Carbon-nitrogen-water Fluxes and Auxiliary Parameters of China's Ecosystems Zone II Versions EN1 Vol 4 (1) 2019
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A dataset of actual evapotranspiration and water use efficiency of typical terrestrial ecosystems in China (2000 – 2010)
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Abstract & Keywords
Abstract: Evapotranspiration (ET) is a central process in the terrestrial hydrological cycle and energy balance. Water use efficiency (WUE) reflects the coupling between the carbon and water cycles. Both ET and WUE have been widely used in the research related to ecology, agriculture, hydrology, and climatology. The eddy covariance (EC) method is regarded as the only standard method for directly measuring exchanges of material and energy between the biosphere and atmosphere, as well as the most important method for observing gross primary productivity (GPP) and ET at an ecosystem scale. By synthesizing eddy-covariance carbon and water flux data in China, using both ChinaFLUX observations and the published literature, we have constructed a dataset recording the actual evapotranspiration and water use efficiency of typical terrestrial ecosystems in China. The dataset contains 143 records of annual actual ET and 96 records of annual mean water use efficiency for 45 ecosystems across China during 2000 – 2010. This dataset can provide data support for analyses on the terrestrial carbon and water cycles, ecosystem management and evaluation, global change, and other related research.
Keywords: actual evapotranspiration; water use efficiency; eddy covariance; terrestrial ecosystem; China; ChinaFLUX
Dataset Profile
TitleA dataset of actual evapotranspiration and water use efficiency of typical terrestrial ecosystems in China (2000 – 2010)
Data corresponding authorYu Guirui (yugr@igsnrr.ac.cn)
Data authorsZheng Han, Yu Guirui, Zhu Xianjin, Wang Qiufeng, Zhang Leiming, Chen Zhi, Sun Xiaomin, He Honglin, Su Wen, Wang Yanfen, Han Shijie, Zhou Guoyi, Zhao Xinquan, Wang Huimin, Ouyang Zhu, Zhang Xianzhou, Zhang Yangjian, Shi Peili, Li Yingnian, Zhao Liang, Zhang Yiping, Yan Junhua, Wang Anzhi, Zhang Junhui, Hao Yanbin, Zhao Fenghua, Zhang Fawei, Zhou Guangsheng, Lin Guanghui, Chen Shiping, Liu Shaomin, Zhao Bin, Jia Gensuo, Zhang Xudong, Zhang Yucui, Gu Song, Liu Wenzhao, Li Yan, Wang Wenjie, Yang Dawen, Zhang Jinsong, Zhang Zhiqiang, Zhao Zhonghui, Zhou Shiqiao, Guo Haiqiang, Shen Yanjun, Xu Ziwei, Huang Hui, Meng Ping
Time range2000 – 2010
Geographical scopeTypical terrestrial ecosystems in China
Data volume143 entries for actual evapotranspiration and 96 entries for water use efficiency
Data format*.xlsx
Data service system<http://www.cnern.org.cn/data/meta?id=40573>;
<http://www.sciencedb.cn/dataSet/handle/610>
Sources of fundingNational Natural Science Foundation of China (31700414, 31500390), National Key Research and Development Program of China (2016YFA0600104), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020302), Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-169).
Dataset compositionThe dataset consists of one data file with four sections in total: (1) basic information for each ecosystem, including ecosystem code, ecosystem name, province, latitude, longitude, altitude, ecosystem type, vegetation type, dominant species, mean annual temperature, and mean annual precipitation; (2) annual actual evapotranspiration data for the observational periods of each ecosystem; (3) annual mean water use efficiency data for the observational periods of each ecosystem; (4) references.
