The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai

Kepeng Xu; Jiayi Fang; Yongqiang Fang; Qinke Sun; Chengbo Wu; Min Liu

doi:10.1007/s13753-021-00377-z

Volume 12 Issue 6

Dec. 2021

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Article Navigation > International Journal of Disaster Risk Science > 2021 > 12(6): 890-902

Kepeng Xu, Jiayi Fang, Yongqiang Fang, Qinke Sun, Chengbo Wu, Min Liu. The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai[J]. International Journal of Disaster Risk Science, 2021, 12(6): 890-902. doi: 10.1007/s13753-021-00377-z

Citation:

Kepeng Xu, Jiayi Fang, Yongqiang Fang, Qinke Sun, Chengbo Wu, Min Liu. The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai[J]. International Journal of Disaster Risk Science, 2021, 12(6): 890-902. doi: 10.1007/s13753-021-00377-z

Citation:

Kepeng Xu, Jiayi Fang, Yongqiang Fang, Qinke Sun, Chengbo Wu, Min Liu. The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai[J]. International Journal of Disaster Risk Science, 2021, 12(6): 890-902. doi: 10.1007/s13753-021-00377-z

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The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai

doi: 10.1007/s13753-021-00377-z

Kepeng Xu^1,2,
Jiayi Fang^{1,2,3
,
,},
Yongqiang Fang^1,2,
Qinke Sun^1,2,
Chengbo Wu^1,2,
Min Liu^{1,2,3
,
,}

1. School of Geographic Sciences, East China Normal University, Shanghai, 200241, China;
2. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China;
3. Institute of Eco-Chongming, East China Normal University, Shanghai, 200241, China

Funds:

Global Development (ECNU-BRGD-202106), and the National Key R&

the China Postdoctoral Science Foundation (2019M651429)

This work is funded by the National Natural Science Foundation of China (42001096, 41730646)

the Shanghai Sailing Program (19YF1413700)

D Program of China (2017YFC1503001, 2017YFE0100700).

East China Normal University

Institute of Belt and Road &

Available Online: 2021-12-27

Abstract

Abstract

Digital Elevation Models (DEMs) play a critical role in hydrologic and hydraulic modeling. Flood inundation mapping is highly dependent on the accuracy of DEMs. Various vertical differences exist among open access DEMs as they use various observation satellites and algorithms. The problem is particularly acute in small, flat coastal cities. Thus, it is necessary to assess the differences of the input of DEMs in flood simulation and to reduce anomalous errors of DEMs. In this study, we first conducted urban flood simulation in the Huangpu River Basin in Shanghai by using the LISFLOOD-FP hydrodynamic model and six open-access DEMs (SRTM, MERIT, CoastalDEM, GDEM, NASADEM, and AW3D30), and analyzed the differences in the results of the flood inundation simulations. Then, we processed the DEMs by using two statistically based methods and compared the results with those using the original DEMs. The results show that: (1) the flood inundation mappings using the six original DEMs are significantly different under the same simulation conditions—this indicates that only using a single DEM dataset may lead to bias of flood mapping and is not adequate for high confidence analysis of exposure and flood management; and (2) the accuracy of a DEM corrected by the Dixon criterion for predicting inundation extent is improved, in addition to reducing errors in extreme water depths—this indicates that the corrected datasets have some performance improvement in the accuracy of flood simulation. A freely available, accurate, high-resolution DEM is needed to support robust flood mapping. Flood-related researchers, practitioners, and other stakeholders should pay attention to the uncertainty caused by DEM quality.
- Digital elevation models,
- Dixon criterion,
- Hydraulic modeling,
- Shanghai,
- Urban flooding

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References(74)

References

Aerts, J.C.J.H., W.J.W. Botzen, K. Emanuel, N. Lin, H. de Moel, and E.O. Michel-Kerjan. 2014. Evaluating flood resilience strategies for coastal megacities. Science 344(6183): 473-475.

Aguilar, F.J., J.P. Mills, J. Delgado, M.A. Aguilar, J.G. Negreiros, and J.L. Pérez. 2010. Modelling vertical error in LiDAR-derived digital elevation models. ISPRS Journal of Photogrammetry and Remote Sensing 65(1): 103-110.

Balica, S.F., N.G. Wright, and F. van der Meulen. 2012. A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Natural Hazards 64(1): 73-105.

Barragán, J.M., and M. de Andrés. 2015. Analysis and trends of the world’s coastal cities and agglomerations. Ocean & Coastal Management 114: 11-20.

Bates, P.D., and A.P.J. De Roo. 2000. A simple raster-based model for flood inundation simulation. Journal of Hydrology 236(1): 54-77.

