GYIG OpenIR  > 环境地球化学国家重点实验室
A new estimation of China's net ecosystem productivity based on eddy covariance measurements and a model tree ensemble approach
Yitong Yao;  Zhijian Li;  Tao Wang;  Anping Chen;  Xuhui Wang;  Mingyuan Du;  Gensuo Jia;  Yingnian Li;  Hongqin Li;  Weijun Luo;  Yaoming Ma;  Yanhong Tang;  Huimin Wang;  Zhixiang Wu;  Junhua Yan;  Xianzhou Zhang;  Yiping Zhang;  Yu Zhang;  Guangsheng Zhou;  Shilong Piao
2018
发表期刊Agricultural and Forest Meteorology
卷号253页码:84-93
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.

关键词Net Ecosystem Productivity (Nep) Model Tree Ensemble China Eddy Covariance Carbon Sink
收录类别SCI
语种英语
文献类型期刊论文
条目标识符http://ir.gyig.ac.cn/handle/42920512-1/8746
专题环境地球化学国家重点实验室
作者单位1.Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
2.Zhan Jiang Urban Planning Bureau, Zhanjiang 524022, China
3.Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, China
4.Department of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
5.Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
6.Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Danzhou 571737, China
7.South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
8.Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan 666303, China
9.Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
10.State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
11.Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing 100085, China
12.The Woods Hole Research Center, Falmouth, MA 02540, USA
13.Laboratoire de Météorologie Dynamique, Institute Pierre-Simon Laplace, 95005 Paris, France
14.Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba 305-8604, Japan
15.CAS Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
16.Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
17.State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
18.Puding Karst Ecosystem Research Station, Chinese Academy of Sciences, Puding 562100, China
推荐引用方式
GB/T 7714
Yitong Yao;Zhijian Li;Tao Wang;Anping Chen;Xuhui Wang;Mingyuan Du;Gensuo Jia;Yingnian Li;Hongqin Li;Weijun Luo;Yaoming Ma;Yanhong Tang;Huimin Wang;Zhixiang Wu;Junhua Yan;Xianzhou Zhang;Yiping Zhang;Yu Zhang;Guangsheng Zhou;Shilong Piao. 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 Yitong Yao;Zhijian Li;Tao Wang;Anping Chen;Xuhui Wang;Mingyuan Du;Gensuo Jia;Yingnian Li;Hongqin Li;Weijun Luo;Yaoming Ma;Yanhong Tang;Huimin Wang;Zhixiang Wu;Junhua Yan;Xianzhou Zhang;Yiping Zhang;Yu Zhang;Guangsheng Zhou;Shilong Piao.(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 Yitong Yao;Zhijian Li;Tao Wang;Anping Chen;Xuhui Wang;Mingyuan Du;Gensuo Jia;Yingnian Li;Hongqin Li;Weijun Luo;Yaoming Ma;Yanhong Tang;Huimin Wang;Zhixiang Wu;Junhua Yan;Xianzhou Zhang;Yiping Zhang;Yu Zhang;Guangsheng Zhou;Shilong Piao."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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