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.
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: 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.
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)
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)
Figure 4: Surface velocity and flow direction obtained by noise-filtered frequency cross-correlation for the summer (left) and winter (middle) period.
Using the software SketchFab we have produced a 3D visualisation of a part of the Langtang glacier in Nepal. The model allows an interactive inspection of the surface features of the glacier. It shows the huge variation on the surafce of the glacier including different types of lakes, ice cliffs, boulders and bare ice. It is also possible to view this 3D model using VR glasses and a Google Cardbox on your smart phone. Enjoy!
Explore Langtang Glacier on SketchFab
Against the unparalleled backdrop of Everest and Nuptse, the late November sun warms the glaciologist slightly as he prepares for an unmanned aerial vehicle (UAV) survey flight. From his coat pockets he pulls batteries that desperately need to stay warm for full power: batteries for the laptop, camera, and UAV that have been stored in his sleeping bag overnight, when temperatures plummeted below -20 C. He checks the wind. He sets up the flight on his laptop, sends the details to the UAV through a radio transmitter, and heads to the nearby launch location. At 5,350 m above sea level, the air has less than half as much oxygen as at sea level, and it can be difficult to launch the UAV as the air pressure is so low. He breathes heavily — partly due to the oxygen depletion, and partly due to nerves. With the UAV in his hands, he starts the motor, heart racing as the propeller whine reaches an intense pitch. He steps forward to throw the UAV and start the flight. He hopes.
In collaboration with Dr Patrick Wagnon, visiting scientist at ICIMOD and researcher at L’Institut du Récherche pour le Développement (IRD, France), and Dr Dibas Shrestha, Research Scientist at the Nepal Academy of Science and Technology (NAST), ICIMOD Glacier Hydrologist Dr Joseph Shea joined a recent field expedition to Sagarmatha National Park (http://www.icimod.org/?q=20187) to conduct UAV surveys of several glaciers in the region. A total of 730 photos were taken from the senseFly eBee (www.sensefly.com) UAV over six successful flights, and the team collected 56 high-precision ground control points to be used in post-processing. The research may have also set an unofficial eBee altitude record, with a maximum flight elevation of 5,900 m. However, the flight conditions were difficult given the altitude and the unpredictable winds, and the eBee was damaged during the course of the fieldwork.
Data obtained during the research will be used to construct detailed mosaics and elevation models of the study sites. Comparisons of the UAV datasets with satellite imagery and terrestrial photography will be used to examine rates of glacier change, glacier flow velocities, and the role of ice cliffs and ponds in the melt rates of debris-covered glaciers. The research was funded by the UK Department for International Development (DFID), ICIMOD, and Utrecht University. The eBee was generously loaned by FutureWater (Netherlands), who have been assured that it will be sent back to the factory for repairs and testing.
Reconciling high-altitude precipitation in the upper Indus basin with glacier mass balances and runoff
Mountain ranges in Asia are important water suppliers, especially if downstream climates are arid, water demands are high and glaciers are abundant. In such basins, the hydrological cycle depends heavily on high-altitude precipitation. Yet direct observations of high-altitude precipitation are lacking and satellite derived products are of insufficient resolution and quality to capture spatial variation and magnitude of mountain precipitation. Here we use glacier mass balances to inversely infer the high-altitude precipitation in the upper Indus basin and show that the amount of precipitation required to sustain the observed mass balances of large glacier systems is far beyond what is observed at valley stations or estimated by gridded precipitation products. An independent validation with observed river flow confirms that the water balance can indeed only be closed when the high-altitude precipitation on average is more than twice as high and in extreme cases up to a factor of 10 higher than previously thought. We conclude that these findings alter the present understanding of high-altitude hydrology and will have an important bearing on climate change impact studies, planning and design of hydropower plants and irrigation reservoirs as well as the regional geopolitical situation in general.
Immerzeel, W. W., Wanders, N., Lutz, A. F., Shea, J. M. and Bierkens, M. F. P., 2015, Reconciling high altitude precipitation with glacier mass balances and runoff, Hydrol. Earth Syst. Sci., 12, 4755–4784.