This paper presents a calibration framework for a precipitation-runoff model for flood prediction in a mesoscale Alpine basin with strongly anthropogenic discharges. The developed methodology addresses two classical hydrological calibration challenges: computational limitations to run optimization algorithms for distributed hourly models and the absence of concomitant meteorological and natural discharge time series. The presented processes-oriented, multi-signal approach is based on hydrologic information coming from various sources and periods, referring to different spatial scales and of different quality. Model parameters are calibrated by sequentially minimizing differences between observed and simulated values for different hydrological signals and signatures such as the phase of precipitations, the time evolution of point-scale snow heights, the mean interannual cycle of daily discharges or timing of snowmelt induced spring runoff. We compare the model performance to a benchmark model obtained by simply using the globally optimal parameter values from the nearest gauged and non perturbed catchment. For prediction of flow seasonality and also extreme events, the calibration methodology outperforms the benchmark.