Study Region: The Washington D.C area. Study Focus: This work investigates the potential of using satellite-based precipitation products in a hydrological model to estimate water quality indicators in the Occoquan Watershed, located in the suburban Washington D.0 area. Three (3) satellite-based precipitation products based on different retrieval algorithms (the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis, TMPA 3B42-V7; the Climate Prediction Center's CMORPH product; and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System, PERSIANN - CCS) are compared to gauge-based records over a 5-year period across the study region. The 3 satellite-based precipitation products and the gaugebased dataset are used as input to the Hydrologic Simulation Program FORTRAN (HSPF) hydrology and water quality model. Each satellite precipitation-forced simulation is compared to the reference model simulation forced with the gauge-based observations, in terms of streamflow and water quality indicators, i.e., stream temperature (TW), total suspended solids (TSS), dissolved oxygen (DO), and biological oxygen demand (BOD). New Hydrological Insights for the Region: Results indicate that the spatiotemporal variability observed in the satellite-based precipitation products has a quantifiable impact on both modeled streamflow and water quality indicators. All 3 satellite products present moderate agreements with the reference precipitation and simulation; CMORPH presenting the best overall performance followed closely by TMPA, and PERSIANN presenting a comparatively inferior performance in terms of correlation, root-mean-square error and bias for streamflow and water quality indicators, such as TW, TSS, DO and BOD concentrations.