# Widespread societal and ecological impacts from projected Tibetan Plateau lake expansion

Fenglin Xu, Guoqing Zhang, R. Iestyn Woolway, Kun Yang, Yoshihide Wada,

Jida Wang, Jean-François Crétaux

► To cite this version:

Fenglin Xu, Guoqing Zhang, R. Iestyn Woolway, Kun Yang, Yoshihide Wada, et al. Widespread societal and ecological impacts from projected Tibetan Plateau lake expansion. Nature Geoscience, 2024, 17 (6), pp.516-523. 10.1038/s41561-024-01446-w. hal-04720377

HAL Id: hal-04720377

https://hal.science/hal-04720377v1

Submitted on 8 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Widespread societal and ecological impacts from projected Tibetan Plateau lake expansion

Received: 28 September 2023

Accepted: 5 April 2024

Published online: 27 May 2024

<figure><img src="image_2.png" /></figure>

Check for updates

Fenglin Xu¹,², Guoqing Zhang¹✉, R. Iestyn Woolway³, Kun Yang⁴, Yoshihide Wada⁵, Jida Wang⁶,⁷ & Jean-François Crétaux⁸

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.

Lakes store approximately 87% of the Earth's accessible surface fresh-water and play a critical role in global hydrological and biogeochemical cycles¹,² that is important for ecosystem balance and socio-economic development³,⁴. In recent decades, large lakes around the world have experienced widespread declines in total water storage due to a combination of climate warming and anthropogenic disturbance⁵. In contrast, lakes residing on the Tibetan Plateau (TP) have exhibited an exceptionally dramatic expansion in response to the effects of a warmer and wetter climate⁶⁻⁸. As such, lake changes within the TP have received considerable attention in studies of global climate change⁹,¹⁰.

The TP, often referred to as the Earth's Third Pole, contains vast stores of both solid (glaciers and permafrost) and liquid (lakes) water¹¹.

It is also one of the regions that is most vulnerable to climate change, acting as an early warning signal for the wider effects of global warming¹²,¹³. The TP has experienced a remarkable expansion of lakes, and the spatial difference is evident¹⁰,¹⁴. The TP is also witnessing a greener and more habitable environment, with a growing population seeking refuge at higher altitudes due to the benefits of warming-induced accessibility to water resources¹⁵,¹⁶. However, the continued expansion of lakes is leading to potential basin mergers or reorganizations, threatening the region's infrastructure and ecological security (Fig. 1 and Extended Data Fig. 1). Despite the existence of some models to study future changes in the surface area and water storage of the TP's endorheic lakes, their applicability to specific lakes remains uncertain due to the
<figure><img src="image_3.png" /><figcaption>**Fig. 1** | **Diagram illustrating future lake development and impacts over the TP.** *a*, Lake status in the past. *b*, Future lake development and impacts. The expansion of lake boundaries and changes in the hydrological connectivity of lake basins are the result of increased net precipitation and glacier/snow melt. Future lake expansion will result in threats to human infrastructure such</figcaption></figure>

as roads and settlements, and ecological components such as grasslands, wetlands and croplands, as well as changes to the lake ecosystem such as algae, fish, zooplankton and microorganism populations. Credit: temperature and evaporation icons, Flaticon.com.

spatiotemporal heterogeneity. Some models have focused on specific large lakes or single case studies but fail to encompass comprehensive future lake changes and their broader impacts on the TP¹⁷⁻²⁰.

In this study we address these knowledge gaps by developing a gen-
eralized data-driven modelling framework that integrates the key driv-
ers (precipitation, glacier meltwater, land surface evapotranspiration

and lake evaporation) (Extended Data Fig. 2) and incorporates field
surveys and remote sensing observations. We quantify the annual
changes in area, water level and storage of individual endorheic lakes
from 2021 to 2100 under Shared Socioeconomic Pathway (SSP) sce-
narios. We then assess the magnitude of the impacts on lake basin
reorganization, infrastructure and the ecological environment. We
<figure><img src="image_4.png" /></figure>

**Fig. 2 |** The spatial patterns of lake surface area, water level and storage changes between 2020 and 2100 under SSP2-4.5. **a,** Lake area change in 2100 relative to 2020. **b,** Lake level change in 2100 relative to 2020. **c,** The ratio of lake storage change between 2020 and 2100 relative to the change between 2000 and 2020. The insets in **a–c** show the trends in the evolution of all lakes under SSP1-2.6, SSP2-4.5 and SSP5-8.5 (solid lines) with the 95% confidence intervals of

the estimations of the bootstrap method (shaded envelopes). **d,** The changes in lake area (green circles and y-axis scale), water level (blue bars and y-axis scale) and storage (colour bars and red y-axis scale) in each subregion. The y-axis scales and values in the 'Total' plot also apply to the plots for each subregion. Additional SSP1-2.6 and SSP5-8.5 scenarios are shown in Extended Data Fig. 3.

highlight the need to develop effective strategies to mitigate water hazards, while protecting biodiversity and safeguarding the well-being of people living in this ecologically sensitive region.

## Future changes in lake area and water storage

The future surface area of endorheic lakes on the TP is projected to increase to 53,657 ± 5,068 km² (+52% compared with the area in 2020, -2,303 km² per decade (dec)), 54,311 ± 5,308 km² (+54%, -2,385 km² dec⁻¹) and 58,716 ± 6,681 km² (+67%, -2,936 km² dec⁻¹) by 2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively (Fig. 2a). Accompanying the expansion of lake surface area, water levels are projected to rise by 10.21 ± 4.14 m (-1.28 m dec⁻¹) under SSP1-2.6, 10.64 ± 4.33 m (-1.33 m dec⁻¹) under SSP2-4.5 and 13.29 ± 5.18 m (-1.66 m dec⁻¹) under SSP5-8.5 by 2100 (Fig. 2b). The future rates of lake surface area expansion and water-level rise are expected to slow down substantially compared with an increase in lake area of -11,400 km² and a rise in water level of -5.25 m between 2000 and 2020, respectively²¹. Over the next 80 years, lake water storage is estimated to increase by 652.97 ± 211 Gt (-81.50 Gt dec⁻¹), 665.32 ± 220 Gt (-83.13 Gt dec⁻¹) and 908.44 ± 282 Gt (-113.50 Gt dec⁻¹) under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively, which correspond to -3.9, -3.9 and -5.4 times the increase in water storage between 2000 and 2020 (Fig. 2c). However, this change in water storage by 2100 is a staggering approximately fourfold increase compared with the period from the 1970s to 2020.

