K a r a k o r a m

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C e n t r a lH i m a l a y a s

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C e n t r a lH i m a l a y a s E a s t e r nH i m a l a y a s

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ReferncesBajracharya, S. R. and Shresta, B.: The status of glaciers in the Hindu Kush–Himalayan region,

ICIMOD, Kathmandu., 2011.Bhambri, R., Bolch, T., Kawishwar, P., Dobhal, D. P., Srivastava, D. and Pratap, B.: Heterogeneity

in glacier response in the upper Shyok valley, northeast Karakoram, The Cryosphere, 7(5), 1385–1398, doi:10.5194/tc-7-1385-2013, 2013.

Bolch, T., Kulkarni, A., Kääb, A., Huggel, C., Paul, F., Cogley, J. G., Frey, H., Kargel, J. S., Fujita, K., Scheel, M., Bajracharya, S. and Stoffel, M.: The State and Fate of Himalayan Glaciers, Science, 336(6079), 310–314, doi:10.1126/science.1215828, 2012.

Frey, H., Machguth, H., Huss, M., Huggel, C., Bajracharya, S., Bolch, T., Kulkarni, A., Linsbauer, A., Salzmann, N. and Stoffel, M.: Ice volume estimates for the Himalaya–Karakoram region: evaluating different methods, Cryosphere Discuss., 7(5), 4813–4854, doi:10.5194/tcd-7-4813-2013, 2013.

Frey, H., Paul, F. and Strozzi, T.: Compilation of a glacier inventory for the western Himalayas from satellite data: methods, challenges, and results, Remote Sens. Environ., 124, 832-843, doi:10.1016/j.rse.2012.06.020, 2012.

Haeberli, W. and Hoelzle, M.: Application of inventory data for estimating characteristics of and regional climate-change effects on mountain glaciers: a pilot study with the Euro-pean Alps, Ann. Glaciol., 21, 206–212, 1995.

Linsbauer, A., Paul, F. and Haeberli, W.: Modeling glacier thickness distribution and bed topography over entire mountain ranges with GlabTop: Application of a fast and robust approach, J. Geophys. Res., 117, F03007, doi:10.1029/2011JF002313, 2012.

Shi, Y., Liu, C. and Kang, E.: The Glacier Inventory of China, Ann. Glaciol., 50(53), 1–4, doi:10.3189/172756410790595831, 2010.

- Beneath the glacierized area (40’800 km2/3360 km3) of the Himalaya and Karakoram Region 16’000 overdeepenings are modelled, covering an area more than 2200 km2 and having a total volume of about 120 km3 (3-4% of the now existing glacier volume).

- Model runs with GlabTop1 and 2 for a test-sample of 788 glaciers in HP, India, reveal that the total volume and the number of overdeepenings are larger using GlabTop2. However, this difference can be diminished with better data (especially DEMs) or different parameter settings in GlabTop2.

- While the modelled overdeepenings based on model runs with different data input differ in shape, the locations of the overdeepenings are robust and the values for the extracted parameters are comparable.

- The role of sediments and debris-covered glaciers is not assessed and needs further investigations.

CONCLUSIONS

Comparison between GlabTop1 and GlabTop2

For 788 glaciers in the Chandra Valley and the Kullu district in Himachal Pradesh, India, covering about 1200 km2, the original GabTop (GlabTop1) and GlabTop2 have been run with the same input data (DEM and glacier outlines). The resulting picture of both modelled ice thickness distribution is similar. However, a closer look reveals that the ice thickness distribution modelled by GlabTop1 is smoother, whereas the values of mean and maximum ice thickness and the total volume resulting from GlabTop2 are larger (cf. Fig. 7 and Table 2).

DISCUSSION

As a consequence of the smoother output from GlabTop1, the number of modelled potential over-deepenings is considerably smaller compared to GlabTop2. This has a direct impact on the total volume of the overdeepenings (cf. Table 3). Fig. 8 shows the higher number of overdeepenings as from the GlabTop2 model run, but it also illustrates the robustness of the locations. Almost at every site where GlabTop1 predicts an overdeepening there is one according to GlabTop2.

Data quality

With better data (especially better DEMs) the robustness of the modelled locations of overdeepen-ings can be increased. This is illustrated in Fig. 9, showing a subset of glaciers from the Swiss Alps, where 4 different model runs with GlabTop 1 and 2 have been completed. While the modelled over-deepenings based on model runs with different data input differ in shape, the locations of the over-deepenings are robust and the values for the extracted parameters are comparable. overdeepenings (> 1mill. m3)

glacier inventory

modelled with GlabTop2modelled with GlabTop1

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explanation for the 4 model runs:73 = glacier outlines 19732k = glacier outlines 2000l1 = DHM25 Level 1 (1985)l2 = DHM25 Level 2 (1995)

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Fig. 7: Ice thickness distribution for glaciers in the Chandra and Parvati Valley in Himachal Pradesh, India, modelled with GlabTop1 (left) and GlabTop2 (rigth).

