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  Dynamics of tipping cascades on complex networks

Krönke, J., Wunderling, N., Winkelmann, R., Staal, A., Stumpf, B., Tuinenburg, O. A., Donges, J. F. (2020): Dynamics of tipping cascades on complex networks. - Physical Review E, 101, 042311.
https://doi.org/10.1103/PhysRevE.101.042311

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 Creators:
Krönke, Jonathan1, Author              
Wunderling, Nico1, Author              
Winkelmann, Ricarda1, Author              
Staal, Arie2, Author
Stumpf, Benedikt1, Author              
Tuinenburg, Obbe A.2, Author
Donges, Jonathan Friedemann1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: Tipping points occur in diverse systems in various disciplines such as ecology, climate science, economy, and engineering. Tipping points are critical thresholds in system parameters or state variables at which a tiny perturbation can lead to a qualitative change of the system. Many systems with tipping points can be modeled as networks of coupled multistable subsystems, e.g., coupled patches of vegetation, connected lakes, interacting climate tipping elements, and multiscale infrastructure systems. In such networks, tipping events in one subsystem are able to induce tipping cascades via domino effects. Here, we investigate the effects of network topology on the occurrence of such cascades. Numerical cascade simulations with a conceptual dynamical model for tipping points are conducted on Erdős-Rényi, Watts-Strogatz, and Barabási-Albert networks. Additionally, we generate more realistic networks using data from moisture-recycling simulations of the Amazon rainforest and compare the results to those obtained for the model networks. We furthermore use a directed configuration model and a stochastic block model which preserve certain topological properties of the Amazon network to understand which of these properties are responsible for its increased vulnerability. We find that clustering and spatial organization increase the vulnerability of networks and can lead to tipping of the whole network. These results could be useful to evaluate which systems are vulnerable or robust due to their network topology and might help us to design or manage systems accordingly.

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 Dates: 2020-03-182020
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: PIKDOMAIN: RD1 - Earth System Analysis
PIKDOMAIN: RD4 - Complexity Science
eDoc: 8992
DOI: 10.1103/PhysRevE.101.042311
Research topic keyword: Tipping Elements
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Ecosystems
Model / method: Nonlinear Data Analysis
Model / method: Open Source Software
Regional keyword: South America
Organisational keyword: FutureLab - Earth Resilience in the Anthropocene
Organisational keyword: RD1 - Earth System Analysis
Working Group: Whole Earth System Analysis
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Physical Review E
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 101 Sequence Number: 042311 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150218
Publisher: American Physical Society (APS)