LISFLOOD-FP 8.2: GPU-accelerated multiwavelet discontinuous Galerkin solver with dynamic resolution adaptivity for rapid, multiscale flood simulation Article Swipe
Alovya Ahmed Chowdhury
,
Georges Kesserwani
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/gmd-18-9827-2025
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/gmd-18-9827-2025
The second-order discontinuous Galerkin (DG2) solver of the two-dimensional shallow water equations in the raster-based LISFLOOD-FP 8.0 hydrodynamic modelling framework is mostly suited for predicting small-scale transients that emerge in rapid, multiscale floods caused by impact events like tsunamis. However, this DG2 solver can only be used for simulations on a uniform grid where it may yield inefficient runtimes even when using its graphics processing unit (GPU) parallelised version (GPU-DG2). To boost efficiency, the new LISFLOOD-FP 8.2 version integrates GPU parallelised dynamic (in time) grid resolution adaptivity of multiwavelets (MW) with the DG2 solver (GPU-MWDG2). The GPU-MWDG2 solver performs dyadic grid refinement, starting from a single grid cell, with a maximum refinement level, L, based on the resolution of the Digital Elevation Model (DEM). Furthermore, the dynamic GPU-MWDG2 adaptivity is driven by one error threshold, ε, against normalised details of all prognostic variables. Its accuracy and efficiency, as well as the practical validity of recommended ε choices between 10−4 and 10−3, are assessed for four laboratory/field-scale benchmarks of tsunami-induced flooding with different impact event complexities (i.e. single- vs. multi-peaked) and L values. Rigorous accuracy and efficiency metrics consistently show that GPU-MWDG2 simulations with ε = 10−3 preserve the predictions of the GPU-DG2 simulation on the uniform DEM grid, whereas ε = 10−4 may slightly improve velocity-related predictions. Efficiency-wise, GPU-MWDG2 yields considerable speedups from L ≥ 10 – due to its scalability on the GPU with increasing L – which can be around 2.0-to-4.5-fold. Generally, the bigger the L ≥ 10, the lower the event complexity over the simulated duration, and the closer the ε to 10−3, the larger the GPU-MWDG2 speedups over GPU-DG2. The LISFLOOD-FP 8.2 code is open source, under the GPL v3.0 licence, as well as the simulated benchmarks' set-up files and datasets, with a video tutorial and further documentation on https://www.seamlesswave.com/Adaptive (last access: 6 July 2025).
Related Topics
Concepts
Solver
Computer science
Grid
Computational science
Scalability
Parallel computing
Algorithm
Flooding (psychology)
Event (particle physics)
Shallow water equations
Discontinuous Galerkin method
Resolution (logic)
Graphics processing unit
CUDA
Mathematical optimization
Graphics
Transient (computer programming)
Speedup
Simulation
Temporal resolution
Supercomputer
Real-time computing
Sequence (biology)
Unstructured grid
Computer simulation
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/gmd-18-9827-2025
- OA Status
- gold
- References
- 50
- OpenAlex ID
- https://openalex.org/W7113913875
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7113913875Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/gmd-18-9827-2025Digital Object Identifier
- Title
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LISFLOOD-FP 8.2: GPU-accelerated multiwavelet discontinuous Galerkin solver with dynamic resolution adaptivity for rapid, multiscale flood simulationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-12-10Full publication date if available
- Authors
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Alovya Ahmed Chowdhury, Georges KesserwaniList of authors in order
- Landing page
-
https://doi.org/10.5194/gmd-18-9827-2025Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.5194/gmd-18-9827-2025Direct OA link when available
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Solver, Computer science, Grid, Computational science, Scalability, Parallel computing, Algorithm, Flooding (psychology), Event (particle physics), Shallow water equations, Discontinuous Galerkin method, Resolution (logic), Graphics processing unit, CUDA, Mathematical optimization, Graphics, Transient (computer programming), Speedup, Simulation, Temporal resolution, Supercomputer, Real-time computing, Sequence (biology), Unstructured grid, Computer simulationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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50Number of works referenced by this work
