Date of Award

2021

Degree Type

Thesis

Degree Name

Master of Science in Ocean Engineering

Department

Ocean Engineering

First Advisor

M Reza Hashemi

Abstract

Coastal erosion and shoreline retreat has become a major challenge due to sea level rise. Green Hill Pond beach is located along the southern shore of Rhode Island, which is made up of multiple wave-dominated barriers fronting coastal ponds. This area is susceptible to flooding due to a combination (hurricanes and Nor’easters), sea level rise, and erosion. The eastern end of Green Hill Pond is particularly vulnerable to erosion and flooding due to it being a relatively low-lying region.

The first part of this thesis assessed the impact of various storms (10, 20, and 30-year return period) on the low-lying region of Green Hill Pond using numerical model XBeach. After the initial assessment was completed, an additional assessment of the impact of a dune restoration in the area was done. This analysis showed a 4 m dune above NAVD88 would help improve the resiliency of the pond and surrounding area during each of the return period storms with the largest impact being during a 10-year storm.

The second part of this thesis looked at using the coupled XBeach-Duna model to simulate the longterm morphodynamics of the area near Green Hill Pond from 2011 to 2018. The focus of the second part was modeling the recovery of the beach following Hurricane Sandy in 2012. This assessment showed that the model has the ability to simulate longterm coastal processes including aeolian driven sediment transport for this region. Results showed a relatively good agreement between calculated and observed unit volume changes as well as morphological changes by comparing beach and dune profiles. There were some discrepancies with observed data that could be the result of almost a year worth of wave data gap or due to simplifications and assumptions made in the model.

Many simplifications and assumptions were made in the pre-processing phase for model set up such as representative single values for wave data and wind speed for each event. These assumptions and simplifications were necessary to create a time series for an eight year period using the available data. Further work can implement more advanced models that include process such as the sediment transport between a nearshore and regional model as well as better simulation of longshore transport.

As this model is a process-based model it can be used to project shoreline changes in the future in response to sea level rise. This is one of the benefits of using a process-based model over a data based model which is more difficult to use to make future predictions.

Available for download on Thursday, August 11, 2022

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