A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models. (April 2019)

• Record Type: Journal Article
• Authors: Razavi, Saman; Gupta, Hoshin V.
• Is Part Of: Environmental modelling & software. Volume 114(2019)
• Page Start: 1
• Page End: 11
• Publication Date: 2019-04
• Subjects: Global sensitivity analysis -- Time-varying sensitivity analysis -- Parameter importance -- Uncertainty -- Variogram analysis of response surfaces (VARS) -- Sobol' -- Morris -- Progressive Latin hypercube sampling (PLHS) -- Dynamical systems models -- Performance metrics -- Sensitivity indices; 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.2018.12.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Many applications of global sensitivity analysis (GSA) do not adequately account for the dynamical nature of earth and environmental systems models. Gupta and Razavi (2018) highlight this fact and develop a sensitivity analysis framework from first principles, based on the sensitivity information contained in trajectories of partial derivatives of the dynamical model responses with respect to controlling factors. Here, we extend and generalize that framework to accommodate any GSA philosophy, including derivative-based approaches (such as Morris and DELSA ), direct-response-based approaches (such as the variance-based Sobol', distribution-based PAWN, and higher-moment-based methods), and unifying variogram-based approaches (such as VARS ). The framework is implemented within the VARS-TOOL software toolbox and demonstrated using the HBV-SASK model applied to the Oldman Watershed, Canada. This enables a comprehensive multi-variate investigation of the influence of parameters and forcings on different modeled state variables and responses, without the need for observational data regarding those responses. Highlights: Introduces a "unifying" multi-method approach to global sensitivity analysis. Properly accounts for the dynamical nature of Earth and environmental system models. Discusses how the full spectrum of sensitivity information may be compressed. Provides software to implement the new approach in the VARS-TOOL toolbox.