Inter-annual surface ice velocities of the Panchi Nala glacier, western Himalaya: trends and controlling factors

doi:10.21203/rs.3.rs-3121630/v1

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Inter-annual surface ice velocities of the Panchi Nala glacier, western Himalaya: trends and controlling factors

https://doi.org/10.21203/rs.3.rs-3121630/v1

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Information on glacier velocity is imperative to understand glacier mass, ice volume, topography, surge events of the glacier and response to climate change. Present study investigates inter-annual surface ice velocity (SIV) of the Panchi Nala Glacier, western Himalaya to understand its dynamics. The SIV has been computed by the feature tracking technique using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) method applied on the multi-temporal Landsat (TM and OLI) and Sentinel − 2 MSI images acquired between 2000 and 2021. Results show that the mean velocity of the debris-covered tongue (4500–4800 m asl) of the Panchi Nala Glacier is 10.6 ± 5.6 m/y during the study period. Additionally, the highest average glacier velocity is 13.8 ± 4.6 m/y, whereas the lowest is 8.9 ± 2.8 m/y, respectively, observed in 2005 and 2015. Also, the 95% confidence interval of the mean annual velocity lies between 9.8 and 11.4 m/y during the entire study period. There is no significant trend in the velocity rather it is highly heterogeneous on the inter-annual scale. Further the influence of several factors namely slope, debris cover, altitude, annual average temperature and precipitation on SIV was also investigated. Results indicate that the annual heterogeneity in SIV is linked with the variation of summer precipitation. Statistically, a 100 mm increment of summer precipitation can reduce the velocity around 1.3 m/y. The main reason behind this is the Panchi Nala glacier is located in high-elevation (4500m to 5600 m asl) where the climate is much colder and during the summer precipitation, the lower temperatures cause the precipitation to take the form of snow, which freezes and accumulates on the glacier. This reduces the process of basal sliding leading to slow movement. Further, detailed investigations using high-resolution remote sensing images and field data along with additional parameters need to be carried out to elucidate the spatial SIV and comprehensive causes for inter-annual fluctuations.

Glacier surface ice velocity

Debris cover

Climate change

Western Himalayas

Panchi Nala glacier

The Himalayan glaciers constitute the largest mass of ice found outside of the polar regions, with an area and volume of ~ 40,000 km² (Azam et al., 2021) and 3500 km³(Azam et al., 2021), respectively. The Himalayan glaciers, which are also aptly known as the "water towers of the world," are the principal sources of perennial rivers like the Ganges, the Indus, and the Brahmaputra, and these rivers provide water for millions of people (Bajracharya et al., 2011; Bolch et al., 2012; Bhattacharya et al., 2016; Pradhan et al., 2021). Currently, the Himalayan glaciers are in a stage of depletion as a result of climate change (Anthwal et al., 2006; Bajracharya et al., 2008; Shrestha et al., 2011; Kaushik et al., 2022), manifested through negative mass balance, dimensional shrinkage, snowline up-shift and velocity reduction (Boltch et al., 2012; Farinotti et al., 2015; Zhou et al., 2018; King et al., 2019; Dehecq et al., 2019; Shukla and Garg, 2020; Hugonnet et al., 2021).

The velocity of a glacier is a crucial factor to comprehend its mass (Dehecq et al., 2019), ice volume (Sattar et al., 2019), surge activity (Quincey et al., 2011), topography (Sam et al., 2018), and its response to climate change (Garg et al., 2019; Zhao et al., 2020; Kaushik et al., 2022). To be more specific, decreasing glacier velocity denotes a reduction in ice mass, whereas increasing glacier velocity denotes an increase in ice mass within the glacier system (Heid and Kääb, 2012a). Reduced glacier velocity causes a noticeable shift in the glacier system because it can encourage the build-up of supraglacial debris-cover and the emergence of ice cliffs and supraglacial ponds (Sakai et al., 2002; Scherler et al., 2011; Miles et al., 2016; Kneib et al., 2021).

Velocity of the glaciers can be estimated in the field by utilizing Differential Global Navigation Satellite System (DGNSS) measurements to follow the position of stakes over time. However, this strategy can be time-consuming and expensive due to the difficult topography, harsh atmosphere, and remoteness of glaciated terrain (Willis, 1995; Watson and Quincey 2015; Dehecq et al 2015). Additionally, the entire glacier is rarely represented in field-based velocity studies since they typically measure point-based surface displacements with restricted geographical and temporal recordings (Taylor et al., 2021). Stake-based surface velocity measurements in the western and central Himalayas have only been the subject of a small number of investigations (Azam et al., 2014; Vincent et al., 2016). Given this, remote sensing technology provides a suitable alternative for determining surface velocities and offers the chance to obtain perfect glacier-wide spatial coverage, even in the most remote regions of the Himalayas (Scherler et al. 2008; Copland et al. 2011; Dehecq et al. 2015; 2019; Kumar et al. 2011; Saraswat et al. 2013; Paul et al. 2017).

A technology that shows promise for determining an object's movement over time is optical satellite data-based image correlation (Singh et al., 2018; Villarroel et al., 2018). The glacier surface velocity has been calculated using a variety of correlation methods, including cross-correlation in the spatial and frequency domains, sub-pixel phase correlation, and orientation correlation (Scambos et al., 1992; Rolstad et al., 1997; Herman et al., 2011). The scientific community has successfully used numerous satellite datasets ranging from low to medium resolution images like AWiFS, MODIS, and Landsat (MSS, TM, and OLI), to determine the displacement fields of glaciers (Scherler et al., 2008; Derkacheva et al., 2020; Kumar et al., 2020; Patel et al., 2021).

