The short-term predictions of annual and seasonal discharge derived by a modified TIPS (Tendency, Intermittency, Periodicity and Stochasticity) methodology are presented in this paper. The TIPS method (Yevjevich, 1984) is modified in such a way that annual time scale is used instead of daily. The reason of extracting a seasonal component from discharge time series represents an attempt to identify the long-term stochastic behaviour. The methodology is applied for modelling annual discharges at six gauging stations in the middle Danube River basin using the observed data in the common period from 1931 to 2012. The model performance measures suggest that the modelled time series are matched reasonably well. The model is then used for the short-time predictions for three annual step ahead (2013–2015). The annual discharge predictions of larger river basins for moderate hydrological conditions show reasonable matching with records expressed as the relative error from –8% to +3%. Irrespective of this, wet and dry periods for the aforementioned river basins show significant departures from annual observations. Also, the smaller river basins display greater deviations up to 26% of the observed annual discharges, whereas the accuracy of annual predictions do not strictly depend on the prevailing hydrological conditions.
Water resource has become a guarantee for sustainable development on both local and global scales. Exploiting water resources involves development of hydrological models for water management planning. In this paper we present a new stochastic model for generation of mean annul flows. The model is based on historical characteristics of time series of annual flows and consists of the trend component, long-term periodic component and stochastic component. The rest of specified components are model errors which are represented as a random time series. The random time series is generated by the single bootstrap model (SBM). Stochastic ensemble of error terms at the single hydrological station is formed using the SBM method. The ultimate stochastic model gives solutions of annual flows and presents a useful tool for integrated river basin planning and water management studies. The model is applied for ten large European rivers with long observed period. Validation of model results suggests that the stochastic flows simulated by the model can be used for hydrological simulations in river basins.