This post contains a list of
recent studies relating to debris-covered glaciers, primarily in the Himalaya. The
main conclusions detailed in the abstract are also listed. The list is a work
in progress but please get in touch if you would like a study added.
Ice
cliffs:
1.
Watson
et al. 2016. Ice cliff dynamics in the Everest region of the Central Himalaya
a.
Ice cliffs predominantly had north-facing
aspects, independent of glacier flow direction
b.
Ice cliff density was positively correlated with
surface lowering and peaked within zones of active ice flow
c.
49% of ice cliffs featured an adjacent
supraglacial pond
2. Buri et al. 2016. A physically-based
3D-model of ice cliff evolution over debris-covered glaciers
a.
Developed a 3D-model of cliff backwasting and
evolution that is validated against observations and an independent dataset of
volume losses
b.
A major factor affecting the survival of steep
cliffs is the coupling with ponded water at its base, which prevents
progressive flattening and possible disappearance of a cliff
3.
Kraaijenbrink
et al. 2016. Object-based analysis of unmanned aerial vehicle imagery to map
and characterise surface features on a debris-covered glacier. Remote Sensing
of Environment. 186, 581-595.
a.
Ice cliffs and ponds are classified using
object-based image analysis (OBIA) and their morphology and spatial
distribution are analysed
b.
Results show that ice cliffs are predominantly
north-facing, and larger ice cliffs are generally coupled with supraglacial
ponds
c.
The spatial distribution of ice cliffs indicates
that they are more likely to form in areas where high strain rates are expected
4.
Brun
et al. 2016. Quantifying volume loss from ice cliffs on debris-covered glaciers
using high-resolution terrestrial and aerial photogrammetry. Journal of
Glaciology. 1-12.
a.
A method was developed to measure ice cliff
volume losses
b.
Ice cliffs lose mass at rates six times higher
than estimates of glacier-wide melt under debris
c.
Ice cliff backwasting was highest in the monsoon
season
5. Buri et al. 2016. A grid-based model of backwasting
of supraglacial ice cliffs on debris-covered glaciers. Annals of Glaciology
57(71), 199-211.
a.
Developed the first grid-based model of cliff
backwasting for two ice cliffs on the debris-covered Lirung Glacier, Nepal
b.
Melt was highly variable in space, suggesting
that simple models provide inaccurate estimates of total melt volumes
c.
Ice cliffs covered 0.09% of the glacier tongue
area, and accounted for 1.23% of the total melt simulated by a
glacio-hydrological model for the glacier’s tongue
6.
Steiner
et al. 2015. Modelling ice-cliff backwasting on a debris-covered glacier in the
Nepalese Himalaya. Journal of Glaciology. 61(229), 889-907.
a.
Longwave fluxes incident to the cliff from
surrounding terrain and effect of local shading on shortwave radiation are
considered in an energy-balance model
b.
Measured ice cliff melt rates varied between
3.25 and 8.6 cm d–1 in May and 0.18 and 1.34 cm d–1 in
October
c.
Disregarding local topography can lead to
overestimation of melt at the point scale by up to ~9 %
Supraglacial
ponds & glacial lakes:
1.
Miles et
al. 2016. Spatial, seasonal and interannual variability of supraglacial ponds in
the Langtang Valley of Nepal, 1999–2013
a.
Analysed 172 Landsat TM/ETM+ scenes (1999–2013)
to identify thawed supraglacial ponds for five debris-covered glaciers in the
Langtang Valley of Nepal.
b.
The ponds showed pronounced seasonality,
appearing in the pre-monsoon as snow melts, peaking at the monsoon onset, then
declining in the post-monsoon as ponds drain or freeze.
c.
Ponds were highly recurrent and persistent, with
40.5% of pond locations occurring for multiple years.
2. Mertes et al. 2016. A conceptual model of
supraglacial lake formation on debris-covered glaciers based on GPR facies
analysis. Earth Surface Processes and Landforms.
a.
Used ground penetrating radar (GPR) surveys to
simultaneously collect supraglacial lake bathymetry and bottom composition data
from Spillway Lake, Ngozumpa Glacier
b.
Present an updated conceptual model of
supraglacial lake evolution
3.
Watson
et al. 2016. The dynamics of supraglacial ponds in the Everest region, central
Himalaya. Global and Planetary Change. 142, 14-27.
a.
Six out of nine study glaciers displayed a net
increase in ponded area
b.
