Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Section 1 

INTRODUCTION 

As required by Section 303(d) of the Federal Clean Water Act, the State of Florida must 

develop total maximum daily loads (TMDLs) for impaired waters (i.e., do not meet their 

designated uses). A TMDL is the maximum loading of a particular pollutant that can be 

discharged in a surface water and still meet its designated uses and applicable water quality 

standards. However, the term TMDL is also used when referring to the overall process 

involved in the assessment and restoration of impaired waters. According to Subsection 

303(d) of the Clean Water Act (CWA) and Section 403.067, Florida Statutes, impaired 

waters are “those not meeting applicable water quality standards, a broad term that includes 

designated uses, water quality criteria, the Florida antidegradation policy, and moderating 

provisions” (Chapter 62-303, FAC). For each of the impaired waters, where the impairment 

is due to abatable, human-induced causes, Florida must define the TMDL. Pursuant to 

United States Environmental Protection Agency (EPA) TMDL regulations, the TMDL is 

expressed as follows: 

TMDL = WLA + LA + MOS 

where: 

WLA = the Wasteload Allocation for point sources, 

LA = a Load Allocation for nonpoint sources including natural background conditions, and 

MOS = a margin of safety (implicit or explicit). 

TMDLs are usually defined using waterbody-specific data, mathematical models and tools 

that consider the magnitude and fate of pollutants discharged from various sources. TMDL 

implementation focuses on assessing point source treatment needs or best management 

practices (BMPs) for nonpoint sources of pollution that can be applied to reduce 

anthropogenic pollutant loads enough to achieve the water quality standards necessary to 

meet designated beneficial uses. 

The CWA and the TMDL process require amplification and interpretation in the context of 

the physical environment and regulatory structure in place within the State of Florida. The 

Florida Department of Environmental Protection (FDEP) has determined that TMDLs should 

be developed using standard protocols for the following reasons: 

 The development of a standard approach will identify appropriate methods to comply 

with the TMDL process and facilitate a technology transfer that will substantially increase 

the understanding of the complex TMDL process; 

 A standard process for developing and applying TMDLs will reduce confusion and 

inappropriate approaches to TMDL implementation in Florida; 

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 Definition of standard practices will improve the consistency of TMDL studies conducted 

by FDEP staff or by local government agencies; 

 TMDL studies consistent with application of standard practices will facilitate the 

comparison of results and extension of data in nearby watersheds; 

 Standardization will result in more efficient implementation of TMDLs in accordance with 

the schedule defined in the TMDL Consent Decree between EPA and EarthJustice, 

while maintaining effective standards in developing those TMDLs; and, 

 Standardization will reduce costs for practitioners responsible for developing and 

reviewing the TDML; 

 A TMDL study conducted using standardized, technically peer-reviewed protocols would 

be more defensible if challenged in court. 

This document presents example standard protocols and references that may be 

considered for use by practitioners for conducting TMDL analyses and preparing 

TMDLs. This protocol report has been developed as a modular tool and reference, 

and includes the following sections (Version 6.0, June 2005):

 Section 1 presents the regulatory requirements for TMDLs and Florida’s watershed 

approach for TMDL implementation, example references, acronyms (Appendix 1A), and 

the Florida Watershed Restoration Act (Appendix 1B). 

 Section 2 presents the applicable water quality regulations and targets. 

 Section 3 presents guidance for data collection and analysis of impaired waters and for 

model preparation. 

 Section 4 discusses model selection. 

 Section 5 defines protocols for model preparation, verification, calibration, and validation. 

 Section 6 discusses model application. 

 Section 7 presents treatment alternatives and Best Management Practices. 

 Section 8 defines example documentation for TMDL analyses. 

This document provides and references standard protocols or practices to develop TMDLs 

for lakes, rivers, streams, and estuaries. The term “water segment” is defined as a portion of 

a waterbody for which FDEP assesses for a TMDL. The Waterbody Identification (WBID) 

number is a unique number assigned to each water segment or waterbody and is used to 

identify the location and extent of water bodies within the state. Each TMDL pertains to only 

one pollutant affecting a given water quality parameter and/or standard in the water segment 

or waterbody. A given waterbody or segment may have several TMDLs that address 

different pollutants (e.g., TN, TP, BOD, and Chl-a effects on DO). Most cases will be 

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adequately addressed by these standard practices. Because of specific circumstances and 

variability, it might be necessary to develop approaches for special or unforeseen cases not 

considered during the development of this standard protocol document to develop the TMDL 

for a given water segment or waterbody. FDEP must approve both the use of any 

nonstandard techniques used for TMDL development, and the application of such 

techniques to the water segment in question. After FDEP review and approval, other 

methods could be added to this protocol at FDEP’s discretion. 

1.1 REGULATORY BACKGROUND 

The TMDL requirements were originally promulgated as a part of the Federal Water 

Pollution Control Act of 1972 and were later expanded by the Clean Water Act (1977) and 

the Water Quality Act (1987). The law requires states to define state-specific water quality 

standards for various designated uses and to identify waterbodies for which the ambient 

water quality has been determined to not meet established standards (Subsection 303(d)). 

Waterbodies that do not achieve such water quality standards as a result of human-induced 

conditions are to be considered impaired. An updated list of impaired waterbodies must be 

presented by the state to the EPA every two years and must designate which of the listed 

impaired waterbodies will require implementation of the TMDL process. Florida issued a full 

303(d) planning list in 2002 and has been producing basin-specific 303(d) impaired waters 

lists recently in accordance with the Florida Watershed Restoration Act (FWRA, Chapter 

403.067, Florida Statute). 

In Florida, the TMDL process is multi-phased and includes the identification, verification, and 

listing of impaired waters, followed by the development and implementation of the TMDL. 

First, FDEP develops a planning list of impaired waters based on existing data. FDEP then 

prepares a verified list following the collection of additional corroborating water quality, 

biological, or other data. The verified list is adopted by the FDEP Secretary as the basin 

specific 303(d) List to be sent to EPA in compliance with the CWA. Once a waterbody is 

adopted on the verified list of impaired waters, then the next phase of the TMDL process is 

the development of a TMDL, including the initial allocation of allowable loads. The next 

phase of the TMDL process is the development of the Basin Management Action Plan 

(BMAP), or the TMDL implementation plan, in which detailed allocations of allowable 

loadings to point and nonpoint sources is done and load reduction strategies are evaluated. 

The following lists Florida's phased approach: 

Phase 1 Data Compilation and Assessment 

Phase 2 Collection and Assessment of Additional Data 

Phase 3 Determination of Total Maximum Daily Load 

Phase 4 Development of Allocation and Basin Management Action Plan 

(BMAP) 

Phase 5 Implementation of the TMDL and BMAP 

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This process for the determination of impaired waters is described in the Impaired Water 

Rule, 62-303 FAC provided in Appendix 2A. In Phase 3, the TMDL is estimated, generally 

with the use of mathematical models associated with water quantity and quality data and 

watershed information. 

Once the TMDL is defined in Phase 3, the allowable loads are allocated in Phase 4 to point 

and nonpoint sources. Point sources would include domestic and industrial wastewater and 

National Pollutant Discharge Elimination System (NPDES) industrial and municipal separate 

storm sewer systems (MS4s) stormwater discharges. Nonpoint sources would include septic 

tanks, agricultural, silvicultural, atmospheric, and natural flora and fauna discharges, as well 

as legacy sediment effects. One or more implementation plans are then developed to define 

how each source will be controlled to achieve the allocated load. For contributing pollutant 

sources under NPDES permits, the allocation will be achieved through permit-specified 

effluent limitations and load reductions. For other sources, such as agricultural areas that 

are not regulated by NPDES permits, load allocations will be achieved through nonregulatory programs based on the implementation of BMPs associated with each agricultural 

type. In Phase 5, the implementation of the BMAPs are initiated. 

1.2 WATERSHED APPROACH 

FDEP has decided to implement the five phases 

of the TMDL program described above using a 

watershed approach (i.e., managing water 

resources within their natural boundaries). To 

implement the watershed approach for all water 

bodies in Florida, FDEP divided each of the six 

FDEP Districts into five geographically based 

groups of watersheds. A map of the groups is 

shown in the inset (Figure 1.1) and a table that 

lists the groups by basin and district is provided 

below (Table 1.1). As illustrated in Table 1.2, 

the five phases of the State’s TMDL program for 

each group are completed in annual cycles, 

starting with Group 1. 

Figure 1.1 

1.3 TECHNICAL BACKGROUND 

For each TMDL completed under the phased watershed management approach outlined 

above, a technical analysis of the assimilative capacity of the subject water segment in 

question may be conducted. The assimilative capacity is the total amount of a pollutant that 

can be discharged into a water segment without causing use impairment. Thus, the 

assimilative capacity, as a result, is numerically equivalent to that segment’s TMDL with a 

MOS. To determine the assimilative capacity, the fate of the total loading to a water 

segment may be compared to water quality criteria and other environmental targets to test 

for use impairment. Such comparisons are usually done using water quality data and 

modeling. 

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YEAR* 00 01 01 02 02 03 03 04 04 05 05 06 06 07 07 08 08 09 09 10

Group 

1 

Phase 

1

Phase 

2 

Phase 

3 

Phase 

4 

Phase 

5 

Phase 

1 

Phase 

2 

Phase 

3 

Phase 

4 

Phase 

5 

Group 

2 

Phase 

1 

Phase 

2 

Phase 

3 

Phase 

4 

Phase 

5 

Phase 

1 

Phase 

2 

Phase 

3 

Phase 

4 

Group 

3 

Phase 

1 

Phase 

2 

Phase 

3 

Phase 

4 

Phase 

5 

Phase 

1 

Phase 

2 

Phase 

3 

Group 

4 

Phase 

1 

Phase 

2 

Phase 

3 

Phase 

4 

Phase 

5 

Phase 

1 

Phase 

2 

Group 

5 

Phase 

1 

Phase 

2 

Phase 

3 

Phase 

4 

Phase 

5 

Phase 

1 

1st Five-year Cycle – High-Priority Waters 2nd Five-year Cycle – Medium-Priority Waters 

Table 1.1 Watersheds Listed by Group and FDEP District 

DEP 

District 

Group 1 

Basins 

Group 2 

Basins 

Group 3 

Basins 

Group 4 

Basins 

Group 5 

Basins 

NW OchlockoneeSt. Marks 

ApalachicolaChipola 

ChoctawhatcheeSt. Andrews Bay 

Pensacola Bay Perdido 

Bay 

NE 

Suwannee Lower St. 

Johns 

Nassau-St. 

Marys 

Upper East 

Coast 

Central 

Ocklawaha Middle St. 

Johns 

Upper St. Johns Kissimmee Indian 

River 

Lagoon 

SW 

Tampa Bay Tampa Bay 

Tributaries 

Sarasota BayPeaceMyakka 

Withlacoochee Springs 

Coast 

S 

Everglades 

West 

Coast 

Charlotte 

Harbor 

Caloosahatchee Fisheating 

Creek 

Florida 

Keys 

SE 

Lake 

Okeechobee 

St. LucieLoxahatchee 

Lake Worth 

Lagoon- Palm 

Beach Coast 

Southeast 

Coast - 

Biscayne Bay 

Everglades 

Table 1.2 Schedule of Phases for Each Group 

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Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Figure 1.2 shows the overall flow chart for a standard TMDL study consisting of four 

elements: Data Acquisition and Assessment; Model Preparation, Verification, Calibration, 

Validation, and Application; Control Strategies Assessments; and Publication and Data 

Transfer. Element V will be produced by FDEP and will cover allocation and implementation. 

Element I - Data Acquisition and Assessment and Model Selection. During this element, 

data necessary for the TMDL analysis are acquired (collected) and assessed (analyzed). 

The first step is the collection and analysis of existing data in conjunction with the 

assessment of the watershed and/or receiving waterbody. An analysis of the variability of 

the available data is completed to define and assess the potential portion of TMDL 

uncertainty attributable to data variability. If the available data are insufficient to apply the 

model or evaluation tool, then additional data may need to be collected. A variety of models 

can also be used to analyze a TMDL for a receiving waterbody. An assessment of 

watershed and receiving waterbody models will generally be performed, and a model (or set 

of models) may be selected on the basis of this assessment. A watershed model is one that 

simulates the loadings of pollutant from land surface runoff and groundwater flows into the 

receiving waters. A receiving waterbody model, or assimilative capacity model, is one that 

simulates the fate of the pollutant once it reaches the receiving water. It should be noted that 

the processes of data acquisition, data assessment, and model selection are iterative in 

nature and, while they are described in series in this document, many of these activities are 

done in parallel. 

Element II - Model Preparation, Verification, Calibration, Validation, and Application. In 

this second element, the model (or set of models) is prepared, verified, calibrated, and 

validated (PVCV in figure) and applied to the subject watershed and receiving waters. The 

model is calibrated using available data to confirm that the model(s) simulate the watershed 

pollutant load generation and receiving water responses to pollutant loading within a defined 

uncertainty and/or error. Model verification and validation refer to the processes and 

techniques that are used to check if the model reasonably matches the specifications and 

assumptions used in applying the model and reasonably matches measured data for the 

watershed and/or receiving waterbody. The data and models are applied to calculate the 

TMDL, uncertainty, and MOS. 

Element III - Control Strategies Assessments. Using the verified, calibrated, and 

validated model(s), design condition and continuous simulations are used to define the total 

allowable loading for the subject pollutant and the potential sources of the pollutants. In 

Florida, industrial and domestic WWTP point sources are required to provide a high degree 

of treatment; therefore, the initial control strategies to be considered may generally pertain 

to stormwater point and nonpoint sources for most pollutants. To help the development of a 

TMDL implementation plan, simulations of three potential control strategies may be 

conducted (FDEP, 2001) as follows: 

 The first strategy simulates application of BMPs to 45 percent of the land area; 

 The second strategy simulates application of BMPs to 90 percent; and 

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 The third strategy simulates the results for some intermediate or higher (e.g. >90 

percent) participation for all sources depending on the need for pollutant discharge 

control. 

Element IV - Publication and Data Transfer. The data collection, model application and 

control strategy assessment should be documented and made available for review by 

stakeholders and the public along with the data, model data sets and simulations. 

Element V – TMDL Allocation and Implementation. The next step is to allocate and 

implement the TMDL. FDEP developed the protocol for the allocation and implementation. 

These data and model results are incorporated into the BMAP process. 

Although this is not a simple process for many TMDL assessments, FDEP must produce a 

significant number of TMDLs to achieve its goals for the watershed approach and also must 

meet the schedule defined in the TMDL Consent Decree between EPA and EarthJustice. In 

order to accomplish these difficult tasks in the short period of time available, the elements 

defined above should be standardized as much as possible. 

1.4 PURPOSE OF TMDL PROTOCOL DOCUMENT 

The purpose of this document is to define standard practices to be considered for use in the 

State of Florida to guide TMDL analyses and to establish TMDL requirements. Initially, the 

general elements of TMDL activities will be defined for the majority of watersheds and 

receiving waters in the State. However, it is recognized that as more TMDL studies are 

completed, additional protocol components may be added and/or refined based on these 

experiences. Therefore, this document may need to be updated periodically. Although this 

document provides protocols for the standard methods of analyzing watersheds and 

receiving waters, there will likely be exceptions. For this reason, where appropriate, this 

document defines methods for consideration by FDEP to use for TMDL analyses. 

Based on the definition of the elements of a standard TMDL (Section 1.3), the following 

purposes are defined for this TMDL Protocol: 

 To define protocols for watershed and receiving water data analysis. While the data for 

each water segment will be highly variable, the analysis techniques are not and can be 

standardized. In particular, data analysis techniques are defined to consider pollutants of 

concern, natural background levels, surrogate parameters, and design conditions. 

 To define watershed and receiving water model selection, preparation, verification, 

calibration, validation, and application protocols. Over the last 30 years, a significant 

number of watershed and receiving water models have been developed. These range 

from very simple and data-minimal models to very complex, data-intensive models. This 

protocol document provides a description of the model selection process and describes 

standard practices for the calibration, verification, and validation of the models, and the 

application of the models to develop TMDLs and pollutant control strategies. 

 To define standard documentation protocols. The last set of protocols is related to the 

documentation and transfer of information to FDEP and the public for review and use 

during the TMDL implementation process. Standard reports for data assessments and 

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Florida Department of Environmental Protection June 2006 

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model application will be addressed, as well as digital data transfers and graphic 

formats. 

1.5 TECHNICAL ADVISORY AND REVIEW COMMITTEE 

(TARC) 

This document has been reviewed at two stages of its development by a Technical Advisory 

Review Committee (TARC) made up of the following: 

Melvin W. Anderson, Ph.D., P.E. 

Gail B. Boyd 

Kenneth L. Campbell, Ph.D., P.E. 

Wendy Graham, Ph.D. 

Susan Morea 

Stephen Nix, Ph.D. 

A. Charles Rowney, Ph.D. 

Martin Wanielista, Ph.D., P.E. 

Gour-Tsyh (George) Yeh, Ph.D., P.E. 

Most of the excellent comments from the TARC have been incorporated into this document 

and this document is envisioned as a living (or dynamic) document which will undergo regular 

updates and improvements as new data, methodologies, and technologies become available. 

1.6 PROTOCOL PREPARATION 

This TMDL Protocol was primarily prepared by CDM with support and review by URS and 

review by the TARC and FAU. Michael F. Schmidt, P.E., DEE served as the chief editor with 

CDM technical review and guidance provided by John Hartigan, P.E., DEE, Richard 

Wagner, P.E., Scott McClelland, and Thomas Quasebarth. 

 1-8 
Model Development and Verification

Notes: 1. Preparation, verification, calibration, a2. Uncertainty and sensitivity analysis ar3. Diamonds ( ) are provided for gene

flow chart. 

4. Data TMDLs may result from adeq

Element I. 

PVCV of 

Current Conditions

Receiving Water 

Model1

Definition of 

Three Control 

Strategies and 

BMP Participation 

Options

Proceed to 

Element IV 

Data 

Adequate 

for TMDL 

Use Existing

No

Yes

Current Conditions 

Receiving Water 

Model, TMDL, 

Uncertainty, and 

Recommendations

Yes

WBID Data

Yes

Clarification/Liaison/Status 

Meeting

Clarification/Liaison/Status 

Meeting

Clarification/Liaison/Sta

Meeting

No

Element I

Data Acquisition and Assessment

Update the

WBID Dataset

FDEP

Acceptance

Review

Comments

TARC

Review

7

Figure 1.2 TMDL PROCESS 

Element 

Control Strategies 

Element II

Assessment of

Initial WBID

 Data Set

Development

Of Initial

WBID Data

Packet

Supplemental

Data Collection or

Monitoring for

WBID

 Uncertainty  Analysis and  Data Needs  for  Selected Model 

Anticipated

Uncertainty of

TMDL

Tech Memo

Assessment of

Watershed and

 Receiving

 Water Models

 Watershed and  Receiving Water  Model Recommendations

Tech Memo

 PVCV of Current Conditions  Watershed Model1

FDEP

Acceptance

No

TARC

Review

Assessment of the

Three Accepted

Control Strategies

FDEP

Acceptance

No

FDEP

Acceptance

No

Yes

Current Conditions

Watershed Model

Tech Memo Yes

Data Adequacy

and Supplemental

Collection Needs

Tech Memo

Supplemental

Data Collection

Review

Comments

TMDL Modeling

Tech Memo

Refinement of

TMDL Objectives

 Definition of

 Conditions

Restatement of

TMDL Modeling

Objectives

Tech Memo

TARC

Review

Project

Kick-Off

Meeting

Issuance of

Notice to

Proceed for

Phased Work

Order

Project Start

1

2

34

5

8

6

9

1011

12

13

16

14

1517

18

Yes

Tech Memo

Commence

No

8

 FDEP

 Decision:

 Adequate  Data? 

 FDEP

 Decision:

 Adequate  Data? 

Yes

No

 Collect Supplemental  Data 

Model Preparation, Verification,  Calibration and Validation 

 Design and  Simulation Design and 

Simulation Conditions
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

SECTION 1 REFERENCES 

Cleland, B. Date unknown. “Observations on Technical Achievability in TMDL 

Development.” From TMDLs.net, TMDL Circuit Rider Program. 

One method to develop a TMDL and to provide for technical achievability by the 

stakeholders in a watershed is a “bottom up” approach, which is developed by the 

definition of BMPs by affected parties in the watershed. The bottom up approach takes 

advantage of already established jurisdictional agreements across city and county 

boundaries to provide achievable improvements which build up to a successful 

recovery of an impaired water segment. To address nonpoint sources, the article 

supports a flow-duration and/or load duration approach as a targeting tool set that is 

supported by adaptive management. 

EPA. 1999. “Protocol for Developing Nutrient TMDLs (First Edition).” EPA 841-B-99-007, 

Office of Wetlands, Oceans, and Watersheds. 

This is a technical guidance produced by EPA to help states, tribes and others develop 

TMDLs for nutrients (primarily, nitrogen and phosphorus. The document provides 

methods to identify target values, to assess sources, to identify links between targets 

and sources, to allocate resultant loads, to develop follow-up monitoring and evaluation 

and to assemble the TMDL document. 

EPA, 1999. “Draft Guidance for Water Quality-based Decisions: The TMDL Process 

(Second Edition).” EPA841-D-99-001, Office of Water. 

This is a technical guidance produced by EPA to help states, tribes and others to 

prepare proper documentation for the establishment and implementation of a TMDL. 

An overall discussion of the TMDL regulatory process is included as well as the 303(d) 

listing protocols and submittal requirements. 

EPA Region 9. January 7, 2000. “Guidance for Developing TMDLs in California.” 

The guidance describes the minimum federal requirements for developing and 

establishing TMDLs in California. The guidance is tailored to California’s unique legal 

and administrative processes and is based on the federal and state requirements for 

TMDLs as of January 2000. Provided in the document are minimum elements required 

for TMDLs, descriptions of the steps necessary to develop a TMDL and a TMDL review 

checklist. 

EPA. 2002. “The Twenty Needs Report: How Research Can Improve the TMDL Program.” 

EPA, Region 10, 2002. “TMDL Review Guidelines.” 

Florida Administrative Code. Impaired Waters Rule. Chapter 62-303. 

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Florida Statutes. Florida Air and Water Pollution Control Act. Section 403. 

Florida Statutes. 1999. Florida Watershed Restoration Act. 

Idaho Division of Environmental Quality, 1999. “State of Idaho Guidance for Development 

of Total Maximum Daily Loads.” 

Indiana Department of Environmental Management, January 2003. “TMDL Strategy 

(Guidelines for TMDL Development)”. IDEM 32/01/003/2003. 

This document defines the strategy to complete TMDLs within Indiana. The document 

includes staffing and resources, definition of an Advisory Group, TMDL development 

schedule, and discussion of the interaction with EPA as well as the technical programs 

for the TMDL. The TMDL process includes three phases: Planning and sampling; 

TMDL development and stakeholder outreach; and TMDP implementation plan. 

Poole, W. Date Unknown. “Uncertainty and Adaptive Management.” From TMDLs.net, 

TMDL Circuit Rider Program. 

Uncertainty is a part of every TMDL, no matter how much data have been collected or 

modeling analysis completed. To address this uncertainty, Dr. Poole stresses the need 

for adaptive management as a strategy for the implementation of TMDLs. Adaptive 

management is a means by which adjustments to the watershed management plan are 

made as new data or analysis refines the strategy. To be successful, Dr. Poole notes 

that the process must be built in to the start of the implementation program, identify 

goals to be checked periodically, and monitoring performed to check movement toward 

the goals. 

Texas Natural Resource Conservation Commission and US Environmental Protection 

Agency. 1999. “Developing Total Maximum Daily Load Projects in Texas: A Guide for Lead 

Organizations.” Austin, TX. 

Prepared by the Texas Natural Resource Conservation Commission (TNRCC), this 

document provides the approach to be used by consultants and governments in the 

state of Texas for TMDLs. The document describes methods for determining the 

pollutant targets, data sources, modeling techniques, public participation, quality 

assurance, and documentation. 

Virginia Department of Environmental Quality, 2000. “Total Maximum Daily Load Program: 

A Ten Year Implementation Plan.” 

  

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APPENDIX 1A 

ACRONYMS AND ABBREVIATIONS 

AC Atypical Coliform Bacteria

BASINS Better Assessment Science Integrating Point and Nonpoint Sources

BMAP Basin Management Action Plan

BMPs Best Management Practices

BOD Biochemical Oxygen Demand

BPJ Best Professional Judgment

C/L/S Clarification/Liaison/Status

CAFO Concentrated Animal Feeding Operation

CBOD Carbonaceous Biochemical Oxygen Demand

CDM Camp Dresser & McKee Inc.

CDS Continuous Deflection Unit

CES Center For Environmental Studies

CFR Code of Federal Regulations

cfs cubic feet per second

CGP Construction General Permit

Chl-a Chlorophyll a

CO-OPS Center for Operational Oceanographic Products and Services

CWA Clean Water Act

DCIA Directly Connected Impervious Area

DEM Digital Elevation Model or Map

DMR Discharge Monitoring Report

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DO Dissolved Oxygen

EFDC Environmental Fluid Dynamics Computer Code

EMC Event Mean Concentration

EPA Environmental Protection Agency

ERP Environmental Resource Permit

ET Evapotranspiration

FAC Florida Administrative Code

FAU Florida Atlantic University

FC Fecal Coliform Bacteria

FDACS Florida Department of Agriculture and Consumer Affairs

FDEP Florida Department of Environmental Protection

FDOH Florida Department of Health

FLUCCS Florida Land Use and Cover Classification System

FS Florida Statutes

FTP File Transfer Protocol

FWRA Florida Watershed Restoration Act

GIS Geographical Information System

GP General Permit

GPS Global Positioning System

GUI Graphical User Interface'

HEC Hydrologic Engineering Center

HEC-RAS HEC River Analysis System

HSG Hydrologic Soil Groups

HSPF Hydrological Simulation Program - FORTRAN

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HUC Hydrologic Unit Code

ID Identification

IFAS Institute of Food and Agricultural Services

IWR Impaired Waters Rule

K-S Kolmogorov-Smirnov

LA Load Allocation

LCI Lake Condition Index

MEP Maximum Extent Practicable

mg/l Milligrams per liter (also parts per million - ppm)

MGD Million Gallons per Day

MIA Marine Industries Association of Florida

MOS Margin of Safety

MS4 Municipal Separate Storm Sewer System

MSGP Multi-Sector General Permit

NAVD North American Vertical Datum 1988

NAWQA National Water Quality Assessment

NCDC National Climatic Data Center

NEP National Estuary Program

NGDC National Geophysical Data Center

NGVD National Geodetic Vertical Datum 1929

NO2 + NO3 Nitrite + Nitrate

NOAA National Oceanic and Atmospheric Administration

NOD Nitrogenous Oxygen Demand

NPDES National Pollutant Discharge Elimination System

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NPS Nonpoint Source

NPSM Nonpoint Source Model

NRCS National Resource Conservation Service

NSIP USGS National Streamflow Information Program

NTU Nephlemetric Turbidity Units

NWFWMD Northwest Florida Water Management District

NWS National Weather Service

O&M Operation and Maintenance

OFW Outstanding Florida Waters

P8 Program for Predicting Pollution Particle Passage through Pits, Puddles and 

Ponds

PCS Permit Compliance System

PCU Platinum-Cobalt Units

PO4 Orthophosphate

PS Point Source

RMS Root Mean Square

SCI Stream Condition Index

SFWMD South Florida Water Management District

SJRWMD St Johns River Water Management District

SLAMM Source Loading and Management Model

SOD Sediment Oxygen Demand

SOP Standard Operating Protocol or Procedure

SRWMD Suwannee River Water Management District

SSO Sanitary Sewer Overflow

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STORET EPA or FDEP STOrage and RETrieval Program

SW Stormwater

SWAT Soil and Water Assessment Tool

SWFWMD Southwest Florida Water Management District

SWMM Storm Water Management Model

TARC Technical Advisory and Review Committee

TBNEP Tampa Bay National Estuarine Program

TKN Total Kjeldahl Nitrogen

TMDL Total Maximum Daily Load

TN Total Nitrogen

TP Total Phosphorus

TSI Trophic State Index

TSS Total Suspended Solids

UAA Use Attainability Analysis

ug/l Micrograms per liter (also parts per billion - ppb)

USACE United States Army Corps of Engineers

USDA United States Department of Agriculture

USGS United States Geological Survey

WAM Watershed Assessment Model

WASP Water Quality Analysis Simulation Program

WBID Water Body Identification

WERF Water Environment Research Foundation

WLA Waste Load Allocation

WMM Watershed Management Model

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WQBEL Water Quality Based Effluent Limit

WWW World Wide Web

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Section 2 

WATER QUALITY STANDARDS AND TARGETS 

A TMDL is the maximum loading of a pollutant allowed to a water segment that will not 

cause an impairment of the water segment. The Federal Clean Water Act (CWA) requires 

states to identify water segments that are impaired, to develop TMDLs that define the 

maximum loading allowed, and to implement programs to control discharges by reducing 

pollutant loads to levels that will eliminate the impairment. A major key in the TMDL program 

is clear and consistent definition of conditions that result in the impairment of designated 

beneficial uses. Discussed in more detail later in this section, the State of Florida defines 

impairment as follows in the Impaired Water Rule (Rule 62-303, Florida Administrative 

Code, or FAC, as shown in Appendix 2A): 

“Subsection 303(d) of the CWA and section 403.067, F.S. describe impaired 

waters as those not meeting applicable water quality standards, which is a 

broad term that includes designated uses, water quality criteria, the Florida 

antidegradation policy and moderating provisions. However, as recognized 

when the water quality standards were adopted, many water bodies naturally 

do not meet one or more established water quality criteria at all times, even 

though they meet their designated uses.” [Rule 62-303.100(2)] 

Accordingly, the purpose of this section of the TMDL Development Protocol is to identify the 

water quality standards which the TMDL analyst will use as a benchmark when making 

TMDL decisions. Water quality standards consist of designated uses (discussed below), 

water quality criteria, antidegradation policies and moderating provisions (e.g., mixing 

zones, site specific alternative criteria, etc.). 

2.1 DESIGNATED USES 

Water quality criteria (generally numerical limits for various pollution related parameters that, 

if exceeded, indicate an undesirable environmental condition) vary depending on the 

designated beneficial uses of the given water body. For example, a water segment that is 

used for swimming requires higher water quality than a water segment that is used to 

receive industrial discharges. The Clean Water Act authorizes states to define water quality 

criteria that vary depending on the water uses designated by the state. In Florida, the term 

“designated use” is defined as “the present and future most beneficial use of a body of water 

as designated by the Environmental Regulation Commission by means of the classification 

system...” [Rule 62-302.200(8), FAC]. Rule 62-302.400, FAC, defines the allowable classes 

within Florida. Generally, Class I waters have more stringent criteria than Class V: 

Class I Potable Water Supplies 

Class II Shellfish Propagation or Harvesting 

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Class III Recreation, Propagation and Maintenance of a Healthy, WellBalanced Population of Fish and Wildlife 

Class IV Agricultural Water Supplies 

Class V Navigation, Utility and Industrial Use (No Florida waters are currently 

listed as Class V) 

Rule 62-302.400(10), FAC, states that all surface waters of Florida are classified as Class III 

unless otherwise defined in the rule. Furthermore, this subsection notes that a Class I, II, or 

III water body also may be designated as Outstanding Florida Waters (OFW, see below). 

As an illustration, Figure 2.1

shows Outstanding Florida Waters 

(OFW) as defined by Rule 62-302, 

FAC. Example areas include the 

Everglades and Florida Keys, 

estuaries along the west coast 

from Charlotte Harbor up to the 

Panhandle to Pensacola Bay. To 

confirm the class of a particular 

water segment, reference Rule 62-

302. 

Waters within Classes I, II and III 

can be further designated as 

OFWs to which the highest 

protection is to be afforded. In 

particular, “no degradation of water 

quality ... is to be permitted” [Rule 62-302.700(1)] except as authorized by Rule 62-4.242(2), 

FAC. Under this rule, requirements for discharge directly to, or which significantly degrades, 

an OFW include that 

 the proposed discharge or activity is “clearly in the public interest” and 

 either a permit was issued to the discharge prior to the OFW designation or the existing 

ambient water quality will not be lowered outside of a mixing zone. 

Additional conditions for discharge to OFWs are provided in Rule 62-4.242(2). 

2.2 APPLICABLE WATER QUALITY STANDARDS 

In this section, water quality criteria, antidegradation policies and moderating provisions are 

discussed as they pertain to TMDL studies. 

In consideration of these water quality standards, the following findings noted in Rule 

62,302.300, FAC, are of particular importance: 

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 Subsection (15) states that “pollution that causes or contributes to new violations of 

water quality standards or to continuation of existing violations is harmful to the waters of 

this State and shall not be allowed.” This is the criterion by which most TMDL analyses 

will be conducted. 

 Subsection (15) also states that “the Department shall not strive to abate natural 

conditions.” Therefore, each TMDL analysis needs to consider natural background 

conditions. 

 Subsection (13) highlights excessive nutrients (e.g., total nitrogen and total phosphorus) 

as “one of the most severe water quality problems facing the State.” The current nutrient 

criteria are narrative, and specific TMDL analyses must therefore be related to other 

criteria (e.g., turbidity or light attenuation) or to resource targets (see Section 2.3 below). 

2.2.1 Minimum Conditions 

State regulations define the minimum conditions for all surface waters in the state. These 

regulations (which are referred to as the “free froms”) require that, at all places and times, 

such waters must be free from man-induced, non-thermal discharges containing pollutants 

that alone or in combination: 

 Settle to form putrescent or nuisance deposits; 

 Floating debris in such amounts to be a nuisance; 

 Produce color, odor, turbidity or other conditions to be a nuisance; 

 Are acutely toxic; 

 Are in carcinogenic, mutagenic or teratogenic concentrations to humans or significant, 

local wildlife; or, 

 Pose a serious danger to public health, safety and welfare. [Rule 62-302.500(1)] 

Thermal discharges are also regulated with one minimum condition: that they do not 

produce nuisance conditions. 

For typical TMDL analyses, minimum conditions are generally not assessed through the 

TMDL process because these conditions are not usually the source of designated use 

impairment and if found in a water segment, are the impetus to specific, local enforcement 

and action. However, where a mixing zone is applicable or where health related pollutants 

are discharged, both the toxic condition and the carcinogenic, mutagenic and teratogenic 

conditions must be considered. Mixing zones are discussed later in this section. Thermal 

effects, on the other hand, are not only highly significant in regulating water quality 

conditions and therefore assimilative capacity, but are very amenable to explicit 

measurement and analysis. 

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2.2.2 Water Quality Criteria 

Water quality criteria are generally numerically-stated concentrations of chemical 

constituents or measurable levels of physical conditions. However, water quality criteria can 

also be expressed as narrative statements that qualitatively describe water quality required 

to support existing or future beneficial uses. The water quality criteria listed in Rule 62-530, 

FAC, apply depending on the use classification except for mixing zones (see below) and 

waters for which relief has been granted by FDEP. Rule 62-302.500(2) also states that an 

exception to the water quality criteria includes the provisions of Section 403.061(7), F.S., 

which states that “Rules... shall not require discharges of waste into waters of the state to 

improve natural background conditions.” Appendix 2B provides the table of non-thermal, 

water quality criteria associated with this rule. It should be noted that Rule 62-302.500(2)(e), 

FAC, states that the “numeric criteria are based on annual average flow conditions” unless 

listed otherwise in Rule 62-530, FAC. 

Where thermal pollution is a potential problem, the criteria listed in Rule 62-302.520, FAC, 

apply. This rule requires all existing or proposed discharges of heated water be assessed 

through a thorough study of the consequences of the heated discharge. This means that, for 

TMDL purposes, most point source thermal discharges (e.g., the cooling water discharges 

from power plants) already have been assessed by significant studies that can be used for 

establishing site-specific TMDL limitations for thermal discharges. More difficult to consider 

are the thermal discharges associated with industrial wastewater and urban stormwater. 

Where potential stress could be caused by such thermal discharges, Rule 62-302.520, FAC, 

must be consulted. 

Finally, some of the criteria are related to “natural background.” Rule 62-302.200, FAC, 

defines natural background to be “the condition of waters in the absence of man-induced 

alterations based on the best scientific information available to the Department.” 

Furthermore, the definition of natural background for a water segment may be based on “a 

similar unaltered water body or on historical pre-alteration data.” 

2.2.3 Moderating Provisions 

Associated with the designated uses and water quality criteria are the moderating provisions 

included as part of the water quality standards to provide “an even-handed and balanced 

approach to attainment of water quality objectives” [Rule 62-302.300(10)(c)]. Moderating 

provisions include “mixing zone, zone of discharge, site-specific alternative criteria, 

exemption, and equitable allocation.” These provisions are discussed below. 

Mixing Zones or Zones of Mixing. A mixing zone is defined by Rule 62-302.200(30) as “a 

volume of surface water containing the point or area of discharge and within which an 

opportunity for the mixture of wastes with the receiving waters has been afforded.” Rule 62-

4.244 considers mixing zones further by authorizing water quality in a mixing zone to be 

degraded below applicable class criteria but no worse than the minimum conditions or “free 

froms” specified under Rule 62-302.500(1). A mixing zone may not, by itself or in 

combination with others, cause significant impairment of the designated use of the water 

segment [Rule 62-4.244(1)(a)]. A mixing zone must meet the following provisions: 

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 It can not include an existing drinking water supply intake if the constituents in the zone 

would impair the use; 

 It can not include a nursery or area approved by FDEP for shellfish harvesting; 

 In canals, rivers, and streams, the maximum length of a mixing zone may not exceed 

800 meters (approximately 0.5 miles). The length of the zone is from the point of 

discharge to the point of complete mixing with the ambient waters. 

 In lakes estuaries, bays, lagoons and other coastal waters, the area of the mixing zone 

“shall be 125,600 square meters (about 31 acres) unless a lesser area is necessary to 

prevent significant impairment of a designated use.” 

 In open ocean waters, the mixing zone “shall be 502,655 square meters” (approximately 

124 acres) unless a smaller area is needed. 

 Cumulatively, mixing zones may not exceed 10 percent of the total length of a stream, 

river, canal or tributary or 10 percent of the area of a lake, estuary, bay, lagoon, bayou or 

sound. 

 Within a mixing zone, besides the minimum conditions requirements, dissolved oxygen 

can not average less than 4.0 mg/l, and turbidity may not average more than 41 NTUs 

above natural background. 

Additional mixing zone limitations related to toxicity are provided in Rule 62-4.244(3). 

Site-Specific Alternative Criteria. The State regulations recognize that, due to “natural 

conditions or man-induced conditions that cannot be controlled or abated”, a water segment 

may not meet the applicable water quality criteria [Rule 62-302.800, FAC]. “Man-induced 

conditions that cannot be controlled or abated” means “conditions influenced by human 

activities” that “would remain after removal of the point sources; would remain after 

imposition of [BMPs] for non-point sources; and cannot be restored or abated by physical 

alteration of the water body, or there is no reasonable relationship between the economic, 

social and environmental costs and the benefits of restoration or physical alteration.” Under 

these circumstances, an “affected person” or FDEP itself may petition the Secretary of 

FDEP to establish a site-specific alternative criterion based upon an “affirmative 

demonstration” that the alternative criterion is more appropriate. The demonstration must 

show that the condition is due to natural conditions or man-induced conditions that cannot 

be controlled or abated. 

According to Rule 62-302.800((4)(d), this provision does not apply to the “free froms” or to 

criteria that are related to the following parameters: 

 Bacteriological quality Oils and grease 

 Biological integrity Radioactive substances 

 BOD Specific Conductance 

 Chlorides Substances that injure or are chronically toxic 

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 Color Substances causing dominance of nuisance species 

 Detergents Total dissolved gases 

 Dissolved Oxygen Transparency 

 Dissolved Solids Turbidity 

 Nutrients Minimum mixing zone parameters 

Odor 

Rule 62-302.500(2)(f) does allow for the alteration of Dissolved Oxygen (DO) levels through 

alternative DO criteria that are based on conditions attributable to natural background or 

man-induced conditions which cannot be controlled or abated. The site-specific criteria may 

not allow a lowering of DO levels in the segment, and daily and seasonal fluctuations must 

be maintained. 

Exemptions. Rule 62-4.243 defines two exemptions that allow water segments to not 

achieve certain applicable water quality criteria. The first is for artificial water bodies that are 

classified as agricultural water supplies. The exemption applies to Class IV waters only and 

applies only for the duration of a permit issued by FDEP. The second is for water segments 

classified for “Navigation, Utility and Industrial Use.” This exemption is for Class V waters 

only and as above applies only for the duration of a permit issued by FDEP. Both 

exemptions require a petition to and approval by FDEP. 

Equitable Abatement. Rule 62-4.242(4) provides additional state policy for waters “whose 

quality has been artificially lowered below the quality to support their designated uses.” 

Under these conditions, FDEP cannot issue permits unless the permit applicant 

“affirmatively demonstrates” that water quality standards once achieved would not be 

violated due to the proposed activity, the activity is “necessary or desirable under federal 

standards,” and the activity is in the public interest. Furthermore, to equitably allocate the 

abatement responsibility, the applicant is required to petition FDEP to instigate an equitable 

allocation process by 1) identifying the contributing sources and 2) quantifying the total 

contribution and individual contributions of each source. FDEP is then to consider previously 

completed abatement programs for the water segment, the costs of alternative abatement 

techniques and the economic impacts of additional abatement on the contributing sources. 

2.3 WATER QUALITY TARGETS 

Some Florida water quality criteria are narrative (rather than numerical) or are related to a 

biological or natural condition. Under these circumstances, the TMDL analysis must focus 

on water quality targets. Water quality targets are surrogate criteria or parameters, the 

violation of which would constitute impairment of the designated use. 

For example, a surrogate may be needed for the narrative nutrients criterion that states, “in 

no case shall nutrient concentrations of a body of water be altered so as to cause an 

imbalance in natural populations of aquatic flora and fauna” [Rule 62-302.530(48)(b)]. 

Another example pertains to Biochemical Oxygen Demand (BOD). While BOD is a pollutant 

that reduces the oxygen dissolved in water, there are no specific criteria for BOD in the 

State regulations. In this case, the criteria itself identifies the target, dissolved oxygen. 

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2.3.1 Nutrients 

Nutrient enrichment is considered to be one of the primary water quality problems facing 

Florida water bodies based on previous water quality assessments. TMDLs to establish 

allowable nutrient loadings may consider water quality targets. FDEP is currently working on 

a set of numerical nutrient criteria, and the associated regulations are expected to be 

available by the end of 2006. In the interim, the following are examples of surrogate 

parameters which can be used to address the narrative criteria for nutrients: 

 For rivers, lakes and estuaries, chlorophyll ‘a’ concentrations can be directly related to 

algal biomass, which in turn is a measure of nutrient enrichment. In general, the higher 

the chlorophyll ‘a’ concentration, the more potential for unnatural alterations to the 

“natural populations of aquatic flora and fauna.” For TMDL purposes, chlorophyll ‘a’ 

concentrations may be used as a surrogate water quality target for nutrients. 

 In estuaries, the relative health and coverage of grass beds represent examples of 

surrogate measures of nutrient enrichment. For example, for the Tampa Bay National 

Estuary Program (NEP), research (TBNEP, 1999) has shown that the protected 

resource in the bay system is grass beds. Since grass beds provide spawning areas for 

juvenile fishes and are a general indication of the health of Tampa Bay, increasing the 

coverage of submerged grass beds became the target of recovery. Further studies have 

shown that nitrogen discharges induced excessive algae growth, which in turn reduced 

the light penetration from the water surface to the bottom of the Bay (as measured by 

Secchi disk depth). The studies also showed that the grass beds could potentially 

recover by increasing light penetration. Therefore, for Tampa Bay NEP, the resourcebased criteria (i.e., criteria based on the protection or enhancement of a defined 

resource) were related to the control of nitrogen. For estuaries, then, similar resourcebased criteria may be defined for nutrient control. 

2.3.2 Background Conditions 

A number of water quality criteria defined in Rule 62-302, FAC, are based on concentrations 

above either “background” or “natural background.” Rule 62-302.200 FAC defines both 

conditions: 

“(3) ‘Background’ shall mean the condition of waters in the absence of the 

activity or discharge under consideration, based on the best scientific 

information available to the Department.” 

“(14) ‘Natural Background’ shall mean the conditions of waters in the absence 

of man-induced alterations based on the best scientific information available 

to the Department. The establishment of natural background for an altered 

waterbody may be based upon a similar unaltered waterbody or on historical 

pre-alteration data. 

The parameters biological integrity, chlorides, and specific conductance are related to 

“background” levels (e.g., “the index for benthic macroinvertibrates shall not be reduced to 

less than 75% of background levels”). The parameters pH, transparency and turbidity are 

also related to “natural background” (e.g., “[pH] shall not vary more then one unit above or 

below natural background”). 

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Since TMDL load allocations must consider both nonpoint sources and natural conditions, 

an assessment of background conditions must be conducted. Where only “background” 

conditions are needed, the analysis may use long-term water quality monitoring data 

collected from a sampling station located upstream, if that station is not affected by any 

point or nonpoint source discharges. For “natural background”, the analysis needs to 

consider water quality conditions that would exist in the absence of any man-induced 

discharges to the water segment. Methods to evaluate these conditions are provided in 

Section 3 (Data Collection and Analysis) and Section 5 (Model Preparation, Verification, 

Calibration, and Validation). 

2.4 IMPAIRED WATERS RULE 

In Florida, a TMDL study is required when a water segment is determined to be impaired. 

This process has been legislatively defined by the Florida Watershed Restoration Act 

(Appendix 1B), and regulations have been promulgated under the Impaired Waters Rule 

(IWR) 62-303, FAC. Provided below is a description of the important elements of the rule. 

The rule defines methods to identify water segments requiring a TMDL. The rule is 

presented here to help the TMDL analyst understand the reasons for the listing of a 

particular water segment. 

The two-step process for the listing of impaired waters is based on the Florida Watershed 

Restoration Act (Chapter 4.3.067, FS). The first step involves developing an initial “planning 

list” that names potentially impaired waters based on existing impairment related data. The 

second step involves developing a focused list of “verified” impaired waters based on 

additional data. It is this latter list that is adopted by the FDEP Secretary and constitutes the 

required 303(d) list. TMDL studies must be conducted for the waters on the verified list. Rule 

62-303, FAC, describes the methods by which the State is to prepare the planning and 

verified lists as well as methods to delist water segments. The process for listing and 

delisting is summarized below. 

2.4.1 Planning List 

The planning list delineates the surface water bodies (or segments) for which additional 

assessments will be conducted to evaluate whether the water is impaired and a TMDL is 

needed, as required under subsection 403.067(2), F.S. A water segment is placed on the 

planning list if it is found not to meet the minimum criteria (“free froms”), designated uses, or 

water quality standards [Rule 62-303.300(1)]. Specific requirements are described in 

separate sections of the rule for each of the following designated uses: aquatic life support, 

primary contact and recreation, fish and shellfish consumption, and drinking water and 

protection of human health. The requirements are summarized below. 

Aquatic Life Support. Water segments are placed on the planning list related to aquatic life 

support (Classes I, II and III) if the segment: 

 Exceeds the water quality criteria appropriate to the classification. This is based on a 

statistical inference that 10 percent of the samples for the parameter exceed the criteria 

with an 80 percent statistical confidence. The EPA STOrage and RETrieval (STORET) 

database is the primary data source for this inference. Data from the last 10 years are to 

be used; however, more recent data may take precedence if a significant change in 

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water quality is indicated and the older data do not meet data quality requirements. A 

minimum of 10, temporally independent (1 week interval) samples within the 10-year 

period are needed for this assessment. Data collected from monitoring locations within 

200 meters of each other are considered to be from the same station unless there is an 

intervening tributary or outfall or significant change in hydrography. Sample data must be 

available for three of the four seasons for a calendar year as defined in the impaired 

waters rule. 

 Does not meet biological assessment thresholds. Bioassessments for the purposes of 

this rule include biological reconnaissance (BioRecons), Stream Condition Indices 

(SCIs) and the benthic macroinvertebrate component of the Lake Condition Index (LCI). 

Water segments that fail one or more bioassessment or biological integrity criteria are 

placed on the planning list. For this rule, “failure” means a poor or very poor rating on the 

bioassessment. 

 Is acutely or chronically toxic. Waters with two samples (taken at least 2 weeks apart) 

indicating acute or chronic toxicity in a 12-month period will be placed on the planning 

list. 

 Exceeds nutrient thresholds. To assess impairment due to nutrients in aquatic life 

supporting segments, trophic state indices (TSIs) and annual mean chlorophyll ‘a’ values 

may be used as surrogates. Other indicators such as algal blooms, excessive 

macrophyte growth, decrease in seagrass distribution (density or areal extent), changes 

in algal species richness and excessive diel oxygen ranges may also be considered. A 

stream segment is placed on the planning list if algal mats are present in quantities 

sufficient to produce a nuisance, the annual mean chlorophyll ‘a’ concentration is greater 

than 20 μg/l, or the annual mean chlorophyll ‘a’ concentration increases by more than 50 

percent over historical values for at least two consecutive years. Lakes are placed on the 

planning list if the TSI exceeds 60 for lakes with color greater than 40 platinum-cobalt 

units (PCU); the TSI exceeds 40 for lakes with color less than or equal to 40 PCU; or for 

any lake, the annual mean TSIs have increased by more than 10 units over historical 

values. The TSI targets can be increased if paleolimnological data exist indicating 

historically higher TSI data. Finally, estuaries are placed on the planning list if the annual 

mean chlorophyll ‘a’ for any year exceeds 11 μg/l or if the annual mean chlorophyll ‘a’ 

has increased by 50 percent over historical values for at least two consecutive years. 

Primary Contact and Recreation Use Support. Water segments in Class I, II or III are 

placed on the planning list if: 

 The segment does not satisfy applicable water quality criteria for bacteriological quality. 

 The segment includes bathing areas that were closed by a local health department for 

more than one week or more than once over a year due to bacteriological data. 

 The segment included bathing areas for which closures, advisories or warnings were 

issued by a local health department for 21 days or more during a calendar year based 

on bacteriological data. 

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 The segment includes bathing areas that were closed or had advisories or warnings for 

more than 12 weeks of a calendar year based on previous bacteriological data or 

derived relationships between bacteria levels and flow or rainfall. 

Closures, warnings and advisories due to non-chronic events such as red tide, rip tides, 

spills, sharks, hurricanes or other factors not related to pollutant discharges are not used to 

identify water segments for the planning list. 

Fish and Shellfish Consumption Use Support. Class I, II or III water segments are placed 

on the planning list if: 

 The segment does not satisfy applicable Class II criteria for bacteriological quality. 

 There is either a “limited consumption” or “no consumption” advisory issued by an 

authorized agency. 

