## 3 Results
### Single-Objective Optimisation Parameters
At the same time, we can clearly observe that there are drastic fluctuations within the top 20 cm of the surface layer and the model is able to capture the variation characteristics of these variables well, especially when using \(\theta\) or \(h\) as the objective variable to simulate \(\theta\) or \(h\). In contrast, the changes in deeper layers are more gradual, with smaller amplitudes, resulting in higher accuracy compared to the surface layer. Based on these observations, we can further speculate that each variable performs best when used as its own objective function in the simulation. In addition, comparing Figure 3a-d, it can be seen that higher-precision simulation results can be generally obtained for \(\theta\) and \(h\), while With respect to the simulations of \(T\) and \(q\), no matter which variable is used as the objective function, such a level of simulation accuracy cannot be achieved. We also find that some of the simulation results in the validation period are good. We speculate that this is due to the reduced radiation and lower temperatures in October, leading to smaller variations and thus better simulation performance. Figure 2: Comparisons of the optimal parameters after normalization under single-objective function. Black, red, blue, and green circles represent the objective variable of \(\theta\), \(T\), \(h\), and \(q\), respectively. during the calibration period in Table 3. Overall, using \(\theta\) or \(h\) as the objective variable provides better simulation of the soil water-vapor-heat processes, as evidenced by the concentration of points near the 1:1 line. However, when \(T\) is used as the objective variable, simulation accuracy decreases, with points more scattered and deviating from the 1:1 line, indicating difficulties in simulating variations in \(\theta\), \(h\), and \(q\). Specifically, the first row shows comparisons between the simulated and \"true\" \(\theta\), where Figure 4a-d correspond to \(\theta\), \(T\), \(h\), and \(q\) as the objective variables, respectively. When \(T\) is used as the objective variable, \(R^{2}\) is less than 0.65 and \(d\) is less than 0.95. However, with \(\theta\), \(h\), or \(q\) as the objective variable, \(R^{2}\) are greater than 0.99 and \(d\) are greater than 0.99. The second row shows comparisons between the simulated and \"true\" \(T\), where Figure 4e-h correspond to \(\theta\), \(T\), \(h\), and \(q\) as the objective variables, respectively. \(R^{2}\) are all above 0.7, and \(d\) are all above 0.92, but the simulation accuracy is lower compared to that for \(\theta\). The third row shows comparisons between the simulated and \"true\" \(h\), where Figure 4i-l correspond to \(\theta\), \(T\), \(h\), and \(q\) as the objective variables, respectively. Similar to the simulation for \(\theta\), when \(T\) is used as the objective variable, the simulation performs the worst, with \(R^{2}\) less than 0.7 and \(d\) less than 0.91. The fourth row shows comparisons between the simulated and \"true\" \(q\), where Figure 4m-p correspond to \(\theta\), \(T\), \(h\), and \(q\) as the objective variables, respectively. Similarly, when \(T\) or \(q\) is used as the objective variable, the simulation performance is the worst, with \(R^{2}\) less than 0.77 and \(d\) less than 0.94. For the other objective variables, \(R^{2}\) are around 0.85 and \(d\) are above 0.96. Furthermore, we find that using \(\theta\) or \(h\) as the objective variable improves the simulation of \(T\) and \(q\). Figure 4: The 1:1 line plots of simulated and observed values for different variables when \(\theta\), \(T\), \(h\), and \(q\) are used as the single-objective variable. (**a–d**) show the 1:1 plots for \(\theta\) under different functions, (**e–h**) for \(T\), (**i–l**) for \(h\), and (**m–p**) for \(q\). Dark blue, red, green, light blue, and orange circles represent observation depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm, respectively.