1.   Introduction
Evapotranspiration (ET) is a central process in the terrestrial hydrological cycle and energy balance, and is closely coupled with the carbon cycle of terrestrial ecosystems.1-2 As an important indicator linking an ecosystem’s coupled carbon and water cycles, water use efficiency (WUE) is generally defined as the ratio of gross primary productivity (GPP) over evapotranspiration (ET) at the ecosystem scale,3-4 which is meaningful for understanding ecosystem-atmosphere interactions and water resource management. Therefore, data on the actual ET and WUE of terrestrial ecosystems is crucial for research into terrestrial carbon-water cycles, ecosystem management, ecosystem service assessment, global change, and other related research fields under the background of global climate change.5-6
A number of in situ observational methods for ascertaining GPP and ET have been widely applied at different spatial and temporal scales. Of these the eddy covariance (EC) method can measure the vegetation-atmosphere carbon and water fluxes at the ecosystem scale on a continuous and long-term basis with almost no empirical hypothesis.7-9 The GPP and ET data obtained from EC method have been widely accepted by micrometeorologists and ecologists. FLUXNET and several regional networks (e.g., AmeriFlux, ChinaFLUX) have been operating9 using EC as the main observation method, which makes it possible to explore the spatial-temporal variations in the regional actual ET and WUE based on the measurements of these networks.
China has utilized the EC method to measure carbon and water fluxes of typical ecosystems since the establishment of the Chinese terrestrial ecosystem flux observation and research network (ChinaFLUX) in 2002,10 filling an observational gap in the Asian monsoon region. Abundant carbon and water flux observations have been collected, which provide an important opportunity to construct a dataset of the actual ET and WUE of typical terrestrial ecosystems in China. Based on the long-term observation of ChinaFLUX sites and published data from other flux sites in China, we synthesized the eddy-covariance flux data in China and constructed a dataset of the actual ET and WUE of typical terrestrial ecosystems in China during 2000-2010. This can be used to provide data support for analyses on terrestrial carbon and water cycles, ecosystem management and evaluation, global change, and other related research at regional and even global scales.
2.   Data collection and processing
By synthesizing ChinaFLUX observations and published data from other sites in China during 2000-2010, we obtained eddy-covariance carbon and water flux data from 45 typical ecosystems in China (Figure 1 shows the location information. Table 1 shows the basic information), and using these constructed an actual ET sub-dataset and a WUE sub-dataset. The actual ET sub-dataset contains 143 entries from 45 ecosystems, covering 14 forests, 12 grasslands, 11 croplands, 6 wetlands and 2 desert ecosystems. The WUE sub-dataset contains 96 entries from 34 ecosystems, covering 12 forests, 10 grasslands, 4 croplands, 6 wetlands and 2 desert ecosystems.


Fig.1   Locations of 45 ecosystems associated with the ET and WUE dataset
Table 1   Basic information of the 45 ecosystems
Ecosystem CodeEcosystem NameLatitude (°N)Longitude (°E)Elevation (m)Ecosystem Type
ALSAilaoshan24.53101.022476Forest
CBSChangbaishan42.4128.1738Forest
CWChangwu35.23107.671200Cropland
DHSDinghushan23.17112.53300Forest
DLCDuolun cropland42.05116.671350Cropland
DLGDuolun grassland42.05116.281350Grassland
DT1Dongtan 131.52121.964Wetland
DT2Dongtan 231.58121.94Wetland
DT3Dongtan 331.52121.974Wetland
DXDangxiong30.8591.084333Grassland
DXCDaxing cropland39.62116.4320Cropland
DXFDaxing forest39.53116.2530Forest
FKFukang44.2887.93475Desert
GQGaoqiao21.57109.7622.8Wetland
GTCGuantao36.52115.1330Cropland
HB1Haibei alpine meadow37.6101.33250Grassland
HB2Haibei shrubland meadow37.66101.333293Grassland
HB3Haibei swamp
meadow
37.61101.313160Grassland
HNHuaining3311715Forest
HTHuitong26.83109.75330Forest
JZJinzhou41.15121.217Cropland
KBQDKubuqi desert40.38108.551169.2Desert
KBQFKubuqi forest40.54108.691033Forest
LCLuancheng37.83114.6750Cropland
LSLaoshan45.33127.57340Forest
MYMiyun40.63117.32350Forest
