Prepared in cooperation with the National Park Service

Development and Implementation of a Regression Model 

for Predicting Recreational Water Quality in the 

Cuyahoga River, Cuyahoga Valley National Park, 

Ohio 2009–11

Scientific Investigations Report 2012–5074

U.S. Department of the Interior

U.S. Geological Survey
Cover photograph:

Bridge at Lock 29, Cayahoga River, 

photograph by Amie Brady.
Development and Implementation of a 

Regression Model for Predicting 

Recreational Water Quality in the 

Cuyahoga River, Cuyahoga Valley 

National Park, Ohio 2009–11

By Amie M.G. Brady and Meg B. Plona

Prepared in cooperation with the National Park Service

Scientific Investigations Report 2012–5074

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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Suggested citation:

Brady, A.M.G., and Plona, M., 2012, Development and implementation of a regression model for predicting recreational water quality in the Cuyahoga River, Cuyahoga Valley National Park, Ohio 2009–11: U.S. Geological Survey 

Scientific Investigations Report 2012–5074, 14 p.
iii

Contents

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

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

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

Sample Collection and Analysis.........................................................................................................3

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

Quality-Control and Quality-Assurance Procedures ......................................................................3

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

Water-Quality Model for Jaite.....................................................................................................................4

Model Development and Validation, 2009.........................................................................................4

Model Refinement and Validation, 2010............................................................................................5

Model Refinement and Validation, 2011............................................................................................8

Water-Quality Model for Lock 29...............................................................................................................10

Model Development and Validation, 2011.......................................................................................10

Implementation of Models at Cuyahoga Valley National Park.............................................................12

Summary and Future Directions................................................................................................................13

Acknowledgments.......................................................................................................................................13

References Cited..........................................................................................................................................13

Figures

1. Map showing Cuyahoga Valley National Park, Ohio, and water-quality sampling 

sites during the May through September recreational seasons of 2009–11 ......................2

2. Scatterplot showing predicted Escherichia coli (E. coli) concentrations from the 

regression model developed using 2005 through 2008 recreationalseason data compared to the measured E. coli concentrations at Jaite, Cuyahoga 

Valley National Park, Ohio, May through August 2009...........................................................6

3. Scatterplot showing predicted Escherichia coli (E. coli) concentrations from the 

regression model developed using 2004 through 2009 recreational-season data 

compared to the measured E. coli concentrations at Jaite, Cuyahoga Valley 

National Park, Ohio, May through August 2010.......................................................................7

4. Scatterplot showing predicted Escherichia coli (E. coli) concentrations from the 

regression model developed using 2004 through 2010 recreational-season data 

compared to the measured E. coli concentrations at Jaite, Cuyahoga Valley 

National Park, Ohio, June through September 2011...............................................................9

5. Scatterplot showing predicted Escherichia coli (E. coli) concentrations from 

the regression model developed using 2004 through 2010 recreational-season 

data compared to the measured E. coli concentrations at Lock 29, Cuyahoga 

Valley National Park, Ohio, June through September 2011.................................................11

6. Sample of the Cuyahoga River Web page of the Ohio Nowcast Web site 

(www.ohionowcast.info) ...........................................................................................................12
iv

Tables

1. Variables and regression statistics for Cuyahoga River models, Cuyahoga Valley 

National Park, Ohio.......................................................................................................................4

2. Comparison of the percentage of correct and false positive and negative 

responses of recreational water-quality conditions using the Jaite 2009 model 

and the traditional method for determining water quality during model 

development and validation, Cuyahoga Valley National Park, Ohio. ...................................5

3. Comparison of the percentage of correct and false positive and negative 

responses of recreational water-quality conditions using the Jaite 2010 model 

and the traditional method for determining water quality during model 

refinement and validation, Cuyahoga Valley National Park, Ohio........................................7

4. Comparison of the percentage of correct and false positive and negative 

responses of recreational water-quality conditions using the Jaite 2011 model 

and the traditional method for determining water quality during model 

refinement and validation, Cuyahoga Valley National Park, Ohio........................................8

5. Comparison of the percentage of correct and false positive and negative 

responses of recreational water-quality conditions using the Lock 29 2011 model 

and the traditional method for determining water quality during model 

development and testing, Cuyahoga Valley National Park, Ohio .......................................10

Conversion Factors

Multiply By To obtain

Length

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Volume

milliliter (mL) 0.06102 cubic inch (in3)

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 milliliters (CFU/100 mL).

Filter pore size refers to the actual opening size of the pores in a membrane filter and is reported 

in microns (μm).

Turbidity is reported in nephelometric turbidity ratio units (NTRU).

