## 3 Results
### Forest Growth Simulations
#### 3.2.2 Model Calibration Forest Growth Simulations  
For the model calibration as a first step the Hydro-PSO model was used, introducing a range of variability for the most sensitive parameters. The number of parameters to be calibrated was reduced according to the results of the sensitivity analysis. The selected parameters for the calibration were limited to the first five parameters. Other parameters were set as constant during this calibration phase. The calibration was carried out using measured data from Bonis catchment: H which is the height and DBH which is the diameter at the breast height. In order to reach better simulation results for both DBH and H, calibrated parameters issued from these \begin{table}
\begin{tabular}{l l l} \hline \hline
**Ranking Number** & **Parameter Name** & **Relative Importance Norm** \\ \hline
1 & f\({}_{\text{pm}}\): Parameter to compute allocation factors & 5.32 \(\times\) 10\({}^{-1}\) \\
2 & S\({}_{\text{pm}}\): Parameter to compute allocation factors & 2.58 \(\times\) 10\({}^{-1}\) \\
3 & GPP-NPP: GPP/NPP ratio & 4.53 \(\times\) 10\({}^{-2}\) \\
4 & Alpha: Parameter to compute allocation factors & 3.88 \(\times\) 10\({}^{-2}\) \\
5 & wood-density & 3.57 \(\times\) 10\({}^{-2}\) \\
6 & agemax: maximum age of the plant & 2.01 \(\times\) 10\({}^{-2}\) \\
7 & hdmin: H/D ratio in carbon partitioning for low density & 1.40 \(\times\) 10\({}^{-2}\) \\
8 & phi-theta: Empirical coefficient of the soil moisture efficiencyfunction & 1.15 \(\times\) 10\({}^{-2}\) \\  & for canopy resistance & \\
9 & k: H/D ratio in carbon partitioning for low density & 1.04 \(\times\) 10\({}^{-2}\) \\
10 & albedo: Plant albedo & 8.76 \(\times\) 10\({}^{-3}\) \\
11 & fpra: Parameter to compute allocation factors & 7.45 \(\times\) 10\({}^{-3}\) \\
12 & Spra: Parameter to compute allocation factors & 7.43 \(\times\) 10\({}^{-3}\) \\
13 & Sla: Specific leaf area & 2.92 \(\times\) 10\({}^{-3}\) \\
14 & phi-ea: Empirical coefficient of the vapor pressure efficiency function & 2.71 \(\times\) 10\({}^{-3}\) \\  & for canopy resistance & 1.67 \(\times\) 10\({}^{-3}\) \\
15 & canopymax: maximum canopy storage capacity & 2.20 \(\times\) 10\({}^{-3}\) \\
16 & laimax: maximum leaf area index used for precipitation interception & 2.00 \(\times\) 10\({}^{-3}\) \\
17 & hdmax: H/D ratio in carbon partitioning for low density & 4.41 \(\times\) 10\({}^{-4}\) \\
18 & tcold-leaf: Temperature threshold that accelerates leaf turnover & 1.67 \(\times\) 10\({}^{-4}\) \\
19 & dbhdcmax: maximum ratio between stem and crown diameters & 3.42 \(\times\) 10\({}^{9}\) \\
20 & dbhdcmin: minimum ratio between stem and crown diameters & 2.79 \(\times\) 10\({}^{9}\) \\
21 & denmax: maximum trees density & 1.67 \(\times\) 10\({}^{9}\) \\
22 & denmin: minimum trees density & 5.48 \(\times\) 10\({}^{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Importance ranking obtained with the sensitivity analysis. Figure 7: Validation of the calibration results: different simulated runoff events (1, 2, 3, and 4) using FEST-WB model Vs measured. two calibrations were combined. All simulations were carried out with daily time steps. Initialization of the model was performed using measured data. The model calculated the increment of the biomass of each tree component. Figure 8 presents a 2D projection of the goodness-of-fit (NSE) surface response for different parameter pairs. The particles tend to converge in the light-blue areas, where NSE reaches its maximum values. After a combination of manual and automatic calibration of parameters, good performances were reached with regards to the simulation of DBH and H.  
Figures 9 and 10 report the results of calibration as compared to measured data. For DBH simulation the coefficient of determination was equal to 0.98 between simulation and observations while for the H was equal to 0.96. The model was calibrated for both hydrological and forest growth simulations.