Complex Environmental Systems Models (CESMs) have been developed and applied as vital tools to tackle the ecological, water, food, and energy crises that humanity faces, and have been used widely to support decision-making about management of the quality and quantity of the Earth’s resources. CESMs are often controlled by many interacting and uncertain factors, and typically integrate data from multiple sources at different spatio-temporal scales, which make them highly complex. Sensitivity Analysis (SA) has proven to be a promising tool for deepening our understanding of the model complexity and interactions between various factors, and providing helpful recommendations for further model development and data acquisition. SA provides a way for systematically explaining how variations in different model input factors and their interactions influence the model output variability, and thus a means of assessing which factor is more responsible for model output variations. The scope of this presentation is to give a general introduction to methods of SA and a tutorial on the use of SA methods to rank input uncertainties.