Carbon-nitrogen-water Fluxes and Auxiliary Parameters of China's Ecosystems Zone II Versions EN1 Vol 4 (1) 2019
Download
A dataset of carbon density in Chinese terrestrial ecosystems (2010s)
 >>
: 2018 - 05 - 20
: 2018 - 06 - 12
: 2018 - 12 - 28
467 5 0
Abstract & Keywords
Abstract: As a major player in the carbon pool of the global terrestrial ecosystem, China contributes considerably to the global carbon cycle in terms of both carbon emission and uptake. We collected data on the carbon density in Chinese terrestrial ecosystems from literature published between 2004 and 2014, and integrated them with relevant experimental data of the same period to build a comprehensive and systematic dataset of vegetation and soil organic carbon density in China. The dataset encompasses forest, grassland, cropland, shrub, and wetland ecosystems in China, and contains carbon density data for the major components, including aboveground biomass, underground biomass, and soil organic carbon density of different soil depths (0–20 cm and 0–100 cm). The dataset is valuable for assessments of regional carbon stocks and ecosystem quality, and provides basic data for ecological model optimization.
Keywords: China; terrestrial ecosystem; carbon density
Dataset Profile
TitleA dataset of carbon density in Chinese terrestrial ecosystems (2010s)
Datacorresponding authorYu Guirui (yugr@igsnrr.ac.cn)
Data authorsXu Li, He Nianpeng, Yu Guirui
Time period2000–2014
Geographical scopeChinese terrestrial ecosystem
Data volume2.71 MB, 15610 entries
Data format*.xlsx
Data service system<http://www.cnern.org.cn/data/meta?id=40579>; <http://www.sciencedb.cn/dataSet/handle/603>
Sources of fundingStrategic Priority Research Program of the Chinese Academy of Sciences (XDA19020302), National Key Research and Development Program of China (2016YFA0600104), Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-169).
Dataset compositionThe dataset consists of 5 subsets in total. It comprises literature data and experimental data, covering forest, grassland, cropland, shrub, and wetland. The subsets are recorded as sheet 1, sheet 2, sheet 3, sheet 4, and sheet 5, used to store data (sheets 1–4) and references (sheet 5). The data are categorized into aboveground carbon density, underground carbon density, soil organic carbon density for 0–20 cm and 0–100 cm.
1.   Introduction
Terrestrial ecosystems are not only huge carbon (C) pools in the earth system,1-2 but also a major C sink for the atmosphere,3 which plays an important role in the global C cycle.4-5 As China covers 6.4% of the global terrestrial area, small changes (increases or decreases) in the C storage of Chinese terrestrial ecosystems may cause significant changes in the global C balance.2,6 However, owing to a lack of basic data, most of existing studies on C storage either assessed vegetation C or soil C separately, or only focused on one specific type of ecosystem. Furthermore, their assessment results are of high uncertainty, due to different data sources and varied estimation methods.7-8 Thus, in order to improve the accuracy of C storage estimate in Chinese terrestrial ecosystems, and to reveal the control mechanism of the C spatial distribution, it is essential to establish a comprehensive and systematic dataset of vegetation and soil C density.
Systematic data of vegetation and soil C storage are important for future research on the changes of the ecological environmental quality in Chinese terrestrial ecosystems. Biomass is an important parameter for assessing changes in vegetation or the ecological environment. Biomass can be estimated using the carbon content constant of 0.45 or 0.50, which can be used to change vegetation C density into the corresponding vegetation biomass.9-10 Similarly, we can obtain data for soil organic matter using a conversion coefficient 0.58, since soil organic C is closely related to soil organic matter.11 The systematic data of soil organic C can reflect Chinese soil quality, thereby providing crucial basic information to reveal the impact of changing patterns of land use on the soil nutrient supply capacity and sustainability in future studies.
