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Supercharging hydrodynamic inundation models for instant flood insight

Nature Water volume 1, pages 835–843 (2023)Cite this article

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Abstract

Floods are one of the most frequent and devastating natural disasters for human communities. Currently, flood response management globally commonly relies on hydrodynamic models for accurate simulation of complex flow patterns of flood events and to provide information on flood risks. However, the computational demand of hydrodynamic models means that they cannot be deployed usefully for real-time flood inundation forecasting over large domains or for situations where simulations need to be run repeatedly for planning purposes. Here we introduce a new modelling approach that supercharges hydrodynamic models for speed while maintaining high accuracy. We found that spatiotemporal patterns of flood inundation simulated using an extremely simplified (and hence superfast) hydrodynamic model can be mathematically transformed to reproduce the results from a high-resolution model. We exploited the efficacy of this transformation to provide high-resolution and accurate flood inundation predictions in a few seconds rather than the many hours required by conventional high-resolution hydrodynamic models, which represents an important practical advancement towards saving lives and protecting assets during flood emergencies.

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Fig. 1: Overview of case studies.
Fig. 2: Maximum flood inundation extent.
Fig. 3: Peak water depth.
Fig. 4: Water depth hydrographs.

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Data availability

The low- and high-fidelity model results used to generate the data in this paper are available from the digital data repository in Fraehr53. The high-fidelity model dataset for the Chowilla floodplain study site was retrieved from the data repository in Fraehr54, and the high-fidelity model dataset for the Burnett River study site was made available by Zhou et al.24.

Code availability

The LSG model framework is coded in Python programming language (v.3.9). The custom code used for data preparation, EOF analysis, LSG model training and prediction, as well as postprocessing, is available from the digital data repository in Fraehr53.

References

  1. Guha-Sapir, D., Below, R. & Hoyois, P. EM-DAT: The CRED/OFDA International Disaster Database (Univ. Catholique de Louvain, accessed 16 February 2023); www.emdat.be

  2. Bates, P. D. Flood inundation prediction. Annu. Rev. Fluid Mech. 54, 287–315 (2022).

    Article  Google Scholar 

  3. Teng, J. et al. Flood inundation modelling: a review of methods, recent advances and uncertainty analysis. Environ. Model. Softw. 90, 201–216 (2017).

    Article  Google Scholar 

  4. Wang, W., Wang, Q. J., Nathan, R. & Velasco-Forero, C. Rapid prediction of flood inundation by interpolation between flood library maps for real-time applications. J. Hydrol. 609, 127735 (2022).

    Article  Google Scholar 

  5. Yang, K., Paramygin, V. A. & Sheng, Y. P. A rapid forecasting and mapping system of storm surge and coastal flooding. Weather Forecast. 35, 1663–1681 (2020).

    Article  Google Scholar 

  6. Linsley, R. K. Flood estimates: how good are they? Water Resour. Res. 22, 159S–164S (1986).

    Article  Google Scholar 

  7. IPCC Climate Change 2021: The Physical Science Basis (eds. Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  8. Coelho, G. D. A. et al. Potential impacts of future extreme precipitation changes on flood engineering design across the contiguous United States. Water Resour. Res. 58, e2021WR031432 (2022).

    Article  Google Scholar 

  9. Schlef, K. E. et al. Incorporating non-stationarity from climate change into rainfall frequency and intensity-duration-frequency (IDF) curves. J. Hydrol. 616, 128757 (2023).

    Article  Google Scholar 

  10. Wasko, C. et al. Incorporating climate change in flood estimation guidance. Phil. Trans. R. Soc. A 379, 20190548 (2021).

    Article  PubMed  Google Scholar 

  11. Luo, P. et al. Urban flood numerical simulation: research, methods and future perspectives. Environ. Model. Softw. 156, 105478 (2022).

