Data for 'Subsidence more than doubles sea-level rise today along densely populated coasts'

Data for: Subsidence more than doubles sea-level rise today along densely populated coasts This dataset accompanies the Nature Communications article: Oelsmann, J. et al. Subsidence more than doubles sea-level rise today along densely populated coasts. Nature Communications (2026).Article DOI: https://doi.org/10.1038/s41467-026-72293-z The dataset provides the output data used to assess contemporary relative sea-level rise along global coastlines, with a focus on the contribution of vertical land motion (VLM), coastal subsidence, and the exposure of coastal populations. The data integrate multiple geodetic and model-based VLM sources, including GNSS/GPS, InSAR, glacial-isostatic-adjustment estimates, and an existing global VLM reconstruction. The main global file is provided on the coastal-segment grid of the Dynamic Interactive Vulnerability Assessment model, consisting of 12,148 global coastal segments. The associated study combines these VLM data with coastal population information and absolute sea-level-change estimates to show that subsidence substantially increases the relative sea-level rise experienced by coastal populations. In the manuscript, the hybrid VLM estimates cover almost 65% of the global coastal population and are used to estimate that modern population-weighted relative sea-level rise is about 6 mm/year, substantially higher than the coastal-length-weighted average. Files included This Zenodo record contains the processed output data associated with the study: File Description Global_VLM_data_Oelsmann_2025_data_supplement.nc Main NetCDF file containing global VLM estimates, uncertainty estimates, source flags, population data, and DIVA coastal-segment coordinates/names. City_VLM_comparison.xlsx Subsidence/VLM estimates for large coastal cities with additional supporting information. SI_Deltas_overview.xlsx Delta subsidence/VLM estimates and supporting information for major coastal deltas. Main NetCDF file The file Global_VLM_data_Oelsmann_2025_data_supplement.nc contains VLM estimates and metadata on the DIVA coastal-segment grid. Dimension x = 12148DIVA coastal segments used for the global coastal analysis. Main variables OE24_GPS_InSAR_GIAHybrid VLM estimate based on OE24, GNSS/GPS, InSAR, and GIA data.Units: mm/year OE24_GPS_InSAR_GIA_unUncertainty estimate for the hybrid VLM product, including formal, cross-validation, and spatial uncertainty components where applicable.Units: mm/year OE24Linear VLM estimate from the Oelsmann et al. 2024 global VLM reconstruction.Units: mm/year OE24_unUncertainty estimate associated with OE24.Units: mm/year GIASolid-Earth deformation due to glacial isostatic adjustment.Units: mm/year GIA_unUncertainty of the GIA estimate.Units: mm/year City subsidence onlyLiterature-based city subsidence estimate from Nicholls et al. 2021.Units: mm/year Delta subsidence onlyLiterature-based delta subsidence estimate from Nicholls et al. 2021.Units: mm/year pop_below_10p0Population living below 10 m elevation, based on Nicholls et al. 2021 / DIVA coastal-segment information.Units: number of people lat, lon, nameLatitude, longitude, and name of each DIVA coastal segment. datatypeFlag indicating which VLM source was used for each coastal segment. The values are: Value Data source 0 InSAR coastal cities 1 InSAR New Zealand 2 InSAR China 3 InSAR USA 4 InSAR Europe / EGMS 5 GNSS 6 InSAR deltas 7 Mississippi Delta dedicated estimate 8 OE24 9 GIA Positive VLM values represent uplift, corresponding to a positive contribution to relative sea-level rise. Negative values represent subsidence, corresponding to a positive contribution to relative sea-level rise. Source data The original input datasets are not republished in this Zenodo record. They are available from their original repositories and were processed, interpolated, or aggregated onto the DIVA coastal grid as described in the associated article and github repository. Please download the following input datasets from their original repositories: OE24 global VLM reconstructionGlobal VLM reconstruction from Oelsmann et al. 2024.https://zenodo.org/records/8308347 GNSS/GPS VLM dataGNSS vertical velocity data from the Nevada Geodetic Laboratory / MIDAS product, based on Blewitt et al.https://geodesy.unr.edu/velocities/midas.IGS14.txt InSAR Europe / EGMSEuropean InSAR VLM estimates from the European Ground Motion Service.https://egms.land.copernicus.eu/Note: this dataset currently needs to be downloaded manually by selecting tiles in the EGMS data explorer. The converted NetCDF file can be obtained from the author on request. InSAR USAInSAR VLM data for the United States from Ohenhen et al. 2024, provided separately for different coastal regions:Pacific coast: https://doi.org/10.7294/17711000Atlantic coast: https://doi.org/10.7294/19350959Gulf coast: https://doi.org/10.7294/22731326 InSAR coastal citiesInSAR-based coastal land-subsidence data from Shirzaei et al. 2024.https://data.lib.vt.edu/articles/dataset/InSAR-Based_Coastal_Land_Subsidence/25864435/1 Tay et al. 2022 city dataData used to obtain a list of some of the largest coastal cities.https://researchdata.ntu.edu.sg/dataset.xhtml?persistentId=doi:10.21979/N9/GPVX0F Mississippi DeltaDedicated Mississippi Delta subsidence data from Nienhuis and Törnqvist 2017.https://osf.io/m83z4/files/osfstorage InSAR deltasDelta-subsidence data from Ohenhen et al. 2025 / 2026.https://doi.org/10.5281/zenodo.15015923 InSAR ChinaSubsidence estimates for Chinese coastal cities from Ao et al. 2024.https://www.science.org/doi/10.1126/science.adl4366#supplementary-materials GIA estimatesGlacial-isostatic-adjustment estimates from Caron et al. 2018.https://vesl.jpl.nasa.gov/solid-earth/gia/ Absolute sea-level change / CMEMSCopernicus Marine gridded absolute sea-level-change data.https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_L4_MY_008_047/description DIVA / Nicholls et al. 2021 dataCoastal-segment location, population, length, and NI21b VLM estimates from Nicholls et al. 2021.https://www.nature.com/articles/s41558-021-00993-z#Sec16 Methods summary The hybrid VLM product combines linear VLM estimates from OE24, InSAR, GNSS/GPS, and GIA. InSAR data were used where available for major coastal cities, deltas, Europe, the United States, New Zealand, and China. GNSS estimates were used in additional densely populated areas and remote islands where appropriate. OE24 was used for remaining coastal segments, and GIA estimates were used where OE24 was unavailable. All datasets were mapped to DIVA coastal segments. High-resolution InSAR data were first interpolated or aggregated on the high-resolution DIVA grid and then averaged to the lower-resolution global coastal-segment grid used for the main analysis. The uncertainty variable combines available formal uncertainties with cross-validation and spatial uncertainty terms for InSAR-based estimates, and uses the published uncertainty estimates for OE24 and GIA where applicable. Software and reproducibility The software used to process the input datasets, combine VLM sources, generate the final coastal-segment products, and reproduce the main and supplementary figures is provided in a separate GitHub repository archived on Zenodo. Please cite both the present dataset DOI and the separate software DOI when using these data and code. Software repository: [GitHub URL to be added]Archived software DOI: [Zenodo software DOI to be added] Recommended citation Please cite both this Zenodo dataset and the associated article: Oelsmann, J., Nicholls, R. J., Lincke, D., Marcos, M., Shirzaei, M., Sánchez, L., Ohenhen, L., Dettmering, D., Hinkel, J., Horton, B. P. & Seitz, F. Subsidence more than doubles sea-level rise today along densely populated coasts. Nature Communications (2026). https://doi.org/10.1038/s41467-026-72293-z Also cite the original source datasets where they are directly used.