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

合作研究：NSFGEO-NERC：使用机器学习和基于物理的模型了解格陵兰冰盖的地表到床层融水路径

• 批准号: 2344690
• 负责人: Ching-Yao Lai
• 金额: $ 41.11万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2344690&HistoricalAwards=false
• 关键词: Collaborative; Research; NSFGEO; NERC; Understanding

This is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries.It is important to understand how melting ice on the surface of ice sheets, caused by a warming climate, affects the movement of the ice sheets. As the climate warms, melting ice at the surface of ice sheets form ponds of meltwater. In order to have an impact on the movement of the Greenland Ice Sheet, the meltwater must reach and lubricate the bottom of the ice sheet. For example, lakes on the surface of the ice sheet can drain through cracks and reach the bottom of the ice sheet within a few hours. To understand the formation of these cracks and the cause of draining lakes on the Greenland Ice Sheet, we plan to use deep learning, an artificial intelligence algorithm, to find the locations of cracks and draining lakes in satellite imagery. Based on this new dataset, we will use mathematical models to understand the formation of new cracks and their impact on the movement of the ice sheet. Our approach contains an exciting mix of observations and mathematical models. The ability to use artificial intelligence to detect cracks and draining lakes offers opportunities to drive new understandings at the ice-sheet scale. Broader Impacts: This project will support (1) a US-UK collaboration; (2) students and junior scientists; (3) the development of open-source artificial intelligence codes for the Arctic sciences community; (4) the production of a comprehensive and freely available database of the Greenland Ice Sheet cracks and draining lakes; and (5) a community-led mentoring program called COMPACT (COmmunity-led Mentoring Program for Advancing Cryosphere Trainees), which will facilitate multi-mentor networks within the US and UK cryospheric communities for minority doctoral students. Overall, this research will help us understand how ice sheets respond to a changing climate.Meltwater that forms on the surface of the ice sheet can seep through moulins and fractures that connect the surface to the bed, lubricating the bottom of the ice sheet and influencing its dynamics. Surface-to-bed meltwater pathways are prevalent across the Greenland Ice Sheet. However, we currently lack the continent-wide maps of moulins, crevasses, and draining lakes needed to understand the formation of surface-to-bed meltwater pathways. Utilizing deep learning techniques for automated detection and mapping of ice sheet surface features can greatly enhance the glaciology community's capacity to analyze high-resolution satellite imagery, leading to new discoveries. By harnessing deep neural networks, this project aims to generate continent-wide databases of surface features that can be used to mechanistically model the ice sheet conditions that create new surface-to-bed pathways and their impact on ice-sheet dynamics. The ability to scale up feature detection to the ice sheet scale can enrich both remote sensing and modeling communities. This project will foster a US-UK collaboration involving junior principal investigators, postdoctoral researchers, and graduate students. The project aims to develop open-source deep learning code, remote-sensing algorithms, and subglacial hydrology model code for the broader glaciological community. The resulting database of Greenland Ice Sheet surface-to-bed pathway locations and supraglacial lake drainage dates and locations will be made open source. The principal investigators will also collaborate to establish a community-led mentoring program within the US and UK cryospheric community to promote the retention of underrepresented doctoral students and junior faculty/scientists. The societal benefit of this research will be a better understanding of ice sheet processes and an improved ability to predict ice sheet change in a warming climate. Understanding the evolving hydrology of the Greenland Ice Sheet remains an important topic given the unknown, but potentially significant, role that meltwater drainage via hydro-fracture may play in the ice-sheet’s dynamic response to an expanding ablation area.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这是一个由国家科学基金会地球科学局（NSF/GEO）和英国国家环境研究委员会（NERC）共同资助的项目，该项目通过NSF/GEO-GEO-NERC领先机构协议。该协议允许该机构提交和同行评审的一项联合提案，其调查员的预算比例最大。成功确定奖励建议后，每个机构都为支持各自国家机构的科学家的预算比例提供了资金，这一点很重要，重要的是要了解如何在冰盖表面上融化冰块在温暖的气候中如何影响冰盖的运动。随着气候变暖，冰片表面融化的冰形成了融化的池塘。为了对格陵兰冰盖的运动产生影响，融化水必须到达并润滑冰盖的底部。例如，冰盖表面上的湖泊可以在几个小时内通过裂缝沥干裂缝并到达冰盖的底部。为了理解这些裂缝的形成和在格陵兰冰盖上切湖的原因，我们计划使用深度学习，一种人工智能算法，以找到碎裂的位置和卫星图像中的排水湖泊。基于这个新数据集，我们将使用数学模型来了解新裂纹的形成及其对冰盖运动的影响。我们的方法包含了令人兴奋的观测和数学模型的组合。使用人工智能检测裂缝和排水湖泊的能力为在冰盖量表上推动新的理解提供了机会。更广泛的影响：该项目将支持（1）US-UK合作； （2）学生和初级科学家； （3）开发北极科学社区的开源人工智能法规； （4）生产格陵兰冰盖裂缝和排水湖泊的全面且免费的数据库； （5）由社区主导的心理计划，名为Compact（社区主导的指导计划，用于推进Cryosphere训练者），该计划将促进美国和英国Cryosphere在少数族裔博士生中的多市场网络。总体而言，这项研究将有助于我们了解冰盖如何应对不断变化的气候。在冰盖表面形成的静水可以通过毛线和裂缝渗入将表面连接到床上的裂缝，从而润滑冰盖的底部并影响其动态。在格陵兰冰盖上，表面上的融水道路普遍存在。但是，我们目前缺乏需要了解地表到床的融化途径形成的毛劳，裂缝和排水湖泊的连续地图。利用深度学习技术来自动检测和映射冰盖表面特征，可以极大地增强冰川学社区分析高分辨率卫星图像的能力，从而导致新发现。通过利用深层神经网络，该项目旨在生成表面特征的连续范围数据库，这些数据库可用于机械地建模冰盖条件，从而创建新的地表到床途径及其对冰盖动态的影响。将特征检测扩展到冰盖量表的能力可以丰富遥感和建模社区。该项目将培养一项美国国际公司的合作，涉及初级首席研究人员，博士后研究人员和研究生。该项目旨在为更广泛的冰川学界开发开源深度学习代码，遥感算法和冰川水文模型代码。 Greenland冰盖表面至床的路径位置以及冰川上湖上排水的日期和位置的数据库将成为开源。首席研究人员还将合作，在美国和英国Cryosphere社区建立一个社区主导的心理计划，以促进代表性不足的博士生和初级教师/科学家的保留。这项研究的社会利益将是对冰盖过程的更好理解，并提高了预测温暖气候变化的冰盖变化的能力。鉴于未知但潜在的作用，通过液压进行融化的作用可能是在冰上对消融区域扩展的动态反应中发挥作用的，理解格陵兰冰盖的不断发展的水文学仍然是一个重要的话题。该奖项反映了NSF的法定任务，这反映了通过基金会的智力评估，并被认为是通过评估的智力效果和宽阔的范围来评估。