Title: Using historical data to improve long-return period flooding estimation
Institution: Centre for Ecology & Hydrology, Wallingford Lead Supervisor: Lisa Stewart ([log in to unmask])
http://nercgw4plus.ac.uk/using-historical-data-to-improve-long-return-period-flooding-estimation/
Estimating flood magnitudes for long return periods, such as the “once-in-a-century” flood is of critical importance to building appropriate flood defences which will withstand the most extreme events, events which may not have been observed within the period of systematic river catchment recordings. Existing methods used in the Flood Estimation Handbook (1999) and subsequent reports extrapolate from existing data to estimate less frequent, more extreme flood events than those observed. By obtaining and including even a small amount of historical data on observed extreme flood events, estimates can be improved and uncertainty decreased.
The project will involve the collection of historical data from a number of sources from both online and physical archives (council records, newspaper archives, anecdotal sources), and then applying existing statistical methods to improve the existing models to observe more accurate flood estimation for the catchments in question. If time permits, this will be supplemented with the use of Approximate Bayesian Computation methods to see if simple simulation methods can improve on existing likelihood based methods.
The student will receive supervision from Dr Adam Griffin on the statistical and computer-based background and Lisa Stewart on the hydrological aspects within the Hydrological Modelling and Risks group. Training will be provided in data management, statistical methodologies regarding analysis and visualisation of censored datasets of unknown quality, and basic hydrological processes. Supervision will be given at the sites finding data from historical archives off-site, and office and computer space will be arranged for the student within the department.
Eligibility
> Be studying for an undergraduate degree in a quantitative discipline outside of NERC’s scientific remit (e.g. mathematics, statistics, computing, engineering, physics).
> Be applying for a placement in a different department to their undergraduate degree.
> Be undertaking their first undergraduate degree studies (or integrated Masters).
> Be expected to obtain a first or upper second class UK honours degree
> Be eligible for subsequent NERC PhD funding (i.e. UK, EU or right to remain in the UK).
Students should submit their application directly to [log in to unmask] submitting a CV (no more than 2 pages) plus reference contact details, transcripts and a brief covering letter (1 page).
The deadline for applications is noon on the 29 May 2017.
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