**STATE of \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_**

**Department of
Environmental\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_**

**Draft Generic Quality Assurance Project Plan for Model Simulations**

**in the Total Maximum Daily Load (TMDL) Program**

**Prepared by**

**STATE of \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_**

**Department of
Environmental\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_**

**Address**

**Prepared for**

**U.S. Environmental Protection Agency**

**New England Office**

**1 Congress Street, Suite 1100**

**Boston, MA 02114**

Approvals Signature (required prior to project start):

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Date: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Date: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

STATE Project Manager

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Date: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

STATE Quality Assurance Officer

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Date: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

U.S. EPA Project Manager/Officer

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Date: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

U.S. EPA QA Manager/Representative

**[Table of Contents]{.underline}**

**Section Page**

Title and Approval
Page\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...1

Table of
Contents\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....2

1.0 PROJECT
MANAGEMENT\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....4

[1.3 Distribution List 4](#__RefHeading___Toc153252849)

[1.4 Project Organization 4](#__RefHeading___Toc153252850)

Key Individuals/Titles and
Responsibilities\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....5

[1.5 Problem Definition/Background 6](#__RefHeading___Toc153252851)

Model Assessment and
Selection\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....7

1.6 Project/Task Description and
Schedule\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....8

[1.7 Quality Objectives and Criteria for Measurement Data and Models
8](#__RefHeading___Toc153252853)

[1.7.1 Objectives and Project Decisions
9](#objectives-and-project-decisions)

[1.7.2 New Data Measurement Performance Criteria/Existing Data
Acceptance Criteria
9](#new-data-measurement-performance-criteriaexisting-data-acceptance-criteria)

New and Existing
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...9

Completeness/Representativeness/Comparability\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....11

Acceptance Criteria for Model Parameterization
(Calibration)\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...11

Model Corroboration
(Validation)\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....13

Model
Sensitivity\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....14

Model
Uncertainty\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....14

[1.8 Special Training Requirements/Certification
15](#refheading___toc153252856)

[1.9 Documents and Records 15](#__RefHeading___Toc153252857)

QAPP and Sampling Plan
Modifications/Archiving\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....15

Modeling
Journal\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....16

[1.9.1 QA Project Plan Distribution 17](#qa-project-plan-distribution)

[2.0 DATA GENERATION AND ACQUISITION
17](#data-generation-and-acquisition)

[2.1 Data Acquisition Requirements (Non-Direct Measurements)
17](#__RefHeading___Toc153252860)

Potential Model Input
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....17

[2.2 Data Management 18](#__RefHeading___Toc153252861)

Potential Model Input
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....19

Model Application
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....19

[3.0 ASSESSMENT AND OVERSIGHT 20](#assessment-and-oversight)

[3.1 Assessments/Oversight and Response Actions
20](#__RefHeading___Toc153252863)

[4.0 MODEL APPLICATION 21](#model-application)

[4.1 Model Parameterization (Calibration))
21](#__RefHeading___Toc153252865)

Parameterization
Considerations\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....21

Parameterization Stop
Criteria\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....22

[4.2 Model Corroboration (Validation and Simulation)
22](#__RefHeading___Toc153252866)

Models for Comparative
Analyses\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....23

[4.3 Reconciliation with User Requirements
24](#__RefHeading___Toc153252867)

Model Limitations and Final Evaluation
Criteria\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...24

[4.4 Reports to Management 25](#refheading___toc153252868)

Existing
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....25

Model Application
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....25

[5.0 MODELING REPORTS 26](#refheading___toc153252869)

Water Quality
Model\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....26

Water Quality Transport and Chemical
Parameterization\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....26

> Model Load
> Inputs\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....26
>
> Model Prediction
> Runs\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....27
>
> Model Sensitivity
> Analysis\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....27

[6.0 REFERENCES 28](#references)

EXAMPLE APPENDICES

**Appendix A.** ME DEP Example Modeling
Report\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....29

**Appendix B.** List of Peer-reviewed Publications of Model Theory and
Model Applications in Other Water
Bodies\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...53

**Appendix C.** Example Technical and User
Manuals\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....54

**Appendix D.** Example Tables from CT DEP Southport Harbor Modeling
Project\...\...\...\...\...\...\...\...\...\...\....55

Table A3: Background
Information\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....56

Table A6: Model Calibration and Validation Targets for CT DEP Southport
Modeling Project\...\...\...\....57

Table A7: Documentation and Records
Retention\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...57

Table B1: Summary of Station
Information\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....58

Table C1: Assessment and Response
Actions\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....58

Table E1: WQMAP
applications\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...59

Table E2: Historical and Non-Directly Measured
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....60

Table E3: Model Calibration Guidance (McCutcheon, et al.,
1990)\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...60

**Appendix E.** Example
Figures\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...61

Figure A1: Modeling Project Organization Chart from CT DEP Southport
Harbor Modeling\...\...\...\...\.....61

Figure E1: Modeling Framework - Relationship of Models and Field Data
Use\...\...\...\...\...\...\...\...\...\...\...\...61

**Appendix F.** Example Modeling Scope of Services from
MassDEP\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....62

**Appendix G.** Technical Memorandum for Calibration/Validation from
MassDEP\...\...\...\...\...\...\...\...\...\...\...67

**Appendix H**. Technical Memorandum for Simulation Scenarios from
MassDEP\...\...\...\...\...\...\...\...\...\...\.....71

# 1.0 PROJECT MANAGEMENT

**[1.1 Title and Approval Page]{.underline}** - See page 1.

**[1.2 Table of Contents]{.underline}** - See page 2.

> []{#__RefHeading___Toc153252849 .anchor}

**[1.3 Distribution List]{.underline}** -- STATE Modeling Quality
Assurance Program Plan Distribution List.

[QAPP Recipient/Title]{.underline} [Organization]{.underline} [Telephone
Number]{.underline}

Mark Scrutinizer, QA Officer Commissioner\'s Office, STATE 207-287-XYZZ

Andrew Fist, Director Bureau of Land & Water Quality, STATE 207-287-XYYY

David Waters, Director Division of Environmental Assessment

Bureau of Land & Water Quality, STATE 207-287-H200

Don Model, Simulation Expert

Rob Simulatar, Modeler

Roy Frogger, Biologist III Lake Assessment Section

Bureau of Land & Water Quality, STATE 207-287-BUGS

Linda Swampus, Biologist II Lake Assessment Section

Bureau of Land & Water Quality, STATE 207-287-SUNK

David Hall, Biologist II Lake Assessment Section

Bureau of Land & Water Quality, STATE 207-287-ZZZZ

Judy Lifcycl, Biologist I Lake Assessment Section

Bureau of Land & Water Quality, STATE 207-287-OVER

Karen Maine, Environmental Science Lake Assessment Section

Specialist III Bureau of Land & Water Quality, STATE 207-287-PINE

John McLake, Biologist I Lake Assessment Section

Bureau of Land & Water Quality, STATE 207-287-POND

Scott Wills, Executive Director STATE Volunteer Lake Monitoring Program
207-783-FREE

Norm Solyds, NPS Coordinator Bureau of Land & Water Quality, STATE
207-287-FLOW

Joan Peacemakker, EPA Project Officer US EPA Region I 617-918-HELP

OTHERS?

[]{#__RefHeading___Toc153252850 .anchor}**[1.4 Project
Organization]{.underline}** -- See key individuals and organizational
chart below. The purpose of this document is to present the QAPP for
conducting modeling to support development of TMDLs. The QAPP provides
general descriptions of the work to be performed to support TMDLs and
the procedures that will be used to ensure that the modeling results are
scientifically valid and defensible and that uncertainty has been
reduced to a known and practical minimum.

A graded approach will be applied to projects in order to apply an
appropriate QA level with the confidence needed in modeling results. The
fundamental requirements that define the QA level include:

> • The Intended Use of the Model -- Higher standards are required for
> projects that
>
> involve potentially large consequences.
>
> • The Scope and Magnitude of the Project -- The more complex the
> project and model,
>
> the more detailed the QA effort that will be necessary.

Although there are no explicit categorizations or guidelines for
applying the graded approach, a generalized methodology has been
identified in QA/G-5M -- Guidance for QAPPs for Modeling (EPA 2002). It
allows QA activities to be adapted to meet the rigor needed for the
project at hand. If a project addresses regulatory compliance or TMDL
implementation, significant QA planning is necessary.

## 1.4.1 Key Individuals/Titles and Responsibilities

Mark Scrutinizer, Quality Assurance Officer: Is responsible for...has
independence from all units generating data and modeling...oversees
training...may issue stop work orders...etc..

Andrew Fist, Director

David Waters, Director: Is responsible for...and maintains the official
approved QA Project Plan.

Don Model

Rob Simulator

Roy Frogger, Biologist III

Linda Swampus, Biologist II

David Hall, Biologist II

Judy Lifcycl, Biologist I

Karen Maine, Environmental Science Specialist III

John McLake, Biologist I

Scott Wills, Executive Director

Norm Solyds, NPS Coordinator

Joan Peacemakker, EPA Project Officer

OTHERS?

**Organizational Chart**

[]{#__RefHeading___Toc153252851 .anchor}**[1.5 Problem
Definition/Background]{.underline}**

This document represents a generic Quality Assurance Program Plan for
the STATE of\_\_\_\_\_\_\_\_, Division of \_\_\_\_\_\_\_\_, Bureau of
\_\_\_\_\_\_\_\_ Total Maximum Daily Load (TMDL) Program. It covers
quality assurance elements for model *applications* only. Modification
of the QAPP will be required when projects will involve new model
*development*.

In STATE, an excess pollutant load can result in a violation of water
quality standards. A TMDL analyses is prepared to estimate the total
load that a water body can accept annually without harming water
quality. Historically, development of TMDLs was first mandated by the
Clean Water Act in 1972, and was applied primarily to point sources of
water pollution. As a result of public pressure to further clean-up
water bodies, lake and stream TMDLs are now being prepared for Non-Point
Sources (NPS) of water pollution. Major land use activities contributing
to the load in water bodies include residential-commercial developments,
agriculture, roadways, and commercial forestry.

Statewide, there are approximately \_\_\_\_\_\_\_\_\_\_\_ water bodies
which do not meet water quality standards. TMDL reports identify
regulatory criteria for water bodies and are based on available water
quality data such as total phosphorus, chlorophyll-a, and dissolved
oxygen. The process includes a public participation component to allow
for public review. Model performance and model outcomes under this QAPP
will address the available regulatory criteria.

The department's TMDL Project Leaders are \_\_\_\_\_\_\_\_\_\_ and
\_\_\_\_\_\_\_\_\_\_\_\_. Their responsibilities are listed on
page\_\_\_\_\_\_\_\_ and include notifying the STATE QA Officer and EPA
Project Officer when new models will be created, justifying the
inability to use existing models and if modifications to the model code
will be necessary.

## Model Assessment and Selection

Model assessment and selection is usually completed at the initiation of
modeling projects by the STATE in order to identify a successful
approach for modeling. As part of the review process, publicly available
simulation models are evaluated in order to identify the most
appropriate modeling tool for characterization of point and non-point
sources. A number of standardized modeling packages are reviewed by the
STATE. They have the following advantages:

> 1\. Comprehensive documentation is distributed including a user\'s
> manual, conceptual
>
> representation of the model process, explanation of theory and
> numerical
>
> procedures, data needs, data input format, and description of model
> output.
>
> 2\. Technical support is typically provided in the form of training,
> use-support, and
>
> continual development from federal or academic research organization
> like EPA,
>
> USDA, and USGS.
>
> 3\. Standardized modeling software has a proven track record,
> providing validity and
>
> defensibility when faced with legal challenges.
>
> 4\. They are readily available to the general public
> (non-proprietary).

Selection criteria include length of model development history,
applicability at the needed scale, and ability to predict the impact of
land management practices on water, sediment, and agricultural chemical
yields. The degree of certainty needed in model outputs is defined on a
project specific basis through model optimization techniques. Certainty
end-points, when specified, are model performance goals as some amount
of irreducible error is inherent in all modeling. If other model
selection tools are applied, such as EPA's Model Selection Tool
(<http://crem.tetratech-ffx.com/index.cfm?action=tool>) their
application will be documented in the modeling journals and reports.
Section 4.3.1 identifies a few assumptions for modeling. Project
specific assumptions in the modeling process will be documented in the
modeling journals and reports.

**[1.6 Project/Task Description and Schedule]{.underline}**

Modeling will be conducted to support TMDL development. TMDLs are
important tools for maintaining and protecting acceptable water quality.
They are primarily designed to \'get a handle\' on the magnitude of the
pollution problem and to develop plans for implementing Best Management
Practices (BMPs) to address the problem.

As a rule, most 303(d) listed TMDL water bodies in STATE are monitored
during STATE summer (August) baseline sampling efforts during which
water chemistry measures are also

collected (e.g., specific conductance, total alkalinity, and color),
along with Secchi disk

transparency, total phosphorus, Chl-a and dissolved oxygen/temperature
profiles. Annual SAPs

produced for TMDL Sampling include the water bodies to be monitored,
frequency and intensity. The sampling data are used to support modeling.
Additional data sources are identified in Section 2.0 and any maps for
modeling projects will be in the modeling reports.

Schedules for modeling work are project specific and will be shared with
QAPP signatories for review and comment. In general, modeling work may
take up to a year or more to complete. However, with many regulating
agencies involved, there may be additional technical evaluations
requested that may require additional time that may impact the schedule.
Regulatory agencies may also require more time for review of model
results and to reach consensus at key decision points. More specific
resource or time constraints cannot be foreseen at this time but, if
significant, will be communicated from the STATE Project Manager to the
EPA Project Officer.

[]{#__RefHeading___Toc153252853 .anchor}

**[1.7 Quality Objectives and Criteria for Measurement Data and
Models]{.underline}**

Quality objectives and criteria for model inputs and outputs are
qualitative and quantitative Statements that (1) clarify study
objectives, (2) define the appropriate type and acceptance criteria of
existing data, (3) establish acceptable model input and parameterization
(calibration) criteria, (4) outline model performance evaluation
obligations, and (5) specify tolerable levels of potential decision
errors. Each is discussed in the following sections.

Assessing whether the DQOs have been achieved for a modeling study is
less straightforward than for a typical sampling and analysis program.
The usual data quality indicators (e.g., completeness,
representativeness, comparability) are difficult to apply and in many
cases do not adequately characterize model output. The ultimate quality
test for the model is whether the output sufficiently represents the
natural system that is being simulated. To a large extent, this is
determined by the expertise of the modelers and the amount of available
data. Nonetheless, there are objective techniques that can be used to
evaluate the quality of the model performance and output. The methods,
and the proposed performance expectations, are discussed in Section
1.7.2 below. Evaluation criteria are also provided in Section 4.3.1.

## 

## 1.7.1 Objectives and Project Decisions

The QAPP has been completed by STATE to ensure that (1) modeling input
data are valid and defensible, (2) model setup and parameterization
(calibration) protocols are followed and documented, (3) model
applications and output data are reviewed and evaluated in a consistent
manner and 4) that models are able to predict hydrologic or water
quality conditions over time in support of TMDL development.

An example overarching purpose may be if modeling indicates that water
quality standards are attainable, then discharge permits may be modified
(or other pollution prevention measures taken) to improve water quality.
To this end, modelers will work with program managers to align model
outputs with the types of decisions to be made.

## 1.7.2 New Data Measurement Performance Criteria/Existing Data Acceptance Criteria

The use of existing data of known quality will help ensure that the
modeling effort yields accurate predictions with an acceptable level of
model uncertainty. All model input or parameterization (calibration)
data sources will have a QAPP in place prior to the use in the modeling
effort. Data with unknown quality (i.e. collected without a documented
QAPP or using unapproved SOPs) will be flagged and noted as either
conditionally acceptable for limited use or not acceptable for use at
all. See also Section 2.1 for additional procedures for excluding data.

[New and Existing Data]{.underline}

As an example of quality control, duplicate water samples are obtained
for one out of every 10^th^ water body sampled. Duplicate results are
expected to be within 10% of each other 75% of the time and 20% of each
other 90% of the time. Laboratories are expected to provide their own
internal approach to quality control for each parameter in the SOP for
each parameter. For example, duplicate filters are routinely submitted
by STATE for analysis so that the labs may perform splits as necessary
to meet their quality objectives. Quality control data is received from
each lab, at minimum, on an annual basis.

As another example, blind splits are provided for inter-lab comparisons
at the beginning of the monitoring season and periodically through the
season to achieve comparisons of 2% of the overall number of samples for
a given parameter. Results from these splits are expected to be within
15% of each other 75% of the time and 25% of each other 90% of the time.

When quality objectives are not met, and best professional judgment
indicates sampling error,

procedures are reviewed to determine which steps are critical for
establishing consistency.

Further detail may be added or modifications made to the SOP. When
quality objectives are not met, and best professional judgment indicates
analytical error, the lab will be contacted and some resolution to the
problem will be sought. Circumstances where best professional judgment
might not indicate evidence of sampling error or analytical error
include results obtained from extremely oligotrophic waters, where
parameter levels are extremely low. Similarly, extremely productive
waters may yield results for duplicate samples that are highly variable
due to the patchy nature of algal cell distribution within the water
column.

Data of known and documented quality are essential to the success of the
modeling projects. All model input or parameterization (calibration)
data sources will have a QAPP in place prior to the use in the modeling
effort. These, in turn generate information for use in decision-making.
STATE has established Data Quality Objectives (DQOs) for modeling
projects in order to specify the acceptance criteria for existing model
input, and parameterization (calibration) or corroboration (validation)
data. DQO's identify the (1) type and quality of data that will be
appropriate for use in the modeling project, (2) spatial and temporal
input data coverage requirements, (3) data quality and currency, and (4)
technical soundness of the collection methodology. A bullet list of
related requirements is shown below.

