{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127493"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127493","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Understanding historical reservoir operation rules, dynamics, and deficiencies - opportunities for improvement","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_has_math":false,"creators":["Chen, Yanan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Cai, Ximing","Stillwell, Ashlynn","Zhang, Zhenxing","Zhu, Tingju"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-05","date_published":"2024-12-05","updated_at":"2026-07-22T22:25:04Z","subjects":["Data-driven Reservoir Simulation Model","Reservoir Operation Rules","Reservoir Operational Changes","Forecast Informed Reservoir Operation"],"languages":["en","eng"],"rights":["Copyright 2024 Yanan Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127493","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Cai, Ximing","Stillwell, Ashlynn","Zhang, Zhenxing","Zhu, Tingju"]},{"key":"dc:creator","label":"Author","values":["Chen, Yanan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-05","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data-driven Reservoir Simulation Model","Reservoir Operation Rules","Reservoir Operational Changes","Forecast Informed Reservoir Operation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Yanan Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127493"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","The student, Yanan Chen, accepted the attached license on 2024-12-04 at 10:00.","The student, Yanan Chen, submitted this Dissertation for approval on 2024-12-04 at 10:17.","This Dissertation was approved for publication on 2024-12-05 at 18:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21494 on 2025-03-28 at 14:55:47","Reservoirs play a vital role in surface water management, serving various purposes such as flood control, irrigation, water supply, and hydroelectricity. However, reservoir operation has been complicated by rapidly changing environments, including climate, water demand, and operation police changes. This dissertation aims to enhance understanding of historical reservoir operation rules, dynamics, and deficiencies across the contiguous United States (CONUS) and explore ways to improve operation performance. The research is organized into three interconnected parts. The first part develops a generic data-driven reservoir operation model (GDROM) using machine learning (ML) to extract real-world reservoir operation rules. Tested on 467 reservoirs of varying capacities and functions across the CONUS, the GDROM demonstrates comparable accuracy in release simulation to other ML models, with improved interpretability due to its transparent structure. The second part identifies reservoir storage and operational changes between 1990 and 2019 for 256 reservoirs across the CONUS and analyzes the underlying causes. The GDROM model is used as a tool to identify operational changes, including the cases of both operational effectiveness and deficiencies through analyzing the relations of storage and operational changes under environmental changes. To address the operational deficiencies detected in the second part, the third part proposes a new forecast-informed reservoir operation (FIRO) decision support framework that integrates analytically derived hedging policies with empirically based flood control rules. Within this framework, the GDROM is integrated to capture real-world operation specifications that reflect operators’ actual response to various hydrologic conditions. The FIRO framework is tested on Folsom Lake in California, which demonstrates significant improvement in water conservation benefit in the post flood season without increasing flood risks during the flood season. Leveraging historical operation records and the developed date-driven reservoir operation model, this dissertation provides insights into real-world operational rules, dynamics, and existing deficiencies within the CONUS, offering guidance for operation design in response to future changes. Moreover, this research establishes a new FIRO decision support framework, which introduces flexibility to address operational deficiencies and improve reservoir performance under varying hydrological conditions."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Understanding historical reservoir operation rules, dynamics, and deficiencies - opportunities for improvement"]}]}],"canonical_facts":{"dc:contributor":["Cai, Ximing","Stillwell, Ashlynn","Zhang, Zhenxing","Zhu, Tingju"],"dc:creator":["Chen, Yanan"],"dc:date":["2024-12-05","2024-12"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","The student, Yanan Chen, accepted the attached license on 2024-12-04 at 10:00.","The student, Yanan Chen, submitted this Dissertation for approval on 2024-12-04 at 10:17.","This Dissertation was approved for publication on 2024-12-05 at 18:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21494 on 2025-03-28 at 14:55:47","Reservoirs play a vital role in surface water management, serving various purposes such as flood control, irrigation, water supply, and hydroelectricity. However, reservoir operation has been complicated by rapidly changing environments, including climate, water demand, and operation police changes. This dissertation aims to enhance understanding of historical reservoir operation rules, dynamics, and deficiencies across the contiguous United States (CONUS) and explore ways to improve operation performance. The research is organized into three interconnected parts. The first part develops a generic data-driven reservoir operation model (GDROM) using machine learning (ML) to extract real-world reservoir operation rules. Tested on 467 reservoirs of varying capacities and functions across the CONUS, the GDROM demonstrates comparable accuracy in release simulation to other ML models, with improved interpretability due to its transparent structure. The second part identifies reservoir storage and operational changes between 1990 and 2019 for 256 reservoirs across the CONUS and analyzes the underlying causes. The GDROM model is used as a tool to identify operational changes, including the cases of both operational effectiveness and deficiencies through analyzing the relations of storage and operational changes under environmental changes. To address the operational deficiencies detected in the second part, the third part proposes a new forecast-informed reservoir operation (FIRO) decision support framework that integrates analytically derived hedging policies with empirically based flood control rules. Within this framework, the GDROM is integrated to capture real-world operation specifications that reflect operators’ actual response to various hydrologic conditions. The FIRO framework is tested on Folsom Lake in California, which demonstrates significant improvement in water conservation benefit in the post flood season without increasing flood risks during the flood season. Leveraging historical operation records and the developed date-driven reservoir operation model, this dissertation provides insights into real-world operational rules, dynamics, and existing deficiencies within the CONUS, offering guidance for operation design in response to future changes. Moreover, this research establishes a new FIRO decision support framework, which introduces flexibility to address operational deficiencies and improve reservoir performance under varying hydrological conditions."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127493"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Yanan Chen"],"dc:subject":["Data-driven Reservoir Simulation Model","Reservoir Operation Rules","Reservoir Operational Changes","Forecast Informed Reservoir Operation"],"dc:title":["Understanding historical reservoir operation rules, dynamics, and deficiencies - opportunities for improvement"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}