Topics Master in Earth Sciences ETH

Background: Ice temperature is a fundamental characteristic of any glacier, directly controlling how ice moves, which has immediate consequences for important large scale issues: For example, glacier thermomechanics dictate the rate at which ice is transported to margins of ice sheets, directly controlling the rate of sea level rise contributions from the ice sheets. Similarly, the thermal state of a glacier critically influences the ice stability and hence determines the threshold at which the glacier becomes a natural hazard. The recently compiled global englacial temperature database (glenglat) holds information about ice temperatures in over 700 boreholes on over 120 glaciers. Typically, glaciers are considered to be “temperate” (all ice is at the pressure melting point), “cold” (all ice is below the pressure melting point), or “polythermal” (a mix of cold and temperate). However, a single borehole is not sufficient to determine the thermal regime of an entire glacier, and not all boreholes reach the glacier bed, despite the importance of basal ice temperature for glacier thermodynamics. This study aims to overcome some of these challenges. Candidate profile: This project deals with a relatively large geospatial dataset (relational database) and benefits from some pre-existing skills in programming (ideally including experience with machine learning and statistics) and/or an interest in broadening these skillsets.

Goals: The goal of this study is to investigate glenglat to expand our knowledge of glacier thermal regimes by categorizing and extrapolating ice temperature profiles and investigating the links between climate and ice temperature through data-driven approaches and machine learning. The thesis aims to address the following questions: 1) How can we estimate the basal ice temperature in boreholes that do not reach the bed? 2) How can we group profiles based on their shape, what categories emerge, and what physical processes do they relate to? 3) Is it possible to predict glacier thermal regimes (or ice temperature) using data-driven approaches such as machine learning?

Ice avalanches are a hazard present in all glacierized areas of the world. For some glaciers, avalanching ice is part of the natural way they stay in balance with the climate. For others, ice avalanches are unusual and unpredictable. Today, climate change is driving changes that have the potential to shift the typical patterns, frequencies, and sizes of ice avalanches in ways we have yet to understand. To fill this knowledge gap, this project will analyze several multi-year datasets of ice avalanche activity available from operational monitoring systems.

The goal of this study is to perform a systematic analysis of camera images and ground based radar data from operational ice-avalanche warning and alarm systems. The available data will be analyzed with regard to ice avalanche frequency, size, timing, and ice flow velocities. The proposed thesis will be guided by the following overarching questions: 1) What is the frequency, volume, and timing of ice avalanches at the monitored sites and how do these compare to known past events? 2) What patterns (e.g., ice avalanche type, volume, timing) emerge from the analyzed datasets? 3) How do short-term weather conditions impact the occurrence of ice avalanches?

Dr. Mylène Jacquemart, Laboratory of Hydraulics, Hydrology, Glaciology (VAW) at ETH D-BAUG, jacquemart@vaw.baug.ethz.ch

High mountain regions are changing at unprecedented rates. With glaciers retreating permafrost thawing, mass movements such as rockfall, rock- or rock-ice avalanches are becoming an increasing concern. Conceptually, the effects of climate change on mass movements are relatively understood. However, we still lack quantitative information about how alpine mass movements are changing, especially with regard to their magnitude and frequency. One way of investigating where rockfalls or anomalous glacier changes have occurred is by analyzing maps of elevation change. In Switzerland, the Federal Office of Topography (Swisstopo) provides regularly update datasets of surface elevation. This study aims leverage these datasets and to expand our knowledge of where, when, and how alpine mass movements are happening in high-mountain regions of the Swiss Alps.

The goal of this study is to perform a systematic analysis of elevation change maps across the Swiss Alps (2018-2025). From this initial analysis we will build an event catalog that compiles information about the type, location, volume, timing, and runout of all events, which can then be used for further analysis. The proposed thesis will be guided by the following overarching questions: 1. Where, when, what kind of changes are occurring in the Swiss Alps (outside glacierized areas)? 2. What patterns (regional, elevation, geologic, timing) patters does a systematic analysis of alpine mass movements reveal across the Swiss Alps? 3. What connection to climatic changes (air temperature, glacier retreat) can be drawn from the event catalog? Candidate profile: This project deals with a relatively large geospatial dataset and benefits from some pre-existing skills in GIS and programming, and/or an interest in broadening these skillsets.

Dr. Mylène Jacquemart, Laboratory of Hydraulics, Hydrology, Glaciology (VAW) at ETH D-BAUG, jacquemart@vaw.baug.ethz.ch Elias Hodel, Laboratory of Hydraulics, Hydrology, Glaciology (VAW) at ETH D-BAUG; hodel@vaw.baug.ethz.ch