Estimating Reservoir Storage Dynamics Using Satellite Remote Sensing and Hypsometric Information

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.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Geosensing Systems Engineering

Grantor

University of Houston

Year dc:date.issued

2026

Author dc:creator

• Nguyen, Ngoc Thi

Advisor dc:contributor.advisor

• Lee, Hyongki

Committee members dc:contributor.committeemember

• Momen, Mostafa
• Li, Hong-Yi
• Xie, Surui
• Glennie, Craig L.

• 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

Language dc:language.iso

English

Handle dc:identifier.uri

https://hdl.handle.net/10657/21019

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/21019