PJCPanjin paddy41.15121.923.8Cropland
PJWPanjin wetland41.14121.917Wetland
QYZQianyanzhou26.74115.06102Forest
SJYSanjiangyuan34.35100.53963Grassland
SNTZSunitezuo44.08113.57974Grassland
TYCTongyu cropland44.57122.92184Cropland
TYGTongyu grassland44.59122.52184Grassland
WLWSWulanwusu44.2885.82469Cropland
WSWeishan36.65116.0530Cropland
XFSXilinhot Stipa krylovii steppe44.13116.331030Grassland
Xi1Xilinhot fenced steppe43.55116.681250Grassland
Xi2Xilinhot degraded steppe43.55116.671250Grassland
Xi3Xilinhot Leymus
chinensis steppe
43.55116.681200Grassland
XLDXiaolangdi35.02112.47410Forest
XSBN1Xishuangbanna rain forest21.93101.27750Forest
XSBN2Xishuangbanna rubber plantation21.93101.27750Forest
YCYucheng36.95116.5728Cropland
YXYunxiao23.92117.4264.5Wetland
YYYuyang29.31112.5131Forest
2.1   Data observation and process of ChinaFLUX
The ChinaFLUX program was launched in 2002, relying on the existing Chinese Ecosystem Research Network (CERN). ChinaFLUX fills an important regional gap and increases the number of ecosystem types in FLUXNET.10-11 The measurement system of ChinaFLUX sites is uniformly composed of an EC system and a meteorological measurement system. The Open-Path Eddy Covariance (OPEC) system is used to measure carbon and water fluxes at ChinaFLUX sites. The OPEC system is composed of an open-path fast response infrared CO2/H2O analyzer (Li-7500, Li-Cor Inc., Lincoln, Nebraska, USA), a three-dimensional sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, Utah, USA), and a data logger (CR5000, Campbell Scientific Inc., Logan, Utah, USA). All signals are sampled with a frequency of 10 Hz and then calculated and recorded at 30 min intervals. Standard meteorological variables are measured synchronously and recorded with a data logger (CR10X and CR23X, Campbell Scientific Inc., Logan, Utah, USA) at 30 min intervals, including solar radiation, net radiation, photosynthetically active radiation, air temperature, air humidity, wind speed, wind direction, precipitation, soil temperature, soil moisture, soil heat flux, etc.
The routine processing procedures recommended by ChinaFLUX were applied to process the raw 30 min eddy flux data (Figure 2), including coordinate rotation, Webb-Pearman-Leuning (WPL) correction, storage term calculation, outlier filter and gap filling10. The coordinate rotation was used to make the average vertical wind speed zero and to force the horizontal wind to the mean wind direction.7,12-13 WPL correction was used to adjust the effects of density change on CO2 and H2O fluxes.7,14 The storage flux below the EC height was calculated for the forest ecosystems.15-16 Spurious data were removed from the data series if the instrument performance and experimental conditions were abnormal, using flux threshold, precipitation events, standard deviation, and low turbulence flux as the criteria. The gaps in the flux data series were filled using the following methods. Short data gaps (<2 h) in the carbon and water flux data were filled with linear interpolation, and longer gaps in the water flux data were filled using a look-up table method. The non-linear regression method was applied for the longer gaps in the carbon flux data,17 so that daytime gaps were filled using a rectangular hyperbola function between carbon flux and photosynthetically active radiation, and the nighttime gaps were filled using the exponential relationship between ecosystem respiration (Re) and temperature.


Fig.2   Flow chart showing the data processing of carbon and water fluxes in ChinaFLUX
Notes: NEE = net ecosystem exchange, GPP = gross primary productivity, ET = evapotranspiration, WUE = water use efficiency. 'wpl' indicates data after WPL correction, and 's' as storage term corresponding to NEE or ET.
In order to obtain the ecosystem GPP data, the net ecosystem exchange (NEE, i.e., carbon flux) data measured using the eddy covariance method was separated into GPP and Re using a non-linear regression method18. First, the nighttime NEE measurements were used to determine the coefficients in the Re equation, which were the same in the gap-filling regression equation, and then the daytime and nighttime Re data were calculated. Second, GPP data was estimated as the sum of daytime NEE and daytime Re for the same time period.