> • All input and parameterization (calibration) data for the model will
> be of a known and documented quality.
>
> • Data will be collected from as many sources as available, and
> provide the maximum
>
> temporal and spatial coverage of the watershed drainage.
>
> • The data will be comparable with respect to previous and future
> studies.
>
> • Modeling data will be representative of the parameters being
> measured with respect
>
> to time, location, and the conditions from which the data are
> obtained.
>
> DQOs for models specifically include:
>
> • The ability to quantify future spatial and temporal distribution of
> sediment, toxics and
>
> nutrients in the watersheds.
>
> • Flexibility to evaluate historical and relative contributions of
> various pollutant sources in the watersheds.
>
> • Adequate resolution to identify the relative in-stream impacts of
> pollutant loading to
>
> the stream system from various urban and non-urban point and non-point
> sources.

DQO's were further refined in order to define performance criteria that
limit the probability of making decision-based errors. They address the
data validity and reliability of the modeling effort and each is briefly
described below in the context of completeness, representativeness, and
comparability. The traditional context of precision and accuracy is not
included due to the fact that, in most cases, the data has already been
collected and analyzed through acceptable analytical procedures.

[Completeness]{.underline} is a measure of the amount of valid input
data obtained during a process. The target completeness for models will
be 100 percent -- e.g. all available sources included. The actual
completeness may vary depending on the intrinsic availability of
monitoring data. Deficiencies in water quality, climatic, or stream flow
data are outside of the control of the modeling effort and will be
addressed as part of the data compilation and assessment effort. In
order to provide surrogate data, the most current statistical or
stochastic methods will be used to extend or fill in missing time-series
data. The normal-ratio will be used to fill precipitation gaps.
Discharges will be linearly interpolated or estimated using other
fitting methods such as regression analysis. STATE will address any data
issues as they develop.

[Representativeness]{.underline} is a measure of how closely the input
or parameterization (calibration) data will reflect the physical
characteristics of hydrology and water quality over time. Standardized
monitoring plan design and the use of Standard Operating Procedures
(SOPs) for discharge measurement, soils identification, land cover
mapping, sample collection and handling, and acquisition of weather data
are crucial to ensuring representative data quality. All model input or
parameterization data sources will have a QAPP in place prior to the use
in the modeling effort.

[Comparability]{.underline} expresses the confidence with which one data
set can be compared to another. Data comparability from external sources
is very much tied to the individual project methodology and time at
which it was collected. For the purpose of the modeling effort,
comparability will be maintained by using consistent units, appropriate
temporal scales, and reproducible methods. Unit conversions (metric may
be the required default), datum transformations, and grid re-projections
will likely be required to make data for the modeling comparable.
Information that exists outside a reasonable temporal scale, has
significantly changed or will potentially alter the modeling results are
not comparable. DEP will make these determinations, as necessary.
Comparability between other model indicators will be evaluated on a
case-by-case basis.

[Acceptance Criteria for Model Parameterization
(calibration)]{.underline}

The acceptance criteria for model parameterization (calibration) define
the procedures whereby the difference between the predicted and observed
values of the model are within an acceptable range, or are optimized.
Often parameterization is the only method to ensure that model
predictions correlate with values observed in the field.
Parameterization uses observed hydrometeorological data in a systematic
search for parameters that yield an acceptable fit of computed results.
This search is performed to find a reasonable best estimate that will
yield the minimum value of an objective function, or variable that is
critical in application.

Parameterization has become increasingly important with the need for
valid and defensible models for TMDL development. Acceptance criteria
for the modeling projects are established by the STATE prior to the
initiation of the effort in order to provide a numerical ruler for
determining whether the model is an appropriate tool for TMDL
decision-making. As an example, the model parameterization criteria are
based on the recommended error percentages for seasonal, annual, and
storm-based water yields (Table 2-1). Generalized information related to
model parameterization criteria, and corroboration considerations,
include the following references: Thomann, 1982; James and Burges, 1982;
Donigian, 1982; ASTM, 1984.

**Table 2-1. Acceptable Model Parameterization (calibration) Hydrology
Criteria**

Errors (Simulated-Observed) Recommended Criteria

Error in Total Volume 10%

Error in 50% Lowest Flows 10%

Error in 10% Highest Flows 15%

Seasonal Volume Error -- Summer 30%

Seasonal Volume Error -- Fall 30%

Seasonal Volume Error -- Winter 30%

Seasonal Volume Error -- Spring 30%

Error in Winter Storm Volumes 20%

Error in Summer Storm Volumes 50%

Graphical comparisons of model performance may also be used including
time series plots of observed and simulated flows and state variables,
and residual scatter plots (observed versus simulated values). Time
series plots are generally evaluated visually for agreement, or lack
thereof, between the simulated and observed values. When observed data
are adequate, or uncertainty estimates are available, confidence
intervals can then be calculated so they can be considered in the model
performance evaluation.

A number of statistical tests are also available for watershed model
evaluation and optimization. The Sum of the Squared Residuals and the
Nash & Sutcliffe Coefficient of Efficiency are two that have been
identified for the purpose of the modeling project. Each is described
below.

Sum of Squared Residuals is a commonly used objective function for
hydrologic model

parameterization (calibration). It compares the difference between the
modeled and observed ordinates, and uses the squared differences as the
measure of fit. Thus a difference of 10 feet^3^/second between the
predicted and observed values is one hundred times worse than a
difference of 1 feet^3^/second. Squaring the differences also treats
both overestimates and underestimates by the model as undesirable. The
function implicitly is a measure of the comparison of the magnitudes of
the peaks, volumes, and times of peak of the hydrographs and water
quality constituents. The equation for calculation of the sum of least
squares is shown below (Diskin and Simon, 1977).

![](media/image1.wmf){width="4.597222222222222in"
height="1.3916666666666666in"}

Nash and Sutcliffe Efficiency is a goodness-of-fit test as a statistical
method for evaluating the hydrologic variability between measured and
predicted model values. The Nash and Sutcliffe Coefficient of Efficiency
(COE) provides a normalized estimate of the relationship between the
observed and predicted model values and is calculated as below (Nash and
Sutcliffe 1970).

![](media/image2.png){width="4.070138888888889in" height="2.0in"}

A COE value of one indicates a perfect fit between measured and
predicted values for all events. COE values between zero and one suggest
a positive relationship between observed and predicted values, thus
allowing for the use of predicted values in lieu of observed data. A
zero value indicates that the fit is as good as using the average value
of all the measured data. See also Section 4.1 for additional
parameterization considerations and stop criteria.

[Model Corroboration (Validation)]{.underline}

Corroboration (validation) is defined as the comparison of modeled
results with independently derived numerical observations from the
simulated environment. Model corroboration is an extension of the
parameterization (calibration) process. Its purpose is to assure that
the calibrated model properly assesses the range of variables and
conditions that are expected within the simulation. Although there are
several approaches to validating a model, perhaps the most effective
procedure is to use only a portion of the available record of observed
values for parameterization. The rest is used for corroboration. Once
final parameterization parameters are developed, simulation is performed
for the remaining period of observed values and the goodness-of- fit
between recorded and simulated values is reassessed. Wherever possible,
this type of split-sample parameterization and corroboration procedure
will be used for modeling projects.

The credibility of the model hinges on the deterministic ability to
predict conditions over the entire range of observed data: in effect,
validating the model. For flow and water quality simulations where
continuous records are available, multiple corroboration techniques will
be used. For example, comparisons of simulated and observed state
variables will be performed for daily, monthly, and annual values.
Statistical procedures mentioned in Section 4.2 will be used to assess
the parameterization. These include error statistics, correlation and
model fit efficiency coefficients, and goodness-of- fit tests. For
sediment and water quality data, model performance will be based
primarily on visual and graphical presentations when the frequency of
observed data is inadequate for accurate statistical measures.

[Acceptance of Model Sensitivity]{.underline}

Sensitivity analysis determines the effect of a change in a model input
parameter or variable on the model outcome. The sensitivity of a model
parameter is typically expressed as a normalized sensitivity coefficient
(Brown and Barnwell, 1987). The methodology for identifying the
sensitivity of a model parameter is shown below.

![](media/image3.png){width="4.6in" height="1.4805555555555556in"}

STATE will qualitatively assess the sensitivity of model parameters
during manual

parameterization (calibration) through parameter perturbation. A summary
of model sensitivity will be included in the modeling journal and final
modeling report. Details will include the variables modified for model
parameterization (calibration), the percent modification (e.g. ± 10%),
percent change in the modeling results, and the normalized sensitivity
coefficient (Example Table 2-2). The format is shown below.

**Example Table 2-2. Sensitivity Coefficient**

[Model Parameter]{.underline} [% Perturbation]{.underline} [%
Change]{.underline} [NSC]{.underline}

Curve Number -15% -29% 2.2

Soil Available Water Capacity 20% 25% 1.3

Channel Erodibility 05% 01% 0.1

Algorithmic techniques for sensitivity and uncertainty assessment are
available through several water quality modeling programs (Monte Carlo
Simulation, first-order error analysis, or automated objective function
optimization).

[Acceptance of Model Uncertainty]{.underline}

Uncertainty is broadly defined as the lack of knowledge regarding model
input parameters and the processes the model attempts to describe. Our
ability to define model uncertainty is marginalized by our limited
ability to accurately describe complex processes. As a result, all
engineering computations are attended to a degree of uncertainty due to
the simplification of natural process and the limitations of input and
parameterization (calibration) data. Computed values differ from
observed ones, and the magnitude and frequency of these differences
characterize the uncertainty of the best model estimate. Uncertainty
analysis is the terminology associated with the examination of how the
lack of knowledge in model parameters, variables, and processes
propagates through the model structure as model output or forecast
error. Sources of model uncertainty are characterized by STATE during
the initial stages of planning in order to better understand how the
model input data and parameters would potentially influence model output
and prediction. Potential sources of model uncertainty include:

\(1\) Estimated model parameter values.

\(2\) Observed model input data.

\(3\) Model structure and forcing functions.

\(4\) Numerical solution algorithms.

STATE will be responsible for conducting the uncertainty analyses,
including statistical procedures in Section 4.2.

[]{#refheading___toc153252856 .anchor}**[1.8 Special Training
Requirements]{.underline}**

The STATE of \_\_\_\_\_\_ job classification system has established
minimum qualifications required for all levels of STATE employment. The
individuals permanently employed in the Sections are either JOB TITLE or
JOB TITLE. Their qualifications range from a \_\_\_\_\_degree to a Ph.D.
in one of the natural sciences or engineering. In addition, most of the
individuals had a number of years of experience in the field
\_\_\_\_\_\_\_\_\_ or a closely related field, prior to their
employment. Because the STATE hiring process establishes training and
experience levels required to be employed by these Sections, there is no
need to include resumes for each individual in the Sections. The only
requirement is that project personnel are expected to read and observe
the QAPP. In addition, the QA officer is responsible for overseeing
training, should it become necessary,

[]{#__RefHeading___Toc153252857 .anchor}

**[1.9 Documents and Records]{.underline}**

[QAPP Modifications]{.underline}. This section addresses procedures to
be followed when modifications are

needed to a) this Program QAPP, including associated SOPs, or b) any SAP
accepted under this

Program QAPP that requires real-time modification to achieve project
goals. Examples of such

modifications include changes in procedures, assessment and reporting.

Discussions involving changes to the Program QAPP may be initiated at
any level. Contact should be made with STATE Quality Assurance Officer
to discern whether modification is warranted. The scope of effect of the
proposed change will determine the formality of the approval process. A
formal QAPP modification will include reference to the section(s) of
text being modified or added to, the reason why the modification is
necessary and the actual replacement/additional language. It will be the
responsibility of the STATE Quality Assurance Officer to seek review and
approval from others within the agency and from EPA. Signatories of the
original Program QAPP will receive such updates for approval. The
electronic files will be updated as proposed using annotation that
indicates reference to the formal amendment in a designated part of the
appendix (e.g., Update I, Appendix 9.10. QAPP Modifications). SOP
modifications, additions and retirements follow the same procedure as
modifications to the QAPP. Additionally, SOPs must be organized and
formatted according to STATE department-wide guidelines. SOPs under
development should be included as part of the QAPP as soon as
practicable.

Water quality data in the STATE datasets are used for project
implementation and

decision making. These data are stored on the STATE computer network
file server and are backed up onto \_\_\_\_\_\_\_\_ after major new data
uploads. Backups are stored off-site WHERE.

[Modeling Journal]{.underline}

A modeling journal will be kept to identify the internal model
parameters that were adjusted during the parameterization process to
meet the criteria identified in Sections 1.7 and 1.7.2. The journal
documents all parameterization iterations made during the project along
with the justification and professional reasoning behind the changes.
For example, each time that a separate model parameterization run is
completed, changes should be documented in the modeling journal. The
level of detail in the modeling journal should be sufficient to allow
another modeler to duplicate the parameterization method given the same
data and model. The modeling journal will include complete recordkeeping
of each step of the modeling process. The documentation will consist of
information addressing the following items:

• Model assessments and selection with references.

• Model assumptions.

• Parameter values and sources.

• Input file notations.

• Output file notations and model runs.

• Parameterization (calibration) and corroboration (validation)
procedures and results from the model.

• Intermediate results from iterative parameterization (calibration)
runs.

• Changes and verification of changes made in code, if any.

• Summary of model sensitivity

The modeling journal and report, all data files, source codes, and
executable versions of the computer software used in modeling studies
will be retained for \_\_\_\_\_\_\_ years in the Section File Room and
STATE Library for auditing or post-project reuse. These files will
include:

• Version and source of the executable code used.

• Parameterization (calibration) input and output data.

• Corroboration (validation) input and output data.

• Model application input and output (i.e., for each scenario studied).

Documentation of any response action taken to correct model
implementation is also described in Section 3.0. Technical Memoranda may
also be used as in the example in Appendix G.

## 1.9.1 QA Project Plan Distribution

### 

### This QAPP will be implemented by STATE once USEPA has given approval. This QAPP is to be considered a "working document". This QAPP will be periodically updated and revised, in accordance with Section 1.9 as technology, policy and protocol change. As required by EPA-NE, an updated QAPP will be formally re-submitted for approval every five years. All QAPP updates will be distributed by the STATE Project Manager according to the distribution list in Section 1.3 and with notification to EPA by the STATE QA Officer. 

### 

### Upon approval and implementation of this QAPP, the original shall be kept \_\_\_\_\_\_\_\_. A copy will also be placed in the STATE Library. All personnel responsible for implementation will be required to review this QAPP within 120 days of approval. As new modeling staff or managers are hired by STATE, they will be required to review this QAPP within 90 days of their hiring date. A copy of the QAPP will be placed on the STATE website, and updated as above.

### 

### 2.0 DATA GENERATION AND ACQUISITION

> []{#__RefHeading___Toc153252860 .anchor}**[2.1 Data Acquisition
> Requirements (Non-Direct Measurements)]{.underline}**

Each project manager under this QAPP will be responsible for summarizing
how well the data quality objectives (DQOs) were met. The reports to
management (Section 4.4) and modeling reports (Section 5.0) and will
document this activity.

[Potential Model Input Data]{.underline}

Water bodies listed on the 303(d) list (designated for TMDLs) should
have a historical dataset that has been evaluated using assessment
criteria. Water bodies that require the development of Total Maximum
Daily Load (TMDL) studies generally receive the most intensive
monitoring by DEP staff, contractors, and volunteers trained to do
advanced monitoring. The intensity of monitoring generally corresponds
to attainment status with respect to water quality standards. Specific
annual monitoring designs are found in the annual TMDL Sampling SAP.

SAPs will remain in the possession of each project manager until a
project is complete, at which time the document will be filed with
copies of results, correspondence and reports produced.

STATE Sections use geographic data derived from U.S.G.S. maps and STATE
GIS coverages the latter of which have metadata associated with them
detailing limitations. U.S. Census data is available by municipality
through the STATE of \_\_\_\_\_\_\_ Planning Office and is used to
evaluate risk of anthropomorphic influences on water quality at the town
level. Vulnerability models developed at STATE are also used. The final
responsibility rests with the individual using data and associated
geographic information to become aware of the limitations inherent in
any information source. These data sources may be used within the
section for decisions regarding trend evaluation, use attainment, and as
historical background for TMDL projects.

A significant amount of watershed input data may be necessary for setup
and parameterization (calibration) of models. Rather than outlining the
input data acquisition process, web links to the direct source providers
are shown below. Quality information can be viewed in subsequent links,
along with information regarding development and disclaimers on use. A
majority of the data originates from published agencies like USGS, EPA,
USDA, STATE and NCDC. The rigor in which these organizations implement
QA/QC fully meet the quality objectives identified in this QAPP.

