Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Stress-testing the cascading economic impacts of urban flooding across 306 Chinese cities

Nature Cities volume 3, pages 89–101 (2026)Cite this article

Subjects

Abstract

Flood research often emphasizes local, direct damages and treats cities as isolated, overlooking development heterogeneity and cascading supply-chain effects. Here we address this gap by coupling flood hazards with a risk-extended multiregional input–output model for 306 Chinese cities across 6 return periods. We quantify direct losses and trace indirect propagation, separating local-indirect losses in the flooded city from ripple losses elsewhere and introduce a spillover indicator for passive losses in nonflooded cities. Losses rise nonlinearly with severity, shifting from direct capital losses in frequent, low-intensity events to local-indirect losses in rare, high-intensity events. Spatial disparities emerge: wealthier cities incur larger absolute but lower loss-to-GDP impacts, whereas poorer cities face higher proportional losses, especially via labor. Spillovers concentrate in major hubs, amplifying systemic risk. Aggregating city stress tests yields conservative lower bounds; a Yangtze River Delta coshock shows strong amplification. Findings motivate sector- and region-specific adaptation and recovery planning.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Spatial distribution of fluvial flood-induced economic losses across Chinese cities under 10- and 500-year floods.
Fig. 2: Top ten regions with the highest losses by category under 10- and 500-year fluvial floods.
Fig. 3: Spatial distribution of spillover impact and ripple effect for five megacities.
Fig. 4: Fluvial flood-related impacts along supply chains of the Transport Equipment and Transportation sectors in megacities.

Similar content being viewed by others

Data availability

Data utilized in this study were collected from the following sources. The flood hazard map with water depth was derived from the Joint Research Centre of Europe Commission (https://data.jrc.ec.europa.eu/dataset/jrc-floods-floodmapgl_rp100y-tif). Urban land-use data, including detailed land-use type information, were obtained from the EULUC-China dataset, available via Zenodo at https://doi.org/10.5281/zenodo.16794007 (ref. 67). The population dataset is from WorldPop (https://www.worldpop.org). The China city-level MRIO table is from CEADs (https://www.ceads.net.cn/). Elevation data were provided by the GEBCO Compilation Group (https://www.gebco.net/data-products/gridded-bathymetry-data). The datasets for building the road network and the river system data were extracted from OpenStreetMaps (https://www.openstreetmap.org/). The monthly precipitation dataset was provided by the National Tibetan Plateau/Third Pole Environment Data Center, available via Zenodo at https://doi.org/10.5281/zenodo.3114194 (ref. 68). The three-dimensional building footprint dataset is available via Zenodo at https://doi.org/10.5281/zenodo.11397015 (ref. 69). We collected economic data, drainage pipeline data and built-up green space data for each city from government documents and reports.

References

  1. Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).

    Article  Google Scholar 

  2. Rentschler, J. et al. Global evidence of rapid urban growth in flood zones since 1985. Nature 622, 87–92 (2023).

    Article  Google Scholar 

  3. Kotz, M., Levermann, A. & Wenz, L. The effect of rainfall changes on economic production. Nature 601, 223–227 (2022).

    Article  Google Scholar 

  4. Ban, J., Sutton, C., Ma, Y., Lin, C. & Chen, K. Association of flooding exposure with cause-specific mortality in North Carolina, United States. Nat. Water 1, 1027–1034 (2023).

    Article  Google Scholar 

  5. Raju, E., Boyd, E. & Otto, F. Stop blaming the climate for disasters. Commun. Earth Environ. 3, 1 (2022).

    Article  Google Scholar 

  6. Rajput, A. A., Liu, C., Liu, Z. & Mostafavi, A. Human-centric characterization of life activity flood exposure shifts focus from places to people. Nat. Cities 1, 264–274 (2024).

    Article  Google Scholar 

  7. Gao, P. et al. Author Correction: Heterogeneous pressure on croplands from land-based strategies to meet the 1.5 °C target. Nat. Clim. Change 15, 682–682 (2025).

    Article  Google Scholar 

  8. Costa, R., Bolte, E., Sharp, C. & Bowers, C. Repeated and localized flooding is an underestimated challenge for urban disaster risk management. Nat. Cities 1, 587–596 (2024).

