On periodic growth and shrinkage of glaciers in the Warwan sub-basin, western Himalaya, between 1990 and 2020

Knowledge about glacier extent, dynamics, and characteristics are important for climate change attribution and prediction. Understanding on long-term dynamics and glacier inventory is crucial, particularly for the melt-dominated and latitudinally-diverse western Himalayan glacier basins. In this study, a temporal inventory is prepared for Warwan-sub basin (WSB), utilizing satellite imageries since the 1993 (Landsat TM: 1993; ETM+: 2001, 2008; OLI: 2020) and elevation model (SRTM DEM: 2000). The base inventory was generated for the year 2001 and systematically adjusted to the glacier situations in 1993, 2008, and 2020. Results indicate that in the year 2001, WSB in the western Himalaya included 84 glaciers (> 0.02 km2) covering an area of 187.9 ± 5.8 km2. The mapping (2001) further revealed a supraglacial debris cover of 15% of the glacierized area (28.2 ± 0.9 km2). Overall, the debris cover increased by 6% between 1993 and 2020. Temporal analyses clearly suggest a period of gain in the glacierized area (2001–2008) interspersed by the two phases of decline (1993–2001 and 2008–2020). Results specify a stronger decline in the glacierized area during 1993 to 2001 (197.03 ± 6.1 to 187.9 ± 5.8 km2) than between 2008 and 2020 (188.4 ± 5.9 to 182.8 ± 5.66 km2). Remarkably, the glacierized area increased from 187.9 ± 5.8 to 188.4 ± 5.8 km2 during 2001 to 2008. In view of widespread recession of regional glaciers, the gain in the area between 2001 and 2008 represents a peculiar characteristic of WSB that needs further detailed investigation. Further analyses suggest that low-altitude, east-facing, debris-free, steep-sloped, and small glaciers experienced greater loss in the area than large, debris-covered, north-facing, gently sloped, and high-altitude glaciers. Overall, the study at the sub-basin scale reveals inherent glacier dynamics with periodic increase and decrease in the glacierized area and a notable influence of non-climatic factors in regulating spatial heterogeneity and the rate of glacier changes.