Fig. 8: Modelled overdeepenings for a subscene of Fig. 6 (Chandra and Parvati Valley, HP, India) detected with GlabTop1 (yellow) and GlabTop2 (red).

Table 2: Key statistics for the test glacier sample

GlabTop1 GlabTop2 Glaciers > 0.1 km2 > 1 km2 > 0.1 km2 > 1 km2 Count 788 216 788 216 Area covered 1200 km2 990 km2 1200 km2 990 km2 Mean ice thickness 49 m 57 m 63 m 70 m Max ice thickness 329 m 329 m 421 m 421 m Total ice volume 60 km3 57 km3 76 km3 70 km3

Table 3: Key statistics for modelled overdeepenings in the glacier beds

Fig. 9: Map showing modelled overdeepenings with a volume larger than 106 m2 based on different model runs with GlabTop1 and 2 for Aletsch, Oberaletsch, Trift and Rhone glaciers in Switzerland.

Debris/Sediments

GlabTop does not take into account whether a glacier is debris covered. Due to the high debris load on some of these glaciers, overdeepenings might be filled with sediment rather than water. The high sediment production in regions with heavily debris-covered glaciers will also strongly influence the characteristics of the future landscape that will be exposed after glacier retreat.

GlabTop1 GlabTop2 Overdeepenings > 104 m2 > 106 m3 > 104 m2 > 106 m3 Count 812 153 1338 300 Area covered 44 km2 27 km2 75 km2 47 km2 Mean depth 11 m 23 m 21 m 28 m Max depth 171 m 171 m 219 m 219 m Total volume 800 106 m3 670 106 m3 1590 106 m3 1294 106 m3

RESULTS

Overdeepenings in the glacier bed topographies of the Himalaya-Karakoram

The glacier coverage for the Himalaya and Karakoram Region is about 40’800 km2 resulting in an estimated total ice volume of ca. 3360 km3. About 16’000 modelled overdeepenings larger than 104 m2 were detected in the modelled glacier bed topography, covering an area of about 2200 km2 and having a total volume of about 120 km3 (3-4% of the now existing glacier volume). About 5000 of these overdeepenings (1800 km2) have a volume larger than 106 m3. The larg-est calculated bed depression (cf. Fig. 3) covers an area of more than 50 km2 and has a volume of about 11 km3. For a statistical analysis concerning the morpho-logical characteristics of these overdeepenings, mean and maximum values of the parameters surface area, length, width, depth, volume, frontal/adverse slope and their statistical interrelations are determined (Table 1).

Modelled overdeepeningsHigh

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Fig. 3: Modelled overdeepenings with GlabTop2 for the glaciers between K2 and the Karakoram-Pass in the Karakoram Range, including the largest mod-elled depression.

Fig. 4: Modelled overdeepenings with GlabTop2 for a glacier sample at the border between Bhutan and Tibet/China.

Fig. 5: Google Earth screenshot of the region around Mount Everest, showing the glacier inven-tory (blue lines) and the modelled overdeepenings with GlabTop2 (light blue).

Bathymetry min mean median max STDev

Area (km2) 0.02 0.44 0.19 56.70 1.42

MaxDepth (m) 12 61 47 744 49

MeanDepth (m) 5 26 20 347 21

Volume (106 m3) 1 25 4 11146 230

MaxLength (m) 201 1069 805 18880 973

MaxWidth (m) 91 440 371 4217 294

MeanWidth (m) 23 311 242 5393 309

Elongation (-) 0.05 0.49 0.44 4.75 0.29

FlowLength (m) 45 961 733 18322 907

opposite slope min mean median max STDev

length (m) 45 547 384 13367 648

mean (deg.) 2 12 11 58 6

ATAN (deg.) 1 9 7 63 7

max (deg.) 2 19 17 70 9

adverse slope min mean median max STDev

length (m) 45 514 407 7358 458

mean (deg.) 1 11 9 52 6

ATAN (deg.) 1 9 7 71 7

max (deg.) 0 17 15 70 9

Table1: Descriptive statistics for all potential overdeepenings from GlabTop2 model runs > 106 m3

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Fig. 6: Scatter plot for modelled overdeepenings from 3 datasets: Maximum depth vs. area.

