Parameter sensitivity and uncertainty analysis of a stormwater runoff model

Abstract

In this study, Generalized Likelihood Uncertainty Estimation (GLUE) methodology was used to perform uncertainty analysis of a stormwater model, which randomly generates parameter sets and identifies behavioral ones with higher likelihood. Latin Hypercube Sampling (LHS) is used to generate parameter sets. The Model for Urban Stormwater Improvement Conceptualization was chosen as an appropriate model for stormwater runoff. Prediction limits are determined by selecting the cutoff threshold for likelihood function. Study area is Goonja Drainage Basin located in the city of Seoul, Korea. From the results, maximum likelihood value for calibration is 0.78 and 0.73 for validation. The p-factors for the calibration and validation are 87 and 83%, respectively. The p-factor for all storm events is 85%. These are all acceptable values as the results are considered good when 60% or more of the observed data are bracketed by prediction limits. Overall, it was shown that, using GLUE methodology with LHS, the model calibrated well for the basin considered in this study.