Snow avalanches pose a significant threat to settlements and their inhabitants. Consequently, hazard maps are a valuable tool for mitigating their impact. Dynamic models have been used to visualize areas affected by avalanches; however, these models require uncertain inputs. This study develops probabilistic hazard maps by quantifying uncertain input variables through probability density functions. These maps represent the probability of model outputs, such as maximum flow thickness, exceeding specific thresholds, allowing for more quantitative hazard assessments. Three uncertainty quantification methods—Monte Carlo, Latin hypercube sampling, and polynomial chaos quadrature (PCQ)—are employed to generate probabilistic hazard maps for snow avalanches. These maps are compared with a reference hazard map created using parameter sets that cover the entire parameter space. Among the three methods, PCQ yields the most accurate results for a given number of simulations, assuming a uniform distribution for each input. The optimal PCQ settings, which deliver superior results with fewer simulations, are then determined. Additionally, a PCQ application is proposed to generate hazard maps based on non-uniform input distributions without requiring extra simulations. This approach reduces the computational cost associated with creating hazard maps for non-uniform distributions if PCQ has already been applied to a uniform case. The application generates two types of probabilistic hazard maps: one considering all potential parameter ranges during the snow season using uniform distributions, and another reflecting non-uniform distributions that account for uncertainty near-term real-world snow cover conditions.

Blasting experiments were performed that investigate multiple explosions that occur in quick succession in the ground and their effects on host material and atmosphere. Such processes are known to occur during volcanic eruptions at various depths, lateral locations, and energies. The experiments follow a multi-instrument approach in order to observe phenomena in the atmosphere and in the ground, and measure the respective energy partitioning. The experiments show significant coupling of atmospheric (acoustic)- and ground (seismic) signal over a large range of (scaled) distances (30–330 m, 1–10 mJ^-1/3). The distribution of ejected material strongly depends on the sequence of how the explosions occur. The overall crater sizes are in the expected range of a maximum size for many explosions and a minimum for one explosion at a given lateral location. The experiments also show that peak atmospheric over-pressure decays exponentially with scaled depth at a rate of d0 = 6.47×10-4 mJ-1/3; at a scaled explosion depth of 4×10-3 mJ-1/3 ca. 1% of the blast energy is responsible for the formation of the atmospheric pressure pulse; at a more shallow scaled depth of 2.75×10-3 mJ-1/3 this ratio lies at ca. 5.5–7.5%. A first order consideration of seismic energy estimates the sum of radiated airborne and seismic energy to be up to 20% of blast energy.