EPA STORET <http://www.epa.gov/storet/dbtop.html>

National Climatic Data Center (NCDC)
<http://www.ncdc.noaa.gov/oa/ncdc.html>

National Elevation Dataset (NED)
<http://nris.state.mt.us/nsdi/nris/el10/dems.html>

National Hydrography Dataset (NHD) <http://nhd.usgs.gov/>

National Land Cover Dataset (NLCD) <http://landcover.usgs.gov/index.asp>

National Water Information System (NWIS)
<http://waterdata.usgs.gov/nwis/>

Remote Automated Weather Stations (RAWS) <http://www.fs.fed.us/raws/>

Snow Telemetry (SNOTEL) <http://www.wcc.nrcs.usda.gov/snotel/>

STATSGO Soils <http://www.ncgc.nrcs.usda.gov/products/datasets/statsgo/>

Flow and water quality parameterization (calibration) data are also
subject to the DQOs identified in Sections 1.7 and 1.7.2. Sources of
effluent, discharge, and in-stream water quality data will have an
approved QAPP from STATE and or the U.S. EPA. Data with unknown quality
(i.e. collected without a documented QAPP or using unapproved SOPs) will
be flagged and noted as either conditionally acceptable for limited use
or not acceptable for use at all. Best professional judgment is used to
exclude data from the dataset. Additional procedures to ensure
consistency in excluding data, outdated data or otherwise, include the
following:

-   
-   
-   

> []{#__RefHeading___Toc153252861 .anchor}
>
> **[2.2 Data Management]{.underline}**

The general approach to data storage and retrieval of electronic media
is as follows:

-   
-   
-   

The following data handling equipment, hardware and software are used.

-   
-   

[Potential Model Input Data]{.underline}

Water quality data are entered on standard field forms that are often
updated annually. Several standard forms exist (Appendix B) and are
updated periodically. Data are entered into databases through various
means. Most of the data are entered into electronic files (dbf) before
arriving at STATE.

\_\_\_\_\_\_\_\_\_\_ staff proof all of the data. Data are proofed for
transcription errors. Quality control readings are examined. Best
professional judgment is used to exclude data from the dataset. Proofed
sheets are initialed and dated by the proof reader. Corrected files or
file sets are merged into one or two large data sets and a number of
error checking procedures are performed to validate that the water body
identification number was used, that parameters are within an expected
range (primarily a check for decimal point errors) and that dates have
been handled properly (to identify conversion errors). Data are uploaded
to 'master files' on the network thereafter. Write access to these files
is restricted. The data currently resides in \_\_\_\_\_\_\_\_\_files
that are not truly relational. \[Data are to be migrated to a relational
database system after undergoing standard normalization procedures.\]

[Model Application Data]{.underline}

Standard parameterization (calibration) and data management procedures
will be implemented during modeling projects to ensure that modeling
results are valid, reproducible, and comparable. The standard procedures
include the following: (1) use of modeling techniques accepted within
the professional industry, (2) parameterization (calibration) methods
that can be performed repeatedly by a qualified person to obtain similar
results, (3) documentation that is clear, concise, and thorough, and (4)
the use of standard units for data management.

Data used during modeling projects will be maintained in either hard
copy or electronic format -- depending on the nature of it. As a result,
database entry and manipulation within a model is identified as one of
the major preventable error sources in the modeling effort. Unlike the
limitations of the model and model driver data itself, user induced
error is correctable under an appropriate level of QA/QC. Multiple steps
will be taken to ensure errors are minimized. Data formatting will be
reviewed prior to the final version of the database being generated,
including the data element type, format, allowable values and ranges,
and other parameters. All data used to populate the modeling database
will be screened before upload to the model application. Manually
entered parameter values from paper sources will be evaluated by
reviewing printouts of summaries and randomly selecting portions of the
model application. The review will include a comparison of the original
data sources and paper documentation. Any record identified as having
problems will be reviewed to determine whether corrected data can be
acquired or the record omitted in accordance with Section 2.1. The model
input files will be checked by STATE for reasonability and correctness
in order to detect errors that may occur during the data management or
transfer process.

# 3.0 ASSESSMENT AND OVERSIGHT

> []{#__RefHeading___Toc153252863 .anchor}**[3.1 Assessments/Oversight
> and Response Actions]{.underline}**

The data generated as part of the modeling results will be evaluated
during the corroboration (validation) process. Model performance
assessments will be made continually by STATE as described in the
parameterization and corroboration processes in Sections 4.1 and 4.2.
Performance audits will consist of review of technical memoranda (as in
Appendix G) comparison of model results with observed historical data,
and general evaluation of model behavior for state variables and other
output lacking historical data. Auditors may include the STATE QA
Officer or Section Manager who are authorized to stop work, for good
cause.

At the end of the parameterization and corroboration period, the STATE
will assess the ability of the model to predict hydrologic and water
quality response over time. Section 1.7.2 identifies criteria that will
be part of the model performance assessment and also assessed are:

• Modeling input and output validity.

• Model parameterization and corroboration performance determinations.

• Sensitivity and uncertainty analysis assessments.

Parameter deviation and post-simulation corroboration of predictions are
major issues in the quality assurance framework. STATE will document the
model data entry, parameter estimation, and parameterization activities,
and will provide this documentation as part of the modeling journal,
model report and the project file and any technical memoranda as in the
example in Appendix G. A typical STATE internal assessment is described
below.

Modeling data, and project deliverables, will be internally quality
controlled by STATE in-house review. Anticipated STATE review staff
members responsible for this process include the Project Manager and QA
Officer (or other-specify). The Project Manager (or other-specify) will
maintain overall responsibility for examining the work to ensure that
methodologies and processes are consistent with the procedures outlined
in the QAPP. He or she will provide advice to the QA Officer of any
deviations from the QAPP so that appropriate actions may be taken either
to correct the problem, or amend the QAPP as needed. The QA Officer (or
other-specify) will monitor the extent to which the QAPP is supporting
its intended use. Other expertise will be called in, as required. For
example\_\_\_\_\_\_\_\_\_\_\_\_\_.

If the quality control audit results in detection of unacceptable
conditions or data, the Project Manager will be responsible for
developing and initiating corrective action. Corrective response actions
may include:

• Review or corroboration of modeling input and parameterization data.

• Re-definition of model extents or spatial distribution.

• Performing additional model runs.

• Editing and modifying report deliverables.

# 4.0 MODEL APPLICATION 

> []{#__RefHeading___Toc153252865 .anchor}**[4.1 Model Parameterization
> (Calibration))]{.underline}**

All models, by definition, are a simplification of the environmental
processes they intend to represent. The optimization of empirical
parameters that form the numerical basis of the model is referred to as
parameterization (calibration). Parameterization iteratively adjusts
model coefficients or parameters until predicted values accurately
reproduce those measured in the field. Some models have internal
parameterization tools that aid the user in managing parameterization
scenarios and refining model runs until acceptable parameterization
criteria are met. Once an acceptable parameterization is reached, the
run can then be verified on an independent data set to judge the extent
to which the model is able to predict hydrologic or water quality
conditions over time.

A complete watershed model parameterization involves a successive
examination of the following characteristics of the watershed hydrology
and water quality: (1) annual and seasonal water balance and streamflow,
(2) sediment, and (3) nutrients. Simulated and observed values for reach
characteristic are examined and critical parameters are adjusted to
attain acceptable levels of agreement. The refinement of parameters
should reflect the scientific literature and not exceed reasonability.
The rationale for any model adjustments should be based on the
parameterization procedures outlined in the QAPP and documented in the
modeling journal and report.

[Parameterization Considerations]{.underline}

Parameterization should consider the most important hydrologic and water
quality response variables. The sensitivity of these parameters has a
significant influence on the uncertainty of the model and should be
equally considered during the parameterization process. Ideally, both
high and low flow years, and the anticipated range of conditions and
scenarios for which the TMDL will be developed will be used. Criteria
for defaulting to non site-specific data include the following:

-   
-   

Considerations for which parameters to keep constant include the
following:

-   
-   
-   

Parameterization should be completed in sequential order, using the most
upstream point first and then moving downstream to the next point of
parameterization (calibration). It is important that parameters of files
associated with the drainage area upstream of a calibrated point, are
not changed during subsequent steps.

[Parameterization Stop Criteria]{.underline}

Stop criteria can be useful to prohibit never-ending parameterization
and allow the introduction of more subjective criteria, if necessary.
This can stop the "ever decreasing circles" that some optimization
methods tend to follow in search of an exact minimum in the objective
function (European Commission 2005). In consultation with the Section
Manager, a prioritized list of stop criteria includes the following:

-   available resources

-   a specified end date

-   a specified maximum number of model runs

-   a specified change in size of a function value (objective function,
    parameter values, etc.) towards an apparent minimum.

Any deficiencies during the parameterization process are worked through
the Section Manager and documented in the modeling journal and report.
See also Section 4.4.

Following parameterization, verification on an independent data set is
necessary to evaluate the effectiveness of the model to represent
physical processes beyond those for which the model was calibrated.
Decisions made during model parameterization and verification will be
documented in the modeling journal and report so that an experienced
user could complete the parameterization process and obtain similar
modeling results. See also Section 1.7 Quality Objectives and 1.7.2
Performance Criteria.

> []{#__RefHeading___Toc153252866 .anchor}
>
> **[4.2 Model Corroboration (Validation and Simulation]{.underline})**

Criteria used to review/validate data are listed in Sections 1.7, 1.7.2
and here. Best professional judgment will also be used in conjunction
with these criteria.

Once an acceptable parameterization is reached, the model corroboration
(validation) is run on an independent data set to judge the extent to
which the model is able to predict hydrologic or water quality
conditions over time.

The quality of the model fit is examined by: 1) the coefficient of
determination (r^2^) of the linear regression between simulated and
observed; 2) the coefficient of model-fit efficiency, which measures the
proportion of variance in the observed as explained by the simulated
(Nash and Sutcliffe, 1970). The coefficient of determination (r^2^) and
the coefficient of model-fit efficiency are similar because both provide
a measure of the variation in the simulated value explained by the
observed value. The coefficient of model-fit efficiency, however,
provides a more rigorous evaluation of the fit quality than does the
(r^2^) because the model-fit efficiency measures the magnitude of the
differences between simulated and observed values, whereas the (r^2^)
measures the difference between the mean values (Duncker and Melching,
1998). In cases were the observed values and model residuals are
normally distributed, the value of the (r^2^) and the model-fit
efficiency should be equal. The difference between simulated and
observed values may also be reported as the (1) standard error, in
ft^3^/s; (2) root mean square error, in percent; (3) percent of time
differences were within 10 percent, and (4) percent of time differences
were within 25 percent; (5) median percent error, (6) minimum percent
error, and (7) maximum percent error.

See also Section 1.7. and 1.7.2

[Models for Comparative Analyses]{.underline}

Occasionally, comparative modeling is used to evaluate potential water
flow and water quality benefits from combinations of storm water
management practices and designs that have yet to be implemented. A cost
benefit analysis of varying designs and design combinations may be the
basis for this type of modeling. In these instances, the following will
be addressed and included in the modeling report.

-   [Definition of the Base Line Conditions]{.underline} - the specific
    conditions, parameters and values that define the baseline
    condition.

-   [Criteria for Comparisons]{.underline} - the terms for comparing the
    model simulation results to the base line condition. For example,
    the terms may be found in quantities or percentages of runoff,
    infiltration or storm water contaminant loads.

-   [Identify Significant Change from Baseline]{.underline} - the
    application of statistical tools and criteria used to determine if
    there are significant differences between the baseline condition and
    model simulation results.

-   [Identify Simulation Scenarios from Sensitivity
    Analysis]{.underline} - how the simulation scenarios take into
    account what is understood from the model sensitivity analysis.

-   [Corroboration of Model Outputs]{.underline} - use of literature
    searches, calculations and the growing number of storm water
    performance databases to "ground truth" the projected water flow
    and/or water quality benefits from storm water management designs.
    Some examples include the following:

EPA Urban Best Management Practices Performance Tool
<http://cfpub.epa.gov/npdes/stormwater/urbanbmp/bmpeffectiveness.cfm>

University of New Hampshire Stormwater Center
<http://www.unh.edu/erg/cstev/pubs_specs_info.htm>

University of Massachusetts Stormwater Technologies Clearinghouse
<http://www.mastep.net/>

International Stormwater Database <http://www.bmpdatabase.org/>

National Pollutant Removal Performance Database, September 2007
<http://www.cwp.org/Downloads/bmpwriteup_092007_v3.pdf>

Center for Watershed Protection
<http://www.cwp.org/PublicationStore/special.htm#pollut2>

Boston Metropolitan Area Planning Council - Massachusetts Low Impact
Development Tool Kit
<http://www.mapc.org/regional_planning/LID/LID_Links_References.html#national>

EPA Low Impact Development Literature Review
<http://www.epa.gov/owow/nps/lid/lid.pdf>

and:
<http://newmoa.org/prevention/webconferences/stormwaterweb/stormwaterresources.pdf>

> []{#__RefHeading___Toc153252867 .anchor}**[4.3 Reconciliation with
> User Requirements]{.underline}**

STATE is committed to developing a representative modeling product and
will ensure that: (1) complete documentation is maintained, (2)
departures from corroboration (validation) criteria are addressed, (3)
corroboration (validation) methods are properly documented, and (4) the
modeling data is properly reviewed. In this context, reconciliation with
user requirements connotes establishing how model results will be tested
and evaluated in order to ensure that the models are producing results
of sufficient quality.

As part of the reconciliation process, the model deliverables (modeling
reports, technical memoranda, etc.) will be reviewed by the STATE
Project Manager to assess whether the quality requirements of the QAPP
have been met. A comprehensive review of the final model files and
documentation will be completed and recommendations provided regarding
the effectiveness of the model to be used in watershed planning and TMDL
decision-making. The determination will largely be based on the
effectiveness of the model to predict hydrologic and water quality
response.

Each project manager under this QAPP will be responsible for summarizing
how well the data quality objectives (DQOs) were met, including whether
there have been departures from the assumptions in the planning process.
Any significant departures from the QAPP and initial assumptions will be
reported to the Section Manager. See also Section 4.4.

[4.3.1 Model Limitations and Final Evaluation Criteria]{.underline}

It should be noted that all models are a simplification of the
environmental processes they intend to represent. Although there is no
consensus on model performance criteria in the literature, a number of
basic statements are likely to be accepted by most professional
modelers.

• Models are approximations of reality and cannot precisely represent
natural systems.

• There is no single, accepted test that determines whether or not a
model is validated.

• Models cannot be expected to be more accurate than the sampling and
statistical error

(e.g., confidence intervals) in the input and observed data.

These considerations must be included in the development of appropriate
procedures for quality assurance of the models. Despite a lack of
agreement on how models should be evaluated, the following principles
provide a final set of evaluation criteria for the modeling projects.

• Exact duplication of observed data is not possible, nor is it a
performance criterion for projects. The model corroboration (validation)
process will measure the ability of the model to simulate measured
values.

• No single procedure or statistic is widely accepted as measuring, nor
capable of establishing, acceptable model performance. Therefore the
combination of graphical comparisons, statistical tests and professional
judgment are proposed to provide sufficient evidence upon which to base
a decision of model acceptance or rejection.

• All model and observed data comparisons must recognize, either
qualitatively or quantitatively, the inherent error and uncertainty in
both the model and the observations. Model sensitivity and uncertainty
will be documented, where possible, as part of each modeling study.

A margin of safety will be built as part of the modeling process to
blanket model limitation and assumptions, and gage the impact on the
usability of the results toward decision-based management. This will be
addressed in the modeling report. See Also Section 1.7.2.

> []{#refheading___toc153252868 .anchor}**[4.4 Reports to
> Management]{.underline}**

[Existing Data]{.underline}

TMDL reports include: a water body description, watershed GIS assessment
and estimation of pollutant sources, identification of a total target
goal (acceptable amount), allocation of watershed/land-use loadings, and
uncertainty concerns and seasonal variation. These reports include a
public participation component to allow for public review prior to
submission to EPA.

An Integrated Water Quality Report is submitted to EPA on a biannual
cycle. This satisfies

reporting requirements in Sections 314, 305(b) and 303(d).

[Model Application Data]{.underline}

The STATE Project Manager will report the status of project activities
at the end of each month (or specify other) to the Section Manager and
the STATE QA Officer. Problems encountered during performance
evaluations, system assessments and data quality evaluations will be
identified and appropriate corrective actions will be determined and
implemented, if necessary. These problems and corrective actions will
also be documented in the modeling journal and final modeling report.

[]{#refheading___toc153252869 .anchor}**5.0 MODELING REPORTS**

Modeling Reports will contain the following information, if applicable.
Where not applicable, an explanation will be provided. An example of a
modeling report used in the past that contains much of the information
below is included in Appendix A.

