The assessment of water resources and their change in time as well as many other applications from hydrology to meteorology require the successful modelling of the dynamics of the seasonal snow cover. Especially if extrapolation in time, e.g. for climate change scenario assessment, is aimed for, the use of energy balance models has been proven to be more trustworthy than simpler temperature index models. The disadvantage of the energy balance method is the requirement of a variety of input quantities, which are not always but become increasingly accessible. What is missing, however, is an in-depth assessment of the influence of typical errors in the individual input quantities on model performance. This contribution presents a sensitivity study, which compares the influence of typical errors and uncertainties in radiation, wind, temperature and humidity input, both measured and modeled, with errors caused by insufficient knowledge of solid precipitation rates. The analysis is carried out with the widely used SNOWPACK model and focuses on the build-up and melt of a mid-latitude seasonal snow cover. While mass input uncertainties still dominate the overall performance especially at high elevations (alpine zone), errors in wind and radiation can cause very significant effects as well. Wind is considered to be more critical in this context, because its spatial variation is both more pronounced and more difficult to estimate than the spatial variation in short- and longwave radiation. In absence of reliable radiation input, its value can successfully be calculated or parameterized if e.g. cloud information is available. Errors in air temperature input or a failure of a correct assessment of its spatial variability can be locally very important because of the feed-back mechanism via atmospheric stability. For the overall mass balance of a seasonal snow cover, humidity errors are less important. Notable exceptions are applications such as the correct assessment of surface hoar formation or prediction of melt rates during rain on snow events, for which good humidity input is required. A main conclusion is that input data through a combination of measurement stations and weather models are now widely available with sufficient quality to even allow a small-scale distributed application of energy balance models.