Regionalization of watersheds by hybrid-cluster analysis

Abstract

Regionalization methods are often used in hydrology for regional trend analysis and frequency analysis of floods, low flows and other variables. During the last two decades considerable effort has gone into analysis and development of regionalization procedures. However, as no single procedure has been demonstrated to yield universally acceptable results, several methods of regionalization are in use. In this paper, three hybrid-clustering algorithms, which use partitional clustering procedure to identify groups of similar catchments by refining the clusters derived from agglomerative hierarchical clustering algorithms, are investigated to determine their effectiveness in regionalization. The hierarchical clustering algorithms used are single linkage, complete linkage and Ward's algorithms, while the partitional clustering algorithm used is the K-means algorithm. The effectiveness of the hybrid-cluster analysis in regionalization is investigated by using data from watersheds in Indiana, USA. Further, four cluster validity indices, namely cophenetic correlation coefficient, average silhouette width, Dunn's index and Davies–Bouldin index are tested to determine their effectiveness in identifying optimal partition provided by the clustering algorithms. The regions given by the clustering algorithms are, in general, not statistically homogeneous. The hybrid-cluster analysis is found to be useful in minimizing the effort needed to identify homogeneous regions. The hybrid of Ward's and K-means algorithms is recommended for use. The hybrid method provides enough flexibility and it offers prospects for improvement in regionalization studies.