Describe the content of modeling reports as including each of the
following:

Introduction and Background

[Water Quality Model]{.underline}

Purpose of Modeling/Modeling Objectives *([Note: Missing in Appendix
A-Example Report]{.underline})*

Scope and Approach for Each Model Used (including):

Physical Setting (and Hydrology, if applicable)

Observational Data Used to Support Modeling

Quality of Acquired Data (and references to data quality reports)

Achievement in Meeting Acceptance Criteria *([Missing in Appendix
A-Report]{.underline})*

References to Monitoring Data

Discussion on Excluded Data and Basis for Exclusion *([Missing in
Appendix A-Report]{.underline})*

Description of Model(s) (including):

Documentation of Candidate Model Assessments and Use of EPA's Model
Selection Tool at *<http://crem.tetratech-ffx.com/> ([Missing in
Appendix A-Report]{.underline})*

Model Configuration (discusses how model was applied, including):

Spatial and Temporal Resolution*([Missing in Appendix
A-Report]{.underline})*

Nature of Grid, Network Design or Sub-watershed Delineation

Application of Sub-models

Model Inflows, Loads and Forcing Functions

Key Assumptions (associated limitations, if any) *([Missing in Appendix
A-Report]{.underline} )*

Changes and Verification of Changes Made in Code, if any

[Water Quality Model Transport and Chemical Parameterization
(calibration)]{.underline}

Model Parameterization (calibration)) and Corroboration (validation)

Objectives, Activities and Methods

> Parameter Values and Sources
>
> Rational for Parameter Values Estimated in the Absence of Data
>
> Parameterization Variables and Targets *([Missing in Appendix
> A-Report]{.underline})* Measures of Parameterization Performance

[Model Load Inputs]{.underline}

Parameterization (calibration) Input, Output and Results Analysis

Model Corroboration Results

[Model Prediction Runs]{.underline}

Model Use Scenario Analysis and Results (should relate to purpose)

Output of Model Runs and Interpretation

Summary of Assessments and Response Actions, if any

Soundness of the Parameterization, Corroboration and Simulations

Review of Initial Assumptions and Model Suitability Evaluation
*([Missing in Appendix A]{.underline})*

Performance Against the Performance Criteria Including:

> Model Parameterization and Corroboration *([Missing in Appendix
> A]{.underline})*

[Model Sensitivity Analysis and Components of Impact]{.underline}

Model Sensitivity and Uncertainty Analyses *([Missing in Appendix
A-Report]{.underline})*

Pre- and Post-Processing Software Development, if any

Maps, Photographs and Drawings (if appropriate)

Deviations from the QAPP Including a List of Non-Applicable Reporting
Elements with Explanations *([Missing in Appendix
A-Report]{.underline})*

Conclusions

Recommendations

References and Appendices *([Missing in Appendix A-Report]{.underline})*

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 6.0 REFERENCES 

**ASTM. 1984.** Standard Practice for Evaluating Environmental Fate
Models of Chemicals.

Designation E978-84. American Society of Testing and Materials.
Philadelphia, PA. 8 p.

**Brown, C.L., and T.O. Barnwell, Jr. 1987.** The Enhanced Stream Water
Quality Models QUAL2E and QUAL2E-UNCAS -- Documentation and User's
Manual. Environmental Research

Laboratory. EPA/600/3-87/007. Athens, GA.

**Donigian, Jr., A.S. 1982.** Field Validation and Error Analysis of
Chemical Fate Models. In:

Modeling Fate of Chemicals in the Aquatic Environment . Dickson et al.
(eds.), Ann Arbor

Science Publishers, Ann Arbor, MI. 303-323 p.

**Duncker, J.J. and Melching C.S., 1998**, Regional rainfall runoff
relations for simulations of streamflow for watersheds in DuPage county,
Illinois: U.S. Geological Survey Water-Resources Investigations Report
98-4035, 80 p.

**Diskin, M.H. and Simon, E. 1977**. "A procedure for the selection of
objective functions for

hydrologic simulation models." Journal of Hydrology, 34, 129-149.

**European Commission, 2005** -- Harmonising Quality Assurance
(HarmoniQuA) in model based catchment and river basin management.
<http://harmoniqua.wau.nl/>

**James, L.D. and S.J. Burges. 1982.** Selection, Calibration, and
Testing of Hydrologic Models. In: Hydrologic Modeling of Small
Watersheds. ASAE Monograph No. 5. C.T. Haan, H.P. Johnson,

**D. L. Brakensiak** (eds). American Society of Agricultural Engineers,
St Joseph, MI. Chap 11. pp

437-474.

**Maine DEP. 2004.** Maine Lake Assessment Quality Assurance Project
Plan. August.

**Montana DEQ. 2005**. Bitteroot Soil Water Assessment Tool (SWAT) Model
Quality Assurance Project Plan. Montana Department of Environmental
Quality-Water Quality Planning Bureau, Data Management Section. March.
[http://www.deq.state.mt.us/wqinfo/Modeling/SWAT%20MODELING%20QAPP-REV2.pdf](http://www.deq.state.mt.us/wqinfo/Modeling/SWAT MODELING QAPP-REV2.pdf)

**Nash, J.E. and Sutcliffe, J.V., 1970.** River flow forecasting through
conceptual models; Part-I, A discussion of principals: Journal of
Hydrology, v. 10, p. 282--290.

**Thomann, R.V. 1982.** Verification of Water Quality Models. Jour. Env.
Engineering Div. (EED)

Proc. ASCE, 108:EE5, October.

**U.S. EPA. 2002**. QA/G-5M - Guidance for Quality Assurance Project
Plans for Modeling.

EPA/240/R-02/007- December.
<http://www.epa.gov/quality/qs-docs/g5m-final.pdf>

**U.S. EPA Region I. 2007**. Draft Generic Modeling Quality Assurance
Project Plan Template, June.
<http://www.epa.gov/region1/lab/qa/qamodeling.html>

**US Geological Survey. 2007** - Draft Quality Assurance Project Plan
for Modeling Effects of Low-Impact Development on the Hydrology of the
Upper Ipswich River Basin, MA

**Appendix A**

# Penobscot River Modeling Report

# Draft

# April 2003

# 

# 

# ![](media/image4.png)

# 

# Paul Mitnik, P.E.

# Bureau of Land and Water Quality

# Division of Environmental Assessment

#### DEPLW-0582

# [Executive Summary]{.underline}

# 

# A study of the Penobscot River from Millinocket to Bucksport (103 miles) began in the summer of 1997 involving the DEP and a number of stakeholders such as the Penobscot Nation, Great Northern Paper, International Paper, USEPA, and the Lincoln Sanitary District

# Data was collected in the summers of 1997 and 2001 to calibrate and verify a water quality model. The lack of runoff prior to the survey, presence of low flow conditions (about 5 year low flow and 97% flow duration), and utilization of good QA/QC measures resulted in excellent quality data to calibrate the water quality model. 

# Non-attainment of class B dissolved oxygen criteria was observed at only one location in 1997, but at ten of fourteen locations sampled in 2001. Chlorophyll a results exceeded the algae bloom threshold (8 ug/l) at only one location in 1997 but five of the fourteen locations sampled in 2001. For detailed descriptions of the data, one should consult the Penobscot River Data Report (MDEP, April 1998 and May 2002).

# MDEP's version of the EPA supported model, QUAL2EU, (QUAL2MDEP) was used to model the Penobscot River and estuary. Some of the important changes to QUAL2EU include the addition of a periphyton module and benthic BOD component, an enhanced dissolved oxygen saturation calculation that adds salinity as a dependent variable, and alteration to phosphorus output units to the nearest 0.1 ppb.

# The model was calibrated and verified with comparisons of the model output of salinity, BOD, phosphorus, nitrogen, chlorophyll-a and dissolved oxygen to the data observed in the summers of 1997 and 2001. Good comparisons resulted. All values assigned as parameter rate inputs were within recommended ranges in the literature. The model is considered to be a good predictive tool for estimating river dissolved oxygen and algae levels. 

# The model run at worse case conditions of 7-day-10-year low flow (7Q10), high water temperatures, and point sources at licensed loads predicts that minimum dissolved oxygen criteria (7 ppm) will not be met in approximately 51 class B river miles or about ½ of the 103 miles modeled. In addition algae blooms are projected to occur in about 25 miles or about ¼ of the 103 miles. 

# Point sources account for about 74% and 94% of the total BOD and phosphorus loads, respectively, that enter the Penobscot River. Paper mills are about 80% and 70% of the total point source loads for BOD and phosphorus, respectively.

# A component analysis was undertaken at three strategic points on the river to determine the causes of dissolved oxygen depletion. The following causes were determined to be the most significant:

> [Above Rockabema Dam]{.underline} -- Sediment Oxygen Demand (37%) and
> Background (37%)
>
> [Passadumkeag - Greenbush]{.underline} -- Point Source Nutrients (45%)
> Sediment Oxygen Demand (35%)
>
> [Orrington]{.underline} -- Point Source BOD (43%) and Sediment Oxygen
> Demand (37%)

1.  Point source reductions of 60% for BOD5 from current licensed
    amounts (slightly higher than actual performance levels) and
    reductions of 40% of total phosphorus from actual levels are needed
    to achieve dissolved oxygen criteria on the entire 103 mile segment.
    Algae blooms would also be eliminated with these reductions

10\. There are many methods that could be used to allocate point source
reductions. The following is offered as a starting point for discussions
on how to implement point source reductions in waste discharge licenses.

**Table 14 Point Source BOD5 and Phosphorus Allocation Municipal
Discharges**

  -------------- -------------- --------------- -------------- ---------------
  Point Source   Weekly Average                 Total          
  Discharge      / Daily                        Phosphorus     
                 Maximum BOD5                   (PPD)          
                 (PPD)                                         

                 Allocate by    Allocate by     Allocate by    Allocate by
                 current        equal           current        equal
                 discharge      concentration   discharge      concentration

  Millinocket    180 / 200      210 / 230       28             24

  Lincoln        50 / 55        90 / 100        12             10

  Old Town       400 / 480      220 / 250       36             26

  Orono          100 / 110      180 / 200       18             21

  Veazie         16 / 18        28 / 31         3              3

  Bangor         900 / 1000     1470 / 1630     212            169

  Brewer         230 / 250      620 / 690       15             71

  Winterport     Primary Plant                  No Restriction 

  Bucksport      Primary Plant                  No Restriction 

  **Paper                                                      
  Mills**                                                      

  Point Source   Weekly Average                 Total          
  Discharge      / Daily                        Phosphorus     
                 Maximum BOD5                   (PPD)          
                 (PPD)                                         

                 Allocate by    Allocate by     Allocate by    Allocate by
                 current        equal           equal %        equal
                 discharge      concentration   Reduction      concentration

  GNP West       8200 / 10800   8700 / 11500    96             87

  GNP East       1450 / 2600    1800 / 3200     97             88

  E Paper        5000 / 6800    2700 / 3700     30             45
  Lincoln                                                      

  G Pacif Old    3600 / 5100    4000 / 5600     63             66
  Town                                                         

  IPCo Bucksport 7100 / 10000\* No Restriction                 
  -------------- -------------- --------------- -------------- ---------------

\* Current licensed levels

## 

## 

## 

## Table of Contents

page

# Introduction....................................................................................1

Summary of 1997 and 2001
Data...........................................................\...1

Water Quality Model
...........................................................................8

Model
Transport...............................................................................10

Chemical Calibration of Water Quality
Model............................................10

# Model Prediction Runs at 10-Year Low Flow..........................................30

Sensitivity
Analysis...........................................................................
40

Component
Analysis..........................................................................
40

Investigation of Pollutant
Abatement....................................................... 47

## Figures

1\. Point Source Pollutant Discharge Comparisons 1997 Vs
2001.......................5

2\. Comparisons of Temperature 1997 Vs
2001............................................6

3\. Comparisons of Dissolved Oxygen 1997 Vs
2001.....................................7

4\. Penobscot River Model
Setup.............................................................9

4a. Ln Salinity Vs River
Mile\................................................................12

5\. Dispersion Calibration Penobscot
Estuary..............................................14

6\. Sample Station Location Vs River
Mile.................................................18

7\. Ultimate BOD Calibration
................................................................20

8\. Chlorophyll a Calibration
............................................................\...21

9\. Total Phosphorus
Calibration.............................................................22

10\. Orthophosphorus
Calibration............................................................23

11\. Total Nitrogen
Calibration...............................................................24

12\. Diurnal Dissolved oxygen Adjustment Used in
Model..............................25

13\. Sediment Oxygen Demand Measured Vs
Model.....................................27

14\. Daily Average Dissolved Oxygen
Calibration........................................28

15\. Diurnal Dissolved Oxygen
Calibration................................................29

16\. Determination of 10-Year Low
Flows.................................................31

17\. Determination of Design River
Temperature.........................................32

18\. Model Predicted Daily Minimum Dissolved Oxygen
..............................36

Point Sources at Licensed and Zero Discharge

19\. Model Predicted Monthly Average Dissolved
Oxygen...........................\...37

Point Sources at License

20\. Licensed Vs Actual BOD5 of Point
Sources..........................................38

21\. Model Predicted Daily Minimum Dissolved
Oxygen................................39

Point Sources at Licensed and Actual Discharge

22\. Summary of Sensitivity
Analysis.......................................................41

23\. Pollutant Sources tp Penobscot River as Pie Chart
Comparisons..................43

24\. Licensed Point Source BOD5 -- Pie Chart Percentage
Comparison................44

25\. Total Point Source Phosphorus Discharged During Intensive
Surveys............45

Pie Chart Percentage Comparison

26\. Components of Dissolved Oxygen Depletion -- Pie Chart
Comparison............46. 27. Various Point Source Allocations at 7Q10
Flow......................................50

28\. Licensed BOD5 Vs Equal BOD5 Concentration
Allocation........................53-55

**[Tables]{.underline}**

1\. Major Point Sources on the
Penobscot...................................................2

2\. Dams on the
Penobscot.....................................................................2

3\. Penobscot River Model Hydraulic
Coefficients........................................11

4\. Dispersion
Calculations....................................................................12

5\. Flow Balance for Penobscot
River........................................................13

6\. Environmental Parameter Rates Used in Model -- Variable by
Reach...............16

7\. Environmental Constants and Parameter Rates Used in
Model.......................17

> Constant by Reach

8\. Dilution of Riverine Point Source
Discharges...........................................30

9\. Point Load Inputs at 7Q10 and 30Q10
Flow.............................................34

10\. Point Load Nutrient Concentration Inputs at
7Q10....................................34

11\. Model Input for WWTP Performance
Run..............................................40

12\. Summary of Model Prediction Runs -- Inputs and
Results........................... 48

13\. Summary of Point Source Loads Used in Model Prediction
Runs...................49

14\. Point Source BOD5 and Phosphorus
Allocation.......................................52

## 

## Introduction

The Penobscot River Basin is the largest river basin lying entirely
within the State of Maine. It has a drainage area of 8592 square miles
at its mouth. The river segment of interest on the Penobscot River
begins in Millinocket below Ferguson Lake as the West Branch, where
after 10 miles it joins with the East Branch. It then flows an
additional 69 miles before reaching head of tide at the Veazie dam, and
then over 24 additional miles of tidal waters to Bucksport. In this
103-mile segment, there are 15 point source discharges, 11 dams, and 9
tributaries that have a drainage area of over 100 square miles. A list
of dams and point sources are illustrated in tables 1 and 2.

The Penobscot River model is a result of an ongoing effort by DEP and
stakeholders. A model for this 103 mile segment was first set up by DEP
in 1991 (Penobscot River Basin Waste Load Allocation, Jan 1991). This
report revealed that the river was at its limit for receiving point
source discharges while still maintaining water quality standards.

The effort undertaken from 1997 to current updates the model to modern
conditions. Two separate Penobscot River Data Reports (April 1998, and
May 2002, MDEP) discuss the data that were collected by DEP and a number
of stakeholders such as the Penobscot Nation, Great Northern Paper,
International Paper, USEPA, and the Lincoln Sanitary in the summers of
1997 and 2001. The 1997 data were collected to calibrate the water
quality model. The Penobscot River Modeling Report (June 2000) discusses
the modeling effort derived from the calibration (calibration) of the
model to the 1997 data. This modeling effort revealed that the Penobscot
River was beginning to develop some water quality non-attainment issues
(lower than required dissolved oxygen and algae blooms). An additional
data set for model verification was recommended in the summer of 2001 to
more accurately assess the situation and consider cleanup alternatives.
This report represents the final recommendations for the Penobscot River
based upon a completed modeling effort.

**[Summary of 1997 and 2001 Data]{.underline}**

The overall quality of both the 1997 and 2001 data are considered
excellent due to good QC measures utilized throughout the sampling
effort that involved such practices as cross checking of dissolved
oxygen meters and duplicate sampling. The three-day intensive surveys
were undertaken on August 5,6, and 7 of 1997 and August 7,8,and 9 of
2001 and were specifically for calibration (calibration) and
verification of the water quality model. It is desirable to collect the
model calibration (calibration) data sets under conditions of low flow
and high water temperature. This represents conditions of worse case
when river dissolved oxygen levels are most likely to be the lowest. At
lower river flow, the dilution of waste loads is reduced resulting in
river pollutant concentrations of higher strength. At high water
temperatures, dissolved

oxygen saturation decreases and the biological activity increases
resulting in a greater amount of oxygen demand in the water column as
BOD (biochemical oxygen demand) and greater amount

of oxygen demand from bottom sediments (SOD). Thus water column
dissolved oxygen depletion is maximized under these conditions.

A goal of sampling at less than 4400 cfs as measured at the USGS gage in
West Enfield (90% flow duration) was used as a target flow for the
three-day intensive survey. This goal was met in both intensive surveys.
The three-day average flow was 3620 cfs in 1997 and 3400 cfs in 2001.
Both data sets represent about a 97 % flow duration or about a 5-year
low flow event.

Another preferable sampling condition is having no runoff during and
prior to the survey. Runoff is undesirable due to the difficulty of
quantifying it as input to the model. One of the water quality model's
underlying assumptions requires steady state conditions. This would not
be met if significant runoff occurred during or two to three days prior
to the sampling event. There was no runoff three weeks prior to August
5,6,7 of 1997 and no runoff ten days prior to August 7,8,and 9 of 2001.

The upper 22 miles of the study reach from Millinocket to the confluence
of the Mattawamkeag River (River Miles or RM 83 -- 61) are classified C
requiring minimum dissolved oxygen to not be less than 5 ppm and 60 % of
saturation. Six locations were sampled in this class C reach for
dissolved oxygen and temperature. The next 67 miles from the
Mattawamkeag River to Reeds Brook in Hampden (RM 61 to --6) are
classified B waters with the exception of 1 mile directly above the
Enfield dam (RM 38 to 37) which is classified C. Fourteen locations were
sampled for dissolved oxygen and temperature in the class B reach and
one location in the class C reach above the Enfield dam. Class B waters
require that a minimum dissolved oxygen level of 7 ppm and 75% of
saturation be maintained at all times. The final 22 miles of the study
reach are tidal waters and are classified SC. Nine locations were
sampled for dissolved oxygen and temperature in the class SC reach.
Class SC requires that minimum dissolved oxygen of 70% of saturation be
maintained at all times.

