A decade of detailed observations (2008–2018) in steep bedrock permafrost at the Matterhorn Hörnligrat (Zermatt, CH)

• Published in: Earth System Science Data
• Main Authors: S. Weber, J. Beutel, R. Da Forno, A. Geiger, S. Gruber, T. Gsell, A. Hasler, M. Keller, R. Lim, P. Limpach, M. Meyer, I. Talzi, L. Thiele, C. Tschudin, A. Vieli, D. Vonder Mühll, M. Yücel
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2019
• Subjects: geo; envir; permafrost
• Online Access: https://doi.org/10.5194/essd-11-1203-2019; https://www.earth-syst-sci-data.net/11/1203/2019/essd-11-1203-2019.pdf; https://doaj.org/article/f4159cb5f738450b9bda96a2faf50ec0

The PermaSense project is an ongoing interdisciplinary effort between geo-science and engineering disciplines and started in 2006 with the goals of realizing observations that previously have not been possible. Specifically, the aims are to obtain measurements in unprecedented quantity and quality based on technological advances. This paper describes a unique >10-year data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500 ma.s.l. Through the utilization of state-of-the-art wireless sensor technology it was possible to obtain more data of higher quality, make these data available in near real time and tightly monitor and control the running experiments. This data set (https://doi.org/10.1594/PANGAEA.897640, Weber et al., 2019a) constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over a period of 10 or more years. By documenting and sharing these data in this form we contribute to making our past research reproducible and facilitate future research based on these data, e.g., in the areas of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. Finally, the cross-validation of four different data types clearly indicates the dominance of thawing-related kinematics.