globalchange  > 气候减缓与适应
DOI: 10.1007/s10533-016-0219-3
Scopus记录号: 2-s2.0-84973171146
论文题名:
Carbon and energy fluxes in cropland ecosystems: a model-data comparison
作者: Lokupitiya E.; Denning A.S.; Schaefer K.; Ricciuto D.; Anderson R.; Arain M.A.; Baker I.; Barr A.G.; Chen G.; Chen J.M.; Ciais P.; Cook D.R.; Dietze M.; El Maayar M.; Fischer M.; Grant R.; Hollinger D.; Izaurralde C.; Jain A.; Kucharik C.; Li Z.; Liu S.; Li L.; Matamala R.; Peylin P.; Price D.; Running S.W.; Sahoo A.; Sprintsin M.; Suyker A.E.; Tian H.; Tonitto C.; Torn M.; Verbeeck H.; Verma S.B.; Xue Y.
刊名: Biogeochemistry
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2016
卷: 129, 期:2018-01-02
起始页码: 53
结束页码: 76
语种: 英语
英文关键词: Carbon and energy fluxes ; Cropland carbon and energy exchange ; Cropland ecosystems ; Land–atmosphere exchange ; Model-data comparison
Scopus关键词: agricultural land ; atmosphere-biosphere interaction ; carbon flux ; comparative study ; crop plant ; data interpretation ; ecophysiology ; ecosystem modeling ; eddy covariance ; energy flux ; growing season ; irrigation ; latent heat flux ; net ecosystem exchange ; rainfed agriculture ; United States ; Glycine max ; Zea mays
英文摘要: Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO2 seasonal uptake over agricultural regions. © 2016, Springer International Publishing Switzerland.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/83376
Appears in Collections:气候减缓与适应
气候变化事实与影响

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作者单位: Department of Zoology and Environment Sciences, Faculty of Science, University of Colombo, Colombo 03, Sri Lanka; Department of Atmospheric Science, Colorado State University, 1371 Campus Delivery, Fort Collins, CO, United States; National Snow and Ice Data Center (NSIDC), University of Colorado, Boulder, CO, United States; Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT, United States; School of Geography and Earth Sciences and McMaster Centre for Climate Change, McMaster University, Hamilton, ON, Canada; Science and Technology Branch, Environment Canada, National Hydrology Research Centre, Innovation Boulevard, Saskatoon, SK, Canada; Ecosystem Dynamics and Global Ecology Laboratory, School of Forestry and Wildlife Science, Auburn University, Auburn, AL, United States; Department of Geography, University of Toronto, Toronto, ON, Canada; Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA CNRS UVSQ, Gif-sur-Yvette, France; Environmental Science Division, Argonne National Laboratory, Lemont, IL, United States; Department of Earth and Environment, Boston University, Boston, MA, United States; Energy, Environment and Water Research Center, The Cyprus Institute, Nicosia, Cyprus; Lawrence Berkley National Laboratory, Berkeley, CA, United States; Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada; Northern Research Station, USDA Forest Service, Durham, NH, United States; Pacific Northwest National Laboratory and University of Maryland, College Park, MD, United States; Department of Atmospheric Sciences, University of Illinois, Urbana, IL, United States; Department of Agronomy & Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin - Madison, Madison, WI, United States; Teleobservation Research LLC, Columbia, MD, United States; U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, United States; School of Life Sciences, University of Technology Sydney, PO Box 123, Broadway, NSW, Australia; Natural Resources Canada, Northern Forestry Centre, 5320-120 Street, Edmonton, AB, Canada; Department of Civil and Environmental Engineering, Princeton University, E324 Engineering Quad, Princeton, NJ, United States; Forest Management and GIS Department, Jewish National Fund-Keren Kayemet LeIsrael, Jerusalem, Israel; School of Natural Resources, University of Nebraska, 807 Hardin Hall, Lincoln, NE, United States; Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, United States; CAVElab – Computational and Applied Vegetation Ecology, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; Department of Geography, University of California, Los Angeles, Los Angeles, CA, United States

Recommended Citation:
Lokupitiya E.,Denning A.S.,Schaefer K.,et al. Carbon and energy fluxes in cropland ecosystems: a model-data comparison[J]. Biogeochemistry,2016-01-01,129(2018-01-02)
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