Shallow ground-penetrating radar (GPR) surveys are used to characterize the small-scale spatial variability of supraglacial debris thickness on a Himalayan glacier. Debris thickness varies widely over short spatial scales. Comparison across sites and glaciers suggests that the skewness and kurtosis of the debris thickness frequency distribution decrease with increasing mean debris thickness, and we hypothesize that this is related to the degree of gravitational reworking the debris cover has undergone and is therefore a proxy for the maturity of surface debris covers. In the cases tested here, using a single mean debris thickness value instead of accounting for the observed small-scale debris thickness variability underestimates modelled midsummer sub-debris ablation rates by 11 %–30 %. While no simple relationship is found between measured debris thickness and morphometric terrain parameters, analysis of the GPR data in conjunction with high-resolution terrain models provides some insight into the processes of debris gravitational reworking. Periodic sliding failure of the debris, rather than progressive mass diffusion, appears to be the main process redistributing supraglacial debris. The incidence of sliding is controlled by slope, aspect, upstream catchment area and debris thickness via their impacts on predisposition to slope failure and meltwater availability at the debris–ice interface. Slope stability modelling suggests that the percentage of the debris-covered glacier surface area subject to debris instability can be considerable at glacier scale, indicating that up to 32 % of the debris-covered area is susceptible to developing ablation hotspots associated with patches of thinner debris.
Debris-covered glaciers are the dominant form of glaciation in the Himalaya
(e.g. Kraaijenbrink et al., 2017) and are common in other tectonically
active mountain ranges worldwide (Benn et al., 2003). Supraglacial debris
cover alters the rate at which underlying ice melts in comparison to clean
ice in a manner primarily governed by the thickness of the debris cover (e.g.
Østrem, 1959; Loomis, 1970; Mattson et al., 1992; Kayastha et al., 2000;
Nicholson and Benn, 2006; Reid and Brock, 2010): A thin supraglacial debris
cover (
Examples of the relationships between supraglacial debris thickness and underlying ice ablation rate at different glacier sites, redrawn from Mattson et al. (1993). The exact form of this relationship at each site varies with prevailing meteorological conditions and debris properties, but its general character is preserved.
Both theory and observations indicate that the spatial variability of supraglacial debris thickness typically has both a systematic and a non-systematic component. Debris thickness tends to increase towards the glacier margins and terminus due to concentration by decelerating ice velocity and increasing background melt-out rate (e.g. Kirkbride, 2000). This systematic variation is evident in field measurements of debris cover thickness (e.g. Zhang et al., 2011) and in characterizations of debris thickness as a function of the surface temperature distribution observed from satellite imagery (e.g. Mihalcea et al., 2006, 2008a, b; Foster et al., 2012; Rounce and McKinney, 2014; Schauwecker et al., 2015; Gibson et al., 2017). At local scales, debris thickness varies less systematically according to the input distribution, local melt-out patterns and gravitational and meltwater reworking of the supraglacial debris. Manual excavations (e.g. Reid et al., 2012), observations of debris thickness made above exposed ice cliffs (e.g. Nicholson and Benn, 2012; Nicholson and Mertes, 2017) and debris thickness surveyed by ground-penetrating radar (McCarthy et al., 2017) demonstrate that debris thickness varies considerably over short horizontal distances. Thus, the thickness of debris over a sampled area of glacier surface is better expressed as a probability density function than a single value (e.g. Nicholson and Benn, 2012; Reid et al., 2012).
Exposed ice faces within debris-covered glacier ablation areas are known to contribute disproportionately to glacier ablation compared to their area (e.g. Sakai et al., 2000; Juen et al., 2014; Buri et al., 2016; Thompson et al., 2016), and it has been proposed that such “ablation hotspots”, along with stagnation, are the reasons for the observed similarity in surface lowering rates of otherwise comparable clean and debris-covered ice surfaces (e.g. Kääb et al., 2012; Nuimura et al., 2012). Given the strongly non-linear relationship between ablation rate and debris thickness (Fig. 1), patches of thinner debris within a generally thicker supraglacial debris cover can similarly be expected to contribute disproportionately to glacier ablation, but this has only rarely been considered (Reid et al., 2012). The implication of this would be that calculations of sub-debris ice ablation rate and meltwater production using spatially averaged mean debris thickness may differ substantially from the actual meltwater generated from a debris layer of highly variable thickness within the same area. Therefore, there remains a critical need to be able to quantify not only mean supraglacial debris thickness, but also local debris thickness variability in order to understand how debris cover is likely to impact glacier behaviour, meltwater production and contribution to local hydrological resources and global sea level rise.
