available

only for relatively restrictive conditions, usually one

dimensional, constant parameters and steady state,

although solutions for some time variable inputs exist,

again for restrictive situations.

It is interesting to note that the accompanying case

studies do not indicate any use of analytical solutions

to determine initial expected reasons or to check on

numerical model results. I do know, however, that

analytical solutions were used for Saginaw Bay as a

completely mixed bay exchanging with Lake Huron

and the results provided important initial guidance for

further model development. Similarly, analytical solutions were often used in the Potomac case to check on

numerical model output in the initial stages of model

construction. One wonders whether some of the calibration difficulties of some of the case studies would

not have been alleviated by initial analytical checks on

the order of water quality response to “close in” on

which particular phenomena were of importance in

describing the observed data.

In spite of the severe assumptions that must be invoked, it is strongly suggested that:

ANALYTICAL SOLUTIONS SHOULD BE USED

TO COMPUTE INITIAL RESPONSE AND TEST

NUMERICAL MODEL COMPUTATIONS.

Such computations provide the first approximations to

the order of water quality response that might be expected from input loading under different hydrological

regimes and model parameters. Also, the use of analytical models provides a first order check on more

complicated numerical models to determine whether the

numerical computations are approximately correct.

C. Model Evolution

The use of models in decision making must recognize

that, very often, the understanding of estuarine processes, and the availability of data and model frameworks for a given estuary are always changing. Models

are not static, but rather continually evolving. Decision

makers must be apprised of this fact and must, to some

degree, be prepared for new input into the decision

process.

The Saginaw Bay and Potomac estuary case studies

are good examples of models that began at relatively

simple levels of complexity and have subsequently progressed to more complex kinetics and spatial and temporal detail. The progression was dictated by an ever

increasing level of complexity in the questions being

asked of the model. For example, the early Potomac

estuary models did not explicitly include phytoplankton

dynamics. But after issues of nutrient controls (e.g.

should phosphorus or nitrogen be removed?) were

raised, an expansion of existing models was required.

However, as noted below, it is not always clear that

adding additional complexity improves credibility. Thus,

for the Saginaw Bay model, it is not clear that the

addition of an internal nutrient pool state variable improved the model performance, whereas the inclusion

of phytoplankton functional groups was important in

predicting the occurrence of nuisance odors.

The Calcasieu estuary case study, on the other hand,

seems to be an example of a modeling framework that

needs to be substantially restructured (e.g. inclusion of

a vertical dimension and non-steady state) in order to

provide more credible results. Yet the original model

(albeit with some updates) continued to be used with

results that were less than desirable.

It should then be clearly recognized by all concerned

(decision makers, model analysts and scientists and

engineers) that:

ALL MODELS MUST CONTINUALLY BE UPDATED: IF NOT, MODEL “ATROPHY” SETS IN

AND CREDIBILITY DETERIORATES. ESTIMATED

MODEL “HALF-LIFE” IS ABOUT 1-2 YEARS.

14-2
Existing models must therefore never be “frozen” in

time and continue to be used in the face of obvious

model inadequacies. As painful as it may be, some

model frameworks need to be restructured, expanded

or even abandoned as new information becomes available.

14.1.2. Appropriate Spatial and Temporal Scales

Unfortunately, because of the ready availability of

computer programs that are fully time variable and

three dimensional, there is a tendency to believe that

more complexity is better since it approaches the real

world more closely. But, increasing complexity does

not usually result in increased model credibility. Figure

14-1 illustrates this opinion. In general, increasing

model complexity requires specification of more and

more parameters and state variables, both in absolute

number and over space and time. Even more importantly, increased model complexity requires a detailed

data base across all state variables and over space

and time for a complete assessment of model adequacy. As a result, what appears to be more realistic

is actually a model that has hidden within it a large

degree of uncertainty. Because of a generally sparse

data base, the uncertainty is not visible and it is assumed that the model is more realistic when in fact it

is not.

On the other hand, the model may be so crude in

spatial, temporal or kinetic definition that key mechanisms or issues associated with the problem are completely missed. Thus, a representation of a longitudinal

estuary as a single completely mixed body of water is

quite inappropriate since the impact of a load over

distance is lost. Similarly, a steady state approximation

may be completely incorrect because of the dynamic

nature of the problem (e.g. time variable phytoplankton

behavior).

The “art” of water quality modeling in general, is to

carefully evaluate the relevant scales of the problem.

This evaluation requires an assessment of the requisite

degree of complexity as opposed to merely assuming

that fully time variable, fine space scale models with

extensive kinetic detail are always the best choice.

A. Temporal Scale Issues

Estuaries exhibit a variety of time scales: hour to hour,

tidal and diurnal fluctuations, week to week and seasonal variations and year to year differences. From a

modeling point of view, what are the choices? One can

try to represent the entire time spectrum from short term

to long term behavior, but this is clearly impractical. A

model may concentrate on the short term, intra-tidal and

diurnal variations, with a possible loss of focus on the

longer term phenomena. Conversely, a steady state

model may miss the transient effects of storm water

inputs or transient hydrologic events. The choice of

relevant model temporal scale in my opinion centers

about the use of estuarine modeling for WLA purposes.

A WLA may be a constant (over time) effluent concentration or a seasonal variation may be allowed (as in

seasonal nitrification). These specifications are usually

assigned to meet water quality objectives during some

critical flow and temperature period. It is not usual to

assign a WLA on a short time scale with the exception

of a probabilisitic assignment of maximum values not be

exceeded. Also, WLA analyses often need to be conducted with relatively limited data, which are usually not

of sufficient density in time and

Figure 14-1. Illustration of relationship between model credibility and model complexity.

14-3
space to calibrate a fully time variable intra-tidal model.

Rather, data are more frequently available at irregular

time intervals, but with some spatial definition. Finally,

developing fully time variable models at an intra-tidal

level is a complex time consuming effort with a necessity to conduct extensive data analysis and output

processing in order to display model results in a defensible manner.

The case studies show a range of temporal scales,

from the steady state analyses of the Calcasieu estuary to the intra-tidal models of the Manasquan estuary

and DO and total residual chlorine in the Potomac

estuary.

The intra-tidal choice for the Manasquan (over two

4-day periods) is not considered to be the correct

choice since the water quality problem under study

involved kinetic behavior over time scales of weeks

and seasons. Key behavior is therefore not captured

by the temporal scale of the Manasquan model. Also,

the fact that intra-tidal computations were performed

does not, in itself, provide for an accurate representation of the actual variability in the data. Indeed, it

is not clear from the comparisons to data presented in

the case study that the intra-tidal calculations captured

the actual variability with any substantive degree of

success.

The choice of an intra-tidal scale for the total residual

chlorine in the Potomac is correct since the kinetics of

the disappearance of chlorine are quite rapid. The time

variable behavior of the chlorine state variable thus

need to be calculated over short time intervals in order

to model the expected transient behavior.

B. Spatial Scale Issues

The choice of spatial dimensionality and scale involves

evaluation of available data (to determine significant

gradients) and the expected geographical extent of the

problem. The fineness of the spatial extent of the

model is to some degree coupled to the temporal

issues noted above. Generally, long time scale problems may involve larger scales and less detailed spatial definition.

The chlorine model of the Potomac is an example of

where cross-estuary gradients needed to be computed

necessitating a spatially detailed model in the lateral

and longitudinal direction. The Saginaw Bay model

consisting of five segments is a good example of a

reasonable grid since a finer spatial definition would

probably not contribute to any improved model credibility.

Finally, a remark should be made about model

boundaries. The extent of the model should always be

sufficiently far removed from any existing or proposed

inputs that may be subject to a WLA. The boundaries

should be at a point where the flows and exchanges and

state variable concentrations can be specified and are

independent of the model output. For example, it is not

entirely clear from the Manasquan case study that the

model boundary is proper, i.e. the extent of the model

may have to be extended out past the inlet in order to

provide a proper independent boundary condition. This

may be especially true if the model had ever been used

for analysis of the proposed regional input at the head

of tide.

C. Suggested Strategy for Temporal-Spatial Scales

Since the principal reason for estuary modeling in the

context of this opinion is a WLA, the following strategy

for choosing a relevant temporal-spatial modeling scale

is offered.

TEMPORAL-SPATIAL SCALES

BEGIN WITH STEADY STATE,

“LARGE” SPACE SCALES,

THEN SEASONAL,

MORE DETAILED SPATIAL DEFINITION,

THEN INTRA-TIDAL, FINE SCALE.

It is suggested that the temporal scale of most WLA

estuarine models should begin at steady state to determine overall relationships between input loads and

resulting water quality. Steady state is suggested

even for highly reactive variables since the steady

state modeling helps to define overall response levels and spatial extent of the input loadings.

Following steady state analyses, if time variable analyses need to be done in estuaries (as a result, e.g. of a

need to specify phytoplankton dynamics for nutrient

control or a seasonal WLA) then a seasonal time scale

(with a model framework representing an average over

a tidal cycle) should be used.

Only if the justification is quite clear, (e.g., transient

storm water input analyses or a complicated hydrodynamic regime as in the Potomac estuary chlorine model)

should an intra-tidal model be constructed. The fact that

the estuary has a tidal oscillation is in itself not justification for constructing an intra-tidal model. The reason is

threefold: (a) as noted earlier, the focus here is on WLA

problems which are normally limited in resources, time

and money, (b) most WLA problems involve processes

that have longer time scales than tidal, and (c), there are

many other sources of temporal variability in water

quality that are not captured by intra-tidal calculations

(e.g. hour to hour and highly local changes in solar

radiation, suspended solids, wind, or velocity, among

others).

