## 4 _Article_ Modeling Snow Depth and Snow Model  
**Modeling Snow Depth and Snow Model**  
**Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China.**_Appl. Sci._**2021**, _11_, 8056. **[[https://doi.org/10.3390/app11188365](https://doi.org/10.3390/app11188365)]([https://doi.org/10.3390/app11188365](https://doi.org/10.3390/app11188365))  
**Collage of Geography Science, Qinghai Normal University, Xining 810008, China**  
* 5 MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, China
* 6 MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, China
* 7 MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, China
* 8 MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, China
* 9 MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, China
* 10 Memb  
Modeling is an important means to study snow cover change [6]. Snow models can be generally divided into two categories: one is an empirical model based on simple statistical methods; the other is physical models based on the energy and mass balance processes [7; 8; 9]. The advantage of the empirical model is that it requires fewer input parameters. Therefore, it has been widely used to simulate snow and glacier melting in Northern Europe, the Alps, the Greenland ice sheet, the Tibetan Plateau, and other regions [10; 11]. Some hydrological models, such as Snowmelt Runoff Model (SRM) [12; 13] and the HBV model [14; 15], also use an empirical model to describe the melting process of ice, snow and glacier. These snowmelt runoff simulations also achieved good results [16; 17; 18; 19]. However, the empirical model simulation accuracy decreases with the improvement in time resolution, and it is impossible to describe the spatial variation of snow surface ablation [20]. Compared with the empirical model, the snow model based on energy balance can better reflect the physical process, the exchange of energy and water between snow cover and atmosphere, the snow melt infiltration, the dynamic change in snow surface albedo, the compaction of snow cover, and other processes [21; 22; 23]. Therefore, physical models have a wide range of applications. There are many snowmelt models based on energy balance, such as the Utah Energy Balance model (UEB) [24] and the SNOWPACK model [25; 26]. Some hydrological models, such as VIC [27] and WEB-DHM [28], also use physical models to describe snowmelt runoff. Land surface models, such as CLM [29], Noah-MP [30; 31] and SURFX [32], have continuously evolved according to the requirements of atmospheric and hydrological disciplines and can also effectively simulate snow processes. [PERSON] et al. [33] combined the Weather Research and Forecasting (WRF) regional climate model with the Noah-MP model to simulate the snow cover fraction (SCF) and snow water equivalent (SWE) over the central Sierra Nevada Mountains and demonstrated that the models can be an efficient approach to simulate snow processes. Irtysh River is the second largest river in Xinjiang, and it is also an international river. It flows through China, the Republic of Mongolia, Kazakhstan, and Russia, which plays an important role in the social and economic development of these countries [23]. Snow has an important contribution to the hydrological process in the basin. However, due to the lack of systematic observation, there is little research on snow cover in the basin. [PERSON] et al. [34] used a UEB model to simulate the snowmelt process at a site in the upper reaches of Irtysh River Basin. [PERSON] et al. [35] coupled the WRF model with the temperature-index model to simulate snow melt in the Kajiertesi River Basin, which is in the upper reaches of the Irtysh River Basin. [PERSON] et al.