U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2009–5192

In cooperation with the National Park Service and the Ohio Lake Erie Commission

Predicting Recreational Water Quality Using Turbidity in the 

Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7

Predicting Recreational Water Quality 

Using Turbidity in the Cuyahoga River, 

Cuyahoga Valley National Park, Ohio, 

2004–7

By Amie M.G. Brady, Rebecca N. Bushon, and Meg B. Plona

In cooperation with the National Park Service and the Ohio Lake Erie Commission

Scientific Investigations Report 2009–5192

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2009

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Suggested citation:

Brady, A.M.G., Bushon, R.N., and Plona, M.B., 2009, Predicting recreational water quality using turbidity in the 

Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7: U.S. Geological Survey Scientific Investigations Report 

2009–5192, 16 p.

Cover photo:

The Cuyahoga River at the State Route 82 (Chippewa Rd.) bridge. (Photo courtesy of the National Park Service.)
iii

Contents

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

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

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

Water-Quality Sample Analyses.........................................................................................................3

Environmental Data Collection ..........................................................................................................3

Quality-Control and Quality-Assurance Practices..........................................................................3

Statistical Methods...............................................................................................................................3

Relation of Escherichia coli Concentrations to Environmental Variables............................................4

Predictive Models..........................................................................................................................................8

A. Jaite....................................................................................................................................................8

B. Old Portage......................................................................................................................................11

C. Jaite Model Results at Other River Locations...........................................................................12

Model Applications and Suggestions for Future Work .........................................................................13

Summary and Conclusions.........................................................................................................................14

Acknowledgments.......................................................................................................................................14

References Cited..........................................................................................................................................15

Figures

1. Map showing location of study sites, Cuyahoga Valley National Park, 

northeastern Ohio.........................................................................................................................2

2–5. Graphs showing:

2. Relation between Escherichia coli concentrations and turbidity at A) Jaite 

and B) Old Portage, Cuyahoga Valley National Park, Ohio, during the 

recreational seasons (May through August) of 2004 through 2007.............................6

3. Relation between Escherichia coli concentrations and instantaneous 

streamflow at Old Portage, Cuyahoga Valley National Park, Ohio, during 

the recreational seasons (May through August) of 2004 through 2007......................7

4. Predicted Escherichia coli concentrations based on the turbidity model 

developed using 2004 through 2006 recreational-season data compared to 

actual Escherichia coli concentrations at Jaite, Cuyahoga Valley National 

Park, Ohio, May through August, 2007 ...........................................................................10

5. Relation between actual Escherichia coli concentrations at Independence 

and probability of exceeding the primary-contact recreation maximum at 

Jaite using the turbidity model developed using 2004 through 2007 

recreational-season data, Cuyahoga Valley National Park, Ohio .............................13
iv

Tables

1. Summary statistics for results of sample analyses and measured 

environmental parameters at time of sampling at Jaite and Old Portage, 

Cuyahoga Valley National Park, Ohio, for the recreational seasons (May

 through September) of 2004 through 2007...............................................................................5

2. Pearson’s r correlation coefficients of environmental variables to log10-

transformed Escherichia coli (E. coli) concentrations by recreational seasons 

(May through September) as indicated by year......................................................................8

4. For the Jaite site, comparison of the percentage of correct and false positive 

and negative responses of recreational water-quality conditions using a 

regression model (with turbidity as the sole explanatory variable) and the 

traditional method for determining water quality (using the previous day’s 

E. coli concentrations) during model development, testing, and revisions, 

Cuyahoga Valley National Park, Ohio........................................................................................9

5. For the Old Portage site, comparison of the percent of correct and false 

positive and negative responses of recreational water-quality conditions using 

regression models with indicated variables and the traditional method for 

determining water quality (using the previous day’s E. coli concentrations) 

during model development, testing, and revisions, Cuyahoga Valley National 

Park, Ohio.....................................................................................................................................11

6. For the additional sites (Old Portage, Bath, and Independence), comparison 

of the percent of correct and false positive and negative responses of 

recreational water-quality conditions using the results of the Jaite simple 

linear regression model based solely on turbidity and the traditional method 

for determining water quality (using the previous day’s E. coli concentrations), 

Cuyahoga Valley National Park, Ohio......................................................................................12

Conversion Factors

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

mile (mi) 1.609 kilometer (km)

Volume

ounce, fluid (fl. oz) 0.02957 liter (L)

Area

acre 4,047 square meter (m2)

Flow rate

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

Bacteria concentrations are given in colony-forming units per 100 millilliters (CFU/100 mL).
Predicting Recreational Water Quality Using Turbidity in 

the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 

2004–7

By Amie M.G. Brady1, Rebecca N. Bushon1, and Meg B. Plona2

1

 U.S. Geological Survey.

2

Cuyahoga Valley National Park.

