Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions

Traditional hydrological objective functions may penalize models that reproduce hydrograph shapes well, but with some shift in time; especially for urban catchments a fast response. Hydrograph timing is not always critical, so this paper investigates alternative (based on the Matching Algorithm) try to mimic visual comparison. A modified version of Generalized Likelihood Uncertainty Estimation proposed compare regular those account errors. This applied 2-year calibration and validation data sets from an catchment. Results show such provide equally reliable model predictions (they envelop same fraction observations), more precision, i.e. smaller estimated uncertainty predictions. Additionally, identifiability parameters improved. Therefore based Algorithm can be useful reduce uncertainties drainage modelling.