Streaming Media

Abstract

The goals of this research are to expand our understanding and improve predictions of bed shear stress in estuarine environments using both observational datasets and numerical modeling. To accurately predict sediment transport, a good understanding of the bed shear stress that drives the sediment erosion, suspension and deposition is essential. Shear stress is a function of both the hydrodynamics in the system and the characteristics of the sediment that comprise the bed itself. The hydrodynamic forcing is determined by tides, waves, meteorological effects, rivers, or some combination that can change on time scales of a few minutes to a few days. The sediment characteristics are site specific, and often vary spatially within a given estuary. The size, shape, material type, organic content, and time in a given location can determine whether the sediment will move, and what mode of transportation is probable (i.e. bed load or suspended load). The temporal and spatial variability of these factors make it difficult to collect comprehensive observational datasets, and often only represent a small portion of the overall processes of interest. Numerical models become useful tools to predict how the interactions of different hydrodynamic conditions and sediment characteristics can change the bed shear stress on a variety of scales. Consequently, these models require parameterizing sub-grid scale processes, and suppressing noise associated with numerical discretization. A useful model then becomes a balance between capturing the processes of interest within a particular grid scale and the available computational resources. The purpose of this research is to use the observational datasets from both the hydrodynamics and sediment and bed characteristics of a particular estuary, and 1) verify the hydrodynamic model, and 2) use that model to characterize and predict the spatial and temporal variability of bed shear stress and sediment transport under different hydrodynamic conditions (tides, waves, meteorological forcing, etc.) and in the presence/absence of vegetation (eelgrass). Ultimately this knowledge will useful for more accurate estimates of sediment transport and nutrient fluxes under varying hydrodynamic conditions in the Great Bay estuary, New Hampshire (and inform similar estuarine mudflat environments), which has been previously difficult.

Presenter Bio

Salme Cook is originally from Fairfax, VA, daughter of a naval submarine commander and IT manager. She completed a B.E. in Environmental Engineering and M.E. in Ocean Engineering at Stevens Institute of Technology in Hoboken, NJ. In that time, she worked part-time for the Army Corps of Engineers in their Construction Division, assisting on dredging projects in New York harbor and building construction in Alexandria VA. During her master’s, she was introduced to the exciting world of coastal oceanography and estuarine modeling with her master’s advisor Alan Blumberg. Before deciding on whether or not to pursue a doctorate, she worked for a NJ environmental and geotechnical consulting firm sampling and delineating contaminated sites across New Jersey. Following Superstorm Sandy, she was motivated to return to academia to continue her studies on coastal oceanography, and working to incorporate both observational datasets with numerical models. Tom Lippmann’s lab and collaborators at UNH provided the observational equipment, boats, and know-how to successfully combine the worlds of collecting observational data, with running numerical models to better understand shallow coastal environments like the Great Bay and Hampton-Seabrook estuary in New Hampshire. Salme will go on to work as a Research Oceanographer with the USGS in Woods Hole, MA along with her partner Mike Hitchcock and dog Nanook at the beginning of 2020.

Publication Date

10-7-2019

Document Type

Presentation

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