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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-113
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 13 Mar 2018

Research article | 13 Mar 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis

Ankit Agarwal1,2,3, Norbert Marwan2, Maheswaran Rathinasamy2, Ugur Ozturk1,2, Bruno Merz1,3, and Jürgen Kurths1,2 Ankit Agarwal et al.
  • 1Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany
  • 2Research Domain Transdisciplinary Concepts & Methods, Potsdam Institute for Climate Impact Research, Telegrafenberg, Potsdam, Germany
  • 3GFZ German Research Centre for Geosciences, Section 5.4: Hydrology, Telegrafenberg, Potsdam, Germany

Abstract. Hydrometric networks play a vital role in providing information for decision-making in water resources management. They should be set up optimally to provide as much and as accurate information as possible, and at the same time, be cost-effective. We propose a new measure, based on complex network analysis, to support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum in the last years in different areas such as brain networks, social networks, technological networks or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node ranking measure, the weighted degree-betweenness, to evaluate the importance of nodes in a network. It is compared to previously proposed measures on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to check its applicability in the optimal design of hydrometric networks. The proposed measure is evaluated using the decline rate of network efficiency and the kriging error. The results suggest that it effectively quantifies the importance of rain stations. The new measure is very useful in identifying influential stations which need high attention and expendable stations which can be removed without much loss of information provided by the station network.

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