Abstract
Flood hazard assessment of cities gained significance globally due to rise in frequency of flood events and rapid urbanisation. Uncertainties in flood inundation models largely depend on the quality of input datasets, among which topography plays a vital role. This study demonstrates the effectiveness of global terrain models in simulating accurate flooding and generating hazard maps while considering the influence of land-use dynamics focussing on data-scarce regions. Open-source forest and building removed digital elevation model (FABDEM) and a terrain model TDX-12 DTM derived from TanDEM-X 12 DSM using a simple-morphological-filtering technique are considered for comparing their performance in simulating a flood event occurred in Surat city during the year 2006. Spatially varying short-term urban growth scenario for the year 2035 is developed by utilizing historical land-use maps of the study area and urban growth indicators. These are combined using multi-criteria-decision-making techniques and Cellular-Automata-Markov-Chain model. The FABDEM based hydrodynamic model performed better (Root-Mean-Squared-Error RMSE of 1.59 m) than TDX-12 DTM based model (RMSE: 1.88 m). Intercomparison of hazard maps of the FABDEM and TDX-12 DTM with ground-surveyed TopoDEM based-model showed an overall accuracy of 71.8% and 72.8%; for the future scenario 71.8% and 75.5% respectively. In a span of 29 years, a notable increase in hazard magnitude of 7.5% is solely attributed to change in land dynamics. In this study, though the FABDEM based-model showed better RMSE than TDX-12 DTM, the model is relatively less successful in capturing high-hazard regions. The DEMs processed for removal of non-ground objects yield accurate models than globally trained models.
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References
Alipour A, Jafarzadegan K, Moradkhani H (2022) Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping. Environ Model Softw 152:105398. https://doi.org/10.1016/j.envsoft.2022.105398
Archer L, Neal JC, Bates PD, House JI (2018) Comparing TanDEM-X data with frequently used DEMs for flood inundation modeling. Water Resour Res 54(12):10–205. https://doi.org/10.1029/2018WR023688
Basse RM, Omrani H, Charif O, Gerber P, Bódis K (2014) Land use changes modelling using advanced methods: cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl Geogr 53:160–171. https://doi.org/10.1016/j.apgeog.2014.06.016
Baugh CA, Bates PD, Schumann G, Trigg MA (2013) SRTM vegetation removal and hydrodynamic modeling accuracy. Water Resour Res 49(9):5276–5289. https://doi.org/10.1002/wrcr.20412
Brunner GW (2016). HEC-RAS hydraulic reference manual, version 5.0. Rep. No. CPD, 69. Accessed from https://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS%205.0%20Reference%20Manual.pdf
Costabile P, Costanzo C, De Lorenzo G, Macchione F (2020) Is local flood hazard assessment in urban areas significantly influenced by the physical complexity of the hydrodynamic inundation model? J Hydrol 580:124231. https://doi.org/10.1016/j.jhydrol.2019.124231
Cox RJ, Shand TD, Blacka MJ (2010) Australian rainfall and runoff revision project 10: appropriate safety criteria for people. Water Res 978:085825–9454
Devi NN, Sridharan B, Kuiry SN (2019) Impact of urban sprawl on future flooding in Chennai city, India. J Hydrol 574:486–496. https://doi.org/10.1016/j.jhydrol.2019.04.041
Devitt L, Neal J, Coxon G, Savage J, Wagener T (2023) Flood hazard potential reveals global floodplain settlement patterns. Nat Commun 14(1):2801. https://doi.org/10.1038/s41467-023-38297-9
Gehlot LK, Jibhakate SM, Sharma PJ, Patel PL, Timbadiya PV (2021) Spatio-temporal variability of rainfall indices and their teleconnections with El Niño-Southern oscillation for Tapi basin. India Asia-Pacific J Atmos Sci 57(1):99–118. https://doi.org/10.1007/s13143-020-00179-1
Geiß C, Wurm M, Breunig M, Felbier A, Taubenböck H (2015) Normalization of TanDEM-X DSM data in urban environments with morphological filters. IEEE Trans Geosci Remote Sens 53(8):4348–4362. https://doi.org/10.1109/TGRS.2015.2396195
Geiß C, Schrade H, Pelizari PA, Taubenböck H (2020) Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data. ISPRS J Photogramm Remote Sens 170:57–71. https://doi.org/10.1016/j.isprsjprs.2020.10.004