Given its relative lack, our team collected lots of field-measured C storage data for China’s terrestrial ecosystems from literature published between 2004 and 2014. The dataset encompasses the main ecosystems in China, namely, forest, grassland, cropland, wetland, and shrub, and includes different components (above-ground biomass C density, below-ground biomass C density, and soil organic C density at depths of 0–20 cm and 0–100 cm). The dataset provides fundamental vegetation and soil C density data for accurate estimation of C storage in Chinese terrestrial ecosystems, and can be used for the construction of relevant models and simulations. Furthermore, this dataset provides basic information for the assessment and management of regional and national environmental quality.
2.   Data collection and processing
2.1   Data sources
The vegetation and soil data are mainly collected from two sources: 1) unpublished field data obtained by personal correspondence; and 2) field investigations published in existing literature from 2004 to 2014 retrieved from the Institute for Scientific Information (ISI) (http://apps.webofknowledge.com) and China National Knowledge Infrastructure (CNKI) (http://www.cnki.net) databases (Figure 1). The keywords we used for locating relevant literature included “soil organic C”, “biomass”, “C density”, and “C storage”. Then, the collected papers were further screened for suitability based on the following rules: 1) whether the data on biomass and soil C content were obtained through field measurements, rather than through literature review or model simulation; 2) whether the investigation was performed after 2000; and 3) whether the methods used for measuring biomass and C content were comparable. A total of 1036 papers were selected after the screening. The collected data covered the main ecosystems in China, including forest, grassland, cropland, wetland, and shrub. Wasteland, bare land, and sandy lands were classified as “other” in our dataset.


Fig.1   Flow diagram illustrating the C density dataset construction
2.2   Data processing
For samples without detailed geographical information, we extracted their latitude and longitude using a digital map (http://map.tianditu.com) based on the description of the study site. Reported field measurements of above-ground biomass C and below-ground biomass C density were added to the dataset directly, labeled as “Direct” in the method section of the dataset; for samples reported only as vegetation biomass, a coefficient of 0.45 was used to convert the vegetation biomass density to C density, and the resulting values were labeled as “Indirect” in the method section. Similarly, soil samples that reported the soil organic C density were labeled as “Direct” in the method section; for soil samples that merely had their bulk density, soil organic C content, or soil depth recorded, we used the following equation to calculate soil organic C density and labeled the resulting values as “Direct” in the method section.
\[\rho _{soc}=\sum_{i=1}^{n}SOC_{i}\times BD_{i}\times D_{i}\times \left ( 1-C_{i} \right )\div 10\]
where SOCi , BDi , Di , and Ci represented soil organic C content (%), bulk density (g cm–3), soil depth (cm), and the volumetric percentage of the fraction > 2 mm (%), respectively, in soil layer i, and n was the number of soil layers. For soil depths ≥ 100 cm, we extracted the data down to 100 cm; for soil depths <100 cm, we used the recorded soil depth to calculate the soil organic C density. Soil organic matter was converted to soil organic C using a constant of 0.5811. A classic pedotransfer function was used to estimate the bulk density from the SOC concentration when records were not available.12-13 For soil samples whose rock fragments were not recorded, we used the mean value of the rock fragment volume for the same soil type. The samples calculated by the above method were labeled as “Indirect” in the method section. For samples with multi-year measurement data, we sorted the observed values for each year in the dataset. However, vegetation C density in croplands was not included in this dataset owing to the harvest cycles.
3.   Sample description
The dataset of C density in China’s terrestrial ecosystem consists of two main subsets, namely, one of vegetation biomass carbon density and one of soil organic carbon density. There are 7927 records in the vegetation biomass C density subsets, including 4485 entries for above-ground biomass C density (3328 from existing literature and 1157 from experimental data) and 3442 entries for below-ground biomass C density (2635 from existing literature and 807 from experimental data). There is a total of 7683 records of soil organic C density, including 4536 for soil organic C density at a depth of 0–20 cm (4356 from literature and 180 from experimental data), and 3147 soil organic C density records at a depth of 0–100 cm (2974 from existing literature and 173 from experimental data). Moreover, the dataset includes information on ecosystem type, geographic location (longitude and latitude), C density (kg m–2), sampling time, data type, and data source. There are two data types: one is direct data, which means that the C density was either directly obtained from literature or experiments, or was directly calculated by using relevant parameters based on existing literature; the other is indirect data, which means that neither C densities nor relevant parameters for calculating them were provided.
