# Effects of Cyclical Climate Change on Water Temperature Characteristics of Permafrost Slopes
## 3 Results and Discussion  
### Model Verification  
Figure 3 shows the comparison results of the measured and simulated values of temperature changes with depth during January, February, and March [16]. From the overall trend, the change laws of the measured curves and the simulated curves in different months are consistent, all showing that the temperature tends to be stable as the depth increases. This indicates that the model has high accuracy in capturing the law of soil liquid temperature changes with depth, especially under the winter conditions of January and February, where the simulation results match well with the measured data, verifying the adaptability of the model to the heat transfer process in low-temperature environments. However, in March, the simulated values of the shallow layer (regional surface layer humidity changes) are slightly lower than the measured values. This may be related to the rapid surface temperature rise and the characteristics of cover materials (such as snow melting and surface layer humidity changes) not being fully considered. Neglecting the snowmelt process may lead to inaccuracies in the thermal boundary conditions of the surface layer of frozen soil, causing the model to overestimate the stability of the frozen soil. In reality, when the snow cover melts, the ground surface turns into bare soil with a low albedo, enhancing its heat absorption capacity, which results in the simulated temperature being lower than the measured temperature. Furthermore, failing to consider the regulatory effects of precipitation, surface runoff, and snowmelt water on the distribution of surface humidity can lead to the accumulation of water at the freeze-thaw interface, forming ice lenses. These ice lenses release latent heat, slowing down the cooling of the frozen layer. This situation may cause an underestimation of the temperature of the frozen soil, making  
Figure 2: Permafrost slope modeling. (**a**) Model (3D); (**b**) meshing (2D). (In (**b**), a is Top center of slope, b is Slope top, c is Bottom of slope, d is Bottom center of slope, e is Right end of slope bottom). the simulated values lower than the measured ones. This discrepancy echoes findings by [PERSON] et al. [12], who emphasized the insulating role of snow cover in delaying thawing processes. Compared to [PERSON]'s [22] phase-lag model, our framework better resolves latent heat release during ice-water transitions, particularly in shallow layers, where pore water phase changes dominate thermal responses. Overall, the model can accurately simulate the winter soil liquid temperature distribution, but there are limitations in representing the dynamic thermal process of the surface layer in spring. Further improvements are needed to enhance the model's ability to describe the hydrothermal coupling mechanism under complex climate changes. Figure 4 shows the comparison between the simulated and measured values of soil liquid water content during January, February, and March [16]. The results show that the model can relatively accurately reflect the general trend that the water content gradually decreases with increasing depth. At the same time, it reveals significant seasonal changes in water content distribution in different months. Specifically, in shallow-layer soil, water content fluctuations are more obvious due to the greater influence of external climate factors (such as evaporation and precipitation), and there is a certain deviation between the simulated values and the measured values. In deep-layer soil (>2 m), the water content change tends to be stable, and the degree of fitting between the simulated values and the measured values is relatively high, indicating that the model can accurately capture the deep-layer water migration law.