&EPA
            United States
            Environmental Protection
            Agency
            Office of Water
            (Mail Code 4305)
EPA 823-B-95-007
September 1995
Technical Guidance Manual
for Developing Total
Maximum Daily Loads
            Book II: Streams and Rivers
            Part 1:  Biochemical Oxygen Demand/Dissolved
                  Oxygen and Nutrients/Eutrophication

-------
                             Click here for
                             DISCLAIMER

                      Document starts on next page
TITLE: Technical Guidance Manual for Performing Wasteload Allocations,
       Book II: Streams and Rivers -
       Part 1: Biochemical Oxygen Demand/Dissolved Oxygen and
       Nutrients/Eutrophication

EPA DOCUMENT NUMBER: EPA-823-B-97-002     DATE: March 1997

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:

   •  Emphasizes the need for water quality managers to consider key water
      quality interactions and ecological responses to point and nonpoint source
      loadings in streams and rivers;

   •  Provides technical guidance on modeling, reaction rate coefficients, and
      field measurement techniques and

   •  Provides the recommended TMDL procedures for biochemical oxygen
      demand (BOD), dissolved oxygen (DO), and nutrients discharged into
      streams and rivers.

Book II Part 1 presents the technical basis for analysis of BOD, DO, nutrient, and
eutrophication impact. It also discusses some of the mathematical models
available to perform TMDL calculations,  provides guidance on model selection,
and uses case studies to illustrate key steps in constructing a site-specific for a
TMDL.  Detailed appendices provide additional discussions of important fate and
transport processes, quality assurance for field monitoring, and uncertainty
analysis.

KEYWORDS: Wasteload Allocations, Rivers, Streams, Biochemical Oxygen
             Demand, Dissolved Oxygen, Eutrophication, Nutrients, Modeling,
             Water Quality Criteria

-------

-------
MEMORANDUM

SUBJECT:     Final Technical Guidance Manual for Developing
               Total Maximum Daily Loads (TMDLs)

FROM:         Tudor T. Davies,  Director
               Office of Science and Technology (4301)

TO:            Regional Water Division Directors
               Regional Environmental Services Division Directors
               Regional TMDL Coordinators

      Attached for national use,  is the final Technical Guidance manual for Developing Total Maximum Daily
Loads, Book II:  Streams and Rivers, Part 1: Biochemical Oxygen Demand/Dissolved Oxygen and Nutri-
ents/Eutrophication. We are sending extra copies of this manual to the Regional TMDL coordinators for
distribution to the States to use on performing TMDLs.
      Section 303(d) of the Clean Water Act requires States to perform wasteload allocations (WLAs) and Total
Maximum  Daily Loads (TMDLs) for waters where technology-based treatment is found to be inadequate
to meet State water quality standards (WQS).  As a part of our technical assistance effort in performing
WLAs, primarily involving controls of point source discharges, the Office of Water issued a series of techni-
cal guidance manuals.  More recently, we issued guidance for the 303(d) program Guidance for water
quality-based decisions: The TMDL Process, 1991 in response to the U.S. General Accounting Office
(GAO) report Water Pollution - Greater EPA Leadership Needed to Reduce Nonpoint Source Pollution,
October 1990.
      We are issuing this TMDL technical guidance manual to support the implementation of the 1991 TMDL
guidance mentioned above. This document provides guidance on how to assess water quality impacts of
point and nonpoint source discharges of biochemical oxygen demanding (BOD) pollutants and nutrients to
streams and rivers. More details of what this guidance includes are stated in the document under the
heading "To the Reader" on page iii.
      The earlier drafts of this document have been reviewed by your staff, and some of them made signifi-
cant contribution to its development. Also, the document has been peer reviewed by technical ex-
perts. This final guidance reflects all comments and suggestions received on the earlier drafts.
      If you have any questions, comments or desire additional information, please contact Hiranmay Biswas,
Standards  and Applied Science Division (4305), Telephone: (202) 260-7012.

Attachment

-------

-------
                                      TO THE READER
This guidance manual represents the consolidation of the following two documents in the U.S. Environmental
Protection Agency's series of Technical Guidance Manuals for Performing Waste Load Allocations:


    USEPA. 1983.  Technical Guidance Manual for Performing Waste Load Allocations, Book II:  Streams
    and Rivers, Chapter 1: Biochemical Oxygen Demand and Dissolved Oxygen.

    USEPA. 1983.  Technical Guidance Manual for Performing Waste Load Allocations, Book II:  Streams
    and Rivers, Chapter 2: Nutrients and Eutrophication.

The revised single manual, renamed Technical Guidance Manual for Developing Total Maximum Daily
Loads, Book II: Streams and Rivers, Part I: Biochemical Oxygen Demand/Dissolved Oxygen and Nutri-
ents/Eutrophication, eliminates duplicative information on hydrodynamics and physical characteristics of
streams and rivers, and on the interactions of nutrients and dissolved oxygen dynamics, that was included
in the above- cited manuals. The availability of a single manual also helps to meet the needs of water
quality managers to adequately consider the key water quality interactions and  ecological responses to
pollutant loadings in streams and rivers. In addition, this manual includes updated information on model-
ing, reaction rate coefficients, field measurement techniques, etc. and includes  several examples using
EPA-supported models. More specifically, these changes and  updates include:


Integration of principles and concepts on waste load and load allocations for nutrients/eutrophication with those for
carbon (BOD) and oxygen balances in aquatic ecosystems (see Chapter 2 - Basic Principles and Concepts).
    •   Update of model identification and selection, with emphasis on the EPA-supported water quality model
       QUAL2E (see Chapter 3 - Model Selection and Review) and additional mention of watershed models.

    •   Update of water quality reaction rate coefficients and field measurement techniques (see Chapter 4
       - River and Stream Modeling Procedures, Appendix A - Development of Model Coefficients and
       Constants,  and Appendix C - Quality Assurance for TMDL Studies).

    •   Update of technical literature citations (see Chapter 5 - References and Appendix E, Supplemental
       Bibliography).

    •   Inclusion of a TMDL example using QUAL2Eand WASPS (see Appendix B - Example Total Maximum
       Daily Load  Analys\s).

    •   Inclusion of an uncertainty analysis example using QUAL2E-UNCAS (see Appendix D - Uncertainty
       Analysis).

Comments and suggestions from the user community help us in improving our guidance manuals, and we
invite the user community to send their comments and suggestions to:
                                    Hiranmay Biswas
                                    U.S. EPA
                                    Office of Science and Technology
                                    Standards and Applied Science Division (4305)
                                    Washington, DC  20460

-------
                               ACKNOWLEDGEMENTS



The contents of this section have been removed to comply with current EPA practice.
                                         IV

-------
                                  TABLE OF CONTENTS

MEMORANDUM  	 i
TO THE READER	  iii
ACKNOWLEDGMENTS	  iv
1.  . .  INTRODUCTION	   1-1
 1.1         Purpose	   1-1
 1.2         Relationship to Other Guidance Documents	   1-2
 1.3         Organization and Scope of Manual	   1-4
2.            BASIC PRINCIPLES AND CONCEPTS	   2-1
 2.1         Purpose	   2-1
 2.2         Overview	   2-1
 2.3         Concepts in Biochemical Oxygen Demand, Dissolved Oxygen, and Nutrient
             Analyses	   2-2
             2.3.1   Pollution Source Characteristics	   2-2
             2.3.2   In-Stream Fate and Transport of Pollutants  	   2-3
             2.3.3   Receiving Water Conditions	   2-4
             2.3.4   Biochemical Oxygen Demand and Dissolved Oxygen Reaction Kinetics   . .   2-8
             2.3.5   Eutrophication Kinetics	2-15
 2.4         Governing Equations	2-20
             2.4.1   Mass Balance Principle	2-20
             2.4.2   Dissolved Oxygen Equation   	2-22
             2.4.3   Separate Mass Balance Equations by Constituent	2-24
3.            MODEL SELECTION AND REVIEW	   3-1
 3.1         Purpose	   3-1
 3.2         Overview	   3-1
 3.3         Model Selection  	   3-1
             3.3.1   Study Objectives and Constraints   	   3-3
             3.3.2   Pollutant Loadings, Spatial and Temporal Resolution, and
                   Transport Mechanisms	   3-3
             3.3.3   Water Quality Pollutant Interactions	   3-8
 3.4         Model Review   	   3-9
4.            RIVER AND STREAM MODELING PROCEDURES	   4-1
 4.1         Purpose	   4-1
 4.1.1        Modeling Goals	   4-1
             4.1.2   General Requirements of a Stream Water Quality Modeling Analysis ....   4-3
 4.2         Initial Assessment  	   4-3
             4.2.1   Study Area Evaluation	   4-3
             4.2.2   Compilation and  Review of Existing Data  	   4-7

-------
             4.2.3  Preliminary Analysis	  4-7
             4.2.4  Selection of Modeling Framework	4-14
 4.3         Site-Specific Stream Survey	4-16
             4.3.1  Hydraulic Geometry Survey	4-16
             4.3.2  Time-of-Travel Study	4-17
             4.3.3  Stream Water Quality Sampling	4-17
             4.3.4  Wastewater Monitoring	4-17
             4.3.5  Biological Assessment	4-21
 4.4         Model Calibration	4-21
             4.4.1  Model Coefficient Assignment	4-21
             4.4.2  Component Analyses	4-22
             4.4.3  Quantifying the Comparison Between Model Results and Data	4-22
 4.5         Model Validation	4-25
             4.5.1  Model Coefficient Adjustment	4-25
             4.5.2  Model Sensitivity Analysis  	4-25
             4.5.3  Model Accuracy	4-25
 4.6         Model Application and Total Maximum Daily Loads	4-25
             4.6.1  Development of Management Scenarios	4-27
             4.6.2  Total Maximum Daily Loads	4-27
             4.6.3  Uncertainty Analysis	4-29
5.            REFERENCES	  5-1
APPENDICES
      Appendix A:  Development of Model Coefficients and Constants  	A-1
      Appendix B:  Example Total Maximum  Daily Load Analysis	  B-1
      Appendix C:  Quality Assurance for TMDL Studies	C-1
      Appendix D:  Uncertainty Analysis	D-1
      Appendix E:  Supplemental Bibliography	  E-1
      Appendix F;  Glossary  	  F-1
      Appendix G:  Abbreviations	G-1
      Appendix H:  Conversion Factors	H-1
      Appendix I:   Symbols  	1-1
      Appendix J:  BOD-DO Nutrient Guidance input files for QUAL2E
                   and WASP5-EUTRO5 example problems. Diskette EPA 823-C-95-004  ..  J-1
                                             VI

-------
                                      LIST OF  FIGURES
2-1     Interaction of transport mechanisms for loads in a stream	   2-5
2-2     Interrelationship of major kinetic processes for BOD and
       DO as represented by water quality models	   2-7
2-3     Comparison of stream BOD and laboratory BOD for various incubation times	2-10
2-4     Steps in nitirification and utilization of dissolved oxygen	2-10
2-5     Interrelationship of major kinetic processes for BOD, DO, and nutrient analysis
       as represented by water quality models	2-14
2-6     Specific algal growth rate as a function of temperature	2-16
2-7     Effect of light intensity on algal  growth	2-16
2-8     Effect of nutrients of algal growth  	2-17
2-9     Effects  of nutrient limitation on algal growth	2-18
2-10   Ammonia preference structure for algal growth	2-20
2-11   Mass balance equations for dissolved oxygen	2-21
2-12   Components of DO profile  (sag curve) downstream of waste discharge   	2-23
3-1     Dissolved oxygen response as  a function of estuary number	   3-8
3-2     Effect of pH and temperature on un-iodized ammonia	3-10
4-1     Steps in the use of a water quality model for a site-specific TMDL application  	   4-2
4-2     Range of chlorophyll a average concentrations and target "objectives" to
       regulatenutrient inputs for eutrphication control for various water bodeis	   4-5
4-3     Time and  space scales for assessment of water quality problems	   4-6
4-4     Diurnal model vs. observed oxygen in Senix Creek, Long Island	4-15
4-5     Catawa River study area and major point sources	4-18
4-6     Preliminary water quality sampling network	4-19
4-7     Component analysis of DO for Rock Creek, Pennsylvania  	4-23
4-8     Numerical tagging of James River	4-24
4-9     Some relative errors of dissolved oxygen models	4-26
4-10   TMDL procedure for  BOD/DO problem  	4-28
                                              VII

-------
VIM

-------
                                      LIST OF TABLES
1-1    Technical Guidance Manuals for Performing Waste Load Allocations	  1-3
1-2    Available guidance and other references for TMDL development	  1-4
2-1    Comparison of typical point and nonpoint sources	  2-2
2-2    Decision situations requiring watershed models	  2-3
2-3    Nonpoint source modeling options	  2-4
2-4    Separate mass balance equations used for each constituent in BOD, DO, and
      nutrient analyses	2-25
3-1    Methods of analysis for phytoplankton and aquatic plants	3-10
3-2    Comparison of models: constituents modeled	3-13
3-3    Comparison of models: summary of capabilities	3-14
3-4    Comparison of models: reaeration formulations	3-15
3-5    Comparison of models: input data requirements	3-18
3-6    Comparison of models: ease of application-output form and content	3-20
3-7    Comparison of models: ease of application-sources, support, and documentation	3-21
3-8    Comparison of models: ease of application-equipment and programming requirements  .  . 3-22
3-9    Comparison of models: operating costs	3-22
3-10  Hierarchy of models based on selected features   	3-23
4-1    Identification of potential water quality problems:  Dissolved oxygen depletion,
      nutrient enrichment, and eutrophication	  4-5
4-2    Data types and possible sources for stream total maximum daily load  	  4-7
4-3    Data for stream eutrophication calculation	4-11
4-4    Water quality survey for the Catawba River	4-20
4-5    Point source sampling program  	4-20
                                              IX

-------

-------
1.    INTRODUCTION
1.1    PURPOSE


The purpose of this guidance manual is to present the
most recent information and techniques for use in pre-
paring total maximum daily loads (TMDLs) when exces-
sive biochemical oxygen demand (BOD), low dissolved
oxygen (DO), and excessive nutrients and eutrophica-
tion impair the water quality of streams and rivers. This
manual reflects current policy on TMDL development
as outlined  in the Guidance for Water Quality-based
Decisions: The TMDL  Process (USEPA, 1991 a), and
represents the consolidation of the following two docu-
ments in the U.S. EPA's series of Technical Guidance
Manuals for Performing Waste Load Allocations:

   USEPA. 1983. Technical Guidance Manual for
   Performing Waste Load Allocations, Book II:
   Streams and Rivers, Chapter 1: Biochemical Oxy-
   gen Demand and Dissolved Oxygen
and
    USEPA. 1983. Technical Guidance Manual for
    Performing Waste Load Allocations, Book II:
    Streams and Rivers, Chapter 2: Nutrients and Eu-
    trophication.

This revised manual, renamed Technical Guidance
Manual for Performing  Total Maximum Daily Loads,
Book II: Streams and Rivers, Part 1: Biochemical Oxy-
gen Demand/Dissolved Oxygen and Nutrients/Eutro-
phication,  eliminates duplicated  information on
hydrodynamics and physical characteristics of streams
and rivers.  The objectives of the manual  are (1) to
emphasize the needs of water quality managers to
adequately consider the key water quality interactions
and ecological responses to both  point and nonpoint
source loadings  in streams and rivers; (2) to provide
technical guidance on modeling, reaction rate coeffi-
cients, and field  measurement techniques; and (3) to
provide the recommended TMDL procedures for bio-
chemical oxygen demand, dissolved oxygen, and nutri-
ents for point sources and nonpoint sources discharging
into streams and rivers.  This manual includes several
examples using EPA-supported models. Much of the
information  needed by the water quality analyst to
design and develop a TMDL for a stream or river (i.e.,
model  selection  and design,  field  measurements,
assignment of reaction rate coefficients, and analysis
of the TMDL) is contained within this manual.

This guidance reflects the current policy on TMDL
development for streams and rivers, which requires
the consideration of pollutant loads from all sources
within a watershed. TMDLs should be developed
to provide more stringent water quality-based con-
trols when technology-based controls are inade-
quate to achieve water quality standards  (USEPA,
1991 a). TMDLs  are composed of waste  load allo-
cations (WLAs) for point sources, load allocations
(LAs) for nonpoint sources,  and a margin of safety
(MOS).  The MOS accounts for scientific uncer-
tainty involved in establishing the TMDL.  This un-
certainty  can  be caused  by  insufficient  or
poor-quality data or a lack of knowledge about the
receiving water and pollution effects.  The TMDL
process consists of the following steps:

    (1) Identifying water quality-limited waters still
       requiring  TMDLs.

    (2) Prioritizing and targeting water quality-limited
       waters.

    (3) Developing the TMDL.

    (4) Implementing the TMDL through control ac-
       tions.

    (5) Assessing whether step 4 actions are suffi-
       cient to meet water quality standards.

The  Guidance for Water Quality-based Decisions:
The  TMDL Process (USEPA, 1991 a) discusses
these procedures for TMDL development within the
context of a  water quality-based watershed ap-
proach. The following guidance is intended primar-
ily to assist the water quality  analyst in the  third step
of the TMDL  process with regard to developing
TMDLs to control BOD,  DO,  and  nutrients in
streams and rivers.

The  level  of effort  required  to develop a  TMDL is
highly dependent on the complexity  and magnitude
of the receiving water problems. In general,  to assess
the anticipated level of effort, site-specific conditions
                                             1-1

-------
need to be evaluated in terms of the type and com-
position of loads, the variability and characteristics of
pollutant sources and their response to local hydro-
logic events, and the characteristics of receiving
water.   Additional considerations may also involve
the local or  regional value of the resources being
protected and the phase of the TMDL process. Since
the TMDL program has directed water and watershed
managers toward adoption of a phased approach to
address controls on  both point and nonpoint source
loads under both dry-weather and wet-weather con-
ditions,  simplified modeling techniques for low-flow
conditions may be of limited use for developing first-
phase TMDLs.  As water quality goals for a water-
shed  are more clearly defined by  first-phase
assessments and additional monitoring efforts, inter-
mediate or complex  modeling techniques may be
required for advanced phases of the TMDL process.

This manual calls for an intermediate level of effort to
develop a TMDL for typical cases associated with
oxygen depletion and nutrient loadings. Although the
models reviewed in this manual accommodate multi-
ple discharges and  complex inflow characteristics,
the emphasis is limited  to less complex scenarios.
More detailed modeling techniques are described in
the Compendium of Watershed-scale Models for
TMDL Development (USEPA, 1992b), Principles of
Surface Water Quality Modeling and Control
(Thomann and  Mueller,  1987), and  Water Quality
Modeling, Volume  1, Transport and Surface Ex-
change in Rivers (McCutcheon,  1989).  In special
cases where a level of effort less than that presented
in this document is deemed necessary, the following
documents may be of interest: Water Quality Assess-
ment: A Screening Procedure for Toxic and Conven-
tional Pollutants in Surface and Ground Water (Mills
et  al., 1985), and Simplified Analytical Method  for
Determining NPDES Effluent Limitations for POTWs
Discharging into Low Flow Streams (USEPA, 1980).
1.2    RELATIONSHIP TO OTHER
       GUIDANCE DOCUMENTS

Table 1-1 summarizes the relationship of the various
documents that make up the series of technical guid-
ance manuals for waste load allocations. These manu-
als describe approaches for allocating point source
waste loads in rivers and streams, lakes and impound-
ments, and estuaries.  The pollutants addressed in the
manuals listed in Table 1-1 include biochemical oxygen
demand/dissolved oxygen,  nutrients, and toxic sub-
stances (ammonia, organic chemicals, and metals).
The manual Simplified Analytical Method for Determin-
ing NPDES Effluent Limitations for POTWs Discharging
into Low-Flow Streams (Table  1-1) may be used to
assist in waste load allocation procedures when such
simplifications are valid.  In addition, ammonia toxicity
is addressed in more detail in the Simplified Methods
Manual, which now includes methods for evaluating the
interactions of multiple discharges.

Table 1-2 lists available guidance for TMDL develop-
ment.  These documents include guidance on  the
allocation  of nonpoint source  loads.  These docu-
ments assist the water quality analyst in selecting and
using appropriate models for development of TMDLs.
In addition, an EPA report entitled Technical Guidance
for Estimating Total Maximum Daily Loads (TMDLs):
Integrating Nonpoint and Episodic Point Source Load-
ing from Stormwater and Combined Sewer Overflows
(CSOs) is currently in development and should be
available by October 1994. This document is intended
to  provide technical guidance  on the integration of
point  and  nonpoint, steady  state and episodic dis-
charges into a waterbody. The guidance will provide
several examples of evaluating  these discharges
within the TMDL process.

Users of this manual also can consult the latest State
water quality standards before developing TMDLs.
These standards  provide applicable water quality
criteria for pollutants of concern in the state. Federal
water quality criteria for many pollutants are listed in
the EPA "Gold Book":

    USEPA. 1987. Quality criteria for 1986 (with up-
   dates  1 and 2 included).  "Gold Book." EPA
   440/5-86-001. U.S. Environmental Protection
   Agency, Office of Water  Regulations and
   Standards, Washington,  DC.

The "Gold  Book" is available from:

    U.S. Government Printing Office
   Superintendent of Documents
   North Capitol and H Street, NW
   Washington, DC 20401
   (202) 783-3238
   Order  No. 955-002-00000-8

Usually, however, TMDL developers consult  state
water quality standards first.  Several  manuals on
modeling and parameter selection are also available.
These documents, listed in Table 1-2,  are available
from:
                                              1-2

-------
 TABLE 1-1. TECHNICAL GUIDANCE MANUALS FOR PERFORMING WASTE LOAD ALLOCATIONS
Technical Guidance Manual for Performing Waste Load Allo-
cations - Book II Streams and Rivers - Chapter 1 Biochemi-
cal Oxygen Demand/Dissolved Oxygen (EPA 440/4-84-020)
This chapter presents the underlying technical basis for perform-
ing WLA and analysis of BOD/DO impacts.  Mathematical mod-
els to calculate water quality impacts are discussed, along with
data needs and data quality.

Technical Guidance Manual for Performing Waste Load Allo-
cations - Book II Streams and Rivers - Chapter 2 Nutrient/Eu-
trophication Impacts (EPA 440/4-84-021)
This chapter emphasizes the effect of photosynthetic activity
stimulated  by nutrient discharges on the DO of a stream or river.
It is principally directed at calculating DO concentrations using
simplified estimating techniques.

Technical Guidance Manual for Performing Waste Load Allo-
cations - Book II Streams and Rivers - Chapter 3 Toxic Sub-
stances (EPA 440/4-84-022)
This chapter describes mathematical models for predicting toxi-
cant concentrations in rivers. It covers a range of complexities,
from dilution calculations to complex, multi-dimensional, time-
varying computer models. The  guidance includes discussion of
background information and assumptions for specifying values.

Technical Guidance Manual for Performing Waste Load Allo-
cations - Simplified Analytical Method for Determining
NPDES Effluent Limitations for POTWs Discharging into
Low-Flow Streams
This document describes methods primarily intended for "desk
top" WLA investigations or screening studies that use available
data for streamflow, effluent flow, and water quality. It is in-
tended for  circumstances where resources for analysis and data
acquisition are relatively limited.

Technical Guidance Manual for Performing Waste Load Allo-
cations - Book IV Lakes and Impoundments - Chapter 2 Nu-
trient/Eutrophication Impacts (EPA 440/4-84-019)
This chapter discusses lake eutrophication processes and some
factors that influence the performance of WLA analysis and the
interpretation of results.  Three classes of models are discussed,
along with  the application of models and interpretation of result-
ing calculations.  Finally, the document provides guidance on
monitoring  programs and simple statistical procedures.

Technical Guidance Manual for Performing Waste Load Allo-
cations - Book IV Lakes, Reservoirs and Impoundments -
Chapter3  Toxic Substances  Impact (EPA 440/4-87-002)
This chapter reviews the basic  principles of chemical water qual-
ity modeling frameworks. The guidance includes  discussion of
assumptions and limitations of  such modeling frameworks, as
well as the type of information required for model  application.
Different levels of model complexity are illustrated in step-by-
step examples.

Technical Guidance Manual for Performing Waste Load Allo-
cations - Book VI Design Conditions - Chapter 1 Stream De-
sign Flow for Steady-State Modeling  (EPA 440/4-87-004)
Many state water quality standards (WQS) specify specific de-
sign flows.  Where such design flows are not specified in WQS,
this document provides a method to assist in establishing a
maximum design flow for the final chronic value (FCV) of any
pollutant.

Final Technical Guidance on Supplementary Stream Design
Conditions for Steady State Modeling
WQS for many pollutants are written as a function of ambient en-
vironmental conditions, such as temperature, pH, or hardness.
This document provides guidance on selecting values for these
parameters when performing steady-state WLAs.

Technical Guidance Manual for performing Waste Load Allo-
cations - Book VII: Permit Averaging (EPA 440/4-84-023)
This document provides an innovative approach to determining
which types of permit limits (daily maximum, weekly, or monthly
averages) should be specified for the steady-state model output,
based on the frequency of acute criteria violations.

Handbook - Stream Sampling for Waste Load Allocation Ap-
plications (EPA 625/6-86/013)
This handbook provides guidance in designing stream surveys
to support modeling applications for waste load allocations.  It
describes the data collection process for model support,  and it
shows how models can be used to help design stream surveys.
In general, the handbook is intended to educate field  personnel
on the relationship between sampling and modeling require-
ments.

Technical Guidance Manual for Performing Waste Load Al-
locations - Book III Estuaries - Part 1 - Estuaries and Waste
Load Allocation Models (EPA 823/R-92-002)
This document provides technical information and policy guid-
ance for preparing estuarine WLAs. It summarizes the impor-
tant water quality problems, estuarine characteristics, and the
simulation models available for addressing these problems.

Technical Guidance Manual for Performing Waste Load Al-
locations Book III Estuaries - Part 2 - Application of Estu-
arine Waste Load Allocation Models (EPA 823-R-92-003)
This document provides a guide to monitoring and model calibra-
tion and testing,  and a case study tutorial on simulation of WLA
problems in simplified estuarine systems.

Technical Support Document for Water Quality-based Tox-
ics Control (EPA 505/2-90-001)
This document discusses assessment approaches, water qual-
ity standards, derivation of ambient criteria, effluent charac-
terization, human health hazard assessment, exposure
assessment, permit requirements, and compliance monitoring.
An example is used to illustrate the recommended procedures.

Technical Guidance Manual for Performing Waste Load Al-
locations - Book III - Estuaries - Part 4 - Critical Review of
Coastal Embayment and Estuarine Waste Load Allocation
Modeling (EPA 823-R-92-005)
This document summarizes several historical case studies of
model use in one freshwater coastal embayment and a number
of estuarine discharge situations.

Technical Guidance Manual for Performing Waste Load Al-
locations - Book III:  Estuaries - Part 3 - Use of Mixing Zone
Models in Estuarine Waste Load Allocations (EPA 823-R-92-
004)
This technical guidance manual describes the initial mixing
wastewater in estuarine and coastal environmental and mixing
zone requirements. The important physical processes that gov-
ern the hydrodynamic mixing of aqueous discharges are de-
scribed, followed by application of available EPA-supported
mixing zone models to four case study situations.

These documents are available from the Office of Science and
Technology (4305), Washington, DC 20460.  See Standards
and Applied Science Division Clearinghouse Request Form for
document completion dates.
                                                       1-3

-------
    TABLE 1-2. AVAILABLE GUIDANCE AND

           OTHER REFERENCES FOR

              TMDL DEVELOPMENT
Rates, Constants, and Kinetics Formulations in Surface
Water Quality Modeling (Bowie etal., 1985, EPA/600/3-85/040)
This report provides formulations used in surface water quality
modeling along with accepted values for rate constants and coeffi-
cients. Topics covered include dispersion, heat budgets, dis-
solved oxygen saturation, reaeration, alkalinity, nutrients, algae,
zooplankton, and coliform bacteria.

Water Quality Assessment: A Screening Procedure for Toxic
and Conventional Pollutants in Surface and Groundwater,
Parts I and II (Mills et al., 1985, EPA 600/6-85/002a and EPA
600/6-85/002b)
Part I of this series describes the aquatic fate of toxic organic sub-
stances, waste loading calculations, and the assessment of water
quality parameters in rivers and streams. Part II describes the as-
sessment of impoundments, estuaries, and groundwater.

Compendium of Watershed-scale Models for TMDL Develop-
ment (USEPA, 1992b, EPA 841-R-92-002)
This document identifies and summarizes the most widely used
watershed-scale models and is intended to assist in model selec-
tion.

Modeling ofNonpoint Source Water Quality in Urban and
Non-urban Areas (Donigan and Huber, 1991, EPA/600/3-91/039)
This document presents detailed reviews of established nonpoint
source assessment procedures, methodologies, and modeling
techniques. Simple procedures (e.g.,  constant concentration, re-
gression, statistical, and loading function approaches) and  com-
plex models (e.g., SWMM, HSPF,  CREAMS, SWRRB) are
described.

A Quick Reference Guide:  Developing Nonpoint Source
Load Allocations for 7/WDLs (USEPA, 1992a, EPA841-B-92-
001)
This document directs TMDL developers to existing technical guid-
ance from other programs while more detailed TMDL technical
guidance is developed.

TMDL Case Study Series
This series of case studies published by EPA illustrates real-world
TMDL applications that the user may consult when appropriate.

The Enhanced Stream Water Quality Models QUAL2E and
QUAL2E-UNCAS: Documentation and User Manual (Brown
andBarnwell, 1987, EPA/600/3-87/007)
This manual describes the water quality models QUAL2E, which
can be operated as a steady-state or dynamic model, and
QUAL2E-UNCAS, which is an enhancement of QUAL2E that in-
cludes uncertainty analysis. QUAL2E allows the user to model
the effects of diurnal variations and to examine diurnal dissolved
oxygen variations caused by algal growth and respiration.

The water quality analysis simulation program,  WASPS, Part
A: Model documentation, Version 5.10 and The water quality
analysis simulation program, WASPS, Part B: The WASPS in-
put dataset, Version 5.10 (Ambrose, et al., 1993a and 1993b)
This manual describes the use of the Water Quality Analysis
Simulation Program Version 5.10 (WASP5). The WASP5 model-
ing system covers hydrodynamics, conservative mass transport,
eutrophication-dissolved oxygen kinetics, and toxic chemical-sedi-
ment dynamics.
    USEPA Center for Exposure Assessment
      Modeling (CEAM)
    Environmental Research Laboratory
    960 College Station Road
    Athens, GA 30605-2720
    (706) 546-3549
    Bulletin Board (706) 546-3402


1.3    ORGANIZATION AND SCOPE OF
        MANUAL

The remainder of this document is organized into three
chapters and five appendices, as summarized below.

Chapter 2, Basic Principles and Concepts, presents the
underlying technical basis for analyzing stream  bio-
chemical  oxygen demand (BOD),  dissolved oxygen
(DO), nutrient, and eutrophication impacts. The basic
theory on transport and fate and the nature of stream
system responses to oxygen-demanding loads are de-
scribed using equations and basic relationships.

Chapter 3, Model Selection and Review, discusses
mathematical models available to perform TMDL cal-
culations, with  emphasis given to EPA-supported
models including Multi-SMP, QUAL2E-UNCAS,  and
WASP5. Guidance is also provided to assist in iden-
tifying and selecting appropriate models for varying
levels of complexity  (e.g., steady-state vs. dynamic).

Chapter 4, River and Stream Modeling  Procedures,
presents the following procedures to construct a site-
specific model for a TMDL:  initial assessment, site-
specific  stream  survey, model calibration,  model
validation, and  model  application. Examples from
actual case studies are  given to illustrate key steps in
the procedures.

Appendix A,  Development of Model Coefficients  and
Constants, provides a detailed discussion on various
fate and transport processes and  reaction rates af-
fecting biochemical oxygen demand, dissolved oxy-
gen,  and nutrients  in rivers and  streams such as
reaeration,  oxidation,  nitrification, photosynthesis,
respiration, settling,  sediment oxygen demand,  and
ammonia flux.  Environmental factors that influence
fate and  transport processes and  technical  ap-
proaches for determining model parameters are  pre-
sented. This appendix supplements the overview
material presented in Chapter 2.

Appendix B, Sample  Total Maximum  Daily Load
Analysis,  presents an  example that  illustrates  the
TMDL process applied in settings using an analytical
                                                  1-4

-------
solution and  the  EPA-supported QUAL2E  and
WASPS models. The example TMDL problem in-
cludes problem setting, river characteristics, treatment
plant and effluent characteristics, ambient river water
quality data review,  model calibration, and model pro-
jections. The example illustrates an analysis of the
same  water quality problem using two different
models: an analytical, screening level model and
the WASPS model.  The solutions consider non-
zero  background  sources, nonpoint source in-
puts,  and eutrophication  problems.  The second
example problem is based  on a  study of the Wil-
lamette River basin in Oregon. The Willamette River
example highlights  the use of the QUAL2E model in
assessing water quality for a large river.

Appendix C, Quality Assurance for Field Monitor-
ing Programs, provides an overview of objectives
and components of a quality assurance plan for
field monitoring.

Appendix D, Uncertainty Analysis, provides a dis-
cussion on  uncertainty analysis  as  it applies to
waste  load allocation modeling. An example demon-
strating various aspects of uncertainty analysis using
QUAL2E-UNCAS is included. Also, through this ex-
ample, techniques in uncertainty analysis, first-order
error analysis,  and Monte Carlo simulation are de-
scribed.

Appendix E, Supplementary Bibliography, includes ad-
ditional references relevant to oxygen depletion, nutri-
ent enrichment,  and eutrophication processes in
freshwater and  marine ecosystems. These references
are not cited in the guidance manual.

Appendix  F presents a glossary of technical terms
related to the guidance document.

Appendix G presents a list of abbreviations used in
the document.  Appendix H provides a list of conver-
sion factors for metric and US equivalent units.

Appendix I provides a list of symbols used as nomen-
clature in the document.

Appendix J provides an attached MS-DOS 3.5 inch
diskette containing input files for the QUAL2E and
WASPS example problems presented in Appendix B.
                                              1-5

-------

-------
2.   BASIC  PRINCIPLES AND  CONCEPTS
2.1     PURPOSE
This chapter provides an introductory discussion of
the primary concepts in conducting analyses of river
and stream dissolved oxygen responses to loadings
of BOD and nutrients.  Section 2.2 provides an over-
view of the basic  principles of the  total  maximum
daily load (TMDL)  process.  Section 2.3 presents a
discussion of the key relationships (i.e., loading, fate
and  transport  by physical  and chemical/biological
processes) that determine  the effect of a pollutant
load on  oxygen demand and  eutrophication in a
stream or river. Section 2.4 presents the mass bal-
ance principle and governing equations that form the
basis for most water quality models used to simulate
the key processes  of interest.
2.2     OVERVIEW
EPA defines the total loading capacity (LC) or total
maximum daily load (TMDL) as the greatest amount of
pollutant loading that a waterbody can receive without
violating water quality standards. A TMDL is the portion
of the LC or TMDL that is allocated to one of its existing
or future point sources of pollution.  A load allocation
(LA) is the portion of the TMDL that is allocated to one
of its existing or future nonpoint sources of pollution and
natural background.  The sum of the individual WLAs
for point sources and LAs for nonpoint sources (includ-
ing natural background sources and tributaries) plus the
margin of safety (MOS) is equivalent to the TMDL (i.e.,
TMDL= LC = WLA+LA+MOS). TMDL studies utilizing
field monitoring data  and predictive models provide
quantitative information to assist managers in making
effective decisions to protect water quality. Models and
water quality equations are used to establish cause-
and-effect relationships  correlating incremental
changes in stream water quality to changes in pollutant
loading. From this correlation, optimum and desirable,
but not required cost-effective treatment levels can be
specified to achieve water quality standards and criteria.
The MOS can be included implicitly in the TMDL model
calculations to account for the uncertainty about the
relationship between the  allocated waste  loads and
loads and the predicted quality of the receiving water-
body. A reserve capacity for future development can
be included in the TMDL at this stage.  Wastewater
treatment plant designers can then evaluate various
combinations  of alternative unit  processes to select
an optimum treatment scheme to meet the require-
ments of the WLA.  Likewise, land use planners and
engineers may need to analyze various management
scenarios to meet the requirements of the nonpoint
source LA.  This analysis may include an  evaluation
of the cost-effectiveness of different  combinations of
best management practices (BMPs).

Knowledge of the quantitative cause-and-effect  rela-
tionship between receiving water quality and pollutant
loads is the key to making reliable determinations of the
total loading capacity.  This relationship is quite sensi-
tive to natural  environmental conditions. These condi-
tions include physical characteristics such  as stream
flow, velocity, depth, slope, time of travel, and tempera-
ture  and chemical/biological characteristics such  as
in-place sediment oxygen demand, algal photosynthe-
sis and respiration, and nitrification. The determination
of the rates at which various water quality reactions take
place in the receiving waterbody  introduces additional
complications  in establishing cause-and-effect relation-
ships and projecting water quality impacts. In some
instances, the water quality response can be as sensi-
tive to the reaction rates as it is to the total amount of
pollutant loadings.   This  is particularly  important in
BOD/DO reactions where the resulting dissolved oxy-
gen concentration is determined by competing reac-
tions of oxygen consumption from BOD, nitrification,
and sediment oxygen demand (SOD) and oxygen re-
plenishment from reaeration and photosynthesis.


Models not only are used to determine rigorous relation-
ships between pollutant loads and the resulting water
quality  response, but  also are necessary to predict
future water quality conditions and conditions that may
not have been monitored for in the past (e.g., 7Q10
critical low-flow conditions).1  Models are also useful to
IThere is a 10 percent chance that the 7Q10 critical low flow (7-day average low flow that occurs once in 10 years) will occur during a 1-year monitoring period. In a 10-year
 monitoring period, there is only a 65 percent chance that critical low-flow conditions will occur.
                                               2-1

-------
evaluate the array of variables (temperature, stream
flow, load,  reaction rates, etc.)  that simultaneously
influence water quality response, especially where
the system is relatively complex as a result of multiple
sources, varying stream geometry, flow changes due
to tributaries and storm events, and other factors.
2.3     CONCEPTS IN BIOCHEMICAL
        OXYGEN DEMAND, DISSOLVED
        OXYGEN, AND NUTRIENT
        ANALYSES
This section examines the relationships between pol-
lutant fate and transport processes in natural waters
and the response of dissolved oxygen in streams and
rivers to point and nonpoint source loads. An appre-
ciation of these relationships and related  factors
should help regulatory staff and water quality and
watershed  managers to assess the technical com-
plexity associated with the development of a given
site-specific TMDL and to recognize the level of mod-
eling  and monitoring effort that may be  required.
Factors that govern the fate and transport processes
of pollutant loadings in streams and rivers and deter-
mine the effects on dissolved oxygen include those
factors related to the magnitude and variability of the
pollution  sources, the  hydrologic  conditions of the
receiving water, and the in-stream transport of pollut-
ants.  Detailed discussion  of these factors can  be
found in Appendix A.  In addition, detailed derivation
of BOD, DO, and nutrient relationships can be found
in Thomann and Mueller (1987).

2.3.1   Pollution Source Characteristics

An  important task in watershed and water quality
modeling is to characterize the pollution sources and
estimate the associated pollutant loadings. Pollution
sources can be characterized as either point sources
or nonpoint sources. These two categories of sources
are governed by different  mechanisms,  resulting in
different impacts on  the receiving water. Table 2-1
identifies typical differences between point and non-
point source characteristics.

Point  Sources - Point source pollutant loads  include
effluent discharges  from  municipal  and  industrial
wastewater treatment plants. Point sources are also
characterized by pollutant inputs to surface waters
from tributaries and other watercourses that aggre-
gate into major surface water systems. Point sources
are defined in the Clean Water Act ) as  ".  . . any
 TABLE 2-1. COMPARISON OF TYPICAL POINT
           AND NONPOINT SOURCES
       Point Sources

 Fairly steady flow
 Variability changes less than
 one order of magnitude

 Most severe impacts occur
 during low flow conditions
                                                  Fairly predictable
                                                  concentrations
     Nonpoint Sources

Highly dynamic flow occurring
at random intermittent intervals

Variability changes several
orders of magnitude

Most severe impacts occur
during or following storm
events

Unpredictable, variable
concentrations
discernible, confined, and discrete conveyance,  in-
cluding  but not limited to, any pipe, ditch, channel, tunnel,
conduit, well, discrete fissure, container,  rolling stock, con-
centrated animal feeding operation, landfill leachate collec-
tion system, vessel or otherfloating craftfrom which pollutants
are or may be discharged. This term does not include return
flows from  irrigated agriculture or agricultural storm water
runoff "(40 CFR Vol. 1 7-1-1990 edition).

Point source loading rates from permitted discharges,
such as publicly owned treatment works (POTWs)
and combined sewer systems, may be determined
from discharge monitoring reports (DMRs) available
from many state regulatory agencies or EPA regional
offices. Most of these DMRs contain information on
many conventional pollutants such as BOD, ammo-
nia, Kjeldahl nitrogen, suspended solids, and fecal
coliform bacteria.  Not all nutrients are reported in
DMRs. For example, facilities that do not have phos-
phorus removal (e.g., secondary treatment  plants)
may not measure or report total phosphorus and/or
orthophosphate concentrations in their effluents.  In
this case, the field monitoring program should include
measuring these effluents. When plant-specific data
are not available, literature values (see Appendix A)
can be used for first approximations.

Nonpoint  Sources -  Nonpoint loading of pollutants
results from the transport of pollutants into receiving
waters via overland surface runoff within a drainage
basin.  Land use and hydrologic characteristics of a
basin are major determinants of the magnitude of
pollutant loading contributed  from  nonpoint  source
inputs. The general long-term trend of deforestation
and the subsequent transition to agricultural, urban,
                                               2-2

-------
and suburban land uses has resulted in large-scale
changes in nonpoint source pollutant loading to the
Nation's rivers and coastal waters.

As the magnitude of the nonpoint source pollution
problem has become better understood over the past
10-20 years, a number of urban and agricultural
management practices have been proposed, investi-
gated, and implemented to reduce pollutant loading
from these very diverse sources.  A major emphasis
of ongoing and future national water quality manage-
ment objectives for  EPA and the states  will be the
basin-scale implementation of best management
practices to reduce nonpoint source pollutant loads.


Several modeling techniques have been developed
for estimating pollutant  loadings  from diffuse and
storm-driven sources. Recent reviews of these tech-
niques  are  presented in USEPA (1992b) and
Donigian  and Huber (1991). Although  watershed
modeling  techniques were originally developed to
estimate loading  and to provide input to receiving
water models, the TMDL program has widened the
range of application of these models to include the
development and  evaluation of various components
of watershed management plans. Since not all deci-
sion situations regarding the development of TMDLs
are the same, some models are more suitable than
others under certain conditions. Simple and screen-
ing watershed models have been extensively used to
support preliminary  assessment  and planning-level
activities,  while applications of detailed simulation
models  are most cost-effective when dealing with

      TABLE 2-2. DECISION SITUATIONS
      REQUIRING  WATERSHED MODELS

Screening Level (simple  models)
    Relative comparison of watersheds
    Preliminary estimates of discharge quantity and quality
    Delineation of the geographical extent and analysis of the
    temporal variability of major pollution sources
    Identification of pollutants and governing processes of
    concern
    Identification of modeling and monitoring needs
Planning Level  (mid-range to detailed models)
    Prioritization and targeting of specific watersheds or
    pollution sources
    Evaluation and selection of control strategies
Post-Planning Level (detailed models)
    Siting criteria for implementation of management measures
    Design criteria for sizing control practices
development of design criteria or evaluating manage-
ment programs.  Table 2-2  presents a set of com-
monly encountered decision situations associated
with TMDL development for which the use of a water-
shed model may be considered. These situations are
presented in increasing order of complexity and mod-
eling requirements.

Other nonpoint pollution sources that may influence
the development of TMDLs include  groundwater
seepage, atmospheric deposition, and natural weath-
ering of rocks and soils. These sources are difficult to
control, and  under  natural conditions they represent
the background  concentrations of the waterbody.
When developing  TMDLs,  uncontrollable sources
need to be identified and their magnitude evaluated
to characterize the available assimilative capacity of
the waterbody. The weathering and dissolution pro-
cesses of rocks and soil are natural mechanisms and
should be considered  as  part of the uncontrolled
loads. Atmospheric deposition is in part  a result of
industrial and development  activities at the regional
or national scale. Therefore, their control at the site-
specific or watershed scale is not possible. The con-
trol of atmospheric deposition is usually addressed in
regional and national programs and should  be con-
sidered as part of the uncontrollable load for typical
TMDL development.

Groundwater contributions  to the nonpoint source
loads are a main concern if the groundwater is con-
sidered contaminated. In general, since groundwater
interfaces natural geological formations that undergo
dissolution and weathering processes under natural
conditions, pollutant discharges caused by noncon-
taminated groundwater seeping  to surface water-
bodies should be considered  a  part  of the
uncontrollable load. When dealing with contaminated
groundwater, seepage to surface water can repre-
sent a major concern requiring an identification of
contamination sources and the  pollutant(s)  of con-
cern and an evaluation of the magnitude of  the dis-
charge. Potential groundwater assessment methods
are summarized in A Review of Methods for Assess-
ing Nonpoint  Source Contaminated  Ground-water
Discharges to Surface Water (USEPA,  1991c).

2.3.2  In-Stream  Fate and Transport of
       Pollutants

When a pollutant load  is  discharged into a  flowing
stream or river,  it  is subject to fate and transport
processes that modify stream concentrations.  The
                                               2-3

-------
principal factors determining stream concentrations
are advection, dispersion, and reaction.

Advection  - Advection represents the primary trans-
port process of pollutant inflow in the downstream
direction.   Lateral  advective transport across  a
stream is typically neglected. Usually complete mix-
ing between the pollutant load and the ambient
stream flow in the vertical and lateral direction  has
been achieved  within  a relatively  short distance
downstream of the outfall.

Dispersion  - If all water elements in a stream were
traveling at a uniform speed over each cross-section
of the river, they would arrive at a given location at
the same time.   In reality, however, lateral velocity
differences cause each element to arrive at a different
time, resulting in an apparent mixing due to vertical
and lateral velocity gradients. For example, the cen-
ter of the stream near the surface moves faster than
the flow  near the banks and streambed.  This phe-
nomenon is called  longitudinal dispersion (see  Fis-
cher et al., 1979).  When analyzing the effects of a
continuous  pollutant load, the effect of dispersion
may be ignored since the contribution of dispersion
to the resulting  in-stream pollutant concentration is
usually small in  comparison to the contribution from
advection.  On the other hand, when analyzing trans-
port of storm-driven loadings during wet-weather pe-
riods, longitudinal dispersion also must be considered
since the pollutant loading is represented as a single
"pulse"  input rather than a continuous series of
"pulse" inputs. When a water quality-analysis is con-
ducted over a "long" distance with a short time "pulse"
interval of discharge,  then longitudinal dispersion
must be considered in  the analysis (Thomann  and
Mueller,  1987).

Reaction - The  biodegradable materials discharged
to a stream or river (e.g., oxygen-demanding or-
ganics)  undergo decomposition  by  bacteria in the
water column. In the presence of dissolved oxygen,
bacteria  convert organic materials to end products
such as CO2, NO3, and H2O, stabilizing the pollutant
load. In  addition, algae take  up nutrients such as
inorganic phosphorus and nitrogen during photosyn-
thesis and reduce the nutrient concentrations in the
stream.  Algal  biomass is then  recycled back  into
inorganic nutrients.  A number of chemical, biological,
and biochemical reactions contribute to the flux  and
attenuation of waste material concentrations.

The interactions of these factors are shown schemati-
cally in  Figure  2-1,  which presents what would be
observed if a single slug of waste load were injected
and could be followed downstream over a period of
time. Conservative materials in the waste (those not
subject to  reaction  and decay,  such as chloride)
would track as shown in the sketch of advection, or
advection and dispersion.  Reactive materials, such
as oxygen-consuming materials, would  behave  as
shown in the sketches that include reaction.  Thus,
the behavior of a dissolved substance in the stream
is the result of the velocity and mixing action  of the
water and the resulting transformation from biological
and chemical reactions.

2.3.3    Receiving Water Conditions

The local impact  of pollutant loadings to receiving
waters is largely determined by the relative magni-
tudes of the loading and the receiving water flow rate.
Assimilation of the pollutant load  is a function  of the
hydrologic conditions of the stream or river defined in
terms of flow rate, transport characteristics, and back-
ground water quality. As a result, seasonal and storm-
driven variations  in  the watershed hydrology and
pollutant buildup and washoff characteristics can ex-
ert very different impacts on the receiving water dur-
ing and immediately following wet-weather episodes.
The significance  of storm-driven discharges from
nonpoint sources  on dissolved oxygen is very site-
specific and difficult to characterize explicitly. During
wet-weather conditions, stream flow rate  is generally
high,  allowing for higher transport and assimilative
capacity. However, pollutant deposition and accumu-
        TABLE2-3. NONPOINT SOURCE
              MODELING OPTIONS

Stochastic/Probabilistic models: Mathematical models that
include consideration of hydrologic uncertainty and probability.
Models rely on statistical characteristics of the hydrologic
process to predict the behavior of the hydrologic system.
Transport and fate processes are represented using
compounded parameters describing multiple processes.

Deterministic models:  Model variables are thought to follow a
predictable, certain behavior, and the probability of hydrologic
data is generally ignored. Models rely on series of algorithms
that simulate actual physical or chemical processes, and are
sometimes referred to as physically based models.

Design storm simulation: Focuses on a detailed simulation of a
single storm event, often selected as representative, with a
given frequency and duration of rainfall (e.g., 1-year, six -hour
storm).

Continuous simulation: Simulates the behavior of a system over
an extended period of time (months to years) with relatively
short time steps, providing continuous runoff and loading values.
                                                 2-4

-------
             ADVEC11GN
  ADVECT1ON AMD BEftCtiON
                                                   LIJQ
      IT
      T MB
T
THI
       ADVECT1QMAND DISPERSiOM
              L-3*3
              1
      I
      1ME
                                              ADVECT1ON, REACTION
                                                AND DISFEflSI ON
                                           •nut
FIGURE 2-1. INTERACTION OF TRANSPORT MECHANISMS FOR LOADS IN A STREAM
                           (After USEPA, 1983a)
                                   2-5

-------
lation on streambed and pool areas may exert a
critical demand on dissolved oxygen during low flow
conditions. When nonpoint sources are identified as
the major sources of oxygen depletion, storm event
or continuous and time-dependent modeling may be
required. Nonpoint source modeling options that cap-
ture, to a certain extent, the impacts of wet-weather
conditions are summarized in Table 2-3.

Often when oxygen  demand  is the mechanism of
concern, the modeling effort focuses primarily  on
pollutant discharges during dry periods and the base
flow rate, which depends on the magnitude of the flow
recession following wet-weather recharge conditions.
Although pollution discharges during dry periods are
characterized by fairly constant rates  and  composi-
tion, the base flow  rate fluctuates seasonally and
annually. In the continental United States, seasonally
high flow normally occurs during the colder period of
winter and in early spring from snowmelt and spring
rains, while seasonally low flow typically occurs dur-
ing the warmer summer and early fall drought peri-
ods.  Because of these seasonal hydrologic and
climatological patterns of low flow, minimum dilution,
and  high temperature,  summer  and  early fall are
typically the critical  periods for evaluating the worst-
case impact of pollutant loads on  water quality. Dur-
ing this  period, flow conditions approaching steady
state are achieved.  The analysis and evaluation of
data collected during this period become more mean-
ingful because the  mathematical assumption of
steady state is frequently made when evaluating dis-
solved oxygen in streams and rivers due to  fairly
constant point sources.

Rapid transport of pollutants by high flow and mixing
conditions results in  a short residence time with typi-
cally minimal ecological damage.  Conversely, slow
removal of pollutants in waters characterized  by a
long residence time because  of low-flow conditions
can  result  in adverse ecological  impacts such  as
severe oxygen depletion, nutrient enrichment, and
eutrophication problems.

Factors  that affect the time of travel in  a natural
stream include stream depth,  width, cross-sectional
area, and  bed slope-hydraulic geometry.   In some
cases, stream hydraulic geometry and time-of-travel
information are available from studies performed by
the U.S. Geological  Survey  (USGS), the U.S. Army
Corps of Engineers, or other State and Federal agen-
cies.  Sometimes a field program  may be needed to
collect the hydraulic geometry data and measure the
time of travel (usually by a dye study).
Projections of water quality impacts for some future
critical low-flow condition are normally required in
TMDL studies.  The change in  hydraulic geometry
caused by flow fluctuation must  be predicted.  Flow
variance, in turn, results from  changes in  stream
velocity and depth (both of which strongly affect the
stream reaeration capacity).

Basically, two  approaches are used  to quantify hy-
draulic geometry and time of travel for future condi-
tions.  First, Leopold  and Maddock (1953)  have
examined  various  rivers  and developed  empirical
relationships between flow (Q),  velocity (U), depth
(H), and width  (W) using the following functions with
flow as the  independent variable.
                                           (2-1)


                                           (2-2)


                                           (2-3)
,m
where a, b, and c are constants for the stream and n,
m, and  f are exponents defining the basic relation-
ships.

These constants vary with size of the  river basin.
More detailed information on these constants can be
found in Appendix A.

The  second approach is to independently calculate
stream velocity, depth, and width for different flows.
Typically, hydrodynamic models based  on momen-
tum and continuity equations are used. Many stream
water quality models (see Chapter 3) include hydrau-
lic components that can be used to  model flow in
addition to water quality.

A simpler modeling approach is to use the Manning
equation relating velocity, depth, slope, and a chan-
nel roughness coefficient (Brown and Barnwell, 1987;
McCutcheon,  1989) to  simulate the velocity  and
stream flow rate for different depths. It is typically
easier  to  estimate  and extrapolate the  Manning
roughness coefficient than  to estimate the hydraulic
constants  and exponents in Equations  2-1 through
2-3.  However, the Manning equation is an empirical
formulation that may not reflect actual conditions of
natural streams. While both equations are  semi-em-
pirical,  the Manning equation involves only one co-
efficient (vs.  six in the  Leopold and  Maddock
equations), and that coefficient is well understood by
hydraulic engineers. The Manning equation also pro-
vides better physical insight by integrating  the effect
                                               2-6

-------
                       Atmospheric Ot
j """" ^
NH;
f
Nifrlfieatten
.
I i
V
NO;
PhotoiyrrtlM
0 _ S*ttilr»i
I S*d!n*«rt W
Si 0*tnt«f — -» —
	 ^- SOD
O
L
¥
E
D
O
X
Y
G
E
N 1
i
• It FUtplratlon
Chi a
; ALGAE I
FIGURE 2-2. INTERRELATIONSHIP OF MAJOR KINETIC PROCESSES FOR BOD AND DO
              AS REPRESENTED BY WATER QUALITY MODELS
                        (After McCutcheon, 1989)
                                2-7

-------
of slope and elaborate stream geometry information.
EPA Region 6, however, has shown Manning's n to
greatly overestimate velocity.  Although not exten-
sively used in TMDLs, other backwater and dynamic
routing simulations are feasible (see McCutcheon
(1985) and French (1985) for a review).

2.3.4   Biochemical Oxygen Demand and
       Dissolved Oxygen Reaction Kinetics

Figure 2-2 shows the interrelationship of the following
major BOD/DO kinetic processes for a water column
as commonly represented by water quality models:

    •   Carbonaceous deoxygenation
    •   Nitrogenous deoxygenation (nitrification)
    •   Reaeration
    •   Sediment oxygen demand
    •   Photosynthesis and respiration

Prior to describing these processes,  a brief discus-
sion of the biochemical oxygen demand concept is
necessary.  BOD is  a  measure of the amount of
oxygen required to stabilize organic matter  in waste-
water. As such, BOD is an equivalent indicator rather
than a true physical or chemical substance.  It meas-
ures the total concentration of dissolved oxygen that
would eventually be demanded as wastewater  de-
grades in the stream. The validity of BOD as a gauge
of wastewater quality has often been questioned;
nevertheless, the concept of BOD remains the stand-
ard for dissolved oxygen modeling analysis.

BOD is determined from a standardized test measur-
ing the amount of oxygen available after incubation
of the sample at  20 °C for a specific  length of time,
usually 5  days.  The oxidation  process is usually
carried out in two stages: carbonaceous and nitroge-
nous (nitrification).  The first stage is accomplished
by saprophytic organisms, which derive their energy
from the  breakdown of organic carbon compounds;
the second  stage,  by  autotrophic bacteria, which
require simple inorganic nitrogen compounds.

Each stage is characterized by two steps: synthesis
and respiration.  In the carbonaceous stage, the en-
ergy required for synthesis is obtained from the de-
struction of complex organic carbon compounds,
liberating carbon dioxide and water. After the organic
matter has  been converted to bacterial cells,  the
endogenous  respiration  of the synthesized organ-
isms occurs, also yielding carbon dioxide, water, and
usually ammonia. In the BOD test, there  is a pro-
nounced lag between the carbonaceous oxidation
and the nitrification step, the latter following by as
much as 10 days. The lag is less for treated (stabi-
lized) wastewaters and is on the order of 1 to 2 days
for  highly treated effluents.   In streams, the two
stages frequently proceed simultaneously, although
there may be lags in the nitrification stage for highly
polluted streams or those with low dissolved oxygen.

2.3.4.1  Carbonaceous Deoxygenation

The  first phase of the BOD reaction  involves the
oxidation of the carbonaceous organic material. The
reaction is approximated by a first-order  reaction.
The oxygen required, y, approaches the total demand
of the overall process, Lo, and the rate is assumed to
be proportional to the amount of oxygen-demanding
material (Lo - y), either substrate or cells:
                 dt
                   = Ki (L0-y)
Integration of this expression yields:
                             (2-4)
                                           (2-5)
or, if the relationship is put in terms of the organic
matter remaining,
                  L=L0e
                         -Kit
                             (2-6)
where
       K1 =
       y

       L   =

       Lo  =
BOD reaction rate coefficient (day-1)
oxygen consumed
oxygen  equivalence of the organic
matter remaining
total oxygen demand
time (day)
Equation 2-4 indicates that the rate at which  the
oxygen  is consumed (dy/dt) is proportional to  the
concentration of biologically degradable organic ma-
terial, as well as chemically oxidizable substances.
The  coefficient,  K1, depends on  the state of  the
material and the  degree of treatment. A typical  do-
mestic wastewater may have the  following values:
raw sewage (0.35 day  ), settled sewage (0.40 day"
 ), and treated effluent (0.25 day" ). Because of the
nature and composition of wastewater, these values
vary significantly.  Industrial wastewaters are known
for their widely varied  Ki rates.  Coefficients deter-
mined from samples taken from rivers indicate
                                              2-8

-------
that many factors affect the rate at which the reaction
proceeds. In many natural settings, the reaction rate
coefficient for river water is usually less than that of
an undiluted wastewater sample and decreases with
distance downstream (Thomann and Mueller, 1987).
Decreasing coefficients indicate the progressive re-
sistance to the oxidation of the more stable (refrac-
tory) end products.

By U.S. convention, BOD measurements are typically
conducted for 5 days.  In fact, regulatory agencies
write  wastewater  discharge  permits (NPDES)  in
terms of 5-day BOD. In addition, many of the tests
are run with a nitrification inhibitor so that the test
measures the oxidation of  carbonaceous material
only.  When total BOD is measured after 5 days (an
inhibitor is not used), these tests are designated as
BODs. When the 5-day BOD test employs a nitrifica-
tion inhibitor, the results are designated as CBODs
(Hall and Foxen, 1984).  More and more frequently,
long-term tests  of 20 to 30 days are employed  to
measure ultimate BOD (BODU) to reflect the potential
strength of the oxygen consumption. Some pulp and
paper mill  wastewater samples  are analyzed for
much longer  periods (in excess of 100 days), but
measurements over an extended period of time are
of limited value in streams where the time of travel
from the waste source to the dissolved oxygen sag is
only a few hours or days, or where the stream is
diluted by tributaries within a few hours or days. Such
tests are very useful, though, in  converting model
results in CBODU to a CBODs NPDES limit.  In a
standard test, the values of oxygen used, y, at the end
of specified intervals of time, t, are determined. Given
a set of such values, the coefficient, Ki, and the
ultimate value, l_o, may be determined (Metcalf and
Eddy, 1991).
Another important concept for stream BOD is illus-
trated in Figure 2-3.  When water samples are taken
from a stream to the laboratory for analysis of their
biochemical  oxygen  demand, the results may be
represented  by a family of curves (Equation  2-5) of
oxygen consumed vs. time of incubation (see Figure
2-3b).  Each  of these curves has a different KI value.
As suggested above, the Ki value decreases in the
downstream direction.   If CBODs values  (either
measured during  the analysis or calculated using
Equation 2-5)  are plotted against the longitudinal
stream distance (Figure 2-3a), a decreasing trend for
CBODs is obtained.  This trend follows an exponen-
tial decay and usually can be approximated  by the
following equation in terms of ultimate CBOD:
         L (CBODu) = L0 (CBODu) e Kru     (2-7)
where
     L(CBODu)  =   oxygen equivalence of the
                    organic matter at any given
                    location in the stream
                    (measured as CBODU)
     Lo(CBODu) =   total oxygen demand
                    measured at the source of
                    waste load following
                    complete mixing (measured
                    as CBODu)
     Kr         =   CBODu removal rate in the
                    stream (day"1)
     x          =   distance below the
                    wastewater discharge
     U          =   average stream velocity
The time of travel, t, is equal to x/U. The meaning of
Equation 2-7 is that the oxygen-consuming materials
are removed from the water column at an overall loss
rate  of  Kr.   It should be noted that Kr is  used to
characterize the overall loss of dissolved  organic
materials in  the water  column  due to biochemical
oxidation and  settling.   It is highly  empirical and,
therefore, is usually quantified by fitting an exponen-
tial decay curve through the field data.

The rate of removal of organic material from the water
column  is not necessarily equal to the rate at which
dissolved oxygen is utilized. The coefficient describ-
ing this oxygen utilization may be identified as K
-------
     w

     f
0 -»-
       Laboratory
       {Nitrification Suppressing
    to
                       CBOO
                           L
                       CBOO.
                                                                            Jffj  ^
 CBOD
 (mg/L)
                      L =
            River Data
            Tlm» of Trav»l (dayi)
                 or D!*tanc*


                  (Figure 2-3a)
                                   •»- x or t
                                                                 - L_ fx) •' J - s
0       5       10    IS

     Tim* of lncufc.itIon (dayi)


    (Figure 2-3b)
           FIGURE 2-3. COMPARISON OF STREAM BOD AND LABORATORY BOD
                           FOR VARIOUS INCUBATION TIMES
                               (Manhattan College, 1983)
                                                                                20
         0' i«u:
           T!(Pt, f
                                                                NlIritm>Ni;iBi> Ni
    fff/mfff.
       FIGURE 2-4. STEPS IN NITRIFICATION AND UTILIZATION OF DISSOLVED OXYGEN
                            (After Thomann and Mueller, 1987)
                                        2-10

-------
may also contribute to the difference between the
laboratory rate, Ki, and the field rates, Kr and K NO 3
Stoichiometrically, 3.43 and 1.14 grams of oxygen
are required to transform each gram of ammonia
nitrogen to nitrite nitrogen (Equation 2-8) and nitrite
nitrogen to nitrate nitrogen (Equation 2-9), respec-
tively.  The decay of organic nitrogen indirectly re-
quires oxygen after the organic nitrogen is hydrolyzed
into ammonia.  Some researchers (e.g., Wezernak
and Gannon, 1967; Adams and Eckenfelder, 1977)
have suggested that the oxygen requirement could
be reduced to 3.22 and 1.11 grams, respectively, due
to cell synthesis.

The most common approach to modeling nitrification
is to use first-order kinetics (similar to BOD described
earlier) to characterize Equations 2-8 and 2-9.  That
is, the rate of accumulation or depletion is linearly
dependent on the amount of nitrogen available in a
specific pool.  Factors affecting the rate of nitrification
include temperature, pH,  nitrogen concentrations,
dissolved oxygen, suspended solids, and organic and
inorganic compounds.

Because of the ease of measuring organic nitrogen,
ammonia, nitrite, and nitrate, waste load allocation
modeling of nitrification involves a mass balance and
a description  of each species decay.  Nitrification is
best simulated as a cascade process involving hy-
drolysis of organic nitrogen,  oxidation of ammonia,
and oxidation of nitrite. In some models, the interme-
diate step of nitrite  oxidation is combined with the
overall oxidation of ammonia to nitrate, but only little
computational efficiency is gained. Furthermore, the
conversion of nitrite to nitrate is very rapid; therefore,
the combination of  the corresponding rates  is not
unreasonable.

A number of studies  have demonstrated that nitrifica-
tion and denitrification in the water column may  be
dominated by benthic processes, particularly in fast-
moving shallow streams and rivers. Enumerations of
nitrifier organisms have demonstrated that benthic
populations can be two to three orders of magnitude
greater than water column populations (Williams and
Lewis, 1986). Several studies have shown that up to
80 to 95 percent of total nitrification can be accounted
for by benthic processes.  Selected studies include
the James River in  Virginia (Cerco,  1981), shallow
streams in North Carolina  (Kreutzberger and Fran-
cisco, 1977; Lewis, 1983), the Trent River in England
(Curtis et al. 1975; Garland, 1978), and the Passaic
River in New Jersey (Matulewich and Finstein, 1978).

The sequential forward reactions of mineralization of
organic nitrogen and nitrification suggest that nitrate
should accumulate as an end product of the reactions.
Several data sets, however, suggest the removal of
nitrate from the water column along with the conver-
sion of ammonia to nitrite and nitrate (see Seitzinger,
1988). Simultaneous benthic nitrification and denitri-
fication have been  observed in the James River
(Cerco, 1981) and in shallow streams in  North Caro-
lina (Williams  and Lewis, 1986) and incorporated into
water quality models of oxygen and nitrogen distribu-
tions (Cerco, 1981; Williams and Lewis, 1986).

Seitzinger (1988) has observed that measured rates
of denitrification in most river,  lake, estuarine, and
coastal sediments (i.e., production of NzO gas) are
higher than the corresponding rates of nitrate loss to
                                              2-11

-------
the sediments.  The major source of nitrate for sedi-
ment denitrification underlying an aerobic water col-
umn is  nitrate  produced in the sediments during
nitrification rather than nitrate diffusing from the over-
lying water column into the sediments.

Some earlier stream models made the cascade proc-
ess a single process by combining Equations 2-8 and
2-9 and combining all nitrogenous oxygen demands
(3.43  +  1.14  =  4.57 grams of oxygen per gram of
nitrogen) as NBOD. Modeling NBOD and CBOD as
separate demands is not  as useful as  modeling
CBOD, organic nitrogen, ammonia, nitrite, and nitrate
as separate demands to  track the sequential reac-
tions of the nitrogen cycle,  which is widely used in
waste load allocation studies.  Nevertheless, NBOD
modeling  has been determined to  be  useful.  For
example, the  Simplified Analytical Method for Deter-
mining NPDES  Effluent Limitations for POTWs Dis-
charging into Low-Flow Streams (see Table 1-1) uses
this approach after ensuring that none  of the con-
straints of the method are violated.

2.3.4.3  Reaeration

In general, oxygen may be removed from or added to
water by  various physical,  chemical, or biological
reactions. If oxygen is removed from the water col-
umn and the  concentration drops below the satura-
tion level, there is a tendency to make up this deficit
by the transfer of the gas from the atmosphere
through the surface into the  stream at a certain rate.
If oxygen is added and the water column concentra-
tion is greater than the saturation level, the supersatu-
ration is reduced by the transfer of oxygen from the
stream to the air. Such interactions between the gas
phase and liquid  phase  are  driven by the partial
pressure gradient in the gas phase and the concen-
tration gradient in the liquid phase (see Thomann and
Mueller, 1987). In  general, oxygen transfer in natural
waters depends on:

    •    I nternal mixing and turbulence due to velocity
       gradients  and fluctuation.
    •   Temperature.
    •   Wind mixing.
    •   Waterfalls, dams, and rapids.
    •   Surface films.
    •   Water column depth.

The  rate  of  transfer to  be  quantified  in stream
BOD/DO modeling analyses is expressed as:
where

     dC/dt

     Cs

     C

     Ka
                                          (2-10)
rate of change of oxygen
concentration
saturation concentration of
dissolved oxygen
dissolved oxygen concentration
in stream
stream reaeration rate
coefficient (day"1)
Many empirical formulations have been developed
for  estimating stream reaeration rate coefficients.
For example, the QUAL2E model offers eight differ-
ent formulations reported in the literature.  Stream
reaeration rate coefficients span a wide  range of
values (typically 0.1 to 10 day" or even greater) and
have a greater magnitude than BOD reaction rate
coefficients.   Appendix A  presents reaeration rate
coefficients reported in the literature for a number of
waterbodies with guidance for selection of an appro-
priate equation.

2.3.4.4 Sediment Oxygen Demand

Benthic decomposition of organic material is defined
as the stabilization of the volatile suspended solids
that have settled to the streambed.  These deposits
are stabilized by the biological activity of many different
organisms including bacteria. As these organic ma-
terials are associated with suspended solids, the
discharge of settleable waste components may form
a sludge blanket below a wastewater outfall.  After a
period of time, organic materials may accumulate,
since the deposition rate of particulate material is
greater than the decomposition and  physical loss
rate.

The demand of oxygen by sediment  and benthic
organisms can, in some instances, be a significant
fraction of the total oxygen demand. This is particu-
larly true  in small streams.  The effects may be par-
ticularly acute during low-flow and high-temperature
conditions.   Decomposition of organic matter and
respiration of resident invertebrates form the major
oxygen demands from the sediment.  In addition to
biological decomposition and respiration of benthic
invertebrates, net  photosynthetic oxygen production
of attached benthic algae (periphyton) can also be a
significant component of the total SOD. The oxygen
balance of shallow  streams, in particular, can be
                                              2-12

-------
influenced by this process since attached algae are
frequently present in shallow streams (see Terry and
Morris, 1986; Jeppesen  and  Thyssen, 1984).  Al-
though these processes are distinct, they are typically
quantified together because in situ measurements
combine oxygen uptake and separation of the proc-
esses would result in added model complexity.

Because  of its complexity, it  is difficult to estimate
SOD analytically and  independently.  In situ meas-
urements of SOD  are usually conducted  using  a
chamber  at the bottom of the stream.  Continuous
measurement of oxygen uptake over a certain period
of time provides data to derive the oxygen consump-
tion rate.  In some cases,  samples of river sediments
(undisturbed) are taken to the laboratory to measure
the oxygen uptake of  bottom muds. The amount of
oxygen used over the test period is  calculated as
grams of oxygen per square meter per day (g O2/m2-
day).  In a modeling analysis, SOD is typically formu-
lated as a zero-order process:
                 at
                                          (2-11)
where
     dC/dt

     SOD

     H
rate change of oxygen
concentration (g O2/m3-day)
sediment oxygen demand (g
O2/m2-day)
average river depth (m)
Appendix A presents various values of SOD reported
in the literature for a  number of streams and rivers.

Like many other reaction rate coefficients, the SOD
values can be determined by model calibration if
direct measurements from the field are not available.
The difficulty arises  when SOD values need to be
predicted for future conditions. In recent years, cred-
ible interactive sediment-water column models have
appeared to independently quantify the oxygen up-
take rates of sediments. For example, Di Toro et al.
(1990) have developed a SOD model based on di-
agenesis of paniculate organic materials to predict
the production of hydrogen sulfide (H2S), ammonium
(NhU), phosphate (PO4), and silicon (Si).  Such a
framework explains  some,  but not all, of the proc-
esses associated with SOD and is still being tested.
As a result, EPA recommends that conservative es-
timates of  SOD  be used  for future conditions in
TMDLs.
                            2.3.4.5 Photosynthesis and Respiration

                            Through  photosynthesis and  respiration, phyto-
                            plankton,  periphyton, and rooted  aquatic plants
                            (macrophytes) could  significantly  affect  the dis-
                            solved oxygen levels in the water column. Because
                            phytoplankton growth requires sunlight and nutri-
                            ents, quantifying photosynthetic oxygen production
                            would need to address phytoplankton-nutrient dy-
                            namics.   That  is,  phytoplankton and  nutrients
                            should be modeled concurrently  to address this
                            problem.  In many simple stream BOD/DO models,
                            however, the  oxygen production rate due to photo-
                            synthesis and consumption rate due to respiration
                            are assigned, thereby uncoupling  the calculation
                            from the phytoplankton-nutrient dynamics. In this
                            section, the simple approach is presented; the full
                            discussion of phytoplankton-nutrient dynamics is
                            provided in Chapter 4.

                            In a stream water quality model, the daily average
                            oxygen production due to photosynthesis and reduc-
                            tion due to respiration is formulated as follows:
where

     dC/dt
                                                     R
                                                                                         (2-12)
rate of change of oxygen
concentration (mg O2/L-day)
average gross
photosynthesis production
(mg O2/L-day)
average respiration (mg
O2/L-day)
                            Note that R is considered to be plant respiration
                            only, excluding microbial respiration for carbona-
                            ceous deoxygenation  and  nitrification.   In  a
                            model such as QUAL2E, the mass flux term for
                            the aquatic plant contributions to the oxygen bal-
                            ance  is typically modeled as a  zero-order proc-
                            ess:
                                         -  = (0.3 \i ~ OC4 p ) Ag
                                                                      (2-13)
                            where

                                 dC/dt

                                 Aa
                    rate of change of oxygen
                    concentration (mg O2/L-day)
                    algal biomass concentration
                    (mg/L)
                                             2-13

-------
                                Atmoaphvrle
/w_t
               Organic
            Sic1«rt«i
            Mirtr:*rt Uptik*
     la iarttMc S«dlm*nt
                                             Cirbon*o*CKj«
	 •%
- /'/',' 7'
1

H -*—
3 I
j rr^m-
/ V / ','' '
r

°*

f
°'
?
S :
S
o
L
V
E
D
O
X

                                             Otnmttd
                                                          CBOO
                                                              **""«
                                         7777
                                                           SOD
  FIGURE 2-5. INTERRELATIONSHIP OF MAJOR KINETIC PROCESSES FOR BOD, DO, AND
        NUTRIENT ANALYSES AS REPRESENTED BY WATER QUALITY MODELS
                           (After McCutcheon, 1989)
                                    2-14

-------
     p

     as



     04
   algal growth rate coefficient
   (day^
   algal respiration rate
   coefficient (day"1)
   the stoichiometric ratio of
   oxygen production per unit
   of algal photosynthesis
   (mg/ mg)
   the stoichiometric ratio of
   oxygen uptake per unit of
   algae respired (mg/mg)
2.3.5    Eutrophication Kinetics

Figure  2-5  shows the major  kinetic processes
usually considered in a complete DO, BOD,  and
nutrient analysis.  The reader should  note the
similarities  between  Figures 2-2 and 2-5.   The
following processes are discussed in this section:

    •    Algal growth  and  nutrient uptake
    •    Algal death and settling
    •    Nutrient mineralization
    •    Sediment nutrient release

2.3.5.1  Algal Growth and Nutrient  Uptake

Phytoplankton growth  is directly related to tempera-
ture in moderate climates, nutrient effect, and  light
intensity up to a saturating  condition:
Gp = GT
                             rn
(2-14)
where
     Gp          =  phytoplankton growth rate
                    (day'1)
     GT          =  temperature dependent
                    growth rate (day'1)
     ri_           =  light effect (dimensionless)
     rn           =  nutrient effect
                    (dimensionless)

Temperature dependence, GT, is approximated by:

              Gr=Gmax(1.066)T-20

where
     Gmax
     T
   maximum growth rate(day ~1)
   temperature (°C)
                                                 The value suggested for Gmax under average condi-
                                                 tions for a mixed phytoplankton population is ap-
       proximately 1.8 day"
       1987).
                                                                     (Thomann and Mueller,
      Auer and  Canale (1980) and Canale  and Vogel
      (1974) summarized data from phytoplankton growth
      experiments conducted at  various  temperatures.
      These results, plotted in  Figure  2-6, illustrate  the
      different temperature optimums for different  phyla
      (species groups) of  phytoplankton (diatoms, green
      and blue-green  algae) as well as the influence of
      temperature on the growth rate.  The growth rate at
      saturating light condition can be expected to be spe-
      cies group dependent, as shown in Figure 2-7.  Be-
      cause light energy available to phytoplankton varies
      with depth and time of day, an appropriate expression
      of light availability for use in models should account
      for these changes.