Meeting this need requires a better understanding of debris thickness variability and the controls upon it, ideally by means of more readily observable properties. Topographic data have been used to predict soil thickness on hilly, extraglacial terrain under the assumption of steady-state conditions (e.g. Pelletier and Rasmussen, 2009). However, associated soil thickness relationships as a function of slope curvature (Heimsath et al., 2017) are based on progressive creep processes, while reworking of supraglacial debris cover occurs mainly as a result of gravitational instabilities such as “topples, slides and flows” (Moore, 2017). Nevertheless, as the debris thickness that can be supported on a slope is related to slope angle, debris texture and saturation conditions (Moore, 2017), it might still be possible to find explicit relationships between topography and debris thickness. If high-resolution topography data, which are increasingly widely available, could be used to indicate local debris thickness variability, this information would complement spatially averaged mean supraglacial debris thickness values derived by other methods (cf. Arthern et al., 2006).
This study investigates the evidence for small-scale debris thickness variability, assesses the impact of local debris thickness variability on calculated sub-debris ice ablation rates and explores the potential for predicting local debris thickness variability from morphometric terrain parameters. First, debris thickness data from shallow ground-penetrating radar surveys are used to characterize the small-scale spatial variability of debris thickness on a Himalayan glacier, examine evidence of gravitational reworking processes and compare the observed variability to previously published data. Second, the impact of the observed small-scale debris thickness variability on modelled sub-debris ablation rates is assessed. Third, a contemporaneous high-resolution terrain model and optical imagery are employed to determine whether the observed thickness variability can be predicted from more readily measured surface terrain properties. Finally, a slope stability model is calibrated with the GPR and ablation model data and used to determine the percentage of our study areas in the debris-covered ablation zone that are subject to debris instability and potentially the formation of ablation hotspots in mid-ablation season (August) conditions.
The Ngozumpa Glacier is a large dendritic debris-covered glacier of the
eastern Himalaya, located in the upper Dudh Kosi catchment, Khumbu Himal,
Nepal (Fig. 2a). The glacier has a total area of 61 km
Debris thickness over much of the debris-covered area is in excess of 1.0 m
precluding widespread manual excavation. However, in 2001 measurements of
debris thicknesses exposed above ice cliffs were made by theodolite survey at
Details of processing steps applied to radargrams
in order of use from left to right, using REFLEXW software.
GPR measurements were made between 31 March and 20 April 2016 broadly following the methods of McCarthy et al. (2017). Debris thickness was sampled in 36 individual radar transects, covering sloping and level terrain with coarse and fine surface material. The GPR system was a dual-frequency 200/600 MHz IDS RIS One, mounted on a small plastic sled and drawn along the surface. Data were collected to a Lenovo Thinkpad using the IDS K2 FastWave software. This system produces two simultaneous radargrams for each acquisition. The 200 and 600 MHz antennas have separation distances of 0.230 and 0.096 m respectively. Data acquisition used a continuous step size, a time window of 100 ms and a digitization interval of 0.024 ns. The location of the GPR system was recorded simultaneously at 1 s intervals by a low-precision GPS integrated with the IDS, which assigns a GPS location and time directly to every twelfth GPR trace and by a more accurate differential GPS (dGPS) system consisting of a Trimble XH and Tornado antenna mounted on the GPR and a local base station of a Trimble Geo7X and Zephyr antenna.
