U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2013–3069

October 2013

Real-Time Assessments of Water Quality—Expanding 

Nowcasting Throughout the Great Lakes

Printed on recycled paper

The overall mission of this work is to provide science-based information and methods that will allow beach managers to more 

accurately make beach closure and advisory decisions, understand the sources and physical processes affecting beach contaminants, 

and understand how science-based information can be used to mitigate and restore beaches and protect the public. 

The U.S. Geological Survey (USGS), in collaboration with many Federal, State, and local agencies and universities, has conducted research on beach health issues in the Great Lakes Region for more than a decade. The work consists of four science elements 

that align with the USGS Beach Health Initiative Mission: real-time assessments of water quality; coastal processes; pathogens and 

source tracking; and data analysis, interpretation, and communication. The ongoing or completed research for real-time assessments 

of water quality is described in this fact sheet.

Why was the Study Done?

• Fecal indicator bacteria (FIB), such as 

Escherichia coli (E. coli) and enterococci, are currently used in beach 

water-quality monitoring to determine 

whether to post a beach advisory or 

closing. Although not necessarily 

pathogenic themselves, FIB indicate 

the possible presence of pathogens 

(disease-causing microorganisms). Traditional culture methods for FIB can 

take up to 18 hours to obtain results so 

that advisory decisions are made based 

on the previous day’s FIB concentration. Water quality can change rapidly 

at beaches in response to varying 

meteorological conditions and other 

factors. This may result in beach advisories that are issued too late to protect 

visitor health, or unnecessarily issued 

the next day, after the water quality has 

improved.

• Agencies that monitor beaches need 

tools that can provide real-time, reliable measures of recreational waterquality conditions. Rapid methods 

for FIB, such as the quantitative 

polymerase chain reaction (qPCR) 

molecular method included in new 

recreational water-quality criteria 

(U.S. Environmental Protection 

Agency, 2012a), are one such tool, but 

are not always practical or cost effective for inclusion in local monitoring 

programs. Predictive modeling is a tool 

for assessing water quality in real-time 

using easily measured environmental 

and (or) water-quality variables—such 

as turbidity (water clarity) or rainfall—

to make management decisions in 1 

or 2 hours on whether to post beach 

advisories or closures. The USGS and 

collaborators have been investigating 

predictive modeling for more than 

a decade (Francy and Darner, 1998; 

Nevers and Whitman, 2005; Zimmerman, 2008). Nowcast systems that use 

predictive models to post advisories or 

closings are one means to provide realtime assessments of water quality. 

When and Where was the Study Done?

During the 2010–12 recreational 

seasons, the USGS, in cooperation with 

23 local and State agencies, worked 

to refine existing operational nowcast 

systems at four beaches and to expand 

the use of operational predictive models 

at 45 beaches throughout the Great Lakes 

Region (fig. 1). These included beaches 

(with numbers of beaches in parentheses) 

in Illinois (8), Indiana (1), Michigan (4), 

New York (7), Ohio (12), Pennsylvania (6), and Wisconsin (11). 

Beach manager changing the nowcast 

sign before 9:30 a.m. at Edgewater 

Beach, Cleveland, Ohio.

Figure 1. Locations of beach study sites.

CANADA

OHIO

MINNESOTA

ILLINOIS

WISCONSIN

NEW YORK

INDIANA

MICHIGAN

PENNSYLVANIA

LAKE SUPERIOR

Erie

Toledo

Duluth

Buffalo

Cleveland

Rochester

Michigan City

0 100 200 MILES

0 100 200 KILOMETERS

EXPLANATION

Beach sampling site

Nowcast operational in 2010

Nowcast in development

Base modified from U.S. Geological Survey

1:2,000,000 scale digital data.

Great Lakes Basin Albers Equal Area Projection

Grand Rapids

Detroit

Port Huron

Chicago

Milwaukee

Madison

Sheboygan

Green Bay

LAKE HURON

LAKE 

MICHIGAN

LAKE ERIE

LAKE ONTARIO

90° 80°

42°

48°

45°

What are Nowcasts?

