Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4603-2022
https://doi.org/10.5194/hess-26-4603-2022
Research article
 | 
16 Sep 2022
Research article |  | 16 Sep 2022

Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis

Haiyang Shi, Geping Luo, Olaf Hellwich, Mingjuan Xie, Chen Zhang, Yu Zhang, Yuangang Wang, Xiuliang Yuan, Xiaofei Ma, Wenqiang Zhang, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde

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Latest update: 28 Mar 2024
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Short summary
There have been many machine learning simulation studies based on eddy-covariance observations for water flux and evapotranspiration. We performed a meta-analysis of such studies to clarify the impact of different algorithms and predictors, etc., on the reported prediction accuracy. It can, to some extent, guide future global water flux modeling studies and help us better understand the terrestrial ecosystem water cycle.