Radargrams were processed in REFLEXW (Sandmeier software) by applying the
steps shown in Table 1. The reflection at the ice surface was picked
manually wherever it was clearly identifiable and was not picked if it was
indistinct. The appropriate signal velocity for the supraglacial debris was
obtained by burying a 1.5 m long steel bar to a known depth and then passing
the GPR over the buried target and picking the two-way travel time to its
reflection (Fig. 3a and b). Both fine and coarse material gave similar wave
speeds (0.15 and 0.16 m ns
Reflector used to identify signal velocity on Ngozumpa Glacier in
During processing, the integrated GPS locations (typical accuracy of
In the absence of suitable field measurements of sub-debris ice ablation, a model of ice ablation beneath a debris cover was applied to assess the impact of debris thickness variability on calculated ablation rates. As recent, high-quality, local meteorological data are not available to force a time-evolving numerical model, typical ablation season conditions measured at the nearby Pyramid weather station were used to force a steady-state model of sub-debris ice ablation that has been previously published and evaluated against field data (Evatt et al., 2015).
Ice ablation conditions are generally restricted to the summer months in the
eastern Nepalese Himalaya (Wagnon et al., 2013). For the illustrative
simulations performed here, the model was forced with mean August
meteorological conditions from 2003 to 2009 (
The model is used to generate an Østrem curve and associated surface
debris temperature for the stated inputs, as a function of debris thickness.
The model does not account for variability in surface energy receipts due to
local or surrounding terrain, or the effects of spatially or temporally
variable debris properties other than thickness, and the chosen input
properties are only approximate. However, this does not preclude its
illustrative use in investigating the influence of variable debris thickness
on calculated ablation rate. Modelling was carried out for three sites for
which local debris thickness data are available: (i) the margin study area
In order to assess the static relationship between the debris distribution and terrain properties, we used a 5 m resolution digital terrain model (DTM) derived from Pléiades optical tri-stereo imagery taken during the field campaign on 12 April 2016. The DTM was generated from photogrammetric point clouds extracted from the Pléiades imagery, using a semi-global matching (SGM) algorithm (Hirschmüller, 2008) within the IMAGINE photogrammetry suite of ERDAS IMAGINE. The three images of each triplet were imported and the rational polynomial coefficients (RPCs) provided with the Pléiades data were used to define the initial functions for transforming the sensor geometry to image geometry. With those transformation functions, individual geometries of each image in the triplet were orientated relative to each other. To obtain the most accurate exterior orientation possible, initial RPC functions were refined using automatically extracted tie points. The calculated point clouds were then filtered for outliers, mainly found in very steep and shaded areas, using local topographic 3-D filters applied in SAGA GIS software, and converted into a 5 m resolution DTM using the average elevation of all points within one raster cell as the elevation value for the cell. Gaps were present in very steep areas, where there was cloud, and in areas with low contrast because of fresh snow or liquid water.
Terrain properties were extracted using the ArcGIS tools Slope, Aspect and Curvature. GPR data were resampled to the same resolution as these rasters (5 m) by taking the mean of the measurements that occurred within each pixel. This was done using the Point to Raster tool in ArcGIS. GPR data within 5 m of ice cliffs were excluded for comparisons made between debris thickness and topography in order that their slope, aspect and curvature were not misrepresented. Similarly, GPR data for which dGPS locations were not available were excluded due to their lack of positional accuracy.
Ponded water at the surface is associated with the deposition of layers of fine sediments and rapid sedimentation by marginal slumping (Mertes et al., 2017). The recent history of ponded water on the parts of the glacier surface sampled by the radar transects was mapped using air photographs from 1984 and seven cloud-free optical satellite images spanning 2008–2016. These images consisted of six DigitalGlobe images, one CNES/Astrium image, all obtained via Google Earth, and the optical image from the 2016 Pleiades acquisition used to generate the DTM.
Slope stability modelling was carried out following Moore (2017). For the three study areas shown in Fig. 2, debris was classified as either stable or unstable. Unstable debris was further classified as being unstable due to the following:
oversteepening, where surface slope exceeds the debris–ice interface
friction coefficient; saturation excess, where the modelled water table height is greater than the
debris thickness; and meltwater weakening, where the modelled water table height is less than the
debris thickness, but debris pore pressures are sufficiently raised to cause
instability.
