A stochastic methodology for generation of seasonal time series reproducing overyear scaling

In generating synthetic time series of hydrologic processes at sub-annual scale it is important to preserve seasonal characteristics and short-term persistence. At the same time, it is equally important to preserve annual characteristics and over year scaling behaviour. This scaling behaviour, which is equivalent to the Hurst phenomenon, has been detected in a large number of hydroclimatic series and affects seriously planning and design of hydrosystems. However, when seasonal models are used, the preservation of annual characteristics and overyear scaling is a difficult task and is often ignored. Disaggregation techniques are the only way to produce synthetic series that are consistent with historical series in several time scales, from seasonal to multiyear, simultaneously. Such techniques involve two or more steps, where in the first step annual series are generated, which are subsequently disaggregated to finer scales. However, disaggregation involves several difficulties (e.g. in parameter estimation), inaccuracies and is a slow procedure. As an alternative, a new methodology is proposed that directly operates on seasonal time scale, avoiding disaggregation, and simultaneously preserves annual statistics and the scaling properties on overyear time scales thus respecting the Hurst phenomenon.