Regional Environmental Change

, Volume 13, Supplement 1, pp 25–33 | Cite as

The impact of climate change on rainfall Intensity–Duration–Frequency (IDF) curves in Alabama

  • Golbahar Mirhosseini
  • Puneet Srivastava
  • Lydia Stefanova
Original Article


Changes in the hydrologic cycle due to increase in greenhouse gases are projected to cause variations in intensity, duration, and frequency of precipitation events. Quantifying the potential effects of climate change and adapting to them is one way to reduce vulnerability. Since rainfall characteristics are often used to design water management infrastructures, reviewing and updating rainfall characteristics (i.e., Intensity–Duration–Frequency (IDF) curves) for future climate scenarios is necessary. This study was undertaken to assess expected changes in IDF curves from the current climate to the projected future climate. To provide future IDF curves, 3-hourly precipitation data simulated by six combinations of global and regional climate models were temporally downscaled using a stochastic method. Performance of the downscaling method was evaluated, and IDF curves were developed for the state of Alabama. The results of all six climate models suggest that the future precipitation patterns for Alabama are expected to veer toward less intense rainfalls for short duration events. However, for long duration events (i.e., >4 h), the results are not consistent across the models. Given a large uncertainty existed on projected rainfall intensity of these six climate models, developing an ensemble model as a result of incorporating all six climate models, performing an uncertainty analysis, and creating a probability based IDF curves could be proper solutions to diminish this uncertainty.


Climate change Intensity–Duration–Frequency (IDF) curve Temporal downscaling General Circulation Models (GCMs) 



We wish to thank National Oceanic and Atmospheric Agency (NOAA) Regional Integrated Sciences and Assessments (RISA) program for funding this project, the North American Regional Climate Change Assessment Program (NARCCAP) for providing the data, and two anonymous reviewers for providing valuable comments that helped to improve the quality of the manuscript.

Supplementary material

10113_2012_375_MOESM1_ESM.docx (357 kb)
Supplementary material 1 (DOCX 356 kb)


