Kinematic observations of the mountain cryosphere using in-situ GNSS instruments

• Main Authors: Beutel, Jan, Biri, Andreas, Buchli, Ben, Cicoira, Alessandro, Delaloye, Reynald, Da Forno, Reto, Gärtner-Roer, Isabelle, Gruber, Stephan, Gsell, Tonio, Hasler, Andreas, Lim, Roman, Limpach, Phillipe, Mayoraz, Raphael, Meyer, Matthias, Noetzli, Jeannette, Phillips, Marcia, Pointner, Eric, Raetzo, Hugo, Scapoza, Cristian, Strozzi, Tazio, Thiele, Lothar, Vieli, Andreas, Vonder Mühll, Daniel, Weber, Samuel, Wirz, Vanessa
• Format: Report
• Language: English
• Published: Copernicus 2021
• Subjects: permafrost
• Online Access: https://hdl.handle.net/20.500.11850/526899; https://doi.org/10.3929/ethz-b-000526899

Permafrost warming is coinciding with accelerated mass movements, talking place especially in steep, mountainous topography. While this observation is backed up by evidence and analysis of both remote sensing as well as repeat terrestrial surveys undertaken since decades much knowledge is to be gained about the specific details, the variability and the processes governing these mass movements in the mountain cryosphere. This dataset collates data of continuously acquired kinematic observations obtained through in-situ Global Navigation Satellite Systems (GNSS) instruments that have been designed and implemented in a large-scale multi field-site monitoring campaign across the whole Swiss Alps. The landforms covered include rock glaciers, high-alpine steep bedrock bedrock as well as landslide sites, most of which are situated in permafrost areas. The dataset was acquired at 54 different stations situated at locations from 2304 to 4003 m a.s.l and comprises 209’948 daily positions derived through double-differential GNSS post-processing. Apart from these, the dataset contains down-sampled and cleaned time series of weather station and inclinometer data as well as the full set of GNSS observables in RINEX format. Furthermore the dataset is accompanied by tools for processing and data management in order to facilitate reuse, open alternate usage opportunities and support the life-long living data process with updates. To date this dataset has seen numerous use cases in research as well as natural-hazard mitigation and adaptation due to climate change. ISSN:1866-3591