Snowline Altitude Research Articles

Leveraging Machine Learning and Google Earth Engine for Snowline Altitude Analysis: Insights from the Parbati Basin, India

Leveraging Machine Learning and Google Earth Engine for Snowline Altitude Analysis: Insights from the Parbati Basin, India

Summer snowline altitude gradients in Western Norway are influenced by maritime climate

ABSTRACT Mountain snowline dynamics are relatively underreported with few studies exploring spatial snowline dynamics. Whilst clear regional-scale relationships between snowline location and temperature exist in European mountains, recent research at higher latitudes reports no response to climate change. In maritime mountains, snowlines occupy complex environmental gradients. Using timeseries of satellite data from Landsat missions 5–8 (151 images between 1984 and 2021), we explored sub-regional summer snowline dynamics across the maritime-continental climate gradient in the Western Norwegian mountains. We characterize spatio-temporal snowline altitude dynamics and investigate the climate factors altering snowline patterns. Summer snowline altitudes were found to increase inland at around double the rate of the 0°C summer isotherm. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) land component of the fifth generation of European Reanalysis (ERA5-Land), showed a potential ‘maritime-mountain’ effect with coastal orographic snowfall and cloud cover-induced surface solar downwelling radiation amplifying maritime-continental snowline altitude gradients alongside surface atmospheric temperature. This was replicated in the Canadian Rocky Mountains. Between 1984 and 2021, we found spatial summer snowline gradients in Norway decreased and propose multiple climate forcings are responsible, potentially masking links between snowlines and climate change. Although non-significant, the data also suggest regional summer snowline altitudes increased. This study demonstrates the complex spatial heterogeneity in snow-climate relationships and highlights how long-term snow dynamics can be queried using fine-grain (Landsat) resolution satellite data. We share our approach through a Google Earth Engine web-app that rapidly executes spatial snowline analyses for global mountain regions via a graphical user interface.

Determining the cooling magnitude in the east of China during the Last Glacial Maximum using a degree-day model

There is a great controversy regarding the cooling magnitude and the existence of ancient glaciers during the Last Glacial Maximum (LGM) in the east of China. The cooling magnitude during the LGM can be seen as a basis for determining whether glaciers developed during the Quaternary. In this study, we chose 9 mountain areas where there are long-term meteorological records as the research sites. And there are different glacier-climate conditions at these 9 sites. The degree-day model was conducted to estimate the snow mass and the snowmelt at the altitude of the bottom of ancient cirques (snow line) based on the mean multiple-year-day temperature and mean multiple-year-day precipitation. The results showed that: (1) The altitude of the modern snow line of Tianshan Mountains was similar to the result estimated by the degree-day model, which indicated this model can be used to determine the altitude of ancient cirques, and it can also be used to calculate the cooling magnitude required to meet a condition for cirques to develop at a given altitude. (2) For the sites where ancient cirques existed in the monsoon zone, the cooling magnitude during the LGM was 6.4 °C, 6.4 °C, and 8.0 °C in Garze, Taibi Mountain, and Yu Mountain, respectively. For the site where ancient cirques existed in the non-monsoon zone, the cooling magnitude during the LGM was 3.3 °C in Tianshan Mountains. The mean cooling magnitude during the LGM was 5.9 °C in China. (3) A much greater cooling magnitude was required to develop cirques during the LGM than the average cooling magnitude of China in Huanggangliang, Wutai Mountain, Tai Mountain, Shenlongjia, and Lu Mountain. (4) The cooling magnitude was underestimated by the TraCE-21ka model compared with this study. The differences between the results from the TraCE-21ka model and this study were approximately 3.3 °C, 2.9 °C, and 5.7 °C for Garze, Taibai, and Yu Mountain, respectively, averaging 4.0 °C. (5) There was no sufficient evidence showing that the cooling magnitude during the LGM decreased with latitude.

Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

Studying the dynamics of snowline altitude at the end of the melting season (SLA-EMS) is beneficial in predicting future trends of glaciers and non-seasonal snow cover and in comprehending regional and global climate change. This study investigates the spatiotemporal variation characteristics of SLA-EMS in nine glacier areas of the Himalayas, utilizing Landsat images from 1991 to 2022. The potential correlations between SLA-EMS, alterations in temperature, and variations in precipitation across the Himalayas region glacier are also being analyzed. The results obtained are summarized below: (1) the Landsat-extracted SLA-EMS exhibits a strong agreement with the minimum snow coverage at the end of the melting season derived from Sentinel-2, achieving an overall accuracy (OA) of 92.6% and a kappa coefficient of 0.85. The SLA-EMS can be accurately obtained by using this model. (2) In the last 30 years, the SLA-EMS in the study areas showed an upward trend, with the rising rate ranging from 0.4 m·a−1 to 9.4 m·a−1. Among them, the SLA-EMS of Longbasaba rose fastest, and that of Namunani rose slowest. (3) The SLA-EMS in different regions of the Himalayas in a W-E direction have different sensitivity to precipitation and temperature. However, almost all of them show a positive correlation with temperature and a negative correlation with precipitation.

Short-term glacier area changes, glacier geometry dependence, and regional climatic variations forcing, King George Island, Antarctica.

This study investigates the transient snowline (TSL) altitude for summer 2020, as well as glacial area loss in King George Island Icefields since 1988 using Sentinel-1 and 2 and Landsat Thematic Mapper (TM) imagery. Trends and anomalies in atmospheric temperature, U-wind, and V-wind were examined using ERA5 solutions. Results show the wet-snow zone corresponds to values of ≤ -13dB, and 44.3% of the glacial area is located above the TSL (≥ 300 m). Glacial area for 2020 is 999.95 km², and losses in the period represent 104.9 km² (error <1%) - a retreat of 3.17 km² / year. Glaciers in Keller Peninsula and Bellingshausen Dome lost the most area (28% and 17%, respectively) and did not have a TSL in 2020; followed by Warszawa (15%), Kraków (13%), and Eastern (10%), where the TSL was verified. Percentage area loss values increased with decreases in dimensions, area above TSL, and maximum elevation. Calving glaciers with ice-flow toward deeper and steeper submarine sectors (Bransfield Strait) exhibited greater glacier variations. The trend in warming atmospheric temperature was greater in the Bransfield Strait than in the Drake Passage. TSL and retreat difference between glaciers were influenced by climatic and ocean input, as well as multiple environmental factors.

Surface Albedo and Snowline Altitude Estimation Using Optical Satellite Imagery and In Situ Measurements in Muz Taw Glacier, Sawir Mountains

Glacier surface albedo strongly affects glacier mass balance by controlling the glacier surface energy budget. As an indicator of the equilibrium line altitude (ELA), the glacier snowline altitude (SLA) at the end of the melt season can reflect variations in the glacier mass balance. Therefore, it is extremely crucial to investigate the changes of glacier surface albedo and glacier SLA for calculating and evaluating glacier mass loss. In this study, from 2011 to 2021, the surface albedo of the Muz Taw Glacier was derived from Landsat images with a spatial resolution of 30 m and from the Moderate Resolution Imaging Spectroradiometer albedo products (MOD10A1) with a temporal resolution of 1 day, which was verified through the albedo measured by the Automatic Weather Station (AWS) installed in the glacier. Moreover, the glacier SLA was determined based on the variation in the surface albedo, with the altitude change along the glacier main flowline derived from the Landsat image at the end of the melt season. The correlation coefficient of &gt;0.7, with a risk of error lower than 5%, between the surface albedo retrieved from remote sensing images and the in situ measurement data indicated that the method of deriving the glacier surface albedo by the remote sensing method was reliable. The annual average albedo showed a slight upward trend (0.24%) from 2011 to 2021. A unimodal seasonal variation in albedo was demonstrated, with the downward trend from January to August and the upward trend from August to December. The spatial distribution of the albedo was not entirely dependent on altitude due to the dramatic effects of the topography and glacier surface conditions. The average SLA was 3446 m a.s.l., with a variation of 160 m from 2011 to 2021. The correlation analysis between the glacier SLA and annual mean temperature/annual precipitation demonstrated that the variations of the average SLA on the Muz Taw Glacier was primarily affected by the air temperature. This study improved our understanding of the ablation process and mechanism of the Muz Taw Glacier.

