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Seasonal flow variability as an indicator of mountain glacier basal conditions


School of Geography

About the Project

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 transport [1], the nature of subsurface hydrological networks [2], and patterns and rates of glacier flow [3]. Evidence suggests that rising air temperatures are influencing englacial and subglacial thermal conditions even at high-altitude [4], meaning that in a future warming world polythermal and temperate ice conditions will become more widespread. Being able to characterise (changing) glacier basal conditions will therefore be critical for predicting landscape evolution, meltwater storage and conveyance, glacier discharge, and ultimately for forecasting glacier evolution.

Given that direct observations of subglacial conditions are limited to localised exposures or speleological investigations [5], a remote sensing approach that serves as a proxy for subglacial water storage would be a useful tool for mountain glaciologists. Previous work [2,3] has suggested that seasonal changes in glacier velocity can indicate subglacial water storage, associated with glacier sliding, and therefore temperate ice.

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.

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org for more information on funding and the full application process.

Funding Notes

This 3 year 9 month long NERC SENSE CDT award will provide tuition fees (£4,409 for 2020/21), tax-free stipend at the UK research council rate (£15,285 for 2020/21), and a research training and support grant to support national and international conference travel.

References

1. 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.

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