A Bayesian ice thickness estimation model for large-scale applications

Accurate estimations of ice thickness and volume are indispensable for ice flow modelling, hydrological forecasts and sea-level rise projections. We present a new ice thickness estimation model based on a mass-conserving forward model and a Bayesian inversion scheme. The forward model calculates flu...

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Bibliographic Details
Published in:Journal of glaciology 2020-02, Vol.66 (255), p.137-152
Main Authors: Werder, Mauro A., Huss, Matthias, Paul, Frank, Dehecq, Amaury, Farinotti, Daniel
Format: Article
Language:English
Subjects:
Bayesian analysis
Computer simulation
Elevation
Estimates
glacier flow
Glacier modelling
glacier volume
Glaciers
Hydrologic forecasting
Hydrologic models
Hydrology
Ice
Ice cover
Ice thickness
Markov chains
Monte Carlo simulation
Mountain glaciers
Mountains
Physics
Probability theory
Regions
Sea level
Sea level forecasting
Sea level rise
Statistical methods
Thickness measurement
Topography
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Summary:Accurate estimations of ice thickness and volume are indispensable for ice flow modelling, hydrological forecasts and sea-level rise projections. We present 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.
ISSN:0022-1430
1727-5652
DOI:10.1017/jog.2019.93