Recent studies have examined the glacier velocity in the Himalayas on a regional basis and revealed a consistent slowdown (Scherler et al., 2011; Sam et al., 2018; Dehecq et al., 2019). With the aid of 220 scenes from Landsat-7 panchromatic images taken between 1999 and 2000 and Sentinel-2 panchromatic images taken between 2017 and 2018, Zhou et al. (2021) examined 36,722 glaciers in the Himalayas and analysed the flow patterns of glaciers. According to the investigation, 32% of glaciers have accelerated, 24.5% have slowed down, and 43.5% have remained unchanged (Zhou et al., 2021). Literature suggests that, in comparison to the Western Himalayas, glaciers in the Eastern and Central Himalayas move more slowly (Bolch et al., 2012; Kaushik et al., 2022). Interestingly, western Himalayan glaciers accelerated during the final decade of the 20th century followed by a steady slow down (Scherler et al., 2011; Azam et al., 2014; Sam et al., 2018; Sahu and Gupta 2019; Garg et al., 2022), however, with intra-regional heterogeneity. The distribution of the Western Himalayas over a wide range of longitudes may be responsible for this notable divergence. Nevertheless, the long-term velocity of the glaciers in the Himalayan region has been analysed in restricted number of studies (Kaushik et al., 2022; Shukla and Garg 2019; Das et al., 2022; Patel et al., 2021; Singh et al., 2018; Shukla and Garg 2020; Yellala et al., 2019).

In the western Himalaya, Das et al. (2021) estimated glaciers velocity in the Chandra Bhaga basin over four-time frames (19992-94, 2000–2002, 2009–2011 and 2013–2019). They reported that the area of crevasses and slope shift was where clean glaciers displayed their highest velocity (> 80 m/y). Additionally, the study found that surface velocity near the front of the lake-terminating glaciers (32.3 m/y) was almost twice as high as the land-terminating glaciers (12.5 m/y). Earlier, Tiwari et al. (2014) analysed the Chhota Shigri glacier's surface velocity fluctuations between 2003 and 2009 and reported that the velocity fluctuates from about 20 m/y to about 40 m/y. Likewise, Garg et al. (2017) investigated the velocity of the Sakchum, Bara Shigri, and Chhota Shigri glaciers between 2003 and 2014 and found high heterogeneity in flow patterns. Yellala et al. (2019) estimated the annual and seasonal surface velocity of the Chhota Shigri and Bara Shigri glacier. Their findings revealed that the average SIV of the glaciers was 19.25m/y and 25.62m/y, respectively between 2016 and 2018. Patel et al. (2019) employed the particle image velocimetry (PIV) technology to calculate the Chhota Shigri glacier's surface velocity. According to their research, the SIV from 2006 to 2016 varied between 22.5 and 27 m/y. Based on data from more than two time periods, Sahu and Gupta (2019) assessed the velocities of four glaciers (Bara Shigri, Chhota Shigri, Gepang Gath, and Hamta) from 2000 to 2018 and they found that the annual surface ice velocity of the glaciers decreased by approximately 34% during the 18-year period.

Above discussion demonstrates that glacier velocity studies have always been conducted at the basin scale and mostly on the quinquennial or decadal scale. The knowledge about long-term inter-annual velocity on individual glacier and its drivers remains unconfirmed. In light of this, the current study concentrates on (a) estimating annual glacier velocity changes of 21st century (2000 to 2020) on one of the debris-covered glaciers of the western Himalaya and (b) comprehends the potential controls that affect the spatial and inter-annual velocity variations of the glacier.

The Panchi Nala glacier (32°43′ 21.49″N, 77°19′48.08″E) is situated in the Bhaga River basin of the Lahaul Spiti District, Himachal Pradesh, western Himalaya (Fig. 1). The glacier features clearly defined ablation and accumulation zones, with a total length of 4.99 km, and a surface area of 4.44 km² (as of 2021). The glacier terminates at 4536 metres above sea level (m asl) after descending from 5597 m asl. The debris mantle covers around 60% of the glacier's surface and comes in a range of particle sizes, from tiny silts to enormous boulders measuring several metres (Fig. 10). Near its snout, the Panchi Nala glacier has a proglacial lake.

The Indian summer monsoon during the summer (July-September) and the Northern Hemisphere mid-latitude westerlies during the winter (January-April) are the two atmospheric circulation systems that have an impact on the glaciers in the Lahaul-Spiti region (Bookhagen and Burbank, 2010; Mandal et al., 2014; Singh et al., 2020). The region's annual average temperature varied between 7 and 10.5°C between 1983 and 2011, according to statistics from the Snow and Avalanche Study Establishment (SASE) observatory, and its winter season snowfall (from November to April) ranged between 200 and 800 cm. (Shukla and Garg, 2019). From 1983 to 2011, the winter (November to April) snow fall in the area fell at a rate of 8.3 cm each year (Negi et al., 2013). On the other hand, throughout the same period (1993–2011), the mean annual temperature of the region scaled at a magnitude of + 0.07 degrees each year (Negi et al., 2013).

3.1 Dataset

The glacier velocity calculation has been done using Landsat-7, Landsat- 8 and Sentinel- 2, multi-temporal satellite data from 2000 to 2021. Landsat-Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) images, obtained from https://earthexplorer.usgs.gov/, were selected for their panchromatic band (15m) capabilities. Sentinel-2 MultiSpectral Instrument (MSI) data, acquired from the https://scihub.copernicus.eu/dhus/#/home, were used, specifically leveraging the 10 m near-infrared band.