 The segment includes a Class III water body that, although once approved for harvesting 

at some previous time has since been downgraded to a more restrictive classification. 

Downgrading from “restricted” to “unclassified” does not constitute impairment. 

Drinking Water Use Support and Protection of Human Health. A Class I water segment 

is placed on the planning list if: 

 The segment does not satisfy applicable Class I water quality criteria. 

 A public water system demonstrates that treatment costs have increased by 25 percent 

or more to treat contaminants or algae in the source water or has changed to an 

alternative water supply because of the treatment costs of the original source. 

 The water segment does not satisfy applicable human health-based criteria expressed 

as maximums. 

 The water segment has average annual water quality that does not satisfy the annual 

average human health-based criteria. 

2.4.2 Verified List 

The verified list identifies the impaired water bodies or segments for which TMDLs will be 

calculated, as provided in subsection 403.067(4), F.S., and which will be submitted to EPA 

pursuant to paragraph 303(d)(1) of the CWA. The verified list is a subset of the planning list. 

After the planning list is prepared, additional data are compiled to further assess the 

identified waters leading to the verified list. Except for data used for historical comparisons, 

only the most recent 7.5 years of data are to be used for the verified list assessment. Similar 

to the planning list, the requirements for placing a water segment on the verified list depend 

on the designated uses as discussed below. 

Aquatic Life Support. A water segment on the planning list is placed on the verified list if 

the segment: 

 Exceeds the water quality criteria appropriate to the classification. For the verified list, 

FDEP must re-examine the data used for the planning list augmented with newer data 

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collected by FDEP or other local agencies. If the exceedances are not a result of 

pollutant discharges and are related to either physical alterations that cannot be abated 

or to natural background, the segment will not be placed on the verified list. If less than 

20 samples were used to put the segment on the planning list, additional samples must 

first be collected in order to have at least 20 samples for the assessment. As with the 

planning list, if the water segment exceeds the applicable water quality criteria for the 

classification, it will be placed on the verified list. This is based on a statistical inference 

that 10 percent of the samples for the parameter exceed the criteria with a 90 percent 

statistical confidence. Data from the preceding 5 years are used for this assessment. If 

worst-case data were used to place the segment on the planning list (i.e., the worst data 

were used from a common site collected within a small window of time), the FDEP must 

re-evaluate the validity of these data. 

 Does not meet biological assessment thresholds. Water segments that fail two or more 

bioassessment or biological integrity criteria within five years preceding listing on the 

planning list are placed on the verified list. For this rule, “failure” means a “poor” or “very 

poor” rating on the bioassessment. If there were less than two bioassessments during 

the preceding five years, then additional bioassessments must be conducted. If other 

indicators of biological impairment are used to place the segment on the planning list, 

then additional bioassessments of the segment must be conducted. Failure of the new 

bioassessments provides confirmation that the segment must be placed on the verified 

list. However, the pollutant causing the failed bioassessment must be identified before 

the water body can be included on the verified list. 

 Is acutely or chronically toxic. Water segments are placed on the verified list if acute 

toxicity data were the basis for placing the segments on the planning list, or if chronic 

toxicity data were used, the impairment is confirmed by a failed bioassessment 

completed within 6 months of chronic toxicity data. Toxicity due to spills, upsets, 

bypasses or rainfall in excess of the 25-year, 24-hour storm event are to be excluded 

from the assessment. 

 Exceeds nutrient thresholds. If the data used to place the segment on the planning list 

were collected during the last 5 years, along with historical data that were sufficient to 

meet the data sufficiency requirements of the planning list rules related to nutrients, then 

the segment will be placed on the verified list. If the data are not sufficient, sufficient 

additional data must be collected and a re-evaluation is required. Data collected for the 5 

years prior to the planning list assessment are to be used for the verified list 

assessment. 

Primary Contact and Recreation Use Support. Water segments in Class I, II or III are 

placed on the verified list if it is verified that the local closures, warnings or advisories have 

been due to chronic pollutant discharges (e.g., not red tides, storms). After re-evaluation of 

the dataset that excludes non-discharge related closures, the segment will be placed on the 

verified list if it still meets the criteria identified for the planning list. If the segment was 

placed on the planning list due to exceedances of the bacteriological criteria, the source of 

the contamination will be evaluated to confirm that the source is not natural background (i.e., 

bacteria sources are sanitary sewer leakage, septic tank seepage, or domestic animals). 

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Fish and Shellfish Consumption Use Support. For the verified list, fish consumption data 

for the segment are re-evaluated to confirm that the fish consumption advisories were 

based on evaluation of fish tissue from at least 12 fish, the data were collected according to 

FDEP standard practices, and there are sufficient data within 7.5 years of placing the water 

body on the planning list. If each of these conditions is confirmed, the water segment is 

placed on the verified list. 

Drinking Water Use Support and Protection of Human Health. If data from the last 5 

years were determined to be insufficient, additional health-based data are to be collected 

and re-evaluated. If the water segment is still found to exceed the conditions identified for 

the planning list, then the segment is placed on the verified list. 

The Impaired Water Rule provides additional information related to impaired waters 

prioritization and evaluation of existing pollution control measures. Water segments 

identified on the verified list are prioritized according to the severity of impairment of 

designated uses, accounting for serious water quality problems, valuable and threatened 

resources, and risk to human health and aquatic life. Highest priority is given to segments 

listed due to threats to potable water or human health and to segments with impairment 

associated with the federally listed threatened and endangered species. Lowest priority is 

given to segments with impairment due to mercury-related fish consumption data prior to 

2010, human-made canals and ditches impaired due to dissolved oxygen and segments 

identified after the development of the planning list (unless the impairment is related to a 

high priority issue). The rest of the segments are considered medium priority, and these are 

prioritized based on OFWs, multiple designated use impairment, water quality criteria 

exceedance of 25 percent of the samples with a 90 percent confidence, multiple criteria 

exceedances, and FDEP TMDL priorities. 

2.4.3 Pollution Control Mechanisms 

Since many governments and private agencies in Florida already have embarked on major 

pollution control plans and strategies, the IWR contains a provision [Rule 62-303.600] that 

allows FDEP to consider whether the plan would be likely to result in the attainment of water 

quality standards within the impaired water body or segment. If FDEP determines that 1) the 

water segment is expected to attain water quality standards in the future; and 2) “reasonable 

progress” toward attainment is expected to be achieved within 5 years (i.e., the next time the 

water segment is considered in the basin rotation schedule), then the water segment is not 

to be listed as a verified water segment for TMDLs. Water segments that are already making 

reasonable progress toward achieving the applicable water quality standards do not need to 

be restudied under a TMDL process. 

The key to this provision is that there must be “reasonable assurance” that the proposed 

pollution control measures (plan or strategies) would indeed be very likely to result in 

attainment of the water quality standards for the segment. To more closely define this 

phrase, FDEP issued a draft memorandum providing guidance to address reasonable 

assurance for such pollution control measures. A very important consideration of this review 

by FDEP is that the pollution control program must be “under local, state, or federal 

authority” to assure that the program is implemented. The guidance states that this provision 

would be satisfied if the following conditions occur: 1) the program is subject to or required 

by ordinance, regulation, rule, or law; or, 2) the program is subject to a written agreement 

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signed by local stakeholders and one or more governmental entities. The agreement must 

include measurable goals, performance criteria, benchmarks, and a corrective action plan. 

Documentation of reasonable assurance must include the following elements: 

 A description of the impaired water segment including the type of water, description of 

the impairment and the suspected source(s) of the impairment; 

 A description of the water quality or ecological goals to which the water segment is to be 

restored including a schedule when the goals are to be met; 

 A description of the pollution control actions that will be implemented including the 

responsible participating agencies, the actions to be taken, the estimated pollution load 

reductions, written agreements and sources of funding; and, 

 A description of corrective actions to be taken in the event water quality does not 

improve to the degree or according to the schedule defined. 

2.4.4 Delisting 

The last major element of the Impaired Waters Rule is the process for removing a stream 

segment from the TMDL program. This process is known as delisting. The fundamental 

premise is that waters that are “verified to not be impaired during development of the verified 

list shall be removed from the State’s planning list” [Rule 62-303.720(1)]. There are two 

ways to delist a water segment from the planning list: 1) completion of a TMDL for all of the 

pollutants for which the segment was placed on the planning list in the first place, or 2) 

verification that the water quality of the segment under scrutiny meets applicable water 

quality standards. As with the listing procedure, the procedure for delisting is based on an 

assessment of the segment’s ability to support the designated uses. 

 For waters that were originally listed for failure to meet aquatic life support: 

 For listings that were based on water quality exceedances, the segment is 

delisted when: the number of exceedances indicate less than a 10 percent 

exceedance frequency with a minimum of a 90 percent statistical confidence 

(minimum sample size is 30 samples); following implementation of pollution 

control activities, new water quality data show that the segment no longer meets 

the conditions for listing; or demonstration that the water segment was 

inappropriately assessed during the original listing process and reanalysis of the 

data shows that the segment no longer meets the conditions for listing. 

 For listings that were based on biological data, the segment is delisted when it 

passes two independent bioassessments with no failed bioassessments for at 

least one year. 

 For listings that were based on toxicity data, the segment is delisted when the 

segment passes two toxicity tests with no failed toxicity tests for a year. 

 For listings based on fish consumption advisories, the segment is delisted when the 

advisory is lifted or removed and the reasons for the advisory are no longer applicable. 

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 For listings based on shellfish, the segment is delisted when the segment is reclassified 

to its original or higher harvesting classification. 

 For listings based on bathing closures or advisories, the segment is delisted when the 

listing thresholds do not apply for five consecutive years. 

 For listings based on threats to potable water supplies, the segment is delisted when 

applicable water quality criteria are met (see sampling requirements above) and the 

costs for higher treatment are removed. 

 For listings based on human health based annual criterion, the segment is delisted when 

there are no exceedances of the criterion for a period of three consecutive years (see 

sampling requirements above). 

 For listings based on nutrient impairment, the segment is delisted if the listing criteria are 

not exceeded for a period of three consecutive years (see sampling requirements 

above). 

 Finally, for any listed waters, segments can be delisted if there is a change in the 

approved analytical procedures that results in the segment no longer meeting the listing 

criteria. 

The FDEP Secretary must issue an order to approve the decision for the segment to be 

formally delisted. 

  

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Section 3 

DATA ACQUISITION AND ASSESSMENT 

Yes

Yes 

Proceed to 

Element IV 

Data 

Adequate 

for TMDL 

Use Existing 

WBID Data

No

Yes

Yes

No

Clarification/Liaison/Status 

Meeting 

Update the 

WBID Dataset 

Review 

Comments 

TARC 

Review 

8

7

Assessment of 

Initial WBID Data 

Set 

Supplemental 

Data Collection or 

monitoring for 

WBID 

Uncertainty 

Analysis and 

Data Needs for 

Selected Model

Anticipated 

Uncertainty of 

TMDL 

Assessment of

Watershed and 

Receiving 

Water Models

Watershed and 

Receiving Water 

Model 

Recommendations 

Tech Memo

Data Adequacy and 

Supplemental 

Collection Needs 

Tech Memo

Supplemental 

Data Collection 

3

4

5 Tech Memo

No

FDEP 

Decision 

Adequate 

Data 

FDEP 

Decision: 

Adequate 

Data? 

No

Collect 

Supplemental 

Data 

6

2

FDEP 

Acceptance 

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3.1 INTRODUCTION 

The purpose of this section is to describe the data that may be needed to conduct a TMDL 

assessment of specific pollutants for specified water segments. Data are required for the 

estimation of point and nonpoint pollution loadings and receiving water effects. This section 

presents a wide range of data types to be considered; however, not all may be required for 

specific application, and in some cases other specific data may be needed that are not listed 

here. Typically, both water quantity and quality data are needed, as well as data related to 

pollutant sources, inputs, background and natural sources, atmospheric deposition, 

geomorphic conditions and other pertinent information. Table 3A.1 in Appendix 3A 

provides an inventory of data types and sources that may be used in a TMDL study. 

This section provides potential sources of pertinent data to support TMDL assessments and 

provides general protocols for compiling, generating, and analyzing these data. Data may be 

required for multiple purposes. For example, water quality data may be used to develop 

model coefficients, calibrate and validate model results, and provide a basis for pollutant 

loading reduction assessments. During many TMDL modeling studies, the need for 

additional data can be iterative. Initial data collection efforts may be sufficient to develop a 

TMDL model that allows screening-level load allocations. As the TMDL is refined and 

targeted to specific pollutant processes, additional data collection and/or analysis may be 

required to refine modeling efforts aimed toward identifying the amount of pollutant reduction 

required to achieve water quality standards. At each step, the TMDL analyst must evaluate 

whether the available data are adequate to support the level of modeling required to develop 

the TMDL. The TMDL analyst also must consider data uncertainty and accuracy. The TMDL 

process includes sensitivity and uncertainty analysis to help define a margin of safety (MOS) 

to account for uncertainty in the data and in the models that are prepared from these data. 

Data are available in various forms and spatial and temporal scales. Daily time series data 

are often available for many hydrologic and water quality parameters. However, for TMDL 

assessments, rainfall, stage, flow, and velocity may be needed at smaller time scales (e.g. 

hourly). Therefore, daily time series data as well as more frequent data time series may be 

considered for acquisition and evaluation. All data used in TMDL assessments may be 

inspected and evaluated for reasonableness and quality (check ranges of typical values and 

attempt to verify extremes and outliers). 

The first major step in the TMDL process is to identify the relevant and necessary data. The 

next step is to collect and analyze existing data including identification of existing models 

and studies for an area. Much of the required data may already be available through 

Federal, State, city, county, and private entity files and may have already been analyzed 

and assessed using the methods consistent with the principles described in this document. 

For this reason, the initial effort for a TMDL assessment is generally a data definition, 

collection, and evaluation task to obtain locally available pertinent watershed and receiving 

water data and to maximize, to the extent practical, the use of these data throughout the 

TMDL development process. In some cases, TMDLs will be developed with a concurrent 

stakeholder outreach process. The stakeholder outreach process for the TMDL program 

should start well before the definition of TMDL loading reductions in order to facilitate the 

initial data collection efforts. 

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This section presents water quantity and water quality data and methods to support TMDL 

analyses. 

3.2 WATER QUANTITY DATA 

Water quantity and water quality data are required to estimate pollutant mass loadings from 

the watershed to receiving water bodies. This section establishes protocols for determining 

and analyzing water quantity data as a part of the TMDL process. 

3.2.1 Streamflow Data 

Understanding the hydrologic cycle and different flow regimes under dry, average, and wet 

weather conditions in natural and manmade systems is critical to reliably estimate pollutant 

load transport and deposition. Flow is rarely measured directly. It is generally estimated by 

measuring water depths and velocities for given cross-sections to produce rating curves. 

The longest-term or most complete data set available may be considered for TMDL analysis 

because a longer period of record will better account for year-to-year or month-to-month 

hydrologic variability. In some cases use of shorter-term current data may be more 

representative, particularly if there have been recent changes in the upstream watershed 

that might affect flows (e.g., new reservoirs or flow diversions). In the absence of reliable 

historical data, alternate methods for flow estimation may need to be employed. These may 

include: 

 Application of regression equations for estimating flow in ungaged areas; 

 Tributary area ratio techniques; or, 

 Development of hydrologic models to synthesize (estimate) flow records. 

Flow data for riverine (non-tidal) systems in Florida are available from a number of sources, 

including United States Geological Survey (USGS), Water Management Districts (WMDs), 

and County, City or privately operated gages. Potential data sources listed above are 

discussed below in the general order of preference/reliability. 

3.2.1.1 United States Geological Survey 

Where available, data from USGS streamflow gaging stations are typically the first choice for 

long-term records of daily average stream flow. The USGS subjects all officially published 

data to rigorous review. The United Stated Geological Survey (USGS) Office of Surface 

Water (OSW) currently publishes streamflow data for 619 locations in Florida. Time series 

datasets are retrievable online. Many sites have historical streamflow data spanning 

decades and up-to-the-hour real time streamflow data are also available for many sites 

(http://nwis.waterdata.usgs.gov/usa/nwis/discharge). Data may also be obtained from the 

USGS National Streamflow Information Program (NSIP) web site 

[http://water.usgs.gov/nsip], through review of annually published Water Resources Data 

Reports, by request from local USGS offices, and through EarthInfo data CDs or web site 

[http://www.earth-info.org]. In some cases more recent data, including real time data, can 

also be obtained from the USGS. However, some such data may not have undergone the 

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full quality-control review. Such data are released by the USGS as “provisional” (i.e., subject 

to revision) and must be used with caution. Using provisional data for TMDL analyses could 

increase the level of uncertainty in pollutant loading estimates and could require a greater 

margin of safety. This may be a tradeoff with the need to use data that are applicable to 

current land use and hydraulics conditions where major changes could make calibration 

difficult and/or incorrect. Figure 3.1 and Figure 3.2 show plots of stage and flow data, while 

Figure 3.2 includes a comparison from a hydrologic model to the USGS gage data 

(sometimes called measured). 

Figure 3.1 Example Plot of Stage versus Time and Rainfall 

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Figure 3.2 Example Plot of USGS Daily Streamflow 

3.2.1.2 Water Management and Flood Control Districts 

Florida’s WMDs participate in water resource management activities that include the 

installation and monitoring of stream-gaging stations. WMDs publish periodic Surface Water 

Reports that present collected data. Some of the WMDs also maintain databases that 

include information from other sources, such as the U.S. Army Corps of Engineers 

(USACE), USGS, National Oceanic and Atmospheric Administration (NOAA), FDEP, and 

county/city/private data collection efforts. Data are subject to revision and are not 

guaranteed to be up-to-date. However, there is a wealth of information available for 

establishing general flow trends and stage boundaries or trends for a variety of Florida 

streams and rivers. The following five WMD data sources can be accessed from the internet 

by simple keyword searches. Data requests also can be made via telephone, fax or e-mail 

requests to the applicable District. 

 Northwest Florida Water Management District – Relies on USGS and USACE, Mobile 

District data collection 

 Suwannee River Water Management District – Keyword “Water Data” 

 St. Johns River Water Management District – Keyword “Hydrologic Conditions and 

Controlled Water Levels” 

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 Southwest Florida Water Management District – Keyword “Hydrologic Data” 

 South Florida Water Management District – Keyword “DBHYDRO database” 

3.2.1.3 County/City/Private Data 

Data collected by local governments or private entities can be useful for supplementing 

other primary data sources. Local data collection efforts tend to be shorter-term and more 

narrowly focused in scope. Data are often collected and reported by professional 

consultants or by trained municipal staff. In some cases, trained citizen volunteers are used 

for data recording. Such data may be treated as provisional and TMDL pollutant loading 

estimates based on these data may have a higher level of uncertainty. 

3.2.1.4 Flow Data Collection 

In the absence of historical data, a flow measurement and data collection program may be 

required. Short-term flow data may be collected during the preparation of the TMDL; 

however, such data would cover only a limited period of time and generally would not 

sufficiently characterize long-term hydrologic variability. In these cases, the TMDL 

implementation plan may require development and implementation of a long-term flow data 

collection program. 

Efforts to obtain and manage field measured flow data should carefully follow standard field 

protocols such as those established by the USGS or Bureau of Reclamation, as 

documented in the publications referenced below: 

 “Techniques of Water Resources Investigations of the United States Geological Survey, 

Discharge Measurements at Gaging Stations, General Procedure for Gaging Streams, 

and Stage Measurements at Gaging Stations”; and, 

 “Department of the Interior Bureau of Reclamation Water Measurement Manual.” 

Field instream flow measurements generally rely on probes and dataloggers that 

continuously measure and record depth or velocity. A channel rating curve must be 

developed that relates flow depth to discharge. Application of the channel rating 

relationships to depth measurements can be used to develop estimates of continuous 

discharge records. It is preferable to obtain channel/rating field measurements at various 

flow levels including both dry- and wet-weather conditions. This is not, however, always 

practical and estimates of flows, particularly high flows, are commonly slope-area 

relationships or other indirect measurements. The following USGS Techniques of Water 

Resources Investigations may be consulted (http://usgs.gov): 

 Measurements of Peak Discharge by the Slope Area Method 

 General Field and Office Procedures for Indirect Discharge Measurements 

 Measurement of Peak Discharge at Width Contractions by Indirect Methods 

 Measurement of Peak Discharge at Dams by Indirect Methods 

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 General Procedure for Gaging Streams 

 Stage Measurement at Gaging Stations 

 Discharge Measurements at Gaging Stations 

 Measurement of Time of Travel and Dispersion by Dye Tracing 

3.2.1.5 Flow Data Analysis 

Flow data obtained using the methods cited above may undergo an analyses process 

consisting of the following steps: 

1. Evaluation of the data set for completeness and consistency 

2. Filling data gaps 

3. Time series analysis 

4. Baseflow analysis 

5. Determination of hydrological characteristics 

6. Development of coefficients and data required for modeling 

Described below are the required steps for the above flow data analyses. 

Evaluation for Completeness and Consistency 

Data may be collected electronically where feasible, or data may be entered in a database if 

obtained in a hard-copy format. The database may be checked for completeness of record 

and statistically analyzed. Simple x-y plots of flow hydrographs are often the best initial 

check for data completeness and consistency. In addition simple statistics such as means, 

standard deviations, maximum and minimum values can be used to determine data outliers, 

and for comparison with other streamflow or rainfall data to ensure that the data make 

“hydrological sense.” For example, such a review of data would expect to indicate that large 

rainfall events that are evenly distributed over a watershed may result in large streamflow 

events, and large streamflow events in upstream tributaries of a river system may result in 

large streamflow events in a downstream channel of the same river system. Other factors, 

such as antecedent moisture conditions may influence these general relationships and may 

be considered when evaluating hydrologic data. 

Actual or even apparent inconsistencies can serve as “red flag” indicators of problems with 

data. Plots that superimpose time-series of streamflow and rainfall data will facilitate data 

comparisons and data quality assessments. Plots that superimpose the daily or monthly 

means, maximums, minimums, and standard deviations can also be developed for rainfall 

and streamflow for the study watershed or for watersheds within the region that have similar 

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tributary areas and land use patterns. Estimates of monthly or annual runoff volumes may 

also be converted to units of depth (e.g., inches of water depth over the entire tributary area) 

for comparison with runoff measured at other gages or incipient precipitation. Typically, the 

annual runoff volumes should not exceed rainfall volumes unless large springs are feeding 

groundwater inflows from adjacent basins. 

Data found to be inconsistent, without a plausible explanation, may be flagged and may 

need to be eliminated from the database. Inconsistent data typically include flow volumes 

that exceed monthly or annual precipitation volumes or flows that do not appear to be a 

response to a precipitation event or a known discharge in the upstream watershed. 

Potential Hysteresis 

Florida has a relatively high potential for hysteresis (more than one value of streamflow 

possible for a given stage) in stage-discharge datasets due to a relatively flat topography, 

urbanization and locations with backwater potential. For a given stage, the expected 

discharge may vary depending on whether the measurement was taken on the rising or 

receding limb of the event hydrograph and the degree of tailwater. This is also called a 

“looped rating curve”. A highly varied flow regime can result in even wider variety of 

elevations that correspond to a given discharge magnitude, resulting from changes in water 

surface slope due to complex storage and constraint relationships with upstream and 

downstream conditions (Bedient and Huber, 1992). This is most evident in urban areas with 

relatively flat terrain where significant variations in headwater and tailwater conditions can 

occur. 

For locations with hysteresis potential, additional information regarding the instrumentation 

is in order. The USGS typically does not measure flow directly at many sites where the 

agency collects streamflow data. Pressure transducers are used to measure stage and 

estimate flow based on a site-specific physically determined stage-discharge relationship. 

Standard protocol requires collection of a significant number of stage-discharge data pairs 

across a range of flow events. Potential hysteresis is accounted for by conducting separate 

rising and falling hydrograph analysis, and by incorporating stage rate of change into the 

development of the rating curve. These are used to create regression relationships between 

stage and flow. In practice, data are seldom available to create a comprehensive 

relationship incorporating multiple independent variables and differentiating low flow, 

bankfull, and flood conditions. 

For larger rivers and areas with identified hysteresis, multiple velocity meters or acoustic 

Doppler technologies are also employed by the USGS in conjunction with pressure 

transducers to develop velocity-discharge relationships. For these locations, the implications 

of unidentified hysteresis are less pronounced. If hysteresis is evident, the TMDL analyst 

should calibrate to stage, velocity, and flow where such data are available. 

Figure 3.3 shows an example of a mild hysteresis condition compared to a rating curve as 

derived from a calibrated hydraulic model to measured stages. One option in this case 

would be to adjust the rating curve using a hydraulic model (e.g., SWMM EXTRAN or HECRAS) with consideration of measured depths to better average the “looped data” for flows. 

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Figure 3.3 Florida Example of Flow Hysteresis

Filling Data Gaps 

Correlation or hydrologic synthesis techniques may be considered as a means to address 

incomplete hydrologic records or data gaps if this would result in more accurate TMDL 

analysis. If long-term records of streamflow data are available with very few missing data, 

the data may be adequate to complete the analysis described in this section, and filling of 

data gaps may not be necessary. However, if filling data gaps could significantly extend the 

time series to provide a more representative long-term hydrologic record, then gaps may be 

filled. Standard statistical or linear interpolation/extrapolation techniques may be used. 

Correlation methods include the following: 

 Precipitation-runoff relationships, 

 Ratio of tributary (drainage) areas method, 

 Correlation of records from similar gaging stations, and 

 Comparison of flow duration curves 

More sophisticated hydrologic synthesis techniques include: 

 Markov process or autoregressive models, and 

 Moving average models. 

Detailed procedures to implement each method are well documented in standard hydrology 

handbooks (e.g. Handbook of Hydrology, D.R. Maidment, McGraw-Hill, 1993). 

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The statistical techniques can be conducted in spreadsheet or statistical software packages. 

For example, regression analyses can be conducted using the streamflow data (with 

missing data) as the dependent variable, and corresponding streamflow data for another 

station as the independent variable. The number of paired data points to be used in the 

regression analysis may be statistically significant. The regression analysis will result in a 

correlation coefficient (e.g., “R”) or coefficient of determination (R2

) that may be tested for 

statistical significance. If the correlation is determined to be significant, the regression 

equation may be used to approximate the missing values. Figures 3.4a and 3.4b show 

example plots comparing two gages in South Florida and Central Florida, respectively. 

y = 1.6127x + 4E+07

R2

 = 0.9716

0.0E+00

1.0E+09

2.0E+09

3.0E+09

0.0E+00 5.0E+08 1.0E+09 1.5E+09 2.0E+09

Cumulative flow at S-308 (acre-ft/year)

S-80=f(S-308) Linear (S-80=f(S-308))

Cumulative Flow at S-80 (acre-ft-year)

Figure 3.4a Cumulative Flow Comparison of Two Gages 

0

1000

2000

3000

4000

5000

6000

7000

0 1000 2000 3000 4000 5000 6000 7000 8000

USGS Cocoa Daily Flow (cfs)

USGS Christmas Daily Flow (cfs)

Figure 3.4b Paired Daily Flow Comparison of Two Gages 

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Time Series Analysis 

If long-term streamflow records are available, a time series analysis may be conducted to 

determine if there are trends in the data. Trends may indicate changing conditions in the 

watershed that could affect TMDLs. For example, changing land use may cause increased 

flow rates, velocities, and pollutant loads. As a result, even if concentrations of water quality 

constituents were to remain constant, pollutant loading to a waterbody may be increasing. 

The establishment of TMDLs needs to take such factors into consideration. 

Time series analysis may be conducted on annual time series of data and can be 

accomplished by using statistical software packages with procedures such as regression or 

Kendall-Tau, or by regression analysis in a spreadsheet platform. Trend analyses may be 

conducted with the year as an independent variable and flow as a dependent variable. 

Regardless of the procedures used, the results must be tested for significance. Other 

methods include Spearman, Mann-Whitney, and Pearson Chi-square (Snedecor and 

Cochran, 1973). These methods test the association between variables via cross-tabulation. 

Figure 3.5a and Figure 3.5b show example time series analysis by use of frequency 

distributions, illustrating the distribution of probabilities for a range of flows being equaled or 

exceeded during a given year and for a given day, respectively. 

 

Figure 3.5a Example Average Flow Exceedance Curve 

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0

1000

2000

3000

4000

5000

6000

7000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Time Flow Exceeded

Daily Flow (cfs)

USGS Christmas

Figure 3.5b Example Daily Flow Duration Curve 

Baseflow Analysis 

Baseflow analysis is required to distinguish between the in-stream effects of surface runoff 

and groundwater flow, in preparation for modeling analysis. Standard techniques that are 

presented in hydrology textbooks may be used. The USGS has a computer program called 

HYSEP, which performs baseflow separation for its gage data 

(http://water.usgs.gov/software). 

Estimation of Hydrologic Characteristics 

Definition of hydrologic characteristics may provide an understanding of the principal 

hydrologic processes that take place in the TMDL study watershed. Local hydrologic 

characteristics may be used to develop a framework for analyses and modeling. Prior to 

conducting modeling studies, it is important to thoroughly understand the hydrologic 

processes that pertain to the local environment that must be reflected in the development of 

a model framework, input, and simulation scenarios for modeling. The selection of an 

appropriate model for a TMDL analyses is dependent not only on the project requirements, 

but also on the data available for model calibration. 

Streamflow data may be compared with available data regarding rainfall, geology, soils, 

meteorology, and groundwater. Characteristics such as closed basins, sinkholes and 

springs that can complicate typical runoff/rainfall relationships may be investigated. 

Seasonal attributes may be determined by examining monthly flow averages. The tools for 

examining and properly accounting for local hydrological characteristics generally would 

include the following: spreadsheets, published maps including geologic maps (hydrologic, 

hydrogeologic, climatologic, topographic) and other computer software. These tools can be 

used to develop runoff/rainfall coefficients, flow duration curves, low-flow and high-flow 

frequency analysis, or other indices of flow that describe the hydrological system. Maps may 

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be used to identify locations of sinkholes, springs, runoff, rainfall patterns, potentiometric 

surfaces and water table elevations, and aquifer properties. 

If adequate streamflow data are available at the point of interest where the river enters the 

receiving waterbody, analyses described above may be adequate for defining flow 

conditions for a TMDL. If the point of interest is not gaged, but a large portion of the 

watershed is gaged, simple extrapolation techniques (e.g., multiplying streamflow by 

tributary area ratios) may be used to simulate a time series of flow data for use in 

establishing TMDLs. If a large portion of the watershed is not gaged, then modeling will be 

necessary. Typically, modeling is necessary because of a limitation of data. For TMDL 

refinement, additional supplemental flow monitoring may be conducted to check or verify 

this extrapolation. 

Development of Hydrologic Coefficients and Data for Modeling 

Definition of hydrologic characteristics will assist in developing an appropriate model 

framework and coefficients for modeling. The coefficients and data depend on the model 

framework but could include runoff/rainfall coefficients, infiltration rates, land slopes, channel 

slopes, soil moisture antecedent conditions, curve numbers, unsaturated soil storage 

values, roughness coefficients, and universal soil loss equation coefficients. Appropriate 

hydrologic coefficients must be selected so that the model simulates flows that reasonably 

mimic the watershed. Sections 3.4, 5, and 6 discuss model coefficients and data in more 

detail. 

3.2.2 Stream Stage, Elevation and Velocity Data 

For the TMDL process, stage and elevation data are used primarily for estimating flow by 

the development of stage-discharge rating curves as described below. Stage is usually 

expressed as an elevation of the water surface above a common datum (e.g., National 

Geodetic Vertical Datum [NGVD]). Flow depth is the difference between the stage and the 

elevation of the channel bottom at the point of interest. For specific points where a time 

series of stage and surveyed cross sections are available, a time series of depth can be 

developed and statistically analyzed. A depth-duration curve may be developed to show the 

percentage of time that specific depths of stream flow are exceeded or equaled in a system. 

The procedure may be accomplished in a spreadsheet that has an option to rank and 

simulate percentiles of exceedances. Stage data also can be used to define model 

boundary conditions, and to establish water column depths and subsequently depthdependent lighting conditions for aquatic plant use. Velocity is also sometimes measured 

continuously and provides a valuable parameter to apply with flow depth and area to 

calculate flow and/or rating curves. 

Stream stage data may be obtained from the same sources previously recommended for 

streamflow data. Important factors include the proximity of the monitoring locations to the 

study area, length of record (long-term), and whether available records reflect current 

watershed conditions. Potential data sources are discussed below in their general order of 

preference/reliability. 

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United States Geological Survey - USGS data from recording gage stations are less 

available for lakes than for streams. Where available, lake stage data are reported by USGS 

as a water surface elevation above some reference datum. Measurements are typically 

instantaneous readings (not continuous records). The USGS subjects all officially published 

data to rigorous review. Lake stage data may be obtained from the USGS National 

Streamflow Information Program (NSIP) web site [http://water.usgs.gov/nsip], but are more 

easily found through review of annually published Water Resources Data Reports, by 

request from local USGS offices, or through EarthInfo data CDs or web site 

[http://www.earth-info.org]. 

Water Management and Flood Control Districts - Water Management District staff gages 

and control structures provide much of the available historical data regarding lake stages. 

WMDs publish periodic Surface Water Reports that present collected data. Some of the 

WMDs also maintain databases that include information from other sources such as the 

USACE, USGS, NOAA, FDEP, and County/City/Private data collection efforts. Data are 

subject to revision and are not guaranteed to be up-to-date. Water Management District 

data sources can be accessed from the Internet by a simple keyword search as described 

previously. Data requests also can be made by telephone, fax or e-mail request to the 

applicable District. 

County/City/Private Data - Data collected by local governments and private entities can be 

useful for supplementing other primary data sources. Local data collection efforts tend to be 

shorter-term and more narrowly focused. Such data are often collected and reported by 

consultants or by trained municipal staff. In some cases, trained citizen volunteers are used 

for data recording. 

Field Measured Data - Measurement protocols for stage measurement in lakes are similar to 

those described in Section 3.2.2 for riverine systems. 

3.2.2.1 Field Measured Data 

It is important that stage measurements at stream gaging stations be conducted over an 

adequate data collection period to characterize hydrologic variability. Typically, at least one 

year of data are required to define seasonal characteristics, but monitoring periods of 

several years are typically more appropriate. 

Stage may be measured by standard protocols established by the USGS and described in 

documents such as “Techniques of Water Resources Investigations of the United States 

Geological Survey, General Procedure for Gaging Streams, and Stage Measurements at 

Gaging Stations” (USGS). Stage measurements may be coupled with rating curves and flow 

measurements to provide estimates of continuous discharge records. It is preferable to 

obtain stage measurements at various flow rates that encompass a wide spectrum of dryweather and wet-weather flow conditions for the system. However, this is not always 

practical and estimates of stage (particularly at high stages) are made from historical flood 

levels as indicated by high water marks. Valuable information can sometimes be obtained 

from residents who are able to recall landmarks that indicate the maximum or minimum 

water levels experienced during historical floods or droughts. USGS “Techniques for Water 

Resources Investigations” may be consulted. 

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3.2.3 Lake Volume, Depth, and Residence Time 

Lake depth data are used to determine the volume of water in a lake and to establish depthdependent light penetration conditions that are used in more complex water quality models. 

Depths are calculated as the difference between the water surface elevation and the 

elevation of the bed of the lake at a given location. Lake volume is important in determining 

residence times, mixing values, and concentrations. For example, a small lake with long 

residence times may not have sufficient assimilative capacity for a large pollutant loading 

mass without resulting in exceedances of water quality standards. 

As discussed previously, lake elevation (static water) data may be obtained from the USGS, 

WMDs, and County or City operated gages. Longest-term and most complete record sets 

may be used. Large lakes may have significant wave action which can affect gage results 

(e.g., Lake Okeechobee). 

Residence time is an estimate of the length of time that inflowing waters and associated 

pollutants are likely to remain within a lake system before being discharged through an 

outfall. For steady-state conditions, residence time can be estimated by dividing the lake 

volume (cubic-feet) by the annual inflow rate (cubic-feet per year). While residing in the lake, 

pollutants may be expected to undergo changes, such as chemical transformation, 

sedimentation and biological uptake. The fraction of pollutant mass stored or lost is 

therefore dependent on the residence time. Short circuiting effects may be considered in the 

analysis. 

Information concerning water residence time for Florida lakes is not generally available in 

public databases, but may be estimated through dye tracing studies, or by storage-routing 

computations, provided suitable data are available regarding lake bathymetry, inflow rates 

and outfall rates. Field review of the lake subject to a TMDL can assist in assessing whether 

the general lake configuration and inflow/outflow locations are likely to result in shortcircuiting or stagnant areas. 

Residence time can also be estimated from linked hydrodynamic and water quality models. 

If models are used, a particle of a conservative substance can be injected (conceptually) 

into a remote part of the waterbody within the model, and the time that it takes to exit the 

system can be simulated. 

Potential data sources are cited in Section 3.2.2. 

3.2.4 Estuarine Tides and Circulation 

In cases where TMDLs are to be established for estuarine systems, data regarding tides 

and circulation are important because pollutants are mixed and diluted at rates that are 

tidally influenced. Consequently, information on circulation and driving tidal forces is 

required. Estuarine systems are typically more complex than riverine systems because, in 

addition to freshwater inflows from a watershed, there are saltwater inflows that occur when 

tides flow in an opposing direction from the sea. Consequently, reverse flows and split-flow 

sometimes occur. For example, flows may be split with near-surface flows consisting of 

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freshwater flowing downstream in a seaward direction, and near-bottom flows consisting of 

saltwater flowing in an upstream direction. 

Long-term historical data for tidal water levels are available from NOAA stations located 

along both coasts of Florida. Available data may include hourly heights, daily heights, and 

high and low water levels. Data may be requested through the Center for Operational 

Oceanographic Products and Services (CO-OPS) databases [http://co-ops.nos.noaa.gov]. 

The nearest station to the study area with adequate data availability may be selected for 

modeling. Should tidal stages need to be field-measured, a full tidal cycle over a 25-hour 

period or longer may be appropriate. 

Pollutant transport, transformation, settling, and removal in an estuary are dependent on 

estuary hydrodynamics, which may be analyzed with complex hydrodynamic models or with 

more simplified flushing analyses. Models are described in Section 4. An example of a Tidal 

Prism Calculation Method is described in Appendix 3B. 

3.3 WATER QUALITY DATA 

As a part of the TMDL process, water quantity data and water quality data are required to 

estimate the pollutant mass loading from the watershed to receiving water bodies, and to 

estimate pollutant masses and concentrations present in the waterbody. This section 

describes the protocols for collection and analyses of water quality data in support of 

TMDLs. 

3.3.1 Historical Data Collection and Analysis 

3.3.1.1 Watershed Land-Based Load Data 

In urbanized areas, pollutant transport is typically modeled using “build up” and “washoff” 

coefficients that describe how pollutants accumulate in dry conditions, are washed off in 

rainfall events, and are conveyed into the storm drain and natural systems. For agricultural 

areas, pollutant transport is typically associated with soil erosion processes from cropland 

areas, as well as with irrigation methods. 

Groundwater contributions of flow and load may also be considered in the analysis. The 

mass load of pollutant transport from a river to a receiving waterbody is a function of the 

water quality constituent-concentrations and the flow rates. Mass loads are calculated by 

multiplying pollutant concentration data by the corresponding flow rates. Flow is usually 

expressed in units of volume per unit of time (e.g., cubic feet per second) and pollutant 

concentration in units of mass per unit of volume (e.g., milligrams per liter). A conversion 

factor is necessary to obtain unit and mass load results (e.g., pounds per year or pounds per 

acre per year). 

Data are required for determining land use-based wash-off coefficients, establishing 

concentrations and loading from streams, and assessing concentrations in waterbodies 

subject to TMDLs for comparison with water quality standards. Land use-based “build-up” 

and “wash-off” coefficients are typically obtained from published literature, calibrated 

models, the Nationwide Urban Runoff Program (NURP), NPDES MS4 monitoring data, 

and/or other measurements. Build-up may consist of atmospheric, automobile, application 

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(fertilizer, pesticide, herbicide), and other sources which are commonly included implicitly in 

event mean concentration (EMC) and loading factors. These literature values are derived 

from measurements of particular pollutant concentrations for specific land uses at various 

locations. There are several Florida references for EMCs (e.g., Harper, 1992; CDM, 1995; 

ERD, 2003; and ERD, 2006). Some federal and state environmental regulatory agency 

databases also can provide information on water quality of specific discharges. Some of 

these discharges are associated with specific land uses and may be an acceptable source 

of data for pollutant wash-off rates. 

3.3.1.2 Ambient Receiving Water Data 

Historical water quality data for rivers, lakes, streams and bays may be obtained from 

several public water quality databases. At the national level, the EPA maintains the 

STORET database. Requests for data may be made online at www.epa.gov/storet. The 

STORET database is split into “Legacy” data (before 1999) and “Modernized” data. The 

joining of data from the two databases into one dataset for TMDL analysis may be required. 

If so, the user may verify the combined dataset (e.g., check mean, median, standard 

deviation, minimum, maximum for each “piece”, and for the “whole”). The USGS National 

Water Quality Assessment (NAWQA) Program maintains a database that contains 

information collected through its activities. That database can be accessed through the 

website [http://water.usgs.gov/nawqa/]. 

Florida water quality data may also be obtained, as available, through the Generalized 

Water Information System (GWIS3), Florida STORET (storet.dep.state.fl.us), FDEP NPDES 

MS4 long-term monitoring data and published reports, and through WMDs. Other relevant 

water quality data also may be available through local counties, cities or other municipalities, 

other government institutions or private firms. 

As with each data source, analyses of water quality data coverage, period of record, 

accuracy uncertainty, and quality may be performed. Detection methods and limits may be 

confirmed for each parameter. 

3.3.2 Data Gap Analysis 

Data gaps may be filled with caution by using some of the protocols described in Section 3.2 

for water quantity. However, water quality data are often more complex, and interpolation or 

regression techniques should be used with caution. For example, a constituent such as 

dissolved oxygen is related to many environmental factors including time of day, solar 

radiation, water temperature, photosynthetic activity, availability of nutrients, respiration of 

plants and animals, growth and death rates of plants, and depth of water. These factors may 

cause a diurnal fluctuation. Data regarding dissolved oxygen is difficult to interpolate 

accurately across highly variable temporal and spatial conditions. Based on the complexity 

of the issues, water quality data gaps are not typically filled; rather, deterministic water 

quality modeling is used to predict water quality. 

Data identified as below detection limit (BDL) should be evaluated with caution and may be 

appropriate for sensitivity analyses. For example, the practitioner may consider the use of 

the detection limit, one-half the detection limit, or zero for the BDL values for sensitivity 

analyses to determine the effect on calibration and validation. 

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3.3.3 Field Data Collection 

Protocols for developing and implementing field programs for water quality monitoring 

should be executed in accordance with Operating Procedures DEP-SOP-001/01. 

3.3.4 Data Analyses 

Water quality analysis may include compilation of available data and subsequent statistical 

analysis. Many of the analytical approaches described in Section 3.2.1 regarding flow can 

also be used to analyze water quality data. 

Natural Background 

Definition of natural background conditions is generally dependent on the length of the water 

quality records, yielding two possible conclusions: 

1. Historical records extend back to a “pre-development” condition, or 

2. Historical records are not sufficient (either too short or too incomplete). 

If records extend to a pre-development era that corresponds to (pristine) conditions, then the 

natural background data may be adequately represented by the data for that period. These 

data may be assessed by considering: 

 The amount of human impact related to the pollutant(s) of concern; 

 Whether the historical conditions identified are achievable or not; and, 

 Statistical measurements of normal daily and seasonal variability of the data. 

Reference Sites 

Records are typically not available for pre-development conditions. Under these conditions, 

other techniques such as modeling or importation of data for undeveloped areas in similar 

environments (e.g., reference sites) may be used. The State of Florida has identified 47 

ecoregions for lakes as well as a compilation of bioregions (Figure 3.6). These regions can 

be used to identify potential waters in the same ecoregion or bioregion that may be used as 

reference. 

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Design Conditions and Time Periods 

a may be used to establish design conditions. For 

Seasonality 

 concentrations, maximums, minimums, variation, as well as data trends may 

Variability and Long-term Statistics 

, various statistical techniques are used to estimate 

Figure 3.6 Ecoregions and Sub-ecoregions of Florida 

The statistical analysis of long-term dat

example, a time series of data may provide information for trends and cyclic components of 

water quality conditions and/or to identify exceedances of standards. Data may be analyzed 

to provide time series for design conditions used in modeling. For example, indices such as 

chlorophyll-‘a’ and dissolved oxygen may be statistically analyzed to determine statistical 

description. A time period with “good”, “poor”, and “average” conditions can then be selected 

and if possible, this may be in the most recent 5 to 10 years to account for current 

conditions, land use, and hydraulics. Wet, average, and dry year conditions may also be 

considered for the choice of the simulation period.

Monthly mean

be analyzed from long-term historical data using the same procedures described in Section 

3.2.1. Monthly means may be used to establish seasonality. It should be recognized, 

however, the water quality data may not be normally distributed and thus, non-parametric 

statistical techniques may be needed. In many parts of Florida, a wet and dry season 

distinction may be adequate (as opposed to four seasons). 

As discussed with water quantity data

status (e.g., compliances, trends, central tendency and variability). Standard deviation, 

mean, median, minimum, maximum, and coefficient of variation may be calculated for each 

parameter of interest to assess the water quality data, its variability (locally, spatially, 

temporally) and interaction with watershed hydrology, hydraulics, and biology.

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TMDL Calculation from Data 

It may be possible to calculate the TMDL (termed “Data TMDL”) and necessary load 

reductions directly if there are adequate long-term flow and water quality data that cover 

both periods of standards compliance and exceedance. The data may also be used to 

develop appropriate coefficients for model simulations to establish TMDLs. If adequate 

historical streamflow and water quality data are available spatially and temporally, 

establishment of TMDLs may be accomplished for a specific waterbody by analysis for 

conservative substances as follows: 

1. Multiply the allowable concentration (considering a margin of safety) by the 

long-term flow series and plot the allowable load distribution. The TMDL may 

be calculated for a time period applicable to the current watershed and 

receiving water conditions. 

2. Multiply measured concentrations for each pollutant of concern by flow (or 

inflow) rates to the receiving water to calculate existing pollutant mass loads. 

3. Use the plotted relationships to determine the pollutant load reduction that will 

be necessary to obtain the necessary reductions of pollutant concentrations 

in the receiving water. 

TMDL Model Data Analyses 

Sections 5 and 6 present details on TMDL model analyses. This section presents 

considerations for water quality model data preparation. 

Short-term as well as long-term data may provide information for modeling. Data may be 

used to calibrate and validate a water quality model. The assessment may therefore include 

the arrangement of data time series in a form that is appropriate for model calibration and 

validation. The water quality model time series could consist of watershed and receiving 

water components. 

For the watershed component, soil erosion and wash-off coefficients are often required. The 

published literature values and data are typically highly variable, even for the same 

geographic region and land use. These values, therefore, may be used to establish ranges 

for model calibration and indicate model variability. 

Analyses of receiving waters require data to establish pollutant transformation as a pollutant 

travels in a river system may include the calculation of relevant model coefficients. The data, 

if available, could be used to calculate nitrification/de-nitrification rates, phytoplankton 

growth and death rates, and other water quality parameters, depending on the model being 

used. Typically, many of these parameters would be estimated as a part of the model 

calibration process. In-stream water quality data may be used to establish coefficients for 

pollutant transformation. For example, if there are two monitoring points along a channel, 

and there are no significant tributary or groundwater inflows, then changes in pollutant 

concentration are likely attributed to transformation. Some models simulate the process as a 

first-order decay, and the data may be used to calculate the decay coefficient. Such 

coefficients may also be established by model calibration and sensitivity. 

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Examples of these techniques are described in EPA publications, including “Rates, 

Constants, and Kinetics Formulations in Surface Water Quality Modeling (2nd Edition)” (EPA, 

1985) and the US EPA Storm Water Management Model (SWMM) Users Manual (Version 4, 

Huber and Dickinson, 1992; and recent Version 5 release). 

3.4 WATERSHED DATA 

As part of the TMDL process, watershed data are typically required to conduct analysis or to 

develop watershed models for water quantity and water quality. 

3.4.1 Land Use Data 

Land use data are required to define impervious area and provide or establish relationships 

for determining nonpoint source and point source loadings. For example, nonpoint source 

loadings can be estimated based upon relationships between land use and pollutant buildup and/or wash-off rates. 

GIS land use data are available from many Florida counties, FDEP, WMDs, Natural 

Resources Conservation Service - formerly the Soil Conservation Service (NRCS) and the 

University of Florida GEOPlan center. Depending on the location of the site, data may be 

obtained from any of the above organizations. The data originate from the NRCS, using the 

Florida Land Use and Cover Classification System (FLUCCS). Statewide FLUCCS data in 

GIS coverages are available for 1988, 1995 and in some areas, 2000 

[ftp.dep.state.fl.us/pub/gis/data]. 

Available information regarding land use data may be assessed to determine if the data are 

current and whether aerial photography updates are necessary. Updating may be performed 

either by obtaining more recent land use GIS coverages, and/or by ground-truthing the 

watershed. The GIS maps can be “marked up” in the field to identify differences are noted 

between the maps and the existing conditions. The new information may then be updated in 

the GIS database. If recent aerial photographs are available, they can also be used to 

update the GIS layer. 

Depending on the model framework selected, similar land use categories with similar 

hydrological and water quality characteristics may be aggregated into representative 

categories. Percent imperviousness is typically a good parameter for aggregating land use 

categories. An example of impervious cover per land use is shown in Table 3.1. 

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Table 3.1 Impervious Cover per Land Use 

Land Use Category Range of Typical 

Percent Impervious 

Forest/Open 0 to 1% 

Agriculture/Pasture 0 to 1% 

Cropland 0 to 1% 

Low Density Single Family Residential 5 to 15% 

Medium Density Single Family residential 25 to 35% 

High Density Single Family Residential 40 to 60% 

Commercial 80 to 90% 

Office/Light Industrial 60 to 80% 

Heavy Industrial 80 to 95% 

Waterbodies 100% 

Wetlands 25 to 100% 

Major Roads 80 to 90% 

 Source: CDM, 1992 

It may be necessary to obtain land use maps for both historical and projected future 

conditions. Historical land use conditions often can be interpreted from historical aerial 

photographs, and future land use patterns may be interpreted from county comprehensive 

plans or zoning maps. 

3.4.2 Meteorological Data 

Meteorological data are required to quantify rainfall, evaporation, transpiration, and other 

parameters for modeling or analysis of nonpoint source pollutant loads. 

Rainfall 

Rainfall is the main input that drives watershed runoff during wet weather conditions. It is 

also a direct source of fresh water, as it falls on lake or other waterbody surfaces. Annual 

rainfall totals can be used to define average, wet, or dry years. 

