IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v272y2022ics0378377422003742.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422003742
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.107827?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Allen, Richard G. & Pereira, Luis S. & Howell, Terry A. & Jensen, Marvin E., 2011. "Evapotranspiration information reporting: I. Factors governing measurement accuracy," Agricultural Water Management, Elsevier, vol. 98(6), pages 899-920, April.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    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. Feng, Jiaojiao & Wang, Weizhen & Che, Tao & Xu, Feinan, 2023. "Performance of the improved two-source energy balance model for estimating evapotranspiration over the heterogeneous surface," Agricultural Water Management, Elsevier, vol. 278(C).
    3. Feng, Yu & Gong, Daozhi & Mei, Xurong & Hao, Weiping & Tang, Dahua & Cui, Ningbo, 2017. "Energy balance and partitioning in partial plastic mulched and non-mulched maize fields on the Loess Plateau of China," Agricultural Water Management, Elsevier, vol. 191(C), pages 193-206.
    4. Darouich, Hanaa & Karfoul, Razan & Ramos, Tiago B. & Moustafa, Ali & Shaheen, Baraa & Pereira, Luis S., 2021. "Crop water requirements and crop coefficients for jute mallow (Corchorus olitorius L.) using the SIMDualKc model and assessing irrigation strategies for the Syrian Akkar region," Agricultural Water Management, Elsevier, vol. 255(C).
    5. Escarabajal-Henarejos, D. & Fernández-Pacheco, D.G. & Molina-Martínez, J.M. & Martínez-Molina, L. & Ruiz-Canales, A., 2015. "Selection of device to determine temperature gradients for estimating evapotranspiration using energy balance method," Agricultural Water Management, Elsevier, vol. 151(C), pages 136-147.
    6. Gao, Yang & Yang, Linlin & Shen, Xiaojun & Li, Xinqiang & Sun, Jingsheng & Duan, Aiwang & Wu, Laosheng, 2014. "Winter wheat with subsurface drip irrigation (SDI): Crop coefficients, water-use estimates, and effects of SDI on grain yield and water use efficiency," Agricultural Water Management, Elsevier, vol. 146(C), pages 1-10.
    7. 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.
    8. Yang, Yanmin & Yang, Yonghui & Han, Shumin & Li, Huilong & Wang, Lu & Ma, Qingtao & Ma, Lexin & Wang, Linna & Hou, Zhenjun & Chen, Li & Liu, De Li, 2023. "Comparison of water-saving potential of fallow and crop change with high water-use winter-wheat – summer-maize rotation," Agricultural Water Management, Elsevier, vol. 289(C).
    9. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    10. Jovanovic, N. & Pereira, L.S. & Paredes, P. & Pôças, I. & Cantore, V. & Todorovic, M., 2020. "A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods," Agricultural Water Management, Elsevier, vol. 239(C).
    11. Fuentes, Sigfredo & Ortega-Farías, Samuel & Carrasco-Benavides, Marcos & Tongson, Eden & Gonzalez Viejo, Claudia, 2024. "Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling," Agricultural Water Management, Elsevier, vol. 297(C).
    12. Jiang, Hou & Lu, Ning & Huang, Guanghui & Yao, Ling & Qin, Jun & Liu, Hengzi, 2020. "Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data," Applied Energy, Elsevier, vol. 270(C).
    13. Shahadha, Saadi Sattar & Wendroth, Ole & Zhu, Junfeng & Walton, Jason, 2019. "Can measured soil hydraulic properties simulate field water dynamics and crop production?," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    14. Wang, Weishu & Rong, Yao & Dai, Xiaoqin & Zhang, Chenglong & Wang, Chaozi & Huo, Zailin, 2024. "Variation and attribution of energy distribution for salinized sunflower farmland in arid area," Agricultural Water Management, Elsevier, vol. 297(C).
    15. Escarabajal-Henarejos, D. & Molina-Martínez, J.M. & Fernández-Pacheco, D.G. & Cavas-Martínez, F. & García-Mateos, G., 2015. "Digital photography applied to irrigation management of Little Gem lettuce," Agricultural Water Management, Elsevier, vol. 151(C), pages 148-157.
    16. Zhao, Nana & Liu, Yu & Cai, Jiabing & Paredes, Paula & Rosa, Ricardo D. & Pereira, Luis S., 2013. "Dual crop coefficient modelling applied to the winter wheat–summer maize crop sequence in North China Plain: Basal crop coefficients and soil evaporation component," Agricultural Water Management, Elsevier, vol. 117(C), pages 93-105.
    17. Wen Zhang & Jing Li & Yunhao Chen & Yang Li, 2019. "A Surrogate-Based Optimization Design and Uncertainty Analysis for Urban Flood Mitigation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4201-4214, September.
    18. Hu, Xinyu & Zhao, Jinfeng & Sun, Shikun & Jia, Chengru & Zhang, Fuyao & Ma, Yizhe & Wang, Kaixuan & Wang, Yubao, 2023. "Evaluation of the temporal reconstruction methods for MODIS-based continuous daily actual evapotranspiration estimation," Agricultural Water Management, Elsevier, vol. 275(C).
    19. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
    20. Elfarkh, Jamal & Simonneaux, Vincent & Jarlan, Lionel & Ezzahar, Jamal & Boulet, Gilles & Chakir, Adnane & Er-Raki, Salah, 2022. "Evapotranspiration estimates in a traditional irrigated area in semi-arid Mediterranean. Comparison of four remote sensing-based models," Agricultural Water Management, Elsevier, vol. 270(C).

    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:eee:agiwat:v:272:y:2022:i:c:s0378377422003742. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.