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dc.contributor.advisorDiMarco, Steven F
dc.contributor.advisorGold-Bouchot, Gerardo
dc.creatorIles IV, Robert Lee
dc.date.accessioned2022-07-27T16:55:48Z
dc.date.created2021-12
dc.date.issued2022-01-04
dc.date.submittedDecember 2021
dc.identifier.urihttps://hdl.handle.net/1969.1/196469
dc.description.abstractBoth marine and terrestrial dissolved organic matter can play a major role in regulating the biogeochemistry and oxygen demand of the coastal ocean and continental shelf region. Where organic matter originates is not the only factor controlling ocean biogeochemistry, but also how recently it was created (i.e. freshness). Samples from two northern Gulf of Mexico cruises in 2019 were analyzed to determine dissolved organic matter fluorescence and subsequently used to create a predictive algorithm for estimating organic matter freshness and source (i.e. terrestrial vs. marine) using a number of variables, including depth, salinity, temperature, and oxygen among others. Additionally, a novel approach for using an established machine learning method, k-means clustering, was used to partition towfish and glider datasets into constituent groups. The derived algorithms were applied to multiple northern Gulf of Mexico datasets to determine the source and freshness of modeled organic matter components. Results showed that, in the early summer near the Mississippi River delta, dissolved organic matter is primarily fresh and terrestrial in origin. By the late summer, the freshest organic matter was found on the western shelf. Riverine impacts to shelf organic matter are reduced when river discharge is lower. Oxygen demand was well-correlated (R² > 0.9) to freshness, regardless of organic matter source. Both terrestrial and marine organic matter were found to correlate to observations of hypoxia, implying that northern Gulf of Mexico hypoxia can be driven by organic matter from sources other than byproducts from algal blooms exclusively. Results suggest that mixing and photodegradation were more impactful to organic matter variability in the upper layers, whereas in the bottom layers, microbial uptake, as deduced by measurements of oxygen demand, was more impactful. Dataset cluster decomposition reveals both the physical structure of the water column as well as allows for quantification of the biogeochemical processes therein. The clusters produced in this study are shown to represent water masses distinguished by river plumes, wind-induced upwelling effects, shifts in currents, density-induced stratification, and microbial activity. This research shows the impacts that organic matter source and freshness can have on the biogeochemistry of a continental shelf region, in particular with respect to oxygen demand. The methods and algorithms produced in this study could also be applied to other river-dominated regions to investigate further the role of dissolved organic matter in ocean biogeochemistry.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectCDOM
dc.subjectunderwater gliders
dc.subjectmachine learning
dc.subjectcontinental shelf
dc.subjectcoastal
dc.subjectriver influence
dc.titleDiagnosing CDOM Dynamics in a River-Dominated Coastal Margin: Application to the Texas-Louisiana Shelf Using Machine Learning and Autonomous Vehicles
dc.typeThesis
thesis.degree.departmentOceanography
thesis.degree.disciplineOceanography
thesis.degree.grantorTexas A&M University
thesis.degree.nameDoctor of Philosophy
thesis.degree.levelDoctoral
dc.contributor.committeeMemberPotter, Henry
dc.contributor.committeeMemberQuigg, Antonietta
dc.type.materialtext
dc.date.updated2022-07-27T16:55:49Z
local.embargo.terms2023-12-01
local.embargo.lift2023-12-01
local.etdauthor.orcid0000-0003-3452-0365


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