CaMa-Flood-GPU: A GPU-based hydrodynamic model implementation for scalable global simulations

Abstract. Floods are among the costliest natural hazards, demanding scalable models to simulate river and floodplain dynamics at a global scale. The Catchment-based Macro-scale Floodplain (CaMa-Flood) model is a leading system for this purpose, but its CPU-based implementation is computationally demanding. This paper introduces CaMa-Flood-GPU, a fundamental refactoring of the model optimized for Graphics Processing Unit (GPU) architectures. We systematically reinterpreted its core algorithms—including river routing on irregular networks, runoff interpolation, and water depth diagnosis—into highly parallel, GPU-native operations. Key challenges were addressed by implementing scatter-add for flux updates, sparse matrix multiplication for runoff mapping, and branchless kernels for floodplain dynamics, all while preserving the original model's physical fidelity. Implemented in Python with Triton kernels and PyTorch, CaMa-Flood-GPU achieves multi-GPU scalability through optimized communication patterns that minimize synchronization overhead. The software adopts a modular structure with optional components (e.g., bifurcation routing, adaptive time stepping) and flexible data interfaces. Benchmarks demonstrate an order-of-magnitude speedup over a 192-core CPU baseline and near-linear scaling on multiple GPUs, with negligible numerical differences from the original model. This performance leap reduces simulation times for high-resolution global runs from days to hours, enabling larger ensembles and rapid scenario analysis. By providing a reproducible and efficient tool, CaMa-Flood-GPU lowers the barrier for adopting GPU acceleration in large-scale hydrology. The released implementation provides a reproducible reference for future method development.