Introduction
The cryosphere is one of the important components of energy and water cycle of the Earth (Immerzeel et al., 2010). Mountain glaciers are of particular importance because they act as sensitive indicators of climatic variability and provide fresh water to large population (Benn & Evans, 2010;Bolch et al., 2012;Immerzeel et al., 2010). Furthermore, the mountain glaciers (though comprise only 4% of the cryosphere; Berthier et al., 2004) hold immense importance with respect to (a) sea level rise (Gardner et al., 2013), (b) climate change assessment in areas lacking appropriate meteorological data (Stocker, 2014), (c) diagnosing future water availability Immerzeel et al., 2010), and (d) glacial hazards (Benn et al., 2012;Quincey et al., 2009).
The Himalayan glaciers form one of the largest concentrations of ice outside the polar region (Benn et al., 2012;Pfeffer et al., 2014). As per national glacial inventory (Space Applications Centre, ISRO), there are 34,919 glaciers, distributed over the three basins: the Indus (western), the Ganga (central), and the Brahmaputra (eastern) (https:// www. isro. gov. in/ earth-obser vation/ snow-and-glaci er). Large number of studies have reported that Himalayan glaciers (except Karakoram region) have been depleting since Little Ice Age (LIA ~ 1850s) (Bhambri & Bolch, 2009;Chand et al., 2017;Gardelle et al., 2013;Shekhar et al., 2010). Snout monitoring records of about ~ 100 glaciers in the Himalaya reveal notable retreat which ranges between ~ 2 and ~ 60 m/year during the last 3-5 decades Gardelle et al., 2013;Kulkarni & Karyakarte, 2014;Scherler et al., 2011). Proxy-based and geodetic assessments have also revealed an acceleration in the mass loss since the last few decades (Maurer et al., 2019;Shean et al., 2020;Shekhar et al., 2010;Zhou et al., 2018). Thus, considering the dynamic nature of glaciers, accurate information of their extent and temporal variation is of utmost importance for different aspects of glaciological research including prediction and modeling (Paul et al., 2013). There are several inventories for the Himalayan region such as Randolph glacier inventory (RGI; Pfeffer et al., 2014), Glacier Area Mapping for Discharge from the Asian Mountains (GAMDAM; Nuimura et al., 2015), and International Centre for Integrated Mountain Development (ICIMOD; Bajracharya & Shrestha, 2011). Most of these inventories are static for a single time period, having large uncertainties and do not assess temporal changes in the glaciers (Chand & Sharma, 2015;Das & Sharma, 2019). There are also some efforts made by Indian national institutions (e.g., Geological Survey of India) to complete a national glacier inventory for the Indian Himalayan Region (IHR) (Raina & Srivastava, 2008;Sangewar & Shukla, 2009). These inventories dealing with IHR are in tabular form having topographic information (Frey et al., 2012). For these inventories, glacier outlines were obtained from topographic maps and aerial photographs (satellite imagery used if available) (Frey et al., 2012). However, as these outlines are not available in a digital form, it is difficult to assess the accuracy and quality of these datasets (Chand & Sharma, 2015;Frey et al., 2012). Moreover, an assessment of temporal glacier variations based on these existing inventories is difficult as they differ regarding mapping method, format, purpose, and data source (Racoviteanu et al., 2009). Therefore, area change computation using these datasets might be an artifact and not the real change (Bolch et al., 2010;Frey et al., 2012;Racoviteanu et al., 2009). Previously, attempts have been made in the IHR to compile temporal glacier inventories of appropriate time span and accuracy, but these are limited to few watersheds/basins (Bhambri et al., 2011;Chand & Sharma, 2015;Das & Sharma, 2019). Nevertheless, these temporal inventories are useful in inferring glacier number, area, topographic characteristics, and magnitude and trend of glacial changes. However, for a reliable climate prediction and attribution, there is a need of accurate sub-basin scale inventories.
Present study focuses on the Warwan sub-basin (WSB) in the western Himalaya which is a highly glacierized basin. The lobe-shaped snouts of the major glaciers in this basin ( Fig. 1) indicate towards an advancement in the past. However, detailed information about this anomalous behavior is not available. Glaciers are generally located in harsh environmental conditions (Paul et al., 2013). Therefore, field-based mapping of glaciers and determining their various topographic characteristics is challenging and not suitable on the regional scale (Bhardwaj et al., 2016). Satellite data, in this context, provide an alternate and suitable platform to investigate spatiotemporal glacier variations on regular basis (Bhambri & Bolch, 2009;Racoviteanu et al., 2009). The use of multispectral remote sensing data for glacier studies is highly advantageous over traditional methods as the synoptic coverage facilitates a large number of glaciers to be viewed periodically (Bahuguna et al., 2014;Bhardwaj et al., 2016;Brahmbhatt et al., 2012;Racoviteanu et al., 2009). In view of the above, the present study utilizes time-series remote sensing data to assess characteristics and temporal glacier evolution. The objectives of the present study are as follows: • To prepare a comprehensive temporal glacier inventory (on the decadal scale) in the WSB for the past 30 years (~ 1990 to 2020). • To assess the trend and magnitude of glacier changes in the WSB.
• To assess the influence of various glacier characteristics (size, slope, aspect, altitude) and debris cover on the glacier changes in the WSB.

Study region
Extent of the study region The current study focuses on WSB in the Chenab Basin, Western Himalaya. The sub-basin is located in Kishtwar district of Jammu and Kashmir Union Territory (UT) (Fig. 1). The WSB (33°29′55″N to 33°45′09″N; 75°48′46″E to 76°12′02″E) covers a total area of 554.5 km 2 . The highest peak (6559 m above sea level (m asl)) of this basin is Bharanzar peak (popularly known as the "Sickle Moon Peak") and the lowest altitude is 1682 m asl. there are 84 glaciers in the basin covering about 187.9 ± 5.8 km 2 of the area. The largest glacier in this basin covers 49.6 ± 1.5 km 2 . The majority of the large sized glaciers are debris-covered, whereas the smaller glaciers are debris-free. In total, ~ 15% of the glacierized area is debris covered. The stream originating from WSB is locally known as Kiar Nal which ultimately joins the Chenab River; which in turn is a tributary of the Indus river system.

