Topics Master in Earth Sciences ETH

**Background** Glaciers worldwide are retreating due to climate change, leading to diverse consequences such as sea level rise, changes in water availability, increased natural hazards, and disruptions to tourism. In the European Alps, glaciers are a key attraction, drawing millions of visitors annually. One of the touristic attractivities of glaciers is their role in skiing. However, climate change in and especially recent extreme negative mass balance years have significantly impacted summer skiing (e.g., Abegg and Mayer, 2023). This MSc project aims to assess the effects of climate change on glacier-based ski resorts across the Alps. Potential study sites include Hintertux, Les Deux Alpes, Saas Fee, and Tignes, though other locations may also be considered. **Goal** The goal of this MSc thesis is to apply an ice flow and mass balance model (Van Tricht and Huybrechts, 2023) to various Alpine glaciers that host ski resorts. Using existing models, ice thickness datasets, satellite imagery, mass balance observations, ski resort maps, and meteorological records, the research will simulate historical and future glacier evolution under climate change while accounting for the impact of ski resort activities. **Methodology & Workflow** 1. Familiarisation with the mass balance and ice flow model. 2. Data Collection, including: o Satellite data (snowlines) o Ice thickness measurements and reconstructions (what is available, should we collect?) o Meteorological records o Historical observations o Information from ski resorts (closing dates of the resorts, piste locations) 3. Model Adaptation to incorporate artificial snowmaking and snow farming (similar to tarps). 4. Model Execution & Evaluation of glacier mass balance and ice flow dynamics. 5. Future Simulations considering climate change scenarios and ski resort impact. **References** Abegg, B., & Mayer, M. (2023). The exceptional year of 2022: “deathblow” to glacier summer skiing in the Alps? Frontiers in Human Dynamics, 5, 1154245. Van Tricht, L., & Huybrechts, P. (2023). Modeling the historical and future evolution of six ice masses in the Tien Shan, Central Asia, using a 3D ice-flow model. The Cryosphere, 17, 4463–4485. https://doi.org/10.5194/tc-17-4463-2023

The goal of this MSc thesis is to apply an ice flow and mass balance model (Van Tricht and Huybrechts, 2023) to various Alpine glaciers that host ski resorts. Using existing models, ice thickness datasets, satellite imagery, mass balance observations, ski resort maps, and meteorological records, the research will simulate historical and future glacier evolution under climate change while accounting for the impact of ski resort activities.

For further information please contact Dr. Lander Van Tricht (vantricht@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

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)

Seismologists record seismic waves generated by earthquakes to monitor seismic activity and understand earthquake dynamics. However, earthquake recordings are often contaminated by a range of noise signals, which complicate the analysis and interpretation of earthquake signals, especially in automated processing workflows. With the rapid growth of earthquake data, reliable denoising techniques are becoming increasingly important. Effectively separating earthquake signals from noise is a game-changer for automated workflows, allowing for faster and more accurate signal processing across numerous recordings with minimal latency. You will develop state-of-the art methods to effectively “clean” time series of earthquake signals by building on existing deep learning models [e.g.,1,2,3] and incorporating your own creative solutions to design an optimal approach. The denoising performance will be evaluated using metrics relevant to seismic data analysis, benchmarked against conventional methods, and demonstrated on earthquake sequences. **Related Literature** - [1] https://doi.org/10.1029/2022JE007503 - [2] https://doi.org/10.1029/2024JH000179 - [3] https://doi.org/10.48550/arXiv.2309.05975

- Assess existing denoising models, identify their limitations, and propose improvements. - Design real-time monitoring solutions for earthquake early warning applications. - Integrate denoising methods into operational earthquake monitoring with continuous data streams. - Contribute your own ideas to advance earthquake monitoring! **Requirements** - You are excited to bring your strong data science and machine learning skills to advance earthquake monitoring through a Master’s thesis. - You enjoy programming in Python, TensorFlow / PyTorch, and are passionate about developing and implementing machine learning approaches to solve complex problems **Hosted by:** Nikolaj Dahmen, Men-Andrin Meier (Department of Earth and Planetary Sciences) **In cooperation with:** Simon Dirmeier (Swiss Data Science Center)

nikolaj.dahmen@eaps.ethz.ch