{
"id": "epxqax5h0p",
"slug": "inundation-model",
"title": "Inundation model",
"updated": "2026-10-15",
"topic_path": [
{
"id": "physical",
"label": "Physical world and mathematics",
"api_url": "https://www.edgepedia.org/api/v1/topics/physical"
},
{
"id": "physical.earth",
"label": "Earth sciences",
"api_url": "https://www.edgepedia.org/api/v1/topics/physical.earth"
},
{
"id": "physical.earth.water",
"label": "Hydrology and ocean science",
"api_url": "https://www.edgepedia.org/api/v1/topics/physical.earth.water"
},
{
"id": "physical.earth.water.hydrology",
"label": "Hydrology",
"api_url": "https://www.edgepedia.org/api/v1/topics/physical.earth.water.hydrology"
},
{
"id": "physical.earth.water.hydrology.modeling-software",
"label": "Hydrological modeling and software",
"api_url": "https://www.edgepedia.org/api/v1/topics/physical.earth.water.hydrology.modeling-software"
}
],
"geo": [
{
"id": "geo.nongeo.t1946.physical.earth.water",
"label": "Non-geographic · 1946 to 2000: Hydrology and ocean science",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical.earth.water",
"path": [
{
"id": "geo.nongeo",
"label": "Non-geographic",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo"
},
{
"id": "geo.nongeo.t1946",
"label": "Non-geographic · 1946 to 2000",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946"
},
{
"id": "geo.nongeo.t1946.physical",
"label": "Physical world and mathematics",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical"
},
{
"id": "geo.nongeo.t1946.physical.earth",
"label": "Earth sciences",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical.earth"
},
{
"id": "geo.nongeo.t1946.physical.earth.water",
"label": "Hydrology and ocean science",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical.earth.water"
}
]
},
{
"id": "geo.nongeo.t2001.physical.earth.water",
"label": "Non-geographic · 2001 to 2020: Hydrology and ocean science",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.physical.earth.water",
"path": [
{
"id": "geo.nongeo",
"label": "Non-geographic",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo"
},
{
"id": "geo.nongeo.t2001",
"label": "Non-geographic · 2001 to 2020",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001"
},
{
"id": "geo.nongeo.t2001.physical",
"label": "Physical world and mathematics",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.physical"
},
{
"id": "geo.nongeo.t2001.physical.earth",
"label": "Earth sciences",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.physical.earth"
},
{
"id": "geo.nongeo.t2001.physical.earth.water",
"label": "Hydrology and ocean science",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.physical.earth.water"
}
]
}
],
"excerpt": "An inundation model is a computational model that simulates how floodwater spreads over land, predicting water depth and flooded extent from river, coastal, or rainfall events.",
"snippet": "An inundation model is a computational model that simulates how floodwater spreads over land, predicting water depth and flooded extent from river, coastal, or rainfall events.",
"node": "physical.earth.water.hydrology.modeling-software",
"markdown": "# Inundation model\n\nAn inundation model is a computational model that simulates how floodwater spreads over land, predicting time-varying water depth and inundated extent from river, coastal, or rainfall events. A typical code outputs raster maps of water depth in every grid square at each time step and, for fluvial flooding, stage and discharge hydrographs at the reach outlet.[1](https://www.bristol.ac.uk/media-library/sites/geography/migrated/documents/lisflood-manual-v2.6.2.pdf) Combining appropriate physics, efficient numerical algorithms, high-performance computing, and new big-data sources has extended these models from a handful of well-studied locations toward fluid-mechanics models of flooding over the entire terrestrial land surface.