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Constructing rainfall depth-frequency curves considering a linear trend in rainfall observations

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Abstract

Comprehensive flood prevention plans are established in large basins to cope with recent abnormal floods in South Korea. In order to make economically effective plans, appropriate design rainfalls are critically determined from the rainfall depth-frequency curves which take the occurrence of abnormal floods into consideration. Conventional approaches to construct the rainfall depth-frequency curves are based on the stationarity assumption. However, this assumption has a critical weak aspect in that it cannot reflect non-stationarities in rainfall observations. As an alternative, this study suggests the non-stationary Gumbel model (NSGM) which incorporates a linear trend of rainfall observations into rainfall frequency analysis to construct the rainfall depth-frequency curves. A comparison of various schemes employed in the model found that the proposed NSGM permits the estimation of the distribution parameters even when shifted in the future by using linear relationships between rainfall statistics and distribution parameters, and produces more acceptable estimates of design rainfalls in the future than the conventional model. The NSGM was applied at several stations in South Korea and then expected the design rainfalls to increase by up to 15–30% in 2050.

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References

  • Communities and Local Government (2010) Planning policy statement 25: Development and flood risk, London

  • Conover WJ (1971) Practical nonparametric statistics. Wiley, New York

    Google Scholar 

  • Cunderlik JM, Burn DH (2003) Non-stationary pooled flood frequency analysis. J Hydrol 276:210–223

    Article  Google Scholar 

  • Dooge JCI, Kuusisto E, Askew A, Bergström A, Bonnell M, Burn D, Edmunds WM, Feddes RA, Hall A, Kundzewicz Z, Liebscher HJ, Lins H, Llasat MC, Lucero O, Pilon P, Roald L, Saelthun NR, Schädler B, Shiklomanov I, Strupczewski WG, Vailmäe R, Varis O, Vorosmarty C (1998) Climate and water—a 1998 perspective. In: Proceedings of the second international conference on climate and water, Espoo

  • Ekström M, Fowler HK, Kilsby CG, Jones PD (2005) New estimates of future changes in extreme rainfall across the UK using regional climate model integrations 2. Future estimates and use in impact studies. J Hydrol 300:234–251

    Article  Google Scholar 

  • El Adlouni S, Ouarda TBMJ, Zhang X, Roy R, Bobée B (2007) Generalized maximum likelihood estimators for the nonstationary generalized extreme value model. Water Resour Res 43:W03410. doi:10.1029/2005WR004545

    Article  Google Scholar 

  • Fowler HK, Ekstöm M, Kilsby CG, Jones PD (2005) New estimates of future changes in extreme rainfall across the UK using regional climate model integrations 1. Assessment of control climate. J Hydrol 300:212–233

    Article  Google Scholar 

  • Hamilton LC (1992) Regression with graphics: a second course in applied statistics. Duxbury Press, Belmont

    Google Scholar 

  • He Y, Bárdossy A, Brommundt J (2006) Non-stationary flood frequency analysis in southern Germany. In: Proceedings of the seventh international conference on hydroscience and engineering, Philadelphia

  • Jung I-W, Bae D-H, Kim G (2011) Recent trends of mean and extreme precipitation in Korea. Int J Climatol 31:359–370

    Article  Google Scholar 

  • Katz RW, Parlange MB, Naveau P (2002) Statistics of extremes in hydrology. Adv Water Resour 25:1287–1304

    Article  Google Scholar 

  • Khaliq MN, Ouarda TBMJ, Ondo J-C, Gachon P, Bobée B (2006) Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: a review. J Hydrol 329:534–552

    Article  Google Scholar 

  • Kuusisto E, Lemmelã R, Liebscher H, Nobilis F (1994) Climate and water in Europe; some recent issues. Technical report of Rapporteurs—Working Group on Hydrology, World Meteorological Organization, Helsinki

  • Mathworks (2010) Optimization toolbox: user’s guide. The Mathworks Inc., Natick

  • McCuen RH (2002) Modeling hydrologic change: statistical methods. Lewis Publishers, Washington

    Book  Google Scholar 

  • Ministry of Construction and Transport (MOCT) (2000) Maps for design rainfalls in South Korea. Report for the research and development of water resources management in 1999, Seoul

  • Park J-S, Kang H-S, Lee YS, Lim M-K (2010) Changes in the extreme daily rainfall in South Korea. Int J Climatol doi:10.1002/joc.2236

  • Ramesh NI, Davison AC (2002) Local models for exploratory analysis of hydrological extremes. J Hydrol 256:106–119

    Article  Google Scholar 

  • Renard B, Lang M, Bois P (2006) Statistical analysis of extreme events in a non-stationary context via a Bayesian framework: case study with peak-over-threshold data. Stoch Environ Res Risk Assess 21:97–112

    Article  Google Scholar 

  • Sankarasubramanian A, Lall U (2003) Flood quantiles in a changing climate: seasonal forecasts and causal relations. Water Resour Res 29(5). doi:10.1029/2002WR001593

  • Stakhiv G (2010) Practical approaches to water management under climate change uncertainty. Workshop on nonstationarity, hydrologic frequency analysis, and water management. Colorado Water Institute Information Series, No. 109

  • Strupczewski WG, Kaczmarek Z (2001) Non-stationary approach to at-site flood frequency modeling. II. Weighted least squares estimation. J Hydrol 248:143–151

    Article  Google Scholar 

  • Strupczewski WG, Singh VP, Feluch W (2001a) Non-stationary approach to at-site flood frequency modeling. I. Maximum likelihood estimation. J Hydrol 248:123–142

    Article  Google Scholar 

  • Strupczewski WG, Singh VP, Mitosek HT (2001b) Non-stationary approach to at-site flood frequency modeling. III. Flood analysis of Polish rivers. J Hydrol 248:152–167

    Article  Google Scholar 

  • Zhang X, Zweirs FW, Li G (2004) Monte Carlo experiments on the detection of trends in extreme values. J Clim 17:1945–1952

    Article  Google Scholar 

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Acknowledgments

This work was supported by grants from the National Research Foundation (No. 2010-0015578), Ministry of Education, Science and Technology, and Natural Hazard Mitigation Research Group (NEMA-NH-2010-35), National Emergency Management Agency, South Korea. All supports are gratefully acknowledged. Dr. Ungtae Kim at the University of Tennessee is thanked for his technical assistance on the modeling. The authors also thank the anonymous reviewers for their constructive comments and corrections.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Hanyang University, Seoul, 133-791, Korea

    Lynn Seo & Minha Choi

  2. Department of Civil and Environmental Engineering, Hanyang University, Ansan, 426-791, Korea

    Tae-Woong Kim

  3. Department of Civil Engineering, Chonbuk National University, Jeonju, 561-756, Korea

    Hyun-Han Kwon

Authors
  1. Lynn Seo
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  2. Tae-Woong Kim
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  3. Minha Choi
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  4. Hyun-Han Kwon
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Correspondence to Tae-Woong Kim.

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For Stochastic Environmental Research and Risk Assessment November 29, 2011.

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Seo, L., Kim, TW., Choi, M. et al. Constructing rainfall depth-frequency curves considering a linear trend in rainfall observations. Stoch Environ Res Risk Assess 26, 419–427 (2012). https://doi.org/10.1007/s00477-011-0549-6

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  • DOI: https://doi.org/10.1007/s00477-011-0549-6

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