Our analysis showed a pronounced heterogeneity in the spatial distribution of future lake changes (Fig. 2, Extended Data Figs. 3 and 4 and Supplementary Figs. 1 and 2). The most substantial change was observed in the northern TP, where the total lake area was projected to increase twofold by 2100 under SSP5-8.5. Albeit to a lesser extent,

lakes in the southeastern, central and northwestern endorheic TP were expected to expand notably (51–71%). Historically, lake changes in the southern TP have followed a shrinking trend¹⁰,²². However, our projections indicated a remarkable transition from shrinkage to expansion in -2021 (Extended Data Fig. 4 and Supplementary Figs. 1–3).

There were notable differences in the evolution of lake surface area, water level and water storage. During the period 2020–2100, changes in lake storage (level and area) ranged from about -0.61 Gt (corresponding to -11.89 m and -8 km²) to 66 Gt (59 m and 903 km²). Most of the increases were between -0.86 Gt (3.96 m and 7.83 km², 25th percentile) and -1.29 Gt (14.21 m and 47.14 km², 75th percentile). Notably, Selin Co (the largest lake in Tibet and the second largest lake in the TP) exhibited the largest substantial water gain of -66 Gt, along with corresponding increases in area of ~800 km² and level of -21 m under SSP2-4.5.

## Changes in the hydrological connectivity of lake basins

Under SSP1-2.6, SSP2-4.5 and SSP5-8.5, 70 (23%), 70 (23%) and 79 (26%) lake basins were reorganized into 28, 28 and 31 basins, respectively, by 2100 (Fig. 3a). New reorganizations will occur mainly in the northern, eastern and southern endorheic TP (Fig. 3c). In addition, 21, 19 and 23 lakes will merge (that is, two or more lakes merging into one) under these scenarios, forming 10, 9 and 11 lakes, respectively (Fig. 3b). Lake mergers were predicted to occur mainly in the northeastern and southeastern TP (Fig. 3d). With the anticipated growth of lakes in the future, the reconfiguration of the drainage system—spurred by the interconnected nature of these lakes—is expected to become even more extensive.
<figure><img src="image_5.png" /><figcaption>**Fig. 3** | **Future reorganization of lake basins and lake mergers.** a, Number of basins before and after reorganization under the different scenarios. b, Number of lakes before and after mergers under the different scenarios. c, Spatial distribution of basin reorganization under SSP2-4.5. d, Spatial distribution of lake mergers under SSP2-4.5. The merged lakes are shown as merged basins because each lake basin has only one endorheic lake.</figcaption></figure>

Seven different types of basin reorganization were identified. each characterized by distinct processes, quantities and types of lake (endorheic and exorheic) (Extended Data Fig. 5). In the future, most basin reorganizations were projected to follow cascading overflow (type I), and the progression from the amalgamation of endorheic lakes (type II) to the inflow of lakes into a merged basin (type III) will occur when merged lakes experience overflow. The outflow of a merged basin into a lake (type IV) and the convergence of several lakes into a single basin (type V) were less common, with only one reorganized basin in each case. In addition, future lake expansion could lead to shifts in lake type, such as an endorheic lake becoming exorheic (type VI). The confluence of endorheic and exorheic lakes (type VII) showed a particular type of merging, where three endorheic and two exorheic lakes merged into one endorheic lake. Different reorganization types could occur for the same lake over different time periods, given the intricate dynamics of continuous expansion. The future evolution of lakes is thus poised to be very complex.

## Impact of future lake expansion

By the end of this century, 1,023 ± 281, 959 ± 274 and 1,481 ± 421 km of roads will be inundated under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively, (Fig. 4a). Under SSP2-4.5 (SSP1-2.6 and SSP5-8.5), ~118 km (149 km and 136 km), 215 km (264 km and 258 km) and 331 km (382 km and 390 km) of roads are projected to be at risk of flooding in 2030, 2040 and 2050, respectively. The inundated roads were mainly concentrated in the southern endorheic TP. Despite the most notable lake expansion projected in the northern endorheic TP, the presence of seasonal permafrost limits road construction and human access, resulting in fewer flooded roads. Conversely, although less lake expansion

is anticipated in the northeastern TP (Fig. 2 and Extended Data Fig. 4), a high number of road segments were expected to be inundated due to increased human activity and the prevalence of roads adjacent to lakes (Extended Data Fig. 6). At the lake scale, 12 lakes (including the three largest lakes, Selin Co, Nam Co and Qinghai Lake) were identified as potentially more vulnerable (Extended Data Fig. 7). Selin Co has the most submerged roads, with the projected length of submerged roadways expected to reach about 84.13–119.08 km by 2100. Due to the rapid expansion of Selin Co, the S208 highway was broken by the outflow flood at the end of September 2023 (Fig. 5). Nam Co, which is almost surrounded by roads, is expected to have ~73.27, ~34.02 and ~118.91 km of roads submerged under the three scenarios, respectively. Owing to the substantial expansion of Qinghai Lake, some roads in the northwest have been inundated (Fig. 5). Approximately 46.9 km of road is expected to be inundated under SSP5-8.5. In summary, a large number of roads are potentially at risk of being submerged by future lake expansion, which is a serious threat that should be considered in future rail and road planning.

Given the importance of water resources and religious culture in the TP, many settlements such as villages, herders' houses and livestock pens are located around lakes (Fig. 1 and Extended Data Fig. 1a). Projections indicate that by the year 2100, an estimated 5.66, 7.40 and 5.78 million people will inhabit the TP under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively²³. Approximately 462, 458 and 615 settlements will be inundated, respectively, and most of them are in the southern endorheic TP (Fig. 4b and Extended Data Fig. 6). Moreover, between 83 and 93 lakes are anticipated to inundate settlements, with Selin Co posing the greatest risk, inundating 64 to 81 settlements across various SSP scenarios. Some settlements already face inundation or are at heightened risk, such
<figure><img src="image_6.png" /><figcaption>**Fig. 4** | The roads, settlements and grasslands submerged by expanding lakes by 2100 under SSP2-4.5. a, Spatial distribution of the length of submerged roads across the lake basin. b, Spatial distribution of the year settlements are submerged. c, Spatial distribution of the extent of submerged grasslands. The insets show the annual variation in the length of submerged roads (a), the number</figcaption></figure>

of submerged settlements (b) and annual variation in the extent of submerged grasslands (c) under the different climate scenarios; the ranges of the error bands were estimated based on 95% confidence intervals for the lake area. Additional SSP1-2.6 to SSP5-8.5 scenarios are shown in Extended Data Fig. 6. Credit: map in a, OpenStreetMap under a Creative Commons license CC BY-SA 2.0.

as the village and building near Zhari Namco and Padu Co (Fig. 5), and this impending threat is expected to increase in the future.

