NERC GW4+ DTP Studentship: A spatio-temporal extreme rainfall generator for surface flooding and landslide risk assessment

University of Exeter - College of Life and Environmental Science

Main supervisor: Dr Theodoros Economou (College of Life and Environmental Sciences, University of Exeter)

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 the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus six Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Met Office, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see

The impact of aerosol particles on the climate system remains highly uncertain (Boucher et al., 2013). A key natural aerosol precursor found over the ocean is Dimethyl Sulphide (DMS), a biogenic volatile gas produced through biological activity in the surface ocean. The natural background marine emission of this gas would have been an important source of aerosols in the pre-industrial era. Knowledge of how ‘dirty’ the pre-industrial climate was is important in order to determine how sensitive our climate system is to aerosol particles emitted by human activities over the industrial era (Carslaw et al., 2013, Regayre et al., 2015). We are currently developing a state-of-the-art Earth System climate model for UK’s contribution to the next IPCC assessment. Early results show that magnitude of the pre-industrial marine DMS emissions can be the difference between no climate change occurring by the present day and strong climate change occurring. During this project you will:

  • Make novel DMS measurements in the Southern Ocean, where the pristine environment provides the best window into our pre-industrial atmosphere.
  • Use these along with existing measurements and model results to estimate what preindustrial DMS emissions may have looked like using established statistical techniques.
  • Perform new climate model experiments at the Met Office to quantify how sensitive the climate system is to DMS emission uncertainty.
  • Use this information to interpret climate model results and improve the representation of DMS in the Earth System model.

This is an exciting multidisciplinary opportunity to make a unique set of measurements in a rarely visited part of the world. You will gain a fundamental understanding of how our planet works using both observations and models. In doing so, you will contribute to improved UK climate predictions.


  1. T. Economou, T. C. Bailey and Z. Kapelan, MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications. Statistics and Computing Statistics and Computing,24(5), 739-752 (2014).
  2. F. Serinaldi and Kilsby C. G., A modular class of multisite monthly rainfall generators for water resource management and impact studies. Journal of Hydrology,25, 464-465 (2012).
  3. B. Ghimire, A. S. Chen, M. Guidolin, E. C. Keedwell, S. Djordjevi and D. A. Savi, Formulation of a fast 2D urban pluvial flood model using a cellular automata approach. Journal of Hydroinformatics,15(3), 676-686 (2013).
  4. S. Almeida, E. Holcombe, F. Pianosi and T. Wagener. Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change. Natural Hazards and Earth System Sciences,In Review (2016).

Please see for more information regarding applications.

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