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: 2235052
• 负责人: Leigh Stearns
• 金额: $ 19.19万
• 依托单位: University of Kansas Center for Research Inc
• 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: 2023-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235052&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理事会（NSF/GEO）和联合王国国家环境研究理事会（NERC）通过NSF/GEO-NERC牵头机构协议联合资助的项目。该协议允许美国/英国提交一份联合提案，并由研究者拥有最大预算比例的机构进行同行评审。一旦成功地共同确定了一项奖励建议，每个机构就为支助各自国家机构科学家的预算部分提供资金。了解由气候变暖引起的冰盖表面的冰融化如何影响冰盖的运动是很重要的。随着气候变暖，冰盖表面的融冰形成了融水池。为了对格陵兰冰盖的运动产生影响，融水必须到达冰盖底部并润滑冰盖。例如，冰盖表面的湖泊可以通过裂缝排水，并在几个小时内到达冰盖的底部。为了了解这些裂缝的形成以及格陵兰冰盖上排水湖的原因，我们计划使用深度学习，一种人工智能算法，在卫星图像中找到裂缝和排水湖的位置。基于这个新的数据集，我们将使用数学模型来了解新裂缝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察和数学模型的组合。利用人工智能来检测裂缝和排水湖泊的能力，为在冰盖规模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）美英合作;（2）学生和初级科学家;（3）为北极科学界开发开源人工智能代码;（4）制作一个全面和免费的格陵兰冰盖裂缝和排水湖数据库;以及（5）社区主导的名为COMPACT的辅导计划（社区领导的促进冰冻圈受训者的指导计划），这将促进美国和英国冰冻圈社区内少数民族博士生的多导师网络。总的来说，这项研究将帮助我们了解冰盖如何对气候变化做出反应。冰盖表面形成的融水可以通过连接表面和床的冰臼和裂缝渗透，润滑冰盖底部并影响其动态。地表到冰层的融水路径在格陵兰冰盖上很普遍。然而，我们目前缺乏大陆范围内的冰臼，裂缝和排水湖的地图，需要了解表面到床融水路径的形成。利用深度学习技术自动检测和绘制冰盖表面特征可以大大提高冰川学社区分析高分辨率卫星图像的能力，从而带来新的发现。通过利用深度神经网络，该项目旨在生成整个大陆范围的表面特征数据库，这些数据库可用于对冰盖条件进行机械建模，这些冰盖条件会产生新的表面到河床的路径及其对冰盖动力学的影响。将特征检测扩展到冰盖规模的能力可以丰富遥感和建模社区。该项目将促进美英合作，涉及初级主要研究人员，博士后研究人员和研究生。该项目旨在为更广泛的冰川学社区开发开源深度学习代码、遥感算法和冰下水文模型代码。由此产生的格陵兰冰盖表面到河床路径位置和冰上湖泊排水日期和位置的数据库将开放源代码。主要研究人员还将合作在美国和英国的冰冻圈社区内建立一个社区主导的指导计划，以促进代表性不足的博士生和初级教师/科学家的保留。这项研究的社会效益将是更好地了解冰盖过程，并提高预测气候变暖中冰盖变化的能力。了解不断变化的格陵兰冰盖水文仍然是一个重要的课题，因为通过水力压裂的融水排水可能在冰盖对不断扩大的消融区域的动态响应中发挥未知但潜在的重要作用。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。