The subglacial landscape and hydrology of Antarctica mapped from space

• Main Author: Wei Ji Leong
• Format: Thesis
• Language: unknown
• Published: 2021
• Subjects: Glaciology; Earth Sciences not elsewhere classified; Knowledge Representation and Machine Learning; Computer Vision; Pattern Recognition and Data Mining; Antarctica; remote sensing; deep learning; digital elevation model; ice sheet modelling; laser altimetry; subglacial hydrology; Siple Coast; School: School of Geography; Environment and Earth Sciences; Unit: Antarctic Research Centre; 040602 Glaciology; 049999 Earth Sciences not elsewhere classified; 080104 Computer Vision; 080109 Pattern Recognition and Data Mining; 170203 Knowledge Representation and Machine Learning; 960901 Antarctic and Sub-Antarctic Land and Water Management; 960306 Effects of Climate Change and Variability on Antarctic and Sub-Antarctic Environments (excl. Social Impacts); Degree Discipline: Physical Geography; Degree Level: Doctoral; Degree Name: Doctor of Philosophy; Antarctic; New Lakes; Pine Island Glacier; Siple; The Antarctic; West Antarctic Ice Sheet; Antarc*; Ice Sheet; Pine Island
• Online Access: https://doi.org/10.26686/wgtn.14956062.v1; https://figshare.com/articles/thesis/The_subglacial_landscape_and_hydrology_of_Antarctica_mapped_from_space/14956062

To narrow uncertainties in the Antarctic ice sheet's contribution to sea level rise, we present a collection of novel machine learning and automated satellite remote sensing methods which use ice surface observations to infer the subglacial nature of Antarctica. A super-resolution deep neural network called DeepBedMap was designed and trained to produce a high-resolution (250 m) bed elevation model of Antarctica called DeepBedMap_DEM that preserves bed roughness details useful for catchment- to continent-scale ice sheet modelling. This DeepBedMap_DEM is compared with a smoother, medium-resolution (500 m) BedMachine topography in a basal inversion experiment over Pine Island Glacier, with results motivating more research into the interacting roles of subglacial hydrology which influences skin drag and high resolution bed topographies which influences form drag. Active subglacial lakes in Antarctica were mapped using an unsupervised density-based classification method on ICESat-2 point cloud data from 2018-2020, yielding 194 active subglacial lakes, including 36 new lakes in the 86-88°S area not detected by the previous ICESat (2003-2009) mission. This thesis showcases both the rich diversity in subglacial landscapes and the dynamic nature of subglacial hydrology in Antarctica, forming a foundation enabling the accurate modelling of overland ice flow in critical regions of the vulnerable West Antarctic Ice Sheet. Plain language summaryAntarctica has a lot of ice, but we're unsure how fast ice can slide into the sea and cause water to go up in beaches around the world. So we teach computers to solve hard math problems that tell us how fast sea water might go up. These computers are fed with lots of pictures taken from cameras up in the sky and space. Ice sits on top of rock in Antarctica, and with practice, the computers get pretty good at telling us how high and bumpy the rock is. The rock under the ice appears quite bumpy, and ice probably doesn't like sliding over bumpy rocks since it's rough. Sometimes though, ...