      Averaging  relative photosynthesis as a function of
      light intensity over a given depth of water and over a
      fixed interval of time yields
                     2.718 /
                     KeHT
                                                     [e
                                                                              - e
']
                                                    0.2 =
                               Jr_
                               lsf
                                                                           (2-15)
                                                                          (2-15a)
                                                                                           (2-15b)
                                                 where
                                                      Is
                                                      IT
                                                      f
                                                      Ke
                                                      H
                                                      T
                                                     light effect
                                                     saturating light intensity (ly/
                                                     day)
                                                     total daily solar radiation (ly)
                                                     photoperiod (day)
                                                     extinction coefficient (m~1)
                                                     depth (m)
                                                     average period (day)
       The extinction coefficient,  Ke, is dependant on inor-
       ganic  solids, detrital particles, and  phytoplankton
       biomass in the water body.  Values of Ke in natural
       water  bodies typically vary from 0.05 to 6.9 m"1.
       Typical values for IT range from 250 to 500 ly. The
       corresponding range of values for ri_ is 0.1 to 0.5, so
       the overall daily effect of light extinction with depth is
       to reduce the growth rate by about  50-90 percent
       (Thomann and  Mueller, 1987).
                                              2-15

-------
                            G ,= 1,SU,066)

                             '
                                          T~aCl
  EC
  9
  Q.
  V}

  x
  rfl

  s
                                            Diatoms
    o t-
                    10
                                      •
                                      C)
FIGURE 2-6.  SPECIFIC ALGAL GROWTH RATE AS A FUNCTION OF TEMPERATURE
                      (After Canale and Vogel, 1974)
       I
       *
       a
       w
       I
                  ]  '   j-  1   n  i  i  f  «  i  TO
        FIGURE 2-7. EFFECT OF LIGHT INTENSITY ON ALGAL GROWTH
                          (After Ryther, 1956)
                                2-16

-------
                                                                                 0.0
                               Nutrient Concentration (pg/L)

                    FIGURE 2-8.  EFFECT OF NUTRIENTS ON ALGAL GROWTH
                                   (After Ambrose et al., 1993a)
The phytoplankton growth rate is also a function of
nutrient concentrations up to a saturating condition,
greater than which it remains constant with nutrient
concentration (Figure 2-8).  At zero nutrient concen-
tration, there is no growth. As the nutrient level is
increased, the growth rate is linearly proportional to
the availability of nutrients.  However,  as nutrient
levels continue to increase, the effect on the growth
rate of the phytoplankton is saturated. Such a rela-
tionship is described by a Michaelis-Menton formula-
tion where  the nutrient reduction factor, or nutrient
effect, for algal growth, rn, is:
                  rn =
                        Nut
                      Km + Nut
                                           (2-16)
where
     Nut
     K
=   the nutrient concentration
    half saturation (Michaelis)
    constant (|ig/L)
The Michaelis half-saturation constant, a function of
the algal species group, is the nutrient concentration
for which the nutrient reduction factor is 0.5 or half the
maximum growth rate.   The value usually ranges
from 5 |ig/L to 25 |j,g/L for nitrogen and from 1 |j,g/L to
5 |ig/l_ for phosphorus, depending on the species.
                                 With more than one nutrient accounted  for in the
                                 model (i.e., nitrogen, phosphorus, silica), the nutrient
                                 effect is given by:
                                   rn= min
                                               DIN
                    DIP
               Si
                                 where
DIN



DIP



Kmn


Kmp


Si


Ksi
                                            Kmn + DIN ' Kmp + DIP ' KS! + Si""
                                                                             /
                                                                            (2-17)
                                                     limiting nutrient reduction
                                                     factor
                                                     inorganic nitrogen
                                                     concentration (sum of
                                                     ammonia, nitrate, and nitrite)
                                                     dissolved inorganic
                                                     phosphorus concentration
Michaelis-Menton constant
for nitrogen (|ig/L)
Michaelis-Menton constant
for phosphorus (jj,g/l_)
dissolved inorganic silica
concentration (|ig/L)
Michaelis-Menton constant
for silica (|ig/L)
                                 The minimum ratio of the nutrients considered in the
                                 model thus controls the computation of the nutrient
                                               2-17

-------
           10.0
             7.5
             5.0
             2.5
                                         Nitrogen
                                         Limitation
                                                     Phosphorus
                                                      Limitation
60          120
        DIN (ug/L)
                                                       180
                                                                       0.1
                                                 o
                                                 to
                                                LL

                                          0.15  I
                                                 o
                                                TJ
                                                 0)
                                          0,2    g
                                                 QC
                                          0.3    i
                                          0.4    2
                                          0.5    U
240
             FIGURE 2-9. EFFECTS OF NUTRIENT LIMITATION ON ALGAL GROWTH
                                  (After Ambrose et al., 1993a)
reduction factor and is described as the nutrient lim-
iting algal growth.  Nitrogen and phosphorus are re-
quired by all algal species while silica is required only
by diatoms.

Figure 2-9 shows the Michaelis-Menton formulation
in a slightly different format.  In this figure, Kmn = 25
|ig/L and Kmp = 1 |^g/L are used.  For a stream with
a DIN concentration of 100 |ig/L, this corresponds to
a 20 percent reduction in the growth rate (rn = 0.8).
For phosphorus to become the limiting nutrient in the
stream, dissolved inorganic phosphorus must reach
a level of 4 |ig/L or less.  It should also be pointed out
that if upstream nitrogen controls were instituted such
that DIN was reduced to 60 |ig/L for the same stream
reach, a further reduction in DIP to 2.5 |ig/L would be
required to keep phosphorus as the limiting nutrient.
In other words, as the water column concentrations
of DIP begin to approach  growth-limiting levels due
to  continued reduction  in  point source phosphorus
effluents, any nitrogen control strategies that might
be instituted would  require additional levels of phos-
                   phorus removal to keep phosphorus as the limiting
                   nutrient by keeping an upstream N/P ratio below 10.

                   2.3.5.2 Algal Death

                   Decreases in algal biomass are brought about by two
                   processes:  algal respiration and death.  Algal respi-
                   ration is caused by endogenous respiration, in which
                   algal biomass  is oxidized to  generate CO2. Algal
                   death includes grazing by zooplankton (for diatoms
                   and greens only) and cell destruction through bacte-
                   rial  attack,  disease, physical damage, the natural
                   aging process, or other mechanisms. The distinction
                   between  phytoplankton reductions through death
                   and  reductions through  respiration, grazing  by
                   zooplankton, or settling is that upon death all the
                   carbon, nitrogen, and phosphorus  contained in the
                   algal biomass is returned to the carbonaceous BOD
                   (CBOD), organic nitrogen, and organic phosphorus
                   pools,  respectively.  During  respiration, carbon is
                                             2-18

-------
given off as CO2 rather than CBOD; through grazing,
only a portion of the organic contents of the algal cells
is  returned to  the respective organic pools.  (The
remaining portion is lost from the phytoplankton mass
balance as zooplankton biomass.)

The algal reduction rate, Dp, can be expressed as:

                DP=DP1(T) + DZ           (2-18)
where
     DP1(T)
     Dz
temperature-dependent
endogenous respiration rate
(day1)
death rate (day'1) (grazing
and natural mortality)
The  phytoplankton death rate, Dz, is  a  function of
zooplankton  population and zooplankton grazing
rate.  Zooplankton control the population of phyto-
plankton through predation.  They also recycle the
nutrient  content of their  prey.   Limited  data on
zooplankton usually do not allow elaborate formula-
tion of zooplankton grazing.  Given the concentration
of zooplankton and their filtering rate, the loss rate of
phytoplankton due to  zooplankton grazing may be
formulated.

2.3.5.3  Algal Settling

Phytoplankton are lost from the water column through
settling.  In a vertically  mixed water column, the net
settling rate (i.e., settling to the bottom less resuspen-
sion from the bottom) is expressed as:
where
     S
     Vs

     H
                         H
net settling rate (day ),
phytoplankton settling
velocity (m/day)
average depth (m)
                       (2-19)
Through settling, none of the organic cell material is
returned to the organic  nutrient pools in the water
column  unless the model incorporates  an explicit
dynamic link between the sediments and the water
column  (e.g.,  Di Toro et al. 1990). As with  other
parameters in a eutrophication model, the phyto-
plankton settling velocity is dependent on the algal
species group and size of the organism.  Settling
velocities can range from 0.1 to 10.0 m/day with small
cells (e.g., chlorophytes) characterized by low veloci-
ties of 0.1  m/day  and larger cell size diatoms by
higher velocities (1-10 m/day).

2.3.5.4  Nitrogen Components

The  major components of the nitrogen system are
detrital organic nitrogen, ammonia, nitrite,  and ni-
trate. In natural waters, there is a stepwise transfor-
mation from organic nitrogen to ammonia, nitrite, and
nitrate, yielding nutrients for phytoplankton growth as
shown in Figure 2-5.  The kinetics of the transforma-
tions are temperature-dependent.

During algal respiration and death, the cellular nitro-
gen is returned to the organic nitrogen pool. Organic
nitrogen undergoes a bacterial decomposition whose
end product is ammonia.  Ammonia, in the presence
of nitrifying bacteria and oxygen, is oxidized to nitrite
and to nitrate (nitrification).  Both ammonia and ni-
trate are available for uptake and use in algal growth;
however, for physiological reasons the preferred form
of nitrogen  is ammonia  (Conway, 1977; Garside,
1981).  The ammonia preference  term is  charac-
terized in  Figure 2-10. As the available nitrate in-
creases  above  approximately  the Michaelis
limitation,  for a given ammonia concentration, the
preference for ammonia reaches a plateau. Also, as
the concentration of available ammonia increases,
the plateau  levels off at values closer to unity, i.e.,
total preference for ammonia.

2.3.5.5  Phosphorus Components

In many stream water quality models, phosphorus is
accounted for in two forms: dissolved and paniculate.
A fraction of the phosphorus released during phyto-
plankton respiration and death is in the inorganic form
and readily available for uptake by other viable algal
cells. The remaining fraction released  is in the or-
ganic form and must undergo a mineralization or
bacterial decomposition into inorganic  phosphorus
before it can be used by phytoplankton.

There  is an adsorption-desorption  interaction be-
tween dissolved  inorganic  phosphorus  and sus-
pended paniculate matter in the water column.  The
subsequent settling of the suspended solids together
with  sorbed  inorganic phosphorus can act as a sig-
nificant loss mechanism in the water column and is a
source of phosphorus to the sediment. Compared
with  the reaction rates for the algal and  biological
                                              2-19

-------
                                                - 25/jg/L
c
Q
£
c

u_
o
t—

o

J3

al
        1,0
        0.8  -'
        0,6  -
        0,4  -
        0.2
                                                       NH  - 200
                                                             100 /jg/L
                                                              SO jugfi.
                                                              25
                                                              SO
                                       N0
                                                                    150
                                                                                     200
           FIGURE 2-10.  AMMONIA PREFERENCE STRUCTURE FOR ALGAL GROWTH
                             (After Thomann and Fitzpatrick, 1982)
kinetics, which are on the order of days, the adsorption-
desorption rates are much faster, permitting an  in-
stantaneous  equilibrium assumption  for the
calculation. In the model formulation, the concentra-
tions of dissolved and paniculate phosphorus need to
be repartitioned at every time step.  A wide range of
partition coefficients for phosphorus have been found
in the literature.  Schreiber and Rausch (1979)  re-
ported partition coefficients ranging  from  4,540 to
15,900  for a  flow  detention  reservoir (see  also
Thomann and Fitzpatrick, 1982).


2.3.5.6  Sediment Nutrient Release

In addition to the external sources of nutrients, the
release of nutrients  from the sediments may also be
important.  Such releases occur as a result  of a
gradient in nutrient concentration between the over-
lying water and the  interstitial water of the sediment.
In some systems, the impact  of sediment nutrient
release can be significant and can result in continuing
eutrophication  problems even after point  sources
have been substantially  reduced through control
measures.   Sediment nutrient releases can  be
                                              treated as nutrient sources to the stream in waste
                                              load allocation modeling studies.  In the absence of
                                              site-specific field data describing sediment nutrient
                                              release, approximations can be made on the basis of
                                              sediment  oxygen demand estimates (see Appendix
                                              A).
                                              2.4    GOVERNING EQUATIONS
                                              2.4.1   Mass Balance Principle

                                              The basic principle used to formulate a stream water
                                              quality model is mass balance.  That is, for a given
                                              segment of the stream, the accumulation of a water
                                              quality constituent over a finite period of time is equal
                                              to the mass entering the segment plus the mass
                                              added to the segment, less the mass  leaving the
                                              segment and the mass lost within the segment (Fig-
                                              ure 2-11).

                                                     Accumulation = Mass In - Mass Out

                                                                 + Source - Sink        (2-20)
                                             2-20

-------

Oxyger. aadM 10 segment by
• Atmosphere feaerattsfi
K] a :

=  river flow rate (LIT)
Dimension Code

L   =  length
M  =  mass
T   =  time
Q
C       =   concentration of dissolved oxygen (M/LJ)
Cs      =   saturation concentration of dissolved oxygen (MIL3)
L       =   CBOD concentration (MIL3)
H       =   mean water depth (L)
dV      =   segment volume AAx(L3)
as      =   the stoichiometric ratio of oxygen production per unit of algal photosynthesis (M/M
014      =   the stoichiometric ratio of oxygen uptake per unit of algae respired (M/M)
as, ae   =   the stoichiometric ratio of oxygen uptake per unit of ammonia and nitrite-nitrogen oxidation,
            respectively (M/M)
PI, f>2   =   ammonia and nitrite oxidation rate coefficient, respectively (T 1)
|i       =   algal growth rate coefficient (T1)
p       =   algal respiration rate coefficient (T 1)
Ni, N2   =   ammonia and nitrite-nitrogen concentration, respectively (MIL3)
Ag      =   algal biomass concentration (MIL3)
SOD    =   temperature-adjusted rate constant for SOD (M/L2T)
Ka      =   atmospheric reaeration rate: reflects first-order reaction whereby a fraction of oxygen deficit is
            satisfied, eK*l= e~^w(T~1)
Ka(Cs-C) =   change in dissolved oxygen concentration in a segment that when, multiplied by segment volume
            (dV), yields change in dissolved oxygen mass in segment (M/L3T)
Kd      =   BOD oxidation rate where oxidation accounts for all CBOD removal (T 1 )

       FIGURE 2-11.  MASS BALANCE EQUATIONS  FOR DISSOLVED OXYGEN
                               (See Equations 2-21 and 2-24)
                                   (After USEPA, 1983a)
                                    2-21

-------
Applying the mass balance principle and considering
a small segment of a stream, one may develop:

       d VAC = QCAt- [Q + AQ] [C- Ax] Af
                                dx
             + WAt-dVKCAt
                                           (2-21)
where

     dV

     AC

     Q
     C
     At
     AQ

     ac
     dx

     w
     K
    volume of the segment and
    is equal to AAx(L3)
    change of concentration
    (MIL3)
=   flow rate (L3/t)
    concentration (MIL3)
    small increment of time (t)
    change of flow rate over the
    length
    concentration gradient over
    Ax (MIL4)
    direct loading rate (Mt~1)
    first-order reaction rate (t"1)
Dividing Equation 2-21 by dVAf results in
          d£ = ^<3dC_£dQ   W__Kr
          dt~ A dx   A  dx + dV
                                           (2-22)
Assuming steady-state conditions and neglecting the
flow gradient, the above equation becomes
                   QdC   W
                                           (2-23)
Note that the reaction term KC may represent formu-
lations for carbonaceous deoxygenation, nitrogenous
deoxygenation, reaeration,  or any  other first-order
reactions.

2.4.2    Dissolved Oxygen Equation

Using the notation in Figure 2-11, the distribution of
dissolved oxygen may be formulated by including all
dissolved  oxygen sources  and  sinks  described in
Section 2.3:
              - 0.5 (3l A/1 - OC6 (32 A/2
                              SOD
                               H
                                                                           (2-24)
The terms on the right side of Equation 2-24 repre-
sent, respectively: the downstream transport of oxygen
with the stream flow, atmospheric reaeration, biologi-
cal oxidation of CBOD, biological oxidation of ammo-
nia, biological oxidation of nitrite photosynthesis less
respiration, and the biological oxidation of sediment
materials. If CBOD is removed only by direct oxida-
tion, the deoxygenation rate coefficient, K
-------
                      wut« Biiehkr-g* :>: * 0, T • o)
Q

O
o «-





      V^-l
                                         (K
      3    Z    *    *     •    10   12   14    it    tg    20

                         TIME Ways)
               Distance (Miles) (U = 2mJ/day)

      fl    4    a     12   IV   29   !•*    39    32    36
   „ .,	,	j	....  __._._



3,,-\      '
 O5      \       3»««t

j
Z 8 -   •-,    i
LU
0
X *
o
o
yj
             f
O            i   x-^
C/3            '      """---_         DOProlll* wl1*N»
Q

              fer>
           2    4    fl     8     10   12   14   If    II    26

                        TIME  (days)

        FIGURE 2-12. COMPONENTS OF DO PROFILE (SAG CURVE)
                DOWNSTREAM OF WASTE DISCHARGE
                             2-23

-------
0, the initial CBODU concentration  is 10 mg/L  fol-
lowing complete mixing between the waste load and
stream flow.  After 10 days  all of the CBODU has
been exerted.  Since the CBOD test measures the
amount of organic material present  in terms of the
amount of  oxygen required  for its stabilization  by
bacteria, the reduction of CBOD concentration is
equivalent  to the dissolved oxygen consumption.
The bottom plot in Figure 2-12 shows two calculated
dissolved oxygen profiles associated with the CBOD
profile in the top plot. The lower profile represents the
dissolved oxygen concentration in the river if oxygen
were not replenished by reaeration.  In this case, the
assumed initial dissolved oxygen concentration of 12
mg/L is  ultimately reduced to 2 mg/L to compensate
for the  CBOD reduction  (in top  plot).  The upper
profile indicates the net effect of reaeration providing
a source of oxygen.

The characteristic  shape of the stream dissolved
oxygen  profile (called the DO sag curve) is the result
of interplay of the biological oxidation and reaeration
rates. Each is represented by first-order kinetics.  In
the early stages, oxidation greatly exceeds reaeration
because of high CBOD concentrations and river dis-
solved  oxygen  concentrations close to saturation
(i.e., small deficit).  Oxygen is used faster than it is
resupplied, and stream dissolved oxygen concentra-
tions decrease. As the waste moves downstream,
the consumption of oxygen decreases with the stabi-
lization of waste and the supply of oxygen from the
atmosphere increases because of  greater deficits.
The driving force to replenish oxygen by atmospheric
reaeration is directly proportional to the oxygen defi-
cit, (i.e., low oxygen concentration).  At some point
downstream from the waste discharge, the decreas-
ing utilization and the  increasing supply are equal.
This is the critical location, where the lowest concen-
tration of dissolved oxygen occurs.  Further down-
stream,  the rate of supply exceeds the utilization rate,
resulting in a full recovery of the dissolved  oxygen
concentration.   The above discussion  is a simple
illustration of the BOD/DO modeling analysis concept
when it  is assumed that organic decomposition and
reaeration are the dominant pro-cesses affecting the
organic  balance.  In reality, many other factors such
as nitrification and SOD can significantly change the
shape of the profile.  Many streams receive nonpoint
sources upstream or other point sources that depress
the upstream dissolved oxygen below a saturation
value. Natural background loading also may depress
dissolved oxygen in certain streams. Note that con-
stant  hydraulic  geometry is also assumed in the
above illustration. In a natural stream, it is difficult to
find constant hydraulic geometry for more than a few
miles.   In this case, the stream is divided into  a
number of reaches with uniform geometry.

2.4.3    Separate Mass Balance Equations by
        Constituent

Dissolved  oxygen dynamics depend on the interac-
tions of several  constituents and processes.  The
constituents that directly  influence  oxygen include
BOD, ammonia  nitrite, and  nitrate.  Nitrogen and
phosphorous determine growth  of phytoplankton,
periphyton, and aquatic plants and subsequently af-
fect dissolved oxygen via  photosynthesis and respi-
ration. For each constituent that is  in the dissolved
oxygen  mass balance, a separate mass balance
equation is used to account for the reactions of that
parameter. Using  the notation developed thus far,
these constituents may be modeled by the mass
balance equations summarized in  Table 2-4. The
mass balance equations in Table 2-4 can be found in
many stream water quality models  (e.g., QUAL2E)
that have been used inTMDL studies. Thomann and
Mueller  (1987)  present a simplified version of the
eutrophication equation for river and stream eutrophi-
cation analysis.

One should note that the major difference between
the BOD/DO modeling and  nutrient/eutrophication
modeling is in terms of the model formulations.  That
is, the equations governing phytoplankton growth are
nonlinear functions of nutrients and  light availability,
whereas the BOD/DO equations are all linear. In fact,
the phytoplankton/nutrient problems are the most
difficult models to work with because of the complex-
ity of the  algal biology, the nonlinear interactions
between nutrients and aquatic plants, and the inter-
actions of the sediment-water column interface. As
a result, the superposition of results from BOD/DO
equations  is appropriate to isolate the effects of the
various  linear reaction terms, whereas the same is
not true of the eutrophication results.
                                              2-24

-------
 TABLE 2-4.  SEPARATE MASS BALANCE EQUATIONS USED FOR EACH CONSTITUENT IN BOD,
                               DO, AND NUTRIENT ANALYSES
Carbonaceous BOD (CBOD)

Ammonia Nitrogen

Nitrite Nitrogen

Nitrate Nitrogen

Organic Nitrogen

Algae

Organic Phosphorus

Dissolved Phosphorus

                                   d{ = ps A/4 - Pi A/1 +-fi- Fa^iAg
                                  C/A/2
                                   dt
                                  dN3
                                   dt
                                  C/A/4
= pi A/1 - p2 A/2

= p2 A/2 - (1 - F)ai |i Xlfl

        - PS A/4 - 04 A/4
                                   cff
                                              dt
                                      = OC2 p Ag - P4 Pi - 05 Pi
                                      = P4Pl+^-(
Variables and coefficients not previously identified in Figure 2-11:
A/3


A/4


Pr
0.2

Ks
nitrate nitrogen concentration             F
(MIL3)
organic nitrogen concentration            pg
(MIL3)
organic phosphorus concentration         $4
(MIL3)
dissolved phosphorus concentration       02
(MIL3)
fraction of algal biomass that is            o3
nitrogen (M/M)
phosphorus content of algae              04
(M/M)
effective loss rate due to settling (T ~1)       05
            fraction of algal nitrogen uptake
            from ammonia pool
            organic nitrogen hydrolysis rate
            coefficient (T"1)
            organic phosphorus decay rate
            (I'1)
            benthos source rate for dissolved
            phosphorus (M/L2T)
            benthos source rate for ammonia
            nitrogen (M/L2T)
            rate coefficient for organic nitrogen
            settling (T"1)
            rate coefficient for organic
            phosphorus settling (T ~1)
                                             2-25

-------

-------
3.  MODEL SELECTION   AND REVIEW
3.1    PURPOSE
The  purpose of this chapter is to provide general
guidance and some specific procedures for selecting
an appropriate model(s) to support the development
of TMDLs for BOD, DO, and nutrients in streams and
rivers. Section 3.2 presents an overview of Chapter
3. Section 3.3 identifies and discusses the steps of
model selection. A brief review of selected models is
presented in Section 3.4. As stated earlier, the mod-
els reviewed in this guidance emphasize the fate and
transport of BOD, DO, and nutrients in streams and
rivers.
3.2    OVERVIEW
The  success of a modeling effort  to support the
development of TMDLs is highly dependent on under-
standing the complexity of the water quality problems.
This understanding will assist in defining the required
accuracy, analyzing the implication of various simpli-
fying assumptions, and eventually selecting an ap-
propriate modeling strategy and modeling tools. It is
generally known that the preferred and most cost-ef-
fective approach is to use the simplest model that
includes all the important processes affecting water
quality in the stream or river. Problem understanding,
normally gained through characterization studies us-
ing available data, provides answers to questions
such as the following: Are nonpoint sources an impor-
tant contributor to water quality impairment? Are non-
point sources or a  portion of nonpoint  sources
controllable? Is watershed modeling  necessary, and
if so, what is the sufficient level of detail? What are
the temporal and spatial boundaries of impaired wa-
ters? In general, the results of watershed and water
quality characterization define the modeling needs as
well  as the need for  monitoring, field surveys, and
other support activities. The selection of too simple
a model may result in inaccurate predictions of man-
agement needs and their implications on water qual-
ity.  The cost implications of  decisions are also
important factors. Furthermore, inaccurate projec-
tions from present to future cond itions may be caused
by an insufficient sensitivity of the selected model(s)
to changes in watershed or water quality processes
such as the balance of sediment oxygen demand in
specific reaches or seasons.

On  the other hand, the selection of too complex a
model can result in misdirected resources, delays in
the  study, and unnecessary costs. Predictive uncer-
tainty may increase because of extra "free" model
parameters that cannot be estimated with available
data and resources.  Study costs will increase be-
cause of the additional data needs, as well as model
calibration and validation requirements.  When water
quality impairment is characterized as the result of
both controllable point  and  nonpoint loadings, the
selection of the modeling tools should be compatible
so that watershed  modeling results provide the data
necessary for analysis  of the water quality in the
receiving water.
3.3    MODEL SELECTION
Successful model selection results from achieving a
close match of the primary site-specific physical,
hydrologic, and water quality features of interest to a
model's capabilities to simulate these features. Two
categories of models are available for use throughout
the TMDL process.  The first category consists of
watershed models that can be used to derive pollut-
ant loadings from both point and nonpoint sources.
Watershed models rely on (1) hydrologic processes
and water balance over the watershed and (2) the
physiographic characteristics of the watershed in-
cluding land  use and land cover, soils, topography,
water uses, and discharges from municipal and in-
dustrial facilities. A detailed review of these models
in terms of their potential application in the develop-
ment of TMDLs is presented in USEPA (1992b).  The
second category consists of receiving water models
that can be used to assess the impact of pollutant
loadings on the waterbody. These  models rely on
(1) transport characteristics of  the receiving water
including flow rate, stream morphology and bounda-
ries, and reaeration and dispersion parameters and
(2) fate of the pollutant within reaches of the receiving
water. Available watershed and water quality models
range from simple empirical and statistical  proce-
                                              3-1

-------
dures to more deterministic  and multidimensional
models.  In addition, these models can be differenti-
ated based on a number of criteria including:

    •   Water quality constituents modeled.

    •   Spatial and temporal resolution of the results.

    •   Level  of detail used to simulate hydrologic
        and water quality processes.

    •   Level of effort and data requirements for the
        specific application.

    •   Ease of application including input and out-
        put data processing, user support, documen-
        tation, and operating requirements.

Most importantly, the selection process should focus
on determining which watershed and water  quality
processes  closely match the site-specific charac-
teristics. As  mentioned  earlier, the results  of the
characterization of watershed and water quality con-
ditions can facilitate this selection process by  provid-
ing  information  for  (1)  establishing  the  study
objectives  and  constraints,  (2) determining  the
needed  detail to represent the pollutant loadings and
the problem boundaries  and identifying the  critical
conditions in terms of their temporal and spatial reso-
lution, and  (3) determining the pollutant of concern
and the  required mathematical formulations of hydro-
logic and water quality interactions.

A preliminary step of the characterization study con-
sists of reviewing existing information about the water
body so that  the dominant  physical and chemical
processes can be defined. This information includes
available site-specific analyses, monitoring data, past
modeling studies that  identify pollution sources and
their magnitude, stream  flow  data,  hydrologic and
statistical characteristics,  and ambient water  quality
impairment. In certain instances, simple calculations
and statistical procedures may be required prior to the
modeling selection process.  These procedures may
include pollutant loading estimates using simple load-
ing functions,  pollutant transport predictions using
analytical steady-state methods, and prediction of
water quality violations using standard excursion and
trend analysis techniques. The result of this  step is
a detailed description of the modeling objectives and
the potential and anticipated constraints. The mag-
nitude of nonpoint source loadings  and the  signifi-
cance of their impact  on the receiving water may
dictate whether a watershed model is necessary or
whether a steady-state water quality model is suffi-
cient to  represent the dominant transport processes.
A second step may involve further evaluation of the
variability of  pollution sources  and the hydrologic
regime to assess the level of modeling effort required
to represent the temporal and spatial resolution of the
water impairment under consideration. Analysis of
the variability of pollution loadings  from  various
sources  and  identification  of critical  water quality
impairment conditions will result in identification of the
temporal and spatial resolution  to be considered in
model development in order to  ensure an accurate
representation of the system. At this stage, the analy-
sis should ensure that the watershed model and the
receiving water quality model are properly addressing
the key decisions and that all assumptions are within
the acceptable range.  Although most streams and
rivers can be represented using a one-dimensional
steady-state model, certain wide or deep reaches
may exhibit significant lateral and vertical water qual-
ity gradients,  therefore requiring a two-dimensional
configuration.  In both of these cases, a simple wa-
tershed loading model  may be  sufficient to provide
input  data for the water quality  model.  However, a
more  detailed continuous or design storm watershed
simulation model may be required if water impairment
is characterized as storm-driven events, if a dynamic
representation of water quality is required to capture
daily variabilities, or if conditions where specific pol-
lutant concentrations violate certain criteria must be
defined.
In a third step, an initial assessment of the dominant
water quality interactions is necessary to ensure that
the proper combination of constituent  and kinetics
formulation is represented by the model. For exam-
ple, where algal photosynthesis and respiration are a
small component of the dissolved oxygen balance,
the corresponding terms and rate coefficients can be
ignored in the model equations. Similarly, sequential
reaction of the various forms of organic and inorganic
nitrogen may be highly nonlinear, resulting in time and
space lags in the resultant dissolved oxygen profile
and therefore making it inadvisable to select a model
that combines all nitrogen reactions in a single term.
The steps listed above are addressed in more detail
in the following sections, with special  emphasis on
BOD, DO, and nutrients in streams and rivers. Model
selection may require a phased  approach in which
simple formulations are considered initially in Phase
1 ofTMDLdevelopment. As new monitoring data and
characterization studies become available, a more
detailed modeling effort can be considered.
                                               3-2

-------
3.3.1   Study Objectives and Constraints

The first  step  in selecting an appropriate model to
support the development of TMDLs is to review the
existing data on pollutant loadings, stream flows, and
ambient water quality regarding the designated uses
of the stream and the applicable water quality stand-
ards.  Estimation of pollutant loadings from a water-
shed may also require land use distribution data, soil
characteristics, information on existing management
practices, and pollutant buildup and washoff parame-
ters in addition to climatic and hydrologic charac-
teristics.  These data should be reviewed to indicate
whether standards violations or water quality prob-
lems  are associated with diel fluctuations, storm
events, flow variations, or seasons of the year.

In selecting a receiving water quality  model, the
modeler  can use this information to determine the
temporal resolution  (steady-state,  quasi steady-
state, real time)  and to specify the magnitude and
variability of point and nonpoint sources that must be
included  in  the selected modeling approach.  Ambi-
ent water quality data should also indicate where
violations or impairment problems are occurring and
whether  significant spatial gradients  in  concentra-
tions exist.  The combined information collected  on
the watershed and hydrologic characteristics and
water quality problems will help determine the level
of effort  needed and the type of water and water
quality processes that must be considered.  Exam-
ples of processes of major concern when modeling
BOD, DO, and nutrients in stream and rivers include
CBOD oxidation, nitrite oxidation, sediment oxygen
demand,  ammonia oxidation, atmospheric  reaera-
tion, and  algal  photosynthesis and respiration.

The modeling framework should include preliminary
mass balance calculations using simple models or
analytical equations to help define water quality proc-
esses. These simple models provide analytical solu-
tions for  various load scenarios under varying flow
conditions.  In-stream sinks and sources can also be
represented using simplified  formulations  such  as
zero- and first-order decay equations.

It is also  desirable to anticipate the technical issues
associated  with  pollution control scenarios, overall
control levels, and other changes in watershed char-
acteristics such  as changes in  land use  and the
addition of  new sources  of pollution.  These issues
can be summarized in terms of how these changes
affect the magnitude of pollutant problems and there-
fore the  modeling and  monitoring  needs.  These
needs actually represent the project objectives and
define a number of criteria to assist in selecting the
appropriate model.

3.3.2   Pollutant Loadings, Spatial and Temporal
       Resolution, and Transport Mechanisms
3.3.2.1  Pollution Sources

Various loads, sources, and sinks influence the dis-
solved oxygen distribution in streams and rivers. Up-
stream  sources of oxygen  demand or dissolved
oxygen  deficit can be caused  by point source dis-
charges from  municipal and industrial waste treat-
ment  plants; combined and separate sewer system
discharges and urban runoff;  and runoff from for-
ested, agricultural, and suburban drainage areas.
In-stream  processes that affect dissolved oxygen
distribution include sediment oxygen demand, ben-
thic regeneration, and oxygen production and utiliza-
tion by phytoplankton and other aquatic plants.

All sources that are explicitly included  in the TMDL
analysis require direct measurements on appropriate
time and space scales to define the magnitude of the
individual  source  by contaminant.  Under diverse
conditions, receiving water quality data are required
to evaluate the effects of both point and nonpoint
sources.

The primary contaminants of concern associated with
point  sources  are organic carbon compounds that
produce carbonaceous biochemical oxygen demand
(CBOD) and the reduced forms  of nitrogen that result
in nitrification.  For each source type, it is necessary
to define the  magnitude of the ultimate oxygen de-
mand for both classes of contaminants. In addition,
field and laboratory data  may be required to distin-
guish between the forms of organic nitrogen that can
hydrolyze  to ammonia and the nitrogen that is effec-
tively  refractory.   This distinction can be important
when nitrification is a  concern.  The effluents from
treatment  plants without nitrification can contain po-
tentially significant concentrations of organic  nitro-
gen.  The degree of nitrification required can be
influenced by the organic nitrogen level in the effluent
that  can  be  transformed to  ammonia and  sub-
sequently  oxidized in the stream or river.  Although
considered point sources, combined sewer overflows
(CSOs) are storm-driven and  contribute additional
pollutants of concern to stormwater discharges.
Groundwater may also contribute a significant portion
of nitrate to surface water, although unless proven
                                               3-3

-------
contaminated, this portion of the loading is uncon-
trollable within the short term.

Nonpoint source pollutants come primarily from agri-
cultural lands, forested watersheds, and urban storm-
water runoff.  Agricultural areas can contribute a
significant amount of nutrients depending on fertiliza-
tion programs. Organic loads also can be significant
during certain periods of the year. The main concern
associated with  agricultural  sources is  the storm-
driven aspect of the pollutant loadings and the direct
relationship between the occurrence of storm events
and  agricultural  practices (e.g., timing  and rate of
fertilizer application, soil plowing  and tillage tech-
niques, etc.).  Many researchers recognize that for-
ested watersheds represent  pristine conditions and
that pollutant loadings from these areas are the result
of natural processes.   These  loads  represent the
background  condition and should be considered un-
controllable.  However, some silvicultural  activities
(e.g., road  construction, timber  harvesting, pre-
scribed burning) may  result  in soil  disturbance and
erosion processes that represent concerns similar to
those for agricultural areas.  Pollutants  of concern
include high  suspended sediment and organic debris
from destruction of topsoils. Urban stormwater runoff
is also a complex, storm-driven source.  Stormwater
runoff transports significant amounts  of metals.
Build-up of dust and dirt on impervious areas repre-
sents a major process  characterizing urban sources.
Where combined sewer overflows (CSOs) are con-
tributing to stormwater runoff, additional pollutants of
concern  involve those  associated with municipal
waste.
the need to simulate the three-dimensional mixing of
an effluent in a stream.  This is especially true for
CBOD and dissolved oxygen, for which it can be
shown that only small errors result from treating the
effluent plume as  being  immediately mixed  at the
point of discharge (McCutcheon, 1989).  For ammo-
nia toxicity, as well as other toxic constituents, it is the
usual practice to meet chronic toxicity criteria at the
edge of the mixing zone and acute criteria at the end
of the discharge pipe and in the mixing zone of initial
dilution (ZID).  However, it  is also important to ac-
count for  the far-field effect of a  potentially toxic
discharge, since the extent of the toxicity is related to
ambient and effluent levels of pH, temperature, and
hardness.  An effluent discharge from an activated
sludge municipal wastewater plant that is charac-
terized by low pH in the effluent may not cause an
ammonia  toxicity  problem in the near-field mixing
zone. However, further downstream, after the pH has
returned to ambient conditions, ammonia toxicity can
occur in the far-field region.  Refer to the Technical
Support Document for Water Quality-based Toxics
Confro/(USEPA, 1991b) for additional information.

Certain rivers may  require a framework that encom-
passes a two-dimensional analysis. These situations
are generally associated with deep rivers or run-of-
the-river impoundments where vertical or lateral gra-
dients can be significant.  Depending on the
geomorphology, the upstream regions of lakes and
impoundments may be characterized by significant
lateral, as well as longitudinal, variations in dissolved
oxygen that would  require a two-dimensional analy-
sis.
3.3.2.2  Model Dimensions

Most receiving water modeling projects that address
dissolved oxygen in streams and rivers under sum-
mer, low-flow conditions can be adequately repre-
sented  as  one-dimensional,  steady-state
calculations.  Both theory and practice demonstrate
that dissolved oxygen gradients in streams and rivers
are most significant along the longitudinal axis. There
are only relatively minor vertical and lateral gradients
except in the initial mixing zone at the  point of dis-
charge. Near the shore of streams and rivers, lateral
gradients can occur from low oxygen conditions re-
sulting from groundwater inflow depleted of oxygen,
photosynthetic and respiratory processes of attached
algae,  elevated temperature, and reduced  velocity
(and atmospheric reaeration) of the shallower near-
shore area. Most, if not all, State laws and regula-
tions have  defined initial mixing zones that alleviate
If a second dimension (i.e., depth or width) is re-
quired, the  analyst should  provide justification  in
terms of the specific decision-making elements relat-
ing to controls and  treatment.  This requirement is
necessary  since the  additional  dimension  in the
analysis  for streams or rivers will usually require
substantially more data collection efforts and gener-
ally will result in a more complex model whose pa-
rameter values  cannot be  determined reliably in all
cases. Thus, if the study is not done well, the addi-
tional dimension can tend to weaken the analysis and
may adversely affect the ability to make decisions.
However, if significant vertical or lateral oxygen  gra-
dients are apparent in observed data, a two-dimen-
sional model should be used.

Three-dimensional analysis of stream and river  sys-
tems is still under development. These complex mod-
els are recommended only for TMDL decisions that
                                               3-4

-------
cannot be addressed in any other fashion.  If three-
dimensional models are required, they must be de-
veloped by experts in the field.

3.3.2.3  Spatial Extent

The spatial extent of the modeling analysis should
extend downstream of the dissolved oxygen recovery
zone  (see  Figure 2-12).  This spatial coverage is
necessary for several reasons:

    •    Reaeration is a dominant factor in the zone
        of recovery, and analysis can provide infor-
        mation on the value of the reaeration coeffi-
        cient.
    •    In many situations, a key issue is the pres-
        ence of nitrification and the rate at which it
        may  occur following treatment upgrades.
        Observations of nitrification in the zone of
        dissolved oxygen recovery could be valuable
        in defining bounds for nitrification rates to be
        considered in making projections under fu-
        ture conditions.
    •    Indications of phytoplankton or other aquatic
        plant growth can be obtained  by examining
        the dissolved  oxygen recovery zone  after
        treatment upgrades.

The information obtained from the zone of dissolved
oxygen recovery will depend, to a large extent, on the
uniformity of the stream.

3.3.2.4  Time Scale

The time scale selected for the analysis should be a
function of  both the observed water quality and the
dissolved oxygen standards or criteria for the system
being  analyzed.  Dissolved oxygen  analysis in
streams  and  rivers usually can be performed on a
seasonal time scale, employing either a steady-state
or time-variable analysis.  It is desirable to evaluate
water quality  data collected during several seasons
to determine the critical period to be analyzed.   The
most frequent critical period is the low-flow, high-tem-
perature summer period.  Winter periods, however,
may also be  critical because of ice cover (physical
restriction of  reaeration).  Fall may be significant if
upstream organic carbon sources from  phytoplank-
ton and/or aquatic plants result in large depressions
in dissolved oxygen. Also, spring floods that pick up
large amounts of organic debris from adjacent flood-
plains can result in severe dissolved oxygen deple-
tion or phytoplankton blooms.  Some Great Plains
streams experience a low flow in spring before snow-
melt occurs. A rigorous hydraulic analysis is needed
to define the critical  flow periods in relation to the
oxygen balance.

Next, the analyst must determine the time interval to
be  used in the water  quality analysis.   Several
choices available are listed below in order of increas-
ing complexity:
    •   Steady-state.

    •   Quasi-steady-state, including
            Constant loads—constant stream flow
            and diel dissolved oxygen production
            by phytoplankton or aquatic plants
            Constant loads—variable stream flow
            Variable loads—constant stream flow
            Other combinations of the above
    •   Fully time-variable dynamic analysis.

In a steady-state analysis, a spatial profile of concen-
tration is calculated, such as would result at equilib-
rium (stream flows, waste loads, temperature, etc.).
To the extent that actual variations in pollutant load,
stream  flow, and other factors can  be realistically
approximated  by constant conditions for the period
covered  by the analysis, the calculated  receiving
water concentration profile will approximate an aver-
age of the actual concentrations during that period.

A fully time-variable analysis performs successive
calculations at relatively short time steps and accepts
variable input values for parameters such as  stream
flow, pollutant load, and temperature. The results are
a record of both temporal and spatial fluctuations in
the calculated water quality concentrations. Practical
considerations of cost and operating time usually limit
the duration that can be covered by such an analysis
to critical conditions.

"Continuous" versions of time-variable  models  ex-
tend the calculations over longer periods of time by
using larger time steps and  averaging the variable
input over that period.  As a result, the calculated
receiving water concentrations will not reflect short-
term variations but will reproduce the  longer-term
fluctuation trends. Also available are complex kinetic
systems that relate oxygen levels to phytoplankton
populations (chlorophyll a), which in turn are control-
led by light, nutrients, zooplankton, and other factors.
These latter frameworks are time-variable and  re-
quire extensive data for model calibration and valida-
tion.
                                               3-5

-------
Quasi-steady-state analyses usually have one time-
variable element incorporated into what basically re-
mains a steady-state calculation. For example, the
QUAL2E program assumes that flow and loads are
constant and simulates the dynamic effect of sunlight,
temperature, and wind. Quasi-steady-state analyses
that use steady-state calculations supplemented by
time-variable  calculations of diel oxygen variations
are also available for streams and rivers (Chapra and
DiToro, 1991).

Continuous monitoring data (usually dissolved oxy-
gen and flow measurements) are useful to determine
the time resolution required for TMDL modeling.  For
example, if dissolved oxygen levels reach a constant
low value for  long periods and if flow and loads are
relatively constant, then a steady-state analysis
should be employed. Accordingly, if dissolved oxy-
gen levels fluctuate, then a quasi-dynamic or dynamic
analysis  may be in order.  However,  steady-state
averaging using  a daily averaging period should be
attempted before complex dynamic models are ap-
plied. If an average condition is investigated, then the
fluctuations about the mean and relationships be-
tween  the standard  (i.e., daily or  hourly average
dissolved oxygen standard) and the mean values
must be  investigated as well.  If necessary, some
safety  factor  must be  incorporated when  using  a
steady-state analysis to estimate the mean trends  in
a dynamically varying stream.

Variability in loads and upstream conditions does not
necessarily dictate a  dynamic analysis.  Only a sig-
nificant variable response in water quality during criti-
cal low dissolved oxygen periods justifies a dynamic
analysis to determine the exact critical conditions.

Diel variability due to photosynthesis also does not
necessarily justify a dynamic or quasi-dynamic simu-
lation.  It has been consistently proven possible to
simulate the average net effect of dissolved oxygen
production minus respiration (P-R).  However, the
fluctuation about the mean must be measured or
estimated by  alternative means and taken into ac-
count (see Thomann and Mueller, 1987).

In general, a steady-state analysis should be widely
useful.  Point sources, sediment oxygen demand,
groundwater  inflows, and  upstream  background
loads are approximately  constant or can be ade-
quately averaged. A dynamic analysis may be justi-
fied only if standards require that minimum dissolved
oxygen  levels be maintained  at  all times or for  a
significant portion of the time (i.e., 95 percent of the
time)  and loads are known to cause variable dis-
solved oxygen levels in the stream.  The effects of
photosynthesis  can normally be taken into account
with a steady-state analysis, or a dynamic  analysis
may occasionally be useful.  The dynamic simulation
is expected to provide more reliable predictions but
will require more data collection and more computa-
tions.  The increased amount of data input to perform
a dynamic analysis also creates difficulty with proper
interpretation of the results.

The issue of the time interval of the analysis  is in part
controlled by the  major  pollution sources.   Point
sources,  sediment oxygen demand, and upstream
conditions usually  can be represented by steady-
state modeling, which employs time-averaged values
for the loads from these sources. The same type of
analysis  can be appropriate for some nonpoint
sources, such as those associated with groundwater
inflow, leaching  from bottom deposits, and drainage
not directly related to transient events such as storm
runoff or  spills.  By contrast, event-related inputs of
mass, such  as  those associated  with storms that
produce urban runoff and runoff from other  land use
types, can require either a time-variable analysis or a
quasisteady-state analysis.  The quasi-steady-state
analysis often can  be considered in situations when
the receiving water is large and the incremental flow
associated with the study area  being  modeled is
small. For most  of these situations, however, a time-
variable analysis has been necessary.

The time-variable analysis can be applied satisfacto-
rily if sufficient data exist or can be obtained. Projec-
tions  present a  special set of problems in  terms of
identifying the storms or storm sequences to be used
to develop the TMDL. Furthermore, the event-related
dissolved oxygen problem can be influenced strongly
by the hydrograph  after the event and the  geomor-
phology of the downstream segments of the water-
body.  In addition, the basic technical, economic, and
environmental issues  associated with wet-weather
standards for dissolved oxygen have not yet  been
addressed fully.

3.3.2.5 Transport Mechanisms

The transport mechanisms that influence the distribu-
tion of wastes discharged into free-flowing  streams
and tidally mixed streams include advective transport
and dispersive transport.  Advective transport repre-
sents  the bulk transport,  by flow, and is often the
dominant net transport mechanism except in certain
                                               3-6

-------
tidally mixed streams where strong flow reversals
occur.   Dispersive transport represents the  mixing
(lateral  and longitudinal) caused by local velocity
gradients within the bulk fluid and is normally a small
portion of the net  transport  except in tidally mixed
streams where the net advective transport is over-
shadowed by the longitudinal mixing caused by peri-
odic, strong tidally reversing flows.

Dispersion is present, to some extent, in all bodies of
water.  However, water quality profiles, such  as dis-
solved oxygen concentrations, may not be influenced
when the dispersive mixing is small and/or the  advec-
tive transport is large.  In these situations, decisions
will not be influenced by inclusion of dispersion in the
analysis. For certain slow-moving streams with com-
plex configurations (e.g.,  bayous), the dispersion
process may be a major transport component. Con-
sequently, the complexity of the calculations and data
collection programs will be reduced. The importance
of dispersion is site-specific and can be estimated by
the following procedure:

STEP 1 - Calculate the approximate longitudinal dis-
persion coefficient  (Fischer et al., 1979).
c = 0.011 U2W2/HU*
                                            (3-D
where
      U

      W
      H
      U*
      longitudinal dispersion
      coefficient (ft2/sec)
      average stream velocity
      (ft/sec)
      stream width (ft)
      stream depth (ft)
      shear velocity (ft/sec)
The Shear velocity (O) for many streams is approxi-
mately one-tenth of the average stream velocity and
can be estimated by:
where
      9

      S
  =   gravitational constant (32.2
      ft/sec2)
  =   stream slope (ft/ft)
        - Calculate the estuary number (n) as de-
fined by O'Connor (Hydroscience, 1971). The longi-
                                  tudinal dispersion coefficient can be employed with
                                  stream velocity and oxidation rate (Kd) to develop this
                                  dimensionless number.
                                                     n =
                                                           U
                                            (3-2)
                                  The estuary number (n) and the ratio (O) of the
                                  reaeration rate coefficient (Ka) to the oxidation rate
                                  coefficient (Kd) or
                                                      0= Ka/Kd
                                            (3-3)
can be used, with Figure 3-1, to provide a basis for
judging the significance of dispersion in calculations
of dissolved oxygen concentration.

Figure 3-1 indicates that for advective streams with
values for n of about 0.1 or less, neglecting dispersion
effects will affect the  calculation of the maximum
dissolved oxygen deficit (critical deficit, Dc) by less
than  10  percent.  When  considering steady-state
conditions,  dispersion can be  ignored.  Where
reaeration is high relative to  deoxygenation rates
(high values of O), the lack of sensitivity to dispersion
extends  to higher values  of n, as indicated by the
essentially horizontal lines for the higher values of O.

It should  be noted that the estimates of the dispersion
coefficient and the ratio of the maximum DO deficit to
the initial BOD concentration (DC/L0) incorporate sev-
eral simplifying  assumptions.   The foregoing  ap-
proach must therefore  be considered  to be an
approximation.  It should, however, be adequate for
use in most studies.

There may be situations where dispersion is consid-
ered significant by the investigator even though the
foregoing analysis suggests otherwise.   Examples
could include swamps, tidal rivers, or upstream seg-
ments of impoundments. If the computational frame-
work employed in the analysis  introduces dispersion
due to spatial segmentation or numerical approxima-
tions  (called numerical dispersion or  numerical mix-
ing), the study should contain an evaluation of the
influence of dispersion on calculations of water qual-
ity.  Finally,  the  influence of  dispersion on TMDL
decisions should also be  supplied in this  situation.
The requirement relating  to numerical mixing can
often be  met by comparisons of analytical solutions
with computer output under comparable conditions.

A  flow balance is required  for the modeling  effort;
                                               3-7

-------
 o i-
 £ z
 UJ Vi
       1,4
       24.
       2.0 L
         0.01
                                                                                    100.0
     FIGURE 3-1. DISSOLVED OXYGEN RESPONSE AS A FUNCTION OF ESTUARY NUMBER,
                      n = KdDx/U2 (Equation 3-2),  = Ka/Kd (Equation 3-3)
                                     (Hydroscience, 1971)
tial importance of groundwater inflow and outflow. In
addition, flow from significant tributaries and waste
sources must be included in the model. The compu-
tation of a flow balance is not a trivial aspect of model
development. Since USGS stream-flow gauge data
are often used to compute time-averaged steady-
state streamflow (e.g., monthly) at successive down-
stream station locations, the flow balance can
become somewhat difficult because of the  down-
stream travel time required for propagation of the flow
wave and the transient response of a drainage basin
to precipitation  events between two  successive
stream gauges. Any discrepancy in the downstream
balance of the USGS gauge time-averaged stream-
flow  data with known (or estimated) point source
inputs from waste discharges and gauged tributaries
is usually attributed to either ungauged tributaries or
groundwater flow. Each of the sources included in
the model must also be supported by data (or best
estimates) to characterize  the concentrations of sig-
nificant constituents, such as dissolved  oxygen,
BOD, and NH3, to compute the pollutant mass flux
rate.
Data on the cross-sectional area, depth, and time of
travel (or velocity), as a function of flow, are required
for the flows at which observed water quality data are
collected and at the critical flow regimes used for
projections.

3.3.3   Water Quality Pollutant Interactions

Dissolved oxygen dynamics depend on the interac-
tions of several constituents and processes.  The
constituents include dissolved oxygen,  carbona-
ceous BOD, ammonia,  nitrite, nitrate, temperature,
and in some cases phytoplankton, periphyton, and
aquatic plants.

These constituents and  processes may  be modeled
by a set of coupled mass balance equations such as
those in Table 2-4. The selection of constituents and
processes should be based on site- and problem-
specific  factors.   Documentation of the rationale for
selection of a particular combination of variables
should be provided in an early stage of the study and
should include an examination of observed water
quality data, considering each variable supplemented
                                             3-8

-------
by calculations and relating the selected  analysis
framework to the decisions to be made at the conclu-
sion of the project.

Ranges of the specific first-order rates for the various
reactions  are discussed in Appendix A, as are the
procedures for defining site-specific reaction rates for
various levels of treatment. The necessity for depar-
tures from this norm should be documented at an
early point in the project and should particularly ad-
dress the additional information required in the deci-
sion-making process.

There are  circumstances, particularly in systems with
low dissolved oxygen, in which nonlinear kinetic for-
mulations  can  be considered. The nonlinear formu-
lation usually employed is Michaelis-type kinetics, in
which the overall rate  of reaction decreases as  a
chemical species is depleted.  Dissolved oxygen is
one of the  chemical species that controls these types
of kinetic formulations.  In particular, the rate of nitri-
fication has been shown to be controlled by dissolved
oxygen levels  at and below 2 mg/L  (Hydroscience,
1971; Carlucci and MacNally, 1969).

One  method of approximating the nonlinear nitrifica-
tion reactions has been to use lower values for first-
order reaction rates in areas of low dissolved oxygen
concentrations.  Low dissolved oxygen concentra-
tions can also reduce the rate of BOD oxidation  and
sediment oxygen utilization and increase the release
of contaminants from the benthos. These latter reac-
tions are influenced only at very low dissolved oxygen
levels such as 0.1 mg/L or lower. In bodies of water
with  large detention times,  feedback  reaction  se-
quences  have occasionally been employed
(Thomann, 1972).  For  example, the death and de-
composition of algal cells returns organic nitrogen to
the system. Feedback reactions can utilize first-order
kinetics in  dissolved oxygen analysis and have been
used to model larger estuaries  (see Thomann  and
Mueller, 1987; O'Connor et  al., 1973).  The usual
reaction sequence employed for dissolved oxygen
investigation is shown in Figures 2-2 and 2-5. This
feedback reaction sequence  may be appropriate for
larger river systems.

Many models use a simplified framework that ignores
or combines some of the processes in Figure 2-5. For
example, in systems where photosynthesis and  res-
piration are small components of the overall oxygen
balance, the corresponding terms and equations can
be left out  of the analysis (e.g., Wu and Ahler,  1979).
Simple models and hand calculation techniques often
represent the nitrogen cycle using a single nitrification
equation (e.g., Wu and Ahler, 1979) or combine the
nitrogenous  and carbonaceous BOD into a  single
constituent representing total BOD.

Even when the nitrogen cycle is not combined into a
single BOD equation, models differ in the number of
stages included in the cycle.  The complete sequence
should include hydrolysis of organic nitrogen to am-
monia and oxidation of ammonia to nitrite and nitrite
to nitrate.  However, many  models do not include
organic nitrogen as a separate constituent (e.g., Wu
and Ahler, 1979; Johanson et al., 1984).  It will be
important in  many situations to distinguish between
organic nitrogen and ammonia concentrations, rather
than to define the nitrogenous oxygen demand (NOD
or NBOD) load on the basis of total Kjeldahl nitrogen
(TKN) concentrations,  which are composed of both
these forms.  Time and space lags in the resultant
dissolved oxygen profile, due to this sequential reac-
tion, may be significant. If the two species of nitrogen
are combined in the calibration and validation effort,
the apparent nitrification rate (Kn) will be lower than
the actual first-order nitrification rate of ammonia.
The ratio of  TKN to NHs-N  affects the value  of the
overall oxidation rate. Where this ratio changes after
treatment, the modeler is faced with additional uncer-
tainty.  Several models (e.g., Brown and  Barnwell,
1987; Ambrose et al.,  1988) include both organic
nitrogen and organic phosphorus capability.  Many
models also leave out nitrite  so  that ammonia  is
oxidized  directly to nitrate in the  model equations
(e.g., Ambrose et al., 1988).  In many situations, the
NO2 concentration level observed  and calculated  is
very low or tends to be uniform, reducing the uncer-
tainty  of this simplification.  It should also be noted
that where algal problems are severe, NHs may be
taken  up directly by algae.


Several levels of analysis can be used for considering
the influence  of phytoplankton and other aquatic
plants.  These are summarized in  Table 3-1.  Level
A, which uses measured  values of photosynthesis
and respiration (P-R) and diel dissolved oxygen data
may be satisfactory in many cases. When significant
changes in nutrients or light extinction coefficient are
anticipated, the Level B analysis should be consid-
ered.
Level C represents a full-scale eutrophication ap-
proach, which increases the project costs for data and
modeling by several orders and should be used when
the problem is dominated by photosynthetic oxygen
                                              3-9

-------
   TABLE 3-1.  METHODS OF ANALYSIS FOR
   PHYTOPLANKTON AND AQUATIC PLANTS

A.    Measure P-R and/or diurnal swings in DO: employ
      measured value in steady-state or quasi-steady-state
      models.
B.    Measure chlorophyll a, light, light extinction, nutrients:
      employ the results in steady-state or quasi-steady-state
      models.
      Calculate P-R.
      Compare to P-R data and diurnal swings.
C.    Model chlorophyll a, nutrients, dissolved oxygen, etc.
      With calibration and validation, a time-variable,
      nonlinear modeling framework is required.
production and utilization and where environmental
or control costs are significant.

Eutrophication analyses require models that simulate
nutrient and algal dynamics.  Phosphorus and nitro-
gen are generally the only nutrients considered, al-
though silica  can be considered  if diatoms  are a
dominant component of the phytophankton commu-
nity.  The major processes include algal uptake, algal
excretion,  sediment release,  and nitrification.
Periphyton and aquatic plants are rarely included in
water quality models  because of the difficulty in pre-
dicting biomass of these parameters although these
photosynthetic organisms can be significant compo-
nents of the oxygen and nutrient balance, specifically
in shallow rivers (e.g., Jeppesen and Thyssen, 1984;
Horner and Welch, 1981).  An  analytical framework
described by  Level A in Table 3-1 can be used to
estimate the diel fluctuations in dissolved oxygen due
to aquatic plants.  More quantitative modeling ap-
proaches would require equations analogous to
those used  for algae  except that the settling term is
replaced  by a sloughing or nonpredatory mortality
term (e.g., Welch et  al.  1989).  The  alternative ap-
proach is to use field data to account for the net
photosynthetic contributions of water column algae,
periphyton, and rooted aquatic plants as a combined
(P-R) term in the oxygen balance model.

In addition to dissolved oxygen analyses,  ammonia
toxicity may be important. Ammonia toxicity is due to
the un-ionized form  of ammonia.  The un-ionized
fraction of total ammonia increases with pH and tem-
perature. Figure 3-2  shows this relationship.  Most
currently available water quality models do not simu-
late un-ionized ammonia or pH. Therefore, TMDLs
that involve ammonia toxicity must usually be based
on total ammonia simulations in combination with field
measurements of pH and temperature (e.g., Szumski

                5    10    IS    20    25    TO
                                                       •3.015
                                                        O.O1
      Q.OO1
  FIGURE 3-2.  EFFECT OF pH AND TEMPERA-
       TURE ON UN-IONIZED AMMONIA
          (Novotny and Krenkel, 1975)

et al., 1982; Yake and James,  1983).  Un-ionized
ammonia concentration  can be calculated  from
mod el-projected total  ammonia and  a  relationship
such as that shown in Figure 3-2. There are models
available for ammonia toxicity (e.g.,  STREAM DO
from EPA Region VIM).
3.4    MODEL REVIEW
In this section the term model, following commonly
used terminology, is used to describe computer pro-
grams. However, computer programs are not models
until the user structures them with  site-specific
boundaries, topography, hydrology, pollution buildup
and washoff,  stream configuration, and pollutant in-
teractions representative  of the contributing water-
shed, sources, and the receiving waterbody being
analyzed.

As stated earlier, TMDL development may require the
development  of a watershed or water quality model
or both,  depending on the  results of the charac-
terization study. The TMDL process creates a frame-
work for considering the management of both point
and nonpoint  pollution sources that contribute to wa-
terbody  impairment.  Although  in most cases dis-
                                              3-10

-------
solved oxygen problems are observed during low-
flow conditions  in streams and rivers where point
sources are the major pollutant load contributor, spe-
cial consideration may be required in instances where
nonpoint sources have a significant impact on dis-
solved oxygen levels. In these cases, the review and
selection of appropriate watershed models are nec-
essary.   The model selected should represent the
dominant processes inthewaterbody, should provide
the necessary management information on the mag-
nitude and variability of pollutant loading, and even-
tually should  allow for an evaluation of the
implications of various watershed management alter-
natives. Watershed models are not considered in the
present review.  However, interested readers are re-
ferred to USEPA (1992b) and Donigian  and Huber
(1991), where detailed reviews of these models are
presented to assist water quality analysts in selecting
the appropriate model for a specific TMDL problem.

Selected receiving water quality models with potential
application to analysis of dissolved oxygen variations
in  streams and  rivers  are reviewed in this section.
The criteria used for reviewing these models as part
of this document are as follows:

    •  They are in the public domain.
    •  They are available at a minimal cost from
       various public agencies.
    •  They are supported on a limited  basis by
       Federal and/or State agencies. The form of
       support is generally telephone contact to a
       staff  of engineers  and programmers who
       have experience with the model and provide
       guidance (usually free of charge).
    •  They have been used extensively for various
       purposes and are generally accepted profes-
       sionally.
    •  They represent a wide range of complexity.
       The more complicated models take into ac-
       count additional processes  and  simulate a
       given process in more detailed manner.

The selection procedure should  not be limited to
those models discussed in  this document.  Other
computer programs (models) that are available to a
project or organization should be given consideration.
USEPA (1979c) and  Hinson  and  Basta  (1979) de-
scribe many  other  available  water quality models.
The discussions and criteria presented in this docu-
ment can be employed as  major elements in the
selection process.  One additional consideration in
this process is the experience and familiarity of the
technical staff with a particular computer program.

It is suggested, however, that where project staffs do
not have access to or familiarity with other computer
programs, effort would be most effectively focused on
the computer programs selected for discussion in this
document. A brief description of the selected  com-
puter programs follows.  The models are listed  in
order of increasing complexity.  Source  code, ex-
ecutable files and sample  input  files for  EPA-sup-
ported models can be downloaded from E PA'sCEAM
electronic Bulletin Board Service (BBS); the phone
number for the BBS  is (706) 546-3402.  The BBS
system operator (SYSOP) can be contacted by tele-
phone at (706) 546-3524.


Simplified  Method  Program  for Multiple Dis-
chargers (Multi-SMP) (USEPA,  1992d) is a steady-
state, one-dimensional water quality  model  that
implements  EPA's Simplified Analytical Method for
Determining NPDES Effluent Limitations for POTWs
Discharging into Low Flow Streams (see Table 1-1).
The model predicts four water quality variables: dis-
solved oxygen, CBOD, NBOD, and un-ionized am-
monia.  Water quality processes  include reaeration,
deoxygenation, nitrification, and sediment oxygen
demand. The model considers up to 10 point source
discharges.  Multi-SMP can be  obtained  from the
Center for Exposure Assessment Modeling (CEAM),
Athens, Georgia (requires one diskette).


Enhanced Stream Water Quality Model QUAL2E
and QUAL2E-UNCAS (Brown and Barnwell, 1987)
are one-dimensional (longitudinal) water quality mod-
els that assume steady flow (steady-state hydraulics)
but allow simulation of diel variations in temperature
or algal photosynthesis and respiration.   QUAL2E
simulates a  series of piecewise, nonuniform seg-
ments that  make up  a river reach.  The  effects  of
withdrawals, branches, and tributaries can also be
included. Water quality variables simulated include
conservative substances;  temperature;   bacteria;
CBOD;  DO; ammonia; nitrite, nitrate, and organic
nitrogen; phosphate and organic phosphorus; and
algae.  QUAL2E is widely  used  for stream TMDLs
and discharge permit determinations in the United
States and other countries.  It has a 15-year history
of application and is a proven, effective analysis tool
(e.g., Crabtree et al., 1986).  QUAL2E Version 3
incorporates several uncertainty analysis techniques
useful in risk assessment.   This model can be ob-
tained from CEAM (requires four  diskettes).
                                             3-11

-------
Water Quality Analysis Simulation Program
(WASPS) is a dynamic compartment modeling sys-
tem that can be used to analyze a variety of water
quality problems in one, two,  or three dimensions
(Ambrose et al., 1993). WASPS simulates the trans-
port  and transformation  of conventional and toxic
pollutants in the water column and benthos of ponds,
streams,  lakes, reservoirs, rivers,  estuaries, and
coastal waters.  The WASPS modeling system cov-
ers four major subjects: hydrodynamics, conservative
mass transport, eutrophication-dissolved oxygen ki-
netics (EUTRO5), and toxic chemical-sediment dy-
namics (TOXI5). The modeling system also includes
a stand-alone link-node hydrodynamic program,
DYNHYD5, that simulates the transport of water.
WASPS, along with the associated programs TOXI5,
EUTRO5,  and DYNHYD5,  can be obtained from
CEAM.
Hydrological Simulation Program-FORTRAN
(HSPF)  is a comprehensive package that performs
continuous simulation of watershed hydrology and
water quality for both conventional and toxic organic
pollutants.  HSPF incorporates the watershed-scale
Agriculture Runoff Model (ARM) and  Non-Point
Source model into a basin-scale analysis framework
that includes fate and transport and transformation in
one-dimensional  stream channels (Johanson et al.,
1984).  It is the only comprehensive model of water-
shed hydrology and water quality that  allows the
integrated simulation of land and soil contaminant
runoff processes with in-stream hydraulic and sedi-
ment-chemical interactions.  HSPF,  however, is  an
extremely complex model that requires enormous
resources for development and application.  HSPF
can be obtained from CEAM (requires six diskettes).
CE-QUAL-RIV1 (US Army Corps of Engineers, 1990)
is  a  fully dynamic one-dimensional riverine water
quality model. The model comprises two submodels:
a  hydrodynamic model, RIV1H,  which  can stand
alone, and a water quality model,  RIV1Q, which re-
quires output from Rl V1H or another routing model to
drive it. Ten water quality variables can be simulated:
temperature, DO, carbonaceous BOD, organic nitro-
gen,  ammonia,  nitrate, phosphate, dissolved iron,
dissolved manganese, and coliform bacteria. Addi-
tionally,  algae/macrophyte  photosynthesis, respira-
tion,  and nutrient  interactions are  included.
CE-QUAL-RIV1  can be obtained from the US Army
Corps of Engineers, Waterways Experiment Station,
Vicksburg, Mississippi.
RIVMOD is a numerical, hydrodynamic,  and sedi-
ment transport riverine model that describes the lon-
gitudinal  distributions of flows and sediment
concentrations in a one-dimensional waterbody
through time. It can be used as an alternative to the
EPA-supported  link-node model, DYNHYD5.
RIVMOD is based on a 4-point implicit  numerical
integration scheme whereas DYNHYD5 is based on
an explicit numerical scheme.  RIVMOD is available
from CEAM, although EPA does not currently provide
support or documentation for the model.  RIVMOD
has been used by CEAM to link the transport output
data files  as input to the  previous version  of the
general WASP model, WASPS.

Three of the models discussed (WASPS, HSPF, and
CE-QUAL-RIV1), when operated in the fully dynamic
modes, are quite  complex and require well-trained
analysts.


The salient features of the first five models selected
for discussion are summarized in Tables 3-2 through
3-10.  Since QUAL2E is probably the most  widely
used computer model for  predicting the  effects of
conventional pollutants in  streams,  the tables use
QUAL2E as a reference point  against which other
models can be compared. The tables presented are
as follows:

Table 3-2   Constituents Modeled
Table 3-3   Summary of Capabilities
Table 3-4   Reaeration Formulations
Table 3-5   Input Data Requirements
Table 3-6   Ease of Application—Output Form and Content
Table 3-7   Ease of Application—Sources, Support, and
          Documentation
Table 3-8   Ease of Application—Equipment and
          Programming Requirements
Table 3-9   Operating Costs
Table 3-10  Hierarchy of Models Based on Selected Features

Information presented under the first four table sub-
jects (Constituents, Capabilities, Reaeration Formu-
lations, and Input  Data Requirements) is primarily
technical and is required to evaluate whether the
model  simulates  the  important physical and bio-
chemical features  of a problem.  Information pre-
sented under the table subjects Ease of Application
and Operating Costs is primarily nontechnical or re-
lated to operational features of the models.  This infor-
mation is needed to evaluate the cost associated with
and  the ease of  acquiring the model, getting the
model  running on the user's system, calibrating and
validating the model, and finally applying the model.
                                             3-12

-------







Q
HI
UJ
n
o
S
SA
z
p
p
Of
I
-i
UJ
Q
0
S
u,
o
z
1
1
b
o
S
ul
J
m











C^J •'— iff
1 I | £ | S
5 frfll f£
J£ m n S *" ^
.= » u S S 5"
f S § •§ s S
< 0 C » Q E
i .9
Jf
Q K
O 53
Q
1
I
e,

c
* e
£
M

5i5
<

.
o
*r

S

x x

X








X X



X X

X
IX X

fz x x
2* '
f X X
1* *
5*
a.
i x x

S
tu
a
Q
I'll
O
a




• *


<3
X X
X X
_

UJ rf
M 3
< 9
3 IU
O 0
e
>
User can add ov
subroutines to
modal osher
coftstilucrrts



x

4


^







x


X

x
X
X
X
X

*

*•



-------





IIUT1ES
flMARYOFCAPAi
«c
tfS
3
UJ
O
o
S

IL
o
g
cc
S
S
o
n
LU
CQ

r Ounlity
Psr«nwt«r Mod*lfld

n. I
1IJ


S
S "°
51
a *
° 0
p
?

i-s
1
I

n


Ii
im Of
o
I*
"3
M W
jj
S
0
F

1" S
i s „-
g- a 5 & i !
fe I ? ? 1 j


S


IS

11
III
111


i
CT






'
O

*
i_g|
t l^f
tfgi
iS fin S-

U^
cv
^
S
f I
|l
s-i
I §
"5
X-
-s'5,
ml
ml
i sj-8N»r 'JIK»V ur BOD.
i»4Js. C8OD,
HWIIlfifliS!. tmlll*lH-*B«
:voi>
ifl *
a e *
1 if.-
S3 | * | C
il fifi
1 !ftf


i
TJ
S


•g

if
MB
Hi
•e
1
g
1


ft

& |
11
jj i
a



1
S
1

* 8
> L.
*' S
•= ^

S 1 5 =
3 'is 1
1 si.-ui*f slway uf
, CBOD, ixijpwJ OO,
MWlhg, LHHMiht. 
-------
             TABLE 3-4.  COMPARISON OF MODELS: REAERATION FORMULATIONS
Model

QUAL2E

CE-QUAL-RIV1
WASPS
HSPF
Multi-SMP
Number of Options
             Options

A, B, F (after L), G, H, I, J, K, model
accounts for C
E (after M), F (after H), C (after N)
A, D, E (after M)
A, D, E, K
A, F (after H), J	
Sources of Stream Reaeration Options
A    Input directly
B    As a power function
C    Structural reaeration due to dams
D    Covar's method (automatic selection among H, I, and L)
E    Wind-driven reaeration
F    Calculated as a function of velocity and depth
G    Langbien and Durum (1967)
H    O'Connor and Dobbins (1958)
I     Owens etal., (1964)
J    Thackston and Krenkel (1969)
K    Tsivoglou-Wallace method (Tsivoglou and Wallace, 1972)
L    Churchill etal., (1962)
M    O'Connor (1983)
N    Wilhelms and Smith (1981)
The information provided in these tables is primarily
qualitative  and sufficient to determine whether a
model  may be suitable  for a particular application.
For some of the models, more quantitative informa-
tion is given in Evaluation of Water Quality Models: A
Management Guide for  Planners (USEPA, 1976b).
For complete information the potential user must
consult the appropriate  user's manuals and other
supporting documentation. The Center for Exposure
Assessment Modeling (CEAM, EPA Environmental
Research Laboratory, Athens,  Georgia) is a source
of information and limited technical support.  Brief
descriptions of the contents of Tables 3-2 through
3-10 follow.

Table  3-2.  Constituents  Modeled.   As a basis for
comparison, QUAL2E simulates  the following con-
stituents:

    •   Dissolved oxygen
    •   Carbonaceous biochemical oxygen demand
    •   Temperature
    •   Algae (as chlorophyll a)
    •   Organic nitrogen
    •   Ammonia
            •   Nitrite
            •   Nitrate
            •   Organic phosphorus
            •   Dissolved phosphorus
            •   Coliforms
            •   Arbitrary nonconservative constituents
            •   Three conservative constituents

         Table 3-2 compares QUAL2E with other models com-
         monly used in TMDL analyses with respect to the
         constituents  simulated.  The models vary  signifi-
         cantly in terms of the number and type of constituents
         for which calculations are performed. The number of
         constituents  analyzed  usually reflects the number
         and complexity of biochemical processes simulated
         and is shown in Table 3-3.  In the more complex
         models (e.g., QUAL2E, WASPS), provision is made
         for selecting  only those constituents (and therefore
         processes) of interest.

         Table 3-3. Summary  of Capabilities.  The model
         equations and process formulations in QUAL2E are
         the same as  those discussed in Section 2.3 for dis-
         solved oxygen, nutrients, and  phytoplankton.  Figure
                                              3-15

-------
2-5 shows the interactions of the various constituents
inQUAL2E.

Table 3-3 compares the general features of QUAL2E
(i.e., temporal and spatial resolution, hydraulics,
types of loads, and processes simulated) with other
computer models used in  TMDL analyses.   Multi-
SMP  is limited to steady-state DO/BOD  analyses,
whereas QUAL2E, WASPS, and CE-QUAL-RIV1 can
be used for eutrophication  analyses as well as dis-
solved oxygen analyses.   The latter three models
simulate the effects of photosynthesis, respiration,
and temperature on diel variations of dissolved oxy-
gen. WASPS, HSPF, and CE-QUAL-RIV1 are truly
dynamic since they simulate continuous temporal
variations in stream hydraulics and waste loadings.
QUAL2E assumes these features remain constant,
but allows the meteorology and water quality condi-
tions downstream  of the upstream boundaries  to
vary, making it a quasi-dynamic model.

Table 3-4. Reaeration Formulations.  Most models
permit direct  input of  the  reaeration coefficient  or
selection from several commonly used correlations or
methods. Appendix A provides a discussion of this
parameter.

Table 3-5. Input Data Requirements.  All models re-
quire data for input, calibration, and validation. It is
best if model selection  is not restricted by availability
of data and the decision  is  made to acquire the
specific type of data required  for the model. On the
other hand, if data availability is a constraint, selection
of a  less sophisticated model than would be war-
ranted on technical grounds  may be  appropriate.
Table 3-5 compares the input data requirements for
the models discussed.  Input data requirements in-
crease with the complexity of the stream hydraulics
and water quality mathematical formulations.  For
example, Multi-SMP,  QUAL2E, and WASPS (de-
scriptive transport mode)  assume steady-state hy-
draulics formulas which then require specification of
regression coefficients (see Equations 2-1 through
2-3) to estimate  velocity and depth required in the
reaeration formulas. The more complex models such
as WASPS (linked with  DYNHYD5)  or CE-QUAL-
RIV1 solve a form of the momentum equation, which
requires more detailed characterization of the stream
geometry and roughness.  Similarly, extensive data
are required to simulate the nonlinear nutrient-algal-
DO linkage.

Table 3-6. Ease of Application—Output Form and
Content.  All of the computer programs print results
of the simulation and the input data to standard ASCII
files.  The more complex programs require scratch
disks or tapes for storing intermediate results to be
read subsequently in submodels or for storing infor-
mation to be plotted. Post-processing of model out-
put is a major task in the application of a model,
requiring software for statistical data summaries and
graphical display of observed and modeled data sets
(Stoddard, 1988; Stoddard et al., 1990).

Table 3-7. Ease of Application—Sources.  Support.
and Documentation. Two of the most important fac-
tors  in facilitating  the use of a new model are the
adequacy of the documentation and the adequacy of
the support available.   The documentation should
state the theory and assumptions in adequate detail,
describe the program organization, and clearly pre-
sent the input data requirements and format. A well-
organized  input data scheme is essential.  Limited
technical support  is typically provided by agencies
responsible for distribution of models.  For example,
EPA's  Center for Exposure Assessment Modeling
(CEAM) in Athens, Georgia and the U.S. Army Corps
of Engineers, Waterways  Experiment  Station  in
Vicksburg, Mississippi will provide very limited tech-
nical consultation to users  experiencing problems
with  operations of models. It may be possible that
special support  arrangements (including  short
courses or informational or personnel exchanges) are
available under existing intra- or interagency agree-
ments or could be made available to the  potential
user. The support agency may also be able to pro-
vide  the potential  user with a list of local users who
could be contacted for information regarding their
past or current experience with the computer program
associated with the model.

