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Climate benefits of lake nutrient management in China

Nature Geoscience (2026)Cite this article

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Abstract

Anthropogenic nutrient discharges drive lake eutrophication and can consequently enhance greenhouse gas (GHG) emissions, amplifying climate change. Managing nutrient levels can substantially reduce GHG emissions from lakes, yet the climate benefits and the cost-effectiveness of managing diverse nutrient sources remain insufficiently quantified. To address this limitation, we develop a machine learning-based integrated assessment framework that synthesizes multisource datasets across China, encompassing lake GHG fluxes, trophic status, morphometric parameters, temperature, hydrological conditions and basin-scale anthropogenic nutrient discharges. Here we show that, compared with a high-discharge trajectory, a strategically managed reduction in anthropogenic nutrient loads could lower cumulative GHG emissions by 251–307 TgCO2 equivalents between 2021 and 2100 under a moderate warming scenario, which is equivalent to avoiding global climate damages valued at US$32–50.1 billion (2020 US$, discounted at 1.5%). A cost–benefit analysis indicates that controlling nutrient discharges from industrial sources is the most cost-effective. This study highlights the necessity of integrating lake nutrient management into global climate strategies and provides a science-based tool for policymakers to optimize both eutrophication control and GHG reduction.

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Fig. 1: Geographical patterns of GHGs, CO2, CH4 and N2O emission rates (mg m−2 per day) in Chinese lakes in 2020 derived from the RF model.
The alternative text for this image may have been generated using AI.
Fig. 2: Changes in GHG rates of Chinese lakes under five anthropogenic nutrient-discharge scenarios during 2020–2100 under the SSP2‑4.5 scenario.
The alternative text for this image may have been generated using AI.
Fig. 3: Estimated social costs of lake GHG emissions under five anthropogenic nutrient-discharge scenarios from 2020 to 2100 under the SSP2‑4.5 scenario, with a discount rate of 1.5%.
The alternative text for this image may have been generated using AI.
Fig. 4: Integrated mitigation framework for reducing anthropogenic nutrient discharges and its impacts on lake eutrophication and GHG emissions.
The alternative text for this image may have been generated using AI.

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Data availability

The greenhouse gas flux and nutrient data compiled for analysis are available via figshare at https://doi.org/10.6084/m9.figshare.31742041 (ref. 75). Water depth and hydrological data are sourced from HydroLAKES (www.hydrosheds.org/products/hydrolakes). Lake area data are available at https://global-surface-water.appspot.com. Temperature and trophic status datasets are available from the National Earth System Science Data Center (www.geodata.cn), the Qinghai-Tibet Plateau Science Data Center (www.data.tpdc.ac.cn) and the National Environmental Monitoring Center of China (www.cnemc.cn). The CMIP6 projection data are available at https://aims2.llnl.gov/search/cmip6. The Social Cost data are available at https://www.canada.ca/en/environment-climate-change/services/climate-change/science-research-data/social-cost-ghg (ref. 66). The anthropogenic nutrient discharge dataset data are available via figshare at https://doi.org/10.6084/m9.figshare.c.6787500.v1 (ref. 76). Source data are provided with this paper.

Code availability

All machine learning models, including k-nearest neighbours, support vector regression, random forest, generalized linear model, XGBoost and LightGBM, were implemented using standard, publicly available packages in Python: Scikit-learn (https://doi.org/10.5281/zenodo.14627164), XGBoost (https://github.com/dmlc/xgboost) and LightGBM (https://github.com/microsoft/LightGBM).

References

  1. Climate Change 2023: Synthesis Report (Intergovernmental Panel on Climate Change, 2023).

  2. Solomon, S. et al. Persistence of climate changes due to a range of greenhouse gases. Proc. Natl Acad. Sci. USA 107, 18354–18359 (2010).

    Article  CAS  Google Scholar 

  3. DelSontro, T., Beaulieu, J. J. & Downing, J. A. Greenhouse gas emissions from lakes and impoundments: upscaling in the face of global change. Limnol. Oceanogr. Lett. 3, 64–75 (2019).

    Article  Google Scholar 

  4. Li, S. et al. Large greenhouse gases emissions from China’s lakes and reservoirs. Water Res. 147, 13–24 (2018).

    Article  CAS  Google Scholar 

  5. Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).

