The mitigation of rapid mass movements involves a subtle interplay between field surveys, numerical modelling, and experience. Hazard engineers rely on a combination of best practices and, if available, historical facts as a vital prerequisite in establishing reproducible and accurate hazard zoning. Full-scale field tests have been performed to reinforce the physical understanding of debris flows and snow avalanches. Rockfall dynamics are - especially the quantification of energy dissipation during the complex rock-ground interaction - largely unknown. The awareness of rock shape dependence is growing, but presently, there exists little experimental basis on how rockfall hazard scales with rock mass, size, and shape. Here, we present a unique data set of induced single-block rockfall events comprising data from equant and wheel-shaped blocks with masses up to 2670 kg, quantifying the influence of rock shape and mass on lateral spreading and longitudinal runout and hence challenging common practices in rockfall hazard assessment.

Abstract-Spontaneous occurring rockfalls are a serious danger, especially nowadays as global warming leads to a retrogression of the permafrost, which stabilized terrain in mountainous regions. In order to perform risk assessments and develop mitigation strategies, advanced simulation tools and models have been developed over the last years. These models come with many parameters and need to be calibrated and validated with realworld data to produce reliable estimates.To this end, we developed StoneNode, a rugged, small, lowpower sensor device which can be embedded into boulders to measure accelerations and angular velocities. The node employs low-power MEMS sensors with high dynamic range and has a maximum operating time of more than 56 h. First field experiments confirm that the StoneNode is a reliable, easy-to-use device, which greatly facilitates the data acquisition process.