in

Emergent constraints on the hydrological impacts of land use and land cover change


Abstract

Land use and land cover changes have substantial effects on the terrestrial water cycle, but their sign and magnitude remain elusive at large scales. State-of-the-art Earth system models disagree on how these changes affect terrestrial evapotranspiration. Here we use the observation-based transpiration-specific Bowen ratio to correct modelled evapotranspiration changes induced by land use and land cover changes globally and regionally within a hierarchical emergent constraint framework. We show that the constraint reverses the sign of the original model estimates at the global scale and over Central and South America, and narrows the inter-model spread. The misrepresentation of transpiration-specific Bowen ratio and its variations across plant functional types in models is the main source of this bias. Applying an analogous constraint framework to a future afforestation scenario, the constrained simulations project stronger evapotranspiration enhancements and weaker decreases in terrestrial water availability compared to the original simulations, particularly in tropics and subtropics.

Similar content being viewed by others

Large biases in the frequency of water limitation across Earth system models

Neglecting land–atmosphere feedbacks overestimates climate-driven increases in evapotranspiration

Global water availability boosted by vegetation-driven changes in atmospheric moisture transport

Data availability

All datasets used in this study are publicly available as referenced in “Methods”. Source data are provided with this paper.

Code availability

The custom MATLAB (R2024a) codes written to read and analyze data and generate figures are publicly available at https://doi.org/10.5281/zenodo.17020036.

References

  1. Luyssaert, S. et al. Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat. Clim. Change 4, 389–393 (2014).

    Google Scholar 

  2. Winkler, K., Fuchs, R., Rounsevell, M. & Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 12, 2501 (2021).

    Google Scholar 

  3. Piao, S. et al. Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. Proc. Natl. Acad. Sci. USA 104, 15242–15247 (2007).

    Google Scholar 

  4. Sterling, S., Ducharne, A. & Polcher, J. The impact of global land-cover change on the terrestrial water cycle. Nat. Clim. Change 3, 385–390 (2013).

    Google Scholar 

  5. Ellison, D. et al. Trees, forests and water: cool insights for a hot world. Glob. Environ. Change 43, 51–61 (2017).

    Google Scholar 

  6. Ellison, D., Pokorný, J. & Wild, M. Even cooler insights: on the power of forests to (water the Earth and) cool the planet. Glob. Change Biol. 30, e17195 (2024).

    Google Scholar 

  7. Wang, Y. et al. Soil moisture decline in China’s monsoon loess critical zone: more a result of land-use conversion than climate change. Proc. Natl. Acad. Sci. USA 121, e2322127121 (2024).

    Google Scholar 

  8. Yang, Y. et al. Evapotranspiration on a greening Earth. Nat. Rev. Earth Environ. 4, 626–641 (2023).

    Google Scholar 

  9. Tang, T., Ge, J., Cao, J. & Shi, H. Land water availability altered by historical land use and land cover change. npj Clim. Atmos. Sci. 8, 230 (2025).

    Google Scholar 

  10. Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Change 8, 539–543 (2018).

    Google Scholar 

  11. Shi, H. et al. Terrestrial biodiversity threatened by increasing global aridity velocity under high-level warming. Proc. Natl. Acad. Sci. USA 118, e2015552118 (2021).

    Google Scholar 

  12. Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).

    Google Scholar 

  13. Chen, Z., Wang, W., Forzieri, G. & Cescatti, A. Transition from positive to negative indirect CO2 effects on the vegetation carbon uptake. Nat. Commun. 15, 1500 (2024).

    Google Scholar 

  14. Schewe, J. et al. Multimodel assessment of water scarcity under climate change. Proc. Natl. Acad. Sci. USA 111, 3245–3250 (2014).

    Google Scholar 

  15. Grassi, G. et al. Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Change 8, 914–920 (2018).

    Google Scholar 

  16. Xu, H., Yue, C., Zhang, Y., Liu, D. & Piao, S. Forestation at the right time with the right species can generate persistent carbon benefits in China. Proc. Natl. Acad. Sci. USA 120, e2304988120 (2023).

    Google Scholar 

  17. Ellison, D., Futter, M. & Bishop, K. On the forest cover–water yield debate: from demand- to supply-side thinking. Glob. Change Biol. 18, 797–1196 (2012).

