Reconstructing the pattern of the Bara Shigri Glacier fluctuation since the end of the Little Ice Age, Chandra valley, north-western Himalaya

Abstract

The pattern of glacial records since the end of the Little Ice Age (LIA) are essential for evaluating glacier fluctuations and their link to post-LIA climate change. Although recession of the Himalayan glaciers is well-documented in this period, debate continues as to the magnitude and accuracy of estimated recession rates. This study presents a reconstruction of the pattern of fluctuations at the Bara Shigri Glacier in the Himachal Himalaya during the termination of the LIA (∼1850). A multi-data integrative analysis (MDIA) technique consisting of repeat terrestrial photographs, historical archives and reports, geomorphological evidence and maps, and high to medium spatial resolution satellite images (Corona, Hexagon, Landsat and WorldView-2) was used with supplemented by extensive field validation. The results indicate that during the early part of the 19th century the terminus of Bara Shigri Glacier was at ∼3900 m asl. Following this, there was a continuous recession with a total retreat of 2898 ± 50 m, which corresponds to a frontal areal loss of 4 ± 1 km2 in the last 151 years (1863–2014). Compared to this, during the last half century (1965–2014), the glacierised area was reduced by 1.1 ± 0.02 km2 with a concomitant terminus retreat of 1100 ± 32 m. The early 19th century advance is ascribed to a combination of cooling during this period, glacier topographical characteristics and contributions from steep-fronted avalanching tributaries. The late 19th century recession can be attributed to an overall increase in the temperature with a corresponding decrease in precipitation in the north-western Himalaya. Results are at variance with earlier, larger estimates of the frontal area loss for the Bara Shigri Glacier using either Survey of India (SoI) topographic maps or coarse spatial resolution satellite images (e.g. Landsat MSS) as historical datasets, and demonstrate the utility of mixed method approaches including higher-resolution satellite imagery for accurate estimation of glacier behaviour in this region.

Recession and Morphological Changes of the Debris-Covered Milam Glacier in Gori Ganga Valley, Central Himalaya, India, Derived From Satellite Data

“…The historical plane-table and topographical maps were obtained from various sources, while the satellite data were obtained from the website of the United States Geological Survey (USGS) (https://earthexplorer.usgs.gov). The satellite scenes with low cloud and snow cover at the end of ablation season (September-November) were selected as these factors can complicate the identification of glacier outlines and lead to erroneous results (Bhambri et al, 2011a(Bhambri et al, ,b, 2012Pandey and Venkataraman, 2013;Shukla and Qadir, 2016;Chand et al, 2017;Paul et al, 2017).…”

“…Further, the subset of Corona image and other multispectral images covering the Milam glacier were co-registered with Sentinel 2 image (14 October 2017) using a total of ∼36 ground control points (GCPs) taken from stable geomorphic features (Hall et al, 2003;Pandey et al, 2011;Bhambri et al, 2012;Pandey and Venkataraman, 2013;Raj et al, 2017). It has been observed in previous studies that the corona images have significant distortions, which must be corrected before using them for glacier mapping and further analysis (Bolch et al, 2008;Bhambri et al, 2011bBhambri et al, , 2012Chand and Sharma, 2015;Chand et al, 2017). Thus, for precise co-registration with low error, the Corona images were co-registered with Sentinel 2 image using the two-steps approach proposed by Bhambri et al (2011bBhambri et al ( , 2012.…”

“…Thus, for precise co-registration with low error, the Corona images were co-registered with Sentinel 2 image using the two-steps approach proposed by Bhambri et al (2011bBhambri et al ( , 2012. In the first step, the projective transformation from Sentinel 2 to Corona image based on GCPs was done, followed by the spline adjustment (Bhambri et al, 2011a;Chand et al, 2017). The co-registration errors (RMS e ) for Corona (1968), Landsat 5 TM (1990), and Landsat 7 ETM+ (2001) were 1.8 (7.2 m), 0.5 (14.25 m), and 0.5 (7.5 m), respectively.…”

“…In addition, the terminus positions of some central Himalayan glaciers have been surveyed and mapped (plane-table) in the field since the early twentieth century [e.g., the Gangotri (Auden, 1937;Jangpangi, 1958); the Pindari (Cotter and Brown, 1907;Tewari and Jangpangi, 1962;Tewari, 1972); the Satopanth and Bhagirath (Jangpangi, 1958) and the Milam, Poting, Burphu, Pacchu, Shanunkalpa (Cotter and Brown, 1907;Jangpangi and Vohra, 1959;Jangpangi, 1975;Kumar et al, 1975;Shukla and Siddiqui, 2001)] (Bhambri and Bolch, 2009). These historical field-based plane-table maps of glacier terminus provide important information about their past extents, which can be compared with recent satellite images to assess the glacier recessions over a considerable time period (Bhambri and Bolch, 2009;Chand et al, 2017), for instance, Gangotri (Bhambri et al, 2012) and Satopanth and Bhagirath glaciers (Nainwal et al, 2016).…”

Reviews and syntheses: Anthropogenically breaking macro-ecospatial chains? – case review of HU Line

“…Large terrain elevation gradients affect atmospheric circulations, leading to spatially-varying precipitation richness (Zheng and Liou, 1986); heterogeneous temperature accumulations alter the growth phases of plants, resulting in regionally-different crop productivity and food supplies (Hou et al, 2014); some kinds of ecosystems are preferred by life, as they can provide more livable conditions for the species (Millennium Ecosystem Assessment, 2005); disasters tend to result in the transfers of livable residential areas (Li et al, 2014). A systematic analysis of the specific eco-effects caused by such geo-factors, which should play the dominant roles in causing and maintaining HU Line, was expanded by using the approach of integrative data analysis (Curran and Hussong, 2009;Chand et al, 2017) as follows.…”