Articles | Volume 18, issue 23
https://doi.org/10.5194/gmd-18-9827-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-9827-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
LISFLOOD-FP 8.2: GPU-accelerated multiwavelet discontinuous Galerkin solver with dynamic resolution adaptivity for rapid, multiscale flood simulation
Alovya Ahmed Chowdhury
CORRESPONDING AUTHOR
Department of Civil and Structural Engineering, University of Sheffield, Sheffield, S10 2TN, United Kingdom
Georges Kesserwani
CORRESPONDING AUTHOR
Department of Civil and Structural Engineering, University of Sheffield, Sheffield, S10 2TN, United Kingdom
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Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
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This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Mohammad Shirvani and Georges Kesserwani
Nat. Hazards Earth Syst. Sci., 21, 3175–3198, https://doi.org/10.5194/nhess-21-3175-2021, https://doi.org/10.5194/nhess-21-3175-2021, 2021
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Flooding in and around urban hubs can stress people. Immediate evacuation is a usual countermeasure taken at the onset of a flooding event. The flood–pedestrian simulator simulates evacuation of people prior to and during a flood event. It provides information on the spatio-temporal responses of individuals, evacuation time, and possible safe destinations. This study demonstrates the simulator when considering more realistic human body and age characteristics and responses to floodwater.
James Shaw, Georges Kesserwani, Jeffrey Neal, Paul Bates, and Mohammad Kazem Sharifian
Geosci. Model Dev., 14, 3577–3602, https://doi.org/10.5194/gmd-14-3577-2021, https://doi.org/10.5194/gmd-14-3577-2021, 2021
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LISFLOOD-FP has been extended with new shallow-water solvers – DG2 and FV1 – for modelling all types of slow- or fast-moving waves over any smooth or rough surface. Using GPU parallelisation, FV1 is faster than the simpler ACC solver on grids with millions of elements. The DG2 solver is notably effective on coarse grids where river channels are hard to capture, improving predicted river levels and flood water depths. This marks a new step towards real-world DG2 flood inundation modelling.
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.
Blaise, S., St-Cyr, A., Mavriplis, D., and Lockwood, B.: Discontinuous Galerkin unsteady discrete adjoint method for real-time efficient tsunami simulations, J. Comput. Phys., 232, 416–430, https://doi.org/10.1016/j.jcp.2012.08.022, 2013.
Bonev, B., Hesthaven, J. S., Giraldo, F. X., and Kopera, M. A.: Discontinuous Galerkin scheme for the spherical shallow water equations with applications to tsunami modeling and prediction, J. Comput. Phys., 362, 425–448, https://doi.org/10.1016/j.jcp.2018.02.008, 2018.
Borrero, J. C., LeVeque, R. J., Greer, S. D., O'Neill, S., and Davis, B. N.: Observations and modelling of tsunami currents at the port of Tauranga, New Zealand, in: Australasian Coasts & Ports Conference 2015: 22nd Australasian Coastal and Ocean Engineering Conference and the 15th Australasian Port and Harbour Conference, ISBN 9781922107794, 2015.
Castro, C. E., Behrens, J., and Pelties, C.: Optimization of the ADER-DG method in GPU applied to linear hyperbolic PDEs, Int. J. Numer. Methods Fluids, 81, 195–219, https://doi.org/10.1002/fld.4179, 2016.
Caviedes-Voullième, D. and Kesserwani, G.: Benchmarking a multiresolution discontinuous Galerkin shallow water model: Implications for computational hydraulics, Adv. Water Resour., 86, 14–31, https://doi.org/10.1016/J.ADVWATRES.2015.09.016, 2015.
Caviedes-Voullième, D., Gerhard, N., Sikstel, A., and Müller, S.: Multiwavelet-based mesh adaptivity with Discontinuous Galerkin schemes: Exploring 2D shallow water problems, Adv. Water Resour., 138, 103559, https://doi.org/10.1016/J.ADVWATRES.2020.103559, 2020.
Chowdhury, A.: Dataset for the paper “LISFLOOD-FP 8.2: GPU-accelerated multiwavelet discontinuous Galerkin solver with dynamic resolution adaptivity for rapid, multiscale flood simulation” [data set], Zenodo, https://doi.org/10.5281/zenodo.13909072, 2024.
Chowdhury, A.: Simulation run guide for the GPU-MWDG2 solver in LISFLOOD-FP 8.2, Zenodo [video], https://doi.org/10.5281/zenodo.15851523, 2025.
Chowdhury, A. A., Kesserwani, G., Rougé, C., and Richmond, P.: GPU-parallelisation of Haar wavelet-based grid resolution adaptation for fast finite volume modelling: application to shallow water flows, Journal of Hydroinformatics, 25, 1210–1234, https://doi.org/10.2166/hydro.2023.154, 2023.
