Rainfall–runoff phenomena are among the main processes within the hydrological cycle. In urban zones, the increases in imperviousness cause increased runoff, originating floods. It is fundamental to know the sensitivity of parameters in the modeling of an urban basin, which makes the calibration process more efficient by allowing one to focus only on the parameters for which the modeling results are sensitive. This research presents a formal sensitivity analysis of hydrological and hydraulic parameters—absolute–relative, relative–absolute, relative–relative sensitivity and R2—applied to an urban basin. The urban basin of Tuxtla Gutiérrez, Chiapas, in Mexico is an area prone to flooding caused by extreme precipitation events. The basin has little information in which the records (with the same time resolution) of precipitation and hydrometry match. The basin model representing an area of 355.07 km2 was characterized in the Stormwater Management Model (SWMM). The sensitivity analysis was performed for eight hydrological parameters and one hydraulic for two precipitation events and their impact on the depths of the Sabinal River. Based on the analysis, the parameters derived from the analysis that stand out as sensitive are the Manning coefficient of impervious surface and the minimum infiltration speed with R2 > 0.60. The results obtained demonstrate the importance of knowing the sensitivity of the parameters and their selection to perform an adequate calibration.
Abstract:The evaluation of the uncertainties in model predictions is key for advancing urban drainage modelling practice. This paper investigates, for the first time in Mexico, the effect of parameter sensitivity and predictive uncertainty in an application of a well-known urban stormwater model. Two of the most common methods used for assessing hydrological model parameter uncertainties are used: the Generalised Likelihood Uncertainty Estimation (GLUE) and a multialgorithm, genetically adaptive multi-objective method (AMALGAM). The uncertainty is estimated from eight selected hydrologic parameters used in the setup of the rainfall-runoff model. To ensure the reliability of the model, four rainfall events varying from 20 mm to 120 mm from minor to major count classes were selected. The results show that, for the selected storms, both techniques generate results with similar effectiveness, as measured using well-known error metrics; GLUE was found to have a slightly better performance compared to AMALGAM. In particular, it was demonstrated that it is possible to obtain reliable models with an index of agreement (IAd) greater than 60 and average Absolute Percentage Error (EAP) less than 30 percent derived from the uncertainty analysis. Thus, the quantification of uncertainty enables the generation of more reliable flow predictions. Moreover, these methods show the impact of aggregation of errors arising from different sources, minimising the amount of subjectivity associated with the model's predictions.
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