Introduction
Thaw of ice-rich permafrost, termed thermokarst, has widespread impact on
terrain, local ecosystems and the global climate, but the processes that
control its rates remain poorly understood .
High-frequency observations of terrain modification are necessary to
elucidate the drivers of thermokarst and to develop models for simulating
permafrost degradation in a changing climate
. The sub-seasonal dynamics of
thermokarst are also important because they have a direct impact on the local
hydrology, biogeochemistry and sediment budget . In
particular, the seasonal timing and magnitude of the thaw-induced
mobilization of organic carbon and nutrients influence their lateral
transport and chemical fate, and hence the type and magnitude of greenhouse
gases released .
Rapid permafrost degradation in ice-rich regions is associated with
characteristic landforms. Depending on the topographic position, these are
shaped by a wide range of physical processes, which we here include under the
umbrella term thermokarst. In flat to gently rolling terrain, thermokarst can
be closely coupled to changes in local hydrological conditions, with
impoundment of water leading to the formation of thermokarst pits, lakes and
wetlands . On steeper terrain, water also plays a key role
in initiating or enhancing thaw degradation in landforms such as
thermo-erosional gullies and thaw slumps . In this
paper we focus on thaw slumps, which develop when icy sediments are exposed
in a steep ice-rich headwall (Fig. ). As the headwall retreats
upslope a low-angled scar zone or slump floor develops. Thaw slumps commonly
form adjacent to streams, lakeshores or coastlines where thermal, fluvial and
coastal erosion can initiate these disturbances by exposing ice-rich
permafrost .
(a) Schematic of a thaw slump labelling
features in black and processes in blue. (b) Retrogressive thaw
slump in the Tuktoyaktuk coastlands surrounded by relict, now densely
vegetated scars on either side (photo by S. Zwieback). (c) Map of the
study regions. (d) Headscarp of an elongated cliff thaw slump
along Bykovsky's coast (photo by G. Grosse). (e) Thaw slump along the
east coast of Kurungnakh (photo by J. Boike). Note the clearly visible ice-poor
sand unit in the lower half of the approximately 30 m high exposure down
to the river level.
Thaw slumps are shaped by thermal, hydrological and mechanical processes over
their entire life cycle from initiation to stabilization. Once initiated,
active thaw slumps can grow upslope by several metres per year
as the headwall retreats into the upslope terrain
(Fig. ). Headwall retreat is linked to energy flux
and processes in the scar zone. The mass wasting at the
headwall releases meltwater and sediment, which accumulates at the base of
the headwall as a saturated slurry and must be removed via downslope
fluidized flow in order for backwasting to proceed unabatedly
. If accumulated material insulates the ice-rich permafrost
or if the head scarp retreats into ice-poor terrain, the thaw slump can
stabilize . The accumulation and transport of sediment are
coupled to the hydrological conditions, as meltwater and thawing debris
typically form a saturated slurry in the slump scar zone .
Depending on the water content, the sediment input and the slope and
material properties, this can be a zone of net accumulation or of net volume
loss. Net accumulation occurs when the sediments cannot be removed
sufficiently quickly: close to the headwall, buttresses of accumulated
material can protect the ground ice and reduce retreat rates
. Conversely, downslope removal of thawed material at the
foot of the headwall can accelerate retreat by exposing ice-rich soil and by
increasing the local relief . Thermal processes in the scar
zone can also help sustain thaw slump activity by effecting height losses,
which are caused by the melting of ground ice in the warm scar zone
. Many of these same processes that reinforce mass wasting are
also central to re-initiating thaw slump activity within stabilized slumps
. Such slumps are called polycyclic, as re-initiation results
in the formation of a new, actively ablating headwall.
The most important processes in driving headwall mass wasting is the ablation
of ground ice. To melt the ground ice at the aerially exposed headwall,
energy is required. Ablation increases with insolation and air temperature,
as the key terms in the surface energy balance are the radiation input and
the turbulent exchange with the atmosphere . If the mass
wasting is limited by the available energy, the sub-seasonal rates of volume
losses will track the incoming radiation and air temperature. On sub-seasonal
timescales of days to weeks, the temporal signature of volume loss is
typically steady and slowly declining towards the end of summer
. Despite the recognized importance of this process, the
prevalence of energy-limited mass wasting has not been assessed at the
landscape scale, thus limiting the skill of current thermokarst predictions.
Headwall mass wasting is not always energy limited, and such conditions may
be detected using observations of sub-seasonal volume losses. One such
exception occurs when an insulating veneer protects the ground ice, thus
slowing down volume losses . Such an insulating cover,
principally derived from the thawing sediments themselves, as well as from
late-lying snow, commonly persists in early summer
. Early summer mass wasting may also be subdued
because the incoming energy is used to warm the cold permafrost to the
melting point before ablation can set in. A separate agent that can govern
mass wasting rates is the availability of liquid water from melting ground
ice, snowmelt or precipitation . Intense precipitation
events may accelerate mass losses in the headwall area in some slumps, both
via the removal of debris on or at the base of the headwall and by water
supplying additional energy . As the
additional water input can also liquify the sediments in the scar zone and
induce downslope flow, thus lowering the base level for erosion and
facilitating the evacuation of the headwall area, precipitation can also feed
back positively on headwall mass losses via scar zone processes. Finally,
failure related to mechanical instabilities is an important mass-wasting
process . Mechanical failure is common when the base of a
permafrost exposure is temporarily in contact with open water, the strong
thermal and mechanical influence of which (thermo-abrasion) leads to
undercutting, niche formation and subsequent block failure .
On coastal cliffs, niche formation is closely tied to open water conditions
and sea temperatures. It sets in later than energy-limited ablation but can
remain effective for longer in autumn : its prevalence can
thus be detected based on a late-summer continuation or speed-up in elevation
losses. All three processes have been previously observed in field studies,
but little is known about their prevalence and spatial association, as
landscape-scale observations of the sub-seasonal mass loss dynamics have up
to now not been available.