Bhuyian, M., and A. Kalyanapu. 2018. Accounting digital elevation uncertainty for flood consequence assessment. Journal of Flood Risk Management 11(S2): S1051-S1062.

Cobby, D.M., D.C. Mason, and I.J. Davenport. 2001. Image processing of airborne scanning laser altimetry data for improved river flood modelling. ISPRS Journal of Photogrammetry and Remote Sensing 56(2): 121-138.

Cook, A., and V. Merwade. 2009. Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping. Journal of Hydrology 377(1): 131-142.

Coveney, S., and A.S. Fotheringham. 2011. The impact of DEM data source on prediction of flooding and erosion risk due to sea-level rise. International Journal of Geographical Information Science 25(7): 1191-1211.

Crippen, R., S. Buckley, P. Agram, E. Belz, E. Gurrola, S. Hensley, M. Kobrick, M. Lavalle, et al. 2016. NASADEM global elevation model: Methods and progress. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4: 125-128.

Dang, Y., L. Chen, W. Zhang, D. Zheng, and D. Zhan. 2020. How does growing city size affect residents’ happiness in urban China? A case study of the Bohai rim area. Habitat International 97: Article 102120.

Deng, J.-L., S.-L. Shen, and Y.-S. Xu. 2016. Investigation into pluvial flooding hazards caused by heavy rain and protection measures in Shanghai, China. Natural Hazards 83(2): 1301-1320.

Dixon, W.J. 1950. Analysis of extreme values. The Annals of Mathematical Statistics 21(4): 488-506.

Dovern, J., M.F. Quaas, and W. Rickels. 2014. A comprehensive wealth index for cities in Germany. Ecological Indicators 41: 79-86.

Du, S., H. Gu, J. Wen, K. Chen, and A. Van Rompaey. 2015. Detecting flood variations in Shanghai over 1949-2009 with Mann-Kendall tests and a newspaper-based database. Water 7(5): 1808-1824.

Du, X., H. Guo, X. Fan, J. Zhu, Z. Yan, and Q. Zhan. 2016. Vertical accuracy assessment of freely available digital elevation models over low-lying coastal plains. International Journal of Digital Earth 9(3): 252-271.

Elkhrachy, I. 2018. Vertical accuracy assessment for SRTM and ASTER Digital Elevation Models: A case study of Najran city, Saudi Arabia. Ain Shams Engineering Journal 9(4): 1807-1817.

Fang, J., D. Lincke, S. Brown, R. Nicholls, C. Wolff, J. Merkens, J. Hinkel, A. Vafeidis, et al. 2020. Coastal flood risks in China through the 21st century-An application of DIVA. Science of the Total Environment 704: Article 135311.

Fereshtehpour, M., and M. Karamouz. 2018. DEM resolution effects on coastal flood vulnerability assessment: Deterministic and probabilistic approach. Water Resources Research 54(7): 4965-4982.

Fewtrell, T., P. Bates, M. Horritt, and N. Hunter. 2008. Evaluating the effect of scale in flood inundation modelling in urban environments. Hydrological Processes 22(26): 5107-5118.

Gallant, J. 2011. Adaptive smoothing for noisy DEMs. Geomorphometry 2011, 07-11 September 2011, Redlands, California. https://gis-lab.info/docs/gallant2011_adaptive_smoothing_for_noisy_dems.pdf. Accessed 18 Oct 2021.

Gesch, D. 2018. Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure. Frontiers in Earth Science 6: Article 230.

Gu, X., Q. Zhang, V.P. Singh, C. Song, P. Sun, and J. Li. 2019. Potential contributions of climate change and urbanization to precipitation trends across China at national, regional and local scales. International Journal of Climatology 39(6): 2998-3012.

Hawker, L., P. Bates, J. Neal, and J. Rougier. 2018. Perspectives on Digital Elevation Model (DEM) simulation for flood modeling in the absence of a high-accuracy open access global DEM. Frontiers in Earth Science 6: Article 233.

Hawker, L., J. Rougier, J. Neal, P. Bates, L. Archer, and D. Yamazaki. 2018. Implications of simulating global digital elevation models for flood inundation studies. Water Resources Research 54: 7910-7928.

Holmes, K.W., O.A. Chadwick, and P.C. Kyriakidis. 2000. Error in a USGS 30-meter digital elevation model and its impact on terrain modeling. Journal of Hydrology 233(1): 154-173.

Hutchinson, M.F. 1989. A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. Journal of Hydrology 106(3-4): 211-232.

Jafarzadegan, K., and V. Merwade. 2017. A DEM-based approach for large-scale floodplain mapping in ungauged watersheds. Journal of Hydrology 550: 650-662.