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| abstract_inverted_index.The | 1, 96, 274 |
| abstract_inverted_index.all | 141 |
| abstract_inverted_index.and | 146, 160, 180, 185, 260, 294, 300 |
| abstract_inverted_index.are | 162 |
| abstract_inverted_index.can | 44, 240 |
| abstract_inverted_index.due | 228 |
| abstract_inverted_index.for | 24, 48, 164 |
| abstract_inverted_index.its | 63, 230 |
| abstract_inverted_index.may | 56, 213 |
| abstract_inverted_index.new | 75 |
| abstract_inverted_index.one | 133 |
| abstract_inverted_index.the | 8, 14, 74, 92, 117, 120, 126, 151, 198, 201, 205, 233, 245, 247, 251, 253, 257, 261, 263, 267, 269, 282, 289 |
| abstract_inverted_index.vs. | 178 |
| abstract_inverted_index.ε, | 136 |
| abstract_inverted_index.– | 227, 238 |
| abstract_inverted_index.≥ | 225, 249 |
| abstract_inverted_index.(MW) | 90 |
| abstract_inverted_index.July | 308 |
| abstract_inverted_index.code | 277 |
| abstract_inverted_index.even | 60 |
| abstract_inverted_index.four | 165 |
| abstract_inverted_index.from | 104, 223 |
| abstract_inverted_index.grid | 53, 85, 101, 107 |
| abstract_inverted_index.like | 38 |
| abstract_inverted_index.only | 45 |
| abstract_inverted_index.open | 279 |
| abstract_inverted_index.over | 256, 272 |
| abstract_inverted_index.show | 189 |
| abstract_inverted_index.that | 28, 190 |
| abstract_inverted_index.this | 41 |
| abstract_inverted_index.unit | 66 |
| abstract_inverted_index.used | 47 |
| abstract_inverted_index.v3.0 | 284 |
| abstract_inverted_index.well | 149, 287 |
| abstract_inverted_index.when | 61 |
| abstract_inverted_index.with | 91, 109, 171, 193, 235, 296 |
| abstract_inverted_index.(DG2) | 5 |
| abstract_inverted_index.(GPU) | 67 |
| abstract_inverted_index.(i.e. | 176 |
| abstract_inverted_index.(last | 305 |
| abstract_inverted_index.Model | 123 |
| abstract_inverted_index.based | 115 |
| abstract_inverted_index.boost | 72 |
| abstract_inverted_index.cell, | 108 |
| abstract_inverted_index.error | 134 |
| abstract_inverted_index.event | 174, 254 |
| abstract_inverted_index.files | 293 |
| abstract_inverted_index.grid, | 208 |
| abstract_inverted_index.lower | 252 |
| abstract_inverted_index.time) | 84 |
| abstract_inverted_index.under | 281 |
| abstract_inverted_index.using | 62 |
| abstract_inverted_index.video | 298 |
| abstract_inverted_index.water | 11 |
| abstract_inverted_index.where | 54 |
| abstract_inverted_index.which | 239 |
| abstract_inverted_index.yield | 57 |
| abstract_inverted_index.(DEM). | 124 |
| abstract_inverted_index.10−3 | 196 |
| abstract_inverted_index.10−4 | 159, 212 |
| abstract_inverted_index.2025). | 309 |
| abstract_inverted_index.around | 242 |
| abstract_inverted_index.bigger | 246 |
| abstract_inverted_index.caused | 34 |
| abstract_inverted_index.closer | 262 |
| abstract_inverted_index.driven | 131 |
| abstract_inverted_index.dyadic | 100 |
| abstract_inverted_index.emerge | 29 |
| abstract_inverted_index.events | 37 |
| abstract_inverted_index.floods | 33 |
| abstract_inverted_index.impact | 36, 173 |
| abstract_inverted_index.larger | 268 |
| abstract_inverted_index.level, | 113 |
| abstract_inverted_index.mostly | 22 |
| abstract_inverted_index.rapid, | 31 |
| abstract_inverted_index.set-up | 292 |
| abstract_inverted_index.single | 106 |
| abstract_inverted_index.solver | 6, 43, 94, 98 |
| abstract_inverted_index.suited | 23 |
| abstract_inverted_index.yields | 220 |
| abstract_inverted_index.10−3, | 161, 266 |
| abstract_inverted_index.Digital | 121 |
| abstract_inverted_index.GPU-DG2 | 202 |
| abstract_inverted_index.access: | 306 |
| abstract_inverted_index.against | 137 |
| abstract_inverted_index.between | 158 |
| abstract_inverted_index.choices | 157 |
| abstract_inverted_index.details | 139 |
| abstract_inverted_index.dynamic | 82, 127 |
| abstract_inverted_index.further | 301 |
