Swim advisories 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 modeling to obtain near-real-time estimates of recreational water quality.

Techniques such as multiple linear regression (MLR) are used to develop multivariable statistical models on the basis of relations between fecal-indicator bacteria concentrations and variables known or suspected to affect their concentrations in a particular water body. The sources of fecal contamination do not need to be identified in order to develop and use statistical models. Multivariable statistical models (hereinafter “predictive models”) are being developed and tested in many areas of the USA; however, they are used for beach closure or advisory decisions at only three locations, all at Great Lakes beaches. These model-based advisories are the Swim Advisory Forecast Estimate (Project SAFE), SwimCast, and the Ohio Nowcast.

The Ohio Nowcast is the result of multi-year partnerships on several projects between the U.S. Geological Survey (USGS) Ohio Water Science Center (OWSC), and other federal, state, and local agencies. Current and past partners include Cleveland Metroparks, Cuyahoga County Board of Health (CCBH), Northeast Ohio Regional Sewer District (NEORSD), Ohio Department of Health (ODH), Ohio Department of Natural Resources (ODNR), Ohio Lake Erie Office, Ohio Water Development Authority, U.S. Environmental Protection Agency, and the U.S. National Park Service.

In Ohio, the use of predictive models was first explored with one year data at three Lake Erie beaches (Francy and Darner, 1998). This effort was expanded to include three additional Lake Erie beaches, one inland lake, and two seasons of data collection (Francy and others, 2002). Predictive models were subsequently developed for five Ohio Lake Erie beaches with 2-4 years of data, depending on the beach (Francy and others, 2006). The best model for each beach was based on a unique combination of variables that explained changes in E. coli concentrations. The “variables” included turbidity (water clarity), rainfall, wave height, water temperature, day of the year, and lake level. The model from Huntington (Bay Village) was validated using data collected during a independent year, leading to implementation of the Ohio Nowcast for Huntington in 2006. At the same time, a rapid analytical method was being tested at river sites within the Cuyahoga Valley National Park (Brecksville, Oh) and a model with turbidity and rapid method results as explanatory variables was developed (Brady, 2007). Continuing the work in Lake Erie, data were collected during the recreational season of 2007 to test and refine predictive models at Huntington, Edgewater (Cleveland), and Villa Angela (Cleveland) (Francy and Darner, 2007). The Huntington and Edgewater models performed well, and Edgewater was added to the Ohio Nowcast in 2008. At Villa Angela, however, the model resulted in correct responses only 61.3 percent of the days monitored; this percentage was lower than that achieved by use of the current method (74.6 percent). Because of these results, Villa Angela was not added to the nowcast.

The steps to develop statistical models are data collection; exploratory data analysis; model development, selection, and diagnosis; determination of model output values; and model validation and refinement. These steps are described in detail with examples in Francy and Darner (2006). Predictive modeling is a dynamic process; that is, models should be continuously validated and refined to improve predictions and better protect public health.

Brady, A.M.G., 2007, Rapid method for Escherichia coli in the Cuyahoga River: U.S. Geological Survey Open-File Report 2007–1210, 5 p.

Francy, D.S., 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., Gifford, A.M., Darner, R.A., 2002, Escherichia coli at Ohio bathing beaches— distribution, sources, wastewater indicators, and predictive modeling: U.S. Geological Survey Water-Resources Investigations Report 02-4285. 120 p. 

Francy, D.S. and Darner, R.A., 2006, Procedures for developing models to predict exceedance of recreational water-quality standards at coastal beaches: U.S. Geological Survey Techniques and Methods 6-B5, 34 p.

Francy, D.S., Darner, R.A., and Bertke, E.E., 2006, Models for predicting recreational water quality at Lake Erie beaches: U.S. Geological Survey Scientific Investigations Report 2006-5192, 13 p. 

Francy, D.S., and Darner, R.A., 2007, Nowcasting beach advisories at Ohio Lake Erie beaches: U.S. Geological Survey Open-File Report 2007–1427, 13 p.