Beneath the Greenland Ice Sheet, 1,600 subglacial lakes are predicted to exist, but fewer than 5% have been found, and only seven are known to be actively filling or draining. This matters because active lakes beneath Greenland are roughly 100 times smaller than those in Antarctica, making them invisible to most satellite methods. Without knowing where these lakes are, how they behave, or when they drain, scientists cannot predict their effect on ice flow or sea-level rise. GLOBE will build an autonomous observatory that detects, monitors, and forecasts lake activity across the entire Greenland Ice Sheet. The team will use super-high-resolution satellite data, machine learning to map 25 years of lake history, deep learning to search historical imagery, and video surveillance techniques to spot new drainage events as they happen. Statistical models will then produce the first probabilistic forecasts of future lake evolution. This is fundamental science. It will not directly change a daily routine or a supply chain. But understanding subglacial lake dynamics is essential for predicting how Greenland’s ice will respond to a warming climate—and that has implications for global sea-level rise, coastal infrastructure, and flood defences worldwide.
View original technical description
Theoretical predictions suggest that 1600 subglacial lakes lie hidden beneath the Greenland Ice Sheet. Yet less than 5% have been discovered to date, and studies have found only 7 that are actively filling or draining. Tracking active lakes beneath Greenland is problematic because they are 100 times smaller than those beneath Antarctica, rendering them undetectable by most standard satellite methods. Here, I will radically advance the ability to detect, monitor, forecast and understand the dynamics of active Greenland subglacial lakes at scale. GLOBE will transform the field from its current state, where almost nothing is known about their distribution, dynamics, drivers and impacts; to a position where all lakes are monitored in real time, their histories are resolved, and probabilistic forecasts predict their future drainage. GLOBE will achieve this through innovation in three key areas, 1) exploiting new, super-high resolution satellite data at scale, 2) developing new methods that exploit unconventional data, and 3) developing novel deep learning and statistical forecasting methodologies. Specifically, I will 1) use high-volume digital elevation models and machine learning to map lake activity, for the first time, over the entire continent and the past 25 years, 2) train deep learning models to search for active lakes in historical satellite imagery, 3) use video surveillance methods to autonomously identify new lake activity as it happens, and 4) use statistical techniques to make the first forecasts of future lake evolution. Ultimately, GLOBE will catalyse an entirely new approach to large-scale lake monitoring, creating a Greenland Subglacial Lake Observatory that autonomously tracks, forecasts and disseminates lake activity, thus transforming understanding of the drivers, dynamics and impacts of elusive lake drainage events.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know