Karamuz, E., R. Romanowicz, and J. Doroszkiewicz. 2020. The use of unmanned aerial vehicles in flood hazard assessment. Journal of Flood Risk Management 13(4): Article e12622.

Kenward, T., D.P. Lettenmaier, E.F. Wood, and E. Fielding. 2000. Effects of Digital Elevation Model accuracy on hydrologic predictions. Remote Sensing of Environment 74(3): 432-444.

Kim, Y., Y. Tak, M. Park, and B. Kang. 2020. Improvement of urban flood damage estimation using a high-resolution digital terrain. Journal of Flood Risk Management 13(S1): Article e12575.

Kulp, S., and B. Strauss. 2018. Coastal DEM: A global coastal digital elevation model improved from SRTM using a neural network. Remote Sensing of Environment 206(2): 231-239.

Kulp, S., and B. Strauss. 2019. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nature Communications 10(1): Article 4844.

Kyriakidis, P., M. Ashton, and F. Michael. 1999. Geostatistics for conflation and accuracy assessment of digital elevation models. International Journal of Geographical Information Science 13(7): 677-707.

Lesser, G., J. Roelvink, J. Kester, and G. Stelling. 2004. Development and validation of a three-dimensional morphological model. Coastal Engineering 51(8): 883-915.

Lim, N., and S. Brandt. 2019. Flood map boundary sensitivity due to combined effects of DEM resolution and roughness in relation to model performance. Geomatics, Natural Hazards and Risk 10(1): 1613-1647.

Lin, N., R.E. Kopp, B.P. Horton, and J.P. Donnelly. 2016. Hurricane Sandy’s flood frequency increasing from year 1800 to 2100. Proceedings of the National Academy of Sciences 113(43): Article 12071.

Mukherjee, S., P.K. Joshi, S. Mukherjee, A. Ghosh, R.D. Garg, and A. Mukhopadhyay. 2013. Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). International Journal of Applied Earth Observation and Geoinformation 21(1): 205-217.

Najibi, N., and N. Devineni. 2018. Recent trends in the frequency and duration of global floods. Earth System Dynamics 9(2): 757-783.

Niu, G., and G. Zhao. 2018. Living condition among China’s rural-urban migrants: Recent dynamics and the inland-coastal differential. Housing Studies 33(3): 476-493.

Pedrozo-Acuña, A., J. Rodríguez-Rincón, M. Arganis-Juárez, R. Domínguez-Mora, and F. González Villareal. 2015. Estimation of probabilistic flood inundation maps for an extreme event: Pánuco River, México. Journal of Flood Risk Management 8(2): 177-192.

Reuter, H.I., A. Nelson, and A. Jarvis. 2007. An evaluation of void-filling interpolation methods for SRTM data. International Journal of Geographical Information Science 21(9): 983-1008.

Rodriguez, E., C.S. Morris, and J.E. Belz. 2006. A global assessment of SRTM performance. Photogrammetric Engineering & Remote Sensing 72(3): 249-260.

Saksena, S., and V. Merwade. 2015. Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. Journal of Hydrology 530: 180-194.

Samela, C., T.J. Troy, and S. Manfreda. 2017. Geomorphic classifiers for flood-prone areas delineation for data-scarce environments. Advances in Water Resources 102: 13-28.

Sampson, C., A. Smith, P. Bates, J. Neal, and M. Trigg. 2016. Perspectives on open access high resolution Digital Elevation Models to produce global flood hazard layers. Frontiers in Earth Science 3: Article 85.

SAMR (State Administration for Market Regulation). 2008. Statistical interpretation of data-Detection and treatment of outliers in the normal sample. Beijing: SAMR. (in Chinese).

Sanders, B.F. 2007. Evaluation of on-line DEMs for flood inundation modeling. Advances in Water Resources 30(8): 1831-1843.

Savage, J., F. Pianosi, P. Bates, J. Freer, and T. Wagener. 2016. Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model. Water Resources Research 52(11): 9146-9163.

Schumann, G., and P. Bates. 2018. The need for a high-accuracy, open-access global DEM. Frontiers in Earth Science 6: Article 225.

SMSB (Shanghai Municipal Statistics Bureau). 2020. Shanghai statistical yearbook 2020. Beijing: China Statistics Press. (in Chinese).

Soille, P., J. Vogt, and R. Colombo. 2003. Carving and adaptive drainage enforcement of grid digital elevation models. Water Resources Research 39(12): Article 1366.

Stephens, E., G. Schumann, and P. Bates. 2014. Problems with binary pattern measures for flood model evaluation. Hydrological Processes 28(18): 4928-4937.