Table 3-8.  Ease of Application—Equipment and Pro-
gramming  Requirements. The models are written in
FORTRAN 77, with the exception of Multi-SMP,
which is written in Turbo  Pascal. Most models are
machine-independent though pre- and post-proces-
sors are important to ease of application.  Storage
requirements  increase with program complexity.

Table 3-9,  Operating Costs.  It is difficult to estimate
overall costs involved in a model application because
applications differ in scope and complexity and the
ability to solve or avoid  certain  problems  is highly
dependent on the experience and technical  back-
ground of the analysts  involved.  However, machine
requirements  and costs associated with typical runs
are usually estimated in the program documentation.
As a rule, the simpler the model, the less expensive
                                             3-16

-------
it is to apply.  It is essential that the support agency
and other experienced professionals be contacted for
information or assistance.

Once the cost of application  has  been estimated, it
should be compared with the benefits of using the
program as part of the water quality modeling effort
and  the overall importance  of the problem.  The
TMDL  study  costs  should be consistent with the
economic, social, or environmental values associ-
ated with the problem and its  solution.

Table 3-10. Hierarchy of Models Based on Selected
Features. To assist in initial model selection, TableS-
10 shows a hierarchy of models based on important
distinguishing features.  As shown in  this table, the
programs increase in complexity.  One of these pro-
grams should be adequate for most TMDL studies and,
in general, the simpler program should  be chosen if it
contains all the features needed to simulate the impor-
tant processes in the prototype. On the other hand, use
of a more complex model may be justified.  Often, a
complex model can be used with no more additional
effort than that required for a simple model by "turning
off processes (i.e.,  set coefficients to zero values).
This procedure allows easy upgrading of the model as
more information becomes available.  QUAL2E, for
example, can be used at the same analysis level as
Multi-SMP and requires no additional information.
                                              3-17

-------
                                                   TABLE 3-5. COMPARISON OF MODELS:  INPUT DATA REQUIREMENTS
Mtxlri
                                                                                   Hydrwlle
                                                                                                                                                                   DUMf
          m*L2t    Blrem lenglh.       lopltonalt Claud      Headwaler and
                    oonnedion setiania,  exw6«. isaramatrtc     bibulaiy «ftows,
                                       ptessurt. aef and     withdrawals
                    iiii-iiniyiil
CE.
DUAL-
HIV I
                    COfliieclioii schuing
00

o>
                    Channel fanpri,
                        i and dlraciKsn,
                 ! Surface
          area and daptti
                                            i, evapOTBlfon
                                                 , r*1 solar
                                       reach-vBiirtbte
                                       chratotogy inpul tof
                                       sleady- state.
    ir. biirotrmlri;
piessure, dry awl
h-ja-Sy,-dl'jr ai>J
liilfultiiY iiillows.
wtrsdrawate
lime senas ol soter  Tima sanas o)
radifiiKo wind EHSMJ hciairv-atsr and
and dlracbon,        mbulary inll&ws
photo pa Jtod,
tempera tura
                                                                    CoerAcients lor

                                                                    re§res£tofi, or bottom 6OQ,
                                                                                                                     FlfiM 1 Jt'J'i
                                                                     .DO,
                                                                     rvalive
                                                                     , initial
                                                                           ft's ft).
                                                                    l:oundai>'
                                                                                Icftian
I M.inn i IIJ'K ;i'|, liirHi
series ol tew.
                                                                    velocity-ttow
                                                                    regre&Kjn
                                                                              cross ^
                                                                                      , bo-Horn
                                                                              How, i
                                                                              rmidjinass
                                                                              (Mannng's n|
Irttow CMoanlraiion,
llXflparaniia, CO.
ROD, inJitol and
                                                                                                                     Flow rites and
                                                                                                  tor al conduits to*
                                                                                                  ii I riiuiiilu.:! :
                                                                                       I Fiila
                                                                              oo«ific»rts;
                                                                              hart- salurattin
                                                                              oaertictanls, and
                                                                                                                                        corrBcfiw te-dors
Baaeraliop,
o»dAlk>n, leac&on,
•jiiHjsuiiinrjnm*
                                                                                                                               liall-saluralion
                                                                                                                                        , and
                                       InMow tonosnlralion,  lime series erf
                                       lemperatiite, OO,    load«§s tor al water  owdalon, readion.
                                       BOD, initial and      quality stele ranablas antf settling rale
                                       boyndary condltlwia                      cxserfioanls, ruinem
                                       tor ai modeled stale                      hall-saliiralion
                                                                              oaerhoents, and
                                                                              ElH-llfHM.lhir^
                                                                              ai>d etevaton ol
                                                                              Lj.d!;m, day at yL'di,
                                                                                                                            9, tiirgui
                                                                                                                     minimum DO
                                                                                                                     i::yii::
-------
           ModiH
                                          TABLE 3-5.  COMPARISON OF MODELS; INPUT DATA REQUIREMENTS {Continued)
                                         Maln-orolayii;
                                                                    Hydjaulk
Watitr Quality
                                                                                                                   ElflU«nt
                                                                                                                                  Rancllara Rates
                                                                                                                                                        Other
          Note: Waleo;cik:.:iii: inpu! Is optional.
IISPF     R?i&ig laftlss. length  Sotar rwJalioni, dcwd Iniamglly computed  Volurnaarid         Inttow and runoi'
         of cwnpuiatjonal     cover, air                            diwlwsjaAteiMh (or   cfficerrtralon,
         ataman), diainage    iBrnperaliurs, de-w                      ea* neaoti, avarige  lemperatura, twntrvc
         area               pC'Inl lempanalure,                      chanrwl slope,       domand, and toxic
                                                               towthraugri *na lor   organic pdulart
                                    n, aad                      mean How, v«toefly
                                       JP                       ah
                                                                                                              Flow ralss suxl
                                 fly* rales and
                                 concentrabon

MulU-
SMP



Stream Jengttis,
•.iniluirii i(i,ji:lii!:;


(Miimng's n\
I't^Kfw^til^ unij Intkyw cofic^inlriiiiori, Fkiw ^lii^ ^n
-------
                                  TABLE 3-6. COMPARISON OF MODELS; EASE OF APPLICATION—OUTPUT FORM AND CONTENT
                  Model
              Output Fonn
                                           Output Content
            QUAL2E
Compyter pnntout, plots, screen graphics
            Gi-QUAL-fNV1
            WASPS
Computer printout lahte and output plel
lila
Computer printoul/ASCII iile. screen
graphics, and screen tables.
aj  Listing of input data
b)  Concentrations and temperature ty reach and computational elements at spedfted
    time steps
c)  Maximum, minimum, and average concentration, temperature, flow, velocity, and
    (fepft al reaches
d)  Final summary, which inclucfes components of DO ctelieit and plot ol DO isfti BOD
e)  Local cllmatotogical data
fj   Detailed summary oi hyckatulic calculations
9)  Pfols of observed and pireciicted DO concentrations (option)

a)  Listing of input dale
DS  Coficenlraiion of a oonsttluenl at any bnoeflocalion

a   Usiing ol input daia
b)  Concenlraiion of constituent arid otriw water quality variablwsj. al any lhfWi1c«aion
c)  pQst-pfoces£ar is. available to create tables for import io graphics routines
d)  values of brar/ispon and varirtles al any time/locaiion
e)  Mass balance table lar selected cortslituanls
rv:
O
            HSPF
            Mul5i-SMP
Computer printout
Con^puler printout of graphs and tables
displayed (o sown or printer
a)  Time iiisuxy of rurtoffi flow rat* sediment load, nuirieni CGooerifra&ons and toads
D)  Water quality and quantity at any pdnt in a wateished
c)  Display of on« at more Uirne series on a plottei and tabular display o1 inpul/ouipui
    iraqpency LaDlcE

a)  ualirig D( input data
b)  DO, CBOD, and MBOO cowentrallws a? munipl«i points along slrearn reach

-------
















00
tVi













TABLE 3-7. COMPARISON OF MODELS: EASE OF APPLICATION—SOURCES, SUPPORT,

WotW SourewM o< Mo*t
QUAU2€ Caniar for Exposure
Assessment Modeling
U.S. ErwifrqnmentBl
Athens, QA 30605
(J7CN3S S46-3649
CE-QUAL-RIV1 US Army Engineer
Waterways Experiment
Station
Vsckstouna, MS 39180
{601)634-3670
WASPS Caniar for Exposure
Assessment Modeling
U.S.Enwwi menial
Protection Agsncy
Attws, CiA 30505
1706) 546-3549
HSPF Centef for Exposure
Assessment rvtedaing
U.S.Environmantel
Pr-oiedion Agency
Alhans, GA 30605
{706) 546-3549
Multi-SMP Canlar (or Ettpo&ura
Ass&ssment Modfiing
U.S.ErtvirWMWiiaJ
Proteclioo Agency
Athens, GA 30605
(706) 54&-3S49
OocurmniUitJijn S
-------
Model
TABLE 3-8. COMPARISON OF MODELS: EASE OF APPLICATION-EQUIPMENT AND
                           PROGRAMMING REQUIREMENTS

         Requirements
QUAL2E         QUAL2E is written in FORTRAN 77 and is compatible with both mainframe and personal computer systems
                equipped with 640 KB RAM. Can be executed from floppy disk. User interface and capabilities to interface
                with graphics display, laser printers or dot matrix printers, or pen plotters.

CE-QUAL-RIV1    The program is written in FORTRAN 77 and is compatible with mainframe and personal computers.

WASPS          The program is written in  FORTRAN 77 and is compatible with mainframe and personal computer systems
                equipped with 640 KB RAM. A 10-MB hard disk and a printer are generally required.  A math coprocessor is
                highly recommended.

HSPF           The program is written in  FORTRAN 77 and is compatible with mainframe and personal computer systems
                equipped with 640 KB RAM. A 10-MB hard disk and a printer are generally required.  A math coprocessor is
                needed.

Multi-SMP        Executable Turbo Pascal file. Requires IBM-compatible PC and EGA card for graphic capability.
                   TABLE 3-9.   COMPARISON OF MODELS: OPERATING COSTS
  Dimensionality
                             Water Quality Problem
Approximate Level of Effort
  1-D steady state


  1-D, 2-D, steady state
                             DO, BOD, nutrients


                             DO, BOD, nutrients,
                             phytoplankton, toxics
1-6 person-months


0.5-1 person-years
  1-D, 2-D time variable
                             DO, BOD, nutrients,
                             phytoplankton, toxics
0.5-2 person-years
  3-D time variable
                                     DO, BOD, nutrients,
                                     phytoplankton, toxics
                                                                1-5 person-years
                                                  3-22

-------
     TABLE 3-10. HIERARCHY OF MODELS BASED ON SELECTED FEATURES

Models Multiple
(Ordered from Point
Least to Sources of
Most Complex) BOD
Multi-SMP X
QUAL2E X
CE-QUAL-RIV1 X
WASPS X
HSPF X
X Available feature

Distributed Benthic
Sources of Oxygen Net Algal Longitudinal
CBOD Demand Production Dispersion
V •
X « D X
x • •
X * A
XXX

Time-
variable
Waste Loads
BOD and Water
Settling Quality

.
X
X X
X X


Time-
variable
Flow


X
X
X

» Specified (i.e., input to the model as forcing function)
A Simulated in a nutrient-algal cycle
V Can be simulated approximately by input of load at beginning of each multiple segment
Can be simulated by making Kr
Meteorology only
                                 3-23

-------

-------
4.    RIVER AND  STREAM MODELING PROCEDURES
4.1    PURPOSE


The purpose of Chapter 4 is to review briefly the
various steps associated with the development of a
site-specific water quality  model. These steps are
generic and can be modified according to available
data and the type and complexity of the water impair-
ment being analyzed. Furthermore, the level  of detail
required within each step may depend on the phase
of the TMDL. Simple analyses are usually sufficient
during the first phase of TMDL development, whereas
more  detailed analysis  may be required  for later
phases.  Examples illustrating the use of these steps
using the EPA-supported water quality  models
QUAL2E and WASPS are provided in Appendix B.
The five steps suggested for model development,
illustrated in Figure 4-1, are as follows:

    •   Initial assessment
    •   Site-specific stream survey
    •   Model calibration
    •   Model validation
    •   Model application

4.1.1   Modeling Goals

Prior to detailing each step, it is necessary to present
the overall goals of model development. A phased
TMDL may require only simple modeling tools in the
early stages of development. However, an  increas-
ing level of model complexity may be needed in later
phases when additional  data become available.  At
either stage, the overall modeling goals should  re-
main  consistent.   For  determining  a TMDL  for
streams and rivers, the following goals are applicable:

    •   Development of a technically credible quan-
       titative cause-effect representation of  in-
       stream processes.
    •   Ensuring that modeling results are defensi-
       ble for use in determining the loading capac-
       ity and load and  TMDLs.
    •   Provision of analytical or modeling tools suf-
       ficient for evaluating the  implications of vari-
       ous pollution reduction alternatives.
    •   Definition of the level of uncertainty for deter-
       mination of the margin of safety.

When developing TMDLs, it is reasonable to separate
the water quality impacts caused by a given pollution
source. This is best achieved through the use of one
model or a combination of models (watershed and/or
receiving water models). For example, in some sys-
tems where nonpoint source loadings are significant,
control of point sources may provide only a marginal
improvement in water quality.

Consider a typical case in which the dissolved oxygen
profile does not meet a water quality standard of 5 mg/L
using  summer temperatures and a 7Q10 low flow.
Separation of the dissolved oxygen profile into com-
ponent responses may show that  the discharger is
responsible for a minor portion of the predicted dis-
solved oxygen depression. The major depression in
dissolved oxygen could be created  by upstream con-
ditions and sediment oxygen demand, for example.
This perspective is  important because it demon-
strates that the discharger may have a minor impact
on the dissolved oxygen  resources, and  increased
treatment at the point source may have only a minor
effect on the dissolved oxygen balance.

Consider a second case in which critical conditions
for both dissolved  oxygen and un-ionized ammonia
occur during the summer when the flow is low and the
river temperature is  high, and where nitrification is
occurring in the river. In this case,  it is necessary to
be able to separately evaluate the effects of carbona-
ceous and nitrogenous BOD  on dissolved  oxygen
and the effects of nitrification on ammonia in order to
optimize decisions on controlling nutrient loads and
on selecting wastewater treatment  schemes (i.e.,
nitrification facilities vs. advanced  CBOD removal).
Appendix  B presents an  example TMDL modeling
analysis using QUAL2E that illustrates this type  of
optimization.

In this context, typical questions to be addressed in a
stream BOD/DO  and nutrient/eutrophication TMDL
may include the following:

    •   How can the effects of two  or multiple pollut-
       ant loads be differentiated?
                                             4-1

-------
            INITIAL ASSESSMENT

         Study Area Evaluation
         Compilation and Review of Existing Data
         Preliminary Analysis
         Selection of Modeling Framework
       SITE-SPECIFIC STREAM SURVEY

             Hydraulic Geometry Survey
             Time-of-Travel Study
             Stream Water Quality Sampling
             Wastewater Monitoring
             Biological Assessment
            MODEL CALIBRATION
  Model Coefficient Assignment
  Component Analysis
  Quantifying Comparison Between Model Results and Data
                       I
              MODEL VALIDATION

              Model Coefficient Adjustment
              Model Sensitivity Analysis
              Model Accuracy Check
                       I
      MODEL APPLICATION AND TMDL

          Development of Evaluation Scenarios
          Waste Load Allocations
          Load Allocations
          Margin of Safety
          Uncertainty Analysis
FIGURE 4-1. STEPS IN THE USE OF A WATER QUALITY MODEL
         FOP A QiTF.QPFriFir TMni  APPI
                        4-2

-------
    •   How can the individual impacts of sediment
        oxygen demand, nonpoint sources, and point
        sources be quantified?
    •   Is nitrification occurring in the stream, and
        would it occur under future conditions?
    •   Which nutrient should be controlled to reduce
        the algal biomass?
    •   What is the magnitude of dissolved oxygen
        fluctuations that cannot be accounted for by
        the present analysis?
    •   How do these fluctuations vary with time and
        space?
    •   Is dissolved oxygen or ammonia toxicity (un-
        ionized ammonia) the limiting water quality
        parameter?

In summary, the goal of the TMDL modeling analysis
is to obtain a quantitative  assessment  of system
behavior that will support decision making.  To ac-
complish this task, a number of general requirements
are listed below.

4.1.2   General Requirements of a Stream
        Water Quality Modeling Analysis

The following are examples of the basic requirements
of a TMDL study:

    •   A quantitative analysis of all pollutant loads
        and inputs.
    •   Sufficient data  to support the derivation of
        model coefficients.
    •   A consistent set of model coefficients deter-
        mined from independent derivation,  model
        calibration, and validation using available
        data.
    •   Assignment of reasonable values for  model
        coefficients in model projections under future
        conditions.

The suggested requirements should be flexible to
meet site-specific needs.
4.2    INITIAL ASSESSMENT


An essential element of a TMDL study is a quantita-
tive assessment of the relative impacts of different
types and sources of pollutant loads on specific water
quality parameters. This assessment will help to en-
sure that all participants in the TMDL process under-
stand the relative importance  of various  pollutant
sources at an early stage and that appropriate priori-
ties are defined.   Another advantage of an  initial
assessment is to provide a check that all significant
waste loads have been identified. It will also help to
ensure that subsequent field monitoring programs are
cost-effective and responsive to planning and deci-
sion-making requirements.  The following  analysis
particularly addresses water quality in streams and
rivers.

4.2.1    Study Area Evaluation

The study area evaluation defines the study area and
problem by determining applicable  water quality
standards as well  as existing  and potential water
quality problems. A more detailed description of the
evaluation is presented in the following subsections.

4.2.1.1  Water Quality Standards

First, a desirable water use, or uses, for the stream
system  (e.g., recreation, water supply, agriculture)
must  be designated.   State  regulatory agencies
should be consulted to define the designated uses,
as well as specific water quality criteria.  In addition,
EPA has published a series of water quality criteria
since the first "Green Book" issued by the Federal
Water Pollution Control Administration in 1968.  The
current edition is EPA's "Gold Book," Quality Criteria
for 79S6(USEPA, 1987). In all cases, however, State
criteria should be consulted first.

In the United States there are no standards regulating
CBOD concentrations in streams. Instead, there are
extensive standards for dissolved oxygen levels that
are affected by CBOD deoxygenation. As  a result,
CBOD and oxidizable nitrogen (NBOD) are regulated
on the basis of dissolved  oxygen standards.   Dis-
solved oxygen  standards  have been set by State
regulatory agencies to protect designated use(s) for
individual streams  or segments of streams.  State
dissolved oxygen standards may be  expressed as
any one or all of the following:

    •    Average daily concentration.
    •    Minimum or  lower percentile concentration
        (usually used for streams that have signifi-
        cant diel variations due to algae).
    •    Percent saturation.

There are  no specific algal biomass standards for
eutrophication analyses since it is difficult to deter-
mine whether a particular chlorophyll a concentration
will be a problem.  Figure 4-2 compares chlorophyll a
concentration ranges with perceived water quality
                                               4-3

-------
conditions and target objectives for several different
waterbodies.  These cases may be used as a guide
to regulate nutrient inputs for eutrophication control.

4.2.1.2  Identifying Existing and Potential Water
        Quality Problems

Table  4-1  summarizes the constituents, waste
sources, and  consequences associated with  dis-
solved oxygen, nutrient enrichment, and eutrophica-
tion problems.  Nutrient enrichment and subsequent
algal growth  are  a concern in  rivers  and streams
because of their effect on dissolved oxygen concen-
trations.  Growing plants provide a net addition of
dissolved oxygen  to the stream on an average daily
basis, yet respiration can cause low dissolved oxygen
levels at night that can affect the survival of  less
tolerant fish species.   Also, if environmental condi-
tions cause a die-off of either microscopic or macro-
scopic plants, the decay of biomass can cause severe
oxygen depressions.   Therefore,  excessive  plant
growth can affect a  stream's  ability to meet both
average daily and instantaneous dissolved oxygen
stream standards.

Biological assessments can also be designed to es-
tablish baseline conditions and assess impacts from
point and nonpoint pollution sources. They can  play
a fundamental role in establishing biocriteria, which
are numerical or narrative expressions that describe
the reference biological condition of aquatic commu-
nities inhabiting waters of a given designated aquatic
life  use (Barbour et al., 1992).  Biocriteria are often
presented as measures such as species composi-
tion, abundance, and diversity (Gallant et al., 1989).
Biological communities reflect overall biological integ-
rity  and integrate the effects of different pollutant
stressors.  In cases where specific impacts are ab-
sent or unknown (e.g., nonpoint source impacts that
degrade habitat),  bioassessments may be the  only
practical assessment tool.

The individual water quality problems can be associ-
ated with specific  time and space scales, which can
be used to identify the most appropriate method of
analysis. Several different time and space scales are
required for effective water quality evaluation (see
Figure 4-3).  In general, the dissolved oxygen prob-
lem associated with organic waste discharges has a
significant time scale of days to weeks, with a signifi-
cant space scale of impacts up to 20 miles. Nutrients
are usually associated with a  longer time scale of
seasons to years and  a space scale  of up to  100
miles.  It is essential to recognize these time  and
space scales in order to address questions and prob-
lems in the most economical manner and to provide
meaningful analysis.  The selection of a steady-state
or time-variable model should be determined on the
basis of the water quality variable, the available data
base, and the major mechanisms affecting that vari-
able.

In evaluating dissolved oxygen water quality effects,
including situations where algal influences are impor-
tant, a steady-state analysis can be used.   Phyto-
plankton chlorophyll a concentrations will commonly
be sufficiently constant over the period covered by a
steady-state analysis tojustify this approach. In such
cases, a steady-state analysis of dissolved oxygen
response to point source BOD discharges is super-
imposed over the algae-induced diel fluctuations.
These fluctuations can be calculated  by simplified
analytical approximations.  QUAL2E uses  steady-
state  hydrology and  allows simulation of diel vari-
ations  in temperature or algal photosynthesis and
respiration.

Time-variable  approaches to  eutrophication  prob-
lems are sometimes employed when a time-variable
data base exists (or can be developed) to calibrate
the model  dynamically over a range of conditions.
Models such as HSPF, CE-QUAL-RIV1, and WASPS
are run in the time-variable mode. When using these
models,  the computation can  be continued,  using
constant input values, until a steady-state condition is
reached.

A general guideline for determining the appropriate-
ness of a steady-state vs. a time-variable approach is
summarized below:

    •    If phytoplankton chlorophyll a concentrations
        are relatively constant over a time period of
        1 or 2 weeks, then a steady-state approach
        is justified. This period should coincide with
        the critical season in terms of stream flow and
        temperature for dissolved oxygen analyses.
        Spatial  variations in algal biomass can be
        handled by averages over appropriate river
        reaches.

    •    Where the principal water quality issue is the
        level of biomass rather than oxygen deple-
        tion, longer time periods (covering one or
        more seasons)  are  usually selected.  On
        such a time scale, expected  changes are
        large and time-variable eutrophication mod-
        els are the most appropriate modeling ap-
        proach.
                                               4-4

-------
       140
       120
                        Asprerf-
                        , nmt*
                                                                i
                                                                f
                                                4
                                            EPA f
                                                                             40
                                                                             X A
                  Wsi»m
                  i_ak» Erie
Sin Joaqun
 Datta, Calif.
                                   PotomsG
                                    ««u«ry
        FIGURE 4-2. RANGE OF CHLOROPHYLL a AVERAGE CONCENTRATIONS AND
               TARGET "OBJECTIVES" TO REGULATE NUTRIENT INPUTS FOR
                EUTROPHICATION CONTROL FOR VARIOUS WATER BODIES
                            (After Thomann and Mueller, 1987)
    TABLE 4-1. IDENTIFICATION OF POTENTIAL WATER QUALITY PROBLEMS: DISSOLVED
           OXYGEN DEPLETION, NUTRIENT ENRICHMENT, AND EUTROPHICATION
Sources:
Consequences:
Organic material, ammonia in:
wastewater
runoff and CSOs
benthic oxidation
algal production
marinas, boating
heated effluent

Fish kills
Reduced fish productivity
Less desirable aquatic community
                            Nitrogen, phosphorus, carbon in:
                            wastewater
                            runoff and CSOs
                            atmospheric deposition
                            benthic recycling
                            marinas, boating
                            Nuisance levels of phytoplankton
                            Less desirable aquatic community
                            Large dissolved oxygen fluctuations
                            Dissolved oxygen depletion
                                         4-5

-------
           ID
               _
10s
               Hour
                                  OxvOIH
                                          SCUOi
                                        NUTRIITfTa
                                    Month  Y**r
                                       S*«ton
                               Time Sea ISA


                             Effsctlva Distance (mites)

                            tr1   icP     1Q1     to*
                          FLOATAtLIt
                                           •HUTP.IINTI
             (5 ft)   (SO ft)  (500 ft)
                                                         •OUBI
                                     .-^—Rtgig?

                                              !--•
                                    Spies Scal*t


FIGURE 4-3. TIME AND SPACE SCALES FOR ASSESSMENT OF WATER QUALITY PROBLEMS

                               (After USE PA, 1983b)
                                      4-6

-------
4.2.2   Compilation and Review of Existing Data  ily queried using interactive tools and the Reach File.
A thorough characterization of the river or stream is
necessary for any water quality study. Table 4-2 lists
the types of data to be gathered  and their possible
sources.

Stream flow and geometry are typically available at
specific gaging stations for large  rivers through the
USGS and/or the US Army Corps of Engineers.
For analysts with access to EPA's mainframe com-
puter, USGS streamflow data available can be eas-
                             The EPA STORE! system is a good  source  for
                             water  quality data.  STORE! data are usually ap-
                             propriate to describe long-term trends for water
                             quality problem identification.  However, STORET
                             does  not always  have synoptic surveys of the
                             stream system, which are most useful for water
                             quality modeling.

                             EPA's Permit Compliance  System (PCS) is the  re-
                             pository of NPDES permitted loads and all reported
           TABLE 4-2. DATA TYPES AND POSSIBLE DATA SOURCES FOR STREAM
                               TOTAL MAXIMUM DAILY LOADS
Data Type
                                              Source
Federal Agencies
State Agencies
Local Groups
Stream Geometry   USGS                      Special studies
                  US Army Corps of Engineers'
                  Division/District Offices
                  EPA
                                                   Planning agencies
Stream Flow
USGS gage records
and low flows (available
through EPA)
Publications on low flows
Basin plans
Universities
Planning agencies
Water Quality Data  EPA STORET
                  USGS
                  US Fish & Wildlife Service
                            Regulatory agencies'
                            TMDL studies
                            State Dept. of Health
                       Studies by regional
                       planning groups
                       Discharger's studies
                       Universities
Wastewater Loads
EPA Permit Compliance
System (PCS)
Discharge Monitoring
Reports (DMR)
Municipal and industrial
discharger's plant records
Nonpoint Loads
EPA STORET, USGS and
US Fish & Wildlife Service;
urban runoff data available
from EPA National Urban
Runoff Program (NURP);
precipitation and meteoro-
logical data available from
Urban runoff data
from special studies;
precipitation and
meteorological data
from State planning
agencies and local
airports; land use and
soils characteristics
                  NOAA National Climatic Data
                  Center; land use data available data from State planning,
                  from USGS; soils charac-      agricultural and geological
                  teristics data available from    agencies.
                  USDA Soil Conservation Service.
Urban runoff data from
regional, city and county
studies; precipitation and
meteorological data from
local and county planning
agencies and local airports;
land use and soils
characteristics data from
regional and county
planning, agricultural and
geological agencies.
                                             4-7

-------
information from NPDES discharge  monitoring re-
ports (DMRs).

4.2.3    Preliminary Analysis

Preliminary screening analysis of available informa-
tion described in the previous section may be per-
formed by employing analytical equations, simplified
models, or a preliminary version of the water quality
model.  Several simplified analyses are presented in
the following paragraphs.

4.2.3.1  Screening Procedure for Determining Algal-
        Nutrient Relationship

This simple  procedure may be used to  provide an
appropriate indication of a nonproblem. That is, if the
maximum possible chlorophyll a level that could be
achieved is extremely low,  it will usually be safe to
conclude that nutrients do not pose a  problem in
relation  to water column algae.  The guidance in
Section  2.3.4.5 and Section A.9 of Appendix A, which
relates chlorophyll  a levels to dissolved  oxygen ef-
fects, can be used to determine how low the concen-
tration of chlorophyll a must be in a particular situation
to be considered insignificant.

On the  other hand, it is not appropriate  to use this
screening procedure to conclude that there is a prob-
lem.  In most natural systems, especially flowing
streams, the actual chlorophyll a levels that occur will
be substantially less  than  the  maximum potential
under a combination of ideal conditions.   Collection
of chlorophyll a data could be used to verify the
estimated chlorophyll  a levels and to  determine
whether a problem exists.
                                                 in the presence of light to synthesize algal proto-
                                                 plasm. Nitrogen and phosphorus are the only essen-
                                                 tial elements that can be controlled since carbon is
                                                 often (but not always) readily available in solution and
                                                 the various trace elements  are usually plentiful in
                                                 natural systems. When considering cell stoichiometry
                                                 of aquatic plants or phytoplankton, for example, cells
                                                 contain approximately 0.5 - 2.0 jig phosphorus per jig
                                                 chlorophyll a and 7 -10 |ig nitrogen per \ig chlorophyll
                                                 a. Although the weight ratio of each nutrient to chlo-
                                                 rophyll a varies with the age of an algal population,
                                                 species composition, and nutritional state, the follow-
                                                 ing ratios are commonly used  to represent typical
                                                 conditions:

                                                      7 jig N/ jig chlorophyll a
                                                      1 jig PI jig chlorophyll a

                                                 The chlorophyll a-to-carbon and  carbon-to-nutrient
                                                 stoichiometry of algal  cells is not precise, and ratios
                                                 that are somewhat different from those used in this
                                                 manual may be  preferred  by other analysts  (see
                                                 Bowie et al., 1985).   Such preferences are usually
                                                 based on local data, which should be used whenever
                                                 possible.

                                                 For example, consider the following nutrient concen-
                                                 trations:
                                                      N =0.35 mg/L = 350 \ig NIL
                                                      P = 0.02 mg/L =  20 jig P/L
                                                 Using the above stoichiometric ratios, the maximum
                                                 potential chlorophyll a concentration would be either:
   350 \
                                                           N/L) - - AJ        = 50 \ig Chi a/L
                                                                / \ig N/\ig Chi a
                                                 (Nitrogen)

                                                 or

                                                    (20 tig P/L)
Stoichiometric  ratios  can be  used  in preliminary
screening analyses to make two useful initial assess-
ments that can help focus subsequent data acquisi-
tion, testing, and analysis activities.  The first of these
is to determine the limiting nutrient (nitrogen or phos-
phorus) and therefore the most appropriate for con-
trol.   The second is an estimate of the  maximum
potential chlorophyll a level that could result and the
implications on the need for nutrient control. In either
case, it should be recognized that such a screening
is  relatively  imprecise and results  should be inter-  (Phosphorus)
preted with care. When indicated conditions are mar-
ginal  rather than being dramatically in favor  of one
result over another, additional analyses  should be
performed as indicated in the discussion that follows.
                     1
                                                               1 \ig P/\ig Chi a
                              = 2Q\ig Chi a/L
Algae require inorganic carbon, nitrogen, phospho-
rus, silica (for diatoms), and various trace elements
Since each concentration represents a maximum po-
tential, the lower of the two is the maximum result and
phosphorus is therefore the limiting nutrient.  The
maximum possible chlorophyll a concentration that
could result from the waste discharge in combination
with the background stream concentration is 20 jig/L
                                               4-8

-------
This level might be  achieved  if there  is adequate
residence time in the study area, optimal environ-
mental conditions (i.e., temperature and light) exist,
and all of the phosphorus is in a form  available for
algal uptake. Stream conditions, however, are usu-
ally considerably less than optimal. Stream turbidity,
shading by a forest canopy, or self-shading by the
algae usually restrict the available light.

If the ratio of ambient nitrogen (mg N/L) to phospho-
rus (mg P/L) is greater than 12 to 1,  phosphorus is
considered to  be the limiting nutrient; if the N-to-P
ratio is less than 5 to 1, nitrogen is considered limiting.
However,  a  number of factors must  be considered
when interpreting the  results of the type of analysis
illustrated above, particularly when the outcome is not
at one extreme or the other.

    •    Nutrient availability  is an important issue.
        Organic and paniculate forms  of the nutrients
        cannot be used directly by algae. Although
        a  relatively slow conversion  to available
        forms takes place in natural water systems,
        the residence time  in most stream systems
        is too short to make this a significant factor.
    •    The lack of precise stoichiometric ratios can
        be an  important consideration when N-to-P
        ratios are only marginally in favor of one  or
        the other as a limiting nutrient.
    •    Nitrogen-fixing blue-green algae may negate
        the  impact of a control program based on
        nitrogen being the limiting nutrient because
        they can draw on  a source  (atmospheric)
        other than the wastewater discharge.

The first two of these issues can be addressed more
reliably by the use of algal growth potential (AGP) tests
to supplement or substitute for the simple analysis
based on  stoichiometric ratios.  Properly performed
AGP tests are generally preferred because they will
provide more accurate results than  the use  of
stoichiometric ratios.   The Selenastrum capricornu-
tum Printz Algal Assay Bottle Test described by Miller
et al. (1978)  is  an example of a suitable AGP test.

4.2.3.2  Phytoplankton Analysis of Short Streams

The previous  section describes the estimation  of
maximum chlorophyll a  concentrations based on
given nutrient  concentrations  under  optimum light
and temperature conditions. This section illustrates
procedures to develop estimates of maximum chloro-
phyll a concentrations in a stream under specific light,
temperature, and nutrient conditions.  A  short stream
is defined as one in which nutrients are in excess of
growth-limiting concentrations over the entire length
of interest. The distance and hence the time of travel
for stream nutrient problem contexts generally tend to
be short, perhaps on the order of less than 10 days.
This travel time is equivalent to distances of less than
160 miles for streams with a velocity of about 1 ft/sec
(0.3 m/sec). As a result, the phytoplankton biomass
may not have enough time to grow to the maximum
level calculated from the N-to-P ratio.  The rate of
growth of the phytoplankton and the travel time of the
stream length are therefore of specific importance.

Thomann and Mueller (1987) describe a simplified set
of differential equations for chlorophyll a and  inor-
ganic phosphorus and nitrogen under a steady-state
condition:
where
     A

     P,N

     r

     X

     U
     ap

     a/v

     Gn



     GP

     DP
                  dA
                  off*
          =  Gn A
                — = aP  Gp A
                dt*
                — = a/v  GP A
                dt*
                                (4-1)
                                            (4-2)
                                            (4-3)
=  concentration of chlorophyll a
   (H9/L)
=  concentrations of inorganic
   phosphorus and nitrogen (mg/L)
=  travel time in stream (= x/U)
   (days)
=  distance downstream of effluent
   (miles)
=  stream velocity (miles/day)
   phosphorus:chlorophyll ratio
   (0.001 mg P/jig A)
=  nitrogen:chlorophyll ratio (0.007
   mg N/|ig A)
   phytoplankton net growth rate
   (day ^)
=  [Gp -Dp - Vs/H]
=  phytoplankton growth rate (PN =
   1.0)  (day'1)
=  phytoplankton death rate (day ~1)
                                               4-9

-------
      Vs    =   phytoplankton net settling velocity
                (ft/day)
     H     =   average stream depth (ft)

In these equations, inorganic phosphorus is assumed
not to settle and  is not recycled from respired algae.
Solutions of Equations 4-1 through 4-3 are:
                 A =  Ao e
                           Gnt
                                  (4-4)
                               G,,,^
              ( for P >  0.025mg/L )

                      and
                                  (4-5)
              (for N>0.-\25 mg/L)
                                  (4-6)
Note that these equations are valid only in the region
where nutrients  are  in  excess of phytoplankton
growth needs.  A0, Po, and  No are the in-stream
concentrations of chlorophyll a  (|ig/L), inorganic
phosphorus (mg/L), and inorganic nitrogen (mg/L) at
the outfall after mixing of the upstream and effluent
flows.  The travel time to the location  in the stream
where nutrients begin to significantly affect the phy-
toplankton growth rate can be calculated from Equa-
tion 4-5 or 4-6 by substituting P = 0.025 mg/L for
inorganic phosphorus and N = 0.125 mg/L for organic
nitrogen:
In summary, "short" streams are defined  as those
streams where actual travel times are less than t*p or
t*N as calculated from Equations 4-7 and  4-8.  For
such streams, phytoplankton concentrations vary ex-
ponentially according to Equation 4-4 and are essen-
tially independent of nutrient concentrations (which
are in excess of growth-limiting concentrations). Nu-
trient removals  at a  point source will  reduce the
in-stream concentrations P0 and/or N0 and will de-
crease the travel times t*p and/or t*N.  If  t*p or t*N
becomes less than the actual stream travel time, peak
chlorophyll concentrations will be reduced.

For small streams, 10 to 20 miles long with  velocities
of 0.5 to 1.0 ft/sec (8 to 16 miles/day), resulting travel
times are from 1  to 2.5 days.  If a high-rate activated
sludge (HRAS) plant flow with effluent P = 5 mg/L (75
percent of which is available for  uptake) mixes with
an equal upstream flow with ambient P = 0.02 mg/L,
Po = 25 |j,g/L, Gp =  1/day, and Gn = 0.5/day, t*p will
equal approximately 7 days.  If phosphorus removal
were instituted and  the effluent were reduced to 1
mg/L, t*p would  become approximately 4  days.  In
both cases, t*p exceeds the actual travel time and the
stream would be classified as a "short" stream, with
phytoplankton concentrations varying exponentially
throughout its length.

The following procedure for analysis is suggested:
                                       1.
                                       2.
                  .
         tp = -pr In
               Gn
        tN =  -7-  In
              Gn
     1_ |n [Ao + Po - 0.025 1     (4-7)       pirical relationships.
    Determine the limiting nutrient (inorganic phos-
    phorus or nitrogen).  Include an estimate for the
    fraction of the inorganic  nutrients available for
    uptake (for example, 0.75).

    For present conditions, estimate Gn, Gp, Dp, and
    Vs using observed phytoplankton data and
     irii~al rolatinnchinc
                                                                                              em-
                                                 3.
           Ao"  + No - 0.125
                  Ao"
                                            (4-8)
where

t*P, t*N =
travel times to stream  locations where
inorganic phosphorus and nitrogen con-
centrations begin to significantly limit phy-
toplankton growth (days)
    Calculate t*p or t*N for present conditions from
    Equation 4-7 or 4-8.

    •    If t*p (or t*i\i) is greater than the actual travel
        time in the stream reach under consideration
        (t*a), then nutrients are in excess and

                A max  « A0eGnta

    •    If t*p or t*N is less than t*a, nutrients have the
        potential to limit at t*p or t*N and
                Ao' =
             ap GpAp
                G~n
                                           (mg/L)
                                                               i max :
                    Ao e
                                                                          Gn(tPortN)
                       a/y GpA0
                       	7^	
                          Gn
                                 (mg/L)
                                               4-10

-------
               TABLE 4-3. DATA FOR STREAM EUTROPHICATION CALCULATION
Parameter
Flow Rates:
Ambient Stream
Wastewater
Total Flow
Hydraulic Geometry:
Stream Depth
Velocity
Velocity
Water Temperature
Solar Radiation:
Daily Solar Radiation (If)
Optimum Light Intensity (ls)
Photoperiod (/)
Averaging Period (T)
Light Extinction Coef. (Ke)
KeH
Inorganic Phosphorus Cone:
Upstream
Wastewater
Maximum Limiting Phosphorus Cone
Chlorophyll a Cone:
Upstream (x < 0)
Downstream (x = 20 mi)
Algal Growth Rate, Gmax(20 °C)
Algal Respiration Rate, \IR (20 °C)
Net Algal Settling Rate, Vs
Unit

cfs
cfs
cfs
ft
ft/sec
mi/day
°C

iy
ly/day
day
day
ft'1
mg/L
mg/L
H9/L
jig/L
day'1
day'1
ft/day
Present

20.0
0.39
20.39
3.0
0.5
8.2
23.0

600
300
0.5
1.0
0.33
0.99
0.02
5.0
0.025
25.0
65.0
1.8
0.1
0.327
Design

12.0
0.49
12.49
2.2
0.4
6.56
25.0

600
300
0.5
1.0
0.33
0.73
0.02
1.0
0.025
25.0
7
1.8
0.1
0.327
4.   Under projected conditions and future removal
    programs, repeat steps 1 through 3. If the new
    t*p (or t*i\i) is greater than the new t*a, nutrients
    would still be in excess.

The data given for the example calculation for a short
stream are summarized in Table 4-3 and Thomann
and Mueller  (1987).  The underlying assumption is
that nutrients are not limiting the algal growth in the
stream.
Analysis
   Use observed chlorophyll a data at x= 0 and
   x = 20 miles  and assume an exponential
   increase.  P(x=20) = P(x=0)exp(+Gn x/U)

   Travel time for reach: x/U= t* = 20 mi/8.2
   mi/day = 2.44 days

   Chlorophyll a at end of 20 miles: P(x=20)= 65
   = 25 e<2-*4)(Gn)

   Net growth rate: Gn = [In (65/25)]  / 2.44 =
   0.391 day'
                                                               ,-1
       Estimate net phytoplankton growth rate (Gn):
•   Determine algae population dynamics rate
    factors:
                                             4-11

-------
Gp = Gmax 1 .066
                      r\erl I

                   = GT ri_ rn
                                            Nut
                    rn = 1.0
         (assuming no nutrient limitation)
                                          Km + Nut


                                           (2-14)


                                           (2-17)
GT= (1 .8day~1) (1 .066) (23~20) =        ~1
                          = 2.18 day~   (Figure 2-6)
OC1 =
0.2 =
           600 ly
                        e-(0.33)(3) = r47    (2_15a)
/L =
     300 ly/day (0.5day)

     	^04	 = 4.00           (2-15b)
     300 /y/day (0.5day)
     (2.718X0.5)  (e-i.47_e-4.oo) = 0287
    (0.33)(3.0)(1.0)

    Gp = (2.18day~1)(0.287)(1)  = 0.626 day'1
     Dp = (0.1day~1)1.C
                              = 0.1 26 day
                                         ,-1
     Since Gn = [Gp - Dp - VS/H], the net settling loss
     rate can be estimated from

  Vs = H (Gp - Dp - Gn) = 3.0 (0.626-0. 126-0.371)

                Vs = 0.327 ft/day

     Summary of population dynamics rates:
                                      -1
                                       -1
                                          ,-1
                                       -1
    Specific growth rate, Gp = 0.626 day
    Respiration loss rate, Dp = 0.126 day
    Algal settling loss rate, Vs/H = 0.109 day
    Net algal growth rate, Gn = 0.391 day'

    •    Check for nutrient limitation:
       Using  a phosphorus-to-chlorophyll  a ratio
       (ap) of 1 .0, the amount of inorganic phospho-
       rus required to generate a net 40 |ig/L chlo-
       rophyll a is
      Po' = Ao'/ap=  apGPA°aP= 40
                       Gn
       The initial phosphorus concentration follow-
       ing complete mixing between the waste input
       and stream flow is
                         -   = 0.115 mg/L= 115
       By the end of the 20-mile reach, the inorganic
       phosphorus concentration would be equal to
       115 - 40 or 75 \ig/L, which is much higher than
       the  maximum  limiting concentration of 25
   [ig/L (see Table 4-3). Thus, the above analy-
   sis is appropriate.

   Estimate algal population dynamics rate fac-
   tors under future design conditions:

   Assume that the phytoplankton settling rate
   (Vs)  and light  extinction coefficient (Ke) will
   not change under future design conditions.

   Modify  pertinent  rate  factors  for design
   stream flow, temperature, and depth.

   Using design conditions summarized in Ta-
   ble 4-3  and the pertinent relationships de-
   fined earlier, the rate factors for algal growth
   dynamics become:

   Light limiting factor, ri_ = 0.236
   Nutrient limiting factor, rn = 1.0
    (initial assumption)
   Specific algal growth rate, Gp = 0.585 day"1
   Algal respiration rate, Dp  = 0.147 day"1
   Algal settling rate, Vs/H  = 0.149 day"1
   Net algal growth rate, Gn = 0.289 day"1

   The projected  algal chlorophyll a concentra-
   tion at x = 20 miles would be
25e(0.289cfay  )(20m//6.56 mi/datf = 6Q 3 ng/L

   which would require the following amount of
   inorganic phosphorus to support it:
         00)^X25)
                                                                              = 50.6
                                                         Yet, the inorganic phosphorus concentration
                                                         following complete mixing at x= 0 is only
                                                      (12.0)(0.02)+(0.47)(1.0)
                                                            12.0+0.49
                                                                          - = 0.0568 mg/L = 56.8 \ig/L
                                                         Although this inorganic phosphorus concen-
                                                         tration  is slightly more than the amount re-
                                                         quired  for algal growth, it is not sufficient to
                                                         maintain a no limitation condition while ap-
                                                         proaching the end  of the  20-mile  stream.
                                                         [Note that an inorganic phosphorus concen-
                                                         tration  of 25 |ig/L (see Table 4-3) is required
                                                         for a no limitation condition in the water col-
                                                         umn.]  In other words, phosphorus limitation
                                                         will occur in the stream prior to the end of the
                                                         20-mile reach. Because of this limitation, the
                                                         chlorophyll a concentration at x = 20 miles,
                                                         60.3 |ig/L as calculated earlier,  will  be the
                                                         upper bound for the algal biomass.  A lower
                                               4-12

-------
bound  can be estimated by first calculating  where
the time it takes to reach a potential phospho-          n
rus limitation (i.e., inorganic  phosphorus
cone. = 25 \ig/L):
                                                            = number of days

                                                           n = cos(nnf/T)
          1  50.6+56.8-25
         Gn
          50.6
                          = 1.628 days
                                                                        (nT/f)2 - (2nn)2
                                                and the average daily value of this function is equated
       The lower bound of the chlorophyll a concen- |° the average daily algal oxygen production calcu-
       tration is therefore
                                         lated as:
Based on the above analysis, the maximum chloro-
phyll a concentration at x = 20 miles would be be-
tween 41 and 60 |j,g/L. The analysis also indicates
that the short stream  assumption is violated under
future design conditions.   More rigorous analyses
(i.e., using a computer model) are required to address
this issue.

4.2.3.3  Diel Dissolved Oxygen Variation Due to Al-
        gae

If only average daily dissolved oxygen concentrations
are of concern in a TMDL, the above analysis may be
used to determine the daily average net dissolved
oxygen  production due to algal photosynthesis and
respiration. In cases where minimum daily standards
are of concern, an estimate of the diel variation in
dissolved oxygen must be made.  A brief theoretical
analysis is presented in the following paragraphs and
followed by an example  using  the data from the
previous example in Section 4.2.3.1.

Algal oxygen production as a function of time during
the day can be approximated as  (Di Toro, 1975;
Chapra and Di Toro, 1991):
       P(t) = PM sin(?i t/f)
       P(0 =
                         0
-------
(Cmax-Cmin)
                                [1-6
                          fKal"\ -
                                           (4-12)

where Cmax and Cmin represent the maximum and
minimum daily (24 hr) oxygen levels.

Thomann and Mueller (1987) and Chapra and Di Toro
(1991) present complete documentation of derivation
of the diel oxygen production model and Di Toro's
(1975) delta method of determining the diel range of
oxygen from algal photosynthesis.

In shallow  streams and  rivers, attached epiphytic
algae and benthic macrophytes can account for sig-
nificant components of observed primary production
and oxygen and nutrient distributions (Jeppesen and
Thyssen, 1984). In particular, steep gradient reaches
of rivers with high current velocity (ca. 50 cm/s) and
sufficient nutrient supply are typically characterized
by maximum rates  of  benthic primary  productivity
from periphyton (Hynes,  1970; Horner and Welch,
1981; Welch et al. 1989). Consistent with other stud-
ies reported in the  literature, stream  velocity in-
creases of  up to -50 cm/s have been observed to
result in enhanced biomass  accumulation  and  pro-
ductivity of attached periphyton (Horner and Welch,
1981).  Velocities higher than -50 cm/s tend to result
in reduced biomass accumulation because of physi-
cal scouring and removal of attached biomass.

Since data  to  describe benthic biomass or benthic
primary productivity in a river are typically not avail-
able, literature values can be used to estimate pa-
rameter values for gross benthic  algae  production
and production/respiration ratios (P/R) (e.g., Bott et
al., 1985).  From these literature values,  gross ben-
thic algae productivity appears to be on the order of
0.5 to 5.0 g C/m2-day.  Based on photosynthetic
efficiency and  assuming that 1  mole 62 = 112 kcal,
gross benthic production is on the order of 0.5 to 5.0
percent of total incoming solar radiation (Thomann
and Mueller, 1987).

In a  comparative  long-term seasonal study of four
rivers across the United States (Oregon, Michigan,
Pennsylvania,  and Idaho), Bott et al. (1985) reported
summer benthic algae productivity rates of 0.25 to 2.5
g C/m -day. Bott et al. (1985) also reported a range
of values of P/R ratios consistent with  the "River
Continuum  Concept" (Williams, 1981) where transi-
tions in community metabolism (i.e., P/R) tend to
occur over  the domain of a  river as small streams
develop into larger rivers  over a drainage basin. In
general, the data of Bott et al. (1985) tend to support
this hypothesis, as summarized below:
                                                  •   Upper reach of river: predominant heterotro-
                                                      phy (P/R <1 )
                                                  •   Middle reach of river:  predominant autotro-
                                                      phy(P/R> 1)
                                                  •   Lower reach of river: predominant heterotro-
                                                      phy (P/R <1 )

                                              In shallow systems, accurate representation of the
                                              observed  diel oxygen range might require a daily
                                              average and diel benthic algae component in addition
                                              to the phytoplankton component.

                                              In order to account for benthic algae, or macrophytes,
                                              Equation 4-11 can be easily modified to include addi-
                                              tional daily average and diel terms. This approach has
                                              been used with hydrographic data for Flanders Bay and
                                              Senix Creek (Tetra Tech, 1989; Morton et al. 1990),
                                              shallow estuarine ecosystems in eastern Long Island .
                                              Data from a 48-hour time series in Senix Creek (Ryther
                                              et al., 1958) are  presented for comparison to the diel
                                              model  results assuming  (a)  phytoplankton  production
                                              only and  (b)  phytoplankton  and benthic macrophyte
                                              production (Figure 4-4).

                                              Using the hydrographic and  nutrient data obtained for
                                              Senix Creek, the computed average phytoplankton
                                              primary production rate of 5.3 g C/m2-day falls within
                                              the observed range of 3.2-6.2 g C/m2-day. If phyto-
                                              plankton were the dominant primary producer, then
                                              the diel analysis  should  have adequately reproduced
                                              the observed 48-hour time series of oxygen data where
                                              sediment  oxygen demand was assumed constant at
                                              4.1g O2/m2-day at the ambient temperature of 25 °C.
                                              Like the diel oxygen model results reported for the
                                              Shenandoah River (Deb  and Bowers, 1983), the com-
                                              puted amplitude  and phase  of the phytoplankton diel
                                              oxygen model do not adequately reproduce the ob-
                                              served data (Figure 4-4(a)).  Incorporation of an aver-
                                              age benthic macrophyte production (4 g C/m2-day) and
                                              respiration (3.75 g C/m2-day), however,  results in
                                              much better agreement with the observations from
                                              Senix Creek (Figure 4-4(b)).

                                              The results of the diel analysis clearly demonstrate
                                              that benthic photosynthetic oxygen production can be
                                              a significant factor in the observed  diel variability of
                                              oxygen in shallow systems  such  as Senix Creek. It
                                              is also likely that benthic macrophytes would account
                                              for a significant component  of total nutrient uptake in
                                              the water column.

                                              4.2.4   Selection of Modeling Framework

                                              Obtaining a simulation model that effectively imple-
                                              ments the conceptual model is important.  If availab
                                              4-14

-------
s
I
a
    * !-
                           Ryttwf « nix 1958) Jua a&-27, S9S7


                              SUNRISE

                                                     u
                         Diuraal
     (a) ONLY ALGAL PRODUCTION INCLUDED IN MODEL (BENTHIC MACROPHYTE
                PRODUCTION AND RESPIRATION EQUAL TO ZERO)
                           Rytber tlri41958) Jun 26-27.19S7
  S
  e
  c
\
     \
                                \&
                                \
                                                                \
                     TME FROM SUNRISE (flay)
                     	DiutMl       &  Ot»

   (b) BOTH ALGAL AND MACROPHYTE PRODUCTION INCLUDED IN MODEL (BENTHIC
       MACROPHYTE PRODUCTION AND RESPIRATION GREATER THAN ZERO)
   FIGURE 4-4. DIEL MODEL VS. OBSERVED OXYGEN IN SENIX CREEK, LONG ISLAND
                           (After Tetra Tech, 1989)
                                   4-15

-------
models do not fully implement a specific conceptual
model, the analyst may:

    •   Refine the model code.

    •   Make calculations or assumptions external to
       the model code.

    •   Explore consequences with model sensitivity
       analyses.

It should be pointed out that how a modeling frame-
work is used is typically more important to a TMDL
modeling study than  exactly which model is used.
Selection of an appropriate modeling framework in-
creases the probability of accurate results.

The nature of the problem and, specifically, the time
and space scales of the problem dictate the simplicity
or complexity of the  modeling analysis.  Given  or
assuming these scales, a specific question is posed,
and the purpose of the modeling analysis is to answer
this question in the simplest, most efficient, and most
realistic manner. For example, if the dissolved oxy-
gen depression in the stream is primarily due to point
source BOD discharges and no significant algae have
been detected in the  study area, a simple model of
BOD/DO without eutrophication is sufficient  for the
analysis.  By contrast, if the dissolved  oxygen prob-
lem is caused by decomposition of algal biomass
following  nutrient inputs, the next level of analysis,
incorporating algal-nutrient dynamics, should be con-
sidered.

Furthermore,  many stream  water quality problems
can be and have been answered by a steady-state
analysis with linear  kinetics and simple transport
(one-dimensional)  components.  The simple dis-
solved oxygen models of streams are  typical exam-
ples.  Such problems may be approximated with
sufficient accuracy to yield an analysis that is ade-
quate for making decisions regarding  the treatment
level of wastewater. On the other hand, at this stage
of development, an analysis  of the eutrophication
problem usually requires time-variable  and nonlinear
terms in  order  to determine the effect of  nutrient
removal from wastewaters.

The relative complexity of the model is an important
factor in TMDL studies—the  more complex, the
greater the degree of model validation required. The
complexity of the model is determined by the number
of transport, kinetic, and input terms  in the model
equations.  Compare the simplicity of the equations
used to describe the steady-state distribution of dis-
solved oxygen in streams to the complexity of those
describing the time-variable distribution of nutrients
and phytoplankton in streams.  For each additional
component included  in the analysis, an additional
degree  of validation is  necessary.   As a conse-
quence, additional data are required. If data are not
available on a specific component, it is questionable
whether the component  should be included  in the
analysis.

Most of the stream water quality models for TMDLs
can be run on personal  computers. Therefore, it is
essential that the modeling framework selected be
user-friendly.  Technical support to operate the model
is also crucial. Finally, graphic display of the model
results can significantly increase the productivity of
the TMDL study.  With  the rapid advancement of
microcomputer hardware and software, user-friendly
features should be considered in model selection.
4.3    SITE-SPECIFIC STREAM SURVEY
Following the initial assessment, including review of
existing data, preliminary analyses, if any,  and the
selection of a modeling framework, a field survey may
be conducted to fill any data gaps.  The additional
data are key to the model calibration and validation
for the TMDL study.  In fact, the initial assessment
should determine:

    •   What pollution sources will be monitored?

    •   What is the extent of water quality data to be
        collected?

In general, a  stream survey  includes three basic
components:

    •   Measuring stream physical parameters such
        as hydraulic  geometry,  velocity,  flow, and
        time of travel.

    •   Receiving water quality (physical, chemical,
        and biological) data  collection.

The following special studies may be conducted, as
needed, if the budget and time schedule allow:

    •   Time-of-travel and dye dispersion studies.

    •   Measurement of reaeration coefficients.

    •   Light and dark bottle tests.

    •   Diel oxygen measurements.
                                             4-16

-------
    •   Nitrifying bacteria counts in the water column
       and sediments.
    •   Long-term BOD tests of wastewater and re-
       ceiving water.
    •   Field measurements of sediment oxygen de-
       mand and nutrient fluxes.

It is extremely important that these components be
synchronized  to form a synoptic survey, making the
data most useful for the water quality modeling analy-
sis. The handbook on stream sampling for wasteload
allocation applications  by Mills et al. (1986) provides
complete information for designing a  stream survey
program for point sources.  A complete  QA program
for the stream survey should be developed in ad-
vance of any sampling  activities to ensure docu-
mented evidence that  a data product of known and
acceptable quality is produced. Appendix C presents
the basic elements of a quality assurance program for
field monitoring programs.

4.3.1   Hydraulic Geometry Survey

Physical stream data include stream cross-sectional
area, average stream depth, stream flow, and aver-
age stream slope. These data must  be collected at
each of the sampling stations selected.  If the river is
constricted or not reflective of the natural channel at
these stations, then area and depth data should be
measured slightly upstream of the location or where
the channel is reflective of natural conditions.

About 5  percent accuracy should be  required for
cross-sectional area data, while 10 percent accuracy
should be required for  flow measurements. In addi-
tion, some self-checking procedure should be estab-
lished so that  any vandalism or change  in staff gage
elevation can  be  determined and corrected.

4.3.2   Time-of-Travel Study

Time-of-travel data are useful to define the time of
passage  between various sampling stations and to
determine the magnitude of longitudinal dispersion in
the stream.  Time-of-travel studies should be  con-
ducted  under different flow conditions  (see  Figure
A-4). These flow conditions could be low flow or dry
weather  flow, and  high  flow.  Each time-of-travel
study should take place during a constant flow period
of a few days. During  the survey, fluorescent dye is
released  at each selected location on the  river and
fluorescence is  measured  at the next  downstream
station.   Stations should be selected  to take into
account any features  in the river basin that might
change the time of travel such as present and future
wastewater inputs, tributary inputs, and changes in
channel characteristics. The dye is released instan-
taneously at the upstream station and collected over
time at the downstream station.  Dye samples can be
collected by an automatic sampler for fluoresence
measurement.  The sampling interval from start of
sampling to end of sampling should be determined
before the dye is released, based on the river flow and
the length of  the stream reach. Although most dye
studies are conducted using a single instantaneous
dye dump, continuous dye injection can also be used.
When conducting  a time-of-travel study for multiple
reaches in a river, the analyst must always start with
the most downstream  reach and proceed in the up-
stream direction to avoid any influence from upstream
dye releases.

Rhodamine WT dye is normally used in stream sur-
veys.  This dye should be diluted, one to one, with
methyl alcohol  to  bring the solution  to a  specific
gravity of approximately 1.0. The mass of dye re-
leased should be recorded, as well as the instantane-
ous flow at the upstream  and downstream stations.
After dye samples have been collected, they should
be analyzed for percent transmittance.  In addition, a
calibration curve should be presented,  showing the
percent transmittance vs.  dye concentration (Wilson
etal., 1986).

4.3.3   Stream Water Quality  Sampling

Water quality sampling should  be conducted to as-
sess the  point and  nonpoint  source impacts on
BOD/DO levels in a stream or river. When monitoring
water quality, all  samples should be  checked for
residual chlorine. If substantial levels are found, then
BOD samples must be dechlorinated and reseeded.
In addition, when planning a stream  water quality
survey, three key items should be determined: (1)
sampling locations, (2) sampling time and frequency,
and (3) sampling protocols.

A survey program designed for the Catawba River,
South Carolina, illustrates the  scope  of a stream
survey to support  a  BOD/DO  TMDL  study (Lung,
1990). Figure 4-5 shows the study area and major
point sources along the Catawba  River.  Figure 4-6
presents the sampling  network along the river.  Note
that the water quality sampling stations were selected
to reflect the major point source  discharges and non-
point source tributary  inputs.   The receiving water
quality parameters sampled are shown  in Table 4-4,
including the sampling frequency. The number and
locations of the stations could vary slightly depending
                                             4-17

-------
                                                                            s

      riilis PLictt-  S.34
                     0*43
                             s,
                                                    —r    	**>y
                                                      !?       7/
  «. ST?
aawnter*
Man*tt* Mills
Lane»sTi*T
Spring Mills Gftce  1L.50-
       hini
       Cotton Mills  0.10
35,.50   15.000
 ES.3'0      100'
 3.«
           FIGURE 4-5.  CATAWBA RIVER STUDY AREA AND MAJOR POINT SOURCES
                                     (Lung, 1990)
                                        4-18

-------
4
5
           of the »yil«
           sf €
                   Hill-
            r STP
S-igar Creek
Ucscreaa of jurctton  with
     Craek
           of junccton
Caw Cre«k
    FIGURE 4-6.  PRELIMINARY WATER QUALITY SAMPLING NETWORK
                          (Lung, 1990)
                              4-19

-------
              TABLE 4-4. WATER QUALITY SURVEY FOR THE CATAWBA RIVER
       Parameter
Stations
Sampling Frequency
       Temperature3
       pHa
       Dissolved Oxygen3
       Specific Conductivity3
       Diurnal Dissolved Oxygen
       CBODu
       TBODud
       CBODs
       Total Kjeldahl Nitrogen
       Ammonia Nitrogen
       Nitrite and Nitrate
       Total Suspended Solids
All
All
All
All
3, 5, 7,9
3, 5, 7,9
3, 5, 7,9
All
All
All
All
All
Twice a day for 2 days
Twice a day for 2 days
Twice a day for 2 days
Twice a day for 2 days
Grab samples every 2 hr for 24 hr
Grab samples0
Grab samples0
Twice a day for 2 days
Twice a day for 2 days
Twice a day for 2 days
Twice a day for 2 days
Twice a day for 2 days
3 Using field sampling unit.
 Light and dark bottle rig was used.
c Both total BOD and nitrification inhibited BOD for days 1, 2, 3, 5, 7, 10, 15, 20, 30, 40, and 50.
d Total BOD.
                     TABLE 4-5. POINT SOURCE SAMPLING PROGRAM
       Parameter
Sources
Sampling Frequency
       Temperature
       pH
       Flow

       Dissolved Oxygen
       CBODs
       Total Kjeldahl Nitrogen
       Ammonia Nitrogen
       Nitrite and Nitrate
       Total Suspended Solids
       CBODu
       TBODu
All
All
All

All
All
All
All
All
All
All
All
Instantaneous
Instantaneous
24-hour composites taken 4 times during
the survey
Same as above
Same as above
Same as above
Same as above
Same as above
Same as above
Grab samples3
Grab samples3
3 Both total BOD and nitrification inhibited BOD for days 1, 2, 3, 5, 7, 10, 15, 20, 30, 40, and 50.
                                           4-20

-------
on the actual field conditions.  A similar summary is
shown in Table 4-5 for point source sampling.  The
survey lasted for approximately 4 days to cover the
entire length of time required  for dissolved sub-
stances  to travel the designated  section of the river.
The sampling of the upstream stations began 2 days
prior to  the stream sampling to establish accurate
boundary conditions for the modeling analysis.

4.3.4    Wastewater Monitoring

Different types of point sources require an analysis of
wastewater characeristics to accurately  determine
the ultimate  dissolved oxygen demand.   The most
important factor for this determination is the CBODu-
to-CBOD5 ratio.  In the Catawba River, a  number of
municipal and industrial point sources were sampled
during dry weather conditions.  Table 4-5 lists the
water quality parameters sampled and the sampling
frequency.

Because of  insufficient CBODu  and CBOD5  data,
conservative values (high ratio) were used during the
study to avoid  criticism.   An overly conservative
CBODu-to-CBOD5 ratio  would result in wasteload
allocations more stringent than necessary.  There-
fore, if industries want to have adequate allocations,
sufficient data are required to justify a lower CBODu-
to-CBOD5 ratio.  A long-term BOD test of the efflu-
ents and the stream samples is needed.   Long-term
BOD tests usually take about 90 to 100 days and are
very straightforward, but time-consuming.

4.3.5    Biological Assessmen*

Several  methods exist for evaluating the biological
attributes of a stream system (USEPA, 1993a).  Habi-
tat Evaluation Procedures (HEPs) are used to docu-
ment the quality and quantity of available habitat by
providing information for comparing in-stream and
riparian  habitat in different areas or in one area under
different conditions. Rapid Bioassessment Protocols
(RBPs) for habitat are screening tools for determining
whether a stream is supporting a designated aquatic
life use.  One component of these protocols  is an
in-stream habitat assessment procedure that meas-
ures physical characteristics of the stream reach.
RBP III,  an RBP for benthic macroinvertebrates, fo-
cuses on quantitative sampling in riffle/run habitat or
on other submerged, fixed structures where  riffles
may not be available. The data collected are used to
calculate various metrics pertaining to benthic com-
munity structure, community balance, and functional
feeding groups.  The Index of Biological Integrity (IBI)
has been used in many States to assess a wide range
of impacts in streams and rivers.  The IBI includes 12
matrices in three major categories offish assemblage
attributes: species composition, trophic composition,
and fish abundance and  condition.  Any of these
methods can be useful in  determining the effects of
pollutant  loadings  on  biological  communities in
streams and rivers.
4.4    MODEL CALIBRATION
Model calibration is the first stage of testing and tuning
a model to a set of field data, preferably a set of field
data not used  in the original model  construction
(Thomann and Mueller, 1987).  Given the external
parameters of a modeled stream system, an initial
estimate is made of the appropriate transport and
reaction rate coefficients in the model.  These coeffi-
cients may be determined from a fundamental analy-
sis relating to each specific coefficient (i.e., hydrologic
or hydraulic analyses). The coefficients may also be
determined from a statistical analysis, as is usually
done with biological  and chemical kinetic terms.  In
any case, if a range of these values is known, a best
estimate is made for each, the model is run, and the
output is compared to the data.  Successive iterations
and adjustments are required to obtain a reasonable
fit of the model and data. This procedure is known as
model calibration.

4.4.1    Model Coefficient Assignment

Model calibration is also part of the process of deter-
mining model coefficients.  A simple example  is the
derivation of the stream deoxygenation coefficient,
Kd, using the measured CBOD5 or ultimate CBOD
data (e.g., CBOD20).

In many stream BOD/DO modeling analyses, sedi-
ment oxygen demand was not included in the models.
To calibrate the model to actual in-stream dissolved
oxygen data, the effect of SOD was incorporated into
other modeling  rates such as Kd, Kn, and Ka.  In
cases where Ka was determined using a reaeration
formula and  was not adjusted, the oxygen demand
from SOD could be  incorporated into the Kd or Kn
rates. This approach would have the effect of over-
estimating the dissolved oxygen impact from removal
of nitrogenous or   carbonaceous BOD.   Sub-
sequently, this derived value must be  incorporated
into the site-specific  model to check the model-com-
puted CBOD5 and dissolved oxygen profiles against
the field data.
                                             4-21

-------
Incorrect calibration of models could also arise from
wrong steps in model calibration.  For example, Kd
can be adjusted until the calculated dissolved oxygen
matches the measured data, rather than adjusting Kd
to correspond to the CBODu data.  Sediment oxygen
demand, not considered in the model, was measured
at a  certain value.  In this case, the effect of SOD
could inadvertently be included in the Kd rate in order
to match the  model output with the observed data.
Thus, the Kd  rate used in the model could be sub-
stantially higher than the Kd  estimated  from the
CBOD data. Since the Kd  used in the model exceeds
its likely value, the dissolved oxygen increase result-
ing from CBOD removal would be overestimated.

In model calibration analyses, adjustments of model
coefficients should not  exceed a predetermined
range for each individual rate constant.  For example,
if CBOD5 data show that Kd could range from 0.25
to 0.30  day-1 (depending on how the slope of the
CBOD decay curve was  drawn), Kd should not be
adjusted beyond this range.  Another approach  in
calibrating a model is to set all rates other than Ka
equal to their most reasonable value based on avail-
able data, and then to vary Ka under various  flow
regimes within the range indicated by the applicable
reaeration formulas. If adjustments within this range
of Ka do not produce a good match with the data, then
the  other rate constants may  be  adjusted further
within their range of uncertainty. If these adjustments
still do not produce a good match, the analyst should
reevaluate available data, the  reaeration  formulas
used, and the receiving stream itself to identify factors
that may be preventing model results from correspond-
ing with actual in-stream  data.   It should be  noted,
however, that if Ka is calculated or measured using
site-specific data, it is not advisable to vary Ka values
and other estimated coefficients  such as SOD should
be adjusted.

A more difficult case is the assignment of the model
coefficients involving eutrophication. As indicated in
Section  2, many kinetic processes related to  phyto-
plankton growth  and nutrient recycling in the water
column are difficult, if not  impossible, to obtain inde-
pendently because of cost or time constraints.  The
practical approach of assigning them in a  TMDL
analysis is to rely on model calibration and sensitivity
analyses. That is, model coefficient values are se-
lected from literature values, preferably from previous
studies  at the specific site location, or from water
bodies with similar problem settings.  Subsequent
model runs are performed to fine tune these model
coefficients by matching the field data. Although a
number of model coefficient values are derived from
literature data, independent estimates of the exoge-
nous variables such as streamflows, time of travel,
boundary conditions, and environmental conditions
should still be derived from the field data to minimize
the degree of tuning.

When field measurements are available, model coef-
ficients can be determined using curve-fitting proce-
dures.  However, it should be pointed out that model
calibration is not a curve-fitting exercise. The model
coefficients (e.g., algal growth rate) are also adjusted
through a series of model runs with reasonable and
narrow ranges of their values derived from the litera-
ture. The model is designed to mimic the steady-state
algal growth and nutrient dynamics and should be
shown to accomplish the task of reproducing the algal
biomass and nutrient concentrations in the stream.  I n
model sensitivity analyses, adjusting the kinetic coef-
ficients  and constants (within  their predetermined
ranges) to  improve the calibration of certain water
quality constituents often results in adverse outcomes
for other water quality constituents.  These  are the
constraints in the model  calibration process  that
would eventually lead to the determination of a unique
set of credible model coefficients.

4.4.2    Component Analyses

In the steady-state  stream BOD/DO modeling analy-
sis, the amount of dissolved oxygen deficit produced
by each of the oxygen-demanding components can
be calculated.  The dissolved oxygen deficit is calcu-
lated for each source of deficit and then is  plotted.
Figure 4-7 shows the component analysis results from
the modeling analysis of Rock Creek, Pennsylvania,
for the August 1979 conditions (Lung, 1990).  The
results suggest that the Gettysburg wastewater treat-
ment plant and the sediment oxygen demand contrib-
uted the most oxygen deficit in the stream. All other
sources had a much smaller effect on the dissolved
oxygen concentrations. This type of analysis is impor-
tant in both the model calibration/validation and was-
teload allocation analyses.  The component analysis
gives  the analyst  a  graphical presentation  of the
cause-and-effect relationship for in-stream water qual-
ity-

While component analyses are routinely performed to
quantify the contribution of individual sources to dis-
solved oxygen deficits, a similar component analysis
is not appropriate for eutrophication modeling analy-
sis because of the nonlinear nature of the phytoplank-
ton growth-nutrient dynamics in the model.   That  is,
results from a component analysis would not predict
algal biomass accurately  in terms  of  the  various
                                              4-22

-------
        1 I

TJ
C
"l;
s
f— ™ "T- 	
CP
5
J3
-i1
1 	 T 	

*

tfl
\ r~~~
ft C
_- 3
-
•S ri
™™HWT 	
e
y
oc

2
	 1 	 1 	 '-f~
c •*
52

— s 3L
                   o
                                               C
                 So lure Led DO  cone e
                                               = 8 . 1 3  n- q/ _
Q
C
                                                   (I>  Curr oer ana
                                                   ©  GeHysburg Plant
                           .    4      5     6
                         Mia*  (roffi Cumb«irlan4
                                                                               11
                                                            Plant
        FIGURE 4-7. COMPONENT ANALYSIS OF DO FOR ROCK CREEK, PENNSYLVANIA
                                         (Lung, 1990)
sources of phosphorus without taking into considera-
tion other factors that also control algal production.
Lung and  Testerman (1989)  have demonstrated a
technique called numerical tagging to determine how
much phosphorus in the algal biomass at a certain
location in the James River, Virginia, is from a par-
ticular wastewater source. The technique is similar to
using a radioactive tracer in limnological studies to
track the fate and transport of phosphorus in systems.
Instead of using a radioactive tracer such as 32PO4,
Lung and Testerman used a numerical tracer injected
at the particular wastewater source studied. That is,
that source of phosphorus was  numerically labeled
and added to the river. Figure 4-8 shows the numeri-
cal tagging results for the James River. The results
show that POTWs in the James River are major
contributors of orthophosphate as  well as  algal
biomass.   One interesting observation is that while
the Richmond POTW contributes more than 80 per-
cent of the orthophosphate to the river, its contribution
to the biomass is only about 50 percent (a dispropor-
tionate share compared with  its phosphorus input).
Such a result further emphasizes the nonlinear nature
of eutrophication models.
                                          4.4.3   Quantifying the Comparison Between
                                                  Model Results and Data

                                          Research activities (Thomann, 1982,1987) in model-
                                          ing eutrophication in lakes have begun to explore the
                                          use of simple statistical comparisons in an attempt to
                                          quantify model adequacy.  These techniques could
                                          be a supplement to the qualitative comparisons of
                                          observed and calculated water quality profiles. Three
                                          techniques that have been used are:

                                              •   Comparison of means
                                              •   Regression analysis
                                              •   Relative  error

                                          In the first technique, the mean of the observed data
                                          is compared to the mean of the computed profile for
                                          the comparable conditions of loading, transport, and
                                          temperature.  The  Student's t-probability density
                                          function is  employed  for the  comparison of the
                                          means.

                                          In regression analysis, calculated concentrations and
                                          observed data are considered as paired points in the
                                          test equation:
                                             4-23

-------
   Freshwater Flow:  1100 cfs (31.2 m'/s)   T«nnp«raturt; 26*C
0
             Upstream & Ncmpoint Input
                                             Bey Exchange
                                50  40  30
              upstream at NonoQint Input
                   Other POTWs
          Richmond
          POTW
                        Appomcrttox Rwer Input
                         Upstreqm at Nanpcfrrt Phoaphorua Input
                          Indiatrfd Phosphorus input
                                      from Other POTWs
                 Richmond
                 ROTW .
  Bay Exdwnga

30   20   10    0
                      70   60   50   40

                      River Mtea from Mouth
           FIGURE 4-8. NUMERICAL TAGGING OF JAMES RIVER
                    (After Lung and Testerman, 1989)
                                4-24

-------
                   = a
                          C + E
where   and o are the true intercept and slope, respectively,
between the calculated value Cand the observed data X Eis the
error associated with the observed dataX The regression analysis
assumes that the calculated value C is known with certainty and
that the error Eis in the measured data, which is not necessarily
a realistic assumption. Standard linear regression methods can
be used to compute the square of the correlation coefficient (r2\
and the standard error of estimate representing the
residual error between data and model.  Estimates of
the slope and intercept are calculated and a test of
significance  developed.  Well-calibrated  models
would have a zero intercept and a slope of one.

Relative error is the absolute value of the difference
between the observed  and the  calculated  values
divided by the observed value. The relative error may
be aggregated across time or space,  and the cumu-
lative frequency of error can be computed. Estimates
can be made of the median relative error as well as
the 10 percent and 90  percent frequency of error.
This statistic  is poorly behaved at the upper tail and
at low values of X.  The median  error can be easily
understood;  therefore, it is the suggested measure if
statistical representations of model adequacy are to
be employed in a TMDL study.

Statistical measures of adequacy are in the early
stages of research and should be employed recog-
nizing  that they provide, at the  very best,  a lower
bound on the magnitude of the error.


4.5     MODEL VALIDATION

4.5.1    Model Coefficient Adjustment

Once a set of coefficients representative of one set
of external conditions (e.g., with respect to tempera-
ture, flow, and loading inputs) has been established,
the model is  rerun for a different set  of input condi-
tions.  If the  model output agrees in a reasonable,
qualitative way with the second set of data, the model
is considered to be validated in the first degree.

In some instances, if sufficient data are available,  a
quantitative comparison is possible. Then a measure
such as the standard error of the  mean can be used,
and the model  is considered  validated if the model
results fall within one standard error. Additional com-
parisons with different combinations of the  exoge-
nous parameters yield higher degrees of validation.

