Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 23
Articles | Volume 18, issue 23
https://doi.org/10.5194/gmd-18-9855-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-9855-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 23
Development and technical paper
 | 
10 Dec 2025
Development and technical paper |  | 10 Dec 2025

rsofun v5.1: a model-data integration framework for simulating ecosystem processes

Josefa Arán Paredes, Fabian Bernhard, Koen Hufkens, Mayeul Marcadella, and Benjamin D. Stocker

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 7,919 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
7,578 242 99 7,919 78 60 67
  • HTML: 7,578
  • PDF: 242
  • XML: 99
  • Total: 7,919
  • Supplement: 78
  • BibTeX: 60
  • EndNote: 67
Views and downloads (calculated since 01 Apr 2025)
Cumulative views and downloads (calculated since 01 Apr 2025)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 7,919 (including HTML, PDF, and XML) Thereof 7,858 with geography defined and 61 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 30 May 2026
Download
Short summary
Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse conditions. Here, we demonstrate and evaluate the rsofun R package, which provides a computationally efficient implementation of the P-model for site-scale simulations of ecosystem photosynthesis. Bayesian model fitting to observed fluxes and traits and evaluation on an independent test data set indicated robust calibration and unbiased prediction capabilities.
Read more
Share