A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022

文献类型： 外文期刊

第一作者： Li, Wangyipu

作者： Li, Wangyipu;Yao, Zhaoyuan;Qu, Yifan;Cui, Yaokui;Li, Wangyipu;Yao, Zhaoyuan;Qu, Yifan;Cui, Yaokui;Yang, Hanbo;Song, Yang;Song, Lisheng;Wu, Lifeng

作者机构：

期刊名称：EARTH SYSTEM SCIENCE DATA （ 影响因子：11.6； 五年影响因子：13.9 ）

ISSN： 1866-3508

年卷期： 2025 年 17 卷 8 期

页码：

收录情况： SCI

摘要： Evapotranspiration (ET) is a crucial component of the terrestrial hydrological cycle. Latent heat flux (LE, equivalent to ET in Wm(-2)) observed by the eddy covariance (EC) technique, commonly known as LEEC, has been widely recognized as a highly accurate benchmark for global ET estimation. Currently, there is an increasing need for long-time-series benchmark data to support climate change analysis, construction of new models, and validation of new products. However, existing LEEC datasets, like FLUXNET2015, face significant challenges due to limited observation periods and extensive data gaps, which hinders their application in ET modeling and global change analysis. To address these issues, we developed a gap-filling and prolongation framework for LE(EC )data and established a benchmark dataset for global ET estimation from 2000 to 2022 across 64 sites at various timescales. The framework mainly includes three parts: site selection and data pre-processing, generation of gap-filled half-hourly/hourly LE data, and generation of prolonged daily LE data. We selected 64 sites from FLUXNET2015 based on rigorous filtering criteria. A novel bias-corrected random forest (RF) algorithm was used for gap-filling and prolongation in the framework to produce seamless half-hourly and daily LE data. After analysis, the framework using the novel bias-corrected RF algorithm achieves excellent performance in both hourly gap-filling and daily prolongation, with mean root mean square error values of 33.86 and 16.58 Wm(-2), respectively. The algorithm significantly improves the gap-filling performance for long gaps and extreme values compared with the original RF and marginal distribution sampling algorithm. The results demonstrate robust prolongation performance of our framework in both prolongation directions and temporal stability. Furthermore, our gap-filled dataset demonstrates strong consistency with FLUXNET2015 in terms of data distribution. In conclusion, we have published the first benchmark dataset for global ET estimation based on FLUXNET2015 from 2000 to 2022. This dataset can effectively provide data support for ET modeling, water-carbon cycle monitoring, and climate change analysis. It is made freely available via the following repository: 10.5281/zenodo.13853409 (Li et al., 2024b).

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