Spatially explicit sensitivity and uncertainty analysis for multicriteria-based vulnerability assessment

Abstract

This research analyses the application of spatially explicit sensitivity and uncertainty analysis for GIS (Geographic Information System) multicriteria decision analysis (MCDA) within a multi-dimensional vulnerability assessment regarding flooding in the Salzach river catchment in Austria. The research methodology is based on a spatially explicit sensitivity and uncertainty analysis of GIS-CDA for an assessment of the social, economic, and environmental dimensions of vulnerability. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimise the associated uncertainty within each dimension of the vulnerability assessment. The methodology proposed for achieving this objective is composed of four main steps. The first step is computing criteria weights using the analytic hierarchy process (AHP). In the second step, Monte Carlo simulation is applied to calculate the uncertainties associated with AHP weights. In the third step, the global sensitivity analysis (GSA) is employed in the form of a model-independent method of output variance decomposition, in which the variability of the different vulnerability assessments is apportioned to every criterion weight, generating one first-order (S) and one total effect (ST) sensitivity index map per criterion weight. Finally, in the fourth step, an ordered weighted averaging method is applied to model the final vulnerability maps. The results of this research demonstrate the robustness of spatially explicit GSA for minimising the uncertainty associated with GIS-MCDA models. Based on these results, we conclude that applying the variance-based GSA enables assessment of the importance of each input factor for the results of the GIS-MCDA method, both spatially and statistically, thus allowing us to introduce and recommend GIS-based GSA as a useful methodology for minimising the uncertainty of GIS-MCDA.

Keywords:

• Global sensitivity and uncertainty analysis
• Monte Carlo simulation
• multiple criteria evaluation
• vulnerability assessment
• Salzach basin

Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and suggestions on the earlier versions of this article. The research leading to these results has received funding from the Austrian Science Fund (FWF) through the Doctoral College GIScience (DK W 1237-N23).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Austrian Science Fund (FWF): Doctoral College GIScience [grant number DK W 1237-N23].