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dc.contributor.authorSmith, Christopher,1956-
dc.creatorSmith, Christopher,1956-en_US
dc.date.accessioned2011-11-28T14:09:37Z
dc.date.available2011-11-28T14:09:37Z
dc.date.issued1986en_US
dc.identifier.urihttp://hdl.handle.net/10150/191886
dc.description.abstractIn automatic calibration, a fitting criteria, which is some function of the difference between the observed and the model generated flows, is optimized to get the best parameter set. The purpose of this investigation was to calibrate the U. S. Geological Survey Precipitation Runoff Modeling System (PRMS) model using three different fitting criteria; ordinary least squares (OLS), Ln transformation of the discharges using the OLS on the transformed flows (LOG), and maximum likelihood estimator for the heteroscedastic errors (HMLE). The performance of each criteria in terms of their ability to produce reliable forecasts was examined. The results of the research showed that the winter storms were reproduced best by the parameter sets chosen by the OLS fitting criteria and the summer storms were reproduced best by the HMLE parameter sets. However, the performance in terms of percent bias in different flow groups suggests that HMLE estimator is superior.
dc.language.isoenen_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.subjectHydrology.
dc.subjectRunoff -- Mathematical models.
dc.subjectRunoff -- Mathematical models -- Calibration.
dc.titleEvaluating three fitting criteria for the calibration of the U.S. Geological Survey precipitation runoff modeling system (PRMS)en_US
dc.typeThesis-Reproduction (electronic)en_US
dc.typetexten_US
dc.contributor.chairSorooshian, Sorooshen_US
dc.identifier.oclc213341156en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.nameM.S.en_US
dc.description.notehydrology collectionen_US
refterms.dateFOA2018-06-17T00:50:42Z
html.description.abstractIn automatic calibration, a fitting criteria, which is some function of the difference between the observed and the model generated flows, is optimized to get the best parameter set. The purpose of this investigation was to calibrate the U. S. Geological Survey Precipitation Runoff Modeling System (PRMS) model using three different fitting criteria; ordinary least squares (OLS), Ln transformation of the discharges using the OLS on the transformed flows (LOG), and maximum likelihood estimator for the heteroscedastic errors (HMLE). The performance of each criteria in terms of their ability to produce reliable forecasts was examined. The results of the research showed that the winter storms were reproduced best by the parameter sets chosen by the OLS fitting criteria and the summer storms were reproduced best by the HMLE parameter sets. However, the performance in terms of percent bias in different flow groups suggests that HMLE estimator is superior.


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