Getachew B, Manjunatha BR, Bhat HG (2021) Modeling projected impacts of climate and land use/land cover changes on hydrological responses in the lake Tana Basin, upper Blue Nile river basin. Ethiopia J Hydrol 595:125974. https://doi.org/10.1016/j.jhydrol.2021.125974
Gidey E, Dikinya O, Sebego R, Segosebe E, Zenebe A (2017) Cellular automata and Markov Chain (CA_Markov) model-based predictions of future land use and land cover scenarios (2015–2033) in Raya, northern Ethiopia. Model Earth Syst Environ 3:1245–1262. https://doi.org/10.1007/s40808-017-0397-6
Grohmann CH (2018) Evaluation of TanDEM-X DEMs on selected Brazilian sites: comparison with SRTM, ASTER GDEM and ALOS AW3D30. Remote Sens Environ 212:121–133. https://doi.org/10.1016/j.rse.2018.04.043
Guan M, Guo K, Yan H, Wright N (2023) Bottom-up multilevel flood hazard mapping by integrated inundation modelling in data scarce cities. J Hydrol 617:129114. https://doi.org/10.1016/j.jhydrol.2023.129114
Hagenauer J, Helbich M (2012) Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks. Int J Geogr Inf Sci 26(6):963–982. https://doi.org/10.1080/13658816.2011.619501
Hawker L, Bates P, Neal J, Rougier J (2018) Perspectives on digital elevation model (DEM) simulation for flood modeling in the absence of a high-accuracy open access global DEM. Front Earth Sci 6:233. https://doi.org/10.3389/feart.2018.00233
Hawker L, Neal J, Bates P (2019) Accuracy assessment of the TanDEM-X 90 digital elevation model for selected floodplain sites. Remote Sens Environ 232:111319. https://doi.org/10.1016/j.rse.2019.111319
Hawker L, Uhe P, Paulo L, Sosa J, Savage J, Sampson C, Neal J (2022) A 30 m global map of elevation with forests and buildings removed. Environ Res Lett 17(2):024016. https://doi.org/10.1088/1748-9326/ac4d4f
Jibhakate SM, Timbadiya PV, Patel PL (2023) Flood hazard assessment for the coastal urban floodplain using 1D/2D coupled hydrodynamic model. Nat Hazards 116(2):1557–1590. https://doi.org/10.1007/s11069-022-05728-7
Li J, Zhao Y, Bates P, Neal J, Tooth S, Hawker L, Maffei C (2020) Digital Elevation Models for topographic characterisation and flood flow modelling along low-gradient, terminal dryland rivers: a comparison of spaceborne datasets for the Río Colorado. Bolivia J Hydrol 591:125617. https://doi.org/10.1016/j.jhydrol.2020.125617
Liu Y, Bates PD, Neal JC, Yamazaki D (2021) Bare-earth DEM generation in urban areas for flood inundation simulation using global digital elevation models. Water Resour Res 57(4):e2020WR028516. https://doi.org/10.1029/2020WR028516
Loli M, Kefalas G, Dafis S, Mitoulis SA, Schmidt F (2022) Bridge-specific flood risk assessment of transport networks using GIS and remotely sensed data. Sci Total Environ 850:157976. https://doi.org/10.1016/j.scitotenv.2022.157976
Loliyana VD, Patel PL (2020) A physics based distributed integrated hydrological model in prediction of water balance of a semi-arid catchment in India. Environ Model Softw 127:104677. https://doi.org/10.1016/j.envsoft.2020.104677
Malgwi MB, Ramirez JA, Zischg A, Zimmermann M, Schurmann S, Keiler M (2021) A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood. Nat Hazards 108:1781–1805. https://doi.org/10.1007/s11069-021-04756-z
Maranzoni A, D’Oria M, Rizzo C (2023) Quantitative flood hazard assessment methods: A review. J Flood Risk Manag 16(1):e12855. https://doi.org/10.1111/jfr3.12855
McClean F, Dawson R, Kilsby C (2020) Implications of using global digital elevation models for flood risk analysis in cities. Water Resour Res 56(10):e2020028241. https://doi.org/10.1029/2020WR028241
Mohamed A, Worku H (2020) Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Clim 31:100545. https://doi.org/10.1016/j.uclim.2019.100545
Mohanty MP, Vittal H, Yadav V, Ghosh S, Rao GS, Karmakar S (2020) A new bivariate risk classifier for flood management considering hazard and socio-economic dimensions. J Environ Manage 255:109733. https://doi.org/10.1016/j.jenvman.2019.109733
Mudashiru RB, Sabtu N, Abustan I, Balogun W (2021) Flood hazard mapping methods: A review. J Hydrol 603:126846. https://doi.org/10.1016/j.jhydrol.2021.126846