Based on the data collection and processing methods of ChinaFLUX above, we obtained the complete time series of 30 min GPP and ET (i.e., water flux) data from eight ecosystems. The 30 min GPP and ET data were collected to obtain annual GPP and ET data for a given ecosystem, and the ratio of annual GPP to annual ET was regarded as the annual mean WUE value for the corresponding year.
2.2   Collection of published flux data in China
In addition to the ChinaFLUX observations, we also collected published carbon and water flux data of other sites in China from the literature. We adopted the following methods to screen these data. First, GPP and ET data were uniformly measured using the EC method, and subsequently passed through a series of processing procedures performed by individual site researchers, including coordinate rotation, WPL correction, outlier filter and gap filling. Second, annual mean WUE value for a given ecosystem was calculated only when the annual GPP and ET were both available for the same year. Third, only sites with at least one year of continuous flux measurements were selected, and the actual observational period was recorded. For example, the observational period for the ecosystems of DLC and DLG is from December 2005 to November 2006.
The units of the GPP and ET data were uniformly transformed into g C m-2 yr-1 and kg H2O m-2 yr-1 (i.e., mm yr-1), respectively, and thus the WUE data had a unit of g C kg-1 H2O. We also collected the basic information for each ecosystem, including geographical factors (i.e., latitude, longitude and elevation), vegetation type, the dominant species, the mean annual air temperature, and the mean annual precipitation.
3.   Sample description
This dataset is composed of four Excel sheets as showed in Table 2:
(1) The ‘Ecosystem basic information’ sheet contains information on ecosystem code, ecosystem name, latitude, longitude, elevation, ecosystem type, vegetation type, dominant species, references, etc. Among them, the ecosystem was coded based on the initial letters of the ecosystem name; for instance, CBS is the acronym for Chang Bai Shan. The code representing ecosystem management measure or ecosystem type has been applied to distinguish between ecosystems with the same acronym. For instance, DLC and DLG represent the cropland and grassland ecosystem in Duolun, respectively. The vegetation type for each ecosystem was determined according to the relevant literature and vegetation map of China19. ‘√’ indicates that actual ET or WUE data is available for the given ecosystem in this dataset. The reference corresponds to the reference with the same number in the ‘Reference’ sheet.
(2) The ‘Actual evapotranspiration data’ contains annual actual ET values during observational period for each ecosystem. For example, the annual actual ET of CBS site in the year of 2003 is 520.56 mm.
(3) The ‘Water use efficiency data’ sheet contains annual mean WUE values during the observational period for each ecosystem. For example, the annual mean WUE of the CBS site in the year of 2003 is 2.62 g C kg-1 H2O.
(4) The ‘Reference’ sheet contains the detailed reference for the ‘Reference’ item in the ‘Ecosystem basic information’ sheet.
Table 2   Data table structure of ET and WUE dataset
Data ItemData TypeSample
Ecosystem basic information
IDNumber3
Ecosystem codeCharacterCBS
Ecosystem nameCharacter长白山
ProvinceCharacter吉林
Latitude (°N)Number42.40
Longitude (°E)Number128.10
Elevation (m)Number738
Ecosystem typeCharacter森林
Vegetation typeCharacter温带针阔混交林
Dominant speciesCharacter红松、紫椴、蒙古栎、水曲柳、色木槭
Mean annual temperature (℃)Number3.6
Mean annual precipitation (mm yr1)Number695.3
Actual evapotranspiration dataCharacter
Water use efficiency dataCharacter
ReferenceCharacter[2]
Actual evapotranspiration data
Ecosystem codeCharacterCBS
DurationDate2003
Annual actual ET (mm yr1)Number520.56
Water use efficiency data
Ecosystem codeCharacterCBS
DurationDate2003
Annual mean WUE (g C kg-1 H2O)Number2.62
References
IDCharacter[2]
ReferenceCharacterZHANG et al. (2006)
4.   Quality control and assessment
ChinaFLUX has established a series of strict measures to guarantee the quality of flux data and the operation of long-term network observations. First, the observation equipment, elements and methods are uniform and have been standardized at different sites. Second, multi-level data verification was established to guarantee the data quality. The ChinaFLUX sites turn over raw high frequency flux observations to the Synthesis Research Center of CERN after elementary examinations each year. The Synthesis Research Center of CERN then conducts further verification and assessment on the equipment performance and data quality, and exports the 30 min flux data with uniform formats. Finally, the routine processing procedures of ChinaFLUX were applied to the 30 min flux data (Figure 2) to ensure the reliability and consistency of the flux data from different ChinaFLUX sites.