Distance along a river is measured along the course of the river and is reported in river miles.
Development and Implementation of a Regression Model 

for Predicting Recreational Water Quality in the Cuyahoga 

River, Cuyahoga Valley National Park, Ohio 2009–11

By Amie M.G. Brady1 and Meg B. Plona2

Abstract

The Cuyahoga River within Cuyahoga Valley National 

Park (CVNP) is at times impaired for recreational use due to 

elevated concentrations of Escherichia coli (E. coli), a fecalindicator bacterium. During the recreational seasons of midMay through September during 2009–11, samples were collected 4 days per week and analyzed for E. coli concentrations 

at two sites within CVNP. Other water-quality and environmental data, including turbidity, rainfall, and streamflow, were 

measured and (or) tabulated for analysis. Regression models 

developed to predict recreational water quality in the river 

were implemented during the recreational seasons of 2009–11 

for one site within CVNP–Jaite. For the 2009 and 2010 seasons, the regression models were better at predicting exceedances of Ohio’s single-sample standard for primary-contact 

recreation compared to the traditional method of using the previous day’s E. coli concentration. During 2009, the regression 

model was based on data collected during 2005 through 2008, 

excluding available 2004 data. The resulting model for 2009 

did not perform as well as expected (based on the calibration 

data set) and tended to overestimate concentrations (correct 

responses at 69 percent). During 2010, the regression model 

was based on data collected during 2004 through 2009, including all of the available data. The 2010 model performed well, 

correctly predicting 89 percent of the samples above or below 

the single-sample standard, even though the predictions tended 

to be lower than actual sample concentrations. During 2011, 

the regression model was based on data collected during 2004 

through 2010 and tended to overestimate concentrations. The 

2011 model did not perform as well as the traditional method 

or as expected, based on the calibration dataset (correct 

responses at 56 percent). At a second site—Lock 29, approximately 5 river miles upstream from Jaite, a regression model 

based on data collected at the site during the recreational 

1

 U.S. Geological Survey.

2

 National Park Service.

seasons of 2008–10 also did not perform as well as the 

traditional method or as well as expected (correct responses 

at 60 percent). Above normal precipitation in the region and 

a delayed start to the 2011 sampling season (sampling began 

mid-June) may have affected how well the 2011 models 

performed. With these new data, however, updated regression 

models may be better able to predict recreational water quality 

conditions due to the increased amount of diverse water quality conditions included in the calibration data.

Daily recreational water-quality predictions for Jaite 

were made available on the Ohio Nowcast Web site at www.

ohionowcast.info. Other public outreach included signage at 

trailheads in the park, articles in the park’s quarterly-published 

schedule of events and volunteer newsletters. A U.S. Geological Survey Fact Sheet was also published to bring attention to 

water-quality issues in the park.

Introduction

Cuyahoga Valley National Park (CVNP), located in 

northeastern Ohio between the cities of Akron and Cleveland, 

is a popular destination for recreationists (fig. 1). Over 2.5 

million people visit the park annually to hike, ride the scenic 

railroad, bike the Towpath Trail, or enjoy the many recreational opportunities provided by the Cuyahoga River and 

its tributaries. Recent studies have shown that the Cuyahoga 

River is at times impaired for recreational use due to elevated 

concentrations of fecal-indicator bacteria (Bushon and Koltun, 

2003; Bushon and others, 2007; Brady, 2007; Brady and others, 2009; Brady and Plona, 2009). Because of uncertain water 

quality, park managers have discouraged canoeing, swimming, 

or wading, but would like to promote the use of the river when 

the water quality is considered acceptable.
2 Regression Model for Predicting Recreational Water Quality in the Cuyahoga River, Ohio 2009–11

#

#

#

§ ̈¦271

§ ̈¦77

§ ̈¦80

Cuyahoga River

Furnace Run

Lock 29

411433081330000

Old Portage

04206000

Jaite 

(Brecksville)

411747081341300

81°35'W 81°30'W

41°20'N

41°10'N

!

!

Akron

Cleveland

Study area

OHIO

EXPLANATION

Approximate area of park

# Sampling site and number

Base from Cuyahoga Valley

National Park, 2011 digital data 0 1 2 MILES

0 1 2 KILOMETERS

Figure 1. Cuyahoga Valley National Park, Ohio, and water-quality 

sampling sites during the May through September recreational 

seasons of 2009–11. In this figure, the Cuyahoga River generally 

flows southeast to northwest.

When traditional methods of assessing water-quality 

conditions are used, concentrations of fecal-indicator bacteria 

are not available until the day after sample collection, and 

this period of time is too long to assess water quality and take 

adequate control measures. This is especially true in the river 

where concentrations can change greatly over short periods 

of time in response to decay, dilution, dispersion, and transport of bacteria (Myers and others, 1998). Predictive models 

have been shown to work well in other river and lake systems 

(Christensen and others, 2002; David and Haggard, 2011; 

Elaria and Vogel, 2005; Francy and Darner, 2007; Francy and 

others, 2009; Nevers and Whitman, 2006; Nevers and others, 

2007; Rasmussen and Ziegler, 2003; Chattahoochee Riverway 

Project, 2011), and may be able to provide reliable results of 

the current day’s fecal-indicator bacteria concentrations at 

CVNP, thus allowing park managers to determine whether 

they might promote river use.

