Abstract
The article aims to show how some dissimilarity criteria, the Mahalanobis distance between regression coefficients and the Euclidean distance between Autoregressive weights, can be applied to hydrologic time series clustering. Specifically, the temporal dynamics of streamflow time series are compared through the estimated parameters of the corresponding linear models which may include both short and long memory components. The performance of the proposed technique is assessed by means of an empirical study concerning a set of daily streamflow series recorded at sites in Oregon and Washington State.
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