- Title
- A general Bayesian framework for calibrating and evaluating stochastic models of annual multi-site hydrological data
- Creator
- Frost, Andrew J.; Thyer, Mark A.; Srikanthan, R.; Kuczera, George
- Relation
- Journal of Hydrology Vol. 340, Issue 3-4, p. 129-148
- Publisher Link
- http://dx.doi.org/10.1016/j.jhydrol.2007.03.023
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2007
- Description
- 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.
- Subject
- stochastic rainfall; long-term persistence; parameter and model uncertainty; hidden Markov models; lag-one autoregressive models; box–cox transformation
- Identifier
- http://hdl.handle.net/1959.13/33909
- Identifier
- uon:3368
- Identifier
- ISSN:0022-1694
- Language
- eng
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