陆地表面模式（LSM）应该有可靠的强迫、验证和表面属性数据，这是有效开发和改进模式的基础。涡协方差通量塔数据被认为是 LSM 的基准数据。然而，目前可用的通量塔数据集在应用于 LSM 之前往往需要进行多方面的处理，以确保数据质量。更重要的是，这些数据集缺乏现场观测属性数据，限制了它们作为基准数据的使用。在此，我们对现有的再处理通量塔数据集进行了全面的质量筛选，包括缺口填充数据比例、外部干扰和能量平衡闭合（EBC），最终得出了 90 个高质量站点。对于这些站点，我们从文献、区域网络和生物、辅助、干扰和元数据（BADM）文件中收集了植被、土壤、地形信息和风速测量高度。然后，我们通过全球数据产品补充和植物功能类型（PFTs）分类获得了最终的通量塔属性数据集。", "ds_source": "
本研究中使用的数据可分为四组。首先，PLUMBER2作为数据质量筛选的数据集。第二组是属性源，包括 113 篇与站点相关的文献、7 个通量区域网络以及 FLUXNET 和 AmeriFlux 提供的生物、辅助、干扰和元数据 （BADM） 文件。\n
网址为：a ：https://ameriflux.lbl.gov/\n
b： http://www.biomet.co.at/\n
c ：http://www.chinaflux.org/\n
d： http://www.europe-fluxdata.eu/\n
e ：https://www.gml.noaa.gov/\n
f： https://ozflux.org.au/\n
g： https://www.swissfluxnet.ethz.ch/.）", "ds_process_way": "
为建立最终数据集，我们主要采取了三个处理步骤：地点和时间段选择、属性收集和数据处理。首先，数据选择过程包括挑选通量（潜热和显热）和水汽压差（VPD）缺口填充率较低的年份，同时剔除受到干扰的年份。(潜热和显热）和蒸汽压力亏损（VPD）的年份，同时排除受外部干扰和无法通过EBC的站点。随后，我们收集了现场观测到的植被、土壤和地形数据。植被属性包括FVC、最大LAI和平均冠层高度。土壤属性包括土壤质地、容重和有机碳浓度。地形属性包括海拔、坡度和坡向。此外，参考测量高度（用于模拟大气模型的最低层，LSM 将与之耦合）在可能的情况下根据风速测量高度进行修订。然后，我们利用全球数据补充了植被属性和土壤质地。最后，将 FVC 进一步细分为不同的 PFT。", "ds_quality": "
模型模拟显示，现场观测到的属性数据与模型默认值之间的输出差异很大，这突出了现场观测到的属性数据的关键作用，并提高了 LSM 界对通量塔属性数据的重视程度。该数据集在一定程度上解决了现场属性数据缺乏的问题，减少了 LSM 数据源的不确定性，并有助于诊断参数和过程缺陷。", "ds_acq_start_time": "2015-01-01 00:00:00", "ds_acq_end_time": "2015-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 2261187, "ds_files_count": 2, "ds_format": " NetCDF", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "39b63a52-7c6d-47e4-805f-bcf77fb5fd41.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15", "170.45" ], "quality_level": 3, "publish_time": "2024-05-22 11:33:08", "last_updated": "2025-06-30 16:20:58", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6474.2024", "i18n": { "en": { "title": "A flux tower site attribute dataset intended for land surface modeling", "ds_format": " NetCDF", "ds_source": "
The data used in this study can be divided into four groups. Firstly, PLUMBER2 serves as the dataset for data quality screening. The second group consists of attribute sources, including 113 literature related to the site, 7 flux area networks, and biological, auxiliary, interference, and metadata (BADM) files provided by FLUXNET and AmeriFlux.\n
The website is: a: https://ameriflux.lbl.gov/\n
b： http://www.biomet.co.at/\n
c ： http://www.chinaflux.org/\n
d： http://www.europe-fluxdata.eu/\n
e ： https://www.gml.noaa.gov/\n
f： https://ozflux.org.au/\n
g： https://www.swissfluxnet.ethz.ch/. ）", "ds_quality": "
The model simulation shows that there is a significant difference in the output between the observed attribute data on site and the default values of the model, highlighting the crucial role of observed attribute data on site and increasing the importance of LSM boundary on flux tower attribute data. This dataset to some extent solves the problem of lack of on-site attribute data, reduces the uncertainty of LSM data sources, and helps diagnose parameter and process defects.", "ds_ref_way": "", "ds_abstract": "
The Land Surface Model (LSM) should have reliable forcing, validation, and surface attribute data as the foundation for effective development and improvement of the model. The vortex covariance flux tower data is considered as the benchmark data for LSM. However, currently available flux tower datasets often require multi-faceted processing to ensure data quality before being applied to LSM. More importantly, these datasets lack on-site observational attribute data, which limits their use as benchmark data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including gap filling data ratio, external interference, and energy balance closure (EBC), and ultimately identified 90 high-quality sites. For these sites, we collected vegetation, soil, terrain information, and wind speed measurement heights from literature, regional networks, and biological, auxiliary, interference, and metadata (BADM) files. Then, we obtained the final flux tower attribute dataset through global data product supplementation and classification of plant functional types (PFTs).
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "To establish the final dataset, we mainly took three processing steps: location and time period selection, attribute collection, and data processing. Firstly, the data selection process involves selecting years with low filling rates for flux (latent and sensible heat) and vapor pressure difference (VPD) gaps, while removing years that are subject to interference. The years of latent heat and sensible heat, as well as vapor pressure deficit (VPD), while excluding stations affected by external interference and unable to pass through EBC. Subsequently, we collected vegetation, soil, and terrain data observed on site. Vegetation attributes include FVC, maximum LAI, and average canopy height. Soil properties include soil texture, bulk density, and organic carbon concentration. The terrain attributes include altitude, slope, and aspect. In addition, the reference measurement height (used to simulate the lowest layer of the atmospheric model, with which LSM will be coupled) is revised based on wind speed measurement height whenever possible. Then, we supplemented vegetation attributes and soil texture with global data. Finally, FVC is further subdivided into different PFTs.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "陆地表面模式（LSM）", "涡协方差通量塔", "能量平衡闭合（EBC）" ], "ds_subject_tags": [ "大气科学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2015 ], "ds_contributors": [ { "true_name": "袁华", "email": "yuanh25@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "袁华", "email": "yuanh25@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "袁华", "email": "yuanh25@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "category": "大气本底" }