POST-BLACKENING APPROACH FOR MODELING PERIODIC STREAMFLOWS

Abstract

The post-blackening (PB) approach introduced by the authors for modeling annual streamflows in an earlier work is extended to model periodic streamflows. This is basically a semi-parametric approach that blends a simple low-order, linear periodic parametric model with the moving block resampling scheme. The first part of the paper demonstrates the hybrid character of the PB model through Monte-Carlo simulations performed on hypothetical data sets drawn from a known population. Following this, the PB model is used for stochastic simulation of periodic streamflows of Beaver and Weber rivers in the US. The results show that the PB model is more consistent in reproducing a wide variety of statistics of periodic streamflows, compared to low-order linear periodic parametric models (Box-Jenkins type) and the periodic k-nearest-neighbor bootstrap (nonparametric) method. In addition, the PB model is able to preserve cross-year serial correlations as well as the month-to-year cross-correlations. This hybrid approach seems to offer considerable scope for improvement in hydrologic time series modeling.