Automated detection of unstable glacier flow and a spectrum of speedup behavior in the Alaska Range

Herreid, Sam; Truffer, Martin

doi:10.1002/2015jf003502

Cited by 42 publications

(40 citation statements)

References 76 publications

(126 reference statements)

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“…In cases where a surging glacier interacts with another glacier (i.e., as either an ephemeral or permanent tributary), surface morphological features, such as distorted or tear-dropped moraines, sometimes develop. These have been used widely to infer past surge-activity and identify surge-type glaciers in satellite imagery, (e.g., [8][9][10]). …”

Section: Introductionmentioning

confidence: 99%

The 2015 Surge of Hispar Glacier in the Karakoram

et al. 2017

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Abstract:The Karakoram mountain range is well known for its numerous surge-type glaciers of which several have recently surged or are still doing so. Analysis of multi-temporal satellite images and digital elevation models have revealed impressive details about the related changes (e.g., in glacier length, surface elevation and flow velocities) and considerably expanded the database of known surge-type glaciers. One glacier that has so far only been reported as impacted by surging tributaries, rather than surging itself, is the 50 km long main trunk of Hispar Glacier in the Hunza catchment. We here present the evolution of flow velocities and surface features from its 2015/16 surge as revealed from a dense time series of SAR and optical images along with an analysis of historic satellite images. We observed maximum flow velocities of up to 14 m d −1 (5 km a −1 ) in spring 2015, sudden drops in summer velocities, a second increase in winter 2015/16 and a total advance of the surge front of about 6 km. During a few months the surge front velocity was much higher (about 90 m d −1 ) than the maximum flow velocity. We assume that one of its northern tributary glaciers, Yutmaru, initiated the surge at the end of summer 2014 and that the variability in flow velocities was driven by changes in the basal hydrologic regime (Alaska-type surge). We further provide evidence that Hispar Glacier has surged before (around 1960) over a distance of about 10 km so that it can also be regarded as a surge-type glacier.

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Section: Introductionmentioning

confidence: 99%

The 2015 Surge of Hispar Glacier in the Karakoram

et al. 2017

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“…Susitna Glacier last surged in 1951/52. A recurrence interval of 50-60 years has been estimated [16,18,19], but no signs of an imminent surge have been observed since. West Fork Glacier surged in 1935 and again in 1987/88 [18].…”

Section: Study Sitementioning

confidence: 99%

“…West Fork Glacier surged in 1935 and again in 1987/88 [18]. The East Fork, MacLaren, and Eureka Glaciers have been described to be of weak surge character, with only little vertical change seen during a surge [16,18,19].…”

Section: Study Sitementioning

confidence: 99%

Glacier Changes in the Susitna Basin, Alaska, USA, (1951–2015) using GIS and Remote Sensing Methods

et al. 2017

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Abstract:The Susitna River draining from the highly glacierized Central Alaska Range has repeatedly been considered a potential hydro-power source in recent decades, raising questions about the effect of glacier changes on the basin's river runoff. We determine changes in the glacier area , elevation (1951-2010, 1951-2005 and 2005-2010), equilibrium line altitude (ELA, 1999(ELA, -2015, and accumulation area ratio (AAR, 1999(AAR, -2015 This study focuses on the glaciers in the Susitna River basin in the Central Alaska Range (Figure 1), and aims to merge data from various satellite and airborne products in order to examine glacier changes during the period 1951-2015. The study area was initially chosen because the Susitna River has been Remote Sens. 2017, 9, 478; doi:10.3390/rs9050478 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 478 2 of 17 subject to plans for hydro-power extraction for several decades [9][10][11]; however, these projects are no longer being pursued. We determine the glacier area changes between 1951 and 2010, glacier elevation changes, and mass losses for the periods 1951-2005, 2005-2010, and 1951-2010. Additionally, we derive annual late summer snow line altitudes as an approximation for the equilibrium line altitudes (ELAs) and determine the accumulation area ratios (AARs) for the period 1999-2015. These variables provide valuable proxies to assess the response of glaciers to climate change [12][13][14].Remote Sens. 2017, 8, 478 2 of 15 projects are no longer being pursued. We determine the glacier area changes between 1951 and 2010, glacier elevation changes, and mass losses for the periods 1951-2005, 2005-2010, and 1951-2010. Additionally, we derive annual late summer snow line altitudes as an approximation for the equilibrium line altitudes (ELAs) and determine the accumulation area ratios (AARs) for the period 1999-2015. These variables provide valuable proxies to assess the response of glaciers to climate change [12][13][14].

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“…Some sophisticated post-processing procedures are 25 available (Maksymiuk et al, 2016), but rely on the coupling with models based upon the Navier-Stokes equations. Also geometric properties can be taken into account during the matching to improve robustness and reduce post-processing efforts, such as reverse-correlation (Scambos et al, 1992;Jeong et al, 2017) or triangle closure (Altena and Kääb, 2017a).…”

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Extracting recent short-term glacier velocity evolution over Southern Alaska from a large collection of Landsat data

Altena

Scambos

Fahnestock

et al. 2018

Preprint

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The measurement of glacier velocity fields using repeat satellite imagery has become a standard method of cryospheric research. However, the reliable discovery of important glacier velocity variations on a large scale is still problematic, because time-series span different time intervals and are partly populated with erroneous velocity estimates. In this study we build upon existing glacier velocity products from the GoLIVE data set (https://nsidc.org/data/golive) and compile a multi-temporal stack of velocity data over the Saint Elias Mountain range and vicinity. Each layer has a time separation of 32 days, making 5 it possible to observe details such as within-season velocity change over an area of roughly 150 000 km 2 . Our methodology is robust as it is based upon a fuzzy voting scheme applied in a discrete parameter space. In this way it is able to filter multiple outliers. The multi-temporal data stack is then smoothed to facilitate interpretation. This results in a spatio-temporal dataset where one can identify short-term glacier dynamics on a regional scale. The goal is not to improve accuracy or precision, but being able to extract the timing and location of ice flow events such as glacier surges. Our implementation is fully automatic 10 and the approach is independent of geographical area or satellite system used. We demonstrate this automatic method on a large glacier area in Alaska/Canada. Within the Saint Elias and Kluane mountain ranges, several surges and their propagation characteristics are identified and tracked through time, as well as more complicated dynamics in the Wrangell's mountains.Copyright statement. TEXT

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Automated detection of unstable glacier flow and a spectrum of speedup behavior in the Alaska Range

Cited by 42 publications

References 76 publications

The 2015 Surge of Hispar Glacier in the Karakoram

The 2015 Surge of Hispar Glacier in the Karakoram

Glacier Changes in the Susitna Basin, Alaska, USA, (1951–2015) using GIS and Remote Sensing Methods

Extracting recent short-term glacier velocity evolution over Southern Alaska from a large collection of Landsat data

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