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https://hdl.handle.net/2440/64925
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dc.contributor.author | Frost, A. | - |
dc.contributor.author | Thyer, M. | - |
dc.contributor.author | Srikanthan, R. | - |
dc.contributor.author | Kuczera, G. | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | Journal of Hydrology, 2007; 340(3-4):129-148 | - |
dc.identifier.issn | 0022-1694 | - |
dc.identifier.issn | 1879-2707 | - |
dc.identifier.uri | http://hdl.handle.net/2440/64925 | - |
dc.description.abstract | Multi-site simulation of hydrological data are required for drought risk assessment of large multi-reservoir water supply systems. In this paper, a general Bayesian framework is presented for the calibration and evaluation of multi-site hydrological data at annual timescales. Models included within this framework are the hidden Markov model (HMM) and the widely used lag-1 autoregressive (AR(1)) model. These models are extended by the inclusion of a Box–Cox transformation and a spatial correlation function in a multi-site setting. Parameter uncertainty is evaluated using Markov chain Monte Carlo techniques. Models are evaluated by their ability to reproduce a range of important extreme statistics and compared using Bayesian model selection techniques which evaluate model probabilities. The case study, using multi-site annual rainfall data situated within catchments which contribute to Sydney’s main water supply, provided the following results: Firstly, in terms of model probabilities and diagnostics, the inclusion of the Box–Cox transformation was preferred. Secondly the AR(1) and HMM performed similarly, while some other proposed AR(1)/HMM models with regionally pooled parameters had greater posterior probability than these two models. The practical significance of parameter and model uncertainty was illustrated using a case study involving drought security analysis for urban water supply. It was shown that ignoring parameter uncertainty resulted in a significant overestimate of reservoir yield and an underestimation of system vulnerability to severe drought. | - |
dc.description.statementofresponsibility | Andrew J. Frost, Mark A. Thyer, R. Srikanthan, George Kuczera | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Science BV | - |
dc.rights | Crown Copyright © 2007 Published by Elsevier B.V. All rights reserved. | - |
dc.source.uri | http://dx.doi.org/10.1016/j.jhydrol.2007.03.023 | - |
dc.subject | Stochastic rainfall | - |
dc.subject | Long-term persistence | - |
dc.subject | Parameter and model uncertainty | - |
dc.subject | Hidden Markov models | - |
dc.subject | Lag-one autoregressive models | - |
dc.subject | Box–Cox transformation | - |
dc.title | A general Bayesian framework for calibrating and evaluating stochastic models of annual multi-site hydrological data | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.jhydrol.2007.03.023 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Thyer, M. [0000-0002-2830-516X] | - |
Appears in Collections: | Aurora harvest Civil and Environmental Engineering publications Environment Institute publications |
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