Articles | Volume 7, issue 3
Geosci. Model Dev., 7, 981–999, 2014
Geosci. Model Dev., 7, 981–999, 2014

Model description paper 26 May 2014

Model description paper | 26 May 2014

Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model

Q. Zhu1,3, J. Liu2, C. Peng1,3, H. Chen1,3, X. Fang3,4, H. Jiang5, G. Yang1, D. Zhu3, W. Wang3, and X. Zhou3 Q. Zhu et al.
  • 1State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China
  • 2Contractor, Western Geographic Science Center, U.S. Geological Survey, Menlo Park, CA 94025, USA
  • 3Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada
  • 4School of Earth Science and Engineering, Hohai University, Nanjing 210098, China
  • 5International Institute for Earth System Science, Nanjing University, Hankou Road 22, Nanjing 210093, China

Abstract. A new process-based model TRIPLEX-GHG was developed based on the Integrated Biosphere Simulator (IBIS), coupled with a new methane (CH4) biogeochemistry module (incorporating CH4 production, oxidation, and transportation processes) and a water table module to investigate CH4 emission processes and dynamics that occur in natural wetlands. Sensitivity analysis indicates that the most sensitive parameters to evaluate CH4 emission processes from wetlands are r (defined as the CH4 to CO2 release ratio) and Q10 in the CH4 production process. These two parameters were subsequently calibrated to data obtained from 19 sites collected from approximately 35 studies across different wetlands globally. Being heterogeneously spatially distributed, r ranged from 0.1 to 0.7 with a mean value of 0.23, and the Q10 for CH4 production ranged from 1.6 to 4.5 with a mean value of 2.48. The model performed well when simulating magnitude and capturing temporal patterns in CH4 emissions from natural wetlands. Results suggest that the model is able to be applied to different wetlands under varying conditions and is also applicable for global-scale simulations.