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Weak asthenosphere beneath the Eurasian interior inferred from Aral Sea desiccation

Nature Geoscience volume 18, pages 351–357 (2025)Cite this article

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Abstract

The rheology of the lower crust and upper mantle influences Earth’s plate tectonic style of mantle convection, yet its spatial variability is poorly resolved, particularly in continental interiors. Here we use satellite radar interferometry to map the delayed uplift resulting from the desiccation of the Aral Sea, which has lost ~1,000 km3 of water since 1960. From this we constrain the rheology of the underlying upper mantle by elastic and viscoelastic modelling. We find a long-wavelength uplift of up to ~7 mm yr–1 between 2016 and 2020 that decays radially from the Aral Sea. This uplift pattern is best explained by viscoelastic relaxation of the asthenosphere below a strong lithospheric mantle. We estimate that the asthenosphere has an effective viscosity of 4–7 × 1019 Pa s below 130–190 km depth, slightly larger than the values inferred from post-seismic deformation at subduction zones, but 1–2 orders of magnitude smaller than estimates from glacial isostatic adjustment in other tectonically stable regions. Such uplift highlights the potential for human activities to influence deep-Earth dynamics and the interconnectedness of surface and mantle processes.

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Fig. 1: Global upper-mantle Maxwell viscosities and the tectonic setting of the Aral Sea.
Fig. 2: Desiccation of the Aral Sea.
Fig. 3: Five-year cumulative deformation (2016–2020) from InSAR observations and four-layer preferred model prediction.
Fig. 4: Lower-crust and upper-mantle rheology inferred from the desiccation of the Aral Sea.

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Data availability

Sentinel-1 data are available from the European Space Agency through the Copernicus Data Space Ecosystem (https://dataspace.copernicus.eu). AW3D DEM was downloaded from the Japan Aerospace Exploration Agency (https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm). InSAR observation data, best model and model input files can be obtained via Zenodo (https://doi.org/10.5281/zenodo.7856136) (ref. 57).

Code availability

The viscoelastic simulation software Relax is open source and can be obtained via GitHub (https://github.com/geodynamics/relax)27,28.

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Acknowledgements

This work is funded by the National Science Foundation of China 42021003 (T.W.) and National Science Foundation EAR-1848192 (S.B.). We thank H. Xu and Z. Li from Peking University and G. Xu from East China University of Technology for their help in data processing and model simulation.

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Authors and Affiliations

  1. School of Earth and Space Sciences, Peking University, Beijing, China

    Wenzhi Fan, Teng Wang & Heng Luo

  2. State Key Laboratory of Earthquake Dynamics and Forecasting, Peking University, Beijing, China

    Teng Wang

  3. Department of Earth Sciences, University of Southern California, Los Angeles, CA, USA

    Sylvain Barbot

  4. Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, China

    Dong Fang & Jiangjun Ran

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Contributions

T.W. conceived the study, supervised and acquired funding for the project and provided advice on InSAR processing. W.F. performed the InSAR processing and viscoelastic modelling. S.B. supervised and provided advice on viscoelastic modelling. D.F. and J.R. performed gravity data processing and hydrological model interpretation. H.L. provided the large-scale InSAR processing software. W.F., T.W. and S.B. wrote the paper. All the authors contributed to the interpretation of the observations and the preparation of the paper.

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Correspondence to Teng Wang.

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Nature Geoscience thanks Juliet Biggs, Simon Lamb and Tim Wright for their contribution to the peer review of this work. Primary Handling Editors: Louise Hawkins, Xujia Jiang and Stefan Lachowycz, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Interferometric configuration.

Spatial-temporal baselines of the Sentinel-1 SAR images we processed with track number indicated to the top left with AT and DT indicating ascending and descending tracks, respectively. Black circles represent SAR images with x-axis the acquisition dates and y-axis the perpendicular baselines with respect to the reference image. Black lines indicate short-term interferograms for estimating atmospheric phase and red line indicate long-term interferograms for estimating cumulative deformation.

Extended Data Fig. 2 Uncertainty represented as 5-year displacement estimated from overlapping areas of different tracks.

Colored overlapping areas are imaged with at least three tracks. The uncertanties are the differences between one track and precition from randomly selected one ascending and one descending track.

Extended Data Fig. 3 Uncentainty evaluation.

a, Standard deviation of the 5-year vertical deformation calculated within the 5-by-5 km grid from the 500 m-resolution data points, indicating for the local noise level. b, and c, show the decomposed uncertainties from the six tracks of reference areas (Supplementary Fig. 7) with different sizes and from error propagations during the ramp correction (Supplementary Fig. 11), respectively.

Extended Data Fig. 4 Changes of water depths.

Spatial distribution of the water depths (water level minus the elevation of the lake bottom) of the Aral Sea on each three years, derived from reported water volumes and AW3D DEM. White represents no water.

Extended Data Fig. 5 Misfits of observation and model prediction.

a–f, misfit between the 6 tracks in LOS direction of InSAR observation and the viscoelastic model, shown as a function of the asthenosphere viscosity and depth. g, shows their average. h, misfit between the vertical InSAR observation and the viscoelastic model. The color map represents the RMSE of difference of observation and model. The dashed line contours show the low-misfit area and display specific values.

Extended Data Fig. 6 Prediction from three-layer model without weak asthenosphere.

a, Cumulative vertical displacements (2016-2020) from InSAR with model prediction and residual. Red indicates uplift. b, Profile of observation and model (dashed areas and lines in a), superimposed with model of optimal 4-layer model (red). The error bar is the same as in Fig. 3c.

Extended Data Fig. 7 Time series of uplift predicted from the best-fit model.

Simulated vertical cumulative deformation changes of the 4-layer model with linear Maxwell rheology. Warm color indicates uplift movement. Black line shows the boundary of the Aral Sea in 1960.

Extended Data Fig. 8 Comparison of model predictions with and without asthenosphere.

5-year cumulative deformation produced by three- and four-layer model without (a) and with (b) asthenosphere relaxation. Warm color indicates uplift, arrows indicate horizontal motion. Black line shows the boundary of the Aral Sea in 1960.

Extended Data Fig. 9 Changes in groundwater in the Aral Sea region.

a, changes of the total water storage Mascon product (the average from CSR, JPL and GSFC) provided by GRACE satellite. b, changes of ground water storage provided by the WaterGAP Global Hydrology Model (WGHM) and PCRaster GLOBal Water Balance model.

Extended Data Fig. 10 Model predicated cumulated vertical deformation around the Aral Sea.

The colored image shows the vertical deformation predicated from our best-fit 4-layer model due to the desiccation of the Aral Sea from 1960 to 2020. Warm color means uplifts.

Supplementary information

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Supplementary Figs. 1–26, Tables 1–4 and Text 1.

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Fan, W., Wang, T., Barbot, S. et al. Weak asthenosphere beneath the Eurasian interior inferred from Aral Sea desiccation. Nat. Geosci. 18, 351–357 (2025). https://doi.org/10.1038/s41561-025-01664-w

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