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, sensitivity analysis is used to limit the number of parameters. However, the current sensitivity analytical methods ignore the effect of the temporal distribution and intensity of precipitation in a rainfall event on the catchment outflow hydrograph. This article presents a methodology of constructing a simulator of catchment outflow hydrograph parameters (volume and maximum flow). For this purpose, uncertainty analytical results obtained with the use of the GLUE (generalized likelihood uncertainty estimation) method were used. A novel analysis of the sensitivity of the hydrodynamic catchment models was also developed, which can be used in the analysis of the operation of stormwater networks and underground infrastructure facilities. Using the logistic regression method, an innovative sensitivity coefficient was proposed to study the impact of the variability of the parameters of the hydrodynamic model depending on the distribution of rainfall, the origin of rainfall (on the Chomicz scale), and the uncertainty of the estimated simulator coefficients on the parameters of the outflow hydrograph. The developed model enables the analysis of the impact of the identified SWMM (Storm Water Management Model) parameters on the runoff hydrograph, taking into account local rainfall conditions, which have not been analyzed thus far. Compared with the currently developed methods, the analyses included the impact of the uncertainty of the identified coefficients in the logistic regression model on the results of the sensitivity coefficient calculation. This aspect has not been taken into account in the sensitivity analytical methods thus far, although this approach evaluates the reliability of the simulation results. The results indicated a considerable influence of rainfall distribution and intensity on the sensitivity factors. The greater the intensity and rainfall were, 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. In the context of the sensitivity analysis, the obtained results have a significant effect on the interpretation of the relationships obtained. The approach presented in this study can be widely applied at the model calibration stage and for appropriate selection of hydrographs for identification and validation of model parameters. The results of the calculations obtained in this study indicate the suitability of including the origin of rainfall in the sensitivity analysis and calibration of hydrodynamic models, which results from the different sensitivities of models for normal, heavy, and torrential rain types. In this context, it is necessary to first divide the rainfall data by origin, for which analyses will be performed, including sensitivity analysis and calibration. Considering the obtained results of the calculations, at the stage of identifying the parameters of hydrodynamic models and their validation, precipitation conditions should be included because, for the precipitation caused by heavy rainfall, the values of the sensitivity coefficients were much lower than for torrential ones. Taking into account the values of the sensitivity coefficients obtained, the calibration of the models should not only cover episodes with high rainfall intensity, since this may lead to calculation errors at the stage of applying the model in practice (assessment of the stormwater system operating conditions, design of reservoirs and flow control devices, green infrastructure, etc.).

Highlights

  • Climate change and progressive urbanization result in an increase in the volume of stormwater outflow from catchments, which leads to flooding and deterioration of water quality in receivers (Crocetti et al, 2020; Fletcher et al, 2013)

  • Following the developed computational algorithm, the “Results and discussion” section includes the following steps: determining the threshold values of the outflow hydrograph parameters using the hydrodynamic model of the catchment, as well as uncertainty analysis; developing the logistic regression model and its verification; sensitivity analysis, in which the influence of rainfall origin and temporal rainfall distribution on the parameters of the hydrograph is analyzed; and analysis of the impact of uncertainty of the estimated coefficients in the logit model on the determined sensitivity coefficients

  • This study showed that the logistic regression model can be used for analyses of the sensitivity of the maximum flow in a hydrograph and hydrograph volume in a rainfall event

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Summary

Introduction

Climate change and progressive urbanization result in an increase in the volume of stormwater outflow from catchments, which leads to flooding and deterioration of water quality in receivers (Crocetti et al, 2020; Fletcher et al, 2013). To reduce the incidence of these phenomena, runoff model generation is needed. This can be achieved using hydrodynamic models based on physical equations representing stormwater outflows. The program allows for simulation of surface runoff from a drainage basin including the flow in a network of pipes and analysis of interaction between hydraulic conditions in the system and sewage flooding on the ground (Fraga et al, 2016). Nonlinear models enable a more accurate description of hydrological processes in urban catchments, which results from the physics of the analyzed phenomena and is confirmed in the literature (Zoppou, 2001)

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