Modeling river hydrodynamics across continental-scale basins is challenging due to their inherently multiscale nature. On one hand, we must account for the water budget along river systems that extend over 1,000 km. On the other hand, water movement within channels and floodplains is governed by topographic features smaller than 100 meters. The global river model CaMa-Flood addresses this complexity by employing the Catchment-based Macro-scale Floodplain modeling approach (CMF approach). This method approximates the relationship between water volume, flood extent, and water depth through sub-grid scale parameterizations. These parameters, derived from high-resolution satellite-based digital elevation models (DEMs) and hydrography datasets, enable realistic simulation of river discharge and flood stages -without explicitly resolving small-scale floodplain dynamics. To further accelerate simulations, recent developments in CaMa-Flood have introduced several performance optimizations, including MPI/OpenMP parallelization, SIMD vectorization, sparse matrix implementation, and a GPU-enabled Python version. These enhancements make the model more suitable for large-scale and near-real-time applications such as global flood monitoring and climate impact assessment.

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