Bayesian Network Approaches for Vulnerability Assessment and Management of Diffuse Groundwater Pollution

BACKGROUND:
Groundwater (GW) is an important source of drinking water in many countries and plays a vital role in the natural water cycle, providing the baseflow of surface water ecosystems. Protecting GW resources 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 most vulnerable to pollution to help target resources and improve the effectiveness of mitigation measures. Physical modelling approaches are often used in this context to better understand GW vulnerability and to predict contaminant behaviour at catchment scale. However, physical approaches are often hampered by insufficient hydrogeological and water quality data and usually ignore the various conflicting environmental and socio-economic interests. Alternative 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 pesticide but can be expanded to consider other pollutants.

OBJECTIVES:
The main aim is 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.

METHODS & OUTLINE PLAN:
We envisage that the BN initially is developed and applied to the River Ugie, a SEPA priority catchment, for which extensive pesticide monitoring data exist. The project involves the following work-packages:

• WP1 (months 1-6): Data compilation and review of GW vulnerability approaches. Exhaustive compilation of Ugie site-specific data and literature on pesticide usage, land use, climate, hydrogeological settings, potential and existing management schemes and other socio-economic information etc.; review of existing approaches for GW vulnerability. This will be synthesized in a conceptual model for the study catchment.
• WP2 (months 6-18): BN development. Based on the conceptual model, a BN for probabilistic GW vulnerability assessment and mapping will be designed.
• WP3 (months 18-24+): BN application and scenarios. Apply BN to study catchment to assess and map GW vulnerability; analyse parameter sensitivity and compare model outputs to existing monitoring data. If relevant, further monitoring data will be collected for validation. Carry out scenarios to investigate the impact of different management actions on pesticide sources in high risk areas as well the associated socio-economic impacts of implementing such management schemes (e.g. trade-offs, such as loss of crop yield).
• WP4 (on-going): Knowledge Exchange. Interaction with key stakeholders to develop and communicate the BN for systematic GW vulnerability assessment. Communicate management and policy recommendations. Scientific dissemination via presentations at international conferences and 3 targeted journal publications.
The final years are expected to focus on further BN development and to test and apply it to other GW catchments and/or contaminants. There is also scope for applying and comparing the BN to other statistical methods and/or physical groundwater modelling.

THE SCHOLARSHIP:
This PhD provides an exciting opportunity to join a collaborative environment between the James Hutton Institute (JHI), the Northern River Institute (NRI) at University of Aberdeen (UoA) and the British Geological Survey (BGS). The student will be based in Aberdeen, Scotland and will join the vibrant PhD/Postdoc community at both JHI and UoA. The student will benefit from training in state-of-the-art approaches in catchment and groundwater sciences, statistics, and agricultural pollution management and policy provided by three partner institutions. We also host annual postgraduate conferences, providing the student with an opportunity to present and discuss the work with fellow students.

This project would most suit an individual with a background in quantitative sciences (particularly engineering and environmental/earth science) and interest in modelling.