    Google Scholar 

  18. Hoek van Dijke, A. et al. Shifts in regional water availability due to global tree restoration. Nat. Geosci. 15, 363–368 (2022).

    Google Scholar 

  19. Zhou, G. et al. Global pattern for the effect of climate and land cover on water yield. Nat. Commun. 6, 5918 (2015).

    Google Scholar 

  20. Feng, X. et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Change 6, 1019–1022 (2016).

    Google Scholar 

  21. Ceballos-Barbancho, A., Morán-Tejeda, E., Luengo-Ugidos, M. & Llorente-Pinto, J. Water resources and environmental change in a Mediterranean environment: the south-west sector of the Duero river basin (Spain). J. Hydrol. 351, 126–138 (2008).

    Google Scholar 

  22. Zhou, G. et al. Forest recovery and river discharge at the regional scale of Guangdong Province, China. Water Resour. Res. 46, W09503 (2010).

    Google Scholar 

  23. Zhang, B., Tian, L., Yang, Y. & He, X. Revegetation does not decrease water yield in the Loess Plateau of China. Geophys. Res. Lett. 49, e2022GL098025 (2022).

    Google Scholar 

  24. Brown, A., Zhang, L., McMahon, T., Western, A. & Vertessy, R. A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation. J. Hydrol. 310, 26–61 (2005).

    Google Scholar 

  25. Zhang, M. et al. A global review on hydrological responses to forest change across multiple spatial scales: importance of scale, climate, forest type and hydrological regime. J. Hydrol. 546, 44–59 (2017).

    Google Scholar 

  26. Buechel, M., Slater, L. & Dadson, S. Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios. Commun. Earth. Environ. 3, 6 (2022).

    Google Scholar 

  27. Wang, D. & Zeng, Z. Urgent need to improve modelled sensitivity of evaporation to vegetation change. Nat. Water 2, 211–214 (2024).

    Google Scholar 

  28. Rodell, M. & Reager, J. Water cycle science enabled by the GRACE and GRACE-FO satellite missions. Nat. Water 1, 47–59 (2023).

    Google Scholar 

  29. Chen, L. & Dirmeyer, P. Reconciling the disagreement between observed and simulated temperature responses to deforestation. Nat. Commun. 11, 202 (2020).

    Google Scholar 

  30. Alkama, R. et al. Vegetation-based climate mitigation in a warmer and greener World. Nat. Commun. 13, 606 (2022).

    Google Scholar 

  31. Bonan, G. & Doney, S. Climate, ecosystems, and planetary futures: the challenge to predict life in Earth system models. Science 359, eaam8328 (2018).

    Google Scholar 

  32. Zeng, Z. et al. Impact of Earth greening on the terrestrial water cycle. J. Clim. 31, 2633–2650 (2018).

    Google Scholar 

  33. Duveiller, G. et al. Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations. Earth Syst. Sci. Data 10, 1265–1279 (2018).

    Google Scholar 

  34. Forzieri, G. et al. Evaluating the interplay between biophysical processes and leaf area changes in land surface models. J. Adv. Model. Earth Syst. 10, 1102–1126 (2018).

    Google Scholar 

  35. Gentine, P. et al. Coupling between the terrestrial carbon and water cycles—a review. Environ. Res. Lett. 14, 083003 (2019).

    Google Scholar 

  36. Green, J. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).

    Google Scholar 

  37. Bowman, K., Cressie, N., Qu, X. & Hall, A. hierarchical statistical framework for emergent constraints: application to snow-albedo feedback. Geophys. Res. Lett. 45, 13050–13059 (2018).

    Google Scholar 

  38. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Google Scholar 

  39. Iturbide, M. et al. An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets. Earth Syst. Sci. Data 12, 2959–2970 (2020).

    Google Scholar 

  40. Lawrence, D. et al. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci. Model Dev. 9, 2973–2998 (2016).

    Google Scholar 

  41. Hurtt, G. et al. Harmonization of global land use change and management for the period 850-2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).

    Google Scholar 

  42. Danabasoglu, G. et al. The Community Earth System Model Version 2 (CESM2). J. Adv. Model. Earth Syst. 12, e2019MS001916 (2020).

    Google Scholar 

  43. Sellar, A. et al. Implementation of U.K. Earth system models for CMIP6. J. Adv. Model. Earth Syst. 12, e2019MS001946 (2020).