Seasonal changes in ponded area were large
c.
Khumbu Glacier is developing a chain of connected
ponds in the lower ablation area, which is indicative of a trajectory towards
large lake development
d.
Using medium-resolution imagery (e.g. 30 m
Landsat) will lead to large classification omissions of supraglacial ponds
4.
Miles
et al. 2016. Refined energy-balance modelling of a supraglacial pond, Langtang
Khola, Nepal. Annals of Glaciology. 57(71), 29-40.
a.
Mass and energy balance is modelled for a
supraglacial pond by applying a free-convection approach to account for energy
exchanges at the subaqueous bare-ice surfaces
b.
The pond acts as a significant recipient of
energy for the glacier system, and actively participates in the glacier’s
hydrologic system during the monsoon.
c.
The majority of absorbed atmospheric energy
leaves the pond system through englacial conduits and rapidly promotes the
downwasting process
5. Thakuri et al. 2016. Factors controlling
the accelerated expansion of Imja Lake, Mount Everest region, Nepal. Annals of
Glaciology. 57(71), 245-257.
a.
Between 1962 and 2013, Imja Lake expanded from
0.03 ±0.01 to 1.35 ±0.05 km2 at a rate
of 0.026 ±0.001 km2
a–1
b.
The mean glacier-wide flow velocity was 37±30 m a–1 during
1992–93 and 23±15 m a–1
during 2013–14, indicating a decreasing velocity
Mass
balance/ surface lowering/ glacier dynamics:
1.
Ragettli
et al. 2016. Heterogeneous glacier thinning patterns over the last 40 years in
Langtang Himal, Nepal. The Cryosphere, 10, 2075-2097.
a.
DEM differencing using stereo satellite imagery
from the period 1974-2015.
b.
Accelerated glacier thinning between 2006 and
2015 when compared to the period 1974-2006.
c.
The April 2015 earthquake has a clear impact on
glaciers of the area, with avalanche deposits compensating for 40% of 1-year
average mass loss.
2.
Thompson
et al. 2016. Stagnation and mass loss on a Himalayan debris-covered glacier:
processes, patterns and rates. Journal of Glaciology. 1-19.
a.
Most mass loss occurs by melt below supraglacial
debris, and melt and calving of ice cliffs
b.
Although ice cliffs cover only ∼5%
of the area of the lower tongue, they account for 40% of the ablation
c.
Spillway Lake underwent a period of rapid
expansion from 2001 to 2009, but later experienced a reduction of area and
volume as a result of lake level lowering and sediment redistribution
3.
Vincent
et al. 2016. Reduced melt on debris-covered glaciers: investigations from
Changri Nup Glacier, Nepal. The Cryosphere. 10(4), 1845-1858.
a.
Ground-based measurements of surface elevation
and ice depth were combined with terrestrial photogrammetry, unmanned aerial
vehicle (UAV) and satellite elevation models to derive the surface mass balance
of the debris-covered tongue of Changri Nup Glacier, located in the Everest
region
b.
The insulating effect of the debris cover had a
larger effect on total mass loss than the enhanced ice ablation due to
supraglacial ponds and exposed ice cliffs
4. Kääb et al. 2015. Brief
Communication: Contending estimates of 2003–2008 glacier mass balance over the
Pamir–Karakoram–Himalaya, The Cryosphere, 9, 557-564, doi:10.5194/tc-9-557-2015.
a.
Ice surface elevation change between 2003 and
2008 using ICESat satellite altimetry data
b.
Spatially inconsistent mass change. The eastern Nyainqêntanglha Shan showed highest surface
lowering rates, whereas glaciers of the western Kunlun Shan are gaining volume
c.
Mass loss of −24 ±
2 Gt yr−1 in the Ganges, Indus and Brahmaputra basins- about 10% of
current glacier contribution to sea level rise.
5. Lama et al. 2015. Glacier area and volume
changes of Hidden Valley, Mustang, Nepal from ~1980s to 2010 based on remote
sensing. Proc. IAHS. 368, 57-62.
a.
The study Mapped
10 glaciers of
the Hidden Valley
covering an area
of 19.79 km
using object-based image
classification, an automatic method, and manual delineation
b.
Average estimated glacier ice reserves lost is
0.326 km3 (26.26 %) and the total glacier area loss is 4.33 km2
(21.87 %) from the 1980s to 2010 based on manual delineation.
c.
