Bayesian inference of hydrological model parameters is crucial for improving the accuracy and reliability of hydrological model executions. This thesis presents the implementation of the Markov Chain Monte Carlo (MCMC) approach to enhance the computational efficiency of Bayesian parameter estimation, with a predominant focus on the parallel version of the algorithms. Results regarding the accuracy and efficiency of the Bayesian inference are analyzed through comparison metrics and displayed using detailed visualizations so that the relationship between algorithm implementation variants and the results can be comprehended. Besides, the relationship between the training time series for the Markov chain Monte Carlo algorithms is also considered. By investigating these aspects of the algorithms and the data set, more insights regarding the performance and the result of Bayesian inference can be gained, enabling more practical and scalable applications in hydrological modeling.