Each model relies on parameters determined through calibration, a process involving adjusting these values to match the model’s behaviour with real-world systems, such as in hydrologic modelling, where simulated discharge aligns with observed data from a river basin. Manual calibration, known for its laborious nature, has led to the development of automatic procedures, leveraging computer power for efficiency. However, both manual and automatic methods face challenges in finding the optimal parameter values, often encountering multiple local optimal sets through optimization algorithms. As a result, identifying a definitive “best” parameter set remains a complex task, as no single set consistently outperforms others. At the core of Monte Carlo methods, utilized for numerical computations, lies the repeated random sampling process. This method involves conducting multiple simulations to acquire distribution of a stochastic variable, when obtaining a closed-form expression is challenging or impossible in various physical and mathematical challenges. Monte Carlo methods prove invaluable in scenarios where deterministic algorithms are impractical. They find application in three primary problem categories: generating samples from probability distributions, numerical integration and optimization. In hydrology, Monte Carlo simulations’ results hold crucial significance, contributing significantly to tasks like parameter optimization, conducting sensitivity analysis and estimating uncertainty. The structured method of incorporating uncertainties related to model inputs using probability distributions and propagating these uncertainties through the models of the system to obtain model outputs is termed uncertainty analysis. In hydrologic modelling, uncertainty analysis closely mirrors the principles of Monte Carlo simulation. This method involves defining probability distributions for model parameters and running the model iteratively for multiple scenarios to establish probability distributions for model outcomes. Meanwhile, sensitivity analysis evaluates how changing individual parameters, one at a time from a starting point, affects the output of a model. It calculates sensitivity coefficients by comparing output changes to input changes, thereby revealing the relative sensitivities of input parameters. This chapter provides a thorough overview of performing Monte Carlo simulations and subsequently leveraging the outcomes of these simulations to refine uncertainty estimations. This refinement is accomplished by constraining the range of model parameters through sensitivity analysis.