In general, calibration of a hydrologic model is essential to better simulate the basin processes and behaviour by fitting the model simulated fluxes to observed fluxes. A major challenge in the calibration process is to choose the appropriate length of the observed data series and spatio-temporal resolution of the model schematization. We present a multi-case calibration approach for determining three pillars of an optimum hydrological model configuration: calibration data length, spin-up period and spatial resolution of the hydrological model. The approach is evaluated for the Moselle River basin using calibration and validation results from the spatially distributed meso-scale Hydrological Model (mHM) for 105 different cases representing the combinations of three calibration data lengths, seven spin-up periods and five spatial model resolutions. A metaheuristic global optimization method, i.e. Dynamically Dimensioned Search (DDS) algorithm, and a well-known hydrological performance metric, i.e. Nash Sutcliffe Efficiency (NSE), are utilized for each of the 105 calibration cases. The results show that a 10-year calibration data length, 2-year spin-up period and a 4 km model resolution are appropriate for the Moselle basin to reduce the computational burden. Analyzing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in hydrological modeling.