A new approach to evaluate spatiotemporal dynamics of controlling parameters in distributed environmental models. (January 2017)

• Record Type: Journal Article
• Authors: Chen, Zhao; Hartmann, Andreas; Goldscheider, Nico
• Is Part Of: Environmental modelling & software. Volume 87(2017)
• Page Start: 1
• Page End: 16
• Publication Date: 2017-01
• Subjects: Distributed environmental models -- Model evaluation -- Spatiotemporal parameter sensitivity analysis -- Sobol's method -- Storm Water Management Model (SWMM) -- Karst flow modelling; 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.2016.10.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Distributed environmental models are usually high-dimensional and non-linear. To comprehensively evaluate the spatiotemporal dynamics of model controls, we propose a novel multi-step approach based on Sobol's method to evaluate parameter sensitivity as well as interactions with respect to different model outlet points, using different objective functions to assess different hydrodynamic conditions; all varying through time. This complete sensitivity analysis can be performed for prior and posterior parameter ranges. The difference between them can be used to assess the influence of parameter constraints on the results of sensitivity analyses. We applied this holistic approach to an existing distributed karst watershed model. The results demonstrated that 1) a limited number of spatially-distributed parameters control the varying flow pattern, 2) the model is nonlinear and the influential parameters are highly correlated in the model domain and 3) the spatial patterns of identified parameter sensitivity and interactions are strongly influenced by the specified parameter bounds. Highlights: We presented a novel model evaluation approach based on Sobol's sensitivity analysis. We combined multi-objective and time-varying sensitivity analysis approaches. We identified and evaluated spatiotemporal dynamics of model controls. Sensitive parameters are found to be highly correlated in model space and time domains. We assessed the influence of parameter constraints on the results of the sensitivity analyses.