U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2013–5166

Coastal and Marine Geology Program 

Prepared in cooperation with the U.S. Environmental Protection Agency, 

Great Lakes Restoration Initiative

Developing and Implementing Predictive Models for 

Estimating Recreational Water Quality at 

Great Lakes Beaches 
Cover: Ontario Beach in Rochester, New York. (Photograph by Brett Hayhurst, USGS).
Developing and Implementing Predictive 

Models for Estimating Recreational Water 

Quality at Great Lakes Beaches 

By Donna S. Francy, Amie M.G. Brady, Rebecca B. Carvin, Steven R. Corsi, 

Lori M. Fuller, John H. Harrison, Brett A. Hayhurst, Jeremiah Lant, 

Meredith B. Nevers, Paul J. Terrio, and Tammy M. Zimmerman

Coastal and Marine Geology Program 

Prepared in cooperation with the U.S. Environmental Protection Agency, 

Great Lakes Restoration Initiative

Scientific Investigations Report 2013–5166

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Francy, D.S., Brady, A.M.G., Carvin, R.B., Corsi, S.R., Fuller, L.M., Harrison, J.H., Hayhurst, B.A., Lant, J., Nevers, M.B., 

Terrio, P.J., and Zimmerman, T.M., 2013, Developing and implementing predictive models for estimating recreational 

water quality at Great Lakes beaches: U.S. Geological Survey Scientific Investigations Report 2013–5166, 68 p., 

http://dx.doi.org/10.3133/sir20135166/.

 ISSN 2328-031X (print)

 ISSN 2328-0328 (online)

 ISBN 978-1-4113-3715-2
iii

Acknowledgments

The authors acknowledge assistance from many individuals and agencies in collecting data and 

developing and testing models.

In Ohio, thanks are extended to Mark Citriglia, Jill Novak, Nicole Schafer, and Kristen Greenwood of the Northeast Ohio Regional Sewer District; Barry Grisez, Becky Gawelek, and Jill Lis of 

the Cuyahoga County Board of Health; Pam Struffolino of the University of Toledo; Bob England 

and Craig Ward of the Erie County Health Department; and Susan Bell, Dan Sinclair, and Laura 

Kuns of the Lake County General Health District. 

In New York, thanks go to Dave Eckhardt of the U.S. Geological Survey; Eric Wiegert of the New 

York State Department of Health; Karen Terbush, Kristen Husson, Lauren Jorgensen, and Evyn 

Costanzo of the New York State Office of Parks, Recreation, and Historic Preservation; Charles 

Knauf of the Monroe County Health Department; and Scott Zimmerman of the Erie County 

Health Department.

In Pennsylvania, thanks go to Harry Leslie and Holly Best of the Pennsylvania Department 

of Conservation and Natural Resources at Presque Isle State Park; Jeanette Schnars of the 

Regional Science Consortium at the Tom Ridge Environmental Center at Presque Isle; and 

Andrew Glass and Karin Tobin of the Erie County Department of Health.

In Illinois and Indiana, we thank Katarzyna Przybyla-Kelly, U.S. Geological Survey; Cathy Breitenbach, Chicago Park District; Michelle Caldwell, Indiana Department of Environmental Management; and Carl Caneva, Evanston Health Department.

In Michigan, thanks are extended to Scott Hankus, Stacey McFarlane, and Laura Pobanz of the 

Macomb County Health Department.

Finally, for manuscript review, we thank John Wathen, U.S. Environmental Protection Agency; 

and Lisa Senior, U.S. Geological Survey. 

v

Contents

Abstract...........................................................................................................................................................1

Introduction ....................................................................................................................................................2

Model Definitions and Measures of Performance..........................................................................2

Existing Nowcast Systems..................................................................................................................2

Expanding Nowcasts............................................................................................................................3

Purpose and Scope ..............................................................................................................................3

Study Areas.....................................................................................................................................................4

Illinois—Evanston Beaches..............................................................................................................11

Illinois—Chicago Beaches ...............................................................................................................11

Indiana..................................................................................................................................................11

Michigan...............................................................................................................................................11

New York ..............................................................................................................................................11

Ohio .......................................................................................................................................................12

Pennsylvania........................................................................................................................................13

Wisconsin.............................................................................................................................................13

Methods.........................................................................................................................................................14

Recreational Water-Quality Standards and Criteria for Closings and Advisories...................14

Illinois...........................................................................................................................................14

Indiana.........................................................................................................................................15

Michigan......................................................................................................................................15

New York .....................................................................................................................................15

Ohio ..............................................................................................................................................15

Pennsylvania...............................................................................................................................15

Wisconsin....................................................................................................................................15

Data Collection and Sample Analysis .............................................................................................16

Field and Laboratory Measurements ....................................................................................16

Nearshore Buoys.......................................................................................................................16

Weather Stations and Other Equipment Installed by the U.S. Geological Survey..........17

Compilation of Data From Existing Sources ..................................................................................17

Weather Data From the National Weather Service.............................................................17

Stream Measurements From a Nearby U.S. Geological Survey Streamgage ................18

Measurements or Predictions From the National Oceanic and Atmospheric 

Administration...............................................................................................................18

Local Sources of Weather Data..............................................................................................18

Lake Level Spreadsheet—A Tool for Compiling Lake-Level Data ....................................18

PROCESSNOAA—A Tool for Compiling Weather Data ......................................................19

EnDDaT—A Tool for Compiling Data From Multiple Sources ............................................19

Development of Predictive Models .................................................................................................19

Data Management and Analysis .....................................................................................................20

Quality Assurance and Quality Control ...........................................................................................21
vi

Identification of Variables and Development of Predictive Models....................................................21

Identification of Variables Used to Improve Operational Nowcasts in Ohio and Wisconsin 25

Ohio ..............................................................................................................................................25

Wisconsin....................................................................................................................................29

Development of Predictive Models .................................................................................................29

Validation of Predictive Models and Implementation of Nowcast Systems......................................38

Illinois—Evanston Beaches..............................................................................................................38

Illinois—Chicago Beaches ...............................................................................................................38

Michigan...............................................................................................................................................39

New York ..............................................................................................................................................39

Ohio .......................................................................................................................................................39

Pennsylvania........................................................................................................................................40

Wisconsin.............................................................................................................................................40

Future Work ..................................................................................................................................................46

Summary........................................................................................................................................................47

References Cited..........................................................................................................................................48

Appendixes

Appendixes 4 and 5 follow immediately in this document. Appendixes 1, 2, and 3 are separate 

documents, available for downloading from the indicated Web address.

1. Obtaining National Oceanic and Atmospheric Administration lake-level data ............................

http://pubs.usgs.gov/sir/2013/5166 

2. Graphical User Interface—PROCESSNOAA.......................................................................................

http://pubs.usgs.gov/sir/2013/5166 

3. Example Spreadsheets for Compiling Beach Water-Quality and Environmental Data ................

http://pubs.usgs.gov/sir/2013/5166

4. Example Protocol for Collecting Data and Running a Predictive Model for 

Nowcasting at Great Lakes Beaches .........................................................................................57

5. Equations for the selected model(s) for each Great Lakes beach...............................................61 

Figures

1. Map showing locations of 49 beach sampling sites in the Great Lakes region of 

Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania, and Wisconsin.......................5

2. Graphs showing relations between log10 Escherichia coli concentrations and 

photosynthetically active radiation (PAR) for 2008–12 at two Ohio beaches...................25

3. Graph showing piezometer water level elevations (“foreshore head”), lake-level 

elevations, wave heights, radar rainfall, and concentrations of Escherichia coli at 

Edgewater, Cleveland, Ohio, May 23–June 30, 2012.............................................................27

4. Graphs showing model responses as compared to using previous day’s 

Escherichia coli concentration (persistence model) during validation in 2012 ...............40

5. Graph showing differences in percentages between paired predictive- and 

persistence-model responses for 42 beach models during validation in 2012 ................41
vii

Tables

1. List of 49 study beaches, cooperating agencies, and nowcast status in the 

Great Lakes region of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania, 

and Wisconsin...............................................................................................................................6

2. Beach sites and identification numbers, sampling locations, and equipment 

installed by the U.S. Geological Survey or local agencies for predictive-model 

data collection, 2010–12, at 49 beaches in the Great Lakes region .....................................8

3. Sampling information and summary statistics at Great Lakes beaches for period 

of sampling and exceedances for calibration and validation years ..................................22

4. Pearson’s correlations between log10 Escherichia coli concentrations and 

photosynthetically active radiation (PAR) or explanatory variables used in predictive 

models at Huntington, Bay Village, and Edgewater, Cleveland, Ohio, 2008–12................26

5. Pearson’s correlations between log10 Escherichia coli concentrations and foreshore 

head or to explanatory variables used in the predictive model at Edgewater, 

Cleveland, Ohio, 2008–12 ...........................................................................................................26

6. Pearson’s correlations between log10 Escherichia coli concentrations and new 

variables or explanatory variables used for the current predictive model at 

Upper Lake Park, Port Washington, Wisconsin ....................................................................29

7. Summary of variables, by State, used in beach-specific predictive models ...................30

8. Variables and statistics for beach-specific predictive models in calibration 

datasets........................................................................................................................................32

9. Predictive-model responses as compared to use of previous day’s Escherichia coli

concentration (persistence model) during validation in 2012 .............................................37

Multiply By To obtain

Length

inch (in) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square meter (m2

) 10.76 square foot (ft2)

Volume

milliliter (mL) 0.03382 ounce, fluid (fl. oz)

Precipitation and flow rate

inch per hour (in/h) 25.4 millimeter per hour (mm/h)

cubic foot per second (ft3

/s) 0.02832 cubic meter per second (m3/s)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

Bacteria concentrations are given in colony-forming units per 100 milliliters (CFU/100 mL) or 

most-probable number per 100 milliliters (MPN/100 mL).

Conversion Factors

Developing and Implementing Predictive Models 

for Estimating Recreational Water Quality at 

Great Lakes Beaches

By Donna S. Francy, Amie M.G. Brady, Rebecca B. Carvin, Steven R. Corsi, Lori M. Fuller, John H. Harrison, 

Brett A. Hayhurst, Jeremiah Lant, Meredith B. Nevers, Paul J. Terrio, and Tammy M. Zimmerman

Abstract

Predictive models have been used at beaches to improve 

the timeliness and accuracy of recreational water-quality 

assessments over the most common current approach to waterquality monitoring, which relies on culturing fecal-indicator 

bacteria such as Escherichia coli (E. coli.). Beach-specific 

predictive models use environmental and water-quality 

variables that are easily and quickly measured as surrogates 

to estimate concentrations of fecal-indicator bacteria or to 

provide the probability that a State recreational water-quality 

standard will be exceeded. When predictive models are used 

for beach closure or advisory decisions, they are referred to as 

“nowcasts.” During the recreational seasons of 2010–12, the 

U.S. Geological Survey (USGS), in cooperation with 23 local 

and State agencies, worked to improve existing nowcasts at 4 

beaches, validate predictive models at another 38 beaches, and 

collect data for predictive-model development at 7 beaches 

throughout the Great Lakes. This report summarizes efforts to 

collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and 

beach-monitoring programs into one comprehensive report. 

Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave 

height, turbidity, water temperature, and numbers of birds at 

the time of sampling. In addition to these field measurements, 

equipment was installed by the USGS or local agencies at or 

near several beaches to collect water-quality and metrological 

measurements in near real time, including nearshore buoys, 

weather stations, and tributary staff gages and monitors. 

The USGS worked with local agencies to retrieve data from 

existing sources either manually or by use of tools designed 

specifically to compile and process data for predictive-model 

development.

Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches 

that had at least 2 years of data (2010–11 and sometimes 

earlier) and for 1 beach that had 1 year of data. For most 

models, software designed for model development by the 

U.S. Environmental Protection Agency (Virtual Beach) was 

used. The selected model for each beach was based on a 

combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 

hours, wave height, wind direction and speed, and antecedent 

rainfall for various time periods. Forty-two predictive models 

were validated against data collected during an independent 

year (2012) and compared to the current method for assessing 

recreational water quality—using the previous day’s E. coli

concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 

percent greater than the persistence model and overall correct 

responses greater than or equal to 80 percent, sensitivities 

(percentage of exceedances of the bathing-water standard that 

were correctly predicted by the model) greater than or equal 

to 50 percent, and specificities (percentage of nonexceedances 

correctly predicted by the model) greater than or equal to 85 

percent. Out of 42 predictive models, 24 models yielded overall correct responses that were at least 5 percent greater than 

the use of the persistence model. Predictive-model responses 

met the performance goals more often than the persistencemodel responses in terms of overall correctness (28 versus 

17 models, respectively), sensitivity (17 versus 4 models), 

and specificity (34 versus 25 models). Gaining knowledge of 

each beach and the factors that affect E. coli concentrations is 

important for developing good predictive models. Collection 

of additional years of data with a wide range of environmental 

conditions may also help to improve future model performance. The USGS will continue to work with local agencies 

in 2013 and beyond to develop and validate predictive models 

at beaches and improve existing nowcasts, restructuring 

monitoring activities to accommodate future uncertainties in 

funding and resources.
2

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Introduction 

Currently, swim advisories or closings issued by beach 

managers in the United States are based on standards for concentrations of Escherichia coli (E. coli) or enterococci. Standard culture methods for these fecal-indicator bacteria (FIB) 

take at least 18–24 hours before results are available. The 

beach is posted with an advisory or closing or is determined to 

be acceptable for swimming on the basis of the previous day’s 

bacteria concentration (persistence model); however, sanitary 

conditions may change overnight and even throughout the day 

(Boehm and others, 2002). Because of the time lag inherent in 

this approach, water-resource managers are seeking solutions 

that provide near-real-time estimates of recreational water 

quality. 

One solution to improve the timeliness and accuracy 

of recreational water-quality assessments is to use predictive models. Predictive models employ environmental and 

water-quality variables that are easily and quickly measured 

to either estimate the probability that the State standard will 

be exceeded or directly estimate concentrations of FIB, such 

as E. coli. In new recreational water-quality criteria recently 

promulgated by U.S. Environmental Protection Agency (EPA), 

the use of predictive models to facilitate rapid notification of 

potential water-quality problems and supplement beach notification programs is encouraged (U.S. Environmental Protection 

Agency, 2012a). 

Model Definitions and Measures of 

Performance

• Persistence models use the previous day’s E. coli concentration to estimate the current day’s E. coli concentration.

• Predictive models are statistical models that use environmental and water-quality variables to estimate the 

probability that the State standard will be exceeded or 

to directly estimate concentrations of E. coli.

• Measures of model performance include the following: 

• The overall percentage of correct responses 

(exceedances and nonexceedances) that are predicted 

by the predictive or persistence model. 

• The sensitivity of the model; that is, the percentage 

of exceedances of the bathing-water standard that are 

correctly predicted by the predictive or persistence 

model. 

• The specificity of the model; that is, the percentage 

of nonexceedances of the bathing-water standard 

that are correctly predicted by the predictive or persistence model. 

Existing Nowcast Systems

When predictive models are used for beach closure or 

advisory decisions, they are referred to as “nowcasts.” In 

2010–11, operational nowcasts (those used daily by beach 

managers to inform the public) were used at only a few 

locations within the Great Lakes, including three beaches in 

Ohio (http://www.ohionowcast.info), three beaches in Illinois 

(Lake County Health Department, 2013), and two beaches in 

Wisconsin (http://www.wibeaches.us/). Of these operational 

nowcast systems, the U.S. Geological Survey (USGS) was 

involved in development and testing of predictive models in 

Ohio and Wisconsin. The USGS has also been involved in past 

nowcasts in Indiana. Nowcasts with USGS involvement have 

used water-quality and environmental variables in predictive 

models developed using statistical techniques to estimate current water-quality conditions based on E. coli concentrations.

The Ohio Nowcast, a cooperative project between the 

USGS and local agencies, has been providing near-real-time 

beach advisories to the public on the basis of predictive models since 2006 (http://www.ohionowcast.info). The probability 

of exceeding the bathing-water standard is posted on the Ohio 

Nowcast Web site by 9:30 a.m. during the recreational season. 

Local agencies that collect daily data and run the nowcast 

include the Cuyahoga County Board of Health, Northeast 

Ohio Regional Sewer District, and the University of Toledo, 

with postings for Huntington, Edgewater, Maumee Bay State 

Park, and Villa Angela beaches. Predictive models have 

included such variables as turbidity, wave height, antecedent 

rainfall, change in lake level in the past 24 hours, and day of 

the year. At Huntington—in Bay Village, Ohio, a suburb of 

Cleveland—nowcasts were provided to the public for 581 

days during the recreational seasons of 2006–11. A hindsight 

examination of the Huntington nowcast results in which 

the same-day cultured E. coli concentration in beach-water 

samples was used as the criterion for correct response indicated that the nowcast yielded a correct response 84.2 percent 

of the time. By comparison, use of the previous day’s E. coli

concentration (the persistence model) resulted in a correct 

response 76.1 percent of the time. The predictive model exhibited greater sensitivity and specificity (54.9 and 89.6 percent) 

than the persistence model (22.4 and 85.9 percent). Nowcasts 

for other Ohio beaches yielded similar results, showing that 

they can better estimate current water-quality conditions than 

can the persistence model used by most beach managers, especially in regard to sensitivity (http://www.ohionowcast.info).

In Wisconsin, various predictive models have been used 

operationally since 2008. A predictive model was implemented 

in 2008 for Upper Lake Park Beach (Port Washington, Wis.) 

using rainfall, turbidity and discharge thresholds to predict 

E. coli concentrations. In 2009, the Wisconsin Department 

of Natural Resources (WDNR) began assisting the Ozaukee 

County Health Department by developing a predictive model 
Introduction 

3

for Upper Lake Park Beach. For the 2011 season, WDNR 

extended their modeling assistance, working with the City of 

Racine Health Department to include North Beach in Racine. 

Although both of these beaches had few exceedances of the 

bathing-water standard during 2011, the predictive model still 

yielded slightly more accurate information than the persistence 

model. 

In Indiana, the Swim Advisory Forecast Estimate (Project 

SAFE), providing real-time data on E. coli concentrations 

based on predictive models, was first used during the recreational season of 2005 (U.S. Geological Survey, 2010). The 

USGS created models for five beaches (Ogden Dunes, West, 

Wells Street, Lake Street, and Marquette Beaches) in Lake and 

Porter Counties in Indiana using water-quality data collected 

in 2004 (Nevers and Whitman, 2005). These beaches are 

directly affected by fecal contamination from nearby Burns 

Ditch, particularly during prevailing north winds. Because of 

the similar impact of Burns Ditch, wind direction, and rainfall 

on E. coli concentrations at these beaches, data from all five 

beaches were combined for model development. Depending 

on the prevailing wind direction, different weather and water 

variables were used. Predicted E. coli concentrations by stepwise linear regression techniques were more accurate during 

north winds (R2

 approximately 0.7 compared to approximately 

0.3 during south winds), and the model incorporated parameters from the lake (chlorophyll and turbidity), from Burns 

Ditch (gage height), and rainfall in the previous 48 hours. 

SAFE was used for nowcasting in 2005, and beach managers were notified of probabilities for beach closures through 

email by 10 a.m. each day. In subsequent years, the program 

has been managed by Gary Sanitary District and the town 

of Ogden Dunes, with varying numbers of beaches included 

in the prediction, depending on funding and interagency 

cooperation. 

The USGS developed an amended predictive model 

for Ogden Dunes in 2011, using water quality and detailed 

hydrometeorological data collected in 2008 (unpublished 

data). The Ogden Dunes beaches are made up of three adjacent beaches. A complex empirical model using a complete 

set of hydrometeorological variables, including wave height, 

wind directions, wind velocity, current velocity, and rainfall, 

turned out to have a similar predictive capacity as models 

using turbidity only. The simple turbidity model yielded an 

adjusted R2

 of 0.55 when the three beaches at Ogden Dunes 

were considered as independent locations and an adjusted R2

of 0.67 when all three beaches were averaged. The turbidity 

model (averaged for three beaches) was used for nowcasting 

by the Town of Ogden Dunes in 2011 for the first time and 

yielded a correct response 90 percent of the time. The persistence model resulted in 83.7 percent of correct responses. The 

predictive model had a higher sensitivity and specificity (61.2 

and 97.4 percent, respectively) than the persistence model (59 

and 90.1 percent). 

Expanding Nowcasts

Given the success of existing operational nowcasts, 

expanding the use of predictive models to other areas is seen 

as a welcome addition to many beach monitoring programs. 

Coordinated leadership, technical expertise, funding, and automated systems are needed to accomplish this expansion. Work 

is also needed to identify new variables to improve existing 

operational models and install and maintain onsite equipment 

for automated measurements of model variables. Automated 

measurements of wave height or turbidity by use of nearshore 

buoys offer real-time measurements of model variables without the need to send personnel to the beach. 

The 2010–12 predictive-modeling efforts by the USGS 

described in this report were designed to provide technical 

expertise on predictive-model development and data collection 

whenever requested by local and State agencies. These USGS 

efforts were funded by the EPA through the Great Lakes 

Restoration Initiative and by the USGS Coastal and Marine 

Geology Program through the Oceans Research Priority 

Plan. The cooperation of State and local agencies was made 

possible through funding from a variety of sources including 

EPA Beaches Environmental Assessment and Coastal Health 

(BEACH) Act Funding (U.S. Congress, 2000) and the Great 

Lakes Restoration Initiative and from services provided by the 

agencies themselves. 

Purpose and Scope

This report describes results of efforts by the USGS and 

State and local agencies to improve and expand operational 

nowcasting at beaches throughout the Great Lakes region. 

During the recreational seasons (May–September) of 2010–12, 

the USGS, in cooperation with 23 local and State agencies, 

worked to improve existing operational nowcast systems at 4 

beaches, validate predictive models at another 38 beaches, and 

collect data for predictive-model development at 7 beaches in 

Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania, 

and Wisconsin. Data were collected during 2010–11 to identify and compare the explanatory variables that best described 

E. coli concentrations at each beach. For some beaches, data 

collected before 2010 were also used. Real-time measures 

of turbidity, wave height, specific conductance, water temperature, and other variables were obtained from equipment 

installed in swimming areas at several beaches. Water-level 

and water-quality data in tributaries expected to affect beach 

water quality also were collected by use of similar equipment. 

Additionally, data on photosynthetically active radiation measured by a sensor and shallow groundwater levels measured 

in a piezometer were evaluated to determine whether these 

variables could improve existing operational nowcasts at Ohio 

beaches. 
4

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Software routines or spreadsheets were designed by the 

USGS to automate the compilation of data from other agencies 

including lake level, rainfall, wind direction and speed, predicted current direction, and stream discharge. A free software 

tool designed by EPA for model development, called Virtual 

Beach, was used to develop or update models by means of 

multiple linear regression techniques (U.S. Environmental 

Protection Agency, 2012b). An alternative statistical technique, 

partial least squares regression (Brooks and others, 2013), 

was tested and incorporated into Virtual Beach. The selected 

model for each beach was based on a combination of explanatory variables including (most commonly) turbidity, day of the 

year, change in lake level over 24 hours, wave height, wind 

direction and speed, and antecedent rainfall for various time 

periods. Updated or new models for 42 beaches were validated 

during an independent year (2012). Model performance was 

compared to use of the persistence model and was examined to 

determine whether model results met established criteria and 

could be used for future public notifications. 

This report summarizes efforts to collect data and 

develop predictive models by multiple agencies. Some models 

have been published previously, and this report compiles existing information into one comprehensive report on past and 

current USGS modeling efforts throughout the Great Lakes. 

Background information on study areas was obtained from 

previous studies whenever available and through discussions 

with beach managers and public health officials, resulting in 

different amounts of information among the study beaches. 

The report is somewhat modular in approach. In several 

sections, information is presented by State to help the reader 

focus on specific areas without having to read the report from 

cover to cover.

Study Areas

Studies were done at 49 Great Lakes beaches in Illinois, 

Indiana, Michigan, New York, Ohio, Pennsylvania, and Wisconsin (fig. 1) in cooperation with 23 local agencies (table 1). 

The USGS Site Identification Numbers (table 2) correspond 

to latitudes and longitudes of each site (with two additional 

digits) and can be used to pinpoint site locations by using any 

digital map system. Altnernatively, detailed site information 

can be obtained for most beaches through EPA’s Beach Web 

site (http://watersgeo.epa.gov/beacon2/). All of the study sites 

are popular swimming beaches, and their locations span a variety of land-use settings. Beaches were placed in the following 

categories based on nowcast status during the recreational 

season of 2012 (table 1):

• Operational.—An existing nowcast was used to issue 

advisories or closings to the public before 2012 and 

continued to be used during 2012.

• Recently implemented.—Models were developed and 

used to issue advisories or closings to the public for the 

first time in 2012. 

• Validation.—At least 2 years of data were collected, 

and models were developed and validated during 2012 

but not used to issue advisories or closings to the public.

• Development.—At least 1 year of data was collected, 

and agencies continued to collect data during 2012 for 

model development.
Study Areas

5

Figure 1. Locations of 49 beach sampling sites in the Great Lakes region of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania, 

and Wisconsin (site details are listed in table 1).

CANADA

OHIO

MINNESOTA

ILLINOIS

WISCONSIN

NEW YORK

INDIANA

MICHIGAN

PENNSYLVANIA

Erie

Toledo

Duluth

Buffalo

Cleveland

Rochester

Michigan City

0 100 200 MILES

0 100 200 KILOMETERS

EXPLANATION

Beach sampling site

Nowcast operational in 2010

Nowcast in development

Base modified from U.S. Geological Survey

1:2,000,000 scale digital data.

Great Lakes Basin Albers Equal Area Projection

Grand Rapids

Detroit

Port Huron

Chicago

Milwaukee

Madison

Sheboygan

Green Bay

90° 80°

42°

48°

45°

Lake Erie

Lake Ontario

Lake Superior

L

a

ke

Michigan

Lake Huron
6

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Beach name and location Beach short name 

(for this report) Cooperating agencies Nowcast status in 2012a

Illinois

Evanston South, Evanston South Beach City of Evanston Health Dept. Validation

Greenwood, Evanston Greenwood Beach City of Evanston Health Dept. Validation

Lee, Evanston Lee Beach City of Evanston Health Dept. Validation

Montrose Beach, Chicago Montrose Chicago Park District Recently implemented

Jackson Park/63rd Street Beach, 

Chicago

63rd Street Chicago Park District Recently implemented

Foster Beach, Chicago Foster Chicago Park District Recently implemented

Calumet Beach, Chicago Calumet Chicago Park District Recently implemented

Oak Street Beach, Chicago Oak Chicago Park District Recently implemented

Indiana

Jeorse Park, East Chicago Jeorse Park Indiana Dept. of Environmental 

Management; East Chicago Health Dept.

Validation

Michigan

Metropark Beach, 

Mount Clemens

Metropark Macomb County Health Dept. Validation

Memorial Beach, St. Clair Shores Memorial Macomb County Health Dept. Validation

New Baltimore Beach, 

New Baltimore

New Baltimore Macomb County Health Dept. Development

Grand Haven State Park Beach, Grand 

Haven

Grand Haven County of Ottawa Health Dept. Development

New York

Hamlin Beach State Park 

Area 3, Hamlin

Hamlin SP 

Area 3 

New York State Office of Parks, 

Recreation, and Historic Preservation; 

Monroe County Public Health Laboratory

Development

Hamlin Beach State Park 

Area 4, Hamlin

Hamlin SP 

Area 4

New York State Office of Parks, 

Recreation, and Historic Preservation; 

Monroe County Public Health Laboratory

Development

Ontario Beach Park, Rochester Ontario Monroe County Dept. of Health Validation

Durand-Eastman Park, Rochester Durand Monroe County Dept. of Health Validation

Woodlawn Beach State Park, 

Hamburg

Woodlawn SP New York State Office of Parks, 

Recreation, and Historic Preservation; 

Erie County Public Health Laboratory

Validation

Evans Town Park, Evans Center Evans TP Erie County Health Dept. Validation

Lake Erie Beach, 

Lake Erie Beach

Lake Erie Erie County Health Dept. Validation

Ohio

Edgewater, Lakefront 

Reservation, Cleveland

Edgewater Northeast Ohio Regional Sewer District Operational

Huntington Reservation, 

Bay Village

Huntington Cuyahoga County Board of Health Operational

Maumee Bay State Park, Oregon Maumee Bay SP University of Toledo Operational

Villa Angela, Cleveland Villa Angela Northeast Ohio Regional Sewer District Recently implemented

Vermillion West, Vermillion Vermillion West Erie County Health Dept. Validation

Nickel Plate, Huron Nickel Plate Erie County Health Dept. Validation

Table 1. List of 49 study beaches, cooperating agencies, and nowcast status in the Great Lakes region of Illinois, Indiana, Michigan, 

New York, Ohio, Pennsylvania, and Wisconsin. 
Study Areas

7

Beach name and location Beach short name 

(for this report) Cooperating agencies Nowcast status in 2012a

Ohio—Continued

Lake Front, 

Huron West Beach, Huron

Lakefront Erie County Health Dept. Validation

Fairport Harbor Lakefront Park 

Beach, Fairport Harbor

Fairport Harbor Lake County General Health District Validation

Headlands Beach State Park, Mentor Mentor Headlands Lake County General Health District Validation

Lakeview, Lorain Lakeview Cuyahoga County Board of Health Validation

Lakeshore, Ashtabula Lakeshore Lake County General Health District Development

Walnut Beach, Ashtabula Walnut Lake County General Health District Development

Pennsylvania

Presque Isle State Park 

Beach 1 West Extension, Erie

PISP Beach 1 

West Extension

Erie County Department of Health; 

Presque Isle State Park; 

Regional Science Consortium

Validation

Presque Isle State Park 

Beach 2, Erie

PISP Beach 2 Same as above Validation

Presque Isle State Park 

Beach 6, Erie

PISP Beach 6 Same as above Validation

Presque Isle State Park Mill Road 

Beaches, Erie

PISP Mill Rd Beach Same as above Validation

Presque Isle State Park 

Beach 9, Erie

PISP Beach 9 Same as above Validation

Presque Isle State Park 

Beach 11, Erie

PISP Beach 11 Same as above Validation

Wisconsin

Upper Lake Park Beach, 

Port Washington

Upper Lake Park Ozaukee County Operational

Hika Park Bay Beach, Cleveland Hika Park University of Wisconsin–Manitowoc Validation

Fisher Park Beach, Cleveland Fischer University of Wisconsin–Manitowoc Validation

Red Arrow Park Beach, 

Manitowoc

Red Arrow University of Wisconsin–Manitowoc Validation

Neshota Beach, Two Rivers Neshotah University of Wisconsin–Manitowoc Validation

Point Beach State Forest 

Lighthouse Picnic Area South

Point South University of Wisconsin–Manitowoc Validation

Point Beach State Forest 

Lakeshore Picnic Area Central

Point Central University of Wisconsin–Manitowoc Validation

Point Beach State Forest 

concession Stand North

Point North University of Wisconsin–Manitowoc Validation

Kreher Park Beach, Ashland Kreher Ashland County Validation

Maslowski Beach, Ashland Maslowski Ashland County Validation

Thompson's West End Park, 

Washburn

Thompson Bayfield County Validation

a

 Operational—an existing nowcast was used to issue advisories or closings to the public before 2012 and continued to be used during 2012.