During 2009 through 2011, the U.S. Geological Survey 

(USGS) and the National Park Service (NPS) implemented the 

use of models to predict E. coli concentrations at a centralized 

location within CVNP. Beginning in the recreational season 

of 2009, results from the predictive model developed during 

a previous study (Brady and Plona, 2009) were made available to the public through the Ohio Nowcast Web site (www.

ohionowcast.info). Signage was placed throughout the park 

to provide the public with information and the address of the 

Web site. Each subsequent year, the regression model was 

refined to include the data from the previous season in the calibration dataset. This report summarizes the performance of the 

predictive models during 2009–11, information on the usage 

of the CVNP page of the Ohio Nowcast Web site, and future 

directions for predictive models within CVNP.

Methods

Escherichia coli (E. coli) concentrations were used to 

assess recreational water quality. E. coli are fecal-indicator 

bacteria whose concentrations have been shown to be correlated with occurrences of swimming-related gastrointestinal illness in epidemiological studies (Dufour, 1984). The 

U.S. Environmental Protection Agency recommends the use 

of E. coli for monitoring recreational water quality of fresh 

waters (U.S. Environmental Protection Agency, 1986). The 

Ohio primary-contact single-sample maximum of 298 colonyforming units per 100 milliliters (CFU/100 mL) was used as 

a benchmark to evaluate water quality and will be henceforth 

called the “water-quality standard” (WQS). On days when 

measured concentrations of E. coli in river samples were 

below or above the WQS, the river was considered safe or 

unsafe for recreation, respectively. 
Methods 3

Sample Collection and Analysis

Water samples were collected by USGS or NPS staff 

from the Cuyahoga River at Jaite (USGS site number 

411747081341300), a site centrally located in the park, and 

at Lock 29 (USGS site number 411433081330000), a site 

approximately 5 river miles upstream from Jaite (fig. 1). 

Recreational season (mid-May–September) sampling at Jaite 

started in 2004 and has continued through the present (2011). 

Recreational season sampling at Lock 29 started in late July 

2008 and also has continued through the present. This stretch 

of the river was targeted for study because it is currently being 

used (unofficially) by recreational paddlers. During 2011, 

actual sampling was delayed until mid-June due to student 

scheduling.

Samples were collected at one location near the center of 

flow by use of a weighted-bottle sampler fitted with a sterile 

1-liter (L) bottle (Wilde, 2006). The weighted sampler was 

submerged and allowed to fill. The bottle was capped and 

placed on ice in a cooler until analyses were performed. Samples were collected 4 days per week, and a replicate sample 

was collected once per week.

Samples were processed within 6 hours of sample collection following the modified mTEC method (U.S. Environmental Protection Agency, 2006). Aliquots of water were 

filtered through a 0.45-micron (μm) filter and then placed onto 

an agar plate. Inverted plates were incubated at 35 degrees 

Celsius (oC) for 2 hours and then transferred to an incubator at 

44.5 ± 0.5oC for 20–22 hours. Magenta-colored colonies were 

counted as E. coli, and results were reported as CFU/100 mL.

Turbidity provides an indication of the clarity of the 

water. A laboratory turbidimeter (Hach 2100P) was used to 

measure turbidity within 2 hours of sample collection so that 

results were available to run predictive models. The average of 

two consecutive measurements that agreed within 10 percent 

was reported for each sample in units of nephelometric turbidity ratio units (NTRU).

Environmental Data Collection

River gage height was collected by means of a weighted, 

steel tape measure. The distance to the water surface was 

measured with the tape from a designated point on the bridge 

with a known elevation. This tape-down measurement was 

recorded in feet (ft). In addition, instantaneous streamflow at 

time of sampling as measured at Old Portage, Ohio (USGS 

streamgage 04206000), upstream from Lock 29, was compiled. Instantaneous streamflow was reported in units of cubic 

feet per second.

Rainfall data were compiled from the National Weather 

Service local climatology data stations at Akron Fulton 

International Airport (call sign AKR) and Akron-Canton 

Airport (call sign CAK) (National Oceanic and Atmospheric 

Administration, 2011). The airports are located approximately 18 and 27 miles, respectively, south of the Jaite site 

and approximately 15 and 24 miles, respectively south of the 

Lock 29 site. Everyday during the recreational season, hourly 

precipitation totals were summed for the 24-hour period prior 

to 8:00 AM (Rain24). A weighted rainfall variable (Rw48) 

was calculated by multiplying the sampling day’s Rain24 by 

two and adding the previous day’s Rain24 (provides more 

weight to the most recent rainfall). Rainfall was recorded in 

inches (in.).

Quality-Control and Quality-Assurance 

Procedures

Quality-control and quality-assurance (QA/QC) procedures are important to ensure the integrity of the data being 

collected. Therefore, strict QA/QC procedures were followed 

throughout this study. Written protocols were distributed to 

USGS and NPS staff prior to the onset of sample collection. 

