Our doctoral candidates participated in an activity to make their research better accessible to the broad public. Many of them therefore created short videos where they briefly present themselves and their work. Check it out here:
(the webcam video shown in Manuels video is taken from www.foto-webcam.eu)
Do you wonder what mechanism caused the massive rock avalanche at Piz Scerscen (Bernina, CH) in spring 2024 (link to DAV-report)? Read the paper: >here<.
Felix Pfluger (TUM, funded by M³OCCA) and colleagues investigated glacier changes, conducted fieldwork on permafrost at 3200 m asl, rock mechanical laboratory studies (Joseph Steinhauser as part of his Bachelor Thesis) and mechanical modeling on a slope scale to infer permafrost–glacier interactions and their implications for triggering high-volume rock slope failures. Using the Bliggspitze rock slide as a case study (Austria, Tyrol), we demonstrate a new type of rock slope failure mechanism triggered by the uplift of the cold–warm dividing line in polythermal alpine glaciers, a widespread and currently under-explored phenomenon in alpine environments worldwide. The publication features a holistic discussion on the role of meltwater and water infiltration/migration in bedrock enabling the buildup of hydrostatic pressure that eventually triggered the rock slide.
With this research, we advance our understanding of coupled processes in complex slope failures situated in the cryosphere.
It was realized as a joint collaboration with researchers from TUM, SLF, BOKU, and FAU.
Nora Gourmelon was interviewed as part of the series FAU Alumni #MyStory. In the interview she talks about her passion for working with “Green AI”, her research and her path to becoming a doctoral student at FAU.
Click here to watch the full interview!
This week, Professor Doug Benn from the University of St. Andrews visited our institute and gave a guest lecture as part of the weekly M3OCCA Research Seminar.
The topic of his lecture was “Icy Oscillators: Understanding glacier surges”
Glacier surges are important but often misunderstood glacier accelerations, which have been observed in many parts of the Arctic, High Mountain Asia, and a few other areas of the world. In this talk, Professor Benn will discuss the geographical distribution of surge-type glaciers and its relationships with climate and glacier geometry. He will go on to show how these patterns can be understood using enthalpy balance theory, a new unifying framework for modelling glacier dynamics. The talk will conclude with a discussion of unsolved problems and possible new directions for research.
During his visit, we had lively discussions on our various research topics and on potential future collaboration. A social gathering with some local cuisine in the evening rounded off his visit.
Thank you again for your wonderful lecture!
This year’s annual workshop of the International Doctoral Program M3OCCA took place at ‘Krone Kinding’ located in the Altmühltal. The doctoral researchers presented the current status of their research projects and gave an outlook on upcoming activities. The M3OCCA-affiliated project ‘Deep-Learning-Informed Glacio-Hydrological Threat’ (DELIGHT), led by Dr. Samual Cook, was officially introduced and the new colleagues presented to all participants. An important point for discussion within the group of doctoral researchers and PIs was the prolongation proposal for the programme. One of the highlights was the guest lecture from Chad Greene (JPL/Caltech, USA) on ‘Remote Sensing of Glaciers and Ice Sheets’. Socializing activities included a hike through the Altmühltal including a stop at the geographical center of Bavaria and a quiz night on the second evening.
Photo copyright: Oskar Herrmann & Akash Patil
At the “Klimatag” (Climate Day) at Paul-Pfinzing-Gymnasium Hersbruck, Thorsten Seehaus (M3OCCA PI) and Philipp Malz from the Institute of Geography at FAU presented the latest findings from glacier research and informed the pupils about the effects of climate change on glaciers. An interactive glacier quiz rounded off the event and ensured fun and lively interest among the pupils. With this participation, the Institute of Geography is committed to raising awareness of climate change and getting young people interested in climate protection and geography.
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.
Our team had the opportunity to represent the M3OCCA doctoral program at the “Meile der Wissenschaft” during the Schlossgartenfest in Erlangen. This unique event, which brings together FAU staff and prominent figures from the region, was a blend of science, culture, alongside dancing and international cuisine until late into the night.
We were one of three scientific booths, where we presented our research on glacier modeling to a broader public. Visitors engaged in insightful conversations about our work, exploring the importance of glaciers and the impacts of climate change. We showcased the instruments and methods we use to monitor glacier changes, and participants enjoyed an interactive 3D visualization dashboard, which demonstrated glacier projections under various climate scenarios.
Our tent provided a cool space for discussions on a record-breaking hot night (thanks to an ice cube machine!), all while being dressed up in style for the occasion. The event offered a wonderful chance to share our work with the public and highlight the crucial role of glaciers in understanding our changing planet.
Photo copyright: Veena Prasad
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).
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).