Prepared in cooperation with the National Park Service

Towards Automating Measurements and Predictions of 

Escherichia coli Concentrations in the Cuyahoga River, 

Cuyahoga Valley National Park, Ohio, 2012–14

Scientific Investigations Report 2015–5094

U.S. Department of the Interior

U.S. Geological Survey
Cover photo. Cuyahoga River at Boston Mills Road, Peninsula, Ohio. (Photograph by Amie Brady, USGS).
Towards Automating Measurements and 

Predictions of Escherichia coli Concentrations in 

the Cuyahoga River, Cuyahoga Valley National Park, 

Ohio, 2012–14

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

Prepared in cooperation with the National Park Service

Scientific Investigations Report 2015–5094

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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Suggested citation:

Brady, A.M.G., and Plona, M.B., 2015, Towards automating measurements and predictions of Escherichia coli concentrations in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2012–14: U.S. Geological Survey Scientific 

Investigations Report 2015–5094, 30 p., http://dx.doi.org/10.3133/sir20155094.

ISSN 2328-0328 (online)
iii

Acknowledgments

The authors would like to thank the National Park Service seasonal interns Lindsey Steinwachs, 

Michael Kosmos, and Yeyzy Vargas for their hard work collecting and processing samples.

v

Contents

Acknowledgments........................................................................................................................................iii

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

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

Study Area.......................................................................................................................................................2

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

In-Stream Water-Quality and Streamflow Measurements at Jaite .............................................2

Water-Quality Standard for Primary-Contact Recreation..............................................................4

Discrete Sample Collection and Analyses .......................................................................................4

Data Compilation From Existing Sources..........................................................................................4

Quality Assurance and Quality Control .............................................................................................5

Statistical Methods...............................................................................................................................5

Running the Nowcast...........................................................................................................................6

Comparison of Laboratory-Measured and In-Stream Turbidity.............................................................6

Comparison of Escherichia coli Enumeration Methods..........................................................................7

Quality-Control Sample Results...................................................................................................................8

Afternoon Samples........................................................................................................................................8

Escherichia coli Concentrations and Environmental and Water-Quality Variables...........................8

Development and Testing of Nowcasts Using Near-Real-Time Data at Jaite...................................12

Using Jaite Nowcasts To Predict Water Quality at Lock 29 .................................................................15

Reaching the Public.....................................................................................................................................16

Summary........................................................................................................................................................18

References Cited..........................................................................................................................................19

Appendix 1.....................................................................................................................................................21

Figures

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

sampling sites (U.S. Geological Survey streamgages) for the recreational 

seasons (May through September) of 2012–14 .......................................................................3

2. Graph showing the relation between turbidity measured in-stream with a 

Forest Technology Systems model DTS–12 sensor and turbidity measured 

in the laboratory with a Hach model 2100P turbidimeter for the Cuyahoga River 

at Jaite site (U.S. Geological Survey streamgage 04206425) in Cuyahoga Valley 

National Park, Ohio, May 2012 through August 2012..............................................................6

3. Graph showing the relation between Escherichia coli (E. coli) concentrations at 

Cuyahoga Valley National Park, Ohio, as determined by membrane filtration using 

a modified membrane thermotolerant E. coli (modified mTEC) agar method 

and the Colilert method for the Cuyahoga River at Jaite site (U.S. Geological 

Survey streamgage 04206425), May 2012 through September 2014 ....................................7
vi

4. Graphs showing Echerichia coli (E. coli) concentrations at Cuyahoga Valley 

National Park, Ohio, as determined by membrane filtration using a modified 

membrane thermotolerant E. coli (modified mTEC) agar method for samples 

collected in the morning and in the afternoon at the A, Cuyahoga River at Jaite 

(U.S. Geological Survey streamgage 04206425) and B, Cuyahoga River 

near Lock 29 near Peninsula OH (U.S. Geological Survey streamgage 

411433081330000) sites, May 2012 through September 2014.................................................9

5. Boxplot showing Escherichia coli (E. coli) concentrations in samples 

collected from the Cuyahoga River at two sites within the Cuyahoga Valley 

National Park, Ohio, from 2012 to 2014....................................................................................11

6. Scatter plot showing predicted Escherichia coli (E. coli) concentrations from 

the predictive models compared with measured E. coli concentrations for the 

Cuyahoga River at Jaite site (U.S. Geological Survey streamgage 04206425) 

during the recreational seasons (May through September) of 2012–14 in 

Cuyahoga Valley National Park, Ohio......................................................................................13

7. Scatter plot showing predicted Escherichia coli (E. coli) concentrations from 

the predictive models for the Cuyahoga River at Jaite site (U.S. Geological Survey 

streamgage 04206425) compared with the measured E. coli concentrations 

at the Cuyahoga River near Lock 29 near Peninsula OH (U.S. Geological Survey 

streamgage 411433081330000) site in Cuyahoga Valley National Park, Ohio, 

during the recreational seasons (May through September) of 2012–14 ...........................15

8. Graph showing the number of visits to the Cuyahoga River Web page of the 

Ohio Nowcast Web site (http://www.ohionowcast.info/), by month, 

from 2012 through 2014 ..............................................................................................................17

Figures—Continued

Tables

1. Results of sample analyses and measured environmental variables at 

time of sampling at Jaite and Lock 29 sampling sites in Cuyahoga Valley 

National Park, Ohio, for the recreational seasons (May through September) 

of 2012 through 2014...................................................................................................................10

2. Pearson’s r correlation coefficients between log10-transformed 

Escherichia coli (E. coli) concentrations as determined by membrane filtration 

using a modified membrane thermotolerant E. coli ( modified mTEC) agar 

method and environmental and water-quality variables by recreational 

season (May through September) from 2012 through 2014.................................................10

3. Explanatory variables and regression statistics for models used to predict 

Escherichia coli (E. coli) concentrations at the Cuyahoga River at Jaite sampling 

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

4. Correct and false positive and negative responses for recreational 

water-quality conditions using the nowcasts and the persistence model 

for determining water quality during model development and testing at 

the Jaite sampling site in Cuyahoga Valley National Park, Ohio........................................14

5. Correct and false positive and negative responses for recreational 

water-quality conditions using the nowcasts at Jaite and the persistence 

model for determining water quality at Lock 29, 

Cuyahoga Valley National Park, Ohio......................................................................................16
vii

Conversion Factors

[Inch/Pound to International System of Units]

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

mile (mi) 1.609 kilometer (km)

Area

square mile (mi2) 2.590 square kilometer (km2) 

Volume

gallon (gal) 3.785 liter (L) 

cubic inch (in3) 16.39 cubic centimeter (cm3) 

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:

°F = (1.8 × °C) + 32.

Supplemental Information

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

most-probable number per 100 milliliters (MPN/100 mL), depending on the method used.

Distance along a river is measured along the course of the river and is reported in river miles.

Abbreviations

CFU colony-forming unit

CVNP Cuyahoga Valley National Park

E. coli Escherichia coli

FNU formazin nephelometric unit

MPN most-probable number

NTRU nephelometric turbidity ratio unit

NWS National Weather Service

USGS U.S. Geological Survey

WQS water-quality standard

Towards Automating Measurements and Predictions of 

Escherichia coli Concentrations in the Cuyahoga River, 

Cuyahoga Valley National Park, Ohio, 2012–14

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

Abstract

Nowcasts are systems that can provide estimates of the 

current bacterial water-quality conditions based on predictive models using easily-measured, explanatory variables; 

nowcasts can provide the public with the information to 

make informed decisions on the risk associated with recreational activities in natural water bodies. Previous studies 

on the Cuyahoga River within Cuyahoga Valley National 

Park (CVNP) have found that predictive models can be used 

to provide accurate assessments of the recreational water 

quality. However, in order to run the previously developed 

nowcasts for CVNP, manual collection and processing of 

samples is required on a daily basis to acquire the required 

explanatory variable data (laboratory-measured turbidity). 

The U.S. Geological Survey and the National Park Service 

collaborated to develop a more automated approach to provide 

more timely results to park visitors regarding the recreational 

water quality of the river.

In May 2012, an in-stream water-quality sensor was 

installed by the U.S. Geological Survey at Jaite, Ohio 

(a site centrally located in CVNP on the Cuyahoga River), to 

provide near-real-time measurements of turbidity and water 

temperature. To transition from methods used during previous 

studies at CVNP, a relation between laboratory- and in-stream 

measured turbidity was developed after the recreational season 

of 2012. During the recreational seasons of 2012 through 

2014, discrete water samples were collected and processed to 

determine Escherichia coli (E. coli) concentrations at Jaite and 

one site upstream of Jaite (Lock 29) within CVNP. Predictive 

models, using in-stream turbidity measurements, were developed for the recreational seasons of 2013 and 2014 to estimate 

recreational water quality in regards to Ohio’s single-sample 

water-quality standard for primary-contact recreation.

A computer program was developed to manage the nowcasts by running the predictive models and posting the results 

to a publicly accessible Web site daily by 9 a.m. The nowcasts 

were able to correctly predict E. coli concentrations above 

or below the water-quality standard at Jaite for 79 percent of 

1

U.S. Geological Survey.

2

National Park Service.

the samples compared with the measured concentrations. In 

comparison, the persistence model (using the previous day’s 

sample concentration) correctly predicted concentrations 

above or below the water-quality standard in only 68 percent 

of the samples. To determine if the Jaite nowcast could be 

used for the stretch of the river between Lock 29 and Jaite, the 

model predictions for Jaite were compared with the measured 

concentrations at Lock 29. The Jaite nowcast provided correct 

responses for 77 percent of the Lock 29 samples, which was 

a greater percentage than the percentage of correct responses 

(58 percent) from the persistence model at Lock 29.

Introduction

Monitoring recreational water quality to determine concentrations of fecal-indicator bacteria can take many forms, 

from traditional culture-based methods that can take up to 

18 hours for results to become available to molecular methods (for example, quantitative polymerase chain reaction, or 

qPCR) that can be completed within 3 hours. Ultimately, the 

goal of monitoring is to protect the public from coming into 

contact with water that contains microorganisms that may 

cause illness. There are drawbacks to many of the current 

monitoring and detection methods. Culture-based methods do 

not provide results in a timely fashion, and molecular methods 

are not easy to complete, may not work at all locations, and 

may not be practical for monitoring agencies due to limitations 

with cost and expertise. Nowcasts are an alternative to culture 

or molecular methods. Nowcasts are systems that can inform 

the public of estimates of the bacteriological water-quality 

conditions in near real time based on predictive models using 

easily-measured explanatory variables. Predictive models 

provide predictions of concentrations of fecal-indicator bacteria or probabilities of exceeding a specified water-quality 

standard (WQS).
2 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Elevated concentrations of Escherichia coli (E. coli), 

a fecal-indicator bacterium, in the Cuyahoga River within 

Cuyahoga Valley National Park (CVNP; fig. 1) can result in 

a failure of the water to meet the Ohio single-sample WQS 

for primary-contact recreational use. Between 2004 and 

2011, the WQS was exceeded in more than 50 percent of the 

samples collected (U. S. Geological Survey, 2015). Sections 

of the river, including the 22 miles that flow through the park, 

have been designated by the U.S. Environmental Protection 

Agency (EPA) as one of the Great Lakes areas of concern 

because of beneficial-use impairments, including impairments caused by bacterial contamination (Ohio Environmental 

Protection Agency, 2003). Water-quality concerns have long 

limited the ability of park managers to promote recreational 

use of the river within CVNP. The ability to rapidly estimate 

bacterial levels in the most efficient and cost-effective way 

would provide the public with information needed to make 

informed recreational-use decisions about water quality and 

possibly encourage greater use of the river.

