Authors: Jun Ma*, Fudan University, Xiangming Xiao, University of Oklahoma, Qin Qin, Eco-environmental Protection Research Institute, Shanghai Academy of Agricultural Sciences
Topics: Global Change, China, Land Use and Land Cover Change
Keywords: Vegetation productivity, Breakpoints, Trends shift, Ecological restoration projects, Climate change and land cover change
Session Type: Poster
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded
Terrestrial gross primary productivity (GPP) is an important flux that drives global carbon cycle. However, quantifying the trend and the control factor of GPP from pixel level to regional level is still a challenge. We generated monthly GPP dataset using the vegetation photosynthesis model (VPM) and calculated the interannual linear trend for China during 2000-2016. The Breaks For Additive Seasonal and Trend (BFAST) method was applied to detect the timing of breakpoint and trends shift of monthly GPP, while Boosted Regression Tree (BRT) analysis was used to identify the most important factor and its relative influence on GPP based on gridded leaf area index (LAI), aerosol optical thickness (AOT), and NCEP-DOE Reanalysis-2 meteorological data. The results show that annual mean GPP is significantly (P < 0.001, R2=0.78) increased from 2000 to 2016. The change rate of annual mean GPP is declined from 18.82 g C m-2 year-1 in 2000-2008 to 3.48 g C m-2 year-1 in 2008-2016. About 55.4% of the breakpoints occur between 2009 and 2011 and is mainly distributed in Qinghai-Tibet Plateau, Central China, Southwestern China, and South China. Although most China show a continuous positive GPP trends shift, negative oriented GPP trends change type still accounts for about 28.76%.Significant vegetation recovery projects in China may induce the most obvious increase trend, while the rapid urbanization may lead to the most obvious decrease trend. Land cover change and climate change are the main reasons for GPP dynamics in the north and south parts of China, respectively.