Long-term rainfall time series data are available from local WMDs, the USGS, the National 

Weather Service (NWS), and the National Climatic Data Center (NCDC), as well as from 

county and municipality gauges. The NWS and NCDC are good sources of data for short 

time-step electronically stored rainfall (e.g., 15 minutes and hourly). As rule of thumb, hourly 

rainfall time series may be sufficient for most TMDL studies. Data from major airports and 

NWS stations are typically stored in the NWS database. The WMDs typically store daily 

rainfall data, but also have been recently storing short-timestep rainfall data. The USGS has, 

in recent years, begun installing short time-step rainfall gauges at some of its streamflow 

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monitoring stations. Counties and municipalities often monitor rainfall for special studies, 

such as estimating seepage into municipal sanitary sewer pipes during flood events. An 

additional source of short time-step rainfall data is the University of Florida IFAS FAWN 

website (http://fawn.ifas.ufl.edu/) that has data starting in 1998 for stations throughout 

Florida. Depending on the analytical or modeling framework chosen, data may be obtained 

by requesting data from the above referenced agencies. In addition, the FDEP has 

contracted with Florida State University to develop methods to allow Doppler radar records 

to be used in TMDL modeling. 

The level of rainfall analysis that will be needed will depend on its intended use. For 

example, long-term simulations could use data from one representative rain gauge, but 

calibration would require data from at least three rain gauges to define spatial and temporal 

variation. Typically, the availability of rainfall data for the watershed may first be 

investigated. All rain gauges may be located and labeled to indicate if daily or shortincremental rainfall data are available, and the period of availability noted. If a detailed 

watershed model is being constructed for nonpoint source pollutant evaluation, then shorttimestep rainfall is desirable (5 to 60 minute). If rain gauges with short-timestep rainfall are 

located on the watershed with adequate distribution to define rainfall spatially, then a 

Theissen polygon network may be developed with the short-timestep rain gauges. If the 

daily rain gauges would add significant improvement to the spatial distribution, they may be 

used to supplement the short-time step rain gauges, by using the hourly temporal 

distribution of a nearby rain gauge. 

Rainfall data can be used to develop runoff/rainfall relationships, frequency analysis, double 

mass flow relationships (Figure 3.7), or wet-pollutant load estimation from atmospheric 

deposition. Runoff/rainfall relationships are described in Section 3.2. Frequency analyses 

may be performed on annual rainfall totals from long-term datasets to determine “wet”, “dry”, 

and “average” years. Years that have short-time step data for the “wet”, “dry”, and “average” 

year may be selected for modeling normal and extreme conditions. The terms “wet” and 

“dry” are relative terms and in this instance means “wetter than average annual total rainfall” 

and “drier than average annual total rainfall”, respectively. 

Double mass curves can be developed to detect and correct inconsistencies or changes in 

rainfall datasets. They are created by developing a cumulative plot with the cumulative 

rainfall of one gauge on the x-axis and cumulative rainfall of another nearby gauge on the yaxis. A constant slope in the line indicates a constant relationship between the gauges over 

time relationship, whereas a change in the slope indicates that a divergence in cumulative 

rainfall catch (Figure 3.7). 

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Figure 3.7 Double Mass Curve 

Solar Radiation and Air Temperature 

Solar radiation data and air temperature data are the important components of a watershed 

or receiving waterbody. They influence evaporation and evapotranspiration, described 

below. In addition, they are required input for some receiving water quality models because 

they influence the simulated respiration and growth of the biota. 

Solar radiation and temperature data are available from the NWS and several universities, 

e.g., Institute of Food and Agricultural Science (IFAS) of the University of Florida. Data also 

may be available from specific studies sponsored by the WMDs, USGS, FDEP, counties and 

municipalities. These studies typically report the monthly average duration of daylight hours 

and the maximum solar radiation that can be expected during those months. 

The analysis may result in the preparation of time series datasets of air temperature and 

solar radiation data suitable for modeling. If solar radiation data are not available, latitudedependent cloud cover can be used to estimate solar radiation. Some texts provide 

information regarding typical seasonal latitude-dependent cloud cover. 

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Evaporation and Evapotranspiration 

Evaporation and evapotranspiration (ET) rates provide estimates of atmospheric losses from 

the watershed land surfaces or receiving waterbody surface, which are important 

components in annual hydrologic balances and in modeling. ET is sometimes established by 

subtracting other known terms from rainfall and other inflows in a mass balance. 

Evaporation data for Class-A evaporation pans are available from the same sources as 

described above regarding solar radiation and air temperature. Evapotranspiration rates are 

dependent not only on seasonal conditions, but also on soil moisture availability. Generally, 

there are only limited measurements of evapotranspiration conducted by lysimeter or other 

instruments, and typically evapotranspiration is calculated from an upper limit, potential 

evapotranspiration (PET). PET may be estimated from pan evaporation rates, or from 

meteorological indices such as humidity, air temperature, wind speed, and solar radiation. 

Figure 3.8 shows an example of the seasonal variation of daily evaporation for an 

application in northeast Florida. 

The USGS has published several references on ET, including direct measurement in some 

areas (USGS, 1996), and the Handbook of Hydrology (Maidment, 1993) which provide 

several methods to estimate ET and PET. 

Figure 3.8 Example of Potential Evapotranspiration Data 

3.4.3 Topographic Data 

Topographic data are required for watershed delineation and runoff modeling. The data will 

provide information on watershed slopes, channel slopes, basin and subbasin tributary 

areas. 

Topography 

Topographic data are typically obtained from hard copy or digital topographic maps 

available from the USGS, the WMDs, counties, cities and other municipalities. USGS 

quadrangle series maps are available for the entire state at a scale of 1 inch = 2,000 feet 

(1:24,000). USGS maps typically do not provide a contour interval of less than 5 feet. More 

detailed maps are available for specific Section/Township/Ranges from the WMDs at a scale 

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of 1 inch = 200 feet (1:2400). Digital elevation model (DEM) data are often available from 

the WMDs or USACE and Light Detection and Ranging (LiDAR) datasets are becoming 

more common. It should be cautioned that all topographic data may be checked against 

verification sources (e.g., survey), and LiDAR may not accurately represent topography in 

heavily forested areas and does not provide bathymetry. 

Cross Sections 

Analysis of topographic data is performed in preparation for modeling. Typically, information 

is extracted from hard copy maps by defining channels and cross sections and obtaining 

measurements with a map odometer or by digitization and measurement in a GIS system. 

DEM may be used to define cross sections with GIS software. It should be cautioned that 

DEMs may not have adequate contour interval resolution to define incised channel or 

bathymetry of water bodies. 

Surveys 

Channel or river cross sectional data may also be obtained from surveys. Field visit and 

desk studies may be conducted to determine appropriate sites to be surveyed. The desk 

studies may include a review of available topographic maps to define points where survey 

information is required to ensure adequate spatial coverage and to define places where 

there are significant changes in channel geometry. The purpose of the field studies is to 

confirm or modify the findings of the desk study. 

Basin/Subbasin Boundaries 

Basin/subbasin boundaries may be defined by examining topographic ridges on a contour 

map. The definition of subbasins may be consistent with the modeling requirements as 

described in Section 5. Measurements of basin and subbasin areas may be made with a 

planimeter or may be digitized in a GIS. 

Overland Flow Slopes and Roughnesses 

Many hydrologic models consider the overland flow slope, length, and roughness (e.g., 

Manning n) in the estimation of runoff. Slopes and lengths are derived from topographic 

maps and Table 3.2 presents shallow overland flow Manning n values. Note that the 

roughness values for overland flow are higher than streamflow since roughness features are 

generally higher than overland flow depths (1 inch to 6 inches). 

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Table 3.2 Manning n Roughness Values for Shallow Overland Flow 

Source Ground Cover n Range

Crawford and Linsley (1966) a Smooth asphalt 0.012 

 Asphalt of concrete paving 0.014 

 Packed clay 0.03 

 Light turf 0.20 

 Dense turf 0.35 

 Dense shrubbery and forest

litter

 0.4 

 Engman (1986) a Concrete or asphalt 0.011 0.01-0.013

 Bare sand 0.01 0.01-0.016

 Graveled surface 0.02 0.12-0.03

 Bare clay-loam (eroded) 0.02 0.12-0.033

 Range (natural) 0.13 0.01-0.32

 Bluegrass sod 0.45 0.39-0.63

 Short grass prairie 0.15 0.10-0.20

 Bermuda grass 0.41 0.30-0.48

 CDM (1989, 1992)c Pine palmetto flatwoods 0.4 0.3-0.6 

Various grass 0.2 0.2-0.3 

Forested wetlands 0.4 0.3-0.6 

a

 Obtained by calibration of Stanford Watershed Model.

b

 Computed by Engman (1986) by kinematic wave and storage analysis of measured rainfall-runoff 

data. 

c

 Values from calibration of models in Volusia County and Duval County.

Bathymetry 

The NOAA’s National Geophysical Data Center (NGDC) compiles bathymetric data for 

coastal areas and provides public domain DEMs. A data search may be run using their web 

site [www.ngdc.noaa.gov/mgg/bathymetry] and available mapping ordered in hardcopy or 

digital formats. DEM files may be directly downloaded for some areas, but DEMs are not 

always reliable sources of bathymetry data. Reliable coastal bathymetry data are also 

available through USGS mapping. Inland waterbody bathymetric data may be available from 

special studies performed by the WMDs or by local municipalities. 

3.4.4 Soils Data 

Soils data are required to establish infiltration characteristics for hydrologic modeling and for 

estimating water storage in the soils profile. For agricultural TMDL studies, detailed soils 

information is needed to estimate erodilibity and nutrient/metals content of the soils (e.g., 

soils potency factors). Soils texture and grain size distribution data are needed for sediment 

generation models. 

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Soil Types 

Data on soil types are available from the NRCS Soil Survey, published for each county. The 

Soil Survey manual provides a name for each soil type as well as information on the 

characteristics of each soil type. Such information includes the soil type, infiltration rate, 

permeability, water holding capacity, water table conditions, classification (e.g., sand, clay, 

silt), and hydrologic soil groups. The National Engineering Handbook Section 4 – Hydrology 

(SCS now NRCS, 1985) presents further information on soils and runoff properties as well 

as WMD ERP applicant’s Handbooks (e.g., SJRWMD, 2005; 

http://www.sjrwmd.com/programs/regulation/handbooks/pdfs/msswhdbk.pdf). The soil types 

may be aggregated into Hydrologic Soil Groups (HSGs) for some hydrologic models. For 

other models, infiltration coefficients must be estimated based on soil characteristics. 

The soil-type database for each watershed typically consists of many different soil types 

developed by the NRCS. For modeling purposes the soil types may be aggregated into the 

HSGs A, B, C, and D, as described below: 

Group A. Soils having a high infiltration rate (and therefore a low runoff potential) 

when thoroughly wet. These consist mainly of deep, well drained to excessively 

drained sands or gravelly sands. These soils have a high rate of water transmission. 

Group B. Soils having a moderate infiltration rate when thoroughly wet. These 

consist chiefly of moderately deep or deep, moderately well drained or well-drained 

soils that have moderately fine texture to moderately coarse texture. These soils 

have a moderate rate of water transmission. 

Group C. Soils having a slow infiltration rate when thoroughly wet. These consist 

chiefly of soils having a layer that impedes the downward movement of water or soils 

of moderately fine texture or fine texture. These soils have a slow rate of water 

transmission. 

Group D. Soils having a very slow infiltration rate (and therefore a high runoff 

potential) when thoroughly wet. These consist chiefly of clays that have high shrinkswell potential, soils that have a permanent high water table, soils that have a 

claypan or clay layer at or near the surface, and soils that are shallow over nearly 

impervious material. These soils have a very slow rate of water transmission. 

Dual class soils (e.g. A/D) indicate that a clay or hard pan may limit infiltration. For 

drained conditions, these soils can function as a class C, B, or A depending on 

degree of drainage. 

Soil storage values can be estimated as identified in a Guide to SCS Runoff Procedures 

(SJRWMD, 1985). Table 3.3 presents examples soil storage and infiltration rate values for 

Florida applications (CDM, 1989 and 1991). 

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Table 3.3 

Typical Ranges of Values for Infiltration Rates (f), Horton Infiltration Shape Factor (k), 

and Soil Storage Capacity (S) for Different Antecedent Moisture Conditions (AMCs) 

USDA NRCS Hydrologic Soil Group 

(HSG) Designation

 A B C D

Final constant infiltration rate, fc

(inches/hour) 

 15 - 0.30 2 - 0.15 0.25 - 0.05 0 – 0.10 

 Initial infiltration rate, fo (inches/hour) 8 - 25 6 - 10 3 - 7 1 – 5 

 Shape factor, k for Horton infiltration 

curve (1/hour) 

 2 - 5 2 - 5 2 - 5 2 – 5 

HSG A B C D

AMC 1, Dry (inches) 12.0 - 4.3 9.0 - 3.4 7.0 – 2.3 1.3 - 3.0 

 AMC 2, Normal (inches) 5.4 - 3.4 4.0 - 2.8 3.0 - 1.8 1.0 - 1.3 

 AMC 3, Wet (inches) 2.1 - 1.8 1.5 - 1.3 1.0 - 0.8 0.5 - 0.7 

 AMC 4, Saturated (inches) 0 0 0 0 

Note: This soil storage S can be related to Curve Number (CN) if desired using the standard SCS 

equation S= (1000/CN)-10.

Defined Antecedent Moisture Conditions (AMCs) for Pervious Areas

Condition Description Total Rainfall during 5 Days Preceding Storm 

(inches)

1 Dry <1.4

2 Normal 1.4 - 2.1

3 Wet >2.1

4 Saturated Saturation

Soil Maps 

Soil maps may contain the detailed soil types or aggregated hydrologic soil groups. 

Electronic GIS maps are available from sources such as the WMDs or NRCS. Depending on 

the model framework selected, the soils map may provide a basis for developing a GIS 

overlay of soils and land uses that is useful for establishing runoff and water quality 

characteristics. 

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Soil Sorption Capacity for Organic Chemicals 

Soils exhibit sorption capacity for pollutants and particularly for organic compounds (e.g. 

pesticides). The propensity of a soil to retain or adsorb organic chemical is typically 

proportional to its organic carbon content. Among the sorption models, the most common 

tool to measure this propensity is the retardation factor, R, which is defined as: 

θ

Kd ρ R 1+=

where Kd is the sorption partition coefficient, ρ is the soil bulk density and θ is the average 

soil moisture content. Kd is also expressed as a function of the soil organic carbon content. 

The physical significance of the retardation factor is that it measures how much slower the 

solute migrates than water because the solute spends part of its time sorbed on the soil 

matrix and immobile. This popular adsorption model assumes that the sorption mechanism 

is a linear function of the concentration and is isothermal. The Geographic Information 

System Department of the Soil and Water Science Department at the University of Florida 

(http://grunwald.ifas.ufl.edu/GIS_Lab/gis_laboratory.htm#mission) has developed a large 

database that relates pesticides properties (e.g., Kd, half-life period) with soil properties 

(e.g., organic carbon content, clay content). 

3.4.5 Groundwater Data 

Groundwater contributes (and removes) flow and associated pollutant loads to surface water 

systems (e.g., springs or lateral seepage). To develop TMDLs, it is important to identify the 

fraction of pollutant loads that are contributed by groundwater. Relevant data include local 

geology, aquifer structure and properties and interactions between surface and groundwater 

in the vicinity of the TMDL study. The potentiometric surface of the deep aquifer and the 

water table elevation of the surficial aquifer in comparison to the surface water elevation are 

factors in determining the rates of groundwater contribution. 

Potentiometric Surfaces 

Maps of potentiometric surface are available from the USGS and WMDs. Data are also 

available for specific wells. The USGS web site [http://www.usgs.gov] has data that can be 

readily downloaded. 

Potentiometric data may be analyzed by constructing or updating maps of groundwater 

contours. Software contour packages can be used to construct contours from point data. 

Water Table and Seasonal Depth to Groundwater 

Data regarding water table elevation are also available from the USGS and WMDs. 

Groundwater level contours for the surficial aquifer may be constructed. 

Seasonal depth to groundwater may be established by examining a time series of 

groundwater level data for the surficial aquifer or the Floridan Aquifer in geographic 

locations where the Floridan is under water table conditions. The data are typically 

presented as elevation above the National Geodetic Vertical Datum (NGVD) or other datum. 

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These elevation data may be subtracted from land surface elevations, as determined from 

topographic maps or surveying, to determine the depth to water table. 

Information on depth to seasonal high groundwater is also published in the Soil Survey 

Manuals developed by the NRCS. 

3.4.6 Geomorphology 

Streams and topographic features change from erosion and deposition of eroded material. 

Many of the lakes in Florida result from sinkhole erosion and subsequent collapse. Channels 

are scoured in locations by the erosional force of water, and sediments are deposited in 

other locations. Such geomorphologic processes may result in dynamic changes in crosssections and bathymetric data which may be required for detailed modeling of channels and 

receiving waters. 

Cross-sectional data are described in Section 3.4.3. If available data were not based on 

recent surveys (past 5-10 years), then they may be field-verified to determine if 

morphological changes are significant enough to require new surveys. 

3.4.7 Biological Data 

3.4.7.1 Biological Assessment Techniques 

Waters may be listed for biological impairment and biological data may be used in 

conjunction with water quality and hydrologic data to support the TMDL analyses. 

Biological assessment techniques may consist of various tools used to measure the types, 

numbers, and diversity of species as well as the level of pollutants in the environment as an 

indicator of potential exposure. These pollutant levels do not necessarily indicate the 

occurrence of deleterious biological effects. Measures of exposure may be associated with 

the measures of effect in order to establish a causal relationship. Example biological 

assessment techniques are provided in the remainder of this section, and Appendix 3 

provides further details. 

3.4.7.1.1 Biological, Physiological and Histological Techniques 

Tissue Analysis- Histopathology 

Chemical analysis of contaminants in biological tissue is a very common technique of 

investigation. It is often related to environmental parameters such as water quality 

parameters, physiography, hydrology, and hydraulics to estimate the bioavailability of a 

constituent. These cause-and-effect relationships are very often valid for the specific site 

under investigation and may be transposed elsewhere for similar conditions. Parameter 

concentrations and their potential negative effect on tissue may be only comparable within 

the same species, for organisms at the same life stage, reproductive condition, size, weight, 

and sex. Many factors have to be considered when evaluating the tissue exposure to 

environmental contaminants. The most significant ones include i) measurable parameter 

concentrations that vary with environmental conditions, ii) organisms that be collected and 

that are large enough to meet sampling requirement, iii) organisms that are easy to find and 

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that can withstand captivity during laboratory experiments, and iv) species with established 

body of knowledge on biological and toxicological effects. 

It has been established that pathologic development of tissue has been related to the 

negative effect of acute as well as chronic exposure to environmental contaminants. Due to 

their xenophobic and filtering functions, the liver and the kidneys are the primary target of 

interest for mammals (Couch, 1975). Of additional interest are the gonads and the immune 

system. For aquatic organisms, the gills are particularly sensitive to the contaminant 

exposure because they are the first uptake site, and fish tissue testing is often used as an 

indicator of cumulative toxicity and potential impacts for human consumption (e.g., fish 

advisory for mercury). 

Semi Permeable Membrane Devices 

The Semi Permeable Membrane Device (SPMD) is an in-situ water sampling technique that 

reproduces the bioconcentration of organic contaminants in lipids of aquatic organisms 

(Huckins et al., 1993). It is not per se a biological assessment technique since it merely 

measures the concentration and not the level of exposure. This sampler made of a thin film 

of neutral lipid enclosed in a flat, and semi permeable membrane tube, passively 

sequestrates environmental contaminants. Whereas some organisms that metabolize and 

excrete toxic contaminants can eliminate the causal link between residue and adverse 

effect, the SPMD will continue to accumulate contaminants to their maximum partitioning 

limit. The trapped constituents can be later analyzed with routine laboratory techniques. 

3.4.7.1.2 Whole Organism and Single Species Techniques 

Mortality, growth, and reproduction are the common measurements of toxicity tests applied 

to whole organism or single-species. Toxicity test procedures have been provided for a large 

variety of species including microbes, algae, vascular plants, and aquatic and terrestrial 

invertebrates and vertebrates. For whole organism, the choice depends upon the species, 

life stage, and route of exposure. For a single-species, the choice depends upon sensitivity 

to contaminant, ease of culture and maintenance, and local relevance to the geographic 

area. 

Microbial Assays 

These type of assays use bioluminescent bacteria to detect the presence of cytotoxic and 

genotoxic environmental contaminants. Microtox® and Mutatox® are the most commonly 

used assays (Kaiser and Palabrica, 1991). Microtox® is used to evaluate the cytoxicity by 

quantifying light reduction and Mutatox® is used to detect the presence of DNA-damaging 

chemical. 

Algal Assays 

Algal assays measure the effect of contaminants and nutrients on algal growth. A large 

variety of responses can be measured including optical density, oxygen production, carbon 

dioxide uptake, cell counts, gravimetric cell determination, and measurement of chlorophylla (American Society for Testing and Materials, 1997a). The behaviors of these parameters 

are then evaluated against some control responses. 

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Aquatic Invertebrate Assays 

Growth of aquatic invertebrates is commonly used to evaluate the effect of environmental 

conditions on receiving water and aquatic sediments. The effect of waterborne contaminants 

is commonly evaluated using the Daphnia magna and Ceriodaphnia dubia (American 

Society for Testing and Materials, 1997b) whereas the effect of contaminant present in 

sediment is evaluated for several aquatic invertebrate such as Hyalella azteca and 

chironomus tentans (American Society for Testing and Materials, 1997c). 

In-situ Toxicity Assessment 

In-situ toxicity assessments consist of observing survival, growth and behavior of indigenous 

species when exposed directly in water of the site of interest using environmental chambers 

(Finger and Bulak, 1988). Unlike laboratory toxicity studies that frequently focus on a single 

contaminant, the in-situ assessments provide insight into the cumulative and synergetic 

effect of contaminant mixtures. 

3.4.7.1.3 Population and Community Techniques 

The effects of environmental contaminants have been measured on tissue, organs, single 

species and whole organism. Evaluating their effect on populations and communities has 

focused on the structural and functional aspects of the biotic components of the ecosystem. 

Structural analysis techniques seek to identify the abundance and distribution of various 

taxa as indicators of population and community stress. Functional analyses are more 

interested in the ecological processes of ecosystems such as community respiration, 

nutrient cycling, decomposition rates, colonization rates, and trophic level analysis. Since 

these techniques seek to establish cause and effect relationship, it is important to integrate 

in the final analysis watershed geochemistry, water chemistry of the streams (pH, alkalinity, 

water temperature, dissolved oxygen), and the physical characteristics of the receiving water 

such as geomorphic channel unit, riparian vegetation, bank stability, stream depth, current 

velocity, and bed-sediment particle distribution. 

Because of regional differences in habitat, species, thermal and hydrological regimes and 

water-quality problems, it may be difficult to make objective comparisons between sites. 

However, a large body of knowledge on population and community information for many 

areas of the country is readily available from the USGS, USEPA, and the US Fish and 

Wildlife Service. 

Algal Population and Community Assessments 

Algae are an indicator for population and community assessment studies. They have a short 

life span, and they readily exchange constituents between the water column and the 

sediment bed. Since they are sedentary, they tend to reflect the local water quality. An 

example of protocols for design, sample collection, documentation and QA/QC for 

population and community assessment of algae is given by Porter et al. (1993). 

Aquatic Invertebrate Population and Community Assessments 

Like the algae, aquatic invertebrates are a good indicator in assessing the long term effect 

of environmental contaminants on the indigenous community living in water column and 

sediments. They have longer life span than algae, live in close association with sediment 

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beds, and are relatively sedentary (e.g., benthic macroinvertebrates). An example of 

protocol design for regional and national scale is provided by Cuffney et al. (1993). 

Fish Population and Community Assessments 

The Index of Biotic Integrity is one of the most commonly used tools to measure the effect of 

contaminants on fish populations and community (Karr, 1981). That method develops 

different metrics related to fish composition, trophic composition, fish abundance and 

condition to measure the structural and functional status of the fish community. Meador et 

al. (1993) have developed a protocol for design, sample collection, and QA/QC for 

population and community assessment of fish. “Fish scores” have been used as a biological 

indicator in Georgia (CDM, 2001). 

3.4.7.2 Trophic State Index 

Naumann (1929) developed the concept of trophic state as the biological response of a 

waterbody to forcing factors such as nutrient additions that can vary with time and space. 

The trophic state of a waterbody can also be defined as the total weight of living biological 

material (biomass) at a specific location and time. The Trophic State Index (TSI) developed 

by Carlson (1977) uses algal biomass as the basis of trophic state classification. 

Chlorophyll-a (CHLa), Secchi-disk depth (SD), and total phosphorous concentration (TP) are 

the three initial parameters used to independently estimate TSI; Total Nitrogen (TN) was 

added later as a fourth indicator. The measured values of these four parameters (CHLa, SD, 

TP, and TN) are used to calculate the four TSIs: TSI(CHLa), TSI(SD), TSI(TP), and TSI(TN). 

The range of the index is from 0 to 100 for convenience. The four index variables are 

interrelated by linear regression models and may produce the same index value for a given 

combination of variable values. Therefore, any of the four variables can theoretically be 

used to classify a waterbody. For the purpose of waterbody classification, a preference may 

be given to CHLa because it is directly related to biomass. The TSI based on CHLa is better 

than those of TP and TN which are better than that of SD. 

The initial calculations of the TSI for each variable are straightforward: 

1. TSI(CHLa)=9.81 Ln(CHL) + 30.6 (Carlson, 1977) 

2. TSI(TN)=14.43 Ln(TN) + 54.45 (Kratzer and Brezonik, 1981) 

3. TSI(TP)=14.42 Ln(TP) + 4.15 (Carlson, 1977) 

4. TSI(SD)=60 – 14.41 Ln(SD) (Carlson, 1977) 

Table 3.4 exhibits the TSI and its relationship with the trophic state of the waterbody 

according to Kratzer and Brezonik (1981). The TSI is an indicator of trophic state and not of 

water quality. TSI is an indicator of trophic conditions related to the biological status of the 

waterbody whereas water quality is related to specific water quality standards based on 

human needs or values. For example, an oligotrophic or eutrophic lake has attributes of 

production that remain constant regardless of the use of the lake or the location of the lake. 

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Table 3.4 State of Waterbody and Trophic State 

(adapted from Kratzer and Brezonik, 1981) 

TSI Trophic State 

0 – 19 Ultraoligotrophic 

20 – 39 Oligotrophic 

40 - 59 Mesotrophic 

60 – 79 Eutrophic 

80 - 100 Hypereutrophic 

The Trophic State Index is one of several methods used to describe the biological 

productivity of a waterbody. Forsberg and Ryding (1980) developed another method called 

the Trophic State Classification System (Figure 3.9). Using this method, waterbodies can be 

grouped into one of four trophic states: 

1. Oligotrophic: Waterbodies have the lowest level of productivity; 

2. Mesotrophic: Waterbodies have a moderate level of biological productivity; 

3. Eutrophic: Waterbodies have a high level of biological productivity; and 

4. Hypereutrophic: Waterbodies have the highest level of biological productivity. 

Figure 3.9 Trophic States, Concentrations of Constituents, and Typical 

Waterbody Uses (Source: Forsberg and Ryding) 

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3.5 EXISTING POLLUTANT SOURCES 

Existing sources of pollutants or other causes of impaired water quality must be quantified 

as part of the TMDL process. Existing sources include point and nonpoint sources that may 

impact a receiving waterbody and must be included in developing water quality models of 

the waterbody. Excluding background sources (see Section 3.3), these sources are 

categorized as: FDEP Regulated Sources, Water Management District Regulated Sources, 

and Sources Not Regulated by FDEP or WMDs. Each of these is described in more detail 

below. 

3.5.1. Sources Regulated by FDEP 

The FDEP has implemented two primary permitting programs for domestic and industrial 

wastewater facilities – the state permitting program for facilities that do not discharge to 

surface waters and the NPDES permitting program for facilities that discharge to surface 

waters. 

There are approximately 4,130 individually permitted wastewater facilities in Florida. These 

facilities represent both domestic and industrial wastewater facilities which are authorized to 

discharge wastewater either to ground water or surface water in the state. 

At present, there are approximately 560 facilities authorized to discharge to surface water. 

As surface water dischargers, these facilities are subject to the Department’s federally 

authorized NPDES requirements. However, many of the NPDES facilities also discharge to 

ground water. The remaining 3,570 facilities are only authorized for groundwater discharges. 

About two-thirds, or 2,750 of the individually permitted wastewater facilities in Florida are 

classified as domestic wastewater facilities. In general, domestic wastewater facilities are 

those principally designed to collect and treat sanitary wastewater or sewage from dwellings 

or homes, business buildings, institutions, and the like. The remaining one-third, or about 

1,380 individually permitted facilities, is classified as industrial wastewater facilities. 

Currently, FDEP permits over 17,000 sources including domestic wastewater, industrial 

wastewater, and stormwater facilities discharging to surface and ground water. Appendix 

3D provides summaries of the permits for these three types of waters, respectively, for the 

counties in Florida. The largest type is Construction Stormwater permits at over 10,000, 

followed by Multi-Sector General Permits (MSGP) and Domestic Wastewater permits at 

about 2,500 each. Individual permit data for each of the facility types may be obtained from 

FDEP (www.dep.state.fl.us/water/wastewater/download). 

Regulated pollutant sources, depending upon size and activity, are currently required to be 

registered with FDEP, and may be subject to discharge monitoring and reporting. Discharge 

Monitoring Reports (DMRs) submitted to EPA and FDEP are public records and may be 

requested for use in developing TMDLs. NPDES-regulated pollutant sources include the 

following: 

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Domestic Wastewater Sources 

Domestic wastewater facilities which discharge to surface waters and have design 

capacities of 10,000 gallons per day (gpd) domestic or 5,000 gpd domestic and commercial 

wastewater are regulated through the NPDES program. NPDES domestic wastewater 

permits require periodic submittal of Discharge Monitoring Reports (DMRs), generally 

monthly or quarterly. Reported parameters may include flow, pH, temperature, total residual 

chlorine, CBOD, TSS, nitrogen (TN, TKN, ammonia), phosphorus, and coliforms. Values 

may be reported as loads or concentrations based on effluent grab samples and flow 

measurements. DMR sampling requirements are typically weekly or monthly for parameters 

requiring lab analysis. Historical DMR data may be reviewed and copied from permit files by 

appointment with the local FDEP office. Records can be located using either the permit 

number or the Facility ID number. A full listing, by county, of FDEP Domestic Wastewater 

NPDES permittees may be downloaded from the FDEP wastewater web site 

[www.dep.state.fl.us/water/ wastewater]. FDEP Tallahassee maintains a DMR database and 

can perform data retrievals for specified time periods and parameters. Requests may be 

sent via fax or e-mail to the FDEP Tallahassee database manager depending on the type of 

activity. 

Industrial Wastewater Sources 

Industrial wastewater facilities that discharge to surface waters are regulated through the 

NPDES permitting program while those that discharge to ground waters are regulated 

through the state permitting program. Individual industrial wastewater discharges are highly 

variable in the amount and types of pollutants they may contain and the type of industrial 

activity. As such, NPDES permit discharge monitoring requirements are tailored specifically 

to each individual industrial facility, and reported parameters vary widely depending on the 

waste streams being treated. Permits typically require periodic submittal of DMRs (generally 

monthly or quarterly). Values may be reported as either loads or concentrations. Historical 

DMR data may be reviewed and copied from permit files by appointment with the local 

FDEP office. Records can be located using either the permit number or the Facility ID 

number. A full listing, by county, of FDEP Industrial Wastewater permittees may be 

downloaded through the FDEP industrial wastewater web site 

[www.dep.state.fl.us/water/wastewater/iw]. FDEP Tallahassee maintains a DMR database 

and can perform data retrievals for specified time periods and parameters. Requests may be 

sent via fax or e-mail to the FDEP Tallahassee database manager. 

Municipal Separate Storm Sewer Systems (MS4s) 

MS4s in Florida are currently regulated by FDEP under the Phase I MS4 (large and medium 

systems) and Phase II MS4 (small) permitting system. Phase II MS4 systems are not likely 

to be good sources for monitored water quality data since the program was only recently 

initiated. However, except for relatively small municipal governments, all urban stormwater 

sources of pollutants are regulated through the NPDES Program in Florida. Phase I MS4 

permits require development and implementation of stormwater monitoring programs, which 

may provide valuable data on runoff water quality from urbanized areas. Each Phase I MS4 

permittee submits a monitoring plan for FDEP review and approval. The schedule of 

implementation, frequency of sampling, number of outfalls monitored, and parameters 

analyzed vary. Parameters that might be expected to be included in MS4 monitoring 

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programs include; dissolved oxygen, pH, specific conductance, TSS, metals, oil and grease, 

total nitrogen, total phosphorus, and possibly total and/or fecal coliform. Municipal storm 

water agencies may be contacted directly to determine the scope and extent of MS4 permitrelated water quality data potentially available. Available data may be obtained from the 

municipality directly or through FDEP permit file review/data retrieval. 

Industrial Stormwater 

Industrial facilities in Florida that have on-site materials or processes exposed to rainfall or 

to stormwater runoff may be regulated by FDEP under the NPDES Individual or Multi-Sector 

General Permit (MSGP) for “stormwater discharges associated with industrial activity.” This 

permit covers discharges from several specified categories of industrial activity such as; 

heavy manufacturing, mining/oil and gas exploration, hazardous waste facilities, landfills and 

open dumps, recycling facilities, steam electric power generation facilities, transportation 

facilities, treatment works, and some light industry. The Industrial MSGP coverage is the 

most common. The discharge monitoring requirements vary by industrial activity. Monitoring 

frequency is typically once per quarter or biannual. Sampling is storm event-driven. Permits 

typically require periodic submittal of DMRs. Values are generally reported as 

concentrations with a total estimated storm volume recorded on the form. NPDES DMR data 

are forwarded to Tallahassee FDEP’s office and may be reviewed and copied from permit 

files by appointment, using either the permit number or the Facility ID number. A full listing, 

by county, of FDEP Industrial Stormwater permittees may be downloaded through the FDEP 

industrial wastewater web site [www.dep.state.fl.us/water/wastewater/iw]. The FDEP 

Tallahassee DMR database can be tapped for data retrievals for specified time periods and 

parameters. Requests may be sent via fax or e-mail to the Tallahassee FDEP database 

manager. 

Construction Site Runoff 

Phase I NPDES permitting regulates construction site runoff from projects that are planned 

to disturb five acres or greater of land from the time of ground breaking to site stabilization. 

Originally promulgated and implemented for Florida under EPA Region IV, the program has 

since been delegated to FDEP. Phase II NPDES permitting for construction sites between 

one and five acres commenced in 2003, under FDEP. Discharge monitoring requirements 

under the FDEP Construction Generic Permit (CGP) emphasize visual evaluation of BMP 

effectiveness and do not require analytic monitoring of discharge for TSS. Quantitative data 

from this program are expected to be sparse. The EPA Region IV version of the CGP 

promulgated in April 2000 does require periodic, event-driven sampling and reporting of 

settleable solids, total suspended solids and turbidity in discharges from active construction 

sites that discharge into 303(d) listed waters. Estimates of potential pollutant contribution 

from active construction sites may be derived from data thus collected in other Region IV 

states by requesting DMR data from EPA Region IV. New agreements between FDEP and 

EPA have removed these sampling requirements. 

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Concentrated Animal Feeding Operations – (This description may be added by FDEP 

later) 

Mining Permits – (This description may be added by FDEP later) 

3.5.2 Sources Regulated by Water Management Districts 

Except for NWFWMD, the WMDs regulate stormwater discharges through Environmental 

Resource Permits (ERPs). NWFWMD does not have ERP authority, and stormwater 

permitting is accomplished through FDEP using Chapter 62-25, FAC. Permits are for the 

management and storage of surface waters as well as for dredge and fill activities. The 

WMDs have various levels of information available, such as the Southwest Florida Water 

Management District site that provides ERP information: 

[http://www.swfwmd.state.fl.us/data/gis/libraries/regulatory.htm]. Information generally can 

include the permit number, location, type, owner, and date of permit. However, the data 

quality is variable, as some permit data are complete while others are not. 

3.5.3 Nonpoint Sources 

There are a number of sources of pollutants that are not regulated with the same programs 

as point sources but are nevertheless regulated; these include septic tanks, agricultural 

sources, and diffuse sources such as atmospheric deposition, groundwater and 

sediment/legacy sources. 

Septic Tanks 

Septic tanks in Florida are regulated by the Florida Department of Health (FDOH) in 

conjunction with county Health Departments and are generally referred to as onsite sewage 

treatment or disposal. According to the FDOH Bureau of Onsite Sewage Programs, 31 

percent of Florida’s population is served by about 2.3 million septic tanks. This represents 

about 426 million gallons per day (mgd) to be discharged to subsurface soils 

[http://www.doh.state.fl.us/environment/ostds/intro.html]. The location, condition, type and 

maintenance frequencies data for septic tanks are very difficult to find and may require 

specific data inquiries to local Health Departments. Of particular interest to the TMDL 

program are data related to the number of septic tanks within a certain distance of surface 

waters and the frequency of failure. Septic tanks in close proximity (less than 300 ft) of lakes 

and streams have the greatest affect on water quality. 

In the absence of specific septic tank data, local health and/or public works staff may be 

able to identify residential areas in a community that are not served by public or private 

sewer systems. Historical evidence indicates that approximately 5 to 15 percent of septic 

tanks fail thereby producing high nutrient concentrations and loads to nearby water bodies 

through shallow ground water flows (EPA, 1986; CDM, 1989). If specific failure rates are not 

known, modelers can use a 10 percent failure rate and perform sensitivity analyses pending 

site-specific data collection efforts. Pollutant loading from septic tank leachate monitoring 

studies can be used to estimate loads contributed by failing septic tanks 

(doh.state.fl.us/environment/ostds/research). The range of concentrations for total surface 

loads from total-P and total-N based upon literature values is presented in Table 3.5. 

 3-39 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Table 3.5 Example Septic Tank Seepage Nutrient Concentrations 

Total-P Total-N 

Low 1.0 mg/L 7.5 mg/L 

Medium 2.0 mg/L 15.0 mg/L 

High 4.0 mg/L 30.0 mg/L 

Source: Watershed Management Model User’s Manual Version 3.30, CDM, 1994 

Septic tank failures are a particular concern along lakes and waterbodies where there may 

be a direct hydraulic connection. 

Agricultural Sources 

Except for concentrated animal feeding operations, agricultural discharges are exempt from 

NPDES permitting pursuant to Federal law. However, several types of agricultural 

operations which alter hydrology are permitted by the WMDs. In Florida, agricultural sources 

are primarily the responsibility of the Department of Agriculture and Consumer Affairs 

(DACS), an agency that has been working with FDEP for a number of years to increase 

agricultural pollution control. The document “Florida’s Agricultural Water Policy” (FDACS, 

July 2003) defines the policy of the state regarding water quality: “Agricultural production 

practices should be protective of surface and ground water quality and employ as many 

appropriate BMPs as are economically and technically feasible.” The Florida Watershed 

Restoration Act continues the “nonregulatory” approach to BMP implementation. However, 

the Act requires DACS to adopt, by rule, BMPs to reduce agricultural NPS pollution. If DEP 

verifies the BMPs are effective in reducing pollution, a farmer that has submitted a notice of 

intent to be covered by the DACS BMP rule and that has implemented the required BMPs 

will receive a presumption of compliance similar to that received by an urban developer who 

obtains a ERP permit. In addition, the legislature funded FDACS to research and implement 

BMP strategies. To date, FDACS and FDEP have produced a number of BMP manuals for 

specific agricultural programs including Citrus, Cow-Calf Programs, Agrichemical Handling, 

and Blended Fertilizer Plants. The BMP documents generally describe methods to control 

irrigation, fertilization (for nutrient load reduction as appropriate) and other land 

management practices to reduce pollutant loading. The documents also provide basic 

information about the agricultural practices to aid in the understanding of the pollution 

loading potential. Documents are available through FDACS 

(www.floridaagwaterpolicy.com/publications.com) or FDEP (www.dep.state.fl.us/water/ 

nonpoint/pubs.htm). Furthermore, DACS has adopted some of the BMP manuals and their 

associated checklists and record keeping requirements 

(http://www.floridaagwaterpolicy.com/rules.html). 

Diffuse Sources 

Three other types of sources are diffuse in nature, with no confined point of discharge: 

atmospheric groundwater and sediment/legacy sources. 

Atmospheric deposition sources refer to pollutants that are contributed via dry deposition 

(e.g., leaf litter, airborne particles, etc.) and wet (i.e., via rainfall) pollutants. For both wet and 

dry deposition, pollutants are deposited on land and open water. The pollutants delivered to 

 3-40 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

land contribute to the parameter loads and concentrations in runoff measured in the field. 

There are studies in Florida on the expected pollutant concentrations of wet- and dry-fall 

(e.g., “Tampa Bay Atmospheric Deposition Study: Final Interim Report”, June 2000, 

TBNEP); however, a more precise set of data may need to be collected in the local area as 

rainfall and its pollutant load vary within Florida, especially where power plants, incinerators, 

or other industrial air discharges may occur. Section 6 provides additional information on 

wet-and dry-fall loads in the absence of locally collected data. 

Groundwater sources refer to the influence of spring water or shallow aquifer water flowing 

diffusely into rivers, streams, lakes and estuaries. Groundwater and surface water flows in 

Florida are often interconnected and may be indistinguishable. Consideration of these 

sources was provided in subsections 3.2 and 3.3 

Sediment/Legacy sources refer to the pollutant loading from bottom sediments in a stream, 

river, lake or estuarine sediments as a legacy of sedimentation from the past. While 

sedimentation and settling of particulates are natural phenomena, water bodies with higher 

particulate pollution loading may have a greater legacy of pollutants associated with the 

sediments. In general, the slower the velocities and/or the less flushing a system 

experiences, the greater the potential for sediment/legacy sources to be a significant 

percentage of the overall pollutant loading to the system. Therefore, information on the 

history of pollutant loading to the system, the net downstream velocity or flushing rate of the 

waterbody and the nature of the sediments are important to the characterization of the 

potential for pollutant loading of such sources. These sediment sources can cause SOD, 

nutrient cycling, and other potential adverse impacts. 

As noted above, diffuse sources can be difficult to quantify for a particular waterbody. In the 

absence of local data, it is strongly recommended that a pollutant loading balance may be 

completed as part of the screening portion of the TMDL study. For this balance, an estimate 

for all possible pollutant inputs and outputs, plus storage, may be provided to determine 

significant sources, as illustrated in Figure 3.10 from the document “Charting the Course” 

produced by the Tampa Bay National Estuary Program (TBNEP, December 1996). 

 3-41 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Figure 3.10 Existing Annual Nitrogen Loadings to Tampa Bay by Bay Segment 

(1992-1994 Average) 

 3-42 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

SECTION 3 REFERENCES 

American Society for Testing and Materials. 1997a. D 3978- 80: Standard practice for algal 

growth potential testing With Selenastrum capricornutum: West Conshohocken, Pa., Annual 

Book of ASTM Standards, v. 11.05, p. 29–33. 

American Society for Testing and Materials. 1997b. E 1295-89: Standard guide for 

conducting three-brood, renewal toxicity tests with Ceriodaphnia dubia: West 

Conshohocken, Pa., Annual Book of ASTM Standards, v. 11.05, p. 626–644. 

American Society for Testing and Materials. 1997c. E 1706-95b: Standard test methods for 

measuring the toxicity of sediment-associated contaminants with fresh water invertebrates: 

West Conshohocken, Pa., Annual Book of ASTM Standards, v. 11.05, p. 1138–1220. 

Bedient, P.B. and W.C. Huber. 1992. Hydrology and Floodplain Analysis. 

Brown, D.A., and Parsons, T.R. 1978. Relationship between cytoplasmic distribution of 

mercury and toxic effects to zooplankton and chum salmon, Oncorhynchus keta, exposed to 

mercury in a controlled ecosystem: Journal of the Fisheries Research Board of Canada, v. 

35, p. 880–884. 

Buchanan, T.J. and W. Somers. 1968. “Stage measurement at gaging stations.” USGSTWRI Book 3. Chapter A7. 

Buchanan, T.J. and W. Somers. 1969. “Discharge measurements at gaging stations.” 

USGS-TWRI Book 3. Chapter A8. 

Carlson, R. E. 1977. A trophic state index for lakes. Limnology and Oceanography 

22:361:369. 

Carlson, R. E. 1980. More complications in the chlorophyll-Secchi disk relationship. 

Limnology and Oceanography 25:378:382. 

Carlson, R. E. 1983. Discussion on “Using differences among Carlson’s trophic state index 

values in regional water quality assessment”, by Richard A. Osgood. Water Resources 

Bulletin. 19:307-309. 

Carter, R.W. and J. Davidian. 1968. “General procedure for gaging streams.” USGS-TWRI 

Book 3. Chapter A6. 

CDM. 1989. City of Daytona Beach Stormwater Master Plan. 

CDM. 1991. SWMM RUNOFF, TRANSPORT, and EXTRAN Applications. 

CDM. 1992. City of Jacksonville Master Stormwater Management Plan. 

CDM. 1994. Watershed Management Model. 

CDM. 1995. Tomoka River Watershed Management Plan for Volusia County. 

 3-43 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Couch, J.A. 1975. Histopathological effects of pesticides and related chemicals on the livers 

of fishes, in Ribelin, W.E., and Migaki, G., eds., Pathology of Fishes: Madison, Wis., 

University of Wisconsin Press, p. 559–584. 

Cuffney, T.F., Gurtz, M.E., and Meador, M.R. 1993. Guidelines for the processing and 

quality assurance of benthic invertebrate samples collected as part of the National WaterQuality Assessment Program: U.S. Geological Survey Open-File Report 93-407, 80 p. 

EPA. 1983. Nationwide Urban Runoff Program. 

EPA. 1985. Rates Constants, and Kinetics Formulations in Surface Water Quality Modeling 

(Second Edition). 

EPA. 1986. “Forecasting onsite soil adsorption failure rates.” EPA/600/2-86/060. 

ERD. 2003. Evaluation of Alternative Stormwater Regulations for Southwest Florida. 

ERD. 2006. Evaluation of Current Stormwater Design Criteria within the State of Florida. 

Finger, S.E., and Bulak, J.S. 1988. Toxicity of water from three South Carolina rivers to 

striped bass: Transactions of the American Fisheries Society, v. 117, p. 521–528. 

Harper, H. 1992. Estimation of Stormwater Loading Rate Parameters for Central and South 

Florida. 

Health and Rehabilitative Services. 1989. City of Jacksonville, Storm Water Improvement 

Management (SWIM) Program. 

Huber, W and R. Dickinson. 1992. US EPA SWMM Users Manual. 

Huckins, J.N., Manuweera, G.K., Petty, J.D., Mackay, Donald, and Lebo, J.A. 1993. Lipidcontaining semipermeable membrane devices for monitoring organic contaminants in water: 

Environmental Science and Technology, v. 27, p. 2489–2496. 

Kaiser, K.L.E., and Palabrica, V.S. 1991. Photobacterium phosphoreum toxicity data index: 

Water Pollution Research Journal of Canada, v. 26, p. 361–431. 

Karr, J.R. 1981. Assessment of biotic integrity using fish communities: Fisheries, v. 6, p. 21–

27. 

Maidment, D.R. 1993. Handbook of Hydrology. 

Meador, M.R., Cuffney, T.F., and Gurtz, M.E. 1993. Methods for sampling fish communities 

as part of the National Water Quality Assessment Program: U.S. Geological Survey OpenFile Report 93-104, 40 p. 

National Resource Conservation Service (NRCS, formerly SCS). 1985. National Engineering 

Handbook. Section 4 Hydrology. 

 3-44 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Naumann, E. 1929. The scope and chief problems of regional limnology. Int. Revue ges. 

Hydrobiol. 21:423. 

Novotny, V. 2004. “Simplified Databased Total Maximum Daily Loads, or the World is LogNormal.” ASCE Journal of Environmental Engineering, 130(6), pp. 674-683. 

Porter, S.D., Cuffney, T.F., Gurtz, M.E., and Meador, M.R. 1993. Methods for collecting algal 

samples as part of the National Water-Quality Assessment Program: U.S. Geological Survey 

Open-File Report 93-409, 39 p. 

Rutledge, A.T. 1998. Computer programs for describing the recession of ground-water 

discharge and for estimating mean ground-water recharge and discharge from streamflow 

records—Update. USGS Water-Resources Investigations Report 98–4148. U.S. Geological 

Survey. 

SFWMD. 2004. Environmental Resource Permit Information Manual. Volume IV. 

SJRWMD. 1988. A Guide to SCS Runoff Procedures. TP 85-5. 

Snedecor, G.W. and W. Cochran. 1973. Statistical Methods. 

Stegeman, J.J., Brouwer, M., Di Giuilo, R.T., Förlin, Lars, Fowler, B.A., Sanders, B.M., and 

van Veld, P.A. 1992. Molecular responses to environmental contamination: Enzyme and 

protein systems as indicators of chemical exposure and effect, in Huggett, R.J., Kimerle, 

R.A., Mehrle, P.M., Jr., and Bergman, H.L., eds., Biomarkers: 

Thomas, J.A.; Bovee, K.D. 1993: Application and testing of a procedure to evaluate 

transferability of habitat suitability criteria. Regulated Rivers: Research and Management 8: 

285–294. 

Thomas, P. 1990. Teleost model for studying the effects of chemicals on female 

reproductive endocrine function: Journal of Experimental Zoology, v. 4, p. 126–128. 

United States Department of the Interior - Bureau of Reclamation. 2001. Water 

Measurement Manual. 

USGS and U.S. Atomic Energy Commission. 1967. Techniques of Water Resources 

Investigations of the United States Geological Survey. 

USGS. 1996. Estimation of evapotranspiration in the Rainbow Springs and Silver Springs 

Basins in North-Central Florida. USGS WRI 96-4024. 

USGS. 1996. Evapotranspiration from successional vegetation in a deforested area of the 

Lake Wales Ridge, Florida. USGS Water-Resources Investigations Report 96-4244. 

Note: A number of Internet addresses are identified for data sources in the section and in 

Appendix 3A. 

