Accurate estimation of percolation is crucial for assessing landfill final cover effectiveness, designing leachate collection/treatment systems, and many other applications, such as in agriculture. Despite the importance, percolation is seldom measured due to the high cost and maintenance of lysimeters, underlining the need for skillful simulation. Process-based numerical models, despite requiring validation and numerous parameters, present an alternative for percolation simulation, though few studies have assessed their performance. This study compares percolation measured from three fully instrumented large-scale experimental plots to simulate percolation using a new version of the Soil Vegetation and Snow (SVS) land-surface model with an active soil-freezing module. Previous research indicates numerical model performance may significantly vary based on soil-related parameter values. To account for input data and parameter uncertainty, we use an ensemble simulation strategy incorporating random perturbations. The results suggest that SVS can accurately capture the seasonal patterns of percolation, including significant events during snowmelts in spring and fall, with little to no percolation in winter and summer. The continuous ranked probability skill score values for the three plots are 0.13, -0.13, and 0.33. SVS simulates near-surface soil temperature dynamics effectively ( R 2 values 0.97-0.98) but underestimates temperature and has limitations in simulating soil temperature in snow-free situations in the cold season. It also overestimates soil freezing duration, revealing discrepancies in the onset and end of freezing periods compared to observed data. This study highlights the potential of land surface models for the simulation of percolation, with potential applications in the design of systems such as leachate collection and treatment. While the SVS model already provides an interesting outlook, further research is needed to address its limitations in simulating soil temperature dynamics during soil freezing periods.