Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2391-2023
© Author(s) 2023. 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-16-2391-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
05 May 2023
Model description paper |
| 05 May 2023
| 05 May 2023
LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations
Mohammad Kazem Sharifian
CORRESPONDING AUTHOR
Department of Civil and Structural Engineering, University of
Sheffield, Western Bank, Sheffield, UK
currently at: Risk Management Solutions, Inc., London, UK
Georges Kesserwani
CORRESPONDING AUTHOR
Department of Civil and Structural Engineering, University of
Sheffield, Western Bank, Sheffield, UK
Alovya Ahmed Chowdhury
Department of Civil and Structural Engineering, University of
Sheffield, Western Bank, Sheffield, UK
Jeffrey Neal
School of Geographical Sciences, University of Bristol, Bristol, UK
Paul Bates
School of Geographical Sciences, University of Bristol, Bristol, UK
Related authors
No articles found.
Hamish Wilkinson, Paul Bates, Chris Lucas, Niall Quinn, Ivan D. Haigh, Tom Collings, and Peter Watson
EGUsphere, https://doi.org/10.5194/egusphere-2026-781, https://doi.org/10.5194/egusphere-2026-781, 2026
Short summary
Short summary
Climate change increases storm surge risks, but traditional hydrodynamic models are too expensive for full risk assessment. Accordingly, we have developed a machine learning emulator 1500 times faster, with high accuracy (R2 = 0.91). Feature engineering is used to improve performance and increase generalisability across regions. Shapley values are used to interpret predictions, ensuring the model respects physical laws. This paper presents a robust framework for understanding storm surge risk.
Alovya Ahmed Chowdhury and Georges Kesserwani
Geosci. Model Dev., 18, 9827–9854, https://doi.org/10.5194/gmd-18-9827-2025, https://doi.org/10.5194/gmd-18-9827-2025, 2025
Short summary
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.
Thomas P. Collings, Callum J. R. Murphy-Barltrop, Conor Murphy, Ivan D. Haigh, Paul D. Bates, and Niall D. Quinn
Nat. Hazards Earth Syst. Sci., 25, 4545–4562, https://doi.org/10.5194/nhess-25-4545-2025, https://doi.org/10.5194/nhess-25-4545-2025, 2025
Short summary
Short summary
Determining the threshold above which events are considered extreme is an important consideration for many modelling procedures. We propose an extension of an existing data-driven method for automatic threshold selection. We test our approach on tide gauge records, and show that it outperforms existing techniques. This helps improve estimates of extreme sea levels, and we hope other researchers will use this method for other natural hazards.
Jeffrey Neal, Anthony Cooper, Stephen Chuter, Leanne Archer, Laura Devitt, Stephen Grey, Laurence Hawker, James Savage, Elisabeth Stephens, Calum Baugh, Tim Sumner, Katherine Marsden, and Tamara Janes
EGUsphere, https://doi.org/10.5194/egusphere-2025-3473, https://doi.org/10.5194/egusphere-2025-3473, 2025
Short summary
Short summary
East Africa has recently experienced a series of devastating tropical cyclone landfalls characterised by hundreds of fatalities, millions of displaced people and substantial economic damage. This paper describes an approach to forecasting the number of people directly exposed to flooding from tropical cyclones and documents experience gained communicating these forecasts to practitioners via emergency bulletins.
Songtang He, Zhenhong Shen, Jeffrey Neal, Zongji Yang, Jiangang Chen, Daojie Wang, Yujing Yang, Peng Zhao, Xudong Hu, Yongming Lin, Youtong Rong, Yanchen Zheng, Xiaoli Su, and Yong Kong
EGUsphere, https://doi.org/10.5194/egusphere-2025-3004, https://doi.org/10.5194/egusphere-2025-3004, 2025
Preprint archived
Short summary
Short summary
We explored why landslides still happen in areas with dense vegetation. Using data from a mountainous region in China, we combined large-scale mapping with detailed field analysis. We found that while plants can help prevent landslides, their weight and interaction with rainfall and wind can sometimes make slopes more unstable. This research highlights the complex role of vegetation and helps improve landslide prediction and prevention in green mountain areas.