It is suggested that initially a relatively crude spatial

representation (e.g. a numerical grid size of several

miles) be used for estuaries in the longitudinal direction

14-4
in order to provide a rapid understanding of the expected order of water quality variations. If vertical

gradients are significant, the model should include a

vertical dimension at the outset. Only if warranted by

the problem context should a spatially detailed (e.g. on

the order of hundreds of feet) model be constructed.

14.1.3. Need for Hydrodynamic Models

Several of the case studies (e.g., Potomac,

Manasquan and Calcasieu) make use of hydrodynamic models. Indeed, the case study reviews seem

to imply rather consistently that a water quality model

is always better when a hydrodynamic model is included. I do not agree that this is always true. It seems

that a mathematical model of the hydrodynamics of the

estuarine system is necessary when:

(a) the transport regime is complex in space and time

and cannot be easily specified a priori,

(b) the transport regime will be changed under some

future WLA condition, such as occasioned by channel

deepening or straightening, or construction of barriers

(c) the absence of hydrodynamic model would weaken

water quality model credibility in the eyes of a peer

scientific review.

It is not clear that hydrodynamic modeling was crucial

and essential for the Potomac DO and the Manasquan

models. Indeed, the issues of water quality model

credibility for a WLA often have little to do with the

hydrodynamic calculation. Rather model credibility

centers around (a) issues of water quality model calibration that do not depend on hydrodynamics (e.g.

parameter specification), (b) inclusion of correct

mechanisms (e.g. appropriate state variable or sediment source/sink interactions) and (c) point and nonpoint input load estimates. An alternate to a full

hydrodynamic model calculation on an intra-tidal basis

is to calculate the net transport from the fresh water

flow and estimate tidal dispersion coefficients by using

salinity (or dye) as a tracer. Many estuarine WLA

models have been successfully constructed using this

type of average across tide approach.

14.1.4. Appropriate Level of Kinetic Complexity

In addition to temporal and spatial issues, one must

also consider the need to include various levels of

kinetic complexity in the model. Specifically a choice

must be made of the relevant state variables to be

included in the model and the nature of the interaction

between the state variables. For example, for a DO

model, should phytoplankton be explicitly modeled or

input? For a phytoplankton model, should various

functional groups be modeled or should total chlorophyll be used? Should sediment nutrient fluxes be

calculated or input?

As a general rule, I would advise to:

KEEP STATE VARIABLES TO A MINIMUM;

MODEL ONLY THOSE FOR

WHICH DATA EXISTS;

BUT ALWAYS INCLUDE THOSE STATE VARIABLES WHICH WILL BE

IMPACTED BY A WLA.

The case studies seem to have implicitly recognized this

general rule, although there are some exceptions. The

Manasquan model is clearly over-specified with state

variables and kinetic interactions for the nature of the

problem under study and the available data sets. The

inability to calibrate to the phytoplankton state variables

severely limits the utility of the model.

On the other hand, the initial Potomac estuary DO model

did not explicitly include organic nitrogen, nor ammonia

uptake by phytoplankton. Also photosynthetic DO

sources and sinks were externally inputted, but these

inputs were to be extensively impacted by a WLA for

nutrients. The model could not therefore respond to the

WLA questions associated with the affect of nutrient

control on DO.

Sometimes a state variable must be included even if

data are not available. For example, for a toxic chemical

model, both dissolved and particulate chemical must be

modeled. But data may not be available for the dissolved

component because of concentrations below a detection limit. Nevertheless, both components need to be

included in the modeling framework.

14.1.5. Calibration and Verification Issues

Of course, all of the above only has relevance when the

model is considered to be “representative” of the actual

estuary. Thus, the question of the calibration and verification of the model must be addressed. This is an area

about which much has been written and discussed for

several decades, all centered about the issue of whether

a model has adequately reproduced the observed data.

A. When Is A Model “Calibrated” And “Verified?”

In my opinion, a model is considered representative of

the real estuary when the key model state variables

reproduce the observed data over a range of expected

conditions and within expected statistical variability. Of

course, this definition may not help at all. For example,

what is the “expected statistical variability?” Perhaps the

only answer is that model “unrepresentativeness” is

obvious. We know when a model is not repre14-5
sentative. The Calcasieu case study is offered as an

example of a model that is claimed by the analyst to

be “good” for the hydrodynamic model and “fair” for the

water quality model. But even a casual examination of

the model comparisons to data indicate severe problems. The DO profile is not captured and a sag is

calculated where it does not exist. This, in my opinion,

is “unrepresentative” and outside the bounds of statistical variability.

Similarly, the Manasquan model simply fails in several

state variables to bound the data. Also, the spatial

profiles for this case are not presented so one cannot

judge the adequacy of the model in reproducing longitudinal variability.

The Potomac estuary DO model was compared to

various data sets by readjusting the model parameters

for each calibration. This is unacceptable. The purpose

of model calibration and verification is not to “force fit”

the model to the data. Rather, the model parameter

numerical assignment should obey the

PRINCIPLE OF PARSIMONY:

BE “STINGY” WITH THE SPATIAL AND TEMPORAL SPECIFICATION OF MODEL PARAMETERS

AND HAVE A REASON

FOR ALL ASSIGNMENTS.

The Potomac estuary phytoplankton and Saginaw Bay

models offer extensive calibration and verification

analyses, including various statistical measures of comparisons. Both spatial and temporal comparisons and

statistics of comparisons are given. These case studies

provide some measure then of an adequate representation of the data by the model and can be profitably

used as a “model” of a model calibration. Two caveats

are in order, however: (1) extensive data sets and

resources were available in both cases, and (2) even

with the extensive calibration and verification of the

Potomac eutrophication model, a bloom in 1983 was not

captured because of presumed pH mediated sediment

phosphorus release, a mechanism not previously included in the model.

14.1.6. Summary

Figure 14-2 summarizes all of the above comments.

As indicated, the suggested procedure is to begin with

simple representations of the estuarine system. This

should always include some investigation of the estuary

water quality problem with analytical solutions. This is

true for all problem contexts. For DO, simple steady

state solutions should be used to provide estimates of

the impact on carbonaceous and nitrogenous loads,

sediment oxygen demand, and photosynthesis and respiration on the DO. For nutrient problems, total nutrient

calculations should be perFigure 14-2. Suggested strategy for determining appropriate level of model complexity.

14-6
formed to determine importance of sediment fluxes

and net loss from the water column. For toxics problems, total, particulate and dissolved chemical can be

easily estimated.

If the estuarine system is too complex for initial analytical solutions (e.g. when vertical and lateral gradients must be defined) then a steady state numerical

model is recommended. The spatial definition is determined from the gradients that need to be captured.

Following the structuring of the simple model, initial

determinations should be made of the model credibility. Comparisons to data should be presented over the

spatial dimensions of the model. Where appropriate,

statistical measure of model adequacy should be computed.

The degree of model credibility should then be assessed in the light of the WLA.

THE SIMPLE MODEL MAY TURN OUT TO BE ENTIRELY SUFFICIENT FOR WLA PURPOSES.

If a determination is made that the simple model provides only “marginal” model credibility, then model

complexity should be increased. This increase in

model complexity often needs to proceed in the following order: (a) additional state variables, (b) additional

kinetic interactions, (c) increased temporal and spatial

definition. It is in the latter that hydrodynamic modeling

may be necessary.

Additional calibration and verification is then conducted with the hope that model credibility is increased. This step should include, whenever possible,

comparisons to data sets collected over a range of

environmental and input loading conditions.

After a determination has been made, then a full WLA

analysis can be conducted. This analysis should include evaluation of water quality response under critical design conditions, sensitivity analysis, projection of

expected loads in the future and components analysis

of individual inputs. This latter analysis is aimed at

describing the relative contribution to the calculated

response from individual components, e.g. particular

point source inputs, and distributed sources (such as

sediment sources). The analysis often provides key

insights into which inputs and mechanisms are most

important in the WLA. (None of the case studies displayed any components analysis).

The final outcome is then the recommended WLA for

an input or region with associated permit specifications. It is this final outcome that should always be kept

in perspective when assessing the need for various

levels of model complexity. Ultimately, of course, the

measure of success of the model is the degree to

which the model projections are actually realized after

the WLA has been implemented. But that is a topic for

another opinion at a different time.

14.1.7. Case Study Review

Case Study 1 - Saginaw Bay

This case study is a very good example of a proper mix

of spatial and temporal specification together with

proper representation of kinetic detail. Illustrations of

the extensive calibration of the model are given and the

post audit of the model is unique. The statistical comparison between model output and data as shown in

Table 10-4 is a very good example of what should be

expected from a water quality model.

The use of a five segment model is entirely appropriate

since the proper exchanges and transport were determined from measured salt concentrations. In this reviewer’s opinion, a representation of the system with a

finer grid operating at finer time and space scales would

not improve the model performance and indeed may

have considerably delayed and obscured the interpretation of model output.

It is concluded that the overall analysis of Saginaw Bay

eutrophication as given in this case study is a paradigm

analysis for water quality modeling. The modeling provided considerable insight into the dynamic behavior of

phytoplankton functional groups, incorporated a detailed calibration and verification analysis and uniquely

conducted a post-audit analysis after nutrient controls

were implemented.

Case Study 2 - Potomac Estuary

This case study, a summary of three efforts on the

Potomac estuary, illustrates a range of modeling approaches to estuarine water quality.