The future expansion of TP lakes will also inundate many ecological components, including grasslands, wetlands, croplands, forests and sparse vegetation (Figs. 4 and 5 and Supplementary Fig. 4). By the year 2100, the anticipated inundation areas are projected to reach 8,533, 9,132 and 11,576 km² by 2100 under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively. More than 500,000 head of livestock could be disturbed, assuming even distribution of livestock (Supplementary Fig. 5).

Grassland areas of 4,241 ± 1168, 4,459 ± 1241 and 5,968 ± 1823 km² will be inundated by 2100 in each of the SSP scenarios (Fig. 4c). This will directly lead to a decline in livestock production, severely affecting the livelihoods of local pastoralists and further exacerbating poverty levels. We also identified 291 lakes widely distributed across the plateau that pose a threat to the security of the grasslands (Fig. 4c), such as the large grasslands near Peng Co (Fig. 5). Loss of cropland could disrupt food production, affecting both local food security and the regional agricultural economy.
<figure><img src="image_7.png" /></figure>

**Fig. 5** | Examples of roads, bridges, settlements and grasslands threatened by expanded lakes. a, The S208 road was damaged by flooding due to Selin Co expansion around 25 September 2023. Field photographs taken on 2 October 2023. The broken road caused by the Selin Co flood is shown in photos 1 and 2. The dashed arrows show the direction of the flooding. b, Roads inundated by Qinghai Lake expansion (yellow outline). c, Original S301 road (red line in yellow outline) inundated and roads that may be inundated. d, Village at high risk near

Zhari Namco (yellow outline). e, Settlement inundated near Padu Co (yellow outline). f, High-risk grasslands near Peng Co. The insets show the evolution of lake levels (unit in m) to 2100 under SSP1-2.6. The number in orange in the inset is the predicted lake level by 2050 and 2100. The locations of these lakes are indicated in Extended Data Fig. 1. Credit: c,d, Landsat-8 images courtesy of the US Geological Survey.

## Implications and mechanisms of future lake expansion

As lakes on the TP continue to expand, there is growing concern about increased emissions of greenhouse gases, including carbon dioxide and methane, into the atmosphere²⁴. These emissions could further exacerbate global warming, creating a positive feedback loop that amplifies the effects of climate change. In addition, the submergence of grasslands due to flooding can lead to the decomposition of organic matter, releasing additional carbon dioxide into the atmosphere²⁵. The interaction between these lakes and the atmosphere, as well as their role in regional hydrological cycles such as extreme rainfall or snowfall events²⁶,²⁷, is expected to intensify as lakes expand in size in the future.

Future increases in lake water volume, with an increase influx of freshwater, can lead to decreases in salinity²⁸,²⁹, which could substantially impact the physical environment of lakes³⁰,³¹, altering species richness, composition and the trophic structure of lake ecosystems³². Rising lake surface water temperatures and increasing heatwaves have cascading effects on the physical structure and chemical properties of aquatic systems, threatening lake biodiversity³³,³⁴. In addition, the overflow water carrying eroded sediments will increase the turbidity and sedimentation in the receiving lakes³⁵, which could disrupt the ecological balance of the lakes and lead to species loss. The newly formed channels resulting from lake basin reorganization and lake merging can lead to a cascade of hydrogeomorphic processes along their paths, such as incision, lateral erosion or aggradation, thermal erosion and increased infiltration, which affect the regional ecological

environment and disrupt ecological migration. For example, Zonag Lake in Hoh Xil Nature Reserve burst in September 2011, blocking the Tibetan antelope migration route and affecting their survival and reproduction³⁶. The effects of this drainage reorganization can spill over into larger downstream basins.

From a socio-economic perspective, the direct economic loss due to inundated roads by 2100 under the SSP2-4.5 scenario is estimated at RMB 20–50 billion based on investment costs in 2022³⁷. The future expansion of lakes could enhance the natural landscapes and increase sustainable tourism development and local income. The Tibet Autonomous Region plans to build ~5,600 km of new highways and three railways³⁷ (Supplementary Fig. 6). The expanding lakes pose challenges to existing and planned infrastructure and communities and requires urgent implementation of effective adaptation and sustainable management strategies to mitigate socio-economic repercussions.

Glacier meltwater contributed ~9% to the increase in lake storage from 1995 to 2020³⁸, and although the temperature of the TP will continue to rise, the contribution of glacier meltwater is estimated to be only 7–15% by 2100 (Extended Data Fig. 8) due to the limited storage of the remaining glaciers³⁹⁻⁴². Lake evaporation is also an important driver of water loss, and the reduction in lake ice this century is likely to lead to an increase in lake evaporation⁴³,⁴⁴; however, the relative contribution is small¹⁹, with an estimated loss contribution of about ~34% under SSP2-4.5 and SSP5-8.5. Owing to future increases in temperature and radiation, precipitation is likely to remain the main controlling factor for evapotranspiration on the TP⁴⁵. Lake expansion on the TP
is primarily influenced by net precipitation⁷,²²,⁴⁶, and the substantial increase in future net precipitation will act as a greater water gain, leading to further lake expansion, with an estimated relative contribution of 109–116%.

Given the current context of global warming and climate change, it is crucial to comprehensively understand future lake changes on the TP to effectively manage water resources, mitigate hazards and preserve the ecology of this crucial region. Our study serves as a scientific guide for future planning and provides valuable insights to avoid the devastating consequences of the impending lake expansion.

## Online content

Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41561-024-01446-w.

## References

1. Williamson, C. E., Saros, J. E., Vincent, W. F. & Smol, J. P. J. L. Lakes and reservoirs as sentinels, integrators, and regulators of climate change. *Limnol. Oceanogr.* **54**, 2273–2282 (2009).

2. Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. *Nature* **503**, 355–359 (2013).

3. Reynaud, A. & Lanzanova, D. A global meta-analysis of the value of ecosystem services provided by lakes. *Ecol. Econ.* **137**, 184–194 (2017).

4. Loewen, C. J. G. Lakes as model systems for understanding global change. *Nat. Clim. Change* **13**, 304–306 (2023).

5. Yao, F. et al. Satellites reveal widespread decline in global lake water storage. *Science* **380**, 743–749 (2023).

6. Pekel, J. F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. *Nature* **540**, 418–422 (2016).

7. Zhang, G. Q. et al. Lake volume and groundwater storage variations in Tibetan Plateau's endorheic basin. *Geophys. Res. Lett.* **44**, 5550–5560 (2017).

8. Yao, F. et al. Lake storage variation on the endorheic Tibetan Plateau and its attribution to climate change since the new millennium. *Environ. Res. Lett.* **13**, 064011 (2018).

