A machine learning approach to constraining ice volume and potential loss in High Mountain Asia

限制亚洲高山冰量和潜在损失的机器学习方法

• 批准号: 2890090
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890090
• 关键词: machine; learning; approach; constraining; ice

Project Background. Glaciers in High Mountain Asia (HMA) are experiencing mass loss [1], with implications for the hundreds of millions of people who depend on them for critical water resources [2]. Projections of the likely trajectory of Himalayan glacier mass balance, and associated runoff, are highly uncertain - due in part to lack of knowledge of glacier thickness, which determines glacier response to climate change [3]. With an ever-growing remote-sensing record for the 90,000 glaciers in the region [e.g., 4], there is potential to compute thicknesses regionally and model glacier response to climate change [5], but until now, very few measurements were available to constrain the thickness models. With the completion of the first airborne [6] ice-thickness survey in the Himalayas, covering the glaciers of the Khumbu basin, these models can finally be constrained. This project will investigate HMA glacier sensitivity to climate warming by combining new field and satellite data products with advanced modelling and machine learning methods. More specifically, 1. Can ML-trained models assimilate/invert for HMA thickness data from satellite data?2. How do field observations inform and improve such inverse models?3. How does the improved assessment of glacier thickness and ablation aid in modelling the future behaviour of Asian glaciers in response to climate change?Methodology. The method for inferring thickness will be based around the python assimilation framework of [5], which makes use of the Instructed Glacier Model, a deep learning emulator [7]. The framework has been applied successfully to Alpine glaciers, but not to HMA glaciers where type and availability of observations differs. The work of the PhD will involve modifying the framework for application to HMA glaciers; preparing and experimenting with inputs based on potential Level-2 and Level-3 EO datasets: elevation change (WorldView [8] and ASTER [9] and Cryosat [1] based data);as well as elevation and glacier velocities (ITS_LIVE). Airborne thickness measurements of select glaciers will be provided by BAS supervisors, allowing validation and refinement of the methodology. Importantly, as IGM has only been trained on and applied to Alpine glaciers, its performance will also be tested on a small subset against a physical glacier model [10], with potential to improve the IGM through further deep learning. The impacts of the improved thickness on future glacier loss will be examined through multidecadal modelling using the IGM.Context: This PhD project will engage strongly with The Big Thaw, a recently-funded BAS-led, cross-institutional NERC Highlight Topics grant which aims to fill key gaps in knowledge of global mountain water resources, but does not encompass novel ML approaches to thickness estimation.The efforts of this PhD will feed into and inform The Big Thaw, and the student will be strongly involved in project meetings and discussions, enabling strong interaction with scientists at BAS, Leeds, and CEH that extend beyond the supervisory team and industry partner.

项目背景。亚洲高山 (HMA) 的冰川正在经历大规模损失 [1]，这对依赖冰川获取重要水资源的数亿人产生了影响 [2]。对喜马拉雅冰川质量平衡和相关径流的可能轨迹的预测具有高度不确定性，部分原因是缺乏对冰川厚度的了解，而冰川厚度决定了冰川对气候变化的响应[3]。随着该地区 90,000 座冰川的遥感记录不断增长 [例如 4]，有可能计算区域厚度并模拟冰川对气候变化的响应 [5]，但到目前为止，可用于预测冰川厚度的测量数据非常少。约束厚度模型。随着喜马拉雅山首次机载[6]冰厚调查的完成，覆盖昆布盆地的冰川，这些模型终于可以得到约束。该项目将通过将新的现场和卫星数据产品与先进的建模和机器学习方法相结合，研究 HMA 冰川对气候变暖的敏感性。更具体地说，1. 机器学习训练的模型能否同化/反演卫星数据中的 HMA 厚度数据？2.现场观察如何为此类逆模型提供信息和改进？3．对冰川厚度和消融的改进评估如何帮助模拟亚洲冰川应对气候变化的未来行为？方法论。推断厚度的方法将基于 [5] 的 python 同化框架，该框架利用深度学习模拟器 [7] Instructed Glacier Model。该框架已成功应用于高山冰川，但不适用于观测类型和可用性不同的 HMA 冰川。博士的工作将涉及修改 HMA 冰川的应用框架；根据潜在的 2 级和 3 级 EO 数据集准备和试验输入：海拔变化（基于 WorldView [8] 和 ASTER [9] 和 Cryosat [1] 的数据）；以及海拔和冰川速度 (ITS_LIVE)。 BAS 主管将提供选定冰川的机载厚度测量，从而验证和完善方法。重要的是，由于 IGM 仅在高山冰川上进行了训练和应用，因此其性能还将在物理冰川模型的一小部分子集上进行测试 [10]，有可能通过进一步的深度学习来改进 IGM。厚度增加对未来冰川损失的影响将通过使用 IGM 的多年代模型进行研究。背景：该博士项目将与 The Big Thaw 密切合作，The Big Thaw 是最近资助的一项由 BAS 领导的跨机构 NERC 重点主题赠款，旨在填补全球山区水资源知识的关键空白，但不包含厚度估计的新颖机器学习方法。该博士的努力将融入并为大解冻提供信息，学生将积极参与项目会议和讨论，与 BAS、利兹和 CEH 的科学家进行强有力的互动，其范围超出了监督团队和行业合作伙伴的范围。