United States Office of Science and 823-R-99-002

Environmental Protection Technology March 1999

Agency

Review of Potential

Modeling Tools and

Approaches to Support

the BEACH Program
Review of Potential Modeling

Tools and Approaches to

Support the BEACH Program

United States Environmental Protection Agency

Office of Science and Technology

Standards and Applied Science Division

401 M Street, SW

Washington, DC 20460

Prepared under Contract No. 68-C-98-010

March 1999
Acknowledgments iii

Acknowledgments

The authors would like to thank the following agencies for providing information used in the

development of this report: Delaware Department of Natural Resources and Environmental Control;

Virginia Department of Health; Rhode Island Department of Environmental Management; Washington

Department of Health, Interstate (New Jersey, New York, Connecticut) Sanitation Commission; City of

Milwaukee (Wisconsin) Health Department; City of Stamford (Connecticut) Health Department; City of

Petersburg (Florida) Leisure Services Department; Darien (Connecticut) Health Department; Orange

County (California) Environmental Health Division; Town of Fairfield, Connecticut; Monroe County

(New York) Department of Health; and Village of Sherewood (Wisconsin) Health Department. The

authors would also like to thank the staff of those agencies who provided critical review and comments

on this document as it was being developed.
iv Review of Potential Modeling Tools and Approaches to Support the BEACH Program

Abstract

The objective of EPA’s Beach Environmental Assessment, Closure and Health (BEACH) Program

is to significantly reduce the risk of infection to users of the nation’s recreational waters. Waterborne

pathogens reaching recreational areas can originate from various sources located either within the

proximity of the beach or at upstream locations within the drainage area or watershed. These sources can

be grouped into three categories: (1) nonpoint source-dominated systems, where pathogen contamination

is governed by rainfall events; (2) point source-dominated systems, where pathogen impact is due to

either continuous or intermittent discharges; and (3) episodic releases of untreated wastewater due to

uncontrolled discharges and accidental spills.

States and local agencies that operate recreational beaches rely on monitoring of water quality for

the presence of pathogens to support their beach advisory decisions. When pathogen concentrations

exceed the water quality standard or local action level, beach advisories or closures are issued. To further

support beach advisory decisions and to optimize water quality monitoring and testing activities, several

agencies use mathematical models to predict increased pathogen concentrations. This document reviews

predictive models currently used by beach operators as management tools to minimize exposure to

pathogens. It also reviews available techniques that potentially can be used to further enhance pathogen

prediction capabilities. This review is based first on the results of the 1998 National Health Protection

Survey of Beaches, which asked respondents about predictive tools to support beach closure decisions.

Available models developed and used to support other water quality-related programs are also reviewed,

and their applicability to real-time prediction of pathogen concentrations is evaluated.
Contents v

Contents

Part I. Identification of Methods Currently Used to Predict Beach Advisories ........ 1 - 1

1. Introduction ............................................................ 1 - 1

2. Waterborne Pathogens ................................................... 2 - 1

2.1 Indicator Organisms ................................................... 2 - 1

2.2 Water Quality Standards ................................................ 2 - 2

3. Predictive Methods Currently Used in the BEACH Program ..................... 3 - 1

3.1 Model Categories ..................................................... 3 - 1

3.2 Use of Predictive Tools ................................................. 3 - 1

3.3 Overview of Predictive Tools ............................................ 3 - 2

3.3.1 Rainfall-based Alert Curves ......................................... 3 - 2

3.3.1.1 State of Delaware Closure Guidelines ............................ 3 - 5

3.3.1.2 City of Milwaukee Closure Guidelines ........................... 3 - 5

3.3.1.3 City of Stamford Closure Guidelines ............................. 3 - 6

3.3.1.4 Preemptive Closure .......................................... 3 - 7

3.3.2 Point Source-Dominated Steady-State Predictive Tools .................... 3 - 7

3.3.2.1 Simple Mixing and Transport Model ............................. 3 - 7

3.3.3 Point Source-Dominated Dynamic Predictive Tools ...................... 3 - 9

3.3.3.1 Regional Bypass Model ....................................... 3 - 9

3.3.4 Hydrodynamic Mixing Zone Models ................................. 3 - 11

3.3.4.1 CORMIX ................................................. 3 - 11

3.3.4.2 PLUMES ................................................. 3 - 15

3.3.4.3 JPEFDC Model ............................................ 3 - 17

4. Review of Applicability and Key Characteristics ............................... 4 - 1

4.1 Evaluation of Models Currently Applied to Beach Advisory or Closure ............ 4 - 1

4.2 Criteria for the Evaluation of Potential Models ............................... 4 - 2

Part II. Review of Other Potential Modeling Tools Available for Beach Advisory

 or Closure ............................................................ 1 - 1

1. Introduction ............................................................ 1 - 1

2. Potential Models ........................................................ 2 - 1

2.1 Watershed-scale Loading Estimates ....................................... 2 - 2

2.2 Receiving Water Models ................................................ 2 - 3

2.2.1 Rivers and Streams ................................................ 2 - 3

2.2.2 Lakes and Estuaries ............................................... 2 - 4
vi Review of Potential Modeling Tools and Approaches to Support the BEACH Program

References .................................................................. R - 1

Appendix A: Model Fact Sheets

City of Milwaukee Closure Guidelines .......................................... A - 1

City of Stamford Closure Guidelines ........................................... A - 2

CORMIX: Cornell Mixing Zone Expert System ................................... A - 3

EFDC: Environmental Fluid Dynamics Computer Code ............................. A - 5

HSPF: Hydrological Simulation Program-Fortran ................................. A - 7

PLUMES: Dilution Models for Effluent Discharges ................................ A - 9

QUAL2E: The Enhanced Stream Water Quality Model ............................ A - 11

Regional Bypass Model .................................................... A - 13

SMTM: Simple Mixing and Transport Model .................................... A - 15

State of Delaware Closure Guidelines ......................................... A - 17

STORM: Storage, Treatment, Overflow, Runoff Model ............................ A - 18

SWMM: Storm Water Management Model ..................................... A - 20

TPM: Tidal Prism Model ................................................... A - 22

Appendix B: BASINS Fact Sheet
Contents vii

Tables

I. Identification of Methods Currently Used to Predicted Beach Advisories

Table 2.1 Waterborne pathogens ............................................... 2-2

Table 3.1 Rainfall-based beach advisory ......................................... 3-7

Table 3.2 Comparison of CORMIX and PLUMES submodels and JPEFDC model ........ 3-18

Table 4.1 Evaluation of model capabilities and applicability .......................... 4-4

II. Review of Other Potential Modeling Tools Available for Beach Closure

Table 2.1 Watershed-scale loading models ........................................ 2-2

Table 2.2 Potential pathogen fate and transport models .............................. 2-3

Figures

I. Identification of Methods Currently Used to Predict Beach Advisories

Figure 3.1 Example steps illustrating development and use of guidelines or decision rules

for beach closure based on real-time rainfall observations .................... 3-4

Figure 4.1 Summary of pathogen predictive tools currently in use ....................... 4-1

II. Review of Other Potential Modeling Tools Available for Beach Closure

Figure 1.1 Components of pathogen modeling ..................................... 1-2

Figure 2.1 Potential predictive tools applicable to pathogens ........................... 2-1
Introduction I.1-1

Part I. Identification of Methods Currently Used to

Predict Beach Advisories

1. Introduction

The objective of the EPA Beach Environmental Assessment, Closure and Health (BEACH)

Program is to significantly reduce the risk of infection to users of the nation’s recreational waters. Users

of recreational waters are exposed to waterborne pathogens because of inadequate monitoring or delayed

notification during periods of poor water quality. Currently, most notifications are based on monitoring of

the beach water quality. Local government agencies test for the presence of increased levels of pathogens

that may pose a threat to the health of beachgoers. The current laboratory methods used to detect

potentially harmful microorganisms take up to 48 hours. During these 48 hours of water sample

processing, beachgoers might be exposed to harmful pathogens.

To reduce exposure to pathogens, local government agencies need tools that can provide a quick

and reliable indication of the water quality conditions. EPA Method 1600 for enterococcus is one such

tool. This laboratory method reduces the test time to 24 hours. Another tool is the use of computer

models or other information that can give an indication of the water quality conditions. The overall goal

is to provide local governments with a range of predictive tools to determine the water quality conditions

so that based on this information, decision makers can determine the need for beach advisories or

closures.

In spring of 1998, EPA conducted the first annual National Health Protection Survey of Beaches.

The objective was to collect information on beach health activities. Information on about 1,000 beaches

was collected as result of the 350 questionnaires distributed by EPA to beach health agencies. A summary

of the information from the 159 respondents was put on the Internet to enable public access. It is located

at EPA’s “Beach Watch” web site at www.epa.gov/ost/beaches.

The objective of this study is to inventory predictive models or tools currently in use by agencies

responsible for evaluating the need for closing beaches or issuing advisories and warnings. A description

of the predictive tools currently in use and their attributes is provided, as well as a discussion of the

limitations, input data requirements, and availability for each of the predictive tools.
Waterborne Pathogens I.2-1

2. Waterborne Pathogens

Microorganisms are rampant in both the aquatic and the terrestrial environment, and many perform

important functions. The survival of ecosystems is impossible without the decomposers. These

microorganisms are responsible for converting organic matter to inorganic nutrients that can be used by

other plants and animals. Also, microorganisms are an essential component of the nitrogen cycle and

other biogeochemical cycles. In humans and animals high on the food chain, microorganisms resident in

the digestive tract aid in the digestion process and are excreted in high numbers. A small group of

microorganisms have been identified and linked with diseases and death. These pathogens can infect the

body through skin contact or ingestion of contaminated water or food.

Pathogens are small in size, and once released to the environment they are easily transported by

water. These characteristics are a primary concern of water resources managers, whose goal is to protect

the public from coming into contact with pathogens through ingestion or swimming. Illnesses such as

respiratory illness, fever, cryptosporidiosis, gastroenteritis, and hepatitis have been associated with

waterborne microorganisms. These pathogens have been grouped into three subcategories—bacteria,

protozoa, and viruses. Table 2.1 shows some of the waterborne diseases associated with each category.

Bacteria are microscopic, unicellular organisms that reproduce by binary fission (Chapra, 1997).

Not all bacteria are pathogenic. Table 2.1 shows some of the major pathogenic waterborne bacteria of

concern. Pathogenic bacteria found in surface water are often attributed to excretions from the bodies of

warm-blooded animals. The coliform group, Streptococcus, Lactobacillus, Staphylococcus, and

Clostridium are some of the pathogenic bacteria excreted.

Protozoans are also unicellular organisms that reproduce by binary fission. Pathogenic protozoans

exist in the environment as cysts to protect themselves from harsh conditions of temperature and salinity.

Once ingested, the cysts hatch, grow, and multiply, manifesting the associated disease. Giardia lamblia

and Cryptosporidium are the two major groups of pathogenic protozoans associated with waterborne

diseases in the United States. A cryptosporidiosis outbreak in Milwaukee in 1993 was caused by

Cryptosporidium contamination of drinking water.

