Subglacial bedload export quantification and subglacial drainage network evolution inferred using environmental seismology techniques

Alpine glaciers have been retreating at increasing rates in recent decades due to climate warming. As a consequence, large amounts of suspended and bedload flux are exported from subglacial channels to proglacial environments, such as proglacial forefields. To date, our understanding of subglacial sediment export by subglacial streams has been predominantly shaped by suspended sediment dynamics recorded in front of shrinking glaciers, primarily due to difficulties in measuring bedload transport. Bedload transport is typically monitored far downstream from glacier termini at permanent monitoring stations (e.g. water intakes), leaving significant uncertainties regarding the absolute quantities and temporal patterns of transport in both glacial and proglacial environments, as well as its relative importance compared to suspended sediment in the context of proglacial morphodynamic filtering. Recent advancements in environmental seismology have addressed this knowledge gap. Given this, the aim of this project was to develop a novel technique for calibrating the Fluvial Model Inversion (FMI) model of Dietze et al. (2019) to quantify, for the first time, the total subglacial bedload export from an Alpine glacier and to investigate the physical mechanisms driving it.

This work focuses on a large Alpine glacier, the Glacier d’Otemma, located in the Southwestern Swiss Alps (Canton Valais). Continuous seismic data were collected in close proximity to the glacier terminus using a DATA-CUBE type 2 datalogger connected to a three-component PE-6/B geophone, over two entire melt seasons (June to September 2020 and 2021) experiencing different climatic conditions: the first year was warm and relatively dry, while the second was cold and relatively wet.

The seismic ground parameter values of the FMI model used to invert the raw seismic data into bedload transport were determined by adopting a Monte Carlo simulation based on a Generalized Likelihood Uncertainty Estimation (GLUE) approach. This involved iteratively running thousands of inversions within predefined ranges of possible ground seismic parameter values. The methodology was validated by comparing parameter values and model outputs to those obtained using a more conventional active seismic survey.

Results indicate that the developed methodology for calibrating the inversion model is promising and comparable to those derived from the more demanding active seismic survey technique. Scientifically, findings reveal a strong agreement between subglacial bedload export rates and the snowline altitude during the melt season. Extremely warm summers are associated with the exhaustion of subglacial bedload sources as the progressive rise of the snowline altitude fully exposes the glacier's bare ice, while cooler summers show the opposite pattern. This highlights the existence of a link between atmospheric temperature, subglacial drainage network extension, and bedload output rates. These results are crucial for advancing our understanding of the relationship between subglacial sediment export and meteorological conditions in a warming climate.