The paper by D. M. Hamby provides a comprehensive review of various techniques for parameter sensitivity analysis in environmental models. Mathematical models are used to approximate complex phenomena across multiple disciplines, and sensitivity analysis is crucial for identifying the most influential parameters. The review covers over a dozen methods, ranging from basic partial differentiation to advanced regression techniques. These methods help modelers understand which parameters require further research, which can be eliminated, and which contribute most to output variability. The paper discusses the practicality and usefulness of each method, including differential analysis, one-at-a-time design, factorial design, correlation coefficients, and regression techniques. It also briefly touches on more specialized techniques for highly complex models, such as structural identifiability, adjoint equations, Fourier analysis, and Green's functions. The goal is to provide a practical guide for researchers conducting sensitivity analyses, helping them choose the most suitable method based on calculational ease and the relevance of the results.The paper by D. M. Hamby provides a comprehensive review of various techniques for parameter sensitivity analysis in environmental models. Mathematical models are used to approximate complex phenomena across multiple disciplines, and sensitivity analysis is crucial for identifying the most influential parameters. The review covers over a dozen methods, ranging from basic partial differentiation to advanced regression techniques. These methods help modelers understand which parameters require further research, which can be eliminated, and which contribute most to output variability. The paper discusses the practicality and usefulness of each method, including differential analysis, one-at-a-time design, factorial design, correlation coefficients, and regression techniques. It also briefly touches on more specialized techniques for highly complex models, such as structural identifiability, adjoint equations, Fourier analysis, and Green's functions. The goal is to provide a practical guide for researchers conducting sensitivity analyses, helping them choose the most suitable method based on calculational ease and the relevance of the results.