Abstract

The Cuyahoga River within Cuyahoga Valley National 

Park (CVNP) in Ohio is often impaired for recreational use 

because of elevated concentrations of bacteria, which are indicators of fecal contamination. During the recreational seasons 

(May through August) of 2004 through 2007, samples were 

collected at two river sites, one upstream of and one centrallylocated within CVNP. Bacterial concentrations and turbidity 

were determined, and streamflow at time of sampling and 

rainfall amounts over the previous 24 hours prior to sampling 

were ascertained. Statistical models to predict Escherichia coli 

(E. coli) concentrations were developed for each site (with 

data from 2004 through 2006) and tested during an independent year (2007). At Jaite, a sampling site near the center of 

CVNP, the predictive model performed better than the traditional method of determining the current day’s water quality 

using the previous day’s E. coli concentration. During 2007, 

the Jaite model, based on turbidity, produced more correct 

responses (81 percent) and fewer false negatives (3.2 percent) 

than the traditional method (68 and 26 percent, respectively). 

At Old Portage, a sampling site just upstream from CVNP, 

a predictive model with turbidity and rainfall as explanatory 

variables did not perform as well as the traditional method. 

The Jaite model was used to estimate water quality at three 

other sites in the park; although it did not perform as well as 

the traditional method, it performed well—yielding between 

68 and 91 percent correct responses. Further research would 

be necessary to determine whether using the Jaite model to 

predict recreational water quality elsewhere on the river would 

provide accurate results.

Introduction

The Cuyahoga Valley National Park (CVNP) is a valuable national resource, encompassing more than 33,000 acres 

of green space between the cities of Akron and Cleveland, 

Ohio (fig. 1). Nearly 3 million people visit the park annually, making it a major destination and recreational resource 

within the Lake Erie watershed. In all, 22 mi of the Cuyahoga 

River and more than 190 mi of tributary streams are within the 

park boundaries. The Cuyahoga River drains approximately 

400 mi2 prior to entering the park, and another 300 mi2 of 

drainage area are added before the river leaves the park. However, because of variable water quality, recreation in the river, 

including canoeing, swimming, and wading, currently (2009) 

is discouraged.

The Ohio Environmental Protection Agency (Ohio EPA) 

has established minimum water-quality requirements for 

all state surface waters based on assigned use designations. 

These requirements are imposed to “protect public health 

and welfare; and to enhance, improve and maintain water 

quality” (Ohio Environmental Protection Agency, 2008). 

The Cuyahoga River within the park has been assigned a use 

designation of primary-contact recreation, meaning the river is 

suitable for full-body contact such as swimming and canoeing. 

However, previous studies within the park have found concentrations of Escherichia coli (E. coli) and fecal coliforms in the 

river and its tributaries above the Ohio EPA standards (Myers 

and others, 1998; Bushon and Koltun, 2003; Brady, 2007). 

Potential sources of contamination to the river have been 

identified and include combined sewer overflows and other 

upstream sources such as poorly functioning septic systems, 

farm runoff, and urban stormwater runoff.

Park managers want to promote the use of the river when 

the water quality is acceptable. Traditional methods used to 

monitor the concentrations of fecal-indicator bacteria in the 

water take at least 18 hours to obtain results. The elapsed 

time between the occurrence of elevated fecal-indicator 

bacteria (such as E. coli and fecal coliforms) concentrations in recreational waters and their detection is too long 

to assess water quality and take adequate control measures. 
2 Predicting Recreational Water Quality Using Turbidity in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7

This is especially true in the river, where decay, dilution, 

dispersion, and transport of fecal-indicator bacteria can cause 

concentrations to change greatly over short periods of time 

(Myers and others, 1998). Predictive models have been shown 

to work well in other systems (USGS BacteriALERT Program, http://ga2.er.usgs.gov/bacteria/default.cfm; Francy and 

Darner, 2007; Francy and others, 2003; Nevers and Whitman, 

2006; Nevers and others, 2007), and may be able to provide 

reliable results of the current day’s fecal-indicator bacteria 

concentrations in the Cuyahoga River, allowing park managers 

to determine whether they might encourage river use.

During 2004 through 2007, the U.S. Geological Survey 

(USGS), in cooperation with the National Park Service (NPS), 

conducted a series of studies within the CVNP to develop 

predictive models to provide near-real-time (within a couple 

hours of sample collection) estimates of E. coli concentrations 

in the Cuyahoga River. Water-quality and environmental data 

were collected to facilitate model development. This report 

describes how the predictive models were developed and how 

well the models predicted E. coli concentrations in the river.

Methods

Samples were collected 3 to 7 days per week, and a replicate sample was collected once per week during the recreational seasons (May through August) of 2004 through 2007. 

On the basis of previous water-quality sampling within the 

park, sites at Old Portage and Jaite were selected for further 

investigation and as potential locations for development of 

predictive models (fig. 1). Both sites are accessible to pedestrians and have a bridge over the river from which to sample. 

Old Portage is just upstream from the park (USGS station 

number 04206000), and Jaite is centrally located within the 

park (USGS station number 411747081341300). During the 

recreational seasons of 2004 and 2005, periodic samples were 

collected at two other sites along the river—Bath and Independence. Bath (USGS station number 04206200) is downstream 

from Old Portage and the Akron Water Pollution Control Station but upstream from Jaite and Independence. Independence 

(USGS station number 04208000) is at the downstream edge 

of the park and was sampled only during 2005. At each sampling site, grab samples were collected from the center of the 

river with a weighted sampler containing a sterile 1-L bottle. 

Samples were kept in the dark at 4°C until they were analyzed. 

