BITE, the Bayesian Ice Thickness Estimation model
- 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