Table 1

List of Landsat and Sentinel satellite data used to estimate glacier velocity. TM = thematic mapper, ETM + = enhanced thematic mapper, OLI = operational land imager, PAN = panchromatic, MSI = multi-spectral Instrument.
Pupose	Satellite name	Sensor	Spatial resolution (m)	Scene ID	Date of acquisition
SIV	Landsat − 7	ETM+	30, 15 PAN	LE07_L1TP_147037_20000828_20200918_02_T1	28-08-2000
	Landsat − 7	ETM+	30, 15 PAN	LE07_L1TP_147037_20020802_20200916_02_T1	02-08-2002
	Landsat − 7	ETM+	30, 15 PAN	LE07_L1TP_147037_20030922_20200916_02_T1	22-09-2003
	Landsat − 7	ETM+	30, 15 PAN	LE07_L1TP_147037_20040908_20200915_02_T1	08-09-2004
	Landsat − 7	ETM+	30, 15 PAN	LE07_L1TP_147037_20050826_20200914_02_T1	26-08-2005
	Landsat − 7	ETM+	30, 15 PAN	LE07_L1TP_147037_20060930_20200913_02_T1	30-09-2006
	Landsat − 7	ETM+	30, 15 PAN	LE07_L1TP_147037_20070816_20200913_02_T1	16-08-2007
	Landsat − 7	ETM+	30, 15 PAN	LE07_L1TP_147037_20080903_20200912_02_T1	03-09-2008
	Landsat − 8	OLI	30, 15 PAN	LE07_L1TP_147037_20140819_20200905_02_T1	28-09-2014
	Landsat − 8	OLI	30, 15 PAN	LC08_L1TP_147037_20150830_20200908_02_T1	30-08-2015
	Landsat − 8	OLI	30, 15 PAN	LC08_L1TP_147037_20161003_20200906_02_T1	03-10-2016
	Sentinel- 2A	MSI	10 NIR	S2A_OPER_MSI_L1C_TL_SGS__20161002T054530_20161002T104830_A006680_T43SGS_N02_04_01	02-10-2016
	Sentinel- 2B	MSI	10 NIR	L1C_T43SGS_A003277_20171022T053409	22-10-2017
	Sentinel- 2B	MSI	10 NIR	L1C_T43SGS_A007710_20180828T053642	28-08-2018
	Landsat − 8	OLI	30, 15 PAN	LC08_L1TP_147037_20180822_20200831_02_T1	22-08-2018
	Landsat − 8	OLI	30, 15 PAN	LC08_L1TP_147037_20190910_20200826_02_T1	10-09-2019
	Landsat − 8	OLI	30, 15 PAN	LC08_L1TP_147037_20200928_20201006_02_T1	28-09-2020
	Sentinel- 2B	MSI	10 NIR	L1C_T43SGS_A027415_20200921T053331	21-09-2020
	Sentinel- 2B	MSI	10 NIR	L1C_T43SGS_A032849_20211006T053218	06-10-2021
Elevation, Slope	SRTM DEM-v3		30		01-02-2000

The selection of satellite images focused on acquiring data during the ablation period (August, September, and October) with minimal snow and cloud cover. The study area encompassed a single scene of Landsat and Sentinel imagery, and detailed information on the specific satellite datasets can be found in Table 1. For topographic analysis, the study employed the Shuttle Radar Topographic Mission Digital Elevation Model Version-3 (SRTM DEM-v3), acquired from https://earthexplorer.usgs.gov/, as an elevation reference data. The SRTM DEM provided global coverage and served as a valuable resource. Furthermore, climate data, including temperature and precipitation, were derived from the Climate Research Unit Time Series 4.06 (CRU-TS 4.06) dataset. The gridded datasets, with a resolution of 0.5°*0.5°, offered comprehensive temporal coverage from 1901 to 2021, enabling the study to investigate long-term climate change and its impact on glaciers. The annual temperature and precipitation data were correlated with the annual glacier velocity to explore the interrelationship between climate factors and glacier dynamics.

3.2 Methodology

Because there are more measurable features in the ablation area than in the accumulation area, the glacier's velocity in the ablation area is generally more accurate (Quincey et al., 2009; Sun et al., 2017). In addition, a glacier's accumulation area frequently has steep topography and is extensively crevassed, which causes miscorrelation (Sun et al., 2017). Therefore, in order to determine the average yearly surface velocity with relatively higher accuracy, only the ablation area (stretched between elevations of 4500 m to 4800 m asl) has been taken into account in the current study. Over the years 2000 to 2021, a total of fourteen spatiotemporal average annual surface ice velocities are determined.

3.2.1 Glacier Velocity Estimation

The Co-registration of Optically Sensed Images and Correlation (COSI-Corr), an add-on module for the ENVI software, was used to estimate the glacier SIV (Leprince et al., 2007; Ayoub et al., 2009). The COSI-Corr can measure the horizontal displacement with a pixel resolution accuracy of 1/20 to 1/10 (Scherler et al., 2008; Tiwari et al., 2014). Surface displacement is evaluated by COSI-Corr utilising a configurable window size (initial and final). The final window size seeks finer detail while the initial window size is typically a huge window that maximises the correlation between pairs of images. East-West (EW), North-South (NS), and Signal-to-Noise Ratio (SNR) displacements are outputs of the common COSI-Corr algorithm (Bhushan et al., 2017). It is also known as the sub-pixel image correlation approach, and was used in the current work to obtain surface displacements from repetitive Landsat ETM+ (2000 to 2008), OLI (2014 to 2016 and 2018 to 2020), and Sentinel 2 MSI (2016 to 2021) images. The panchromatic (PAN 15 m) bands of the ETM + and OLI images and the NIR (10 m) band of the MSI were used for the correlation analysis.

A moving window of 32 × 32 pixels was used for correlation, and it was scaled down to a final size of 16 x 16 pixels. Between neighbouring correlations, there is a step size of 4 pixels for both the 15 m Pan and the 10 m MSI. The correlations resulted into NS, EW and SNR images. The SNR image is a measure of correlation accuracy. Here, any pixel with an SNR value of less than 0.9 is identified as being erroneous, and is consequently removed (Maanya et al., 2016). Then, an NLM (Non-Local Means) filter, with H (Noise parameter) set to 1.4 and a patch size of 5 × 5, was applied to remove erroneous values. Subsequently, the vector field is created using the NS and EW displacements. Finally, the SIVs were determined by taking the square root of the sum of the squares of the NS and EW displacements after the vector field had been determined to be adequate. The velocity pixels were all finally adjusted to 365 days (m/y). (Fig. 2)

3.2.2 Uncertainty Estimation

Inaccuracies in orthorectification, poor correlation brought on by snow and cloud cover, and low contrast in the image can all contribute to uncertainties in glacier velocity calculation using remotely sensed data (Heid & Kääb, 2012b; Bhushan et al., 2017). Images with a minimum amount of clouds and a seasonal snow cover were utilised to reduce the inaccuracies caused by scene features. Errors in orthorectification cannot be mitigated since Landsat and Sentinel image products are pre orthorectified (Bhushan et al., 2017). Nevertheless, Landsat images are acceptable for glaciological studies (Dehecq et al., 2015; Bhattacharya et al., 2021). Additionally, for the same path and set of imageries, inaccuracy resulting from modest horizontal changes is regarded as minimal in glacier velocity studies (Kääb et al., 2016; Paul et al., 2017). Finally, using the standard techniques, the entire uncertainty in the glacier velocity was determined over the stable terrain (Table 2). The displacements' mean and standard deviation over a stable area are added up to arrive at error quotient (Scherler and Strecker, 2012; Shukla and Garg, 2019).

