Chris Banks (NOC), Simon Williams (NOC), Dougal Lichtman (NOC), Ivan Haigh (UoS)
To apply for this project please click here. Tick programme type – Research, tick Full-time or Part-time, select Academic year – ‘2024/25, 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.
Rationale:
Sea level variability has a major impact on flood risk at the coast, and understanding this risk requires detailed knowledge of sea level. The UK water level gauge network forms the basis of the UK coastal flood predictions service, with the areas between gauges filled in by model data. However, these data are poorly validated away from the tide gauge measurements. The problem is greater in areas of complex coastal morphology, such as estuaries, which are difficult to model accurately due to nearshore hydrodynamics and river flow. However, the quality of sea level measurements from satellite altimeters has improved markedly over recent years close to the coast, through coastal retracking, SAR altimetry and novel 2D altimetry. This project seeks to improve the accuracy of water level estimates around the UK coast, better determine the uncertainty for extreme water levels and, therefore, better manage risk of flooding at the coast. To address key issues in the coastal zone, it is proposed to develop a methodology for a coastal sea level product. By building a model linking sea level data from tide gauges and altimetry to flood models, the areas between tide gauges will be better resolved for determining risk to coastal communities, habitats, and infrastructure.
Methodology:
This project will make use of novel 2D water level measurements from the Surface Water and Ocean Topography (SWOT) satellite altimeter, as well as the large archive of existing satellite altimetry data, and data from the UK water level gauge network. SWOT is designed to measure water levels for rivers as well as the sea (different product resolutions from 50 m to 2 km), allowing the effects of river and sea to be studied for joint effects on water level at the coast. Training will be given in the use of these state-of-the-art 2D altimetry measurements and machine learning & statistical methods, to produce predictions and uncertainty estimates of extreme water levels that result in coastal flooding. The approach would incorporate multiple satellite altimeters, tide gauges and auxiliary data, via some form of spatio-temporal model. The key data sources to be exploited in this project are:
- Satellite altimetry sea level data (CryoSat-2, Jason series, Sentinel 3, Sentinel 6 and SWOT)
- Tide gauge data
- Auxiliary satellite and model data (e.g., wind, air pressure, river flow)
Location:
Hosted at the National Oceanography Centre, degree awarded by University of Southampton
Background Reading:
Neumann, B., Vafeidis, A. T. , Zimmermann, J. and Nicholls, R. Future Coastal Population Growth and Exposure to Sea Level Rise and Coastal Flooding – A Global Assessment. PLOS ONE, 10(3): e0118571, doi:10.1371/journal.pone.0118571, 2015.
Cazenave, A., Gouzenes, Y., Birol, F. et al. Sea level along the world’s coastlines can be measured by a network of virtual altimetry stations. Commun Earth Environ 3, 117 (2022). https://doi.org/10.1038/s43247-022-00448-z
Kirezci, E., Young, I.R., Ranasinghe, R. et al. Projections of global-scale extreme sea levels and resulting episodic coastal flooding over the 21st Century. Sci Rep 10, 11629 (2020). https://doi.org/10.1038/s41598-020-67736-6
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