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

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  • 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
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    References listed on IDEAS

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