Abstract
In natural hazard modeling, underestimation is not acceptable, since it will produce unreliable and inadequate information for decision makers to work on during hazard mitigation planning. Including tsunami inundation modeling, result of the natural hazard models is highly sensitive to the accuracy of input data. High resolution elevation data, such as light detection and ranging (LiDAR) Digital Elevation Models (DEMs) can provide modeling results that are closer to the reality. However, gathering such high-resolution datasets is expensive, time consuming and is usually not open to public access. Moreover, required computational time with those high-resolution datasets is significantly long. Therefore, open-source DEMs such as, Advanced Land Observing Satellite (ALOS) World 3D (AW3D30), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Shuttle Radar Topography Mission (SRTM) are usually the first preferred data sets. This study aims to analyse the performances of AW3D30, ASTER GDEM, and SRTM for tsunami modeling by comparing them with high-resolution LiDAR DEM tsunami modeling results. For the selected test site of this study, among these three open-source DEMs, tsunami simulations performed with ASTER GDEM exhibits an underestimated coverage of tsunami inundation areas and the maximum flow depth values in those areas and should not be preferred. Whereas, the comparisons of the simulation results using SRTM and AW3D30 as input DEMs revealed that, inundated areas are not less than the LiDAR reference and their error is lower at land use based flow depth analysis. Therefore, AW3D30 and SRTM can be accepted to use in tsunami modeling for the test site of this study considering their limited accuracy and low resolutions, when there is no available higher resolution data with good accuracy or if a rapid hazard assessment is required. However, it should be noted that open-source DEMs may have substantial errors or inadequacy on other coastal areas with different settings. Therefore, it is advised to be cautious in the usage of open-source DEMs in tsunami inundation modeling.
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References
Abrams M, Crippen R, Fujisada H (2020) ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD). Remote Sens 12:1156. https://doi.org/10.3390/rs12071156
ASTER GDEM Validation Team (2009) ASTER GDEM Validation Summary Report
Aytore B, Yalciner AC, Zaytsev A, Cankaya ZC, Suzen ML (2016) Assessment of tsunami resilience of Haydarpaşa port in the sea of Marmara by high-resolution numerical modeling. Earth Planet Sp 68:1–12. https://doi.org/10.1186/s40623-016-0508-z
Bimtaş (2013) İstanbul il sınırları içinde hava Lidar teknolojisiyle elde edilecek Lidar verilerinden sayısal yüzey modelleri ve 3 boyutlu kent modelinin üretilmesi işi
Cankaya ZC, Suzen ML, Yalciner AC, Kolat C, Zaytsev A, Aytore B (2016) A new GIS-based tsunami risk evaluation: MeTHuVA (METU tsunami human vulnerability assessment) at Yenikapı, Istanbul. Earth Planet Sp 68:1–22. https://doi.org/10.1186/s40623-016-0507-0
Carrera-Hernández JJ (2021) Not all DEMs are equal: an evaluation of six globally available 30 m resolution DEMs with geodetic benchmarks and LiDAR in Mexico. Remote Sens Environ 261:112474. https://doi.org/10.1016/j.rse.2021.112474
Courty LG, Soriano-Monzalvo JC, Pedrozo-Acuña A (2019) Evaluation of open-access global digital elevation models (AW3D30, SRTM, and ASTER) for flood modelling purposes. J Flood Risk Manag 12. https://doi.org/10.1111/jfr3.12550
Del González-Moradas MR, Viveen W (2020) Evaluation of ASTER GDEM2, SRTMv3.0, 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
Dogan GG, Pelinovsky E, Zaytsev A, Metin AD, Ozyurt Tarakcioglu G, Yalciner AC, Yalciner B, Didenkulova I (2021a) Long wave generation and coastal amplification due to propagating atmospheric pressure disturbances. Nat Hazards 106:1195–1221. https://doi.org/10.1007/s11069-021-04625-9
Dogan GG, Annunziato A, Hidayat R, Husrin S, Prasetya G, Kongko W, Zaytsev A, Pelinovsky E, Imamura F, Yalciner AC (2021b) Numerical simulations of December 22, 2018 Anak Krakatau tsunami and examination of possible submarine landslide scenarios. Pure Appl Geophys 178:1–20. https://doi.org/10.1007/s00024-020-02641-7
Fereshtehpour M, Karamouz M (2018) DEM resolution effects on coastal flood vulnerability assessment: deterministic and probabilistic approach. Water Resour Res 54:4965–4982. https://doi.org/10.1029/2017WR022318
Gesch D, Oimoen M, Danielson J, Meyer D (2016) Validation of the ASTER global digital elevation model version 3 OVER the conterminous UNITED STATES. Int Arch Photogramm Remote Sens Spatial Inf Sci XLI-B4:143–148. https://doi.org/10.5194/isprsarchives-XLI-B4-143-2016
