Spatiotemporal Dynamics and Multi-Scale Diagnosis of Urban Resilience to Typhoon Disaster Chains in Fujian, China

Xiaoliu Yang , Laiyin Zhu , Xiaochen Qin , Xiang Zhou , Miaomiao Ma , Ying Chen , Jianhui Wei , Lu Gao , Harald Kunstmann

International Journal of Disaster Risk Science ›› 2026, Vol. 17 ›› Issue (4) : 772 -790.

PDF
International Journal of Disaster Risk Science ›› 2026, Vol. 17 ›› Issue (4) :772 -790. DOI: 10.1007/s13753-026-00763-5
Article
research-article
Spatiotemporal Dynamics and Multi-Scale Diagnosis of Urban Resilience to Typhoon Disaster Chains in Fujian, China
Author information +
History +
PDF

Abstract

Coastal cities in southeastern China face increasing threats from typhoon-induced compound disasters (for example, torrential rainfall, urban waterlogging, and storm surges) that can cascade into interconnected disaster chains under climate change and rapid urbanization. However, dynamic multi-scale assessments of resilience to such compound disasters remain limited. This study develops an integrated framework that combines multi-scale geospatial analysis with explainable machine learning (XGBoost-SHAP). Using Fujian Province as a case study, we assess typhoon disaster chain urban resilience (TDCUR) in 2010, 2015, and 2020 across grid, administrative unit, and watershed scales, characterize spatiotemporal patterns, and apply XGBoost-SHAP as a post hoc diagnostic to summarize nonlinear indicator-TDCUR association patterns and their spatial concentration under the predefined TDCUR framework. The results indicate that: (1) Provincial TDCUR increased by 6.9% and regional disparities converged, yet major coastal cities experienced declining resilience despite strong economic development; (2) Resilience showed pronounced spatial polarization, with low-resilience cold spots expanding by 48% and clustering in the Xiamen-Quanzhou area; (3) Machine learning diagnostics indicate that typhoon-strong wind-storm surge sensitivity (B8), typhoon-rainfall-flood sensitivity (B7), and impervious surface proportion (A2) show the strongest model-based associations with the spatial variation of TDCUR and display significant interaction effects; and (4) SHAP-based spatial diagnosis identifies the Xiamen-Quanzhou-Fuzhou coastal belt and the Jinjiang Basin as priority areas with concentrated low TDCUR and high cumulative SHAP magnitudes. The proposed framework is transferable and can support spatial screening for targeted resilience actions in coastal regions, with implications for SDG 11.

Keywords

Fujian / Multi-scale assessment / SHapley additive exPlanations / Typhoon disaster chains / Urban resilience

Cite this article

Download citation ▾
Xiaoliu Yang, Laiyin Zhu, Xiaochen Qin, Xiang Zhou, Miaomiao Ma, Ying Chen, Jianhui Wei, Lu Gao, Harald Kunstmann. Spatiotemporal Dynamics and Multi-Scale Diagnosis of Urban Resilience to Typhoon Disaster Chains in Fujian, China. International Journal of Disaster Risk Science, 2026, 17 (4) : 772-790 DOI:10.1007/s13753-026-00763-5

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Agboola OP, Tunay M. Urban resilience in the digital age: The influence of information-communication technology for sustainability. Journal of Cleaner Production, 2023, 428: 139304

[2]

AghaKouchak A, Huning LS, Chiang F, Sadegh M, Vahedifard F, Mazdiyasni O, Moftakhari H, Mallakpour I. How do natural hazards cascade to cause disasters?. Nature, 2018, 561(7724): 458-460

[3]

Alam A, Sammonds P, Ahmed B. Cyclone risk assessment of the Cox’s Bazar district and Rohingya refugee camps in southeast Bangladesh. Science of the Total Environment, 2020, 704: 135360

[4]

Amirzadeh M, Sobhaninia S, Sharifi A. Urban resilience: A vague or an evolutionary concept?. Sustainable Cities and Society, 2022, 81: 103853

[5]

Bevacqua E, Vousdoukas MI, Zappa G, Hodges K, Shepherd TG, Maraun D, Mentaschi L, Feyen L. More meteorological events that drive compound coastal flooding are projected under climate change. Communications Earth & Environment, 2020, 1: 1-11

[6]

Bristow G, Healy A. Innovation and regional economic resilience: An exploratory analysis. The Annals of Regional Science, 2018, 60(2): 265-284

[7]

Chatti W, Majeed MT. Information communication technology (ICT), smart urbanization, and environmental quality: Evidence from a panel of developing and developed economies. Journal of Cleaner Production, 2022, 366: 132925

