摘要：More recently, snow accumulation and snowmelt models for their calculations are forced to apply data from numerical weather prediction (NWP) models. This approach allows improvement the accuracy of calculating snow water equivalent (SWE) values especially in remote and mountain regions. In this study, we compared the numerical results of SWE calculations performed by two independent models. The first one is the SnoWE model and the second one is the ICON NWP model. During the period from November 2018 to May 2019, the simulation results of SWE compared with in-situ data from 64 snow surveys, which are located in the Kama river basin. We found that both models (SnoWE and ICON) allow getting satisfactory estimates of the maximum values of SWE (the accuracy of data is sufficient for their practical using). The root mean square error was equal 14-18% from the average measured SWE. Moreover, we got reliable maximum values of SWE for forested areas. At the same time, both models underestimate SWE values during spring snowmelt season. Probably, this underestimation is due to the shortcomings of the models and a sparse snow course-measuring network.
其他摘要：More recently, snow accumulation and snowmelt models for their calculations are forced to apply data from numerical weather prediction (NWP) models. This approach allows improvement the accuracy of calculating snow water equivalent (SWE) values especially in remote and mountain regions. In this study, we compared the numerical results of SWE calculations performed by two independent models. The first one is the SnoWE model and the second one is the ICON NWP model. During the period from November 2018 to May 2019, the simulation results of SWE compared with in-situ data from 64 snow surveys, which are located in the Kama river basin. We found that both models (SnoWE and ICON) allow getting satisfactory estimates of the maximum values of SWE (the accuracy of data is sufficient for their practical using). The root mean square error was equal 14-18% from the average measured SWE. Moreover, we got reliable maximum values of SWE for forested areas. At the same time, both models underestimate SWE values during spring snowmelt season. Probably, this underestimation is due to the shortcomings of the models and a sparse snow course-measuring network.