    Google Scholar 

  44. Wei, Z. et al. Revisiting the contribution of transpiration to global terrestrial evapotranspiration. Geophys. Res. Lett. 44, 2792–2801 (2017).

    Google Scholar 

  45. Miralles, D. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453–469 (2011).

    Google Scholar 

  46. Mianabadi, A., Coenders-Gerrits, M., Shirazi, P., Ghahraman, B. & Alizadeh, A. A global Budyko model to partition evaporation into interception and transpiration. Hydrol. Earth Syst. Sci. 23, 4983–5000 (2019).

    Google Scholar 

  47. Zhang, X. et al. Greening-induced increase in evapotranspiration over Eurasia offset by CO2-induced vegetational stomatal closure. Environ. Res. Lett. 16, 124008 (2021).

    Google Scholar 

  48. Compo, G. et al. The twentieth century reanalysis project. Q. J. R. Meteorol. Soc. 137, 1–28 (2011).

    Google Scholar 

  49. Muñoz Sabater, J. ERA5-Land monthly averaged data from 1950 to present [Data set]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.68d2bb30 (2019).

  50. Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9, 269–278 (2019).

    Google Scholar 

  51. Boisier, J., de Noblet-Ducoudré, N. & Ciais, P. Historical land-use-induced evapotranspiration changes estimated from present-day observations and reconstructed land-cover maps. Hydrol. Earth Syst. Sci. 18, 3571–3590 (2014).

    Google Scholar 

  52. Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. 9, 679 (2018).

    Google Scholar 

  53. Curtis, P. et al. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).

    Google Scholar 

  54. Nelson, J. et al. Ecosystem transpiration and evaporation: insights from three water flux partitioning methods across FLUXNET sites. Glob. Change Biol. 26, 6916–6930 (2020).

    Google Scholar 

  55. Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).

    Google Scholar 

  56. Cox, P. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).

    Google Scholar 

  57. Winkler, A., Myneni, R., Alexandrov, G. & Brovkin, V. Earth system models underestimate carbon fixation by plants in the high latitudes. Nat. Commun. 10, 885 (2019).

    Google Scholar 

  58. Nosetto, M., Jobbágy, E., Brizuela, A. & Jackson, R. The hydrologic consequences of land cover change in central Argentina. Agric. Ecosyst. Environ. 154, 2–11 (2012).

    Google Scholar 

  59. Doelman, J. et al. Exploring SSP land-use dynamics using the IMAGE model: regional and gridded scenarios of land-use change and land-based climate change mitigation. Glob. Environ. Change 48, 119–135 (2018).

    Google Scholar 

  60. Ning, T. et al. Effects of forest cover change on catchment evapotranspiration variation in China. Hydrol. Process. 34, 2219–2228 (2020).

    Google Scholar 

  61. Teuling, A. et al. Climate change, reforestation/afforestation, and urbanization impacts on evapotranspiration and streamflow in Europe. Hydrol. Earth Syst. Sci. 23, 3631–3652 (2019).

    Google Scholar 

  62. Ukkola, A. et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat. Clim. Change 6, 75–78 (2016).

    Google Scholar 

  63. O’Conner, J. et al. Forests buffer against variations in precipitation. Glob. Change Biol. 27, 4686–4696 (2021).

    Google Scholar 

  64. Humphrey, V. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021).

    Google Scholar 

  65. McKay, D. et al. Exceeding 1.5 °C global warming could trigger multiple climate tipping points. Science 377, eabn7950 (2022).

    Google Scholar 

  66. Zeng, Z. et al. Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nat. Clim. Change 7, 432–436 (2017).

    Google Scholar 

  67. Lian, X. et al. Partitioning global land evapotranspiration using CMIP5 models constrained by observations. Nat. Clim. Change 8, 640–646 (2018).

    Google Scholar 

  68. Forzieri, G. et al. Increased control of vegetation on global terrestrial energy fluxes. Nat. Clim. Change 10, 356–362 (2020).

    Google Scholar 

  69. Chen, Z., Wang, W., Cescatti, A. & Forzieri, G. Climate-driven vegetation greening further reduces water availability in drylands. Glob. Change Biol. 29, 1628–1647 (2023).

    Google Scholar 

  70. Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 6273 (2016).

    Google Scholar 

  71. Schlesinger, W. & Jasechko, S. Transpiration in the global water cycle. Agric. For. Meteorol. 189-190, 115–117 (2014).

