Chapter Seven Integrated Modelling Frameworks for Environmental Assessment and Decision Support

Publication Type:

Book Chapter


Developments in Integrated Environmental Assessment, Elsevier, Volume 3, p.101-118 (2008)




environmental integrated modelling frameworks, knowledge representation, model engineering, model management, modelling frameworks


In this chapter we investigate the motivation behind the development of modelling frameworks that explicitly target the environmental domain. Despite many commercial and industrial-strength frameworks being available, we claim that there is a definite niche for environmental-specific frameworks. We first introduce a general definition of what is an environmental integrated modelling framework, leading to an outline of the requirements for a generic software architecture for such frameworks. This identifies the need for a knowledge layer to support the modelling layer and an experimentation layer to support the execution of models.

The chapter then focuses on the themes of knowledge representation, model management and model execution. We advocate that appropriate knowledge representation and management tools can facilitate model integration and linking. We stress that a model development process adhering to industry standards and good practices, called “model engineering,” is to be pursued. We focus on the requirements of the experimental frame, which can ensure transparency and traceability in the execution of simulation scenarios and optimisation problems associated with complex integrated assessment studies.

A promising trend for knowledge representation is the use of ontologies that have the capacity to elicit the meaning of knowledge in a manner that is logical, consistent and understandable by computers and the knowledge worker community. This new path in knowledge-based computing will support retention of institutional knowledge, while putting modelling back in the hands of modellers. Environmental modelling will then become a conceptual activity, focusing on model design rather than model implementation, with code generation being delegated to some degree to ontology-aware tools. In this respect, we envision the whole model lifecycle to change drastically, becoming more of a theoretical activity and less of a coding-intensive, highly engineering-oriented task.