Prediction of Return Periods of Sewer Flooding Due to Climate Change in Major Cities

기후변화에 따른 주요 도시의 하수도 침수 재현기간 예측

Park, Kyoohong;Yu, Soonyu;Byambadorj, Elbegjargal
박규홍;유순유;뱜바도지 엘베자르갈

  • Received : 2015.11.23
  • Accepted : 2016.01.18
  • Published : 2016.02.28


In this study, rainfall characteristics with stationary and non-stationary perspectives were analyzed using generalized extreme value (GEV) distribution and Gumbel distribution models with rainfall data collected in major cities of Korea to reevaluate the return period of sewer flooding in those cities. As a result, the probable rainfall for GEV and Gumbel distribution in non-stationary state both increased with time(t), compared to the stationary probable rainfall. Considering the reliability of ${\xi}_1$, a variable reflecting the increase of storm events due to climate change, the reliability of the rainfall duration for Seoul, Daegu, and Gwangju in the GEV distribution was over 90%, indicating that the probability of rainfall increase was high. As for the Gumbel distribution, Wonju, Daegu, and Gwangju showed the higher reliability while Daejeon showed the lower reliability than the other cities. In addition, application of the maximum annual rainfall change rate (${\xi}_1{\cdot}t$) to the location parameter made possible the prediction of return period by time, therefore leading to the evaluation of design recurrence interval.


Generalized extreme value (GEV) distribution;Gumbel distribution;climate change;return period;sewer flooding


  1. Byambadorj, E. (2013). Evaluation of sewer flooding and drought by estimating design rainfalls of cities in Korea and Mongolia due to climate change, Mater's Thesis. Chung-Ang University.
  2. Bell, V. A., Kaya, A.L., Jonesb, R.G., Moorea, R.J., and Reynard, N.S. (2009). Use of soil data in a grid-based hydrological model to estimate spatial variation in changing flood risk across the UK. Journal of Hydrology, 377(3-4), 335-350.
  3. Choi, D. (2010), Analysis of Impact of Future Climate Change and Evaluation on Its Effect on Water Resources in Watershed. Master's thesis, Pukyong University.
  4. Coles, G.S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer.
  5. Fowler, H.J. and Kilsby, C.G. (2003). A regional frequency analysis of United Kingdom extreme rainfall from 1961 to 2000. International Journal of Climatology, 23, 1313-1334.
  6. Grum M, Jorgensen A.T, Johansen R.M, and Linde J.J. (2006). The effect of climate change on urban drainage: an evaluation based on regional climate model simulation.
  7. He, Y., Bardossy, A., and Brommundt, J. (2006). Non-stationary flood frequency in southern Germany. The 7th International Conference on Hydroscience and Engineering.
  8. Mailhot, A. and Duchesne, S. (2010). Design criteria of urban drainage infrastructures under climate change. Journal of Water Resources Planning and Management, 136(2), 201-208.
  9. Ministry of Construction and Transportation, (2000) Study on Development of 1999 Water Resources Management Methodology - Map of Probable Rainfall in Korea.
  10. Na, Y. (2009). A Study on the Design Rainfall and Variation of Floods Due to Climate Change. Master's thesis, Sejong Univ.
  11. Kim, B., Kim, B., Kyung, M., and Kim, H. (2008). Evaluation of the effect of climate change on the extreme rainfall and IDF analysis, Korean Water Resources Association, 41(4), 379-394.
  12. Kim, E., Lee, D., and Yoo, C., (2004). Analysis on effect of runoff in Dam Daecheong watershed due to climate change, Korean Water Resources Association, 37, 305-314.
  13. Korean Association of Water and Wastewater (2011). Standard on Sewerage Facilities. 31-33.
  14. Korean Meteorological Adminstration (2011). White Paper on Monsoon Seasons.
  15. Kwon, Y., Park, J., and Kim, T. (2009). Estimation of design probable rainfall considering increasing trend of rainfall depth, Korean Society of Civil Engineers, 29(2), 131-139.
  16. Kwon, J. (2009). Detailing of GCM Climate Change Simulation Scale Using Climate Change Trend Analysis and Random Cascade Model. Master's thesis, Dankook Univ.
  17. Lim, H., Kwon, H., Bae, D., and Kim, S. (2006). Hydrologic Analysis on Watershed of Dam Soyan Due to Climate Change Using CA-Markov technique, Korean Water Resources Association, 39(5), 241-245.
  18. Lee, H. (2012) Analysis of Generalized Extreme Distribution on Design Rainfall Standard Due to Climate Change, Master's thesis, Chung-Ang University.
  19. Lee, H., Ryu, J., Yoo, S., and Park, K. (2012) Analysis of generalized extreme value distribution to estimate stormwater capacity of sewer system under climate change, Korean Society of Water and Wastewater, 26(2), 321-329.
  20. Lee, S. (2010). Regional Variation of Probable Rainfall Due to Climate Change, Master's thesis, Dankook University.
  21. Oh, S. (2004). Change of Flood Runoff in Rivershed Due to Climate Change. Master's thesis, Korea University.
  22. Ouarda, T.B.M.J. and Adlouni, S.E. (2008). Bayesian inference of non-stationary flood frequency models, World Environmental and Water Resources Congress 2008.
  23. Park, K. (2012) Annual Report on Development of Analysis System on Level of Sewer Service and Investigation on Planning and Evaluation System on the Integrated Sewer Asset Management System. Research Center of Urban Sewer and Drainage System.
  24. Ryu, J., Lee, H., Yoo, S, and Park, K. (2014). Statistical evaluation on storm sewer design criteria under climate change in Seoul, South Korea, Urban Water Journal, 11(5), 370-378.
  25. Semadeni-Daviesa, A., Hernebringb, C., Svenssonb, G., and Gustafssonc, L.G. (2008). The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden : Combined sewer system. Journal of Hydrology, 350(1-2), 100-113.
  26. Seo, L. (2011). Development of Method to Estimate the Probable Rainfall Considering Climate Change, Master's thesis, Hanyang University.
  27. The Seoul Institute (2011). Forum on Flood Prevention Policy in Seoul Due to Climate Change.


Supported by : 하수관거관리기술연구단