Soil Temperature, Organic-Carbon Storage, and Water-Holding Ability Should Be Accounted for the Empirical Soil Respiration Model Selection in Two Forest Ecosystems

Soil respiration (SR) is a main component of the carbon cycle in terrestrial ecosystems, and being strongly affected by changes in the environment, it is a good indicator of the ecosystem’s ability to cope with climate change. This research aims to find better empirical SR models using 25-year-long SR monitoring in two forest ecosystems formed on sandy Entic Podzol and loamy Haplic Luvisol. The following parameters were considered in the examined models: the mean monthly soil or air temperatures (Tsoil or Tair), the amount of precipitation during the current (P) and the previous (PP) months, and the storage of soil organic carbon (SOC). The weighted non-linear regression was used for model parameter estimations for the normal, wet, and dry years. To improve the model resolutions by magnitude, we controlled the slope and intercept of the linear model comparison between the measured and modeled data through the change in R0—SR at zero soil temperature. The mean bias error (MBE), root-mean-square error (RMSE), and determination coefficient (R2) were used for the estimation of the goodness of model performances. For the sandy Entic Podzol, it is more appropriate to use the models dependent on SOC (TPPC). While for the loamy Haplic Luvisol, the Raich–Hashimoto model (TPPrh) with the quadratic Tsoil or Tair dependency shows the better results. An application of Tsoil for the model parameterization gives better results than Tair: the TPPC model was able to adequately describe the cold-period SR (Tsoil ≤ 2 °C); the TPPrh model was able to avoid overestimations of the warm-period SR (Tsoil > 2 °C). The TPPC model parameterized with Tsoil can be used for the quality control of the cold-period SR measurements. Therefore, we showed the importance of accounting for SOC and the water-holding ability when the optimal SR model is chosen for the analysis.