Glacier thickness and volume estimation in the Upper Indus Basin using modeling and ground penetrating radar measurements
In the Himalaya, ice thickness data are limited, and field measurements are even scarcer. In this study, we employed the GlabTop model to estimate ice reserves in the Jhelum (1.9 ± 0.6 km3) and Drass (2.9 ± 0.9 km3) sub-basins of the Upper Indus Basin. Glacier ice thickness in the Jhelum ranged up t...
| Published in: | Annals of Glaciology |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Cambridge University Press
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| Subjects: | |
| Online Access: | https://doi.org/10.1017/aog.2024.2 https://doaj.org/article/942cb4d00fa94f33836c859042a8a22f |
| Summary: | In the Himalaya, ice thickness data are limited, and field measurements are even scarcer. In this study, we employed the GlabTop model to estimate ice reserves in the Jhelum (1.9 ± 0.6 km3) and Drass (2.9 ± 0.9 km3) sub-basins of the Upper Indus Basin. Glacier ice thickness in the Jhelum ranged up to 187 ± 56 m with a mean of ~24 ± 7 m, while the Drass showed ice thickness up to 202 ± 60 m, with a mean of ~17 ± 5 m. Model results were validated using Ground Penetrating Radar measurements across four profiles in the ablation zone of the Kolahoi glacier in the Jhelum and nine profiles across the Machoi glacier in the Drass sub-basin. Despite underestimating ice-thickness by ~10%, the GlabTop model effectively captured glacier ice-thickness and spatial patterns in most of the profile locations where GPR measurements were taken. The validation showed high correlation coefficient of 0.98 and 0.87, low relative bias of ~ −13% and ~ −3% and a high Nash–Sutcliffe coefficient of 0.94 and 0.93 for the Kolahoi and Machoi glaciers, respectively, demonstrating the model's effectiveness. These ice-thickness estimates improve our understanding of glacio-hydrological, and glacial hazard processes over the Upper Indus Basin. |
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