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Detecting groundwater flows from land to sea: a remote sensing perspective

Ramos Caineta, Júlio Alexandre (2022) Detecting groundwater flows from land to sea: a remote sensing perspective. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Submarine groundwater discharge (SGD) represents an important component of the global water cycle. Improved assessment of SGD occurrences provides information to constrain current estimates of total freshwater discharges, which are crucial for evaluation of changes in the global water cycle.

Groundwater usually exhibit distinct and less variable temperature than seawater. Consequently, thermally anomalous plumes often occur in coastal areas with SGD. Nutrient-enriched groundwater flows have been linked to changes in ocean color as a consequence of algae growth and of the presence of other solids. Under such conditions, SGD presence may also manifest as plumes of different colors. The main goal of this research was to demonstrate the improved ability of detecting SGD when combining observations of sea surface temperature (SST) and color. To date, only the thermal response was used from a remote sensing perspective.

A method based on clustering and spectroscopy techniques is applied to detect SGD plumes. SST data are clustered to map thermal anomalies. Derivative analysis and angular distance are applied to identify a color signature linked to SGD. In a novel approach, SST and color are combined to improve the reliability of remote sensing-based SGD detection. The identified plumes are compared to field surveys of SGD occurrences, and verified that the novel methods contribute effectively to map SGD. These methods are further applied to build a time series of SGD detections, whereby time persistent signatures provide stronger SGD evidence. Whereas field measurements provide the strongest evidence of SGD presence, results demonstrate that the developed methods narrow the gap between the well supported field surveys and model-based assessments, by improving the reliability of remote sensing-derived indicators. An alternative approach based on deep learning is proposed to tackle the complex relation between temperature, color, and SGD flows, while also addressing two constraining factors of the initial methods (reliance on one observed spectrum and requirement of significant thermal gradients between groundwater and seawater).

These methods are applied to provide further evidence for SGD occurrences in south Ireland and in Hawaiʻi, and showcase that satellite data can be utilized to better identify SGD occurrences.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Ramos Caineta, Júlio Alexandrejulio.caineta@pitt.edujur300000-0002-7216-4114
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairBain, Daniel
Committee CoChairThomas, Brian
Committee MemberShelef,
Committee MemberNewman, Jeffrey
Committee MemberGardner, John
Committee MemberRamsey, Michael
Committee MemberHarbert,
Date: 12 October 2022
Date Type: Publication
Defense Date: 28 July 2022
Approval Date: 12 October 2022
Submission Date: 23 July 2022
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 175
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computational Modeling and Simulation
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Submarine groundwater discharge, Sea surface temperature, Ocean color, Coastal waters
Date Deposited: 12 Oct 2022 16:14
Last Modified: 12 Oct 2023 05:15

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