Modélisation à fine échelle de la dynamique saisonnière, intrasaisonnière et spatiale des flux de CO₂ des sols en sapinière boréale

Authors: Harel, Antoine
Advisor: Thiffault, Évelyne
Abstract: In the boreal forest, total soil CO₂ efflux (F[indice CO2]) is the second most important carbon flux after photosynthesis and can account for 48-71% of total ecosystem respiration. F[indice CO2]depends on the small-scale condition of the soil, i.e., its temperature, water content, quantity and quality of organic matter and root and microbial activity. Other factors, called "external factors", such as topography, weather events and vegetation height influence the soil condition and thus indirectly F[indice CO2]. Predicting F[indice CO2] is complex due to the large number of factors to be considered and the different patterns of temporal variation to be modeled. The objective of the study was to quantify the effect of external factors that may influence the seasonal, intra-seasonal and spatial variability of F[indice CO2] in the boreal forest. F[indice CO2] measurements were taken in the Montmorency Forest (Quebec, Canada) between June and October 2020. A random forest regression model was used to predict F[indice CO2] as a function of external factors such as topography, vegetation and recent weather. The developed model was able to explain 79% of the variation in F[indice CO2] measurements. Our results demonstrated the importance of spatial and intra-seasonal variation (44%) in F[indice CO2] measurements compared to seasonal variation (35%), which has implications for F[indice CO2] measurement and modeling. Tree height and air temperature were the two most important factors in predicting F[indice CO2] value. The proposed methodology would allow F[indice CO2] to be predicted from external factors that can be obtained on a large scale from LiDAR surveys and climate data.
Document Type: Mémoire de maîtrise
Issue Date: 2022
Open Access Date: 9 May 2022
Grantor: Université Laval
Collection:Thèses et mémoires

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