Uncertainty in operational weather forecasts, particularly in predicting storm location and precipitation patterns, presents challenges for flood inundation mapping. As hurricane forecasts evolve, spatial discrepancies in precipitation estimates lead to misalignment between forecasted and observed rainfall, affecting flood prediction accuracy. This study implements a storm-position–conditioned Quantitative Precipitation Forecast (QPF) displacement ensemble using HRRR forecasts and propagates those precipitation placement perturbations through a 2D hydrodynamic model (SFINCS) to quantify impacts on deterministic and probabilistic flood inundation mapping. The analysis, focused on Hurricane Beryl (July 2024), evaluates the impact of multiple storm location scenarios over 24-hour forecast periods for cycles generated in 6-hour intervals. QPF fields are used as input to the Super-Fast INundations of CoastS (SFINCS) model, applied to the highly urbanized central Houston area, to generate forecast maps of flood extent and water surface elevations. Results show that incorporating HRRR forecasts and spatial displacement of QPF fields improves the correlation with in-situ meteorological and water elevation observations, reduces biases in predicted flood depths, and better represents the spatial variability of flood impacts across the study area. The probabilistic flood inundation mapping generated through this approach highlights the benefits of ensemble-based forecast integration, providing decision-makers with a range of plausible flooding scenarios. By accounting for uncertainties in storm position, rainfall intensity, and timing, the methodology enhances the reliability and operational value of short-term flood forecasts in urban environments, supporting improved flood risk management and emergency preparedness….Read more