Viruses are submicroscopic infectious agents that require a host to live. The nucleic acid core is

protected by a protein or lipoprotein shell that determines the surface to which a virus can adhere. Once

inside the host, the virus reproduces, manifesting the associated illness. Viruses such as hepatitis A,

rotaviruses, and Norwalk-type viruses are then excreted in the feces of infected individuals. These enteric

viruses present a major threat to human health.

2.1 Indicator Organisms

Waterborne pathogens pose a threat to human health when people come into contact with

contaminated water through ingestion of water, ingestion of fish or shellfish harvested from contaminated

water, or skin contact (swimming). Identification and enumeration of pathogens such as viruses in water

is a difficult process. Instead, laboratory methods have been developed to test for the presence and

density of indicator organisms. The presence of the indicator organisms shows that a water body might be

contaminated, and the concentration of the indicator organisms can be correlated to the concentration of

the pathogen to give an indication of the extent of pollution (Thomann and Mueller, 1987). The coliform

bacteria group is found in the intestines and feces of warm-blooded animals. Therefore, their presence

indicates that pathogens from untreated or partially treated sewage or contaminated runoff may be

present in the water. Bacteria in the coliform group, which includes total coliform, fecal coliform, and

fecal streptococci bacteria, are used as indicators because (1) they are easily detected by simple

laboratory methods, (2) they are not usually present in unpolluted waters, and (3) they appear in

concentrations that can be correlated with the extent of contamination.
I.2-2 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

Table 2.1 Waterborne pathogens

Pathogen Disease Effects

Bacteria Escherichia coli

(enteropathogenic)

Gastroenteritis Vomiting, diarrhea, death in

susceptible populations

Legionella pneumophila Legionellosis Acute respiratory illness

Leptospira Leptospirosis Jaundice, fever (Weil’s disease)

Salmonella typhi Typhoid fever High fever, diarrhea, ulceration of

the small intestine

Salmonella Salmonellosis Diarrhea, dehydration

Shigella Shigellosis Bacillary dysentery

Vibrio cholerae Cholera Extremely heavy diarrhea,

dehydration

Yersinia enterolitica Yersinosis Diarrhea

Protozoans Balantidium coli Balantidiasis Diarrhea, dysentery

Cryptosporidium Cryptosporidiosis Diarrhea

Entamoeba histolytica Amedbiasis (amoebic

dysentery)

Prolonged diarrhea with bleeding,

abscesses of the liver and small

intestine

Giardia lamblia Giardiasis Mild to severe diarrhea, nausea,

indigestion

Naegleria fowleri Amoebic

meningoencephalitis

Fatal disease; inflammation of the

brain

Viruses Adenovirus (31 types) Respiratory disease

Enterovirus (67 types,

e.g., polio, echo, and

Coxsackie viruses)

Gastroenteritis Heart anomalies, meningitis

Hepatitis A Infectious hepatitis Jaundice, fever

Norwalk agent Gastroenteritis Vomiting, diarrhea

Reovirus Gastroenteritis Vomiting, diarrhea

Rotavirus Gastroenteritis Vomiting, diarrhea

2.2 Water Quality Standards

In response to widespread public concern about the condition of our nation’s waters, the United

States Congress enacted landmark legislation in 1972. This statute, the Federal Water Pollution Control

Act Amendments of 1972 (referred to as the Clean Water Act of 1972, or CWA), expanded and built

upon existing laws designed to control and prevent water pollution. Successive amendments to the 1972

CWA (the Clean Water Act of 1977 and the Water Quality Act of 1987) have continued to strengthen the

law to better protect our nation’s waters.

Water quality standards are the cornerstone of a state’s water quality management program. States,

territories, and Indian tribes set water quality standards for waters within their jurisdictions. Water quality

standards define a use for a water body and describe the specific water quality criteria to achieve that use.

The water quality standards also contain antidegradation policies to protect existing water quality. These

are the goals by which success is ultimately gauged for a given water body or watershed.
Waterborne Pathogens I.2-3

The water quality standards program is administered by the U.S. Environmental Protection Agency

(EPA). Congress has mandated that EPA is responsible for providing water quality criteria

recommendations, approving state-adopted standards for interstate waters, evaluating adherence to the

standards, and overseeing enforcement of standards compliance. Guidance for the development of

standards by individual states, tribes, and territories is contained in the EPA documents Water Quality

Standards Handbook, second edition (1983) and Ambient Water Quality Criteria for Bacteria (1986).

However, because of the difficulties in analyzing for and detecting the many possible pathogens or

parasites, concentrations of fecal bacteria, including fecal coliforms, enterococci, and Escherichia coli,

are used as the primary indicators of fecal contamination. The latter two indicators are considered to have

a higher degree of association with outbreaks of certain diseases than fecal coliforms and were

recommended as the basis for bacterial water quality standards in the 1986 Ambient Water Quality

Criteria for Bacteria document (both for fresh waters, enterococci for marine waters). The standards are

defined as a concentration of the indicator above which the health risk from waterborne disease is

unacceptably high.

Prior to the 1986 revision to the national critera, recommendations were made by the National

Technical Advisory Committee to the Secretary of the Interior in Water Quality Criteria (1967) and by

EPA in Quality Criteria for Water (1976). In both of these documents criteria were based on fecal

coliforms, and both recommended that maximum densities not exceed geometric means of 200 organisms

per 100 ml in recreational waters (USEPA, 1998).

The 1986 criteria statement for bacteriological criteria follows:

EPA Criteria for Bathing (Full Body Contact)

Recreational Waters

Freshwater

Based on a statistically sufficient number of samples (generally not less than 5 samples equally spaced

over a 30-day period), the geometric mean of the indicated bacterial densities should not exceed one or the

other of the following:1

E. coli 126 per 100 mL; or

Enterococci 33 per 100 mL.

No sample should exceed a one sided confidence limit (C.L.) calculated using the following as guidance:

Designated bathing beach 75% C.L.

Moderate use for bathing 82% C.L.

Light use for bathing 90% C.L.

Infrequent use for bathing 95% C.L.

based on a site-specific log standard deviation, or if site data are insufficient to establish a log standard

deviation, then using 0.4 as the log standard deviation for both indicators.

Marine Water

Based on a statistically sufficient number of samples (generally not less than 5 samples equally spaced

over a 30-day period), the geometric mean of the enterococci densities should not exceed 35 per 100 mL.

No sample should exceed a one sided confidence limit using the following as guidance:

Designated bathing beach 75% C.L.

Moderate use for bathing 82% C.L.

Light use for bathing 90% C.L.

Infrequent use for bathing 95% C.L.

based on a site specific log standard deviation, or if site data are insufficient to establish a log standard

deviation, then using 0.7 as the log standard deviation.

1Only one indicator should be used. The regulatory agency should select the appropriate indicator for its conditions.

Source: USEPA, 1998.
Predictive Methods Currently Used in the BEACH Program I.3-1

3. Predictive Methods Currently Used in the BEACH

Program

The process of identifying predictive tools currently in use by local agencies responsible for beach

advisories was based on three approaches. First, using the 1998 National Health Protection Survey of

Beaches (or beach survey), agencies that are currently basing their beach advisories on water quality

model prediction were identified. The agencies were then contacted regarding the types of models being

used and information about extent of use, who developed the model, and the model’s availability.

The second approach was a review of literature and information from related EPA programs. This

approach included a review of the models and guidelines provided in the Clean Water Act (CWA) 301(h)

program, identification of tools used in the Total Maximum Daily Load (TMDL) program, and review of

other EPA publications that relate to water quality modeling.

The third approach in the process was networking within the modeling community. Model

developers were queried about their models’ availability and applicability to the BEACH Program. This

approach was the most fruitful.

3.1 Model Categories

The overall objective of all beach advisory predictive tools is to reduce the risk of illness due to

exposure to elevated levels of pathogens. The tools currently in use by responsible agencies vary in their

complexity and approach to minimizing exposure. In the case of the City of Milwaukee, City of

Stamford, and Delaware Department of Natural Resources and Environmental Control (DNREC), the

approach taken was regression analysis to relate rainfall to pathogen concentration. Models developed

based on this approach are site-specific since they are derived from locally observed relationships

between water quality and rainfall data.

Simulation of water quality conditions under a variety of scenarios of untreated or partially treated

wastewater can also be used. Comparison of the resulting water quality conditions to the established

action level, such as the water quality standard, can serve as the basis for the beach advisory or closure.

For the New York-New Jersey Harbor, a model was developed to predict water quality conditions that

result from the bypassing of sewage at preselected locations. Beaches surrounding the discharge location

are closed whenever the predicted pathogen concentrations exceed a locally specified threshold level.

Water quality models are used to establish closure zones in the shellfish sanitation programs of

several states. Although the models used differ in their description of the dominant mixing and transport

processes and their applicability to local conditions, the same basic approach is followed. The models are

used to predict pathogen concentration in the waters surrounding a pathogen source, such as a wastewater

treatment plant outfall. The boundary of the closure zone is then delineated based on these predicted

pathogen concentrations. Dye studies may also be conducted in conjunction with the water quality

modeling to refine the closure zone boundaries.

3.2 Use of Predictive Tools

Review of the 1998 beach survey results indicated that few local agencies are currently using

models for beach closure. Some of the models used are based on simple relationships between the

observed rainfall and pathogen concentration, and others are based on complex modeling of the dominant

mixing and transport processes. The objective of either approach is to reduce the risk of illness due to

exposure to water high in pathogen concentrations. It is important to note that in most cases predictive

tools are not used alone; they are usually combined with real-time monitoring of water quality conditions.

These two processes are dependent on one another. The frequency of water quality sampling might be
I.3-2 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

reduced in the future once a reliable modeling approach is in place. Detailed descriptions and analyses of

models now in use are provided in the next section.

3.3 Overview of Predictive Tools

3.3.1 Rainfall-based Alert Curves

The objective of rainfall-based alert curve models is to establish a statistical relationship between

rainfall events and pathogen concentrations. This relationship can then serve as a management tool for

developing operating guidelines or predicting pathogen concentrations requiring a beach advisory or

closure. Several agencies have developed beach operating rules based on analysis of site-specific

relationships between rainfall and water quality monitoring data. Examples from Delaware (DNREC,

1997), Wisconsin (City of Milwaukee Health Department, 1998), and Connecticut (Kuntz, 1998) are

presented in this section to illustrate the development and application of this approach. These types of

models are based on simple regression and frequency of exceedance analysis of simultaneous

observations of pathogen concentration at representative monitoring stations located near the beach and

rainfall events at one or several locations in the upstream watershed. The development of rainfall-based

models consists of three phases, as described in the following box. These phases include data collection,

data analysis and development of predictive tool(s), and development of operating rules.

For relatively small watersheds, it is common to use a single rainfall station selected to be

representative of storm conditions of the

upstream drainage area. The selection of a

representative rainfall station takes into

consideration its location within the

watershed and its ability to capture the most

dominant rainfall events (magnitude and

duration) that generate relatively high storm

runoff volumes and transport high pathogen

loadings to the beach. For example,

DNREC selected a rainfall station based on

its central location in the watershed and

strong statistical correlation with observed

pathogen concentrations at the beach site of

interest (DNREC, 1998). The city of

Milwaukee compared rainfall data from

various stations and elected to use a NOAA

rainfall database that consistently provided

a relatively better correlation with observed

levels of pathogens. The city is currently

performing further data collection and

analysis to refine rainfall station selection

for use in prediction of pathogen levels at

three beach sites (City of Milwaukee Health

Department, 1998).

