A Raster–Vector Framework for Multi-Scale Hydrological–Hydraulic Modeling Across Large Domains

Abstract. Hydrological models are widely used for large-scale flood modeling but typically rely on simplified routing schemes and a grid-based discretization that poorly capture river flow dynamics and geometry. Conversely, hydraulic models enable more accurate representation of flow dynamics through physically based routing schemes and vector-based geometry, but their application to large domains is constrained by significant parametrization challenges. Bridging these limitations requires integrated hydrological-hydraulic (H&H) modeling approaches capable of reconciling the spatial scale and structural mismatch between the two models while ensuring seamless coupling and computational efficiency for large-scale applications. We present an integrated raster-vector H&H modeling framework that leverages sub-grid representation of both river networks and drainage areas derived from high-resolution topography. The framework is implemented within the open-source smash modeling platform and integrates an end-to-end workflow from DEM preprocessing to internally coupled H&H simulations. The framework was evaluated over the Garonne river basin (France, 50 100 km²) through one-directional coupling of a grid-based conceptual hydrological model with a vector-based 1D hydrodynamic model solving the zero-convective inertia approximation of the shallow water equations. Geometric preprocessing analysis performed across spatial resolutions ranging from 0.5 km to 10 km demonstrates that sub-grid information enable to maintain high spatial accuracy of DEM-derived sub-grid networks across scales in relation to the mapped hydrography, and reduces significantly catchment area errors compared to the default grid-based area delineation method. H&H simulations conducted at 1 km, 5 km, and 10 km without recalibration show robust preservation of flow timing across scales and demonstrate more stable streamflow bias across resolutions when using sub-grid drainage areas, while grid-based areas exhibit scale-dependent volume biases reflecting drainage area misrepresentation. The proposed H&H framework demonstrates scalable and efficient simulations at large domains within a unified modeling environment, offering promising perspectives for assimilation of multi-source water surface observations to infer key model parameters, addressing critical parametrization challenges in data-sparse regions.