    Google Scholar 

  72. Yuan, K. et al. Deforestation reshapes land-surface energy-flux partitioning. Environ. Res. Lett. 16, 024014 (2021).

    Google Scholar 

  73. McDermid, S., Mearns, L. & Ruane, A. Representing agriculture in Earth system models: approaches and priorities for development. J. Adv. Model. Earth Syst. 9, 2230–2265 (2017).

    Google Scholar 

  74. Chen, L., Dirmeyer, P., Guo, Z. & Schultz, N. Pairing FLUXNET sites to validate model representations of land-use/land-cover change. Hydrol. Earth Syst. Sci. 22, 111–125 (2018).

    Google Scholar 

  75. Liu, H. et al. Nature-based framework for sustainable afforestation in global drylands under changing climate. Glob. Change Biol. 28, 2202–2220 (2022).

    Google Scholar 

  76. van der Ent, R., Savenije, H., Schaefli, B. & Steele-Dunne, S. Origin and fate of atmospheric moisture over continents. Water Resour. Res. 46, W09525 (2010).

    Google Scholar 

  77. Zan, B. et al. Spatiotemporal inequality in land water availability amplified by global tree restoration. Nat. Water 2, 863–874 (2024).

    Google Scholar 

  78. Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).

    Google Scholar 

  79. Fu, Z. et al. Atmospheric dryness reduces photosynthesis along a large range of soil water deficits. Nat. Commun. 13, 989 (2022).

    Google Scholar 

  80. IPCC. Climate Change 2021: The Physical Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2021).

  81. Friedlingstein, P. et al. Global carbon budget 2023. Earth Syst. Sci. Data 15, 5301–5369 (2023).

    Google Scholar 

  82. Doelman, J. et al. Afforestation for climate change mitigation: potentials, risks and trade-offs. Glob. Change Biol. 26, 1576–1591 (2020).

    Google Scholar 

  83. Windisch, M., Davin, E. & Seneviratne, S. Prioritizing forestation based on biogeochemical and local biogeophysical impacts. Nat. Clim. Change 11, 867–871 (2021).

    Google Scholar 

  84. Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).

    Google Scholar 

  85. Pan, S. et al. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. Hydrol. Earth Syst. Sci. 24, 1485–1509 (2020).

    Google Scholar 

  86. Collins, W. et al. AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6. Geosci. Model Dev. 10, 585–607 (2017).

    Google Scholar 

  87. Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).

    Google Scholar 

  88. Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).

    Google Scholar 

  89. Jung, M. et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data 6, 74 (2019).

    Google Scholar 

  90. Zhou, S., Yu, B., Zhang, Y., Huang, Y. & Wang, G. Partitioning evapotranspiration based on the concept of underlying water use efficiency: ET partitioning. Water Resour. Res. 52, 1160–1175 (2016).

    Google Scholar 

  91. Perez-Priego, O. et al. Partitioning eddy covariance water flux components using physiological and micrometeorological approaches. J. Geophys. Res. Biogeosci. 123, 3353–3370 (2018).

    Google Scholar 

  92. Nelson, J. et al. Coupling water and carbon fluxes to constrain estimates of transpiration: the TEA algorithm. J. Geophys. Res. Biogeosci. 123, 3617–2632 (2018).

    Google Scholar 

  93. Boysen, L. et al. Global climate response to idealized deforestation in CMIP6 models. Biogeosciences 17, 5615–5638 (2020).

    Google Scholar 

  94. Luo, X. et al. The biophysical impacts of deforestation on precipitation: results from the CMIP6 model intercomparison. J. Clim. 35, 3293–3311 (2022).

    Google Scholar 

  95. Li, Y. et al. Deforestation-induced climate change reduces carbon storage in remaining tropical forests. Nat. Commun. 13, 1964 (2022).

    Google Scholar 

  96. Eyring, V. et al. Taking climate model evaluation to the next level. Nat. Clim. Change 9, 102–110 (2019).

    Google Scholar 

  97. Winkler, A., Myneni, R. & Brovkin, V. Investigating the applicability of emergent constraints. Earth Syst. Dynam. 10, 501–523 (2019).

    Google Scholar 

  98. Keenan, T. et al. A constraint on historic growth in global photosynthesis due to rising CO2. Nat. Clim. Change 13, 1376–1381 (2023).

