The importance of model error assessment during physica model calibration is widely recognized. We highlight the challenges arising in conventional statistical methods accounting for model error, and develop a density estimation framework to quantify and propagate uncertainties due to model errors in presence of sparse and noisy data. The reformulated calibration problem is then tackled with Bayesian techniques. We demonstrate the key strengths of the method on synthetic cases and on a few practical applications.