9. Woolway, R. I. et al. Global lake responses to climate change. *Nat. Rev. Earth Environ.* **1**, 388–403 (2020).

10. Zhang, G. et al. Response of Tibetan Plateau lakes to climate change: trends, patterns, and mechanisms. *Earth Sci. Rev.* **208**, 103269 (2020).

11. Yao, T. et al. The imbalance of the Asian water tower. *Nat. Rev. Earth Environ.* **3**, 618–632 (2022).

12. Chen, D. et al. Assessment of past, present and future environmental changes on the Tibetan Plateau. *Chin. Sci. Bull.* **60**, 3025–3035 (2015).

13. Immerzeel, W. W. et al. Importance and vulnerability of the world's water towers. *Nature* **577**, 364–369 (2019).

14. Ma, R. et al. A half-century of changes in China's lakes: global warming or human influence? *Geophys. Res. Lett.* **37**, L24106 (2010).

15. Zhang, W., Zhou, T. & Zhang, L. Wetting and greening Tibetan Plateau in early summer in recent decades. *J. Geophys. Res. Atmos.* **122**, 5808–5822 (2017).

16. Zhong, L., Ma, Y., Xue, Y. & Piao, S. Climate change trends and impacts on vegetation greening over the Tibetan Plateau. *J. Geophys. Res. Atmos.* **124**, 7540–7552 (2019).

17. Jia, B., Wang, L. & Xie, Z. Increasing lake water storage on the Inner Tibetan Plateau under climate change. *Sci. Bull.* **68**, 489–493 (2023).

18. Adnan, M. et al. Prediction of changes in water balance of Nam Co Lake under projected climate change scenarios. *Hydrol. Sci. J.* **66**, 1712–1727 (2021).

19. Yang, K. et al. Quantifying recent precipitation change and predicting lake expansion in the Inner Tibetan Plateau. *Climatic Change* **147**, 149–163 (2018).

20. Cheng, J. et al. Regional assessment of the potential risks of rapid lake expansion impacting on the Tibetan human living environment. *Environ. Earth Sci.* **80**, 166 (2021).

21. Zhang, G., Bolch, T., Chen, W. & Cretaux, J. F. Comprehensive estimation of lake volume changes on the Tibetan Plateau during 1976-2019 and basin-wide glacier contribution. *Sci. Total Environ.* **772**, 145463 (2021).

22. Lei, Y. et al. Response of inland lake dynamics over the Tibetan Plateau to climate change. *Climatic Change* **125**, 281–290 (2014).

23. Chen, Y. et al. Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. *Sci. Data* **7**, 83 (2020).

24. Pi, X. et al. Mapping global lake dynamics reveals the emerging roles of small lakes. *Nat. Commun.* **13**, 5777 (2022).

25. Marcé, R. et al. Emissions from dry inland waters are a blind spot in the global carbon cycle. *Earth Sci. Rev.* **188**, 240–248 (2019).

26. Su, D. et al. Simulation of the potential impacts of lakes on glacier behavior over the Tibetan Plateau in summer. *Clim. Dynam.* **60**, 3435–3454 (2022).

27. Yao, X. et al. Surface friction contrast between water body and land enhances precipitation downwind of a large lake in Tibet. *Clim. Dynam.* **56**, 2113–2126 (2021).

28. Liu, C. et al. In-situ water quality investigation of the lakes on the Tibetan Plateau. *Sci. Bull.* **66**, 1727–1730 (2021).

29. Song, C. et al. Widespread declines in water salinity of the endorheic Tibetan Plateau lakes. *Environ. Res. Commun.* **4**, 091002 (2022).

30. Boehrer, B. & Schultze, M. Stratification of lakes. *Rev. Geophys.* **46**, RG2005 (2008).

31. Ladwig, R., Rock, L. A. & Dugan, H. A. Impact of salinization on lake stratification and spring mixing. *Limnol. Oceanogr. Lett.* **8**, 93–102 (2021).

32. Lin, Q. et al. Responses of trophic structure and zooplankton community to salinity and temperature in Tibetan lakes: implication for the effect of climate warming. *Water Res.* **124**, 618–629 (2017).

33. Woolway, R. I. et al. Lake heatwaves under climate change. *Nature* **589**, 402–407 (2021).

34. Wang, X. et al. Climate change drives rapid warming and increasing heatwaves of lakes. *Sci. Bull.* **68**, 1574–1584 (2023).

35. Lu, S. et al. Drainage basin reorganization and endorheic-exorheic transition triggered by climate change and human intervention. *Glob. Planet. Change* **201**, 103494 (2021).

36. Pei, J. et al. Recovered Tibetan antelope at risk again. *Science* **366**, 194–194 (2019).

37. Transport investment costs of the Tibet Autonomous Region in 2022. *Department of Transportation of Tibet Autonomous Region* https://jtt.xizang.gov.cn (2023).

38. Chen, W. et al. What controls lake contraction and then expansion in Tibetan Plateau's endorheic basin over the past half century? *Geophys. Res. Lett.* **49**, e2022GL101200 (2022).

39. Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. *Nat. Clim. Change* **8**, 135–140 (2018).

40. Rounce, D. R. et al. Global glacier change in the 21st century: every increase in temperature matters. *Science* **379**, 78–83 (2023).
41. Hugonnet, R. et al. Accelerated global glacier mass loss in the early twenty-first century. *Nature* **592**, 726–731 (2021).

42. Rounce, D. R., Hock, R. & Shean, D. E. Glacier mass change in High Mountain Asia through 2100 using the open-source Python Glacier Evolution Model (PyGEM). *Front. Earth Sci.* **7**, 331 (2020).

43. Wang, X. et al. Continuous loss of global lake ice across two centuries revealed by satellite observations and numerical modeling. *Geophys. Res. Lett.* **49**, e2022GL099022 (2022).

44. Sharma, S. et al. Widespread loss of lake ice around the Northern Hemisphere in a warming world. *Nat. Clim. Change* **9**, 227–231 (2019).

45. Ma, N. & Zhang, Y. Increasing Tibetan Plateau terrestrial evapotranspiration primarily driven by precipitation. *Agric. Meteorol.* **317**, 108887 (2022).

46. Tong, K., Su, F. & Xu, B. Quantifying the contribution of glacier meltwater in the expansion of the largest lake in Tibet. *J. Geophys. Res. Atmos.* **121**, 11158–11173 (2016).

