Taylor, Jackie2022-11-142022-11-142022-06https://hdl.handle.net/11299/243095University of Minnesota Ph.D. dissertation. June 2022. Major: Civil Engineering. Advisors: MIki Hondzo, Vaughan Voller. 1 computer file (PDF); xvi, 138 pages.Harmful algal blooms (HABs) occur when algae populations accumulate in large concentrations near the air-water interface in aquatic environments. HABs---which, due to climate change, are increasing in severity, frequency, and global distribution---are hazardous to both environmental and public health. A particularly concerning algal species is the ubiquitous cyanobacterium Microcystis, which produces toxic microcystins and is especially competitive in thermally stratified lakes. Despite the growing body of evidence that suggests vertical heterogeneity of Microcystis can be a precursor to HAB formation, the abiotic drivers of vertical distribution of Microcystis are poorly understood in the field environment. The prediction of subsurface peaks in cyanobacteria concentrations is also pertinent because subsurface concentrations are not easily recognizable to the public or lake system managers, creating a risk of exposure to harmful algal toxins. To understand how vertical distributions of Microcystis are impacted by lake temperature profiles and hydrodynamics, we conducted a field study with novel monitoring technology. High-frequency temporal and vertical data were collected from a research station anchored in a stratified and eutrophic lake for five months, which is detailed in Chapter 2. Using a combination of dimensional analysis and machine learning approaches, data show that the magnitude of the subsurface Microcystis concentration peak and the center of gravity of the deep cyanobacteria layer are statistically significantly mediated by the thermal structure of the lake. The peak subsurface cyanobacteria biovolume is related to the thermocline depth and temperature, whereas the center of gravity of the subsurface cyanobacteria biovolume is related to the mixed layer depth and temperature. Furthermore, our data suggest there is a seasonal evolution of the subsurface cyanobacteria center of gravity that could potentially indicate timing of HAB onset. Based on easily measured parameters associated with the vertical lake temperature profile and meteorological conditions, we provide evidence of predictable trends in subsurface cyanobacteria variables. The field investigation revealed observations connecting lake hydrodynamics and Microcystis vertical distributions that make it possible to construct a mathematical model to understand the underlying physical phenomena behind the field observations (i.e, the lake thermal profile mediation of subsurface peaks in cyanobacteria concentration). For many harmful algae species in eutrophic lakes, the formation of such blooms is controlled by three factors: the lake hydrodynamics, the vertical motility of the algae organisms, and the ability of the organisms to form colonies. Here, using the common cyanobacterium Microcystis aeruginosa as an example, we develop a model that accounts for both vertical transport and colony dynamics. At the core of this treatment is a model for algal aggregation, described using Smoluchowski dynamics containing parameters related to Brownian motion, turbulent shear, differential setting, and cell-to-cell adhesion. To arrive at a complete description of bloom formation, we place the Smoluchowski treatment as a reaction term in a set of one-dimensional advection diffusion equations which account for the vertical motion of the algal cells through molecular and turbulent diffusion and self-regulating buoyant motion. This model is rich with interesting mathematics, both analytical and numerical. We have depth-dependent dispersion, spatiotemporal oscillatory advective velocities, a mixture of discrete and continuous variables, and all of this in a complex system of hundreds of partial differential equations. Before interpreting the implications of model results on Microcystis vertical distributions in the field, we must understand how the model behaves numerically. To this end, we investigate the accuracy and stability of various numerical schemes in Chapter 3. We offer estimations of numerical dispersion for an upwind scheme with temporally oscillating velocity fields. The numerical dispersion of the first-order upwind is then compared to a quadratic upwinding scheme with a flux-limiter, switching between first-order and quadratic upwind to improve accuracy while retaining stability. Finally, we draw conclusions on the impacts of various model features on numerical accuracy and stability. Namely, while first-order upwind schemes underestimate peak concentrations and overestimate concentration pulse widths in advection-dominated flow regimes, upwinding still predicts peak location and time to large colony appearance accurately in all flow regimes. Thus, due to the robustness and simplicity of the scheme, first-order upwind is an appropriate numerical scheme for the transport and aggregation of harmful algae. Once the numerics are clearly understood, Chapter 4 investigates model performance for predicting Microcystis vertical distributions in a field environment. Results indicate Smoluchowski aggregation qualitatively describes the colony dynamics of M. aeruginosa. Further, the model demonstrates wind-induced mixing is the dominant aggregation process and the rate of aggregation is inversely proportional to algal concentration. Essentially, a large wind event and high algal concentrations are necessary and sufficient conditions for the rapid aggregation of M. aeruginosa. Because blooms of Microcystis typically consist of large colonies, both of these findings have direct consequences to harmful algal bloom formation. While the theoretical framework outlined in this chapter was derived for M. aeruginosa, both motility and colony formation are common among bloom-forming algae. As such, this coupling of vertical transport and colony dynamics is a useful step for improving forecasts of surface harmful algal blooms, which is the guiding motivation of this thesis. Using a combination of data-driven and mechanistic models, the work described herein can help stakeholders assess the risk of toxic HAB formation, thereby mitigating the adverse environmental and public health impacts.encyanobacteriaharmful algal bloomsMicrocystis aeruginosaThesis or Dissertation