    Article  Google Scholar 

  12. Neal, J., Fewtrell, T. & Trigg, M. Parallelisation of storage cell flood models using OpenMP. Environ. Model. Softw. 24, 872–877 (2009).

    Article  Google Scholar 

  13. Sanders, B. F. & Schubert, J. E. PRIMo: parallel raster inundation model. Adv. Water Resour. 126, 79–95 (2019).

    Article  Google Scholar 

  14. Schubert, J. E., Luke, A., AghaKouchak, A. & Sanders, B. F. A framework for mechanistic flood inundation forecasting at the metropolitan scale. Water Resour. Res. 58, e2021WR031279 (2022).

    Article  Google Scholar 

  15. Ming, X., Liang, Q., Xia, X., Li, D. & Fowler, H. Real‐time flood forecasting based on a high‐performance 2‐D hydrodynamic model and numerical weatherpredictions. Water Resour. Res. 56, e2019WR025583 (2020).

    Article  Google Scholar 

  16. Morales-Hernández, M. et al. TRITON: a Multi-GPU open source 2D hydrodynamic flood model. Environ. Model. Softw. 141, 105034 (2021).

    Article  Google Scholar 

  17. Buttinger-Kreuzhuber, A. et al. An integrated GPU-accelerated modeling framework for high-resolution simulations of rural and urban flash floods. Environ. Model.Softw. 156, 105480 (2022).

    Article  Google Scholar 

  18. Bates, P. D. & De Roo, A. P. J. A simple raster-based model for flood inundation simulation. J. Hydrol. 236, 54–77 (2000).

    Article  Google Scholar 

  19. Sridharan, B., Bates, P. D., Sen, D. & Kuiry, S. N. Local-inertial shallow water model on unstructured triangular grids. Adv. Water Resour. 152, 103930 (2021).

    Article  Google Scholar 

  20. Wu, W. Y. et al. Ensemble flood forecasting: current status and future opportunities. Wiley Interdiscip. Rev. Water 7, e1432 (2020).

    Article  Google Scholar 

  21. Merz, B. et al. Impact forecasting to support emergency management of natural hazards. Rev. Geophys. 58, e2020RG000704 (2020).

    Article  Google Scholar 

  22. Kabir, S., Patidar, S. & Pender, G. A machine learning approach for forecasting and visualising flood inundation information. Proc. Inst. Civil. Eng. Water Manag. 174, 27–41 (2021).

    Article  Google Scholar 

  23. Kabir, S. et al. A deep convolutional neural network model for rapid prediction of fluvial flood inundation. J. Hydrol. 590, 125481 (2020).

    Article  Google Scholar 

  24. Zhou, Y., Wu, W., Nathan, R. & Wang, Q. J. A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction. Environ. Model. Softw. 143, 105112 (2021).

    Article  Google Scholar 

  25. Ivanov, V. Y. et al. Breaking down the computational barriers to real‐time urban flood forecasting. Geophys. Res. Lett. 48, e2021GL093585 (2021).

    Article  Google Scholar 

  26. Chu, H. B., Wu, W. Y., Wang, Q. J., Nathan, R. & Wei, J. H. An ANN-based emulation modelling framework for flood inundation modelling: application, challenges and future directions. Environ. Model. Softw. 124, 104587 (2020).

    Article  Google Scholar 

  27. Donnelly, J., Abolfathi, S., Pearson, J., Chatrabgoun, O. & Daneshkhah, A. Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model. Water Res. 225, 119100 (2022).

    Article  CAS  PubMed  Google Scholar 

  28. Xie, S. et al. Artificial neural network based hybrid modeling approach for flood inundation modeling. J. Hydrol. 592, 125605 (2021).

    Article  Google Scholar 

  29. Razavi, S., Tolson, B. A. & Burn, D. H. Review of surrogate modeling in water resources. Water Resour. Res. https://doi.org/10.1029/2011WR011527 (2012).

