Topographic data is a fundamental input to flood hazard models and controls the quality of the outputs. However, open-access global digital elevation models (DEMs) are dated and limited to 30 m resolution, which hinders modelling efforts in urban or topographically complex environments. We used the flood prone and expanding city of Kathmandu, Nepal, to evaluate the impact of topographic data source and resolution on flood model outputs. All DEMs evaluated featured spatially correlated topographic sinks with depths exceeding 20 m that required hydrological conditioning before being used in flood modelling. Incomplete hydrological conditioning appeared related to the overestimation of flood extent and therefore limited agreement when comparing a global 90 m resolution flood hazard model with a bespoke city-scale model at 10 m resolution (F1 score = 0.40). Instead, we found that the height above nearest drainage (HAND) metric was better able to replicate the higher resolution flood map as an indicator of flood susceptibility requiring only topographic information as an input. We also found that the computationally efficient FastFlood model was able to match the inundation extent (F1 score = 0.79) and flood depths (mean absolute error and root mean square error of 0.46 and 0.76 m respectively) of a published 10 m physics-based flood hazard model whilst requiring 212 times less computation time. Our analysis demonstrated that mapping city-scale flood inundation required hydrologically conditioned high-resolution topographic data but not physically complex flood models, highlighting the need for greater availability of high quality open access topographic data.