Disaggregating geodetic glacier mass balance to annual scale using remote-sensing proxies

AbstractDecadal-scale, high-resolution geodetic measurements of glacier thinning have transformed our understanding of glacier response to climate change. Annual glacier mass balance can be estimated using remote-sensing proxies like snow-line altitude. These methods require field data for calibration, which are not available for most glaciers. Here we propose a method that combines multiple remotely-sensed proxies to obtain robust estimates of the annual glacier-wide balance using only remotely-sensed decadal-scale geodetic mass balance for calibration. The method is tested on Chhota Shigri, Argentière and Saint-Sorlin glaciers in the Himalaya and the Alps between 2001 and 2020, using four remotely-sensed proxies – the snow-line altitude, the minimum summer albedo over the glacier and two statistics of normalised difference snow index over the off-glacier area around the ablation zone. The reconstructed mass balance compares favourably with the corresponding glaciological field data (correlation coefficient 0.81 − 0.90, p &lt; 0.001; root mean squared error 0.38 − 0.43 m w.e. a−1). The method presented may be useful to study interannual variability in mass balance on glaciers where no field data are available.

Positive mass budgets of high-altitude and debris-covered fragmented tributary glaciers in Gangotri Glacier System, Himalaya

Glacier-wide mass balances (MBs) of the Gangotri, Chaturangi, Raktavaran, Meru, and Gangotri Glacier System are reconstructed with a temperature-index (T-index) model using bias-corrected ERA5 data at a daily temporal resolution over 1979–2020. The model output is calibrated against available geodetic MB for Gangotri Glacier System and validated with satellite-derived snow line altitudes (SLAs) for Gangotri Glacier. Gangotri and Meru glaciers show mean mass wastage of –0.88 ± 0.31 m w. e. a‒ˡ (meter water equivalent per year) and ‒0.17 ± 0.29 m w. e. a‒ˡ, respectively whereas the mass budgets of fragmented tributary Chaturangi and Raktavaran glaciers are positive with the mean values of 0.49 ± 0.17 m w. e. a‒ˡ and 0.62 ± 0.15 m w. e. a‒ˡ, respectively over 1979–2020. Gangotri Glacier’s tongue is covered by thick debris having several supra-glacial lakes and ice cliffs (considered as melting hotspots); therefore, despite the presence of thick debris, we assume the melting over this area as of a clean glacier. The whole Gangotri Glacier System shows a moderate wastage of ‒0.27 ± 0.25 m w. e. a‒ˡ. The positive MBs of the Raktavaran and Chaturangi glaciers are due to their high area-elevation distribution and heavily debris-covered tongues. The positive MBs on these fragmented tributary glaciers are due to non-climatic topographic reasons and should not be misunderstood as climate change deniers or compared with Karakoram Anomaly. Modelled MBs are most sensitive to the threshold temperature for melt. The altitudinal MB sensitivities to all model parameters become negligible above 6,200 m a.s.l.

Mapping Samudra Tapu glacier: A holistic approach utilizing radar and optical remote sensing data for glacier radar facies mapping and velocity estimation

Himalayan glaciers have shown more sensitivity and visible changes to the climate change and global warming in the last 150 years. The highly rugged topography and inaccessible remote areas makes satellite images as the most appropriate source of information retrieval. We performed remote sensing based glacier change study for Samudra Tapu glacier, located in the Chandra basin of North-West Himalaya. In the present study, the capabilities of both optical and microwave remote sensing data was analysed in glacier change study in terms of its coverage, shift in equilibrium line altitude (ELA) and surface velocity over a period from 2000 to 2021. Multi Sensor (RISAT-1, Sentinel-1) time series of C-band SAR data along with a object oriented classification technique were used to identify different glacier facies such as percolation facies, icefalls, bare ice facies, refreeze snow and supraglacial debris. These classified maps were also used to detect the snow line and firn line along with ELA, aided with elevation information from digital elevation model (DEM). It was identified that more than 50 % of the total glacier area still lies into accumulation region. Further, we estimated the glacier surface velocity using Differential Interferometric Synthetic Aperture Radar (DInSAR) technique using European Remote Sensing Satellite (ERS-1/2) tandem data of 1996. High value of coherence was observed from the SAR return signal for one-day temporal difference. A mean velocity of 17–24 cm/day was found for the months of March and May 1996, highest flow rates were seen in the high accumulation area located in the Eastern and Southern Aspect of glacier. Spatial analysis of velocity patterns with respect to slope and aspect show that high rates of flow was found in southern slopes and movement rates generally increase with increase in slope. Feature tracking approach was used to estimate the glacier flow for long term and seasonal basis using optical and SAR datasets (IRS-1C, 1D PAN, Landsat-7, 8 PAN, and TANDEM-x) during 1999–2020 period. The results suggest that glacier flow varies with season, i.e., high velocity during spring-summer season, as compared to late summer or winter and, the rate of ice flow changes over the years. The mean glacier velocity reduced to 49.5 m/year during 2013–2020 time, as compared to 67.67 m/year during 1999–2003 time. These results of reducing glacier velocity and changing snow line altitude indicates enhanced glacier’s melt rate and overall negative mass balance for Smudra tapu glacier.

