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A new estimation of China's net ecosystem productivity based on eddy covariance measurements and a model tree ensemble approach
Yao, Yitong1; Li, Zhijian2; Wang, Tao3,4; Chen, Anping5; Wang, Xuhui1,6; Du, Mingyuan7; Jia, Gensuo8; Li, Yingnian9; Li, Hongqin9; Luo, Weijun10,11; Ma, Yaoming3,4; Tang, Yanhong12; Wang, Huimin13; Wu, Zhixiang14; Yan, Junhua15; Zhang, Xianzhou13; Zhang, Yiping16; Zhang, Yu17; Zhou, Guangsheng18; Piao, Shilong1,3,4
2018-05-01
Source PublicationAGRICULTURAL AND FOREST METEOROLOGY
ISSN0168-1923
Volume253Pages:84-93
Corresponding AuthorWang, Tao(twang@itpcas.ac.cn)
AbstractAccurate assessment of the strength of China's terrestrial ecosystem carbon sink is key to understanding its regional carbon budget. However, large uncertainties in current carbon sink estimations still exist, which hinder the prediction of future climate change trajectories. In this study, we generated a high-resolution (1 km x 1 km) dataset of China's net ecosystem productivity (NEP) in the last decade via a model tree ensemble approach combined with data from 46 flux sites in China and neighboring regions. The upscaling also included detailed information on nitrogen (N) deposition and forest age that have often been neglected in previous studies. The performance of MTE algorithm in simulating NEP at the site level is relatively high for both training (R-2 = 0.81, RMSE = 0.73 gC m(-2) day(-1)) and validation datasets (R-2 = 0.76, RMSE = 0.81 gC m(-2)day(-1)). Our data-driven estimation showed that roughly 70% of the area is a carbon sink, and the largest carbon sinks are found in the southeast and southwest monsoon regions. The total annual NEP in China in the last decade was 1.18 +/- 0.05 Pg C yr(-1), which is similar to the results found by another foundational global-scale study. Yet, the two studies significantly differ in the spatial distribution of carbon sink density. The seasonality of China's NEP is characterized by region-specific kurtosis and skewness in most areas. Furthermore, ecosystem carbon use efficiency (CUE), defined as the annual NEP/GPP ratio, also showed high spatial variation. For example, the Xiaoxing'anling and Changbai Mountains in northeastern China, the eastern edge of the Tibetan Plateau, and bordering areas of the southeast and southwest monsoon regions have a larger CUE than the rest of China. On average, China's terrestrial ecosystem CUE is approximately 0.17. Our data-driven NEP and CUE estimates provide a new tool for assessing China's carbon dioxide flux. Our study also highlights the necessity to incorporate more environmental variables related to vegetation growth and more data derived from flux sites into NEP upscaling to reduce uncertainties in carbon budget estimations.
KeywordNet ecosystem productivity (NEP) Model tree ensemble China Eddy covariance Carbon sink
DOI10.1016/j.agrformet.2018.02.007
WOS KeywordTERRESTRIAL CARBON UPTAKE ; LAST 30 YEARS ; NITROGEN DEPOSITION ; SPATIAL-DISTRIBUTION ; FOREST ECOSYSTEMS ; ATMOSPHERIC CO2 ; DIOXIDE UPTAKE ; CLIMATE-CHANGE ; NORTH-AMERICA ; CYCLE
Indexed BySCI
Language英语
Funding ProjectNational Basic Research Program of China[2013CB956303] ; National Natural Science Foundation of China[41530528] ; National Youth Top-notch Talent Support Program in China
Funding OrganizationNational Basic Research Program of China ; National Natural Science Foundation of China ; National Youth Top-notch Talent Support Program in China
WOS Research AreaAgriculture ; Forestry ; Meteorology & Atmospheric Sciences
WOS SubjectAgronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS IDWOS:000430783800008
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/55002
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Tao
Affiliation1.Peking Univ, Coll Urban & Environm Sci, Sinofrench Inst Earth Syst Sci, Beijing 100871, Peoples R China
2.Zhan Jiang Urban Planning Bur, Zhanjiang 524022, Peoples R China
3.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Alpine Ecol & Biodivers, Beijing 100085, Peoples R China
4.Chinese Acad Sci, Ctr Excellence Tibetan Earth Sci, Beijing 100085, Peoples R China
5.Woods Hole Res Ctr, Falmouth, MA 02540 USA
6.Inst Pierre Simon Laplace, Lab Meteorol Dynam, F-95005 Paris, France
7.Natl Agr & Food Res Org, Inst Agroenvironm Sci, Tsukuba, Ibaraki 3058604, Japan
8.Chinese Acad Sci, Inst Atmospher Phys, CAS Key Lab Reg Climate Environm Temperate East A, Beijing 100029, Peoples R China
9.Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining 810008, Qinghai, Peoples R China
10.Chinese Acad Sci, Inst Geochem, State Key Lab Environm Geochem, Guiyang 550081, Peoples R China
11.Chinese Acad Sci, Puding Karst Ecosyst Res Stn, Puding 562100, Peoples R China
12.Peking Univ, Dept Ecol, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
13.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
14.Chinese Acad Trop Agr Sci, Rubber Res Inst, Danzhou 571737, Peoples R China
15.Chinese Acad Sci, South China Bot Garden, Guangzhou 510650, Guangdong, Peoples R China
16.Chinese Acad Sci, Key Lab Trop Forest Ecol, Xishuangbanna Trop Bot Garden, Mengla 666303, Yunnan, Peoples R China
17.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China
18.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
Recommended Citation
GB/T 7714
Yao, Yitong,Li, Zhijian,Wang, Tao,et al. A new estimation of China's net ecosystem productivity based on eddy covariance measurements and a model tree ensemble approach[J]. AGRICULTURAL AND FOREST METEOROLOGY,2018,253:84-93.
APA Yao, Yitong.,Li, Zhijian.,Wang, Tao.,Chen, Anping.,Wang, Xuhui.,...&Piao, Shilong.(2018).A new estimation of China's net ecosystem productivity based on eddy covariance measurements and a model tree ensemble approach.AGRICULTURAL AND FOREST METEOROLOGY,253,84-93.
MLA Yao, Yitong,et al."A new estimation of China's net ecosystem productivity based on eddy covariance measurements and a model tree ensemble approach".AGRICULTURAL AND FOREST METEOROLOGY 253(2018):84-93.
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