Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-6957-2022
https://doi.org/10.5194/gmd-15-6957-2022
Model description paper
 | 
16 Sep 2022
Model description paper |  | 16 Sep 2022

Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest

Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar

Viewed

Total article views: 2,994 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,381 561 52 2,994 124 39 42
  • HTML: 2,381
  • PDF: 561
  • XML: 52
  • Total: 2,994
  • Supplement: 124
  • BibTeX: 39
  • EndNote: 42
Views and downloads (calculated since 02 May 2022)
Cumulative views and downloads (calculated since 02 May 2022)

Viewed (geographical distribution)

Total article views: 2,994 (including HTML, PDF, and XML) Thereof 2,843 with geography defined and 151 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Jun 2024
Download
Short summary
Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.