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Environmental variables are often auto-correlated and a principal component analysis (PCA) can be used to find an axis or dimension that summarizes this redundancy130. We applied a PCA using R software packages FactoMineR and ggplot to evaluate the relationships between rockfall variables similar to previous approaches131,132. For each rockfall event, we extracted (i) elevation as a proxy for frost cracking, (ii) MARST as a proxy for permafrost, (iii) deglaciation age as a proxy for paraglacial adjustment, (iv) slope angle as a proxy for shear stress, and (v) rockfall volume. In using deglaciation age as a proxy for paraglacial process intensity we assumed that paraglacial processes were most intense at the start of deglaciation and decreased thereafter in the form of an exhaustion curve9,84. We used the rockfall elevation as a proxy for frost cracking, which at the Hungerli Valley has been shown to strongly scale with elevation32 despite variability in lithology and other local influences73,87. For each rockfall event, we extracted slope angle and rockfall volume from our laserscanning-derived DEM. All parameters were standardised using 2ff7e9595c
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