ORCID ID
0000-0001-8213-4843
Date of Award
12-2025
Degree Type
Dissertation
Degree Name
Ph.D.
Degree Program
Engineering and Applied Science - Earth & Environmental
Department
Earth and Environmental Sciences
Major Professor
Robert C. Mahon
Second Advisor
Allison M. Penko
Third Advisor
Madeline Foster-Martinez
Fourth Advisor
Ioannis Y. Georgiou
Fifth Advisor
Juliette W. Ioup
Abstract
This dissertation presents three studies that exemplify a framework-based research approach to coastal modeling, addressing parameter optimization, nearshore current estimation, and sediment mobility characterization. While these studies focus on distinct modeling objectives, they share a common systematic methodology and unified goal: extending the utility of coastal modeling to data-scarce settings. The framework-based approach emphasizes systematic parameter space exploration, modular architecture, automated workflows, and comprehensive quality control to extract maximum insight from available data while quantifying limitations and establishing guidance for use. The first study develops a parameter sensitivity analysis and optimization framework which is applied to the northern Gulf of Mexico during Hurricane Michael (2018) and to the Oceanside, California nearshore during the ExCaliBur field experiment (2022). This systematic approach reveals patterns of spatial- and enery-dependence in optimal hydrodynamic and wave model parameters. The second study assesses when globally available forcing can adequately predict nearshore currents by systematically comparing simplified-input models against comprehensively-forced models. Applied to Oceanside, California, this analysis provides operational guidance for determining when simplified approaches yield acceptable accuracy versus when comprehensive time-series forcing becomes necessary for reliable current predictions. The third study estimates continental shelf sediment mobility using only globally available datasets through development of an automated workflow that processes multi-decadal reanalysis data into representative climatological forcing. Applied across the northern Gulf of Mexico continental shelf, this framework reveals spatial patterns of sediment mobility, seasonal variability, and episodic event impacts that inform marine mineral extraction planning. This work advances coastal modeling in data-scarce settings by developing systematic approaches to enable coastal process characterization worldwide. The system in the first study can be used to determine appropriate parameter settings for locations where model calibration is not feasible. The second and third studies demonstrate frameworks that can be deployed without local observational data or regional forecast models, providing first-order understanding of coastal processes where none previously existed.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Allen, Sunni Siqueira, "Adapting Coastal Modeling Techniques for Data-Scarce Settings" (2025). University of New Orleans Theses and Dissertations. 3329.
https://scholarworks.uno.edu/td/3329
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.