Collection and (or) analysis that followed protocols established in Francy and others (2010) were done daily for filter 

blanks, weekly for replicate samples, and two or three times 

each season for field blanks. Quality-control checks were 

performed onsite monthly to ensure that sample collection 

and analyses were done according to protocols. No QA/QC 

problems were identified during quality-control checks. All 

filter and field blanks were negative for bacterial growth. Variability of E. coli concentrations, defined as the absolute value 

of the difference between log10-transformed replicate pair 

concentrations divided by the average of the log10-transformed 

concentrations, ranged from 0 to 11 percent. This variability 

was similar to what was reported during previous studies at 

the park (0 to 10 percent) (Brady and others, 2009; Brady and 

Plona, 2009).

Statistical Methods

Correlation analysis and scatter plots were used to 

examine the relations between E. coli concentrations and 

environmental and water-quality variables. A linear correlation 

coefficient (Pearson’s r) was used to determine the degree to 

which parameters and (or) variables were related to covariates. The more the coefficient differed from 1 or -1, the weaker 

the relation. Linear regression analysis was used to estimate 

log10-transformed E. coli concentrations in the river based on 

one explanatory variable (simple linear regression or SLR) or 

more than one variable (multiple linear regression). For model 

selection, the adjusted coefficient of determination (adj. R2) 

and the Mallows’ Cp statistic were used to narrow the subset 

of all possible models. The root mean squared error (RMSE), 

also referred to as the standard error of the estimate (SEE), 

was determined to assess the variation in the predicted versus 

the actual concentrations. Assumptions of linear regression 

(homoscedasticity, autocorrelation, and normality) were 

tested by examining plots of the residuals against predicted 

values and over time and by using normal probability plots. 

For all models, heteroscedasticity was not observed. Some 
4 Regression Model for Predicting Recreational Water Quality in the Cuyahoga River, Ohio 2009–11

autocorrelation (based on Durbin-Watson tests, D ranged from 

1.5 to 1.6) and non-normality (based on Shapiro-Wilk tests, 

W ranged from 0.92 to 0.95) were observed and indicate that 

the models could be improved upon. The methods utilized in 

model development and (or) refinement and selection steps are 

further described in Francy and Darner (2006) and specifically 

for CVNP in Brady and others (2009). The level of significance for statistical tests was set at α = 0.05 for this report.

For model development and (or) refinement and comparisons between models and the traditional method (using the 

previous day’s E. coli concentrations), the method’s effectiveness for determining recreational water quality was based on 

several response statistics. The predicted E. coli concentration 

for each day was compared to the measured concentration 

as determined by the membrane-filtration technique. Rates 

of correct predictions above or below the WQS, false positives, and false negatives were calculated. False positives 

were defined as model predictions above the WQS when the 

measured concentrations were below the WQS (nonexceedance). False negatives were defined as model predictions 

below the WQS when the measured concentrations exceeded 

the WQS. Model sensitivity and specificity were also determined. Sensitivity is the proportion of correctly predicted 

exceedances of the WQS out of all exceedances. Specificity is 

the proportion of correctly predicted nonexceedances out of all 

nonexceedances. In terms of public health protection, the frequency of false negatives and the sensitivity of the model were 

the principle 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 

unnecessarily discouraged from recreating in the river. 

Water-Quality Model for Jaite

Model Development and Validation, 2009

During the recreational season of 2009, a regression 

model with log10 turbidity as the sole explanatory variable was 

used to predict recreational water quality of the Cuyahoga 

River at Jaite (table 1). This model was based on data collected during the recreational seasons of 2005 through 2008. 

During model development, this model, which excluded the 

2004 data from the calibration dataset, was determined to 

provide more accurate results compared to the model including the 2004 data (81 and 77 percent correct for the calibration 

set that excluded and included the 2004 data, respectively). 

Precipitation during the 2004 season (as observed at CAK), 

was greater than 7 in. above normal, and storms during the 

season resulted in record flooding in the park. The greatest 

river stage recorded in over 80 years was 13.74 ft on May 22, 

2004 as observed at the Old Portage USGS streamgage. The 

extreme conditions resulted in unusual observations, which 

may explain why the model performed better during model 

development when data from 2004 were not included. 

Table 1. Variables and regression statistics for Cuyahoga River models, Cuyahoga Valley National Park, Ohio.

