Carbon-nitrogen-water Fluxes and Auxiliary Parameters of China's Ecosystems Zone II Versions EN1 Vol 4 (1) 2019
Carbon and water fluxes observed by the Chinese Flux Observation and Research Network (2003 – 2005)
: 2019 - 05 - 18
: 2018 - 07 - 09
: 2019 - 01 - 11
939 11 0
Abstract & Keywords
Abstract: Based on micrometeorological theory, eddy covariance technique (EC) is an approach for direct measurement of ecosystem-scale productivity, energy balance and greenhouse gas exchange. EC-generated flux data importantly support studies on ecosystem carbon & water cycling mechanism and the spatio-temporal patterns of carbon sink capacity and water use. Resulting from the first batch observations by the Chinese Flux Observation and Research Network (ChinaFLUX)founded in 2002, this dataset collected field measurements from eight sites during 2003–2005, including 4 forests, 3 grasslands and 1 cropland. The dataset follows the ChinaFLUX data processing protocols, and it provides important data basis for analyzing responses to environmental changes.
Keywords: eddy covariance technique, flux measurement, meteorological factors, carbon and water cycling, terrestrial ecosystem
Dataset Profile
Chinese title2003–2005年中国通量观测研究联盟(ChinaFLUX)碳水通量观测数据集
English titleCarbon and water fluxes observed by the Chinese Flux Observation and Research Network (2003 – 2005)
Corresponding data authorYu Guirui (
Data producersStationObserverFormer DirectorCurrent DirectorAffiliation
Inner MongoliaHao YanbinWang YanfenWang YanfenCollege of Resources and Environment, University of Chinese Academy of Sciences
Dinghu MountainZhang DeqiangZhou GuoyiZhou GuoyiSouth China Botanical Garden, Chinese Academy of Sciences
HaibeiZhang FaweiZhao XinquanLi YingnianNorthwest Institute of Plateau Biology, Chinese Academy of Sciences
Changbai MountainWu JiabingHan ShijieWang AnzhiInstitute of Applied Ecology, Chinese Academy of Sciences
YuchengLi Fadong,
Zhao Fenghua
Ouyang ZhuOuyang ZhuInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
DangxiongHe YongtaoZhang XianzhouZhang YangjianInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
XishuangbannaSong QinghaiZhang YipingLin LuxiangXishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences
QianyanzhouDai XiaoqinLiu QijingWang HuimingInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
Data CenterZhang Leiming,
Su Wen
Yu GuiruiYu GuiruiInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
Time range2003 – 2005
Observation scopeEight ChinaFLUX terrestrial stations in China
Data amount78.94 MB
Data format*.xls
Data service system<>;
Sources of fundingNational Key Research and Development Program of China (2017YFC0503801, 2016YFA0600104), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020302), Science and Technology Service Network Initiative of the Chinese Academy of Sciences (STS Plan,KFJ-SW-STS-169).
Dataset compositionThe dataset consists of the flux and meteorological measurement of eight ChinaFLUX stations, including Changbai Moutain, Qianyanzhou, Dinghu Mountain, Xishuangbanna, Inner Mongolia, Haibei, Yucheng and Dangxiong, each station corresponding to a subset. Each subset consists of half-hour and daily data products. The half-hour dataset of flux measurement includes CO2 flux, latent flux and sensible flux after quality control, while that of meteorological measurement includes air temperature, precipitation, global radiation and photosynthetic active radiation. The daily dataset includes integrated daily CO2 flux, latent flux and sensible flux.
1.   Introduction
Long-term observation of terrestrial ecosystem has attracted worldwide scholarly attention. By measuring the carbon and water exchange between vegetation and atmosphere based on micrometeorological theory, eddy covariance technique (EC) realized direct monitoring of the ecological processes and functions, including productivity, energy budget and greenhouse gases (GHGs) at the ecosystem scale,1-3 and EC has been accepted as the core technical method for global flux observation network (FLUXNET).4EC-generated flux data have been widely utilized to validate different kinds of models and remote sensing observation. Moreover, the long-term coordinated observation on multi-processes and multi-factors supports analyses of ecosystem carbon-water-nitrogen cycling mechanism and spatial-temporal distributions of carbon sink/source.5-6Especially, the global-scale united flux observation breaks grounds in its extension of the observation from ecological phenomena or elements to global ecosystem functions.7
Sponsored by the Knowledge Innovation Project of the Chinese Academy of Sciences “Study on the carbon budget of terrestrial and offshore ecosystems in China”, the Chinese Terrestrial Flux Observation and Research Network was launched relying on Chinese Ecosystem Research Network (CERN) in 2001. In 2003, eight sites, including Changbai Moutain, Qianyanzhou, Dinghu Mountain, Xishuangbanna, Inner Mongolia, Haibei and Yucheng, started their coordinated measurement on ecosystem carbon and water fluxes in China,8 which filled a regional gap in the Asian Monsoon Zone. In 2014, a new ChinaFLUX was formed by combining ChinaFLUX with other flux stations in China.