Recently implemented—models were developed and were used to issue advisories or closings to the public for the first time in 2012. 

Validation—at least 2 years of data were collected and models were developed and validated during 2012, but not used to issue advisories or closings to the 

public.

Development—at least 1 year of data was collected and agencies continued to collect data during 2012 for model development.

Table 1. List of 49 study beaches, cooperating agencies, and nowcast status in the Great Lakes region of Illinois, Indiana, Michigan, 

New York, Ohio, Pennsylvania, and Wisconsin.—Continued.
8

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Table 2. Beach sites and identification numbers, sampling locations, and equipment installed by the U.S. Geological Survey (USGS) or 

local agencies for predictive-model data collection, 2010–12, at 49 beaches in the Great Lakes region.

Beach short name USGS station name

USGS site 

identification number 

(incorporates latitude 

and longitude)

Sampling locations and equipment

Illinois

South Beach South Beach at Evanston, IL 420140087400301 Single sampling location.

Greenwood Beach Dempster-Greenwood Beach 

at Evanston, IL

420235087401601 Single sampling location.

Lee Beach Lee Beach at Evanston, IL 420211087401001 Single sampling location.

Montrose Lake Michigan 

at Montrose Beach 

at Chicago, IL

415800087381301 Geometric mean E. coli for two sampling locations; 

buoy for turbidity, waves, and water temperature 

(data transmitted in near real-time).

Calumet Lake Michigan at Calumet Park 

at Chicago, IL

414250087314001 Same as above.

63rd Street Lake Michigan 

at 63rd St Beach 

at Chicago, IL

414657087342501 Geometric mean E. coli for two sampling locations; 

buoy for turbidity, waves, and water-temperature 

(data transmitted in near real time); weather 

station for winds, rainfall, air temperature, solar 

radiation, relative humidity, barometric pressure. 

Foster Lake Michigan at Foster Ave 

at Chicago, IL

415844087385601 Same as above.

Oak Lake Michigan at Oak St Beach 

at Chicago, IL

415412087371901 Same as above.

Indiana

Jeorse Park Lake Michigan at Jeorse Park 

at East Chicago, IN

413902087260001 Single sampling location.

Michigan

Metropark Metropark Beach on Lk St Clair 

at Mount Clemens, MI

423415082474601 Geometric mean E. coli for multiple sampling 

locations as required by State of Michigan.

Memorial Memorial Beach on Lk St Clair 

at St. Clair Shores, MI

423138082521601 Same as above.

New Baltimore New Baltimore Beach on Lk St 

Clair at New Baltimore, MI

424039082440201 Same as above.

Grand Haven Grand Haven State Park Beach 

on Lk Michigan at 

Grand Haven, MI

430307086144301 Same as above.

New York

Hamlin SP Area 3 Hamlin Beach State Park 

at Area 3, NY

432155077572101 Single sampling location.

Hamlin SP Area 4 Hamlin Beach State Park 

at Area 4, NY

432203077574701 Single sampling location.

Ontario Ontario Beach Park, 

Rochester, NY

431535077363101 Single sampling location.

Durand Durand-Eastman Park, 

Rochester, NY

431428077341901 Single sampling location.

Woodlawn SP Woodlawn Beach State Park, 

Hamburg, NY

424722078510901 Single sampling location; Rush Creek staff gage.

Evans TP Evans Town Park, NY 423837079035301 Single sampling location; Fern Brook staff gage.

Lake Erie Lake Erie Beach, NY 423729079054501 Single sampling location; Muddy Creek staff gage.
Study Areas

9

Table 2. Beach sites and identification numbers, sampling locations, and equipment installed by the U.S. Geological Survey or local 

agencies for predictive-model data collection, 2010–12, at 49 beaches in the Great Lakes region.—Continued.

Beach short name USGS station name

USGS site 

identification number 

(incorporates latitude 

and longitude)

Sampling locations and equipment

Ohio

Edgewater Lake Erie at Edgewater, Main 

Beach East and West, 3 ft

412922081442303

412920081442603

Average for two sampling locations.

Edgewater Lake Erie at Edgewater Beach 412923081442600 Buoy for turbidity, waves, and water-temperature 

(data transmitted in near real time).

Huntington Lake Erie at Huntington 

Reservation 2 (central 

location) and West

412928081560220

412929081561100

Average E. coli for two sampling locations; buoy 

for turbidity, waves, and temperature (central 

location, data transmitted in near real time).

Huntington Huntington Beach Tower 412926081560100 Sensor for photosynthetically active radiation. 

Maumee Bay SP Maumee Bay at Maumee Bay 

State Park Cove 2, Cove 3, 

and Cove 4

414110083222700

414111083223200

414110083223900

Average E. coli for three sampling locations.

Villa Angela Lake Erie at Villa Angela beach 

east, OH

413510081340100 Single sampling location.

Vermilion West Lake Erie at Vermilion West 

Beach nr Vermilion, OH 

412531082215800 Single sampling location.

Nickel Plate Lake Erie at Nickel Plate Beach 

at Huron, OH

412348082323900 Single sampling location.

Lake Front Lake Erie at Huron River West 

Beach near Huron OH

412355082331800 Single sampling location.

Fairport Harbor Lake Erie at Fairport Harbor 

Beach

414530081163100 Single sampling location.

Mentor Headlands Lake Erie at Headlands SP 

Beach East and West

414527081172800

414524081173100

Average E. coli for two sampling locationsa

; 

buoy for turbidity, waves, and temperature 

(east location, data transmitted in near real time).

Mentor Headlands Weather station near Headlands 

State Park, OH

414514081174400 Weather station for precipitation, wind speed and 

direction, barometric pressure, air temperature, 

net solar radiation, and photosynthetically active 

radiation (data transmitted in near real time).

Lakeview Lake Erie at Lakeview Beach 

East

412753082114200 Single sampling location.

Lakeshore Lake Erie at Lakeshore Central 

Beach

415430080462903 Single sampling location.

Walnut Lake Erie at Walnut Beach 

at Ashtabula, OH

415407080483200 Single sampling location.

Pennsylvania

PISP Beach 1 

West Extension

Presque Isle 1 West Ext Center 420652080091901 Single sampling location.

PISP Beach 2 Presque Isle Beach 2 East 420755080084501 Buoy for waves, water currents, turbidity, pH, 

dissolved oxygen, conductivity, water temperature; weather station for winds, rainfall, air 

temperature, solar radiation, relative humidity, 

barometric pressure (data transmitted in near 

real time).

PISP Beach 6 Presque Isle Beach 6 Center 420839080081801 Single sampling location; buoy for turbidity, pH, 

dissolved oxygen, conductivity, and water 

temperature (data transmitted in near real time).
10

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Beach short name USGS station name

USGS site 

identification number 

(incorporates latitude 

and longitude)

Sampling locations and equipment

Pennsylvania—Continued

PISP Mill Rd Beach Presque Isle Millroad Beach 

Center

420944080071501 Single sampling location.

PISP Beach 9 Presque Isle Pine Tree Beach 

Center

421012080062101 Single sampling location.

PISP Beach 11 Presque Isle Beach 11 Center 420945080044201 Single sampling location.

Wisconsin

Upper Lake Park Lake Park Beach 

at Port Washington, WI

432345087514501 Single sampling location.

Hika Park Hika Park Beach 

at Cleveland, WI

435457087432401 Single sampling location; rain gage, 

Centerville Creek staff gage.

Fischer Fischer Cr Conservation Area 

Beach nr Cleveland, WI

435613087430801 Single sampling location; 

Fischer Creek staff gage.

Red Arrow Red Arrow Park Beach 

at Manitowoc, WI

440434087392001 Single sampling location; rain gage; 

storm sewer staff gage.

Neshotah Neshotah Beach 

at Two Rivers, WI

440903087331001 Single sampling location; rain gage; 

storm sewer staff gage.

Point South Point Beach State Forest Rawley 

Point Beach, WI

441243087302401 Single sampling location; 

Molash Creek staff gage.

Point Central Point Beach State Forest 

Lakeshore Picnic Beach, WI

441303087302401 Single sampling location.

Point North Point Beach State Forest 

Concession Area Beach, WI

441323087302401 Single sampling location; rain gage.

Kreher Kreher Park Beach 

at Ashland, WI

463549090525401 Single sampling location; 

Bay City Creek staff gage.

Maslowski Maslowski Park Beach 

at Ashland, WI

463502090551801 Single sampling location; 

Fish Creek and 

Whittlesey Creek staff gages.

Thompson's West 

End Park

Thompson's West End Park 

Beach at Washburn, WI

463957090541601 Single sampling location; rain gage; 

Thompson Creek staff gage.

a

 If a sample from one sampling location exceeded the single-sample standard of 235 colony-forming units per 100 milliliters (235 CFU/100 mL) and the 

average was less than 235 CFU/100 mL, the highest value among the two locations was used to err on the safe side.

Table 2. Beach sites and identification numbers, sampling locations, and equipment installed by the U.S. Geological Survey or local 

agencies for predictive-model data collection, 2010–12, at 49 beaches in the Great Lakes region.—Continued.
Study Areas

11

Illinois—Evanston Beaches

The City of Evanston, Ill., adjacent to the northern border 

of Chicago, operates six public beaches on Lake Michigan. All 

of the beaches have been formed or built up with groins at the 

southern ends. These groins generally extend 350 to 500 feet 

(ft) out from the shoreline. One beach is designated for dog 

activity and is not used for public bathing. The most southern 

beach, aptly named South Beach, has historically been closed 

more often than the other beaches because of elevated E. coli

concentrations. The Evanston Health Department has been 

collecting samples for E. coli concentrations and environmental data at its beaches since 2007. Data collection for predictive models, in cooperation with the USGS, began in 2010 

at three beaches: South Beach, Greenwood Beach, and Lee 

Beach. 

Illinois—Chicago Beaches

Chicago’s Lake Michigan beaches have been the subject 

of many previous investigations to identify causes for periodic 

beach closures. All of the beaches are in an urban environment with no known point-source contamination. The situation of these beaches is varied, with many partially enclosed 

by breakwaters, a factor that may facilitate the accumulation 

of FIB. The 63rd Street Beach has elevated concentrations of 

FIB more frequently than the other 22 Chicago beaches and 

is largely enclosed by groins. Attempts to develop predictive 

models for 63rd Street Beach have met with varying success 

(Olyphant and Whitman 2004), but more recent research has 

identified the hydrodynamic processes that may be influencing 

this beach (Ge and others, 2010, 2012) and could lead to the 

refinement of predictive capabilities. Other beaches that have 

elevated bacteria concentrations and may be potential candidates for predictive models are Foster, Montrose, Calumet, 

and Oak Street. Predictive models have been developed for 

these beaches by using historical monitoring data (Nevers and 

Whitman, 2011), and a regional model that attempts to explain 

bacteria fluctuations along the entire Chicago coast was developed that highlighted relationships of FIB with wave height, 

day of year, and barometric pressure (Whitman and Nevers, 

2008). One year of data to develop predictive models was collected for Chicago beaches (2010), and a second year of data 

was collected with slight modifications to the methods supported by a cooperative grant that facilitated the installation 

of automated buoys and weather systems (2011). Predictive 

models were used to issue advisories at five Chicago beaches 

for the first time in 2012, with considerable testing and refinement expected to be needed.

Indiana

Jeorse Park Beach on Lake Michigan in East Chicago, 

Ind., is one of the most frequently closed beaches in the Great 

Lakes. Since beach monitoring was initiated in 2004, the frequency of beach closings has continued to increase each year. 

Bird populations have been identified as a potential source of 

contamination (M.N. Byappanahalli, U.S. Geological Survey, 

written commun., 2011), but source-tracking attempts have 

been inconclusive. Predictive-model testing was initiated in 

2010 as part of this study, but because of poor model performance and other reasons, the effort was discontinued in 2011. 

Although the beach was never a strong candidate for predictive modeling given the high rate of exceedances (78.1 percent 

in 2010), predictive modeling was explored to determine 

whether any particularly strong variable was related to E. coli

concentrations. Predictive models do not work as well as the 

persistence model at beaches where the standard is exceeded 

on the majority of days (Francy, 2009). Initiating the modeling 

helped with focusing activities toward eliminating potential 

contamination sources. 

Michigan

In Macomb County, Mich., three beaches are occasionally impaired because of high E. coli concentrations: 

Metropark, Memorial, and New Baltimore, all on Lake 

St. Clair. The USGS conducted a previous study at two of 

these beaches, where investigators found that rainfall, water 

temperature, and turbidity were related to E. coli concentrations (Holtschlag and others, 2008). In Ottawa County, Grand 

Haven State Park Beach on Lake Michigan is a popular beach 

for citizens and tourists, with attendance in the hundreds on 

most summer days. High E. coli concentrations are rare at 

Grand Haven, but because of its popularity, there remains 

interest in protecting the public by the most cost effective and 

timely methods. Data collection and model validation were 

done by Macomb County Health Department at two beaches 

during this study: Metropark and Memorial. At New Baltimore 

and Grand Haven State Park, data were collected for model 

development by Macomb County and the County of Ottawa 

Health Department, respectively. 

New York

Hamlin State Park (SP) is on Lake Ontario in Monroe 

County, north of the town of Hamlin and west of the city of 

Rochester. Two beaches were included for predictive-model 

development during this study, both operated by New York 

State Office of Parks, Recreation, and Historic Preservation: 

Hamlin SP Area 3 and Hamlin SP Area 4. The two beaches are 

separated by jetties and Spring Brook Marsh. Hamlin SP Area 

3 has 2 miles (mi) of lakefront with swim areas bounded by 
12

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

stone jetties. Yanty Creek Marsh, to the east of Area 3, drains 

a predominantly agricultural watershed. Hamlin SP Area 4 has 

approximately 1.6 mi of shoreline west of Area 3. Potential 

bacteria sources and modeling parameters for Hamlin SP were 

documented through a detailed sanitary survey (Kirsten Husson, New York State Office of Parks, written commun., 2012). 

Monroe County Department of Health operates two public beaches on Lake Ontario, northwest of the city of Rochester: Ontario and Durand beaches. Both beaches were included 

for model validation in 2012. Ontario is a popular urban beach 

located west of the mouth of the Genesee River. Durand is a 

smaller urban beach at Durand Eastman Park and 1.8 mi east 

from the mouth of the Genesee River. The Genesee River 

is likely the greatest contributor of fecal contamination and 

sediments to these beaches, with inputs from septic systems, 

wastewater-treatment-plant effluents, stormwater runoff, and 

agriculture. Monroe County has been using decision-tree models for beach closure decisions since 1976. These models are 

based on thresholds for Genesee River discharge (greater than 

5,000 cubic feet per second (ft3

/s)) and rainfall totals (greater 

than 0.7 inches (in.) for 24 hours or greater than 0.4 inch per 

hour (in/h) of peak rainfall intensity) and include qualitative 

estimates of algal mats and water clarity. The current decisiontree model produced accurate predictions 60 to 80 percent of 

the time over the operational periods (1976 to 2011) for E. coli

exceedances (Charles Knauf, Monroe County Department of 

Health, oral commun., 2011). 

Three public beaches on Lake Erie in New York were 

included for predictive-model validation during this study: 

Woodlawn SP, Evans Town Park (TP), and Lake Erie Beach. 

Woodlawn SP contains approximately 1 mi of Lake Erie 

shoreline and is approximately 7.2 mi south of the city of 

Buffalo in the town of Hamburg. The natural areas of the park 

consist of vegetated sand dunes, wetlands, and wooded areas. 

The day-use park is bordered by heavy industry and residential 

apartments and a wastewater treatment plant south of the park. 

Rush Creek and Blasdell Creek, directly north of the park, are 

considered the major sources of fecal contamination to the 

beach from sewage overflows, stormwater, and urban runoff 

(Eric Wiegert, New York State Health Department, written 

commun., 2012). Potential pollution sources and possible 

modeling parameters for Woodlawn SP were documented 

previously through a detailed sanitary survey (Terbush and 

Husson, 2009). During 2000–2008, closures based on rainfall amounts (greater than 0.5 in. for 24 hours) resulted in 16 

percent false negatives (E. Costanzo, New York State Office of 

Parks, oral commun., 2011). Woodlawn SP beach closures are 

currently based on the persistence model. The Evans TP and 

Lake Erie beaches are south of Buffalo and less than a mile 

apart between the moderately populated towns of Evans Center and Lake Erie Beach. Evans TP beach is a recreational area 

with a small swimming section along the shoreline surrounded 

by residential housing and wooded areas. Fern Brook contains 

stormwater runoff and seepage from rural septic systems and 

is likely the main source of fecal contamination to Evans TP. 

Fern Brook empties directly on to the sands along the south 

side of the swimming area and is often channelized during 

beach operation and after a storm event. Lake Erie Beach is a 

small beach directly north of Evangola State Park encompassing two small tributaries that discharge directly adjacent to the 

swimming area. Muddy Creek to the south is the larger of the 

two and the likely source of fecal contamination at this beach. 

Muddy Creek flow contains inputs from septic systems, wastewater-treatment-plant effluent, combined-sewer overflows, 

storm runoff, and agricultural runoff. Beach closures for Evans 

TP and Lake Erie beaches are based on the persistence model 

and (or) a rainfall threshold (greater than 0.5 in. for 24 hours). 

The beach operators may also close the beaches if a chemical 

substance sheen or physical deposition that may affect water 

quality is present, as described in the New York State Department of Health Sanitary Code (Scott Zimmerman, Erie County 

Department of Health, oral commun., 2011).

Ohio

Studies were done at 12 Ohio Lake Erie beaches: 3 with 

existing operational nowcasts, 1 with a recently implemented 

nowcast, 6 with model validation, and 2 with model development during 2012. 

Edgewater, Huntington, and Maumee Bay State Park 

(Maumee Bay SP) have been part of the Ohio Nowcast since 

2008, 2006, and 2011, respectively. Edgewater is an urban 

recreational area in Lakefront Reservation where water circulation is restricted by groins to the west and east of the main 

swimming area. Earlier work at Edgewater indicated that local 

sources of fecal contamination were dominant, the Edgewater 

outfall (to the west of the beach) was not a major source of 

fecal contamination, and elevated E. coli concentrations were 

found in shallow groundwater. The infiltration of bird feces 

through beach sands and interaction of shallow groundwater 

and waves were identified as a likely mechanism of E. coli

storage and accumulation in beach sands and transport to 

lake water (Francy and others, 2006a). At Huntington in Bay 

Village, a western suburb of Cleveland, two outfalls discharge 

stormwater runoff from a parking lot into the lake, and a creek 

to the east drains a heavily populated area (Francy and others, 

2006b). Wave height, turbidity, antecedent rainfall, and day 

of the year have typically been used in the Ohio Nowcast for 

Edgewater and Huntington. Maumee Bay SP is in the southwest corner of Lake Erie near the cities of Toledo and Oregon. 

This Lake Erie beach is in a popular state park and consists 

of five separate coves used for swimming. In an earlier study, 

remote sources were identified as minor contributors of E. 

coli to the beach, and a ditch to the east of the beach (Berger 

Ditch) was identified as a principal source of E. coli (Francy 

and others, 2005). At Maumee Bay SP, wind direction has 

been a dominant explanatory variable used in the Ohio 

Nowcast. Wind directions from the north and east (sweeping 

contamination from Berger Ditch into the beach area) have 

been associated with higher E. coli concentrations at Maumee 

Bay SP. 
Study Areas

13

Villa Angela is part of Lakefront Reservation and is on 

the east side of Cleveland. At Villa Angela, four breakwaters 

and a groin to the west stabilize the beach area. Overflow from 

a sanitary-sewage pump station near Euclid Creek, directly to 

the east of the beach, is a probable source of fecal contamination. Previous work at Villa Angela indicated that predictive models did little to improve upon the persistence model 

(Francy and others, 2003). As a result, Villa Angela was not 

included in the Ohio Nowcast before 2012. Recent work, however, showed that a rapid analytical method for E. coli concentrations, quantitative polymerase chain reaction (qPCR), could 

be used to derive a measure that improved correct responses 

over using the persistence model (Mark Citriglia, Northeast 

Ohio Regional Sewer District, written commun., 2012). With 

qPCR results as a predictive variable, Villa Angela was added 

to the Ohio Nowcast in 2012.

Two years of data were collected for model validation in 

2012 at three beaches in Erie County (Vermillion West, Nickel 

Plate, and Lake Front) by the Erie County Health Department, 

two beaches in Lake County (Fairport Harbor and Mentor 

Headlands) by the Lake County General Health District, and 

one beach in Lorain County (Lakeview) by the Cuyahoga 

County Board of Health. The Erie County beaches are in two 

small cities along the Lake Erie shoreline: Vermillion and 

Huron. Vermillion West lies between the Vermillion River 

to the west, which is affected by combined-sewer overflows, 

and a groin to the east. Nickel Plate is a 1⁄2-mi-long popular 

recreational area east of the Huron River. Lake Front is a small 

swimming area in a Huron city park, west of the mouth of the 

Huron River. The Erie County beaches are potentially affected 

by stormwater runoff and discharges of wastewater. Fairport 

Harbor and Mentor Headlands are east of Cleveland in a less 

populated area. Fairport Harbor is a small beach protected by 

breakwaters with a shallow swimming area and little wave 

action. Mentor Headlands is a 1-mi-long, heavily used beach 

that is open to the lake and susceptible to high waves. Possible sources of fecal contamination to Lake County beaches 

include effluents from a wastewater treatment plant, sanitarysewer overflows, and sanitary-sewer exfiltration to the stormwater drainage system (Susan Bell, Lake County General 

Health District, oral commun., 2012). The Grand River drains 

into Lake Erie directly west of Fairport Harbor and east of 

Mentor Headlands. In an earlier study (Francy and others, 

2003), a model was developed for Mentor Headlands and 

was based on wave height, rainfall, and turbidity; however, 

because of too few exceedances that year, a model was not 

developed for Fairport Harbor. At Lakeview, an urban beach in 

Lorain, Ohio, three breakwaters restrict water circulation, and 

large populations of waterfowl frequent the swimming area. In 

an earlier study, data indicated that wind direction, day of the 

year, and turbidity were significantly related to E. coli concentrations at Lakeview (Francy and others, 2006b). 

One year of data were collected by the Lake County 

General Health District for development of models at Lakeshore Park and Walnut Beach. Walnut Beach is in the city of 

Ashtabula in northeast Ohio, and Lakeshore Park is just east 

of Ashtabula. The mouth of the Ashtabula River lies to the 

east of Walnut and to the west of Lakeshore. Sources of fecal 

contamination to Lakeshore include a waterfowl pond that 

drains to the west of the beach and stormwater from a parking 

lot outfall that drains directly to the beach (Francy and others, 

2006a). Rainfall, turbidity, and water temperature were found 

to be significantly related to E. coli concentrations at Lakeshore (Francy and others, 2006b). Walnut Beach has a small 

swimming area open to the lake and has not been previously 

investigated by the USGS. 

Pennsylvania

Pennsylvania has 40 mi of Lake Erie shoreline, 8 mi of 

which are permitted public bathing beaches (Dorfman and 

Rosselot, 2011). Monitored public bathing beaches at Presque 

Isle State Park in Erie, Pennsylvania (Pa.), make up less than 

1 mi. Previous studies by the Erie County Department of 

Health (ECDH) and other agencies (Erie County Department 

of Health, 2007) identified sources of fecal contamination at 

the Presque Isle beaches that included stormwater runoff and 

combined-sewer overflows. These are likely the same type 

of sources that continue to influence concentrations of FIB 

at Presque Isle beaches, especially during periods of heavy 

rainfall.

 A model was developed as part of a previous study using 

data collected at one Presque Isle State Park (PISP) beach during three recreational seasons (2004–6). Investigators found 

that log10 turbidity, bird count, and wave height were related to 

E. coli concentrations at PISP Beach 2 (Zimmerman, 2008). In 

2010–11, data were collected at PISP Beach 2 and five additional PISP beaches (Beach 1 West Extension, Beach 6, Mill 

Road Beach, Beach 9, and Beach 11) for model development 

and validation in 2012. 

Wisconsin

Studies were done at eight Lake Michigan beaches and 

three Lake Superior beaches in Wisconsin. Lake Michigan 

beaches are Upper Lake Park in Port Washington and seven 

beaches in Manitowoc County. The Manitowoc County 

beaches are, from south to north, Hika, Fischer, Red Arrow, 

Neshotah, and three beach areas along the shore of Point 

Beach State Park (Point South, Point Central, and Point 

North). The Lake Superior beaches are all within Chequamegon Bay, with Kreher and Maslowski in the City of Ashland 

and Thompson immediately south of the city of Washburn. 

At Upper Lake Park, a predictive model had already 

been developed by WDNR and the Ozaukee County Health 

Department. Previous predictive models included variables 

for rainfall, water temperature, cloud conditions, wave height, 

currents, and date. Influences on Upper Lake Park include 

two nearby small tributaries to the south with a mix of urban 

and agricultural land use, and one small tributary to the north 

with primarily agricultural land use. Predictive models were 
14

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

developed for nowcast validation in 2012 for the remaining 10 

Wisconsin beaches. 

The Manitowoc County beaches represent a range of 

different land use influences from the surrounding drainage 

areas. Hika and Fischer are south of the city of Manitowoc 

near the small community of Cleveland, Wis. Both have small 

streams draining to the lake adjacent to each beach that are 

dominated by agricultural land use. Centerville Creek drains 

at the northern boundary of Hika, and Fischer Creek drains 

within the boundaries of Fischer. Red Arrow is within the city 

of Manitowoc. This beach has numerous potential influences 

on water quality, including two storm sewers that drain urban 

areas from Manitowoc, the mouth of the Manitowoc River 1 

mi north of the beach, and a sewage treatment plant that discharges 0.75 mi to the northeast of the beach. The Manitowoc 

River is dominated by agricultural land use, but there is some 

urban influence from Manitowoc. Neshota is in the small community of Two Rivers. Small storm sewers drain to the north 

and to the south directly adjacent to the beach boundaries, 

and the mouth of the Twin River is 0.5 mi south of Neshota. 

The Twin River drains an agricultural watershed. Point Beach 

State Park is approximately 11 mi north of Manitowoc. These 

three beaches are situated approximately along a 1-mi section 

of shore that is approximately 2.5 mi north from the mouth of 

Molash Creek, whose watershed encompasses a mix of agricultural land use and wetland area. The mouth of Twin River 

is 6.3 mi south and the mouth of the Kewaunee River is 16 mi 

north of Point Beach State Park. The Kewaunee River is also 

dominated by agricultural land use.

The three Chequamegon Bay/Lake Superior beaches are 

influenced by nearby streams that drain into Chequamegon 

Bay, as well as by urban runoff from Ashland and Washburn. 

Chequamegon Bay is approximately 12 mi long and ranges 

from 2 to 6 mi in width, with a maximum depth of 35 ft. 