Surface slope (see Sect. 4.3), modelled midsummer ablation rate (see
Sect. 4.2), upstream contributing area and mean debris thickness (see
Sect. 4.1) were used as inputs to the model. Upstream contributing area
was determined from the DTM in ArcGIS using the Flow Direction and Flow
Accumulation tools. Sinks in the DTM were filled if they were less than 3 m
deep, following Miles et al. (2017), using the ArcGIS Sink and Fill tools.
Surface water flow paths were also determined using the Stream To Feature
tool.
The model also requires input values for the debris–ice interface friction
coefficient, the densities of water and wet debris, and the saturated
hydraulic conductivity of the debris. A value of 0.5 was used for the
debris–ice interface friction coefficient, following Barrette and
Timco (2008) and Moore (2017). Values of 1000 and 2190 kg m
The percentage areal coverage of debris instability was calculated for each of the three study areas (Fig. 2). This was done both including and excluding ice cliffs and ponds, where ice cliffs and ponds were manually digitized from the orthophoto associated with the DTM.
The GPR data, DTM and associated orthophoto were collected in March–April 2016, while slope stability modelling was carried out using midsummer (August) ablation rates. It is likely that the debris on a given slope becomes more or less stable seasonally with changes in ablation rates. However, GPR observations of debris instability in March–April are likely to be representative of midsummer debris instability for saturated hydraulic conductivity as maximum melt is expected in midsummer. Similarly, while pond incidence and area vary seasonally on Himalayan glaciers, seasonal ponds commonly reform at the same sites (Miles et al., 2016), so manually digitized ponds and ice cliffs for March–April are assumed to be broadly representative of ponds and ice cliffs in midsummer for percentage area debris instability calculations excluding ponds and ice cliffs. Finally, model results should be treated only as a best approximation because the model assumes debris thickness and ablation rate are spatially homogeneous in each study area, which, as discussed by Moore (2017), is clearly not the case.
Statistics of sampled debris thickness variability measured at different locations on Ngozumpa and other glaciers by a range of methods.
Overview map of GPR debris thickness sampled on Ngozumpa Glacier in
2016 overlaid on the hillshade from the Pleiades DTM, recent surface pond
evolution and surface flow paths for the Gokyo
Percentage frequency histograms of debris thickness (
The quality of the GPR data is generally high. The ice surface was clearly identifiable through the debris in the majority of the radargrams collected. This is likely because the GPR system was used in continuous mode and appropriate acquisition parameters were used. For those radargrams in which the ice surface was not easily identifiable, the debris was generally too thick. This means there is the possibility of a slight thin bias in the data. However, penetration depth was often greater than 7 m, which is likely near the maximum debris thickness. Debris thickness was found to be highly variable with a total range of 0.18 to 7.34 m (Fig. 4 and examples in Fig. 5). There is coherent structure to the debris thickness variation along transects (Fig. 4): In some areas, changes in debris thickness along the transect are gradual, while in a number of cases, there are abrupt changes in debris thickness along a transect associated with pinning points or topographic hollows and cavities in the underlying ice, which the debris cover fills (see Sect. 5.3 and Fig. 6).
Simple statistics of the debris thickness derived from the GPR samples of this study compared with debris thickness data sets available from other glaciers are given in Table 2. Mean debris thickness measured by GPR towards the glacier margin is thicker, and shows wider spread and lower skewness and kurtosis, than the GPR thickness data collected at the Gokyo study area (Table 2; Figs. 4, 5a–c). The percentage frequency histogram of GPR debris thickness from the glacier margin has a similar shape, but a positive offset compared to data obtained by surveying ice faces about 1 km from the glacier terminus in 2001, while the GPR data from Gokyo agree closely with the estimates of debris thickness from the photographic terrain model (Nicholson and Mertes, 2017). The 2001 surveyed debris thickness data from further upglacier (Nicholson and Benn, 2012) are thinner, more skewed and have higher kurtosis than the sites further downglacier (Fig. 5a–c).