For more details on the analysis of the morphological char-acteristics of these overdeepenings --> cf. Poster 1245 on board B885 (same session)METHODS

Fig. 1: Oblique view of Rhone gla-cier, Switzerland: (a) glacier sur-face (original DEM), (b) glacier bed modelled with GlabTop1, (c) bed topography with overdeep-enings shown in blue, and (d) photograph from August 2012 showing the currently forming lake at the terminus of Rhone glacier (swisseduc.ch/glaciers J. Alean).

glacier-adjecent cell

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non-marginal cell

claculation cell (random)

glacier outline

glacier outline (raster)

enlarging buffer

Modelling ice thickness, bed topography and potential overdeepenings

GlabTop (Glacier bed Topography) is an ice dynamical approach, based on the assumption of perfect plasticity of ice, which relates glacier thick-ness to its local surface slope via the basal shear stress estimated for each glacier based on an empirical relation between shear stress and elevation range as a governing factor of mass turnover (Linsbauer et al., 2012). While the original approach (GlabTop1) relies on so called glacier branch lines that had to be digitized manually, GlabTop2 is fully automated and requires only a DEM and glacier outlines as an input. Thereby ice thick-ness is calculated for random glacier points (defined percentage of all gla-cier cells). For the determination of the slope a buffer of variable size is used (buffer is enlarged until a certain elevation extent is reached, here 50 m), the ice thickness is calculated for the random points using the empiri-cal elevation range/basal stress-function from Haeberli and Hoelzle (1995) and then interpolated between the random points and the glacier-adjacent cells. The entire procedure is repeated three times for each glacier; final results are obtained by averaging.

Fig. 2: Schematic sketch of the functionality of GlabTop2: glacier polygons (blue curved line) are converted to a raster matching the DEM cells (red outline). Cells are discriminated as inner glacier cells (light blue), glacier marginal cells (powder-blue), glacier adjacent cells (yellow), and non-glacier cells (white). Pink cells represent randomly selected cells (r) for which local ice thickness is calculated; the blue square symbolizes the buffer of variable size, which is enlarged (dashed blue square), until an elevation extent of hmin is reached within the buffer.

DATADEM: SRTM, version 4 (void-filled SRTM data provided by CGIAR); 3 arc seconds resolution (≈90 m); mosaic of 20 tiles.

Glacier outlines: Updated dataset, mostly based on satellite imagery from between 2000 to 2010. Composed by the fol-lowing sources (text colors correspond to glacier polygons in the background image): ICIMOD (Bajracharya and Shresta, 2011), GlobGlacier (Frey et al., 2012), Chinese Glacier Inventory (Shi et al., 2009), and Bhambri et al. (2012). This inventory in-cludes more than 28’000 glaciers, covering an area of about 40’800 km2 (the same dataset has been used by Bolch et al., 2012).

Ice thickness distribution: Modelled using GlabTop2 based on DEM and glacier outlines (Frey et al., 2013) with a total volume of 3360 km3 (with an uncertainty range of ±30%).

Calculating ice thickness distribution and bed topographies for large glacier samples is an essential task to es-timate stored ice volumes with their potential for sea level rise and to model possible future retreat scenarios of glacier evolution under conditions of continued warming. Modelling such bed topographies to become exposed in the near future by continued glacier retreat also enables modelling of future landscapes with their landforms, processes and interactions. As the erosive power of glaciers can form numerous and sometimes large closed topographic bed depressions, many overdeepenings are commonly found in formerly glaciated mountain ranges. Where such overdeepend parts are becoming exposed and filled with water rather than sediments new lakes can come into existence.In this study, the potential bed overdeepenings for the 28’000 glaciers of the Himalaya-Karakoram region are modelled based on the ice thickness distribution estimated with GlabTop2 in the study of Frey et al. (2013).

INTRODUCTION

This study has been funded by the National Cooperative for the Disposal of Radioactive Waste (Nagra) in Switzerland and the Swiss Agency for Development and Cooperation (SDC) project IH-CAP. Contact: Andreas Linsbauer, Departement of Geography, University of Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland. (andreas.linsbauer@geo.uzh.ch)

Modelling bed overdeepenings for the glaciers in the Himalaya-Karakoram region using GlabTop2A. Linsbauer 1,2, H. Frey 1, W. Haeberli 1 and H. Machguth 3

1 Department of Geography, University of Zurich, Switzerland2 Department of Geosciences, University of Fribourg, Switzerland

3 Centre for Arctic Technology, Danish Technical University, DK-2800, Kgs. Lyngby Swiss Agency for Developmentand Cooperation SDC