Ryan Searcy is currently a PhD candidate at Stanford University. His research focuses on monitoring and modeling threats to coastal health. His current projects involve developing data-driven models to predict beach water quality, using environmental DNA (eDNA) to assess native salmonid populations in a coastal stream, and collecting physical oceanographic data to measure circulation and retention at a chronically-polluted beach. Prior to Stanford, Ryan was the beach water quality modeler for Heal the Bay. In that position, he built and managed the NowCast system which provides beachgoers and health agencies daily water quality predictions for dozens of California beaches. Ryan is a frequent user of the coast and surfs whenever the weather and work allow.

Honors & Awards

  • Stanford Graduate Fellowship, Stanford University (2021-Present)
  • Sea Grant Traineeship, U. of Southern California Sea Grant (2020-2021)
  • School of Engineering Graduate Fellowship, Stanford University (2019-2021)
  • Robert L. Wiegel Scholarship, California Shore and Beach Preservation Association (2019)

Professional Affiliations and Activities

  • Executive Committee Member, Surfrider Foundation (2014 - 2023)

Education & Certifications

  • M.S., UC San Diego, Mechanical Engineering (2014)
  • B.S., UC San Diego, Environmental Engineering (2013)

Lab Affiliations

All Publications

  • Know Before You Go: Data-Driven Beach Water Quality Forecasting. Environmental science & technology Searcy, R. T., Boehm, A. B. 2022


    Forecasting environmental hazards is critical in preventing or building resilience to their impacts on human communities and ecosystems. Environmental data science is an emerging field that can be harnessed for forecasting, yet more work is needed to develop methodologies that can leverage increasingly large and complex data sets for decision support. Here, we design a data-driven framework that can, for the first time, forecast bacterial standard exceedances at marine beaches with 3 days lead time. Using historical data sets collected at two California sites, we train nearly 400 forecast models using statistical and machine learning techniques and test forecasts against predictions from both a naive "persistence" model and a baseline nowcast model. Overall, forecast models are found to have similar sensitivities and specificities to the persistence model, but significantly higher areas under the ROC curve (a metric distinguishing a model's ability to effectively parse classes across decision thresholds), suggesting that forecasts can provide enhanced information beyond past observations alone. Forecast model performance at all lead times was similar to that of nowcast models. Together, results suggest that integrating the forecasting framework developed in this study into beach management programs can enable better public notification and aid in proactive pollution and health risk management.

    View details for DOI 10.1021/acs.est.2c05972

    View details for PubMedID 36472482

  • High-frequency and long-term observations of eDNA from imperiled salmonids in a coastal stream Environmental DNA Searcy, R. T., et al 2022

    View details for DOI 10.1002/edn3.293

  • A Day at the Beach: Enabling Coastal Water Quality Prediction with High-Frequency Sampling and Data-Driven Models. Environmental science & technology Searcy, R. T., Boehm, A. B. 2021


    To reduce the incidence of recreational waterborne illness, fecal indicator bacteria (FIB) are measured to assess water quality and inform beach management. Recently, predictive FIB models have been used to aid managers in making beach posting and closure decisions. However, those predictive models must be trained using rich historical data sets consisting of FIB and environmental data that span years, and many beaches lack such data sets. Here, we investigate whether water quality data collected during discrete short duration, high-frequency beach sampling events (e.g., samples collected at sub-hourly intervals for 24-48 h) are sufficient to train predictive models that can be used for beach management. We use data collected during six high-frequency sampling events at three California marine beaches and train a total of 126 models using common data-driven techniques. Tide, solar irradiation, water temperature, significant wave height, and offshore wind speed were found to be the most important environmental variables in the models. We validate the predictive performance of models using withheld data. Random forests are consistently the top performing model type. Overall, we find that data-driven models trained using high-frequency FIB and environmental data perform well at predicting water quality and can be used to inform public health decisions at beaches.

    View details for DOI 10.1021/acs.est.0c06742

    View details for PubMedID 33471505

  • An ASBPA White Paper: U.S. beach water quality monitoring Shore and Beach Hannides, A., Elko, N., Roberts Briggs, T., Kim, S., Mercer, A., Park, K., Rosov, B., Searcy, R. T., Walther, M. 2021; 89 (3)

    View details for DOI 10.34237/1008933

  • Implementation of an automated beach water quality nowcast system at ten California oceanic beaches JOURNAL OF ENVIRONMENTAL MANAGEMENT Searcy, R. T., Taggart, M., Gold, M., Boehm, A. B. 2018; 223: 633–43


    Fecal indicator bacteria like Escherichia coli and entercococci are monitored at beaches around the world to reduce incidence of recreational waterborne illness. Measurements are usually made weekly, but FIB concentrations can exhibit extreme variability, fluctuating at shorter periods. The result is that water quality has likely changed by the time data are provided to beachgoers. Here, we present an automated water quality prediction system (called the nowcast system) that is capable of providing daily predictions of water quality for numerous beaches. We created nowcast models for 10 California beaches using weather, oceanographic, and other environmental variables as input to tuned regression models to predict if FIB concentrations were above single sample water quality standards. Rainfall was used as a variable in nearly every model. The models were calibrated and validated using historical data. Subsequently, models were implemented during the 2017 swim season in collaboration with local beach managers. During the 2017 swim season, the median sensitivity of the nowcast models was 0.5 compared to 0 for the current method of using day-to-week old measurements to make beach posting decisions. Model specificity was also high (median of 0.87). During the implementation phase, nowcast models provided an average of 140 additional days per beach of updated water quality information to managers when water quality measurements were not made. The work presented herein emphasizes that a one-size-fits all approach to nowcast modeling, even when beaches are in close proximity, is infeasible. Flexibility in modeling approaches and adaptive responses to modeling and data challenges are required when implementing nowcast models for beach management.

    View details for PubMedID 29975890