    Article  Google Scholar 

  6. Ho, J. C., Michalak, A. M. & Pahlevan, N. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574, 667–670 (2019).

    Article  CAS  Google Scholar 

  7. Conley, D. J. et al. Controlling eutrophication: nitrogen and phosphorus. Science 323, 1014–1015 (2009).

    Article  CAS  Google Scholar 

  8. Kumar, A., Mishra, S., Bakshi, S., Upadhyay, P. & Thakur, T. K. Response of eutrophication and water quality drivers on greenhouse gas emissions in lakes of China: a critical analysis. Ecohydrology 16, e2483 (2022).

    Article  Google Scholar 

  9. Li, Y. et al. The role of freshwater eutrophication in greenhouse gas emissions: a review. Sci. Total Environ. 768, 144582 (2021).

    Article  CAS  Google Scholar 

  10. Xiao, Q. et al. Eutrophication and temperature drive large variability in carbon dioxide from China’s Lake Taihu. Limnol. Oceanogr. 67, 379–391 (2021).

    Article  Google Scholar 

  11. Peñuelas, J., Sardans, J., Rivas-ubach, A. & Janssens, I. A. The human-induced imbalance between C, N and P in Earth’s life system. Global Change Biol. 18, 3–6 (2011).

    Article  Google Scholar 

  12. Carpenter, S. R. Phosphorus control is critical to mitigating eutrophication. Proc. Natl Acad. Sci. USA 105, 11039–11040 (2008).

    Article  CAS  Google Scholar 

  13. Yang, Q., Chen, S., Li, Y., Liu, B. & Ran, L. Carbon emissions from Chinese inland waters: current progress and future challenges. J. Geophys. Res. Biogeosci. 129, e2023JG007675 (2024).

    Article  Google Scholar 

  14. Liu, S. et al. Human activities reshape greenhouse gas emissions from inland waters. Global Change Biol. 31, e70139 (2025).

    Article  CAS  Google Scholar 

  15. Yu, W. et al. Human-induced N:P imbalances will aggravate GHG emissions from lakes and reservoirs under persisting eutrophication. Water Res. 276, 123240 (2025).

    Article  CAS  Google Scholar 

  16. Sinha, E., Michalak, A. M. & Balaji, V. Eutrophication will increase during the 21st century as a result of precipitation changes. Science 357, 405–408 (2017).

    Article  CAS  Google Scholar 

  17. West, W. E., Creamer, K. P. & Jones, S. E. Productivity and depth regulate lake contributions to atmospheric methane. Limnol. Oceanogr. 61, s51–s61 (2015).

    Google Scholar 

  18. Li, M. et al. The significant contribution of lake depth in regulating global lake diffusive methane emissions. Water Res. 172, 115465 (2020).

    Article  CAS  Google Scholar 

  19. Bastviken, D., Cole, J., Pace, M. & Tranvik, L. Methane emissions from lakes: dependence of lake characteristics, two regional assessments, and a global estimate. Global Biogeochem. Cycles 18, GB4009 (2004).

    Article  Google Scholar 

  20. Bastviken, D., Cole, J. J., Pace, M. L. & Van de Bogert, M. C. Fates of methane from different lake habitats: connecting whole-lake budgets and CH4 emissions. J. Geophys. Res. Biogeosci. 113, G02024 (2008).

    Article  Google Scholar 

  21. Beaulieu, J. J., DelSontro, T. & Downing, J. A. Eutrophication will increase methane emissions from lakes and impoundments during the 21st century. Nat. Commun. 10, 1375 (2019).

    Article  Google Scholar 

  22. 2030 Agenda for Sustainable Development (United Nations Development Programme, 2015).

  23. Tong, Y. et al. Improvement in municipal wastewater treatment alters lake nitrogen to phosphorus ratios in populated regions. Proc. Natl Acad. Sci. USA 117, 11566–11572 (2020).

    Article  CAS  Google Scholar 

  24. The United Nations World Water Development Report 2019: Leaving No One behind (United Nations, 2019).

  25. Ministry of Housing and Urban–Rural Development China. Statistical Yearbook of Urban Construction in China (China Statistics Press, 2018).

  26. Huang, J. et al. Characterizing the river water quality in China: recent progress and on-going challenges. Water Res. 201, 117309 (2021).

    Article  CAS  Google Scholar 

  27. Tong, Y. et al. Establishment of season-specific nutrient thresholds and analyses of the effects of nutrient management in eutrophic lakes through statistical machine learning. J. Hydrol. 578, 124079 (2019).

