Modeling long-term persistence in hydroclimatic time series using a hidden state Markov model

A hidden state Markov (HSM) model is developed as a new approach for generating hydroclimatic time series with long-term persistence. The two-state HSM model is motivated by the fact that the interaction of global climatic mechanisms produces alternating wet and dry regimes in Australian hydroclimatic time series. The HSM model provides an explicit mechanism to stochastically simulate these quasi-cyclic wet and dry periods. This is conceptually sounder than the current stochastic models used for hydroclimatic time series simulation. Models such as the lag-one autoregressive (AR(1)) model have no explicit mechanism for simulating the wet and dry regimes. In this study the HSM model was calibrated to four long-term Australian hydroclimatic data sets. A Markov Chain Monte Carlo method known as the Gibbs sampler was used for model calibration. The results showed that the locations significantly influenced by tropical weather systems supported the assumptions of the HSM modeling framework and indicated a strong persistence structure. In contrast, the calibration of the AR(1) model to these data sets produced no statistically significant evidence of persistence.