PLEASE APPLY ONLINE TO THE SCHOOL OF ENGINEERING, PROVIDING THE PROJECT TITLE, NAME OF THE PRIMARY SUPERVISOR AND SELECT THE PROGRAMME CODE "EGPR" (PHD - SCHOOL OF ENGINEERING)
This is a project within the multi-disciplinary EPSRC and ESRC Centre for Doctoral Training (CDT) on Quantification and Management of Risk & Uncertainty in Complex Systems & Environments, within the Institute for Risk and Uncertainty. The studentship is granted for 4 years and includes, in the first year, a Master in Decision Making under Risk & Uncertainty. The project includes extensive collaboration with prime industry to build an optimal basis for employability.
Many rivers systems are affected by the excess supply of fine-grained sediment and the resulting adverse effects on water resources, infrastructure and aquatic ecosystems. River catchments containing significant areas of intensively-managed agricultural land are particularly vulnerable to these sediment-related impacts. The current project will focus on ‘sediment source fingerprinting’ - a method which allows river managers to identify major sources of fine sediment, and apply resources in these key areas to reduce excess sediment inputs to rivers. By quantifying the uncertainties involved in this procedure, the outcomes of the project will provide key information which will aid the management of the UK’s river systems.
Sources of sediment pollution in agricultural river catchments may include various land uses, riverbanks, roads and urban surfaces. Sediment fingerprinting involves selecting tracers which are found in soils and sediments and can be used to statistically distinguish sediment sources. The selected tracers are then used to quantitatively determine the contributions from these sources to the mixture of sediment transported downstream. Successful ‘un-mixing’ of sediment sources requires tracers that behave conservatively during transport or for non-conservative behaviour to be predictable.
Whilst sediment fingerprinting techniques have very considerable potential to generate valuable scientific and management information, recent work has identified significant challenges that require attention. These challenges relate to procedures for tracer selection, the detection of non-conservative behaviour and the choice of mixing model structures for quantifying contributions from sediment sources.
This research will address these challenges by focusing on aspects of method development, particularly in relation to the propagation of errors and uncertainty through the complete sediment fingerprinting procedure - this will include sampling, tracer selection and source un-mixing. The study will make use of both an existing dataset as well as new data to be collected from several sub-catchments in the River Ribble, north-west England.
The research aims to develop these revised sediment fingerprinting procedures for use in support of the management objectives of the River Ribble Trust. Fine sediment pollution is a significant management challenge in parts of the Ribble catchment. The Trust requires tools to support very significant investments in restoration measures designed to improve the riverine environment with benefits for water supply, aquatic habitats and the recreational use of this river system.
Any special features: The project requires a strong numerical background and a keen interest to work at the interface of environmental, modelling and statistical approaches. Applicants should have a background in Engineering, Environment Sciences or in relevant fields of the Applied Mathematical Sciences. A combined education in these fields would be a significant advantage. The results of this work will be of direct relevance to river and water resource managers.
For further details, please send of a copy of your curriculum vitae to Dr Peter Green ([email protected]
) and Dr Hugh Smith (email: [email protected]