From integration to fusion: the challenges ahead
J. Sutherland, I. H. Townend, Q. K. Harpham, G. R. Pearce, 2017. "From integration to fusion: the challenges ahead", 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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The increasing complexity of numerical modelling systems in environmental sciences has led to the development of different supporting architectures. Integrated environmental modelling can be undertaken by building a ‘super model’ simulating many processes or by using a generic coupling framework to dynamically link distinct separate models during run-time. The application of systemic knowledge management to integrated environmental modelling indicates that we are at the onset of the norming stage, where gains will be made from consolidation in the range of standards and approaches that have proliferated in recent years. Consolidation is proposed in six topics: metadata for data and models; supporting information; Software-as-a-service; linking (or interface) technologies; diagnostic or reasoning tools; and the portrayal and understanding of integrated modelling. Consolidation in these topics will develop model fusion: the ability to link models, with easy access to information about the models, interface standards such as OpenMI and software tools to make integration easier. For this to happen, an open software architecture will be crucial, the use of open source software is likely to increase and a community must develop that values openness and the sharing of models and data as much as its publications and citation records.
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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.