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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 11 | Copyright
Hydrol. Earth Syst. Sci., 18, 4565-4578, 2014
https://doi.org/10.5194/hess-18-4565-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 20 Nov 2014

Research article | 20 Nov 2014

Complex networks for streamflow dynamics

B. Sivakumar2,1 and F. M. Woldemeskel1 B. Sivakumar and F. M. Woldemeskel
  • 1School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
  • 2Department of Land, Air and Water Resources, University of California, Davis, CA, USA

Abstract. Streamflow modeling is an enormously challenging problem, due to the complex and nonlinear interactions between climate inputs and landscape characteristics over a wide range of spatial and temporal scales. A basic idea in streamflow studies is to establish connections that generally exist, but attempts to identify such connections are largely dictated by the problem at hand and the system components in place. While numerous approaches have been proposed in the literature, our understanding of these connections remains far from adequate. The present study introduces the theory of networks, in particular complex networks, to examine the connections in streamflow dynamics, with a particular focus on spatial connections. Monthly streamflow data observed over a period of 52 years from a large network of 639 monitoring stations in the contiguous US are studied. The connections in this streamflow network are examined primarily using the concept of clustering coefficient, which is a measure of local density and quantifies the network's tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels, which are based on correlations in streamflow data between the stations. The clustering coefficient values of the 639 stations are used to obtain important information about the connections in the network and their extent, similarity, and differences between stations/regions, and the influence of thresholds. The relationship of the clustering coefficient with the number of links/actual links in the network and the number of neighbors is also addressed. The results clearly indicate the usefulness of the network-based approach for examining connections in streamflow, with important implications for interpolation and extrapolation, classification of catchments, and predictions in ungaged basins.

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This study introduces the theory of networks, and in particular complex networks, to examine connections in streamflow dynamics. Monthly streamflow data from a network of 639 stations in the United States are studied. The connections are examined primarily using the concept of clustering coefficient, which quantifies the network’s tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels based on correlations in streamflow between the stations.
This study introduces the theory of networks, and in particular complex networks, to examine...
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