Non-linear failure patterns in urban road networks exposed to flooding
4 affiliations
- 1Risk and Environmental Studies, Karlstad University, Karlstad, Sweden
- 2Centre of Natural Hazards and Disaster Science (CNDS), Uppsala, Sweden
- 3Department of Mathematics and Computer Science, Karlstad University, Karlstad, Sweden
- 4Department of Civil Engineering, School of Engineering, Democritus University of Thrace, Xanthi, Greece
Abstract
Urban road networks are highly sensitive to flooding, yet the systemic consequences of inundation on their structural and functional organization remain insufficiently understood. This study introduces a scale-independent analytical framework that integrates complex network measures such as degree, closeness, and betweenness centrality with community-structure analysis to quantify how flooding transforms network connectivity, accessibility, and mesoscale cohesion. This study assesses the impact of river flooding on the urban road network under two scenarios: a 100-year return period flood and the maximum probable scenario (BHF- Beräknat Högsta Flöde), which corresponds to an estimated extreme event with an approximate return period of 10,000 years. Our analysis shows that inundation under the two flood scenarios can generate non-linear and disproportionate impacts on connectivity, accessibility, and overall network cohesion. Specifically, our method: (a) localizes critically isolated nodes exposed to high risk; (b) identifies structural changes, showing that the flooded network is divided into three main large subnetworks with specific epicenters, along with 263 smaller subnetworks in the 100-year case and 553 in the BHF case; (c) estimates that network inefficiency, interpreted as analogous to mean travel time to a destination, increases by 120% and 170% for the 100-year and BHF scenarios, respectively; and (d) localizes critical roads that may serve as potential corridors during evacuation planning. These results offer actionable insights for disaster risk reduction, including planning for neighborhood-scale isolation, identifying vulnerable corridors, and designing redundant and decentralized emergency access routes. The framework is transferable to other urban areas where flood maps and road network data are available, supporting risk-informed spatial planning and strengthened civil protection.
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BibTeX
@article{karagiorgos2026nonlinear,
title = {{Non-linear failure patterns in urban road networks exposed to flooding}},
author = {Konstantinos Karagiorgos and Nikos I. Kavallaris and Ole Sönnerborn and Fotis Maris and Konstantinos Spiliotis},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0354204},
url = {https://doi.org/10.1371/journal.pone.0354204},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Non-linear failure patterns in urban road networks exposed to flooding
AU - Konstantinos Karagiorgos
AU - Nikos I. Kavallaris
AU - Ole Sönnerborn
AU - Fotis Maris
AU - Konstantinos Spiliotis
PY - 2026
DA - 2026/09/01
JO - PLOS One
DO - 10.1371/journal.pone.0354204
UR - https://doi.org/10.1371/journal.pone.0354204
AB - Urban road networks are highly sensitive to flooding, yet the systemic consequences of inundation on their structural and functional organization remain insufficiently understood. This study introduces a scale-independent analytical framework that integrates complex network measures such as degree, closeness, and betweenness centrality with community-structure analysis to quantify how flooding transforms network connectivity, accessibility, and mesoscale cohesion. This study assesses the impact of river flooding on the urban road network under two scenarios: a 100-year return period flood and the maximum probable scenario (BHF- Beräknat Högsta Flöde), which corresponds to an estimated extreme event with an approximate return period of 10,000 years. Our analysis shows that inundation under the two flood scenarios can generate non-linear and disproportionate impacts on connectivity, accessibility, and overall network cohesion. Specifically, our method: (a) localizes critically isolated nodes exposed to high risk; (b) identifies structural changes, showing that the flooded network is divided into three main large subnetworks with specific epicenters, along with 263 smaller subnetworks in the 100-year case and 553 in the BHF case; (c) estimates that network inefficiency, interpreted as analogous to mean travel time to a destination, increases by 120% and 170% for the 100-year and BHF scenarios, respectively; and (d) localizes critical roads that may serve as potential corridors during evacuation planning. These results offer actionable insights for disaster risk reduction, including planning for neighborhood-scale isolation, identifying vulnerable corridors, and designing redundant and decentralized emergency access routes. The framework is transferable to other urban areas where flood maps and road network data are available, supporting risk-informed spatial planning and strengthened civil protection.
SN - 1932-6203
ER -