EGU24-13124, updated on 09 Mar 2024
https://doi.org/10.5194/egusphere-egu24-13124
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Exploring englacial hydrology with surface nuclear magnetic resonance

Laura Gabriel1,2, Marian Hertrich3, Raphael Moser1,2, Christophe Ogier1,2, Hansruedi Maurer4, and Daniel Farinotti1,2
Laura Gabriel et al.
  • 1Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland (laura.gabriel@vaw.baug.ethz.ch)
  • 2Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
  • 3Bedretto Underground Laboratory for Geosciences and Geoenergies, ETH Zurich, Zurich, Switzerland
  • 4Institute of Geophysics, ETH Zurich, Zurich, Switzerland

The amount and distribution of liquid water inside a glacier are relevant for its dynamics, related natural hazards or for sediment transport. Experimentally investigating the glacier's hydrology is challenging because of restricted accessibility, investigation depth, material properties, and environmental factors. In addition, the subglacial drainage network is highly dynamic and undergoes diurnal and seasonal changes.

This contribution investigates the application of surface nuclear magnetic resonance (SNMR) to characterize the liquid water distribution within Swiss Alpine glaciers. Analogous to magnetic resonance imaging (MRI) in medicine, SNMR utilizes an oscillating magnetic field to excite nuclear spins of hydrogen atoms within water molecules. The subsequent spin relaxation is then analyzed, providing insights into the probed material. In simpler terms, this process allows us to directly detect liquid water in ice and gain information on its spatial distribution.

We conducted a first SNMR field survey on Rhonegletscher in the summer of 2023. During this survey, we tested various measurement configurations, including separate-loop measurements and the application of noise-compensation loops. The latter proved crucial for subsequent data processing. After carefully optimizing the processing scheme, we extracted SNMR signals in several recordings despite the poor signal-to-noise ratio. The results were compared to 1D forward-modelled data, suggesting that the average water content in the survey area lay between 0.7 and 1.2 %. In addition, we could show that a homogenous water distribution over the entire ice column cannot explain the observed data and that we need to consider more complex subsurface models including at least one additional water layer. Specifically, our ongoing research aims to identify which configurations of the subglacial water distribution (e.g., homogenous water distribution vs layered water-ice structure resulting from an englacial water channel) are distinguishable experimentally. Moreover, the study seeks to optimize measurement design and data processing methodologies to acquire information more efficiently, and effectively handle the expected low signal-to-noise ratios.

In future field campaigns, we intend to deploy SNMR for selected glaciological case studies within the Swiss Alps. A primary focus will be on efficiently detecting water pockets that may pose a potential risk of downstream flooding upon rupture. Similarly, we want to investigate the extent to which we can distinguish cold from temperate ice.

How to cite: Gabriel, L., Hertrich, M., Moser, R., Ogier, C., Maurer, H., and Farinotti, D.: Exploring englacial hydrology with surface nuclear magnetic resonance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13124, https://doi.org/10.5194/egusphere-egu24-13124, 2024.

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