In response to these concerns, the U.S. Geological 

Survey (USGS) and the National Park Service (NPS) collaborated to develop a method to provide more timely results 

to park visitors regarding the recreational water quality of the 

river. In previous research at CVNP, a predictive model based 

on turbidity measured in the laboratory accurately predicted 

water-quality conditions at one site on the river more than 

80 percent of the time when compared with results of physical 

samples collected at the same site (in terms of exceedances or 

nonexceedances of the WQS; Brady and Plona, 2009; Brady 

and others, 2009). To expand on these capabilities and to 

decrease demand on staff time for field data collection, a study 

was initiated to test the use of a continuous in-stream measurement of turbidity for use in a predictive model for recreational 

water quality. Continuous in-stream measurements of turbidity 

make it possible for park managers to assess recreational water 

quality and notify the public from the office without having to 

visit the site.

This report describes the results of efforts by the USGS 

and the NPS to improve the operational nowcast at CVNP 

during the recreational seasons (May through September) of 

2012 through 2014. The report describes the procedures used 

in and results of the (1) acquisition and analysis of the data 

required to develop the nowcasts (including the transition 

from laboratory-measured turbidity to in-stream measured 

turbidity), (2) analysis of results of testing the nowcast, and 

(3) examination of the feasibility of using the nowcast predictions for a specific reach of the river and (or) for an extended 

period of time.

Study Area

The CVNP comprises approximately 33,000 acres along 

the Cuyahoga River between the cities of Cleveland and Akron 

in northeastern Ohio (fig. 1). Prior to entering CVNP, the river 

drains approximately 400 square miles (mi2), and another 

300 mi2

 of drainage area is added before the river leaves the 

park. In all, 22 miles of the Cuyahoga River and more than 

228 miles of tributary streams are within the park boundaries. 

More than 2.2 million people visit the CVNP annually to enjoy 

the park’s historical, cultural, natural, and recreational activities all in one setting (National Park Service, 2015).

The USGS sampling site at streamgage 411433081330000, 

Cuyahoga River near Lock 29 near Peninsula Ohio (Lock 29), is 

along the Ohio and Erie Canal Towpath Trail in Peninsula, Ohio. 

The Towpath Trail is busy year-round due to its proximity to 

shops, restaurants, bicycle rentals, and public parking. The 

USGS streamgage station 04206425, Cuyahoga River at Jaite 

(Jaite) is approximately 5.3 river miles downstream from 

Lock 29, near the center of CVNP. The streamgage at Jaite 

was installed in May 2012 on an abandoned bridge abutment 

just downstream from the Highland Road bridge. The 

Cuyahoga River is quickly becoming a popular destination for 

kayaking as efforts are underway to designate the entire length 

of the river as a water trail. These two sites bracket one of the 

most popular stretches of the river for paddling within CVNP.

Methods

In-Stream Water-Quality and Streamflow 

Measurements at Jaite

In May 2012, a water-quality monitor and telemetry 

instrumentation were added at the Cuyahoga River at Jaite 

streamgage. The station was equipped with an in-stream 

Forest Technology Systems Ltd. DTS–12 water-quality sensor 

that provided continuous measurements of turbidity and temperature. Turbidity and water temperature were recorded every 

15 minutes. (The in-stream sensor reports turbidity based on 

measurements of the forward and back scattered light at a 

90-degree (o) angle to the light beam; results are reported as 

formazin nephelometric units). A USGS hydrologic technician 

visited the site at approximately 2-week intervals to service 

the monitor. Except for periods where the monitor was being 

serviced, data were collected continuously during the recreational season (May through September). The in-stream sensor 

was removed from the river in early November each year to 

minimize damage from freezing temperatures as well as to 

have the instrument recalibrated by the manufacturer. Operation, record computation, and data reporting were done as 

described in Wagner and others (2006).

Instantaneous streamflow measurements were made 

periodically, and stream stage was recorded at 15-minute intervals. The stream stage data were used with the instantaneous 

streamflow measurements to develop stage-discharge rating 

curves using methods described in Rantz and others (1982a,b). 

The stage-discharge rating curves were used to compute a time 

series of streamflows. Near-real-time data were made available 

through the USGS National Water Information System (NWIS) 

Web interface (http://waterdata.usgs.gov/oh/nwis) beginning in 

late April (streamflow) and May 2012 (water quality).
Methods 3

¦

0 1 2

0 1 2 KILOMETERS

MILES

41°10'

41°20'

81°35' 81°30'

OHIO

Study area

Brecksville

Lock 29

411433081330000

Jaite 

04206425

Peninsula

Cuyahoga River

Fu rnace Run

Base from National Park Service 

digital data, 2014

80

77

EXPLANATION

Area of park

Sampling site and number

Akron

Cleveland

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

(U.S. Geological Survey streamgages) for the recreational seasons (May through September) 

of 2012–14. In this study area, the Cuyahoga River generally flows southeast to northwest.
4 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Water-Quality Standard for Primary-Contact 

Recreation

The Ohio Environmental Protection Agency assigns minimum water-quality requirements for all state surface waters 

on the basis of use designations. Within CVNP, the Cuyahoga 

River has been assigned a Class A use designation of primarycontact recreation, meaning waters that “support, or potentially support, frequent primary contact recreation activities,” 

such as wading, swimming, canoeing and kayaking (Ohio 

Environmental Protection Agency, 2014). The specific not-toexceed numeric criterion for E. coli for this use designation in 

a single sample is 298 colony-forming units per 100 milliliters 

(CFU/100 mL). This WQS was used as a benchmark to evaluate recreational water quality in this study.

Discrete Sample Collection and Analyses

The NPS collected discrete water samples from the 

Cuyahoga River at both Jaite and Lock 29, one to three times 

per week during the recreational season. A pedestrian bridge 

over the river at Lock 29 and the bridge at Highland Road at 

Jaite were selected as the sampling locations. Samples were 

collected from a bridge near the center of flow by using a 

weighted-bottle sampler fitted with a sterile, 1-liter bottle 

(U.S. Geological Survey, 2006). The weighted sampler was 

submerged, and the bottle was allowed to fill. To allow for 

adequate mixing during analysis, sample water was immediately poured off as needed to provide 1 to 2 inches of headspace in the bottle. The bottle was then capped and placed on 

ice in a cooler until analyses were performed.

Samples collected before 9 a.m. were designated as the 

morning samples, and E. coli concentrations for these samples 

were compared with daily nowcast predictions. Several 

samples were also collected during the afternoon to determine if the morning E. coli concentrations were accurately 

representing the concentrations in the river later in the day. 

Because visitors to CVNP are engaged in recreational activities throughout the day, nowcast predictions would be most 

valuable if they provided information for the whole day.

Observations of weather and river condition were 

noted at the time of sample collection. Water temperature 

was recorded at Lock 29 using either a digital thermometer 

(±0.2 °C) or an alcohol thermometer (±0.5 °C) placed in a second sample collected using the weighted sampler so as to not 

contaminate the primary sample used for bacteria analyses. At 

Jaite, in-stream turbidity and water temperature was recorded 

by manually querying the streamgage instrumentation.

Samples were transported to the NPS laboratory, and 

analyses to determine E. coli concentrations were started 

within 8 hours of sample collection in accordance with compliance requirements for nonpotable waters (Rice and others, 

2006). Two methods were used during this study for determining E. coli concentrations—the membrane filtration method 

using modified membrane thermotolerant E. coli (modified 

mTEC) agar method (membrane filtration method; U.S. Environmental Protection Agency, 2009) and, starting in August 

2013, the Colilert Quanti-Tray/2000 method (Colilert method; 

IDEXX Laboratories, Inc.). Previous predictive model studies 

in CVNP used the membrane filtration method to determine 

E. coli concentrations. Because of its ease-of-use, CVNP personnel were interested in transitioning to the Colilert method 

from the membrane filtration method. Membrane filtration was 

used during the entire study to ensure the two tested methods 

were comparable. For the Colilert method, results are reported 

as most-probable number per 100 milliliters, and for the membrane filtration method, results are reported as colony-forming 

units per 100 milliliters.

Turbidity for discrete samples was measured by use 

of a turbidimeter in the laboratory (Hach Company model 

2100P). Previous predictive model studies in CVNP used the 

laboratory-measured turbidity as an explanatory variable. 

To transition to using the in-stream turbidity measurements, 

the laboratory-measured turbidity was measured during the 

entire study. The turbidimeter used during this study reported 

turbidity measurements based on a ratio of the signals from 

its 90o and transmitted light detectors. Results are reported as 

nephelometric turbidity ratio units.

Data Compilation From Existing Sources

Data from existing sources were compiled and used to 

develop and test predictive models during the study. Throughout the recreational seasons, precipitation data were manually 

retrieved for the National Weather Service (NWS) AkronFulton International Airport station (National Oceanic and 

Atmospheric Administration, 2014c) for input into predictive 

models. At the end of each season, data were retrieved in bulk 

for the entire season to ensure that the data used for model 

development for the upcoming season were the final approved 

data (National Oceanic and Atmospheric Administration, 

2014b). To enable a more automated approach to predicting recreation water quality, hourly radar rainfall data were 

obtained from the NWS (National Oceanic and Atmospheric 

Administration, 2014a) for a 4-kilometer grid that included the 

Akron-Fulton International Airport. The sums of the hourly 

rainfall values for the 24-hour periods prior to 8 a.m. on the 

day of sampling (Rd−1) and prior to 8 a.m. on the day before 

sampling (Rd−2) were tallied. Previous research at beaches and 

at CVNP has shown that weighted rainfall variables may be 

important explanatory variables in predictive models 

(Francy and others, 2006; Brady and Plona, 2012; Francy and 

others, 2013). A weighted 48-hour rainfall variable (RW48) 

was calculated as shown in equation 1, which gives more 

weight to the most recent rainfall.

RW48 = (2 × Rd−1) + Rd−2 (1)

All rainfall variables were tallied or calculated for both airport 

and radar data.
Methods 5

Quality Assurance and Quality Control

Use of quality-control and quality-assurance (QA/QC) 

procedures is important to ensure the integrity of the data 

being collected. Therefore, strict QA/QC procedures were followed throughout the study. Written protocols were prepared 

and distributed to all staff prior to the onset of sample collection. The QA/QC procedures for sample collection and 

analysis of E.coli concentrations followed protocols described 

in Francy and others (2014) and Myers and others (2014) and 

were done daily for filter blanks, weekly for replicate samples, 

and seasonally for field blanks. At least twice during each recreational season, a USGS hydrologist visited the sites and the 

NPS laboratory to observe the NPS personnel collecting and 

processing samples to ensure sampling and analytical protocols were being followed properly. Problems identified during 

these QA/QC checks were minor (for example, calculation and 

rounding errors), and corrective actions were taken immediately. To quantify sampling and analytical variability for this 

study, the relative error for each replicate pair was calculated 

as shown in equation 2.

