An understanding of the clogging of fine sediments within gravel substrates is advanced through the use of dimensional analysis and macro-analysis of clogging experiments in hydraulic flumes. Dimensional analysis is used to suggest that the dimensionless clogging depth can be collapsed using the original and adjusted bed-to-grain ratios, substrate porosity, roughness Reynolds number, and Peclet number. Macro-analysis followed by statistical analysis of 146 experimental test results of fine sediment deposition in gravel substrates suggests that the dimensionless clogging depth can be collapsed using the substrate porosity and roughness Reynolds number reflecting the processes of gravity settling and turbulence induced fluid pumping between substrate particles. In addition, a clear cutoff of fine sediment unimpeded static percolation and sediment clogging is found using the adjusted bed-to-grain ratio. Thereafter, a physics-based approach is used to predict the clogging depth of fine sediment in gravel and in turn approve upon the preliminary findings in the empirical analysis. A momentum-impulse model that accounts for the critical impulse of a particle bridge is balanced with a fluid pulse resulting from turbulent pumping. The momentum-impulse model reduces the number of unknown parameters in the clogging problem and increases the model predictability as quantified using k-fold validation and model comparison with the empirical approach. A nomograph derived from applying the momentum-impulse model is provided herein, which will be useful for stream restoration practitioners interested in estimating embeddedness. Also, prediction of the clogging profile is shown using the clogging depth predicted with the momentum-impulse model.