Are vegetation-specific model parameters required for estimating gross primary production? (Articolo in rivista)

Type
Label
  • Are vegetation-specific model parameters required for estimating gross primary production? (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.5194/gmdd-6-5475-2013 (literal)
Alternative label
  • W. Yuan1,2, S. Liu3, W. Cai1, W. Dong1, J. Chen4,5, A. Arain6, P. D. Blanken7, A. Cescatti8, G. Wohlfahrt9, T. Georgiadis10, L. Genesio11, D. Gianelle12, A. Grelle13, G. Kiely14, A. Knohl15, D. Liu1, M. Marek16, L. Merbold17, L. Montagnani18, O. Panferov15, M. Peltoniemi19, S. Rambal20, A. Raschi11, A. Varlagin21, and J. Xia1 (2013)
    Are vegetation-specific model parameters required for estimating gross primary production?
    in Geoscientific model development discussions; European Geosciences Union (EGU), "Katlenburg-Lindau ; Munich" (Germania)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • W. Yuan1,2, S. Liu3, W. Cai1, W. Dong1, J. Chen4,5, A. Arain6, P. D. Blanken7, A. Cescatti8, G. Wohlfahrt9, T. Georgiadis10, L. Genesio11, D. Gianelle12, A. Grelle13, G. Kiely14, A. Knohl15, D. Liu1, M. Marek16, L. Merbold17, L. Montagnani18, O. Panferov15, M. Peltoniemi19, S. Rambal20, A. Raschi11, A. Varlagin21, and J. Xia1 (literal)
Pagina inizio
  • 5475 (literal)
Pagina fine
  • 5488 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 6 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 14 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China 2State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, The Chinese Academy of Sciences, Lanzhou, Gansu 730000, China 3State Engineering Laboratory of Southern Forestry Applied Ecology and Technology, Central South University of Forestry and Technology, Changsha, Hunan 410004, China 4International Center for Ecology, Meteorology and Environment, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China 5Department of Environmental Sciences, University of Toledo, Toledo, OH 43606, USA 6School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario, Canada 7Department of Geography, University of Colorado at Boulder, Boulder, CO 80309, USA 8European Commission, Joint Research Center, Institute for Environment and Sustainability, Ispra, Italy 9Institute of Ecology, University of Innsbruck, Sternwartestrasse 15, 6020 Innsbruck, Austria 10IBIMET-CNR, Institute of Biometeorology, National Research Council, Via Gobetti, 101, Bologna, 40129, Italy 11IBIMET-CNR, Institute of Biometeorology, National Research Council, Via G. Caproni, 8, Firenze, 50145, Italy 12Sustainable Agro-ecosystems and Bioresources Department, Research and Innovation Centre, Fondazione E. Mach, San Michele all'Adige, Italy 13Department of Ecology, Swedish University of Agricultural Sciences, 750 07, Uppsala, Sweden 14Civil & Environmental Engineering Dept and Environmental Research Institute, University College Cork, Cork, Ireland 15Bioclimatology Group, Büsgen Institute, Georg-August University of Göttingen, Göttingen, Germany 16Department of Forest Ecology, Mendel University Brno, Zemed?lská 3, 603 00 Brno, Czech Republic 17ETH Zurich, Institute of Agricultural Sciences, 8092 Zurich, Switzerland 18Forest Services of Autonomous Province of Bolzano, Bolzano, Italy 19Finnish Forest Research Institute, 01301 Vantaa, Finland 20DREAM, CEFE, CNRS, UMR5175, 1919 route de Mende, 34293 Montpellier Cedex 5, France 21A. N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow 119071, Russia (literal)
Titolo
  • Are vegetation-specific model parameters required for estimating gross primary production? (literal)
Abstract
  • Models of gross primary production (GPP) are currently parameterized with vegetation-specific parameter sets and therefore require accurate information on the distribution of vegetation to drive them. Can this parameterization scheme be replaced with a vegetation-invariant set of parameter that can maintain or increase model applicability by reducing errors introduced from the uncertainty of land cover classification? Based on the measurements of ecosystem carbon fluxes from 150 globally distributed sites in a range of vegetation types, we examined the predictive capacity of seven light use efficiency (LUE) models. Two model experiments were conducted: (i) a constant set of parameters for various vegetation types and (ii) vegetation-specific parameters. The results showed no significant differences in model performances to simulate GPP while using both sets of parameters. These results indicate that a universal set of parameters, which is independent of vegetation cover type and characteristics can be adopted in prevalent LUE models. Availability of this well tested and universal set of parameters would help to improve the accuracy and applicability of LUE models in various biomes and geographic regions. (literal)
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