To study the sub-seasonal dynamics of thaw slump mass wasting, we use
synoptic measurements of topographic changes with a nominal temporal repeat
period of 11 days. Our estimates of topographic changes are obtained from
repeated topographic observations using the radar remote-sensing technique
single-pass interferometry . By repeated application of
single-pass measurements, time series of the topography and hence topographic
changes can be derived. Repeated observations of two permafrost regions with
high ground ice content were made by the TanDEM-X satellite pair in the
science phase (June–August 2015). The frequent acquisitions every 11 days,
the high precision of 20–40 cm and the planimetric resolution of 12 m make
this data set an excellent opportunity to study the sub-seasonal dynamics of
rapid permafrost degradation.
Single-pass interferometry is a promising technique for observing
thaw-induced topographic changes on the landscape scale. While it has not
been employed in permafrost environments, the technology is mature, as
evidenced by the widespread use of the digital elevation models (DEMs) obtained from
the Shuttle Radar Topography Mission or TanDEM-X, or the application of such
data for quantifying temporal changes in volcanology and glaciology
. The ability to cover large areas and to do so
frequently are key advantages over in situ measurements, photogrammetry and
lidar . A further advantage is that reliable
height measurements can also be made when the soil moisture changes and when
the surface structure is disrupted – a common occurrence in rapid mass
movements. This is in contrast to the closely related technique differential
radar interferometry, which is capable of providing synoptic estimates of
more subtle elevation changes associated with seasonal and secular thaw
subsidence . Single-pass data, by contrast, are
typically not sensitive enough to capture these slow processes over yearly
timescales, but instead are ideal for more rapid thermokarst phenomena.
Here, we pursue two objectives:
to derive synoptic estimates of topographic changes and their
uncertainty in two ice-rich permafrost regions in the Northwest Territories,
Canada, and in the Sakha Republic, Russia (around 5000 km2), using
TanDEM-X data acquired during the science phase in 2015;
to analyse the sub-seasonal dynamics of topographic changes at slump
headwalls and their variability between features with the aim of attributing
the observed patterns to potential drivers.
Our guiding hypothesis is that the volume losses are governed by the ablation
of ice, and hence limited by the available energy. To test this hypothesis on
timescales of one to several weeks, we compare the observed temporal
dynamics to the energy available for melting ice, which we estimate using a
model driven by ground measurements and additional satellite data. Despite
the comparatively large measurement noise, significant deviations from
energy-limited dynamics are common during the entire thaw period. To
attribute these deviations to additional controls, we compare the
sub-seasonal fluctuations of mass losses to potential drivers such as
precipitation and insulation by snow based on the distinct temporal
signatures of these drivers.
Study areas
The two study areas are underlain by continuous ice-rich permafrost, and they
are locally affected by hillslope thermokarst. The Tuktoyaktuk coastlands in
the Mackenzie Delta region in the Northwest Territories, Canada
(Fig. ), are a glacially shaped landscape that contains areas
rich in massive ground ice, where retrogressive thaw slumping is common along
lake shores . Conversely, the Lena River delta area, Sakha Republic, Russia, was not
glaciated. Our data cover two sites in this region, both of which are
characterized by extensive yedoma uplands that are underlain by fine-grained,
ice-rich Pleistocene deposits. Their very high total ground ice content of up
to 90 % by volume makes them vulnerable to rapid coastal and riverbank
erosion and thaw slumping . In summary, the two study
areas provide contrasting climatic conditions and geological histories for
analysing the sub-seasonal dynamics of thaw slump activity.
The Tuktoyaktuk coastlands were covered by two crossing TanDEM-X orbits that
mainly extended in the north–south direction (> 100 km),
yielding a total area of 4500 km2. Climatic conditions change along a
steep gradient from Inuvik in the south to Tuktoyaktuk on the Beaufort Sea
coast . The southern part of the study area is about 3∘
warmer in summer than the north (mean July temperature of 14.1 ∘C in
Inuvik vs. 11.0 ∘C in Tuktoyaktuk , whereas
the temperature is more uniform in winter. Annual precipitation decreases
from 240 mm in the south to 161 mm in the north. The vegetation reflects
the climatic gradient, as forest transitions to shrub tundra. The transition
zone is characterized by a northward decrease in the density and height of
tall shrubs. The gradients in climate and vegetation combine to shape the
ground thermal regime, as the minimum near-surface ground temperature
decreases from about -3 ∘C in the south to -7 ∘C in the
north .
Retrogressive thaw slumps can be abundant where the relief position and
surficial geology are favourable (Fig. ). They almost
exclusively occur in immediate proximity to lakes, which are widespread
throughout the study area . The surficial geology varies from
hummocky moraine in the south to an increasing proportion of lacustrine
plains in the north, interspersed with hummocky moraine and glaciofluvial
deposits, both of which may host massive ground ice . Thaw slumps can grow to areas exceeding several hectares. Headwall
heights can reach up to about 15 m, depending on geology and topography
. In addition to the mass wasting at thaw slumps, areas of
notable slope erosion also occur along the Beaufort Sea coast
, and in ice-poor sandy bluffs exposed at large water bodies
such as the Husky (Eskimo) Lakes.
The second study area, the Lena River delta area in north-east Siberia,
consists of two sites (Fig. ). The first site, the Bykovsky
Peninsula, is located south-east of the delta close to the harbour town of
Tiksi on the Laptev Sea coast. The climate is subpolar, with mean monthly
temperatures varying from -31 ∘C in February to 8 ∘C in
July in Tiksi . Geologically, it is characterized by very
ice-rich yedoma uplands consisting of thick Pleistocene deposits,
interspersed with thermokarst lakes and drained thaw lake basins (alases;
). The Bykovsky Peninsula is subject to
continual coastal erosion both along yedoma and the alas coasts
. Yedoma uplands that are exposed along the coast form
elongated retrogressive thaw slumps . The upper part of
these bluffs, whose height can exceed 40 m, consists of a vertical icy
headwall (Fig. d). Below the slump headwall, the slopes are
more graded but still comparatively steep, shaped by the balance between
sediment supply and removal along the bluff.
