Comparing high spatial and temporal resolution snow depth measurements and modelling results in an avalanche release area

Abstract. Accurate representation of snow depth distribution within avalanche release areas is critical for understanding avalanche formation and supporting operational avalanche mitigation measures. In this study, we investigate the spatial variability of snow depth in an avalanche release area using high spatial (0.5 m) and temporal (hourly) resolution measurements obtained from a low-cost terrestrial laser scanner (TLS). The TLS data provide detailed snow depth distribution maps. We analyse three selected snow accumulation events, including sub-event evolution, enabling an event- and sub-event-based analysis of snow deposition patterns. We assess the ability of three terrain-based modelling approaches to reproduce observed snow depth patterns: the topographic position index (TPI), a wind shelter index (Sx), and a statistical preferential deposition model. The results indicate that simple topography-derived indices generally achieve the highest correlations with measured snow depths across most analysed events. The correlations reach maximum values of up to 0.57 (Spearman correlation), indicating that topographic predictors are able to partially, but not fully explain the present snow depth variability at sub-metre spatial resolution. These findings emphasise the dominant role of local terrain in shaping snow accumulation patterns within avalanche release areas, demonstrate the value of TLS data for event-scale model evaluation, and highlight the potential to complement incomplete observations using simple terrain-based modelling approaches. The collection of additional snow depth distribution data with such high spatio-temporal resolution in different avalanche release areas would enable the development of machine learning approaches in the future. This fosters event-based avalanche forecasting by improving the spatial completeness of snow depth observations in complex terrain at slope scale.