Clearly, while debris thickness shows small-scale variability in all cases on the Ngozumpa Glacier, the details of that variability differ from site to site. This is also observed when considering data from other glaciers (Table 2; Fig. 5). Debris thickness at the Lirung Glacier in central Nepal shows a bimodal distribution not replicated at the other sites. This is suspected to be due at least partly to sampling bias, as the measurements were made to test the GPR method rather than to characterize typical debris thickness at this glacier. At Suldenferner, in the Italian Alps, debris thickness measured across the whole debris-covered area by excavation and along cross- and downglacier transects by GPR shows a substantially thinner mean than the Himalayan cases, with greater skewness and kurtosis. The debris cover on the medial moraine of Haut Glacier d'Arolla in the Swiss Alps is even thinner with yet more pronounced skewness and kurtosis. Thus, debris thickness variability at the Alpine sites shown here is more comparable to that of the upper Ngozumpa, while the Lirung Glacier measurements appear broadly more similar to sites further downglacier on the Ngozumpa Glacier.
The medial moraine on Haut Glacier d'Arolla emerged during glacial recession
in the second half of the 20th century (Reid et al., 2012), offering an
example of a recently developed debris cover. The debris-covered part of
Suldenferner developed its continuous debris cover since the beginning of the
19th century, when the glacier was mapped with debris cover below
The percentage frequency distributions shown in Fig. 5, viewed in the context of the relative “maturity” of the debris covers sampled, are suggestive of a progressive change in skewness and kurtosis of debris thickness variability over time, as debris accumulates and undergoes progressively more gravitational reworking. The more mature debris covers on the Ngozumpa and Lirung glaciers are generally thick and characterized by hummocky terrain (cf. Fig. 2b), dissected with ponds and ice faces, whereas the less mature debris cover on Suldenferner is generally thinner and the terrain is less hummocky, with relief primarily associated with incision by supraglacial streams. Similarly, the observed progressive change in thickness and skewness/kurtosis of the debris sites downglacier on the Ngozumpa Glacier would reflect the downglacier increase in maturity of the debris-covered surface.
Ablation was calculated for three locations on the Ngozumpa Glacier (Fig. 2) encompassing different mean debris thickness and debris thickness variability (Figs. 5, 6a), which might reflect different stages in debris cover maturity (see Sect. 5.1), but it should be noted that the sampling method and sample number differ between locations (Table 2).
The ablation calculated for typical August conditions using the mean debris thickness for each location on the glacier totalled 0.07, 0.11 and 0.32 m of ice surface lowering over the month at the 1, 2 and 7 km sites respectively. This agrees with the general expected patterns of ablation gradient reversal towards the terminus of a debris-covered glacier (e.g. Benn and Lehmkuhl, 2000; Bolch et al., 2008; Benn et al., 2017). Accounting for the percentage frequency distribution of debris thickness increased the monthly total surface lowering due to ablation to 0.08, 0.16 and 0.46 m, at 1, 3 and 7 km respectively. In these illustrative examples, using a mean debris thickness instead of the local frequency distribution of debris thickness underestimates the ablation rate at these sites by 11 %–30 % over typical August conditions (Fig. 6c). These values are specific to the cases presented here but can be considered indicative of the order of the effect of using mean debris thickness instead of the local variable debris thickness. Considering the maximum and minimum error bounds of the debris thickness distribution (Fig. 6a and c) increases the range of this underestimate to 10 %–40 %. This suggests that local mean debris thickness, and also other measures of central tendency (tested but not shown), are likely to be poor metrics for ablation modelling for typical debris cover. Instead, sufficient data points of debris thickness used to capture the local variability are likely to give a more reliable ablation estimate from model simulations. As the melt rate in the thin debris part of the Østrem curve responds more sensitively to changes in debris thickness than it does in the thick debris part of the curve, the impact of accounting for local spatial variability in debris thickness varies inversely with debris thickness (Fig. 6c). This is compounded by the fact that thinner debris appears to have more skewness and kurtosis in the percentage frequency distribution of debris thickness, meaning that the offset between the calculated mean debris thickness and the typical debris thickness is likely to be greater.
