Modeling of seasonal hydrologic time series often requires the identification of periodic functions of its basic parameters such as the mean, standard deviation, and autocorrelation coefficients. The consequences of incorrect estimation of harmonics on dependence models of the stochastic component are investigated. The underremoval of harmonics in the periodic mean introduces four additional terms in the inferred autoregressive moving average model of stochastic component for each underestimated harmonic. Overremoval has a small effect on the dependence structure. In both cases, however, the total number of parameters to be estimated increases. The underremoval of harmonics in the periodic standard deviation decreases the values of autocorrelation coefficients of the stochastic component, whereas the overremoval may increase them. In the case of the periodic standard deviations the effect on model identification of stochastic component appears to be minimal. The neglect of periodicity in the auto‐ correlation coefficients can lead to erroneous stationary models of the stochastic component. Copyright 1985 by the American Geophysical Union.
