Volcanically-triggered changes in glacier surface velocity 

<p>Many (about 250) volcanoes worldwide are occupied by glaciers. Often glaciers are regarded as problematic for volcano monitoring, since glacier ice potentially masks evidence of volcanic activity. The most devastating volcanic eruptions of the last 100 years involved volcano-glacier interactions. The 1985 eruption of Nevado del Ruiz killed 23000 people, and the 2010 eruption of Eyjafjallajökull led to the closure of many European airports. Therefore, it is imperative to minimize these impacts on society by improving methods for monitoring of glacier-clad volcanoes. Amongst several methods, optical satellite remote sensing techniques are perhaps most auspicious, since they frequently have a relatively high temporal and spatial resolution, and are mostly freely available. They often clearly show the effects of volcanic activity on glaciers, including ice cauldron formation, ice fracturing and glacier terminus changes potentially due to subglacial melt or subglacial dome growth. This study has the objective to link pre-, syn- and post-eruption glacier behaviour to the type and timing of volcanic activity, and to develop a satellite based predictive tool for monitoring future eruptions. Despite several studies that link volcanic activity and changing glacier behaviour, the potential of using the latter to predict the former has yet to be systematically tested. Our approach is to observe how glaciers responded to past volcanic events using mostly, but not exclusively optical satellite imagery, and to build a database of examples for potential automated detection and forecasting on a global scale.</p>

Our understanding of identifying and monitoring surge-type glacier distribution patterns, fluctuations, periodicities, and occurrence mechanism under the changing climate is challenging and scarce due to small numbers, limitations on the spatiotemporal coverage of remote sensing observations, and insufficient field-based glaciological data from the High Mountain Asia. The surging glaciers have caused major hazards, and their movement can destroy peripheral and downstream areas like roads, connecting bridges, villages, and hydropower stations and trigger a glacial lake outburst flood or form a dammed (moraine or ice) lake in High Mountain Asia (HMA) in the recent past. Many glaciers have experienced a mass loss and retreat due to ongoing climate change in HMA in recent decades, whereas studies conducted in the Karakorum, Pamir, Tien Shan, and Kunlun Shan regions have reported the surging of the glaciers. Whereas, in the central Himalayan region, very limited studies have been able to identify and explain in detail the surging glaciers and their surge mechanism. In this study, we identified an unnamed glacier surge in the central Himalaya, triggered between 12 September and 14 October 2019 (on a monthly scale) using multi-source high-resolution remote sensing data (CARTOSAT-1 [2011 and 2012], LISS-IV-2A [2011, 2017, and 2020], Landsat-5 [TM], 7 [ETM +], 8 [OLI/TIRS], and Sentinel [2A and 2B]) in conjunction with shuttle radar topography mission [SRTM], Advanced Spaceborne Thermal Emission and Reflection Radiometer [ASTER], and High Mountain Asia digital elevation model (DEM) database. We used a series of algorithms package named MicMac ASTER (MMASTER) tool for generating DEMs from data of two telescopes for the estimation of the surface elevation change, and to calculate the surface velocity, we employed the "Co-registration of Optically Sensed Images and Correlation" (COSI-Corr), a Fourier-based, highly advanced matching program. Based on the observations of the glacier terminus fluctuation, surface velocity, and surface elevation change from 1993 to 2022, this study revealed that the unnamed glacier underwent a surge for the first time in the past three decades. The glacier's surface velocity increased from 7 ± 3 m year-1 during quiescence (2001-2002) to 163 ± 1 m year-1 during the surge (2019-2020) and then decreased to 17 ± 2 m year-1 between 2021 and 2022. Between 12 September and 14 October 2019, there was a sudden and significant increase in surface velocity of 863m within a month (i.e., 27 m/day compared to the month prior), indicating the initiation of the surge. Overall, the present study results suggest that the glacier's velocity varied considerably during the observed period, with periods of gradual increase, sudden increase, and subsequent decrease. Further, the changes in glacier surface suggest a total mean elevation change of 0.26 ± 0.2 m year-1 between 2000 and 2020. In this study, we present novel observations of a glacier surge in the central Himalaya, compare its characteristics to surge-type glaciers reported elsewhere, and discuss the possible mechanisms controlling its behavior.