    Article  Google Scholar 

  9. Kephart, J. L. et al. Social disparities in neighborhood flood exposure in 44,698 urban neighborhoods in Latin America. Nat. Cities 2, 246–253 (2025).

    Article  Google Scholar 

  10. Dottori, F. et al. Increased human and economic losses from river flooding with anthropogenic warming. Nat. Clim. Change 8, 781–786 (2018).

    Article  Google Scholar 

  11. Wang, W., Yang, S., Stanley, H. E. & Gao, J. Local floods induce large-scale abrupt failures of road networks. Nat. Commun. 10, 2114 (2019).

    Article  Google Scholar 

  12. Tian, Z. et al. Dynamic adaptive engineering pathways for mitigating flood risks in Shanghai with regret theory. Nat. Water 1, 198–208 (2023).

    Article  Google Scholar 

  13. Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628, 551–557 (2024).

    Article  Google Scholar 

  14. Willner, S. N., Otto, C. & Levermann, A. Global economic response to river floods. Nat. Clim. Change 8, 594–598 (2018).

    Article  Google Scholar 

  15. Rentschler, J., Salhab, M. & Jafino, B. A. Flood exposure and poverty in 188 countries. Nat. Commun. 13, 3527 (2022).

    Article  Google Scholar 

  16. Smiley, K. T. et al. Social inequalities in climate change-attributed impacts of Hurricane Harvey. Nat. Commun. 13, 3418 (2022).

    Article  Google Scholar 

  17. Palagi, E., Coronese, M., Lamperti, F. & Roventini, A. Climate change and the nonlinear impact of precipitation anomalies on income inequality. Proc. Natl Acad. Sci. USA 119, e2203595119 (2022).

    Article  Google Scholar 

  18. Botzen, W. J. W., Deschenes, O. & Sanders, M. The economic impacts of natural disasters: a review of models and empirical studies. Rev. Environ. Econ. Policy 13, 167–188 (2019).

    Article  Google Scholar 

  19. Ward, P. J. et al. A global framework for future costs and benefits of river-flood protection in urban areas. Nat. Clim. Change 7, 642–646 (2017).

    Article  Google Scholar 

  20. Aerts, J. C. J. H. et al. Integrating human behaviour dynamics into flood disaster risk assessment. Nat. Clim. Change 8, 193–199 (2018).

    Article  Google Scholar 

  21. Haer, T., Botzen, W. J. W., de Moel, H. & Aerts, J. C. J. H. Integrating household risk mitigation behavior in flood risk analysis: an agent-based model approach. Risk Anal. 37, 1977–1992 (2017).

    Article  Google Scholar 

  22. Hallegatte, S. An adaptive regional input–output model and its application to the assessment of the economic cost of Katrina. Risk Anal. 28, 779–799 (2008).

    Article  Google Scholar 

  23. Carrera, L., Standardi, G., Bosello, F. & Mysiak, J. Assessing direct and indirect economic impacts of a flood event through the integration of spatial and computable general equilibrium modelling. Environ. Model. Softw. 63, 109–122 (2015).

    Article  Google Scholar 

  24. Koks, E. E. et al. The macroeconomic impacts of future river flooding in Europe. Environ. Res. Lett. 14, 084042 (2019).

    Article  Google Scholar 

  25. Hirabayashi, Y. et al. Global flood risk under climate change. Nat. Clim. Change 3, 816–821 (2013).

    Article  Google Scholar 

  26. Chen, W. et al. Integrated urban flood vulnerability assessment using local spatial dependence-based probabilistic approach. J. Hydrol. 575, 454–469 (2019).

    Article  Google Scholar 

  27. Jiao, S. S., Li, W. J. & Wen, J. H. Spatiotemporal changes of manufacturing firms in the flood prone Yangtze Delta. Environ. Hazards Hum. Policy Dim. 21, 334–360 (2022).

    Article  Google Scholar 

  28. Marin, G., Modica, M., Paleari, S. & Zoboli, R. Assessing disaster risk by integrating natural and socio-economic dimensions: a decision-support tool. Socio-Econ. Plan. Sci. 77, 101032 (2021).

    Article  Google Scholar 

  29. Mendoza-Tinoco, D., Guan, D., Zeng, Z., Xia, Y. & Serrano, A. Flood footprint of the 2007 floods in the UK: the case of the Yorkshire and The Humber region. J. Clean. Prod. 168, 655–667 (2017).

