Rock temperature prior to failure: Analysis of 209 rockfall events in the Mont Blanc massif (Western European Alps). (16th June 2021)

• Record Type: Journal Article
• Authors: Legay, Alexandre; Magnin, Florence; Ravanel, Ludovic
• Is Part Of: Permafrost and periglacial processes. Volume 32:Number 3(2021)
• Page Start: 520
• Page End: 536
• Publication Date: 2021-06-16
• Subjects: alpine rock wall -- Mont Blanc massif -- permafrost -- rockfall -- thermal modeling; Frozen ground -- Periodicals; Sols gelés -- Périodiques; Périglaciaire -- Périodiques; 551.3805
• DOI: 10.1002/ppp.2110
• Languages: English
• ISSNs: 1045-6740

Abstract

Abstract: Periglacial rock walls are affected by an increase in rockfall activity attributed to permafrost degradation. While recent laboratory tests have asserted the role of permafrost in bedrock stability, linking experimental findings to field applications is hindered by the difficulty in assessing bedrock temperature at observed rockfall locations and time. In this study, we simulated bedrock temperature for 209 rockfalls inventoried in the Mont Blanc massif between 2007 and 2015 and 209, 000 random events artificially created at observed rockfall locations. Real and random events are then compared in a statistical analysis to determine their significance. Permafrost conditions (or very close to 0°C) were consistently found for all events with failure depth > 6 m, and for some events affecting depths from 4 to 6 m. Shallower events were probably not related to permafrost processes. Surface temperatures were significantly high up to at least 2 months prior to failure, with the highest peaks in significance 1.5–2 months and 1–5 days before rockfalls. Similarly, temperatures at scar depths were significantly high, but steadily decreasing, 1 day to 3 weeks before failure. The study confirms that warm permafrost areas (> −2°C) are particularly prone to rockfalls, and that failures are a direct response to extraordinary high bedrock temperature in both frozen and unfrozen conditions. The results are promising for the development of a rockfall susceptibility index, but uncertainty analysis encourages the use of a greater rockfall sample and a different sample of random events.