Monitoring glacier flow is vital to understand the response of mountain glaciers to environmental forcing in the context of global climate change. Seasonal and interannual variability of surface velocity in the temperate glaciers of the Parlung Zangbo Basin (PZB) has attracted significant attention. Detailed patterns in glacier surface velocity and its seasonal variability in the PZB are still uncertain, however. We utilized Landsat-8 (L8) OLI data to investigate in detail the variability of glacier velocity in the PZB by applying the normalized image cross-correlation method. On the basis of satellite images acquired from 2013 to 2020, we present a map of time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu, and Azha) in the PZB. Next, we explored the driving factors of surface velocity and of its variability. The results show that the glacier centerline velocity increased slightly in 2017–2020. The analysis of meteorological data at two weather stations on the outskirts of the glacier area provided some indications of increased precipitation during winter-spring. Such increase likely had an impact on ice mass accumulation in the up-stream portion of the glacier. The accumulated ice mass could have caused seasonal velocity changes in response to mass imbalance during 2017–2020. Besides, there was a clear winter-spring speedup of 40% in the upper glacier region, while a summer speedup occurred at the glacier tongue. The seasonal and interannual velocity variability was captured by the transverse velocity profiles in the four selected glaciers. The observed spatial pattern and seasonal variability in glacier surface velocity suggests that the winter-spring snow might be a driver of glacier flow in the central and upper portions of glaciers. Furthermore, the variations in glacier surface velocity are likely related to topographic setting and basal slip caused by the percolation of rainfall. The findings on glacier velocity suggest that the transfer of winter-spring accumulated ice triggered by mass conservation seems to be the main driver of changes in glacier velocity. The reasons that influence the seasonal surface velocity change need further investigation.

Glacier evolution with time provides important information about climate variability. Here, we investigated glacier velocity changes in the Himalayas and analysed the patterns of glacier flow. We collected 220 scenes of Landsat-7 panchromatic images between 1999 and 2000, and Sentinel-2 panchromatic images between 2017 and 2018, to calculate surface velocities of 36,722 glaciers during these two periods. We then derived velocity changes between 1999 and 2018 for the early winter period, based on which we performed a detailed analysis of motion of each individual glacier, and noted that the changes are spatially heterogeneous. Of all the glaciers, 32% have sped up, 24.5% have slowed down, and the rest 43.5% have remained stable. The amplitude of glacier slowdown, as a result of glacier mass loss, is significantly larger than that of speedup. At regional scales, we found that glacier surface velocity in winter has uniformly decreased in the western part of the Himalayas between 1999 and 2018, while increased in the eastern part; this contrasting difference may be associated with decadal changes in accumulation and/or melting under different climatic regimes. We also found that the overall trend of surface velocity exhibits seasonal variability: summer velocity changes are positively correlated with mass loss, i.e., velocity increases with increasing mass loss, whereas winter velocity changes show a negative correlation. Our study suggests that glacier velocity changes in the Himalayas are spatially and temporally heterogeneous, in agreement with studies that previously highlighted this trend, emphasising complex interactions between glacier dynamics and environmental forcing.

