IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-35414-y.html
   My bibliography  Save this article

Recent global decline in rainfall interception loss due to altered rainfall regimes

Author

Listed:
  • Xu Lian

    (Columbia University)

  • Wenli Zhao

    (Columbia University)

  • Pierre Gentine

    (Columbia University
    Columbia University)

Abstract

Evaporative loss of interception (Ei) is the first process occurring during rainfall, yet its role in large-scale surface water balance has been largely underexplored. Here we show that Ei can be inferred from flux tower evapotranspiration measurements using physics-informed hybrid machine learning models built under wet versus dry conditions. Forced by satellite and reanalysis data, this framework provides an observationally constrained estimate of Ei, which is on average 84.1 ± 1.8 mm per year and accounts for 8.6 ± 0.2% of total rainfall globally during 2000–2020. Rainfall frequency regulates long-term average Ei changes, and rainfall intensity, rather than vegetation attributes, determines the fraction of Ei in gross precipitation (Ei/P). Rain events have become less frequent and more intense since 2000, driving a global decline in Ei (and Ei/P) by 4.9% (6.7%). This suggests that ongoing rainfall changes favor a partitioning towards more soil moisture and runoff, benefiting ecosystem functions but simultaneously increasing flood risks.

Suggested Citation

  • Xu Lian & Wenli Zhao & Pierre Gentine, 2022. "Recent global decline in rainfall interception loss due to altered rainfall regimes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35414-y
    DOI: 10.1038/s41467-022-35414-y
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-35414-y
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-35414-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Markus G. Donat & Andrew L. Lowry & Lisa V. Alexander & Paul A. O’Gorman & Nicola Maher, 2016. "More extreme precipitation in the world’s dry and wet regions," Nature Climate Change, Nature, vol. 6(5), pages 508-513, May.
    2. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    3. Xu Lian & Shilong Piao & Chris Huntingford & Yue Li & Zhenzhong Zeng & Xuhui Wang & Philippe Ciais & Tim R. McVicar & Shushi Peng & Catherine Ottlé & Hui Yang & Yuting Yang & Yongqiang Zhang & Tao Wan, 2018. "Partitioning global land evapotranspiration using CMIP5 models constrained by observations," Nature Climate Change, Nature, vol. 8(7), pages 640-646, July.
    4. Mirco Migliavacca & Talie Musavi & Miguel D. Mahecha & Jacob A. Nelson & Jürgen Knauer & Dennis D. Baldocchi & Oscar Perez-Priego & Rune Christiansen & Jonas Peters & Karen Anderson & Michael Bahn & T, 2021. "The three major axes of terrestrial ecosystem function," Nature, Nature, vol. 598(7881), pages 468-472, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Feng, Jiaojiao & Wang, Weizhen & Xu, Feinan & Wang, Shengtang, 2024. "Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces," Agricultural Water Management, Elsevier, vol. 291(C).
    2. Aryal, Kishor & Maraseni, Tek & Apan, Armando, 2023. "Examining policy−institution−program (PIP) responses against the drivers of ecosystem dynamics. A chronological review (1960–2020) from Nepal," Land Use Policy, Elsevier, vol. 132(C).
    3. Licheng Liu & Wang Zhou & Kaiyu Guan & Bin Peng & Shaoming Xu & Jinyun Tang & Qing Zhu & Jessica Till & Xiaowei Jia & Chongya Jiang & Sheng Wang & Ziqi Qin & Hui Kong & Robert Grant & Symon Mezbahuddi, 2024. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Zhu, Shihua & Fang, Xia & Cao, Liangzhong & Hang, Xin & Xie, Xiaoping & Sun, Liangxiao & Li, Yachun, 2023. "Multivariate drives and their interactive effects on the ratio of transpiration to evapotranspiration over Central Asia ecosystems," Ecological Modelling, Elsevier, vol. 478(C).