 3-45 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

  

 3-46 
Appendix 3A

DATA TABLE

Table 3A: FDEP TMDL Protocol: Example of Inventory D

Data Category Data Source Date or 

Origin Period of Record

Biology Habitat Suitability Curve

http://www.swfwmd.state.fl.us/documents/reports/files/peace

%20river%20review2.pdf, 

http://www.doc.govt.nz/Publications/004~Science-andResearch/Science-for-Conservation/PDF/sfc231.pdf Varies 

Trophic State Index http://dipin.kent.edu/tsi.htm Varies 

Biological Assessment Techniques

http://www.epa.gov/owow/monitoring/rbp/wp61pdf/ch_02.pdf, 

http://ma.water.usgs.gov/fhwa/products/ofr99-240.pdf, 

http://www.nmfs.noaa.gov/habitat/ecosystem/habitatdocs/efhwkshp

doc.PDF

Varies 

Climate and Meteorology Rainfall

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7; http://www.noaa.org/; 

http://www.ncdc.noaa.gov/oa/ncdc.html

Varies 

Temperature

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7; http://www.noaa.org/; 

http://www.ncdc.noaa.gov/oa/ncdc.html

Varies 

Solar Radiation

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7; http://www.noaa.org/; 

http://www.ncdc.noaa.gov/oa/ncdc.html

Varies 

Wind Speed

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7; http://www.noaa.org/; 

http://www.ncdc.noaa.gov/oa/ncdc.html

Varies 
Appendix 3A

DATA TABLE

Table 3A: FDEP TMDL Protocol: Example of Inventory D

Data Category Data Source Date or 

Origin Period of Record

Pan evaporation

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7; http://www.noaa.org/; 

http://www.ncdc.noaa.gov/oa/ncdc.html

Varies 

Groundwater Models and Data Sink Hole ftp://ftp.dep.state.fl.us/pub/gis/data/ -- Point s

Groundwater wells

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7

Varies 

Potentiometric Surface

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7

Varies 

Recharge ftp://ftp.dep.state.fl.us/pub/gis/data/; http://sjr.state.fl.us -- Point s

Report on Floridan Aquifer USGS #1403-G, Frederick W. Meyer, http://sofia.usgs.gov/publications/papers/pp1403g -- 
Appendix 3A

DATA TABLE

Table 3A: FDEP TMDL Protocol: Example of Inventory D

Data Category Data Source Date or 

Origin Period of Record

Hydrology Hydrographic basin

ftp://ftp.dep.state.fl.us/pub/gis/data/; 

http://www.usgs.gov/;http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7; 

2003 Polygon 

Bathymetry

ftp://ftp.dep.state.fl.us/pub/gis/data/; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7;

--

Polygon 

Land Use Landuse

ftp://ftp.dep.state.fl.us/pub/gis/data/; http://www.sfwmd.gov; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7;

-- Polygon 

Roads

ftp://ftp.dep.state.fl.us/pub/gis/data/; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7;

2003 Arc sh

Aerial Photogrametry

http://data.labins.org/2003/MappingData/DOQQ/doqq.cfm; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7;

1999 

Non-Point Source Pollution Agriculture http://www.dep.state.fl.us/water/nonpoint/ Varies 
Appendix 3A

DATA TABLE

Table 3A: FDEP TMDL Protocol: Example of Inventory D

Data Category Data Source Date or 

Origin Period of Record

Best Management Practices http://www.dep.state.fl.us/water/nonpoint/ Varies 

Permit Groundwater/Surface water/Stormwater/Wetland

http://www.dep.state.fl.us/secretary/info/permitting.htm; 

http://www.srwmd.state.fl.us/services/permitting/default1.htm; 

https://permitting.sjrwmd.com/epermitting/jsp/start.jsp; 

http://www.swfwmd.state.fl.us/permits/; 

http://www.sfwmd.gov/site/index.php?id=25

Varies 

Point-Source Pollution Public Facilities (Wastewater/Water Treatment Facilities)

http://www.dep.state.fl.us/secretary/info/permitting.htm; 

http://www.srwmd.state.fl.us/services/permitting/default1.htm; 

https://permitting.sjrwmd.com/epermitting/jsp/start.jsp; 

http://www.swfwmd.state.fl.us/permits/; 

http://www.sfwmd.gov/site/index.php?id=25

Varies 

Industrial Facilities

http://www.dep.state.fl.us/secretary/info/permitting.htm; 

http://www.srwmd.state.fl.us/services/permitting/default1.htm; 

https://permitting.sjrwmd.com/epermitting/jsp/start.jsp; 

http://www.swfwmd.state.fl.us/permits/; 

http://www.sfwmd.gov/site/index.php?id=25

Varies 

Septic Tank

http://www.dep.state.fl.us/secretary/info/permitting.htm; 

http://www.srwmd.state.fl.us/services/permitting/default1.htm; 

https://permitting.sjrwmd.com/epermitting/jsp/start.jsp; 

http://www.swfwmd.state.fl.us/permits/; 

http://www.sfwmd.gov/site/index.php?id=25

Varies 

Best Management Practices

http://www.dep.state.fl.us/water/; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7

Varies 
Appendix 3A

DATA TABLE

Table 3A: FDEP TMDL Protocol: Example of Inventory D

Data Category Data Source Date or 

Origin Period of Record

Soils/Geotechnical Soils Series ftp://ftp.dep.state.fl.us/pub/gis/data/; http://www.nrcs.usda.gov/ 2003 Polygon 

Surface Water Models and Data Floodplain ftp://ftp.dep.state.fl.us/pub/gis/data/; http://www.fema.gov/ Varies Point s

Flow

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7

Varies 

Surface Water

ftp://ftp.dep.state.fl.us/pub/gis/data/; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7

Varies Polygon 

Gaging stations

ftp://ftp.dep.state.fl.us/pub/gis/data/; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7

Varies Point s

Wetlands

ftp://ftp.dep.state.fl.us/pub/gis/data/; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7; 

http://www.nwi.fws.gov/

Varies Point s
Appendix 3A

DATA TABLE

Table 3A: FDEP TMDL Protocol: Example of Inventory D

Data Category Data Source Date or 

Origin Period of Record

Structure(s)

http://www.usace.army.mil/; 

http://www.dot.state.fl.us/rddesign/; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7

Varies 

Stage

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7

Varies 

Topography Topography/Survey

http://data.labins.org/2003/MappingData/USGS_Download/q

uad.cfm?quad=2402; 

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.h

tml; http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7

Varies 

Water Quality Models and Data Pesticide leaching potential in aquifers ftp://ftp.dep.state.fl.us/pub/gis/data/ Not Available Point s

Impaired Water 303d ftp://ftp.dep.state.fl.us/pub/gis/data/ 1998 Polygon 

Outstanding Florida Water ftp://ftp.dep.state.fl.us/pub/gis/data/ Not Available Polygon 
Appendix 3A

DATA TABLE

Table 3A: FDEP TMDL Protocol: Example of Inventory D

Data Category Data Source Date or 

Origin Period of Record

Water Quality

http://www.state.fl.us/nwfwmd/isb/isb.htm; 

http://www.srwmd.state.fl.us/; 

http://sjr.state.fl.us/programs/plan_monitor/gis/docs/themes.html; 

http://www.swfwmd.state.fl.us/data/; 

http://www.sfwmd.gov/site/index.php?id=7; 

http://www.epa.gov/storet/

Varies 

Notes: 1 This table provides an overview of typical data considered for TMDL evaluations. It is meant 

2 Local county, city, and water control district data should also be considered to augment data.3 Other local and anecdotal data (citizen input and public meetings) should also be considered

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Appendix 3B 

TIDAL PRISM CALCULATIONS 

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Tidal Prism Calculation Method 

Flushing may be analyzed by either of the following methods: 

A. The Fraction of Freshwater Method (EPA, 1985) is a six-step method as follows: 

1. Graph the estuarine salinity profile by constructing a graph showing salinity on the yaxis and the distance from the head of the estuary on the x-axis. 

2. On the graph, divide the estuary into segments. The segmentation should be such 

that in areas where there are steep salinity gradients, the segments will be smaller; 

and in areas where salinity is constant, the segments may be longer. 

3. Calculate each segment’s fraction of freshwater by: 

fi = (Ss – Si)/ Ss;

where fi is the fraction of freshwater in segment i, Ss is the salinity of local 

seawater, and Si is the mean salinity for segment i. 

4. Calculate the quantity of freshwater in each segment by: 

WI = fi x Vi; 

where Wi is the quantity of freshwater in segment i, and Vi is total volume of 

segment i at mean tide. 

5. Calculate the exchange time (flushing time) for each segment by: 

Ti = Wi / R; 

where Ti is the segment flushing time in tidal cycles, and R is the river 

discharge over one tidal cycle. 

6. Calculate the entire estuary flushing time by summing the exchange 

times for the individual segments: 

Tf = Σ Ti, i=1 to n; 

where Tf is the estuary flushing time, in tidal cycles, and n is the number of 

segments. 

B. The Tidal Prism method does not require knowledge of the salinity distribution. The 

method uses the volume of water in the estuary at low tide, the volume of water in 

the estuary at high tide, and the volume of freshwater flowing into the estuary over 

one tidal cycle to compute the flushing time, as follows: 

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1. Use streamflow data to estimate the volume of freshwater (Vf) flowing into the 

estuary during a tidal cycle (one rising tide and one falling tide). 

2. Use tide charts and physical dimensions to estimate the difference in volume of 

water (Vp, the prism) in the estuary between high and low tide. 

3. Calculate the flushing time as: 

T f = T x V /(Vt + Vr); 

where T is the time of the tidal cycle (typically 12.5 or 25 hours), V is the total 

volume of water in the estuary, Vt is the difference in estuarine volume 

between high and low tide, and Vr is the volume of freshwater inflow 

(streamflow rate x T). 

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Appendix 3C 

BACTERIA CHARACTERIZATION 

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Appendix 3C 

Fecal Coliform Bacteria Characterization and 

Evaluation 

3.C.1 Background 

Monitoring of ambient waters for microbial pathogens (microorganisms that cause 

disease in humans) in Florida and across the United States is conducted to assess the 

suitability of the waters to support a variety of beneficial uses as described in the federal 

Clean Water Act of 1972 (CWA). In the State of Florida, surface waters are classified as 

one of the following: 

(i) Class I (Potable Water Supply); 

(ii) Class II (Shellfish Propagation or Harvesting); 

(iii) Class III (Recreation, Fish, and Wildlife); 

(iv) Class IV (Agricultural Water Supplies); and 

(v) Class V (Navigation, Utility and Industrial Use) – Although there are no waters 

currently classified as Class V in Florida. 

In addition, the classification of Outstanding Florida Waters (OFW) is designated for 

water quality protection. 

For practical reasons, regulatory agencies throughout most of the United States, 

including the FDEP, have historically used and continue to use fecal coliform bacteria 

(FC) as an indicator to assess the potential of the existence of pathogenic 

microorganisms (bacteria and viruses) in those waters. FC exists in the intestinal tracts 

of all warm-blooded animals, including humans, and is discharged in their feces in large 

numbers. However, the presence of FC bacteria does not definitely prove the presence 

of pathogenic microorganisms, but it does provide an indication of the probability that 

such pathogens might be present. Elevated levels of FC in surface waters may trigger 

the regulatory agencies to close recreational beaches to avoid human contact with the 

suspect waters and to close shellfish harvesting areas to avoid consumption of 

pathogens, thereby reducing the possibility of exposure to a waterborne disease. FC 

data are commonly available in most databases (e.g., STORET). 

Other indicators have been and are being investigated by agencies including US EPA, 

Food and Drug Administration (FDA), and the USGS. These other indicators are 

documented in example references and include: 

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 Escherichia Coliform (E. coli) 

 Fecal Streptococci (FS) 

 Atypical Coliform (AC) 

 Total Coliform (TC) 

 Enterococci 

For Class I and Class III waters, the applicable State of Florida water quality standard 

with respect to FC is a 30-day geometric mean of 200 MPN/100 ml (based upon a 

minimum of 5 to 10 ambient water samples in a 30-day period), and any single sample is 

not to exceed 800 MPN/100 ml. The term “MPN” is the abbreviation for “most probable 

number”. In addition, no more than 10 percent of the samples can exceed 400 MPN/100 

ml. The Class II limits are more restrictive, with a maximum geometric mean of 14 

MDN/100 ml, and no more than 10 percent of samples can exceed 43 MPN/100 ml. 

Exceedance of applicable ambient water quality standards for FC is an issue for ambient 

waters throughout the entire Unites States (US) (CDM, 2001), and in Florida 

approximately 17 percent of impaired waters are listed for pathogens 

(www.dep.state.fl.us/water). WBIDs defined as impaired in Florida are either “partiallysupporting” or “not-supporting” designated uses per Section 305(b) of the CWA. Ambient 

water bodies classified as “impaired” are subject to development and implementation of 

a Total Maximum Daily Load (TMDL) for each pollutant listed as the cause of the 

impairment. 

This FC Bacteria Characterization and Evaluation Appendix focuses on the 

characterization and evaluation of FC bacteria. It is necessary to identify potential 

sources of FC for an accurate TMDL and for a cost-effective implementation. Typically, 

point sources are representative of municipal wastewater treatment facilities, while 

nonpoint sources are often representative of stormwater runoff (urban, rural, and 

agricultural), septic tank seepage, and sanitary sewer overflows (SSOs). In order to limit 

or reduce FC levels and comply with the applicable FC water quality standard, the TMDL 

data evaluation may need to include data coordinated with field and laboratory 

investigations to identify and characterize FC sources and locations. The investigations 

may also include processes to differentiate human from non-human FC sources, as well 

as differentiation between types of animals. 

3.C.2 Goals 

The goals of this appendix are to: 

 Summarize the types of microorganisms typically measured in laboratory analyses 

as indicators of pathogenicity and provide references. 

 Summarize alternative methods used for identifying general sources of FC, such as: 

failing septic tanks, SSOs, illicit connections, illegal discharges (e.g., “blue water” 

dumping), agricultural practices, and domesticated animals (horses, chickens, pigs, 

cows), pet waste, and wildlife (ducks, geese, raccoons, etc.). 

 Summarize alternative biological and chemical methods used to track potential FC 

sources. 

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 Document this information for a potential protocol to assist in FC source 

identification. 

 Identify modeling issues for consideration. 

3.C.3 Sources and Magnitudes 

Monitoring programs throughout the Unites States have documented the high variability 

of FC levels in ambient water samples, even at the same monitoring location from one 

sampling period to another. Numerous factors are responsible for this, including wet 

weather variations in stormwater runoff, antecedent moisture conditions, sewer 

infiltration and inflow, and septic tank performance and maintenance. 

Pitt (2004) published a report with urban stormwater runoff statistics from the NPDES 

MS4 monitoring programs. The report summarizes measured runoff concentrations from 

10 years of monitoring in 66 urban areas within 17 states. For FC, Pitt reports a median

value band of 8,000 to 11,000 MPN/100 ml for residential land use, and 4,300 to 5,000 

MPN/ml for commercial land use. 

Other sources have reported mean (average) FC levels for stormwater runoff in urban 

areas. The mean values are higher than the median or geometric mean values because 

they are skewed by the highest measured values. 

Pitt (1998) reported a mean1

 count of 20,000 MPN/100ml using the results of the 

Nationwide Urban Runoff Program (NURP) completed in the 1980s by the EPA (1983). 

Under the NURP, more than 2,220 storm events were monitored at 28 urban locations 

across the U.S. Pitt (1998) reported on 25 studies conducted since the NURP evaluation 

and reported a mean coliform concentration of 20,000 MPN/100ml. In Florida, Tampa 

was a NURP site and the mean value for 15 samples was 24,400 MPN/100 ml. The 

Center for Watershed Protection (Schueler, 1999) evaluated 34 urban monitoring 

programs and reported a mean of 15,000 MPN/100ml. 

There are numerous sources of FC in urbanized and rural watersheds. Human sources 

may include SSOs, illicit connections, illegal dumping, septic systems, landfills, marinas, 

pumpout facilities, and inadequately treated direct discharges. Examples of non-human 

sources include waterfowl, pets, livestock (including small farms), rats, raccoons, 

pigeons, gulls, otters, beavers, and other animals commonly found along urban and rural 

streams. Table 1 compares the daily discharge rate of FC for humans and typical 

urban/domesticated animals. 

 

1

It should be noted that coliform data are not normally distributed. Consequently, mean values 

are frequently quite different from the median, or the geometric mean. Unfortunately, most 

authors cite only one summary statistic making inter-study comparisons difficult. 

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Table 1 

Daily Discharge of Fecal Coliform 

(Counts/day/individual) 

Waterfowl 3.97E+09 

Dog 3.34E+09 

Duck 2.25E+09 

Human 2.07E+09 

Cow 1.61E+09 

Cat 5.38E+08 

Rat 5.81E+06 

 Source: Geldrich et al., 1962; Godfrey, 1992; Pitt, 1998) 

Two studies (Bannerman et al, 1993. Steuer et al., 1997) indicated that residential 

lawns, driveways and streets are a major source of bacteria in runoff. Both studies also 

indicated that end-of-pipe concentrations were an order of magnitude higher than the 

runoff source, suggesting that either illicit connections from sanitary sewer lines are a 

major contributor to the problem, or the stormwater conveyance system itself was a 

major source. 

Genetic studies (Alderiso et al., 1996; Trial et al., 1993) for some areas have indicated 

that large percentages of FC in stormwater was from non-human sources, further 

suggesting that the conveyance system itself may be a significant contributor. Indeed, 

studies by Burton et al. (1987) and Marino and Gannon (1991) have confirmed that FC 

bacteria can both survive and reproduce in the highly organic sediments frequently 

found in stormwater ditches and retention ponds, and growth in closed stormwater 

systems (dark, warm water, nutrients) has been documented for some Best 

Management Practice (BMP) evaluations. 

Recent evidence also indicates that FC can survive and even multiply in the sediments 

after runoff events. Surcharged stream conveyance systems that cause streambed 

erosion may actually re-suspend colonies deposited during previous events. 

3.C.4 Source Investigation Process 

The objectives of the source investigation process are first to identify locations, land 

uses, and/or specific discharges of FC bacteria; second to differentiate human sources 

from non-human sources; and third to identify the specific animal species responsible for 

the FC, if necessary. 

Some sources may be present in runoff during storm events, while others may be 

present in both runoff and dry weather baseflow. A hydrograph separation technique is 

are presented in Section 3 of the TMDL Protocol if the practitioner needs to evaluate the 

FC data versus flow conditions. Examples of runoff FC can include SSOs, pet waste in 

parks, and/or farm pastures. An example of a source which would be present in both 

runoff and baseflow is a continuous illicit discharge. While the initial effort should focus 

on the elimination of human sources, “windshield” and sometimes marine (boat) surveys 

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should be conducted early in the source identification program to identify obvious human 

and non-human sources. These obvious sources should be eliminated when identified, if 

possible (e.g., buildup of pet wastes at or near bathing beaches, apparent septic tank 

seepage). Non-human sources should be investigated after the human sources have 

been identified and eliminated, if possible, although some sources may be from natural 

fauna and cannot be eliminated. Use of existing data can help focus field work by 

prioritizing high probability source areas. 

Although some sources will also contribute during dry weather, the protocol is intended 

primarily for identification of significant urban sources contributing during wet weather 

and runoff events. The purpose of the initial survey is to identify probable major sources 

prior to resorting to additional sampling involving one or more chemical or biological 

tracers. 

One source that may be particularly difficult to isolate is the re-suspension of FC that 

survive and reproduce in the sediments of the stormwater conveyance system and 

treatment ponds. Areas of high sediment deposition may be suspect, and measurement 

of coliform in the sediment instead of the water column may be required to isolate this 

source. 

To assist in this investigation, a number of procedures and methods are currently in use. 

Some are basic and complementary to progressively more complex investigations, and 

others provide an alternative mechanism to identifying specific sources of FC. The 

following process is suggested. 

Step 1 - Compile Data: The source investigation should begin by compiling available 

watershed and receiving water specific coliform data (e.g., FDEP, STORET, USGS, and 

local programs) and cataloging the data into dry and wet weather data. Geometric 

means should be developed for each station (or subbasin) and the results entered into a 

GIS database or plotted on a map of the sub-basins. Geographic areas where either wet 

or dry weather bacteria concentrations are unusually high should be targeted for further 

inspection. The initial effort should be identification of the human sources. 

Step 2 - Evaluate Domestic Wastewater Treatment Systems: The second step 

should be to perform a detailed evaluation of domestic wastewater treatment systems. 

Sub-basin characteristics such as known data on septic tanks and permits, streams 

(hydrography) and wetlands, land use and/or aerial photography (different time periods 

can be correlated to data trends), SSO records for overflows, WWTP outfalls, and if 

necessary sanitary system pipes and pump stations, maintenance (for both regional 

sewer and septic tanks), suitability of soils for septic systems, and proximity to 

waterbodies should be evaluated for potential sources and compared to the coliform 

database. If available, pump-out records for septic systems and/or complaints about 

malfunctioning septic systems should be investigated although these are rarely 

available. GIS applications can assist in locating and prioritizing the number of potential 

sources and focusing the source investigation. Areas which exhibit both a high source 

potential and high historical coliform counts should be targeted for additional windshield 

surveys and/or follow-up FC sampling. 

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Step 3 - Identify Non-Human Sources: After identifying the more apparent human 

sources, the source identification should target the remaining areas of elevated FC 

counts using the same basic methodology. Farms, kennels, and equestrian centers 

should be identified and mapped. Waterbodies with high known densities of waterfowl 

should be identified. Public parks should be evaluated for density of urban wildlife and 

the intensity of use as pet walks. Animal control authorities may be able to identify areas 

of high animal density, and pet waste ordinance enforcement may assist with specific 

WBIDs. 

Step 4 - Design a Follow-up Monitoring Program: If necessary, the next step is to 

design a follow-up monitoring program to confirm the source(s) of FC bacteria. The 

monitoring plan can be designed to isolate a source by progressively sampling at 

upstream locations until the source is identified. The follow-up program will likely involve 

sample collection and lab analyses of additional tracer chemicals that are discussed 

below. 

3.C.5 Indicators, Tracers, and Differentiators 

A variety of chemical and biological methods may be used to indicate, trace, and/or 

differentiate bacteria and sources. While most tracers detect the presence of either 

treated water or “used” potable water (targeting a parameter that would only be present 

in wastewater), some are direct measures of FC bacteria as described below. Examples 

of direct evidence wastewater tracers include caffeine and pharmaceutical drugs. 

Currently there is no tracer that will directly distinguish between a septic tank source and 

wastewater treatment facility, although Pitt (2004) has identified potential indicators such 

as the ammonia to potassium ratio (NH3/K). 

3.C.5.1 Biological Methods 

Microbial Source Tracking (MST) 

Collectively referred to as “microbial source tracking” (MST), the fundamental premises 

of these methods are that: 

(1) FC of ambient waters can be traced back to the originating source; 

(2) These sources can further be characterized as being either human or animal; and 

(3) Identification of the specific animal species is possible. 

Currently, MST is being applied at the international, national, regional, state, and local 

level, with research continuing in the development of more reliable, faster, practical, and 

cost-effective methods. All current MST methods, which are generally classified as 

microbiological, genotypic, or phenotypic have the common goal of characterizing 

groups of microorganisms that can be used as FC indicator organisms and can further 

lead to identification of a specific source species. Specifically, the methods are designed 

to detect a specific microorganism “signature” in an effort to distinguish and identify its 

originating source (for example, human, dog, goose, duck, deer, raccoon, etc.). 

Microorganisms under consideration include specific FC bacteria (e.g., Escherichia coli, 

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Citrobacter freundi, Enterobacter aerogenes, Klebsialla pneumonia), fecal streptococci 

(e.g., Enterococcus avium, Enterococcus faecalis, Enterococcus faecium, Enterococcus 

gallinarum, Streptococcus bovis, Streptococcus equines), animal specific bacteriophage 

(e.g., male bacteriophage), human entero viruses (e.g., enteroviruses, adenoviruses), 

and bacteroides-Prevotella. 

Biochemical approaches attempt to identify unique characteristics of different 

microorganisms and the methods based on these approaches are called “phenotypic’ 

(Vikas et al, 2004; Meays et al, 2004). The more popular phenotypic MST methods 

include: antibiotic resistance analysis (ARA) (Wiggins, 1996; Harwood et al., 2000; 

Hagedorn et al., 1999; Geary and Davies, 2003), carbon source utilization (CSU) (Griffith 

et al., 2003); F+

 coliphage serotyping (Messer, 2004), host-specific indicators (for 

example, Enterococcus avium) (Kuntz et al., 2004); and bacteriophage indicators 

(Burkhardt et al., 1998; Noble et al., 2003). 

AC/TC Ratio 

Rationale: Atypical coliform bacteria have been identified by several researchers as an 

additional indicator. Brion (2003) “has identified a relatively inexpensive tool for 

indicating microbial quality in watersheds utilizing the conventional total coliform test, 

comparing the relative concentration of different colonies that form on a membrane filter 

fed by m-Endo media. These bacterial colonies can be classified into 3 types: typical 

(TC), atypical (AC), and background. The ratio of the concentrations of AC colonies to 

those of TC is related to water quality, fecal loadings, and fecal age. The AC/TC ratio 

relies upon shifts in populations between indigenous and introduced bacteria, with the 

indigenous bacteria providing a baseline against which the concentrations of the 

introduced are evaluated. When the AC/TC ratio is low (<5), fresh fecal material is in the 

water and pathogen risk can be expected to be higher. As time passes, the AC/TC ratio 

increases (>20) and can be related to healthier water quality conditions. Different types 

of runoff have different AC/TC values with human sewage at the lowest end of the 

spectrum with a value of 1.5 under normal conditions.” 

FC/FS Ratio 

Rationale: Traditionally, the ratio of FC to fecal streptococci (FS) had been used to 

assess whether the source is likely to be human or animal (APHA, 1998). However, 

guidance in the form of testing methods has been unclear on this point and water 

resources managers have been discontinuing the use of this ratio. Feachem (1975) 

reports that the ratio is constantly changing with time due to differing rates of die-off 

between the two bacterial groups. If the source is human, the ratio will start out high and 

decrease with time. In contrast, a non-human source begins with a low ratio, which 

increases with time. 

As a result, testing results are only valid if taken within 24 hours following deposition of 

feces. Consequently, the limitations of testing prevent practical application to storm 

water monitoring field studies. 

Coprostanol 

Rationale: Coprostanol is a fecal sterol that exists in human and animal wastes. It exists 

as a series of chemical isomers. The ratio of certain isomers is relatively specific for 

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humans. Because of coprostanol’s affinity for particulates, coprostanol measurements 

are typically performed on sediments. Coprostanol is more persistent in sediments than 

other indicators such as Linear Alkylbenze Sulfonates (LAS). Sediment concentrations in 

excess of 0.1 mg/kg and containing the specific mix of isomers indicate human sewage 

sources. 

Lab Issues: Coprostanol is not commonly analyzed by commercial environmental 

laboratories, although several universities and research organizations do conduct this 

test. 

DNA and RNA 

Rationale: More definitive source identification can be obtained by matching the genetic 

code of coliform bacteria found in stormwater or baseflow. Coliform bacteria are filtered 

from the suspect water source and incubated (Coastlines, 1998). A DNA pattern is 

obtained from ribosomal RNA of each isolate and compared to the DNA patterns of 

various sources. Large libraries of isolates have been developed for the scat of urban 

animals (pets, raccoons, beaver, migrating fowl etc.) and marine mammals. The 

similarity of the DNA patterns is used to assess the likelihood and variety of the source. 

To some extent, the patterns remain consistent from region to region for a given source 

(Dr. Samadpour, personal communication). Therefore, within limitations, it is possible to 

use the DNA ribotype of a raccoon isolate from Florida, for example, to suggest a source 

of raccoon coliform in waters elsewhere. 

Lab Issues: Once the candidate sources have been short-listed, local specimens can be 

collected and used to refine the testing. The cost of incubating coliform and preparing an 

RNA fingerprint from a local source is about $150 to $200 per coliform and higher. For a 

typical urban evaluation, approximately 200-400 coliform cells are isolated, incubated 

and their RNA mapped. Only a handful of research laboratories are capable of providing 

this type of analysis. The USGS began RNA ribotyping in metropolitan Atlanta in early 

2000 (USGS, 1999-present). 

Previous Studies: Genotypic or molecular methods are designed to obtain a specific 

microorganism’s DNA genetic sequence (commonly referred to as “DNA fingerprint”). 

These methods include host-specific 16S ribosomal DNA genetic markers (Bernhard 

and Field, 2000a), repetitive extragenic palindromic-polymerase chain reaction or repPCR (Carson et al., 2003), ribotyping (Hartel et al., 2002), length heterogeneity-PCR or 

LH-PCR (Bernhard and Field, 2000b), terminal restriction fragment length polymorphism 

or T-RFLP (Bernhard and Field, 2000b), and pulse-filed gel electrophoresis or PFGE 

(Parveen et al., 2001). 

Enteric Virus 

Rationale: Enteric viruses and pathogens have also been used as direct evidence of 

contamination with sanitary wastewater. Organisms such as coliphage and bacteroides 

that reproduce only in bacteria found in the human digestive system can be used as 

definitive tracers. FDA (Burkhardt et al., 1998) has identified male bacteriophage as a 

cost-effective indicator for human versus non-human sources. 

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Lab Issues: Specialized laboratories provide analysis of enteric viruses. 

ARA 

The ARA uses the scat from the variety of animals along with a number of antibodies to 

identify a characteristic resistance pattern by animal type (Harwood, 2000; CDM, 2001). 

Previous Studies: The ARA has been successfully applied in Atlanta (CDM, 2001), 

Denver (CDM, 2003), and other locations to identify potential human and nonhuman 

sources. In Atlanta, human, cat, dog, chicken, pig, cow, horse and wild animal scat 

samples were used with the antibodies to characterize sources for management plan 

controls. The resultant data were consistent with land use, animal types by area, soils 

and stream conditions, and age of septic and sanitary systems. Consideration for future 

enhancement included adding one or two more antibodies to improve confidence 

intervals, taking human samples from a stadium where only human waste is expected, 

and potential differentiation for wild animals. 

3.C.5.2 Chemical Methods 

Surfactants 

Rationale: Surfactants are widely used in both domestic and industrial detergents and 

are commonly measured as Methylene-Blue Active Substances (MBAS). However, the 

MBAS test is not specific to the LAS, the predominant class of anionic surfactants. 

 

Lab Issues: MBAS is a commonly measured water quality parameter and is a useful 

tracer in the absence of naturally occurring interferences. Direct measurement of LAS is 

a more complex analysis requiring infrared spectroscopy or gas chromatographic 

techniques and is not commonly analyzed by commercial environmental laboratories. 

Previous Studies: In studies of the Mississippi River (Barber et al., 1995), interference 

from naturally occurring MBAS substances (e.g. humic acids) resulted in poor correlation 

between MBAS and direct measurement of LAS. Thus, direct testing for LAS is preferred 

over the more common MBAS measurement of surfactants. LAS degrades easily under 

aerobic conditions and can be measured in either aqueous or sediment samples. 

Results of the Mississippi River studies indicate aqueous concentrations ranging from 

0.01 to 0.00005 mg/l and sediment concentrations ranging from 0.1 to 1 mg/kg 

downstream of major urban centers. Surfactants are present in domestic wastewater in 

the range of 1-20 mg/l (Standard Methods, 18th Edition). As with all of the non-specific 

tracers, the presence of LAS alone is not confirmatory for domestic sources, but is more 

likely with proximity to either a domestic or industrial wastewater source. 

Caffeine 

Rationale: Caffeine is naturally present in coffee and is an additive in many soft drinks 

and food products, which makes caffeine a fairly specific tracer of domestic wastewater. 

Concentrations in municipal wastewater range from 20-300 ug/l. 

Lab Issues: The USGS (NWQL, 1997) has modified the standard GC/MS technique 

developed for priority pollutants to achieve a method detection limit (MDL) of 0.02 ug/l 

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for caffeine. The USGS is currently monitoring caffeine in the Atlanta area as part of their 

wastewater tracer study. Initial sampling showed caffeine in most samples. 

EDTA 

Rationale: EDTA (ethylene diamine tetra-acetic acid) is widely used in domestic and 

industrial applications to solublize metals. Uses include bleaching agents in domestic 

laundry soaps and prevention of boiler scale in industry. It is also an additive to many 

food products. Like LAS and other non-specific tracers, its presence is suggestive of 

anthropogenic sources, but cannot differentiate sanitary wastewater from other types of 

sources. 

Fluoride 

Rationale: In some circumstances, fluoride may be a useful tracer. The American Dental 

Association recommends a concentration range of 0.7-1.2 mg/l in potable water for 

dental health and many communities add fluoride to potable water. Where potable water 

is fluoridated and where ambient fluoride concentrations are low, the presence of 

elevated fluoride values may be used to assist in identifying waters of anthropogenic 

origin. However, it should be noted that fluoride levels will also be elevated in grey water, 

car wash water and other non-sanitary sources. 

Lab Issues: Fluoride is an inexpensive analysis commonly provided by commercial 

laboratories. 

Previous Studies: Typical values for Birmingham, Alabama ranged from 0.8-1.0 mg/ for 

potable water, sanitary wastewater and septic tank effluent, compared to 0.031 mg/l for 

spring water. Industrial sources can be much higher, however, ranging from 12 mg/l for 

car wash water to 150 mg/l for radiator flush water in Birmingham. 

Ammonia/Potassium Ratio 

Rationale: Dry-weather stormwater outfall flows typically indicate a non-stormwater 

source and water samples from these outfalls can be laboratory analyzed to confirm the 

identity of the source. If tests indicate that the water samples contain compounds 

commonly found in detergents, then the ratio of ammonia to potassium can be used as 

an indicator of sanitary wastewater contamination. Ratios of ammonia concentrations to 

potassium concentrations greater than 0.60 likely indicate the presence of sanitary 

wastewater (Pitt, 2001). 

Lab Issues: Both ammonia and potassium are inexpensive analyses commonly provided 

by environmental laboratories. 

Previous Studies: A search of EPA’s STORET database indicates Tracer concentration 

studies conducted in Birmingham, Alabama (Pitt et al., 1993; Lalor, 1994) that included 

the ammonia/potassium ratio found that the mean ratio values for sanitary wastewater, 

septic effluent, and laundry wastewater were 1.7, 5.2, and 0.24, respectively. The mean 

ratios for spring water, treated potable water, and car wash water were 0.011, 0.018, 

and 0.006, respectively. 

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Brighteners 

Rationale: Brightening agents used in laundry products have also been used as tracers 

for the detection of failing septic systems. These compounds fluoresce under ultraviolet 

light and can be detected in the field with portable fluorometers. However, other nonsanitary sources (such as laundries and car washing facilities) may introduce significant 

quantities of brighteners. 

Lab Issues: There is no EPA approved methodology for the analysis of brighteners, and 

commercial environmental laboratories do not commonly provide this test. However, the 

fluorometric technique is expected to have a low per-sample cost. It has the added 

advantage that it can be conducted in the field, providing immediate results if a field 

fluorometer is available. 

Isotopes 

Rationale: Although not widely used, the enrichment of isotope ratios of nitrogen 

(N15/N14) and oxygen (O18/O16) over a standard ratio have also been used (Aravena et al. 

1993) to characterize the source of nitrogen. The ratio of heavier isotopes changes as 

nitrogen passes through trophic levels. Thus, it is possible to differentiate between “new” 

atmospheric nitrogen and nitrogen from fertilizer, or nitrogen from wastewater sources. 

Lab Issues Commercial laboratories do not commonly provide this type of isotope 

analysis. 

Multiple Chemical Tracers

The analytical cost of tracing the source of FC varies widely, although combining simple 

tracers can result in an effective methodology based on the “body of evidence”. One 

such protocol involving analysis of detergents, ammonia and potassium was developed 

by Pitt (2004) and is reproduced as Figure 1. This protocol is primarily for source 

identification of baseflow contamination measured at the end-of-pipe. A similar protocol 

could be developed depending on the ambient levels of fluoride, potassium, and 

ammonia and whether natural MBAS interferences exist. 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 Task Assignment 003.03/05-003 

 3C-13 

Start 

Land use 

Industrial or 

industrial/ 

commercial 

Residential 

Check for dry-weather flow

Flow? 

Recheck per permit 

requirements 

Detergent 

>0.25 mg/L 

or Boron 

>0.35 mg/L

Fluorescence

>25 mg/L 

Fluoride 

 >0.6 mg/L

E.coli >12,000 

MPN/100mL 

or Enterococci 

>5,000 

MPN/100mL 

Ammonia/

potassium 

ratio >1.0

Fluoride 

>0.25 

mg/L 

No

Yes No No 

No 

No

No

Yes

Yes

Yes

Yes

Yes

No

Figure 1 Flow Chart for Identifying Base Flow Components Adapted from Pitt (200

Parameter Check Land Use Flow Check
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

3.C.6 Modeling Issues for Consideration 

Dry Weather: Dry weather or baseflow quantities (concentrations) can be estimated by 

evaluating the water quality data in conjunction with flow (or stage) and rainfall. These 

can also be varied seasonally (e.g., dry weather period during the wet season). 

Wet Weather: Wet weather sources can vary from pet waste washoff from lawns and 

parks to SSOs and agricultural discharges. These are often variable, as is rainfall, and 

sensitivity analyses are recommended since SSO records are rarely available and the 

other sources can have great variability. 

Decay and Transformation Coefficient: Often a “first-order loss” coefficient is applied to 

account for the cumulative loss over time of bacteria levels in receiving waters. It is 

important to note that this coefficient includes decay (die-off), transformation (e.g., FC to 

AC), and re-growth. Table 2 lists ranges for these first order loss rates. 

Table 2 

Typical First Order Loss Rates for Bacteria Modeling 

Range of 

First-Order 

Loss Rates 

Coliform Type (1/day) Comments Source 

Total coliform 1.0 - 5.5 Freshwater Thomann and Mueller, 1987 

 0.7 - 3.0 Seawater Thomann and Mueller, 1987 

 0.2 - 2.5 Lombardo, 1972 

Fecal coliform 0.2 - 3.1 Lombardo, 1972 

E Coli 0.08 - 2.0 Seawater Thomann and Mueller, 1987 

Septic Tanks: Due to voluntary participation and cost, septic tanks often remain in 

service along lakes, wetlands, and creeks even when areas are sewered. Therefore, 

either WBID specific field reconnaissance for septic tanks should be completed and/or a 

sensitivity analysis should be performed using the WBID perimeter as septic tank 

coverage (i.e., all homes and buildings along the stream or lake are on septic tanks). 

Failure rates are referenced in Section 3 of the TMDL Protocol. 

SSOs: SSO data are often sparse and difficult to quantify. SSOs can be a major 

contribution during wet weather conditions and these loads can be a calibration 

parameter. 

Animal Sources: Animal sources can be diffuse and random and/or concentrated and 

persistent (urban ducks and geese). Therefore, field confirmation is often important to 

define this input. 

 3C-14 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

3.C.7 Conclusions 

A source identification process based on the evaluation of existing FC data coupled with 

development of GIS layer(s) to map probable sources has been presented. Other 

considerations for indicators of human and non-human sources have also been 

referenced. If the source(s) cannot be determined with existing data or through simple 

field inspections, it is recommended that simple and inexpensive chemical and biological 

tracers be measured to assist in identifying the source during dry and wet weather 

conditions. 

An array of potential tracers was presented which may be used to identify the sources of 

FC bacteria and differentiators, and an example flow chart used by Pitt (2004) in 

Birmingham has been presented. A pilot study could be conducted to confirm the validity 

of the source investigation protocol by correlating the coliform counts with the 

concentrations of the tracer compounds. In the event that the simple tracers do not 

provide the source identification needed, then the more complex and expensive MST 

methods may be used. These have been used in a variety of evaluations to identify likely 

sources. 

TMDL implementation plans should identify cost-effective practices and BMPs based on 

the magnitude and source of the FC indicator causing impairment. Often this is a plan to 

reduce human and controllable animal waste. 

3.C.8 References 

APHA, 1998. Standard Methods for the Examination of Water and Wastewater. 20th

Edition. American Public Health Association. 

Bannerman, R., D. Owens, R. Dodds, and N. Hornewer. 1993. Sources of pollutants in 

Wisconsin stormwater. Water Science and Technology 28(3-5):241-259. 

Barber, L.B., II, Leenheer, J.A., Pereira, W.E., Noyes, T.I., Brown G.K., Tabor, C.F., and 

Writer, J.H., 1995. Organic Contaminants in the Mississippi River from Municipal and 

Industrial Wastewater in Contaminants in the Mississippi River. USGS Circular 1133. 

Edited by R.H. Meade. 

Brion, G.M. 2003. The AC/TC Bacterial Ratio: A Tool for Watershed Quality 

Management. 

Burkhardt, W., III, K.R. Calci, W.D. Watkins, S.R. Rippey. 1998. Decay of Microbial 

Indicators in Estuarial Waters. 

Burkhardt, W., III, K.R. Calci, W.D. Watkins, S.R. Rippey. 1998. Occurrence of MaleSpecific Bacteriophage in Feral and Domestic Animal Wastes, Human Feces, and 

Human-Associated Wastewater. 

 3C-15 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Burton, A., D. Gunnison and G. Lanza. 1987. Survival of pathogenic bacteria in various 

freshwater sediments. Applicant and Environmental Microbiology 53(4) 633-638. 

CDM. 2001. Big Creek Water Resource Management Plan. Fulton County, Georgia. 

CDM. 2003. Rouge River National Wet Weather Demonstration Project. 

CDM. 2005. Merrimack River Water Assessment Study. 

Coastline, Spring 1998 Issue 8.2. Pinning Down Sources of Coliform Bacteria. Pg. 4-5. 

Feacham, R. 1975. An improved role for faecal coliform to faecal streptococci ratios in 

the differentiation between human and non-human pollution sources. Water Res. 9:689. 

Geary, P.M. and Davies, C.M. (2003).”Bacterial source tracking and shellfish 

contamination in a coastal catchment”, Water Sci. Technol., 47(7-8),95-100. 

Geldrich, E., C. Boreen, C. Huff, H. Clark, P. Kabler. 1962. Type distribution of coliform 

bacteria in the feces of warm-blooded animals. Journal Water Pollution Control 

Federation (34):295. 

Godfrey, A. 1992. Sources and fate of microbial contaminants. In Recreational Water 

Quality Management Volume 2: Freshwaters. eds. D. Kay and R. Hanbury. Ellis 

Horwood, New York NY, pp.137-154 

Griffith,J. F., et al. (2003). “Evaluation of microbial source tracking methods using mixed 

fecal sources of aqueous test samples”, J. Water Health, 4(1), 141-151. 

Hagedorn, C. et al. (1999). “Determining sources of fecal pollution in a rural Virginia 

watershed with antibiotic resistance patterns in fecal streptococci”, Appl. Environ. 

Microbiolog., 65(12), 5522-5531. 

Harwood, V.J. et al. (2000). “Classification of antibiotic resistance patterns of indicator 

bacteria by discriminant analysis: use in predicting the source of fecal contamination in 

subtropical waters”, Appl. Environ. Microbiol., 66(9), 3698-3704. 

Kuntz, R. L. et al. (2004). “Presence of Enterococcus faecalis in broiler litter and wild bird 

feces for bacterial source tracking”, Water Res., 38, 3551-3557. 

Lalor, M. 1994. Assessment of non-stormwater discharges to storm drainage systems in 

residential and commercial land use areas. Ph.D. dissertation. Dept. of Environmental 

and Water Resources Engineering. Vanderbilt Univ. Nashville, TN. 256 pp. ( Watershed 

Protection Techniques 3(1): Page 591.). 

Lalor, M and R. Pitt. 1999. Use of Tracers to Identify Sources of Contamination in Dry 

Weather Flow. Wat. Prot. Techniques. 3(1): page 585-592. 

 3C-16 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Marino, R. and J. Gannon. 1991. Survival of fecal coliforms and fecal streptococci in 

storm drain sediment. Water Resources 25(9): 1089-1098. 

Messer, D.F. (2004). Microbial source tracking: a review of current methods and 

recommendations, Technical Memorandum sent to Amin Kazemi, City of Stockton. 

Meays, C. L. et al. (2004). “Source tracking fecal bacteria in water: a critical review of 

current methods, J. Environ. Manage., 73, 71-79. 

Noble, R. T. et al. (2003). “Use of viral pathogens and indicators to differentiate between 

human and non-human fecal contamination in a microbial source tracking comparison 

study”, J. Water Health, 4(1), 195-207. 

NWQL, 1997. National Water Quality Laboratory Newsletter. Vol. 5, No. 1. U.S. Dept. 

Interior/USGS. 

Parveen, S. et al. (2001). “Phenotypic and genotypic characterization of human and nonhuman Escherichia coli’, Water Res., 35(2),379-386. 

Pitt, R., et al. (1993). A User’s Guide for the Assessment of Non-Stormwater Discharges 

into Separate Storm Drainage Systems. Jointly published by the center of 

Environmental Research Information, US EPA, and the Urban Waste Management & 

Research Center. EPA/600/R-92/238. PB93-131472. Cincinnati, Ohio. 

Pitt, R. 1998. Epidemiology and Stormwater Management. In Stormwater Quality 

Management. CRC/Lewis Publishers. New York, NY. 

Pitt, R. 2001. Source Tracking of Inappropriate Discharges to Storm Drainage systems. 

NWRI National Urban Conference, Costa Mesa, California. October 17-19. 

Pitt, R., et al. (2004). The National Stormwater Quality Database (NSQD, Version 1.1). 

Dept. of Civil and Environmental Engineering, University of Alabama, Tuscaloosa, AL. 

Samadapour, M.. Dept. Env. Health. Univ. of Washington. Seattle, WA. (206-543-5120). 

Telephone interview. 1999. 

Schueler, T.R. 1999. Microbes and Urban Watersheds. II. Concentrations, Sources, and 

Pathways. Watershed Protection Techniques. 3(1): page 554. 

Schueler, T.R. 1999. Microbes and Urban Watersheds. IV. Implications for Watershed 

Managers. Watershed Protection Techniques. 3(1): page 581. 

Steuer, J., W. Selbig, N. Hornewer and J. Prey. 1997. Sources of contamination in an 

urban basin in Marquette, Michigan and an analysis of concentrations, loads and date 

quality. USGS Water Resources Investigation Report 97-4242. Middleton, MI. 26 pp. 

USGS Chattahoochee River National Recreation Area Monitoring Program 

(1999 – present ga.water.usgs.gov/projects/chatm/publications.html) 

 3C-17 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Vikas G.S., Dunstan, H. and Geary, P.M. (2004) Application of Emerging Bacterial 

Source Tracking (BST) Methods to Detect and Distinguish Sources of Fecal Pollution in 

Waters, In Proceedings of International Conference on Emerging Technologies (ICET2004), Eds D.K. Mishra and P.N. Ramachandranan, December 22-24, Bhubaneswar, 

India,117-126, (ISBN 81 7764 727 X).

Watershed Protection Techniques. Volume 3, No. 1. Pages 551- 596. 

