Groundwater (GW) is an important source of drinking water in many countries and plays a vital part of the natural water cycle, providing the baseflow of surface water ecosystems. Protecting GW resources and keeping them free from contamination is therefore essential. The EU Water Framework Directive (WFD) seeks to ensure good chemical and ecological status of both GW and surface waters. A key aspect of the WFD is to identify areas vulnerable to pollution to ensure resources are targeted to areas at greatest risk and help improve the effectiveness of mitigation measures. Physical modelling approaches are often used in this context to better understand and map GW vulnerability and to predict contaminant behaviour and transport at catchment scale. However, assessing GW vulnerability is complex and typically hampered by limited hydro-geological and water quality data and will furthermore often be faced with a number of conflicting interests (environmental, economic and social). New modelling approaches are required to enable integration of the physical and socio-economic factors influencing GW vulnerability, while also accounting for the associated uncertainties. This project will explore the use of Bayesian Networks (BN) for vulnerability assessment and management of diffuse GW pollution. BNs are graphical probabilistic models that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The project focuses on diffuse pesticide pollution, but will potentially be expanded to consider other pollutants
The PhD research aims to develop and apply a BN, combining hydrogeological and socio-environmental factors, as a novel probabilistic method for assessing and mapping of the vulnerability of GW to diffuse pesticide pollution. The aims are subsequently: (i) to test/validate the BN with actual monitoring data; (ii) to apply the BN to explore the effectiveness of different management measures on reducing diffuse GW pollution. Later stages will explore if the BN can be modified and applied to other contaminants and/or catchments and the feasibility of including socio-economic factors and impacts on ecosystem services will be investigated.
The project will be implemented through collaborative supervision between the hydrology groups at the University of Aberdeen (UoA) and the James Hutton Institute (JHI). Both UoA and JHI are centres of research excellence in environmental hydrology and delivers internationally leading catchment science to help underpin sustainable water management.
The project would most suit an individual with a background in quantitative sciences (particularly engineering and environmental science) and interest in modelling.
The studentship is funded under the James Hutton Institute/University Joint PhD programme, in this case with the University of Aberdeen. The student is expected to work 60% at the James Hutton Institute in Aberdeen and 40% at the University of Aberdeen. Applicants should have a first-class honours degree in a relevant subject or a 2.1 honours degree plus Masters (or equivalent).Shortlisted candidates will be interviewed in Jan/Feb 2018. A more detailed plan of the studentship is available to candidates upon application. Funding is available for European applications, but Worldwide applicants who possess suitable self-funding are also invited to apply