Autocorrelation structure of monthly streamflows

Abstract

The autocorrelation structure of monthly streamflows, a nonstationary process, is developed from a mathematical model that assumes that monthly precipitation is an independent series and that the base flow of the stream is derived from a linear aquifer. Under these assumptions the first‐order autocorrelation coefficients of streamflow are found to vary seasonally, as do other statistics such as monthly means and standard deviations. Comparison of the autocorrelation coefficients predicted by the model with those computed from an actual streamflow record of 58 years indicates that the seasonality of streamflow is well represented by the model.