In some cases, a model coefficient value may have
to be varied slightly to match the data in the validation
process.  Then the changed value must be tested
again with the data set for the model calibration run.
A good example of such an exercise is the calibration
of the nitrification rate, Kn. When data from a winter
survey are first used to calibrate  a model, the dis-
solved oxygen balance is not sensitive to the nitrifica-
tion rate and, therefore, Kn cannot be determined
accurately.  During model validation, another set of
data collected in the summer months is used. Since
the nitrification process is highly sensitive to tempera-
ture, the modeling analysis is able to tune the nitrifi-
cation rate with greater accuracy. Now, this modified
Kn  should be checked again with the winter run.
Because of the cold temperature in the winter months,
the model results are not affected. This procedure is
also valid for model calibration and validation analy-
ses between  two  drastically different stream flow
conditions.  Under high flows, some  kinetic coeffi-
cients may not be important and, therefore, cannot be
accurately calibrated  until the low-flow conditions,
when  the  kinetics become much more significant in
relation to stream flow.

4.5.2    Model Sensitivity Analysis

When validating or calibrating a mathematical water
quality model, the  analyst  selectively determines
some model input parameters that, when used in the
model, yield reasonable simulations  of observed
water quality data.  Some of these input parameters,
such as stream geometry, cross-sectional areas, and
depths, are directly measured. Other model parame-
ters, such as  system  transport,  oxidation rates,
reaeration rates, and nitrification rates, are not directly
measured. These parameters are determined  from
empirical formulations, literature searches, or itera-
tive model simulations.  The purpose of a sensitivity
analysis is to test the sensitivity of the model to some
of these input parameters.  Some of the common
model sensitivity analyses in a stream TMDL include:

    •    Sensitivity of model to transport coefficients.
    •    Sensitivity of model to Ka rates.
    •    Sensitivity of model to Ka rates.
    •    Sensitivity of model to Kn rates.
    •    Sensitivity of model to the  net  algal oxygen
        production rate (P-R).
    •    Sensitivity to SOD
                                               4-25

-------
                                      Rity pcicev
                                       had met&aii relative
FIGURE 4-9. SOME RELATIVE ERRORS OF DISSOLVED OXYGEN MODELS
                        (Thomann, 1980)
                             4-26

-------
4.5.3   Model Accuracy

The question of model accuracy is often crucial in
situations where a given allocation is being negoti-
ated or contested. Thomann (1980) has discussed
this question and compiled a distribution of relative
errors between model calibration results and the ob-
served data. Figure 4-9 displays the median relative
error in measured versus simulated (modeled) dis-
solved oxygen for waterbodies of varying complexity.
The models represented in Figure 4-9 generally rep-
resent state-of-the-art  models,  applied  by experi-
enced practitioners  using best judgment on loads,
parameters, and model structure. That is, the calibra-
tions were conducted based on defensible theoretical
assumptions rather than simply an attempt to match
the measured dissolved oxygen values by arbitrarily
adjusting model coefficients. With this consideration
in mind, Figure 4-9 indicates that for the 20 models
represented, 50 percent had a median relative error
in dissolved oxygen of 10 percent (plus or minus),
with maximum errors of up to 60 percent occurring in
the smaller streams/estuaries simulated. This com-
parison is useful in suggesting the present ability to
reproduce the observed data with a credible model.
4.6    MODEL APPLICATION AND
       TOTAL MAXIMUM DAILY LOADS


An integral part of the TMDL process is the analysis
of cause-effect relationships via a  mathematical
model  of loading  input and resulting water quality
response. The TMDL rests heavily on the credibility
and predictive capability of the mathematical model-
ing framework (Thomann and Mueller, 1987). How-
ever, the adequacy of the modeling framework is only
one of many issues that must be considered in a
TMDL  process (Chadderton and  Kropp, 1985).  To
develop an actual TMDL, a number of tasks need to
be conducted. The following sections provide a brief
description of these tasks.

4.6.1   Development of Management Scenarios

In many  cases,  management  alternatives can  be
evaluated by using model applications, particularly in
river systems that  receive loadings  from multiple
sources.  Usually a regional or State planning agency
is responsible for soliciting input from dischargers, the
public,  and other interested parties to determine the
most feasible management alternatives. In all cases,
depending on scenario,  all point  and  nonpoint
sources should be considered when developing allo-
cation scenarios.

When developing an allocation scenario for a TMDL, the
water resource manager should select the best combi-
nation  of  point and  nonpoint source  controls that
achieves water quality standards. The selection of an
allocation alternative largely depends on available tech-
nical and financial resources. The best combination of
pollution reduction controls is that which is the most
cost-effective  and  feasible to implement.   Allocation
scenarios  typically  reduce point source  discharges
through NPDES permitting,  reduce nonpoint source
loads through the implementation of best management
practices (BMPs), or use a combination of both.

Cost trade-offs are an important consideration  when
developing alternative pollution allocation scenarios.
Point and  nonpoint source trading  is one cost-effec-
tive alternative for meeting  water  quality criteria or
other appropriate TMDL endpoints. Although  it can
be implemented in many different forms, essentially
trading allocates pollutant loading reductions across
point and  nonpoint sources using  least cost as the
criterion (USEPA, 1992c).  For  example, in lieu of
upgrading their pollution control technology,  point
source dischargers may be allowed to pay for equiva-
lent or greater reductions in nonpoint source loadings
within their watersheds.  Trading is applicable  when
implementation of nonpoint source BMPs is less
costly per unit of pollution reduction than upgrading
point source treatment technology.

4.6.2    Total Maximum Daily Loads

Application of a model to allocate waste loads and
nonpoint loads is usually done under 7-day, 10-year
low-flow conditions depending on the WQS being im-
plemented, and the type  of waterbody  (see Section
A.3.2).   A temperature condition needs to be estab-
lished as well.  There is no standard procedure  in the
model application analysis. Figure 4-10 is a suggested
allocation procedure for BOD/DO in streams, the steps
of which are discussed below. The procedure does not
address cost/benefit issues.

The determination of the dissolved oxygen standard or
endpoint as the first step includes an evaluation  of the
statistical requirements of the standard. Thus,  if the
standard indicates that the dissolved oxygen should
never be less than 5 mg/L, then recognition should be
given to random uncontrollable variations in dissolved
oxygen. For streams and rivers, these fluctuations may
be on the order of a standard deviation  of 0.25  mg/L.
Thus, if 0.5 mg/L is added to the standard, then the
                                              4-27

-------
                  YES
   ALLOCATION IS AS
GIVEN BY APPLIED LOADS
 INCREMENT TREATMENT
    LEVEL UNTIL DO
 STANDARD IS VIOLATED
I
     "EQUIVALENT"
   RESERVE CAPACITY'
  "See glossary for definition
                                  DO STANDARD
                                      I
                               DETERMINE UPSTREAM
                              AND BACKGROUND FLOW,
                              BOD AND DO CONDITIONS
                                      I
                               INPUT ALTERNATIVES
                               LOADINGS SOURCES
                                      I
                               APPLY WATER QUALITY
                                     MODEL
                                      i
                                 7S DO STANDARD
                                   ACHIEVED?
I
                                                NO
                                                            NO
                        INCREMENT TREATMENT
                          LEVEL WITH EACH
                            SOURCE LOAD
                                                                 IS DO STANDARD
                                                                  ACHIEVED?
                           YES
                                                            I
                                                              MAXIMUM ALLOWABLE
                                                                     LOAD
                                1
                                                                    SELECT
                                                               "MARGIN OF SAFETY"
                                                                     I
                                                                CHECK FOR UPPER
                                                                 TECHNOLOGICAL
                                                                  CONSTRAINT
           FIGURE 4-10. TMDL PROCEDURE FOR BOD/DO PROBLEM
                                      4-28

-------
resulting level of 5.5 mg/L represents the target mini-
mum  level that, if attained,  will  meet the absolute
minimum  level  of 5  mg/L with only  a 2.5  percent
chance of dropping  below the  standard.  This does
not imply that the short-term fluctuations may or may
not be damaging to the ecosystem.  That determina-
tion is part of the interpretation  of the standard.

The selection of a background dissolved oxygen defi-
cit is subject to wide variation depending on the spe-
cifics of the area, such as urban, suburban, or rural
land use.  Some States have determined background
percent saturation for specific ecoregions. The deficit
may be determined  from upstream BOD and dis-
solved oxygen conditions and calculated through the
region of interest. This approach requires assignment
of BOD deoxygenation coefficients. A minimum effort
analysis would  simply assign  a constant dissolved
oxygen deficit throughout the river reach of 0 -1  mg/L
depending on the problem conditions.  This step is
clearly subject to potentially widely varying engineer-
ing judgment.  It should be noted that the use of a
1 -mg/L dissolved oxygen deficit may result in a signifi-
cantly higher degree of required treatment than that
resulting if no background is assigned.


The inputs from each of the point source discharges
are then  estimated  following general guidelines for
expected  effluent concentrations.  Nonpoint source
loadings are estimated from existing or collected wa-
tershed data. Often, nutrient budget studies are con-
ducted as part  of the TMDL process to  determine
approximate pollutant loadings contributed by non-
point sources. The remaining steps are as indicated.
The application  of the water quality model may also
vary widely, depending on the level of effort involved,
from simplified desktop calculations to full-scale field
and calibration studies. If the dissolved oxygen stand-
ard is  achieved with presently mandated effluent lev-
els, then the allocation is  as given by those levels and
an equivalent reserve capacity  can be estimated.  In
some  cases the dissolved oxygen standard may be
achieved  by incrementing point source treatment by
discrete levels.   However, nonpoint source controls
may be needed when further reductions in point source
waste loads are not  possible or are cost-prohibitive.
The technological upper  bound should be checked
here.  The maximum allowable discharge load is then
the load needed to achieve the standard. However, this
is not necessarily the load to be  allocated.

If relatively rapid growth is forecasted for an  area,
then it is  recommended that some fraction of the
maximum allowable load be placed in reserve for
future growth. A fraction of the maximum allowable
load can be set aside explicitly, or implicitly, as  a
margin of safety to account for scientific uncertainty
about whether the TMDL reflects the actual loading
capacity of the waterbody. This uncertainty can be
caused by insufficient or poor-quality data or a lackof
knowledge about the water resource and pollutant
effects.  Thus, if a margin of safety of 0.8 is chosen,
then 20 percent of the allowable load is placed in
reserve.  The allocation is given by the margin of
safety times the maximum allowable load. However,
a final check  should be  made to ensure that the
required treatment level is technologically feasible. If
an upper technological treatment bound has been
exceeded, the margin of safety may  have to be ad-
justed.

4.6.3   Uncertainty Analysis

Uncertainty analysis should be included as an integral
component of water quality modeling. One of the
primary purposes is to quantify the error in predicting
water quality and evaluate the effect of input parame-
ters on model  output. Better management decisions
can be made by quantifying this error. Such quanti-
fication also facilitates subsequent studies, such as
risk assessments, to evaluate alternative allocations.

In addition, uncertainty analysis may  provide insight
into the need  for additional data collection to refine
the estimate of  certain loads, initial conditions, or
reaction rates. For example, if the model is sensitive
to the reaeration rate (that is, a small change in
reaeration rate results in large changes in the predic-
tion of critical water quality parameters such as dis-
solved oxygen), it  may  be  appropriate to  allocate
resources to more accurately estimate the reaeration
rate of that stream or river.

Appendix D presents a discussion of the three tech-
niques for performing uncertainty analysis: sensitivity
analysis, first-order error analysis, and Monte Carlo
simulation. Each technique has advantages and dis-
advantages  in terms of  applicability  and computa-
tional burden that will make one method more suitable
than another for a  particular analysis.  In  many in-
stances, the modeler may need to explore the results
from all three  procedures.  The three methods may
produce  discrepancies in their results because the
methodologies and assumptions differ.   Each of
these techniques is available  in QUAL2E-UNCAS,
and  the discussion and  example in  Appendix D is
limited to the features available in that model.
                                              4-29

-------

-------
5.    REFERENCES
Adams, C.E., and W.W. Eckenfelder.  1977.  Nitrifi-
cation design approach for high strength ammonia
wastewater. J.  WPCF 49(3):413-421.

Alvarez-Montalvo, A., R.A. Ferrara, and D.R.F. Har-
leman. Undated. Simulation of water quality in the
Willamette River using the  M.I.T.  Nitrogen  Cycle
Model.
Baca, R.G., W.W. Waddel, C.R. Cole, A. Brandstet-
ter, and D.B. Cearlock. 1973. EXPLORE-I: A river
basin water quality model. Battelle Pacific Northwest
Lab,  Richland, WA.

Baity, H.G. 1938. Some factors affecting the aerobic
digestion of sludge deposits. Sewage Works Journal
10:539-568.
Ambrose, R.B., T.A. Wool, J.P. Connolly, and R.W.
Schanz. 1988. WASP4, a hydrodynamic and water
quality model: Model theory, user's manual and pro-
grammers guide.  EPA/600/3-87/039. U.S. Environ-
mental Protection Agency, Environmental Research
Laboratory, Athens, GA.

Ambrose, R.B., T.A.  Wool, and J.L. Martin. 1993a.
The  water  quality analysis simulation program,
WASP5, Part A: Model documentation, Version 5.10.
United States Environmental Protection Agency, En-
vironmental Research Laboratory, Athens, GA.

Ambrose, R.B., T.A.  Wool, and J.L. Martin. 1993b.
The  water  quality analysis simulation program,
WASP5, Part B: The WASP5 input dataset, Version
5.10.  United  States Environmental  Protection
Agency, Environmental Research  Laboratory, Ath-
ens, GA.

Ambrose, R.B., T.A.  Wool, and J.L. Martin. 1993c.
The dynamic estuary model hydrodynamics program,
DYNHYD5 model documentation and user manual.
United States Environmental Protection Agency, En-
vironmental Research Laboratory, Athens, GA.

APHA. 1989.  Standard methods for the examination
of water and wastewater.  1 7th ed. American Public
Health Association, Washington, DC.

Auer,  M.T.,  and R.P. Canale.  1980.  Phosphorus
uptake dynamics as related to mathematical model-
ing at a site on Lake Huron. J. Great  Lakes Res.
Baca, R.G., and R.C. Arnett.  1976.  A limnological
model for eutrophic lakes and impoundments. Bat-
telle Pacific Northwest Lab, Richland, WA.
Bannerman, R.T., D.E. Armstrong, R.F. Harris, and
G.C. Holdren.  1975.  Phosphorus uptake and re-
lease by Lake Ontario sediments.  EPA-660/3-75-
006. U.S. Environmental Protection Agency, Office
of Research and Development, NERC.

Bansal, M.K. 1973.  Atmospheric reaeration in natu-
ral streams. Water Res. 7:769-782.

Bansal,  M.K.  1975.   Deoxygenation in  natural
streams.  Water Res. Bull. 11:491-504.

Bansal, M.K.  1976. Nitrification in natural streams.
J. WPCF 48(10):2380-2393.

Barbour,  M.T., J.B. Stribling,  and J.R. Karr. 1992.
Biological criteria: Technical guidance for streams.
Draft. U.S. Environmental Protection Agency, Office
of Water, Washington, DC.

Barnwell, T.O., C.B. Linfield, and W.  Marek.  1989.
Application of expert systems technology in water
quality modeling.  Water Sci. Tech. 21:1045-1056.

Bauer, D.P.,  R.E.  Rathbun,  and H.W.  Lowham.
1979. Travel time, unit-concentration, longitudinal
dispersion,  and reaeration characteristics of up-
stream reaches of the Yampa  and Little Snake Riv-
ers, Colorado and  Wyoming.  USGS  Water
Resources Investigations 78-122.

Bedford, K.W., R.M. Sylees, and C.  Libicki.  1983.
Dynamnic advective water quality model for rivers. J.
Env. Eng., ASCE  109:535-554.

Bennett, J.P.,  and R.E. Rathbun. 1972. Reaeration
in open channel flow. Professional Paper 737. U.S.
Geological Survey, Reston, VA.
                                            5-1

-------
Bhargava, D.S. 1983.  Most rapid BOD assimilation
in Ganga and Yamura Rivers.  J. Env. Eng., ASCE
109:174-188.

Bierman, V.J., Jr., D.M. Dolan,  E.F. Stoermer, J.E.
Gannon, and V.E. Smith.  1980.  The development
and calibration of a multi-class phytoplankton model
for Saginaw Bay, Lake Huron. Great Lakes Environ-
mental Planning Study. Contribution No. 33.  Great
Lakes Basin Commission, Ann Arbor, Ml.

Blain, W.A. 1969.  Discussion of evaluation of nitrifi-
cation in streams. J. San. Eng. Div., ASCE/SA 5:956-
958.

Bott, T.L., J.T. Brock, C.S. Dunn, RJ. Naiman, R.W.
Quink, and R.C. Petersen.  1985.  Benthics commu-
nity metabolism in four temperate stream systems:
An inter-biome comparison and evaluation of the river
continuum concept. Hydrobiologia 123:3-45.

Bowie, G.L., C.W. Chen, and D.H. Dykstra.  1980.
Lake Ontario ecological modeling, Phase III. Report
TC-3942.  Prepared by Tetra Tech, Inc., for National
Oceanic and Atmospheric Administration.

Bowie, G.L., W.B. Mills, D.B. Porcella, C.L. Campbell,
J.K. Pagenkopf, G.L.  Rupp, K.M. Johnson, P.W.H.
Chan, and S.A. Gherini. 1985. Rates, constants and
kinetics formulations in surface water quality model-
ing. 2ded. EPA/600/3-85/040.  U.S. Environmental
Protection Agency, Environmental Research Labora-
tory, Athens, GA.

Brandes, RJ. 1976.  An aquatic ecologic model for
Texas bays and estuaries.  Contract No. 14-30027.
Texas Water Development Board, Austin, TX.

Bridle, T.R., D.C. Climenhage, and A. Stelzig.  1979.
Operation of a full-scale nitrification-denitrification in-
dustrial waste treatment  plant.  J.  WPCF
51(1):127139.

Brown, L.C.,  and  T.O. Barnwell.  1987.   The en-
hanced  stream water quality model  QUAL2E and
QUAL2E-UNCAS documentation and user manual.
EPA-600/3-87/007. U.S. Environmental Protection
Agency, Athens, GA.

Buswell, A.M., et al. 1957. Laboratory studies on the
kinetics of the growth of nitrosomonas with relation to
the nitrification phase of the BOD test. Appl. Micro-
biol. 2:21-25.

Butts, T.A.  1974.  Measurements of sediment oxy-
gen demand characteristics of the Upper Illinois wa-
terway.  Illinois State Water Survey Report of Inves-
tigation 76, NTIS Publication No. PB-240 992. NTIS,
Springfield, VA.

Butts, T. A., and R.L. Evans. 1978.  Sediment oxygen
demand studies of selected North-Eastern  Illinois
streams.  Illinois State Water Survey Circular 129,
NTIS Publication No. PB-285-788.  NTIS, Springfield,
VA.

Butts, T.A., and R.L.  Evans. 1983.  Small stream
channel dam aeration characteristics. J. Env. Eng.,
ASCE 109(3):555-573.

Cadwallader, T.E., and AJ. McDonnell.  1969.  A
multivariate analysis of reaeration  data.  Water Res.
2:731-742.

Calendar, E., and  D.E. Hammond.  1982.  Nutrient
exchange across the sediment-water interface in the
Potomac River estuary.   Est. Coast. Shelf Sci.
15:395-413.

Camp, T.R.  1965.  Field estimates of oxygen bal-
ance parameters. J. Sani. Eng. Div., ASCE 91 (S5A).

Canale, R.P., L.M. Depalma, and A.H. Vogel. 1976.
A plankton-based food  web model for  Lake Michi-
gan. In Modeling biochemical processes in aquatic
ecosystems, ed. R.P. Canale, pp.  33-74. Ann Arbor
Science Publishers, Ann Arbor, Ml.

Canale, R.P., and A.H. Vogel.  1974. The effects of
temperature on phytoplankton growth. J. Env. Eng.,
ASCE 100(EE1):231-241.

Carlucci, A.F.,  and P. MacNally. 1969.  Nitrification
by marine bacteria in low concentrations of substrate
and oxygen.  Limnol. Oceanogr. 14 (5):736-739.

Cerco, C.F.  1981.   Nitrification in the upper tidal
James River.   In Estuaries and Nutrients, ed.  B.
Neilson and EJ.  Cronin,  pp. 413-424.   Humana
Press, Clifton, NJ.

Chadderton, R.A.,  and I.S. Kropp.   1985. An evalu-
ation of eight wasteload allocation methods.  Water
Res. Bull. 21(5):833-839.

Chairo, P.S., and D.A. Burke. 1980. Sediment oxy-
gen demand and nutrient release.  J. Env. Eng. Div.,
ASCE, pp. 177-195.

Chapra, S.C., and D.M. Di Toro.  1991.  Delta mod-
eling for estimating primary production, respiration,
                                             5-2

-------
and reaeration in
116(5):640-655.
streams.   J.  Env.  Eng., ASCE
Chen, C.W. 1970. Concepts and utilities of ecologi-
cal model. J. Sani. Eng. Div., ASCE 96(SA5).

Chen, C.W., and G.T. Orlob. 1972. Ecologic simu-
lation for aquatic environments. Final Report to U.S.
Department of Interior, Office of Water  Resources
Research. Water Resource Engineers, Inc., Walnut
Creek, CA.

Chen, C.W., and G.T. Orlob. 1975. Ecologic simu-
lation of aquatic environments.  In Systems analysis
and simulation in ecology, Vol. 3, ed. B.C. Patten, pp.
476-588. Academic Press, New York, NY.

Chen, C.W., and J.T. Wells, Jr.  1975.  Boise River
water quality-ecological model  for urban planning
study. Prepared by Tetra Tech, Inc.,  Lafayette, CA for
US  Army  Engineering District, Walla Walla, WA,
Idaho Water Resources Board,  and Idaho Dept. of
Environmental and Community Services.

Chen, C.W., and J.T. Wells, Jr.  1976.  Boise River
modeling.   In Modeling biochemical processes in
aquatic ecosystems, ed. R.P. Canale, pp. 171-204.
Ann Arbor Science Publishers, Ann Arbor, Ml.

Churchill, M.A.,  H.L.  Elmore, and R.A. Buckingham.
1962. The prediction of stream reaeration rates. J.
San. Eng.  Div., ASCE 88(SA4):1-46.

Cirello, J., R.A.  Rapaport, P.F. Strom, V.A. Matule-
wich, M.L.  Morris, S. Goetz, and M.S. Einstein. 1979.
The question of nitrification in the Passiac River, New
Jersey:  Analysis of historical data and experimental
investigation. Water Res. 13:525-537.

Clark, J.W., W. Viessman, and MJ. Hammer. 1977.
Water supply and pollution control.  3d ed. Harper &
Row, New York, NY.

Committee on  Sanitary  Engineering  Research.
1960. Solubility of atmospheric oxygen in water. J.
San. Eng.  Div., ASCE 86(SA4):41-53.

Conway, H.L.  1977.  Interactions of inorganic nitro-
gen in the uptake and assimilation by marine phyto-
plankton.  Mar. Biol. 39:221-232.

Courchaine, RJ. 1968.  Significance of nitrification
in stream analysis—Effects on the  oxygen balance.
J. WPCF 40:835.
Crabtree, R.W., I.D. Clukie, C.F. Forste, and C.P.
Crockett. 1986.  A comparison of two river quality
models. Water Res. 20(1):53-61.

Crane, S.F., and  R.F. Malone.   1982.   Reliabiltiy
analysis of BOD kinetics in a small southern stream
governed by the discharge of an oxidation pond.
Louisiana Water Resources Research Institute,  Ba-
ton Rouge, LA.

Crim, R.L.,  and N.L. Lovelace.  1973.  Auto-Qual
modeling system.  EPA-440/9-73-003. U.S. Environ-
mental Protection Agency.

Curtis, E.J., K. Durant, and M.I. Harman. 1975. Ni-
trification in rivers in the Trent basin.   Water Res.
9:225-268.

Curtis, M.D. 1983.  Oxidative and non-oxidative am-
monia modeling.  In  Proceedings of the ASCE 1983
National Conference on Environmental Engineering,
Boulder, Colorado, ed. A. Medine and M. Anderson,
pp. 493-499.

Deb,  A.K., and  D. Bowers.  1983.  Diurnal water
quality modeling: A case  study.  J. WPCF
55(12):1476-1488.

Deb,  A.K., J. Klafter-Snyder,  and W.G.  Richards.
1983.  Water quality modeling of shallow surface
active streams - a case study.  In Proceedings of the
1983  National Conference on  Environmental Engi-
neering, Boulder Colorado, ed. A. Medine and M.
Anderson, pp. 486-492.

Di Toro, D.M. 1975.  Algae and dissolved oxygen.
Summer Institute in Water Quality Modeling, Manhat-
tan College, Bronx, NY.

Di Toro, D.M. 1978.  Optics of turbid estuarine wa-
ters:   Approximations and  applications.  Water Res.
12:1059-108.

Di Toro, D.M.  1986. A diagenetic oxygen equiva-
lents  model of sediment oxygen demand. In Sedi-
ment oxygen demand: Processes, modeling  and
measurement, ed. KJ. Hatcher,  pp. 171-208. Inst.
of Natural Resources, University of Georgia, Athens,
GA.

Di Toro, D.M., and J.P. Connolly.  1980. Mathemati-
cal models of water quality in  large lakes.  Part II:
Lake  Erie.  U.S.  EPA Ecological Research Series,
EPA-600/3-3-80-065.  U.S.  Environmental  Protec-
tion Agency, Washington, DC.
                                             5-3

-------
Di Toro, D.M., and W.F. Matystik, Jr. 1980. Mathe-
matical models of water quality in large lakes. Part I:
Lake Huron and Saginaw Bay.  U.S. EPA Ecological
Research Series, EPA-600/3-80-056.  U.S. Environ-
mental Protection Agency, Washington, DC.

Di Toro, D.M.,  J.A. Mueller, and MJ. Small. 1978.
Rainfall-runoff and statistical receiving water models.
NYC  208 Task Report 225.  Prepared by Hy-
droscience, Inc. for Hazen & Sawyer Engr. and  New
York City Department of Environmental Protection.
March.

Di Toro, D.M., DJ. O'Connor, and R.V.  Thomann.
1971.  A dynamic model of the phytoplankton popu-
lation  in the Sacramento-San Joaquin Delta. In Ad-
vances in chemistry, No. 169, pp. 131 -180. American
Chemical Society, Washington, DC.

Di Toro, D.M.,  P.R. Paquin, K. Subburamu, and  D.
Gruber. 1990.   Sediment oxygen demand model:
Methane and ammonia oxidation. J. Env. Eng. ASCE
116(5):945-987.

Dobbins, W.E. 1964. BOD and oxygen relationships
in streams.  J. San. Eng. Div., ASCE 90 (SA3):53-78.

Dolan, D.M., A.K. Yui, and R.D. Geist.  1981. Evalu-
ation of river load estimation methods for total phos-
phorus. J. Great Lakes Res. 7(3):207-214.

Donigian, A.S., and W.C. Huber. 1991. Modeling of
nonpoint source water quality in urban and non-urban
areas. EPA/600/3-91/039. U.S. Environmental  Pro-
tection Agency, Office of Research and  Develop-
ment,  Washington, DC.

Downing, A.L., and G.A. Truesdale.  1955. Some
factors affecting the rate of solution  of  oxygen  in
water. J. of Appl. Chem. 5:570-581.

Duke, J.H., Jr., and F.D. Masch. 1973.  Computer
program documentation for the stream quality model
DOSAG3.  Vol. I. Prepared for U.S. Environmental
Protection Agency, Systems Development Branch,
Washington, DC.

Edberg, N., and B.V. Hofsten.  1973. Oxygen uptake
of bottom  sediments studied in in situ and in the
laboratory.  Water Res. 7:1285-1294.

Edwards, R.W., and H. Rolley. 1965. Oxygen  con-
sumption in river muds. J. Ecol. 53:1-18.
Elmore, H.L., and W.F. West. 1961. Effects of water
temperature on stream reaeration.  J. of San. Eng.
Div., ASCE 87(SA6):59.

Eloubaidy, A.F., and EJ. Plate.  1972.  Wind shear-
turbulence  and  reaeration coefficient.  J. Hydraul.
Div., ASCE 98(HY1):153-170.

Eppley, R.W.  1972.  Temperature and  phytoplank-
ton growth in the sea. Fish. Bull. 70(4):1063-1085.

Erdmann,  J.B.  1979a.  Systematic  diurnal curve
analysis.  J. WPCF 15(1):78-86.

Erdmann,  J.B.   1979b.   Simplified diurnal curve
analysis. J. of Env. Eng. Div., ASCE 105(EE6):1063-
1074.

Fair, G.M. et al. 1941. The natural purification of river
muds and  pollutional sediments.  Sewage Works
Journal 13(4):756-779.

Fair, G.M., J.C. Geyer, and D.A. Okun. 1968. Water
and wastewater engineering. John Wiley and Sons,
New York, NY.

Fair, G.M., J.C. Geyer, and D.A. Okun.  1971.  Ele-
ments  of water supply and wastewater disposal.
John Wiley and Sons, New York, NY.

Fillos, J., and W.R. Swanson.  1975.  The release
rate of nutrients from river and lake sediments.  J.
WPCF47(5):1032-1042.

Finstein, M.S., and V.A. Matulewich. 1974. Distribu-
tion of autotrophic  nitrifying bacteria in a polluted
stream. Report A-030-NJ.  Water Resources Re-
search Institute, Rutgers University, New Brunswick,
NJ.

Fischer, H.B.  1968. Dispersion predictions in natural
streams. J. San.  Eng. Div.1, ASCE, 94:927-944.

Fischer, H.B.  1975.  Discussion of "Simple method
for predicting dispersion  in streams" by R.S.
McQuivey  and T.N. Keefer.  J.  Env. Eng., ASCE
101:453-455.

Fischer, H.B., EJ. List, R.C.Y. Koh, J. Imberger, and
N.H. Brooks.  1979.  Mixing in inland and coastal
waters. Academic Press, New York, NY.

Foree,  E.G.  1976.  Reaeration and velocity predic-
tion for small streams. J. Env.  Eng. Div., ASCE
102(EE5):937-951.
                                             5-4

-------
Foree, E.G. 1977.  Low-flow reaeration and velocity
characteristics of small streams, (Update). J. of Env.
Eng. Div., ASCE, EE5.

Freedman, P.L., and  R.P. Canale.  1977.  Nutrient
release from  anaerobic sediments.  J.  Env.  Eng.,
ASCE 103(EE2):233-244.

Freedman, P.L., R.P.  Canale, and J.F. Pendergast.
1980. Modeling storm overflow impacts on eutrophic
lake. J. Env. Eng., ASCE 106:335-349.

French, R.H.  1985.   Open-channel hydraulics.
McGraw-Hill, New York, NY.

Frexes, P., G.H. Jirka, and W. Brutsaert.  1984.  Ex-
amination of recent field data on stream reaeration.
J. Env. Eng. Div., ASCE 110(6):1179-1183.

Gakstatter, J.H., M.O. Allum, S.E. Dominquez, and
M.R. Grouse.   1978.  A survey of phosphorus and
nitrogen levels in treated municipal wastewater.  J.
WPCF 50(4).

Gallant, A.L., T.R. Whittier, D.P. Larsen,  J.M.
Omernik, and R.M. Hughes. 1989. Regionalization as
a tool for managing environmental resources.
EPA/600/3-89/060. U.S. Environmental  Protection
Agency, Environmental  Research Laboratory,  Cor-
vallis, OR.

Garber,  J.H.  1990. Sediment-water flux measure-
ments in the Peconic Bay estuarine ecosystem: July
and October 1989.  Data report submitted to Suffolk
County Dept. Health Services, Riverhead, NY. Final
report for Agreement No. 01-4400-456-29-00022.

Garland, J.N.H. 1978. Nitrification in the River Trent.
In  Mathematical models in water pollution control, ed.
A.  James, pp. 167-192.  John Wiley and Sons, New
York, NY.

Garside, C. 1981.  Nitrate and ammonia uptake in
the apex of the New York Bight. Limnol. Oceanogr.
26(4):731-739.

Genet, L.A.,  DJ. Smith and M.B. Sonnen.  1974.
Computer program documentation for the dynamic
extuary model. Prepared for U.S. Environmental Pro-
tection Agency, Systems  Development  Branch,
Washington, DC.

Godfrey, R.G., and BJ. Frederick. 1970. Dispersion
in natural streams. USGS Professional Paper 433-K.
U.S. Geological Survey.
Gowda, T.P.H.  1983.  Modeling nitrification effects
on the dissolved oxygen regime of the Speed River.
Water Res.  17:1917-1927.

Grant, R.S.  1976. Reaeration-coefficient measure-
ments of 10 small streams in Wisconsin using radio-
active  tracers...  with  a  section  on  the
energy-dissipation model. U.S. Geological Survey,
Water Resources Investigations 76-96.

Grant, R.S.  1978. Reaeration capacity of the Rock
River between  Lake Koshonong, Wisconsin and
Rockton, Illinois. U.S. Geological Survey, Water Re-
sources Investigations 77-128.

Grenney, W.J.,  and  A.K.  Kraszewski.   1981.  De-
scription and application of the stream simulation and
assessment model:  Version  IV  (SSAM  IV).  (In-
stream flow information paper).   Fish and Wildlife
Service, Fort Collins,  CO, Cooperative Instream Flow
Service Group.

Gulliver, J.S.,  T.W. Mattke, and H.G. Stefan. 1982.
Numerical and graphical procedures for estimation of
community photosynthesis and respiration  in experi-
mental streams.  EPA-600/3-82-052.  NTIS No.
PB82-220765. Prepared by University of Minnesota,
St. Anthony Falls Hydraulic Laboratory, Minneapolis,
MN, for U.S. Environmental Protection Agency.

Gulliver, J.S.,  and H.G.  Stefan. 1981.  Air-water sur-
face exchange of oxygen:  Theory and application to
the USEPA Monticello Experimental Field channels.
St. Anthony  Falls Hydraulic  Laboratory  External
Memorandum No. 173, University of Minnesota.

Gunnerson, C.G., et  al.  1982. Management of do-
mestic  wastes.  In Ecological  stress  and  the  New
York Bight:   Science and management,  ed.  G.F.
Mayer, pp. 91-112. Estuarine Research Federation,
Columbia, SC.

Haith,  D.A.,  R. Mandal,  and R.S. Wu.  1992.
GWLF—Generalized watershed  loading functions,
version 2, user's manual. Department of Agricultural
and Biological Engineering,  Cornell  University, Ith-
aca, NY.

Haith, D.A., and L.L.  Shoemaker. 1987. Generalized
watershed loading functions for stream flow nutrients.
Water Res.  Bull. 23(3):471-477.

Hall,  J.C.,  and RJ.  Foxen.   1984.  Nitrification in
BODs test  increases  POTW noncompliance.  J.
WPCF 55(12) 1461:1469.
                                             5-5

-------
Harleman, D.R.F., J.E. Dailey, M.L. Thatcher, T.O.
Najarian, D.N. Brocard, and  R.A.  Ferrara.   1977.
User's manual for the M.I.T. Transient Water Quality
Network Model—Including Nitrogen-Cycle Dynamics
for Rivers and Estuaries. EPA-600/3-77-010. Pre-
pared by R.M. Parsons Laboratory for Water Re-
sources and Hydrodynamics,  Massachusetts
Institute of Technology, Cambridge, MA, for U.S.
Environmental Protection Agency,  Corvallis, OR.
January.

Hartwig, E.G.  1975. The  impact of nitrogen and
phosphorus release from a siliceous sediment on the
overlying water. Third International  Estuarine Con-
ference, Galveston, TX. Paper no. COO-3279-20.

Hatcher, K.J., ed. 1986.  Sediment oxygen demand:
Processes, modeling and measurement. Institute of
Natural Resources,  University of Georgia, Athens,
GA.

Hinson, M.O., and DJ. Basta.  1979. Analyzing re-
ceiving water systems. In Analysis for residuals—en-
vironmental quality management: Analyzing natural
systems, ed. DJ. Basta and B.T. Bower.  Prepared
by Resources for the Future for U.S. Environmental
Protection Agency, Office of Research and Develop-
ment.

Holmes,  R.W.  1970.   The  Secchi disk in  turbid
coastal waters. Limnol. Oceanogr.  15:668-694.

Horner, R.R., and  E.B. Welch.   1981.   Stream
periphyton development in relation to current velocity
and nutrients. Can. J. Fish. Aquat. Sci. 38:449-457.

Hubbard, E.F., F.A. Kilpatrick, L.A. Martens, and J.F.
Wilsons.  1982. Measurement of time of travel and
dispersion in streams by dye tracing. U.S. Geological
Survey techniques of water-resources investigations.
Chapter A9 of Book 3, Applications of hydraulics. U.S.
Geological Survey.

Hunter, J.V., M.A. Harnett, and A.P.Cryan. 1973. A
study of the factors determining the oxygen uptake of
benthal  stream deposits.   NTIS No. PB-226-238.
Water Resources Research Institute, Rutgers Uni-
versity, New Brunwsick, NJ.

Hvitved-Jacobsen, T.  1982.  The impact of com-
bined-sewer overflows on the dissolved oxygen con-
centration of a river.  Water Res. 16:1099-1105.

HydroQual Inc.  1982.  Contaminant inputs to the
Hudson-Raritan Estuary.  Prepared by J.A. Mueller,
T.A.  Gerrish, and M.C. Casey for National Oceanic
and Atmospheric Administration,  Office of  Marine
Pollution Assessment.  NOAA Technical Memoran-
dum OMPA-21.

HydroQual, Inc.  1987. Evaluation of sediment oxy-
gen demand in the Upper Potomac estuary. Techni-
cal  report prepared for  Metropolitan Washington
Council of Governments, Washington, DC.

Hydroscience, Inc. 1968. Water quality analysis of the
Mohawk River -  Barge Canal. New York State Dept.
of Health.

Hydroscience,  Inc. 1971. Simplified mathematical
modeling of water quality. 444-368/392. U.S. Envi-
ronmental Protection Agency, Office of Water Pro-
grams.   U.S.  Government Printing Office,
Washington, DC.

Hydroscience, Inc.  1972. Addendum to simplified
mathematical modeling of water quality.  U.S. Envi-
ronmental Protection Agency, Washington, DC.

Hydroscience, Inc. 1975. Time variable water qual-
ity analysis  and related  studies,  Upper Delaware
River. Westwood, NJ.

Hydroscience, Inc.   1976.   Areawide assessment
procedure manual. EPA-600/9-76-014. Prepared for
U.S. Environmental Protection Agency, Office of Re-
search and Development, Municipal Environmental
Research Laboratory, Cincinnati, OH.

Hydroscience, Inc. 1978.  PCB analysis.  Prepared
by W. M. Leo for Hazen & Sawyer, Engrs. and New
York City Department of Water Resources. NYC 208
Task Report 317 Addendum.

Hynes, H.B.N. 1970. The ecology of running waters.
Liverpool University Press, Liverpool, UK. pp. 53-77.

Ice, G.G., and G.W. Brown.  1978.  Reaeration in a
turbulent stream system. Prepared for Office of Water
Research and Technology, Washington, DC.

Imhoff, J.C., J.L. Kittle, Jr., A.S. Donigian, Jr., and
R.C. Johanson.  1981.  User's manual for Hydrologi-
cal Simulation Program -  Fortran (HSPF). Prepared
under contract 68-03-2895 for U.S. Environmental
Protection Agency, Athens, GA. (Revision 7.0).

Isaacs, W.P., and A.F. Gaudy, Jr.  1968.  Atmos-
pheric oxygenation in a simulated stream.  J. San.
Eng. Div., ASCE 94(SA2): 319-344.
                                             5-6

-------
James, A. 1974. The measurement of benthal res-
piration. Water Res. 8:955-959.

Jennings, M.E., S.C. McCutcheon, and K.M. Flynn.
1982. Discussion: Least-squares estimates of BOD
parameters.  J. Env. Eng., ASCE 108:215-217.

Jeppesen, E., and N. Thyssen.  1984.  Modeling the
seasonal variation in structural biological  compo-
nents and oxygen in macrophyte dominated streams:
a summary of work in progress.  Water Sci. Tech.
16:533-537.

Johanson, R.C., J.C. Imhoff, J.L. Kittle, and A.S.
Donigian.  1984. Hydrological simulation program-
FORTRAN (HSPF):  Users manual for release 8.0.
EPA-600/3-84-066.  U.S.  Environmental Protection
Agency, Athens, GA.

Jorgensen, S.E. 1976. A eutrophication model for a
lake. Ecol. Model. 2:147-165.

Jorgensen, S.E., ed.  1979.  Handbook of environ-
mental data and ecological parameters. International
Society for Ecological Modeling.

Jorgensen, S.E., H. Mejer, and  M. Friis.  1978.  Ex-
amination of a lake model. Ecol. Model. 4:253-278.

JRB Associates. 1983.  User's  manual for Vermont
QUAL-II model. SAIC, Inc., McLean, VA.

Karlgren, L. 1968. Fibersediment och vattendragens
syrebalans — IVL-Konferensen, 1967. Institutot for
Vatten-Och Luftsvardforskning, B28, Stockholm.

Kaushik, N.K., J.B. Robinson, W.N. Stammers, and
H.R. Whiteley. 1981.  Aspects of nitrogen transport
and transformations in headwater streams.  In Per-
spectives in running water ecology, ed. M.A. Lock and
D.D. Williams, pp.  113-140.  Plenum Press, New
York, NY.

Kelly, M.G., G.M. Hornberger, and BJ. Cosby. 1975.
A  method for monitoring eutrophication in rivers.
NTIS No. PB-252-058. Prepared by Department of
Environmental Sciences, University of Virginia, Char-
lottesville,  VA, for Office of  Water Research  and
Technology.

Ketchum, B.H.  1982.  Man's impact on the coastal
environment: Nutrients in the marine environment.
In  Impact of man on the  coastal environment, ed.
T.W. Duke, pp. 68-84. NTIS No. PB 85-193506. U.S.
Environmental  Protection Agency, Environmental
Research Laboratory, Gulf Breeze, FL.
Knowles, C., A.L. Downing, and MJ. Barrett. 1965.
Determination of kinetic constants for nitrifying bac-
teria in mixed culture, with the aid of an electronic
computer. Gen.  Microbiol. 38:263-278.

Koenig, M. 1986. Withlacoochee River - QUAL2E
model calibration from Clyatville,  GA to Ellaville, FL.
U.S. Environmental  Protection Agency,  Region  IV,
Environmental Services Division, Athens, GA.

Koltz, BJ.  1982.  Nitrogen transformations in the
Iowa and Cedar Rivers.  Master's thesis, University
of Iowa, Iowa City, IA.

Kremer, J.N., and S.W. Nixon.  1978.  A  coastal
marine ecosystem:  Simulation and analysis.  Sprin-
ger-Verlag, Berlin, W. Germany.

Krenkel, P.A. 1960. Turbulent diffusion and the kinet-
ics  of oxygen adsorption. Ph.D.  diss.,  University of
California, Berkeley.

Krenkel, P.A., and G.T. Orlob.  1962. Turbulent dif-
fusion and the  reaeration coefficient. J. San. Eng.
Div., ASCE 88(SA2):53-83.

Kreutzberger, W.A.,  and D.E. Francisco.  1977.  An
investigation of the distribution of nitrifying bacteria in
shallow stream  and downstream from wastewater
treatment plants. ESE Publication No. 483.  Univer-
sity of North Carolina, Chapel Hill, NC.

Langbein, W.B., and W.H. Durum. 1967.  The aera-
tion capacity of streams.  Circular S42. U.S. Geologi-
cal Survey, Reston, VA.

Larsen, D.P., H.T. Mercier, and K.W. Malueg. 1973.
Modeling algal growth dynamics in Shagawa Lake,
Minnesota, with  comments concerning projected res-
toration of the lake.  In Modeling the eutrophication
process,  ed.  EJ. Middlebrooks, D.H.  Falkenborg,
and T.E. Maloney, pp. 15-31.  Utah State University,
Logan, UT, September 5-7, 1973.

Lau, Y.L. 1972.  Prediction equation for reaeration in
open-channel  flow, J. San. Eng. Div.,  ASCE
98(SA6):1061-1068.

Laudelout, H., and L. Van Tichelen.  1960. Kinetics
of the nitrite oxidation by nitrobacterwinogradskyi. J.
Bact. 79:39-42.

Leo, W.M.,  R.V. Thomann,  and T.W.  Gallagher.
1984. Before and after case studies: Comparisons
of water quality following municipal treatment plant
improvements. Report no. 430/9-007. Technical  re-
                                              5-7

-------
port prepared by HydroQual,  Inc. for U.S. Environ-
mental Protection Agency, Office of Water Programs,
Facility Requirements Div., Washington, DC.

Leopold, L.B., and T.G. Maddock.  1953.  The hy-
draulic geometry of  stream  channels and some
physiographic implications.  USGS Professional Pa-
per 252. Washington, DC.

Lewis, M.L. 1983. Determination of sediment nitrifi-
cation rates in a Piedmont stream and nitrification-de-
nitrification  dependency on dissolved  oxygen.
Technical  report submitted to University  of North
Carolina, Chapel Hill,  NC, in partial fulfillment of re-
quirements for degree of Master of Science in Public
Health.

Linsley, R.K. Jr., M.A. Kohler, and  J.L.H.  Paulhus.
1958. Hydrology for engineers. McGraw-Hill Book
Company,  New York, NY.

Lombardo, P.S.  1972.  Mathematical model of water
quality in  rivers  and  impoundments.  Hydrocomp,
Inc., Palo Alto, CA.

Long, E.G. 1984.  Letter to Ray Whittemore of Tufts
University  from Texas Department of Water Re-
sources.

Lung, W.S. 1980. Unpublished data.

Lung, W.S. 1986a. Assessing phosphorus control in
the James  River basin.   J. Env.  Eng.,  ASCE
112(1):44-60.

Lung, W.S.   1986b.  Phosphorus loads to the
Chesapeake Bay:   A perspective.  J.  WPCF
58(7):749-756.

Lung. W.S., and H.W. Paerl.  1988. Modeling  blue
green algal blooms in the lower Neuse River.  J.
Water Res. 22(7): 895-905.

Lung, W.S., and N. Testerman. 1989. Modeling fate
and transport of nutrients in James estuary. J.  Env.
Eng., ASCE 115(5):978-991.

Lung, W.S.  1990. Course notes for water quality
modeling, University of Virginia, Charlottesville,  VA.

Manhattan College.  1980.  Mathematical modeling
of natural systems.  Manhattan College Summer In-
stitute Notes.  Manhattan College, Bronx, NY.

Manhattan College. 1983.  Quality models of natural
water sytems. Course notes for the Summer Institute
in  Water  Pollution  Control, Manhattan  College,
Bronx, NY.

Martens, C.S., G.W. Kipphut, and J.V. Klump. 1980.
Sediment-water chemical exchange in the coastal
zone traced by in situ Radon-222 flux measurements.
Science 208:285-288.

Masch, F.D. et al. 1971. Theory and description of the
QUAL-I mathematical modeling system. Report 128.
Texas Water Development Board, Austin TX.

Mattingly,  G.E.  1977.  Experimental study of wind
effects on reaeration, J.  Hydraul. Div.,  ASCE
103(HY3):311.

Matulewich, V.A., and M.S.  Finstein. 1978. Length
of  incubation for enumerating nitrifying bacteria pre-
sent in various  environments.   Appl. Microbiol.
29:265-268.

McCutcheon, S.C.  1983.  Evaluation of selected
one-dimensional stream water-quality models  with
field data.  Technical  Report E-83-11.   US Army
Corps of Engineers Waterways Experiment Station,
Vicksburg, MS.

McCutcheon, S.C. 1985. Water quality and stream-
flow data for the West Fork Trinity River in Fort Worth,
TX.  U.S.  Geological Survey, Water Resources In-
vestigation Report 84-4330, NTSL, MS.

McCutcheon, S.C. 1989.  Water quality modeling:
Volume I, Transport and surface exchange in rivers.
CRC Press, Inc., Boca  Raton, FL.

McCutcheon, S.C., and M.E. Jennings.  1982.  Dis-
cussion of: Stream reaeration by Velz Method.  J.
Env. Eng.  Div., ASCE 108(EE1):218-220.

McDonnell, A.S., and  S.D. Hall.  1969.   Effect  of
environmental factors on benthal oxygen uptake.  J.
WPCF41:R353-R363.

McQuivey, R.S., and  T. Keefer.  1974.  Simple
method for predicting dispersion in streams.  J. Hy-
draul. Div., ASCE 100:997-1011.

Medina, M.A.,  Jr.  1979.  Level ll-receiving water
quality modeling for urban stormwater management.
EPA-600/2-79-100.   U.S. Environmental Protection
Agency, Municipal Environmental Research Labora-
tory, Cincinnati, OH.

Metcalf and Eddy. 1972. Wastewater engineering.
McGraw-Hill, Inc., New York, NY.
                                             5-8

-------
Metcalf and Eddy.  1977.  Urban stormwater man-
agement and technology.  EPA-600/8-77-014. U.S.
Environmental Protection Agency, Washington, DC.

Metcalf and Eddy.  1989.  Boston Harbor Project-
Deer Island related facilities: Trailer pilot plant report.
Draft report submitted to Massachusetts Water Re-
sources Authority  (MWRA), Program Management
Division, Boston, MA.  November 15.

Metcalf and Eddy. 1991. Wastewater engineering,
treatment, disposal, reuse.  3d ed. McGraw Hill, New
York, NY.

Miller, W.E., J.C. Greene, and T. Shiroyama. 1978.
The Salenastrum capricornutum Printz algal assay
bottle test:   Experimental design, application and
data interpretation protocol.  EPA-600/9-78-018.
U.S. Environmental Protection Agency, Office of Re-
search and Development, Washington, DC.

Mills, W.B., D.B. Porcella,  M.J. Ungs, S.A. Gherini,
K.V. Summers, L. Mok, G. L. Rupp, G.L. Bowie, and
D.A. Haith.  1985. Water quality assessment:  A
screening procedure for toxic and conventional pol-
lutants in surface and  ground water - Parts I and  II.
EPA/600/6-85/002a and EPA/600/6-85/002b. U.S.
Environmental Protection Agency,  Office of Re-
search and Development, Athens, GA.

Mills, W.B., G.L. Bowie, T.M. Grieb,  K.M. Johnson,
and R.C. Whittemore.   1986.  Handbook:  Stream
sampling for  waste load allocation applications.
EPA/625/6-86/013. U.S. Environmental Protection
Agency, Office of Research  and  Development,
Washington, DC.

Morton, M.R., A. Stoddard, and J. Pagenkopf. 1990.
Eutrophication and nutrient enrichment in the Pe-
conic Bay: Numerical model of historical conditions
of the mid-1970s.  In Proceedings of ASCE Specialty
Conference on Estuarine and Coastal Modeling, No-
vember 15-17, 1989, Newport, Rl, ed. M.L. Spauld-
ing.

Mueller, J.A., T.A. Gerrish, and M.C.  Casey. 1982.
Contaminant inputs to the Hudson-Raritan estuary.
NOAA technical memorandum OMPA-21.  National
Oceanic and Atmospheric Administration,  Boulder,
CO.

Mueller, J.A.,  J.S. Jeris, A.R. Anderson, and C.F.
Hughes. 1976. Contaminant inputs to the New York
Bight. NOAA technical memorandum ERL MESA-6.
National Oceanic and Atmospheric Administration,
Marine Ecosystems Analysis Program, Boulder, CO.

Murphy, P.J., and D.B. Hicks.  1986.  In situ method
for measuring sediment oxygen demand.  In  Sedi-
ment oxygen  demand:  Processes,  modeling and
measurement, ed. KJ. Hatcher, pp. 305-328. Inst. of
Natural Resources, University of Georgia, Athens,
GA.

MWCOG.  1982. Application of HSPF to Seneca
Creek Watershed. Metropolitan Washington Council
of Governments, Washington,  DC.

MWCOG.  1987. Potomac River water quality 1985:
Conditions and trends in metropolitan Washington.
Annual report.  Metropolitan Washington Council of
Governments,  Department of Environmental Pro-
grams, Washington, DC. April.

NCASI.  1971.  An investigation of the effects of bark
leaching and  benthal decomposition on receiving
water quality.   NCASI  Technical Bulletin No. 247.
National Council for Air and Stream Improvement,
Inc., New York, NY.

NCASI.  1978.  Interfacial  velocity effects on the
measurement of sediment oxygen demand. NCASI
Technical Bulletin No. 317.  National  Council for  Air
and Stream Improvement, Inc., New York, NY.

NCASI.  1979. Further studies of sediment oxygen
demand measurement and its variability.  NCASI
Technical Bulletin No. 321.  National  Council for  Air
and Stream Improvement, Inc., New York,  NY.

NCASI.  1981. The effect of  temperature on  sedi-
ment oxygen  demand  as related to water quality
modeling.  NCASI  Technical Bulletin No. 351. Na-
tional Council for Air and Stream Improvement, Inc.,
New York,  NY.

NCASI.  1982a. A comparison of reaeration estima-
tion techniques for the Ouachita River Basin. NCASI
Technical Bulletin No. 375.  National Council of the
Paper Industry for Air and Stream Improvement, Inc.

NCASI.  1982b. A study of the selection, calibration
and verification of mathematical water quality models.
NCASI Tech. Bull.  No. 374. National Council of the
Paper Industry for Air and Stream Improvement, New
York, NY.

Negulescu, M., and V. Rojanski.  1969.  Recent  re-
search to  determine reaeration coefficient. Water
Res. 3(3):189.
                                             5-9

-------
Novotny, V.  1991.  Urban diffuse pollution; sources
and abatements. Water Env. Tech. 3(12):60-65.

Novotny, V.  1992.  Unit pollutant loads. Water Env.
Tech. 4(1):40-43.

Novotny, V., and G. Chesters.  1981.  Handbook of
nonpoint pollution.  Van Nostrand Reinhold Com-
pany, New York, NY.

Novotny, V., and P.A. Krenkel.  1975.  A waste as-
similative capacity model for  a shallow turbulent
stream.  Water Res. 9:233-241.

NURP. 1983. Final report of the National Urban Run-
off Program. U.S. Environmental Protection Agency,
Water Planning Division, Washington, DC.

O'Connell, R.L., and N.A. Thomas.  1965.  Effect of
benthic algae on stream dissolved oxygen. J. San.
Eng. Div., ASCE 91 (SA3):1-16.

O'Connor, DJ. 1958. The measurement and calcu-
lation of stream reaeration rates.  In Seminar on oxy-
gen relationships in streams, pp. 35-45. Robert A.
Taft Sanitary Engineering Center Technical Report
58-2.

O'Connor, DJ.  1983.  Wind effects on gas-liquid
transfer coefficients, J. Env. Eng. 109 (3):731-752.

O'Connor, D.J., and D.M. Di Toro. 1970. Photosyn-
thesis and the  diurnal dissolved oxygen variation in
streams.  J. ASCE 96 (SA2) April.

O'Connor, D.J., D.M.  Di Toro, and R.V. Thomann.
1975.  Phytoplankton models  and eutrophication
problems.   In  Ecological modeling in a resource
management framework, ed.  C.S.  Russell. Re-
sources for the Future, Inc., Washington, DC.

O'Connor, D.J., and W.E.  Dobbins.  1958.  Mecha-
nism of reaeration in natural streams. ASCE trans.,
paper no. 2934, pp. 641-684.

O'Connor, D.J., J.L. Mancini,  and J.R. Guerriero.
1981. Evaluation of factors influencing the temporal
variation of dissolved oxygen in the  New York Bight,
Phase II. Manhattan College, Bronx, NY.

O'Connor, D.J., R.V. Thomann,  and D.M.  Di Toro.
1973. Dynamic water quality forecasting and man-
agement. EPA-660/3-73-009. Technical report pre-
pared for  U.S. Environmental  Protection  Agency,
Office of Research and  Development, Washington,
DC.
Owens, M., R.W. Edwards, and J.W. Gibbs.  1964.
Some reaeration studies in streams.  Inter. J. Air and
Water Pollut.  8(819):469-486.

Padden, T.J., and E.F. Gloyna. 1971. Simulation of
stream processes in a model river. Report No. EHE-
70-23, CRWR-72.  University of Texas, Austin.

Pamatmat, M.M. 1971. Oxygen consumption by the
sealed-VI seasonal cycle of chemical oxidation and
respiration in  Puget Sound. Int. Revue Ges. Hydro-
biol. Hygdrogr. 56:769.

Parkhurst, J.E., and R.D. Pomeroy.  1972.  Oxygen
absorption in  streams. J. San. Eng. Div., ASCE 98
(SA1).

Phoel,  W.C.   1982.  A comparison of the seabed
oxygen consumption rates of different benthic envi-
ronments along the northeast United States continen-
tal shelf. Int. Coun.  Explor.  Sea, CM 1982/E:23,
Marine Environmental Quality Committee.

Porcella, D.B., T.M. Grieb, G.L. Bowie, T.C. Ginn, and
M.W. Lorenzen. 1983. Assessment methodology for
new cooling  lakes,  Vol.  1:  Methodology to assess
multiple uses for new cooling lakes. Report EPRI
EA-2059. Electric Power Research Institute.

Rathbun, R.E.  1977.  Reaeration coefficients of
streams - state of the art.  J.  Hydraul. Div., ASCE
103(HY4):409-424.

Rathbun, R.E., and R.S. Grant. 1978. Comparison
of the radioactive and modified tracer techniques for
measurements of  stream  reaeration coefficients.
USGS  Water Resources Investigation 78-68. U.S.
Geological Survey, Reston, VA.

Rathbun, R.E.,  DJ.  Shultz,  and D.W.  Stephens.
1975. Preliminary experiments with a modified tracer
technique for measuring stream  reaeration coeffi-
cients.  USGS Open-File Report 75-256.  U.S. Geo-
logical Survey, Reston, VA.

Redfield, A.C., B.H. Ketchum, and F.A. Richards.
1963. The influence of organisms on the composition
of seawater.   In The sea, vol.  2,  ed. M.N. Hill, pp.
26-77.  Wiley-lnterscience, New York, NY.

Rich, L.G. 1973. Environmental systems engineer-
ing. McGraw Hill Book Company,  New York, NY.

Rinella, F.A., S.W. McKenzie, and S.A. Wille.  1981.
Dissolved oxygen  and algal conditions in selected
locations of  the Willamette River Basin,  Oregon.
                                             5-10

-------
USGS Open-File Report 81-529.
Survey.
U.S.  Geological
Roesner, L.A.,  P.R. Giguere, and  D.E.  Evenson.
1977 (rev. 1981). Computer program documentation
for the stream quality model QUAL-II. EPA 600/9-81 -
014. U.S. Environmental Protection Agency, Athens,
GA.

Roesner, L.A.,  P.R. Giguere, and  D.E.  Evenson.
1981.  User's manual for  stream quality model
(QUAL-II). EPA-600/a-81-015.  U.S. Environmental
Protection Agency Environmental Research Labora-
tory, Athens, GA.

Rolley, H.S., and M. Owens.  1967.  Oxygen con-
sumption rates and same chemical properties of river
muds.  Water Res. 1:759-766.

Ruane, R.J., and P.A. Krenkel. 1978.  Nitrification
and other factors affecting nitrogen in the Holston
River. J. WPCF 50:2016.

Ryther, J.H.  1956.  Photosynthesis in the ocean as
a function of light  intensity.  Limnol. Oceanogr.
Ryther, J.H., C.S., Tentsch, E.M. Hurlburt, and R.F.
Vaccaro. 1958.  The dynamics of a diatom bloom.
Biol. Bull. 115(2):257-268.

Salas, H.J., and  R.V. Thomann.  1978.  A steady-
state phytoplankton model of Chesapeake Bay.  J.
WPCF 50(1 2):2752-2770.

Salisbury, O.K., J.V. DePinto, and T.C. Young. 1 983.
Impact of algal-available phosphorus on Lake Erie
water quality:  Mathematical modeling. Prepared for
U.S. Environmental  Protection Agency, Environ-
mental research Laboratory, Duluth, MN.

Scavia, D. 1980.  An ecological model of Lake On-
tario.  Ecol. Model. 8:49-78.

Schreiber, J.D., and D.L. Rausch. 1979. Suspended
sediment-phosphorus relationships for the inflow and
outflow of a flood detention reservoir. J. Env. Qual.
8(4):510-514.

Seitzinger, S.B.  1988.  Denitrification in freshwater
and coastal marine ecosystems: ecological and geo-
chemical significance. Limnol. Oceanogr. 33(4, Part
2), Special publication no. 4.
Shahane, A.N. 1982. Estimation of pre- and post-de-
velopment nonpoint water quality  loadings.  Water
Res. Bull. 18(2):231-237.

Sharma, B., and R.C. Ahlert. 1977. Nitrification and
nitrogen removal. Water Res. 11:897-925.

Shindala, A.,  and D.D. Truax.  1980.  Reaeration
characteristics of small streams.  Engineering and
Industrial Research Station, Mississippi State Univer-
sity.

Simonsen, J.F., and P. Harremoes.  1978.  Oxygen
and pH fluctuations in rivers. Water Res. 12:477-489.

Slayton, J.L.,  and E.R. Trovato. 1978.  Simplified
N.O.D. determination.  NTIS No. PB-297-995. U.S.
Environmental Protection Agency,  Region 3, An-
napolis Field Office, Annapolis, MD.

Slayton, J.L., and E.R.  Trovato. 1979. Biochemical
studies of the Potomac  Estuary-summer, 1978. EPA
903/9-79-005.  U.S.  Environmental Protection
Agency, Region 3, Annapolis Field Office, Annapolis,
MD.

Smayda, T.I.  1970.  The suspension and sinking of
phytoplankton in the sea. Oceanogr. Mar. Biol. Ann.
Rev. 8:353-414.

Smith, DJ.  1978a. Water quality for river-reservoir
systems. Prepared by Resource Management Asso-
ciates, Inc., Lafayette,  CA,  for US Army Corps of
Engineers,  Hydrologic  Engineering Center (HEC),
Davis, CA.

Smith, DJ.  1978b.  WORRS, generalized computer
program for river-reservoir systems. User's manual
401-100, 100A. US Army Corps of Engineers, Hy-
drologic Engineering Center (HEC), Davis, CA.

Stamer, J.K., R.N. Cherry, R.E. Faye, and R.L. Kleck-
ner.  1979.  Magnitudes, nature, and effects of point
and nonpoint discharges in the Chattahoochee River
Basin, Atlanta to West  Point Dam, Georgia. USGS
Water Supply Paper 2059.  U.S. Geological Survey.

Stoddard, A. 1988.  An innovative approach for the
synthesis of large oceanographic data sets with pre-
processing  and post-processing of an ecosystem
model of the New York Bight. In OCEANS '88 con-
ference rescord. IEEE  and Mar. Tech. Soc., Wash-
ington, DC.  pp. 942-947.

Stoddard, A., M.R. Morton, and J. Pagenkopf. 1990.
Innovative approaches for pre-processing and post-
                                             5-11

-------
processing  for a eutrophication  model of Peconic
Bay, Long Island. In Proceedings of ASCE Estuarine
and  Coastal Transport Modeling Conference,  No-
vember 1989, ed. M.L. Spaulding, pp. 341-350.

Stratton,  F.E. 1966.  Nitrification effects on oxygen
resources in streams, Ph.D.  dissertation,  Stanford
University, Stanford, CA.

Stratton,  F.E., and P.L. McCarty.  1969.  Discussion
of evaluation of nitrification in streams. J. San. Eng.
Div., ASCE (SA5):952-955.

Streeter,  H.W.   1926.  The  rate of atmospheric
reaeration of sewage polluted streams. Trans. ASCE
89:1351.

Sullivan, J., R. Young, and S.  Rogers.  1978.  Meth-
ods and results of field surveys collected in 1977 and
1978 on the Upper Wisconsin River for the develop-
ment of a water quality computer model.  Wisconsin
Dept. Natural Resources, Rhinelander, Wl.

Sweeten, J.M., and S.W. Melvin. 1985.  Controlling
water pollution from nonpoint source livestock opera-
tions. In Perspectives on nonpoint source pollution,
ed.  U.S. Environmental Protection Agency, pp. 215-
217. EPA 440/5-85-001. U.S. Environmental Protec-
tion Agency, Office of Water, Washington, DC.

Szumski,  D.S., D.A. Barton, H.D. Putnam, and R.C.
Polta. 1982. Evaluation of EPA un-ionized ammonia
toxicity criteria. J. WPCF 54(3):281-191.

Terry, J.E.,  and E.E. Morris.  1986.  Deriving  repre-
sentative benthic  oxygen  demands by stream
reach—a modeling approach.  In Sediment oxygen
demand:  Processes, modeling and measurement,
ed.  KJ. Hatcher, pp.  409-426. Inst. of Natural Re-
sources,  University of Georgia, Athens, GA.

Terry, J.E.,  E.E. Morris, and  C.T.  Bryant.  1983.
Water-quality assessment of  White  River  between
Lake Sequoyah and  Beaver Reservoir, Washington
County, Arkansas.  WRI 82-4063.  U.S.  Geological
Survey, Little Rock, AR.

Tetra Tech, Inc. 1976. Documentation of water qual-
ity models for the Helms Pumped Storage Project.
Tetra Tech, Inc., Lafayette, CA.

Tetra Tech, Inc. 1977. Water quality assessment: a
screening method for  nondesignated  208 areas.
EPA-600/9-77-023.   Prepared by S.W. Zison, K.F.
Haven, and W.B. Mills for U.S. Environmental Protec-
tion Agency,  Environmental Research Laboratory,
ERL, Athens,  GA.

Tetra Tech, Inc.  1980.  Methodology for evaluation
of multiple power plant cooling system effects, Vol. V.
Methodology application to prototype - Cayuga Lake.
EPRI  EA-1111.  Prepared for  Electric Power  Re-
search Institute.

Tetra Tech, Inc.  1982.  A screening procedure for
toxic and conventional  pollutants—part I.  EPA-
600/6-82-004a.  Prepared by W.B. Mills, J.D. Dean,
D.B. Porcella, S.A. Gherini, R.J.M. Hudson, W.E.
Frick, G.L. Rupp, and G.L.  Bowie for U.S. Environ-
mental Protection Agency, Environmental Research
Laboratory, Athens, GA.

Tetra Tech, Inc.  1989. Water quality modeling for the
Peconic Bay  BTCAMP.  Interim Progress Reports
No. 1  and No.  2.  Prepared by Tetra Tech,  Inc.,
Fairfax,  VA,  for  Dvirka and Bartilucci  and  Suffolk
County Department of Health Services, Riverhead,
NY.

Tetra Tech, Inc.  1992. Water quality modeling for the
Peconic Bay  BTCAMP.  Prepared  by  Tetra Tech,
Inc., Fairfax, VA, for Dvirka and Bartilucci and Suffolk
County Department of Health Services, Riverhead,
NY.

Thackston, E.L.,  and P.A. Krenkel.  1969.  Reaera-
tion prediction in natural streams. J. San.  Eng.  Div.
ASCE 95(SA1):65.

Thomann, R.V.  1972.  Systems analysis and water
quality management.  Env.  Sci.  Serv. Div., Environ-
mental Research and Applications, Inc., New York,
NY.

Thomann, R.V.  1980.  Measures of verification. In
Workshop on verification of water quality models, pp.
37-61.  EPA-600/9-80-016.  U.S. Environmental Pro-
tection Agency, Environmental Research Laboratory,
Athens, GA.

Thomann, R.V.   1982. Verification of water quality
models. J. Env. Eng. Div., ASCE 108(EE5):923-940.

Thomann, R.V.   1987.  Systems analysis in water
quality management—a 25 year retrospective.  Key-
note address to IAWPRS, London, UK.  In Proceed-
ings of Systems  Analysis and  Water Quality
Management, ed. M.B. Beck.

Thomann, R.V., D.M. Di Toro, R.P. Winfield, and DJ.
O'Connor. 1975. Mathematical modeling of phyto-
                                             5-12

-------
plankton in Lake Ontario. Part I. Model development
and verification.  EPA-600/3-75-005.  Prepared by
Manhattan College,  Bronx,  NY,  for U.S. Environ-
mental Protection Agency.

Thomann, R.V., and JJ. Fitzpatrick. 1982. Calibra-
tion and verification of a mathematical model of the
eutrophication of the Potomac estuary. Prepared by
HydroQual, Inc. for the Government of the District of
Columbia, Washington, DC.

Thomann, R. V., and J.A. Mueller. 1987.  Principles
of surface water quality modeling and control. Harper
& Row, New York, NY.

Thomann, R.V., J. Segna, and R.P. Winfield. 1979.
Verification analysis of Lake Ontario and  Rochester
Embayment three-dimensional  eutrophication mod-
els. Prepared by Manhattan College, Bronx, NY, for
U.S. Environmental  Protection Agency, Office of Re-
search and Development.

Thomas,  N.A., and R.L. O'Connell. 1966.  A method
for measuring primary production by stream benthos.
Limnol. and Oceanogr. 11:386-392.

Truesdale, G.A.,  and  K.G. Van Dyke.  1958.  The
effect  of temperature on the aeration of flowing wa-
ters. Water and Waste Treat. J. 7:9.

Tsivoglou, E.G.  1967.   Tracer measurement of
stream reaeration. PB-229 923. Federal Water Pol-
lution  Control Administration, Washington, DC.

Tsivoglou, E.G., and L.A. Neal.  1976.  Tracer meas-
urement of reaeration: 3. Predicting the  reaeration
capacity of inland streams.   J. WPCF 48(12):2269-
2689.

Tsivoglou, E.G., and J.R. Wallace.  1972. Charac-
terization of stream reaeration capacity. EPA-R3-72-
012. Prepared for  U.S.  Environmental  Protection
Agency, Office of Research  and  Monitoring, Wash-
ington, DC.

US Army Corps of Engineers, Environmental Labo-
ratory. 1990.  CE-QUAL-RIV1:  A dynamic, one-di-
mensional (longitudinal) water  quality  model for
streams:  User's  manual. Instruction Report E-90-,
US Army Corps of Engineers Waterways Experiment
Station, Vicksburg, MS.

USE PA. 1976a.  Areawide assessment procedures
manual.  Vol. I.  EPA 600/9-76-014. U.S. Environ-
mental Protection Agency, Cincinnati, OH.
USE PA. 1976b. Evaluation of water quality models:
A management guide for planners. EPA 600/5-76-
004. U.S. Environmental Protection Agency, Office
of Air, Land, and Water Use and Office of Research
and Development, Cincinnati, OH.

USEPA.  1977.  Federal guidelines, State and local
pretreatment programs. Vol. 1.  EPA 430/9-76-017a.
U.S. Environmental Protection Agency, Section F,
Washington,  DC.

USEPA. 1979a. Biochemical studies of the Potomac
Estuary - summer, 1978.  EPA 903/9-79-005. U.S.
Environmental Protection Agency,  Annapolis Field
Office, Annapolis, MD.

USEPA.  1979b.  Carbonaceous  nitrogenous  de-
mand studies of the Potomac Estuary. EPA 903/9-
79-003.  U.S.  Environmental Protection Agency,
Annapolis Field Office, Annapolis, MD.

USEPA. 1979c. Environmental modeling catalogue,
abstracts of  environmental models.   Prepared by
Management Information and  Data Systems Divi-
sion, under EPA contract no. 68-01-4723.

USEPA. 1980. Technical guidance manual for per-
forming waste load allocations, Simplified analytical
method  for determining  NPDES effluent limitations
for POTWs discharging  into low-flow streams. U.S.
Environmental Protection Agency,  Office of Water
Regulations and Standards, Washington, DC.

USEPA. 1983a. Technical guidance manual for per-
forming waste load allocations,  Book II: Streams and
rivers, Chapter 1: Biochemical oxygen demand/dis-
solved  oxygen.  EPA-440/4-84-020. U.S. Environ-
mental Protection  Agency, Office of Water
Regulations and Standards, Washington, DC.

USEPA. 1983b. Technical guidance manual for per-
forming waste load allocations,  Book II: Streams and
rivers, Chapter  2: Nutrient/eutrophication impacts.
EPA-440/4-84-021.  U.S. Environmental  Protection
Agency, Office of Water Regulations and Standards,
Washington,  DC.

USEPA. 1984. Water quality standards handbook.
U.S. Environmental  Protection Agency, Office of
Water Regulations and Standards, Washington, DC.

USEPA.  1987.  Quality criteria for 1986 (with  up-
dates 1  and  2 included). EPA 440/5-86-001. U.S.
Environmental Protection Agency,  Office of Water
Regulations and Standards, Washington, DC.
                                            5-13

-------
USEPA. 1990. RIVMOD: A one-dimensional hydro-
dynamic and sediment transport model, Model theory
and user's manual.  U.S. Environmental Protection
Agency, Center for Exposure Assessment Modeling,
Athens, GA.

USEPA. 1991 a. Guidance for water quality-based
decisions: The TMDL process. EPA 440/4-91-001.
U.S. Environmental Protection Agency, Office of
Water,  Washington, DC.

USEPA.  1991b.  Technical support document for
water quality-based toxics control. EPA/505/2-90-
001. NTIS No. PB91-127415. U.S. Environmental
Protection Agency, Office of Water, Washington, DC.

USEPA. 1991c. A review of methods for assessing
nonpoint source  contaminated ground-water dis-
charge to surface water. EPA 570/9-91-010. U.S.
Environmental Protection Agency, Office of Water,
Washington, DC.

USEPA. 1992a. A quick reference guide: Developing
nonpoint source load allocations for TMDLs. EPA
841-B-92-001.   U.S.  Environmental  Protection
Agency, Office of Water, Washington, DC.

USEPA. 1992b. Compendium of watershed-scale
models for TMDL development. EPA 841-R-92-002.
U.S. Environmental Protection Agency, Office of
Water,  Washington, DC.

USEPA. 1992c. Incentive analysis for Clean Water
Act reauthorization:  Point source/nonpoint source
trading for nutrient discharge reductions. U.S. Envi-
ronmental  Protection Agency, Office of Water and
Office of Policy, Planning, and Evaluation, Washing-
ton, DC.

USEPA. 1992d.  Multi-SMP simplified method pro-
gram for multiple dischargers. U.S. Environmental
Protection Agency, Center for Exposure Assessment
Modeling, Athens, GA.

USEPA. 1993a. Guidance specifying management
measures for sources of nonpoint pollution in coastal
waters. EPA840-B-92-002. U.S. Environmental Pro-
tection Agency, Office of Water, Washington, DC.

USEPA. 1993b. Technical guidance for estimating
total maximum daily loads (TMDLs): Integrating non-
point and episodic point source loadings from storm-
water and combined sewer overflows (CSOs). Draft.
U.S. Environmental Protection Agency, Office of
Water,  Washington, DC.
Velz, CJ.  1984. Applied stream sanitation.  John
Wiley and Sons, New York, NY.

Virginia Water Control Board. 1989. Effects of phos-
phate detergent ban in Virginia. Final report prepared
by the Chesapeake Bay Office, Richmond, VA.

Walton, W.C.  1970. Groundwater resource evalu-
ation. McGraw-Hill Book Company, New York, NY.

Welch, E.B., R.R. Horner, and C.R. Patmont. 1989.
Prediction of nuisance periphyton biomass: A man-
agement approach. Water Res. 23(4):401-405.

Wezernak, C.T., and JJ.  Gannon. 1967.  Oxygen-
nitrogen relationships  in  autotrophic nitrification.
Appl. Microbiol. 15:1211-1215.

Wezernak, C.T., and JJ. Gannon.  1968. Evaluation
of nitrification  in streams.  J. San. Eng. Div. ASCE
94:883-895.

Whittemore, R.C.  1984.   Implementation of in situ
and laboratory SOD measurements in water quality
modeling.  In press.

Whittemore, R. 1986. The significance of interfacial
velocity effects on the exertion of SOD. In Sediment
oxygen demand:  Processes, modeling and meas-
urement, ed. J.K. Hatcher, pp. 329-341. Institute of
Natural Resources, University of Georgia, Athens,
GA.

Whittemore, R.  1990a. Non-radioactive method for
measurement of reaeration rates.  NCASI technical
report.

Whittemore, R. 1990b.  Large database of reaeration
measurements for streams and rivers.  NCASI tech-
nical report.

Wild, H.E., C.N.  Sawyer, and T.C. McMahon. 1971.
Factors  affecting  nitrification kinetics.  J. WPCF
43(9):1845-1854.

Wilhelms, S.C., and D.R.  Smith.  1981.  Reaeration
through gaged-conduit outlet works. Report E-81 -5,
US Army Corps of Engineers Waterways Experiment
Station, Vicksburg, MS.

