Articles | Volume 15, issue 18
Geosci. Model Dev., 15, 6957–6984, 2022
https://doi.org/10.5194/gmd-15-6957-2022
Geosci. Model Dev., 15, 6957–6984, 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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-87', Anonymous Referee #1, 27 Jun 2022
    • AC1: 'Reply on RC1', Bahar Bahrami, 13 Aug 2022
  • RC2: 'Comment on gmd-2022-87', Anonymous Referee #2, 12 Jul 2022
    • AC2: 'Reply on RC2', Bahar Bahrami, 14 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Bahar Bahrami on behalf of the Authors (14 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (16 Aug 2022) by David Lawrence
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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.