In Cooperation With the Ohio Lake Erie Office, Northeast Ohio Regional Sewer District, 

Cuyahoga County Board of Health, and U.S. Environmental Protection Agency Region 5, 

Water Division 

Nowcasting Beach Advisories at Ohio Lake Erie Beaches 

By Donna S. Francy and Robert A. Darner 

Open File Report 2007–1427 

U.S. Department of the Interior 

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

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia 2007 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Francy, D.S., and Darner, R.A., 2007, Nowcasting beach advisories at Ohio Lake Erie beaches: 

U.S. Geological Survey Open-File Report 2007–1427, 13 p. 

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

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report. 

ii
Contents 

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

Introduction .........................................................................................................................................................................1

Methods ...............................................................................................................................................................................2

Analytical Methods ........................................................................................................................................................2

Variables for Model Development...............................................................................................................................4

Testing and Development of Predictive Models.......................................................................................................5

Results ..................................................................................................................................................................................5

Performance of the Models in 2007.............................................................................................................................5

Exploratory Data Analysis.............................................................................................................................................6

Model Development, Diagnosis, and Selection for 2008.........................................................................................6

Next Steps............................................................................................................................................................................7

Acknowledgments..............................................................................................................................................................7

References...........................................................................................................................................................................8

Figures 

1. Map showing location of study area. ........................................................................................................................3

2. Photo of wave-height measuring buoy in the nearshore area at Edgewater, Cleveland, Ohio.....................3

Tables

1. Variables, model properties, and model responses as compared to using previous day's 

Escherichia coli (E. coli) concentrations in 2007................................................................................................9

2. False-negative and false-positive responses for predictive models as a percentage of total exceedances 

and non-exceedances of the bathing-water standard, respectively, for different data segments, by 

date ..........................................................................................................................................................................10

3. Pearson's r correlations between log10 Escherichia coli (E. coli) concentrations and explanatory 

variables at three Lake Erie beaches, 2000–2007.............................................................................................11

4. Examination of candidate Huntington 2000–2007 models.....................................................................................12

5. Examination of candidate Edgewater models for 2004–7 .....................................................................................13

iii
Conversion Factors 

Multiply By To obtain 

inch (in.) 2.54 centimeter (cm) 

inch (in.) 25.4 millimeter (mm) 

foot (ft) 0.3048 meter (m) 

mile (mi) 1.609 kilometer (km) 

kilometer (km) 0.6214 mile (mi) 

millliter (mL) 0.03381 ounce, fluid (fl. oz) 

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

°F=(1.8×°C)+32 

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

Pore sizes of filters are given in micrometers (μm). 

iv
Nowcasting Beach Advisories at Ohio Lake Erie Beaches 

By Donna S. Francy and Robert A. Darner

Abstract 

Data were collected during the recreational season of 2007 to test and refine predictive models at 

three Ohio Lake Erie beaches. In addition to Escherichia coli (E. coli) concentrations, field personnel 

collected or compiled data for environmental and water-quality variables expected to affect E. coli

concentrations including turbidity, wave height, water temperature, lake level, rainfall, and antecedent dry 

days and wet days. At Huntington (Bay Village) and Edgewater (Cleveland) during 2007, the models gave 

correct responses 82.7 and 82.1 percent of the time; these percentages were greater than percentages 

obtained by use of the previous day’s E. coli concentrations (current method). In contrast, at Villa Angela 

during 2007, the model gave correct responses only 61.3 percent of the days monitored; this percentage 

was lower than that achieved by use of the current method (74.6 percent). To refine the Huntington and 

Edgewater models, data from 2007 were added to existing datasets, and the larger datasets were split into 

two or three segments by date. Models were developed for dated segments and for combined datasets. At 

Huntington, the summed responses for separate best models for dated segments resulted in a greater 

percentage of correct responses (85.6 percent) than the one combined best model (83.1 percent). Similar 

results were found for Edgewater. Water-resource managers will determine how to apply these models to 

the Internet-based “nowcast” system for issuing water-quality advisories during 2008. 

