Knowledge Management System of Northwest Institute of Plateau Biology, CAS
Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites | |
Zhou, Yanlian1,2; Wu, Xiaocui2,3; Ju, Weimin3,4; Chen, Jing M.2,3; Wang, Shaoqiang5; Wang, Huimin5; Yuan, Wenping6; Black, T. Andrew7; Jassal, Rachhpal7; Ibrom, Andreas8; Han, Shijie9; Yan, Junhua10; Margolis, Hank11; Roupsard, Olivier12,13; Li, Yingnian14; Zhao, Fenghua5; Kiely, Gerard15; Starr, Gregory16; Pavelka, Marian17; Montagnani, Leonardo18,19; Wohlfahrt, Georg20,21; D'Odorico, Petra22; Cook, David23; Arain, M. Altaf24,25; Bonal, Damien26; Beringer, Jason27; Blanken, Peter D.28; Loubet, Benjamin29; Leclerc, Monique Y.30; Matteucci, Giorgio31; Nagy, Zoltan32; Olejnik, Janusz33,34; U, Kyaw Tha Paw35,36; Varlagin, Andrej37 | |
2016-04-01 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES |
卷号 | 121期号:4页码:1045-1072 |
文章类型 | Article |
摘要 | Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (epsilon(msh)) was 2.63 to 4.59 times that of sunlit leaves (epsilon(msu)). Generally, the relationships of epsilon(msh) and epsilon(msu) with epsilon(max) were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR. |
WOS标题词 | Science & Technology ; Life Sciences & Biomedicine ; Physical Sciences |
DOI | 10.1002/2014JG002876 |
关键词[WOS] | NET ECOSYSTEM EXCHANGE ; PHOTOSYNTHETICALLY ACTIVE RADIATION ; CARBON-DIOXIDE EXCHANGE ; TERRESTRIAL PRIMARY PRODUCTION ; EDDY COVARIANCE TECHNIQUE ; WATER-VAPOR EXCHANGE ; NCEP-NCAR REANALYSIS ; LAND-SURFACE MODEL ; DECIDUOUS FOREST ; DIFFUSE-RADIATION |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(41371070) ; Special climate change fund(CCSF201412) ; Chinese Academy of Sciences(XDA05050602-1) ; Department of Energy's (DOE) National Institute for Climate Change Research (NICCR)(07-SC-NICCR-1059) ; National Science Foundation(NSF) Division of Atmospheric and Geospace Sciences (AGS), Atmospheric Chemistry program(1233006) ; NSF(EF1137306/MIT ; NSF through the Florida Coastal Everglades Long Term Ecological Research program(DBI-0620409 ; AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program)(DE-FG02-04ER63917 ; CFCAS ; NSERC ; BIOCAP ; Environment Canada ; NRCan ; CarboEuropeIP ; FAO-GTOS-TCO ; iLEAPS ; Max Planck Institute for Biogeochemistry ; National Science Foundation ; University of Tuscia ; Universite Laval and Environment Canada ; U.S. Department of Energy ; 5710003122) ; DEB-9910514) ; DE-FG02-04ER63911) |
WOS研究方向 | Environmental Sciences & Ecology ; Geology |
WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary |
WOS记录号 | WOS:000378702800002 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://210.75.249.4/handle/363003/6483 |
专题 | 中国科学院西北高原生物研究所 |
作者单位 | 1.Nanjing Univ, Sch Geog & Oceanog Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210008, Jiangsu, Peoples R China 2.Joint Ctr Global Change Studies, Beijing, Peoples R China 3.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210008, Jiangsu, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Res, Nanjing, Jiangsu, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China 6.Beijing Normal Univ, Future Earth Res Inst, State Key Lab Earth Surface Proc & Resource, Beijing 100875, Peoples R China 7.Univ British Columbia, Fac Land & Food Syst, Vancouver, BC V5Z 1M9, Canada 8.Tech Univ Denmark DTU, Dept Environm Engn, Lyngby, Denmark 9.Chinese Acad Sci, Inst Appl Ecol, Shenyang 110016, Peoples R China 10.Chinese Acad Sci, South China Bot Garden, Guangzhou, Guangdong, Peoples R China 11.Univ Laval, Fac Forestry Geog & Geomat, Ctr Forest Studies, Quebec City, PQ, Canada 12.SupAgro CIRAD INRA IRD, UMR Ecol Fonctionnelle & Biogeochim Sols & Agroec, CIRAD Persyst, Montpellier, France 13.CATIE Trop Agr Ctr Res & Higher Educ, Turrialba, Costa Rica 14.Chinese Acad Sci, Northwest Inst Plateau Biol, Xining, Peoples R China 15.Univ Coll Cork, Civil & Environm Engn Dept, Environm ntal Res Inst, Cork, Ireland 16.Univ Alabama, Dept Biol Sci, Tuscaloosa, AL USA 17.Inst Syst Biol & Ecol AS CR, Lab Plants Ecol Physiol, Prague, Czech Republic 18.Forest Serv, Autonomous Prov Bolzano, Bolzano, Italy 19.Free Univ Bolzano, Fac Sci & Technol, Bolzano, Italy 20.Univ Innsbruck, Inst Ecol, A-6020 Innsbruck, Austria 21.European Acad Bolzano, Bolzano, Italy 22.Swiss Fed Inst Technol, Inst Agr Sci, Grassland Sci Grp, Zurich, Switzerland 23.Argonne Natl Lab, Div Environm Sci, Atmospher & Climate Res Program, 9700 S Cass Ave, Argonne, IL 60439 USA 24.McMaster Univ, McMaster Ctr Climate Change, Hamilton, ON, Canada 25.McMaster Univ, Sch Geog & Earth Sci, Hamilton, ON, Canada 26.INRA Nancy, UMR EEF, Nancy, France 27.Univ Western Australia, Sch Earth & Environm, Crawley, Australia 28.Univ Colorado, Dept Geog, Boulder, CO 80309 USA 29.Univ Paris Saclay, AgroParisTech, INRA, UMR ECOSYS, Thiverval Grignon, France 30.Univ Georgia, Coll Agr & Environm Sci, Dept Crop & Soil Sci, Athens, GA 30602 USA 31.Univ Tuscia, Viea San Camillo Ed LellisViterbo, Viterbo, Italy 32.Szent Istvan Univ, MTA SZIE Plant Ecol Res Grp, Godollo, Hungary 33.Poznan Univ Life Sci, Meteorol Dept, Poznan, Poland 34.Global Change Res Ctr, Dept Matter & Energy Fluxes, Brno, Czech Republic 35.Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA 36.MIT, Joint Program Sci & Policy Global Change, 77 Massachusetts Ave, Cambridge, MA 02139 USA 37.Russian Acad Sci, AN Severtsov Inst Ecol & Evolut, Moscow, Russia |
推荐引用方式 GB/T 7714 | Zhou, Yanlian,Wu, Xiaocui,Ju, Weimin,et al. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites[J]. JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES,2016,121(4):1045-1072. |
APA | Zhou, Yanlian.,Wu, Xiaocui.,Ju, Weimin.,Chen, Jing M..,Wang, Shaoqiang.,...&Varlagin, Andrej.(2016).Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites.JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES,121(4),1045-1072. |
MLA | Zhou, Yanlian,et al."Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites".JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES 121.4(2016):1045-1072. |
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