Catchment Science Centre, Kroto Research Institute, University of Sheffield, North Campus, Broad Lane, Sheffield S3 7HQ, UK.
J Environ Manage. 2012 Apr 15;96(1):116-27. doi: 10.1016/j.jenvman.2011.10.016. Epub 2011 Dec 11.
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 decision-makers 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.
综合流域管理（ICM），如最近的欧洲水框架指令等法规所提倡的，给规划者和决策者带来了困难的挑战。为了在面对高复杂性和不确定性时支持决策，需要能够整合评估替代管理方案所需的证据基础，并促进沟通和社会学习的工具。在本文中，我们提出了一种实用的方法来开发综合决策支持工具，其中可用的信息来源非常多样化，不可能进行紧密的模型耦合。在最初阶段，开发了一个松散耦合的模型，其中包括数值子模型和基于知识的子模型。然而，对于没有建模技能的决策者和利益相关者来说，这样的模型不容易操作。因此，我们从它导出一个基于贝叶斯网络方法的元模型，这是一种针对决策者需求定制的决策支持工具，操作快速简便。元模型可以根据决策者的要求在不同的详细程度和复杂程度上进行推导。在我们的案例中，元模型是为高级决策者设计的，用于探索流域尺度管理行动之间的冲突和协同作用。由于预测不确定性在模型结果中得到传播和明确表示，因此可以识别重要的知识差距，并为稳健决策提供证据基础。该框架旨在通过提供综合的科学证据基础和促进沟通和学习过程，促进支持 ICM 的建模工具的发展。
