Preprints
https://doi.org/10.5194/essd-2024-77
https://doi.org/10.5194/essd-2024-77
10 Apr 2024
 | 10 Apr 2024
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

A flux tower site attribute dataset intended for land surface modeling

Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai

Abstract. Land surface models (LSMs) should have reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are considered the benchmarking data for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets lack site-observed attribute data, limiting their use as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, external disturbances, and energy balance closure (EBC), leading to 90 high-quality sites. For these sites, we collected vegetation, soil, topography information, and wind speed measurement height from literature, regional networks, and Biological, Ancillary, Disturbance, and Metadata (BADM) files. Then we obtained the final flux tower attribute dataset by global data product complement and plant functional types (PFTs) classification. This dataset is provided in NetCDF format complete with necessary descriptions and reference sources. Model simulations revealed substantial disparities in output between the attribute data observed at the site and the defaults of the model, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of site attribute data to some extent, reduces uncertainty in LSMs data source, and aids in diagnosing parameter as well as process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.10939725 (Shi et al., 2024).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-77', Anonymous Referee #1, 17 May 2024
    • AC1: 'Reply on RC1', Jiahao Shi, 26 Jun 2024
  • RC2: 'Comment on essd-2024-77', Anonymous Referee #2, 05 Jun 2024
    • AC2: 'Reply on RC2', Jiahao Shi, 10 Aug 2024
  • RC3: 'Comment on essd-2024-77', Anna Ukkola, 13 Jun 2024
    • AC3: 'Reply on RC3', Jiahao Shi, 10 Aug 2024
  • RC4: 'Comment on essd-2024-77', Lingcheng Li, 09 Jul 2024
    • AC4: 'Reply on RC4', Jiahao Shi, 10 Aug 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-77', Anonymous Referee #1, 17 May 2024
    • AC1: 'Reply on RC1', Jiahao Shi, 26 Jun 2024
  • RC2: 'Comment on essd-2024-77', Anonymous Referee #2, 05 Jun 2024
    • AC2: 'Reply on RC2', Jiahao Shi, 10 Aug 2024
  • RC3: 'Comment on essd-2024-77', Anna Ukkola, 13 Jun 2024
    • AC3: 'Reply on RC3', Jiahao Shi, 10 Aug 2024
  • RC4: 'Comment on essd-2024-77', Lingcheng Li, 09 Jul 2024
    • AC4: 'Reply on RC4', Jiahao Shi, 10 Aug 2024
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai

Data sets

A flux tower site attribute dataset intended for land surface modeling Jiahao Shi et al. https://doi.org/10.5281/zenodo.10939725

Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai

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Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
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