title: “Validation of 3D-CMCC Forest Ecosystem Model (v. 5.1) against eddy covariance data for 10 European forest sites” authors:

  • Collalti et al. date: “2016-02-07T00:00:00Z” doi: ""

Schedule page publish date (NOT publication’s date).

publishDate: “2016-02-07T00:00:00Z”

Publication type.

Legend: 0 = Uncategorized; 1 = Conference paper; 2 = Journal article;

3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;

7 = Thesis; 8 = Patent

publication_types: [“2”]

Publication name and optional abbreviated publication name.

publication: “Geoscientific Model Development” publication_short: ""

abstract: “This study evaluates the performances of the new version (v.5.1) of 3D-CMCC Forest Ecosystem Model (FEM) in simulating gross primary productivity (GPP), against eddy covariance GPP data for 10 FLUXNET forest sites across Europe. A new carbon allocation module, coupled with new both phenological and autotrophic respiration schemes, was implemented in this new daily version. Model ability in reproducing timing and magnitude of daily and monthly GPP fluctuations is validated at intra-annual and inter-annual scale, including extreme anomalous seasons. With the purpose to test the 3D-CMCC FEM applicability over Europe without a site-related calibration, the model has been deliberately parametrized with a single set of species-specific parameterizations for each forest ecosystem. The model consistently reproduces both in timing and in magnitude daily and monthly GPP variability across all sites, with the exception of the two Mediterranean sites. We find that 3D-CMCC FEM tends to better simulate the timing of inter-annual anomalies than their magnitude within measurements' uncertainty. In six of eight sites where data are available, the model well reproduces the 2003 summer drought event. Finally, for three sites we evaluate whether a Published by Copernicus Publications on behalf of the European Geosciences Union. 480 A. Collalti et al. Validation of 3D-CMCC Forest Ecosystem Model (v.5.1) more accurate representation of forest structural characteristics (i.e. cohorts, forest layers) and species composition can improve model results.”

Summary. An optional shortened abstract.



  • Forest Ecosystem Modeling
  • Land Cover change

featured: false


Featured image

To use, add an image named featured.jpg/png to your page’s folder.

image: caption: focal_point: "" preview_only: false

Associated Projects (optional).

Associate this publication with one or more of your projects.

Simply enter your project’s folder or file name without extension.

E.g. internal-project references content/project/internal-project/index.md.

Otherwise, set projects: [].



Slides (optional).

Associate this publication with Markdown slides.

Simply enter your slide deck’s filename without extension.

E.g. slides: "example" references content/slides/example/index.md.

Otherwise, set slides: "".


#Supplementary notes can be added here, including code and math.

Dr. Sergio Marconi (He/Him)
Dr. Sergio Marconi (He/Him)
Post-Doc at University of Florida

My research focuses on AI for forest cross-scale ecology and conservation