4.   Quality control and assessment
The dataset was derived from published literature and experimental findings. To extract data from published literature, we followed a rigorous process of database selection, keyword search and literature screening, and data collection and compilation; to obtain experimental data, we performed quality control throughout the whole process (including sampling site selection, plot setting, sample collection, and indoor analysis). In addition, expert verification was adopted to further ensure the accuracy and reliability of the obtained data.
4.1   For data from existing literature
First of all, we should clarify that this dataset is a set of field-measured data reflecting the current status (2010s) of C density of China’s major ecosystem types. The data extraction forms are tailored to the ecosystem types. The data table contains basic information on the sample (including its location, monitoring time, geographical location, climatic conditions, ecosystem type, dominant species, etc.), vegetation C (plant biomass and C content in different organs), and the soil (soil type, depth, bulk density, soil organic C content, etc.)
For literature screening and data extraction, two authoritative databases (ISI and CNKI) were chosen as the data sources, from which data were extracted by using keywords designating vegetation and soil C density, assisted by uniform screening criteria. Entries with vague or incomplete basic information were excluded. We built a document for each piece of literature, which contained the original paper and a data extraction form for the corresponding ecosystem type. Getdata software was then used to extract the graph data for C density, and screenshots of relevant charts and graphs were stored into respective folders for subsequent data verification and query. Furthermore, the data units were carefully checked to ensure no anomaly.
During data collation, samples were sorted into different data tables according to their integrity status, and missing information was completed using a unified interpolation method. We checked the unit and values of the intermediate data and the C density data, and eliminated outliers. Finally, the completed dataset was checked by the data organizer before being delivered to experts for a final review and revision to ensure the authenticity and reliability of the dataset. All files were backed up during the collation of the dataset.
4.2   For data from experimental findings
The other part of the data comes from the findings of experiments conducted by our research group and associated groups. With rich experience in field survey, we organized many field investigations and samplings, and used uniform normative standards for the indoor analyses to ensure the quality of the experimental data. During the process of data summarization, we carefully checked the basic information (survey time, ecosystem type, C density unit, etc.) and corrected any problematic data. The completed dataset was checked by the data organizer before being delivered to experts for a final review and revision to ensure the authenticity and reliability of the dataset.
5.   Usage notes
The dataset of C density in China’s terrestrial ecosystems (2010s), which was constructed based on experimental findings and published literature, covers the main ecosystems in China, including forest, grassland, cropland, wetland, and shrub. This dataset can be used to assess regional ecosystem carbon stocks. Currently, our team has assessed the C storage, spatial pattern and control mechanism of China’s terrestrial ecosystems using this dataset.14-17 Furthermore, this dataset provides data support for ecosystem quality assessment and ecological model optimization. However, certain “indirect” vegetation and soil C density data, which were calculated or converted using relevant parameters or methods, are worth attention as there may be uncertainties.
The dataset can be accessed via the Synthesis Research Center of CERN (http://www.cern.org.cn) or Science Data Bank (http://www.sciencedb.cn/dataSet/handle/603). The former platform requires users to log in, where a click of the “data paper data” icon on the homepage, or “paper data” in the “data resources” section, will direct users to a download page.
Acknowledgments
This study is supported by the leaders of CERN. We are very grateful to Dr. Peng Shunlei, Dr. Ma Anna, as well as graduate students Wen Ding, Chai Hua, Zhao Hang, Liu Yuan, Yu Haili, Zhu Jianxing, Wang Chunyan, Li Ying, Liu Congcong, and Song Guangyan. We also thank Professor Chen Quansheng, Professor Hu Zhongmin, Associate Professor Huang Mei, Associate Professor Wang Qiufeng, Associate Professor Wang Changhui, Dr. Li Jie and Dr. Xue Jingyue for sharing their experimental data!