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

© The Author(s), under exclusive licence to Springer Nature Limited 2024

<footnote n="1">¹State Key Laboratory of Tibetan Plateau Earth System Science, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China. ²University of Chinese Academy of Sciences, Beijing, China. ³School of Ocean Sciences, Bangor University, Menai Bridge, UK. ⁴Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, China. ⁵Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.</footnote>

<footnote n="6">⁶Department of Geography and Geographic Information Science, University of Illinois Urbana-Champaign, Urbana, IL, USA. ⁷Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, USA. ⁸Laboratoire d'Études en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES-IRD-CNRS-UT3, Centre National d'Études Spatiales (CNES), Toulouse, France. e-mail: guoqing.zhang@itpcas.ac.cn</footnote>
# Methods

## Historical lake storage changes

Weekly to annual historical changes in lake water storage from five data sources were used to project the evolution of as many lakes as possible (Supplementary Table 1): (1) Hydroweb (http://hydroweb.theia-land.fr/hydroweb)47,48, which contains time series of lake water levels, surface area and storage changes, and hypometric curves covering 42 endorheic lakes in the TP; (2) the annual area and water storage of 976 lakes larger than 1 km² on the TP for 1991–2018⁴⁹; (3) the annual area of ~300 lakes larger than 10 km² in the TP for 1991–2018 and hypometric curves⁷; (4) water storage changes for 1,132 lakes with ~5 year intervals and hypometric curves²¹; (5) weekly to monthly water-level and storage changes for 52 lakes on the TP during 2000–2017 and hypometric curves⁵⁰. These different types of dataset, which convert area or water level to water storage change by combining hypometric curves, were integrated. In this study, 307 lakes representing ~97% of the total area of endorheic lakes in the TP were selected on the basis of the availability of historical annual lake volume change.

## Precipitation product

The average of two sets of historical precipitation products was used (Supplementary Table 2). The first, ERA5-Land, a state-of-the-art reanalysis precipitation, is an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5) produced by the European Centre for Medium-Range Weather Forecasts³¹, spanning 1950 to the present day with hourly to monthly resolution. ERA5-Land data with a spatial resolution of ~9 km were interpolated from the ERA5 data with a spatial resolution of ~31 km using a linear interpolation method based on a triangular mesh. Although ERA5-Land overestimates the amount of precipitation, it provides a good representation of the spatiotemporal variation patterns over the TP⁵². The second, TPHiPr, is a high-precision precipitation dataset for the Third Pole region (1/30°, daily) obtained by merging the atmospheric simulation-based ERA5_CNN product with data from over 9,000 rain gauges using climatologically aided interpolation and random forest methods⁵³. This dataset has better accuracy than ERA5-Land, IMAGE, MSWEP v2 and AERA5-Asia data, with a root mean square error (r.m.s.e.) of 5.0 mm d⁻¹. A comparison of precipitation time series between TPHiPr and ERA5-Land (Supplementary Fig. 7) suggested good agreement. Importantly, the offset between these two datasets did not contribute to uncertainties, as the precipitation anomaly served as the basis for lake modelling. However, we used the average of TPHiPr and ERA5-Land to minimize the uncertainty of the lake projections. To obtain the future precipitation products, the monthly outputs of the 32 Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system models (ESMs) under SSP1-2.6, SSP2-4.5 and SSP5-8.5 were used, calibrated against ERA5-Land and TPHiPr data, respectively, during the overlap period. The top 15 ESMs in each basin were selected using historical references from ERA5-Land and TPHiPr data, respectively, and subsequently the ensemble mean of the 30 ESMs served as future precipitation. Further details of the downscaling and selection method for the CMIP6 ESMs are available in Supplementary Text 1 (Supplementary Figs. 8 and 9 and Supplementary Tables 3 and 4). Detailed information on the evapotranspiration product and glacier mass balance data used in this study can be found in Supplementary Texts 2 and 3.

## Lake evaporation forcing data

The forcing variables used to calculate evaporation included air temperature, specific humidity, pressure, downward shortwave radiation, downward longwave radiation and wind speed. These variables were obtained from the China Meteorological Forcing Dataset (CMFD) with a 3 h time step and grid resolution of 0.1° (ref. 54) and bias-corrected CMIP6 ESMs. It should be noted that although CMFD is considered to be the most accurate meteorological data for the TP, errors have been identified based on observed data. Therefore, linear corrections were

applied to air temperature, specific humidity, air pressure, downward shortwave radiation and wind speed using correction equations from previous studies55,56 that could reduce the bias of the CMFD. Details of the correction equations are available in Supplementary Text 4. The bias-corrected CMFD was then used to correct the 15–36 CMIP6 ESMs. For each lake, the area-weighted mean of the forcing data was calculated for the grids intersecting the lake. Further details of the methods used for downscaling the CMIP6 ESMs are available in Supplementary Text 1.

## Estimate of lake surface evaporation

During the unfrozen period, the monthly lake evaporation rates were calculated using the Penman–Monteith equation (equation (1)), which is widely used to estimate open water evaporation⁵⁷⁻⁵⁹.

$$E_w = \frac{\Delta}{\Delta + \gamma} \frac{R_n}{\lambda} + \frac{\gamma}{\Delta + \gamma} E_a \quad (1)$$

where $E_w$ is the daily evaporation rate (mm d⁻¹) from open water, where w refers to water; $R_n$ is the net radiation (W m⁻²); Δ is the slope of the saturated vapour pressure curve (kPa °C⁻¹); γ is the psychrometric constant (kPa °C⁻¹); and λ is the latent heat of vaporization (2.45 MJ kg⁻¹).

$E_a$ (mm d⁻¹), where a denotes the aerodynamic component, is the evaporative component from the turbulent movement of water vapour by eddy diffusion⁶⁰ and is calculated as follows:

$$E_a = 0.26(1 + 0.54u)(e_s - e_a) \quad (2)$$

where u is the wind speed at 2 m, $e_s$ – $e_a$ is the vapour pressure deficit (KPa), $e_s$ is the saturation vapour pressure and $e_a$ is the actual vapour pressure in the air. During the freeze period, lake evaporation occurs in the form of sublimation and is estimated by equation (2)⁶¹. Further details of the historical and future lake ice phenology are available in Supplementary Text 5.

Net radiation ($R_n$) over the lake surface was estimated using equation (3):

$$R_n = (1 - \alpha)K^1 + L^1 - L^\uparrow \quad (3)$$

where α is the lake surface albedo (0.07); K¹ is the downward shortwave radiation (W m⁻²); L¹ is the downward longwave radiation (W m⁻²); L¹ is the upward longwave radiation (W m⁻²) estimated using L¹ = $\varepsilon_w\sigma(T_a + 273.15)^4$; $\varepsilon_w$ is the emissivity of the lake surface (0.98); σ is the Stefan–Boltzmann constant (5.67 × 10⁻⁸ W m⁻² K⁻⁴); and $T_a$ denotes air temperature, which ideally should be the lake surface temperature, but due to the unavailability of future data we used air temperature as a proxy. As we used the relative change in evaporation, the effect of this term on evaporation is minimal.