E. coli concentrations were used to monitor recreational 

water quality. The Ohio primary-contact single-sample standard for E. coli of 298 colony-forming units per 100 milliliters 

(CFU/100 mL) was used as a benchmark to evaluate water 

quality in this study and will be herein referred to as the 

“water-quality standard” (WQS). In order to meet the recreational-use designation, this standard cannot be exceeded 

in more than 10 percent of the water samples collected 

during any 30-day period (Ohio Environmental Protection 

Agency, 2008).

Figure 1. Location of study sites, Cuyahoga Valley 

National Park, northeastern Ohio. The Cuyahoga River 

flows north through the park. 

I-71

41°20'

41°10'

OHIO

Study area

Cleveland

Akron

I-80

I-271

Cuyahoga

River

EXPLANATION

Approximate area of 

 Cuyahoga Valley National Park

Urban areas

 Sampling site

0 5 KILOMETERS

¦I-77

¦

¦

Independence

Jaite

Bath

Old Portage 







81°40' 81°30'
Methods 3

Water-Quality Sample Analyses

Turbidity was determined in the laboratory with a Hach 

Model 2100AN turbidimeter (Hach Company, Loveland, 

Colo.) and was reported in nephelometric turbidity ratio units 

(NTRU). Standard membrane-filtration techniques were 

used to determine E. coli concentrations by using modified 

mTEC agar (U.S. Environmental Protection Agency, 2006). 

An aliquot of sample was run through a 0.45-μm filter onto 

which bacteria were concentrated. The filter was transferred 

to a modified mTEC agar plate and incubated at 35°C for 

2 hours and then transferred to a 44.5°C incubator for another 

22 hours. Magenta-colored colonies were counted as E. coli.

Environmental Data Collection 

Streamflow data were collected for the Old Portage 

site from the nearby streamgage. Because there is no active 

streamgage near the Jaite site, streamflow was determined by 

using standard USGS techniques based on river stage (Rantz 

and others 1982). Rainfall information was obtained from 

the Automated Flood Warning System (AFWS), a network 

of rain gages supported by numerous communities and State 

and Federal agencies. Data are available online at (www.afws.

net). On the day of sample collection, the total rainfall over 

the previous 24 hours at AFWS sites closest to the sampling 

sites was recorded. For the Jaite sampling site, the Macedonia 

AFWS site, approximately 3 mi northeast of Jaite, was used 

(AFWS site ID 8431). For the Old Portage and Bath sampling 

sites, the Bath AFWS site, approximately 4.5 mi northwest of 

Old Portage and 2.5 mi west of Bath, was used (AFWS site 

ID 8433). The Walton Hills AFWS site, approximately 3 mi 

southeast of Independence, (AFWS site ID 8314) was used for 

the Independence sampling site.

Quality-Control and 

Quality-Assurance Practices

Quality-control and quality-assurance (QA/QC) procedures were adopted as set forth by the American Public Health 

Association and others (1998), Britton and Greeson (1989), 

and Francy and others (2007). Several QA/QC procedures 

were performed for the membrane-filtration method. These 

included field, filter, and procedure blanks and replicate samples as described in Francy and others (2007). Field blanks—

sterile buffered water transferred in the field and treated as a 

sample—were collected once during every recreational season. Every day that samples were processed, a filter blank—

which was an aliquot of sterile buffered water filtered prior 

to sample analysis—was completed. Each week that samples 

were analyzed, a procedure blank—which was an aliquot 

of sterile buffered water filtered after sample analysis—and 

a replicate sample were analyzed. If either a filter blank or 

procedure blank showed bacterial growth, immediate action 

was taken to resolve the issue. A new buffer lot or improved 

rinsing between filtered aliquots reconciled these issues. All 

replicate samples agreed within 10 percent, indicating that 

10 percent or less of the variability of the sample results was 

due to analytical and(or) sampling variability. (The agreement, 

or percent difference, was calculated as the difference of the 

log-transformed replicate pairs divided by the log of the average of the two pairs and multiplied by 100.) 

Turbidity was analyzed in duplicate for each sample. If 

results for subsamples did not agree within 10 percent of the 

average of the subsamples, samples were reanalyzed until 

results agreed for two consecutive subsamples. Reported 

sample turbidities were an average of these two results.

Statistical Methods

Correlation analysis was performed to determine the 

strength of the relation between two variables. Pearson’s correlation coefficient (r) was calculated. Linear regression analysis was used to estimate E. coli concentrations in the river 

based on one explanatory variable (simple linear regression, or 

SLR) or more variables (multiple linear regression, or MLR). 

For model selection, the coefficient of determination (R2) and 

the Mallows’ Cp statistic were used to narrow the subset of all 

possible models. The methods utilized in model development 

and selection steps are further described in Francy and Darner 

(2006). The alpha (α) level for all tests of significance in this 

report was set at 0.05.

The effectiveness of the models was based on several 

response statistics. The predicted E. coli concentration for 

each day was examined and compared to the actual concentration as determined by the membrane-filtration technique. Rates of correct predictions above or below the WQS 

(298 CFU/100 mL), false positives, and false negatives 

were calculated. False positives are defined as model predictions above the WQS when the actual concentrations were 

below the WQS; false negatives are defined as model predictions below the WQS when the actual concentrations were 

above the WQS. Model sensitivity and specificity were also 

determined. Sensitivity is the proportion of correctly predicted exceedances out of all exceedances. Specificity is the 

proportion of correctly predicted nonexceedances out of all 

nonexceedances.

