作者
Alessandro Cicoira,Samuel Weber,Andreas Biri,Ben Buchli,Reynald Delaloye,Reto Da Forno,Isabelle Gärtner‐Roer,Stephan Gruber,Tonio Gsell,Andreas Hasler,Roman Lim,Philippe Limpach,Raphaël Mayoraz,Matthias Meyer,Jeannette Noetzli,Marcia Phillips,Eric Pointner,Hugo Raetzo,Cristian Scapozza,Tazio Strozzi,Lothar Thiele,Andreas Vieli,Daniel Vonder Mühll,Vanessa Wirz,Jan Beutel
摘要
Abstract. Monitoring of the periglacial environment is relevant for many disciplines including glaciology, natural hazard management, geomorphology, and geodesy. Since October 2022, Rock Glacier Velocity (RGV) is a new Essential Climate Variable (ECV) product within the Global Climate Observing System (GCOS). However, geodetic surveys at high elevation remain very challenging due to environmental and logistical reasons. During the past decades, the introduction of low-cost global navigation satellite system (GNSS) technologies has allowed us to increase the accuracy and frequency of the observations. Today, permanent GNSS instruments enable continuous surface displacement observations at millimetre accuracy with a sub-daily resolution. In this paper, we describe decennial time series of GNSS observables as well as accompanying meteorological data. The observations comprise 54 positions located on different periglacial landforms (rock glaciers, landslides, and steep rock walls) at altitudes ranging from 2304 to 4003 ma.s.l. and spread across the Swiss Alps. The primary data products consist of raw GNSS observables in RINEX format, inclinometers, and weather station data. Additionally, cleaned and aggregated time series of the primary data products are provided, including daily GNSS positions derived through two independent processing tool chains. The observations documented here extend beyond the dataset presented in the paper and are currently continued with the intention of long-term monitoring. An annual update of the dataset, available at https://doi.org/10.1594/PANGAEA.948334 (Beutel et al., 2022), is planned. With its future continuation, the dataset holds potential for advancing fundamental process understanding and for the development of applied methods in support of e.g. natural hazard management.