Publication Abstracts

Hermoso de Mendoza et al. 2021, submitted

Hermoso de Mendoza, I., E. Boucher, F. Gennaretti, A. Lavergne, L. Andreu-Hayles, and R.D. Field, 2021: A new snow module improves predictions of isotope-enabled MAIDENiso forest growth model. Geosci. Model Dev., submitted.

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.

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BibTeX Citation

@unpublished{he01400p,
  author={Hermoso de Mendoza, I. and Boucher, E. and Gennaretti, F. and Lavergne, A. and Andreu-Hayles, L. and Field, R. D.},
  title={A new snow module improves predictions of isotope-enabled MAIDENiso forest growth model},
  year={2021},
  journal={Geosci. Model Dev.},
  note={Manuscript submitted for publication}
}

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RIS Citation

TY  - UNPB
ID  - he01400p
AU  - Hermoso de Mendoza, I.
AU  - Boucher, E.
AU  - Gennaretti, F.
AU  - Lavergne, A.
AU  - Andreu-Hayles, L.
AU  - Field, R. D.
PY  - 2021
TI  - A new snow module improves predictions of isotope-enabled MAIDENiso forest growth model
JA  - Geosci. Model Dev.
ER  -

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