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

Long Qian, Lifeng Wu (), Xiaogang Liu, Yaokui Cui and Yongwen Wang
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Long Qian: School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, 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

Sustainability, 2022, vol. 14, issue 21, 1-24

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.

Keywords: reference evapotranspiration; reanalysis dataset; CLDAS; machine learning; limited meteorological data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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