## 4 Discussion
### Influence Degree Threshold Analyses  
Based on the feature importance extraction, the most influential factors affecting GPP in relation to soil hydrothermal conditions are the deep soil moisture and the surface soil temperature. Although it provides a more intuitive depiction of the contribution degree of soil hydrothermal conditions to GPP, the influence degree threshold analyses also need to be conducted to trace the changing trend and clarify the internal regulations mechanism.  
Due to the \"black box\" nature of machine learning models, the Shapley Additive exPlanation (SHAP) method is utilized to evaluate the concise causality relationships between feature variables [62]. The SHAP method is an additivity interpretation model inspired by the Shapley value from cooperative probability theory [63]. Its fundamental concept involves calculating the marginal contribution of features to the model output, thereby enabling the interpretation of the \"black box\" model at both global and local levels. The SHAP method has gained widespread adoption due to its desirable properties, including local accuracy, missingness, and consistency [64; 65]. To better investigate the threshold effects and the degree of influence between soil hydrothermal conditions and GPP values, scatter analyses are conducted for different dominant vegetation areas using the \"SHAP package\" in Python 3.9.  
The zero value of SHAP is commonly used as a threshold. A positive SHAP value indicates that the sample point positively contributes to the predicted result. The magnitude of the SHAP value corresponds to the extent of the positive contribution, with larger values indicating greater contributions and vice versa. The analyses for soil moisture and soil temperature are shown in Figures 10 and 11, respectively. The linear regression analysis of soil moisture values in the vegetation zone has demonstrated that positive contributions to GPP only occur when the surface soil moisture SM-L1 exceeds 34.06%. However, SM-L2 and SM-L3 exhibit positive contributions even before reaching the threshold values of 33.80% and 34.32%, respectively (Figure 10a-c). The higher number of positive sample points further confirms the stronger facilitation effect of the deep soil. The scatter analyses for grassland (Figure 10d-f), which is the dominant vegetation type with the widest distribution, are similar to those for the vegetation zone, with threshold points of 33.45%, 31.22%, and 35.78%, respectively. In the shrub (Figure 10g-i), the SHAP values of soil moisture in different soil layers do not exhibit a well-fitting linear trend, and the threshold effect is not significant. For alpine vegetation (Figure 10)-l), the limited number of sample  
Figure 9: Interannual fluctuation of normalized values of GPP in the Qilian Mountains vegetation zone.  
points and low SHAP values can be attributed to the low coverage area of this vegetation type. The most pronounced linear fitting trend in the vegetation zone is observed in Level 2, where a clear positive contribution is evident once the soil temperature exceeds \(-0.58\)\({}^{\circ}\)C (Figure 11a-c). In the grassland (Figure 11d-f), both ST-L1 and ST-L2 exhibit significant threshold turning points at \(-0.35\)\({}^{\circ}\)C and \(0.23\)\({}^{\circ}\)C, indicating that the positive contribution of ST-L1 is more extensive and influential for GPP. The positive and negative evaluations are most pronounced at the threshold point of ST-L1 in shrubs (Figure 11g-i), demonstrating consistent facilitation when the soil temperature exceeds 2.78 \({}^{\circ}\)C. Similar to soil moisture, the analyses of soil temperature for alpine vegetation are also limited by the small number of sample points (Figure 11j-l).  
Figure 10: Influence degree threshold analysis of soil moisture values in different dominant vegetation areas: (**a–c**) vegetation zone, (**d–f**) grassland, (**g–i**) shrub, and (**j–l**) alpine.