[RMSE, root mean square error; GH, gage height; Rw48, rainfall weighted for 48-hour period prior to sampling]

Site and 

test year

Calibration data 

seasons (number 

of samples)

Adjusted R2 RMSE Variables in Model Parameter 

estimate

Significance of 

variable

Jaite 2009 2005–8 0.75 0.33218 y-intercept 1.361 <0.0001

(151) log10 turbidity 1.056 <0.0001

Jaite 2010 2004 –9 0.68 0.35168 y-intercept 1.608 <0.0001

(287) log10 turbidity * log10 GH 1.046 <0.0001

Jaite 2011 2004–10 0.69 0.34405 y-intercept 1.277 <0.0001

(335) log10 turbidity 1.125 <0.0001

Lock 29 2011 2008–10 0.76 0.27396 y-intercept 1.295 <0.0001

(100) log10 turbidity 1.147 <0.0001

Rw48 0.201 0.0022
Water-Quality Model for Jaite 5

Forty-nine samples were collected at Jaite between May 

26 and August 20, 2009. Twenty-six of the samples, or 53 percent, exceeded the WQS. The Jaite 2009 model correctly predicted exceedances or nonexceedances for 34 samples, or 69 

percent (table 2). This percentage is lower than expected—81 

percent of the calibration data sample results were predicted 

correctly (n = 151, data not shown), and similar percentages 

were expected for the validation year. Precipitation during 

the 2009 season was a little above normal (approximately 

1 in.), which may help explain why the Jaite 2009 model did 

not perform as well as expected. An additional contributor 

to the unexpected performance of the Jaite 2009 model was 

the exclusion of the 2004 data, which included wet-weather 

sampling, from the calibration dataset.

The higher error rate for the Jaite 2009 model resulted 

from a greater percentage of false positives, indicating that 

the model was overestimating concentrations in the river as 

can be seen in figure 2. Compared to the traditional method 

of determining advisories by using the previous day’s E. coli

concentration, the Jaite 2009 model had fewer false negatives 

and more false positives (table 2). The Jaite 2009 model had 

a higher sensitivity but a lower specificity than that of the 

traditional method, which indicates that the model provides 

better protection in terms of public health, but could be deterring park visitors from using the river when the water is safe 

for recreation.

Model Refinement and Validation, 2010

In keeping with conventional procedures for maintaining operational Nowcast models (Francy and Darner, 2006; 

Francy and others, 2009; Stidson and others, 2012) the entirety 

of the data, including data collected during the validation year, 

was tabulated for model refinement for the upcoming season. 

Scatter plots and correlation analyses were done to examine 

the data in detail. Due to the decrease in predictive capabilities observed during the 2009 validation year, subsets of data 

based on year of collection were not explored during model 

refinement for the 2010 recreational season. 

Statistically significant correlations were observed 

between log10-transformed E. coli concentrations and log10 

turbidity (r = 0.83), the mathematical product of log10 turbidity and log10 gage height (r = 0.82), and Rw48 at AKR (r = 

0.72). These variables and selected others were included as 

potential explanatory variables during model refinement. The 

new model prepared for 2010 (Jaite 2010) was a SLR model, 

but was based on the mathematical product of log10 turbidity and log10 gage height (table 1). This cross-product model 

was chosen over the SLR model with log10 turbidity as the 

sole explanatory variable, because the cross-product model 

provided more correct responses and higher sensitivity and 

specificity compared to the log10 turbidity model for the calibration data set.

Table 2. Comparison of the percentage of correct and false positive and negative responses of recreational water-quality conditions 

using the Jaite 2009 model and the traditional method for determining water quality during model development and validation, Cuyahoga 

Valley National Park, Ohio.

[Jaite 2009 model based on log10 turbidity; traditional method uses the Escherichia coli concentration from the previous day; WQS, water quality standard; 

%, percent]

Number of 

samples

Exceedances 

of WQS

Correct 

responses

False 

positive

False 

negative Sensitivity Specificity

Value (number of samples)

Development—calibration data

Model 151 56% (85) 81% (122) 11% (17) 7.9% (12) 86% 74%

Traditional 

method

101 58% (59) 68% (69) 13% (13) 19% (19) 68% 69%

Validation—2009 data

Model 49 53% (26) 69% (34) 28% (14) 2.0% (1) 96% 39%

Traditional 

method

35 54% (19) 71% (25) 14% (5) 14% (5) 74% 69%
6 Regression Model for Predicting Recreational Water Quality in the Cuyahoga River, Ohio 2009–11

10 100 1,000 10,000 100,000

Predicted Escherichia coli, in colony-forming units per 100 milliliters

10

100

1,000

10,000

100,000

Measured Escherichia coli, in colony-forming units per 100 milliliters

Figure 2. Jaite - 2009

Correct 

nonexceedances

Correct exceedances

False positives

False negatives

Ohio single-sample standard for

 primary-contact recreation

1:1 line

Figure 2. Predicted Escherichia coli (E. coli) concentrations from the regression model developed using 2005 through 2008 recreationalseason data compared to the measured E. coli concentrations at Jaite, Cuyahoga Valley National Park, Ohio, May through August 2009. 

