Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 5
Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-1931-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1931-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Data sets

Meteorology, MAIDENiso parameter files and observational data for Tungsten, Caniapiscau, and associated GPP stations Ignacio Hermoso de Mendoza, Etienne Boucher, Laia Andreu-Hayles; Fabio Gennaretti, and Aliénor Lavergne https://doi.org/10.5281/zenodo.5599091

Model code and software

MAIDENiso (version with snow) Ignacio Hermoso de Mendoza, Etienne Boucher, Fabio Gennaretti, and Aliénor Lavergne https://doi.org/10.5281/zenodo.5597877

MAIDENiso (version without snow) Ignacio Hermoso de Mendoza, Etienne Boucher, Fabio Gennaretti, and Aliénor Lavergne https://doi.org/10.5281/zenodo.5598076

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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.
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