Kurungnakh Island is located in the southern central Lena River delta,
Russia, around 120 km west of the Bykovsky Peninsula. Its location further
inland is associated with slightly colder February air temperatures
(-33 ∘C) and slightly warmer July air temperatures
(10 ∘C, ). The part of Kurungnakh we focus on is
also largely covered by yedoma sediments . Along its
eastern margin, bordering the Olenyekskaya channel, the
yedoma sediments (around 25–30 m thick) and the underlying ice-poor fluvial
sands form steep cliffs of up to 40 m height above river level
(; see Fig. e). In addition,
thaw slumping also occurs on slopes surrounding to thermokarst lakes within
alases .
Methods and data
Height changes and rates from interferometry
Estimating height changes
TanDEM-X bistatic image pairs acquired during the science phase in 2015 (June to
August) served as input for the topographic mapping. The image pairs were
acquired with particularly large across-track baselines corresponding to
heights of ambiguity of 8–14 m, with which height precisions of better than
0.5 m can be achieved (Table S1 in the Supplement). The topographic
information was derived from the Co-registered Single-look Slant-range Complex
(CoSSC) products. They have a native planimetric resolution of around 3 m,
depending on the study area (Table S1), which was reduced to 10–12 m during
the interferometric processing.
Estimates of topographic changes Δh were derived from time series of
TanDEM-X CoSSC image pairs. Topographic changes were computed between
successive image pairs that are usually separated by 11 days; the
corresponding elevation loss rates (cm day-1) are referred to as
sub-seasonal rates r. To also characterize the seasonal, time-average
elevation loss rates, we stacked the time series of individual Δh
measurements and computed the stacked elevation loss rate rs
using generalized least squares. rs lends itself to spatial
analyses and visualizations.
The interferogram for every CoSSC image pair was formed by standard methods
(range spectral filtering, removal of the flat Earth/topographic phase from
the input EM, multilooking) and this time series was co-registered
. We then directly differenced consecutive interferograms.
Our rationale was that the baselines were essentially constant for all
acquisitions, so that the differencing of interferograms yielded a direct estimate of Δh and
phase unwrapping was greatly facilitated. The differencing of interferograms m and n
yielded the phase difference Δϕ=ϕn-ϕm, which contains
the required information about the height difference Δh=hn-hm:
Δϕ=kz,nhn-kz,mhm+ϕmov,mn+ϕoffset,mn=kz,nΔh-(kz,m-kz,n)hm+ϕmov,mn+ϕoffset,mn.
Δh is related to this observable via the height sensitivity kz,n
of interferogram n. In order to estimate Δh, the other terms had to
be quantified and removed. The second term is a small residual topographic
contribution that we removed using the auxiliary DEM. The third term
ϕmov,mn due to the along-track baseline is zero over dry
land but can be non-zero over moving water surfaces (see Fig. S2 for
details). The fourth term ϕoffset,mn is a phase offset, e.g.
due to orbital errors which changes only slowly across the interferogram. We
removed it by mild high-pass filtering with a length scale
of 600 m that is much larger than the individual thermokarst disturbances.
The filtering procedure was robust to outliers as it was based on the median
and was not applied to masked pixels like radar shadow or water surfaces.
Note that this filtering also largely cancelled seasonal thaw subsidence,
which we will generally neglect in the following because of its small
magnitude compared to the uncertainties .
Uncertainties
The uncertainty of the observed height changes were typically between
30 and 60 cm. As these uncertainties are comparable to the signal magnitude, a
detailed uncertainty analysis is required. We estimated the uncertainty from
the phase noise, which in turn was estimated from the observed coherence
magnitude |γ|. The coherence magnitude is an indicator of the
similarity of the image pair: it takes on values between 0 (no similarity)
and 1 (perfect similarity and high phase quality; ). We
employed standard techniques to translate the phase noise inferred from the
observed coherence to an uncertainty in Δh (see Sect. S1.1 for
details).
The coherence magnitude and with it the phase noise are influenced by surface
characteristics and measurement properties in several ways. Firstly, a loss
of coherence can be induced by the additive measurement noise
. Secondly, temporal changes between the two acquisitions
also reduce the coherence. While this effect is minimized in the single-pass
system TanDEM-X, a short effective temporal baseline remains, which is
associated with decorrelation over water surfaces. The decorrelation is
expected to increase with wind speed: simple modelling further indicated that
it may also be relevant over mixed pixels that contain sub-resolution water
bodies (Fig. S2a). Finally, the height variability within the resolution cell
is associated with geometric decorrelation (Fig. S2b). For extended planar
slopes, this can be largely compensated for by spectral filtering, but the
impact of vegetation and irregular terrain cannot be cancelled
. Vegetation also biases the height estimates (i.e. the
estimated height will not coincide with the terrain height); we will assess
the impact on estimating Δh in the shrub tundra separately when we
consider measurement biases.
The coherence-based uncertainty estimates were assessed independently and
found to be accurate to within approximately 30 % and generally
conservative (see Sect. S1.3 for details). The rationale of the assessment
was to compare the predicted uncertainty to the observed variability within
areas that could be assumed stable and homogeneous . The
analysis of stable areas further allowed us to assess residual biases due to
ϕoffset, which were found to be small (2 cm) compared to
the overall uncertainty (> 30 cm, Fig. S6). Also, the uncertainty
due to errors in the input DEM or the orbit information was estimated to be
small by comparison. In other words, the phase noise is the limiting factor
in the precision of estimated height changes.