## Development of the future lake modelling framework

Future lake modelling is limited by the following factors: (1) region-specific methods exist, but individual lake-specific modelling methods for large scales are lacking¹⁹; (2) the limitation of large-scale applications of land surface or hydrological models due to the scarcity of runoff inflow observations for lakes (available only for Selin Co and Nam Co)¹⁸; (3) the absence of consideration for the topography constraints on future lake changes and hydrological connectivity between lakes; (4) machine or deep learning methods are limited by coarse temporal resolution due to cloud contamination of satellite imagery, and lack of interpretability¹⁷. The storage changes of endorheic lakes accounted for 95–100% (161.9 ± 14 Gt) of the total lake change observed from the 1970s to 2020, while exorheic lakes remained relatively stable (7.8 ± 5.8 Gt)²¹. We therefore developed a data-driven generalized modelling framework specifically for endorheic lakes to model their future annual changes, including lake surface area, water level and lake water storage (Extended
Data Fig. 2). Lake changes were mainly controlled by four dominant forcings: precipitation, glacier meltwater (Supplementary Text 2), land evapotranspiration (Supplementary Text 3) and lake evaporation$^{7,62}$. The framework consists of two main steps: (1) projecting future changes in lake storage in response to climate change (Extended Data Fig. 2a) and (2) integrating inundation models and digital elevation models (DEMs), considering lake connectivity, to estimate actual surface area, water level and lake storage (Extended Data Fig. 2b).

Step 1 of the modelling framework defines the concept of a stable period, where the size of the lake remains relatively constant during the beginning and end years of this period, allowing for fluctuations in lake size during this period (Extended Data Fig. 2a). The stable period can vary for each lake and was determined on the basis of the changes in lake area from 1988 to 2018. Lakes on the TP experienced a contraction from the 1970s to 1995, followed by a substantial expansion from 1995 to 2020. Most lakes had a stable period around 1995, such as the stable period 1989–1995 for Selin Co. However, there are also some lakes that had a stable period after 2000, such as Peiku Co for 2008–2015. During the stable period, the lake is in a state of equilibrium and then the equilibrium states of each factor were calculated as follows:

$$ \bar{x}^k = \sum_{i=1}^{t_2} \frac{x_i^k}{n} \qquad (4) $$

where k is the kth variable, including land precipitation (PL), glacier meltwater (G), land evapotranspiration (ET), lake water surface precipitation (PW) and lake surface evaporation (E), respectively. t1 and t2 represent the start and end years of the stable period, n is the number of years in the stable period and i represents the years within the stable period. The concept of a change period was also defined: the period outside the stable period for which lake storage data are available, mainly after 1990. The anomaly relative to the equilibrium state was calculated for each contributing factor during the change period as follows:

$$ \delta x_i^k = x_i^k - \bar{x}^k \qquad (5) $$

where i represents the years within the change period. Subsequently, the annual cumulative anomalies relative to the equilibrium state (equation (4)) for each contribution factor were calculated, starting from the first year of the change period as follows:

$$ \chi_i^k = \sum_{i=1}^{2018} \delta x_i^k \qquad (6) $$

where c1 represents the starting years of the change period; the form of each contribution factor can be expressed as land precipitation (PL<sub>i</sub><sup>cum</sup>), glacier meltwater (G<sub>i</sub><sup>cum</sup>), land evapotranspiration (ET<sub>i</sub><sup>cum</sup>), lake surface precipitation (PW<sub>i</sub><sup>cum</sup>) and lake surface evaporation (E<sub>i</sub><sup>cum</sup>), respectively. We also calculated the annual cumulative changes in lake storage starting from the first year of the change period as follows:

$$ V_i^{\text{cum}} = \sum_{i=1}^{2018} (V_i - V_{i-1}) \qquad (7) $$

By integrating the cumulative changes in lake storage and the cumulative anomalies relative to the equilibrium state for each contribution factor calculated from equation (6), the lake storage modelling framework can be expressed as:

$$ V_i^{\text{cum}} = \alpha (\theta (\text{PL}_i^{\text{cum}} + G_i^{\text{cum}} - \text{ET}_i^{\text{cum}}) + \text{PW}_i^{\text{cum}} - E_i^{\text{cum}}) + \beta \qquad (8) $$

Equation (8) can be decomposed into the following two parts:

$$ U_i = \theta (\text{PL}_i^{\text{cum}} + G_i^{\text{cum}} - \text{ET}_i^{\text{cum}}) + \text{PW}_i^{\text{cum}} - E_i^{\text{cum}} \qquad (9) $$

$$ V_i^{\text{cum}} = \alpha(U_i) + \beta \qquad (10) $$

where PL<sub>i</sub><sup>cum</sup> + G<sub>i</sub><sup>cum</sup> - ET<sub>i</sub><sup>cum</sup> represents the land component, which includes the combined contributions of surface runoff, soil water and groundwater to the lake change, and θ is a land-specific parameter. PW<sub>i</sub><sup>cum</sup> - E<sub>i</sub><sup>cum</sup> represents the lake surface component. U<sub>i</sub> denotes a variable that integrates land surface precipitation, land evapotranspiration, glacier meltwater, lake surface precipitation and lake surface evaporation, and is considered as net input.

The parameters in equation (8) were trained for each lake on the basis of historical lake storage changes. Initially, θ was set to a fixed value of 1, and then α and β were determined using the Theil–Sen non-parametric method. If the R<sup>2</sup> value between α and β was greater than or equal to 0.60, the constructed model was used to estimate future changes in lake storage. However, if the R<sup>2</sup> value was less than 0.6, θ was adjusted in increments of 0.01 between 0.9 and 1.1, and then the Theil–Sen non-parametric method was used again to determine α and β until R<sup>2</sup> exceeded 0.60. If R<sup>2</sup> remained consistently below 0.6, the lake was extrapolated using the ratio of the average storage change of the nearest five lakes. It is worth emphasizing that U<sub>i</sub> and V<sub>i</sub><sup>cum</sup> showed a highly robust linear synchronization that was prevalent among endorheic lakes on the TP (Extended Data Fig. 9a and Supplementary Fig. 10). The established model for each lake was then used to project future changes in lake storage in response to climate change by using the cumulative anomalies relative to the equilibrium state for each contribution factor from 2021 to 2100 as input.

