EGU2020-13513
https://doi.org/10.5194/egusphere-egu2020-13513
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

BITE, the Bayesian Ice Thickness Estimation model

Mauro Werder1,2, Matthias Huss1,2,3, Frank Paul4, Amaury Dehecq1,2, and Daniel Farinotti1,2
Mauro Werder et al.
  • 1Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
  • 2Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
  • 3Department of Geosciences, University of Fribourg, Fribourg, Switzerland
  • 4Department of Geography, University of Zurich, Zurich, Switzerland

Accurate estimations of ice thickness and volume are indispensable for ice flow modelling, hydrological forecasts and sea-level rise projections. We present BITE, a new ice thickness estimation model based on a mass-conserving forward model and a Bayesian inversion scheme. The forward model calculates flux in an elevation-band flow-line model, and translates this into ice thickness and surface ice speed using a shallow ice formulation. Both ice thickness and speed are then extrapolated to the map plane. The model assimilates observations of ice thickness and speed using a Bayesian scheme implemented with a Markov chain Monte Carlo method, which calculates estimates of ice thickness and their error. We illustrate the model's capabilities by applying it to a mountain glacier, validate the model using 733 glaciers from four regions with ice thickness measurements, and demonstrate that the model can be used for large-scale studies by fitting it to over 30 000 glaciers from five regions. The results show that the model performs best when a few thickness observations are available; that the proposed scheme by which parameter-knowledge from a set of glaciers is transferred to others works but has room for improvements; and that the inferred regional ice volumes are consistent with recent estimates.

How to cite: Werder, M., Huss, M., Paul, F., Dehecq, A., and Farinotti, D.: BITE, the Bayesian Ice Thickness Estimation model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13513, https://doi.org/10.5194/egusphere-egu2020-13513, 2020

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