Study of temporal streamflow dynamics with complex networks: network construction and clustering

Abstract

Applications of the concepts of complex networks for studying streamflow dynamics are gaining momentum at the current time. The present study applies a coupled phase space reconstruction–network construction method to examine the clustering property of the temporal dynamics of streamflow. The clustering of the temporal streamflow network is determined using clustering coefficient, which quantifies the tendency of a network to cluster (a measure of local density). Monthly streamflow time series observed from each of 639 stations (i.e. 639 networks) in the United States are studied. The presence of links between nodes (i.e. phase space reconstructed vectors) in each streamflow network (i.e. station) is identified using the Euclidean distance. Different distance thresholds are used to examine the influence of threshold on the clustering coefficient results and to identify the critical threshold. The results indicate that the distance threshold has significant influence on the clustering coefficient values of the temporal streamflow networks. With the critical distance threshold values, the clustering coefficients for the 639 stations are found to be between 0.15 and 0.81, suggesting very different types of network connections and dynamics. The clustering coefficient values are found to provide useful information on the influence of a given month (i.e. timestep) of the year on the temporal dynamics. Reliable interpretations of the clustering coefficient values in terms of catchment characteristics and flow properties are also possible.