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.
| Identifer | oai:union.ndltd.org:wm.edu/oai:scholarworks.wm.edu:etd-7373 |
| Date | 01 January 2022 |
| Creators | Cai, Xun |
| Publisher | W&M ScholarWorks |
| Source Sets | William and Mary |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations, Theses, and Masters Projects |
| Rights | © The Author, http://creativecommons.org/licenses/by/4.0/ |
Page generated in 0.0022 seconds