The traditional method for determining the current day’s 

water quality is to use the previous day’s E. coli concentration. 

By examining this data in comparison to the actual concentrations, response statistics as described above could be calculated. Because samples were not collected every day throughout the study period, this dataset was smaller than the dataset 

for the models. 
4 Predicting Recreational Water Quality Using Turbidity in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7

To determine whether the Jaite model could predict 

recreational water quality at other sites along the river, another 

method to determine exceedances of the WQS—the thresholdprobability method—was used. Briefly, the t-distribution was 

used to test the hypothesis that the model-predicted value is 

greater than the WQS. The t-value is determined as:

t=[Y′ - log10(298)]/SEP

where Y′ is the predicted concentration in log10 units and SEP 

is the standard error of the prediction. The computed t-value 

is used along with the number of degrees of freedom for the 

regression model to determine the one-tailed probability that 

Y′ is greater than the WQS (298 CFU/100 mL). This method 

is described in more detail elsewhere (Francy and others, 

2003; Francy and Darner, 2006).

A scatterplot of the actual E. coli concentrations (x-axis) 

in comparison to the probability of exceeding the WQS 

(y-axis) is examined. A vertical line representing the WQS 

is also plotted. The analyst can then use a horizontal line to 

determine a threshold probability, above which model predictions are considered exceedances of the WQS. The threshold 

probability is decided upon at a probability that maximizes the 

number of correct responses and (or) minimizes the number of 

false negative responses. Response statistics were calculated 

as described previously.

In terms of public health protection, the frequency of 

false negatives and the sensitivity of the model were the 

principal statistics considered. However, false positives and 

the specificity of the model are important statistics that could 

cause a decrease in river use because the public may be 

deterred from recreating on the river. Therefore, when deciding which models to implement at the park, all of the response 

statistics were examined and compared.

Relation of Escherichia coli

Concentrations to 

Environmental Variables

During the recreational seasons of 2004 through 2007, 

211 samples were collected at Jaite, and 180 samples were collected at Old Portage. Concentrations of E. coli ranged from 

67 to 46,000 CFU/100 mL at Jaite and from 110 to 49,000 

CFU/100 mL at Old Portage. Summary statistics for E. coli

concentrations in samples, rainfall in the 24-hour period prior 

to sample collection, streamflow at time of sampling, and 

sample turbidity are listed for both sites in table 1.

Log10 E. coli concentrations at both sites were significantly related to rainfall (table 2). Log10 turbidities were 

strongly correlated to E. coli concentrations at Jaite (fig. 2A). 

Although the correlation between turbidities and E. coli 

concentrations was significant at Old Portage, the relation 

was weaker than at Jaite (fig. 2B). Streamflow was related 

to E. coli concentrations at Jaite (r = 0.57 for all years), but 

not at Old Portage (r = 0.12 for all years). The highest E. coli

concentrations at Old Portage, however, were observed with 

relatively low corresponding streamflow at time of sampling 

(fig. 3), implying that there may be sources of E. coli that 

affect concentrations at this site that are not related to hydrologic factors.

The relation between the current day’s and the previous 

day’s measured log10 E. coli concentrations were significant at 

both sites (r = 0.43 for Jaite and r = 0.38 for Old Portage for 

all years) (table 2). For the predictive models, this variable 

was not considered for inclusion because ease-of-use was a 

major priority for an implemented model. Ideal models would 

be those that could be used without or with limited site visits; 

that is, model parameters that could be assessed via online 

sources, for example. By including this variable, daily samples 

analyzed for E. coli concentrations would be necessary to predict current conditions and may not be readily available every 

day; for example, daily samples may be difficult to obtain during a weekend or holiday when staff are limited or occupied 

with other duties.
Relation of Escherichia coli Concentrations to Environmental Variables 5

Table 1. Summary statistics for results of sample analyses and measured environmental 

parameters at time of sampling at Jaite and Old Portage, Cuyahoga Valley National Park, Ohio, for 

the recreational seasons (May through September) of 2004 through 2007. Rainfall previous 24 hrs. is 

defined as the rainfall during the 24 hours prior to sample collection.

[E. coli, Escherichia coli; CFU/100 mL, colony-forming units per 100 milliliters; ft3/s, cubic feet per second; NTRU, 

nephelometric turbidity ratio units]

Variable Number of 

samples Minimum Median Maximum Mean

Jaite

E. coli, in CFU/100 mL 211 67 420 46,000 2,300

Rainfall previous 24 hours, in inches 210 0 0 2.5 0.20

Streamflow, in ft3/s 195 171 412 1,900 563

Turbidity, in NTRU 211 4.1 18 1,200 56

Old Portage

E. coli, in CFU/100 mL 180 110 500 49,000 1,800

Rainfall previous 24 hours, in inches 172 0 0 1.48 0.12

Streamflow, in ft3/s 180 50 186 3,510 387

Turbidity, in NTRU 180 3.9 9.2 100 12

Table 2. Pearson’s r correlation coefficients of environmental variables to log10-transformed 