Williams, D.D. 1981. Migrations and distributions of
stream benthos.  In Perspectives in running water
ecology, ed. A. Lock and D.D. Williams, pp. 155-208.
Plenum Press, New York.
                                            5-14

-------
Williams, R.E., and M.S. Lewis. 1986. Stream model  Yotsukura, N., H.B. Fischer, and W.W. Sayre.  1970.
of benthic nitrification and denitrification.  J.  Env.  Measurement of mixing characteristics of the Mis-
Eng., ASCE 112(2):367-386.                       souri River between  Sioux  City, Iowa and
                                                Plattsmouth, Nebraska.  Water-Supply Paper 1899.
Wilson, J.F., E.D. Cobb, and  F.A. Kilpatrick. 1986.  |j s Geological Survey, Reston, VA.
Fluorometric procedures for dye tracing.  U.S. Geo-
logical Survey techniques of water-resources inves-  Youngberg, B.A. 1977.   Application of the aquatic
tigations. Chapter 12A of Book 3,  Applications of  model CLEANER to stratified resevoir  system. Re-
hydraulics, U.S. Geological Survey, Reston, VA.      port #1.  Center for Ecological Modeling, Rensselaer
                                                Polytechnic Institute, Troy, NY.
Wright and McDonnell. 1979.  In stream deoxygena-
tion rate prediction. J.  Env.  Eng.,  ASCE,  Zison, S.W., W.B. Mills, D. Deimer, and C.W. Chen.
105(EE2):323-335.                                1978. Rates, constants and kinetics formulations in
                                                surface water quality modeling. EPA-600/3-78-105.
Wu, J.,  and R.C. Ahler.  1979.  Application  of a  Prepared by Tetra Tech, Inc., Lafayette, CA, for U.S.
steady-state  one dimensional water quality model.  Environmental Protection Agency,  Environmental
Water Res. Bull. AWRA 15(3):660-670.              Research Laboratory, Athens, GA.

Yake, W.E., and R.K. James.  1983.  Setting effluent
ammonia limits to meet in-stream toxicity criteria. J.
WPCF  559(3):303-310.
                                             5-15

-------

-------
APPENDIX A.  DEVELOPMENT OF MODEL
                         COEFFICIENTS AND CONSTANTS
A.1  OVERVIEW

As demonstrated in Chapter 2, a number of model coef-
ficients and constants are formulated in a stream water
quality model. Coefficient values can be obtained in four
ways:
    •  Direct measurement
    •  Estimation from field data
    •  Literature values
    •  Model calibration
Model calibration is usually required regardless of the
approach selected. However, coefficients that are site-
specific or those that can take on a wide range of values
should be  measured directly or  estimated  from  field
samples.   The purpose of Appendix A is to provide
sufficient information and data to develop a consistent set
of model coefficients and parameter values for a TMDL
model analysis.  For some model coefficients, additional
discussions are presented to  address subtle technical
issues associated with the determination. This appendix
is organized to follow the materials presented in Chapter
2 and is summarized below.
A.1   Overview
A. 2   Loads
A.3   Physical  Parameters
A.4   Carbonaceous Deoxygenation Rate
A.5   Nitrogenous Deoxygenation (Nitrification)
      Rate
A.6   Stream Reaeration Rate
A.7   Sediment Oxygen Demand
A.8   Photosynthesis and Respiration
A.9   Phytoplankton Kinetic  Rates
A.10 Nutrient Recycling Rates
A.11  Sediment Nutrient Release Rate
A.12 Temperature Effects on Reaction Rate
      Coefficients

A.2    LOADS

A.2.1  Effluent Concentrations

As suggested in Chapter  2, point source inputs from
municipal wastewater treatment plants or publicly owned
treatment works (POTWs) and industrial facilities should
be measured for site-specific situations.  However, if the
data are not readily available,  typical effluent charac-
teristics reported for POTWs in the  literature may be
used as a first approximation.   In a study by Leo et al.
(1984), an extensive  amount of POTW effluent infor-
mation was gathered  and compiled to assess effluent
BOD5, CBODs, ammonia, CBODu-to-BODs ratios, and
CBODu-to-CBODs ratios for various treatment levels. In
total, information on these parameters was available from
approximately 114treatmentfacilities. TableA-1 presents
a summary of the effluent BODs, CBODs, and ammonia
concentrations for POTWs with various treatment levels.
Effluent BODs and CBODs concentrations are signifi-
cantly different (see Table A-1), reinforcing the findings by
Hall and Foxen (1984) that significant nitrification occurs
during BOD tests for many POTWs with secondary treat-
ment.  For the 26 secondary treatment facilities in the
above study, the ammonia data were gathered during
intensive summer water quality surveys,  indicating that
many secondary POTWs achieve some nitrification dur-
ing summer periods. It is likely that with in-plant nitrification
occurring, nitrifying bacteria present in the effluent can
cause oxygen consumption during the BODs test.  The
BODs test would  therefore tend to underestimate the
ability of the POTW to remove  carbonaceous oxidizing
materials.
In general, only POTWs that practice phosphorus removal
to meet their NPDES permit effluent limits measure and
report phosphorus concentrations in the effluent.  Lung
(1986b) reported an average total phosphorus concentra-
tion of 6.25 mg/L in the effluents of 18 secondary POTWs
in the Chesapeake Bay area (plants in Virginia, Maryland,
and Pennsylvania) that did not have phosphorus removal.
Phosphate detergents have been progressively banned
in the Chesapeake Bay region since that study. Recent
data collected from a number  of POTWs in Hampton
Roads  Sanitation District, Virginia, indicated up to a 50
percent phosphorus load reduction following the phos-
phate detergent ban, which became effective on January
1,1988 (Virginia Water Control Board, 1989).  One should
note that phosphate detergent bans would have no effect
on the effluent concentrations at POTWs that remove
phosphorus to meet NPDES permits.
A number of texts, technical reports, and other literature
document influent and effluent characteristics of munici-
pal wastewater for various treatment levels.  Tables A-1
through A-17 present data summarized  by a number
                                             A-1

-------
                   TABLE A-1.  SUMMARY OF EFFLUENT CHARACTERISTICS
                                      (After Leo et al., 1984)
                                                    POTW Effluent Concentrations (mg/L)
                                              BODs
                CBODs
                                                                                  Ammonia-N
Treatment Type
Primary
Trickling Filter
Secondary
Secondary + P-Removal
Secondary + Nitrification
Secondary + P-Removal + Nitri-
ficaton
Secondary + P-Removal + Fil-
ters
Number of
Locations3
2
13
38
9
10
3
3

Mean
101.0
41.2
19.1
16.2
11.5
13.6
3.9

Standard
Deviation Mean
21.2 —
27.8 —
16.3 10.3
14.0 14.6
11.8 4.8
18.6 —
2.0 —

Standard
Deviation Mean
— —
— 16.6
6.4 8.9
9.3 7.9
3.9 1.0
— 0.9
— 4.8

Standard
Deviation
—
12.2
6.3
8.9
1.4
0.7
8.2

 s Number of locations with BODs data. In some cases, number with CBODs or Nhb data may be less.
of investigators that can be used to estimate effluent
characteristics.

A.2.2   Effluent CBODu-to-BODs or CBODu-to-
        CBODs Ratios

The effluent CBODu-to-BODs or CBODu-to-CBODs ra-
tio is required in dissolved oxygen modeling analyses
to estimate  POTW CBODU from effluent BODs or
CBODs data. This data is also needed to convert model
output (as CBODu) to NPDES permit limits (as CBODs).
A summary of this information is presented in Figure A-1
(Leo et al., 1984), suggesting a mean value of 2.47 for
CBODu-to-BODs and  2.84 for CBODu-to-CBODs.
Thomann and Mueller (1987) summarize the CBODu-
to-CBODs ratios for municipal wastes as 1.2 for no
treatment, 1.6 for primary/secondary, 3.2 for activated
sludge, and 2.84 for advanced primary. In the absence
of site-specific data, these ratios are reasonable ap-
proximations for a dissolved oxygen modeling analysis.
EPA strongly recommends that, whenever possible,
data from existing plant or pilot plant effluents be used
in the modeling analysis.  In this case, long-term BOD
tests should be run to determine the Ki coefficient from
Equation 2-5 and consequently the CBODu-to-CBODs
ratio.   However, caution should be exercised when
using data from an existing plant that has a treatment
level significantly less than that of a proposed plant.
In this case, the existing data should  be used as a
guide. A model sensitivity analysis  of the final WLA
with respect to the  ratio should help  the analyst to
judge the need for additional data.
The CBODu-to-CBODs ratio of industrial wastewater is
highly dependent on the type of industry manufacturing
processes, treatment schemes or operation, measure-
ment techniques, and other factors. Pulp and paper mill
effluent, for example, is characterized by very high ratios
of CBODu to CBODs because of the refractory nature
of the cellulose and compounds in the wastewater. For
many industrial wastewaters, the ratios also may vary
with BOD concentration.

A.2.3   Nonpoint Source Loads

Other loading rates for nonpoint loads such as combined
sewer overflows, urban storm runoff, and upstream back-
ground loads vary from one study  area to another.
Thomann and Mueller (1987) and Novotony (1991,1992)
provide a brief summary of these loading rates. Mills et
al.  (1985) present information on  determining these
loads. Table A-2  lists some of the typical ranges as
described in the literature.
                                              A-2

-------
               TABLE A-2. TYPICAL RANGES OF POLLUTANT LOAD FOR SOURCES
Source
                                   Range
                                   Supplemental References
Domestic and Industrial
Point Sources

Upstream Background Levels:
     Dissolved Oxygen Deficit
     BOD5
     NH3
     N03
     Organic N

Combined Sewer Overflow, BOD
     Organic N
     TN

Nonpoint sources (kg/ha/yr)
Urban
     General
         TN
         TP
         BODS

    Residential
         TN
         TP
         BODS

    Commercial
         TN
         TP
         BODS
NPDES Permits
Compliance Reports
0.5-2.Omg/L
0.5-3.0 mg/L
0.05-.27 mg/L
0.07-0.37 mg/L
0.05-0.50 mg/L

115 mg/L
3.8 mg/L
9.1 mg/L
6.69
6.37-8.00
8.12
5.62-7.14
17.23
1.57
0.40-3.19
1.20
0.89-4.46
1.33
58.97
34.23

0.87
4.77-7.16
6.69
0.17
0.48-0.79
1.03
28.55
28.67

7.34
2.39-9.56
17.83
0.55
0.07-0.71
2.70
13.03
78.03
a, b, c, d, e
g, i, Use STORET
f, g, h, Use STORET
f, g, h, Use STORET
f, Use STORET
f, g, Use STORET

d, h,j
d, h,j
d, h,j
m
n
o
k
I
m
n
o
k
m
m
k
I
m
k
m
m
k
I
m
k
m
                                                  A-3

-------
                                        TABLE A-2. (Continued)
Source
Agricultural
General

TP
Cropland
TN



TP



Improved Pasture
TN

TP

Pasture
TN

TP

Forested TN


Range

0.63-59.71
0.08-7.16

20.46
4.77-47.77
0.09-10.35
18.71
0.83
0.24-5.58
0.04-2.32
5.45

5.02
3.98-11.95
0.92
0.08-0.48

4.17
2.31
0.24
0.44
2.44
0.79-6.37
2.41-10.35
Supplemental References

I
I

k
I
n
o
k
I
n
0

k
I
k
I

k
0
k
0
k
I
n
a  Leo et al., 1984
b  Metcalf and Eddy, 1972
c  Mueller et al., 1976
d  Thomann and Mueller, 1987
e  Mueller et al., 1982
f  Hydroscience, 1975
g  Hydroscience, 1968
h  Metcalf and Eddy, 1977
i   Manhattan College, 1980
j   USEPA,  1976a
k  Shahane, 1982
I   Novotny  and Chesters, 1981
m NURP, 1983
n  Sweeten and Melvin,  1985
o  Haith and Shoemaker, 1987
                                                    A-4

-------
                 TABLE A-3. REPORTED VALUES OF SELECTED WASTE INPUT
                              PARAMETERS IN THE UNITED STATES
                                 (after Thomann and Mueller, 1987)
Variable
Average daily flow
Total suspended solids
CBODs9
CBODu9
NBOD9
Total nitrogen
Total phosphorus
Total coliforms
Cadmium
Lead
Chrome
Copper
Zinc
Total PCB
Units3
gcd
mg/L
mg/L
mg/L
mg/L
mg-N/L
mg-P/L
106/100mL
mg/L
mg/L
M9/L
|ig/L
mg/L
mg/L
Municipal
Influent"
125
300
180
220
220
50
10
30
1.2
22
42
159
241
0.9
csoc

410
170
240
290
9
3
6
10
190
190
460
660
0.3
Urban Agriculture
Runoff11 (Ib/mi2-day)e

610 2500
27 40
2.3 15
0.5 1.0
0.3
13
280
22
110
500

Forest Atmosphere
(Ib/mi2-day)e (Ib/mi2-day)f

400
8
4 8.9-18.9
0.3 0.13-1.3
0.015
1.3
0.088

1.8
0.002-0.02
 Units apply to municipal influent, combined sewer overflow (CSO), and urban runoff sources; gcd = gallons per capita per day.
b Thomann (1972); heavy metals and PCB, HydroQual (1982).
c Thomann (1972); total coli, Tetra Tech, (1977); heavy metals DiToroetal. (1978); PCB, Hydroscience (1978).
d Tetra Tech (1977); heavy metals, Di Toro et al. (1978).
e Hydroscience (1976).
f Nitrogen and phosphorus, Tetra Tech (1982); heavy metals and PCB, HydroQual (1982).
9 CBOD5 = 5 day carbonaceous biochemical oxygen demand (CBOD); CBODu = ultimate CBOD; NBOD = nitrogenous BOD.
 TABLE A-4. APPROXIMATE COMPOSITION OF AN AVERAGE DOMESTIC WASTEWATER (mg/L)
                                      (after Clark etal., 1977)
                            Before Sedimentation
             After Sedimentation
             Biologically Treated
Total solids
Total volatile solids
Suspended solids
Volatile suspended solids
BOD
Ammonia nitrogen as N
Total nitrogen as N
Soluble phosphorus as P
Total phosphorus as P
800
440
240
180
200
 15
 35
  7
 10
680
340
120
100
130
 15
 25
  7
530
220
 30
 20
 30
 20
 20
  7
  7
                                                A-5

-------
          TABLE A-5.  MUNICIPAL WASTE CHARACTERISTICS BEFORE TREATMENT
                                   (after Thomann, 1972)
Variable
Unit
                                                   Approx. Average
Normal Range
Avg. Daily Flow
Solids -Total
Total Volatile
Total Dissolved
Total Suspended
Volatile Suspended
Settleable
CBOD (5-day)a
CBOD (ultimate)
NBODb
Total Nitrogen
Organic Nitrogen
Ammonia Nitrogen
Nitrate + Nitrite
Total Phosphate
Ortho Phosphate
Poly-Phosphate
Total Coliforms
Fecal Coliforms
gal/cap/day
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/LN
mg/L N
mg/LN
mg/L N
mg/L PO4
mg/L PO4
mg/L PO4
million/1 OOmL
million/1 OOmL
125
800
400
500
300
130
150
180
220
220
50
20
28
2
20
10
10
30
4
100-200
450-1200
250-800
300-800
100-400
80-200
—
100-450
120-580
—
15-100
5-35
10-60
0-6
10-50
5-25
5-25
2-50
0.3-17
 CBOD - Carbonaceous oxygen demand.
bNBOD - Nitrogenous oxygen demand, ultimate; exclusive of CBOD.


              TABLE A-6. TYPICAL COMPOSITION OF RAW DOMESTIC SEWAGE
                  (All values except settleable solids are expressed in mg/L)
                                (after Metcalf & Eddy, 1972)
Concentration Before Treatment
Constituent
Solids, total
Dissolved, total
Fixed
Volatile
Suspended, total
Fixed
Volatile
Settleable solids, (mL/L)
Biochemical oxygen demand, 5-day, 20
°C (BOD5@20°C)
Total organic carbon (TOC)
Chemical oxygen demand (COD)
Nitrogen, (total as N)
Organic
Free Ammonia
Nitrites
Nitrates
Phosphorus (total as P)
Organic
Inorganic
Chlorides8
Alkalinity (as CaCO3)a
Grease
Strong
1200
850
525
325
350
75
275
20
300

300
1,000
85
35
50
0
0
20
5
15
100
200
150
Medium
700
500
300
200
200
50
150
10
200

200
500
40
15
25
0
0
10
3
7
50
100
100
Weak
350
250
145
105
100
30
70
5
100

100
250
20
8
12
0
0
6
2
4
30
50
50
 Values should be increased by amount in carriage water.
                                           A-6

-------
               TABLE A-7. TYPICAL MUNICIPAL WASTEWATER CHARACTERISTICS
                                       (After Mueller et al., 1976)
                                                         Concentration (mg/L)
Parameter
                                          NYC
                                      Raw Sewage
                               NJ
                          Primary Effluent
                               NYC
                        Secondary Effluent
SS
ALK
BOD5
COD
TOC
MBAS
Oil & Grease
NH3-N
Org-N
NO2+NO3-N
Total N
Ortho-P
Total P
Cd
Cr
Cu
Fe
Hg
Pb
Zn
Fecal Coll (cells/100ml_)
Total Coll (cells/1 OOmL)
Total Coll after Chlorination
(cells/100mL)
      139
      190
      131
 2.5xBOD5
       83
       10
       36
       10.6
       10.4
        0.68
       21.7
        3.27
        4.70
        0.018
        0.15
        0.23
        2.5
        0.033
        0.26
        0.39
0.44xT.Coli
    50x1O6
       93
      190
      158
 2.5XBOD5
 0.68x BODs
       10
       23
 0.58xTot.N
0.69xNH3-N
 0.02 x Tot. N
       22
  0.7xTot.P
        6.14
        0.012
        0.057
        0.105
        0.70
        0.025
        0.190
        0.185
 0.44xT.Coli
     15x106
      357
       43
      170
       36
  4.7xBOD5
0.94XBOD5
        1.0
       15
 0.64xTot.N
0.53xNH3-N
 0.02 x Tot. N
       22
  0.7xTot.P
        3.30
        0.012
        0.057
        0.105
        0.70
        0.025
        0.190
        0.185
0.44xT.Coli
     2.5X106
      357
                                                   A-7

-------
            TABLE A-8. TYPICAL MUNICIPAL WASTEWATER CHARACTERISTICS
                   FOR CONVENTIONAL POLLUTANTS AND MOST METALS
                                   (after Mueller et al., 1982)
Parameter
SS
BOD
TOC
NH3-N
ORG-N
N02-N
NO3-N
Ortho-P
Total-P
Fecal Coliform
(MPN/100ml_)e
Winter
Summer
Cadmium
Chromium
Copper
Cyanide
Lead
Mercury
Nickel
Zinc
NYC Raw
Sewage3 (mq/L)
110
104
93
10
13
0.07
0.38
2.0
3.2


3x106
3x106
1.2
42
159
92
22
1.3
45
241
Middlesex County
NJ Primary Secondary
Effluent" (mq/L) Effluent0
105
218
151
22
13
0.06
0.51
7.7
9.3


33
33
14
68
185
92'"
211
0.62
105
365
43
39
128
11
17
0.16
3.6
3.5
2.3


33
33
12
34
334
57
77
0.2
37
4800
NYC
Secondary
Effluentd (mq/L)
20
15
39
7.9
6.1
0.19
1.3
1.6
2.1


1.5x105
33
1.1
16
93
52
11
0.57
37
101
Not including Newton Creek and Bowery Bay (high industry).
14 New Jersey plants.
SS, BOD, TOC, ORG-N, and ortho-P from ISC data.  All other from Middlesex County (NJ) quarterly reports.
Not including Coney Island, Newton Creek, and Owls Head (intermediate treatment) and Bowery Bay (high industry).
NYC raw from 208 study, NJ primary, Middlesex County, and NYC secondary from NYC summer 1980 and NYC winter
from Lake Tahoe, California.
NYC raw concentration assumed.
                                              A-8

-------
O
LU


3

Cft
Cfi
UJ

o
O
<
w


Q

<
CE
LU
IX
O
H
Zf


g|


S5

|S


Z fE
O
o
< o
> LU

il
*>
5§
m
x
u


w

UJ

LU
LU
<
UJ
J
m

)-
15 31 L?
' *K 1*™* I ^7
3«
*d

" C * t
! o .£1 *
°- .0,
z^
fit
4J

ca
A

| Ed] $
E "5 El !2
uj K — *<
8 !
£o5is
HstS
i 1
in '"7"
Q<
0 f
o>S
»1













o
V
SD











g
1
1
ii
'• c
0 "
O c
§ f
C ' #?
a
*•«
ra
01
hw
"5
t)
'7
C7
O
•a

4

{SI
in

8
th


10
if)

V
rt
V





i
i
s

S
S

s
1
i
!!
m
3
13
I
HI
CD
3
Ml
-a
S
0!
B
»rt
•^
«
o

(0

LD
T~

8
o
w


to
"T
o

S
tf5
O












&
ffl.
IS
f
I
s
a
c
1
f

T3
3
IS
D
irt
1 —
A
o

»a

rxi
•^~

0
ia


«
6
(M

IA
w
p






s
ra
9
3
CD
t
s
frifksalion,
'c
.1
"3
,-fc;
c
en

•o
£
CO
ti
C5
•^~
in

OJ
V

CJ
Ift

8
^2


o
s

s
6
o
1












•o
•o
g
>•
1
o
i
1

1
g

*?
cc-
d


V

ftl
'"

Id
m


c
CT3
d

IA
V
5
c
o
1
5
•f
c
D
s
-ISS
c

g
1
•c
'c
&
T3
01

Q
1
i

1
i
o
*"
no


V

O
5

m
in


o

in
o
o











jn
I
at
a
g
a

a

1
10
c
jp
*?
n
d


V


*v

m
tf


o
CM

V
0
V




c

















e
£
I

^ 2
5 ^
•OH 5
i ! i- ^'
fa l™ o

l* ^1 O
2 if
^ 9 "^
o 3 ^5
f 1 1
i N 1
I * s
"ra
5
;a
XI

1
ra
ra
1 P
P 3
r 1
* fl
a g
C CL

S >
1 i
? f
                                A-9

-------
          TABLE A-10.  MASSACHUSETTS WATER RESOURCES AUTHORITY (MWRA)
              BOSTON HARBOR PILOT PLANT WASTEWATER EFFLUENT LEVELS
                                  (After Metcalf & Eddy, 1989)
                              (in mg/L unless otherwise specified)
Effluent Parameter
Total CBODs
Soluble CBODs
Paniculate CBODs
Particulate/Total CBODs
Total COD
Soluble COD
Particulate COD
Particulate/Total COD
Total SS
TKN
NH3-N
Total N
N03-N + N02-N
Total P
MWRA Primary
Effluent
112
44.80
67.20
60%
253
93.61
159.39
63%
136
16
11
16.33
0.33
4.13
MWRA Secondary
Effluent
9
3.05
5.94
66%
50.50
37.87
12.62
25%
15
11
8.10
11.22
0.22
1.88
Data Source
MWRA Pilot
TotarParticulate
Total'Particulate/Total
MWRA Pilot
MWRA Pilot
TotarParticulate
Total'Particulate/Total
MWRA Pilot
MWRA Pilot
MWRA Pilot
MWRA Pilot
TN=TKN/0.98
NO3=TN*0.02
MWRA Pilot
              TABLE A-11. EFFECT OF VARIOUS TREATMENT OPERATIONS AND
                          PROCESSES ON PHOSPHORUS REMOVAL3
                                  (after Metcalf & Eddy, 1991)
Treatment Operation or Process
Removal of Phosphorus Entering System (%)
Conventional treatment
     Primary
     Activated-sludge
     Trickling-filter
     Rotating biological contactors

Biological phosphorus removal only
     Mainstream treatment
     Sidestream treatment

Combined biological nitrogen and phosphorus removal

Chemical removal
     Precipitation with metal salt
     Precipitation with lime

Physical removal
     Filtration
     Reverse osmosis
     Carbon adsorption
               10-20
               10-25
               8-12
               8-12
               70-90
               70-90

               70-90
               70-90
               70-90
               20-50
               90-100
               10-30
 Adapted in part from Ref. 24 cited in Metcalf & Eddy (1991).
                                             A-10

-------
                  TABLE A-12.  EFFECT OF VARIOUS TREATMENT OPERATIONS
                           AND PROCESSES ON NITROGEN COMPOUNDS3
                                       (After Metcalf & Eddy, 1991)
Nitrogen Compound
Treatment Operation
or Process
Conventional treatment
Primary
Secondary
Organic Nitrogen
10-20% removed
15-50% removed0 urea
— > NH3-NH4d
NH3-NH4
No effect
< 1 0% removed
N03
No effect
Slight effect
Removal of Total
Nitrogen Entering
Process, %b
5-10
10-30
Biological processes
    Bacterial assimilation
    Denitrification
    Harvesting algae

    Nitrification
    Oxidation ponds

Chemical processes
Breakpoint chlorination
    Chemical coagulation
    Carbon adsorption
    Selective ion exchange for
    ammonium
    Selective ion exchange for
    nitrate
No effect
No effect
Partial transformation to
NH3-NH4d
Limited effect
Partial transformation to
NH3-NH4

Uncertain
50-70% removed
30-50% removed
Slight, uncertain

Slight effect
40-70% removed
No effect
—>  Cells

—> N03
Partial removal by
stripping

90-100% removed
Slight effect
Slight effect
80-97% removed

Slight effect
Slight                  30-70
80-90% removed        70-95
—>  Cells             50-80

No effect               5-20
Partial removal by       20-90
nitrification/denitrification

No effect               80-95
Slight effect            20-30
Slight effect            10-20
No effect               70-95

75-90% removed        70-90
Physical operations
    Filtration

    Air stripping
    Electrodialysis

    Reverse osmosis
30-95% of suspended
organic N removed
No effect
100% of suspended
organic N removed
60-90% removed
Slight effect

60-95% removed
30-50% removed

60-90% removed
Slight effect

No effect
30-50% removed

60-90% removed
20-40%

50-90%
40-50%

80-90%
                                                   A-11

-------









of
LU
_
Z
i •


!- f:
>." f
ce ij)
i *
E §
a. t,i
hi
> Q
ffl C
FUENTS
977; Gun
H T"
r™ r
Z i
O UJ
23
llBBB "~ "
fefi
gz
c?2
3 g
< s
> a
S oq
UJ O

S P
O Q
QC Z
UJ <
CL
ri
ty
J
(Q
£






0)
C
n


D

55
T3
«
I
<<










sr Plants
=
il
D

—
^
H








I
H
ffl
-c
a.






•»!
o ^
zC


s~
•c' "Sl»

•

u «*
i- O
ffl 3
11
S o

1
u
z


^1
o 5
z a.



"3d
|I

•o *=
, I*
"5 «
11
(ft a


i


Q *^
O O
ZE

S*^
|I
PS
||
S§ 1$3
if) Q


C

*
a
ra
a
rv s_ni ^ o ^0 — h>. sgj iS*y ^f ^ "^r ci ••«• so j^i sn
y^ca^^S — *r£- ^.ft^rty^-SS^-




3; KO ru •**• o Ji ** o o a O o o O ^5 cdi O
"J^, ,_ ,* j-y — — -~ ~^. *- -^ -_- ~» -«— -^. -^". cr; tc -^,
y«' "->. --, *^ CJ CO Qfl> w w 013 i,f> G> iQ O ^5 w ^1
yti* CTDi^'QQiiijRCrS' QJiCSCDOCniiSliOCTdQ
O 3C3 0>



^X|T-P«» CJcacyQ'—
'ID CO OJ ET31 CM CSJ "*F i~* F? '^F "<& C^ •"(f CTl UTJ C^l f!\J




iSd ffl?

D5 SZf QD " C\l f^- O '061 Uf>lQiir"r-i'*a'(7>EVCD(73
i—f— r— cycycnirM cMrjcnryc^c^EVOJCj





irt ptap T*- fw CQ Of F™ O C^ f*- J^»» ^3 "tih •«• tO D «•
N,h*wh-c\i«^^>-0 wc^-^Ovjs-oc^^ioW1


^* CNl C5 SU h"*> f1*" Cxi "C^ i'™4 fl& ^ •"* ^ ^O1 'SB* S*** ^J1
"^ tfS ^1 <^ "^ 'isT^1 C\? "?n! CJ ^3" sSj t"j' Cy T~i



|lillii| liilliiil

*-CvJ*«' S*~ ^evoiw^^-Hcxievi



*™ ^S1 '1^ ijp CQ CSJ Q EO DO* K5 "U 'f^- U2* 'fi lf~ O1 ^O
lift £v OJ ^5 *~ Cy ^4 ?!# OJ *^N '^J EM1 ^^ "*J 'r~


.-^/"sQ-^-,, ^CTH^a (._ o D^ZI — c djjg
EoSBolifEi^^S^^2^^2
lAaQ^-UQ-KZCL.E






d
X
•c
3
s
I
•3
|

5
H
=
"i
s
f

_.
2
K
yf
•g
1
S
s

1
i"
S

1
QL
"b"
i
E
JE
^
c
1
1
-5
8
o

13
C
g
s-
o
JE;
1
P
A-12

-------
             TABLE A-14. REMOVAL OF NITROGEN FROM SEWAGE EFFLUENTS
                                    (After Ketchum, 1982)
                                              Effect on Constituent
Treatment process
Organic-N
                                              Ammonia-N
    Nitrate-N
                                               Removal of Total
                                               Nitrogen Entering
                                                 Process (%)
Conventional treatment processes
    Primary
    Secondary
10-20% removed   no effect          no effect
15-20% removed   <10% removed     nil
urea —> NH3/NH4
                   5-10
                   10-20
Advanced wastewater treatment
processes
Filtration
Carbon sorption
Electrodialysis
Reverse osmosis
Chemical coagulation
Other nitrogen removal processes
Selective ion exchange for nitrate
Oxidation ponds
Algae stripping
Bacterial assimilation

30-95% removed
30-50% removed
100% of suspended
organic N removed
100% of suspended
organic N removed
50-70% removed
nil
partial
transformation to
NH3/NH4
partial transforma-
tion to Nhb/NhU
no effect

nil
nil
40% removed
85% removed
nil
nil
partial removal by
stripping
— > cells
40-70% removed

nil
nil
40% removed
85% removed
nil
75-90% removed
partial removal by
nitrification-
denitrification
— > cells
limited effect

20-40
10-20
35-45
80-90
20-30
70-90
20-90
50-85
30-70
  TABLE A-15. TOTAL NITROGEN AND PHOSPHORUS CONCENTRATION (MEDIAN) IN WASTE-
      WATER EFFLUENTS FOLLOWING FOUR CONVENTIONAL TREATMENT PROCESSES
                                    (After Ketchum, 1982)
                         Primary
                                                 Treatment Type
          Trickling Filter
Activated Sludge
                                                                            Stabilization Pond
No. of plants sampled            55
Total P (mg/L)              6.6 ± 0.66
Total N (mg/L)              22.4 ±1.30
N:P (weight) (mg N/mg P)         3.39
N:P (atoms)                  7.52
(mg-at N/mg-at P)
              244
            6.9 ±0.28
           16.4±0.54
              2.38
              5.26
     244
   5.8 ±0.29
   13.6 ±0.62
     2.34
     5.19
   149
 5.2 ±0.45
11.5± 0 .84
   2.21
   4.90
                                            A-13

-------
        TABLE A-16. AVERAGE WASTE REDUCTION EFFICIENCIES OF VARIOUS
                   CONTROLS OPERATING ON MUNICIPAL WASTE
                           (After Metcalf & Eddy, 1991)
Percent Reduction3
Control
Conventional Treatment
Primary settling
Intermediate-chemical treatment
Secondary-primary and trickling filter
Secondary-primary and
conventional activated sludge
Advanced Treatment
Secondary, nitrification and
denitrification
Secondary and coagulation,
settling filtration
Secondary and coagulation and
adsorption
Secondary and coagulation and
adsorption and electrodialysis
CBOD

25-40
40-65
65-85
85-90


90-95

95

99C

99C

Suspended
Solids

20-60
60-80
60-80
90


95

99

99

99

Total
Nitrogen

10-20
20-30
20-40
20-40b


90

50

55

75

Dissolved
Solids

-
-
-
5


5

10

15

50

Total
Phosphorus

-
-
-
10


10

95

95

97

Percent reduction based on raw waste concentration.
Longer aeration times convert oxidizable nitrogen to nitrate.
Minimum CBOD of about 2-5 mg/L.	
 TABLE A-17.  MEDIAN AND MEAN PHOSPHORUS AND NITROGEN CONCENTRATIONS AND
           MEDIAN LOADS IN WASTEWATER EFFLUENTS FOLLOWING FOUR
                     CONVENTIONAL TREATMENT PROCESSES
                           (After Gakstatter et al., 1978)

Number of Sampled Plants
Total Population Served
Ortho-P Cone, (mg/l)
Total-P Cone, (mg/l)
Total-P Load (cap-y)
Total-N Load (kg/cap -y)
Inorganic-N Cone, (mg/l)
Total-N Cone, (mg/l)
Total-N Load
TN:TP Ratio
Per Capita Flow (1/cap • d)




Median
Mean
Median
Mean
Median
Median
Median
Mean
Median
Mean
Median
Median
Median

Primary
55
1,086,784
3.5±0.29a
4.0 ±0.62
6.6 ±0.66
7.7±1.19
1.1 ±0.10
3.7
6.4 ±1.00
8.3 ±1.40
22. 4± 1.30
23. 8 ±3. 48
4.2 ±0.40
3.4
473 ±72
Treatment Type
Trickling
Filter
244
3,459,893
5.1 ±0.21
5.4 ±0.38
6.9 ±0.28
7. 2 ±0.50
1.2 ±0.05
2.9
7.1 ±0.38
8.2 ±0.60
16.4±0.54
17. 9 ±1.23
2. 9 ±0.1 7
2.4
439 ±19

Activated
Sludge
244
4,357,138
4.6 ±0.24
5. 3 ±0.40
5. 8 ±0.29
6.8 ±0.51
1.0 ±0.06
2.4
6.5 ±0.45
8.4 ±0.69
13.6±0.62
15.8±1.16
2.2±0.15
2.4
394 ±26

Stabilization
Pond
119
270,287
3. 9 ±0.34
4.8 ±0.62
5. 2 ±0.45
6.6 ±0.81
0.9 ±0.10
2.0
1.3±0.29
5. 5 ±1.95
11. 5 ±0.84
17.1 ±3.59
2.0 ±0.26
2.2
378 ±38
                                     A-14

-------
                                (a)
nj.U
5.5
O1 4.0
O
£0 3,0
•s,
J 2.0
3
O
00
u 1.0
0,8
1.
« 50
0 4'°
e 3.0
*v
5 2,0
3
Q
O
CO
u 1.0
rt«l
•
- *
rM{.&H +S $TQ OEV A
" 1 ..----... •
mm * * »
~~ » r-ME&N- 2.4?
. • •••• ... . ••
* »
I ^.MEAN-I STO, DEV.
JL i I 1 I i ( | J t I 1 1 ! 1 1 !

0 3.0 10.0 50.O SOO.O
EFFLUENT B008 (mi/1 )
(b)
•
[_ r-MEAM4§l STO.D6V. * «
^* * • * * ^-MEAN= 2.84
_ » . • • •
""**" V **^-*-% **
^-MEAN -i STO, DEV, *
i i i 1 i ' i i 1 i i i 1 i i i i

                         5.0    10.0             50.0    IOO.O
                    EFFLUENT CSOOS (mg/l 1
FIGURE A-1. POTW EFFLUENT ULTIMATE CBOD AS A FUNCTION OF CBODs AND BOD5
                         (After Leo et al., 1984)
                               A-15

-------
These loading rates represent those rates found at
specific sites across the country.  In any water quality
modeling analysis, site-specific data should be col-
lected.


A.3   PHYSICAL PARAMETERS

A.3.1  Hydraulic Geometry Data

Hydraulic data include the velocities, flows, and water
depths. Flows include the flows at upstream bounda-
ries of all channels, as well as all significant tributary
inflows, lateral inflow (from groundwater or runoff),
flow diversions, return flows, and stage at some loca-
tions.  If wastewater flows represent a significant
portion (i.e., greater than 5 to 10 percent) of the total
stream flow, they should  be included in the hydraulic
analysis.
While the upstream boundary, tributary, and  diver-
sion flows can be measured directly, lateral inflows
from groundwater or runoff can be estimated from
differences in measured flow  at different  locations
along the stream channel. USGS official flow records
are annually published several  months to a year after
the end of the water year, which ends September 30.
All  USGS flow data are available through EPA's
mainframe computer. Under special contract, USGS
is able to furnish the records for the period of a stream
study as soon as the  stream stage data can be
converted to flow.  These records may be designated
as tentative or provisional, but are adequate for all but
the strictest  legal uses.  The U.S. Army  Corps of
Engineers and the U.S. Bureau of Reclamation main-
tain stream flow records on streams for which they
have special responsibilities. In addition, some State
regulatory agencies only rarely make stream flow
measurements for selected streams.
As indicated in Section 2.3.3,  mathematical models
such  as Manning's Equation and  stream  hydrody-
namic models can be used to quantify the stage-flow
relationships for  each channel reach in a stream.
One of the simple stream  hydrodynamic models is
RIVMOD (USEPA, 1990). Power functions may also
be developed relating flow with velocity, depth, and
cross-sectional area (see Equations 2-1 through 2-3).
It can be shown that the sum of the exponents (m +
n + f) and the product of the coefficients (abc) from
Equations 2-1 through 2-3  are unity because  of the
continuity equation.  Using  this and experience from
a variety of streams, the value of the exponents can
be approximated as follows (Barnwell et al., 1989):

    n  =    range (0.4 - 0.6); typical 0.43
    m  =
range (0.3 - 0.5); typical 0.45
    f  =     range (0 - 0.2); typical 0.12
Impounded reaches in rivers have exponents m and
f = 0,  and n = 1. Where the analyst has more than
one set of data, a log-log plot of area, depth, and
velocity against stream flow will permit extrapolation
to other flows of interest.  The slope  of such  plots
provides the local value of the exponents (see Figure
A-2).  When data at only a single flow regime are
available, estimates for other flows can be developed
by the following ratios, derived from  the foregoing
relationships:
    velocity:U2/Ui =
                         0.43
    depth:
   H2/Hi = (Q2/Qi)
                             0.45
    cross-sect.  A2/Ai = (Q2/Qi)
    area:
                             0.43
    travel time:  T2/Ti = (Q2/Qi)
                             0.43
(A-1)


(A-2)



(A-3)


(A-4)
It should be recognized that these relationships exist
only in free-flowing streams and care should be exer-
cised  when upstream dams or hydropower facilities
are present that may interfere with the assumption of
the analysis. It is also common to collect site-specific
data since the exponents may vary by 50 percent for
any river.

A.3.2   Low-Flow Analysis

Generally, the minimum average 7-day flow expected
to occur once every  10 years is used in  modeling
analyses depending on the state WQS and  water
body assessed.  However, several different types of
flows can be estimated from a hydrologic record.  For
example, the minimum average 7-day flow occurring
at any time in a year can  be estimated, or the mini-
mum average flow in a given month or season can be
computed. Figure A-3 shows the low-flow frequency
curves for the Potomac River at Point of Rocks de-
veloped using the USGS flow records from 1891 to
1981.   Table A-18 presents a summary of data ob-
tained from Figure A-3. The analysis to determine the
low-flow frequency curve  is simple and straightfor-
ward;  Thomann and Mueller (1987) provide an easy-
to-follow procedure.
                                             A-16

-------
    River Geometry
    10
     1 -r
                        Shallow Channel
              Depth =0,565
                           •vQ.48
      0,1        1.0        10.0       100.0
                   Flow (m1,'*)
                                                     Deep
                                                    10
                                                 1
                                                 I
                                                     1
                                                              Depth «1,412 Qfl
                                                     0.1         1.0        IO-.0       100-0
   100
I
    River Velocity      sha||ow
  1,00
I
  0.10
  0.01
              Velocity « 0.065 Q'
      0,1
                   Rovrtm1/*)
                                     1000
10.0       100JO
                                                  1COO
                                               1
                                                  100
                                                               Araa -S7.6Si QQSf
                                                     Deep Channel
                                                 i.oo
                     I
                     I
                                                  0.10 • •
                                                  0,01
                                                           Velocity » 0,017
                                                     0.1
                                                               1.0         10.0
                                                                 Flowfn'lk)
                                                    O.I         1.0         10.0        100.0
                                                          1CO.O
            FIGURE A-2.  EMPIRICAL RELATIONSHIPS FOR HYDRAULIC GEOMETRY
                                          A-17

-------


"""" " ^— "" •! - 1 1 <- W |
	 	 r^ 	 -— |p~U-i '
" ' — —• 	 _L: - -L- - '
_. .' - \\~ "i~: "
\1

. „_. ^*^3
^ , 	 »'^ i'=«- -"'-=» r- .i..«i.^i.-a 	 .
t \ ' ' ' '
rbt= 	 ~t===
\i X . ,. 	 	 	 	
V : - -i li \
, — y 	 v 	 ^ 	 ,---. ;-r^ _._;
                         :t|

              1 "'" ""
           _:	vi  iv
           —	-VM-V—^  i
                 Ct
                 a
                                o>
                               O
                               O
                                 00
                               2
                               LU LU
                               > D
                               o
                               Q.
A-18

-------
                  TABLE A-18.  LOW FLOW RECURRENCES, POTOMAC RIVER
                               AT POINT OF ROCKS, MARYLAND
                                          (1891 -1981)
Recurrence
Interval
(years)
5
10
50
Probability
of Occurrence
(% time flow
< flow plotted)
20%
10%
2%


7-day
950
825
625

Mean Annual Low Flow (cfs)
30-day 90-day
1200 1500
1005 1250
800 910


365-day
6600
5600
4000
A.3.3  Time-of-Travel Survey

Time of travel can be determined by several different
methods. The three principal methods involve use of
surface floats, use of a tracer  such as a  dye or
radionuclide,  and measurement of cross-sectional
areas.
Surface floats can be followed downstream and timed
for known distances to determine times of travel. This
approach  requires  considerable  judgment since
floats tend to travel into eddies or become trapped on
tree  limbs, stream banks, or other obstacles.  The
floats must be frequently retrieved and returned to the
current of the stream. The principal judgment factors
are how long the floats should be left in quiet areas
before  retrieval and where they should  be  placed in
the current. The surface water velocity is greater than
the average for the entire stream,  and  a correction
factor must  be applied to the surface velocity; an
average velocity  equal to 85 percent of the surface
velocity is a reasonable rule of thumb. Oranges make
very  good floats since their density is such that they
float  with only a small portion of their tops exposed to
wind action  and  their  color is easily detected and
followed in the water.
Measurement of cross-sections at frequent longitudi-
nal intervals and calculation of average velocity from
the average cross-section and stream flow at the time
of measurement constitute a time-consuming method
of obtaining time  of travel. This method does, how-
ever, produce information that  is  useful  for other
purposes.  For example,  reaeration coefficients may
be calculated by one of the formulas based on aver-
age depths and velocities.
The  most accurate method of  measuring time of
travel, and a good way to measure  velocity, involves
following a tracer downstream.  An industrial waste
may  include an occasional discharge of some  con-
stituent that can  serve as a tracer.  Radioisotopes
have given good  results, but their safe handling can
present problems. Several kinds of dyes have been
used, including the recent trend of using Rhodamine
WT.  This dye can be detected in concentrations as
low as 0.05 part per billion (|ig/L) by a fluorometer.
The dye  is distributed across the stream at the up-
stream point,  as nearly instantaneously as possible.
Because of longitudinal mixing (see Figure 2-1), the
dye arrives downstream in a wide band. The time of
travel to  a downstream  point is the difference be-
tween the time the dye was released to the stream
and the time the centroid of the dye mass arrives at
the downstream point. Two handbooks by the USGS
(Hubbard et al., 1982 and Wilson et al., 1986) provide
step-by-step procedures to conduct a time-of-travel
study.  Figure A-4 shows the hydraulic geometry and
time-of-travel  data for the Kalamazoo River in Michi-
gan under various stream flow conditions.

A.3.4  Longitudinal Dispersion Coefficient

Although the mechanics of longitudinal dispersion are
well understood, general analytical treatment is ex-
tremely difficult, if not impossible. Theoretical devel-
opment has led to a number of equations to calculate
longitudinal dispersion  in one-dimensional stream
models.  One equation suggested  by Fischer et al.
(1979) is:
               Dx
                    0.011L/2 I/I/2
                       HU*
                                           (A-5)
where
    Dx
           =   longitudinal dispersion
               coefficient (ft2/sec)
    U       =  average velocity (ft/sec)
    I/I/     =   river width (ft)
    H      =   river depth (ft)
    U*     =   shear velocity (ft/sec)
Since the exact effect of irregularities cannot be in-
cluded and, for most applications, results are insen-
sitive to the  exact value used, it is generally not
                                             A-19

-------
§1
H* ^

  *
  u
tt >
O
o

               Al
                                          O
                                  JWU
        FIGURE A-4. HYDRAULIC GEOMETRY AND TIME OF TRAVEL
                            A-20

-------
necessary to achieve a high accuracy in predicting
dispersion coefficients (Fischer et al., 1979).  Equa-
tion A-5 provides a good approximation to longitudinal
dispersion coefficients for a  number of rivers, as
shown in Table A-19.

A.4   CARBONACEOUS
       DEOXYGENATION RATE

A.4.1   Using Field Data to Determine Krand Kd

The carbonaceous  deoxygenation  rate coefficient
can be estimated from field data  by plotting CBOD
measurements versus time of travel on semi-log pa-
per, based on Equation 2-7. The rate of deoxygena-
tion is estimated as the slope of the line that best fits
the data points:
                Kr=-
                      In (L/Lo)
                          t
                                           (A-6)
where
    L
              oxygen equivalence of the
              organic matter at any given
              location in the stream (mg/L)
    Lo     =   total oxygen demand measured
              at the source of waste load
              following complete mixing
              (mg/L)
    t       =  time of travel (day)
The above equation,  based on concentrations,  is
applicable for constant hydraulic geometry.  In prac-
tice, the river channel changes frequently and addi-
tional flow from tributaries may provide dilution to the
river flow, all affecting the  BOD  concentrations.  A
more  practical approach to estimating the deoxy-
genation rate coefficient is  to plot the mass loading
rate (kg/day) of BOD instead of concentration.  The
mass loading rate at any given location in the river is
the product of measured BOD concentration and river
flow. Figure A-5  shows such a plot for Rock Creek,
Pennsylvania.  Note that the tributaries provide dilu-
tion along the stream.
In Rock Creek, a two-stage curve is obtained.  The
first part of the curve is steep, showing that the total
removal rate (Kr)  results when both settling and bio-
logical oxidation are significant.  The  second part, a
more gradual slope, generally represents the CBOD
deoxygenation rate (Kd) after most of the settling has
taken place. The settling rate (Ks) can then be esti-
mated as the difference between  Krand Kd.
The above procedure  is  valid for either CBODs or
CBODu  provided  that the laboratory coefficients, Ki,
are identical for all  stations. If Ki values vary from
station to station,  it  is necessary to use CBODU.
Leo et al. (1984) evaluated the change in Kd following
the installation of higher treatment levels at POTWs.
The data  indicate  that Kd values associated with
advanced treatment levels  are generally lower than
those determined for lesser treatment levels.
Algae can affect the CBOD data used to calculate Kd.
Algal respiration and decay in the CBOD  bottle can
              TABLE A-19.  LONGITUDINAL DISPERSION COEFFICIENT IN RIVERS
                                   (After Fischer et al., 1979)
River
Missouri River
Clinch River,
Tennessee

Bayou Anacoco

Nooksack River
Wind/Bighorn Rivers

John Day River

Comite River
Sabine River

Yadkin River

H
(m)
2.70
0.85
2.10
2.10
0.94
0.91
0.76
1.10
2.16
0.58
2.47
0.43
2.04
4.75
2.35
3.84
W
(m)
200
47
60
53
26
37
64
59
69
25
34
16
104
127
70
72
U
(m/sec)
1.55
0.32
0.94
0.83
0.34
0.40
0.67
0.88
1.55
1.01
0.82
0.37
0.58
0.64
0.43
0.70
if
(m/sec)
0.074
0.067
0.104
0.107
0.067
0.067
0.27
0.12
0.17
0.14
0.18
0.05
0.05
0.08
0.10
0.13
Dx(m2/sec)
Measured
1500
14
54
47
33
39
35
42
160
14
65
14
315
670
110
260
Dx(m2/sec)
Using Eq. A-5
5290.8
43.7
100.2
94.7
13.6
39.5
98.6
224.6
342.7
86.4
23.5
17.9
392.4
191.2
42.4
66.0
References
Yotsukura et al.
(1970)
Godfrey and
Frederick (1970)
(predicted Dx
from Fischer,
1968)
McQuivey and
Keefer(1974)
(predicted Dx
from
Fischer, 1975)




                                             A-21

-------
93
•q
C3
3

Q
O
CO
u
19
        J3



        O
          en

          1
August 8-9,1979

Temperatyri =•
      i •-
                 2          «          *         t         ia.
        MILiS FROM CUMBERLAND WASTEWATER TREATMENT PLANT
FIGURE A-5. DETERMINATION OF CBOD REMOVAL AND DEOXYGENATION RATES FOR ROCK

                          CREEK, PENNSYLVANIA
                                 A-22

-------
cause  higher  measured CBOD values and thus
higher Ki rates compared to samples without algae.
In addition, if the concentration of suspended algae
is not constant  in the stream below the discharge, the
measured CBOD concentrations would not indicate
a defined Kd. Where the concentration of suspended
algae increases in  the stream, there may be a  net
increase of measured CBOD  below the discharge.
To minimize these  effects, filtered CBOD and total
CBOD samples should be analyzed at several loca-
tions downstream of the discharge.  The oxygen  de-
mand resulting from settleable  (i.e., filtered) organics
is then accounted for separately in establishing  the
Ks (BOD settling) and SOD rates.  However, even
with filtered and unfiltered CBOD data, it is difficult to
select rates for model  calibration and  projection
analyses of algae-dominated streams.  Where large
concentrations of algae occur in the receiving stream,
a range of Kd  rates should  be estimated based on
filtered and unfiltered CBOD  data. Some general
rules of thumb  follow:
   •   Algal impacts on Kd  occur wherever high
       concentrations of chlorophyll a or large diur-
       nal dissolved  oxygen fluctuations are meas-
       ured.
   •   10 |j,g/L of chlorophyll a will increase  the
       CBODu concentration  by 1 mg/L above lev-
       els without algae. Rough estimates can be
       obtained from multiples of this  relationship;
       i.e., 100 |j,g/L of chlorophyll a may increase
       the concentration by 10 mg/L.
   •   If the stream is effluent-dominated with most
       of the CBOD  originating from the discharge
       rather than  algae, filtering may not be needed
       or the number of filtered CBOD analyses may
       be decreased. If the stream is not effluent-
       dominated  and most of the CBOD is from
       algae,  filtered and unfiltered samples should
       be run.

A.4.2  Projection of Carbonaceous
       Deoxygenation  Rates

In wasteload allocations, Kd rates  are projected for
future conditions.   Literature data  have been com-
piled (Figure A-6)  to correlate Kd with the stream
depth in lieu of any other parameters. The rationale
behind this correlation is that the greater the wetted
perimeter, the greater the contact with the biological
community in  the  streambed, the most important
factor in natural oxidation processes. The tendency
for this relation to hold  is greater for rocky streambeds
than  for silty beds.  However,  the general trend  ap-
pears reasonable up to depths of about 5 to 10 ft.
Hydroscience (1971) developed the following rela-
tionship:
           .  n o \H-. -0.434
           f= U.6 [—\
forO8
                                          (A-7a)
                                          (A-7b)
where H is the depth in feet.  Wright and McDonnell
(1979) suggested the following:
                Kd=10.3Q
                           -0.49
                (A-8)
where Q is stream flow in cfs.  They also indicated
that for flow conditions greater than 800 cfs, Kd rates
were consistent with Ki for  the effluent.  In other
words,  for larger and deeper streams (greater than
10 ft), the characteristics of the streambed become
less of a factor and the  level of  treatment would
dictate the following Kd values:
    •   Primary      0.4  day"1
    •   Intermediate  0.3  day"1
    •   Secondary   0.2  day"1
    •   Advanced    0.1  day"1
That is, for increasing levels of treatment, the residual
waste contains a large proportion of refractory organ-
isms and will be less easily oxidized since the treat-
ment processes are designed to oxidize the labile
components of the organic matter.

A.4.3   Literature Values of Kd

Bowie et al. (1985) summarized a large number of
carbonaceous deoxygenation rate coefficients  re-
ported in the literature (Table A-20). Leoetal. (1984)
provided  a comparison of Kd rates before and after
the improvement in stream dissolved oxygen condi-
tions following treatment upgrades (Table A-21).

A.5    NITROGENOUS DEOXYGENATION
       (NITRIFICATION) RATE

A.5.1   Using Field Data to Determine Kn

The procedure described in Section A.4.1  (see Figure
A-5) may be used to develop the nitrification coeffi-
cient, Kn, for NBOD.  Either the nitrogenous compo-
nents of the laboratory BOD  test or the directly
measured ammonia concentrations at the various
locations in the stream may be used.  Each  should
yield the  same value of Kn following the first-order
reaction.   The displacement  of the curves is due to
the stoichiometric  relation between oxygen and am-
                                             A-23

-------
 0.3    0.5      1     1,6         5      10

                          DiPTH IHH'Mt)
SO     100
                                       O'Connor Dili
                                                     0*t*
FIGURE A-6.  DEOXYGENATION COEFFICIENT (Kd) AS A FUNCTION OF DEPTH
      (A benthic deoxygenation component is included in these values)
                      (After Hydroscience, 1971)
                                A-24

-------
TABLE A-20. VALUES OF KINETIC COEFFICIENTS FOR DECAY OF CARBONACEOUS BOD
                          (After Bowie et al., 1985)
Location
Potomac Estuary 1977
Potomac Estuary 1978
Willamette River, OR
Chattahoochee River, GA
Ganga River, India
Yamuna River, India
S. Fork, Shenandoah River
Merrimack River, MA
Gray's Creek, LA
Onondaga Lake, NY
Yampa River, CO
Skravad River, Denmark

Seneca Creek
Kansas (6 rivers)
Michigan (3 rivers)
Truckee River, NV
Virginia (3 rivers)
N. Branch, Potomac, WV
South Carolina (3 rivers)
New York (2 rivers)
New Jersey (3 rivers)
Houston Ship Channel, TX
Cape Fear R. Estuary, NC
Holston River, TN
New York Bight
White River, AR
N. Fork Kings River, CA
Lake Washington, WA
Ouachita River, AR


36 U.S. river reaches plus
laboratory flume
San Francisco Bay Estuary
Boise River, ID
W. Fork, Trinity River, TX
Wilamette River, OR
Arkansas River, CO
Lower Sacramento River, CA
Delaware River Estuary
Wappinger Creek Estuary, NY
Potomac Estuary
Speed River, Ontario
Kd
(day 1 @20 °C,
base e)
0.14±0.023
0.16±0.05
0.1-0.3
0.16
3.5-5.6(Kr)
1.4
0.4(Kr)
0.01-0.1
1.44(Kr)
0.10
0.40
0.15
0.90(Kr)
0.008
0.20-0.60
0.56-3.37
0.36-0.96
0.30-1.25
0.4
0.3-0.35
0.125-0.4
0.2-0.23
0.25
0.23
0.4-1.5
0.05-0.25
0.004-0.66(Ki)
0.2
0.2
0.15
0.17(Ki)
0.02(Ki)
0.08-4.24

0.2
0.75
0.06-0.30
0.07-0.14
1.5
0.41
0.31
0.31
0.16, 0.21
1.0
Method
Determining
Coefficient
field study



field study

field study
field study
model calibration
model calibration
model calibration
field study


various methods









model calibration

laboratory study


calibration

laboratory study
field studies




laboratory study
lab and field study




field study
Reference
USEPA(1979b)
USEPA(1979a)
Bacaetal. (1973)
Bauer etal. (1979)
Bhargava (1983)

Deb and Bowers (1 983)
Camp (1965)
Crane and Malone (1 982)
Freedman etal. (1980)
Grenney and Kraszewski (1981)
Hvitved-Jacobsen (1982)

MWCOG (1982)
Reported by Bansal (1975)









Novotny and Krenkel (1975)
O'Connor etal. (1981)
Terry etal. (1983)
TetraTech (1976)
Chen and Orlob (1975)
Hydroscience (1979)

NCASI(1982a)
Wright and McDonnell (1979)

Chen (1970)
Chen and Wells (1975)
Jennings etal. (1982)
McCutcheon (1983)

Hydroscience (1972)


Thomann and Fitzpatrick (1982)
Gowda (1983)
                                  A-25

-------
        TABLE A-21. SUMMARY OF PRE- AND POST-IMPROVEMENT OXIDATION RATES
                                      (After Leo et al., 1984)
Pre-lmprovement
River
Main Stem
Patuxent
Wilsons Creek


Hurricane Creek
Cibolo Creek
Clinton River

Hudson River
South River

Treatment
Secondary

Secondary


Trickling Filter
Secondary
Secondary

Primary
Secondary

CBOD
Oxidation
Rate (day 1)
0.61a

0.30


0.10-0.50
0.18
2.20

0.25


NBOD
Oxidation
Rate (day 1)
0.76a

0.40


0.10-0.50
0.25
0

0
1.6-2.0

Treatment
Secondary and
Nitrification
Secondary and
Nitrification and
Polishing
Secondary15
Secondary0
Secondary and
P- Removal
Secondary
Secondary and
Nitrification
Post-Improvement
CBOD
Oxidation
Rate (day 1)
0.30a

0.30


0.35
0.18
0.20

0.15


NBOD
Oxidation
Rate (day 1)
0.46a

0.40


0.70
0.25
2.5-3.8

0
1.6-2.0

  From reference (23) cited in Leoetal. (1984), not from calibration analysis.
b Oxidation ditch achieving nitrification.
c New facility achieving nitrification.
monia, which is  approximately 4.57.  Figure A-7
shows the determination of Kn for the Shenandoah
River.
Benthic ammonia regeneration, benthic nitrification,
and large concentrations of algae, either suspended
or attached, markedly affect values obtained for Kn.
The approach shown in Figure A-7 to determine Kn
reflects only the net loss of ammonia. Such an ap-
proach can result in the overestimation of Kn where
significant algal or attached periphyton effects occur.
Algae consume ammonia as a nutrient; therefore, a
Kn determination based only on the loss of ammonia
would include uptake of ammonia by algae as well as
ammonia oxidation.  In some cases, using the rate of
nitrate increase is a better approach for estimating Kn
because nitrate increases result directly from ammo-
nia oxidation.  However, as a cautionary note, under
some conditions algae can consume nitrate as well
as ammonia.  In addition, benthic denitrification can
account for a significant component of the total nitro-
gen balance as a loss mechanism for nitrate (Seitz-
inger, 1988).  Therefore, the Kn rate derived from
nitrate data would represent the minimum Kn.  A
critical issue in determining Kn involves the time of
year.  Although ammonia and nitrate data may indi-
cate relatively high Kn rates during July and August,
Kn rates for the same stream may be negligible during
the winter months and even during transitional peri-
ods such as April, May, June, September, October,
and November.  Seasonal adjustments in Kn using
the temperature correction relationships can be found
in Section A.12.  During months when the tempera-
ture falls below 20 °C, nitrogen series data collected
during these cooler  periods may be necessary to
select appropriate Kn rates to support cool-weather
nitrification.

A.5.2   Projection of Nitrogenous
        Deoxygenation Rates

Kn rates applied to  deep and slow-moving rivers,
without site-specific data, are higher than those nor-
mally expected for such rivers.  The availability of
surfaces for nitrifier attachment can affect Kn rates.
These surfaces include the stream bottom and  sus-
pended particles in the water column.   Therefore,
shallow streams with  rocky bottoms favor the growth
of nitrifying bacteria with associated high Kn rates.
Deep rivers composed of sands,  silts, or clays  gen-
erally have fewer attached nitrifiers. Although these
streams may support significant populations of nitrifi-
ers in the water column, they tend to have lower Kn
values than shallow streams.

One of the difficult issues related to the determination
of Kn is the onset  of the nitrification process.  It is
known that there are lags in the nitrification process
in highly polluted streams or those with low dissolved
oxygen. In projection analyses, a critical question is
                                              A-26

-------
4.0 T
3.0-
2,0-
 1,0
0.9
0,8
0.5
                          •Kn-l.25/day
               OJ          1.0          1,5         2,0
                       TIME OF TRAVEL.LWS
2,5
       FIGURE A-7. NITROGENOUS BIOCHEMICAL OXYGEN DEMAND VERSUS
                   TRAVEL TIME IN SHENANDOAH RIVER
                         (Deb and Bowers, 1983)
                                A-27

-------
when and where nitrification should occur following
improved treatment such as nitrification at the plant.

A.5.3  Literature Values of Kn Rates

A large body of literature exists for case studies of Kn
rates in streams and rivers. Tables A-22 and A-23,
taken from Bowie et al. (1985), summarize Kn rates
measured in the field and used as parameter values
for models from a number of investigations.


A.6   STREAM REAERATION RATE

A.6.1  Measuring Stream Reaeration Rate

Several methods of measuring Ka rates have been
reported  in the  literature.  These methods are dis-
cussed in detail by Bennett and  Rathbun (1972).
Churchill et al. (1962) used  a dissolved oxygen bal-
ance method to measure reaeration in several Ten-
nessee rivers.  Owens et al. (1964) used a similar
technique for small English  streams.  Both required
that all other factors influencing the oxygen balance
be known or negligible.  Because of the difficulty in
accurately measuring the other factors, these tech-
niques are subject to considerable error.  Tsivoglou
(1967) developed a gas-tracer method for directly
measuring gas  transfer in streams,  eliminating the
need for the oxygen balance  information.  In this
method, a fluorescent tracer is used for determining
time of travel and longitudinal dispersion, tritium is
used as an indicator of total  dispersion, and krypton-
85 is used as  a  gaseous  tracer.  Of the various
methods, the gas-tracer method is superior since it
does not require estimating any other factor affecting
the oxygen balance. This method, however, requires
the handling and the injecting of radioactive tracers
into  streams.  Application of the method  is limited
because the use of radioactive tracers in the natural
environment is  subject to public health restrictions.
Rathbun et al. (1975) modified Tsivoglou's method to
use  nonradioactive hydrocarbons (ethylene or pro-
pane) as the gaseous tracers. Additional information
about the method is given in Rathbun and  Grant
(1978). Whittemore (1990a) has recently reported a
nonradioactive technique for determining reaeration
rates.

A.6.2 Predicting Stream  Reaeration Coefficient

Various predictive equations are discussed in Ben-
nett and Rathbun (1972) and Rathbun (1977). One
of the most popular theoretical formulas was that of
O'Connor and Dobbins (1958):
                 Ka =
                      12.9LT
                                           (A-9)
where
    U      =   average stream velocity (ft/sec)
    H      =   average stream depth (ft)
This formulation was derived from theoretical consid-
erations regarding surface renewal of the liquid film
through internal turbulence.   Table A-24 lists  a
number of other conceptual, empirical, and semi-em-
pirical predictive equations found in the literature.
The model QUAL2E offers eight different formula-
tions.
The variability of Ka estimates using these formulas
is illustrated in Figure A-8. As shown in this figure,
the range of Ka values calculated with the O'Connor,
Owens, Churchill, and Tsivoglou equations is signifi-
cant at all flow levels in the Muskingum River. Al-
though these formulas may not be equally applicable
to this particular river, this result illustrates the differ-
ences in the Ka values calculated using these four
equations.  For some allocations, Ka values derived
from empirical formulas could result in the degree of
uncertainty exceeding the degree of projected water
quality improvement from the proposed treatment
plant upgrades.  Using  a predictive equation devel-
oped  for the receiving water conditions similar to
those being modeled is extremely important (see the
applicability column in Table A-24).
In additiontothe numerous studies compiled in Bowie
et  al., (1985), Whittemore (1990b) has  compiled  a
data base of stream reaeration measurements ob-
tained over a 30-year period under a wide range of
environmental and hydraulic conditions. The data
base and computer software  for querying the data
base are available to interested  investigators from
NCASI for  a modest cost.  Using a  personal  com-
puter, the data base can be queried to  extract field
study  measurements obtained under a range of en-
vironmental criteria specified  by  the analyst (i.e.,
depth, velocity, temperature, etc.).

A.6.3  Dam  Reaeration

The QUAL2E model uses the following equation de-
scribed by Butts and Evans (1983) to estimate oxy-
gen input from dam reaeration:
Da-Db= 1-
1 + 0.116 a £>H(1-0.034H) 0+0.0467)J  a

                              (A-10)
                                              A-28

-------
                    TABLE A-22.  SUMMARY OF NITRIFICATION RATES (day )
                                     (After Bowie et al., 1985)
River
Grand River, Ml
Clinton River, Ml

Truckee River, NV
South Chickamaugo Creek, TN
Oostanaula Creek, TN
Town Branch, AL
Chattahoochee River, GA
Willamette River, OR
Flint River, Ml
Upper Mohawk River, NY
Lower Mohawk River, NY
Barge Canal near Upper
Mohawk River, NY
Ohio River, OH
Big Blue River, NB
Delaware River Estuary, DE
Willamette River, OR
Ouachita River, AR and LA
Potomac Estuary
Lake Huron and Saginaw Bay
New York Bight, NY
Maximum
3.9
15.8
4.0
2.4
1.9
0.8


0.7
2.5
0.3
0.3
0.25
0.25
0.25
0.54
-



Average
2.6
5.7
1.9
1.9
-
-
0.7
0.44
-
1.4
0.25
0.3
0.25
0.25
0.11
0.3
0.75a
1.05b
0.1a
0.5b
0.09-0.13
0.20
0.025
Minimum
1.9
2.2
0.4

1.1
0.1
0

0.4
0.1
0.25
0.3
0.25
0.25
0.03
0.09
-



Reference
Courchaine (1968)
Wezernak and Gannon (1968)

O'Connell and Thomas (1965)
Ruane and Krenkel (1978)
Ruane and Krenkel (1978)
Ruane and Krenkel (1978)
Stameretal. (1979)
Rinellaetal. (1981)
Bansal (1976)
Bansal (1976)
Bansal (1976)
Bansal (1976)
Bansal (1976)
Bansal (1976)
Bansal (1976)
Alvarez-Montalvo et al.
(undated)
NCASI(1982a)
Thomann and Fitzpatrick (1982)
DiToro and Matystik (1 980)
O'Connor etal. (1981)
Note: Nitrification rates are in units of day"
   a Ammonia oxidation.
   b Nitrate oxidation.
                                              A-29

-------
               as

               2 $Q


               ^'•5 S



               *Z C -ni
               CL O ,£T

               Cl t? ,*s



               111
          a c at c '

          •s 2S 1
               *** CE?


               II
X *

— E
Q- •&
                                                   i§
                                                   o .£
                                                     c

                                                     "5
      .J1 Of T*

      m ^ ^p,

      > « O
      c a "5
      c s o
        B _
                                    et
                                  u, a
                                  s i
TABLE A-23.  CASE STUDIES OF NITRIFICATION IN NATURAL WATERS

                      (Bowie etal., 1985)
                            A-30

-------
II
                     o


                     .8
                     CJ
                     c

                     e
                     Uk

                     "C
                     c:
                     01

                     %

                     S>


                     &
                     ajr
                     ^ h-

                     S »
                     ^ ^.
                                 e*




                                 <5



                                 1

                                 O
                                                      2


                                                      •D
                                                       i/: r-

                                                       S 5
                                                                  o

                                                                  5
£
c
O

O
O- 2




  z
o

s
«3
                                                                 9
                                                                 o
                        £ S
                                                           1 | .5


                                                           111


                                                           oil
                                                           I- TJ S
     I

     W
                    O w
                                    >

                                   £

                                   .9
                                   a. z
                                                                  o
                                                                  a.
                        IABLL A-23. (Continued)
                                  A-31

-------
     TABLE A-24. REAERATIQN COEFFICIENTS FOR RIVERS AMD STREAMS
                                   (Bowfe ei al,, 1 MS)
K(1 base e (day"1 at 20 "C)
 23.3 O5
   7.fi 0
            H


      Ll-O-^^K
             H


            s ...J*35



       24.9 < IT
             H
234
                             Units
                         U-lps
                         U-lps
H-ISit


U-lps
                         U-lps
                         H-lwt
                         U-nVa
                         S-nvWi
                         U-tps
                         H-lMt


                         u-lpa
                         w-ipa
U-ips
S-l¥?t
                         Oi-tffmr
                         LMps
                         Applicability
                                                            Auttior(s)
                                                     'Cewnw anil Daebfea ft 9SS J
                           to deep channels:
                    i 30 ft, 0 5_£ U i.i.a ips.
             0-S ^ te i.T2.a'aav. O'Comof snd
             afet> -slLi;hi.
             Ri^rission analysis psrlwrr^d on. 0-sts
                                                                                             ii362)
                                                          w*d Ortob (1 962)
                                                    P'addon arhJ CSIflyna 1 1 97 1 J
                         S-IMl
             B^Sed On m iltt'.'ri.Tnlrj ;i-,Tlyr,L-. of rixia: alion   Ca.d\vu.liiJ'Ji and
             aata.                                    I^Dcfirwl r, 1369}
                                                 A-32

-------
                                            TABLE A-24. (Continued)
     K., base e {day -1 at 20 °C)         U nits
                 , , I
                  .««
                                      H-fart
S-ltfl
H-fe«
                                      AppJicabJIity
                                                            Author(s)
                                                   Ess*! on -aaralysis of raewrati&i daia in     Bansal (<973)
                                                   nLfrHrujs nvarS.
                         These t*o eauattsfra art ijaseo w a
                         rMnalysis 0! hiSlos'iSal daia, vsilh CIQ
                         equation 5«wg abnj^ as good a pradctor
                         as *.a first, bud run hawng the slcea arm.
                                                                                          Swnffft arel
                                                                                                            i'i973)
1 17 I  1 H-F % 6'Sl3 a?*
                 ,5
                 0
                   *1
      -in
           1-2
                )- 0.0052
       ° ra  "J-
            W-leet

            U-fca
            Q-tF/see

            ^teel
            Ct-
                                      T-aC
                                      U-nv'wc
                               i n>*asur»r«nte m     Oob&nB (1964]
             nalurtl sa^aim, ana llyin* dam ol Kninkel
             arx! •
                                 in sflvenu small     *C9 and B^w (1979)
                                                   Sasad on Ssl3
                                                          streams.
                                                   Based on me VB*I nwfliod (1 970) ar«J
                                                              for JiS rnix irrtKnpl I nrs
                                                   on an aooumuMon ol aKHteauofls of tfw
                          af 25 "C
                tot q> 1

      D.4J {Q.63 i"' 0-4S )
                  <& fi 1
            S-ltfrrt
                                      flJvfwt
                         ftadkjacflva
                         5tr*arn4 m Kentucky.
                         0 < $*
                                                      on wrall
                                                               Pom's f l W6J (tea
                                                   Baa«l «i sunmaiy s< redtoacdva iracw
                                                   ;ippscal:ann la 5 rfvfifl.
                         Bae«J on dasa coflectee on ?4
                         streaf« using radssacUve Iras&r imlhod.
                                                                            Wnllacs (1972)
                                                   Tsivoglou and Naa! 11976)
          0.09
                      25* C
                         Based on data from iO sm^y stfoains in      Grant [1876}
                         Wisconsin uwrg radtoactwa tracer techniqmw:
                                                      1.25 S 5 70 Wmilii
                                                      0.3
                                                            A-33

-------
                                             TABLE A-24. {Continued)
                      '1 at 2®
OJOS'^5 '»t2S-C
           046;

Units
                                       Mays
                                       L-tfaya
                                                      Applicability
                                         3as«J en
                                         an flock
                                            0.0"
                                            0.25 S U<1.€lpB
                                            0.2 <  S £ 3.5
                                            260
                           tracer data
                     , 'Wisconsin antf Illinois;
AuUiorfs}
                                                                                           3raoc(197B)
         L!;L:;I>I qn r«-.nr--r.l
         (x»tfoents l
-------
                                                        •IS

                                                        _
                                                        *
                                                        o
it
9 O
| I
• I
1 1
1 <3
t l
i
XI
i
4
Q
i
b
•z
1 *"
_*


FIGURE A-8. STREAM REAERATION ACCORDING TO SIX FORMULAS
                         A-35

-------
where
    Da
    Db

    T
    H
           =   dissolved oxygen deficit above
               dam (mg/L)
           =   dissolved oxygen deficit below
               dam (mg/L)
           =   temperature (°C)
           =   height through which water
               falls (ft)
    a      =   1.80 in clean water
           =   1.60 in slightly polluted water
           =   1.00 in moderately polluted water
           =   0.65 in grossly polluted water
    b      =   0.70 to 0.90 for flat, broad
               crested weir
           =   1.05 for sharp crested weir with
               straight slope face
           =   0.80 for sharp crested weir
               with vertical face
           =   0.05 for sluice gates with
               submerged discharge
The parameters H, a, and b need to be assigned for
each dam.

A.6.4  Saturated Dissolved Oxygen
       Concentration

The solubility of oxygen in water is dependent on
water temperature,  barometric pressure (i.e.,  alti-
tude), and the concentration of dissolved solids in the
water.  Oxygen saturation concentration decreases
with the increase in temperature and salts and in-
creases  with  barometric pressure.   Freshwater
streams, where temperature ranges from about 0 to
30 °C, typically have oxygen saturation concentra-
tions ranging from 7.5 to 15 mg/L.  The following
equation is recommended to calculate the saturated
dissolved oxygen concentrations as a function of
temperature for freshwater streams:
                 Cs =
                        468
                     316.6+7
                                          (A-11)
where Tis water temperature in degrees Celsius (°C).
This equation is accurate to within 0.03  mg/L com-
pared with the Benson-Krause equation on which the
Standard Methods tables are based (see McCutch-
eon, 1985).

A.7   SEDIMENT OXYGEN DEMAND

A.7.1  Field Measurement of SOD

Direct field  measurement of sediment oxygen con-
sumption upstream and downstream of the discharge
is the preferred approach for obtaining model input
data.   Consistent field  techniques for determining
SOD in natural waters are evolving with investigators
in the southeast United States using an approach
developed by Murphy and Hicks (1986).  The two
basic measurement techniques are (1) in situ cham-
bers and (2) sediment core extraction and laboratory
measurement (see Bowie et al., 1985; Hatcher, 1986;
and Whittemore, 1986).  The in situ method requires
submersion of a chamber on the bottom with periodic
measurements of oxygen to  determine the uptake
rate in the chamber. Laboratory  measurements are
based on a sample core  from the sediment (hopefully
undisturbed) being placed in a well-oxygenated col-
umn with oxygen measurements taken over time to
determine the uptake rate.   Different investigators
have varying opinions on the  relative merits of each
technique; however, the use of in situ chambers, with
minimal disturbance of  the natural  sediments, ap-
pears to be the preferred technique (Murphy and
Hicks, 1986).


A.7.2  Predicting SOD
Projections of the expected water quality impact of a
waste discharge alternative for some future low-flow
condition are normally required during waste load
allocation studies.   Projections  of the  expected
change in SOD that might result from a change in
waste loading to a stream is a complex evaluation
(e.g.,  DiToro et al., 1990).  HydroQual (1987),  for
example, demonstrated that a reduction of total or-
ganic  carbon loading to the Potomac estuary from
92,540 Ib/day (1.56 g C/m2-day) in  1969  to 57,800
Ib/day (0.98 g C/m2-day) in 1985 resulted in a reduc-
tion of the mean SOD from 2.2 to 1.8 g O2/m2-day.
The relationship used to infer the long-term coupling
between carbon loading and SOD in the Potomac is
not a simple  formulation.

Until such time when models (such as that  of Di Toro
et al.,  1990)  are readily available to explicitly couple
paniculate carbon deposition and  sediment oxygen
demand, it is beyond the scope of most waste load
allocation studies to predict future SOD rates with any
credibility since SOD is not linearly proportional to the
waste loading of organic carbon in  freshwater sys-
tems.  For projecting future water quality conditions,
it  is preferable to  use  the  same SOD parameter
values that were used in verification of the  model. At
the least, this approach will result  in a somewhat
conservative projection of future oxygen levels since
SOD is likely to be reduced following improvements
in waste management.
                                             A-36

-------
A.7.3 Literature Values of SOD

Model parameter values for SOD could be specified
using field measurements reported for streams and
rivers with similar hydraulic and waste loading char-
acteristics.   A fairly large body of literature (e.g.,
Phoel, 1982; Butts and Evans, 1978; Butts, 1974) is
available for the analyst to review actual field meas-
urements obtained under a wide range of conditions
that might be similar to the study area for a waste load
allocation.   Table  A-25,  taken  from Bowie  et al.
(1985), summarizes a large number of investigations
of SOD rates that have been reported for streams and
rivers in the literature.  Table A-26, taken from Mur-
phy  and Hicks (1986),  also summarizes a large
number of in situ chamber SOD measurements ob-
tained from 1977 to 1984.

