A global optimization strategy for efficient and effective calibration of hydrologic models.

Persistent Link:
http://hdl.handle.net/10150/185655
Title:
A global optimization strategy for efficient and effective calibration of hydrologic models.
Author:
Duan, Qingyun.
Issue Date:
1991
Publisher:
The University of Arizona.
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 or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The successful application of a CRR model depends on how well we handle each phase of model calibration. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not impossible, to obtain a unique set of optimal parameters for a CRR model. Unless the best set of parameters associated with a given calibration data set can be found, it is impossible to determine how sensitive the parameter estimates (and hence the model forecasts) are to factors such as input and output data error, model error, quantity and quality of data, objective function used, and so on. In this dissertation, results that clearly establish the nature of the problem of multiple optima in CRR models are presented. Based on these results it is shown why currently used optimization procedures have little chance of successfully finding the optimal parameter sets. This understanding is then used to develop a new global optimization procedure, the Shuffled Complex Evolution (SCE) method, which can efficiently and effectively identify the optimal values for the model parameters. The efficiency and effectiveness of the SCE method is first demonstrated on some theoretical test functions. It is then used to calibrate a research version of the SMA-NWSRFS model--the SIXPAR model. The SCE method is compared to other available methods used in practice on the theoretical test functions and the SIXPAR model. Finally, the SCE method is applied to the full scale SMA-NWSRFS model using both synthetic data and real data. The test results clearly indicate that the SCE method is superior to other methods tested in this research.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Dissertations, Academic; Hydrology
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Hydrology and Water Resources; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Sorooshian, Soroosh

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleA global optimization strategy for efficient and effective calibration of hydrologic models.en_US
dc.creatorDuan, Qingyun.en_US
dc.contributor.authorDuan, Qingyun.en_US
dc.date.issued1991en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThe successful application of a CRR model depends on how well we handle each phase of model calibration. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not impossible, to obtain a unique set of optimal parameters for a CRR model. Unless the best set of parameters associated with a given calibration data set can be found, it is impossible to determine how sensitive the parameter estimates (and hence the model forecasts) are to factors such as input and output data error, model error, quantity and quality of data, objective function used, and so on. In this dissertation, results that clearly establish the nature of the problem of multiple optima in CRR models are presented. Based on these results it is shown why currently used optimization procedures have little chance of successfully finding the optimal parameter sets. This understanding is then used to develop a new global optimization procedure, the Shuffled Complex Evolution (SCE) method, which can efficiently and effectively identify the optimal values for the model parameters. The efficiency and effectiveness of the SCE method is first demonstrated on some theoretical test functions. It is then used to calibrate a research version of the SMA-NWSRFS model--the SIXPAR model. The SCE method is compared to other available methods used in practice on the theoretical test functions and the SIXPAR model. Finally, the SCE method is applied to the full scale SMA-NWSRFS model using both synthetic data and real data. The test results clearly indicate that the SCE method is superior to other methods tested in this research.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectDissertations, Academicen_US
dc.subjectHydrologyen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSorooshian, Sorooshen_US
dc.contributor.committeememberDavis, Donald R.en_US
dc.contributor.committeememberWoolhiser, David A.en_US
dc.contributor.committeememberYakowitz, Sidney J.en_US
dc.contributor.committeememberSen, Suvrajeeten_US
dc.identifier.proquest9208054en_US
dc.identifier.oclc711880731en_US
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