Introduction 

Swim advisories are issued by beach managers on the basis of standards for concentrations of 

bacterial indicators—Escherichia coli (E. coli) or enterococci for freshwaters and enterococci for marine 

waters. At Ohio beaches, advisories are issued if the previous day’s E. coli concentration exceeds the 

single-sample bathing-water standard of 235 colony-forming units per 100 milliliters (CFU/100 mL) 

(Ohio Department of Health, 2007). Because it takes 18–24 hours to obtain results and recreational waterquality conditions may change overnight, water-resource managers may issue inappropriate advisories 

based on erroneous assessments of current public-health risk. As a result of this time-lag issue, some 

agencies have turned to predictive modeling to obtain near-real-time estimates of recreational water 

quality. 

In an earlier study, predictive models were shown to be real-time alternatives to traditional 

monitoring methods at Ohio beaches (Francy and others, 2006). The best model for each beach was based 

on a unique combination of “variables” that explained changes in E. coli concentrations. The variables 

included turbidity (water clarity), rainfall, wave height, water temperature, day of the year, and lake level.

The output from each model was the probability that the Ohio single-sample bathing water standard would 

be exceeded. A threshold probability established for each beach was based on historical data. The 

threshold was the probability associated with too great a risk to allow swimming—a probability that 

would warrant the issuance of a bathing-water advisory. This approach results in estimated probabilities 

similar to those in a weather forecast. At one beach, Huntington (Bay Village), predictions based on a 
model have been available to the public through an Internet-based nowcasting system 

(www.Ohionowcast.info) since May 30, 2006. 

The U.S. Geological Survey (USGS), in cooperation with the Ohio Lake Erie Office, Northeast 

Ohio Regional Sewer District, and Cuyahoga County Board of Health, continued the development, testing, 

and refinement of predictive models at Huntington and two other Lake Erie beaches—Edgewater and 

Villa Angela (Cleveland) (fig. 1). The Huntington model (2000–2006) used for the nowcast during 2007 

contained the variables log turbidity, wave height, rainfall (radar and airport), and day of the year. The 

Huntington model resulted in more correct responses than use of the previous day’s E. coli during 2007. 

The Edgewater model (2004–6), with variables log turbidity, wave height, radar rainfall, and lake level, 

also resulted in more correct responses than use of the previous day’s E. coli during 2007. At Villa 

Angela, however, the model (2004–6) with variables log turbidity, rainfall, and water temperature did not 

result in more accurate predictions than use of the previous day’s E. coli during 2007. Investigators 

continued to work on improving the predictive abilities of the Huntington and Edgewater models for the 

nowcast by developing bi-phase seasonal models for 2008. 

Methods 

Daily data were collected during the recreational season of 2007 (late May through early 

September) and included analyses of water samples for E. coli concentrations and measurements of 

explanatory variables for model development and testing. Data were collected at Huntington 7 days per 

week by Cuyahoga County Board of Health (CCBH) personnel and at Edgewater and Villa Angela on 

Monday−Friday by Northeast Ohio Regional Sewer District (NEORSD) personnel. These agencies were 

also responsible for running the predictive models each morning using Fortran programs developed by the 

USGS. The CCBH updated the nowcast Web site daily by 9:30 a.m. with water-quality advisories and 

other information based on the model; model results at Edgewater and Villa Angela were compiled in a 

spreadsheet. 

Samples were collected between 8 and 10 a.m. where the water was 2−3 ft deep in two areas of 

each beach used for swimming. All water-sample bottles were filled about 1 ft below the water surface by 

means of a grab-sampling technique (Myers and Wilde, 2003). Water samples were kept on ice and 

analyzed for concentrations of E. coli and turbidity within 6 hours of collection. The average value from 

the two water samples was used for data analysis and model development. 

Analytical Methods 

In the Cuyahoga County Sanitary Engineers (CCSE) Laboratory for Huntington or the NEORSD 

Laboratory for Edgewater and Villa Angela, samples were analyzed by use of the modified mTEC 

membrane-filtration method (U.S. Environmental Protection Agency, 2006). In accordance with this 

method, bacteria were concentrated from the water sample by filtration through a 0.45-μm-pore-size filter. 

Filters, representing varying volumes of sample, were then placed onto modified mTEC agar plates and 

incubated at 35°C for 2 hours and then at 44.5°C for an additional 22 to 24 hours. Magenta-colored 

colonies visible after incubation were counted as E. coli and reported as CFU/100 mL. Turbidity was 

determined in water samples with a turbidimeter onsite (Huntington) or in the laboratory (Edgewater and 

Villa Angela) and reported in nephelometric turbidity ratio units (NTRUs). 

