Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 15
Articles | Volume 18, issue 15
https://doi.org/10.5194/gmd-18-4915-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-4915-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 15
Methods for assessment of models
 | 
11 Aug 2025
Methods for assessment of models |  | 11 Aug 2025

Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849

Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, Ricardo Dalagnol, and Wei Li

Viewed

Total article views: 6,076 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,734 1,104 238 6,076 398 177 247
  • HTML: 4,734
  • PDF: 1,104
  • XML: 238
  • Total: 6,076
  • Supplement: 398
  • BibTeX: 177
  • EndNote: 247
Views and downloads (calculated since 21 Mar 2025)
Cumulative views and downloads (calculated since 21 Mar 2025)

Viewed (geographical distribution)

Total article views: 6,076 (including HTML, PDF, and XML) Thereof 5,984 with geography defined and 92 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 25 Jun 2026
Download
Short summary
This study enhances the accuracy of modeling the carbon dynamics of the Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to use in refining global carbon cycle models.
Read more
Share