IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14577-d964627.html
   My bibliography  Save this article

Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data

Author

Listed:
  • Long Qian

    (School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Lifeng Wu

    (School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China)

  • Xiaogang Liu

    (Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Yaokui Cui

    (School of Earth and Space Sciences, Institute of RS and GIS, Peking University, Beijing 100871, China)

  • Yongwen Wang

    (School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China)

Abstract

The accurate calculation of reference evapotranspiration (ET 0 ) is the fundamental basis for the sustainable use of water resources and drought assessment. In this study, we evaluate the performance of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) and two simplified machine learning models to estimate ET 0 when meteorological data are insufficient in China. The results show that, when a weather station lacks global solar radiation (R s ) data, the machine learning methods obtain better results in their estimation of ET 0 . However, when the meteorological station lacks relative humidity (RH) and 2 m wind speed (U 2 ) data, using RH CLD and U 2CLD from the CLDAS to estimate ET 0 and to replace the meteorological station data obtains better results. When all the data from the meteorological station are missing, estimating ET 0 using the CLDAS data still produces relevant results. In addition, the PM–CLDAS method (a calculation method based on the Penman–Monteith formula and using the CLDAS data) exhibits a relatively stable performance under different combinations of meteorological inputs, except in the southern humid tropical zone and the Qinghai–Tibet Plateau zone.

Suggested Citation

  • Long Qian & Lifeng Wu & Xiaogang Liu & Yaokui Cui & Yongwen Wang, 2022. "Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data," Sustainability, MDPI, vol. 14(21), pages 1-24, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14577-:d:964627
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14577/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14577/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Blankenau, Philip A. & Kilic, Ayse & Allen, Richard, 2020. "An evaluation of gridded weather data sets for the purpose of estimating reference evapotranspiration in the United States," Agricultural Water Management, Elsevier, vol. 242(C).
    2. Xiaolong Huang & Shuai Han & Chunxiang Shi, 2021. "Multiscale Assessments of Three Reanalysis Temperature Data Systems over China," Agriculture, MDPI, vol. 11(12), pages 1-20, December.
    3. Huan Wang & Jiejun Huang & Han Zhou & Lixue Zhao & Yanbin Yuan, 2019. "An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature," Sustainability, MDPI, vol. 11(15), pages 1-11, July.
    4. Feng, Yu & Cui, Ningbo & Gong, Daozhi & Zhang, Qingwen & Zhao, Lu, 2017. "Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling," Agricultural Water Management, Elsevier, vol. 193(C), pages 163-173.
    5. Masoud Karbasi, 2018. "Forecasting of Multi-Step Ahead Reference Evapotranspiration Using Wavelet- Gaussian Process Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1035-1052, February.
    Full references (including those not matched with items on IDEAS)

    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. Malik, Anurag & Jamei, Mehdi & Ali, Mumtaz & Prasad, Ramendra & Karbasi, Masoud & Yaseen, Zaher Mundher, 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection," Agricultural Water Management, Elsevier, vol. 272(C).
    2. Tao, Hai & Diop, Lamine & Bodian, Ansoumana & Djaman, Koffi & Ndiaye, Papa Malick & Yaseen, Zaher Mundher, 2018. "Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso," Agricultural Water Management, Elsevier, vol. 208(C), pages 140-151.
    3. Zhang, Yitong & Hao, Zengchao & Zhang, Yu, 2023. "Agricultural risk assessment of compound dry and hot events in China," Agricultural Water Management, Elsevier, vol. 277(C).
    4. 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).
    5. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
    6. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    7. Weiwei Lin & Yanping Shi, 2023. "A Study on the Development of China’s Financial Leasing Industry Based on Principal Component Analysis and ARIMA Model," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    8. Mohammadi, Babak & Mehdizadeh, Saeid, 2020. "Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 237(C).
    9. Tomáš Mikita & Zdeněk Patočka & Elizaveta Avoiani, 2023. "Sap flow modelling based on global radiation and canopy parameters derived from a digital surface model," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 69(8), pages 348-359.
    10. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
    11. Phon Sheng Hou & Lokman Mohd Fadzil & Selvakumar Manickam & Mahmood A. Al-Shareeda, 2023. "Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    12. Tianao Wu & Wei Zhang & Xiyun Jiao & Weihua Guo & Yousef Alhaj Hamoud, 2020. "Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    13. Cécile Couharde & Rémi Generoso, 2023. "The financial cost of stabilizing US farm income under climate change," Working Papers hal-04159823, HAL.
    14. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    15. Allen, Richard G. & Dhungel, Ramesh & Dhungana, Bibha & Huntington, Justin & Kilic, Ayse & Morton, Charles, 2021. "Conditioning point and gridded weather data under aridity conditions for calculation of reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 245(C).
    16. Chen, Xiuzhi & Liu, Chang & van Oel, Pieter & Mergia Mekonnen, Mesfin & Thorp, Kelly R. & Yin, Tuo & Wang, Jinyan & Muhammad, Tahir & Li, Yunkai, 2022. "Water and carbon risks within hydropower development on national scale," Applied Energy, Elsevier, vol. 325(C).
    17. Xing, Liwen & Cui, Ningbo & Liu, Chunwei & Zhao, Lu & Guo, Li & Du, Taisheng & Zhan, Cun & Wu, Zongjun & Wen, Shenglin & Jiang, Shouzheng, 2022. "Estimation of daily apple tree transpiration in the Loess Plateau region of China using deep learning models," Agricultural Water Management, Elsevier, vol. 273(C).
    18. Bellido-Jiménez, Juan A. & Estévez, Javier & García-Marín, Amanda P., 2022. "A regional machine learning method to outperform temperature-based reference evapotranspiration estimations in Southern Spain," Agricultural Water Management, Elsevier, vol. 274(C).
    19. Mohammad Taghi Sattari & Halit Apaydin & Shahaboddin Shamshirband, 2020. "Performance Evaluation of Deep Learning-Based Gated Recurrent Units (GRUs) and Tree-Based Models for Estimating ETo by Using Limited Meteorological Variables," Mathematics, MDPI, vol. 8(6), pages 1-18, June.
    20. Yan, Shicheng & Wu, Lifeng & Fan, Junliang & Zhang, Fucang & Zou, Yufeng & Wu, You, 2021. "A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China," Agricultural Water Management, Elsevier, vol. 244(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:gam:jsusta:v:14:y:2022:i:21:p:14577-:d:964627. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.