Nowcasts are systems that inform 

the public of current bacterial waterquality conditions at beaches and are 

based on predictive models. A nowcast 

estimates conditions in real-time, similar 

to the way a weather forecast looks to 

the future. Nowcasts use environmental and water-quality variables that are 

easily and quickly measured, such as 

rainfall and turbidity (water clarity), as 

surrogates in beach-specific statistical 

models to estimate densities of bacterial 

indicators, such as E. coli, or to provide 

the probability that a State standard 

will be exceeded. At the time this study 

began in 2010, nowcasts were used for 

beach closure or advisory decisions 

(operational nowcasts) at only a few 

locations within the Great Lakes Region 

including three beaches in Ohio (http://

www.ohionowcast.info), three beaches in 

Illinois (Lake County Health Department, 

2013), and two beaches in Wisconsin 

(http://www.wibeaches.us/). 
How Were Data Collected and Compiled?

Local agencies measured E. coli 

concentrations and variables expected to 

affect E. coli concentrations including 

wave height, turbidity, water temperature, and numbers of birds at the time of 

sampling (fig. 2). In addition to field measurements, equipment was installed at or 

near several beaches to measure variables 

in real-time—nearshore buoys and local 

weather stations (fig. 3) and streamflow 

and water-quality gaging stations. Data 

from existing sources were compiled 

manually or by use of new tools developed by the USGS, which were designed 

specifically to compile and process data 

for predictive model development. These 

new tools included the following: 

• The Lake Level spreadsheet that 

compiles hourly lake-level data from 

the National Oceanic and Atmospheric 

Administration (2012a). 

• PROCESSNOAA, a software program 

that compiles and processes hourly 

rainfall, wind direction and speed, and 

barometric pressure data from the nearest airport site. Data are obtained from 

the National Oceanic and Atmospheric 

Administration and U.S. Department 

of Commerce (2012). 

• Environmental Data Discovery and 

Transformation (EnDDaT), which 

retrieves and processes data from 

multiple sources including streamflow from the U.S. Geological Survey 

(2012); radar rainfall from the National 

Oceanic and Atmospheric Administration (2012b); and forecasted winds, 

waves, and currents from the National 

Oceanic and Atmospheric Administration (2012c).

Virtual Beach, a software package 

designed by the U.S. Environmental 

Protection Agency (EPA) for model 

development (U.S. Environmental 

Protection Agency, 2012b) was used to 

update or develop new models using multiple linear-regression techniques. The 

USGS worked with the EPA to develop 

and enhance the use of Virtual Beach, 

including the addition of an alternative 

statistical technique—partial least squares 

(Brooks and others, 2013)—into the 

Virtual Beach software.

Figure 2. U.S. Geological Survey employee 

measuring field variables at Presque Isle 

State Park Beach 2, Erie, Pennsylvania.

Figure 3. Weather station at Headlands 

State Park, Ohio (left), and nearshore buoy 

at Calumet Beach, Chicago, Illinois (right). 

PROCESSNOAA for Compiling 

Weather Data

PROCESSNOAA, a graphical user 

interface (GUI), assists in processing 

large quantities of weather data provided 

by National Oceanic and Atmospheric 

Administration (NOAA). Hourly precipitation totals and predominant wind speed 

and direction data are routinely collected 

at airports across the country. These data 

often are used in predictive modeling at 

beaches but could require hours for the 

modeler to synthesize manually. PROCESSNOAA synthesizes the hourly data 

and saves these data in a format ready for 

direct import into Virtual Beach. 

For further information on PROCESSNOAA, contact Jeremiah Lant 

(jglant@usgs.gov).

EnDDaT Tool for Compiling Data 

from Multiple Sources

A Web tool, Environmental Data 

Discovery and Transformation (EnDDaT), was developed to facilitate 

retrieval of data from multiple agencies 

and combine them into one location 

(http://cida.usgs.gov/enddat/). Currently, 

this tool includes access to national 

sources of data for variables including streamflow, radar precipitation, and 

forecasts of surface-current magnitude 

and direction, depth-integrated current 

magnitude and direction, wave height, 

wave direction, deviation from average 

lake level, surface-water temperature, and 

wind speed and direction. 

For further information on the EnDDaT, contact Laura De Cicco (enddat@

usgs.gov)
What Were the Study Results?

What Types of Variables Did the Models 

Contain? 

Models were refined for existing 

nowcasts at 4 beaches, models were 

newly developed for 39 beaches, and 

data were collected for future predictive 

models at 6 beaches. The best model 

for each beach was based on a unique 

combination of explanatory variables 

including most commonly, turbidity, day 

of the year, wave height, wind direction and speed, antecedent rainfall for 

various time periods (prior to sampling), 

and change in lake level over 24 hours. 