Climate pattern in study region
The climate in the western Himalaya is regulated by the western disturbance and the Indian Summer Monsoon alternatively, and the western disturbance is the major source of precipitation . The study area is situated at the end of monsoon conveyor-belt and, hence, it is sensitive to strength of the Indian Summer Monsoon (Frey et al., 2012;Li et al., 2018). The temperature patterns in the region are as follows: the maximum temperature ranges between 6.5 and 20.8 °C, whereas the minimum temperature ranges between − 20 and 8.6 °C (Joshi et al., 2012). It is reported that in the western Himalaya, the seasonal mean, maximum, and minimum temperatures increased by ∼2, 2.8, and 1 °C, respectively (Shekhar et al., 2010). Bhutiyani (2018) also reported a significant rate of warming during the winter season (1.4 °C/100 years) than the monsoon temperature (0.6 °C/100 years) with annual warming rate (1.1 °C/100 years) higher than the global rate (about 0.7 °C/100 years) during 1866-2012. The average annual rainfall for Kishtwar district (where the study site is situated) for the year 2020 is ~ 1245.17 mm. In the year 2020, the rainfall statics of Kishtwar district for the monsoon months are approximately 82.13 mm, 118.33 mm, 116.87 mm, and 64.775 mm for June, July, August, and September respectively (https:// imdpu ne. gov. in/ hydro logy/ rainf all% 20var iabil ity% 20page/ jk_ final. pdf) which clearly shows the maximum rainfall in the month of July. Shekhar et al. (2010) noticed a decrease in total seasonal snowfall of 280 cm over the entire western Himalaya between 1988 and 2008. The snowfall decreased by 280, 80, 440, and ~ 0 cm over the Pir Panjal, Shamshawari, Greater Himalaya, and Karakoram ranges, respectively (Shekhar et al., 2010).

Datasets
The USGS archive (https:// earth explo rer. usgs. gov/) was explored to find suitable Landsat scenes for the study region. Multi-temporal orthorectified level-1 Landsat images (Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM +), and Operational Land Imager (OLI)) were utilized to map the glaciers and quantify glacier changes. The seasonal snow and cloud cover greatly impacts the accurate demarcation of glacier outlines . Therefore, satellite scenes of the ablation season (July-October) having minimum cloud cover were used in this study to obtain the accurate results. We selected six appropriate scenes, in which 4 scenes were taken from Landsat 4-5 TM sensor, acquired in 1993, 1994, 2000, 2008; one scene from Landsat 7 ETM + sensor, acquired in 2001; and one scene from Landsat 8 OLI/TIRS sensor, acquired in 2020 (Table 1). The 2001 ETM + image was taken as a base and congruence between it, and the rest of the images was ensured. All the processed images were projected in the Universal Transverse Mercator (UTM) coordinate system. Further, the SRTM DEM version 3 having spatial resolution of 30 m (1 arc second) was also acquired from https:// earth explo rer. usgs. gov/ and used here to derive topographic information of the glaciers. In mountainous region, SRTM DEM provides a vertical accuracy of ± 10 m (Rodríguez et al., 2006).