The 1997 data indicated that minimum statutory dissolved oxygen criteria
were met and often greatly exceeded at all locations, except North
Lincoln, where minor non-attainment of class B dissolved oxygen criteria
sometimes occurred. Of significance, however was the fact that point
source discharges were at only 10% of their licensed permitted BOD5
(five-day biochemical oxygen demand[^1]) limits. Hence the potential for
lower dissolved oxygen levels than measured is possible, and worse case
levels must be determined by the model. The 2001 data indicated that
dissolved oxygen criteria were not met in 10 of the 14 locations sampled
in class B waters.

About 50 river miles are estimated to currently not attain class B
minimum dissolved oxygen criteria. The BOD5 discharged by point sources
was about 17% of licensed amounts during the

2001 sampling event. In class C and class SC waters, the 2001 data
indicate that dissolved oxygen criteria were maintained.

A chlorophyll-a[^2] level of 8 ug/l is used as a threshold level
indicating the occurrence of an algae bloom. When chlorophyll a levels
approach this threshold, the water may begin to appear green tainted
from plankton that are floating in the water. The plankton may also be
visible within the water column. Only one location exceeded 8 ug/l in
1997; the average three-day chlorophyll a was 9 ug/l at the Weldon dam.
Other locations at Dolby dam and three locations within the estuary had
levels approaching 8 ug/l. The data and modeling reports indicated that
a eutrophication problem on the Penobscot River could be forthcoming.

The 2001 data indicates a further deterioration in eutrophic state in
the Penobscot. Chlorophyll a levels exceeded the threshold of 8 ug/l at
five of the fourteen locations sampled; including above Dolby,
Rockabema, and Weldon dams on the West Branch and upper Penobscot and
Orrington center and South Orrington in the estuary.

Both the chlorophyll-a levels and dissolved oxygen readings indicate
deterioration in water quality when compared to the 1997 data.
Conditions of river flow, water temperature and waste load inputs were
examined as an initial attempt to explain the lower water quality
experienced in 2001. River flow was not significantly different in both
data sets (3620 Vs 3400 cfs at Enfield in 1997 and 2001, respectively).

A comparison of point source inputs (Figure 1) indicates that point
sources discharged higher amounts of pollutants in 2001 when compared to
1997. Point sources collectively were discharging 739 ppd. of total
phosphorus in 1997 and 1250 ppd. of total phosphorus in 2001
representing an increase of 69%. Point sources collectively were
discharging 30,600 ppd. of total ultimate BOD[^3] in 1997, and 45,300
ppd. of total ultimate BOD in 2001 representing an increase of 48%.

A comparison of water temperature (figure 2) indicates that levels in
2001 were typically 3 to 4 ^o^C higher than 1997. As explained earlier
in the text, higher water temperatures generally result

in lower dissolved oxygen. Higher water temperatures also result in
conditions more favorable for algae growth.

The higher water temperatures, and higher inputs of BOD and phosphorus
collectively result in lower dissolved oxygen at virtually all locations
in 2001 than 1997 (figure 3). The higher levels of algae can be
explained by the higher phosphorus inputs and higher water temperatures.
Algae creates a diurnal cycle of the lowest dissolved oxygen in the
early morning after extended respiration and the highest dissolved
oxygen in mid to late afternoon during extended photosynthesis and
respiration. A larger range (diurnal dissolved oxygen) of the AM and PM
dissolved oxygen readings usually indicates more algal activity. The
larger diurnal dissolved oxygen in the 2001 data is evident when
compared to the 1997 data (figure 3).

**[Water Quality Model]{.underline}**

The EPA supported model, QUAL2EU was used in the analysis of the
Penobscot. Steady state flows and load inputs are required and major
transport mechanisms of advection and dispersion must be
one-dimensional. The lack of runoff that was previously discussed
satisfies the steady state condition. The uniformity of the dissolved
oxygen and temperature readings in the vertical profiles indicates that
the Penobscot is a well-mixed system and hence one-dimensional flow
occurs. The Penobscot River should be well suited to this model.

Many changes were recently incorporated into MDEP's version of QUAL2EU
or more appropriately named QUAL2MDEP. The changes are as follows:

1.  [Addition of a periphyton module with links to the nutrient and
    dissolved oxygen modules]{.underline}. A major shortcoming of
    QUAL2EU is bottom attached algae can not be directly modeled. The
    majority of impacts now experienced in rivers involve low early
    morning dissolved oxygen from bottom attached algae. The QUAL2MDEP
    model can now be used to model bottom attached algae and the
    resulting diurnal dissolved oxygen swings.

2.  [Addition of a benthal BOD component.]{.underline} QUAL2EU models
    the direct oxygen demand from bottom sediments, but the sediment may
    also add BOD to the water column. This is particularly significant
    in long river systems like the Penobscot with long travel times to
    accurately model non-point source impacts. This was identified as a
    deficiency in QUAL2EU (see page 6, Penobscot Modeling Report, June
    2000).

3.  [Enhancement of the dissolved oxygen saturation
    calculation.]{.underline} QUAL2EU calculates dissolved oxygen
    saturation as a function of temperature. This results in unnecessary
    error in marine situations, since salinity also affects dissolved
    oxygen saturation. Salinity is now included into the dissolved
    oxygen saturation calculation.

4.  [Alteration to phosphorus output units.]{.underline} QUAL2EU's
    output for organic phosphorus and dissolved phosphorus is rounded
    off to the nearest 10 ppb. This has been changed in

> QUAL2MDEP so the output for phosphorus components are now rounded off
> to the nearest 0.1 ppb.

5.  [Revisions to the simulation output formats.]{.underline} The
    diurnal output was enhanced so that all dynamic output can now be
    observed. An EXCEL VBA post processor was created. The output for a
    dynamic model run is quite large and not easily managed. The
    postprocessor

> allows the selection of specific output specified by the user, which
> can be transferred to an EXCEL spreadsheet for observation and easy
> plotting.

The model reach structure was set up identical to the 2000 modeling
effort. The model has 39 reaches, and 34 point source inputs (figure 4).
In the model non-point source tributary inputs are modeled as point
sources. There are 15 point source inputs and 19 tributary inputs. The
estuary was simulated as a tidally averaged steady state model.
Phytoplankton as chlorophyll-a, nutrients as nitrogen and phosphorus,
carbonaceous BOD, periphyton, and dissolved oxygen were simulated as the
chemical parameters of interest.

**[Model Transport]{.underline}**

In the hydraulic component of the model, river velocity and depth
relationships are developed as a function of flow. Transect and time of
travel data are used as a basis for deriving the relationships. QUAL2EU
offers two options for the transport of pollutant parameters; a power
equation and the Manning equation for open channel flow. The power
equation option was chosen for the Penobscot River model. This computes
velocity and depth as a function of flow with the following equation:

> **V = A~1~Q^B^~1~ and D = A~2~Q^B^~2~**
>
> Where:
>
> V = velocity;
>
> D = depth;
>
> Q = flow, and
>
> A~x~, B~x~ are coefficients that are empirically derived from transect
> and time of travel data

The hydraulic coefficients were already calculated from a previous MDEP
modeling effort (see Penobscot River Basin Waste Load Allocation, P.
Mitnik, 1991). No changes were made to the 1991 model hydraulic
coefficients (table 3).

Dispersion or longitudinal spreading becomes very significant in the
estuary and must be appropriately considered. A conservative parameter
such as the salinity data is generally used to calibrate the dispersion
rates to use in the estuary. Initial estimates of dispersion can be
obtained

by plotting Ln salinity Vs river mile. The dispersion is then the
estuary advective or flushing velocity divided by the slope of the Ln
salinity Vs river mile. Initial estimates of dispersion rates used in
the estuary ranged from 5 to 150 mi^2^/day and resulted in a good fit of
the salinity data to measured values (figure 4a, 5, table 4).

Flow data is available at a number of locations throughout the Penobscot
River watershed. USGS gages that were used include the Penobscot River
at West Enfield; Mattawamkeag River at Mattawamkeag; and Piscataquis
River at Medford. A flow balance was calculated for the watershed (table
5) using this available flow information and a proration of watershed
drainage

area for tributary inputs to the Penobscot. The larger tributaries were
input to the model as point sources and the smaller tributaries were
grouped as incremental flow inputs or distributed loads.

**[Chemical Calibration of the Water Quality Model]{.underline}**

The chemical calibration of the model involves inputting measured
tributary and treatment plant effluent as point source loads, measured
upstream and downstream boundary conditions and measured water
temperature as initial conditions. The model output of various
parameters, such as BOD, chlorophyll a, and dissolved oxygen are
compared to measured values and adjustments are made to the model
parameter rate coefficients until a good match of model and observed
data occur. The model parameter rates that are adjusted include many
inputs (see Tables 6, 7). Default values are used as initial estimates
and adjusted within the ranges recommended in the literature until
satisfactory results are achieved. The model is verified after
satisfactory results are obtained from a comparison of modeled Vs
observed data of a second independent data set. After this process, the
model can then be reliably used for model predictions of water quality.

The 1997 data collected on August 3, 4, and 5 were used to calibrate the
Penobscot River water quality model. This is discussed in the 2000
modeling report. The 2000 modeling report stated that "*calibration
ordinarily involves verification with a second independent data set. A
second three-day data set was not collected in 1997 and for this reason
the update of the model is considered incomplete. An additional
three-day data set is recommended for the next year MDEP is scheduled to
be in the Penobscot River watershed, which is the summer of 2001.*"

The 2001 data are used in this report to verify the model. The
verification effort actually involves re-consideration of parameter
rates in both data sets. The lack of a satisfactory calibration for
chlorophyll-a on the West Branch locations, in particular, was
considered a weakness of the original calibration effort. The addition
of the periphyton module and the capability to simulate daily dissolved
oxygen fluctuations in QUAL2MDEP should result in better model
calibration. Many of the algae component parameters were changed in this
modeling effort. The parameter rates used in the model calibration /
verification are displayed in

tables 6 (rates variable by model reach) and 7 (rates constant in all
model reaches). The rates used for the Penobscot River were within
ranges recommended in the literature.

The model calibration / verification are plotted for each chemical
parameter (figures 8 to 14) in a river mile Vs chemical parameter
format. The model output is displayed as a line and the data as an
average (unshaded square) and range (high and low error bars). To aid
the reader, a column plot (figure 6) shows the river mile of all
sampling locations.

Due to the very low level of ammonia measured in the river, BOD was
modeled as total ultimate BOD and not partitioned into the carbonaceous
and nitrogenous fractions. A benthic CBOD

source rate of 30 mg / ft^2^-day was assigned to all model reaches. This
value was obtained by a trial an error procedure in the modeling that
resulted in UBOD values throughout the entire river in the model output
that were similar to background values, after all point sources were
removed.

In a large river with many impoundments where currents are not
significant, the UBOD decay rates derived in the laboratory test often
give satisfactory results for an estimation of the actual ambient rates.
The Penobscot falls into this category river type. The laboratory rates
are derived from a least square regression line fit of many UBOD values
measured over the 60 day time period. The following equation is used in
this analysis.

**BOD~t~ = UBOD (1-e-^kt^)**

Where:

BOD~t~ = BOD in ppm at any given time

BOD = The final ultimate BOD in ppm

K = The BOD decay rate (/day)

T = Time in days.

Depending upon the data set, the UBOD decay rates varied from 0.03 to
0.05 /day. The 97 data set was assigned a rate of 0.05 /day and the 01
data set 0.04 /day in fresh waters and 0.03 /day in tidal waters. This
results in a satisfactory fit of modeled to observed UBOD values (figure
7).

An examination of the data reveals that a large loss of phytoplankton
occurs immediately below the Weldon dam impoundment. The majority of the
loss is probably due to the die-off of algae. This may be due to the
change in river environment from impounded to free flowing waters. The
algae in the impoundments are not suited to thrive in the flowing
environment and hence the rapid die-off. There is no direct input for an
algae die-off rate in QUAL2, but this can be simulated as settling to
compensate for this deficiency in the model (Some of the algae loss may
actually be settling.)

There appears to be a large uptake of dissolved phosphorus from the
Rockabema dam to the Weldon dam in excess of that needed for algae
growth. An additional PO4-P uptake rate was assigned to three model
reaches (9 to 11) here. Both QUAL2 versions have a direct input for an
orthophosphorus source to the water column from the sediment, but not a
direct input for uptake, or orthophosphorus loss from the water column
to the sediment. Orthophosphorus uptake, this can be indirectly
simulated as a negative source rate from the sediment.

When these and some other adjustments were made to the model, a good
calibration of chlorophyll-a and nutrients results (figures 7 to 11).

The dissolved oxygen calibration involves both a daily average
calibration and a daily minimum calibration. The former involves running
the model in the steady state mode and comparing

the model output to the daily average dissolved oxygen observed in both
data sets. The latter involves running the model in the dynamic mode and
comparing the model output to the AM and PM dissolved oxygen observed in
both data sets.

In the 2000 modeling effort, periphyton and the resulting diurnal
dissolved oxygen swings could not be directly modeled. To simulate the
daily minimum dissolved oxygen, a diurnal adjustment was made to the
model run in steady state mode. The diurnal adjustment was based the
difference observed in the data between the daily average and daily
minimum dissolved oxygen (Figure f2, Penobscot River Modeling Report,
June 2000). Since periphyton can now be modeled, this diurnal adjustment
is no longer necessary except in tidal waters. The difference in river
depth and water chemistry in downstream boundary (ocean) when comparing
the low and high tide data results in the necessity of a diurnal
dissolved oxygen adjustment. Simulation of time variable boundaries and
depth is not possible in QUAL2. The diurnal adjustment applied to tidal
waters ranged from 0.10 to 0.50 ppm (Figure 12).

The calibration of dissolved oxygen involves the initial steps of
calibrating BOD, chlorophyll a, and nutrient and subsequent steps of
estimating the reaeration rate (K~a~) and sediment oxygen demand rate
(SOD) for each modeled reach of river[^4]. K~a~ and SOD are typically
very variable over the length of a river and the rates assigned can be
quite different reach by reach. The rates assigned to the model are
identical to those assigned in the 2000 modeling effort (Table 6).

There are a number of formulas to estimate reaeration based upon
research by experts. Up to eight different formulations can be specified
by the user in QUAL2. The O-Connor Dobbins reaeration formula which
calculates reaeration as a function of velocity and depth was used in
most reaches.

**k~a~ = 12.85 V^.5^/D^1.5\ ^**

where v = velocity in fps, and D = depth in ft

In the deeper and lower velocity reaches, k~a~ was calculated by an
impoundment reaeration formula which is considered a lower bound for
k~a~ whenever the O Connor-Dobbins formula results in a lower estimate.

**k~a~ = 3/D**

This option is not directly available in QUAL2, but can be calculated
outside the model and input as a user specified rate.

SOD analysis at eight river and four estuary locations was undertaken in
the autumn of 2001 led by USEPA with field assistance from MDEP and the
Penobscot Nation. The data report of May 2002 describes the SOD sample
collection as follows: *"In most sample locations of the Penobscot, it
was difficult to collect sediment samples in the main channel, due to
the lack of adequate sediments. There is, no doubt, great scouring of
sediments in a large river such as the Penobscot occurs during high flow
periods. Sediment samples were collected in known depositional areas
that were often outside the main channel."* The results of the SOD
analysis resulted in high levels at many locations when compared to
other river systems in Maine. The model inputs were often much lower
than those reported in the analysis (Figure 13). It is deduced that the
SOD in depositional areas may be much higher than the average value
throughout the river bottom. The SOD measured in depositional areas is a
good upper boundary of the maximum amount that can be expected on the
Penobscot.

The parameter rates used for each model reach are summarized in table 6.
The calibration of dissolved oxygen with these parameter rates results
in a good fit of the model output to the daily average (Figure 14) and
daily minimum of the measured data (Figure 15). Of all sample locations
compared, 71% and 87% of average dissolved oxygen were within 0.2 and
0.3 ppm, respectively, of the observed data.

**Model Predictions Runs at 10-Year Low Flow**

After the water quality model is calibrated to observed data, a
prediction run is made at worst case conditions to assure dissolved
oxygen criteria will be achieved at all locations. Worse case conditions
are defined by low river flows, when dilution of wastewater is at a
minimum; by high water temperatures, when the saturation of dissolved
oxygen is lower and BOD decay and oxygen demand from the sediment are
higher; and by point sources discharging at licensed limits. Non- point
source loads are accounted for as tributary loads with pollution
concentrations as

measured in the August 1997 survey, distributed load inputs in the model
incremental flow, and as sediment oxygen demand (which results partially
as sediment that has settled during runoff events prior to low flow).

The 7-day 10-year low flow (7Q10)[^5] is used to assess compliance with
dissolved oxygen criteria. Prior estimates of 7Q10 were based upon USGS
gages at the period of record up to 1991. This analysis was updated to
also include the years from 1992 to 2002. The updates resulted in new
7Q10's of 3070 cfs at the West Enfield gage and 3170 cfs at a
discontinued USGS gage at Eddington (Figure 16). A flow balance was
derived to determine various 7Q10 flows and 1Q10

flows at different locations (Table 5). Dilutions for the toxics program
regulation for point source discharges (Chap. 530.5) will be changed
based upon this updated information (Table 8).

