About the Project
Supervisory Team
- Lead Supervisor: Dr. Katie Miles, Lancaster University
- Co-Supervisor: Dr. Henry Moss, Lancaster University
- Co-Supervisor: Prof. Duncan Quincey, University of Leeds
- Co-Supervisor: Prof. Mal McMillan, Lancaster University
Project Summary
The Greenland Ice Sheet (GrIS) is one of the largest contemporary contributors to global sea-level rise, containing enough ice to raise sea level by around 7 m. While GrIS mass loss has accelerated in recent decades, its projected contribution to future sea-level rise remains uncertain. In summer, supraglacial lakes form on the surface of the ice sheet, where the water generated by melted ice ponds in surface depressions. These lakes are important because when the water stored in them drains through fractures in the ice, in can serve to lubricate the bed of the ice sheet, and allow the ice to flow more rapidly towards its margin.
Satellite remote sensing provides a crucial, low-carbon means of monitoring dynamic processes at scale, compared to field-based monitoring and techniques. Supraglacial lake presence, area, and drainage have been well documented during the summer melt season using optical satellite imagery, but winter behaviour remains poorly constrained due to snow cover and lack of solar illumination. Recent advances in Synthetic Aperture Radar (SAR) have revealed that liquid water can persist through winter and that some lakes drain during this period, influencing ice velocity. Yet, year-round lake behaviour (including the frequency, timing, and impact of winter drainage) on longer timescales remains largely unknown.
This PhD project will develop and apply a novel, energy-efficient remote sensing framework using a range of Earth Observation data to monitor supraglacial lakes year-round across the GrIS. Cloud-based platforms powered by renewable energy will be utilised to contribute to the net-zero aims of the project. Machine learning will be used to efficiently handle and extract information from large data volumes, reducing computational expense. The project will also evaluate satellite-based ice-velocity and uplift products instead of field-based GNSS networks as a low-carbon alternative for assessing the impact of drainage events.
Research Objectives
- Develop a multi-sensor (e.g., optical, synthetic aperture radar, and laser altimetry) satellite-based methodology to characterise the magnitude and frequency of supraglacial lake drainages year-round over a sub-area of the GrIS.
- Identify and train a machine-learning approach using the observations from objective 1 to detect lakes and drainage events year-round.
- Apply the machine-learning multi-sensor approach developed in objectives 1 and 2 to scale-up the analysis spatially and temporally, using the existing archive of satellite data and evaluating the impact of drainage events using additional Earth Observation data instead of field-based GNSS networks.
- Net Zero Case Study Objective: Assess the potential of multi-sensor remote sensing methods for characterising supraglacial lake drainage events, reducing the need for field-based observations and making use of satellite-based ice-velocity and uplift products instead of field-based GNSS networks.
Technical Approaches
This project will employ multi-sensor satellite Earth Observation to develop the first year-round supraglacial lake detection and drainage algorithm for the GrIS. The approach will integrate synthetic aperture radar (SAR), optical, and altimetry data within scalable, cloud-based workflows designed for energy efficiency and reproducibility. C-band and L-band SAR will be used for winter detection, while optical imagery will be merged with SAR during the melt season. Machine-learning methods will be implemented to detect spatial and temporal drainage patterns. The resulting workflow will be open source and transferable to other settings.
Training Opportunities
The PhD student will have the opportunity to attend glaciological, remote sensing, and machine learning training courses run by various providers, which will be highlighted throughout the PhD by the supervisors. There is also a possibility for fieldwork to conduct ground-truthing of the remote sensing results and thus quantify net-zero savings, if the student were interested, and supervisors will provide relevant training and support if this were undertaken.
Academic Background
This project would be suitable for students with a degree in physical sciences or a closely related subject. Students should be interested in glaciology, remote sensing, and supraglacial lake processes and monitoring. Experience of working with geospatial data, particularly remote sensing methods, is desirable but not essential. Applicants would normally be expected to hold at least a 2:1 UK Honours degree or equivalent, but experience in relevant fields through non-traditional routes is also encouraged. We welcome applications from candidates from all backgrounds. Interested applicants may contact Dr Katie Miles (k.miles1@lancaster.ac.uk) for more information.
Eligibility
For entry to PhD study, applicants are expected to have at least one of the following:
• a first or upper second (2:1) class honours undergraduate degree in a relevant subject, or an equivalent international qualification,
• a relevant master’s qualification or equivalent evidence of prior professional practice.
International applicants and candidates from non-English speaking countries will need to meet the minimum language requirements for admission onto the programme of study for their Home institution.
How to Apply
To apply for a NZPS DTP studentship, please follow the guidance on the NZPS application process webpage.
Informal enquiries about the project and your application should be addressed to the project supervisor, Dr Katie Miles – k.miles1@lancaster.ac.uk
After you have discussed your application with the project supervisor and read the NZPS application guidance, you should:
1) Complete the online NZPS Application Form by 17.00GMT 7th January 2026.
2) Submit any additional application documents in the requested format to NZPS@northumbria.ac.uk by the closing date.
If you require any additional assistance in submitting your application or have any queries about the application process, please don’t hesitate to contact us at nzps@northumbria.ac.uk
This PhD is part of the Net Zero Polar Science DTP, which aims to make polar science possible in a net zero world. For further details visit https://nzps-dtp.ac.uk/
Funding Notes
Funding is available to Home/UK and international (including EU) students, subject to the successful completion of quality assurance checks and UK Visa and Immigration (UKVI) compliance requirements. This includes a full stipend at UKRI rates (for 2025/26 FT study this is £20,780 per year), full tuition fees and an annual Research Training and Support Grant (RTSG). Studentships are also available for Home applicants who wish to study part-time in combination with work or personal responsibilities. Please note: additional costs may apply for international applicants.
References
1. Miles, K. E., Willis, I. C., Benedek, C. L., Williamson, A. G., & Tedesco, M. (2017). Toward Monitoring Surface and Subsurface Lakes on the Greenland Ice Sheet Using Sentinel-1 SAR and Landsat-8 OLI Imagery. Frontiers in Earth Science, 5, 58. https://doi.org/10.3389/feart.2017.00058
2. Fitzpatrick, A., Hubbard, A., Box, J., Quincey, D., et al. (2014). A decade (2002–2012) of supraglacial lake volume estimates across Russell Glacier, West Greenland. The Cryosphere, 8, 1, 107-121. doi.org/10.5194/tc-8-107-2014
3. McMillan, M., et al. (2007). Seasonal evolution of supraglacial lakes on the Greenland Ice Sheet. Earth and Planetary Science Letters, 262, 3-4, 484-492. https://doi.org/10.1016/j.epsl.2007.08.002
