This paper provides a comprehensive review of Sensitivity Analysis (SA) in environmental modeling, aiming to offer a systematic classification of SA methods and practical guidelines for their application. SA is a crucial tool for understanding how variations in model inputs affect outputs, and it is widely used for uncertainty assessment, model calibration, diagnostic evaluation, and robust decision-making. The authors define key concepts such as input factors, output variables, and different types of SA (local vs. global, quantitative vs. qualitative, one-at-a-time vs. all-at-a-time). They also discuss the connections between SA and other methodologies like uncertainty analysis, model calibration, and emulators. The paper is divided into three main sections: an introduction to SA, a systematic review of SA methods, and a discussion of a practical workflow for applying SA. The review covers five broad classes of SA methods based on computational complexity and purpose, including perturbation and derivatives methods, multiple-start perturbation methods, correlation and regression analysis methods, regional sensitivity analysis, and variance-based methods. Each method is described in terms of its underlying principles, advantages, and limitations. The paper emphasizes the importance of computational complexity and provides a classification system to guide users in selecting the most appropriate method for their specific needs.This paper provides a comprehensive review of Sensitivity Analysis (SA) in environmental modeling, aiming to offer a systematic classification of SA methods and practical guidelines for their application. SA is a crucial tool for understanding how variations in model inputs affect outputs, and it is widely used for uncertainty assessment, model calibration, diagnostic evaluation, and robust decision-making. The authors define key concepts such as input factors, output variables, and different types of SA (local vs. global, quantitative vs. qualitative, one-at-a-time vs. all-at-a-time). They also discuss the connections between SA and other methodologies like uncertainty analysis, model calibration, and emulators. The paper is divided into three main sections: an introduction to SA, a systematic review of SA methods, and a discussion of a practical workflow for applying SA. The review covers five broad classes of SA methods based on computational complexity and purpose, including perturbation and derivatives methods, multiple-start perturbation methods, correlation and regression analysis methods, regional sensitivity analysis, and variance-based methods. Each method is described in terms of its underlying principles, advantages, and limitations. The paper emphasizes the importance of computational complexity and provides a classification system to guide users in selecting the most appropriate method for their specific needs.