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Widespread societal and ecological impacts from projected Tibetan Plateau lake expansion

Abstract

Lakes on the Tibetan Plateau are expanding rapidly in response to climate change. The potential impact on the local environment if lake expansion continues remains uncertain. Here we integrate field surveys, remote sensing observations and numerical modelling to assess future changes in lake surface area, water level and water volume. We also assess the ensuing risks to critical infrastructure, human settlements and key ecosystem components. Our results suggest that by 2100, even under a low-emissions scenario, the surface area of endorheic lakes on the Tibetan Plateau will increase by over 50% (~20,000 km2) and water levels will rise by around 10 m relative to 2020. This expansion represents approximately a fourfold increase in water storage compared with the period from the 1970s to 2020. A shift from lake shrinkage to expansion was projected in the southern plateau around 2021. The expansion is primarily fuelled by amplified lake water inputs from increased precipitation and glacier meltwater, profoundly reshaping the hydrological connectivity of the lake basins. In the absence of hazard mitigation measures, lake expansion is projected to submerge critical human infrastructure, including more than 1,000 km of roads, approximately 500 settlements and around 10,000 km2 of ecological components such as grasslands, wetlands and croplands. Our study highlights the urgent need for water hazard mitigation and management across the Tibetan Plateau.

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Fig. 1: Diagram illustrating future lake development and impacts over the TP.
Fig. 2: The spatial patterns of lake surface area, water level and storage changes between 2020 and 2100 under SSP2-4.5.
Fig. 3: Future reorganization of lake basins and lake mergers.
Fig. 4: The roads, settlements and grasslands submerged by expanding lakes by 2100 under SSP2-4.5.
Fig. 5: Examples of roads, bridges, settlements and grasslands threatened by expanded lakes.

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Data availability

The lake boundaries from 2021 to 2100 produced during this study are available via Figshare at https://doi.org/10.6084/m9.figshare.24873747 (ref. 69). Precipitation and evapotranspiration data from ERA5-Land can be accessed at https://www.ecmwf.int/en/era5-land. The TPHiPr precipitation dataset was acquired from https://doi.org/10.11888/Atmos.tpdc.272763. Noah_GL can be accessed at https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_M_2.0/summary?keywords=GLDAS_NOAH025_M_2.0. GLEAM can be accessed at https://www.gleam.eu. CMFD can be accessed at http://poles.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49. NASADEM data were downloaded from https://search.earthdata.nasa.gov. HydroSHED can be accessed at https://www.hydrosheds.org. The outputs of CMIP6 ESMs can be accessed at https://esgf-node.llnl.gov/search/cmip6. OpenStreetMap can be accessed at https://www.openstreetmap.org. Settlement data can be accessed at http://www.webmap.cn.

Code availability

The codes associated with this study are available from the corresponding author upon request.

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Acknowledgements

This study was supported by grants from the Second Tibetan Plateau Scientific Expedition and Research Program (grant number 2019QZKK0201) and the Basic Science Center for Tibetan Plateau Earth System (BSCTPES; NSFC project number 41988101-03). R.I.W. was supported by a UKRI Natural Environment Research Council (NERC) Independent Research Fellowship (grant number NE/T011246/1) and NERC grant number NE/X019071/1 ‘UK EO Climate Information Service’. The contributions of J.-F.C. were funded by the ESA Climate Change Initiative project on the Lake Essential Variables (contract number 4000125030/18/I-NB).

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G.Z. designed the study. F.X. and G.Z. drafted the paper. R.I.W., K.Y., Y.W., J.W. and J.-F.C. edited the paper. All authors contributed to the final form of the study.

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Correspondence to Guoqing Zhang.

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Nature Geoscience thanks Xuehui Pi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 State of the lakes together with adjacent roads, settlements, grassland, and population on the TP.

a, State of the lakes in 2020. The distribution of roads, settlements and grassland is respectively shown. Lakes mentioned in the text are also labelled. The inset shows the location of the TP in the world. b, State of the lakes in 2100. The projected lake boundaries in 2100 under the SSP2-4.5 scenario are shown. The population density in 2100 under the SSP5-8.5 scenario is aggregated per basin23. The westerlies, Indian monsoon, and East Asian monsoon with the plus sign indicate that these monsoons are expected to intensify in the future70,71,72. The workflow of this study is shown at the top of this panel. Glacier boundaries are from the RGI glacier inventory73. Credit: The icons at the top of panel b are from Flaticon.com.

Extended Data Fig. 2 The framework for projecting future lake changes on the TP.

a, Considering land surface precipitation, land surface evapotranspiration, glacier meltwater, lake surface precipitation and lake surface evaporation as drivers, the future change in annual lake water storage from 2021 to 2100 is projected. b, Based on the topographic constraints on the lake changes, the actual lake area, level and storage are estimated.

