Theses glaciology

Snow accumulation is a critical factor influencing the mass balance of alpine glaciers. To enhance our understanding of accumulation processes and their representation in advanced mass-balance models, as well as to effectively calibrate these models, extensive field measurements are essential. Traditionally, such measurements have relied on snow probing and density sampling at a limited number of locations, followed by extrapolation to achieve spatial coverage. In recent years, advancements in remote sensing technologies have enabled the generation of high-resolution elevation change maps with exceptional accuracy (Sold et al., 2013). These innovations offer the potential to estimate snow depths at an unprecedented spatial resolution, providing critical data for calibrating mass-balance models and gaining new insights into the spatial variability of snow deposition.

The aim of this MSc thesis is to create a very high-resolution snow depth map of the ablation zone of the **Pers and Morteratsch glaciers** using UAV (drone) data. This map will then be compared with snow depth measurements obtained from probings during GLAMOS winter surveys. A key objective will be to determine the precision required for accurate measurements and to identify and address potential pitfalls. A significant part of the research will also involve optimising and calculating the vertical ice-flow velocity to correct for ice movement. Once the snow depth map is obtained, spatial patterns can be analyzed, and their underlying causes can be investigated. **Intermediate steps:** i. Get familiar with UAV data and snow probing techniques. ii. Collect UAV data during the GLAMOS winter surveys by flying a DJI Phantom 4 RTK drone. On the same day, perform snow probings and snow density measurements to infer local snowdepths over the glacier. iii. Reconstruct the Digital Elevation Model (DEM) of the ablation area of the Pers and Morteratsch glaciers and calculate elevation changes with respect to previous DEMs (existing since 2017). iv. Determine vertical ice velocities over the glacier using a mass balance model through applying the continuity equation method (vertical velocity = mass balance – ice thickness change). For Morteratsch glacier, this mass balance model needs to be setup by calibrating it with mass balance observations that exist since 2001. v. Derive snow depth from the UAV inferred DEMs and the vertical velocities and compare with snow probing measurements. vi. Analyse snow depth patterns and explore their relationship with geometric and meteorological variables. vii. Improve the mass balance model by implementing different snow distribution patterns. **Potential opportunities:** - Join GLAMOS winter surveys to perform drone surveys and snow probings - Collaborate with Belgian partner (Vrije Universiteit Brussel) and possibility for a short-term research stay in Belgium

For further information please contact Dr. Lander Van Tricht (vantricht@vaw.baug.ethz.ch)

In this MSc thesis you will apply a mass balance/ice flow model for two glaciers in Kyrgyzstan (Central Asia), Bordu and Sary-Tor, between 2013 and 2024. You will make use of an (existing) mass balance model (Van Tricht et al., 2021), drone data, satellite data (albedo) and temperature and precipitation measurements in the vicinity of the glaciers. The main goal is to analyse the (potential) role of the nearby Kumtor gold mine on the glacier’s mass balance. The preliminary steps of the thesis are: i. Getting familiar with the surface mass balance model ii. Collecting all data (satellite, drone, meteorological, surface samples) iii. When fieldwork can be performed, laboratories analysis of surface samples iv. Model runs and evaluation v. Sensitivity analysis and role of dust vi. Prognostic simulations taking into account the influence of the gold mine **Potential opportunities:** - Collaborate with Swiss partners (Fribourg) that performed a similar analysis on glaciers in the Andes (Barandun et al., 2022). - Join fieldwork in Kyrgyzstan (August 2025) to perform drone surveys and collect surface samples on several glaciers - Collaborate with Belgian partner (Vrije Universiteit Brussel) and possibility for a short-term research stay in Belgium **References:** - Barandun, M., Bravo, C., Grobety, B., Jenka, T., Fang, L., Naegeli, K., Rivera, A., Cisternas, S., Münster, T., and Schwikowski, M.: Anthropogenic influence on surface changes at the Olivares glaciers; Central Chile, Sci. Total Environ., 833, 155068, 2022 - Van Tricht, L., Paice, C. M., Rybak, O., Satylkanov, R., Popovnin, V., Solomina, O., and Huybrechts, P.: Reconstruction of the Historical (1750–2020) Mass Balance of Bordu, Kara-Batkak and Sary-Tor Glaciers in the Inner Tien Shan, Kyrgyzstan, Front. Earth Sci., 9, 734802, 2021

Investigate the role of a gold mine on the albedo and (future) evolution of a Kyrgyz glacier.

For further information please contact Dr. Lander Van Tricht (vantricht@vaw.baug.ethz.ch)

The thesis focuses on the application of a glacier mass balance (MB) model based on machine learning, driven by climate variables and topographical features. The model is part of the Mass Balance Machine (https://github.com/ODINN-SciML/MassBalanceMachine), a collaborative project that aims to create an open-source package for machine learning MB modeling. While the Mass Balance Machine is currently being applied to the Swiss Alps and Norwegian glaciers, the thesis aims at investigating its transferability to the French Alps. The project requires knowledge in programming (Python) and while it is not restricted to computer science students, it expects some familiarity with data-sciences and machine learning methods.

Package development: participate in developing the Mass Balance Machine package by tailoring it to the French Alps and producing unit tests. Science: make predictions of glacier-wide and regional mass balance for the French Alps. This will encompass data gathering, pre-processing, and the development of a testing and validation pipeline.

For further information please contact Marijn van der Meer

Anthropogenic climate warming is causing rapid melting and retreat of mountain glaciers worldwide, which is of particular concern for regions that depend on them for water resources. One of the main uncertainties in modelling the long-term mass balance of mountain glaciers and their contribution to downstream hydrology is related to accumulation processes. Indeed, glaciers accumulate mass at high elevations in the form of snow, which densifies into ice and then flows downstream where it melts. However, precipitation volumes are very difficult to constrain in mountain regions. Moreover, snow does not accumulate evenly on the entire glacier surface: wind-driven transport and avalanching redistribute snow horizontally, which results in spatially variable accumulation rates. The effect of these processes is particularly difficult to quantify.

The goal of this project is to determine the accumulation rate of 4 glaciers in the Swiss Alps and Central Asia by combining measurements of ice thickness variations and surface velocity fields derived from satellite images. The Instructed Glacier Model (IGM), which uses deep learning constrained by physics, will be applied to invert the ice flux based on surface velocity observations and thus obtain 3D velocity fields. Net accumulation can then be calculated as the difference of the vertical component of the velocity and the elevation changes obtained from high resolution Digital Elevation Models. These results will be validated with field measurements and compared to the outputs from a spatially distributed snow mass and energy balance model, which will help identify the contribution of the different snow accumulation and redistribution processes to the spatial variability in winter accumulation and glacier mass balance.

For further information please contact Marin Kneib (marin.kneib@univ-grenoble-alpes.fr) or Giulia Mazzotti (mazzotti@vaw.baug.ethz.ch).

Accurate modelling of glacier mass balance requires the representation of wind-driven snow processes. Yet, the physical representation of these processes is computationally too expensive to include in standard mass balance models and, therefore, requires simplification via parameterization schemes.

This thesis will compare existing but not widely used parameterizations for different climatic regions (continental Alpine glaciers, maritime glaciers in New Zealand, and seasonal snow cover). The aim is to consolidate existing parameterizations for applications in standard energy and mass balance models.

For further information please contact Dr. Ruzica Dadic

In the framework of the Swiss glacier monitoring, mass balance is measured on Rhonegletscher. Since 2011 extensive surveys with snow depth probings in spring are complemented with GPR measurements along a longitudinal transect. Density information is needed to determine the water equivalent of the snow layer. In addition, the propagation of the electromagnetic wave is required to transform GPR travel times into correct snow thicknesses. This depends on the material properties essentially defined again by the bulk density of the snow layer. The task will be the processing of the GPR data recorded in April 2025 based on the strategy established for previous surveys. Evaluation will include the interpretation of the subsurface structure and the analysis of the snow layer accumulated during the winter 2024/25. Results will be compared to the independent snow depth sampling and the results of the previous surveys. This will allow to investigate the inter-annual variations of the spatial distribution. In addition an assessment of the density and electromagnetic wave velocity, both critical parameter of the data evaluation, is envisaged.

