A nonparametric approach for representing interannual dependence in monthly streamflow sequences

[1] 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 timescales are found lacking in two main respects: inability in preserving dependence attributes particularly at large (seasonal to interannual) time lags and a poor representation of observed distributional characteristics, in particular, representation of strong asymmetry 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, while the use of a "variable kernel'' ensures accurate modeling of streamflow data sets that contain a substantial number of zero-flow values. 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 monthly flows from the Beaver River near Beaver, Utah, and the Burrendong dam inflows, New South Wales, Australia.