For the quality control and assessment on the published data from other sites in China, the following methods were adopted to screen the published data: (1) The carbon and water flux data were uniformly measured using the EC method, and subsequently passed a series of data processing procedures performed by individual site researchers; (2) Only sites with at least one year of continuous flux measurements were selected; (3) Only peer-reviewed literature was used to guarantee the credibility of published data. At the same time, self-examination and expert review were utilized to further ensure the accuracy and reliability of the GPP and ET data from the literature.
5.   Usage notes
The dataset can be used for research into terrestrial carbon and water cycles, ecosystem management and evaluation, global change, and other related research, and also provides ground-based observations for model verification. Readers can also conduct multi-site comparison studies by selecting sites from typical regions or ecosystem types.
It should be noted that the data processing techniques and methods for eddy flux data have not been universally recognized due to diverse land surfaces, vegetation characteristics, and climatic conditions. A researcher’s preferences (e.g., for parameter setting) could affect the calculation results even with the same data processing procedures. In this dataset, the carbon and water fluxes of ChinaFLUX sites were calculated with the routine processing procedures recommended by ChinaFLUX, which may differ from the calculations by the researchers from a given site. The GPP and ET data from the literature were calculated by different researchers using independent data quality control and data processing procedures. Thus, the differences in processing carbon and water fluxes may result in deviations in the actual evapotranspiration and water use efficiency data to some extent. The readers can refer to our published papers5-6 when having problems in using this dataset.
The dataset is available in the data resources service website of CERN (http://www.cnern.org.cn). After logged in, click ‘data for papers’ icon on the home page or select ‘paper data’ in the data resources section and then the reader can download data. The dataset is also available in the website of Science Data Bank online (http://www.sciencedb.cn/dataSet/handle/610).
Acknowledgments
We acknowledge CERN, ChinaFLUX, and all eddy-covariance flux sites involved in this dataset for contributing data and other support. We thank Professor He Nianpeng for suggestions about how to improve this paper.
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Yu G, Wang Q & Yu Z. Study on the coupling cycle of water-carbon and process management in terrestrial ecosystem. Advances in Earth Science 19(2004): 831-839.
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Yu G, Song X, Wang Q et al. Water-use efficiency of forest ecosystems in eastern China and its relations to climatic variables.New Phytologist 177(2008): 927-937.
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Zheng H, Lin H, Zhou W et al. Revegetation has increased ecosystem water-use efficiency during 2000–2014 in the Chinese Loess Plateau: Evidence from satellite data. Ecological Indicators. 102(2019): 507-518.
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Zheng H, Yu G, Wang Q et al. Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: Results from eddy covariance measurements.Journal of Geographical Sciences 26(2016): 1391-1411.
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Zhu X, Yu G, Wang Q et al. Spatial variability of water use efficiency in China's terrestrial ecosystems.Global and Planetary Change 129(2015): 37-44.
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Yu G & Sun X. Principles of Flux Measurements in Terrestrial Ecosystems. Beijing: Higher Education Press, 2006.
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Baldocchi D. 'Breathing' of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems.Australian Journal of Botany 56(2008): 1-26.
9.
Baldocchi D, Falge E, Gu L et al. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities.Bulletin of the American Meteorological Society 82(2011): 2415-2434.
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Yu G, Wen X, Sun X et al. Overview of ChinaFLUX and evaluation of its eddy covariance measuremen.Agricultural and Forest Meteorology 137(2006): 125-137.
