Ice fractures when subject to stress that exceeds the material failure strength. Previous studies have found that a von Mises failure criterion, which places a bound on the second invariant of the ...deviatoric stress tensor, is consistent with empirical data. Other studies have suggested that a scaling effect exists, such that larger sample specimens have a substantially lower failure strength, implying that estimating material strength from laboratory-scale experiments may be insufficient for glacier-scale modeling. In this paper, we analyze the stress conditions in crevasse onset regions to better understand the failure criterion and strength relevant for large-scale modeling. The local deviatoric stress is inferred using surface velocities and reanalysis temperatures, and crevasse onset regions are extracted from a remotely sensed crevasse density map. We project the stress state onto the failure plane spanned by Haigh–Westergaard coordinates, showing how failure depends on mode of stress. We find that existing crevasse data are consistent with a Schmidt–Ishlinsky failure criterion that places a bound on the absolute value of the maximal principal deviatoric stress, estimated to be 158±44 kPa. Although the traditional von Mises failure criterion also provides an adequate fit to the data with a von Mises strength of 265±73 kPa, it depends only on stress magnitude and is indifferent to the specific stress state, unlike Schmidt–Ishlinsky failure which has a larger shear failure strength compared to tensile strength. Implications for large-scale ice flow and fracture modeling are discussed.
The 17 June 2017 rock avalanche in the Karrat Fjord, West Greenland, caused a tsunami that flooded the nearby village of Nuugaatsiaq and killed four people. The disaster was entirely unexpected since ...no previous records of large rock slope failures were known in the region, and it highlighted the need for better knowledge of potentially hazardous rock slopes in remote Arctic regions.
The landslide of 17 June 2017 at Karrat Fjord, central West Greenland, triggered a tsunami that caused four fatalities. The catastrophe highlighted the need for a better understanding of landslides ...in Greenland and initiated a recent nation-wide landslide screening project led by the Geological Survey of Denmark and Greenland (GEUS; see also Svennevig (2019) this volume).
This paper describes an approach for compiling freely available data to improve GEUS’ capability to monitor active landslides in remote areas of the Arctic in near real time. Data include seismological records, space borne Synthetic Aperture Radar (SAR) data and multispectral optical satellite imagery. The workflow was developed in 2018 as part of a collaboration between GEUS and scientists from the Technical University of Denmark (DTU). This methodology provides a model through which GEUS will be able to monitor active landslides and provide relevant knowledge to the public and authorities in the event of future landslides that pose a risk to human life and infrastructure in Greenland.
We use a minor event on 26 March 2018, near the site of the Karrat 2017 landslide, as a case study to demonstrate 1) the value of multidisciplinary approaches and 2) that the area around the landslide has continued to be periodically active since the main landslide in 2017.
Arctic sea ice has a significant impact on the global radiation budget, oceanic and atmospheric circulation and the stability of the Greenland ice sheet (Vaughan et al. 2013). Prior to the era of ...aircraft and satellite, information on sea-ice extent relied on observations from ships and people living at the coast. This information is a valuable contribution to better understand the history of sea ice. However, the information exists in a range of formats, e.g., sea-ice extent before the late 1800s is typically reported in the literature as an annual index from a single geographical point or as hand-drawn maps. This makes it difficult to assess and compare data across time and space. The combination of digitised historical maps and single-point data makes the information more accessible and provides a record that can help understand the dynamics and processes of the climate and its interactions with the cryosphere (Chapman & Walsh 1993). In this study, maps of sea-ice extent by Koch (1945) were digitised. We use these maps in combination with sea-ice charts from the Danish Meteorological Institute (DMI) and Koch’s sea-ice index from 1820 to 1939, to map estimated sea-ice extent between Iceland and Greenland going back to 1821. This information has not been included in even the most recent databases of Arctic sea ice (Walsh et al. 2015, 2017). Furthermore, we extract time series of sea-ice extent at a number of locations and investigate the relationship between them. Our observation area is along eastern Greenland, between the southern tip of Greenland at 59°46´N northwards to 77°21´N.
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