The 2016 Lamplugh rock avalanche, Alaska: deposit structures and emplacement dynamics

Dufresne, Anja; Wolken, G. J.; Hibert, Clément; Bessette-Kirton, Erin K.; Coe, Jeffrey A.; Geertsema, Marten; Ekström, Göran

doi:10.1007/s10346-019-01225-4

Cited by 45 publications

(52 citation statements)

References 55 publications

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“…Rock avalanche data do not include possible earthquake-triggered events (three) and each two-part failure is only counted once. Dufresne et al, 2019). This finding highlights the possibility that numerous rock avalanches in glaciated areas may go undetected without systematic examination of repeat satellite imagery.…”

Section: Implications For Glacier Composition and Dynamicsmentioning

confidence: 87%

A 36-Year Record of Rock Avalanches in the Saint Elias Mountains of Alaska, With Implications for Future Hazards

Bessette-Kirton

Coe

2020

Front. Earth Sci.

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Glacial retreat and mountain-permafrost degradation resulting from rising global temperatures have the potential to impact the frequency and magnitude of landslides in glaciated environments. Several recent events, including the 2015 Taan Fiord rock avalanche, which triggered a tsunami with one of the highest wave runups ever recorded, have called attention to the hazards posed by landslides in regions like southern Alaska. In the Saint Elias Mountains, the presence of weak sedimentary and metamorphic rocks and active uplift resulting from the collision of the Yakutat and North American tectonic plates create landslide-prone conditions. To differentiate between the typical frequency of landsliding resulting from the geologic and tectonic setting of this region, and landslide processes that may be accelerated due to changes in climate, we used Landsat imagery to create an inventory of rock avalanches in a 3700 km 2 area of the Saint Elias Mountains. During the period from 1984 to 2019, we identified 220 rock avalanches with a mean recurrence interval of 60 days. We compared our landslide inventory with a catalog of M ≥ 4 earthquakes to identify potential coseismic events, but only found three possible earthquake-triggered rock avalanches. We observed a distinct temporal cluster of 41 rock avalanches from 2013 through 2016 that correlated with above average air temperatures (including the three warmest years on record in Alaska, 2014-2016); this cluster was similar to a temporal cluster of recent rock avalanches in nearby Glacier Bay National Park and Preserve. The majority of rock avalanches initiated from bedrock ridges in probable permafrost zones, suggesting that ice loss due to permafrost degradation, as opposed to glacial thinning, could be a dominant factor contributing to rock-slope failures in the high elevation areas of the Saint Elias Mountains. Although earthquake-triggered landslides have episodically occurred in southern Alaska, evidence from our study suggests that area-normalized rates of non-coseismic rock avalanches were greater during the period from 1964 to 2019, and that the frequency of these events will continue to increase as the climate continues to warm. These findings highlight the need for hazard assessments in Alaska that address changes in landslide patterns related to climate change.

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Section: Implications For Glacier Composition and Dynamicsmentioning

confidence: 87%

A 36-Year Record of Rock Avalanches in the Saint Elias Mountains of Alaska, With Implications for Future Hazards

Bessette-Kirton

Coe

2020

Front. Earth Sci.

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“…This RA occurred on 11 June 2012 and was deposited onto a tributary of the John Hopkins Glacier (Geertsema, 2012). The upper portion of the deposit was sequestered into the ice after its deposition in 2012, as is common of debris inputs in glacier accumulation areas (Dunning et al, 2015). However, the deposit toe remained visible on the surface, likely because it was below the snow line.…”

Section: Tracking New Debris Transportationmentioning

confidence: 96%

“…Without the associated deposit, bedrock source areas are easily mistaken for glacial cirques (Turnbull and Davies, 2006). Fresh snowfall or wind redistribution of snow can rapidly cover a RA deposit that is many kilometres square in area (Dunning et al, 2015). If this occurs within the accumulation zone the deposit is essentially lost to all surface investigation and nonice-penetrating remote sensing and ground-based techniques until its eventual re-emergence in the ablation zone, after potentially considerable modification by transport processes.…”

Section: Introductionmentioning

confidence: 99%

“…Seismic methods have identified some of the largest supraglacially deposited RAs in recent times (e.g. Lamplugh glacier RA; Dufresne et al, 2019) which are compiled in a database (IRIS DMC, 2017) and, when combined with manual analysis of satellite imagery, give information on duration, momenta, potential energy loss, mass, and runout trajectory. However, landslides are challenging to detect using seismic methods, and event positional accuracy is limited to a 20-100 km radius, due to the lack of high-frequency waves when compared to earthquakes, further inhibited by the low frequencies and long wavelengths of dominant seismic waves worldwide (Ekström and Stark, 2013).…”