DYNAMIC ESTUARY MODEL

The first effort, the use of the Dynamic Estuary Model

(DEM) examined the DO resources of the estuary. The

one dimensional hydrodynamic model was used to provide the transport and was calibrated to hydraulic properties as well as the longitudinal extent of chloride

concentration in the estuary. This effort is a good example of calibration of the model to observed data, but also

indicates the hazards of calibration where the underlying kinetic structure is too simple. The DO calibration

reset initial conditions for each survey. This is not considered a proper calibration method. As inidicated during a post-audit, the DEM failed to properly account for

nitrification phenomena by assuming that all ammonia

that was lost was due to nitrification, rather than through

some measure of up14-7
take by the phytoplankton. The intratidal hydrodynamic model, while initially appearing to provide a

more realistic “real time” modeling framework, in actuality added little to understanding of the overall water

quality behavior of the estuary.

The history of the DEM is a useful example of model

evolution in the midst of decision making. With initial

emphasis on intra-tidal calculations to a shift towards

more detailed kinetic evaluations during the post audit

stage, the DEM illustrates the need to properly include

necessary phenomena that link various water quality

constiituents.

POTOMAC EUTROPHICATION MODEL

The Potomac Eutrophication Model (PEM) is an example of an intermediate scale of estuarine water quality

model. The use of a coarse grid in the lower estuary

was justified on the basis of the lack of any significant

gradients in water quality constituents of interest. Vertical homogeneity is a key assumption and undoubtedly influenced the ability of the model to properly

calculate water quality in the region of the turbidity

maximum. This time variable model (on a time scale

of weeks to seasons) properly did not rely on a detailed

intra-tidal hydrodynamic calculation on a fine time and

space scale. Emphasis was rather placed on the role

of the sediment on the overlying nutrient concentrations and the interactions of the various nutrient forms

with phytoplankton and DO.

The PEM study is a good example of extensive calibration and verification analyses, illustrations of which

are shown in the case summary. Also, the PEM analysis made use of extensive statistical comparisons (see

11.4.6) between the model output and the observed

data.

Like the DEM, the PEM was subjected to a post audit

analysis. The analysis was prompted by a major algal

bloom in the summer of 1983. As noted in the case

study summary, pg. 11-25 ff., the PEM was not able to

predict the full extent of the observed bloom, due in

some measure to a significant source of phosphorus

that was not incorporated in PEM. Subsequent work

indicated that such a source may have been from a pH

mediated release of sediment phosphorus. Additional

input may have resulted from upstream transport of

phosphorus from downstream bottom waters. This

latter effect was also not included in PEM because of

the vertically homogeneous nature of the model.

Overall, the PEM is a good example of calibration and

verification of a time variable eutrophication estuarine

system. It also illustrates the hazards of apparently

“best” calibration of the model that misses a phenomena which only appears after certain conditions ensue.

Nevertheless, the PEM proved useful in a variety of

decision making contexts, not the least of which was to

assess the reasons for the major 1983 algal bloom.

FINITE ELEMENT CHLORINE MODEL

This model is a very good example of the proper choice

of time and space scales. Because the decay rate of

chlorine is so rapid, the zone of influence of the chlorine

residual would be expected to be highly local. As a

result, this model has as its spatial focus a region of

about five miles centered at the location of the major

input. Detailed lateral specification is required because

of the need to calculate lateral movement of the chlorine. Model calibration of transport and dispersion was

first accomplished by comparisons to dye study results.

The results shown in Figure 11-14 are a good example

of what one can expect. The general shape is captured,

but not all of the details even though the grid is relatively

fine. As noted in the text, further work using dye decay

would be necessary to improve the calibration. It was

concluded however, that the dispersion was properly

captured in general.

That conclusion is a good example of a judgement

made by the analysts on the suitability of a model

calculation. This reviewer believes that the judgement

made here is correct, but only because of the calibration

analysis to the observed dye data.

A similar conclusion can be drawn with respect to the

calibration of the total residual chlorine model to survey

data. What was required here was approximate representation of the general field of the chlorine, to approximate order of magnitude. This was achieved. More

importantly, the sensitivity analysis indicated the degree of model uncertainty and this is clearly discussed.

That uncertainty did not affect the basic conclusions.

SUMMARY

These three modeling efforts of the Potomac estuary

water quality illustrate a good range of spatial and

temporal scales, level of model complexity and the

need for extensive calibration and verification to observed data. Two major points seem to emerge:

• Uncertainty in the model coefficients sometimes

does not affect the conclusions, i.e., the decisions that are reached. But under certain situations, a model that is believed to be properly

calibrated can miss entire phenomena or linkages. Such a model may then fail in varying degrees during a post audit. The experience of the

Potomac esturary models summarized in

14-8
this case study should be borne in mind by any

analyst.

• Each problem requires its own spatial, temporal

and kinetic level of detail. Finer spatial and temporal resolution is often not the issue especially

when the problem context is over a larger time

and space scale. Funding and project completion times are realities that must be faced in

any modeling effort. Such constraints must be

balanced against more and more detail in the

modeling framework with perhaps less than desired return in improving the certainty of decision making.

Case Study 3 - Manasquan Estuary

This model illustrates the use of an intra-tidal calculation to describe estuarine water quality. This reviewer

believes that the proper temporal scale was not used.

By focusing in on two 4 day periods as examples of

“calibration” and “verification,” the model does not

capture the longer term, i.e., week to month, behavior

of the water quality constituents of interest. Further, the

analysis is flawed in several ways. The August 1980

period is used as a calibration set and July 1980 is

used as a verification data set. What would be much

more convincing is to use the model in one complete

calculation extending from prior to July 1980 through

the August 1980 data. By restarting the calculation

each time before August and July and then extending

the calculation for only four days, the credibility of the

model is severely compromised.

Also, this model is presented as a demonstration of a

“successful calibration and verification of a real-time

estuary model.” This reviewer does not agree that this

model is successfully calibrated and verified even for

a brief period of four days. The “real time” model is

presented in a fashion that seems to indicate that

because the model calculated at a time scale of hours

or less that it is more realistic than averaged models.

Ostensibly, the “real time model was selected to predict photosynthesis effects on diurnal DO.” But the

model fails to reproduce the observed DO range (see

e.g., Figure 12.9 and 12.10). Also, the CBOD, NBOD

and nitrogen forms are not calibrated. For example,

Figure 12-18 shows comparisons of computed nitrogen forms to observed data. The computed forms vary

approximately sinusoidally with an apparent look of

reality and certainty. But the comparison to the observed ammonium data, for example, show some significant over-calculation of the data. One wonders how

well the model would have done if the model were not

restarted for the July 1980 data set but rather was run

for a several month period.

It is recognized that this model was apparently constructed with only limited data and under apparently

tight constraints. As such, the exercise is useful in

showing how a model can be used to delineate data

and input load deficiencies. However, the modeling

framework is not considered to be adequately calibrated and verified over the time scales necessary for

the water quality constituents under investigation. The

model spatial extent may also be inadequate for evaluating certain alternatives and may have to be extended

into the ocean.

Case Study 4 - Calcasieu River Estuary

This case study is adequately presented as an example

of a modeling context with problems in credibility and

in application. The modeling structure is flawed in not

adequately representing phytoplankton interactions on

the DO, no settling of particulate forms and a lack of

vertical detail. (No data are presented however to indicate the extent of any vertical stratification in salinity or

DO). The model is not considered to be adequately

calibrated and verified because of a failure to capture

the salinity and DO profiles on several occasions. More

critically, the conclusion on a total maximum daily load

of 83,130 lbs UOD/day is not justified by the model

analysis. Since the data already indicate DO violations

below a standard of 4 mg/L, it is hard to see how the

stated allowable load was determined.

This case study should be seen as an example of model

evolution under different analysts with final results that

are marginal at best. The difficulty stems from differences in the opinions of analysts as to what constitutes

a satisfactorily calibrated and verified model. One analyst described the hydrodynamic calibration and verification as good, but this reviewer sees a very poor

comparison. At several of the stations, the computed

stage differs from the observed stage by several feet,

an apparent clear inability of the model to properly

represent the easiest of hydrodynamic variables. The

adequacy of the hydrodynamic model can also be

judged by examination of the salinity profiles which are

erratic in comparison to observed data. For example,

the July 1978 salinity profile is adequately captured, but

the computed July 1980 profile is significantly below the

observed data. A zero DO concentration is calculated

in this vicinity that is not representative of the observed

data.

In general, this case study indicates a modeling framework that is not entirely credible and as such, the

application to a waste load allocation is somewhat

problematical. The inconsistency of the computed allowable UOD load with the observed data, as noted

above, is illustrative of the tenuous nature of the model

for use in decision making.

14-9
14.2. Donald R.F. Harleman, Ph.D.

Professor, Department of Civil Engineering

Massachusetts Institute of Technology

Cambridge, Massachusetts

14.2.1. Introduction

The concept of a technical manual for performing

wasteload allocation in estuaries is an excellent one.

Part IV of the manual is intended to be a “critical review

of estuarine wasteload allocation modeling.” It consists

of four case studies “representing various levels of

complexity.” The task assigned to the reviewer is to

provide a general discussion of the “appropriate level

of estuarine model complexity” and to comment on the

case studies within the context of the reviewer’s philosophy of environmental modeling.

14.2.2. Statement of the Problem

Many environmental problems require the development of models in order to answer management questions related to the effectiveness of various control

scenarios. Such an effort requires a careful statement

of the problem and applicable regulatory constraints.

Decision makers need to be able to assess the importance of controlling point or non-point sources of pollutants, and they usually need to know the time scale

at which the estuary can be expected to respond to the

implementation of source controls. Effective environmental modeling would avoid the Lake Mead fiasco

where the City of Las Vegas operates a tertiary waste

treatment plant designed to minimize phosphorus discharges to Lake Mead while the Fish and Wildlife

Service periodically adds phosphorus to the lake to

promote the growth of fish.

