Glacier speed-up as a possible precursor to volcanic eruptions at Mount Veniaminof, Alaska

<p>Many (~250) volcanoes worldwide are occupied by glaciers. This can be problematic for volcano monitoring, since glacier ice potentially masks evidence of volcanic activity. However, some of the most devastating and costly volcanic eruptions of the last 100 years involved volcano-glacier interactions (e.g. Nevado del Ruiz 1985, Eyjafjallajökull 2010). Therefore, improving methods for monitoring glacier-covered volcanoes is of clear societal benefit. Optical satellite remote sensing datasets and techniques are perhaps most promising, since they frequently have a relatively high temporal and spatial resolution and are often freely available. These sources often show the effects of volcanic activity on glaciers, including ice cauldron formation, ice fracturing, and glacier terminus changes. In this study, we use satellite sources to investigate possible links between volcanic activity and changes in glacier velocity. Despite some studies reporting periods of glacier acceleration triggered by volcanic unrest, the potential of using the former to monitor the latter has yet to be investigated. Our approach is to observe how glacier surface velocity responded to past volcanic events in Alaska and Chile by applying feature-tracking, mostly using optical satellite imagery. The overall aim is to systematically track changes in the glacier velocity, with hope of improving volcano monitoring and eruption prediction. </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.

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.

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.

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.

<p>The contribution of glacier mass loss to future sea level rise is still poorly constrained (Lemke and others, 2007). One of the remaining unknowns is how water inputs influence glacier velocity. Short-term variations in glacier velocity occur when a water input exceeds the capacity of the subglacial drainage system, and the subglacial water pressure increases. Several studies (Van de Wal and others, 2008; Sundal and others, 2011) have suggested that high ice-flow velocities during these events are later offset by lower ice-flow velocities due to a more efficient subglacial drainage system. This study combines in-situ velocity measurements with a full Stokes glacier flowline model to understand the spatial and temporal variations in glacier flow on the lower Franz Josef Glacier, New Zealand. The Franz Josef Glacier experiences significant water inputs throughout the year (Anderson and others, 2006), and as a result, the subglacial drainage system is likely well-developed. In March 2011, measured ice-flow velocities increased by up to 75% above background values in response to rain events and by up to 32% in response to diurnal melt cycles. These speed-up events occurred at all survey locations across the lower glacier. Through flowline modelling, it is shown that the enhanced glacier flow can be explained by a spatially-uniform subglacial water pressure that increased during periods of heavy rain and glacier melt. From these results, it is suggested that temporary spikes in water inputs can cause glacier speed-up events, even when the subglacial hydrology system is well-developed (cf. Schoof, 2010). Future studies should focus on determining the contribution of glacier speed-up events to overall glacier motion.</p>

<p>The contribution of glacier mass loss to future sea level rise is still poorly constrained (Lemke and others, 2007). One of the remaining unknowns is how water inputs influence glacier velocity. Short-term variations in glacier velocity occur when a water input exceeds the capacity of the subglacial drainage system, and the subglacial water pressure increases. Several studies (Van de Wal and others, 2008; Sundal and others, 2011) have suggested that high ice-flow velocities during these events are later offset by lower ice-flow velocities due to a more efficient subglacial drainage system. This study combines in-situ velocity measurements with a full Stokes glacier flowline model to understand the spatial and temporal variations in glacier flow on the lower Franz Josef Glacier, New Zealand. The Franz Josef Glacier experiences significant water inputs throughout the year (Anderson and others, 2006), and as a result, the subglacial drainage system is likely well-developed. In March 2011, measured ice-flow velocities increased by up to 75% above background values in response to rain events and by up to 32% in response to diurnal melt cycles. These speed-up events occurred at all survey locations across the lower glacier. Through flowline modelling, it is shown that the enhanced glacier flow can be explained by a spatially-uniform subglacial water pressure that increased during periods of heavy rain and glacier melt. From these results, it is suggested that temporary spikes in water inputs can cause glacier speed-up events, even when the subglacial hydrology system is well-developed (cf. Schoof, 2010). Future studies should focus on determining the contribution of glacier speed-up events to overall glacier motion.</p>