Better understanding of the spatial snow depth distribution. Assessment of snow density and the potential of electromagnetic wave velocity for bulk density estimates.

For further information please contact: Andreas Bauder (bauder@vaw.baug.ethz.ch) or Hansruedi Maurer (hansruedi.maurer@erdw.ethz.ch)

The Great Aletsch Glacier lies in central Switzerland, and has been selected as a super test site for the TanDEM-​X mission. Since 2011, dense time series of SAR data has been collected over the glacier, allowing for a detailed examination of the glacier status. In the project, the student is expected to 1) generate DEM of the glacier from the TanDEM-​X imagery using the provided toolchain, 2) generate velocity maps of the glacier with the same dataset using a developed offset tracking algorithm, and 3) analyse the time series of DEM and velocity maps to characterize the glacier height changes and velocity dynamics. The student will learn essential SAR data processing techniques and get hands-​on experience of using advanced SAR image processing software. He/she also finds the project a good opportunity to apply remote sensing data to real-​world problem solving.

Shiyi Li (Shiyi.li@ifu.baug.ethz.ch), Irena Hajnsek (hajnsek@ifu.baug.ethz.ch)

Antarctic ice streams feed approximately 90% of Antarctica’s ice into the world’s oceans. These ice streams flow to the ocean through a combination of internal deformation and by sliding over and deforming the rock and sediment that underlies them. Processes at the base of the ice are particularly important to understand as they can result in rapid changes in ice flow velocity at time scales of hours to days and years to centuries. Many of these processes emit acoustic signals which can be recorded at the ice surface making passive-source seismology an ideal tool with which to study them. This project will use existing data to examine the transition where the grounded Kamb Ice Stream crosses into the Ross Ice Shelf and contributes to global sea level.

Project aims include identifying, locating, and characterising seismicity from sources at the base of the ice stream and within the ice shelf. These sources include water flow in a large subglacial drainage channel, and crevasse formation due to flexure of the floating ice shelf as it rises and falls with the tide. To achieve the aims you will use existing tools (e.g. obspy) to process and analyse the data. Basic programming skills, or the desire to develop them, are required. The ideal candidate would also have an interest in glaciology and applied geophysics. Should time allow, the results will be compared with other complementary data (e.g. Global Navigation Satellite System positioning) and model output (e.g. subglacial water routing.)

For further information please contact Dr. Huw Horgan (horganh@ethz.ch)

This project will investigate the acceptance of covering glaciers with geotextiles to slow down their melting process. A survey should lead to understand how people react to such engineering solutions depending on both the spatial extent and the geographical positioning of the intervention, as well as to auxiliary information that is provided on e.g. the effectiveness and drawbacks of such measures as well as people’s relationships to the place. The survey should be based on visual reconstructions of the change caused by the covering at various scales.

The survey should involve scenario visualization using 3D models based on LiDAR surveys, distributed as the open dataset “SwissSurface3D” by Swisstopo and time series of both digital elevation models and repeated photographies acquired over glacier sections that have been covered in the past. A digital model will be created to visually simulate scenarios of "melt with cover" and "melt without cover" according to Representative Concentration Pathways (RCP) of climate change scenarios. Information about the effectiveness of such measures in suppressing glacier melt, as well as potential negative impacts of the measures, shall be retrieved from existing literature.

For further information please contact Prof. Dr. Adrienne Grêt-Regamey (gret@ethz.ch) or Prof. Dr. Daniel Farinotti (daniel.farinotti@ethz.ch)