CUDA C++ Programming Guide: https://docs.nvidia.com/cuda/cuda-c-programming-guide/, last access: 23 March 2023.
de la Asunción, M. and Castro, M. J.: Simulation of tsunamis generated by landslides using adaptive mesh refinement on GPU, J. Comput. Phys., 345, 91–110, https://doi.org/10.1016/J.JCP.2017.05.016, 2017.
Ferreira, C. R. and Bader, M.: Load Balancing and Patch-Based Parallel Adaptive Mesh Refinement for Tsunami Simulation on Heterogeneous Platforms Using Xeon Phi Coprocessors, in: Proceedings of the Platform for Advanced Scientific Computing Conference, 12, https://doi.org/10.1145/3093172.3093237, 2017.
Gao, S., Collecutt, G., Syme, W. J., and Ryan, P.: High resolution numerical modelling of tsunami inundation using quadtree method and GPU acceleration, in: Proceedings of the 22nd IAHR-APD Congress (IAHR-APD 2020), ISBN 9781713827030, https://www.iahr.org/library/info?pid=7668 (last access: 30 November 2025), 2020.
Gerhard, N. and Müller, S.: Adaptive multiresolution discontinuous Galerkin schemes for conservation laws: multi-dimensional case, Computational and Applied Mathematics, 35, 321–349, https://doi.org/10.1007/s40314-014-0134-y, 2016.
Gerhard, N., Caviedes-Voullième, D., Müller, S., and Kesserwani, G.: Multiwavelet-based grid adaptation with discontinuous Galerkin schemes for shallow water equations, J. Comput. Phys., 301, 265–288, https://doi.org/10.1016/J.JCP.2015.08.030, 2015.
Hajihassanpour, M., Bonev, B., and Hesthaven, J. S.: A comparative study of earthquake source models in high-order accurate tsunami simulations, Ocean Model., 141, 101429, https://doi.org/10.1016/j.ocemod.2019.101429, 2019.
Hajihassanpour, M., Kesserwani, G., Pettersson, P., and Bellos, V.: Sampling-Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice, Water Resources Research, 59, e2022WR034011 https://doi.org/10.1029/2022WR034011, 2023.
Hunter, N. M., Horritt, M. S., Bates, P. D., Wilson, M. D., and Werner, M. G. F.: An adaptive time step solution for raster-based storage cell modelling of floodplain inundation, Adv. Water Resour., 28, 975–991, https://doi.org/10.1016/j.advwatres.2005.03.007, 2005.
Kesserwani, G.: Topography discretization techniques for Godunov-type shallow water numerical models: a comparative study, Journal of Hydraulic Research, 51, 351–367, https://doi.org/10.1080/00221686.2013.796574, 2013.
Kesserwani, G. and Liang, Q.: Dynamically adaptive grid based discontinuous Galerkin shallow water model, Adv. Water Resour., 37, 23–39, https://doi.org/10.1016/J.ADVWATRES.2011.11.006, 2012.
Kesserwani, G. and Liang, Q.: RKDG2 shallow-water solver on non-uniform grids with local time steps: Application to 1D and 2D hydrodynamics, Applied Mathematical Modelling, 39, 1317–1340, https://doi.org/10.1016/j.apm.2014.08.009, 2015.
Kesserwani, G. and Sharifian, M. K.: (Multi)wavelets increase both accuracy and efficiency of standard Godunov-type hydrodynamic models: Robust 2D approaches, Adv. Water Resour., 144, 103693, https://doi.org/10.1016/J.ADVWATRES.2020.103693, 2020.
Kesserwani, G. and Sharifian, M. K.: (Multi)wavelet-based Godunov-type simulators of flood inundation: Static versus dynamic adaptivity, Adv. Water Resour., 171, 104357, https://doi.org/10.1016/j.advwatres.2022.104357, 2023.
Kesserwani, G. and Wang, Y.: Discontinuous Galerkin flood model formulation: Luxury or necessity?, Water Resour. Res., 50, 6522–6541, https://doi.org/10.1002/2013WR014906, 2014.
Kesserwani, G., Caviedes-Voullième, D., Gerhard, N., and Müller, S.: Multiwavelet discontinuous Galerkin h-adaptive shallow water model, Comput. Methods Appl. Mech. Eng., 294, 56–71, https://doi.org/10.1016/j.cma.2015.05.016, 2015.
Kesserwani, G., Ayog, J. L., and Bau, D.: Discontinuous Galerkin formulation for 2D hydrodynamic modelling: Trade-offs between theoretical complexity and practical convenience, Comput. Methods Appl. Mech. Eng., 342, 710–741, https://doi.org/10.1016/J.CMA.2018.08.003, 2018.
