  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 of the case study 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. Sobol' sensitivity indices are
obtained directly from the expansion coefficients by a mere post-processing.
Bayesian inference via Markov chain Monte Carlo posterior sampling is
drastically accelerated.
