In low-lying coastal regions, flooding often arises from more than one drive (e.g. oceanographic, fluvial and/or pluvial), a phenomenon that is known as ‘compound flooding’. This PhD will use a new km-scale system coupling atmosphere, land, waves and ocean. Modelling experiments across weather and climate timescales will further our understanding and improve the prediction of compound events and their potential changes in the future.
Coastal hazards (e.g., estuarine flooding, coastal overtopping, coastal erosion) mainly occur in response to meteorological events that drive multiple hazards, such as strong winds, heavy rain, storm surge, high waves, high river flows (Bevacqua et al., 2020). Current guidance and coastal flood prediction generally doesn’t account for interdependencies and potential non-linear amplification of these hazards. Understanding both the present-day combined multi-hazard flood risk and the potential changes under future sea-level rise and changes in storminess are fundamental to robust coastal adaptation planning. This PhD will gain new insight into past multi-hazard dependencies and their future evolution, as well as define multi-hazard thresholds for weather forecasting, by answering these questions:
(i) what are the relationships between tides, waves, storm surges and river flows observed in the historical past?
(ii) How important are the interactions between these flood hazard drivers in modifying the overall risk?
(iii) How might these drivers and their interactions change under future sea-level rise?
This PhD project will make use of a new regional coupled modelling system developed for the Northwest European Shelf, including atmospheric, wave, ocean and river models at km-scale (Lewis et al. 2019, Lewis & Dadson, 2021). A unique feature of this system is its ability to run in both weather forecast and climate projection modes. The PhD student will evaluate this new coupled modelling capability, with a particular focus on wave, surge and river flows. They will also test new developments in the land-surface scheme (e.g., groundwater scheme) relevant to compound flood events. The project will use a first-of-its-kind 20-year simulation to better quantify multi-hazard dependencies. This will enable the definition of multi-variate thresholds for weather forecasting and inform multi-variable co-variances for statistical models used to infer return levels for coastal resilience. The student will make use of future projections (10-year time slices, complemented by targeted storm-event downscaling) to better quantify future changes in multi-variate coastal hazards. The project will be supported by a strong multidisciplinary team and benefit from the many collaborations around the Regional Environmental Prediction project, between the University of Southampton, Met Office, National Oceanography Centre, UK Centre for Ecology and Hydrology, British Geological Survey, Plymouth Marine Laboratory.
The MFC CDT programme provides comprehensive training in the theory of climate science, physical sciences, scientific computing, statistics and data analysis to address pressing problems and challenges posed by climate change. The CDT programme also affords extensive opportunities for personal and professional development training, and for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. Specific training will include:
* Training in data analysis in python language, statistics and meteorology / oceanography (depending on candidate's background).
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, select Academic year – ‘2024/25', search text – ‘PhD Ocean&EarthSci (Mathematics for our Future Climate 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 acng@soton.ac.uk.
* Bevacqua, E., Vousdoukas, M. I., Zappa, G., Hodges, K., Shepherd, T. G., Maraun, D., et al. (2020). More meteorological events that drive compound coastal flooding are projected under climate change. Communications Earth & Environment, 1(1), 47. https://doi.org/10.1038/s43247-020-00044-z
* Lewis, H. W., Castillo Sanchez, J. M., Arnold, A., Fallmann, J., Saulter, A., Graham, J., et al. (2019). The UKC3 regional coupled environmental prediction system. Geoscientific Model Development, 12(6), 2357–2400. https://doi.org/10.5194/gmd-12-2357-2019
* Lewis, H. W., & Dadson, S. J. (2021). A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom. Hydrological Processes, 35(12), e14438. https://doi.org/10.1002/hyp.14438