3.3.3 Inter-annual Trend Analysis

According to Ali et al. (2019), the Mann-Kendell and Sen method is frequently used in trend analysis. The management of missing data, the need for few assumptions, and the independence of the data distribution are just a few benefits of these non-parametric methods (Kisi 2015; Öztopal and Sen 2017; Wu and Qian 2017; Ali et al., 2019).The current study's null (H₀) and alternative hypothesis (H_a) are described below:

H₀: There is no trend in the SIV in Panchi Nala glacier

H_a: There is a trend in the SIV in Panchi Nala glacier

3.3.4 Parameter subset selection and estimation of co-efficient

A crucial tool for anticipating a quantitative response is linear regression. For this study, in order to determine the association between the various morphological features as predictors (independent variables) and response (dependent variables), i.e., SIV, multiple linear regression is utilised. Eq. (1) can be used to represent the equation for multiple linear regression:

Y = β₀ + β₁X₁ + β₂X₂+ · · · + βpXp (1)

where Y is the dependent variable. X₁, X₂, and X_p are independent variables that the subset selection method can select. β₀, β₁, β₂, and β_p are the coefficients of the model.

In this work, adjusted R² has been chosen as a model accuracy measure in addition to R². Since R² only accounts for the training error, which can be eliminated by including additional independent variables or over fitting, adjusted R² is preferable to R² (James et al., 2013). The crucial test error is defined by the adjusted R², on the other hand. According to James et al. (2013), a model with a low-test error has a high adjusted R² value. The adjusted R² parameter also aids in removing unnecessary predictors. As a result, adjusting R² is essential to reduce model complexity. In the last section, the coefficients of the model have been computed using the minimum least square error. The values that minimise the sum of squares between the data and the model are the estimated model parameters according to the least-square method (Abdi, 2007).

The aim of the present study is to assess the annual SIV of the Panchi Nala Glacier over a 21-year period from 2000 to 2021. Unfortunately, due to the unavailability of suitable snow and cloud free images during a few years from 2009 to 2013, the SIV for these years could not be computed. Thus, spatio-temporal SIV variation for a period of 14 years could be determined (Fig. 3). During the study period, the glacier has moved at an average speed of 10.6 ± 5.6 m/y (Table 2). The highest average glacier velocity of 13.7 ± 4.6 m/y was noticed in 2005, while the lowest SIV of 8.9 ± 2.8 m/y was recorded in 2015 (Fig. 4 and Table 2). With a confidence level of 95%, the mean annual velocity ranged from 9.7 and 11.4 m/y for the time period of 2000-02 to 2020-21. A highly heterogeneous velocity pattern on the inter-annual scale is observed between 2000 and 2021 (Table 2). The movement rate of the clean ice part of the glacier was found to be higher than that of the debris-covered part, with SIV of 19.8 ± 3.8 m/y and 15.1 ± 3.5 m/y, respectively (Fig. 7). Additionally, the steeper parts (4900m to 5100m asl) of the glacier demonstrated higher movement rates, with 24.1 m/y in upper ablation zone, as compared to the gentle parts (4500m to 4800m asl) at 10.8 m/y (Fig. 3) located in the lower ablation zone of the glacier.

Table 2

Estimated average surface ice velocity (SIV) and uncertainty in velocity of the Panchi Nala Glacier for each image pair from 2000 to 2021. The uncertainties were computed over non-glaciated stable terrain.
Period	Sensor	Velocity Pair		Average SIV (m/y)	Uncertainty in velocity estimation (± m/y)
Period	Sensor	Pre	Post	Average SIV (m/y)	Uncertainty in velocity estimation (± m/y)
2000–2002	ETM+	28-08-2000	02-08-2002	9.2	6.3
2002–2003	ETM+	02-08-2002	22-09-2003	10.8	6.5
2003–2004	ETM+	22-09-2003	08-09-2004	12.2	5.2
2004–2005	ETM+	08-09-2004	26-08-2005	10.1	7.7
2005–2006	ETM+	26-08-2005	30-09-2006	13.8	4.6
2006–2007	ETM+	30-09-2006	16-08-2007	10.4	5.6
2007–2008	ETM+	16-08-2007	03-09-2008	11	6.6
2014–2015	OLI	28-09-2014	30-08-2015	9	3.5
2015–2016	OLI	30-08-2015	03-10-2016	8.9	2.8
2016–2017	MSI	02-10-2016	22-10-2017	9.2	7.4
2017–2018	MSI	22-10-2017	28-08-2018	11	4.4
2018–2019	OLI	22-08-2018	10-09-2019	9.6	6.3
2019–2020	OLI	10-09-2019	28-09-2020	10.7	4.2
2020–2021	MSI	21-09-2020	06-10-2021	12.4	6.9

4.1 Inter Annual Velocity and Climate Trend

The detailed chorological study reveals that the average SIV over 2000-02 to 2007-08 and 2014-15 to 2020-21, respectively, was 11.07 ± 6.06m/y and 10.13 ± 5.06 m/year. The Mann-Kendell test result demonstrates that the study's null hypothesis was maintained, indicating that there is no detectable SIV trends in the Panchi Nala Glacier (Table 3).