The glaciers of Hidden Valley are shrinking and
fragmented due to decrease in glacier area and ice reserves
6. Pellicciotti et al. 2015. Mass-balance
changes of the debris-covered glaciers in the Langtang Himal, Nepal, from 1974
to 1999. Journal of Glaciology. 61(226), 373-386.
a.
Elevation and mass changes were reconstructed
for the debris-covered glaciers of the upper Langtang valley, Nepalese
Himalaya, using a digital elevation model (DEM) from 1974 stereo Hexagon
satellite data and the 2000 SRTM (Shuttle Radar Topography Mission) DEM
b.
Thinning occurred in areas of low velocity and
low slope
c.
These areas were prone to a general, dynamic
decay of surface features and to the development of supraglacial lakes and ice
cliffs, which may be responsible for a considerable increase in overall glacier
ablation
7. Neckel et al. 2014. Glacier mass changes on the Tibetan Plateau 2003–2009 derived from ICESat
laser altimetry measurements. Environmental Research Letters, (9).
a.
Ice surface elevation change between 2003 and
2009 using ICESat satellite altimetry data over the Tibetan Plateau.
b.
Total annual mass budget of −15.6 ± 10.1 Gt yr−1 estimated for eight
sub-regions across the Tibetan Plateau.
c.
−13.9 ± 8.9 Gt yr−1
of the total mass budget contributed directly to global sea level rise.
Glacial
lake outburst floods:
1.
Rounce
et al. 2016. A new remote hazard and risk assessment framework for glacial
lakes in the Nepal Himalaya. Hydrol. Earth Syst. Sci. 20(9), 3455-3475.
a.
The remote hazard assessment analyses mass
movement entering the lake, the stability of the moraine, and lake growth in
conjunction with a geometric GLOF to determine the downstream impacts to
quantify present and future risk
b.
Eight proglacial lakes in the Nepal Himalaya
were assessed
2.
Watson
et al. 2015. An improved method to represent DEM uncertainty in glacial lake
outburst flood propagation using stochastic simulations. Journal of Hydrology.
529, Part 3, 1373-1389.
a.
A new stochastic first-pass GLOF assessment
technique MC-LCP (Monte Carlo Least Cost Path) was evaluated against an
existing GIS-based model (MSF) and an existing 1D hydrodynamic model
b.
SRTM DEM exhibited fewer artefacts compared to the
ASTER GDEM
c.
The MSF model exhibited large linear artefacts
resulting from the pre-processing requirement to ‘fill’ the DEM
d.
Incorporation of a stochastic variable in the
MC-LCP provides an illustration of uncertainty that is important when modelling
and communicating assessments of an inherently complex process
3. Westoby et al. 2015. Numerical modelling of
glacial lake outburst floods using physically based dam-breach models. Earth
Surf. Dynam. 3(1), 171-199.
a.
Multiple breach scenarios were produced by
differing parameter ensembles associated with a range of breach initiation
mechanisms, including overtopping waves and mechanical failure of the dam face
b.
The material roughness coefficient was found to
exert a dominant influence over model performance
c.
The downstream routing of scenario-specific breach
hydrographs revealed significant differences in the timing and extent of
inundation
d.
A methodology for constructing probabilistic
maps of inundation extent, flow depth, and hazard is presented and provides a
useful tool for communicating uncertainty in GLOF hazard assessmen
Hydrology:
1.
Ragettli
et al. 2015. Unraveling the hydrology of a Himalayan catchment through
integration of high resolution in-situ data and remote sensing with an advanced
simulation model. Advances in Water Resources.
a.
A new set of detailed ground data from the upper
Langtang valley in Nepal was used to systematically guide a state-of-the art
glacio-hydrological model
b.
The role played by avalanching for runoff was
assessed for the first time for a Himalayan catchment
c.
Snowmelt was the most important contributor to
total runoff during the hydrological year 2012/2013 (representing 40% of all
sources), followed by rainfall (34%) and ice melt (26%)
Glacier
velocity:
1. Kraaijenbrink et al. 2016. Seasonal surface
velocities of a Himalayan glacier derived by automated correlation of unmanned
aerial vehicle imagery. Annals of Glaciology. 57(71), 103-113.
a.
Unmanned aerial vehicle (UAV) deployed three
times (May 2013, October 2013 and May 2014) over the debris-covered Lirung
Glacier in Nepal
b.
The acquired data are processed into
orthomosaics and elevation models by a Structure from Motion workflow, and
seasonal surface velocity is derived using frequency cross-correlation
c.