Wiggins, B. A.. (1996). “Discriminant analysis of antibiotic resistance patterns in fecal 

streptococci, a method to differentiate human and animal sources of fecal pollution in 

natural waters”, Appl. Environ. Microbiol., 62(11), 3997-4002

 3C-18 
County

Animal 

Feeding 

Operation

Concentrated 

Animal 

Feeding 

Operation

Concrete 

Batch GP

Industrial 

Wastewater

Petroleum 

Cleanup GP 

(long term) Total

Alachua 2 6 8 16

Baker 1 1 2

Bay 8 10 4 22

Bradford 1 3 4

Brevard 10 43 2 55

Broward 13 25 38

Calhoun 1 4 5

Charlotte 8 16 24

Citrus 5 15 20

Clay 1 7 14 22

Collier 9 13 5 27

Columbia 2 10 1 13

Desoto 1 7 8

Dixie 1 1

Duval 20 57 6 83

Escambia 5 28 6 39

Flagler 3 1 4

Franklin 1 1

Gadsden 3 9 4 16

Gilchrist 3 1 1 5

Glades 1 3 4

Gulf 4 4

Hamilton 5 1 6

Hardee 1 1 7 9

Hendry 2 9 1 12

Hernando 3 9 12

Highlands 4 1 5 7 1 18

Hillsborough 2 22 83 6 113

Indian River 4 26 30

Jackson 12 12

Jefferson 1 1 1 3

Lafayette 2 2

Lake 8 35 2 45

Lee 18 19 4 41

Leon 4 11 15

Levy 4 5 9

Liberty 3 3

Madison 1 2 3

Manatee 7 11 18

Marion 11 37 48

Table 3D.2

TMDL Model Development Protocol

Summary of NPDES-Regulated Facilities

Number of Industrial NPDES Facilities by County in Florida

Table 3D-2 NPDES WW Sources.xls
County

Animal 

Feeding 

Operation

Concentrated 

Animal 

Feeding 

Operation

Concrete 

Batch GP

Industrial 

Wastewater

Petroleum 

Cleanup GP 

(long term) Total

Table 3D.2

TMDL Model Development Protocol

Summary of NPDES-Regulated Facilities

Number of Industrial NPDES Facilities by County in Florida

Martin 1 3 15 3 22

Miami-Dade 20 22 2 44

Monroe 23 23

Nassau 3 5 1 9

Okaloosa 6 4 10

Okeechobee 10 7 1 8 1 27

Orange 21 64 4 89

Osceola 6 5 2 13

Palm Beach 11 37 1 49

Pasco 3 13 19 2 37

Pinellas 10 13 6 29

Polk 3 22 93 5 123

Putnam 2 17 3 22

Santa Rosa 5 10 3 18

Sarasota 7 24 3 34

Seminole 5 8 1 14

St. Johns 4 4 8

St. Lucie 1 2 26 2 31

Sumter 1 3 10 14

Suwannee 1 4 1 6

Taylor 1 8 9

Union 2 2

Volusia 7 24 2 33

Wakulla 1 6 7

Walton 3 9 12

Washington 3 3

Totals 32 9 356 1,017 86 1,500

Table 3D-2 NPDES WW Sources.xls
County

Domestic 

WWTP

Residuals 

Application 

Facility

Residuals/ 

Septage 

Management 

Facility

Reuse/ 

Distribution 

System Total

Alachua 30 10 1 41

Baker 4 7 11

Bay 16 16

Bradford 6 4 10

Brevard 66 5 71

Broward 19 19

Calhoun 1 1

Charlotte 35 3 38

Citrus 77 7 1 85

Clay 27 2 29

Collier 27 1 28

Columbia 22 7 29

Desoto 21 9 1 31

Dixie 3 1 4

Duval 42 10 3 55

Escambia 12 4 16

Flagler 15 4 1 20

Franklin 12 12

Gadsden 11 8 19

Gilchrist 5 3 8

Glades 25 25

Gulf 5 2 7

Hamilton 11 4 15

Hardee 17 4 21

Hendry 15 22 37

Hernando 39 8 3 50

Highlands 69 7 76

Hillsborough 140 2 142

Holmes 4 5 9

Indian River 14 3 17

Jackson 9 3 12

Jefferson 5 2 7

Lafayette 2 2 4

Lake 122 10 132

Lee 90 13 103

Leon 15 2 17

Levy 14 4 18

Liberty 3 3

Madison 8 6 14

Manatee 10 15 3 28

Table 3D.1

TMDL Model Development Protocol

Summary of NPDES-Regulated Facilities

Number of Domestic Wastewater Permited Facilities in Florida

Table 3D-1 NPDES WW Sources.xls
County

Domestic 

WWTP

Residuals 

Application 

Facility

Residuals/ 

Septage 

Management 

Facility

Reuse/ 

Distribution 

System Total

Table 3D.1

TMDL Model Development Protocol

Summary of NPDES-Regulated Facilities

Number of Domestic Wastewater Permited Facilities in Florida

Marion 132 18 1 151

Martin 51 51

Miami-Dade 27 27

Monroe 297 1 298

Nassau 20 2 22

Okaloosa 18 2 20

Okeechobee 24 2 1 27

Orange 73 2 75

Osceola 42 9 51

Palm Beach 67 1 1 69

Pasco 84 1 1 1 87

Pinellas 26 2 28

Polk 177 23 5 205

Putnam 30 8 1 39

Santa Rosa 11 1 12

Sarasota 61 2 63

Seminole 28 5 33

St. Johns 34 3 37

St. Lucie 42 12 1 55

Sumter 41 5 46

Suwannee 12 3 15

Taylor 7 2 9

Union 1 2 1 4

Volusia 124 13 2 139

Wakulla 7 7

Walton 10 5 15

Washington 6 6

Total 2,520 316 30 5 2,871

Table 3D-1 NPDES WW Sources.xls
County

Constrution 

SW GP

Individual 

SW MSGP MS4 Totals

Alachua 70 40 110

Baker 10 6 16

Bay 86 65 151

Bradford 4 6 10

Brevard 238 119 357

Broward 336 2 188 3 529

Calhoun 3 3 6

Charlotte 51 12 63

Citrus 16 4 20

Clay 87 1 27 115

Collier 254 40 294

Columbia 15 1 15 31

Desoto 6 3 9

Dixie 3 4 7

Duval 748 2 222 3 975

Escambia 145 54 1 200

Flagler 41 6 47

Franklin 15 15

Gadsden 18 18 36

Gilchrist 3 3

Glades 6 6

Gulf 19 5 24

Hamilton 6 3 9

Hardee 14 2 16

Hendry 9 15 24

Hernando 33 4 37

Highlands 29 16 45

Hillsborough 1,000 2 230 3 1,235

Holmes 7 2 9

Indian River 82 33 115

Jackson 18 10 28

Jefferson 7 1 8

Lafayette 4 1 5

Lake 111 23 134

Lee 765 113 1 879

Leon 185 28 2 215

Levy 6 2 8

Liberty 7 2 9

Madison 8 7 15

Manatee 275 1 46 2 324

Marion 48 31 79

Table 3D.3

TMDL Model Development Protocol

Summary of NPDES-Regulated Stormwater Facilities

Number of Regulated Facilities by County

Table 3D-3 NPDES WW Sources.xls
County

Constrution 

SW GP

Individual 

SW MSGP MS4 Totals

Table 3D.3

TMDL Model Development Protocol

Summary of NPDES-Regulated Stormwater Facilities

Number of Regulated Facilities by County

Martin 68 40 108

Miami-Dade 125 114 3 242

Monroe 6 24 30

Nassau 51 10 61

None 408 3 411

Okaloosa 105 27 132

Okeechobee 14 10 24

Orange 1,202 208 3 1,413

Osceola 191 27 218

Palm Beach 536 121 1 658

Pasco 282 21 1 304

Pinellas 304 109 2 415

Polk 579 2 139 1 721

Putnam 19 17 36

Santa Rosa 69 8 77

Sarasota 446 65 1 512

Seminole 342 40 1 383

St. Johns 150 23 173

St. Lucie 104 29 133

Sumter 18 6 24

Suwannee 6 6

Taylor 8 3 11

Union 5 10 15

Volusia 151 52 203

Wakulla 13 1 3 17

Walton 48 9 57

Washington 26 3 29

Totals 10,064 12 2,527 28 12,631

Table 3D-3 NPDES WW Sources.xls
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Section 4 

MODEL EVALUATION AND SELECTION 

Notes: 

1. Preparation, verification, calibration, and validation (PVCV) 

Tech Memo

PVCV of Current 

Conditions 

FDEP

Acceptance

C/L/S Meeting

No

TARC

Review

Current Conditions

Watershed Model

Tech Memo

Review

Comments

12

13

14

Current Conditions 

Receiving Water 

Model, TMDL, 

Uncertainty, and 

Recommendations

PVCV of Current 

Conditions 

Receiving Water 

Model1

Yes

11

TMDL Modeling

Tech Memo

Refinement of

TMDL Objectives

10

 Definition of

 Conditions

Design and 

Simulation 

Conditions

Design and 

Simulation

9

Tech Memo

Restatement of

TMDL Modeling

Objectives

 4-1 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

4.1 INTRODUCTION 

This section of the protocol discusses the methods to be used for selecting the modeling 

tools to evaluate flows and loads, to define the total allowable load, and evaluate the 

benefits (e.g., load reduction) to be obtained from the implementation of BMPs. This section 

provides guidance on model selection based on the available data and models, processes 

being modeled, level of complexity, and the temporal and spatial scales. Figure 1.2 shows 

the overall flowchart for a TMDL project. 

4.2 MODEL EVALUATION AND SELECTION 

METHODOLOGY 

A variety of models and related tools may be used for TMDL analyses. The following 

process can be considered for model evaluation and selection. It should also be noted that 

some tasks can be performed in parallel with others. 

Task 1: Consider Classification of the Selected Receiving Water Body

From the 303(d) list of impaired watersheds, the user should consider the classification of 

the type of receiving water body for model selection: 

 Lake; 

 River, canal, stream, or ditch; 

 Estuary; 

 Sink hole; and/or 

 Wetland. 

This will assist with the decisions for level of detail, necessary model features, and temporal 

scale. For many TMDLs, multiple classifications may be necessary (e.g., river with lakes and 

wetland flow ways). 

Task 2: Define Parameters and Processes

From the 303(d) list of impaired water bodies, the user will need to evaluate one or more 

parameter(s) associated with the impaired watershed. The grouping of parameters may 

include: 

 Oxygen demand in various forms, including 5-day biochemical oxygen demand (BOD5), 

ultimate BOD, carbonaceous oxygen demand (CBOD), nitrogenous oxygen demand 

(NOD), plant dark cycle respiration, and sediment oxygen demand (SOD); 

 Nutrients and related parameters, including nitrogen (total nitrogen - TN, nitrite - NO2, 

nitrate - NO3, total Kjeldahl nitrogen - TKN, ammonia - NH3, also called unionized 

ammonia, ammonium - NH4) and phosphorous (total - TP and orthophosphate - PO4); 

 4-2 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

 Pathogens and Bacteria, including fecal and total coliform, atypical coliform, enterococci, 

streptococcus, and others; 

 Toxics, which may include priority pollutants; and 

 Sediments, including sands (coarse, medium, fine), silts, and clays. 

The hydrological, physical, chemical, and geological processes related to the impairment 

should be considered to define necessary model features, capabilities, and associated data. 

Multi-dimensional needs for estuaries, lakes, and/or large rivers; wetlands (empirical wetland 

equations and data); and other special cases should be identified to assist in model 

selection and data refinements. For example, it may be necessary to model and/or estimate 

processes such as groundwater interactions using integrated or coupled surface and 

groundwater models of sinkholes in Karst regions of central Florida or highly permeable 

aquifers such as the Biscayne in southeast Florida. 

Task 3: Determination of Temporal and Spatial Scales

As part of the model selection process, it is important to identify the temporal and spatial 

scales of the water quality or biological impairment to be evaluated. The temporal scale 

refers to two related issues: the time over which measurable water quality or quantity 

changes occur in the environment and the associated time step for which the simulation 

model is best suited. For example, a steady-state model may be useful for TMDL application 

in simulations of very slowly changing environmental conditions in a large lake or estuary 

(annual or seasonal); a continuous simulation model may be useful for TMDL application 

requirements for assessment of streams in urban nonpoint sources (time scales of seconds, 

minutes, or hours); and an estuarine model should consider tidal variation (hours). 

Water quality based problems that occur during a low-flow period may require a different 

assessment approach than waters where longer-term loadings are of concern. Therefore, it 

is important to identify the time scale of the water quality problem prior to selecting the 

appropriate TMDL assessment tool. For instance, examining a water quality problem that 

occurs only at low-flow periods may be based upon gaging station records or a simplified 

analysis of contributing sources. Low-flow conditions are often dominated by point source 

discharges, septic tank seepage, and baseflow sources (e.g., groundwater). For low-flow 

conditions, source loadings may be quantified by a combination of in-stream monitoring and 

point source discharge records instead of more detailed water quality modeling. If the 

problem is a result of longer-term pollutant loadings, then it may be appropriate to estimate 

loads on a monthly, seasonal, and/or annual basis. Techniques appropriate for these 

situations include unit-load rating calculations and use of simple model methods (e.g., 

spreadsheet-based loading estimates). For waterbodies that have relatively short residence 

times, consideration of a series of individual storms or a continuous (hourly or daily) 

simulation of the loading processes may be warranted. Interpretation of time variable loads 

can be used to examine the frequency and magnitude of the loading. 

Water quality model time scale and time steps can greatly affect simulation times. Some 

models run in a few minutes and others require hours or days for long-term continuous 

simulations. To accurately represent watershed hydrology, time steps should be less than, 

 4-3 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

and ideally an integer multiple of, the rainfall interval. Time steps should be less than 

overland flow, conduit, and WBID reach travel times to maintain continuity (i.e., conservation 

of mass) for each model process. Table 4.1 shows a general characterization of the time 

scale necessary for the noted application. 

Table 4.1 Time Scale Examples

Application Type Residence or Travel Time Time Scale 

Tributary Model Short (<24 hours) Minutes, Hours 

Watershed Model Medium (24 hours to 7 days) Hours, Days, Weeks 

Receiving Water Model Long (days to months) Days, Months, Seasons 

The term “spatial scale” refers to the suitable watershed size and/or levels of detail for 

discretization for model applications. Certain models (e.g., P8 and SLAMM) are appropriate 

for use in many applications (e.g., simulations of development sites and small watersheds), 

but they may not be appropriate for simulations of large watersheds because they do not 

directly account for instream flow routing and pollutant parameter transformation and decay. 

Other models can generally be applied to watersheds ranging in size from small to large, as 

would be required for the watershed planning studies. Therefore, it is important to account 

for the spatial scale of the problem during the model selection and preparation processes. 

The level of detail incorporated into the model will affect computer simulation times and the 

amounts of data that need to be processed, explained, and documented. 

Task 4: Screening Analysis

This task can assist the TMDL practitioner in the selection of appropriate modeling tools 

based on the available spatial data for the watershed as well as on their respective scales. 

Data gaps and needs also can be identified at this stage. Data priorities can be established 

using sensitivity analysis guidelines described in Section 3 and model testing for sensitivity 

described in Section 5. 

After the data evaluation, the screening analysis involves identifying and obtaining existing 

model(s) for the subject watershed. The uncertainty or range of parameter variation for 

existing models may also be defined. Depending upon the outcome of this check, three 

options are available to the practitioner: 

 Option 1: A previously prepared model exists with the necessary features and pertinent 

input data at the correct resolution. The practitioner should perform a peer review of the 

model input as well as the verification, calibration, and validation. If the model is not 

verified, calibrated, or validated, then the practitioner should perform these tasks. 

 Option 2: A previously prepared model exists with the necessary features, but there is an 

input data deficiency or limitation in model scale (detail) and/or accuracy. As a result, the 

 4-4 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

practitioner may need to collect more data and refine the model before starting the 

TMDL analysis. The uncertainty analysis and necessary levels of detail help define data 

needs. The peer review described in Option 1 should be performed. 

 Option 3: There is no previously prepared model. Therefore, the practitioner would need 

to select the model for preparation. 

Task 5: Model Selection

In this task, the practitioner selects an appropriate modeling tool. Using the results of the 

screening analysis, the practitioner can select from a wide range of tools to provide the 

necessary features and capabilities. A number of sources exist to assist in this effort, 

including the Water Environment Research Foundation Water Quality Selection Tool 

(WERF, 2001) and EPA documents. Table 4.2 presents the capabilities and relative 

complexity of more than 40 watershed models as listed by EPA in the Compendium of Tools 

for Watershed Development and TMDL Development (EPA, 1997). Table 4.3 presents the 

same information for over 30 receiving water models. If the practitioner is faced with a data 

deficiency for the selected model, further input data may have to be collected or another 

model may need to be considered. Existing hydrologic and hydraulic models may also be 

used to define flows for a water quality model. 

4.3 SELECTION EXAMPLE: LOWER HILLSBOROUGH 

RIVER 

The model selection decision process has been applied to the Lower Hillsborough River in 

Tampa, Florida. The various water bodies have been selected for TMDL evaluation because 

they are on the verified 303(d) list for water quality impacts to: 

 Nutrients 

 DO 

 Ammonia (unionized) 

 Bacteria 

Task 1: Identify the Receiving Water Body Type 

For the Hillsborough River, two principal receiving water body types are present: a lake 

(Lake Thonotosassa) and river reaches (e.g., Flint and Baker Creeks and Lower 

Hillsborough River). Several of the models listed in Table 4.3 are capable of water quality 

simulation of these two water body types with one model (e.g., AESOP, CE-QUAL-ICM, 

WASP) and several models can be used to generate the watershed loads (e.g., SWMM, 

BASINS-HSPF, WAM, SWAT, SLAMM). The watershed also contains many wetlands and 

significant groundwater inflows and withdrawals in the northwest, so the selected watershed 

model must address these related processes. 

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Florida Department of Environmental Protection June 2006 

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Task Assignment 003.03/05-003 

Task 2: Define Parameter(s) and Processes 

For the seven WBIDs that comprise the Lower Hillsborough River, nutrients, dissolved 

oxygen, unionized ammonia, and bacteria have been identified as parameters of concern. 

Table 4.4 associates each of the WBIDs within the Lower Hillsborough River as well as 

other basin information with parameters of concern. It was decided by FDEP and EPA that 

bacteria would be modeled by EPA as part of this TMDL evaluation. 

Table 4.4 Available Data for the Lower Hillsborough River TMDL Model Selection 

Water Segment Type Miles

Parameters of 

Concern 

Hydraulic Model 

(Yes/No) 

Flow 

Data 

Water 

Quality 

Data 

Baker Creek River 2.3 DO, nutrients Y (SWMM EXTRAN) Y Y 

Cow House Creek River 3 DO Y (SWMM EXTRAN) Y Y 

Flint Creek River 2.3 DO, nutrients Y (SWMM EXTRAN) N Y 

Upper Hillsborough 

River River 6 DO Y (HEC-RAS) Y Y 

Lower Hillsborough 

River River 5 Nutrients Y (UNET, HEC-1, FLDWAV) Inlet only Y 

Lake Thonotosassa Lake 1.3 Unionized 

ammonia Y (SWMM EXTRAN) Y Y 

Sparkman Branch River 5.4 DO N N Y 

In order to evaluate these parameters, processes for oxygen demand (e.g., BOD, SOD), 

nutrient cycles, and algal growth and decay will be required. 

Task 3: Determination of Temporal and Spatial Scales 

Because the parameters of concern in the Lower Hillsborough River are expected to change 

within a daily time-step, the selected model must be able to simulate hourly or shorter time 

steps. Existing hydraulic models were available for the majority of the study area, allowing a 

finer discretization of the hydraulics of the primary reaches within the WBIDs. Sufficient 

water quality monitoring data were available to observe in-stream water quality processes at 

the reach scale. Within the 7 WBIDs, there are approximately 9 miles of primary reaches 

and 16 miles of tributaries. 

Task 4: Screening Analysis 

For the Lower Hillsborough River, relevant data such as hydrologic, water quality monitoring, 

point source discharge locations and loads (NPDES data), and existing hydraulic model 

data were compiled and evaluated for relevance to the specific WBID TMDL evaluations. 

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Task Assignment 003.03/05-003 

As discussed in Section 3, a variety of data analysis tasks were performed, including: USGS 

flow gages evaluation, USGS water quality monitoring stations summaries for period of 

record, number of sample events available per parameter of concern, minimum, maximum, 

and average result values; and point sources identification. The selection of viable points of 

comparison for model results prior to preparation of the model itself is required. The 

impaired reach lengths and spatial distribution of instream data points are important 

determinants of the appropriate level of model resolution. 

In previous work, a WAM had been prepared for the Hillsborough River watershed for flows 

and loads. The majority of the study area is also represented in UNET, HEC-RAS, and 

SWMM EXTRAN hydraulic models. These models were used to define system crosssections, volumes, and hydraulics for the WAM application of the Lower Hillsborough TMDL 

models. 

Other critical factors for model analyses include budgetary constraints and schedule 

constraints. In this case, schedule constraints did not allow any additional data collection 

beyond what was readily available. Associated uncertainty was quantitatively defined for 

data gaps and limitations. 

Task 5: Model Selection 

The chosen simulation models for the Lower Hillsborough River were WAM and WASP 

because they best met the criteria to evaluate the impaired reaches and related parameters. 

A WAM application was already established at an appropriate level of detail, and it was 

refined with the best available data to improve its accuracy. WASP was chosen because it 

can simulate the necessary water quality processes (oxygen demands, nutrient cycles, and 

algal growth and decay) with continuous simulation to address the parameters of concern 

(DO, nutrients, and unionized ammonia). 

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Florida Department of Environmental Protection June 2006 

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Task Assignment 003.03/05-003 

 4-8 

SECTION 4 REFERENCES 

EPA. 1986. Forecasting onsite soil adsorption failure rates. EPA/600/2-86/060. 

EPA. 1997. Compendium of Tools for Watershed Assessment and TMDL Development. 

EPA841-B-97-006. 

Water Environment Research Foundation. 2001. Water Quality Models: A Survey and 

Assessment. Project 99-WSM-5.
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 Task Assignment 003.03/05-003 

Table 4.2 

Florida TMDL Protocol 

Watershed Models and Packages 

Time Scale Polluta

Index Short Name Full-Name

Events 

Continuous 

Annual / 

Seasonal 

Sediments 

Nutrients 

1 AGNPS-98 AGricultural Non Pollution - Source Model 98 9 9 9 9 9 2 ANSWERS

Areal Non-point Source Watershed Environment Response Simulation 9 9 9 9 9 

3 APEX Automatic Parameter Estimation eXrention 9 9 9 9 9 4 BASINS (HSPF)

Better Assessment Science Integrating point and Non-point Source 9 9 9 9 9 

5 BASINS (SWAT) 

Better Assessment Science Integrating point and Non-point Source 9 9 9 9 9 

6 CREAMS

Chemicals, Run-off, Erosion, from Agricultural Management Systems 9 9 9 9 9 

7 DR3M-QUAL Distributed Routing Runoff Rainfall Model - Quality 9 9 9 9 9 8 EPIC Erosion Productivity Impact Calculator 9 9 9 9 9 9 ETD Enhanced Trickle-Down 9 9 9 

10 FHWA Not Available 9 9 9 11 GISPLM

Geographical Information System Phosphorous Loading Model 9 9 9 9 

12 GLEAMS

Groundwater Loading Effect of Agricultural Management Systems 9 9 9 9 9 

13 GWLF Generalized Watershed Loading Function 9 9 9 9 9 14 HEC-1/HMS Hydrologic Engineering Center-1/Hydrologic Modeling System 9 9 15 HSPF Hydrologic Simulation Program-Fortran 9 9 9 9 9 16 HUMUS Hydrologic Unit Model for the United States 9 9 9 9 9 

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17 KINEROS KINEmatic Runoff and erOSion Model 9 9 9 9 18 LSPC Loading Simulation Program in C++ 9 9 9 9 9 19 LWWM Linked Watershed/Waterbody Model 9 9 9 9 9 20 MAGIC Model of Acidification of Groundwater In Catchments 9 9 21 NAPRA National Agricutural Pesticide Risk Assessment 9 9 9 9 9 22 NLEAP Nitrate Leaching and Economic Analysis Package 9 9 9 9 23 Opus Opus 9 9 9 9 9 24 P8-UCM (P8) Urban Catchment Model 9 9 9 9 9 25 PRMS Precipitation Runoff Modeling System 9 9 9 9 26 PRMS/ANNIE Precipitation Runoff Modeling System 9 9 9 27 PRZM-3 Pesticide Root Zone Model-3 9 9 9 9 9 28 SLAMM Source Loading And Management Model 9 9 9 9 9 29 SLOSS-PHOSPH Not Available 9 9 9 9 30 SLURP Simple Lumped Reservoir Parameter 9 9 9 31 SPUR-91 Simulation of Production and Utilization of Rangelands 9 9 9 9 9 32 SSARR Streamflow Synthesis and Reservoir Regulation 9 9 9 33 STORM Storage Treatment Overflow Runoff Model 9 9 9 9 9 34 SWAT Soil and Water Assessment Tool 9 9 9 9 9 35 SWMM Stormwater Management Model (RUNOFF and EXTRAN) 9 9 9 9 9 36 SWRRBWQ Simulation for Water Resource in Rural Basin Water Quality 9 9 9 9 9 37 TR-55/20 Technical Release 55 9 38 USGSREGR United States Geological Survey REGRession 9 9 9 9 39 UTM-TOX-WHTM Unified Transport Model for Toxic Materials 9 9 9 9 9 40 WAM Watershed Assessment Model 9 9 9 9 9 41 WATERSHED WATERSHED 9 9 9 9 42 WEPP Water Erosion Prediction Program 9 9 9 9 43 WMM Watershed Management Model 9 9 9 44 WSTT Watershed Screening and Targeting Tool 9 9 9 9 9 

(Source - EPA 1997 Compendium of Tools for Watershed Assessment and TMDL Development) (EPA841-B-97-006)

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Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 Task Assignment 003.03/05-003 

Table 4.3 

Florida TMDL Pro

Receiving Water M

Time Scale Dimensions Type 

Index Short Name Full-Name

Steady-State 

Time-variable 

1-D 

2-D 

3-D 

Reservoir/Lake 

Stream/River 

Estuary 

Pathogens 

1 ADCIRC ADvanced CIRculation Model 9 9 9 9 9 9 9 2 AESOP Not Available 9 9 9 9 9 9 9 9 9 3 BATHTUB BATHTUB 9 9 9 9 4 Branch Branch-Network Dynamic Flow Model 9 9 9 9 9 5 CE-QUAL-ICM 

Corps of Engineer-Quality-Integrated Compartment Model 9 9 9 9 9 9 9 9 

6 CE-QUAL-R1 Corps of Engineer-Quality-Reservoir 1 9 9 9 9 9 7 

CE-QUALRIV1 Corps of Engineer-Quality-River 1 9 9 9 9 9 

8 CE-QUAL-W2 Corps of Engineer-Quality-Water 2 9 9 9 9 9 9 9 CH3D-WES

Curvilinear Hydrodynamics in 3-Dimensions - Waterways Experiment Station 9 9 9 9 

10 CONCEPTS 

CONservational Channel Evolution and Pollutants Transport System 

11 CORMIX CORnell MIXing zone expert system 9 9 9 9 9 9 9 12 DYNHYD5 DYNHYD5 9 9 9 9 13 EFDC Environmental Fluid Dynamics Code 9 9 9 9 9 9 9 9 9 14 EPDRiv1 

A Dynamic One-Dimensional Model of Hydrodynamics and Water Quality 9 9 9 9 9 

15 EUTROMOD EUTROphication MODel 9 9 9 16 FESWMS-2DH

Finite Element Surface-Water Modeling System:Two-Dimensional Flow in a Horizontal 9 9 9 9 9 

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Plane 

17 HEC-5Q Hydrologic Engineering Center 5 Q 9 9 18 HEC-6 Hydrologic Engineering Center - 6 9 9 9 19 HEC-RAS 

Hydrologic Engineering Center - River Analysis System 9 9 9 

20 HEM-1D Hydrodynamic Eutrophication Model 1-D 9 9 9 9 9 21 HEM-2D Hydrodynamic Eutrophication Model 2-D 9 9 9 9 22 HEM-3D Hydrodynamic Eutrophication Model 3-D 9 9 9 9 9 23 

HSPFRCHRES Hydrologic Simulation Program-Fortran 9 9 9 9 9 9 

24 MECCA Not Available 9 9 9 9 9 25 PHOSMOD PHOSphorous MODel 9 9 9 26 POM Princeton Ocean Model 9 9 9 9 9 9 9 27 QUAL2E Enhanced Water Quality 2 9 9 9 9 28 RIVMOD-H RIVer MODel-H 9 9 9 9 9 29 RMA-10 Resource Management Associates 10 9 9 9 9 9 9 9 9 30 RMA2-WES

Resource Management Associates 2 - Waterways Experiment Station 9 9 9 9 9 9 9 

31 SWMM Stormwater Management Model (Transport) 9 9 9 9 9 9 32 TWQM Tail Water Quality Model 9 9 9 9 9 33 WASP Water Quality Analysis Simulation Program 9 9 9 9 9 9 9 9 9 

(Source - EPA 1997 Compendium of Tools for Watershed Assessment and TMDL Development) (EPA841-B-97-006)

  

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Task Assignment 003.03/05-003 

Section 5 

 MODEL PREPARATION, VERIFICATION, CALIBRATION, 

AND VALIDATION 

Notes: 

1. Preparation, verification, calibration, and validation (PVCV) 

Tech Memo

 PVCV of 

Current Conditions 

Watershed Model1

Yes FDEP

Acceptance

C/L/S Meeting

TARC

Review

12

13

14

PVCV of 

Current Conditions

Receiving Water 

Model1

No

Current Conditions

Watershed Model

Tech Memo

Review

Comments

Current Conditions 

Receiving Water 

Model, TMDL, 

Uncertainty, and 

Recommendations

TMDL Modeling

Tech Memo

Refinement of

TMDL Objectives

 Definition of

 Conditions

Design and 

Simulation 

Conditions

 Design and 

 Simulation

11

10

9

Tech Memo

Restatement of

TMDL Modeling

Objectives

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5.1 INTRODUCTION 

As described in Section 1, the TMDL process includes the identification of impaired waters, 

followed by the analysis and development of the TMDL. For Florida, the development and 

allocation of a TMDL is part of a five-element process (see flow chart on cover of this 

section), in which the second element process leads to the definition of the allowable load. 

This section of the report discusses the protocols for developing the preparation, verification, 

calibration, and validation of the chosen watershed and/or impaired waterbody-specific 

models to define the total allowable loads and associated water quality and hydrologic 

statistics. 

5.2 MODEL SETUP AND DATA INPUT 

After the model selection process, the selected model(s) need to be prepared (setup). In this 

process, care must be taken to prepare the model at an appropriate level of detail, giving 

consideration to the features of the study area and the resolution necessary to understand 

the nature of the water quality problem (i.e., parameters, sources, and magnitudes) and to 

evaluate reduction measures (e.g., BMPs) within available data limitations. For example, 

subbasin and stream reach delineation should consider factors such as variability of rainfall, 

land use, topography, potential pollutant sources, coverages and efficiencies of BMPs, 

locations of monitoring stations, water control structures, stream crossings, municipal and 

state jurisdictions, the time scale associated with the pollutant(s) of concern (e.g., minutes, 

hours, days, months, years), and other local conditions that contribute to the water quality 

impairment and listing. There is often a tradeoff between model detail and model run times 

as well as the resultant data processing. This data refinement consistent with the 

discretization of model(s) is often an iterative process coordinated with the data evaluations 

in Section 3. 

Model numerical (or mathematical) stability should be considered in the model preparation, 

verification, calibration, and application. The time step for each model should be chosen to 

maintain conservation of mass (continuity) for rainfall, runoff, and routing through short, 

medium, and long systems (e.g., ditches and channels, culverts and bridges, and rivers, 

respectively). Short reaches often tend to be bridges or culverts which may have limited flow 

restriction for 90 percent of rainfall events or greater, and in many cases may be neglected 

for these long-term water quality simulations or may be combined with adjacent conduits to 

maintain overall system length. Some models provide warning messages or other indicators 

of model instability in the model output. For example, SWMM output provides a continuity 

warning in the form of an error value that the model generates by comparing the sum of the 

system's initial water volume and water inflow to the sum of the system's final water volume 

and total outflow. Thus, it indicates the extent to which the model is calculating a net gain or 

net loss of water during the simulation period. Some models like SWMM EXTRAN provide 

warning messages regarding "short" reaches where numerical instabilities are most likely to 

occur. Besides model output messages, graphical plots of flow, stage, concentration, and/or 

load results should be reviewed to look for "spikes" or rapid variations that may indicate 

model instability. Another method of evaluating model stability is to conduct a model run in 

which a conservative parameter is modeled, assigning all initial concentrations, inflow 

concentrations, and boundary concentrations with a constant concentration. If the model is 

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Florida Department of Environmental Protection June 2006 

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stable, the concentration in the entire system should stay equal to the same constant 

concentration, and mass should be conserved (water and conservative parameters). 

5.2.1 Model Discretization 

The model(s) should be discretized so that each model segment represents a part of the 

receiving waterbody and/or watershed having similar physical, chemical, and biological 

characteristics, although parameter variation is a necessity in some cases (e.g., Manning n 

roughness variation between main channel and overbanks). For hydrologic routing, the 

discretization should account for major changes in slope, cross-sectional geometry, depth, 

and other physical characteristics. In addition, the discretization should be fine enough to 

allow spatial differences in concentrations of water quality parameters to be calculated, 

especially for impaired segments. The time step should be consistent with the expected or 

necessary travel time of a unit flow volume passing through the segment control volume. In 

many cases, the receiving water may be a river that is fairly well-mixed laterally and 

vertically, and the receiving water can be represented as a 1-dimensional system with 

concentration differences only in the longitudinal direction. However, in larger rivers, 

tributary inflows or point source discharges may tend to “hug the shore” or the surface in 

tidal estuaries and not get dispersed or become “well mixed”. In such cases, it may be 

necessary to apply a 2-dimensional model to better define the lateral mixing downstream of 

the inflow. In lakes and estuaries, it is possible that both lateral and vertical differences (e.g., 

stratification) need to be defined by a 3-dimensional model. 

The discretization of the receiving water tributary area into subbasins should go hand-inhand with the discretization (segmentation) of the receiving water. Figure 5.1a and Figure 

5.1b show examples of the tributary watershed discretization and segmentation levels of 

detail for the lower Hillsborough River. The tributary area should be identified for each of the 

receiving water segments, and flows and loads would be calculated from each tributary 

area. If these tributary areas are large enough to have travel times greater than 12 to 24 

hours, the watershed model should consider the attenuation and the reduction of total load 

due to instream processes (e.g., settling, first-order decay). 

5.2.2 Boundary Conditions 

Information regarding boundary conditions is required by most surface water and ground 

water hydraulic and water quality models. Boundary conditions define hydrologic conditions 

(e.g., flow, stage) and water quality conditions (e.g., concentrations) at locations where 

external system flows enter or exit the watershed and/or receiving water model. 

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Figure 5.1a Subbasins and Reaches for the Lower Hillsborough River 

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Florida Department of Environmental Protection June 2006 

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Figure 5.1.b Receiving Water Segmentation for the Lower Hillsborough River 

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Florida Department of Environmental Protection June 2006 

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As an example, the surface water hydraulic boundary condition at the downstream end of a 

receiving water body can be a free discharge point or a fixed stage, or the boundary 

condition can follow tidal fluctuations. Modelers can define boundary conditions based on 

tidal data from NOAA or normal water levels from USGS or WMD data. Normal water 

surface elevations are often used as the boundary conditions for large lakes that respond 

very slowly to rainfall events. For smaller lakes, a statistical analysis may be conducted (as 

data permit) to estimate stages for various recurrence intervals, which can be used as a 

fixed boundary condition. In all cases, the models selected for watershed analyses should 

be capable of representing the boundary condition for the study area using the appropriate 

time scale for the analysis. 

Water quality boundary concentrations are required as model inflows for water quality loads 

that are not calculated in the receiving water TMDL model. For example, downstream water 

quality concentrations need to be defined for tidal receiving waters, where the tidal inflow 

and associated boundary concentration will determine the mass of constituent entering the 

receiving water with the incoming tide. Boundary concentrations also need to be set for 

constituents that are not calculated by the watershed model. Constituents that may not 

necessarily be evaluated using a watershed loading model could include DO and 

chlorophyll-a, which are often responses in a receiving water that are dependent upon 

watershed stream network characteristics, surface runoff and loads, and baseflow loads. 

5.2.3 Model Coefficients 

Model coefficients come in various forms that differ from model to model. For watershed 

hydrologic models, coefficients can range from a single Rational Method runoff coefficient 

(“C” value) to a set of coefficients that fully represent the hydrologic cycle (e.g., surface 

storage and runoff, infiltration, evapotranspiration, groundwater outflow). Watershed load 

model parameters can be as simple as constant concentrations associated with runoff and 

baseflow, or models can include buildup and washoff coefficients that are used to calculate 

runoff concentrations as a function of runoff intensity and antecedent surface mass load. 

Receiving water quality models can range in complexity from simple mixing and dilution 

models, to models with complex sets of coefficients that characterize the physical, chemical, 

and biological process that affect the concentration of constituents in the receiving water. 

Coefficient choices and sensitivity analyses should be documented. 

Coefficient values are initially based on measurement or are assigned a value within a 

reasonable range of values. Subsequent model calibration may involve coefficient 

adjustments such that the model accurately represents measurements from the study area. 

Literature sources may provide the coefficient values used in the model. Potential sources 

include: U.S. EPA documents (e.g., Rates, Constants and Kinetic Formulations in Surface 

Water Quality Modeling), the respective model User's Manual, and previous studies. It may 

be especially useful to compile coefficient values used in previous model applications to 

have a database of typical values that can be used to set initial coefficient values in future 

studies. Table 5.1 shows an example of a HSPF data set with ranges of values for the 

Upper St. Johns River. 

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Florida Department of Environmental Protection June 2006 

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Table 5.1 Example Subset of HSPF Hydrologic Parameters for Agricultural Land Uses 

Typical Values Possible Values1 Values by Land Use 

Parameter Description Units Minimum Maximum Minimum Maximum Citrus2 Pasture2 Row 

Crop2

LZSN Lower zone 

nominal soil 

moisture 

storage 

inches 3.0 8.0 2.0 15.0 4.0 6.5 4.0 

INFILT Index to 

infiltration 

capacity 

in/hr 0.01 0.25 0.001 0.5 0.05 0.05 0.05 

LSUR Length of 

overland flow 

feet 200 500 100 700 300 300 300 

SLSUR Slope of 

overland flow 

plane 

ft/ft 0.01 0.15 0.001 0.30 0.001 0.001 0.001

UZSN Upper zone 

nominal soil 

moisture 

storage 

inches 0.10 1.00 0.05 2.00 0.40 0.65 0.40 

1

Values published in BASINS Technical Note # 6. 

2

Upper St. Johns River calibrated values (CDM, 2003) 

5.2.4 Initial Conditions 

Information regarding initial conditions is required to define hydrologic and hydraulic 

conditions (e.g., watershed antecedent moisture conditions for soils, initial water levels for 

wetlands and waters, receiving water flows and stages) and water quality conditions (e.g., 

concentrations of pollutants) in receiving water segments. Measured data, if available, can 

be used to establish initial conditions. If measured data are not available, an initial model run 

can be conducted for a period prior to the period of interest, such that the model itself 

calculates the initial conditions at the beginning of the period of interest. This can range from 

weeks to months (and occasionally years) depending on the model, size of the system, and 

processes affecting impairment. Sensitivity analyses can be used to identify data needs 

and/or determine a starting point (average or conservative value). For the antecedent 

moisture condition (AMC) in a hydrologic model, a 2- to 4-week period in advance of the 

simulation period has often been shown to adequately allow for the model simulation to 

come to equilibrium for the actual AMC at the start of the simulation, although some systems 

may require months or even years to establish appropriate conditions (e.g., large wetland 

systems such as the Tomoka River). Therefore estimates of initial soil and surface water 

levels are important to reduce the antecedent simulation period required. 

5.3 MODEL OUTPUT 

Typical continuous simulation model outputs will include time series of flows and 

concentrations, and associated characteristics (e.g., velocity, stage, depth), as well as mass 

balances and flow balances that identify total flow volumes and constituent loads. Outputs 

may also include average and maximum values for hydraulic parameters (e.g., flow, depth, 

velocity) and flow-weighted concentrations. 

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Florida Department of Environmental Protection June 2006 

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For both hydrologic/hydraulic and water quality models, the overall flow and mass balance 

should be evaluated to compare model inflows to model outflows for model elements 

(conduits, reaches, junctions). Table 5.2 shows an example mass balance for a lake. 

Output data from hydrologic and hydraulic modeling should be evaluated to determine 

whether the results are reasonable. For high-flow conditions in streams, measured flow 

values can be compared to modeled peak flows in the segment corresponding to the flow 

gage location. In the absence of measured flow values, unit peak flow values (i.e., discharge 

rates in cfs per square mile) can be compared to local or regional unit peak flow values. 

Modeled peak velocities can be evaluated with respect to literature values to assess 

potential for erosion (generally less than 2.5 to 3.0 feet per second; Chow, 1959), and 

limited field reconnaissance and/or anecdotal evidence can be used to validate model 

velocity values for areas with known streambank erosion problems. This can help to define 

potential TSS loads from scour. For continuous simulation periods including low-flow and 

high-flow conditions, it is appropriate to evaluate the relative contributions of stormwater 

runoff and baseflow, and the general distribution of flows in streams. If there are measured 

streamflows, baseflow separation techniques referenced in Section 3 can be applied to 

calculate the relative measured quantities of runoff and baseflow, which can be compared to 

modeled streamflows (using the same baseflow separation techniques) and/or the 

distribution of runoff and baseflow calculated by the hydrologic watershed model. 

Similarly, the calculated watershed model loads and receiving water model concentrations 

should be evaluated to determine if the results are reasonable. In many cases, the 

comparison of measured and modeled concentrations in streams cannot be as rigorous as 

the comparison for hydrology and hydraulics (stages and flows) due to data limitations. 

Measured streamflow data generally reflect continuous monitoring, but water quality 

monitoring data are often developed by analyzing “grab samples” at pre-determined time 

intervals (e.g., monthly, weekly) and/or defined flow conditions. Consequently, the 

monitoring data may not reflect the full range of concentrations that are simulated by the 

model. However, the general distribution of concentrations calculated by the model may be 

similar to the distribution of concentrations from grab sampling, particularly if the period of 

sampling is relatively long and samples are collected relatively frequently and randomly 

(e.g., wet weather and dry weather conditions). It is most appropriate to compare measured 

concentrations with modeled concentrations that correspond to the times when the samples 

were taken, rather than the entire simulation period. It may also be useful to analyze 

measured concentrations with respect to local rainfall and streamflow records to determine 

whether the samples were collected during a period when streamflow consisted entirely of 

baseflow, were a combination of baseflow and runoff, or were primarily runoff, as shown in 

Figure 5.2.

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Table 5.2 Example of Long Term Yearly Water Budget Analysis for a Lake 

 Inflows Outflows 

Change 

in 

 Storage 

 Annual Lake Area= 

359 

acres 

 Pervious Impervious Direct Lake Outlet Seepage Net 

 Inflow Inflow Rainfall Evaporation Outflow Outflow Flow 

Seepage 

Outflow 

Year (ac-ft) (ac-ft) (ac-ft) (ac-ft) (ac-ft) (ac-ft) (ac-ft) 

Rough 

Estimate 

in inches 

 

1961 

 

462 438 1,448 

 

1,503 

 

1,180 

 

97 -432 3.2 3.3 

1962 

 

248 431 1,311 

 

1,263 

 

806 

 

157 -235 5.2 1.9 

1963 

 

144 331 943 

 

1,203 

 

469 

 

171 -426 5.7 1.1 

1964 

 

1,276 781 2,209 

 

1,339 

 

1,397 

 

216 1,314 7.2 9.9 

1965 

 

1,441 630 2,116 

 

1,554 

 

2,627 

 

201 -194 6.7 10.6 

1966 

 

971 533 1,799 

 

1,505 

 

1,899 

 

150 -251 5.0 7.2 

1967 

 

523 518 1,576 

 

1,461 

 

1,098 

 

187 -130 6.3 4.0 

1968 

 

470 493 1,388 

 

1,292 

 

852 

 

288 -80 9.6 3.5 

1969 

 

538 492 1,484 

 

1,268 

 

861 

 

294 90 9.8 4.2 

1970 

 

1,364 600 1,970 

 

1,536 

 

2,197 

 

215 -14 7.2 10.1 

1971 

 

291 432 1,385 

 

1,331 

 

845 

 

241 -309 8.1 2.2 

1972 

 

1,228 705 2,105 

 

1,458 

 

1,539 

 

238 804 7.9 9.3 

1973 

 

813 478 1,639 

 

1,523 

 

1,780 

 

182 -554 6.1 6.1 

1974 

 

370 472 1,383 

 

1,297 

 

829 

 

218 -119 7.3 2.8 

1975 

 

381 461 1,391 

 

1,278 

 

772 

 

294 -112 9.8 3.1 

1976 

 

381 454 1,252 

 

1,195 

 

630 

 

384 -122 12.8 2.7 

1977 

 

161 294 787 

 

1,163 

 

247 

 

397 -565 13.3 1.1 

1978 

 

288 466 1,159 

 

1,274 

 

219 

 

291 127 9.7 2.1 

1979 

 

399 574 1,491 

 

1,202 

 

445 

 

254 562 8.5 3.0 

1980 

 

448 360 1,153 

 

1,323 

 

787 

 

235 -385 7.9 3.3 

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1981 

 

41 326 856 

 

1,104 

 

137 

 

251 -269 8.4 0.3 

1982 

 

705 596 1,602 

 

1,178 

 

700 

 

240 786 8.0 5.3 

1983 

 

1,185 641 2,099 

 

1,462 

 

1,598 

 

157 708 5.2 9.3 

1984 

 

444 347 1,222 

 

1,508 

 

1,322 

 

75 -892 2.5 2.9 

1985 

 

273 460 1,315 

 

1,222 

 

622 

 

109 96 3.6 2.2 

1986 

 

589 493 1,512 

 

1,331 

 

950 

 

94 219 3.1 4.6 

1987 

 

704 435 1,411 

 

1,401 

 

1,284 

 

97 -231 3.2 5.0 

1988 

 

750 618 1,807 

 

1,401 

 

1,155 

 

141 478 4.7 5.6 

1989 

 

273 415 1,270 

 

1,352 

 

862 

 

202 -459 6.8 2.1 

1990 

 

361 441 1,256 

 

1,353 

 

703 

 

327 -325 10.9 2.6 

1991 

 

416 427 1,294 

 

1,297 

 

663 

 

290 -113 9.7 3.1 

1992 

 

549 556 1,507 

 

1,217 

 

540 

 

289 566 9.7 4.1 

1993 

 

421 413 1,223 

 

1,281 

 

809 

 

295 -328 9.8 3.2 

1994 

 

986 598 1,844 

 

1,299 

 

1,037 

 

238 854 7.9 7.5 

1995 

 

990 517 1,812 

 

1,440 

 

1,833 

 

170 -124 5.7 7.3 

1996 

 

658 495 1,648 

 

1,465 

 

1,223 

 

144 -31 4.8 5.0 

1997 

 

1,121 688 2,164 

 

1,513 

 

1,531 

 

185 745 6.2 8.7 

1998 

 

1,042 501 1,660 

 

1,485 

 

2,573 

 

40 -896 1.4 7.5 

1999 

 

383 414 1,325 

 

1,322 

 

1,053 

 

154 -407 5.1 2.9 

2000 

 

364 473 1,306 

 

1,297 

 

677 

 

289 -120 9.6 2.7 

2001 

 

185 357 992 

 

1,161 

 

380 

 

333 -340 11.1 1.4 

2002 

 

147 433 1,172 

 

1,207 

 

123 

 

362 61 12.1 1.4 

Average 

 

590 490 1,483 

 

1,340 

 

1,030 

 

219 -25 7.3 4.4 

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Figure 5.2 Measured and Modeled Streamflow and Total N 

5.4 MODEL QUALITY MANAGEMENT 

A modeling quality management process (also called Quality Assurance and Control) is a 

key component of successful model application. It is important that quality assurance 

reviews occur throughout the project including model input data, verification, calibration, and 

validation points. This process should include considerations and decisions such as the 

following: 

 Screening/selecting the right model for the intended applications – As part of the 

model selection process, the project team should identify the factors causing impairment 

and identify the key stakeholders. This will provide a general understanding of the 

desired result of the application, which will help with model selection. Also, model 

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selection should consider time and cost constraints, data availability, and familiarity of 

the project team and model technical reviewer(s) with the model selected. 

 Checking model parameter values – The person(s) responsible for technical review of 

the model preparation should verify that the model parameter input values used to run 

the model are within accepted ranges of values (literature or measured) or document 

reasons why values vary from accepted ranges. As part of this process, the sensitivity of 

the model results to changes in model parameters on the predicted results may also 

warrant investigation. Results can be used to determine confidence intervals and to 

identify where data refinements would be warranted (or even necessary). This was 

discussed in Section 3 and here in Section 5. 

 Checking model results – Model results should first be checked for numerical stability 

and reasonableness. Figure 5.3 shows an example plot of model numerical instability. 

Instabilities, if not corrected, can lead to significant under- or overpredictions of flows, 

loads, and constituent concentrations. Stability checks should be done prior to using the 

model results to assess a problem or influence decisions, requirements, or actions. 

Continuity checks alone do not fully confirm model numerical stability. As an example, 

an instability that oscillates equally above and below zero can “cancel out” continuity 

error and cause the model to appear to be stable. Visual plots of flow and concentration 

versus time are commonly used as an additional check. It is also important to check the 

predicted model results for reasonableness (e.g., annual or seasonal hydrologic and 

water quality parameters mass balance checks), especially if the results will eventually 

be used for pollutant load allocations. This is often done through model calibration, 

verification, and validation as described later in this section. 

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Figure 5.3 Example of Numerical Instability in Flow 

5.4.1 Model Verification 

As defined by EPA, model verification refers to the examination of the numerical technique 

in the computer code to ascertain that it represents the conceptual model with no inherent 

numerical problems. The models described previously in Section 4 have gone through a 

verification process by the owning entities (e.g., EPA, USGS), in which solutions calculated 

by the model were compared with results calculated by the owning entities. 

Verification steps for the practitioner can include examination of the “input echo” to confirm 

that input data is read correctly, comparisons of mass balance terms for chosen control 

volumes (e.g., entire model domain versus individual segments or reaches), continuity 

checks, routing connectivity, and detailed plots of flows and stages to confirm numerical 

stability. Water quality model verification could include test routings of a conservative 

parameter at a constant rate, as described in Section 5.2. 

5.4.2 Model Calibration to Measured Data 

The term model calibration refers to the adjustment of model parameters (within reasonable 

limits) so that the simulated response will approximate corresponding measured data. This 

exercise can serve as a reality check on model results and can help to define model 

reliability, credibility, and uncertainty. It is important during model calibration that parameter 

adjustments beyond normal limits are verified or documented with data to establish the 

basis for the parameter (see Table 5.1). 

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It is also important to make sure that land use, floodplain storage, and hydraulic (structures 

and gates) conditions in the model are applicable to the calibration period. As examples, 

long term simulations over many years may have significant changes in land use and 

impervious area, WWTP discharge location changes, and/or major hydraulic changes to a 

structure. Each of these may warrant modifications for the time periods as part of calibration 

such as varying impervious area by year (with model “hot starts”), changing WWTP 

discharge locations, or modifying hydraulics and flows for a structure change at the 

appropriate time period. Model calibration is a critical element of a detailed modeling 

process. It is also important to keep in mind the uncertainty of input data and comparative 

stage, flow, and water quality data during comparisons. 

Several statistical analysis methods for data were presented in Section 3 and Appendix 3A. 

These sections present further details and methods to compare model results to data. 

5.4.2.1 Water Quantity 

Model calibration for water quantity requires comparisons with measured data. Such data 

may be anecdotal or measured. Anecdotal data can include descriptive information obtained 

from the public or staff (or even news stories or photographs) regarding observed minimum 

low-flow levels or maximum high-flow levels that occurred on a certain date or following a 

specific storm event. Measured data may include surveys of high-water marks or 

continuously recorded data at monitoring stations (e.g., time-rainfall, time-stage, timevelocity) within the project area. In some instances, recording stations may be installed to 

collect rainfall, stage, and flow data for future model calibration if it is determined that 

detailed modeling is warranted. 

Water quantity (hydrology) model calibration also can be time-scale dependant. Depending 

upon the nature of the problem being assessed, models may be calibrated to one or more 

discrete storm events, or to a longer period of record (e.g. weeks, months, years). Long-term 

model calibration is usually done for hydrologic models to support water quality models or 

for maximum and/or minimum flows and levels within a waterbody over the long term. 

In general, the following types of data are needed and may be modified for hydrologic model 

calibration: 

 Historical rainfall data (short-term or long-term) across the study area (hourly or daily) in 

adequate resolution to account for spatial and temporal changes; 

 Evaporation/potential evapotranspiration (PET) data (daily or monthly); 

 Antecedent moisture condition information (for both event and continuous simulations); 

 Measured stream stages, velocities, and flows (hourly or daily); 

 Measured or design event model boundary conditions (vary as necessary); 

 Soils data in terms of infiltration, runoff potential, soil storage, and potential interflow; 

 Land use data for the time periods of interest to estimate impervious area; 

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 Groundwater flow (daily or monthly); 

 Baseflow (daily or hourly); 

 Channel cross-sections and Manning n roughness values; 

 Culvert and bridge geometries and properties; and 

 Lake stage-area-storage-discharge relationship. 

Data such as rainfall, evaporation, antecedent moisture conditions, and boundary conditions 

directly affect the amount of flow and stages that are generated in the study area receiving 

water network. Modeled stages and flows calculated by the model are compared to the 

measured values to evaluate the accuracy of the model. 

There are a number of methods for comparing the model results to measured flows, stages, 

and velocities. Several of the more common analysis techniques are described below. 

Time series plots - Modeled results and measured data are plotted against time, to see 

how closely the modeled values match the measured values, and to see if the modeled 

values follow the upward or downward movement of the measured values (trends). Such 

comparisons would be appropriate for event or continuous simulations. For discrete events 

using a dynamic model, results might be printed every 5 to 20 minutes, but this would rarely 

be required. For a continuous simulation hydrologic model, results might be printed hourly, 

daily, or monthly. An example of comparative time series plots for a 4-year flow simulation is 

presented in Figure 5.4. 

Figure 5.4 Example Time Series Plot of Modeled and Measured Daily Streamflows

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Flow volume comparison - Comparison of simulated and measured flow volumes for a 

range of durations can provide information on model performance over different time scales. 

The following comparisons of measured and modeled streamflow volume should be 

considered, and are typically reported in acre-ft and percent difference from measured data 

(volume error): 

 Total simulation period 

 Annual 

 Seasonal (typically wet season vs. dry season for Florida) 

 Monthly 

 Daily 

It is also recommended that minimum and maximum monthly flow volumes be evaluated. 

