Modeling Urban Flood Disaster Chain Propagation Based on Coupling Pathways of Exposed Units

Abstract

To investigate the spatial transmission patterns of flood disaster chains and the coupling mechanisms of lifeline facilities in complex urban environments, this study proposed a flood disaster chain propagation model based on coupling pathways among exposed units, focusing on the coupling pathways of power, transportation, and communication infrastructures. By integrating remote sensing and GIS data, we identified the spatial distribution and functional dependencies of exposed units. A functional degradation model was developed by combining water-depth vulnerability functions with inter-facility coupling pathways to simulate direct flood impacts and cascading failures. Taking the main urban area of Chongqing as a case study, we simulated the evolution characteristics and spatial transmission patterns of the disaster chain during a typical 2020 flood event. The results indicate that as the disaster chain shifted from isolated failure pathways to compound coupling cascades, the chain effect significantly amplified both the scope and severity of system disruptions. Model outputs show strong consistency with historical damage records in both spatial distribution patterns and magnitude, thereby validating the model’s effectiveness in disaster chain identification, vulnerable node detection, and risk prediction. These findings provide a scientific basis for systematic prevention, risk assessment, and emergency decision making in urban flood management.

Keywords

Chongqing / Exposed units / Flood disaster chain / Functional failure / Spatial propagation / Urban lifeline system

References

[1] Acosta-Coll M, Ballester-Merelo F, Martinez-Peiro M. Early warning system for detection of urban pluvial flooding hazard levels in an ungauged basin. Natural Hazards, 2018, 92(2): 1237-1265.

[2] Arrighi C, Pregnolato M, Castelli F. Indirect flood impacts and cascade risk across interdependent linear infrastructures. Natural Hazards and Earth System Sciences, 2021, 21(6): 1955-1969.

[3] Asaridis, P., D. Molinari, F. Di Maio, F. Ballio, and E. Zio. 2025. A probabilistic modeling and simulation framework for power grid flood risk assessment. International Journal of Disaster Risk Reduction 120: Article 105353.

[4] Bermudez, M., J.F. Farfan, P. Willems, and L. Cea. 2021. Assessing the effects of climate change on compound flooding in coastal river areas. Water Resources Research 57(10): Article e2020WR029321.

[5] Brunner, L.G., R.A.M. Peer, C. Zorn, R. Paulik, and T.M. Logan. 2024. Understanding cascading risks through real-world interdependent urban infrastructure. Reliability Engineering & System Safety 241: Article 109653.

[6] Choo, K.-S., D.H. Kang, and B.-S. Kim. 2020. Impact assessment of urban flood on traffic disruption using rainfall-depth-vehicle speed relationship. Water 12(4): Article 926.

[7] Cirianni, F., F. Fonte, G. Leonardi, and F. Scopelliti. 2012. Analysis of lifelines transportation vulnerability. Procedia – Social and Behavioral Sciences 53: 29–38.

[8] de Bruijn, K.M., C. Maran, M. Zygnerski, J. Jurado, A. Burzel, C. Jeuken, and J. Obeysekera. 2019. Flood resilience of critical infrastructure: Approach and method applied to Fort Lauderdale, Florida. Water 11(3): Article 517.

[9] Du Z, Gu J. A domain ontology construction method of disaster chain – Case study of rainstorm flood disaster chain. Geomatics World, 2016, 23(4): 7-13. (in Chinese).

[10] Duenas-Osorio L, Craig JI, Goodno BJ. Seismic response of critical interdependent networks. Earthquake Engineering & Structural Dynamics, 2007, 36(2): 285-306.

[11] Girgin, S., A. Necci, and E. Krausmann. 2019. Dealing with cascading multi-hazard risks in national risk assessment: The case of Natech accidents. International Journal of Disaster Risk Reduction 35: Article 101072.

[12] Goldbeck N, Angeloudis P, Ochieng WY. Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models. Reliability Engineering & System Safety, 2019, 188: 62-79.

[13] Guo H, He X, Lv X, Wu Y. Risk analysis of rainstorm-urban lifeline system disaster chain based on the PageRank-risk matrix and complex network. Natural Hazards, 2024, 120(12): 10583-10606.

[14] He, K., X. Chen, X. Yu, C. Dong, and D. Zhao. 2024. Evaluation and prediction of compound geohazards in highly urbanized regions across China’s Greater Bay Area. Journal of Cleaner Production 449: Article 141641.

[15] Heracleous C, Kolios P, Panayiotou CG, Ellinas G, Polycarpou MM. Hybrid systems modeling for critical infrastructures interdependency analysis. Reliability Engineering & System Safety, 2017, 165: 89-101.

[16] Hilly, G., Z. Vojinovic, S. Weesakul, A. Sanchez, D.N. Hoang, S. Djordjevic, A.S. Chen, and B. Evans. 2018. Methodological framework for analysing cascading effects from flood events: The case of Sukhumvit area, Bangkok, Thailand. Water 10(1): Article 81.

[17] Huang W, Ling M. System resilience assessment method of urban lifeline system for GIS. Computers, Environment and Urban Systems, 2018, 71: 67-80.

[18] Huang, S., H. Wang, Y. Xu, J. She, and J. Huang. 2021. Key disaster-causing factors chains on urban flood risk based on Bayesian network. Land 10(2): Article 210.

[19] Jamali B, Löwe R, Bach PM, Urich C, Arnbjerg-Nielsen K, Deletic A. A rapid urban flood inundation and damage assessment model. Journal of Hydrology, 2018, 564: 1085-1098.

