Application of a lower-fidelity surrogate hydraulic model for historic flood reconstruction. (July 2019)

• Record Type: Journal Article
• Authors: Bomers, A.; Schielen, R.M.J.; Hulscher, S.J.M.H.
• Is Part Of: Environmental modelling & software. Volume 117(2019)
• Page Start: 223
• Page End: 236
• Publication Date: 2019-07
• Subjects: Lower-fidelity model -- Sensitivity analysis -- Uncertainty -- Historic flood reconstruction; 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.2019.03.019
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Two dimensional hydraulic models are useful to reconstruct maximum discharges and uncertainties of historic flood events. Since many model runs are needed to include the effects of uncertain input parameters, a sophisticated 2D model is not applicable due to computational time. Therefore, this papers studies whether a lower-fidelity model can be used instead. The presented methodological framework shows that a 1D-2D coupled model is capable of simulating maximum discharges with high accuracy in only a fraction of the calculation time needed for the high-fidelity model. Therefore, the lower-fidelity model is used to perform the sensitivity analysis. Multiple Linear Regression analysis and the computation of the Sobol' indices are used to apportion the model output variance to the most influential input parameters. We used the 1926 flood of the Rhine river as a case study and found that the roughness of grassland areas was by far the most influential parameter. Highlights: A novel framework is set up to enable historic flood reconstruction. A lower-fidelity physically based surrogate hydraulic model is developed which is capable of simulating maximum discharges with high accuracy compared to a sophisticated 2D model. Sensitivity analysis can be applied for factor prioritization to determine on which input parameter to focus during historical geometry reconstruction to reduce model output variance. The uncertainty of the maximum discharge is mostly influenced by the roughness of grasslands which is in this study the roughness class with the largest surface area.