| abstract_inverted_index.improve | 215 |
| abstract_inverted_index.maximum | 111 |
| abstract_inverted_index.metrics | 187 |
| abstract_inverted_index.shallow | 10 |
| abstract_inverted_index.single- | 177 |
| abstract_inverted_index.source, | 280 |
| abstract_inverted_index.uniform | 52, 206 |
| abstract_inverted_index.values. | 182 |
| abstract_inverted_index.version | 69, 78 |
| abstract_inverted_index.whereas | 209 |
| abstract_inverted_index.GPU-DG2. | 273 |
| abstract_inverted_index.Galerkin | 4 |
| abstract_inverted_index.However, | 40 |
| abstract_inverted_index.Rigorous | 183 |
| abstract_inverted_index.accuracy | 145, 184 |
| abstract_inverted_index.assessed | 163 |
| abstract_inverted_index.flooding | 170 |
| abstract_inverted_index.graphics | 64 |
| abstract_inverted_index.licence, | 285 |
| abstract_inverted_index.performs | 99 |
| abstract_inverted_index.preserve | 197 |
| abstract_inverted_index.runtimes | 59 |
| abstract_inverted_index.slightly | 214 |
| abstract_inverted_index.speedups | 222, 271 |
| abstract_inverted_index.starting | 103 |
| abstract_inverted_index.tutorial | 299 |
| abstract_inverted_index.validity | 153 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Elevation | 122 |
| abstract_inverted_index.GPU-MWDG2 | 97, 128, 191, 219, 270 |
| abstract_inverted_index.datasets, | 295 |
| abstract_inverted_index.different | 172 |
| abstract_inverted_index.duration, | 259 |
| abstract_inverted_index.equations | 12 |
| abstract_inverted_index.framework | 20 |
| abstract_inverted_index.modelling | 19 |
| abstract_inverted_index.practical | 152 |
| abstract_inverted_index.simulated | 258, 290 |
| abstract_inverted_index.tsunamis. | 39 |
| abstract_inverted_index.(GPU-DG2). | 70 |
| abstract_inverted_index.Generally, | 244 |
| abstract_inverted_index.adaptivity | 87, 129 |
| abstract_inverted_index.benchmarks | 167 |
| abstract_inverted_index.complexity | 255 |
| abstract_inverted_index.efficiency | 186 |
| abstract_inverted_index.increasing | 236 |
| abstract_inverted_index.integrates | 79 |
| abstract_inverted_index.multiscale | 32 |
| abstract_inverted_index.normalised | 138 |
| abstract_inverted_index.predicting | 25 |
| abstract_inverted_index.processing | 65 |
| abstract_inverted_index.prognostic | 142 |
| abstract_inverted_index.refinement | 112 |
| abstract_inverted_index.resolution | 86, 118 |
| abstract_inverted_index.simulation | 203 |
| abstract_inverted_index.threshold, | 135 |
| abstract_inverted_index.transients | 27 |
| abstract_inverted_index.variables. | 143 |
| abstract_inverted_index.LISFLOOD-FP | 16, 76, 275 |
| abstract_inverted_index.benchmarks' | 291 |
| abstract_inverted_index.efficiency, | 73, 147 |
| abstract_inverted_index.inefficient | 58 |
| abstract_inverted_index.predictions | 199 |
| abstract_inverted_index.recommended | 155 |
| abstract_inverted_index.refinement, | 102 |
| abstract_inverted_index.scalability | 231 |
| abstract_inverted_index.simulations | 49, 192 |
| abstract_inverted_index.small-scale | 26 |
| abstract_inverted_index.(GPU-MWDG2). | 95 |
| abstract_inverted_index.Furthermore, | 125 |
| abstract_inverted_index.complexities | 175 |
| abstract_inverted_index.considerable | 221 |
| abstract_inverted_index.consistently | 188 |
| abstract_inverted_index.hydrodynamic | 18 |
| abstract_inverted_index.parallelised | 68, 81 |
| abstract_inverted_index.predictions. | 217 |
| abstract_inverted_index.raster-based | 15 |
| abstract_inverted_index.second-order | 2 |
| abstract_inverted_index.discontinuous | 3 |
| abstract_inverted_index.documentation | 302 |
| abstract_inverted_index.multi-peaked) | 179 |
| abstract_inverted_index.multiwavelets | 89 |
| abstract_inverted_index.tsunami-induced | 169 |
| abstract_inverted_index.two-dimensional | 9 |
| abstract_inverted_index.2.0-to-4.5-fold. | 243 |
| abstract_inverted_index.Efficiency-wise, | 218 |
| abstract_inverted_index.velocity-related | 216 |
| abstract_inverted_index.laboratory/field-scale | 166 |
| abstract_inverted_index.https://www.seamlesswave.com/Adaptive | 304 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.82352463 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |