EPA researchers are working to develop predictive models to relate nutrient loads, land use, land cover, socioeconomic factors, and climate to the frequency, location, and severity of cyanobacterial blooms in lakes of the United States.

EPA researchers are improving scientific approaches to create a variety of tools that will help decision makers, watershed managers and community leaders make better decisions.

Predictive models for water quality and indices of cyanobacteria

Computational ecologists at the US EPA have used machine learning methods to predict chlorophyll a concentrations in lakes. These approaches rely on large data sets, emphasize open science practices and are well suited to extract information about the potential drivers of water quality. This work used chlorophyll a, a widely used measure of water quality. Chlorophyll a has the additional benefit of being a proxy for cyanobacteria concentrations.    

Additional Resources

Software and Analyses: The following links exit the site Exit

• LakeTrophicModelling. [GitHub]
• Lakemorpho. [GitHub]

Blog Posts:

• Opening our Science: Open Science and Cyanobacterial Research at EPA
• Modeling Cyanobacteria Ecology to Keep Harmful Algal Blooms at Bay
• SPARROWs, Lakes, and Nutrients?

Publications:

• Modelling Lake Trophic State: A Data Mining Approach. (In Review) Submitted to Ecosphere.
• Estimating Summer Nutrient Concentrations in Northeastern Lakes from SPARROW Load Predictions and Modeled Hydraulic Residence Time Exit