Forty-four samples were collected from May 27 through 

August 25, 2010. Twenty-seven of the samples, or 61 percent, 

exceeded the WQS. The Jaite 2010 model correctly predicted 

exceedances or nonexceedances for 39 samples, or 89 percent 

(table 3). The 2010 model tended to underestimate E. coli concentrations when measured concentrations were greater than 

the WQS (fig. 3), but generally the predicted concentrations 

were still above the WQS, resulting in correct classification 

of the samples as exceedances. Compared to the traditional 

method, the Jaite 2010 model predicted correct exceedances 

and nonexceedances more often. The model statistics indicate 

that, compared to the traditional method, the Jaite 2010 model 

is better able to provide protection in terms of public health as 

well as less often discourage park visitors from using the river 

when the water is safe for recreation.
Water-Quality Model for Jaite 7

Table 3. Comparison of the percentage of correct and false positive and negative responses of recreational water-quality conditions 

using the Jaite 2010 model and the traditional method for determining water quality during model refinement and validation, Cuyahoga 

Valley National Park, Ohio.

[Jaite 2010 model based on the mathematical product of log10 turbidity and log10 gage height; traditional method uses the previous day’s Escherichia coli 

concentration; WQS, water quality standard; %, percent]

Number of 

samples

Exceedances 

of WQS

Correct 

responses False positive False negative Sensitivity Specificity

Value (number of samples)

Refinement—calibration data

Model 287 60% (173) 78% (224) 16% (46) 5.9% (17) 90% 60%

Traditional 

method

221 62% (137) 72% (158) 13% (29) 15% (34) 75% 65%

Validation—2010 data

Model 44 61% (27) 89% (39) 4.5% (2) 6.8% (3) 89% 88%

Traditional 

method

32 59% (19) 78% (25) 9.4% (3) 12% (4) 79% 77%

10 100 1,000 10,000 100,000

10

100

1,000

10,000

100,000

False negatives

Correct 

nonexceedances

Correct exceedances

False positives

Ohio single-sample standard for

 primary-contact recreation 

Measured Escherichia coli, in colony-forming units per 100 milliliters

Predicted Escherichia coli, in colony-forming units per 100 milliliters

1:1 line

Figure 3. Predicted Escherichia coli (E. coli) concentrations from the regression model developed using 2004 through 2009 recreationalseason data compared to the measured E. coli concentrations at Jaite, Cuyahoga Valley National Park, Ohio, May through August 2010.
8 Regression Model for Predicting Recreational Water Quality in the Cuyahoga River, Ohio 2009–11

Model Refinement and Validation, 2011

The data collected during 2010 were added to the dataset, 

and scatter plots and correlation analyses were done to examine the 2004–10 data in detail. Similar to what was observed 

during the previous year’s data analysis, statistically significant relations were observed between log10-transformed E. coli

concentrations and log10 turbidity (r = 0.83), the mathematical 

product of log10 turbidity and log10 gage height (r = 0.82), and 

Rw48 at AKR (r = 0.70). These variables and selected others 

were included as potential explanatory variables during model 

refinement. The new model prepared for 2011 (Jaite 2011) 

was a SLR model, but it was based solely on log10 turbidity 

(table 1). Other models that included additional variables were 

explored, but there was little to no improvement in the model’s 

predictive performance compared to the chosen model.

Precipitation for the 2011 season was above normal, and 

the year was ranked as the second highest for precipitation 

(National Weather Service Forecast Office, 2012). Previous 

years with above normal precipitation for the season tended 

to have percentages of WQS exceedance greater than those 

closer to normal or below normal precipitation (data not 

shown). Thirty-nine samples were collected from June 13 

through September 19, 2011. Fifteen samples, or 38 percent, 

exceeded the WQS. The delayed season may help explain why 

percentages of WQS exceedances were lower (38 percent) 

than in previous years (60 percent) (table 4). Further, the Jaite 

2011 model correctly predicted exceedances or nonexceedances of the WQS for 56 percent of the samples (fig. 4), which 

is much lower than expected based on the calibration dataset 

(78 percent) (table 4). Together, these general observations 

imply that the Jaite 2011 model needs to be improved upon. 

With the addition of the 2011 data to the calibration dataset, an 

updated regression model may be able to better predict recreational water quality during more diverse weather conditions, 

such as those observed during the 2011 season.

Table 4. Comparison of the percentage of correct and false positive and negative responses of recreational water-quality conditions 

using the Jaite 2011 model and the traditional method for determining water quality during model refinement and validation, Cuyahoga 

Valley National Park, Ohio.

