Working with the Environment Agency on improving estimates of the frequency of extreme events (e.g. flooding) by using non-systematic records. Tom graduated from the University of Bath in 2017 with an MMath degree, which included a year-long industrial placement doing consumer loyalty analytics. His final-year project involved using Conditional Autoregressive Bayesian Spatial models and Monte-Carlo methods to quantify uncertainty in estimates of the impact which air pollution has on health. He has a particular interest in the application of statistics, and in the role that statistics plays in society. Outside of mathematics, he has an appreciation for computing, music, and cats.

Research project title:
Estimating the Frequency of Extreme Events in the Presence of Non-Systematic Records

Supervisor(s):
Simon Shaw, Thomas Kjeldsen, Ilaria Prosdocimi, Sean Longfield

Project description:
Extreme flood events can be devastating, so having good estimates of how often floods of a given size might occur at a specified location is of clear importance. However, the systematically-collected river flow time series from which these estimates may be derived are short, being typically just 40-50 years long in the UK. Consequently, the flood frequency estimates have large uncertainties. The systematic record may be extended by utilising non-systematic records such as newspaper reports, photographs, and flood marks carved into buildings. Working with the Environment Agency, Tom is developing methodology to allow these non systematic records to be routinely used in flood frequency analyses, with a particular focus on the importance of accounting for the many sources of uncertainty that such an analysis involves. He is also investigating the utility of non-systematic records in ‘regional’ flood frequency analysis, wherein river flow series from hydrologically similar catchments are combined in order to reduce uncertainty. The methodology developed during this research will be applicable to other natural hazards.