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Development of non-homogeneous and hierarchical hidden markov models for modelling monthly rainfall and streamflow time series

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dc.contributor.authorWhiting, J.
dc.contributor.authorLambert, M.
dc.contributor.authorMetcalfe, A.
dc.contributor.authorKuczera, G.
dc.contributor.conferenceWorld Water and Environmental Resources Congress (2004 : Salt Lake City, Utah)
dc.date.issued2004
dc.description.abstractHidden Markov models (HMMs) offer a plausible representation of long-term hydroclimatic persistence in rainfall and streamflow observations. Persistent climate processes influence hydrological observations at various time scales. This paper develops the stochastic framework of two-state HMMs to better represent climate-rainfall interactions at both monthly and annual levels. Two new models, a hierarchical HMM and a non-homogeneous HMM are introduced, and fitted to monthly rainfall and streamflow observations from Australia. The value of these models to identify two-state persistence is compared to that of existing two-state HMMs.
dc.description.statementofresponsibilityJulian Whiting, Martin Lambert, Andrew Metcalfe, and George Kuczera
dc.identifier.citationCritical transitions in water and environmental resources management [electronic resource] : proceedings of the World Water and Environmental Resources Congress : June 27-July 1, 2004, Salt Lake City, UT / sponsored by Environmental and Water Resources Institute (EWRI) of the American Society of Civil Engineers ; Gerald Sehlke, Donald F. Hayes, and David K. Stevens (eds.): pp. 1-10
dc.identifier.doi10.1061/40737(2004)212
dc.identifier.isbn0784407371
dc.identifier.orcidLambert, M. [0000-0001-8272-6697]
dc.identifier.orcidMetcalfe, A. [0000-0002-7680-3577]
dc.identifier.urihttp://hdl.handle.net/2440/41502
dc.language.isoen
dc.publisherAmerican Society of Civil Engineers
dc.source.urihttps://doi.org/10.1061/40737(2004)212
dc.titleDevelopment of non-homogeneous and hierarchical hidden markov models for modelling monthly rainfall and streamflow time series
dc.typeConference paper
pubs.publication-statusPublished

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