Predicting the frequency and aerial extent of flooding in river valleys is essential for infrastructure design, environmental management, and risk assessment. Such flooding occurs when the discharge of water within a river channel exceeds its maximum capacity and the extra water submerges the adjoining floodplain surface. The maximum capacity of a channel is controlled by its geometry, gradient, and frictional resistance. Conventional flood prediction methods rely on assumptions of unchanging flood probabilities and channel capacities. However, changes in climate, land cover, and water management have been shown to systematically shift the magnitude and variability of flood flows in many systems. Additionally, alluvial river channels continually adjust their geometries according to characteristics of flow and sediment regimes. For example, channels can expand their geometry during high-energy flows through erosion, then contract their geometry through sediment deposition during low-energy flows. This means that changes in flow magnitudes, frequencies, or durations can cause changes in a channel’s maximum capacity due to adjustments in river channel geometry. Therefore, future changes in river flow regimes and channel geometry may amplify or attenuate the frequency and magnitude of flood inundation in unexpected ways. The focus of this thesis is the development of a novel simulation model to investigate potential changes in the frequency and aerial extent of floodplain inundation due to systematic changes in peak flows and subsequent adjustments in channel geometry and capacity. The model was run using six hypothetical flow scenarios to explore how changes in the mean and variance of an annual peak flow series influences the frequency and magnitude of floodplain inundation. In order to qualitatively simulate the various mechanisms controlling channel adjustment across a continuum of different river environments, each scenario was run multiple times while gradually varying model parameters controlling the amount of permissible adjustment in channel geometry. Results suggest that systematic shifts in peak flows cannot be translated directly to changes in the frequency or magnitude of floodplain inundation due to the non-linear factors controlling the rate and trajectory of channel adjustment. Insights gained from these results demonstrate the need to account for potential changes in both peak flows and channel capacities in the prediction and mitigation of flood hazards.

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There is mounting evidence that microplastics are a persistent and increasing hazard for aquatic organisms. The effects of microplastics on organisms and ecosystems are complex, however, and may be linked to a wide variety of particle characteristics including size, shape, polymer, additive chemistry, and degree of weathering. Assessing risk is complicated by the fact that many known effects of microplastics are sublethal, and that plastics have been postulated to interact with other stressors, such as pathogens. The work presented here expands our understanding of these complex effects. First, the impacts of microplastics on sedimentary microbial ecosystems and biogeochemical carbon and nitrogen cycles were investigated. A microcosm experiment using salt marsh sediment amended with polyethylene (PE), polyvinyl chloride (PVC), polyurethane foam (PUF) or polylactic acid (PLA) microplastics was conducted. We found that the presence of microplastics altered sediment microbial community composition and nitrogen cycling processes. Compared to control sediments without microplastics, PUF- and PLA-treated sediments promoted nitrification and denitrification, while PVC inhibited both processes. These results indicate that nitrogen cycling processes in sediments can be significantly affected by different microplastics, which may serve as organic carbon substrates for microbial communities. Second, we probed the virus-related mortality of a commercially important salmonid species under chronic exposure to nylon microfibers, polystyrene microplastics, and natural marsh grass microparticles. Mortality increased when fish were co-exposed to pathogen and microparticle, particularly nylon microfibers. This correlated with host viral load and mild gill inflammation. As such, we speculated that chronic exposure microplastics may create opportunities for pathogens to bypass defenses and colonize hosts via sensitive tissues. To investigate if this was enhanced by the physical properties of plastic microfibers, we assessed differences in mortality following chronic exposure to nylon microfibers and powder, finding that fibers had a greater effect than powdered counterparts. The importance of the timing of microplastic exposure was also confirmed by completing viral/microplastics co-exposures where microplastics were dosed before, after, or before and after viral introduction. Indeed, virulence was most enhanced when fish were exposed to microplastics pre-virus or chronically, significantly more so than post-virus only. Finally, we tested whether UV-weathering changed the effect of natural and plastic microparticles on disease-related mortality. We observed changes in the virulence effects of microparticles following UV-weathering, but the pattern of that change was inconsistent and merits further research. Considering their ubiquity and increasing concentrations globally, further research on the effects of microplastics is warranted. Particularly, the work here demonstrates that microplastics may influence entire communities and inorganic nutrient cycling systems, classifying microplastics as a potential planetary boundary threat. Further, we illustrate that even when microplastics alone may not have substantial effects on a fish population, when combined with disease they may amplify pathogen-related mortality significantly. More research on the interplay between microplastics and infectious disease is recommended, particularly as it may inform researchers on the risks of microplastics to human health.

Chesapeake Bay is one of the most productive ecosystems on the US east coast which supports various living resources and habitat, and therefore has significant impacts on human beings and ecosystem health. Developing the capability of accurately simulating the water quality condition in the Chesapeake Bay, such as seasonal hypoxia, phytoplankton production, and nutrient dynamics, helps to better understand the interactions of hydrodynamical and biochemical processes, and more importantly, to predict conditions under changing climate and human intervention. Currently, most Chesapeake Bay models use structured grids that lack the flexibility for local refinements to fit complex geometry over both large and small scales, which hampers the allocation of local TMDLs for shallow water and small tributaries. In addition, few of them extend their simulations beyond the water column state variables, such as dissolved oxygen and nutrients, to include other living resources such as vegetation. These limitations motivate the model developments in this dissertation of: (1) a new comprehensive water quality model using high-resolution unstructured grids, which possesses the cross-scale capability to study interactions among water bodies and processes of different scales; and (2) a tightly coupled tidal marsh model, which is linked to the water quality model for water column to study the interactions between the marshes and surrounding aquatic system. The new modeling tool can be effectively utilized as a powerful tool for adaptive management in the Chesapeake Bay and can also be exported to other estuaries in the world.In this dissertation, Chapter 2 focuses on the development of a high-resolution water quality model in the water column and sediment flux part of the water quality model. This part of this study also demonstrates the importance of the correct representation of geometry, and the detrimental effects of artificial bathymetry smoothing on model simulations. Chapter 3 of this dissertation studies the impacts of sea-level rise (SLR) on seasonal hypoxia and phytoplankton production in the Chesapeake Bay with the newly developed water quality model. SLR is predicted to increase the hypoxic volume in the Chesapeake Bay by altering the physical processes and enhancing the estuarine respirations. Phytoplankton production in the shallow shoals is also predicted to increase under SLR, as a result of increased light utilization. Chapter 4 of this dissertation focuses on developing a new marsh model in the hydrodynamic-water quality model framework. This new model extends the model coverage to the tidal wetlands which are periodically inundated. The tidal marshes are suggested to affect the estuarine oxygen, carbon, and nutrient dynamics through tidal exchange, e.g., contributing the diel DO cycle. Chapter 5 studies the impacts of SLR on the biochemical processes in the York River Estuary, a tributary of the Bay that has extensive tidal marshes, with the fully-coupled hydrodynamic-water quality-marsh model. The SLR is predicted to enhance the exchanges between the marshes and the adjacent channel, which in turn further impacts the estuarine biochemical processes.

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