## 3 Results
### Model Parametrization  
To parameterize the models, in addition to \(\mathrm{Ph_{V}}\) and \(\mathrm{Ph_{L}}\), the following were used as independent variables: photosynthetically active radiation (PAR) and (or) WTL for GEE; surface soil temperature (\(\mathrm{T_{0}}\)) and/or WTL for \(\mathrm{R_{eco}}\). Figures 5 and 6 show the results of model parametrization comparing to the results of the field measurements (see parameters of models in Table 10). The correlation coefficient shown in the figure illustrates the relationship between the modeled and measured fluxes as a whole for the model and not as a single independent variable. The correlation coefficient (R) between the simulated and measured GEE fluxes decreased in the model 4, 5, 3, 2 and 1 and amounted to 0.68, 0.66, 0.63, 0.62 and 0.55; RMSE was similar for models 4, 5, 3 and 1 and amounted to 27, 26, 26 and 27, respectively, but 40 for model 2. The GEE-PAR relationship is characterized by an accumulation curve; the half-saturation constants \(\mathrm{k_{1}}\)\(\div\)\(\mathrm{k_{5}}\) were 202, 248, 487, 330 and 275 \(\mathrm{\SIUnitSymbolMicro mol^{-2}s^{-1}}\) for models 1-5, respectively. The GEE-\(\mathrm{Ph_{V}}\) relationship in models 4 and 5 is also represented by an accumulation curve (the GEE ceases to change by more than 5% that is achieved at \(\mathrm{Ph_{V}}\) = 125 and 75 g/m\({}^{2}\), respectively); in other cases, it is continu  
Figure 5: Relationship between modelled (Y-axis; using different models—see Section 2.4) and observed (X-axis) gross ecosystem exchange (GEE). Figure 6: Relationship between modelled (Y-axis; using different models—see Section 2.4) and observed (X-axis) ecosystem respiration (\(\mathrm{R_{eco}}\)). 2, 3) or accelerating (model 1) to phytomass reserves. Finally, the relationship between GEE and WTL is shown in model 5, which has a bell-shaped curve. Unfortunately, considering the GEE-WTL connection did not significantly improve the simulation results: models 2, 3 and 4 showed the similar R when describing the field measurement data. As a result of the parameterization of the R\({}_{\rm eco}\) models, the correlation coefficient (R) decreased in the series model 11, 9, 6, 8, 10, 7 and amounted to 0.83, 0.82, 0.77, 0.76, 0.74, 0.55, respectively; RMSE was 12, 30, 22, 19, 22, 29, respectively (Figure 6). The temperature dependence in the models is described by a power-law (model 6) or exponential function (models 7, 8, 10 and 11): the coefficient Q\({}_{10}\) for model 6 was 4.9, and in other cases, the respiration rate increased by 1.2-1.9 with an increase in temperature by 10 \({}^{\circ}\)C for the range of 10-20 \({}^{\circ}\)C and 1.2-2.0 times for the range of 18-28 \({}^{\circ}\)C. In contrast to the GEE, the account in the R\({}_{\rm eco}\) models of the connection between the processes of respiration and the WTL played a more important role: it was the models that took into account WTL variability in one way or another that were better parameterized. In addition, accounting for the litter mass of vascular plants played an important role in the R\({}_{\rm eco}\) variability. In models 7 and 8, which are identical in structure, the best parameterization result was achieved in the latter, including the dependence on Ph\({}_{\rm L}\) (Table A2). However, a significant improvement in the results of parameterization of model 8 in comparison with model 7 can be explained by the fact that the amount of litter was correlated with WTL (R = 0.61). In summary, it can be noted that the models that consider the relationship with 1-2 independent variables (models 2 and 9) showed an R that is not inferior to the more complex models (models 5 and 11). Successful parameterization of the GEE relationship model with PAR and aboveground phytomass will allow in the future, as data are accumulated, to disseminate the results of modeling in time and space; however, this will require considering the daily dynamics of PAR and the spatiotemporal variability of phytomass stocks.