Publications

Ground penetrating radar (GPR) is an effective tool in cryosphere and climate research, as it can provide detailed, non-invasive insights into ice thickness, internal structures, and subglacial conditions. This technology uncovers critical data on glacier dynamics and climate change impacts, enhancing our understanding of past, present, and future environmental shifts. In this contribution, the design and experimental verification of a lightweight, surface-based GPR platform intended for imaging glacial subsurface structures is presented. Therein, the system requirements for glaciological applications and the design implications for the developed platform and its components are described. In addition, a detailed overview of the utilized radar system, including the 3D-printed horn antennas and the localization concept, is provided. Furthermore, the imaging properties of the developed system are introduced, and the processing chain to retrieve subsurface images from the raw radar data using synthetic aperture radar concepts is presented. The platform was tested during a field campaign in March 2024 on the Jungfraufirn glacier in Switzerland. The data from this field campaign provide detailed imaging results of the glacier subsurface, including its stratification with high resolution and contrast. Moreover, a comparison of our rather broadband ultra-high frequency GPR measurements to the data acquired with a high-performance state-of-the-art low frequency GPR system is provided. Finally, this contribution concludes with current limitations and an outlook on future improvements.

https://ieeexplore.ieee.org/document/10669622

The regional climate of New Zealand’s South Island is shaped by the interaction of the Southern Hemisphere westerlies with the complex orography of the Southern Alps. Due to its isolated geographical setting in the south-west Pacific, the influence of the surrounding oceans on the atmospheric circulation is strong. Therefore, variations in sea surface temperature (SST) impact various spatial and temporal scales and are statistically detectable down to temperature anomalies and glacier mass changes in the high mountains of the Southern Alps. To enable future studies on the processes that govern the link between large-scale SST and local-scale high-mountain climate, we utilized dynamical downscaling with the Weather Research and Forecasting (WRF) model to produce a regional atmospheric modelling dataset for the South Island of New Zealand over a 16-year period between 2005 and 2020. The 2 km horizontal resolution ensures realistic representation of high-mountain topography and glaciers, as well as explicit simulation of convection. The dataset is extensively evaluated against observations, including weather station and satellite data, on both regional (in the inner domain) and local (on Brewster Glacier in the Southern Alps) scales. Variability in both atmospheric water content and near-surface meteorological conditions is well captured, with minor seasonal and spatial biases. The local high-mountain climate at Brewster Glacier, where land use and topographic model settings have been optimized, yields remarkable accuracy on both monthly and daily time scales. The data provide a valuable resource to researchers from various disciplines studying the local and regional impacts of climate variability on society, economies and ecosystems in New Zealand. The model output from the highest resolution model domain is available for download in daily temporal resolution from a public repository at the German Climate Computation Center (DKRZ) in Hamburg, Germany (Kropač et al., 2023; 16-year WRF simulation for the Southern Alps of New Zealand, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/NZ-PROXY_16yrWRF).

https://rmets.onlinelibrary.wiley.com/doi/10.1002/gdj3.263

Glaciers across the globe react to the changing climate. Monitoring the transformation of glaciers is essential for projecting their contribution to global mean sea level rise. The delineation of glacier-calving fronts is an important part of the satellite-based monitoring process. This work presents a calving-front extraction method based on the deep learning framework nnU-Net, which stands for no new U-Net. The framework automates the training of a popular neural network, called U-Net, designed for segmentation tasks. Our presented method marks the calving front in synthetic aperture radar (SAR) images of glaciers. The images are taken by six different sensor systems. A benchmark dataset for calving-front extraction is used for training and evaluation. The dataset contains two labels for each image. One label denotes a classic image segmentation into different zones (glacier, ocean, rock, and no information available). The other label marks the edge between the glacier and the ocean, i.e., the calving front. In this work, the nnU-Net is modified to predict both labels simultaneously. In the field of machine learning, the prediction of multiple labels is referred to as multi-task learning (MTL). The resulting predictions of both labels benefit from simultaneous optimization. For further testing of the capabilities of MTL, two different network architectures are compared, and an additional task, the segmentation of the glacier outline, is added to the training. In the end, we show that fusing the label of the calving front and the zone label is the most efficient way to optimize both tasks with no significant accuracy reduction compared to the MTL neural-network architectures. The automatic detection of the calving front with an nnU-Net trained on fused labels improves from the baseline mean distance error (MDE) of 753±76 to 541±84 m. The scripts for our experiments are published on GitHub (https://github.com/ho11laqe/nnUNet_calvingfront_detection, last access: 20 November 2023). An easy-access version is published on Hugging Face (https://huggingface.co/spaces/ho11laqe/nnUNet_calvingfront_detection, last access: 20 November 2023).

https://tc.copernicus.org/articles/17/4957/2023/

Transport of snow by the wind can have high impact on local glacier mass changes as it leads to non-uniform amounts of snow on the ground. In order to simulate and better understand this process we introduce a new modeling framework that is included into the widely used atmospheric ‘Weather Research and Forecasting (WRF)’ model. Test simulations and sensitivity experiments show the physical consistency of the model. Complex interactions between different processes like snow erosion, drifting snow sublimation and the wind field show the necessity of coupling the snow and atmospheric models.

