Accurate soil moisture and streamflow data are an aspirational need of many hydrologically-relevant fields. Model simulated soil moisture and streamflow hold promise but numerical models require calibration prior to application to ensure sufficient model performance. Manual or automated calibration methods require iterative model runs and hence are computationally expensive. In this study, we leverage the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to help constrain soil parameter uncertainties in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. After calibration, WRF-Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in-situ soil moisture observing stations. Compared to four well-established soil moisture datasets including Soil Moisture Active Passive Level 4 data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-calibrated WRF-Hydro produces the highest mean KGE (0.67) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases especially in the case where the model domain is set up with an SSURGO-informed total soil thickness. Both the magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean Nash-Sutcliffe Efficiency across seven of the nine gages increases from 0.19 in default WRF-Hydro to 0.63 after calibration. Our soil data-informed calibration approach, which is transferable to other spatially-distributed hydrological models, uses open-access data and non-iterative steps to improve model performance and is thus operationally and computationally attractive.