Table 3

Mann-Kendall test statics of surface ice velocity (SIV) and average annual temperature trend for the Panchi Nala Glacier between 2000 and 2021.The Kendall's tau value for SIV indicates a weak positive correlation. The high two-tailed p-value suggests that there is no significant trend in SIV at a significance level of 0.05. The Kendall's tau value for temperature also indicates a weak negative correlation. The two-tailed p-value is 0.165 indicate that there is no significant trend in temperature at a significance level of 0.05.
Mann-Kendall trend test parameters	SIV	Average annual temperature
Kendall's tau	0.033	-0.232
S	3	-44
Var(S)	333.667	950.000
p-value (Two-tailed)	0.913	0.165
alpha	0.05	0.05

The annual temperature and precipitation trend between 2000 and 2021 have been estimated using reanalysis dataset from CRU-TS 4.06. The Mann-Kendall test result demonstrates that, though statistical insignificant, there has been an increasing trend in the temperature (Table 3). According to Sen’s slope estimate, the mean annual temperature has increased with a magnitude of 0.007°C/y during 2000–2021 (Table 4). On the other side, the precipitation has showed a declining trend (1.35mm/y), though statistically insignificant. Previous studies have noticed strong linkages between increasing temperature, declining precipitation and glacier dynamics (Yao et al. 2004; Yang et al. 2014; Mir et al. 2018; Kaushik et al. 2019; de Kok et al., 2020).

Table 4

Temperature statistics of Sen’s Slope. The analysis of Sen's slope for temperature reveals a calculated slope value of -0.295. The lower and upper bounds (95% confidence interval) for the slope are − 0.650 and 0.123, respectively. The intercept is estimated to be 620.128, with lower and upper bounds of 200.635 and 977.260, respectively.
Parameter	Value	Lower bound (95%)	Upper bound (95%)
Slope	-0.295	-0.650	0.123
Intercept	620.128	200.635	977.260

The results of the multiple linear regression demonstrate a substantial negative correlation between the Panchi Nala Glacier's velocity and the amount of summer precipitation (Fig. 5). The temperature, debris cover and velocity of the glacier, however, do not significantly correlate (Fig. 9). In Table 5, the P value of summer precipitation is less than 0.1 which indicates that the null (Null hypothesis = no relationship exists between the parameter and velocity) hypothesis rejected in favor of the alternative hypothesis. Therefore, we can conclude that summer precipitation is correlated with velocity (p = 0.06). Also, negative coefficient (-0.012941) value shows that the summer precipitation is inversely proportional to the velocity of the glacier. This implies that if summer precipitation increases, the velocity of the glacier decreases. Numerically, the glacier velocity can reduce by about 1.3 m/y for every 100 mm increase in summer precipitation.

Table 5

Detail statistics of the significant parameter which is controlling the inter-annual SIV in Panchi Nala glacier. The statistical analysis reveals that the intercept has an estimated coefficient of 15.550140, with a standard error of 2.448954. The corresponding t-value is 6.350, indicating a significant impact. Additionally, the parameter "Summer Precipitation" shows a coefficient of -0.012941, with a standard error of 0.06335. The associated t-value is -2.043, suggesting a significant relationship, albeit at a p-value of 0.0637. This detailed statistic provides valuable insights into the summer precipitation influence on surface ice velocity of the glacier.
Coefficients	Estimate	Std. Error	t value	Pr(>\|t\|)
Intercept	15.550140	2.448954	6.350	3.67
Summer Precipitation	-0.012941	0.06335	-2.043	0.0637

5.1 Comparison of SIV with previous studies

Rigorous comparison shows that the estimated SIVs of the Panchi Nala Glacier are in line with the previous estimates (Table 6). The findings of the present study reveal a high degree of heterogeneity in the spatio-temporal pattern of SIV over the glacier. This outcome aligns with previous studies that have documented varying flow rates of glaciers in the Western Himalayas across different time periods (Table 6) (Scherler et al., 2011; Azam et al., 2014; Sam et al., 2018; Sahu and Gupta, 2019; Garg et al., 2022). Only one study (Shukla and Garg, 2019) has previously attempted to examine the velocity of the Panchi Nala glacier using remotely sensed data that is too on the decadal scale. Shukla and Garg (2019) assessed the SIV of the glacier between 1992 and 2016. Their findings indicated that the SIV values in 2000/02 and 2015/16 was 11.55 ± 3.2 and 9.7 ± 4.18 m/y, respectively. However, the current study noticed an average SIV of 9.2 ± 6.2 m/y and 8.9 ± 2.8 m/y for the same periods i.e. (2000/02 and 2015/16), which seems comparable with the findings of Shukla and Garg (2019). The slight differences in SIV can be attributed to the fact that the earlier study only calculated SIV along the central flow line, whereas the present study considered all the pixels within a specific altitude band of the glacier.

Velocity of a glacier largely depends on its size and other topographical factors such as slope, altitude and orientation (Quincey et al., 2009; Benn et al., 2012; Garg et al., 2017; Shukla and Garg, 2019). The available velocity estimations in the western Himalaya is for the glaciers having different sizes and topographic characteristics restricting direct comparison of their movements. Nevertheless, the average velocity of Chhota Shigri, Sakchum, Patsio and Pensilungpa glacier, which are comparable in size to the Panchi Nala glacier, ranges between 9.76 m/y to 11.43 m/y which is in line with the observed velocity magnitude in western Himalaya.

It is also evident from the Table 6 that there have been a clear trend of velocity reduction in the region. Notably, all the glaciers included in the Table 6 have experienced velocity slowdown to some extent. On an average the velocity has been reduced by 34.26% over the Chandra-Bhaga basin glaciers. Specifically for the Panchi Nala Glacier, Shukla and Garg (2019) reported a velocity reduction of 33.87% between 1993 and 2016. For post-2000 period only, the reported slowdown is 26.41% during 2000–2016 (Shukla and Garg, 2019). If we consider the velocity values for the corresponding years only (i.e., 2000 and 2016), the current study found a slowdown of only 3.3% which again differ from the previous estimates (Shukla and Garg, 2019). This departure can again be attributed to methodological difference. It is notable that present study found a large variation in inter-annual average velocity with standard deviation of 1.34 m/y. It indicates that the decadal estimates are less likely representative of accelerating or decelerating glacier velocity. Rather, evaluation of inter-annual or near annual velocity can provide a more reliable picture about the glacier dynamics. In this regard, the findings of present study forms a baseline to understand the state of the Panchi Nala Glacier.