**Table 8 Dilution of Riverine Point Source Discharges**

  ------------- -------- -------- -------- ---------- -------- ------------- -----------
                Effl     River    River    Old                 **New         
                Flow mgd 7Q10 cfs 1Q10 cfs Dilution            Dilution**    

                                           Chronic    Acute    **Chronic**   **Acute**

  GNP West      43       2216     2000     30.1       7.5      **33.2**      **7.8**

  Millinocket   2.33     2219     2000     556        140      **615**       **139**

  GNP East      33       2226     2007     39.2       9.8      **43.5**      **10.1**

  Eastern Paper 16.3     2822     2703     111        24.6     **112**       **27**

  Lincoln       1.07     2822     2703     1626       349      **1703**      **408**

  Old Town      1.7      2795     2521     1191       254      **1062**      **239**

  GP Old Town   24.4     2802     2527     83.5       17.7     **74.1**      **17**

  Orono         1.84     3178     2867     1329       283      **1115**      **252**

  Veazie        .35      3183     2871     12251      2604     **5868**      **1323**

  Bangor        18       3206     2892     139        30.2     **116**       **26.2**

  Brewer        5.19     3243     2925     478        102      **404**       **91.2**
  ------------- -------- -------- -------- ---------- -------- ------------- -----------

To determine the appropriate river temperature that should be used for
the design in the model prediction runs, historical temperature data was
first examined at Eddington. This data appears to indicate a trend of
increasing river temperature from 1979 to 1994. It was determined that
water temperatures of 25 ^o^C and 24 ^o^C were appropriate to use as
weekly average and monthly averages values, respectively (Figure 17).
Since the river temperature can vary by as much as 9 ^o^C when comparing
all sample locations on any given day, adjustments were derived for
other locations based upon the 1997 and 2001 data (Figure 17). Hence the
design weekly average river temperature is about 23 ^o^C at Millinocket;
increases to 25 ^o^C in Bangor; and eventual decreases to about 15 ^o^C
at Bucksport (Figure 17). Note that the water temperatures experienced
in the 2001 survey for three consecutive days are considered extreme,
and would probably not occur for seven consecutive days simultaneously
at a 7Q10 flow event.

Two tests are run with the water quality model to check dissolved oxygen
compliance with statutory criteria; one to test compliance of minimum
dissolved oxygen criteria and a second to test compliance with the
monthly average criteria of 6.5 ppm. In the first test assessing
compliance with minimum dissolved oxygen criteria, river flows are
inputted as 7Q10; river temperatures are inputted as a weekly average;
and point sources are inputted at their weekly average licensed loads.
Since the paper mill licenses have no weekly average BOD5 on their
permit, ratios of weekly average to daily maximums were derived, based
on three years of discharge monitoring data provided by the mill
personnel. In the second test assessing compliance with monthly average
dissolved oxygen criteria, river flows are inputted as 30Q10; river
temperatures are inputted as a monthly average; and point sources are
inputted at their monthly average licensed loads.

In both these runs, pollutants that are not included in the license such
as nitrogen or phosphorus are ordinarily inputted as measured in the
calibration data (August 1997 and 2001). The ultimate point source BOD
must be derived from the product of a BODu/BOD5 ratio (which is derived
from data) and the licensed BOD5 concentration. Point source inputs to
the model and related information is summarized in tables 9 and 10.

The classification of the Penobscot River changes from B to C in the
riverine portion and is classified SC in the estuarine portion of the
river. Although the classification and dissolved oxygen criteria applied
to each classification are discussed earlier in the text, it is repeated
here for convenience sake. The following five segments define its
classification:

1.  From the Ferguson Lake outlet to the Mattawamkeag River -- Class C

2.  From the Mattawamkeag River to 1 mile above the West Enfield Dam --
    Class B

3.  From 1 mile above the West Enfield Dam to the West Enfield Dam--
    Class C

4.  From the West Enfield Dam to Reed Brook in Hampden -- Class B

5.  From Reeds Brook to Bucksport -- Class SC

The dissolved oxygen criteria is as follows:

Class B Daily minimum [\>]{.underline} 7.0 ppm and 75% of saturation

Class C Daily minimum [\>]{.underline} 5.0 ppm and 60% of sat.; monthly
average [\>]{.underline} 6.5 ppm

Class SC Daily minimum \> 70% of saturation

The model prediction run of point sources discharging licensed amounts
indicates that minimum dissolved oxygen criteria should be met in class
C and SC segments. However

about 51 river miles are not expected to meet the minimum class B
criteria (figure 18). The following locations are projected not to meet
minimum dissolved oxygen criteria:

1.  A forty-eight-mile class B segment (RM 61 to 13) from the
    Mattawamkeag River confluence in Winn to the Milford dam. The lowest
    dissolved oxygen level predicted is 6.3 ppm, which is within 0.7 ppm
    of minimum class B criteria

2.  A three-mile segment (RM -3 to -6) in tidal waters from the
    approximate location of the Bangor and Brewer outfall pipe to the
    Reeds Brook confluence. The lowest dissolved oxygen level predicted
    here is 6.3 ppm, which is within 0.7 ppm of minimum class B criteria

In addition, algae blooms (chlorophyll a \> 8 ug/l) are projected for 11
miles of the river including impoundments from Dolby dam to Weldon dam
with chlorophyll a levels as high as 14 ug/l predicted. About 11
addition miles of estuarine waters are projected to have chlorophyll a
levels between 8 and 9 ug/l, slightly over the bloom threshold .

The model prediction run at 30 Q10 flow to check compliance with monthly
average dissolved oxygen criteria of 6.5 ppm indicates that criteria
will be met everywhere except above the

Rockabema dam, reaching a low of 6.4 ppm (figures 19). Since this is
within 0.1 ppm or measurement error, it can be considered to be
marginally complying with the monthly average criteria.

Model prediction runs with point sources at licensed conditions are
compared with point sources at zero discharge levels (figure 17) and
point sources at actual discharge levels (Figure 19) as indicates by
summer discharge monitoring report from 1999 to 2002. It should first be
pointed out that the actual discharge levels are typically much less
than licensed discharge levels. For example, point sources collectively
discharged about 38% of their licensed BOD5 in the summers from 1999 to
2002 (Figure 21).

The model runs indicate that dissolved oxygen criteria are met at zero
discharge conditions, although only marginally at the beginning (RM 61)
and end (RM --6) of the class B segment. Point sources collectively
contribute up to 1.1 ppm of the dissolved oxygen depletion, which are
about 56% of the total deficit from saturation. Of the remaining 44%,
there may be additional dissolved oxygen depletion attributable to point
sources contribution to sediment oxygen demand.

The model runs comparing licensed conditions to actual conditions
indicate that the length of non-attainment is similar with actual
discharge levels (47 miles) when compared to licensed discharge levels
(51 miles). Dissolved oxygen levels should improve by about 0.2 ppm, and
the minimum dissolved oxygen of 6.5 ppm would be 0.5 ppm within Class B
minimum criteria. The model inputs for actual discharge levels are
summarized in Table 11.

![](media/image5.wmf)

**[Sensitivity Analysis]{.underline}**

In a sensitivity analysis, some of the parameter rates can be tested to
determine which are more important in the development of the model. The
model prediction run at 7Q10 was used as a basis for the sensitivity
analysis runs. Four different parameter inputs are tested; the sediment
oxygen demand rate, reaeration rate, BOD decay rate, and algae
(phytoplankton and periphyton) growth rates. Each parameter was
multiplied by a factor of 0.5 and 2 and the model output for dissolved
oxygen was then compared as a range at three strategic locations (figure
22). The three locations chosen are above the Rockabema dam, a point
midway between the Greenbush and Passadumkeag sampling locations, and at
Orrington at the end of the class B segment. It is at these locations
that the lowest dissolved oxygen readings are predicted and hence the
most sensitivity is expected.

From this analysis it appears that the order of sensitivity in the
calibration of the model dissolved oxygen are the atmospheric reaeration
rate, followed by the sediment oxygen demand rate, the BOD decay rate,
and finally the algae growth rate. The reaeration rate and sediment
oxygen demand rate appears to be sensitive at all locations. The algae
growth rate appears to be more sensitive in shallower flowing segments
(Greenbush-Passadumkeag). The BOD decay rate appears to be more
sensitive in the deeper reaches (Rockabema dam and Orrington).

**[Component Analysis]{.underline}**

Components of potential river dissolved oxygen depletion are compared in
two ways. First pollutant loads inputs to the Penobscot River as a whole
can be computed and compared in pie chart diagrams. The larger loads
have more potential for impact.

Second, the actual impact each load has to the river dissolved oxygen
depletion can be determined with model prediction runs. In this
analysis, load inputs are individually subtracted from the model and the
difference in dissolved oxygen predicted by the model from a base case
is then observed. The model prediction run at 7Q10 flow and licensed
point source loads was used as a base case for the component analysis.

Point source, tributary, and background input loads are compared as pie
chart diagrams for total ultimate BOD and total phosphorus (figure 23).
The tributary and background loads are computed using pollutant
concentrations as measured during the intensive surveys and represent
both natural and non-point source pollutant loads. From this analysis it
can be observed that point source inputs when discharging at maximum
licensed conditions are overwhelmingly the largest source of pollution
representing about 74% and 94% of the total input of BOD and phosphorus,
respectively.

Point source input loads are compared for BOD5 (Figure 24) and total
phosphorus (Figure 25). From this analysis, it can be observed that
paper mills collectively are the largest pollutant source that accounts
for more than 80% and 70% of the BOD5 and total phosphorus, respectively
of all point source discharges. The city of Bangor accounts for about
11% and 18% of the total BOD5 and phosphorus, respectively. Other
municipal sources (Millinocket, Lincoln, Old Town, Orono, Veazie, and
Brewer) individually seem insignificant, since each discharges is
typically less than 3% of the total point source loads. However, when
considered collectively, they are significant representing about 8% and
10% of the total point source inputs for BOD and phosphorus,
respectively.

The component analysis of the dissolved oxygen deficit is analyzed at
the same three strategic locations as the sensitivity analysis; above
the Rockabema dam, Passadumkeag -- Greenbush midpoint, and Orrington at
end of class B segment. Five components of dissolved oxygen depletion
were investigated:

1.  Sediment oxygen Demand (SOD) -- Includes all SOD collectively from
    natural, point source, and non-point sources.

2.  Point Source BOD -- Includes nitrogenous and carbonaceous BOD from
    all industrial and municipal sources.

3.  Non-point Source BOD - Includes nitrogenous and carbonaceous BOD
    from tributary and incremental drainage. Includes both natural and
    non-point source pollution.

4.  Background -- Model run with no background impact. Dissolved oxygen
    is adjusted to 100% saturation and background BOD is adjusted to
    zero. Collectively includes impacts from the initial DO deficit and
    background BOD from natural and non-point sources.

5.  Point Source Nutrients -- Diurnal dissolved oxygen impacts from
    attached and floating algae. Includes nutrient impacts from point
    sources.

Above the Rockabema dam (Figure 26), sediment oxygen demand and
background conditions are the largest factors contributing to dissolved
oxygen depletion resulting in about 37% each of the total dissolved
oxygen deficit. Point source BOD is responsible for about 18% of the
total dissolved oxygen depletion. Point source nutrients and non-point
source BOD are less important, contributing about 7% and 1%,
respectively to the total dissolved oxygen deficit. At the Passadumkeag
-- Greenbush midpoint (Figure 26), point source nutrients and sediment
oxygen demand are the largest factors contributing to dissolved oxygen
depletion resulting in about 45% and 35% of the total dissolved oxygen
deficit, respectively. Point source BOD contributes about 11%;
background conditions about 5%; and non-point BOD about 4% of the total
dissolved oxygen deficit.

At Orrington (Figure 26), point source BOD and sediment oxygen demand
and are the largest factors contributing to dissolved oxygen depletion
resulting in about 43% and 37% of the total dissolved oxygen deficit,
respectively. Non-point source BOD and background conditions are

responsible for about 13% and 7%, respectively, of the total dissolved
oxygen deficit. Nutrients are unimportant resulting in less than 1% of
the total dissolved oxygen depletion.

**[Investigation of Pollutant Abatement]{.underline}**

Model prediction runs with point sources at both licensed and actual
conditions resulted in widespread non-attainment (up to ½ of the 103
river miles investigated) of minimum class B dissolved oxygen criteria.
In addition, the model predicts that algae blooms should occur in up to
¼ of the length of the 103 miles investigated. Point sources are the
main cause of the dissolved oxygen depletion and the algae blooms.
Clearly reductions of point source inputs are necessary for attaining
compliance of dissolved oxygen criteria.

Additional model runs are made with point source reductions of BOD and
phosphorus. Phosphorus is the limiting nutrient responsible for the
growth of benthic algae and phytoplankton (floating algae). Limiting
phosphorus inputs to the river will limit algae production, which will
also alleviate the early morning low dissolved oxygen readings that
result from extended evening respiration. The algae typically produce
excess oxygen when exposed to light during the daytime through
photosynthesis, and the maximum daily dissolved oxygen is reached at mid
to late afternoon. Conversely, at night in the absence of light,
extended respiration results in a continuing depletion of dissolved
oxygen until minimum daily values are achieved at dawn.

The point source reductions can be accomplished in a number of methods.
One method could be requiring the larger point sources to undertake most
of the reductions, since they are responsible for most of the impact.
Another method could be requiring all point source inputs to do some
abatement with the larger inputs doing more abatement than the smaller
inputs. **In summary, it has been determined that if point source
discharges are reduced to levels slightly higher**

**than actual BOD input levels**[^6] **(about a 60% collective reduction
from licensed amounts), and point source total phosphorus reduced by
about 40% from actual levels, all criteria could be marginally
attained.**

A summary of model run inputs and prediction results (table 12) and
point source load inputs for each model run (table 13) illustrate the
expected water quality for the various load reductions investigated.
Model runs are also plotted in a river mile Vs dissolved oxygen level
format (Figure 27). Phosphorus for point source discharges is typically
regulated as a monthly average discharge in the summer months (May 15 to
Sept 30). BOD5 is regulated in the summer months as a monthly and weekly
average or possibly a daily maximum.

  ------------- ---------- ----------- ---------- ----------- ----------- ----------- ----------- ----------- ----------- ----------- ----------- -----------
  **Table 13                                                                                                                                      
  Summary of                                                                                                                                      
  Point Source                                                                                                                                    
  Loads (PPD)                                                                                                                                     
  in Model                                                                                                                                        
  Prediction                                                                                                                                      
  Runs at 7Q10                                                                                                                                    
  Flow**                                                                                                                                          

                Pen.7qt                PenP.7qt               PenP2.7qt               PenP3.7qt               PenP4.7qt               PenP6.7qt   

                TP         BOD5 WA\*   TP         BOD5 DM\*\* TP          BOD5        TP          BOD5        TP          BOD5        TP          BOD5

  GNP West      502        13680       244        8247        87          8247        87          8247        87          8247        87          8715

  Millinocket   80         874         28         177         28          177         80          874         28          874         28          205

  GNP East      385        6710        246        1451        88          1451        88          1451        88          1451        88          1760

  Eastern Pap   114        8191        76         5070        45          5070        45          5070        45          5070        45          2702

  Lincoln       35         402         12         51          12          51          35          402         12          402         12          90

  Old Town      69         638         36         403         36          403         69          638         36          638         36          223

  GP Old Town   244        12780       159        3576        66          3576        66          3576        66          3576        66          3978

  Orono         47         690         18         99          18          99          47          690         18          690         18          180

  Veazie        11         131         3          16          3           16          11          131         3           131         3           28

  Bangor        650        6755        212        907         212         907         650         6755        212         907         212         1471

  Brewer        31         1947        15         232         15          232         31          1947        15          1947        15          620

  Totals        **2168**   **52799**   **1050**   **20229**   **611**     **20229**   **1209**    **29781**   **611**     **23933**   **611**     **19973**

  \* Weekly                                                                                                                                       
  Average                                                                                                                                         
  Loads. For                                                                                                                                      
  paper mills,                                                                                                                                    
  derived by                                                                                                                                      
  daily maximum                                                                                                                                   
  load times                                                                                                                                      
  WA/DM ratio                                                                                                                                     
  from                                                                                                                                            
  discharge                                                                                                                                       
  monitoring                                                                                                                                      
  reports.                                                                                                                                        

  \*\* Daily                                                                                                                                      
  maximum load                                                                                                                                    
  used for                                                                                                                                        
  performance                                                                                                                                     
  runs.                                                                                                                                           
  ------------- ---------- ----------- ---------- ----------- ----------- ----------- ----------- ----------- ----------- ----------- ----------- -----------

It should first be mentioned that the discharges in Winterport and
Bucksport are well below the impacted river and estuary segments. Even
though some portion of their effluent discharge may reach the impacted
segments during an incoming (flood) tide, model runs assessing their
impact determined that their inputs collectively is negligible. Hence no
point source controls other than

those that currently exist within licenses are recommended for
Bucksport, Winterport, and the International Paper mill in Bucksport.
The dilution and dispersion of the Penobscot estuary in the location of
the outfalls of these discharges is very large and adds insurance that
regulation of these discharges will do little to improve the
non-attainment areas. Also, the estuary becomes nitrogen limited rather
than phosphorus limited in Winterport and Bucksport. If nutrient
controls were implemented here, nitrogen reductions, rather than
phosphorus reductions would be implemented.

It would appear that the most equitable way of achieving the phosphorus
reductions would be restricting paper mill discharges to mass loads
calculated by actual flow and phosphorus treated to a 0.5 ppm level.
Municipal discharges should be capped at current phosphorus input levels
based upon actual flow and phosphorus as measured in the 97 and 01
surveys. The required monthly average phosphorus loads for each
discharge under this case are summarized in Table 13, run PenP6.7qt
(2^nd^ column from far right).