Numerous small streams draining to the bay have potential 

to affect the water quality at these three beaches. The closest 

streams to the study beaches are the following: City Creek is 

adjacent to Kreher and is influenced by urban land use and 

forested areas, Fish Creek and Whittlesey Creek are near 

Maslowski, and Thompson Creek is adjacent to Thompson. 

All three of these streams are influenced by a mix of agricultural land use and forested areas. 

Methods

Data were collected during the recreational seasons 

(May–September) of 2010–12 for most beaches, with earlier years’ data included for some beaches. Daily data were 

collected by local agencies (table 1) and included analysis 

of water samples for E. coli concentrations that were part of 

existing monitoring programs designed to conform to State 

water-quality standards, described below. At the time of E. 

coli sample collection, local agencies made field measurements and recorded observations for model development 

and validation. Nearshore buoys and other equipment were 

installed or monitored by the USGS and (or) local agencies 

specifically for this project to collect site-specific waterquality and meterological data. Data on explanatory variables 

from other sources were compiled by local agencies or by the 

USGS. 

Recreational Water-Quality Standards and 

Criteria for Closings and Advisories

Water-quality standards for E. coli concentrations and 

associated actions taken (advisories and closings) differ by 

State and (or) locality but were all based on the 1986 criteria 

at the time of this study (2010–12) (U.S. Environmental Protection Agency, 1986). The EPA published new recreational 

water-quality criteria in December 2012 (U.S. Environmental 

Protection Agency, 2012a). New recreational water-quality 

criteria include 30-day geometric means and statistical 

threshold values (STV) for two estimated illness rates. The 

EPA also recommended that States could use a Beach Action 

Value (BAV) as a “precautionary tool for making beach notification decisions.” The BAV is not a “component of EPA’s 

recommended criteria” but could be used by States to trigger a notification if any single sample goes above the BAV. 

Two BAVs were identified for E. coli—235 colony-forming 

units per 100 milliliters (CFU/100 mL) and a stricter, 190 

CFU/100 mL—both corresponding to the same illness rates 

as in the new geometric mean and STV criteria. At the time of 

this study (2010–12) and as described below, most States used 

the single-sample bathing water standard of 235 CFU/100 mL 

as a lower threshold for posting beach advisories or closings. 

Therefore, models based on culturable E. coli data collected 

during the present study would fit into new BAV State waterquality standards, and model thresholds could be adjusted if 

the stricter BAV was adopted into a State standard. 

Illinois

In Chicago, a swim advisory is posted at a beach when 

the geometric mean E. coli concentration of two water samples 

from the previous day is equal to or greater than 235 CFU/100 

mL. A swim ban is posted at a beach when the geometric 

mean is equal to or greater than 1,000 CFU/100 mL. This 

system was revised during the course of this study, such that 

swimming advisories were issued when E. coli concentrations 

exceeded 235 CFU/100 mL, but swim bans were eliminated. 

Precautionary swim bans are also issued for 2 days at all 

beaches following any storm event that initiates the opening 

of the locks that separate Lake Michigan from the Chicago 

River system for flood control. Updated beach information is 

available at the Chicago Park District Web site or by calling 

312–74–BEACH or by sending a text message with the name 

of the beach to 312–715–SWIM. In Evanston, the beach is 

closed to swimming when the E. coli concentration from the 

previous day is equal to or greater than 235 CFU/100 mL. 
Methods

15

Indiana

In Indiana, beaches are generally monitored and managed 

by use of the single-sample bathing water standard for E. coli

of 235 CFU/100 mL. If this concentration is exceeded, determinations for issuing a swimming advisory or closing for the 

beach are at the discretion of the beach manager. Results and 

advisories using the previous day’s E. coli concentration are 

posted through the BeachGuard system (Indiana Department 

of Environmental Management, 2012). 

Michigan

In Michigan, county health departments are not required 

to conduct beach monitoring; but if they do, they are required 

to collect a minimum of three samples each time a beach 

area is monitored (Michigan Department of Environmental 

Quality, 2012a). The daily geometric mean for E. coli concentrations calculated from these three samples must be below 

300 CFU/100 mL for the water to be considered safe for 

swimming. The county health departments frequently sample 

more than once a month. A minimum of five sampling events 

(consisting of at least three samples per event) must be collected within a 30-day period for the results to be considered a 

reliable indicator of water quality. After 30 days, a geometric 

mean is calculated for all the individual samples collected 

within that timeframe. The 30-day geometric mean for E. coli

concentrations must be below 130 CFU/100 mL for the water 

to be considered safe for swimming. A beach is closed if either 

the single-day or 30-day concentration exceeds the established 

limit. If a beach closure is due to bacterial contamination, 

county health departments will continue to monitor the water 

quality at the beach and will permit the beach to reopen when 

bacteria levels fall to within acceptable levels. It is possible 

that a beach could be closed for swimming but that other 

recreational activities at the beach may still be available. The 

State maintains a Web site where each public beach is identified, and monitoring data, as well as beach contact information, are provided throughout the swimming season (Michigan 

Department of Environmental Quality, 2012b). 

New York

 In New York, swim advisories are issued by beach 

managers if the previous day’s E. coli concentration is greater 

than or equal to the single-sample bathing water standard of 

235 CFU/100 mL. The condition of the beach is also considered in the decision process. An advisory may also be 

issued if there are other indications of chemical contamination, visible deposits or oil sheens, or hazardous algal or 

other aquatic growth (New York State Department of Health, 

2010). If the E. coli concentration is equal to or greater than 

1,000 CFU/100 mL, beaches are posted with a swimming 

ban, and swimming is not permitted. In addition, beaches are 

closed when a threshold of 0.5 in. of rain in a 24-hour period 

is exceeded at locations with nearby tributary or outflow 

influences. 

Ohio

In Ohio, a swimming advisory is issued at all beaches 

(except the Ohio Nowcast beaches) if the previous day’s 

E. coli concentration exceeds the single-sampling bathing 

water standard of 235 CFU/100 mL (Ohio Department of 

Health, 2012a). Results and advisories using the previous 

day’s E. coli concentration or the nowcast are posted through 

a BeachGuard system (Ohio Department of Health, 2012b) by 

local agencies that monitor their beaches. 

Pennsylvania

In Pennsylvania, swimming advisories are issued by 

beach managers if the previous day’s E. coli concentration 

is greater than or equal to the single-sample bathing water 

standard of 235 CFU/100 mL but less than 1,000 CFU/100 

mL (Erie County Department of Health, 2012). If the E. coli

concentration is equal to or greater than 1,000 CFU/100 mL, 

beaches are posted with a swimming restriction, and swimming is not permitted. In addition, precautionary advisories are 

issued at the beach manager’s discretion prior to receiving any 

bacteria results when conditions are observed that historically 

produced elevated E. coli concentrations. 

Wisconsin

In Wisconsin, beaches are prioritized into high, medium, 

and low priority with monitoring conducted most frequently 

at high-priority beaches and least frequently at low-priority 

beaches (Wisconsin Department of Natural Resources, 2012a). 

Advisories are posted at all of these beaches when a single 

sample concentration for E. coli exceeds 235 CFU/100 mL. 

Advisories are also posted for high-priority beaches when 

a geometric mean of at least five samples collected over a 

30-day period exceeds 126 CFU/100 mL. In addition, beaches 

are closed under any of the following four conditions: whenever a human health hazard, such as a reported illness, exists 

as determined by the local health department; after a major 

pollution event; after a significant rainfall event that is determined to affect a beach area; and whenever the E. coli concentration in a beach water sample exceeds 1,000 CFU/100 mL. 

Beach advisories are posted with signs directly on the beach 

as well as on the Wisconsin Beach Health Web site (Wisconsin 

Department of Natural Resources, 2012b). An option to be 

alerted by email for specific beach conditions also is available. 
16

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Data Collection and Sample Analysis

Water samples for E. coli concentrations and turbidity 

(all beaches), chlorophyll a (Illinois and Indiana, only), and 

other field observations and measurements were collected 

during the recreational season at 49 beaches (table 2). Other 

field measurements included discrete data collected at the time 

of water-sample collection and continuous data collected by 

instruments mounted on buoys, weather stations, streamgages, 

or other equipment near the beaches. At most beaches, USGS 

site identification numbers for equipment measurements 

were the same as site identification numbers for the sampling 

locations (for example, 63rd Street, Ill.). In Ohio, two USGS 

site identification numbers were established specifically for 

measurements at a buoy (Edgewater) and a weather station 

(Mentor Headlands) (table 2). 

Field and Laboratory Measurements 

Daily samples (3 to 7 consecutive days each week) were 

collected during the morning hours throughout the recreational 

seasons (May–September) from the area(s) used for swimming. Sampling techniques and locations were based on EPA 

guidance (U.S. Environmental Protection Agency, 2002) and 

State requirements. Samples were collected into sterile bottles 

or bags by use of grab-sampling techniques in water depths 

consistent for each beach, ranging from 1.5 to 3 ft. Samples 

were collected from one to six sampling sites at each beach 

(table 2), depending on the length of the beach and (or) State 

requirements.

Bird counts and debris assessments were made upon 

arrival at each beach. Wave heights were measured by placing 

a graduated rod at the sampling location and noting minimum and maximum water heights for 1 minute. The wave 

height was computed as the difference between the maximum 

and minimum water height. Specific conductance and water 

temperature were measured at the sampling location by using 

a digital thermometer and (or) an in situ sensor by use of 

standard USGS methods (Wilde, variously dated). Turbidity 

was measured by use of a turbidimeter and an aliquot from 

the E. coli sample bottle or a second subsample bottle, or by 

use of a an in situ turbidity sensor. At Ontario and Durand 

beaches, New York (N.Y.), Secchi disk measurements were 

included at sampling locations to account for transparency of 

the entire water column. 

 Water samples were kept on ice and processed within 6 

hours for E. coli concentrations by use of the Colilert QuantiTray/2000 method (IDEXX Laboratories, Inc., Westbrook, 

Maine) or the modified mTEC method (U.S. Environmental 

Protection Agency, 2009). For the Colilert Quanti-Tray/2000 

method, results are reported in most-probable number per 

100 milliliters (MPN/100 mL). These results are considered 

equivalent to the CFU/100 mL results reported from the modified mTEC method.

At Villa Angela, Ohio, samples were analyzed by the 

local agency using the quantitative polymerase chain reaction 

(qPCR) method for E. coli as described in Noble and others (2010). The qPCR method was designed to provide rapid 

analytical results within 4 hours. For this method, a target 

genetic sequence of DNA present in E. coli is amplified into 

an amount that can be quantified. The direct output from the 

qPCR method is a cycle threshold (Ct), which is the intersection of the amplification curve and a designated threshold 

level. The Ct value was used as an explanatory variable in the 

model. A sample-processing control was run with each sample 

to detect any inhibition of the qPCR. If the results indicated 

inhibition, the Ct value was not used and the model was not 

run. Instead, the ∆∆Ct method was used to calculate results 

(Haugland and others, 2005) in calibrator cell equivalents 100 

milliliters (CCE/100 mL), which was then compared to the 

water-quality standard of 235 CFU/100 mL.

At Chicago beaches in Illinois and at Jeorse Park, Ind., 

water samples collected at the beach were analyzed in the 

laboratory for relative fluorescence (a surrogate for chlorophyll a) using a fluorometer. Results are reported as relative 

fluorescence units (RFU). 

Nearshore Buoys

At some beaches in Illinois, Pennsylvania, and Ohio, 

automated measurements were made from buoys placed 

outside the swimming areas (table 2). The buoys were custom 

made by environmental equipment manufacturers for use in 

nearshore shallow waters. Buoys were equipped with dataloggers to store data and a battery and (or) solar panels for power. 

Equipment for measurements included pressure transducers 

to measure wave heights and wave periods; thermisters to 

measure water temperature; and sensors to measure pH, dissolved oxygen, conductivity, and turbidity. Most buoys were 

equipped with a radio, amplifier, and antenna to transmit the 

data for display on the Web in near real time. Information 

specific to each location is the following:

• In Chicago, buoys were installed and maintained by 

the Chicago Park District and USGS at five beaches in 

2011 and seven beaches in 2012, usually at the middle 

point outside of the swimming area at the depth of 4–6 

ft. Data were transmitted in near real time during the 

recreational season and displayed at http://v4.wqdata.

com/webdblink/chicagopark.php. Parameters were 

measured and updated every hour.
Methods

17

• In Ohio, buoys were installed and maintained by the 

USGS at Huntington in 2008–11, Edgewater in 2005–

12, and Mentor Headlands in 2011–12. Wave heights 

and wave periods were compiled every 30 minutes, 

and water-quality data were recorded every hour. Buoy 

data were transmitted in near real time during the recreational season and displayed on the USGS National 

Water Information System Web site (NWISWeb) (U.S. 

Geological Survey, 2012). 

• At Presque Isle State Park, Erie, Pa., buoys were 

placed outside the Beach 2 and Beach 6 swimming 

areas. The buoys were installed by the Regional Science Consortium and were maintained with help from 

the USGS. The buoy at Beach 2 has been operational 

since 2008 for most parameters, and instrumentation 

to measure wave height and water current speed and 

direction were added in 2011. The buoy at Beach 6 was 

newly deployed in 2011. All buoy data were transmitted in near real time every 10 minutes during the recreational season and displayed at http://v4.wqdata.com/

webdblink/trec.php?tab2=overview&t=classic_web. 

Parameters were measured every 10 minutes. 

Weather Stations and Other Equipment Installed 

by the U.S. Geological Survey

A weather station at one beach in Ohio and rain gages at 

several beaches in Wisconsin were installed and maintained 

by the USGS to obtain site-specific weather data for model 

development. A weather station was installed near the western edge of Mentor Headlands State Park, Ohio, to measure 

precipitation, wind speed and direction, barometric pressure, 

air temperature, net solar radiation, and photosynthetically 

active radiation. Weather data from Mentor Headlands (table 

2) were transmitted during the recreational season for nearreal-time display on NWISWeb (U.S. Geological Survey, 

2012). In Wisconsin, tipping-bucket rain gages yielded rainfall 

data on a fine time scale (5 minutes or less). These included 

four rain gages in Manitowoc County that measured rainfall 

near all seven Manitowoc County beaches and one rain gage 

in Washburn, Wis., to measure rainfall at Thompson West End 

Park Beach (table 2). 

Staff gages were installed by the USGS on seven tributaries that were expected to influence water-quality at beaches in 

New York and Wisconsin. A gage-height measurement, to the 

tenth of an inch, was obtained by visible observation by the 

beach sampler near the time of sample collection, and the difference from the previous day’s measurement was calculated. 

Previous-day gage-height variables represented changes in 

discharge over a 24-hour period up to the time of sampling.

Other types of equipment were installed and maintained 

by the USGS at two beaches in Ohio. A sensor to measure 

photosynthetically active radiation (PAR) and datalogger 

were mounted on top of a 50-ft water tower at Huntington 

to record PAR data every 3 minutes. The total PAR for the 

previous day (midnight to midnight) was calculated by summing all recorded measurements. At Edgewater, a temporary 

piezometer (shallow water well) equipped with a pressure 

transducer and datalogger to measure and record water levels 

every 30 minutes was installed 20 ft inland from the edge of 

water. The screened interval was from 1.3 to 4.3 ft below land 

surface. To aid in beach grooming, the piezometer was buried 

1 ft deep; the elevation of the top of the casing was surveyed. 

Water-level data from the piezometer were corrected for barometric pressure and are referred to as “foreshore head.” The 

foreshore head varies as a function of pressure changes due 

to the local water table, fluctuations in barometric pressure, 

the water level in the lake, and the pressure variance due to 

wave action (Francy and others, 2009). The rate of change in 

foreshore head, in feet per hour (ft/h), was calculated for each 

30-minute time interval. Average rates were then calculated by 

averaging the rate of change in foreshore-head measurements 

over the antecedent 6-, 12-, 24-, and 48-hour periods up to the 

time of sampling. 

Compilation of Data From Existing Sources 

Data from existing sources were compiled and used to 

develop and validate predictive models for this study. Data 

were retrieved manually by the USGS and local agencies, or 

by use of tools described below and designed specifically to 

compile and process data for predictive-model development. 

A lake-level spreadsheet organizes hourly lake-level data 

retrieved from the nearest offshore buoy; PROCESSNOAA 

processes hourly rainfall, wind direction and speed, and barometric pressure data retrieved from the nearest airport site; and 

Environmental Data Discovery and Transformation (EnDDaT) 

retrieves and processes data from multiple sources. 

Weather Data From the National Weather 

Service

Rainfall, wind direction and speed, and air-temperature 

data were obtained from the nearest National Weather Service 

(NWS) airport site by compiling data daily throughout the 

season (National Oceanic and Atmospheric Administration, 

2012a) or in batch for the entire season(s) (National Oceanic 

and Atmospheric Administration, 2012b). Hourly rainfall 

values were totaled for 24 hours up to the time of sampling 

and lagged and weighted as described below; running 30-day 

totals were calculated for one site. Wind direction and speed 

were recorded for the approximate time of sampling (generally 8 a.m.) or compiled for the past 24 hours and summed as 

described below.

In order to obtain information from a more widespread 

area, hourly radar rainfall data were obtained from the NWS 
18

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

(National Oceanic and Atmospheric Administration, 2012c) 

for 4-kilometer (km) grids, or “cells.” The number of cells 

used for calculations ranged from 1 to 102, depending on the 

configuration of the beach and the areal extent of the watershed. For some beaches, radar rainfall values were totaled for 

each cell for the previous 24 hours up to the time of sampling. 

Data were expressed as the maximum value among all the 

cells (radarMax24) or the summed total for all cells (radarTotal24); data were lagged and weighted as described below 

in the section “PROCESSNOAA—A Tool for Compiling 

Weather Data.” For other beaches, watershed areas contributing to the beach were delineated in a geographic information 

system (GIS), as were watershed boundaries for nearby tributaries. The watershed average rainfall was computed by using 

all radar rainfall cells that influenced these watersheds, as 

described below in the section “EnDDaT—A Tool for Compiling Data from Multiple Sources.” 

Stream Measurements From a Nearby 

U.S. Geological Survey Streamgage 

For streams in proximity to beaches (less than 5 mi 

away), stream-discharge or stream-stage data were obtained 

from established USGS streamgages. These data are available 

online (U.S. Geological Survey, 2012) or can be obtained by 

contacting a state USGS office. The mean, minimum, or maximum stream discharge or stage for the previous 24–72 hours 

up to the time of sampling or the instantaneous stream discharge at the time of sampling was used in predictive models.

Measurements or Predictions From the National 

Oceanic and Atmospheric Administration

Lake-level data were obtained from the nearest offshore 

station (National Oceanic and Atmospheric Administration, 

2012d). Based on the time of sampling, change over the past 

24 hours or change from spring average values were used. 

Hourly lake-level data and change over the past 24 hours were 

compiled for some sites by using the lake-level spreadsheet, 

as discussed later in the report in the section “Lake-Level 

Spreadsheet—A Tool for Compiling Lake-Level Data.” To 

compute change from the prerecreational season average 

(spring), hourly lake levels for April 1–May 15 for the current 

year were compiled, the prerecreational season average was 

calculated, and the instantaneous value at the time of sampling 

was subtracted from the prerecreational season average. Lake 

level was not used directly as an explanatory variable. Previous work indicated that direct lake levels may be poor predictors of E. coli concentrations if lake levels are very different in 

the calibrator years from the nowcast year (Francy and others, 

2009); change in lake level over the past 24 hours or change 

from prerecreational season averages were used in this study 

because they may be better predictors over several years. 

Wind direction and speed data for the time of sampling 

were obtained for one site (Nickel Plate, Ohio) from the 

National Estuarine Research Reserve System (National Oceanic and Atmospheric Administration, 2012e). 

Nowcast or forecast results from Great Lakes Coastal 

Forecasting System (GLCFS) were available on a 2-km grid 

throughout the Great Lakes (National Oceanic and Atmospheric Administration, 2012f). Data used from the GLCFS 

for predictive modeling, which were compiled by using the 

EnDDat system, included nowcast or forecast winds, wave 

heights, surface temperatures, and currents.

Local Sources of Weather Data

In addition to established regional or national sources of 

weather data, local data were obtained from nearby sources 

near some Illinois and Pennsylvania beaches. Hourly rainfall 

and wind speed and direction were collected at the Evanston, 

Ill., water treatment plant on the Lake Michigan shoreline 

just north of the study beaches. In Chicago, Ill., weather 

stations were installed by the Chicago Park District at 63rd 

Street, Oak Street, and Foster Beaches to measure precipitation, wind speed and direction, barometric pressure, air 

temperature, and solar radiation. Weather data from this site 

were transmitted in near real time during the recreational 

season and displayed on the Web at http://v4.wqdata.com/

webdblink/chicagopark.php. At Presque Isle State Park, Pa., 

two weather stations were installed in 2008 by the Regional 

Science Consortium. One weather station is on a tower on 

top of the Tom Ridge Environmental Center (TREC) at the 

entrance to Presque Isle State Park; the other weather station is 

on a tower at Beach 2. Weather data were measured every 10 

minutes and were transmitted in near real time year round and 

displayed on the Web at http://v4.wqdata.com/webdblink/trec.

php?tab2=overview&t=classic_web.

Lake Level Spreadsheet—A Tool for Compiling 

Lake-Level Data 

Instructions and a spreadsheet for compiling hourly lakelevel data from the nearest offshore station (National Oceanic 

and Atmospheric Administration, 2011d) are included in this 

report as appendix 1. The spreadsheet was used by study 

personnel; after retrieving data from the source, instantaneous 

lake-level data for the time of sampling were compiled, and 

change in lake level from the previous day’s sampling time 

was calculated. 
Methods

19

PROCESSNOAA—A Tool for Compiling Weather 

Data 

Rainfall, wind direction and speed, and barometric pressure data from National Oceanic and Atmospheric Adminsitration (NOAA) airport locations can be compiled in batch by 

use of a software program called PROCESSNOAA. Instructions for PROCESSNOAA (appendix 2) and the downloadable 

program are available at http://pubs.usgs.gov/sir/2013/5166/. 

Rainfall totals were compiled for the 24-hour period 

preceding the morning sampling (Rd−1). The PROCESSNOAA 

program was used to lag rainfall data and calculate weighted 

rainfall variables from airport rainfall data. These calculations 

can also be done manually with rainfall from other sources. 

The “Rw48” and “Rw72” variables were 48 hours and 72 

hours of cumulative rainfall, respectively, giving more weight 

to the most recent rainfall amount as follows:

Rw48 = (2* Rd−1) + Rd−2

Rw72 = (3* Rd−1) + (2* Rd−2) + Rd−3

where

Rd−1 is the amount of rain, in inches, that fell in 

the 24-hour period preceding the morning 

sampling; 

Rd−2 is the amount of rain that fell in the 24-hour 

period 2 days preceding the morning sampling; 

and 

Rd−3 is the amount of rain that fell in the 24-hour 

period 3 days preceding the morning sampling.

The PROCESSNOAA software was also used to compile 

and process wind direction and speed data for the time of sampling (approximately 8 a.m.) and for the preceding 24-hour 

period. The 24-hour wind speed and direction variables were 

calculated by averaging hourly wind vectors for the 24-hour 

period preceding sampling and determining the direction 

and speed of the resultant vector. The instantaneous 8 a.m. 

and 24-hour wind speed and direction variables were used 

directly or were used in Virtual Beach to calculate alongshore 

and offshore wind components (U.S. Environmental Protection Agency, 2012b). The 24-hour wind speed and direction 

variable, however, could not be easily obtained on a daily 

basis and was determined to be impractical for an operational 

nowcast; this variable was omitted from the latest version of 

PROCESSNOAA.

For some sites, wind directions compiled from PROCESSNOAA were placed into categories by examining patterns in plots of E. coli concentrations in lake-water samples 

as a function of wind direction. Wind codes were calculated 

by assigning the highest value to the range of wind directions 

associated with the highest E. coli concentrations. Processes 

affecting E. coli concentrations were also considered to 

ensure that the wind direction categories could be reasonably 

explained as a function of the nature and locations of potential 

E. coli sources. 

EnDDaT—A Tool for Compiling Data From 

Multiple Sources

A Web tool, Environmental Data Discovery and Transformation (EnDDaT), was developed by the USGS to facilitate retrieval of data from multiple Web-based sources and 

combine them into one dataset (U.S. Geological Survey, 2013). 

Currently, this tool can access national sources of data such as 

the USGS NWISWeb, the GLCFS (a 2-km grid throughout the 

Great Lakes), radar-indicated precipitation from the NOAA 

National Mosaic Quantitative Precipitation Archive (4-km 

grid for the United States), the National Data Buoy Center, the 

National Weather Service Surface Summary of Day data, and 

a link to nearby meteorological data from the National Climatic Data Center. In addition, this tool is currently evolving 

and incorporating other relevant sources of environmental data 

that become available. 

Variables retrievable from the GLCFS included surface 

current magnitude and direction, depth-integrated current 

magnitude and direction, wave height, wave direction, deviation from average lake level, surface water temperature, and 

wind speed and direction. Access to these model results was 

facilitated by use of EnDDaT, including a mapping interface 

for choice of grid points, definition of sample times, specifying the beach orientation resolution of velocity vectors and 

direction of waves to the beach orientation, and statistical 

processing of the model results time series. Additional detail 

on the use of EnDDaT is available in the user manual (http://

cida.usgs.gov/enddat/). 

Development of Predictive Models

At least 2 years of data on E. coli concentrations and field 

measurements (except for Jeorse Park where only 1 year of 

data was used) and data from existing sources were compiled 

into spreadsheets and formatted to develop site-specific predictive models for E. coli. Data exploration, model development, and model validation were done by use of Virtual Beach 

(U.S. Environmental Protection Agency, 2012b) or, less often, 

by use of other statistical software. Procedures for developing models are described in Francy and Darner (2006), and 

these steps generally were followed whether Virtual Beach or 

other software was used. Briefly, because of the wide range 

of expected values and to improve linear relations between 

E. coli concentrations and explanatory variables, concentrations of E. coli were log10-transformed before any statistical 

testing and modeling. The relations between explanatory 

variables and E. coli concentrations were examined by using 

Pearson’s correlation analysis and by associated scatterplots. 

Explanatory variables that showed statistically significant 

linear relations to E. coli concentrations, or nonlinear relations in scatterplots, were selected for model development. 

Statistical methods for model development included linear 

regression and partial least squares (PLS) techniques described 

elsewhere (U.S. Environmental Protection Agency, 2012b). 
20

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

For each beach, a model was selected to be validated and (or) 

used in nowcasts during the 2012 recreational season. Beach 

managers selected one of two model outputs: (1) a predicted 

E. coli concentration or (2) the probability of exceeding the 

State standard. If using the probability as the output, threshold 

probabilities for advisories or closings were established by 

examining model-output sensitivities and specificities at different probability levels. 

Data Management and Analysis 

Daily data (from field and lab measurements or compiled 

from other sources) and model results (from Virtual Beach) 

were compiled by local agencies into spreadsheets or entered 

into a sanitary survey database for Wisconsin data, described 

below. 

Example spreadsheets for compiling daily data are 

included in appendix 3. Daily data for sample times, field 

observations, model variables, E. coli concentrations, and 

exceedance probabilities of a State standard or predicted 

E. coli concentrations (“model prediction” in Virtual Beach 

software and in appendix 3) were entered into the spreadsheet. 

The exceedance probability was used in the example for 

Beach 1 and was obtained by entering values for explanatory 

variables into the Virtual Beach software and obtaining an 

exceedance probability as output from the model. The predicted E. coli concentration was used in the example for Beach 

2 and was similarly obtained from Virtual Beach software. 

The spreadsheet was used to compile responses to facilitate 

comparisons of the model output (exceedance probability 

or predicted E. coli concentration), threshold (probability or 

E. coli concentration), and the measured E. coli concentration. 

Assessment of a correct response was based on the measured 

E. coli concentration determined by the culture method for the 

day of prediction. Model responses were classified as follows:

• Correct nonexceedance (Correct -).—The exceedance 

probability (or predicted E. coli) was below the threshold, and the measured E. coli concentration was below 

the bathing-water standard.

• False positive (false +).—The exceedance probability 

(or predicted E. coli) was equal to or above the threshold, and the measured E. coli concentration was below 

the bathing-water standard.

• Correct exceedance (Correct +).—The exceedance 

probability (or predicted E. coli) was equal to or above 

the threshold, and the measured E. coli concentration 

was above the bathing-water standard.

• False negative (false -)—The exceedance probability 

(or predicted E. coli) was below the threshold, and the 

measured E. coli concentration was above the bathingwater standard.

Data on E. coli concentrations, wave heights, turbidity, and water temperature measured at the time of sampling 

were entered into the USGS database for water-quality data 

available to the public through NWISWeb (U.S. Geological 

Survey, 2012). Data were entered in batch format at the end 

of the season, with codes for local collecting and analyzing 

agencies and station identification numbers for each sampling 

location (table 2). Data were also available in near real time on 

NWISWeb during the recreational season for measurements 

obtained from selected water-quality sensors installed to support the development of nowcasts (table 2). 