The glacier has considerable spatial and
seasonal differences in surface velocity, with maximum summer and winter
velocities 6 and 2.5ma–1, respectively, in the upper part of the tongue, while
the lower part is nearly stagnant
d.
It is hypothesized that the higher velocities
during summer are caused by basal sliding due to increased lubrication of the
bed
Modelling
studies:
1.
Soncini
et al. 2016. Future hydrological regimes and glacier cover in the Everest
region: The case study of the upper Dudh Koshi basin. Science of The Total
Environment. 565, 1084-1101.
a.
Investigated the impact of climate change until
2100 using IPCC AR5 scenario
b.
Stream flows will be largely reduced (−30% or
so) until 2100
c.
Ice volume in the catchment will largely
decrease (−50% or so) until 2100
2.
Chand
et al. 2015. Seasonal variation of ice melting on varying layers of debris of
Lirung Glacier, Langtang Valley, Nepal. Proc.
IAHS. 368, 21-26.
a.
Seasonal melting of ice beneath different
thicknesses of debris on Lirung Glacier in Langtang Valley, Nepal, was studied 2013–14.
b.
The melting rates of ice under 5 cm debris
thickness are 3.52, 0.09, and 0.85 cm d-1 during the monsoon, winter
and pre-monsoon season, respectively.
c.
Maximum melting is observed in dirty ice (0.3 cm
debris thickness) and the rate decreases with the increase of debris thickness.
d.
The energy balance calculations on dirty ice and
at 40 cm debris thickness show that the main energy source of ablation is net
radiation.
3. Parajuli et al. 2015. Modified temperature
index model for estimating the melt water discharge from debris-covered Lirung
Glacier, Nepal. Proc. IAHS. 368, 409-414.
a.
This paper presents a glacier melt model
developed for the Lirung sub-basin of Langtang valley.
b.
Used a temperature index approach to estimate
sub-daily melt water discharge for a two week period at the end of monsoon, and
the melt factor is varied according to the aspect and distributed to each grid
processed from the digital elevation model.
c.
The model uses easily available data and simple
extrapolation techniques capable of generating melt with limited data.
4.
Rowan
et al. 2015. Modelling the feedbacks between mass balance, ice flow and debris
transport to predict the response to climate change of debris-covered glaciers
in the Himalaya. Earth and Planetary Science Letters. 430, 427-438.
a.
Developed a numerical model that couples the
flow of ice and debris and includes important feedbacks between debris accumulation
and glacier mass balance
b.
Supraglacial debris prolongs the response of the
glacier to warming and causes lowering of the glacier surface in situ
c.
Since the Little Ice Age, Khumbu Glacier has
lost 34% of its volume while its area has reduced by only 6%
d.
Predict a decrease in Khumbu Glacier volume of
8–10% by AD2100, accompanied by dynamic and physical detachment of the
debris-covered tongue from the active glacier within the next 150yr.
5. Rounce et al. 2015. Debris-covered glacier
energy balance model for Imja–Lhotse Shar Glacier in the Everest region of
Nepal. The Cryosphere. 9(6), 2295-2310.
a.
Combined fieldwork with a debris-covered glacier
energy balance model to estimate debris temperatures and ablation rates on
Imja–Lhotse Shar Glacier located in the Everest region of Nepal
b.
The debris properties that significantly
influence the energy balance model are the thermal conductivity, albedo, and
surface roughness
6.
Shea
et al. 2015. Modelling glacier change in the Everest region, Nepal Himalaya.
The Cryosphere. 9(3), 1105-1128.
a.
Applied a glacier mass balance and ice
redistribution model to examine the sensitivity of glaciers in the Everest
region of Nepal to climate change
b.
Sustained mass loss from glaciers in the Everest
region is expected through the 21st century
Climate:
1. Viste et al. 2015. Snowfall in the
Himalayas: an uncertain future from a little-known past. The Cryosphere. 9(3),
1147-1167.
a.
The study combined present-day observations and
reanalysis data with climate model projections to estimate the amount of snow
falling over the basins today and in the last decades of the 21st century
b.
With the strongest anthropogenic forcing
scenario (RCP8.5), the climate models projected reductions in annual snowfall
by 30– 50% in the Indus Basin, 50–60% in the Ganges Basin and 50–70% in the
Brahmaputra Basin by 2071–2100
Debris-cover:
