Connecting delta morphology, surface processes, and subsurface structure

dc.contributor.advisorPassalacqua, Paola
dc.contributor.committeeMemberHodges, Ben
dc.contributor.committeeMemberWerth, Charles
dc.contributor.committeeMemberMichael, Holly
dc.creatorHariharan, Jayaram Athreya
dc.creator.orcid0000-0002-1343-193X
dc.date.accessioned2023-11-07T01:33:13Z
dc.date.available2023-11-07T01:33:13Z
dc.date.created2022-08
dc.date.issued2022-08-08
dc.date.submittedAugust 2022
dc.date.updated2023-11-07T01:33:14Z
dc.description.abstractHome to a disproportionate population relative to their areas, river deltas are critically important landscapes. Their locations at the interface of the land and sea make them particularly susceptible to sea level rise, while their vast extents limit characterization based on in situ observations alone. Consequently, remote sensing and numerical modeling methods and studies are needed to better understand the current processes occurring within these systems, as well as to estimate their future evolution. In this dissertation, a suite of remote sensing and numerical modeling methods are developed and applied to better understand current processes within deltas, and to project future changes. The first study is an assessment of the accuracy of discharge partitioning estimation from remotely sensed imagery of river delta networks. This analysis aggregates data from 15 site-specific studies to find that errors associated with graph-theoretic estimates of discharge partitioning are consistent across a diverse set of delta landscapes. In the second study, reduced-complexity modeling simulates the evolution and anthropogenic modification of idealized river deltas. Simulation of different hydrodynamic scenarios, and routing of passive particles through the landscape, enables characterizations of the impact that natural morphological differences, anthropogenic modifications, and different flow conditions have on material transport. The results suggest that material type exerts a first-order control over particle behavior, and human modifications to the landscape reduce hydrological connectivity. The third and fourth studies present reduced complexity modeling of deltaic evolution over hundreds of years to investigate the relationship between surface processes and subsurface form within deltaic environments in the context of their future evolution. In the third study, testing of different input sediment compositions and steady rates of sea level rise suggests that both variables influence surface morphology and subsurface connectivity. The fourth study considers the impact of sea level rise acceleration, and finds that the dynamic response of surface channels to an unsteady rate of sea level rise changes based on its magnitude and trajectory. These changes on the surface are mirrored to an extent in the subsurface, which can only be estimated from surface information if the sea level change is relatively steady for some period of time. The results of these studies provide guidance for both policy makers and managers of deltas, as it is clear that humans are significantly impacting the natural processes of these landscapes. Taken together, research conducted as part of this dissertation provides information about current processes and potential future evolution of delta landscapes.
dc.description.departmentCivil, Architectural, and Environmental Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/122484
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/49288
dc.language.isoen
dc.subjectRiver delta
dc.subjectGeomorphology
dc.subjectGraph theory
dc.subjectSurface processes
dc.subjectSubsurface structure
dc.subjectReduced-complexity modeling
dc.subjectNumerical simulation
dc.subjectHydrology
dc.subjectParticle transport
dc.titleConnecting delta morphology, surface processes, and subsurface structure
dc.typeThesis
dc.type.materialtext
local.embargo.lift2024-08-01
local.embargo.terms2024-08-01
thesis.degree.departmentCivil, Architectural, and Environmental Engineering
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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