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

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  • 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
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    References listed on IDEAS

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    1. 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.
    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. 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.
    4. 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.
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