1 Introduction
Unlithified Arctic coastlines are situated on the boundary of three
rapidly changing and intertwined systems – terrestrial, oceanic, and
atmospheric. Near surface terrestrial permafrost temperatures are
increasing (Biskaborn et al., 2019), and active layer depths (ALDs) are
growing (Letterly, 2018). Sea ice cover is in a state of rapid decline,
and ocean temperatures are increasing (Markus et al., 2009; Steele et
al., 2008; Stroeve et al., 2014). Surface air temperatures are warming
at an accelerated rate relative to the rest of the planet (Johannessen
et al., 2016; Serreze and Francis, 2006). All these changes are expected
to continue through the 21st century in response to anthropogenic
climate change (AMAP, 2017; Collins et al., 2013; Comiso, 2006). These
profound transformations of the Arctic environment are already resulting
in significant widespread degradation of coastal permafrost (Fritz et
al., 2017; Günther et al., 2013, Günther et al., 2015; Jones et al.,
2018; Lewkowicz and Way, 2019; Mars and Houseknecht, 2007; Novikova et
al., 2018; Pizhankova et al., 2016; Ramage et al., 2018). One of the
most active forms of thermokarst are Retrogressive Thaw Slumps (RTSs), a
form of slope failure in which thawed soils and ice melt water flow
along a massive ice (MI) body or layer of ice rich permafrost. Active
thaw slumps are traditionally characterised by a distinctive “C”
shaped scar zone up to 1,000 m wide (Lantuit et al., 2012), containing
three main elements (Figure 1);
- A near vertical headwall consisting of ice poor permafrost and the
active layer
- A steep angled headscarp with exposed ice – the ablation of which
drives the back wasting of the RTS
- A low angled slump floor, where thawed permafrost material from the
headwall combines with meltwater to form a muddy mixture which flows
downslope
The areal extent of RTS affected terrain has undergone a dramatic
increase in the last two decades across the western Canadian Arctic,
where it is now believed to be the dominant driver of geomorphic change
in the region (Kokelj et al., 2015; Lantuit et al., 2012; Lewkowicz and
Way, 2019; Ramage et al., 2018; Segal et al., 2016). However, attempts
to link RTS activity (using metrics such as headwall retreat rates
[HWR]) to temperature indices, such as thawing degree days, have
proved inconsistent and typically suited to a narrow range of
meteorological and geomorphic conditions (Heginbottom, 1984; Jones et
al., 2019; Lewkowicz, 1987a; Robinson, 2000, Zwieback et al., 2018). Two
key limitations have constrained our understanding of RTS development
and evolution, namely: (1) a lack of topographic data with sufficient
spatial and temporal resolution and (2) knowledge of subsurface
variability in overburden and MI thicknesses.
The recent increase in LiDAR use (Obu et al., 2016; Ramage et al., 2017)
and Structure from Motion-Multiview Stereo (SfM-MVS) derived digital
elevation models (Cunliffe et al., 2018, Westoby et al., 2012) have
provided new opportunities to better constrain topographic controls on
RTSs activity. However, detection and mapping of subsurface MI and
overburden variability remains primarily limited to visual observations
of their exposures along cliffs and headwalls, sporadic borehole
measurements and identification of surface features that may act as
proxy indicators for the presence of MI. Interpolation and extrapolation
of these values to produce regional MI models (Couture and Pollard,
2017; Couture et al., 2018) may result in significant inaccuracies,
especially on local scale, placing a limitation of estimates of carbon
loss and management of vulnerable infrastructure. Only recently have new
methods emerged for mapping massive ice non-invasively using passive
seismic recordings (Lim et al., 2020). Here we present a combination of
inter-annual, high spatial resolution SfM-MVS data in combination with
passive seismic monitoring of subsurface variability to address these
critical problems.