Clustering streamflow time series for regional classification

SummaryThe 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.

Highlight
► A technique based on time series modeling is proposed to group similar river flow series.
► Mahalanobis distance between harmonic regression coefficients and AR metric are introduced.
► Mahalanobis distance measures the dissimilarity of dominant seasonal components.
► AR metric between time series models measures dissimilarity among other dynamics components.
► The statistical properties of the AR distance locates clustering in an inferential framework.