Natural Hazards

, Volume 63, Issue 2, pp 1203–1217 | Cite as

Flood risk assessment using regional regression analysis

Original Paper


This study aimed to create a flood risk map for ungauged regions, which have limited flood damage data and other relevant data. The fact that there is a shortage of data that are critical for the establishment of a flood assessment and mitigation plan is not surprising even in developed countries like South Korea. To address this problem, the regional regression concept in statistical hydrology was introduced to the flood risk assessment field in this study, and it was framed with a series of two regression functions: flood damage and regional coefficients. As the second regression function utilizes the local socioeconomic variables, the resulting flood risk map can reflect the spatial characteristics well. The proposed methodology was applied to create flood risk maps for the three metropolitan areas in South Korea. The comparison of the proposed methodology with the existing methods revealed that only the proposed methodology can produce a statistically meaningful flood risk map based on a recent major flood in 2001.


Regional regression Flood damage Multiple regression Flood risk map 



The research presented in this paper was carried out as part of the Climate Change Assessment & Projection for Hydrology in Korea (CCAPH-K) project. And, this project was funded by the Korea Institute of Construction & Transportation Technology Evaluation and Planning (KICTEP).


  1. Abdulla FA, Lettenmaier DP (1997) Development of regional parameter estimation for a macroscale hydrologic model. J Hydrol 197:230–257CrossRefGoogle Scholar
  2. Boyd E, Levitan M, Van Heerden I (2005) Further specification of the dose-response relationship for flood fatality estimation. National Science Foundation and Ministry of Disaster and Relief, Government of Bangladesh. Dhaka, 19–21 Dec 2005Google Scholar
  3. DEFRA (Department of the Environment, Food, and Rural Affairs) (2006) Flood risks to people: a guide document. Environment Agency, LondonGoogle Scholar
  4. Fernadez W, Vogel RM, Sankarasubramanian A (2000) Regional calibration of a watershed model. Hydrol Sci J 45–5:689–707CrossRefGoogle Scholar
  5. Hirsch RM, Helsel DR, Cohn TA, Gilroy EJ (1993) Statistical analysis of hydrologic data. In: Maidment DR (ed) Handbook of hydrology, Chap. 17. McGraw-Hill, New YorkGoogle Scholar
  6. Ikeda S (2006) An integrated risk analysis framework for emerging disaster risks: towards a better risk management of flood disaster in urban communities. Terrapub, TokyoGoogle Scholar
  7. Jung SW, Lee DH, Moon YJ, Kim KH (2001) Potential flood damage (PFD) assessment. Proceedings of the Korea water resources association conference (in Korean), pp 601–606Google Scholar
  8. Kim HS (2006) Potential risk and damage estimation of urban flood II: MOCT core technology development project report. Ministry of Construction and Transportation, Korea (in Korean)Google Scholar
  9. Kim DS, Kang NJ (2008) Regression analysis: basics and applications. Nanam, Korea (in Korean)Google Scholar
  10. Kottegoda NT, Rosso R (1997) Statistics, probability, and reliability for civil and environmental engineers. McGraw-Hill, New YorkGoogle Scholar
  11. Munich Re (2003) Topics annual review: 2002 natural catastrophes. Munich Reinsurance Company, GermanyGoogle Scholar
  12. National Emergency Management Agency (2006) Rainfall frequency analysis using FARD. NEMA, Korea (in Korean)Google Scholar
  13. National Research Council (1983) Risk assessment in the federal government: managing the process. National Academy Press, Washington DCGoogle Scholar
  14. Olds EG (1938) Distributions of sums of squares of rank differences for small numbers of individuals. Ann Math Stat 9(2):133–148CrossRefGoogle Scholar
  15. SCEMD (2002) State of South Carolina hazards assessment. South Carolina Emergency Management Division, South CarolinaGoogle Scholar
  16. Schmidt-Thome P (ed) (2005) The spatial effects and management of natural and technological hazards in general and in relation to climate change. ESPON Project 1.3.1, Geological Survey of Finland, FinlandGoogle Scholar
  17. Seoul Development Institute (2002) A study of the management system for areas prone to floods in South Korea. SDI, Seoul (in Korean), pp 12–36Google Scholar
  18. SPSS INC (2009) SPSS base 17.0 user guide. SPSS INC, ChicagoGoogle Scholar
  19. Stedinger JR, Vogel RM, Foufoula-Georgiou E (1993) Frequency analysis of extreme events. In: Maidment DR (ed) Handbook of hydrology, Chap 18. McGraw-Hill, New YorkGoogle Scholar
  20. United Nations Development Programme (2004) Reducing disaster risk: a challenge for development. United Nations Development Programme, Bureau for Crisis Prevention and Recovery, New YorkGoogle Scholar
  21. Zou LL (2009) Impact assessment using DEA of coastal hazards on social economy in Southeast Asia. Natl Hazards 48:167–189CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringSeoul National UniversitySeoulRepublic of Korea
  2. 2.Daelim Industrial Co., LTDSeoulRepublic of Korea

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