Key rainfall characteristics considered

in the development of these rainfall-based

alert curve models include (1) amount of

rainfall expressed in inches, (2) storm

duration expressed in hours, (3) interevent

periods expressed in dry days, (4) lag time

between rainfall record event and receiving

Development of Rainfall-based Models

1. Data Collection

• Define pathogen species to serve as an indicator

• Define applicable standards and their expression

• Identify all rainfall monitoring stations within and

surrounding the contributing watershed. (Define

stations as hourly or daily stations. Hourly stations

are preferred especially when dealing with small

to medium-sized watersheds.)

• Define season of concern and collect

corresponding rainfall data concurrently with

pathogen data

2. Development of Predictive Tool

• Analyze rainfall data by storm events and identify

representative data set and storm characteristics to

consider in tool development (i.e., rain station,

storm duration, intensity, interevent duration, etc.)

• Test various prediction relationships between

storm characteristics and pathogen concentration

at beach locations of concerns

• Perform statistical and validation tests to select

most appropriate prediction model(s)

3. Development of Beach Advising Operating Rules

• Use the selected prediction model(s) to develop a

set of beach operating rules

• Operating rules may range from warning and

increased water testing frequency to beach

advisory or closure
Predictive Methods Currently Used in the BEACH Program I.3-3

beach response, and (5) the season(s) of high usage of the beach resources. When dealing with nonpoint

source-dominated systems, antecedent rainfall conditions can be very significant factors in explaining the

relationship between the rainfall and pathogen concentrations. Kuntz (1998) found higher pathogen

concentrations during periods of low rainfall or near-drought conditions than during seasons of normal

rainfall. Storm event duration is also a key factor in explaining rainfall-water quality relationships. For a

watershed in Delaware, examination of the relationship between the cumulative rainfall obtained based

on two durations (24 hours and 3 days) and pathogen concentrations showed that the 24-hour cumulative

rainfall data yielded a statistically stronger relationship than the 3-day cumulative rainfall data (DNREC,

1997).

Pathogen data supporting the development of rainfall-based models are generated from water

column concentrations obtained from ambient or targeted monitoring programs. Pathogen concentrations

can be used in the regression model or frequency of exceedance analyses as direct observations or can be

transformed to a set of geometric mean values of a defined step. Transformation of pathogen observations

prior to development of regression models can allow direct comparison to state water quality standards

for recreational uses, which are usually expressed as limits on geometric mean values of observed

concentrations. The regression model can be developed for one or several pathogen species. Fecal

coliform, E. coli, and enterococcus bacteria are common indicator species used in these models. The city

of Milwaukee uses E. coli and fecal coliform in the regression analysis, whereas the city of Stamford uses

the geometric mean of enterococcus bacteria (City of Milwaukee Health Department, 1998; Kuntz,

1998).

Because of the seasonality of recreation activities, as well as of rainstorm characteristics, rainfallpathogen models can be developed for targeted seasons. In addition, developing predictive tools for

various seasons can significantly enhance the predictive capability of the tool. The city of Milwaukee

developed beach closure rules based on analysis of fecal coliform and E. coli concentrations collected

daily (Monday-Friday) during the June-September season (City of Milwaukee Health Department, 1998).

Rainfall-based models are site-specific, and their development requires relatively large monitoring

data sets of both rainfall and water quality. The overall relationship can be described by a statistical

regression/estimation model. Depending on the number of rainfall stations considered and the number of

rainfall characteristics (amount, duration, lag time, etc.), the relationship may require a more complex

multiple-regression model. Because of their statistical nature, these types of models do not distinguish

between point sources or nonpoint sources of pathogens and do not explicitly incorporate the advection,

transport, and decay processes. Since their use is also limited to assisting in the development of beach

operating guidelines, they do not attempt to provide the spatial and vertical distribution of pathogens.

Frequency of exceedance analysis is another rainfall-based method that can be used to develop

rainfall-based alert curvers. An exceedance is defined as any time the observed pathogen concentration

exceeds the action level, such as the state water quality standard specified by the responsible agencies.

The objective of this method is to determine the minimum amount of rainfall that causes the pathogen

concentration to exceed the action level. This determination can be accomplished by dividing cumulative

rainfall amounts over a 24-hour period or more into segments that range from no rainfall to an upper limit

that is representative of the rainfall record, types of storms, and season. For each rainfall amount

category, either the observed pathogen concentration or the geometric mean is compared to the action

level. A guideline is developed based on the least amount of rainfall that would result in a violation of the

action level. This method applies to situations where historical rainfall data and water quality records

exist. Guidelines or closure rules should also be developed to include seasonal variation in rainfall data.

After establishing a relationship between rainfall amounts and pathogen concentrations, developing

guidelines or decision rules for beach advisory and closure is the next step. The process can be developed

in three phases, which are explained here for the hypothetical cumulative rainfall data shown in Figure

3.1.
I.3-4 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

Time (hr)

0 0.5 1 2 2.5 3 6 7 8 9 10

0

0.25

0.5

0.75

1

1.25

1.5

1.75

2

Phase 3: Beach Closure

- Rainfall 1.0 inch or more

- Beach advisory or closure is issued until

pathogen concentration is back to level that

does not pose a threat to public health

- Beach advisory or closure is communicated

through media and signs posted on the beach

Phase 2: Alert Stage

- Rainfall 0.75 to 1.00 inch

- Beach advisories may be issued

- Sampling frequency is increased

Phase 1: Cautionary Stage

- Rainfall 0.5 to 0.75 inch

- Advisory may be issued

- Increased sampling frequency

Figure 3.1 Example steps illustrating development and use of guidelines or decision rules

for beach closure based on real-time rainfall observations
Predictive Methods Currently Used in the BEACH Program I.3-5

For this example the exceedance criterion is 1 inch of rainfall in a 24-hour period. Phase 1 is the

cautionary stage, which starts when cumulative rainfall exceeds 0.5 inch. At this point sampling

frequency is increased and an advisory may be issued to notify the public of potential health risks

associated with an increase in pathogen concentration.

Phase 2 is the alert stage, which begins at cumulative rainfall of 0.75 inch. Beach water quality

samples are taken at a higher frequency, and a beach advisory may be issued if not issued during the

cautionary stage. The advisory to the public should stress the potential health risks associated with

recreation in pathogen-contaminated waters.

Phase 3 is beach advisory or closure. This stage is reached when the cumulative rainfall is 1 or

more inches. The public is notified about the risk and strongly advised not to recreate. Also, water quality

samples are collected and analyzed at a higher frequency. Rainfall-pathogen data collected following this

approach can then be entered into the existing database and can be used in future regression analysis.

This process would keep the database up-to-date and help to strengthen the rainfall-pathogen relationship.

3.3.1.1 State of Delaware Closure Guidelines

The state of Delaware’s Department of Natural Resources and Environmental Control (DNREC)

investigated the effect of rainfall on bacteria levels in natural waterways (DNREC, 1997). The objective

was to establish rainfall conditions that lead to increased levels of pathogens in water that might result in

beach advisory or closure. The DNREC obtained rainfall data from two gaging stations, the Georgetown

station and the Lewes station. Twelve freshwater sites were selected in Sussex County, Delaware. These

sites were selected based on watershed characteristics such as implementation of nonpoint source best

management practice (BMP) conditions (good or poor) and land use (DNREC, 1997). Eighteen sets of 12

water samples were collected and analyzed for enterococcus bacteria.

Linear regression methods were used to evaluate the relationship between the observed

enterococcus bacteria concentrations at the 12 sites. The Georgetown rainfall data were selected over the

Lewes station data since the Georgetown station was centrally located to the 12 sampling sites and

regression analysis yielded a positive and more statistically significant relationship. Three-day and 24-

hour cumulative rainfall data prior to sampling were correlated with pathogen concentrations at the 12

sampling sites. The relationship between water quality and rainfall was positive for cumulative 3 days

and 24 hours prior to sampling. A stronger positive relationship was found for cumulative rainfall 24

hours prior to sampling.

Antecedent conditions prior to sampling were defined as wet weather conditions (rainfall event of

0.15 inch in 24 hours or less or 0.25 inch of rain in a 3-day period prior to sampling) (DNREC, 1997).

Eleven of the eighteen sample sets collected were taken during wet weather conditions. Correlation

coefficients describing the relationship between total enterococcus level and rainfall data ranged from

0.0232 to 0.7299 (DNREC, 1997). Five sites did not meet the DNREC freshwater primary contact

geometric mean standard of 193 enterococcus/100 mL.

The DNREC used a similar approach to evaluate water quality conditions at marine and fresh water

beach areas after rainfall events. Using regression methods, the DNREC determined the level of rainfall

above which a beach advisory will be issued. For marine water, e.g., Rehoboth Beach, a beach advisory

for 12 hours may be issued following 3.5 inches of rain or more in 24 hours or 3 inches of rain in 12

hours at one or more rainfall observation sites. A freshwater beach advisory may be issued at Lums Pond

following 2.5 inches of rain in a 24-hour period or less and at Lake Como, Silver Lake, and Trap Pond

following a rainfall event of 1.5 inches in a 24-hour period. The duration of freshwater advisories is 24

hours (DNREC, 1998).

3.3.1.2 City of Milwaukee Closure Guidelines

A rainfall model for beach closure in the city of Milwaukee was developed in the early 1970s.

Recently, the City of Milwaukee Health Department in collaboration with the Milwaukee Metropolitan
I.3-6 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

Sewerage District investigated the predictive accuracy of the model and planned to update the model to

reflect changes in the land use in the Milwaukee River basin. The observed fecal coliform concentrations

were compared at different sites and at the wastewater treatment plant outfall using regression methods.

The objective of these correlations was to determine whether the wastewater treatment plant significantly

contributes to observed pathogen concentrations at the beach area, as well as to investigate the

relationships between rainfall and poor water quality due to high bacteria (E. coli and fecal coliform)

concentrations.

Two sources of rainfall data, General Mitchell Field and the river watershed monitoring stations,

were used to investigate the relationship between rainfall and fecal coliform concentrations at South

Shore beach. Analysis of rainfall data, fecal coliform data, and dates of beach closings and openings for

the period from 1991 to 1993 indicated that insufficient data were available to establish a statistically

significant relationship. Therefore, one of the conclusions was to increase the frequency of bacterial

sampling during 1995.

Analysis of the 1995 single-sample data collected by the City of Milwaukee Health Department

indicated that 55 percent of water quality samples exceeded the fecal coliform criterion (235 colonies/100

mL) following 0.1 inch of rain. In addition, after 0.3 inch of rainfall, 30 percent of E. coli single samples

and 15 percent of fecal coliform single samples violated the water quality criteria. The model was not

validated since violations in the water quality standards occurred following a rainfall event of 0.1 inch

instead of 0.3 inch.

