Natural Hazards

, Volume 60, Issue 2, pp 715–726 | Cite as

Climate change impacts on extreme floods II: improving flood future peaks simulation using non-stationary frequency analysis

Original Paper


In the companion paper, Seidou et al. (2011, submitted) have shown that when adequate meteorological data are available to calibrate rainfall-runoff models, using a non-stationary GEV model with the simulated flows can provide a better description of flood peaks distributions than directly using the simulated peaks. Their methodology is extended in this paper to improve future flood peaks simulation under a changing climate. In this case, the rainfall-runoff model is forced with the downscaled outputs of the Canadian General Circulation Model CGCM3. Special attention is paid to the statistical downscaling of precipitations, as the choice of the transfer function has a significant influence on the performance of non-stationary GEV model. Stepwise regression was initially used to describe precipitation occurrence and intensity, but the patterns of the simulated hydrographs were found to be unsatisfactory. After precipitation occurrence model was successfully replaced with an ensemble of regression trees, the non-stationary GEV model was shown to provide a better description of flood peaks in the observation period. The non-stationary GEV model shows that exceedance probabilities on the Kemptville Creek will gradually rise up to 34% above current levels in 2100 for a 20-year service life.


Climate change Deterministic simulations Non-stationary distribution Flood peak 


  1. Boyer C, Chaumond D, Chartier I, Roy A (2010) Impact of climate change on the hydrology of St. Lawrence tributaries. J Hydrol 384(1–2):65–83CrossRefGoogle Scholar
  2. Cheng S, Auld H, Li G, Klaassen J, Li Q (2007) Possible impacts of climate change on freezing rain in south-central Canada using downscaled future climate scenarios. Nat Hazards Earth Sci Syst 7(1):71–87CrossRefGoogle Scholar
  3. Coulibaly P, Dibike YB, Anctil F (2005) Downscaling precipitation and temperature with temporal neural networks. J Hydrometeorol 6(4):483–496CrossRefGoogle Scholar
  4. DAI CGCM3 Predictors (2008) Sets of predictor variables derived from CGCM3 T47 and NCEP/NCAR Reanalysis, version 1.1, November 2009, Montreal, QC, Canada, (
  5. Dibike YB, Coulibaly P (2006) Temporal neural networks for downscaling climate variability and extremes. Special Issue Neural Netw 19(2):135–144Google Scholar
  6. Giorgi F, Hewitson B, Christensen J, Fu C, Jones R, Hulme M, Mearns L, Von Storch H, Whetton P (2001) Regional climate information evaluation and projections. In: Houghton JT et al (eds) Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, p 944Google Scholar
  7. Hessami M, Gachon P, Ouarda T, St-Hilaire A (2008) Automated regression-based statistical downscaling tool. Environ Model Softw 23:813–834CrossRefGoogle Scholar
  8. Kharin VV, Zwiers FW (2005) Estimating extremes in transient climate change simulations. J Clim 18(8):1156–1173CrossRefGoogle Scholar
  9. Mearns LO, Rosenzweig C, Goldberg R (1997) Mean and variance change in climate scenarios: Methods, agricultural applications, and measures of uncertainty. Clim Change 35:367–396CrossRefGoogle Scholar
  10. Seidou O, Ramsay A, Nistor I (2011) Climate change impacts on extreme floods: combining of imperfect deterministic simulations and non-stationary frequency analysis. SubmittedGoogle Scholar
  11. Wang Jiafeng, Zhang Xuebin (2008) Downscaling and projection of winter extreme daily precipitation over North America. J Clim 21:923–937CrossRefGoogle Scholar
  12. Wilby RL, Dawson CW, Barrow EM (2002) SDSM: a decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17:147–159Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity of OttawaOttawaCanada

Personalised recommendations