Snow cover evolution at Qasigiannguit Glacier, southwest Greenland: A comparison of time-lapse imagery and mass balance data

In a warming climate, understanding seasonal fluctuations in snowline position is key to accurately predicting the melt contribution of glaciers to sea-level rise. Snow and ice conditions have a large impact on freshwater availability and supply on seasonal and multi-annual timescales. Factors such as snow extent and physical characteristics affect predictions in snowmelt- and glacier-fed catchments, influencing the potential of hydropower and drinking water supply in these areas, as well as ecosystems and fjord waters. Summer snow monitoring on glaciers and ice caps peripheral to the Greenland Ice Sheet are limited, and are typically excluded from ice-sheet wide assessments. Here, we analyse snow extent evolution on Qasigiannguit Glacier (QAS), a small coastal mountain glacier in Kobbefjord, southwest Greenland, with the aim of obtaining a baseline dataset of snow and ice conditions. Maximum snowline altitude and bare ice extent are extracted using terrestrial time-lapse photogrammetry, and compared to mass balance and automated weather station observations since 2014. The number of days of visible bare ice, cumulative Positive Degree Days (PDD) and mass balance are closely linked, with 2016 and 2019 experiencing the most negative mass balance, earliest onset of PDDs and greatest cumulative PDDs. 2021 had a relatively small negative mass balance (−0.072 m w.e.) despite having the longest bare ice exposure (112 days). This is attributed to the timing of bare ice exposure relative to the mean 90% cumulative PDD (28th August). Longer periods of bare ice exposure precede the mean 90% cumulative PDD in both 2016 and 2019, which reflects differences in the amount of melt energy available at different times in the melt season. This has far reaching implications for mass balance modelling efforts as this study demonstrates that spatial and temporal variability in snow/bare ice cover are linked to differences in melt factors and energy required to melt snow and ice. Snowline position provides a coarse indication of surface conditions, but future modelling efforts need to incorporate the complex spatial evolution of snow-to-bare ice ratios in order to improve estimates of mass loss from glaciarised mountain catchments.

Machine Learning Approaches to Automatically Detect Glacier Snow Lines on Multi-Spectral Satellite Images

Documenting the inter-annual variability and the long-term trend of the glacier snow line altitude is highly relevant to document the evolution of glacier mass changes. Automatically identifying the snow line on glaciers is challenging; recent developments in machine learning approaches show promise to tackle this issue. This manuscript presents a proof of concept of machine learning approaches applied to multi-spectral images to detect the snow line and quantify its average altitude. The tested approaches include the combination of different image processing and classification methods, and takes into account cast shadows. The efficiency of these approaches is evaluated on mountain glaciers in the European Alps by comparing the results with manually annotated data. Solutions provided by the different approaches are robust when compared to the ground truth’s snow lines, with a Pearson’s correlation ranging from 79% to 96% depending on the method. However, the tested approaches may fail when snow lines are not continuous or exhibit a strong change of elevation. The major advantage over the state of the art is that the proposed approach does not require one calibration per glacier.

The Snowline and 0°C Isotherm Altitudes During Precipitation Events in the Dry Subtropical Chilean Andes as Seen by Citizen Science, Surface Stations, and ERA5 Reanalysis Data