[8]

Chen A, Pokhrel Y, Chen D, Huang H, Dai Z, He B, Wang J, Li J, et al.. Impact of tropical cyclones and socioeconomic exposure on flood risk distribution in the Mekong Basin. Communications Earth & Environment, 2024, 5: 704

[9]

Davtalab M, Byčenkienė S. Synergistic effects of spatial urban form on PM2.5 concentration and urban heat islands: A multi-scale explanatory machine learning. Journal of Cleaner Production, 2026, 538: 147226

[10]

Folke C. Resilience: The emergence of a perspective for social-ecological systems analyses. Global Environmental Change, 2006, 16(3): 253-267

[11]

Gori A, Lin N, Xi D, Emanuel K. Tropical cyclone climatology change greatly exacerbates US extreme rainfall-surge hazard. Nature Climate Change, 2022, 12: 171-178

[12]

Huang M, Wang Q, Liu M, Lin N, Wang Y, Jing R, Sun J, Murakami H, et al.. Increasing typhoon impact and economic losses due to anthropogenic warming in Southeast China. Scientific Reports, 2022, 12: 14048

[13]

Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin JP, et al.. Tropical cyclones and climate change. Nature Geoscience, 2010, 3: 157-163

[14]

Kou R, Hunter R, Cleland C, Ferguson S, Schipperijn J, Peng Q, Ellis G. Built environment influences on park visits for older adults: Insights from a machine learning approach. Cities, 2025, 165: 106143

[15]

Laurien F, Hochrainer-Stigler S, Keating A, Campbell K, Mechler R, Czajkowski J. A typology of community flood resilience. Regional Environmental Change, 2020, 20: 24

[16]

Li Y, Qiu L. A comparative study on the quality of China’s eco-city: Suzhou vs Kitakyushu. Habitat International, 2015, 50: 57-64

[17]

Li J, Hu J, Yao Y, Ren N. Copula-based joint assessment of the extreme precipitation and storm surge along the coast of mainland China: Impact of tropical cyclones. Journal of Hydrology, 2025, 662: 134124

[18]

Liang Y, Wang C, Chen G, Xie Z. Evaluation framework ACR-UFDR for urban form disaster resilience under rainstorm and flood scenarios: A case study in Nanjing. China. Sustainable Cities and Society, 2024, 107: 105424

[19]

Lin J, He P, Yang L, He X, Lu S, Liu D. Predicting future urban waterlogging-prone areas by coupling the maximum entropy and FLUS model. Sustainable Cities and Society, 2022, 80: 103812

[20]

Lin Y, Peng C, Shu J, Zhai W, Cheng J. Spatiotemporal characteristics and influencing factors of urban resilience efficiency in the Yangtze River Economic Belt. China. Environmental Science and Pollution Research, 2022, 29(26): 39807-39826

[21]

Liu D, Feng J, Li H, Fu Q, Li M, Faiz MA, Ali S, Li T, et al.. Spatiotemporal variation analysis of regional flood disaster resilience capability using an improved projection pursuit model based on the wind-driven optimization algorithm. Journal of Cleaner Production, 2019, 241: 118406

[22]

Liu W, Zhou J, Li X, Zheng H, Liu Y. Urban resilience assessment and its spatial correlation from the multidimensional perspective: A case study of four provinces in North-South Seismic Belt. China. Sustainable Cities and Society, 2024, 101: 105109

[23]

Mallick SK, Das P, Maity B, Rudra S, Pramanik M, Pradhan B, Sahana M. Understanding future urban growth, urban resilience and sustainable development of small cities using prediction-adaptation-resilience (PAR) approach. Sustainable Cities and Society, 2021, 74: 103196

[24]

Massaro E, Schifanella R, Piccardo M, Caporaso L, Taubenbock H, Cescatti A, Duveiller G. Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes. Nature Communications, 2023, 14: 2903

[25]

Mohtat N, Khirfan L. Distributive justice and urban form adaptation to flooding risks: Spatial analysis to identify Toronto’s priority neighborhoods. Frontiers in Sustainable Cities, 2022, 4: 919724

[26]

Mu X, Fang C, Yang Z. Spatio-temporal evolution and dynamic simulation of the urban resilience of Beijing-Tianjin-Hebei urban agglomeration. Journal of Geographical Sciences, 2022, 32(9): 1766-1790

[27]

Pee LG, Pan SL. Climate-intelligent cities and resilient urbanisation: Challenges and opportunities for information research. International Journal of Information Management, 2022, 63: 102446

[28]

Pesaresi M, Guo H, Blaes X, Ehrlich D. A global settlement layer from optical HR/VHR RS data: Concept and first results. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6: 2102-2131