Statistics of thaw slumps in the Tuktoyaktuk coastlands.
(a) Headwall orientations (darker colour represents slumps whose
area exceeds 2 ha). (b) Size distribution (histogram and scatter
plot).
(c) Predicted probability of activity plotted with black line for
each explanatory variable (all other explanatory variables set to their median)
with observed distribution of each variable for the active slumps above and the
inactive ones below (white line: median; dark colour: interquartile
range; light colour: 10th and 90th percentile; dots: remaining values). The direction
factor c is the cosine between look direction and headwall orientation.
(d) Distribution of topographic and geometric slump properties
for all three clusters of sub-seasonal activity C1–C3. a is the headwall aspect.
The observed height change does not necessarily reflect the true height
change within the resolution cell. We found three important sources of bias
in the tundra: late-lying snow packs, shrub phenology and water surfaces. The
ablation of snow packs induced an apparent lowering of the surface of more
than 1 m (Fig. S1), which could be mistaken for thermokarst. Conversely,
over tall shrubs the single-pass observations indicated positive elevation
changes of several decimetres in June, coincident with leaf-out
(Cory Wallace, personal communication, 2017). Finally, over water bodies, the
sign of the measurement bias depended on the wind conditions, and its
magnitude exceeded several metres. We believe that the best way to mitigate
all these biases is to mask them where necessary. Our focus here is on
hillslope thermokarst phenomena, so that open water surfaces were of minor
concern. By contrast, snow was a potential error source, at least in June,
when late-lying snow patches persisted in many slumps. To assess the role of
snow on the measurements, we mapped the presence of snow patches within
slumps using medium-resolution satellite imagery in June and July. The
details of this analysis, as well as an in-depth assessment of biases, can be
found in Sec. S1.2.
Mapping of disturbances
To identify and map disturbances in the study regions, we used high- to
medium-resolution optical data. In the Tuktoyaktuk coastlands, we inventoried 160
thaw slumps that showed signs of recent activity with Sentinel-2 imagery from
2016 (10 m visible and near infrared). The slumps were identified based on
their distinctive appearance caused by exposed mineral soils and limited
vegetation cover, their shape and the presence of a head scarp
. In polycyclic slumps we mapped those units that showed
signs of recent activity. Within the inventory, all slumps were located in
immediate proximity to lakes and their distribution was non-uniform, with
higher abundance on morainal deposits . The slump size varied
by about two orders of magnitude and could reach up to more than 5 ha
(Fig. a); 14 % of the slumps exceeded
1 ha in area, but the median size was considerably smaller (0.4 ha). Slopes
of all aspects are affected by slumping, but features with north-east and
north-west orientations are more common
(Fig. b), which is consistent with
previous findings by . To further characterize the slumps, we
extracted diverse attributes from the satellite image (normalized difference
vegetation index: NDVI) and from topographic data sets (elevation and
drainage for the slump centroid from a pre-disturbance DEM, ;
local relief as a proxy for headwall height from the TanDEM-X data). To
quantify the decadal-scale dynamics, we analysed orthophotos from 2004
(< 1 m resolution). For each slump (except one, which was not
covered by the aerial imagery), we determined if the location had
been undisturbed in 2004 (14 % of the slumps), if the same slump had
already been present and had continued activity (21 %) or if there
had been an earlier generation slump (65 %). During this time interval, the
areal expansion of the slumps was 0.3 ha on average for the latter two
categories. In addition to the thaw slumps, we also mapped several disturbed
shorelines along the Husky Lakes, the largest lake system in the study region. On
the Bykovsky Peninsula and on Kurungnakh Island, we mapped active elongated
coastal/riverbank thaw slumps as well as thaw slumps along lake shores.
To quantify the thaw-induced volume losses, we manually delineated active
areas within the previously identified thaw slumps in the TanDEM-X data.
Despite the clear signal of change in the headwall area of active slumps (see
Fig. S8 for an example), constraints associated with the 12 m resolution of
the TanDEM-X data have to be considered. The mapped resolution cells may thus
also contain undisturbed terrain and scar zone surfaces on either side of the
ablating headwall. Thaw slumps were labelled inactive when no volume losses
could be detected along the headwall. For the active landforms,
representative subseasonal r and seasonal (stacked) rs volume
loss rates were computed by aggregating the TanDEM-X resolution cells within
these active parts and forming the median. Their uncertainty is reduced by
the aggregation processes and was estimated from the pixel-level
uncertainties using parametric bootstrap analysis . Its
objective is to mimic the measurement process by generating many potential
data sets (aggregate rate estimates) from which the standard error can be
estimated; see supplement for details. The measurement process of the
aggregate rate depends on the pixel-level TanDEM-X height changes and their
uncertainties. To explore the activity and the subsidence rates of all the
inventoried thaw slumps as a function of potential controls such as aspect,
we employed logistic regression (activity) and standard regression analysis
(rs). In addition to mapping areas of volume loss along slump
headwalls, we also delineated active areas within the scar zones where volume
changes could be detected in the TanDEM-X data. These scar zone changes often
indicated an elevation gain (Fig. S8), but they were difficult to map because
they were generally an order of magnitude smaller than those along the
headwall. To compare thaw slump activity with other volume losses, we also
estimated volume losses at retreating ice-poor bluffs along the Husky Lakes,
Tuktoyaktuk coastlands.
Time series analysis of sub-seasonal dynamics
To explore and interpret the sub-seasonal dynamics of height changes, we used
observations of meteorological parameters. In the Tuktoyaktuk coastlands, we
analysed in situ measurements from Inuvik (precipitation, air temperature,
humidity, wind speed and air pressure) and Trail Valley Creek (also incoming
and outgoing long and shortwave radiation). Both sites are located in the
southern half of the study area at a distance of 45 km. Despite the
proximity, the precipitation records did not match well, as persistent and
large deviations are common (≈ 20 mm week-1, or up to 100 %;
see Fig. S9). Radiation fluxes were also taken from the Ceres SYN1deg-3Hour
Ed3A product (incoming shortwave radiation partitioned into direct and
diffuse terms), which contains flux estimates from a radiative transfer model
and satellite observations . The total incoming shortwave
radiation and the net longwave radiation compared well with the in situ
measurements (Fig. S9). The Ceres flux data were also employed in the
Bykovsky study area, where they were complemented by in situ meteorological
measurements from Tiksi (WMO 21824, 20 km from the study area).
