In mountainous terrain, reliable snow simulations are crucial for many applications. However, except in highly instrumented research catchments, meteorological data are usually limited, and so the interpolated spatial fields used to force snow models are uncertain. Moreover, certain potentially important processes cannot presently be simulated at catchment scales using entirely physical algorithms. It is therefore often appropriate to introduce empirical parameters into otherwise physically-based snow models. Many opportunities to incorporate snow observations into the parameter estimation process now exist, but they remain to be fully exploited. In this context, a novel approach to the calibration of an energy balance-based snow model that additionally accounts for gravitational redistribution is presented. Several important parameters were estimated using an efficient, gradient-based method with respect to two complementary observation types – Landsat 8-derived snow extent maps, and reconstructed snow water equivalent (SWE) time-series. When assessed on a per-pixel basis, observed patterns were ultimately reproduced with a mean accuracy of 85%. Spatial performance metrics compared favourably with those previously reported, whilst the temporal evolution of SWE at the stations was also satisfactorily captured. Subsequent uncertainty and data worth analyses revealed that: i) the propensity for model predictions to be erroneous was substantially reduced by calibration, ii) pre-calibration uncertainty was largely associated with two parameters which modify the longwave component of the energy balance, but this uncertainty was greatly diminished by calibration, and iii) a lower elevation SWE series was particularly valuable, despite containing comparatively few observations. Overall, our work demonstrates that contemporary snow models, observation technologies, and inverse approaches can be combined to both constrain and quantify the uncertainty associated with simulations of alpine snow dynamics.