Spatial Global Sensitivity Analysis of High Resolution classified topographic data use in 2D urban flood modelling. (March 2016)

• Record Type: Journal Article
• Authors: Abily, Morgan; Bertrand, Nathalie; Delestre, Olivier; Gourbesville, Philippe; Duluc, Claire-Marie
• Is Part Of: Environmental modelling & software. Volume 77(2016:Mar.)
• Page Start: 183
• Page End: 195
• Publication Date: 2016-03
• Subjects: Urban flood -- Uncertainties -- Shallow water equations -- FullSWOF_2D -- Sensitivity maps -- Photogrammetry -- Classified topographic data; 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.2015.12.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper presents a spatial Global Sensitivity Analysis (GSA) approach in a 2D shallow water equations based High Resolution (HR) flood model. The aim of a spatial GSA is to produce sensitivity maps which are based on Sobol index estimations. Such an approach allows to rank the effects of uncertain HR topographic data input parameters on flood model output. The influence of the three following parameters has been studied: the measurement error, the level of details of above-ground elements representation and the spatial discretization resolution. To introduce uncertainty, a Probability Density Function and discrete spatial approach have been applied to generate 2, 000 DEMs. Based on a 2D urban flood river event modelling, the produced sensitivity maps highlight the major influence of modeller choices compared to HR measurement errors when HR topographic data are used. The spatial variability of the ranking is enhnaced. Highlights: Sobol index maps produced by a spatial GSA rank in space the weight of each uncertain parameter on the variability of output parameter of interest. The weight of the modeller choices, with respect to the influence of HR dataset accuracy is enhanced. Added value is for modeller to better understand limits of his model. Requirements and limits for this approach are related to subjectivity of choices and to computational cost.