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Climate change threatens terrestrial water storage over the Tibetan Plateau

Nature Climate Change volume 12, pages 801–807 (2022)Cite this article

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Abstract

Terrestrial water storage (TWS) over the Tibetan Plateau, a major global water tower, is crucial in determining water transport and availability to a large downstream Asian population. Climate change impacts on historical and future TWS changes, however, are not well quantified. Here we used bottom-up and top-down approaches to quantify a significant TWS decrease (10.2 Gt yr–1) over the Tibetan Plateau in recent decades (2002–2017), reflecting competing effects of glacier retreat, lake expansion and subsurface water loss. Despite the weakened trends in projected TWS, it shows large declines under a mid-range carbon emissions scenario by the mid-twenty-first century. Excess water-loss projections for the Amu Darya and Indus basins present a critical water resource threat, indicating declines of 119% and 79% in water-supply capacity, respectively. Our study highlights these two hotspots as being at risk from climate change, informing adaptation strategies for these highly vulnerable regions.

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Fig. 1: Lakes, glaciers and major river basins on the TP.
Fig. 2: Component contributions and agreement in water-storage changes.
Fig. 3: Reconstructed and projected changes in water storage and climate drivers.
Fig. 4: Projected changes in water demand and supply capacity in key Asian basins.

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Data availability

Shapefiles of hydrologic basin boundaries63 used in this study are provided at http://data.tpdc.ac.cn. GRACE and GRACE-FO data are provided by the NASA MEaSUREs Program, where SH and JPL-M data can be accessed at https://grace.jpl.nasa.gov/; and the CSR-M data can be accessed at http://www2.csr.utexas.edu/grace. The elevation-change maps over the glacierized areas64 are provided at https://doi.org/10.1594/PANGAEA.876545, and RGI 6.0 glacier mask can be accessed at http://www.glims.org/RGI/. Datasets of lake storage changes46,65 are available at https://doi.org/10.5281/zenodo.5543615 and https://doi.org/10.1594/PANGAEA.898411. Data from GLDAS land surface models and IMERG precipitation can be accessed at https://disc.gsfc.nasa.gov/. ERA5 reanalysis data are available at https://cds.climate.copernicus.eu/. GLEAM ET data are available at https://www.gleam.eu/. Data from CMIP6 models can be found at https://esgf-node.llnl.gov/. Irrigation water demand is derived from the LPJml model provided by the ISIMIP portal (https://www.isimip.org/outputdata/). Industrial and domestic water demand is available on request from Y. Wada (wada@iiasa.ac.at). Monsoon monitor results for generating Supplementary Fig. 8 are derived at http://apdrc.soest.hawaii.edu/projects/monsoon/. Model-simulated TWS for generating Supplementary Fig. 10 is available at https://www.isimip.org/outputdata/. Reservoir information for generating Supplementary Fig. 18 can be accessed at https://globaldamwatch.org. Data for generating Supplementary Figs. 1, 6 and 11 are accessible on request from Y. Pokhrel (ypokhrel@egr.msu.edu), G. Zheng (zhengguanheng163@163.com) and Z. Sun (sunzhangli@gmail.com), respectively. Projected TWS changes by the mid-twenty-first century, generated by this study66, are available at https://doi.org/10.5281/zenodo.6784501.

Code availability

All analysis was performed using functions in MATLAB. The key portions of the computer code used to process the results and develop the figures67 are available at https://doi.org/10.5281/zenodo.6784641.

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Acknowledgements

D.L. and F.T. acknowledge support from the National Natural Science Foundation of China (grant no. 92047301). D.L. also acknowledges support from the Second Tibetan Plateau Scientific Expedition and Research (STEP) programme (2019QZKK0105). The authors sincerely thank X. Xu from the Chinese Academy of Meteorological Sciences for guiding expeditions to the Tibetan Plateau, Y. Wada from the International Institute for Applied Systems Analysis for providing datasets of global industrial and domestic water demand, Y. Pokhrel from Michigan State University for providing the multimodel weighted mean of TWS projections as a comparison with this study and G. Zheng from Tsinghua University for providing the frozen soil map that supports analysis of component contributions to TWS changes.

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Authors and Affiliations

  1. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

    Xueying Li, Di Long, Xingdong Li, Fuqiang Tian, Zhangli Sun & Guangqian Wang

  2. Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA

    Bridget R. Scanlon

  3. Department of Meteorology and Atmospheric Science, Penn State University, University Park, PA, USA

    Michael E. Mann

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Contributions

D.L. and Xueying Li developed the methodology of this study. Xueying Li and D.L. performed the analysis with additional support from B.R.S., M.E.M., Xingdong Li, F.T., Z.S. and G.W. All authors discussed the results and improved the writing of this manuscript.

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Correspondence to Di Long.

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Nature Climate Change thanks Mark Giordano, Xingong Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Projected changes in climate variables over the Tibetan Plateau up to 2100.

(a) Annual average temperature, (b) annual precipitation, and (c) annual surface short-wave radiation for the 2002–2100 period were estimated by the ensemble mean of nine CMIP6 models under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Both temperature and surface short-wave radiation were bias corrected using the delta approach, and precipitation was corrected using the quantile mapping approach. Shadows represent uncertainty range of ±1 standard deviation among outputs from different models.

Extended Data Fig. 2 Trends in terrestrial water storage over the Tibetan Plateau during 2002–2017.

Results were derived from four GRACE solutions, that is, (a) JPL-M, (b) CSR-M, (c) JPL-SH, and (d) CSR-SH. Stippling marks regions that have a significant trend (the Mann-Kendall test at a 5% significance level).

Extended Data Fig. 3 Terrestrial water storage anomalies and decomposed long-term variabilities.

Monthly time series of (a–d) terrestrial water storage anomaly (TWSA) and (e–h) long-term variability are shown in twelve basins during Apr 2002–Jun 2017. Solid lines are the mean of four GRACE solutions (JPL-M, CSR-M, JPL-SH, and CSR-SH), and shadows represent ±1 standard deviation among different solutions. In particular, TWSA derived from different GRACE solutions in the Inner TP is shown in (i) as an example, where TWSA is shown in solid lines and long-term variability is shown in dash lines.

Extended Data Fig. 4 Observed and projected terrestrial water storage anomalies.

Red lines show GRACE observations from JPL-M during 2002–2017, whereas blue lines show the ensemble mean of machine-learning outputs from nine CMIP6 forcings during 2002–2060 in the (a) Amu Darya and (b) Indus basins. Shadows represent uncertainty range of ±1 standard deviation among outputs from different CMIP6 forcings.

Extended Data Fig. 5 Trends in climate drivers over the Tibetan Plateau during 2002–2017.

Precipitation, temperature, and surface short-wave radiation are analyzed during (a–c) annual, (d–f) summer (June–August), and (g–i) winter (December–February) periods. Stippling marks regions that have a significant trend (the Mann-Kendall test at a 5% significance level). Precipitation data were derived from Integrated Multi-satellitE Retrievals for GPM (IMERG) V06, and temperature and radiation data were derived from reanalysis ERA5 data.

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Supplementary Sections 1–5, Figs. 1–19 and Tables 1–8.

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Li, X., Long, D., Scanlon, B.R. et al. Climate change threatens terrestrial water storage over the Tibetan Plateau. Nat. Clim. Chang. 12, 801–807 (2022). https://doi.org/10.1038/s41558-022-01443-0

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