University of Leeds: Geography
Supervisor: Scott Watson c.s.watson @ leeds.ac.uk
As glaciers disappear, many are leaving behind glacial lakes, which can span several kilometres in length and reach depths of hundreds of metres. These lakes act as a positive feedback mechanism, accelerating glacier mass loss. In some cases, they can also pose a downstream flood risk in the event of a glacial lake outburst flood (GLOFs). This hazard is particularly pronounced in High Mountain Asia, including countries like Nepal, where the exposure and vulnerability of people and infrastructure to GLOFs is high. Therefore, there is a requirement to closely monitor changes at glacial lakes using satellite data, owing to their remote and high-altitude locations.
The researcher will use Surface Water Ocean Topography (SWOT) data to analyse changes in the water surface elevation of glacial lakes in Nepal. This data, generated by the Ka-band Radar Interferometer (KaRIn), provides water elevation measurements at approximately 20-meter spacing. The researcher will build on our existing scripts to analyse the spatial and temporal changes in lake water levels across Nepal. The dataset will reveal novel insights into water storage and will contribute to the Glacial Lake Observatory project. The researcher will also explore comparisons with other datasets such as ICESat-2 altimetry and our field-derived Global Navigation Satellite System observations. The researcher will have the opportunity to discuss their work at Glacial Lake Observatory team meetings and will be trained to access and analyse the data, and present their findings.
The candidate should be familiar with a GIS package (e.g. QGIS or ArcGIS) and have an interest in satellite data. Some coding experience (e.g. R or Python) would be beneficial but is not required. Full training in the methods required to complete the project will be provided.
The researcher will access SWOT data over glacial lake catchment where we have field observations of lake bathymetry and water level elevations. Data will be filtered using geolocation quality and classification flags. Water surface elevations will be computed by subtracting tidal loading corrections and referencing the heights to the EGM2008 geoid. These results will be integrated with field-measured bathymetry and compared with GNSS water level observations to derive glacial lake water storage, associated uncertainties, and seasonal changes. The methodology will be scripted in R or Python to support reproducibility and produce semi-automated time series analysis of water level changes at priority glacial lakes.
Through regular supervision and research group meetings, the researcher will be supported to write up their methodology into a reproducible script and short report with high-quality figures to summarise their findings. The research will disseminate their findings through Glacial Lake Observatory social media channels and our upcoming website.
The researcher will work within the broader Glacial Lake Observatory team and will be supported to present their findings to one of our school research cluster meetings.
