Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations

Abstract

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.