The doctoral dissertation deals with the field of hydrology and hydrological models. Hydrological phenomena are formed in a hydrological system determined by nature and are stochastic. Phenomena are unique, unrepeatable, and changing in space and time. We simulate these natural processes with the help of various hydrological models with which we try to integrate complex processes illustrated by continuous functions. In the past, different models have been developed to simulate different hydrological processes. However, discrepancies between simulated and measured values are still significant and pose a challenge to many researchers. The models contain many parameters that cannot be measured directly. The values of most of these parameters are determined in the calibration process, which is conditioned by the efficiency of such models. The process of model calibration is burdened by the lack of data and simplifications with which we solve the mathematical model. These all cause noise, or deviations between the results of the calculation given to us by the model and the measurements obtained in nature. In the process of calibrating the model, we try to determine the parameters contained in the individual functions so that the differences between the simulation and measurements are as small as possible. The doctoral dissertation presents the use of the enhanced Gauss-Levenberg-Marquardt (GLM) procedure in combination with singular value decomposition (SVD) and Tikhonov regularization. With such a procedure, we improve the calibrations of the hydrological model. The procedure was tested on a freely accessible hydrological model using synthetic data and on real models of the Savinja river basin in Slovenia and the Bosna river in Bosnia. Based on several model calibration efficiency indicators, it was found that the GLM process in combination with SVD and Tikhonov regularization ensures efficient matching of the history of the synthetic model and almost complete calibration of the parameters. With the help of Tikhonov parameter regularization, the calibration of the model was significantly improved, and a minimum error variance was achieved. Also, by comparing the results of the proposed process with the results of global evolutionary calibration processes, it was found that the calibration with the combined GLM process alone fully simulated low flows. Last but not least, the noise in the calculation results of the combined GLM method with Tikhonov regularization was practically the same either in the calibration or validation process, which shows that only the calculation noise remained in the results.