Escherichia coli (E. coli) concentrations by recreational seasons (May through September) as 

indicated by year. [Relations significant at α = 0.05 are indicated in bold. The number of samples for 

each relation are indicated in parentheses. Rainfall previous 24 hours is defined as the rainfall during 

the 24 hours prior to sample collection]

[NTRU, nephelometric turbidity ratio units; ft3/s, cubic feet per second; E. coli, Escherichia coli; CFU/100 mL, colonyforming units per 100 milliliters]

Year Rainfall previous 24 hours,

 in inches

Log10 turbidity, 

in NTRU

Streamflow, 

in ft3/s

Previous day’s Log10 E. coli, 

in CFU/100 mL

Jaite

2004–6 0.59 (179) 0.82 (180) 0.56 (164) 0.44 (147)

2007 0.61 (31) 0.87 (31) 0.77 (31) 0.36 (19)

2004–7 0.59 (210) 0.83 (211) 0.57 (195) 0.43 (166)

Old Portage

2004–6 0.52 (162) 0.47 (170) 0.13 (170) 0.37 (137)

2007 0.47 (10) 0.71 (10) 0.03 (10) 0.68 (6)

2004–7 0.51 (172) 0.49 (180) 0.12 (180) 0.38 (143)
6 Predicting Recreational Water Quality Using Turbidity in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7

Figure 2. Relation between Escherichia coli concentrations and turbidity at A) Jaite and 

B) Old Portage, Cuyahoga Valley National Park, Ohio, during the recreational seasons (May through 

August) of 2004 through 2007. The solid line indicates the best-fit line. [r, Pearson’s correlation 

coefficient; p, significance of the relation; CFU/100 mL, colony-forming units per 100 milliliters; NTRU, 

nephelometric turbidity ratio units]

100 101 102 103 104

Turbidity, in NTRU

101

102

103

104

105

Escherichia coli, in CFU/100 mL

2004 - 2006

2007

A. Jaite

r = 0.83

p < 0.0001

Water-quality standard

100 101 102 103 104

Turbidity, in NTRU

101

102

103

104

105

Escherichia coli, in CFU/100 mL

2004 - 2006

2007

B. Old Portage

r = 0.49

p < 0.0001

Water-quality standard
Relation of Escherichia coli Concentrations to Environmental Variables 7

Figure 3. Relation between Escherichia coli concentrations and instantaneous streamflow at Old Portage, 

Cuyahoga Valley National Park, Ohio, during the recreational seasons (May through August) of 2004 through 

2007. Samples where the actual concentrations were above the estimated upper confidence limit for the 

predicted concentrations based on the multiple linear regression model (outliers) are indicated. [CFU/100 

mL, colony-forming units per 100 milliliters]

101 102 103 104

Instantaneous streamflow, in cubic feet per second

102

103

104

105

Escherichia coli, in CFU/100 mL

All samples

Outliers

Water-quality standard
8 Predicting Recreational Water Quality Using Turbidity in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7

Predictive Models

Data collected during the recreational seasons of 2004 

through 2006 were used to develop predictive models that 

were tested during the recreational season of 2007. Models 

were chosen on the basis of predictive capabilities and ease of 

use. Model statistics are listed in table 3.

A. Jaite

When examining all of the potential models using the 

model calibration data (data collected during the recreational 

seasons of 2004 through 2006), the SLR model based solely 

Table 3. Statistics for models for the Jaite and Old Portage sites.

[<, less than; R2, coefficient of determination; p, significance of the relation]

Site Data used for model 

development

Adjusted R2 

of model

Variables in 

model

Parameter 

estimate p value

Jaite 2004–6 0.68 log10 turbidity 1.006 <0.0001

y-intercept 1.433 <0.0001

Old Portage 2004–6 0.34 log10 turbidity 0.812 <0.0001

rainfall 0.665 <0.0001

discharge -0.0002 0.0144

y-intercept 2.006 <0.0001

Old Portage 2004–6 0.32 log10 turbidity 0.535 <0.0001

rainfall 0.710 0.0003

y-intercept 2.196 <0.0001

Jaite 2004–7 0.69 log10 turbidity 1.041 <0.0001

y-intercept 1.382 <0.0001

on log10 turbidity showed the most promise for use within 

the park. When rainfall and (or) discharge was included with 

turbidity, the parameter estimates for the additional variables 

were not statistically significant, indicating that these variables 

(singly or in combination) did not improve the model. As 

compared to the traditional method of determining the current 

day’s conditions based on the previous day’s bacterial count, 

the SLR turbidity model predicted conditions correctly more 

often: 77 percent compared to 74 percent of the time for the 

traditional method (table 4). Although the specificity of the 

model was lower than that of the traditional method, the sensitivity of the SLR model was greater, demonstrating that the 

model was better at predicting exceedances of the WQS than 

the traditional method. 
Predictive Models 9

Table 4. For the Jaite site, comparison of the percentage of correct and false positive and negative responses of recreational waterquality conditions using a regression model (with turbidity as the sole explanatory variable) and the traditional method for determining 

water quality (using the previous day’s E. coli concentrations) during model development, testing, and revisions, Cuyahoga Valley 

National Park, Ohio.

