17 Jul 2023
 | 17 Jul 2023
Status: this preprint is currently under review for the journal ESSD.

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data

Changming Li, Ziwei Liu, Wencong Yang, Zhuoyi Tu, Juntai Han, Sien Li, and Hanbo Yang

Abstract. Land evapotranspiration (ET) plays a crucial role in Earth's water-carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land-atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in-situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits excellent performance across various vegetation coverage types, as validated against in-situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square-errors (RMSE) of 0.81 and 0.73 mm/d, unbiased root-mean-square-errors (ubRMSE) of 1.20 and 1.04 mm/d, mean absolute errors (MAE) of 0.81 and 0.73 mm/d, and Kling-Gupta efficiency (KGE) of 0.60 and 0.65 on average over resolutions of 0.1° and 0.25°, respectively.

Changming Li et al.

Status: open (until 05 Nov 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-226', Anonymous Referee #1, 05 Sep 2023 reply
    • AC1: 'Reply on RC1', Changming li, 28 Sep 2023 reply

Changming Li et al.

Data sets

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data Changming Li; Ziwei Liu; Wencong Yang; Zhuoyi Tu; Juntai Han; Li Sien; Yang Hanbo

Changming Li et al.


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Short summary
Using a collocation-based approach, we developed a reliable global land evapotranspiration product (CAMELE) by merging multi-source datasets. The CAMELE product outperformed individual input datasets and showed satisfactory performance compared to reference data. It also demonstrated superiority for different plant functional types. Our study provides a promising solution for data fusion. The CAMELE dataset allows for detailed research and a better understanding of land-atmosphere interactions.