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This data was collected over 34 sampling trips during four summers (May-October), 2019-2022. 35 grid boxes were generated over Lake Mendota. Before each sampling effort, sample point locations were randomized within each grid box. Surface measurements were taken with an EXO multi-parameter sonde at a subset of the 35 grid boxes throughout Lake Mendota during each sampling trip. Measurements include temperature, conductivity, chlorophyll, phycocyanin, turbidity, dissolved organic material, ODO, pH, and pressure. 

As anthropogenic eutrophication and the associated increase of cyanobacteria continue to plague inland waterbodies, local officials are seeking novel methods to proactively manage water resources. Cyanobacteria are of particular concern to health officials due to their ability to produce dangerous hepatotoxins and neurotoxins, which can threaten waterbodies for recreational and drinking-water purposes. Presently, however, there is no cyanobacteria outlook that can provide advance warning of a potential threat at the seasonal time scale. In this study, a statistical model is developed utilizing local and global scale season-ahead hydroclimatic predictors to evaluate the potential for informative cyanobacteria biomass and associated beach closure forecasts across the June–August season for a eutrophic lake in Wisconsin (United States). This model is developed as part of a subseasonal to seasonal cyanobacteria forecasting system to optimize lake management across the peak cyanobacteria season. Model skill is significant in comparison to June–August cyanobacteria observations (Pearson correlation coefficient = 0.62, Heidke skill score = 0.38). The modeling framework proposed here demonstrates encouraging prediction skill and offers the possibility of advanced beach management applications.