Cyanobacterial harmful algal blooms (cHABs) have become more frequent and intense over the past few decades and are projected to continue to increase in severity and toxicity due to a warming climate and anthropogenically-enhanced nutrient loading. As such, detecting and monitoring cHAB development and toxicity are of growing importance, especially for freshwater systems such as the Laurentian Great Lakes that supply drinking water to many municipalities. Traditional sampling and analysis methods are time-consuming, labor intensive, and generally implemented on only a weekly or bi-weekly basis, which may fail to detect ephemeral yet highly toxic bloom events. Fortunately, novel, fit-for-purpose detection technologies are becoming available to address previous constraints by providing near-real time data. This project directly addresses four research priorities listed in the COHH3 RFA: (1) compare and correlate current observing systems for monitoring ocean and Great Lakes properties including Harmful Algal Blooms, (2) evaluate long-term field application potential of newly developing in situ sensors for monitoring ocean and Great Lakes properties, (3) evaluate real-time, in-water observations of physicochemical properties, as well as the detection of HAB species and toxins, to provide data streams for assimilation by predictive models, (4) develop appropriate and efficient monitoring strategies for algal toxins (particularly in drinking water) that are protective of public health.
The specific aims of the proposed project are to integrate in-situ sensing and sampling technologies with data assimilation strategies to improve forecast accuracy, provide regional stakeholders with advanced warning of cHAB development and toxic events, and evaluate the impacts of climate change on cHABs and internal phosphorus loading in Lake Erie. We will accomplish these aims by integrating an autonomous, in-situ Environmental Sample Processor, Solid Phase Adsorption Toxin Tracking devices, water quality probes, and field-portable sampling methods, along with satellite remote sensing with the broader outcome of improving bloom forecasting models and to develop a more timely and complete spatio-temporal picture of developing cHAB toxicity and biomass as well as internal phosphorus loading in Lake Erie. Collectively, GLERL's long-term water quality monitoring and NOAA's advanced cHAB forecasting model (HAB tracker), which integrates satellite data, physicochemical, biological, molecular, and toxicity (this project) data to forecast bloom location, size and toxicity with a 5-day lead time, will facilitate informed, timely decisions to reduce the impacts of toxic cHABs on public health, natural resources, and local economies. Project outputs will also contribute to the Center Program's goal of better understanding the influence of climate change on the frequency and severity of cHABs in Lake Erie and other Great Lakes' regions, and thereby inform long-term planning for development of land use as well as management and mitigation strategies.
