Description: Anisotropic crystal fabrics in ice sheets develop as a consequence of deformation and hence record information of past ice flow. Simultaneously, the fabric affects the present-day bulk mechanical properties of glacier ice because the susceptibility of ice crystals to deformation is highly anisotropic. This is particularly relevant in dynamic areas such as fast-flowing glaciers and ice streams, where the formation of strong fabrics might play a critical role in facilitating ice flow. Anisotropy is ignored in most state-of-the-art ice sheet models, and while its importance has long been recognized, accounting for fabric evolution and its impact on the ice viscosity has only recently become feasible. Both the application of such models to ice streams and their verification through in-situ observations are still rare. Ice cores provide direct and detailed information on the crystal fabric, but the logistical cost, technical challenges, particularly in fast-flowing ice and shear margins, difficulty in reconstructing the absolute orientation of the core, and their limitation of being a point measurement, make ice cores impractical for a spatially extensive evaluation of the fabric type. Indirect geophysical methods applied from or above the ice surface create the link between the small scale of laboratory experiments and ice–core observations to the large-scale coverage required for ice flow models and the complete understanding of ice stream dynamics. Here, we present a comprehensive analysis of the distribution of the ice fabric in the upstream part of the North-East Greenland Ice Stream (NEGIS). Our results are based on a combination of methods applied to extensive airborne and ground-based radar surveys, ice- and firn-core observations, and numerical ice-flow modelling. They show that in the onset region of NEGIS and around the EGRIP ice core drilling site, the fabric is horizontally strongly anisotropic, forming a horizontal girdle perpendicular to the ice flow, while the horizontal anisotropy reduces quickly over distances of less than five ice thicknesses outside of the ice stream’s shear margins. Downstream of the drill site, the fabric develops into a more vertically symmetric configuration on a time scale of around 2 ka, the first observation of this kind. Our study shows how ice-core based fabric observations, geophysical surveys and ice-flow modelling complement each other to obtain a more comprehensive picture of the spatially strongly varying fabric.

Description: Ice shelves play a key role in the stability of the Antarctic Ice Sheet due to their buttressing effect. A loss of buttressing as a result of increased basal melting or ice shelf disintegration will lead to increased ice discharge. Some ice shelves exhibit channels at the base that are not yet fully understood. In this study, we present in situ melt rates of a channel which is up to 330 m high and located in the southern Filchner Ice Shelf. Maximum observed melt rates are 2 m yr−1. Melt rates inside the channel decrease in the direction of ice flow and turn to freezing ∼55 km downstream of the grounding line. While closer to the grounding line melt rates are higher within the channel than outside, this relationship reverses further downstream. Comparing the modeled evolution of this channel under present-day climate conditions over 250 years with its present geometry reveals a mismatch. Melt rates twice as large as the present-day values are required to fit the observed geometry. In contrast, forcing the model with present-day melt rates results in a closure of the channel, which contradicts observations. The ice shelf experiences strong tidal variability in vertical strain rates at the measured site, and discrete pulses of increased melting occurred throughout the measurement period. The type of melt channel in this study diminishes in height with distance from the grounding line and is hence not a destabilizing factor for ice shelves.

Description: Future sea-level predictions require that the history and physical state of the Antarctic ice sheet is well understood and constrained by observations. Much of the ice sheets’ ice-dynamic properties are governed by processes at the ice-bed interface which can be imaged with radar sounding surveys. Moreover, certain processes at the ice-sheet base can have an effect all the way to the ice surface, which in turn can be observed with satellites. Here we use a combination of ultra-wideband radio-echo sounding data, satellite radar and laser altimetry data to characterize the evolution of the subglacial morphology of the Jutulstraumen drainage basin (western Dronning Maud Land, Antarctica). Based on the classification of the bed topography, we reconstruct the step-by-step modifications the subglacial landscape has experienced since the beginning of the glaciation of Antarctica, 34 million years ago. In addition, between 2017 and 2020, we find evidence of active episodic cascade-like subglacial water transport along the subglacial valley network. The combination of these observations will represent an important step towards a better understanding of large-scale ice-sheet dynamics in western Dronning Maud Land.

