@article{han_aziz_del giudice_hall_obenour_2021, title={Exploring nutrient and light limitation of algal production in a shallow turbid reservoir}, volume={269}, ISSN={["1873-6424"]}, DOI={10.1016/j.envpol.2020.116210}, abstractNote={Harmful algal blooms are increasingly recognized as a threat to the integrity of freshwater reservoirs, which serve as water supplies, wildlife habitats, and recreational attractions. While algal growth and accumulation is controlled by many environmental factors, the relative importance of these factors is unclear, particularly for turbid eutrophic systems. Here we develop and compare two models that test the relative importance of vertical mixing, light, and nutrients for explaining chlorophyll-a variability in shallow (2–3 m) embayments of a eutrophic reservoir, Jordan Lake, North Carolina. One is a multiple linear regression (statistical) model and the other is a process-based (mechanistic) model. Both models are calibrated using a 15-year data record of chlorophyll-a concentration (2003–2018) for the seasonal period of cyanobacteria dominance (June–October). The mechanistic model includes a novel representation of vertical mixing and is calibrated in a Bayesian framework, which allows for data-driven inference of important process rates. Both models show that chlorophyll-a concentration is much more responsive to nutrient variability than mixing, light, or temperature. While both models explain approximately 60% of the variability in chlorophyll-a, the mechanistic model is more robust in cross-validation and provides a more comprehensive assessment of algal drivers. Overall, these models indicate that nutrient reductions, rather than changes in mixing or background turbidity, are critical to controlling cyanobacteria in a shallow eutrophic freshwater system.}, journal={ENVIRONMENTAL POLLUTION}, author={Han, Yue and Aziz, Tarek N. and Del Giudice, Dario and Hall, Nathan S. and Obenour, Daniel R.}, year={2021}, month={Jan} }
 @article{han_smithheart_smyth_aziz_obenour_2020, title={Assessing Vertical Diffusion and Cyanobacteria Bloom Potential in a Shallow Eutrophic Reservoir}, volume={36}, ISSN={["2151-5530"]}, url={http://dx.doi.org/10.1080/10402381.2019.1697402}, DOI={10.1080/10402381.2019.1697402}, abstractNote={Abstract Han Y, Smithheart JW, Smyth RL, Aziz TN, Obenour CR. 2019. Assessing vertical diffusion and cyanobacteria bloom potential in a shallow eutrophic reservoir. Lake Reserv Manage. 36:169–185. Harmful blooms of cyanobacteria are an increasing threat to many lakes and reservoirs. While vertical mixing has been shown to be an important control on cyanobacteria dominance in some lakes, the relevance of mixing in relatively shallow turbid systems remains unclear. To explore mixing and its impact on cyanobacteria bloom potential, we leveraged data from a multiyear field campaign of a central North Carolina reservoir where artificial circulators were installed to (1) implement a parsimonious one-dimensional (1D) turbulent diffusion model with an artificial circulation term, (2) introduce a novel multi-objective calibration approach considering both water column temperature and stability, and (3) explore how mixing affects cyanobacteria bloom potential through changes in cyanobacteria light exposure relative to other algal taxa. Our multi-objective calibration approach is shown to realistically simulate both water temperature (R2 = 0.99) and water column stability (R2 = 0.62) throughout the year. Analysis of artificial mixing demonstrates the relative insignificance of the circulator deployment in our study area and suggests that at least eight times the implemented circulation rate would be required to substantially reduce the ability of buoyant cyanobacteria to outcompete other algal taxa for light. Overall, this study demonstrates an efficient and systematic approach for characterizing vertical mixing in lakes and reservoirs, which can be used to assess the viability of artificial circulation prior to deployment.}, number={2}, journal={LAKE AND RESERVOIR MANAGEMENT}, publisher={Informa UK Limited}, author={Han, Yue and Smithheart, Jeremy W. and Smyth, Robyn L. and Aziz, Tarek N. and Obenour, Daniel R.}, year={2020}, month={Apr}, pages={169–185} }