This paper presents a hybrid algorithmic framework for nonparametric multisite daily streamflow simulation, evaluated on 52 years of observed data from three hydrological stations. The method generates streamflow data of 1000 synthetic years while preserving marginal distributions, daily rank structures, inter-station consistency, annual-sum behavior, and dependence across consecutive years. The workflow integrates Monte Carlo sampling, Schaake-shuffle reordering, block-mosaic reconstruction with partial freezing, Hungarian assignment optimization, annual-sum matching, and an adaptive permutation genetic algorithm for year-order optimization. The results show that the proposed algorithm improves aggregate hydrological diagnostics, particularly annual-sum autocorrelation, hydrological indices, persistence, seasonality, and timing of extremes, while reducing runtime in the final optimization phase by 45.2% compared to the benchmark algorithm. The study therefore formulates daily streamflow simulation as a constrained time-series reconstruction and permutation-optimization problem, making the method suitable for further algorithmic development and other multisite environmental time-series applications.
Pitulić et al. (Fri,) studied this question.