Clean ice mapping
The recommendations for the compilation of glacier inventories Racoviteanu et al., 2009) and methodologies from previous papers (Bolch et al., 2010;Frey et al., 2012) were followed to map the glaciers. We applied a semi-automated approach to map the clean ice parts of the glaciers which involves rationing of the Red and Shortwave-Infrared (SWIR) spectral bands (Frey et al., 2012;. Threshold values varying between 1.7 and 2.2 Page 5 of 16 390 Vol.: (0123456789) were used to separate snow/ice from rest of the objects and, consequently, binary images were generated. Band rationing is a robust method of glacier mapping which is also effective in shadow regions (Paul et al., 2002). A median filter (3 × 3 kernel size) was applied on binary images to eliminate isolated pixels and to fill small voids (Chand & Sharma, 2015;Frey et al., 2012;Racoviteanu et al., 2009), and then, the binary images were converted into vector polygons. All the polygons derived from the ratio method were carefully checked and improved in a Geographic Information System (GIS) environment. Finally, the minimum size threshold of the glaciers to be registered for the inventory was set as 0.02 km 2 (Chand & Sharma, 2015;Frey et al., 2012).
Frequent orographic clouds on the southern edge of the Himalayan range often cover glaciers partly or entirely making it challenging to demarcate glaciers boundary accurately (Frey et al., 2012). Thus, in some cases, we had to use an image from another relatable month/year to map the missing parts. To delineate upper parts of glaciers, where perennial snow is prevalent, GLIMS glacier boundary (http:// www. glims. org/ MapsA ndDocs/ guides. html) were used as a guide. DEM data was also used to separate individual glacier (Chand & Sharma, 2015). The tributaries connected to main trunk through ice were considered as a single glacier entity .

Mapping of debris-covered part of the glaciers
Delineation of debris-covered glacier parts is a timeconsuming and error prone task in generation of glacier inventory (Bhambri et al., 2011;Paul et al., 2009). Spectral signature of supraglacial debris (i.e. debris-covered ice) does not differentiate it with periglacial debris or valley rocks (Bhambri et al., 2011;Paul et al., 2009). Therefore, we preferred manual digitization to map the debris-covered parts of glaciers. The presence of certain characteristic features such as supraglacial ice cliffs and ponds, sign of movements (identified on temporal images), break in slope, and emergence of melt water streams have facilitated demarcation of debris-covered glacier margins (Chand & Sharma, 2015;Garg et al., 2019;. Further, since the clean and debris-covered glacier parts were mapped using separate methodologies, separate polygons for both the parts were readily available (Frey et al., 2012;Garg et al., 2019;. This facilitated in estimation of total debris cover for all the time periods as well as percentage debris cover of the total glacierized area and on individual glacier (Frey et al., 2012;Garg et al., 2017).
Following the recommendations for compilation of glacier inventory ), a baseline inventory was generated for the WSB based on the ETM + image of 2001 in the first step. In case of higher seasonal snow cover and shadows, we used TM image of 2000 as additional information. Then, the glacier outlines were manually adjusted to glacier situation in 1993, 2008, and 2020 based on the respective images (Table 1) (Bolch et al., 2010;. Besides, various topographic characteristics of glaciers viz. size, surface slope, aspect, and altitude (mean, minimum, maximum, and range) were determined using SRTM DEM and glacier outlines of 2001 in a GIS environment.

Results
In the present study, a temporal glacier inventory is generated for the WSB for the years 1993, 2001, 2008, and 2020 and the analysis of glacier area changes is carried out. The fluctuation of the glacier area changes has also been analyzed on a decadal scale, for which the total time period has been subdivided into three time frames : 1993-2000/2001 (8 years), 2001-2008 (7 years), and 2008-2020 (12 years).