As the first dataset of coordinated flux measurement in China, this dataset is composed by the ecosystem carbon and water fluxes and routine meteorological records from the most initially established eight sites. The data indicators mainly include CO2 flux, sensible flux, latent flux, friction, air temperature, soil temperature, soil moisture, precipitation, global radiation, net radiation and photosynthetic radiation at both half-hour and daily scales. This dataset was released in October 2013, and since then has received wide attention at home and abroad, but there is still a lack of systemic description of it. This is published as an endeavor of ChinaFLUX to promote data sharing and data (re)use. ChinaFLUX will further strengthen the coordination with field stations to promote long-term, multi-site data sharing while dedicated to protecting data property by means of data publishing.
2.   Data collection and processing
2.1   Data sources
Based on top-level design and scientific appraisal, we considered the spatial distribution of the terrestrial transects and the field stations of CERN, and conducted observation system design, instrument selection, site investigation,, on-site installation and instrument debugging. Before the end of 2002, the first six sites were established, including Changbai Moutain, Qianyanzhou, Dinghu Mountain, Xishuangbanna, Haibei and Yucheng sites, while Inner Mongolia and Dangxiong sites were completed in May 2003. The eight sites constituted the first version of ChinaFLUX (Tables 1 & 2, Figure 1).
Table 1   Profile of the name and location of the flux sites
CodeNameFull nameLongitudeLatitudeVegetation
CBSChangbai Mountain SiteTemperate broad-leaved Korean pine mixed forest site in Changbai Mountain128°06'E42°24'NForest
QYZQianyanzhou SiteSubtropical evergreen plantation flux site in Qianyanzhou115°03'E26°44'NForest
DHSDinghu Mountain SiteSubtropical monsoon mixed forest flux site in Dinghu Mountain112°30'E23°09'NForest
XSBNXishuangbanna SiteTropical forest flux site in Xishuangbanna101°16'E21°54'NForest
NMGInner Mongolia SiteTemperate grassland flux site in Inner Mongolia116°18'E44°08'NGrassland
HBGCTHaibei SiteAlpine grassland flux site in Haibei101°20'E37°40'NGrassland
DXDangxiong SiteAlpine meadow flux site in Dangxiong91°03'E30°29'NGrassland
YCYucheng SiteTemperate cropland flux site in Yucheng116°38'E36°58'NCropland

Fig.1   Spatial distribution of the flux sites
Table 2   Characteristics of the vegetation and soil in each flux site
Site codeDominant speciesCanopy height1)
(m2 m-2)
Soil type
CBSPinus koraiensis, Tiliaamurensis, Quercus mongolica, Fraxinus mandshurica , etc.266.1Upland dark brown forest soil
QYZPinus massoniana, Pinus elliottii, Cunninghamia lanceolata and Schimasuperba etc.123.5Typical red earth
DHSCastanopis chinensis , Schimasuperba, Cryptocarya chnensis, Pinus massoniana, etc.175.6Lateritic red-earth, yellow-earth,
and mountain shrubby-meadow soil
XSBNTerminalia myriocarpa , Pometia tomentosa, Barringtoniamacrostachya, Gironnierasubaequalis, Mitrephoramaingayi, Garciniacowal, Knemaerratica, Ardisiatenera, Mezzettiopsiscreaghii, Dichmpetalum gelonioides, etc.366.0Lateritic and red lateratic soil
NMGAgropyron cristatum , Stipa grandis, Cleistogenes squarrosa and Carex duriuscula, etc.0.51.4Chernozem soil
HBGCTPotentilla fruticosa , Kobresia humilis, Stipa aliena, Poa orinosa, Helictotrichon tibeticum, Elymus nutans, Aster flaccidus, Polygonum viviparum, and Leontopodium nanum, etc.0.32.8Alpine meadow soil,
alpine scrubby meadow soil,
and swamp soil
DXKobresiapygmaea , Stipa capillacea, Carex montiseverestii, etc.0151.8Meadow soil with sandy loam
YCWinter wheat and summer maizeWinter wheat:0.9
Summer maize: 2.5
Winter wheat: 6.5
Summer maize:4.6
Aquox and salt aquox; surface soil is rich in light-mid loam
Notes:1). The canopy height of grassland and cropland indicates the maximum vegetation height during the growing season; 2). LAI denotes the maximum LAI (leaf area index) during the growing season.
