A STOCHASTIC SEDIMENT YIELD MODEL FOR BAYESIAN DECISION ANALYSIS APPLIED TO MULTIPURPOSE RESERVOIR DESIGN

Persistent Link:
http://hdl.handle.net/10150/620119
Title:
A STOCHASTIC SEDIMENT YIELD MODEL FOR BAYESIAN DECISION ANALYSIS APPLIED TO MULTIPURPOSE RESERVOIR DESIGN
Author:
Smith, Jeffrey Haviland
Affiliation:
Department of Hydrology & Water Resources, The University of Arizona
Publisher:
Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ)
Issue Date:
1975-07
Rights:
Copyright © Arizona Board of Regents
Collection Information:
This title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.
Abstract:
This thesis presents a methodology for obtaining the optimal design capacity for sediment yield in multipurpose reservoir design. A stochastic model is presented for the prediction of sediment yield in a semi -arid watershed based on rainfall data and watershed characteristics. Uncertainty stems from each of the random variables used in the model, namely, rainfall amount, storm duration, runoff, peak flow rate, and number of events per season. Using the stochastic sediment yield model for N- seasons, a Bayesian decision analysis is carried out for a dam site in southern Arizona. Extensive numerical analyses and simplifying assumptions are made to facilitate finding the optimal solution. The model has applications in the planning of reservoirs and dams where the effective lifetime of the facility may be evaluated in terms of storage capacity and of the effects of land management on the watershed. Experimental data from the Atterbury watershed are used to calibrate the model and to evaluate uncertainties associated with our knowledge of the parameters of the joint distribution of rainfall and storm duration used in calculating the sediment yield amount.
Keywords:
Sedimentation and deposition -- Mathematical models.; Reservoirs -- Design and construction.; Water resources development -- Mathematical models.; Bayesian statistical decision theory.
Series/Report no.:
Technical Reports on Hydrology and Water Resources, No. 24
Sponsors:
This report constitutes the Master of-Science thesis of the same title completed by the author in January 1975, and accepted by the Department of Systems and Industrial Engineering. It is the result of two joint research projects, one on "Decision Analysis of Watershed Management Alternatives," supported in part by the United States Department of the Interior, Office of Water Resources Research, as authorized under the Water Resources Research Act of 1964, and one on "Practical Uses of Bayesian Decision Theory in Hydrology and Engineering," under a grant from the National Science Foundation (No. GK- 35791).

Full metadata record

DC FieldValue Language
dc.contributor.authorSmith, Jeffrey Havilanden
dc.date.accessioned2016-09-13T22:06:24Z-
dc.date.available2016-09-13T22:06:24Z-
dc.date.issued1975-07-
dc.identifier.urihttp://hdl.handle.net/10150/620119-
dc.description.abstractThis thesis presents a methodology for obtaining the optimal design capacity for sediment yield in multipurpose reservoir design. A stochastic model is presented for the prediction of sediment yield in a semi -arid watershed based on rainfall data and watershed characteristics. Uncertainty stems from each of the random variables used in the model, namely, rainfall amount, storm duration, runoff, peak flow rate, and number of events per season. Using the stochastic sediment yield model for N- seasons, a Bayesian decision analysis is carried out for a dam site in southern Arizona. Extensive numerical analyses and simplifying assumptions are made to facilitate finding the optimal solution. The model has applications in the planning of reservoirs and dams where the effective lifetime of the facility may be evaluated in terms of storage capacity and of the effects of land management on the watershed. Experimental data from the Atterbury watershed are used to calibrate the model and to evaluate uncertainties associated with our knowledge of the parameters of the joint distribution of rainfall and storm duration used in calculating the sediment yield amount.en
dc.description.sponsorshipThis report constitutes the Master of-Science thesis of the same title completed by the author in January 1975, and accepted by the Department of Systems and Industrial Engineering. It is the result of two joint research projects, one on "Decision Analysis of Watershed Management Alternatives," supported in part by the United States Department of the Interior, Office of Water Resources Research, as authorized under the Water Resources Research Act of 1964, and one on "Practical Uses of Bayesian Decision Theory in Hydrology and Engineering," under a grant from the National Science Foundation (No. GK- 35791).en
dc.language.isoen_USen
dc.publisherDepartment of Hydrology and Water Resources, University of Arizona (Tucson, AZ)en
dc.relation.ispartofseriesTechnical Reports on Hydrology and Water Resources, No. 24en
dc.rightsCopyright © Arizona Board of Regentsen
dc.sourceProvided by the Department of Hydrology and Water Resources.en
dc.subjectSedimentation and deposition -- Mathematical models.en
dc.subjectReservoirs -- Design and construction.en
dc.subjectWater resources development -- Mathematical models.en
dc.subjectBayesian statistical decision theory.en
dc.titleA STOCHASTIC SEDIMENT YIELD MODEL FOR BAYESIAN DECISION ANALYSIS APPLIED TO MULTIPURPOSE RESERVOIR DESIGNen_US
dc.typetexten
dc.typeTechnical Reporten
dc.contributor.departmentDepartment of Hydrology & Water Resources, The University of Arizonaen
dc.description.collectioninformationThis title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.en
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