Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/64925
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Type: Journal article
Title: A general Bayesian framework for calibrating and evaluating stochastic models of annual multi-site hydrological data
Author: Frost, A.
Thyer, M.
Srikanthan, R.
Kuczera, G.
Citation: Journal of Hydrology, 2007; 340(3-4):129-148
Publisher: Elsevier Science BV
Issue Date: 2007
ISSN: 0022-1694
1879-2707
Statement of
Responsibility: 
Andrew J. Frost, Mark A. Thyer, R. Srikanthan, George Kuczera
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.
Keywords: Stochastic rainfall; Long-term persistence; Parameter and model uncertainty; Hidden Markov models; Lag-one autoregressive models; Box–Cox transformation
Rights: Crown Copyright © 2007 Published by Elsevier B.V. All rights reserved.
RMID: 0020109363
DOI: 10.1016/j.jhydrol.2007.03.023
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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