To understand the increased pathogen concentrations during low rainfall, the effects of the

antecedent moisture condition, lag time, number of dry days, individual and cumulative river flow,

wastewater treatment outfalls, and day of the week the violation occurred were investigated. Based on

correlation analysis, the increased fecal coliform concentrations were explained by total river flow,

Menomonee River flow, or total rain at General Mitchell Field.

The General Mitchell Field total rainfall (NOAA data) was selected as the best indicator of the

water quality conditions at South Shore beach. Guidelines for beach closure for the period from June 1 to

September 30, 1998, were established as follows:

C 48-hours closure after 0.3 to 0.69 inch of rainfall

C 72-hour closure following 0.7 to 1.49 inches of rainfall

C 96-hour closure following 1.5 inches of rain or more

C 96-hour closure in cases where a 48-hour or a 72-hour advisory was already in effect

3.3.1.3 City of Stamford Closure Guidelines

The state of Connecticut adopted enterococcus bacteria as an indicator of beach water quality in

1990. The established limit for beach closure is based on a geometric mean of 33 colonies/100 mL

enterococcus bacteria. Water quality data collected between 1989 to 1996 at Stamford beaches on Long

Island Sound indicated that enterococcus bacteria concentrations were directly related to rainfall and that

this correlation was statistically significant (Kuntz, 1998). Rainfall levels at or greater than 1 inch could

result in an enterococcus bacteria count that would exceed the standard.

The effect of rainfall on water quality was evaluated at Cove Island Beach, Cummings Beach, West

Beach, and Southfield Beach. Rainfall amount data were divided into five increments, and the geometric

mean was calculated for each increment. Rainfall data and enterococcus bacteria counts collected by the

City of Stamford Health Department indicate that in cases where there were no rainfall events, the

geometric mean of enterococcus bacteria at the five beaches did not exceed 7 counts/100 mL.

Exceedance of the 33 counts/100 mL at four of the five beaches was observed following a rainfall event

of 1 inch or more. Southfield Beach was the site most sensitive to rainfall; enterococcus exceeded the

standard following a rainfall event of 0.25 inch or more (Kuntz, 1998).
Predictive Methods Currently Used in the BEACH Program I.3-7

Beach closure due to elevated levels of enterococcus bacteria following rainfall events was also

influenced by periods of low rainfall or drought conditions. The City of Stamford Health Department

observed that during drought conditions, enterococcus exceeded the standard following 0.7 inch of rain

and in some cases 0.5 inch of rain (Kuntz, 1998). The geometric mean of enterococcus bacteria during

periods of low rainfall exceeded the standard at four beaches following a rainfall event in the range of 0.5

to 0.99 inch.

Based on the above information, the current policy of the city of Stamford is to close beaches for

24 hours following a rainfall event of 1 inch or more under normal conditions. During periods of low

rainfall or drought conditions, advisories are issued following a rainfall event greater than or equal to 0.5

inch (Kuntz, 1998).

3.3.1.4 Preemptive Closure

Several agencies are using closure guidelines based on rainfall amounts. The agencies listed in

Table 3.1 indicated that beach advisories or closure may be posted based on rainfall events. These

guidelines are used by local health agencies to minimize beach users exposure pathogens. No analytical

methods were used in developing these guideline; they are based on the responsible agency’s

observations and expertise.

3.3.2 Point-Source-Dominated Steady-State Predictive Tools

3.3.2.1 Simple Mixing and Transport Model

The Simple Mixing and Transport Model (SMTM) was developed in 1989 for the Virginia Bureau

of Shellfish Sanitation by the Virginia Institute of Marine Science, Gloucester Point, Virginia (Hamrick

and Neilson, 1989). This model was designed to assist in the determination of marina buffer zones, as

well as buffer zones for other point source discharges. The potential for pathogenic contamination of

shellfish beds in areas around marinas and other point discharges is determined based on the

concentration of total or fecal coliform in the water column. Although SMTM was developed for

determining marina buffer zones, it can be applied to evaluate the impacts of point source discharges or

prolonged accidental spills on other recreational activities such as swimming.

Table 3.1 Rainfall-based beach advisory

Agency Rainfall Amounts

Beach Advisory

Duration

City of St. Petersburg, Leisure Services Department,

Florida

Maximo Beach

North Shore Beach

0.8 in./24 hr

1 in./24 hr

Until samples

indicate low

bacterial count

Darien Health Department, Connecticut >1 in./24 hr 24 hr

Orange County Environmental Health Division, California 0.2 in. /24 hr 72 hr

Town of Fairfield, Connecticut 1 in. /24 hr 24 hr

Monroe County Department of Health, New York 0.3 to 0.7 in./24 hr

0.7 to 1.5 in./24 hr

$ 1.5 in./24 hr

24 hra

24 hr

48 hr

Village of Shorewood Health Department, Wisconsin 1-2 in. /24 hr

with strong easterly

winds

Until samples

indicate low

bacterial count

aA 24-hour closure if the lake shore current is west or there is a northwest wind. No beach advisories are issued under other

conditions.
I.3-8 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

SMTM is a computerized steady-state/tidally averaged simulation model based on a simple

analytical mixing and transport formulation. Because of its analytical solution, it requires readily

available site data from tidal tables and navigation charts. Pathogen kinetics are represented by a firstorder decay function. Model simulation algorithms predict the horizontal distribution of pathogen

concentrations and assume that the concentration in the vertical direction is uniformly distributed. SMTM

consists of three modeling options developed to address various physical characteristics of the marina

sites. The three modeling options are described below.

Wide river module. This module addresses marinas located along a shoreline of a wide channel

with measurable net freshwater discharge in addition to tidally driven flow. Wide channels are described

as channels wider than 100 meters in which the water depth changes are assumed to be negligible during

tidal cycles. The pathogen concentration is set constant across the water column, and the channel-end

effects are neglected based on the assumption that the contaminant will decay or die off before it reaches

either end of the channel. The longitudinal and lateral dispersion coefficients are based on a flow rate that

is tidally dominated; however, the advection of pathogen organisms is dominated by the freshwater flow

in the channel (Hamrick and Neilson, 1989).

Narrow channel module. This module addresses marinas located within narrow channels with

insignificant net freshwater discharge. An example of this type would be a tidal creek. Since the net

freshwater flow is negligible, both advection and dispersion processes are dominated by the tidal flow.

The model formulation uses a one-dimensional advection-dispersion equation to estimate the horizontal

distribution of pathogen concentration along the channel. In cases where the distinction between wide

and narrow sites is not clear, it is recommended that the analyst apply both the wide and narrow module

and select the most conservative results.

Semi-enclosed bays and basins. This module addresses marinas located in semi-enclosed bays

characterized by narrow entrances. Contaminant mixing and transport in semi-enclosed systems assumes

that mixing of pathogens is complete and that change in depth due to tidal flow is negligible. If the

predicted concentration in the entire basin exceeds the water quality standard, use of the one- or twodimensional advection-dispersion module is suggested (Hamrick and Neilson, 1989).

Parameters required for use of these modules are simple and can often be easily obtained. Input

parameters include the following:

Site characterization

C Mean water depth (meters).

C Upstream distance to closed end (meters), applicable to narrow channel and semi-enclosed bays

or basins.

C Downstream distance to open end (meters), applicable to narrow channel and semi-enclosed

bays or basins.

C Channel width (meters).

C Entrance channel cross-sectional area (square meters), applicable to narrow channel and semienclosed bays or basins.

C Basin surface area (square meters).

C Maximum tidal velocity magnitude (meters/second).

C Tidally averaged mean discharge velocity (meters/second)

C Tidal elevation range (meters) change in depth between high and low water.

C Tidal period (44712 seconds).

Discharge characterization

C Loading rate (organisms/second). Constant loading rate can be estimated from discharge flow

rate and the fecal coliform concentration.
Predictive Methods Currently Used in the BEACH Program I.3-9

Kinetics

C Decay rate coefficient (1/second); first-order microbial death or decay rate that is applicable to

water body conditions (fresh or marine water).

Model parameters required to characterize the site are obtained from navigation or topographic

charts and tidal tables. The pathogen loading rate can be estimated based on the size and occupancy of

the marina, discharge monitoring data, or permit information. The model uses a constant

discharge/loading rate for each simulation run.

The Virginia Department of Health, Division of Shellfish Sanitation uses SMTM to determine the

size of the required buffer zone surrounding marinas or wastewater discharge locations. The objective is

to minimize potential pathogenic contamination of shellfish harvested in these areas. Therefore, direct

shellfish harvesting is banned from waters where fecal coliform concentrations are 14 organisms/100 mL

surrounding any source of fecal material (e.g., waste from boats). These criteria are used to delineate the

buffer zone boundaries. Two examples of model application to a marina site and a wastewater treatment

site are presented below for illustration purposes.

3.3.3 Point Source-Dominated Dynamic Predictive Tools

3.3.3.1 Regional Bypass Model

The Regional Bypass Model (RBM) was developed for USEPA Region 2 and part of USEPA

Region 1 to determine the impact of bacterial discharge on beaches and shellfish beds due to untreated

wastewater bypass in the New York-New Jersey Harbor, including the Hudson River, East River, New

York Bay, Raritan Bay, and Apex of New York Bight. In addition, Long Island Sound beaches and

shellfish beds in Westchester and Nassau County, New York, as well as southwest Connecticut, are

included in the model grid. The objective of the model is to allow a rapid evaluation of water quality

condition and determine impact areas where either increased monitoring frequency or temporary closure

of beach recreational activities and shellfish harvesting is needed.

The model simulates a discharge of a known quantity of bacteria at a specified location and

calculates the water quality response at sensitive areas including existing and potential beaches and

shellfish harvesting areas. Application to the New York-New Jersey-Connecticut metropolitan area

included 29 discharge location points and 53 beaches/shellfish areas distributed throughout the tristate

metropolitan area. The bacteria selected for the modeling analysis are total coliform. However, other

bacteria such as fecal coliform bacteria or enterococci can be approximated using proportionality ratios

(HydroQual, Inc., 1998).

The RBM is based on a modified version of the System-Wide Eutrophication Model (SWEM)

developed for the New York City Department of Environmental Protection. The hydrodynamics were

represented using an existing three-dimensional SWEM developed under a previous eutrophication study

of the harbor. The hydrodynamic conditions for various temperature scenarios were generated to serve as

input to the water quality model. Temperature was considered a significant factor affecting the bacterial

kinetics. However, for the purpose of developing a quick-reference tool, the effects of wind, which were

estimated to have negligible influence compared to tidal influences, were not considered a key variable in

the model. The water quality representation includes a first-order kinetic model for total coliform.
I.3-10 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

Example Illustrating the Use of SMTM

Buffer Zone for Mythical Marina

Mythical Marina is located on the south shoreline of the Nice River in Great County, Wonderland.

The marina is located on a major estuarine channel and has 40 boat slips.

Input data are:

M = loading rate = 1.2 x106

 organisms/second

Kd

 = decay coefficient = 10-5 /second

qm = maximum tidal velocity magnitude = 0.57 meter/second

h = mean water depth = 3.3 meters

B = channel width = 3550 meters

u = tidally averaged mean discharge velocity = 0.0002 meter/second

The steps taken to estimate the buffer zone for Mythical Marina are:

(1) Make initial estimate of the dispersion coefficients.

