Date of Award
5-2019
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Ocean Engineering and Marine Sciences
First Advisor
Steven Lazarus
Second Advisor
Pallav Ray
Third Advisor
Nezamoddin Nezamoddini-Kachouie
Fourth Advisor
Richard Aronson
Abstract
While wind driven waves affect erosion, sediment resuspension, and flow in shallow estuaries like the Indian River Lagoon (IRL), neither winds nor waves are well observed in this environment. In order to calculate accurate significant wave heights, the winds used for the calculation must be consistent with the observed winds over the lagoon. Given the complex land/water geometry and subsequent limited fetch, a probabilistic approach is used to produce a representative wind field over the IRL. Observed winds, near the IRL, are used to sample wind distributions obtained from 180 high resolution atmospheric model simulations in order to generate a synthetic wind time series. Significant wave heights are then calculated using a well-known wave height parameterization, modified for shallow water, which depends on the synthetic winds, fetch, and bottom topography. Results within the IRL indicate that significant wave heights appear to be sensitive to wind direction in addition to wind speed. The bathymetry also has an impact on significant wave heights in the shallow estuary. Due to the lack of observations, the parameterized significant wave heights are compared to wave model simulations. Statistics indicate that the parameterized wave heights are biased low compared to the model. However these differences can, in part, be explained by a mismatch between the model and parameterization topography. The wave parameterization is a less expensive alternative to running probabilistic, long term, hydrodynamic-wave model simulations within shallow water estuaries such as the IRL.
Recommended Citation
Haley, Vanessa Maria, "A Probabilistic Approach to Generating Representative Wind Forcing and Wave Heights within an Estuarine Environment" (2019). Theses and Dissertations. 1153.
https://repository.fit.edu/etd/1153