  1. Bhunya P, Jain S, Ojha C, Agarwal A (2007) Simple parameter estimation technique for three-parameter generalized extreme value distribution. J Hydrol Eng 12(6):682–689CrossRefGoogle Scholar
  2. Brown SA, Stein SM, Warner JC (1996) Urban drainage design manual, hydraulic engineering circular no. 22Google Scholar
  3. Choi J, Socolofsky S, Olivera F (2008) Hourly disaggregation of daily rainfall in Texas using measured hourly precipitation at other locations. J Hydrol Eng 13(6):476–487CrossRefGoogle Scholar
  4. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, BerlinGoogle Scholar
  5. Dai A (2006) Precipitation characteristics in eighteen coupled climate models. J Clim 19:4605–4630CrossRefGoogle Scholar
  6. Durrans SR, Brown PA (2001) Estimation and internet-based dissemination of extreme rainfall information. Transportation Research Record 1743, Transportation Research Board, National Research Council, pp 41–48Google Scholar
  7. Feddersen H, Andersen U (2005) A method for statistical downscaling of seasonal ensemble predictions. Tellus Series A Dyn Meteorol Oceanogr 57:398–408CrossRefGoogle Scholar
  8. Hansen JW, Challinor A, Ines A, Wheeler T, Moron V (2006) Translating climate forecasts into agricultural terms: advances and challenges. Clim Res 33(1):27–41CrossRefGoogle Scholar
  9. Hershfield DM (1961) Technical Paper No. 40, Rainfall Frequency Atlas of the United States. Cooperative Studies Section, Hydrologic Services Division for Engineering Division, Soil Conservation Service U.S. Department of Agriculture, WashingtonGoogle Scholar
  10. Hosking JRM, Wallis JR, Wood EF (1985) Estimation of the generalized extreme value distribution by method of probability weighted moments. Technometrics 27(3):251–261CrossRefGoogle Scholar
  11. Islam S, Entekhabi D, Bras RL (1990) Parameter estimation and sensitivity analysis for the modified Bartlett–Lewis rectangular pulses model of rainfall. J Geophys Res 95(D3):2093–2100CrossRefGoogle Scholar
  12. Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res 115, D10101. doi: 10.1029/2009JD012882
  13. Massey FJ (1951) The Kolmogorov–Smirnov test for goodness of fit. J Am Stat Assoc 46(253):68–78CrossRefGoogle Scholar
  14. McCuen R (1998) Hydrologic analysis and design. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  15. Mearns LO et al (2007, updated 2011) The North American Regional Climate Change Assessment Program dataset. National Center for Atmospheric Research Earth System Grid data portal, Boulder, CO. Data downloaded 2011-01-03. doi: 10.5065/D6RN35ST
  16. Mearns LO, Gutowski WJ, Jones R, Leung LY, McGinnis S, Nunes AMB, Qian Y (2009) A regional climate change assessment program for North America. EOS 90(36):311–312CrossRefGoogle Scholar
  17. NCDC Online Climate Data Directory. NOAA National Climatic Data Center (NCDC).
  18. Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192CrossRefGoogle Scholar
  19. Prodanovic P, Simonovic SP (2007) Development of rainfall intensity duration frequency curves for the City of London under the changing climate. Water Resour Res Report, LondonGoogle Scholar
  20. Richard J, Wilfran MO, Simon T (2007, updated 2011) The North American Regional Climate Change Assessment Program dataset, National Center for Atmospheric Research Earth System Grid data portal, Boulder, CO. Data downloaded 2011-05-13.
  21. Rodriguez-Iturbe I, Cox DR, Isham V (1987) Some models for rainfall based on stochastic point processes. Proc Royal Soc A London 410:269–288CrossRefGoogle Scholar
  22. Schneider SH et al (2007) Assessing key vulnerabilities and the risk from climate change. Climate change 2007: impacts, adaptation and vulnerability contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. ML Parry, OFGoogle Scholar
  23. Sebastien B, Daniel C, René L (2007, updated 2011) The North American Regional Climate Change Assessment Program dataset, National Center for Atmospheric Research Earth System Grid data portal. Boulder, CO. Data downloaded 2011-05-14.
  24. Sharma D, Das Gupta A, Babel MS (2007) Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping river basin, Thailand. Hydrol Earth Syst Sci 11(4):1373–1390CrossRefGoogle Scholar
  25. Socolofsky S, Adams E, Entekhabi D (2001) Disaggregation of daily rainfall for continuous watershed modeling. J Hydrol Eng 6(4):300–309CrossRefGoogle Scholar
  26. Von Storch H (1999) Representation of conditional random distributions as a problem of “spatial” interpolation. In: Gòmez-Hernàndez J, Soares A, Froidevaux R (eds) geoENV II—Geostatistics for Environmental Applications, Kluwer Academic Publishers, Dordrecht, Boston, pp 13–23. ISBN 0-7923-5783-3Google Scholar
  27. Wolcott SB, Mroz M, Basile J (2009) Application of Northeast Regional Climate Center Research results for the purpose of evaluating and updating Intensity-Duration-Frequency (IDF) Curves. Case Study: Rochester, New York. In: Proceedings of world environmental and water resources congress 2009. Kansas City, MissouriGoogle Scholar
  28. Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate outputs. Clim Change 62(1–3):189–216CrossRefGoogle Scholar
  29. Wright P, DeGeatano A, Merkel W, Metcalf L, D. Quan Q, Zarrow D (2010) Updating rainfall intensity duration curves in the Northeast for runoff prediction. In: Proceedings of ASABE annual international meeting. Pittsburgh, PennsylvaniaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Golbahar Mirhosseini
    • 1
  • Puneet Srivastava
    • 2
  • Lydia Stefanova
    • 3
  1. 1.Civil/Biosystems Engineering DepartmentAuburn UniversityAuburnUSA
  2. 2.Biosystems Engineering DepartmentAuburn UniversityAuburnUSA
  3. 3.Center for Ocean-Atmospheric Prediction Studies (COAPS)Florida State UniversityTallahasseeUSA

Personalised recommendations