Author(s): Ashish Sharma; Robert O’Neill
Keywords: Stochastic; Streamflow; Water resources management
Abstract: The estimation of risks associated with water management plans requires generation of synthetic streamflow sequences. The mathematical algorithms used to generate these sequences at monthly time scales are found lacking in two main respects: inability in preserving dependence attributes particularly at large (seasonal to inter-annual) time lags; and, a poor representation of observed distributional characteristics, in particular, representation of strong assymetry or multimodality in the probability density function. Proposed here is an alternative that naturally incorporates both observed dependence and distributional attributes in the generated sequences. Use of a nonparametric framework provides an effective means for representing the observed probability distribution. A careful selection of prior flows imparts the appropriate short-term memory, while use of an “aggregate” flow variable allows representation of interannual dependence. The nonparametric simulation model is applied to the Burrendong dam inflows, New South Wales, Australia.