Project details
A great challenge in flood risk management to better model extreme events, particularly as flood frequency and severity are likely to increase in the UK as climate changes. A critical limitation for high-resolution flood modelling is inadequate information about the shape and roughness of the bed of our river channels.
This PhD project is ambitious and exploratory, yet based on a simple breakthrough idea: Water surface motion visibly reflects the key factors shaping flow in a river, almost by definition, so information for enhanced flood prediction should be observable from short videos of rivers (e.g. on a mobile phone). Its vision is to create a handheld, easily and widely deployable ‘tool’ for improving real-time flood risk assessment. It will have one critical conceptual advantage over other approaches (e.g. green lidar, structure from motion); it does not require light or sophisticated, expensive, equipment to penetrate to the river’s bed. It potentially reveals how riverbed shape changes during high flow events (i.e. when the water is entirely opaque), and Storm Desmond in 2015 demonstrated how important riverbed shape changes during high flows (i.e. River Greta) are in influencing flooding.
Methodology:
The PhD will use short video-clips of the water surface of a river to get a surface velocity field (established techniques) and spatially classify flow regimes (e.g. pool, riffle). Then, these data will be inverted via flow modelling & machine learning (physics-informed neural network – PINN) to get a detailed & spatially varying bathymetry and/or riverbed roughness. As such the methodology has four elements
1. Flume tank: In controlled conditions, data will be collected relating surface observables from videos, with underlying riverbed characteristics designed and known at 1-10 m scale.
2. Hydraulic modelling: A large number of simulations will be run, for known riverbeds, yielding predictions of surface observables.
3. PINN construction: Will be designed, using adversarial training and semi-supervised learning strategies to blend these data types, enabling prediction from this (what is for machine learning) sparse data.
4. Fieldwork: Building on pilot data collected of 10 days in 2024 between on the river Greta near Keswick in the Lake District, videos and field surveys will serve to make this work applicable to the real rivers (10-100m scale). Pragmatically, a variety of video capture technology will be investigated (i.e. mobile phone, fixed GoPro camera, aerial drone).
References:
Environment Agency (2021) Understanding river channel sensitivity to geomorphological changes, Report number – FRS17183/R1, ISBN: 978-1-84911-479-0.
Muste et al. (2009) Large-scale particle image velocimetry for measurements in riverine environments. Water Resources Research, 44, W00D19. doi:10.1029/2008WR006950.
Xia et al. (2019) A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS). Advances in Water Resources 132, 103392.
94% of Loughborough’s research impact is rated world-leading or internationally excellent. REF 2021
Supervisors
Primary Supervisor: John Hillier
Secondary Supervisor: Qiuhua Liang
Entry requirements
Our entry requirements are listed using standard UK undergraduate degree classifications i.e. first-class honours, upper second-class honours and lower second-class honours. To learn the equivalent for your country, please choose it from the drop-down below.
Entry requirements for United Kingdom
Applicants must already have, or expect to shortly graduate with, a very good undergraduate degree or Master’s degree (at least a UK 2:1 honours degree) – or an equivalent international qualification from a high ranking university – in a relevant subject. EU and Overseas applicants should achieve an IELTS score of 6.5 with at least 6.0 in each competency.
English language requirements
Applicants must meet the minimum English language requirements. Further details are available on the International website.
Fees and funding
Tuition fees for 2025-26 entry
UK fee
Fully funded Full-time degree per annum
International fee
Fully funded Full-time degree per annum
Fees for the 2025-26 academic year apply to projects starting in October 2025.
Find out more about research degree funding
Studentship type – UKRI through Flood-CDT (flood-cdt.ac.uk) The studentship is for 3.5 years and provides a tax-free stipend of £19,237 per annum plus tuition fees at the UK rate. Excellent International candidates are eligible for a full international fee waiver however due to UKRI funding rules, no more than 30% of the studentships funded by this grant can be awarded to International candidates.
How to apply
All applications should be made online. Under programme name, select Geography and Environment. Please quote the advertised reference number: FCDT-25-LU3 in your application. 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 found here. 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 1 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
- academic transcripts and degree certificates (translated if not in English)
- a IELTS/TOEFL certificate, if applicable.
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.
