4.7 Article

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

Journal

SOIL BIOLOGY & BIOCHEMISTRY
Volume 184, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.soilbio.2023.109094

Keywords

Automated chamber system; Environmental control; Fine root dynamics; Gaseous diffusion; Random forest; Water-filled pore space

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This study measured CH4 flux with and without plant roots in a regenerating deciduous forest, and found that the soil was a CH4 sink throughout the experimental period. The presence of roots significantly enhanced CH4 uptake by improving soil aeration. The CH4 uptake rate varied seasonally and showed significant spatial variation due to differences in soil properties. Machine learning approaches, such as Random Forest, showed potential for investigating the dynamic variation in soil CH4 flux using continuous and high-resolution data.
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 waterfilled 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.

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