Relative error = ( ) − ( )

( ) + ( ) × log log

log log

10 1 10 2

10 1 10 2

2

100

R R

R R (2)

where

R1 is the value of the first replicate sample of the 

replicate sample pair; and

R2 is the value of the second replicate sample of 

the replicate sample pair.

Replicate samples were two bottles collected one after the 

other using the weighted bottle sampler. Turbidity was measured and E. coli concentrations were determined for each 

bottle.

Statistical Methods

Exploratory scatter plots and correlation analyses were 

used to examine the relations between environmental and 

water-quality variables, including E. coli concentrations 

derived from both membrane filtration and Colilert methods. 

A linear correlation coefficient (Pearson’s r) was used to 

determine the degree to which parameters and (or) variables 

were related to covariates. The Wilcoxon signed-rank test 

(Wilcoxon, 1945) was used to compare paired groups of data 

(for example, a comparison between turbidity values measured 

in the laboratory versus values measured in the stream at the 

time of sample collection). When comparing two independent 

groups of data (for example, E. coli concentrations for samples 

collected at Jaite compared with those for samples collected at 

Lock 29), the Wilcoxon rank-sum test (Wilcoxon, 1945) was 

used. When comparing more than two groups of data 

(for example, E. coli concentrations in samples collected at 

Jaite during the different sampling years), the Kruskal-Wallis 

test (Helsel and Hirsch, 2002) was used, followed by a posthoc Dunn’s (multiple comparison) test (Dunn, 1964), when 

applicable. McNemar’s test (McNemar, 1947) was used to 

examine the marginal frequencies of two binary outcomes 

(for example, this test was used to compare the frequencies of 

sample results when the two culture methods were discordant 

in regards to E. coli concentrations above or below the WQS).

To predict E. coli concentrations in the river, predictive 

models were developed and tested annually. The methods used 

for model development and testing are described in Francy 

and Darner (2006). For this study, the models were developed 

using the Virtual Beach 2.4.3 software (U.S. Environmental 

Protection Agency, 2014). Concentrations of E. coli were 

log10-transformed before any statistical analyses or modeling 

was done. Relations between potential explanatory variables 

(including transformations of explanatory variables) and 

E. coli concentrations were examined by using Pearson’s correlation analysis (Helsel and Hirsch, 2002) and by associated 

scatterplots. Linear regression methods were used for model 

development (U.S. Environmental Protection Agency, 2014). 

Models were selected for testing based on several response 

statistics of the calibration dataset—accuracy, sensitivity, and 

specificity (description below). Selected models were tested 

on an independent year (for example, the model calibrated 

using data from 2004 to 2011 was tested using data collected 

in 2012). The level of significance (α) for all statistical tests 

was set at 0.05.

Model effectiveness for determining recreational water 

quality was judged based on several response statistics. 

Exceedance or nonexceedance of the WQS based on the 

predicted E. coli concentration for each day was compared 

with the same based on concentrations as determined with 

the membrane-filtration method. Rates of correct predictions 

above and below the WQS (accuracy), as well as rates of false 

positives and false negatives, were calculated. False positives 

occur when the model predicts an exceedance of the WQS 

and the measured concentration was below the WQS (nonexceedance). False negatives occur when the model predicts 

a nonexceedance of the WQS and the measured concentration was above the WQS (exceedance). Model sensitivity 

and specificity were also calculated. Sensitivity is the ratio 

of correctly predicted exceedances of the WQS to the total 

number of exceedances. Specificity is the ratio of correctly 

predicted nonexceedances of the WQS to the total number 

of nonexceedances. These response statistics were used to 

select models during model development and also to compare 

predictive models and the persistence model (using the previous day’s E. coli concentration to determine the current day’s 

water quality.
6 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Running the Nowcast

Predictive models, based mainly on turbidity, were 

developed for Jaite in previous studies (Brady and Plona, 

2009, 2012; Brady and others, 2009). Each year, models were 

revised and prepared for use during the next recreational season. For the 2012 recreational season, a model was developed 

by the USGS and run manually by NPS personnel at CVNP. 

During subsequent years (2013–14), a computer program was 

set up to run automatically each day at 9 a.m. to (1) retrieve 

predictor-variable data from NWIS and the NWS, (2) calculate 

the predicted E. coli concentration for that day, and (3) post 

results to the Ohio Nowcast Web site (http://www.ohionowcast.info/). The program also sent a daily email to appropriate 

USGS and NPS staff with the values for the model variables 

and the predicted E. coli concentration for that day so the 

model calculations could be checked in Virtual Beach. Confirmation was essential to ensuring the most accurate results 

were posted to the Web site. Each year, minor glitches, such as 

rounding errors and incorrect model variables retrieved, were 

caught early because of the confirmation procedures used.

Comparison of Laboratory-Measured 

and In-Stream Turbidity

Turbidity explained the largest proportion of the variation 

in the predictive models used at CVNP, therefore, it was essential that the parameter was measured and reported consistently 

and accurately. Because the in-stream sensor used a different technique to measure turbidity than that of the laboratory 

turbidimeter used during previous research at CVNP, it was 

necessary to compare the results from the two instruments. 

The relation between the 2012 measurements was strong 

(r = 0.97, p < 0.0001; fig. 2). The median for the in-stream 

sensor measurements was not statistically greater than that 

for the laboratory turbidimeter (Wilcoxon signed-rank test, 

p = 0.1752). However, modifications to the models developed 

using laboratory-measured turbidity were necessary to ensure 

E. coli concentrations predicted with in-stream sensor measurements were as accurate as possible.

100 101 102 103

Laboratory-measured turbidity, in nephelometric turbidity ratio units

10

In-stream turbidity, in formazin nephelometric units

0

101

102

103

r = 0.97

p < 0.0001

Figure 2. Relation between turbidity measured in-stream with a Forest Technology Systems model DTS–12 sensor 

and turbidity measured in the laboratory with a Hach model 2100P turbidimeter for the Cuyahoga River at Jaite site 

(U.S. Geological Survey streamgage 04206425) in Cuyahoga Valley National Park, Ohio, May 2012 through August 

2012. r, 0.97; p, <0.0001; <, less than.
Comparison of Escherichia coli Enumeration Methods 7

After the recreational season of 2012, an equation was 

developed to describe the relation between the laboratorymeasured and in-stream measured turbidity (equation 3; 

assumption of normality was not rejected based on ShapiroWilk test of the residuals). This equation was used to estimate 

an in-stream turbidity value from turbidity measurements 

made with the laboratory turbidimeter during previous years 

(2004–11).

log10(in-stream turbidity) = 1.01225 × 

 log10(laboratory turbidity) – 0.02413 

The estimated in-stream turbidity values (before 2012) and the 

in-stream turbidity data (2012 and later) were then used during 

development of the 2013 and 2014 predictive models.

(3)

Comparison of Escherichia coli 

Enumeration Methods

In August 2013, the Colilert method for determining 

E. coli concentrations was added to the study. Compared 

with the membrane filtration method, the Colilert method 

requires less hands-on time to complete. The Colilert method 

was included in this study so that it could ultimately replace 

the membrane filtration method that historically had been 

used to test the nowcast predictions. Both enumeration 

methods are approved for monitoring ambient fresh waterquality by the EPA, and results from previous studies showed 

that the two methods were highly correlated (Francy and 

Darner, 2000; Kinzelman and others, 2005). Results similar to those in the previous studies were found during the 

study described in this report (r = 0.98, p < 0.0001; fig. 3); 

101 102 103 104 105

r = 0.98

p < 0.0001

101

102

103

104

105

Escherichia coli as determined by the membrane filtration method, 

in colony-forming units per 100 milliliters

Escherichia coli as determined by the Colilert method, 

in most-probable number per 100 milliliters

1(n =2) 2 (n =50)

3 (n =31) 4 (n =7)

Figure 3. Relation between Escherichia coli (E. coli) concentrations at Cuyahoga Valley National Park, Ohio, as determined 

by membrane filtration using a modified membrane thermotolerant E. coli (modified mTEC) agar method and the Colilert 

method for the Cuyahoga River at Jaite site (U.S. Geological Survey streamgage 04206425), May 2012 through September 

2014. The Ohio single-sample water-quality standard (WQS) of 298 colony-forming units per 100 milliliters is shown on the 

plot as red lines. The plot was split into quadrants where (1) results from the membrane filtration method were below the 

WQS but from the Colilert method exceeded the WQS, (2) results from both methods exceeded the WQS, (3) results from 

both methods were below the WQS, and (4) results from the membrane filtration method exceeded the WQS but from the 

Colilert method were below the WQS. n, number of samples; r, 0.98; p, <0.0001; <, less than.
8 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

however, E. coli concentrations obtained using the membrane 

filtration method tended to be greater than those obtained 

using the Colilert method. Upon examination of the data to 

determine if both methods predicted nonexceedances and 

exceedances of the WQS, seven of the membrane filtration 

method results exceeded the WQS when the Colilert method 

results did not (fig. 3, quadrant 4) and two of the Colilert 

method results exceeded the WQS when the membrane filtration method results did not (fig. 3, quadrant 1). Based on 

McNemar’s test, the observed discordance between the results 

of the two methods was not significant (S = 2.78, p = 0.0956).

Quality-Control Sample Results

All filter and field blanks for this study were negative 

for bacterial growth. For the turbidity replicate sample pairs 

(n = 41), relative errors ranged from 0 to 11 percent. Relative 

errors for replicate sample pairs processed for E. coli by membrane filtration (n = 41) ranged from 0 to 14 percent and by the 

Colilert method (n = 17) ranged from 0 to 7.9 percent. This 

variability is similar to that reported during previous studies 

at CVNP for bacteriological analyses (0 to 11 percent; Brady 

and Plona, 2009, 2012; Brady and others, 2009). For E. coli

replicate samples with relative errors greater than 10 percent 

(membrane filtration analyses only), sample concentrations 

were always less than the WQS. For 9.7 percent of replicate 

pairs, sample E. coli concentrations as determined using 

membrane filtration showed disparate results, meaning that the 

concentration from one replicate sample was above the WQS 

where that of the other replicate sample was below the WQS. 

There were no disparate results for replicate samples analyzed 

using the Colilert method.

Afternoon Samples

To determine if the morning E. coli concentrations were 

accurately representing concentrations in the river later in 

the day, afternoon samples were also collected and processed 

by the membrane filtration method. A total of 58 afternoon 

samples were collected at Jaite from 2012 to 2014. Compared 

with the morning samples collected on the same date, there 

were only eight afternoon samples (14 percent) that did not 

agree with the morning water-quality condition (concentrations above or below the WQS; fig. 4A), and all eight samples 

had concentrations that were below the WQS when those of 

the morning samples were above the WQS. At Lock 29, afternoon samples processed using the membrane filtration method 

were only collected in 2012 and 2013 (n = 46). Similar to 

what was observed for samples collected at Jaite, the morning samples tended to have higher concentrations than the 

afternoon samples (fig. 4B). Six afternoon samples (13 percent) had concentrations that were below the WQS when the 

associated morning sample concentration was above the WQS. 

In terms of protecting the public, the greatest concern would 

be to not post an advisory when the river water exceeds or is 

expected to exceed the WQS. Study results did not indicate 

that the river water-quality conditions worsened from morning to afternoon samples. However, the sampling schedule for 

afternoon samples was limited and may have excluded dates 

when storms or other rapidly changing hydrologic conditions 

altered the water-quality conditions during the day.