14.2.3. Data

Available data, both hydrodynamic and water quality,

must be studied in order to understand the spatial

complexity of the problem. In the hydrodynamic area,

it is important to understand the factors influencing the

currents and circulation pattern. These include: the

degree of vertical stratification within the salinity intrusion zone, the extent of changes in longitudinal salinity intrusion due to tides, wind and seasonal changes

in fresh water inflows, and the degree of lateral stratification due to fresh water inputs from tributaries located on one side of the estuary. Temperature

stratification may also influence the vertical mixing and

circulation pattern. The main stem of Chesapeake Bay

is an excellent example of an estuary with distinctly

three-dimensional characteristics.

In the water quality area, vertical stratification significantly affects the vertical flux of nutrients to and from

the bottom sediments and may contribute to the formation of anoxic regions along the bottom of the

estuary. The objective of the data analysis is a decision

on the dimensionality of the model. It would obviously

be inappropriate to use a two-dimensional depth averaged model in an estuary having a history of bottom

anoxia.

14.2.4. Spatial Resolution of Models

In terms of spatial resolution, environmental models

may be classified as box models or as one-, two-, or

three-dimensional hydrodynamic models. The distinction between box models and the hierarchy of dimensional hydrodynamic models is an important one that is

not clear in the presentation of the four case studies of

Part 4.

A. Box Models

Box models require an empirical, rather than an analytical (or numerical), specification of the flow field. Thus

there is no hydrodynamic model component in a box

type model. Box models may be arranged in a longitudinal, lineal array or boxes may be arranged in pseudo

two-dimensional depth-averaged arrays. Two examples of this are contained in the case studies. Case

study 10.0 of Saginaw Bay on Lake Huron shows the

entire Bay represented by five boxes (see Fig. 10.2).

Case study 11.4, Potomac Eutrophication Model

(PEM), uses a box network consisting of 23 main

channel longitudinal segments and 15 lateral tidal embayment segments. In the lower saline portions of the

estuary, these box segments are as much as 15 miles

in length. This is mind-boggling when it is realized that,

by definition, each box is a fully-mixed compartment.

Case study 10.0 (Saginaw Bay, Lake Huron) contains

no information on how the flow between boxes (the

largest of which has as surface area of about 400

square miles) or how the dispersive mixing parameters

are determined. In addition, there is no information on

the sensitivity of model results to these important transport quantities. The time scale of the model is seasonal,

that is, it deals with monthly variations in water quality

parameters. In terms of spatial and temporal resolution,

it is difficult to see how this model would be applicable

to estuary studies.

Case study 11.4 (Potomac Eutrophication Model) is

similar to the Saginaw Bay study in that there is no

information on how the daily averaged flow and dispersion between boxes is obtained.

In this reviewer’s opinion, box models represent a

“black art.” Specification of empirical advective and

dispersive transport between boxes can only be accomplished reliably by using a conservative substance

such as salinity. Determining the spatial distribution of

advection and dispersion for each box segment that

14-10
satisfies a given salinity distribution requires the solution of an inverse problem for which there is no unique

solution. Furthermore, the spatial distribution of advection and dispersion coefficients will change in time in

relation to factors such as fresh water inflow, which

change the longitudinal and vertical distribution of salinity.

B. Hydrodynamic models

The state-of-the-art of numerical hydrodynamic modeling is extremely well advanced in two-dimensional,

both laterally averaged and depth averaged, applications. Limited, but reasonably good experience exists

at the three-dimensional level. An excellent review of

the status of two- and three-dimensional hydrodynamic modeling has been prepared by the ASCE Task

Committee on Turbulence Models in Hydraulic Computation (ASCE, 1988). The review contains a discussion of various turbulence closure models, lists of

available two- and three-dimensional hydrodynamic

computer codes, code selection guides and case study

examples.

The only case study in Part 4 which falls within the

realm of multi-dimensional hydrodynamic modeling is

11.5 Neleus (Potomac Residual Chlorine Model). This

is a two-dimensional, depth-averaged, finite element

model of the upper, fresh water, tidal portion of the

Potomac. The case study is deficient in not providing

a list of references. The two-dimensional model grid

shown in Fig. 11-13 consists of more than 1,100 elements covering a 15-mile portion of the river. Calibration of the model for the 1980 dye study (Fig. 11-14)

shows reasonably good agreement. In general, the

model is well-suited to provide information on residual

chlorine levels.

The group of case studies in Part 4 is deficient in not

providing an example of a two-dimensional, laterallyaveraged hydrodynamic model. This type of model is

well-suited to estuaries that exhibit some degree of

salinity of temperature stratification over the depth.

Bloss et al (1988) describes the application of a twodimensional, laterally-averaged hydrodynamic and salinity model to the Trave estuary in Germany. A

long-term simulation of 85 days reproduced total mixing events and strong stratification. The model showed

good agreement with extensive field data. A similar

2-D model study of stratification and wind-induced

destratification in Chesapeake Bay has been reported

by Blumberg and Goodrich (1990).

One-dimensional hydrodynamic and salinity models

are in an advanced state of development. These crosssectionally averaged models are applicable to wellmixed estuaries - those having strong tidal regimes

and relatively small fresh water inflows. The Delaware

and Hudson estuaries are examples of reasonably

well-mixed estuaries.

Case study 12.0 (MIT-Dynamic Network Model) applied to the Manasquan River in New Jersey is a good

example of a one-dimensional hydrodynamic and salinity model. Longitudinal dispersion is modeled as a

function of magnitude of the local salinity gradient and

the degree of vertical stratification. Thus this model is

able to track longitudinal salinity changes due to variations in fresh water inflow. (Thatcher and Harleman,

1981).

The remaining case studies of Part 4 are 11.3 (Dynamic

Estuary Model) applied to the upper portion of the

Potomac estuary and 13.0 (RECEIV-II-EPA) applied to

the Calcasieu Estuary. These models are pseudo onedimensional tidal models employing a link-mode schematization. Tidal motion is represented, but the models

do not include hydrodynamic and salinity interactions.

The primary disadvantage of this class of models is that

dispersion effects are not modeled and therefore must

be calibrated using conservative tracers. A characteristic of this class of model is their inability to simulate

the steepest portion of the longitudinal salinity gradient

due to excessive longitudinal numerical dispersion

(See Fig. 11-3).

The Calcasieu estuary case study (13.0) states that the

model contains no dispersion. The so-called hydrodynamic verification for tidal stages is very poor (See Fig

13.4). This reviewer would not recommend further use

of this model for estuarine studies.

14.2.5. Temporal Resolution of Models

The prevalent modeling philosophy throughout this

manual (and one that is widely held) is that the temporal

resolution of a model should be determined by the time

scale of interest to the user of the model output. This

usually leads to the conclusion that time steps averaged over a tidal period or longer are desirable. The

result is a model far removed from the physics (fluid

mechanics) of the relevant transport and mixing process. Thus the modeler is required to “select” multi-dimensional dispersion coefficients which must be

“adjusted” by calibration to inadequate data. This approach is based on the mistaken assumption that here

is some inherent law stating that there must a correspondence between the time scale of the model input

(and the computational time scale) and that of the

output.

An alternative approach is to take advantage of the

powerful hydrodynamic computational tools that are

available in one, two or three dimensions. These require temporal resolution at the intratidal level. The

question then arises as to how to interact the small time

14-11
step hydrodynamic model with the longer time step

water quality model (This is the subject of a separate

discussion below). The philosophical point is that

model output can be averaged temporally in any way

that is desired to produce a result at the time scale of

interest to the user. In other words, one should not

average the input in order to produce an averaged

output.

14.2.6. Time and Space Scales for Interfacing

Hydrodynamic and Water Quality Models

The rational methodology for waste load allocation

makes use of water quality models that are capable of

predicting the response of a water body to various

loading scenarios. We are increasingly called upon to

model water bodies that have high degrees of temporal

and spatial complexity. Examples are unsteady,

strongly advective flow systems with density stratification due to temperature, suspended and/or dissolved

substances. Such systems are at least two-dimensional and more often three-dimensional in nature.

There exists, on one hand, a number of 2- and 3-dimensional models that include baroclinic (i.e., stratification) effects and sophisticated hydrodynamic

turbulence closure components. On the other hand,

there are a number of ecologically sound, multi-parameter water quality models. These two types of

models have evolved independently of one another

through the efforts of hydrodynamicists and aquatic

scientists. A great deal of research support has gone

into these separate model development efforts. However, there has been little effort directed to the crucial

problem of interfacing or coupling of hydrodynamic

transport and water quality models. The coupling problem arises because of the following dichotomy.

The dynamic nature of multi-dimensional hydrodynamic models and the associated numerical stability

requirements usually dictate a small spatial grid and a

computational time step of the order of minutes. Water

quality models typically involve longer time scales

ranging from a day in the case of nutrient recycling in

the water column to months or years for sedimentwater column interactions. There is an obvious disinclination to interface water quality models at the small

spatial and temporal resolution of the hydrodynamic

model because of the enormous computational burden. Yet there do not exist generic guidelines for

interfacing water quality models at larger spatial and

temporal increments.

The state of the art of interfacing hydrodynamic and

water quality models has evolved in two directions.

The first makes use of large boxes where, because of

the large spatial grid, it is impossible to apply numerical

hydrodynamic models. The user is then faced with the

problem of empirically calibrating transport and mixing

to an observed distribution such as temperature or

dissolved solids. It is impossible to carry out such “large

box” calibrations in stratified, strongly time-varying,

multidimensional systems.