Approximately 17% of the Earth’s 1,413 Holocene volcanoes are glacier covered or possess at least one glacier within a radius of 5 km. Glacier-volcano interactions are therefore relatively common, yet our understanding of these interactions is hindered by a sparsity of observations and a lack of quantitative data. Furthermore, glaciovolcanicanism has been implicated in a number of particularly deadly and costly volcanic eruptions in recent decades. Documenting and quantifying the impacts of glacier-volcano interactions is therefore increasingly needed to both accurately forecast the future dynamics of volcanic glaciers and mitigate associated glaciovolcanic hazards.Encouragingly, recent research has shown that optical satellite imagery can be used to detect volcanic impacts on glacier surface morphology, such as the development of ice cauldrons and widespread crevassing. However, to date the capacity of volcanic activity to influence glacier velocities and wider glacier geometry remains relatively unexplored. Here, we present a comparative study of volcanic and non-volcanic glacier velocities and geometries. We apply descriptive and multivariate statistical analyses to a broad range of glacial, volcanic and climate records in order to: i) compare volcanic and non-volcanic glacier parameters globally for the year 2017/18, and ii) investigate relationships between volcano properties and volcanic glacier characteristics.Our final dataset comprises ~2,700 volcanic glaciers and ~210,000 non-volcanic glaciers. We reveal that volcanic glaciers typically exhibit greater and more variable velocities than their non-volcanic counterparts, with an average median velocity of 18.09 ma‑1 versus 7.94 ma-1 for non-volcanic glaciers. We also find that volcanic glaciers are typically larger, longer and thicker than non-volcanic glaciers, and are more likely to be situated at lower elevations, on more gentle slopes in warmer, wetter climates than their non-volcanic counterparts. However, when controlling for these differences in glacier geometry, situation and climate, we find that the greater velocities observed for volcanic glaciers remain statistically significant. Relationships between volcano properties and volcanic glacier characteristics tentatively indicate that volcano type and tectonic setting may also act as controls on volcanic glacier velocities, and that the greatest volcanic glacier velocities are typically found in glaciers situated closest to volcanoes.The enhanced velocities documented here, particularly for glaciers most proximal to volcanoes, are hypothesised to be a consequence of locally increased geothermal heat inputs. Consequently, we contend that the velocities of volcanic glaciers may be a valuable proxy for volcanic activity and, with further investigation, may provide considerable potential for monitoring and forecasting volcanic activity, and for improving the mitigation of glaciovolcanic hazards.

Glacier evolution with time provides important information about climate variability. Here we investigate glacier surface velocity in the Himalayas and analyse the patterns of glacier flow. We collect 220 scenes of Landsat-7 panchromatic images between 1999 and 2000, and Sentinel-2 panchromatic images between 2017 and 2018, to calculate surface velocities of 36,722 glaciers during these two periods. We then derive velocity changes between 1999 and 2018, based on which we perform a detailed analysis of motion of each individual glacier, and noted that the changes are spatially heterogeneous. Of all the glaciers, 32 % have speeded up, 24.5 % have slowed down, and the rest 43.5 % remained stable. The amplitude of glacier slowdown, as a result of glacier mass loss, is remarkably larger than that of speedup. At regional scales, we found that glacier surface velocity in winter has uniformly decreased in the western part of the Himalayas between 1999 and 2018, whilst increased in the eastern part; this contrasting difference may be associated with decadal changes in accumulation and/or melting under different climatic regimes. We also found that the overall trend of surface velocity exhibits seasonal variability: summer velocity changes are positively correlated with mass loss, whereas winter velocity changes show a negative correlation. Our study suggests that glacier velocity changes in the Himalayas are more spatially and temporally heterogeneous than previously thought, emphasising complex interactions between glacier dynamics and environmental forcing.

Glacier evolution with time provides important information about climate variability. Here we investigate glacier surface velocity in the Himalayas and analyse the patterns of glacier flow. We collect 220 scenes of Landsat-7 panchromatic images between 1999 and 2000, and Sentinel-2 panchromatic images between 2017 and 2018, to calculate surface velocities of 36,722 glaciers during these two periods. We then derive velocity changes between 1999 and 2018, based on which we perform a detailed analysis of motion of each individual glacier, and noted that the changes are spatially heterogeneous. Of all the glaciers, 32 % have speeded up, 24.5 % have slowed down, and the rest 43.5 % remained stable. The amplitude of glacier slowdown, as a result of glacier mass loss, is remarkably larger than that of speedup. At regional scales, we found that glacier surface velocity in winter has uniformly decreased in the western part of the Himalayas between 1999 and 2018, whilst increased in the eastern part; this contrasting difference may be associated with decadal changes in accumulation and/or melting under different climatic regimes. We also found that the overall trend of surface velocity exhibits seasonal variability: summer velocity changes are positively correlated with mass loss, whereas winter velocity changes show a negative correlation. Our study suggests that glacier velocity changes in the Himalayas are more spatially and temporally heterogeneous than previously thought, emphasising complex interactions between glacier dynamics and environmental forcing.