Kesserwani, G., Shaw, J., Sharifian, M. K., Bau, D., Keylock, C. J., Bates, P. D., and Ryan, J. K.: (Multi)wavelets increase both accuracy and efficiency of standard Godunov-type hydrodynamic models, Adv. Water Resour., 129, 31–55, https://doi.org/10.1016/J.ADVWATRES.2019.04.019, 2019.
Kevlahan, N. K.-R. and Lemarié, F.: wavetrisk-2.1: an adaptive dynamical core for ocean modelling, Geosci. Model Dev., 15, 6521–6539, https://doi.org/10.5194/gmd-15-6521-2022, 2022.
Lee, H. S.: Tsunami run-up modeling with adaptive mesh refinement method: A case study for Monai Village run-up experiment, in: Proceedings of the 20th IAHR-APD Congress (Colombo, 2016), https://www.iahr.org/library/infor?pid=9478 (last access: 30 November 2025), 2016.
LeVeque, R. J., George, D. L., and Berger, M. J.: Tsunami modelling with adaptively refined finite volume methods, Acta Numerica, 20, 211–289, https://doi.org/10.1017/S0962492911000043, 2011.
Liang, Q., Hou, J., and Amouzgar, R.: Simulation of Tsunami Propagation Using Adaptive Cartesian Grids, Coastal Engineering Journal, 57, 1550016, https://doi.org/10.1142/S0578563415500163, 2015.
LISFLOOD-FP developers: LISFLOOD-FP v8.2 hydrodynamic model (8.2), Zenodo [code], https://doi.org/10.5281/zenodo.13121102, 2024.
Liu, P. L. F., Yeh, H., and Synolakis, C. E.: Advanced Numerical Models for Simulating Tsunami Waves and Runup, World Scientific, 344 pp., https://doi.org/10.1142/6226, 2008.
Lynett, P. J., Gately, K., Wilson, R., Montoya, L., Arcas, D., Aytore, B., Bai, Y., Bricker, J. D., Castro, M. J., Cheung, K. F., David, C. G., Dogan, G. G., Escalante, C., González-Vida, J. M., Grilli, S. T., Heitmann, T. W., Horrillo, J., Kânoğlu, U., Kian, R., Kirby, J. T., Li, W., Macías, J., Nicolsky, D. J., Ortega, S., Pampell-Manis, A., Park, Y. S., Roeber, V., Sharghivand, N., Shelby, M., Shi, F., Tehranirad, B., Tolkova, E., Thio, H. K., Velioğlu, D., Yalçıner, A. C., Yamazaki, Y., Zaytsev, A., and Zhang, Y. J.: Inter-model analysis of tsunami-induced coastal currents, Ocean Model., 114, 14–32, https://doi.org/10.1016/j.ocemod.2017.04.003, 2017.
Macías, J., Castro, M., Ortega, S., Escalante Sánchez, C., and González Vida, J.: Tsunami currents benchmarking results for Tsunami-HySEA, 115–135, https://doi.org/10.13140/RG.2.2.22999.47527, 2015.
Macías, J., Castro, M. J., and Escalante, C.: Performance assessment of the Tsunami-HySEA model for NTHMP tsunami currents benchmarking. Laboratory data, Coastal Engineering, 158, 103667, https://doi.org/10.1016/J.COASTALENG.2020.103667, 2020a.
Macías, J., Castro, M. J., Ortega, S., and González-Vida, J. M.: Performance assessment of Tsunami-HySEA model for NTHMP tsunami currents benchmarking. Field cases, Ocean Model., 152, 101645, https://doi.org/10.1016/j.ocemod.2020.101645, 2020b.
Matsuyama, M. and Tanaka, H.: An experimental study of the highest run-up height in the 1993 Hokkaido Nansei-Oki earthquake tsunami, in: Proceedings of the National Tsunami Hazard Mitigation Program Review and International Tsunami Symposium (ITS), 879–889, https://www.researchgate.net/publication/236646065_An_experimental_study_of_the_highest_run-up_height_in_the_1993_Hokkaido_Nansei-Oki_earthquake_tsunami (last access: 30 November 2025), 2001.
Nandi, S. and Reddy, M. J.: An integrated approach to streamflow estimation and flood inundation mapping using VIC, RAPID and LISFLOOD-FP, J. Hydrol., 610, 127842, https://doi.org/10.1016/j.jhydrol.2022.127842, 2022.
NVIDIA Corporation: CUDA C++ Programming Guidehttps://docs.nvidia.com/cuda/cuda-c-programming-guide/ (last access: 30 November 2025), 2024.
Park, H., Cox, D. T., Lynett, P. J., Wiebe, D. M., and Shin, S.: Tsunami inundation modeling in constructed environments: A physical and numerical comparison of free-surface elevation, velocity, and momentum flux, Coastal Engineering, 79, 9–21, https://doi.org/10.1016/j.coastaleng.2013.04.002, 2013.
