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

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Graduate College

Grantor dc:publisher

The University of Arizona.

Year dc:date.issued

2024

Author dc:creator

• Venegas Quiñones, Héctor Leopoldo

Advisor dc:contributor.advisor

• Ferre, Paul A.

Committee members dc:contributor.committeemember

• Gupta, Hoshin V.
• Condon, Laura
• Korgaonkar, Yoga

• Flood Hazard Mapping
• Groundwater Data Analysis
• Hydrogeological Innovation
• Machine Learning in Hydrology
• Sustainable Water Technologies
• Water Resource Management

Statement dc: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.

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10150/675795

OAI identifier oai:identifier

oai:repository.arizona.edu:10150/675795