Abstract
The adverse effect of climate change continues to expand, and the risks of flooding are increasing. Despite advances in network science and risk analysis, we lack a systematic mathematical framework for road network percolation under the disturbance of flooding. The difficulty is rooted in the unique three-dimensional nature of a flood, where altitude plays a critical role as the third dimension, and the current network-based framework is unsuitable for it. Here we develop a failure model to study the effect of floods on road networks; the result covers 90.6% of road closures and 94.1% of flooded streets resulting from Hurricane Harvey. We study the effects of floods on road networks in China and the United States, showing a discontinuous phase transition, indicating that a small local disturbance may lead to a large-scale systematic malfunction of the entire road network at a critical point. Our integrated approach opens avenues for understanding the resilience of critical infrastructure networks against floods.
Highlights
The adverse effect of climate change continues to expand, and the risks of flooding are increasing
We find that 90.6% of reported road closures and 94.1% of reported flooded streets are covered by the total failures obtained using the failure model and that the reported failures of each county in Houston strongly correlate with the coverage of total failures obtained using the failure model
Before we use this model, we take the catastrophic flooding in Houston and South East Texas due to Hurricane Harvey[51] as a case study to validate the results of the proposed failure model
Summary
The adverse effect of climate change continues to expand, and the risks of flooding are increasing. Risk analyses have focused on methodologies of the road network absorption capacity[9], hazard impact framework[19], level of service remaining, and cascading effects within critical infrastructure systems[10,11]. These analyses help clarify the propagation of flood risk among infrastructure systems at different spatial scales[12]. The largest connected component, called the giant connected component, is assumed to be functional[30,31,32] This measure is especially relevant to the robustness of a system, representing the function of mutual navigability under shocks[33]. The percolation approach was shown to be extremely useful in addressing other scenarios, such as efficient damage and immunization[34,35,36,37], in obtaining optimal paths[38], and in designing robust networks[39]
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