Articles | Volume 15, issue 5
Model evaluation paper
09 Mar 2022
Model evaluation paper |  | 09 Mar 2022

A new snow module improves predictions of the isotope-enabled MAIDENiso forest growth model

Ignacio Hermoso de Mendoza, Etienne Boucher, Fabio Gennaretti, Aliénor Lavergne, Robert Field, and Laia Andreu-Hayles


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2021-275', Ru Huang, 21 Sep 2021
  • RC1: 'Comment on gmd-2021-275', Anonymous Referee #1, 15 Oct 2021
  • CEC1: 'Comment on gmd-2021-275', Juan Antonio Añel, 25 Oct 2021
  • AC1: 'Comment on gmd-2021-275', Ignacio Hermoso de Mendoza, 26 Oct 2021
  • RC2: 'Comment on gmd-2021-275', Anonymous Referee #2, 17 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ignacio Hermoso de Mendoza on behalf of the Authors (06 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (07 Jan 2022) by Tomomichi Kato
RR by Anonymous Referee #1 (14 Jan 2022)
RR by Anonymous Referee #2 (25 Jan 2022)
ED: Publish as is (03 Feb 2022) by Tomomichi Kato
Short summary
We modify the numerical model of forest growth MAIDENiso by explicitly simulating snow. This allows us to use the model in boreal environments, where snow is dominant. We tested the performance of the model before and after adding snow, using it at two Canadian sites to simulate tree-ring isotopes and comparing with local observations. We found that modelling snow improves significantly the simulation of the hydrological cycle, the plausibility of the model and the simulated isotopes.