Joshua Green, Ivan D. Haigh, Niall Quinn, Jeff Neal, Thomas Wahl, Melissa Wood, Dirk Eilander, Marleen de Ruiter, Philip Ward, and Paula Camus
Nat. Hazards Earth Syst. Sci., 25, 747–816, https://doi.org/10.5194/nhess-25-747-2025, https://doi.org/10.5194/nhess-25-747-2025, 2025
Short summary
Short summary
Compound flooding, involving the combination or successive occurrence of two or more flood drivers, can amplify flood impacts in coastal/estuarine regions. This paper reviews the practices, trends, methodologies, applications, and findings of coastal compound flooding literature at regional to global scales. We explore the types of compound flood events, their mechanistic processes, and the range of terminology. Lastly, this review highlights knowledge gaps and implications for future practices.
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
Short summary
Short summary
This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
Thomas P. Collings, Niall D. Quinn, Ivan D. Haigh, Joshua Green, Izzy Probyn, Hamish Wilkinson, Sanne Muis, William V. Sweet, and Paul D. Bates
Nat. Hazards Earth Syst. Sci., 24, 2403–2423, https://doi.org/10.5194/nhess-24-2403-2024, https://doi.org/10.5194/nhess-24-2403-2024, 2024
Short summary
Short summary
Coastal areas are at risk of flooding from rising sea levels and extreme weather events. This study applies a new approach to estimating the likelihood of coastal flooding around the world. The method uses data from observations and computer models to create a detailed map of where these coastal floods might occur. The approach can predict flooding in areas for which there are few or no data available. The results can be used to help prepare for and prevent this type of flooding.
Laurence Hawker, Jeffrey Neal, James Savage, Thomas Kirkpatrick, Rachel Lord, Yanos Zylberberg, Andre Groeger, Truong Dang Thuy, Sean Fox, Felix Agyemang, and Pham Khanh Nam
Nat. Hazards Earth Syst. Sci., 24, 539–566, https://doi.org/10.5194/nhess-24-539-2024, https://doi.org/10.5194/nhess-24-539-2024, 2024
Short summary
Short summary
We present a global flood model built using a new terrain data set and evaluated in the Central Highlands of Vietnam.
Leanne Archer, Jeffrey Neal, Paul Bates, Emily Vosper, Dereka Carroll, Jeison Sosa, and Daniel Mitchell
Nat. Hazards Earth Syst. Sci., 24, 375–396, https://doi.org/10.5194/nhess-24-375-2024, https://doi.org/10.5194/nhess-24-375-2024, 2024
Short summary
Short summary
We model hurricane-rainfall-driven flooding to assess how the number of people exposed to flooding changes in Puerto Rico under the 1.5 and 2 °C Paris Agreement goals. Our analysis suggests 8 %–10 % of the population is currently exposed to flooding on average every 5 years, increasing by 2 %–15 % and 1 %–20 % at 1.5 and 2 °C. This has implications for adaptation to more extreme flooding in Puerto Rico and demonstrates that 1.5 °C climate change carries a significant increase in risk.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
Short summary
Short summary
A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Paul D. Bates, James Savage, Oliver Wing, Niall Quinn, Christopher Sampson, Jeffrey Neal, and Andrew Smith
Nat. Hazards Earth Syst. Sci., 23, 891–908, https://doi.org/10.5194/nhess-23-891-2023, https://doi.org/10.5194/nhess-23-891-2023, 2023
Short summary
Short summary
We present and validate a model that simulates current and future flood risk for the UK at high resolution (~ 20–25 m). We show that UK flood losses were ~ 6 % greater in the climate of 2020 compared to recent historical values. The UK can keep any future increase to ~ 8 % if all countries implement their COP26 pledges and net-zero ambitions in full. However, if only the COP26 pledges are fulfilled, then UK flood losses increase by ~ 23 %; and potentially by ~ 37 % in a worst-case scenario.
Yinxue Liu, Paul D. Bates, and Jeffery C. Neal
Nat. Hazards Earth Syst. Sci., 23, 375–391, https://doi.org/10.5194/nhess-23-375-2023, https://doi.org/10.5194/nhess-23-375-2023, 2023
Short summary
Short summary
In this paper, we test two approaches for removing buildings and other above-ground objects from a state-of-the-art satellite photogrammetry topography product, ArcticDEM. Our best technique gives a 70 % reduction in vertical error, with an average difference of 1.02 m from a benchmark lidar for the city of Helsinki, Finland. When used in a simulation of rainfall-driven flooding, the bare-earth version of ArcticDEM yields a significant improvement in predicted inundation extent and water depth.