[R2, coefficient of determination; SLR, simple linear regression model; %, percent; —, not applicable]

Number of 

samples R2

Correct responses False positive False negative

Specificity Sensitivity

Value (number of samples)

Development—2004–6 data

SLR model 180 0.68 77% (138) 18% (32) 5.6% (10) 54% 91%

Traditional method 147 — 74% (109) 13% (19) 13% (19) 66% 79%

Testing—results of 2007 data in above models

SLR model 31 — 81% (25) 16% (5) 3.2% (1) 64% 94%

Traditional method 26 — 68% (13) 5.3% (8) 26% (5) 83% 62%

Revisions—2004–7 data 

SLR model 211 0.69 77% (162) 17% (35) 6.6% (14) 58% 89%

Traditional method 166 — 73% (122) 12% (20) 14% (24) 68% 77%

During the recreational season of 2007, the previously developed model was tested. The SLR turbidity model 

performed better than the traditional method (table 4), correctly predicting water-quality conditions 81 percent of the 

time compared to 68 percent for the traditional method. The 

sensitivity was higher for the SLR model compared to the 

traditional method. These results support previous research 

that concentrations of bacteria in a river can change greatly 

over short periods of time (Myers and others, 1998) causing 

inaccurate predictions based on the previous day’s concentration. The predicted E. coli concentrations from the SLR model 

compared to the actual E. coli concentrations are shown in 

Figure 4. 

When all of the data collected during 2004 through 2007 

were compiled, new models were created. Parameter estimates 

and response statistics for the SLR model (table 3 and table 4) 

changed only slightly with the additional data. Although the 

parameter estimate for rainfall was significant in the MLR 

model (with turbidity), the addition of this variable did not 

improve the response statistics compared to the SLR model. 

The parameter estimate for discharge was not significant in 

the MLR model (with turbidity). The SLR model yielded 

more correct responses than the traditional method. Although 

the traditional method had a somewhat higher specificity—68 percent compared to 58 percent for the SLR model—

the SLR model had higher sensitivity: 89 percent compared to 

77 percent for the traditional method. 
10 Predicting Recreational Water Quality Using Turbidity in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7

Figure 4. Predicted Escherichia coli concentrations based on the turbidity model developed using 2004 

through 2006 recreational-season data compared to actual Escherichia coli concentrations at Jaite, Cuyahoga 

Valley National Park, Ohio, May through August, 2007. The dotted lines indicate the primary-contact 

recreation standard for a single sample (298 CFU/100 mL) as set by the Ohio Environmental Protection Agency 

(Ohio Environmental Protection Agency, 2008). The solid line indicates the best-fit line. [r, Pearson’s correlation 

coefficient; p, significance of the relation; CFU/100 mL, colony-forming units per 100 milliliters]

101 102 103 104 105

Predicted Escherichia coli concentration, in CFU/100 mL

101

102

103

104

105

Measured Escherichia coli concentration, in CFU/100 mL

r = 0.87

p <0.0001

False

negatives

Correct exceedances

False positives

Correct 

non-exceedances

1:1 line
Predictive Models 11

B. Old Portage

The two models developed for the Old Portage site 

contained two variables (turbidity and rainfall) or three variables (turbidity, rainfall, and discharge) (table 3). For models 

developed and tested using the data from the recreational 

seasons of 2004 through 2006, there were fewer correct 

responses (72 percent) compared to the traditional method 

(81 percent) (table 5). Further, the sensitivities of the models 

were 100 percent because the models predicted all samples to 

be above the standard (WQS), whereas the traditional method 

had a lower sensitivity (87 percent) but a higher specificity 

(64 percent compared to 0 percent for both models). When the 

models were tested in 2007, the models again predicted all of 

the samples above the standard.

New models were developed using all of the data (2004–

7), but very similar results were found. The models predicted 

the majority or all of the samples above the standard. For one 

model, two predictions were below the standard, but these 

were both false negatives. Although the traditional method had 

a higher percentage of false negatives, this method had higher 

correct responses and fewer false positives.

As shown in figure 3, the highest E. coli concentrations 

were observed when streamflow was relatively low. Outliers 

were defined as samples with actual E. coli concentrations 

above the upper 90-percent confidence limit of the predicted 

concentration produced by the MLR models. The rainfall 

amounts and turbidity values for these samples ranged widely 

but had relatively low medians (0 to 0.36 in. with a median 

of 0.08 in., and 5.8 to 27 NTRU with a median of 10 NTRU, 

respectively). This information supports the previously mentioned hypothesis that sources of E. coli to the river upstream 

of this site may not be affected by hydrologic factors; instead, 

they might be affected by industrial and domestic discharges 

or broken sewerlines. These sources may also have contributed bacteria to the river at other times throughout the study 

but bacteria may not have been observed when streamflow 

was greater because of dilution.

Table 5. For the Old Portage site, comparison of the percent of correct and false positive and negative responses of recreational 

water-quality conditions using regression models with indicated variables and the traditional method for determining water quality 

(using the previous day’s E. coli concentrations) during model development, testing, and revisions, Cuyahoga Valley National Park, Ohio.

