An early-warning forecast model for red tide (Karenia brevis) blooms on the southwest coast of Florida

Abstract

Karenia brevis blooms occur nearly annually along the southwest coast of Florida, and effective mitigation of ecological, public health, and economic impacts requires reliable real-time forecasting. We present two boosted random forest models that predict the weekly maximum K. brevis abundance category across the Greater Charlotte Harbor estuaries over one-week and four-week forecast horizons. The feature set was restricted to data available in near-real time, consistent with adoption of the models as decision-support tools. Features include current and lagged K. brevis abundance statistics, Loop Current position, sea surface temperature, sea level, and riverine discharges and nitrogen concentrations. During cross-validation, the one-week and four-week forecasts exhibited 73 % and 84 % accuracy, respectively, during the 2010–2023 study period. In addition, we assessed the models’ reliability in forecasting the onset of 10 bloom events on time or in advance; the one-week and four-week models anticipated the onset eight times and five times, respectively.