Author Archives: Philip Kraaijenbrink

New paper: investigations on debris-covered glaciers

Glaciers covered by debris – rocks, dirt, silt, and sand – are common in the Himalayas. Depending on who’s counting (and where you are looking), debris covers nearly 25% of the total glacierized area in the region.  Experiments and previous studies have shown that really thin debris enhances melt, but that anything over 2 cm thick insulates the ice melt.  But what is the net effect of debris cover on glacier melt rates? Our recently published (open access) paper in the Cryosphere tries to answer this question.

 

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Khumbu Glacier (center) is debris covered. So is the bottom 2/3 of Changri Nup Glacier, located to the west. Everest is at the far right of this Landsat scene.

 

Unfortunately, the answer is not so easy to obtain. Traditional mass balance stake measurements are (a) difficult to install and maintain on debris-covered glaciers, and (b) impossibly biased towards locations where it is possible to drill. You could look at surface elevation changes over part of the glacier with either photogrammetry, UAV, or satellite (we use all three), but if you do this you also need to consider the emergence velocity (or increase in elevation) of the glacier as it flows downhill. On any given point in the ablation zone, the total surface elevation change is a function of both emergence and melt. And to estimate the mean emergence velocity, you need to measure the ice flux through a cross-section of the glacier.

 

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Rates of surface elevation change at Changri Nup Glacier for different periods and data sources: (A) 2011 – 2014 (photogrammetry); (B) 2011 – 2015 (photogrammetry and UAV); (C) 2009 – 2014 (satellite and photogrammetry)

 

Christian Vincent and Patrick Wagnon, French glaciologists from Laboratoire de Glaciologie et Geophysique (LGGE) and Institut de Recherche pour le Development (IRD), have collected multiple datasets over 4 years to estimate the mass gain and loss over the debris-covered Changri Nup Glacier. I’d remind you that debris-covered glaciers at 5400 m of elevation are not among the easiest places to work.

But together with a team of co-authors they have measured surface velocities and surface melt rates with ablation stakes; developed digital elevation models from photogrammetry in 2011 and 2014, from unmanned aerial vehicle surveys in 2015, and from high-resolution satellite data in 2009; measured ice depths with ground-penetrating radar, and mapped ground control points and elevation profiles with differential GPS.

 

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The lead author C. Vincent uses a differential GPS to measure a ground control point for UAV flights over the clean Changri Nup.

 

And the overall result: melt rates on the debris-covered glacier are about 60% less than what they would be if the glacier was free of debris. Ice cliffs and ponds enhanced melt locally, but not enough to offset the overall reduction in melt caused by the debris. The surface mass balance (in m of water equivalent, or m w.e.) over the debris-covered tongue, inferred from average surface lowering of -0.81 m w.e./yr and an average emergence velocity of +0.37 m w.e./yr, is -1.21 m w.e./yr. If the glacier were debris-free, we would expect to see an average mass balance rate of -3.00 m w.e./yr.

This field-based study provides strong evidence that the ‘debris-cover anomaly’ (where satellite data show that debris-covered glaciers appear to be lowering at the same rate as clean-ice glaciers) is an artifact. It also shows that, in this location at least, the effects of ponds and ice cliffs are minimal.

Why is this important? If debris-covered ice (low-angle and thick) occupies 25% of the total glacierized area, it probably contains an even greater percentage of the total ice volume. Better estimates of the net insulating effect of debris will help us improve simulations of future ice loss, and its impacts on water resources downstream.

 

This is a re-post of a recent blog by Joseph M. Shea.

Contribution to EGU General Assembly 2016

This year’s EGU General Assembly has passed and we presented a number of topics in 4 different sessions.

In a session with numerous outstanding talks on Mountain Climates on Wednesday, Joseph Shea presented initial results from an analysis of glaciological and hydrological sensitivities in modeling in the Hindukush Himalaya region.

On Thursday, Walter Immerzeel opened the session on debris covered glaciers with a solicited talk, summarizing our recent efforts in quantifying mass changes on the debris covered glaciers. During the same row of talks Jakob Steiner looked at the spatial and temporal evolution of ice cliffs and lakes in the Langtang catchment in the recent decade.