Understanding the variability of the snowline and the closely related 0°C isotherm during infrequent precipitation events in the dry Andes in Chile is fundamental for precipitation, snow cover, and discharge predictions. For instance, it is known that on the windward side of mountains, the 0°C isotherm can be several hundreds of meters lower than on the free air upwind counterpart, but little is understood about such effects in the Andes due to missing in situ evidence on the precipitation phase. To bridge this gap, 111 photographs of the snowline after precipitation events between 2011 and 2021 were gathered in the frame of a citizen science programme to estimate the snowline altitude. Since photographs of the mountain snowline are in good agreement with Sentinel-2 imagery, they have great potential to validate empirical snowline estimations. Using the snowline altitude from the photos, we evaluated different methods to estimate the snowline and 0°C isotherm altitude during precipitation events based on surface meteorological observations and ERA5 reanalysis data. We found a high correlation between the observed snowline altitude and the extrapolated 0°C isotherm based on constant lapse rates (−5.5 to −6.5°C km−1) applied to air temperature from single, near stations. However, uncertainty increases for distances &amp;gt;10 km. The results also indicate that the linear regression method is a good option to estimate ZSL, but the results strongly depend on the availability of high-elevation station datasets. During half of the precipitation events, the 0°C isotherm lies between ∼1,800 and ∼2,400–2,500 m asl. in winter, and the snowline is on average ∼280 m below this altitude. Our results indicate the presence of a mesoscale lowering of the 0°C isotherm over the windward slopes compared to the free-air upwind value during precipitation events and a possible isothermal layer of near-freezing air temperatures comparable to other mountain ranges. Due to this mesoscale and local behavior, ERA5 data generally overestimate the snow–rain transition in high-elevation areas, especially for relatively intense events. On the other hand, the 0°C isotherm altitude is underestimated if only low-elevation valley stations are considered, highlighting the importance of high-altitude meteorological stations in the network.

Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform

Glacier snow line altitude (SLA) at the end of the ablation season is an indicator of the equilibrium line altitude (ELA), which is a key parameter for calculating and assessing glacier mass balance. Here, we present an automated algorithm to classify bare ice and snow cover on glaciers using Landsat series images and calculate the minimum annual glacier snow cover ratio (SCR) and maximum SLA for reference glaciers during the 1985–2020 period in Google Earth Engine. The calculated SCR and SLA values are verified using the observed glacier accumulation area ratio (AAR) and ELA. We select 14 reference glaciers from High Mountain Asia (HMA), the Caucasus, the Alps, and Western Canada, which represent four mountainous regions with extensive glacial development in the northern hemisphere. The SLA accuracy is ~73%, with a mean uncertainty of ±24 m, for 13 of the reference glaciers. Eight of these glaciers yield R2 &gt; 0.5, and the other five glaciers yield R2 &gt; 0.3 for their respective SCR–AAR relationships. Furthermore, 10 of these glaciers yield R2 &gt; 0.5 and the other three glaciers yield R2 &gt; 0.3 for their respective SLA–ELA relationships, which indicate that the calculated SLA from this algorithm provides a good fit to the ELA observations. However, Careser Glacier yields a poor fit between the SLA calculations and ELA observations owing to tremendous surface area changes during the analyzed time series; this indicates that glacier surface shape changes due to intense ablation will lead to a misclassification of the glacier surface, resulting in deviations between the SLA and ELA. Furthermore, cloud cover, shadows, and the Otsu method limitation will further affect the SLA calculation. The post-2000 SLA values are better than those obtained before 2000 because merging the Landsat series images reduces the temporal resolution, which allows the date of the calculated SLA to be closer to the date of the observed ELA. From a regional perspective, the glaciers in the Caucasus, HMA and the Alps yield better results than those in Western Canada. This algorithm can be applied to large regions, such as HMA, to obtain snow line estimates where manual approaches are exhaustive and/or unfeasible. Furthermore, new optical data, such as that from Sentinel-2, can be incorporated to further improve the algorithm results.

Glacial equilibrium line–based relationship for paleoclimate reconstructions (Sierra Nevada, USA)

AbstractWe use PRISM climatic data (1981–2010) and Landsat images (2012–2013) to establish an empirical relationship linking annual temperature and precipitation to the equilibrium line altitude (ELA) of glaciers in the Sierra Nevada (36–41°N, California, USA). For this, we determined the present-day ELAs of 57 glaciers and the local 0°C isotherms elevation Iso0, averaged over the 1981–2010 period. The difference, for each glacier, is Y, the normalized snowline altitude (Y = ELA – Iso0). We then empirically calibrated a logarithmic relationship between this normalized snowline altitude and mean annual precipitation using data from partially covered glaciers. Our calibration is statistically distinct from that previously established for the tropical and midlatitude Andes (Fox and Bloom [1994], Journal of Geography (Chigaku Zasshi), 103, 867–885; Condom et al. [2007], Global and Planetary Change, 59, 189–202). This new relationship for North America is an easy-to-use tool to permit paleoclimatic reconstructions from paleo-ELAs. For a specific paleoglacial site, paleotemperature can be computed knowing the paleoprecipitation range, and vice versa. We also performed a test showing that, if precipitation is well known, the uncertainty associated with paleotemperature is about 1°C (1σ).

