ORCID ID

0000-0001-8213-4843

Date of Award

8-2025

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Earth & Environmental Science

Department

Earth and Environmental Sciences

Major Professor

Robert Mahon

Second Advisor

Juliette Ioup

Third Advisor

Madeline Foster-Martinez

Abstract

This thesis presents a framework for parameter sensitivity analysis and optimization of coastal hydrodynamic models. The research uses the Generalized Likelihood Uncertainty Estimation (GLUE) method to quantify parameter sensitivity and identify optimal values for currents, wave height, and water level. A variance-based technique guides parameter selection when GLUE yields competing values or when observations are unavailable.

The framework is demonstrated through two case studies: Hurricane Michael (2018) and the ExCaliBur field study (2022) near Oceanside, CA. Results from Hurricane Michael show higher optimal hydrodynamic model bottom friction values near the hurricane's landfall location. The Oceanside study identifies the hydrodynamic model's bottom friction coefficient as a key parameter, with an optimized value of 0.043 improving model skill for this location and conditions. The wave model's bottom friction coefficient is important for accurately modeling significant wave height, while horizontal eddy viscosity affects alongshore currents.

This thesis discusses implications for data-scarce settings and outlines future research, including transferring optimized parameter sets and developing predictive relationships. This research improves coastal model accuracy where observations are available and establishes a foundation for enhanced predictions in data-scarce coastal regions through informed parameter selection.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Included in

Oceanography Commons

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