Supervisors: Hachem Kassem (UoS), Jenny Brown (NOC), Charlie Thompson (UoS), Charlotte Lyddon (University of Liverpool), Doug Pender (JBA Consulting)
Contact email: hachem.kassem@soton.ac.uk
Location: Southampton
Project Rational: Gravel barrier shorelines offer widespread, critically important natural flood protection to many coastal communities. Their management, creation and enhancement are increasingly seen as sustainable, while providing nature-based adaptation options that boost natural capital. But these assets must be well managed to ensure they continue serving such functions in the face of increased risk of coastal erosion and flooding.
This project aims to identify the controls on gravel barrier overwash at a national scale to develop a framework of indicators that support the development of adaptation pathways and monitor performance over time.
The objectives are to:
1) Schematise all gravel beaches around the UK using numerical simulations to create a classification dataset for training Machine Learning tools.
2) Apply the new tools to identify trigger thresholds in geomorphic and environmental conditions that cause a state change in gravel beach response to forcing.
3) Develop an approach to process long-term coastal monitoring data into the tools to provide early warning of a shift in state.
Methodology: This project will apply Machine learning (ML) to forecast gravel-beach overwash vulnerability in a changing climate. Working alongside the NERC highlight topic research project #gravelbeach it will deliver new tools for the National Network of Regional Coastal Monitoring Programmes. Using nation-wide information collected within #gravelbeach, a classification to support a Frame of Reference1 for gravel barrier management will be prepared. A library of numerical simulations will be generated, using XBeach-G2, to understand key parameters that control the system’s state and thresholds3 that trigger a state change. This numerical database will train a supervised ML algorithm to predict physics-informed barrier overwash in response to coastal forcing (e.g., beach profile characteristics, and overtopping in response to wave/storm conditions and antecedent morphology, etc.). The ML techniques will be chosen in collaboration with JBA consulting to ensure they are appropriate for coastal management needs. The ML model will be applied in a hypothetical decadal (and potentially real-time) forecasting scenario of relevance to key management interests identified with #gravelbeach stakeholders. The model will be tested with operational monitoring (beach surveys, wave buoy, tide gauge) to demonstrate how it can be used with existing observations to provide coastal flood and erosion risk managers with early warnings.
Background Reading:
- van Koningsveld, M., Mulder J.P.M. (2004); Sustainable Coastal Policy Developments in The Netherlands. A Systematic Approach Revealed. Journal of Coastal Research 1 April 2004; 20 (2) 375–385. https://doi.org/10.2112/1551-5036(2004)020[0375:SCPDIT]2.0.CO;2
- Ions, K., Karunarathna, H., Reeve, D.E., Pender, D. (2021); Gravel Barrier Beach Morphodynamic Response to Extreme Conditions. Journal of Marine Science and Engineering, 9 (2), 135. https://doi.org/10.3390/jmse9020135
- Prime, T., Brown, J.M., Plater, A.J. (2016); Flood inundation uncertainty: the case of a 0.5% annual probability flood event. Environmental Science & Policy. 59, 1 – 9.
FLOOD-CDT
This PhD is being advertised as part of the Centre for Doctoral Training for Resilient Flood Futures (FLOOD-CDT). Further details about FLOOD-CDT can be seen here https://flood-cdt.ac.uk. Please note, that your application will be assessed upon: (1) Motivation and Career Aspirations; (2) Potential & Intellectual Excellence; (3) Suitability for specific project and (4) Fit to FLOOD-CDT. So please familiarise yourselves with FLOOD-CDT before applying. During the application process candidates will need to upload:
• a 1 page statement of your research interests in flooding and FLOOD-CDT and your rationale for your choice of project;
• a curriculum vitae giving details of your academic record and stating your research interests;
• name two current academic referees together with an institutional email addresses; on submission of your online application your referees will be automatically emailed requesting they send a reference to us directly by email;
• academic transcripts and degree certificates (translated if not in English) - if you have completed both a BSc & an MSc, we require both; and
• a IELTS/TOEFL certificate, if applicable.
Please upload all documents in PDF format. You are encouraged to contact potential supervisors by email to discuss project-specific aspects of the proposed prior to submitting your application. If you have any general questions please contact floodcdt@soton.ac.uk.
Apply
To apply for this project please click here: https://student-selfservice.soton.ac.uk/BNNRPROD/bzsksrch.P_Search. Tick programme type - Research, tick Full-time or Part-time, select Academic year – ‘2025/26, Faculty Environmental and Life Sciences’, search text – ‘PhD Ocean & Earth Science (FLOOD CDT)’.
In Section 2 of the application form you should insert the name of the project and supervisor(s) you are interested in applying for.
If you have any problems please contact: fels-pgr-apply@soton.ac.uk.
