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  Optimal design of hydrometric station networks based on complex network analysis

Agarwal, A., Marwan, N., Maheswaran, R., Öztürk, U., Kurths, J., Merz, B. (2020): Optimal design of hydrometric station networks based on complex network analysis. - Hydrology and Earth System Sciences, 24, 5, 2235-2251.
https://doi.org/10.5194/hess-24-2235-2020

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Agarwal, Ankit1, Autor              
Marwan, Norbert1, Autor              
Maheswaran, Rathinasamy 2, Autor
Öztürk, Ugur2, Autor
Kurths, Jürgen1, Autor              
Merz, Bruno2, Autor
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Zusammenfassung: Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few 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 (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.

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 Datum: 2020-04-232020
 Publikationsstatus: Final veröffentlicht
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.5194/hess-24-2235-2020
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
MDB-ID: yes - 3042
Research topic keyword: Complex Networks
Research topic keyword: Weather
Model / method: Nonlinear Data Analysis
Regional keyword: Germany
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Art des Abschluß: -

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Titel: Hydrology and Earth System Sciences
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, oa
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 24 (5) Artikelnummer: - Start- / Endseite: 2235 - 2251 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals208
Publisher: Copernicus