Escherichia coli Concentrations and 

Environmental and Water-Quality 

Variables

During the recreational seasons of 2012 through 2014, 

196 samples were collected at Jaite, and 162 samples were 

collected at Lock 29. Summary statistics for E. coli and 

measured environmental and water-quality variables are listed 

in table 1. A subset of data that only included samples concurrently collected at both sites was used to compare E. coli, 

turbidity, and water temperature between the sites. Based on 

Wilcoxon rank-sum tests, no significant differences in median 

E. coli concentrations, turbidity, or water temperature were 

observed between sites. Further, of the 146 sample pairs (concurrent samples collected at Jaite and Lock 29) with E. coli

concentration results, only 14 pairs disagreed when comparing 

if concentrations exceeded the WQS at one site but not the 

other. This observed discordance was not significant (based on 

McNemar’s test; S = 1.14, p = 0.2850).

Radar-indicated rainfall for the 24-hour period prior to 

8 a.m. on the day of sampling (Rd-1) had a maximum value 

(that coincided with sample collection) of 2.6 inches observed 

on July 11, 2013 (data not shown). For this event, only the 

Lock 29 sample was collected due to closure of the bridge at 

Jaite. Each consecutive summer (May through September) 

during this study had more precipitation than the previous 

summer (based on radar-indicated rainfall)—totals were 

12.8 inches in 2012, 20.5 inches in 2013, and 29.7 inches 

in 2014.

Correlations between log10-transformed E. coli concentrations and square root of Rd−2 (radar), log10-transformed 

turbidity, and log10-transformed streamflow were statistically 

significant at both sites for each year, except square root of 

Rd−2 (radar) at Lock 29 for 2014, possibly due to the small 

sample size (table 2). Correlations between these variables 

were also statistically significant by site when all data for the 

study were combined. Correlations between E. coli and water 

temperature were not statistically significant at either site for 

any year or all three years combined.

Median concentrations of E. coli (fig. 5) increased during 

each year of this study. Similar to radar rainfall data, monthly 

mean streamflow data indicated that streamflows during summers 2013 and 2014 were greater than those during summer 

2012. Increased rainfall and subsequent increased streamflows 
Afternoon Samples 9

102

103

104

10 A 5

101

10 2 103 104 10 5 101

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

Afternoon Escherichia coli, in colony-forming units 

per 100 milliliters

10 2 103 104 10 5

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

102

103

104

10 B 5

101

10

Afternoon Escherichia coli, in colony-forming units 

per 100 milliliters

1

1:1 Line

1:1 Line

2012

2013

2014

1(n = 0) 2 (n =16)

3 (n = 31) 4 (n = 8) 

1(n = 0) 2 (n =14)

3 (n = 25) 4 (n = 6)

2012

2013

Figure 4. Escherichia coli (E. coli) concentrations at Cuyahoga Valley National Park, Ohio, as 

determined by membrane filtration using a modified membrane thermotolerant E. coli (modified mTEC) 

agar method for samples collected in the morning and in the afternoon at the A, Cuyahoga River at Jaite 

(U.S. Geological Survey streamgage 04206425) and B, Cuyahoga River near Lock 29 near Peninsula 

OH (U.S. Geological Survey streamgage 411433081330000) sites, May 2012 through September 2014. 

The Ohio single-sample water-quality standard (WQS) of 298 colony-forming units per 100 milliliters is 

shown on the plot as red lines. The plot was split into quadrants where (1) results from morning samples 

were below the WQS but from afternoon samples exceeded the WQS, (2) results from both samples 

exceeded the WQS, (3) results from both samples were below the WQS, and (4) results from morning 

samples exceeded the WQS but from afternoon samples were below the WQS. n, number of samples.
10 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Table 1. Results of sample analyses and measured environmental variables at time of sampling at Jaite and Lock 29 sampling sites in 

Cuyahoga Valley National Park, Ohio, for the recreational seasons (May through September) of 2012 through 2014.

[Reported streamflow is the instantaneous streamflow at 8 a.m. or 1 p.m. associated with morning or afternoon samples, respectively. A turbidity value was 

considered an estimate when the sample had to be diluted to read within the instrument’s operational range. CFU/100 mL, colony-forming units per 100 milliliters; MPN/100 mL, most probable number per 100 milliliters; NTRU, nephelometric turbidity ratio units; FNU, formazin nephelometric units; ft3

/s, cubic 

feet per second; E, estimated value]

Variable Number of 

samples Minimum Median Maximum Mean

Lock 29

Escherichia coli, in CFU/100 mL 150 33 300 44,000 2,200

Escherichia coli, in MPN/100 mL 35 88 1,000 24,000 2,700

Turbidity, in NTRU 162 2.9 12 1,500 47

Water temperature, in degrees Celsius 162 16.0 21.5 24.5 21.2

Jaite

Escherichia coli, in CFU/100 mL 179 19 260 78,000 2,800

Escherichia coli, in MPN/100 mL 82 63 340 82,000 3,300

Turbidity, in NTRU 196 4.0 13 E1,900 63

Turbidity, in FNU 196 3.4 14 E1,600 63

Water temperature, in degrees Celsius 196 15.9 21.4 25.9 21.3

Instantaneous streamflow, in ft3

/s 186 131 366 4,490 557

Table 2. Pearson’s r correlation coefficients between log10-transformed Escherichia coli (E. coli) concentrations as determined by 

membrane filtration using a modified membrane thermotolerant E. coli ( modified mTEC) agar method and environmental and waterquality variables by recreational season (May through September) from 2012 through 2014.

[Relations significant at α = 0.05 are indicated in bold. The number of samples for each relation is indicated in parentheses. Rd-2 (radar) is the sum of the hourly 

totals for radar rain for the 24 hours prior to 8 a.m. on the day prior to the day of sampling. NTRU, nephelometric turbidity ratio units; FNU, formazin nephelometric units; ft3

/s, cubic feet per second; --, no data available]

Year Square root of Rd-2

(radar), in inches

Log10 turbidity, 

in NTRU

Log10 turbidity, 

in FNU

Log10 streamflow, 

in ft3

/s

Water temperature, 

in degrees Celsius

Jaite

2012 0.35 (66) 0.86 (66) 0.88 (66) 0.88 (60) -0.057 (66)

2013 0.37 (67) 0.92 (68) 0.93 (68) 0.87 (65) 0.063 (68)

2014 0.35 (45) 0.90 (45) 0.91 (45) 0.77 (45) -0.12 (45)

2012–2014 0.40 (178) 0.88 (179) 0.88 (179) 0.76 (170) -0.11 (179)

Lock 29

2012 0.29 (63) 0.75 (63) -- 0.83 (57) -0.082 (63)

2013 0.45 (63) 0.91 (64) -- 0.86 (61) 0.030 (64)

2014 0.38 (23) 0.88 (23) -- 0.72 (23) 0.0022 (23)

2012–2014 0.42 (149) 0.84 (150) -- 0.75 (141) -0.046 (150)
Afternoon Samples 11

Jaite

Lock 29

2012 2013 2014

101

102

103

104

105

Escherichia coli, in colony-forming units per 100 milliliters

(66) (63) (68) (64) (45) (23)

EXPLANATION

(66) Number of samples

Outlier 1

Upper whisker 2

75th percentile

25th percentile

Lower whisker

Median of detections

1 Outliers are defined as values outside 1.5 

times the interquartile range beyond the ends 

of the box.

2 The whiskers are the largest and smallest 

values within 1.5 times the interquartile range 

beyond the 75th and 25th percentiles, 

respectively. 

Figure 5. Escherichia coli (E. coli) concentrations in 

samples collected from the Cuyahoga River at two sites 

within the Cuyahoga Valley National Park, Ohio, from 2012 

to 2014. The Ohio single-sample water-quality standard 

(WQS) of 298 colony-forming units per 100 milliliters is 

shown on the plot as a red line. During the 2013 season, 

the median concentrations for both sites are just below 

the WQS.
12 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

may explain some of the observed increase in median E. coli 

concentrations each year (fig. 5). Other changes in the watershed, including removal of upstream dams or alterations to 

wastewater management practices, could also affect concentrations of E. coli in the river. A Kruskal-Wallis test followed 

by a post-hoc Dunn’s test showed that median E. coli concentrations in 2013 and 2014 were statistically larger than the 

median E. coli concentration in 2012 for both sites.

Development and Testing of Nowcasts 

Using Near-Real-Time Data at Jaite

Prior to each recreational season, predictive models were 

developed for use in a nowcast at Jaite (table 3). The variables 

chosen for each year’s model always included turbidity and a 

rainfall variable. A radar rainfall variable was used as an alternative variable to the NWS airport weather station-acquired 

rainfall data starting in 2013 because the radar rainfall data 

were in electronic format readily available for use by the computer program used to make daily predictions. Radar data were 

not available prior to 2008; therefore, the models for 2013 and 

2014 were calibrated using data starting in 2008. For the 2014 

model, the day of the year was added even though it was not 

statistically significant because the resulting percentage of correct responses for this model was greater than that for a model 

that did not include this variable (data not shown). Alternate 

models were created for use during 2013 and 2014 in case the 

radar rainfall data were not available; however, only the 2014 

alternate model was used (table 3).

During 2012, the nowcast was run manually by NPS 

personnel because the relation between the two instruments 

used to measure turbidity was not yet available. Starting in 

2013, the nowcast was run automatically using the computer 

program as described in the “Running the Nowcast” section. 

Results were posted to the Ohio Nowcast Web site by 9 a.m. 

each morning. The predicted E. coli concentrations were 

double-checked manually. Morning and afternoon samples 

were collected to compare E. coli results (membrane filtration method only) with the morning predictions from the 

predictive models (n = 120 for morning samples and n = 57 

for afternoons samples). All predictions and corresponding 

measured E. coli concentrations (when applicable) are listed in 

appendix 1.

Table 3. Explanatory variables and regression statistics for models used to predict Escherichia coli concentrations at the 

Cuyahoga River at Jaite sampling location in Cuyahoga Valley National Park, Ohio.

