NSFGEO-NERC: Understanding surface-to-bed meltwater pathways across the Greenland Ice Sheet using machine-learning and physics-based models

Greenland Ice Sheet (GrIS) mass loss acceleration is driven by increasing rates of surface melt and calving of marine-terminating outlet glaciers. The links between increasing surface melt and ice-flow dynamics are poorly understood, in part because we do not mechanistically understand where and under what conditions meltwater accesses the ice-sheet bed at a continental scale. Surface meltwater must reach the ice-bed interface via a surface-to-bed meltwater pathway for meltwater to affect GrIS flow dynamics and, in most cases, for meltwater to contribute to sea level. Surface-to-bed pathways have been manually mapped in local regions (<500 km2), but these methodologies are not practical at the continental scale (~10 to the power of 6 km2). Automated characterization and mapping of ice-sheet surface features is required to fill this gap in knowledge and advance our understanding of the features and processes driving meltwater's influence on ice-sheet dynamics.

To understand the formation of surface-to-bed meltwater pathways across the GrIS and their impact on ice-flow dynamics, this three-year project will use a combination of remote-sensing observations, deep learning, and physics-based models to: (1) detect continent-wide surface fractures, moulins and supraglacial lake drainage events with satellite imagery; (2) determine the ice-sheet conditions required to trigger supraglacial lake drainage via hydrofracture and create surface-to-bed pathways; and (3) model the impact of supraglacial lake drainage events on ice-flow dynamics at a regional scale. These objectives will produce the first comprehensive, continental-scale database of GrIS surface-to-bed meltwater pathways and supraglacial lake drainage dates and mechanisms.