    Google Scholar 

  99. Clark, M. et al. Improving the representation of hydrologic processes in Earth system models. Water Resour. Res. 51, 5929–5956 (2015).

    Google Scholar 

  100. McDowell, N. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, 964 (2020).

    Google Scholar 

  101. Chen, Z. et al. Observationally constrained projection of Afro-Asian monsoon precipitation. Nat. Commun. 13, 2552 (2022).

    Google Scholar 

  102. Shiogama, H., Watanabe, M., Kim, H. & Hirota, N. Emergent constraints on future precipitation changes. Nature 602, 612–616 (2022).

    Google Scholar 

  103. Dai, P., Nie, J., Yu, Y. & Wu, R. Constraints on regional projections of mean and extreme precipitation under warming. Proc. Natl. Acad. Sci. USA 121, e2312400121 (2024).

    Google Scholar 

  104. Chen, X. et al. Emergent constraints on future projections of the western North Pacific Subtropical High. Nat. Commun. 11, 2802 (2020).

    Google Scholar 

  105. Wang, T. et al. Atmospheric dynamic constraints on Tibetan Plateau freshwater under Paris climate targets. Nat. Clim. Change 11, 219–225 (2021).

    Google Scholar 

  106. Swann, A., Fung, I. & Chiang, J. Mid-latitude afforestation shifts general circulation and tropical precipitation. Proc. Natl. Acad. Sci. USA 109, 712–716 (2012).

    Google Scholar 

  107. Zhou, S. et al. Soil moisture-atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Change 11, 38–44 (2021).

    Google Scholar 

  108. Fisher, J., Tu, K. & Baldocchi, D. Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ. 112, 901–919 (2008).

    Google Scholar 

  109. Cheng, L. et al. Recent increases in terrestrial carbon uptake at little cost to the water cycle. Nat. Commun. 8, 110 (2017).

    Google Scholar 

  110. Wang, L., Good, S. & Caylor, K. Global synthesis of vegetation control on evapotranspiration partitioning. Geophys. Res. Lett. 41, 6753–6757 (2014).

    Google Scholar 

  111. Pitman, A. et al. Importance of background climate in determining impact of land-cover change on regional climate. Nat. Clim. Change 1, 472–475 (2011).

    Google Scholar 

  112. Zeng, Z. et al. Deforestation-induced warming over tropical mountain regions regulated by elevation. Nat. Geosci. 14, 23–29 (2021).

    Google Scholar 

Download references

Acknowledgements

This work has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Actions (grant agreement No. 101152010, TYPIC). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This is ClimTip contribution #89; the ClimTip project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101137601. R.X. was supported by the Fundamental Research Funds for the Central Universities (grant agreement No. B240207078) and China Scholarship Council (CSC) Grant (grant agreement No. 202406710180). Furthermore, we thank Dr. Zhongwang Wei (Sun Yat-Sen University) for providing the observational ET dataset derived by LAI-based upscaling. Certain Esri ® ArcGIS ® Imagery in this work are owned by Esri and/or its data contributors and are used herein with permission. Copyright © 2026 Esri and its data contributors. All rights reserved.

Author information

Authors and Affiliations

Authors

Contributions

Z.C. conceived and designed the research; Z.C. and R.X. collected and processed raw data, and implemented the data analysis; A.C. and G.F. contributed analysis ideas; Z.C. interpreted the results and drafted the initial manuscript; A.C. and G.F. provided suggestions and further improved writing. All authors approved the final version of this manuscript.

Corresponding author

Correspondence to
Zefeng Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks David Ellison and Alexander Winkler for their contribution to the peer review of this work. A peer review file is available.

Additional information

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

Supplementary information

Supplementary Information

Description of Additional Supplementary Files

Supplementary Data 1

Supplementary Data 2

Supplementary Data 3

Supplementary Data 4

Supplementary Data 5

Transparent Peer Review file

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Chen, Z., Cescatti, A., Xing, R. et al. Emergent constraints on the hydrological impacts of land use and land cover change.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-69883-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41467-026-69883-2


Source: Ecology - nature.com

Climate-driven Avicennia germinans expansion reduces marsh edge erosion in coastal Louisiana (USA)

The role of habitat mosaics on biological communities at hydrothermal vents and their periphery

Back to Top