# Environmental Effects on Normalized Gross Primary Productivity in Beech and Norway Spruce Forests
## 2 Materials and Methods
### Soil Water Content Simulations  
As the soil volumetric water content (SVWC) was not measured over the entire period of the study (the measurements started at the beginning of 2016), the simulated daily SVWC was used. The simulations were performed by soil water balance model R-4 ET (R package for Empirical Estimate of Ecosystem EvapoTranspiration, [33]). This soil water balance model was calibrated using the Bayesian statistics implemented in the R package BayesianTools [34] with Differential-Evolution Markov chain Monte Carlo sampler. In calibration using the Bayesian statistics, the model input parameters were updated iteratively to provide a probability distribution of the calibrated parameters representing the uncertainty in the measured data and structure of the model. These simulations were repeated several times (4.8 \(\times\) 10\({}^{6}\)), with the first 1.8 \(\times\) 10\({}^{6}\) simulations based on the prior distribution and the remaining 3 \(\times\) 10\({}^{6}\) runs restricted by the posterior distribution that resulted from the first set of processes. This high number of iterations was significant in attaining a good input parameter convergence with narrow distribution. The Gelman diagnostics based on a criterion for potential scale reduction factor (less than 1.2 for all parameters) as used in [35; 36] was used to inspect the convergence. Additionally, a final selection of parameters from their probability distributions was made using a maximum posteriori probability estimate. Moreover, the input variables for the soil water balance model comprised meteorological data and leaf area index (see in [33] for more details). The soil at each of the sites was then stratified into 12 layers, increasing in thickness with depth to \(\sim\)3 m. These soil layers were selected in a manner that matched the depths of all sensors with an extra \(\pm\)2.5 cm due to the volume measured by these sensors. At all sites, the available SVWC measurements were available throughout the main root zone region. At the wet spruce forest site in Bily Kriz, the CS616 (Campbell Scientific, Inc., Logan, UT, USA) sensors were placed and measured at depths of 0.05, 0.1, 0.22, 0.34, and 0.42 m. Furthermore, at the dry spruce forest site in Rajec, the same type of sensors was placed and measured at depths of 0.05, 0.1, 0.2, 0.5, and 0.8 m. At the beech forest site in Stitna, the ThetaProbe (ML2x 355, Delta-T, UK) sensors were placed and measured at depths of 0.05, 0.1, 0.3, 0.6, and 0.9 m. The optimized model parameters included soil parameters such as the SVWC at saturation, field capacity, wilting point, and saturated hydraulic conductivity--all optimized at all 12 depths [33]. There were also additional single parameters of relevance to the SVWC simulations, which were optimized. These included the rooting depth with the Beta parameter, which described the root profile shape [37], surface resistance and the degree of isohydricity [38], the water interception capacity of leaf and bark area, and curve number representing a runoff parameter [33]. A uniform distribution of priors was applied if the lower and upper limits were set to be within \(\pm\)50% of the values based on field measurements of wilting point, field capacity, and saturated water content and \(\pm\)100% for the remaining parameters that were estimated from literature or previous anecdotal analysis. In providing the model with sufficient spin-up time to stabilize and provide reliable and robust parameterization, the model simulations were initiated at the start of 2010 with initial conditions of SVWC set to the field capacity estimated from soil texture (note that 2010 was one of the wettest years across all forest sites according to a computed standardized precipitation-evapotranspiration index over the region). Overall, the entire simulation was conducted from 2010-2019, with the observed data for the Bayesian calibration spanning from 2016 to 2019.