A method for coupling daily and monthly time scales in stochastic generation of rainfall series

Stochastic generation of hydrological time series is a useful tool for the design and management of water resources systems. One of the shortcomings of many available models for stochastic generation of daily rainfall data is that they are unable to preserve satisfactorily key statistical properties simultaneously at daily, monthly and annual time scales. In this paper, a method for coupling two different time scales of stochastic hydrological time series models is introduced. The key feature of the method is to first generate two resembling time series, one preserving key statistical properties at a finer time scale and the other at a coarser time scale. Adjustment is then made to the finer time scale series so that this series becomes consistent with the coarser time scale series. Because the initial two time series resemble each other, the adjustment is kept small. In the paper, the technique for generating two resembling time series is described. The implementation of the method for coupling daily and monthly rainfall series is demonstrated. Test results of the method using rainfall data from a number of sites around Australia showed that the coupling method was able to generate daily rainfall time series that preserved satisfactorily some key statistical properties at daily, monthly and even annual time scales.