    Article  CAS  Google Scholar 

  28. Wang, M. et al. Seasonal pattern of nutrient limitation in a eutrophic lake and quantitative analysis of the impacts from internal nutrient cycling. Environ. Sci. Technol. 53, 13675–13686 (2019).

    Article  CAS  Google Scholar 

  29. Downing, J. A., Polasky, S., Olmstead, S. M. & Newbold, S. C. Protecting local water quality has global benefits. Nat. Commun. 12, 2709 (2021).

    Article  CAS  Google Scholar 

  30. Zhang, H., Sun, H., Zhao, R., Tian, Y. & Meng, Y. High resolution spatiotemporal modeling of long term anthropogenic nutrient discharge in China. Sci. Data 11, 283 (2024).

    Article  Google Scholar 

  31. Tong, Y. et al. Decline in Chinese lake phosphorus concentration accompanied by shift in sources since 2006. Nat. Geosci. 10, 507–511 (2017).

    Article  CAS  Google Scholar 

  32. Bastviken, D. & Johnson, M. S. Future methane emissions from lakes and reservoirs. Nat. Water 3, 1397–1410 (2025).

    Article  CAS  Google Scholar 

  33. Carlson, R. E. A trophic state index for lakes1. Limnol. Oceanogr. 22, 361–369 (1977).

    Article  CAS  Google Scholar 

  34. Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).

    Article  CAS  Google Scholar 

  35. Korver, M. C., Lehner, B., Cardille, J. A. & Carrea, L. Surface water temperature observations and ice phenology estimations for 1.4 million lakes globally. Remote Sens. Environ. 308, 114164 (2024).

    Article  Google Scholar 

  36. Gundlach, J. & Paul, I. The Social Cost of Greenhouse Gases: A Guide for State Officials (Institute for Policy Integrity, 2022).

  37. Yindong, T. et al. Lake warming intensifies the seasonal pattern of internal nutrient cycling in the eutrophic lake and potential impacts on algal blooms. Water Res. 188, 116570 (2021).

    Article  Google Scholar 

  38. Meerhoff, M. et al. Feedback between climate change and eutrophication: revisiting the allied attack concept and how to strike back. Inland Waters 12, 187–204 (2022).

    Article  Google Scholar 

  39. Scholz, M. J., Obenour, D. R., Morrison, E. S. & Elser, J. J. A critical eutrophication–climate change link. Nat. Sustain. 8, 222–223 (2025).

    Article  Google Scholar 

  40. Jenny, J.-P. et al. Scientists’ warning to humanity: rapid degradation of the world’s large lakes. J. Great Lakes Res. 46, 686–702 (2020).

    Article  Google Scholar 

  41. Paerl, H. W., Otten, T. G. & Kudela, R. Mitigating the expansion of harmful algal blooms across the freshwater-to-marine continuum. Environ. Sci. Technol. 52, 5519–5529 (2018).

    Article  CAS  Google Scholar 

  42. Moss, B. et al. Allied attack: climate change and eutrophication. Inland Waters 1, 101–105 (2011).

    Article  Google Scholar 

  43. Data from the National Aquatic Resource Surveys. Environmental Protection Agency https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys (2026).

  44. Dai, M. et al. Persistent eutrophication and hypoxia in the coastal ocean. Camb. Prisms Coast. Futures 1, e19 (2023).

  45. Le, C. et al. Eutrophication of lake waters in China: cost, causes, and control. Environ. Manage. 45, 662–668 (2010).

    Article  CAS  Google Scholar 

  46. Feng, K. et al. Eutrophication induces functional homogenization and traits filtering in Chinese lacustrine fish communities. Sci. Total Environ. 857, 159651 (2023).

    Article  CAS  Google Scholar 

  47. Tong, Y. et al. Human activities altered water N:P ratios in the populated regions of China. Chemosphere 210, 1070–1081 (2018).

    Article  CAS  Google Scholar 

  48. Qin, B. et al. Polluted lake restoration to promote sustainability in the Yangtze River Basin, China. Natl Sci. Rev. 9, nwab207 (2022).

