Periodic Cyanobacterial Harmful Algal Blooms (CyanoHAB) in Kansas reservoirs such as Cheney Lake have the potential to produce toxins and taste-and-odor compounds that may cause substantial economic, public health, and environmental concerns. Predictive tools are needed to better manage CyanoHAB outbreaks, including predictive simulation models, fine-scale remote sensing data, and bacteria detection floating devices. So far, this project has developed and calibrated a comprehensive watershed model for Cheney Lake Watershed to predict inflows and influent nutrient concentrations in the lake; conducted correlation analysis of cyanobacteria concentration against environmental parameters based on multi-year sub-daily USGS dataset from Cheney Reservoir; and began development of a mechanistic modeling framework considering watershed modeling of contributing catchment, process-based modeling of cyanobacteria growth in a lake, and rapid lake assessment. In the next part of the project, a forecasting tool linking with climate and reservoir watershed models would help to conceptualize future CyanoHAB prevention strategies, and its relation with climatic change, watershed condition, and nutrient abundance in the lake.
Contact: Aleksey Sheshukov, Department of Biological and Agricultural Engineering, Kansas State University