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Fusing and disaggregating models, data and analysis tools for a dynamic science-society interface

Rowe, E.C.; Wright, D.G.; Bertrand, N.; Reis, S.. 2017 Fusing and disaggregating models, data and analysis tools for a dynamic science-society interface. In: Riddick, A.T.; Kessler, H.; Giles, J.R.A., (eds.) Integrated environmental modelling to solve real world problems: methods, vision and challenges. London, Geological Society of London, 235-244. (Geological Society Special Publication, 408).

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Abstract/Summary

Society requires rapid, most-probable predictions for specific and/or multifaceted questions related to environmental and geological science. In principle, models that encapsulate disciplinary knowledge are useful tools for making predictions and testing theory, but academic rewards favour disciplinary specialism and a proliferation of often insufficiently tested models. Decision makers have to assess the quality and robustness of predictions for complex environmental issues, and may prefer a model that performs accurately in a case study to a more parsimonious and generalizable model. Predictive ecosystem models tend to grow, as more processes are considered, even when a simpler model may be more appropriate and give results that are easier to interpret within a policy-relevant timeframe. Model fusion provides a practical way to combine knowledge from different disciplines, but can accelerate model growth. How then can we facilitate the evolution of useful predictive models? Coherent design is essential. When combining models it is often necessary to resolve overlapping scope, so tools need to allow for the disaggregation of model implementations as well as their fusion. Modelling software and integration frameworks can help resolve technical constraints, but to make models useful and used it is essential to involve stakeholders in their design and interpretation.

Item Type: Publication - Book Section
Digital Object Identifier (DOI): 10.1144/SP408.1
CEH Sections: Dise
Emmett
Parr
ISBN: 9781862396876
NORA Subject Terms: Data and Information
Date made live: 26 Jun 2014 13:01 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/507556

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