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

A hidden state Markov (HSM) model is developed as a new approach for genera ting hydroclimatic time series with long-term persistence. The two-state HS M model is motivated by the fact that the interaction of global climatic me chanisms produces alternating wet and dry regimes in Australian hydroclimat ic time series. The HSM model provides an explicit mechanism to stochastica lly simulate these quasi-cyclic wet and dry periods. This is conceptually s ounder than the current stochastic models used for hydroclimatic time serie s 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 da ta sets. A Markov Chain Monte Carlo method known as the Gibbs sampler was u sed for model calibration. The results showed that the locations significan tly influenced by tropical weather systems supported the assumptions of the HSM modeling framework and indicated a strong persistence structure. In co ntrast, the calibration of the AR(1) model to these data sets produced no s tatistically significant evidence of persistence.