Extended Data Fig. 3 The spatial patterns of changes in lake area, water level, and storage between 2020 and 2100 under the SSP1-2.6 and SSP5-8.5 scenarios.

The percentage of area change in 2100 compared to 2020 under SSP1-2.6 (a) and SSP5-8.5 (b) scenarios. The change in lake level in 2100 compared to 2020 under SSP1-2.6 (c) and SSP5-8.5 (d) scenarios. The ratio of the change in lake storage changes between 2020 and 2100 relative to the change between 2000 and 2020 under SSP1-2.6 (e) and SSP5-8.5 (f) scenarios.

Extended Data Fig. 4 Temporal evolution of lake area relative to 2020 in different regions under SSP1-2.6 to SSP5-8.5 scenarios.

a, The temporal evolution of the area of all lakes. bh, The temporal evolution of the area of lakes in each subregion. The location of each subregion is shown in the top panel. The range of the error bands shows the 95% confidence intervals of the estimations of the bootstrap method.

Extended Data Fig. 5 Basin reorganization types as future lake expansion under the SSP5-8.5 scenario.

a, Spatial distribution of basin reorganization types (b to h). b‒h are schematic diagrams of different types. i, Statistics of basin reorganization types. The number of different types occurring in the last event was calculated to determine the frequency of reorganizations. The following delineates the diverse categories of basin reorganizations identified: (b, type I) cascading overflow, (c, type II) the amalgamation of endorheic lakes, (d, type III) the inflow of lakes into a merged basin, (e, type IV) the outflow of a merged basin into a lake, (f, type V) the convergence of several lakes into a single basin, (g, type VI) transition from endorheic to exorheic lakes, and (h, type VII) the confluence of endorheic and exorheic lakes.

Extended Data Fig. 6 The roads, settlements, and grasslands submerged as lakes expand by 2100 under SSP1-2.6 and SSP5-8.5 scenarios.

Spatial distribution of length of basin-wide submerged roads under SSP1-2.6 (a) and SSP5-8.5 (b) scenarios. Spatial distribution and time of submerged settlements under SSP1-2.6 (c) and SSP5-8.5 (d) scenarios. Spatial distribution of area of basin-wide submerged grasslands under SSP1-2.6 (e) and SSP5-8.5 (f) scenarios. Credit: map in a,b, OpenStreetMap under a Creative Commons license CC BY-SA 2.0.

Extended Data Fig. 7 The 12 lakes with high risk under SSP2-4.5 scenario.

a, Spatial distribution of the combined impact of lake changes on roads and grasslands. 12 examples showing expanding lake boundaries and submerged roads, settlements and grasslands. 1. Qinghai Lake, 2. Hala Lake. 3. Xiaochaidan Hu, 4. Ayagekumukuli, 5. Saibu Co, 6. Dong Co, 7. Laguo Co, 8. Zhari Namco, 9. Angzi Co, 10. Selin Co, 11. Nam Co, 12. Peng Co. Credit: map in a, OpenStreetMap under a Creative Commons license CC BY-SA 2.0; images 1–12, background world imagery maps, Esri, DigitalGlobe, GeoEye, i-cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo and the GIS User Community.

Extended Data Fig. 8 Attribution of future lake volume changes.

P‒ET is the precipitation minus evapotranspiration. R1 to R7 are the number of corresponding subregions.

Extended Data Fig. 9 Robustness of the framework as measured by the coefficient of determination between cumulative lake water storage change and net input.

a, Spatial distribution of the determination coefficient (R2), which indicates the ability of the model to explain historical changes in water volume. R2 ≥0.6 indicates good model performance. b, The number of all lakes and lakes with R2 ≥0.6 across different lake sizes. The black number represents the corresponding lake number. c, Water volume change during 2000–2020 of all lakes and lakes with R2 ≥0.6 across different lake sizes. The black number represents the corresponding lake water volume change. d, Number and water volume change (%) of lakes with R2 ≥0.6‒0.9, and the black number indicates the corresponding cumulative number and water volume change.

Extended Data Fig. 10 Validation of modelling change in lake water storage.

a, Scatter plot of observed and modelling change in lake water storage during the validation period. Distribution of correlation coefficients (b) and bias (c) for each lake during the validation period.

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Xu, F., Zhang, G., Woolway, R.I. et al. Widespread societal and ecological impacts from projected Tibetan Plateau lake expansion. Nat. Geosci. 17, 516–523 (2024). https://doi.org/10.1038/s41561-024-01446-w

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