NWIPB OpenIR
Large-scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands
He, Honglin1; Liu, Min2; Xiao, Xiangming3; Ren, Xiaoli1,4; Zhang, Li1; Sun, Xiaomin1; Yang, Yuanhe5; Li, Yingnian6; Zhao, Liang6; Shi, Peili1; Du, Mingyuan7; Ma, Yaoming8; Ma, Mingguo9; Zhang, Yu9; Yu, Guirui1
2014-03-01
发表期刊JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
ISSN2169-8953
卷号119期号:3页码:466-486
文章类型Article
摘要Gross primary production (GPP) is an important parameter for carbon cycle and climate change research. Previous estimations of GPP on the Tibetan Plateau were usually reported without quantitative uncertainty analyses. This study sought to quantify the uncertainty and its partitioning in GPP estimation across Tibetan alpine grasslands during 2003-2008 with the modified Vegetation Photosynthesis Model (VPM). Monte Carlo analysis was used to provide a quantitative assessment of the uncertainty in model simulations, and Sobol' variance decomposition method was applied to determine the relative contribution of each source of uncertainty to the total uncertainty. The results showed that the modified VPM successfully reproduced the seasonal dynamics and magnitude of GPP of 10 flux tower sites on the plateau (R-2=0.77-0.95, p<0.001). The 6 year mean GPP in Tibetan alpine grasslands was estimated at 223.3 Tg C yr(-1) (312.3g C m(-2)yr(-1)). The mean annual GPP increased from western to eastern plateau, with the increase of annual temperature and precipitation and the decrease of elevation, while the decrease of GPP from southern to northern plateau was primarily driven by air temperature. Furthermore, the mean relative uncertainty of the annual GPP was 18.30%, with larger uncertainty occurring in regions with lower GPP. Photosynthetic active radiation, enhanced vegetation index, and the maximum light use efficiency (LUE) are the primary sources of uncertainty in GPP estimation, contributing 36.84%, 26.86%, and 21.99%, respectively. This emphasizes the importance of uncertainty in driving variables as well as that of maximum LUE in LUE model simulation. Key Points A methodology for regional GPP estimation and uncertainty analysis was evaluated The 6-year GPP in Tibetan grasslands are estimated using modified VPM model The uncertainty of modeled GPP is quantified and traced to specific model input; Gross primary production (GPP) is an important parameter for carbon cycle and climate change research. Previous estimations of GPP on the Tibetan Plateau were usually reported without quantitative uncertainty analyses. This study sought to quantify the uncertainty and its partitioning in GPP estimation across Tibetan alpine grasslands during 2003-2008 with the modified Vegetation Photosynthesis Model (VPM). Monte Carlo analysis was used to provide a quantitative assessment of the uncertainty in model simulations, and Sobol' variance decomposition method was applied to determine the relative contribution of each source of uncertainty to the total uncertainty. The results showed that the modified VPM successfully reproduced the seasonal dynamics and magnitude of GPP of 10 flux tower sites on the plateau (R-2=0.77-0.95, p<0.001). The 6 year mean GPP in Tibetan alpine grasslands was estimated at 223.3 Tg C yr(-1) (312.3g C m(-2)yr(-1)). The mean annual GPP increased from western to eastern plateau, with the increase of annual temperature and precipitation and the decrease of elevation, while the decrease of GPP from southern to northern plateau was primarily driven by air temperature. Furthermore, the mean relative uncertainty of the annual GPP was 18.30%, with larger uncertainty occurring in regions with lower GPP. Photosynthetic active radiation, enhanced vegetation index, and the maximum light use efficiency (LUE) are the primary sources of uncertainty in GPP estimation, contributing 36.84%, 26.86%, and 21.99%, respectively. This emphasizes the importance of uncertainty in driving variables as well as that of maximum LUE in LUE model simulation.
关键词Tibetan Alpine Grasslands Vegetation Photosynthesis Model (Vpm) Eddy Covariance Uncertainty Analysis
WOS标题词Science & Technology ; Life Sciences & Biomedicine ; Physical Sciences
关键词[WOS]LIGHT-USE-EFFICIENCY ; NET ECOSYSTEM EXCHANGE ; SATELLITE SENSOR DATA ; TERRESTRIAL ECOSYSTEMS ; FLUXNET MEASUREMENTS ; SENSITIVITY-ANALYSIS ; DATA ASSIMILATION ; COMBINING MODIS ; AMERIFLUX DATA ; CO2 EXCHANGE
收录类别SCI
语种英语
WOS研究方向Environmental Sciences & Ecology ; Geology
WOS类目Environmental Sciences ; Geosciences, Multidisciplinary
WOS记录号WOS:000334534500020
引用统计
被引频次:46[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://210.75.249.4/handle/363003/4247
专题中国科学院西北高原生物研究所
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China
2.E China Normal Univ, Shanghai Key Lab Urban Ecol Proc & Ecorestorat, Shanghai 200062, Peoples R China
3.Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
5.Chinese Acad Sci, State Key Lab Vegetat & Environm Change, Inst Bot, Beijing, Peoples R China
6.Chinese Acad Sci, Northwest Plateau Inst Biol, Xining, Qinghai, Peoples R China
7.Natl Inst Agroenvironm Sci, Tsukuba, Ibaraki 305, Japan
8.Chinese Acad Sci, Key Lab Tibetan Environm Changes & Land Surface P, Inst Tibetan Plateau Res, Beijing, Peoples R China
9.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou, Gansu, Peoples R China
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He, Honglin,Liu, Min,Xiao, Xiangming,et al. Large-scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands[J]. JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES,2014,119(3):466-486.
APA He, Honglin.,Liu, Min.,Xiao, Xiangming.,Ren, Xiaoli.,Zhang, Li.,...&Yu, Guirui.(2014).Large-scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands.JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES,119(3),466-486.
MLA He, Honglin,et al."Large-scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands".JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES 119.3(2014):466-486.
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