Munthali MG, Mustak S, Adeola A, Botai J, Singh SK, Davis N (2020) Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid cellular automata and Markov model. Remote Sens Appl: Soc Environ 17:100276. https://doi.org/10.1016/j.rsase.2019.100276
Nandam V, Patel PL (2022) A novel hybrid approach using SVM and spectral indices for enhanced land use land cover mapping of coastal urban plains. Geocarto Int 37(16):4714–4736. https://doi.org/10.1080/10106049.2021.1899300
Nandam V, Patel PL (2024) A framework to assess suitability of global digital elevation models for hydrodynamic modelling in data scarce regions. J Hydrol 630:130654. https://doi.org/10.1016/j.jhydrol.2024.130654
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66
Papaioannou G, Loukas A, Vasiliades L, Aronica GT (2016) Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach. Nat Hazards 83:117–132. https://doi.org/10.1007/s11069-016-2382-1
Park S, Jeon S, Kim S, Choi C (2011) Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landsc Urban Plan 99(2):104–114. https://doi.org/10.1016/j.landurbplan.2010.09.001
Patel DP, Ramirez JA, Srivastava PK, Bray M, Han D (2017) Assessment of flood inundation mapping of Surat city by coupled 1D/2D hydrodynamic modeling: a case application of the new HEC-RAS 5. Nat Hazards 89:93–130. https://doi.org/10.1007/s11069-017-2956-6
Pingel TJ, Clarke KC, McBride WA (2013) An improved simple morphological filter for the terrain classification of airborne LIDAR data. ISPRS J Photogramm Remote Sens 77:21–30. https://doi.org/10.1016/j.isprsjprs.2012.12.002
Rentschler J, Salhab M, Jafino BA (2022) Flood exposure and poverty in 188 countries. Nat Commun 13(1):3527. https://doi.org/10.1038/s41467-022-30727-4
Rizzoli P, Martone M et al (2017) Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS J Photogramm Remote Sens 132:119–139. https://doi.org/10.1016/j.isprsjprs.2017.08.008
Del Rosario Gonzalez-Moradas M, Viveen W (2020) Evaluation of ASTER GDEM2, SRTMv, ALOS AW3D30 and TanDEM-X DEMs for the Peruvian Andes against highly accurate GNSS ground control points and geomorphological-hydrological metrics. Remote Sens Environ 237:111509. https://doi.org/10.1016/j.rse.2019.111509
Said M, Hyandye C, Komakech HC, Mjemah IC, Munishi LK (2021) Predicting land use/cover changes and its association to agricultural production on the slopes of Mount Kilimanjaro. Tanzania Annals GIS 27(2):189–209. https://doi.org/10.1080/19475683.2020.1871406
Sampson CC, Smith AM, Bates PD, Neal JC, Trigg MA (2016) Perspectives on open access high resolution digital elevation models to produce global flood hazard layers. Front Earth Sci 3:85. https://doi.org/10.3389/feart.2015.00085
Schreyer J, Geiß C, Lakes T (2016) TanDEM-X for large-area modeling of urban vegetation height: evidence from Berlin, Germany. IEEE J Sel Top Appl Earth Obs Remote Sens 9(5):1876–1887. https://doi.org/10.1109/JSTARS.2015.2508660
Shafizadeh-Moghadam H, Tayyebi A, Helbich M (2017) Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation. Environ Monit Assess 189:1–14. https://doi.org/10.1007/s10661-017-5986-3
Shikhteymour SR, Borji M, Bagheri-Gavkosh M, Azimi E, Collins TW (2023) A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods. Appl Geogr 158:103035. https://doi.org/10.1016/j.apgeog.2023.103035
Singh SK, Mustak S, Srivastava PK, Szabó S, Islam T (2015) Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environ Process 2:61–78. https://doi.org/10.1007/s40710-015-0062-x
Smith GP, Davey E, Cox KR (2014) Flood hazard WRL Technical report 2014/07 Water Research Laboratory. Accessed from https://knowledge.aidr.org.au/media/2334/wrl-flood-hazard-techinical-report-september-2014.pdf
Tayyebi A, Pijanowski BC (2014) Modeling multiple land use changes using ANN, CART and MARS: comparing tradeoffs in goodness of fit and explanatory power of data mining tools. Int J Appl Earth Obs Geoinf 28:102–116. https://doi.org/10.1016/j.jag.2013.11.008
Teng J, Jakeman AJ, Vaze J, Croke BF, Dutta D, Kim S (2017) Flood inundation modelling: a review of methods, recent advances and uncertainty analysis. Environ Model Softw 90:201–216. https://doi.org/10.1016/j.envsoft.2017.01.006
Teng J, Penton DJ, Ticehurst C, Sengupta A et al (2022) A comprehensive assessment of floodwater depth estimation models in semiarid regions. Water Resour Res 58(11):e2022WR032031. https://doi.org/10.1029/2022WR032031
Timbadiya PV, Patel PL, Porey PD (2015) A 1D–2D coupled hydrodynamic model for river flood prediction in a coastal urban floodplain. J Hydrol Eng 20(2):05014017. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001029
Vassilaki DI, Stamos AA (2020) TanDEM-X DEM: comparative performance review employing LIDAR data and DSMs. ISPRS J Photogramm Remote Sens 160:33–50. https://doi.org/10.1016/j.isprsjprs.2019.11.015
Vora A, Sharma PJ, Loliyana VD, Patel PL, Timbadiya PV (2018) Assessment and prioritization of flood protection levees along the lower Tapi river. India Nat Hazards Rev 19(4):05018009. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000310
Wessel B, Huber M, Wohlfart C, Marschalk U, Kosmann D, Roth A (2018) Accuracy assessment of the global TanDEM-X digital elevation model with GPS data. ISPRS J Photogramm Remote Sens 139:171–182. https://doi.org/10.1016/j.isprsjprs.2018.02.017
Xu K, Fang J, Fang Y, Sun Q, Wu C, Liu M (2021) The importance of digital elevation model selection in flood simulation and a proposed method to reduce dem errors: a case study in Shanghai. Int J Disaster Risk Sci 12:890–902. https://doi.org/10.1007/s13753-021-00377-z
Yacoubi SE (2008) A mathematical method for control problems on cellular automata models. Int J Syst Sci 39(5):529–538. https://doi.org/10.1080/00207720701847232
Yamazaki D, Ikeshima D, Tawatari R, Yamaguchi T, O’Loughlin F, Neal JC, Sampson CC, Kanae S, Bates PD (2017) A high-accuracy map of global terrain elevations. Geophys Res Lett 44(11):5844–5853. https://doi.org/10.1002/2017GL072874
Zhang K, Gann D, Ross M, Robertson Q et al (2019) Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for Hispaniola and implications for mapping vulnerability to coastal flooding. Remote Sens Environ 225:290–306. https://doi.org/10.1016/j.rse.2019.02.028
Zhang K, Shalehy MH, Ezaz GT, Chakraborty A, Mohib KM, Liu L (2022) An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis. Environ Model Softw 148:105279. https://doi.org/10.1016/j.envsoft.2021.105279
Zink M, Moreira A, Hajnsek I, Rizzoli P et al (2021) TanDEM-X: 10 years of formation flying bistatic SAR interferometry. IEEE J Sel Top Appl Earth Obs Remote Sens 14:3546–3565. https://doi.org/10.1109/JSTARS.2021.3062286
Zope PE, Eldho TI, Jothiprakash V (2016) Impacts of land use–land cover change and urbanization on flooding: A case study of Oshiwara river Basin in Mumbai, India. CATENA 145:142–154. https://doi.org/10.1016/j.catena.2016.06.009
Acknowledgements
The first author is supported by the Department of Science and Technology, Ministry of Science and Technology, Government of India vide their letter no. DST/INSPIRE Fellowship/2018/[IF180589] dated July 24, 2019. TanDEM-X data was provided by the German Aerospace Centre (DLR) through an announcement of opportunity and proposal call with reference to the proposal “DEM_HYDR3378”. The authors thank data disseminating agencies such as Central Water Commission (CWC), Tapi Division, Surat, Surat Municipal Corporation (SMC), and Surat Irrigation Circle (SIC) for providing necessary data for hydrodynamic modelling. The authors acknowledge the infrastructural and computational facility provided by the Centre of Excellence (CoE) on “Water Resources and Flood Management”, SVNIT Surat under TEQIP-II funded by World Bank Grant through the Ministry of Education, Government of India. The authors sincerely thank the editor and anonymous reviewers for helping us to improve the quality of this manuscript.
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All authors contributed to the study conception and design. Conceptualization, methodology, data curation, software, validation, writing original draft and reviewing and editing were performed by NV under the supervision of PPL All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Nandam, V., Patel, P.L. On the role of digital terrain topography and land use dynamics in flood hazard assessment of urban floodplain. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06664-4
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DOI: https://doi.org/10.1007/s11069-024-06664-4