About the project
Flooding is the UK’s costliest natural disaster, particularly in estuaries where rivers meet the sea. Rising seas, storms, and heavy rainfall increase flood risks, threatening homes, infrastructure, and economies. Traditional monitoring is expensive and limited, this project combines satellites and AI to sustainably track water levels and reduce flood impacts.
Flooding is the costliest natural disaster in the UK, with coastal flooding alone causing around £540 million in damages each year. Estuaries, where rivers meet the sea, are particularly vulnerable, threatening critical infrastructure, homes, and local economies. Understanding how flood risks in estuaries may evolve under climate change is vital for effective flood risk management, which requires continuous monitoring of water levels along the estuary. However, traditional monitoring is expensive, spatially limited, and prone to data quality and reliability issues. Our project addresses this challenge by integrating satellite remote sensing, in-situ measurements, and AI, to make a step change in sustainable estuarine water level monitoring.
You will leverage the recently launched SWOT satellite, which provides near-complete coverage of UK water levels, alongside high-frequency tidal and river gauge data.
The project involves building an AI model to simulate estuarine water levels in densely monitored systems such as the Thames and Severn, refining the model using observed data, and evaluating performance under diverse environment conditions in that estuary. The framework will then be tested in other estuaries, such as the Humber, which experience both tidal and compound flooding.
You will work with leading coastal scientists at the National Oceanography Centre, University of Southampton, and estuary managers from the Environment Agency, gaining hands-on experience with satellite data, AI techniques, and helping real-world flood risk management.
This project is ideal for master’s graduates in environmental science, hydrology, computer science, civil engineering, or related fields, with a strong interest in AI applications for environmental monitoring.
Supervisors
As well as Masashi Watanabe from the University of Southampton, you will also receive supervision from:
- Xiaoyan Wei: xwei@noc.ac.uk from the National Oceanography Centre (lead supervisor).
- Ian Dougal Lichtman: doulich@noc.ac.uk from the National Oceanography Centre.
- Philip Staley: philip.staley@environment-agency.gov.uk from the Environment Agency.
Please contact the lead supervisor if you require further information about the project.
References
Zhu, R., Zhang, W. and Wei, X., 2025. Impact of intertidal habitats on hydrodynamics in tidally energetic, well-mixed estuaries. Journal of Physical Oceanography.
Lichtman, I. D., Bell, P. S., Gommenginger, C., Banks, C., Calafat, F. M., Brown, J., & Williams, S. D. P. (2025). Evaluating water levels from the surface water and ocean topography (SWOT) mission in a hyper‐tidal coastal and estuarine environment. Earth and Space Science, 12, e2024EA004104. https://doi.org/10.1029/2024EA004104
Shirai, T., Enomoto, Y., Watanabe, M. and Arikawa, T., 2022. Sensitivity analysis of the physics options in the Weather Research and Forecasting model for typhoon forecasting in Japan and its impacts on storm surge simulations. Coastal Engineering Journal, 64(4), pp.506-532.