Glacier inventory and statistics
The WSB covers an area of 554.6 km 2 , out of which 187.9 ± 5.8 km 2 or 33.9% (year 2001) of the area is glacierized. According to the statistics of our base layer (2001), WSB contains 84 glaciers covering an area of 187.9 ± 5.8 km 2 . Out of these 84 glaciers, 7 glaciers are covered with debris at their ablation zone. In total, the extent of supraglacial debris in the basin is 28.2 ± 0.9 km 2 (15% of the total glacierized area).
Within the sub-basin, the size range of glaciers varies from 0.04 km 2 to 49.6 km 2 . Considering this, the glaciers have been categorized as small-(< 1 km 2 ), medium-(1-7 km 2 ), and large-(> 7 km 2 ) sized glaciers (Fig. 2). Out of these categories, the small-sized glaciers have the highest count (65 or 77.4%), but they contribute much less to the glacierized area of the basin (17.8 ± 0.6 km 2 (9.49%) (Fig. 2). The overall elevation of the glaciers of this subbasin ranges from 3394 to 6559 m asl, with mean elevation being 4976 m asl (Fig. 3a), whereas the elevation ranges depending on size class are 4030-6126 m asl for small-(mean 4858), 3394-6247 m asl for medium-(mean 4866), and 3520-6559 m asl for large-(mean 4794.6) sized glaciers (Fig. 3c). The aspect analysis shows that 47 (56%) glaciers are oriented towards northward aspect (N/NW/NE) and 34 (41%) glaciers are facing south (S/SW/SE). In contrast, only 2 glaciers are facing west and only one glacier is facing in the east direction (Fig. 3b). Furthermore, the mean slope of the glaciers ranges from 13.64 to 43.66° (Fig. 3d), with an average of 26.16°. The glacier statistics for other time frames (1993, 2008, and 2020) are summarized in Supplementary  Table S1. It is notable that most of the glacier characteristics viz. total number, number of debris-covered glaciers, mean elevation change, and mean slope showed low changes in different years (Supplementary Table S1) and the major changes were seen in glacierized area and debris cover which are discussed in "Area changes" and "Debris cover area change."

Area changes
We observed a notable loss in the glacierized area of WSB since the 1993 (Table 2). During the period from 1993 to 2020, total glacier area reduced from 197.03 ± 6.1 km 2 to 182.8 ± 5.8 km 2 , indicating an overall deglaciation of 14.3 ± 0.4 km 2 (0.3 ± 0.1%/ year) ( Table 2). The percentage loss in the area of individual glacier ranges from 1.4 to 78.4% during this period. Fortunately, no glacier disappeared due to area loss. The analysis of decadal (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001) changes shows that glacier area declined from 197.03 ± 6.1 km 2 to 187.9 ± 5.8 km 2 , with a total loss of 9.14 ± 0.3 km 2 (0.6 ± 0.4%/year) ( Table 2). Interestingly, between 2001 and 2008, an increase in glacier area of 0.04 ± 0.4%/year has been observed where the total glacier area increased from 187.9 ± 5.8 to 188.4 ± 5.8 km 2 , indicating a net gain of 0.5 ± 0.02 km 2 (0.04 ± 0.4%/year) (Table 2; Fig. 4). Out of 84, 33 glaciers showed an area gain. Interestingly, the glaciers that showed are characterized by higher mean slope (5036 m asl) and a steep average slope (28.3°). This area gain is morphologically visible through advancement of large glaciers and their lobe shape of their snout in the basin (Fig. 4). In view of widespread recession of Himalayan glacier since the onset of twenty-first century, the area gain during 2001-2008 represents peculiar characteristics of the WSB. Nevertheless, during 2008-2020, glacier area again reduced from 188.4 ± 5.9 km 2 to 182.8 ± 5.7 km 2 exhibiting a loss of 5.6 ± 0.2 km 2 (0.3 ± 0.3%/ year) (Table 2). Thus, the assessment shows that the rate of glacier recession has been highly fluctuating on the decadal scale (Table 2; Fig. 4). The intraregional heterogeneity also prevailed in the sub-basin.

Debris cover area change
As estimated for the year 2001, total debris covered area in the basin is estimated to be 28.2 ± 0.9 km 2 (15% of the total glacierized area) ( Table 2). A temporal observation (Fig. 5) reveals that debris cover increased by 2 ± 0.1 km 2 (2.4%) during 1993 to 2020 at a rate of 0.2 ± 0.1%/year (Table 2). We also observed decadal scale fluctuations in the debris-covered area (Fig. 5).