The model responses for the calibration dataset (data used 

to develop the model) and validation dataset (data collected 

during an independent year) were evaluated in terms of the 

correct predictions, sensitivities, and specificities. The validation dataset included a comparison to the use of the persistence 

model. To determine whether responses for correct predictions, sensitivities, and specificities were significantly different 

between the nowcast and persistence models, responses were 

compared by use of the paired student’s t-test in SAS 9.2 (SAS 

Institute Inc., Cary, North Carolina).

Data from Wisconsin sites were managed through a 

USGS daily sanitary survey database designed specifically for 

nowcast data. The daily sanitary survey database was project 

specific and not available to the public. It included access to 

online forms for entering water-quality and beach-observation 

data. Data were entered at the “beach level” for data elements 

with only one sampling point per beach. Data were entered 

at the “monitoring point level” for data elements that were 

collected at more than one site per beach. Data were entered 

for relevant tributaries for data elements such as water level, 

turbidity, and specific conductance. All beach and tributary 

data were stored in a relational database. Access to all data 

residing in the database was available through online query 

forms by specifying the beach, choosing parameters of interest, choosing the file format, and specifying the date range. 

Final file format options included XML, tab delimited, comma 

delimited, or Microsoft Excel. 
Identification of Variables and Development of Predictive Models

21

Quality Assurance and Quality Control

Quality-assurance and quality-control (QA/QC) procedures were implemented to facilitate the collection of highquality datasets for model development. Written protocols, 

similar to the example presented in appendix 4, were distributed to local agencies if locally written protocols were not 

available. The USGS performed QA/QC checks of procedures 

performed by field and laboratory personnel and provided 

written comments and recommended corrective actions. For 

most sites, the USGS checked spreadsheets compiled by 

local agencies against daily field forms. Field quality-control 

(QC) samples collected for E. coli concentrations included 

field blanks and replicates. Laboratory QC samples for E. coli

included reference samples provided by the USGS, filter 

blanks, and processing blanks (Myers and others, 2007). For 

turbidity, duplicate aliquots were measured from the same 

bottle, and duplicate measurements that did not agree within 

15 percent (or 1.5 nephelometric turbidity units (NTU) for 

values less than 10 NTU) were repeated. Turbidity reference 

samples were sent to all participating laboratories. Results 

from QC samples were monitored, and retests were done and 

corrective measures taken when needed. 

Automated measurements from buoys placed outside the 

swimming areas at some beaches in Illinois, Pennsylvania, 

and Ohio were monitored throughout the season for abnormal 

fluctuations. If an abnormal fluctuation occurred, the equipment was removed, checked, and replaced (if needed). At 

Chicago beaches, turbidity, wave height, and temperature 

sensor measurements were checked several times throughout 

the season per manufacturer’s instructions. At the end of the 

season, data obtained from Chicago-area buoys were screened 

for abnormal or negative values, and these data points were 

removed for predictive modeling. For example, unusually high 

turbidity measurements randomly occurring within datasets 

were compared against measurements from other buoys and 

removed if needed. At Pennsylvania beaches, the continuous 

water-quality instrumentation on the buoys was recalibrated 

every 4 to 6 weeks. 

The USGS installed and operated a weather station at 

Mentor Headlands, Ohio, and rain gages at several beaches in 

Wisconsin. Rain gages were calibrated at the beginning and 

end of the season in accordance with standard USGS methods 

(U.S. Geological Survey, 2009). The Chicago Park District 

installed three onsite weather stations and made repairs as 

needed. Weather data from the Chicago sites were screened 

for abnormal and negative values, which were subsequently 

removed from datasets. If there were long periods of missing 

data due to malfunctioning of the equipment, missing data 

were substituted with readings from the closest onsite weather 

station. 

Identification of Variables and 

Development of Predictive Models

Summary statistics for E. coli concentrations for the 

sampling period for this project at 49 Great Lakes beaches and 

percent exceedances for the calibration and validation year(s) 

are listed in table 3. For this study, the number of samples 

collected ranged from 32 samples at Jeorse Park, Ind., to 829 

samples at Edgewater, Ohio. E. coli concentrations ranged 

from less than 1 CFU or MPN/100 mL (both units will be 

reported hereinafter as “CFU/100 mL”) at several sites to 

at least 13,000 CFU/100 mL at Ontario, N.Y. Median concentrations of E. coli ranged from 10 CFU/100 mL at Lake 

Erie, N.Y., to 570 CFU/100 mL at Jeorse Park, Ind. During 

the recreational season of the calibration year(s), the percentage of days that the E. coli bathing-water standard was 

exceeded ranged from 3.9 percent at Upper Lake Park, Wis., to 

78.1 percent at Jeorse Park, Ind. During the recreational season of the validation year (2012), the percentage of days that 

the E. coli bathing-water standard was exceeded ranged from 

0 percent at Lake Erie, N.Y., and Oak Street, Ill., to 51 percent 

at Lakeview, Ohio.
Table 3. Sampling information and summary statistics at Great Lakes beaches for period of sampling and exceedances for calibration and validatio[CFU, colony-forming units; MPN, most-probable number; mL, milliliters; na, not applicable because model was not validated during 2012] Beach short name Sampling years and frequency/week Number of samples Daily E. coli concentrations during calibration and validation years in CFU or MPN/100 mL Percentage of days standard was exceeded during calibration years (2010, 2010–11, or earlier) Percentage of days standard was exceeded during validation year (2012) 

Median Minimum Maximum Illinois South Beach 2010-12, 5 days 186 72 1 1,000 20.0 16.2 

Greenwood Beach 2010-12, 5 days 179 27 1 1,100 11.0 6.2 Lee Beach 2010-12, 5 days 186 32 1 960 7.0 4.4 Montrose 2010-12; 3–5 days 192 76 3 >2,400 22.3 26.3 63rd Street 2010-12; 3–5 days 184 47 1 >2,400 15.6 24.0 Foster 2010-12; 3–5 days 182 26 1 1,800 6.5 10.8 Calumet 2010-12; 3–5 days 190 57 2 1,800 16.4 16.3 Oak Street 2011-12; 5 days 138 14 1 1,800 4.5 0.0 

Indiana Jeorse Park 2010; 3 days 32 570 15 >2,400 78.1 na 

Michigan Metro 2010-12, 2–4 days 187 27 1 >2,400 16.0 1.5 

Memorial 2010-12, 2–4 days 179 63 1 >2,400 20.7 4.4 New Baltimore 2010-12, 2–4 days 181 52 1 >2,400 7.0 na Grand Haven 2011, 4 days 65 77 6 1,400 12.3 na 

New York Hamlin SP Area 3 2011-12; 3-5 days 107 84 1 >3000 27.1 na 

Hamlin SP Area 4 2011-12; 3-5 days 104 99 4 >3000 29.8 na Ontario 2010-12; 7 days 253 125 1 13,000 36.4 31.8 Durand 2010-12; 7 days 229 43 1 4,200 20.6 15.9 Woodlawn SP 2010-12; 5 days 266 80 4 >7,600 30.8 12.1 Evans TP 2010-12; 5 days 201 20 4 1,000 13.4 3.5 Lake Erie 2010-12; 5 days 201 10 4 4,800 16.9 0.0 
Table 3. Sampling information and summary statistics at Great Lakes beaches for period of sampling and exceedances for calibration and validatio[CFU, colony-forming units; MPN, most-probable number; mL, milliliters; na, not applicable because model was not validated during 2012]

Beach 

short name

Sampling years 

and 

frequency/week

Number 

of 

samples

Daily E. coli

concentrations 

during calibration 

and validation years 

in CFU or MPN/100 mL

Percentage of days 

standard was 

exceeded during 

calibration years 

(2010, 2010–11, or earlier)

Percentage of days 

standard was 

exceeded during 

validation year 

(2012)

Median Minimum Maximum

Ohio

Edgewater 2005-12; 5–7 days 829 67 3 4,800 23.8 13.0 

Huntington 2005-12; 4–7 days 692 45 1 5,600 15.5 19.0 Maumee Bay SP 2008-12; 4 days 157 57 4 3,600 16.2 22.4 Villa Angela 2011-12, 7 days 190 190 2 12,000 48.5 44.8 Vermillion West 2010-12; 3–4 days 143 45 1 >2,400 29.7 19.2 Nickel Plate 2010-12; 3–4 days 143 45 1 >2,400 15.4 17.3 Lake Front 2010-12; 3–4 days 143 96 3 >2,400 28.6 19.2 Fairport Harbor 2010-12; 7 days 310 49 1 >2,400 14.6 17.1 Mentor Headlands 2010-12; 7 days 310 33 1 1,700 16.6 14.3 

Lakeview 2005, 11-12; 4, 7 days 242 240 11 6,000 50.7 51.0 Lakeshore 2011-12, 4 days 111 140 1 >2,400 35.2 na Walnut 2011-12, 4 days 111 23 <1 870 9.3 na 

Pennsylvania

Beach 1 West Extension 2010-12; 4–7 days 223 38 <1 1,800 18.3 18.5 Beach 2 2004-6, 10-12; 4, 4–7 days 372 20 <1 1,200 7.5 12.0 Beach 6 2010-12; 4–7 days 223 40 <1 1,300 8.4 14.1 Mill Road 2010-12; 4–7 days 221 28 <1 720 7.8 15.6 Beach 9 2010-12; 4–7 days 219 28 <1 700 10.6 7.9 Beach 11 2010-12; 4–7 days 221 22 <1 5,500 5.6 10.0 

Wisconsin

Upper Lake Park 2009-2012; 4 days 140 12 <1 1,300 3.9 25.0 Hika Park 2010-2012; 4 days 128 79 <1 >2,400 28.2 18.6 Fischer 2010-2012; 4 days 132 52 <1 >2,400 19.0 20.8 Red Arrow 2010-2012; 4 days 140 112 <1 >2,400 34.2 25.0 Neshotah 2010-2012; 4 days 127 29 <1 1,300 6.8 1.9 
Table 3. Sampling information and summary statistics at Great Lakes beaches for period of sampling and exceedances for calibration and validatio[CFU, colony-forming units; MPN, most-probable number; mL, milliliters; na, not applicable because model was not validated during 2012] Beach short name Sampling years and frequency/week Number of samples Daily E. coli concentrations during calibration and validation years in CFU or MPN/100 mL Percentage of days standard was exceeded during calibration years (2010, 2010–11, or earlier) Percentage of days standard was exceeded during validation year (2012) 

Median Minimum Maximum Wisconsin—Continued Point Beach South 2010-2012; 4 days 136 46 <1 >2,400 16.7 15.5 

Point Beach Central 2010-2012; 4 days 136 41 <1 >2,400 15.4 13.8 Point Beach North 2010-2012; 4 days 127 33 <1 >2,400 10.1 12.1 Thompson's West End Park 2010-2012; 4 days 128 22 <1 >2,400 16.9 5.9 

Kreher Park 2010-2012; 4 days 116 29 <1 >2,400 18.8 10.6 

Maslowski 2010-2012; 4 days 137 76 2 >2,400 11.5 32.0 
Identification of Variables and Development of Predictive Models

25

Identification of Variables Used to Improve 

Operational Nowcasts in Ohio and Wisconsin

Data were examined to determine the potential for new 

variables to improve performance of three nowcasts in Ohio 

and Wisconsin that were operational at the start of the study in 

2010. In Ohio, correlations between E. coli concentrations and 

photosynthetically active radiation (PAR) and foreshore-head 

variables were evaluated. In Wisconsin, correlations between 

E. coli concentrations and cloud cover, air temperature, water 

temperature, and directional wave height variables were 

evaluated.

Ohio

The data representing the linear regression relations 

between log10 E. coli concentrations and sum of the PAR 

for the previous day are shown for Huntington (fig. 2A) and 

Edgewater (fig. 2B) for 2008–12. The PAR sensor was operating at Huntington; however, data for Edgewater were included 

because Edgewater is approximately 10 mi east of Huntington 

along the shore of Lake Erie. The E. coli concentrations were 

negatively related to PAR at Huntington and Edgewater with 

similar slopes and y-intercepts; however considerable scatter 

is present in these relations. 

Pearson’s correlations between log10 E. coli concentrations and the sum of the PAR for the previous day are listed in 

table 4 for each year and for all years combined. At Huntington, the correlation was stronger during 2010 (r = −0.57) than 

for the other years, and the only year where a significant correlation was not found was in 2012 (r = 0.02). At Edgewater, 

correlations were strongest during 2011 (r = −0.37), and the 

only year where a significant correlation was not found was 

in 2008 (r = −0.15). By using datasets with observations that 

had PAR data, the correlations between E. coli concentrations 

and PAR were compared qualitatively to the relations between 

E. coli and the variables used in predictive models. At Huntington and Edgewater, these relations were not as strong for 

PAR as they were for sum of the log turbidity and wave height 

or for radar rain 48 hours weighted (Rw48). At Huntington, 

the relation between E. coli and PAR was similar to that for 

the wind variable or Rd−1 for most years and was stronger than 

that for airport rainfall Rd−2 for all years except 2012. Airport 

rainfall Rd−2 was a variable used in the Huntington backup 

model. At Edgewater, the relation between E. coli and PAR 

was stronger than day of the year (a weakly related variable, if 

at all) and was stronger than lake-level change for most years.

A piezometer with a pressure transducer to measure water 

levels in shallow groundwater (“foreshore head”) was installed 

at Edgewater during 2008–09 and 2011–12. An example of 

one time period (May 23–June 30, 2012) is shown in figure 3. 

Elevated E. coli concentrations (greater than the bathing-water 

standard of 235 CFU/100 mL) were usually associated with 

periods of increasing foreshore head. Because the foreshore 

head can be influenced by rainfall, lake level elevations, and 

wave heights, these ancillary measurements are included on 

the plot. The patterns of lake levels and wave heights were 

similar to those of foreshore head, but significant rainfall 

events were not usually associated with spikes in foreshore 

head. 

Pearson’s r correlations between log10 E. coli concentrations and the average rate of change in foreshore head for different time intervals are listed in table 5 for each year and for 

all years combined. Over all years, the 12-hour average was 

most highly correlated with E. coli, with the highest singleyear correlation for the average rate of 12 hours in 2009 

(r = 0.53). Significant correlations were found for each year 

and all years combined using the average rates of 6, 12, 24, 

and 48 hours, except during 2008 using the average 6 hour rate 

(r = 0.14). Using datasets with foreshore-head data, the correlations between E. coli concentrations and foreshore head 

were compared qualitatively to the relations between E. coli

and the variables used in predictive models. The relations 

between E. coli concentrations and the 12-, 24-, and 48-hour 

average rates were comparable to or slightly weaker than the 

relations between E. coli and the radar rain 48 hours weighted 

(Rw48) or sum of the log turbidity and wave height variables. 

Stronger correlations were found between E. coli concentrations and these three foreshore lake variables than with day of 

the year or lake-level change.
26

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Figure 2. Relations 

between log10

Escherichia coli

concentrations and 

photosynthetically 

active radiation 

(PAR) for 2008–12 at 

two Ohio beaches. 

A, Huntington. 

B, Edgewater. (r, 

Pearson’s correlation 

coefficient)

A.

B.

0

4.0

3.5

3.0

2.5

2.0

1.5

0.5

0

1.0

10,000 20,000 30,000 40,000 50,000 60,000

R2 = 0.10

Sum of the PAR for previous day, in millimoles per meter squared

Log 10 Escherichia coli, in colony-forming units per 100 milliliters

y = 10-5x + 2.32

y = 10-5x + 2.41

R2

 = 0.08

0

4.0

3.5

3.0

2.5

2.0

1.5

0.5

0

1.0

10,000 20,000 30,000 40,000 50,000 60,000

Sum of the PAR for previous day, in millimoles per meter squared

Log 10 Escherichia coli, in colony-forming units per 100 milliliters
Identification of Variables and Development of Predictive Models

27

Table 4. Pearson’s correlations between log10 Escherichia coli concentrations and photosynthetically active radiation (PAR) or 

explanatory variables used in predictive models at Huntington, Bay Village, and Edgewater, Cleveland, Ohio, 2008–12.

Variable 2008 2009 2010 2011 2012 All years

Huntington

Number of samples 81 103 57 54 58 353

PAR, sum of the 

previous day

-0.35 (0.0012) -0.39 (<0.0001) -0.57 (<0.0001) -0.33 (0.0136) 0.02 (0.8554) -0.32 (<0.0001)

Sum, log turbidity and 

wave height

0.59 (<0.0001) 0.68 (<0.0001) 0.63 (<0.0001) 0.60 (<0.0001) 0.65 (<0.0001) 0.62 (<0.001)

Radar rain, 48 hours 

weighted (Rw48)a

0.45 (<0.0001) 0.59 (<0.0001) 0.32 (0.0184) 0.62 (<0.0001) 0.28 (0.0325) 0.46 (<0.0001)

Wind code x wind 

speed, instantaneous

0.42 (0.0001) 0.39 (<0.0001) 0.25 (0.0615) 0.46 (0.0004) 0.20 (0.1229) 0.34 (<0.0001)

Airport rainfall, Rd-1b 0.31 (0.0048) 0.51 (<0.0001) 0.57 (<0.0001) 0.53 (<0.0001) 0.31 (0.0188) 0.40 (<0.0001)

Airport rainfall, Rd-2cd 0.07 (0.5131) 0.27 (0.0067) 0.18 (01888) 0.20 (0.1484) 0.19 (0.1555) 0.16 (0.0022)

Edgewater

Number of samples 88 117 86 59 114 464

PAR, sum of the 

previous day

-0.15 (0.1560) -0.30 (0.0009) -0.30 (0.0048) -0.37 (0.0035) -0.23 (0.0123) -0.28 (<0.0001)

Sum, log turbidity and 

wave height

0.42 (<0.0001) 0.58 (<0.0001) 0.53 (<0.0001) 0.54 (<0.0001) 0.32 (0.0006) 0.48 (<0.0001)

Radar rain, 48 hours 

weighted (Rw48)a

0.35 (0.0008) 0.39 (<0.0001) 0.25 (0.0221) 0.65 (<0.0001) 0.38 (<0.0001) 0.39 (<0.0001)

Day of the year 0.00 (0.9374) -0.07 (0.4778) 0.10 (0.3637) 0.14 (0.2761) 0.31 (0.0009) 0.16 (0.0004)

Lake level, change in 

24 hours

0.30 (0.0040) 0.17 (0.0658) 0.31 (0.0042) 0.31 (0.0154) 0.20 (0.0311) 0.22 (<0.0001)

a

 Radar rainfall sum from multiple cells for the 24-hour periods 1 and 2 days before sampling. The most recent rainfall receives the most weight. 

b

 The total rainfall, in inches, for the 24-hour period before sampling .

c

 The total rainfall, in inches, for the 24-hour period 2 days before sampling.

d

 Variable was used in the backup model when radar rainfall data were not available.
28

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Figure 3. Piezometer water level elevations (“foreshore head”), lake-level elevations, wave heights, radar rainfall, and concentrations 

of Escherichia coli at Edgewater, Cleveland, Ohio, May 23–June 30, 2012. (CFU/100 mL, colony-forming units per 100 milliliters)

Piezometer water-level elevation

Lake-level elevation

Wave height

24-hour radar rainfall

E. coli in lake water

EXPLANATION

Water elevation, in feet

Escherichia coli, in cfu/100 mL

5/24/2012

570

571

572

573

574

575

0

1

2

3

100

101

102

103

5/31/2012 6/7/2012

Date

6/14/2012

Bathing-water standard

6/21/2012 6/28/2012

Water height, in feet

24-hour radar rainfall, in inches

Variable 2008 2009 2011 2012 All years

Number of samples 71 96 70 100 337

Foreshore head, average rate of change, 

6 hours

0.14 (0.2392) 0.34 (0.0007) 0.33 (0.0053) 0.30 (0.0025) 0.27 (<0.0001)

Foreshore head, average rate of change, 

12 hours

0.25 (0.0326) 0.53 (<0.0001) 0.46 (<0.0001) 0.22 (0.0298) 0.35 (<0.0001)

Foreshore head, average rate of change, 

24 hours

0.36 (0.0020) 0.44 (<0.0001) 0.37 (0.0016) 0.26 (0.0077) 0.34 (<0.0001)

Foreshore head, average rate of change, 

48 hours

0.25 (0.0391) 0.40 (<0.0001) 0.37 (0.0018) 0.35 (0.0003) 0.33 (<0.0001)

Sum, log turbidity and wave height 0.43 (0.0002) 0.51 (<0.0001) 0.55 (<0.0001) 0.36 (0.0002) 0.47 (<0.0001)

Radar rain, 48 hours weighted (Rw48) 0.31 (0.0086) 0.42 (<0.0001) 0.66 (<0.0001) 0.38 (0.0001) 0.41 (<0.0001)

Day of the year 0.11 (0.3737) -0.06 (0.5641) 0.27 (0.0221) 0.27 (0.0064) 0.13 (0.0192)

Lake-level, change in 24 hours 0.21 (0.0813) 0.12 (0.2473) 0.28 (0.0180) 0.20 (0.0452) 0.20 (0.0002)

Table 5. Pearson’s correlations between log10 Escherichia coli concentrations and foreshore head or to explanatory variables used in 

the predictive model at Edgewater, Cleveland, Ohio, 2008–12. 

[The p values are in parenthesis; the highest correlations among foreshore head variables for each year are in bold]
Identification of Variables and Development of Predictive Models

29

Wisconsin

Several new variables from the GLCFS were explored 

for potential utility as predictor variables in the E. coli models 

for Upper Lake Park, Port Washington, Wis. The current 

model used in 2012 at Upper Lake Park included 10 different variables with various transformations of those variables 

(table 6). New variables from GLCFS explored for use in 

the Upper Lake Park predictive model included time-based 

means of cloud cover and directional wave height (parallel 

and perpendicular components of a vector using wave height 

as the magnitude along with wave direction) and the standard 

deviation of air temperature and water temperature over several time spans (table 6). Time-averaged cloud cover was used 

as a surrogate for UV-light exposure and subsequent adverse 

impact of UV light on E. coli survival. The standard deviation 

of air and water temperature was used to explore potential 

impacts on E. coli concentrations resulting from fluctuations 

in temperature. For Upper Lake Park data, correlations of log10

E. coli concentrations to these new variables varied depending 

on the year and timespan. It did not appear that any of the new 

variables would result in improvement over those used in the 

current model; however, several of them proved to be valuable 

as predictors in other Wisconsin beach models (table 7). 

Development of Predictive Models

Predictive models were developed by using data collected 

through 2011 for 43 beaches. Models were not developed 

for six beaches that were in “development” nowcast status 

(table 1). The same types of data were not available at all 

beaches, accounting for some differences in the selection of 

variables for the predictive models.

Among all variables, turbidity (measured with a turbidimeter) was used most often (79.1 percent of models) and in 

models from all six States (table 7). Wave heights (wave rod) 

were used in models from all States, and day of the year was 

used in models from all States except Wisconsin. Because field 

measurements and observations are easy to obtain and were 

were included in monitoring programs at all beaches, they 

were used in 95.3 percent of models, the highest percentage 

among general categories of variables. Lab measurements of 

quantitative polymerase chain reaction or chlorophyll a were 

made and used at only one beach each, or 2.3 percent of the 

models. Weather data from the NWS nearest airport site were 

used in 44.2 percent of models; these data were relied on in 

models for several beaches in Michigan, New York, Ohio, and 

Pennsylvania. Radar rainfall data were used in 18.6 percent of 

the models and only at beaches in Ohio and Wisconsin. Radar 

data were available for only Ohio and Pennsylvania beaches 

by request to the USGS through a special computer program. 

Although radar data were also available through EnDDat, 

these data were accessed only for Wisconsin beaches. Data 

from nearby USGS gages were used in 32.6 percent of models. Among the measurements and predictions from NOAA, 

24-hour lake-level change was used most often (34.9 percent). 

The variables based on NOAA predictions were used often in 

models for Wisconsin beaches, and local sources of data were 

used in Illinois/Indiana and Pennsylvania, owing to the availability of these data.

The equations for the model(s) selected for each beach 

are presented in appendix 5. Table 8 lists the variables, 

adjusted R2

 values, predicted E. coli concentration or threshold 

probability, number of observations, and response characteristics for the calibration datasets. The adjusted R2

 (coefficient 

of multiple determination) is a measure of the fraction of the 

variability in E. coli concentrations that can be explained 

by each model. The adjusted R2 values ranged from 0.22 at 

Lake Front, Ohio, to 0.71 at Thompson, Wis., with a median 

adjusted R2 of 0.43 (median data not listed in table 8). Beach 

managers in Illinois and Indiana used the predicted E. coli

concentration as output from the models, along with a threshold of 235 CFU /100 mL. At all other beaches, the probability 

of exceeding the water-quality standard was computed, and 

a threshold probability was established from the calibration dataset. At Wisconsin beaches, a threshold probability 

of 50 percent was used for all beaches. Excluding Illinois, 

Indiana, and Wisconsin, threshold probabilities were established to maximize the correct percentage while, at the same 

time, obtaining a compromise between false positives and 

false negatives. The threshold probabilities established ranged 

from 15 percent at Metropark, Mich., to 45 percent at Villa 

Angela, Ohio, with a median of 28 percent. For all beaches, 

the percentages of correct responses ranged from 71.9 percent 

at Jeorse Park, Ind., to 97.3 percent at Neshotah, Wis., with 

a median of 86.7 percent. Sensitivities were 0 percent or 

very low for most of the beaches in Illinois and Indiana; at 

all of the other beaches except for Kreher, sensitivities were 

greater than or equal to 50 percent. Low specificities were 

found at Jeorse Park, Ind. (40 percent), and at Lakeview, Ohio 

(74.6 percent); otherwise specificities were greater than or 

equal to 82 percent. 
30

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Category Variable 2009 2010 2011 2012 All

Variables used in current model

Upper Lake Park, 

2012 model 

variables

High algae 

(binary variable)

0.17 (0.27) NA (NA) NA (NA) 0.15 (0.4) 0.12 (0.1)

Onshore current 

(quadratic)

0.15 (0.35) 0.12 (0.41) 0.23 (0.1) 0.09 (0.59) 0.08 (0.27)

Gull count 

(quadratic)

-0.14 (0.39) -0.04 (0.78) -0.71 (<0.01) -0.22 (0.2) -0.35 (<0.01)

Turbidity (1/3 power) 0.51 (<0.01) 0.56 (<0.01) 0.49 (<0.01) 0.64 (<0.01) 0.47 (<0.01)

Tubidity x 48-hour radar rainfall 0.39 (0.02) 0.49 (<0.01) 0.43 (<0.01) 0.52 (<0.01) 0.25 (<0.01)

24-hour radar rainfall 

(1/4 power)

0.45 (<0.01) 0.19 (0.19) 0.23 (0.11) 0.33 (0.05) 0.26 (<0.01)

Water temperature (manual) 0.16 (0.31) 0.39 (0.01) 0.21 (0.14) 0.23 (0.18) 0.33 (<0.01)

Wave height 0.5 (<0.01) 0.25 (0.08) 0.4 (<0.01) 0.29 (0.09) 0.38 (<0.01)

Perpendicular wave height 0.32 (0.04) 0.23 (0.11) 0.19 (0.2) 0.3 (0.08) 0.28 (<0.01)

Water temperature 

(square; Great Lakes Coastal 

Forecasting System.)

0.34 (0.03) 0.37 (0.01) 0.23 (0.12) 0.37 (0.03) 0.46 (<0.01)

Potential new variables

Cloud cover, 

mean

1-hour 0.44 (<0.01) -0.04 (0.79) 0.21 (0.15) 0.24 (0.16) 0.15 (0.05)

2-hour 0.46 (<0.01) 0.01 (0.95) 0.13 (0.36) 0.3 (0.08) 0.14 (0.06)

6-hour 0.5 (<0.01) 0.06 (0.68) 0.08 (0.6) 0.42 (0.01) 0.16 (0.04)

12-hour 0.41 (0.01) 0.08 (0.59) 0.01 (0.96) 0.42 (0.01) 0.13 (0.09)

24-hour 0.33 (0.03) 0 (0.98) 0.1 (0.5) 0.37 (0.03) 0.1 (0.18)

Air temperature, 

standard 

deviation

6-hour -0.23 (0.14) 0.28 (0.05) 0.1 (0.48) 0 (0.99) 0.01 (0.93)

12-hour -0.36 (0.02) 0.08 (0.58) 0.11 (0.44) 0.08 (0.65) 0.01 (0.85)

24-hour -0.34 (0.03) -0.15 (0.29) -0.17 (0.24) -0.02 (0.91) -0.13 (0.09)

48-hour -0.14 (0.39) -0.31 (0.03) -0.16 (0.28) 0.02 (0.92) -0.12 (0.12)

72-hour -0.14 (0.36) -0.31 (0.03) 0 (1) 0.33 (0.05) -0.02 (0.77)

120-hour -0.2 (0.2) -0.25 (0.08) -0.03 (0.81) 0.41 (0.01) 0 (1)

Water temperature, 

standard 

deviation

6-hour -0.16 (0.32) 0.14 (0.34) -0.02 (0.91) 0.11 (0.52) -0.01 (0.86)

12-hour -0.08 (0.6) 0.03 (0.84) -0.21 (0.14) -0.27 (0.11) -0.15 (0.05)

24-hour -0.03 (0.83) 0.15 (0.3) -0.26 (0.07) -0.12 (0.47) -0.06 (0.41)

48-hour -0.07 (0.64) -0.09 (0.52) -0.15 (0.32) -0.1 (0.55) -0.09 (0.22)

72-hour -0.01 (0.95) -0.02 (0.91) -0.06 (0.67) -0.09 (0.62) -0.06 (0.45)

120-hour 0.12 (0.46) 0.15 (0.29) -0.08 (0.59) 0.11 (0.52) 0.01 (0.88)

Directional 

wave height, 

mean

Parallel: 1-hour 0.04 (0.8) 0.33 (0.02) -0.36 (0.01) -0.23 (0.17) -0.14 (0.07)

2-hour 0.04 (0.78) 0.22 (0.13) -0.35 (0.02) -0.27 (0.12) -0.16 (0.04)

6-hour 0.05 (0.74) 0.19 (0.18) -0.4 (<0.01) -0.36 (0.03) -0.24 (<0.01)

12-hour -0.12 (0.46) -0.01 (0.93) -0.4 (<0.01) -0.36 (0.03) -0.31 (<0.01)

24-hour -0.2 (0.2) -0.09 (0.55) -0.28 (0.05) -0.4 (0.01) -0.33 (<0.01)

Perpendicular: 1-hour -0.31 (0.05) -0.25 (0.08) -0.24 (0.1) -0.3 (0.08) -0.29 (<0.01)

2-hour -0.31 (0.04) -0.17 (0.23) -0.22 (0.13) -0.27 (0.11) -0.27 (<0.01)

6-hour -0.33 (0.03) -0.24 (0.09) -0.15 (0.3) -0.33 (0.05) -0.3 (<0.01)

12-hour -0.32 (0.04) -0.26 (0.06) -0.22 (0.13) -0.23 (0.17) -0.32 (<0.01)

24-hour -0.23 (0.14) -0.2 (0.16) -0.19 (0.2) -0.25 (0.14) -0.3 (<0.01)

Table 6. Pearson’s correlations between log10 Escherichia coli concentrations and new variables or explanatory variables used for the 

current predictive model at Upper Lake Park, Port Washington, Wisconsin.

[The p values are in parenthesis]
Identification of Variables and Development of Predictive Models

31

Table 7. Summary of variables, by State, used in beach-specific predictive models.

[Rd-1, Rd-2, Rd-3: the total rainfall for the 24-hour period before sampling, 2 days before sampling, or 3 days before sampling; Rw48, Rw72: the amount of 

rainfall for the 48-hour or 72-hour period before sampling with the most recent rainfall receiving the most weight; h, hrs: hour(s); min: minimum; max: maximum; USGS, U.S. Geological Survey; NOAA, National Oceanic and Atmospheric Administration]

Ill./ Ind. Mich. N.Y. Ohio Pa. Wis. Totals Used in all 

models (percent)

Number of beaches 9 2 5 10 6 11 43

Field measurements and observations

Day of the year 7 1 3 3 2 0 16 37.2

Subseason category 0 0 0 2 0 0 2 4.7

Water temperature 3 0 0 0 0 1 4 9.3

Wave height (wave rod) 2 1 2 7 1 1 14 32.6

Turbidity, turbidimeter, lake or nearby tributary 1 2 5 9 6 11 34 79.1

Specific conductance, lake or nearby tributary 0 0 0 0 0 2 2 4.7

Debris category 0 0 1 0 0 0 1 2.3

Algae category 2 0 0 0 0 1 3 7.0

Bird count, time of sampling 2 0 1 0 0 1 4 9.3

People count, time of sampling 2 0 0 0 0 0 2 4.7

Weather categories 3 0 0 0 0 0 3 7.0

Total number of models that included field 

measurements and observations

8 2 5 9 6 11 41 95.3

Laboratory measurements

Quantiative polymerase chain reaction 0 0 0 1 0 0 1 2.3

chlorophyll a (fluorometric sensor) 1 0 0 0 0 0 1 2.3

Total number of models that included 

lab measurements

1 0 0 1 0 0 2 4.7

Weather data from the National Weather Service nearest airport site

Rainfall: Rd-1, Rd-2, and (or) Rd-3 0 1 4 5 2 0 12 27.9

Rainfall, weighted: Rw48 or Rw72 0 0 1 3 1 0 5 11.6

Rainfall: 30-day sum 0 1 0 0 0 0 1 2.3

Air temperature: 24 h mean 0 0 1 0 0 0 1 2.3

Wind direction and (or) speed, instantaneous 1 2 4 5 0 0 12 27.9

Wind direction and (or) speed, 24 h 0 2 2 0 1 0 5 11.6

Total number of models that included weather 

data from National Weather Service nearest airport site

1 2 5 8 3 0 19 44.2

Radar rainfall (sum from several 4-kilometer grids)

Radar rainfall: 12 h, Rd-1, and (or) Rd-2 0 0 0 0 0 5 5 11.6

Radar rainfall, weighted: Rw48 0 0 0 2 0 0 2 4.7

Radar rainfall: 5 day sum 0 0 0 0 0 1 1 2.3

Total number of models that included radar rainfall 0 0 0 2 0 6 8 18.6

Measurements from a nearby USGS gage or buoy

Rainfall: Rd-1 and (or) Rd-2 0 0 0 0 0 3 3 7.0

Rainfall: 5-day sum 0 0 0 0 0 1 1 2.3

Wave height or period, nearshore buoy 0 0 0 0 0 0 0 0.0

Turbidity, nearshore buoy 1 0 0 0 0 0 1 2.3

Water temperature, nearshore buoy 0 0 0 0 0 0 0 0.0

Stream discharge, time of sampling 0 0 0 4 0 0 4 9.3
32

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Table 7. Summary of variables, by State, used in beach-specific predictive models.—Continued

[Rd-1, Rd-2, Rd-3: the total rainfall for the 24-hour period before sampling, 2 days before sampling, or 3 days before sampling; Rw48, Rw72: the amount of 

rainfall for the 48-hour or 72-hour period before sampling with the most recent rainfall receiving the most weight; h, hrs: hour(s); min: minimum; max: maximum; USGS, U.S. Geological Survey; NOAA, National Oceanic and Atmospheric Administration]

Ill./ Ind. Mich. N.Y. Ohio Pa. Wis. Totals Used in all 

models (percent)

Number of beaches 9 2 5 10 6 11 43

Measurements from a nearby USGS gage or buoy—Continued

Stream discharge, mean 24, 48, or 72 h 0 0 0 1 0 0 1 2.3

Stream discharge, min or max, 24–72 h 0 0 0 0 0 1 1 2.3

Stream discharge, change, 6 h 0 0 0 0 0 3 3 7.0

Stream turbidity, change in 24 h 0 0 1 0 0 0 1 2.3

Total number of models that included measurements 

from a nearby USGS gage or buoy

1 0 1 5 0 7 14 32.6

Measurements or predictions from NOAA

Lake level, 24 h change 2 1 3 4 5 0 15 34.9

Lake level, change from spring average 0 0 0 1 0 0 1 2.3

Wind direction and speed, time of sampling 0 0 0 1 0 0 1 2.3

Current direction, 24 h prediction 0 0 1 0 0 0 1 2.3

Air temperature, mean, 1–120 h prediction 0 0 0 0 0 3 3 7.0

Air temperature, st. deviation, 1–144 h prediction 0 0 0 0 0 3 3 7.0

Cloud cover, mean, 1–48 h prediction 0 0 0 0 0 3 3 7.0

Depth averaged current vector, mean 1–120 h prediction 0 0 0 0 0 1 1 2.3

Lake height above sea level, mean 1 h prediction 0 0 0 0 0 2 2 4.7

Lake height above sea level, mean, max, min or change, 

24 h prediction

0 0 0 0 0 5 5 11.6

Surface current vector, mean, 1–120 hrs prediction 0 0 0 0 0 6 6 14.0

Water temperature, mean, 1-24 h prediction 0 0 1 0 0 4 5 11.6

Water temperature, st. deviation, 1–120 h prediction 0 0 0 0 0 2 2 4.7

Wave direction, mean, 1–24 h prediction 0 0 0 0 0 3 3 7.0

Wave height, mean, 1–24 h prediction 0 0 2 0 0 5 7 16.3

Wind vector, mean, 1–24 h prediction 0 0 0 0 0 3 3 7.0

Total number of models that included measurements 

or predictions from NOAA

2 1 3 5 5 10 26 60.5

Local sources, including on-site weather stations

Rainfall: 4 or 6 h 2 0 0 0 0 0 2 4.7

Rainfall: Rd-1, Rd-2, and (or) Rd-3 3 0 0 0 0 0 3 7.0

Solar radiation, 4–6 h 5 0 0 0 0 0 5 11.6

Wind speed and (or) direction 2 0 0 0 1 0 3 7.0

Offshore or alongshore wind component 4 0 0 0 0 0 4 9.3

Air temperature 2 0 0 0 0 0 2 4.7

Humidity 1 0 0 0 0 0 1 2.3

Barometric pressure 1 0 0 0 5 0 6 14.0

Total number of models that included local sources 8 0 0 0 6 0 14 32.6
Table 8. Variables and statistics for beach-specific predictive models in calibration datasets.—Continued[R2, fraction of the variation of E. coli concentrations that is explained by the model; threshold probability is based on meeting and exceeding the single-sample bathing standar

percentage of exceedances of the bathing-water standard that are correctly predicted by the model; specificity is the percentage of nonexceedances of the bathing-water standarthe model; model performance is based on the calibration dataset]

Number (calibration dataset) 

Beach 

short name General variables Adj. R2

Predicted E. coli 

concentration 

or threshold 

probabilitya

Observations Correct + Correct − False + False − Corre

Illinois

South Beach Day of the year, water temperature, 

people count, bird count, 

weather categories, local rainfall

0.43 235 109 4 84 4 17 80

Greenwood Beach Day of the year, wave-height rod, 

bird count, people count, algae, weather category, lake-level change, local rainfall, local air temperature

0.36 235 110 0 99 0 11 90

Lee Beach Day of year, water temperature, turbidity, 

lake-level change, algae, weather category, local wind

0.40 235 112 3 102 0 7 93

Montrose Day of the year, local solar radiation, 

local offshore wind component, local humidity 

0.36 235 79 8 61 2 8 87

63rd Street Day of the year, local rainfall, local solar 

radiation, local offshore wind component

0.38 235 76 3 64 3 6 88

Foster Day of the year, local solar radiation, 

local offshore wind component, local air temperature

0.33 235 76 0 73 0 3 96

Calumet Day of the year, local rainfall, local solar 

radiation, local offshore wind component

0.38 235 78 2 64 4 8 84

Oak Turbidity from nearshore buoy, local 

rainfall, local solar radiation, local wind speed, local barometric pressure

0.28 235 67 0 64 0 3 95

Indiana

Jeorse Park Field water temperature, wave-height rod, 

chlorophyll a (fluorometric sensor), airport wind speed

0.28 235 32 19 4 6 3 71
Table 8. Variables and statistics for beach-specific predictive models in calibration datasets.—Continued [R2, fraction of the variation of E. coli concentrations that is explained by the model; threshold probability is based on meeting and exceeding the single-sample bathing standar

percentage of exceedances of the bathing-water standard that are correctly predicted by the model; specificity is the percentage of nonexceedances of the bathing-water standarthe model; model performance is based on the calibration dataset] Number (calibration dataset) 

Beach short name General variables Adj. R2 Predicted E. coli concentration or threshold probabilitya Obser- vations Correct + Correct − False + False − Corre

Michigan Metropark Wave-height rod, turbidity, airport rainfall, airport wind, lake-level change 0.33 15 70 10 53 4 3 90

Memorial Day of the year, turbidity, airport rainfall, airport wind 0.55 39 72 7 57 4 4 88

New York Ontario Day of the year, wave-height rod, turbidity, airport wind, lake-level change, current direction prediction, wave-height prediction 0.67 27 153 45 84 15 9 84

Durand Day of the year, turbidity, debris category, bird count, airport rainfall, airport wind, lake-level change, wave-height prediction 0.59 25 131 24 89 15 3 86

Woodlawn SP Day of the year, wave-height rod, turbidity, airport rainfall, airport wind, stream turbidity change 0.39 40 156 34 89 19 14 78

Evans TP Turbidity, airport rainfall, airport wind, lake- level change, water temperature prediction 0.58 20 95 12 70 11 2 86

Lake Erie Turbidity, airport rainfall, airport wind, airport air temperature 0.42 27 94 10 70 6 8 85

Ohio Edgewater (radar available) Day of the year, subseason, wave-height rod, turbidity, radar rainfall, lake-level change 0.41 30 681 99 448 67 67 80

Edgewater (no radar available) Day of the year, subseason, wave-height rod, turbidity, airport rainfall, lake-level change 0.39 30 714 98 462 82 72 78

Huntington (radar available) Subseason, wave-height rod, turbidity, air- port rainfall, airport wind, radar rainfall 0.53 27 606 61 464 49 32 86

Huntington (no 

radar available)

Subseason, wave-height rod, turbidity, airport rainfall, airport wind 0.52 31 606 59 461 47 39 85
Table 8. Variables and statistics for beach-specific predictive models in calibration datasets.—Continued[R2, fraction of the variation of E. coli concentrations that is explained by the model; threshold probability is based on meeting and exceeding the single-sample bathing standar

percentage of exceedances of the bathing-water standard that are correctly predicted by the model; specificity is the percentage of nonexceedances of the bathing-water standarthe model; model performance is based on the calibration dataset]

Number (calibration dataset) 

Beach 

short name General variables Adj. R2

Predicted E. coli 

concentration 

or threshold 

probabilitya

Observations Correct + Correct − False + False − Corre

Ohio—Continued

Maumee Bay SP 

(positive flow in ditch)

Airport rainfall, airport wind 0.43 20 70 6 58 5 1 91

Maumee Bay SP 

(negative flow in ditch)

Turbidity, airport wind, stream discharge, 

lake-level change

0.45 25 29 7 18 2 2 86

Villa Angela Quantiative polymerase chain reaction 

(qPCR) for E. coli

0.47 45 257 92 119 26 20 82

Vermillion West Wave-height rod, turbidity, airport rainfall, 

stream discharge

0.63 39 87 19 56 5 7 86

Nickel Plate Day of the year, wave-height rod, turbidity, wind speed and direction, stream 

discharge

0.26 29 87 7 63 10 7 80

Lake Front Wave-height rod, turbidity, lake-level change 0.22 37 86 15 56 5 10 82

Lakeview Day of the year, turbidity, airport rainfall, 

airport wind, lake-level change 

0.43 41 144 64 53 18 9 81

Fairport Harbor Wave-height rod, turbidity, airport rainfall, 

airport wind, stream discharge

0.38 28 204 16 158 16 14 85

Mentor Headlands Wave-height rod, turbidity, airport rainfall, 

airport wind, stream discharge

0.47 22 204 22 154 16 12 86

Pennsylvania

Beach 1 West 

Extension

Turbidity, lake-level change, local wind 0.52 29 129 13 96 10 10 84

Beach 2 Turbidity, lake-level change, local barometric pressure 0.51 28 130 6 112 10 2 90

Beach 6 Turbidity, lake-level change, local barometric pressure 0.36 21 130 7 106 13 4 86

Mill Road Wave height, turbidity, airport rainfall, lakelevel change, local barometric pressure 0.39 26 127 5 106 11 5 87
Table 8. Variables and statistics for beach-specific predictive models in calibration datasets.—Continued [R2, fraction of the variation of E. coli concentrations that is explained by the model; threshold probability is based on meeting and exceeding the single-sample bathing standar

percentage of exceedances of the bathing-water standard that are correctly predicted by the model; specificity is the percentage of nonexceedances of the bathing-water standarthe model; model performance is based on the calibration dataset] Number (calibration dataset) 

Beach short name General variables Adj. R2 Predicted E. coli concentration or threshold probabilitya Obser- vations Correct + Correct − False + False − Corre

Pennsylvania—Continued Beach 9 Day of the year, turbidity, airport rainfall, airport wind, lake-level change, local barometric pressure 0.39 29 136 14 104 11 7 86

Beach 11 Day of the year, turbidity, airport rainfall, local barometric pressure 0.36 18 131 6 114 9 2 91

Wisconsin Upper Lake Park Turbidity, algae category, bird count, wave height, radar rainfall, surface-current vec- tor, water temperature, and wind-vector predictions 0.49 30 104 2 99 1 2 97

Red Arrow Turbidity, cloud cover, water temperature, and wave-height predictions 0.31 50 76 16 43 7 10 77

Neshotah Turbidity, air temperature, cloud cover, lake height above sea level, surface-current vector, water temperature, and wave- height predictions 0.57 50 73 4 67 1 1 97

Point Beach South Turbidity, radar rainfall, stream discharge, lake height above sea level, surface- current vector, water temperature, wave direction, and wave-height predictions 0.57 50 225 19 190 3 13 92

Point Beach Central Same variables as Point Beach South 0.57 50 225 19 190 3 13 92

Point Beach North Same variables as Point Beach South 0.57 50 225 19 190 3 13 92

Fischer Turbidity, gage rainfall, depth-averaged- current vector and surface-current vector predictions 0.51 50 79 8 61 3 7 87

Hika Turbidity, specific conductance, gage rainfall (PLS model) 0.29 50 85 14 52 9 10 77
Table 8. Variables and statistics for beach-specific predictive models in calibration datasets.—Continued[R2, fraction of the variation of E. coli concentrations that is explained by the model; threshold probability is based on meeting and exceeding the single-sample bathing standar

percentage of exceedances of the bathing-water standard that are correctly predicted by the model; specificity is the percentage of nonexceedances of the bathing-water standarthe model; model performance is based on the calibration dataset]

Number (calibration dataset) 

Beach 

short name General variables Adj. R2

Predicted E. coli 

concentration 

or threshold 

probabilitya

Observations Correct + Correct − False + False − Corre

Wisconsin—Continued

Thompson Turbidity, water temperature, gage rainfall, 

radar rainfall, air temperature, cloud cover, and wind-vector predictions

0.71 50 77 10 64 0 3 96

Kreher Turbidity, stream discharge, gage rainfall, 

air temperature, and lake height above sea level predictions 

0.49 50 82 6 67 0 9 89

Maslowski Turbidity, tributary specific conductance, 

radar rainfall, air temperature, lake height above sea level, and wind-vector predictions 

0.43 50 74 4 62 4 4 89

a The predicted E. coli concentration is 235 colony-forming units per 100 milliliters; the other values are threshold probabilities, in percent.
38

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Validation of Predictive Models and 

Implementation of Nowcast Systems

Models for 42 beaches were validated during an independent year (table 9). At Jeorse Park, the predictive model 

was not validated and the modeling effort was discontinued 

in 2011, owing to poor model performance and various other 

reasons. Models that performed at least 5 percent better than 

the persistence model in yielding correct responses, sensitivities, or specificities were considered promising models; these 

responses are highlighted in bold on table 9. Out of 42 predictive models, 24 models (57 percent) yielded overall correct 

responses that were at least 5 percent greater than the use of 

the persistence model. For the purposes of this report, overall 

correct responses greater than or equal to 80 percent, sensitivities greater than or equal to 50 percent, and specificities 

greater than or equal to 85 percent were goals for good model 

performance. 

The predictive-model responses were compared to use 

of the persistence model during validation in 2012 in terms of 

overall percent correct, sensitivities, and specificities (fig. 4). 

Scatterplots of the data as compared to the 1:1 lines show that 

predictive-model responses were often higher than persistence-model responses. Using the percentage goals set forth 

in the previous paragraph, the plots can be split into quadrants 

where (1) the predictive model met the goal but the persistence 

model did not, (2) both models met the goal, (3) neither model 

met the goal, and (4) the persistence model met the goal but 

the predictive model did not. For overall percent correct, the 

predictive model met the goal while the persistence model did 

not at 13 beaches (fig 4A, quadrant 1), whereas the opposite 

was true at only 2 beaches (fig 4A, quadrant 4). The persistence model met the goal for sensitivity only 4 times (fig. 4B, 

quadrants 2 and 4), whereas the predictive model met the goal 

17 times (fig. 4B, quadrants 1 and 2). The specificity goal was 

met by both models 22 times (fig. 4C, quadrant 2), but the 

predictive model met the goal more often when the persistence 

model did not (fig. 4C, quadrants 1 and 3, respectively). 

The differences between predictive- and persistencemodel paired responses (that is, the predictive-model percentage minus the persistence-model percentage) for each beach 

model are shown in figure 5. In all cases, the median difference was above zero, indicating typically better performance 

of the predictive models. By use of a paired student’s t-test, 

mean percentages of overall correct responses, sensitivities, 

and specificities were significantly higher for the predictive 

model than for the persistence model (p = 0.0018, 0.0006, and 

0.0241, respectively). 

For all models, data from 2012 will be added to previous 

years’ datasets to refine the models for use in 2013. During the 

recreational season of 2013, plans are underway to continue 

operational nowcasts at 10 beaches, initiate new nowcasts at 3 

beaches, and refine and retest models at 29 beaches (table 9). 

Validation results are discussed below for beaches by State. 

Illinois—Evanston Beaches

Predictive models for South, Greenwood, and Lee 

Beaches in Evanston, Ill., yielded slightly higher percentages 

of correct predictions than the persistence models, ranging 

from 77.3 to 96.9 percent correct (table 9). Although overall 

correct predictions by the nowcast and the persistence models 

for South Beach were similar, the predictive model yielded a 

lower sensitivity and a higher specificity than the persistence 

model.

For both Greenwood Beach and Lee Beach, the predictive models yielded higher percentages correct (93.8 and 96.9 

percent, respectively) but resulted in sensitivities of 0 percent and specificities of 100 percent. The persistence models 

for these beaches also yielded sensitivities of 0 percent and 

slightly lower specificities than the predictive models. These 

results indicate that elevated E. coli concentrations are likely 

ephemeral at these beaches, accounting for low sensitivities of 

the persistence models and the possibility that some factor or 

factors are not being accounted for in the predictive models. 

The 2012 validation period used for the predictive models represented a period with less rainfall and lower stream 

discharge and direct runoff into Lake Michigan than normal. 

Therefore, 2012 validation datasets might not adequately represent the more normal conditions during the calibration years 

(2010–11). Refinement of the predictive models for Evanston, 

Ill., beaches will continue, and refined models will be validated during 2013. 

Illinois—Chicago Beaches

Predictive models for four Chicago beaches (Montrose, 

63rd Street, Foster, and Calumet) generally performed better 

than persistence models in terms of percentage of correct 

responses and specificity (table 9). In both of these categories, however, the predictive model for Oak Street Beach 

had a slightly lower response than the persistence model. All 

models for Chicago beaches had lower sensitivities than the 

persistence models, and none of the Chicago models reached 

the targeted sensitivity level of 50 percent. Only the model for 

Foster had ≥80 percent correct responses (86.1 percent), and 

all models met the goal for specificity (≥85 percent). 

The high number of false negative responses from all 

Chicago predictive models in 2012 was of concern, but several factors should be considered. Three out of five Chicago 

models included rainfall as a variable (appendix 5), and the 

2012 summer was very dry in comparison to the 2010–11 

calibration years for predictive models. Lack of rain may have 

contributed to the lower number of exceedances, indicating the 

importance of rainfall in the success of these models. Beaches 

in Chicago are generally influenced by nonpoint sources of 

FIB (Whitman and Nevers, 2008) because no river flows 

into Lake Michigan in the Chicago area. Thus, presumptive 

sources of high E. coli concentrations are beach sands (Whitman and Nevers, 2003), shorebirds, and nearshore processes 
Validation of Predictive Models and Implementation of Nowcast Systems

39

such as current circulation, wave action, resuspension, and 

transport (Ge and others, 2010, 2012). 

Interestingly, 63rd Street Beach had a higher percentage of E. coli exceedances in the validation year (2012) than 

in the calibration years (2010–11) (24 and 15.6 percent, 

respectively, table 3). Historically, high concentrations of 

E. coli have occurred at the 63rd Street Beach, but the annual 

average concentrations fluctuate, and several management 

approaches in recent years have likely helped to decrease the 

number of swimming advisories. Gull fecal droppings contribute to higher E. coli concentrations in the sand, which in 

turn becomes a nonpoint source of E. coli to nearshore beach 

water (Whitman and Nevers, 2003). Any bacterial contamination that enters the beach water tends to remain in the beach 

embayment; current and wave actions limit water exchange 

with the open lake, leading to frequent closures at this beach 

(Ge and others, 2010, 2012).

Of the five Chicago Beaches, Montrose had the highest 

number of E. coli exceedances in calibration and validation 

years (22.3 and 26.3 percent, respectively; table 3). The model 

in 2012 did not perform well, missing E. coli exceedances 

on 17 out of 21 occasions. The current model contains three 

meteorological variables measured at a neighboring onsite 

weather station and day of the year. More studies are needed 

to determine causes for E. coli exceedances at this beach.

Michigan

Predictive models were created for Metropark and 

Memorial Beaches, both on Lake St. Clair in Macomb County, 

Mich., by using data collected in 2010–11. There were too few 

exceedances during 2012 to adequately evaluate the Metropark 

model. The Memorial predictive model performed better than 

the persistence model in terms of sensitivity (table 9). There 

were a high number of false positives for Memorial, however, 

which lowered the percentage of correct responses. 

Metropark Beach had only one closure in 2012 (1.5 percent of the sample events exceeding the daily standard of 300 

MPN/100 mL or the 30-day geometric mean standard of 130 

MPN/100 mL) compared to 19 closures (16 percent) in 2010 

and 2011. Memorial Beach was closed only 3 times in 2012 

(4.4 percent) compared to 23 times (20.7 percent) in 2010 and 

2011. The 2012 swimming season was marked by very low 

precipitation, which likely contributed to fewer beach closures 

than in previous years. 

Future plans for Metropark and Memorial Beaches are 

to refine and retest the models during the 2013 season, with 

plans to pursue operational nowcast systems at both beaches. 

The county health department is also looking at developing 

a predictive model for a third Lake St. Clair beach in New 

Baltimore.

New York

During the 2012 season, predictive models at Ontario 

and Durand beaches performed better than the persistence 

models, with percentages of correct responses of 77.0 and 

83.5, respectively (table 9). Performance in the calibration 

years (table 8, 2010–11) was comparable to performance during the 2012 validation. Both models will be used for nowcasting in 2013.

Woodlawn SP has been a difficult model to define 

because of the potential input of E. coli loads from beach 

sands. Additionally, combined-sewer and stormwater overflows have contributed to fecal contamination in Rush Creek, 

a tributary that discharges north of the bathing area. Woodlawn 

beach has a gentle slope eastward into small dunes separating the beach from a low-lying wetland area that may have 

direct interactions with Rush Creek. Most likely, the bathing 

area is influenced by both groundwater interactions and direct 

bacterial loading from Rush Creek. The model validated during 2012 resulted in an increase in sensitivity as compared to 

the persistence model; however, the percentage correct and 

specificity for the predictive model were 4 to 5 percent lower 

than for the persistence model. Work will continue in 2013 to 

refine the Woodlawn model by considering variables for 24- 

and 48-hour changes to both lake turbidity and Rush Creek 

turbidity, as well as multiday changes in lake levels, wave 

directions, and wave heights. 

The Evans TP and Lake Erie models performed well considering that there were only three exceedances at Evans TP 

and no exceedances at Lake Erie. A period of drought affected 

western New York, drying up the tributaries to both beaches. 

The model sensitivity at Evans TP was 100 percent and the 

overall correctness was 95.3 percent, but this level of performance may change during a season of nondrought conditions. 

Plans are to include the Evans TP model in a new nowcast and 

refine and retest the Lake Erie model during 2013. 

Ohio

Predictive models for three of the four beaches with 

operational nowcasts in Ohio performed better than the persistence models during 2012 in all categories. Overall correct 

predictive-model responses were at least 5 percentage points 

higher than those for the persistence model at Huntington, 

Maumee Bay SP, and Villa Angela, but not at Edgewater, 

owing to a lower specificity at Edgewater (table 9). Edgewater 

has been a challenge to model because of the probable influence of E. coli in beach sands on lake water quality (Francy 

and others, 2006a). The beach has a gentle slope, and beach 

sands are often saturated. Change in lake level and subseason 

variables have improved nowcast predictions at Edgewater, 

but more work needs to be done to quantify the interaction 

of shallow groundwater and waves and their influence on E. 

coli concentrations in the lake. As more data become available, adding foreshore head as a variable may improve model 
40

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

performance at Edgewater. The Ohio Nowcast for Huntington yielded correct predictions more than 83 percent of the 

time since 2006. The inclusion of a subseason variable and 

radar rainfall improved model performance from earlier years 

(Francy and others, 2009). Water quality at Maumee Bay SP 

is influenced by Berger Ditch to the east of the beach (Francy 

and others, 2005). After an unsuccessful test of the nowcast in 

2011, it was determined that the inclusion of two models—one 

for negative flow and one for positive flow in Berger Ditch—

resulted in better nowcast predictions during 2012. Because 

of the high percentage of exceedances of the bathing-water 

standard at Villa Angela (greater than 45 percent), models 

developed in past studies based on environmental variables 

did not perform any better than the persistence model (Francy 

and others, 2003). As a result, a model based on the Ct value 

from qPCR as the sole predictive variable was validated during 2012. Although the predictive model yielded 81.6 percent 

correct responses at Villa Angela, 40 percent of the responses 

were based on the ∆∆Ct and were not based on the Ct model 

because of inhibition of the qPCR reaction (data not shown). 

