Simple models for soil CO2, CH4, and N2O fluxes calibrated using a Bayesian approach and multi-site data

Emissions and uptake of soil greenhouse gases (GHG) are controlled by soil biogeochemical processes. We developed simple models, which were termed SG models, for soil CO2 efflux, CH4 uptake, and N2O efflux in forest soils. We described each gas flux in terms of three functions: soil physiochemical properties (C/N ratio for CO2 and N2O, bulk density for CH4; 0–5-cm soil layer), water-filled pore space (WFPS, 5-cm depth) and soil temperature (5-cm depth). Multi-site data, which were gathered monthly in Japanese forests over 3 years, were used for model calibration (36 sites, n=768 in total for each gas flux). We used Bayesian calibration for optimization of the models. The functions for soil physiochemical properties were as follows. As soil C/N ratio increases, CO2 flux increases, but N2O flux rapidly decreases. CH4 uptake decreases with increasing bulk density. Calibration clearly revealed the different sensitivities of each gas flux to WFPS and soil temperature. The estimated optimum WFPS for CO2 flux was around 0.5 (intermediate), whereas CH4 flux decreased with increasing WFPS, and N2O flux increased with increasing WFPS. The Q10 values for CO2, CH4, and N2O fluxes were 1.9, 1.1, and 3.4, respectively. Our models reproduced observed GHG fluxes well, both in comparison to each observation and the site average. The SG models require only three inputs, which are easily measurable and are therefore suitable for regional application and incorporation into other models as GHG submodels.