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metarepo: Subsidence more than doubles sea-level rise today along densely populated coasts.

Authors: Oelsmann, Julius;

metarepo: Subsidence more than doubles sea-level rise today along densely populated coasts.

Abstract

Software for Oelsmann et al. 2026 Nature Communications 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 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: https://github.com/oelsmann/global_hybrid_vlm_estimates 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.

If you use this software, please cite it as below.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average