Assistant Professor FAMU-FSU College of Engineering
Harmful algal blooms (HABs), driven by excess nutrient export from various sources such as agricultural runoff and wastewater treatment discharges, pose significant risks to public health, wildlife, and local economies. Considering the risks associated with HABs, there is an urgent need for a comprehensive tool to understand and mitigate the increasing occurrence of HABs in freshwater lakes. This study aims to develop an interactive web-based tool that assesses and indicates the relationship between cyanobacteria concentrations (indicators of HABs) and a range of watershed variables including land use patterns, climate variables and streamflow metrics in 134 of Florida’s lakes. To achieve this goal, we have utilized satellite remote sensing data from 2002 to 2022 across Florida derived from different sensors including MERIS and OLCI on Sentinel-3A/3B for cyanobacteria concentrations.
The platform employs machine learning algorithms to correlate these datasets providing insights into cyanobacteria concentrations. Our findings from Lake Munson, reveal that significant blooms predominantly occurred during the summer season. Additionally, we observed high-value outliers for TP, turbidity, and Chl-a concentrations, with TP spikes primarily occurring in summer, and turbidity and Chl-a spikes mainly observed in fall. These observations suggest seasonal fluctuations in water quality parameters which could influence HAB management strategies. The developed interactive tool enables decision-makers to formulate sustainable watershed management strategies such as best management practices (BMPs) adapted specifically for regions experiencing HABs. The proposed tool will not only facilitate informed decision-making but also contribute significantly towards understanding the complex interactions between land use changes; water quality variations; meteorological factors; nutrient loading rates; streamflow metrics; and their combined impact on cyanobacteria bloom occurrences.