Glacier inventory and comparative evaluation of area changes
According to the statistics, there are 84 glaciers in the WSB, covering 187.9 ± 5.8 km 2 of the area in the year 2001. We compared our results with RGI (RGIv6.0) (Pfeffer et al., 2014) and GAMDAM inventories (Nuimura et al., 2015). Results indicate that though the RGI inventory reported almost similar glacier count (85 glaciers), it underestimated the total area by 7.9% (173.1 km 2 ). On the other hand, the GAM-DAM inventory though reported an area (184.3 km 2 ) which is similar to our inventory (with deviation of only 1.9%), it overestimated the glacier count (109 glaciers). The observed variations can be attributed to (a) misinterpretation of debris-free and debriscovered glaciers, (b) temporal differences in the terms of acquired images and mapping period, and (c) differences in classification of glacier area/boundary (Bhambri & Bolch, 2009;Das & Sharma, 2019;Garg et al., 2017). In this study, an overall deglaciation rate of the glaciers in the WSB is estimated to be 0.27 ± 0.2%/ year, during 1993-2020. Studies on other basins of the western Himalaya estimated lower deglaciation rates (0.1 ± 0.1%/year (1971-2010/13) in the Ravi basin (Chand & Sharma, 2015), 0.17 ± 0. 01%/year (1971 to 2016) in the Jankar Chhu watershed (Das & Sharma, 2019), and 0. 13%/year (1971-2017) in the Suru sub-basin )) compared to this study. In contrast, the results derived by Brahmbhatt et al. (2017) collectively for the Warwan and Bhut basins (western Himalaya) show a higher area loss rate (0.44%/year from 1962 to 2011).
The decadal rate of deglaciation in WSB is calculated to be 0.58 ± 0.7%/year and 0.25 ± 0.4%/year, from 1993 to 2001 and 2008 to 2020, respectively, whereas the glaciers slightly gained area at a rate of 0.037 ± 0.4%/year from 2001 to 2008. This peculiar gain in the area could be a result of less negative mass balance condition observed during 1990s in the western Himalaya (Mukherjee et al., 2018;Zhou et al., 2018). Azam et al. (2012) and Vincent et al. (2013) investigated field observations from the Chhota Shigri Glacier in the western Himalaya between 1988 and 2010 and found a positive mass balance (± 0.09 m w.e./year) during 1988-1999 and a highly negative mass balance (− 0.44 m w.e./year) during 1999-2010. It may be possible that these mass gain or positive mass balance conditions during 1990s were not limited  (2023) 195:390 to the Chhota Shigri Glacier and were widespread in the western Himalaya. Notably, glacier shrinkage and growth are determined by its mass balance (Srivastava et al., 2022). Glaciers tend to adjust their geometry for establishing an equilibrium with the ensuing mass balance regime. However, there may exist a time lag from a few years to centuries (based on glacier size) between the changing mass balance and its corresponding adjustment in glacier geometry (Cuffey & Paterson, 2010;Jóhannesson et al., 1989;Mehta et al., 2014). Therefore, the observed slight area gain in the WSB may be a result of prevailing positive mass balance regime during 1990s which reflected with time lag of a decade. The topographical settings of the glaciers with area gain is also conducive for an advancement as these have higher mean slope which likely promotes higher mass accumulation (Quincey et al., 2009) and steep slope which likely promote comparatively higher velocity (Salerno et al., 2017;). Further, pre-2001and post-2001 deglaciation rates were noted as 0.58 ± 0.7%/year and 0.14 ± 0.09%/year, respectively. Our results are in line with the long-term results obtained by Brahmbhatt et al. (2017) for Warwan and Bhut basins during 1962 to 2011, which shows a reduction in deglaciation rate during post-2001 (0.13%/year) as compared to pre-2001 (0.28%/year). Das and Sharma (2019) examined decadal area changes for 41 glaciers of the Jankar Chuu watershed. Their results reveal increasing deglaciation rates (0.1 ± 0.1%/year, 0.14 ± 0.3%/year, and 0.2 ± 0.1%/year for the periods 1971 to 1989, 1989 to 2000, and 2000 to 2016, respectively). Decadal deglaciation rates of 54 mapped glaciers of the Ravi basin (Chand & Sharma, 2015) are 0.2 ± 0.2%/year, 0.2 ± 0.4%/year, and 0.2 ± 0.4%/year for the periods 1971 to 1989, 1989 to 2002, and 2002 to 2010/2013, respectively. Thus, the above comparisons show that the rate of glacier changes is highly heterogeneous in the western Himalaya. This could be possibly related to the debris cover and various topographic factors (Brahmbhatt et al., 2017;Garg et al., 2017;Shukla & Qadir, 2016). The influence of these factors is described in the following section.
Influence of debris cover and topography on glacier changes

