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Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study
Citation:
FRICK, W. E., Z. GE, AND R. G. ZEPP. Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 42(13):4818-4824, (2008).
Impact/Purpose:
Objective is to refine and expand the validation of existing statistical models (MLR models such as Virtual Beach) for marine beach notification and advisories/closures, and develop method for model calibration for broader site specific applications.
Description:
Public concern over microbial contamination of recreational waters has increased in recent years. A common approach to evaluating beach water quality has been to use the persistence model which assumes that day-old monitoring results provide accurate estimates of current concentrations. This model is frequently incorrect. Recent studies have shown that, frequently, statistical regression models based on least squares fitting are more accurate. To make such models more generally available, the Virtual Beach (VB) tool was developed. VB is public-domain software that prescribes site-specific predictive models. In this study we used VB as a tool to evaluate statistical modeling for predicting E coli levels at Huntington Beach, on Lake Erie. The models were based on readily available weather and environmental data, plus U.S. Geological Service onsite data. Although models for Great Lakes beaches have frequently been fitted to multi-year data sets, this work demonstrates that useful statistical models can be based on limited data sets collected over much shorter time periods, leading to dynamic models that are periodically refitted as new data become available. Comparisons of the resulting nowcasts (predictions of current, but yet unknown, bacterial levels) with observations verified the effectiveness of VB and showed that dynamic models are about as accurate as long-term static models. Finally, fitting models to forecasted explanatory variables, bacteria forecasts were found to compare favorably to nowcasts.