Seasonal flow variability as an indicator of mountain glacier basal conditions
Lead supervisor: Duncan Quincey* (University of Leeds)
Co-supervisors: Francesca Pellicciotti (WSL), John Elliott (Leeds), Noel Gourmelen (Edinburgh)
Glacier thermal regime, and in particular the presence or absence of meltwater at the bed, is a key control of subglacial processes. It governs rates of sediment erosion, entrainment and transport1, the nature of subsurface hydrological networks2, and patterns and rates of glacier flow3. Evidence suggests that rising air temperatures are influencing englacial and subglacial thermal conditions even at high-altitude4, meaning that in a future warming world polythermal and temperate ice conditions will become more widespread. Being able to characterise glacier basal conditions, and how they change, will therefore be critical for predicting landscape evolution, meltwater storage and conveyance, glacier discharge, and ultimately for forecasting glacier decay.
Given that direct observations of subglacial conditions are limited to localised exposures or speleological investigations5, a remote sensing approach that serves as a proxy for subglacial water storage would be a useful tool for mountain glaciologists. Previous work2,3 has suggested that seasonal changes in glacier velocity can indicate subglacial water storage, associated with glacier sliding, and therefore temperate ice (Figure 1).
Figure 1: The Baltoro Glacier, Karakoram (left), and its annually averaged centreline velocity. Seasonal velocities (right) indicating warm ice at higher elevations as the profiles diverge at ~10 km from the terminus.
This PhD project will therefore seek to test and expand this hypothesis, by initially deriving seasonal velocity data for mountain glaciers with known thermal characteristics, to describe spatial patterns of flow, and their change through time. It will build on previous work from within the supervisory group, primarily making use of image feature tracking and radar interferometry for bespoke velocity analyses, supplemented by freely available datasets from existing repositories such as ITS_LIVE. It will explore a range of space-based SAR (e.g. Sentinel-1) and optical (e.g. Planet) imagery, as well as off-the-shelf and custom-built digital elevation datasets, and will develop existing and derive new routines for automating processing workflows. Subsequent steps will assimilate regional datasets of speed-up/slow-down, supported by modelled climate, ice thickness and energy balance data, to infer broader subglacial conditions. There is also the opportunity to collect complementary field-based velocity data, using micro-dgps, depending on the interests/skills of the successful applicant.
- Cook et al., (2020) 10.1038/s41467-020-14583-8. 2. Benn et al., (2017) 10.5194/tc-11-2247-2017. 3. Quincey et al., (2009) 10.3189/002214309790794913. 4. Vincent, C. et al., (2020) 10.5194/tc-14-925-2020. 5. Temminghoff et al., (2018) 10.1080/04353676.2018.1545120.
*email@example.com; @duncanquincey; +44 (0)113 34 33312