[2](https://doi.org/10.1146/annurev-fluid-030121-113138)\n\n| Key fact | Detail |\n|---|---|\n| Outputs | Raster water depth per cell per time step; stage and discharge hydrographs at the reach outlet [1](https://www.bristol.ac.uk/media-library/sites/geography/migrated/documents/lisflood-manual-v2.6.2.pdf) |\n| Equation sets in common use | Saint-Venant, diffusion wave, kinematic wave, and dynamic wave [3](https://ijneam.unimap.edu.my/images/PDF/ISSTE2022/Vol_15_SI_March_2022_81-100.pdf) |\n| Floodplain resolution guidance | About 25–100 m grid cells; DEM vertical accuracy generally below ±0.25 m [1](https://www.bristol.ac.uk/media-library/sites/geography/migrated/documents/lisflood-manual-v2.6.2.pdf) |\n| Inertial time-step gain | Stable steps 1–3 orders of magnitude larger than diffusive storage-cell models [4](https://doi.org/10.1016/j.jhydrol.2010.03.027) |\n| Typical extent skill | F of 81% (LISFLOOD-FP) and 78% (HEC-RAS) on the Secchia River floodplain [5](https://cris.unibo.it/retrieve/649073/2019_HSJ_Shustikova_et_al_HECRAS_LISFLOODFP_HSJ-2019-0168-OpenAccess.pdf) |\n| Modern speed | RIM2D produced Berlin-wide forecasts 347× faster than real time at 10 m resolution [6](https://nhess.copernicus.org/articles/26/85/2026/nhess-26-85-2026.html) |\n| Operational forecasting | The US National Water Model forecasts flooding in more than 2 million river reaches [7](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022RG000788) |\n\n## How it works\n\n**Governing equations.** [Flood routing](https://www.edgepedia.org/flood-routing) models solve part or all of the 1D Saint-Venant continuity and momentum equations in the longitudinal direction; quasi-2D and 2D models additionally solve the lateral direction.[8](https://www.sciencedirect.com/science/article/abs/pii/S0022169416000378) The 1D system is\n\n\\[ \\frac{\\partial A}{\\partial t} + \\frac{\\partial Q}{\\partial x} = 0 \\]\n\n\\[ \\frac{1}{A}\\frac{\\partial Q}{\\partial t} + \\frac{1}{A}\\frac{\\partial \\left(Q^{2}/A\\right)}{\\partial x} + g\\frac{\\partial w}{\\partial x} = g\\left(S_{o} - S_{e}\\right) \\]\n\nwhere \\( Q \\) is discharge, \\( A \\) wetted area, \\( g \\) gravity acceleration, \\( w \\) water depth, \\( S_{o} \\) bed slope, and \\( S_{e} \\) energy slope.[8](https://www.sciencedirect.com/science/article/abs/pii/S0022169416000378) Four equation sets dominate inundation modeling: Saint-Venant, diffusion wave, kinematic wave, and dynamic wave.[3](https://ijneam.unimap.edu.my/images/PDF/ISSTE2022/Vol_15_SI_March_2022_81-100.pdf) Excluding the inertial terms creates the diffusion wave model of the original LISFLOOD formulation.[9](https://etheses.whiterose.ac.uk/id/eprint/7493/1/TDMW_Phd_SystematicAnalysisofUncertaintyinFloodModels.pdf) Because the inertial formulation's stable time step scales with \\( 1/D_{x} \\) rather than \\( (1/D_{x})^{2} \\), it permits stable steps 1–3 orders of magnitude larger for typical cell sizes than diffusive storage-cell models.[4](https://doi.org/10.1016/j.jhydrol.2010.03.027)\n\n**Solver choices.** HEC-RAS 2D offers three equation sets: 2D Diffusion Wave (the default), SWE-ELM with Eulerian-Lagrangian advection, and SWE-EM with Eulerian advection, which is more momentum conservative but may need smaller time steps.[10](https://www.hec.usace.army.mil/confluence/rasdocs/r2dum/latest/running-a-model-with-2d-flow-areas/2d-computation-options-and-tolerances) The diffusion wave set runs faster and is inherently more stable, but full shallow water equations are needed for rapid gate closures, wave run-up on walls or around bridge piers and buildings, and super-elevation around tight bends.[11](https://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS_2D_Users_Manual_v6.6.pdf) HEC-RAS solves these equations with an implicit finite-volume scheme under a CFL condition.[12](https://mdpi-res.com/d_attachment/water/water-12-02326/article_deploy/water-12-02326-v2.pdf?version=1597906381) The LISFLOOD-FP framework carries a diffusive wave solver, a local inertial solver, a first-order finite volume solver, and a second-order discontinuous Galerkin (DG2) solver, with mass conserved to machine precision.[13](https://gmd.copernicus.org/articles/18/9827/2025/gmd-18-9827-2025.html)\n\n## How it is done\n\n**Inputs.