2
Figure 1. Location of study area. 

Figure 2. Wave-height measuring buoy in the nearshore area at Edgewater, 

Cleveland, Ohio. 

3
Variables for Model Development 

During the data-collection period, field personnel collected or compiled data for environmental and 

water-quality variables expected to affect E. coli concentrations. 

• Wave height. At the time of sample collection, wave heights were measured by use of a graduated 

rod. The rod was placed at the sampling location, and minimum and maximum water heights were 

noted over the course of 1 minute. Before 2005, wave-height data were measured by placing wave 

heights into categories through visual observations (Francy and others, 2006), not by use of the 

wave-height measuring rod. 

 At Edgewater, hourly wave heights were also obtained from a wave-height buoy placed just 

outside the swimming area (fig. 2). The 3-ft-diameter buoy was custom made by an environmental 

equipment manufacturer for use in nearshore shallow waters. The buoy was equipped with a 

pressure transducer to measure wave heights; a datalogger to store the data; a radio, amplifier, and 

antenna to transmit the data; and a battery and solar panel for power. For the Edgewater model, 

data from the wave buoy at the time of sampling were used when available. 

• Water temperature. Water temperature was measured at the sampling location at the time of 

sampling using an alcohol-filled thermometer. 

• Lake level. Lake-level data were obtained from the National Oceanic and Atmospheric 

Administration (NOAA) station in Cleveland (NOAA ID 9063053) at http://www.coops.nos.noaa.gov/. 

• Airport rainfall. Rainfall data were obtained from National Weather Service (NWS) stations at 

Cleveland Hopkins International Airport for Huntington and Edgewater and at Burke Lakefront 

Airport for Villa Angela at http://www.erh.noaa.gov/cle/ 

• Radar rainfall. In order to obtain rainfall data from a more widespread area, radar data were 

obtained from the NWS. The data were provided for 4-km grids (“cells”) for each hour of the day. 

From an examination of the watershed boundaries and correlation analysis, combinations of radar 

configuration (2-cell, 4-cell, 6-cell, etc.) and timeframe (24 or 48 hours) most related to E. coli

concentrations at each beach were included as possible variables in model development (described 

below). Following model development procedures, only one radar variable was chosen to be 

included in each beach model. For the area around Huntington, data from 6 cells were summed to 

estimate rainfall amounts in the past 24 hours (“radar6cell-24”). For the area around Edgewater, 

data from 2 cells were summed to estimate rainfall amounts in the past 48 hours (“radar2cellw48”); data were weighted as indicated in the example below. At Villa Angela, data in the past 24 

hours from 16 cells were summed; however, these data were not used in the model because they 

did not produce a stronger relation to E. coli than did airport rainfall. 

 A weighted rainfall variable was calculated and used in predictive model development. 

Rainfall weighted 48 hours (Rw48) is 48 hours of cumulative rainfall and gives more weight to the 

most recent rainfall amount as follows: 

 (Rw48) = (2* Rd-1 + Rd-2) 

where Rd-1 is the amount of rain, in inches, that fell in the 24-hour period (9 a.m. to 9 a.m.) 

preceding the morning sampling and Rd-2 is the amount of rain that fell in the 24-hour period 2 days 

preceding the morning sampling. 

4
• Antecedent dry days and wet days. The variable “antecedent dry days” was calculated by counting 

the number of consecutive days without measureable rainfall up to and including 9 a.m. on the date 

of the sampling. “Antecedent wet days” was calculated by counting the number of consecutive 

days with measureable rainfall up to and including 9 a.m. on the date of the sampling. 

Quality-assurance and quality-control (QA/QC) procedures were implemented to ensure the 

collection and documentation of high-quality data. The USGS did several onsite QA/QC checks of 

procedures performed by field and laboratory personnel, and any needed corrective actions were taken. 

Duplicates, field blanks, and positive-control reference cultures for E. coli, described elsewhere (Francy 

and others, 2007), were analyzed by CCSE and NEORSD. For turbidity, duplicate aliquots were measured 

from the same bottle, and measurements that did not agree within 10 percent were repeated. Turbidity 

reference standards were sent to both laboratories. Results from QC samples were carefully monitored by 

the USGS, and retests were done or corrective measures taken when needed. 

