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NERC GW4+ DTP PhD studentship: Linking large-scale oceanic and atmospheric forcings to local scale flooding via machine learning

  • Full or part time
  • Application Deadline
    Monday, January 06, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of an alliance between the Universities of Bath, Bristol and Exeter and Cardiff University plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology & Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad multi-disciplinary training, designed to produce tomorrow’s leaders in earth and environmental science.

Lead Supervisor: Dr Thomas Kjeldsen, University of Bath, Department of Architecture and Civil Engineering
Co-Supervisor: Dr Cecilia Svensson, Centre for Ecology & Hydrology (CEH), Wallingford

THE PROJECT:

In their Global Risk Report 2018, the World Economic Forum ranks extreme weather events, natural hazards, and failure of climate-change mitigation and adaptation among the global risks with the highest likelihood and impact. Flood frequency analysis is routinely used for quantifying the link between frequency and magnitude of extreme events for planning purposes (e.g. calculation of the 100-year event). Traditionally, frequency analysis of hydro-climatological events is conducted by fitting simple statistical distributions (black box) to observations of, for example, rainfall and river flow. However, this approach neglects that such events are not generated locally, but are the result of regional- to global-scale oceanic and atmospheric processes that in combination with the pre-existing hydrological state of the catchment lead to runoff. Establishing links between these large-scale climate forcings and the frequency and magnitude of local extreme events could potentially provide a step change in our understanding of flood risk in the UK.

This interdisciplinary project will combine skills in hydrology, climatology/meteorology, statistical extreme value analysis, and big-data analytics. It will develop a new process-based approach to flood frequency analysis through the use of a novel combination of existing long-term flood flow observations and large-scale atmospheric and oceanic data sets. By applying machine learning techniques, such as cluster analysis and pattern recognition, the large-scale conditions prevailing at the time of historical extreme events will be analysed and classified to understand moisture sources and weather patterns associated with extreme floods. The outcome of this analysis will be a new set of flood typologies, such as, for example, convective flash floods, or atmospheric rivers feeding moisture into frontal systems on a variety of storm tracks. Trends in the occurrence and magnitude of the different event-types will be investigated to assess possible effects of climate change. The different types of flood events will be combined into a single flood frequency framework using statistical mixture models. Finally, the performance gains of the new process-based models will be compared to traditional flood frequency estimation methods.

There will be a large degree of freedom for the right candidate to design the project according to interest and progress.

Through the co-supervisor, the student will have the opportunity to gain experience of a non-university work environment. CEH is the UK’s Centre for Excellence for integrated research in terrestrial and freshwater ecosystems and their interaction with the atmosphere and it is anticipated that the student will spend part of their time working there.

APPLICATIONS:

The ideal candidate will have an interest in environmental science and risk analysis. The project will require an interest in computer programming, statistical analysis and machine learning, and hydrology. A good first degree (first or 2:1) in a relevant quantitative subject, for example (but limited to) engineering, physics, physical geography, environmental and atmospheric science, or computer science.

Enquiries relating to the project should be directed to Dr Thomas Kjeldsen, . Enquiries relating to the application process should be directed to .

In order to apply, you should select the relevant University of Bath PhD online application form form:
https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUAR-FP01&code2=0014

When completing the form, please state in the ‘Finance’ section that you wish to be considered for NERC GW4+ DTP funding and quote the project title and lead supervisor’s name in the ‘Your research interests’ section. You may apply for more than one project within the same application but you should submit a separate personal statement for each one.

Further information about the application process may be found here: http://www.bath.ac.uk/topics/postgraduate-research/

Interviews will take place between 10 and 21 February 2020.

For more information about the NERC GW4+ DTP, please visit https://nercgw4plus.ac.uk.

Anticipated start date: 28 September 2020.

Funding Notes

NERC GW4+ DTP funding is for 3.5 years and is open to UK and EU applicants who have been resident in the UK since September 2017.

A studentship will provide UK/EU tuition fees, maintenance in line with the UKRI Doctoral stipend rate (£15,009 per annum, 2019/20 rate) and a generous budget for research expenses and training.

References

Schlef, K.E., Moradkhani, H. and Lall, U., 2019. Atmospheric Circulation Patterns Associated with Extreme United States Floods Identified via Machine Learning. Scientific reports, 9(1), p.7171.

How good is research at University of Bath in Architecture, Built Environment and Planning?

FTE Category A staff submitted: 28.38

Research output data provided by the Research Excellence Framework (REF)

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