    Article  Google Scholar 

  49. The United Nations World Water Development Report 2018: Nature-Based Solutions for Water (United Nations, 2018).

  50. Zhou, X. et al. Review of global sanitation development. Environ. Int. 120, 246–261 (2018).

    Article  Google Scholar 

  51. Stackpoole, S. M., Stets, E. G. & Sprague, L. A. Variable impacts of contemporary versus legacy agricultural phosphorus on US river water quality. Proc. Natl Acad. Sci. USA 116, 20562–20567 (2019).

    Article  CAS  Google Scholar 

  52. Feng, J. et al. Optimization of fertilization combined with water-saving irrigation improves the water and nitrogen utilization efficiency of wheat and reduces nitrogen loss in the Nansi Lake Basin, China. J. Integr. Agr. 24, 4034–4047 (2025).

    Article  CAS  Google Scholar 

  53. Zhao, G. et al. Quantitative assessment sustainability of the river-irrigation district-lake system under global climate change. Ecol. Indic. 177, 113802 (2025).

    Article  CAS  Google Scholar 

  54. Chen, L. et al. Nitrogen removal in an ecological ditch receiving agricultural drainage in subtropical central China. Ecol. Eng. 82, 487–492 (2015).

  55. Massoud, M. A., Tarhini, A. & Nasr, J. A. Decentralized approaches to wastewater treatment and management: applicability in developing countries. J. Environ. Manage. 90, 652–659 (2009).

    Article  Google Scholar 

  56. Wald, C. The urine revolution: how recycling pee could help to save the world. Nature 602, 202–206 (2022).

    Article  CAS  Google Scholar 

  57. Wang, W. et al. Managing liquid digestate to support the sustainable biogas industry in China: maximizing biogas-linked agro-ecosystem balance. GCB Bioenergy 13, 880–892 (2021).

    Article  CAS  Google Scholar 

  58. Gan, T., Zhou, Z., Li, S. & Tu, Z. Carbon emission trading, technological progress, synergetic control of environmental pollution and carbon emissions in China. J. Clean. Prod. 442, 141059 (2024).

    Article  Google Scholar 

  59. Pan, J., Cross, J. L., Zou, X. & Zhang, B. To tax or to trade? A global review of carbon emissions reduction strategies. Energy Strateg. Rev. 55, 101508 (2024).

    Article  Google Scholar 

  60. Zhou, L., Li, L.-Z. & Huang, J.-K. The river chief system and agricultural non-point source water pollution control in China. J. Integr. Agr. 20, 1382–1395 (2021).

    Article  Google Scholar 

  61. Tong, Y. et al. Influence of social and environmental drivers on nutrient concentrations and ratios in lakes: a comparison between China and Europe. Water Res. 227, 119347 (2022).

    Article  CAS  Google Scholar 

  62. Deemer, B. R. et al. Greenhouse gas emissions from reservoir water surfaces: a new global synthesis. Bioscience 66, 949–964 (2016).

  63. 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).

    Article  CAS  Google Scholar 

  64. Ran, L. et al. Substantial decrease in CO2 emissions from Chinese inland waters due to global change. Nat. Commun. 12, 1730 (2021).

    Article  CAS  Google Scholar 

  65. O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Article  Google Scholar 

  66. Social cost of greenhouse gas emissions. Government of Canada https://www.canada.ca/en/environment-climate-change/services/climate-change/science-research-data/social-cost-ghg (2023).

  67. Peng, J. T., Zhu, X. D., Sun, X. & Song, X. W. Identifying external nutrient reduction requirements and potential in the hypereutrophic Lake Taihu Basin, China. Environ. Sci. Pollut. R. 25, 10014–10028 (2018).

    Article  CAS  Google Scholar 

  68. Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).

    Article  CAS  Google Scholar 

  69. Rocher-Ros, G. et al. Global methane emissions from rivers and streams. Nature 621, 530–535 (2023).

    Article  CAS  Google Scholar 

  70. Zhao, F. et al. New insights into eutrophication management: importance of temperature and water residence time. J. Environ. Sci. 111, 229–239 (2022).

    Article  CAS  Google Scholar 

  71. Sun, K., Jia, J., Wang, S. & Gao, Y. Real-time and dynamic estimation of CO2 emissions from China’s lakes and reservoirs. Innov. Geosci. 1, 100031 (2023).

    Article  CAS  Google Scholar 

  72. Neubauer, S. C. & Megonigal, J. P. Moving beyond global warming potentials to quantify the climatic role of ecosystems. Ecosystems 18, 1000–1013 (2015).