The dispersion coefficients are calculated assuming uniform channel conditions ((x=1) and using the

following equations:

D

h

8

q and D

h

60

q x = m x y = mγ

Dx = 0.23 square meters/second

Dy

 = 0.03 square meters/second

(2) Run the model based on the initial estimates of the dispersion coefficients.

The model was run using the above dispersion coefficients. The boundaries of the buffer zone are

selected from the tabular output of the model and based on an action level concentration of fecal

coliform of 14 organisms /100 mL. The across the channel mixing zone width, Ym, is 43 meters and

the channel nonuniformity factor (x

 is 100.

(3) Estimate D x based on the new (x

 value.

Using the above equation and a (x

 value of 100, the calculated new D x is 23 m2 /s.

(4) Perform final iteration.

Finally the model was run while setting the velocity equal to zero to ensure a conservative prediction

of the upstream buffer zone and D x = 23 m2

 /s.

The model predicts that the boundary of the 14 organisms/100 mL buffer zone is 852 meters along

the channel and 142 meters across the channel. This corresponds to a buffer zone area of 47 acres

surrounding Mythical Marina.
Predictive Methods Currently Used in the BEACH Program I.3-11

The discharge characteristics considered to have a significant influence on the modeling results

include (1) discharge quantity and quality represented by discharge flow rate and corresponding coliform

concentrations and (2) spill duration expressed in hours. Because of the significant influence of the

timing of the spill (flood tide vs. ebb tide), the minimum spill duration was assumed to extend over a full

tidal cycle. This assumption provides a conservative bound on the modeling results.

Because of the linear relationship between the discharge flux and the resulting concentration at

receptor sites, the modified SWEM model was applied to simulate various discharge and temperature

scenarios. The results were processed to develop a quick-reference tool that allows estimation of total

coliform concentration (MPN/100 mL) at the 53 beaches/shellfish areas as a result of a spill at 1 of the 29

discharge points. The scenarios used to develop the RBM include the following:

C Bypassing of untreated wastewater at a rate of 100 million gallons per day and a fecal coliform

concentration of 20x106

 MPN/100 mL. Assuming a linear relationship between the discharge

and resulting concentration, model results can be used to calculate other flow rates using a direct

proportionality calculation. The discharge rate of 100 MGD serves as a reference value that

allows users to scale down to specifically fit the actual wastewater bypass flow rate while

preserving a significant number of digits.

C Three different bypass durations (12 hr, 24 hr, and 96 hr). Each duration represents a multiple

number of tidal cycles. The range of discharge durations considered represents the most likely

spill scenarios. 

C Three different temperatures (-4 oC, 12 oC, and 22 oC). Temperature values were selected to

represent winter, spring/fall, and summer conditions, respectively.

The RBM package consists of a computer program that uses the results of presimulated scenarios

and performs all the necessary interpolation to adjust for the actual flux rate and temperature conditions

and calculate the bacterial concentration response at the specified receptor sites. The user does not

actually run SWEM.

Key input data required by the model include:

C Discharge location

C Receptor site location

C Water temperature

C Volume of discharges (million gallons)

C Discharge concentration (most probable number, MPN x106)

C Bacteria type to analyze (total coliform is the default)

The program output can be viewed in a tabular or graphical form. In either case the results given

are maximum bacteria concentrations for 12-hour intervals beginning at the start of the discharge. Also,

the results are displayed for 12-, 24-, and 96-hour discharge durations. If a threshold concentration were

selected, the program would highlight receptor site locations to allow a visual screening of the receptor

site locations exceeding the threshold. The principle of super position applies to multiple untreated

wastewater bypasses.

3.3.4 Hydrodynamic Mixing Zone Models

3.3.4.1 CORMIX

CORMIX is a microcomputer-based hydrodynamic mixing zone modeling and decision support

system that is widely used for near-field water quality studies. The current version of CORMIX (Jirka et

al., 1996) includes three submodels—CORMIX1 for submerged single-port discharges, CORMIX2 for

submerged multi-port discharges, and CORMIX3 for surface discharges. The CORMIX system also

includes a number of utility programs: CORJECT is a single-port buoyant jet model, FFLOCATR is a
I.3-12 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

far-field plume locator model, and CMXGRAPH is a graphical utility for displaying CORMIX submodel

predictions. The three primary CORMIX submodels are based on integral solutions of the Eulerian

momentum and transport equations. The CORMIX1 and CORMIX2 submodels can be used to analyze

steady-state buoyant submerged discharges from single-port outfalls and multi-port diffusers,

respectively, into flowing, density stratified ambient environments. The CORMIX3 submodel can be

used to analyze steady-state buoyant surface discharge from a discharge canal or pipe into a flowing,

homogeneous, density-stratified environment.

Use of CORMIX1 requires the specification of an idealized rectangular cross section to

characterize the geometry of the receiving water, a steady and uniform ambient flow normal to the crosssection plane, and a piecewise linear vertical density structure to characterize the ambient flow

environment. The discharge is specified by its position and orientation (angles with respect to the

horizontal and vertical), volume flow rate, and discharge velocity, the density of the discharged water,

and the contaminant concentration in the discharged water. To simulate coastal discharges, the width of

the rectangular cross section is set to a large value such that the plume trajectory does not reach the far

bank. For nonconservative contaminants, a first-order decay rate can be specified. From 35 possible

classes, the decision support system implemented in CORMIX1 selects the solution class most

representative of the specified situation and provides the user with graphical and tabular summaries of the

solution results that are used to define the mixing zone. The major advantage of CORMIX1 is its ease of

use. The primary disadvantages or limitations of CORMIX1 are related to its use of idealized receiving

water geometry and spatially and temporally uniform ambient conditions. For example, the use of a

constant depth precludes accurate representation of discharges offshore from sloping beaches. The

constant ambient flow condition, including flow direction alignment parallel to the lateral boundary,

precludes modeling the effects of vertical and horizontal spatial variations in ambient current speed and

direction associated with wind-, wave-, and tide induced currents in coastal regions. Because the ambient

current and stratification are time-invariant, multiple analyses must be conducted for tidal receiving

waters to identify variations in mixing boundaries at different tidal phases.

The CORMIX2 submodel idealizes a multi-port diffuser as a finite-length, buoyant slot jet with the

slot width and orientation chosen to give volume, momentum, and buoyancy fluxes equivalent to the

vector sums of the corresponding port fluxes. The ambient flow environment is specified by an idealized

rectangular cross section, a steady and uniform ambient flow, and a piece-wise linear vertical density

structure, consistent with CORMIX1. The discharge is specified by its position and orientation (angles

with respect to the horizontal and vertical), the slot equivalent volume flow rate and discharge velocity,

and the density of the discharged water and its contaminant concentration. From 24 possible classes, the

decision support system implemented in CORMIX2 can select the solution class most representative of

the specified situation and provide graphical and tabular summaries of the solution results for dilution

and near-field the mixing zone definition. The major advantage of CORMIX2 is its ease of use. The

primary disadvantages or limitations of CORMIX2 are the same as those discussed for CORMIX1 and

are related to the use of idealized receiving water geometry and spatially and temporally uniform ambient

conditions.

The CORMIX3 submodel simulates a surface discharge from a canal or pipe into a semi-infinite

ambient environment. The ambient flow environment is represented by a constant shoreline depth and

bottom sloping downward away from the shoreline. The spatially and temporally constant ambient

current is aligned parallel to the shoreline. The discharge is specified by its orientation (angle with

respect to the shoreline), canal width or pipe diameter, and volume flow rate and discharge velocity, and

the density of the discharged water and its contaminant concentration. CORMIX3 is particularly suited

for the analysis of thermal discharges and includes a wind speed-dependent atmospheric heat exchange

formulation. From nine possible classes the decision support system implemented in CORMIX3 can

select the solution class most representative of the specified situation and provide graphical and tabular

summaries of the solution results for dilution and near-field mixing zone definition. The major advantage

of CORMIX3 is its ease of use. The primary disadvantages or limitations of CORMIX3 are similar to
Predictive Methods Currently Used in the BEACH Program I.3-13

those of the other two CORMIX submodels and are related to the use of idealized receiving water

geometry and spatially and temporally uniform ambient conditions.

All three of the CORMIX models include limited far-field capabilities, in that when the discharge

plume mixing characteristics are no longer described by the appropriate solution classes, additional

mixing and dilution can occur by user-specified ambient turbulent diffusion in the horizontal and vertical

as appropriate.

The utility program CORJET complements the CORMIX1 and CORMIX2 submodels, allowing the

user to simulate greater details of the discharge's dynamic behavior in the immediate vicinity of the

discharge point. CORJET allows arbitrary specification of vertical variations in ambient current speed

and ambient density. However, since CORJET is designed to investigate the immediate vicinity of the

discharge, the ambient environment is assumed to be of infinite extent in the horizontal and of constant

depth. For single-port discharges, CORJET can be used to investigate anomalous discharges and ambient

conditions identified by the CORMIX1 decision support system and as well as the effects of the

idealizations inherent in ORMIX1’s representation of ambient current and stratification conditions. For

multiple port discharges, CORJET can be used to analyze the discharge and mixing characteristics of

individual ports and to verify the appropriateness of CORMIX2’s representation of multi-port discharges

by line jets. The utility program FFLOATR uses a superposition procedure to extend the results of

CORMIX submodels to more complex flow environments such as meandering rivers or sloping beaches.

The CORMIX system is characterized by a user-friendly interface and a variety of output options

including graphical display. The user interface allows the CORMIX system to be efficiently used by

relatively inexperienced users, with the built-in decision support capability providing ample warnings if

further detailed analysis or interpretation is required. The version 3 user’s manual (Jirka et al., 1996)

includes a variety of documented examples for each submodel and appropriate references to research

reports and papers providing greater technical details regarding the submodel’s underlying formulations.

The major limitations of the CORMIX submodels are related to the use of idealized representations

of ambient geometry, currents, and stratification. To further illustrate these limitations, consider a

submerged single- or multi-port discharge offshore from a recreational beach and the necessity of

predicting the risk of bathers being exposed to contaminants issuing from the discharge. Under such

conditions, the ambient flow could include tidal, wind- and wave-driven components having significant

horizontal and vertical variations, and not aligned parallel to the shoreline in contrast to CORMIX's

idealization of a spatially uniform, shore-parallel current. Current magnitudes and direction could also

change significantly over the course of a few hours due to tidal phase, sea breeze effects, and incident

wave direction changes in contrast to CORMIX’s assumption of steady current. The nearshore

bathymetry profile would likely be characterized by increasing depth in the offshore direction, but could

include isolated features such as bars and depressions in contrast to CORMIX’s spatially uniform

bathymetry. A continuous discharge would also contribute to the establishment of time and spatial

variations in ambient contaminant concentrations in contrast to CORMIX’s assumption of no existing

ambient contaminant levels. Under these realistic conditions, the use of the CORMIX models to

determine probable distributions of discharged contaminants is subject to a high degree of uncertainty

and would at best require that a user having considerable knowledge of nearshore dynamics evaluate a

variety of conditions to estimate the risk of exposure.

