- 1IGE-CNRS, Université Grenoble Alpes, Grenoble France (juliette.bonnet2@univ-grenoble-alpes.fr)
- 2ONF-RTM, Grenoble, FRANCE
The thermal regime of glaciers plays a critical role in their dynamics and potential hazards. In particular, impermeable cold ice can trap and store liquid water leading to the formation of intraglacial water pockets, as in the Tête Rousse Glacier (France), where the sudden drainage of over 100,000 m³ of water in 1892 caused 175 fatalities and significant damage to the village of Saint-Gervais.
The objective of this work is to produce a detailed thermal regime map of Alpine glaciers to identify those most susceptible to host this type of thermic water pockets. To achieve this, we use a thermo-mechanical ice flow model based on the Elmer/Ice code to simulate the thermal structure of synthetic 2D glacier profiles. The model is based on the enthalpy formulation, where the surface boundary conditions are computed by a subgrid model solving for meltwater percolation and refreezing in the firn. The synthetic 2D profiles are chosen to be representative of the morphological and climatic diversity of the Alps with various length, slope, bedrock shape, elevation range, aspect, and snow accumulation distribution.
The outputs of these simulations form a large database of glacier thermal structures, which will serve as the training dataset for a machine learning emulator currently under development. This emulator will provide a tool to infer thermal regimes to the scale of the entire Alpine region, predicting basal temperatures based on glacier morphology.
The results from the 2D simulations suggest that snow accumulation patterns play a dominant role in shaping glacier thermal regimes: (i) upstream over-accumulation promotes percolation and refreezing of liquid water, releasing latent heat and warming the glacier locally, while (ii) exposed ice downstream acts as an impermeable thermal barrier, creating favorable conditions for water storage.
This integrated approach, combining detailed physical modeling with machine learning techniques, provides a way to build a tool that can be easily applied at a large scale while accounting for the complex interactions that determine the thermal regime of glaciers. In the longer term, it seeks to deliver a comprehensive thermal regime map of Alpine glaciers, providing a valuable resource for identifying glaciers most at risk of hosting intraglacial water pockets and improving the prevention and management of glacier-related hazards.
How to cite: Bonnet, J., Gilbert, A., Ozenda, O., and Gagliardini, O.: Regional scale thermal regime mapping of Alpine glaciers inferred from 2D thermo-mechanical modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6245, https://doi.org/10.5194/egusphere-egu25-6245, 2025.