{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/21019"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/21019","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Estimating Reservoir Storage Dynamics Using Satellite Remote Sensing and Hypsometric Information","abstract":"Reservoirs are crucial components of the global water cycle, accounting for over half of the seasonal variability in global surface water storage. Their operation significantly influences downstream hydrology, ecosystem integrity, and water availability. However, a persistent lack of publicly accessible reservoir operation data, especially in transboundary and developing regions, hampers our understanding of storage dynamics and limits the development of adaptive, integrated water management strategies. As dam construction accelerates globally, particularly in developing countries, there is an urgent need for promising solutions to monitor and analyze reservoir behavior across diverse landscapes and political boundaries. This dissertation aims to leverage the strengths of remote sensing technologies to enhance the estimation of reservoir outflows both in space and time, especially in highly regulated, transboundary regions. To achieve this objective, Chapter 2 investigates the feasibility of using remote sensing techniques to evaluate the impact of ungauged reservoirs on downstream hydrology. Chapter 3 introduces a novel approach for deriving reservoir hypsometry by combining satellite observations with geometry-based methods, thereby improving the accuracy and scalability of global reservoir storage estimates. Chapter 4 introduces a data fusion framework to generate sub-weekly time series of reservoir surface area, which improves the temporal continuity of reservoir monitoring and evaluates the reliability of fusion-based area and storage estimation. The findings from this dissertation advance future research in accurately studying and quantifying hydrological processes. They help address the critical lack of in situ observations of reservoir storage dynamics, particularly in developing countries, and provide improved information for water resources management and operational decision-making.","abstract_html":"Reservoirs are crucial components of the global water cycle, accounting for over half of the seasonal variability in global surface water storage. Their operation significantly influences downstream hydrology, ecosystem integrity, and water availability. However, a persistent lack of publicly accessible reservoir operation data, especially in transboundary and developing regions, hampers our understanding of storage dynamics and limits the development of adaptive, integrated water management strategies. As dam construction accelerates globally, particularly in developing countries, there is an urgent need for promising solutions to monitor and analyze reservoir behavior across diverse landscapes and political boundaries. This dissertation aims to leverage the strengths of remote sensing technologies to enhance the estimation of reservoir outflows both in space and time, especially in highly regulated, transboundary regions. To achieve this objective, Chapter 2 investigates the feasibility of using remote sensing techniques to evaluate the impact of ungauged reservoirs on downstream hydrology. Chapter 3 introduces a novel approach for deriving reservoir hypsometry by combining satellite observations with geometry-based methods, thereby improving the accuracy and scalability of global reservoir storage estimates. Chapter 4 introduces a data fusion framework to generate sub-weekly time series of reservoir surface area, which improves the temporal continuity of reservoir monitoring and evaluates the reliability of fusion-based area and storage estimation. The findings from this dissertation advance future research in accurately studying and quantifying hydrological processes. They help address the critical lack of in situ observations of reservoir storage dynamics, particularly in developing countries, and provide improved information for water resources management and operational decision-making.","abstract_has_math":false,"creators":["Nguyen, Ngoc Thi"],"institution":"University of Houston","degree_name":"Doctor of Philosophy","degree_level":null,"degree_discipline":"Geosensing Systems Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Lee, Hyongki"],"committee_chairs":[],"committee_members":["Momen, Mostafa","Li, Hong-Yi","Xie, Surui","Glennie, Craig L."],"year":2026,"date_issued":"2026-05","date_published":"2026-05","updated_at":"2026-07-24T02:31:47Z","subjects":["Reservoir Dynamics","Synthetic Aperture Radar imagery","Satellite Remote Sensing","Hypsometric Relationship","Global Hydrological Model","Transboundary River Basin","Data fusion","Surface area","Reservoir Geometry","DEM","Optical imagery"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/21019","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lee, Hyongki"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Momen, Mostafa","Li, Hong-Yi","Xie, Surui","Glennie, Craig L."]