    Article  Google Scholar 

  73. Silverthorn, T. et al. The importance of ditches and canals in global inland water CO2 and N2O budgets. Global Change Biol. 31, e70079 (2025).

    Article  CAS  Google Scholar 

  74. Brandão, M., Kirschbaum, M. U. F. & Cowie, A. L. Evaluating metrics for quantifying the climate-change effects of land-based carbon fluxes. Int. J. Life Cycle Ass. 29, 328–343 (2023).

    Article  Google Scholar 

  75. Zhao, F. Greenhouse gas flux and nutrient data compiled for analysis in this study. figshare https://doi.org/10.6084/m9.figshare.31742041 (2026).

  76. Tian, Y. High-resolution spatiotemporal modeling of long-term anthropogenic nutrient discharge in China from 1980-2020. figshare https://doi.org/10.6084/m9.figshare.c.6787500.v1 (2024).

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant nos. U24A20640 and 42377399 to Y.T.); the Key Research and Development Project of Xizang Autonomous Region (grant nos. XZ202501ZY0091, XZ202502ZY0047, XZ202502JD0025 and XZ202502ZY0019 to Y.T.); the Scientific Research Innovation Capability Support Project for Young Faculty (award no. SRICSPYF-ZY2025153 to W.Z.); and the Basic Research Program of Jiangsu (award no. BK20250013 to W.Z.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Authors and Affiliations

  1. School of Environmental Science and Engineering, Tianjin University, Tianjin, China

    Feng Zhao, Qirui Wang, Zhao Huang, Jiawen Xie, Guangshuo Chai, Xiaoyu Cui & Yindong Tong

  2. State Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Hohai University, Nanjing, China

    Wei Zhi

  3. International Centre for Integrated Mountain Development, Kathmandu, Nepal

    Rongkun Liu

  4. School of Ecology and Environment, Tibet University, Lhasa, China

    Yindong Tong

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Contributions

F.Z. and Y.T. designed the research; Y.T. and W.Z. acquired the funding needed to complete the study; F.Z., Q.W., Z.H., J.X., G.C. and X.C. performed data collection and measurements; F.Z. and Q.W. conducted data processing and modelling; F.Z., Q.W. and Y.T. wrote the original paper in close discussion with W.Z. and R.L.

Corresponding author

Correspondence to Yindong Tong.

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Nature Geoscience thanks Sarian Kosten, Amit Kumar, Chi Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Comprehensive schematic representation of the integrated Nutrient-Greenhouse Gas-Cost Benefit Analysis (N-GHG-CBA) methodological framework.

This figure illustrates the key components of the framework, including characterizing watershed-scale anthropogenic nutrient loadings, predictive modeling of lake trophic status dynamics and greenhouse gas emissions using machine learning approaches, and systematic economic valuation of mitigation strategies through cost-effectiveness and climate damage assessment.

Extended Data Fig. 2 Comparative projections of lake greenhouse gas emission rates (CO2eq, CO2, CH4, N2O) in Chinese lakes under a High Nutrient Reduction Scenario across Four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5).

Total CO2‑equivalent emissions are calculated by converting CH4 and N2O fluxes to CO2 equivalents using the Sustained Global Warming Potential (SGWP) metric. The definition of the ‘High Nutrient Reduction Scenario’ is provided in the Methods section.

Source data

Extended Data Table 1 Model performances of three greenhouse gas emission rates and trophic status on the testing dataset
Full size table
Extended Data Table 2 The cost-effectiveness of greenhouse gas emission reductions achieved by cutting nutrient inputs from a specific source while keeping other discharge sources unchanged
Full size table

Supplementary information

Supplementary Information (download PDF )

Supplementary Text 1. Linkage between greenhouse gas emissions and nutrient enrichment. Supplementary Text 2. Mechanistic interpretation of partial dependence patterns. Supplementary Text 3. Limitations and future perspectives of this study. Supplementary Text 4. Methodological details. Supplementary Figs. 1–12 and Tables 1–5.

Supplementary Data (download XLSX )

This table contains raw data on GHGs, trophic status, SC-GHGs and GHG data source.

Source data

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Zhao, F., Wang, Q., Huang, Z. et al. Climate benefits of lake nutrient management in China. Nat. Geosci. (2026). https://doi.org/10.1038/s41561-026-01971-w

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