Testing and Development of Predictive Models 

The models previously developed at Huntington (2000–2006) and Edgewater and Villa Angela 

(2004–6) were tested during 2007 (table 1). Personnel from local agencies entered daily data into a Fortran 

program, written by the USGS, that computed the probability of exceeding 235 CFU/100 mL based on 

each beach-specific model. At the end of the 2007 season, model responses were tallied. After testing, the 

2007 data were added to the existing dataset, and refined models were developed for future use, when 

applicable. Steps to developing new models included (1) examining performance of the models during 

2007, (2) performing exploratory data analysis, and (3) model development, diagnostics, and selection. 

Multiple linear regression (MLR) techniques were used for model development. In MLR, a unique set of 

variables is used to develop a model that best explains the variation in E. coli concentrations, leaving as 

little variation as possible to unexplained “noise.” Detailed information on the steps for developing 

predictive models can be found in Francy and Darner (2006). 

Results 

Performance of the Models in 2007 

The Huntington 2000–2006 model and Edgewater and Villa Angela 2004–6 models were 

evaluated for their abilities to yield correct predictions and sensitivities and specificities of responses 

during 2007 and were compared to use of the previous day’s E. coli concentrations (table 1; all tables are 

at back of report). The sensitivity is the percentage of events where the model correctly predicts 

exceedance of the bathing-water standard among days where the standard is exceeded. The specificity is 

the percentage of events where the model correctly predicted nonexceedance of the standard. At 

Huntington and Edgewater, the models resulted in greater percentages of correct responses than use of the 

previous day’s E. coli concentrations (current method); the percent difference for Huntington, however, 

was small and not as great as that found during 2006 (see http://www.ohionowcast.info/ 

ohionowcastfindoutmore.htm). Also at Huntington, the model resulted in better sensitivity but similar 

specificity as the current method. The improved sensitivity for the model over the current method resulted 

in four more correct predictions when the standard was exceeded. This is important in terms of protection 

of public health. At Edgewater, the model resulted in both higher sensitivity and specificity than the 

current method. In contrast, at Villa Angela, the model resulted in correct responses only 61.3 percent of 

the days monitored; this percentage was lower than that achieved by use of the current method (74.6 

percent). Although specificity of the model was high at Villa Angela, the sensitivity was only 32.6 percent 

and considerably lower than that for the current method. 

5
Because of the uncertainty in the transmission of radar data, an alternative model with the same 

variables but without radar6cell-24 was developed for Huntington for the daily nowcast. The alternative 

model without radar data yielded the same percentage of correct responses, same sensitivity, and very 

similar specificity as the original model with radar data (data not shown). 

At Huntington during 2007, 7 out of 7 false negatives occurred before July 29, and 9 out of 11 

false positives occurred after July 22. A false negative occurs when the E. coli concentration exceeds the 

standard but the model predicts the concentration to be below the standard. A false positive occurs when 

the E. coli concentration meets the standard but the model predicts exceedance of the standard. The data 

for 2000–2007 were split into segments based on the occurrence of false negatives and positives that 

would have occurred in responses from the Huntington 2000–2006 model (table 2). For the data split into 

two or three segments, the percentages of false negatives decreased over time and the percentages of false 

positives increased over time. The two-segment split was used in model refinement because it was simpler 

than the three-segment split and it divided the season into reasonably sized segments: (1) May 21 through 

July 23 (“season 1”) and (2) July 24 through September 3 (“season 2”). 

At Edgewater, the data for 2004–7 were similarly split into segments based on occurrence of false 

negatives and positives that would have occurred in responses from the Edgewater 2004–6 model. As was 

found for Huntington, the percentages of false negatives decreased over time, whereas the percentages of 

false positives increased over time (table 2). The three-segment split clearly showed this trend, and this 

division was later used in model refinement. 

Exploratory Data Analysis 

The correlations between E. coli concentrations and explanatory variables are shown for data 

collected in previous years and in 2007 (table 3). Correlations for wave heights measured by use of the 

wave rod in previous years were not included because they were measured as categorical variables before 

2005. Similarly, the wave-height buoy at Edgewater became operational in late summer 2005. 

At Huntington, for the variables used in the 2000–2006 model—day of the year, Rw48, radar6cell24, and log turbidity—correlations in 2007 were the same or stronger than in previous years. Interestingly, 

the correlation between E. coli concentrations and wave heights measured by the buoy at Edgewater 

(r=0.67) was stronger than wave heights measured by use of the measuring rod at Huntington (r=0.60). 

Edgewater is approximately 10 mi east of Huntington along the Lake Erie shoreline. 