Asian High Mountain glaciers play an important role in climate change and water cycle on both a global and regional scale. The Puruogangri is the largest modern ice field in the Tibetan Plateau with an area of 400 km2 in total, where the information on ice topography and glacier velocity are scarce due to the difficulty to reach. In this study, the glacier surface elevation and velocity in the Puruogangri were measured using satellite SAR interferometry applied to a pair of ERS-1/2 SAR Tandem images acquired during 1998. A maximum surface velocity of 0.12 m/day was observed in glacier tongues of the eastern portion of the Puruogangri, with an averaged velocity of 0.07 m/day. The results are consistent to the filed observations obtained in 2002. The elevation of the Puruogangri estimated by the Tandem InSAR is ranged from 5200 m to 6200 m which is comparable to the SRTM DEM.

Remote sensing is a key tool to derive glacier surface velocities but existing mapping methods, such as cross-correlation techniques, can fail where surface properties change temporally or where large velocity variations occur spatially. High-resolution datasets, such as UAV imagery, offer a promising solution to tackle these issues and to study small-scale glacier dynamics, but new workflows are required to handle such data. Therefore, we tested the potential of new deep learning-based image-matching algorithms for deriving glacier surface velocities across the ablation area of a glacier with strong spatial variability in surface velocities (<5 m/yr to >100 m/yr) and substantial changes in surface properties between image acquisitions. For a thorough comparison of state-of-the-art methods and sensors, we applied three different techniques (cross-correlation using geoCosiCorr3D, feature tracking with ORB using SeaIceDrift and the new deep learning-based method using ICEpy4D) and three different platforms (Sentinel-2, PlanetScope, UAVs) to estimate glacier surface velocities. Results showed lowest errors for velocities derived with the deep learning-based approach applied to UAV imagery (RMSE = 2.17 m/yr, R2 = 0.99), followed by cross-correlation using Sentinel-2 images (RMSE = 21.0 m/yr, R2 = 0.59) and the deep learning-based approach with PlanetScope data (RMSE = 21.28 m/yr, R2 = 0.36). Cross-correlation with geoCosiCorr3D resulted in comparably high errors with the UAV dataset (RMSE = 36.22 m/yr, R2 = 0.24), whereas ORB-based feature tacking showed lowest performance with all sensors. Spatial patterns of computed velocities indicate that applying existing cross-correlation methods for areas with regular displacements or low glacier velocities yields suitable results on UAV data, but innovative deep learning-based approaches are required for resolving rapid changes in velocities or in surface properties. This novel method benefits from improved keypoint detection and matching through training using neural networks and data characterized by challenging geometries, outlier minimization and more robust descriptors by applying cross-attention layers. We conclude that continued development of deep learning-based feature tracking approaches for glacier velocity computations may substantially improve UAV-based velocity derivations applied to challenging situations. This method is able to deliver reliable displacement data in situations where traditional methods fail, which implies a new level of detail in understanding and interpreting glacier dynamics.

Identifying early indicators of volcanic eruptions is a fundamental part of natural hazard management but is notoriously difficult. Here we consider whether monitoring changes in glacier velocity can help. We use satellite images to investigate changes in the surface velocity of Cone Glacier (Alaska) between November 2017 and January 2022, a period encompassing two eruptions of Mount Veniaminof on which the glacier sits. Our data show high glacier velocities months prior to these eruptions and low velocities immediately before, during and after the 2018 eruption, likely caused by volcanically triggered ice melt and associated changes in subglacial water pressures. Evidence for elevated velocities months prior to eruptions is particularly important and indicates that glacier speed-up might be an early indicator of volcanic unrest. Thus, glaciers could serve as tools for volcano monitoring and eruption forecasting since more than 2500 glaciers globally are located within 5 km of an active volcano.

