Low-complexity terrain-based models are increasingly utilized for their rapid simulation time and low data requirements. The Height Above Nearest Drainage terrain index coupled with Synthetic Rating Curves (HAND-SRC) emerges as a prominent model for mapping floods from a digital elevation model (DEM). However, the DEM requirements for its implementation remain unclear in many geographic settings. In this study, we evaluate the terrain conditions necessary for HAND-SRC flood mapping in rural low-relief terrain. This was investigated in the Ostouane River, Northern Lebanon, where an intensive field investigation was conducted to collect a high-resolution DEM (25 cm), bathymetric cross-sections, and a crowdsourced dataset reconstituting the January 2019 flood event. Specifically, we scrutinize both the terrain’s geometric representation and its resolution. An adapted hydro-conditioning process was introduced to assess flood mapping performance. This process integrated surveyed and theoretical bathymetry, enforced drainage into both bathymetry and floodplain and removed levees in an unorthodox approach. The generated terrain was then tested after resampling it into coarser DEM resolutions. The hydro-conditioned terrain with integrated surveyed bathymetry demonstrated reliable flood mapping accuracy against crowdsourced data (CSI = 0.64 and RMSE = 0.54 m) and HEC-RAS extents (CSI = 0.66). Introducing a theoretical trapezoidal bathymetry based on hydraulic geometry power laws produced improved metrics due to enhanced drainage continuity between the channel and floodplains. Analysis of the resampled terrains highlights a random loss of terrain convergence and geometric accuracy, disrupting the model’s implementation and accuracy at coarser resolutions (> 1 m). Overall, the comprehensive hydro-conditioning approach allows the model to depict the full inundation extent and retain the topographic accuracy in the HAND index raster. A sufficient grid resolution that maintains terrain convergence and drainage continuity is essential to overcome the challenges of low-relief topography. While the model admits limitations in cell-by-cell flood depth estimations, we suggest that it can be highly beneficial for rapid and accurate flood mapping.