At Edgewater, for two of the variables used in the 2004–6 model—radar2cell-48 and lake level—

correlations in 2007 were stronger than in previous years. For log turbidity, however, the correlation was 

weaker in 2007 (r=0.33) than in previous years (r=0.47). The wave-height buoy and wave-rod 

measurements were equally correlated with E. coli concentrations. At Villa Angela, for the variables used 

in the 2004–6 model, the correlation for water temperature was slightly stronger in 2007 than for previous 

years. For log turbidity and Rd-1, however, the correlations were weaker during 2007 than in previous 

years. 

Model Development, Diagnosis, and Selection for 2008 

Huntington. At Huntington, explanatory variables consistently related to E. coli during 2007 and 

previous years were used for model development for the 2000–2007 dataset—day of the year, Rd-1, Rw48, 

radar6cell-24, log turbidity, and wave height. Two new variables, antecedent dry days and antecedent wet 

days, also were included. For data collected in 2000–2007, dry days and wet days were significantly 

related to E. coli (r=-0.32 and r=0.30, respectively). Data from 2000–2007 were split into two segments by 

date, as previously described. Models were developed for season 1, season 2, and combined datasets. Two 

types of models were developed: (1) simple models that used the same variables as those in the 2000–

2006 model, omitting radar rainfall (log turbidity, wave height and w48) and (2) the best model for each 

dataset as identified by MLR techniques (Francy and Darner, 2006). Threshold probabilities were 

established for each simple and best model by identifying the probabilities that were reasonable balances 

6
between achieving a high number of correct responses and low number of false negative responses 

(Francy and Darner, 2006). 

In addition to standard model diagnostic procedures, such as R2 values and partial residual plots 

(Francy and Darner, 2006), candidate models were evaluated by examining the numbers and percentages 

of correct responses, false positives, and false negatives for 2000–2007 data (table 4). The summed 

responses for separate simple models for seasons 1 and 2 resulted in a greater percentage of correct 

responses (83.0 percent) than one combined simple model (81.0 percent); sensitivities were higher and 

specificities only slightly higher for the former; this translated into 10 more correct responses for the 

summed simple model over the combined simple model. Similarly, the summed responses for separate 

best models for seasons 1 and 2 resulted in a greater percentage of correct responses (85.6 percent) than 

the one combined best model (83.1 percent), translating to 19 more correct responses for the summed best 

model over the combined best model. The summed responses for the season 1 and 2 best models provided 

a higher specificity but slightly lower sensitivity than the combined best model. Of the two summed 

models, the best model had a greater percentage of correct responses, higher specificity, and a slightly 

lower sensitivity (but the same number of false negatives) as the simple summed model. 

One more model was developed for Huntington. This model was the season 1 best model without 

radar rainfall for possible use in the event radar rainfall data are not available. A threshold probability of 

20 was set to achieve similar percentages of responses as those found for the season 1 best model with 

radar rainfall. 

 

Edgewater. At Edgewater, explanatory variables consistently related to E. coli during 2007 and 

previous years were used for model development for the 2004–7 dataset—day of the year, Rd-1, Rw48, 

radar2cell-w48, log turbidity, lake level, and wave-height rod. Antecedent dry days and antecedent wet 

days were not included because the correlations to log E. coli (r=-0.21 and r=0.25, respectively) were 

lower than for the other available variables. Data from 2004–7 were split into three segments by date, as 

previously described. Best models and threshold probabilities were developed for season 1, season 2, 

season 3, and for the combined dataset (table 5). 

Model diagnostics were performed in the same manner as described above for Huntington. For 

Edgewater, as was found for Huntington, the summed responses for separate simple models for seasons 1, 

2, and 3 provided a greater percentage of correct responses (86.9 percent) than one combined simple 

model (84.9 percent); specificities and sensitivities were also higher for the former. 

Next Steps 

The beach manager at Huntington will determine which 2000–2007 model to use to issue 

advisories for the nowcast system during 2008. Similarly, from results in 2007, water-resource managers 

will decide whether predictions based on the Edgewater 2004–7 model can be used to issue advisories 

through the nowcast system in 2008. Because modeling is a dynamic process, models for Huntington and 

Edgewater should be validated and refined to improve predictions during 2008. At Villa Angela, other 

options will be examined, such as the use of a rapid analytical method for E. coli for issuing beach 

advisories. 

Acknowledgments 

The authors would like to thank Jill Lis and Lena Kavaliauskas of the Cuyahoga County Board of 

Health for sampling and running the nowcast. Thanks are also extended to collaborators Mark Citriglia, 

Eva Hatvani, and Lester Stumpe at the Northeast Ohio Sewer District. This project was funded in part 

through the Lake Erie Protection Fund, administered by the Ohio Lake Erie Commission. 

7
References 

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 6–B5, 34 p., available at http://pubs.usgs.gov/tm/2006/tm6b5/

Francy, D.S., Darner, R.A., and Bertke, E.E., 2006, 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., Bushon, R.N., Bertke, E.E., Brady, A.M.G., Kephart, C.M., Likirdopolus, C.A., and 

Stoeckel, D.M., 2007, Quality-assurance/quality-control manual for the Ohio Water Microbiology 

Laboratory, accessed December 2007 at http://oh.water.usgs.gov/micro_qaqc.htm

Myers, D.N., and Wilde, F.D., eds., 2003, Biological indicators (3d ed.): U.S. Geological Survey 

Techniques of Water-Resources Investigations, book 9, chap. A7, accessed March 2006 at 

http://pubs.water.usgs.gov/twri9A7/

Ohio Department of Health, 2007, Bathing beach monitoring program, accessed October 2007 at 

http://www.odh.state.oh.us/odhPrograms/eh/bbeach/beachmon.aspx

U.S. Environmental Protection Agency, 2006, Method 1603—Escherichia coli in water by membrane 

filtration using modified membrane-thermotolerant Escherichia coli agar: Washington, D.C., EPA 821–

R–06–011, 42 p. 

8
Table 1. Variables, model properties, and model responses as compared to using previous day's Escherichia coli (E. coli) concentrations in 

[E. coli , Escherichia coli; NA, not applicable; R2, fraction of the variation of E. coli concentrations that is explained by the model; threshold probability is based on meetin

the single-sample bathing standard for E. coli]

Percentage of 

Beach Model variables

Data used to 

develop model R2 

Threshold 

probability

Sample 

size Correct

Sensitiv

(numbe

Huntington

Log turbidity, wave height, Rw48a, 

radar6cell-24b, day of the year 2000−2006 0.43 31 104 82.7

53.3

(8/15)

Previous day's E. coli NA NA 101 80.2 30.8 (4/1

Edgewater

Log turbidity, wave height, radar2cell-w48c, 

Lake level 2004−6 0.42 29 78 82.1

50

(11/22

Previous day's E. coli NA NA 66 66.7

33.3

(6/18) 

Villa Angela Log turbidity, Rd-1d, water temperature 2004−6 0.32 29 80 61.3

32.6

(14/43

Previous day's E. coli NA NA 63 74.6

74.3

(26/35

a Rw48 was the amount of rainfall, in inches, that accumulated in the 48-hour period up to 9 a.m. on the date of sampling at Cleveland Hopkins International Airport, Ohio, w

recent rainfall receiving the greatest weight.

b Radar6cell-24 was the amount of rainfall, in inches, that accumulated in six 4-km grids surrounding the beach in the 24-hour period preceding the 9 a.m. sampling.c Radar2cell-w48 was the amount of rainfall, in inches, that accumulated in two 4-km grids surrounding the beach in the 48-hour period up to 9 a.m. on the date of sampling, 

recent rainfall receiving the greatest weight.

d Rd-1 was the amount of rainfall, in inches, at Burke Lakefront Airport, Cleveland, Ohio, in the 24-hour period preceding the 9 a.m. sampling.

9
Table 2. False-negative and false-positive responses for predictive models as a 

percentage of total exceedances and nonexceedances of the bathing-water standard, 

respectively, for different data segments, by date.

Huntington 2000−2006 model with data from 2000−2007

1

May 21−June 30

2

July 1−July 30

3

July 31−Sept. 3

False negatives 56.0 42.9 35.7

False positives 5.4 8.3 17.6

1

May 21−July 23

2

July 24−Sept. 3

False negatives 53.8 30.6

False positives 6.3 16.0

Edgewater 2004−6 model with data from 2004−7

1

May 21−June 17

2

June 18−Aug. 13

3

Aug. 14−Sept. 7

False negatives 66.7 30.0 25.0

False positives 1.7 9.4 24.1

1

May 21−Aug 13

2

Aug. 14−Sept. 7

False negatives 38.5 25.0

False positives 7.0 24.1

10
Table 3. Pearson's r correlations between log10 Escherichia coli (E. coli) concentrations and explanatory 

variables at three Lake Erie beaches, 2000−2007.

[Relations that were significant at p < 0.05 are in bold; ND is not determined]

Huntington Edgewater Villa Angela

Variable 2000−2006 2007 2004−6 2007 2004−6 2007

Birds, number at time of sampling -0.014 ND 0.16 0.002 0.16 0.13

Day of the year 0.15 0.23 0.26 0.37 -0.14 0.008

Rd-1a 0.36 0.36 0.22 0.33 0.37 0.25

Rd-2 a 0.20 0.28 ND 0.34 0.11 0.015

Rw48 b 0.39 0.42 0.28 0.40 ND ND

Radar6cell-24 0.35 0.35 ND ND ND NDRadar2cell-w48 ND ND 0.33 0.41 ND ND

Radar16cell-24 ND ND ND ND 0.36 NDAntecedent dry days ND ND NDAntecedent wet days ND ND NDTurbidity 0.48 0.51 0.40 0.37 0.38 0.27

Log turbidity 0.53 0.53 0.47 0.33 0.35 0.30

Water temperature 0.026 0.17 0.052 0.22 0.27 0.28

Lake level -0.087 ND -0.25 -0.30 -0.031 ND

Wave-height buoy at Edgewater ND 0.67 ND 0.60 ND ND

Wave-height rod ND 0.60 ND 0.60 ND 0.40

a Rd-1 was the amount, in inches, at Cleveland Hopkins International Airport or Burke Lakefront Airport, Cleveland, Ohio in the 24-hour 

period preceding sampling; Rd-2 was the amount 2 days before sampling. 

b Rw48 was the amounts, in inches, at Cleveland Hopkins International Airport or Burke Lakefront Airport, Cleveland, Ohio, in the 48-

hour period before sampling, with the most recent rainfall receiving the most weight.

11
Table 4. Examination of candidate Huntington 2000-2007 models.

[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 standard 

23 and season 2 is July 24-Sept 3; calculated sums are shaded below] 

Number 

Model Variables R2

Threshold probability Correct False + False - Correct 

Simple models

Season 1 Log turbidity, wave height, Rw48 0.38 21 242 35 19 81.8 

Season 2 Log turbidity, wave height, Rw48 0.47 28 168 19 11 84.8 

Sum of seasons 1 and 2 410 54 30 83.0 

Combined Log turbidity, wave height, Rw48 0.40 31 400 58 36 81.0 

Best models

Season 1 best Rw48a, radar6cell-24b, log turbidity, dry days, day of the year 0.44 25 251 27 17 85.1 

Season 2 best

Rw48, log turbidity, wave height, day of the year 0.48 27 170 14 13 86.3 

Sum of seasons 1 and 2 421 41 30 85.6 

Combined Rw48, radar6cell-24, log turbidity, wave height, 

dry days, wet days, 0.44 27 402 53 29 83.1 

Model without radar data

Season 1 best

Rw48, wave height, dry days, day of the year 0.44 20 253 25 18 85.5 

a Rw48 was the amount, in inches, at Cleveland Hopkins International Airport, Ohio, in the 48-hour period before sampling, with the most recent rainfall receiving the most weigbRadar6cell-24 was the amount of rainfall in the previous 24-hour period that fell in six 4-km grids as determined from radar data.

12
Table 5. Examination of candidate Edgewater models for 2004-7.

[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 standard 

shaded below] 

Number 

Model Variables R2

Threshold probability Correct False + False - Correct 

Best models

Season 1, May 21-June 17 Wave height, log turbidity, radar2cell-w48 0.43 27 64 3 2 92.8 

Season 2, June 18-August 13

Log turbidity, radar2cell-w48, lake level, day of the year 0.43 28 138 14 9 85.7 

Season 3, August 14 - Sept 7 Wave height, log turbidity, lake level 0.44 32 51 6 4 83.6 

Sum of seasons 1, 2, and 3 253 23 15 86.9 

Combined Wave height, log turbidity, radar2cell-w48, lake level 0.44 26 247 26 18 84.9 

bRadar2cell-w48 was the amount of rainfall in the 48-hour period preceding sampling that fell in two 4-km grids as determined from radar data, with the most recent rainfall recei

13