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Title: Detection and attribution of vegetation greening trend in China over the last 30 years

Journal Article · · Global Change Biology
DOI:https://doi.org/10.1111/gcb.12795· OSTI ID:1348302
 [1];  [2];  [2];  [3];  [2];  [2];  [4];  [5];  [6];  [2];  [7];  [4];  [8];  [9];  [2];  [3]
  1. Peking Univ., Beijing (China). Sino-French Inst. for Earth System Science, College of Urban and Environmental Sciences; Chinese Academy of Sciences (CAS), Beijing (China). Inst. of Tibetan Plateau Research, Center for Excellence in Tibetan Earth Scicence
  2. Peking Univ., Beijing (China). Sino-French Inst. for Earth System Science, College of Urban and Environmental Sciences
  3. CSIRO Marine and Atmospheric Research, Aspendale, Vic. (Australia). Centre for Australian Weather and Climate Research
  4. Chinese Academy of Sciences (CAS), Beijing (China). Inst. of Geographic Sciences and Natural Resources Research
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Climate Change Science Inst. and Environmental Sciences Division
  6. Boston Univ., MA (United States). Dept. of Earth and Environment
  7. Montana State Univ., Bozeman, MT (United States). Inst. on Ecosystems and the Dept. of Ecology
  8. Beijing Normal Univ., Beijing (China). School of Geography
  9. Univ. of Maryland, College Park, MD (United States). Dept. of Atmospheric and Oceanic Science
+ Show Author Affiliations

Abstract The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite‐derived Leaf Area Index ( LAI ) datasets for detection as well as five different process‐based ecosystem models for attribution. Rising atmospheric CO 2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing‐season LAI trend ( LAI GS ) estimated by satellite datasets (average trend of 0.0070 yr −1 , ranging from 0.0035 yr −1 to 0.0127 yr −1 ), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAI GS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai–Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAI GS ( P  < 0.05), and marginally significantly ( P  = 0.07) correlated with the residual of LAI GS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO 2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
AC05-00OR22725; 2013CB956303; 41125004; 31321061; B14001
OSTI ID:
1348302
Alternate ID(s):
OSTI ID: 1464436
Journal Information:
Global Change Biology, Vol. 21, Issue 4; ISSN 1354-1013
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 506 works
Citation information provided by
Web of Science

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Related Subjects

54 ENVIRONMENTAL SCIENCES
afforestation
attribution
China
CO2 fertilization effect
detection
greening trend
nitrogen deposition