Debris cover variability and its influence
Assessment of supraglacial debris cover is important as it exerts strong influences on dynamics and ablation processes of the glaciers (Benn et al., 2012;Nicholson & Benn, 2006;Östrem 1959;Patel et al., 2018). Debris is generally added to the glacier system through rockfalls and small landslides from adjacent mountainsides, avalanches, dust blown from exposed moraines, thrusting from the bed, or solifluction from ice-cored moraines (Evatt et al., 2015;Miles et al., 2020). If the debris transfer mechanism of the glaciers is not efficient, it allows plentiful debris to accumulate on the ablation zone (Shroder et al., 2000). In the WSB, 15% of the glacierized area is covered by the debris. As per Scherler et al. (2011), glaciers having steep accumulation zone (slope > 25°) and gentle ablation zone (slope < 8°) likely accumulate more debris cover. In present study, we analyzed slope characteristics of debris-covered glaciers which reveals that the average slope of over their clean parts is steep (22.6°), while the average slope of only debris-covered parts is gentle (8.7°). Such slope settings can largely be attributed for debris accumulation on debris-covered glaciers. Also, previous studies reported a negative correlation between glacier area and debris cover (Ali et al., 2017;Shukla & Qadir, 2016) which indicates receding glaciers become debris-covered glaciers more rapidly.
In the present study, we also noticed a negative correlation (r = − 0.50; p > 0.05) between glacier area and debris cover. The only deviation from the trend was seen in the year 2001 which is possibly because of huge area (and corresponding debris) loss over debriscovered glacier tongues (Fig. 4). Nevertheless, the debris cover has exerted considerable influence on glacier variation. The overall deglaciation rates of clean glaciers were almost four times higher (0.60 ± 0.1%/ year) than the debris-covered glaciers (0.14 ± 0.1%/ year) during the study period . It reveals a strong control of debris cover on ablation and overall dimensional changes. The debris cover has also an influence at the decadal scale glacier area changes.

Elevation
The altitudinal characteristics of glaciers affect mass input and ablation pattern (Bhambri & Bolch, 2009;Brahmbhatt et al., 2017;DeBeer & Sharp, 2009;Garg et al., 2017;Kulkarni et al., 2007;Quincey et al., 2009). According to our study, the mean elevation of glaciers of WSB is 4976.5 m asl. During 1993-2020, the glaciers situated at higher elevations (> 4976.5 m asl) experienced lesser rates of deglaciation (0.26 ± 0.1%/ year) as compared to the glaciers situated at lower elevations (< 4976.5 m asl) (0.28 ± 0.1%/year). In general, the glaciers reaching to higher altitudes are subjected to more mass input and likely experience low temperature which leads to sustaining their mass and results in comparatively less area loss, whereas glacier reaching low altitude are subjected to warm temperature which leads to high ablation and consequently higher area loss (Brahmbhatt et al., 2017;Garg et al., 2017;Pandey & Venkataraman, 2013;Quincey et al., 2009;Tangborn et al., 1990). Similar inference were drawn by Brahmbhatt et al. (2017), Das and Sharma (2019), Pandey and Venkataraman (2013), and  in their study on Warwan and Bhut basins, Jankar Chhu Watershed, Chandra basin, and Suru subbasin in western Himalaya, respectively.

