Exposed Built-Up Lands Grew Faster than Total Flood Areas in China During 2000–2020

Hanru Shen , Weiyue Li , Jingwei Li , Haoyuan Wu , Yongqiang Duan , Chengjie Zhou , Yukun Lin , Shiqiang Du

International Journal of Disaster Risk Science ›› 2026, Vol. 17 ›› Issue (2) : 269 -280.

PDF
International Journal of Disaster Risk Science ›› 2026, Vol. 17 ›› Issue (2) :269 -280. DOI: 10.1007/s13753-026-00713-1
Article
research-article
Exposed Built-Up Lands Grew Faster than Total Flood Areas in China During 2000–2020
Author information +
History +
PDF

Abstract

Exposed built-up land in flood areas is a vital indicator determining flood losses. It potentially changes in the context of rapid urbanization and climate change particularly in hotspot countries like China. However, a comprehensive understanding of the spatiotemporal patterns of flood extent and exposed built-up lands is hampered due to limited information of historical floods. To fill the research gap, this study developed the Spatial Dataset of Historical Floods in China (SDHFC) with event-explicit exposure from 2000 to 2020 using Google Earth Engine, based on internationally available flood event inventories and remote sensing-based surface water data. The Mann-Kendall test and rectified Theil-Sen trend analysis were applied to quantify the changes in flood extent and exposed built-up lands. The SDHFC delineated the inundation extent for 212 flood events in China during 2000–2020, demonstrating a notable improvement compared with global database. Both flood extent and exposed built-up lands increased significantly, with the latter growing at a rate of 9.65%·a-1, approximately 1.8 times that of the former. Continuous expansion of built-up lands was identified as the primary driver (62%) for the rapid increase in their exposure to floods, much higher than the contribution of the observed flood extent (38%). The findings are crucial for understanding the complex interactions between flood patterns and urbanization processes in China in 2000–2020. The methodology could be applied in various regions for investigating long-term sequences of flood extent and exposed built-up lands.

Keywords

Flood exposure / Historical floods / Satellite observation / Urbanization

Cite this article

Download citation ▾
Hanru Shen, Weiyue Li, Jingwei Li, Haoyuan Wu, Yongqiang Duan, Chengjie Zhou, Yukun Lin, Shiqiang Du. Exposed Built-Up Lands Grew Faster than Total Flood Areas in China During 2000–2020. International Journal of Disaster Risk Science, 2026, 17(2): 269-280 DOI:10.1007/s13753-026-00713-1

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Bai, X., and P. Shi. 2025. China’s urbanization at a turning point – Challenges and opportunities. Science 388(6747): Article eadw3443.

[2]

Bates PD, Neal JC, Alsdorf D, Schumann GJP. Observing global surface water flood dynamics. Surveys in Geophysics, 2013, 35(3): 839-852

[3]

Brakenridge, G.R. 2024. Global active archive of large flood events. Dartmouth Flood Observatory, University of Colorado, Boulder, CO, USA. http://floodobservatory.colorado.edu/Archives/index.html. Accessed 10 Jan 2024.

[4]

Bryan BA, Gao L, Ye Y, Sun X, Connor JD, Crossman ND, Stafford-Smith M, Wu J, et al. . China’s response to a national land-system sustainability emergency. Nature, 2018, 559(7713): 193-204

[5]

Cian F, Marconcini M, Ceccato P. Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data. Remote Sensing of Environment, 2018, 209: 712-730

[6]

Ding M, Lin P, Gao S, Wang J, Zeng Z, Zheng K, Zhou X, Yamazaki D, et al. . Reversal of the levee effect towards sustainable floodplain management. Nature Sustainability, 2023, 6(12): 1578-1586

[7]

Du S, Cheng X, Huang Q, Chen R, Ward PJ, Aerts JCJH. Brief communication: Rethinking the 1998 China floods to prepare for a nonstationary future. Natural Hazards and Earth System Sciences, 2019, 19(3): 715-719

[8]

Du, S., C. He, Q. Huang, and P. Shi. 2018. How did the urban land in floodplains distribute and expand in China from 1992–2015? Environmental Research Letters 13(3). https://doi.org/10.1088/1748-9326/aaac07.