<p>Climate induced glacier change has important implications for global sea level rise, freshwater availability and geomorphologic hazards. Changes in ice dynamics and mass flow can globally be observed by long- and short-term changes in ice surface velocity. Consistent and continuous data on glacier surface velocity are important inputs to time series analyses, numerical ice dynamic modelling and glacier mass balance calculations. Therefore, glacier surface velocities have been identified as an Essential Climate Variable (ECV) that should be monitored on a regular and global scale. Since 2014, repeat-pass Synthetic Aperture Radar (SAR) data, acquired by the Sentinel-1 constellation as part of ESA’s (European Space Agency) Copernicus program, enable global, near real time-like and fully automatic processing of glacier velocity fields at up to 6-day temporal resolution, independent of weather conditions, season and daylight.</p><p>We present a new near-global data set of Sentinel-1 glacier velocities that comprises continuously updated image pair velocity fields, as well as monthly and annually averaged velocity mosaics at 200 m spatial resolution, derived from applying intensity feature tracking on both archived and new acquisitions. The data set covers all major glaciated regions outside the polar ice sheets and is generated in an HPC (High Performance Computing) environment at the University of Erlangen-Nuremberg. By the beginning of January 2021, we processed more than 110.000 Sentinel-1 scenes, amounting to roughly 450 TB of data. The velocity products are freely accessible via an interactive web portal (http://retreat.geographie.uni-erlangen.de) that provides capabilities for download and simple online analyses. We give information on the procedures of data generation, as well as on how to access the data and demonstrate the capabilities of our products for velocity time series analyses at very high temporal resolution. We compare our data to velocity products generated from very high resolution TerraSAR-X SAR (Synthetic Aperture Radar) and Landsat-8 optical (ITS_LIVE, GoLIVE) data. For this comparison we selected Svalbard as an example region, as it includes glaciers of a broad variety of sizes, different velocitiy magnitudes and seasonal velocity patterns, as well as very fast flowing surging glaciers and almost featureless ice caps.</p>

Abstract. En masse application of feature tracking algorithms to satellite image pairs has produced records of glacier surface velocities with global coverage, revolutionizing the understanding of global glacier change. However, glacier velocity records are sometimes incomplete due to gaps in the cloud-free satellite image record (for optical images) and failure of standard feature tracking parameters, e.g., search range, chip size, or estimated displacement, to capture rapid changes in glacier velocity. Here, we present a pipeline for pre-processing commercial high-resolution daily PlanetScope surface reflectance images and for generating georeferenced glacier velocity maps using NASA's autonomous Repeat Image Feature Tracking (autoRIFT) algorithm with customized parameters. We compare our velocity time series to the NASA Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) global glacier velocity dataset, which is produced using autoRIFT, with regional-scale feature tracking parameters. Using five surge-type glaciers as test sites, we demonstrate that the use of customized feature tracking parameters for each glacier improves upon the velocity record provided by ITS_LIVE during periods of rapid glacier acceleration (i.e., changes greater than several meters per day over 2–3 months). We show that ITS_LIVE can fail to capture velocities during glacier surges but that both the use of custom autoRIFT parameters and the inclusion of PlanetScope imagery can capture the progression of order-of-magnitude changes in flow speed with median uncertainties of <0.5 m d−1. Additionally, the PlanetScope image record approximately doubles the amount of optical cloud-free imagery available for each glacier and the number of velocity maps produced outside of the months affected by darkness (i.e., polar night), augmenting the ITS_LIVE record. We demonstrate that these pipelines provide additional insights into speedup behavior for the test glaciers and recommend that they are used for studies that aim to capture glacier velocity change at sub-monthly timescales and with greater spatial detail.

Volcanic heating is predicted by theory to affect the velocity of nearby glaciers. However, conclusive studies on a large scale are lacking. Here, we conduct a global comparison of the velocities of glaciers near active volcanoes (i.e. within 5 km) and those located elsewhere ( > 5 km from an active volcano). Our findings show that, when considered over an annual scale (e.g. 2017-2018) and controlling for other factors, glaciers near volcanoes flow 46% faster than those located elsewhere (based on median values). This finding strongly suggests that volcanic heating impacts glacier velocity at a global scale, and supports the idea that glacier velocity monitoring could be a valuable indirect tool to help volcano monitoring and eruption prediction, particularly where volcanic heating (and therefore subglacial melt) intensifies months or years prior to eruptions.

ABSTRACTSince the 1960s, warming air and sea surface temperatures have led to decreasing sea ice extent and longer periods of open water in the Canadian Arctic Archipelago (CAA), together with changes in glacier discharge patterns. An important question, therefore, is whether there is a relationship between changing sea ice conditions, glacier dynamics, and iceberg production in this region. Using synthetic aperture radar (SAR) (Radarsat-1, Radarsat-2, and ALOS PALSAR) and optical (Landsat 7 and 8) satellite imagery, iceberg plume events and sea ice break-up/freeze-up dates between 1997 and 2015 are investigated for 40 tidewater glaciers around the Prince of Wales (POW) Icefield, Ellesmere Island. Results show a clear relationship between the presence of sea ice and the production of icebergs, with ~49% of total iceberg plume events occurring during the 3–4 month long summer open water season and ~51% of events when sea ice was present the remaining 8–9 months of the year. There is no clear evidence of recent increases in iceberg production on a regional basis, but on a local, individual glacier scale there has been a connection between periods of increased iceberg plume events and: (a) acceleration in the surface velocity of Trinity and Wykeham glaciers; (b) increase in terminus retreat rates for glaciers which have not accelerated in flow speed over the past ~5–10 years. Comparisons with ocean temperature, surface air temperature from NCEP-NCAR reanalysis, and tidal data showed no clear relationship with iceberg plume events.

