Implementation of an automated beach water quality nowcast system at ten California oceanic beaches

Abstract

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.