Example of radargrams showing debris thickness variability and internal structures in relation to local topography and surface meltwater flow pathways.
Highly variable debris thicknesses can be expected to impact methods of
mapping debris thickness using thermal-band satellite imagery, as our data
show that the debris thickness variability within individual pixels of a
thermal band satellite image may be large. The modelled surface temperatures
for mean August conditions were 19.5, 19.0 and 16.6
Visual inspection of the radargrams indicates that the thinnest debris cover occurs on steep slopes (Fig. 7a and b). On the basis that slope failure typically redistributes mass from areas of high slope angle and that debris sliding was often experienced while collecting the GPR data, it seems likely that this is the result of high debris export rates in these areas due to frequent or recent slope failure in the form of sliding events (cf. Lawson, 1979; Heimsath et al., 2012). Here, the debris surface is approximately parallel to the ice surface, and this appears to be a characteristic of debris covers at or near the limits of gravitational instability. Localized areas of thick debris are found below steep slope sections in the form of infilled ice-surface depressions. Modelled surface flow paths (Fig. 7b) cross-cut the GPR transects where these depressions are located, indicating that they were likely incised by meltwater. This suggests that meltwater is transported in sub-debris supraglacial channels (cf. Miles et al., 2017), but also that meltwater routing has local control over debris thickness by providing topographic lows that become infilled by debris. Additionally, it seems likely that meltwater channels undercut steep slopes, thereby causing debris failure. Steep slopes on debris-covered glaciers are relatively short, so undercutting would have the combined effect of increasing slope angle and also reducing the confining force (or buttressing effect) imparted by downslope debris cover. In some places, thick debris is contained behind pinning points of the underlying ice (Fig. 7a and b), which results in the occurrence of talus slopes (Fig. 7a). This stabilizes the debris and increases the confining force. Thick debris on convex, divergent terrain provides evidence of topographic inversion due to differential ablation (Fig. 7c).
The single glacier margin transect shows increasing debris thickness towards the glacier margin (Figs. 4b and 7e). This is expected as a result of (i) material delivered onto the glacier from the inner flanks of the lateral moraines as they are progressively debuttressed by glacier surface lowering, and (ii) lower surface velocities at the glacier margins; hence debris advection rates are slower. The Ngozumpa Glacier and others in the region typically have troughs at the boundary between the glacier and the lateral moraine, and evidence of thicker debris here reinforces the idea that these troughs are eroded by meltwater routed along the glacier margins (Benn et al., 2017).
Summary of relationships between measured debris thickness and
terrain properties:
Since 1984, the development of supraglacial ponds within the Gokyo study area is likely to have affected two areas of radar transects: several transects towards the north of the Gokyo study area, which were partially affected by lakes in 2012 and 2014, and a single transect towards the east of the Gokyo study area, which was partially affected by lakes in all sampled years except 2014 and 2016 (Fig. 4). One of the transects towards the north of the Gokyo study area shows thick debris and some internal structures (Fig. 7e) including what may be a relict slump structure, where a package of sediment fell into the lake from its margin as the lake expanded (e.g. Mertes et al., 2016). Thick debris in former supraglacial lakes is likely due to high sedimentation rates in the ponds and slumping at lake margins during lake expansion (Mertes et al., 2016). Modelling suggests that subaqueous sub-debris melt rates are low (Miles et al., 2016), so debris thickening caused by the melt-out of englacial debris is likely to be minimal. The radar stratigraphy over former lake beds suggests multiple near-surface reflectors that can reasonably be interpreted as fine lake sediments overlying coarser supraglacial diamict, suggesting that the locally thicker sediments associated with lakes are due to deposition from sediment-rich supraglacial and englacial meltwaters flowing into a more sluggishly circulating pond.