Abstract. Glaciers all over the world are experiencing changes at varying stages due to changing climatic conditions. Minuscule changes in the glaciers in Antarctica can thus have major implications. The velocity of glaciers is important in several aspects of glaciology. A glacier’s movement is caused by different factors such as gravity, internal deformation present in the ice, pressure caused by accumulation of snow, basal sliding etc. The velocity of a glacier is an important factor governing mass balance and the stability of the glacier. A glacier which moves fast generally brings more ice towards the terminus than a slow moving glacier. Thus, the glacier velocity can determine its load carrying capacity and gives indication on the ‘health’ of the glacier. Measurement of the ice flow velocity can help model glacier dynamics and thus provide increasing insights on different glacier subtleties. However, field measurements of velocity are limited in spatial and temporal domains because these operations are manual, tedious and logistically expensive. Remote sensing is a tool to monitor and generate such data without the need for physical expeditions. This study uses optical satellite imagery to understand the mechanisms involved in the movement of a glacier. Optical image correlation method (COSI-Corr module) is chosen here as the promising method to derive displacement of a moving glacier. The principle involved in this technique is that two images acquired at different times are correlated to find the shift in the position of moving ice, which is then treated as displacement in the time interval. Employing this technique we estimated the velocity of Mellor glacier (73°30′S, 66°30′E), a tributary glacier of the Amery Ice Shelf, Antarctica, over a span of four years from 2014 to 2017. Correlation is performed using Landsat-8 panchromatic images of 15 m resolution. Optical images from Landsat 8 often have noise due to atmospheric conditions such as cloud cover, so we used only those images cloud with cloud cover less than 10%. The glacier is covered in 128 path frame and 112 by Landsat-8. The correlation frequency was calculated using the correlator engine. Window size taken here is 256 and step sizes is 64 for both x and y dimensions. Once the correlation is calculated for an image pair for a specific time-period, we obtain three different outputs. Two of them indicated displacement (one in x direction and another in y direction) and the remaining output provided signal to noise ratio. The band math tool using displacement outputs in ENVI software performed velocity calculations. This gives us a raster image showing velocity at each point or pixel. Some errors such as noise persist and their correction is performed in ArcGIS software. In order to get pure signals, we removed all the signals with a signal to noise ratio less than 0.9 and this was carried out using raster calculator tool. All the resultant velocity rasters were interpolated and bias was calculated between seasons of two consecutive years. Two maps were generated for each year, one for early summer i.e. from January to April and one from September to December using the resultant velocity raster. The mean values of velocities found for Mellor glacier from Jan-April 2014, 2015, 2016 and 2017 were 374.06 ma−1, 413.59 ma−1, 278.62 ma−1 and 406.66 ma−1, respectively. Velocities for September-December 2014, 2015, 2016 and 2017 were found to be 334.63 ma−1, 334.43 ma−1, 367.37 ma−1 and 381.31 ma−1, respectively. The biases are computed for all the seasons of four years and root mean square (RMSE) values are estimated. These RMSE values signify the season-wise variations in the velocities. RMSE values for season of Jan–April 2014–15, 2015–16 and 2016–17 were 75.92 ma−1, 147.82 ma−1, and 133.33 ma−1, respectively. Similarly, RMSE values for season of September-December 2014–15, 2015–16 and 2016–17 are 35.7 ma−1, 51.29 ma−1 and 35.84 ma−1 respectively. The results showed variations in velocities for different seasons. We plan to integrate this data with the discharge rates, to estimate mass balance and melting rates of the glacier to decipher mechanisms at work for the Mellor glacier.