[20] Lam, C.Y., and T. Shimizu. 2021. A network analytical framework to analyze infrastructure damage based on earthquake cascades: A study of earthquake cases in Japan. International Journal of Disaster Risk Reduction 54: Article 102025.

[21] Li, D., Y. Hou, S. Du, and F. Zhou. 2024. Cascading failure and resilience of urban rail transit stations under flood conditions: A case study of Shanghai Metro. Water 16(19): Article 2731.

[22] Lin L, Wu Z, Liang Q. Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework. Natural Hazards, 2019, 97(2): 455-475.

[23] Lin I-C, Yagan O, Joe-Wong C. Dynamic coupling strategy for interdependent network systems against cascading failures. IEEE Transactions on Network Science and Engineering, 2023, 10(4): 2265-2282.

[24] Liu K, Wang M, Zhu W, Wu J, Yan X. Vulnerability analysis of an urban gas pipeline network considering pipeline-road dependency. International Journal of Disaster Risk Reduction, 2018, 23: 79-89

[25] Lyu H-M, Sun W-J, Shen S-L, Arulrajah A. Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach. Science of the Total Environment, 2018, 626: 1012-1025.

[26] Mei C, Chen Y, Liu J, Wang H, Wang D, Shao W. Impact assessment of urban waterlogging on road traffic based on scenario simulation. Water Resources Protection, 2022, 119: 31-38. (in Chinese).

[27] Ouyang M. Review on modeling and simulation of interdependent critical infrastructure systems. Reliability Engineering & System Safety, 2014, 121: 43-60.

[28] Ouyang M, Hong L, Mao Z-J, Yu M-H, Qi F. A methodological approach to analyze vulnerability of interdependent infrastructures. Simulation Modelling Practice and Theory, 2009, 17(5): 817-828.

[29] Qin X-L, Wang S-F, Meng M. Flood cascading on critical infrastructure with climate change: A spatial analysis of the extreme weather event in Xinxiang. China. Advances in Climate Change Research, 2023, 14(3): 458-468.

[30] Sanchez-Munoz, D., J.L. Dominguez-Garcia, E. Martinez-Gomariz, B. Russo, J. Stevens, and M. Pardo. 2020. Electrical grid risk assessment against flooding in Barcelona and Bristol Cities. Sustainability 12(4): Article 1527.

[31] Schneider, M., L. Halekotte, A. Mentges, and F. Fiedrich. 2025. Dependent infrastructure service disruption mapping (DISruptionMap): A method to assess cascading service disruptions in disaster scenarios. Scientific Reports 15(1): Article 5736.

[32] Serre D, Heinzlef C. Assessing and mapping urban resilience to floods with respect to cascading effects through critical infrastructure networks. International Journal of Disaster Risk Reduction, 2018, 30: 235-243.

[33] Shi P. Theory and practice of disaster study. Journal of Natural Disasters, 1996, 5(4): 6-17. (in Chinese).

[34] Shi, Y., J. Yang, and P. Shen. 2020. Revealing the correlation between population density and the spatial distribution of urban public service facilities with mobile phone data. ISPRS International Journal of Geo-Information 9(1): Article 38.

[35] Wang, X. and Y.-M. Shen. 2023. A framework of dependence modeling and evaluation system for compound flood events. Water Resources Research 59(8): Article e2023WR034718.

[36] Wang Y, Gao G, Zhai J, Liu Q, Song L. Evolution characteristics of the rainstorm disaster chains in the Guangdong-Hong Kong-Macao Greater Bay Area. China. Natural Hazards, 2023, 119(3): 2011-2032.

[37] Wang, Y., F. Meng, H. Liu, C. Zhang, and G. Fu. 2019. Assessing catchment scale flood resilience of urban areas using a grid cell based metric. Water Research 163: Article 114852.

[38] Wu J, Duenas-Osorio L. Calibration and validation of a seismic damage propagation model for interdependent infrastructure systems. Earthquake Spectra, 2013, 29(3): 1021-1041.

[39] Yan, P., F. Zhang, F. Zhang, and L. Geng. 2025. A systematic review and conceptual framework of urban infrastructure cascading disasters using scientometric methods. Buildings 15(7): Article 1011.

[40] Yang, L., Z. Gu, Y. Dang, and P. He. 2022. Analysis of vulnerability on weighted power networks under line breakdowns. Entropy 24(10): Article 1449.

[41] Yang, Y., H. Guo, D. Wang, X. Ke, S. Li, and S. Huang. 2021. Flood vulnerability and resilience assessment in China based on super-efficiency DEA and SBM-DEA methods. Journal of Hydrology 600: Article 126470.

[42] Yin Y, Val DV, Zou Q, Yurchenko D. Resilience of critical infrastructure systems to floods: A coupled probabilistic network flow and LISFLOOD-FP model. Water, 2022, 14(5): 683.

[43] Zhang, K., C. Mei, J. Liu, J. Wang, T. Song, H. Shi, A. Zhang, and X. Cao. 2025. Spatiotemporal evolution analysis of urban lifeline infrastructure disaster chain network based on numerical simulation and cascading effects. Sustainable Cities and Society 133: Article 106880.

[44] Zhao X, Chen Z, Xu J, Tang H. Seismic vulnerability of urban double-layer interdependent lifeline network. Journal of Zhejiang University (Engineering Science), 2020, 4: 767-777. in Chinese).

[45] Zhou Y, Zhao Z, Louet Y, Ying Q, Li R, Zhou X, Chen X, Zhang H. Large-scale spatial distribution identification of base stations in cellular networks. IEEE Access, 2015, 3: 2987-2999.

RIGHTS & PERMISSIONS

The Author(s)