Reconstructed annual glacier surface mass balance in the Ányêmaqên Mountains, Yellow River source, based on snow line altitude

Reconstructed annual glacier surface mass balance in the Ányêmaqên Mountains, Yellow River source, based on snow line altitude

Surface evolution and dynamics of the Kangriz glacier, western Himalaya in past 50 years

Glacier specific studies, in a relatively unexplored terrain of Ladakh, hold immense importance tocomprehend not only the glacier response but also its synchronicity with the general regional trend. Accordingly, in this study, the Kangriz glacier in the Suru sub-basin, western Himalaya, has been taken up for multiparametric (area, terminal retreat, debriscover, snow line altitude, surface dynamics) assessment for the period 1971–2018. Results reveal an overall shrinkage of 3.3 ± 1.6%, with an expansion in the supraglacial debris cover by 45% (1971–2018). Concomitantly, the glacier surface velocity has reduced by 10.85 ± 5.68 ma−1 (35%), from 31.2 ± 5.8 ma−1 (1993/94) to 20.3 ± 1.7 ma−1 (2017/18), with mass wastage of ‐0.52 ± 0.19 m w.e.a−1 during 2000–17. The notable glacier degeneration is synchronous with regional warming (Tmax increase by 7%, Tmin increase by 43%, Tavg increase by 64%) and a decrease in precipitation by 3% (significant at α <0.05). Besides, frontal dynamics have changed recently, with an enhanced intensity of terminal retreat (2016–2018: 57 ± 13(Stdev) ma−1). The overall glacier status suggests a degenerative pattern of the glacier, which is in sync with the other western Himalayan glaciers. In view of the recently amplified ice-calving events and rapid mass loss observed in the snout region, the frontal glacier morphology may change drastically in the coming years .

Variability of the snowline altitude in the eastern Tibetan Plateau from 1995 to 2016 using Google Earth Engine

The glacier snowline can be used as an indicator of a glacier’s equilibrium line, which is a pivotal parameter for studying the effect of climate change on glaciers. However, the relationship between snowline altitudes (SLAs) and climatic regime, as well as the comparison between different glacier types, has received less attention. Using Google Earth Engine, we first developed an automated algorithm that employs the Otsu thresholding method to distinguish snow-covered areas from clean ice on a near-infrared band of Landsat imagery available from 1995 to 2016 and further to delineate glacier SLAs in the three regions of the eastern Tibetan Plateau (TP). The three study regions, Sepu Kangri (maritime), Bu’Gyai Kangri (continental), and western Qiajajima (continental), in the eastern TP have different climate regimes and are on a latitudinal transect from south to north. We then investigated the impacts of climatic factors on the SLA and its variability over the period studied. The results over the eastern TP indicate that (1) the SLA increased by 94, 55, and 49 m from south to north during the 22-year period, with the SLA variations of maritime glaciers being the most pronounced; (2) the southern maritime glaciers were mainly affected by precipitation, whereas the northern continental glaciers were influenced by temperature. Owing to the difference in primary climatic factors affecting snowlines, continental glaciers were found to have higher SLAs on the south slope, whereas maritime glaciers had higher SLAs on the north slope.

Recent Spatiotemporal Trends in Glacier Snowline Altitude at the End of the Melt Season in the Qilian Mountains, China