2.2   Data collection
The same instruments were applied in each site for measuring the ecosystem fluxes and meteorological elements. The data measurement and collection was completed automatically.
Composition and models of the instruments: The sensors, analyzers of the instruments and their manufacturers are presented in Table 3.
Table 3   The sensors, analyzers of the instruments and their manufacturers
Measurement systemMeasured elementsModel of sensor or analyzerManufacturers
Routine meteorological systemAir temperatureHMP45CVAISALA
Precipitation5220/TE525MMRM YOUNG
Global radiationCM11KIPP&ZONEN
Photosynthetic radiationLI190SBLI-COR
Eddy covariance systemSonic anemometerCSAT3CAMPBELL
CO2 and H2O densitiesLI7500LI-COR
Data collection and transmissionMeteorological elementsCR10X/CR23XCAMPBELL
Eddy fluxesCR5000CAMPBELL
Instrument installation and deployment: Considering the local terrain and vegetation height at each site, different sensors and analyzers were installed on the flux towers for continuous, real-time measurement of CO2, H2O, energy fluxes and meteorological elements (Table 4).
Table 4   The installation height of different sensors (m)
Routine meteorological systemAir temperature*1323927422222
Air temperature*2283124381111
Global radiation323936422222
Photosynthetic radiation323936422222
Eddy covariance systemCO2 and H2O fluxes40392748.82222
Canopy height26. wheat)
3.0(summer maize)
Notes: 1 and 2 indicate the two different heights for air temperature at one site.
Data collection and transmission: The raw data observed by Eddy covariance system and routine meteorological system were recorded at 10Hz and 30 min, respectively. Both were collected and saved with the dataloggers as indicated in Table 4. According to the local conditions, the data was downloaded through the cable (Changbai Mountain site, Qianyanzhou site, Dinghu Mountain site, Xishuangbanna site and Yucheng Site) or by using memory card (Inner Mongolia site, Haibei site and Dangxiong site) from the datalogger for subsequent quality control, standard processing and product formation.
2.3   Data processing and product formation
ChinaFLUX developed standard protocols to process the raw observed data of ecosystem carbon and water fluxes (Figure 2).
Data quality control and quality assurance: Widely-recognized methods were applied for data quality control and quality, including raw data analysis,2 sonic temperature correction,9 coordinate rotation,10 WPL correction,11 frequency loss correction,12 canopy storage estimation,13 steady state test and turbulent characteristics analysis,14 friction velocity threshold filtering,15outlier removal,16 and energy balance closure evaluation.17Among them, the double coordinate rotation was applied to grassland and cropland, while the planar fit method was applied to forest. The canopy storage of CO2, H2O and heat was estimated with the single point method, which only considered forests with high canopy.
Gap filling: Short gaps (< 2 hour) in the missing flux and meteorological data were filled through interpolation. Large gaps ( >= 2 hour) in the meteorological data were first filled with records from their adjacent meteorological field operated by CERN. Remaining gaps would be filled by the mean diurnal variation method.
Nonlinear regression method was applied to fill large gaps ( >= 2 hour) in the CO2 flux data,18-19while the Arrhenius model was utilized to fill the nighttime gaps.20 If the site suffered from drought stresses (e.g., Qianyanzhou site, Inner Mongolia site and Dangxiong site), Q10 in the Arrhenius model was modified as the linear regression of both soil temperature and soil moisture.21 The rectangular hyperbola function was adopted to fill the daytime gaps within a time window of 7 days.
