Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, we evaluated simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (1/16th degree) and fine (1/120th degree) spatial resolutions against observed streamflows from 217 watersheds. In particular, we examine the adequacy of VIC simulations in groundwater- versus runoff-dominated watersheds using a range of flow metrics relevant for water supply and aquatic habitat. These flow metrics were: a) total annual streamflow, b) total fall, winter, spring, and summer season streamflows, and c) 5th, 25th, 50th, 75th, and 95th flow percentiles. We also evaluated the effect of climate on model performance by comparing the observed and simulated streamflow sensitivities to temperature and precipitation. We evaluated model performance using four quantitative statistics: nonparametric rank correlation (ρ), Normalized Nash-Sutcliffe efficiency (NNSE), Root Mean Square Error (RMSE), and Percent BIAS (PBIAS). The VIC model captured the sensitivity of streamflow for temperature better than for precipitation, and was in poor agreement with the corresponding temperature and precipitation sensitivities derived from observed streamflow. The model was able to capture the hydrologic behavior of the study watersheds with reasonable accuracy. Both total streamflow and flow percentiles, however, are subject to strong systematic model bias. For example, summer streamflows were under predicted (PBIAS = -13%) in groundwater- and over predicted (PBIAS = 48%) in runoff-dominated watersheds. Similarly, 5th flow percentile was under predicted (PBIAS = -51%) in groundwater- and over predicted (PBIAS = 19%) in runoff-dominated watersheds. These results provide a foundation for improving model parameterization and calibration in ungaged basins.