Land Surface Models (LSMs) are commonly subject to various sources of uncertainties including parametric uncertainty and forcing uncertainty. However, there is a lack of understanding of how forcing uncertainties interact with parametric uncertainties to influence LSMs simulated key terrestrial water budget components. To address this research need, we use the Community Land Model version 5 (CLM5), the land component of the Community Earth System Model, at a spatial scale of 12-km to simulate the land surface and subsurface processes for 464 watersheds, selected from the Catchment Attributes for Large-Sample Studies (CAMELS) dataset to be representative of physiographic and climatic gradients across the conterminous United States (CONUS). For each watershed, CLM5 experiments are driven by five most commonly used, gridded forcing datasets: NLDAS-2, Livneh, PRISM, Daymet, and WRF-based dynamical downscaling of ERA5 reanalysis data, in combination with a large ensemble (> 1300) of key CLM5 hydrologic parameters to represent parametric uncertainty. We develop 25 hydrological signatures to diagnose the statistical and dynamic properties of CLM5 simulated water budget components, and disentangle the contribution of forcing selection to parametric uncertainty for hydrologic signatures including extreme events (drought and flood) and seasonal water balance. Preliminary results suggest that uncertainty in lower streamflow quantiles is dominated by parametric uncertainty, while forcing uncertainty contributes more to higher streamflow quantiles. The results from this study support the emerging needs of understanding and incorporating uncertainties in Earth system models in hydrologic applications particularly for hazard and risk assessments under extreme conditions.