For the large gaps ( >= 2 hour) in energy flux data, the marginal distribution sampling method was adopted.15
CO2 flux partitioning: The marginal distribution sampling method was utilized to partition the net CO2 flux into gross ecosystem productivity and respiration.15 First, the Arrhenius model, which was used for gap filling previously, was used to determine the coefficients of the aspiration equation based on nighttime data, so as to estimate the daytime and nighttime ecosystem respiration. After that, both the interpolated daytime CO2 flux and daytime respiration were summed to obtain the daytime ecosystem gross productivity. The nighttime gross productivity was assumed as zero due to the lack of plant photosynthesis during nighttime.

Fig.2   Data processing and quality control developed by ChinaFLUX (modified from Reference 8)
3.   Sample description
3.1   Naming scheme and data size
This dataset covers data observed from eight sites, corresponding to eight subsets that total66 excel files and 78.94 MB (Table 5). The half-hour scale data files are named in the format "AAATL30MIN_GFYY.xls" and "AAAQX30MIN_GFYY.xls", respectively, where AAA indicates site code, TL means flux, QX means meteorology,30MIN means time scale of the data, GF means gap filled data, and YY means observation year (e.g., 2003 was abbreviated as 03).
For daily scale data, a similar naming scheme is applied, except that “30MIN” was replaced by “DAILY” in these cases.
Table 5   The naming of each subset and their sizes
Site codeYearHalf-hour scaleSize(MB)Daily scaleSize(KB)
SumNumber offiles66Size(MB)78.94
3.2   Data items
Tables 6 and 7 present items in the flux data file and the meteorological data file at half hour scale, respectively. Table 8 presents items in the daily flux data file.
Table 6   Items of half hour-scale flux data
CO2 FluxNumbermgm-2 s-1Corrected CO2 flux without gap filling0.03017
Latent fluxNumberW m-2Corrected latent flux without gap filling−0.28914
Sensible fluxNumberW m-2Corrected sensible flux without gap filling−9999*
Notes:-9999 indicates that the original data was either lost during measurement or was removed during data quality control.
Table 7   Items of half hour-scale meteorological data
Air temperature*NumberAir temperature under canopy−18.53
Air temperature*NumberAir temperature above canopy−18.81
Global radiationNumberW m-2Mean global radiation277.2
Photosynthetic radiationNumberμmol m-2 s-1Mean photosynthetic radiation483.2
PrecipitationNumbermmAccumulative rainfall0.2
Notes: For grassland and cropland, both air temperatures indicate air temperature above canopy, while the installation height for the former was lower than that of latter. Specific installation heights are listed in Table 4.
Table 8   Items of daily-scale flux data
NEENumberg C m-2 d-1Daily accumulated net ecosystem exchange-0.03325
GEENumberg C m-2 d-1Daily accumulated gross ecosystem exchange-0.52953
ReNumberg C m-2 d-1Daily accumulated ecosystem respiration0.49628
LENumberKg H2O m-2d-1Daily accumulated latent heat flux0.02922
HNumberKg H2O m-2d-1Daily accumulated sensible heat flux0.46578
Notes: “-99999”indicates that the daily accumulation could not be accounted because of too many unfilled gaps for that day.
4.   Data quality control and assessment
4.1   Data quality control
Based on generally accepted techniques and methods for global flux observation, ChinaFLUX established its standard protocols based on technical flows widely recognized by the global flux observation circle,8for field measurement, data collection, quality assurance (QA), quality control (QC), gap filling and flux partitioning to support the long-term and continuous multi-site coordinated observation (Figure 2). According to a systemic comparison among ChinaFLUX, JapanFlux and KoFlux organized by AsiaFlux, the calculated fluxes showed a great consistency.22
4.2   Data quality assessment
Using the general methods for flux data quality assurance, the spectrum analysis indicated that the power spectra of 3D wind velocity, CO2, H2O and temperature within the inertia subrange abided by the -2/3 law, and the co-spectrum of CO2, H2O, temperature with vertical wind velocity abided by the -4/3 law.23 The energy closure analysis indicated that the energy balance ratio (EBR) varied from 0.57 to 0.95, averaged 0.73,24similar to the variation range of global flux observations.17 The uncertainty analysis showed that the annual CO2 flux was mainly influenced by the threshold of friction wind speed set during the nighttime data processing. Under the impact of the uncertainty of data processing, annual carbon exchange and annual ecosystem respiration had a relative deviation of 3.88%–11.41%and 6.45%–24.91%, respectively.25
At the half hour scale, the mean valid data coverage for CO2 flux, latent heat flux and sensible heat flux was 42.6%±4.6%, 54.1%±8.8% and 54.7%±9.0% across the different sites and years, respectively. However, when the daytime data was concerned only, the ratio for CO2 flux increased to more than 60%–70% (Table 9). Flux data missing could be attributed to two major reasons: one was site-specific, including power breakdown, instrument failure, maintenance or disturbance, and the other resulted from data control, including outlier removal, nighttime data filtering, of which nighttime data filtering and quality control accounted for the main reasons of the lower data validity.
