Location
National Oceanography Centre (Liverpool) and University of Southampton

Supervisors: Jeff Polton (NOC), Ivan Haigh (UoS), Ed Steele (Met Office), Jenny Sansom (EA)

Contact email: jelt@noc.ac.uk

Project rationale

Coastal flooding is the second largest non-malicious risk to the UK. Accurate coastal forecast information is critical to enabling EA Incident Managers to assess coastal flood risk in real time and take appropriate mitigating actions. Coastal water level forecast errors are currently twice the target accuracy in key locations due to complex local coastal processes. Forecast uncertainty at Thames Barrier, for example, forces operational teams to be overly cautious and more frequently close the barrier. This increases the probability of closures (together with sea level rise) exceeding that which is feasible before early 2030s, at which point a multi-billion pound upgrade will be needed. EA water level forecasts are based on a dynamical ocean model. This approach is skillful away from the coast but is fundamentally limited in effectiveness at the coastline, where processes are complex. Remnant errors/model bias have necessitated post-processing of raw model outputs. Current post processing techniques are simplistic and focus on correcting long-term bias. By contrast, machine learning techniques better represent error from complex or poorly understood processes and constrain model trajectories to real time observations. Machine learning techniques have shown promising potential to make a significant contribution to surge forecast skill but have yet to be explored in an operational context by the EA.

Methodology

In the first year, the student will review:

  1. Storm surge dynamics. NOC will develop the student’s knowledge through hosting the PhD, as international leading experts in storm surge modelling and tidal analysis.
  2. Requirements for operational forecasting. The student will work with MO/EA supervisors to identify key challenges and case studies, including operational considerations that shape forecast performance.
  3. Available data and tools.

The student will familiarise themselves with these aiming for use in following years – supported by all supervisors, including Southampton University who provide expertise in analysing and interpreting the observational record.

The PhD will have access to operational surge model, tidal prediction and observational data (e.g., tide, river, mean sea level, meteorological). Key activities will include (lead support):

  • Investigate the role of tidal harmonics and bed friction in complex coastal environments (NOC)
  • Develop metrics to assess existing model skill (EA)
  • Run existing surge machine learning code to explore process and principles (NOC)
  • Review modelling and machine learning tools and techniques (NOC/MO)
  • Review observational data handling, challenges and quality control (SU).

Following years will develop a machine learning problem which addresses a challenge identified in year one, provide a proof of concept for operational implementation including success evaluation.

Background reading

Paper about using neural networks to predict surge:

Bruneau, N., Polton, J., Williams, J., Holt, J. (2020), Estimation of Global Coastal Sea level extremes using Neural Networks, ERL, https://doi.org/10.1088/1748-9326/ab89d6.

Polton, J.A., O’Neill, C.K., Gomez, B., Warder, S., Piggott, M., Saulter, A. (2024) The dynamical forecast model: AMM7-surge: A 7-km resolution Atlantic Margin Model surge configuration using NEMOv4.0.4. https://doi.org/10.5281/zenodo.10605585

Tool and reports for existing example of machine learning developed for improving water level prediction (in research phase), developed by Oxford University and currently being tested operationally by the EA in partnership with NOC: GitHub – thomasmonahan/RTide: A Python implementation of the ML Response Framework for tidal analysis and prediction of complex tidal phenomena.

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 one-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 and 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.