The round of talks was followed by a poster session with 20 submissions specifically on the topic of debris covered glaciers, underlining the increased attention the issue has received recently. Philip Kraaijenbrink presented his work on the monitoring of glaciers using unmanned aerial vehicles. Pascal Buri presented some progress on the distributed modeling of ice cliff backwasting in the catchment. Evan Miles provided insight into the temporal change of supraglacial lakes and how to extract this information from Landsat imagery. He also presented recent work on deriving surface roughness oft he glacier with photogrammetric analysis. The contribution to this session was rounded off by Pascal Egli’s work on deriving debris thickness from remotely sensed temperature data. Jakob Steiner also presented some insights from analysis of the development of a large alluvial fan in the Langtang catchment in a session on sediment transport in pro-glacial environments.

The week ended with Tobias Bolch presenting Silvan Ragettli’s recent work on mass balance change in the Langtang catchment during the last decade.

 

Contribution to Alpine Glaciology Meeting

At the recent Alpine Glaciology Meeting (AGM) 2016 held in Munich we presented some of the ongoing work in the Langtang catchment.


Pascal Egli presented his work on the reconstruction of debris thickness on debris covered glaciers from thermal satellite images and meteorological data via an energy balance approach. We used three previously published methods based on the energy balance to compute debris thickness maps of Lirung Glacier, assess their performance and determined their sensitivity to several input parameters. We developed a new time-integrating energy balance model with which we intend to obtain more accurate estimates of debris thickness by accounting for the heat transport and storage in the debris layer. Debris thickness reconstruction is essential for the determination of melt rates of debris covered glaciers in the Himalayas.


Figure 1: Modelled debris thickness on Lirung Glacier using an energy-balance approach, with distributed surface temperature data from a Landsat 8 image from 23rd October 2015.


 

Pascal Buri presented a new 3D-modelling approach of ice cliff backwasting. Whereas some supraglacial cliffs remain stable in terms of shape over the melting season, some cliffs flatten considerably and disappear. We tested our physically-based model on selected cliffs on Lirung Glacier in order to simulate cliff evolution over one season. Both atmospheric melt and the interaction with adjacent lakes and debris slopes are implemented. This allows us to dynamically simulate the cliff geometry applying monthly geometric corrections. Simulated volume losses and melt rates for each cliff roughly agree with a TIN-approach used for validation but give also further insight into the inter-annual variability of melt processes and mechanisms behind cliff dynamics.

Figure 2: Simulated ice cliff outlines from May to October for cliffs 1-4. Observed outlines from UAV orthoimage in the background.


 
Jakob Steiner talked about traces of the Gorkha earthquake from April 2015 left on the cryosphere in the catchment. We could observe that the ice released mainly came from the top ridges pointing at topographic amplification playing a possibly significant role in this case. This could help to assess which hanging ice seracs pose a danger in future earthquakes. The massive deposits on the glacier tongues of two debris covered glaciers in the catchment, could provide a chance to observe how the heterogeneous debris cover develops over time.

Figure 3: The debris surface on Lirung glacier is nearly level on the upper parts after the earthquake. Observing how this surface changes into the hummocky terrain normally observed on such glaciers could provide insights into the general development of debris cover.

Special issue of Annals of Glaciology

In the recent special issue of the Annals of Glaciology on ‘Glaciology in High Mountain Asia’ our research team contributed four papers focused on the surface of debris-covered Lirung Glacier, located in the Langtang Valley in the Nepalese Himalaya.


Variation in environmental lapse rate

In a study of the distributed air and surface temperature of the debris surface (link) we propose to adapt the normally used environmental lapse rate when using off-glacier data for an energy balance of the glacier, as the debris surface heats up much more than the surrounding environment (Fig. 1). We also show that the lapse rate has a diurnal cycle and the relation between air and surface temperature changes between day and night and for the dry and wet season. This has implications for using surface temperature data acquired from satellite products used to determine local air temperature over a debris covered glacier.

An additional paper looking at the wider catchment (link) shows a strong seasonality of the environmental lapse rate. Lateral variability at transects across valley is high and dominated by aspect, with south-facing sites being warmer than north-facing sites and deviations from the fitted lapse rates of up to several degrees.

