Supervisors: Alessandro Silvano (UoS), Ivan Haigh (UoS) Marc Walraven (RWS), Stefano Libardo (Consorzio Venezia Nuova), Chao Zheng (UoS)
Contact email: a.silvano@soton.ac.uk
LocationUniversity of Southampton
Project Rational:
Coastal flooding is one of the most dangerous and costly natural hazards that humanity faces globally and yet it will become even more frequent and challenging to manage because of climate change and other factors. In densely populated estuarine settings, a storm surge barrier is often an attractive and economical solution for flood protection. There are many surge barriers in operation today around the world protecting tens of million people and trillions of pounds of property and infrastructure. However, with accelerating rates of sea-level rise being observed and changes in storminess, surge barriers are starting to have to close more and more frequently, with critical implications for barrier management, maintenance, and operation. This has also negative ramifications for shipping and the health of the estuary behind barriers and the important ecosystems they support. This project will focus on areas of Europe most vulnerable to coastal flooding: Venice, Netherlands and London. The overall aims of this PhD are to: 1) improve forecast of flooding through Artificial Intelligence and develop early warning systems essential for storm barrier management and risk mitigation; 2) assess the impact of sea level rise and changes in storminess on flooding in these areas, considering near and long-term implications for the storm surge barriers that protect them. Results will be used to guide future barrier management, maintenance, operation, and upgrade/replacement planning.
Methodology:
The study will have three main components. First a broad scale assessment will be carried out determining how both mean and extreme sea-levels have changed in the past in Venice, London and Netherlands, using tide gauge records, satellite observations and model re-analysis. Second, the student will use new AI approaches to improve forecast of storm surges in these areas, developing early warning systems. Knowledge obtained during the initial assessment will be essential to select training data for AI. Finally, future changes in sea level and extremes will be assessed using a range of climate projections. A statistical method will be developed (building on existing work being carried out at the University of Southampton) to estimate how many more times the barriers will have to close each year and when in the year, in the future. Changes in weather predictability in a warming world will be assessed as part of this exercise. Today storm surge barriers typically close between 1 and 30 times per year, but with a sea level rise of 1 m, this might increase to >100 closures per year. An assessment will thus be made of implications of increased closures on storm surge barrier management, maintenance and operation.
Background reading
• Umgiesser, G., Bajo, M., Ferrarin, C., Cucco, A., Lionello, P., Zanchettin, D., Papa, A., Tosoni, A., Ferla, M., Coraci, E., Morucci, S., Crosato, F., Bonometto, A., Valentini, A., Orlić, M., Haigh, I. D., Nielsen, J. W., Bertin, X., Fortunato, A. B., Pérez Gómez, B., Alvarez Fanjul, E., Paradis, D., Jourdan, D., Pasquet, A., Mourre, B., Tintoré, J., and Nicholls, R. J.: The prediction of floods in Venice: methods, models and uncertainty (review article), Nat. Hazards Earth Syst. Sci., 21, 2679–2704, https://doi.org/10.5194/nhess-21-2679-2021, 2021. https://nhess.copernicus.org/articles/21/2679/2021/
• Trace-Kleeberg, S., Haigh, I.D., Walraven, M., Gourvenec, S. (2023) How should storm surge barrier maintenance strategies be changed in light of sea-level rise? A case study, Coastal Engineering, 184, 104336, https://doi.org/10.1016/j.coastaleng.2023.104336.
• Ebel, P., Victor, B., Naylor, P., Meoni, G., Serva, F., Schneider, R. (2024). Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 471-480.
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.