In Step 2 of the modelling framework (Extended Data Fig. 2b), the inundation model and DEM were combined, considering the connectivity of lakes$^{63}$, to accurately estimate the actual lake storage, water level and surface area. If a lake continues to expand in the future until it reaches the lowest point of the basin boundary, it will overflow from its basin and flow into adjacent lakes, resulting in a stable phase. Conversely, lakes that receive overflow recharge from other lakes will experience accelerated expansion. Therefore, the constraint of topography on future lake change is essential.

NASADEM (30 m pixel size) was selected to estimate the distribution of the projected lake water volume changes because it was acquired instantaneously (11 day mission in February 2000) and offers better accuracy than other available 30 m DEMs$^{64,65}$. First, considering that NASADEM corresponds to the year 2000, the average elevation of the lake boundary in 2020 was extracted from NASADEM and then used to replace all the elevations within the lake boundary in 2020. Then, using the regional growth algorithm with the centre of the lake as the seed point, growth of 60 m was initiated to determine the corresponding changes in area, water level and water storage for each step of 1 m growth. The maximum storage capacity of the lake basin was also determined and compared with the projected water storage from Step 1 to determine whether the lake would overflow from the basin or receive inflow from other lakes. The estimated water storage in Step 1 was also matched with the water storage change corresponding to the 1–60 m growth to determine the flow paths of water between adjacent lakes each year, redistributing the water storage and obtaining the actual annual variations in lake storage, as well as the corresponding annual water level and area under the SSP1-2.6, SSP2-4.5 and SSP5-8.5. For lakes experiencing a reduction in size, the approach involved utilizing a robust empirical linear volume–area–level relationship<sup>21</sup> when the projected water volume change is less than the volume loss recorded between 2000 and 2020. The lake outlines from 2020 were used to conservatively estimate the potential impact of lake inundation. When the projected lake volume change is greater than the recorded volume loss between 2000 and 2020, the estimation of the extent was instead performed using a regional growth algorithm. Further details of the method used for analysing the impacts of lake changes, auxiliary data and the attribution of future lake changes are available in Supplementary Texts 6–8.
## Validation and error estimation of future lake modelling

To evaluate the robustness of our approach, the $R^2$ between $U_i$ (net input) and $V_{cum}$ was employed to assess the generalizability of the framework (see 'Development of the future lake modelling framework'). The modelling framework developed in this study using four drivers performed well, explaining more than 94% of the historical lake storage change in -70% of the lakes with a uniform distribution across the TP (Extended Data Fig. 9). Among the 307 endorheic lakes, there were 214, 189, 154 and 91 lakes with $R^2$ values higher than 0.60 (70%), 0.70 (62%), 0.80 (50%) and 0.90 (30%), respectively, which could explain 94%, 92%, 85% and 61% of the historical lake storage changes, respectively (Extended Data Fig. 9). Furthermore, the performance of the modelling framework varied between lakes of different sizes, performing better for larger lakes, leading to a higher proportion of lakes and water storage that can be explained. In addition, the samples for each lake were split in time, with the initial 70% allocated for model training and the remaining 30% for validation. The model's performance was assessed using the correlation coefficient ($R$), bias and r.m.s.e. The simulated and observed changes in lake water storage showed good agreement during the validation period, with an overall $R$ of 0.97 and a bias of 0.05 (Extended Data Fig. 10). The correlation coefficients and bias distribution for different lakes also demonstrated good accuracy of the modelling framework.

The bootstrap method, a non-parametric approach to estimate confidence intervals of sample statistics through repeated resampling of the original dataset, was used to assess the uncertainty of projected future lake storage changes. For each lake, the historical data used for parameter training were resampled with replacement 1,000 times, with each sample comprising 50% of the total dataset. These 50% samples were used to train 1,000 sets of corresponding models, which were then used to estimate 1,000 sets of future lake storage changes. Finally, the 95% confidence interval of the annual results was taken as the uncertainty of the estimate. Subsequently, the uncertainty in the lake storage changes was converted into the uncertainty in the water level and area; for example, if the error of lake storage for a given year was -1 Gt, the uncertainty in the water level and area corresponded to the changes in the water level and area for that year. The uncertainty of submerged roads and grasslands was estimated on the basis of lake area. First, the submerged roads and grasslands corresponding to the area changes from 2021 to 2100 were calculated. Then the uncertainty in area for each year was proportionally converted to the uncertainty in submerged roads and grasslands.

## Limitations and uncertainties of future lake modelling

Our model did not account for the effects of thermodynamic changes due to mixing regimes resulting from basin reorganization and lake mergers in future changes. Given the 30 m spatial resolution of NASA-DEM, changes in lake boundaries within a year were occasionally less than one pixel, in which case the previous year's boundaries were used. Selin Co, which has the largest lake surface area in Tibet and the largest historical extent change on the TP, was selected as a case study to evaluate the impact of DEM accuracy on the projected lake water volume distribution. The DEM accuracy could introduce a small uncertainty due to the influence of elevation uncertainty, especially at the lake basin outlet (Supplementary Fig. 11). However, our analysis showed only a small discrepancy in the projected lake volume change from NASADEM compared with the high-accuracy AW3D30 DEM⁶⁶. The time series of future volume changes in both are in good agreement ($R > 0.98$). Furthermore, the DEM-derived lake volume trends showed small errors ($R^2 = 0.97$ and bias = -5%) compared with the altimetry-derived trends for 18 Tibetan lakes⁸. It is important to emphasize that our projections were highly dependent on the climate projection used under the CMIP SSP scenarios. However, due to differences in the representation of physical processes, parameterization schemes and the ability to simulate climate systems across climate models, there were discrepancies

in projections under the same scenario. To reduce the uncertainty in future projections, the average of multiple models was used.

A direct estimate of the groundwater contribution to lake change is limited by the lack of groundwater observations. Basin-wide groundwater change in the inner TP, estimated from decomposed terrestrial water storage based on GRACE observations, suggested a notable, but smaller magnitude, water mass gain relative to lake change⁷. Our model accounts for groundwater contribution indirectly, with $P - ET + G$ representing the combined contributions of surface runoff, soil water and groundwater without distinguishing their individual effects on lakes. In addition, our model did not include the influence of permafrost on future lake changes due to the current limited understanding of freeze-thaw and hydrothermal processes of permafrost on the TP⁶⁷, which could also introduce uncertainties in future lake modelling. However, we consider this effect to be limited due to the small contribution of ground ice meltwater to the historical increase in lake water storage⁶⁸.

## 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.

## References

47. Crétaux, J. F. et al. Lake volume monitoring from space. *Surv. Geophys.* **37**, 269–305 (2016).

48. Crétaux, J. F. et al. SOLS: a lake database to monitor in the near real time water level and storage variations from remote sensing data. *Adv. Space Res.* **47**, 1497–1507 (2011).