No phosphorus requirements were also investigated for municipal point
sources (model run PenP3.7qt) along with the mentioned paper mill
reductions, but it was determine that this would

not be enough, since 26 river miles would still not attain dissolved
oxygen criteria and 10 river miles still experience algae blooms.
Additional phosphorus reductions from mill discharges would be necessary
without regulating municipal discharges. A 0.5 ppm TP level may be
achievable at each paper mill by undertaking process controls, i.e.
optimizing phosphorus addition, and levels lower than 0.5 ppm could
require large capital investments.[^7]

For the BOD reductions many methods are possible. One possible method is
holding all point source discharges to performance based standards
(model run PenP2.7qt). This may not be the most equitable method, since
dischargers who are performing well will be regulated at much lower
levels than discharges who are performing poorly (hence good operation
of a plant is penalized). Another method that appears to be more
equitable is requiring BOD5 loads based upon an equal concentration and
actual treatment plant flow. A BOD5 concentration of 30 ppm would be
required as a weekly average to meet the necessary load reductions. The
loads for this method for each point source are summarized in table 13,
model run PenP6.7qt (in the column to the far right).

For the phosphorus reductions, two allocation methods are presented for
municipal discharges. The first involves capping all municipal
discharges at their current mass load. A more equitable method analogous
to the BOD allocation method may be calculating required TP loads based

upon an equal concentration. The concentration that would be required at
actual flow using this method is 3.45 ppm.

Actual treatment plant BOD5 performance data for the years 1999 -- 2002
are plotted and compared to current licensed levels and allocation based
upon the 30 ppm BOD5 allocation

(Figure 28a, 28b, 28c). Note that the daily maximum allocation is
adjusted to reflect a daily maximum rather than the weekly average
inferred in the model run. The ratio used for this adjustment are the
inverse of those appearing in table 9 (weekly/daily maximum BOD ratio).
It appears that Eastern Paper and Old Town would have the most
difficulty meeting this allocation. Old Town is currently undertaking a
plant upgrade, which could result in significant improvements in
performance.

Another possibility, which should be mentioned, is a water quality
trading system. Hence a discharge that could have difficulty meeting a
required phosphorus or BOD level could possibly trade pollutant credits
with another discharge who is expected to be well under future
requirements. Note that in the model runs provided, GNP west and GNP
east have already been modeled assuming pollutant trading that is more
consistent with how the plants actually perform

(West is based upon BOD5 = 50 ppm; East BOD5 = 10 ppm). Note that in a
pollutant trading system, pollutants are not necessarily traded in a 1:1
ratio, since where each input is located in relation to impacted river
areas must also be considered. Finally it should be stated that DEP is
open to other allocation methods, so long as the desired goal of
attainment of water quality standards is achieved. The following
allocations are offered for starting points of discussion.

**Table 14 Point Source BOD5 and Phosphorus Allocation** Municipal
Discharges

  -------------- -------------- --------------- -------------- ---------------
  Point Source   Weekly Average                 Total          
  Discharge      / Daily                        Phosphorus     
                 Maximum BOD5                   (PPD)          
                 (PPD)                                         

                 Allocate by    Allocate by     Allocate by    Allocate by
                 current        equal           current        equal
                 discharge      concentration   discharge      concentration

  Millinocket    180 / 200      210 / 230       28             24

  Lincoln        50 / 55        90 / 100        12             10

  Old Town       400 / 480      220 / 250       36             26

  Orono          100 / 110      180 / 200       18             21

  Veazie         16 / 18        28 / 31         3              3

  Bangor         900 / 1000     1470 / 1630     212            169

  Brewer         230 / 250      620 / 690       15             71

  Winterport     Primary Plant                  No Restriction 

  Bucksport      Primary Plant                  No Restriction 

  **Paper                                                      
  Mills**                                                      

  Point Source   Weekly Average                 Total          
  Discharge      / Daily                        Phosphorus     
                 Maximum BOD5                   (PPD)          
                 (PPD)                                         

                                                               

                 Allocate by    Allocate by     Allocate by    Allocate by
                 current        equal           equal %        equal
                 discharge      concentration   Reduction      concentration

  GNP West       8200 / 10800   8700 / 11500    96             87

  GNP East       1450 / 2600    1800 / 3200     97             88

  E Paper        5000 / 6800    2700 / 3700     30             45
  Lincoln                                                      

  G Pacif Old    3600 / 5100    4000 / 5600     63             66
  Town                                                         

  IPCo Bucksport 7100 / 10000\* No Restriction                 
  -------------- -------------- --------------- -------------- ---------------

\* Current licensed level

> **Appendix B -- List of Peer-reviewed Publications**

**Boundary-Fitted Hydrodynamic and Pollutant Transport Model
Bibliography (Journal Articles and Conference Proceedings)**

**2001**

Kim, H.-S. and J. C. Swanson, 2001. Modeling of double flood currents in
the Sakonnet River.

Submitted for publication in 7th Annual International Conference on
Estuarine and Coastal

Modeling (ECM 7), St. Pete Beach, Fl, November 5-7, 2001.

Swanson, C. and M. Ward, 2001. Linking landside nutrient loading and
water quality models: application

to Nantucket waters. Submitted for publication in 7th Annual
International Conference on

Estuarine and Coastal Modeling (ECM 7), St. Pete Beach, Fl, November
5-7, 2001.

Ward, M. and M. Spaulding, 2001. A nowcast/forecast system of
circulation dynamics for Narragansett

Bay. Submitted for publication in 7th Annual International Conference on
Estuarine and Coastal

Modeling (ECM 7), St. Pete Beach, Fl, November 5-7, 2001.

**1999**

Mendelsohn, D., S. Peene, E. Yassuda, S. Davie, 1999. A hydrodynamic
model calibration study of the

Savannah River Estuary with an examination of factors affecting salinity
intrusion. Estuarine and

Coastal Modeling 6 (ECM6), New Orleans, LA, 3-5 November 1999.

Spaulding, M., D. Mendelsohn and J.C. Swanson, 1999. WQMAP: An
integrated three-dimensional

hydrodynamic and water quality model system for estuarine and coastal
applications. Marine

Technology Society Journal, Volume 33, Number 3, Fall 1999, pp. 38-54.

Spaulding, M., J.C. Swanson, D. Mendelsohn, 1999. Application of
quantitative model -- data calibration

measures to assess model performance. Estuarine and Coastal Modeling 6
(ECM6), New

Orleans, LA, 3-5 November 1999.

Swanson, J. C. and M. C. Ward, 1999. Improving coastal model predictions
through data assimilation. In

proceedings of The 6th International Conference on Estuarine and Coastal
Modeling (ECM6),

November 3-5, 1999, New Orleans, LA.

Yassuda, E., S. Davie, D. Mendelsohn, T. Isaji, and S. Peene, 1999
Development of a waste load

allocation model within Charleston Harbor estuary phase II: water
quality. Estuary, Coastal and

Shelf Science, Vol. 50.

Yassuda, E., S. Davie, D. Mendelsohn, T. Isaji, and S. Peene, 1999
Development of a waste load

allocation model within Charleston Harbor Estuary phase III: project
application. In proceedings

6th Annual International Conference on Estuarine and Coastal Modeling
(ECM 6), New Orleans,

LA, November 3-5, 1999.

**1998**

Hunkins, K., D. Mendelsohn and T. Isaji, 1998. Numerical hydrodynamic
models of Lake Champlain.

Published in American Geophysical Union Monograph, 1998.

> **Appendix C -- Technical and User Manuals**

**WQMAP User Manual**

**Version 5.0**

**January 2004**

**Headquarters:**

Narragansett, RI

Phone: 401-789-6224

Fax: 401-789-1932

asa@appsci.com

www.appsci.com

**Worldwide Offices:**

Castetbon, France

St. Andrews, Scotland

São Paulo, Brazil

Gold Coast, Australia

Perth, Australia

**APPLIED SCIENCE**

**ASSOCIATES, INC.**

**Appendix D -- Example Tables**

**Southport Harbor Modeling Project**

**Connecticut**

![](media/image6.wmf){width="6.2652777777777775in"
height="6.729166666666667in"}

![](media/image7.png){width="4.53125in"
height="3.675in"}![](media/image8.png){width="5.264566929133858in"
height="3.340944881889764in"}

![](media/image9.png){width="5.514583333333333in" height="3.53125in"}

![](media/image10.png){width="5.35625in" height="2.9118055555555555in"}

![](media/image11.png){width="6.166666666666667in"
height="7.2131944444444445in"}

![](media/image12.png){width="5.206944444444445in"
height="5.666666666666667in"}

![](media/image13.png){width="4.85625in" height="3.4375in"}

Appendix E -- Example Figures

Appendix E- Example Figures - Southport Harbor, CT

![](media/image14.png){width="4.772222222222222in" height="3.0625in"}

![](media/image15.png){width="4.855555555555555in" height="3.59375in"}

Appendix F - Example Scope of Services from MassDEP

#### SCOPE OF SERVICES

***Assabet River Nutrient Total Maximum Daily Loading (TMDL) Project***

**I. Purpose**

The purpose of this Contract is to provide Assabet River nutrient Total
Maximum Daily Loading (TMDL) modeling services to the Massachusetts
Department of Environmental Protection (the Department). Nutrient Total
Maximum Daily Loading (TMDL) modeling in the Assabet River is necessary
because of the presence of summertime eutrophic conditions in the river
system. As a result of water quality problems associated with these
conditions, the Assabet River was placed on a list of impaired
waterbodies requiring water quality improvement, known as a Section
303(d) list and implementation of a nutrient TMDL allocation plan is
required.

**II. Scope of Services**

The Scope of Services for this Contract shall consist of the following
deliverables as outlined below. In order for a deliverable to be
complete under the Contract, the deliverable must be done in accordance
with the Contract specifications (and the Contract schedule).
Deliverables must be approved by the Department and otherwise satisfy
the Contract provisions, as determined by the Department. The scope of
services shall consist of the following tasks. As specified in the
deliverables paragraph of each task below, the Contractor will develop
technical memorandum that will address both the in-stream and watershed
components of the Assabet River model.

**Task 1: Literature Review**

The Contractor has performed a detailed literature review as part of
another Department sponsored project and will review any additional
literature that may become available as part of the model set-up and
development task (Task 5).

**Deliverables:** Copy of previously completed literature review with
any additional literature reviewed as part of this project.

**Task 2: Identification of Appropriate Models**

Based on the modeling requirements for the Assabet River system and
potentially critical modeling factors identified by the Contractor in
their proposal, the Contractor has tentatively identified two models as
most appropriate for the Assabet River nutrient TMDL modeling project.
The model to be applied will be selected with the concurrence of the
Department. The two models recommended for consideration are:

-   **HSPF11**, the USGS/EPA combined watershed and instream water
    quality model Hydrological Simulation Program -- Fortran, and

-   **WASP5**, the EPA instream Water Quality Analysis Simulation
    Program. The WASP modeling package would be coupled with a separate
    watershed land use based model.

Each of these models has the modeling capabilities necessary for this
project and is well suited to successfully complete the Assabet River
nutrient TMDL allocation modeling application.

**Deliverables:** A Technical Memorandum (TM#1) will be submitted to the
Department describing the model selection process, including a detailed
description of HSPF and WASP. TM#1 will provide documentation of the
model selection process including both the in-stream and watershed
model. TM#1 will also identify the selected model and provide the
rationale for its selection. The contractor will seek input and comment
from the Consortium relative to the model selection and inputs.

**Task 3: Hydrologic and Nutrient Budgets**

The Contractor shall develop water quality predictions using the
selected model and apply these predictions to establish appropriate TMDL
allocations for sources throughout the Assabet River system and to
evaluate alternative loading allocation options. Development of the
model including model set-up, calibration, and validation shall follow
the approach outlined in the Contractor's proposal or as otherwise
approved by the Department.

The hydrologic and nutrient budget models will accurately simulate the
hydrology and nutrient budgets of the Assabet River system over time.
The model will provide a mass balance of nutrients at each time step and
will simulate nutrient kinetic processes observed in the field. The
developed hydrologic and nutrient budget will be fully capable of
providing the predictions required to perform the TMDL allocation.

**Deliverables:** A Technical Memorandum (TM#2) will be submitted to the
Department describing the approach to be applied to the modeling
process. TM#2 will include a description of all key model settings and
the rationale to be applied in assigning values for each. TM#2 will also
include a description of the approach to be applied in the model
calibration and validation process. The TM

will address the modeling approaches to be used for both the water
quality model as well as land use model. The Contractor will coordinate
these efforts with the Consortium who will be collecting

data and mapping the stormwater systems for each town so that the same
subwatersheds and physical attributes are used in both studies where
feasible.

**Task 4: Land Use Based Modeling to Determine Non-point Source
Loadings**

The Contractor shall develop a land use based model using a GIS platform
that will be fully integrated with the hydrologic nutrient budget model
as described in their proposal. The process of developing an

effective land use model for the Assabet River watershed will involve
identification of subwatersheds, assignment of a set of physical
attributes and functional coefficients to each subwatershed, and
adjustment of these parameters to improve the correlation between model
predictions and field measurements. Land use based model set-up,
calibration, and validation tasks will be provided to the Department in
a Technical Memorandum.

The land use based model will provide dynamic predictions of nutrient
loadings in each Assabet River subwatershed in support of TMDL modeling
goals. The land use based model will be applied to provide quantitative
predictions of water quality improvements associated with hypothetical
modifications to land use in each subwatershed. For example, the model
may be applied to predict reductions in nutrient loadings over time
associated with implementation of specific BMPs at golf courses.

**Deliverables:**

A Technical Memorandum (TM#3) will be submitted to the Department
providing the results of the model calibration and validation process
for both the water quality and watershed model applications. TM#3 will
include a discussion of all key model parameters adjusted during the
calibration process and the technical basis for each calibration
adjustment.

**Task 5: Develop the nutrient TMDL and evaluate alternative loading
options.**

The Assabet River TMDL model developed by the Contractor will include
the hydrodynamic and nutrient budget model and land use based watershed
model components described above. The Contractor shall apply the
developed Assabet River TMDL model to provide dynamic predictions of the
hydrology, water quality, and biology of the Assabet River and
impoundments along its course with emphasis on quantification of ambient
nutrient concentrations and associated water quality (*e.g.*, dissolved
oxygen concentration) and biological (*e.g.*, alga and macrophyte
biomass) conditions. The Contractor will apply the model to predict
water quality, at a minimum, under the following scenarios:

Present summertime conditions during low flow (7Q10) and average flow
conditions. The scenarios will account for time-varying nutrient
loadings such as those associated with wet-

weather point and non-point source loadings. Nutrient TMDL allocation
conditions including identification of all point source (WLA) and
non-point source (LA) allocations necessary to

achieve the TMDL allocation. The TMDL scenario will be specified by
performing numerous model simulations with incrementally reduced
loadings from present levels towards acceptable levels until the model
predicts that both narrative and numerical water quality standards for
the Assabet River will be met. Nutrient TMDL modeling will quantify the
nutrient assimilation loading capacity of the Assabet River.

-   Alternative loading options. The Contractor will perform simulations
    of 20 alternative loading scenarios, selected collaboratively with
    the Department. Each alternative loading option will provide an
    alternative means of achieving the TMDL allocation using a different
    set of WLAs and LAs. Other methods of improving water quality, such
    as dam removal and land use modifications, may also be evaluated as
    alternative loading options. The contractor will seek the input of
    the Consortium during the selection of alternative scenarios.

Modeling results for each of the scenarios outlined above will include
all necessary predictions including time-varying flows, nutrient
concentrations, dissolved-oxygen concentration and algal and macrophyte
biomass. Since the proposed modeling package includes a river model
integrated with a land use based watershed model, the in-stream water
quality impacts of land use changes in specific subbasins may be
quantified using the model.

The nutrient TMDL allocation modeling application will result in
identification of a nutrient TMDL allocation for the Assabet River and
20 alternative loading scenarios to support the TMDL planning and
implementation process. TMDL allocations will include all required
factors, such as allocation of both point and non-point source loadings,
seasonal considerations, margin of safety and future growth allowances.

**Deliverables:**

1\) Draft Set of Alternative Loadings Scenarios. A Technical Memorandum
(TM#4) will be submitted describing a set of draft model application
scenarios. Each scenario would likely feature an alternative set of
nutrient loading reductions (e.g., different tradeoffs between point and
non-point source reductions). TM#4 will be written in a format suitable
for delivery to interested parties in order to seek input and reach
consensus on a reasonable set of model application scenarios. Scenarios
described in TM#4 will likely include a set of alternative approaches to
reducing nutrient loadings to the river. TM#4 will also include the
rationale for each proposed model application scenario.

2\) Final Set of Alternative Loadings Scenarios. A Technical Memorandum
(TM#5) will be submitted to the Department describing the final set of
model application scenarios. TM#5 will provide a final

list of model application scenarios to be run and will provide a
rationale for each. TM#5 will also include a response to feedback
provided by interested parties. Scenarios added or modified based

on input from interested parties will be noted and the rationale for
declining to add scenarios suggested by interested parties if deemed
necessary, will also be included. TM#5 will provide a list of modeling
scenarios to be applied and will provide a technical basis for each.

**Task 6: Develop a draft nutrient TMDL report for review.**

The Contractor will prepare and submit a clear and concise draft TMDL
report in accordance with EPA guidance and input provided by the
Department. The draft TMDL report will provide, at a minimum a narrative
with accompanying tables and charts that address the 12 items listed in
the RFR.

