Observational data and neural network-accelerated modelling provide valuable insights for glacier thickness reconstruction in High Mountain Asia

Mountain glaciers provide an irreplaceable water resource in High Mountain Asia, with a significant proportion of water input to rivers coming from glacial meltwater. However, the volume of water held in these glaciers and their evolution over the coming decades is subject to great uncertainty. The reliability of existing glacier ice thickness estimates in High Mountain Asia is limited by the use of low-order models, known to be locally unreliable on mountain glaciers, to describe the relationship between ice velocity and thickness, and by the scarcity of measured thicknesses available for constraint and validation at the time those estimates were produced. We use the Instructed Glacier Model (v2.2.3), a deep-learning-based high-order ice flow model with the capability to invert observed glacier surface velocity for ice thickness, to construct an estimated thickness map of Bhote Kosi glacier. Our thickness inversion is constrained using data collected via a novel airborne radar method for measuring ice thickness. We perform an in-depth case study, carefully justifying inversion parameter choices and quantifying the accuracy of our results. We demonstrate that in the absence of thickness observations, results can be optimized via the use of an L-curve to select the regularization parameter, with significant bias in the unconstrained results, but comparable accuracy to leading thickness estimates. We find that while thickness-constrained inversions are able to correct the modelled thickness field where there is limited information from observed surface velocity, cross-validation experiments demonstrate that the “interpolative power” of thickness observations is weak.