## 2 Materials and Methods
### Eddy Covariance and Ancillary Measurements
#### 2.3.2 Soil Water Content Simulations  
Since the soil volumetric water content (SVWC) was not measured throughout the entire period of our analyses (the measurements started at the beginning of 2016), we used a simulated daily SVWC instead. We applied the soil water balance model R-4 ET, an R package for Empirical Estimate of Ecosystem EvapoTranspiration as used in [50]. The calibration of the soil water balance model was carried out using Bayesian statistics implemented in the R package BayesianTools [51] with a Differential-Evolution Markov chain Monte Carlo sampler. In Bayesian calibration, the model input parameters are iteratively updated to provide a probability distribution of the calibrated parameters that represents the uncertainty in the measured data and in the model structure. The simulations were repeated 4.8 \(\times\) 10\({}^{6}\) times, where the first 1.8 \(\times\) 10\({}^{6}\) runs were based on the prior distribution, while the remaining 3 \(\times\) 10\({}^{6}\) were constrained by the posterior distribution resulting from the first set of runs. A number of iterations treated as burn-in were set to 0, and the thinning parameter determining the interval in which values are recorded was set to 1. The high number of iterations was necessary to attain a good input parameter convergence with a narrow distribution. For inspecting the convergence, we used Gelman diagnostics with a criterion for the potential scale reduction factor being less than 1.2 for all parameters [52; 53]. The final selection of parameters from their probability distributions was conducted by using a maximum a posteriori probability estimate, i.e., the value representing the mode of the posterior distribution. The main model input variables included meteorological data and the leaf area index (see [50], for more details). The soil was stratified into 12 layers, which increased in thickness with increasing depth down to 3 m. Note that the soil layers were selected in the way that the layers used for optimization matched the depths of all sensors with an extra \(\pm\)2.5 cm, considering the volume measured by the sensors. At both sites, the available SVWC measurements were available throughout the main part of the root zone. At CZ-BK1, the sensors CS616 (Campbell Scientific, Inc., Logan, UT, U.S.A.) were placed at 0.05, 0.1, 0.22, 0.34, and 0.42 m soil depths and measured since 2016. At CZ-RAJ, the same type of sensors were also placed at 0.05, 0.1, 0.2, 0.5, and 0.8 m depths and measured since 2016. The soil parameters, including SVWC at saturation, field capacity, wilting point, and saturated hydraulic conductivity, were optimized at all 12 depths [50]. Additional single parameters relevant for the SVWC simulations that were optimized included the following: rooting depth with the Beta parameter describing the root profile shape [54], surface resistance and the degree of isohydicity [55], the water interception capacity of leaf and bark area, and curve number representing a runoff parameter [50]. The uniform distribution of priors was used, where the lower and upper limits were set to be within \(\pm\)50% of the values based on field measurements of the wilting point, field capacity, and saturated water content and \(\pm\)100% for the remaining parameters that were estimated from the literature or from previous anecdotal analyses. To provide the model with sufficient spin up time to stabilize and ensure reliable and robust parametrization, the simulation was initiated at the beginning 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 at both sites, according to a computed standardized precipitation-evapotranspiration in index over the region). The overall simulation was done for the period 2010-2019 where the observed data for Bayesian calibration spanned the period 2016-2019.