Glaciers in the Qilian Mountains, China, play an important role in supplying freshwater to downstream populations, maintaining ecological balance, and supporting economic development on the Tibetan Plateau. Glacier snowline altitude (SLA) at the end of the melt season is an indicator of the Equilibrium line altitude (ELA), and can be used to estimate the mass balance and climate reconstruction. Here, we employ the height zone-area method to determine the SLA at the end of the melt season during the 1989–2018 period using Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) SLA and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data. The accuracy of glacier SLA obtained in 1989–2018 after adding MODIS SLA data to the years without Landsat data increased by about 78 m. The difference between the remote-sensing-derived SLA and measured equilibrium line altitude (ELA) is mostly within 50 m, suggesting that the SLA can serve as a proxy for the ELA at the end of the melt season. The SLA of Qiyi Glacier in the Qilian Mountains rose from 4690 ± 25 m to 5030 ± 25 m, with an average of 4900 ± 103 m during the 30 year study period. The western, central, eastern sections and the whole range of the Qilian Mountains exhibited an upward trend in SLA during the 30 year study period. The mean glacier SLAs were 4923 ± 137 m, 4864 ± 135 m, 4550 ± 149 m and 4779 ± 149 m for the western, central, eastern sections and the whole range, respectively. From the perspective of spatial distribution, regardless of the different orientation, grid scale and basin scale, the glacier SLA of Qilian Mountains showed an upward trend from 1989 to 2018, and the glacier SLA is in general located at a comparably higher altitude in the southern and western parts of the Qilian Mountains while it is located at a comparably lower altitude in its northern and eastern parts. In an ideal condition, climate sensitivity studies of ELA in Qilian Mountains show that if the summer mean temperature increases (decreases) by 1 °C, then ELA will increase (decrease) by about 102 m. If the annual total solid precipitation increases (decreases) by 10%, then the glacier ELA will decrease (rise) by about 6 m. The summer mean temperature is the main factor affecting the temporal trend of SLA, whereas both summer mean temperature and annual total precipitation influence the spatial change of SLA.

Stagnation of the Pensilungpa glacier, western Himalaya, India: causes and implications

AbstractThis study investigates stagnation conditions of the Pensilungpa glacier, western Himalaya. Multiple glacier parameters (length, area, debris extent and thickness, snowline altitude (SLA), velocity, downwasting and ice cliffs) were studied using field measurements (2016–18), high-resolution imagery from GoogleEarth (2013–17) and spaceborne Landsat, ASTER and SRTM data (1993–2017) to comprehend the glacier's current state. Results show a moderate decrease in length (6.62 ± 2.11 m a−1) and area (0.11 ± 0.03% a−1), a marked increase in SLA (~6 m a−1) and debris cover (2.86 ± 0.29% a−1) and a slowdown of ~50% during 1993–2016. Notable thinning of −0.88 ± 0.04 m a−1 was observed between 2000 and 2017 showing a similar trend as field measurements during 2016–17 (−0.88 m) and 2017–18 (−1.54 m). Further, results reveal a stagnation of the lower ablation zone (LAZ). Less mass supply and heterogeneous debris growth (6.67 ± 0.41% a−1) over the previous decade resulted in slowdown, margin insulation and slope-inversion, leading to stagnation. Stagnation of LAZ caused bulging in the dynamic upper ablation zone and favored the development of supraglacial ponds and ice cliffs. Ice cliffs have grown significantly (48% in number; 41% in area during 2013–17) and their back-wasting now dominates the ablation process.

Evolution of Surface Characteristics of Three Debris-Covered Glaciers in the Patagonian Andes From 1958 to 2020

A number of glaciological observations on debris-covered glaciers around the globe have shown a delayed length and mass adjustment in relation to climate variability, a behavior normally attributed to the ice insulation effect of thick debris layers. Dynamic interactions between debris cover, geometry and surface topography of debris-covered glaciers can nevertheless govern glacier velocities and mass changes over time, with many glaciers exhibiting high thinning rates in spite of thick debris cover. Such interactions are progressively being incorporated into glacier evolution research. In this paper we reconstruct changes in debris-covered area, surface velocities and surface features of three glaciers in the Patagonian Andes over the 1958–2020 period, based on satellite and aerial imagery and Digital Elevation Models. Our results show that debris cover has increased from 40 ± 0.6 to 50 ± 6.7% of the total glacier area since 1958, whilst glacier slope has slightly decreased. The gently sloping tongues have allowed surface flow velocities to remain relatively low (&amp;lt;60 m a−1) for the last two decades, preventing evacuation of surface debris, and contributing to the formation and rise of the ice cliff zone upper boundary. In addition, mapping of end of summer snowline altitudes for the last two decades suggests an increase in the Equilibrium Line Altitudes, which promotes earlier melt out of englacial debris and further increases debris-covered ice area. The strongly negative mass budget of the three investigated glaciers throughout the study period, together with the increases in debris cover extent and ice cliff zones up-glacier, and the low velocities, shows a strong linkage between debris cover, mass balance evolution, surface velocities and topography. Interestingly, the presence of thicker debris layers on the lowermost portions of the glaciers has not lowered thinning rates in these ice areas, indicating that the mass budget is mainly driven by climate variability and calving processes, to which the influence of enhanced thinning at ice cliff location can be added.