Location
Loughborough University

Supervisors: John Hillier (LU), Quihua Liang (LU), Hui Fang (LU), Andrew Pledger (Previsico)

Contact email: j.hillier@lboro.ac.uk

Project rationale

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 and machine learning (physics-informed neural network – PINN) to get a detailed and 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 1–10m 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).

Background reading

Environment Agency (2021) Understanding river channel sensitivity to geomorphological changes, Report number – FRS17183/R1, ISBN: 978-1-84911-479-0. [Includes floods of storm Desmond 2015]

Muste, M., Fujita, I. and Hauet, A. (2009) Large-scale particle image velocimetry for measurements in riverine environments, Water Resources Research, 44, W00D19. http://doi.org/10.1029/2008WR006950

Xia, X., Liang, Q., Ming, X. (2019) A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS), Advances in Water Resources 132, 103392.

FLOOD-CDT

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 seen here https://flood-cdt.ac.uk. 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 one-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;
  • name two current academic referees together with an institutional email addresses; on submission of your online application your referees will be automatically emailed requesting they send a reference to us directly by email;
  • academic transcripts and degree certificates (translated if not in English) – if you have completed both a BSc and an MSc, we require both; and
  • a IELTS/TOEFL certificate, if applicable.

Please upload all documents in PDF format. 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.

Apply

To apply for this project, please apply through the Loughborough University application portal (available on this link: https://www.lboro.ac.uk/study/postgraduate/research-degrees/phd-opportu…) and quote reference number FCDT-25-LU3