Calibration of model parameters is of utmost importance to ensure the good performance of hydraulic simulation models. In this work, calibration is conceived within a joint multi-period parameter and state estimation approach, where model parameters (i.e. roughness coefficients) and hydraulic variables should be computed from available measurements at different times. The aim of this paper is twofold: (1) to present a novel methodology for the calibration of water networks via multi-period state estimation, and (2) to adapt observability analysis to this approach. The novelty of this work is that such a large-scale non-linear optimisation problem is here solved using mathematical programming decomposition techniques. On the other hand, observability analysis requires the construction of the multi-period measurement and parameter Jacobian matrix of the problem. The proposed approach enables computation of the observable roughness coefficients from available readings over time, making possible the periodic reassessment of roughness values based on recent online measurements. The potential of the method is illustrated by means of a case study, which shows how such a methodology would contribute to make the most of telemetry data for calibration purposes.