Spatial resolution strongly influences how flood hazards and exposed assets are represented, yet this source of uncertainty is often overlooked in impact-oriented flood studies. This study examines how hydraulic model resolution and exposure-data aggregation affect flood hazard classification and estimates of exposed buildings and population in the downstream reach of the Upper Narmada Basin, India. Two-dimensional flood simulations were performed for the August 2022 and 2020 events and evaluated using radar-derived inundation extent and observed discharge data. A 30 m hydraulic model was used as the reference and compared with coarser 140 m and 280 m resolutions. Building exposure was derived from combined OpenStreetMap and Open Buildings datasets, while population exposure was estimated using WorldPop data. Coarser resolutions systematically overestimated flood extent, hazard zones, and exposed elements. The overpredicted inundation area increased from 51.30 km² at 30 m to 58.45 km² at 140 m and 73.36 km² at 280 m. Similarly, the estimated number of affected buildings increased from 894 (2.5 times) at 30 m to 3051(8.8 times) at 140 m and 11 537 (33 times) at 280 m, while the affected population increased from 1612 to 5982 and 24 733, respectively. These results show that coarse-resolution hazard and exposure data can substantially inflate exposure estimates and may distort emergency planning, infrastructure prioritization, and resource allocation. Spatial resolution should therefore be treated as a key source of uncertainty in flood hazard and exposure assessment, and model/data resolution should be aligned with the research question and intended decision scale.