5 Discussion
5.1 Passive Seismic for Subsurface Layer Mapping
The MI surface model was shown to accurately represent the internal
surface variations of the MI body and OT, demonstrating the efficacy of
the passive seismic device and analysis method presented. The layer
model also revealed variability not detectable from headwall exposures,
their extrapolations or surface features, such as the rapid reduction in
ice surface elevation and the bowl-like depression in the western
section. Using the 2018 headwall exposures, a maximum difference of 1.4
m was found between the modelled and observed MI surface. When comparing
the headwall exposures closest to the passive seismic measurement
points, where the ice surface is not modelled below the headwall base,
the layer boundaries were accurate to within 0.5 m. These validations
demonstrate that the technique can be used to accurately detect
subsurface layering depth and suggest that a greater density of
measurements may be required to accurately capture the fine scale
variations. Furthermore, knowledge of the MI variability and overburden
thickness have been noted as critical to understanding the geomorphic
response of Arctic landscapes to climate change and other anthropogenic
disturbances (Pollard, 1990; Segal et al., 2016). On Banks Island,
observations show that there has been a 60-fold increase in RTS numbers
between 1984 and 2015 (Lewkowicz and Way, 2019). It has been suggested
that a thin overburden layer, as little as 1 m in places (Lakeman &
England, 2012), is a significant contributor to the observed landscape
sensitivity on Banks Island. This thin overburden exacerbates the
relatively slow warming trend by allowing the surface energy to more
easily reach the near surface MI, thus triggering the formation of new
RTSs (Rudy et al., 2017; Segal et al., 2016). This highlights the need
for locally detailed maps of overburden and MI thickness variability in
vulnerable areas, to aid in determining the likelihood of rapid
geomorphic disturbances under continued warming in the near future.
Finally, more accurate models of the relative proportions of ice and OT
can allow for improved estimates of future soil losses, and thus better
constrained predictions of carbon, nutrient and pollutant fluxes. They
also have important implications for determining future levels of thaw
subsidence, a significant contributor towards the cost of building and
infrastructure maintenance across permafrost terrain (Clement, 2013;
Couture et al., 2018; Couture and Pollard, 2017; Jones et al., 2008).
5.2 Factors Governing Short-Term HWR Rates
Severalprevious studies have focused on thaw indices and simple
geometric models in an attempt to understand the drivers and controls on
rates of HWR, but the results have been spatially and temporally
inconsistent, with several authors citing a lack of locally accurate
data on MI and OT as a significant constraint (Heginbottom, 1984; Jones
et al., 2019; Lewkowicz, 1987a; Robinson, 2000, Zwieback et al., 2018).
Here, we identify two dominant in-situ controls upon HWR, firstly
a persistent exposure of MI inland of the current headwall position,
regardless of its thickness, and secondly an average OT of under 4 m.
Along transects where these criteria were met HWR rates were more than
twice as fast as otherwise. It appears that on Peninsula Point the
absolute thickness and the proportion of the headwall that consists of
ice play little role in determining the HWR rates. Rather, the presence
of ice allows for the initiation of HWR, with the resulting rate of
retreat being largely dictated by the OT.
By using detailed observations of exposed headwall properties, it has
been possible to predict the rate of HWR more accurately than simply
extrapolating based on recent or historical averages. The accuracy of
these predictions are further improved by incorporating the mapping of
MI and OT inland of the headwall, with a reduction in the RMSE of 58%
compared to predictions based on historical averages, and 36% compared
to predictions based on the HWR rates recorded in the previous year.
This is especially important in areas where HWR has not yet been
initiated, but where the criteria necessary for HWR to occur can now be
identified using passive seismic mapping. For example, in the western
portion of Peninsula Point, 16 transects contained no visible exposures
of MI in 2016 (Figure 6a). However, 11 of those transects experienced
HWR > 10 m between 2016 and 2017, four of which exceeded 20
m. Had passive seismic monitoring been carried out in this region in
2016, it may have been possible to identify the region as having the
requisite layering (MI and thin OT) to produce rapid rates of HWR once
the MI was exposed. This provides the potential to better predict and
therefore mitigate the dramatic geomorphic changes that occur in
ice-cored terrain and highlights the need to detect and map MI
variability, not only to improve forecasts of HWR rates, but to identify
areas where new RTSs are likely to develop.
6 Conclusion
Passive seismic monitoring has been shown to be an effective tool for
detecting OT and MI surface variability. The resulting models showed
variations in MI not apparent from headwall exposures or surface
topographic features, and the opposite of those inferred from simple
extrapolations of headwall exposures.
OT and IT were found to exert a significant control over short term HWR
rates from 2016 to 2018. Where the OT remained below 4 m and MI was
present inland of the headwalls, HWR rates were more than twice as fast
the headwalls with differing properties.
By conducting detailed surveys of headwall properties, it is possible to
predict HWR rates more accurately than extrapolating based on past rates
alone. Furthermore, by mapping and incorporating OT and MI variations
inland of the current headwall position, the errors in HWR predictions
can be more than halved compared to predictions based on historical
averages, and reduced by more than a third compared to extrapolations
from the previous years HWR rates
This research brings into focus the need for accurate data on MI and OT
in order to understand the spatial and temporal variability in RTS
activity. Better knowledge of MI variability can contribute to improved
forecasts of coastal change, such as rates of shoreline retreat and
volume loss. These findings have the potential to provide more accurate
estimates of nearshore carbon and sediment fluxes and to improve
assessments of the susceptibility of local landscapes to rapid
geomorphic changes. We emphasise the need for widespread testing of
these headwall metrics and passive seismic monitoring ensure their
efficacy and robustness across different ice-cored permafrost terrains.