Separating snow, clean and debris covered ice in the Upper Indus Basin, Hindukush-Karakoram-Himalayas, using Landsat images between 1998 and 2002

Abstract

The Hindukush Karakoram Himalayan mountains contain some of the largest glaciers of the world, and supply melt water from perennial snow and glaciers to the Upper Indus Basin (UIB) upstream of Tarbela dam, which constitutes greater than 80% of the annual flows, and caters to the needs of millions of people in the Indus Basin. It is therefore important to study the response of perennial snow and glaciers in the UIB under changing climatic conditions, using improved hydrological modeling, glacier mass balance, and observations of glacier responses. However, the available glacier inventories and datasets only provide total perennial-snow and glacier cover areas, despite the fact that snow, clean ice and debris covered ice have different melt rates and densities. This distinction is vital for improved hydrological modeling and mass balance studies. This study, therefore, presents a separated perennial snow and glacier inventory (perennial snowcover on steep slopes, perennial snow-covered ice, clean and debris covered ice) based on a semi-automated method that combines Landsat images and surface slope information in a supervised maximum likelihood classification to map distinct glacier zones, followed by manual post processing. The accuracy of the presented inventory falls well within the accuracy limits of available snow and glacier inventory products. For the entire UIB, estimates of perennial and/or seasonal snow on steep slopes, snow-covered ice, clean and debris covered ice zones are 7,238 ± 2 724, 5,226 ± 522, 4,695 ± 469 and 2,126 ± 212 km 2 respectively. Thus total snow and glacier cover is 19,285 ± 1,928 km 2 , out of which 12,075 ± 1,207 km 2 is glacier cover (excluding steep slope snow-cover). Equilibrium Line Altitude (ELA) estimates based on the Snow Line Elevation (SLE) in various watersheds range between 4,800-5,500 m, while the Accumulation Area Ratio (AAR) ranges between 7-80%. 0 o C isotherms during peak ablation months (July and August) range between ~ 5,500-6,200m in various watersheds. These outputs can be used as input to hydrological models, to estimate spatially-variable degree day factors for hydrological modeling, to separate glacier and snow-melt contributions in river flows, and to study glacier mass balance, and glacier responses to changing climate.

Heterogeneous glacier thinning patterns over the last 40 years in Langtang Himal, Nepal

“…Currently the estimated AAR of Yala Glacier is 40 % (Table 1), which is a common value in the HKH region . The estimated AAR of 86 % at Kimoshung Glacier, in contrast, corresponds to an exceptionally high value for the HKH (Khan et al, 2015). The differences in thinning rates point to the role of glacier hypsometry for the response of debris-free glaciers to climatic changes (e.g., Jiskoot et al, 2009).…”

Adjustment of measurement errors to reconcile precipitation distribution in the high‐altitude Indus basin

“…The estimates for mean ELA at sub‐basin scale are taken from Khan et al . (), who estimated ELA values based on SLE.…”

“…In temperate glaciers, usually the snow line elevation (SLE) and ELA are often assumed to be the same. The estimates for mean ELA at sub-basin scale are taken from Khan et al (2015), who estimated ELA values based on SLE.…”

Decadal Change in Supraglacial Debris Cover in Baspa Basin, Western Himalaya

“…Supervised classification of VNIR and SWIR bands (0.5-1.7 m) are used in the characterization of features of a glacier 28,30,31 . Therefore, the same band combination was used in the present study.…”

“…Therefore, it is essential to monitor the changes in SDC to better understand the glacier dynamics, such as changes in glacier extent, hydrology and mass balance due to climate change 2,7,18 . Monitoring changes in SDC in the field is a challenge and therefore, many studies have effectively utilized remotely sensed data 2,6,[25][26][27][28][29][30] . Previously developed semiautomatic approaches for mapping SDC area use multispectral data augmented by information like topography, thermal data, surface texture and geomorphometric features 2,6,18,25,27,[29][30][31][32] .…”