**Task 7: Draft and Final Report**

The Contractor will prepare and submit a clear and concise draft and
final report in accordance with the RFR. The draft report will be
submitted at least two months prior to the scheduled termination date of
the contract. The Contractor will receive comments from reviewers,
perform necessary edits, and submit a final report, including a
"Response to Comments" document for inclusion in the final report.

**Task 8: Coordination and Timelines**

The Contractor will work with MA DEP to ensure that the TMDL allocation
process (WLA and LA) is closely coordinated with the comprehensive
wastewater management planning process currently underway with the
Assabet River Consortium. Coordination with Consortium will include
regular communication throughout the project in order to work closely
with the Consortium to develop environmentally acceptable and
implementable TMDL allocation strategies. The Contractor will attend and
participate in five (5) meetings on a quarterly basis for the duration
of the project to inform and receive comments from the consortium
regarding the progress of the TMDL process.

**Task 9: Public Meetings**

During the TMDL development process The Contractor will hold six public
meetings to inform the public of the TMDL process, schedule, and
findings. The Contractor will coordinate each meeting with the
Consortium Program Manager, EOEA SuAsCo Watershed Team Leader, the
Organization for the Assabet River (OAR), the Department, and EPA Region
I. At the conclusion of the project, the Contractor shall hold up to two
public meetings to summarize the findings of the TMDL allocation
project.

**Task 10: Quarterly and Final Reports**

The Contractor will prepare and submit brief quarterly progress reports,
as well as draft documents or models required for each task. The
Contractor shall also provide a final camera-ready copy of the Final
Report; electronic versions of all data, models, model runs and modeling
results in formats approved by the Department (e.g. on CD disk); and 25
copies of the Final Report.

Appendix G - Technical Memorandum for Calibration Validation from
MassDEP

**Date:** 10 April, 2001

**To:** MADEP

**From**: Contractor

**RE:** Assabet River Nutrient TMDL Model: Water Quality Model
Calibration

This memo contains a summary of the water quality model calibration and
validation components of the HSPF numerical modeling application
performed in support of the Assabet River Nutrient TMDL project. The
water quality model calibration includes water quality, sediment
quality, and biological components and represents the final task prior
to model simulation. The model calibration process involves using field
measurements of hydrologic, water quality, sediment quality, and biology
to support parameterization of the selection the model to accurately
simulate present water quality conditions.

The objective of the water quality model was to develop a simulation
tool to provide a reasonable numerical representation of the numerous
interrelated physical and biological processes that occur in the Assabet
River. The model could then be applied to simulate the present water
quality conditions and a set of alternative conditions in the Assabet
River system. Thus, the model may provide a technical basis for
evaluating alternative river management scenarios for improving water
quality.

Specific calibration goals were to predict:

• Concentrations of orthophosphorus, nitrate and ammonia along the river
profile;

• Diurnally varying DO concentrations with focus on minimum daily DO
values; and

• Quantities of biomass of free-floating and rooted aquatic vegetation
within the impoundments.

These metrics were considered indicative of the Assabet River system
water quality status. The water quality model was calibrated to field
data collected in August of 2000. This data set was chosen for
calibration purposes because the data set is more extensive and the flow
conditions were more readily simulated than those of the July 1999 data
set. Field data collected during the August 2000 survey included
nutrient concentrations (nitrate, ammonia, total and ortho-phosphorus)
and DO concentration measurements at 21 river locations, continuous,
in-situ DO concentration measurements in six reaches and biomass
measurements in each of the impoundments. This was the primary data used
to calibrate the water quality model. The calibration model simulation
was run from June 1997 to September 2000.

**Boundary Conditions**

Boundary conditions specific to the water quality segment of the model
were applied to specify nutrient concentrations, dissolved oxygen
concentrations and subsequent biological reactions at model boundaries.
Boundary conditions are specified were based on measured or observed
values. Data was analyzed to determine the appropriate resolution for
accurate model predictions. External boundary conditions include point
source loads, non-point source loads, rainfall, evapotranspiration and
solar radiation.

Point source loads in the Assabet watershed are contributed by the four
major wastewater treatment plants on the mainstem of the River. The
treatment plants are Westboro (RM 30), Marlboro (RM 23.6), Hudson (RM
15.7) and Maynard (RM 6.7). Monthly data for each plant was available
for January 2000 to October 2000. Missing data was interpolated among
available values. Monthly average values were used at each plant to
specify flow and nutrient loads. Daily discharge values were averaged
over each month. Nutrient concentrations, including ammonia, nitrate,
orthophosphorus, dissolved oxygen, BOD, TSS and alkalinity were measured
periodically throughout the period of record, generally with two or
three measurements each month. Monthly average nutrient values, in
lbs/hr, were added to the appropriate model reach for each treatment
plant. Table 1 presents the model input values of flow and nutrient
concentrations at each treatment plant. Although flow and concentrations
vary diurnally and daily, variations are not great, and monthly averaged
values were assumed representative of the loadings.

Non-point source loads were determined for each watershed, based on land
use distribution and export coefficients. One-half of the predicted
nutrient load from each watershed was assumed to reach the river based
in literature source recommendations. Each of the watersheds delineated
in the model contributes flow and nutrient loadings, including
particulate phosphorus, orthophosphorus, nitrate, ammonia and sediment,
to the model reaches of the Assabet River. Nutrients are washed in with
flow during rainfall events throughout the year.

Figures 1 through 3 present the model inputs of point and non-point
source loadings for total phosphorus, orthophorus and nitrate. The point
source dissolved phosphorus nutrient loadings are significantly higher
than the non-point source loadings. Non-point source loadings of total
phosphorus and dissolved nitrate are generally greater than or equal to
the point source loads.

Meteorological boundary conditions include rainfall, evapotranspiration
and solar radiation. The data sets for rainfall and evapotranspiration
have been discussed in earlier communications. Hourly solar radiation
values were computed from a data set for percent cloud cover recorded at
Worcester, MA. The data set consisted of daily values ranging from 0 to
10, representing percent cloud cover. The daily solar radiation was
computed from this data set, as a function of latitude. The daily solar
radiation values were disaggregated into hourly values.

After initial model runs, the solar radiation data set was further
modified to better represent the amount and timing of solar radiation
that reaches the river. Initial model results revealed that predicted DO
concentration began to rise much earlier each day than measured values.
This was attributed to trees along the river bank that shade the river
in the morning hours. The solar radiation data was modified to reflect
this by setting the first few hours to zero, in effect having the sun
rise later and more quickly.

This resulted in predicted diurnal DO variation the more closely matched
observed values.

**Calibration Procedure**

To begin the model calibration, initial values were selected for each of
the model parameters. Initial conditions were also established for each
of the predicted parameters. However, the model was fairly insensitive
to initial conditions, and equilibrium values were achieved within a few
time steps (ΔT = 1 hour). When available, field data were used to
establish initial parameter values and initial conditions. When field
data were unavailable, average literature values were selected.

To complete the calibration, model parameters were adjusted to achieve
reasonable agreement between measured and predicted values. In general,
a single parameter was adjusted at a time to isolate its effect on the
system. Parameters were adjusted within reasonable limits, based on
allowable model ranges or literature values. Of the parameters
calibrated, a few had a significant effect on the simulation results.
Table 2 presents significant model calibration parameters and supporting
information.

**Calibration Results**

The results of the model calibration are presented in Figures 4 through
6. Where model results are presented with rivermile in the x-axis and
parameter value in the y-axis. Model predictions are compared with
measured values in Figures 4 through 6. Model predictions were averaged
over the week of the field program, in order to eliminate diurnal
variation in the data. Temporal comparisons were made at the actual time
of collection.

Figure 4 presents the measured and week-averaged predicted nutrient
concentrations. The predicted values are in the range of the measured
values, and follow the spatial trend of nutrient concentrations with
downstream distance. Spikes in observed nutrient concentrations occur at
the locations of the WWTPs. Modeled treatment plant loadings were
assigned based on data provided by the treatment plants, and monthly
average values were established. It is possible that these data may not
be representative of the actual plant discharge during the period of the
field program, as indicated by the absence of a spike in predicted
orthophosphorus concentration at the Hudson treatment plant.

Figure 5 presents the modeled and measured aquatic biology in each of
the impoundments. Phytoplankton, or floating algae, is presented in
units of mg/L, and the benthic algae, or rooted plants, is presented in
units of mg/m2. In general, model predictions match the observed
concentrations and follow the observed spatial trends.

Figure 6 presents the modeled and measured dissolved oxygen in several
reaches along the river. The diurnal DO variation is captured in the
model predictions, with the exception of the most upstream sampled
location, School Street. The maximum DO concentrations are slightly
under or overpredicted, but the minimum concentrations are well
predicted, as well as the duration of low DO and the rates of diurnal
increase and decrease.

**Validation Results**

Figures 7 through 9 present the results of the model validation. Data
from the July 1999 field programwas used for model validation. The
figures follow the same presentation format described above. The
predicted validation values do not provide as good a match to the field
data as the calibration values. However, general spatial trends in
nutrients and biomass are predicted. In the downstream reaches, minimum
DO concentrations are well predicted, but less so in the upstream
reaches. Validation difficulty is likely attributable, at least in part,
to the extremely low flows that complicated the hydrologic and hydraulic
calibration.

**Summary**

We are generally pleased with the Assabet River model calibration and
believe that the model has been successfully calibrated. We hope that
you find this memorandum informative and thought provoking. We look
forward to meeting with you to discuss this important process and to
moving forward with model simulations. Please do not hesitate to call
with any questions or comments.

**Appendix H -- Example Technical Memorandum from MassDEP**

**To:** MA DEP

**Date:** 6 June, 2003

**From:** Contractor

**File:**

**RE:** Assabet River Nutrient TMDL: Water Quality Modeling Results for
4 Alternative Management Scenarios

This technical memorandum provides Assabet River water quality model
predictions associated with a set of 4 alternative management scenarios.
The alternative management scenarios are described briefly below,
followed by a presentation of water quality modeling results.

**Introduction**

The 4 alternative management scenarios consist of reductions in WWTP
effluent phosphorus and of reductions in benthal release of phosphorus
at both permitted and projected WWTP flows, as described below and
summarized in Table 1. Projected flows (Table 2) were provided by the
Assabet River Consortium. The alternative scenarios presented in this
memo are:

1\. Permitted flows -- In two scenarios, effluent dissolved phosphorus
concentrations at the four treatment plants were reduced concurrently
with reductions in the benthal release of phosphorus. These scenarios
were run with the permitted effluent flows from the WWTPs (Table 2). The
scenarios are:

a\. WWTP dissolved phosphorus at 0.2 mg/l and benthal release of
phosphorus at 25% of existing flux rate; and b. WWTP dissolved
phosphorus at 0.2 mg/l and benthal release of phosphorus at 10% of
existing flux rate.

2\. Projected flows -- In two scenarios, effluent dissolved phosphorus
concentrations at the four treatment plants were reduced concurrently
with reductions in the benthal release of phosphorus. These scenarios
were run with the projected effluent flow rates from the WWTPs (Table
2). The scenarios are:

c\. WWTP dissolved phosphorus at 0.2 mg/l and benthal release of
phosphorus at 25% of existing flux rate; and

d\. WWTP dissolved phosphorus at 0.2 mg/l and benthal release of
phosphorus at 10% of existing flux rate.

**Assabet River Water Quality Modeling Results**

Model results for each scenario are presented and discussed below. Model
results are compared to the existing conditions scenario. Model results
are reported for the low flow period observed during July 12 -- 18,
1999. Model scenario results are presented in nine figures and two
tables. Three figures of instream nutrient concentration predictions
(total and dissolved phosphorus and nitrate), three figures of DO
predictions (maximum, minimum and range) and three figures of biomass
predictions (total, floating and rooted). One table presents a tally of
the number of hours that the model predicted DO concentration is below
5.0 mg/L and the number of hours that DO exceeds saturation. The second
table presents the total predicted biomass (in kg) in each impoundment.

*Nutrient Concentration Predictions*

Figures 1 --3 present total phosphorus, orthophosphorus, and nitrate
concentration predictions vs. river mile during low flow conditions.
Model predictions indicate that in-stream phosphorus concentrations are
greatly reduced for all alternative scenarios compared to existing
conditions. In general, predicted phosphorus concentrations are similar
for all alternative scenarios. Predicted phosphorus concentrations are
slightly lower for the scenarios with a 90% reduction in benthal release
of phosphorus. The projected flow scenarios result in the slightly lower
predictions of phosphorus than the corresponding permitted flow
scenarios. Predicted nitrate concentrations rise with reductions in WWTP
dissolved phosphorus concentrations and/or benthal release of phosphorus
due to decreased nitrate consumption and constant nitrate loading.

*Dissolved Oxygen Concentration Predictions*

Figures 4 - 6 present ambient DO max, min, and range predictions vs.
river mile during low flow conditions. Model predictions of DO indicate
that, for all alternative scenarios, the minimum DO concentration is
generally increased, while the maximum DO concentration is generally
reduced -- reducing the overall DO range, as compared to existing
conditions. Table 3 presents the number of hours over the one-week
period with DO below 5.0 mg/L and with DO above saturation in all
reaches. Model predictions indicate the minimum DO falls below 5.0 mg/L
for less than 1% of the period of record for all alternative scenarios.
The scenario with 90% reduction in benthal release of phosphorus

and projected flows results in achievement of the 5.0 mg/L water quality
standard for the entire period of record.

*Biomass Predictions*

Figures 7 -- 9 present total biomass (floating and rooted), total
floating biomass, and total rooted biomass predictions vs. river mile
during low flow conditions. Model predictions indicate that, when
phosphorus loads are reduced, predicted biomass for all alternative
scenarios is reduced relative to existing conditions. Table 4 presents
the total biomass (in kg) for existing and alternative scenarios in each
of the Assabet River impoundments and provides a numerical
representation of the model predictions provided in Figure 7. Predicted
total biomass in the alternative scenarios is reduced 32% to 45% from
existing conditions. Greater reductions in biomass are predicted under
the projected flow scenarios than the corresponding permitted flow
scenarios. It should be noted that biomass predictions are less certain
than other model predictions due to several factors including the
model's simplifying assumptions and the complex and changing species
composition characteristics of river systems.

**Summary**

The Assabet River water quality model predicts reductions in phosphorus
concentrations, improvements in DO dynamics, and reductions in total
biomass associated with implementation of the alternative management
scenario evaluated (as shown in the attached figures and tables).

Increased water quality improvement is realized with a decrease in WWTP
dissolved phosphorus concentrations and/or a decrease in the benthal
release of phosphorus, indicated by predictions of lower phosphorus and
biomass concentrations. Both of these reductions in phosphorus loads are
predicted to contribute significantly to the overall improvement in
water quality. Projected flow scenarios, with slightly higher effluent
flows at two of the WWTPs, results in slightly greater improvements in
water quality over the corresponding permitted flow scenarios.

**Table 1 WWTP Phosphorus Reduction Series: Alternative Management
Scenarios**

**Table 2 WWTP Permitted Flows**

**Table 3 WWTP Phosphorus Reduction Series: Prediction of Number of
Hours (in all reaches) that DO falls below 5.0 mg/L or Exceeds
Saturation Under Low Flow Conditions**

**Table 4 WWTP Phosphorus Reduction Series: Predicted Total Biomass (kg,
Floating and Rooted) Under Low Flow Conditions**

[^1]: Biochemical oxygen demand (BOD) is a laboratory test estimating
    the amount of oxygen demanding substances in water samples. The
    oxygen depletion of a water sample is measured over a time
    increment. The five-day test or BOD5 is typically used to measure
    BOD in effluent samples from wastewater treatment plants. Hence,
    this test measures the potential of discharges to deplete oxygen
    within a river.

[^2]: The chlorophyll-a test is used as an indicator to quantify the
    amount of phytoplankton or floating algae within a water sample.

[^3]: The ultimate BOD test (UBOD) involves observing oxygen depletion
    in a water sample in a laboratory over a period of 60 days or more
    until nearly all of the oxygen demand is utilized. It is a more
    accurate representation of oxygen demand than the five-day test, and
    is typically used in modeling studies. The five-day test was
    originally thought to capture about 60% of the total UBOD, but
    studies have shown that the five-day test typically captures much
    less than 60% of the UBOD. Total ultimate BOD (TBODu) is the sum of
    both the carbonaceous and nitrogenous components of BOD.

[^4]: The reaeration rate, K~a~, is the rate at which oxygen from the
    atmosphere enters the water column at the surface. K~a~ is typically
    high in stretches of rapids or shallow water, and low in impounded
    or sluggish water. Sediment oxygen demand is the oxygen demand
    exerted by bottom sediments to the water column.

[^5]: The 7-day 10-year low flow (7Q10) is the lowest 7-day average flow
    expected to occur at a frequency of once in ten years. The 1-day ten
    year low flow (1Q10) is the lowest single day flow expected to occur
    at a frequency of once in ten years.

[^6]: For model runs at performance or actual levels, the average of the
    daily maximum pollutant load inputs for each summer month from 1999
    to 2002 were used to simulate weekly average loads at 7Q10 flow (no
    weekly average load input data was available). The daily maximum
    load set for point sources would be slightly higher than the inputs
    used in the model which are a weekly average.

[^7]: Paper mill effluent is typically nutrient deficient and phosphorus
    is added to the treatment process to optimize BOD removal. Often
    much more phosphorus is added than what is needed (see GNP study
    appendix). Studies should be undertaken at each paper mill to
    determine the minimum amount of phosphorus that needs to be added
    while still insuring good BOD removal. In some paper mills, studies
    have determined that no nutrient addition is actually needed.