Advanced sensitivity analysis of the impact of the temporal distribution and intensity in a rainfall event on hydrograph parameters in urban catchments: a case study

Abstract

Abstract. Knowledge of the variability of the hydrograph of outflow from urban catchments is highly important for measurements and evaluation of the operation of sewer networks. Currently, hydrodynamic models are most frequently used for hydrograph modeling. Since a large number of their parameters have to be identified, there may be problems at the calibration stage. Hence, the sensitivity analysis is used to limit the number of parameters. However, the current sensitivity analysis methods ignore the effect of the temporal distribution and intensity of precipitation in a rainfall event on the catchment outflow hydrograph. The article presents the methodology of construction of a simulator of catchment outflow hydrograph parameters (volume, maximum flow). For this purpose, uncertainty analysis results obtained with the use of the GLUE (Generalized Likelihood Uncertainty Estimation) method were used. An innovative sensitivity coefficient has been proposed to study the impact of the variability of hydrodynamic model parameters depending on rainfall distribution, rainfall genesis (in the Chomicz scale), and uncertainty of estimated simulator coefficients on the parameters of the outflow hydrograph. The results indicated a considerable influence of rainfall distribution and intensity on the sensitivity factors. The greater the intensity and temporal distribution of rainfall, the lower the impact of the identified hydrodynamic model parameters on the hydrograph parameters. Additionally, the calculations confirmed the significant impact of the uncertainty of the estimated coefficient in the simulator on the sensitivity coefficients, which has a significant effect on the interpretation of the relationships obtained. The approach presented in the study can be widely applied at the model calibration stage and for appropriate selection of hydrographs for identification and validation of model parameters.