Uncertainty in the parameterization of sediment build-up and wash-off processes in the simulation of sediment transport in urban areas. (January 2019)

• Record Type: Journal Article
• Authors: Gorgoglione, Angela; Bombardelli, Fabián A.; Pitton, Bruno J.L.; Oki, Lorence R.; Haver, Darren L.; Young, Thomas M.
• Is Part Of: Environmental modelling & software. Volume 111(2019)
• Page Start: 170
• Page End: 181
• Publication Date: 2019-01
• Subjects: Model uncertainty -- Sensitivity analysis -- Model performance -- Sediment build-up and wash-off -- SWMM model; 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.2018.09.022
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Drainage-system management relies on results from urban stormwater models; errors in these models may have serious implications. Inaccurate field data or overly simplified models may cause the complex response of an urban basin to a rainfall event to be inadequately represented. Before undertaking expensive studies to gather and analyze additional data, it is reasonable to understand what enhancement in model performance would result if individual uncertainties could be decreased. This paper uses data collected during field monitoring campaigns to calibrate and validate a hydrologic and sediment-transport model within the Storm Water Management Model (SWMM). Solid accumulation and disappearance rates were identified as factors generating the highest model sensitivity. A generalized evaluation matrix is presented that considers both the uncertainty in input variables and the associated sensitivity in model response to inform model performance expectations and guide investments in model improvement toward actions with maximum benefit. Highlights: The impact that the uncertainty associated with sediment build-up and wash-off processes has on water quality model predictions was analyzed. A method based on model uncertainty and sensitivity was developed to assess and predict the accuracy of water quality models. This approach will assist users in making appropriate model performance conclusions.