TY - BOOK

T1 - Monitoring Greenland and Antarctic supraglacial hydrology from space

AU - Corr, Diarmuid

PY - 2024/1/6

Y1 - 2024/1/6

N2 - Ice sheets, large masses of glacial ice covering polar regions, influence global sea level and ocean currents. The study of surface water on these ice sheets, supraglacial hydrology, is essential to understand the effects of climate change on ice sheet stability, sea-level rise, and climate systems. This thesis examines supraglacial hydrological systems in Antarctica and Greenland by developing novel methods to classify them using optical satellite imagery (Sentinel-2 and Landsat-8). Chapter 2 reveals the presence of supraglacial hydrology features, such as lakes and channels, on the West Antarctic Ice Sheet through a novel dual-NDWI and k-means clustering approach. A total of 10,478 features covering 119.4 km² were identified, broadening our knowledge of Antarctica's supraglacial hydrology. Chapter 3 uses random forest and radiative transfer models to analyse the extent and volume of surface meltwater on the Greenland Ice Sheet from 2014 to 2022. This study assesses supraglacial hydrological features Greenland wide, on a decadal scale, for the first time. The results imply that reductions in firn air content and increases in ice slab content are drivers of increasing meltwater in various drainage basins, particularly in the north, east, and south.Chapter 4 presents an innovative algorithm that quantifies uncertainty in the prediction of supraglacial hydrology using Bayesian inference with spatial statistics. This probabilistic approach provides predictions for the presence of water at the pixel level with associated standard deviations, which signify uncertainty. By quantifying uncertainty, this approach is important for understanding the quantity and trends of meltwater flowing into the ocean. This research advances our understanding of the distribution and dynamics of supraglacial hydrology on ice sheets, providing data and tools for the wider scientific community. These findings contribute to our understanding of the impacts of climate change on polar regions and support machine learning models to map surface water.

AB - Ice sheets, large masses of glacial ice covering polar regions, influence global sea level and ocean currents. The study of surface water on these ice sheets, supraglacial hydrology, is essential to understand the effects of climate change on ice sheet stability, sea-level rise, and climate systems. This thesis examines supraglacial hydrological systems in Antarctica and Greenland by developing novel methods to classify them using optical satellite imagery (Sentinel-2 and Landsat-8). Chapter 2 reveals the presence of supraglacial hydrology features, such as lakes and channels, on the West Antarctic Ice Sheet through a novel dual-NDWI and k-means clustering approach. A total of 10,478 features covering 119.4 km² were identified, broadening our knowledge of Antarctica's supraglacial hydrology. Chapter 3 uses random forest and radiative transfer models to analyse the extent and volume of surface meltwater on the Greenland Ice Sheet from 2014 to 2022. This study assesses supraglacial hydrological features Greenland wide, on a decadal scale, for the first time. The results imply that reductions in firn air content and increases in ice slab content are drivers of increasing meltwater in various drainage basins, particularly in the north, east, and south.Chapter 4 presents an innovative algorithm that quantifies uncertainty in the prediction of supraglacial hydrology using Bayesian inference with spatial statistics. This probabilistic approach provides predictions for the presence of water at the pixel level with associated standard deviations, which signify uncertainty. By quantifying uncertainty, this approach is important for understanding the quantity and trends of meltwater flowing into the ocean. This research advances our understanding of the distribution and dynamics of supraglacial hydrology on ice sheets, providing data and tools for the wider scientific community. These findings contribute to our understanding of the impacts of climate change on polar regions and support machine learning models to map surface water.

KW - Glaciology

KW - Antarctica

KW - Greenland

KW - Remote Sensing

KW - Machine Learning

KW - Supraglacial Hydrology

U2 - 10.17635/lancaster/thesis/2222

DO - 10.17635/lancaster/thesis/2222

M3 - Doctoral Thesis

PB - Lancaster University

ER -