Variability of summer cyanobacteria abundance: can season-ahead forecasts improve beach management?

Abstract

Beal MRW, O’Reilly BE, Soley CK, Hietpas KR, Block PJ. 2022. Variability of summer cyanobacteria abundance: can season-ahead forecasts improve beach management? Lake Reserv Manage. 39:37–52.

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.

Keywords:

• Blue-green algae
• cyanobacteria
• Lake Mendota
• season-ahead forecasting

Acknowledgments

We acknowledge Madison & Dane County Public Health for providing beach closing data and management applications of forecasts. We also acknowledge the North Temperate Lakes Long Term Ecological Research Program for providing cyanobacteria abundance data. Datasets for this research are available in these in-text data citation references: Magnuson et al. (2020), NCEP/NWS/NOAA/USDC (1994), PHMDC (2020), USGS (2021a, 2021b), Wuertz et al. (2018). The model code used in this study is available at https://github.com/mrwbeal/MendotaCyanobacteriaForecast.

Additional information

Funding

This work is supported by an NSF CAREER project grant (1845783), a NOAA SARP project grant (NA14OAR4310270), and the UW–Madison Graduate School.