[The type of instrument used to measure turbidity and the source of rainfall data are indicated in parentheses. The 2014 alternate model was used only when 

radar rainfall information was not available.R2, fraction of the variation in Escherichia coli concentrations that is explained by the model; RMSE, root mean 

square error; RW48, rainfall weighted for 48-hour period prior to sampling; Rd-2, sum of the hourly rainfall values for the 24-hour period prior to 8 a.m. on the 

day prior to sampling; <, less than]

Test year

Calibration data 

seasons

(number of 

samples)

Adjusted 

R2 RMSE Model parameter Parameterestimate p-value

2012 2004–2011

(373)

0.77 0.2999 y-intercept 1.480 <0.0001

log10 turbidity (laboratory) 0.809 <0.0001

Square root of RW48 (airport) 0.507 <0.0001

2013 2008–2012

(229)

0.74 0.3294 y-intercept 1.146 <0.0001

log10 turbidity (in-stream) 1.128 <0.0001

Square root of Rd-2 (radar) 0.632 <0.0001

2014 2008–2013

(292)

0.77 0.3107 y-intercept 1.341 <0.0001

log10 turbidity (in-stream) 1.122 <0.0001

Square root of Rd-2 (radar) 0.492 <0.0001

Day of the year -0.00091 0.1217

2014

(alternate)

2008–2013

(292)

0.75 0.3197 y-intercept 1.407 <0.0001

log10 turbidity (in-stream) 1.080 <0.0001

RW48 (airport) 0.1770 <0.0001

Day of the year -0.00092 0.1299
Development and Testing of Nowcasts Using Near-Real-Time Data at Jaite 13

Model predictions during testing were compared with 

available measured E. coli concentrations (fig. 6). Almost 

47 percent of the samples collected at Jaite during this 

study exceeded the WQS. The nowcasts correctly predicted 

79.1 percent of samples above or below the WQS. There were 

more false positives during 2013 than for the other years, 

but the percentages of false positives were similar for 2013 

and 2014. Conversely, there was a decrease in the percentage of false negatives observed each season with the greatest 

percentage observed during the 2012 season (11 percent) and 

the lowest observed during the 2014 season (0 percent). The 

observed changes in the percentages of false positives and 

false negatives may not be indicative of any improvements in 

the models, but may be due to the tendency of the models to 

overpredict E. coli concentrations (fig. 6) in conjunction with 

the increased number of exceedances each year (that is, there 

is a greater probability of predicting an exceedance if there are 

more exceedances).

Response statistics varied slightly from year to year 

(table 4), but the predictive models were more effective at 

correctly predicting E. coli concentrations above or below 

the WQS than the persistence model. During model development, the predictive models always performed better than 

the persistence model in terms of percent correct responses, 

sensitivity, and specificity. During model testing, for all years, 

the percentages of correct responses for the predictive models 

were greater than that for the persistence model. Specificity 

for the predictive models during testing decreased each year 

and was less than the persistence model during 2013 and 2014. 

However, sensitivity for the predictive models during testing 

was always higher than that for the persistence model and 

showed an increase each year. Overall (looking at all 3 years 

of this study), the predictive models performed better than 

the persistence model, correctly predicting 89 percent of the 

exceedances and 70 percent of the nonexceedances of the 

WQS.

101 102 103 104 105 106

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

1

102

103

104

105

106

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

10

Correct

nonexceedances

(n = 66)

False positives

(n = 28)

False negatives

(n = 9)

Correct exceedances

(n = 74)

1 1 L ne i :

2012

2013

2014

Figure 6. Predicted Escherichia coli (E. coli) concentrations from the predictive models compared with measured E. coli

concentrations for the Cuyahoga River at Jaite site (U.S. Geological Survey streamgage 04206425) during the recreational 

seasons (May through September) of 2012–14 in Cuyahoga Valley National Park, Ohio. The Ohio single-sample water-quality 

standard of 298 colony-forming units per 100 milliliters is shown on the plot as red lines. n, number of samples.
14 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14 Table 4. Correct and false positive and negative responses for recreational water-quality conditions using the nowcasts and the persistence model for determining water quality during model development and testing at the Jaite sampling site in Cuyahoga Valley National Park, Ohio. [The greatest percentages (correct, sensitivity, and specificity) by year are shown in bold. All samples (morning and afternoon) are reported. WQS, water-quality standard; the persistence model is the previous day’s Escherichia coli concentrations; sensitivity is the percentage of exceedances of the WQS that are correctly predicted by the model; specificity is the percentage of nonexceedances of the WQS that cor- rectly predicted by the model] Dataset Model Number of samples Exceedances of WQS, in percent Number Percentage Correct negative Correct positive False positive False negative Correct responces Sensitivity Specificity 2012 Development (2004–2011) Nowcast 373 58.2 109 190 47 27 80.2 87.6 69.9 Persistence 278 58.6 78 125 37 40 73.0 75.8 67.8 Testing (2012) Nowcast 65 35.4 34 16 8 7 76.9 69.6 81.0 Persistence 39 28.2 16 6 12 5 56.4 54.5 57.1 2013 Development (2008–2012) Nowcast 230 48.7 97 91 21 21 81.7 81.3 82.2 Persistence 151 46.4 52 49 29 21 66.9 70.0 64.2 Testing (2013) Nowcast 67 49.2 22 31 12 2 79.1 93.9 64.7 Persistence 40 50.0 14 17 6 3 77.5 85.0 70.0 2014 Development (2008–2013) Nowcast 292 48.3 131 119 20 22 85.6 84.4 86.8 Persistence 191 47.1 66 66 35 24 69.1 73.3 65.3 Testing (2014) Nowcast 45 60.0 10 27 8 0 82.2 100.0 55.6 Persistence 27 59.2 7 12 4 4 70.4 75.0 63.6 2012–2014 Testing (2012–2014) Nowcast 177 46.9 66 74 28 9 79.1 89.2 70.2 Persistence 106 44.3 37 35 22 12 67.9 74.4 62.7
Using Jaite Nowcasts To Predict Water Quality at Lock 29 15

Using Jaite Nowcasts To Predict Water 

Quality at Lock 29

Comparison of E. coli concentrations (membrane filtration method only) in samples collected at Lock 29 with those 

collected at Jaite showed some variations in concentrations, but 

there were no significant differences in median concentrations 

between the sites or when examining if concentrations exceeded 

the WQS at one site but not the other. Brady and others (2009) 

showed that the predictive models for the CVNP have a limited 

ability to accurately predict concentrations at upstream locations. However, the nearest upstream site in Brady and others 

(2009) is more than 14 river miles upstream from Jaite whereas 

Lock 29 is 5.3 river miles upstream. Given that the sample concentrations at Lock 29 and Jaite did not differ markedly, that the 

sites are physically close, and that the Jaite nowcasts performed 

well at determining water-quality conditions at Jaite, the model 

predictions were used to determine whether the Jaite nowcasts 

could predict water-quality conditions at Lock 29.

A total of 148 samples were collected at Lock 29 for 

times when a model prediction was also available at Jaite 

(fig. 7). Of these samples, 50.7 percent exceeded the WQS. 

The 2013 predictive model tended to overpredict E. coli

concentrations at Lock 29 compared with the other years, and 

therefore, the percentage of false positives was higher during 

2013 than for the other years. Similar to what was observed 

for Jaite, there was a decrease in the percentage of false 

negatives observed each season at Lock 29. For all years, the 

Jaite predictive models correctly predicted 77 percent of the 

Lock 29 samples above or below the WQS with a sensitivity 

of 82.7 percent and a specificity of 71.2 percent (table 5). In 

comparison, the persistence model predicted only 57.8 percent 

of the samples correctly. During each year, the Jaite nowcasts 

predicted water-quality conditions at Lock 29 better than the 

persistence model based on samples collected at Lock 29. 

Predictive model response statistics were similar between 

the two sites, implying that the Jaite nowcasts could be used 

to predict E. coli concentrations for the stretch of the river 

between Lock 29 and Jaite.

101 102 103 104 105 106

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

10

102

103

104

105

106

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

1

False positives

(n = 21) 

Correct exceedances 

(n = 62)

False negatives

(n = 13) 

Correct 

nonexceedances 

(n = 52)

1:1 Line

2012

2013

2014

Figure 7. Predicted Escherichia coli (E. coli) concentrations from the predictive models for the Cuyahoga River at Jaite site 

(U.S. Geological Survey streamgage 04206425) compared with the measured E. coli concentrations at the Cuyahoga River 

near Lock 29 near Peninsula OH (U.S. Geological Survey streamgage 411433081330000) site in Cuyahoga Valley National Park, 

Ohio, during the recreational seasons (May through September) of 2012–14. The Ohio single-sample water-quality standard of 

298 colony-forming units per 100 milliliters is shown on the plot as red lines. n, number of samples.
16 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Table 5. Correct and false positive and negative responses for recreational water-quality conditions using 

the nowcasts at Jaite and the persistence model for determining water quality at Lock 29, Cuyahoga Valley 

National Park, Ohio.

[The greatest percentages (correct, sensitivity, and specificity) by year are in bold. All samples (morning and afternoon) are 

reported. The persistence model is the previous day’s Escherichia coli concentrations; sensitivity is the percentage of exceedances 

of the water-quality standard (WQS) that are correctly predicted by the model. Specificity is the percentage of nonexceedances of 

the WQS that correctly predicted by the model]

Model Number of 

samples

Exceedances of 

WQS, in percent

Percentage

Correct Sensitivity Specificity

2012

Nowcast 62 43.5 79.0 66.7 88.6

Persistence model 39 28.2 51.3 54.5 50.0

2013

Nowcast 63 49.2 73.0 90.3 56.2

Persistence model 37 45.9 62.2 70.6 55.0

2014

Nowcast 23 73.9 82.6 94.1 50.0

Persistence model 14 78.6 64.3 72.7 33.3

2012–2014

Nowcast 148 50.7 77.0 82.7 71.2

Persistence model 90 43.3 57.8 66.7 51.0

Reaching the Public

To ensure information regarding water quality and the 

nowcasts were available to the public, the NPS and the USGS 

worked collaboratively to produce products designed to reach 

the public. These products included a news release, business 

cards with the Ohio Nowcast Web site address and quick 

response (QR) code (a matrix barcode that can be scanned 

using a smart phone that resolves to the Ohio Nowcast Web 

site [http://www.ohionowcast.info/]), and seasonal signage 

at CVNP trailheads that provides a basic overview of water 

quality issues in the park and a link to the Ohio Nowcast Web 

site. Links to the Ohio Nowcast Web site are also posted on 

the CVNP Web and Facebook sites. The NPS also created and 

presented programs on water quality to park visitors. The news 

release was prepared prior to the recreational season of 2013 

to publicize that nowcast predictions would be available daily 

on the Ohio Nowcast Web site using the automated process 

described in the “Running the Nowcast” section. The story 

was picked up by local news outlets in the northeastern Ohio 

region and, surprisingly, in Seattle, Washington, and 

San Francisco, California. The business cards, which were 

carried by NPS interns while sampling so that these could be 

handed out as needed to CVNP visitors, were also available 

at the CVNP visitor centers as a quick reference to the Ohio 

Nowcast Web site.

The number of visitors to the Ohio Nowcast CVNPspecific Web page by month is shown in figure 8. Not surprisingly, there was a large increase in the number of visitors to 

the Web page in June 2013 after the news release. For the 

other months of the recreational season, each consecutive 

year shows an increase in the number of visits. Because the 

Ohio Nowcast Web site’s main page also contains nowcast 

results for locations other than CVNP, the number of site visits 

to the main page was not tallied. The data shown in figure 8 

demonstrate the number of visitors interested in more detailed 

information on the water quality of the river.
Reaching the Public 17

na na

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

Number of visits to Web site

2012

2013

2014

Figure 8. Number of visits to the Cuyahoga River Web page of the Ohio Nowcast Web site (http://www.ohionowcast.info/), by 

month, from 2012 through 2014. na, data not available.
18 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Summary

The U.S. Geological Survey (USGS) and the National 

Park Service have worked in collaboration since 2004 to 

develop a method to provide more timely results to the public 

regarding the recreational water quality of the Cuyahoga River 

within the Cuyahoga Valley National Park (CVNP). Previous 

research has shown that a turbidity-based predictive model 

could accurately predict recreational water quality above or 

below Ohio’s single-sample standard for primary-contact 

recreation (water-quality standard, WQS) at one site on the 

river at Jaite, Ohio, for more than 80 percent of the samples 

collected. Prior to development of the predictive model, the 

estimates of recreational water quality were based on bacteria 

concentrations in previously collected samples, which has 

proven to be less accurate than the predictive model and also 

requires a sample to be collected and analyzed each day.