Potential outliers or questionable values in the data or model can be discovered by 

evaluation of trends in volume. 

Frequency-exceedance plots - The modeled and measured values are plotted against the 

percent of time the value is exceeded in the dataset. This is appropriate for continuous 

simulation results. For streamflow, the plots can be used to see how well the model results 

represent the range of flows that were measured in the stream. This approach is also useful 

for comparing measured and modeled concentrations. Because water quality data are 

typically limited, it may be appropriate to plot model results for the full-modeled dataset, and 

for a model dataset that includes only results corresponding to the dates when the 

measured data were collected. Figure 5.5 shows an example frequency-exceedance plot 

for measured and modeled streamflow data. 

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Figure 5.5 Example Flow Frequency Exceedance Plot of Modeled and 

Measured Streamflows 

Plot of paired measured vs. modeled data - Paired values of measured data and 

corresponding model output data are plotted against one another. If the measured and 

modeled data were in perfect agreement, the plotted data would fall along a straight line with 

a slope of 1.0 and an intercept value of 0.0. This type of analysis would be appropriate for 

continuous simulation efforts. 

Linear regression analysis can be done in conjunction with plotting the paired measured and 

modeled data. A regression line can be drawn to determine the slope of the regression line, 

the intercept, and the coefficient of determination (R2

). The slope of the line indicates how 

well the model captures the trends of the recorded data. An R2 of 0.8 to1.0 indicates close 

agreement between modeled and measured data, and an ideal match would be an R2 of 1. 

The intercept can point out systematic errors in the model, and it should be as close to 0.0 

as possible. The R2

 indicates the percent of variance in the recorded data that is explained 

by the model. Additional statistics can be calculated to test the hypotheses that (a) the slope 

of the line is significantly different than 1.0, and (b) the intercept is significantly different than 

0.0. An example of a paired-point plot is presented in Figure 5.6. In this example, 

approximately 91% of the variation is explained by the regression equation. 

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USJR at Christmas June 1996 - May 2000

y = 1.0765x - 27.748

R2

 = 0.9081

0

2000

4000

6000

8000

10000

12000

0 1000 2000 3000 4000 5000 6000 7000

Measured Streamflow (cfs)

Modeled Streamflow (cfs)

Figure 5.6 Paired-point Plot with Regression Analysis

Relative error - This simple statistic is a direct comparison of the means for the measured 

and modeled datasets. A good starting point for evaluating this statistic is to estimate that 

the relative error should ideally be no greater than potential stream gauging errors, sampling 

errors and laboratory errors possible in the measured data. The USGS often rates the 

quality of its flow data, and laboratories can report error based on “blank” samples. 

Root mean square error (RMS) - The RMS error measures the precision with which the 

model tracks measured data variations. As with relative error, the RMS error should ideally 

be no greater than potential gaging, sampling, and laboratory errors inherent in the 

measured data. 

Nash-Sutcliffe Model Efficiency 

The Nash Sutcliffe score (Nash and Sutcliffe, 1970) is a measure of model efficiency 

typically used for evaluating rainfall-runoff models. It is the initial variance less the sum of 

squared errors, divided by the initial variance: 

2 2

0

2

0

F F E

F

− =

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Where 

 E = Nash-Sutcliffe score (dimensionless) 

( )2 2 F QQ 0 = − ∑ ( )

2 2 F Q ˆ = − ∑ Q 

And 

 Q = observed flow in ft3

/s or m3/s 

Q = average flow in ft3/s or m3/s 

 = predicted flow in ft Qˆ 3

/s or m3/s 

It is analogous to the coefficient of determination (R2

), but there are important differences to 

be noted. The Nash Sutcliffe score (E) discriminates better than R2

 for systematic model 

bias, consequently reporting a lower score. The range of E is -∞ < E > 1, but it is typically 

reported between 0 and 1, as is R2

. A standard reporting scale for this diagnostic statistic is 

as follows: 

 > 0.8 very good 

 0.6 – 0.8 good 

 < 0.6 fair 

0 model accuracy is no better than taking the average of all measured 

data 

Nonparametric comparison of means and distributions - Measured flow data and 

concentration data generally exhibit skewed (i.e., non-normal) frequency distributions. 

Therefore, nonparametric tests (which assume no shape for the population distribution) are 

often used to compare measured data with model results. An example of a nonparametric 

test for comparing measured and modeled medians is the Wilcoxon Rank Sum Test 

(Scheaffer and McClave, 1984). The two-sided Kolmogorov-Smirnov (KS) Test (Maidment, 

1993) is an example of a nonparametric test that can be used for comparing the distributions 

of measured and modeled dataset distributions. For both tests, the calculated test statistic 

value is used to determine if the measured and modeled medians or distributions are 

significantly different at a selected critical level-of-significance (e.g., 0.05). 

Wilcoxon Rank Sum Test 

The Wilcoxon Rank Sum Test is a substitute of the parametric T-test used for median 

comparison between two paired samples (Wilcoxon, 1945). In this test, absolute differences 

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between the measured and the predicted values are ranked from the smallest to the largest 

before the signs are restored. Then the smallest sum between the negative and positive 

differences is compared to a theoretical distribution at a given confidence level to check the 

null hypothesis of this test. The null hypothesis states that the medians of the measured and 

predicted values are identical. Readers interested in further details and examples are 

directed to Snedecor and Cochran (1967). 

Two-Sided Kolmogorov-Smirnov Test 

The Two-Sided Kolmogorov-Smirnov (K-S) Test is a method for evaluating the difference 

between the distributions of the measured data and model results. Again, the populations of 

measured data and model results are combined and then arranged in ascending order. 

Each observation is assigned either a value of 1 or -1 depending on which population the 

observation is from, and the cumulative sum of the assigned values is calculated. Using the 

number of measured and modeled data observations, and the maximum absolute 

cumulative sum of assigned values, a statistical value can be calculated and used to 

determine the level of significance of the difference between the measured and modeled 

concentration distribution. If the level of significance is less than a defined critical level of 

significance (e.g., 0.05), then the conclusion is that the measured and modeled data have 

significantly different distributions. 

5.4.2.2 Water Quality 

Model calibration for water quality simulations also requires comparisons with measured 

data available for this purpose. Such data may be anecdotal or measured. Anecdotal data 

can include descriptive information obtained from the public or staff (or even news stories or 

photographs) regarding fish kills (low dissolved oxygen), observed eutrophication impacts 

(e.g., such as algae blooms), high levels of visible contaminants such as suspended solids, 

or a visible sheen from oil and grease. Measured data would include event and/or periodic 

samples at monitoring stations (i.e., time-rainfall, time-stage or time-flow) within the project 

area. In some instances, recording stations may be installed to collect rainfall data, flow data 

and water quality data (e.g., dissolved oxygen) for future model calibration if it is determined 

that detailed modeling is warranted. 

Water quality model calibration also can be time-scale dependant. Depending upon the 

nature of the problem being assessed, models may be calibrated to one or more design 

conditions (e.g., 7-day, 10-year low-flow condition or first flush), or to longer periods of 

record (e.g., weeks, months, years). Long-term model calibration is sometimes done if the 

problem is more seasonal in nature (wet season water quality problem, or problems in lakes 

and estuaries with long residence times). 

In general, the following types of data are needed for water quality model calibration in 

addition to those listed for the water quantity (hydrologic) model: 

 Measured watershed concentrations or loads and receiving water concentrations of 

parameters of concern, as well as other relevant water quality data if available (i.e., DO 

may not be a parameter of concern, but would need to be modeled correctly to correctly 

represent nutrient dynamics in a system); 

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 Point source discharge flows and concentrations (daily or monthly); 

 Load contributions from other intermittent sources such as failing septic tanks or sanitary 

sewer overflows (SSOs) (hourly or daily); and 

 Measured or design event model boundary conditions (hourly or daily). 

The concepts discussed previously regarding water quantity model calibration apply to water 

quality model calibration. Time series plots, frequency-exceedance plots, plots of paired 

values, and statistical tests as presented previously for data and model results are 

appropriate for evaluating the similarity of measured data and model results. In addition, 

seasonal or annual loads calculated by the model can be compared with loads estimated 

from flow data and concentration monitoring data. 

In general, water quality calibration is more difficult than streamflow data or stage data 

calibration. There are several reasons for this: 

 Measured water quality data are typically more limited and more costly to obtain than 

streamflow or stage data. Flow or stage data are routinely monitored on a continuous 

basis, but constituent concentrations are often collected as periodic grab samples. Thus, 

it is customary to compare measured data to modeled data only on the days 

corresponding to sample collection days. The statistical tests described in this section 

compare measured and modeled concentrations only on measured days. 

 Models often have difficulty in replicating the natural variability in measured water quality 

due to data and/or model process limitations. For example, water quality models often 

specify a constant value for baseflow concentration, whereas measured baseflow 

concentrations could exhibit day-to-day variability due to a number of factors. For 

stormwater runoff quality, many models rely on mean runoff concentrations (EMCs) or 

simplified buildup-washoff relationships that do not account for the random factors that 

influence runoff concentrations. Other potential load sources such as failing septic tanks 

also represent highly variable sources that are difficult to account for on a day-to-day 

basis. For evaluations in Florida and Georgia (CDM, 2002 and 2001), it was found that 

sewered areas can have septic tanks, since residents are not always required to convert 

to sewer. It was also determined at public meetings that many residents did not know 

their house or business was on a septic tank, especially for homes that had been sold 

after the sewer project. 

 Concentrations of sediment and other water quality constituents may increase or 

decrease in the receiving water network. The characterization of the processes that 

effect the increase (e.g., benthic release) or decrease (e.g., decay) of constituent 

concentration in the receiving water network should be considered. 

As discussed, the methods previously documented for water quantity data and model results 

can be applied for water quality data model and results comparisons and evaluations. The 

following example of a box and whisker plot is provided for reference. This method can also 

be used for water quantity comparisons. 

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Box and Whisker Plots - Figure 5-7 shows a typical Box and Whisker plot for total nitrogen 

(TN). The “box” shows the range of data between the 25th and 75th percentile, with the lower 

end of the box representing the 25th percentile and the top of the box representing the 75th

percentile. The line inside the box shows the 50th percentile value. The “whiskers” show the 

range of values that fall within 1.5 times the “spread” of the high and low end of the box 

(where spread is defined as the difference between the 25th and 75th percentile values). 

Typically, values that are 1.5 to 3 times the spread from the upper or lower end of the box 

are called “outside values” and are plotted as points. 

1

1.5

2

2.5

3

3.5

4

TN (mg/l)

TN (obs) TN (mod)

HSPF RCHRES 665 - Total N

 Figures 5-7 Example Box and Whisker Plot

5.4.3 Model Validation 

Model validation refers to the comparison of the model results to measured data using a 

continuous simulation period that is completely independent of the event(s) or simulation 

period used for model calibration. This technique is often used to validate a “calibrated” 

model prior to its application to a known critical design period or condition. For example, if a 

high flow period or condition is used for calibration, then a low flow period or condition could 

be used for validation, especially if the pollutant of concern is a problem during different 

periods, conditions, or seasons. In other words, the calibration and validation period should 

cover the potential “design conditions” that are pertinent to the pollutant(s) of concern. 

Preferably, the combination of calibration and validation periods will include at least one 

“wet” year, one “average” year, and one “dry” year, and the water quality calibration and 

validation periods should ideally be the same as the water quantity calibration periods. 

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As previously discussed, anecdotal information can be used for limited model validation but 

should never be given a preference or priority over the use of model calibration using 

measured data. These types of data are more descriptive in nature and can be used to see 

if the predicted results approximate reported flooding problems. Examples of anecdotal 

information can include reported fish kills, algal blooms, turbidity problems, and low or high 

water marks or debris lines. Anecdotal information is often collected from the public, and 

therefore, validation of these data to the model results can often lead to public support of the 

predictive tool being used to address a problem. However, it should be noted that a model 

should never be considered fully calibrated based upon anecdotal data alone. 

It may be necessary to repeat or refine calibration and validation if the validation process 

yields unsatisfactory results in terms of the water quality and/or water quantity models 

matching observed data. Uncertainty should be defined at this point. 

5.4.4 Uncertainty Analysis and Margin of Safety 

Based on EPA guidelines (40 CFR 130.2(I)), the calculation of the TMDL should contain an 

uncertainty allowance referred to as the “margin of safety” (MOS). The MOS allowance may 

be addressed using either “implicit” or “explicit” methods. For the implicit approach, the MOS 

is assumed to be embedded in the analyses. Examples would include selection of 

conservative parameter values for the water quality model used to develop the TMDL, or 

achieving a standard that is more stringent than the actual standard. For the explicit 

approach, the MOS is quantified (e.g., expressed as number of pound per year of 

pollutants). This value can be established as a percentage of the allowable load (say 10 

percent to 20 percent) or by applying uncertainty analysis to the calibrated water quality 

model. 

5.4.4.1 Implicit and Explicit Margins of Safety 

Implicit Margin of Safety 

An implicit margin of safety is not expressly stated, but rather is implied and/or is inherent in 

the data and/or analyses performed for the TMDL. In many cases, practitioners “compound” 

implicit MOS by choosing “conservative” numbers for rainfall, soils, initial abstractions, ET, 

land use and impervious area, surface storage, groundwater flow, event mean concentration 

(EMC) or related pollutant load data, and BMP removal efficiencies. 

Data, model limitations in processes, budget, and schedule can each constrain the 

practitioner’s ability to provide a very accurate result with a small band of uncertainty. As 

discussed in Sections 3, additional data or model refinements may be necessary in 

subsequent rounds of TMDL refinement and implementation. Where possible, the 

practitioner should document the data decisions on “conservative” choices (e.g. use 40 

versus 30 percent impervious area due to variations in land use over a data collection or 

simulation period) in case explicit uncertainty and margin of safety must be estimated later in 

the TMDL implementation. 

Sensitivity analyses can be performed with “compound” implicit MOS decisions to see the 

impact on the resultant flows, loads, and concentrations. It should be noted that 

compounding implicit conservatism can potentially lead to oversized treatment facilities (e.g. 

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BMPs) to achieve the PLRG from a “conservative” TMDL. When put in the context of “right 

sizing”, the more defined and explicit the MOS, the more public funds can be used for other 

programs to achieve state-wide water quality goals. 

Explicit Margin of Safety 

The three most common methods for uncertainty analysis of the calibrated/validated TMDL 

model are as follows: 

 Sensitivity Analysis 

 First-Order Error Analysis (FOEA) 

 Monte Carlo Simulations 

Each of these methods is described below. The use of these uncertainty analyses to define 

the MOS allowance for the TMDL is described in Section 6.4. 

5.4.4.2 Sensitivity Analysis 

To conduct a sensitivity analysis, selected model input parameters are varied within a range 

of realistic values to evaluate the resulting change in modeled watershed and/or receiving 

water output response. Ultimately, these responses or variations in sensitivity may be used 

to determine the impact of model uncertainty on management plan (implementation 

strategy) decisions. The sensitivity analysis provides an understanding of how the model 

responds to changes in input parameters. In many cases, this could identify the need for 

better data to define the unknown parameters. Knowing the response and relative degree of 

sensitivity of individual parameters, a combination of model parameter changes can be 

tested as a means of achieving improved calibration, less uncertainty, and greater model 

reliability. For many hydrologic applications in Florida, rainfall and tributary area are the most 

sensitive parameters for wet weather evaluations, followed by percent of DCIA, and 

subsequently by soils (CDM, 1992). 

For sensitivity analysis and for model calibration, the range over which a parameter is 

adjusted should reflect how much confidence the practitioner has in their original parameter 

estimate. For example, some hydrologic parameters may be empirical (e.g. curve number), 

theoretical and/or difficult to measure for full representation of the watershed, whereas many 

hydraulic parameters (e.g., structure geometry) can be accurately surveyed. As discussed, 

parameters in which the practitioners have the least confidence should be targeted for 

adjustment (e.g., decay coefficients). In many cases, the sensitivity analysis will identify 

parameters that have little effect on the system response; in which case, the confidence in 

estimating such parameters would be less important. 

The TMDL model sensitivity analysis should result in a table of values for parameter 

sensitivity coefficients (Si) which represent the percentage change in the model output for a 

one percent change in input parameter values. Table 5.3 shows an example of a summary 

table of sensitivity parameters for a 30% adjustment of parameters for the Upper St Johns 

River. These sensitivity coefficients can be derived by performing multiple sensitivity 

simulations of the TMDL model (individual model runs for each parameter) and averaging 

the ratios of the output and parameter value changes, as illustrated in the example below for 

a calibrated instream rate coefficient. 

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Task Assignment 003.03/05-003 

 Sensitivity Model Run No. 1: 25 percent increase (or another range) in rate coefficient 

reduces output concentration by 5 percent (sensitivity coefficient = 5%/25% = 0.20) 

 Sensitivity Model Run No. 2: 25 percent decrease in rate coefficient increases output 

concentration by 3 percent (sensitivity coefficient = 3%/25% = 0.12) 

 Average Sensitivity Coefficient (Si) for Rate Coefficient: (0.20 + 0.12)/(2) = 0.16 

Another similar approach would be to define an uncertainty “band” for input data and model 

results by varying parameters to the high and low extreme possible values, note sensitivity, 

and choose the parameter range to be used for simulation. 

Table 5.3 Example Summary Table of Sensitivity Parameters and Results 

Percent Change over Entire Simulation 

6E Jane Green 

6F Lake 

Washington 6G Lake Poinsett Parameter Adjustment 

Total N Total P Total N Total P Total N Total P 

Land Based +30% 28% 26% 17% 14% 19% 15% 

Loads (lbs) 

-30% -30% -30% -16% -14% -20% -16% 

+30% -1.3% -2.7% -0.1% -2.6% -1.0% -3.5% PHYSET: 

Phytoplankton 

settling (ft/hr) 

-30% 1.3% 0.5% 2.0% 4.4% 1.0% 4.5% 

+30% 1.4% 0.4% 1.5% 3.4% 0.8% 3.8% 

CMMP: 

MichaelisMenton 

Constant for 

Phosphorus -30% 0.8% -1.5% -0.3% -3.3% -1.3% -4.8% 

Those model input parameters which exhibit the greatest sensitivity factors for the 

calibrated/validated model should be screened by the practitioner to determine whether 

recalibration and/or the collection of additional back-up field data is justified. In many cases, 

recalibration and/or the collection of additional field data will not be necessary, assuming the 

calibrated parameter value is within the range of typical literature values and was selected 

by calibrating to a sufficient amount of observed water quality data. The sensitivity 

coefficient table of values can be used to perform the FOEA evaluations described below. 

Listed below are the key variables which should be included in the sensitivity analysis of the 

calibrated/validated TMDL models (both water quantity models and water quality models): 

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Task Assignment 003.03/05-003 

Quantity 

 Rainfall and its temporal and spatial variation 

 Tributary areas for flat interconnected systems 

 Directly connected impervious areas 

 Pervious and impervious area runoff coefficients 

 Representation of overland flow and channel friction 

 Soil infiltration rates and soil storage values 

 Baseflow and groundwater inputs 

 Channel cross-section data and values where detailed survey is not available 

 Bathymetry of lakes and wetlands where detailed survey is not available 

Water Quality - General 

 Baseflow and groundwater concentrations 

 Nonpoint sources (e.g., surface runoff, failing septic tanks, sanitary sewer overflows) 

 Point source discharges and leaking sewers (e.g., stream crossings) 

 Settling and stream scour rates 

 General first-order decay rate or delivery ratio 

Water Quality – Dissolved Oxygen/BOD 

 Watershed BOD loads 

 Reaeration coefficient (often calculated from modeled depth and velocity) 

 CBOD decay rate 

 NBOD decay rate 

 Sediment Oxygen Demand (SOD) 

 Algal growth and death rates, and oxygen-to-biomass ratios (if algal biomass is 

modeled) 

 Water temperature (affects decay rates, saturated DO concentration) 

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Water Quality – Nutrients 

 Watershed nutrient loads and variability 

 Net flux rate of nutrients between sediment and water column 

 Atmospheric wet- and dry-fall 

 Decay rates (e.g., breakdown of organic N and P, nitrification) 

 Settling rates for particulate nutrients 

 Algal growth and death rates, algal settling rates, and nutrient-to-biomass ratios (if algal 

biomass is modeled) 

 Water color/light extinction (if algal biomass is modeled) 

Water Quality - Pathogens 

 Watershed pathogen loads (event and seasonal variability) 

 Net first-order bacteria loss rate (base decay rate plus impacts due to light and 

temperature mortality, transformation and re-growth) 

 Potential septic tank and SSO load contributions 

5.4.4.3 First-Order Error Analysis (FOEA) 

FOEA uses the first-order approximation to compute the overall variance for a multivariate 

model. First, the fraction of model output variance contributed by each key input parameter 

is determined. Next, a first-order approximation is used to calculate the overall model 

variance from the variance and sensitivity coefficient for each model parameter. 

The following steps in an FOEA for a TMDL study are outlined and demonstrated by Zhang 

and Yu (2004): 

1. Determine the key input parameters in the calibrated/validated model: For the TMDL 

water quality model, these will typically include: rate coefficients for growth (algae), 

decay (BOD/DO), die-off (bacteria), and transformation to other forms (nitrification); 

temperature correction coefficients; settling rates; adsorption rates; and release rates 

(benthic). 

2. Calculate the sensitivity coefficient (Si) for key input parameters: The table of values for 

the sensitivity coefficients derived by the sensitivity analysis (see Section 5.4.4.1) can 

serve as an approximation of the partial derivative of the model with respect to each 

parameter. The sensitivity coefficient for each input parameter is derived by dividing the 

percent change in model output (e.g., maximum or minimum instream concentration) by 

the percent change in the parameter value based on single parameter sensitivity runs of 

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Task Assignment 003.03/05-003 

the TMDL model). The sensitivity coefficient (Si) for each variable is expressed as a 

decimal fraction. 

3. Estimate the Coefficient of Variation (COV) for each key model input parameter: The 

COV (ratio of standard deviation to the mean) for model parameters can be based on 

ranges reported in the general literature and the judgment of the TMDL modeler. 

Examples of COV ranges reported in the literature are as follows: 

 Continuous simulation hydrologic model (early version of HSPF): COVs range from 

0.1 to 0.5 for soil moisture parameters, with typical values in the range of 0.1 to 0.25 

(Garen and Burges, 1981); 

 Rate coefficients for QUAL2E model (Brown and Barnwell, 1987): COVs typically 

range from 0.02 to 0.25; 

 Lake eutrophication model (Scavia, et al., 1981): COVs range from 0.16 to 0.33 for 

algal growth rates and chlorophyll-a composition ratios and range from 0.1 to 0.4 for 

other model parameters. 

4. Perform the FOEA analysis based on results of steps 1 through 3: Compute the overall 

variance of the TMDL model based upon the following approximation of the first-order 

expansion equation (Zhang and Yu, 2004): 

[ ] ( ) [ ] ( ) ( )2

i

p

i

2

i

2 CCOV = ∑ S*XCOV

 where COV(C) = The overall COV for the calibrated TMDL model’s simulation of the 

instream concentration “C” which is the focus of the TMDL study 

 COV (Xi) = The COV for each key model parameter (ranging from “i” to “p” 

parameters) 

 Si = The sensitivity coefficient for each key model parameter (ranging 

from “i” to “p”) as derived in Step 2 

The COVs and the Si values in the expansion equation are all expressed as decimal 

fractions. 

The calculated COV(C) value for the TMDL model’s simulation of instream concentration 

provides a quantitative assessment of overall uncertainty associated with the TMDL model. 

A COV(C) value less than 0.1 is an indication of fairly low model uncertainty, whereas a 

value greater than 0.4 to 0.5 is an indication of fairly high model uncertainty. The COV(C) 

value calculated from the FOEA can be used to establish the Margin of Safety (MOS) for the 

TMDL, with larger values associated with a greater MOS and vice versa. Section 6.4 

summarizes procedures for incorporating the FOEA analysis into the determination of the 

MOS allowance for the TMDL. Table 5.4 and Figure 5.8 present an example application 

from the Hillsborough River. 

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TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Table 5.4 Summary of First-order Error Analysis for Lower Limit (-50%) on DO 

Variability (Flint Creek and Hillsborough River Below Lake T) 

 Below the Lake Component of Variance

Input Variable 

Standard 

Dev. #6 #13 #16 #6 #13 #16 

1st order BOD decay rate 0.175 0.036 0.017 0.185 0.1% 0.0% 2.2% 

1st order nitrification rate 0.175 0.0219 0.000568 0.0155 0.0% 0.0% 0.0% 

Sediment Oxygen Demand 0.175 0.934 0.412 0.819 46.4% 27.2% 43.4% 

1st order reaeration rate 0.175 -0.868 -0.611 -0.717 40.1% 59.7% 33.3% 

DO boundary concentration 0.175 -0.078 -0.285 -0.482 0.3% 13.0% 15.0% 

Chl-a growth rate 0.175 -0.452 -0.018 -0.302 10.9% 0.1% 5.9% 

Chl-a settling rate 0.175 0.205 0.01 0.0461 2.2% 0.0% 0.1% 

Coef. of Var.2 0.058 0.019 0.047 100.0% 100.0% 100.0%

DO Median (mg/L) 4.609 5.832 4.668 

DO Standard Deviation 

(mg/L) 1.106 0.807 1.015 

0

1

2

3

4

5

6

7

8

9

10

11

12

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Cumulative Distribution

DO less or equal to (mg/L) 

Cum. Dist. of predicted DO with high (+50%) SOD at segment # 13

Cum. Dist. of predicted DO with average SOD at segment # 13

Cum. Dist. of M easured DO at Segment # 13 

Cum. Dist. of predicted DO with low (-50%) SOD at segment # 13

DO Water Quality Standard (5 mg/L)

Figure 5.8 Effect of SOD on DO Distribution at Segment #13 (Below the Lake Model) 

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Task Assignment 003.03/05-003 

The calculated COV(C) value can be converted to a standard deviation (SD) for evaluations 

of error bounds for the TMDL model simulations. This conversion can be calculated by 

multiplying the model’s COV(C) value by the mean concentration (C ) for the TMDL model 

simulations (i.e., SD = COV(C) * C . For steady-state model applications where the TMDL 

model output matches the instream water quality standard (WQS), the SD can be calculated 

by multiplying the COV(C) by the WQS (i.e., the maximum or minimum concentration limit) 

which is the basis of the TMDL. If the TMDL model’s simulation of the instream 

concentration is consistent with an approximately normal distribution, there is an 84 percent 

probability of complying with the TMDL water quality standard (WQS) if the TMDL is based 

on an allocated load which produces a mean concentration that is one SD less than the 

WQS (or one SD greater than the WQS for a minimum concentration standard). Similar error 

bounds can be developed for a safety factor based on other multiples of SD (e.g., 90 

percent compliance probability for 1.3 SD and 95 percent for the 1.64 SD). 

5.4.4.4 Monte Carlo Simulations 

Monte Carlo simulations can be used to develop uncertainty bands for assessments of the 

calibrated/validated TMDL model. The Monte Carlo simulation method relies upon statistics 

on the TMDL model’s input parameters (e.g., mean, SD) to develop a set of randomly 

sampled parameters for the TMDL model which is repeatedly executed with each new set of 

parameters. Following numerous model runs with the different parameter values (on the 

order of several hundred to several thousand model runs), the summary statistics are 

calculated for the simulated instream concentrations (mean, SD, COV). The summary 

statistics for the model output can be used to develop uncertainty bands for the TMDL 

model (e.g., plots of mean concentration plus or minus one SD). As discussed in Section 

6.4, the uncertainty bands based upon the Monte Carlo simulations can also be used to 

develop an MOS for the TMDL study. 

Monte Carlo simulations are repetitive runs to test a model over the full probability 

distribution of the input parameters. These distributions are either derived from field 

measurement or estimated by the modeler. A Monte Carlo simulation includes repetitive 

runs during which input parameter values are randomly selected from their distribution 

before the model calculation. As a result, each run is a unique representation of reality with 

its associated probability of occurring. By definition, extreme values have a low probability of 

occurring compared to central values. Model outputs from the large number of runs are 

ultimately analyzed to determine statistical moments of transport and flow characteristics 

(e.g., mean, standard deviation of resident concentration, water flux, and stream flow) 

and/or to estimate their frequency distribution. 

Monte Carlo techniques are included in some models, such as QUAL2E and EUTROMOD, 

and provide the most robust estimate of uncertainty estimates. Such simulations should be 

included in the most complex of analyses if no other uncertainty elements are included and 

the results of the probability distribution should be included in the model documentation. The 

QUAL2E user’s manual (Brown, L.C. and T.O. Barnwell, 1987) provides an explanation of 

the calculations used by the model in conducting Monte Carlo simulation, as well as general 

ranges of expected variability for various model input parameters, and an example 

application using the model. 

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TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Compared to FOEA methods, Monte Carlo simulations represent a more complex type of 

model uncertainty analysis. As discussed, previous studies which have compared these two 

uncertainty analysis methods have found that the FOEA method can produce similar results 

which are sufficiently accurate for assessments of overall model variance (Zhang and Yu, 

2004; Warwick, 1997; Scavia, et al., 1981). Therefore, the simpler FOEA method should 

provide an adequate evaluation of uncertainty for most TMDL modeling applications. 

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TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

SECTION 5 REFERENCES 

Beven, K. J. 2001. Rainfall-Runoff Modelling: The Primer, Wiley, Chichester. 

Brown, L.C. and T.O. Barnwell, The Enhanced Stream Water Quality Models QUAL2E and 

QUAL2E-UNCAS: Documentation and User Manual, EPA/600/3-87/007, May 1987. 

CDM. 1992. City of Jacksonville Master Stormwater Management Plan. 

CDM. 2001. Big Creek Water Resource Management Plan. 

CDM. 2002. Evaluation of High Water Impacts to Stormwater Systems and Other Facilities 

on the Harris Chain of Lakes. 

Chow, V.T. 1959. Open Channel Hydraulics. McGraw-Hill. 

Garen, D.C. and Burges, S.J. 1981. “Approximate Error Bounds for Simulated 

Hydrographs.” ASCE Journal of Hydraulics Division, 107(11), pp. 1519-1534. 

Maidment, D.R. 1993. Handbook of Hydrology. 

Nash, J.E., and I.V. Sutcliffe. 1970. River Flow Forecasting through Conceptual Models. 

Journal of Hydrology. 273, 282-290. 

Scavia, D. 1981. “Comparison of First-Order Error Analysis and Monte Carlo Simulation in 

Time-Dependent Lake Eutrophication Models.” Water Resources Research, 17(4), pp. 

1051-1059. 

Scheaffer, R.L. and J. McClave. 1994. Probability and Statistics for Engineers. 

Snedecor, G. W. and W. Cochran. 1967. Statistical Methods, Sixth Edition, The Iowa State 

University Press, Iowa, USA. 

Warwick, J.J. 1997. “Use of First-Order Uncertainty Analysis to Optimize Successful Stream 

Water Quality Simulation.” Journal of American Water Resources Association, 33(6), pp. 

1173-1185. 

Wilcoxon, F. 1945. Biometrics Bul., 1:80. 

Zhang, H.X. and Yu, S.L. 2004. “Applying the First-Order Error Analysis in Determining the 

Margin of Safety for Total Maximum Daily Load Computations.” ASCE Journal of 

Environmental Engineering, 130(6), pp. 664-673. 

  

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Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Section 6 

MODEL APPLICATION 

Notes: 

1. Preparation, verification, calibration, and validation (PVCV) 

Tech Memo

PVCV of Current 

Conditions 

FDEP

Acceptance

C/L/S Meeting

No

TARC

Review

Current Conditions

Watershed Model

Tech Memo

Review

Comments

TMDL Modeling

Tech Memo

Refinement of

TMDL Objectives

 Definition of

 Conditions

Restatement of

TMDL Modeling

Objectives

Tech Memo

9

10

11

12

13

14

Design and 

Simulation Design and 

Simulation 

Conditions

Current Conditions 

Receiving Water 

Model, TMDL, 

Uncertainty, and 

Recommendations

PVCV of Current 

Conditions 

Receiving Water 

Model1

Yes

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Task Assignment 003.03/05-003 

6.1 INTRODUCTION 

The previous sections presented examples of how data are collected and analyzed, 

appropriate model(s) are selected, the chosen model(s) are prepared for the specific 

parameters and conditions for the water body under study, and the model(s) are verified, 

calibrated and validated. The model(s) may now be used to simulate various conditions to 

provide estimates of the total amount of pollutants that can be discharged to the water body 

to meet water quality standards and to achieve the designated uses. The practitioner can 

use the model(s) to identify the critical loading(s) at which the designated use(s) would not 

be met (i.e., the level at which applicable standards would be exceeded), reduce the 

allowable loading to account for uncertainty and a margin of safety, and designate the 

reduced loading as the total maximum daily load (TMDL) allowed to be discharged by 

human and non-human or background sources. The identification of the process of 

determining the critical loading may require several model simulations to determine the 

maximum load. The purpose of this section is to describe the simulations that may be 

conducted for the TMDL analysis and definition. 

6.2 SIMULATIONS OF DESIGN CONDITIONS AND 

PERIODS 

Information from Section 3 (Data Acquisition and Assessment) and Section 5 (Model 

Preparation, Verification, Calibration, and Validation) may be used to identify the conditions 

under which the pollutant(s) of concern have the greatest impacts. For example, stormwater 

sources generally have a greater impact on receiving water quality during high rainfall 

conditions, and WWTP point sources generally have a greater impact during extended dryweather, low-flow conditions. The design conditions and periods may also depend upon the 

type of receiving water body. Streams may have more short-term impacts in comparison 

with lakes and estuaries with extended residence times, where seasonal conditions may be 

more important. Therefore, the practitioner may identify critical loadings on an hourly, daily, 

monthly, seasonal, and/or annual basis for determination of a TMDL. 

In general, design or critical time period conditions can be identified and considered by 

parameter as follows: 

 Dissolved oxygen: For free-flowing streams, the design condition for dissolved oxygen 

and biochemical oxygen demand is generally during periods of high water temperature 

and low-flow conditions. Higher temperatures result in lower DO saturation, which in turn 

limits the rate of stream reaeration. Because reaeration is also a function of turbulence 

and velocity (reaeration increases as velocity increases), low-flow conditions may also 

limit the rate of reaeration. Concurrently, reaeration rates can increase to some degree 

as depth decreases due to an increase in the ratio of surface area to volume in a reach. 

High temperatures can also increase the rates of other processes (e.g., SOD, BOD, 

nitrification) which use oxygen in the stream, and SOD impacts tend to increase as the 

depth of water decreases. Low-flow conditions also limit the amount of dilution of point 

source loads, thus concentrations of oxygen-demanding substances from point sources 

in low flows will be high relative to stream concentrations under medium and high-flow 

conditions. 

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Lake and estuary design conditions for DO/BOD analyses would also consider high 

temperature, low-flow conditions, but the focus is more likely to be monthly, seasonal, 

and/or annual due to the extended residence times in these types of water bodies. 

Because of the longer residence times, algae may be a concern and likely will be a 

necessary process to model. Though algae may provide a net increase in water body 

oxygen, these plants will also result in greater diurnal swings in DO concentration, and 

thus may result in exceedance of the minimum DO standard especially at night during 

the dark cycle, even when the average DO standard is being met. Robust colonies of 

aquatic plants (e.g., hydrilla) can dramatically affect DO ranges. Anecdotal reports of fish 

kills from low DO can also be used as part of the validation process. 

 Nutrients: For free-flowing streams with short travel times (<12 hours), instream nutrient 

concentrations in and of themselves may not be a concern. The exception would be the 

concentration of ammonia nitrogen, because the un-ionized fraction of ammonia nitrogen 

can be toxic to aquatic organisms. In such cases, low-flow conditions with less 

opportunity for point source dilution would be a critical design condition. The fraction of 

ammonia that is un-ionized also depends upon the water pH, with higher pH values 

yielding a higher fraction of un-ionized ammonia. Otherwise, nutrient loads in streams 

are generally of concern as they affect downstream lakes or estuaries. 

Lake and estuary design conditions for nutrients would typically consider both high- and 

low-flow conditions. In many cases, the most critical design condition would be a wet 

period (with high flows discharging nutrients to lakes and estuaries) followed by an 

extended dry period with extended residence times providing the opportunity for 

excessive algal growth. The algal growth can raise the pH in the water, which in turn 

may result in higher nutrient releases from the sediments, further stimulating algal 

growth. In tidal estuaries, seasonal changes in tidal fluctuation can affect the tidal 

flushing, thus causing greater nutrient impacts during periods with less flushing. 

 Pathogens: Pathogen concentrations can be high relative to typical standards, even in 

undeveloped areas. This suggests that natural background may be an important 

consideration (Schueler, 1999; Pitt, 1998; CDM, 2001). The geometric mean of 

pathogens in surface runoff (wet-weather conditions) is typically an order of magnitude 

higher than the geometric mean of baseflow (dry-weather) conditions. In addition, runoff 

concentration may vary significantly by season, with summer concentrations being an 

order of magnitude higher than winter concentrations based on data from the NURP 

study (USEPA, 1983) and select NPDES Phase II and other local monitoring data (CDM, 

2001). This is offset to some degree by the fact that summer stream conditions also 

include higher water temperature, greater solar radiation and lower stream depth 

(greater light penetration), which would cause higher pathogen die-off rates relative to 

winter conditions. 

For lakes and estuaries, the extended residence times are likely to result in significant 

potential for pathogen die-off and transformation. Lakes with residence times of weeks to 

months may remove or transform as much as 60–90 percent of incoming pathogens. 

However, lakes may also have natural background loads (e.g., from warm-blooded 

aquatic wildlife) that raise pathogen concentrations. Estuaries generally have more 

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Task Assignment 003.03/05-003 

restrictive standards for pathogens due to potential shellfish and fish consumption, but 

also provide more opportunity for dilution due to tidal mixing. In both lakes and estuaries, 

the die-off of pathogens due to light mortality can be limited by algal growth that reduces 

light penetration. Atypical coliform bacteria may also be considered, as discussed in 

Section 3. 

 Metals and Other Parameters: For parameters such as heavy metals and organic 

chemicals, both high- and low-flow conditions may be critical, since these pollutants can 

be discharged by both point and nonpoint sources. Since metals standards are hardness 

based, hardness may be evaluated or modeled if possible to correlate metals 

concentrations if necessary with published standards in order to consider potential 

bioaccumulation. Pesticides and herbicides can also dramatically affect water quality 

and biota. Discharges of these compounds can cause toxic reactions (e.g., fish kills) or 

in the case of herbicides, may artificially lower Chl-a levels. 

Regardless of the design conditions pertinent to the pollutant(s) of concern, if necessary, the 

water segment may be simulated with and without human impacts to identify the relative 

contributions of human impacts and natural background sources. For point sources, such 

simulations are relatively straightforward – the point source can simply be set to zero flow. 

For non-point sources, such as surface runoff from urban and agricultural development, this 

requires further analyses. A discussion of various methods to conduct such an analysis is 

provided below. 

6.2.1 Natural Background Conditions 

For the purposes of this protocol, the term natural background refers to the pollutant loading 

condition of the water segment in the absence of anthropogenic pollutant loads. For point 

sources, this condition can be evaluated by simulating the water segment with zero point 

source discharge. For nonpoint sources, the watershed model can be adjusted based on the 

condition that the study area was undeveloped (i.e., no urban or agricultural development). 

Specific data on wildlife contributions (historic and current) may be difficult to obtain, but 

such loads can dominate a system (e.g., ducks, geese, wading birds). Local data are 

recommended for systems with significant wildlife interaction, especially for pathogens. 

The discussion below focuses on suggested adjustments to watershed models and 

receiving water models. Many of the issues discussed below would have been considered in 

the initial model evaluations (e.g., preparation, verification, calibration, and validation), and 

therefore may be repetitive or iterative as refinements are made. 

Watershed Baseflows: Baseflow rates from study area watersheds (or nearby areas) can 

be identified through analysis of gaged flow data using readily available baseflow separation 

techniques (Section 3). Such analyses would identify the portion of gaged flow that can be 

attributed to baseflow versus surface runoff. Analysis of data for a “critical” period would 

determine the baseflow quantities during that period, and long-term flow analysis would be 

used to estimate average annual and seasonal baseflow rates. If gaged flows are not 

available, a literature review may yield information on long-term hydrologic information (e.g., 

annual average water budget of rainfall, evapotranspiration, runoff, and baseflow), and local 

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rainfall data may be used to designate wet-weather and dry-weather condition during a 

“critical” evaluation period. Some watershed models (e.g., WMM, SWMM) can accept 

baseflow quantity as input values or time series, whereas some other models (e.g., HSPF) 

would need to be calibrated to calculate the water balance (including baseflow discharge) on 

a continuous basis. 

It should be noted that most models consider that baseflow is contributed only by pervious 

land surfaces in the study area. Consequently, measured baseflow rates may be evaluated 

with respect to the fraction of the study area that is pervious land. For example, in a study 

area that is 10 percent impervious and has a measured baseflow of 6.3 inches per year, the 

baseflow rate from a totally undeveloped (natural background) condition would be 7.0 inches 

(i.e., 6.3 divided by 90% perviousness). 

Baseflow water quality is often estimated using instream water quality data because 

groundwater quality data are typically scarce. The instream data would be evaluated in 

conjunction with evaluation of baseflow rates and/or analysis of wet-weather/dry-weather 

periods. Ideally, instream water quality data would be separated into dry weather (baseflow) 

samples and wet weather samples (i.e., samples affected by storm water runoff and/or 

interflow). Dry-weather samples would be evaluated to establish appropriate baseflow 

concentrations for the pollutants of concern. The exception would be dissolved oxygen 

(DO), for which the baseflow concentration to the stream is often established as a 

percentage of saturation DO at the design water temperature. 

The above discussion is based upon the condition that baseflow concentrations at the 

sampling location would not be affected by point source discharges. It is relatively simple to 

determine if point source discharges are contributing upstream of the sampling station, but 

their effect on instream concentrations requires evaluation. The practitioner may be able to 

factor out the point source discharge(s) from the baseflow quantity and quality if sufficient 

data regarding the point source discharge rate and quality are known. Another alternative 

would be to analyze water quality data from a “reference” site (i.e., site monitoring an area 

without point source discharges). 

Even if conditions at the monitoring site are not affected by point source discharges, the 

water quality measured at the site may be affected by human impacts. For example, 

baseflow nitrogen and phosphorus concentrations in a residential area may be higher than 

in a forested area because fertilizers and other human impacts have raised local 

groundwater concentrations. Literature values may be useful to estimate how much one 

could expect groundwater concentrations of the constituent of interest to change due to 

development (Section 3). Again, a “reference” site in an undeveloped area may provide a 

suitable basis for estimating baseflow concentrations that have not been affected by human 

impacts. 

Watershed Runoff: Runoff rates from study area watersheds (or nearby areas) can also be 

identified through analysis of gaged flow data using readily available baseflow separation 

techniques (Section 3). The portion of gaged flow that is not contributed by baseflow would 

be surface runoff and interflow. Data for a “critical” period would be analyzed to estimate the 

runoff quantities during that period, and long-term flow analysis would be used to estimate 

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long-term average annual and seasonal runoff rates. If data regarding gaged flows are not 

available, a literature review may yield information on long-term hydrologic information (e.g., 

annual average water budget of rainfall, evapotranspiration, runoff, and baseflow) and local 

rainfall data may be used to designate wet-weather and dry-weather conditions during a 

“critical” evaluation period. Some watershed models (e.g., WMM) can accept runoff 

coefficients for pervious and impervious surface as an input value, whereas more complex 

models (e.g., HSPF) would need to be calibrated to calculate the water balance (including 

runoff and interflow discharge) on a continuous basis. 

Because runoff is generated by both pervious and impervious land surfaces, measured 

runoff rates may be evaluated with respect to the fractions of the study area that are 

pervious and impervious land. For example, consider a study area that is 10 percent 

impervious and has a measured runoff quantity of 9.0 inches per year and is located in an 

area that receives 50 inches of rain. If it is expected that a high fraction (90-95 percent) of 

rainfall is converted to runoff for impervious areas; in this study area, the runoff due to 

impervious land would be 4.5 inches per year (50 inches of rain * 90 percent conversion of 

rainfall to runoff * 10 percent impervious area). The remaining runoff (9.0 – 4.5 = 4.5 inches 

per year) is attributed to pervious land area, and the unit runoff from pervious land would be 

5.0 inches per year (4.5 inches / 90 percent of area = 5.0 inches). 

Runoff quality is typical defined based on land use, using single land use monitoring data 

from sources such as the NURP study or NPDES Phase I and Phase II data collection. 

Unfortunately, monitoring data from such sources tends to reflect mainly urban land uses, 

and limited monitoring of undeveloped land is conducted by most stormwater management 

programs, although data are being collected and analyzed as part of the Comprehensive 

Everglades Restoration Program (CERP). If there is a “reference” site in an area of little or 

no development, it may be possible to estimate runoff concentrations using instream water 

quality data (CDM, 2001). The instream data would be evaluated in conjunction with 

evaluation of runoff rates and/or analysis of wet-weather/dry-weather periods. Ideally, 

instream water quality data would be separated into dry-weather (baseflow) samples and 

wet-weather samples (samples affected by storm water runoff and interflow). Data from wetweather samples would be evaluated to establish appropriate runoff concentrations for the 

pollutants of concern. The exception would be dissolved oxygen (DO), for which the runoff 

concentration to the stream is often established as a percentage of saturation DO at the 

design water temperature. 

Receiving Water: In general, the receiving water model prepared as discussed in Section 5 

would be applied using watershed flows and loads with no development, as discussed 

above. However, some receiving water input values may be modified if natural background 

conditions are being used. Examples of these would include: 

 Boundary conditions. Upstream, downstream, or lateral inflow boundary concentrations 

under existing conditions may reflect human impacts. It may be appropriate to reduce 

the boundary concentrations based on typical concentrations from a “reference” site; 

 Sediment oxygen demand (SOD). In many cases, existing SOD rates are in part 

dependent upon oxygen-demanding substances that in the past were generated due to 

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human impacts. It may be appropriate to review literature values or measured SOD 

values from a “reference” site to determine whether background SOD values would be 

expected to be lower than existing values; 

 Other sediment fluxes. Similarly, human impacts may have caused higher loads of 

nutrients, metals, or other compounds in the sediments, with resulting increases in flux 

from the sediment to the water column; 

 Water temperature. Impervious cover tends to produce runoff of a higher temperature 

than pervious area (James, 1997). Consequently, stream receiving water temperatures 

may have risen due to urbanization. It would be difficult to quantify this without long-term 

data. 

 Light extinction coefficient. The light extinction in a stream, lake or estuary generally is 

related to suspended solids and algae in the water. Therefore, the light extinction under 

existing conditions may be greater than under background conditions. 

 Plant respiration. The respiration of submerged aquatic vegetation can cause large 

fluctuations in DO, especially at night. 

6.2.2 Existing Conditions 

For the purposes of this document, the term existing conditions refers to the pollutant 

loading condition of the water segment based on “permitted” discharges to receiving waters. 

This condition would include permitted discharge flows and water quality constituent 

concentrations, stormwater runoff flows and loads under existing land use and implemented 

water quality controls (BMPs), and baseflow quantity and quality based on existing land use. 

Existing conditions could vary over time due to changes in permitted discharges, land use, 

hydraulics, and other factors. 

In general, analysis of existing conditions would use the models and approaches as 

described in Section 5. These models would have been calibrated and validated through 

comparison of measured and modeled flows and concentrations, and the practitioner would 

adjust model input parameters to provide an adequate match between measured data and 

model output data. For this analysis, however, the period of simulation would be based on 

the “critical” design conditions and/or time periods discussed earlier. 

Potential differences between the input to the calibrated/validated model and the “existing 

conditions” model are discussed below: 

 Point source discharges: The calibrated/validated model would typically use measured 

values of flow and concentration for the simulation. In contrast, the “existing conditions” 

model should use the maximum permitted flows and concentrations, which in most 

cases will provide a greater load than actual measured flows and concentrations. 

 Meteorological data: If the designated critical design condition reflects a period other 

than the periods used for model calibration and validation, then meteorological data for 

the critical period needs to be used as model input. For continuous simulation, 

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metrological data that may be used for watershed and receiving water analysis includes 

rainfall and evapotranspiration for hydrologic analysis, and potentially solar radiation, 

wind and air temperature for water quality simulation. For steady-state conditions, it may 

be appropriate to establish a “design” value for the meteorological data based on 

analysis of historical data. For example, if the analysis is for a steady-state analysis for 

low flow conditions, the air temperature or solar radiation data can be established based 

on historical data for periods corresponding to the month(s) in which low flows most 

commonly occur. For a continuous simulation, a time period and associated 

meteorological dataset may be defined to reflect the full range of hydrologic and water 

quality variation (5 to 10 years or longer, such as 40 to 50 years). 

 Water temperature data: If the designated critical design condition reflects a period 

other than the periods used for model calibration and validation, then water temperature 

data for the critical period need to be used as model input, unless water temperature is 

being modeled. In the absence of measured or modeled water temperature, a typical 

time series of water temperatures based on time of year can be developed from existing 

monitoring data. For steady state conditions, it would be appropriate to establish a 

“design” value for the meteorological data based on analysis of historical data. 

 Land uses and BMPs: The existing conditions model would likely simulate the most 

current land use, imperviousness, and BMPs. 

 Hydraulic features: Changes in bridges, culverts, structures and/or operation of 

structures may cause these conditions to be different for the existing conditions 

model(s). 

Other model input parameters (e.g., first-order decay rates, temperature correction factors) 

should remain the same as the values developed in model calibration and validation unless 

specific variations are applicable for the simulation time period or condition. 

6.3 CALCULATION OF THE TMDL 

If the analysis of critical design conditions shows that receiving water quality standards 

would be exceeded under existing loading conditions, the model may be applied to 

determine the total maximum daily load (TMDL). The TMDL can be calculated by several 

methods depending on whether the parameter is conservative (e.g., freshwater, chloride, or 

atrazine) or non-conservative (e.g., DO or fecal coliform bacteria). The choice of design 

conditions and periods may include consideration of: 

 Continuous longer term dynamic conditions (e.g., 20 to 50 years of hydrologic and water 

quality variability), and 

 Discrete steady-state extreme conditions (e.g., 7-day 10-year low-flow). 

This choice may include one or both of these conditions as appropriate for the parameter(s), 

watershed(s), and receiving water(s) in question. 

As discussed previously in Section 3, long-term datasets for water quality and flow for 

conservative parameters may allow the calculation of the TMDL based on the water quality 

standard less the MOS times the long-term flow record divided by the critical time period(s). 

