Development of Non-Homogeneous and Hierarchical Hidden Markov Models for Modelling Monthly Rainfall and Streamflow Time Series

Abstract

Hidden Markov models (HMMs) offer a plausible representation of long-term hydroclimatic persistence in rainfall and streamflow observations. Persistent climate processes influence hydrological observations at various time scales. This paper develops the s tochastic framework of two-state HMMs to better represent climate-rainfall interactions at both monthly and annual levels. Two new models, a hierarchical HMM and a non-homogeneous HMM are introduced, and fitted to monthly rainfall and streamflow observatio ns from Australia. The value of these models to identify two-state persistence is compared to that of existing two -state HMMs.