The second interfacing approach averages the advective and diffusive output of the short time step hydrodynamic model over larger spatial grids and time periods

(e.g., 24 hours) that are thought to be appropriate to the

water quality model. The problem is that important

advective and diffusive information from the hydrodynamic model is lost in direct proportion to the length of

the spatial and temporal averaging period. There are

no quantitative guidelines for multi-dimensional models

to indicate the extent of information loss by averaging.

Therefore, we are again faced with the necessity of

difficult empirical calibration procedures.

A number of studies have addressed the hydrodynamic-water quality interfacing problem in the context

of one-dimensional lake and reservoir models: Ford

and Thornton (1979), Walters (1980), Imboden et al.

(1983), Wang and Harlemen (1983), and Shanahan

and Harlemen (1984). Systematic studies of interfacing

for time-varying, multi-dimensional, stratified water

bodies are now underway in connection with the

EPA/COE Chesapeake Bay modeling program.

14.2.7. Water Quality-Eutrophication Models

The water quality components of most of the waste load

allocation models in Part 4 are fairly similar in that they

model BOD, DO, ammonia, nitrite, nitrate orthophosphate and chlorophyll. In some case, more than one

class of algae are included, and some models include

zooplankton although data for this component is usually

sparse or non-existent.

Three important waste load allocation and management issues are virtually ignored by the water qualityeutrophication models presented in Part 4. They are:

(a) The question of nitrogen or phosphorus limitation in

the eutrophication process together with the role of

point versus non-point sources as sources of N and P

is of crucial importance in waste load allocation. The

issue of major investments in advanced waste treatment plants as opposed to control of agricultural fertilizer runoff depends on the model’s ability to deal with

nutrient limitation kinetics. The problem is complicated

by the fact that most estuaries include upstream fresh

water portions as well as the downstream salinity intrusion region. Algal species and nutrient preferences may

shift between the fresh-salt water zones of an estuary.

14-12
(b) A significant number of estuaries experience summer anoxic conditions in deep bottom zones. Very low

or zero dissolved oxygen is known to trigger major

increases in the release of nutrients from the bottom

sediment to the overlying water. Eutrophication models applied to estuaries having low DO problems must

have the ability to simulate the vertical stratification and

vertical mixing processes that affect vertical oxygen

transport and dissolved oxygen gradients and benthic

nutrient fluxes.

(c) The determination of the time scale at which an

estuary responds to changes in waste load inputs

depends on how sediment-water column interactions

are modeled. Waste load (nutrient) inputs generally

result in algal production in the upper euphotic zone.

Dead algae sink and are incorporated as organic material into bottom sediment. Sediment diagenesis occurs in the sediment and results in nutrient fluxes and

sediment oxygen demand. The rate at which the sediment diagenesis occurs controls the rate at which the

estuary responds to loading changes. Important papers in this modeling area are contained in Hatcher

(1986).

Attention should be given in this document to a report

prepared by ASCE Task Committee on the Verification

of Models of Hydrologic Transport and Dispersion

(Ditmars et al,1987). The objective of the report is to

identify, collate, and define the procedures required to

evaluation of performance of an analytical or numerical

surface water model. The essential elements are:

identification of the problem; relationship of the model

to the problem; solution scheme examination, model

response studies, model calibration; and model validation. Literature examples are used to define the techniques that have been used to address each of the

elements above. Emphasis in the six elements is

placed on moving the evaluation of models, particularly

those in journal publication, towards more quantitative

or objective measures of calibration and validation.

14.2.8. References

ASCE Task Committee on Turbulence Models in Hydraulic Computations, ASCE Journal of Hydraulic Engineering 114(9), September, 1988.

Bloss, Siegried; Rainer Lehfeldt and John C. Patterson. Modeling turbulent transport in stratified estuary.

ASCE Journal of Hydraulic Engineering 114(9), September, 1988.

Blumberg, Alan F. and David M. Goodrich. Modeling

of wind-induced destratification in Chesapeake Bay,

Estuaries, March, 1990.

Ditmars, J.D., E. Eric Adams, Keith W. Bedford, and

Dennis E. Ford. Performance evaluation of surface

water transport an dispersion models. ASCE Journal of

Hydraulic Engineering 113(8), August, 1987.

Ford, D. and K.W. Thornton. Time and length scales for

one-dimensional assumptions and its relation to ecological models. Water Resources Research 15 (1) February 1979.

Hatcher, Kathryn J. (editor). Sediment Oxygen Demand: Processes, Modeling & Measurement. Athens,

Georgia, Institute of Natural Resources, University of

Georgia, 1986.

Imboden, D.M. et al., Mixing processes in lakes:

Mechanisms and ecological relevance. Schwerz. Z.

Hydrol., 45(1), 1983.

Shanahan, P., and D. R. F. Harleman. Transport in lake

water quality modeling. Proceedings, ASCE Env. Eng.

110(1), February 1984.

Thatcher, M. Llewellyn and Donald R.F. Harleman.

Long-term salinity calculation in Delaware estuary,

ASCE Journal of the Environmental Engineering Division 107(EE1), February, 1981.

Wang, M. and D.R. F. Harleman. Modelling phytoplankton concentrations in a stratified lake. Proceedings,

Ecology Modeling Conference, Colorado State University, 1983.

14-13
14.3. Gerald T. Orlob, Ph.D., P.E.

Professor, Department of Civil Engineering

University of California

Davis, California

14.3.1. Introduction

This assessment of selected case studies of estuarine

modeling was prefaced by an opportunity afforded by

the Environmental Protection Agency to review drafts

of the proposed technical guidance manual for waste

load allocation in estuarine systems. It draws on this

background to some extent, but is based primarily on

the experience of the reviewer in developing and applying mathematical models as tools in support of

decision making in water quality management, much

of which has been concerned with estuaries. Naturally,

the views expressed here are reflective of this experience and are uniquely those of the reviewer, for which

he takes full responsibility.

Before examining the specifics of the selected case

studies, it is appropriate to identify a few of the characteristics or attributes to the modeling process that

need special attention in bringing models to a level of

effective application as decision support tools. Among

the more important of these are the following:

• Specific goals or objectives of users to be met by

the use of models and related decision support

capabilities.

• Basic data and information required for construction, calibration, and verification of model(s).

• Temporal and spatial scales appropriate to the

intended use of model(s).

• Hydrodynamics input to water quality models.

• Model structure and complexity.

• Calibration and verification.

• Some brief comments concerning each of these

will provide a reference for the succeeding case

study critiques.

• Goals and Objectives.

In the present context, models are to serve as useful

tools in the decision process, i.e. they are to enable a

decision maker to make better, more defensible

choices among alternatives for waste load allocation.

Although some users would like to use estuarine models in predictive modes, this is rarely feasible at the

present state of the art. Most of models currently

available for water quality simulation are inherently

uncertain to a degree that absolute prediction is exceedingly risky. However, after careful calibration and

verification estuarine water quality models can usually

be applied with confidence in assessment of incremental changes between simulated solutions for different structural or operational alternatives.

14.3.2. Basic Data and Information

The weakest aspect of most modeling projects is the

data base. Most often data are gathered without a view

to future development or application of a model, and

the modeler is forced to adapt to an existing but inadequate body of data. This has prompted some modelers

to resort to construction of simple box or statistical

models rather than design and implement a data base

to serve model application. A well designed data collection program is the best confidence builder for modeling. It should be a continuing activity in any situation

where models are to serve future management of estuarine water quality.

14.3.3. Temporal and Spatial Scales

Selection of time and space scales for modeling is an

activity that is closely related to definition of objectives.

If decisions are to be based on long term (monthly or

more) means, then the dynamics of water quality/ecologic processes on a diurnal or tidal basis may

not be necessary, although it may still be risky to

smooth short term data on these processes, thereby

eliminating important information on extremes. Often it

is the extreme values, occurring during daily or tidal

periods, that are of greatest importance in waste load

allocation. Temporal or spatial averaging may be justified in cases where the data are sparse or where the

decision process does not require great detail. In today’s world of computers the cost of simulation is fast

becoming a non issue, that is, the degree of temporal

or spatial discretization is virtually at the discretion of

the user. If model detail is required, it is more likely to

be controlled by the availability of data for calibration

and verification than computation cost.

14.3.4. Hydrodynamics

In the judgement of this reviewer inadequate description of advective transport is probably the most common cause of poor calibration and verification in water

quality models. This need not be the case, however,

since good hydrodynamic models exist for virtually all

types of estuarine systems, from simple one-dimensional channel networks to complex stratified estuaries

of broad lateral extent where three-dimensional representation is required. Most of these models are relatively easy to calibrate and verify, compared to their

water quality companions, and produce descriptions of

water levels and current structure that are useful for

“driving” water quality simulators.

It is good to recognize in this connection that there is

an important trade off between improving simulation of

advective processes, which entails additional spatial

14-14
and temporal detail, and depending on empirically

derived dispersive fluxes to describe transport of pollutants. Model simplification usually means greater

uncertainty, which in water quality simulation is usually

manifest in empirical dispersion coefficients which represent the aggregate effects of many ill-defined quantities.

14.3.5. Model Structure and Complexity

The trend in water quality modeling has generally been

toward increasing complexity, i.e. more state variables

and an even greater number of additional rate constants, coefficients, etc. While this is a commendable

trend in the sense of improved understanding of the

aquatic system, it also introduces increased uncertainty in model output, due in major part to the inherent

uncertainties in the parameters that have to be estimated or empirically determined. There is probably a

level of detail that is “best” for a given situation, somewhere between a simple black box and the detailed

model, which produces the most reliable result from

the decision maker’s viewpoint. Uncertainty analysis,

e.g. first order error analysis, Monte Carlo simulation,

etc., may provide some guidance as to best structure

for the model in relation to decision goals.