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Also as discussed previously, consideration of changes in land uses and hydraulics and 

their effects on flows and loads may be factored into this data TMDL assessment (e.g., 

applicable land use, impervious area, bridge-culvert, and gate operations conditions), since 

many watersheds have development and hydrologic-hydraulic changes occurring over the 

monitoring and modeling period which can significantly affect results. 

In many cases, changes that result in significant variations in non-conservative parameters 

may require the application of long term continuous models to evaluate the TMDL by 

evaluating the pollutant load reduction goal options to determine the threshold at which the 

standard is met (e.g., reduce BOD by a range of amounts to meet a DO standard). The 

implicit MOS would be included in these results, and the explicit MOS would be calculated 

and subtracted from the resultant TMDL. Section 6.5 presents further discussion on MOS. 

Figure 6.1 shows an example frequency plot for daily load based on a water quality 

numerical standard (as applicable) and time series results for three model simulations which 

include the expected model parameters (most applicable dataset from calibration and 

validation) and both high and low ranges of compound implicit uncertainty simulations in 

order to define an uncertainty "band" and assist in the definition of MOS. Data and model 

refinements may also be identified at this point to reduce uncertainty. The TMDL would be 

calculated based on the load to meet the applicable standard minus the MOS. 

Figure 6.1 Example Plot of Water Quality Standard, Monitored Data, and Model Results 

0

50

100

150

200

250

0% 20% 40% 60% 80% 100%

Percentile

Parameter

(lb/day)

Water Quality Standard

Monitored Data and Flow

Model Simulation (Expected)

Model Simulation (High Band)

Model Simulation (Low Band)

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The consideration of residence time and the potential for in-system response(s) may be 

considered in the time period for the load that is used to calculate the daily load estimate. 

For example, the TMDL could be a weekly (rivers), monthly (lakes), seasonal (estuaries), 

and/or annual (very large systems) issue for the receiving water. The practitioner may 

consider the various potential time periods of interest and calculate the daily loads for 

several different time periods to evaluate the uncertainty of the calculation in order to better 

define and document the MOS. 

6.4 EVALUATION OF LOAD REDUCTIONS AND 

ALLOCATIONS 

The next step in the TMDL process is the evaluation of the pollutant load reduction required 

to achieve instream concentrations that meet the water quality standards with a margin of 

safety. The evaluations described in Section 6.1 reflect two options for scenarios in the 

study area: no human impacts (totally undeveloped) and existing human impacts. Model 

results for the two cases could be used as a comparison for the load reduction analysis, 

although in many cases, the analysis will identify allowable load and/or load reductions to 

meet the water quality standard. 

If significant loads are contributed by both point sources and nonpoint sources, the load 

reductions need to be allocated between the two source types. The point sources would 

include domestic and industrial wastewater and NPDES MS4 discharges, and these sources 

would comprise the WLA as described in Section 1. The nonpoint sources would include 

flora and fauna, atmospheric, agricultural, and silvicultural discharges along with legacy 

sediment effects. These sources would comprise the load allocation (LA). Such an allocation 

may consider the relative contributions of each source type, and the impacted receiving 

water type. For example, if the receiving water is a stream that frequently does not meet DO 

standards during periods of extreme low-flow conditions, then a reduction of point source 

loads would likely be more appropriate than reduction of stormwater and/or nonpoint loads 

which would provide little or no oxygen-demand load under the critical low-flow design 

conditions. Conversely, it may be inappropriate to allocate load reductions to point sources 

that are already subject to very high levels of treatment. Comparative cost-benefit analyses 

could be performed to compare options. 

To clarify potential cost-benefit questions about point source controls versus nonpoint 

source controls, the FDEP Allocation TAC (FDEP, 2001) has recommended that since 

domestic and industrial wastewater point sources already treat pollutants to a high degree, 

the first step in reducing loads should be a consideration of the impacts when 45% of 

nonpoint sources participate in current BMP requirements. The second step would consider 

90% participation. If neither of these alternatives reduces loads to below impaired 

requirements, then load reduction requirements for both point and nonpoint sources may be 

considered. The practitioner could identify potential BMPs (Section 7) and coverages to 

model these load reduction scenarios. 

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6.5 DETERMINATION OF MARGIN OF SAFETY (MOS) FOR 

TMDL STUDY 

Based on EPA guidelines discussed in Section 5.4, the TMDL for each receiving water is 

supposed to include an MOS allowance which reflects the uncertainty in the TMDL 

determination and provides a safety factor for water quality protection. Simply stated, the 

MOS is the unallocated portion of the TMDL which is derived from the model applications 

described in Section 6.3. The MOS can be “explicitly” defined in the TMDL as unallocated 

assimilative capacity. Alternatively, the MOS can be “implicitly” factored into the TMDL 

through the use of conservative model input parameter values in the TMDL water quality 

model, as previously discussed in Section 5. 

A National Research Council (NRC) evaluation of the scientific basis for the TMDL program 

found that, out of 172 completed TMDL studies, 70 percent specified an explicit MOS 

(almost all were based on fairly arbitrary selection of a safety factor), 23 percent relied upon 

an implicit MOS (none of which documented the degree of protection required), and 7 

percent did not specify an MOS at all (Dilks and Freedman, 2004). Based on this review, the 

NRC TMDL report recommended that the practice of arbitrarily assigning a MOS should end 

and uncertainty analyses should be used as the basis for MOS determination (NRC, 2001). 

Three methods for determining the MOS for a TMDL study are summarized below. 

6.5.1 Implicit Method 

The implicit method is the simplest approach to addressing the MOS. As discussed in 

Section 5.4.4.1, it involves selecting TMDL model parameter values and other assumptions 

(e.g., TMDL flow conditions or loading characteristics) which are sufficiently conservative to 

include an appropriate safety factor. Unfortunately, there is insufficient guidance on how the 

practitioner may select “sufficiently conservative” model input values. Therefore, the 

magnitude of the implicit MOS is difficult to quantify. 

If the implicit method is used for a TMDL study, an approximate MOS may be backcalculated for the TMDL study reporting. For example, the TMDL water quality model could 

be rerun using less conservative input values which will produce a lower (or higher as 

appropriate) simulated instream concentration and a greater “allowable load” (Dilks and 

Freedman, 2004). The difference between the final TMDL simulations and the simulations 

with less conservative parameter values could then be used to back-calculate an MOS (in 

units of concentration or mass loadings) from the water quality standard which can be 

expressed as a percentage of the TMDL. Model sensitivity studies (Section 5.4.4.1) may 

suffice to define the changes in model output and associated uncertainty using less 

conservative parameter values. 

6.5.2 Intermediate Explicit Method: FOEA 

The FOEA analysis described in Section 5.4.4.2 can be incorporated into a quantitative 

assessment of the MOS for the TMDL study. 

This explicit method relates the MOS to the variance calculated for the TMDL model output 

using the FOEA analysis described in Section 5.4.4.2. The FOEA uncertainty analysis 

calculates the COV(C) value for the TMDL model output (e.g., instream concentration or 

daily load). The TMDL model’s COV(C) value is then converted to a standard deviation (SD) 

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based on the mean concentration (C ) for the TMDL model simulations (i.e., SD = COV(C) * 

C ). For steady-state TMDL model applications which vary the TMDL load until the instream 

concentration matches the instream water quality standard (WQS), the SD can be calculated 

from the WQS (i.e., SD = COV(C) * WQS). 

The unallocated fraction (i.e., MOS) of the TMDL would then be based upon the percentage 

of the TMDL load associated with the selected model variance. Larger values for COV(C) 

and SD would result in a larger back-calculated MOS, and vice versa. The MOS can be 

expressed as the SD times a factor (Z) and back-calculated from the mean instream 

concentration simulated with the TMDL model. The factor “Z” is the SD multiplier from the 

standard normal distribution table. 

Figure 6.2 illustrates this explicit method for back-calculating a probability-based MOS from 

the TMDL model output of instream concentration. For this example, the steady-state TMDL 

model simulates an instream concentration equal to the WQS. The instream concentration 

simulated for the TMDL is then broken down into the unallocated portion (∆ CMOS) and an 

allocated portion (∆ CALL). The MOS fraction (in units of mg/L in Figure 6.2) is based upon 

the SD times the factor Z associated with the selected probability of WQS compliance based 

on a standard normal distribution. 

Figure 6.2 Explicit Method for Back-calculating a MOS 

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Table 6.1 summarizes the probability of compliance based on a MOS ranging from 1 to 2.33 

SDs below (or above) the WQS: 

Table 6.1 Probability of Compliance for Various Margins of Safety 

TMDL Margin of Safety 

(Z * SD) 

Probability of Compliance 

with Water Quality Standard 

1.0 SD 84% 

1.3 SD 90% 

1.64 SD 95% 

2.33 SD 99% 

These WQS compliance probabilities are based on a TMDL that is an allocated load 

(WLA + LA) which produces the mean instream concentration, CALL, plus an MOS allowance 

(∆ CMOS) equivalent to 1.0 to 2.33 SDs. For the published TMDL and allocation, the MOS 

based upon the instream concentration (Figure 6.2) should be converted to a mass load. 

After quantifying potential MOS values based upon the FOEA statistics, the practitioner may 

compare the MOS load for alternate SD factors with the TMDL and the allocated loads (WLA 

+ LA). Recognizing that an excessively large MOS may not be cost-effective, the selection 

of the MOS should balance factors such as increased compliance costs, required degree of 

protection, and acceptable risk. MOS factors reported in the general literature are typically 

equal to or less than 10 percent of the TMDL (Zhang and Yu, 2004). 

It may be appropriate to use a MOS ranging from a safety factor of one SD below the WQS 

(or above the WQS for a minimum WQS limit) up to a percentage of the TMDL (for a safety 

factor greater than one SD). For models exhibiting greater than average uncertainty, this 

percentage could range from 10 percent of the TMDL up to the percentage of model 

uncertainty about the applicable standard or critical loading causing impairment. Decisions 

about the need for greater MOS factors (i.e., greater than 10 percent of the TMDL) can be 

based on future post-audits which will benefit from a larger database on completed TMDL 

studies. 

6.5.3 Advanced Explicit Method: Monte Carlo Simulation 

The Monte Carlo simulation method described in Section 5.4.4.3 can also be incorporated 

into a quantitative assessment of the MOS, similar to the FOEA approach described above. 

As discussed in Section 5, Monte Carlo simulations represent a more complex analysis of 

TMDL model uncertainty compared to the FOEA method which produces similar results for 

most applications. Therefore, use of Monte Carlo simulation for MOS evaluations should 

probably be reserved for selected TMDL applications where a more complex approach is 

merited. 

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The determination of potential MOS allowances with Monte Carlo simulations follows the 

same steps outlined above for the FOEA method. The TMDL model is executed numerous 

times with a set of randomly sampled parameters developed by the Monte Carlo 

simulations. The summary statistics (mean, SD, COV) for the numerous TMDL model runs 

are used to develop the breakdown shown in Figure 6.2 for alternate MOS allowances 

(based on Z times SD for the assumed probability). The selection of the final MOS should 

balance compliance costs with degree of protection and risk. 

The MOS range suggested above for the FOEA method should also be applicable to the 

Monte Carlo simulation method: from one SD below (or above) the WQS up to a maximum 

of 10 percent (for a safety factor greater than one SD). 

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SECTION 6 REFERENCES 

CDM. 2001. Big Creek Water Resource Management Plan. Fulton County, GA. 

Dilks, D.W. and Freedman, P.L. 2004. “Improved Consideration of the Margin of Safety in 

Total Maximum Daily Load Development.” ASCE Journal of Environmental Engineering. 

130(6), pp. 690-694. 

James, W. and M.K. Thompson. 1997. Contaminants from Four New Pervious and 

Impervious Pavements in a Parking Lot. Advances in Modeling the Management of 

Stormwater Impacts - Volume 5, CHI Publishers. 

National Research Council. 2001. “Assessing the TMDL Approach to Water Quality 

Management.” Washington, D.C. 

Pitt, R. 1998. Epidemiology and Stormwater Management . In Stormwater Quality 

Management. CRC/Lewis Publishers, New York, NY. 

Rutledge, A.T. 1998. Computer programs for describing the recession of ground-water 

discharge and for estimating mean ground-water recharge and discharge from streamflow 

records—Update. USGS Water-Resources Investigations Report 98–4148. U.S. Geological 

Survey. 

Schueler, T.R. 1999. Microbes and Urban Watersheds, II. Concentrations, Sources, and 

Pathways. Watershed Protection Techniques, 3(1): page 554. 

Schueler, T.R. 1999. Microbes and Urban Watersheds, IV. Implications for Watershed 

Managers. Watershed Protection Techniques. 3(1): page 581. 

USEPA. 1983. Results of the Nationwide Urban Runoff Program. Volume 1 - Final Report, 

Washington D.C. 

Zhang, H.X. and Yu, S.L. 2004. “Applying the First-Order Error Analysis in Determining the 

Margin of Safety for Total Maximum Daily Load Computations.” ASCE Journal of 

Environmental Engineering, 130(6), pp. 664-673. 

  

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Section 7 

TREATMENT ALTERNATIVES AND BEST 

MANAGEMENT PRACTICES 

Notes: 

1. Preparation, verification, calibration, and validation (PVCV) 

Yes 

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This section describes treatment alternatives and best management practices (BMPs) that 

can be used to manage and reduce existing and future point and nonpoint source 

discharges to address the requirements of the TMDL process. BMPs are considered in the 

evaluation of existing loads (Element II), in the control of loads from new development 

(Element III), and in the evaluation of retrofit facilities to reduce loads and meet PLRGs 

(Element III). This section provides information regarding typical BMP treatment efficiencies 

and design parameters based on mass removal (load reduction) to be used for existing 

sources in the absence of sufficient monitoring data. Local BMP monitoring data should be 

used whenever possible. Concurrently with this Protocol, FDEP is developing a BMP 

Database. 

The following discussion of sources and associated BMPs is divided into regulated point 

sources (Section 7.1) and nonpoint sources that are not regulated by a permit (Section 7.2) 

7.1 REGULATED POINT SOURCES 

As noted in the previous chapter, the regulated point sources in Florida consist of domestic 

wastewater, industrial wastewater, municipal separate storm sewer systems (MS4s), 

industrial NPDES stormwater discharges, and construction site runoff. Not all of these 

sources discharge the types or amounts of pollutants that may cause impairments, so the 

discussion below addresses the major pollutants discharged by systems using standard 

treatment technologies. More specific data on pollutant mass removal and reductions of 

pollutant concentrations should be obtained from discharge monitoring reports, if available. 

7.1.1 Domestic Wastewater 

There are three basic ways domestic 

wastewater is treated in Florida: by 

onsite sewage treatment and disposal 

(i.e., septic tanks); by package plants; 

or by centralized wastewater treatment 

facilities. Roughly 2.3 million septic 

tanks in Florida treat approximately 0.4 

billion gallons per day of domestic 

wastewater. Most of these facilities are 

privately owned and operated. The rest 

of the domestic wastewater, about 1.7 

billion gallons per day (bgd) with a 

permitted flow of 2.3 bgd, is treated by 

approximately 2,700 centralized and 

package plant wastewater treatment 

facilities (WWTFs). Most of these are 

owned and operated by municipal 

agencies. According to FDEP, 90 percent 

of this daily volume is treated by WWTFs that have capacities of greater than 0.5 million 

gallons per day (mgd). About half of the facilities discharge their treated wastewater to 

surface waters, and the other half discharge to groundwater (Figure 7.1). Therefore, for 

inland waters (e.g., lakes, rivers, streams), there are approximately 800 surface water 

Effluent Disposal in Florida

Surface Water

31%

Ocean Outfall

22%

Deep Well Injection

24%

Ponds, RIBs and 

Land Application

23%

Figure 7.1 Effluent Disposal in Florida 

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WWTF discharges that must be considered in TMDL processes. Each of these facilities is 

required to provide quantitative discharge reports documenting discharge flow and quality. 

These discharge reports are generally the principle source of information regarding pollutant 

loads from point sources. WWTF discharge records representing existing flows and quality 

are generally appropriate to use when assessing actual existing conditions. However, the 

TMDL analysis should consider the WWTF’s maximum discharge capacity and permitted 

quality for alternatives analyses and for the process of distributing the total load among 

point, nonpoint, and background sources. 

7.1.2 Industrial Wastewater 

Although the State of Florida is not heavily industrialized, there are industrial wastewater 

discharges to surface waters that can, and have, caused significant environmental concern. 

Industrial discharges range from the pulp and paper mills in northern Florida, to the 

phosphate mines in central Florida and the power plants and smaller facilities throughout the 

state. FDEP requires each industrial discharge to obtain a permit that explicitly prescribes 

conditions for allowable discharges of treated effluent, including monitoring and 

documentation of discharge flow and quality. It should be noted that discharge records 

document existing flows and quality for existing conditions. However, TMDL analyses should 

be based on each permitted facility’s maximum discharge capacity and permitted quality. A 

sensitivity analysis could be performed during the TMDL analyses to determine the impact 

on critical parameter concentrations and loads from the permitted and actual WWTF flow 

rates and loads. 

7.1.3 BMPs for Use in Municipal Separate Storm Sewer Systems 

(MS4s) 

Municipal stormwater programs typically employ structural and nonstructural controls to 

manage flooding, erosion, and water quality degradation due to increased runoff and 

associated pollutants. As a class, methods to control stormwater runoff are called Best 

Management Practices, or BMPs. BMPs include techniques, approaches, devices, and 

constructed facilities that control runoff velocities, volumes, peak discharge rates, and/or 

pollutant loads. BMPs can be categorized as follows: 

 Source controls reduce runoff and pollutant generation at or near the source. 

 Treatment controls are constructed or manufactured facilities that control or reduce 

runoff and load at a downstream point. 

BMPs can also be categorized as: 

 Structural – constructed facilities such as detention ponds 

 Non-structural – policies, programs, or ordinances 

This section provides several information sheets on many of the more common BMPs that 

have been used in Florida and elsewhere in the United States. The intent of this section is to 

describe BMPs that are applicable to urban environments in Florida, providing information 

on benefits and limitations of their application. 

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The application of a specific BMP depends on site-specific conditions and objectives, such 

as water quality protection, flood control, aquifer recharge, and/or volume control. In many 

cases, there are multiple goals or needs for a given project. Therefore, BMPs can be "mixed 

and matched" to develop a "treatment train" (Florida Development Manual - FDER, 1988). 

The treatment train concept maximizes the use of available site conditions from the point of 

runoff generation to the receiving water discharge in order to maximize benefits associated 

with water quantity (flood control and stream channel protection), water quality (pollutant 

load reduction), aquifer recharge, and wetlands protection and enhancement. Figure 7.2 is 

an example schematic flowchart of the treatment train concept. 

Figure 7.2 Treatment Train Concept 

Online versus Offline 

An important factor that can affect BMP effectiveness is whether the BMP is “online” or 

offline. An “online” BMP is configured so that all of the stormwater runoff is routed through 

the facility (i.e., no bypass). An “offline” BMP is typically configured such that a control 

structure diverts a design volume to the facility but not all runoff from larger storms is 

treated. Capture and treatment of smaller, colloidal particles such as silts and clays may 

require offline facilities to avoid resuspension and flushing from large volume storm events. 

Whenever possible, pretreatment or offline treatment facilities should be considered and 

may be required (e.g., for OFW, Class I and Class II waters). Refer to local WMD and 

City/County requirements for offline treatment. 

Reuse 

Stormwater reuse provides benefits of pollutant load and runoff volume reduction while 

reducing potable demand for irrigation. Reuse should be considered whenever possible (ref. 

Stormwater Management, M.P. Wanielista and Y.A. Yousef, 1993). 

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BMP Information Sheets 

The BMP Information Sheets provided in this section provide basic information on the 

benefits and limitations of a broad variety of BMPs that are in fairly common use, including 

applicable ranges of expected pollutant mass removal efficiencies. Table 7-1 is a list of the 

information sheets provided in this section for urban BMPs. 

Table 7-1 

Urban BMPs 

Wet Detention Ponds 

Dry Detention Basins 

Exfiltration Trenches 

Retention Basins and Swales 

Conveyance Swales 

Buffers and Filter Strips 

Baffle Boxes 

Alum Treatment 

Hydrodynamic Separators 

Pervious Pavement 

Stormwater Wetlands and STAs 

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Task Assignment 003.03/05-003 

Wet Detention Ponds – Information Sheet 

Detention refers to the temporary storage of excess runoff onsite prior to gradual release after the peak of 

the storm inflow has passed. Runoff is held for a period of time and is slowly released to a natural or 

manmade watercourse, usually at a rate no greater than the pre-development peak discharge rate (and 

often for multiple design storms). A wet detention system includes a permanent pool of water, a shallow 

littoral zone with aquatic plants, and the capacity to provide detention for an extended time necessary for 

the treatment of a required volume of runoff. Pollutant removal occurs by solids settling, algal and 

vegetative uptake, and adsorption primarily within a permanent pool during the period of time between 

storm events. They are typically sized to provide at least a 2-week residence time during the wet season. 

Potential Benefits 

 Reduction of downstream flooding problems by attenuating the peak rate of flow 

 Reduction in pollutant loadings to receiving waters for dissolved and suspended pollutants 

 Can be used in areas with high water tables and less permeable soils 

 Low frequency of failure if properly maintained 

 Reduction in cost for downstream conveyance facilities 

 Creation of local wildlife habitat 

 Possible higher property values as an aesthetic annuity for adjacent lots 

 Creation of fill that can be used on site or sold 

 Can be used for irrigation sources (reuse) 

Potential Limitations 

 Potential safety hazards if not designed & constructed properly (gradual sideslopes are desirable) 

 Occasional nuisance problems such as odors, algae, debris, and mosquitoes 

 Regular maintenance of the littoral zone required to prevent nuisance plant species 

 Eventual need for sediment removal from the permanent pool or sediment forebay 

Design Considerations 

 Provide a live pool (volume and depth of 1.0 to 2.5 inches - refer to local WMD and FDEP 

guidelines) 

 Storage recovery of first 1/2 inch of runoff is generally required in no less than 24 hours 

 Provide a permanent pool with forebay (volume and depth with hydraulic residence time of 14 to 

21 days - refer to local WMD guidance) 

 Prevent short circuiting with minimum 3:1 length:width ratio or use of baffles 

 Maximum depth as per local WMD or fountains or aeration (depth > 12 ft.) 

 Control structure above the average groundwater elevation 

 Sideslopes (3H:1V or milder) with 6:1 littoral zone as per WMD and FDEP guidelines 

 Skimmer for oil and trash at the outlet 

 Open waters or wetlands 

Typical Mass Removal Efficiencies 

BOD5 20%-40% 

COD 20%-40% 

TSS 80%-90% 

TDS 20%-40% 

Total-P 40%-60% 

Dissolved-P 30%-50% 

TKN 20%-30% 

NO2+NO3 30%-40% 

Lead 70%-80% 

Copper 60%-70% 

Zinc 40%-50% 

Cadmium 70%-80% Α 

 7-6 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Dry Detention Basins – Information Sheet 

Dry detention ponds are primarily a flood prevention BMP which slows the stormwater such that the 

allowable peak discharge rate is met. Dry detention has been used widely in the area served by the 

SFWMD. Additionally, the SJRWMD allows a modified form of dry detention for some small projects. 

Any treatment within a dry detention system is via infiltration or sedimentation. 

Potential Benefits

 Reduction of downstream flooding problems by attenuating the peak rate of flow 

 Limited removal of pollutant loadings to receiving bodies of water for suspended pollutants 

 Reduction in cost for downstream conveyance facilities 

 Creation of local wildlife habitat 

 Creation of fill that can be used on site or sold 

 Low frequency of failure if properly maintained 

Potential Limitations 

 Very limited dissolved pollutant removal (nutrients) 

 Potential safety hazards if not designed and constructed properly 

 No permanent pool to store sediment inflow 

 Occasional nuisance problems such as debris and mosquitoes 

 Regular maintenance required to prevent nuisance plant species and to remove accumulated 

sediments 

 Must be off-line to be effective 

Design Considerations 

 Tributary areas: Generally, the dry pond is considered for areas smaller than 10 acres 

 Pond volume and water quantity control requirements 

 Runoff is directed to the pond and detained for 24 to 48 hours before discharge to downstream 

 The first 0.5 to 1.0 in of runoff is typically detained for treatment and attenuation 

 Detention time: The minimum detention time of 24 to 48 hours should be targeted 

 Sideslopes (3H:1V or milder) 

Typical Mass Removal Efficiencies 

BOD5 20%-30% 

COD 20%-30% 

TSS 20%-80% 

TDS 0%-10% 

Total-P 10%-30% 

Dissolved-P 0%-10% 

TKN 10%-20% 

NO2+NO3 0%-10% 

Lead 30%-50% 

Copper 20%-40% 

Zinc 20%-40% 

Cadmium 20%-40% 

Α 

 7-7 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Exfiltration Trenches – Information Sheet 

An exfiltration trench is the onsite retention of stormwater accomplished through underground 

exfiltration. The trench should always be designed off-line to minimize clogging and failure. The 

subsurface retention facilities most commonly used are excavated trenches with perforated pipe 

backfilled with coarse graded aggregate. Exfiltration trenches are used to retain the "first flush" of 

stormwater runoff. This promotes pollutant load reductions to receiving waters, reduces the runoff 

volume and peak discharge rate from a site, filters suspended pollutants out of groundwater 

discharges, and promotes the recharge of groundwater. Exfiltration trenches are practical in highly 

permeable soils (Hydrologic Group A or B) where the subsoil is sufficiently permeable to provide a 

reasonable rate of infiltration, and where the water table is sufficiently lower than the design depth of 

the facility to allow for recovery of the storage prior to the next storm event (generally required in 72 

hours). 

Potential Benefits

 They mimic the natural groundwater recharge capabilities of the site 

 Relatively easy to fit into the margins, perimeters, and other space constrained areas 

 Can provide offline treatment for environmentally sensitive waters (e.g., Class I, Class II, or OFW) 

 Can be used to retrofit already developed sites where space is limited 

Potential Limitations 

 Require highly permeable soils to function properly 

 Difficulties in keeping sediment out of the structure during site construction 

 Not recommended for clayey or highly erodible soils 

 Have relative short life spans before replacement or extensive restoration/maintenance is 

required 

 Often more costly than other treatment alternatives, especially when O&M costs are considered 

Design Considerations 

 Tributary areas: Generally, exfiltration is considered for areas smaller than 10 acres 

 Exfiltration volume of 0.5 to 1.0 inch over the tributary area (see local WMD guidelines) 

 Groundwater table should be at least 2 to 4 feet below the bottom of the trench in the wet season 

(minimum 1.0 inch/hour using a safety factor of 2) 

 Drain time should be no less than 24 hours and not greater than 72 hours 

 Trench should be at least 100 ft from any drinking water supply 

 Trench should be at least 10 ft downgradient and 100 ft upgradient from building foundations 

 Trench is not feasible on sites with 

a slope greater than 20 percent 

Typical Mass Removal Efficiencies 

BOD5 80%-95% 

COD 80%-95% 

TSS 80%-95% 

TDS 80%-95% 

Total-P 80%-95% 

Dissolved-P 80%-95% 

TKN 80%-95% 

NO2+NO3 80%-95% 

Lead 80%-95% 

Copper 80%-95% 

Zinc 80%-95% 

Cadmium 80%-95% 

 7-8 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Conveyance Swales – Information Sheet 

Shallow grassed swales are constructed shallow trenches shaped or gradually graded to required 

dimensions and established in suitable vegetation for the safe conveyance, storage, and treatment of 

runoff. Swales are normally used for conveyance systems to transport runoff offsite or to a 

stormwater facility. They also may be used as pretreatment in the overall treatment train stormwater 

system. They are best suited at sites with soils of moderate-to-high infiltration capacity (usually 

Hydrologic Groups A or B). With slight modification (e.g., check dams, raised inlets, or swale blocks), 

swales can be used to add retention storage, control erosion, provide aquifer recharge, and/or reduce 

the pollutant load from concentrated stormwater runoff in urban areas and provide landscape 

amenities. 

Potential Benefits 

 Usually less expensive than installing curb and gutters 

 Hardly noticeable if shallow swales (0.5 to 1.0 ft max depth) have gradual side slopes (4:1 to 6:1) 

 Reduction in cost for downstream conveyance facilities 

 Can provide treatment for environmentally sensitive waters (e.g. Class I, Class II, or OFW) 

 Can reduce peak rates of discharge by storing, detaining, or attenuating flows 

 Can be used in space constrained areas such as along lot lines, rear of lots, and along roadside

 

Potential Limitations 

 Effective only as a conveyance system in unsuitable soils 

 Possible nuisances such as odors, mosquitoes, or nuisance plant species can occur if not 

designed, constructed, or maintained properly 

 Aesthetically unpleasing if improperly designed and constructed 

 May not be suitable or may require geotextile matting in areas that serve as vehicle parking areas

 

Design Considerations 

 Typically designed to convey the runoff from the 3-year, 1- hour storm 

 Recovery time should not be greater than 72 hours 

 Swale depth ranges from 0.5 to 2 ft. max with gradual side slopes (4:1 to 6:1) 

 Swale slope should range from 2 percent to 6 percent to achieve average flow velocity of <0.5 ft/s 

 Swale invert shall be at least 1-2 feet above the seasonal high water table 

 Swales shall be grassed 

Typical Mass Removal Efficiencies 

(Conveyance Swales) 

BOD5 20%-40% 

COD 20%-40% 

TSS 70%-90% 

TDS 10%-20% 

Total-P 30%-50% 

Dissolved-P 10%-30% 

TKN 20%-40% 

NO2+NO3 10%-30% 

Lead 60%-90% 

Copper 40%-60% 

Zinc 40%-50% 

Cadmium 50%-80% 

Α 

 1. For retention swales, use retention efficiencies 

 7-9 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Retention Basins and Swales – Information Sheet 

A retention basin or swale is an infiltration system designed to retain stormwater onsite, thus reducing 

pollution, recharging groundwater, and controlling flood waters. Typically, these basins have dry 

bottoms covered with native grasses. The site characteristics where retention basins function best are 

where soils are highly permeable and the seasonal high water table is situated well below the soil 

surface (at least 2 to 3 feet below pond bottom). Infiltration controls are best suited for onsite 

applications (off-line from the primary stormwater management system) where the contributing area is 

limited to a single development site or subdivision (e.g., 1 to 50 acres). To be most effective, retention 

controls must be an integral part of the initial design and construction of a site. 

Potential Benefits 

 Mimics the natural water balance of a site by promoting groundwater recharge 

 Can provide offline or on-line infiltration treatment for environmentally sensitive waters (Class I, 

Class II, or OFW) 

 Online systems can reduce peak rate and volume of flood discharge by retaining water onsite 

 Creation of fill that can be used on site or sold 

 Reasonably cost-effective in comparison with other BMPs for both construction and maintenance 

costs 

Potential Limitations 

 Require well-drained soils to function properly 

 Soluble pollutants can be conveyed into groundwater 

 Possible nuisances such as odors, mosquitoes, or nuisance plant species can occur if not 

designed, constructed, or maintained properly 

Design Considerations 

 Pond volume and water quantity control requirements (first 0.5 to 1.0 in of runoff is typically 

detained for treatment and attenuation) 

 Retention time: The maximum retention time of 72 hours 

 Sideslopes (3H:1V or milder) 

 Swales with check dams or raised outlets 

Typical Mass Removal Efficiencies 

BOD5 80%-95% 

COD 80%-95% 

TSS 80%-95% 

TDS 80%-95% 

Total-P 80%-95% 

Dissolved-P 80%-95% 

TKN 80%-95% 

NO2+NO3 80%-95% 

Lead 80%-95% 

Copper 80%-95% 

Zinc 80%-95% 

Cadmium 80%-95% 

 Α 

 7-10 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Buffers or Filter Strips – Information Sheet 

Buffer strips or filter strips, consisting of grass or other close-growing vegetation, are designed to 

accept overland flows of runoff. They are usually composed of dense vegetation such as high 

grasses, ground covers, or shrubs. Buffers can be combined with underlying stone layers to enhance 

infiltration. Buffers can be effective in slowing stormwater runoff rates and velocities, reducing 

downstream sediment loading by settling and physical entrapment. They can increase infiltration of 

stormwater and can provide some nutrient removal through uptake of plants. Buffer strips with berms 

or weirs are efficient for controlling roadway runoff discharges to sensitive surface waters. 

Buffer strips are typically located between pollutant source areas and a downstream receiving water 

body. They also can be used as outlet or pretreatment devices for other stormwater control BMPs. A 

buffer strip is not a complete stormwater management system. 

Potential Benefits 

 Reduces sediment loading and pollutants associated with sediment load to receiving waterbody 

 Contributes to groundwater recharge 

 Prevents soil erosion 

Potential Limitations 

 Require routine maintenance (weed and pest control, mowing, fertilizing and irrigation) to ensure 

reliability and effectiveness 

 Not considered a complete stormwater management system – should be used as a component in 

the BMP treatment train. 

 These systems are very vulnerable to large storms because the high-velocity flows can erode the 

vegetated cover 

Design Considerations 

 Buffer widths should increase as slope increases and are typically a minimum of 15 feet wide for 

grassed buffer strips and a minimum of 35 to 50 feet wide for stream buffers 

 Design should include a level spreader along the top edge of the buffer strip to prevent erosion 

channel formation 

Typical Mass Removal Efficiencies 

BOD5 0%-80% 

COD 0%-80% 

TSS 20%-80% 

TDS No Data 

Total-P 0%-60% 

Dissolved-P No Data 

TKN No Data 

NO2+NO3 0%-60% 

Lead 20%-80% 

Copper 20%-80% 

Zinc 20%-80% 

Cadmium 20%-80% 

Source: Modified from Metropolitan Government of Nashville and Davidson County, 2000. Stormwater 

______ Management Manual. 

 Source: SFWMD, 2002. Best Management Practices for 

South Florida Urban Stormwater Management Systems

 7-11 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Baffle Boxes – Information Sheet 

Baffle boxes are BMP devices that are mainly used at the end of pipes in order to provide additional 

treatment of runoff. The original baffle box is similar to a three chambered septic tank and is designed 

to treat sediment, TSS, large debris and oil and grease. The effectiveness of these devices at 

removing the individual pollutant depends upon the type of device used, the residence time, and 

whether the device is online or offline. Baffle boxes, when used in conjunction with pretreatment 

measures such as street sweeping, may be the most feasible water quality control device in areas 

where the other more traditional measures discussed previously may not be applicable due to various 

constraints. 

Potential Benefits 

 Provide primary treatment at outfalls where no other option exists 

 Require very little space 

 Baffle boxes are a modification of the existing stormwater system, and therefore are typically less 

expensive as compared to other treatment methodologies 

 Can have significant treatment benefits when offline 

Potential Limitations 

 Primarily most effective at removing suspended solids, although second generation models 

remove nutrients 

 Require maintenance at least quarterly 

 Do not achieve removal of most dissolved solids 

Design Considerations 

 Should be used based on settling velocities for expected particle sizes and flow rates for 80% 

capture on an annual basis 

 Must incorporate easy vacuum truck accessibility (multiple manholes-cleanouts) 

 Flow through velocity should be less than the settling velocity of the target material to ensure 

proper removal 

 Offline systems are superior to online systems for removal 

 Concrete box culverts can be used by bolting in aluminum or stainless steel weirs in series 

Typical Mass Removal Efficiencies 

BOD5 0%-50% 

COD No data 

TSS 70%-90% 

TDS No data 

Total-P 20%-40% 

Dissolved-P 0%-20% 

TKN 0%-10% 

NO2+NO3 0%-20% 

Lead 60%-80% 

Copper 40%-60% 

Zinc 20%-40% 

Cadmium 50%-70% 

Α 

Exit 

pipe Sediment 

Chamber II Chamber III 

Baffles 

Chamber I Inlet 

pipe 

Access manholes for pumping out sediments 

 7-12 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Alum Treatment – Information Sheet 

Aluminum sulfate, Al2(SO4)3, commonly called alum, has been used extensively as a flocculating 

agent in the treatment of wastewater for over 100 years. The addition of alum to water results in the 

production of chemical precipitates which remove pollutants by two primary mechanisms. Removal of 

suspended solids, algae, phosphorus, heavy metals and bacteria occurs primarily by enmeshment 

and adsorption onto aluminum hydroxide precipitate according to the following net reaction: 

 Al+3 + 6H2O → Al(OH)3(s) + 3H3O+

 (1) 

Removal of additional dissolved phosphorus occurs as a result of direct formation of AlPO4 by: 

 Al+3 + HnPO4

n-3 → AlPO4(s) + nH+

 (2) 

The aluminum hydroxide precipitate, Al(OH)3, is a gelatinous floc which attracts and adsorbs colloidal 

particles onto the growing floc, thus clarifying the water. Phosphorus removal or entrapment can 

occur by several mechanisms, depending on the solution pH. Inorganic phosphorus is also effectively 

removed by adsorption to the Al(OH)3 floc. Removal of particulate phosphorus is most effective in the 

pH range of 6-8 where maximum floc occurs (Cooke and Kennedy, 1981). 

Potential Benefits 

 Provides very high levels of stormwater pollutant removal except for nitrogen 

 Requires very little space 

 Construction costs for alum stormwater treatment systems are largely independent of the 

watershed area to be treated and depend primarily upon the number of outfalls to be retrofitted 

 Alum treatment of stormwater runoff is often substantially less expensive than other stormwater 

treatment retrofitting alternatives with respect to both initial capital construction costs and annual 

O&M costs. 

Potential Limitations 

 Require routine maintenance and trained maintenance staff 

 Do not achieve high removal of some dissolved pollutants (especially nitrogen) 

 Can create free aluminum ion which is potentially toxic 

 Do not provide any flood control benefits (unless in association with wet pond) 

 Creates sludge that should be removed regularly 

Design Considerations 

 Must maintain pH between 6.0 and 8.0 to minimize potential for aluminum toxicity 

 Floc (sludge) must be captured and disposed of properly. Conical chambers can be used to 

collect sludge and it can be sent to wastewater treatment plant. 

 Jar tests must be done to determine proper dosage of alum (typically 5 to 15 mg/l) 

 Lightning protection for the controls and dosage equipment is recommended 

Typical Mass Removal Efficiencies 

BOD5 45% - 65% Total N 40% - 70% 

COD 45% - 65% NO2+NO3 10% - 40% 

TSS 80% - 99% Lead 60% - 95% 

TDS 50% - 90% Copper 40% - 75% 

Total-P 45% - 95% Zinc 40% - 75% 

Dissolved-P 20% - 90% Cadmium 40% - 75% 

 7-13 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Hydrodynamic Separators – Information Sheet 

Hydrodynamic separators are flow-through structures with a settling or separation unit to remove 

sediments. The energy of the flowing water allows the sediments to efficiently separate so no outside 

power source is required. Depending on the type of unit, this separation may be by means of swirl 

action or indirect filtration. 

Potential Benefits 

 Provide primary treatment at outfalls where no other option exists 

 Require very little space 

 Typically a modification of the existing stormwater system, and therefore are typically less 

expensive as compared to other treatment methodologies 

 Can have significant treatment benefits when offline 

Potential Limitations 

 Primarily most effective at removing suspended solids 

 May export nutrients due to anaerobic conditions within them 

 Require maintenance at least quarterly 

 Do not achieve removal of most dissolved solids 

Design Considerations 

 Must incorporate easy vacuum truck accessibility (multiple manholes-cleanouts) 

 Offline systems are superior to online systems for removal 

Typical Mass Removal Efficiencies 

BOD5 0%-20% 

COD 0% 

TSS 70%-90% 

TDS 0% 

Total-P 25%-45% 

Dissolved-P 0%-20% 

TKN 0%-10% 

NO2+NO3 0%-20% 

Lead 60%-85% 

Copper 40%-60% 

Zinc 25%-45% 

Cadmium 50%-70% 

Source: Fenner and Tyack, 1997. 

OVERFLOW 

CENTER 

CONE 

SOLIDS C

HOPPER 

OLLECTION 

O

UNDERFLOW 

UTLET PIPE

SEPARATOR 

BODY 

INLET PIPE 

DIP 

PLATE 

CENTRAL SHAFT 

 7-14 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Pervious Pavement – Information Sheet 

A pervious pavement generally consists of a layer of porous or pervious concrete overlying an 

underground reservoir filled with stone aggregates. It is mainly designed to treat rainfall that falls on 

the pavement. After stormwater runoff infiltrates through the pavement, it is collected in reservoirs 

where it infiltrates into the subsoil. Pervious pavement is typically used in the construction of parking 

lots as a built-in stormwater treatment device. 

The design of a pervious pavement can be modified to enable the system to accept runoff from 

surrounding areas and rooftops. This modification includes the installation of perforated inflow pipes 

to distribute the runoff throughout the stone reservoir. In addition, a pretreatment system is needed to 

remove trash, sediment, oil, and grease to prevent them from clogging the reservoir. 

Potential Benefits

 Reduces effective impervious area 

 Provides groundwater recharge 

 Less need for curbing and storm sewers 

Potential Limitations 

 Need contractors certified in pervious concrete use 

 Subject to clogging from sediments and therefore requires routine maintenance 

 Not suitable for areas with high water tables 

 Potential for introducing pollutants to groundwater 

Design Considerations 

 Pervious material mixture can be adjusted to achieve desired infiltration rate 

 Traffic volume must be considered – not appropriate for high traffic volume 

 Groundwater table should be at least 4 feet below the surface 

 Installation should be avoided near drinking water supply 

Typical Mass Removal Efficiencies 

BOD5 No data 

COD 60%-90% 

TSS 60%-100% 

TDS No data 

Total-P 40%-90% 

Dissolved-P No data 

TKN 80%-95% 

NO2+NO3 40%-90% 

Lead 60%-90% 

Copper No data 

Zinc 60%-90% 

Cadmium No data 

Source: Modified from Metropolitan Washington Council of Governments, 1987. Controlling Urban 

Runoff: A Practical Manual for Planning and Designing Urban BMPs. 

Undisturbed Soils with a Field Capacity > 0.27 

Inches/Hour Preferably > 0.50 Inches/Hour 

Gravel Course or 

6-Inch Sand 

Layer

Pervious Pavement 

Stone Reservoir Drains in 48-72 Hours 

Perforated Pipe Discharges 

Only When 2-Year Storage 

Volume Exceeded 

Filter Fabric Lines Sides 

of Reservoir to Prevent 

Sediment Entry 

Overflow 

Pipe

Berm Keeps Off-Site Runoff 

and Sediment Out, Provides 

Temporary Storage POSTED 

Sign Posted to Prevent 

Resurfacing and Use of 

Abrasives, and to Restrict 

Truck Parking Vacuum Swept, Followed by 

Jet Hosing to Keep Pores 

Open 

References: 

1. U.S. EPA, 1999. Stormwater Technology Fact Sheet – Porous Pavement 

2. Wade, D., Janicki, A., Pribble, R. 1997. Watershed Management Model for Optimal Allocation of BMPs User’s Guide and 

-- Case Study. Tampa Bay National Estuary Program Technical Publication No. 07-96. 

 7-15 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Stormwater Wetlands and STAs – Information Sheet 

Wetlands improve the quality of stormwater runoff, and can also control runoff rate and volume. They 

are one of the more reliable BMPs for removing pollutants and are adaptable to most locations where 

the hydrology supports wetlands. Wetlands remove pollutants from stormwater through physical, 

chemical, and biological processes. Chemical and physical assimilation mechanisms include 

sedimentation, adsorption, filtration, and volatilization. In general, wetlands remove pollutants about 

as effectively as conventional pond systems and wetlands can provide higher levels of treatment for 

nutrients, such as the stormwater treatment areas (STAs) in South Florida which use combinations of 

emergent, submergent, and periphyton systems to treat urban and agricultural runoff. 

Wetlands used for stormwater treatment can be incidental, natural, or constructed. Incidental 

wetlands are those that were created as a result of previous development or human activity. The use 

of natural wetlands for stormwater treatment is prohibited except as allowed by Florida statute and 

regulations. 

Potential Benefits 

 Opportunity to incorporate natural systems for water stormwater treatment 

 Protection of downstream receiving waters 

 Can provide wetland creation, restoration, or enhancement 

 Can provide wildlife and waterfowl habitat if priority pollutants are below levels that would cause 

adverse impacts to the biota (e.g., very low concentrations, treating for TSS only, hydrologic 

regulation, and/or large wetland to tributary area ratio 

 Can be used in areas with high water table and/or low permeable soils 

Potential Limitations 

 Area requirements may be significant 

 May have limited application in urban setting due to available land limitations. 

 Potential for short-circuiting, which can lead to poor performance 

 Pre-treatment is required if natural wetlands are incorporated into the stormwater system 

 May be a wildlife and waterfowl attractant, therefore caution should be considered if priority 

pollutants are in the inflow to be treated 

 Constructed wetlands, by themselves, do not provide flood control benefits 

Design Considerations 

 The BMP treatment train is essential to provide pretreatment 

 Specific design and pretreatment requirements for wetland stormwater treatment systems are set 

forth in DEP stormwater rules and WMD environmental resource permitting rules 

Typical Mass Removal Efficiencies 

Source: Modified from Metropolitan Washington Council of 

Governments, 1992. Design of Stormwater Wetland Systems.

Max storm elevation 

normal water table 

Max storm elevation 

Seasonal high water table 

D. Pocket Wetland 

C. Pond/Wetland System 

wetland 

pond 

normal pool elevation 

normal pool elevation micropool 

B. Extended Detention (ED) 

forebay micropool 

normal pool elevation 

A. Shallow Marsh 

BOD5 40%-60% 

COD 40%-60% 

TSS 60%-80% 

TDS No Data 

Total-P 20%-60% 

Dissolved-P No Data 

TKN No Data 

NO2+NO3 20%-40% 

Lead 30%-80% 

Copper 20%-60% 

Zinc 20%-60% 

Cadmium 20%-60% 

 Source: SFWMD, 2002. Best Management Practices for 

South Florida Urban Stormwater Management Systems

References:1. U.S. EPA, 1999. Stormwater Technology 

Fact Sheet – Storm Water Wetlands

 7-16 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

7.1.4 Nonstructural BMPs to Reduce Urban Stormwater Pollution 

Land Use Planning 

Land use planning and management presents an important opportunity to reduce and/or 

minimize pollutants in stormwater runoff and control flooding. A comprehensive planning 

process can be used to integrate municipal stormwater management (i.e. flood control and 

water quality improvement) goals into the development and redevelopment process. 

Management measures may include modification or restrictions of certain land use activities, 

control over the placement of certain land uses, and control over drainage patterns within 

the land area. Greater restrictions may be warranted where development can affect 

impaired, threatened, or significant water bodies. Increases in impervious cover from urban 

development typically result in increased pollutant loadings and flooding, land use planning 

practices than consider water quality impacts can be an effective control measure. 

Public Information Program 

Public information (PI) and participation programs can inform employees, the public, and 

businesses about the importance of protecting stormwater from improperly used, stored, 

and discarded pollutants. Many people do not realize that yard debris or trash thrown into 

ditches can worsen flooding and pollute surface waters. PI programs may be directed to 

training municipal employees, especially those that work in departments not directly related 

to stormwater but whose actions affect stormwater. PI programs can also increase public 

awareness about a variety of hazardous products used in the home whose improper use 

and disposal can pollute stormwater. Examples include improper disposal of oils, antifreeze, 

paints, and solvents that can end up in streams and lakes, poisoning fish and wildlife. 

Increased public awareness also facilitates public scrutiny of industrial and municipal 

activities and will likely increase public reporting of incidents. PI programs can help inform 

businesses, particularly smaller ones that may not be regulated by Federal, State, or local 

regulations, about ways to reduce their potential to pollute stormwater. 

Examples of public information programs: 

Florida Clean Marina Program 

FDEP has designed the Florida Clean Marina program to introduce citizens to simple, 

innovative BMPs for boatyards. Other participating organizations include the University of 

Florida Sea Grant, Marine Industries Association (MIA) of Florida (including numerous local 

chapters), International Marine Institute, Marine Resource Council, Florida Marine Trades 

Association, Marina Operators Association of America, Marina Owner/Operators and 

Consultants. Guiding FDEP’s actions are the principles of ecosystem management, 

foremost of which is that all things are connected. Ecosystem management takes a holistic 

approach to environmental protection, where air, water, land and living things are all 

considered together, not in isolation. 

The Clean Marina program applies to boatyards that repair and convert recreational and 

small commercial vessels for Florida’s waterways. U.S. shipyards are categorized as either 

first-tier or second-tier. This program addresses the second-tier that includes many small 

and medium-size businesses that construct and repair vessels smaller than 122 meters (383 

feet). 

 7-17 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

The Clean Marina Program promotes hurricane preparations, petroleum control, boat 

cleaning, solid waste management, hazardous waste management, fish waste management 

and sewage management BMPs to prevent or reduce pollutant discharge. 

Florida Yards and Neighborhoods Program 

The Florida Yards & Neighborhoods Program were developed to address serious problems 

of pollution and disappearing habitats by enlisting citizens efforts and activities to save the 

natural environment. The program provides special educational and outreach activities 

directed at the community to help residents reduce pollution and enhance their environment 

by improving home and landscape management. 

Florida Green Industries BMP Program 

The Florida Green Industries program has published a 60 page BMP manual developed 

jointly by the Florida Green Industries, FDEP, FDACS, DCA, water management districts, 

and the University of Florida (June 2002). The manual provides information and guidance on 

turfgrass and landscape management practices for the purpose of conserving and 

protecting Florida’s water resources. Practices cover establishment of new turf and 

landscapes and the care of existing turf and landscapes, including construction activities, 

irrigation, nutrient management, and pest management. 

Adopt a Pond Program 

The Adopt a Pond Program is an educational program developed and implemented by 

Hillsborough County to improve proper operation and maintenance of stormwater systems. 

The restoration or rehabilitation of stormwater ponds can have a positive impact on water 

quality in the downstream health of our lakes, creeks, rivers and bays. The program requires 

all stormwater ponds to have a landscape design. This design is critical to the success of 

the pond. Aquatic vegetation used to stabilize a pond and to remove pollutants is a vital link 

that helps maintain water quality. Other amenities that a properly designed pond may have 

are increased wildlife habitat, recreation areas, and aesthetic vistas. 

Stormwater Management Ordinance Requirements 

In general, municipal stormwater management ordinance establish guidelines for the safe 

management and discharge of stormwater runoff from developed areas. Ordinances are 

applicable to all new development, generally excluding single family dwellings, and require 

that all applications for building permits must contain a stormwater management plan. Such 

ordinances are necessary for the control of new development (to control future runoff) and 

significant redevelopment (to retrofit older developments to new standards). 

Fertilizer Application Control 

Fertilizer application control is a voluntary control mechanism by citizens who use fertilizer 

as part of their landscaping activities. Fertilizer application controls are implemented through 

a public information program by making the public aware of the principles of environmental 

landscape maintenance and the problems associated with overuse of fertilizers. Overuse of 

fertilizers can contribute to excessive runoff of nutrients to surface waters thereby wasting 

money for the homeowner and potentially degrading the receiving water body. Information 

programs should also be extended to professional fertilizer users. 

