文献类型： 外文期刊

作者： Yao, Yitong ¹ ; Li, Zhijian ² ; Wang, Tao ³ ; Chen, Anping ⁵ ; Wang, Xuhui ¹ ; Du, Mingyuan ⁷ ; Jia, Gensuo ⁸ ; Li, Yingn ¹ ;

作者机构： 1.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

关键词： Net ecosystem productivity (NEP); Model tree ensemble; China; Eddy covariance; Carbon sink

期刊名称：AGRICULTURAL AND FOREST METEOROLOGY （ 影响因子：5.734； 五年影响因子：5.964 ）

ISSN： 0168-1923

年卷期： 2018 年 253 卷

页码：

收录情况： SCI

摘要： Accurate 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.