  30. Liu, H. T., Ong, Y. S., Cai, J. F. & Wang, Y. Cope with diverse data structures in multi-fidelity modeling: a Gaussian process method. Eng. Appl. Artif. Intell. 67, 211–225 (2018).

    Article  Google Scholar 

  31. Apel, H., Vorogushyn, S. & Merz, B. Brief communication: impact forecasting could substantially improve the emergency management of deadly floods: case study July 2021 floods in Germany. Nat. Hazards Earth Syst. Sci. 22, 3005–3014 (2022).

    Article  Google Scholar 

  32. Asher, M. J., Croke, B. F. W., Jakeman, A. J. & Peeters, L. J. M. A review of surrogate models and their application to groundwater modeling. Water Resour. Res. 51, 5957–5973 (2015).

    Article  Google Scholar 

  33. Willis, T., Wright, N. & Sleigh, A. Systematic analysis of uncertainty in 2D flood inundation models. Environ. Model. Softw. 122, 104520 (2019).

    Article  Google Scholar 

  34. Fraehr, N., Wang, Q. J., Wu, W. & Nathan, R. Upskilling low‐fidelity hydrodynamic models of flood inundation through spatial analysis and Gaussian process learning. Water Resour. Res. 58, e2022WR032248 (2022).

    Article  Google Scholar 

  35. Fraehr, N., Wang, Q. J., Wu, W. & Nathan, R. Development of a fast and accurate hybrid model for floodplain inundation simulations. Water Resour. Res. 59, e2022WR033836 (2023).

    Article  Google Scholar 

  36. Carreau, J. & Guinot, V. A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment. Adv. Water Resour. 147, 103821 (2021).

    Article  Google Scholar 

  37. 2D Modeling User’s Manual (US Army Corps of Engineers, 2021); https://www.hec.usace.army.mil/confluence/rasdocs/r2dum/6.2

  38. Hunter, N. M., Bates, P. D., Horritt, M. S. & Wilson, M. D. Simple spatially-distributed models for predicting flood inundation: a review. Geomorphology 90, 208–225 (2007).

    Article  Google Scholar 

  39. Mudashiru, R. B., Sabtu, N., Abustan, I. & Balogun, W. Flood hazard mapping methods: a review. J. Hydrol. 603, 126846 (2021).

    Article  Google Scholar 

  40. Nathan, R. et al. Impact of natural variability on design flood flows and levels. In Proc. 37th Hydrology & Water Resources Symposium 2016: Water, Infrastructure and the Environment 335–345 (Engineers Australia, 2016); https://www.tuflow.com/media/5014/2016-impact-of-natural-variability-on-design-flood-flows-and-levels-nathan-et-al-hwrs-nz.pdf

  41. Guan, M., Guo, K., Yan, H. & Wright, N. Bottom-up multilevel flood hazard mapping by integrated inundation modelling in data scarce cities. J. Hydrol. 617, 129114 (2023).

    Article  Google Scholar 

  42. Casulli, V. A high-resolution wetting and drying algorithm for free-surface hydrodynamics. Int. J. Numer. Methods Fluids 60, 391–408 (2009).

    Article  CAS  Google Scholar 

  43. Casulli, V. Computational grid, subgrid, and pixels. Int. J. Numer. Methods Fluids 90, 140–155 (2019).

    Article  Google Scholar 

  44. Bomers, A., Schielen, R. M. J. & Hulscher, S. J. M. H. The influence of grid shape and grid size on hydraulic river modelling performance. Environ. Fluid Mech. 19, 1273–1294 (2019).

