This work describes the implementation of the distributed GEM-Hydro runoff modeling platform, developed at Environment and Climate Change Canada (ECCC) over the last decade. The latest version of GEM-Hydro combines the SVS (Soil, Vegetation and Snow) land-surface scheme and the WATROUTE routing scheme in order to provide streamflow predictions on a gridded river network. SVS is designed to be two-way coupled to the GEM (Global Environmental Multi-scale) atmospheric model exploited by ECCC for operational weather and environmental forecasting. Although SVS has been shown to accurately track soil moisture during the warm season, it has never been evaluated before for hydrological prediction. This paper presents a first evaluation of its ability to simulate streamflow for all major rivers flowing into Lake Ontario. The skill level of GEM-Hydro is assessed by comparing the quality of simulated flows to that of two established hydrological models, MESH and WATFLOOD, which share the same routing scheme (WATROUTE) but rely on different land–surface schemes. All models are calibrated using the same meteorological forcings, objective function, calibration algorithm, and watershed delineation. Results show that GEM-Hydro performs well and is competitive with MESH and WATFLOOD. A computationally efficient strategy is proposed to calibrate the land-surface model of GEM-Hydro: a simple unit hydrograph is used for routing instead of its standard distributed routing component. The distributed routing part of the model can then be run in a second step to estimate streamflow everywhere inside the domain. Global and local calibration strategies are compared in order to estimate runoff for ungauged portions of the Lake Ontario watershed. Overall, streamflow predictions obtained using a global calibration strategy, in which a single parameter set is identified for the whole watershed of Lake Ontario, show skills comparable to the predictions based on local calibration. Hence, global calibration provides spatially consistent parameter values, robust performance at gauged locations, and reduces the complexity and computational burden of the calibration procedure. This work contributes to the Great Lakes Runoff Inter-comparison Project for Lake Ontario (GRIP-O) which aims at improving Lake Ontario basin runoff simulations by comparing different models using the same input forcings.