    Article  Google Scholar 

  30. Zhang, Z. et al. Economic ripple effects of individual disasters and disaster clusters. Int. J. Disast. Risk Sci. 13, 948–961 (2022).

    Article  Google Scholar 

  31. León, J. A., Ordaz, M., Haddad, E. & Araújo, I. F. Risk caused by the propagation of earthquake losses through the economy. Nat. Commun. 13, 2908 (2022).

    Article  Google Scholar 

  32. Wang, D. et al. Economic footprint of California wildfires in 2018. Nat. Sustain. 4, 252–260 (2021).

    Article  Google Scholar 

  33. Huizinga, J., De Moel, H. & Szewczyk, W. Global Flood Depth–Damage Functions: Methodology and the Database with Guidelines (Joint Research Centre, 2017).

  34. Jin, X. Y., Fang, D. L., Chen, B. & Wang, H. Multiobjective layout optimization for low impact development considering its ecosystem services. Resour. Conserv. Recycl. 209, 2986 (2024).

    Article  Google Scholar 

  35. He, Y., Yang, Y. & Wang, S. Network and mechanism of China’s new energy vehicle industry from the perspective of value chain. J. Geogr. Sci. 34, 779–803 (2024).

    Article  Google Scholar 

  36. Development Index Report on China’s Aviation Logistics Hubs (‌Social Sciences Academic Press, 2024).

  37. Zhang, X., Lu, J. & Peng, Y. Hybrid MCDM model for location of logistics hub: a case in China under the Belt and Road Initiative. IEEE Access 9, 41227–41245 (2021).

    Article  Google Scholar 

  38. Zhao, L. et al. Optimizing logistics hub selection in the integrated network of the China–Europe Railway Express and New International Land-Sea Trade Corridor. J. Ind. Manag. Optim. 21, 4384–4411 (2025).

    Article  Google Scholar 

  39. La Rosa, D. & Pappalardo, V. Planning for spatial equity—a performance based approach for sustainable urban drainage systems. Sustain. Cities Soc. 53, 101885 (2020).

    Article  Google Scholar 

  40. Brillinger, M., Scheuer, S. & Albert, C. Deliberating options for nature-based river development: insights from a participatory multi-criteria evaluation. J. Environ. Manag. 317, 115350 (2022).

    Article  Google Scholar 

  41. Landry, C. E., Turner, D. & Petrolia, D. Flood insurance market penetration and expectations of disaster assistance. Environ. Resour. Econ. 79, 357–386 (2021).

    Article  Google Scholar 

  42. Bradt, J. T., Kousky, C. & Wing, O. E. J. Voluntary purchases and adverse selection in the market for flood insurance. J. Environ. Econ. Manag. 110, 102515 (2021).

    Article  Google Scholar 

  43. Vitale, C., Meijerink, S. & Moccia, F. D. Urban flood resilience, a multi-level institutional analysis of planning practices in the Metropolitan City of Naples. J. Environ. Plan. Manag. 66, 813–835 (2023).

    Article  Google Scholar 

  44. Tellman, B. et al. Satellite imaging reveals increased proportion of population exposed to floods. Nature 596, 80–86 (2021).

    Article  Google Scholar 

  45. Wu, X., Zhou, L., Gao, G., Guo, J. & Ji, Z. Urban flood depth-economic loss curves and their amendment based on resilience: evidence from Lizhong Town in Lixia River and Houbai Town in Jurong River of China. Nat. Hazards 82, 1981–2000 (2016).

    Article  Google Scholar 

  46. Tanoue, M. et al. Estimation of direct and indirect economic losses caused by a flood with long-lasting inundation: application to the 2011 Thailand flood. Water Resour. Res. 56, 120068 (2020).

    Article  Google Scholar 

  47. Wu, Z., Lv, H., Meng, Y., Guan, X. & Zang, Y. The determination of flood damage curve in areas lacking disaster data based on the optimization principle of variation coefficient and beta distribution. Sci. Total Environ. 750, 142277 (2021).

    Article  Google Scholar 

  48. Koks, E. E., Bočkarjova, M., de Moel, H. & Aerts, J. C. J. H. Integrated direct and indirect flood risk modeling: development and sensitivity analysis. Risk Anal. 35, 882–900 (2015).