Input data required to use CORMIX are divided into four groups, which include ambient data,

discharge port data, effluent characteristics, and mixing zone data.

Ambient data

C Water body depth (meters)

C Water body depth at discharge (meters)

C Ambient flow rate if steady (cubic meters/second)

C Water body width if bounded (meters)
I.3-14 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

C Tidal period (hours)

C Maximum tidal velocity (meters/second)

C Manning’s n or Darcy-Weisback f

C Wind speed (meters/second)

C Density of water body (fresh or marine water)

C Units of density

C Stratification data: pycnocline height (meters)

C Density/temp at surface

C Density/temp at bottom

Discharge data

C Single port discharge: CORMIX1

- Location of nearest bank

- Distance to nearest bank (meters)

- Vertical angle (degrees)

- Horizontal angle (degrees)

- Port diameter (meters)

- Port height (meters)

- Port area (square meters)

C Submerged multi-port discharge: CORMIX2

- Nearest bank orientation

- Distance to endpoints

- Diffuser length

- Total number of openings

- Port diameter

- Port height

- Concentration ratio

- Diffuser arrangement type

- Alignment angle

- Horizontal angle

- Vertical angle

- Relative orientation

C Buoyant surface discharge: CORMIX3

- Discharge location

- Discharge configuration

- Horizontal angle

- Distance form bank

- Depth at discharge

- Bottom slope

- Discharge width and channel depth if rectangular

- Discharge diameter and bottom invert pie if circular
Predictive Methods Currently Used in the BEACH Program I.3-15

Effluent Characteristics

C Flow rate (cubic meters/second)

C Effluent velocity (meters/second)

C Effluent temperature (oC)

C Heat loss coefficient in cases of heated discharge

C Effluent concentration and units

C Decay rate coefficient in case of nonconservative substances

Mixing zone data

C Value of water quality standard

C Toxicity of pollutant

C CMC and CCC for toxic pollutants

C Distance, width, or area of mixing zone in case specified.

The state of Washington’s Department of Health, Shellfish Program, is currently using the

CORMIX model to estimate shellfish closure areas around wastewater treatment plant outfalls

(Meriwether, 1998). Shellfish harvesting is banned in water bodies that contains fecal coliform bacteria

equal to or greater than 14 organisms/100 mL. This criterion is used to delineate the boundaries of the

closure area. In addition, the Washington State Department of Health uses a conservative approach in

estimating the closure zone by setting the input parameters to reflect maximum fecal coliform

concentration in the plume. The assumptions used in estimation of the closure zone include the

following:

C Use of the larger of treatment plant average monthly flow or design flow.

C Use of maximum fecal coliform concentration. This can be obtained from sampling the

wastewater treatment plant effluent prior to disinfection, or from plant DMR.

C No fecal coliform decay.

C Use of ambient adverse water conditions, usually based on the winter season conditions, since

most treatment plant upsets occur during the winter months.

3.3.4.2 PLUMES

PLUMES (Baumgartner et al., 1994) is a microcomputer-based hydrodynamic mixing zone

modeling system that has many functional features similar to those of the CORMIX system. The

PLUMES system has five main components: the PLUMES interface, which allows interactive

construction of input files describing the discharge and ambient conditions and visualization of model

predictions; the RSB model, which is appropriate for submerged multi-port discharges, and the UM

model, which is appropriate for both single- and multi-port submerged discharges; a far-field mixing

modeling that automatically extends the dilution calculates of RSB and UM; and a discharge

classification system based on the CORMIX1 and CORMIX2 classification systems. From a functional

perspective, the UM model includes capabilities similar to those of CORMIX1 and CORMIX2, whereas

the RSB model is similar to CORMIX2. For multi-port discharges, the RSB and UM model complement

each other in that their theoretical bases and formulations represent two entirely different approaches to

near-field mixing zone analysis.

The RSB and UM models in the PLUMES system use identical input data to define discharge and

ambient conditions. The ambient flow environment is assumed to be of infinite horizontal extent and

constant depth. The ambient current is steady and unidirectional but can vary arbitrarily in the vertical

over the depth of the water column. The ambient density and background contaminant concentrations are

steady but can vary arbitrarily in the vertical. The assumption of infinite or unbounded conditions in the

horizontal generally precludes the use of these models for rivers and narrow estuaries. The discharge is
I.3-16 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

specified by its position and orientation (angles with respect to the horizontal and vertical), volume flow

rate, and number of ports and port spacing, and the density of the discharged water and its contaminant

concentration. The UM model also allows inclusion of ambient concentration levels of discharged

contaminants.

The RSB (Roberts, Snyder and Baumgartner, 1989a, 1989b, 1989c) model is a semi-empirical

model based on the principles of dynamic similitude and dimensional analysis applied to an extensive set

of laboratory and field observations of multi-port discharge behavior. Input parameters specifying

discharge and ambient conditions are used to calculate a set of dimensionaless parameters, which are in

turn used to evaluate observational data correlation functions and predicted plume trajectory and dilution

characteristics. The RSB model is recommended primarily for large-volume freshwater discharges into

marine environments.

The UM model is the latest in a family of plume models that includes OUTPLM, MERGE,

UOUTPLM, and UMERGE. The UM model is based on a Lagrangian formulation incorporating the

projected area entrainment hypothesis. The UM model is applicable to both single- and multi-port

discharges. For multi-port discharges, UM simulates each individual port discharge and merging of

plumes from adjacent discharges. In contrast to CORMIX1, CORMIX2, and RSB, the UM model can be

executed in batch mode with multiple input files to facilitate the automatic analysis of mixing and

dilution under a range of ambient conditions. The UM model is appropriate for both marine and

freshwater environments, but due to its infinite horizontal extent formulation it is not recommended for

narrow rivers and estuaries.

The PLUMES system is characterized by a graphical user interface and a variety of output options

including graphical display. The inclusion of the CORMIX classification system allows diagnostics of

unusual plume conditions to be made and provides a means of cross checking PLUMES analyses using

the CORMIX system. The user’s manual includes background information, a tutorial on use of the

interfaces, and a variety of documented examples for each submodel, as well as appropriate references to

research reports and papers providing greater technical details regarding the submodels’ underlying

formulations. The major limitations of the PLUMES system are similar to those previously discussed for

the CORMIX system and are related to the use of idealized representations of ambient geometry,

currents, and stratification. A notable exception is the UM model’s arbitrary specification of vertically

varying current and density. However, the UM assumption of constant depth and infinite horizontal

extent limits its applications to discharges not influenced by shorelines. The batch mode capability of

UM does allow for investigation of a range of ambient conditions for a specific discharge in a more

efficient manner than does the CORMIX system.

The data required to use PLUMES are listed below:

Ambient data

C Water body depth

C Far-field distance

C Far-field increment

C Current speed

C Density

C Salinity

C Temperature

C Ambient concentration

C Farfield dispersion coefficient 

C Average current speed in the farfield
Predictive Methods Currently Used in the BEACH Program I.3-17

Outfall structure

C Total diffuser flow

C Number of ports in the diffuser

C Spacing between ports

C Port depth

C Port diameter

C Port elevation

C Vertical angle

C Contraction coefficient cell

C Horizontal diffuser angle

Effluent characteristics

C Effluent density

C Pollutant concentration

C Effluent salinity

C Effluent temperature

C First-order decay coefficient

The Rhode Island Department of Environmental Management (RIDEM) has used the PLUMES

model to predict water quality conditions surrounding wastewater treatment plant outfalls (Goblick,

1995). In particular, RIDEM was interested in protecting shellfish growing areas in Narragansett Bay

from the impacts of a potential 6-hour failure in the chlorination process at wastewater treatment plants.

RIDEM needed to delineate buffer zones for the 12 wastewater treatment plants discharging into the bay

within a short duration of time and thus chose PLUMES to determine initial buffer zones. RIDEM has

since begun to refine the initial buffer zones through dye dilution studies of dye-tagged effluent.

PLUMES was set up to provide the most conservative estimate of pathogen concentrations by

simulating chlorination failure occurring during the start of the ebb tide. Minimum mixing due to

dispersion occurs during this stage because of the minimum velocity during the flood tide. The tidal

current will sweep the plume the longest distance from the outfall beyond the near-field initial dilution.

The actual input parameters used in PLUMES were obtained from Rhode Island Pollutant

Discharge Elimination System Permits, operation and maintenance records of wastewater treatment

plants, and tidal charts. In addition, RIDEM developed Assessment of Analytical Model PLUMES for

Sizing Prohibitive Shellfish Closure Zones - A Technical Guidance Manual, which includes a detailed

sensitivity analysis of model parameters, application and limitations of the model, two case studies

comparing modeling and dye study results, and derivation of wastefield width equations that were later

incorporated into the model (Goblick, 1995).

3.3.4.3 JPEFDC Model

The JPEFDC (Jet, Plume-EFDC) is a buoyant jet near-field dilution and mixing zone submodel

(Hamrick, 1998) that is incorporated directly into the EFDC (Environmental Fluid Dynamic Code) threedimensional hydrodynamic and transport model (Hamrick and Wu, 1997). The JPEFDC model simulates

single-port and merging multi-port discharges using a three-dimensional extension of the Lagrangian

formulations used in the UM model (Baumgartner et al., 1994) and the JETLAG model (Lee and Cheung,

1990). The JPEFDC model is unique in its use of unsteady, fully three-dimensional ambient velocity

density and concentration fields and realistic bathymetry for trajectory, entrainment, and dilution

calculations. (See Table 3.2.) For multi-port discharges, the merging of individual port plumes into

multiple coalesced plumes is simulated. In addition to simulating the near-field and far-field 
I.3-18 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

Table 3.2 Comparison of CORMIX and PLUMES submodels and JPEFDC model

Model Discharge Ambient Geometry Ambient Current

Ambient Density

and Concentration

CORMIX1 single submerged rectangular channel constant piecewise linear

CORMIX2 multi-port

submerged

rectangular channel constant piecewise linear

CORMIX3 surface rectangular channel constant constant

UM single or multi-port

submerged

infinite, constant

depth

arbitrary vertical

variations

arbitrary vertical

variations

RSB multi-port

submerged

infinite constant

depth

arbitrary vertical

variations

arbitrary vertical

variations

JPEFDC single submerged

(can be extended

to multiple

submerged)

arbitrary depth

variations and lateral

boundaries

arbitrary 3-D

variations and timevariable

arbitrary 3-D

variations and timevariable

concentration of dissolved contaminants, EFDC and JPEFDC simulate sediment transport and the

transport and fate of sorptive contaminants, including the settling and bed exchange of the suspended and

sorbed material.

Operating in the EFDC model, JPEFDC automatically updates multiple outfalls as ambient

conditions and outfall discharges evolve in time. The near-field JPEFDC solution is automatically

coupled to the EFDC model's far-field transport and fate simulation, allowing the ambient concentration

field to represent the historical influences of all unsteady discharges.