Nowcasts are systems that can provide estimates of the 

current bacterial water-quality conditions based on predictive models using easily-measured, explanatory variables; 

nowcasts can provide the public with the information to make 

informed decisions on the risk associated with recreational 

activities in natural water bodies. To streamline current 

implementation procedures for nowcast models at CVNP, 

three time-saving mechanisms were initiated during this study: 

(1) a USGS stream monitor with an in-stream turbidity sensor 

and near-real-time data transmission was installed at Jaite in 

May 2012; (2) a less labor-intensive method (Colilert) for 

determining Escherichia coli (E. coli) concentrations was 

instituted for use during nowcast testing; and (3) a computer 

program was developed to automatically run and post nowcast 

predictions on the Ohio Nowcast Web site (http://www.ohionowcast.info/) each day at 9 a.m.

During the recreational season of 2012, a relation 

between laboratory- and in-stream-measured turbidity was 

developed so nowcasts could be developed using the in-stream 

turbidity measurements. Starting during the recreational season of 2013, the Colilert method was used side-by-side with 

the membrane filtration method (used during previous studies) 

to determine E. coli concentrations in water samples. Although 

the membrane filtration appeared to report larger concentrations more frequently than the Colilert method, no significant differences in exceedances of the WQS were observed 

between the two methods during this study. The automated 

computer program ran during the recreational seasons of 2013 

and 2014. The program retrieved required data and made and 

posted predictions to the Ohio Nowcast Web site by 9 a.m. 

each morning.

Afternoon samples were collected on several days of 

the recreational season of each year to determine if the daily 

predicted E. coli concentrations based on morning samples 

could accurately represent concentrations later in the day. The 

morning sample concentrations were a good representation of 

what the afternoon sample concentrations were, but due to the 

limited sampling schedule, more research would be required to 

better understand the diurnal changes in water quality.

Nowcasts were developed and tested each year of the 

study (2012 through 2014). Discrete water samples were 

collected at Jaite and at one upstream site, at Lock 29, to test 

nowcasts. Concentrations of E. coli were determined, and 

measurements of turbidity using a laboratory turbidimeter 

were made for all samples collected. Median concentrations of 

E. coli were statistically higher during the recreational seasons 

of 2013 and 2014 compared with those in the recreational season of 2012 for both sites. Relations between E. coli concentrations and water-quality and environmental variables were 

ascertained. Significant relations were found between E. coli

and turbidity (laboratory- and in-stream measured), streamflow as reported at Jaite, and rainfall during the 24-hour period 

prior to 8 a.m. on the day prior to sampling. Nowcasts developed at Jaite varied somewhat each year, but always included 

turbidity and rainfall as explanatory variables. In comparison 

to the persistence model, the nowcasts consistently performed 

better based on two response statistics—percent correct 

responses and sensitivity. Specificity for the nowcast compared with the persistence model was lower during the 2013 

and 2014 recreational seasons. The nowcasts also performed 

better than the persistence model when the Jaite nowcast 

predictions were compared with to the Lock 29 sample results. 

Understanding of the water quality in this stretch of the river 

is important because it is becoming a popular destination 

for kayaking. With the implementation of the scheduled and 

automated run of the computer program to post predicted recreational water quality conditions in the river 7 days per week, 

a news release was issued in June 2013 to provide the public 

information on how to understand and access the water-quality 

predictions and, therefore, allow the public to better assess 

potential recreational water-quality risks

Continued implementation of the CVNP nowcast is 

dependent on the availability of operating funds but is anticipated for summer 2015. Future studies at CVNP could include 

a more in-depth look at using the single nowcast to predict 

recreational water quality in the stretch of the river from 

Lock 29 to Jaite and (or) research to determine if a single nowcast could be used consistently for several years, eliminating 

the need for annual updates to the models.
References Cited 19

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Ohio—USGS 04206425 Cuyahoga River at Jaite OH: 

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Smith, B.A., 2006, Guidelines and standard procedures 

for continuous water-quality monitors—Station operation, 

record computation, and data reporting: U.S. Geological 

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methods: Biometrics Bulletin, v. 1, no. 6, p. 80–83.
Appendix 1.

Nowcast Predictions for the Cuyahoga River at Jaite, 

Cuyahoga Valley National Park, 2012–14
22 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Table 1–1. Nowcast predictions for the Cuyahoga River at Jaite sampling site in the Cuyahoga Valley National Park, 2012–14.—Continued

[Indicated in the notes are dates when the predicted water quality was not reported to the Ohio Nowcast Web site (http://www.ohionowcast.info/) due to issues with 

the Web site or scheduled run of the computer program used to update the Web site. Morning samples were generally collected prior to 9 a.m.; afternoon samples 

were collected after 12 p.m. NTRU, nephelometric turbidity ratio unit; FNU, formazin nephelometric unit; E. coli, Escherichia coli; --, no data available; CFU/100 

mL, colony-forming unit per 100 milliliters; MNP/100 mL, most-probable number per 100 milliliters]

Date

Observed 

turbidity, 

in NTRU (2012) 

or FNU 

(2013, 2014)

Morning 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Afternoon 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Predicted 

E. coli, in 

CFU/100 

mL

Predicted 

water 

quality1

Nowcast 

used2 Notes

05/23/12 6.9 77 -- 140 Good 2012

05/25/12 6.2 110 60 130 Good 2012

05/30/12 59 27,000 -- 4,600 Poor 2012

06/01/12 13 2,100 1,900 420 Poor 2012

06/04/12 15 580 450 270 Good 2012

06/06/12 13 340 -- 460 Poor 2012

06/11/12 8 180 130 160 Good 2012

06/13/12 8.1 140 63 210 Good 2012

06/15/12 7.6 170 73 160 Good 2012

06/17/12 8.2 120 67 330 Poor 2012

06/20/12 16 670 550 470 Poor 2012

06/22/12 12 380 200 230 Good 2012

06/25/12 12 200 93 220 Good 2012

06/27/12 10 85 73 200 Good 2012

06/29/12 8.3 70 110 170 Good 2012

07/02/12 7.2 80 28 190 Good 2012

07/04/12 19 1,500 -- 630 Poor 2012

07/06/12 9 110 210 320 Poor 2012

07/09/12 6 77 40 130 Good 2012

07/11/12 7 82 19 150 Good 2012

07/13/12 12 480 500 530 Poor 2012

07/16/12 11 260 180 470 Poor 2012

07/20/12 53 12,000 4,600 3,800 Poor 2012

07/23/12 12 190 -- 230 Good 2012

07/25/12 12 130 53 230 Good 2012

07/27/12 450 78,000 -- 18,000 Poor 2012

07/30/12 23 780 -- 380 Poor 2012

08/01/12 13 620 210 280 Good 2012

08/03/12 10 170 200 200 Good 2012

08/06/12 16 4,800 1,400 280 Good 2012

08/08/12 9.2 150 180 180 Good 2012

08/10/12 10 420 -- 690 Poor 2012

08/13/12 18 490 300 380 Poor 2012

08/15/12 11 310 120 210 Good 2012

08/17/12 13 160 130 240 Good 2012

08/20/12 11 100 180 540 Poor 2012

08/24/12 9.7 140 -- 190 Good 2012

05/18/13 4.8 -- -- 82 Good 2013

05/19/13 4.3 -- -- 84 Good 2013
Appendix 1 23

Table 1–1. Nowcast predictions for the Cuyahoga River at Jaite sampling site in the Cuyahoga Valley National Park, 2012–14.—Continued

[Indicated in the notes are dates when the predicted water quality was not reported to the Ohio Nowcast Web site (http://www.ohionowcast.info/) due to issues with 

the Web site or scheduled run of the computer program used to update the Web site. Morning samples were generally collected prior to 9 a.m.; afternoon samples 

were collected after 12 p.m. NTRU, nephelometric turbidity ratio unit; FNU, formazin nephelometric unit; E. coli, Escherichia coli; --, no data available; CFU/100 

mL, colony-forming unit per 100 milliliters; MNP/100 mL, most-probable number per 100 milliliters]

Date

Observed 

turbidity, 

in NTRU (2012) 

or FNU 

(2013, 2014)

Morning 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Afternoon 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Predicted 

E. coli, in 

CFU/100 

mL

Predicted 

water 

quality1

Nowcast 

used2 Notes

05/20/13 4.6 -- -- 78 Good 2013

05/21/13 4 -- -- 67 Good 2013

05/22/13 3.6 83 -- 59 Good 2013

05/23/13 4.9 250 -- 84 Good 2013

05/24/13 7.1 330 -- 150 Good 2013

05/25/13 6.1 -- -- 190 Good 2013

05/26/13 5.5 -- -- 96 Good 2013

05/27/13 6.2 -- -- 110 Good 2013

05/28/13 17 -- -- 340 Poor 2013

05/29/13 14 93 -- 690 Poor 2013

05/30/13 7.2 330 -- 200 Good 2013

05/31/13 8 180 -- 150 Good 2013

06/01/13 94 -- -- 2,400 Poor 2013

06/02/13 300 -- -- 20,000 Poor 2013

06/03/13 42 -- -- 3,400 Poor 2013

06/04/13 27 -- -- 710 Poor 2013

06/05/13 26 380 -- 550 Poor 2013

06/06/13 24 400 -- 500 Poor 2013

06/07/13 93 7,700 -- 9,600 Poor 2013

06/08/13 49 -- -- 4,600 Poor 2013

06/09/13 30 -- -- 650 Poor 2013

06/10/13 25 -- -- 530 Poor 2013

06/11/13 670 -- -- 32,000 Poor 2013

06/12/13 32 420 -- 1,400 Poor 2013

06/13/13 140 4,500 -- 3,700 Poor 2013

06/14/13 38 2,000 -- 2,000 Poor 2013

06/15/13 25 -- -- 530 Poor 2013

06/16/13 25 -- -- 430 Poor 2013

06/17/13 50 -- -- 2,100 Poor 2013

06/18/13 26 -- -- 1,100 Poor 2013

06/19/13 19 250 -- 860 Poor 2013

06/20/13 16 -- -- 320 Poor 2013

06/21/13 12 250 -- 230 Good 2013

06/22/13 9.5 -- -- 180 Good 2013

06/23/13 8 -- -- 150 Good 2013

06/24/13 7.1 -- -- 130 Good 2013

06/25/13 6.4 -- -- 110 Good 2013

06/26/13 850 41,000 -- 28,000 Poor 2013

06/27/13 75 3,000 -- 17,000 Poor 2013
24 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Table 1–1. Nowcast predictions for the Cuyahoga River at Jaite sampling site in the Cuyahoga Valley National Park, 2012–14.—Continued

[Indicated in the notes are dates when the predicted water quality was not reported to the Ohio Nowcast Web site (http://www.ohionowcast.info/) due to issues with 

the Web site or scheduled run of the computer program used to update the Web site. Morning samples were generally collected prior to 9 a.m.; afternoon samples 

were collected after 12 p.m. NTRU, nephelometric turbidity ratio unit; FNU, formazin nephelometric unit; E. coli, Escherichia coli; --, no data available; CFU/100 

mL, colony-forming unit per 100 milliliters; MNP/100 mL, most-probable number per 100 milliliters]