Park, J., Yuk, J.-H., Joo, W., and Lee, H. S.: Wave Run-up Modeling with Adaptive Mesh Refinement (AMR) Method in the Busan Marine City during Typhoon Chaba (1618), J. Coast. Res., 91, 56, https://doi.org/10.2112/SI91-012.1, 2019.
Popinet, S.: Quadtree-adaptive tsunami modelling, Ocean Dyn., 61, 1261–1285, https://doi.org/10.1007/s10236-011-0438-z, 2011.
Popinet, S.: Adaptive modelling of long-distance wave propagation and fine-scale flooding during the Tohoku tsunami, Nat. Hazards Earth Syst. Sci., 12, 1213–1227, https://doi.org/10.5194/nhess-12-1213-2012, 2012.
Popinet, S. and Rickard, G.: A tree-based solver for adaptive ocean modelling, Ocean Model., 16, 224–249, https://doi.org/10.1016/j.ocemod.2006.10.002, 2007.
Qin, X., Motley, M., LeVeque, R., Gonzalez, F., and Mueller, K.: A comparison of a two-dimensional depth-averaged flow model and a three-dimensional RANS model for predicting tsunami inundation and fluid forces, Nat. Hazards Earth Syst. Sci., 18, 2489–2506, https://doi.org/10.5194/nhess-18-2489-2018, 2018.
Rannabauer, L., Dumbser, M., and Bader, M.: ADER-DG with a-posteriori finite-volume limiting to simulate tsunamis in a parallel adaptive mesh refinement framework, Comput. Fluids, 173, 299–306, https://doi.org/10.1016/j.compfluid.2018.01.031, 2018.
Sharifian, M. K., Kesserwani, G., and Hassanzadeh, Y.: A discontinuous Galerkin approach for conservative modeling of fully nonlinear and weakly dispersive wave transformations, Ocean Model., 125, 61–79, https://doi.org/10.1016/j.ocemod.2018.03.006, 2018.
Sharifian, M. K., Hassanzadeh, Y., Kesserwani, G., and Shaw, J.: Performance study of the multiwavelet discontinuous Galerkin approach for solving the Green-Naghdi equations, Int. J. Numer. Methods Fluids, 90, 501–521, https://doi.org/10.1002/fld.4732, 2019.
Sharifian, M. K., Kesserwani, G., Chowdhury, A. A., Neal, J., and Bates, P.: LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations, Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, 2023.
Shaw, J., Kesserwani, G., Neal, J., Bates, P., and Sharifian, M. K.: LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs, Geosci. Model Dev., 14, 3577–3602, https://doi.org/10.5194/gmd-14-3577-2021, 2021.
Sun, X., Kesserwani, G., Sharifian, M. K., and Stovin, V.: Simulation of laminar to transitional wakes past cylinders with a discontinuous Galerkin inviscid shallow water model, Journal of Hydraulic Research, 61, 631–650, https://doi.org/10.1080/00221686.2023.2239750, 2023.
Velioglu Sogut, D. and Yalciner, A. C.: Performance Comparison of NAMI DANCE and FLOW-3D® Models in Tsunami Propagation, Inundation and Currents using NTHMP Benchmark Problems, Pure Appl. Geophys., 176, 3115–3153, https://doi.org/10.1007/s00024-018-1907-9, 2019.
Violeau, D., Ata, R., Benoit, M., Joly, A., Abadie, S., Clous, L., Martin Medina, M., Morichon, D., Chicheportiche, J., Le Gal, M., Gailler, A., Hébert, H., Imbert, D., Kazolea, M., Ricchiuto, M., Le Roy, S., Pedreros, R., Rousseau, M., Pons, K., Marcer, R., Journeau, C., and Silva Jacinto, R.: A database of validation cases for tsunami numerical modelling, in: Proceedings of the 4th IAHR Europe Congress (IAHR Europe Congress, Liège), CRC Press, ISBN 9781138029774, https://www.iahr.org/library/infor?pid=10483 (last access: 30 November 2025), 2016.
Zeng, Z., Wang, Z., and Lai, C.: Simulation Performance Evaluation and Uncertainty Analysis on a Coupled Inundation Model Combining SWMM and WCA2D, International Journal of Disaster Risk Science, 13, 448–464, https://doi.org/10.1007/s13753-022-00416-3, 2022.
Ziliani, L., Surian, N., Botter, G., and Mao, L.: Assessment of the geomorphic effectiveness of controlled floods in a braided river using a reduced-complexity numerical model, Hydrol. Earth Syst. Sci., 24, 3229–3250, https://doi.org/10.5194/hess-24-3229-2020, 2020.
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.
LISFLOOD-FP 8.2 is a framework for running real-world simulations of rapid, multiscale floods...