A collaborative process of developing mathematical models for water quality management

在水质管理的背景下，转移开发决策支持数学模型的协作和可持续过程英国的农村社区在管理国家自然资源方面面临相当大的挑战。食品系统的长期可持续性是英国政府食品战略的核心问题1，而欧盟水框架指令（WFD; 2000/60/EC）要求到2015年该国大多数水体的生态状态为“良好”。这些国家政策优先事项和环境立法限制因区域关切事项而得到加强，包括农村景观的娱乐价值、农村社区的生活质量和健康、农村企业和农村就业的生存能力，特别是在粮食生产和旅游业方面，以及生物多样性2。除了私营和公共部门的责任和任务外，志愿部门在谈判和满足相互矛盾的需要方面尤其重要。众多的压力和环境的复杂性需要科学的工具来支持自然资源的管理。通过使复杂性易于理解和管理，数学模型对于实现这一目标至关重要。然而，越来越多的证据表明，仅从研究角度开发的模型通常不会在实践中使用，尤其是因为Defra（2010）。2030年食品。http://www.defra.gov.uk/foodfarm/food/pdf/food2030strategy.pdf (24.05.2010);受制于新政府的政策和优先事项2 RELU（2005）。展开中的研究议程。农村经济和土地利用计划简报系列第三号。http://www.relu.ac.uk/news/briefings/RELU%20Brief3%2024pp.pdf (24.05.2010)3 Smith, l.e.d.， H. Cook， A. Inman， D. Benson和A. Jordan（2010）。下沉还是游泳？在英格兰和威尔士调查以社区为基础的集水区小组。[4]张晓明，张晓明，张晓明，张晓明（2008）.水与环境，中国环境科学与工程学会年会，北京：北京：北京)。政策评估工具在公共政策制定中的使用与不使用：对三个欧洲国家和欧盟的分析。政策科学41(4):335-355最终用户期望与模型可交付成果之间的不一致性。目前由申请人资助的英国研究委员会的研究表明，如果采用一种新的模型开发协作过程，这种情况是可以逆转的，在这种过程中，产品是根据最终用户的需求定制的，而那些在决策和交付必要行动中起关键作用的利益相关者是参与的5。这个过程确保了模型的可信性和科学严谨性，允许通过整合涉众知识来改进模型，并从那些决定或受决策影响的人那里建立对模型的信任。在转移这一模式开发的合作过程、加强公共和志愿部门的能力以及在研究机构和这些部门之间建立长期关系以确保这一过程的可持续性方面，存在着相当大的可持续发展机会。

Transferring a collaborative and sustainable process of developing mathematical models for decision support in the context of water quality managementRural communities in the UK face considerable challenges in managing the country’s natural resources. Long-term sustainability of the food system is a central concern of the UK Government’s food strategy1, whereas the EU Water Framework Directive (WFD; 2000/60/EC) demands ‘good’ ecological status of most of the country’s water bodies by 2015. These national policy priorities and environmental legislation constraints are augmented by regional concerns, including the recreational value of the rural landscape, the quality of life and the health of rural communities, the viability of rural businesses and rural employment, especially in food production and tourism, and biodiversity2. Along with the responsibilities and mandates of the private and public sectors, the voluntary sector in particular is seen as important in negotiating and meeting conflicting needs3. The multitude of pressures and the complexity of the environment require scientific tools to support the management of natural resources. Mathematical models are essential for achieving this by making complexity comprehensible and manageable. However, there is growing evidence that models developed from a research perspective alone are often not used in practice4, not least because of1 Defra (2010). Food 2030. http://www.defra.gov.uk/foodfarm/food/pdf/food2030strategy.pdf (24.05.2010); subject to policies and priorities of the new government2 RELU (2005). The Unfolding Research Agenda. Rural Economy and Land Use Programme Briefing Series No. 3. http://www.relu.ac.uk/news/briefings/RELU%20Brief3%2024pp.pdf (24.05.2010)3 Smith, L. E. D., H. Cook, A. Inman, D. Benson and A. Jordan (2010). Sinking or swimming? Surveying community based catchment groups in England and Wales. Water and Environment 2010, CIWEM’s Annual Conference, April 28th-29th, London, UK4 Nilsson, M., A. Jordan, J. Turnpenny, J. Hertin, B. Nykvist and D. Russel (2008). The use and non-use of policy appraisal tools in public policy making: an analysis of three European countries and the European Union. Policy Sciences 41(4): 335-355Page 3 of 13incoherence between end-user expectations and model deliverables. Current UK Research Councils-funded research by the applicants shows that this situation can be reversed if a novel collaborative process of model development is adopted where the product is tailored to the end-user needs and those stakeholders key in making decisions and delivering the necessary actions are involved5. This process ensures credibility and scientific rigour of models, allows improvement of models by incorporating stakeholder knowledge, and builds trust in models from those deciding or affected by the decisions. Considerable KE opportunities exist for transferring this collaborative process of model development, strengthening capacity in the public and voluntary sectors to take it up, and building long-term relationships between research institutions and these sectors to ensure sustainability of the process.