Surjan, A., G.A. Parvin, Atta-ur-Rahman, and R. Shaw. 2016. Expanding coastal cities: An increasing risk. In Urban disasters and resilience in Asia, ed. R. Shaw, Atta-ur-Rahman, A. Surjan, and G.A. Parvin, 79-90. Amsterdam: Elsevier.

Tachikawa, T., M. Hato, M. Kaku, and A. Iwasaki. 2011. Characteristics of ASTER GDEM version 2. In Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, 24-29 July, Vancouver, BC, Canada, 3657-3660.

Tachikawa, T., M. Kaku, A. Iwasaki, D. Gesch, M. Oimoen, Z. Zhang, J. Danielson, and T. Krieger et al. 2011. ASTER global Digital Elevation Model version 2-Summary of validation results. Washington, DC: NASA.

Tadono, T., H. Ishida, F. Oda, S. Naito, K. Minakawa, and H. Iwamoto. 2014. Precise global DEM generation by ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-4: 71-76.

Takagi, M. 1998. Accuracy of digital elevation model according to spatial resolution. International Archives of Photogrammetry and Remote Sensing 32(4): 613-617.

Takaku, J., T. Tadono, K. Tsutsui, and M. Ichikawa. 2016. Validation of "AW3D" global DSM generated from ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences III-4: 25-31.

Talchabhadel, R., H. Nakagawa, K. Kawaike, K. Yamanoi, and B.R. Thapa. 2021. Assessment of vertical accuracy of open source 30m resolution space-borne digital elevation models. Geomatics, Natural Hazards and Risk 12(1): 939-960.

Vaze, J., J. Teng, and G. Spencer. 2010. Impact of DEM accuracy and resolution on topographic indices. Environmental Modelling & Software 25(10): 1086-1098.

Wang, J., W. Gao, S. Xu, and L. Yu. 2012. Evaluation of the combined risk of sea level rise, land subsidence, and storm surges on the coastal areas of Shanghai, China. Climatic Change 115(3): 537-558.

Wechsler, S.P. 2007. Uncertainties associated with digital elevation models for hydrologic applications: A review. Hydrology and Earth System Sciences 11(4): 1481-1500.

Wechsler, S.P., and C.N. Kroll. 2006. Quantifying DEM uncertainty and its effect on topographic parameters. Photogrammetric Engineering & Remote Sensing 72(9): 1081-1090.

Wen, K., and Y. Xu. 2006. China meteorological disaster dictionary Shanghai volume. Beijing: Meteorological Press. (in Chinese).

Wong, W., S. Tsuyuki, K. Ioki, and M. Phua. 2014. Accuracy assessment of global topographic data (SRTM & ASTER GDEM) in comparison with lidar for tropical montane forest. In Proceedings of the 35th Asian Conference on Remote Sensing 2014, 27-31 October 2014, Nay Pyi Taw, Myanmar, 722-727.

Yamazaki, D., D. Ikeshima, R. Tawatari, T. Yamaguchi, F. O’Loughlin, J.C. Neal, C. Sampson, S. Kanae, and P.D. Bates. 2017. A high-accuracy map of global terrain elevations. Geophysical Research Letters 44(11): 5844-5853.

Yang, Y., J. Yin, M. Ye, D. She, and J. Yu. 2020. Multi-coverage optimal location model for emergency medical service (EMS) facilities under various disaster scenarios: A case study of urban fluvial floods in the Minhang district of Shanghai, China. Natural Hazards and Earth System Sciences 20(1): 181-195.

Yin, J., D. Yu, and B. Liao. 2020. A city-scale assessment of emergency response accessibility to vulnerable populations and facilities under normal and pluvial flood conditions for Shanghai, China. Environment and Planning B: Urban Analytics and City Science. https://doi.org/10.1177/2399808320971304

Yin, J., D. Yu, Z. Yin, J. Wang, and S. Xu. 2013. Multiple scenario analyses of Huangpu River flooding using a 1D/2D coupled flood inundation model. Natural Hazards 66(2): 577-589.

Yu, D., and S.N. Lane. 2006. Urban fluvial flood modelling using a two-dimensional diffusion-wave treatment, part 1: Mesh resolution effects. Hydrological Processes 20(7): 1541-1565.

Yuan, Z. 1999. Floods and drought in Shanghai. Nanjing: Hohai University Press. (in Chinese).

Zhang, K., D. Gann, M. Ross, Q. Robertson, J. Sarmiento, S. Santana, J. Rhome, and C. Fritz. 2019. Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for Hispaniola and implications for mapping vulnerability to coastal flooding. Remote Sensing of Environment 225: 290-306.

Zhao, G., P. Bates, and J. Neal. 2020. The impact of dams on design floods in the conterminous US. Water Resources Research 56(3): e2019WR025380.

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