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

Abstract

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..