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Sea-level rise from land subsidence in major coastal cities


Coastal land can be lost at rapid rates due to relative sea-level rise (RSLR) resulting from local land subsidence. However, the comparative severity of local land subsidence is unknown due to high spatial variabilities and difficulties reconciling observations across localities. Here we provide self-consistent, high spatial resolution relative local land subsidence (RLLS) velocities derived from Interferometric Synthetic Aperture Radar for the 48 largest coastal cities, which represent 20% of the global urban population. We show that cities experiencing the fastest RLLS are concentrated in Asia. RLLS is also more variable across the 48 cities (−16.2 to 1.1 mm per year) than the Intergovernmental Panel on Climate Change estimations of vertical land motion (−5.2 to 4.9 mm per year). With our standardized method, the identification of relative vulnerabilities to RLLS and comparisons of RSLR effects accounting for RLLS are now possible across cities worldwide. These will better inform sustainable urban planning and future adaptation strategies in coastal cities.

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Fig. 1: Peak RLLS velocities across the 48 coastal cities.
Fig. 2: The range of RLLS across the 48 coastal cities.
Fig. 3: InSAR-derived velocities of RLLS in a coastal city with fast-subsiding areas, Jakarta (Indonesia).
Fig. 4: InSAR-derived velocities of RLLS in a coastal city with minimal subsidence, New York (USA).

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Data availability

All replication data of RLLS velocities in this study are available in the following DR-NTU Data repository: The vertical land motion rates from the IPCC AR6 are publicly available at Glacial isostatic adjustment rates from ICE-6G_C (VM5a) is publicly available at Plate tectonic boundaries plotted in Fig. 2 are downloadable from GNSS velocities plotted in Fig. 4 are downloadable from Source data are provided with this paper.

Code availability

All codes for the analysis of the datasets are available in the following DR-NTU Data repository: ARIA-SG algorithms, which contain ISCE algorithms, are accessible at: Other InSAR processing algorithms including ARIA-Tools v1.1.1 and MintPy v1.3.0 are open-source and freely available through Github.


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We thank the European Union Copernicus and European Space Agency for acquiring Sentinel-1 data, and Amazon Web Services for their provision of the Public Dataset Program to enable the download of Sentinel-1 data in the Asia region ( This research is supported by the Earth Observatory of Singapore (EOS), the National Research Foundation (NRF), Singapore, and the Ministry of Education (MOE), Singapore, under the Research Centres of Excellence initiative, by a Singapore NRF Investigatorship (Award ID NRF-NRFI05-2019-0009) and a Singapore MOE Tier 3 grant (Award ID MOE2019-T3-1-004) awarded to E.M.H. This research is also supported by the NRF, Singapore, and National Environment Agency, Singapore, under the National Sea Level Programme Initiative as part of the Urban Solutions & Sustainability – Integration Fund (Award No. USS-IF-2020-5) by a grant (Award ID NSLP-2021-3R-05) awarded to E.M.H. The research was carried out, in part, at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. T.L. and B.P.H. are supported by the Singapore MOE Academic Research Fund (MOE-T2EP50120-0007). This work comprises EOS contribution number 445.

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Authors and Affiliations



C.T., E.O.L., E.M.H. and J.W.M. were responsible for the conceptualization of the work. C.T., E.O.L., D.B. and M.N. were responsible for the methodology. Data processing was carried out by C.T. and S.T.C. S.T.C., C.T., D.B., H.H., G.M., M.K. and M.N. were responsible for the software. C.T., E.O.L., D.B., M.N., B.P.H., T.L. and E.M.H. undertook the formal analysis and investigation. C.T. prepared the original draft. E.O.L, E.M.H., J.W.M, M.N., D.B., B.P.H, T.L. and S.T.C reviewed and edited the manuscript. E.M.H. and D.B. were responsible for funding acquisition.

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Correspondence to Cheryl Tay.

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Nature Sustainability thanks Gonéri le Cozannet, Julia Pfeffer and Shimon Wdowinski for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Sections A–G (each section with accompanying text/figure/table) and references.

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Source data

Source Data Fig. 1

Peak velocities shown in Fig. 1a and histogram bin counts for specific cities shown in Fig. 1b.

Source Data Fig. 2

Median velocities shown in Fig. 2a and 16th/50th/84th percentile velocities shown in Fig. 2b.

Source Data Fig. 3a,b

Velocities in Jakarta and standard deviations of velocities in Jakarta.

Source Data Fig. 3c

Displacements at data points in Jakarta.

Source Data Fig. 4a,b

Velocities in New York and standard deviations of velocities in New York.

Source Data Fig. 4c,d

Displacements at data points in New York and InSAR and GNSS velocities at data points in New York.

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Tay, C., Lindsey, E.O., Chin, S.T. et al. Sea-level rise from land subsidence in major coastal cities. Nat Sustain 5, 1049–1057 (2022).

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