Table 9   The valid data coverage of corrected fluxes for each site (%)
Site codeYearCO2 fluxLatent heat fluxSensible heat flux
5.   Usage notes and suggestions
This dataset was publicized simultaneously by the synthesis center of CERN and the synthesis center of ChinaFLUX. Users can login in the Data Source and Service website ( to submit on-line requests for dataset access, or visit Science Data Bank (
As there has been a lack of consensus regarding the data processing and quality control of the eddy covariance flux measurements, five points are suggested here for the usage of this dataset.
(1) Subject to a combined influence of terrain, vegetation and climate conditions, no consensus has been reached as to relevant data processing techniques or methods.
(2) In order to minimize the bias from different data processing methods, this dataset was produced following the ChinaFLUX protocols. As a result, the data presented here might deviate from those provided by respective sites.
(3) With the theoretical and technical development of data processing, ChinaFLUX will update its protocols, which may also results in data deviations.
(4) Affected by the instrument status and quality control measures, Some data are missing. Considering possible uncertainties resulting from data interpolation, model validation and modification are advised first for checking valid data coverage, where dates with less missing data should be selected. For the studies on ecosystem carbon and water exchange processes, flux data without interpolation should be selected.
(5) For the detailed data control and processing, please refer to References [3, 8 & 23].
Each field station of ChinaFLUX provided important support to ournetwork operation and data accumulation. Particularly, staff of the following stations made great contribution to on-site equipment maintenance and data collection: Changbai Moutain, Qianyanzhou, Dinghu Mountain, Xishuangbanna, Inner Mongolia, Haibei and Yucheng.
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Data citation
1. Yu G, He Y, Wang Y et al. Carbon and water fluxes observed by the Chinese Flux Observation and Research Network (2003 – 2005). Science Data Bank. DOI: 10.11922/sciencedb.600 (2018).
Article and author information
How to cite this article
Zhang L, Luo Y, Liu M et al. Carbon and water fluxes observed by the Chinese Flux Observation and Research Network (2003 – 2005). China Scientific Data 4(2019). DOI: 10.11922/csdata.2018.0028.zh
Zhang Leiming
integrated data processing methods and manuscript writing.
Associate Professor.
Luo Yiwei
data processing and manuscript preparation.
Master's candidate.
Liu Min
data quality control.
Associate Professor.
Chen Zhi
data quality assurance.
Associate Professor.
data format standardization, data management and service.
Associate Professor.
He Hongling
general database structural design
Zhu Zhilin
flux data quality control.
Associate Professor.
Sun Xiaomin
technical framework design for ChinaFLUX.
Wang Yanfen
Zhou Guoyi
Zhao Xinquan
Han Shijie
Ouyang Zhu
Zhang Xianzhou
Zhang Yiping
Liu Qijing
Hao Yanbin
Yan Junhua
Zhang Deqiang
Li Yingnian
Wang Anzhi
Wu Jiabing
Li Fadong
Zhao Fenghua
Shi Peili
He Yongtao
Song Qinghai
Liu Yunfen
Yu Guirui
overall operation and development of ChinaFLUX.
National Key Research and Development Program of China (2017YFC0503801, 2016YFA0600104), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020302), Science and Technology Service Network Initiative of the Chinese Academy of Sciences (STS Plan,KFJ-SW-STS-169).
Publication records
Published: Jan. 11, 2019 ( VersionsEN1
Released: July 9, 2018 ( VersionsZH3
Published: Jan. 11, 2019 ( VersionsZH4