Uncertainty analysis in a GIS-based multi-criteria analysis tool for river catchment management

  • Authors:

  • H. Chen;M. D. Wood;C. Linstead;E. Maltby

  • Affiliations:

  • Institute for Sustainable Water, Integrated Management and Ecosystem Research, School of Environmental Sciences, University of Liverpool, Liverpool L69 3GP, UK;Institute for Sustainable Water, Integrated Management and Ecosystem Research, School of Environmental Sciences, University of Liverpool, Liverpool L69 3GP, UK;Institute for Sustainable Water, Integrated Management and Ecosystem Research, School of Environmental Sciences, University of Liverpool, Liverpool L69 3GP, UK;Institute for Sustainable Water, Integrated Management and Ecosystem Research, School of Environmental Sciences, University of Liverpool, Liverpool L69 3GP, UK

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2011

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Abstract

The importance of uncertainty analysis has been increasingly recognised, due to the influence of uncertainties in data, models and expert judgements. However, the successful integration of uncertainty analysis into multi-criteria analysis (MCA) has rarely been achieved. This paper analyses uncertainty sources in MCA. General methods of uncertainty analysis in MCA are reviewed, including probabilistic methods, indicator-based methods and fuzzy logic. Building on this review, an uncertainty analysis module developed for use within a GIS-based MCA tool for catchment management is presented. In this module, the influence of uncertainties on decision-making can be visually explored using an indicator-based method. The indicator-based method provides a pragmatic approach to communicating areas of uncertainty to decision-makers without assuming any prior knowledge of uncertainty analysis techniques. This enables uncertainty analysis to be more effectively operationalised within the decision-making process. An application example in the Tamar catchment, southwest UK, is used to illustrate the capability of the uncertainty analysis module when applied in a decision-making context.