[29]

Qin X, Wu Y, Huang H, Yang X, Gao L. Impact of impervious surface spatial morphologies on urban waterlogging: Insights from a cascade modeling chain at catchment scale. Sustainable Cities and Society, 2025, 130: 106912

[30]

Ranjan R, Karmakar S. Compound hazard mapping for tropical cyclone-induced concurrent wind and rainfall extremes over India. Npj Natural Hazards, 2024, 1: 15

[31]

Ruan J, Chen Y, Yang Z. Assessment of temporal and spatial progress of urban resilience in Guangzhou under rainstorm scenarios. International Journal of Disaster Risk Reduction, 2021, 66: 102578

[32]

Shafiei-Dastjerdi M, Lak A. Towards resilient place emphasizing urban form: An assessment framework in urban design. Sustainable Cities and Society, 2023, 96: 104646

[33]

Sun Y, Wang Y, Zhou X, Chen W. Are shrinking populations stifling urban resilience? Evidence from 111 resource-based cities in China. Cities, 2023, 141: 104458

[34]

Tian Z, Zhang Y, Udo K, Lu X. Regional economic losses of China’s coastline due to typhoon-induced port disruptions. Ocean & Coastal Management, 2023, 237: 106533

[35]

Tiggeloven T, Moel H, Winsemius H, Eilander D, Erkens G, Gebremedhin E, Loaiza A, Kuzma S, et al.. Global-scale benefit-cost analysis of coastal flood adaptation to different flood risk drivers using structural measures. Natural Hazards and Earth System Sciences, 2020, 20(4): 1025-1044

[36]

UNISDR (United Nations International Strategy for Disaster Reduction). Terminology on disaster risk reduction, 2009, Geneva, UNISDR

[37]

Wang W, Kim D, Kin G, Kim KT, Kim S, Kim H. Flood risk assessment of the Naeseongcheon Stream Basin, Korea using the grid-based flood risk index. Journal of Hydrology: Regional Studies, 2024, 51: 101619

[38]

Wu N, Zhou Y, Yin S, Gong H, Zhang C. Revealing the nonlinear impact of environmental regulation on ecological resilience using the XGBoost-SHAP model: Evidence from the Yangtze River Delta region. China. Journal of Cleaner Production, 2025, 514: 145700

[39]

Xi L, Qi Z, Feng Y, Cao X, Zou J, Han J. An explainable machine learning approach (SHAP) to assessing desertification risk and its drivers in the Ring-Tarim Basin, 1990–2020. Environmental Impact Assessment Review, 2026, 118: 108309

[40]

Xie W, Sun C, Lin Z. Spatial-temporal evolution of urban form resilience to climate disturbance in adaptive cycle: A case study of Changchun City. Urban Climate, 2023, 49: 101461

[41]

Yang X, Yan Y, Zhou L, Ma M, Zhang J, Chen Y, Gao L. Risk of compound typhoon disaster chains: Insights from Southeastern China. International Journal of Disaster Risk Science, 2025, 16(5): 870-887

[42]

Yin S, Rourou S, Nannan W, Jun Y. Measuring the impact of technological innovation on urban resilience through explainable machine learning: A case study of the Yangtze River Delta region. China. Sustainable Cities and Society, 2025, 127: 106457

[43]

Yu S, Kong X, Wang Q, Yang Z, Peng J. A new approach of robustness-resistance-recovery (3Rs) to assessing flood resilience: A case study in Dongting Lake Basin. Landscape and Urban Planning, 2023, 230: 104605

[44]

Zhang T, Sun Y, Yin L, Tian Y, Sun Y, Zhuang B. Examining heat risk inequality from the perspective of urban resilience. Sustainable Cities and Society, 2025, 131: 106743

[45]

Zhu S, Li D, Huang G, Chhipi-Shrestha G, Nahiduzzaman KM, Hewage K, Sadiq R. Enhancing urban flood resilience: A holistic framework incorporating historic worst flood to Yangtze River Delta, China. International Journal of Disaster Risk Reduction, 2021, 61: 102355

[46]

Zscheischler J, Westra S, van den Hurk BJJM, Seneviratne SI, Ward PJ, Pitman A, AghaKouchak A, Bresch DN, et al.. Future climate risk from compound events. Nature Climate Change, 2018, 8: 469-477

[47]

Zyl CV, Ye X, Naidoo R. Harnessing explainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of GradCAM and SHAP. Applied Energy, 2024, 353: 122079.a

Rights & permissions

The Author(s)

PDF

4

Accesses

0

Citation

Detail

Sections
Recommended

/