Modeling of CO₂ Efflux from Forest and Grassland Soils Depending on Weather Conditions

Carbon dioxide (CO₂) efflux from soil (or soil respiration, SR) is one of the most important yet variable characteristics of soil. When evaluating large areas, CO₂ efflux modeling serves as a viable alternative to direct measurements. This research aims to identify site-specific differences and their effects on empirical CO₂ efflux modeling. The experimental data from 25 years of field observations were utilized to identify the optimal site- and weather-specific models, parameterized for normal, wet, and dry years, for the forest and grassland ecosystems located on similar Entic Podzols (Arenic) in the same bioclimatic coniferous–deciduous forest zone. The following parameters were considered in the examined models: mean monthly soil or air temperatures (Tsoil and Tair), amount of precipitation during the current (P) and the previous (PP) months, and the storage of soil organic carbon (SOC) in the top 20 cm of soil. The weighted non-linear regression method was employed to estimate the model parameters for the normal, wet, and dry years. To increase the magnitude of the model resolutions, we controlled the slope and intercept of the linear model comparison between the measured and modeled data through the change in R₀—CO₂ efflux at Tsoil = 0 °C. The mean bias error (MBE), root-mean-square error (RMSE), and determination coefficient (R²) were employed to assess the quality of the model’s performance. The measured Tsoil, Tair, and P, as well as the litter (for forest) or sod (for grassland) horizon (modeled by the Soil SCLmate Statistical Simulator (SCLISS)), and soil temperatures (Tlit_m, Tsoil_m) and moistures (Mlit_m, Msoil_m), were used for SR simulation. For the CO₂ efflux in the forest ecosystem with the lower SOC availability for mineralization, the direct Tsoil and Tair measurements in combination with SOC storage provided better parameterization for the empirical TPPC model. For the CO₂ efflux in the grassland ecosystem with the high SOC availability for mineralization, the temperature became the governing factor, and the TPPrh model provided better performance over all the considered models. The model’s performance was the best for the wet years, and the worst for the dry years for both ecosystems. For forest ecosystems, the model performance for average precipitation years was equivalent to that in wet years. For grassland ecosystems, however, the model performance was equivalent to that in dry years due to differing exposure and hydrothermal regimes. The wet-year R₀ obtained for both forest and grassland ecosystems differed from the normal- and dry-year values. The measured SR values relevant for the R₀ estimations distribute along the precipitation range for the forest and along the temperature range for the grassland. The SCLISS-modeled Tlit_m and Mlit_m provide good alternatives to direct atmospheric measurements, and can be used as initial temperature and moisture data for CO₂ efflux modeling when direct soil and moisture observations are not available on site.