No two flood events are the same. They can vary over time caused by different storm events and weather types. They can also vary over space, where different catchments respond to rainfall in different ways. Furthermore, flood generating processes can exhibit spatio-temporal variations e.g. storm track from certain direction activating sub-catchments in certain sequence and resulting in tributary synchronisation.
Understanding how floods are caused helps us to develop strategies for more effective management. Whilst traditional flood defence (walls, embankments) protect all flood up to a certain level, newer approaches of Nature Based Solutions applied upstream throughout the catchment are much more uncertain in terms of their effectiveness. This is because it depends on the storage capacity of ponds or the soil at that specific time, so the sequencing of storms and rainfall becomes more important, along with their spatial variability.
It is hoped using improved understanding of UK flood generating processes will allow the development of probabilistic predictions for locations where NBS can be implemented so that the distributed approach can be spatially optimised.
Aims and Objectives:
To understand how flood generating processes vary over space and time, and the consequences of this for catchment scale Nature-Based Solutions.
- For UK catchments what are the dominant flood generating (Lamb) Weather types?
- What are the relationships between the weather types and the sub-catchments response in terms of peak flow magnitude and relative timing?
- Develop probabilistic predictions for how NBS will be beneficial in reducing flood risk when positioned in certain parts of the catchment.
Methods:
The project will use the Big Data from the NRFA database on River Flows in the UK. This is 15-minute resolution and is available for over 1500 gauging stations, extending back to the 1970s in most cases. The Peak over Threshold (POT) values will allow flood events to be focussed upon.
Deep Learning techniques and statistics e.g. Neural Networks, Support Vector Machines, will be used to identify spatial and temporal trends in the Big Data, and to analyse the effectiveness of potential NBS management strategies.
Eligibility
To be eligible, applicants should have a first-class honours degree in a relevant subject or a 2.1 honours degree plus Masters (or equivalent experience). Additional criteria may apply so please check the specific project details before applying. Scholarships will be awarded by competitive merit, taking into account the academic ability of the applicant.
We recognise that not every talented researcher will have had the same opportunities to advance their careers. We therefore will account for any particular circumstances that applicants disclose (e.g. parental leave, caring duties, part-time jobs to support studies, disabilities etc.) to ensure an inclusive and fair recruitment process.
How to Apply
Please complete our online application form. Please select PhD Civil Engineering programme and include the project reference, title and supervisor names on your application. If these details are not included your application may not be considered. Please note that applicants may only submit ONE proposal.
Please also provide a supporting statement outlining how you would approach the research and upload this to the research proposal section of the online application. You will also be required to upload a CV, a copy of your degree certificate and relevant transcripts and one academic reference. Until your nominated referee has uploaded their statement, your application will not be marked as complete and will not be considered by the review panel. You must also provide proof of your ability in the English language (if English is not your mother tongue or if you have not already studied for a degree that was taught in English). We require an IELTS certificate showing an overall score of at least 6.5 with no component scoring less than 6.0 or a TOEFL certificate with a minimum score of 90 points.
Timetable
Applications will be reviewed throughout March and applicants will be notified of the outcome of their application by the end of April 2021. Applicants MUST be available to start the course of study on a full-time basis in September 2021.
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