## 5 Discussion
### Uncertainty Analysis  
Upon finalizing the construction of the SWAT model, it is inevitable that the input data will exhibit varying levels of resolution accuracy, leading to potential errors [41]. The accuracy of the land use data utilized in this research is constrained to 1 km because of the limitations of data acquisition methods, which may influence the results. Additionally, the geographical characteristics of the study area, along with the prolonged freezing and thawing periods during winter, result in the SWAT model not fully accounting for the elements specific to cold regions, which may affect the accuracy of the simulations. In contrast to the more extreme cold weather conditions found in Russia, the study area is situated in the cold temperate zone. Therefore, the inherent systematic errors by the SWAT model are unlikely to significantly affect the simulation results [42]. Compared to other models, the SWAT model provides a more comprehensive representation of geographical elevation, soil type, land use, and weather simulation. Consequently, the influence of extreme weather on the modeling outcomes is overlooked during the modeling process [43].  
In comparison to the uncertain relationship between the SWAT model and extreme weather, the InVEST model also faces challenges regarding parameter adaptability. Certain parameters within the water production module of the InVEST model are established based on research or experiences from other regions and may not be entirely suitable for application in colder regions. The presence of frozen soil in the study area will influence the infiltration and storage of soil water, significantly impacting water yield. To facilitate broad application, the InVEST model utilizes a standard default parameter; however, ecosystems vary significantly across regions, exhibiting distinct geographical environments, climate conditions, and soil characteristics. In the mountain ecosystem, utilizing default values for parameters like soil permeability and vegetation root depth fails to accurately represent the actual local conditions of water infiltration and retention. This leads to discrepancies in the assessment of water conservation services [44]. It is important to note that the InVEST model water yield module may not, by default, accurately account for the effects of frozen soil, potentially resulting in biased water yield estimates. During the winter months, water bodies such as rivers and lakes in colder regions may experience freezing, and precipitation is predominantly in the form of snow. This presents challenges for the monitoring and estimation of water yield [45]. The water production module of the InVEST model necessitates precise precipitation and evaporation data to effectively calculate water production. However, acquiring these data during winter poses challenges, potentially resulting in inaccurate estimations of water production within the model during that season [46].  
The SWAT model and InVEST model demonstrate applicability in cold regions; however, it is essential to thoroughly consider the unique climate, terrain, and hydrological conditions present in these areas during their application. Consequently, the InVEST model is integrated with other models in the research to address its parameter limitations, including the coupled hydrological model (i.e., SWAT). The accurate watershed hydrological parameters of the SWAT offer enhanced flow path and confluence time parameters for the InVEST water conservation module, thereby optimizing the output effectiveness of InVEST. The model underwent local parameter adjustment and validation to enhance the precision of water yield estimation. The current accuracy of land use data obtainable in the research region is restricted to 1 km, constrained by the data collection methods and approaches, hence preventing the acquisition of more precise data. Consequently, the incorporation of higher resolution data is a primary emphasis of our future research endeavors.