Nuclear power remains essential to the UK’s energy security and is seen by government as a cost-competitive, low carbon component of the UK’s current and future energy mix, with new nuclear power stations currently under construction along the coast, and more planned to replace aging reactors. The coast offers easy access to cooling waters, but coastal erosion and flooding hazards can pose a significant threat to nuclear facilities and infrastructure, with potentially severe consequences. Implementing effective protection measures over the lifecycle (construction, operation, decommissioning) of these assets is essential and requires robust predictions of shoreline change and flood risk in current and future climates. Nuclear regulations require a Coastal Process Assessment of nearshore wave and water levels, sediment transport and bathymetric change, and predictions of flood depths and coastal erosion/accretion under various climate change projections . Such models are expensive and often carry large uncertainty due to limited datasets and complex interactions between oceanographic and meteorological processes acting at global, regional, and local scales. Hybrid, multivariate statistical-dynamical models are emerging as a skillful tool for probabilistic assessment of coastal hazards  and may offer an efficient means for assessing exposure and vulnerability of nuclear sites to climate-mediated coastal hazards.
The aim of the project is to implement hybrid statistical-dynamical modelling to probabilistic assessment of exposure to coastal hazards at nuclear power station development sites in the UK. This will be realized via three objectives. The first objective will be to infer regional-scale synoptic weather-types across NW Europe by identifying dominant modes of spatial variability in multivariate oceanographic (e.g. waves, tides, storm surge, sea level rise, etc.) and meteorological (sea level pressure, wind, precipitation, etc.) forcing at annual and seasonal timescales . Statistical down-scaling and machine-learning tools will be then used to identify representative combinations of forcing conditions, including extreme, compound, and clustered events, and develop probabilistic input for simulating coastal change . These extreme climate types will be used to explored ‘extreme’ morphological responses and map hazard and vulnerability at UK nuclear installations and development sites. The final objective will use these inputs in dynamic, coastal evolution models to simulate the impacts of coastal flooding and erosion at a specific case study site.
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the School of Ocean and Earth Science, Graduate School of the National Oceanography Centre, Southampton with a placement at EDF R&D UK Centre.
Specific training will include coastal oceanography, big data analysis and machine learning in Matlab/Octave or Python, and numerical modelling of coastal processes, as well as opportunities to develop written and verbal communication.
Please see https://inspire-dtp.ac.uk/how-apply for details.
 ONR (2018). Analysis of coastal flood hazards for nuclear sites. NS-TAST-GD-012 Annex 3 Reference Paper: ONR Expert Panel on Natural Hazards Paper No: GEN-MCGH-EP-2017-2, Sub-Panel on Meteorological and Coastal Flood Hazards, Office for Nuclear Regulation. 33 pp.
 Anderson, D., Rueda, A., Cagigal, L., Antolinez, J.A.A., Mendez, F.J., Ruggiero, P. (2019). Time‐varying emulator for short and long‐term analysis of coastal flood hazard potential. Journal of Geophysical Research: Oceans, 124, 9209–9234.
 Camus, P., Haigh, I.D., Nasr, A. Wahl, T., Darby, S.E., Nicholls, R.J. (2021) Regional analysis of multivariate compound flooding potential: sensitivity analysis and spatial patterns. Preprint [Discussions]. Natural Hazards and Earth System Sciences.