A hidden Markov model for modelling long-term persistence in multi-site rainfall time series. 2. Real data analysis

Abstract

The hidden Markov model (HMM) provides an attractive framework for modelling long-term persistence in hydrological data because it can produce time series with long-term wet and dry periods. In this study, the Bayesian calibration procedure for the multi-site HMM developed by Thyer and Kuczera [J. Hydrol. (2003)] is used to calibrate the model to multi-site rainfall data from the Warragamba, Central Coast and Williams River catchment regions - all important water supply catchments located on the east coast of Australia. This methodology is used to verify the majority of the HMM assumptions. The results for the Warragamba and Central Coast catchment region provided strong evidence that a model with a two-state persistence structure was more consistent with the data than a one state model with no persistence. These findings may have considerable implications for water resources management and drought risk assessment in both these regions. In addition, the results suggested that the multi-site framework exploits space-for-time substitution and the sampling of missing data to better identify the long-term persistence structure. For the Williams River catchment rainfall data difficulties were experienced with achieving convergence of the calibration procedure because of bimodal posterior distributions. While the results suggested a two-state persistence structure exists for the Williams, the difficulties indicate there is scope for further refinement of the implementation of the HMM concept. © 2003 Published by Elsevier Science B.V.