Nowcasting Beach Advisories at Ohio Lake Erie Beaches

Francy, Donna S.; Darner, Robert A.

doi:10.3133/ofr20071427

Cited by 13 publications

(24 citation statements)

References 1 publication

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“…Considering all model duration categories, it is the best explanatory variable, followed by dew point temperature and cloud cover. In contrast, the wave height variable, important in the USGS study (19,21), only appears intermittently.…”

Section: Resultsmentioning

confidence: 93%

“…Data obtained by the USGS from Huntington Beach, Ohio, on Lake Erie west of Cleveland, were used in this study. The site is described in several reports by Francy and co-workers (19)(20)(21) and also by Haugland et al (22).…”

Section: Methodsmentioning

confidence: 87%

“…Thus, models prescribed by VB were based on more explanatory variables than were used by Francy et al (19)(20)(21). This expanded set of explanatory variables potentially could yield improved predictive capacity.…”

Section: Methodsmentioning

confidence: 99%

“…The sampling time was nominally 0900 but ranged from 0752 to 0952. Samples were analyzed for E. coli concentration as described elsewhere (19,21). The explanatory variables turbidity, wave height, and water temperature were measured simultaneously.…”

Section: Methodsmentioning

confidence: 99%

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Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study

Frick

Zepp

2008

Environ. Sci. Technol.

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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 statistical regression models based on least-squares fitting often are more accurate. To make such models more generally available, the Virtual Beach (VB) tool was developed. VB is publicdomain software that prescribes site-specific predictive models. In this study we used VB as a tool to evaluate statistical modeling for predicting Escherichia coli (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 multiyear 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, yielding adjusted coefficients of determination (adjusted R 2 ) of about 0.40.

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Section: Resultsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study

Frick

Zepp

2008

Environ. Sci. Technol.

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“…For example, managers can use real-time estimates of chloride to determine if elevated levels are toxic to aquatic organisms (Corsi and others, 2010). Realtime estimates can be used for public health notices, such as whether fecal coliform concentrations may be above waterquality standards that may present public health risks (Francy and Darner, 2007). Real-time concentration estimates in conjunction with stream discharge data can be used to calculate loads of water-quality constituents of interest.…”

Section: Use Of Real-time Monitoring To Predictmentioning

confidence: 99%

Use of real-time monitoring to predict concentrations of select constituents in the Menomonee River drainage basin, Southeast Wisconsin, 2008-9

Baldwin¹,

Graczyk²,

Robertson³

et al. 2012

Scientific Investigations Report

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Real-time observation and prediction of physical processes in a typhoon-affected lake

Kimura

Liu

Chiu

et al. 2011

Paddy Water Environ

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A nowcasting system integrating observations and a numerical model was developed to better understand the spatial distributions and temporal variations in the dynamics of a small, subtropical lake occasionally influenced by severe rainstorms. The nowcasting system was used to monitor real-time meteorological conditions and thermal structures, to provide spatial information on the thermal and flow dynamics from model predictions, and to compare the predictions with the observations. The system was also able to determine whether the instruments collecting field data were performing correctly using telemetry operations. The nowcasting system was initially operated in the spring of 2009 in Yuan-Yang Lake, Taiwan. Rainstorm-induced mixing occurred due to Typhoon Morakot during August 7-9, 2009. The mixing was observed by the instruments, and the spatial distributions and temporal variations during the mixing were successfully predicted by the three-dimensional hydrodynamic lake model. A quantitative comparison of the energy balances among the heat, wind, and water inflow inputs to the lake implied that the typhoon-induced mixing was primarily caused by strong winds. The model predictions showed that the lake experienced mixing and flooding (large amounts of inflow/ outflow discharges), resulting in homogenous temperatures and flows that moved to the outlet of the lake.

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Nowcasting Beach Advisories at Ohio Lake Erie Beaches

Cited by 13 publications

References 1 publication

Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study

Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study

Use of real-time monitoring to predict concentrations of select constituents in the Menomonee River drainage basin, Southeast Wisconsin, 2008-9

Real-time observation and prediction of physical processes in a typhoon-affected lake

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