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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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    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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