The embedded JPEFDC near-field dilution model is currently being used to determine sediment

contamination zones in the vicinity of wastewater treatment plant and combined sewer overflow

discharges in Elliott Bay and Duwamish River, Washington. In this application, 15 unsteady outfalls are

simulated at hourly intervals for one year to determine probability of exposure statistics. An attempt to do

this type of analysis for 15 discharges over the course of one day would require 360 CORMIX

simulations. The EFDC-JPEFDC model is particularly well suited to near-shore coastal simulations. The

hydrodynamic component of the EFDC model is capable of simulating unsteady tide-, wind- and wavedriven currents including long-shore and across-shore currents associated with incident wave

transformation and surf zone wave breaking. A graphical user interface and automated input file creation

system are being developed for the EFDC model, including the JPEFDC submodel.
Review of Applicability and Key Characteristics I.4-1

Washington State

CORMIX

Rhode Island

PLUMES

Mixing Zone Models 

New York-New Jersey

Bypass Model

Virginia 

Simple Mixing and Transport

Deterministic Models

Stamford, CT Milwaukee Delaware

Regression Analysis

Simple Methods

Predictive Tools Currently in Use

Figure 4.1 Summary of pathogen predictive tools currently in use

4. Review of Applicability and Key Characteristics

4.1 Evaluation of Models Currently Applied to Beach Advisory or Closure

The review of current beach closure techniques identified a wide variety of model types,

complexities, and application protocols used across the country. The various model types employed can

be summarized as shown in Figure 4.1. The models employed can be grouped into two broad

categories— simple methods and deterministic models. Simple methods, as discussed previously, use

statistical analysis to build relationships between indicators and closure actions. Deterministic models

include a range of simple to complex modeling techniques. The range of model complexity can be

evaluated based on the analytical framework, the calibration requirements, and the degree to which

variability (i.e., dynamic loadings) is incorporated.

Several observations can be made regarding the choice of modeling tools currently used in beach

advisories. One of the essential features is the short time frame under which decisions must be made.

Real- time data collection efforts and meteorologic and/or pathogen sampling are usually the basis for

supporting beach and shellfish closure decisions. Regardless of the computational or technical complexity

of the approach, the application to day-to-day decision making must be very quick. The use of models to

support advisories must therefore be adapted to this quick-turnaround requirement. Simple models are

most often used because of the development procedures required and the relative ease of use. Mid-range

or complex models are also used, although more typically in the development of decision rules, not for

real-time application. The New York/New Jersey Harbor study is an illustration of the use of a complex

model in the development of closure rules.
I.4-2 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

4.2 Criteria for the Evaluation of Potential Models

Selection of the appropriate model for beach advisories will depend on the site conditions of the

water body of concern. Some of the site-specific considerations include the types of sources (point

source/nonpoint source), water body types, transport and circulation patterns, severity of impairment, and

frequency of exceedances. Other issues to consider are the modeling development and application cost,

the accuracy required, the use of the system, training of staff, and public outreach and education

requirements. Economies of scale may be identified when related analysis and modeling efforts have

been initiated in the water body of concern. In some cases multiple models may be needed to address the

various components of the system.

A wide range of models are available and could be adapted to support beach advisory decisions.

Clearly, simple models for dilution and mixing zone evaluations are candidates for such analyses. More

complex models should also be considered in light of their ability to assess dynamic loading and transport

processes. Detailed models can be used in the development of a range of decision rules for categories of

loading or environmental conditions. These decision rules can be used to address day-to-day operations in

a cost-effective and timely manner.

In some cases objectives can best be met by using one model, and in others a combination of

models is needed. Factors such as data needs, application cost, pollutant type, and required accuracy are

important when considering the type of model to use. The selection of the appropriate model can be

based on the following screening factors:

C Combined point and nonpoint sources. Combined point and nonpoint sources criteria relate to

how the model handles the loadings from point and nonpoint sources. Models based on water

quality data implicitly take into account the point and nonpoint sources, whereas models that use

continuous simulation of the water quality directly account for the sources. Typically, the

sources are part of the input parameters. For example, the rainfall-based alert curves discussed

earlier are models based on the water quality conditions. Those models do not explicitly account

for the point and nonpoint sources. Instead, the sources affect the water quality parameters used

in the models. In the case of the CORMIX and PLUME models, point sources are a component

of the model input; the flow and concentration must be included.

C Pathogen source characterization. Pathogens found at a beach site of interest might be from

point sources (e.g., wastewater treatment plants, combined sewer overflows, etc.) or nonpoint

sources. Accounting for the different sources of pathogens requires the use and integration of a

variety of models. Once pathogen loads from point and nonpoint sources are determined, the

next step is the routing of the pathogen through the system using a representative model of the

dominant mixing and transport processes to estimate the pathogen concentration at the location

of interest.

C Dominant mixing and transport processes. The water body type dictates the dominant mixing

and transport processes of a pollutant. In rivers and streams, the dominant processes are

advection and dispersion. In estuaries, these processes are influenced by tidal cycles and flows.

Factors such as water body size and net freshwater flow are key in determining the dominant

processes.

C Pathogen concentration prediction. This criterion evaluates the ability of the model to predict

the likelihood that the pathogen concentration will exceed the action level in the receiving water

at the location of interest, which in this case is a beach site. Transformation processes such as

bacterial kinetics must also be accounted for in the model to allow for a realistic prediction.

C Real-time analysis, decision making, and guideline development. Real-time analysis is needed

for timely closure. Models applied to predict water quality conditions can be used as a basis for

decision making and as management tools. For example, beach authorities can use such tools as

a basis for beach advisories following a rainfall event or accidental sewage spills.
Review of Applicability and Key Characteristics I.4-3

C Real-time use. Under this category the input data needed, processing time, and post processing

abilities of the model are evaluated. Potential predictive tools for beach closure are required to

predict pathogen concentration at the site of interest in a relatively short amount of time. This

means that the data input requirements and processing time must be maintained to a minimum.

Also crucial to the success of the predictive tool is the postprocessing of the output data. Tabular

or graphical representation of the output data provides a quick and easy way to interpret the

results and provides a basis for the decision making concerning beach closure.

C Evaluation of unplanned and localized spills. Spills or bypassing of a pollutant can be accidental

due to equipment failure or rainfall. In either case, this criterion evaluates how the model

handles the additional loading. Models that are based on water quality data do not account for

this increased loading unless samples were collected during the rainfall or spill event, samples

were analyzed, and data were entered into the model database. On the other hand, models that

account for point sources can easily account for the increased loading by including the spill as an

input parameter.

C Documented application to beach and shellfish closure. This criterion evaluates the applicability

of the model to predict the water quality condition surrounding swimming and shellfish areas.

Models can be used as water quality predictive tools and as a basis for decision making. In the

previous section, rainfall models and the Regional Bypass model were shown to be effective

tools to protect people from exposure to pathogens following rainfall events or sewage spills.

C Ease of use. The level of user experience is an important factor in determining whether a model

is easy to use. Some complex models require a great deal of training and experience; simple

methods require only a conceptual understanding of the processes.

C Input data requirements. Input data requirements are a function of a model’s complexity. In

general, complex models require more specific and complex input data than simple models.

Some of these data might not be readily available, and acquiring such data might require

expenditure of resources. Therefore, the objective of the model application can be very

important in this step to eliminate unnecessary expenditure of time resources.

C Calibration requirements. Decision making and management alternatives based on modeling

results require that the model outcome be acceptable and reliable. Not all models can be

calibrated. Models that simulate water quality conditions are calibrated against instream

monitoring stations. Simple models such as the rainfall alert curves should be continuously

updated to provide accurate results by continuously updating the model’s database.

C Pollutant routing. Pollutant routing addresses how a model deals with the fate and transport of

pollutants. Simpler models may not include processes that describe pollutant transformation.

More complex models vary in their description of the processes. The range can be from a gross

or a net estimate of the process to a detailed mechanism of the process. The focus is on bacterial

processes. In general, most environmental models use the first-order decay rate to represent the

microbial death rate.

C Kinetics of pathogen decay. The survival of pathogens in the environment is influenced by many

variables such as age of the fecal deposit, temperature, sunlight, pH, soil type, salinity, and

moisture conditions. In general, the death rate of pathogens can be estimated as a first-order rate,

which is incorporated into water quality models.

An evaluation of the models discussed in Section 3 based on the criteria above is summarized in

Table 4.1.
I.4-4 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

Table 4.1 Evaluation of model capabilities and applicability

Model

Combined

PS/NPS

Real Time

and

Decision

Making Spills

Application

to Beach or

Shellfish

Closure

Ease

of Use

Input Data

Requirements Calib.

Developing

Guidelines

Pollutant

Routing

RainfallBased Alert

Curves

x x x x x x 0 x x x x x x x x x x x 0

Bypass

Model

x (PS) x x x x x x x x x x x x x x x x x x x x x

SMTM x (PS) x x x x x x x x x x 0 x

PLUMES x (PS) x x x x x x x x x x x

CORMIX x (PS) x x x x x x x x x

JPEFDC x x

(NPS/PS)

x x x x x x x x x x x x x x x x x

0 Not applicable

x Low

x x Medium

x x x High
Introduction II.1-1

Part II. Review of Other Potential Modeling Tools

Available for Beach Advisory or Closure

1. Introduction

The identified beach closure predictive tools were characterized based on their modeling or

prediction application techniques and their modeling components. Rainfall-based alert curves, which are

based on regression analysis, are simple, reliable tools that have been used in Milwaukee, Wisconsin,

Stamford, Connecticut, and Delaware. Computer models that predict pathogen concentration by

simulating the dominant mixing and transport processes in the receiving water range from simple to very

complex. A simple mixing and transport model is used by the Virginia Department of Health to predict

water quality conditions surrounding wastewater treatment plant outfalls. More complex models such as

CORMIX and PLUMES are used by the states of Washington and Rhode Island, respectively, to predict

water quality conditions surrounding wastewater treatment outfalls. Part I of this report provided a

detailed description of these tools and their attributes, limitations, data requirements, and availability.

The objective of Part II of this document is to identify water quality predictive tools that could be applied

to beach advisories but are not currently in use by local agencies.

A description of the general process of modeling pathogens is presented in Figure 1.1. The first

component in the process is characterizing the point and nonpoint sources of pathogens and establishing

the loading rates. The second component is estimating the dominant fate and transport processes to

estimate the pathogen distribution. The third component is interpreting the model output to find the

pathogen concentration at the point of interest to determine the need for a beach advisory. This

determination can be accomplished by comparing the model results with a preestablished action level,

such as the state water quality standard for primary contact recreation. If the predicted pathogen

concentration exceeds the action level, a beach advisory is issued. The advisory period depends on the

length of time it takes for the pathogen levels to return to less than the established action level.
II.1-2 Review of Potential Modeling Tools and Approaches to Support the BEACH Program

Nonpoint Sources Point Sources

Pathogen Loading

Source Characterization

Fate and Transport

Rivers and Streams Estuaries and Coastal

Areas

Pathogen Concentration Prediction

Pathogen Loading Estimates

Concentration Predictions

Develop Beach Advisory

Decision Rules and Guidelines

Figure 1.1 Components of pathogen modeling
Potential Models II.2-1

Watershed-Scale

Loading Models

Components Potential Models

• HSPF

• SWMM

• STORM

Receiving Water

Models

Stream (Run of the river):

• CE-QUAL-RIV1

• QUAL-2E

• HSPF

Lake-Estuary:

• CE-QUAL-ICM

• CE-QUAL-W2

• WASP

• TPM

• EFDC

Figure 2.1 Potential predictive tools applicable to pathogens

2. Potential Models

The results of searching the literature and consulting with experts in the modeling field indicate that

potential models for use as predictive tools to determine the need for beach advisories include the

following modules:

C Pathogen loadings from point and nonpoint sources

C Pathogen fate and transport

Figure 2.1 shows potential predictive tools that can be used to determine the need for beach

advisories. The listed models were divided into two categories: watershed-scale loading models and

receiving water models. The latter category was divided into two additional groups to reflect the water

body types—rivers and streams, and lakes and estuaries.
II.2-2 Review of Potential Modeling Tools and Approaches to Support the Beach Program

2.1 Watershed-scale Loading Estimates

Potential watershed loading models that can be used to estimate pathogen loadings to receiving

waters are presented in Table 2.1. Three considerations are taken into account in Table 2.1—real-time

prediction, source types, and land use types.

