## 3 Results and Discussion
### An Optimal-Model Selection and the Winter Soil Respiration Control  
The obtained above conclusions on the quality of the SR models parameterized with the different temperature sources (\(T_{soil}\) and \(T_{air}\)) for Entic Podzol (Figure 9) and Haplic Luvisol (Figure 10) are well illustrated by the time series generated from the TPPC (red) and TPPrh (blue) models. It should be noted that the TPPC model generates higher values than the TPPrh model and by this, the TPPC model better approximates the winter extremes but overestimates the summer SR pixes. It can be seen that an application of \(T_{soil}\) for the model parameterization allows more accurate estimations of the winter SR (both models) and reduces overestimations in summer (both models), improving SR estimates in the warm periods. Figure 7: The comparison-statistics: slope—the slope of the lm; \(R^{2}\)—the determination coefficient of the lm; \(|\)MBE—the absolute mean-bias error; and RMSE—the root-mean-square error between the modeled and measures soil respiration values for the normal (**blue**), wet (**red**), and dry (**green**) environmental conditions; in each panel: top—\(T_{soil}\) (local), bottom—\(T_{air}\) (monitoring station) used for parameterizations; in each panel: left—for Entic Podzol, right—for Haplic Luvisol. Figure 8: The mean annual SR (gC m\({}^{-2}\) day\({}^{-1}\)) over 25-year-long periods—blue bars—measured (SR) and modeled (labels on the \(x\)-axis) with the standard deviation ranges (orange bars) of the individual-year distributions of the mean annual SR; top—\(T_{\text{soll}}\) (local), bottom—\(T_{\text{ait}}\) (monitoring station). Figure 9: The SR time series modeling for Entic Podzol: SR measurements—black line, TPPC model—dashed red line, and TPPrh model—dashed blue line; top—for \(T_{\text{soll}}\) (local) and bottom—for \(T_{\text{ait}}\) (monitoring station); green—normal, blue—wet, beige—dry years. As an explanation of such model behavior, we can point to the soil condition difference between the winter and summer periods: when there is enough water in winters, the SR becomes more dependent on the \(T_{soil}\) interconnected with microbial activity and \(SOC\) presence as a substrate for the microbial community [24], while in dryer summer periods, the presence of precipitation and the respective temperature and humidity changes though the evapotranspiration of vegetation [60; 61] start playing an important role in the soil water balance and SR regulation. All those mentioned above are illustrated by Figure 4 (Section 3.1) highlighting different regimes of dependency between the \(T_{soil}\) and \(T_{air}\) in the cold and warm times of the year--blue and red lines, respectively. We combine the TPPC and TPPrh models by the regime-change condition (\(T_{soil}\approx 2\)\({}^{\circ}\)C) from Figure 4, comparing the soil and air temperatures:  
* with the \(T_{soil}>2\)\({}^{\circ}\)C--choose the TPPrh model;
* with the \(T_{soil}\leq 2\)\({}^{\circ}\)C--choose the TPPC model. Figure 10: The SR time series modeling for Haplic Luvisol: SR measurements—black line, TPPC model—dashed red line, and TPPrh model—dashed blue line; top—for \(T_{soil}\) (local) and bottom—for \(T_{air}\) (monitoring station); green—normal, blue—wet, beige—dry years. For the comparison analysis of the combined TPPC, the TPPrh models (Table 2) show the best statistical values for \(R^{2}\), MBE, and RMSE for the following model parameterizations:  
All data: for the combined TPPC[\(T_{soil}\)], the TPPrh[\(T_{soil}\)] model parameterized by the soil temperature;  
Cold period: for the TPPC[\(T_{soil}\)] model parameterized by the soil temperature;  
Warm period: for the TPPrh[\(T_{soil}\)] or TPPrh[\(T_{air}\)] model parameterized by the soil or the air temperature.