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Complex Networks for Streamflow Dynamics : Volume 18, Issue 11 (20/11/2014)

By Sivakumar, B.

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Book Id: WPLBN0004011280
Format Type: PDF Article :
File Size: Pages 14
Reproduction Date: 2015

Title: Complex Networks for Streamflow Dynamics : Volume 18, Issue 11 (20/11/2014)  
Author: Sivakumar, B.
Volume: Vol. 18, Issue 11
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Woldemeskel, F. M., & Sivakumar, B. (2014). Complex Networks for Streamflow Dynamics : Volume 18, Issue 11 (20/11/2014). Retrieved from

Description: School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia. 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.

Complex networks for streamflow dynamics

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