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Harmful algal blooms (HABs) of cyanobacteria are a growing problem for freshwater reservoirs in the United States. Issues related to these cyanobacterial blooms include aesthetic problems, hypoxia, taste and odor, and the potential release of cyanotoxins. Enhanced circulation technologies have been proposed as an on-site treatment method to suppress the formation of HABs in freshwater bodies. In this project, physical, chemical, and biological data were collected from summer 2015 to present from three Piedmont North Carolina reservoirs with different levels of artificial circulation (Jordan Lake, University Lake, and City Lake). The timing and severity of cyanobacterial blooms were determined using measurements of algal cell density, algal biovolume, and cyanobacterial dominance from phytoplankton assemblages and chlorophyll grab-samples, which were synthesized with profiles of in vivo phycocyanin and chlorophyll to produce a more comprehensive data set for predictive modeling. A multi-level (hierarchical) modeling framework was then used to account for the spatial and temporal variability of the algal dataset using meteorological conditions, physical lake characteristics, nutrients, and presence or absence of artificial mixing as candidate predictor variables. Modeling results indicate the strength and significance of various factors controlling cyanobacteria dominance.

Jeremy Smithheart (Primary Presenter/Author), NC State University,;

Daniel Obenour ( Co-Presenter/Co-Author), NC State University,;

Yue Han ( Co-Presenter/Co-Author), NC State University, ;

Tarek Aziz ( Co-Presenter/Co-Author), NC State University,;