A.8    PHOTOSYNTHESIS AND
        RESPIRATION

A.8.1 Estimation Techniques

Three methods for estimating  photosynthesis (P) and
respiration (R) in waste load allocation modeling stud-
ies are:
    •   Estimation from observed chlorophyll a lev-
        els.
    •   Measurements of diel variations of dissolved
        oxygen concentrations.
    •   Light and dark bottle measurements of dis-
        solved oxygen.
The first method is addressed by the following prob-
lem: given the concentration of phytoplankton in a
stream,  estimate the average daily oxygen produc-
tion. A technique for performing this estimate, devel-
oped by Di Toro (1975), can be found  in Thomann
and Mueller (1987). In summary, the following equa-
tions are used:
                 P= 0.25 Chi a
(A-12)
and
                 Pi = 0.025CWa            (A-13)

in which Chi a is the chlorophyll a concentration in
For the second method, Di Toro (1975) has devel-
oped an analytical method to calculate P based on
the measured diurnal dissolved oxygen range:
       where
          f
          Ka
           =   photo period (0 < f < 1)
           =   stream reaeration rate coefficient
               (day1)
                 fKa(\-e-KsT)
                                          (A-14)
    T      =1 -day period
    A      =   diurnal dissolved oxygen range
               (mg/l_)(max-min)
Note that Equation A-14 can be used to estimate the
diurnal range of dissolved oxygen with an estimate of
P from the first two methods (Thomann and Mueller,
1987). Table A-27 presents a summary of stream
photosynthesis studies compiled  by  Bowie  et al.
(1985).
The light and dark bottle method is described in detail
by Standard Methods (APHA, 1989).  As  shown in
Figure A-9, clear glass (light) and foil-wrapped glass
(dark) bottles are stationed or suspended at various
fixed depths in a stream and filled with water collected
at  their respective depths.  Usually, an attempt is
made in deep rivers to suspend the bottles at least to
the depth of the euphotic zone, taken to be the 1 percent
light penetration depth. Based on Equation A-20, the
depth to 1 percent remaining light can be estimated
as 4.6/Ke.  Since Ke is approximated by I.HSecchi
depth (Equation A-21), the approximate depth of the
euphotic zone is 2.9 times the Secchi depth.
Dissolved oxygen measurements are made at regu-
lar time intervals, with the light bottles that receive the
solar radiation, measuring  net photosynthetic oxygen
production (P-R). The dark bottles,  in the absence of
light,  measure  gross respiration  (R)  as shown in
Figure A-9. It should be noted that:
    •   In contrast to the diurnal method where water
       column and  benthic algae or macrophytes
       contribute to the observed  oxygen balance,
       only the photosynthetic activity of the algae
       in the water column (phytoplankton) is meas-
       ured by this technique. If there are significant
       attached algae or rooted plants, no measure-
       ment of their photosynthetic contribution is
       made.
    •   The estimate of respiration (R) made from
       the dark bottle studies includes both algal
       respiration and bacterial respiration from oxi-
       dation  of carbonaceous  and  nitrogenous
       compounds.
    •   Both P and  R are temperature-dependent.
       Since  they are essentially expressions of
       growth  rate  and respiration rate in oxygen
       equivalents,  the temperature-rate relation-
       ships discussed in Sections 2.3.5 and A.9 for
       growth and respiration apply directly to Pand
                                             A-37

-------
            TABLE A-25.  MEASURED VALUES OF SEDIMENT OXYGEN DEMAND
                                     IN RIVERS AND STREAMS
                                      (After Bowie etal., 1985)
SOD, g02\m2 day
0.022-0.92
0.09 ±0.02 (@12°C)
0.1 5 ±0.04 (@20°C)
0.20 ±0.03 (@28°C)
0.29 ±0.07 (@36°C)
0.18±0.05 (@12°C)
0.55 ±0.22 (@20°C)
0.60 ±0.28 (@28°C)
0.87 ±0.23 (@36°C)
3.2-5.7
0.52-3.6
Environment
Upper Wisconsin river
Eastern U.S. river
Southeastern U.S. river



Fresh shredded tree bark
Aged shredded tree bark
Experimental Conditions
60-hour laboratory core incubation
periodic mixing, 4 °C, dark
45-day incubation of 0.6 liter
sediment in 3.85-liter BOD
dilution water, light




10-liter incubations in aged tap
water, room temperature, light
References
Sullivan et al. (1978)
NCASI (1981)




NCASI (1971)
2-33
0.9-14.1
<0.1-1.4(@20°C)
0.27-9.8
0.1-5.3 (@20°C)
1.1-12.8
0.3-1.4
0.2-1.2
1.7-6.0
1.5-9.8
4.6-4.4
Four eastern U.S. rivers   In situ chamber respirometers,
downstream of paper mill  22-27 °C, light, stirred at varying
discharges              rates

                       Open-ended tunnel respirometer,
                       in situ 22-27 °C, dark
                                NCASI (1978)
Eastern U.S. river
downstream of paper
mill discharge

Northern Illinois rivers
(N = 89 stations)

Six stations in eastern
Michigan rivers
                      New Jersey rivers
                      (10 stations)
                      Swedish rivers
                      Swedish rivers
                      Spring Creek, PA
74 samples from
21 English rivers

Streams
In situ respirometer stirred at
various rates, 9-16 °C, dark,
0 = 1.08

In situ respirometer, dark,
T = 5-31 °C, time = 1 1/2-3 hours

In situ respirometer in stirred
chambers, 15-27 hours, dark,
19-25 °C, 0 = 1.08 0 = constant
for temperature adjustment

In situ respirometer, dark 30
minutes-8 hours, stirred.
Temperature unknown

In situ respirometer, light,
stirred, 0-10 °C

Laboratory incubations,
stirred, dark, 20 °C

Laboratory incubators in dark,
stirred, 20 °C

Laboratory incubation of
cores; 15 °C

Oxygen mass balance
NCASI (1979)



Butts & Evans (1978)


Chiaro & Burke (1980)




Hunter etal. (1973)



Edberg & Hofsten (1973)


Edberg & Hofsten (1973)


McDonnell & Hall (1969)


Rolley & Owens (1967)


James (1974)
                                                     A-38

-------
         TABLE A-26.  SOD RATES MEASURED USING EPA IN SITU CHAMBERS, 1977-1984
                                       (After Murphy and Hicks, 1986)

Location
Indian River, FL
Sykes Creek at Merritt Is, FL

Description
High-salinity lagoon
Saltwater tidal creek subjf

Mean
g 02/m2-hr
.12
=ct to urban .31
SOD Rates
Range
g 02/nr-hr
.10-. 14
.12-. 69

C,V
%
23.6
81.0

Mean
Temp. °C
30.0
31.8
Turkey Creek at Melbourne, FL
Inidan River at Turkey Creek,
Melbourne, FL
Sugarloaf Key, FL
Wilson Creek, SC
Wilson Creek, SC
Wilson Creek, SC
Mobile Bay, AL
St. Andrews Bay, FL
Savannah  River at Savannah, GA
Savannah  River at Savannah, GA
Savannah  River at Savannah, GA
Hillsborough River at Tampa Bay, FL
Hillsborough River at Tampa Bay, FL
Hillsborough River at Tampa Bay, FL
Hillsborough River at Tampa Bay, FL
Sarasota Bay, FL (Summer)
Sarasota Bay, FL (Summer)
Sarasota Bay, FL (Summer)
Sarasota Bay, FL (Summer)
Sarasota Bay, FL (Winter)
Sarasota Bay, FL (Winter)
Sarasota Bay, FL (Winter)
Sarasota Bay, FL (Winter)
Whitaker Bayou, FL (Summer)

Whitaker Bayou, FL (Summer)

Sarasota Bay, FL at Whitaker Bayou
(Summer)
Whitaker Bayou, FL (Winter)

Whitaker Bayou, FL (Winter)

Sarasota Bay, FL, at Whitaker Bayou
(Winter)
Lake Myakka, FL (Summer)

Lake Myakka, FL (Winter)

Lake Myakka, FL (Winter)

Gulf Shores, AL
Gulf Shores, AL
Gulf Shores, AL
Guntersville Reservoir, AL
Guntersville Reservoir, AL
Pickwick Reservoir, AL
Pickwick Reservoir, AL
runoff and STP
Density-stratified tidal creek stream,
residential development, heavy organic
deposit
Estuary

Dead-end canal, hypersaline
Shallow, flashy, piedmont creek
Shallow, flashy, piedmont creek
Shallow, flashy, piedmont creek
Low-salinity estuary
Estuary
Density-stratified, high-velocity river/estuary
Density-stratified, high-velocity river/estuary
Density-stratified, high-velocity river/estuary
Density-stratified river/estuary
Density-stratified river/estuary
Density-stratified river/estuary
Density-stratified river/estuary
Shallow bay, grass flat
Open bay, sandy bottom
Open bay, sandy bottom
Deep bay channel, coarse sand
Shallow bay, grass flat
Open bay, sandy bottom
Shallow bay, grass flat
Deep bay channel, coarse sand
Density-stratified creek, thick  organic
deposits.  Subject to urban runoff and STP
Density-stratified creek, thick  organic
deposits.  Subject to urban runoff and STP
Bay near mouth of tidal creek. Subject to
urban runoff and STP
Density-stratified creek, thick  organic
deposits.  Subject to urban runoff and STP.
Density-stratified creek, thick  organic
deposits.  Subject to urban runoff and STP
Bay near mouth of tidal creek. Subject to
Urban runoff and STP
Shallow freshwater lake with bottom of
dense organic matter
Shallow freshwater lake with bottom of
dense organic matter
Shallow freshwater lake with bottom of
dense organic matter
Gulf Intracoastal Waterway
Gulf Intracoastal Waterway
Gulf Intracoastal Waterway
TVA lake
TVA lake
TVA lake
TVA lake
                                                                            .54
                                                                            .12
                                                                                         .49-.60
                                                                                         .13-.22
                                                                                                    14.3
                                                                                                    36.4
                                                                                                              32.4
                                                                                                              32.0
.12
.10
.08
.12
.12
.05
.027
.061
.036
.41
.18
.11
.17
.165
.227
.156
.128
.122
.106
.077
.086
.155
.140
.264
.154
.240
.160
.049
.070
.124
.086
.070
.112
.163
.099
.037
.099
.10-. 14
.09-. 11
.05-. 14
.07-. 18
.10-. 13
.03-.04
.21-. 32
.38-. 78
.25-. 44
.37-. 45
.16-. 19
.09-. 14
.12-. 19
.148-. 183
.172-. 357
.145-. 166
.107-. 152
.070-. 202
.094-. 116
-
.063-. 110
.050-. 121
.117-. 157
.098-. 604
.138-. 169
.134-. 300
.123-. 214
.020-.064
.063-0.75
.081-. 172
.070-. 109
.066-.078
.072-. 154
.136-. 222
.078-. 120
.028-.056
.093-. 104
23.6
10.7
36.0
30.4
16.7
40.0
22.2
29.5
19.4
8.37
6.55
19.72
14.92
14.9
34.3
9.6
17.5
39.6
9.5
-
27.2
43.0
12.9
66.0
14.3
35.4
26.3
51.0
7.1
36.3
18.8
9.9
32.7
20.0
18.4
27.0
5.7
25.0
23.4
24.6
25.1
28.0
20.0
24.8
21.2
23.2
22.8
22.8
22.9
23.2
29.6
29.0
28.2
28.9
20.3
21.1
21.1
22.0
28.9
27.7
28.4
22.0
20.5
20.5
28.5
19.7
19.2
22.5
21.5
22.0
25.5
24.8
23.5
23.0
                                                       A-39

-------
TABLE A-26. (Continued)
Location
Hillsborough Bay, FL
Hillsborough Bay, FL
Hillsborough Bay, FL

Sowashee Creek, MS
Sowashee Creek, MS
Hillsborough Bay, FL
Tampa Bay, FL
Hillsborough Bay, FL
Tampa Bay, FL
Old Tampa Bay, FL
Tampa Bay, FL
Manatee River, FL
Manatee River, FL
Manatee River, FL
Boone Lake
Boone Lake
Boone Lake
Boone Lake
Boone Lake
Boone Lake
Boone Lake
Boone Lake
Boone Lake
Boone Lake
Calcasieu River
Calcasieu River
Calcasieu River
Calcasieu River
Calcasieu River
Calcasieu River
Calcasieu River
Charlotte Harbor
Pine Island Sound
Ft. Loudoun Res.
Ft. Loudoun Res.
Ft. Loudoun Res.
Tellico Reservoir
Tellico Reservoir
Description
Density-stratified bay, dynamic murk
Density-stratified bay, sand/silt
Density-stratified bay at river mouth and
STP density
Shallow creek, upstream of STP
Shallow creek, downstream of STP
Shallow bay, dark sand & silt
Open Bay, sand
Shallow bay, dark sand & silt
Open bay, sand
Shallow bay, nearshore
Shallow bay, nearshore
Tidal stratified river
Tidal stratified river
Tidal stratified river
TVA lake, sludge bank
TVA lake, upstream of sludge bank
TVA lake, downstream of sludge bank
TVA lake, HRM19.7
TVA lake, HRM 26
TVA lake, HRM 29
TVA lake, HRM 31
TVA lake, WRM 3
TVA lake, WRM 7
TVA lake, WRM 11. 2 5
Stratified river/estuary
Stratified river/estuary
Stratified river/estuary
Stratified river/estuary
Stratified river/estuary
Stratified river/estuary
Stratified river/estuary
Estuary; seasonal stratification
Estuary; shallow
TVA Lake, TRM-608
TVA Lake, TRM-608
TVA Lake, TRM-638
TVA Lake, LTRM-16.5
TVA Lake, LTRM-21
Mean
g 02/m2-hr
.033
.046
.094

.097
.102
.131
.074
.131
.195
.111
.203
.077
.181
.075
.346
.072
.041
.031
.064
.078
.109
.050
.023
.072
.027
.027
.027
.067
.055
.027
.035
.062
.044
.046
.044
.043
.048
.047
SOD Rates
Range
g 02/nr-hr
.025-.048
.039-.052
.085-. 100

.079-. 116
.090-. 124
.074-. 176
.061-.087
.074-. 176
.161-. 276
.107-. 114
.137-. 284
.064-. 107
.160-. 193
.062-.087
.171-. 465
.071-.073
.037-0.44
.018-.036
.055-.077
.062-. 10
.066-. 14
.040-.060
.020-.027
.64-. 81
.019-.031
.019-.039
.019-.039
.061 -.083
.038-.073
.024-.038
.029-.043
.054-.070
.038-.048
.045-.049
.033-.050
.038-.051
.042-.054
.028-.065
c,v
%
39.3
20.2
8.4

19.2
18.7
33.2
24.8
33.2
21.5
3.2
26.5
25.9
10.2
13.7
44.7
1.4
8.8
11.9
12.1
30.7
30.7
17.3
13.3
8.4
20.1
32.8
22.0
13.9
27.3
22.3
14.6
10.4
11.8
5.0
17.2
13.9
12.5
33.3
Mean
Temp. °C
16.5
16.0
18.0

26.2
29.5
30.2

30.2
31.0
31.0
30.5
31.5
32.0
31.0



14.0
10.5
10.5
10.5
11.8
14.3
24.8
24.1
24.0
24.2
24.2
26.0
24.5
23.0
17.0
19.5
24.2
24.8
24.2

18.1
         A-40

-------
 TABLE A-27.  PHOTOSYNTHETIC OXYGEN PRODUCTION AND RESPIRATION RATES IN RIVERS
                                  (After Bowie et al., 1985)
River
Grand, Michigan
Clinton, Michigan
Truckee, Nevada
Ivel, Great Britain
Flint, Michigan
North Carolina Streams
Laboratory Streams
Charles, Massachusetts
Shenandoah, Virginia
Baker, Virginia
Rappahannock, Virginia
S. Fork Rivanna, Virginia
Rivanna Virginia
South, Virginia
Mechums, Virginia
Havelse, Denmark
Experimental Channels
T Pm Pav
°C g/m2-day g/m2-day
28 12.7-37.6 4.4-13.0
21 13.2-22.9 4.2-7.3
28 12.9-26.0 4.8-9.6
16 24 9.0
28 4.0-40.0 1.3-18.
9.8
3.4-4.0
19-25 - 0.0-12.0
23 4.8-17.4
0.45
6.1
2.1
2.3
2.0
1.3
0.2-25.9C
9-24 5-45 1.5-14.8
R
g/m2-day
9.3-12.73
9.3a
3.6-6.2a
. ra
4.6
4.-203
21. 5b
2.4-2.9b
0.0-36.b
0.9-5.93
1.9b
7.3b
3.4b
5.4b
5.3b
2.6b
4.8-22.9b
2.6-10.7b
Reference
O'Connor and Di Toro (1970)
O'Connor and Di Toro (1970)
O'Connor and Di Toro (1970)
O'Connor and Di Toro (1970)
O'Connor and Di Toro (1970)
Thomas and O'Connell (1966)
Thomas and O'Connell (1977)
Erdmann (1979a, b)
Deb and Bowers (1983)
Kelly etal. (1975)
Kelly etal. (1975)
Kelly etal. (1975)
Kelly etal. (1975)
Kelly etal. (1975)
Kelly etal. (1975)
Simonsen & Harremoes (1978)
Gulliver etal. (1982)
aAlgal respiration only.
bTotal community respiration.
cMeasurements were made over the period of 1 year, and solar radiation varied by more than a factor of 10.
                                           A-41

-------
                              EJGHT
                              BOTTLB
                                          *  O
                                             *f.  "*
                                          c  O
                                                  I
          12
          10 -

               Chi a - 14 uf/L
             S     Id     fit
                                                          12
                                                       j  10 -
                                                                                 pa,
                                                                          TIME
EXAMPLE CALCULATION (for each bottle)
(1) Slope of light DO data
      6 hr      1 day
(2) Slope of dark bottle DO data
   (6.1-7.5) v  (24 hr)    , A   ,.   .
   	 X	 = 1.4 mq/L-day
     10 hr     1 day        M     J
(3) R = Slope of dark bottle data = 1.4 mg/L-day
(4) P = Slope of light bottle + Slope of dark bottle
   P = 22.0 + 1.4 = 23.4mg/L/day
(5) Calculate P for each depth sampled
   (A, B, C, D) and plot as shown
(6) Equate area  under curve Ai to A2 to determine
   depth averaged production rate
                                                                  P Cmi/L) 0,
                                                      X

                                                      Q  C -
                          FIGURE A-9. LIGHT AND DARK BOTTLE STUDIES
                                  (After Thomann and Mueller, 1987)
                                                  A-42

-------
       R measurements derived from light and dark
       bottle tests.
The  productivity vs. depth  relationship developed
from the  light and dark bottle test data, shown  in
Figure A-9, provides a determination of the depth-av-
eraged primary productivity rate. The extent to which
it is time-averaged depends on the period of the day
covered by the measurements.  Because of the sig-
nificant variations in Pwith depth and time, care must
be taken that light and dark bottle test results are
interpreted correctly.
When conducting the light and dark bottle work, it is
essential  that the light bottles not be allowed to pro-
gress to  the point where saturation is exceeded.
Losses of dissolved oxygen during sample handling
and performing  the analytical measurements would
introduce errors into the test results. The maximum
hourly increase in dissolved oxygen in the light bottle
can be computed as follows:
 .„
AC =
                    Gmax 1 .066(    '
                   (1000) (24)
                                  Ch
                                          (A-15)
where
    AC
            =  maximum hourly increase in
               dissolved oxygen (mg/L-hr)
    aoc    =   stoichiometric ratio of oxygen to
               carbon = 2.67 (mg  O2/mg C)
    ac     =   stoichiometric ratio of carbon
               to chlorophyll a (mg C/mg Chi)
    Gmax  =   maximum algal growth rate (day-1)
    T     =   water temperature  (°C)
    Chi    =   instantaneous chlorophyll a
               concentration (|ig/L)
Equation A-15 can be  used to estimate appropriate
sampling intervals  and maximum  duration  of  light
bottle measurements.

A.8.2  BOD Deoxygenation in Bottles

The following example illustrates how to calculate the
algal respiration from the light and dark bottle results
with significant BOD: The initial DO in a light and dark
bottle test is 7.0 mg/L. After 1 day, the DO in the dark
bottle is 2.0 mg/L and the DO in the light bottle is 9.0
mg/L.  The BODs of the water sample without algae
is 10 mg/L and Ki  is 0.3 day"1. Using Equation 2-5,
                        - e
yielding BODU equal to 12.9 mg/L.  Thus, BODi can
be calculated as
                                                     = 1 2.9 [1 - e ~
                                                                                 = 3.34
                                                That is, the amount of oxygen consumed by bacteria
                                                for BOD decay is 3.34 mg/L. The algal respiration is
                                                then equal to 7.0 - 2.0 - 3.34 or 1.66 mg/L.

                                                A.9   PHYTOPLANKTON KINETIC
                                                       RATES

                                                A.9.1  Growth Rate

                                                Eppley (1972) summarized algal growth data from a
                                                variety of sources as a function of temperature and
                                                developed the following equation:
                                                                GT= Gmax©
                                                                           T-20
                                                                                 (A-16)
                                                where
                                                    GT
Gmax

0

r
temperature-adjusted growth rate
(day1)
maximum growth rate at 20°C
(day1)
                                                      constant for temperature
                                                      adjustment
                                                      temperature (°C)
                                        Both  GT and Gmax are specific  growth rates under
                                        optimum light and  nutrient conditions.  Reported
                                        ranges for Gmax and 0 are:

                                                   Gmax = 1 to 3 day"1 at 20°C
                                                      0=1.01 to 1.18

                                        As a  first approximation, Gmax = 1.8 day'1 and 0 =
                                        1.066 may be used (Thomann and Meuller, 1987).
                                        Thus,
                                                              GT= 1.8 (1.066) (T~20)
                                                                                 (A-17)
                                                Equation A-17 is shown in Figure 2-5.  This relation-
                                                ship (Eppley, 1972) can be viewed as an envelope
                                                representing the maximum growth rate at any tem-
                                                perature, under optimum light and nutrient condi-
                                                tions.

                                                A.9.2   Light Effect on Phytoplankton Growth

                                                A depth- and time-averaged effect of available light
                                                energy on phytoplankton growth rate can be obtained
                                                (Di Toro et al., 1971), by integrating the light intensity
                                                relationships over depth and time. This reduces to
                                                      KeHT
                                                                           ' - e
                                                                                -OC2N
                                                                                          (A-18)
                                                where
                                             A-43

-------
                           -KeH
                      _
                  oci =  is
                    0.2=  -
                      IT
                      Is
    r\_      =   light limitation factor
    f      =   photoperiod - daylight fraction of
               averaging period (day)
    T      =   averaging period (day)
    Ke     =   light extinction coefficient (m~1)
    H      =   average depth of segment (m)
    la      =   average of incident light on water
               surface over a 24-hour day (ly/day)
     IT     =   average of incident light over
               photoperiod (=la/f)0y)
    Is      =   saturated light intensity (ly/day)
               (see Figure 2-6)

The full expression for algal growth can be synthe-
sized from Equations A-16, A-17, and  A-18 as fol-
lows:
Gp=Gmax1.066(T-20^
                     AS

         DIN        DIP
      mm
                                   Si
          Kmn + DIN ' Kmp + DIP ' KSi + si    (A-19)
Solar radiation is measured routinely at selected
weather stations in the United States.  It is usually
reported as langleys (ly), which is a measure of the
total radiation of all wavelengths that reach the sur-
face of the earth.  One ly is equal to 1 g cal/cm2.
Algae  and other  photosynthetic plants respond to
solar radiation in the visible  range of the  spectrum.
Visible light energy  was historically  measured in
terms of intensity as footcandles. A common conver-
sion used in calculations is  2000  ft-candles = 350
ly/day.  Contemporary primary  production studies
generally report incident  light intensity with units of
jj£m~2- sec (micro einstein)  where the appropriate
conversion factors are:
                                                    TABLE A-28. TYPICAL LIGHT EXTINCTION
                                                               COEFFICIENTS
Ke = 1.5/SD
(m1)
0.02 - 0.06

0.2
0.2 -0.5
0.5-5.0
Secchi
Depth (m)
30-80

8-10
3 -8
0.3-3.0
Types of Waterbodies
Clear, mid-ocean
oligotrophic waters
Clear lake waters
Coastal zone marine waters
Rivers and estuaries
                                             condition for algal growth when compared with turbid
                                             and deep waters. KeH, a dimensionless number, is
                                             also referred to as the light extinction factor.
                                             Typical /
-------
The nonvolatile suspended solids (the inorganic par-
ticulates) both absorb and scatter the light, whereas
the organic detritus and phytoplankton  chlorophyll
mainly absorb the light.  Di Toro has shown that
Equation A-22 applies to Ke values of approximately
less than 5.0 m"  and phytoplankton biomass up to
approximately 15 jj,g/l_.

A.9.3  Death Rate

The endogenous respiration rate,  Dpi (Equation 2-
16) is given approximately by
               Dpi = [IR (1.08)
                            T-20
(A-23)
where |j,f? varies from 0.05 to 0.25 day"  (Thomann
and Mueller, 1987).  A value of 0.15 day"1 is usually
used as a first approximation.

A.9.4  Settling Rate

Phytoplankton settling rate is estimated  by dividing
the settling velocity by the stream depth. Phytoplank-
ton settling velocities are presented in Table A-29.
Additional data are available in a review by Smayda
(1970).  Some phytoplankton such as blue-green al-
gae develop gas vacuoles, which result in buoyancy
and subsequent aggregation at the water surface.
The proliferation of such species is a particular prob-
lem because the settling velocity may be zero or even
negative and phytoplankton tend to remain  in the
water column or at the surface (e.g., 1983 Microcystis
bloom in Potomac estuary).

A.9.5  Biomass Stoichiometry

Dry weight biomass  is related to the major nutrients
(carbon, nitrogen, and phosphorus) and chlorophyll a
through stoichiometric ratios that give the ratios of
each nutrient to the total biomass.   Typical algal
nutrient compositions are summarized in Table A-30.
Ratios  for different  algal groups or species (e.g.,
blue-green, diatom, etc.) can be found in the literature
(O'Connor et al., 1973; Bowie et al., 1985) but are not
included in Table A-29 as this manual addresses the
algal modeling only on a total population basis.

A.9.6  Half Saturation Constants

Half saturation constants are required to describe the
nutrient dependence of the phytoplankton growth rate
(Equation A-19). Table A-31 summarizes an exten-
sive compilation of phytoplankton half saturation con-
stants  for  populations of diatoms, flagellates,
chlorophytes and chrysophytes.
A.10  NUTRIENT RECYCLING RATES

A.10.1 Phosphorus Mineralization Rate

The rate of transformation from paniculate phospho-
rus to orthophosphate in the water column ranges
from 0.02 to 0.10 day"1 (Bowie etal., 1985). As a first
approximation, a value of 0.03 day"1 may be used.

A.10.2 Organic Nitrogen Hydrolysis Rate and
       Ammonia Nitrification Rate

Table A-32 presents the rate coefficients for nitrogen
transformations reported in  a number of modeling
studies.
       A.11   SEDIMENT NUTRIENT RELEASE
              RATE

       Sediment nutrient releases measured in the field are
       usually reported in  mg/m2-day.  Table A-33 (from
       Thomann and Mueller, 1987) shows some reported
       nutrient fluxes from the sediments under both aerobic
       and anaerobic conditions. When the overlying water
       is anaerobic, the flux of phosphorus from the sedi-
       ment increases significantly as a result of increased
       diffusion between the sediment and the water. Such
       increased diffusion results from changes in the iron
       complexes at the water-sediment interface.   Table
       A-34  presents data for two stations in the Peconic
       Bay in Long Island (Garber, 1990).
       Since data are usually not available to characterize
       sediment nutrient processes for many streams and
       rivers, aerobic sediment flux rates of ammonia and
       phosphate can be estimated  as the stoichiometric
       equivalent of the biochemical component of SOD
       using  Redfield ratios (by weight) for O:C:N:P
       (109:41: 7.2:1) (Redfield etal., 1963). UsingtheO:N
       ratio of 109 mg 62: 7.2 mg N benthic regeneration of
       ammonia can be estimated as:

       jNHs = SOD [1000 mg O2/g 02] [1 mg N/15.14 mg
                            02]

       where SOD is in units of g O2/m2-day and jNHs has
       units  of mg N/m~2-day.  Di Toro (1986) has summa-
       rized  paired measurements of SOD and jHNs flux to
       substantiate the  relationship of SOD and jHNs under
       aerobic conditions.
       Although the sediment-water interactions for phopho-
       rus recycling are complex, Redfield Stoichiometry is
       appropriate for a preliminary estimate of phosphate
       flux from the sediments  under  aerobic conditions.
                                             A-45

-------
  TABLE A-29. TOTAL PHYTOPLANKTON SETTLING VELOCITIES
                        (After Bowie etal., 1985)
Settling Velocity (m/day)	References
      0.05 - 0.5                       Chen and Orlob (1975)
                                     TetraTech (1976)
                                     Chen (1970)
                                     Chen and Wells (1975, 1976)

      0.05 -0.2                       O'Connor et al. (1975, 1981)
                                     Thomann et al. (1975, 1979)
                                     Di Toro and Matystik (1980)
                                     Di Toro and Connolly (1980)
                                     Thomann and Fitzpatrick (1982)

      0.02-0.05                       Canale etal. (1976)

         0.4                         Lombardo(1972)

      0.03-0.05                       Scavia(1980)

         0.04                         Bierman etal. (1980)

      0.2 - 0.25                       Youngberg (1977)

      0.04 - 0.6                       Jorgensen (1976)
                                     Jorgensen et al. (1978, 1981)

      0.01 - 4.0                       Baca and Arnett (1976)

       0.0 - 2.0                        Chen and Orlob (1975)
                                     Smith (1978a)

      0.15-2.0                       Duke and Masch (1973)
                                     Roesneretal. (1977)

       0.0-0.2                        Brandes (1976)

      0.0 - 30.0                       Jorgensen (1979)
                                  A-46

-------
 TABLE A-30. NUTRIENT COMPOSITION OF ALGAL CELLS - RATIO TO CHLOROPHYLLa (as \igl\ig)
                                     (After Bowie et al., 1985)
C N P Si
Algal Type Cr|l a Chi a Ch la Chi a
Total Phytoplankton 50 - 1 00 7-15 0.5-1.0




0.5
7.2 0.63
25-112a 7-29a 1.0a
10-100a 2.7-9.18
Diatoms 100 1.-15 0.5-1.0 40-50


0.5
5.-200b
18-500a 2.2-74.6a 0.27-1 9.2a 2.4-50.7a
Green Algae 20-1 00b
Blue-green Algae 14-67b
Dino flagellates 275 19.3
Dry Weiaht (ma/ma DW) percentaae of five phvtoplankton function aroups. (O'Connor et al.
Total
Phytoplankton % Carbon % Nitrogen
Average % SD(N=5) 39 % 4.4 6.1 % 1 .9
Range 19150 2.719.1

References
Thomannetal. (1975, 1979)
O'Connor etal. (1981)
DiToro&Matystik(1980)
DiToro & Connolly (1980)
Salas and Thomann (1978)
Salisbury etal. (1983)
Larsenetal. (1973)
Jorgensen (1979)
O'Connor etal. (1981)
DiToro & Connolly (1980)
Di Toro & Matystik (1 980)
Thomann etal. (1979)
Salisbury etal. (1983)
Baca& Arnett (1976)
DiToro etal. (1971)
Baca& Arnett (1976)
Baca& Arnett (1976)
O'Connor etal. (1981)
, 1973)

% Phosphorus
1.6% 0.8
0.4I3.3
3 Literature values.
b Model documentation values.

N = available inorganic nitrogen concentration, mass/volume
C = available inorganic carbon concentration, mass/volume
Si= available inorganic siliconcentration mass/volume.
                                               A-47

-------
TABLE A-31. LITERATURE SUMMARY OF PHYTOPLANKTON HALF-SATURATION CONSTANTS
                    FOR NITROGEN, PHOSPHORUS, AND SILICA
                           (After Tetra Tech, Inc., 1992)
Species
mixed/natural populations
mixed/natural populations
mixed/natural populations
mixed/natural populations
Skeletonema costatum
Skeletonema costatum
Skeletonema costatum
Skeletonema costatum
mixed/natural populations
mixed/natural populations
mixed/natural populations
mixed/natural populations
Dunalliella tertiolecta
Dunalliella tertiolecta
Dunalliella tertiolecta
Dunalliella tertiolecta
Monchrysis lutheri
Monchrysis lutheri
Monchrysis lutheri
Monchrysis lutheri
Taxa
DIATOM:AVG
DIATOM:MIN
DIATOM:MAX
DIATOM:OBS
DIATOM:AVG
DIATOM:MIN
DIATOM:MAX
DIATOM:OBS
FLAG ELL: AVG
FLAGELLMIN
FLAGELLMAX
FLAGELL:OBS
CHLORO:AVG
CHLORO:MIN
CH LORD: MAX
CHLORO:OBS
CHRYSO:AVG
CHRYSO:MIN
CHRYSO:MAX
CHRYSO:OBS
N03
HgN/L
15.25
1.40
71.40
43
6.30
5.60
7.00
2
64.12
1.40
144.20
5
7.14
1.82
19.60
4
7.14
5.88
8.40
2
NhU-N
ngN/L
22.46
0.28
130.20
30
19.74
6.16
50.40
4
56.47
15.40
79.80
3
2.17
0.28
4.90
6
4.37
0.42
7.42
9
Si(OH>4
jigSi/L
55.00
2.46
158.20
39
24.98
11.76
50.68
9



ND



ND



ND
PCM-P
ngP/L
27.46
10.00
53.32
5



ND



ND



ND



ND
      TABLE A-32. NITROGEN TRANSFORMATION RATES IN WATER COLUMN (day-1).
                             (After Bowie etal., 1985)
PONa to NH3
0.035
0.03
0.03
0.03
0.075
0.1-0.15
0.1
0.003
0.1
0.1-0.4
0.02-0.04
NH3 to N02






0.05
0.02
0.02
0.1-0.5
0.1-0.5
NH3 to N03
0.04

0.12
0.20
0.09-0.13
0.05-0.15





NO2 to NOs







0.25
0.25
5.0-10.0
3.0-10.0
Reference
Thomann etal. (1975)
Thomann etal. (1979)
Di Toro and Connolly (1 980)
Di Toro and Matystik (1 980)
Thomann and Fitzpatrick (1982)
Lung (1986a)
Lung and Paerl (1988)
Tetra Tech, Inc. (1980)
Porcella et al. (1983)
Bacaetal. (1973)
Bacaand Arnett (1976)
PON = particulate organic nitrogen.
                                    A-48

-------
                      TABLE A-33. SEDIMENT NUTRIENT RELEASE RATES
                                (After Thomann and Mueller, 1987)
                       Flux-Aerobic Conditions
                      	(mg/m2 -day)	
                            Flux-Anaerobic Conditions
                           	(mg/m2 -day)	
 Location
Total Dissolved
 Phosphorus
                                     NH3-N
Total Dissolved
 Phosphorus
                                                               NH3-N
Si-silicon
Reference
Muddy River, Boston,
MA
Lake Warner, Amherst,
MA
Lake Ontario
Lake Erie
Western Basin
Central Basin
Eastern Basin
White Lake, Muskegon
Co., Ml
Cape Lookout Bight,
NC
LaJolla Bight

Potomac Estuary


9.6 (96 Max)

1 .2 (26 Max)

0.2

6.0 44
3.0 30
2.0 22
34 32

40 (winter)
325 (summer)
2.4
(-13to 16)
1-10


Fillos and
Swanson (1975)
Bannerman
etal. (1975)


DiToro and
Connolly (1980)

297 Freedman and
Canale (1977)
Martens et al.
(1 980)
Hartwig (1975)

Calendar and
Hammond
(1982)
       TABLE A-34. SELECTED MEASUREMENTS
          OF SOD AND BENTHIC AMMONIA
              FLUX IN PECONIC BAY,
           JULY 1989  (after Garber, 1990)
                             rates of benthic nitrification and benthic denitrification
                             are summarized in Table A-35.
Region
Gardiner Bay
Little Peconic
Bay
Station
BT-6A
BT-3
SOD
(g 02/m2
-day)
0.76
0.8
NH3 flux
(gN/m2
-day
14.6
33.3
Temp
(°C)
23.7
24.8
Using the N:P ratio of 7.2 mg N:1.0 mg P, benthic
regeneration of phosphate can be estimated as:

         j PCM =JHN3  (1mg P/7.2 mg N)

wherejPO4 is in units of mg P/m2-day and jNHs has
units of mg N/m2-day.  In addition to benthic release
of nitrogen, benthic uptake of ammonia and nitrate
can be a potentially significant component of the
overall nitrogen balance in a stream  or river (e.g.,
Williams  and  Lewis, 1986).  Reported measured
                                             A-49

-------
  TABLE A-35.  REPORTED RATES OF BENTHIC NITRIFICATION AND BENTHIC NITRATE LOSS
      (as mg N/m2-day)(Negative values indicate water column loss to the sediments; positive
                       values indicate sediment source to the water column)

Study Site	Range	Reference	

Benthic Nitrification

Lake Mendota                          -540to-900                Kaushiketal. (1981)
Sewage-enriched stream                   Oto-150                  Kaushiketal. (1981)
Laboratory stream                        -29a to - 69b                Kaushik et al. (1981)


Benthic Nitrate Loss

Sewage effluent/canals                   -913c                      Kaushik etal. (1981)
Swifts  Brook/Ontario                     -480                       Kaushik etal. (1981)
Duffin Creek/Ontario                     -40 to-300                  Kaushik etal. (1981)
Laboratory columns                      -61 to-166                  Kaushik etal. (1981)
Silt-enriched columns                    -100to-251                 Kaushik etal. (1981)
Sand/gravel columns                     -20 to-60                   Kaushik etal. (1981)
Streams                               -50a to-90b                 Kaushik etal. (1981)
Upper Potomac estuary                   -266 to +23                 MWCOG (1987)
Gunston Cove, Potomac                  .35                       Seitzinger (1988)

Notes:
3 Absence of tubificid worms.
b Presence of tubificid worms enhances nitrification and denitrification/nitrate loss rates.
c Reported units are mg NO3-N/m2-day.
               TABLE A-36. REPORTED VALUES FOR ATMOSPHERIC REAERATION
                      TEMPERATURE COEFFICIENT (After Bowie et al., 1985)

              Temperature
              Coefficient, B	Reference	


                 1 -047                          Streeter (1926)
                 1-0241                         Elmore and West (1961)
                 1-0226                         Elmore and West (1961)
                 1 -020                          Downing and Truesdale (1955)
                 1 -024                          Downing and Truesdale (1955)
                 1-016                          Downing and Truesdale (1955)
                 1-016                          Streeter (1926)
                 1.018                          Truesdale and Van Dyke (1958)
                 1.015                          Truesdale and Van Dyke (1958)
                 1 -008                          Truesdale and Van Dyke (1958)
                 1.024                          Churchill etal. (1962)
                 1-022                          Tsivoglou (1967)
                 1-024                          Committee on Sanitary Engineering Research (1960)
                                                 A-50

-------
A.12  TEMPERATURE EFFECTS ON                       „   „  nr-2o            ,A  N
       REACTION RATE COEFFICIENTS                   "T= Kw &               (A 24)
                                              in which KT and Kzo are values of the kinetic coeffi-
Water temperature plays an important role in affect-  cient at temperatures T and 20 °C, respectively.
ing many first-order kinetic coefficients because the  Tables A~36 tnrou9n A-41 Present ran9es of © values
reactions are temperature-dependent following the  for a varietV of kmetlc coefficients.
usual van't Hoff-Arrhenius temperature correlation:
                                           A-51

-------
                 TABLE A-37. VALUES OF THE TEMPERATURE COEFFICIENT
                             FOR CARBONACEOUS BOD DECAY
                                   (After Bowie et al, 1985)
Temperature coefficient, Q
                               Temperature
                               Limits (°C)
                                                                          Reference
1.047
1.05
Chen (1970)
Harleman et al. (1977)
Medina (1979)
Genet etal. (1974)
Bauer etal. (1979)
JRB Associates (1983)
Bedford etal. (1983)
Thomann and Fitzpatrick (1982)
Velz(1984)
Roesneretal. (1981)

Crim and Lovelace (1973)
Rich (1973)
1.03-1.06
1.075
1.024
1.021-1.06
1.04
1.05-1.15
(0-5)-(30-35) Smith (1978b)
Imhoff etal. (1981)
MWCOG (1982)
Bacaand Arnett (1976)
Bacaetal. (1973)
Di Toro and Connolly (1980)
5-30 Fair etal. (1968)
              TABLE A-38. TEMPERATURE COEFFICIENT, 0, FOR NITRIFICATION
                                   (After Bowie etal. 1985)
Reference
Stratton (1 966)
Knowlesetal. (1965)
Buswelletal. (1957)
Wild etal. (1971)
Bridle etal. (1979)
Sharma and Ahlert (1977)
Ammonia Oxidation
1.0876
1.0997
1.0757
1.0548
1.1030
1.0689
Nitrite Oxidation
1.0576
1.0608
-
-
-
1.0470
Laudelout and Van Tichelen (1960)

Mean
                                        1.0850
     1.0689

     1.0586
                                            A-52

-------
          TABLE A-39. TYPICAL VALUES OF THE TEMPERATURE COEFFICIENT FOR
                        SOD USED IN WATER QUALITY MODELS
                                (After Bowie et al., 1985)
      Model
                        0
                                       Qioa
          Reference
DOSAG-3
QUAL-II
Vermont QUAL-II
Lake Erie Model
WASP
WASP
LAKECO
WQRRS
ESTECO
DEM
EAM
EAM
USGS - Steady
AQUA-IV
EXPLORE-I
Laboratory/Field Studies
1.047
1.047
1.047
1.08
1.08
1.1
1.02
1.02-1.04
1.02-1.04
1.04
1.02
1.047
1.065
1.02-1.09
1.05
1.040-1.130
1.58
1.58
1.58
2.16
2.16
2.59
1.22
1.22-1.48
1.22-1.48
1.48
1.22
1.58
1.88
1.22
1.63
1.5-3.4
Duke and Masch (1973)
Roesneretal. (1977)
JRB Associates (1983)
Di Toro and Connolly (1980)
Thomann and Fitzpatrick (1982)
O'Connor etal. (1981)
Chen and Orlob (1972, 1975)
Smith (1978)
Brandes (1976)
Genet etal. (1974)
Bowie etal. (1980)
Tetra Tech (1980); Porcella et al.
Bauer etal. (1979)
Baca and Arnett (1976)
Bacaetal. (1973)











(1983)



Zison etal. (1978); Whittemore (1984)
1 Qio = ratio of K (20°C)/K(10°C) = e10from K(T) = K2o OT 20
forT = 10°C
 TABLE A-40. TYPICAL EXPERIMENTAL VALUES OF THE EFFECT OF TEMPERATURE ON SOD
                               (After Whittemore, 1986)
Reference
Edbergand Hofsten (1973)


Edwards and Rolley (1965)
Karlgren (1968)
McDonnell and Hall (1969)
Pamatmat (1971)
Thomann (1972)
Fair etal. (1941)
Baity (1938)
Masch etal. (1971)
Range, °C
5-15
10-20
15-25
10-20
2-22
5-25
5-10
10-30
10-25
22-29
_
Coefficient, 0
1.130
1.180
1.040
1.077
1.090
1.067
1.088
1.065
(1.07)a
(1.05)a
1.0159
 Estimated based upon author's conclusion.
                                       A-53

-------
     TABLE A-41.  TEMPERATURE DEPENDENCE OF BENTHIC AMMONIA REGENERATION
                                 IN ESTUARINE WATERS
Location
Narragansett Bay
Peconic Bay
mean
range (N = 8)
Chesapeake Bay
(Anoxic mainstem)
Qio
4.48
7.0
0.8-11.6
3.0
0
1.16
1.215
0.98-1.28
1.116
Reference
Kremerand Nixon (1978)
Garber (1990)
Garber (1990)
Note:    Relationship of Qio to & and reaction rates at 10°C (Kio) and 20°C (K2o)
       Qio =  K2o/Kio
       0   =  10allogQ10
       0   =  exP(0.1 In Qio)
                                         A-54

-------
APPENDIX  B:   SAMPLE TOTAL  MAXIMUM DAILY
                            LOAD ANALYSES
B.1     INTRODUCTION
Two studies, the Rivanna River study and the Willamette
River study, are included in this appendix to illustrate the
application of WASPS and QUAL2E—two EPA supported
stream water quality  models. Additionally, an analytical
solution approach is included in the Rivanna River study to
demonstrate how different water quality parameters can be
directly calculated, without the use of a complex simulation
model, for a relatively simple BOD-DO and eutrophication
problem. The Willamette River Basin modeling study high-
lights the use of the QUAL2E model in assessing dissolved
oxygen (DO), nutrients, and phytoplankton biomass for a
large river system in Oregon.

Readers are cautioned that site-specific data must be used
when  performing TMDL analyses, and that values  pre-
sented in this (or any  other) example must not be substi-
tuted for site-specific data.  When  such data are not
available, applicable values should be developed by follow-
ing the procedures detailed  in the text.
B.2    RIVANNA RIVER STUDY
B.2.1   Problem Setting

This example is based on the earlier example problem
presented in the Technical Guidance Manual for Perform-
ing Waste Load Allocations (USEPA, 1983) in calculating
simple biochemical oxygen demand and dissolved oxygen
conditions in streams.  This example extends further to
include a eutrophication problem and assessment of vari-
ations in channel geometry.  Addressing the algal growth
as a component of this example provides a more compre-
hensive analysis of production and respiration processes
and their effect on dissolved oxygen in rivers.

A city of approximately 60,000 people discharges its waste-
water into a relatively small river, Rivanna River, with an
average annual flow of about 250 cfs. The city's wastewa-
ter is presently treated by a trickling filter plant that provides
about 85 percent BOD removal and has reached its design
capacity of 7.5 mgd. The population is projected to increase
by more than 50 percent to 92,000 people (with a range of
75,000 to 120,000 people) by the year 2000. Expansion
of the treatment  plant to a capacity of 11.5 mgd and
provision  of an activated sludge system for secondary
treatment has been proposed.

Rivanna River, for 60 miles downstream of the treatment
plant outfall, is classified  as B1, which has a designated
water use of fish  and wildlife propagation. The pertinent
State water quality standards for this example are a mini-
mum DO level of 5.0 mg/L, a maximum un-ionized ammo-
nia concentration as specified by EPA Ambient Water
Quality Criteria for Ammonia (USEPA, 1984), and a narra-
tive nutrient criteria as specified in the State water quality
standards.  The river  is used locally for fishing and is
bordered  by several campgrounds and a State park. Ap-
proximately 30  miles downstream of the treatment plant
outfall is a wider, slow  moving section of the river, which
under low flow conditions has experienced algal blooms,
with chlorophyll a  proliferation in the range of 30-40 ug/L.
Occasional violations of DO and un-ionized ammonia have
been observed  downstream of the wastewater treatment
plant. The watershed  consists of approximately 60 per-
cent agricultural, 35 percent forested, and 5 percent urban
areas under  existing conditions.  The size of the urban
portion of the watershed is 9.4  square miles which is
projected to increase to  14.4 square  miles  under future
conditions (2000 A.D.).  Future land use conditions are
expected to show a conversion of a portion of agricultural
land  to urban areas. This more developed watershed is
estimated to consist of 58 percent agricultural, 35 percent
forested, and 7 percent urban areas.  A summary of the
problem setting and treatment plant data is  presented in
Figures B-1 and B-2.

B.2.2   River  Characteristics

The river flow is gaged by the United States Geological
Service (USGS) 5 miles  upstream of the treatment plant
discharge. The average monthly flows for a 30- year period
are summarized in Figure B-3(A).  The
                                               B-1

-------
       POPULATION PROJECTION:         •
                                                                     -
            1900     If25    :S*6
                             mm USE
1980   2000     29W
                                         ftW
5,
              DC- CcrowtraEcn • O^-jaw ^an 5 1 ^
              -JfM€P-z«I Atnncnia • L*SS sfan o -32
               and Use
       a.     Active and locally poputof fishery
       b.     Several campgrounds aod     parks have river as an attfftctton
             Limited water quality     thaw occasional violations ot
             DO and un-ionized ammonia Biandards under low flow
             conditions; parlodic      Wo0ms occur in the wide
             section of the stream.

                                               B-2

-------
A/TREATMENT FACILITIES
Tn ck! ing filler plant constructed in
Plant -now at design capacity of 7.5 MGO
Effluent does not meet iNPQES permit for
          treatment
        Present:
        Proposed;   Activated sludge s^aiam to provide s-econdary treatment
B. EFFLUENT CHARACTERISTICS

Fit™
MQD
ODD*
Kg/day
n*g,'l
rrgrt
NEJOQ
nxj;1
Present Deafgn • •'
7.5
40
1136
80
2271
15
426
68
1930
111.5
30-
.1306
60
2612
15
653
2960
    PrBlimiruiry baais • fitDr.dnriS M^LiJidjri,1 Irontmont

    oat Ifie SQ-fn^fi. BOO? u«6d in *i§ eitaitfihS repfMfiflB l» ieconSanf !T&a(mgrY) 0ffluvil standard, and tha? bertlnr parfann.
   a niay occur during wdrfn weather. TIjarBloro. affluanl Cha«C5arrSliea uSWiwn nwdilir!g rsal-lilo sfeaftsn^ shogld rflftncl Hi a pa"
               ffl Old prcpaaafl Tanlity dunng ffi6 crilkaJ parked.
                                              =2,0.
             SOD tfl?1» l
    NQOD m SlDklvidnMilrlc oxygan f o-q uirenmn'j for aiidatl
    nitrogen fornu = 4.JS? z NH^N (atfluvnt axldiuifilo organic ni1ro-g«n if
           FIGURE B-2. TREATMENT FACILITIES AND £f FLUENT CHARACTERISTICS.
                                                  B-3

-------
                                                              .
                                                                         Vbmfa
   10
 1
             Depth =0,565 Q
                           **
                          us
                                                    Deep Charm*)
                                                  10
                                                I
                                                f
                                                i
                                                    C.I
*"

                                                               t«        W.0
                                                                 («« y,',,

s

     st         10
                          wte
C.
I
  Q.td -
  a m
             Velocity «• 0. 065 a
                              "*1
                      pw*<»|

                                                 "
                                              1
                                                • O.i0
                                                 DO1
                                                    Q1
                                                                        «4 C
                                                                 flow Wl'
                                                                         IDG       1DC.O
                                             B-4

-------
average annual flow  is about 250 cfs with a minimum
monthly average low flow of 100  cfs, which occurs in
September.  However, the State requires that minimum
DO standards must be met for the  minimum 7 consecu-
tive-day flow with a return period of once every 10 years
(7Q10). (As discussed in Book VI, Design Conditions,
other design flows may apply under summer conditions.)
From a statistical analysis of the flow records, the 7Q10 is
determined to be 30 cfs and occurs between August and
October.  (For further discussion of critical conditions, refer
to Section 3.3 of this document.)

Critical conditions of DO, un-ionized ammonia concentra-
tion and nutrient enrichment in the river occur during the
summer when the flow is low and the river's water tempera-
ture is  high.  From 11 years of water temperature data
collected as part of a limited river monitoring program, the
maximum average monthly river temperature is calculated
to be  27°C and occurs in August.  The river condition
selected to represent critical conditions assume a river flow
of 30 cfs and a river temperature of 27°C.

Note that for this example, both the critical low flow (7Q10)
and the maximum average monthly temperature  have
been used in the projection, even though historical records
(summarized  in Figure B-3A) show  minimum average
monthly flow and temperature to occur in different months.
This tacitly assumes that although the minimum average
monthly flow occurs in September, the critical 7Q10 could
occur in August, the month of maximum average tempera-
tures.   In areas where it can be shown that the 7Q10 will
occur in a month with lower temperature, then the appro-
priate combination should be used rather than each of the
extreme values. For example, critical low flows frequently
occur  during October in the northeast.   An appropriate
approach in such cases would be to define the 7Q10 and
temperature conditions for each of the critical months (e.g.
June-October), determine which month is most critical, and
use that month in allocation calculations.

For this example, assume that three  surveys were con-
ducted to  measure stream cross-sectional area under
different flow conditions. Cross-sections were measured
at 20 locations within the 65-mile long study area. From
analysis of cross-sectional area measurements, it can be
concluded that the study area may be divided into three
relatively homogeneous reaches as shown in Figure B-1.
The first and the  third reaches have almost the same
channel geometry, while the second reach has a wider and
deeper channel.  Throughout the length of each of these
reaches the river is assumed to have uniform geometry.
The first reach stretches from the STP outfall to 30 miles
downstream.  The second reach is 10 miles long  and
begins 30 miles downstream of the STP outfall.  The final
reach is 20 miles long. Two representative cross-sectional
areas are used to characterize the reaches for each set of
flow conditions; one cross-sectional area at a given  flow
represents the first and the third  reaches, and another
represents the second reach.

The average river velocity during each of the cross-sec-
tional area survey periods was computed by application of
the equation VELOCITY = FLOW/AREA. The average
flow for each survey period is obtained from USGS records.
Alternatively, dye study techniques could be used to more
accurately determine average velocity  for a given river
section.

River  cross-sectional area, depth, and velocity generally
form linear relationships with flow when the data are plotted
on log-log scales.  Figure B-3B shows two sets of log-log
plots derived from stream cross-sectional data. One set
represents the relationships between the channel geome-
try and flow for the narrow and shallow sections between
0-30 miles and 40-60 miles downstream of the discharge.
Another set represents the wide and deep channel be-
tween 30-40 miles. Figure B-3C shows the relationship
between stream velocity and flow. Interpolations and ex-
trapolations of river geometry and velocity at specific flows
can be made directly from the log-log plots or can be
computed from the equation of the line of best fit.  The
equation for the line of best fit has the form  Y =IQs where
I is the intercept at Q = 1 cfs and s is the slope scaled directly
from the plot. A summary of the mathematical expressions
of the graphs presented in  Figure B-3 are as follows:

For the narrow and shallow sections

   AREA (m2) = 15.358 [Q (m3/s)f'57      (B'1>
                           3,_\i0.45
  DEPTH (m) = 0.565 [Q (m3/s)]
(B-2)
   VELOCITY (mis) = 0.065[Q (m3/s)f'43    (B-3)

For the wide and deep sections

 AREA (m2) = 57.659 [Q (m3/s)]0.57         (B-4)

 DEPTH (m) = 01.413 [Q (m3/s)]0.45         (B-5)

 VELOCITY (mis) = 0.017 [Q (m3/s)]0.43     (B-6)
                                                 B-5

-------
River area, depth, and velocity can be computed for any
flow in the appropriate section of the river by using the
equations listed above. If river geometry data are available
for only one flow condition, the relationship presented in
Section A.3.1 (Equations A-1 through A-4) can be used to
calculate river depth, area, and velocity at other flows.

B.2.3   Review of Water Quality Data

Historic river water quality data within the study area are
limited. As part of the State environmental department's
overall monitoring program for this river basin, water sam-
ples are periodically collected at stations  located at river
miles 25 and 55. These data represent approximately one
grab sample per month during the summer over a 5 year
period.  A review  of these data reveal occasional water
quality problems with regard to dissolved oxygen and
un-ionized ammonia. Further downstream, periodic  algal
blooms violate the State's narrative nutrient criteria. Prob-
lems appear to occur only under extreme low flow condi-
tions. Since there are indications of occasional violations
of water quality criteria, a TMDL is needed to assess load
allocations  under  future conditions.  The TMDL should
address the occasional DO, un-ionized  ammonia prob-
lems, and the eutrophication in the downstream recrea-
tional area.  The TMDL should consider both upstream
nonpoint source loadings and the local point source dis-
charge.
Considering the conditions under which problems occur,
an appropriate level of effort for a TMDL study initially can
be limited to the analysis of a single river water quality data
set collected during summer low-flow conditions. Accord-
ingly, a survey was conducted during two days in August
when the river flow averaged 100 cfs and the river water
temperature was 25oC. The results of this survey and the
State environmental data are presented in Figure B-4.
The DO data in Figure B-4, both August 1979 data and
historical data, show stream DO levels above the standard
of 5.0 mg/L at a flow condition of 100 cfs. The increase in
river BOD5 and the ammonia concentrations at zero mile
point showthe impact of the treatment plant discharge. The
gradually decreasing ammonia profile and increasing nitrite
and nitrate profile suggest that nitrification is occurring in the
river.  There is evidence that a natural nitrification process,
in which nitrate and some oxygen-demanding material are
removed from the water, may occur in some streams.

Un-ionized ammonia  has been demonstrated to be the
principal form of ammonia toxic to biological life.  Tempera-
ture and pH have been shown to affect ammonia toxicity.
The  EPA Ambient Water Quality Criteria for Ammonia
(USEPA, 1984) requires two conditions to be met—a 4-day
average for chronic toxicity and an 1 -hour average concen-
tration for acute toxicity. For a river temperature of 25°C
and pH of 7.75, with salmonids or other sensitive coldwater
species absent, the 4-day average standards are 0.043
mg/L un-ionized ammonia (0.0353 mg/L un-ionized NH3-
N) and 1.39 mg/L total ammonia (1.142 mg/L NH3-N). For
a 1 -hour averaging period standards are 0.32 mg/L un-ion-
ized ammonia (0.263 mg/L un-ionized NH3-N) and 10.384
mg/L total ammonia  (8.384 mg/L NH3-N).  During the
August 1979 survey, the ambient total ammonia concen-
tration was less than  the standard  of 8.384  mg/L  total
NH3-N for the 1 -hour average condition. The 1 -hour aver-
age values are used for calibration so that the worst case
scenario can be portrayed.  Historical river water quality
data collected near the USGS gage provides concentra-
tions under 7Q10  flow conditions. Table B-1 shows  esti-
mated  data for upstream boundary conditions used in
modeling. The boundary conditions take into account the
proportionally highly nonpoint source loads under the 100
and 250 cfs flow conditions.  Monitored concentrations of
water quality constituents inthewastewater treatment plant
effluent  are listed  in Table  B-2. Table B-2 also shows
estimated effluent concentrations for management alterna-
tives used in modeling (e.g., activated sludgetreatmentand
advanced water treatment). These values were obtained
through a literature search of typical loadings from various
levels of treatment.

B.2.4   The Simplified Analytical Solution
        Approach

The Simplified Analytical Solution Approach uses the exact
solutions to differential equations presented in this docu-
ment to analyze the Rivanna River example. This approach
provides a better insight into the fundamentals of modeling
DO and eutrophication problems in rivers. The analytical
solution approach is similar to the methods described in the
Technical Guidance Manual for Performing Waste Load
Allocation, Simplified Analytical Method for  Determine
NPDES Effluent Limitation for POTWs Discharging into
Low FlowStreams (USEP'A, 1980) in calculating ammonia
toxicity and DO concentrations. The analytical solution
presented for DO presented here expands on the modified
Streeter-Phelps equation to  account for phytoplankton
production and respiration
                                                  B-6

-------
                                              i r-
£ '* t~
J p I -far i*j. SJB^B^
! ;
z L * * »
~ A *
r ~ 4*1,.. ,.,,,,.>, .V. ..• 4......
10 •) tO 10 j<3 tO io 'X5 r
Dist^oe (ttulet)
-? 	 ~~*- - 	 	 — "=™ 	
1 2 —
1
\ .-
i . • T
t * *^.«.
= L,....i.. , .«.. , .." f -.
• iO fO 10 2*3 3O *Q SO ^
|1»' 	 LUJJ— ,,1U.
*V £ 	 Lm_— ^ . .- 	
c
«( ^ ^a
iJ p
£ ao r
1 is E- '
I3£ .!»
S so f- »
i; 	 *;i},ir,k.,^,,.,.
1
s
_
z
i
*
2
z
0

I
a
S
0
1
5
n
1
s
i
1
Dissolved
5 E-
4 f-
' E"
2§~ 4 . • f
P » '*
Ar ..* *
»-* *° * f i
-10 U MJ 10 34 -JO 50 oG T'
Pattwoe (suJe)
30 -
20 h T
*"
c '
10 1 T f
^ .••*•.."•
3 !" !*( , ,.,«.. -*
-id ** t<> r, 50 *? 50 to *;
I4|
HP
10 ~ T
Sttutwioa
3 rT~' ~~ ~ ~" "" *~4*~ ~~
4 ^- Standand
2 r
H l,ullllnlt.-M.4-.|.,,IMi.IMlMt4.Jl-».-.t.., ,
• 10   0   10   20   30  4t}   SO  60   70

-10  0    10   20   30  40   50
             DistMJM <£Bilei                                   DwtuBce *,i

                           SjlTSiOGEN AND PHOSPHORUS, CHUWPHYLL. A,
                         AWO ^O DATA i August 23-24  1'
                                           B-7

-------
                      TABLE 8-1. UPSTREAM BOUNOARV CONDITIONS.
1
$>
P
Pargurre'er Uncs
L,
CBOD., . mg/L
TN 2 mg/L
(S*H , " mg/L
NO, ^ NQj i mg/L,
Og N j mg/L
ITQT - PAR ^ y,'d
PholQpenoc ' 'Jays
Temperature I :C
pH
PO4 mg/L
Pl-ylwjlarnton ^g/L
Existing Faw Ccndil.ons1
2SO ds*
{7.070 mVs)
9.6^CO
2.5782
0.1437
0,7779
1 .6565
144.1
0.61
15
7.75
o.osa
2409
1 30 c4s"
(2.932 nj,'s;.
S.C663
2.^126
C.134S
0,728
1.SS01
94.6
0.55
25
7.7S
0.099
2.702
30 Sis1
(C.B49 m'/s)
20C72
1.7907
0.10
R.1'-rB Flew
Contfrtions^
30 cfas
«;0,849 mVs)
2.CO?2
1.7907
0.10
0.5*02 i 0.5403
1.1505
1136
1.1505
113.6
0.5 SS [ 0.58S
arc
7,75
0.14
3.80
37'=C
7.76
0.14
3.BO
" Existing Conditions (Plant sflluanl = 7,5 mgd; Land usos: AgncuNuffl 60%, Fixeslry 35%. Urban S%) Pop. = &3.QQQ
: Future CondiiiorfS i Plant atfluani = * 1.5 mgd; Land uses: Agriculture 5B%. Fcirescrv 35%, Uftjan 7%J Pop, = 92,000
1 Repr0$rJfliaiivQ May data
' Repreaeri1a1iva September data
5 Eslimaled August data

 TABLE B-2. CHARACTERISTICS OF EFFLUENT TREATMENT FOR DIFFERENT PROCESSES.
Water Quality
Constituent '
NH3
NO3
PC*
Clil_a (iiglL)
caoo*
CBOO^
: Oxygen
OrgN
\ Org P
I 	
TM
TP
Tricklirg Ffflar
15.0
,0-
4.6S8
,3.
^0
SO
a.o
-3-
I.SS3
15.0
625
Activated Sludge Trealmenl
1S.O
-0-
4.B69
-0-
3C
60
B.O
-0-
1.379
15.0
5.§4
AdvAncetl Waler Trealmenl
I
1.3
-0-
0.70
-0-
30
60
a.o
-0-
0.3
1
..5
1
                                              B-8

-------
as well as, BOD, SOD, and reaeration (Thomann and
Mueller, 1987). Although ammonia toxicity is likely to occur
near the  vicinity of the wastewater treatment plant dis-
charge, this approach also  shows how to calculate the
ammonia concentrations at  downstream locations. The
allowable instream total ammonia concentration is  based
on the un-ionized ammonia concentration as a function of
pH  and temperature. The EPA Ambient Water Quality
Criteria for Ammonia—1984 (USEPA, 1984) describes
how to calculate allowable chronic and acute toxicity levels
for  un-ionized and total ammonia  at  a given pH and
temperature.  This approach also shows clearly the effect
of changing channel geometry on DO sag.  A simplified
analysis calculates the location of DO sag due to a waste-
water discharge for BOD decay only.  If the problem  is
further compounded by conditions associated with lower
reaeration coefficients and phytoplankton growth,   a sec-
ond DO sag may occur. Which of the two sags produces
the minimum DO depends  on overall conditions. The
analytical solution approach provided here includes a more
elaborate process for dealing with nonuniform channels
and eutrophication issues. In the absence of phytoplank-
ton growth and any significant variation in channel geome-
try, the analytical approach reduces to the method
presented in the above mentioned document.

The analytical solution approach also addresses some of
the limitations of reaeration  models.  The method pre-
sented here uses a discrete  segment approach similar to
that employed by more sophisticated computer simulation
programs. To avoid the tedious work of repetitive calcula-
tions, a spreadsheet or a short computer program can be
set up to solve the appropriate mathematical equations.
Repeated solutions of the equations presented here were
used  to generate solutions  at  desired  locations  of the
stream.   Application of this approach is, however,  limited
to steady state conditions. A set of sample calculations are
shown in Table B-3. The first step in the analytical  model
application is to divide the system under study into reaches
with relatively uniform physical characteristics. The stream
is divided into four reaches based on wastewater treatment
plant discharge location and channel geometry. The up-
stream boundary conditions were used as initial conditions
for the first reach. Concentrations of water quality constitu-
ents at the downstream end of each reach are used as
initial  conditions for the next reach.  Therefore, for the
second reach, initial  conditions are the concentrations
resulting  from mixing of the  treatment plant effluent with
instream concentrations at the end of the first reach. Efflu-
ent from the wastewater treatment plant is assumed to mix
completely with stream water immediately after discharge.
Since the second reach is 30 miles long, a more detailed
assessment could be performed by calculating concentra-
tions at various locations along the stream, possibly more
densely near the discharge.  The calculations shown in
Table B-3 can be grouped into six steps, 1)  calculating
physical  parameters,  2)  calculating net phytoplankton
growth rate, 3) checking nutrient limits for phytoplankton
growth,  4)  estimating  reaction  rates, 5)  calculating DO
components and DO concentration,  and 6)  calculating
concentrations of nutrients, BOD, and chlorophyll a. This
simplified approach does  not include analysis of organic
nitrogen, organic phosphorus, and exchange of nutrients
between water and sediment.  Additional equations can be
used to incorporate these  factors in the analysis of phyto-
plankton growth. Alternatively, more detailed water quality
models, such as WASPS, can explicitly consider a wider
range of nutrient species, interactions, sources, and sinks.

As stream water quality data are available for 100 cfs flow
conditions, it is used for calibration. For an analytical solu-
tion approach, calibration consists of the determination of
the reaction rate coefficients (presented in Chapter 2 and
Appendix A) that describe the spatial distribution of CBOD,
ammonia, nitrite and  nitrate,  phytoplankton growth, and
DO.  The first set of calculations must be made based on
an educated guess of reaction rates. Then a comparison
between the calculated values and observed data will allow
the modeler to make a better estimate of reaction rates.
The overall loss rate of N BOD can be estimated solely by
matching observed ammonia and nitrate data. The overall
loss rate of CBO D and the effective deoxygenation rate can
be estimated by matching  observed CBOD and DO data,
respectively.  Calibration of the  analytical  solution model,
as shown in Figure B-5, provides 0.15 day-1, 0.30 day-1,
0.30day-1 and0.20day-1  for the overall loss rate of N BOD
(Kn), the effective deoxygenation rate (Kd), the overall loss
rate of CBOD (Kr) and endogenous respiration of phyto-
plankton, respectively. Loss rates are determined by finding
a value that provides  the best  fit with the August  1979
CBOD data. The CBOD removal rate by settling (Ks) is
assumed to be zero for the secondary effluent, and the
CBOD oxidation rate (Kd) equals the total removal rate (Kr).
These values can be adjusted for simulating water quality
constituents under different management conditions. The
atmospheric reaeration rate (Ka) is determined in accord-
ance with Table A-24.
                                                  B-9

-------
          TA3LE B-3.  SAMPLE CALCULATIONS FOR REACH 1,
Lei us a$wrne IM a $ttgi-sp«cific investigation provided followm^ additional
  Background Light sxtinraion oMffitierrt, K, = I -SO at'
  Endogenous respra1ic.il of phytoptonkton, y f = 0,20 dby"'
  Pfiytoplankloni settling rate, Vt =05 m>'" day
  Cartson 1o chlorophyll a raila, ac - 33 tzg C/ ,T^ C"W a
  Nitrogen la chlorophyll a ratio, a v = 5 & W£ iV / JJf CW «
  Phosphorus, to clilOTOp*)iyll a ranc-, a^ = 0 "9 ng  P f fsg Cki a
  Phytoplanklon maximumgrawlh rate, (!M - ISdrt' ' af 30'"C

At x = S.O miles, wltldi is |r-!D = 1.8- L066':I3-3IJ| = 1.47* (Ay-1                          12-
Ligrit extinction coflfficient considering Ihe self shading or a(ga« (Rn«y, 1956).
^ = K.i + 0.008 S X9 + 0.054 . A^ ,      wh»w Aa n the Chi a
    = L5 -t- Q.QQ8S- 2.702 4- 0,054 • 2 7-Q2" = I 63 w"1
Ugiil dependent gfowtlt redudii«i factor,
           -*«      l136
                                     ^ = 0 223                            <

                                                                         12-1 Sb)
                         t
        \25 + (I3S+- 728}  ]+• 99.2
                                                                          (2-17)
                                  = mMi{G971,G99) = 0971
                                        B-10

-------
                         TABLE B-3. (CONTINUED)
            growth rait after adjusting farlernperatu™. liphl arxi nutrianl liffirt


6' P = (JrfLrx =2478- 0.4 56 • 097 1. = 1.097 itoy ' '

Temperature adjusted endogenous feiara-ion raw


D,. = .HJ, • LOS**"" =02-10*'= 0294
            death tut,
              D 31 0 294 + 0,0 = 0.294
               aretwih raw,
C =6*?-D,<-— =
       ?     P   H
ni] Ch«ek nutrtont limn
                                  O.S


                                 0.902
= 02
                                                                       {2- 1 4)




                                                                       
-------
                            TABLE 8-3, (CONTINUED)
f'.v =  --In
       ?" artd t\ > t' no nutrient WmH occurs in the flrS reach,
                                                                           (4-9)
           nt«*

Almoapheilc reaeratlon rala using Bennett and RaChburj i1§72j famiula :
202
               01017
                      '— -
1-3281  i-
                        { ft
                        [m

       adjuiiment maerailon rale.
               0.902m -3.281
                             \ m
                                          - = 1.66 day~
                                                              A'24)
U 45 important to check lh« valy* of nsieratien f»t* at eacft raaeft. Hydres4S«iiea (19?1) suggeas a
minimum value of the owpen transftr «effici«fH (ojc^^fl iffl^tor ewfflcieni , K<_ « K, H) ol D.s to 1 .0
rwday. if KL H IMS than 0.6 m/diy at »y (*«eli, ¥* shOuW 6« adjust^

TM folitswirw valuis of Kg. Kt arid K v^ere deiennined ihrouoh cainaraftlon j
 K, =
           l 2QX

 J^tf  = 0,22 • 1 OT:^" = 0.
 JC, =038- 1,047 &1S = 0,

 JC.,  = O.J8 • l.047ilj"" = 0,30
                                        B-12

-------
TABLE B-3. (CONTINUED)
          v) Disioivid
          Saturated cancenif alien of Do al mem s«a level and z«ra salinily is given in APHA (1985) as,
          ,-_,,_

          Al T » 25
                                        L^i**' *" -* *: '*** "' j
                                        :r*:7! i »;• ii'iT, y,' I
                  . initial
          a = c, - q, = a 263 - a 3 = -o 017 «^ / /.


          DO deficit at 1fie end of the reach ts gwcn by

                                                               " -K-EX
                «                           ST = -0.00646 m£ / £.

          CEOD
          CBODU,   L» = 2 0 /„ = 2 0 • 4;03 = 8.06 mg f L
                             L-*.'1 »e-*V j
                     *,-A;
                      0,38-8.06  r H:H*O*ISJ    -19*0*1^1
                   _ 	1^-,,-OU    _ g <   F, ,    I

                   =  0.53l5rag/L
          NBOD
                »,  IJ' = 4.S7 [ Ar//j ] - 4.S 7 • 0. t J 5 = 0 6 1 1 mg t L
                       if  rw t
                       ^,v^>    -*^'  _ -K
          "5*
                      0.22-0.617 r^ji, », 3 r _  -
                      1.906- 022
                   =  0.051 8 mg/L
                                  (i.e.
                            rallo.
          a0 s 1.67 ^ a 2.67-33 (J4^ O, t tig Q (wr C i m Chf} = 88 1 1 tig Q f
                                               B-13

-------
TABLE B-3. (CONTINUED)
                                                0*7-0 2fl«l-
                                                          •
                                                                       I
                                                                       (.   L
                                                  (1906-024?);
                                      _  -
                                              |Jt 1
         SOQ eai^rti
                                               = 05
                                                               ^
         £3
         ^
                   -[I-.
                                   .906
         Sybstltuling the values in lh« DO

         /> = ^O.OO646 + 05315 + O.OS ] S - Q.04fi + 0.3 59 = d.*9 my / L

         TTlsnjforB. Ihe dissolvad oxygen coneertration ju«t upttrtam of tti* discfiai^e (i.e. x * 5 miles)

         C = Ct - D = S.263, - O.S? = 737 M^ f L
        vi) COirte»nUitiQ« of oth*r w«t»r quality eenftHutoll*

        CBODS Oincantrailon
                             as
f  =403e"1'3i:<"'!S"=2S46m^.,'£


               = O.I I tag ft' f L
        Nitrate and nitrita
= O.L35«H"-

 JSJUUMM
                       = I NO - NO 1   4- r VJV 1   -rw 1   - SH^fAL13-"' - i\
                         ^j T'.J'I   J * l,,jrt JL.rt ' I > T4 J j,ju.«fl  I J TJ!,I! i J.™fl     _    1 ^      L J
                             *      . M J  1    J^^^U  *    l-i-lTM!     ^J    ^        jp
                                                                 T

                       = 0.72S 4 0135 - 0 JI - •"• "}m 'l^LJtoi4w^»7 __ ^
                                              0146-1000   x            '
                                                B-14

-------
TABLE B-3. (CONTINUED)
   inorganic Qhostfiorus fs$sti«riifna no recycling from
                                   ••?. .1"   si
                                       ~
                            ~
                           G
                         0246-1000
            = 0 0966 ing P ; L
  N'ole that whusn a nulnerti limitirtg siluasion -0«aj*S due 10 -3^-pleiiQn of inofBJrtie pho$pfeOH4S H
  critical to include recycling of TO^tere ptosphonj* from ftspifKl algas  Otherwise,
                population will drop atonistly due io nytrient limrted gncwrth.
                population
                 = 2 704 • ^=*3"">   = 1388 «y CM /
   H1Jief« were no effluent di-so'iange from Iji* was«ewat*f treaimunl planl al the end of first naacii, all
   DQtneantraliOTS cateulaled at llie end of first react* w3uW h*v* been u£*d as boundary eo«i«n(r*1i4*
   for ?he $«00nd reach  &«aus* of the wastewalar ireatmant plant discharge i1 x = 5 miles, all water
   quality constituent concentration* JiHv* l.o b* cal&lafad assumirsa a campjet* rnlxlrtg of efnuenrt
   wrth stream walec and the new values will b® «s«o<« 50 th«
   Tli« foil-owing forrrtula is u&ad 10 calculated
                              r«5._ T Ud


        , »c«S- and x=S* indicate upareim and dovrtisartirn of milt peirtt 5, i.s. tha disdwQa looiton,
                                               B-15

-------
    - L
 t
£
5
I   ,
    * r-
            •**
20
                                 W
                             3    t
                             *   ~ r
                             i   ~ r
                                                1
                                                «

                                                2
                                                                 '
-ly   §   10   20  3O   40  SO   fiO  TO

                    (tmle)
O     £
>  ^
      L:S-
                                    *
                                                a
                                                •=  20 \-
                                                    10 i-
>


,J 10
s
§ s



n
-
:



i 1 1
*~ i \
= M ^^
• X -

Ciii, i , , , , I , M , 1 , ,', .trtftLk-u^j^lii i Jj

f.
c
i
o
1
1
a


"" r-
12 E-

10 ^-
-a " » 1
a ~ ' „.— —'*' 1
""* »* *'~ 1
6 L J'l,^*-^'l ^"£ _ 	 _ . 	 -
4 1, * DO Standard
~
2 p-
rt bjOjJjJAlliJJLLlj_LLj.l,l 1 i >> 1 ' 1 1 1 M 1 1 1 U U-
     40   0    i§   20  JO   40  SO   60  70
              B-&. CALJBfiATiON OF
                                   -10   0   10   20   M  40   50  56   TO

                                                DiitHiee (mile)
                                              B-16

-------
(Ka) is determined in accordance with Table A-24. In the
wider section of the river,  Ka drops below 0.5 day-1.
Hydroscience (1971) suggests that the minimum value for
oxygen transfer coefficient is 0.6 to 1.0 m/d. The minimum
Ka is then determined by dividing the minimum oxygen
transfer coefficient by the corresponding river depth.

