Characterizing the kinematics of river deltas using the automatic extraction and analysis of channel networks from remotely sensed imagery

dc.contributor.advisorPassalacqua, Paola
dc.contributor.committeeMemberFaust, Kasey
dc.contributor.committeeMemberBovik, Alan
dc.contributor.committeeMemberMohrig, David
dc.contributor.committeeMemberHodges, Ben
dc.creatorJarriel, Teresa Marie
dc.creator.orcid0000-0002-0344-4569
dc.date.accessioned2022-07-29T21:34:07Z
dc.date.available2022-07-29T21:34:07Z
dc.date.created2021-05
dc.date.issued2021-05-07
dc.date.submittedMay 2021
dc.date.updated2022-07-29T21:34:08Z
dc.description.abstractRiver deltas are globally ubiquitous networks that form where a river system meets the sea and deposited sediment cannot be fully dispersed. River deltas’ low elevation gradients, coastal locations, and dense populations make them particularly susceptible to the adverse effects of flooding, subsidence, sea-level rise, and human structures such as dams, embankments, and dikes. With hundreds of millions of people living on these dynamic systems, it is critically important to understand and predict how delta channel networks will evolve over time. In this dissertation, a new methodology for the automatic extraction of channel kinematics is developed and applied to experimental and real-world delta systems in order to better understand how changing internal and external forcings affects the kinematics of river delta channels. While many delta studies use hydrologic models to study delta kinematics, in this dissertation we use only remotely sensed imagery analyses so that the work is globally replicable and independent of in-situ data availability. In the first study, a methodology for the automatic extraction of channel kinematics is developed and applied to imagery of an experimental delta basin subjected to five different sets of forcings. We find that increasing magnitudes of water and sediment inflow result in corresponding increases in channel movement. We find that increasing the ratio of sediment to water inflow results in increased number of channelized areas. Adding cohesion to the exposed sediment surface of the experimental delta results in a decreased number of channelized areas. Finally, by detecting changes in channel presence over time, we are able to quantify the time scale of internal channel reorganization events as the experimental delta evolves under constant forcings. In the second study, the same methodology is applied to real-world imagery of the Ganges Brahmaputra Meghna Delta (GBMD), a particularly large and complex delta in Bangladesh and India, in order to relate delta kinematics to processes acting on the system. We find that the distributions of channel kinematics are related to the spatial distribution of the dominant physiographic forcings in the system (tidal and fluvial influence, channel connectivity to the primary source rivers, and anthropogenic interference). We find that the anthropogenically modified embanked regions have much higher levels of geomorphic change than the adjacent natural Sundarban forest due to channel infilling and increased rates of channel migration. In the final study, we present a new methodology for the automatic extraction of channel migration vectors from remotely sensed imagery by combining deep learning and principles from particle image velocimetry. This new methodology is implemented on 48 river delta systems to create the first global dataset of decadal scale delta channel migration. By comparing delta channel migration distributions to a variety of known external forcings, we find that global patterns of channel migration can largely be reconciled with the level of fluvial forcing acting on the delta, average annual sediment flux, and frequency of flood events. The results of these three studies provide a comprehensive analysis of the drivers of deltaic channel kinematics across different scales. This dissertation provides critical information needed to successfully predict future changes to delta systems and inform decision makers striving for deltaic resilience.
dc.description.departmentCivil, Architectural, and Environmental Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/115031
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/41934
dc.language.isoen
dc.subjectRiver deltas
dc.subjectCoastal networks
dc.subjectRemote sensing
dc.subjectMorphodynamics
dc.titleCharacterizing the kinematics of river deltas using the automatic extraction and analysis of channel networks from remotely sensed imagery
dc.typeThesis
dc.type.materialtext
local.embargo.lift2023-05-01
local.embargo.terms2023-05-01
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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