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Riparian vegetation covers only about 0.4% of the land surface whereas rivers and streams cover about 0.3 to 0.56% but riparian vegetation still provides substantial ecosystem services including many that affect hydrodynamic processes. The ability to estimate the flow resistance caused by vegetation and the consequences for hydrodynamic processes has long been a vexing problem for engineers. Flow resistance is strongly influenced by local hydraulic conditions (water velocity and depth) and vegetation structures, and in turn, vegetation affects mass and momentum exchange processes. However, riparian vegetation conditions change continuously due to external stressors including climate change (e.g., extended drought in New Mexico) and engineered structures (e.g., dams and levees), which in turn have impacts on river morphology. In my dissertation, first I investigated novel techniques for characterizing riparian vegetation through field-based and remote sensing techniques and modeling hydraulic roughness due to riparian vegetation in a two-dimensional hydrodynamic model (Chapter 2). Next, I studied the influence of riparian vegetation on channel/floodplain connectivity in terms of mass and momentum transport (Chapter 3). Finally, I explored the use of machine learning techniques to characterize spatiotemporal variations in riparian vegetation and river morphology in response to external hydrodynamic drivers of change (Chapter 4). The overarching goal of this research in general was to advance understanding of the dynamics of river systems in relation to riparian vegetation. In chapter 2, spatial variations in Manning’s roughness were observed based on vegetation species and discharge that effects the hydraulic parameters in presence of riparian vegetation when comparing the user assigned and iteratively computed hydraulic roughness approaches. Thus, the method proposed here is beneficial for describing the hydraulic conditions for the

Riparian vegetation covers only about 0.4% of the land surface whereas rivers and streams cover about 0.3 to 0.56% but riparian vegetation still provides substantial ecosystem services including many that affect hydrodynamic processes. The ability to estimate the flow resistance caused by vegetation and the consequences for hydrodynamic processes has long been a vexing problem for engineers. Flow resistance is strongly influenced by local hydraulic conditions (water velocity and depth) and vegetation structures, and in turn, vegetation affects mass and momentum exchange processes. However, riparian vegetation conditions change continuously due to external stressors including climate change (e.g., extended drought in New Mexico) and engineered structures (e.g., dams and levees), which in turn have impacts on river morphology. In my dissertation, first I investigated novel techniques for characterizing riparian vegetation through field-based and remote sensing techniques and modeling hydraulic roughness due to riparian vegetation in a two-dimensional hydrodynamic model (Chapter 2). Next, I studied the influence of riparian vegetation on channel/floodplain connectivity in terms of mass and momentum transport (Chapter 3). Finally, I explored the use of machine learning techniques to characterize spatiotemporal variations in riparian vegetation and river morphology in response to external hydrodynamic drivers of change (Chapter 4). The overarching goal of this research in general was to advance understanding of the dynamics of river systems in relation to riparian vegetation. In chapter 2, spatial variations in Manning’s roughness were observed based on vegetation species and discharge that effects the hydraulic parameters in presence of riparian vegetation when comparing the user assigned and iteratively computed hydraulic roughness approaches. Thus, the method proposed here is beneficial for describing the hydraulic conditions for the

This body of work seeks to advance knowledge of resilience and resilience engineering (RE) from a practical and theoretical perspective. Three distinct applications of resilience and RE that align two fundamental frameworks are presented. The first application, a vulnerability assessment, exemplifies the largest scale of panarchy and the measuring aspect of RE. The second application, a Bayesian network approach, exemplifies the intermediate scale of panarchy and the modeling aspect of RE. The third application, an engineering design project using analytical hierarchy process (AHP), exemplifies the smallest scale of panarchy and the building aspect of RE. Studying these applications enriches understanding of complex-adaptive system, informing decision-making and building system resilience. The diverse socio-ecological systems evaluated, in the Western US and Nepal, differ in vulnerabilities and strengths, providing opportunity to observe variations in linkages and sensitivities. This work aims to increase awareness of RE uses, benefits, and challenges from a demonstrative context.

This body of work seeks to advance knowledge of resilience and resilience engineering (RE) from a practical and theoretical perspective. Three distinct applications of resilience and RE that align two fundamental frameworks are presented. The first application, a vulnerability assessment, exemplifies the largest scale of panarchy and the measuring aspect of RE. The second application, a Bayesian network approach, exemplifies the intermediate scale of panarchy and the modeling aspect of RE. The third application, an engineering design project using analytical hierarchy process (AHP), exemplifies the smallest scale of panarchy and the building aspect of RE. Studying these applications enriches understanding of complex-adaptive system, informing decision-making and building system resilience. The diverse socio-ecological systems evaluated, in the Western US and Nepal, differ in vulnerabilities and strengths, providing opportunity to observe variations in linkages and sensitivities. This work aims to increase awareness of RE uses, benefits, and challenges from a demonstrative context.