Table 6

Overview of some significant glacier velocity studies across the Western Himalaya.
No.	Study Area/Glacier/ Basin	Data Type	Method/Tool/ Database	Period	Observations (average velocity) m/y	Remarks	Reference
1	Chhota Shigri glacier, Western Himalaya	Optical (TM and ETM+)	Particle Image Velocimetry (PIV) Software	2006-/07	21.0 to 36.0	Velocity decrease by 52.38–5.56% during 2006 to 2010.	Patel et al. (2017)
1	Chhota Shigri glacier, Western Himalaya	Optical (TM and ETM+)	Particle Image Velocimetry (PIV) Software	2009/10	10.0 to 38.0	Velocity decrease by 52.38–5.56% during 2006 to 2010.	Patel et al. (2017)
2	Kangriz glacier, Western Himalaya	Optical (Corona, TM, ETM + and OLI)	COSI-Corr	1993/94	31.2 ± 5.8	SIV decrease by 10.58 or 33.9% during 1993–2018	Garg et al. (2022)
2	Kangriz glacier, Western Himalaya	Optical (Corona, TM, ETM + and OLI)	COSI-Corr	2017/18	20.3 ± 1.7	SIV decrease by 10.58 or 33.9% during 1993–2018	Garg et al. (2022)
3	Chandra basin, Western Himalaya	Optical (TM, ETM + and OLI)	Feature tracking	2003/04	23.51	SIV slowdown by 14.50% (2003–2014).	Garg et al., (2017)
3	Chandra basin, Western Himalaya	Optical (TM, ETM + and OLI)	Feature tracking	2013/14	20.10	SIV slowdown by 14.50% (2003–2014).	Garg et al., (2017)
4	Chandra basin, Western Himalaya	SAR (C-band of Sentinel-1)	Cross-correlation (offset tracking)	2014/15	5.87	Velocity reduced by 17.78%.	Yellala et al., (2019)
				2015/16	14.49
				2016/17	7.26
				2017/18	4.85
5	Panchi Nala glacier, Western Himalaya	Optical (Corona KH-4B and MSI, TM,ETM + and OLI)	COSI-corr	1992/93	12.85	Average SIV decrease by 33.87% during 1992–2016.	Shukla and Garg (2019)
				2000/02	11.55
				2015/16	8.50
6	Chhota Shigri glacier, Western Himalaya	SAR(ASTER) and DGPS measurements	Semiautomatic COSI-Corr	2003/04	30.5	SIV reduced by 11.48% from 2003 to 2010.	Tiwari et al., (2014)
				2004/05	28.5
				2005/06	27.5
				2006/07	28
				2007/08	30.5
				2008/09	26
				2009/10	27
7	Chhota Shigri glacier, Western Himalaya	DGPS measurements	The annual stake displacements	2003/04	27.58	Velocity decreased by 38.07%.	Azam et al., (2012)
7	Chhota Shigri glacier, Western Himalaya	DGPS measurements	The annual stake displacements	2004/05	17.08	Velocity decreased by 38.07%.	Azam et al., (2012)
8	Miyar Glacier, Western Himalaya	Optical (TM, ETM + and OLI)	COSI-corr	1992/93	23.5	Speed-up of velocity by 37.17% (1992–2019).	Das et al., (2022)
				2009/10	29.3
				2010/11	16.5
				2017/18	37.4
9	Pensilungpa glacier, Western Himalaya	Optical (TM, ETM + and OLI)	COSI-corr	1993/94	13.94 ± 3.97	Average SIV decreased by 48.28% (1993–2017)	Garg et al., (2022)
				1999/00	9.33 ± 2.76
				2016/17	7.63 ± 3.87
10	Chandra basin, Western Himalaya	Optical (ETM + and OLI)	COSI-corr	1999/00	23.91	Overall annual SIV decrease 37.43% during 1999 to 2017	Sahu and Gupta (2019)
				2000/01	18.25
				2001/02	15.55
				2013/14	15.68
				2014/15	15.04
				2015/16	13.17
				2016/17	14.96
11	Western Himalaya	Optical (ETM+)	Feature-trackingusing JPL auto-RIFT software	2000	21.6	SIV decreases 3.43 ± 0.45%/y (2000–2017) and overall decreases by 54.88%	Dehecq et al., (2018)
11	Western Himalaya	Optical (ETM+)	Feature-trackingusing JPL auto-RIFT software	2016	9.8		Dehecq et al., (2018)
12	Chhota Shigri glacier, Western Himalaya	SAR(ALOSPALSAR)	Differential Interferometry (DInSAR)	2015	~ 5.82	Single-period analysis conducted	Nela et al., (2019)
13	Patseo glacier, Western Himalaya	Optical (LISS IV & OLI)	COSI-corr	2013/14	~ 5.47	Single-period analysis conducted	Singh et al (2018)
14	Baspa basin Western Himalaya	Optical (OLI)	GoLIVE	2013–2017	25.93	No temporal comparison available	Sam et al. (2018)
15	Zaskar basin, Western Himalaya	Optical (ETM + and OLI)	COSI-corr	2013/14	~ 90 ± 5.58	Single-period analysis conducted	Bhushan et al., (2018)
16	Chandrabhaga Basin, Western Himalaya	Optical (TM, ETM + and OLI)	COSI-corr	1992–2019	24.47	No temporal comparison available	Das, (2021)
17	Chandra-Bhaga sub-basins, Western Himalaya	SAR (ALOS-2/PALSAR-2)	Differential Interferometry (DInSAR) and DGPS	2015	~ 7.3	Single-period analysis conducted	Singh et al., (2020)

5.2 Control of surface topography and Climate on glacier velocity

The surface gradient, supraglacial debris and altitudinal characteristics of the glacier may exert considerable influence on glacier movement and its spatial variation (Quincey et al., 2009; Benn et al., 2012; Shukla and Garg 2020). Here, a systematic assessment has been made to understand the influence of surface slope and debris cover on the spatio-temporal variation of SIV of the Panchi Nala Glacier.

