Assessment of Vulnerability of Source Waters to Toxic Cyanobacterial Outbreaks

While it is known that the growth of cyanobacteria in lakes and reservoirs is favored by high nutrient concentrations, elevated temperatures, thermal stratification, and high levels of sunlight, the dynamic seasonal and temporal combinations of these factors is not well understood in individual circumstances. This project will address this challenge by advancing new methods from interpretable artificial intelligence (IAI). The IAI will utilize user-provided water quality data and publicly available data from weather stations and satellite imagery to compute risk metrics of cyanobacterial harmful algal blooms (cHABs) forming in a given waterbody. An interactive web and mobile application will be developed for users to explore the results, including estimating the effect of specific events on future cHAB risk.