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dc.contributor.advisorNewman, Alexandra M.
dc.contributor.authorBurrow, Andrew
dc.date.accessioned2019-02-12T18:04:05Z
dc.date.accessioned2022-02-03T13:18:10Z
dc.date.available2019-02-12T18:04:05Z
dc.date.available2022-02-03T13:18:10Z
dc.date.issued2019
dc.identifierBurrow_mines_0052E_11663.pdf
dc.identifierT 8655
dc.identifier.urihttps://hdl.handle.net/11124/172844
dc.descriptionIncludes bibliographical references.
dc.description2019 Spring.
dc.description.abstractAs populations and economies grow in regions with changing climates, water demand quickly increases beyond what natural supply can sustain. This scenario is playing out in the western United States, including northeastern Colorado, where growth is enhancing an already growing water supply gap. In order to help reduce this growing gap, the state recognizes the need for additional reservoir storage to "bank" water during times of excess for use during times of dearth. Thus, we develop a methodology which uses simulation, combined with optimization, to design reservoir storage based on river flow over a 50-year time horizon. We use the State of Colorado's Stream Simulation Model to identify locations of water excess and unmet demand along 150 miles of the Lower South Platte River which we use as input to a mixed integer-linear optimization model. Model 1 minimizes the cost of meeting demands by designing additional storage for, and assigning network flow of, excess water while adhering to constraints that force the physical and topographical structures of the river. Next, we extend this deterministic work to incorporate characteristics of the food-energy-water nexus by using mixed-integer linear programming to consider the impact of nexus decisions related to agricultural irrigation, water storage, and power generation. Model 2 minimizes the cost of mitigating agricultural water shortages before identifying the location of the highest, most consistent volume to facilitate thermal power generation. We further apply this methodology using a multi-period, two-stage stochastic optimization model to design reservoir storage that is robust against a wide array of future climate scenarios such as historic trends of the past, reduced mean flow, and seasonally shifted flow. Model 3 minimizes the cost of meeting demands and we test solutions against a wide array of climate scenarios; our results indicate the optimal design to be multiple, smaller reservoirs of varying type. Our model develops solutions that mitigate between 90-100\% of all demands over any of our chosen climate scenarios. We also identify solutions which integrate new reservoir construction with the expansion of existing infrastructure to capture the excess water in the river.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2010-2019 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectfood-energy-water nexus
dc.subjectmixed integer-linear
dc.subjectwater resource planning
dc.subjectinfrastructure planning
dc.subjectdeterministic reservoir design
dc.subjectstochastic reservoir design
dc.titleDesigning river basin storage using optimization
dc.typeText
dc.contributor.committeememberIllangasekare, T. H.
dc.contributor.committeememberGuerra, Andres
dc.contributor.committeememberMehta, Dinesh P.
dc.contributor.committeememberWaskom, R. M. (Reagan McTier)
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorColorado School of Mines


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