** A standard fluvial setup needs a DEM, upstream discharge hydrographs, and downstream normal-depth boundary conditions.[14](https://iwaponline.com/wpt/article/18/4/831/94502/HEC-RAS-2D-modeling-for-flood-inundation-mapping-a) Manning's roughness is assigned per land cover class; a Berlin pluvial study used \\( n \\) = 0.04 for vegetation, 0.03 for water bodies, 0.1 for forest, 0.025 for built-up areas, and 0.035 for bare soil and agricultural land, classified from 2020 [Sentinel-2](https://www.edgepedia.org/sentinel-2) imagery.[6](https://nhess.copernicus.org/articles/26/85/2026/nhess-26-85-2026.html) The computation interval must satisfy the Courant condition; reducing it from 1 minute to 1 second raised simulation time by 40–50%.[14](https://iwaponline.com/wpt/article/18/4/831/94502/HEC-RAS-2D-modeling-for-flood-inundation-mapping-a) Subgrid schemes decouple terrain resolution from mesh size: HEC-RAS 5.0.7 stores hydraulic radius, volume, and cross-sectional property tables computed from a high-resolution DEM, so a 2 m DEM can drive 25 m mesh cells.[12](https://mdpi-res.com/d_attachment/water/water-12-02326/article_deploy/water-12-02326-v2.pdf?version=1597906381)\n\n**Validation and skill metrics.** LISFLOOD-FP was validated on the Meuse, Thames, Severn, and Imera reaches against flood extent maps from air photography, satellite SAR, or ground survey.[1](https://www.bristol.ac.uk/media-library/sites/geography/migrated/documents/lisflood-manual-v2.6.2.pdf) The 2000 Meuse application simulated a 35 km reach for the January 1995 flood and correctly predicted 81.9% of inundated and non-inundated areas, against 69.5% for a planar free surface, and 63.8% for a 2D finite element code.[15](https://doi.org/10.1016/s0022-1694%2800%2900278-x) On the Secchia floodplain, validated against 46 surveyed high-water marks with RMSE, LISFLOOD-FP reached a maximum F of 81% and HEC-RAS 78%.[5](https://cris.unibo.it/retrieve/649073/2019_HSJ_Shustikova_et_al_HECRAS_LISFLOODFP_HSJ-2019-0168-OpenAccess.pdf) Extent performance sits close to the error of the observed satellite or air photo data, which are assumed to classify only 90% of the true flooded area correctly.[1](https://www.bristol.ac.uk/media-library/sites/geography/migrated/documents/lisflood-manual-v2.6.2.pdf) For short-term pluvial events, calibration data are often absent and blind, uncalibrated simulation is common practice.[6](https://nhess.copernicus.org/articles/26/85/2026/nhess-26-85-2026.html)\n\n## Origin\n\nStorage-cell precursors came first: early storage-cell floodplain methods discretized floodplains into irregular polygonal compartments of \\( 10^{0} \\)–\\( 10^{1} \\) km² with fluxes from weir or Manning uniform flow formulae.[4](https://doi.org/10.1016/j.jhydrol.2010.03.027) Bates and De Roo (2000) note that a similar raster method exists.[15](https://doi.org/10.1016/s0022-1694%2800%2900278-x)\n\nA two-dimensional finite-element model for river flow inundation by [P. D. Bates](https://www.edgepedia.org/paul-david-bates) and M. G. Anderson appeared in Proceedings of the Royal Society Series A in 1993.[16](https://doi.org/10.1098/rspa.1993.0029) The raster-based model of Bates and De Roo, published in Journal of Hydrology in 2000, couples 1D kinematic wave channel routing to a 2D diffusion wave floodplain on a raster DEM and was written in the PCRaster dynamic modeling language.[15](https://doi.org/10.1016/s0022-1694%2800%2900278-x) The code was later re-written in C++ for efficiency and then as an adaptive time step code.[4](https://doi.org/10.1016/j.jhydrol.2010.03.027) A coastal extension by Bates and colleagues, published in Coastal Engineering in 2005, allowed regional-scale simulations within minutes or a few hours on a desktop PC.[17](https://doi.org/10.1016/j.coastaleng.2005.06.001) The inertial formulation by Bates, Horritt, and Fewtrell followed in Journal of Hydrology in 2010.[4](https://doi.org/10.1016/j.jhydrol.2010.03.027) Sampson and colleagues published a high-resolution global flood hazard model in Water Resources Research in 2015.