14.3.6. Calibration and Verification

Although modeling implicitly requires comparison of

simulation and prototype observations, and most

modelers comply with the two step process, the practice is still largely judgmental. There are comparatively

few examples of rigorous objective assessment of

model reliability. There is a need for formalizing calibration and verification procedures, perhaps along the

lines of the uncertainty analysis approach suggested

above.

14.3.7. Case Study Review

The five case studies were ostensibly selected for the

diversity of modeling approaches to characterization

of estuarine water quality. They were chose also, so it

appears, to represent a range of difficulties encountered in applying existing models to actual estuaries

and further to illustrate varying degrees of success in

overcoming these difficulties. No ideal examples are

provided, since none actually exist. However, while

those chosen for this review can fairly be regarded as

instructive of some of the problems encountered in the

real world of water quality modeling, they may not be

as exemplary of estuarine modeling per se as one

would like. In two of these cases, as we shall see, there

is reasonable doubt that the systems modeled can

even be categorized as estuaries within the definitions

provided in the technical guidance manual.

Yet, the several case studies do represent applications

of a number of different models and it is useful to

examine these for comparative purposes. Because the

type of estuary often dictates the structure of the model

most suitable for simulation of its hydrodynamic and

water quality behavior, this reviewer has chosen to

organize his critique according to the specific geographic situation.

Case Study 1 - Saginaw Bay

This is a non-estuary, at least in so far as classical

definitions apply. The major difficulties here appear to

be most likely associated with characterization of transport rates, both advective and dispersive. Although it is

acknowledged that “water levels and flow directions of

the Bay change” there is no explicit treatment of the

hydromechanical behavior of the water body. Admittedly, this is not a trivial undertaking from a modeling

viewpoint, although there are some excellent examples

of two- and three-dimensional circulation models for the

Great Lakes that would probably be suitable for Saginaw Bay.

The complexity of the ecosystem dynamics represented in this model and the rough “box” configuration

of the embayment, both suggest a greater interest in

ecosystem kinetics than in the practical problem of

waste load allocation. Both of these aspects, simplicity

in the one extreme (5 boxes) and complexity in the

other (18 plus compartments), lead to increased uncertainty in the results of simulations. This reviewer suggests that perhaps a better result from the point of view

of the decision maker might have been obtained with a

somewhat more rigorous description of lake circulation

and some aggregation in ecosystem compartments.

The trends indicated by the results shown seem hardly

sufficient for decision purposed in light of apparent

uncertainties in model parameters and field data.

This is a case where it seems that the third spatial

dimension could be especially important in the model.

To what degree does stratification of water quality

variables play a role in determining primary productivity? What about vertical advection and dispersion? It is

not clear that changes along the vertical axis are important considerations in this case study, although they

should be.

In summary, Saginaw Bay is not an estuary, so as a

case study of estuarine modeling this example leaves

much to be desired. Nevertheless, it is instructive in that

it illustrates the tradeoff between hydrodynamic circulation and dispersion as driving forces in water quality

modeling, as opposed to increased complexity in ecosystem description. However, because of the greatly

increased data requirement that accompanies

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the introduction of more state variables, such as a

model may not be the most cost effective from the

decision viewpoint. Notwithstanding this argument, it

may still be a great learning tool. This is probably its

most important attribute.

Case Study 2 - Potomac Estuary

Here we deal with a real estuary, but only partially. The

focus in this example of the application of the Dynamic

Estuary Model (DEM) and the Potomac Eutrophication

Model (PEM) is on the upper “fresh water” section of

the estuary, where the effects of tidal oscillation are

minimal. In this region dispersive effects induced by

the tide (which is of rather small amplitude, anyway)

are probably negligible and stratification is unlikely to

be a significant consideration.

DYNAMIC ESTUARY MODEL

A redeeming feature of the DEM application is that it

does address directly the hydrodynamics of the estuarine system, producing time-variant water levels, velocities, and discharges as output of a hydrodynamic

model, which are, in turn, utilized in a separate water

quality model to describe the fate of pollutants in the

estuary. A limitation of the model(s) is that the basic

configuration is one-dimensional, that is, flows are

directionally constrained. Pseudo two-dimensional

representations are possible for shallow vertically

mixed embayments, but circulations for such systems

should be regarded as rough approximations.

Calibration and verification of the hydrodynamic model

was achieved in a straight forward manner. Extensive

experience with this model in branching channel estuarine systems, like the Sacramento-San Joaquin Delta

for which it was originally developed, indicate that it is

easy to calibrate and gives a good account of tidal

effects over a wide range of boundary conditions.

The problem of calibrating the DEM for chlorides along

the axis of the estuary is attributed to numerical mixing,

a consequence of the solution procedure. Despite this

difficulty the model appears to give fair results at the

far field level. The practice of varying model coefficients from one survey to the next in an arbitrary

manner in order to assure the “best fit” is purely subjective and should not be encouraged. If such as

procedure is employed, a rational basis for parameter

adjustment must be provided. After calibration and

verification for the Potomac study the DEM model

appears to have been provided with most of the attributes of a useful decision support tool.

POTOMAC EUTROPHICATION MODEL

The PEM has many of the characteristics of QUAL 2E

or WASP 4, in that it is essentially a box model for

which the boundary fluxes are governed by either a

simple hydrologic mass balance or are generated by an

external hydrodynamic model like that in DEM, averaged over a tidal cycle. The contention that the “PEM

was developed because the existing DEM model focused more on spatial resolution than on the kinetic

complexities of eutrophication” implies that spatial

resolution is not of consequence in eutrophication and

that kinetic complexities could not be accommodated

in a modified DEM. This reviewer believes that spatial

resolution of the degree afforded by the DEM, as well

as the hydrodynamic information such a model provides, are indeed desirable for a eutrophication study

such as exemplified by this case. The more detailed

kinetics of PEM are, of course, appropriate. However,

experience has shown (and another of these case

studies illustrates) that the attributes of more complex

kinetics need not be at the expense of realistic hydrodynamics.

Spatial resolution and temporal resolution may be dictated in part by the structure of the basic data used to

calibrate and verify the model. The practice of aggregating data from several stations and smoothing over

time seems in this case to be consistent with a “regional

and seasonal focus,” but it tends to ignore local and

short term events which are often of major concern in

setting goals for wastewater management. It also presents problems in calibration and verification, as evidenced in some of the examples given.

The post audit experience, in which the model was

unable to predict the magnitude or spatial extent of the

1983 blue-green algae bloom, appears to confirm a

need for improved resolution and extension of the

model. It is credit to the model developers that the

model has been periodically revised to improve its

capability as a management tool.

NELEUS - CHLORINE MODEL

The problem presented in modeling the fate of chlorine

in the Potomac Estuary is properly addressed with a

two-dimensional finite element model, capable of representing the irregular configuration of the water body

and providing the essential spatial detail. It is unfortunate that field data were insufficient for thorough calibration, but experience with such models has shown

that hydrodynamics can be closely simulated, even for

very complex geometries and unsteady boundary conditions.

The water quality model in this package is driven by the

hydrodynamic model, but with the added requirement

of estimating lateral and longitudinal dispersion coefficients. Again, model calibration was not carried to a

satisfactory level, due in major part to inadequate field

data. There is insufficient foundation for selection of

either dispersion coefficients or the decay rate for

14-16
chlorine, hence the models at this stage are of questionable use for decision purposes, despite their intrinsic potentials.

The important lesson of this case study is to provide

an adequate data base for complete calibration and

verification of both hydrodynamic and water quality

models.

Case Study - Manasquan Estuary

This case, among all those presented, is probably the

most balanced in the treatment of hydrodynamics and

water quality, and in calibration and verification methodology. Unfortunately, the MIT-Dynamic Network

Model (MIT-DNM) did not reach the stage of actual

application as a management tool, so its performance

cannot be fully assessed.

The calibration-verification sequence of hydrodynamics/conservative tracer (salinity)/nonconservative

water quality is representative of good modeling practice. Because the model is one-dimensional and only

a rough approximation of the estuary, it is necessary

to utilize an empirically derived dispersion coefficient

as a calibration parameter. While the functional relationship between this parameter and geometric and

hydraulic properties of the estuary appears well

founded in theory and experiment it is nevertheless

unique for a particular estuarine system, e.g. constant

K and m. It purports to account for factors that cannot

be adequately represented in a one dimensional model

with such a coarse segmentation, e.g. advective dispersion and stratification. Dependence on this uncertain calibration parameter could probably be reduced

by some additional detail in spatial characterization of

the estuary.

The relatively unsatisfactory results of water quality

calibration point to a need for improving the data base,

particularly the pattern of nutrient loading on the estuary. It seems unlikely that the model will become a

useful tool for waste load allocation to the Manasquan

Estuary until this additional data is developed.

Case Study - Calcasieu River Estuary

This study was allegedly selected in part because it

represents the application of a so-called “canned”

model supported by EPA. This reviewer disagrees with

the implication that such models, exemplified also by

such well documented and supported models as

QUAL 2E, SWMM, DEM, WASP 4, HEC 5Q, TABS II,

etc. are likely to lead to the kind of difficulties encountered in modeling the Calcasieu River Estuary. It is the

responsibility of the modeler to select the most appropriate modeling approach for the particular situation.