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Pesticide Use Control 

Pesticide use control is also a voluntary control by citizens who use pesticides as part of 

their housekeeping and lawn maintenance activities. Some pesticides include priority 

pollutants (e.g., Endrin, Lindane, and Silvex), which can be toxic. Overuse of these 

chemicals can cause excessive runoff to surface waters and entry into the food chain. Many 

professional applicators of pesticides are using approved pesticides in a safe and proper 

manner. An information program on pesticide use will help to reduce the amount of 

pesticides entering the stormwater system. In addition, two model landscaping ordinances 

are available on the DEP web site that can be incorporated into local land development 

regulations. These are Guidelines for Model Ordinance Language for Protection of Water 

Quality and Quantity Using Florida Friendly Lawns and Landscapes and Sample Florida 

Yards and Neighborhoods Ordinance. 

Cisterns and Rainbarrels 

Cisterns are underground storage tanks used to manage water from the rooftops and 

impervious drainage areas. On-lot storage and possible reuse of collected stormwater saves 

potable water and thus may reduce water utility costs. Cisterns are applicable to residential, 

commercial, and industrial low-impact development sites. Larger cisterns are needed at 

commercial and industrial sites due to the size of rooftops and the amount of impervious 

tributary area. Individual cisterns can be located beneath each downspout, or storage 

volume can be provided in one common cistern. Cisterns need be located where they are 

easily accessible for maintenance. 

Rain barrels are low-cost, effective, and easily maintainable retention devices practical in 

both residential and commercial/industrial site applications. Rain barrels first retain a 

predetermined volume of rooftop runoff and then an overflow pipe provides some detention 

beyond the retention capacity of the rain barrel. Like cisterns, rain barrels can also store 

runoff for reuse in irrigation. To be aesthetically acceptable, rain barrels can be incorporated 

into the lot’s landscaping plans or patio or decking designs. Gutters and downspouts are 

used to convey water from rooftops to rain barrels. Filtration screens should be used ton 

gutters to prevent clogging of debris. Rain barrels should be designed so that complete 

draining of the system is possible. Rain barrels should also be equipped with a drain that 

has garden hose threading, suitable for connection to a drip irrigation system. An overflow 

adapter could be used to connect two or more barrels to divert surplus water away from 

foundations. An overflow outlet must be provided to bypass runoff from large storm events. 

Rain barrels must be designed with removable, child-resistant covers and mosquito 

screening on water entry holes. The size of the rain barrel will depend on the rooftop surface 

area that drains to the barrel and the amount of rainfall desired for retention storage. 

Solid Waste Management 

In some instances, problems can arise from trash and other debris flowing into, and 

obstructing, open channels, and culverts, and storm sewers. The public can be informed of 

the adverse impacts of littering and poor solid waste management. This can also include pet 

droppings and illegal dumping into storm drains, wooded areas, and ditches. Pet droppings 

can be a source of coliform bacteria and pathogens. 

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Hazardous Material Management 

A BMP promoting efficient and safe housekeeping practices including storage, use, and 

cleanup when handling potentially harmful materials and the use of less harmful and safe 

alternative products may be implemented to reduce discharge of pollutants to storm water. 

In some cases, safer alternative products exist for such household items as fertilizers, 

pesticides, cleaning solutions, paint products, automotive products, and swimming pool 

chemicals. Municipal employees working with potentially harmful materials should be trained 

in good housekeeping practices and use of safer alternative products. 

Implementation of this BMP will also involve coordination of public education efforts focusing 

on the benefits of good housekeeping practices and use of safer alternative products. The 

cost for this BMP is very minimal. Pubic awareness will promote a willingness to try 

alternatives and to modify old behaviors. One limitation for this BMP may be that alternative 

products may not always be available, suitable, or effective in every case. 

The discharge of pollutants to storm water from material delivery and storage can be 

reduced and prevented by storing materials inside or under cover on paved surfaces, 

minimizing storage and handling of hazardous materials on-site, utilizing secondary 

containment, conducting regular inspections, and training employees and subcontractors. 

The key to this BMP is to design and maintain material storage areas that reduce exposure 

to storm water. Accurate and up-to-date inventories need to be kept of all stored materials. 

A supply of spill cleanup materials needs to be kept near the storage area and the 

employees need to be well trained in proper material storage. Storage sheds are also 

required to meet building and fire code specifications. 

Spill prevention should be part of a comprehensive BMP program to prevent runoff 

contamination. An important tool in preventing spills is a Spill Prevention Plan. A Spill 

Prevention Plan specifies materials handling procedures and storage requirements and 

identifies spill cleanup procedures for areas and processes in which spills may potentially 

occur. The plan standardizes process-operating procedures and employee training in an 

effort to minimize accidental pollutant releases that could contaminate stormwater runoff. 

A Spill Prevention Plan is applicable to facilities that transport, transfer, and/or store 

hazardous materials, petroleum products, or fertilizers that can contaminate stormwater 

runoff. Most businesses and public agencies that generate hazardous water and/or produce 

transport, or store petroleum products are required by State and federal law to prepare spill 

control and cleanup plans. Existing plans should be reevaluated and revised to address 

stormwater management issues. 

Street Sweeping 

Street sweeping can be an effective method of improving street aesthetics in developed 

areas and, depending on the type of equipment used, can be an effective pretreatment 

method of water quality control. Special equipment vacuum or sweep debris and sediment 

accumulated from streets in urban environments. Curbs and gutters are required for street 

sweeping and it is common for such streets to be swept many times during the year. 

Sediment and debris are collected within the equipment and must be properly disposed. 

Recent developments in vacuum sweepers show increased removal efficiencies for smaller 

particles. 

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A recent study compared several different vacuum sweepers and indicated that TSS 

washoff reduction can range from a 20 percent reduction up to a 50 percent reduction, 

depending on the type of street sweeper used, for monthly street sweepings (Sutherland et 

al. 1997). In addition, the Sutherland study indicated that with increased street sweeping 

frequency, even higher reductions could potentially be achieved, ranging upwards to nearly 

80 percent for the most efficient models for weekly street sweepings. 

Vehicle Use Reduction 

A BMP to reduce the discharge of pollutants to storm water from vehicles also exists. The 

amount of pollutant discharge to storm water may be greatly reduced by highlighting the 

storm water impacts, promoting the benefits of alternative transportation on storm water, and 

integrating initiatives with existing or emerging regulations and programs. Integration of this 

BMP practice with other efforts by governmental agencies and businesses to reduce vehicle 

use and improve air quality can greatly enhance this BMP’s applicability, effectiveness and 

efficiency by avoiding possible redundant and/or conflicting programs. Establishing trip 

reduction programs at major employers can be a great staring point. Support efforts to pass 

reasonable regulations at the State and local level aimed at reducing vehicle use and 

developing transit-oriented communities is very critical in achieving the full potential benefits 

of this BMP. Also the public needs to understand that air pollution means water pollution and 

a public education program focusing on the water quality benefits of reduced vehicle use 

needs to be employed. Public participation in ride sharing programs can highly affect the 

success of this BMP. 

There can be two possible limitations. Level of cooperation and integration between 

departments and programs may be challenging and the use of alternative transportation is 

highly dependent on its convenience and relative cost. 

Directly Connected Impervious Area (DCIA) Minimization 

Another nonstructural BMP option is to minimize the amount of directly connected 

impervious area (DCIA) on a site and to promote the use of green buffer zones around 

paved areas for infiltration. For example, roof runoff from structures can be directed to green 

buffer zones or shallow landscaped swales around houses. In addition, parking lots and 

driveways can be graded to landscaped/grassed areas or swales, reducing direct runoff to 

the storm drainage system. Also, commercial parking areas can be reduced by allowing 

parking lots to be sized for average use, not high attendance times (e.g., Christmas). Extra 

parking for such special occasions can be accommodated using pervious parking or other 

less permeable parking spaces. 

Low Impact Development 

Low impact development emphasizes a BMP treatment train and reduced land clearing in 

an environmentally sensitive design. Applying the principles together, planners, developers, 

and local officials can measurably reduce impervious cover, conserve natural areas, and 

reduce the impacts of stormwater from new development while at the same time enhancing 

both the natural environment and community well being. The photograph below shows a 

house and landscaped lot that were designed to capture approximately 98 percent capture 

of annual rainfall volume with zero discharge in a coastal Florida application. Listed below 

are some examples of low impact development ideas: 

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 Clearing and grading of forested and native vegetation at a site should be limited to the 

minimum amount needed to build lots. 

 Conserve trees and other vegetation at each site by planning additional vegetation, 

clustering tree areas, and promoting the 

use of native plants. 

 Residential streets designed for the 

minimum required pavement width 

needed to support travel lanes, on-street 

parking, and emergency vehicle access. 

 Reduction of residential street lengths by 

examining alternative street layouts. 

 Minimization of the number of street culde-sacs and incorporate landscape 

areas to reduce their impervious cover. 

 Where density, topography, soils and 

slope permit, vegetated open channels 

should be used in the street right-of-way to convey and treat runoff. 

 Reduce excessive parking, minimize stall dimensions, encourage shared parking, and 

use pervious materials in spillover parking areas where possible. 

 Provide stormwater treatment for parking lot runoff using bioretention areas, filter strips, 

and/or other practices that can be integrated into required landscaping areas and traffic 

islands. 

 Advocate open space design development and stream buffers incorporating smaller lot 

sizes to minimize total impervious area, conserve natural areas, provide community 

recreational space, and promote watershed protection. 

 Relaxation of setbacks to reduce overall lot imperviousness. 

 Consider locating sidewalks on only one side of the street and providing common 

walkways linking pedestrian areas. 

 Promote alternative driveway surfaces and shared driveways to reduce overall lot 

imperviousness. 

 Direct rooftop runoff to pervious areas. 

 Green roof technologies 

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Illicit Connections (Non-Stormwater Discharges) Identification and Removal 

In the 1987 Clean Water Act Amendments, three major goals were identified: to control 

pollution from municipal stormwater systems to the maximum extent practicable (MEP), to 

eliminate "illicit" discharges to storm sewers and to control the discharge of stormwater from 

industrial activities. These goals have generated the National Pollution Discharge 

Elimination System (NPDES) permitting programs for municipal storm sewer discharges to 

waters of the United States (see Subsection 2.5). For the second goal, "illicit" discharges are 

defined as those that are non-stormwater discharges to the storm sewer. Examples include 

laundromat wastewater, car washing water, restaurant wastes, etc. To achieve this goal, 

EPA has been encouraging municipalities to search for illicit connections and eliminate such 

discharges. The ongoing EPA NPDES illicit connection survey should be used to strengthen 

applicable codes and eliminate these connections which can cause plugs of toxic 

substances to enter surface waters. Aggressive inspection is a key component of this BMP 

program. Emphasis on cooperation and public outreach is also a critical to the effort to 

eliminate illicit discharges. 

Erosion and Sediment Control on Construction Sites 

Erosion and sediment control on construction sites provides for the protection of receiving 

waters from sediment loads. Proper erosion prevention and sediment flow controls during 

construction are listed below: 

 Minimize clearing and grading using site planning, open space, buffer zones, and other 

protections. 

 Constant retention and/or detention systems in first phase. 

 Protect waterways and stabilize drainage ways. 

 Phase construction to reduce soil exposure. 

 Immediately cover, revegetate, and stabilize exposed soils with mulch or other means. 

 Install controls to filter sediments, gravel filter weirs, including sediment fences keyed 

into land surface, temporary berms, swales, exit controls, and inlet filters at the perimeter 

of the site and, on larger sites, throughout the site. 

 Employ advanced sediment settling controls, such as well-designed and maintained 

basins. 

The state of Florida has recently created a sediment and erosion control training process 

that provides education on proper sediment and erosion management of construction sites. 

Certification is also provided. 

Source Controls on Construction Sites 

Source control on construction sites can be accomplished with proper construction practices 

to prevent or reduce the discharge of pollutants from dewatering operations, paving 

operations and structure construction and painting. Contractor and employee training should 

be integrated with existing training programs to encourage appropriate material 

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management, waste management and vehicle and equipment management to also prevent 

or reduce the discharge of pollutants. Dewatering flows should be filtered through a gravel 

filter weir or equivalent (FDOT washed stone of 1-inch diameter wrapped in filter fabric). 

Operation and Maintenance (O&M) 

A recent survey by FDEP has reported that nearly 70 percent of existing stormwater 

treatment facilities in Florida are not properly maintained, and therefore likely do not provide 

the intended pollutant removal effectiveness. Because of this, one of the most effective 

nonstructural BMPs is routine maintenance of existing treatment facilities. For publicly 

owned stormwater treatment facilities, routine maintenance and inspection should be 

performed. For privately owned stormwater facilities, a municipality does not typically 

perform maintenance. There are several options that can be pursued by a municipality to 

help ensure that proper maintenance is being conducted. These options include a 

certification program initiated by a municipality that requires all approved subdivision ponds 

(private) to be recertified by the owner on a predetermined time interval. A state 

certified/trained inspector or engineer may complete the recertification. Enforcement of 

maintenance of privately owned facilities is one of the most difficult problems for privately 

owned facilities. Potential enforcement measures may include governmental intervention 

(after sufficient notification) where the government completes critical maintenance and the 

cost of the maintenance is billed to the owner or by other means as deemed necessary by 

the municipality. Another option would be to consider the assessment of fines. 

Maintenance can be provided on a routine basis or as needed based upon inspection. Many 

governments provide a mixture of routine maintenance for critical facilities and inspectionbased maintenance for less critical facilities. The following paragraphs highlight some 

important maintenance considerations for BMPs. 

Mowing 

The side slopes, embankments, emergency spillways, and other grassed areas of 

stormwater facilities must be periodically mowed to prohibit woody growth and 

control weeds. More frequent mowing may be required in residential areas by 

adjacent homeowners. Mowing usually constitutes the largest routine maintenance 

expense. The use of native or introduced grasses that are water-tolerant, 

pest-tolerant, and slow growing are recommended. 

Debris and Litter Removal 

Debris and litter accumulate near stormwater facility control structures and should be 

removed during regular mowing operations. Particular attention should be paid to 

floatable debris that can eventually clog the control structure or riser. Trash screens 

or racks can be strategically placed near inflow or outflow points to capture debris. 

Erosion Control 

Side slopes, emergency spillways, and embankments all may periodically suffer from 

slumping and erosion. This should not occur often if the soils are properly compacted 

and vegetated during construction. Regrading and revegetation may be required to 

correct any problems that develop. 

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Nuisance Control 

Standing water or soggy conditions within a stormwater facility can create nuisance 

conditions for nearby residents. Odors, mosquitoes, weeds, and litter can all be 

potential problems in stormwater facilities. However, wetland plants established in 

wet detention ponds can harbor birds and predacious insects and fish that serve as a 

natural check on mosquitoes, and regular maintenance to remove debris and ensure 

control structure functionality will help control these potential problems. 

Structural Repairs and Replacement 

Eventually, the facility control structure will deteriorate and must be replaced. For 

ponds, these should be inspected at least once annually. In the case of exfiltration 

trenches and porous pavement, when the trench or pavement becomes clogged, 

part or all of the facility may need replacement. 

7.2 SOURCES NOT REGULATED BY NPDES 

While the sources discussed in Section 7.1 are regulated by NPDES and generally have 

sufficient discharge information, this subsection considers other potentially significant 

pollutant sources that are not regulated by NPDES permits and may not have discharge 

data. These sources can be categorized as septic tanks, agricultural sources and distributed 

sources. 

7.2.1 Septic Tanks 

As discussed in Section 3, roughly 2.3 million septic tanks in Florida treat approximately 0.4 

billion gallons per day of domestic wastewater, serving approximately 30 percent of Florida’s 

population. In areas with correct soils, sufficient distance from groundwater influences and 

where they are properly spaced and maintained, septic tanks can function well. However as 

noted previously, between 5 and 15 percent of such onsite facilities present a pollutant 

source to nearby surface waters when improperly placed, designed, constructed, or 

maintained (EPA, 1986). Section 3 recommends that a value of 10 percent should be used 

for septic tank failures in areas with proximal surface waters. In cases where septic tank 

failures represent a significant pollutant source, there are only two treatment technologies 

available to remediate the source: repair or replacement of the septic tank system, or the 

connection of the homes with septic tanks to a local or regional wastewater treatment 

facility. 

7.2.2 Agricultural Sources 

Information related to agricultural sources is presented in Subsection 3.4.3. To date, FDACS 

and FDEP have produced a number of BMP manuals for specific agricultural programs 

including Citrus, Cow-Calf Programs, Agrichemical Handling, and Blended Fertilizer Plants. 

The BMP documents generally describe methods to control irrigation, fertilization (for 

nutrient load reduction as appropriate) and other land management practices to reduce 

pollutant loading. Included at the end of this section are a series of Information Sheets for 

some of the standard agricultural BMPs to aid in the understanding of the pollution loading 

potential. Table 7-2 presents a list of agricultural BMPs.

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Table 7-2 Agricultural BMPs 

Agricultural BMPs Included in This Document:

Irrigation Management 

Conservation Tillage 

 - No-till/Strip Till 

 - Mulch Till 

 - Ridge Till 

 - Terraces 

Conservation Buffer 

 - Filter Strips 

 - Strip Cropping Contour 

 - Riparian Forest Buffer 

Contour Farming 

Water and Sediment Control Basin 

Animal Waste Management 

Other BMPs for Consideration Include:

Nutrient BMPs 

Soil Testing and Plant Analysis 

Correct Timing and Placement of Fertilizer 

Use of Slow Release Fertilizer 

Pesticide BMPs 

Correct Pesticide Selection 

Correct Pesticide Application 

Correct Container Disposal 

Biologic Control 

Selection of Resistant Crop Varieties 

Water Management BMPs 

Use of Plastic Mulches 

Water Table Management 

Use of Water Tolerant Crops 

Animal Waste BMPs 

Fencing 

Planned (rotational) grazing 

Provide Water/Feeder Locations away from natural water bodies 

Providing Shade sources away from natural water bodies 

Sediment BMPs 

Diversion and Terraces 

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Irrigation Management for Agricultural Operations – Information Sheet 

Proper irrigation scheduling/management will help to assure efficient use of water and energy in crop 

production. Irrigation scheduling methods that are currently applicable in Florida are 1) a water 

budget method requiring estimation of daily ET and soil water content, and 2) the use of soil moisture 

measurement instrumentation. When properly used and combined with efficient methods of water 

application, irrigation management should also result in increased production and profits. (Smajstrla 

et al.) 

Potential Benefits 

 If applied properly can increase productivity and reduce irrigation expenses 

 Efficient irrigation can reduce farm discharges and thus reduce pollutant loading 

 Irrigation management systems can also include a fertilization component 

Potential Limitations 

 May require significant upfront capital investment 

 Depending on the type of system implemented, the grower may have to implement more 

thorough recording keeping practices and increased levels of maintenance 

Design Considerations 

 System designs must include a thorough analysis of the total costs including operating and 

maintenance costs. Typically, a higher initial capital investment can pay for itself in the long-term 

by reducing maintenance costs and achieving improved system performance. 

 System design requires an understanding of many factors, including: crop water needs, soils, 

climate, irrigation method(s), management objectives, and regulatory constraints (FDACS 2004) 

 If coupled with a fertilization component, Florida law requires proper backflow prevention 

 Targeted Parameters 

Sediment and 

Nutrients 

 

Source: Tim McCabe USDA 2004 Image Number: 85cs0114 CD0890-100

References: 

1. A.G. Smajstrla, B.J. Boman, D.Z. Haman, F.T. Izuno, D.J. Pitts and F.S. Zazueta 1997. Basic Irrigation Scheduling in Florida

Bulletin No. 249. Institute of Food and Agricultural Sciences, University of Florida. 

2. FDACS (Florida Department of Agriculture and Consumer Services) 2004. Water Quality/Quantity Best Management 

Practices for Florida Vegetable and Agronomic Crops

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Conservation Tillage: No-till/Strip Till - Information Sheet 

In conservation tillage, previous crop residue is conserved by using only the minimum tillage 

necessary to control pests and successfully plant the next crop. Conservation tillage, other than notill, is not well suited to low residue crops like cotton since not enough surface residue will remain for 

erosion control. No-till is planting crops in a previous crop’s residue or cover crop with no surface 

tillage, except a slot to put the seed in the ground. 

Potential Benefits

 Residue keeps soil from blowing or washing 

away. In favorable conditions, conservation 

tillage can reduce erosion by as much as 90 

percent. 

 Residue helps to promote increased water 

infiltration, slows evaporation, and can reduce 

the need for irrigation. 

 Reduced runoff and increased infiltration 

keep sediment and other pollutants from 

reaching the River. 

 Crop residue contains valuable nutrients that 

can make the soil more fertile. 

 Crop yields may increase because topsoil and nutrients are not washed away. 

 Conservation tillage practices can save time and money because they involve less labor, less 

fuel, and fewer pieces of equipment. 

Potential Limitations 

Properly timed and accurate herbicide application is critical. There are still a few weeds that cannot 

be controlled economically in no-till systems. Conservation tillage is much less effective than no-till in 

reducing erosion. (No-till is often considered to be a type of conservation tillage, but conservation 

tillage usually means more soil disturbance). 

Design Considerations 

A minimum of 30 percent of the soil surface should be left covered by crop residue after planting, 

which helps control erosion. 

Targeted Parameters 

TSS and nutrients 

Typical Mass Removal Efficiencies 

United States Environmental Protection Agency, Report EPA-840-B-92-002, January 1993. 

http://nonpoint.deq.state.la.us/manage7.html 

References 

http://economics.ag.utk.edu/extension/bcstudy/Row%20Crops.p65linked2.pdf

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Conservation Tillage: Mulch Till - Information Sheet 

Mulch-till systems manage crop residue on the soil surface year round, while growing crops where 

the entire soil surface is tilled prior to or during the planting operation. Residue is partially 

incorporated using chisels, sweeps, field cultivators, or similar farming implements. Mulch-till is 

defined as leaving 30 percent crop residue cover after planting. 

Potential Benefits 

The beneficial effects of mulching are attributed to: 

 Physical effects that minimize raindrop impact, improve rainfall acceptance through enhancement 

of soil structure and reduce runoff and erosion; 

 Biological effects that increase the activity and species diversity of soil flora and fauna, notably 

earthworms increased biomass carbon, and improved crop growth; and 

 Chemical effects that alter nutrient status and influence crop growth.

Potential Limitations 

Heavy residue can pose a problem for the implements during seedbed preparation,

Numerous operations are becoming increasingly expensive in terms of fuel and equipment costs, and 

Seeding difficulties due to straw residue. 

Design Considerations 

Not available. 

Targeted Parameters 

Runoff, sediment, pesticide residue 

Typical Mass Removal Efficiencies 

United States Environmental Protection Agency, Report EPA-840-B-92-002, January 1993. 

http://nonpoint.deq.state.la.us/manage7.html 

References

http://www.epa.gov/watertrain/pdf/Agriculture.pdf

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Conservation Tillage: Ridge Till - Information Sheet 

Ridge till is a system in which seeds are planted into a seedbed that is prepared by scraping off the 

top of a ridge. The scraped-off ridge usually provides an excellent environment for planting. Ridges 

are formed during cultivation of the previous year's crop. 

Potential Benefits 

 On poorly drained soil, seeds are planted in a warmer, better-drained seedbed than with most 

other tillage systems. 

 Ridge till can greatly reduce erosion when compared to 

conventional tillage, especially when ridges are on the 

contour. 

 Fuel, equipment, and labor costs are all less with ridge till 

when compared to conventional tillage. 

 Ridge till often allows earlier planting than conventional 

tillage, especially on poorly drained soils. 

 Ridge till may not present as many weed-control problems as 

no-till. Weed seeds on the soil surface, including volunteer 

corn kernels, are pushed to the middle of the rows where 

they can be destroyed by cultivation. Example of Ridge Till1

Potential Limitations 

The combine used on ridge-till fields may need to have its wheel spacings adjusted. It is important 

that the combine wheels be spaced to operate between rows. That way, the wheels will not compact 

the ridges formed during cultivation for the next year's crop. Research has shown a significant yield 

decrease in rows driven upon by a combine. A spreader or chopper is also desirable to distribute the 

residue more uniformly across the field. Residue left in the windrows may hamper subsequent 

operations. 

Design Considerations 

Research shows that a ridge till system is best suited to 

medium-to-heavy textured soils with gentle slopes and 

a continuous corn or corn-soybean cropping system. 

Because corn and soybeans predominate in the state, 

and many of the agricultural soils in Illinois are mediumto-heavy and gently sloping, ridge till is a viable system 

in Illinois. Perhaps it should be used much more 

throughout the state. 

Targeted Parameters 

TSS, nutrients and pesticides 

 Ridge Tillage in Operation

Typical Mass Removal Efficiencies 

United States Environmental Protection Agency, Report EPA-840-B-92-002, January 1993. 

http://nonpoint.deq.state.la.us/manage7.html 

References 

1

http://web.aces.uiuc.edu/vista/pdf_pubs/RIDGING.PDF

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Conservation Tillage: Terraces with Surface or Underground Outlets - Information Sheet 

Terraces are long, low earthen embankments constructed across a slope, as nearly as practical on 

the contour. They intercept runoff water and guide it off the field safely. Terraces are best used to 

control concentrated flow and prevent gullies when no-till, conservation tillage and grassed 

waterways will not work without them. 

Potential Benefits

 Terraces help reduce gullying in fields, reducing wear and tear on machinery. 

 By reducing erosion, terraces help maintain the yield potential and long-term value of land. 

 By slowing water flow, more water is 

absorbed into the soil and is available 

during dry periods. 

Example of a Terrace

Potential Limitations 

 Generally more expensive than no-till.

 Make machinery operation more difficult. 

 Not suited to fields with irregular, short 

slopes. 

 Usually require annual maintenance. 

When not maintained, they can increase 

gully erosion problems. 

Design Considerations 

 Terraces must have stable outlets like 

grassed waterways or underground pipes to 

carry water off the field. 

 Terraces should not be used on slopes of more than 8 percent. 

Targeted Parameters 

 TSS, nutrients, and pesticides 

Typical Mass Removal Efficiencies 

 Terraces can reduce the soil loss by 50 percent or more. 

References 

http://ianrpubs.unl.edu/soil/g750.htm#terplan

http://economics.ag.utk.edu/extension/bcstudy/Row%20Crops.p65linked2.pdf

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Conservation Buffer: Filter Strips for Agricultural Operations - Information Sheet 

Filter Strips are used to remove sediment, organic matter, and other pollutants from runoff or 

wastewater by deposition, infiltration, and sorption, thereby reducing pollution, improving water 

quality, and protecting the environment (NRCS 1999). 

Potential Benefits 

 Can be applied to a variety of agricultural practices including row crops, cattle/calf operations, 

forestry etc. 

 Protection of downstream receiving waters 

Potential Limitations 

 Potential for short-circuiting if rills or small channels are formed within the filter strip 

 May be a wildlife attractant, therefore caution should be considered when conducting weed 

control, mowing and other maintenance activities 

Design Considerations 

 Width of filter strip should vary based on land slope; land slopes of less than 5 percent typically 

require filter strip widths from 5 feet to 35 feet. NRCS recommends a 3-foot increase in filter strip 

width per 1 percent increase in land slope over 5 percent. 

 Care shall be taken to ensure runoff flows perpendicularly across the filter in a uniform manner. 

 If implemented near concentrated livestock areas for lounging which are normally void of 

vegetation during the winter months, the filter strip width should be at least 100 feet wide between 

the lounging area and water bodies. 

 May require fencing to prevent access by farm animals 

 Existing native vegetation should be used when appropriate. 

 Targeted Parameters 

Sediment, 

Nutrients, and 

Organic Matter 

 

Source: EPA 2004. Section 319 Success Stories, Vol. III. Stillwater River Watershed Protection Project

References: 

1. National Resources Conservation Service (NRCS) 1999. Conservation Practice Standard No. 393 – Filter Strip 

 7-32 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Conservation Buffer: Contour Strip Cropping - Information Sheet 

Contour strip cropping divides a steep land into contour strips that cut across the path of overland 

flow and retard its velocity. Contour strip cropping is an alternation of contour buffer strips with 

contour cropped strips. Low-growing soil conserving crops (e.g. cowpea, soybeans, Stylosanthes, 

Pueraria) are grown in alternate strips with open-row soil-degrading crops (e.g. maize, rice). A soilconserving crop is grown in a contour strip down slope of the erosion promoting crop to absorb runoff, 

retard runoff velocity, and encourage sedimentation of entrained sediment. Various categories of strip 

cropping include the following: Contour Strip Cropping, Buffer Strip Cropping, Field Strip Cropping, 

Barrier Strips, and Border Strips.

Potential Benefits 

 Strip cropping is generally effective on gentle slopes (< 7%) on rolling terrain. 

 For steep slopes, strip cropping should be reinforced with engineering structures. 

Potential Limitations 

 Herbicide drift from one crop to another can be a 

problem. 

 Because every crop in the rotation is in every field, 

this practice can increase the amount of 

machinery time spent moving from field to field 

compared to rotating whole fields. This may 

increase costs significantly in operations with 

scattered fields. 

Design Considerations 

 Contour strip cropping works best on fields with 

long, uniform slopes. If the fields have slopes that 

are short and/or not uniform (complex slopes), 

the two problems with using this practice are 

complex row patterns and more turning of 

machinery. 

Example of Contour Strip Cropping in the 

Midwest 

Design Criteria (Source: NRCS 

Strip Width Guidelines) 

 On irregular, complex slopes, strips can be planted across the 

general slope instead of on the contour. This practice, called 

field strip cropping, is less effective in erosion control. % Slope Strip Width

1–2% 150 ft 

3–5% 135 ft 

6–8% 120 ft 

9–15% 105 ft 

16–20% 90 ft 

≥21% 60 ft 

Targeted Parameters 

TSS, Nutrients and Pesticides 

Typical Mass Removal Efficiencies 

United States Environmental Protection Agency, Report EPA-840-B-92-

002, January 1993. 

References 

http://www.unu.edu/unupress/unupbooks/uu27se/uu27se09.htm#1.%20mulch%20farming

http://economics.ag.utk.edu/extension/bcstudy/Row%20Crops.p65linked2.pdf

 7-33 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Conservation Buffer: Riparian Forest Buffer - Information Sheet

Riparian forest buffer is a particular type of conservation buffer. There are approximately 10 

conservation practices commonly thought of as buffers: alley cropping, contour buffer strips, field 

borders, filter strips (or riparian grass buffer), grassed waterways with filters, herbaceous wind 

barriers, riparian forest buffers, vegetative barriers, and windbreaks/shelterbelts. Unlike filter strips, 

riparian forest buffers are woody areas (trees and shrubs) maintained in permanent vegetation 

between the edge of fields and streams, lake, rivers or tidal waters. 

Potential Benefits

 They help filter nutrients, sediments, 

and other pollutant from runoff, as well 

as remove nutrients from groundwater. 

They also offer food and shelter for 

wildlife. 

 Buffers can vary widely in their 

vegetation and location on the 

landscape in order to enhance specific 

ecological functions that achieve 

conditions landowners and other 

stakeholders want. 

 The ecological functions of buffers 

include creating stable and productive 

soils, providing cleaner water, 

enhancing wildlife populations, 

protecting crops and livestock, 

enhancing aesthetics and recreation opportunities, and creating sustainable landscapes. 

Potential Limitations 

 Reduction of acreage available for crops. 

Design Considerations (1) 

Buffer Goal Buffer Width (feet)

Water Temperature Moderation 75 – 100 

Sediment Removal 40 – 200 

Nutrient Removal 25 – 300 

Diversity 10 – 350 

Targeted Parameters 

Nitrogen, Phosphorous, Sediment, Pesticides, Herbicides 

Typical Mass Removal Efficiencies (2) 

Nitrogen: 17-58% 

Phosphorous: 50-78% 

Sediment: 50-75% 

References 

(1) http://www.epa.gov/watertrain/pdf/Agriculture.pdf

(2) http://dnrweb.dnr.state.md.us/watersheds/surf/bmp/info/ripariangrass.html

 7-34 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Contour Farming - Information Sheet 

The simplest approach to erosion control is contour farming (e.g., performing all farm operations on 

the contour rather than up and down the slope or parallel to field boundaries). Important farm 

operations to control soil erosion include plowing, ridging, sowing, fertilizer application, and inter-row 

cultivation. 

Potential Benefits

On fields with long, smooth slopes, contour 

farming can be one of the least expensive 

ways to reduce erosion. 

Potential Limitations 

The major drawbacks of contour farming are 

frequent turning involving extra labor and 

machinery time, and loss of some area that 

may have to be put out of production. 

Design Considerations 

 Contour farming is most effective on 

slopes between 2 and 10 percent. 

 This practice will be less effective in 

achieving the stated purpose(s) on slopes exceeding 10 percent and in areas with 10-year 

frequency, single storm energy intensity (EI) values greater than 140. (EI = total storm energy 

times the maximum 30 minute intensity). 

Practice of Contour Farming 

 The practice is not well suited to rolling topography having a high degree of slope irregularity 

because of the difficulty meeting row grade criteria. 

 A contour farming layout shall not occur on a hill slope that is 

longer than the critical slope length, unless supported by other 

practices (e.g., terraces, diversions) that either reduce slope 

length below the critical length or reduce overland flow 

velocities. 

Targeted Parameters 

TSS, Nutrients and Pesticides 

Typical Mass Removal Efficiencies 

The effectiveness of contour farming decreases with increase in slope 

gradient and slope length, and increasing intensity of rains. If the rainfall 

exceeds the surface detention capacity of the contouring system, 

concentrated runoff flowing downstream unchecked can lead to 

accelerated erosion and even severe gulling. Therefore, contour farming alone is not sufficient to 

control erosion on steep, long slopes, erodible soils, and during erosive rains. 

% Slope Strip Width

1–2% 150 ft 

3–5% 135 ft 

6–8% 120 ft 

9–15% 105 ft 

16–20% 90 ft 

≥21% 60 ft 

Design Criteria (Source: 

NRCS Strip Width Guidelines) 

United States Environmental Protection Agency, Report EPA-840-B-92-002, January 1993. 

References 

http://lubbock.tamu.edu/irrigate/documents/2074410-L5205.pdf

http://efotg.nrcs.usda.gov/references/public/SD/330final.pdf

http://www.unu.edu/unupress/unupbooks/uu27se/uu27se09.htm#1.%20mulch%20farming

http://economics.ag.utk.edu/extension/bcstudy/Row%20Crops.p65linked2.pdf

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Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Water and Sediment Control Basins - Information Sheet 

Water and sediment control basins are erosion control structures commonly installed to prevent bank 

and gully erosion on farmland. These structures control erosion due to concentrated water flows and 

are not effective in combating sheet erosion. This erosion control structure may also be referred to as 

a "channel terrace" or a "floodwater storage for a smaller 

watershed”. A water and sediment control basin in a 

conservation system can achieve the same objective as a 

grassed waterway. This structure is comprised of an earthen 

berm, constructed across a low draw in a field, and built to a 

height to pond the runoff water from the upstream watershed for 

the maximum expected storm which may occur once every 10 

years. 

Potential Benefits 

 The peak runoff flows from a watershed are buffered 

reducing their erosive capability downstream. Sediment 

settles out of the runoff water in the storage pond 

decreasing pollution and silting problems in watercourses 

below. 

 Outlet flows from the storage pond are small in comparison to a system with no storage available, 

resulting in smaller diameter, lower cost outlet pipes. 

 Potential plugging of the pond tile inlet system is minimized, as compared to a tile/catch basin 

system with no storage, since debris has time to settle out before reaching the inlet pipe. 

Potential Limitations 

 Flooding of the land in the ponding area will occur for a period up to 24 hours after the runoff 

event. A higher risk is associated with this structure because of the possibility of berm washout or 

water overtopping the berm causing problem further down in the watershed. 

 There is an inconvenience factor associated with working around the berms with farm equipment. 

Berms (especially narrow-based) are vulnerable to rodent damage which could result in failure. 

 A water and sediment control basin system requires a thorough design by a qualified person 

before being constructed. 

Design Considerations 

 Before a water and sediment control system can be designed, a field survey should be 

undertaken to determine the topographical features which will affect the berm locations and 

ponding volumes available. Soil types, watershed design features and outlet availability should 

also be investigated. 

 Having established the berm locations in the field the following factors should be considered in 

the design of the structure.

 The peak flow and total volume of runoff water for the watershed is to be determined. 

Targeted Parameters 

TSS, nutrients, and pesticides 

Typical Mass Removal Efficiencies 

 United States Environmental Protection Agency, Report EPA-840-B-92-002, January 1993. 

 See also retention, dry detention, and wet detention urban BMP information sheets. 

References 

http://www.gov.on.ca/OMAFRA/english/engineer/facts/89-167.htm

Sediment Control Basin 

 7-36 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Animal Waste Management - Information Sheet 

Animal waste management is a generic term that encompasses waste management from different 

farm animal as diverse as swine, dairy/beef cattle, chicken, and turkey production. Depending upon 

the producer's choice, it can be included into an irrigation management plan as well as a nutrient 

conservation plan for maximum fertilization or nutrient reduction plan for ease of management. For 

large and small livestock operations, storage lagoons are the common animal waste management. 

They are designed and operated either anaerobically or aerobically. Anaerobic lagoons are preferred 

over aerobic lagoons because they are cheaper to operate and they offer a better pretreatment for 

concentrated organic waste. However, in aerobic lagoons, bacterial degradation is more complete 

with relatively odor-free end products. 

Potential benefits 

 Storage and disposal flexibility 

 Less land for total treatment system 

 Liquid recycling for pit waste removal 

 Land application by simple irrigation 

 Lower labor needs and operating costs 

Potential Limitations 

 Appreciable loss of nutrient value 

 Offensive odor if improperly designed and managed 

 Frequent sludge removal if undersized 

 Groundwater protection consideration 

 High mechanical aeration cost if used 

Design Considerations 

Liquid volume rather than surface area is the basis for anaerobic lagoon design. 

Anaerobic lagoon 

 Accumulation rate design criteria in table 2 for each livestock 

 Storage for a 25-year, 24-hour rainfall and an additional foot of freeboard to prevent embarkment 

and a 180-day storage capacity 

 Any shape but without any deep bays and pockets 

 For rectangular shape, the length to width ration should not exceed 4:1 

 Side slope varies from 1:1 in clay soils to 3:1 in sandy soils 

 Minimum liquid depth of 6 feet at all times (Maximum up to 20 feet to minimize surface area and 

encourage dissolution of gases) 

Aerobic lagoon 

 Liquid depth ranges from 3 to 5 feet and daily load of 50 pounds of BOD5 per acre of lagoon 

surface 

Targeted Parameters 

Nutrient, BOD and TSS 

Typical Mass Removal Efficiencies 

Reduction of nutrient for land application up to 85%. 

References 

http://www.bae.ncsu.edu/programs/extension/publicat/wqwm/ebae18593.html

http://www.bae.ncsu.edu/programs/extension/publicat/wqwm/ebae103 83.html

 7-37 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

7.2.3 Distributed Sources 

As noted above and in Subsection 3.4.3, distributed sources include atmospheric deposition 

through wet and dry fall, groundwater sources, and sediment/legacy sources. While each of 

these sources can be quantified to some degree through water and/or pollutant balance 

calculations, reduction of the pollutant load from distributed sources is complicated and 

sometimes not possible. For example, in the Tampa Bay National Estuary Program (NEP) 

atmospheric deposition can range from 8 percent of the total nitrogen loading in 

Hillsborough Bay to 83 percent of the total nitrogen loading in Lower Tampa Bay, for an 

overall nitrogen loading of potentially 29 percent (TBNEP, Dec 1996). However, researchers 

have not agreed on how much of the locally generated nitrogen is deposited in Tampa Bay 

and how much of the nitrogen generated outside of the Tampa area is locally deposited. 

Nitrogen is also generated by mobile sources (e.g., automobiles), so that treatment of 

atmospheric nitrogen appears to need a “shotgun” approach – treat everything in a hope 

that the major source will be affected. 

Treatment for groundwater and sediment/legacy sources may be better defined. For 

groundwater sources, treatment is better stated as prevention – that is, prevention of 

pollutant sources by best management practices. Such practices include: fertilization 

controls; sediment and toxic material controls at marinas, ports and other near shore 

activities; and pesticide controls on agricultural activities. Sediment/legacy sources can be 

reduced using a combination of two activities: the reduction of the source of sediment (e.g., 

runoff from construction sites, excess erosion, etc.) and elimination of built-up sediment in 

slow moving surface waters (e.g., lake sediments or stream deposits). Elimination or 

reduction of one without a concomitant reduction of the other will not result in a long-term 

control of the pollutant source. 

 7-38 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

SECTION 7 ADDITIONAL REFERENCES 

CDM. 1993. California Best Management Practice Handbooks. 

FDACS (Florida Department of Agriculture and Consumer Services) 2004. Water 

Quality/Quantity Best Management Practices for Florida Vegetable and Agronomic Crops 

FDER. 1988. The Florida Development Manual: A Guide to Sound Land and Water 

Management. 

http://dnrweb.dnr.state.md.us/watersheds/surf/bmp/info/ripariangrass.html

http://economics.ag.utk.edu/extension/bcstudy/Row%20Crops.p65linked2.pdf

http://efotg.nrcs.usda.gov/references/public/SD/330final.pdf

http://ianrpubs.unl.edu/soil/g750.htm#terplan

http://lubbock.tamu.edu/irrigate/documents/2074410-L5205.pdf

http://web.aces.uiuc.edu/vista/pdf_pubs/RIDGING.PDF

http://www.bae.ncsu.edu/programs/extension/publicat/wqwm/ebae103 83.html

http://www.bae.ncsu.edu/programs/extension/publicat/wqwm/ebae18593.html

http://www.epa.gov/watertrain/pdf/Agriculture.pdf

http://www.gov.on.ca/OMAFRA/english/engineer/facts/89-167.htm

http://www.unu.edu/unupress/unupbooks/uu27se/uu27se09.htm#1.%20mulch%20farming

Indian River Citrus. 2002. BMPs. Indian River Research and Education Center. 

Metropolitan Government of Nashville and Davidson County. 2000. Stormwater 

Management Manual. 

National Resources Conservation Service (NRCS). 1999. Conservation Practice Standard 

No. 393 – Filter Strip. 

SFWMD. 2002. Best Management Practices for South Florida Urban Stormwater 

Management Systems. 

Smajstrla, A.G., B.J. Boman, D.Z. Haman, F.T. Izuno, D.J. Pitts and F.S. Zazueta. 1997. 

Basic Irrigation Scheduling in Florida Bulletin No. 249. Institute of Food and Agricultural 

Sciences, University of Florida. 

 7-39 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.03/05-003 

Tampa Bay National Estuary Program (TBNEP). 1996. "Charting the Course: The 

Comprehensive Conservation Management Plan for Tampa Bay."

Tyack, J.N., and R.A. Fenner. 1997. “The Use of Scaling Laws in Characterising Residence 

Time in Hydrodynamic Separators.” Presented at the 1997 IAWQ Conference, Aalborg, 

Denmark. 

U.S. EPA, 1999. Stormwater Technology Fact Sheet – Porous Pavement. 

U.S. EPA, 1999. Stormwater Technology Fact Sheet – Storm Water Wetlands. 

United States Environmental Protection Agency. 1993. Report EPA-840-B-92-002. 

Wade, D., Janicki, A., Pribble, R. 1997. Watershed Management Model for Optimal 

Allocation of BMPs User’s Guide and Case Study. Tampa Bay National Estuary Program 

Technical Publication No. 07-96. 

Wanielista, M.P., and Y.A. Yousef. 1993. Stormwater Management. John Wiley & Sons. 

  

 7-40 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.02/04-003 

Section 8 

MODEL DOCUMENTATION 

TMDL Report

Prepare the Draft 

TMDL Report

Finalize the 

Draft TMDL 

Report TMDL Report

No

23

Duplication and

Distribution of the Final

TMDL Report

Yes

Transfer of Final

Electronic Files and

24

Documentation

FDEP

Acceptance

Clarification/Liaison/Status 

Meeting

19

Draft Report

20

Review

Comments

Independent 

Technical 

Review

21

22

Final Report

Element V 

Protocol to Allocate 

and Implement 

TMDL 

 8-1 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.02/04-003 

The next step in the TMDL process is the documentation of the results. Because the results 

will be used as part of a regulatory process and the potential consequences of the TMDL 

may be stringent, model documentation has three purposes: 

 Provide sufficient information so that the TMDL analysis can be reproduced by others; 

 Provide sufficient information for technical peer review; and 

 Summarize potential data and analysis refinements as may be required. 

8.1 TMDL Documentation Outline 

Based on the review of TMDL requirements by EPA and those suggested by other states 

(EPA, 1999a; EPA Region 9, 2000; Idaho Division of Environmental Quality, 1999; EPA, 

1999b; Virginia Department of Environmental Quality, 2000), Table 8.1 provides the 

recommended general outline of the TMDL documentation. The outline provides information 

on each area identified in this Model Documentation Protocol including data collection; 

standards and targets; watershed and receiving water data assessment; site specific model 

preparation, verification, calibration, and validation; model application; TMDL definition; 

TMDL allocation; and TMDL implementation. 

As part of the appendices of the report, references, correspondence and a complete digital 

record of data and analysis used for the TMDL should be included. The model itself should 

be included as well as all model input datasets and output files in digital form. Example input 

and output may also be valuable in hardcopy form. Finally, the appendix should include a 

digital version of the documents in Adobe Acrobat © format. 

8.2 TMDL Documentation Form 

For quick reference to the TMDL analysis, a TMDL documentation form is recommended, as 

shown in Figure 8.1. The documentation form is relatively simple; however, it allows for 

quick reference to a specific TMDL analysis as well as to the documents related to the 

TMDL and the source of the information about the TMDL analysis. The documentation form 

should be filled out as part of the TMDL analysis and access to the documentation form 

should be available from the FDEP TMDL website as well as project files and library 

references. 

 8-2 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.02/04-003 

Table 8.1 

Florida TMDL Protocol 

Recommended TMDL Documentation Outline 

Executive Summary 

1. Introduction 

1.1. Purpose of Document 

1.2. Problem Identification Including Parameters of Concern 

2. Water Segment Description 

2.1. Name, Location and Mapping 

2.2. 303(d) Listed and Related Parameters 

2.3. General Watershed and Receiving Water Descriptions 

3. Applicable Water Quality Standards and Targets 

3.1. Water Quality Standards 

3.2. Numerical Targets (If Any) 

4. Data Collection and Analysis 

4.1. Watershed Data 

4.1.1. Rainfall, Evaporation, and other Meteorological Data 

4.1.2. Soils 

4.1.3. Topography and Subbasins 

4.1.4. Land Use and Impervious Area 

4.1.5. Point Sources 

4.1.6. Nonpoint Sources 

4.1.7. Tributary Stage and Flow 

4.1.8. Tributary Cross-sections 

4.1.9. Existing Models 

4.1.10. Reports 

4.2. Receiving Water Data 

4.2.1. Water Quantity Data 

4.2.1.1 Bridges, Culverts, Weirs, and Gates 

4.2.1.2 Stages and Flows 

4.2.1.3 Bathymetry 

4.2.1.4 Cross-sections 

 8-3 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.02/04-003 

4.2.1.5 Boundary Conditions 

4.2.2. Water Quality Data 

4.2.2.1 Concentrations 

4.2.2.2 Loads 

4.2.2.3 Sediments 

4.2.2.4 Biology 

4.2.2.5 Other Considerations 

4.2.3. Existing Models 

4.3. Data Analysis Summary 

4.3.1. Stages and Flows 

4.3.2. Concentrations and Loads 

4.3.3. Seasonal Variations 

4.3.4. Uncertainty 

4.3.5. Data Needs and Refinements 

4.3.6. TMDL from Data (if applicable) 

5. Site Specific Model Selection, Preparation, Verification, Calibration, and Validation 

5.1. Model(s) Selection and Description 

5.2. Model Preparation 

5.2.1. Model Parameters 

5.2.2. Model Boundary Conditions 

5.2.3. Model Simulation Periods 

5.3 Model Verification 

 5.3.1 Conservation of Mass 

 5.3.2 Numerical Stability 

 5.3.3 Other Tests 

5.4 Model Calibration 

 5.4.1 Simulation Period 

 5.4.2 Watershed or Receiving Water Variations 

 5.4.3 Goodness of Fit Tests 

 5.4.4 Sensitivity Analysis 

5.5 Model Validation 

 5.5.1 Simulation Period 

 8-4 
Florida Department of Environmental Protection June 2006 

TMDL Protocol Version 6.0 

Task Assignment 003.02/04-003 

 5.5.2 Watershed or Receiving Water Variations 

 5.5.3 Goodness of Fit Tests 

5.6 Model Uncertainty and Margin of Safety 

5.7 Model Quality Control 

5.8 Recommended Data Needs and Model Refinements 

6. Model Application 

6.1. Design Conditions and Periods 

6.2. Natural Background Conditions 

6.3. Simulations Under Design Conditions 

6.3.1. Existing Point Sources and Nonpoint Sources 

6.3.2. Existing Point Sources and Nonpoint Sources at 45% 

Participation 

6.3.3. Existing Point Sources and Nonpoint Sources at 90% 

Participation 

6.4. Model Uncertainty and Margin of Safety 

6.5. Model Conclusions and Recommendations 

7. TMDL Allocation and Implementation 

7.1. Total Loading(s) Allowed and Margin of Safety 

7.2. Total Reduction(s) Required 

7.3. Allocation of Total Load(s) to Point Sources and Nonpoint Sources 

7.4. Recommendations for Updates and Refinements 

Appendices 

A. TMDL Documentation Form 

B. Correspondence 

C. Data Summary and Tables 

D. Digital Copy of Model (CD) and Copy of Input and Output Files and Example 

Hardcopies 

E. Digital Copy of Report (PDF or equivalent format) 

F. References 

  

 8-5 
Figure 8.1 Florida TMDL Protocol Documentation Form

Florida TMDL Protocol

Documentation Form

Water Segment Description:

Water Segment Common Name

Watershed Name

WBID Number

Water Body Type (Lake, River, Estuary)

Pollutants of Concern:

Dissolved Oxygen/BOD Sediments

Nutrients Toxic Materials

Pathogens

Other

List:

General Watershed Description:

Total Area (acres) acres

General Land Uses

Percent Impervious

Annual Rainfall inches

Design Conditions:

Low Flow

High Flow

Other Period

Total Maximum Daily Load Definition:

Load Units

Existing

Load Reduction Required

Point Source Allocation

Nonpoint Source Allocation

Seasonal Variation

Load For Future Growth

Margin Of Safety

Statement of Uncertainty

Potential Refinements

References:

State of Florida Contact

Phone

e-mail

Name of TMDL Document