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Natural Hazards

, Volume 94, Issue 3, pp 1187–1210 | Cite as

Evaluating typical flood risks in Yangtze River Economic Belt: application of a flood risk mapping framework

  • Chengwei Lu
  • Jianzhong Zhou
  • Zhongzheng He
  • Shuai Yuan
Original Paper

Abstract

The Yangtze River Economic Belt is one of the three national strategies of China, while flood risk is one of the most important concerns in the development of Yangtze River Economic Belt. In order to decrease the risks caused by floods, complete flood management system and adequate pre-arranged planning are desiderated to be researched in advance. This study considers two typical situations of flood risk, in which one is sluice-control situation in flood detention area and another is dike-break situation in flood-protected area, and proposes a framework for flood risk mapping. The results show that the losses caused by flood hazards are massive both in the two typical cases when extreme floods happen. The economic losses of different indicators are of great difference in flood detention area and flood-protected area, respectively. The framework effectively handles the complex boundaries in the Yangtze River Economic Belt and provides more accurate flood routing information. The evacuation plan module which has been incorporated in the framework also provides informative assistance for emergent action of evacuation under urgent condition.

Keywords

Flood risk Dike-break Sluice-control Complex boundaries handling techniques Evacuation plan 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 91547208, 51509095 and 51579108).