Inthis example, the calculation of the DO profile agrees with
the measured data quite favorably  without any adjust-
ments. In some cases, the calculated DO profile does not
initially agree with the data because of sources and sinks
of oxygen which may not be accounted for, such as SOD.
Benthic oxygen demand, phytoplankton  production and
respiration are included in the analytical calculations.  Be-
cause no exchange is considered between the benthic
layer and the water column, observed NH3 data are found
to be higher than model predicted values in Figure B-5.  A
good agreement between observed and simulated data is
found in the chlorophyll a calibration. The observed inor-
ganic phosphate data shows a decline with distance down-
stream which is not matched by the analytical solution. This
difference is probably due to the assumption that only
dissolved phosphate is considered in the analytical solution
approach. The loss of phosphate by settling is therefore
omitted.

Having calibrated a  model for CBOD, DO, nutrients, and
chlorophyll a (i.e., having defined site-specific coefficients
and accepting that some reservations on reliability exist
since the model is not tested against an independent data
set), an analyst may use the model to project water quality
impacts that might be expected under conditions of interest.
Three different flow conditions are modeled for existing
loading conditions to evaluate the range of conditions under
which problems may occur.  For each of the three flow
conditions, the upstream boundary condition is varied to
account for changes in nonpoint source loading contribu-
tions. Under the 30 cfs case, upstream flow is considered
to be comprised wholly of baseflow.   Ammonia, nitrate,
inorganic phosphate, chlorophyll a, BODS, and DO profiles
are presented in Figure B-6.

The calculated profiles in Figure B-6 show that present
wastewater loads would result in DO water quality standard
violations over approximately 8  miles of the river under
design 30 cfs drought flow conditions (7Q10 flow and a river
temperature of 27oC).  The lowest DO concentration is
about 3.4 mg/L under 7Q10 flow conditions. Total ammo-
nia violates the standard for approximately 25 miles down-
stream of discharge. For a river temperature of 27oC and
pH of 7.75, the total ammonia standard that corresponds
to an acceptable un-ionized ammonia level is 1.14 mg/L
NH3-N. The highest predicted total ammonia concentra-
tion is more than three times the standard.  It is important
to note here that if the observed effluent ammonia concen-
tration and the 100 cfs flow condition persists for 4 days,
violations  of the standard would occur  over a  25-mile
section of river downstream of the discharge. The BOD5
profile shows a significantly higher concentration immedi-
ately downstream of discharge, but it decreases rapidly
within first 30 miles downstream. Another major concern
is the growth of phytoplankton. The total  chlorophyll a
profile shows that under 7Q10 flow eutrophic conditions
exist in the downstream reaches of the river. Accelerated
growth of algal is  likely to result  in a reduction of the
recreational value of the river.

The analysis of the three flow conditions shows  that the
initial selection of 30 cfs as the critical condition is justified.
Developing the TMDL for the 30 cfs flow condition should
be protective of other flow conditions and result in a con-
servative estimate of required load reduction.

The next step in the analysis is to consider three alternative
levels of treatment for the wastewater treatment plant. The
three different treatment scenarios are simulated using
future population and land use conditions for the low flow
critical condition (e.g.,  30 cfs).  For each  management
alternative, boundary conditions were defined by baseflow
concentrations (equivalent to 30 cfs case under  existing
conditions).  Each treatment alternative is then compared
with the water quality criteria for DO and un-ionized ammo-
nia. The narrative nutrient standard must  be equated to a
numeric  measure for comparison with  model  results.
Based on a review of similar rivers in the State a chlorophyll
a threshold of 20 g/l is selected as a target goal. Continued
monitoring should be used to reevaluate the target in future
years.

Calculated ammonia,  nitrate, phosphate, chlorophyll a,
BOD5, and DO profiles for the projected wastewater loads
are presented in Figure B-7.   No significant difference
exists between the effluent nutrient concentrations that
result from trickling filter and activated sludge treatment
processes under simulated future loading conditions.
Therefore, ammonia, nitrate and chlorophyll a profiles for
these two alternatives coincide.  Effluent discharged from
the
                                                  B-17

-------
&
.*•««, nr
* ?
§ 6
§ c
•= >
is
1 4
o
,o 3

Z 2
i i
n,
U1
-1
•1
4 |
j^* 15
'S***'
^v
'•»*•
f"»
'•™* i
o_ 1

-1

^ c
35
30
^ 25
^ji
f 20
•-«»*
1 1S
a 10

s
fi


	 100 tfs j
ZSO cfs

_

r~ \
I X
— : \
:
i_ \ 4^ay avg. Standard
: """- X\ (pH.-=7,75,"I = 259C)
=- -| ^ -"-=— st^-- 	 	
0 0 10 20 30 40 50 60 ?
Dwl™C* fm'1**>
—
_
_
r ""*"----_
-"— -
*i • Ttl 1 i 1 1 L 1 I..J i 1 1 « i * 1 * i i i 1 i 1 i i 1 ' " t 1 1 1 I I J
00 10 2fl 30 40 50 60 7
Di«4»*>

i
L i
* \
'— i
- \t

— \
: \



ij-i^^t,,^,^! , , ,,
0
fj
sh '
7 *
a
L» 5
2 .
'« 4
'-«-• 1
•^ 3 1
^ s
5 2;
t ]'
ft
U
0 -I

80
s
-S 60
"3 ^
^
20

0
1J
0 -1

I £
ir>
j-^1*
c 12
^ 10

S* S
| 6
i 4
s
2
n



r~



mm
~ , 	 „,,„». 	 ».
r x-'-"" \
: /
E- / ^---'-^r --
: ,• . _- - -~ ~" \
~ — J'" N,
-IlltllllllllllllJIjIj IJ.J Ji-LiilLl H 1 ,| 4 | J,
00 10 20 30 40 50 60 K
Distanw (mile)
—
BBS,
E-
; /
r
» „-*""
00 10 20 30 40 50 60 7C
DislaBBs (nule)

k
'-
"-
— i
	 	 	
i, i __/"" 	
- \ / .DO SlttdwJ
; v
—
~l 1 I I 1 1 1 1 l_j_;J_J!::l 1 1 1 1 I 1 1 1 I I < i . 1 1 1 1 1 1 J J.UJJ.
  -10   0   10  20  30  40  50  60  70

              Distance (mile)
-10   0   10  20  30  40   SO   60   70

            Distance (mile)
RGURE B-6, CONCENTRATIONS OF NUTRIENTS, CHLOROPHYLL A, BODs AMD CO FOR DIFFER-
                  ENT FLOW CONDITIONS UNDER EXISTING LOADS,
                                   B-18

-------
s
J 7
1.
i, «
i »
5 '
| 4l
1 3|
, . I

Z 2
Z 1


-L

,:i
s
1 2
XJ
3^
v
2 i

-3



40
"f 3°
irt
0 20

10
il



: '*


^_


:-


~l 1 -I^L
0 t


-
—
—

-
-
0 C


:
—
—
-
—
»
lax

Advane*d Treatment {30 cfs)
	 Activned Sludge (30 cfs)
\
\
\
\
\

\ 4-day avg. standarxl
'""•-^ (pH = 7.75. T " 27*0
X
I 1 I - 1 r ' 1 ' t L 1 1 1 1 1 'l""Tl-| 1 1 1 " i ! i i 1 1 1 1 f 1
> 10 20 30 40 SO 60 7
Di*l«K)C (miles)



r 	 , 1 _ ,_ -— — — ._ 	
~ — ' — "— "•- -.,_ „
i
_u_jj 1 1 1 1 1 1 1 1 1 1 1 1 1 1 = 1 1 1 1 1 1 j i j i Li-i_u_
) 10 20 30 40 SQ 60 7
DsalMioc (miie)


S
'X
\
\
^\
X
' 1 ' 1 1 ' •* 1 1 1 1 1 1 1 1 ' ifatl 1 1 1 1 1 1 1 1 * ' ' ' 1 1

^
C
O
2
Q*

T3
G
41
c"
Z

c

i

2
w
a
(2

0



1
I
1
I

m
5
a ^
!
!
6
S

4
3


2
1

it
U1
• 1
irm
i i."j^
90
SO
70
60
50
40

30
20
LO
-1

i A
ID
14
12
to
*
6

4
2
f>


—
'-

t"*
~at.
~

I" /
- /
* f
SJOjJ-O-Ll
0 0

i-
E-
1"
z_
—
@
m
= , n-f-m
0 0


1
^->
r
rs
i- I
_. li
f ':;
h , , , 1 1 1



s"~~~-—,
,--"""" """"•--.
s'
S


\


, , . . I , , , , , 1 . , , ! • 1 : , , ,
10 20 30 40 50 60 7C
Distance {mile}
'




/^
"rtTTTT'Ti 1 1 1 1 1 1 > j 1 '• u > 1 ' ' > ' 1 • • ' '
10 20 30 40 50 60 70
Distance hail*;}




' A ^~~^
; 	 ;f£ff 	 : : 	 	 	 ^^ ,„,. 	 	 , 	 :, 	 .

,f
jLjJjj^j 1 t i i i 1 i i i i 1 i i. i 1 1 1 I.J.l.il_LJ_L-
   -10  0   10  20  30  40  SO  60  70

               Dsslance (mD«)
-10  0   LO  20  10  40   50   60   70
                  (mile)
FIGURE B-7, PROJECTED NUTRIENTS. CHLOROPHYLL 4, BQOs AND DO FOR DIFFERENT TREAT-
                       MENT OPTIONS UNDER FUTURE LOADS,
                                      B-19

-------
trickling filter or an activated sludge treatment process is
estimated to violate the total ammonia and the DO stand-
ards under the low flow critical condition. The minimum DO
simulated for trickling filter and activated sludge processes
are 2.4 and 3.5  mg/L,  respectively.  An algal bloom is
predicted to continue up to the end of 65-mile study area
and reaches 30 ug/L of chlorophyll a.  Advanced water
treatment is the only option which allows the river to meet
total ammonia and DO standards at 30 cfs. It also controls
the algal growth to a maximum of 18.6 ug/L.  The lowest
daily average DO concentration of about 5.0 mg/L occurs
at 3 miles downstream of discharge location. A sensitivity
analysis is recommended when differences between dif-
ferent options are small. Sensitivity analysis can also be
used in the determination of Margin of Safety (MOS). More
refined analysis can be used to reduce the MOS and in
some cases increase the allowable discharge.

B.2.5WASP5 EXAMPLE

This example shows an alternative approach to analysis of
the Rivanna River example and considers in greater detail
the in-stream impacts of changes in the contributions from
nonpoint source runoff, baseflow, and effluent concentra-
tions to the river reaches. WASP5 includes more detailed
transformation and exchange processes than an analytical
approach.   For example WASP5 can account for the
settling of inorganic paniculate material, recycling of organic
nutrients to the inorganic pool, and nutrient flux from the
bottom sediment layer to the water column. This applica-
tion illustrates the capabilities of a steady state WASP5
model application,  similar to the  previous example.  In
addition, the development of a WASP5 model allows the
user the flexibility to examine continuous simulation results
should further examination of dynamic nonpoint sources
loadings prove necessary.

The WASP5 model  is developed based on the data
provided  in Problem Setting and River Characterization
(Sections B.2.1 and B.2.2). The WASP5 model is config-
ured as 45 segments. The first 35 miles are represented
by 15 one-mile segments  followed by 10 two-mile seg-
ments. The wider portion of the river is divided into 10
one-mile  segments followed by 20 miles divided into 10
two-mile  segments.  Additional data  required by the
WASP5 model includes downstream boundary conditions,
geometric data for each segment, and air temperature.
Downstream boundary conditions and initial conditions are
estimated based on observed stream data.  Depth and
width for each segment are derived as a function of flow
and hydraulic coefficients.

The model is executed using a timestep of 0.05 days (1.2
hours)  for the three flow conditions and three treatment
scenarios considered earlier.  A 30-day simulation period
is used to allow sufficient time for the model to reach
steady-state conditions. The modeling results are based
on the final 5 days of the simulation period.

The first step in any model application is to calibrate the
model with existing data.  For this example, the WASP5
model  is calibrated to the observed data gathered from
August 23-24,1979. Figure B-8 shows the model calibra-
tion analyses for WASP5 with the observed data.  The
analytical solution, described in the previous section, pro-
vides an additional check for model calibration. Analytical
solutions are generally recommended for evaluation of
model performance.  This comparison demonstrates the
ability of each model to mimic the water quality responses
of the river and shows the similarity between the results of
each model  for a  steady-state application.  Although the
results agree with the measured data, the WASP5 run has
significantly  higher input  requirements and demand a
greater  level of effort for application. Table B-4 shows a
listing of the WASP5 input file.

Similar to the analytical solution, once calibrated, WASP5
was run for three different flow scenarios (30 cfs, 100 cfs,
and 250 cfs)  and three treatment scenarios for future point
source loading (30cfs flow only) foratotalof6runs. Figures
B-9 and B-10 show the resulting (based on the last 5 days
of the simulation period) in-stream concentrations of oxy-
gen,  BOD5,  ammonia, nitrate, total organic nitrogen, and
total  nitrogen for existing  and future conditions, respec-
tively. The worst case, with DO below the recommended
limit of 5 mg/l, occurs under the 30 cfs low flow condition
(Figure  B-9). Under low flow  conditions, the baseflow
contribution is assumed to be constant and changes are
attributed solely to the increase in load  from the treatment
plant.  WASP5 simulation results show problems very
similar to those of analytical solution approach for ammo-
nia, nitrate, chlorophyll a and DO.  Interestingly, with a  20
percent paniculate phosphate and a 0.5 m/d settling rate,
WASP5 is well calibrated for instream phosphate concen-
trations. This is important because it causes nutrient inhibi-
tion for phytoplankton growth  and  a resulting decline of
chlorophyll a
                                                  B-20

-------
«ft
"f 7
£ 6
" * *i
*S *'
1 4
3
1 3
o
•? 1
i i


.

I 2

U
t ]
c
Li.




-1

25
i20
e
« 1S
a
S 10

S


.
;
-
_
_
—

	

"
i
»

0

^
^
H
-
^



In
*~s
0

"-
7"
"~
—
»
—
:
_
"
~t
0

_ _ WASP5 simiiJatioo
* Observed Data






/*"•"•*• -i.
! "• X.
•t
,.,-M , ,.,).., .!,,,,!,, "^ ft TTTM-n-*.!,,,,

0 10 20 30 40 5D 60 7
Distance (miles)





* *

' * " ~~*- •* -* .-_
j •*--*--,*.
« Tnl i 1 M 1 i M s 1 si t i i 1 1 t 1 i 1 i t »» : li 1 1 1 1 1 ! i K
0 LO 20 30 40 50 60 7
Distoace (mile)



*
!">
!\
- ' *%Vv*
^ *-
*V^
itiliifrnlglltllllll'liii li^TT I^Tin T-W1 1 L JL I 1
0 10 20 30 40 S3 SO 7

2
s
u
V*
c
z
^
3
Z
c"
2
!

0

^
—
_
3
(2





0

2
¥
ac
X*
O
1
1
S


0
o

»
5
4

.,.


]
rt
u
•i

16
12

8


4



4
1 H^
JO
14
12
10

8

6
4

2




_
j-
=.

-
-
E_
«s
^ , L , 1 , , ,

0 0

E-
—
K*
"
^

—
— ~"^m^^~"~4
-
Bujjljja.
0 0

^~
—
-
— ^

-_ \
—
"m
	
:, , » , 1 , , ,
0 0







,. ~ ,-i ™- •• «fls.
," "" •
=-- -~ •
'-*"'* *
i 1 , , , , 1 i ; , , t , , , , 1 , , , , 1 , , « « i' , , , ,

10 30 30 40 50 60 7
Distance (mile)




*

• i — -*~ - - 	 - — -" '~ ~"
__—*"•" m
*
S, L L ! i | I 1 I j j 1 i i t i 1 i 1 I I 1 i i i i 1 ( i i i
10 20 30 40 50 60 7<
Distance (mile)



* -*
,-~~ ™
r<-J-"^^-'r



.l,...l,i,,l.,,,l. .,!,,,,!..,,
10 20 30 40 50 60 7
        Distance (mile)
(jnik)
FIGURE e-a. CALIBRATION OF WASPS MODEL USING AUGUST 2M4,1979 OATA,
                               B-21

-------
UJ




w


a


CTJ
                              i !  fl  ---2S4-t=JIS    I  -•"tifx^iJSS   i
                                -  as                 5                *
                          ~-I, — I
                 |    —^^- -. |


                 !              !                   f  i=ffn«?ifsi = if

    i
    | •*!*£«
    * flllflifSii
    *i ^-^e*«raw •o-^it'® !S
                                                                 SS:  -   »p
                                                                 5S  Is  Is*
                   1  SillllS'SSIS  588  fft«fftf«ttf   1 sfsissaassffi  1 s«
           -a®il3SaB  a  £ S a. »S a o a o 25 a  •&-   ,w?**i'"a*s»'*     *4-*.*»^-**^-«   - * « «
                    £f-"*" •»-*--                                             §
                                       B-22

-------
            =8  -jH  «•*!  »ft tiL.  5


            3  *  a  aS"  5

                                          I
                                           ' M       .f
                                           % i     s> a

                                           ^ra     **.
                                                         IF-     ^ j
                                                         11-     ^ 2
                                                                       f
                                                                      .je«
                                                               a?
                                                                              is     Is
                                       _>_^-       ^_


                                 nsl   ?  3- Is  5-   a  t *
                                l :_ i    >.  a    >.  a    >.  -i
                                                                             ?  a-   5  9-   i  s
                                                                             *•  o   >-  -5   > .  .
                                                                             «ft - L.   i^O •-   •*-* Oi
                                                                              ^U  *" S^1 ^   •
     c
              rrr     rri3
2  i  I  S
                              S  5
                                                3

                                                          O  -H   -"^  -K   *•*

                                                           '  i  2& ~  "i  sf^f
                                                          O  -O1  OO-  O  Q*
     •M   •  S
J^.   h)  ,-   .<
                                                       'sr O  •>J>  wr O "-f —f «Q-  <-f  — • f&  ~f  «.n  _•
                                                       "3' o  o  so a oo-  o  o- a  o   a Q  A
                                                       •-# C«  ••>-  yo «% u'cl  H»  »j= *3  *  u o  m.


                                                                        "
*J  hr  fc-MS^i^,

   a     "~ ^



   I  aa|h
   %  •« «*J Q  -2

   S*"8*!
D

LU
z   £
                                     ssr  ssz   -sr   asr
                                     •&•*•    S^*1   R» **   ^^^
                                       i       i       )       i        i
                                     *      •<      •*      -*      *
                                         •   -               •
8
     a     f
             r"
                           *m o -e-a^wn o
                                 -
                                         5-dCt  Ci.aiS'FI  — O"«
                                         '       *  PI  .e '

                                            «  s-     p^
                                                                                                 • _q  •
                                                                                      o  3-
T

CD

LL

_t

CQ
                           •  *  If  >rf*>


                           B  5  *  *
                                 -,352.    a^SS^^S S S    .j  338


                                 •S  i«  ^rt  ^"f  >i  *5l  sfl-  -sijT-  B hfi aft *§5lS  <"  &>  «*• *

                                 -t  3i  ^  3c£  "fcl  iZs  Ji  *S*    3 IK 2       ^E  $  2C

                                 3i        'o1^1     '     &  *         a^T         b
        —^ •—	J -J »— 'r- —  3  ft
         T3£•  I"?
                                                                                             5-,  K*-
                                                                                                 ^
                                                                                             7   s
                                                  S  2    £  S
                                                      2 |£  S  S  s |§  S  |  :  ?S  S  I  5  ?S  S
                                                      Q v-So^lea wllloa  vJAlia  Hjal
s

i
3
                               s
                               *
                                                               a r-4u%     IM •
                                                                              e
                                             £EE$a£     E'SESEJt     as

                                             laisfe  s  PPP  s  Is
                                             $>$>$..  --.  Q, . 131 • "3 -  ^-i  :g t
                                             . C-,. Vli — O  >-  mO«O«i>@>  *^  -i^J
                                          ^(            5B^            I\|            f^f
                               *— • sjj un,   ^«.  •••  ™p  ^Tj   'X'M'
-------
                                          ^    O^   O-d    ^ ^    ^^   ^ *•-    °3 **-

                                          1    IS   11    §3    IS   §3    §3
                                                                                                   3
                                                     ^ ^
                                                                                                  b " 2
        ^^^     ^     ,<^      ^     ^     ^      ^     ^     ^            ,      _                         ^     ^.
         fOI  — O I J  ^ -3 *J3  ^ C« -IJ  ^ O "J  -'^ O "ij  *^ C" "J  ^ CT "J°  ^ C1 •*#  KJ "^ "fl5  ma !© 5iJ  ti 1^1 i,ji  tui^hijt  Jtsi iD %il  na til i^il  ^ Hi IB  ^ Q
         ia -r SE) * i^^ ,-jB S -P ^ if ti -^ ^B S ^ — -ft ^ * ^-» S^ j « H %* ^ ft 3i^- j * tl^ ^ 1 -Ji -j _i4L ^-^> j * »^ ^ * ^--^ ^* i%--if' _. tt S^ _i|[

         |I§%lsf|ls&||5f|isl||snJ|»f^

        a§&iSa»-H!Ano5lA0!-go!^iaoo^  oS-^  5"-3 ^  5fl^  Sill^i  5^S"5 5^5  S-S^  "^3^  S?SW  ^^S  oa^a
        ^"Sffi3^K^^!^^W^^^^^!!M^MJ?!S^K^H^«^fflwS!ff^»WM^^SS^SWM^W«S
                                                                                     "^3^  S?SW  ^^S  oa^aS




                                                                                     ^-4 --1 | ©^T1! ^*y-^ TJ OPJ »lo
                                                                                   -5     '3     3      S      rj
                                                                Kii:ill*C     H     KlUii

                                                                FIL      1%     sS     Ci     ^     i"*J     gj      jr




                                                               "     ~      "           """     ~     ~
                                                                             ^     ^
                                                                                                      ^.
s   ^
                                                          o-      a      >=*«



                                                          ifi'^'»^-o^'-«— «^&ia
                                                          M     'Hit      Ri
o   a
          *  O  B^a^ai^iKAK uo  •  a  •   a  w
          -s^ ij -Z  i^, ^j «S  i, ij ^4  „, ij i3-  B_ijiHi~  i—, ij •—  ^ v 9 F-  st up ^—  lit, 4
          .jimQ  ~i»o  -^ t-i o  -j iS a  -1 ^ -S  -'^•Q  — -ft Q
          ess  >sl  ?si  ?;;"  ^=3  ssS  •s'aa
                                                                               ;••£!»  SUM  S S 3  S SE S  !^1y§l  5p
                                      ao    -5 o   C1 Q*   ~i9'Q    7IT   ^d"   B1'^    !S ^    C<@*    S3!    Q^    0s !§!
                                      !i    11   §i   11    3l   ii   §i    11    ii    Ii    11    fi
(-.
                                       la  p
                                       ^AO  =o-vi
                                                                as    s
                                                                ir>^&  O- "*» <
                                                                            -$-  -s-

3
•••-• J
^' yi
* s
                                              ^     -     a     »     a      3-    a     «
                                                                      sal  ill  n9  iil
                                        u.
                                      £51  sll sit
                                                           B-24

-------
                      '
                                 I  II



3
I
                             *&   ^



                             £j ^  ijt
*1 81
                                               8"  S* 3*  8" 3*  3" 8*  1
s c


  a
  i   i
                                            f  II  SI 51  II |I  I
                                               *   *  -   8  It  -s
                                               tl  tS s   i  §   s

                                               *—  «— *   *  »   <
                                                M   ^_     —  »   ^

                        -  -H   •       «                 M.^-.
                        * 5*  *  i*» 9*  a   ti   J'"*  if--• *~•"  a**» ^-IP*  o«p ^3w

                        » It  I*  if I?  *• it  1*  i~ 3-  I5 I*  I- I5
                        J OW  «!-*  *^ ^» _£  •#* * «* »  W M|  -S* HTarf  *^ Ow»  O_* 3*«

                        «F |pm  ^pi  j-It 0^1  rf w »*S  «" »  *- 3£ •- S  tsK ^P*  oS? a 4*
                                     =  sr 8= st  at §r  gt tr  8t s=
               M»             j

                     JL I,  5-*  f£
             «i  1
                  *
                                                                    3   s
     II   II  ii  II   II  11   II  II   fi  II  H   li
I  i!   II  II  Ii   i!  it

i? P?  Pi Pi Ps  i^s P
j- '> i£Ma  3^3 eSu'-ilt '*^3  ou^
-------





                                                                                          .

D

LU
o
o


                                                                                                                             III
CD

UJ

                                                                                                                 If
                                             i
                                   a o^ru «»i    o KI •*
       i
JM*i    O **1-^1 J*s t*K fc", «:.-->^ ^1. C&1!%1
                                                                -o- o o C3 c--s
                                                                                         c-o oo o-o-oc-ooo
                                                                               I!
                                                                               ^





                                                              B-26

-------
       y.


       I

z

2
3
yj
*   III
I   "MM'
1
        ii
          I


                          B-27

-------
    8 p
                                250 ds
      r   \
->  2 I"   If "" - • X       (pH-7 75, T» 25*C)
2   4 j      ^,_
    |% Pi IJfTl i!t I I I ! i I 1 I I I L L I tVqULt.tTTlT-Jyaj-*-*. J 1411
     • 10  0   10  20   30   40   50  60  70

                       : (miles)
                                             w   5
                                             z

     €
      40   0   10   20   30  40  SO  60  70

                  Dutiooc (mUe)
U
\  I
£
    o
      :. ,fii.,,, i.,,, i,,,, i., i7T7rn4.<-^^i m.
     • 10  0   10  20  30   40   50  60  70

                 Di3lMi*e (miJc)
   100
    SO
    SO


e  60



1  *
2  30
                                                10
      -SO   0    10  20   30   40  50  60  70
    0
     -10   0    10   20   JO   40  50  60  70

                 Distnoe (mik)
                                             ^  14

                                             I  12
                                             "f  10
                                                    JLI IHI f 1 f s ' ' 1 * * ' i lit i ' Itiiil
      -10  0   10   HO  30  40  SO  60   70

                  Distance (railc)
         FIGURE B-9. WASPS PREDtCTION OF INORGANIC NITROGEN. PHOSPHORUS,
 CHLOROPHYLL A BOD AND DISSOLVED OXYGEN FOR DIFFERENT FLOW CONDITIONS UNDER
                                   EXISTING LOADS.
                                         B-28

-------
 o
'•§
O
5J
a
?
6
5
4
3
2
I
ft
&
m
m
r
—
~
~
-
Lui
-i- i > i--i
1. 1 iCKJujg Fllitr
	 Activated Sludge
AdvuKcd Treilnwnl
\
\


4-4av «vg. slandlsrd
\JpH - 7.75, T - 2TC)
t i ' i ! t ! i i I r t i t 1 i r-*_t 1 t i i 5, L •« i & i. { I 1 1 !
     -10   ft    10   20   30  40   SCI   60  70

                 Distance (mites)
=
a
U
                                             -3
                                              C
                                             O
                                             Z
    ;r
      v,

    0 F-,771.. ..I.... i.. 1.1.,..;..,, \_IL1J_LLL!:>
     -10   0    10   10   30  40  SO   60   TO

                 Distance (mile)
u
Q.:
 V
o
     • 10   0   10   20   30  40   SO   60   70

                 Disiinc* (mile)
                                                »

                                            1
                                            •5  60
   40
                                                 20
                                                   r
    0
     -10   0   10   20   30  40  50   60   70

                 Distance {mik)
tin

B
SQ
          0   10  20  30   40   50  60  70
                  Distance :'mi]e)

,—B.
t
1
-Jil
fr
O
-g
>
a
.a
c

iu r 	
I* r
12 f-
10

8

6

4
2
n
—
m
._ ^_ 	 . 	
• 1 ^-" X/
— 1 ^^" ^^
\ 	 ^ ^^ ^itf ^^ ww s)
— fT^-//'^ D° stMidaiid
r
i , I 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 i 1 . iLu-Llll-LJ-LlLUJ-
-10   0   SO  20
                           40  SO  60  70
                  Distance (rfe)
   FIGURE B-tO. PROJECTiD INORGANIC MTRQGEN, PHOSPHORUS, CHLOROPHYLL A, BODs,
        AND DO UNDER DIFFERENT FUTURE LOADINGS UNDER LOW FLOW CONDITIONS.
                                          B-29

-------
concentration at approximately 40 miles downstream for
existing 30 cfs flow conditions.
                                                    B.3    WILLAMETTE RIVER EXAMPLE
                                                            QUAL2E MODEL
                                                    B.3.1   Introduction
For the low flow condition,  the WASPS run results in
approximately the same oxygen and ammonia concentra-
tions as that of the analytical example under both existing
and future conditions.  Oxygen concentrations drop to
approximately 4 mg/L and ammonia concentrations reach
approximately 4.2 mg/L, resulting in predicted violations of
both oxygen and ammonia standards.  The BODS  plot
shown in Figure B-10  includes the demand exerted by
decaying algae which was not considered in the analytical
solution approach. In the WASPS model, benthic denitrifi-
cation  is  not accounted  for  since  it is  assumed to be
negligible.
B.2.6   Conclusions
For this example, the results showed that low flow condi-
tions (7Q10) represent a critical condition for maintenance
of DO and  NH3 standards.  Critical conditions occurred
primarily under low flow (30 cfs) when the system was
dominated by point source loads.  Recommended man-
agement for implementation of a TMDL is to pursue a load
reduction from point sources.  As the flow decreases, an
increasing percentage of the CBOD and nutrient load can
be  attributed to the sewage treatment  plant.  Low flow
conditions are also critical for augmented algal growth. For
other types of pollutants or other site specific conditions,
analyses may result in different conclusions.  In some
cases a continuous simulation of storm inputs and receiving
water response may be  required to determine the fre-
quency and duration of stream impacts.  As shown here,
steady state examination of several flow conditions using
a analytical solution or WASPS can assist in screening for
the range of flow conditions where problems may occur.
For development of an actual TMDL, some additional
investigations are recommended. Calibration and valida-
tion of a nonpoint source loading model and river response
model  may need to be conducted and  additional data
collected if possible. Future development that may cause
an additional expansion of the treatment plant should be
considered. Any model uncertainty and future conditions
should  be built into a margin of safety for the TMDL. A final
TMDL should not be assigned until all of these factors are
considered carefully.
The Willamette River basin modeling study was selected
to show a recent example of a QUAL2E application. This
example  highlights the use of the  QUAL2E  model in
assessing DO (DO), nutrients, and phytoplankton biomass
for a large river system in Oregon. For additional discussion
on the use of QU AL2E in the development of TMDLs, see
Sections 2.3 and 3.4. TheuseofQUAL2E for uncertainty
analysis is shown in Appendix D.

The  Willamette  River Basin Water Quality Study
(WRBWQS) is an ongoing interdisciplinary study that in-
cludes investigations of river hydrology, sediment transport,
toxic organic compounds and trace elements, point and
nonpoint pollution sources, and aquatic ecosystems. The
development of predictive computer models under low flow
conditions of late summer was one goal of the Phase I
study. The low-flow period coincides with the critical period
for DO and is suitable for steady-state DO  modeling. A
model was  needed to more fully assess the interactions
among nutrients,  phytoplankton, and DO, which had not
previously been undertaken. A summary of the selection,
calibration, and preliminary evaluation of the Phase I DO
model is presented below. A water quality management
case scenario involving the effect of variation of the river
flow regime on DO and phytoplankton biomass is also
presented.  A more complete description of the develop-
ment and evaluation of the WRBWQS Phase I DO model
has been provided in a number of technical reports (Tetra
Tech, 1993a, b,c).

B.3.2   Problem Setting

The Willamette River drainage basin area is approximately
30,000 km2 in area and is bounded by  the Coast and
Cascade mountain ranges (Figure B-11).  The mainstem
of the river  meanders in a northerly direction through an
alluvial valley approximately 300 km to the Columbia River.
The problem setting and essential characteristics of the
river system are summarized in Figure B-12.

Although the drainage basin contains the majority of the
State's inhabitants, approximately half of the basin  is for-
ested. However, significant changes have occurred in the
drainage basin since the arrival of European  immigrants
beginning in the early 1890s
                                                B-30

-------
a
Major Poinl Sourca
Wa;*r Quaiiiy Sampling
FIGURE B-11. THE WJLUWIETTE RIVER BASIN STUOV AfiEA, LOCATIONS OF MAJOR POINT
    SOURCE DISCHARGES ALONG THE MAINSTEM OF THE RIVER, AND LOCATIONS
              Of THE AUGUST 1S92 SYNOPTIC SURVEY STATIONS,
                                  B-31

-------
IV
n
             Mudnomaft Chimf*
             Portland
          uiottvnul* BMP
                   Corvallis
        Hi^yt Hhw i
                   MU 111,!
                   AMI 174.7

                   RM 177,7
                                    • ny t f
                                    • Ry T?
                                       £M J
                                    . ._ — -= AJUMQ


                                    - an 71 2
                                       fto
                                 Salem
                                    • =iu H.D
                                 Herri sburg
                                 Springfield
                                                               LEGEND
                                                                    R«»ch
                                                                     Rivar MiM
            FIGURE B-l 2,  RIVER SEGMENTATION, POINT SOURCE ^*JD
                       TRIOUTARY LOCATION SCHEMATIC
                                         B-32

-------
Gleeson, 1972; Sedell and Frogatt, 1984). Approximately
one-third of the basin is currently used for agriculture, and
the forests have  been exploited for timber production.
About 10 percent of the basin has been urbanized or is in
residential use. The river receives direct inputs of treated
municipal wastes  and industrial effluents—primarily from
pulp and paper processing  facilities.  Although nonpoint
source inputs are significant during winter rainfall runoff, this
source is considered minor relative to point sources during
the dry critical period for DO.

Oregon  water  quality standards relevant to the modeling
study include the  state standards for DO and an "action
level" for chlorophyll  a  (Oregon  Administrative Rules,
Chapter 340, Division 41).  The state DO standards vary
for each reach of the river:

    •   The Tidal Reach [river kilometer (RK) 0-43] -
        5 mg/L

    •   Newberg Pool  (RK 43-80) - 6 mg/L

    •   Newberg Pool to Salem (RK 80-137) - 7 mg/L

Above Salem, the  DO standard is a minimum of 90 percent
of the saturation concentration for DO.

The state action level for chlorophyll a is 15 g/L. This action
level applies to natural lakes that do not thermally stratify,
and to reservoirs, rivers, and estuaries. The action level is
intended to identify water bodies where  phytoplankton
might impair beneficial uses.  If it is determined that the
action level is exceeded  in a particular water body, addi-
tional studies might be conducted to determine the causes
of the exceedances and impacts on beneficial uses. Con-
trol strategies,  including additional standards or pollutant
load limitations, could then be developed.

The  need for predictive water quality models was under-
scored by the  continued  industrial and agricultural devel-
opment and population growth withinthe basin. As of 1990,
the population within the nine counties that cover the basin
had almost reached 2 million.  Highest population growth
rates have occurred in the counties that encompass the
large urban centers  of  Eugene, Salem,  and  Portland.
These urban centers and other smaller towns and industrial
facilities are found along the  banks of the mainstem of the
river.
Currently there are 21 major dischargers to the mainstem
of the Willamette River. Twelve additional facilities dis-
charge to tributaries of the Willamette. Discharger name,
type, receiving water, and river mile location are summa-
rized in Table B-5.  Pollutant loading information for the
various facilities is shown in Table B-6.

B.3.3   River Characteristics
At its mouth, the Willamette is the 10th largest river in the
continental U nited States, in terms of total discharge (Sedell
and Frogatt, 1984), and the discharge per unit area is the
highest of the large rivers in the Nation due to the heavy
winter rainfall at lower elevations in the  basin during the
winter months (Rickert and Mines, 1978). At higher eleva-
tions the winter precipitation occurs as snow, which con-
tributes to extended high flow as spring snowmelt runoff.
The climate is temperate and characterized by wet,  mild
winters and dry, moderately warm summers. Most of the
rainfall occurs in the fall, winter, and spring, with little rainfall
during June, July, and August. The period of low river flow
during the late summer coincides with the period of low
rainfall and highest air temperatures.
River discharge is managed for flood control, irrigation, and
navigation  purposes  by impoundments  located on a
number of the large tributaries.  Nonetheless, river dis-
charge varies seasonally, with greatest runoff occurring
during the winter months (December-February) (ca. 1,800
m3/see) and lowest flows occurring in summer (July-Sep-
tember) (ca. 283 m3/sec), with a mean annual flow of
approximately  943 m3/sec (Moffatt et al., 1990).   Low
summer flows are augmented by controlled releases from
tributary impoundments to provide for commercial naviga-
tion. A natural flow control occurs at RK 42—Willamette
Falls—although a lock, powerhouse, and fish ladder have
also been constructed at this location.  Below the falls, the
river is tidally influenced via the confluence with the Colum-
bia River which flows to the Pacific Ocean approximately
160 km to the west. Due to the great d istance to the ocean,
flow reversals in this 42-km reach cause intrusion of only
fresh Columbia River water into the Willamette. Flow in the
reach below the falls is further complicated by the presence
of two channels—the  main  channel, which enters  the
Columbia River at RK  162, and the Multnomah Channel,
which passes between Willamette RK 5 and Columbia RK
140.
Based on hydraulic and physical characteristics, the main-
stem of the river may be divided into three distinct reaches
(Rickert et al., 1976). The upstream reach is 217 km long
and is characterized by fast-moving currents flowing over
a shallow, meandering
                                                  B-33

-------
       a
       y
'A
ts
ai ./•
-4 4
a a
*U -
s5
S£

&t
E oi
QS
^^
dU
LU c
^1
                                    'y '"
khtt
           it *" a H" S *• £' S! »' 5 <#*- a S'lT!^ 1 «*


           •S'l£»Jl',"^> *j5_l < Slrt^Cl^jir.fZiafSIwi,* 'W,^
is'
i if:
jfi 2 J {Id ;=! J
          !Sl
          1 2
        M Ui g
        « *»i M
        f||

fi^av^i.'-j^n
slil^'l^'CS'il
«B«
                                                 !
                                   B-34

-------
  < <
< =5
                                                 B-35

-------
riverbed composed of cobbles and gravel.  The middle
reach (Newberg Pool) is a 54-km-long, deep and slow-
moving portion of the river formed by the natural impound-
ment behind Willamette Falls. The tidally influenced reach
below the falls (Tidal Reach) is also relatively deep and has
the longest estimated travel time—241 hours during critical
flow conditions (Rickert et al., 1975).

B.3.4   Model Application

The objective of the WRBWQS was to develop and cali-
brate a predictive DO model for the Willamette River to
evaluate river basin management alternatives and meet
regulatory mandates. To first identify appropriate predictive
models, several DO models of varying complexity were
identified and evaluated using a number of selection crite-
ria: 1) Dimensionality—a one-dimensional model was con-
sidered  adequate;   2)  Temporal characteristics—a
steady-state model was considered appropriate for the
summer low-flow period of interest; 3) Consideration of
relevant processes—these processes included the capa-
bility to model phytoplankton growth and nutrient interac-
tions;      4)   Suitability   for   a   range   of
applications—temperature or bacteria modeling is an ex-
ample; 5) Data requirements—the data required for model
calibration had to be within the resources of the study; and
6) Ease of use—the selected model needed to be suffi-
ciently easy to use so water quality managers could prac-
tically apply the model as a decision-making tool.

Based  on these selection criteria, the model QUAL2E
(Version  3.14) was selected.   This one-dimensional,
steady-state model incorporates all of the relevant proc-
esses and has a menu-driven input and output system that
facilitates use of the model.  Additionally, the model in-
cludes  applications for component, sensitivity, first-order
error analysis, and Monte Carlo simulations.
B.3.4.1  Database Development and Model
        Calibration
Historical water quality data and previous  DO modeling
efforts were reviewed to identify relevant data and modeling
approaches that could be incorporated into the QUAL2E
model calibration effort (Tetra Tech, 1992a, b). The histori-
cal data review also identified data gaps to support the
design of a synoptic field sampling effort to provide a data
set for calibration of the model.

The field sampling effort was conducted in August 1992
and included diurnal DO and temperature measurements
at 15 stations and measurements of nutrients and CBOD
at 24 stations along the mainstem (Tetra Tech, 1992c).
ODEQ collected single grab samples from 10 locations,
and Tetra Tech collected samples at approximately 6-hr
intervals over a 24-hr period at 15 stations. Data were also
collected by the USGS at RK 20.6 as part of its National
Stream  Quality Assessment Network  (NASQUAN) on
August 17,1992. USGS data were also incorporated into
the model calibration effort. Point source loading data were
compiled for the 21  major municipal and industrial effluent
discharges to the mainstem of the Willamette River using
the permit-required monitoring reports submitted to ODEQ
and additional data collected during the synoptic field study
(Tetra Tech, 1992c, d)  (Table B-4).

The QUAL2E model was first discretized based on river
hydraulic information provided by USGS (M. Fretwell, May
20,1992, personal communication). The river was divided
into 35  model segments with  similar  physical  charac-
teristics, resulting in a  model consisting of 35 reaches
divided into elements 1.2 km long for a total of 249 model
elements. The tributaries and major municipal and indus-
trial point sources were modeled as point sources inputs to
the mainstem of the river.

Since river depths, velocities, and cross-sectional areas in
each model segment vary under different hydrologic con-
ditions, discharge coefficients and exponents were esti-
mated for the model calculation of these variables as a
function of discharge.   (Note:   It was assumed that the
effective channel width would not change under low flow
conditions.) To estimate the coefficients and exponents for
each model segment velocity- and depth-discharge rela-
tionship, low- and high-flow stream channel hydraulic infor-
mation provided by USGS was used (M. Fretwell, May 20,
1992, personal communication). The resulting QUAL2E
model output for discharge, velocity, depth, and cross-sec-
tional area are compared to the channel hydraulics data
provided by USGS for the 2-yr, 60-day recurrence interval
flow in Figure B-13.  The model-predicted discharge, ve-
locity, depth, and cross-sectional area are also shown in
Figure  B-14 for  the August 1992 sampling period.  A
sample of the model input file is shown in Table B-7.

The model was then calibrated to the 1992 synoptic water
quality survey data using a combination of visual best-fit
and error minimization  techniques. A preliminary calibra-
tion was conducted first using best
                                                 B-36

-------
                         £* £~f 13 —
                         5 a - ii
                         U --5 3 O

                         E > 3 2

   2»



   JWJ-



   !M i

_ aw


 - HO






 • 90
FtGURE B-13. WILLAMETTE RIVER FLOW, VELOCITY, DEPTH, AND CROSS-SECTION
                         AREA RELATIONSHIPS
                                B-37

-------

  1C*
                           -E 3 £ 2
                                3 i
                                it.
                                         i'l S..
                                        -2 '*  ?
                                             f   IT — •




                                             f    1
                                                                       •
                                                                      — SSI




                                                                      -EJ*
                                                                       •
I  :*
5  13
*  B»
                                                                      - 61
                                                        •84
                                                                      ~M



                                                                      - 14
•5  01
                                                                       ftG
                                                       •^ 08
                                                                        D4
                                                                       t 02
      100
  290
                           100
                                                 WO
                                Jtr.fr kii MI
                                                                      -'§0
S 50% c~ ~HE CAL-SR.A*=I:. O^AL^E
                                                          -'T»'jr ~o *-E
                                    B-38

-------
            3 o-sap SDO =.£10-00 O OO
<
^
O
-j  j

li
uj

         i
                        •C- —-*PH«3-EXi-^j-J ^w^ p> £
                                      o-a oo o« o-o oa *s? ww «3 "
                                                                 i a-oo-ci B-OO-O c-o-ra oo
c x x x ar :x x i £; i a:
         a  ^ir^ssssJS^^rssis1^

                                                      • —                  -
                                              Ifl«»«"' B  *°a^«I"5llS
                                                                                O =• a i= -j r_- ^ r; .= a r, D c o a •= =1 o
               =5=


                                                             _^.&. — ,...^    a
                                                              11^ *r«^.girvry^fam «    i_
                                      3lui
               «---™          «  "

                                                                  u

                                                                  <-•> SB. «a •; 3 ™t -1 * * "»• "^ Ba ^ lsl "^ "ffi *" *" "^ f" """^ »" ~ "~ ^ *" ~
                                                                                       -'
                                                   B-39

-------
Q
LU
13
O
O.
CD

LU
-J
                                   ooeqaooqno
                                      a e a a-oo a a oo
          pppp ppoppa pop pc
                                         ooooemooo  a i
                                                     j ocsoooooooci i
                                                                 oo 00,00-0
                                                                             oo oo oo oo
                                                                                                      ooo a a
              O O O >Q- O id i
                                                              .
                                                        «n ^rf> V
                                                                             .    B
                                                                          1*1 «•* iri uf* tnui vrs^niti
          o-onO'OoaoE>'Oo--ao o-o o-o oo O'O n c-ocata a-o a  o c-o a o-o c-ooo-ao-oad-cii?h«ii ^.

          **~ W O- O •* IMA^-rf^^Ha ^HDG^ C-^IX*Kl«JI«V O^ B ft O'^-'Vi *^"s|* J*.  •^IXirtsJ'1 A-flH Aft O^i*iirVirkniy
              !— i— «-i-a-i— i-r-i-»- rury run, r«i^( n.f^**u»«»^«i«»«^«l!»*                *-w«r,astii!_s.,

          «••!»!»«• « « • « • •*•*•»•• mmmtmmmmm  * •' * • *! * « * * *> * f f I 1 I i> i
          XEKarseiKaexzxaE, KacacKxxxixxxixxjsxxaef  ^ ^ ^ ^ .s ^ ^ ^ aE m * * ^ •» ffi * -j- E
jj lliiiiiiiiiiiiiiiiiiiiiiiisii  5H555555S5!5!!?!:!5!5!5!5SSH55r!
Sj «. «. a. a. &. a. a. *.&.*. ILL j. 1.1 ^ tn.Li.J-i.i4-i. t ia. tS'S! SS S S; iMSUMS'flSS'SS S 5 SSIS'SSSSS £i SKS'SlS'S'tfS
|
                                           __
                                          jgSjOo
                                           •  •   "
                                                    » o i» d ci a* Ci £3 c
                                                                                         3 OOOOOO O  P OO O 13 &

                                                                                                      a oo e> *i*i
                          '- — -JCN f-J^i — Kl fc^ r***%-
                          - "-    -    —""
       /x,  — £

       *   • _•  * _• _• - _• o

                                                                                                      -o ooo P a-
                                               3 OO O O.O OO OO

                                                OO-Q oooacao
                                                              ooaaa'ci
               ' ooo oo po ooooo
          ^; '^ ^ 3' ^ w; '*^ ^ f] •«• g "P W g W H W H W- -H W; ^

          _™.»-™.,™._,™.™. — — ™ ™ _ ,™ __™.».™, .*. ~. —
                                                                                •

                                                       B-40

-------
                          O OO O-Q O-O «
                                                                  i »3 fO Ci Q Offi O O Q O D O O-O

                                                                 S ^ & d a ci -a is ci a o a- o & a
                                                                                                         I-CIOC3Oa^
                                                                                          &-a oo-o D-Ci-a B-OO o fc
                                                                                                                                         " a <=*-o a
Q
j^lJ    j'i|  I-J?M tor-ji-j

13    ~	
S2    ft
K    £


8    I
1*
03

LU
_l
CQ
           ill
000 a G-a oaa OQ-o-a OQ o-ao o-o a &O-Q Q-QQ-O OQ-Q o
                                                  oa a-ooo-B-o-a a o-ra-oo-ai o-o   «i o-aoa
                                                                                           Str^wi viw^.pnir«m •nn.B^m •<%«*** rtrtnfl •*
                                                                                           c*a o D Qa-a ooo-a c-o-a a O'Sa sa

                                                                                         i|i c* ci d a- o" o Q" o -a o -a o a a a o o n-
                                                                                            •  * i « i  i  • i * 1 If  i »
                                                                                                                                             -»••**•

                                                                                                                                                     '

                                                                                                                    i««*»»ii»a»"aaa»>
                                                                        B-41

-------
          •a O     O
       *U   i 1

        !  O-CS
yu

3 :    gj*  ^"^s
5j     j  io.1

^     S  "-"
   I    ifol  to -M •
           i  -Isssa
m

LU
_l
m
   *a es-oo oooo-oa a-    """•  ?^  ™ ^^^^^^^^^7-^"3 3-33'?3T'''!j: ? 3'33-'j:  w;S3*~^5%<*'i^H''"-*-§'^- - -  "-S  - -S

                                                                                      i;---;j—"--•-•




                                                                                      i






                           ra                       —      -*              a    c-       a e



                           iS  «•« p.£S WI*OP, 0*"«* i&ii

                           ni




   acjac-z.Dc-oDact    '   **  **       K.IK K.ja-0^ i**^fc  •     ••        m  »o  ^B-—   .^ac  ^^—o  o——-a  a-









-^                             «  I  f I  ; I  £  I F «  • f  ?  9 f  •»•-!-!»•!                 r^"        Of—    1-^
                              a Q -a o D- a a- o -B- o -D o -o 0 -a D >s -a o< o a 'z1 -D a- Q!

                           o                 ^        .          .,        "CTCT«CTD-c?ioaiOiwa-|»«?«j "E^wwo a-wra owe?

                                      __   ™ji "..Q j^:   '5 •!  JH  lii   -« -3 X^'mJ  I^O"O^¥lOlHF)|"v^COB1l?^"*r^r%ru'N$*kOdi?>>VM
                           iM    •— «n«  W. f   ^W^M'   ra ^  • **  31   ^ ^ S a!  "i "!  Ki id -Si r-j IH.  i3= h, i -Q   'iQ r^ ^    i, Jfl -^















                                                                                                         ^*fc
._ nn^*nir^^^nn.carin



                     -.^^--^^  -^^^^_  ,J..JJ-J-_-_JJ —— -__..,-_------_---—.-—^^
                                                        B-42

-------
professional judgment and a visual best-fit technique to
arrive at reasonable values for the rate constants in the
following steps:

    •   Nitrogen balance: Adjustment of the ammo-
        nia oxidation nitrification and organic nitro-
        gen hydrolysis  rates to  fit  the model
        predictions to the nitrate, ammonia, and or-
        ganic nitrogen data.

    •   Phytoplankton growth:
        1) Variation of the model  options for algal
        growth limitation and photosynthesis.
        2) Adjustment of the specific maximum algal
        growth rate, algal respiration rate, and  the
        phytoplankton settling rate to fit the model
        predictions to the chlorophyll a and nutrient
        data.

    •   Phosphorus balance:  Adjustment of the or-
        ganic phosphorus decay rate to fit the or-
        ganic phosphorus and soluble  phosphorus
        data.
    •   DO balance: Fixing of the  instream ultimate
        CBOD decay  rate  and  the atmospheric
        reaeration coefficient  based on  previous
        studies of the Willamette River (reported by
        McKenzie et al., 1979) and  adjustment of the
        SOD to fit the DO field data.

Final model calibration was achieved  by minimizing the
cumulative absolute relative error (CARE) between model
output and field data using the ammonia oxidation rate, the
organic phosphorus decay rate, the  maximum specific
algal growth rate, the algal settling rate, and the sediment
oxygen demand rate.

The calibrated model's fit to the synoptic survey data are
shown in Figures B-14, B-15, and B-16. The location and
concentration of the minimum DO measured during the
synoptic survey at  RK 43 was matched by the model (7.3
mg/L) (Figure B-16).  The model-predicted DO concentra-
tions ranged up to 8.6 percent of the 24-hour average DO
concentrations measured at 15 stations, with a mean and
median relative difference of 2.5  and 1.7 percent, respec-
tively. The model-predicted DO  concentrations did not fit
the concentrations measured using single grab samples
collected by ODEQ.  In general, single grab samples for
DO were considered inadequate for the calibration of a
steady-state model,  especially for the upper river reach
where large diurnal fluctuations in DO occur.
The maximum chlorophyll a concentrations measured in
the lower river were also predicted well by the model,
although the model prediction increased exponentially to
much higher levels below RK 11 (Figure B-17). The model
did not predict the relatively high chlorophyll a levels meas-
ured in the upper river.  Suspended algal biomass in the
upper river reach is considered to be derived from slough-
ing of periphyton in this relatively shallow stretch of river.
Because the model does not  consider the  influence of
periphyton growth, the model DO predictions for the upper
river reach  reflect only variation in the steady-state DO
concentration due to point source inputs and reaeration.
B.3.4.2 Model Validation
Validation of a calibrated model with an independent data
set is meant to substantiate the model's predictive power
under environmental conditions  similar to those under
which the model was calibrated. With this goal in mind, the
calibrated model was applied to August 1990 conditions
using point source data provided by HydroQual (1990) and
water quality data  available as part of ODEQ's Ambient
Monitoring  Program.  Although model-predicted  and
measured  conditions were in relatively good agreement
(Figure B-17 shows the model's fitto the DO and chlorophyll
a data), the model was not considered to be fully validated
because the DO concentrations reported by ODEQ are for
single grab samples. These types of samples were not
considered  adequate for the calibration or validation  of a
steady-state model.   The model was considered suffi-
ciently tested for management analysis. Full validation will
be achieved upon completion of additional monitoring and
future updates of the model.
B.3.5   Conclusions
Figure B-18 shows the effect of variation in the Willamette
River flow regime on Willamette River DO and chlorophyll
a concentrations.  The relative effect of various flow re-
gimes, ranging  from  135 to 218 m3/sec measured at
Salem, on the calibrated-model prediction of DO at RK 43
(using the August 1992 model inputs) and chlorophyll a at
RK 16, are presented.  In general, variation in river flow had
a noticeable effect  on DO throughout the river and on
chlorophyll a below RK 80 where the river enters the
Newberg Pool reach.  This analysis substantiates the as-
sumption that flow augmentation of the Willamette River
during the low-flow period of July through September can
be an effective means of water quality
                                                 B-43

-------
      ?!•
   a
   i
     ftDfl

f1
h 	 ' X
I
it
I
-J
i 1
                                                                 , g.OO
  •T CM
     ooa r-
     0.03 -
     ooa
        3CO
                     j i-±.
                                                           T
                                                           T    - a.H
                 250
                                              too
                               River Kfl
       FIGURE B-15,  COMPARISON OF CALIBRATED OUAL2E MODEL OUTPUT
TO THE AUGUST 1992 FILE DATA FOR ULTIMATE CBOD AND PHOSPORUS COMPOUNDS
                                   B-44

-------
     »
     >fc
     Pi
       7 -
       @r
n,

 300
                                               ntB/L
i
State DO jtuMUrdi -""" (
--_,__ S mi1

«j
L ]
j C
j
                 250
200
                                 ISO
                KM
                                 50
       10-
 300
                             CTyoruphjll
                            Li^
ZSO
2M
                                 150
                                                 SO
FIGURE B-16. COMPARISON OF THE CALIBRATED QUALSE MODEL OUTPUT TO THE AUGUST

         19952 FIELD DATA FOR CHLOROPHYLL A AND DISSOLVED OXYGEN,
                              B-45

-------
    S -


      KB
                            ? * g
                            z 5-4
                            5 i i
                               ii
7    !
I    t a


1^  I I
                                           i .i-
                                     y
                                     •a
                                     1_.
                                                             I
                                                             3*
                                                            i.
                                           s=   »
                                                                  - i
i  10
'**   i
    o —	
                1*3
                              R>ITT Kii-qMrB»f
                                                      10
  O-1 7 VSRjRCATtON GF
  ST AUGUST
DATA K3» DtSSOLVED Q*VG©N
                                                 lO


                                                           A.
                                  B-46

-------
 *
 I
 !
250
                    200
                                  t»
f
1ft •-
i
18 -
14 -
,.!-
a I-


1 p
* i i
j ' /:
. 1 } L
-------
management. A minimum flow of 170 m3/sec atthe Salem
gauge (RK134) is maintained during these months to allow
for navigation, and also, to maintain adequate DO levels in
the river (Rickert et al. 1980).  The model-predicted DO
concentrations at RK 43 vary almost linearly from 7.0 to 7.5
mg/L over the range of flow  regimes  evaluated.   The
model-predicted effect of flow on chlorophyll a concentra-
tion was not linear.  The chlorophyll a concentration is
predicted by the model to increase rapidly when river flow
at Salem falls much below 150 m3/see.  These results
support the hypothesis of Rickert et al. (1977) that phyto-
plankton biomass in the lower river is most strongly con-
trolled by variation in the flow (i.e., the water residence time)
and that management of the flow regime is not only an
effective means to control DO  levels, but should also be
effective in the control of phytoplankton biomass.
None of the assumptions associated with the model devel-
opment precluded its application and use as a predictive
tool.

Future work includes a field study conducted during the
summer of 1994 by ODEQ and USGS, including meas-
urements of SOD that will allow further refinement of the
QUAL2E-UNCAS DO model.  Planned  Phase II model
improvements include updating the model to QUAL2E
Version 3.20, which will allow for the incorporation of minor
point sources and the evaluation of the  model-specified
SOD rates. Depending on the results of this evaluation, the
model will be recalibrated and validated  using the more
recently collected field data. The update of the model will
refine estimation and predictive capabilities.
There are several assumptions inherent to the modeling
analysis that should be considered in interpretation of the
calibrated model results.
B.4    REFERENCES
     1)  The model does not incorporate the effect of
     periphyton  production  on DO.  The  effect of
     periphyton  production on DO  might be  signifi-
     cant in the upper reach of the river above RK 80.
     2)   The model does not account for tidal mixing
     with the Columbia River. Therefore, the model
     output below RK 16 should be interpreted with
     caution.
APHA (American Public Health Association), 1985. Stand-
ard Methods for the Examination of Water and Waste
Water, 16th ed., Washington, DC., 874 pp.

Bowie, G.L., W.B. Mills, D.B. Porcella, C.L. Campbell, J.K.
Pagenkopf, G.L. Rupp, K.M. Johnson, P.W.H. Chan, and
S.A. Gherini. 1985. Rates, constants and kinetics formula-
tions in  surface  water quality modeling.   2nd ed.
EPA/600/3-85/040.   U.S.  Environmental  Protection
Agency, Environmental Research Laboratory, Athens, GA.
     3)   The model does  not consider high-flow
     events or dynamic conditions.  The model as-
     sumes steady-state conditions that are appropri-
     ate for  representing  the low-flow conditions
     present when problems in the Willamette River
     typically occur.


     4)   The model does not explicitly consider mi-
     nor point sources of nutrients or oxygen-de-
     manding substances.   Preliminary  estimates
     suggest that minor point sources could contrib-
     ute as much as an additional 10 percent to the
     estimated CBOD demand load to the mainstem
     of the river (Tetra Tech, 1992d).


     5)   The model-predicted DO concentration in
     the lower river was very sensitive to the model-
     specified rate of SOD.  However, no field data
     were collected to establish the reliability of the
     rates of SOD specified  in the model.
Fretwell, M.O.  20 May 1992. Personal Communication
(letter to Mr. Robert Baumgartner, Water Quality Section,
Oregon  Department of Environmental Quality, Portland,
OR). District Chief, U.S. Geological Survey, Portland, OR.

Gleeson, G.W..  1972. The return of a river.  The Wil-
lamette  River, Oregon.  The Willamette River Advisory
Committee on Environmental Science and Technology
and Water Resources Institute, Oregon State University,
Corvallis, OR.

HydroQual.  1990.  DO data analysis and modeling for the
Willamette River, Oregon. HydroQual, Inc., Mahwah, NJ.

McKenzie.S.W., W.G. Hines, D.A. Rickert and F.A. Rinella.
1979. Steady-state DO model of the Willamette River,
Oregon.  U.S. Geological Survey Circular 715-J.

Moffatt,  R. L, R.E. Wellman and J.M. Gordon.  1990.
Statistical summaries of streamflow data in Oregon:
                                                B-48

-------
Volume 1 — Monthly and annual streamflow,  and flow
duration values. U.S. Geological Survey, Open-File Re-
port 90-118, Prepared in cooperation with Oregon Water
Resources Department.

Rickert, DA, and W.G. Mines. 1978. River quality assess-
ment:   implications  of a  prototype project.   Science,
200:1113-1118.
pared for Oregon Department of Environmental Quality,
Portland, OR. Tetra Tech, Inc., Redmond, WA.

TetraTech. 1992d.  Willamette River Basin Water Quality
Study. Component?: Point source discharges and waste
loading to the Willamette River basin during 1991. Pre-
pared for Oregon Department of Environmental Quality,
Portland, OR. Tetra Tech, Inc., Redmond, WA.
Rickert,  D.A., W.G. Mines and S.W. McKenzie.  1975.
Methods and data requirements for river-quality assess-
ment. Water Resources Bulletin 11:1013-1039.

Rickert,  D.A., W.G. Mines and S.W. McKenzie.  1976.
Methodology for river-quality assessment with application
to the Willamette River Basin, Oregon.  U.S. Geological
Survey Circular 715-M.

Rickert,  D.A., R. Petersen, S.W. McKenzie, W.G. Mines
and S.A. Wille. 1977. Algal conditions and the potential for
future algal problems in the Willamette River, Oregon. U.S.
Geological Survey Circular 715-G.

Rickert, D.A., F.A. Rinella, W.G. Mines and S.W. McKenzie.
1980. Evaluation of planning alternatives for maintaining
desirable dissolved-oxygen concentrations in the  Wil-
lamette River, Oregon.  U.S. Geological Survey Circular
715-K.

Riley, G.A.,  1956. Oceanography of Long  Island Sound
1952-1954.  II. Physical Oceanography, Bulletin Bingham.
Oceanog. Collection 15, pp. 15-46.

Sedell, J.R. and J.L. Frogatt.  1984. Importance of stream-
side forests to large rivers: The isolation of the Willamette
River, Oregon, U.S.A., from its floodplain by snagging and
streamside   forest  removal.   Verh.  Internal Limnol.
22:1828-1834.

TetraTech.  1992a.  Willamette River Basin  Water Quality
Study. Components: Data review and summary for DO
modeling on the Willamette River. Prepared for Oregon
Department of Environmental Quality, Portland, OR. Tetra
Tech, Inc., Redmond, WA.

TetraTech.  1992b.  Willamette River Basin Water Quality
Study.  Component 4: Review and summary of nutrient
and phytoplankton growth data for the  Willamette River.
Prepared for Oregon Department of Environmental Qual-
ity, Portland, OR.  Tetra Tech, Inc., Redmond, WA.

TetraTech.  1992c.  Willamette River Basin Water Quality
Study.  Component 11:  Water quality survey data. Pre-
TetraTech. 1993a. Willamette River Basin Water Quality
Study.  Summary report.   Prepared for Oregon Depart-
ment of Environmental Quality, Portland, OR. Tetra Tech,
Inc., Redmond, WA.

TetraTech. 1993b. Willamette River Basin Water Quality
Study.  Willamette River DO modeling component report.
Volumes 1 and 2. Prepared for Oregon  Department of
Environmental Quality, Portland, OR.   Tetra Tech,  Inc.,
Redmond, WA.

TetraTech. 1993c. Willamette River Basin Water Quality
Study.  Willamette River nutrient and phytoplankton growth
modeling component report. Volumes 1 and2. Prepared
for Oregon Department of Environmental Quality, Portland,
OR. Tetra Tech, Inc., Redmond, WA.

Thomann, R.V., and  J.A. Mueller. 1987. Principles of
surface water quality modeling and control.  Harper & Row,
New York, NY.

USEPA. 1980. Technicalguidance manual'forperforming
waste load allocation, Simplified analytical method for de-
termine NPDES effluent limitation for POTWs discharging
into low  flow streams.  U.S. Environmental  Protection
Agency,  Office of Water  Regulations and  Standards,
Washington, D.C.

USEPA. 1983a. Technical guidance manual for performing
waste load allocations, Book II: Streams and rivers, Chap-
ter  1:  Biochemical oxygen demand/dissolved  oxygen.
EPA-440/4-84-020.  U.S.  Environmental  Protection
Agency,  Office of Water  Regulations and  Standards,
Washington, DC.

USEPA. 1983b. Technical guidance manual for perform-
ing waste load allocations, Book II: Streams and rivers,
Chapter2: Nutrinet/eutrophication imacts.  EPA-440/4-84-
021.   U.S. Environmental Protection Agency, Office of
Water Regulations and Standards, Washington, DC.

USEPA.  1984.  EPA Ambient water quality criteria lor
ammonia. U.S. Environmental Protection
                                                B-49

-------
Agency, Office of Water  Regulations  and Standards,   USEPA. 1992.  Compendium of watershed-scale models
Washington, D.C.                                    for ™DL development. EPA 841-R-92-002. U.S. Envi-
                                                   ronmental Protection Agency, Office of Water, Washing-
USEPA. 1987. Quality criteria lor 1986.  EPA 440/5-86-   ton, D.C.
001.  U.S.  Environmental  Protection Agency, Office of
Water Regulations and Standards, Washington, D.C.
                                               B-50

-------
APPENDIX C: QUALITY ASSURANCE  FOR  FIELD
                          MONITORING  PROGRAMS
C.1     OVERVIEW
As used here,  quality assurance (QA)  is a system of
activities used to provide documented assurance that a
data product of known and acceptable quality is produced.

The importance of QA should be evident. However, be-
cause of the additional effort required to provide QA (ad-
vance planning,  management, supervision,  and
resources) it is often neglected or overlooked. This manual
has addressed, at some length, guidelines for the analysis
of data that will lead to the performance of technically
sound, defensible TMDL studies. This is particularly impor-
tant where decisions derived from TMDL studies have
serious economic and environmental impacts.

A properly planned and  implemented QA program will
enable the substantiation of data accuracy and precision
by an outside, impartial review and forestall any attempts
to discredit or impeach the data produced. This section
outlines the minimum QA effort required to ensure a reliable
TMDL study. Its aim is to assist the user in developing a
reliable and effective quality assurance program that will
meet data user requirements for completeness, precision,
accuracy, and comparability of data.  Note that the QA
requirements given  herein are the minimum requirements;
they are to serve as a foundation on which the user can
build a viable QA program.
C.2   ACCURACY AND PRECISION
Accuracy refers to agreement between the measurement
and the true value of the measurand, with the discrepancy
normally referred to as error. Precision refers to the repro-
ducibility (repeatability) of the measurement, when re-
peated on a homogenous, time-stationary measurand,
regardless of the displacement of the observed value from
the true value.

The statistical measures  of location or central tendency
(e.g., the various averages, mean, median, and mode) are
related to accuracy. The statistical measures of dispersion
or variability (e.g., variance, standard deviation, coefficient
of variation, and other measures derived from central
moments of the probability density function) are related to
precision.

Discrepancies between the results of repeated observa-
tions, or errors, are inherent in any measurement process
since it is recognized that the true value of an object of
measurement can never be exactly established.  These
errors are customarily classified into two main groups:
systematic and random (or accidental) errors. Systematic
errors usually enter into records with the same sign and
frequently with either the same magnitude (e.g., a zero
offset) or an establishable relationship between the mag-
nitude of the measurement and the error. The methods of
symmetry and substitution are frequently used to detect
and quantify systematic errors. In the method of symmetry,
the test is repeated in a symmetrical or reversed manner
with respect to the particular condition that is suspect.  In
the method of substitution, the object of measurement is
replaced by one of known magnitude (a calibration stand-
ard); an instrument with a known calibration curve is sub-
stituted for the measuring instrument in question, and so
on. Thus, systematic errors bear heavily on the accuracy
of the measurement.

Random errors, on the other hand, are due to irregular
causes, too many in number and too complex in nature to
allow their origin to be determined. One of the chief char-
acteristics of random errors is thatthey are normally as likely
to be positive as negative and, therefore, are not likely to
have a great effect on the mean of a set of measurements.
The chief aim of a data quality assurance effort is to account
for systematic errors and thereby reduce errors to the
random class, which can be treated by simple probability
theory, in order to determine the most probable value of the
object of observation and a measure of the confidence
placed in this determination.
C.3    ELEMENTS OF A QA PROGRAM
The basic elements of any quality assurance program
include the following:

    •    Management's commitment to provide the
        resources  necessary to implement quality
                                               C-1

-------
        assurance activities (approximately 10 to 20
        percent of total water monitoring resources).
    •   Designation of a quality assurance coordina-
        tor responsible for coordinating and  imple-
        menting necessary quality assurance
        activities.
    •   Documentation of a quality assurance plan
        outlining the specifics of and responsibilities
        for the development and implementation of
        internal  and  external quality  assurance
        checks.

A complete QA program for water quality measurements
would incorporate a variety of specific elements.  These
can be depicted on a quality assurance wheel, as shown
in Figure C-1. The wheel arrangement illustrates the nature
of a quality assurance system that addresses all elements
and at the same time allows program managers the flexi-
bility to emphasize those elements which are most appli-
cable to their  particular program.  Quality assurance
elements are grouped  on the wheel  according to the
organizational level to which responsibility is normally as-
signed. These organizational levels are the quality assur-
ance coordinator (normally a staff function), supervisor (a
line function), and operator. Together the supervisor and
quality assurance coordinator must see that  all these
elements form  a  complete and integrated system and
achieve the desired program objectives.

The following specific elements are suggested as minimal
requirements for structuring  a QA program for a TMDL
study.   Any proposed program should be compared
against these criteria to determine its acceptability.

    •   A written quality assurance plan should be
        prepared.  It should define the oversight role
        of management; identify personnel responsi-
        ble for the quality assurance program;  and
        specify proper sample collection, use of ap-
        proved measurement techniques, calibration
        standards and their verification, internal qual-
        ity control practices,  and appropriate data
        management controls.
    •   An estimate of costs associated with the
        quality assurance program  in terms of per-
        centage of overall project  cost should  be
        developed. Normally, a minimum of 10 per-
        cent of the estimated sample collection and
        analysis costs will be necessary for adequate
        quality control.
    •   A program for demonstration of acceptable
        performance through the use of audit sam-
        ples should be  established and  used
        throughout the duration of the study.
        Provision should be made for performing on-
        site field and laboratory audits  at the option
        of  and on a  schedule established by the
        project officer. Such audits would evaluate
        performance and document the availability of
        all  equipment and supplies necessary  for
        successful execution of the study.
        Documentation  of quality control perform-
        ance should be submitted with the final report
        and otherwise as directed by the project offi-
        cer.
C.4    ASPECTS OF A QA PROGRAM

A number of aspects of a QA program must be addressed
by the QA plan if the minimal requirements are to be met.
These aspects can be aggregated into three general cate-
gories: water chemistry (laboratory), field data collection,
and data handling and reporting.

C.4.1   Water Chemistry

The minimum QA requirements for water chemistry are as
follows:

    •   Quality control management manual
        -   Outline of quality assurance program ob-
           jectives
        -   Outline of the administrative structure of
            the  laboratory  (including  an organiza-
            tional chart)
            Clear identification of the responsibilities
            for implementing the specific quality con-
            trol activities
            Commitment of resources by manage-
            ment to implement the necessary quality
            control activities
            Description  of laboratory training pro-
            gram
            Designation of a laboratory quality assur-
            ance coordinator, including a statement
            addressing coordination responsibilities
            and duties
    •   Laboratory operations manual
        -   Description  of analytical methodologies
            and  procedures
            Description  of laboratory quality control
            activities
                                               C-2

-------
FIGURE C-1. QUALITY ASSURANCE ELEMENTS AND RESPONSIBILITIES
              (THE QUALITY ASSURANCE WHEEL)
                          C-3

-------
       -   Description of the laboratory's internal
           chain-of-custody procedures
       -   Description  of general laboratory  re-
           quirements
       -   Description of laboratory communication
           and coordination
    •   Sample log manual
    •   Quality control records manual
    •   Blind duplicate and spiked field samples
           Sample audits
           Parameters included in the program
    •   Audit sample preparation procedures
    •   Data evaluation
       -   Audit follow-up and corrective action
    •   Estimation of limits for laboratory accuracy
       checks
C.4.2   Data Collection
The minimum QA requirements for field data collection are
as follows:
    •   Sampling network design
    •   Sampling procedures
    •   Calibration of direct-reading field instruments
       and fixed continuous monitoring devices
    •   Record keeping
    •   Quality assurance checks in field sampling
    •   Personnel training
    •   Flow measurements
    •   Records, data storage and retrieval
    •   Sample handling and  identification proce-
       dures (chain of custody)
    •   Collection of samples/field investigations
C.4.3   Handling and Reporting
The minimum QA  requirements for data handling and
reporting are as follows:
    •   Preprinted forms and labels
    •   Data sheets
    •   Data flow
    •   Significant figures and rounding procedures
    •   Calculation checks
    •   Data corrections
    •   Data reviews
    •   Reasonableness and consistency checks
    •   Data acceptance
    •   Data storage and retrieval
                                              C-4

-------
APPENDIX  D: UNCERTAINTY ANALYSIS
D.1    INTRODUCTION

Uncertainty analysis should be included as an inte-
gral component of water quality modeling. One of the
primary purposes is to quantify the error in predicting
water quality and evaluate the effect of input parame-
ters on model output. By quantifying this error, im-
proved management decisions can be made. Such
quantification also facilitates subsequent studies
such as  risk assessments to evaluate  alternative
waste load allocations. In addition, uncertainty analy-
sis may provide insight into the need for additional
data collection to refine the estimate of certain loads,
initial conditions, or reaction rates.  For example, if
the model is sensitive to  the reaeration rate (that is,
a small change in reaeration rate results in large
changes  in the prediction of critical water quality
parameters such as dissolved oxygen), it may  be
appropriate to allocate resources to more accurately
estimate the reaeration rate of that stream or river.

There are three techniques for performing uncertainty
analysis:  sensitivity analysis, first-order error analy-
sis, and Monte Carlo simulation.  Each technique has
advantages and disadvantages in terms of applicabil-
ity and computational burden that  will  make one
method more suitable than another  for a particular
analysis.  In many instances, the modeler may need
to explore the results from all three procedures. The
three methods  may produce discrepancies  in their
results since the  methodologies and assumptions
differ.  Each of these techniques  is available in
QUAL2E-UNCAS, and the following discussion is
limited  to the features available in that model.  An
example uncertainty analysis using  QUAL2E-
UNCAS is  provided at the end of this appendix.


D.2    TECHNIQUES IN UNCERTAINTY
       ANALYSIS

D.2.1   Sensitivity Analysis

Sensitivity analysis is the simplest of the three tech-
niques for assessing the effect of an input variable on
the output variable. This analysis technique can  be
used to evaluate simple alternatives and projections
such as the effect of reducing all pollutant loads  by
10 percent. Simple what-if scenarios are particularly
useful  for managers who must make decisions
among alternative load reduction strategies. For the
modeler, the same analysis can serve as a useful
guide for model calibration.

In the single-factor approach, the modeler varies one
of the input variables, X, and observes the effect in a
particular output variable, Y. A sensitivity coefficient
is then computed as the percentage  change in  Y
divided by the percentage change in X.  In general, a
sensitivity coefficient can be estimated at all points
where the output variable is predicted. However, this
process can  result in an enormous  interpretation
burden and it is generally recommended that the
analysis be limited to critical points along the modeled
stream. This process can then  be repeated for a
number of different perturbations inXaswell as other
input variables.  By evaluating the relative change in
the sensitivity coefficients for different input variable
perturbations, the modeler can determine the model
nonlinearity for that input variable.

Similarly, several input variables can be varied simul-
taneously.   As the number of input variables and
combinations  is increased,  the interpretation of re-
sults is complicated. Experimental design strategies
can be applied  in this situation to elicit main and
interaction effects of input variables. Specifically, in
QUAL2E-UNCAS, the modeler may specify a 22 or
23 factorial design.  In other words, the  modeler may
be able to  examine the main and interactive effects
of two or three variables evaluated at two levels (e.g.,
perturbations). The statistical significance of the in-
teraction and main effects are evaluated by compar-
ing an  appropriate ratio of the sum of  squares to a
critical  F ratio.

