Project Description
Catastrophic flooding and debris flow events in postglacial mountain environments illustrate how sediment mobilisation, transport and deposition can become the dominant drivers of fluvial hazards. The efficacy of hillslope-to-fluvial channel sediment transport via landslides, rockfalls and debris flows, is argued to set the pace of postglacial landscape evolution. Headwater ephemeral channels, which flow only briefly following precipitation or melt events (e.g., rainfall, snowmelt, or glacier melt), are conduits between the hillslopes and fluvial network. However, the dynamics of these transient channels (including colluvial channels, gullies, rills) are poorly understood. High resolution topographic data, collected using UAV-LiDAR, structure from motion photogrammetry and terrestrial laser scanning, provides an exciting opportunity to quantify the modern dynamics of these channels and their connected hillslopes while also constraining their postglacial history.
The Grampian Mountains in Scotland feature impressive postglacial landscapes which are increasingly susceptible to fluvial hazards and their cascading impacts under a changing climate. A suite of catchments within and beyond the Loch Lomond Stadial ice limits will be investigated to allow the project to assess how these channels vary across landscapes with differing glacial histories.
This project will:
1) Create a high resolution topographic record of ephemeral channel morphology across the Grampian Mountains
2) Develop topographic metrics to classify these channels based on their past and future behaviour
3) Reconstruct the ephemeral channel’s role in the postglacial evolution of the Grampian Mountains
Research themes
Climate and Environmental Change
Project Specific Training
The student will receive a bespoke training programme combining specialist scientific training with transferable professional skills. Training in topographic data collection (operation of UAV and TLS) and geomorphological mapping will be delivered by the whole supervisory team, making use of QMUL’s field and laboratory facilities. Software engineering and data science training will be provided by Grieve, focusing on geospatial data science and topographic analysis techniques (Python, C++, QGIS, LSDTopoTools). The student will also recieve training through the QMUL Graduate School in skills including project management, public speaking and science communication.
Potential Career Trajectory
During this project, the successful student will develop software engineering and data science skills. In particular they will develop skills to process and analyse large unstructured datasets using deep learning approaches. Previous students supervised by Grieve have gone on to use these skills to work as a machine learning engineer in an environmental start-up and as an environmental hazard modeller in the insurance industry.
The fieldwork and environmental monitoring skills developed during this project lend themselves well to work in environmental consultancy and land management sectors. The supervisory team have strong connections in these industries and can support the successful student in pursuing this type of career path through internship and networking opportunities.
The interdisciplinary nature of this project also opens up a wide range of postodctoral research opportunities in geomorphology, environmental management, natural hazard modelling and remote sensing. Former students of the supervisory team have secured competititive postdoctoral funding at several UK and international institutions.
Project supervisor/s
Stuart Grieve, Department of Geography and Environment, Queen Mary, University of London, s.grieve@qmul.ac.uk, https://www.qmul.ac.uk/geog/staff/grieves.html
Alex Henshaw, Geography, Queen Mary, University of London, a.henshaw@qmul.ac.uk, https://www.qmul.ac.uk/geog/staff/henshawa.html
