The Possible Transition From Glacial Surge to Ice Stream on Vavilov Ice Cap

An edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.Surge‐type glaciers typically undergo cyclical flow instability due to mass accumulation; however, some recent glacier surges have caused irreversible ice loss in a short period. At Vavilov Ice Cap, Russia, surge‐like behavior initiated in 2013 and by spring 2019 the ice cap had lost 9.5 Gt of ice (11% mass of the entire basin). Using time series of surface elevation and glacier velocity derived from satellite optical and synthetic‐aperture radar imagery, we identify a shift of flow pattern starting in 2017 when shear margins formed within the grounded marine piedmont fan. Multiple summer speedups correlate with warmer summers during 2015–2019 and suggest that surface melt may access the subglacial environment. Force balance analysis and examination of the Péclet number show that glacier thinning propagated upstream in 2016–2017, and diffusion became a significant dynamic response to thinning perturbations. Our results suggest that the glacier has entered a new ice stream‐like regime

INVESTIGATING MASS LOSS AND CHANGING ICE DYNAMICS OF ARCTIC ICE CAPS USING REMOTE SENSING

184 pagesGlacier thinning and retreat have accelerated globally in the last century and are the largest contributor to rising sea levels. For the Arctic region, observations and modeling results have shown that extensive warming is taking place. However, the recent glacier dynamics (mass balance and ice discharge) in many Arctic regions have not been well studied due to the remote nature of these glaciers. This thesis uses multiple types of satellite data to quantify the mass balance and ice discharge for three Arctic regions showing dramatic glacier change in recent decades possibly due to Arctic warming. The objective is to resolve the mass budget and velocity pattern on a per glacier basis and understand the mechanisms driving recent changes. To facilitate the entire workflow, our research team has developed the Cryosphere and Remote Sensing Toolkit (CARST) software, and I am the lead author. CARST provides useful python and bash scripts that use satellite imagery, particularly SAR and optical images, to monitor changes of glaciers and ice caps through time. The first study area is Franz Josef Land (FJL), Russia, which is currently subjected to a rapidly-warming climate in the Arctic. I combine surface elevation data derived from different sources and times, including the WorldView satellite series and the ArcticDEM data set (2011–2015), SPOT-5 (2007), CryoSat-2 (2011–2015), and a digitized cartographic map (1953). I calculate elevation change rate (dh/dt) in two different periods, and the results show a two-fold rate of ice loss over the past 60 years, from -2.18 ± 0.72 Gt/yr (1953–2011/2015 average) to -4.43 ± 0.78 Gt/yr (2011–2015). Despite being spatially variable, a trend of increased thinning from NE towards SW is discovered, suggesting a link to the local gradient in temperature and precipitation. Ice loss is mostly focused on marine-terminating glaciers probably due to the interaction between glaciers and warming ocean water. These retreating glaciers generated a new island in 2016 and more islands are likely to emerge in the foreseeable future as FJL’s ice loss has reached an unprecedented rate. The research focus in the following chapter shifts to the neighboring archipelago called Severnaya Zemlya, Russia. A surge-like collapse initiated in 2013 in Vavilov Ice Cap, one of many ice caps in this region. By spring 2019, this ice cap had lost 9.5 Gt of ice. Using time series of surface elevation and glacier velocity derived from multiple satellite data sets such as WorldView (elevation), ArcticDEM (elevation), ASTER (elevation), Landsat 8 (velocity), Sentinel-1, (velocity), Sentinel-2 (velocity), Radarsat-2 (velocity), and ALOS-2 (velocity), I identify a shift of flow pattern starting in 2017 when shear margins formed within the grounded marine piedmont fan. Multiple summer speedups occurred after the new flow pattern formed, possibly with the aid of basal lubrication due to surface melt. With the analysis using multiple physical models, it is suggested that the collapsed ice cap has entered a new ice stream-like regime in which diffusion of surface thinning controls the glacier dynamics. This is the first documented case of an ice stream-like feature ever being formed, and this glacier now flows at a higher speed and drains the ice cap more efficiently. To publicize the findings and their scientific implications, I made two videos showing the temporal changes of the terminus position and speed pattern, which are available on Youtube. In the last chapter, I further develop a physical framework for the glacier perturbation model to understand how different glaciers respond to basal lubrication. The modified 1-D flowline model suggests two physical quantities, Péclet number (Pe) and a value dubbed J0, governing glacier vulnerability to basal lubrication. To test the model, I use the Ice Thickness Models Intercomparison eXperiment (ITMIX) data set and the NASA MEaSUREs ITS_LIVE data set. ITMIX contains velocity, elevation, and ice thickness data from Austfonna Ice Cap, Svalbard, where multiple glacier collapse events occurred within the past 10 years. I calculate Pe and |J0| using the data from ITMIX and compare them with the speed change revealed by the ITS_LIVE data set. The results show that a low Pe and a high |J0| correspond to the high magnitude of glacier speedup during 1995–2018, as suggested by the model prediction. My analysis implies that basal lubrication can lead to a prolonged or even permanent change of glacier dynamics for some glaciers. These “weak” glaciers might be able to waste ice more rapidly than we thought, posing a warning of an underestimated sea level rise projection

