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。亚洲高山区（High Mountain Asia，HMA）的冰川正在经历质量损失[1]，这对依赖冰川获取关键水资源的数亿人产生了影响[2]。喜马拉雅冰川物质平衡的可能轨迹和相关径流的预测是高度不确定的-部分原因是缺乏对冰川厚度的了解，这决定了冰川对气候变化的反应[3]。随着该地区9万座冰川的遥感记录不断增加[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，很少有测量可用于限制厚度模型。随着首次在喜马拉雅山进行的覆盖昆布盆地冰川的航空[6]冰厚调查的完成，这些模型最终可以得到约束。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. ML训练的模型可以同化/反演来自卫星数据的HMA厚度数据吗？2.实地观察如何为这种逆模型提供信息并加以改进？3.改进后的冰川厚度和消融评估如何有助于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，该框架利用了深度学习模拟器Instructed Glacier Model [7]。该框架已成功地应用于阿尔卑斯山冰川，但没有HMA冰川的类型和可用性的观测不同。博士的工作将涉及修改应用于HMA冰川的框架;准备和试验基于潜在的2级和3级EO数据集的输入：海拔变化（WorldView [8]和ASTER [9]和Cryosat [1]基于数据）;以及海拔和冰川速度（ITS_LIVE）。BAS监督员将提供选定冰川的航空厚度测量结果，以便验证和改进方法。重要的是，由于IGM仅在阿尔卑斯冰川上进行了训练和应用，因此其性能也将在物理冰川模型的一小部分上进行测试[10]，并有可能通过进一步的深度学习来改进IGM。厚度的增加对未来冰川损失的影响将通过使用IGM的多年模型进行研究。这个博士项目将与大解冻，最近资助的BAS领导的，跨机构的NERC突出主题赠款，旨在填补全球山区水资源知识的关键差距，但不包括新的ML方法来估计厚度。这个博士的努力将馈入并告知大解冻，学生将积极参与项目会议和讨论，实现与BAS、利兹和CEH的科学家的强有力互动，而不仅仅是监督团队和行业合作伙伴。