Bringing diverse knowledge sources together--a meta-model for supporting integrated catchment management.

Area of Science:

• Environmental Science
• Water Resource Management
• Decision Science

Background:

• Integrated Catchment Management (ICM) faces challenges due to complexity and uncertainty.
• Existing legislation, like the European Water Framework Directive, necessitates effective decision-support tools.
• Planners and decision-makers need integrated tools to evaluate management scenarios and foster learning.

Purpose of the Study:

• To develop a pragmatic, integrated decision-support tool for ICM.
• To create a user-friendly meta-model from a complex, loosely coupled model.
• To aid high-level decision-makers in exploring management actions and identifying uncertainties.

Main Methods:

• Developed a loosely coupled model with numerical and knowledge-based sub-models.
• Derived a meta-model using a Bayesian Network approach from the initial model.
• Designed the meta-model for ease of operation by decision-makers without extensive modeling skills.

Main Results:

• A fast and easy-to-operate Bayesian Network meta-model was successfully derived.
• The meta-model allows exploration of conflicts and synergies between management actions at the catchment scale.
• Uncertainties are explicitly represented, identifying knowledge gaps and providing an evidence base for robust decisions.

Conclusions:

• The developed framework supports ICM by integrating scientific evidence and facilitating communication.
• Bayesian Network meta-models offer a tailored, accessible approach for decision-makers in complex environmental management.
• This approach enhances the development of modeling tools for effective and robust integrated catchment management.