The main aim of the QUICS ITN is to educate and train researchers capable of operating at academic research institutions, water utilities or other public bodies so as to provide them with a comprehensive understanding of water quality processes, uncertainty issues and knowledge of appropriate decision making strategies for integrated catchment management.
The other aims of this ITN are to:
- Develop and consolidate knowledge on the understanding of uncertainty in integrated water quality modelling studies (WP1).
- Evaluate different approaches for the identification and where possible quantification of uncertainty at significant temporal and spatial scales in catchments (WP2, 3).
- Work with engineering consultants to develop methods that can be used to communicate the implications of uncertainty in water quality predictions to improve the decision making process (WP4).
- Disseminate this understanding in a format that can be used by practitioners’ and public authorities as they implement the WFD (WP4).
The proposed scientific and technological objectives of the ITN are:
- Test and further develop statistical methods that can be used to elucidate the range of uncertainty in model inputs, parameters and structure in sub-models used to simulate water quality in catchments (WP1).
- Test and further develop methods to propagate uncertainty in water quality predictions between models with different spatio-temporal resolutions (WP2).
- Create guidance documents and protocols that advise end users on what type and complexity of integrated water quality model to select for different purposes (WP4).
- Develop tools to provide information on appropriate modelling tools and data requirements to achieve user specified confidence levels of water quality predictions for a specific catchment (WP1, 2).
- Develop tools that enable end users to optimise the resources required for data collection and monitoring efforts (WP3).
- Develop tools that can be deployed by end users to select responses to potential water quality failures whilst considering all aspects of modelling uncertainty (WP4).