Reply on RC1

Abstract

The subglacial bed topography is critical for modeling the evolution of Thwaites Glacier in the Amundsen Sea Embayment (ASE), where rapid ice loss threatens the stability of the West Antarctic Ice Sheet. However, mapping of subglacial topography is subject to high uncertainty. This is mainly because the bed topography is measured by airborne ice-penetrating radar along flight lines with large gaps up to tens of kilometers. Deterministic interpolation approaches do not reflect such spatial uncertainty. While traditional geostatistical simulation can model such uncertainty, it may be difficult to apply because of the significant non-stationary spatial variation of topography over such large surface area. In this study, we develop a non-stationary multiple-point geostatistical approach to interpolate large areas with irregular geophysical data and apply it to model the spatial uncertainty of entire ASE basal topography. We collect 166 high-resolution topographic training images (TIs) to train the gap-filling of radar data gaps, thereby simulating realistic topography maps. The TIs are extensively sampled from deglaciated regions in the Arctic as well as Antarctica. To address the non-stationarity in topographic modeling, we introduce a Bayesian framework that models the posterior distribution of non-stationary training images to the local modeling domain. Sampling from this distribution then provide candidate training images for local topographic modeling with uncertainty, constrained to radar flight line data. Compared to traditional MPS approaches without considering TI sampling, our approach demonstrates significant improvement in the topographic modeling quality and efficiency of the simulation algorithm. Finally, we simulate multiple realizations of high-resolution ASE topographic maps. We use the multiple realizations to investigate the impact of basal topography uncertainty on subglacial hydrological flow patterns.