Section: Introductionmentioning

confidence: 99%

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GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new tool for identifying and monitoring supraglacial landslide inputs

et al. 2020

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Abstract. Landslides in glacial environments are high-magnitude, long-runout events, believed to be increasing in frequency as a paraglacial response to ice retreat and thinning and, arguably, due to warming temperatures and degrading permafrost above current glaciers. However, our ability to test these assumptions by quantifying the temporal sequencing of debris inputs over large spatial and temporal extents is limited in areas with glacier ice. Discrete landslide debris inputs, particularly in accumulation areas, are rapidly “lost”, being reworked by motion and icefalls and/or covered by snowfall. Although large landslides can be detected and located using their seismic signature, smaller (M≤5.0) landslides frequently go undetected because their seismic signature is less than the noise floor, particularly supraglacially deposited landslides, which feature a “quiet” runout over snow. Here, we present GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new free-to-use tool leveraging Landsat 4–8 satellite imagery and Google Earth Engine. GERALDINE outputs maps of new supraglacial debris additions within user-defined areas and time ranges, providing a user with a reference map, from which large debris inputs such as supraglacial landslides (>0.05 km2) can be rapidly identified. We validate the effectiveness of GERALDINE outputs using published supraglacial rock avalanche inventories, and then demonstrate its potential by identifying two previously unknown, large (>2 km2) landslide-derived supraglacial debris inputs onto glaciers in the Hayes Range, Alaska, one of which was not detected seismically. GERALDINE is a first step towards a complete global magnitude–frequency of landslide inputs onto glaciers over the 38 years of Landsat Thematic Mapper imagery.

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“…Seismic methods have identified some of the largest supraglacially deposited RAs in recent times (e.g. Lamplugh glacier RA (Dufresne et al, 2019)) which are compiled in a database (IRIS DMC, 2017), and, when combined with manual analysis of satellite imagery, gives information on duration, momenta, potential energy loss, mass and runout trajectory. However, landslides are challenging to detect using seismic methods and event positional accuracy is limited to a 20 -100 km radius, due to the lack of high frequency waves when compared to earthquakes, further inhibited by the low frequencies and long wavelengths of dominant seismic waves worldwide (Ekström and Stark, 2013).…”

Section: Introductionmentioning

confidence: 99%

GERALDINE (Google earth Engine supRaglAciaL Debris INput dEtector) – A new Tool for Identifying and Monitoring Supraglacial Landslide Inputs

Smith¹,

Dunning²,

Brough³

et al. 2020

Preprint

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Abstract. Rock avalanches, a high-magnitude, long runout form of bedrock landslide, are thought to increase in frequency as a paraglacial response to ice-retreat/thinning, and arguably, due to warming temperatures/degrading permafrost above current glaciers. However, our ability to test these assumptions by quantifying the temporal sequencing of debris inputs over large spatial and temporal extents is limited in areas with glacier ice. Discrete landslide debris inputs, particularly in accumulation areas are rapidly ‘lost’, being reworked by motion and icefalls, and/or covered by snowfall. Although large landslides can be detected and located using their seismic signature, small to medium-sized landslides, particularly supraglacially deposited landslides which feature a quiet runout over snow, frequently go undetected because their seismic signature is less than the noise floor. Here, we present GERALDINE (Google earth Engine supRaglAciaL Debris INput dEtector): a new open-source tool leveraging Landsat 4–8 satellite imagery and Google Earth Engine. GERALDINE outputs maps of new supraglacial debris additions within user-defined areas and time ranges, providing a user with a reference map, from which large debris inputs such as supraglacial rock avalanches can be rapidly identified. We validate the effectiveness of GERALDINE outputs using published rock-avalanche inventories, then demonstrate its potential by identifying two previously unknown, large (> 2 km2) supraglacial debris inputs onto glaciers in the Hayes Range, Alaska, one of which was not detected seismically. GERALDINE is a first step towards a revised global magnitude-frequency of rock avalanche inputs onto glaciers over the 37 years of Landsat Thematic Mapper imagery.

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The 2016 Lamplugh rock avalanche, Alaska: deposit structures and emplacement dynamics

Cited by 45 publications

References 55 publications

A 36-Year Record of Rock Avalanches in the Saint Elias Mountains of Alaska, With Implications for Future Hazards

A 36-Year Record of Rock Avalanches in the Saint Elias Mountains of Alaska, With Implications for Future Hazards

GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new tool for identifying and monitoring supraglacial landslide inputs

GERALDINE (Google earth Engine supRaglAciaL Debris INput dEtector) – A new Tool for Identifying and Monitoring Supraglacial Landslide Inputs

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