This paper presents an efficient surrogate modeling strategy for the uncertainty quantification and Bayesian calibration of a hydrological model. In particular, a process-based dynamical urban drainage simulator that predicts the discharge from a catchment area during a precipitation event is considered. The goal is to perform a global sensitivity analysis and to identify the unknown model parameters as well as the measurement and prediction errors. These objectives can only be achieved by cheapening the incurred computational costs, that is, lowering the number of necessary model runs. With this in mind, a regularity-exploiting metamodeling technique is proposed that enables fast uncertainty quantification. Principal component analysis is used for output dimensionality reduction and sparse polynomial chaos expansions are used for the emulation of the reduced outputs. Sensitivity measures such as the Sobol indices are obtained directly from the expansion coefficients. Bayesian inference via Markov chain Monte Carlo posterior sampling is drastically accelerated.The quantification of uncertainty has become an integral aspect of computational science and engineering in the last two decades [1,2]. Algorithmic advances on the one hand and hardware improvements on the other hand allow for an increasingly detailed simulation of complex systems.That the underlying models are hardly perfect and the model parameters are barely known with certainty prompts scientist and engineers to conduct an end-to-end analysis of the encountered errors. This process, for which one commonly relies on probability theory, is known as uncertainty quantification (UQ).

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