Proglacial lakes and their impact on Himalayan glacier evolution

至少自20世纪初以来，绝大多数山地冰川一直在减少质量，近几十年来一直处于特别明显的衰退期(Wouters等人，2019年；Maurer等人，2019年)。这次冰川衰退最明显的视觉证据是数以千计的冰川湖泊的存在，这些湖泊无处不在，存在于世界上所有主要的冰川化地区，形成于融水占据冰川雕刻盆地和现在存在于冰河大坝后面的空洞(Shugar等人，2020年)。这些冰川湖泊是关键的天然水库，可用于在旱季期间维持河流流量，并为城市地区提供水力发电；然而，许多湖泊也日益受到关注，因为它们对下游社区构成了暴发洪水的风险(Carrivick和Teed，2016)。最近的研究还表明，它们可以对冰川质量平衡产生深远的影响，与陆地终止的湖泊相比，它们加速了冰的流失(King等人，2019年)。冰川湖的存在可以通过三个关键机制来增强消融：通过水下热侵蚀，通过促进冰川崩解(Watson等人，2020年)，以及通过冰加速(或冰减少；Liu等人，2020)。目前，这些过程中的每一个过程导致质量损失加剧的速度仅受到松散的限制(Song等人，2017年)，这意味着目前湖泊形成对冰川质量平衡的影响是不确定的。关于未来湖泊将如何以及在哪里对冰川演化模式做出贡献的信息就更少了，以至于它们在对未来冰冻圈变化的数值模拟中仍然基本上被忽视了。该博士项目将寻求弥合这些重大知识差距。该项目将评估最近和未来山区冰川环境的变化，重点是喜马拉雅山脉，目的是建立所需的经验数据，以阐明湖泊特征(面积、体积、深度)与冰川对气候变化的反应之间的关系。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酌情从历史航空摄影中得出新的遥感数据集。将有机会发展以下方面的技能：自动分类(例如谷歌地球引擎)、冰川速度推算(例如COSI-Corr(LePrince等人，2007年)、GAMA)、统计分析(R Studio)和大地质量平衡计算(例如IMAGINE摄影测量)。对未来湖泊发展的分析将需要估计冰的厚度(例如，使用GLabTop模型(Linsbauer等人，2016年))。最后一步将是进一步发展学生的冰川建模培训，将这些分析纳入监督小组正在开发的冰流模型(例如iSOSIA(Egholm等人，2012年))中，以进行实验，作为在未来冰川变化模拟中明确包括湖泊-冰相互作用的第一步。该项目将结合各种尺度的遥感技术和数值模拟，研究喜马拉雅冰川最近的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实地地点，对卫星数据进行地面真实解释，并收集关键湖泊参数的现场测量结果。学生还将被鼓励与在这一主题领域工作的广泛的国际专家小组建立合作。

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.