49. Zhao, R. et al. Annual 30-m big lake maps of the Tibetan Plateau in 1991-2018. *Sci. Data* **9**, 164 (2022).

50. Li, X. et al. High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000–2017 using multiple altimetric missions and Landsat-derived lake shoreline positions. *Earth Syst. Sci. Data* **11**, 1603–1627 (2019).

51. Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. *Earth Syst. Sci. Data* **13**, 4349–4383 (2021).

52. Wu, X., Su, J., Ren, W., Lü, H. & Yuan, F. Statistical comparison and hydrological utility evaluation of ERA5-Land and IMERG precipitation products on the Tibetan Plateau. *J. Hydrol.* **620**, 129384 (2023).

53. Jiang, Y. et al. TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1/30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations. *Earth Syst. Sci. Data* **15**, 621–638 (2023).

54. He, J. et al. The first high-resolution meteorological forcing dataset for land process studies over China. *Sci. Data* **7**, 25 (2020).
55. Wang, B., Ma, Y., Su, Z., Wang, Y. & Ma, W. Quantifying the evaporation amounts of 75 high-elevation large dimictic lakes on the Tibetan Plateau. *Sci. Adv.* **6**, eaay8558 (2020).

56. La, Z. *The Response of Lake Evaporation to Climate Change and Its Impact on Lake Water Balance Change for Large Lakes in the Tibetan Plateau* (Univ. Chinese Academy of Sciences, 2017).

57. Grossman, R. L. et al. Reservoir evaporation in the western United States: current science, challenges, and future needs. *Bull. Am. Meteorol. Soc.* **99**, 167–187 (2018).

58. Penman, H. L. Natural evaporation from open water, bare soil and grass. *Proc. R. Soc. Lond. A* **193**, 120–145 (1948).

59. Zhao, G., Li, Y., Zhou, L. & Gao, H. Evaporative water loss of 1.42 million global lakes. *Nat. Commun.* **13**, 3686 (2022).

60. Zhou, J. et al. Exploring the water storage changes in the largest lake (Selin Co) over the Tibetan Plateau during 2003-2012 from a basin-wide hydrological modeling. *Water Resour. Res.* **51**, 8060–8086 (2015).

61. Zhang, Y., Ohata, T., Ersi, K. & Tandong, Y. Observation and estimation of evaporation from the ground surface of the cryosphere in eastern Asia. *Hydrol. Process.* **17**, 1135–1147 (2003).

62. Zhou, J. et al. Quantifying the major drivers for the expanding lakes in the interior Tibetan Plateau. *Sci. Bull.* **67**, 474–478 (2021).

63. Liu, K. et al. Ongoing drainage reorganization driven by rapid lake growths on the Tibetan Plateau. *Geophys. Res. Lett.* **48**, e2021GL095795 (2021).

64. Li, H., Zhao, J., Yan, B., Yue, L. & Wang, L. Global DEMs vary from one to another: an evaluation of newly released Copernicus, NASA and AW3D30 DEM on selected terrains of China using ICESat-2 altimetry data. *Int. J. Digit. Earth* **15**, 1149–1168 (2022).

65. Uuemaa, E., Ahi, S., Montibeller, B., Muru, M. & Kmoch, A. Vertical accuracy of freely available global digital elevation models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM). *Remote Sens.* **12**, 3482 (2020).

66. Liu, K. et al. Global open-access DEM performances in Earth's most rugged region High Mountain Asia: a multi-level assessment. *Geomorphology* **338**, 16–26 (2019).

67. Ni, J. et al. Simulation of the present and future projection of permafrost on the Qinghai-Tibet Plateau with statistical and machine learning models. *J. Geophys. Res. Atmos.* **126**, e2020JD033402 (2021).

68. Wang, L. et al. Contribution of ground ice melting to the expansion of Selin Co (lake) on the Tibetan Plateau. *Cryosphere* **16**, 2745–2767 (2022).

69. Xu, F. et al. Future lake changes on the Tibetan Plateau and corresponding social and ecological impacts. *Figshare* https://doi.org/10.6084/m9 figshare.24873747 (2024).

70. Li, Z., Sun, Y., Li, T., Ding, Y. & Hu, T. Future changes in East Asian summer monsoon circulation and precipitation under 1.5 to 5°C of warming. *Earth Future J.* **7**, 1391–1406 (2019).

71. Katzenberger, A., Schewe, J., Pongratz, J. & Levermann, A. Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models. *Earth Syst. Dynam.* **12**, 367–386 (2021).

72. Li, Y. et al. Westerly jet stream controlled climate change mode since the Last Glacial Maximum in the northern Qinghai-Tibet Plateau. *Earth Planet. Sci. Lett.* **549**, 116529 (2020).

73. Randolph Glacier Inventory—a dataset of global glacier outlines, version 6.0. NSIDC https://doi.org/10.7265/4m1f-gd79 (2017).

## 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).

## Author contributions

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.

## Competing interests

The authors declare no competing interests.

## Additional information

*Extended data* is available for this paper at
https://doi.org/10.1038/s41561-024-01446-w.

**Supplementary information** The online version contains supplementary material available at
https://doi.org/10.1038/s41561-024-01446-w.

**Correspondence and requests for materials** should be addressed to
Guoqing Zhang.

**Peer review information** *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.

**Reprints and permissions information** is available at
www.nature.com/reprints.
<figure><img src="image_8.png" /></figure>

**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 future⁷⁰⁻⁷². 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.
<figure><img src="image_9.png" /><figcaption>**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</figcaption></figure>

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.
<figure><img src="image_10.png" /></figure>

**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.
<figure><img src="image_11.png" /><figcaption>**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. b–h, The temporal evolution of the area of lakes</figcaption></figure>

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.
<figure><img src="image_12.png" /></figure>

**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.
<figure><img src="image_13.png" /><figcaption>**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</figcaption></figure>

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.
<figure><img src="image_14.png" /></figure>

**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.
<figure><img src="image_15.png" /><figcaption>**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.</figcaption></figure>
<figure><img src="image_16.png" /></figure>

**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 ($R^2$), which indicates the ability of the model to explain historical changes in water volume. $R^2 \ge 0.6$ indicates good model performance. b, The number of all lakes and lakes with $R^2 \ge 0.6$ 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 $R^2 \ge 0.6$ across different lake sizes. The black number represents the corresponding lake water volume change. d, Number and water volume change (%) of lakes with $R^2 \ge 0.6–0.9$, and the black number indicates the corresponding cumulative number and water volume change.
<figure><img src="image_17.png" /><figcaption>**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.</figcaption></figure>