Ten‐Year Hindcast Assessment of an Improved Probabilistic Forecast System for Cyanotoxin (Microcystins) Risk Level in Lake Erie - Qianqian Liu; Mark D. Rowe; Richard P. Stumpf; Reagan Errera; Casey Godwin; Justin D. Chaffin; Eric J. Anderson; Tongyao Pu (Water Resources Research)
Abstract Toxic harmful algal blooms produce public health hazards in freshwater systems around the world. There is a need for forecast systems that can mitigate risk of public exposure to toxins. We improved an approach to predict the spatially and temporally resolved probability of microcystins (MCs) exceeding a threshold level (6 μg L−1) in western Lake Erie. This approach combines a 5‐day chlorophyll‐a forecast model, a weekly updated regression model predicting MCs from chlorophyll‐a, and an empirical relationship between predicted MCs and observed probability of MCs exceeding the threshold calibrated over a hindcast period. We included additional years in the database for calibration and assessment, applied an empirical bias adjustment to the Moderate Resolution Imaging Spectroradiometer for consistency with Sentinel‐3 satellite imagery, and applied a robust Siegel regression method. Cross‐validation showed reasonable skill over regions including surface water, public water system plant intake sites, and bottom waters. The forecast also presented useful skill when assessed against two intensive sampling events of Microcystis blooms in western Lake Erie in 2018 and 2019. Our results provide a comprehensive assessment of a novel method to forecast MC risk, which may be recalibrated and applied to other systems affected by toxic cyanobacterial blooms, where a similar relationship exists between chlorophyll and toxin concentrations at toxin levels relevant to advisory levels.