Abstract

We present SWEpy, an open-source Python finite volume (FV) software for solving the shallow water equations (SWEs) on unstructured triangular meshes. The framework combines flexibility and high performance through GPU acceleration and a well-balanced, positivity-preserving, higher-order central-upwind (CU) scheme. These features are required for simulation of hydrodynamic phenomena such as tsunami propagation, flooding, and dam-break flows in complex and large geometries. To reduce numerical diffusion, a phenomenon commonly encountered in FV methods, SWEpy incorporates a second-order WENO reconstruction together with a third-order strong stability-preserving Runge-Kutta time integration scheme. These numerical components are particularly well-suited for far-field tsunami modeling, where minimizing artificial diffusion is essential to accurately preserve wave amplitude, phase, and dispersion over long propagation distances. The performance, stability, and accuracy of SWEpy are validated using canonical benchmarks, including Synolakis' conical island and Bryson's flow over a Gaussian bump. Its capabilities are further demonstrated through large-scale simulations of the 1959 Malpasset Dam failure and the 2010 Maule tsunami, highlighting its effectiveness in realistic scenarios. Overall, these results show that SWEpy framework delivers high-resolution solutions on consumer-grade hardware, providing a user-friendly and computationally efficient platform for both research applications and operational forecasting.