Urban Drainage Infrastructure Design Model Calibration and Output Uncertainty Minimization, Are Model Users Pursuing Accuracy and Model Calibration?

Models are just simplifications of the real condition that is going to be simulated or analyzed. However, the series of entries and assumptions of input data reflect the most representative case of the practical conditions. Accurate and reliable modeling of stormwater runoff (i.e. hydrologic, hydraulic, water quality?etc.), and associated phenomena have been and continue to be a challenge, despite the fact that models, model interfaces, and even model math engines have been improved. The designing and planning of major urban drainage infrastructures are significantly depends on the use of those complex, advanced models and their outputs. Therefore, to get the required degree of model output accuracy model calibrations are necessary. The calibration process minimizes the probabilities of inaccuracies that can be generated from different input parameters and model users. This paper aims to briefly discuss urban drainage infrastructure design models? calibration in order to minimize output value uncertainty. A case study, using SWMM-5, was performed in Astoria Heights, New York City, to assess and demonstrate model output uncertainties and the relative importance of calibration. The study involves adjustment of the primary model parameters until the model results are approximately close to the actual observed values as designed under similar conditions. After the model result and the observed values are in reasonable agreement the final model calibration was performed to recommend how to minimize model output uncertainties. In conclusion, the result analysis conformed that it is very crucial to establish model output calibration standards before proceeding to the final design stage of any urban drainage infrastructure. Furthermore, the author also recommends that model users to pursue accuracy and model calibration for reliable model output results.