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A method for generating combined multivariate time series at multiple locations and at different time scales is presented. The procedure is based on three steps: first, the Monte Carlo method generation of data with statistical properties as close as possible to the observed series; second, the rearrangement of the order of simulated data in the series to achieve target correlations; and third, the permutation of series for correlation adjustment between consecutive years. The method is non-parametric and retains, to a satisfactory degree, the properties of the observed time series at the selected simulation time scale and at coarser time scales. The new approach is tested on two case studies, where it is applied to the log-transformed streamflow and precipitation at weekly and monthly time scales. Special attention is given to the extrapolation of non-parametric cumulative frequency distributions in their tail zones. The results show a good agreement of stochastic properties between the simulated and observed data. For example, for one of the case studies, the average relative errors of the observed and simulated weekly precipitation and streamflow statistics (up to skewness coefficient) are in the range of 0.1–9.2% and 0–5.4%, respectively.

A non-parametric method for generating stationary weekly hydrologic time series at multiple locations is presented. The procedure has three distinct steps: first, the Monte Carlo method is used to obtain 1000 years of simulated weekly flows having statistical properties as close as possible to the observed series; second, rearranging the order of simulated data in the series to achieve target spatial and temporal correlations within each simulated year; and third, the permutation of annual partial series to adjust the correlation of weekly streamflows at the beginning of a year with that at the end of a previous year while also adjusting the auto-correlation of annual flows. In this paper the method is applied for the first time on log-transformed data, and contributes to this methodology by introducing an additional criterion related to the possibility to obtain a desired frequency of occurrence of extremely dry years in the simulated series. The method is tested in two case studies, which use data from three hydrologic stations on the Studenica River in Serbia, and from seven stations in the Oldman River basin in Southern Alberta, Canada. The results show that the simulated data correspond to the observed data in all their stochastic properties and that they can be consequently used in the studies related to planning and design of reservoirs and other water management systems.

This thesis describes the main features of a 'Strongly Feasible Evolution Program' ('SFEP') for solving network flow programs that can be non-linear both in the constraints and in the objective function. The approach is a hybrid of a network flow algorithm and an evolution program. Network flow theory is used to help conduct the search exclusively within the feasible region, while progress towards optimal points in the search space is achieved using evolution programming mechanisms such as recombination and mutation. The solution procedure is based on a recombination operator in which all parents in a small mating pool have equal chance of contributing their genetic material to an offspring. When an offspring is created with better fitness value than that of the worst parent, the worst parent is discarded from the mating pool while the offspring is placed in it. The main contributions are in the 'massive parallel initialization' procedure which creates only feasible solutions with simple heuristic rules that increase chances of creating solutions with good fitness values for the initial mating pool, and the 'gene therapy procedure' which fixes "defective genes" ensuring that the offspring resulting from recombination is always feasible. Both procedures utilize the properties of network flows. Tests were conducted on a number of previously published transportation problems with 49 and 100 decision variables, and on two problems involving water resources networks with complex non-linear constraints with up to 1500 variables. Convergence to equal or better solutions was achieved with often less than one tenth of the previous computational efforts.