Date

Observed 

turbidity, 

in NTRU (2012) 

or FNU 

(2013, 2014)

Morning 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Afternoon 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Predicted 

E. coli, in 

CFU/100 

mL

Predicted 

water 

quality1

Nowcast 

used2 Notes

06/28/13 50 1,000 -- 2,200 Poor 2013

06/29/13 43 -- -- 1,200 Poor 2013

06/30/13 86 -- -- 6,700 Poor 2013

07/01/13 43 1,300 -- 1,200 Poor 2013

07/02/13 92 7,900 -- 2,300 Poor 2013

07/03/13 26 650 -- 810 Poor 2013

07/08/13 27 -- -- 850 Poor 2013

07/09/13 490 -- -- 15,000 Poor 2013

07/10/13 1790 12,000 -- 160,000 Poor 2013

07/11/13 370 -- -- 60,000 Poor 2013

07/12/13 180 -- -- 52,000 Poor 2013

07/13/13 120 -- -- 3,100 Poor 2013

07/14/13 79 -- -- 1,900 Poor 2013

07/15/13 66 -- -- 1,600 Poor 2013

07/16/13 57 -- -- 1,300 Poor 2013

07/18/13 42 -- -- 1,800 Poor 2013

07/19/13 36 -- -- 800 Poor 2013

07/20/13 66 -- -- 1,600 Poor 2013

07/21/13 54 -- -- 2,300 Poor 2013

07/22/13 39 -- -- 2,000 Poor 2013

07/23/13 40 -- -- 1,600 Poor 2013

07/24/13 140 -- -- 5,600 Poor 2013

07/25/13 36 1,300 700 1,000 Poor 2013

07/26/13 29 -- 700 620 Poor 2013

07/27/13 27 -- -- 580 Poor 2013

07/28/13 32 -- -- 700 Poor 2013

07/29/13 25 -- -- 1,000 Poor 2013

07/30/13 19 -- -- 390 Poor 2013

07/31/13 14 180 170 270 Good 2013

08/01/13 13 180 190 250 Good 2013

08/02/13 8.9 120 150 160 Good 2013

08/03/13 8.5 -- -- 160 Good 2013

08/04/13 110 -- -- 5,800 Poor 2013

08/05/13 11 -- -- 240 Good 2013

08/06/13 7.1 -- -- 130 Good 2013

08/07/13 6.2 -- -- 110 Good 2013

08/08/13 170 16,000 4,000 4,600 Poor 2013

08/09/13 120 -- -- 7,100 Poor 2013

08/10/13 56 -- -- 2,600 Poor 2013
Appendix 1 25

Table 1–1. Nowcast predictions for the Cuyahoga River at Jaite sampling site in the Cuyahoga Valley National Park, 2012–14.—Continued

[Indicated in the notes are dates when the predicted water quality was not reported to the Ohio Nowcast Web site (http://www.ohionowcast.info/) due to issues with 

the Web site or scheduled run of the computer program used to update the Web site. Morning samples were generally collected prior to 9 a.m.; afternoon samples 

were collected after 12 p.m. NTRU, nephelometric turbidity ratio unit; FNU, formazin nephelometric unit; E. coli, Escherichia coli; --, no data available; CFU/100 

mL, colony-forming unit per 100 milliliters; MNP/100 mL, most-probable number per 100 milliliters]

Date

Observed 

turbidity, 

in NTRU (2012) 

or FNU 

(2013, 2014)

Morning 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Afternoon 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Predicted 

E. coli, in 

CFU/100 

mL

Predicted 

water 

quality1

Nowcast 

used2 Notes

08/11/13 28 -- -- 600 Poor 2013

08/12/13 19 -- -- 520 Poor 2013

08/13/13 17 -- -- 340 Poor 2013

08/14/13 18 280 210 450 Poor 2013

08/15/13 16 260 160 320 Poor 2013

08/16/13 14 260 150 270 Good 2013

08/17/13 13 -- -- 250 Good 2013 Not reported to Web site

08/18/13 11 -- -- 210 Good 2013 Not reported to Web site

08/19/13 10 -- -- 190 Good 2013 Not reported to Web site

08/20/13 9.8 -- -- 180 Good 2013 Not reported to Web site

08/21/13 9.2 210 87 170 Good 2013

08/22/13 8.2 170 110 150 Good 2013

08/23/13 9.3 170 150 170 Good 2013

08/24/13 7.9 -- -- 210 Good 2013 Not reported to Web site

08/25/13 7.7 -- -- 140 Good 2013 Not reported to Web site

08/26/13 8.2 -- -- 150 Good 2013

08/27/13 7.9 -- -- 140 Good 2013

08/28/13 68 -- -- 2,400 Poor 2013

08/29/13 35 -- -- 3,200 Poor 2013

09/04/13 22 650 440 1,700 Poor 2013

09/05/13 19 350 280 390 Poor 2013

09/06/13 18 320 250 360 Poor 2013

09/07/13 18 330 -- 360 Poor 2013

09/08/13 16 -- -- 320 Poor 2013

09/09/13 15 -- -- 300 Poor 2013

09/10/13 14 -- -- 270 Good 2013

09/11/13 14 300 220 350 Poor 2013

09/12/13 38 710 1,000 850 Poor 2013

09/13/13 170 6,000 2,400 5,300 Poor 2013

09/14/13 36 2,200 -- 2,600 Poor 2013

09/15/13 19 -- -- 480 Poor 2013

09/16/13 16 -- -- 320 Poor 2013

09/17/13 14 -- -- 400 Poor 2013

09/18/13 14 290 220 270 Good 2013

09/19/13 16 310 240 320 Poor 2013

09/20/13 14 270 200 270 Good 2013

09/21/13 220 -- -- 6,100 Poor 2013

09/22/13 83 -- -- 9,600 Poor 2013

09/23/13 42 -- -- 4,000 Poor 2013
26 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Table 1–1. Nowcast predictions for the Cuyahoga River at Jaite sampling site in the Cuyahoga Valley National Park, 2012–14.—Continued

[Indicated in the notes are dates when the predicted water quality was not reported to the Ohio Nowcast Web site (http://www.ohionowcast.info/) due to issues with 

the Web site or scheduled run of the computer program used to update the Web site. Morning samples were generally collected prior to 9 a.m.; afternoon samples 

were collected after 12 p.m. NTRU, nephelometric turbidity ratio unit; FNU, formazin nephelometric unit; E. coli, Escherichia coli; --, no data available; CFU/100 

mL, colony-forming unit per 100 milliliters; MNP/100 mL, most-probable number per 100 milliliters]

Date

Observed 

turbidity, 

in NTRU (2012) 

or FNU 

(2013, 2014)

Morning 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Afternoon 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Predicted 

E. coli, in 

CFU/100 

mL

Predicted 

water 

quality1

Nowcast 

used2 Notes

09/24/13 26 -- -- 550 Poor 2013

09/25/13 19 470 290 390 Poor 2013

09/26/13 15 240 -- 300 Poor 2013

09/27/13 14 -- -- 270 Good 2013

09/28/13 14 -- -- 270 Good 2013

09/29/13 12 -- -- 230 Good 2013

09/30/13 11 -- -- 210 Good 2013

10/01/13 9.7 -- -- 210 Good 2013

10/02/13 8.5 -- -- 160 Good 2013

10/03/13 8.4 -- -- 150 Good 2013

10/04/13 43 -- -- 1,200 Poor 2013

10/05/13 13 -- -- 740 Poor 2013

10/08/13 40 -- -- 900 Poor 2013

10/09/13 22 -- -- 460 Poor 2013 Not reported to Web site

10/10/13 17 -- -- 340 Poor 2013 Not reported to Web site

10/11/13 15 -- -- 300 Poor 2013 Not reported to Web site

10/13/13 11 -- -- 210 Good 2013 Not reported to Web site

10/14/13 10 -- -- 190 Good 2013 Not reported to Web site

10/15/13 8.8 -- -- 160 Good 2013 Not reported to Web site

10/16/13 10 -- -- 190 Good 2013 Not reported to Web site

10/17/13 95 -- -- 2,400 Poor 2013 Not reported to Web site

10/18/13 38 -- -- 850 Poor 2013 Not reported to Web site

10/19/13 20 -- -- 410 Poor 2013 Not reported to Web site

10/20/13 26 -- -- 550 Poor 2013 Not reported to Web site

10/21/13 15 -- -- 300 Poor 2013 Not reported to Web site

10/22/13 16 -- -- 320 Poor 2013

10/23/13 13 -- -- 250 Good 2013

10/24/13 12 -- -- 230 Good 2013

10/25/13 11 -- -- 210 Good 2013

10/26/13 9.5 -- -- 200 Good 2013

10/27/13 10 -- -- 190 Good 2013

10/28/13 12 -- -- 300 Poor 2013

10/29/13 13 -- -- 250 Good 2013

10/30/13 13 -- -- 250 Good 2013

10/31/13 12 -- -- 280 Good 2013

11/01/13 110 -- -- 3,600 Poor 2013

05/28/14 120 13,000 -- 3,400 Poor 2014

05/29/14 39 -- -- 2,000 Poor 2014

05/30/14 25 -- -- 910 Poor 2014
Appendix 1 27

Table 1–1. Nowcast predictions for the Cuyahoga River at Jaite sampling site in the Cuyahoga Valley National Park, 2012–14.—Continued

[Indicated in the notes are dates when the predicted water quality was not reported to the Ohio Nowcast Web site (http://www.ohionowcast.info/) due to issues with 

the Web site or scheduled run of the computer program used to update the Web site. Morning samples were generally collected prior to 9 a.m.; afternoon samples 

were collected after 12 p.m. NTRU, nephelometric turbidity ratio unit; FNU, formazin nephelometric unit; E. coli, Escherichia coli; --, no data available; CFU/100 

mL, colony-forming unit per 100 milliliters; MNP/100 mL, most-probable number per 100 milliliters]

Date

Observed 

turbidity, 

in NTRU (2012) 

or FNU 

(2013, 2014)

Morning 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Afternoon 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Predicted 

