Himalayan debris-covered glacier literature

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: