Validation of Urban Surface Water Flood Modeling with Multisource Data: Two Case Studies in Baoji and Linyi Cities, China

Abstract

Urban areas are particularly vulnerable to surface water flooding in a changing environment. A large number of urban surface water flood models have been developed to derive flood inundations and support risk management. However, unlike fluvial and coastal flooding, urban pluvial flooding is often associated with shallow water and thus the model is difficult to validate with traditional monitoring data. In this study, we first developed a full two-dimensional (2D) hydrodynamic model for simulating surface water floods. We further evaluated the model performance with multisource data from flood incidents, including official reports and social media data. The model was tested in the cities of Baoji and Linyi, China, where two surface water flood events recently occurred and caused considerable losses and casualties. In total, 350 localized flooding incidents were obtained for the two cities (220 in Baoji and 130 in Linyi) and 313 reports were retained after data cleaning (202 in Baoji and 111 in Linyi). Over 90% of the reported flood incidents fall in urban areas where water depths are predicted to be higher than 0.15 m. The results demonstrate that the model is able to derive the broad patterns of flood inundation at the city scale. The approach tested here could be applied to other flood-prone cities and future research could include water depth information for more robust model validation.

Keywords

Model validation / Multisource data / Surface water flooding / Urban flood modeling

References

[1] AudusseE, BouchutF, BristeauMO, KleinR, PerthameB. A fast and stable well-balanced scheme with hydrostatic reconstruction for shallow water flows. SIAM Journal on Scientific Computing, 2004, 25(6): 2050-2065.

[2] BatesPD, HorrittM, FewtrellT. A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. Journal of Hydrology, 2010, 387(1): 33-45.

[3] BerkhahnS, FuchsL, NeuweilerI. An ensemble neural network model for real-time prediction of urban floods. Journal of Hydrology, 2019, 575: 743-754.

[4] CalderIR, HardingRJ, RosierPTW. An objective assessment of soil moisture deficit models. Journal of Hydrology, 1983, 60(1–4): 329-355.

[5] FohringerJ, DranschD, KreibichH, SchröterK. Social media as an information source for rapid flood inundation mapping. Natural Hazards and Earth System Sciences, 2015, 15(12): 2725-2738.

[6] Gao, D., J. Yin, Y. Yang, J. Mai, Y. Lu, P. Liang, W. Wang, X. Yuan, and D. Yu. 2025. Validating typhoon-induced rainstorm flood inundation modelling with insurance claims. Journal of Hydrology 657: Article 133178.

[7] GuanM, WrightN, SleighP. A robust 2D shallow water model for solving flow over complex topography using homogenous flux method. International Journal for Numerical Methods in Fluids, 2013, 73(3): 225-249.

[8] Guan, M., N. Wright, and P. Sleigh. 2014. 2D process-based morphodynamic model for flooding by noncohesive dyke breach. Journal of Hydraulic Engineering 140(7): Article 04014022.

[9] GuidolinM, ChenAS, GhimireB, KeedwellEC, DjordjevićS, SavićDA. A weighted cellular automata 2D inundation model for rapid flood analysis. Environmental Modelling & Software, 2016, 84: 378-394.

[10] GuoK, GuanM, YanH, ChanFKSExploring the utility of social media data for urban flood impact assessment in data scarce cities2022Natural Hazards and Earth System Sciences Discussions

[11] Guo, K., M. Guan, H. Yan, and X. Xia. 2023. A spatially distributed hydrodynamic model framework for urban flood hydrological and hydraulic processes involving drainage flow quantification. Journal of Hydrology 625: Article 130135.

[12] GuoK, GuanM, YuD. Urban surface water flood modelling – A comprehensive review of current models and future challenges. Hydrology and Earth System Sciences, 2021, 25(5): 2843-2860.

[13] Hawker, L., P. Uhe, L. Paulo, J. Sosa, J. Savage, C. Sampson, and J. Neal. 2022. A 30m global map of elevation with forests and buildings removed. Environmental Research Letters 17(2): Article 024016.

[14] Herfort, B., J.P. de Albuquerque, S.J. Schelhorn, and A. Zipf. 2014. Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe flood in June 2013. In Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management (ISCRAM’14), 18–21 May 2014, University Park, PA, USA.

[15] HorrittMS. Calibration of a two-dimensional finite element flood flow model using satellite radar imagery. Water Resources Research, 2000, 36(11): 3279-3291.

[16] HorrittMS, Di BaldassarreG, BatesPD, BrathA. Comparing the performance of a 2-D finite element and a 2-D finite volume model of floodplain inundation using airborne SAR imagery. Hydrological Processes, 2007, 21(20): 2745-2759.

[17] IPCC (Intergovernmental Panel on Climate Change)Climate change 2021: The physical science basis, 2021, Cambridge, UK. Cambridge University Press.

[18] LiangQ, MarcheF. Numerical resolution of well-balanced shallow water equations with complex source terms. Advances in Water Resources, 2009, 32(6): 873-884.

[19] MaidmentDRHandbook of hydrology, 1993, New York. McGraw-Hill.

[20] MarkO, WeesakulS, ApirumanekulC, AroonnetS, DjordjevicS. Potential and limitations of 1D modelling of urban flooding. Journal of Hydrology, 2004, 299(3): 284-299.

[21] MasonDC, BatesPD, Dall’AmicoJT. Calibration of uncertain flood inundation models using remotely sensed water levels. Journal of Hydrology, 2009, 368(1–4): 224-236.

[22] PanY, GuJ, XuB, YangS, ShuaiH, ShiC. Advances in multi-source precipitation merging research. Advances in Meteorological Science and Technology, 2018, 8(1): 143-152(in Chinese)

[23] PittMThe Pitt review: Lessons learned from the 2007 floods, 2008, London. Cabinet Office.

[24] RollasonE, BrackenLJ, HardyRJ, LargeARG. The importance of volunteered geographic information for the validation of flood inundation models. Journal of Hydrology, 2018, 562: 267-280.

[25] Rossman, L.A., and M.A. Simon. 2022. Storm water management model user’s manual version 5.2. EPA-600/R-22/030. Cincinnati, OH: United States Environmental Protection Agency.

[26] Songchon, C., G. Wright, and L. Beevers. 2023. The use of crowdsourced social media data to improve flood forecasting. Journal of Hydrology 622: Article 129703.

[27] Xia, X., Q. Liang, and X. Ming. 2019. A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS). Advances in Water Resources 132: Article 103392.

[28] XiaJ, ZhangY, XiongL, HeS, WangL, YuZ. Opportunities and challenges of the Sponge City construction related to urban water issues in China. Science China Earth Sciences, 2017, 60(4): 652-658.

[29] YangY, YinJ, WangD, LiuY, LuY, ZhangW, XuS. ABM-based emergency evacuation modelling during urban pluvial floods: A “7.20” pluvial flood event study in Zhengzhou Henan Province. Science China Earth Sciences, 2023, 66(2): 282-291.

[30] YinJ, LinN, YuD. Coupled modeling of storm surge and coastal inundation: A case study in New York City during Hurricane Sandy. Water Resources Research, 2016, 52(11): 8685-8699.

[31] YinJ, YuD, YinZ, WangJ, XuS. Modelling the anthropogenic impacts on fluvial flood risks in a coastal mega-city: A scenario-based case study in Shanghai, China. Landscape and Urban Planning, 2015, 136: 144-155.

[32] YuD, CoulthardTJ. Evaluating the importance of catchment hydrological parameters for urban surface water flood modelling using a simple hydro-inundation model. Journal of Hydrology, 2015, 524: 385-400.

[33] Yu, D., J. Yin, and M. Liu. 2016. Validating city-scale surface water flood modelling using crowd-sourced data. Environmental Research Letters 11(12): Article 124011.

[34] Zeng, Y.F., M.J. Chang, and G.F. Lin. 2024. A novel AI-based model for real-time flooding image recognition using super-resolution generative adversarial network. Journal of Hydrology 638: Article 131475.

[35] ZischgAP, MosimannM, BernetDB, RöthlisbergerV. Validation of 2D flood models with insurance claims. Journal of Hydrology, 2018, 557: 350-361.

[36] ZouL, LamNS, CaiH, QiangY. Mining Twitter data for improved understanding of disaster resilience. Annals of the American Association of Geographers, 2018, 108(5): 1422-1441.

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