Improved monitoring and prediction of permafrost climate change related landsliding in the Arctic

The Arctic plays a key role in understanding and mitigating the challenge of climate adaptation. Indeed, the observed Arctic warming is more than twice the global mean, implying that the Arctic may serve as an ‘early warning region’ in terms of climate change impacts. Longyearbyen, the Arctic capital settlement situated at 78°N in the archipelago of Svalbard, is located in a geographical hotspot affected by extreme Arctic climate change. Reports show that both experienced changes from 1971-2000 and projected 2071-2100 changes for Svalbard include increased air temperature, increased precipitation (especially in summer and autumn) and more frequent and intense events with heavy rainfall. Immediate and potentially detrimental impacts of these climate changes are seen in the widespread permafrost of the Arctic, which is particularly sensitive to climate change. These impacts include increases in active-layer thickness and melting of ground ice, resulting in increased risk of landslides.

Based in Longyearbyen, Svalbard, the PermaMeteoCommunity project develops a permafrost and meteorological response system that consists of (1) instrumented boreholes for direct observations of ground temperature and pore water pressure in the active layer and top meters of permafrost, (2) a network of meteorological stations, which records key standard parameters such as air temperature and precipitation with high spatial and temporal resolution. Using IoT technology, the observations are to be connected with an open online platform that receives and displays all data in near real-time. The data can thereby be used for local authorities and decision makers, during operational evaluations and extreme weather events such as large amounts of rain, potentially inducing permafrost-related landslides. The platform will also give access to historical data and the system will be highly relevant for use in research, for education, and in outreach as well as for long-term societal infrastructure and overall land area planning. Furthermore, work is being done to include more elements in the response system, among others (1) remote sensing data for monitoring of ground movement (2) high-resolution numerical weather simulations to be employed in preparedness situations on an on-demand basis and (3) a machine learning component for enhanced predictions of landslides.