    Article  Google Scholar 

  49. Jongman, B. et al. Declining vulnerability to river floods and the global benefits of adaptation. Proc. Natl Acad. Sci. USA 112, E2271–E2280 (2015).

    Article  Google Scholar 

  50. Jonkman, S. N., Bočkarjova, M., Kok, M. & Bernardini, P. Integrated hydrodynamic and economic modelling of flood damage in the Netherlands. Ecol. Econ. 66, 77–90 (2008).

    Article  Google Scholar 

  51. Zhu, Z. et al. Historic storms and the hidden value of coastal wetlands for nature-based flood defence. Nat. Sustain. 3, 853–862 (2020).

    Article  Google Scholar 

  52. Jonkman, S. N., Vrijling, J. K. & Vrouwenvelder, A. C. W. M. Methods for the estimation of loss of life due to floods: a literature review and a proposal for a new method. Nat. Hazards 46, 353–389 (2008).

    Article  Google Scholar 

  53. Jonkman, S. N., Maaskant, B., Boyd, E. & Levitan, M. L. Loss of life caused by the flooding of New Orleans After Hurricane Katrina: analysis of the relationship between flood characteristics and mortality. Risk Anal. 29, 676–698 (2009).

    Article  Google Scholar 

  54. Hallegatte, S. Modeling the role of inventories and heterogeneity in the assessment of the economic costs of natural disasters. Risk Anal. 34, 152–167 (2013).

    Article  Google Scholar 

  55. Sun, Y. et al. Global supply chains amplify economic costs of future extreme heat risk. Nature 627, 797–804 (2024).

    Article  Google Scholar 

  56. Wang, D. et al. Supply chains create global benefits from improved vaccine accessibility. Nat. Commun. 14, 1569 (2023).

    Article  Google Scholar 

  57. Zhang, Z. et al. How to integrate labor disruption into an economic impact evaluation model for postdisaster recovery periods. Risk Anal. 39, 2443–2456 (2019).

    Article  Google Scholar 

  58. Li, J., Crawford-Brown, D., Syddall, M. & Guan, D. Modeling imbalanced economic recovery following a natural disaster using input–output analysis. Risk Anal. 33, 1908–1923 (2013).

    Article  Google Scholar 

  59. Noth, F. & Rehbein, O. Badly hurt? Natural disasters and direct firm effects. Fin. Res. Lett. 28, 254–258 (2019).

    Article  Google Scholar 

  60. Xia, Y. et al. Assessment of the economic impacts of heat waves: a case study of Nanjing, China. J. Clean. Prod. 171, 811–819 (2018).

    Article  Google Scholar 

  61. Fang, D., Duan, C. & Chen, B. Average propagation length analysis for carbon emissions in China. Appl. Energy 275, 115386 (2020).

    Article  Google Scholar 

  62. Fang, D. L. et al. Clean air for some: unintended spillover effects of regional air pollution policies. Sci. Adv. 5, 115386 (2019).

    Article  Google Scholar 

  63. Vazquez Santiago, J., Hata, H., Martinez-Noriega, E. J. & Inoue, K. Ozone trends and their sensitivity in global megacities under the warming climate. Nat. Commun. 15, 10236 (2024).

    Article  Google Scholar 

  64. Xu, R. et al. Global population exposure to landscape fire air pollution from 2000 to 2019. Nature 621, 521–529 (2023).

    Article  Google Scholar 

  65. Bakhtsiyarava, M. et al. Potential drivers of urban green space availability in Latin American cities. Nat. Cities 1, 842–852 (2024).

    Article  Google Scholar 

  66. Chen, B. et al. Contrasting inequality in human exposure to greenspace between cities of Global North and Global South. Nat. Commun. 13, 4636 (2022).

    Article  Google Scholar 

  67. Li, Z. & Chen, B. EULUC China 2.0: Essential Urban Land Use Categories Map across China for 2022 (Shapefile and Geodatabase). Zenodo https://doi.org/10.5281/zenodo.16794007 (2025).

  68. Peng, S. High-spatial-resolution monthly precipitation dataset over China during 1901–2017. Zenodo https://doi.org/10.5281/zenodo.3114194 (2019).

