Journal
ECOLOGICAL MODELLING
Volume 355, Issue -, Pages 84-96Publisher
ELSEVIER
DOI: 10.1016/j.ecolmodel.2017.04.012
Keywords
Dynamic global vegetation model; Integrated biosphere simulator; Gross primary production; Above-ground biomass; Global carbon cycle
Categories
Funding
- National Science Foundation of China [41301020]
- National Key Basic Research Program of China [2013CB956604]
- U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program [DE-FG02-04ER63917, DE-FG02-04ER63911]
- CFCAS
- NSERC
- BIOCAP
- Environment Canada
- NRCan
- CarboEuropeIP
- FAO-GTOS-TCO
- iLEAPS
- Max Planck Institute for Biogeochemistry
- National Science Foundation
- University of Tuscia
- Universite Laval
- US Department of Energy
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Dynamic global vegetation models are useful tools for the simulation of global carbon cycle. However, most models are hampered by the poor availability of global aboveground biomass (AGB) data, which is necessary for the model calibration process. Here, taking the integrated biosphere simulator model (IBIS) as an example, we evaluated the modeled carbon dynamics, including gross primary production (GPP) and potential AGB, at the global scale. The IBIS model was constrained by both in situ GPP and plot-level AGB data collected from the literature. Model results showed that IBIS could reproduce GPP with acceptable accuracy in monthly and annual scales. At the global scale, the IBIS-simulated total AGB was similar to those obtained in other studies. However, discrepancies were observed between the model-derived and observed AGB for pan-tropical forests. The bias in modeled AGB was mainly caused by the unchanged parameters over the global scale for a specific plant functional type. This study also showed that different meteorological inputs can introduce substantial differences in modeled AGB in the global scale, although this difference is small compared with parameter-induced differences. The conclusions of our research highlight the necessity of considering the heterogeneity of key model physiological parameters in modeling global AGB. (C) 2017 Elsevier B.V. All rights reserved.
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