Robust global sensitivity analysis under deep uncertainty via scenario analysis. (February 2016)

• Record Type: Journal Article
• Authors: Gao, Lei; Bryan, Brett A.; Nolan, Martin; Connor, Jeffery D.; Song, Xiaodong; Zhao, Gang
• Is Part Of: Environmental modelling & software. Volume 76(2016:Feb.)
• Page Start: 154
• Page End: 166
• Publication Date: 2016-02
• Subjects: Global sensitivity analysis -- Robust sensitivity analysis -- eFAST -- Decision theory -- Land use change -- Deep uncertainty; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.11.001
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Complex social-ecological systems models typically need to consider deeply uncertain long run future conditions. The influence of this deep (i.e. incalculable, uncontrollable) uncertainty on model parameter sensitivities needs to be understood and robustly quantified to reliably inform investment in data collection and model refinement. Using a variance-based global sensitivity analysis method (eFAST), we produced comprehensive model diagnostics of a complex social-ecological systems model under deep uncertainty characterised by four global change scenarios. The uncertainty of the outputs, and the influence of input parameters differed substantially between scenarios. We then developed sensitivity indicators that were robust to this deep uncertainty using four criteria from decision theory. The proposed methods can increase our understanding of the effects of deep uncertainty on output uncertainty and parameter sensitivity, and incorporate the decision maker's risk preference into modelling-related activities to obtain greater resilience of decisions to surprise. Highlights: We performed global sensitivity analyses of a land use model under deep uncertainty. Deep uncertainty was characterised by internally consistent global change scenarios. The influence of scenarios on output uncertainty and parameter sensitivity was significant. Sensitivity indicators robust to deep uncertainty were calculated using four decision criteria. Our methods can better inform efforts to improve model outputs under deep uncertainty.