Identifying persistence in rainfall and streamflow extremes and other hydrological variables

Abstract

Hydrological observations are characterised by protracted wet and dry cycles, a characteristic that is termed hydrological persistence. Interactions between global climate phenomena and the hydrological cycle result in rainfall and streamflow data clustering into wetter and drier states. These states have implications for the management and planning of water resources. Statistical tests constructed from the theory of wet and dry spells indicate that evidence for persistence in monthly observations is more compelling than at an annual scale. Hidden Markov models (HMMs) provide a strong conceptual basis for describing hydrological persistence, and are shown to provide accurate descriptions of fluctuating climate states. These models assume that the broader climate fluctuates between small numbers of climate states, usually two or three, with each state influencing the probability distributions of hydrological observations. The supposition of the climate having a tendency to fluctuate between finite numbers of stable regimes has a long history in meteorological studies. This paper details the recent development of a novel non-parametric (NP) HMM, which provides a more robust and flexible implementation for HMMs by removing the requirement to assume parametric forms for underlying state distributions. Significant hydrological persistence in time series of total monthly rainfall and streamflow as well as in time series of the total number of monthly rain-days and hydrological extremes are illustrated. These results show that hydrological persistence is revealed in a similar manner across a wide range of hydrological variables, providing knowledge of hydroclimatic interactions that will assist future planning and management of water resources.