Spatiotemporal variation in soil methane uptake in a cool-temperate immature deciduous forest

• Published in: Soil biology & biochemistry 2023-09, Vol.184, p.109094, Article 109094
• Main Authors: Hu, Rui, Hirano, Takashi, Sakaguchi, Kaho, Yamashita, Syunpei, Cui, Rui, Sun, Lifei, Liang, Naishen
• Format: Article
• Language: English
• Subjects: aeration; Automated chamber system; automation; biochemistry; biomass; deciduous forests; diffusivity; Environmental control; Fine root dynamics; fine roots; Gaseous diffusion; methane; nitrogen; porosity; prediction; Random forest; soil biology; soil carbon; soil temperature; soil water; soil water content; Water-filled pore space
• ISSN: 0038-0717; 1879-3428
• DOI: 10.1016/j.soilbio.2023.109094

Atmospheric methane (CH4) concentration has been increasing recently, contributing to global warming. As natural sinks, forest soils are expected to mitigate the atmospheric CH4 rise. However, it had been difficult to measure soil CH4 flux continuously and accurately because of the limited stability and precision of CH4 analyzers in the field. In this study, we measured hourly CH4 flux with plant roots (Root) and without roots (Trench) during the growing season in a regenerating deciduous forest using an automated chamber system with an up-to-date analyzer. Combined with a Random Forest (RF) approach, we studied the spatiotemporal variation and control of soil CH4 uptake rates. The results showed that the soil was a CH4 sink throughout the experimental period, and the existence of roots significantly enhanced CH4 uptake, mainly through improving soil aeration. The CH4 uptake rate varied seasonally according to the variations in soil gaseous diffusion caused by soil moisture and temperature differences. In addition, soil CH4 uptake showed a significant spatial variation, mainly resulting from spatial difference in soil porosity, soil carbon and nitrogen contents, and fine root biomass. The RF models showed high performance in soil CH4 flux prediction using the soil O2 diffusion coefficient and soil temperature as explanatory variables. The performance of RF models using ordinary variables of soil water content or water-filled pore space (WFPS) was equal to or slightly better than that of models using the diffusion coefficient. The higher importance of ambient CH4 concentration in Trench chambers indicates an increase in soil CH4 uptake at higher CH4 concentrations, which is predicted in the future. Although there are limitations, we believe that a machine learning approach, such as RF, using a large amount of continuous data with high temporal resolution, has great potential for investigating the dynamic variation in soil CH4 flux. [Display omitted] •Roots increased soil CH4 uptake, mainly by enhancing soil gaseous diffusion.•Variation in CH4 uptake was due to soil properties, moisture, and fine roots.•Random Forest models showed high performance in hourly CH4 flux prediction.•Ordinary soil moisture variables are a good proxy of the diffusion coefficient.