Most often the modeler is well advised to begin with a

package that is well documented (as are those cited

above) and for which there is a considerable body of

experience in adapting to new conditions. If what is

available proves to be unsuitable it can be modified, as

in this case, or a completely new model can be devised.

The test of its capability will be in the processes of

calibration and verification.

The principal difficulty with the Calcasieu estuary is that

it is so complex that virtually no model existing at the

time of the study was fully equal to the task. The

tortuous looping and branching channel configuration

might at first appear to be a candidate for RECEIV–II,

since the model was designed originally for such systems. However, this model assumes vertical homogeneity where the Calcasieu system includes many

sections which are stratified. The system also includes

very broad channel reaches and embayments, even

lakes, which are not well represented hydrodynamically

by the pseudo two-dimensional network approximation

possible with RECEIV–II. The existence of stratified

lakes within the system suggests the need for a model

capable of dealing with hydrodynamics in one, two or

three dimensions, depending on the local conditions. A

finite element approach is probably the most feasible

at present, although in fairness to the modelers of the

Calcasieu estuary it is acknowledged that such a model

was not available at the time of the study.

Hydrodynamic calibration/verification for this study was

described as “good,” although in certain instances elevation differences between model and prototype were

large enough to indicate that system storage was not

well simulated, e.g. 1978. Water quality calibration/verification was fair at best, a result attributed by the

modeler to inadequate input information and dynamics.

Here again the complexity of the system and the water

quality model, with its large number of parameters,

probably preclude a good result. Future modeling efforts for this estuary should be directed to improving

hydrodynamic simulation and estimates of waste loads.

14.3.8. Concluding Comment

This selection of case studies illustrates most of the

problems encountered in modeling of water quality in

estuarine systems. Among the lessons to be learned

from these experiences, the following appear to this

reviewer to be the more significant in directing future

modeling efforts.

1. There is no substitute for hard data from the field.

Data collection programs should be designed with

model requirements in mind.

2. Water quality models of estuarine systems are driven

by hydrodynamics. More attention needs to be given

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to the hydrodynamic driver as an integral part of the

modeling package. In particular, effects of stratification

should be explicitly modeled.

3. Complexity may lead to more uncertainty in model

results. Adding more compartments may improve fundamental understanding of important mechanisms,

but it requires more data and does not necessarily lead

to better decisions.

4. Models should be designed and applied as tools to

support decisions by non-modelers. Output should be

readily interpretable by decision makers.

5. Calibration/verification is still largely a subjective

process. Criteria for acceptance of a verified model

should be developed and related to the intended use

of the model in the decision process.

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Glossary

Acute Toxicity1 - Any toxic effect that is produced

within a short period of time, usually 24-96 hours.

Although the effect most frequently considered is

mortality, the end result of acute toxicity is not necessarily death. Any harmful biological effect may be the

result.

Aerobic1 - Refers to life or processes occurring only

in the presence of free oxygen; refers to a condition

characterized by an excess of free oxygen in the

aquatic environment.

Algae (Alga)1 - Simple plants, many microscopic,

containing chlorophyll. Algae form the base of the

food chain in aquatic environments. Some species

may create a nuisance when environmental conditions are suitable for prolific growth.

Allochthonous1

- Pertaining to those substances, materials or organisms in a waterway which originate

outside and are brought into the waterway.

Anaerobic2 - Refers to life or processes occurring in

the absence of free oxygen; refers to conditions characterized by the absence of free oxygen.

Autochthonous1 - Pertaining to those substances,

materials, or organisms originating within a particular

waterway and remaining in that waterway.

Autotrophic1 - Self nourishing; denoting those organisms that do not require an external source of organic

material but can utilize light energy and manufacture

their own food from inorganic materials; e.g., green

plants, pigmented flagellates.

Bacteria1

- Microscopic, single-celled or noncellular

plants, usually saprophytic or parasitic.

Benthal Deposit2 - Accumulation on the bed of a

watercourse of deposits containing organic matter

arising from natural erosion or discharges of wastewaters.

Benthic Region1 - The bottom of a waterway; the

substratum that supports the benthos.

Benthal Demand2 - The demand on dissolved oxygen

of water overlying benthal deposits that results from

the upward diffusion of decomposition products of the

deposits.

Benthos1 - Organisms growing on or associated principally with the bottom of waterways. These include:

(1) sessile animals such as sponges, barnacles, mussels, oysters, worms, and attached algae; (2) creeping forms such as snails, worms, and insects; (3)

burrowing forms, which include clams, worms, and

some insects; and (4) fish whose habits are more

closely associated with the benthic region than other

zones; e.g., flounders.

Biochemical Oxygen Demand2 - A measure of the

quantity of oxygen utilized in the biochemical oxidation of organic matter in a specified time and at a

specific temperature. It is not related to the oxygen

requirements in chemical combustion, being determined entirely by the availability of the material as a

biological food and by the amount of oxygen utilized

by the microorganisms during oxidation. Abbreviated

BOD.

Biological Magnification1 - The ability of certain organisms to remove from the environment and store in

their tissues substances present at nontoxic levels in

the surrounding water. The concentration of these

substances becomes greater each higher step in the

food chain.

Bloom1 - A readily visible concentrated growth or

aggregation of minute organisms, usually algae, in

bodies of water.

Brackish Waters1 - Those areas where there is a

mixture of fresh and salt water; or, the salt content is

greater than fresh water but less than sea water; or,

the salt content is greater than in sea water.

Channel Roughness2 - That roughness of a channel,

including the extra roughness due to local expansion

or contraction and obstacles, as well as the roughness

of the stream bed proper; that is, friction offered to the

flow by the surface of the bed of the channel in contact

with the water. It is expressed as roughness coefficient in the velocity formulas.

Chlorophyll1 - Green photosynthetic pigment present

in many plant and some bacterial cells. There are

seven known types of chlorophyll; their presence and

abundance vary from one group of photosynthetic

organisms to another.

Chronic Toxicity1 - Toxicity, marked by a long duration, that produces an adverse effect on organisms.

The end result of chronic toxicity can be death although the usual effects are sublethal; e.g., inhibits

reproduction, reduces growth, etc. These effects are

reflected by changes in the productivity and population structure of the community.
Coastal Waters1 - Those waters surrounding the continent which exert a measurable influence on uses of

the land and on its ecology. The Great Lakes and the

waters to the edge of the continental shelf.

Component Tide2 - Each of the simple tides into

which the tide of nature is resolved. There are five

principal components; principal lunar, principal solar,

N2, K, and O. There are between 20 and 30 components which are used in accurate predictions of tides.

Coriolis Effect2

- The deflection force of the earth’s

rotation. Moving bodies are deflected to the right in

the northern hemisphere and to the left in the southern

hemisphere.

Datum2 - An agreed standard point or plane of state

elevation, noted by permanent bench marks on some

solid immovable structure, from which elevations are

measured or to which they are referred.

Density Current2 - A flow of water through a larger

body of water, retaining its unmixed identity because

of a difference in density.

Deoxygenation2 - The depletion of the dissolved oxygen in a liquid either under natural conditions associated with the biochemical oxidation of organic matter

present or by addition of chemical reducing agents.

Diagenetic Reaction - Chemical and physical

changes that alter the characteristics of bottom sediments. Examples of chemical reactions include oxidation of organic materials while compaction is an

example of a physical change.

Dispersion2 - (1) Scattering and mixing. (2) The mixing of polluted fluids with a large volume of water in a

stream or other body of water.

Dissolved Oxygen2 - The oxygen dissolved in water,

wastewater, or other liquid, usually expressed in milligrams per liter, or percent of saturation. Abbreviated

DO.

Diurnal2 - (1) Occurring during a 24-hr period; diurnal

variation. (2) Occurring during the day time (as opposed to night time). (3) In tidal hydraulics, having a

period or cycle of approximately one tidal day.

Drought2 - In general, an extended period of dry

weather, or a period of deficient rainfall that may

extend over an indefinite number of days, without any

quantitative standard by which to determine the degree of deficiency needed to constitute a drought.

Qualitatively, it may be defined by its effects as a dry

period sufficient in length and severity to cause at

least partial crop failure or impair the ability to meet a

normal water demand.

Ebb Tide1

- That period of tide between a high water

and the succeeding low water; falling tide.

Enrichment1 - An increase in the quantity of nutrients

available to aquatic organisms for their growth.

Epilimnion1 - The water mass extending from the

surface to the thermocline in a stratified body of water;

the epilimnion is less dense that the lower waters and

is wind-circulated and essentially homothermous.

Estuary1 - That portion of a coastal stream influenced

by the tide of the body of water into which it flows; a

bay, at the mouth of a river, where the tide meets the

river current; an area where fresh and marine water

mix.

Euphotic Zone1 - The lighted region of a body of water

that extends vertically from the water surface to the

depth at which photosynthesis fails to occur because

of insufficient light penetration.

Eutrophication1 - The natural process of the maturing

(aging) of a lake; the process of enrichment with

nutrients, especially nitrogen and phosphorus, leading to increased production of organic matter.

Firth1 - A narrow arm of the sea; also the opening of

a river into the sea.

Fjord (Fiord)1 - A narrow arm of the sea between

highlands.

Food Chain1 - Dependence of a series of organisms,

one upon the other, for food. The chain begins with

plants and ends with the largest carnivores.

Flood Tide2 - A term indiscriminately used for rising

tide or landward current. Technically, flood refers to

current. The use of the terms “ebb” and “flood” to

include the vertical movement (tide) leads to uncertainty. The terms should be applied only to the horizontal movement (current).

