Preprints
https://doi.org/10.5194/gmd-2021-275
https://doi.org/10.5194/gmd-2021-275

Submitted as: model evaluation paper 17 Sep 2021

Submitted as: model evaluation paper | 17 Sep 2021

Review status: this preprint is currently under review for the journal GMD.

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

Ignacio Hermoso de Mendoza1, Etienne Boucher1, Fabio Gennaretti2, Aliénor Lavergne3, Laia Andreu-Hayles4,5,6, and Robert Field7 Ignacio Hermoso de Mendoza et al.
  • 1Centre de Recherche sur la dynamique du système Terre (GEOTOP) and Centre d’études nordiques, Université du Québec à Montréal (UQAM), Canada
  • 2Institut de Recherche sur les Forêts (IRF), Université du Québec en Abitibi-Témiscamingue (UQAT), Canada
  • 3Carbon Cycle Research Group, Space and Atmospheric Science, Physics Department, Imperial College London, United Kingdom
  • 4Tree-Ring Laboratory, Lamont-Doherty Earth Observatory of Columbia University, United States
  • 5Ecological and Forestry Applications Research Centre (CREAF), Spain
  • 6Catalan Institution for Research and Advanced Studies (ICREA), Spain
  • 7NASA Goddard Institute for Space Studies, Dept. Applied Physics and Applied Mathematics, Columbia University, United States

Abstract. The representation of snow processes in forest growth models is necessary to accurately predict the hydrological cycle in boreal ecosystems and the isotopic signature of soil water extracted by trees, photosynthates and tree-ring cellulose. Yet, most process-based models do not include a snow module, consequently their simulations may be biased in cold environments. Here, we modified the MAIDENiso model to incorporate a new snow module that simulates snow accumulation, melting and sublimation, as well as thermal exchanges driving freezing and thawing of the snow and the soil. We tested these implementations in two sites in East and West Canada for black spruce (Picea mariana) and white spruce (Picea glauca) forests, respectively. The new snow module improves the skills of the model to predict components of the hydrological cycle. The model is now able to reproduce the spring discharge peak and to simulate stable oxygen isotopes in tree-ring cellulose more realistically than in the original, snow-free version of the model. The new implementation also results in simulations with a higher contribution from the source water on the oxygen isotopic composition of the simulated cellulose, leading to more accurate estimates. Future work may include the development of inverse modelling with the new version of MAIDENiso to produce robust reconstructions of the hydrological cycle and isotope processes in cold environments.

Ignacio Hermoso de Mendoza et al.

Status: open (until 21 Nov 2021)

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 reply
  • RC1: 'Comment on gmd-2021-275', Anonymous Referee #1, 15 Oct 2021 reply

Ignacio Hermoso de Mendoza et al.

Ignacio Hermoso de Mendoza et al.

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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.