{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105955"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105955","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Reservoir yield estimates with consideration of uncertainty in used data in Illinois","abstract":"Reservoir yield estimates are necessary and required for better water supply to communities especially during a severe drought. This study provides a framework to estimate reservoir yields with consideration of associated uncertainties in used data. Errors exist in inflow, reservoir capacity, evaporation and precipitation data and contribute to the overall uncertainty in reservoir yield estimates. Before combining optimization with Monte Carlo simulation, errors of each data category are assumed to follow a certain normal distribution. The framework is applied to three reservoirs in Illinois. It is found that the 95% probability intervals surrounding the estimates of reservoir yields range between -29% and +42% of the best estimate and the range is a bit right-shifted; evaporation contributes the most to the overall uncertainty, followed by reservoir capacity of small reservoirs and inflow to large reservoirs.","abstract_html":"Reservoir yield estimates are necessary and required for better water supply to communities especially during a severe drought. This study provides a framework to estimate reservoir yields with consideration of associated uncertainties in used data. Errors exist in inflow, reservoir capacity, evaporation and precipitation data and contribute to the overall uncertainty in reservoir yield estimates. Before combining optimization with Monte Carlo simulation, errors of each data category are assumed to follow a certain normal distribution. The framework is applied to three reservoirs in Illinois. It is found that the 95% probability intervals surrounding the estimates of reservoir yields range between -29% and +42% of the best estimate and the range is a bit right-shifted; evaporation contributes the most to the overall uncertainty, followed by reservoir capacity of small reservoirs and inflow to large reservoirs.","abstract_has_math":false,"creators":["Zhang, Yu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Environ Engr in Civil Engr","degree_department":null,"school":null,"contributors":["Cai, Ximing"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:59:49Z","date_published":"2019-11-26T20:59:49Z","updated_at":"2026-07-22T22:24:45Z","subjects":["yield estimates","data uncertainty"],"languages":["en"],"rights":["Copyright 2019 Yu Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105955","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Cai, Ximing"]},{"key":"dc:creator","label":"Author","values":["Zhang, Yu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:59:49Z","2021-11-27T10:15:16Z","2019-07-16","2019-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Environ Engr in Civil Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["yield estimates","data uncertainty"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Yu Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105955"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Reservoir yield estimates are necessary and required for better water supply to communities especially during a severe drought. This study provides a framework to estimate reservoir yields with consideration of associated uncertainties in used data. Errors exist in inflow, reservoir capacity, evaporation and precipitation data and contribute to the overall uncertainty in reservoir yield estimates. Before combining optimization with Monte Carlo simulation, errors of each data category are assumed to follow a certain normal distribution. The framework is applied to three reservoirs in Illinois. It is found that the 95% probability intervals surrounding the estimates of reservoir yields range between -29% and +42% of the best estimate and the range is a bit right-shifted; evaporation contributes the most to the overall uncertainty, followed by reservoir capacity of small reservoirs and inflow to large reservoirs.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-08-01","The student, Yu Zhang, accepted the attached license on 2019-07-16 at 11:41.","The student, Yu Zhang, submitted this Thesis for approval on 2019-07-16 at 11:51.","This Thesis was approved for publication on 2019-07-16 at 14:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14345 on 2019-11-26 at 14:04:19","Made available in DSpace on 2019-11-26T20:59:49Z (GMT). No. of bitstreams: 2 ZHANG-THESIS-2019.pdf: 1000857 bytes, checksum: 46cfc89a0505c64ed621b23380f2c884 (MD5) LICENSE.txt: 4205 bytes, checksum: c9b4dafa2ca4d427b173e9ee846e8098 (MD5) Previous issue date: 2019-07-16","Embargo set by: Seth Robbins for item 113102 Lift date: 2021-11-26T20:59:54Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 113102 on 2021-11-27T10:15:16Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Reservoir yield estimates with consideration of uncertainty in used data in Illinois"]}]}],"canonical_facts":{"dc:contributor":["Cai, Ximing"],"dc:creator":["Zhang, Yu"],"dc:date":["2019-11-26T20:59:49Z","2021-11-27T10:15:16Z","2019-07-16","2019-08"],"dc:description":["Reservoir yield estimates are necessary and required for better water supply to communities especially during a severe drought. This study provides a framework to estimate reservoir yields with consideration of associated uncertainties in used data. Errors exist in inflow, reservoir capacity, evaporation and precipitation data and contribute to the overall uncertainty in reservoir yield estimates. Before combining optimization with Monte Carlo simulation, errors of each data category are assumed to follow a certain normal distribution. The framework is applied to three reservoirs in Illinois. It is found that the 95% probability intervals surrounding the estimates of reservoir yields range between -29% and +42% of the best estimate and the range is a bit right-shifted; evaporation contributes the most to the overall uncertainty, followed by reservoir capacity of small reservoirs and inflow to large reservoirs.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-08-01","The student, Yu Zhang, accepted the attached license on 2019-07-16 at 11:41.","The student, Yu Zhang, submitted this Thesis for approval on 2019-07-16 at 11:51.","This Thesis was approved for publication on 2019-07-16 at 14:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14345 on 2019-11-26 at 14:04:19","Made available in DSpace on 2019-11-26T20:59:49Z (GMT). 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