Using the ∆∆Ct exclusively would have yielded more correct 

responses (88.5 percent) than using the Ct model. Plans are to 

continue the nowcast for these four beaches in 2013.

The predictive models for beaches in Erie County (Vermillion West, Lake Front, and Nickel Plate) yielded mixed 

responses. Sensitivities were low at Vermillion West and Lake 

Front, and specificity was low at Nickel Plate. The R2

 values 

for the Lake Front and Nickel Plate models were also low 

(0.26 and 0.22; table 8). Before nowcast systems are implemented, more work will be needed to understand the processes 

that affect E. coli concentrations at Erie County beaches in 

order to identify variables to improve model performance. 

The predictive models for beaches in Lake County (Fairport 

Harbor and Mentor Headlands) yielded over 80 percent correct 

responses but had low sensitivities. Inclusion of site-specific 

weather data from the Mentor Headlands weather station may 

help to improve future model performance. 

The predictive model for Lakeview in Lorain County did 

not perform well in 2012, most notably in terms of specificity. 

Lakeview is similar to Edgewater in that it is a gently sloping 

beach with frequently saturated sands, that it has breakwaters 

that restrict water circulation, and that it is frequented by large 

populations of waterfowl in the swimming area. At Lakeview 

during 2012, the bird count at the time of sampling was as 

high as 3,000, with counts of over 1,000 birds on 15 days. The 

median bird count for the season was 500. By comparison, 

bird counts at Edgewater were over 1,000 on only one day 

(2,100 birds), and the median was much lower (165). It may 

be that a model with good performance will be difficult to 

develop for Lakeview unless a way to account for the influence of birds can be identified. 

Pennsylvania

Predictive models based on 2010–11 datasets and tested 

during validation in 2012 performed better than the persistence models for all six Pennsylvania beaches at Presque 

Isle State Park (table 9). Correct responses for the predictive 

models were well over 80 percent in all cases, whereas correct 

responses for the persistence models were above 80 percent 

at only three of the six beaches. Specificities for the predictive models were greater than 85 percent at all six beaches; 

sensitivities were higher than those for persistence models,

but none were greater than 50 percent (ranged from 23.1 to 

42.9 percent). Specificities for the persistence models were 

greater than 85 percent for all but one beach (Beach 1 West 

Extension) but the sensitivities were less than or equal to 

22.2 percent and were 0 percent at two beaches (Beach 2 and 

Beach 9). Turbidity, barometric pressure and change in lake 

level were important variables in predicting when the E. coli

standard would be exceeded (explanatory variables in at least 

five of the six predictive models). Refinement of the predictive 

models for Presque Isle State Park beaches will continue, and 

refined models will be validated during 2013.

Wisconsin

Environmental conditions may have influenced how well 

some of the models performed throughout the 2012 season in 

Wisconsin. During the beginning of the 2012 beach season in 

Wisconsin, there was a substantial rainfall event that generated large stream discharges in tributaries, followed by a long 

drought period. Neither of these conditions were represented 

in the calibration datasets from 2010–11 for either Manitowoc County or the Chequamegon Bay area of Ashland and 

Bayfield Counties. It is hoped that adding 2012 data to the 

calibration datasets and redeveloping models will improve the 

accuracy of future model predictions given that more data are 

available for model calibration and that the extra year of data 

will provide a broader suite of environmental conditions.

The Chequamegon Bay, Lake Superior models had variable results in prediction accuracy, even though they are less 

than 6 mi apart (table 9). The model used in 2012 at Thompson had a very high sensitivity and specificity, performing 

better than the persistence model. The predictive model for 

Kreher also performed better than the persistence model for 

sensitivity but was nearly the same in overall accuracy and 

specificity. The model for Maslowski yielded only 66 percent 

overall correct responses and a lower sensitivity than the 

persistence model. For use in operational decisions, Thompson and Kreher models appear to have promise, the Kreher 

model may benefit from recalibration with the 2012 data, and 

the Maslowski predictive model will need recalibration and 

improvements to be useful. 
Validation of Predictive Models and Implementation of Nowcast Systems

41

Given the substantial differences in model results at 

these three beaches and given their proximity, exploration 

of site information is warranted to help explain potential 

factors involved. A few likely sources appear to be contributors to these differences. The two largest tributaries near 

these beaches are closest to Maslowski. The distances from 

Maslowski to the mouths of Fish Creek and Whittlesey Creek 

are 0.94 mi and 1.5 mi, respectively. Longshore-current direction varies, so the influence of these creeks on water quality 

at Maslowski is variable. Adding to that variability, North 

Fish Creek is affected by seiche from the bay, so flow may 

be from North Fish Creek into the bay or from the bay back 

into North Fish Creek, depending on the exact time. Kreher is 

a mile to the east of Maslowski, resulting in extra traveltime 

and additional dilution (relative to Maslowski) before waters 

from these two tributaries reach Kreher. Thompson is 5 mi to 

the North of Whittlesey Creek. There is more direct influence 

from a much smaller stream, Thompson Creek, and a small 

artesian well (very clean water) that discharges adjacent to the 

beach. 

Birds are also much more prevalent at Maslowski (average number of birds = 39) than at Kreher and Thompson 

(average number of birds = 6 and 8, respectively). Currents at 

the beach, flow direction in North Fish Creek, and the number 

of birds influencing the beach are all variables that could help 

improve predictions at Maslowski. All three of these variables 

can vary quickly, and discrete measurements four times per 

week are not always representative of the influence from tributaries, birds, and currents on beach water quality.

Prediction accuracy of models among the Lake Michigan 

beaches in Wisconsin were also quite variable. Upper Lake 

Park, Red Arrow, and the three Point Beach predictive-model 

results were better than the persistence model for all three 

primary performance measures (total correct, sensitivity, and 

specificity), and other predictive models were better than the 

persistence model in one or two of these performance measures. On the low end, predictive models for Hika and Fischer 

yielded 71.9 and 79.1 percent correct predictions, respectively. 

Both predictive models had the same (Fischer) or better (Hika) 

overall correct predictions as the persistence model. Both 

beaches had substantial Cladophora accumulation at times 

during the season that increased turbidity (one of the explanatory variables) substantially, even though E. coli concentrations did not consistently exceed the decision criteria. In 

general, Wisconsin predictive-model-development protocol 

dictated that ordinary least squares regression models from 

Virtual Beach be considered first to be consistent with other 

models throughout the Great Lakes. For Hika, ordinary least 

squares analyses did not result in an acceptable model, but 

performance was improved by using partial least squares analyses for development of this model. Red Arrow had the most 

exceedances of the Wisconsin beaches in 2012 (31 percent of 

sampled days), most due to influence from multiple potential 

contamination sources. At Red Arrow, the predictive model 

was a substantial improvement over the persistence model. 

The predictive model for Neshotah predicted the only exceedance of 2012 correctly. The Point Beach models were developed by using data from all three beaches. The only difference 

in explanatory variables between the three beaches is that local 

measurements of turbidity were used in each model. Results 

for the Point Beach predictive models were better than those 

from persistence models, but they still have potential for some 

improvement.

For Wisconsin predictive models in the 2013 season, 

plans are to explore the use of a two-tiered modeling approach 

with tier I as the “standard” (using field-measured data) model 

and tier II as the “automated” model for nonsampled days. The 

tier II model would use only data that do not require a visit to 

the beach.
42

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Table 9. Predictive-model responses as compared to use of previous day’s E. coli concentration (persistence model) during 

validation in 2012.—Continued

[The persistence model is the previous day’s E. coli concentration, the current method for assessing water quality; sensitivity is the percentage of exceedances 

of the bathing-water standard that are correctly predicted by the model; specificity is the percentage of nonexceedances of the bathing-water standard that are 

correctly predicted by the model; responses that were at least approximately 5 percent higher for the predictive model than for the persistence model are in 

bold]

Beach 

short name

Model 

used

Number Percentage Nowcast

Observations Correct 

+

Correct 

−

False 

+

False 

− Correct Sensitivity Specificity Status for 2013

Illinois

South Beach Predictive 66 1 50 5 10 77.3 9.1 90.9 Refine and retest

Persistence 68 3 49 8 8 76.5 27.3 86.0

Greenwood 

Beach

Predictive 64 0 60 0 4 93.8 0 100.0 Refine and retest

Persistence 65 0 53 4 4 87.7 0 93.4

Lee Beach Predictive 65 0 63 0 2 96.9 0 100.0 Refine and retest

Persistence 68 0 62 3 3 91.2 0 95.4

Montrose Predictive 78 4 52 5 17 71.8 19.0 91.2 Continue nowcast

Persistence 65 6 32 15 12 58.5 33.3 68.1

63rd Street Predictive 72 1 53 3 15 75.0 6.3 94.6 Continue nowcast

Persistence 59 8 35 10 6 72.9 57.1 77.8

Foster Predictive 72 0 62 2 8 86.1 0 96.9 Continue nowcast

Persistence 57 0 42 8 7 73.7 0 84

Calumet Predictive 77 2 58 6 11 77.9 15.4 90.6 Continue nowcast

Persistence 65 2 47 10 6 75.4 25.0 82.5

Oak Street Predictive 70 0 67 3 0 95.7 0 95.7 Continue nowcast

Persistence 54 0 54 0 0 100.0 0 100

Michigan

Metropark Predictive 61 0 60 0 1 98.4 0 100.0 Refine and retest

Persistence 68 0 67 1 0 98.5 0 98.5

Memorial Predictive 64 1 47 14 2 75.0 33.3 77.0 Refine and retest

Persistence 68 0 65 3 0 95.6 0 95.6

New York

Ontario Predictive 87 18 49 10 10 77.0 64.3 83.1 New nowcast

Persistence 88 11 43 17 17 61.4 39.3 71.7

Durand Predictive 79 6 60 10 3 83.5 66.7 85.7 New nowcast

Persistence 113 3 81 14 15 74.3 16.7 85.3

Woodlawn SP Predictive 72 2 51 11 8 73.6 20 82.3 Refine and retest

Persistence 107 1 82 12 12 77.6 7.7 87.2

Evans TP Predictive 85 3 78 4 0 95.3 100 95.1 New nowcast

Persistence 85 1 80 2 2 95.3 33.3 97.5

Lake Erie Predictive 86 0 84 2 0 97.7 0 97.7 Refine and retest

Persistence 86 0 86 0 0 100.0 0 100.0

Ohio

Edgewater Predictive 115 9 85 15 6 81.7 60.0 85.0 Continue nowcast

Persistence 111 5 89 8 9 84.7 35.7 91.8

Huntington Predictive 58 7 44 3 4 87.9 63.6 93.6 Continue nowcast

Persistence 39 2 26 5 6 71.8 25.0 83.9

Maumee Bay 

SP

Predictive 58 7 38 7 6 77.6 53.8 84.4 Continue nowcast

Persistence 43 1 23 9 10 55.8 9.1 71.9
Validation of Predictive Models and Implementation of Nowcast Systems

43

Table 9. Predictive-model responses as compared to use of previous day’s E. coli concentration (persistence model) during 

validation in 2012.—Continued

[The persistence model is the previous day’s E. coli concentration, the current method for assessing water quality; sensitivity is the percentage of exceedances 

of the bathing-water standard that are correctly predicted by the model; specificity is the percentage of nonexceedances of the bathing-water standard that are 

correctly predicted by the model; responses that were at least approximately 5 percent higher for the predictive model than for the persistence model are in 

bold]

Beach 

short name

Model 

used

Number Percentage Nowcast

Observations Correct 

+

Correct 

−

False 

+

False 

− Correct Sensitivity Specificity Status for 2013

Ohio—Continued

Villa Angela Predictive 87 24 47 1 15 81.6 61.5 97.9 Continue nowcast

∆∆ CTa 87 31 46 2 8 88.5 79.5 95.8

Persistence 69 14 24 13 18 55.1 43.8 64.9

Vermillion 

West

Predictive 52 1 42 0 9 82.7 10.0 100.0 Refine and retest

Persistence 30 4 21 3 2 83.3 66.7 87.5

Nickel Plate Predictive 52 6 23 20 3 55.8 66.7 53.5 Refine and retest

Persistence 30 3 21 3 3 80.0 50.0 87.5

Lake Front Predictive 52 4 35 7 6 75.0 40.0 83.3 Refine and retest

Persistence 30 2 21 4 3 76.7 40.0 84.0

Fairport 

Harbor

Predictive 105 5 80 7 13 81.0 27.8 92.0 Refine and retest

Persistance 104 3 71 15 15 71.2 16.7 82.6

Mentor

Headlands

Predictive 105 3 86 4 12 84.8 20.0 95.6 Refine and retest

Persistance 104 4 78 11 11 78.8 26.7 87.6

Lakeview Predictive 95 39 19 28 9 61.1 81.3 40.4 Refine and retest

Persistence 93 28 24 20 21 55.9 57.1 54.5

Pennsylvania

Beach 1 West 

Extension

Predictive 92 7 73 2 10 87.0 41.2 97.3 Refine and retest

Persistence 89 3 58 14 14 68.5 17.6 80.6

Beach 2 Predictive 92 4 79 2 7 90.2 36.4 97.5 Refine and retest

Persistence 88 0 67 10 11 76.1 0.0 87.0

Beach 6 Predictive 92 3 74 5 10 83.7 23.1 93.7 Refine and retest

Persistance 88 2 66 10 10 77.3 16.7 86.8

Mill Road Predictive 90 3 77 3 7 88.9 30.0 96.3 Refine and retest

Persistance 85 2 69 7 7 83.5 22.2 90.8

Beach 9 Predictive 89 3 79 3 4 92.1 42.9 96.3 Refine and retest

Persistence 84 0 70 7 7 83.3 0.0 90.9

Beach 11 Predictive 90 3 76 5 6 87.8 33.3 93.8 Refine and retest

Persistence 85 1 68 8 8 81.2 11.1 89.5

Wisconsin

Upper Lake 

Park

Predictive 36 6 26 1 3 88.9 66.7 96.3 Continue nowcast

Persistence 36 2 16 7 7 50.0 22.2 69.6

Red Arrow Predictive 64 13 41 7 3 84.4 81.3 85.4 Refine and retest

Persistence 64 7 40 8 9 73.4 43.8 83.3

Neshotah Predictive 54 1 50 3 0 94.4 100.0 94.3 Refine and retest

Persistence 54 0 52 1 1 96.3 0.0 98.1

Point Beach 

South

Predictive 58 4 45 4 5 84.5 44.4 91.8 Refine and retest

Persistence 58 3 43 6 6 79.3 33.3 87.8

Point Beach 

Central

Predictive 58 4 46 4 4 86.2 50.0 92.0 Refine and retest

Persistence 58 2 44 6 6 79.3 25.0 88.0
44

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Table 9. Predictive-model responses as compared to use of previous day’s E. coli concentration (persistence model) during 

validation in 2012.—Continued

[The persistence model is the previous day’s E. coli concentration, the current method for assessing water quality; sensitivity is the percentage of exceedances 

of the bathing-water standard that are correctly predicted by the model; specificity is the percentage of nonexceedances of the bathing-water standard that are 

correctly predicted by the model; responses that were at least approximately 5 percent higher for the predictive model than for the persistence model are in 

bold]

Beach 

short name

Model 

used

Number Percentage Nowcast

Observations Correct 

+

Correct 

−

False 

+

False 

− Correct Sensitivity Specificity Status for 2013

Wisconsin—Continued

Point Beach 

North

Predictive 58 4 46 5 3 86.2 57.1 90.2 Refine and retest

Persistence 58 2 46 5 5 82.8 28.6 90.2

Fischer Predictive 53 10 28 13 2 71.7 83.3 68.3 Refine and retest

Persistence 53 5 33 8 7 71.7 41.7 80.5

Hika Predictive 43 2 32 3 6 79.1 25.0 91.4 Refine and retest

Persistence 43 2 29 6 6 72.1 25.0 82.9

Thompson Predictive 51 3 47 1 0 98.0 100.0 97.9 Refine and retest

Persistence 51 1 46 2 2 92.2 33.3 95.8

Kreher Park Predictive 47 3 37 5 2 85.1 60.0 88.1 Refine and retest

Persistence 47 1 38 4 4 83.0 20.0 90.5

Maslowski Predictive 50 3 30 4 13 66.0 18.8 88.2 Refine and retest

Persistence 50 7 26 8 9 66.0 43.8 76.5

a

 A delta delta Ct calculation, described in Haugland and others (2005) was used instead of the predictive model.

Figure 4. Model responses as compared to using previous day’s Escherichia coli concentration (persistence model) during validation 

in 2012: A, Overall percent correct. B, Sensitivities. C, Specificities. (Each plot was split into quadrants where (1) the predictive model 

met the goal but the persistence model did not, (2) both models met the goal, (3) neither model met the goal, and (4) the persistence 

model met the goal but the predictive model did not. Sensitivity is the percentage of exceedances of the bathing-water standard that 

are correctly predicted by the model. Specificity is the percentage of nonexceedances of the bathing-water standard that are correctly 

predicted by the model.)

EXPLANATION

Illinois (n = 8)

Michigan (n = 2)

New York (n = 5)

Ohio (n = 10)

Pennsylvania (n = 6)

Wisconsin (n = 11)

Persistence model overall percent correct

Predicitve model, overall percent correct

A.

1:1 line
Validation of Predictive Models and Implementation of Nowcast Systems

45

Figure 4.—Continued Model responses as compared to using previous day’s Escherichia coli concentration (persistence model) 

during validation in 2012: A, Overall percent correct. B, Sensitivities. C, Specificities. (Each plot was split into quadrants where (1) the 

predictive model met the goal but the persistence model did not, (2) both models met the goal, (3) neither model met the goal, and (4) 

the persistence model met the goal but the predictive model did not. Sensitivity is the percentage of exceedances of the bathing-water 

standard that are correctly predicted by the model. Specificity is the percentage of nonexceedances of the bathing-water standard that 

are correctly predicted by the model.)

EXPLANATION

Illinois (n = 8)

Michigan (n = 2)

New York (n = 5)

Ohio (n = 10)

Pennsylvania (n = 6)

Wisconsin (n = 11)

Persistence model sensitivity, in percent

Predicitve model sensitivity, in percent 

B.

1:1 line

Persistence model specificity, in percent 

Predicitve model specificity, in percent

C.

1:1 line
Figure 5. Differences in percentages between paired 

predictive- and persistence-model responses for 42 beach 

models during validation in 2012. (Correct is the overall percent 

correct, sensitivity is the percentage of exceedances of the 

bathing-water standard that were correctly predicted, and 

specificity is the percentage of nonexceedances correctly 

predicted as nonexceedance of the standard.)

Future Work

The results of this study indicate that predictive models 

can predict exceedances of E. coli standards with accuracies 

as good as or better than the persistence model at most, but 

not all beaches. Drier conditions during 2012 as compared 

to 2010–11 (most calibration periods) may explain the poor 

model performance in 2012 at Illinois, Michigan, New York 

Lake Erie, and Wisconsin beaches, suggesting possible differences in factors affecting bacteria concentrations in dry 

and wet years. Additional years of data with a wide range of 

environmental conditions may help to improve future model 

performance.

A few beaches were described as gently sloping, with 

reservoirs of E. coli (presumably from bird populations) in the 

sand and shallow groundwater acting as a potential source of 

contamination to the lake; in particular, 63rd Street (Chicago, 

Ill.), Woodlawn SP (N.Y.), and Edgewater and Lakeview 

(Ohio). Inclusion of a variable that quantifies the interaction of 

shallow groundwater with lake water, such as foreshore head, 

may help to improve model performance at this type of beach.

Most importantly, many of the predictive models that 

met goals for percentages of correct responses were those 

where data had been collected for a number of years and the 

beach managers and modelers had gained an understanding 

of the factors affecting E. coli concentrations. For example, 

at Ontario and Durand, N.Y., Monroe County has been using 

decision-tree models for beach closure decisions since 1976. 

The experience gained in applying logic models to beach 

management decisions was the backbone for developing linear 

regression models in the current study that performed well. 

The knowledge gained in developing and testing models over 

several years at Ohio Nowcast beaches resulted in more accurate predictions during 2012 than at those Ohio beaches where 

only 2 years of data were collected. At Ohio Nowcast beaches, 

model performance was improved by the addition of radar 

rainfall data and subseason variables at Huntington, development of two models at Maumee Bay SP (one for negative flow 

and one for positive flow in Berger Ditch), and development of 

a model based on qPCR at Villa Angela. Knowing the specific 

characteristics of each beach is the key to developing models 

that perform well in assessing water quality.

Predictive models are intended to augment existing beach 

monitoring programs, not to replace them. Models must be 

continuously tested and refined, especially if changes are made 

to improve water quality at a particular beach. The enhancement of Virtual Beach software has made it possible for 

beach managers with limited statistical backgrounds to more 

easily take over developing and running predictive models 

from those with more advanced statistical backgrounds. With 

budget uncertainties for beach monitoring activities in the 

future, especially in light of potential reduced funding under 

Correct

Differences, in percent

100

-100

50

-50

0

Sensitivity Specificity

Upper whisker1

75th percentile

25th percentile

Median

Lower whisker1

Outliers2

EXPLANATION

1

The span of the whiskers is the 

largest and smallest values with 

1.5 times the interquartile range

2

Outliers are defined as values 

outside the whisker span
the Beaches Environmental Assessment and Coastal Health 

(BEACH) Act (U.S. Congress, 2000), the future of predictive 

models is also uncertain. At beaches with many years of model 

data (at least 5 years) and (or) with strong understandings of 

the factors that affect E. coli concentrations, it may be possible to reduce sampling to once or twice a week (using field 

measurement data) and use another predictive model (using 

automated data) for beach advisory decisions on nonsampling 

days. However, at beaches with only a few years of data and 

(or) where there is a weak understanding of the factors affecting E. coli concentrations, it would be more difficult to maintain modeling activities with infrequent monitoring. Initiating 

a two-tiered modeling approach, as described previously for 

Wisconsin beaches, may be one solution to reduced funding. 

In the two-tiered system, tier I would be a standard model 

(using field-measured data) and tier II would be an automated 

model for nonsampled days. 

In future years, the USGS plans to continue working with 

local partners to develop and validate predictive models at 

beaches and improve existing nowcasts, restructuring monitoring activities as budget issues arise. 

Summary

Predictive models have been used to overcome the timelag issue that limits the ability of 18–24 hour bacterial culture 

results to accurately assess current recreational water-quality 

conditions at beaches. Predictive models use environmental 

and water-quality variables to compute the probability that 

the State standard will be exceeded or to estimate concentrations of fecal-indicator bacteria (FIB), such as Escherichia 

coli (E. coli). They are recommended for use by the U.S. 

Environmental Protection Agency (EPA) (2012a) to provide 

same-day public health decisions. When predictive models are 

used for beach closure or advisory decisions, they are referred 

to as “nowcasts.” In 2010–11, operational nowcasts (those 

used daily by beach managers to inform the public) were used 

at a few locations within the Great Lakes. Prior to 2010 and 

the current study, the U.S. Geological Survey (USGS) was 

involved in developing and testing predictive models in Ohio, 

Wisconsin, and Indiana. These models were able to provide 

more accurate water-quality information than estimates 

based on the previous day’s E. coli concentration (persistence 

model). 

Building on the success of nowcast systems, the USGS 

worked with 23 local and State agencies during the recreational seasons of 2010–12 to improve existing operational 

nowcast systems and to expand the use of operational predictive models throughout the Great Lakes. This report summarizes existing information on past and current USGS modeling 

efforts, compiles background information on the study areas, 

and presents efforts to collect data and develop predictive 

models by multiple agencies. Studies were done at 49 Great 

Lakes beaches in Illinois, Indiana, Michigan, New York, Ohio, 

Pennsylvania, and Wisconsin. At the start of this overall study, 

the beaches consisted of four with operational nowcasts, six 

with recently implemented nowcasts, 33 in model validation, 

and six in model development. By using data collected during 

the recreational seasons of 2010–11 for most beaches (with 

earlier years’ data included for some beaches), predictive models were refined or developed for 43 beaches, and 42 of these 

models were validated during an independent year (2012). 

Data collection continued during 2012 at the remaining six 

sites to develop predictive models.

Data on E. coli concentrations were collected to conform to State water-quality standards. Swim advisories were 

issued if the concentration in a single sample or the geometric 

mean of multiple samples from the same beach on the most 

recent monitoring day (usually the previous day) exceeded 

235 colony-forming units per 100 milliliters (CFU/100 mL) 

in Illinois, Indiana, New York, Ohio, and Pennsylvania or 300 

CFU/100 mL in Michigan. Swim bans were posted in Illinois, Indiana, New York, Pennsylvania, and Wisconsin if the 

concentration was greater than 1,000 CFU/100 mL. The 235 

CFU/100 mL and 300 CFU/100 mL standards were used as 

thresholds in model development. 

Data on explanatory variables for model development 

and validation included field measurements and observations, 

data from nearshore buoys and other equipment installed 

specifically for this project, and data compiled from other 

sources. Field measurements included wave height, turbidity, 

water temperature, bird and people counts, and categorical 

measures of debris and algae. In nearby laboratories, samples 

from one beach in Ohio (Villa Angela) were analyzed by use 

of the quantitative polymerase chain reaction (qPCR) method 

for E. coli, and samples from several beaches in Chicago 

and one beach in Indiana were analyzed for relative fluorescence as a surrogate for chlorophyll a. Nearshore buoys were 

installed by the USGS or local agencies at beaches in Illinois, 

Pennsylvania, and Ohio to measure wave heights and wave 

periods, water temperature, pH, dissolved oxygen, conductivity, and (or) turbidity in near real time. A weather station 

was installed at one beach in Ohio (Mentor Headlands), three 

beaches in Chicago, and one beach in Pennsylvania (Presque 

Isle Beach 2), and rain gages were installed at several beaches 

in Wisconsin to obtain site-specific weather data. Equipment 

was also installed on nearby tributaries in New York and Wisconsin to more directly investigate their influence on beach 

water quality; these measurements in tributaries included 

water level (staff gages), turbidity, specific conductance, and 

water temperature. A device to measure photosynthetically 

active radiation and a piezometer to measure foreshore head 

were installed at selected Ohio beaches to determine whether 

these variables could improve the performance of operational 

predictive models. 

Data from other sources were compiled manually by the 

USGS or by use of tools designed specifically to facilitate 

the compilation and processing of data for predictive-model 

development. A lake-level spreadsheet developed during this 

study can be used to compile hourly lake-level data retrieved 
from the nearest offshore buoy; PROCESSNOAA compiles 

and processes hourly rainfall, wind direction and speed, and 

barometric pressure data collected at the nearest National 

Oceanic and Atmospheric Administration (NOAA) airport 

site; and Environmental Data Discovery and Transformation 

(EnDDaT) retrieves and processes data from multiple sources. 

Models were developed by use of Virtual Beach, a 

software program designed by USEPA specifically for beach 

model development (U.S. Environmental Protection Agency, 

2012b) or by use of other statistical software. Most models 

were developed by use of multiple linear regression techniques. As one alternative statistical technique, the USGS 

developed software to construct predictive models based on 

partial least squares regression (Brooks and others, 2013). 

The model outputs were the predicted E. coli concentration 

or the probability of exceeding the State standard. If using the 

probability as the output, threshold probabilities for advisories 

were established by examining model output sensitivities and 

specificities at different probability levels. Assuming that the 

actual E. coli concentration measured by the culture method 

determined the correct response, calibration and validation 

datasets were examined in terms of overall correct responses, 

sensitivities (percentage of exceedances of the bathing-water 

standard that were correctly predicted), and specificities (percentage of nonexceedances of the bathing-water standard that 

were correctly predicted). 

 Data on photosynthetically active radiation (PAR) at 

Huntington, Ohio, and foreshore head at Edgewater, Ohio, 

were collected for several years to determine whether they 

could be used to improve performance of models used in the 

Ohio Nowcast. The use of the sum of the PAR for the previous day was examined for both Huntington and Edgewater 

because of the proximity of these two beaches. There was a 

significant negative correlation between PAR and E. coli concentrations for each of the five years tested except for one year 

at each beach. At both beaches, the relations between E. coli

and PAR were not as strong as those between E. coli and two 

other model variables, but were equal to or stronger than three 

weaker model variables. Whether or not PAR can improve 

model performance is still uncertain. Water level in a shallow 

ground water well (piezometer) was used as measurement of 

foreshore head at Edgewater. The rate of change in foreshore 

head (in feet per hour) over the antecedent 12-hour period up 

to the time of sampling showed the strongest relations to E. 

coli concentrations among the foreshore-head variables (6, 

12, 24, and 48 hours). The relations between foreshore-head 

variables and E. coli concentrations were about as strong or 

stronger than most other model variables at Edgewater. 