5.2.1 Impact of slope on glacier velocity

Basal sliding is one of the significant processes that control the glacier SIV, which is in turn a function of slope, rock-type, and availability of water at the base of the glacier (Iken & Bindschadler 1986; Gudmundsson 2003; Cuffey & Paterson 2010; Salerno et al., 2017). Among these, slope is the most crucial factor that influences SIV of the glacier (Salerno et al., 2017). Since the slope of Panchi Nala Glacier tongue is gentle, we included SIV and slope of accumulation area too in order to understand their mutual relationship. Notably, only reliable measurements of SIV over accumulation area have been included in the analysis which are, though spatially scattered, can be indicative of movement rate in this zone. In our analysis, we find that the mean slopes of the accumulation (~ 5100 m to 5600 m) and ablation (~ 4500 m to 5100 m) zones of the Panchi Nala Glacier are 41.14° and 16.46°, respectively (Fig. 6a). Through our observations, we have identified that the glacier velocities are lowest near the terminus and increases with distance from the snout and towards higher elevation (Fig. 6a). Our findings demonstrate a strong and positive correlation (r = 0.74; p = 0.000043) between slope and velocity (Fig. 6b) in the ablation area (up to 5100m) of the glacier. Specifically, we noticed that higher velocities are associated with steeper slopes, while gentler slopes tend to have lower velocities (Fig. 6a). This suggests that surface slope significantly regulates the spatial variation in surface ice velocity of the Panchi Nala Glacier.

5.2.2 Debris cover change and impact on glacier velocity

Glacier surface area can be categorized as debris-covered and clean ice parts. In the Himalayas, lower ablation zones of most of the glaciers have debris cover over them (Scherler et al., 2018; Herried and Pelliciotti, 2020), which significantly affects melting rates as a function of its thickness (Nicholson and Benn, 2006; Zhang et al., 2012; Nicholson et al., 2018; Patel et al. 2018). The debris is transported by ice and affects ice-flow (Banerjee and Shankar 2013; Tiwari et al., 2014). Our analysis shows that clean ice parts of the glacier have consistently higher velocity compared to debris-covered areas in all observed years (Fig. 7b). Previous studies have found that debris-covered parts of glaciers typically have lower velocities and larger stagnant parts near their snouts (Quincey et al. 2009; Scherler et al., 2011; Benn et al., 2012; Tiwari et al., 2014; Garg et al., 2022). In the present study, we estimated that the Panchi Nala Glacier has experienced a 19.74% increase in total debris-covered area from 2000 to 2021 with significant inter-annual variation (Fig. 7a). The inter-annual variation in debris cover is likely because of steep slope at the transition zone between debris-covered and debris-free parts of the glacier which frequently allows to descend clean-ice parts downwards (Fig. 10). In the year 2021, the debris cover was mapped to be 61.01% of the glacier area (Fig. 7a). This suggests that the presence of debris cover drags down the glacier movement (Patel et al. 2018; Kaushik et al., 2022; Shukla and Garg 2020; Das et al., 2022). Overall, our comparison of velocity distribution between debris-covered and clean ice areas provides a better understanding of the influence of debris cover on glacier movement (Banerjee and Shankar 2013; Tiwari et al., 2014).

5.2.3 Influence of altitudinal characteristics on glacier velocity

To examine the impact of altitudinal characteristics on SIV, we conducted a correlation analysis between the altitude and SIV of the Panchi Nala Glacier. We observed a strong positive correlation (r = 0.83; p = 0.0000008) between elevation and SIV (Fig. 8a) in the ablation zone (~ 4500 to 5100m). This indicates that as elevation increases in the ablation zone, the velocity of the glacier also tends to increase. This finding aligns with previous studies that have highlighted the role of elevation in influencing glacier flow dynamics (Kumar et al. 2011; Farinotti et al., 2011; Sam et al. 2018). Therefore, the positive correlation suggests that factors such as ice melt, supraglacial drainage, and basal sliding may be contributing to the accelerated ice movement in the upper ablation zone (Hoffman et al., 2011; Smith et al., 2021).

Though the present study mainly focuses on the SIV of ablation zone (between 4500–5100 m asl), a correlation between altitude and SIV of accumulation zone was also analysed to get an insight into their relationship. Results reveal that, the accumulation zone (~ 5100 to 5600m) exhibited, though poor but, a negative correlation (r = -0.31; p = 0.22) between elevation and velocity (Fig. 8b). This implies that as elevation increases in the accumulation zone, the velocity of the glacier tends to decrease. Previous research suggests that this phenomenon can be attributed to factors such as decreasing ice thickness and lower temperatures at higher elevations, which promotes ice accumulation and reduces ice movement (Cuffey & Paterson, 2010; Braithwaite, 2019).

5.2.4 Glacier velocity variation in relation to Climate Change

Climate change has generally interacted with glaciers, resulting in melting, which represents a particular sensitive process (Wagnon et al., 2001). Here, the velocity results of the Panchi Nala Glacier were analysed with respect to temperature and precipitation variations. The last century experienced a crucial warming, ranging from 0.6°C to 1.6°C in the Himalayan region as well as the global temperature increased by 0.74°C (Shrestha et al., 2011; IPCC, 2007). In the present study, the temperature and precipitation data from CRU TS 4.06 was assessed for the period of 2000–2020 that almost overlaps for the velocity estimation period (2000–2021). The average temperature for 2000–2021 ranges from the − 1.33°C to -2.78°C while the average annual precipitation ranges from 253.7 cm to 472.7 mm. In order to determine the interrelationship between the independent climatic variables (temperature and precipitation) and the dependent variable SIV (Fig. 9), we conducted a multiple linear regression analysis. The findings reveal that there is no significant relationship between temperature and the SIV of the glacier, suggesting that temperature does not exert a considerable influence on the SIV of the Panchi Nala Glacier. However, a noteworthy outcome emerged when examining the impact of summer precipitation on the SIV of the glacier (Fig. 5). We found a significant negative correlation (r = − 0.5; p = 0.06) between the SIV and the amount of summer precipitation which indicates that the SIV is sensitive to precipitation variations. Specifically, it suggests that as summer precipitation increases, the SIV for the Panchi Nala Glacier tends to decrease within the same year.

