Development policy affects coastal flood exposure in China more than sea-level rise

Wang, Yafei, Ye, Yuxuan, Nicholls, Robert J., Olsson, Lennart, van Vuuren, Detlef P., Peterson, Garry, He, Yao, Li, Manchun, Fan, Jie and Scown, Murray (2025) Development policy affects coastal flood exposure in China more than sea-level rise. Nature Climate Change, 15. 1071–1077. ISSN 1758-678X

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Abstract

Effective coastal exposure assessments are crucial for adaptively managing threats from sea-level rise (SLR). Despite recent advances, global and regional assessments are constrained by omitting critical factors such as land-use change, failing to disaggregate potential impacts by land uses and oversimplifying land subsidence. Here we address these gaps by developing context-specific scenarios to 2100 based on a comprehensive analysis of Chinese coastal development policies. We integrate high-resolution simulations of population and land-system changes with inundation exposure assessments that incorporate SLR, land subsidence, tides and storm surges, offering a more nuanced understanding of coastal risks. Across our plausible set of downscaled scenarios of shared socioeconomic and representative concentration pathways, policy decisions have a bigger effect on what is exposed to coastal flooding until 2100 than does the magnitude of SLR. Hence, coastal policy decisions largely influence coastal risk and adaptation needs to 2100, demonstrating the necessity of appropriate policy design to manage coastal risks.

Item Type:	Article
Additional Information:	Data availability: The projected land-system maps for five policy scenarios, together with associated validation datasets and sampling points for projecting land subsidence, are available via figshare at https://doi.org/10.6084/m9.figshare.29263130 (ref. 55). The input data used in CLUMondo for land-system change simulations are cited throughout the paper, with full details provided in Supplementary Note 4 and Supplementary Table 4. The SLR data were obtained from the IPCC AR6 database, available via Zenodo at https://doi.org/10.5281/zenodo.6382554 (ref. 43), while tide and surge data were sourced from the CoDEC dataset, available via Zenodo at https://doi.org/10.5281/zenodo.3660927 (ref. 56). The CoastalDEM were acquired from Climate Central (https://go.climatecentral.org) and MDT data were obtained from AVISO (https://doi.org/10.24400/527896/a01-2023.003). Sources for explanatory factors used in predicting land-subsidence rates are listed in Supplementary Table 7. All data supporting this study are provided with the paper. Code availability: The CLUMondo model is publicly available via GitHub at https://github.com/VUEG/CLUMondo. Python scripts used for projecting land subsidence and generating figures can be accessed via figshare at https://doi.org/10.6084/m9.figshare.29263130 (ref. 55). The improved geometric inundation model, which incorporates hydrological connectivity and attenuation, is available via GitHub at https://github.com/geoye/attenuated_bathtub. Additional code supporting the findings of this study is available from the corresponding authors upon reasonable request.
Uncontrolled Keywords:	sdg 15 - life on land ,/dk/atira/pure/sustainabledevelopmentgoals/life_on_land
Faculty \ School:	University of East Anglia Research Groups/Centres > Theme - ClimateUEA
UEA Research Groups:	University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Groups > Collaborative Centre for Sustainable Use of the Seas
Depositing User:	LivePure Connector
Date Deposited:	30 Oct 2025 16:31
Last Modified:	04 Nov 2025 12:31
URI:	https://ueaeprints.uea.ac.uk/id/eprint/100862
DOI:	10.1038/s41558-025-02439-2

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