Comment on gmd-2022-258

Abstract

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model and need many forcing variables at high temporal resolution. However, when trying to model snow depth on climatological timescales, which is often needed for winter tourism related applications, these preconditions can cause barriers. Often, for these types of applications empirical snow models are used. These can estimate snow accumulation and melt based on daily precipitation and temperature data, only. To convert the resultant snow water equivalent time series into snow depth, we developed the empirical model SWE2HS. SWE2HS has been calibrated on a data set derived from a manual observer station network in Switzerland and validated against independent data from automatic weather stations in the European Alps. The model fits the calibration data with root mean squared error (RMSE) of 8.4 cm, coefficient of determination (R2) of 0.97 and BIAS of 0.2 cm and is able to reach RMSE of 20.5 cm, R2 of 0.92 and BIAS of 2.5 cm on the validation data. The temporal evolution of the bulk density can be reproduced reasonably well on both data sets. Due to its simplicity, the model can be used as post-processing tool for output of any other snow model that provides daily snow water equivalent output. SWE2HS is available as a Python package which can be easily installed and used.