11.
Yu G, Zhu X, Fu Y et al. Spatial patterns and climate drivers of carbon fluxes in terrestrial ecosystems of China.Global Change Biology 19(2013): 798-810.
12.
Finnigan J, Clement R, Malhiy et al. A re-evaluation of long-term flux measurement techniques-Part I: Averaging and coordinate rotation.Boundary-Layer Meteorology 107(2003): 1-48.
13.
Zhu Z, Sun X, Zhou Y et al. Correcting method of eddy covariance fluxes over non-flat surfaces and its application in ChinaFLUX.Science in China Series D-Earth Sciences 48(2005): 42-50.
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Webb E, Pearman G & Leuning R. Correction of flux measurements for density effects due to heat and water vapour transfer.Quarterly Journal of the Royal Meteorological Society 106(1980): 85-100.
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Hollinger D, Kelliher F, Byers J et al. Carbon Dioxide Exchange between an Undisturbed Old-Growth Temperate Forest and the Atmosphere.Ecology 75(1994): 134-150.
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Zhang M, Wen X, Yu G et al. Effects of CO2 storage flux on carbon budget of forest ecosystem. Chinese Journal of Applied Ecology 21(2010): 1201-1209.
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Falge E, Baldocchi D, Olson R et al. Gap filling strategies for long term energy flux data sets[J].Agricultural and Forest Meteorology 107(2001): 71-77.
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Data citation
Zheng H, Yu G, Zhu X et al. A dataset of actual evapotranspiration and water use efficiency of typical terrestrial ecosystems in China (2000 – 2010). China Scientific Data 4(2019). DOI: 10.11922/csdata.2018.0034.zh
Article and author information
Zheng Han
data collection and manuscript preparation.
PhD, postdoctoral researcher, research area: terrestrial carbon and water cycles.
Yu Guirui
overall design and manuscript revision.
PhD, Professor, research area: ecosystem ecology, global change and carbon-nitrogen-water cycle.
Zhu Xianjin
data collection and manuscript preparation.
PhD, Associate Professor, research area: global change and carbon-water cycle.
Wang Qiufeng
manuscript revision.
PhD, Associate Professor, research area: global change and carbon-water cycle.
Zhang Leiming
data quality control.
PhD, Associate Professor, research area: ecosystem carbon-water dynamic and global change.
Chen Zhi
data collection.
PhD, Assistant Professor, research area: global change and carbon cycle.
Sun Xiaomin
technical support of ChinaFLUX.
PhD, Professor, research area: remote-sensing retrieval of regional surface fluxes.
He Honglin
overall design of ChinaFLUX database.
PhD, Professor, research area: ecological informatics.
Su Wen
data processing, data management and services.
Senior Engineer.
Wang Yanfen
Han Shijie
Zhou Guoyi
Zhao Xinquan
Wang Huimin
Ouyang Zhu
Zhang Xianzhou
Zhang Yangjian
Shi Peili
Li Yingnian
Zhao Liang
Zhang Yiping
Yan Junhua
Wang Anzhi
Zhang Junhui
Hao Yanbin
Zhao Fenghua
Zhang Fawei
Zhou Guangsheng
Lin Guanghui
Chen Shiping
Liu Shaomin
Zhao Bin
Jia Gensuo
Zhang Xudong
Zhang Yucui
Gu Song
Liu Wenzhao
Li Yan
Wang Wenjie
Yang Dawen
Zhang Jinsong
Zhang Zhiqiang
Zhao Zhonghui
Zhou Shiqiao
Guo Haiqiang
Shen Yanjun
Xu Ziwei
Huang Hui
Meng Ping
National Natural Science Foundation of China (31700414, 31500390), National Key Research and Development Program of China (2016YFA0600104), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020302), Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-169)
Publication records
Published: Dec. 29, 2018 ( VersionsEN1
Released: June 28, 2018 ( VersionsZH2
Published: Dec. 29, 2018 ( VersionsZH5
References
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