• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Power To The People - Harnessing Innovative Approaches To Address Hydrologic Challenges

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_etd_21928_sip1_m.pdf
    Size:
    22.82Mb
    Format:
    PDF
    Download
    Author
    Venegas Quiñones, Héctor Leopoldo
    Issue Date
    2024
    Keywords
    Flood Hazard Mapping
    Groundwater Data Analysis
    Hydrogeological Innovation
    Machine Learning in Hydrology
    Sustainable Water Technologies
    Water Resource Management
    Advisor
    Ferre, Paul A.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Water resource management faces critical challenges requiring innovative approaches in data collection, technological solutions, and advanced analytical methods. This dissertation presents four interconnected research papers that advance the field through novel methodologies and practical applications.The research begins by addressing a crucial data gap through the development of Chile’s first comprehensive groundwater levels dataset (1970-2021). This study compiles over 120,000 records from 640 wells, establishing a robust foundation for water management strategies. The second study presents a sustainable approach to flood hazard mapping by combining machine learning with free remote sensing data. This methodology demonstrates its effectiveness across four southwestern United States states, offering a cost-effective solution for flood risk assessment. The third paper advances groundwater mapping in Arizona through Random Forest algorithms. This research results in a patented methodology and a publicly accessible ArcGIS platform, enhancing the accessibility and accuracy of groundwater data for practitioners and policymakers. The final study provides the integration of machine learning with MODFLOW through a novel “phantom well” approach and likelihood-based evaluation. This innovative method addresses data sparsity challenges and evaluates model uncertainties, bridging the gap between traditional hydrogeological modeling and modern computational techniques. An additional appendix introduces a patented atmospheric water harvesting technology using Peltier Cells. This study develops an energy-efficient prototype capable of producing up to 5 liters of water daily while consuming only 30-70W of power, as validated through field testing in diverse locations including Tucson, Lima, and Santiago. This comprehensive body of work advances hydrogeological practice by bridging traditional methods with modern computational techniques and innovative technologies. The research progresses from regional-scale data management to advanced methodological developments, offering immediately applicable solutions while establishing frameworks for future advancement. These contributions provide scalable approaches to address critical water resource challenges globally, while ensuring accessibility to practitioners through public platforms and comprehensive methodological guidelines.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Hydrology
    Degree Grantor
    University of Arizona
    Collections
    Dissertations

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.