Fine scale assessment of seasonal, intra-seasonal and spatial dynamics of soil CO2 effluxes over a balsam fir-dominated perhumid boreal landscape

Abstract

Total soil CO2 efflux (FCO2) is the second most important carbon flux after photosynthesis in boreal forests. However, accurate modelling of FCO2 remains challenging because of its high variability, both temporally and spatially. Using an Abies balsamea-dominated boreal landscape in Quebec (eastern Canada) as a case study, we modelled seasonal, intra-seasonal and spatial variability of FCO2 using climate variables and topographic and canopy structure attributes derived from Light Detection and Ranging (LiDAR) and assessed their respective contributions to soil CO2 emissions. Weekly point measurements of FCO2 at 99 sites were taken over an area of 122 ha between June and October 2020. The seasonal component of FCO2 was quantified and subtracted from FCO2 measurements to isolate the spatial and intra-seasonal components of the flux. The two components were then modelled using a Random Forest Regression model and studied using accumulated local effect plots (ALE plots). Our approach explained 81% of the variation in FCO2: the seasonal pattern explained 36% of the variation in FCO2 measurements, while spatial and intra-seasonal patterns together explained 45%. The most important factors explaining spatial variation were vegetation height and the slope height. Average air temperature of the last two days before efflux measurements was the most important factor explaining intra-seasonal variation. The proposed methodology makes it possible to predict FCO2 from external factors derived from climate and remote sensing data and enables the decomposition of FCO2 into its seasonal, intra-seasonal and spatial components. Our results demonstrate the importance of spatial and intra-seasonal variations in FCO2 compared to seasonal variation, a finding that has implications for the measurement and modelling of FCO2 at landscape and global scales.