Hydrological distributed models demand large amounts of data, information and parameters in order to accurately represent the spatial variability of the main hydrological processes and inputs. The parameter estimation is always a complex and expensive task which is often unaffordable and therefore hydrologists are frequently forced to disregard distributed modelling in favour of lumped or semidistributed models. To solve this situation, we propose in this paper to split the effective parameter at each cell in two components: the hydrological characteristic (at point scale and maintaining its physical meaning) and a correction factor (common for all cells and taking into account all modelling errors including the temporal and spatial scale effects). The new split-parameter structure adopted in our distributed model (called TETIS) is coupled with the SCE-UA automatic optimization method in order to obtain the set of optimal correction factors of the model, without loosing the spatial variability described by the a priori estimated hydrological characteristics. Automatic procedures can be easily adapted to the split-parameter structure because the number of variables to be calibrated is dramatically reduced from thousands of cell parameters to a small number of common correction factors (one for each parameter map). Also, the optimum initial values of the state variables can be obtained automatically. In order to show the advantages of the new structure a real application is performed in a case study in which the calibration and the spatial and spatial–temporal validation processes have been carried out. The results can be considered as being excellent and it is concluded that automatic calibration with the SCE-UA algorithm has been shown to be reliable and fast.