globalchange  > 影响、适应和脆弱性
DOI: 10.1111/gcb.14274
Scopus记录号: 2-s2.0-85046536730
论文题名:
Joint structural and physiological control on the interannual variation in productivity in a temperate grassland: A data-model comparison
作者: Hu Z.; Shi H.; Cheng K.; Wang Y.-P.; Piao S.; Li Y.; Zhang L.; Xia J.; Zhou L.; Yuan W.; Running S.; Li L.; Hao Y.; He N.; Yu Q.; Yu G.
刊名: Global Change Biology
ISSN: 13541013
出版年: 2018
卷: 24, 期:7
起始页码: 2965
结束页码: 2979
语种: 英语
英文关键词: data-model comparison ; ecosystem models ; grassland ; gross primary productivity ; interannual variability
Scopus关键词: annual variation ; grassland ; leaf area index ; modeling ; photosynthesis ; physiology ; primary production ; semiarid region ; China
英文摘要: Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data-model comparisons. © 2018 John Wiley & Sons Ltd
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/110339
Appears in Collections:影响、适应和脆弱性
气候变化事实与影响

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作者单位: School of Geography, South China Normal University, Guangzhou, China; Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A & F University, Yangling, China; CSIRO Oceans and Atmosphere, Aspendale, VIC, Australia; Terrestrial Biogeochemistry Group, South China Botanic Garden, Chinese Academy of Sciences, Guangzhou, China; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China; Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China; Tiantong National Forest Ecosystem Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China; Institute of Eco-Chongming (IEC), Shanghai, China; College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, China; School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou, China; NTSG, College of Forestry and Conservation, University of Montana, Missoula, MT, United States; School of Geographic Science, Nanjing Normal University, Nanjing, China; College of Life Sciences, University of Chinese Academy Sciences, Beijing, China; School of Life Sciences, University of Technology Sydney, Sydney, NSW, Australia

Recommended Citation:
Hu Z.,Shi H.,Cheng K.,et al. Joint structural and physiological control on the interannual variation in productivity in a temperate grassland: A data-model comparison[J]. Global Change Biology,2018-01-01,24(7)
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