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Integrated assessment and modelling: features, principles and examples for catchment management

To meet the challenges of sustainability and catchment management requires an approach that assesses resource usage options and environmental impacts integratively. Assessment must be able to integrate several dimensions: the consideration of multiple issues and stakeholders, the key disciplines wit...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2003-01, Vol.18 (6), p.491-501
Main Authors: Jakeman, A.J., Letcher, R.A.
Format: Article
Language:English
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Summary:To meet the challenges of sustainability and catchment management requires an approach that assesses resource usage options and environmental impacts integratively. Assessment must be able to integrate several dimensions: the consideration of multiple issues and stakeholders, the key disciplines within and between the human and natural sciences, multiple scales of system behaviour, cascading effects both spatially and temporally, models of the different system components, and multiple databases. Integrated assessment (IA) is an emerging discipline and process that attempts to address the demands of decision makers for management that has ecological, social and economic values and considerations. This paper summarises the features of IA and argues the role for models and information systems as a prime activity. Given the complex nature of IA problems, the broad objectives of IA modelling should be to understand the directions and magnitudes of change in relation to management interventions so as to be able to differentiate between associated outcome sets. Central to this broad objective is the need for improved techniques of uncertainty and sensitivity analysis that can provide a measure of confidence in the ability to differentiate between different decisions. Three examples of problems treated with an IA approach are presented. The variations in the way that the different dimensions are integrated in the modelling are discussed to highlight the sorts of choices that can be made in model construction. The conclusions stress the importance of IA as a process, not just as a set of outcomes, and define some of the deficiencies to be overcome.
ISSN:1364-8152
1873-6726
DOI:10.1016/S1364-8152(03)00024-0