Maria Pregnolato, Andrew O. Winter, Dakota Mascarenas, Andrew D. Sen, Paul Bates, and Michael R. Motley
Nat. Hazards Earth Syst. Sci., 22, 1559–1576, https://doi.org/10.5194/nhess-22-1559-2022, https://doi.org/10.5194/nhess-22-1559-2022, 2022
Short summary
Short summary
The interaction of flow, structure and network is complex, and yet to be fully understood. This study aims to establish rigorous practices of computational fluid dynamics (CFD) for modelling hydrodynamic forces on inundated bridges, and understanding the consequences of such impacts on the surrounding network. The objectives of this study are to model hydrodynamic forces as the demand on the bridge structure, to advance a structural reliability and network-level analysis.
Gang Zhao, Paul Bates, Jeffrey Neal, and Bo Pang
Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021, https://doi.org/10.5194/hess-25-5981-2021, 2021
Short summary
Short summary
Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.
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
Short summary
Short summary
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.
Peter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, and Hylke E. Beck
Geosci. Model Dev., 14, 4865–4890, https://doi.org/10.5194/gmd-14-4865-2021, https://doi.org/10.5194/gmd-14-4865-2021, 2021
Short summary
Short summary
We present a cascade of models to compute high-resolution river flooding. This takes meteorological inputs, e.g., rainfall and temperature from observations or climate models, and takes them through a series of modeling steps. This is relevant to evaluating current day and future flood risk and impacts. The model framework uses global data sets, allowing it to be applied anywhere in the world.
Cited articles
Amarnath, G., Umer, Y. M., Alahacoon, N., and Inada, Y.: Modelling the
flood-risk extent using LISFLOOD-FP in a complex watershed: case study of
Mundeni Aru River Basin, Sri Lanka, Proc. Intl. Assoc. Hydrol. Sci., 370,
131–138, 2015.
Asinya, E. A. and Alam, M. J. B.: Flood risk in rivers: climate driven or
morphological adjustment, Earth Syst. Env., 5, 861–871, 2021.
Bates, P. D. and De Roo, A. P. J.: A simple raster-based model for flood
inundation simulation, J. Hydrol., 236, 54–77, 2000.
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, 2010.
Beevers, L., Collet, L., Aitken, G., Maravat, C., and Visser, A.: The
influence of climate model uncertainty on fluvial flood hazard estimation,
Nat. Hazards, 104, 2489–2510, 2020.
Bessar, M. A., Choné, G., Lavoie, A., Buffin-Bélanger, T., Biron, P.
M., Matte, P., and Anctil, F.: Comparative analysis of local and large-scale
approaches to floodplain mapping: a case study of the Chaudière River,
Canadian Wat. Resour., 46, 194–206, 2021.
Chaabani, C., Chini, M., Abdelfattah, R., Hostache, R., and Chokmani, K.:
Flood mapping in a complex environment using bistatic TanDEM-X/TerraSAR-X
InSAR coherence, Remote Sens., 10, 1873, https://doi.org/10.3390/rs10121873, 2018.
Choné, G., Biron, P. M., Buffin‐Bélanger, T., Mazgareanu, I., Neal, J. C., and Sampson, C. C.: An assessment of large‐scale flood modelling based on LiDAR data, Hydrol. Process., 35, e14333, https://doi.org/10.1002/hyp.14333, 2021.
Chow, V. T.: Open Channel Hydraulics, McGraw-Hill, New York, ISBN 007085906X, 9780070859067, 1959.
Costabile, P., Costanzo, C., Ferraro, D., and Barca, P.: Is HEC-RAS 2D
accurate enough for storm-event hazard assessment? Lessons learnt from a
benchmarking study based on rain-on-grid modelling, J. Hydrol., 603, 126962, https://doi.org/10.1016/j.jhydrol.2021.126962,
2021.
Coulthard, T. J., Neal, J. C., Bates, P. D., Ramirez, J., de Almeida, G. A.,
and Hancock, G. R.: Integrating the LISFLOOD-FP 2D hydrodynamic model with
the CAESAR model: implications for modelling landscape evolution, Earth
Surf. Process. Landform., 38, 1897–1906, 2013.
De Almeida, G. A. and Bates, P.: Applicability of the local inertial
approximation of the shallow water equations to flood modelling, Water
Resour. Res., 49, 4833–4844, 2013.
De Almeida, G. A. M., Bates, P., Freer, J. E., and Souvignet, M.: Improving
the stability of a simple formulation of the shallow water equations for 2-D
flood modeling, Water Resour. Res., 48, W05528, https://doi.org/10.1029/2011WR011570, 2012.