[Jaite 2011 model based on log10 turbidity; traditional method uses the previous day’s Escherichia coli concentration; WQS, water quality standard; %, percent]

Number of 

samples

Exceedances 

of WQS

Correct 

responses False positive False negative Sensitivity Specificity

Value (number of samples)

Refinement—calibration data

Model 335 60% (202) 78% (262) 16% (53) 6.0% (20) 90% 60%

Traditional 

method

253 62% (156) 72% (183) 13% (32) 15% (38) 76% 67%

Validation—2011 data

Model 39 38% (15) 56% (22) 41% (16) 2.6% (1) 88% 33%

Traditional 

method

25 28% (7) 72% (18) 20% (5) 8.0% (2) 71% 72%
10

10 100 1,000 10,000 100,000

100

1,000

10,000

100,000

Measured Escherichia coli , in colony-forming units per 100

milliliters Figure 4. Jaite - 2011

1:1 line

Correct 

nonexceedances

False positives

Correct exceedances

False negatives

Ohio single-sample standard for

 primary-contact recreation 

Predicted Escherichia coli, in colony-forming units per 100 milliliters

Water-Quality Model for Jaite 9

Figure 4. Predicted Escherichia coli (E. coli) concentrations from the regression model developed using 2004 through 2010 

recreational-season data compared to the measured E. coli concentrations at Jaite, Cuyahoga Valley National Park, Ohio, June 

through September 2011.
10 Regression Model for Predicting Recreational Water Quality in the Cuyahoga River, Ohio 2009–11

Water-Quality Model for Lock 29

Model Development and Validation, 2011

Data collected at Lock 29 during the recreational seasons of 2008 through 2010 were compiled. Scatterplots and 

correlation analyses were done to examine the data in detail. 

Statistically significant relations were observed between 

log10 E. coli concentrations and log10 turbidity (r = 0.86), the 

amount of rain that fell in 24-hour period prior to sample collection at AKR (r = 0.67), Rw48 at AKR (r = 0.73), and log10

instantaneous streamflow at Old Portage (r = 0.54). Several 

models were explored, including models that had streamflow 

as an explanatory variable. Multicollinearity between this and 

other explanatory variables tended to decrease the model’s 

predictive capabilities and was therefore not included in the 

final model. The Lock 29 2011 model was a multiple linear 

regression model with log10 turbidity and Rw48 as explanatory 

variables (table 1).

From June 13 through September 19, 2011, 37 samples were collected. Sixteen of the samples, or 43 percent, 

exceeded the WQS. As observed for samples collected during 

the same season at the Jaite site, fewer WQS exceedances than 

expected were detected in samples collected at Lock 29 during 

the 2011 season (based on the calibration data). Correspondingly, the Lock 29 2011 model correctly predicted only 60 

percent of the samples as nonexceedances or exceedances 

(table 5). Based on correct predictions and the number of false 

positives, the Lock 29 2011 model did not perform as well 

compared to the traditional method. The model did have 100 

percent sensitivity, but this may be due to the model’s tendency to overestimate concentrations (fig. 5). Above normal 

precipitation observed for the region resulted in the 2011 season being a poor year for validating the model. Inclusion of 

the 2011 data in the calibration dataset may allow an updated 

regression model to better predict recreational water quality. 

Table 5. Comparison of the percentage of correct and false positive and negative responses of recreational water-quality conditions 

using the Lock 29 2011 model and the traditional method for determining water quality during model development and testing, Cuyahoga 

Valley National Park, Ohio.

[Lock 29 2011 model based on log10 turbidity and weighted 48-hour rainfall; traditional method uses the previous day’s Escherichia coli concentration; 

WQS, water quality standard; %, percent]

Number of 

samples

Exceedances 

of WQS

Correct 

responses False positive False negative Sensitivity Specificity

Value (number of samples)

Development—calibration data

Model 100 58% (58) 77% (77) 14% (14) 9.0% (9) 84% 67%

Traditional 

method

70 56% (39) 69% (48) 14% (10) 17% (12) 71% 66%

Validation— 2011 data

Model 37 43% (16) 60% (22) 41% (15) 0.0% (0) 100% 29%

Traditional 

method

22 41% (9) 64% (14) 18% (4) 18% (4) 56% 69%
Water-Quality Model for Lock 29 11

10 100 1,000 10,000 100,000

10

100

1,000

10,000

100,000

Figure 5. Lock 29 - 2011

Correct 

nonexceedances

False positives

Correct exceedances

False negatives

Ohio single-sample standard for

 primary-contact recreation 

Measured Escherichia coli, in colony-forming units per 100 milliliters

1:1 line

Predicted Escherichia coli, in colony-forming units per 100 milliliters

Figure 5. Predicted Escherichia coli (E. coli) concentrations from the regression model developed using 2004 through 2010 

recreational-season data compared to the measured E. coli concentrations at Lock 29, Cuyahoga Valley National Park, Ohio, June 

through September 2011.
12 Regression Model for Predicting Recreational Water Quality in the Cuyahoga River, Ohio 2009–11

Implementation of Models at Cuyahoga 

Valley National Park

For the recreational season of 2009, the Ohio Nowcast 

Web site (www.ohionowcast.info) was adapted to include the 

CVNP Nowcast system. From the home page, viewers were 

able to see current predicted conditions at Jaite, and from the 

Cuyahoga River Web page, more detailed information about 

the sampling site and links to previous data (from 2009) were 

available (fig. 6). 

