Insights into sensitivity analysis of Earth and environmental systems models: On the impact of parameter perturbation scale. (September 2017)

• Record Type: Journal Article
• Authors: Haghnegahdar, Amin; Razavi, Saman
• Is Part Of: Environmental modelling & software. Volume 95(2017)
• Page Start: 115
• Page End: 131
• Publication Date: 2017-09
• Subjects: Sensitivity analysis -- Perturbation scale -- VARS -- Environmental modelling -- MESH -- SWAT -- HydroGeoSphere; 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.2017.03.031
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper investigates the commonly overlooked "sensitivity" of sensitivity analysis (SA) to what we refer to as parameter "perturbation scale", which can be defined as a prescribed size of the sensitivity-related neighbourhood around any point in the parameter space (analogous to step size Δ x for numerical estimation of derivatives). We discuss that perturbation scale is inherent to any (local and global) SA approach, and explain how derivative-based SA approaches (e.g., method of Morris) focus on small-scale perturbations, while variance-based approaches (e.g., method of Sobol) focus on large-scale perturbations. We employ a novel variogram-based approach, called Variogram Analysis of Response Surfaces (VARS), which bridges derivative- and variance-based approaches. Our analyses with different real-world environmental models demonstrate significant implications of subjectivity in the perturbation-scale choice and the need for strategies to address these implications. It is further shown how VARS can uniquely characterize the perturbation-scale dependency and generate sensitivity measures that encompass all sensitivity-related information across the full spectrum of perturbation scales. Highlights: Model sensitivity analysis is "sensitive" to parameter perturbation scale (e.g., step size). VARS is a novel global sensitivity analysis (SA) technique that uniquely characterizes perturbation scale dependency of SA. The sensitivity analysis strategies are preferred that take into account the impact of parameter perturbation scale.