More than 50 different variables or groups of variables 

were used in 43 beach-specific models. The number of times 

each variable was used in models was summed by State, and 

the frequency of use for each variable and group of variables 

was calculated as a percentage of all models. The same types 

of data were not available at all beaches, accounting for some 

differences in the selection of variables for the predictive 

models. Among individual variables, turbidity measured with 

a turbidimeter was used most often (79.1 percent), followed by 

day of the year (37.2), lake-level change over 24 hours (34.9 

percent), and wave height measured with a wave rod (32.6 

percent). As a variable group, weather data from the nearest NOAA airport sites were used in 44.2 percent of models 

from all States except Wisconsin, and radar rainfall data were 

used less often (18.6 percent) and only in models from Ohio 

and Wisconsin. The variables based on NOAA predictions 

were used in 2.3–16.3 percent of models, all at beaches in 

Wisconsin and New York. Local sources of data were used in 

32.6 percent of models, all at beaches in Illinois/Indiana and 

Pennsylvania.

Goals for good model performance were set for overall 

correct responses greater than or equal to 80 percent, sensitivities greater than or equal to 50 percent, and specificities 

greater than or equal to 85 percent. By using these goals, the 

model responses during validation (2012) were compared 

to results from the persistence model. For 42 beach models, 

predictive-model responses met the performance goals more 

often than the persistence-model responses in terms of overall 

correct (31 versus 18 models, respectively), sensitivity (18 

versus 4 models), and specificity (34 versus 26 models). 

Results from the paired student’s t-test showed that overall 

correct responses, sensitivities, and specificities were significantly higher for the predictive model than for the persistence 

model. 

Although predictive models performed better than most 

persistence models, there is still room for improvement. 

Drier conditions during the 2012 validation year as compared 

to 2010–11 calibration years may have led to poor model 

performance at Illinois, Michigan, New York Lake Erie, and 

Wisconsin beaches. Measuring foreshore head may improve 

model performance at Edgewater, Ohio, and other gently sloping beaches with sand as reservoirs of E. coli. Most importantly, gaining knowledge of each beach and the factors that 

affect E. coli concentrations is important for developing good 

predictive models. Collection of additional years of data with 

a wide range of environmental conditions may also help to 

improve future model performance. In times of budget restrictions, it may be possible to reduce sampling to once or twice 

a week at beaches with good-performing models and use a 

predictive model for beach advisory decisions on nonsampling 

days. The USGS will continue to work with local agencies in 

2013 and beyond to develop and validate predictive models at 

beaches and improve existing nowcasts, restructuring monitoring activities as budget issues arise. 
References Cited

Boehm, A.B., Grant, S.B., Kim, J.H., Mowbray, S.L., McGee, 

C.D., Clark, C.D., Foley, D.M., and Wellman, D.E., 2002, 

Decadal and shorter period variability of surf zone water 

quality at Huntington Beach, California: Environmental Science & Technology, v. 36, no. 18, p. 3885–3892.

Brooks, W.R., Fienen, M.N., and Corsi, S.R., 2013, Partial 

least squares for efficient models of fecal indicator bacteria 

on Great Lakes beaches: Journal of Environmental Management, v. 114, p. 470–475.

Dorfman, Mark, and Rosselot, K.S., 2011, Testing the 

waters—A guide to water quality at vacation beaches: Natural Resources Defense Council, accessed December 2012 at 

http://www.nrdc.org/water/oceans/ttw/ttw2011.pdf.

Erie County Department of Health, 2007, Bacterial quality 

of waters near Presque Isle—A compilation of relevant 

publications, 1987–2007, accessed December 2012 at 

http://ecdh.org/water-quality.php/Bacterial-WaterQuality/40/2144/446/1453.

Erie County Department of Health, 2012, Environmental 

Health Services, Beach Sampling, Protocol for public notification, accessed December 2012 at http://ecdh.org/waterquality.php/Beach-Sampling/40/2144/446/1420.

Francy, D.S., 2009, Use of predictive models and rapid methods to nowcast bacteria levels at coastal beaches: Aquatic 

Ecosystem Health & Management, v. 12, no. 2, p. 177–182.

Francy, D.S., Gifford, A.M., and Darner, R.A., 2003, Escherichia coli at Ohio bathing beaches—Distribution, sources, 

wastewater indicators, and predictive modeling: U.S. 

Geological Survey Water-Resources Investigations Report 

02–4285, 120 p. (also available at http://oh.water.usgs.gov/

reports/Abstracts/wrir02-4285.html).

Francy, D.S., Struffolino, P., Brady, A.M.G., and Dwyer, 

D.F., 2005, A spatial, multivariable approach for identifying proximate sources of Escherichia coli to Maumee Bay, 

Lake Erie, Ohio: U.S. Geological Survey Open File Report 

2005–1386, 20 p. (also available at http://pubs.usgs.gov/

of/2005/1386/).

Francy, D.S., Bertke, E.E., Finnegan, D.P., Kephart, C.M., 

Sheets, R.A., Rhoades, J., and Stumpe, L., 2006a, Use 

of spatial sampling and microbial source-tracking tools 

for understanding fecal contamination at two Lake Erie 

beaches: U.S. Geological Survey Scientific Investigations 

Report 2006–5298, 29 p., available at http://pubs.usgs.gov/

sir/2006/5298/.

Francy, D.S., Darner, R.A., and Bertke, E.E., 2006b, Models for predicting recreational water quality at Lake Erie 

beaches: U.S. Geological Survey Scientific Investigations 

Report 2006–5192, 13 p., available at http://pubs.usgs.gov/

sir/2006/5192/.

Francy, D.S., and Darner, R.A., 2006, Procedures for developing models to predict exceedance of recreational water-quality standards at coastal beaches: U.S. Geological Survey 

Techniques and Methods book 6, chap. B5, 34 p., available 

at http://pubs.usgs.gov/tm/2006/tm6b5/.

Francy, D.S., Bertke, E.E., and Darner, R.A., 2009, Testing and refining the Ohio Nowcast at two Lake Erie 

beaches—2008: U.S. Geological Survey Open-File Report 

2009–1066, 20 p., available at http://pubs.usgs.gov/

of/2009/1066/.

Ge, Z., Nevers, M.B., Schwab, D.J., and Whitman, R.L., 2010, 

Coastal loading and transport of Escherichia coli at an 

embayed beach in Lake Michigan: Environmental Science 

& Technology, v. 44, p. 6731–6737.

Ge, Z., Whitman, R.L., Nevers, M.B., Phanikumar, M.S., 

and Byappanahalli, M.N., 2012, Evaluating the role of 

an embayed beach as a reservoir and a net source of fecal 

contamination: Limnology and Oceanography v. 57, no. 1, 

p. 362–381.

Haugland, R.A., Siefring, S.C., Wymer, L.J., Brenner, K.P., 

and Dufour, A.P., 2005, Comparison of Enterococcus 

measurements in freshwater at two recreational beaches by 

quantitative polymerase chain reaction and membrane filter 

culture analysis: Water Research, v. 39, p. 559–568. 

Holtschlag, D.J., Shively, Dawn, Whitman, R.L., Haack, 

S.K., and Fogarty, L.R., 2008, Environmental factors and 

flow paths related to Escherichia coli concentrations at two 

beaches on Lake St. Clair, Michigan, 2002–2005: U.S. Geological Survey Scientific Investigations Report 2008–5028, 

38 p. 

Indiana Department of Environmental Management, 2012, 

BeachGuard, accessed December 2012 at https://extranet.

idem.in.gov/BeachGuard/Default.aspx.

Lake County (Illinois) Health Department, 2013, SwimCastData, accessed June 2013 at http://health.lakecountyil.gov/

Population/LMU/Pages/SwimCast-Data.aspx. 

Michigan Department of Environmental Quality, 2012a, 

About beach monitoring in Michigan, accessed March 

2012 at http://www.michigan.gov/deq/0,1607,7-135-

3313_3686_3730-11005--,00.html.
Michigan Department of Environmental Quality, 2012b, About 

beach monitoring in Michigan, accessed March 2012 at 

http://www.deq.state.mi.us/beach.

Myers, D.N., Stoeckel, D.M., Bushon, R.N., Francy, D.S., and 

Brady, A.M.G., 2007, Fecal indicator bacteria: U.S. Geological Survey Techniques of Water Resources Investigations, book 9, chap A7, section 7.1 (version 2.0), available 

at http://pubs.water.usgs.gov/twri9A/.

National Oceanic and Atmospheric Administration, 2012a, 

National Weather Service, accessed December 2012 at 

http://www.nws.noaa.gov/.

National Oceanic and Atmospheric Administration, 2012b, 

National Climatic Data Center—Unedited local climatological data, accessed December 2012 at http://cdo.ncdc.noaa.

gov/ulcd/ULCD. 

National Oceanic and Atmospheric Administration, 2012c, 

National Weather Service—Advanced Hydrologic Prediction Service, accessed December 2012 at http://water.

weather.gov/precip/download.php.

National Oceanic and Atmospheric Administration, 2012d, 

Center for Operational Oceanographic Products and 

Services—Tides and currents, accessed December 2012 at 

http://tidesandcurrents.noaa.gov/.

National Oceanic and Atmospheric Administration, 2012e, 

National Estuarine Research Reserve System—Centralized Data Management Office, accessed December 2012 at 

http://cdmo.baruch.sc.edu/get/realTime.cfm.

National Oceanic and Atmospheric Administration, 2012f, 

Great Lakes Environmental Research Laboratory—Great 

Lakes Coastal Forecasting System, accessed December 

2012 at http://www.glerl.noaa.gov/res/glcfs.

Nevers, M.B., and Whitman, R.L., 2005, Nowcast modeling of Escherichia coli concentrations at multiple urban 

beaches of southern Lake Michigan: Water Research, v. 39, 

p. 5250–5260.

Nevers, M.B., and Whitman, R.L., 2011, Efficacy of monitoring and empirical predictive modeling at improving public 

health protection at Chicago beaches: Water Research, v. 45, 

p. 1659–1668.

New York State Department of Health, 2010, Part 6—Subpart 

6-2 Bathing Beaches, accessed December 2012 at http://

www.health.ny.gov/regulations/nycrr/title_10/part_6/subpart_6-2.htm#operate.

Noble, R.T., Blackwood, A.D., Griffith, J.F., McGee, C.D., 

and Weisberg, S.B., 2010, Comparison of rapid quantitative PCR-based and conventional culture-based methods for 

enumeration of Enterococcus spp. and Escherichia coli in 

recreational waters: Applied and Environmental Microbiology, v. 76, no. 22, p. 7437–7443.

Ohio Department of Health, 2012a, Beach monitoring, 

accessed December 2012 at http://www.odh.ohio.gov/odhprograms/eh/bbeach/beachmon.aspx.

Ohio Department of Health, 2012b, BeachGuard, accessed 

December 2012 at http://publicapps.odh.ohio.gov/BeachGuardPublic/Default.aspx.

Olyphant, G.A., and Whitman, R.L., 2004, Elements of a predictive model for determining beach closures on a real time 

basis―The case of 63rd Street Beach Chicago: Environmental Monitoring and Assessment v. 98, p. 175–190.

Terbush, K., and Husson, K., 2009, Woodlawn Beach State 

Park Beach sanitary survey report: Albany, N.Y., New York 

State Office of Parks, Recreation and Historic Preservation 

Environmental Management Bureau, 26 p.

U.S. Congress, 2000, Beaches Environmental Assessment and 

Coastal Health Act of 2000: U.S. Congress, 106th, Public 

Law 106–284. 

U.S. Environmental Protection Agency, 1986, Ambient water 

quality criteria for bacteria—1986: Washington, D.C., EPA 

440/5–84–002, 18 p. 

U.S. Environmental Protection Agency, 2002, National beach 

guidance and required performance criteria for grants: 

Washington, D.C., EPA–823–B–02–004, 178 p. 

U.S. Environmental Protection Agency, 2009, Method 1603—

Escherichia coli in water by membrane filtration using 

modified membrane-thermotolerant Escherichia coli agar 

(modified mTEC): Washington, D.C., EPA 821–R–09–007, 

36 p., accessed December 2012 at http://www.epa.gov/

waterscience/methods/method/biological/.

U.S. Environmental Protection Agency, 2012a, Recreational water quality criteria: Washington, D.C., EPA–

820–F–12–058, 63 p., accessed December 2012 at http://

water.epa.gov/scitech/swguidance/standards/criteria/health/

recreation/index.cfm.

U.S. Environmental Protection Agency, 2012b, Exposure 

assessment models—Virtual Beach: Center for Exposure 

Assessment Modeling, accessed May 2013 at http://www2.

epa.gov/exposure-assessment-models/virtual-beach-vb.

U.S. Geological Survey, 2009, Collection, quality assurance, 

and presentation of precipitation data: Office of Surface 

Water Technical Memorandum 2006.01 (revised December 

2009), accessed March 2013 at http://water.usgs.gov/osw/

pubs/memo.summaries.html.

U.S. Geological Survey, 2010, Swimming Advisory Forecast 

Estimate, accessed June 2013 at http://www.glsc.usgs.gov/

projectSAFE.php.
References Cited

51

U.S. Geological Survey, 2012, National Water Information 

System—Web interface, USGS Water Data for the Nation, 

accessed December 2012 at http://waterdata.usgs.gov/nwis/.

U.S. Geological Survey, 2013, Environmental Data Discovery 

and Transformation, accessed June 2013 at http://cida.usgs.

gov/enddat/.

Whitman, R.L., and Nevers, M.B., 2003, Foreshore sand as 

a source of Escherichia coli in nearshore water of a Lake 

Michigan beach: Applied and Environmental Microbiology, 

v. 69, p. 5555–5562.

Whitman, R. L. and Nevers, M. B., 2008, Summer E. coli

patterns and responses along 23 Chicago beaches: Environ-

mental Science & Technology, v. 42, p. 9217–9224.

Wilde, F.D., ed., variously dated, Field measurements: U.S. 

Geological Survey Techniques of Water-Resources Investi-

gations, book 9, chap. A6, sec. 6.1, 6.3, and 6.7, available at 

http://pubs.water.usgs.gov/twri9A6/.

Wisconsin Department of Natural Resources, 2012a, Wiscon-

sin Beaches, accessed December 2012 at http://dnr.wi.gov/

topic/beaches/.

Wisconsin Department of Natural Resources, 2012b, Wiscon-

sin Beach Health, accessed December 2012 at http://www.

wibeaches.us/apex/f?p=BEACH:HOME:447869626819978. 

 Zimmerman, T.M., 2008, Modeling to predict Escherichia 

coli at Presque Isle Beach 2, City of Erie, Erie County, 

Pennsylvania: U.S. Geological Survey Scientific Investiga-

tions Report 2008–5039, 13 p.

Appendixes

Appendixes 4 and 5 follow immediately in this document. Appendixes 1, 2, and 3 are separate 

documents, available for downloading from the indicated Web address.

Appendixes:

1. Obtaining National Oceanic and Atmospheric Administration lake-level data ............

http://pubs.usgs.gov/sir/2013/5166 

2. Graphical User Interface—PROCESSNOAA..........................................................................

http://pubs.usgs.gov/sir/2013/5166 

3. Example Spreadsheets for Compiling Beach Water-Quality and Environmental Data...

http://pubs.usgs.gov/sir/2013/5166

4. Example Protocol for Collecting Data and Running a Predictive Model for 

Nowcasting at Great Lakes Beaches .........................................................................................57

5. Equations for the selected model(s) for each Great Lakes beach .................................61
54

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Appendix 1. Obtaining National Oceanic and Atmospheric Administration 

Lake-Level Data

Appendix 1 documents in spreadsheet form are available for downloading from the indicated Web address:

http://pubs.usgs.gov/sir/2013/5166.
Appendix 2

55

Appendix 2. Graphical User Interface: PROCESSNOAA

Appendix 2 documents, including instructions, software, and source code are available for downloading from the indicated Web 

address: http://pubs.usgs.gov/sir/2013/5166. 
56

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Appendix 3. Example Spreadsheets for Compiling Beach Water-Quality and 

Environmental Data

Appendix 3 documents in spreadsheet form are available for downloading from the indicated Web address:

http://pubs.usgs.gov/sir/2013/5166.
Appendix 3

57

Appendix 4. Example Protocol for Collecting Data and Running a Predictive 

Model for Nowcasting at Great Lakes Beaches

This document is an example of a recommended protocol for collecting and recording data for nowcasting at Great 

Lakes Beaches. It is intended to be used as a starting point 

by agencies collecting data and running predictive models 

for nowcasting and can be revised to meet the needs of local 

monitoring programs. The data collected in this protocol are 

entered into a spreadsheet, an example of which is Appendix 3, available for dowloading at http://pubs.usgs.gov/

sir/2013/5166. This protocol is part of a larger document that 

describes the development and implementation of predictive models for estimating recreational water quality at Great 

Lakes beaches. 

Field protocol

Equipment

• Personal floatation device (PFD, mandatory) and chest 

waders (optional)

• Sample bottles—500-milliliter (mL) or 1-liter (L) 

sterile plastic bottle with lid (one for each sampling 

location), labeled with site and date (an extra bottle is 

needed when a replicate or field blank is collected)

• Clipboard with field forms

• Wave rod, thermometer, and a small cooler with ice

• Camera (optional)

Sampling Locations and Frequency

Water samples are collected Monday through Thursday 

by 9 a.m. at Beach1 and Beach 2. Water samples are collected from approximately 1 foot (ft) below the water surface 

at a total water depth of 2–3 ft (not including waves). It is 

important that samples be collected consistently at the same 

depth throughout the summer. Under rough conditions (waves 

greater than 2 ft), the employee is to wade out to a comfortable depth, collect a sample, and record the water depth (not 

including waves). 

Measure and Record Field Observations

• Record the number of birds and swimmers on the field 

form.

• Record any unusual field observations, such as excessive debris, algae, etc.

Measure Wave Height 

Go to the sampling location. Place a wave rod in the 

water and stand there for 1 minute. Note the minimum water 

level and maximum height of waves during that period—

record on field form. Perform the following calculation to get 

the wave height for the model:

Maximum height – minimum height = wave height (in feet)

If you are unable to enter the water because of unsafe conditions caused by high waves, estimate the wave height. Mark 

“estimate” on the field form.

Collect Water Samples 

Aseptically collect water samples using a presterilized 

bottle. To collect a hand-dipped sample, open a sterile bottle 

about 12 to 18 inches (in.) below the water’s surface. Grasp 

the bottle near the base and allow the bottle to fill with the 

opening pointed slightly upward. Remove the bottle with the 

opening pointed upward from the water and tightly cap it, 

allowing some headspace for proper mixing. Do not stir up 

bottom sediments while collecting the water sample. 

• Measure water temperature by placing the thermometer 

about 12 in. below the water surface at one sampling 

location at each beach. 

• If the sample is collected in less than 2 ft of water 

because of rough conditions, indicate so on the field 

form. 

• Immediately place all samples out of the sun in coolers with ice. The sample needs to be kept on ice and 

processed for bacterial indicators and turbidity within 6 

hours of sample collection. 
58

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Turbidity Protocol for Hach 2100Q1

Instructions for the the Hach 2100Q turbidimeter are 

given as an example. Other turbidimeters may be used.

Calibrate the Instrument Using the StablCal® 

Standards

Turbidity is measured in the laboratory. These are 

formazin standards that come with the instrument. Calibrate 

the instrument the first time you use it each season. After that, 

calibrate the instrument only if the verification standard is 

off. Use the 20, 100, and 800 nepholemetric turbidity ratio 

units (NTRU) standards for calibration. 

1. Clean the outside of each vial using silicone oil and a lintfree cloth.

2. Read the three standards (as described in the instruction 

manual) and record the results in the calibration book that 

is kept with the instrument.

Check the Verification Standard 

The verification standard is the 10 NTRU formazin standard. Run the verification standard each week you use the 

instrument. 

1. Clean the outside of the 10 NTRU vial using silicone oil 

and a lint-free cloth.

2. Press “Verify Cal” and insert the vial.

3. Press READ. The machine will indicate if the verification passed. If it did not pass, recalibrate the instrument as 

described above. Press DONE.

4. Record results in the calibration log.

Read the Sample

1. Because the samples are cold, you may have to set up 

aliquots in the sample vials and let them reach room temperature before reading turbidity; otherwise, condensation 

may interfere with your reading.

2. Shake the sample bottle. 

3. Immediately after shaking, pour an aliquot from the 

sample into a clean glass test vial. Fill to the line. Place 

the cap on the glass test vial.

1

 Any use of trade, firm, or product names is for descriptive purposes only 

and does not imply endorsement by the U.S. Government.

4. Shake the sample bottle again and pour an aliquot from 

the sample into a second glass test vial. Place the cap on 

the second vial.

5. Hold the vials by the black lids only. Wipe the outside of 

the glass vials with silicone oil and a lint-free cloth.

6. Invert the vials gently a few times and place in the turbidimeter. Orient the vial in the well so that the diamond 

aligns with the raised orientation mark on the front of the 

well.

7. Close the turbidimeter cover. 

8. Take the measurement by pressing READ. 

9. Repeat for a second aliquot of the same sample. The two 

measurements must meet the following criteria. If they 

don’t, take a third measurement—and a fourth if necessary—until two consecutive measurements meet the 

criteria.

• For values less than 10 NTRU, they must agree within 

1.5 NTRU.

• For values greater than 10 NTRU, they must agree 

within 15 percent.

10. Record both turbidity values on laboratory sheets (the two 

that are within 15 percent) and calculate and record an 

average value.

11. Clean the test vials immediately after use. The glass is 

easily scratched, so be careful in handling and washing. 

Wash the vials with laboratory-grade detergent and rinse 

three times with deionized water. Do not allow to air dry. 

Store the vials filled with deionized water with the caps 

on. 
Appendix 4

59

Colilert Quanti-Tray Method

Instructions for the Colilert Quanti-Tray/2000 method 

(IDEXX Laboratories, Inc., Westbrook, Maine) for Escherichia coli (E. coli) are given below. Other U.S. Environmental Protection Agency (EPA)-approved methods may be used.

Process the Sample

1. Prepare lab forms and label. Fill out appropriate lab 

forms for each sample and check off the dilutions to prepare, if any. Label the bottom of the tray with site name, 

date, and dilution (if any). 

2. Prepare sample and reagent mixture. Measure out 100 

mL of sample in a sterile graduated cylinder and pour into 

a sterile 125-mL bottle. Add one packet of Colilert reagent 

to the sample. Gently shake to distribute reagent throughout the sample.

3. Prepare dilution (if needed). The system will enumerate 

between 1 and 2,400 most probable number (MPN)/100 

mL for the undiluted sample. If you suspect the water 

to have greater than 2,400 MPN/100 mL, make a 1/10 

dilution (10 mL of the supernatant and 90 mL of sterile 

deioinzed water) and mix the diluted sample with Colilert reagent. This will enumerate between 10 and 24,000 

MPN/100 mL.

4. Load and incubate the sample. Pour the reagent/sample 

mixture into the Quanti-Tray. Tap the small wells to 

release any air and allow foam to settle. Seal the QuantiTray by placing in the sealer

Incubate and Count Wells

Incubate for 24 hours (24 to 28 hours max) at 35 °C. 

Count large and small positive wells that fluoresce under a 

long-wave ultraviolet light and refer to the MPN table provided by the manufacturer. For the 1:10 dilution, multiply the 

result in the table by 10 to get MPN/100 mL. 

Models

Below are instructions for running a predictive model, 

using the example spreadsheet presented in appendix 3. Variable names used in appendix 3 are in parentheses below. 

The variables used in the models for nowcast validation 

this year are as follows:

Beaches 1 and 2

1. Log turbidity (Turb_NTU) plus the wave height 

(Waveht_ft)

2. Airport rainfall during the previous 24 hours 

(AirportRainRd-1_in)

3. Lake level change in the past 24 hours (LakeLvl24chg_ft)

The thresholds for issuing an advisory are a 39 percent probability for Beach 1 and 235 MPN/100 mL for Beach 2.

Pull and Enter Data Into the 

Spreadsheets

1. Enter field measurements and sample information into 

the data spreadsheets (see Appendix 3 for examples of 

spreadsheets).

2. Rainfall at the airport. Go to Web page: http://www.nws.

noaa.gov/

a. Enter the location, click “Go,” and click on “3 day 

history.”

b. AirportRainRd-1_in. Hourly data for precipitation are in the right three columns and go back 3 

days. Add up the precipitation for the last 24 hours 

(8:53 yesterday to 7:53 today) and record the rainfall 

amount in the spreadsheet.

3. Lake level. Go to Web page: http://tidesandcurrents.

noaa.gov/station_retrieve.shtml?type=Great+Lakes+Wat

er+Level+Data

a. Click on the appropriate site.

b. Change to “feet.” Change to “local time.”

c. Click on the box that says “view data.”

d. Obtain the water level value for today’s date at 8:00 

from the fourth column for use in the model. Record 

this value in the spreadsheet.

e. The spreadsheet will calculate the change in lake 

level since the previous day.
60

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Run the Models in Virtual Beach (VB)

1. Every day that you collect samples, run the models. (For 

running a nowcast live, this should get done by 9:30 a.m. 

on all sampling days.)

2. Open the VB software. Click on Model, Open, and open 

the Beach 1.vbmx file. 

3. Enter ID (M/DD/YY) and the variables.

4. Click on the box for “IV Data Validation,” click Scan, and 

click Return.

5. Click on the box for “Make Predictions.”

6. Depending on which output you use, record the “Exceedance probability” (Beach 1) or “Model prediction” (for 

Beach 2) into your spreadsheet. 

7. Back in the VB software, click Model, Save, change to 

Log10, click Save; use the same name and replace the file.

8. After obtaining the E. coli results from the Colilert 

Quanti-Tray, enter them into the “MeasuredEcoli_MPN” 

column in the spreadsheet. Note the tally of model results 

(false positive, false negatives, etc.). 

9. Open the model for Beach 2 and repeat steps 2–8.

Data Entry and Management

• Enter data daily in the designated state database, as 

required by the State.

• Every Friday, check over the spreadsheets to be sure 

that all data have been entered. If you are working with 

the USGS, send an updated spreadsheet via email to 

your USGS contact.

• Enter quality-control (QC) data for E. coli and turbidity 

replicates and E. coli field blanks on a separate worksheet on appendix 3. 

Quality Assurance and Quality Control

Duplicate Water Samples and Counting Checks 

Duplicate analyses will be done for one sample every 

other week. Duplicate samples are collected from all sites, and 

some duplicates should be collected after significant changes 

in environmental or weather conditions. A duplicate sample is 

a second sample bottle collected immediately after and at the 

same location as the first bottle. Collection time is reported 

with a difference of 2 minutes. Both bottles are analyzed for 

E. coli and turbidity.

For counting checks, fluorescent well counts are made by 

a second person on approximately 10 percent of samples. If 

results don’t agree within 20 percent, work with a laboratory 

supervisor to reconcile differences.

Field Blanks 

One field blank is collected at the beginning of the sampling period (early June) and one later in the sampling period 

(July). 

• Use a regular sample bottle. 

• Pour about 200 mL of sterile phosphate buffer into 

the bottle under actual field conditions (at the beach). 

Store the sample with the regular sample bottles in the 

cooler.

• Process the field blank for E. coli along with the regular samples. 

Reference Samples for the Culture Method 

Pure culture positive and negative controls, purchased 

from the manufacturer, will be processed in June and again in 

July. In June, every analyst that is expected to process samples 

will analyze pure cultures. If a new analyst is brought into the 

project at some other time, the new analyst also will process 

the pure cultures. 

Turbidity Measurements

The USGS will provide at least one set of reference 

samples for turbidity. If measured results do not agree within 

15 percent of the stated turbidity value, another set of reference samples will be tested. 

Quality-Assurance Checks

Someone from the USGS will perform at least one QA/

QC check of field and laboratory procedures each season. 

USGS contacts are listed below.

Donna Francy, dsfrancy@usgs.gov

Amie Brady, amgbrady@usgs.gov

Equipment

Laboratory staff will maintain a logbook for recording 

equipment checks. The logbook should note the equipment 

model number and serial number, date and time of each check, 

and results and initials of the person performing each check.

Incubators. Incubators must be operated at 35+ 0.5 °C. 

Check the operating temperatures of the incubators by using 

an external thermometer weekly during the recreational 

season. The digital thermometer you use is checked before 

use against a National Institute of Standards and Technology 

(NIST) thermometer. Record operating temperature in the 

logbook.
Appendix 4

61

Appendix 5. Equations for the Selected Model(s) for Each Great Lakes Beach

Montrose

Log10(E. coli) = 4.7 − 0.0069*(Dayofyear) − 

0.0044*(SolarRad_6) − 0.008327*(Humidity_24) + 

0.083*(WindO_comp4) 

Source of local weather and solar radiation data: City of 

Chicago weather station at Foster Avenue Beach

Beach orientation is −68.82°.

