Proglacial lakes and their impact on Himalayan glacier evolution

前冰川湖泊及其对喜马拉雅冰川演化的影响

• 批准号: 2640276
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2640276
• 关键词: Proglacial; lakes; their; impact; Himalayan

The vast majority of mountain glaciers have been losing mass since at least the early part of the 20th Century, and have been in a particularly marked period of recession in recent decades (Wouters et al., 2019; Maurer et al., 2019). The clearest visual evidence of this ice recession is the presence of many thousands of glacial lakes, ubiquitous in all major glacierised regions of the world, formed as meltwater occupies glacially carved basins and the voids that now exist behind moraine dams (Shugar et al., 2020). These proglacial lakes represent critical natural reservoirs that can be utilised to sustain river flows during the dry season and generate hydro-electric power for urban areas; however, many also represent a growing concern because they pose an outburst flood risk to downstream communities (Carrivick and Tweed, 2016). Recent work has also shown that they can have a profound impact on glacier mass balance, accelerating ice loss when compared to their land-terminating counterparts (King et al., 2019).The presence of a proglacial lake can enhance ablation through three key mechanisms: via subaqueous thermal erosion, by promoting glacier calving (Watson et al., 2020), and by ice acceleration (or drawdown; Liu et al., 2020). The rates at which each of these processes contributes to enhanced mass loss are only loosely constrained at present (Song et al., 2017), meaning the current impact of lake formation on glacier mass balance is uncertain. Even less is known about how, and where, future lakes will contribute to patterns of glacier evolution, to the extent that they remain largely ignored in numerical simulations of future cryospheric change. This PhD project will seek to close each of these major knowledge gaps.The project will assess recent and future changes in mountain glacier environments, focussing on the Himalaya, with the aim of establishing the empirical data required to formulate relationships between lake characteristics (area, volume, depth) and glacier response to climate change. This will require a systematic review of existing data within the literature, and the derivation of new remotely sensed datasets from both optical and SAR-based sources, and if appropriate, historical aerial photography. There will be the opportunity to develop skills in automatic classification (e.g. Google Earth Engine), glacier velocity derivation (e.g. Cosi-CORR (LePrince et al., 2007), GAMMA), statistical analysis (R Studio) and geodetic mass balance calculation (e.g. Imagine Photogrammetry). Analysis of future lake development will require estimates of ice thickness to be made (using, for example, the GlabTop model (Linsbauer et al., 2016)). The final step will be to further develop the student's glacier modelling training by incorporating these analyses into ice-flow models (e.g. iSOSIA (Egholm et al., 2012)) being developed by the supervisory team, to make experiments as a first step towards explicitly including lake-ice interactions in simulations of future glacier change. This project will use a combination of remote sensing techniques at a range of scales, and numerical modelling, to study recent changes in Himalayan glaciers and their implications for future cryospheric evolution. Depending on the interests and skills of the successful applicant, there will be exciting opportunities to visit remote field sites, to ground-truth interpretations of satellite data and to collect in-situ measurements of key lake parameters. The student will also be encouraged to build collaborations with the broad group of international experts working in this topical area.

至少自 20 世纪初以来，绝大多数山地冰川的质量一直在减少，并且在近几十年来处于特别明显的衰退期（Wouters 等，2019；Maurer 等，2019）。这次冰退缩最明显的视觉证据是存在数千个冰川湖，它们在世界所有主要冰川地区无处不在，是由于融水占据了冰川雕刻的盆地和现在存在于冰碛坝后面的空隙而形成的（Shugar等人，2020）。这些冰川前湖是重要的天然水库，可用于在旱季维持河流流量并为城市地区提供水力发电；然而，许多也引起了越来越多的关注，因为它们对下游社区构成了洪水爆发的风险（Carrivick 和 Tweed，2016）。最近的研究还表明，与陆地终止湖相比，它们可以对冰川质量平衡产生深远影响，加速冰损失（King等人，2019）。前冰湖的存在可以通过三种关键机制增强消融：通过水下热侵蚀、促进冰川崩解（Watson等人，2020）以及冰加速（或下降；Liu等人， 2020）。目前，每个过程导致质量损失增加的速率仅受到宽松的限制（Song等人，2017），这意味着当前湖泊形成对冰川质量平衡的影响尚不确定。关于未来湖泊如何以及在何处对冰川演化模式做出贡献的了解甚至更少，以至于在未来冰冻圈变化的数值模拟中它们仍然在很大程度上被忽视。该博士项目将寻求弥合这些主要知识差距。该项目将评估山地冰川环境的近期和未来变化，重点关注喜马拉雅山，目的是建立建立湖泊特征（面积、体积、深度）和冰川对气候变化响应之间关系所需的经验数据。这将需要对文献中的现有数据进行系统审查，并从光学和基于合成孔径雷达的来源以及历史航空摄影（如果适用）中推导出新的遥感数据集。将有机会发展自动分类（例如 Google Earth Engine）、冰川速度推导（例如 Cosi-CORR（LePrince 等人，2007）、GAMMA）、统计分析（R Studio）和大地质量平衡计算（例如想象摄影测量）方面的技能。对未来湖泊发展的分析将需要对冰厚度进行估计（例如，使用 GlabTop 模型（Linsbauer 等人，2016 年））。最后一步将是通过将这些分析纳入监督团队正在开发的冰流模型（例如 iSOSIA（Egholm 等人，2012））中，进一步发展学生的冰川建模培训，以进行实验，作为明确将湖冰相互作用纳入未来冰川变化模拟的第一步。该项目将结合使用各种尺度的遥感技术和数值模拟，研究喜马拉雅冰川的近期变化及其对未来冰冻圈演化的影响。根据成功申请者的兴趣和技能，将有令人兴奋的机会访问偏远的现场地点，对卫星数据进行地面实况解释并收集关键湖泊参数的现场测量结果。还将鼓励学生与该主题领域的广泛国际专家建立合作。