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A deep learning-based hybrid model of global terrestrial evaporation

Author

Listed:
  • Akash Koppa

    (Ghent University)

  • Dominik Rains

    (Ghent University)

  • Petra Hulsman

    (Ghent University)

  • Rafael Poyatos

    (CREAF
    Universitat Autònoma de Barcelona)

  • Diego G. Miralles

    (Ghent University)

Abstract

Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, Et) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress (St), i.e., the reduction of Et from its theoretical maximum. Then, we embed the new St formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the St formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate St and E globally. The proposed framework may be extended to improve the estimation of E in Earth System Models and enhance our understanding of this crucial climatic variable.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29543-7
    DOI: 10.1038/s41467-022-29543-7
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    1. Christopher R. Schwalm & William R. L. Anderegg & Anna M. Michalak & Joshua B. Fisher & Franco Biondi & George Koch & Marcy Litvak & Kiona Ogle & John D. Shaw & Adam Wolf & Deborah N. Huntzinger & Kev, 2017. "Global patterns of drought recovery," Nature, Nature, vol. 548(7666), pages 202-205, August.
    2. P. C. D. Milly & K. A. Dunne & A. V. Vecchia, 2005. "Global pattern of trends in streamflow and water availability in a changing climate," Nature, Nature, vol. 438(7066), pages 347-350, November.
    3. 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.
    4. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    5. Martin Jung & Markus Reichstein & Philippe Ciais & Sonia I. Seneviratne & Justin Sheffield & Michael L. Goulden & Gordon Bonan & Alessandro Cescatti & Jiquan Chen & Richard de Jeu & A. Johannes Dolman, 2010. "Recent decline in the global land evapotranspiration trend due to limited moisture supply," Nature, Nature, vol. 467(7318), pages 951-954, October.
    6. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
    7. Goutam Konapala & Ashok K. Mishra & Yoshihide Wada & Michael E. Mann, 2020. "Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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    Cited by:

    1. Zhu, Wenbin & Yu, Xiaoyu & Wei, Jiaxing & Lv, Aifeng, 2024. "Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison," Agricultural Water Management, Elsevier, vol. 291(C).
    2. Jiang, Xiaoman & Wang, Yuntao & A., Yinglan & Wang, Guoqiang & Zhang, Xiaojing & Ma, Guangwen & Duan, Limin & Liu, Kai, 2024. "Optimizing actual evapotranspiration simulation to identify evapotranspiration partitioning variations: A fusion of physical processes and machine learning techniques," Agricultural Water Management, Elsevier, vol. 295(C).

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