[R2, coefficient of determination; MLR, multiple linear regression model; %, percent; —, not applicable]

Number of 

samples R2

Correct responses False positive False negative

Specificity Sensitivity

Value (number of samples)

Development—2004–6 data 

MLR1

turbidity, rainfall, discharge 162 0.34 72% (117) 28% (45) 0% (0) 0% 100%

MLR2

turbidity, rainfall 162 0.32 72% (117) 28% (45) 0% (0) 0% 100%

Traditional method 137 — 81% (111) 9.5% (13) 9.5% (13) 64% 87%

Testing—results of 2007 data in above models

MLR1

turbidity, rainfall, discharge 10 — 90% (9) 10% (1) 0% (0) 0% 100%

MLR2 

turbidity, rainfall 10 — 90% (9) 10% (1) 0% (0) 0% 100%

Traditional method 8 — 75% (6) 12.5% (1) 12.5% (1) 0% 86%

Revisions—2004–7 data 

MLR1

turbidity, rainfall, discharge 172 0.35 72% (124) 27% (46) 1.2% (2) 0% 98%

MLR2

turbidity, rainfall 172 0.32 73% (126) 27% (46) 0% (0) 0% 100%

Traditional method 145 — 81% (117) 9.6% (14) 9.6% (14) 62% 87%
12 Predicting Recreational Water Quality Using Turbidity in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7

C. Jaite Model Results at Other River Locations

Because the Jaite SLR turbidity model performed well 

at determining water-quality conditions at Jaite and the Old 

Portage models did not perform well, the Jaite SLR model 

was used to determine whether it could predict water-quality 

conditions elsewhere along the river. Overlapping sampling 

dates (days samples were collected at Jaite and at another site) 

provided a total of 171, 114, and 44 samples at Old Portage, 

Bath, and Independence, respectively (fig. 1). Model performance for these additional sites was determined in two ways. 

First, the predicted E. coli concentrations from the Jaite SLR 

model were compared to the actual concentrations at the additional sites as described previously (predicted-E. coli method). 

Second, the probability of exceeding the WQS was compared 

to a threshold probability (threshold-probability method). 

For the three additional sites, the regression-based 

prediction methods provided variable results (table 6). For 

Independence (fig. 5), the threshold probability (70 percent) 

was much higher than that at both Old Portage and Bath 

(30 percent and 40 percent, respectively). The response statistics for the probability method at Independence were better 

(more correct responses, fewer false positives, and higher 

specificity) compared to the predicted-E. coli method and the 

traditional method. Although the threshold-probability method 

provided more correct responses, the specificity was lower 

at Old Portage and Bath compared to the predicted-E. coli

method. Further, the traditional method provided more correct 

responses at these two sites than either of the model-based 

prediction methods. 

Table 6. For the additional sites (Old Portage, Bath, and Independence), comparison of the percent of correct and false positive and 

negative responses of recreational water-quality conditions using the results of the Jaite simple linear regression model based solely 

on turbidity and the traditional method for determining water quality (using the previous day’s E. coli concentrations), Cuyahoga Valley 

National Park, Ohio. Two methods of determining how well the model performed were used: the predicted Escherichia coli (E. coli) 

concentration and the probability of exceeding the single-sample maximum in relation to a threshold probability were compared to the 

actual E. coli concentration.

[SLR, simple linear regression modell; %, percent; —, not applicable]

Number of 

samples R2

Correct responses False positive False negative

Specificity Sensitivity

Value (number of samples)

Old Portage—2004–7 data compared to Jaite turbidity model (2004–7) results

Jaite SLR—predicted E. coli 171 — 70% (120) 14% (24) 16% (27) 47% 78%

Jaite SLR—probability 171 30% 73% (125) 20% (35) 6.4% (11) 22% 91%

Traditional method at Old Portage 145 — 81% (117) 9.6% (14) 9.6% (14) 62% 87%

Bath—2004–5 data compared to Jaite turbidity model (2004–7) results

Jaite SLR—predicted E. coli 114 — 68% (77) 12% (14) 20% (23) 50% 73%

Jaite SLR—probability 114 40% 74% (85) 16% (18) 9.6% (11) 36% 87%

Traditional method at Bath 95 — 81% (77) 10% (9) 10% (9) 88% 59%

Independence—2005 data compared to Jaite turbidity model (2004–7) results

Jaite SLR—predicted E. coli 44 — 73% (32) 20% (9) 6.8% (3) 68% 81%

Jaite SLR—probability 44 70% 91% (40) 2.3% (1) 6.8% (3) 96% 81%

Traditional method at Independence 24 — 88% (21) 4.2% (1) 8.3% (2) 93% 80%
Model Applications and Suggestions for Future Work 13

Model Applications and 

Suggestions for Future Work

Although the model results are not as reliable as results 

of the traditional method for Old Portage and Bath, the model 

is based on one site and one variable and therefore is an easier 

and quicker method than sampling several sites along the 

river. Further research and validation of this method would 

be needed before using it to estimate bacterial concentrations along the Cuyahoga River within CVNP. If this method 

is pursued and proves reliable, only Jaite would need to be 

sampled. A turbidity sensor could be used to provide real-time 

information that would further minimize the amount of staff 

hours required.