Environment Agency: Real-time and Near-real-time river level data, Environment Agency [data set], https://data.gov.uk/dataset/0cbf2251-6eb2-4c4e-af7c-d318da9a58be/real-time-and-near-real-time-river-level-data (last access: 28 April 2023), 2020.
Ferraro, D., Costabile, P., Costanzo, C., Petaccia, G., and Macchione, F.: A
spectral analysis approach for the a priori generation of computational
grids in the 2-D hydrodynamic-based runoff simulations at a basin scale, J.
Hydrol., 582, 124508, https://doi.org/10.1016/j.jhydrol.2019.124508, 2020.
Guo, K., Guan, M., and Yu, D.: Urban surface water flood modelling – a comprehensive review of current models and future challenges, Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, 2021.
Hirai, S. and Yasuda, T.: Risk assessment of aggregate loss by storm surge
inundation in ise and mikawa bay, in: Coastal Engineering Proceedings: No. 36 (2018): Proceedings of 36th Conference on Coastal Engineering, Baltimore, Maryland, 30 July–3 August 2018, https://doi.org/10.9753/icce.v36.risk.35, 2018.
Hoch, J. M. and Trigg, M. A.: Advancing global flood hazard simulations by improving comparability, benchmarking, and integration of global flood models, Environ. Res. Lett., 14, 034001, https://doi.org/10.1088/1748-9326/aaf3d3, 2019.
Hoch, J. M., Eilander, D., Ikeuchi, H., Baart, F., and Winsemius, H. C.: Evaluating the impact of model complexity on flood wave propagation and inundation extent with a hydrologic–hydrodynamic model coupling framework, Nat. Hazards Earth Syst. Sci., 19, 1723–1735, https://doi.org/10.5194/nhess-19-1723-2019, 2019.
Hunter, N. M., Horritt, M. S., Bates, P. D., Wilson, M. D., and Werner, M.
G.: An adaptive time step solution for raster-based storage cell modelling
of floodplain inundation, Adv. Water Resour., 28, 975–991, 2005.
Hunter, N. M., Bates, P. D., Horritt, M. S., and Wilson, M. D.: Improved
simulation of flood flows using storage cell models, in: Proceeds. Instit.
Civil Engs. Water Manag., Vol. 159, No. 1, 9–18, Thomas Telford Ltd, https://doi.org/10.1680/wama.2006.159.1.9,
2006.
Hunter, N. M., Bates, P. D., Neelz, S., Pender, G., Villanueva, I., Wright,
N. G., Liang, D., Falconer, R. A., Lin, B., Waller, S., Crossley, A. J., and Mason, D. C.: Benchmarking 2D hydraulic models for urban
flooding, in: Proceeds. Inst. Civil Engs. Water Manag., Vol. 161, No. 1,
13–30, Thomas Telford Ltd, https://doi.org/10.1680/wama.2008.161.1.13, 2008.
Jafarzadegan, K., Abbaszadeh, P., and Moradkhani, H.: Sequential data assimilation for real-time probabilistic flood inundation mapping, Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021, 2021.
Karamouz, M. and Mahani, F. F.: DEM uncertainty based coastal flood
inundation modeling considering water quality impacts, Water Resour. Manag.,
35, 3083–3103, 2021.
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., 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.
LISFLOOD-FP developers: LISFLOOD-FP 8.1 hydrodynamic model (8.1), Zenodo [code], https://doi.org/10.5281/zenodo.6912932, 2022.
Liu, Z. and Merwade, V.: Accounting for model structure, parameter and
input forcing uncertainty in flood inundation modeling using Bayesian model
averaging, J. Hydrol., 565, 138–149, 2018.
Makungu, E. and Hughes, D. A.: Understanding and modelling the effects of
wetland on the hydrology and water resources of large African river basins,
J. Hydrol., 603, 127039, https://doi.org/10.1016/j.jhydrol.2021.127039, 2021.
McCall, I.: Carlisle Flood Investigation Report, Flood Event 5–6th December 2015, Tech. Rep., Environment Agency, https://www.cumbria.gov.uk/eLibrary/Content/Internet/536/6181/42494151257.pdf (last access: 28 April 2023), 2016.