Steiner J. and Pellicciotti F. (2016), On the variability of air temperature over a debris-covered glacier, Nepalese Himalaya. Ann. Glaciol., 57(71), (doi: 10.3189/2016AoG71A066)
Heynen M, Miles E, Ragettli S, Buri P, Immerzeel W and Pellicciotti F (2016) Air temperature variability in a high elevation Himalayan catchment. Ann. Glaciol., 57(71) (doi: 10.3189/ 2016AoG71A076)
Figure 1

Figure 1: Mean air temperatures measured at the T-Loggers in the daytime (red) and at night-time (blue) in all three seasons (a–c) and from 2012 to 2014 (top to bottom). The lines show the temperatures determined with the ELR from the off-glacier AWS Kyanjing. The plots at the bottom show absolute deviation of each value from the ELR.


Distributed modelling of ice cliffs

Investigating ice cliffs further (link) we developed a first distributed model of cliff backwasting for two cliffs. The physically-based model includes an improved representation of shortwave and longwave radiation, and their interplay with the glacier topography. Diffuse radiation is the major shortwave component, as the direct component is strongly reduced through self-shading. Incoming longwave radiation is higher than the total incoming shortwave flux, due to radiation emitted by the surrounding terrain. We could show a considerably high variability in melt rates across the cliff surface and ice cliff melt to be more than 10 times higher than melt under debris per unit area (Fig. 2).

Buri P, Pellicciotti F, Steiner J, Miles E, Reid T and Immerzeel W (2016) A grid-based model of backwasting of supraglacial ice cliffs over debris-covered glaciers. Ann. Glaciol. (doi: 10.3189/ 2016AoG71A059)
Figure 2: Distribution of daily melt rate (cm w.e.) for cliff 1 (left, NW-aspect) and cliff 2 (right, NE-aspect) for pre-monsoon (PRM), monsoon (M) and post-monsoon (POM) periods.

Figure 2: Distribution of daily melt rate (cm w.e.) for cliff 1 (left, NW-aspect) and cliff 2 (right, NE-aspect) for pre-monsoon (PRM), monsoon (M) and post-monsoon (POM) periods.


Modelling of supraglacial lakes

In another study (link) we focus on supraglacial lakes. This research advances previous efforts to develop a model of mass and energy balance for supraglacial ponds by applying a free-convection approach to account for energy exchanges at the subaqueous bare-ice surfaces (Fig. 3). We develop the model using field data from a pond on Lirung Glacier, Nepal, that was monitored during the 2013 and 2014 monsoon periods. Supraglacial ponds efficiently convey atmospheric energy to the glacier’s interior and rapidly promote the downwasting process.

Miles E, Pellicciotti F, Willis I, Steiner J, Buri P and Arnold N (2016) Refined energy-balance modelling of a supraglacial pond, Langtang Khola, Nepal. Ann. Glaciol., 57(71), 29–40 (doi: 10.3189/2016AoG71A421)
A conceptual diagram of the energy balance developed for supraglacial lakes.

Figure 3: A conceptual diagram of the energy balance developed for supraglacial lakes.


Seasonal surface velocities

The last study (link) reveals seasonal differences in velocities of Lirung Glacier in detail using cross-correlation feature tracking of imagery from an unmanned aerial vehicle (UAV).The glacier has considerable spatial and seasonal differences in surface velocity, with maximum summer and winter velocities 6 and 2.5 m a–1, respectively, in the upper part of the tongue, while the lower part is nearly stagnant (Fig. 4). UAVs have great potential to quantify seasonal and annual variations in flow and can help to further our understanding of debris-covered glaciers.

Kraaijenbrink PDA, Meijer SW, Shea JM, Pellicciotti F, Jong SM, Immerzeel WW (2016). Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery, Ann. Glaciol., 57(71), 103-113 (doi: 10.3189/2016AoG71A072)
Surface velocity and flow direction

Figure 4: Surface velocity and flow direction obtained by noise-filtered frequency cross-correlation for the summer (left) and winter (middle) period.


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