Gesch DB (2018) Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure. Front Earth Sci 6. https://doi.org/10.3389/feart.2018.00230
Griffin J, Latief H, Kongko W, Harig S, Horspool N, Hanung R, Rojali A, Maher N, Fuchs A, Hossen J, Upi S, Edi Dewanto S, Rakowsky N, Cummins P (2015) An evaluation of onshore digital elevation models for modeling tsunami inundation zones. Front Earth Sci 3. https://doi.org/10.3389/feart.2015.00032
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
Hawker L, Rougier J, Neal J, Bates P, Archer L, Yamazaki D (2018a) Implications of simulating global digital elevation models for flood inundation studies. Water Resour Res 54:7910–7928. https://doi.org/10.1029/2018WR023279
Hawker L, Bates P, Neal J, Rougier J (2018b) 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. https://doi.org/10.3389/feart.2018.00233
Heidarzadeh M, Krastel S, Yalciner AC (2014) The state-of-the-art numerical tools for modeling landslide tsunamis: a short review. In: Krastel S (ed) Submarine mass movements and their consequences, vol 37. Springer, Cham, pp 483–495
Imamura F, Imteaz M (1995) Long waves in two layers: governing equations and numerical model. Sci Tsunami Hazards 13:3–24
Jakeman JD, Nielsen OM, Putten KV, Mleczko R, Burbidge D, Horspool N (2010) Towards spatially distributed quantitative assessment of tsunami inundation models. Ocean Dyn 60:1115–1138. https://doi.org/10.1007/s10236-010-0312-4
Kaiser G, Scheele L, Kortenhaus A, Løvholt F, Römer H, Leschka S (2011) The influence of land cover roughness on the results of high resolution tsunami inundation modeling. Nat Hazards Earth Syst Sci 11:2521–2540. https://doi.org/10.5194/nhess-11-2521-2011
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. https://doi.org/10.1029/2020WR028516
Løvholt F, Griffin J, Salgado-Gálvez M (2015) Tsunami Hazard and Risk Assessment on the Global Scale. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_642-1
Lynett PJ, Gately K, Wilson R, Montoya L, Arcas D, Aytore B, Bai Y, Bricker JD, Castro MJ, Cheung KF, David CG, Dogan GG, Escalante C, González-Vida JM, Grilli ST, Heitmann TW, Horrillo J, Kânoğlu U, Kian R et al (2017) Inter-model analysis of tsunami-induced coastal currents. Ocean Model 114:14–32. https://doi.org/10.1016/j.ocemod.2017.04.003
Mays LW (2010) Water resources engineering. John Wiley & Sons
Mason I (2003) Binary events. Forecast verification: a Practitioner’s guide in atmospheric science. Chichester, John Wiley and Sons
METU-IMM (2018) İstanbul İli Marmara Kıyıları Tsunami Modelleme, Hasar Görebilirlik ve Tehlike Analizi Güncelleme Projesi Sonuç Raporu. https://depremzemin.ibb.istanbul/calismalarimiz/tamamlanmis-calismalar/istanbul-ili-marmara-kiyilarinda-tsunami-kaynakli-risk-arastirmasi/
Mukul M, Srivastava V, Jade S, Mukul M (2017) Uncertainties in the shuttle radar topography Mission (SRTM) heights: insights from the Indian Himalaya and peninsula. Sci Rep 7:41672. https://doi.org/10.1038/srep41672
NASA (2015) The shuttle radar topography Mission (SRTM). Collection User Guide
Ozer Sozdinler C, Yalciner AC, Zaytsev A, Suppasri A, Imamura F (2015) Investigation of hydrodynamic parameters and the effects of breakwaters during the 2011 great East Japan tsunami in Kamaishi Bay. Pure Appl Geophys 172:3473–3491. https://doi.org/10.1007/s00024-015-1051-8
Prakash Mohanty M, Nithya S, Nair AS, Indu J, Ghosh S, Mohan Bhatt C, Srinivasa Rao G, Karmakar S (2020) Sensitivity of various topographic data in flood management: implications on inundation mapping over large data-scarce regions. J Hydrol 590:125523. https://doi.org/10.1016/j.jhydrol.2020.125523
Rabus B, Eineder M, Roth A, Bamler R (2003) The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar. ISPRS J Photogramm Remote Sens 57:241–262. https://doi.org/10.1016/S0924-2716(02)00124-7
Saksena S, Merwade V (2015) Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. J Hydrol 530:180–194. https://doi.org/10.1016/j.jhydrol.2015.09.069
Sampson CC, Smith AM, Bates PD, Neal JC, Alfieri L, Freer JE (2015) A high-resolution global flood hazard model. Water Resour Res 51:7358–7381. https://doi.org/10.1002/2015WR016954
Santillan JR, Makinano-Santillan M (2016) Vertical accuracy assessment of 30-m resolution ALOS, ASTER, and SRTM global DEMS OVER northeastern MINDANAO, PHILIPPINES. Int Arch Photogramm Remote Sens Spatial Inf Sci XLI-B4:149–156. https://doi.org/10.5194/isprsarchives-XLI-B4-149-2016
Stephens E, Schumann G, Bates P (2014) Problems with binary pattern measures for flood model evaluation. Hydrol Process 28:4928–4937. https://doi.org/10.1002/hyp.9979
Tachikawa T, Hato M, Kaku M, Iwasaki A (2011) Characteristics of ASTER GDEM version 2. In: 2011 IEEE International Geoscience 2011, pp. 3657–3660
Tadono T, Ishida H, Oda F, Naito S, Minakawa K, Iwamoto H (2014) Precise global DEM generation by ALOS PRISM. ISPRS Ann. Photogramm Remote Sens Spatial Inf Sci II-4:71–76. https://doi.org/10.5194/isprsannals-II-4-71-2014
Tadono T, Nagai H, Ishida H, Oda F, Naito S, Minakawa K, Iwamoto H (2016) Generation of the 30 m-mesh global digital surface model by ALOS prism. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLI-B4:157–162. https://doi.org/10.5194/isprsarchives-XLI-B4-157-2016
Takaku J, Tadono T, Doutsu M, Ohgushi F, Kai H (2020) Updates of ‘AW3D30’ ALOS global digital surface model with other open access datasets. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLIII-B4-2020:183–189. https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-183-2020
Talchabhadel R, Nakagawa H, Kawaike K, Yamanoi K, Thapa BR (2021) Assessment of vertical accuracy of open source 30m resolution space-borne digital elevation models. Geomatics, Nat Hazards Risk 12:939–960. https://doi.org/10.1080/19475705.2021.1910575
Taubenböck H, Goseberg N, Setiadi N, Lämmel G, Moder F, Oczipka M, Klüpfel H, Wahl R, Schlurmann T, Strunz G, Birkmann J, Nagel K, Siegert F, Lehmann F, Dech S, Gress A, Klein R (2009) "last-mile" preparation for a potential disaster – interdisciplinary approach towards tsunami early warning and an evacuation information system for the coastal city of Padang, Indonesia. Nat Hazards Earth Syst Sci 9:1509–1528. https://doi.org/10.5194/nhess-9-1509-2009
Tufekci D, Suzen ML, Yalciner AC, Zaytsev A (2018) Revised MeTHuVA method for assessment of tsunami human vulnerability of Bakirkoy district, Istanbul. Nat Hazards 90:943–974. https://doi.org/10.1007/s11069-017-3082-1
Tufekci-Enginar D, Suzen ML, Yalciner AC (2021) The evaluation of public awareness and community preparedness parameter in GIS-based spatial tsunami human vulnerability assessment (MeTHuVA). Nat Hazards 105:2639–2658. https://doi.org/10.1007/s11069-020-04416-8
van de Sande B, Lansen J, Hoyng C (2012) Sensitivity of coastal flood risk assessments to digital elevation models. Water 4:568–579. https://doi.org/10.3390/w4030568
Velioglu Sogut D, Yalciner AC (2019) Performance comparison of NAMI DANCE and FLOW-3D® models in tsunami propagation, inundation and currents using NTHMP benchmark problems. Pure Appl Geophys 176:3115–3153. https://doi.org/10.1007/s00024-018-1907-9
Yalciner B, Zaytsev A (2017) Assessment of efficiency and performance in tsunami numerical modeling with GPU: EGU general assembly, EGU2017, proceedings from the conference held 23-28 April, 2017 in Vienna, Austria., p.1246. EGU general assembly.
Yalçıner AC, Alpar B, Altınok Y, Özbay İ, Imamura F (2002) Tsunamis in the sea of Marmara. Mar Geol 190:445–463. https://doi.org/10.1016/S0025-3227(02)00358-4
Yalciner A, Zaytsev A, Aytore B, Insel I, Heidarzadeh M, Kian R, Imamura F (2014) A possible submarine landslide and associated tsunami at the Northwest Nile Delta. Mediterranean Sea oceanog 27:68–75. https://doi.org/10.5670/oceanog.2014.41
Yurtseven H (2019) Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area. Eurasian. J For Sci 10.31195/ejejfs.597460
Zaytsev A, Kostenko I, Kurkin A, Pelinovsky E, Yalçiner AC (2016) The depth effect of earthquakes on tsunami heights in the Sea of Okhotsk. Turkish J Earth Sci 25:289–299. https://doi.org/10.3906/yer-1509-6
Zhang K, Gann D, Ross M, Robertson Q, Sarmiento J, Santana S, Rhome J, Fritz C (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
Acknowledgements
The authors acknowledge Istanbul Metropolitan Municipality (IMM) for providing high resolution data used in this study. The authors are grateful to Finn Løvholt and one anonymous reviewer for their constructive comments and feedbacks that improved the manuscript.
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The high resolution LiDAR data that support the findings of this study are available from Istanbul Metropolitan Municipality but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Istanbul Metropolitan Municipality.
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Tufekci-Enginar, D., Dogan, G.G., Suzen, M.L. et al. Performance analysis of open-source DEMs in tsunami inundation modelling. Earth Sci Inform 15, 2447–2466 (2022). https://doi.org/10.1007/s12145-022-00852-1
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DOI: https://doi.org/10.1007/s12145-022-00852-1