The satellite observed glacier mass changes over the Upper Indus Basin during 2000–2012

Abstract

Decadal glacier thickness changes over the Upper indus Basin in the Jammu and Kashmir Himalaya were estimated using the TanDEM-X and SRTM-C Digital Elevation Models (DEMs) from 2000 to 2012. In the study area 12,243 glaciers having 19,727 ± 1,054 km 2 area have thinned on an average of − 0.35 ± 0.33 m a −1 during the observation period. The highest thinning of − 1.69 ± 0.60 m a −1 was observed in the Pir Panjal while as the marginal thinning of − 0.11 ± 0.32 m a −1 was observed for the glaciers in the Karakoram. The observed glacier thickness changes indicated a strong influence of the topographic parameters. Higher thickness reduction was observed on the glaciers situated at lower altitudes (− 1.40 ± 0.53 m a −1) and with shallower slopes (− 1.52 ± 0.40 m a −1). Significantly higher negative thickness changes were observed from the glaciers situated on the southern slopes (− 0.55 ± 0.37 m a −1). The thickness loss was higher on the debris-covered glaciers (− 0.50 ± 0.38 m a −1) than on the clean glaciers (− 0.32 ± 0.33 m a −1). The cumulative glacier mass loss of − 70.32 ± 66.69 Gt was observed during the observation period, which, if continued, would significantly affect the sustainability of water resources in the basin. The recent reports on the enhanced glacier-melt in the Himalayan region 1 encompassing the Upper Indus Basin (UIB) and the consequent impacts on the water, food and energy security 2 stimulated various scientific studies to investigate the health and dynamics of glaciers in the UIB 3-5,7-16. However, most of the studies have focussed on the changes in the glacier area, snout recession, Equilibrium Line Altitude (ELA), impacts of the increasing temperatures, the influence of debris-cover 4,5. Glacier mass balance is the most important indicator of glacier health and is regarded as the direct and immediate glacier response to any changes in climate 6. However, glaciological mass balance, though considered more accurate method for glacier mass balance, is restricted to a few glaciers in the UIB owing to its rugged terrain, logistic challenges and security impediments 3. In view of these constraints, the geodetic mass balance has emerged as a credible alternate approach to assess glacier mass balance changes at local and regional scales in the UIB 7-15. The past studies have reported significant variability in glacier mass changes over the High Mountain Asia (HMA) region. Gardelle et al. 15 for instance, provided comprehensive regional mass balance estimates over the Hindu-Kush Karakoram Himalaya (HKH) region between 2000 and 2011, and highlighted the higher mass wastage in the western Himalaya, moderate mass loss in the eastern and central Himalaya, small mass losses in the Hindu-Kush and stability or even slight mass gain in the Karakoram and Pamir sub-regions. The variability in the mass balance of glaciers in various Hindu-Kush Himalayan regions has been largely attributed to the peculiar topographic and climatic setting of each of the mountain ranges 16. However, it has been observed that the...

Evaluation of the global glacier inventories and assessment of glacier elevation changes over north-western Himalaya

“…The minimization process was repeated till either the magnitude of shift was <0.5m or the normalized median absolute difference (NMAD) on off-glacier terrain was <5% 310 than the previous pass (Nuth and Kääb, 2011). The voids in the DEMs were filled using the Natural Neighbour (NN) algorithm (Abdullah et al, 2020).…”

“…The DEMs were corrected for radar penetration bias before generating the DEM difference map at pixel level. The relative penetration bias was calculated as a function of altitude (Vijay and Braun, 2016;Abdullah et al, 2020). KUGI was used to calculate the mean glacier elevation 315 changes between 2000 and 2012 and the volume changes thereof.…”

“…The uncertainties in the estimation of geodetic mass balance due the uncertainty in DEM differencing, radar signal penetration, uncertainty due to void fill, glacier outlines and mass conversion were considered. For the uncertainty assessment, we followed the methodology after Huber et al, (2020) with an additional term to account for the radar penetration error ( σ penetration 2 ) (Abdullah et al, 2020) assuming that all the errors are uncorrelated and random.…”

“…where σΔh is the off-glacier NMAD, A is the glacier area analysed in the study area and Acor = πd 2 , with d being the decorrelation length. We assumed a mean value d=950 for glaciers in the study area (Abdullah et al, 2020). The uncertainty due to the TanDEM-X date (σTDXdate) was assumed to be equal to be ±2 times the annual elevation change rate from 2000 to 2012 355 (Huber et al, 2020).…”

“…The area above 3600 m asl in general remains 135 covered with perennial snow and glaciers. There are 15064 glaciers covering an area 24,022 km 2 (RGI Consortium, 2017;Abdullah et al, 2020) equivalent to ~11% of the geographical area of the region, thus making the study area the most glaciated terrain in the Indian Himalayas. Most of the glaciers in the area are valley glaciers except the glaciers in the Ladakh range where cirque glaciers are more common (Kamp et al, 2011).…”