To perform sensitivity analysis  with QUAL2E-
UNCAS, the user must specify the type of analy-
sis (single/multiple variable or  factorial design),
the input variables to be modified, and the pertur-
bation  as a percent of the  input variable.

D.2.2  First-Order Error Analysis

First-order error analysis can be used in a manner
similar to that used for sensitivity analysis. In addition
                                             D-1

-------
to estimating the change of an output variable with
respect to an input variable, first-order error analysis
provides an estimate of the output variance.  A first-
order approximation (from the Taylor series  expan-
sion) to the relationship for computing variances in
multivariate situations  is used.  Input variables are
assumed to be independent, and the model is as-
sumed to respond linearly to the input variables. (In
some instances, the assumptions may not be cor-
rect.)   The  linear assumption  can be evaluated by
computing the normalized sensitivity coefficients for
several different input parameter perturbations. If the
normalized  sensitivity coefficients are similar or the
difference is small, the model can be assumed to be
linear for that input  parameter.  If the difference in
normalized coefficients is large, it may be more accu-
rate to use  the Monte  Carlo simulation approach to
estimate output parameter variance.

In this analysis, the sensitivity coefficients are normal-
ized such that
              Sij=(AYj/Yjy(AXi/Xi)
                            (D-1)
where
    Sij
    AX\
    Yj
    AYj
normalized sensitivity coefficient
for output  Yy to input X\
base value of input variable
magnitude of input perturbation
base value of output variable
sensitivity  of output variable
The components of variance for each output variable
(Y) are the percentages of output variance attribut-
able to each input variable (X) and are computed in
the following manner.
tainty caused by factors such as spatial and temporal
variation, sampling and analytical error, and bias in
measurement or estimation techniques.   A file of
typical variance estimates is provided with the model.

D.2.3  Monte Carlo Simulation

Monte Carlo simulation is a numerical procedure that
can be used to evaluate linear and nonlinear systems.
Each input variable is defined to have a certain prob-
ability density function (pdf). Before each model run,
an input  variable is  randomly  selected from each
predefined pdf.   By combining  the results from nu-
merous model runs, a pdf can be developed for the
output variable. The pdf is useful in evaluating overall
model predictions and in assessing the likelihood of
violating a water quality standard.

In general, the linear and independence assumptions
of first-order error analysis can be relaxed when using
Monte Carlo simulation techniques. In QUAL2E-UN-
CAS, only the linear assumption is relaxed. To use
QUAL2E-UNCAS, the user must specify the variance
of the input  variable  (a file of typical variance esti-
mates is provided),  the probability  distribution as
either normally or  lognormally  distributed,  and the
number  of simulations to  perform.  As one would
expect, the number of model runs is relatively large
as compared to the number of runs typically done for
sensitivity or first-order error analysis.  Preliminary
experience indicates that about 2000 simulations are
required  to  achieve  estimates of output  standard
deviations with 95 percent confidence intervals of
5 percent.


D.3   EXAMPLE APPLICATION
         Var(Yj)= £ (AY/AX,)2 Var(Xj)
                  /'=1                       (D-2)

where
    Var(Yj) =   variance of output variable Yy
    Var(Xj) =   variance of input variable Xj

Each term  in the summation of Equation D-2 is a
component of the total variance of the output variable.
A particular input variable may be a large  (small)
contributor  to the output variance  if  it has either a
large (small) input variance or a large (small) sensi-
tivity coefficient. This analysis can be used as a guide
for additional field work. To apply this analysis tech-
nique using QUAL2E-UNCAS, the user must specify
the magnitude of input parameter  perturbation and
variance. The variance term is  a measure of uncer-
                                  This section provides an example of how the uncer-
                                  tainty methodologies in QUAL2E-UNCAS can be ap-
                                  plied to a QUAL2E data set. The sole purpose of this
                                  section  is  to demonstrate the  utility of uncertainty
                                  analysis rather than to provide a definitive analysis of
                                  the river system from which the data were obtained.
                                  This appendix is a condensation of Appendix C of the
                                  QUAL2E and QUAL2E-UNCAS User Manual (Brown
                                  and Barnwell, 1987),  entitled  QUAL2E-UNCAS Ex-
                                  ample Application.   The reader is referred to that
                                  manual for a more detailed explanation of QUAL2E-
                                  UNCAS.

                                  The data  used  to  demonstrate the  capabilities of
                                  QUAL2E-UNCAS were obtained  from a U.S.  EPA
                                  Region 4 survey of the Withlacoochee River during
                                  October 1984 (Koenig, 1986).  In this study, water
                                              D-2

-------
quality simulations were examined for portions of the
river subjected to both municipal and industrial waste
loads.  In addition, there is a significant accretion of
flow from groundwater inputs.  The river has a uni-
form low slope but  is characterized by  alternating
shoals and pools (often in excess of 25 feet deep).
Average depths during the survey periods were 5.2
to 14.8  feet; widths  were 90 to 140 feet; and flows
varied from 150 cfs at the headwater to 660 cfs at the
end of the system. Water quality is affected by algae
activity  resulting from municipal  waste discharges
above the section of stream studied.  The addition of
industrial waste  at  RM  24,  however, dramatically
reduces light penetration to the extent that the algae
population diminishes in the downstream direction.

A location map of the basin is shown in Figure  D-1,
and a plot of observed and modeled dissolved oxygen
concentrations is presented in Figure D-2.  Ten state
variables were simulated in this study:  temperature,
dissolved oxygen, carbonaceous BOD, four nitrogen
forms (organic, ammonia, nitrate, and nitrite), two
phosphorus forms (organic and dissolved), and algae
as chlorophyll a.  A summary of the calibrated inputs
and their variance estimates for the uncertainty analy-
sis is shown in Table D-1.  The calibrated values in
general were obtained by adjusting field or laboratory
measurements of the specific model  inputs.   The
variance estimates  were computed from replicate
data taken during the survey period and by inference
from other published data (McCutcheon, 1985; Bowie
etal., 1985).

D.3.1    First-Order Error Analysis (FOEA)

Table D-2 shows the first-order error analysis (FOEA)
results for the  output variables of CBOD  and DO at
three  locations in the Withlacoochee  system:  an
upstream location (RM 26), a midpoint near the dis-
solved oxygen sag (RM 20), and a downstream loca-
tion (RM 2). For the CBOD sensitivity coefficients in
Table D-2(a), it is clear that the input forcing functions
such as point load, headwater flows, and CBOD
dominate model sensitivity.

Table D-2(a) also presents the components of vari-
ance for the modeled CBOD output. These results
show a  pattern similar to the sensitivity coefficients.
The headwater CBOD is the dominant contributor (99
percent) to  CBOD variability in the upper  reaches of
the basin.  The point load  CBOD values are the
primary variance component elsewhere in the river
(84 percent at  RM 20 and 79 percent at RM 2).  The
total variability in simulated CBOD estimated by the
first-order analysis, when expressed as a standard
deviation, varies from 0.35 mg/L to 0.76 mg/L to 0.27
mg/L proceeding through the basin.

The FOEA results for dissolved oxygen are presented
in Table D-2(b). The only forcing functions that have
large DO sensitivity coefficients are the headwater
inputs, not the point  load inputs.  DO is  also very
sensitive to temperature inputs. Next in importance
of DO sensitivity are the reaeration rate and velocity.
Similar patterns are apparent in the components of
variance for dissolved oxygen (Table D-2(b)). CBOD
decay has a relatively small impact on DO variance,
whereas reaeration and SOD have large impacts.
Temperature inputs make a minimum contribution to
DO variance.  The total  variability in simulated DO,
when expressed  as a standard deviation,  increases
in the downstream direction varying from 0.18 mg/L
to 0.30 mg/L and averaging about 5 percent of the
simulated DO.

D.3.2   Effect of Model Nonlinearity

First-order error analysis uses a linear approximation
to  compute an estimate of output variance.  The
validity of that approximation can be assessed  by
computing the sensitivity coefficients for both large
and small values of delta x, the input perturbation.
Small changes in the normalized sensitivity  coeffi-
cient indicate near linearity of the state variable over
the range  of perturbed input values, while large
changes in sensitivity reflect important nonlinear ef-
fects. Table D-3 contains values of the normalized
sensitivity coefficients for the state variables DO and
chlorophyll a for input perturbations, ranging from -20
to  +20 percent.   The input variables selected for
analysis are those having the largest sensitivity coef-
ficients.

For dissolved oxygen (Table D-3(a)), the reaeration
and headwater temperature have the largest non-
linear effects on DO.  The other variables are consid-
ered linear for the conditions of the simulation. The
net effect from all model  input nonlinearities is mani-
fest in the FOEA estimate of dissolved oxygen stand-
ard deviation, which decreases by 7 percent over the
range of input perturbations.

The more pronounced patterns are observed for the
state variable, chlorophyll a (Table D-3(b)). The ratio
of chlorophyll a to algal biomass and headwater flow
exhibit large nonlinear effects.  The maximum algal
growth rate and the algal respiration rate show mod-
est nonlinearities, while  headwater chlorophyll a is
essentially linear. The net FOEA estimate of stand-
                                               D-3

-------
     FIGURE D-1. LOCATION MAP OF THE WITHLACOOCHEE RIVER BASIN
 n
 a
                                                    RM 2
                     EK 20
                                 Spring
               mm
                                   I
20      IS

  Hlvw Location
                                       10
FIGURE D-2. OBSERVED AND PREDICTED DISSOLVED OXYGEN CONCENTRATIONS
                               D-4

-------
            TABLE D-1. SUMMARY OF INPUT DATA FOR QUAL2E-UNCAS SIMULATION-
                               WITHLACOOCHEE RIVER SURVEY 1984

 Input Parameter or                             Base Case (Mean)                    Relative Standard
 Coefficient	Values	Deviations (%)	
 Hydraulic Data (7)*
        Flow (cfs)                                 150-660                               3%
        Depths (ft)                                 5.2-14.8                               8%
        Velocities (fps)                             0.12-0.78                              8%
        Others                                     a, b                              10-20%

 Reaction Coefficients (8)
        CBOD Decay (day1)                         0.04-0.10                              15%
        Reaeration (day )                           0.08-0.08                              13%
        SOD (g02/ft^ - day)                         0.04-0.13                              12%
        N. p. AI9ae                                  a,b                              15-25%

 Algae, Nutrient, Light Coefficients (17)
        Maximum Growth Rate (day 1)                   13                                10%
        Respiration Rate (day1)                        g ^g                                ig%
        Otners                                     a, b                                10%

 Climatology, Temperature Inputs (23)
        Wet, Dry Bulb Air Temps (°F)                  64 3 ?4 5                              2%
        Temperature Coefficients                    1.00-1.083                              3%
        Others                                     a,b                               1-15%
 Headwater, Incremental, Point Loads (27)
        DO, Temperature
        CBOD, N, P, Algae                             a                                 1"3/0
	 '  '  '  *	a	8-25%	
 (a)   Basin-specific values from Koenig, 1986.
 (b)   Typical values from Table III-3 of Koenig, 1986.

 'Value in parentheses is the number of input variables of the type indicated.


      TABLE D-2.  SUMMARY OF FIRST ORDER SIMULATIONS FOR WITHLACOOCHEE RIVER
(a) Simulation Variable:
Input
Variable
CBOD Decay
Incr Flow
HW Flow
HWTemp
HWCBOD
Ptld Flow
Ptld CBOD
Relative
St Dev (%)
15
3
3
1
15
3
15
CBOD (mg/L)

Sensitivity Coefficient
RM26
-0.06(3)a
-0.05
0.05
-0.11(2)
0.98(1)
0.00
0.00
RM20
-0.11
-0.22
-0.44(3)
-0.13
0.24
0.67(2)
0.74(1)
Standard Deviation of Simulated








Components of Variance (%)
RM2
-0.22
-0.37(3)
-0.05
-0.16
0.18
0.43(2)
0.69(1)
(CBOD) (mg/L)
(%)
RM26
1
1
1
1
99
0
0
0.35
15
RM20
2
1
1
1
9
3
84
0~76
12
RM2
8
1
1
1
6
1
79
0.27
12
 (b)      Simulation Variable: Dissolved Oxygen
Velocity
CBOD Decay
SOD
Reaeration
Incr Temp
HE Temp
HWDO
8
15
5
13
1
1
3
0.03
-0.02
-0.05(3)
0.04
-0.01
-0.25(2)
0.92(1)
0.05
-0.12
-0.23
0.31(3)
-0.15
-0.70(1)
0.55(2)
Standard Deviation of Simulated DO




-0.26(2)
-0.03
0.09
0.40(1)
-0.17(3)
-0.13
0.04
(mg/L)
(%)
1
1
5
4
1
1
84
0.18
3
2
9
5
45
1
1
8
O27
6
13
1
3
77
1
1
1
0.30
6
  Value in parentheses is rank, with 1 being highest.
                                                   D-5

-------
        TABLE D-3.  NORMALIZED SENSITIVITY COEFFICIENTS FOR VARIOUS SIZES OF
                      INPUT PERTURBATIONS (WITHLACOOCHEE RM 20)
(a)     Simulation Variable: Dissolved Oxygen (mg/L)


                       -20%
Input Variable

CBOD
SOD
Reaeration
HW Temp
HWDO

Std Dev. (mg/L)
Magnitude of Input Perturbation %
      -1%            +1%
                        -0.12
                        -0.23
                         0.33
                        -0.66
                         0.55

                         O28
      -0.12
      -0.23
      0.31
      -0.69
      0.55

      0.27
-0.12
-0.22
0.31
-0.69
0.55

0.27
+20%

 -0.12
 -0.23
  0.30
 -0.77
  0.55

  0.26
 Relative
Change (%)

      0
      0
     -9
    +16
      0
(b)
       Simulation Variable:  Chlorophyll a (|ig/L)
Max Growth Rate
Respiration
Chi a/Agy-B
HW Flow
HW Chl-a
Std Dev. (ng/L)
0.40
-0.37
-1.24
0.28
0.96
Jj2
0.41
-0.36
-1.01
0.24
0.95
3.12
0.42
-0.35
-0.98
0.25
0.96
3.06
0.43
-0.34
-0.83
0.21
0.94
2.64
+ 7
-8
-33
-25
-2
^29
 TABLE D-4.  DIFFERENCES IN STANDARD DEVIATION ESTIMATES FOR OUTPUT VARIABLES-
                            WITHLACOOCHEE RIVER SURVEY 1984
Output Variables
                                       Between FOE A Input
                                        Perturbations from
                                           -20 to +20%
                                     Between FOE A (5%)
                                       and Monte Carlo
                                      Simulations (2000)
Temperature
Dissolved Oxygen
CBOD
Nitrogen Forms
Phosphorus Forms
Chlorophyll a
Algal Growth Rate
5.4
7.7
0.8
a
a
29
6.9
1.8-4.3
0.6-4.5
1.4-2.6
a
a
16-21
2-4
 Expected values of standard deviations are too small to compute meaningful relative differences, although values are certainly
less than 10% and likely less than 5%.	
ard deviation of chlorophyll a decreases by 29 per-
cent over the range of input perturbations.
                                                the magnitude of the input perturbation increases
                                                over the range of -20 to +20 percent.
The results of the analysis of the other state variables
(Table D-4)  show changes in  FOEA estimates of
standard deviation of about 7 percent for algal growth
rate, 5  percent for temperature, and  less than 5
percent for all others, including CBOD, the nitrogen
forms, and the phosphorus forms.  In all cases, the
FOEA estimate of standard deviation decreases as
                                                 D.3.3   Monte Carlo Simulations

                                                 The Monte Carlo simulation output in QUAL2E-
                                                 UNCAS provides summary statistics and  fre-
                                                 quency d istributions for the state variables at specific
                                              D-6

-------
                TABLE D-5. SUMMARY STATISTICS FROM 2000 MONTE CARLO
                         SIMULATIONS FOR WITHLACOOCHEE RIVER
Statistic
   Dissolved Oxygen (mg/L)
RM26      RM20      RM2
     Chlorophyll a (|ig/L)
RM26      RM20       RM2
Calibrated Mean
Simulated Mean
Minimum
Maximum
Range
Std. Deviation
Coef. Variation
Skew Coef.
Std. Deviation from FOEA
5.83
5.82
5.26
6.41
1.15
0.18
3.0%
0.01
0.18
4.48
4.47
3.47
5.31
1.84
0.28
6.2%
-0.15
0.27
5.06
5.05
3.69
5.89
2.20
0.31
6.2%
-0.20
0.30
18.1
18.9
10.2
53.8
45.6
4.25
23.5%
1.73
3.54
14.4
15.0
2.8
41.4
33.6
3.48
24.2%
1.6
2.94
6.6
6.6
3.0
22.2
19.2
1.87
28.4%
1.46
1.62
locations in the basin.  Table D-5 contains the sum-
mary statistics, based on 2000 Monte Carlo simula-
tions.   The same input variances employed  in the
first-order error analysis were used. Input probability
distributions were assumed to be normal.

There  is very good agreement between the cali-
brated mean and  simulated  mean  for dissolved
oxygen.  For chlorophyll a the differences average
3 percent and may be attributed to the nonlineari-
ties. For dissolved oxygen, the standard deviation
grows  in the downstream direction.  This  is the
result of the fact that dissolved oxygen never recov-
ers to approach saturation, as well as the cumula-
tive effect of  model input uncertainty.   For
chlorophyll a,  the standard  deviation decreases
steadily in the downstream direction because the
algal  biomass concentration  is also decreasing.
This is the result of a lower algal growth rate due to
reduced  light penetration  caused by color  in the
industrial waste discharge at RM 24 and due to the
                        dilution effects from groundwater inflow. As shown
                        in Table D-4, for the output variables of temperature,
                        CBOD, and algae growth rate, the Monte Carlo esti-
                        mate of standard  deviation differs by less than  5
                        percent from the FOEA estimate. These differences
                        are within the 95 percent confidence interval for the
                        Monte Carlo estimates, thus implying negligible non-
                        linear effects for the  conditions of this simulation.
                        The frequency distributions for dissolved oxygen
                        generated by the Monte Carlo analysis are shown
                        graphically  in Figure  D-2.  These distributions are
                        useful in providing a visual representation of the
                        distribution  of model output at different locations in
                        the system.  In the case of dissolved oxygen shown
                        in  Figure B-3, the  distributions appear nearly sym-
                        metric and the dispersion in the upper reaches of the
                        basin is substantially smaller than that in the middle
                        and lower reaches.  Similar plots (not shown) for
                        chlorophyll  a data  in Table D-5 clearly show the
                        decreasing dispersion and pronounced  positive
                        skew in the simulated data.
                                             D-7

-------
D-8

-------
 APPENDIX  E:    SUPPLEMENTAL  BIBLIOG-
                            RAPHY
Bienfang, P.K. 1980. Phytoplankton sinking rates in
oligotrophic waters off Hawaii, USA. Mar. Biol. 61:69-
77.

Bruno, S.F., R.D. Staker, and G.M. Sharma.  1980.
Dynamics  of phytoplankton productivity  in the Pe-
conic Bay Estuary,  Long Island.  In Estuarine and
coastal marine science. Academic Press, Inc., Lon-
don.

Bruno,  S.F.,  R.D. Staker,  G.M. Sharma, and J.T.
Turner. 1983. Primary productivity and phytoplank-
ton  size fraction  dominance in a temperate  North
Atlantic estuary.  Estuaries  6(3):200-211.

Burns, N.M.,  and F. Rosa.   1980.  In situ measure-
ment of the settling velocity of organic  carbon parti-
cles and  10  species  of phytoplankton.  Limnol.
Oceanogr. 25 (5):855-864.

Collins, C.D., and J.H. Wlosinski. 1983. Coefficients
for use in the  U.S. Army Corps of Engineers reservoir
model, CE-QUAL-R1.  U.S. Army Corps of Engi-
neers, Waterways Experiment Station, Vickburg, MS.

Cullen , JJ. 1990. On models of growth and photo-
synthesis in phytoplankton. Deep-Sea.   Res.
37(4):667-683.

Curl, H. 1962. Analysisof carbon in marine plankton
organisms. J. Mar. Res. 20(3):181-188.

Dennison, W.C., GJ.  Marshall,  and  C.  Wigand.
1989.  Effect of "Brown Tide" shading on eelgrass
(Zostera marina  L.) distributions.  In  Novel phyto-
plankton blooms: Causes and impacts of recurrent
brown tides  and other unusual blooms, ed.  E.M.
Cosper, EJ.  Carpenter, and V.M. Bricejj,  pp. 675-
692. Lecture Notes on Coastal and Estuarine Stud-
ies. Springer-Verlag, Berlin.

Durbin, E.G.,  R.W. Krawiec, and TJ. Smayda.  1975.
Seasonal studies on the relative importance of differ-
ent  size fractions of phytoplankton in  Narragansett
Bay (USA). Mar. Biol. 32:271-287.

Eppley, R.W., J.  Rogers, and J. McCarthy. 1969.
Half-saturation constants for the uptake of nitrate and
ammonium  by marine phytoplankton.
Oceanogr. 14:912-920.
Limnol.
Eppley, R.W., and W. Thomas. 1969. Comparison
of half-saturation constants for growth and nitrate
uptake by marine phytoplankton.  J. Phycol. 5:375-
379.

Falkowski, P.G.  1975. Nitrate uptake in marine phy-
toplankton:  Comparison of half saturation constants
from seven  species.  Limnol. Oceanogr.  20(3):412-
417.

Falkowski, P.G., and T.G. Owens. 1978. Effects of
light intensity on photosynthesis and dark respiration
in six species of marine phytoplankton.  Mar. Biol.
45:289-295.

Garber, J., J.M. Barnes, and S. Stammerjohn.  1990.
Sediment water flux measurements in the Peconic
Bay estuarine ecosystem: July and October 1989.
Final  report for  agreement  no.  01-4400-456-29-
00022 submitted to Suffolk County Dept. Health Serv-
ices, Riverhead, New York.

Goering, J.J., D.M. Nelson, and J.A. Carter. 1973.
Silicic acid uptake by natural populations of marine
phytoplankton. Deep-Sea Res. 20:777-789.

Goldman, J.C. and J.H. Ryther. 1976. Temperature
influenced species competition in mass  cultures of
marine phytoplankton.  Biotechnol. and  Bioeng.
18:1125-1144.

Hegseth, E.N., and E. Sakshaug.  1983. Seasonal
variation in light and temperature dependent growth
of marine planktonic diatoms in in situ dialysis cul-
tures in the Trondheimsfjord, Norway (63 °N). J. Exp.
Mar. Biol. Ecol. 67:199-220.

Hitchcock, G.L., and TJ. Smayda.  1977.  The impor-
tance of light in the initiation of the 1972-1973 winter-
spring diatom bloom  in Narragansett Bay.  Limnol.
Oceanogr. 22(1):126-131.

Langdon, C. 1988. On the causes of interspecific
differences  in the growth-irradiance  relationship for
phytoplankton. II. A general  review. J. Plank. Res.
10(6):1291-1312.
                                            E-1

-------
Lehman, J.T., D.B. Botkin, and G.E.  Likens. 1975.
The assumptions and rationales of a computer model
of phytoplankton population dynamics.   Limnol.
Oceanogr. 20(3):343-364.

Li, W.K.W.  1980. Temperature adaptation in phyto-
plankton: cellular and photosynthetic characteristics.
In Primary productivity in the sea, ed. P.G. Falkowski,
pp. 259-280. Environmental Science Research, Vol.
19, No. 31, of the Brookhaven Symposia in Biology.
Plenum Press, New York.

Malone, T.C.  I977a. Plankton systematics and dis-
tribution. MESA New  York Bight Atlas Monograph
13, New York Sea Grant  Institute, Albany, NY.

Malone, T.C.  I977b.  Light-saturated photosynthesis
by phytoplankton size fractions in the New York Bight,
USA.  Mar. Biol. 42:281-292.

Malone, T.C., and M. Chervin. 1979. The production
and fate of phytoplankton size fractions in the plume
of the Hudson  River, New  York Bight.   Limnol.
Oceanogr. 24 (4):683-696.

Malone, T.C., and C. Garside.  1980. Evidence of
silicon limited diatom  growth in the plume of the
Hudson River. Abstr. of papers submitted for Second
Winter Meeting, ASLO, January 1980.

Malone, T.C.,  and PJ.  Neale.  1981.  Parameters of
light-dependent  photosynthesis for  phytoplankton
size fractions in temperate estuarine and coastal
environments.  Mar. Biol. 61:289-297.

Mancini, J.L. 1978. Numerical estimates of coliform
mortality rates under various  conditions.  J.  WPCF
50(11):2477-2484.

Mandelli, E.F., P.R. Burkholder, T.E. Doheny, and  R.
Brody.  1970.   Studies  of primary  productivity  in
coastal waters of southern Long Island, New York.
Mar. Biol. 7:153-160.

Morel, A. 1987.  Chlorophyll-specific scattering coef-
ficient of phytoplankton.   Simplified theoretical ap-
proach.  Deep-Sea Res. 34:1093-1105.

Paasche, E. 1973a. Silicon and the ecology of marine
plankton diatoms: Silicate uptake kinetics in five dia-
tom species. Mar. Biol. 19:262-269.

Paasche, E. 1973b. Silicon and the ecology of ma-
rine plankton diatoms:  Thalassiosira pseudonana
(Cyclotella nana) grown in a chemostat with silicate
as limiting nutrient. Mar. Biol.  19:117-126.
Paasche, E. 1980. Chapter 7. Silicon. In The physi-
ological ecology of phytoplankton., ed. I. Morris, pp.
259-284.   Blackwell Science Publication.  Oxford,
U.K.

Parsons, T., and  M.  Takahashi.  1973.  Biological
oceanographic processes.  Pergamon  Press, Eng-
land.

Peterson, W.T.  1986. The effect of seasonal vari-
ations in stratification on plankton dynamics in Long
Island Sound. In Lecture notes on coastal and estu-
arine studies, tidal mixing  and plankton dynamics,
Vol. 17, ed. M.S. Bowman,  C.S. Yentsch, and W. T.
Peterson, pp. 297-320.

Petipa, T.S.  1966.   Relationship between growth,
energy metabolism and ration. In  Physiology of ma-
rine animals, ed. A. Clausi, pp. 82-91. Akad. Nauk.
USSR, Oceanographical Commission.

Pratt, D.M.  1959. The phytoplankton of Narragan-
sett Bay. Limnol. Oceanogr. 4(4):425-440.

Riley, G.A.   1952.  Phytoplankton of Block Island
Sound, 1949. Bull. Bingham.  Ocean. Coll. 13(3):40-
64.

Riley, G.A.  1959. Environmental control of autumn
and winter diatom flowerings in Long Island Sound.
International Oceanographic Congress.

Riley, G.A.   1970.   Paniculate  organic matter in
seawater. Adv. Mar.  Biol. 8:1-118.

Riley, G.A.,  and S.M.Conover. 1967.  Phytoplankton
of Long Island Sound 1954-1955.  Bull. Bingham.
Ocean. Coll. 19(2):5-34.

Rizzo, W.M. 1990. Nutrient exchanges between the
water column and a subtidal benthic microalgal com-
munity.  Estuaries 13(3).

Ryther, J.H., and  W.M. Dunstan.  1971.  Nitrogen,
phosphorous  and eutrophication  in coastal marine
areas.  Science 171:1008-1013.

Sakshaug, E., and K. Andresen. 1986. Effect of light
regime upon growth rate and chemical composition
of a clone of Skeletonema costatum from the Trond-
heimsfjord,  Norway. J. Plankton Res. 8:619-637.

Sakshaug E., D.A. Kieferand  K. Andresen. 1989. A
steady state description of  growth and light absorp-
tion in  the  marine planktonic diatom, Skeletonema
costatum. Limnol. Oceanogr. 34:198-205.
                                              E-2

-------
Shultz, DJ.  1989.  Nitrogen dynamics in the tidal
freshwater Potomac River, Maryland and Virginia,
water years  1979-81.  USGS Water Supply Paper
2234-J. Dept. Interior, United States Geological Sur-
vey, Reston, VA.

Smayda, TJ. 1957. Phytoplankton studies in lower
Narragansett Bay. Limnol. Oceanogf. 2(4):342-359.

Smayda,  TJ.  1973.  The growth of Skeletonema
costatum during a winter-spring bloom in Narragan-
sett Bay,  Rhode Island. Norwegian J. Botany. 20(2-
3):219-247.

Steele, J.H.   1965.  Primary production in  aquatic
environments. In  Notes  on some theoretical prob-
lems in production ecology, ed. C.R. Goldman, pp.
383-398 Mem. Inst. Idrobiol. 18Suppl. University of
California Press,  Berkeley, CA.

Turner, J.T., S.F.  Bruno, RJ. Larson, R.D.  Staker,
and G.M. Sharma.  1983.  Seasonality of plankton
assemblages in a temperate estuary. P.S.Z.N.I: Mar.
Eco 4(1): 81-99.

Walsh, J.J. 1975.  A spatial simulation model of the
Peru upwelling ecosystem. Deep-Sea Res. 22:201-
236.

Yentsch, C.S., and R.W. Lee.  1966.  A study of
photosynthetic light reactions.   J.  Mar.  Res. 24
(3):319-337.

Yentsch, C.S., and J.H. Ryther. 1959. Relative sig-
nificance of the net phytoplankton and nanoplankton
in the waters of Vineyard Sound. J. Conseil Int. Exp.
Mer.  24:231-238.

Yentsch, C.S. 1974. Some aspects of the environ-
mental physiology of marine phytoplankton; a second
look.  In Oceanogr. Mar. Biol. Ann. Rev. 12, ed. H.
Barnes, pp.  41-75. George  Allen and  Unwin Ltd.,
London, UK.
                                             E-3

-------
E-4

-------
APPENDIX  F.     GLOSSARY
Activated sludge


Acute toxicity
Adsorption-desorpt
ion
Advanced primary
treatment
Advanced
secondary
treatment
Advanced waste
treatment (AWT)
Advection


Aerobic


Algae
Algal bloom


Algal growth
A secondary wastewater treatment process that removes organic matter by mixing
air and recycled sludge bacteria with sewage to promote decomposition.

A chemical stimulus severe enough to rapidly induce an effect; in aquatic toxicity
tests, an effect observed within 96 hours or less is considered acute. When referring
to aquatic toxicology or  human  health, an acute effect is not always measured in
terms of lethality.

Adsorption is the process by which nutrients such as inorganic phosphorous adhere
to particles via a loose chemical bond with the surface of clay particles. Desorption
is the process by which inorganic nutrients are released from the surface of particles
back into solution.

Waste  treatment process that incorporates primary sedimentation of suspended
solids with chemical addition and flocculation to increase the overall removal of
organic solids. Advanced primary treatment typically achieves about 50% removal
of suspended solids and BOD.

Biological or chemical treatment processes added to a secondary treatment plant
including a conventional activated sludge to increase the removal of solids and BOD.
Typical removal rates for advanced secondary plants are on the order of 90%
removal of solids and BOD.

Wastewater treatment process that includes combinations of physical and chemical
operation units designed to remove nutrients, toxic substances, or other pollutants.
Advanced, or tertiary, treatment processes treat effluent from secondary treatment
facilities using processes such as nutrient removal (nitrification, denitrification),
filtration, or carbon adsorption. Tertiary treatment plants typically achieve about 95%
removal of solids and BOD in addition to removal of nutrients or other  materials.

Bulk transport of the mass of discrete chemical or biological constituents by fluid
flow within a receiving water. Advection describes the mass transport due to  the
velocity, or flow, of the waterbody.

Environmental conditions characterized by the presence of dissolved oxygen; used
to describe biological or chemical processes that occur in the presence of oxygen.

Any organisms of a group of chiefly aquatic microscopic nonvascular  plants; most
algae have chlorophyll  as the  primary pigment for carbon fixation. As primary
producers, algae serve  as the  base of the aquatic food web, providing food for
zooplankton and fish resources. An overabundance of algae in  natural waters is
known  as eutrophication.

Rapidly occurring growth and accumulation of algae within a body of water. It usually
results from excessive nutrient loading and/or sluggish circulation regime with a long
residence time. Persistent and frequent bloom can result in low oxygen conditions.

Algal growth is related to temperature, available light, and the available abundance
of inorganic nutrients (N,P,Si). Algal species groups (e.g., diatoms, greens, etc.)  are
typically characterized by different maximum growth  rates.
                                             F-1

-------
Algal respiration


Algal settling
Ambient water
quality
Ammonia
Ammonia toxicity
Anaerobic
Analytical model
Anoxic


Anthropogenic

Aquatic ecosystem
Assimilative
capacity
Attached algae
Autotroph
Process of endogenous respiration of algae in which organic carbon biomass is
oxidized to carbon dioxide.

Phytoplankton cells (algae) are lost from the water column by physical sedimentation
of the cell particles. Algal biomass lost from the water column is then incorporated
as sediment organic matter and undergoes bacterial and biochemical  reactions
releasing nutrients and consuming dissolved oxygen.

Natural concentration of water quality constituents prior to mixing of either point or
nonpoint source load of contaminants. Reference ambient concentration is used to
indicate the concentration of a chemical that will not cause adverse impact to human
health.

Inorganic form of nitrogen; product of hydrolysis of organic nitrogen and denitrifica-
tion. Ammonia is preferentially used  by phytoplankton over nitrate for uptake of
inorganic nitrogen.

Under specific conditions of temperature and pH, the un-ionized component of
ammonia  can be toxic to aquatic  life.  The un-ionized component of ammonia
increases with pH and temperature.

Environmental condition characterized by zero oxygen levels. Describes biological
and chemical processes that occur in the absence of oxygen.

Exact mathematical solution of the differential equation formulation of the transport,
diffusion and reactive terms of a water quality model. Analytical solutions of models
are often  used to check the magnitude of the system response computed using
numerical model approximations.

Aquatic environmental conditions containing zero or little dissolved oxygen. See also
anaerobic.

Pertains to the [environmental] influence of human activities.

Complex of biotic and abiotic components of natural waters. The aquatic ecosystem
is an ecological unit that includes the physical characteristics (such as flow or velocity
and depth), the biological community of the water column and benthos, and the
chemical characteristics such as dissolved solids, dissolved oxygen, and nutrients.
Both living and nonliving components of the aquatic ecosystem interact and influ-
ence the properties and status of each component.

The amount of contaminant load (expressed as  mass per unit time) that can be
discharged to a specific stream or river without exceeding water quality standards
or criteria. Assimilative capacity  is  used to define the ability of a waterbody to
naturally absorb and use waste matter and organic materials without impairing water
quality or harming aquatic life.

Photosynthetic organisms that remain in a stationary location by attachment to hard
rocky substrate. Attached algae,  usually present in shallow hard-bottom environ-
ments, can significantly influence nutrient uptake and diurnal oxygen variability.

Organisms that derive cell carbon from carbon dioxide. The conversion of carbon
dioxide to organic cell tissue is a reductive process that requires a net input of energy.
The energy needed for cell synthesis is provided by either light or chemical oxidation.
Autotroph that use light, phototroph, include photosynthetic algae and bacteria.
Autotroph that use chemical energy, chemotroph, include nitrifying bacteria.
                                               F-2

-------
Background levels
Bacterial
decomposition

Benthic

Benthic ammonia
flux
Benthic
denitrification
Benthic
nitrification
Background levels represent the chemical, physical, and biological conditions that
would result from natural geomorphological processes such  as weathering  or
dissolution.

Breakdown by oxidation, or decay, of organic matter by heterotrophic bacteria.
Bacteria use the organic carbon in organic  matter as the energy source for cell
synthesis.

Refers to material, especially sediment, at the bottom of an aquatic ecosystem. It
can be used to describe the organisms that live on, or in, the bottom of a waterbody.

The decay of organic matter within the sediments of a natural water results in the
release of ammonia nitrogen from the interstitial water of sediments to the overlying
water column. Benthic release, or regeneration, of ammonia is an essential compo-
nent of the nitrogen cycle.

Under anaerobic, or low oxygen conditions, denitrifying bacteria synthesize cellular
material by  reducing nitrate to  ammonia and nitrogen gas.  Denitrification is a
component of the overall nitrogen cycle and has  been shown to account for a
significant portion of the "new" nitrogen loading to freshwater and estuarine ecosys-
tems.

Under aerobic conditions, nitrifying bacteria synthesize cellular material by oxidizing
ammonia to nitrite and  nitrate. Benthic nitrification  is  a component of the overall
nitrogen cycle and has been shown to account for a significant portion of the nitrogen
budget of shallow freshwater and estuarine ecosystems.
Benthic organisms    Organisms living in, or on, bottom substrates in aquatic ecosystems.
Benthic
photosynthesis

Best management
practices (BMPs)
Biochemical
oxygen demand
(BOD)


Biological
Nutrient Removal
(BNR)
Biomass

Boundary
conditions
Calibration
Carbonaceous
Synthesis of cellular carbon  by algae attached to the bottom of a natural water
system. Benthic photosynthesis typically is limited to shallow waters because of the
availability of light at the bottom.

Methods, measures, or practices that are determined to be reasonable and cost-ef-
fective means for a land owner to meet certain, generally nonpoint source, pollution
control needs. BMPs include structural and nonstructural controls and operation and
maintenance procedures.

The amount of oxygen per unit volume of water required to bacterially or chemically
oxidize  (stabilize) the oxidizable  matter  in water.  Biochemical oxygen demand
measurements are usually conducted over specific time intervals (5,10,20,30 days).
The term BOD generally refers to standard 5-day BOD test.

Waste treatment method that employs  natural biological processes to reduce the
quantity of nitrogen and phosphorus  discharged  to  natural  waters. Treatment
processes employ the movement of primary effluent  through aerobic, anoxic/an-
aerobic zones to facilitate bacterially mediated processes of nitrification and denitri-
fication.

The amount, or weight,  of a species, or group of  biological organisms, within a
specific volume or area of an  ecosystem.

Values or functions representing the state of a system at its boundary limits.

Testing and tuning of a model to a set of field data not used in the development of
the model; also includes minimization of deviations between measured field condi-
tions and output of a model by selecting appropriate model coefficients.

Pertaining to or containing carbon derived from  plant and animal residues
                                              F-3

-------
Channel


Channel
improvement


Channel
stabilization

Chloride

Chlorophyll



Chronic toxicity



Coliform bacteria



Combined sewer
overflows (CSOs)


Complete mixing


Concentration


Conservative
substance

Contamination


Conventional
pollutants


Cross-sectional
area
Decay



Decomposition


Denitrification


Design stream
flow
Designated use
A natural stream that conveys water; a ditch or channel excavated for the flow of
water.

The improvement of the flow characteristics of a channel by clearing, excavation,
realignment, lining, or other means in order to increase its capacity. Sometimes used
to connote channel stabilization.

Erosion prevention and stabilization of velocity distribution in a channel usingjetties,
drops, revetments, vegetation, and other measures.

An atom of chlorine in solution, bearing a single negative charge.

A group of green photosynthetic pigments that occur primarily in the chloroplast of
plant cells. The amount of chlorophyll-a, a specific pigment, is frequently used as a
measure of algal biomass in natural waters.

Toxicity impact that lingers or continues for a relatively  long period of time, often
one-tenth of the life span or more. Chronic  effects could include mortality, reduced
growth, or reduced  reproduction.

A group of bacteria that normally live within the intestines of mammals, including
humans. Coliform bacteria are used as an indicator of the presence of sewage in
natural waters.

A combined sewer carries  both wastewater and stormwater runoff.  CSOs dis-
charged to receiving water can result in contamination problems that may prevent
the attainment of water quality standards.

No significant difference in concentration of a pollutant exists across the transect of
the waterbody.

Amount of a substance  or  material  in a  given unit volume of solution. Usually
measured in milligrams per liter (mg/l) or parts per million (ppm).

Substance that does not undergo any  chemical or  biological  transformation or
degradation  in a given ecosystem.

Act of polluting or making impure; any indication of chemical, sediment, or biological
impurities.

As specified under the Clean Water Act, conventional contaminants include sus-
pended solids, coliform bacteria, biochemical oxygen demand, pH, and oil and
grease.

Wet area of a waterbody  normal to the longitudinal component of the flow.

Gradual decrease in the amount of a given  substance  in a  given system due to
various sink processes including chemical and biological transformation, dissipation
to other environmental media, or deposition into storage areas.

Metabolic  breakdown  of  organic materials;  the  by-products formation releases
energy and simple organics and inorganic compounds, (see also respiration)

Describes the decomposition of ammonia compounds, nitrites, and  nitrates  (by
bacteria) that results in the eventual release of nitrogen gas into the atmosphere.

The stream flow used to conduct steady-state wasteload allocation modeling.

Uses specified in water quality standards for each waterbody or segment regardless
of actual attainment.
                                              F-4

-------
Detritus

Diagenesis


Dilution
Discharge permits
(NPDES)
Discharge
Monitoring Report
(DMR)
Dispersion
Dissolved oxygen
(DO)
Dissolved oxygen
sag
Diurnal

Domestic
wastewater
Drainage basin
Dye study

Dynamic model

Dynamic
simulation
Ecosystem

Effluent

Effluent plume


Epiphyte
Any loose material produced directly from disintegration processes. Organic detritus
consists of material resulting from the decomposition of dead organic remains.

Production of sediment fluxes as a result of the flux of paniculate organic carbon in
the sediment and its decomposition. The diagenesis reaction can be thought of as
producing oxygen equivalents released by various reduced species.

Addition of less concentrated liquid (water) that results in a decrease in the original
concentration.

A permit issued by the U.S. EPA or a State regulatory agency that sets specific limits
on the type and amount of pollutants that a municipality or industry can discharge
to a receiving water; it also includes a compliance schedule for achieving those limits.
It is called the NPDES because the permit process  was established under the
National Pollutant Discharge Elimination System, under provisions of the Federal
Clean Water Act.

Report of effluent characteristics submitted by a municipal or industrial facility that
has been granted an NPDES discharge permit.

The spreading of chemical or biological constituents, including pollutants, in various
directions  from a point source, at varying velocities depending on the  differential
instream flow characteristics.

The amount of oxygen that is dissolved  in water. It also refers to a  measure of the
amount of oxygen available for biochemical activity in water body, and as indicator
of the quality of that water.

Longitudinal variation of dissolved oxygen representing the oxygen depletion and
recovery following a waste load discharge into a receiving water.

Actions or processes having a period or a cycle of approximately one tidal-day or
are completed within a 24-hour period and which recur every 24 hours.

Also called sanitary wastewater, consists of wastewater discharged from residences
and from commercial, institutional, and similar facilities.

A part of the land area enclosed by a topographic divide from which direct surface
runoff from precipitation normally drains by gravity into a receiving water. Also
referred to as watershed, river basin, or  hydrologic unit.

Use of conservative substances to assess the physical behavior of a natural system
to given stimulus.

A mathematical  formulation describing the physical behavior  of  a system or a
process and its temporal variability.

Modeling of the behavior of physical, chemical, and/or biological phenomena and
their variation over time.

An interactive system that includes the organisms of a natural community associa-
tion together with their abiotic physical, chemical, and geochemical environment.

Municipal  sewage or industrial liquid waste (untreated, partially treated, or  com-
pletely treated) that flows out of a treatment plant, septic system, pipe, etc.

Delineates the extent of contamination  in a given medium as a result of effluent
discharges (or spills). Usually shows the concentration gradient within the delineated
areas or plume.

A plant growing on another plant; more generally, any organism growing attached
on a plant.
                                               F-5

-------
Estuary


Estuarine number


Eutrophication
Eutrophication
model
Extinction
coefficient
Factor of Safety


Fate of pollutants
Fecal coliform
bacteria

First-order kinetics
Flocculation


Flux


Forcing functions



Geochemical

Gradient


Groundwater
Half-saturation
constant
Heterotroph
Hydrodynamic
model
Brackish-water areas influenced by the tides where the mouth of the river meets the
sea.

Nondimensional  parameter accounting for decay, tidal  dispersion, and advection
velocity. Used for classification of tidal rivers and estuarine systems.

Enrichment of an  aquatic ecosystem with nutrients (nitrates, phosphates)  that
accelerate biological productivity (growth of algae and weeds) and an undesirable
accumulation of algal biomass.

Mathematical formulation that describes the advection,  dispersion, and biological,
chemical, and geochemical reactions that influence the growth and accumulation of
algae in aquatic ecosystems. Models of eutrophication typically include one or more
species groups of algae, inorganic and organic nutrients (N,P), organic carbon, and
dissolved oxygen.

Measure for the reduction (absorption) of light intensity within a water column.

Coefficient used to account for uncertainties in representing, simulating, or designing
a system.

Physical, chemical, and biological transformation in the  nature and changes of the
amount of a pollutant in an environmental system. Transformation processes are
pollutant specific. However, they have comparable kinetics so that different formu-
lations for each pollutant are not required.

Bacteria that are present in the intestines or feces of warm-blooded animals. They
are often used as indicators of the sanitary quality of water. See Coliform bacteria.

Describes a reaction in which the rate of transformation of a pollutant is proportional
to the amount of that pollutant in the environmental system.

The process by which suspended colloidal or very fine particles are assembled into
larger masses or flocules that eventually settle out of suspension.

Movement and transport of mass of any water quality constituent over a given period
of time. Units of mass flux are mass per unit time.

External empirical formulation used to provide input describing a  number of proc-
esses. Typical forcing functions include parameters such as temperature, point and
tributary sources, solar radiation, and waste loads and flow.

Refers to chemical reactions related to earth materials such as soil, rocks, and water.

The rate of decrease (or increase) of one quantity with respect to  another; for
example, the rate of decrease of temperature with depth in a lake.

Phreatic water or subsurface water in the zone of saturation. Groundwater inflow
describes the rate and amount of movement of water from a saturated formation.

Nutrient concentration at which the growth rate is half the maximum rate. Half-satu-
ration constants define the nutrient uptake characteristics of different phytoplankton
species. Low half-saturation constants indicate the ability of the algal group to thrive
under nutrient-depleted conditions.

Organisms  that use organic carbon for the formation of cell tissue.  Bacteria are
examples of heterotroph.

Mathematical formulation used in describing circulation, transport, and deposition
processes in receiving water.
                                               F-6

-------
Hydrograph


Hydrologic cycle



Hydrolysis



In situ


Initial conditions


Initial mixing zone



Interstitial water


Kinetic processes


Light saturation

Loading, Load,
Loading rate

Load allocation
(LA)


Long stream


Longitudinal
dispersion


Low-flow  (7Q10)



Macrophyte

Margin of Safety
(MOS)

Mass balance


Mathematical
model
Mineralization
A graph showing variation of in stage (depth) or discharge of water in a stream over
a period of time.

The circuit of water movement from the atmosphere to the earth and return to the
atmosphere through various stages or processes, such as precipitation, intercep-
tion, runoff, infiltration, storage, evaporation, and transpiration.

Reactions that occur  between chemicals  and water molecules resulting  in the
cleaving of a molecular bond and the formation of new bonds with components of
the water molecule.

In place; in situ measurements consist of measurement of component or processes
in a full-scale system or a field rather than in a laboratory.

A state of a system  prior to an introduction  of  an induced stimulus. Describe
conditions at the start-up of system simulations.

Region immediately downstream of an outfall where effluent dilution processes
occur. Because of the combined effects of the effluent buoyancy, ambient stratifica-
tion, and current, the prediction of initial dilution can be involved.

Water contained in the interstices, which are the pore spaces or voids in soils and
rocks.

Description of the rate and mode of change in the transformation or degradation of
a substance in an ecosystem.

Optimal light level for algae and macrophyte growth and photosynthesis.

The total amount of material  (pollutants) entering the system from one or multiple
sources; measured as a rate  in weight per unit time.

The portion of a receiving water's total maximum daily load that is attributed either
to one of its existing or future nonpoint sources of pollution or to natural background
sources.

A receiving water where nutrients are in excess of growth limiting  conditions, and
where the travel time allows growth and physical accumulation of algal biomass.

The spreading of chemical or biological constituents, including  pollutants,  down-
stream from a point source at varying velocities due to the differential instream flow
characteristics.

Low-flow (7Q10) is the 7-day average  low flow occurring once in 10  years; this
probability-based statistic is used in determining stream design flow conditions and
for evaluating the water quality impact of effluent discharge limits.

Large vascular rooted  aquatic plants.

A required component of the TMDL that accounts of the uncertainty about the
relationship between the pollutant load and the quality of the receiving waterbody.

An equation that accounts for the flux of mass going into a defined area and the flux
of mass leaving the defined area. The flux in must equal the flux out.

A system of mathematical expressions that describe the spatial and temporal
distribution of water  quality constituents resulting from fluid transport and the one,
or more,  individual  processes and  interactions within some  prototype aquatic
ecosystem. A mathematical water quality model is used as the basis for waste load
allocation evaluations.

The transformation of organic matter into a mineral or an inorganic compound.
                                               F-7

-------
Mixing
characteristics
Monte Carlo
simulation
N/P ratio
Natural waters

Nitrate (NO3) and
Nitrite (NO2)
Nitrification
Refers to the tendency for natural waters to blend; i.e. for dissolved and paniculate
substances to disperse into adjacent waters.

A stochastic modeling technique that involves the random selection of sets of input
data for use in repetitive model runs. Probability distributions of receiving water
quality concentrations are generated as the output of a Monte Carlo simulation.

The ratio of nitrogen to phosphorus in an aquatic system. The ratio is used as an
indicator of the nutrient limiting conditions for algal growth; also used as indicator
for the analysis of trophic levels of receiving waters.

Flowing water within a physical system that has developed without human interven-
tion, in which natural processes continue to take place.

Oxidized nitrogen species. Nitrate is the form of nitrogen preferred by aquatic plants.

The oxidation of ammonium salts to  nitrites (via Nitrosomonas bacteria) and the
further oxidation of nitrite to nitrate via Nitrobacter bacteria.
Nitrifier organisms    Bacterial organisms that mediate the biochemical oxidative processes of nitrification.
Nitrobacter

Nitrogenous BOD
(NBOD)
Nitrosomonas
Nonconservative
substance
Nonpoint source
Numerical model


Nutrient


Nutrient limitation

One-dimensional
model (1-D)
Organic matter
Organic nitrogen
Type of bacteria responsible for the conversion of nitrite to nitrate.

Refers to the oxygen demand associated with the oxidation of nitrate.

Type of bacteria responsible for the oxidation of ammonia to the intermediate product
nitrite.

Substances that undergo chemical or biological transformation in a given environ-
ment.

Pollution that is not released through  pipes but rather  originates  from  multiple
sources over a relatively a large area. Nonpoint source can be divided into source
activities related to either land or water use including failing septic tanks, improper
animal-keeping practices, forest practices, and urban and rural runoff.

Models that approximate a solution of governing partial differential equations which
describe a  natural  process. The approximation uses a numerical discretization of
the space and time components of the system or process.

A primary element necessary for the growth of living organisms. Carbon  dioxide,
nitrogen, and phosphorus,  for example, are required  nutrients for phytoplankton
growth.

Deficit of nutrient (e.g., nitrogen and phosphorus) required  by microorganisms in
order to metabolize organic substrates.

A mathematical model defined along one spatial coordinate of a  natural water
system. Typically 1-D models are used to describe the longitudinal variation of water
quality constituents along the downstream direction  of a stream or river. In writing
the model,  it is  assumed that the cross-channel (lateral)  and vertical variability is
relatively homogenous and can, therefore,  be averaged over those spatial coordi-
nates.

The organic fraction that includes plant and animal residue at various stages of
decomposition,  cells and tissues of soil  organisms, and substance synthesized by
the soil population. Commonly determined as the amount of organic  material
contained in a soil or water sample.

Form of nitrogen bound to an organic compound.

-------
Orthophosphate
(O_PO4_P)
Outfall

Oxidation
Oxygen demand


Oxygen depletion

Oxygen saturation
Partition
coefficients

Peak runoff
Periphyton

Photoperiod



Photosynthesis



Phyla


Phytoplankton


Plankton



Point source



Pollutant
Postaudit
Form of phosphate available for biological metabolism without further breakdown.

Point where water flows from a conduit, stream, or drain.

The chemical union of oxygen with metals or organic compounds accompanied by
a removal of hydrogen or another atom. It is an important factor for soil formation
and permits the release of energy from cellular fuels.

Measure of the dissolved  oxygen used  by a system (microorganisms) in the
oxidation of organic matter. See also biochemical oxygen demand.

Deficit of dissolved oxygen in a water system due to oxidation of organic matter.

Natural or artificial  reaeration or oxygenation of a water system (water sample) to
bring the level  of  dissolved oxygen to saturation. Oxygen saturation is greatly
influence by temperature and other water characteristics.

Chemicals in solution are partitioned into dissolved and particulate adsorbed phase
based on their corresponding sediment-to-water partitioning coefficient.

The highest value of the stage or discharge attained by a flood or storm event, also
referred to as flood peak or peak discharge.

Attached benthic algae.

Time period of the  seasonal response by organisms to change in the length of the
daylight period (e.g., flowering, germination of seeds, reproduction, migration, and
diapause are frequently under photoperiod control).

The  biochemical synthesis of carbohydrate based organic compounds from water
and carbon dioxide using light energy in the presence of chlorophyll. Photosynthesis
occurs in all plants, including  aquatic organisms such as algae and macrophyte.
Photosynthesis  also occurs in primitive bacteria such as blue-green algae.

Species  groups of same  family of organisms.  Phyla of phytoplankton  include
diatoms, blue-green algae, dinoflagellates, and green algae.

A group of generally unicellular microscopic plants characterized by passive drifting
within the water column. See Algae.

Group of generally microscopic plants and animals passively floating, drifting or
swimming weakly.  Plankton include the phytoplankton (plants) and zooplankton
(animals).

Pollutant loads discharged at a specific location from pipes, outfalls, and conveyance
channels from either  municipal wastewater treatment  plants  or  industrial waste
treatment facilities. Point sources can also include pollutant loads contributed by
tributaries to the main receiving water stream or river.

A contaminant  in a concentration or amount  that adversely alters the physical,
chemical, or  biological properties of a natural  environment.  The term  include
pathogens, toxic metals,  carcinogens, oxygen demanding substances,  or other
harmful  substances.  Examples of pollutant sources include dredged spoil,  solid
waste, incinerator residue, sewage, garbage, sewage sludge, munitions, chemical
waste, biological material, radioactive materials, heat, wrecked or discharged equip-
ment, sediment, cellar dirt, hydrocarbons, oil, and municipal, industrial, and agricul-
tural waste discharged into surface water or groundwater.

A subsequent examination and verification of model predictive performance follow-
ing implementation of an environmental control program.
                                              F-9

-------
Pretreatment
Primary
productivity
Primary treatment
plant


Priority pollutant
Publicly Owned
Treatment Works
(POTW)
Raw sewage

Reaction rate
coefficient


Reaeration


Receiving waters



Refractory
organics


Reserve capacity


Residence time



Respiration
Rotating
biological
contactors (RBCs)
Roughness
Coefficient
Scour
The treatment of wastewater to remove or reduce contaminants prior to discharge
into another treatment system or a receiving water.

A measure of the rate at which new organic matter is formed and accumulated
through photosynthesis and chemosynthesis activity of producer organisms (chiefly,
green plants). The rate of primary production is estimated by measuring the amount
of oxygen released (oxygen method) or the amount of carbon assimilated by the
plant (carbon method)

Wastewater treatment process where solids are removed from raw sewage primarily
by physical settling. The process typically removes about 25-35% of solids and
related organic matter (BODs).

Substances listed  by the U.S. EPA under the Federal Clean Water Act as harmful
substances and having priority for regulatory controls. The list includes metals (13),
inorganic compounds (2),  and a broad range of naturally occurring  or artificial
organic compounds (111).

Municipal wastewater treatment plant owned and operated by a public governmental
entity such as a town  or city.

Untreated municipal sewage.

Coefficient describing the rate of transformation of a substance in an environmental
medium characterized by a set of physical, chemical, and biological conditions such
as temperature and dissolved oxygen level.

Describe the net flux of oxygen occurring from the atmosphere to a body of water
with a free surface.

Creeks, streams, rivers, lakes, estuaries, groundwater formations, or other bodies
of water into which surface water and/or treated or untreated waste are discharged,
either naturally or in man-made systems.

A broad lumping of man-made organic  chemicals that resist chemical or bacterial
decomposition,  including many  pesticides,  herbicides,  household  and industrial
cleaners and solvents, photofinishing chemicals, and dry-cleaning fluids.

Pollutant loading rate set aside in determining stream waste load allocation account-
ing for uncertainty and future growth.

Length of time that a pollutant remains within a section of a stream or river. The
residence time is determined by the streamflow and the volume of the  river reach
or the average stream velocity and the length of the river reach.

Biochemical process  by means of which cellular fuels are  oxidized with the aid of
oxygen to permit the release of the energy required to sustain life; during respiration
oxygen is consumed and carbon dioxide is released.

A wastewater treatment process consisting of a series of  closely spaced rotating
circular disks of polystyrene or polyvinyl chloride. Attached biological growth is
promoted on the surface of the disks. The rotation  of the disks allows contact with
the wastewater and the atmosphere to enhance oxygenation.

A factor in velocity and  discharge formulas representing the  effects  of channel
roughness on energy losses in flowing  water. Manning's "n" is a  commonly used
roughness coefficient.

To abrade and wear away. Used to describe the weathering away of a terrace or
diversion channel or streambed. The clearing and  digging  action of flowing water,
                                             F-10

-------
Secchi depth


Secondary
treatment plant


Sediment
Sediment oxygen
demand (SOD)
Sedimentation


Short stream


Simulation



Sorption

Spatial
segmentation

Stabilization pond
Steady-state
model

Stoichiometric
ratio

STORE!
Storm runoff
Stratification (of
water body)


Streamflow
especially the downward erosion by stream water in sweeping away mud and silt
on the outside of a meander or during flood events.

A measure of the light  penetration  into the water  column.  Light penetration is
influenced by turbidity.

Waste treatment process where oxygen-demanding organic materials (BOD) are
removed by bacterial oxidation of the waste to carbon dioxide and water. Bacterial
synthesis of wastewater is enhanced by injection of oxygen.

Particulate organic and inorganic matter that accumulates in a loose, unconsolidated
form on the bottom of natural waters.

The solids discharged to a receiving water are partly organics, and upon settling to
the bottom, they decompose anaerobically as well as aerobically, depending on
conditions. The oxygen consumed in aerobic decomposition represents another
dissolved oxygen sink for the waterbody.

Process of deposition of waterborne or windborne sediment or other material; also
refers to the infilling of bottom substrate in a waterbody by sediment (siltation).

A receiving water where nutrients are in  excess of growth-limiting conditions and
where the time of travel within the stream reach is not sufficient to allow growth and
physical accumulation of algal biomass.

Refers to the use of mathematical models to approximate the observed behavior of
a natural water system in response  to a specific known set of input and forcing
conditions. Models that have been validated, or verified, are then used to predict the
response of a natural water system to changes in the input or forcing conditions.

The adherence of ions or molecules in a gas or liquid to the surface of a solid particle
with which they are in contact.

A numerical discretization of the spatial component of a system into one or more
dimensions; forms the basis for application of numerical simulation models.

Large earthen basins  that are used for  the treatment of wastewater by natural
processes involving the use of both algae and bacteria.

Mathematical model of fate and transport that uses constant values of input variables
to predict constant values of receiving water quality concentrations.

Mass-balance-based ratio for nutrients, organic carbon and algae (e.g., nitrogen-to-
carbon ratio).

U.S. Environmental  Protection Agency (EPA) national water quality database for
STORage and RETrieval (STORET). Mainframe water quality database that in-
cludes physical, chemical, and biological data measured in waterbodies throughout
the United States.

Rainfall  that does not evaporate or infiltrate the ground because of impervious land
surfaces or a soil infiltration rate lower than rainfall intensity, but  instead flows onto
adjacent land or waterbodies or is routed  into a drain or sewer system.

Formation of water  layers each with specific  physical, chemical, and biological
characteristics. As the density of water decreases due to surface heating, a stable
situation develops with lighter water overlaying heavier and denser water.

Discharge that occurs  in a natural channel. Although the term "discharge" can be
applied to the flow of a canal, the word "streamflow" uniquely describes the discharge
in a surface stream course.  The term streamflow is more general than "runoff" as
                                              F-11

-------
Substrate
Surface waters
Suspended solids
or load
Temperature
coefficient
Tertiary treatment
Three-dimensional
model (3-D)


Total Kjeldahl
Nitrogen (TKN)

Total Maximum
Daily Load (TMDL)


Total coliform
bacteria
Toxic substances


Transit time

Transport of
pollutants (in
water)
Travel time

Tributary

Trickling filter


Turbidity
Turbulent flow
Turbulence
streamflow may be applied to discharge whether or not it is affected by diversion or
regulation.
Refers to bottom sediment material in a natural water system.

Water that is present above the substrate or soil surface. Usually refers to natural
waterbodies such as rivers, lakes and impoundments, and estuaries.

Organic and inorganic  particles (sediment)  suspended in and carried by a fluid
(water). The suspension  is  governed by the upward components of turbulence,
currents, or colloidal suspension.

Rate of increase  in an  activity or process over a 10 degree Celsius increase in
temperature. Also referred to as the Qio.

Waste treatment processes designed to  remove or  alter the forms of nitrogen or
phosphorus compounds contained in domestic sewage.

Mathematical model defined along three spatial coordinates where the water quality
constituents are considered to vary over all three spatial coordinates of length, width,
and depth.

The total of organic and ammonia nitrogen in a sample, determined by the Kjeldahl
method.

The sum of the individual wasteload allocations and load allocations. A margin of
safety is included with the two types of allocations so that any additional loading,
regardless of source, would  not produce a violation of water quality standards.

A  particular group of bacteria that  are  used as indicators of  possible sewage
pollution. They are characterized as aerobic or facultative anaerobic, gram-negative,
nonspore-forming, rod-shaded bacteria which ferment lactose with gas formation
within 48 hours at 35 degrees Celsius. (See also fecal coliform bacteria)

Those chemical substances, such as pesticides, plastics, heavy metals, detergent,
solvent, or any other material that are poisonous, carcinogenic, or otherwise directly
harmful to human health and the environment.

In  nutrient cycles, average time that a substance remains in a particular form; ratio
of  biomass to productivity.

Transport of pollutants in water involves two main process: (1) advection, resulting
from the flow of water, and (2) diffusion, or transport due to turbulence in the water.

Time period required by a particle to cross a transport route such as a watershed,
river system, or stream reach.

A lower order stream compared to a receiving waterbody. "Tributary to" indicates
the largest stream into which the reported stream or tributary flows.

A wastewater treatment process consisting of a bed of highly permeable medium to
which microorganisms are attached and through which wastewater is percolated or
trickled.

Measure of the amount  of suspended material in water.

A flow characterized by  irregular,  random-velocity fluctuations.

A type of flow in which any particle may move in any direction with respect to any
other particle and in a regular or fixed path. Turbulent water is  agitated by cross
current and eddies. Turbulent velocity is that velocity above which turbulent flow will
always exist and below which the flow may be either turbulent or laminar.
                                              F-12

-------
Two-dimensional
model (2-D)
Ultimate
Biochemical
Oxygen Demand
(UBOD or BODu)
Uncertainty factors
Unstratified
Verification (of a
model)

Volatilization
Waste load
allocation (WLA)

Wastewater
Wastewater
treatment


Water quality
Water quality
criteria (WQC)
Water quality
standard (WQS)
Wind mixing


Zero-order kinetics


Zooplankton
Mathematical model defined along two spatial coordinates where the water quality
constituents are considered averaged over the third remaining spatial coordinate.
Examples of 2-D models include descriptions  of the variability of water quality
properties along:  (a) the length and width of  a river that incorporates vertical
averaging or (b) length and depth of a river that incorporates lateral averaging across
the width of the waterbody.

Longterm oxygen demand required to completely stabilize organic carbon in waste-
water or natural waters.
Factors used in the adjustment of toxicity data to account for unknown variations.
Where toxicity is measured on only one test species, other species may exhibit more
sensitivity to that effluent. An uncertainty factor would  adjust measured toxicity
upward and downward to cover the sensitivity range of other, potentially more or
less sensitive species.

Indicates a  vertically uniform or well-mixed condition in a waterbody. See also
stratified.

Subsequent testing of a precalibrated model to additional field data usually under
different external conditions to further examine model validity (also called validation).

Process by which chemical compounds are vaporized (evaporated) at given tem-
perature and pressure conditions by gas  transfer  reactions. Volatile compounds
have a tendency to partition into the gas phase.

The portion of a receiving water's total maximum daily load that is allocated to one
of its existing or future point sources of pollution.

Usually refers to effluent from a sewage treatment plant. See also domestic
wastewater.

Chemical, biological, and mechanical procedures applied to an industrial or munici-
pal  discharge or to any other sources of contaminated water in order to remove,
reduce, or neutralize contaminants.

The biological, chemical, and  physical conditions of a water body. It is a measure of
a water body to support beneficial uses.

Water quality criteria comprised numeric and narrative criteria. Numeric criteria are
scientifically derived ambient concentrations developed by E PA or States for various
pollutants of concern to protect human health and aquatic life. Narrative criteria are
statements that describe the desired water quality goal.

A water quality standard is  a law or regulation  that consists of  the  beneficial
designated use or uses of a waterbody, the numeric and narrative water quality
criteria that are  necessary to protect the use or uses of that particular waterbody,
and an antidegradation statement.

Refers to a physical  process  occurring when wind over a free water  surface
influences the atmospheric reaeration rate.

Describe the rate of transformation or degradation of a substance; the reaction rate
of change is independent of the concentrations in solution.

Very small animals (protozoans, crustaceans, fish embryos, insect larvae) that live
in a waterbody and are moved passively by water currents and wave action.
                                              F-13

-------

-------
APPENDIX  G.
ABBREVIATIONS
AGP          algal growth potential
ARM          agricultural runoff model
ASCII         American Standard Code for Information
              Interchange
AT           advanced treatment
AWT          advanced water treatment
BOD          biochemical oxygen demand
BODs         5-day biochemical oxygen demand
BODu         ultimate biochemical oxygen demand
BMPs         best management practices
BNR          biological nutrient removal
CBOD        carbonaceous biochemical oxygen demand
CBODs        5-day carbonaceous biochemical oxygen
              demand
CEAM        Center for Exposure Assessment Modeling
              (EPA)
CE-QUAL-     fully dynamic one-dimensional riverine water
  RIV1         quality model
Chi           chlorophyll concentration
COD          chemical oxygen demand
COE          U.S. Army Corps of Engineers
CSO          combined sewer overflow
DIN           inorganic nitrogen concentration (sum of
              ammonia, nitrate, and nitrite)
DIP           dissolved inorganic phosphorus concentration
DMR          discharge monitoring report
DO           dissolved oxygen
DYNHYD5     hydrodynamic model; a submodel of WASP5
EUTRO5      eutrophication/dissolved oxygen kinetics; a
              submodel of WASP5
EPA          Environmental Protection Agency
EPA STORET  U.S. Environmental Protection Agency (EPA)
              national water quality data base for STORage
              and RETrieval (STORET). Mainframe water
              quality data base that includes physical,
              chemical, and biological data measured in
              waterbodies throughout the United States
FOIA          Freedom of Information Act
FOEA         first-order error analysis
FORTRAN     FORmula TRANslation; revised high-level
              programming language for solving problems
              in science and engineering
FORTRAN77   FORmula TRANslation ANSI Standard of
              1977; computer language often used in
              writing scientific equations and models as
              source code for water quality and
              hydrodynamic models.
H            surface-to-bottom depth of the water column;
              units of length
HSPF         Hydrologic Simulation Program - FORTRAN
HRAS         high-rate activated sludge
LA           load allocation
              MBAS        methyl benzene alkyl sulfonate
              MGD          million gallons per day
              Multi-SMP     Simplified Method Program for multiple
                            dischargers
              NBOD        nitrogenous biochemical oxygen demand
              NPDES        National Pollutant Discharge Elimination
                            System
              NTIS          National Technical Information Service
              NTU          nephelometry turbidity units
              NVSS         nonvolatile suspended solids concentration
              O-PO4-P      orthophosphate
              P             average gross photosynthesis production
              PC           personal computer; usually refers to IBM
                            DOS-compatible machines
              PCS          Permit Compliance System
              PDF          probability density function
              POTW        publicly owned treatment works
              P/R           production/respiration ratio
              P-R           photosynthesis and respiration
              Q             streamflow; units of volume/time
              QA/QC        quality assurance/quality control
              QUAL2E      stream water quality model
              QUAL2E-      stream water quality model
              UNCAS
              R             average respiration
              RIVMOD      numerical, hydrodynamic, and sediment
                            transport riverine model
              RIV1H        hydrodynamic model; a submodel of CE-
                            QUAL-RIV1
              RIV1Q        water quality  model; a submodel of CE-QUAL-
                            RIV1
              SOD          sediment oxygen demand
              STP          sewage treatment plant
              TBOD        total biochemical oxygen demand
              TDS          total dissolved solids
              TKN          total Kjedahl nitrogen
              TMDL        total maximum daily load
              TOC          total organic carbon
              TOXI5        toxic chemical-sediment dynamics; a
                            submodel of WASP5
              TP           total phosphorus; sum of all forms of
                            phosphorus: dissolved, particulate, inorganic,
                            and organic phosphorus
              TSS          total suspended solids
              USGS        U.S. Geological Survey
              VSS          detritus concentration
              W            width across a stream channel; units of length
              WASPS        Water Quality Analysis Simulation Program
              WLA          waste load allocation
              1-D           one-dimensional water quality model
              7Q10          7-day average low flow that occurs once in 10
                            years
                                                    G-1

-------

-------
APPENDIX  H:  CONVERSION  FACTORS
           For readers who
              Multiply
 milimeter (mm)
 Meter (m)

 kilometer (km)

 meter per second (mis)
 nanometer (nm)
 centimeter (cm)

 square meter (m2)

 cubic meter (m3)

 liter (L)
 cubic meter per second (m3/s)

 millligram (mg)
 gram (g)

 kilogram (kg)
 metric ton (Mt)  (1000 kg)
 gram per square meter (g/m2)

 degree Celsius (°C)

 milligram per liter (g/L)
 grams per liter  (mg/L)

 langley (ly)
 calorie/square centimeter day
              Symbol
 PAR
|imho
prefer to use U.S. equivalents of metric
 for terms used in this report are listed
                      By
                    Length
                    0.03937
                     3.281
                     1.094
                    0.6214
                    0.5400
                     3.281
                  3.937X108
                    0.3937
                     Area
                     10.76
                     1.196
                   Volume
                     35.31
                     1.308
                     1.057
                     35.31
                     Mass
                  0.00003527
                    0.03527
                   0.002205
                     2,205
                     1.102
                     8.922
                 Temperature
                  1.8[°C] +32
                Concentration
                      1.0
                      1.0
                    Energy
                      1.0
                    3.6867
                   Meaning
        Photosythetically active radiation
        (400-700 nanometer waveband).
        Measured in microeinsteins per
        square meter per second [|iE/m2/s]
        Conductance in micromhos.  A
        measure of the amount of dissolved
        ions present in water
units, conversion factors
below:
                To
              Obtain
 inch (in)
 foot (ft)
 yard (yd)
 mile (mi)
 nautical mile (nmi)
 foot per second (Ills)
 inch (in)
 inch (in)

 square foot (ft2)
 square yard (yd2)

 cubic foot (ft3)
 cubic yard (yd3)
 quart (qt)
 cubic foot per second (ft3/s)

 ounce (oz)
 ounce (oz)

 pound (Ib)
 ton (short)
 pound per acre (Ib/acre)

 degree Fahrenheit (°F)

 parts per million (ppm)
 parts per thousand (ppt)

 calorie/square centimeter (cal/cm2)
 British thermal units/square foor/day
 (Btu/ft2/day)
            Conversion
 1 watt m 2~4.6|iEm 2s 1
 1 ly day1-0.485 watt m 2
 1 part per thousand is approximately
 1,500|imhoat25°C
                                                  H-1

-------

-------
APPENDIX  I.    SYMBOLS
SYMBOL   DESCRIPTION (dimension)
                                                 SYMBOL  DESCRIPTION (dimension)
3N
ap
A
Ag
bn
C
Cs
C(t)
dV
Dp
Dx
Dz
f
F
g
Gmax
Gn
Gp
GT
H
K
Ka
Kd


Ke
Km
Kmn
Kmp
Kn
Kr
Ks
Ksi
Ki
la
IT
L
n
N
Nut
Ni
N2
N3
N4
nitrogen:chlorophyll ratio (MM 1)
phosphorus:chlorophyll ratio (MM^)
chlorophyll a concentration (ML3)
algal biomass concentration (ML 3)
periodic coefficient
dissolved oxygen concentration in stream (ML 3)
saturation concentration of dissolved oxygen (ML )
time varying oxygen value (ML 3)
volume of the segment and is equal to AAX(L 3)
phytoplankton death rate (T 1)
longitudinal dispersion coefficient (L2T 1)
death rate (T  1)
photoperiod (T)
fraction of algal nitrogen uptake from ammonia pool
gravitational constant (L2 T1)
maximum growth rate (T 1)
phytoplankton net growth rate (T ^)
phytoplankton growth rate (T 1)
temperature effect (T 1)
average depth (L)
first-order reaction rate (T 1)
stream reaeration rate coefficient (T  1);
atmospheric reaeration rate: reflects first-order
reaction whereby fraction of oxygen deficit is
satisfied
BOD oxidation rate where oxidation accounts for
all CBOD removal (T  1)
extinction coefficient (L 1)
half saturation (Michaelis) constant (ML 3)
Michaelis-Menton constant for nitrogen (ML 3)
Michaelis-Menton constant for phosphorus (ML  3)
nitrification reaction rate (T 1)
CBODs removal rate in the stream (T 1)
effective loss rate due to settling (T 1)
Michaelis-Menton constant for silica (ML3)
BOD reaction rate (T  1)
average of incident light on water surface over a
24-hour period (ly/day)
saturating light intensity (ly/day)
total daily radiation (ly)
oxygen equivalence of the organic matter
remaining CBOD concentration (ML 3); length; liters
total oxygen demand (ML 3)
langley (incident light intensity)
estuary number (dimensionless)
nitrogen concentration (ML 3)
nutrient concentration (ML 3)
ammonia concentration (ML 3)
nitrite-nitrogen concentration (ML 3)
nitrate-nitrogen concentration (ML 3)
organic nitrogen concentration (ML 3)
Pav

PM

P(t)

Pi
P2
0
PL
rn

R
S
Sb
Si
t
t*
T
U
u*
Vs
w
x
y
a-i
OC2
as

OC4

as

ae

61
62
63
B4
02
03
04
05
average gross photosynthesis production
(ML 3 T1); phosphorus concentration (ML 3)
average daily rate of photosynthetic oxygen
production (ML 3 T 1)
maximum rate of photosynthetic oxygen
production (ML 3 T 1)
algal gross photosynthetic production of oxygen
(ML 3T 1)
organic phosphorus concentration (ML 3)
dissolved phosphorus concentration (ML 3)
river flow rate (L3T 1)
light effect (dimensionless)
nutrient effect (dimensionless); limiting nutrient
reduction factor
average algal oxygen respiration (ML 3T 1)
net settling rate  (T  1); stream slope (LL 1)
sediment oxygen demand (ML2T 1)
dissolved inorganic silica concentration (ML 3)
time (T)
travel time in stream ; =x/U  (T)
temperature (°); average time period
average stream velocity (LT 1)
shear velocity (LT 1)
phytoplankton settling velocity (MT 1)
direct loading rate (MT 1); stream width (L)
distance downstream of effluent (L)
oxygen consumed (ML 3)
fraction of algal biomass that is nitrogen (MM-1)
fraction of algal biomass that is phosphorus (MM 1)
the stoichiometric ratio of oxygen production per
unit of algal photosynthesis (MM-1)
the stoichiometric ratio of oxygen uptake per unit
of algae respired (MM-1)
the stoichiometric ratio of oxygen uptake per unit
of ammonium (MM-1)
the stoichiometric ratio of oxygen uptake per unit
of nitrite-nitrogen oxidation (MM-1)
ammonia oxidation rate coefficient (T 1)
nitrite oxidation rate coefficient (T 1)
organic nitrogen hydrolysis  rate coefficient (T 1)
organic phosphorus decay rate (T 1)
benthos source rate for dissolved phosphorus (ML 2T 1)
benthos source rate for ammonia nitrogen (ML 2T 1)
rate coefficient for organic nitrogen settling  (T -1)
rate coefficient for organic phosphorus settling (T 1)
algal growth rate coefficient (T 1)
algal respiration rate coefficient (T 1)
constant for temperature adjustment (dimensionless)
Dimension codes:
  L=Length    M=Mass    T=Time
                                                         1-1

-------