Among all variables, turbidity (measured 

with a turbidimeter) was used most often 

(79.1 percent of models). Weather data 

from the nearest airport site were used in 

44.2 percent of models. Among unique 

variables, a lab measurement of qPCR 

for E. coli was used at one beach in 

Ohio. Variables based on NOAA predictions, such as predicted current or wave 

direction, often were used in models for 

Wisconsin beaches. Local sources of data 

on rainfall, solar radiation, wind direction and speed, air temperature, humidity, 

and barometric pressure were used in 

32.6 percent of models. 

What Types of Information Does Each 

Model Provide for Beach Managers?

There are two types of output 

available from models: (1) the predicted 

E. coli concentration and (2) the probability of exceeding the single-sample 

standard or Beach Action Value (BAV). 

The BAV is a precautionary tool recommended for use in recently published 

criteria (U.S. Environmental Protection 

Agency, 2012a). The BAVs were not in 

use at the time of this study; however, the 

BAV of 235 colony-forming units per 100 

milliliters (CFU/100 mL) is the same as 

the single-sample bathing-water standards used in all States in this study—

except Michigan—for posting beach 

advisories or closings. At the time of this 

study, Michigan used 300 CFU/100 mL 

as their standard. In Illinois and Indiana, the predicted E. coli concentration 

was used as output from the models. At 

all other beaches, the probabilities of 

exceeding the single-sample standard 

were used as output from the models.

For those beaches where the probability of exceeding the single-sample 

standard was used as output from the 

models, a threshold probability for 

issuing an advisory was established for 

each beach. A threshold probability is 

determined by taking the dataset used 

to develop the model and finding the 

probability that maximized the percentage of correctly predicted exceedances 

and non-exceedances of the standard, 

while at the same time, found a compromise between false positives and false 

negatives (Francy and Darner, 2006). 

Computed probabilities that are less 

than the threshold indicate that bacterial 

water quality is most likely acceptable 

for swimming. Computed probabilities 

equal to or greater than the threshold 

probability indicate that the water quality 

is most likely not acceptable and that a 

water-quality advisory may be needed. 

Established thresholds ranged from 15 to 

50 percent, with a median of 28 percent. 

How Well Did the Models Perform During 

an Independent Year?

Models were developed from data 

collected during 2010–11 for most 

beaches, and included pre-2010 data for 

a few beaches. Updated or new models 

for 42 beaches were validated during an 

independent year (2012); at one beach, 

the predictive modeling effort was 

discontinued. During validation, model 

performance was compared to the current 

method for assessing recreational water 

quality—using the previous day’s E. coli 

concentration—and to determine whether 

model results were good enough to be 

used for public notifications. Goals for 

good model performance were correct 

responses (predictions of exceedances 

and non-exceedances of the standard) that 

were at least 5 percent greater than the 

use of the previous day’s E. coli concentration and overall correct responses 

>80 percent, sensitivities >50 percent, 

and specificities >85 percent. 

• The sensitivity was the percentage of 

exceedances of the bathing-water standard that were correctly predicted by 

the model or the previous day’s E. coli 

concentration. 

• The specificity was the percentage of 

non-exceedances of the standard that 

were correctly predicted by the model 

or the previous day’s E. coli concentration. 

During validation in 2012, 24 models provided overall correct responses 

that were at least 5 percent greater than 

the use of the previous day’s E. coli 

concentration. Predictive models met the 

above goals for overall correct responses, 

sensitivities, and specificities more often 

than the previous day’s E. coli concentrations; this was especially apparent in 

terms of sensitivities (fig. 4). 

0

20

40

60

80

100

Percent

Correct

28 17

17 4

34 25

Sensitivity Specificity

*

*

*

*

*

*

*

*

Nowcast

Previous day's Escherichia coli 

Goal

Number of models meeting goal *

EXPLANATION

Figure 4. Percentages of overall correct, sensitivities, and specificities during validation 

of 42 beach-specific predictive models, 2012. Goals for good model performance are overall 

correct responses ≥ 80 percent, sensitivities ≥ 50 percent, and specificities ≥ 85 percent. 
How is Real-Time Information Being 

Transferred to Beach Managers and 

Others?

