Glacier volume estimates in the Indus Basin

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1 Glacier volume estimates in the Indus Basin Methods and applications Dr. Holger Frey Department of Geography, University of Zurich Indus Forum Workshop, 11 October 2016 University of Zurich, Switzerland

2 Why estimating glacier volumes? Sea level rise P. Rastner Glacier evolution Landscape changes J. Alean J. Alean Hydropower

3 Where? Karakoram ICIMOD Frey et al. (2012) Bhambri et al. (2012) Chinese Glacier Inventory W-Himalayas Estimating the ice volumes in the Himalaya/Karakoram C-Himalayas region using SRTM (void-filled) accurate glacier inventory data three different approaches E-Himalayas

4 How? Volume-area scaling V = c A γ Slope-dependent thickness estimations (according to Haeberli and Hoelzle, 1995, Ann. Glaciol.) h avg = ( τ / f ρ g sin(α)) (π/4) Modeling of ice-thickness distribution Linsbauer et al. (2009) Huss & Farinotti (2012)

5 How? V-A relations Slope-dependent thickness estimations Ice-thickness distribution models Bahr et al., 1997, JGR h avg = p 4 Haeberli and Hoelzle, 1995 t f r g sina Quick to apply Cogley, 2012, in: Barry et al. Weak correlation of area and thickness Large uncertainties Mean ice thickness calculation Rapid calculation with results similar to GlabTop Include 3D terrain information from DEMs Many subsequent applications: Volume per elevation Glacier bed topography Validation with measurements not possible Can be validated with measurements

6 Ice-thickness distribution models: GlabTop Input: digital glacier outlines + DEM contour lines (50 m) ΔH glacier outlines flowlines surface slope base points h = interpolated glacier bed f t r g sina h A. Linsbauer

7 GlabTop2 Input: digital glacier outlines + DEM Further development of the GlabTop approach (Linsbauer et al., 2012) Calculation of ice thickness at random points and interpolation h = f t r g sina

8 Other ice-thickness distribution models Huss and Farinotti (2012)

9 Results: Glacier volumes 5'000 4'000 3'000 2'000 1' Volume (km 3 ) Volume (km 3 ) Chen & Ohmura (1990) Bahr et al. (1997) Arendt et al. (2006) Haeberli & Hoelzle (1995) GlabTop2 ITEM (Huss & Farinotti, 2012) INDUS BASIN (GlabTop2): 1830 km 3 Karakoram W Himalaya C Himalaya E Himalaya TOTAL Frey et al. 2014

10 Results: mean thickness & SLE Mean ice thicknesses (and corresponding Sea Level Equivalents) Region Chen and Ohmura (1990) Bahr et al. (1997) LIGG et al. (1988) Slope-dep. thickness estimate GlabTop2 HF-model Karakoram m (5.56 mm) m (6.82 mm) m (7.03 mm) m (5.28 mm) 93.8 m (4.18 mm) m (4.65 mm) W Himalayas 57.6 m (1.28 mm) 68.2 m (1.52 mm) 78.7 m (1.75 mm) 58.9 m (1.31 mm) 56.3 m (1.25 mm) 60.7 m (1.35 mm) C Himalayas m (1.61 mm) 77.4 m (1.91 mm) 88.8 m (2.19 mm) 51.6 m (1.27 mm) 55.6 m (1.37 mm) 56.4 m (1.39 mm) E Himalayas 59.6 m (0.58 mm) 70.6 m (0.69 mm) 81.7 m (0.80 mm) 50.2 m (0.49 mm) 54.6 m (0.54 mm) 49.2 m (0.48 mm) HK region 89.1 m (9.03 mm) m (10.95 mm) m (11.78 mm) 82.4 m (8.35 mm) 72.5 m (7.35 mm) 77.7 m (7.87 mm) INDUS BASIN (GlabTop2): 44.9 m (4.55 mm SLE)

11 Results: Hypsometric distribution Frey et al. 2014

12 Results: Summary Indus Basin ~1830 km 3 of glacier ice = mean thickness of ~45 m = ~4.55 mm Sea Level Equivalent Large amounts of ice in flat, low-lying glacier tongues (often under debris cover)

13 Validation Validation of mean thickness-estimation approaches is difficult Ice-thickness distributions can be compared to GPR measurements Linsbauer et al. 2015

14 Validation Frey et al Average differences for 6 glaciers (std devs) of -2.9% (89m) (GlabTop2, blue) and % (63m) (Huss & Farinotti, red)

15 Applications: Future lakes Rhone Glacier

16 Applications: Future lakes Linsbauer et al. 2015

17 Applications: RCMs Dynamic integration of glaciers in a regional climate model REMO: First RCM with dynamic glacier integration Ice volumes so far based on V/A-scaling Glacier volumes distributed over elevation bands as model input Kumar et al. 2015

18 Future work: ITMIX IACS Working Group on Glacier Ice Thickness Estimation Ice Thickness Models Intercomparison experiment (ITMIX) 17 model approaches applied to 21 test cases Farinotti et al. in prep. Next step: Application of GlabTop2 to all glaciers in the World Glacier volumes in the Indus Basin Indus Forum, 11/10/16 Zurich Dr. Holger Frey

19 Conclusions Models are able to estimate distributed ice thicknesses of glaciers DEMs and glacier outlines are globally available automated model approaches can be applied to all glaciers in the World Scaling approaches involve very large uncertainties and should only be applied to large ensembles of glaciers, using local scaling parameters Distributed models allow for validation with measurements Applications of ice thickness distribution include future landscape modeling, glacier evolutions models, runoff predictions

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22 Approach I: Volume area relations Quick and easy to apply Based on relatively few measurements Weak correlation of glacier area and thickness Bahr et al., 1997, JGR Cogley, 2012, in: Barry et al. Glacier separation has a strong influence on results Area: -0.03% Vol: -37%

23 Approach II: Slope-dependent thickness estimations Haeberli and Hoelzle (1995): h avg = ( τ / f ρ g sin(α)) (π/4) (τ parameterized with ΔH, max. 1.5 bar) H max Haeberli and Hoelzle, 1995 Different ways of calculating surface slope α: α tan = arctan (ΔH/l) α DEM = average of all DEM cells in most cases α DEM < α tan ΔH l Correction of α DEM to α tan H min 1 km

24 Two ways of calculating mean slope Haeberli and Hoelzle (1995): α = arctan (ΔH/l) [α l ] In GIS: α = average of all DEM cells [α DEM ] α l α DEM! Corrections of α DEM : If Area > 20 km 2 : -10 Area 5-20 km 2 : - 5 Area 2-5 km 2 : Area < 2 km 2 : no correction