Figure 5. Relation between actual Escherichia coli concentrations at Independence and probability of 

exceeding the primary-contact recreation maximum at Jaite using the turbidity model developed using 

2004 through 2007 recreational-season data, Cuyahoga Valley National Park, Ohio. The dotted line indicates 

the primary-contact recreation maximum for a single sample (298 CFU/100 mL) (WQS) as set by the Ohio 

Environmental Protection Agency (Ohio Environmental Protection Agency, 2008)

Implementation of the model for predicting recreational 

water quality at Jaite may begin during the recreational 

season of 2009. Model results are expected to be available 

daily via the World Wide Web. Future studies at the CVNP 

could include an examination of an in situ turbidity probe that 

could provide real-time water-quality estimates. However, 

a closer examination of the relation between model predictions at Jaite and actual concentrations of E. coli at other sites 

along the river would need to be completed prior to use of this 

method in the CVNP. Updates to water-treatment facilities and 

wastewater conveyance systems may affect water quality in 

the river and consequently the ability of the model to correctly 

predict conditions in the river. Therefore, continued waterquality monitoring of the river would be necessary to maintain 

the model’s current predictive capabilities.

101 102 103 104 105

Escherichia coli, in CFU/100 mL at Independence

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Probability of exceeding single-sample maximum at Jaite

Threshold = 70%

False positives

Correct exceedances

False negatives Correct 

non-exceedances
14 Predicting Recreational Water Quality Using Turbidity in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7

Summary and Conclusions

The Cuyahoga River within the Cuyahoga Valley 

National Park (CVNP) is often impaired for recreational use 

due to concentrations of E. coli, an fecal-indicator bacterium, 

above the Ohio primary-contact single-sample standard. During the recreational seasons (May through August) of 2004 

through 2007, the USGS, in cooperation with the National 

Park Service and the Ohio Lake Erie Commission, conducted 

a series of studies within CVNP to develop predictive models 

to provide near-real-time estimates of E. coli concentrations in 

the Cuyahoga River.

Predictive models have been demonstrated to work well 

to determine same-day water quality at beaches (Francy and 

Darner, 2007; Francy and others, 2003; Nevers and Whitman, 

2005; Nevers and others, 2007) and for river sites (USGS BacteriALERT Program, http://ga2.er.usgs.gov/bacteria/default.

cfm). In this study, regression models were developed for Jaite 

and Old Portage, two sites on the Cuyahoga River within and 

just upstream of, respectively, CVNP to predict same-day 

recreational water quality. The model results were compared 

to sampling results from use of the membrane-filtration technique and to results from the traditional method for determining recreational water quality (using the membrane-filtration 

bacteria count from the previous day). The models were based 

on easily measured variables that can provide results in less 

than 1 hour. 

The model developed for Jaite performed better than the 

traditional method and was able to provide same-day waterquality information. This model, based solely on turbidity, 

correctly predicted recreational water quality above or below 

the primary-contact single-sample standard set by the Ohio 

Environmental Protection Agency (WQS, 298 CFU/100 mL) 

in 81 percent of the samples collected during an independent 

year (2007). This correct prediction percentage is similar to what was found in earlier studies at two Lake Erie 

beaches—83 and 82 percent for Huntington and Edgewater 

beaches, respectively (Francy and Darner, 2007). Based on the 

model developed from all years of data collection at Jaite, a 

turbidity measurement greater than 11 NTRU will result in a 

model prediction greater than the WQS. 

The two models developed for the second site just 

upstream of CVNP, Old Portage, did not perform as well as 

the traditional method. The models were able to correctly 

predict exceedances of the standard in 72 or 73 percent of the 

samples as compared to 81 percent for the traditional method. 

Elevated concentrations of E. coli were found when streamflow in the river was relatively low, indicating that some 

sources of bacteria may not be influenced by hydrologic factors. These sources could be point sources that are not directly 

associated with precipitation such as industrial and domestic 

discharges or broken sewerlines. Because of this unknown and 

possibly sporadic source(s) of bacteria, the models developed 

were unable to accurately predict water quality at this site.

The Jaite turbidity model was also used to predict water 

quality at other sites along the river. For the sites upstream 

from Jaite, Old Portage and Bath, the Jaite model did not predict water quality as well as the traditional method. However, 

the predictions were correct 73 percent and 74 percent of the 

time for Old Portage and Bath, respectively. For Independence, the site downstream from Jaite, the predictions were 

correct 91 percent of the time compared to 88 percent of the 

time for the traditional method. The correct percentages are 

relatively high for all three sites, and this method could be 

explored further to determine if predictions at one site can be 

used to predict water quality at other locations on the river.

Implementation of the model for predicting recreational 

water quality at Jaite may begin during the recreational season 

of 2009. If it does, model results may be available four days 

per week on the Ohio Nowcast website (www.ohionowcast.

info). Future studies at the CVNP could include an examination of an in situ turbidity probe that could provide real-time 

water-quality estimates. 

Acknowledgments

The authors acknowledge the assistance of all of the 

USGS and NPS student interns: James Cirreddu, Suzanna 

Dzejachok, Anthony Kinyo, Nickolas Fath, and Caitlin 

Splawski. This project was completed with support in part 

from the USGS/NPS Water-Quality Assessment and Monitoring Program and from the Ohio Lake Erie Commission. 
References Cited 15

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Brady and others—Predicting Recreational Water Quality Using Turbidity in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004–7—SIR 2009–5192

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