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023MS004007

Supraglacial lakes in Greenland are highly dynamic hydrological features in which glacial meltwater cumulates, allowing for the loss and transport of freshwater from a glacial surface to the ocean or a nearby waterbody. Standard supraglacial lake monitoring techniques, specifically image segmentation, rely heavily on a series of region-dependent thresholds, limiting the adaptability of the algorithm to different illumination and surface variations, while being susceptible to the inclusion of false positives such as shadows. In this study, a supraglacial lake segmentation algorithm is developed for Sentinel-2 images based on a deep learning architecture (U-Net) to evaluate the suitability of artificial intelligence techniques in this domain. Additionally, a deep learning-based cloud segmentation tool developed specifically for polar regions is implemented in the processing chain to remove cloudy imagery from the analysis. Using this technique, a time series of supraglacial lake development is created for the 2016 to 2022 melt seasons over Nioghalvfjerdsbræ (79°N Glacier) and Zachariæ Isstrøm in Northeast Greenland, an area that covers 26,302 km² and represents roughly 10% of the Northeast Greenland Ice Stream. The total lake area was found to have a strong interannual variability, with the largest peak lake area of 380 km² in 2019 and the smallest peak lake area of 67 km² in 2018. These results were then compared against an algorithm based on a thresholding technique to evaluate the agreement of the methodologies. The deep learning-based time series shows a similar trend to that produced by a previously published thresholding technique, while being smoother and more encompassing of meltwater in higher-melt periods. Additionally, while not completely eliminating them, the deep learning model significantly reduces the inclusion of shadows as false positives. Overall, the use of deep learning on multispectral images for the purpose of supraglacial lake segmentation proves to be advantageous.

https://www.mdpi.com/2072-4292/15/17/4360

The Antarctic Peninsula Ice Sheet (APIS) has become a significant contributor to rising sea levels, and accurately estimating ice discharge from its outlet glaciers is essential for assessing the mass balance of the region. This study calculates ice discharge from APIS outlet glaciers north of 70°S using five commonly used ice-thickness reconstructions, employing a consistent surface velocity field and flux gates. Results indicate a total volumetric ice discharge ranging from 45 to 141 km3 per year for 2015–2017, with a mean of 87 ± 44 km3 per year. The substantial differences in results highlight the large uncertainty in current ice-discharge estimates, emphasizing the challenge of accurately modeling the ice-thickness distribution in this complex and data-scarce region.

https://www.cambridge.org/core/journals/annals-of-glaciology/article/estimating-ice-discharge-of-the-antarctic-peninsula-using-different-icethickness-datasets/67B2F8FC77CD9342BD07DAACA41497AF

Existing mass budget estimates for the northern Antarctic Peninsula (>70° S) are affected by considerable limitations. We carried out the first region-wide analysis of geodetic mass balances throughout this region (coverage of 96.4 %) for the period 2013–2017 based on repeat pass bi-static TanDEM-X acquisitions. A total mass budget of −24.1±2.8 Gt/a is revealed. Imbalanced high ice discharge, particularly at former ice shelf tributaries, is the main driver of overall ice loss.

https://tc.copernicus.org/articles/17/4629/2023/tc-17-4629-2023.html

Advancements in Deep Learning have enabled the automated identification of glacier calving fronts in satellite imagery. This study improves the accuracy of this process by incorporating a Conditional Random Field (CRF) into the post-processing of the neural network’s predictions. Experiments using the CaFFe dataset showed a 27-meter improvement in mean distance error. The code is available at https://github.com/EntChanelt/GlacierCRF.

https://ieeexplore.ieee.org/document/10282915

Research on surge-type glaciers, although they constitute a small percentage of all glaciers, significantly contributes to understanding glacier flow mechanisms. Recent studies have utilized remote sensing techniques to unravel these processes, highlighting the potential of combining high-performance computing and earth observation. While modeling surge events has gained popularity, there is a lack of comprehensive spatial and temporal data on surge timing. To address this, an algorithm has been developed that not only detects surge-type glaciers but also determines the onset of surges. The algorithm relies on time series analysis of glacier surface velocity using Sentinel-1 data, involving seasonal and trend decomposition and outlier detection via the General Studentized Extreme Deviate Test. Cluster analysis is then applied to identify outlier clusters associated with glacier surges. The method’s effectiveness was demonstrated in the Svalbard archipelago between 2015 and 2021, where 18 glacier surges and their timing were successfully identified.

https://www.mdpi.com/2072-4292/15/6/1545