## 4 Conclusions  
We have demonstrated an importance to account for the \(SOC\) storage and water-holding ability for the selection of the proper SR models. For the sandy Entic Podzol with a coarse texture and good drainage, it is better to use the models accounting for the \(SOC\) storage (TPC and TPPC), while for the loamy Haplic Luvisol, having a finer texture and high water-holding ability, it is better to apply Raich-Hashimoto-type models (TPrrh) with \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{4}{c}{**Entic Podzol**} & \multicolumn{4}{c}{**Haplic Luvisol**} \\ \hline
**Model** & \(\mathbf{R}^{2}\) & **MBE** & **RMSE** & \(\mathbf{R}^{2}\) & **MBE** & **RMSE** \\ \hline  & \multicolumn{4}{c}{(all data)} \\ \hline TPPC[\(T_{soil}\)]:TPrrh[\(T_{air}\)] & **0.734** & \(-\)0.150 & **0.527** & 0.624 & \(-\)0.348 & 0.716 \\ \hline TPPC[\(T_{air}\)]:TPrrh[\(T_{air}\)] & 0.731 & \(-\)0.156 & 0.536 & 0.623 & \(-\)0.357 & 0.723 \\ \hline
**TPPC[\(T_{soil}\)]**TPrrh[\(T_{soil}\)] & **0.735** & \(\mathbf{-0.115}\) & **0.524** & **0.674** & \(\mathbf{-0.287}\) & **0.651** \\ \hline  & \multicolumn{4}{c}{\(T_{soil}\leq 2\) (cold periods)} \\ \hline
**TPPC[\(T_{soil}\)]** & **0.116** & \(\mathbf{-0.225}\) & **0.397** & **0.054** & \(\mathbf{-0.376}\) & **0.553** \\ \hline TPPC[\(T_{soil}\)] & 0.110 & \(-\)0.241 & 0.428 & 0.047 & \(-\)0.402 & 0.580 \\ \hline TPPrh[\(T_{soil}\)] & 0.032 & \(-\)0.224 & 0.411 & 0.110 & \(-\)0.425 & 0.581 \\ \hline TPPrh[\(T_{air}\)] & 0.070 & \(-\)0.288 & 0.480 & 0.040 & \(-\)0.456 & 0.643 \\ \hline  & \multicolumn{4}{c}{\(T_{soil}\) > 2 (warm periods)} \\ \hline TPPC[\(T_{soil}\)] & 0.583 & \(-\)0.124 & 0.638 & 0.465 & \(-\)0.413 & 0.852 \\ \hline TPPrrh[\(T_{soil}\)] & 0.616 & \(-\)0.094 & 0.599 & 0.431 & \(-\)0.412 & 0.856 \\ \hline
**TPrrh[\(T_{soil}\)]** & **0.604** & \(\mathbf{-0.051}\) & **0.584** & **0.512** & \(\mathbf{-0.239}\) & **0.698** \\ \hline
**TPrrh[\(T_{air}\)]** & **0.604** & \(\mathbf{-0.106}\) & **0.589** & 0.431 & \(-\)0.333 & 0.790 \\ \hline \end{tabular}
\end{table} 
Table 2: Quality control of the modeling by the combined TPPC: TPPrh models with the parameterization by different temperature sources ([\(T_{soil}\)] or [\(T_{air}\)]) conducted on the 25-year monitoring for Entic Podzol and Haplic Luvisol; top—all data, middle—cold periods (\(T_{soil}\leq 2\)\({}^{\circ}\)C), bottom—warm periods (\(T_{soil}\) > 2\({}^{\circ}\)C). the quadratic temperature dependency being connected to the water presence in the soil and the reaction of the microbial biomass on temperature change. Both in the dry and in the normal years, accounting for \(SOC\) storage significantly improves the modeling results (TPC and TPPC models) in comparison to the more simple model results (T, TP, and TPP models). In the dry years, the TPPC model is better than the TPC model, but in the normal years, the TPC model is better than the TPPC model, highlighting an importance of the prolonged presence of soil humidity in dry periods. An effect of humidity change becomes the most important in the wet years (TPP in TPPC models). Optimal values for the parameter \(\alpha\) included into the TPP and TPPC models become close to zero or negative in the dry years, highlighting an importance of the (continuous) moisture and precipitation presence from the previous month (PP). We found that TPPC parameterized by the soil temperature adequately describes the SR measured during the cold periods (\(T_{soil}\leq 2~{}^{\circ}\)C), whereas TPPh parameterized by the soil or air temperature is better for describing the SR measured during the warm periods (\(T_{soil}>2~{}^{\circ}\)C). The parameterization of the models with the soil temperature is shown to be an important factor for adequate SR estimates. With this parameterization, the TPPC model can be applied for the control of the winter-time SR measurements conducted at the sites. Conceptualization, I.K. and S.K. ; methodology, I.K. and S.K. ; formal analysis, S.K. ; investigation, S.K. ; resources, V.L.d.G., D.K., T.M. and D.S.