},{"key":"dc:creator","label":"Author","values":["Nguyen, Ngoc Thi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-04-02T19:00:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Geosensing Systems Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Reservoir Dynamics","Synthetic Aperture Radar imagery","Satellite Remote Sensing","Hypsometric Relationship","Global Hydrological Model","Transboundary River Basin","Data fusion","Surface area","Reservoir Geometry","DEM","Optical imagery"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/21019"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Reservoirs are crucial components of the global water cycle, accounting for over half of the seasonal variability in global surface water storage. Their operation significantly influences downstream hydrology, ecosystem integrity, and water availability. However, a persistent lack of publicly accessible reservoir operation data, especially in transboundary and developing regions, hampers our understanding of storage dynamics and limits the development of adaptive, integrated water management strategies. As dam construction accelerates globally, particularly in developing countries, there is an urgent need for promising solutions to monitor and analyze reservoir behavior across diverse landscapes and political boundaries. This dissertation aims to leverage the strengths of remote sensing technologies to enhance the estimation of reservoir outflows both in space and time, especially in highly regulated, transboundary regions. To achieve this objective, Chapter 2 investigates the feasibility of using remote sensing techniques to evaluate the impact of ungauged reservoirs on downstream hydrology. Chapter 3 introduces a novel approach for deriving reservoir hypsometry by combining satellite observations with geometry-based methods, thereby improving the accuracy and scalability of global reservoir storage estimates. Chapter 4 introduces a data fusion framework to generate sub-weekly time series of reservoir surface area, which improves the temporal continuity of reservoir monitoring and evaluates the reliability of fusion-based area and storage estimation. The findings from this dissertation advance future research in accurately studying and quantifying hydrological processes. They help address the critical lack of in situ observations of reservoir storage dynamics, particularly in developing countries, and provide improved information for water resources management and operational decision-making."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Estimating Reservoir Storage Dynamics Using Satellite Remote Sensing and Hypsometric Information"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lee, Hyongki"],"dc:contributor.committeemember":["Momen, Mostafa","Li, Hong-Yi","Xie, Surui","Glennie, Craig L."],"dc:creator":["Nguyen, Ngoc Thi"],"dc:date.accessioned":["2026-04-02T19:00:36Z"],"dc:date.issued":["2026-05"],"dc:description.abstract":["Reservoirs are crucial components of the global water cycle, accounting for over half of the seasonal variability in global surface water storage. Their operation significantly influences downstream hydrology, ecosystem integrity, and water availability. However, a persistent lack of publicly accessible reservoir operation data, especially in transboundary and developing regions, hampers our understanding of storage dynamics and limits the development of adaptive, integrated water management strategies. As dam construction accelerates globally, particularly in developing countries, there is an urgent need for promising solutions to monitor and analyze reservoir behavior across diverse landscapes and political boundaries. This dissertation aims to leverage the strengths of remote sensing technologies to enhance the estimation of reservoir outflows both in space and time, especially in highly regulated, transboundary regions. To achieve this objective, Chapter 2 investigates the feasibility of using remote sensing techniques to evaluate the impact of ungauged reservoirs on downstream hydrology. Chapter 3 introduces a novel approach for deriving reservoir hypsometry by combining satellite observations with geometry-based methods, thereby improving the accuracy and scalability of global reservoir storage estimates. Chapter 4 introduces a data fusion framework to generate sub-weekly time series of reservoir surface area, which improves the temporal continuity of reservoir monitoring and evaluates the reliability of fusion-based area and storage estimation. The findings from this dissertation advance future research in accurately studying and quantifying hydrological processes. They help address the critical lack of in situ observations of reservoir storage dynamics, particularly in developing countries, and provide improved information for water resources management and operational decision-making."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/21019"],"dc:language.iso":["English"],"dc:subject":["Reservoir Dynamics","Synthetic Aperture Radar imagery","Satellite Remote Sensing","Hypsometric Relationship","Global Hydrological Model","Transboundary River Basin","Data fusion","Surface area","Reservoir Geometry","DEM","Optical imagery"],"dc:title":["Estimating Reservoir Storage Dynamics Using Satellite Remote Sensing and Hypsometric Information"],"dc:type":["Thesis"],"thesis:degree_discipline":["Geosensing Systems Engineering"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:31:47Z"}