63rd Street

Log10 (E. coli) = 0.59 + 0.0067*(Dayofyear) + 0.86*

(R6hr_cm) − 0.0026*(SolarRad_6) + 0.068*(WindO_comp6)

Source of local weather and solar radiation data: City of 

Chicago weather station at 63rd Street Beach

Beach orientation is −62.24°.

Foster

Log10 (E. coli) = 1.8 + 0.0040*(Dayofyear) − 

0.0015*(SolarRad_4) − 0.036*(AirTempC_4) + 

0.12*(WindO_comp4)

Source of local weather and solar radiation data: City of 

Chicago weather station at Foster Avenue Beach

Beach orientation is −36.3°.

Calumet

Log10 (E. coli) = −1.8 + 1.6*Log10(Dayofyear) + 0.60*

(R4hr_cm) − 0.0010*(SolarRad_4) + 0.13*(WindO_comp4)

Source of local weather and solar radiation data: City of 

Chicago weather station at 63rd Street Beach

Beach orientation is −23.94°.

Oak Street

Log10 (E. coli) = −50.6 − 0.00061*(Turb1hr) + 0.00098*

(R48_ cm) − 2.1*Log10(SolarRad_24) + 0.16*(WindSp_4) + 

0.076*(BaroPress_6)

Source of turbidity data: City of Chicago nearshore buoy

Source of local weather and solar radiation data: City of 

Chicago weather station at Foster Avenue Beach

Models were used to predict Escherichia coli (E. coli) 

concentrations from measurements of environmental and 

water-quality variables. Equations were generated using Virtual Beach software (U.S. Environmental Protection Agency, 

2012). Variables are described at the end of this appendix.

Illinois

South Beach

Ln(E. coli) = −4.8 + 0.022*(Dayofyear) − 0.16*

(Rain_cat) + 0.26*(Rain_cat_Int) + 0.37*(Cloud_cat) + 

0.061*(WaterTemp_F) − 0.060*(People_no) − 0.0034*

(Gulls_no) − 0.20*(Rw72)

Source of local rainfall data: Evanston, Ill., water treatment 

plant

Greenwood Beach

Ln(E. coli) = −21.5 + 2.5*Ln(Dayofyear) + 

2.1*Ln(AirTempF_INST) + 0.35*(Rd−1)

0.1 + 0.56*

Ln(WaveHt_in) − 0.22*(Rain_cat) − 0.32*(People_no)0.25

+ 0.27*Ln(Gull_no) + 0.45*[3.4 + 1.2*(LakeLevel_48) − 

0.0020*(LakeLevel_48)2] + 0.29*(Algae_cat)

Source of air temperature data: Evanston, Ill., water treatment 

plant

Lee Beach

Ln(E. coli) = −122.6 + 0.029*(Dayofyear) + 

0.045*(WaterTemp_F) − 0.046*(Turb) + 0.31*(Cloud_cat) + 

0.20*(LakeLevel) + 0.92*(Algae_cat) + 0.10*

(WindA_comp_INST)

Source of local wind data: Evanston, Ill., water treatment plant

Beach orientation is −16.82°.
62

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Indiana

Jeorse Park

Log10 (E. coli) = 2.1 − 0.024*(Chloro-A) + 

0.00076*(WaterTempC) + 0.023*(WaveHt_cm) + 

0.10*(WindSp_INST)

Source of airport wind data: Gary/Chicago International 

Airport 

Michigan

Metro

Log10 (E. coli) = 2.2− 0.080*(WindSp_24V) − 

0.049*(WindSp_INST) − 1.0*(Rw48) + 0.061*(R30d) + 

1.1*(LakeLevel_24) + 0.0082*(Turb*WaveHt_ft)

Source of airport weather data: Detroit City Airport and 

Selfridge Air Force Base 

Memorial

Log10 (E. coli) = 3.6 − 0.0017*(WindDir_INST) − 

0.0092*(DayofYear) + 1.29*(Rd−1) + 0.011*(Turb*Rd−1) 

+ 0.047*(WindSp_INST) − 0.040*(WindSp_24V) + 

0.0013*(WindDir_24V)

Source of airport weather data: Detroit City Airport

New York

Ontario

Log10 (E. coli) = −0.15 − 1.4*(CurrentA_comp_24) + 

1.7*[−4.9 + 0.062*(DayofYear) − 0.00014*(DayofYear)2] + 

0.44*[1.6 + 1.38*(WaveHtPred_ft) − 0.32*(WaveHtPred_ft)2] 

+ 0.0014*(WindSp_INST)2 + 0.45*[2.0 + 2.1*(LakeLevel_24) 

+ 15.6*(LakeLevel_24)2] − 0.75*(CurrentO_comp_24)0.5 − 

4.4*(WaveHtPred_24)0.1 + 0.25*(Ln(Turb))

Source of airport weather data: Greater Rochester 

International Airport

Source of current and wave-height prediction data: 

Great Lakes Coastal Forecasting System (GLCFS)

Durand

Log10 (E. coli) = −7.2+ 0.69*(1.6 + 3.7*(LakeLevel_24) + 

16.8*(LakeLevel_24)2) + 2.7*[−2.0 + 0.034*(DayofYear) − 

7.4e−05*(DayofYear)2] + 0.91*(Rd−1)0.5 + 0.55*Log10(Turb) + 

0.86*[1.6 − 0.025*(WindA_comp_Inst) + 0.0023*

(WindA_comp_Inst)2] + 0.12*(Debris_Cat) − 0.60*

[1.2 + 1.6*(WaveHtPred_24) − 0.62*(WaveHtPred_24)2] 

+ 0.96*[2.0 − 0.0067*(WindDir_24V) + 

2.1e−05*(WindDir_24V)2] + 7.3e−05*(Birds_no)2

Source of airport weather data: Greater Rochester 

International Airport

Source of wave-height prediction data: Great Lakes Coastal 

Forecasting System (GLCFS)

Beach orientation is −62.6°.

Woodlawn SP

Log10 (E. coli) = −4.5 + 0.54*(Turb)0.25 + 1.6*

[6.1 − 0.043*(DayofYear) + 0.00011*(DayofYear)2] − 0.14*

( Turb-stream_24)0.1 + 0.21*(WaveHt_ft)0.5 − 0.064*

(WindO_comp24) + 0.83*[2.1 − 0.99*(Rd−3) + 0.99*(Rd−3)2]

Source of airport weather data: Buffalo Niagara International 

Airport

Beach orientation is −180°.

Evans TP

Log10 (E. coli) = −0.076 + 0.63*(Turb)0.25 + 0.73*

[1.4 + 0.00031(WindA_comp_INST) + 0.0026*

(WindA_comp_INST)2] − 70.3*(1/WaterTempPred_24) + 

0.38*[1.5 + 0.39*(LakeLevel_24) + 1.0*(LakeLevel_24)2] 

+ 0.69*[1.6 − 1.6*(Rd−3) + 1.5*(Rd−3)2

] + 0.26*(Rd−1)0.25 + 

0.33*[1.6 − 0.0042*(WindDir_INST) + 1.83e−05*

(WindDir_INST)2]

Source of airport rain and wind data: Buffalo Niagara 

International Airport

Source of water temperature prediction: Great Lakes Coastal 

Forecasting System (GLCFS)

Beach orientation is −123°.
Appendix 5

63

Lake Erie

Log10 (E. coli) = −2.4 + 0.036*(WindSp_INST) + 0.91*

[1.6 − 1.0*(Rd−3) + 0.96*(Rd−3)2] + 0.37*(Rw48)0.5 + 

0.16*(Turb)0.5 + 1.0*[−9.6 + 0.88*(AirTemp_mean24) − 

0.17*(AirTemp_ mean24)2]

Source of airport weather and air temperature data: Buffalo 

Niagara International Airport

Ohio

Edgewater

When radar rainfall data are available—

Log10(E. coli) = 0.55 + 0.24*[WaveHt_ft + Log10(Turb)] + 

0.0042*(DayofYear) + 0.36*(Radarxcell-sum-Rw48)0.5 + 

0.26*(LakeLevel_24) + 0.17*(Subseason1)

When radar rainfall data are not available—

Log10(E. coli)= 0.52 + 0.0045*(DayofYear) + 0.32*(Rw48)0.5

+ 0.19*(Subseason1) + 0.24*[WaveHt_ft + Log10(Turb)] + 

0.30*(LakeLevel_24) 

Source of airport weather data: Cleveland Hopkins 

International Airport

Huntington

When radar rainfall data are available— 

Log10(E. coli) = 0.99 + 0.51*(Rd−1)0.5 + 0.29*

[Log10(Turb) + (WaveHt_ft)0.5] − 0.10*(PreJuly24) + 

0.013*(WindSp_INST*WindCode) + 0.11*

(Radarxcell-sum-Rw48)0.5

When radar rainfall data are not available— 

Log10(E. coli)= 0.92 + 0.75**(Rd−1)0.5 + 0.28*

[Log10(Turb) + (WaveHt_ft)0.5] + 0.012*

(WindSp_INST*WindCode) + 0.19*(Rd−2)0.5 + 

0.096*(PostJuly24) + 0.30*(LakeLevel_24)

Source of airport weather data: Cleveland Hopkins International Airport

Maumee Bay State Park

When discharge from Berger Ditch was positive—

Log10(E. coli) = 1.5+ 0.40*(Rd−2)0.25 + 0.060*WindCode_4

When discharge from Berger Ditch was negative (from backwater from Maumee Bay)—

Log10(E. coli) = 1.1+ 10.9*Ln(LakeLevel_24) + 

0.083*[WindCode_4*Log10(Turb)] − 0.40*(Disch_730am)0.25

Source of airport weather data: Toledo Executive Airport 

(formerly Metcalf Field)

Villa Angela

Log10(E. coli) = 6.4 − 0.11*Ct

Vermilion

Log10(E. coli) = 0.50 + 0.44*[(WaveHt_ft)0.5 + Log10(Turb)] + 

0.33*Log10(Disch_8am) + 0.25*(Rd−1)0.5

Source of airport weather data: Lorain County Regional 

Airport

Source of stream−discharge data: Vermilion River near Vermilion Ohio, USGS site 04199500

Lakefront

Log10(E. coli) = 1.7+ 0.22*[WaveHt_ft + Log10(Turb) + 

LakeLevel_24]

Nickel Plate

Log10(E. coli) = −0.037− 0.19*(WindO_comp_INST) 

+ 0.33*(WaveHt_ft) + 14.5*(1/Disch_Md−2) + 

0.0057*(Dayofyear) + 0.0084*(Turb)

Source of wind data: Old Woman Creek, Ohio, NOAA 

National Estuarine Research Reserve System-wide Monitoring 

Program site OWCOWMET

Source of stream-discharge data: Huron River at Milan Ohio, 

USGS site 04199000

Beach orientation is −58.7°.
64

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Lakeview, Lorain

Log10(E. coli) = 0.09 + 0.13*(WindSp_INST)0.5

− 0.27*(LakeLevel_Spring)2 + 0.32*(Rw48)0.5 + 

0.0080*(Dayofyear) + 0.14*(Turb)0.5

Source of airport weather data: Lorain County Regional 

Airport

Headlands

Log10(E. coli) = 0.35− 0.026*(WindA_comp_INST) + 

0.15*(Rw72)0.5 + 0.36*[(WaveHt_ft)0.5 + 

Log10 (Turb)] + 0.19* Log10 (Disch_8am)

Source of airport wind direction and speed data: Ashtabula 

County Airport

Source of airport rainfall data: Burke Lakefront Airport

Source of stream-discharge data: Grand River near Painesville 

Ohio, USGS site 04212100

Beach orientation is −134°.

Fairport Harbor

Log10(E. coli) = 0.7242 + 0.074*(Turb)0.5 − 0.021*

(WindA_comp) + 0.57*(WaveHt_ft)0.5 + 

0.17*(Log10(Disch_8am) + 0.41*(Rd−1)0.5

Source of airport weather data: Ashtabula County Airport

Source of stream-discharge data: Grand River near Painesville 

Ohio, USGS site 04212100

Beach orientation is −76.6°.

Pennsylvania (Presque Isle State Park)

Beach 1 West Extension

Log10 (E. coli) = 0.60+ 1.0* Log10 (Turb) + 

0.80*(LakeLevel_24) − 0.041*(WindA_comp_Inst)

Beach orientation is −157°.

Beach 2

Log10 (E. coli) = 147.6 + 1.1* Log10 (Turb) − 100.5* Log10

(BaroPress_INST) + 0.52*(LakeLevel_24)

Beach 6

Log10 (E. coli) = 26.1 + 0.86* Log10 (Turb) − 

0.87*(BaroPress_INST) + 0.50*(LakeLevel_24)

Mill Road

Log10 (E. coli) = 29.6+ 0.66* Log10 (Turb) + 

0.57*(LakeLevel_24) − 0.98*(BaroPress_INST) + 

0.14*(WaveHt_ft) − 0.44*(R72)

Beach 9

Log10 (E. coli) = 35.9+ 0.92* Log10 (Turb) + 

0.48*(LakeLevel_24) − 1.3*(BaroPress_INST) − 0.44*(R48) 

+ 0.44* Log10 (WindDir_24V) + 0.0037*(Dayofyear)

Beach 11

Log10 (E. coli) = 27.0 + 0.076*(Turb) + 3.3*

( Log10 (Dayofyear)) − 1.2*(BaroPress_INST) + 

0.1507*(Rw72)

Source of rainfall data: Erie International Airport 

Source of local weather data: Regional Science Consortium
Wisconsin

Source of data for water temperature, air temperature, current 

direction, cloud cover, lake level, wave height, and surface 

current predictions: Great Lakes Coastal Forecasting System 

(GLCFS)

Upper Lake Park 

Log10 (E. coli) = −3.23 + 0.069*(WaveHtPred_ft) + 

0.54*(Turb)1/3 + 0.45*[1.14 − 0.0088*(Gulls_no) + 

9.77e−5*(Gulls_no)2] + 1.04*[1.06 − 1.17*

(CurrentO_comp_1) + 59.8*(CurrentO_comp_1)2] + 

0.00030*(Turb*RadarRain_48) + 0.34*(WaterTempPred_1)1/2

+ 0.35*(WaveO_comp_1) + 0.00014*(WaterTempPred_F)2

 − 

0.012*(RadarRain_24)1/4 + 0.31*(Algae_cat)

Beach orientation is 31.71°.

Red Arrow

Log10 (E. coli) = 1.2 + 0.025* Log10 (Turb) + 

1.0*(CloudCover_24) + 0.15*

(WaterTempPred_SD48) + 1.1*(WaveHtPred_12)

Neshotah

Log10 (E. coli) = 2.12 + 0.35* Log10 (Turb) + 14.0*

(LakeHt_1) − 0.70*(AirTempPred_SD144) − 0.29*

(WaterTempPred_SD6) − 7.0*(CurrentO_comp_144) + 

1.13*(WaveHtPred_1) + 0.26*(CloudCover_2)

Beach orientation is 61.3°.

Point Beach South, Central, and North

Log10 (E. coli) = 0.61 + 0.77* Log10 (Turb) − 

57.8*(LakeHt_max24) + 0.80*(WaveHtPred_2) − 

24.6*(CurrentO_comp_120) − 0.0029*(Disch_diff6) + 

0.050*(WaterTempPred_6) − 0.53*

(WaveO_comp_24) + 0.059*(RadarRain_48)1/2

Source of stream-discharge data: Manitowoc River at 

Manitowoc, WI, USGS site 04085427

Beach orientations are 9.8 °(south), 1.8 °(central), 

356.5°(north).

Appendix 565

Fischer

Log10 (E. coli) = 0.75 + 0.60* Log10 (Turb) + 0.64* Log10

(Turb_stream) + 7.0*(CurrentA_comp_48) + 0.29*(Rain_

gage_120)1/2 − 10.5*(CurrentA_comp_D12)

Source of rain-gage data: Sheboygan River at Sheboygan, 

Wisconsin, USGS site 04086000

Beach orientation is 25.5°.

Hika

Log10 (E. coli) = 0.74 + 0.42* Log10 (Turb) + 

0.0021*(Conductance) + 0.34*(Rain_gage_48)1/2 + 

0.24*(Rain_gage_12)1/2

Source of rain-gage data: Sheboygan River at Sheboygan, 

Wisconsin, USGS site 04086000

Thompson W. End Park

Log10 (E. coli) = −1.8 + 0.080* (Turb) + 0.87*(CloudCover_2) 

+ 0.15*(RadarRain_120) 1/2 − 0.10*(WindOPred_comp_6) − 

0.11*(WindAPred_comp_24) + 0.049*(WaterTempPred_C) + 

0.074*(AirTempPred_2) + 0.55*(Rain_gage_24) 1/2

Source of rain-gage data: Whittlesey Creek near Ashland, 

Wisconsin, USGS site 040263205

Beach orientation is 78.4°.

Kreher Park

Log10 (E. coli) = 1.9 + 0.84* Log10 (Turb) + 

0.078*(AirTempPred_48) − 0.32*

(AirTempPred_SD120) + 12.8*(LakeHt_diff24) + 

0.76*(Rain_gage_120) 1/2 − 0.079*(Disch_min72) + 

13.0*(LakeHt_min24) + 9.5*(LakeHt_1)

Source of rain-gage and stream-discharge data: Whittlesey 

Creek near Ashland, Wisconsin, USGS site 040263205

Maslowski

Log10 (E. coli) = 1.1 + 0.11*(RadarRain_48) 1/2 − 

0.0015*(Conductance-stream) + 0.052*(AirTempPred_2) 

− 0.30*(AirTempPred_SD6) + 0.46* Log10 (Turb) + 

0.044*(WindAPred_comp_2) + 5.6*(LakeHt_diff24)

Beach orientation is 280.4°.
66

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Key to Acronyms and Variables

nr, near

USGS, U.S. Geological Survey

USACE, U.S. Army Corps of Engineers

field, source of data is a field observation or measurement

lab, source of data is a lab measurement

airport, source of data is the National Weather Service nearest 

airport site

radar, source of data is the National Weather Service 

radar data

USGS gage, source of data is a nearby USGS gage

NOAA measurement, source of data is a measurement provided by the National Oceanic and Atmospheric Administration (NOAA)

NOAA prediction, source of data is a prediction provided 

by the NOAA Great Lakes Coastal Forecasting System 

(GLCFS)

Local, source of data is a local weather station or gage

AirTemp: (field) Air temperature in degrees Celsius (_C) 

or Fahrenheit (_F) at the time of sampling (_INST) or the 

mean for the 4-hour period (4) or 24-hour period (24) prior 

to sampling

AirTemp_mean24: (airport) Mean air temperature for previous 24 hours, in degrees Celsius 

AirTempPred: (NOAA prediction) Hourly air temperature 

from the nearest GLCFS grid point used to compute mean 

for the 2-hour (_2) or 48-hour (_48) period before sampling 

or standard deviation for the 6-hour (_SD6), 120-hour 

(_SD120), or 144-hour (_SD144) period before sampling 

Algae_cat: (field) Category of quantity of algae present at the 

beach at time of sampling—none, some, many

BaroPress: (local) Mean barometric pressure, in millimeters of mercury at the time of sampling (_INST) or for the 

6-hour period prior to sampling (_6)

Beach orientation: If the beach lies straight along the north/

south line, beach orientation is 0 degrees with land to the 

west and water to the east or 180 degrees with water to the 

west and land to the east. Beach orientations from 1 to 179 

degrees deviate from the north/south line accordingly, with 

land to the west or north and water to the east or south. 

Beach orientations from −1 to −179 degrees deviate from 

the north/south line accordingly, with land to the east or 

south and water to the west or north.

Birds_no: (field) Number of birds on the beach at the time of 

sampling

Cloud_cat: (field) Categories for sunny (1), partly cloudy (2), 

or cloudy (3) at the time of sampling

Chloro-A: (lab) Chlorophyll a, in relative fluorescence units, 

using a laboratory fluorometric sensor

CloudCover: (NOAA prediction) Hourly cloud cover from 

the nearest GLCFS grid point, mean over 2 (_2) or 24 (_24) 

hours prior to sampling

Conductance: (field) Conductivity of the water at the beach, 

measured at the time of sampling

Conductance-stream: (field) Conductivity of the water in a 

nearby stream, measured near the time of sampling

CurrentO_comp: (NOAA prediction) Measure of the component of the surface current velocity moving perpendicular 

to the shoreline at the nearest GLCFS grid point using mean 

for the 1-hour (_1), 24-hour (_24), 120-hour (_120), or 144-

hour (_144) period before sampling 

CurrentA_comp: (NOAA prediction) Measure of the component of the surface or depth-averaged current velocity 

moving parallel to the shoreline at the nearest GLCFS grid 

point using the mean depth-averaged current for the 12-hour 

period before sampling (_D12) or the mean for the 24-hour 

(_24) or 48-hour (_48) period before sampling 

Ct: (lab) mean sample cycle threshold value (Ct) from quantitative polymerase chain reaction for E. coli

Dayofyear: Number representing the date beginning with 1 

for January 1 and 365 or 366 for December 31 (the latter 

being a leap year)

Debris_cat: (field) Debris categorical variable (1 for no 

debris, 2 for some, 3 for visible floating, 4 for thick layer, 

5 for multiple layers)

Disch: (USGS gage) Discharge, in cubic feet per second:

• at a specific time (_time)

• the mean for the 24-hour period before sampling 

(_Md−1)

• the mean for the 24-hour period 2 days prior to sampling (_Md−2)

• the difference within 6-hour period before sampling 

(_diff6)

• the minimum within 72-hour period before sampling 

(_min72)

E. coli: (lab) Concentration of Escherichia coli, in most-probable number per 100 milliliters
Gulls_no: (field) Number of gulls on the beach at the time 

of sampling

Humidity: (local) Mean relative percent humidity for the 

24 hours prior to sampling (_24)

LakeLevel: (NOAA measurement) Lake level, in feet at 

8 a.m., 

• on the day of sampling

• the change in lake level (today – yesterday) (_24)

• the change in lake level (today – 2 days ago) (_48)

• the deviation from the spring average (_Spring)

LakeHt: (NOAA prediction) Difference of the water level at 

beach location compared to the mean of all GLCFS grid cells 

for the entire lake:

• 1 hour prior to sampling (_1)

• the maximum value within the 24-hour period before 

sampling (_max24)

• the minimum value within 24-hour period before 

sampling (_min24)

• difference between 1 hour and 24 hours prior to sampling (_diff24)

People_no: (field) Number of people at the beach (swimmers 

and on the sand) at the time of sampling

PreJuly24: Binary variable that equals 1 if the date is before 

July 24 and 0 if the date is July 24 or later

PostJuly24: Binary variable that equals 1 if the date is July 24 

or later and 0 if the date is before July 24

RadarRain: (radar) Radar-indicated precipitation from the 

NWS National Mosaic Quantitative Precipitation Archive 

(4-kilometer grid for the United States). Grid cells relevant to the defined watershed were used to compute an 

area-weighted average by using Thiessen polygons. Total 

precipitation using the 24-, 48-, or 120-hour period before 

sampling: (_24), (_48), (_120)

Radarxcell-sum-Rw48: (radar) Sum from multiple cells for 

the 24-hour periods 1 and 2 days before sampling. The most 

recent rainfall receives the most weight. Calculated as: 

(2*Radarxcell-sum-Rd-1) + Radarxcell-sum-Rd-2

Rain_cat: (field) Rain occurred with 24 (category = 1), 

48 (category =2), or 72 (category =3) hours

Rain_cat_Int: (field) If rain occurred, was it light (1), 

moderate/steady (2), or heavy (3)

Rain_gage: (USGS gage) Sum of rainfall, in inches: for the 

12-hour (_12), 24-hour (_24), 48-hour (_48), or 120-hour 

(_120) period before sampling

R4hr_cm: (airport) Total rainfall, in centimeters, for the 4-hour 

period prior to sampling

R6hr_cm: (airport) Total rainfall, in centimeters, for the 6-hour 

period prior to sampling

Rd−1: (airport) Total rainfall, in inches, for the 24-hour period 

before sampling 

Rd−2: (airport) Total rainfall, in inches, for the 24-hour period 

2 days before sampling

Rd−3: (airport) Total rainfall, in inches, for the 24-hour period 

3 days before sampling

R48: (airport) Amount of rainfall, in inches or centimeters, for 

the 48-hour period before sampling

R72: (airport) Amount of rainfall, in inches for the 72-hour 

period before sampling

Rw48: (airport) Amount of rainfall, in inches, for the 48-hour 

period before sampling, with the most recent rainfall receiving the most weight. Calculated as (2*Rd−1)+Rd−2

Rw72: (airport) Amount of rainfall, in inches, for the 72-hour 

period before sampling, with the most recent rainfall receiving the most weight. Calculated as (3*R d−1) + (2*Rd−2) + Rd−3

R30d: (airport) Total rainfall, in inches, for the 30-day period 

before sampling

SolarRad:(local) Mean solar radiation, in watts per square 

meter, for the 4 hours (_4), 6 hours (_6), or the 24 hours 

(_24) prior to sampling

Subseason1:Equals 1 if the sampling date is June 15 or earlier 

and is 0 if the sampling date is after June 15

Turb: (field) Turbidity of the sample from the beach, measured with a turbidimeter, in nephelometric turbidity ratio 

units

Turb1hr:(field) Mean water turbidity for the 1-hour period 

prior to sampling from a nearshore buoy, in nephelometric 

turbidity units

Turb_stream: (field) Turbidity of the water in a nearby 

stream, measured with a turbidimeter, in nephelometric 

turbidity ratio units

Appendix 567
68

Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches

Turb-stream_24:(USGS gage) Change in turbidity in a 

nearby stream (today – yesterday)

WaterTemp:(field) Water temperature at time of sampling, in 

degrees Celsius (_C) or in degrees Fahrenheit (_F)

WaterTempPred:(NOAA prediction) Hourly water temperature, in degrees Celsius, from the nearest GLCFS grid point 

used to compute mean for the 1-hour, 6-hour, or 24-hour 

period before sampling (_1, _6, _24) or standard deviation 

for the 6-hour (_SD6) or 48-hour (_SD48) period before 

sampling

WaveHt: (field) Wave height as measured with a graduated 

rod, in feet (_ft), inches (_in), or centimeters (_cm)

WaveHtPred: (NOAA prediction) Hourly wave height from 

the nearest GLCFS grid point used to compute mean for the 

1-hour (_1), 2-hour (_2), 12-hour (_12), or 24-hour (_24) 

period before sampling 

WaveO_comp: (NOAA prediction) Measure of the component of the wave velocity moving perpendicular to the 

shoreline at the nearest GLCFS grid point using the instantaneous value near 8 a.m. (INST) or the mean for the 1-hour 

(_1) or 24-hour (_24) period before sampling

WindA_comp: (airport) Measure of the component of the 

wind velocity moving parallel to the shoreline, for the 

instantaneous (INST) value near 8 a.m. or a 24-hour vector 

up to 8 a.m. (24), calculated as: 

WindA = −wind speed * cosine ((wind direction – beach 

orientation) * π/180)

A positive value indicates winds are moving from right 

to left across the beach when looking toward the water 

from the shoreline (U.S. Environmental Protection 

Agency, 2012).

WindAPred_comp: (NOAA prediction) Measure of the component of the wind velocity moving parallel to the shoreline 

at the nearest GLCFS grid point using the mean for the 

2-hour (_2) or 24-hour (_24) period before sampling 

WindCode: (airport) Site-specific wind code calculated by 

assigning the most weight to the range of wind directions 

associated with the highest E. coli concentrations:

• at the time of sampling (_INST) 

• summed each hour for the 4-hour period prior to sampling (_4)

WindDir: (airport) Wind direction, in degrees, at:

• the time of sampling (_INST) 

• the 24-hour vector up to 8 a.m. (_24V)

WindO_comp: (airport) Measure of the component of the 

wind velocity moving perpendicular to the shoreline, using:

• the instantaneous (INST) value near 8 a.m. 

• the mean wind speed and median wind direction for the 

previous 4 or 6 hours prior to sampling, or

• a 24-hour vector up to 8 a.m. (24)

calculated as:

WindO = wind speed * sine ((wind direction – 

beach orientation) * π/180)

A positive value indicates winds are moving from 

the water toward shore (U.S. Environmental Protection Agency, 2012).

WindOPred_comp: (NOAA prediction) Measure of the component of the wind velocity moving perpendicular to the 

shoreline at the nearest GLCFS grid point using the mean 

for the 6-hour (_6) period before sampling

WindSp: (airport) Wind speed, in miles per hour, at:

• the time of sampling (_INST) 

• the mean for the 4-hour period prior to sampling (_4)

• the 24-hour vector up to 8 a.m. (_24V)

Reference

U.S. Environmental Protection Agency, Center for Exposure 

Assessment Modeling, 2012, Exposure Assessment Models—Virtual Beach, accessed June 2013 at http://www2.epa.

gov/exposure-assessment-models/virtual-beach-vb.

Francy and others—Developing and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches —SIR 2013-5166

ISSN 2328-0328 

ISSN 2328-031X