[18](https://doi.org/10.1002/2015wr016954) The DG2 solver by Shaw and colleagues appeared in Geoscientific Model Development in 2021.[19](https://doi.org/10.5194/gmd-14-3577-2021)\n\n## Variants\n\n**Named codes.** A review catalog lists LISFLOOD-FP, HEC-RAS, MIKE (11, 21, FLOOD), ISIS, SWMM, RRI, RMA-2, TELEMAC-2D, FloodMap-HydroInundation2D, SOBEK, and FLO-2D for 1D, 2D, and coupled 1D-2D flood nowcasting.[3](https://ijneam.unimap.edu.my/images/PDF/ISSTE2022/Vol_15_SI_March_2022_81-100.pdf) HEC-RAS, a widely used model associated with the US Army Corps of Engineers, gained 2D and coupled 1D-2D capability in version 5.0.[3](https://ijneam.unimap.edu.my/images/PDF/ISSTE2022/Vol_15_SI_March_2022_81-100.pdf) The same storage-cell blueprint underlies commercial packages including JFLOW by JBA Ltd., FlowRoute by Ambiental, and the RMS Ltd UK Flood Risk Model.[4](https://doi.org/10.1016/j.jhydrol.2010.03.027)\n\n**Speed versus physics.** The original raster model needs roughly 100 floating point operations per cell per time step, against about 4000 per node per step in a typical 2D finite element code.[15](https://doi.org/10.1016/s0022-1694%2800%2900278-x) The inertial formulation's maximum reported speed-up is 1120.[4](https://doi.org/10.1016/j.jhydrol.2010.03.027) In a benchmark sharing all subroutines except the flow equation, only the full shallow water LISFLOOD-Roe reproduced flow over a bump correctly; the inertial model overtopped it only with increased roughness, and the diffusive model did not overtop it.[20](https://eprints.whiterose.ac.uk/id/eprint/79279/1/Neal_revised.pdf)\n\n**Machine-learning emulators.** FloodGAN, an image-to-image GAN, ran 106 times faster than 2D hydraulic models but was trained only on synthetic data and limited to maximum extent and depth for up to 1-hour rainfall.[21](https://www.mdpi.com/2073-4441/15/3/566) Simplified and data-driven methods have not been rigorously tested on out-of-sample events unlike their training data.[7](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022RG000788)\n\n## Applications\n\nUse falls into three classes: hazard estimation for a given return period, near-real-time inundation forecasting, and [hindcasting](https://www.edgepedia.org/hindcasting) of past events for calibration and validation.[7](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022RG000788) Operationally, the US National Water Model forecasts flooding in more than 2 million river reaches.[7](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022RG000788) RIM2D, a multi-GPU CUDA Fortran implementation of the local inertia equations, produced Berlin-wide pluvial forecasts 347× faster than real time at 10 m resolution.[6](https://nhess.copernicus.org/articles/26/85/2026/nhess-26-85-2026.html) On the 2021 Ahr valley flood, SERGHEI (full shallow water, multi-GPU) and RIM2D (local inertia, single GPU) both ran at least 99 times faster than the event duration.[22](https://nhess.copernicus.org/articles/24/2857/2024/nhess-24-2857-2024.html) At global scale, CaMa-Flood discretizes basins into irregular unit-catchments of roughly 5–50 km from MERIT Hydro, splits water between channel and floodplain storage, and routes channels with the local inertial momentum equation.[23](https://gmd.copernicus.org/articles/19/5623/2026/)\n\n## Limitations and alternatives\n\n**Topography dominates.** Differences between model results are assigned mostly to the quality of topographic and input data and less to the complexity of the phenomenon.[8](https://www.sciencedirect.com/science/article/abs/pii/S0022169416000378) Changing resolution from 10 m to 100 m altered maximum water surface elevation by up to 20 cm (5% of depth) and peak velocity arrival times by up to 25 minutes, so topographic sampling had a similar or greater effect than model formulation.[20](https://eprints.whiterose.ac.uk/id/eprint/79279/1/Neal_revised.pdf) Hyper-resolution grids below 2 m are not necessary when the model has subgrid capability, with 2 m and 10 m grids performing comparably, but for accurate flood depths DEM or model resolution is the single most critical factor.