Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/114209
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dc.contributor.authorMcInerney, D.-
dc.contributor.authorThyer, M.A.-
dc.contributor.authorKavetski, D.-
dc.contributor.authorBennett, B.-
dc.contributor.authorLerat, J.-
dc.contributor.authorGibbs, M.-
dc.contributor.authorKuczera, G.-
dc.date.issued2018-
dc.identifier.citationEnvironmental Modelling and Software, 2018; 109:306-314-
dc.identifier.issn1873-6726-
dc.identifier.issn1873-6726-
dc.identifier.urihttp://hdl.handle.net/2440/114209-
dc.description.abstractAbstract not available-
dc.description.statementofresponsibilityDavid McInerney, Mark Thyer, Dmitri Kavetski, Bree Bennett, Julien Lerat, Matthew Gibbs, George Kuczera-
dc.language.isoen-
dc.publisherElsevier-
dc.rightsCrown Copyright © 2018 Published by Elsevier Ltd. All rights reserved.-
dc.subjectProbabilistic prediction; rainfall-runoff modelling; method of moments; maximum likelihood-
dc.titleA simplified approach to produce probabilistic hydrological model predictions-
dc.typeJournal article-
dc.identifier.doi10.1016/j.envsoft.2018.07.001-
dc.relation.granthttp://purl.org/au-research/grants/arc/LP140100978-
pubs.publication-statusPublished-
dc.identifier.orcidMcInerney, D. [0000-0003-4876-8281]-
dc.identifier.orcidKavetski, D. [0000-0003-4966-9234]-
dc.identifier.orcidBennett, B. [0000-0002-2131-088X]-
dc.identifier.orcidGibbs, M. [0000-0001-6653-8688]-
Appears in Collections:Aurora harvest 8
Civil and Environmental Engineering publications

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