<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>

Short-term glacier velocity variations typically occur when a water input is accommodated by an increase in the subglacial water pressure. Although these velocity variations have been well documented on many glaciers, few studies have considered them on glaciers where heavy rain and glacier melt occur year-round. This study investigates the relationship between water inputs and glacier velocity on Franz Josef Glacier, New Zealand. We installed six GNSS stations across the lower glacier during austral summer 2010/11 and one station during summer 2012/13. Glacier velocity remained elevated at all stations for ∼7 days following large rain events. During diurnal melt events, we find velocity variations in the early afternoon (12:00–16:00) at 600 m a.s.l. and in the late evening (20:00–01:00) at 400 m a.s.l. We hypothesize that the late-evening velocity variations occurred as an upstream region of high subglacial water pressures and accelerated ice motion propagated downstream. This mechanism may also explain the increased longitudinal compression and transverse extension across the lower glacier during speed-up events. Our results indicate that the subglacial drainage system likely decreases in efficiency upstream and that the water input variability can still cause short-term velocity variations despite the large year-round water inputs.

The Mt.Tomur glaciers, in the Tian Shan mountains of Western China, are usually debris-covered, and due to climate change, glacial hazards are becoming more frequent in this region. However, no changes in the long-time series of glacier surface velocities have been observed in this region. Conducting field measurements in high-altitude mountains is relatively difficult, and consequently, the dynamics and driving factors are less studied. Here, image-correlation offset tracking using Landsat images was exploited to estimate the glacier surface velocity of glaciers in the Mt.Tomur region from 2000 to 2020 and to assess glacier ice thickness. The results show that the glacier surface velocity in the Mt.Tomur region showed a significant slowdown during 2000–2020, from 6.71 ± 0.66 m a−1 to 3.95 ± 0.66 m a−1, an overall decrease of 41.13%. The maximum glacier ice thickness in the Mt.Tomur region was estimated based on the ice flow principle being 171.27 ± 17.10 m, and the glacier average thickness is 50.00 ± 5.0 m. Glacier thickness at first increases with increasing altitude, showing more than 100 ± 10 m ice thickness between 3400 m and 4300 m, and then decreases with further increases in altitude. The reliability of the surface velocity and ice thickness obtained from remote sensing was proved using the measured surface velocity and ice thickness of Qingbingtan glacier No. 72 stall (the correlation coefficient R2 > 0.85). The debris cover has an overall mitigating effect on the ablation and movement rate of Qingbingtan Glacier No. 72; however, it has an accelerating effect on the ablation and movement rate of glacier No. 74.

The motion of glaciers with a temperate base is highly variable in time and space, mainly as a result of glacier basal sliding being strongly modulated by subglacial hydrology. Although transient friction laws have recently been established in order to predict short-term sliding velocity changes in response to water input changes, yet little observations enable fully constraining these laws. Here we investigate short-term changes in glacier dynamics induced by transient rainwater input on the Glacier d’Argentière (French Alps) using up to 13 permanent GPS stations. We observe strong surface acceleration events materialized by maximum downglacier velocities on the order of 2 to 3 times background velocities and associated with significant glacier surface uplift of 0.03 m to 0.1 m. We demonstrate that uplift strikingly coincides with water discharge. In contrast, horizontal speed-up occurs over a timescale shorter than discharge and uplift changes, with a maximum occurring concomitantly with maximum water pressure but prior to maximum discharge or uplift. Our findings suggest that transient acceleration and uplift of the glacier are not necessarily modulated by the same mechanism. We also observe that the horizontal speed-ups propagate downglacier at migrating speeds of 0.04 m s-1 to 0.13 m s-1, suggesting an underlying migration of subglacial water flows through the inefficient, distributed system. We demonstrate that the temporal relationship between water discharge, water pressure, and three-dimensional glacier motions are complex and cannot be directly interpreted by changes in the subglacial water pressure through cavity formation and water storage. 

The velocity of glacier is the most important parameter in the study of glaciers and remote sensing is a powerful tool to calculate their surface velocities. Due to persistent cloud cover in this region, it is impossible to acquire enough optical images to provide measurements. However, measurement of the offsets between two SAR images is an effective way to determine surface velocity. In order to do this, offsets both in slant range and azimuth directions are derived from two SAR images. The movement of the glacier during the SAR data acquisition time is calculated after the global part of offsets has been removed by the polynomial fit method. The offsets used for removing the global part are selected on the basis of the Single-to-Noise ratio (SNR) and correlation in area without glaciers but with large topographic changes. The surface velocity of the whole glacier using SAR data will make a significant contribution to the study of glacier dynamics. The Kekesayi glacier can be divided into four parts, based on the velocity map. The results show that the surface velocity of the Kekesayi glacier is different on the different part of the glacier, and offset measurements are an effective method for the study of glaciers.