[9]

Du S, Shen J, Fang J, Fang J, Liu W, Wen J, Huang X, Chen S. Policy delivery gaps in the land-based flood risk management in China: A wider partnership is needed. Environmental Science & Policy, 2021, 116: 128-135

[10]

Duan Y, Li J, Fang X, Shen J, Shen H, Du S. Future supply-demand relationship of flood regulation service from 2020 to 2050 under ScenarioMIP: A case study in the Yangtze River Delta. China. Chinese Geographical Science, 2025, 35(5): 1139-1152

[11]

Fang Y, Du S, Wen J, Zhang M, Fang J, Liu M. Chinese built-up land in floodplains moving closer to freshwaters. International Journal of Disaster Risk Science, 2021, 12(3): 355-366

[12]

Fang, J., C. Zhang, J. Fang, M. Liu, and Y. Luan. 2021. Increasing exposure to floods in China revealed by nighttime light data and flood susceptibility mapping. Environmental Research Letters 16(10). https://doi.org/10.1088/1748-9326/ac263e

[13]

Giustarini L, Hostache R, Matgen P, Schumann GJP, Bates PD, Mason DC. A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(4): 2417-2430

[14]

Gong, P., X. Li, J. Wang, Y. Bai, B. Cheng, T. Hu, X. Liu, B. Xu, et al. 2020. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment 236: Article 111510.

[15]

Groemping, U. 2006. Relative importance for linear regression in R: The package relaimpo. Journal of Statistical Software 17(1). https://doi.org/10.18637/jss.v017.i01.

[16]

Guha-Sapir, D., R. Below, and P. Hoyois. 2024. EM-DAT: International disaster database. Université Catholique de Louvain, Louvain-la-Neuve, Belgium. www.emdat.be. Accessed 13 Oct 2024.

[17]

Guo, K., M. Guan, and H. Yan. 2023. Utilising social media data to evaluate urban flood impact in data scarce cities. International Journal of Disaster Risk Reduction 93: Article 103780.

[18]

Han, Q., and Z. Niu. 2020. Construction of the long-term global surface water extent dataset based on water-NDVI spatio-temporal parameter set. Remote Sensing 12(17): Article 2675.

[19]

Hao, C., A.P. Yunus, S.S. Subramanian, and R. Avtar. 2021. Basin-wide flood depth and exposure mapping from SAR images and machine learning models. Journal of Environmental Management 297: Article 113367.

[20]

Healey, N.C., C.P. Barber, K. Smith, R. Mital, J.F. Brown, and C. Robison. 2025. Resiliency of land change monitoring efforts to input data resampling. Frontiers in Remote Sensing 6: Article 1570580.

[21]

Jiang, R., H. Lu, K. Yang, D. Chen, J. Zhou, D. Yamazaki, M. Pan, W. Li, et al. 2023. Substantial increase in future fluvial flood risk projected in China’s major urban agglomerations. Communications Earth & Environment 4(1): Article 389.

[22]

Kendall M. Rank correlation methods, 1975, London. Grif

[23]

Li, Z., Y. Xie, W. Hou, Z. Liu, Z. Bai, J. Hong, Y. Ma, H. Huang, et al. 2022. In-orbit test of the polarized scanning atmospheric corrector (PSAC) onboard Chinese environmental protection and disaster monitoring satellite constellation HJ-2 A/B. IEEE Transactions on Geoscience and Remote Sensing 60: Article 4108217.

[24]

Liu F, Zhang Z, Zhao X, Liu B, Wang X, Yi L, Zuo L, Xu J, et al. . Urban expansion of China from the 1970s to 2020 based on remote sensing technology. Chinese Geographical Science, 2021, 31: 765-781

[25]

Mann, H.B. 1945. Nonparametric tests against trend. Econometrica: Journal of the Econometric Society 13(3): 245–259.

