GLOBE: The Greenland Subglacial Lake Observatory

GLOBE：格陵兰冰下湖天文台

• 批准号: EP/Y02642X/1
• 负责人: Malcolm McMillan
• 金额: $ 215.65万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY02642X%2F1
• 关键词: GLOBE; Greenland; Subglacial; Lake; Observatory

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.

理论预测表明，格陵兰冰盖下隐藏着1600个冰下湖泊。然而，迄今为止发现的只有不到5%，研究发现只有7种正在积极填充或排出。跟踪格陵兰岛下的活跃湖泊是有问题的，因为它们比南极洲下的湖泊小100倍，使得大多数标准卫星方法无法检测到它们。在这里，我将从根本上提高检测，监测，预测和了解格陵兰活跃冰下湖泊动态的能力。地球仪将把这个领域从目前的状态转变为一个位置，在这个位置上，对湖泊的分布、动态、驱动因素和影响几乎一无所知;在这个位置上，所有的湖泊都能得到真实的实时监测，它们的历史被解析，概率预报预测它们未来的排水情况。地球仪将通过三个关键领域的创新来实现这一目标，1）大规模开发新的超高分辨率卫星数据，2）开发利用非常规数据的新方法，3）开发新的深度学习和统计预测方法。具体来说，我将1）首次使用大容量数字高程模型和机器学习来绘制整个大陆和过去25年的湖泊活动，2）训练深度学习模型来搜索历史卫星图像中的活跃湖泊，3）使用视频监控方法来自动识别新的湖泊活动，（4）运用统计技术对未来湖泊演变进行初步预测。最终，地球仪将促进一种全新的大规模湖泊监测方法，建立一个格陵兰冰下湖泊观测站，自主跟踪、预测和传播湖泊活动，从而改变对难以捉摸的湖泊排水事件的驱动因素、动态和影响的理解。