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Stochastic modeling of seasonal streamflow

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dc.contributor.authorMendonça, Antonio Sergio Ferreira, author
dc.contributor.authorSalas, Jose D., advisor
dc.contributor.authorFontane, Darrell G., committee member
dc.contributor.authorLoftis, Jim C., committee member
dc.contributor.authorGessler, Johannes, committee member
dc.date.accessioned2007-01-03T06:31:05Z
dc.date.available2007-01-03T06:31:05Z
dc.date.issued1987
dc.description.abstractThis research examines topics on seasonal (monthly, bimonthly, etc.) hydrologic time-series modeling. A family of periodic models was derived by allowing parameters for a particular Multiplicative Autoregressive Integrated Moving Average model (Multiplicative ARIMA) to vary from season to season. The derived model presents parameters relating data for seasons in the same year and parameters relating data for the same season for consecutive years. PARMA models are particular cases of the proposed model, here called Multiplicative Periodic Autoregressive Moving Average (Multiplicative PARMA). Least-squares estimation based on the Powell algorithm for nonlinear optimization was developed for determining the model parameters. Properties such as seasonal variances and autocorrelations were derived analytically for particular cases of the general model. Analysis of sensitivity of the annual autocorrelograms to the parameters of the model showed that the yearly autoregressive parameters are the most important for the reproduction of high annual autocorrelations. Tests of model were made through data generation. The model was applied to four-and six-season series for river discharge presenting distinct characteristics of variabilty and dependence. Tests for goodness-of-fit and selection criteria of models for seasonal series were also discussed. Results from data generation indicate that the estimation procedure is able to estimate parameters for the Multiplicative PARMA models and can also be used for refinement of estimations made by method-of-moments for other models. Application to discharge data from St. Lawrence, Niger, Elkhorn and Yellowstone rivers showed that the proposed modeling technique is able to preserve long term dependence better than models currently used in practical hydrology. Direct consequence of this improvement is better reproduction of floods and droughts and more accuracy in the design and operation of water resource structures.
dc.format.mediumdoctoral dissertations
dc.identifier1987_Fall_Mendonca_Antonio.pdf
dc.identifierETDF1987400030CVEE
dc.identifier.urihttp://hdl.handle.net/10217/82144
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relationCatalog record number (MMS ID): 991011040699703361
dc.relationGB1201.72.M35.M45 1987
dc.relationwwdl
dc.relation.ispartof1980-1999
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectStreamflow -- Mathematical models
dc.subjectStream measurements -- Mathematical models
dc.subjectTime-series analysis
dc.titleStochastic modeling of seasonal streamflow
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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