[26]

Newman, R., and I. Noy. 2023. The global costs of extreme weather that are attributable to climate change. Nature Communications 14(1): Article 6103.

[27]

Opperman JJ, Galloway GE, Fargione J, Mount JF, Richter BD, Secchi S. Sustainable floodplains through large-scale reconnection to rivers. Science, 2009, 326(5959): 1487-1488

[28]

Peng, J., and J. Zhang. 2024. Spatiotemporal assessment of urban flooding hazard using social media: A case study of Zhengzhou “7⋅20”. Environmental Modelling & Software 176. https://doi.org/10.1016/j.envsoft.2024.106021.

[29]

Rentschler, J., M. Salhab, and B.A. Jafino. 2022. Flood exposure and poverty in 188 countries. Nature Communication 13(1): Article 3527.

[30]

Sen PK. Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, 1968, 63(324): 1379-1389

[31]

Shao, W., J. Dong, J. Li, M. Li, J. Shen, Y. Wu, and S. Du. 2025. Varying flood exposure due to uncertain data of flood hazard and population distribution. Environmental Research Letters 20(11): Article 114029.

[32]

Shen, J., J. Li, Q. Ma, D. Wang, and S. Du. 2023. Response of flood regulation service to land use changes and dam construction – A case study in the Yangtze River Basin. Ecological Indicators 154: Article 110715.

[33]

Smith L, Liang Q, James P, Lin W. Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework. Journal of Flood Risk Management, 2017, 10(3): 370-380

[34]

Tellman B, Sullivan JA, Kuhn C, Kettner AJ, Doyle CS, Brakenridge GR, Erickson TA, Slayback DA. Satellite imaging reveals increased proportion of population exposed to floods. Nature, 2021, 596(7870): 80-86

[35]

Tripathy P, Malladi T. Global Flood Mapper: A novel Google Earth Engine application for rapid flood mapping using Sentinel-1 SAR. Natural Hazards, 2022, 114(2): 1341-1363

[36]

Wang J, Li S, Wang Y, Duan B, Zhou J. Data quality inspection method for comprehensive risk survey of natural disasters. Journal of Geo-information Science, 2023, 25(9): 1765-1773

[37]

Wang D, Scussolini P, Du S. Assessing Chinese flood protection and its social divergence. Natural Hazards and Earth System Sciences, 2021, 21(2): 743-755

[38]

Wang, N., F. Sun, D. Koutsoyiannis, T. Iliopoulou, T. Wang, H. Wang, W. Liu, G.F. Sargentis, et al. 2023b. How can changes in the human‐flood distance mitigate flood fatalities and displacements? Geophysical Research Letters 50(20): Article e2023GL105064.

[39]

Wang, Z., C. Zhang, and P.M. Atkinson. 2022. Combining SAR images with land cover products for rapid urban flood mapping. Frontiers in Environmental Science 10. https://doi.org/10.3389/fenvs.2022.973192.

[40]

Wu, Y., J. Li, H. Wu, Y. Duan, H. Shen, and S. Du. 2024. Sustainable urban planning to control flood exposure in the coastal zones of China. Landscape Ecology 39(8): Article 141.

[41]

Wu Y, Wu S-Y, Wen J, Xu M, Tan J. Changing characteristics of precipitation in China during 1960–2012. International Journal of Climatology, 2016, 36(3): 1387-1402

[42]

Yang Y, Yang L, Villarini G, Zhao F, Huang D, Vecchi GA, Wang Q, Sun Y, et al. . Synchronization of global peak river discharge since the 1980s. Nature Climate Change, 2025, 15(10): 1084-1090

[43]

Zhang J, Liu K, Wang M. Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data. Earth System Science Data, 2023, 15(2): 521-540

RIGHTS & PERMISSIONS

The Author(s)

PDF

0

Accesses

0

Citation

Detail

Sections
Recommended

/