PhD position: semi-distributed catchment modelling of C-N-P transfer to evaluate ecosystem services
A PhD scholarship position is available within the project “Channel Payments for Ecosystem Services (CPES)” funded by the European INTERREG programme. CPES is a multidisciplinary action research project aiming at improving water quality in six catchments in France and the United Kingdom, through implementation of Payments for Ecosystem Services. The PhD student will be involved in the “Lac au Duc” reservoir pilot study (Brittany, France), where the effect of improved landscape management in a 375 km² catchment will be evaluated against curative management options in the reservoir itself (in terms of cost-efficiency), to curb nutrient pollution and cyanobacterial blooms. The role of the PhD student will be to implement a semi-distributed catchment model of nutrient (nitrogen and phosphorus) and carbon export that will be used to predict the impact of different landscape management scenarios (types of cropping systems/linear landscape elements and their spatial arrangement) on water quality. The modelling task will have a pivotal role within the CPES project, as the PhD student will collaborate with a post-doc in charge of water quality monitoring and data collection, and a team of economists in charge of implementing a payment of ecosystem services scheme that relies on the modelling results. As a collaborative project, catchment stakeholders will be involved in CPES to develop landscape management scenarios that improve water quality while being economically viable. Collaborations with scientists from various disciplines and local stakeholders are opportunities for enriching exchanges. Several modelling challenges have been identified:
- Identify model structures able to simulate the dominant processes controlling C-N-P cycling in agricultural landscapes in the context of the pilot study (in collaboration with a post-doc in charge of data collection and interpretation).
- Include a description of the landscape spatial arrangement in a semi-distributed model (e.g. SWAT, HYPE, INCA). A spatially-implicit approach (using distribution of indicators rather than spatially explicit description) will be developed.
- Develop a stochastic approach to parameterize the model and estimate uncertainties in modelling results.
- Convert narrative information about landscape management scenarios into a quantitative form to simulate these scenarios with a model.