Supervisors: Steve Darby (UoS), Lee Pimble (Xylem), Julian Leyland (UoS), Nick Everard (CEH)
Contact email: S.E.Darby@soton.ac.uk
Location: Southampton
Project Rational: Flood models are essential for helping to manage and mitigate the annual £2.2B cost of flooding to the UK. However, one of the big uncertainties that remains in these models is how complex floodplains are characterised under a range of real-world conditions, including on floodplains with vegetation. The issue is further complicated by the fact that roughness varies during floods as the flow interacts with vegetation. The flood hydraulics schemes within models are based largely on empirical data, but the challenge of gathering the data needed *during the flood event itself* means that underpinning data are biased towards lower, more manageable, flows.
This project will use new technologies in flow monitoring to meet this challenge. Specifically, Computer Vision Stream Gauging (CVSG) offers a way off characterizing complex flows over floodplains. We will deploy CVSG across a range of floodplains, leveraging NERC’s exciting new multi-million investment in Floods & Droughts Research Infrastructure (F&DRI). The data obtained will be analysed to provide insights into the dynamic adjustments of roughness during floods, and develop roughness parameterisations that will be deployed into flood models to benchmark their improved performance. CVSG best practice will also be developed to understand applications/site limitations of the technology.
Methodology: First, a literature review will develop an in-depth understanding of existing approaches to characterizing roughness across rivers and floodplains and to enable selection of a range of representative UK field sites for the field trials. In these field trials the CVSG approach will be employed, in concert with more traditional approaches, to gather entirely new field data characterizing the spatial distribution of the flow velocity distribution during floods. Best Practice installation methodology will be developed, for example by comparing camera height vs distance, angle of camera to bank, camera facing upstream vs across channel. The data obtained will enable extraction of roughness values actually attained during floods under a range of floodplain and vegetation types.
The field data will be analysed to derive functions linking the vegetative, sedimentological and morphological properties of the channel/floodplain system and the resulting roughness. These relationships will be implemented within hydrodynamic models to evaluate how roughness estimates affect flood routing and inundation extents. A key objective will be to evaluate whether the new relationships provide uplifts in model skill compared to existing approaches. In this way the potential benefits of assimilating the novel CVSG acquired datasets into flood modelling approaches will be demonstrated.
Background Reading:
- Hutley, N. R., Beecroft, R., Wagenaar, D., Soutar, J., Edwards, B., Deering, N., Grinham, A., and Albert, S.: Adaptively monitoring streamflow using a stereo computer vision system, Hydrol. Earth Syst. Sci., 27, 2051–2073, https://doi.org/10.5194/hess-27-2051-2023
- Mishra, A. et al. 2022. An overview of flood concepts, challenges, and future directions. Journal of Hydraulic Engineering, 27(6), https://doi.org/10.1061/(ASCE)HE.1943-5584.0002164
- Schumamm, G., Bates, P.D., Neal, J.C. and Konstantinos, M. 2015. Measuring and mapping flood processes. Hydro-Meteorological Hazards, Risks and Disasters, 2015, 35-64.
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 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;
• 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 & 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 click here: https://student-selfservice.soton.ac.uk/BNNRPROD/bzsksrch.P_Search. Tick programme type - Research, tick Full-time or Part-time, select Academic year – ‘2025/26, 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.
If you have any problems please contact: fels-pgr-apply@soton.ac.uk.
