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| Titel |
Complex networks for streamflow dynamics |
| VerfasserIn |
B. Sivakumar, F. M. Woldemeskel |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 11 ; Nr. 18, no. 11 (2014-11-20), S.4565-4578 |
| Datensatznummer |
250120528
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| Publikation (Nr.) |
 copernicus.org/hess-18-4565-2014.pdf |
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| Zusammenfassung |
| 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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