Froude’s Number2 - A numerical quantity used as an

index to characterize the type of flow in a hydraulic

structure that has the force of gravity (as the only force

producing motion) acting in conjunction with the resisting force of inertia. It is equal to the square of

characteristic velocity (the mean, surface, or maximum velocity) of the system, divided by the product

of a characteristic linear dimension, such as diameter

or expressed in consistent units so that the combinations will be dimensionaless. The number is used in

open-channel flow studies or in cases in which the
free surface plays an essential role in influencing

motion.

Heavy Metals2 - Metals that can be precipitated by

hydrogen sulfide in acid solution, for example, lead,

silver, gold, mercury, bismuth, copper.

Heterotrophic1 - Pertaining to organisms that are

dependent on organic material for food.

Hydraulic Radius2 - The right cross-sectional area of

a stream of water divided by the length of that part of

its periphery in contact with its containing conduit; the

ratio of area to wetted perimeter. Also called hydraulic

mean depth.

Hydrodynamics2 - The study of the motion of, and the

forces acting on, fluids.

Hydrographic Survey2 - An instrumental survey

made to measure and record physical characteristics

of streams and other bodies of water within an area,

including such things as location, areal extent and

depth, positions and locations of high-water marks,

and locations and depths of wells.

Inlet1 - A short, narrow waterway connecting a bay,

lagoon, or similar body of water with a large parent

body of water; an arm of the sea, or other body of

water, that is long compared to its width, and that may

extend a considerable distance inland.

Inorganic Matter2 - Mineral-type compounds that are

generally non-volatile, not combustible, and not biodegradable. Most inorganic-type compounds, or reactions, are ionic in nature, and therefore, rapid

reactions are characteristic.

Lagoon1 - A shallow sound, pond, or channel near or

communicating with a larger body of water.

Limiting Factor1 - A factor whose absence, or excessive concentration, exerts some restraining influence

upon a population through incompatibility with species requirements or tolerance.

Manning Formula2 - A formula for open-channel flow,

published by Manning in 1890, which gives the value

of c in the Chezy formula.

Manning Roughness Coefficient2 - The roughness

coefficient in the Manning formula for determination

of the discharge coefficient in the Chezy formula.

Marsh1 - Periodically wet or continually flooded area

with the surface not deeply submerged. Covered

dominantly with emersed aquatic plants; e.g., sedges,

cattails, rushes.

Mean Sea Level2 - The mean plane about which the

tide oscillates; the average height of the sea for all

stages of the tide.

Michaelis-Menton Equation2 - A mathematical expression to describe an enzyme-catalyzed biological

reaction in which the products of a reaction are described as a function of the reactants.

Mineralization2 - The process by which elements

combined in organic form in living or dead organisms

are eventually reconverted into inorganic forms to be

made available for a fresh cycle of plant growth. The

mineralization of organic compounds occurs through

combustion and through metabolism by living animals. Microorganisms are ubiquitous, possess extremely high growth rates and have the ability to

degrade all naturally occurring organic compounds.

Modeling2 - The simulation of some physical or abstract phenomenon or system with another system

believed to obey the same physical laws or abstract

rules of logic, in order to predict the behavior of the

former (main system) by experimenting with latter

(analogous system).

Monitoring2 - Routine observation, sampling and testing of designated locations or parameters to determine efficiency of treatment or compliance with

standards or requirements.

Mouth2 - The exit or point of discharge of a stream into

another stream or a lake, or the sea.

Nautical Mile2 - A unit of distance used in ocean

navigation. The United States nautical mile is defined

as equal to one-sixteenth of a degree of a great circle

on a sphere with a surface equal to the surface of the

earth. Its value, computed for the Clarke spheroid of

1866, is 1,853.248 m (6,080.20ft). The International

nautical mile is 1,852 m (6,070.10 ft).

Nanoplankton2 - Very minute plankton not retained in

a plankton net equipped with no. 25 silk bolting cloth

(mesh, 0.03 to 0.04 mm.).

Neap Tides1 - Exceptionally low tides which occur

twice each month when the earth, sun and moon are

at right angles to each other; these usually occur

during the moon’s first and third quarters.

Neuston2 - Organisms associated with, or dependent

upon, the surface film (air-water) interface of bodies

of water.

Nitrogenous Oxygen Demand (NOD)2 - A quantitative measure of the amount of oxygen required for the

biological oxidation of nitrogenous material, such as
ammonia nitrogen and organic nitrogen, in wastewater; usually measured after the carbonaceous oxygen

demand has been satisfied.

Nutrients1 - Elements, or compounds, essential as

raw materials for organism growth and development;

e.g., carbon, oxygen, nitrogen, phosphorus, etc.

Organic1 - Refers to volatile, combustible, and sometimes biodegradable chemical compounds containing

carbon atoms (carbonaceous) bonded together and

with other elements. The principal groups of organic

substances found in wastewater are proteins, carbohydrates, and fats and oils.

Oxygen Deficit1 - The difference between observed

oxygen concentration and the amount that would

theoretically be present at 100% saturation for existing conditions of temperature and pressure.

Pathogen1 - An organism or virus that causes a disease.

Periphyton (Aufwuchs)1 - Attached microscopic organisms growing on the bottom, or other submersed

substrates, in a waterway.

Photosynthesis1 - The metabolic process by which

simple sugars are manufactured from carbon dioxide

and water by plant cells using light as an energy

source.

Phytoplankton1 - Plankton consisting of plant life.

Unattached microscopic plants subject to movement

by wave or current action.

Plankton1 - Suspended microorganisms that have

relatively low powers of locomotion, or that drift in the

water subject to the action of waves and currents.

Quality2 - A term to describe the composite chemical,

physical, and biological characteristics of a water with

respect to it’s suitability for a particular use.

Reaeration2 - The absorption of oxygen into water

under conditions of oxygen deficiency.

Respiration1 - The complex series of chemical and

physical reactions in all living organisms by which the

energy and nutrients in foods is made available for

use. Oxygen is used and carbon dioxide released

during this process.

Roughness Coefficient2 - A factor, in the Chezy,

Darcy-Weisbach, Hazen-Williams, Kutter, Manning,

and other formulas for computing the average velocity

of flow of water in a conduit or channel, which represents the effect of roughness of the confining material

on the energy losses in the flowing water.

Seiche1 - Periodic oscillations in the water level of a

lake or other landlocked body of water due to unequal

atmospheric pressure, wind, or other cause, which

sets the surface in motion. These oscillations take

place when a temporary local depression or elevation

of the water level occurs.

Semidiurnal2 - Having a period or cycle of approximately one half of a tidal day. The predominating type

of tide throughout the world is semidiurnal, with two

high waters and two low waters each tidal day.

Slack Water2 - In tidal waters, the state of a tidal

current when its velocity is at a minimum, especially

the moment when a reversing current changes direction and its velocity is zero. Also, the entire period of

low velocity near the time of the turning of the current

when it is too weak to be of any practical importance

in navigation. The relation of the time of slack water

to the tidal phases varies in different localities. In

some cases slack water occurs near the times of high

and low water, while in other localities the slack water

may occur midway between high and low water.

Spring Tide1 - Exceptionally high tide which occurs

twice per lunar month when there is a new or full

moon, and the earth, sun, and moon are in a straight

line.

Stratification (Density Stratification)1 - Arrangement of water masses into separate, distinct, horizontal layers as a result of differences in density; may be

caused by differences in temperature, dissolved or

suspended solids.

Tidal Flat1 - The sea bottom, usually wide, flat, muddy

and nonproductive, which is exposed at low tide. A

marshy or muddy area that is covered and uncovered

by the rise and fall of the tide.

Tidal Prism2 - (1) The volume of water contained in a

tidal basin between the elevations of high and low

water. (2) The total amount of water that flows into a

tidal basin or estuary and out again with movement of

the tide, excluding any fresh-water flows.

Tidal Range2 - The difference in elevation between

high and low tide at any point or locality.

Tidal Zone (Eulittoral Zone, Intertidal Zone)1 - The

area of shore between the limits of water level fluctuation; the area between the levels of high and low tides.

Tide1 - The alternate rising and falling of water levels,

twice in each lunar day, due to gravitational attraction
of the moon and sun in conjunction with the earth’s

rotational force.

Tide Gage2 - (1) A staff gage that indicates the height

of the tide. (2) An instrument that automatically registers the rise and fall of the tide. In some instruments,

the registration is accomplished by printing the heights

at regular intervals; in others by a continuous graph in

which the height of the tide is represented by ordinates

of the curve and the corresponding time by the abscissae.

Toxicant1 - A substance that through its chemical or

physical action kills, injures, or impairs an organism;

any environmental factor which, when altered, produces a harmful biological effect.

Water Pollution1 - Alteration of the aquatic environment in such a way as to interfere with a designated

beneficial use.

Water Quality Criteria1 - A scientific requirement on

which a decision or judgement may be based concerning the suitability of water quality to support a designated use.

Water Quality Standard1 - A plan that is established

by governmental authority as a program for water

pollution prevention and abatement.

Zooplankton2 - Plankton consisting of animal life.

Unattached microscopic animals having minimal capability for locomotion.

1Rogers, B.G., Ingram, W.T., Pearl, E.H., Welter, L.W.

(Editors). 1981, Glossary, Water and Wastewater

Control Engineering, Third Edition, American Public

Health Association, American Society of Civil Engineers, American Water Works Association, Water

Pollution Control Federation.

2

Matthews, J.E., 1972, Glossary of Aquatic Ecological

Terms, Manpower Development Branch, Air and

Water Programs Division, EPA, Oklahoma.
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