[24](https://www.sciencedirect.com/science/article/abs/pii/S0022169425019183)\n\n**Parameter and boundary uncertainty.** On the Secchia floodplain, F varied between 73% and 77% as floodplain roughness ranged from 0.030 to 0.200 \\( \\mathrm{m^{-1/3}\\,s} \\).[5](https://cris.unibo.it/retrieve/649073/2019_HSJ_Shustikova_et_al_HECRAS_LISFLOODFP_HSJ-2019-0168-OpenAccess.pdf) The inertial model performs worse than the diffusive model at low Manning's n of 0.01, with small instabilities.[4](https://doi.org/10.1016/j.jhydrol.2010.03.027) The hydrological boundary condition is among the most uncertain inputs; GEV-fitted 95% confidence intervals on peak inflows span 62.79–172.02 \\( \\mathrm{m^{3}/s} \\) at Dyce and 269.23–759.63 \\( \\mathrm{m^{3}/s} \\) at Glasgow.[25](https://www.mdpi.com/2073-4441/16/9/1309)\n\n**Artefacts and alternatives.** HEC-RAS 5.0 shows a \"leaking\" effect when cell faces are not aligned with elevated linear features, producing hydraulically disconnected flooded areas; LISFLOOD-FP's D4 raster routing can restrain water propagation over low-elevation linear features such as rivers and canals.[5](https://cris.unibo.it/retrieve/649073/2019_HSJ_Shustikova_et_al_HECRAS_LISFLOODFP_HSJ-2019-0168-OpenAccess.pdf) Mass-conservative local-inertial schemes without subgrid wetting tend to under-spread flood extent relative to observations.[26](https://arxiv.org/html/2607.09614) Building-footprint treatment in models (non-inundated blocks) differs from the bare-earth interpolation used in observationally reconstructed maps, hindering comparability.[24](https://www.sciencedirect.com/science/article/abs/pii/S0022169425019183) Flexible-mesh models are superior to regular grids in accuracy and computational time because the mesh aligns with the floodway geometry.[21](https://www.mdpi.com/2073-4441/15/3/566)\n\n## References\n\n1. [LISFLOOD-FP user manual version 2.6.2 (University of Bristol)](https://www.bristol.ac.uk/media-library/sites/geography/migrated/documents/lisflood-manual-v2.6.2.pdf)\n2. [Paul D. Bates (2021). Flood Inundation Prediction. Annual Review of Fluid Mechanics.](https://doi.org/10.1146/annurev-fluid-030121-113138)\n3. [Advance Flood Inundation Model Toward Flood Nowcasting: A Review](https://ijneam.unimap.edu.my/images/PDF/ISSTE2022/Vol_15_SI_March_2022_81-100.pdf)\n4. [Paul D. Bates, Matthew S. Horritt, Timothy J. Fewtrell (2010). A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. Journal of Hydrology.](https://doi.org/10.1016/j.jhydrol.2010.03.027)\n5. [Comparing 2D capabilities of HEC-RAS and LISFLOOD-FP on complex topography (Shustikova et al., Hydrological Sciences Journal)](https://cris.unibo.it/retrieve/649073/2019_HSJ_Shustikova_et_al_HECRAS_LISFLOODFP_HSJ-2019-0168-OpenAccess.pdf)\n6. [Enabling real-time high-resolution flood forecasting for the entire state of Berlin through multi-GPU accelerated physics-based modeling (NHESS, 2026)](https://nhess.copernicus.org/articles/26/85/2026/nhess-26-85-2026.html)\n7. [Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling (Reviews of Geophysics)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022RG000788)\n8. [Comparative evaluation of 1D and quasi-2D hydraulic models based on benchmark and real-world applications for uncertainty assessment in flood mapping (Dimitriadis et al., J. Hydrol. 