PREDICTIVE MODELS FOR RECREATIONAL WATER
or closings are issued by beach managers on the basis of standards for
concentrations of bacterial indicators—Escherichia coli (E. coli) or enterococci
for freshwaters and enterococci for marine waters. The analytical methods for
these organisms, however, take at least 18–24 hours to complete. Recreational
water-quality conditions may change during this time, leading to erroneous
assessments of public-health risk. As a result, some agencies have turned to
predictive modeling to obtain near-real-time estimates of recreational water
quality. Predictive models, developed through statistical techniques such as
multiple linear regression (MLR), use easily measured environmental and
water-quality variables to estimate bacterial-indicator concentrations or the
probability of exceeding target concentrations.
eight Lake Erie beaches and one recreational river, predictions based on models are
available to the public during the recreational season (May-Aug) through an
Internet-based system called the
The nowcast is like a weather forecast, in that it provides the probability (in
percent) that the bathing-water standard for E. coli will be exceeded. (The Ohio
single-sample bathing-water standard for E. coli is 235 colony-forming units/100
milliliters). So on any given morning, there could be from a 1- to 100- percent
probability that the standard would be exceeded. How does one know when the
probability presents too great a risk to go swimming? Would you go swimming if
there was an 80-percent probability that the standard would be exceeded? What
about a 25 percent chance? To help out, beach managers established threshold
probabilities for their beaches based on historical data. If the
probability is greater than or equal to the threshold, than the beach is posted
with an advisory on the Ohio Nowcast.
How did the nowcast system perform in past years? To find out, refer to the
Ohio Nowcast website.
The USGS and its partners will continue to work to improve the predictive
abilities of the Ohio Nowcast models.
How can we develop models for our beaches?
find out how to develop predictive models for your beaches in a step-by-step
fashion, click on the techniques
report. The steps to develop predictive models are data collection;
exploratory data analysis; model development, selection, and diagnosis;
determination of model out values; and model validation and refinement.
The U.S. Environmental Protection Agency
developed a free software program, called
Virtual Beach, that
enables beach managers and others to develop or update models using statistical
techniques. The software is user friendly and can be used by those without a
strong statistics background.
A spreadsheet was designed by the USGS to
automate the compilation of lake level data retrieved from the nearest offshore
buoy operated by the National Oceanic and Atmospheric Administration. The
spreadsheet will organize hourly lake-level data and calculate the change in
lake level over 24 hrs. The spreadsheet is available as
Appendix 1 as part of a USGS
Scientific Investigations Report. Contact Amie Brady
for more information.
A software routine, called PROCESSNOAA, was
designed by the USGS to automate the compilation of weather data from the
nearest National Weather Service airport site. The software processes hourly
rainfall, wind direction and speed, and barometric pressure, and provides lagged
and weighted rainfall variables. The software is available as
Appendix 2 as part of a USGS
Scientific Investigations Report. Contact
Donna Francy for more information.
Collecting better data for predictive models:
Predictive modeling is a dynamic process meant to augment existing
beach-monitoring programs. Models should be continuously validated and refined
to improve predictions.
The USGS Ohio Water Science Center is
working with the Lake County General Health District to collect local weather
data at Mentor Headlands State Park, Ohio. A USGS operated weather station
measures wind speed, wind direction, barometric pressure, air temperature, net
solar radiation, incident light, and rainfall. Data are available in real time
USGS station number 414514081174400.
We are also working to identify additional explanatory variables to include in
the models. For example, a sensor to measure photosynthetically active radiation
(PAR) was installed at Huntington. Increased sunlight, as measured
by PAR, has been shown to result in decreased levels of E. coli. At Edgewater, a temporary piezometer (shallow water well) equipped with a pressure transducer and data logger to measure and record water levels every 30 minutes
was installed 20 ft inland from the edge of water during each recreational season. Edgewater is as gently sloping beach with reservoirs of E. coli (presumably from bird populations) in the sand and shallow groundwater acting as a potential source of contamination to the lake. Including a variable that quantifies the interaction of shallow groundwater with lake water, such as the water level measured in the piezometer, may help to improve model performance at Edgewater.