Developing composite indicators for ecological water quality assessment based on network interactions and expert judgment
Increasingly, composite indicators and multi-criteria approaches are applied in environmental assessment and decision-making, including the EU Water Framework Directive. For example, integrated evaluation of aquatic ecosystem conditions and functioning usually involves a group of criteria, such as b...
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Veröffentlicht in: | Environmental modelling & software : with environment data news 2019-05, Vol.115, p.51-62 |
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Sprache: | eng |
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Zusammenfassung: | Increasingly, composite indicators and multi-criteria approaches are applied in environmental assessment and decision-making, including the EU Water Framework Directive. For example, integrated evaluation of aquatic ecosystem conditions and functioning usually involves a group of criteria, such as biological organisms and communities, physicochemical and hydromorphological variables, which are measured individually and combined by a weighted linear function into an overall ‘score’. We argue that the network interactions of evaluation components are useful information for expert judgments, which have not been sufficiently considered in existing multi-criteria combination strategies in environmental assessment and management. Built upon the Analytic Network Process and demonstrated with the Chishui River Basin in China, this paper introduces a network-based expert judgment approach to construct ecological water quality indicators, and to determine and adjust their variable weight settings with information of interaction networks. This approach has potential to construct composite indicators for a broad environmental context.
•Networks are key features of ecosystems, providing useful information for decision-making.•Interactions are seldom used in setting weights for ecological composite indicators.•We develop a method to adjust indicator weights using ecological network information.•We show an extensible and more justified indicator building strategy. |
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ISSN: | 1364-8152 1873-6726 |
DOI: | 10.1016/j.envsoft.2019.01.011 |