A flux tower site attribute dataset intended for land surface modeling
<p>Land surface models (LSMs) require reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are widely regarded as the benchmark for LSMs. However, currently available flux tower datasets ofte...
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| Language: | English |
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Copernicus Publications
2025-01-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/117/2025/essd-17-117-2025.pdf |
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| author | J. Shi H. Yuan W. Lin W. Dong H. Liang Z. Liu J. Zeng H. Zhang N. Wei Z. Wei S. Zhang S. Liu X. Lu Y. Dai |
| author_facet | J. Shi H. Yuan W. Lin W. Dong H. Liang Z. Liu J. Zeng H. Zhang N. Wei Z. Wei S. Zhang S. Liu X. Lu Y. Dai |
| author_sort | J. Shi |
| collection | DOAJ |
| description | <p>Land surface models (LSMs) require reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are widely regarded as the benchmark 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 frequently lack site-observed attribute data, such as fractional vegetation cover and leaf area index, which limits their utility as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, energy balance closure (EBC), and external disturbances such as irrigation and deforestation, leading to 90 high-quality sites. For these sites, we collected vegetation, soil, and topography data as well as wind speed, temperature, and humidity measurement heights from literature; regional networks; and Biological, Ancillary, Disturbance, and Metadata (BADM) files. We then compiled the final flux tower attribute dataset by filling in missing attributes with global data and classifying plant functional types (PFTs). This dataset is provided in NetCDF (Network Common Data Form) format with necessary descriptions and reference sources. Model simulations revealed substantial disparities in the output between the attribute data observed at the site and those commonly used by LSMs, 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 the site attribute to some extent, reduces uncertainty in LSM data source, and aids in diagnosing parameter and process deficiencies. The dataset is available at <a href="https://doi.org/10.5281/zenodo.12596218">https://doi.org/10.5281/zenodo.12596218</a> (Shi et al., 2024).</p> |
| format | Article |
| id | doaj-art-b09dcd4e0b2a48a9a43fe37f330a0c05 |
| institution | Kabale University |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj-art-b09dcd4e0b2a48a9a43fe37f330a0c052025-01-16T11:33:28ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-01-011711713410.5194/essd-17-117-2025A flux tower site attribute dataset intended for land surface modelingJ. Shi0H. Yuan1W. Lin2W. Dong3H. Liang4Z. Liu5J. Zeng6H. Zhang7N. Wei8Z. Wei9S. Zhang10S. Liu11X. Lu12Y. Dai13Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China<p>Land surface models (LSMs) require reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are widely regarded as the benchmark 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 frequently lack site-observed attribute data, such as fractional vegetation cover and leaf area index, which limits their utility as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, energy balance closure (EBC), and external disturbances such as irrigation and deforestation, leading to 90 high-quality sites. For these sites, we collected vegetation, soil, and topography data as well as wind speed, temperature, and humidity measurement heights from literature; regional networks; and Biological, Ancillary, Disturbance, and Metadata (BADM) files. We then compiled the final flux tower attribute dataset by filling in missing attributes with global data and classifying plant functional types (PFTs). This dataset is provided in NetCDF (Network Common Data Form) format with necessary descriptions and reference sources. Model simulations revealed substantial disparities in the output between the attribute data observed at the site and those commonly used by LSMs, 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 the site attribute to some extent, reduces uncertainty in LSM data source, and aids in diagnosing parameter and process deficiencies. The dataset is available at <a href="https://doi.org/10.5281/zenodo.12596218">https://doi.org/10.5281/zenodo.12596218</a> (Shi et al., 2024).</p>https://essd.copernicus.org/articles/17/117/2025/essd-17-117-2025.pdf |
| spellingShingle | J. Shi H. Yuan W. Lin W. Dong H. Liang Z. Liu J. Zeng H. Zhang N. Wei Z. Wei S. Zhang S. Liu X. Lu Y. Dai A flux tower site attribute dataset intended for land surface modeling Earth System Science Data |
| title | A flux tower site attribute dataset intended for land surface modeling |
| title_full | A flux tower site attribute dataset intended for land surface modeling |
| title_fullStr | A flux tower site attribute dataset intended for land surface modeling |
| title_full_unstemmed | A flux tower site attribute dataset intended for land surface modeling |
| title_short | A flux tower site attribute dataset intended for land surface modeling |
| title_sort | flux tower site attribute dataset intended for land surface modeling |
| url | https://essd.copernicus.org/articles/17/117/2025/essd-17-117-2025.pdf |
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