Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.252 IF 4.252
  • IF 5-year value: 4.890 IF 5-year
    4.890
  • CiteScore value: 4.49 CiteScore
    4.49
  • SNIP value: 1.539 SNIP 1.539
  • SJR value: 2.404 SJR 2.404
  • IPP value: 4.28 IPP 4.28
  • h5-index value: 40 h5-index 40
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 51 Scimago H
    index 51
Discussion papers
https://doi.org/10.5194/gmdd-6-5475-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmdd-6-5475-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 04 Nov 2013

Development and technical paper | 04 Nov 2013

Review status
This discussion paper is a preprint. It has been under review for the journal Geoscientific Model Development (GMD). The revised manuscript was not accepted.

Are vegetation-specific model parameters required for estimating gross primary production?

W. Yuan2,1, 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 W. Yuan et al.
  • 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, Zemedělská 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

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.

W. Yuan et al.
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
W. Yuan et al.
Viewed  
Total article views: 2,558 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,101 1,408 49 2,558 62 65
  • HTML: 1,101
  • PDF: 1,408
  • XML: 49
  • Total: 2,558
  • BibTeX: 62
  • EndNote: 65
Views and downloads (calculated since 04 Nov 2013)
Cumulative views and downloads (calculated since 04 Nov 2013)
Cited  
Saved  
Discussed  
No discussed metrics found.
Latest update: 21 Feb 2019
Publications Copernicus
Download
Citation