1.
Heimann M & Reichstein M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451 (2008): 289-292.
2.
Piao S, Fang J, Ciais P, et al. The carbon balance of terrestrial ecosystems in China. Nature 458(2009): 1009-1013.
3.
Le Quere C, Moriarty R, Andrew R, et al. Global carbon budget 2015. Earth System Science Data 7(2015): 349-396.
4.
Fang J, Guo Z, Piao S, et al. Terrestrial vegetation carbon sinks in China, 1981-2000. Science in China Series D: Earth Sciences 50(2007): 1341-1350.
5.
Pan Y, Birdsey R, Fang J, et al. A large and persistent carbon sink in the world's forests. Science 333(2011): 988-993.
6.
Levine M & Aden N. Global carbon emissions in the coming decades: The case of China. Environmental Energy Technologies Division 33(2008): 19-38.
7.
Li K, Wang S & Cao M. Vegetation and soil carbon storage in China. Science of China 47(2004): 49-57.
8.
Ni J. Carbon storage in grasslands of China. Journal of Arid Environment 50(2002): 205-218.
9.
Fang J, Liu G, Xu S, et al. Carbon Reservoir of Terrestrial Ecosystem in China. Beijing: China Environmental Sciences Publishing House, 1996.
10.
Wang J, Zhou W, Xu K, et al. Quantitative assessment of ecological quality in Beijing-Tianjin-Hebei urban megaregion, China. Chinese Journal of Applied Ecology 28(2017): 2667-2676.
11.
Xie Z, Zhu J, Liu G, et al. Soil organic carbon stocks in China and changes from 1980s to 2000s. Global Change Biology 13(2007): 1989-2007.
12.
Yang Y, Mohammat A, Feng J, et al. Storage, patterns and environmental controls of soil organic carbon in China. Biogeochemistry 84(2007): 131-141.
13.
Xu L, He N, Yu GR, et al. Differences in pedotransfer functions of bulk density lead to high uncertainty in soil organic carbon estimation at regional scales: evidence from Chinese terrestrial ecosystems. Journal of Geophysical Research: Biogeosciences 120(2015): 1567-1575.
14.
Xu L, Yu G, He N, et al. Carbon storage in China’s terrestrial ecosystems: A synthesis. Scientific Reports 8(2018): srep2806.
15.
Wen D & He N. Spatial patterns and control mechanisms of carbon storage in forest ecosystem: evidence from the north-south transect of eastern China. Ecological Indicators 61(2016): 960-967.
16.
Ma A, He N, Yu G, et al. Carbon storage in Chinese grassland ecosystems: Influence of different integrative methods. Scientific Reports 6(2016): srep21378.
17.
Peng S, Wen D, He N, et al. Carbon storage in China’s forest ecosystems: estimation by different integrative methods. Ecology and Evolution 6(2016): 3129-3145.
Data citation
1. Xu L, He N & Yu G. A dataset of carbon density in Chinese terrestrial ecosystems (2010s). Science Data Bank. DOI: 10.11922/sciencedb.603 (2018).
Article and author information
How to cite this article
Xu L, He N & Yu G. A dataset of carbon density in Chinese terrestrial ecosystems (2010s). China Scientific Data 4(2019). DOI: 10.11922/csdata.2018.0026.zh
Xu Li
field investigation, data collation, and quality control.
PhD, Assistant Professor; research area: ecosystem ecology.
He Nianpeng
overall design, data collation, and quality control.
PhD, Professor; research area: ecosystem ecology and ecosystem traits.
Yu Guirui
overall design, data collation, and quality control.
yugr@igsnrr.ac.cn
PhD, Professor; research area: ecosystem ecology.
Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020302), National Key Research and Development Program of China (2016YFA0600104), Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-169).
Publication records
Published: Dec. 28, 2018 ( VersionsEN1
Released: June 12, 2018 ( VersionsZH2
Published: Dec. 28, 2018 ( VersionsZH3
References
中国科学数据
csdata