Modeling ice thickness distribution and storage volume of glaciers in Chandra Basin, western Himalayas

Identification of Potential Sites for Future Lake Formation and Expansion of Existing Lakes in Glaciers of Chandra Basin, Western Himalayas, India

“…The ice-thickness estimates used for extracting glacier bed topographies in this study are associated with relatively low uncertainty (Ramsankaran et al, 2018) compared with previous studies such as Frey et al (2014) and Linsbauer et al (2016). This lower uncertainty was achieved through optimal parameterization of Glabtop2_IITB model and use of high-resolution TDX DEM, as reported in Ramsankaran et al (2018) and Pandit and Ramsankaran (2020). Hence, the spatial and dimensional characteristics/attributes of the PFGLs reported could be considered reliable.…”

“…In this study, for deriving bed topography profiles of the 65 selected study glaciers, we adopted the ice-thickness distribution obtained by Pandit and Ramsankaran (2020), which was estimated using the GlabTop2_IITB version model (referred to as the GlabTop2 model in Ramsankaran et al, 2018). The GlabTop2_IITB model is fully automated and requires only DEM, slope, and glacier outlines as model inputs.…”

“…The GlabTop2_IITB model is fully automated and requires only DEM, slope, and glacier outlines as model inputs. It is an independent implementation of the original GlabTop2 model with minor modifications as reported in Pandit and Ramsankaran (2020). This model is fundamentally based on the physical concept of SIA wherein glacier-specific basal shear stress is estimated from glacier elevation range following Haeberli and Hoelzle (1995) and then related with glacier slope and thickness.…”

“…Further details of the implementation and validation of the GlabTop2_IITB model along with the near-optimal shape factor estimation method carried out for Chhota Shigri Glacier can be obtained from our previous study (Ramsankaran et al, 2018). Using this optimal-model parameterization approach, GlabTop2_IITB model was implemented to estimate icethickness distribution of the 65 study glaciers in Pandit and Ramsankaran (2020). Based on the estimated ice thickness and available DEMs, the glacier bed topographies of all study glaciers were derived, and quantitative information such as area, volume, maximum depth, and mean depth of the potential future lakes were determined.…”

“…In view of the aforementioned, the present research aims to provide a comprehensive, basin-scale, high-resolution database on potential future glacial lake sites in 65 selected glaciers of Chandra basin in the western Himalayas using high-resolution glacier bed topography. Here, the bedrock topography of each study glacier was extracted from our earlier study (Pandit and Ramsankaran, 2020), where the GlabTop2_IITB ice-thickness model was implemented with a high-resolution TanDEM-X DEM and glacier-specific optimal model parameterization. The western Himalayan region was specifically chosen as it has been experiencing more warming as compared with other HKH regions in recent decades (Gautam et al, 2010), and hence, FIGURE 1 | Map of the study area showing locations of the 65 study glaciers in Chandra river basin, Lahaul-Spiti valley, Himachal Pradesh, India.…”