A document by Wei Ji Leong. Click on the document to view its contents.

To understand the movement of subglacial water in Antarctica, we present an updated inventory of active subglacial lakes using ICESat-2 laser altimetry data. The ICESat-2/ATLAS instrument’s six-beam laser array captures denser point measurements than the previous generation ICESat and Cryosat-2 satellites, allowing us to examine the spatial and temporal variability of active subglacial lakes with unprecedented detail. Active subglacial lakes are classified directly from the high density ATL11 land ice time-series (60 m along track spatial resolution) point cloud data using an unsupervised density-based clustering algorithm. The key finding we show is how subglacial lake shorelines can migrate spatially over time through fill-drain cycles. In addition, we examine subglacial water connectivity from coupled fill-drain cycles over lakes at the Whillans Ice Stream on the Siple Coast. This study yields new insights into the dynamic nature of the subglacial water system in Antarctica, and will be of interest to biologists quantifying biogeochemical cycle processes and glaciologists studying the influence of subglacial hydrology on ice dynamics.

To better resolve the bed elevation of Antarctica, we present DeepBedMap - a deep learning method that produces realistic Antarctic bed topography from multiple remote sensing data inputs. Our super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high resolution (250 m) groundtruth bed elevation grids are available, and then used to generate high resolution bed topography in less well surveyed areas. DeepBedMap takes in a low resolution (1000 m) BEDMAP2 dataset alongside other high spatial resolution inputs such as ice surface elevation, velocity and snow accumulation to generate a four times upsampled (250 m) bed topography map even in the absence of ice-thickness data from direct seismic or ice-penetrating radar surveys. Our DeepBedMap model is based on an Enhanced Super Resolution Generative Adversarial Network architecture that is adapted to minimize per-pixel elevation errors while producing realistic topography. We show that DeepBedMap offers a more realistic topographic roughness profile compared to a standard bicubic interpolated BEDMAP2, and also run model inversions to compare the basal traction of our DeepBedMap_DEM with other bed elevation models.