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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Annelie Holzkaemper
  • Vikas Kumar
  • Ben Surridge
  • Achim Paetzold
  • David Lerner
<mark>Journal publication date</mark>04/2012
<mark>Journal</mark>Journal of Environmental Management
Issue number1
Number of pages12
Pages (from-to)116-127
Publication StatusPublished
<mark>Original language</mark>English


Integrated catchment management (ICM), as promoted by recent legislation such as the European Water Framework Directive, presents difficult challenges to planners and decision-makers. To support decision-making in the face of high complexity and uncertainty, tools are required that can integrate the evidence base required to evaluate alternative management scenarios and promote communication and social learning. In this paper we present a pragmatic approach for developing an integrated decision-support tool, where the available sources of information are very diverse and a tight model coupling is not possible. In the first instance, a loosely coupled model is developed which includes numerical sub-models and knowledge-based sub-models. However, such a model is not easy for decision-makers and stakeholders to operate without modelling skills. Therefore, we derive from it a meta-model based on a Bayesian Network approach which is a decision-support tool tailored to the needs of the decision-makers and is fast and easy to operate. The meta-model can be derived at different levels of detail and complexity according to the requirements of the decision-makers. In our case, the meta-model was designed for high-level decisionmakers to explore conflicts and synergies between management actions at the catchment scale. As prediction uncertainties are propagated and explicitly represented in the model outcomes, important knowledge gaps can be identified and an evidence base for robust decision-making is provided. The framework seeks to promote the development of modelling tools that can support ICM both by providing an integrated scientific evidence base and by facilitating communication and learning processes.