Observations of slope movements in mountain landforms using permanent in-situ GNSS instruments

Slope movements in mountain areas are abundant and diverse phenomena, with an extreme range in size and velocity, and constituted from different materials such as bedrock, debris, and ice. In the past two decades, many studies have observed accelerating trends in the surface velocities of these landforms, often attributed to global warming and its amplified impact on high mountains. Detailed data needed for quantitative analysis and modelling, however, remain scarce due to logistic and technical difficulties. In particular, state-of-the-art monitoring strategies of surface displacement in high-mountains rely either on geodetic terrestrial surveys or on remote sensing techniques. While these methods are beneficial for the establishment of long-term time series and distributed datasets of surface displacements, they lack high temporal resolution and are sensitive to data gaps. These characteristics limit their potential for underpinning detailed process understanding and natural hazard management procedures. By contrast, in-situ permanent instruments allow high temporal resolution without observation gaps, providing unprecedented information w.r.t. the processes at hand. Furthermore, continuous observations with short transmission delays are suitable for applications in real-time, essential for many aspects of natural hazard monitoring and early warning systems.

Here, we present a decadal dataset consisting of continuously acquired kinematic data obtained through in-situ global navigation satellite system (GNSS) instruments that have been designed and implemented in a large-scale multi field-site monitoring campaign across the Swiss Alps. The monitored landforms include rock glaciers, high-alpine steep bedrock as well as landslide sites, most of which are situated in permafrost areas. The dataset was acquired at 54 different stations between2304 and 4003 m a.s.l and comprises ~240’000 daily positions derived through double-difference 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 alternative 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 measures. Some of those are presented in order to showcase the fidelity and versatility of the monitoring network.