  69. Che, Y. et al. Building height of Asia in 3D-GloBFP. Zenodo https://doi.org/10.5281/zenodo.11397015 (2024).

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 42230106 to C.S., 72574027 to D.F. and 72174029 to D.F.) and the 10th Young Elite Scientists Sponsorship Program by CAST (to D.F.).

Author information

Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Hazards Risk Governance, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Delin Fang, Fei Xu, Xuanyi Jin, Changqing Song & Peichao Gao

  2. Faculty of Geography, Yunnan Normal University, Yunnan, China

    Delin Fang & Changqing Song

  3. Department of Geographical Sciences, University of Maryland, College Park, MD, USA

    Laixiang Sun

  4. Department of Geography, King’s College London, London, UK

    Daoping Wang

  5. Department of Geography, University of Hong Kong, Hong Kong, China

    Kuishuang Feng

Authors
  1. Delin Fang
    View author publications

    Search author on:PubMed Google Scholar

  2. Fei Xu
    View author publications

    Search author on:PubMed Google Scholar

  3. Xuanyi Jin
    View author publications

    Search author on:PubMed Google Scholar

  4. Changqing Song
    View author publications

    Search author on:PubMed Google Scholar

  5. Peichao Gao
    View author publications

    Search author on:PubMed Google Scholar

  6. Laixiang Sun
    View author publications

    Search author on:PubMed Google Scholar

  7. Daoping Wang
    View author publications

    Search author on:PubMed Google Scholar

  8. Kuishuang Feng
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization: D.F. Data curation and formal analysis: D.F., F.X. and X.J. Writing—original draft: D.F. Interpretation of the results: D.F., P.G. and D.W. Writing—review and editing: D.F., L.S. and K.F. with input from all co-authors. Funding acquisition: D.F. and C.S. Supervision: C.S., L.S. and K.F.

Corresponding authors

Correspondence to Changqing Song or Kuishuang Feng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cities thanks Jlenia di Noia, Yoomi Kim and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Sectoral composition and trends of fluvial flood-induced losses under different flood recurrence intervals.

Stacked bar plots show the sectoral breakdown and evolution of (a) capital loss, (b) labor loss, (c) local indirect loss (that is, supply-chain disruptions within the affected city), (d) ripple effect (that is, supply-chain disruptions affecting other cities), and (e) total loss, under 10, 20, 50, 100, 200, 500-year flood. Each color represents a different economic departmental category (see legend above).

Extended Data Fig. 2 Spatial distribution of fluvial flood-induced spillover impacts under 10 and 500-year floods.

a, c: Spatial distribution of the average spillover impact for each city experiences flooding in other regions under 10 and 500-year floods. b, d: Spatial distribution of the average spillover impact as a percentage of each city’s GDP experiences the flood occurs in other regions under 10 and 500-year floods. Cross-hatched areas indicate regions not included in the analysis due to data limitations. Cited from Tianditu https://www.tianditu.gov.cn/.

Extended Data Fig. 3 Top 10 cities with by spillover impact losses under 10 and 500-year fluvial floods.

(a, b) Cities with the largest absolute spillover impact losses (bars: loss value, left axis; dots: loss as % of GDP, right axis) for 10-year (a) and 500-year (b) floods. (c, d) Cities with the highest spillover impact loss ratios (bars: loss value, left axis; dots: loss as % of GDP, right axis) for 10-year (c) and 500-year (d) floods. Bar colors reflect GDP per capita (red = low, blue = high). Cities with black triangles (▲) are newly ranked in the top 10 under 500-year floods. Asterisks (*) denote provincial or municipal capitals.

Extended Data Fig. 4 Schematic diagram of the methodological framework.

Coupling mechanisms for overlay, direct loss, and economic recovery modules.

Supplementary information

Supplementary Information

Supplementary Methods, uncertainty analysis and parameter calibration, Supplementary Results, model limitations and pathways for enhancement, Supplementary Figs. 1–8 and Supplementary Tables 1–9.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, D., Xu, F., Jin, X. et al. Stress-testing the cascading economic impacts of urban flooding across 306 Chinese cities. Nat Cities 3, 89–101 (2026). https://doi.org/10.1038/s44284-025-00372-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s44284-025-00372-1

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene