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 simulated 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.

The present study focuses on quantifying the impact of the choice of spatio-temporal resolution and hydrology models on the projection of extreme flow and their link to the catchment size. We use two process-based distributed hydrology models forced with a large-ensemble regional climate model (50-member ClimEx dataset) over the 1990-2100 period at different spatio-temporal scales. The extreme summer-fall flow corresponding with each spatio-temporal resolution was extracted by pooling the members together and computing the empirical cumulative distribution function. The results show that by refining the time-step from daily to sub-daily, the summer-fall extreme flow projected over the future period exceeds that of the reference period for the small but not large catchments. By increasing the catchment size, the hydrology model’s contribution to the variability of extreme flow increases. Moreover, the choice of spatial resolution affects the extreme flow’s trend in terms of magnitude, significance, and direction. But no pattern regarding the catchment size and spatial discretization variations exists.

Quantifying the uncertainty linked to the degree to which the spatio-temporal variability of the catchment descriptors (CDs), and consequently calibration parameters (CPs), represented in the distributed hydrology models and its impacts on the simulation of flooding events is the main objective of this paper. Here, we introduce a methodology based on ensemble approach principles to characterize the uncertainties of spatio-temporal variations. We use two distributed hydrological models (WaSiM and Hydrotel) and six catchments with different sizes and characteristics, located in southern Quebec, to address this objective. We calibrate the models across four spatial (100, 250, 500, 1000 $m^2$) and two temporal (3 hours and 24 hours) resolutions. Afterwards, all combinations of CDs-CPs pairs are fed to the hydrological models to create an ensemble of simulations for characterizing the uncertainty related to the spatial resolution of the modeling, for each catchment. The catchments are further grouped into large ($>1000 km^2$), medium (between 500 and 1000 $km^2$) and small ($<500km^2$) to examine multiple hypotheses. The ensemble approach shows a significant degree of uncertainty (over $100\%$ error for estimation of extreme streamflow) linked to the spatial discretization of the modeling. Regarding the role of catchment descriptors, results show that first, there is no meaningful link between the uncertainty of the spatial discretization and catchment size, as spatio-temporal discretization uncertainty can be seen across different catchment sizes. Second, the temporal scale plays only a minor role in determining the uncertainty related to spatial discretization. Third, the more physically representative a model is, the more sensitive it is to changes in spatial resolution. Finally, the uncertainty related to model parameters is dominant larger than that of catchment descriptors for most of the catchments. Yet, there are exceptions for which a change in spatio-temporal resolution can alter the distribution of state and flux variables, change the hydrologic response of the catchments, and cause large uncertainties.