Detection of Agricultural Drainage Infrastructure in Intensively-managed Landscape

Description
Agricultural drainage infrastructure is widely implemented in intensively managed landscapes such as the U.S. Midwest. As a key agricultural water management practice, it commonly comprises components including subsurface drainage pipes and surface drainage ditches, which helps maintain optimal soil moisture,

Agricultural drainage infrastructure is widely implemented in intensively managed landscapes such as the U.S. Midwest. As a key agricultural water management practice, it commonly comprises components including subsurface drainage pipes and surface drainage ditches, which helps maintain optimal soil moisture, enhancing crop yields and boosting farm income. These agricultural water practices transport not only water and sediments but also chemicals, leading to hydrological and environmental issues. Explicit representation of agricultural drainage infrastructure is vital to better understand their impacts on hydrologic processes to improve hydrological and water quality prediction. However, existing studies often simplified the effects of agricultural drainage infrastructure due to insufficient explicit representations of these agricultural practices. Therefore, there is a pressing need to improve the representations of agricultural drainage infrastructure. The dissertation aims to detect both subsurface and surface components of drainage infrastructure in agricultural landscapes to enhance their explicit representations in hydrological models. The first part tackles the lack of specific methods to guide survey times or filter historical remote sensing imageries for subsurface drainage detection. A simulation-guided approach is developed to determine the optimal time window to detect subsurface drainage pipes. The second part focuses on overcoming challenges in preserving surface drainage features while eliminating noise in high-resolution LiDAR DEMs. A smoothing algorithm that accounts for both gradient magnitudes and orientations is introduced, enabling effective noise reduction while preserving key features. This algorithm, combined with a detection method based on the principal slope, is applied to accurately delineate surface drainage networks at the watershed scale in the Midwest. To further enhance detection efficiency, the third part leverages the identified drainage data in the second part to label data and train a UNet model aiming at detecting comprehensive watershed-scale agricultural surface drainages from LiDAR data. The UNet model is successfully applied to identify surface drainage networks and cross-sectional geometries of drainage channels across multiple watersheds in the Midwest. By achieving the explicit representation of these infrastructures, this study will lead to a more accurate understanding of their roles in hydrologic processes, ultimately enhancing agricultural water management, boosting crop production and promoting environmental sustainability.

Details

Contributors
Date Created
2024
Resource Type
Language
  • eng
Note
  • Partial requirement for: Ph.D., Arizona State University, 2024
  • Field of study: Civil, Environmental and Sustainable Engineering

Additional Information

English
Extent
  • 151 pages
Open Access
Peer-reviewed

Inclusive Urban Flood Resilience in a Developing Economy: Case of Georgetown, Guyana

Description
Inequities and exclusions, compounded by the increasing intensity of extreme weather events, pose significant challenges to urban planning for low-elevation coastal zones (LECZ). Inclusive development (ID) and urban flood resilience (UFR) have emerged as widely endorsed solutions by scholars. Granting

Inequities and exclusions, compounded by the increasing intensity of extreme weather events, pose significant challenges to urban planning for low-elevation coastal zones (LECZ). Inclusive development (ID) and urban flood resilience (UFR) have emerged as widely endorsed solutions by scholars. Granting that they gain substantial support and enthusiasm, they have the potential to transform vulnerable urban areas. While their noble intentions are commendable, the intricacies of ID cannot be overlooked, as UFR often inherits and perpetuates the inequalities ingrained in conventional development paradigms. Given the critical importance of ID and UFR in contemporary urban planning, my dissertation research devolved into their fusion by answering my main research question, what constitutes inclusive urban flood resilience? This investigation was carried out through a series of four secondary research questions distributed over three academic papers, each contributing a unique perspective and insights to this burgeoning field. Through a systematic literature review and employing bibliometric and thematic analyses, Chapter 2 offers a comprehensive understanding of inclusive development and a refined definition of the concept. Subsequently, taking Georgetown, the capital city of Guyana, as a case study, Chapter 3 estimates its UFR and employs dimensionality reduction by way of principal component analysis to present these findings in a transparent manner. Chapter 4 builds on the findings of the previous chapters, by first presenting a novel approach to evaluate inclusive development within the framework of the results of Chapter 2, and secondly, together with a systematic meta-analysis of flood resilience measurements, it offers an examination of the ID-UFR nexus. The findings suggest that the concept of inclusive development is nuanced by context-specific definitions, that flood resilience in Georgetown varies among its sub-districts, and that city dimensions (natural, built, social, economic, and institutional), as assessed by pooling global studies, do not share synergistic relationships, being a measure of inclusive development. These findings are critical to urban planning in Georgetown and similar contexts globally as they provide data-driven guidance for understanding these concepts and applying them toward developing inclusive and flood-resilient cities and communities.

Details

Contributors
Date Created
2023
Embargo Release Date
Resource Type
Language
  • eng
Note
  • Partial requirement for: Ph.D., Arizona State University, 2023
  • Field of study: Interdisciplinary Studies

Additional Information

English
Extent
  • 138 pages
Open Access
Peer-reviewed

Complex Hydroclimate System Modeling: Causation, Tipping, and Extremes

Description
Nonlinear responses in the dynamics of climate system could be triggered by small change of forcing. Interactions between different components of Earth’s climate system are believed to cause abrupt and catastrophic transitions, of which anthropogenic forcing is a major and

Nonlinear responses in the dynamics of climate system could be triggered by small change of forcing. Interactions between different components of Earth’s climate system are believed to cause abrupt and catastrophic transitions, of which anthropogenic forcing is a major and the most irreversible driver. Meantime, in the face of global climate change, extreme climatic events, such as extreme precipitations, heatwaves, droughts, etc., are projected to be more frequent, more intense, and longer in duration. These nonlinear responses in climate dynamics from tipping points to extreme events pose serious threats to human society on a large scale. Understanding the physical mechanisms behind them has potential to reduce related risks through different ways. The overarching objective of this dissertation is to quantify complex interactions, detect tipping points, and explore propagations of extreme events in the hydroclimate system from a new structure-based perspective, by integrating climate dynamics, causal inference, network theory, spectral analysis, and machine learning. More specifically, a network-based framework is developed to find responses of hydroclimate system to potential critical transitions in climate. Results show that system-based early warning signals towards tipping points can be located successfully, demonstrated by enhanced connections in the network topology. To further evaluate the long-term nonlinear interactions among the U.S. climate regions, causality inference is introduced and directed complex networks are constructed from climatology records over one century. Causality networks reveal that the Ohio valley region acts as a regional gateway and mediator to the moisture transport and thermal transfer in the U.S. Furthermore, it is found that cross-regional causality variability manifests intrinsic frequency ranging from interannual to interdecadal scales, and those frequencies are associated with the physics of climate oscillations. Besides the long-term climatology, this dissertation also aims to explore extreme events from the system-dynamic perspective, especially the contributions of human-induced activities to propagation of extreme heatwaves in the U.S. cities. Results suggest that there are long-range teleconnections among the U.S. cities and supernodes in heatwave spreading. Findings also confirm that anthropogenic activities contribute to extreme heatwaves by the fact that causality during heatwaves is positively associated with population in megacities.

Details

Contributors
Date Created
2023
Resource Type
Language
  • eng
Note
  • Partial requirement for: Ph.D., Arizona State University, 2023
  • Field of study: Civil, Environmental and Sustainable Engineering

Additional Information

English
Extent
  • 127 pages
Open Access
Peer-reviewed