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

项目背景。亚洲高山冰川（HMA）正在经历大规模消融，这对数亿依赖冰川获取关键水资源的人们产生了影响。喜马拉雅冰川物质平衡和相关径流可能轨迹的预估是高度不确定的，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。随着该地区90000个冰川的遥感记录不断增加[例4]，有可能计算区域厚度并模拟冰川对气候变化的响应[b]，但到目前为止，很少有测量数据可以约束厚度模型。随着首次在喜马拉雅山覆盖昆布盆地冰川的空中b[6]冰厚调查的完成，这些模式最终可以得到约束。该项目将通过将新的野外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从卫星数据中吸收/反演HMA厚度数据？野外观测如何为这种反演模型提供信息和改进？对冰川厚度和消融的改进评估如何有助于模拟亚洲冰川响应气候变化的未来行为？方法。推断厚度的方法将基于[5]的python同化框架，该框架使用了指示冰川模型，这是一个深度学习模拟器[7]。该框架已成功应用于高山冰川，但未应用于观测类型和可用性不同的HMA冰川。博士的工作将包括修改应用于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：高程变化（基于WorldView[8]和ASTER[9]和Cryosat[1]的数据）；以及海拔和冰川速度（ITS_LIVE）。选定冰川的航空厚度测量将由BAS主管提供，允许验证和改进方法。重要的是，由于IGM只在高山冰川上进行了训练和应用，它的性能也将在一小部分物理冰川模型[10]上进行测试，并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的多年代际模拟来检验。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bas领导的跨机构NERC重点主题资助项目，旨在填补全球山地水资源知识的关键空白，但不包括厚度估计的新颖ML方法。该博士的努力将为The Big Thaw提供信息，该学生将积极参与项目会议和讨论，从而与BAS、利兹和CEH的科学家进行强有力的互动，而不仅仅是管理团队和行业合作伙伴。

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.