    Article  Google Scholar 

  45. Chowilla Report Card 2021–22 (Murray–Darling Basin Authority, 2023); https://www.mdba.gov.au/climate-and-river-health/water-environment/progress-and-outcomes/chowilla-report-card-2021-22

  46. Environmental Watering and Monitoring (South Australia, Department for Environment and Water, 2022); https://www.environment.sa.gov.au/topics/river-murray/improving-river-health/wetlands-and-floodplains/chowilla-floodplain/environmental-watering-and-monitoring

  47. Water Data Online (Australian Government, Bureau of Meteorology, 2022); http://www.bom.gov.au/waterdata/

  48. Huxley, C. & Syme, B. TUFLOW GPU – best practice advice for hydrologic and hydraulic model simulations. TUFLOW https://www.tuflow.com/media/5015/2016-tuflow-gpu-best-practice-advice-for-hydrologic-and-hydraulic-model-simulations-huxley-et-al-hwrs-nz.pdf (2016).

  49. Chang, C. H. et al. Hindcast and forecast of daily inundation extents using satellite SAR and altimetry data with rotated empirical orthogonal function analysis: case study in Tonle Sap Lake Floodplain. Remote Sens. Environ. 241, 111732 (2020).

    Article  Google Scholar 

  50. Lin, Q., Leandro, J., Gerber, S. & Disse, M. Multistep flood inundation forecasts with resilient backpropagation neural networks: Kulmbach case study. Water 12, 3568 (2020).

    Article  Google Scholar 

  51. Altenau, E. H., Pavelsky, T. M., Bates, P. D. & Neal, J. C. The effects of spatial resolution and dimensionality on modeling regional‐scale hydraulics in a multichannel river. Water Resour. Res. 53, 1683–1701 (2017).

    Article  Google Scholar 

  52. Schaefer, J. T. The critical success index as an indicator of warning skill. Weather Forecast. 5, 570–575 (1990).

    Article  Google Scholar 

  53. Fraehr, N. Dataset for LSG Models for the Chowilla Floodplain and Burnett River v.1 (Univ. of Melbourne, 2023); https://melbourne.figshare.com/articles/dataset/Dataset_for_LSG_models_for_the_Chowilla_floodplain_and_Burnett_River_/22290850

  54. Fraehr, N. Data from HEC-RAS Models for Training and Validation in ‘Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations’ v.2 (Univ. of Melbourne, 2023); https://melbourne.figshare.com/articles/dataset/Data_from_HEC-RAS_models_for_training_and_validation_in_Development_of_a_fast_and_accurate_hybrid_model_for_floodplain_inundation_simulations_/21235782

  55. Sanders, B. F. et al. Collaborative modeling with fine-resolution data enhances flood awareness, minimizes differences in flood perception, and produces actionable flood maps. Earths Future 8, e2019EF001391 (2020).

    Article  Google Scholar 

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Acknowledgements

N.F. acknowledges support from The University of Melbourne via the Melbourne Research Scholarship, and W.W. acknowledges support from the Australian Research Council via the Discovery Early Career Researcher Award (no. DE210100117). We thank SunWater and the Murray–Darling Basin Authority for their permission to use the case studies for the Burnett River and Chowilla floodplain, respectively. We thank Y. Zhou for making high-fidelity model results available for the Burnett River case study, and BMT for providing a TUFLOW licence to conduct TUFLOW simulations.

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Authors and Affiliations

  1. Department of Infrastructure and Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Victoria, Australia

    Niels Fraehr, Quan J. Wang, Wenyan Wu & Rory Nathan

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  1. Niels Fraehr
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Contributions

N.F., Q.J.W., W.W. and R.N. conceived the study conceptualization. N.F. built and ran the hydrodynamic models, developed the codebase, performed data analysis and created visualizations. The methodology was developed by N.F., Q.J.W., W.W. and R.N., with N.F. and Q.J.W. devising the evaluation metrics. All authors contributed to writing, editing and review.

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Correspondence to Niels Fraehr or Quan J. Wang.

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Nature Water thanks Gustavo Coelho and Tom Wills for their contribution to the peer review of this work.

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Fraehr, N., Wang, Q.J., Wu, W. et al. Supercharging hydrodynamic inundation models for instant flood insight. Nat Water 1, 835–843 (2023). https://doi.org/10.1038/s44221-023-00132-2

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