Aspect
Glacier aspect determines the amount and duration of solar radiation received by them, and windward and leeward slope effect on precipitation (DeBeer & Sharp, 2009;Furbish & Andrews, 1984;Garg et al., 2017; Page 13 of 16 390 Vol.: (0123456789) Jiskoot et al., 2009). In the WSB, most of the glaciers either oriented towards north quadrant or south quadrant and only two glaciers deviating with this trend are facing other directions. Nevertheless, the south facing glaciers retreated at a higher rate (0.4 ± 0.1%/ year) as compared to the glaciers facing towards north (0.2 ± 0.1%/year). This is because the south oriented glaciers are exposed to a longer duration of solar radiation as compared to the north oriented glaciers and, therefore, are prone to higher area loss (Bhambri et al., 2011;DeBeer & Sharp, 2009;Garg et al., 2017;Jiskoot et al., 2009). Similar studies on other Himalayan basins (Bhambri et al., 2011;Das & Sharma, 2019;Garg et al., 2017;Pandey & Venkataraman, 2013; reveal that the glaciers having southward aspect experience more deglaciation as compared to the northward facing glaciers. The number of glaciers facing towards east and west is too minimal to conclude and compare their recession stats with the majority of glaciers facing towards north and south.

Conclusion
Present study provides a comprehensive glacier inventory and multi-temporal glacier change for the WSB of western Himalaya from 1990 to 2020. The following major inferences can be drawn from the present study: • The WSB contains 84 glaciers (> 0.02 km 2 ) in 2001 covering an area of 187.9 ± 5.8 km 2 out of which 15% is covered by debris. The mean elevation and mean slope of the glaciers are 4976.5 m asl and 26.16°, respectively, and the glaciers mainly have northward (N/NE/NW) and southward (S/SE/SW) aspects. • The glacier area decreased from 197.03 ± 6.1 km 2 to 182.8 ± 5.7 km 2 between 1993 and 2020, indicating deglaciation of 0.3 ± 0.1%/year. On decadal scale, the glaciers lost 9.14 ± 0.3 km 2 (0.6 ± 0.4%/ year) area during 1993-2001, gained 0.5 ± 0.02 km 2 (0.04 ± 0.4%/year) area during 2001-2008, and again lost 5.6 ± 0.2 km 2 (0.3 ± 0.3%/year) area during 2008-2020. This reveals periodic area loss and gain in the WSB. • The recession rate of clean ice glaciers is almost 4 times higher (0.6 ± 0.1%/year) than that of debris-covered glaciers (0.14 ± 0.1%/year) during 1993-2020, which indicates a strong influence of debris cover on the deglaciation of glaciers. • The debris cover change was also highly fluctuating. Overall, the debris cover increased at a rate of 0.2 ± 0.1%/year during 1993-2020. However, on the decadal scale, debris-covered area decreased by 1.5 ± 0.4%/year, and 0.03 ± 0.3%/ year during 1993-2001 and 2008-2020, respectively, whereas increased by 2.9 ± 0.4%/year during 2001-2008. • During 1993 to 2020, the small glaciers (< 1 km 2 ) deglaciated at much higher rate (0.85 ± 0.8%/year) as compared to the large glaciers (0.14 ± 0.12%/ year). The glaciers located at higher altitudes (> 4976 m asl) deglaciated at a lower rate (0.5 ± 0.1%/year) as compared to the glaciers located at lower altitudes (0.9 ± 0.1%/year). The deglaciation rate of south facing glaciers was twice (0.4 ± 0.1%/year) than north facing glaciers (0.2 ± 0.1%/year), indicating the influence of solar radiation received by the glaciers facing in different direction. • Reflecting the important role of slopes in the accumulation as well as area loss of glaciers, the glaciers having steep slopes deglaciated at very higher rate (0.7 ± 0.1%/year), as compared to the gently sloping glaciers (0.21 ± 0.1%/year).
It is noble that, although the climate is the prime driver of the glacier changes, non-climate factors may considerably modify the glacier response to climate. The present study, thus, investigates the influence of non-climatic factors viz. glacier size, debris cover, elevation, slope, and aspect on the response of glaciers in the Warwan sub-basin of western Himalaya to the prevailing climate. The response of the glaciers reveals a periodic fluctuating pattern in which the glaciers retreated during 1993-2001, advanced during 2001-2008, and again retreated during 2008-2020, whereas overall response during 1993-2020 reveals the recession of glaciers of WSB. The uncommon response of the glaciers during 2001-2008 needs to be studied in more detail with the combination of climatic factors to learn the factors which supported the advancement of glaciers during this particular period.