Description: With the Earth’s climate rapidly warming, the Arctic represents one of the most vulnerable regions to environmental change. Permafrost, as a key element of the Arctic system, stores vast amounts of organic carbon that can be microbially decomposed into the greenhouse gases CO2 and CH4 upon thaw. Extensive thawing of these permafrost soils therefore has potentially substantial consequences to greenhouse gas concentrations in the atmosphere. In addition, thaw of ice-rich permafrost lastingly alters the surface topography and thus the hydrology. Fires represent an important disturbance in boreal permafrost regions and increasingly also in tundra regions as they combust the vegetation and upper organic soil layers that usually provide protective insulation to the permafrost below. Field studies and local remote sensing studies suggest that fire disturbances may trigger rapid permafrost thaw, with consequences often already observable in the first years post-disturbance. In polygonal ice-wedge landscapes, this becomes most prevalent through melting ice wedges and degrading troughs. The further these ice wedges degrade; the more troughs will likely connect and build an extensive hydrological network with changing patterns and degrees of connectivity that influences hydrology and runoff throughout large regions. While subsiding troughs over melting ice wedges may host new ponds, an increasing connectivity may also subsequently lead to more drainage of ponds, which in turn can limit further thaw and help stabilize the landscape. Whereas fire disturbances may accelerate the initiation of this process, the general warming of permafrost observed across the Arctic will eventually result in widespread degradation of polygonal landscapes. To quantify the changes in such dynamic landscapes over large regions, remote sensing data offers a valuable resource. However, considering the vast and ever-growing volumes of Earth observation data available, highly automated methods are needed that allow extracting information on the geomorphic state and changes over time of ice-wedge trough networks. In this study, we investigate these changing landscapes and their environmental implications in fire scars in Northern and Western Alaska. We developed a computer vision algorithm to automatically extract ice-wedge polygonal networks and the microtopography of the degrading troughs from high-resolution, airborne laserscanning-based digital terrain models (1 m spatial resolution; full-waveform Riegl Q680i LiDAR sensor). To derive information on the availability of surface water, we used optical and near-infrared aerial imagery at spatial resolutions of up to 5 cm captured by the Modular Aerial Camera System (MACS) developed by DLR. We represent the networks as graphs (a concept from the computer sciences to describe complex networks) and apply methods from graph theory to describe and quantify hydrological network characteristics of the changing landscape. Due to a lack of historical very-high-resolution data, we cannot investigate a dense time series of a single representative study area on the evolution of the microtopographic and hydrologic network, but rather leverage the possibilities of a space-for-time substitution. We thus investigate terrain models and multispectral data from 2019 and 2021 of ten study areas located in ten fire scars of different ages (up to 120 years between date of disturbance and date of data acquisition). With this approach, we can infer past and future states of degradation from the currently prevailing spatial patterns and show how this type of disturbed landscape evolves over time. Representing such polygonal landscapes as graphs and reducing large amounts of data into few quantifiable metrics, supports integration of results into i.e., numerical models and thus largely facilitates the understanding of the underlying complex processes of GHG emissions from permafrost thaw. We highlight these extensive possibilities but also illustrate the limitations encountered in the study that stem from a reduced availability and accessibility to pan-Arctic very-high-resolution Earth observation datasets.

Description: Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited.

Description: Anisotropic crystal fabrics in ice sheets develop as a consequence of deformation and hence record information of past ice flow. Simultaneously, the fabric affects the present-day bulk mechanical properties of glacier ice because the susceptibility of ice crystals to deformation is highly anisotropic. This is particularly relevant in dynamic areas such as fast-flowing glaciers and ice streams, where the formation of strong fabrics might play a critical role in facilitating ice flow. This fact is ignored in most state-of-the-art ice sheet models, and while their importance has been recognized years ago, accounting for fabrics evolution and their impact on the ice viscosity has only recently become feasible. Both, the application of such models in ice streams as well as their verification through in-situ observations are, however, still rare. We present an extensive dataset of fabric anisotropy derived from radar data recorded in the onset region of the Northeast Greenland Ice Stream by air-borne and ground-based systems. Our methods yield the horizontal anisotropy and are based on travel time anisotropy and splitting as well as birefringence-induced power modulation of radar signals. They complement each other and show good agreement. We compare these in-situ observations with the results obtained from a fabric-evolution model employed along flow tubes in the ice stream onset to discuss the fabric in light of past flow history and its significance for the current flow mechanics of the ice stream.

Description: Future sea-level predictions require that the history of the Antarctic Ice Sheet is well understood and constrained by observations. Much of the ice sheets’ ice-dynamic properties are governed by processes at the ice-bed interface which can be imaged with radar sounding surveys. Here we use a combination of ultra-wideband radio-echo sounding data, satellite radar and laser altimetry data, as well as electromagnetic waveform modeling to characterize the properties of the ice base and the evolution of the subglacial morphology of the Jutulstraumen drainage basin (western Dronning Maud Land, Antarctica). Based on the classification of the bed topography, we reconstruct the step-by-step modifications the subglacial landscape has experienced since the beginning of the glaciation of Antarctica, 34 million years ago. Between 2017 and 2020, we find evidence of active episodic cascade-like subglacial water transport along the subglacial valley network. In addition, our high-resolution radio-echo sounding data reveal a cluster of anomalous basal ice units whose material properties we constrain by electromagnetic waveform modeling. Through this, we aim to derive the physical conditions at the ice base, and establish a link to the subglacial hydrology system. The combination of these observations will represent an important step towards a better understanding of large-scale ice-sheet dynamics in western Dronning Maud Land.

Description: Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited.

Description: In the face of a warming Arctic, ice-rich permafrost landscapes are undergoing rapid changes. Ice-wedge polygonal networks in Arctic lowlands are especially vulnerable and melting ice wedges can induce widespread subsidence and trough formation. The transition from low-centered to high-centered polygons can have important implications on surface hydrology, as the connectivity of the newly forming trough networks determines the rate of drainage for these lowland landscapes. However, quantifying such dynamics can be challenging, as even small-scale changes can have far-reaching implications for the larger scale hydrology of a region. In this talk, we introduce an automated workflow that enables quantification of trough network dynamics in thaw-affected landscapes. We use methods from traditional computer vision to extract (a) the spatial pattern of the trough network and (b) the morphological parameters of trough width and depth from high-resolution digital terrain models. Finally, we (c) incorporate this information into graphs - a mathematical concept used to represent complex networks - and use graph analysis methods to determine progressing subsidence and trough formation. Based on a study area in the Anaktuvuk River Fire scar on the North Slope, Alaska, USA, we present the potentials and benefits of such graph algorithms for quantifying the erosional development of this thaw-affected landscape. In our study region, we observed an increase (+127%) in the number of discernible troughs as well as their connectivity (number of disconnected networks decreased by 89%) over the observed period of ten years. The average width of troughs has increased (+14.5%), while the depth has decreased (-12.5%). With this approach, for the first time, a large-scale analysis of such detailed ground-ice and hydrological surface dynamics is made possible.