Articles | Volume 18, issue 23
https://doi.org/10.5194/gmd-18-9827-2025
https://doi.org/10.5194/gmd-18-9827-2025
Development and technical paper
 | 
10 Dec 2025
Development and technical paper |  | 10 Dec 2025

LISFLOOD-FP 8.2: GPU-accelerated multiwavelet discontinuous Galerkin solver with dynamic resolution adaptivity for rapid, multiscale flood simulation

Alovya Ahmed Chowdhury and Georges Kesserwani

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Cited articles

Arcos, M. and LeVeque, R.: Validating Velocities in the GeoClaw Tsunami Model using Observations Near Hawaii from the 2011 Tohoku Tsunami, Pure Appl. Geophys., 172, https://doi.org/10.1007/s00024-014-0980-y, 2014. 
Ayog, J. L., Kesserwani, G., and Baú, D.: Well-resolved velocity fields using discontinuous Galerkin shallow water, solutions, arXiv [physics.flu-dyn], https://doi.org/10.48550/arXiv.2104.11308, 2021. 
Bates, P. D., Horritt, M. S., and Fewtrell, T. J.: A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, J. Hydrol., 387, 33–45, https://doi.org/10.1016/j.jhydrol.2010.03.027, 2010. 
Berger, M. J., George, D. L., LeVeque, R. J., and Mandli, K. T.: The GeoClaw software for depth-averaged flows with adaptive refinement, Adv. Water Resour., 34, 1195–1206, https://doi.org/10.1016/J.ADVWATRES.2011.02.016, 2011. 
Blaise, S. and St-Cyr, A.: A Dynamic hp-Adaptive Discontinuous Galerkin Method for Shallow-Water Flows on the Sphere with Application to a Global Tsunami Simulation, Mon. Weather Rev., 140, 978–996, https://doi.org/10.1175/MWR-D-11-00038.1, 2012. 
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Short summary
LISFLOOD-FP 8.2 is a framework for running real-world simulations of rapid, multiscale floods driven by impact events like tsunamis. It builds on the LISFLOOD-FP 8.0 and 8.1 papers published in GMD: whereas LISFLOOD-FP 8.0 focussed on GPU-parallelisation, and LISFLOOD-FP 8.1 focussed on static mesh adaptivity of (multi)wavelets, LISFLOOD-FP 8.2 combines GPU (graphics processing unit)-parallelisation with multiwavelet dynamic mesh adaptivity to drastically reduce simulation runtimes, achieving up to a 4.5-fold speedup.
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