Results of debris stability modelling: upslope catchment area as a
function of slope angle for the three study areas
The debris thickness sampled with GPR in this study does not show distinct
relations with slope, aspect or curvature (Fig. 8a, b, c). Binning the
thickness data with respect to slope indicates a step decrease in debris
thickness above surface slope angles of around 20–23
Slope stability modelling suggests that, under mid-August ablation
conditions, the percentage of the debris-covered area interpreted as
potentially unstable for the three study areas of Ngozumpa Glacier is between
13 % and 34 %, including ponds and ice cliffs and between 13 %
and 32 % if ponds and ice cliffs are excluded (Fig. 9). The percentage of
potentially unstable surface area increases upglacier, as debris thickness
decreases and ablation rates increase (Fig. 6c). Oversteepening was found to
be the dominant cause of instability in all three study areas, meaning that
the debris is most likely to be unstable where surface slope is greater than
On the basis that thin debris is more likely to exist on unstable slopes, or on slopes that have recently failed, and that debris-covered glaciers typically extend to lower elevations than debris-free glaciers, these results have important implications for debris-covered glacier surface mass balance. Debris gravitational instability provides a mechanism by which relatively large parts of debris-covered glaciers can experience high melt rates, even if debris is generally thick.
Debris thickness is known to vary over the surfaces of debris-covered glaciers due to advection, rockfall from valley sides, movement by meltwater and slow cycles of topographic inversion. The debris thickness data presented here suggest that the local debris thickness variability may show characteristic changes in skewness and kurtosis associated with progressive thickening and/or reworking of debris cover over time. On this basis the likely distribution of debris thickness might be predicted by the maturity, or time elapsed since development, of the debris cover found on a glacier surface.
For the thickly debris-covered glaciers of the Himalaya, sub-debris melt
rates across the ablation zones are generally considered to be small
compared to subaerial melt rates at ice cliffs (e.g. up to 5 cm d
On the surface of the Ngozumpa Glacier, our data suggest that topography is important for additional local control on debris thickness distribution via slope and hydrological processes and also that thick sediment deposits at the beds of former supraglacial ponds are an important additional control on the local variability of debris thickness. Surface debris appears to be mobilized and transported by slope- and aspect-dependent sliding caused by sub-debris melting and most likely triggered by meltwater activity. Debris is redistributed from steep slopes to shallow slopes and to ice-surface depressions that are often of hydrological origin. However, the relationship between debris thickness and morphometric terrain parameters is complex. While there is some apparent variation of debris thickness with slope and aspect, whereby thinner debris caused by slope failure is more likely to occur on steeper slopes with aspects that receive more abundant solar radiation, we find no meaningful variation with curvature. This, combined with observations of slide-type debris morphology, suggests that mass movement on the Ngozumpa Glacier occurs on relatively short timescales and predominantly by processes that occur at the limits of gravitational stability (e.g. Moore, 2017). Slope stability modelling suggests that large areas of the glacier are potentially prone to failure, and thus, as failure forms areas of thinner debris, that melting in these areas might be important on the glacier scale.
Debris thickness data measured on Ngozumpa Glacier are
publicly available at
LN, MM and HP contributed to field data collection. LN analysed the debris thickness distributions, performed melt modelling and led the preparation of the manuscript. MM, with guidance from HP and IW, processed the GPR data and performed terrain analysis and slope stability modelling. All authors contributed to finalizing the manuscript.
The authors declare that they have no conflict of interest.
This research is supported by the Austrian Science Fund (FWF) projects V309 and P28521 and the Austrian Space Applications Program of the Austrian Research promotion agency (FFG) project 847999. Michael McCarthy is funded by NERC DTP grant number NE/L002507/1 and receives CASE funding from Reynolds International Ltd. Hamish Pritchard was funded by a British Antarctic Survey collaboration grant. The field team in Nepal was Ursula Blumthaler, Mohan Chand, Costanza del Gobbo, Alexander Groos, Astrid Lambrecht, Christoph Mayer, Hamish Pritchard, Lorenzo Rieg, Anna Wirbel. Christoph Klug generated the DEM. Debris thicknesses data on Haut Glacier d'Arolla were collected by Marco Carenzo, Francesca Pelliciotti and Lene Peterson and provided by Tim Reid. Edited by: Valentina Radic Reviewed by: Peter Moore and one anonymous referee