E. coli, in 

CFU/100 

mL

Predicted 

water 

quality1

Nowcast 

used2 Notes

05/31/14 16 -- -- 360 Poor 2014

06/01/14 13 -- -- 280 Good 2014

06/02/14 10 120 -- 210 Good 2014

06/03/14 11 180 -- 230 Good 2014

06/04/14 8.1 -- 230 280 Good 2014

06/05/14 230 -- -- 7,100 Poor 2014

06/06/14 19 -- -- 1,600 Poor 2014

06/07/14 12 -- -- 260 Good 2014

06/08/14 10 -- -- 210 Good 2014

06/09/14 140 6,200 -- 4,000 Poor 2014

06/10/14 65 1,600 -- 5,500 Poor 2014

06/11/14 37 530 -- 900 Poor 2014

06/12/14 26 -- -- 830 Poor 2014

06/13/14 21 -- -- 630 Poor 2014

06/14/14 19 -- -- 420 Poor 2014

06/15/14 15 -- -- 410 Poor 2014

06/16/14 13 150 -- 280 Good 2014

06/17/14 12 210 -- 300 Poor 2014

06/18/14 220 3,300 -- 8,400 Poor 2014

06/19/14 120 -- -- 14,000 Poor 2014

06/20/14 78 -- -- 7,300 Poor 2014

06/21/14 220 -- -- 9,300 Poor 2014

06/22/14 56 -- -- 2,500 Poor 2014

06/23/14 40 430 370 960 Poor 2014

06/24/14 170 3,500 2,800 4,900 Poor 2014

06/25/14 150 17,000 20,000 10,000 Poor 2014

06/26/14 63 -- -- 4,700 Poor 2014

06/27/14 39 -- -- 1,600 Poor 2014

06/28/14 31 -- -- 710 Poor 2014

06/29/14 29 -- -- 660 Poor 2014

06/30/14 39 5,000 1,300 920 Poor 2014

07/01/14 25 240 120 770 Poor 2014

07/02/14 310 2,200 -- 9,300 Poor 2014

07/03/14 330 -- -- 16,000 Poor 2014

07/04/14 30 -- -- 1,300 Poor 2014

07/05/14 22 -- -- 630 Poor 2014

07/06/14 16 -- -- 330 Poor 2014

07/07/14 14 140 110 290 Good 2014

07/08/14 12 -- -- 280 Good 2014
28 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Table 1–1. Nowcast predictions for the Cuyahoga River at Jaite sampling site in the Cuyahoga Valley National Park, 2012–14.—Continued

[Indicated in the notes are dates when the predicted water quality was not reported to the Ohio Nowcast Web site (http://www.ohionowcast.info/) due to issues with 

the Web site or scheduled run of the computer program used to update the Web site. Morning samples were generally collected prior to 9 a.m.; afternoon samples 

were collected after 12 p.m. NTRU, nephelometric turbidity ratio unit; FNU, formazin nephelometric unit; E. coli, Escherichia coli; --, no data available; CFU/100 

mL, colony-forming unit per 100 milliliters; MNP/100 mL, most-probable number per 100 milliliters]

Date

Observed 

turbidity, 

in NTRU (2012) 

or FNU 

(2013, 2014)

Morning 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Afternoon 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Predicted 

E. coli, in 

CFU/100 

mL

Predicted 

water 

quality1

Nowcast 

used2 Notes

07/09/14 33 670 -- 1,000 Poor 2014

07/10/14 18 -- -- 560 Poor 2014

07/11/14 12 -- -- 290 Good 2014

07/12/14 9.9 -- -- 190 Good 2014

07/13/14 15 -- -- 310 Poor 2014

07/14/14 21 -- -- 610 Poor 2014

07/15/14 550 59,000 -- 28,000 Poor 2014

07/16/14 38 1,900 -- 2,400 Poor 2014

07/17/14 25 600 -- 540 Poor 2014

07/18/14 23 -- -- 490 Poor 2014

07/19/14 20 -- -- 420 Poor 2014

07/20/14 26 -- -- 1,200 Poor 2014

07/21/14 17 210 -- 710 Poor 2014

07/22/14 14 200 -- 280 Good 2014

07/23/14 10 150 -- 190 Good 2014

07/24/14 11 -- -- 240 Good 2014

07/25/14 9.6 -- -- 200 Good 2014

07/26/14 8.7 -- -- 160 Good 2014

07/27/14 670 -- -- 32,000 Poor 2014

07/28/14 410 20,000 -- 41,000 Poor 2014

07/29/14 63 2,000 -- 3,000 Poor 2014

07/30/14 51 570 -- 1,800 Poor 2014

07/31/14 600 -- -- 18,000 Poor 2014

08/01/14 64 -- -- 2,800 Poor 2014

08/02/14 58 -- -- 1,300 Poor 2014

08/03/14 41 -- -- 900 Poor 2014

08/04/14 39 800 -- 1,700 Poor 2014

08/05/14 34 220 -- 1,600 Poor 2014

08/06/14 39 4,900 -- 850 Poor 2014

08/07/14 29 -- -- 1,400 Poor 2014

08/08/14 25 -- -- 510 Poor 2014

08/09/14 20 -- -- 400 Poor 2014

08/10/14 17 -- -- 330 Poor 2014

08/11/14 14 -- -- 270 Good 2014

08/12/14 950 59,000 -- 30,000 Poor 2014

08/13/14 140 4,800 -- 15,000 Poor 2014

08/14/14 69 930 -- 2,500 Poor 2014

08/15/14 43 -- -- 930 Poor 2014

08/16/14 31 -- -- 640 Poor 2014
Appendix 1 29

Table 1–1. Nowcast predictions for the Cuyahoga River at Jaite sampling site in the Cuyahoga Valley National Park, 2012–14.—Continued

[Indicated in the notes are dates when the predicted water quality was not reported to the Ohio Nowcast Web site (http://www.ohionowcast.info/) due to issues with 

the Web site or scheduled run of the computer program used to update the Web site. Morning samples were generally collected prior to 9 a.m.; afternoon samples 

were collected after 12 p.m. NTRU, nephelometric turbidity ratio unit; FNU, formazin nephelometric unit; E. coli, Escherichia coli; --, no data available; CFU/100 

mL, colony-forming unit per 100 milliliters; MNP/100 mL, most-probable number per 100 milliliters]

Date

Observed 

turbidity, 

in NTRU (2012) 

or FNU 

(2013, 2014)

Morning 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Afternoon 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Predicted 

E. coli, in 

CFU/100 

mL

Predicted 

water 

quality1

Nowcast 

used2 Notes

08/17/14 38 -- -- 810 Poor 2014

08/18/14 20 -- -- 440 Poor 2014

08/19/14 17 -- -- 430 Poor 2014

08/20/14 900 -- -- 28,000 Poor 2014

08/21/14 110 -- -- 20,000 Poor 2014

08/22/14 64 -- -- 6,900 Poor 2014

08/23/14 45 -- -- 1,100 Poor 2014

08/24/14 39 -- -- 1,100 Poor 2014

08/25/14 33 220 -- 680 Poor 2014

08/26/14 27 250 -- 540 Poor 2014

08/27/14 28 620 -- 560 Poor 2014

08/28/14 29 200 -- 710 Poor 2014 Not reported to Web site

09/02/14 12 240 -- 210 Good 2014 Not reported to Web site

09/03/14 12 220 -- 250 Good 2014-

backup3

Radar rainfall unavailable

09/04/14 9 -- -- 170 Good 2014-

backup3

Radar rainfall unavailable

09/08/14 19 -- -- 640 Poor 2014

09/09/14 16 140 -- 290 Good 2014 Actual E. coli is in MPN/100mL 

09/10/14 16 -- -- 290 Good 2014

09/11/14 490 -- -- 13,000 Poor 2014

09/12/14 65 -- -- 4,300 Poor 2014

09/13/14 38 -- -- 930 Poor 2014

09/14/14 25 -- -- 530 Poor 2014

09/15/14 22 150 -- 460 Poor 2014 Actual E. coli is in MPN/100mL 

09/16/14 32 580 -- 620 Poor 2014 Actual E. coli is in MPN/100mL 

09/17/14 18 -- -- 590 Poor 2014

09/18/14 17 -- -- 310 Poor 2014

09/19/14 15 -- -- 270 Good 2014

09/20/14 13 -- -- 230 Good 2014

09/21/14 11 -- -- 190 Good 2014

09/22/14 14 330 -- 300 Poor 2014 Actual E. coli is in MPN/100mL 

09/23/14 10 -- -- 220 Good 2014

09/24/14 12 -- -- 260 Good 2014

09/25/14 9.2 -- -- 150 Good 2014

09/26/14 8.6 -- -- 140 Good 2014

09/27/14 8.3 -- -- 160 Good 2014

09/28/14 8.1 -- -- 130 Good 2014

09/29/14 8.1 -- -- 130 Good 2014
30 Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, Ohio, 2012–14

Table 1–1. Nowcast predictions for the Cuyahoga River at Jaite sampling site in the Cuyahoga Valley National Park, 2012–14.—Continued

[Indicated in the notes are dates when the predicted water quality was not reported to the Ohio Nowcast Web site (http://www.ohionowcast.info/) due to issues with 

the Web site or scheduled run of the computer program used to update the Web site. Morning samples were generally collected prior to 9 a.m.; afternoon samples 

were collected after 12 p.m. NTRU, nephelometric turbidity ratio unit; FNU, formazin nephelometric unit; E. coli, Escherichia coli; --, no data available; CFU/100 

mL, colony-forming unit per 100 milliliters; MNP/100 mL, most-probable number per 100 milliliters]

Date

Observed 

turbidity, 

in NTRU (2012) 

or FNU 

(2013, 2014)

Morning 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Afternoon 

actual E. coli, 

in CFU/100 mL 

unless 

otherwise noted

Predicted 

E. coli, in 

CFU/100 

mL

Predicted 

water 

quality1

Nowcast 

used2 Notes

09/30/14 8.5 -- -- 140 Good 2014

10/01/14 21 -- -- 380 Poor 2014

10/02/14 8 -- -- 270 Good 2014

10/03/14 7.2 -- -- 110 Good 2014

10/04/14 71 -- -- 1,500 Poor 2014

10/05/14 13 -- -- 530 Poor 2014

10/06/14 8.3 -- -- 210 Good 2014

10/07/14 12 -- -- 280 Good 2014

10/08/14 14 -- -- 390 Poor 2014

10/09/14 7.7 -- -- 260 Good 2014

10/10/14 7.4 -- -- 110 Good 2014

10/11/14 7.8 -- -- 120 Good 2014

10/12/14 7.5 -- -- 130 Good 2014

10/13/14 8.3 -- -- 130 Good 2014

10/14/14 7 -- -- 110 Good 2014

10/15/14 44 -- -- 840 Poor 2014

10/16/14 180 -- -- 10,000 Poor 2014

10/17/14 22 -- -- 570 Poor 2014

10/18/14 18 -- -- 510 Poor 2014

10/19/14 20 -- -- 510 Poor 2014

10/20/14 16 -- -- 460 Poor 2014

10/21/14 16 -- -- 6,500 Poor 2014-

backup3

Radar rainfall unavailable

10/22/14 45 -- -- 2,100 Poor 2014

10/23/14 26 -- -- 1,000 Poor 2014

10/24/14 19 -- -- 320 Poor 2014

10/25/14 18 -- -- 300 Poor 2014

10/26/14 16 -- -- 260 Good 2014

10/27/14 15 -- -- 240 Good 2014

10/28/14 15 -- -- 240 Good 2014

10/29/14 14 -- -- 230 Good 2014

10/30/14 11 -- -- 210 Good 2014

10/31/14 11 -- -- 170 Good 2014

11/01/14 13 -- -- 210 Good 2014

11/02/14 9.2 -- -- 140 Good 2014

11/03/14 8.7 -- -- 130 Good 2014

1

Predicted water quality is listed as “Good” or “Poor” if it is below or above (respectively) the Ohio single-sample water-quality standard for primary-contact 

recreation (298 CFU/100 ml of E. coli). 

2

The nowcast used refers to the year the nowcast was validated. 

3

The 2014-backup model was used when radar-indicated rainfall observations or in-stream turbidity measurements was not available to run the primary predictive model. 

Brady and Plona—Automating Measurements and Predictions of E. coli Concentrations in the Cuyahoga River, 2012–14—Scientific Investigations Report 2015–5094

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20155094