Markov Autocorrelation Pulse Model for Two Sites Daily Streamflow

Abstract

A Markov autocorrelation pulse (MACP) model for two sites daily streamflow generation is proposed in this study. By using two-component pulses with a one-order Markov autocorrelation deterministic part and a skewed Weibull random part as inputs to the hydrologic system, a simplified model for daily streamflow generation is formulated. Pulses occurring concurrently at two neighboring gauges are assumed to be crosscorrelated processes. The mean, standard deviation, auto- and crosscorrelation of daily, monthly, and annual streamflows are computed for observed and generated flow sequences. The performance of the stochastic streamflow model is evaluated. Results show that the MACP model may relatively and satisfactorily reproduce both linear and nonlinear dependence of daily streamflow processes. The effectiveness of the model is demonstrated through its practical application to the generation of daily streamflow for the Wupper River in Germany. The significance of the findings reported in this paper is that the MACP model provides a flexible and adaptive method for reproducing the historical frequency distribution of daily streamflow sequence.