whyjz/GLAFT: GLAFT 1.0.0-a

<p>The alpha release of GLAFT v1.0.0 corresponds to the version used in the revised manuscript "GLAcier Feature Tracking testkit (GLAFT): A statistically- and physically-based framework for evaluating glacier velocity products derived from satellite image feature tracking," published in The Cryosphere Discuss (<a href="https://doi.org/10.5194/tc-2023-38">https://doi.org/10.5194/tc-2023-38</a>).</p> <p>GLAcier Feature Tracking testkit (GLAFT) is a Python package for assessing and benchmarking feature-tracked glacier velocity maps derived from satellite imagery. To be compatible with as many feature-tracking tools as possible, GLAFT analyzes velocity maps (and optional reliability files used as weight) and calculates two metrics based on statistics and ice flow dynamics. Along with GLAFT's visualization tools, users can intercompare the quality of velocity maps processed by different methods and parameter sets. In the GLAFT publication, we further provide a guideline for optimizing glacier velocity maps by comparing the calculated metrics to an ideal threshold value.</p> <p>GLAFT is an open sourced project and is hosted on Github (<a href="https://github.com/whyjz/GLAFT">https://github.com/whyjz/GLAFT</a>). All documentation and cloud-executable demos are deployed as Jupyter Book pages (<a href="https://whyjz.github.io/GLAFT/">https://whyjz.github.io/GLAFT/</a>).</p&gt

whyjz/GLAFT: GLAFT 1.0.0

GLAFT v1.0.0 corresponds to the version used in the article "GLAcier Feature Tracking testkit (GLAFT): a statistically and physically based framework for evaluating glacier velocity products derived from optical satellite image feature tracking," published in The Cryosphere (https://doi.org/10.5194/tc-17-4063-2023). GLAcier Feature Tracking testkit (GLAFT) is a Python package for assessing and benchmarking feature-tracked glacier velocity maps derived from satellite imagery. To be compatible with as many feature-tracking tools as possible, GLAFT analyzes velocity maps (and optional reliability files used as weight) and calculates two metrics based on statistics and ice flow dynamics. Along with GLAFT's visualization tools, users can intercompare the quality of velocity maps processed by different methods and parameter sets. In the GLAFT publication, we further provide a guideline for optimizing glacier velocity maps by comparing the calculated metrics to an ideal threshold value. GLAFT is an open sourced project and is hosted on Github (https://github.com/whyjz/GLAFT). All documentation and cloud-executable demos are deployed as Jupyter Book pages (https://whyjz.github.io/GLAFT/). GLAFT is also available on Ghub (https://theghub.org/resources?alias=glaft) for cloud access

whyjz/GLAFT: GLAFT 0.2.0

<p>This is the first public release of GLAFT, the GLAcier Feature Tracking testkit.</p> <p>GLAFT is based on the scientific Python ecosystem and focuses on calculating the metrics and benchmarking glacier velocity products derived using the feature tracking technique. To be compatible with most feature tracking tools, GLAFT aims to process only the most common and essential products, which are the velocity maps (and optional input of reliability file used as weight). GLAFT also provides visualization tools for the derived metrics, making scientific communication much easier.</p> <p>GLAFT is an open-source project with all source code hosted on Github (<a href="https://github.com/whyjz/GLAFT">https://github.com/whyjz/GLAFT</a>). Users can find relevant documentation and cloud-executable demos in the same repository and on its Jupyter Book-based Github pages (<a href="https://whyjz.github.io/GLAFT/">https://whyjz.github.io/GLAFT/</a>).</p> <p>Version 0.2.0 is used in the submitted manuscript "GLAcier Feature Tracking testkit (GLAFT): A statistically- and physically-based framework for evaluating glacier velocity products derived from satellite image feature tracking."</p&gt