The NPS managers posted signs at trailheads along the 

river to raise public awareness of water-quality issues and 

referenced the Ohio Nowcast Web site for further information. 

Other public outreach items included articles on the park’s 

Web page (www.nps.gov/cuva) and in the park’s quarterly published schedule of events and volunteer newsletters. A USGS 

Fact Sheet (Brady and Plona, 2010) was published and made 

available at multiple locations within the park. During 2011, 

to further communicate water-quality issues to park visitors, a 

student intern provided public outreach on the Nowcast system 

to over 400 park visitors on busy summer weekends. Business cards were prepared 

with a brief description 

of the Ohio Nowcast 

system and the Web page 

address, and the student 

intern was able to distribute the cards to interested 

park visitors.

During the recreational season of 

2009, there were 1,342 

external visits (excluding 

administrative checks 

and daily updates) to 

the CVNP Web page of 

the Ohio Nowcast Web 

site. During the recreational seasons of 2010 

and 2011, there were 

1,607 and 1,964 external 

visits to the Web page, 

respectively. For all 

years, there were more 

visits to the site during July than any other 

month (May through 

August). Because the 

Ohio Nowcast Web site 

is not specific to the 

Cuyahoga River, visits 

to the home page were 

not tallied. Therefore, 

the external visitor count 

demonstrates the number 

of visitors interested in 

more detailed information on the water quality 

of the river and may be 

an underestimate of the 

actual use of the Web site 

for this purpose. 

Nowcast Web site (www.ohionowcast.info).

Figure 6. Sample of the Cuyahoga River Web page of the Ohio 
References Cited 13

Summary and Future Directions

The Cuyahoga River within the Cuyahoga Valley 

National Park is often impaired for recreation due to elevated 

concentrations of E. coli. To assist park managers who would 

like to encourage the use of the river when it is safe to do so, 

the USGS in collaboration with the park, initiated a monitoring program that enabled the development and (or) refinement 

and implementation of regression models to predict recreational water quality of the river at two sites. At Jaite, a site 

centrally located in the park, water-quality data from 2004 

were available to develop and (or) refine predictive models for 

the recreational seasons of 2009–11. Recreational water-quality predictions were posted daily on the Ohio Nowcast Web 

site during each year. Each year, the predictive models were 

refined to include the previous season’s data in the calibration 

dataset. Compared to the traditional method for determining recreational water quality by using the previous day’s E. 

coli concentrations, the regression models were better able to 

predict exceedances of the WQS and had fewer false negative 

responses during the 2009 and 2010 seasons. During the 2011 

season, however, the model did not perform as well as the traditional method, possibly due to the unusual precipitation. At a 

second site on the river, Lock 29, approximately 5 river miles 

upstream of Jaite, monitoring was done during the 2008–10 

seasons, and a regression model based on turbidity and rainfall 

over the previous 48 hours was developed. Similar to the Jaite 

model for the 2011 season, the Lock 29 model did not perform 

as well as the traditional method. Above normal precipitation 

for the region and a delayed sampling season (sample collection began mid-June 2011), which had not been included in the 

calibration datasets, may account for the decreased performance of both models during the 2011 season.

Continued monitoring during upcoming recreational 

seasons is planned. Utilization of the Ohio Nowcast Web site 

for serving daily predictions at Jaite, and possibly Lock 29, is 

anticipated. In addition, the use of a real-time turbidity sensor 

is being explored for the Jaite site, and this would involve 

establishing a semipermanent stream monitor at the site. 

Data would then be telemetered to the USGS, where it would 

be processed for display on the National Water Information 

System Web Interface (NWIS Web). Park managers could 

access the data as it becomes available and run the regression 

model 7 days per week, eliminating the need to manually collect daily samples. Continued manual sampling for bacteria is 

necessary to ensure that the regression model results continue 

to reflect environmental conditions; however, sampling could 

be reduced to 2 or 3 days per week. 

Afternoon monitoring at Jaite is also planned. Those 

monitoring data would provide information needed to determine if relations between E. coli concentrations and environmental and water-quality variables previously developed, 

based on bacterial data collected in the morning, can also 

be applied to conditions present in the afternoon or if a new 

regression model would be needed. In either case, the ability 

to predict recreational water quality in the afternoon will be a 

benefit to recreationists planning on utilizing the river later in 

the day.

Additional public outreach, including hands-on training 

for park rangers, utilization of science communication interns 

for water-quality interpretation and education, additional signage, and presentations by researchers, is planned.

Acknowledgments

The authors acknowledge the assistance of student interns 

Donald J. Doyle, III (U.S. Geological Survey), Joshua Smith 

(National Park Service), and Bethany Wallace (National 

Park Service) for their hard work collecting and processing 

samples. 

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Brady and Plona—Regression Model for Predicting Recreational Water Quality in the Cuyahoga River, Ohio 2009–11 —Scientific Investigations Report 2012–5074