Forest carbon (C) balance projections rely on models describing tree physiological processes to assess forest growth. The representation of C allocation and xylogenesis is still a source of uncertainty in these projections. The objective of this project is to improve boreal C balance projections by including explicitly tree C allocation and wood formation processes in modeling.
We will use xylogenesis monitoring data from micro-cores and dendrometers over two gradients. A longitudinal gradient, collected since 2000 on black spruce stands. An edaphic gradient including data for two growing seasons (2021-2022) for black spruce and jack pine on clay and sandy soils. An additional dataset contains measurements of ecosystem fluxes from flux tower sites in black spruce stands (2010-2015). All data was collected in Quebec, Canada and will be used to calibrate and validate the ecophysiological modeling of intra-annual forest C dynamics with MAIDEN, a model that simulates photosynthesis, phenology and allocation. We will use data-model fusion approaches and optimization algorithms to achieve this objective. A new module for xylogenesis will be implemented to model each step of wood cell formation and associated biomass accumulation.
This project will reduce uncertainties on growth and C responses of boreal trees to meteorological variability due to, for example, model assumptions of proportionality between stem biomass increase and girth increment. We will quantify how the environmental variability impacts xylogenesis with implication for the C balance at the stand scale. The new model for xylogenesis could also be integrated in land surface models to improve the representation of the C fluxes at the forest-atmosphere interface.