Met Office: Met Office Rain Radar Data from the NIMROD System, Met Office [data set], https://catalogue.ceda.ac.uk/uuid/82adec1f896af6169112d09cc1174499 (last access: 28 April 2023), 2013.
Ming, X., Liang, Q., Xia, X., Li, D., and Fowler, H. J.: Real‐time flood forecasting based on a high‐performance 2‐D hydrodynamic model and numerical weather predictions, Water Resour. Res., 56, e2019WR025583, https://doi.org/10.1029/2019WR025583, 2020.
Muthusamy, M., Casado, M. R., Butler, D., and Leinster, P.: Understanding
the effects of Digital Elevation Model resolution in urban fluvial flood
modelling, J. Hydrol., 596, 126088, https://doi.org/10.1016/j.jhydrol.2021.126088, 2021.
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.
Neal, J., Schumann, G., and Bates, P.: A subgrid channel model for
simulating river hydraulics and floodplain inundation over large and data
sparse areas, Water Resour. Res., 48, W11506, https://doi.org/10.1029/2012WR012514, 2012a.
Neal, J., Villanueva, I., Wright, N., Willis, T., Fewtrell, T., and Bates,
P.: How much physical complexity is needed to model flood inundation?,
Hydrol. Process., 26, 2264–2282, 2012b.
Neal, J., Dunne, T., Sampson, C., Smith, A., and Bates, P.: Optimisation of
the two-dimensional hydraulic model LISFOOD-FP for CPU architecture, Environ.
Modell. Softw., 107, 148–157, 2018.
Néelz, S. and Pender, G.: Benchmarking the latest generation of 2D hydraulic modelling packages, Tech. Rep. SC120002, Environment Agency, Horizon House, Deanery Road, Bristol, BS1 9AH, https://www.gov.uk/government/publications/benchmarking-the-latest-generation-of-2d-hydraulic-flood-modelling-packages (last access: 28 April 2023), 2013.
O'Loughlin, F. E., Neal, J., Schumann, G. J. P., Beighley, E., and Bates, P.
D.: A LISFLOOD-FP hydraulic model of the middle reach of the Congo, J.
Hydrol., 580, 124203, https://doi.org/10.1016/j.jhydrol.2019.124203, 2020.
Özgen-Xian, I., Kesserwani, G., Caviedes-Voullième, D., Molins, S.,
Xu, Z., Dwivedi, D., David Moulton, J., and Steefel, C. I.: Wavelet-based
local mesh refinement for rainfall-runoff simulations, J. Hydroin., 22,
1059–1077, 2020.
Rajib, A., Liu, Z., Merwade, V., Tavakoly, A. A., and Follum, M. L.: Towards
a large-scale locally relevant flood inundation modeling framework using
SWAT and LISFLOOD-FP, J. Hydrol., 581, 124406, https://doi.org/10.1016/j.jhydrol.2019.124406, 2020.
Seenath, A.: Effects of DEM resolution on modeling coastal flood vulnerability, Mar. Geod., 41, 581–604, https://doi.org/10.1080/01490419.2018.1504838, 2018.
Sharifian, M. K. and Kesserwani, G.: LISFLOOD-FP 8.1: New GPU accelerated
solvers for faster fluvial/pluvial flood simulations – video supplement,
Zenodo [video], https://doi.org/10.5281/zenodo.6685125, 2022.
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 – simulation results, Zenodo [data set], https://doi.org/10.5281/zenodo.6907286, 2022.
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.
Shustikova, I., Neal, J. C., Domeneghetti, A., Bates, P. D., Vorogushyn, S.,
and Castellarin, A.: Levee breaching: a new extension to the LISFLOOD-FP
model, Water, 12, 942, https://doi.org/10.3390/w12040942, 2020.
Siqueira, V. A., Paiva, R. C. D., Fleischmann, A. S., Fan, F. M., Ruhoff, A. L., Pontes, P. R. M., Paris, A., Calmant, S., and Collischonn, W.: Toward continental hydrologic–hydrodynamic modeling in South America, Hydrol. Earth Syst. Sci., 22, 4815–4842, https://doi.org/10.5194/hess-22-4815-2018, 2018.
Sosa, J., Sampson, C., Smith, A., Neal, J., and Bates, P.: A toolbox to
quickly prepare flood inundation models for LISFLOOD-FP simulations, Env.
Modell. Soft., 123, 104561, https://doi.org/10.1016/j.envsoft.2019.104561, 2020.
