Advancing an early warning system for California beach water quality
One issue common to populated coastlines is the higher risk of recreational that people who swim or surf in the water have a risk of getting sick from fecal indicator bacteria (FIB) such as enterococci and Escherichia coli. Usually, levels of FIB are low, but events like rain, an increase in stormwater run-off, waste-treatment plant overflows, changes in currents, and even wind can increase FIB levels. Accurate water quality data is critical for California’s resource managers to keep swimmers safe on beaches. Still, there is often a two-day laboratory turnaround of beach water samples, potentially exposing beachgoers to contaminated water. Water quality managers are turning to forecast models, which usually require extensive, multi-year data sets to predict water quality accurately. University of Southern California (USC) Sea Grant funded a study to determine whether high-frequency sampling over a 1-2 day period could be sufficient to more efficiently develop predictive water quality models for “data-poor” beaches where more extensive monitoring is unachievable.
Key Results:
- Models using oceanographic and meteorological data collected every 10 minutes over 1-2 days were predictive of beach water quality at Santa Cruz, Monterey, and Huntington Beach
- Predictive models are accurate for at least one swimming season at a time, enabling efficient and quick water quality forecasts
- For the first time, even a “data-poor” beach could have accurate beach water quality data and forecast relatively quickly
Project Impacts & Application:
- Presented results to the State Water Resources Control Board, the US Environmental Protection Agency BEACH group, the Surfrider Foundation, and the Surfrider San Mateo County Chapter
- The research team has presented and trained many groups to use the models, including the State Water Resources Control Board, the US Environmental Protection Agency BEACH group, the Southern California Coastal Water Research Program, the Environmental Protection Agency, Heal the Bay, and Surfrider which is actively using the data/model in Half Moon Bay
- Results were shared in two major publications in Environmental Science and Technology and PLOS One
Principal Investigator:
Alexandria Boehm, Ph.D., Stanford
Funding:
NOAA, 2020-2022
Additional Info and Publications:
- https://web.stanford.edu/~aboehm/projects.html
- https://dornsife.usc.edu/uscseagrant/ews-water-quality/
- https://news.stanford.edu/2021/01/21/new-way-forecast-beach-water-quality/
- Ryan T. Searcy and Alexandria B. Boehm. 2022. Know Before You Go: Data-Driven Beach Water Quality Forecasting. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.2c05972
- Ryan T. Searcy and Alexandria B. Boehm. A Day at the Beach: Enabling Coastal Water Quality Prediction with High-Frequency Sampling and Data-Driven Models. Environmental Science & Technology 2021 55 (3), 1908-1918. DOI: 10.1021/acs.est.0c06742
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