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Improving predictions of evapotranspiration by integrating multi-source observations and land surface model

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  • He, Xinlei
  • Liu, Shaomin
  • Xu, Tongren
  • Yu, Kailiang
  • Gentine, Pierre
  • Zhang, Zhe
  • Xu, Ziwei
  • Jiao, Dandan
  • Wu, Dongxing

Abstract

Accurate estimation of evapotranspiration (ET) is essential for understanding terrestrial energy, water, and carbon cycles. This study proposes a hybrid model integrating in-situ and remote sensing-derived soil moisture (SM) observations and remote sensing leaf area index (LAI) with the Noah-MP model. The ensemble Kalman filter (EnKF) approach updates the leaf biomass and specific leaf area (SLA) by assimilating the remotely sensed LAI. A machine learning (ML) surrogate model is used to integrate multi-site SM profile observations and remote sensing SM products to estimate the three-layer SM. An iterative coupling of two parts implements the hybrid model: optimization of leaf biomass and SLA by assimilation of LAI in the Noah-MP model and simulation of three-layer SM in the ML surrogate model. The performance of the hybrid model is evaluated in the Heihe River Basin (HRB) in northwest China. The estimated ET from the hybrid model is compared with observations from the large aperture scintillometer (LAS) at the Arou, Daman, and Sidaoqiao sites and up-scaled watershed ET over the HRB. The findings indicate that the hybrid model performs well in vegetated areas but underestimates ET in extreme arid deserts. The three-site unbiased root mean squared errors (ubRMSEs) of ET estimates from the hybrid model are 29.06%, 42.76%, and 50.00% lower than Noah-MP at the Arou, Daman, and Sidaoqiao sites, respectively. The coupling of data assimilation (DA) and ML methods can improve vegetation dynamics and SM transport estimation in the Noah-MP model. The hybrid model can take advantage of DA and ML methods and integrate multi-source observations to improve the accuracy of ET estimation. The results also indicate that the ET predictions are more sensitive to root zone SM (0–40 cm) over croplands, grasslands, and shrublands, while the ET simulations are more affected by deeper rooting depths SM (0–100 cm) and groundwater over forests.

Suggested Citation

  • He, Xinlei & Liu, Shaomin & Xu, Tongren & Yu, Kailiang & Gentine, Pierre & Zhang, Zhe & Xu, Ziwei & Jiao, Dandan & Wu, Dongxing, 2022. "Improving predictions of evapotranspiration by integrating multi-source observations and land surface model," Agricultural Water Management, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:agiwat:v:272:y:2022:i:c:s0378377422003742
    DOI: 10.1016/j.agwat.2022.107827
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    References listed on IDEAS

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    1. Martí, Pau & González-Altozano, Pablo & López-Urrea, Ramón & Mancha, Luis A. & Shiri, Jalal, 2015. "Modeling reference evapotranspiration with calculated targets. Assessment and implications," Agricultural Water Management, Elsevier, vol. 149(C), pages 81-90.
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    3. Li, Xi & Zheng, Yi & Sun, Zan & Tian, Yong & Zheng, Chunmiao & Liu, Jie & Liu, Shaomin & Xu, Ziwei, 2017. "An integrated ecohydrological modeling approach to exploring the dynamic interaction between groundwater and phreatophytes," Ecological Modelling, Elsevier, vol. 356(C), pages 127-140.
    4. 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.
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    Cited by:

    1. 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.
    2. Wei, Jiaxing & Dong, Weichen & Liu, Shaomin & Song, Lisheng & Zhou, Ji & Xu, Ziwei & Wang, Ziwei & Xu, Tongren & He, Xinlei & Sun, Jingwei, 2023. "Mapping super high resolution evapotranspiration in oasis-desert areas using UAV multi-sensor data," Agricultural Water Management, Elsevier, vol. 287(C).

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