Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/24891
- Title
- Modelling Long-Term Persistence in Hydrological Time Series
- Author/Creator
-
Thyer, Mark Andrew
- Institution
- University of Newcastle. Department of Civil, Survey and Environmental Engineering
- Description
- The hidden state Markov (HSM) model is introduced as a new conceptual framework for modelling long-term persistence in hydrological time series. Unlike the stochastic models currently used, the conceptual basis of the HSM model can be related to the physical processes that influence long-term hydrological time series in the Australian climatic regime. A Bayesian approach was used for model calibration. This enabled rigourous evaluation of parameter uncertainty, which proved crucial for the interpretation of the results. Applying the single site HSM model to rainfall data from selected Australian capital cities provided some revealing insights. In eastern Australia, where there is a significant influence from the tropical Pacific weather systems, the results showed a weak wet and medium dry state persistence was likely to exist. In southern Australia the results were inconclusive. However, they suggested a weak wet and strong dry persistence structure may exist, possibly due to the infrequent incursion of tropical weather systems in southern Australia. This led to the postulate that the tropical weather systems are the primary cause of two-state long-term persistence. The single and multi-site HSM model results for the Warragamba catchment rainfall data supported this hypothesis. A strong two-state persistence structure was likely to exist in the rainfall regime of this important water supply catchment. In contrast, the single and multi-site results for the Williams River catchment rainfall data were inconsistent. This illustrates further work is required to understand the application of the HSM model. Comparisons with the lag-one autoregressive [AR(1)] model showed that it was not able to reproduce the same long-term persistence as the HSM model. However, with record lengths typical of real data the difference between the two approaches was not statistically significant. Nevertheless, it was concluded that the HSM model provides a conceptually richer framework than the AR(1) model.
- Description
- PhD Doctorate
- Relation
- University of Newcastle Research Higher Degree Thesis
- Date
- 2001
- Keyword(s)
-
Long-term persistence;
Hidden state Markov model;
Australian rainfall;
El Nino;
Markov chain Monte Carlo methods;
Gibbs sampler;
Parameter uncertainty;
AR(1) model
- Resource Type
- thesis
- Rights
- http://www.newcastle.edu.au/copyright.html)
- Rights
- Copyright 2001 Mark Andrew Thyer
- Identifier
- http://hdl.handle.net/1959.13/24891
- Language
- eng
- Full Text
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