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The Role of Massive Ice and Exposed Headwall Properties on Retrogressive Thaw Slump Activity
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  • Samuel Hayes,
  • Michael Lim,
  • Paul James Mann,
  • Dustin Whalen,
  • Paul Fraser,
  • Roger Penlington,
  • James E Martin
Samuel Hayes
University College Cork

Corresponding Author:[email protected]

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Michael Lim
Northumbria University
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Paul James Mann
Northumbria University
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Dustin Whalen
Natural Resources Canada
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Paul Fraser
Natural Resources Canada
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Roger Penlington
Northumbria University
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James E Martin
Northumbria University
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Abstract

Retrogressive Thaw Slumps (RTSs), a highly dynamic form of mass wasting, are accelerating geomorphic change across ice-cored permafrost terrain, yet the main controls on their activity are poorly constrained. Questions over the spatial variability of environmentally sensitive buried massive ice (MI) bodies and a paucity of high-spatial and temporal resolution topographic data have limited our ability to project their development and wider impacts. This research addresses these key problems by investigating RTS processes on Peninsula Point — the type site for intra-sedimental MI in the Western Canadian Arctic. Utilizing high-resolution topographic data from drone surveys in 2016, 2017 and 2018 we (1) measure the temporal and spatial variations in headwall properties and retreat rates, (2) determine the spatial pattern of subsurface layering using passive seismic monitoring and (3) combine these to analyse and contextualise the factors controlling headwall retreat rates. We find that headwall properties, namely MI thickness and overburden thickness, are significant controls over rates of headwall retreat. Where persistent ice exposures are present and overburden thickness remains < 4 m, headwall retreat is typically more than double that of other headwalls. Furthermore, a 3D site model was created by combining photogrammetric and passive seismic data, highlighting the variability in internal layering, demonstrating the limitations of extrapolations based on headwall exposures, and improving predictions of headwall retreat rates compared to long term averages and extrapolations from the previous year. These results provide fresh insights into the controls on headwall retreat rates and new approaches to improve their predictability.