Analysis and Modelling of Soil CO2 Emissions Within Temperate Coniferous and Deciduous Forests

Abstract

Climate change and extreme weather events have impacted global forest ecosystems’ ability to sequester atmospheric carbon dioxide. In this study, the temporal and spatial dynamics of soil CO2 efflux or soil respiration (Rs) was measured in a temperate coniferous (TP74) and a deciduous forest (TPD) over a six-year period (2014 to 2019). Analysis of Rs trends showed a strong positive correlation with soil temperature (Ts) and soil moisture (SM) at TPD and TP74 causing large pulses of Rs. The average annual temperature sensitivity (Q10) was found to be 2.06 for TPD and 1.87 for TP74. Coherence analysis for both sites from 2017 to 2019 showed that in extreme weather events, TP74’s carbon pool was less stable than that of TPD. Dynamics of Rs at both forest sites was further analyzed using thirteen different Rs models (e.g. Ts only, SM only, Ts and SM models, neural network) to evaluate their performance in simulating observed patterns of soil CO2 effluxes. As compared to other models, the Gaussian – Gamma model consistently reproduced observed dynamics of Rs where on average 70% of variability in Rs was explained. This study showed that Ts and SM are key determinants of Rs in both forests. Models that incorporate the influence of SM on Rs and were able to better simulate Rs dynamics as compared to Ts only models. Results also suggest that coherence analysis can be utilized to understand temporal variations in Rs. The knowledge of environmental drivers of Rs can be used to determine the impact of climate change and extreme weather events on Rs and assist in developing ecosystem models.