References

  1. Ahmadisharaf E, Kalyanapu AJ, Chung ES (2017) Sustainability-based flood hazard mapping of the swannanoa river watershed. Sustainability 9:1–15.  https://doi.org/10.3390/su9101735 CrossRefGoogle Scholar
  2. Alho P, Aaltonen J (2008) Comparing a 1D hydraulic model with a 2D hydraulic model for the simulation of extreme glacial outburst floods. Hydrol Process 22:1537–1547.  https://doi.org/10.1002/hyp.6692 CrossRefGoogle Scholar
  3. Alkema D (2003) Flood risk assessment for EIA: an example of a motorway near Trento, Italy. Med Phys 34:2593–2594Google Scholar
  4. Andrei A, Robert B, Erika B (2017) Numerical limitations of 1D hydraulic models using MIKE11 or HEC-RAS software—case study of Baraolt River, Romania. In: World multidisciplinary civil engineering-architecture-urban planning symposium-Wmcaus.  https://doi.org/10.1088/1757-899x/245/7/072010
  5. Apel H, Merz B, Thieken AH (2009) Influence of dike breaches on flood frequency estimation. Comput Geosci 35:907–923.  https://doi.org/10.1016/j.cageo.2007.11.003 CrossRefGoogle Scholar
  6. Chow VT (1959) In: Chow VT, Harmer ED (eds) Open-channel hydraulics, vol 54, Civil engineering, vol 6, International student edition. McGraw-Hill Book Company, New York, pp 182–192. https://doi.org/ISBN 07-010776-9
  7. DHI (2014a) MIKE 11: a modelling system for rivers and channels user guide. DHI Water and Environment, HørsholmGoogle Scholar
  8. DHI (2014b) MIKE FLOOD: 1D–2D modelling user manual. DHI Water and Environment, HørsholmGoogle Scholar
  9. Glas H, Jonckheere M, Mandal A, James-Williamson S, De Maeyer P, Deruyter G (2017) A GIS-based tool for flood damage assessment and delineation of a methodology for future risk assessment: case study for Annotto Bay, Jamaica. Nat Hazards 88:1867–1891.  https://doi.org/10.1007/s11069-017-2920-5 CrossRefGoogle Scholar
  10. Islam MDM, Sado K (2000) Development of flood hazard maps of Bangladesh using NOAA-AVHRR images with GIS. Hydrol Sci J 45:337–355.  https://doi.org/10.1080/02626660009492334 CrossRefGoogle Scholar
  11. Jancikova A, Unucka J (2015) DTM impact on the results of dam break simulation in 1D hydraulic models. In: GIS Ostrava compilation surface models for geosciences. Tech Univ Ostrava, Ostrava, Czech Republic, pp 125–136.  https://doi.org/10.1007/978-3-319-18407-4_11
  12. Jing R, Nedovic-Budic Z (2016) Integrating spatial planning and flood risk management: a new conceptual framework for the spatially integrated policy infrastructure. Comput Environ Urban Syst 57:68–79.  https://doi.org/10.1016/j.compenvurbsys.2016.01.008 CrossRefGoogle Scholar
  13. Jongman B, Ward PJ, Aerts JCJH (2012) Global exposure to river and coastal flooding: long term trends and changes. Glob Environ Change Hum Policy Dimens 22:823–835.  https://doi.org/10.1016/j.gloenvcha.2012.07.004 CrossRefGoogle Scholar
  14. Jongman B et al (2014) Increasing stress on disaster-risk finance due to large floods. Nat Clim Change 4:264–268.  https://doi.org/10.1038/NCLIMATE2124 CrossRefGoogle Scholar
  15. Kabenge M, Elaru J, Wang HT, Li FT (2017) Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index. Nat Hazards 89:1369–1387.  https://doi.org/10.1007/s11069-017-3024-y CrossRefGoogle Scholar
  16. Kalyanapu AJ, Judi DR, McPherson TN, Burian SJ (2012) Monte Carlo-based flood modelling framework for estimating probability weighted flood risk. J Flood Risk Manag 5:37–48.  https://doi.org/10.1111/j.1753-318X.2011.01123.x CrossRefGoogle Scholar
  17. Kourgialas NN, Karatzas GP (2017) A national scale flood hazard mapping methodology: the case of Greece—protection and adaptation policy approaches. Sci Total Environ 601:441–452.  https://doi.org/10.1016/j.scitotenv.2017.05.197 CrossRefGoogle Scholar
  18. Kundzewicz ZW et al (2014) Flood risk and climate change: global and regional perspectives. Hydrol Sci J 59:1–28.  https://doi.org/10.1080/02626667.2013.857411 CrossRefGoogle Scholar
  19. Leopardi A, Oliveri E, Greco M (2002) Two-dimensional modeling of floods to map risk-prone areas. J Water Resour Plan Manag ASCE 128:168–178.  https://doi.org/10.1061/(ASCE)0733-9496(2002)128:3(168) CrossRefGoogle Scholar
  20. Liu Y, Zhou J, Song L, Zou Q, Guo J, Wang Y (2014) Efficient GIS-based model-driven method for flood risk management and its application in central China. Nat Hazards Earth Syst Sci 14:331–346.  https://doi.org/10.5194/nhess-14-331-2014 CrossRefGoogle Scholar
  21. Lu C, Zhou J, Jiang Y, Weng Z, Liu Y, Yuan S (2017) Flood routing numerical simulation in jingjiang diversion area based on MIKE FLOOD model. J Basic Sci Eng 25:905–916.  https://doi.org/10.16058/j.issn.1005-0930.2017.05.004 CrossRefGoogle Scholar