To test the hypothesis that the observed volume losses were energy limited,
we estimated headwall ablation using the semi-empirical approach by
. This approach uses meteorological forcing data to
estimate the energy available for melting the ground ice by regression
formulae that approximate the energy balance modelled using the gradient
method for turbulent exchange (input in situ measurements: air temperature,
water vapour pressure, wind speed) and the net radiation (derived from
Ceres). The net shortwave radiation was estimated as a function of surface
slope and aspect by considering the diffuse and direct incoming radiation and
using an albedo of 0.15 for the disturbed slump surface .
The hourly estimates of the ablation flux (available energy) were aggregated
to estimate the total ablation between two successive TanDEM-X acquisitions
(typically 11 days), which could then be compared to the observations. The
model predictions have previously been found to be accurate at daily to monthly
timescales, but the model overpredicted the ablation when the ice face was
partially covered by debris/snow in early summer. This
early season bias was likely exacerbated by the model's lack of a conductive
subsurface heat flux term: the entire ground heat flux is used to melt the
ice according to the model, whereas in reality part of the heat flux will
warm the cold permafrost. Furthermore, the model does not consider heat transfer
from liquid water, and the representativeness of the forcing data was
difficult to ascertain (e.g. unmodelled shading effects, variation of
meteorological conditions across the study area). The modelled ablation is
sensitive to the ground ice content, which is generally difficult to obtain
and also varies across a single slump; we used the value by
. The impact of this assumption was, however, considered
to be small because we focused on the relative temporal variability of the
ablation, not its absolute value. The reason for this is that we measured
height changes at 12 m resolution, which were expected to be proportional
to the ablation, but whose factor of proportionality remained unknown as it
depended on the unknown sub-resolution geometry (e.g. how much of the
resolution cell was intersected by the headwall). In addition to this
semi-empirical model, we also considered a second reference model of the
sub-seasonal dynamics, namely one of uniform rate.
Overview of the study area and all mapped thaw slumps. (a) Slumps in the
TanDEM-X data according to whether activity could be detected. The locations of in situ
measurements at Inuvik and Trail Valley Creek (TVC) are also shown. (b) The sub-seasonal
dynamics from mid-July to late August form three clusters. The arrow denotes the slump 152
shown in Fig. . (c) Variability of the seasonal
elevation loss rate rs from mid-July to late August.
We assessed the consistency of these models with the observations using
statistical tests which accounted for the uncertainty of the observations.
The null hypothesis of the test was that the time series of height changes
were proportional to those predicted by the model. The tests were based on
the parametric bootstrapping approach for determining the uncertainties
: a sample of potential measurements under the null
hypothesis was generated and the p value was computed by determining how
extreme the actual observation was compared to this sample (see Sect. S1.5
for details). Small p values (p<0.05) indicated statistically significant
deviations from sub-seasonal dynamics that were either uniform or
proportional to the energy balance.
To explore the synchronicity of the sub-seasonal dynamics of elevation losses
across the Tuktoyaktuk coastlands, we used clustering analysis. The fuzzy
c-means clustering approach found representative time series (the clusters)
so that the normalized dynamics of the landforms within one cluster were as
similar (i.e. the volume loss as synchronous) as possible . In
addition to the clusters, the analysis produced, for each landform, a degree
of membership to all cluster centres; we assigned the landforms to the
cluster to which they had the highest membership. The number of clusters was
determined using the elbow method (see Sect. S1.6 for details).
Results
Tuktoyaktuk coastlands
In the Tuktoyaktuk coastlands study area, elevation losses were commonly
observed in the headwall area of slumps, in contrast to large swathes of the
study area which appeared stable. More than half of the inventoried slumps
(89/160; Fig. a) exhibited detectable activity in the
TanDEM-X data. As the activity and its detectability may be influenced by
slump characteristics and the sensor viewing geometry, we compared the
detected activity to the slump's NDVI, area and its orientation to the
satellite sensor using logistic regression
(Fig. c). Smaller NDVI values,
indicating sparse vegetation cover, were associated with a higher probability
of detected activity. The model predicted a slightly higher chance of
detecting activity when the satellite look direction was parallel to the
strike of the head scarp (c=0) than when the headwall was observed from
behind (c=1; potential shadow) or face-on (c=-1; potential layover). The
areal extent of a slump was not a useful predictor of detectable activity and
neither was the headwall height.
Observed sub-seasonal volume loss rates r at the active slumps varied
throughout the summer season, in a way that did not reflect the energy
available for ablation. The discrepancy was most pronounced in early summer
(early June to mid-July), as volume losses were smaller than in the second
half of summer, despite the ample available energy. The largest slump in the
study area (152, shown in Fig. a) is a case
in point, as the volume loss rate is low in early summer. For all slumps
covered by the ascending orbit, the median volume loss rate increased from
0 cm d-1 in early June to around
3 cm d-1 in late July and August
(Fig. b). The acceleration was also evident
when looking at the slumps individually as 94% exhibited smaller
elevation losses in early June compared to August, indicating the presence of
a negative control (e.g. debris cover) on ablation. After all, potential
ablation fluxes as predicted using the energy balance approach were highest
in June and early July and subsequently dropped by around one-quarter. The
difference between the sub-seasonal dynamics of the observed volume losses
and the hypothesized ablation-driven ones was statistically significant for
the majority of slumps (53 %; from June to August).