Sridharan, B., Bates, P. D., Sen, D., and Kuiry, S. N.: Local-inertial
shallow water model on unstructured triangular grids, Adv. Water
Resour., 152, 103930, https://doi.org/10.1016/j.advwatres.2021.103930, 2021.
Taccone, F., Antoine, G., Delestre, O., and Goutal, N.: A new criterion for
the evaluation of the velocity field for rainfall-runoff modelling using a
shallow-water model, Adv. Water Resour., 140, 103581, https://doi.org/10.1016/j.advwatres.2020.103581, 2020.
Towner, J., Cloke, H. L., Zsoter, E., Flamig, Z., Hoch, J. M., Bazo, J., Coughlan de Perez, E., and Stephens, E. M.: Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin, Hydrol. Earth Syst. Sci., 23, 3057–3080, https://doi.org/10.5194/hess-23-3057-2019, 2019.
Wainwright, H. and Williams, K.: LiDAR collection in August 2015 over the
East River Watershed, Colorado, USA, Watershed Function SFA, ESS-DIVE
repository [data set], https://doi.org/10.21952/WTR/1412542 (last access:
19 February 2023), 2017.
Wang, Y., Liang, Q., Kesserwani, G., and Hall, J. W.: A
positivity-preserving zero-inertia model for flood simulation, Comp. Fluids,
46, 505–511, 2011.
Wing, O. E. J., Bates, P. D., Sampson, C. C., Smith, A. M., Johnson, K. A.,
and Erickson, T. A.: Validation of a 30 m resolution flood hazard model of
the conterminous United States, Water Resour. Res., 53, 7968–7986, 2017.
Wu, X., Wang, Z., Guo, S., Lai, C., and Chen, X.: A simplified approach for
flood modeling in urban environments, Hydrol. Res., 49, 1804–1816, 2018.
Xia, X. and Liang, Q.: A new efficient implicit scheme for discretising the
stiff friction terms in the shallow water equations, Adv. Water Resour.,
117, 87–97, 2018.
Xia, X., Liang, Q., and Ming, X.: A full-scale fluvial flood modelling
framework based on a high-performance integrated hydrodynamic modelling
system (HiPIMS), Adv. Water Resour., 132, 103392, https://doi.org/10.1016/j.advwatres.2019.103392, 2019.
Yang, Q., Ma, Z., and Zhang, S.: Urban Pluvial Flood Modeling by Coupling
Raster-Based Two-Dimensional Hydrodynamic Model and SWMM, Water, 14,
1760, https://doi.org/10.3390/w14111760, 2022.
Yin, Y., Val, D. V., Zou, Q., and Yurchenko, D.: Resilience of Critical
Infrastructure Systems to Floods: A Coupled Probabilistic Network Flow and
LISFLOOD-FP Model, Water, 14, 683, https://doi.org/10.3390/w14050683, 2022.
Zare, M., Schumann, G. J. P., Teferle, F. N., and Mansorian, R.: Generating
Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology
for Precipitation, Atmosphere, 12, 1336, https://doi.org/10.3390/atmos12101336, 2021.
Zeng, Z., Wang, Z., and Lai, C.: Simulation Performance Evaluation and
Uncertainty Analysis on a Coupled Inundation Model Combining SWMM and WCA2D,
Int. J. Dis. Risk Sci., 13, 448–464, https://doi.org/10.1007/s13753-022-00416-3, 2022.
Zhao, G., Bates, P., and Neal, J.: The impact of dams on design floods in
the conterminous US, Water Resour. Res., 56, e2019WR025380, https://doi.org/10.1029/2019WR025380, 2020.
Zhao, J., Pelich, R., Hostache, R., Matgen, P., Wagner, W., and Chini, M.: A
large-scale 2005–2012 flood map record derived from ENVISAT-ASAR data:
United Kingdom as a test case, Remote Sens. Env., 256, 112338, https://doi.org/10.1016/j.rse.2021.112338, 2021.
Zhu, S., Dai, Q., Zhao, B., and Shao, J.: Assessment of population exposure
to urban flood at the building scale, Water, 12, 3253, https://doi.org/10.3390/w12113253, 2020.
Zhu, X., Dai, Q., Han, D., Zhuo, L., Zhu, S., and Zhang, S.: Modeling the high-resolution dynamic exposure to flooding in a city region, Hydrol. Earth Syst. Sci., 23, 3353–3372, https://doi.org/10.5194/hess-23-3353-2019, 2019.
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
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.
This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood...