  22. Meyer V, Scheuer S, Haase D (2009) A multicriteria approach for flood risk mapping exemplified at the Mulde river, Germany. Nat Hazards 48:17–39.  https://doi.org/10.1007/s11069-008-9244-4 CrossRefGoogle Scholar
  23. Morgan A, Olivier D, Nathalie B, Claire-Marie D, Philippe G (2016) High-resolution modelling with bi-dimensional shallow water equations based codes-high-resolution topographic data use for flood hazard assessment over urban and industrial environments. In: 12th international conference on hydroinformatics (Hic 2016)-smart water for the future, vol 154, pp 853–860.  https://doi.org/10.1016/j.proeng.2016.07.453
  24. Osti R (2016) Framework, approach and process for investment road mapping: a tool to bridge the theory and practices of flood risk management. Water Policy 18:419–444.  https://doi.org/10.2166/wp.2015.121 CrossRefGoogle Scholar
  25. Papaioannou G, Loukas A, Vasiliades L, Aronica GT (2016) Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach. Nat Hazards 83:S117–S132.  https://doi.org/10.1007/s11069-016-2382-1 CrossRefGoogle Scholar
  26. Party CCotCC (2016) Outline of Yangtze River economic belt development plan. Party CCotCC, BeijingGoogle Scholar
  27. Pelletier JD, Mayer L, Pearthree PA, House PK, Demsey KA, Klawon JE, Vincent KR (2005) An integrated approach to flood hazard assessment on alluvial fans using numerical modeling, field mapping, and remote sensing. Geol Soc Am Bull 117:1167–1180.  https://doi.org/10.1130/B255440.1 CrossRefGoogle Scholar
  28. Rahmati O, Zeinivand H, Besharat M (2016) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat Nat Hazards Risk 7:1000–1017.  https://doi.org/10.1080/19475705.2015.1045043 CrossRefGoogle Scholar
  29. Resources CWRCotMoW (2012) The comprehensive planning of Yangtze River Basin (2012–2030). Changjiang Water Resources Commission of the Ministry of Water Resources, WuhanGoogle Scholar
  30. Sande CJVD, Jong SMD, Roo APJD (2003) A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. Int J Appl Earth Obs Geoinf 4:217–229.  https://doi.org/10.1016/S0303-2434(03)00003-5 CrossRefGoogle Scholar
  31. Smith L, Liang Q, James P, Lin W (2017) Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework. J Flood Risk Manag 10:370–380.  https://doi.org/10.1111/jfr3.12154 CrossRefGoogle Scholar
  32. Stefanidis S, Stathis D (2013) Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat Hazards 68:569–585.  https://doi.org/10.1007/s11069-013-0639-5 CrossRefGoogle Scholar
  33. Timbadiya PV, Patel PL, Porey PD (2014) A 1D–2D coupled hydrodynamic model for river flood prediction in a coastal urban floodplain. J Hydrol Eng 20:05014017.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0001029 CrossRefGoogle Scholar
  34. Wang W (1998) Urban transportation planning theory and its application. Publishing House of Southeast University, NanjingGoogle Scholar
  35. Wang T, Zhou J, Jiang Y, Weng Z, Liu Y, Zhang C (2016) Flood refuge and migration model based on network flow. J Nat Disasters 25:56–64.  https://doi.org/10.13577/j.jnd.2016.0107 CrossRefGoogle Scholar
  36. Winsemius HC et al (2016) Global drivers of future river flood risk. Nat Clim Change 6:381–385.  https://doi.org/10.1038/NCLIMATE2893 CrossRefGoogle Scholar
  37. Zavattero E, Du MX, Ma Q, Delestre O, Gourbesville P (2016) 2d sediment transport modelling in high energy river—application to Var River, France. In: 12th international conference on hydroinformatics (Hic 2016)-smart water for the future, pp 536–543.  https://doi.org/10.1016/j.proeng.2016.07.549
  38. Zhang W, Zhou JZ, Liu Y, Chen X, Wang C (2016) Emergency evacuation planning against dike-break flood: a GIS-based DSS for flood detention basin of Jingjiang in central China. Nat Hazards 81:1283–1301.  https://doi.org/10.1007/s11069-015-2134-7 CrossRefGoogle Scholar
  39. Zheng N, Lu F, Duan Y (2010) Dynamic dual graph model for turn delays on road networks. J Image Graph 15:915–920Google Scholar
  40. Zhongmin L, Jun W, Ye S, Zhongbo Y (2008) Study on GIS-based flood risk map for flood detention area. In: Geoinformatics 2008 and joint conference on GIS and built environment: monitoring and assessment of natural resources and environments, Guangzhou, China. SPIE, p 71450F.  https://doi.org/10.1117/12.812991

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Chengwei Lu
    • 1
  • Jianzhong Zhou
    • 1
    • 2
  • Zhongzheng He
    • 1
  • Shuai Yuan
    • 1
  1. 1.School of Hydropower and Information EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.Hubei Key Laboratory of Digital Valley Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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