2016)](https://www.sciencedirect.com/science/article/abs/pii/S0022169416000378)\n9. [Systematic Analysis of Uncertainty in Flood Inundation Modelling (PhD thesis, White Rose eTheses)](https://etheses.whiterose.ac.uk/id/eprint/7493/1/TDMW_Phd_SystematicAnalysisofUncertaintyinFloodModels.pdf)\n10. [2D Computation Options and Tolerances (HEC-RAS 2D User's Manual, Confluence)](https://www.hec.usace.army.mil/confluence/rasdocs/r2dum/latest/running-a-model-with-2d-flow-areas/2d-computation-options-and-tolerances)\n11. [HEC-RAS 2D User's Manual v6.6](https://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS_2D_Users_Manual_v6.6.pdf)\n12. [Assessment of 2-D HEC-RAS (5.0.7) for rainfall-runoff simulations at the basin scale (Water, 2020)](https://mdpi-res.com/d_attachment/water/water-12-02326/article_deploy/water-12-02326-v2.pdf?version=1597906381)\n13. [LISFLOOD-FP 8.2: GPU-accelerated multiwavelet discontinuous Galerkin solver with dynamic resolution adaptivity (GMD, 2025)](https://gmd.copernicus.org/articles/18/9827/2025/gmd-18-9827-2025.html)\n14. [HEC-RAS 2D modeling for flood inundation mapping: a case study of the Krishna River Basin (Water Practice & Technology, 2023)](https://iwaponline.com/wpt/article/18/4/831/94502/HEC-RAS-2D-modeling-for-flood-inundation-mapping-a)\n15. [A simple raster-based model for flood inundation simulation (Journal of Hydrology, 2000)](https://doi.org/10.1016/s0022-1694%2800%2900278-x)\n16. [P. D. Bates, M. G. Anderson (1993). A two-dimensional finite-element model for river flow inundation. Proceedings of the Royal Society of London Series A Mathematical and Physical Sciences.](https://doi.org/10.1098/rspa.1993.0029)\n17. [Paul D. Bates and colleagues (2005). Simplified two-dimensional numerical modelling of coastal flooding and example applications. Coastal Engineering.](https://doi.org/10.1016/j.coastaleng.2005.06.001)\n18. [Christopher C. Sampson and colleagues (2015). A high-resolution global flood hazard model. Water Resources Research.](https://doi.org/10.1002/2015wr016954)\n19. [James Shaw and colleagues (2021). LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs. Geoscientific model development.](https://doi.org/10.5194/gmd-14-3577-2021)\n20. [How much physical complexity is needed to model flood inundation? (Neal et al.)](https://eprints.whiterose.ac.uk/id/eprint/79279/1/Neal_revised.pdf)\n21. [A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling (Water, 2023)](https://www.mdpi.com/2073-4441/15/3/566)\n22. [Are 2D shallow-water solvers fast enough for early flood warning? A comparative assessment on the 2021 Ahr valley flood event (NHESS, 2024)](https://nhess.copernicus.org/articles/24/2857/2024/nhess-24-2857-2024.html)\n23. [CaMa-Flood-GPU: a GPU-based hydrodynamic model implementation for scalable global simulations (GMD, 2026)](https://gmd.copernicus.org/articles/19/5623/2026/)\n24. [The mirage of the silver bullet: Exploring the limitations of high-resolution data in flood model validation (Journal of Hydrology, 2025)](https://www.sciencedirect.com/science/article/abs/pii/S0022169425019183)\n25. [Advanced Uncertainty Quantification for Flood Inundation Modelling (Water, MDPI, 2024)](https://www.mdpi.com/2073-4441/16/9/1309)\n26. [Inunda: A GPU-Native, Differentiable Solver for High-Resolution Flood Inundation Modeling (arXiv preprint)](https://arxiv.org/html/2607.09614)\n\n---\n*Topic: Encyclopedia › Physical world and mathematics › Earth sciences › Hydrology and ocean science › Hydrology › Hydrological modeling and software*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n",
"same_as": [],
"url": "https://www.edgepedia.org/inundation-model",
"markdown_url": "https://www.edgepedia.org/inundation-model.md",
"license": {
"name": "Edgepedia Community License 1.0",
"url": "https://www.edgepedia.org/edgepedia/license",
"summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.",
"spdx": "LicenseRef-Edgepedia-Community-1.0"
},
"credit": "\"Inundation model\", Edgepedia (EdgeChat), https://www.edgepedia.org/inundation-model. Edgepedia Community License 1.0.",
"credit_md": "\"[Inundation model](https://www.edgepedia.org/inundation-model)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/inundation-model](https://www.edgepedia.org/inundation-model). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).",
"credit_html": "\"Inundation model\", Edgepedia (EdgeChat), https://www.edgepedia.org/inundation-model. Edgepedia Community License 1.0.",
"speakable": "An inundation model is a computational model that simulates how floodwater spreads over land, predicting water depth and flooded extent from river, coastal, or rainfall events."
}