Integrated environmental modelling: achieving the vision
R. V. Moore, A. G. Hughes, 2017. "Integrated environmental modelling: achieving the vision", Integrated Environmental Modelling to Solve Real World Problems: Methods, Vision and Challenges, A. T. Riddick, H. Kessler, J. R. A. Giles
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Integrated environmental modelling (IEM) is a recent phenomenon that offers the opportunity to solve complex environmental problems. Whilst it has made great strides in recent years, there are still challenges to be met before IEM is universally accepted and used. This paper describes the current state of IEM and sets out a roadmap for achieving its full potential. A multidisciplinary, multi-agency approach will be required, the main goals of which are to: (1) raise awareness and build confidence in IEM; (2) ensure availability and accessibility of IEM techniques, tools and standards; (3) establish a minimum set of standards; (4) build the IEM skills base; (5) establish an underpinning research and development (R&D) programme; (6) co-ordinate and promote collaboration; and (7) foster IEM use by government, industry and the public. Once these goals have been achieved, then IEM can be deployed to help resolve currently intractable environmental issues, and the IEM methodology can be transferred to other fields.
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Integrated Environmental Modelling to Solve Real World Problems: Methods, Vision and Challenges
The discipline of Integrated Environmental Modelling (IEM) has developed in order to solve complex environmental problems, for example understanding the impacts of climate change on the physical environment. IEM provides methods to fuse or link models together, this in turn requires facilities to make models discoverable and also to make the outputs of modelling easily visualized.
The vision and challenges for IEM going forward are summarized by leading proponents. Several case studies describe the application of model fusion to a range of real-world problems including integrating groundwater and recharge models within the UK Environment Agency, and the development of ‘catastrophe’ models to predict better the impact of natural hazards. Communicating modelling results to end users who are often not specialist modellers is also an emerging area of research addressed within the volume. Also included are papers that highlight current developments of the technology platforms underpinning model fusion.