Simulating the generation and movement of water and its pathogen content from the source area to

the receiving water can be a continuous or single-event simulation. Single-event simulation requires

defining and characterizing the antecedent moisture content before each simulation. Continuous

simulation, on the other hand, provides a long time series of water and pathogen loading but requires a lot

of input data and a long run time (USEPA, 1997).

The sources of pathogens can be point sources, nonpoint sources, and combined sewer overflows

(CSOs). Models differ in their ability to account for these various source types. Models that simulate

nonpoint sources are capable of describing the pathogen buildup processes during dry weather and

washoff processes related to rainfall-generated runoff. Accounting for the various land uses is very

important in estimating the nonpoint source loadings since the processes of buildup and washoff are land

use-specific. In addition to land use, CSO loading is a function of the hydraulic routing and the facility’s

storage capacity and operations. Therefore, the model’s ability to deal with the complex land uses in the

watershed is an important factor in model selection and applicability. Key loading models suited for realtime prediction are briefly described below.

HSPF: Hydrological Simulation Program-Fortran. HSPF is a comprehensive watershed-scale

model developed by EPA. The model uses continuous simulation of water balance and pollutant buildup

and washoff processes to generate time series of runoff flow rate, as well as pollutant concentration at

any given point in the watershed. Runoff from both urban and rural areas can be simulated using HSPF;

however, simulation of CSOs is not possible. Because of the model’s comprehensive nature, data

requirements for HSPF are extensive and the model requires highly trained personnel (USEPA, 1997).

The HSPF model has been integrated into BASINS as the Nonpoint Source Model, or NPSM (see

Appendix B).

SWMM: Storm Water Management Model. SWMM is a comprehensive watershed-scale model

developed by EPA. It can be used to model several types of pollutants on either a continuous or storm

event basis. Simulation of mixed land uses is possible using SWMM, but the model’s capabilities are

limited for rural areas. Loadings from CSOs can be simulated using SWMM. The model requires

intensive data input and requires a special effort for validation and calibration. The model output is time

series of flow, storage, and contaminant concentration at any point in the watershed (USEPA, 1997).

Table 2.1 Watershed-scale loading models

Model

Type Model Name

Real-time Prediction Source Type Land Use Type

Data Needs

Processing

Time PS NPS CSO Urban Rural

Watershedscale

loading

HSPF: Hydrological

Simulation Program-Fortran

x x xx x 0 x xxx

SWMM: Storm Water

Management Model

x x x x xx xx x

STORM: Storage,

Treatment, Overflow, Runoff

Model

x x x x x xx 0

0 Not applicable

x Low

xx Medium

xxx High
Potential Models II.2-3

STORM: Storage, Treatment, Overflow, Runoff Model. STORM is a watershed loading model

that was developed by the US Army Corps of Engineers for continuous simulation of runoff quantity and

quality. STORM was primarily designed for modeling storm water runoff from urban areas, but it can

also simulate combined sewer systems. It requires relatively moderate to high calibration and input data.

The simulation output is hourly hydrographs and pollutographs (USEPA, 1997).

2.2 Receiving Water Models

Loading models, depending on the simulation type, provide estimates of either the total water and

pollutant loading or a time series loading of water and pollutants. Pathogen concentration prediction is

the process of describing the response of the water body to pollutant loadings, flows, and ambient

conditions. Since the response is specific to the water body type, different types of models are required

for accurate simulation, as shown in Table 2.2. The models were divided into two categories—rivers and

streams, and lakes and estuaries. The dominant processes for both types are briefly described here.

2.2.1 River and Streams

Prediction of pathogen concentration in rivers and streams is dominated by the processes of

advection and dispersion and the death rate. One-, two-, and three-dimensional models have been

developed to describe these processes, as shown in Table 2.2. Water body type and data availability are

the two most important factors that determine model applicability. For most small and shallow rivers,

one-dimensional models are sufficient to simulate the water body response to pathogen loading.

However, for large and deep rivers and streams, the one-dimensional approach falls short of describing 

Table 2.2 Potential pathogen fate and transport models

Model Name

Real-Time Prediction Water type

Data Needs

Processing

Time Rivers

Lakes &

Estuaries

HSPF: Hydrological Simulation ProgramFortranxx x x N/A

CE-QUAL-RIV1: Hydrodynamic and Water

Quality Model for Streams

xx xx x N/A

CE-QUAL-ICM: A Three-Dimensional TimeVariable Integrated-Compartment

Eutrophication Model

xxx xxx x xx

CE-QUAL-W2: A Two-Dimensional, Laterally

Averaged Hydrodynamic and Water Quality

Model

xxx xx xx x

WASP5: Water Quality Analysis Simulation

Program

xx xx xx xx

EFDC: Environmental Fluid Dynamics

Computer Code

xx xx xx xx

QUAL2E: The Enhanced Stream Water

Quality Model

x x x N/A

TPM: Tidal Prism Model x x N/A x

x Low

xx Medium

xxx High
II.2-4 Review of Potential Modeling Tools and Approaches to Support the Beach Program

the processes of advection and dispersion. Assumptions that the pathogen concentration is uniform both

vertically and laterally are no longer valid. In such cases two- or three-dimensional models that include a

description of the hydrodynamics are used.

HSPF: Hydrological Simulation Program-Fortran. HSPF is a comprehensive watershed-scale

model developed by EPA. The receiving water component allows dynamic simulation of onedimensional stream channels, with several hydrodynamic routing options available. The model output is

time series of runoff flow rate, as well as pollutant concentration at any given point in the watershed.

Because of the model’s comprehensive nature, data requirements for HSPF are extensive and the model

requires highly trained personnel (USEPA, 1997).

CE-QUAL-RIV1: Hydrodynamic and Water Quality Model for Streams. CE-QUAL-RIV1 is a

dynamic, one-dimensional model for rivers and estuaries. The model consists of two codes—one for

hydraulic routing and the other for dynamic water quality simulation. CE-QUAL-RIV1 allows simulation

of unsteady flow of branched river systems. The input data requirements include the river geometry,

boundary conditions, initial instream and inflow boundary water quality concentrations, and

meteorological data. The model predicts time-varying concentration of water quality constituents

(USEPA, 1997).

2.2.2 Lakes and Estuaries

Predicting the response of lakes and estuaries to pathogen loading requires an understanding of

hydrodynamic processes. Shallow lakes can be simulated as a simplified, completely mixed system with

an inflow stream and outflow stream. However, simulating deep lakes with multiple inflows and outflows

that are affected by tidal cycles is not a simple task. Pathogen concentration prediction is dominated by

the processes of advection and dispersion, but these processes are affected by the tidal flow. The size of

the lake or the estuary, the net freshwater flow, and wind conditions are some of the factors that

determine the applicability of the models (USEPA, 1997).

CE-QUAL-ICM: A Three-Dimensional Time-Variable Integrated-Compartment

Eutrophication Model. CE-QUAL-ICM is a dynamic water quality model that can be applied to most

water bodies in one, two, or three dimensions. The model can be coupled with three-dimensional

hydrodynamic and benthic-sediment model components. CE-QUAL-ICM predicts time-varying

concentrations of water quality constituents. The input requirements for the model include 140

parameters to specify the kinetic interactions, initial and boundary conditions, and geometric data to

define the waterbody to be simulated. Model use may require significant expertise in aquatic biology and

chemistry (USEPA, 1997).

CE-QUAL-W2: A Two-Dimensional, Laterally Averaged Hydrodynamic and Water Quality

Model. CE-QUAL-W2 is a hydrodynamic water quality model that can be applied to most water bodies

in one dimension or laterally averaged in two dimensions. The model is suited for simulating long and

narrow water bodies like reservoirs and long estuaries where stratification may occur. The model

application is flexible since the constituents are arranged in four levels of complexity. Also, the water

quality and hydrodynamic routines are directly coupled, which allows for more frequent updating of the

water quality routines. This can reduce the computational burden for complex systems. The input

requirements for CE-QUAL-W2 include geometric data to define the water body, specific initial

boundary conditions, and specification of approximately 60 coefficients for the simulation of water

quality (USEPA, 1997).

WASP5: Water Quality Analysis Simulation Program. WASP5 is a general-purpose modeling

system for assessing the fate and transport of pollutants in surface water. The model can be applied in

one, two, or three dimensions and can be linked to other hydrodynamic models. WASP5 simulates the

time- varying processes of advection and dispersion while considering point and nonpoint source loadings

and boundary exchange. The water body to be simulated is divided into a series of completely mixed
Potential Models II.2-5

segments, and the loads, boundary concentrations, and initial concentrations must be specified for each

state variable (USEPA, 1997).

EFDC: Environmental Fluid Dynamics Computer Code. EFDC is a general three-dimensional

hydrodynamic model developed by Hamrick (1992). EFDC is applicable to rivers, lakes, reservoirs,

estuaries, wetlands, and coastal regions where complex water circulation, mixing, and transport

conditions exist. EFDC must be linked to water quality models to predict the receiving water quality

conditions. HEM-3D is a three-dimensional hydrodynamic eutrophication model that was developed by

integrating EFDC with a water quality model. Considerable technical expertise in hydrodynamics and

eutrophication processes is required to use the EFDC model (USEPA, 1997).

QUAL2E: The Enhanced Stream Water Quality Model. QUAL2E is a steady-state receiving

water model. The basic equation used in QUAL2E is the one-dimensional advective-dispersive mass

transport equation. Although the model assumes a steady-state flow, it allows simulation of diel

variations in meteorological inputs. QUAL2E input requirements include the stream reach physical

representation and the chemical and biological properties for each reach (USEPA, 1997). QUAL2E has

been fully integrated into BASINS (see Appendix B).

TPM: Tidal Prism Model. TPM is a steady-state receiving water quality model applicable only to

small coastal basins. In such locations the mixing and transport of pollutants are dominated by the tidal

cycles. The model assumes that the tide rises and falls simultaneously throughout the water body and that

the system is in hydrodynamic equilibrium. Two types of input data are required to run TPM. The

geometric data that define the system being simulated include the returning ratio, initial concentration,

and boundary conditions. The physical data required include water temperature, reaction rate, point and

nonpoint sources, and initial boundary conditions for water quality parameters modeled (USEPA, 1997).
References R-1

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