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.