Large scale seasonal forecasting of peak season algae metrics in the Midwest and Northeast U.S.

In recent decades, many inland lakes have seen an increase in the prevalence of potentially harmful algae. In many inland lakes, the peak season for algae abundance (summer and early fall in the northern hemisphere) coincides with the peak season for recreational use. Currently, little information regarding expected algae conditions is available prior to the peak season for productivity in inland lakes. Peak season algae conditions are influenced by an array of pre-season (spring and early summer) local and global scale variables; identifying these variables for forecast development may be useful in managing potential public health threats posed by harmful algae. Using the LAGOS-NE dataset, pre-season local and global drivers of peak-season algae metrics (represented by chlorophyll-a) are identified for 178 lakes across the Northeast and Midwest U.S. from readily available gridded datasets. Forecasting models are built for each lake conditioned on relevant pre-season predictors. Forecasts are assessed for the magnitude, severity, and duration of seasonal chlorophyll concentrations. Regions of pre-season sea surface temperature, and pre-season chlorophyll-a demonstrate the most predictive power for peak season algae metrics, and resulting models show significant skill. Based on categorical forecast metrics, more than 70% of magnitude models and 90% of duration models outperform climatology.  Forecasts of high and severe algae magnitude perform best in large mesotrophic and oligotrophic lakes, however, high algae duration performance appears less dependent on lake characteristics. The advance notice of elevated algae biomass provided by these models may allow lake managers to better prepare for challenges posed by algae during the high use season for inland lakes.