Observed time series of elevation losses in the Tuktoyaktuk coastlands.
(a) Sub-seasonal elevation loss rates r at thaw slump 152
(see Fig. ) are lower in June and early July than later in summer.
The rates r are plotted halfway between the differenced successive TanDEM-X
acquisitions (shown by ticks). The elevation losses occur at the arcuate headwall,
as shown below (stacked losses rs between mid-July and August).
(b) (1) Spatially aggregated sub-seasonal elevation loss rate r
at thaw slumps covered by the ascending orbit (N=71; solid black line: median; grey area:
interquartile range), along with observed rates over all slump scar zones with detected changes
(N=25) and over patches with dense shrub cover (N=10). The observed elevation
loss rate is plotted halfway between the earlier and the later acquisition (indicated by ticks).
(2) Available energy for different headwall orientations (grey) and temporal averages
for a horizontal surface (black). (3) Temperature at Inuvik (daily averages in grey;
averages between subsequent acquisitions in black). (4) Daily precipitation measured in Inuvik.
Also, in the second half of summer (mid-July to late August), deviations from
energy-limited ablation-driven volume losses were commonly observed, as only
a subset of the thaw slumps exhibited volume losses that approximately
tracked the energy available for ablation. This subset formed one of three
distinct categories (as suggested by clustering analysis; see Sec. S1.6),
each of which exhibited a large degree of synchronicity. The first cluster C1
corresponded to a fairly steady volume loss, similar to the expected
energy-limited trajectory; it contained almost half the slumps
(Fig. ). Conversely, the other two clusters largely
exhibited volume losses that did not appear to be controlled by the
hypothesized energy-limited variability. Cluster C2 appeared to be related to
intense precipitation events recorded at Inuvik in that it showed two peaks,
in mid-July and mid-August (Fig. ). The degree to
which volume losses increased during these two time intervals varied across
the slumps. As these peaks were at odds with the smoothly varying available
energy from turbulent exchange and radiation, the null hypothesis of
ablation-driven volume losses could be rejected more often than for features
in cluster C1 (Fig. ). Finally, less than a quarter
of the slumps exhibited an end-of-summer acceleration of subsidence (C3), but
a majority of ice-poor lake bluffs did (Fig. ). As
the available energy decreased towards late August, the observed accelerating
volume losses were predominantly significantly different from the
hypothesized energy-limited trajectories.
Slumps and bluffs in the Tuktoyaktuk coastlands were grouped into
three clusters (C1–C3) according to their sub-seasonal elevation loss
dynamics after mid-July. The first row contains the normalized rates of
the three clusters (aggregated over the headwall; absolute elevation changes
sum to one), with the time series of selected slumps and bluffs shown in
blue and green respectively, and the representative cluster dynamics in
black. The second row contains the available energy (grey: daily values for
different headwall orientation; black: aggregate values for horizontal
orientation), the precipitation (daily values measured in Inuvik in
grey; aggregate values for days with intense precipitation >5mm in black)
and the air temperature measured in Inuvik (daily values in grey; maximum thawing
degree days, TDD, between successive acquisitions in black). The third row contains
the percentage of landforms for which the null hypothesis of uniform (U) or energy
balance-limited (EB) elevation losses was rejected (horizontal bars) and the
percentage of landforms that were classified in the respective cluster
(vertical bars). Please note that dates are shown in month/day format.
Not all landforms fitted neatly into this classification, as certain
sub-seasonal dynamics appeared to be mixtures of two clusters or different
altogether. A few illustrative examples are shown in Fig. S10a, whereas Figs.
S11–S16 show all thaw slumps. The examples include slumps that showed
intermittent speed-up in late July (classified as C1) or that accelerated
slowly during all of August (classified as C3). Also, negative volume losses
(apparent uplift) were commonly observed, but their magnitude was rarely (only for 7 %)
larger than the standard error. One example of an unusual
slump is the one previously discussed (152;
Fig. ); it appeared to be a mixture of C1 and
C2 in that it speeded up intermittently in mid-August (like C2) but only to a
small extent in July (unlike C2). The significant acceleration was observed
in the measurements from both orbits and appeared to be limited to around one-quarter of the headwall length, illustrating the potential intra-slump
variability that the sensor resolution did not allow us to study except for
this very large slump (Fig. S10b).
The cluster membership and hence the sub-seasonal dynamics were poorly
related to the geographical location and easily measured slump characteristics
such as the size. All three types of dynamics occurred in the entire study
area, often in close proximity (Fig. ). In addition, they could not
be well distinguished by geometric and topographical properties, as the
head scarp aspect, local relief and catchment size were similar for all three
clusters (Fig. d). The slump area and
elevation provided some insight, as landforms in cluster C3 – the late
speed-up – tend to be smaller and at slightly higher altitudes. Also, a
comparison with each slump's state in 2004 – for instance whether the
location had been undisturbed or not – did not reveal any clear-cut relation to the
cluster membership. Conversely, detectable summertime height changes in the
scar zone were closely related to the cluster membership (Figs. S11–S16), as
they were chiefly observed at slumps that responded to strong precipitation
events (C2). This association, along with the sign (predominant height
increase) and magnitude (decimetres) of the scar zone elevation change,
suggests a strong coupling of headwall mass wasting with downstream sediment
dynamics at these slumps.
To further explore the spatial variability of the volume losses in the second
half of summer, we also investigated the time-averaged volume loss rates
rs (mid-July to late August; four TanDEM-X acquisitions). Across all
slumps rs varied typically between
2 and 5 cm d-1 with a weak dependence on location and
head scarp geometry (Fig. a, both orbits). Regression
modelling of rs in terms of slump properties indicated that
south-facing slumps were slightly more active (by
≈ 0.8 cm d-1) than north-facing ones
(Figs. b, S17), which would be consistent with the
hypothesized dominance of the available energy (especially insolation). For a
given headwall orientation, the elevation loss rates were predicted to
increase very little, if at all, with headwall height and slump area.
However, the regression could explain little of the observed scatter (R2=0.14), and the regression coefficients tended to be comparable or smaller in
magnitude than their standard errors. Apart from natural variability and the
limited precision of the observations, one likely reason for the lack of
spatial consistency was the sub-resolution geometry due to which the observed
volume losses were related to the headwall retreat by an unknown and
spatially variable factor of proportionality.
Seasonal elevation loss rates rs at slumps (both orbits)
in the Tuktoyaktuk coastlands, temporally averaged between 15 July and
28 August 2015. (a) Scatter plot of rs estimates and
their standard error. (b) Dependence of rs on slump
features as predicted by the regression model: the dots show the predicted
change in rs for a change in the property of the annotated magnitude
(e.g. 5 m increase in headwall relief); the bars indicate plus/minus 1
standard error.
Lena River delta area
On the Bykovsky Peninsula, localized volume losses were observed along all
coastal thaw slumps, whereas the interior appeared to be stable. Actively
retreating thaw slump yedoma cliffs were detected on both the east coast
(favourable viewing geometry) and the west coast (problematic viewing
geometry inducing foreshortening and layover). The mean rates of volume loss
were similar for cliffs on either coast
(3–5 cm d-1, Fig. ), as were
the sub-seasonal dynamics as all volume loss rates were fairly constant from
June to August (hence no clustering; Fig. ). The
near-uniform dynamics resembled the available energy, which changed by only
15 % during the summer. However, the similarity of the observations to the
hypothesized energy-limited losses may have been misleading in early summer,
as the volume losses were likely overestimated before mid-July due to
contemporary snow ablation (volume losses were also observed in gullies, and
residual snow was still present in early July; see Fig. S18).
Rapid permafrost degradation occurred at coastal thaw slumps
on the Bykovsky Peninsula. (a) Sentinel-2 image. (b) Mean elevation
loss from 27 July to 28 August 2015 rs estimated from TanDEM-X
is large at yedoma cliff slumps (rectangles). (c) Magnified images
showing three coastal stretches from (b). (d) Time series
of observed elevation loss rates r at cliffs (aggregated along headwall)
and atmospheric conditions similar to Fig. ,
except that the topmost panel shows the dynamics of all cliffs (grey)
and their average (black).
The island of Kurungnakh in the Lena Delta was comparatively stable after the
ablation of most snowbanks (after 11 July, Fig. S19). The only lake-side
retrogressive thaw slump in the area did not show any detectable changes. The
steep yedoma riverbank slumps on the eastern shore were very poorly imaged
because of extreme foreshortening and layover. Consequently, volume losses
were detected in only a few spots, despite the known activity of these
slumps. The viewing geometry was more favourable at the northern shore, where
localized height losses were particularly pronounced in steep gullies.
However, we attributed this signal in the gullies largely to snow, as it was
most pronounced during mid-July (≈10cm d-1) when snow packs were observed to persist
in these deep gullies (Anne Morgenstern, personal communication, 2016).
Discussion
Sub-seasonal mass wasting
Our landscape-scale analyses reveal sub-seasonal patterns of mass wasting
that are common to most features, especially the slow onset of ablation in
early summer, which suggest the widespread presence of a common control.
Conversely, the observed synchronicity of only limited subsets of landforms
indicates the presence of distinct processes whose impact is particularly
pronounced for only those subsets.
The delayed onset of volume losses in early summer despite the large
available energy indicates that mass wasting is not energy limited at this
time. and observed that early season
mass wasting can veneer late lying snowdrifts, protecting ice-rich permafrost
from early season thawing. Snow cover was still widespread in thaw slumps at
the time of the first radar acquisition in early June (Tuktoyaktuk
coastlands), but likely limited in depth due to the preceding weeks of
above-zero temperatures. Snow had largely disappeared by mid-June (Fig. S20),
but subdued elevation losses persisted into July according to the TanDEM-X
data, suggesting the importance of debris cover, possibly also on top of
snow. In addition, even in the absence of a persistent snowbank, a portion of
the available energy must also go into warming cold permafrost behind the
slump headwall. However, our data do not allow us to distinguish between
these two processes. Conversely, we do not believe the observed early season
signal to be spurious, as the snow bias is of the wrong sign. Also, the
influence of shrubs is too small to explain the reduced volume losses by
itself (Sect. S1.2), and it should be negligible in most active slumps, as
shrubs are restricted to adjacent disturbed areas within the 12 m resolution
cells.
Energy-limited mass wasting appears to have been important from mid-July to
late August, as uniform or slowly decreasing activity was typical. Such
mass loss driven by the energy available for ablation has previously been
found to govern sub-seasonal rates of headwall retreat in Alaska and on Banks
Island, Canada . We observed steady mass
wasting at the majority of slumps in the Tuktoyaktuk coastlands (C1), and
also at coastal slumps on the Bykovsky Peninsula. A strong link between
sub-seasonal ablation rates on coastal yedoma cliffs and the energy balance
has previously been observed on Muostakh Island (15 km south of Bykovsky
Peninsula; ).
However, deviations from energy-limited mass wasting were found also in the
second half of summer, as increased activity occurred during periods of
intense rainfall events. We believe the precipitation-linked volume losses of
slumps in cluster C2 (Tuktoyaktuk coastlands) not to be a measurement
artefact, as the magnitudes were large compared to the biases that changing
soil moisture conditions are expected to induce (see Sect. S1.2). Whether
precipitation really was the driver is difficult to say because of the short
time series, the measurement frequency of 11 days and the lack of agreement
among the available precipitation records. It is, however, not implausible as
rainfall could increase volume losses by flowing water delivering extraneous
energy to the headwall , or by additional water
removing insulating debris accumulation from the headwall and evacuating
accumulated sediments from the foot of the headwall and the scar zone via
fluidized flow . However, partitioning these effects
is difficult as the sensor resolution did not allow us to distinguish
headwall retreat from the evacuation of material from the foot of the
headwall. The headwall area appeared to be coupled to the scar zone further
downstream, as suggested by the commonly observed height changes in the scar
zone. However, the temporal patterns in the scar zones were unlikely
mono-causal due to their heterogeneity (e.g. Fig. S12). Many slump floors
showed increased accumulation (positive height changes) during the periods of
increased headwall volume losses while others did not, indicating a
non-uniform balance between the increased sediment supply and net
accumulation of materials in the scar zone, and the increased capacity for
removal following intense rainfall.
The late speed-up characteristic for cluster C3 is also at odds with
energy-limited volume losses. Instead, it may point to an increased
sensitivity to warming as the warm season becomes longer. The mechanisms are
not clear, but a number of processes could give rise to this behaviour. It
may be related to an insulating cover that persists for an
uncharacteristically long time compared to the majority of thaw slumps.
Alternatively, the acceleration could also be due to an internal instability.
For instance, ice-poor parts of slump headwalls can fail upon reaching a
sufficient thaw depth or when undercut by ablating material underneath
, although this also occurs earlier in the thaw
period. A mechanical instability seems particularly plausible for ice-poor
bluffs that do not ablate and for which a late speed-up was commonly observed
(Fig. ). However, the slightly larger peak wind
speeds in late August may also contribute by
increasing wave-induced erosion, which is likely an important factor for
bluff erosion. Irrespective of the origin, the observations highlight the
need for detailed observations and modelling efforts to better characterize
the vulnerability of permafrost to warmer, longer and stormier summers.
Single-pass radar interferometry
The spatial variability only becomes evident in synoptic observations,
highlighting the importance of remote-sensing approaches for understanding
and quantifying permafrost degradation. Single-pass interferometry from
satellites can offer such large coverage, but it is subject to limitations in
terms of precision, spatial resolution and systematic errors. In many ways,
we have pushed the TanDEM-X data to their limits, as evidenced by the
substantial uncertainties of short-term elevation changes. The height
precision of around 50 cm did not allow for more detailed analyses of
changes in the scar zones, and especially for smaller or less active slumps
it is comparable to the elevation changes on sub-seasonal timescales. The
limited resolution of 12 m is a problem for detecting and observing mass
wasting at small slumps. It likely induced sampling biases in this study, as
small slumps with little activity were more difficult to capture than the
bigger ones like that in Fig. . A higher
resolution would also be helpful for distinguishing sediment transport near
the headwall from headwall backwasting. Systematic errors such as biases
induced by the shrub phenology must be considered when using single-pass
interferometry data, especially in large-scale analyses where manual masking
approaches are insufficient.
In summary, single-pass interferometry with its large and potentially
frequent coverage complements more established techniques like airborne lidar
and spaceborne photogrammetry, which can provide higher resolution and often
more accurate elevation measurements. To an even larger degree, it
complements detailed field studies of permafrost degradation and its
consequences. It is only through a nested approach that small-scale field
studies can be put into a regional and continental context by synoptic
satellite observations. To achieve this goal, the observational capabilities
of Earth observation satellites need to be maintained and extended. In this
context, single-pass radar observations at higher radar frequencies such as
Ku band, which are currently not available from satellites, seem promising
owing to the higher resolution and height precision.
Conclusions
This study analysed sub-seasonal dynamics of rapid permafrost thaw in two
ice-rich study sites during summer 2015. Our objectives were to map
thermokarst activity by observing elevation changes using single-pass
interferometry and to analyse the observed sub-seasonal dynamics with respect
to their spatial variability and potential drivers of permafrost degradation.
Our guiding hypothesis was that mass wasting was limited by the energy
required to melt the ground ice on sub-seasonal timescales, so that the
11-day mass losses should track the available energy. Our major findings and
conclusions are as follows.
The synoptic TanDEM-X single-pass observations revealed spatial variability
in rates and in sub-seasonal dynamics of elevation changes which would be difficult
to capture with in situ measurements alone. The observed spatial variability was
only poorly explained by macroscale characteristics such as aspect angle, which
may indicate the importance of local geomorphic influences such as the ground ice
content and soil conditions. Observational limitations also contribute; these are
induced by the small magnitude of the elevation changes (which is commonly comparable
to the instrument precision), by observational biases and by the limited resolution.
During the early thaw period in June, thaw slumps in the Tuktoyaktuk coastlands
were less active than the available energy would suggest, indicating the widespread
presence of an insulating veneer of debris or snow on the headwalls. In addition,
a considerable amount of the ground heat flux has to warm the ground up to the melting
point before ablation can proceed more freely later in the summer.
Later in the summer, these slumps exhibited divergent but relatively distinct
patterns of volume changes. Many showed approximately uniform or slowly decreasing rates,
as would be expected based on the available energy, as did the coastal thaw slump cliffs
on Bykovsky, Russia. Other slumps in the Tuktoyaktuk coastlands showed pronounced and
synchronous peaks, which for one type were possibly associated with strong precipitation events,
coupled to accumulation and downslope removal of sediment in the scar zone. For another type,
the peak occurred at the end of the thaw season, suggesting an acceleration of thaw
rates in late summer. In summary, thaw slump mass wasting was not consistently limited
by the available energy on approximately weekly timescales.
The observed spatial and temporal heterogeneity of thaw slump mass wasting should be
considered when predicting thermokarst rates across spatial and temporal scales.
The spatial and within-season variability has important implications for estimating
the fate of the mobilized carbon, nutrients and sediments. The associated differences
in exposure, lateral transport and re-burial of the thawed material deserve further
attention. Also, they illustrate the need for nested approaches linking local field
investigation with remote sensing to quantify cryospheric change and its consequences
across the landscape.