Dynamics of tipping cascades on complex networks
Dynamics of tipping cascades on complex networks
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DOI：
10.1103/physreve.101.042311
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发表时间：
2020-04-29
影响因子：
2.4
通讯作者：
Donges, Jonathan F.
中科院分区：
文献类型：
--
作者：
Kroenke, Jonathan;Wunderling, Nico;Donges, Jonathan F.
临界点发生在不同学科的不同系统中，如生态学，气候科学，经济和工程。临界点是系统参数或状态变量的临界阈值，在该临界点处，微小的扰动可以导致系统的定性变化。许多具有临界点的系统可以被建模为耦合多稳态子系统的网络，例如，耦合的植被斑块、连接的湖泊、相互作用的气候倾斜要素和多尺度基础设施系统。在这样的网络中，一个子系统中的倾翻事件能够通过多米诺骨牌效应引起倾翻级联。在这里，我们调查的影响，网络拓扑结构的发生这样的级联。在Erdos-Renyi、Watts-Strogatz和Barabasi-Albert网络上进行了基于临界点概念动力学模型的级联数值模拟。此外，我们使用亚马逊雨林的水分循环模拟数据生成更真实的网络，并将结果与模型网络的结果进行比较。我们还使用了一个有向配置模型和一个随机块模型，保留了亚马逊网络的某些拓扑属性，以了解这些属性是负责其增加的脆弱性。我们发现，集群和空间组织增加了网络的脆弱性，并可能导致整个网络的倾斜。这些结果可能有助于评估哪些系统由于其网络拓扑结构而易受攻击或健壮，并可能有助于我们相应地设计或管理系统。
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 Erdos-Renyi, Watts-Strogatz, and Barabasi-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.