Surface soil structure and pore space are very responsive to both natural and anthropogenic impacts such as rainfall or tillage. These changes affect the soil hydraulic properties as well as the soil water budget. Despite available theories and evidence on the nature of these changes, efforts to capture the temporal dynamics of soil pore size distribution (PSD) and incorporate the derived hydraulic properties in modeling studies are quite rare. The objective of this paper is to examine the suitability of an existing pore evolution model to capture the evolution of soil PSD using water retention parameter (WRP) data sets from around the world. The physical processes governing the evolution of soil PSD are incorporated in the coefficients of the pore evolution model. The applicability of the model to predict the evolution of PSD is discussed and evaluated for two cases (1) when there is a change in tillage regime, and (2) as a novel undertaking, for the months following tillage operations. For the second case, the model is also evaluated for the assumption that the WRPs for the intermediate stages following tillage is not known. This enables us to predict the effects of tillage on soil PSD at a given time even without WRP measurements at all stages. Overall, it is seen that the model and its coefficients are adequate in estimating the overall reduction in porosity and loss of inter-aggregate pores (corresponding to pressure head range from 0 to 330 cm) that are characteristic after tillage operations for both scenarios. In most cases, there is a good agreement between the observed and predicted values indicated by the r² and RMSE values. The model seems to be less suitable for pores with radii ≤10 μm in some cases, especially for intense rainfall scenarios which may lead to rapid aggregate breakdown and formation of finer pores at a faster rate in comparison to moderate rainfall events. As a solution, we may provide more recent initial conditions for initial PSD to the model following heavy rainfall events and continue our simulations from there to better capture the effects of rainfall. The main limitation for the application of the model is, however, the lack of adequate datasets to validate and calibrate it for different management practices, soil types, and climate regimes.