Technology transfer is an important component of USGS activities. The 

USGS, EPA, and Wisconsin Department 

of Natural Resources worked together 

to conduct hands-on workshops in 2011 

and 2013 at local USGS offices to train 

stakeholders on how to develop their 

own predictive models for use in nowcast 

systems. Participants had the opportunity 

to use data from their beach of choice and 

work through data compilation and analysis and model development, implementation, and validation procedures. Technology transfer also has included working 

with local agencies on a day-to-day 

basis to collect real-time data and apply 

data-management tools and Virtual Beach 

software to develop and test predictive 

models for beaches.

What is Next for USGS Real-Time 

Activities?

During 2013, the USGS will 

continue to work with local agencies 

to improve real-time predictions and 

implement more operational nowcast 

systems. Although models performed 

better than the current method to assess 

recreational water quality (previous day’s 

E. coli concentration), work is ongoing 

to improve their accuracies. The USGS 

also will continue to work with the team 

of Virtual Beach developers to enhance 

model capabilities and provide technology transfer opportunities with stakeholders. The USGS is committed to helping 

beach managers develop and implement 

nowcast programs. 

References

Brooks, W.R., Fienen, M.N., and Corsi, 

S.R., 2013, Partial least squares for 

efficient models of fecal indicator bacteria on Great Lakes beaches: Journal 

of Environmental Management, v. 114, 

p. 470–475.

Francy, D.S., and Darner, R.A., 1998, 

Factors affecting Escherichia coli concentrations at Lake Erie public bathing 

beaches: U.S. Geological Survey 

Water-Resources Investigations Report 

98–4241, 41 p.

Francy, D.S., and Darner, R.A., 2006, 

Procedures for developing models to 

predict exceedances of recreational 

water-quality standards at coastal 

beaches: U.S. Geological Survey Techniques and Methods 6–B5, 34 p.

Lake County Health Department, 2013, 

SwimCastData, accessed June 2013 

at http://health.lakecountyil.gov/

Population/LMU/Pages/SwimCastData.aspx. 

National Oceanic and Atmospheric 

Administration Satellite and Information Service and U.S. Department of 

Commerce National Climatic Data 

Center, 2012, Unedited local climatological data, accessed January 2012 at 

http://cdo.ncdc.noaa.gov/ulcd/ULCD.

National Oceanic and Atmospheric 

Administration, Center for Operational Oceanographic Products and 

Services, 2012a, Tides and currents, 

accessed January 2012 at http://

tidesandcurrents.noaa.gov/.

National Oceanic and Atmospheric 

Administration, National Weather 

Service, 2012b, Advanced Hydrologic 

Prediction Service, accessed January 2012 at http://water.weather.gov/

precip/download.php.

National Oceanic and Atmospheric 

Administration, Great Lakes Coastal 

Forecasting System, 2012c, accessed 

March 2012 at http://www.glerl.noaa.

gov/res/glcfs.

Nevers, M.B., and Whitman, R.L., 2005, 

Nowcast modeling of Escherichia 

coli concentrations at multiple urban 

beaches of southern Lake Michigan: 

Water Research, v. 39, no. 20, p. 5250–

5260. 

U.S. Environmental Protection Agency, 

2012a, Recreational water quality 

criteria: Washington, D.C., Office 

of Water, EPA-820-F-12-058, 63 p., 

accessed December 2012 at http://

water.epa.gov/scitech/swguidance/

standards/criteria/health/recreation/

index.cfm.

U.S. Environmental Protection Agency, 

2012b, Exposure assessment models—

Virtual Beach: Center for Exposure 

Assessment Modeling, accessed 

May 2013 at http://www2.epa.gov/

exposure-assessment-models/virtualbeach-vb.

U.S. Geological Survey, 2012, National 

Water Information System—Web interface, USGS water data for the Nation, 

accessed January 2012 at http://

waterdata.usgs.gov/nwis/.

Zimmerman, T.M., 2008, Modeling to 

predict Escherichia coli at Presque Isle 

Beach 2, City of Erie, Erie County, 

Pennsylvania: U.S. Geological Survey Scientific Investigations Report 

2008–5039, 13 p.

Program Information 

Funding for USGS beach projects 

and research in the Great Lakes comes 

from a variety of sources including the 

Ocean Research Priority Plan, the USGS, 

the Great Lakes Restoration Initiative, the 

EPA, the NOAA Center for Great Lakes 

and Human Health, and many State and 

local partner agencies and organizations 

throughout the region. 

Contact

James R. Morris, Director 

USGS Michigan and Ohio 

 Water Science Centers

614–430–7700

http://greatlakesbeaches.usgs.gov/