    5. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    6. Florian Reiner & Martin Brandt & Xiaoye Tong & David Skole & Ankit Kariryaa & Philippe Ciais & Andrew Davies & Pierre Hiernaux & Jérôme Chave & Maurice Mugabowindekwe & Christian Igel & Stefan Oehmcke, 2023. "More than one quarter of Africa’s tree cover is found outside areas previously classified as forest," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    7. Wu, Genan & Lu, Xinchen & Zhao, Wei & Cao, Ruochen & Xie, Wenqi & Wang, Liyun & Wang, Qiuhong & Song, Jiexuan & Gao, Shaobo & Li, Shenggong & Hu, Zhongmin, 2023. "The increasing contribution of greening to the terrestrial evapotranspiration in China," Ecological Modelling, Elsevier, vol. 477(C).
    8. Wang, Yangjun & Liu, Kefeng & Zhang, Ren & Qian, Longxia & Shan, Yulong, 2021. "Feasibility of the Northeast Passage: The role of vessel speed, route planning, and icebreaking assistance determined by sea-ice conditions for the container shipping market during 2020–2030," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    9. Richards, Daniel Rex & Lavorel, Sandra, 2022. "Integrating social media data and machine learning to analyse scenarios of landscape appreciation," Ecosystem Services, Elsevier, vol. 55(C).
    10. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    11. Xu Lian & Sujong Jeong & Chang-Eui Park & Hao Xu & Laurent Z. X. Li & Tao Wang & Pierre Gentine & Josep Peñuelas & Shilong Piao, 2022. "Biophysical impacts of northern vegetation changes on seasonal warming patterns," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    12. Guoxiong Chen & Qiuming Cheng & Timothy W. Lyons & Jun Shen & Frits Agterberg & Ning Huang & Molei Zhao, 2022. "Reconstructing Earth’s atmospheric oxygenation history using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    13. Yu, Haichao & Li, Sien & Ding, Jie & Yang, Tianyi & Wang, Yuexin, 2023. "Water use efficiency and its drivers of two typical cash crops in an arid area of Northwest China," Agricultural Water Management, Elsevier, vol. 287(C).
    14. Zhang, Xinru & Hou, Lei & Liu, Jiaquan & Yang, Kai & Chai, Chong & Li, Yanhao & He, Sichen, 2022. "Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining," Energy, Elsevier, vol. 254(PB).
    15. Amin, M.G. Mostofa & Mahbub, S.M. Mubtasim & Hasan, Md. Moudud & Pervin, Wafa & Sharmin, Jinat & Hossain, Md. Delwar, 2023. "Plant–water relations in subtropical maize fields under mulching and organic fertilization," Agricultural Water Management, Elsevier, vol. 286(C).
    16. Zhou, Huanyu & Qiu, Yingning & Feng, Yanhui & Liu, Jing, 2022. "Power prediction of wind turbine in the wake using hybrid physical process and machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 568-586.
    17. Akash Koppa & Dominik Rains & Petra Hulsman & Rafael Poyatos & Diego G. Miralles, 2022. "A deep learning-based hybrid model of global terrestrial evaporation," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    18. Leila M. V. Carvalho, 2020. "Assessing precipitation trends in the Americas with historical data: A review," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(2), March.
    19. Guanyin Shuai & Yan Zhou & Jingli Shao & Yali Cui & Qiulan Zhang & Chaowei Jin & Shuyuan Xu, 2024. "Comparison of Multiple Machine Learning Methods for Correcting Groundwater Levels Predicted by Physics-Based Models," Sustainability, MDPI, vol. 16(2), pages 1-18, January.
    20. Shasha Song & Isaac R. Santos & Huaming Yu & Faming Wang & William C. Burnett & Thomas S. Bianchi & Junyu Dong & Ergang Lian & Bin Zhao & Lawrence Mayer & Qingzhen Yao & Zhigang Yu & Bochao Xu, 2022. "A global assessment of the mixed layer in coastal sediments and implications for carbon storage," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35414-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.