In agricultural areas, Tile Drains (TDs) are often installed by farmers in order to drain any excess of water accumulating in crop fields. This anthropogenic modification to the land surface can have strong effects on streamflow in these areas. Here, a simple technique was employed in order to partly account for the effect that TDs can have on streamflow simulated with the GEM-Hydro physically-based and distributed hydrologic model developed at Environment and Climate Change Canada (ECCC). The technique consists in significantly increasing the horizontal hydraulic conductivity of the soil layer generally containing the TDs, in the land-surface scheme of GEM-Hydro, for the part of the grid-cell that contains TDs. To do so, a multiplying coefficient obtained through automatic calibration was used to increase the appropriate soil layer’s horizontal hydraulic conductivity. The part of the grid-cell containing Tile Drains was obtained from different databases depending on the country (i.e., US or Canada) or the province (Ontario or Quebec). Moreover, a similar strategy was followed to represent the effect that agricultural ploughing practices can have on streamflow, by increasing the model’s vertical hydraulic conductivity for the superficial soil layers. The methodology employed allowed to significantly increase the performance of GEM-Hydro streamflow simulations in the watershed of the Laurentian Great-Lakes when compared to the default (current) version of the model, while maintaining similar performances for other hydrologic variables simulated with GEM-Hydro, such as evapotranspiration, and soil moisture and surface temperature simulations, when comparing for example to the recent Global Land Evaporation Amsterdam Model (GLEAM version 3.5b) reference dataset. These findings are promising in the view of developing land-surface schemes that can be applied both for two-way coupling with atmospheric models and for environmental and hydrologic applications.

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.