Nonparametric Simulation of Single-Site Seasonal Streamflows

Various parametric and nonparametric models have been suggested in literature for stochastic generation of seasonal streamflows. State-of-the-art nonparametric models are reviewed herein and their drawbacks identified. We developed a simple model that employs the k-nearest neighbor resampling algorithm with gamma kernel perturbation (denoted as KGK model), which enables generation of data that are not the same as the historical data. For preserving the annual variability two approaches are developed. The first one employs the aggregate variable concept (KGKA model), and the second one uses a pilot variable that leads the generation of the seasonal data (KGKP model). The pilot variable refers to the annual data that has been previously generated, but its role is not for disaggregation but rather for conditioning the state that guides the generation of seasonal flows. The proposed models have been compared with a currently available nonparametric model that considers the reproduction of the interannual variability. Monthly streamflows of the Colorado and Niger Rivers were utilized and the performance of the alternative models evaluated by comparing a number of statistics such as the mean, variance, and storage capacity determined from the generated data (monthly and annual) and the corresponding historical data. The extensive testing and comparison of the models led to the conclusion that overall, the suggested model structure KGK (particularly the KGKP model) performs quite well in preserving the monthly and annual historical statistics. The possibilities of extensions of the models are also discussed.