The inter-annual SIV magnitude of the Panchi Nala Glacier shows a diverse tendency. However, due to greater unpredictability, there is no discernible trend in the SIV. The SIV of the glacier has a larger inter-annual variability due to periodic fluctuations in summer precipitation. Precipitation has an inverse relationship with SIV, meaning that when precipitation increases, SIV for the Panchi Nala Glacier decreases. The main reason behind this is that the Panchi Nala Glacier is located in high-elevations (4500m to 5600 m asl) where the prevailing temperature is expected to be colder and during the summer precipitation, the lower temperatures cause the precipitation to take the form of snow, which freezes and accumulates on the glacier. This reduces the process of basal sliding and consequent SIV (Quincey et al., 2009; Cuffey and Paterson, 2010; Benn and Evans, 2014; Basnett et al., 2013).

This study presents the spatial and temporal SIV variations of the debris-covered Panchi Nala Glacier during the last two decades (2000–2021) and explores the influence of climatic (precipitation and temperature) and non-climatic (surface slope, debris cover and elevation) factors on glacier movement. Glacier SIVs were derived from the cross-correlation algorithm applied on Landsat and Sentinel images using COSI-Corr open-source software. Results reveal that the Panchi Nala Glacier had a heterogeneous flow pattern during the observation period (2000–2021), with a mean velocity of 10.60 ± 5.56 m/y. The maximum and minimum velocities were observed to be 13.77 ± 4.64 m/y and 8.92 ± 2.78 m/y for the period 2005/06 and 2015/16, respectively. Spatially, the average velocity is low near snout region and keeps increasing with increasing distance from the snout, confirming a typical valley-type glacier movement pattern. Findings also reveal that the SIV is largely controlled by the presence of debris cover wherein, on an average, the clean ice parts moved with an average rate of 19.79 ± 3.78 m/y as compared to debris-covered parts (15.13 ± 3.49 m/y). A notable control of slope on SIV is also evident by a positive correlation (r = 0.74) between them implying higher SIV over steep slopes and low SIV over gentle slopes. Quantitatively, the movement rate over steep glacier parts was found to be almost twice (~ 24.1 m/y) as compared to the gentle parts (~ 10.77 m/y). To understand the inter-annual trend of the SIV, the non-parametric Mann-Kendell test has been applied. The result of the test shows that there exists no discernible trend in SIV of the glacier between 2000 and 2021. Furthermore, the results of the multiple linear regression analysis show that the temporal velocity of lacier is significantly correlated with summer precipitation. However, there is no significant relationship among temperature, debris cover and the velocity of the glacier. It was found that an increase of 100mm in summer precipitation leads to a reduction of around 1.3 m/y in the velocity. Probably the main reason behind this is the high elevation of the glacier where the temperature remains much colder. During the summer precipitation, the lower temperatures cause the precipitation to take the form of snow, which freezes and accumulates on the glacier. This reduces the process of basal sliding. The current study introduces multiple linear regression models to understand the reason for inter-annual fluctuations of SIV in the glacier. Further, detailed investigations with additional parameters need to be carried out to elucidate the comprehensive causes for inter-annual fluctuations in surface velocity. In this perspective, future research may be directed towards higher temporal and spatial scale remote sensing-based investigations and validation of glacier surface velocity by field surveys, to better understand the glacier dynamics and its implications on the surface morphology evolution.

Acknowledgement

PKG thanks the Director, G.B. Pant National Institute of Himalayan Environment for providing the necessary facilities to carry out the work. AS thanks the Secretary, Ministry of Earth Sciences, Government of India for facilitating the study. All the authors deeply acknowledge the US Geological Survey for providing satellite data (Landsat, SRTM) free of cost.

Author Contribution

Purushottam Kumar Garg and Aparna Shukla designed the study and provided guidance on the conceptualization, research content, manuscript structure and figure preparation. Mohit Prajapati and Purushottam Kumar Garg conducted the data collection, analysis and calculations, led the writing of the manuscript and designed the figures. Supratim Guha performed all statistical calculations also editing and review of manuscript. Iram Ali helped in interpretation of the statistics. The results were thoroughly discussed among all the authors, and each author made significant contributions to the final form of the article.

Data availability statement

All data generated or analyzed during this study are included in this published article

Competing interests: The authors declare no competing interests.

Ethical responsibilities of Authors statement: All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors.

Funding declaration: The author(s) received no financial support for the research, authorship, and/or publication of this article

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Inter-annual surface ice velocities of the Panchi Nala glacier, western Himalaya: trends and controlling factors

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Abstract

Figures

1. Introduction

2. Study Area

3. Data and methods

3.1 Dataset

3.2 Methodology

3.2.1 Glacier Velocity Estimation

3.2.2 Uncertainty Estimation

3.3.3 Inter-annual Trend Analysis

3.3.4 Parameter subset selection and estimation of co-efficient

4. Results

4.1 Inter Annual Velocity and Climate Trend

5. Discussion

5.1 Comparison of SIV with previous studies

5.2 Control of surface topography and Climate on glacier velocity

5.2.1 Impact of slope on glacier velocity

5.2.2 Debris cover change and impact on glacier velocity

5.2.3 Influence of altitudinal characteristics on glacier velocity

5.2.4 Glacier velocity variation in relation to Climate Change

6. Conclusion

Declarations

Acknowledgement

Author Contribution

Data availability statement

References

Additional Declarations

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