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DEVELOPING SITE-SPECIFIC MODELS FOR FORECASTING BACTERIA LEVELS AT COASTAL BEACHES
Citation:
FRICK, W. E., D. S. FRANCY, R. A. DARNER, AND Z. GE. DEVELOPING SITE-SPECIFIC MODELS FOR FORECASTING BACTERIA LEVELS AT COASTAL BEACHES. Presented at 18th Biennial Conference of the Estuarine Research Federation, Norfolk, VA, October 16 - 20, 2005.
Impact/Purpose:
A main objective of this task is to combine empirical and physical mechanisms in a model, known as Visual Beach, that
â— is user-friendly
â— includes point and non-point sources of contamination
â— includes the latest bacterial decay mechanisms
â— incorporates real-time and web-based ambient and atmospheric and aquatic conditions
â— and has a predictive capability of up to three days to help avert potential beach closures.
The suite of predictive capabilities for this software application can enhance the utility of new methodology for analysis of indicator pathogens by identifying times that represent the highest probability of bacterial contamination. Successful use of this model will provide a means to direct timely collection of monitoring samples, strengthening the value of the short turnaround time for sampling. Additionally, in some cases of known point sources of bacteria, such as waste water treatment plant discharges, the model can be applied to help guide operational controls to help prevent resulting beach closures.
Description:
The U.S.Beaches Environmental Assessment and Coastal Health Act of 2000 authorizes studies of pathogen indicators in coastal recreation waters that develop appropriate, accurate, expeditious, and cost-effective methods (including predictive models) for quantifying pathogens in coastal recreation waters. The U.S. Geological Survey developed models for predicting concentrations that exceed the bathing-water standard for Escherichia coli at Lake Erie beaches. The statistical models were specific to each beach, and the best were based on a unique combination of environmental and water-quality variables as explanatory factors, including wave height, wind direction, rainfall, and turbidity. The models can be used like weather forecasts to predict the probability that bathing water standards will be exceeded. The strategies and statistical techniques used to develop these methods are applied to a selected coastal beach in the formal, established way. A general modeling framework is also developed that is intended to allow users without extensive training in statistical techniques to create similar models. Results are compared and analyzed.