Water Resources Management

, Volume 30, Issue 13, pp 4603–4616 | Cite as

Potential Impact of Climate Change on Rainfall Intensity-Duration-Frequency Curves in Roorkee, India

  • Ronit Singh
  • D. S. AryaEmail author
  • A. K. Taxak
  • Z. Vojinovic


Intensification and frequency of hydrologic events are attributed to climate change and are expected to increase in coming future. Intensity-Duration-Frequency (IDF) curves quantify the extreme precipitation and are used extensively to assess the return periods of rainfall events. It is expected that climate change will modify the occurrence of extreme rainfall events. Thus a need of updating IDF curves arises under the climate change scenario. This paper aims at updating the IDF curves for a typical Indian town using an ensemble of five General Circulation Models (GCMs) for all the Representative Concentration Pathways (RCP) scenarios. Sub-daily maximum intensities (15-, 30-, 45-, 60-, 120-, and 180 min) were obtained from the observed records. Equidistance quantile method was used to study the relationships between the historical and projected GCM data, and the historical GCM and observed sub-daily data. This relationship was used to obtain projected sub-daily intensities. The IDF curves were developed using observed and projected data. Analysis of the curves indicated increase in precipitation intensities for all the RCP scenarios. It was also found that intensities of all return periods increases with intensifying RCP scenarios. The variation in the intensities across the GCMs was attributed to the driving forces considered in a particular GCM.


Climate change Extreme rainfall IDF curves Statistical downscaling Equidistant quantile matching Gumbel distribution 



Anthropogenic aerosols


Anthropogenic forcing (a mixture that might include GHGS, aerosols, ozone, and land-use changes).


black carbon




well-mixed greenhouse gases


land-use change


mineral dust


natural forcing (a combination, not explicitly defined here, that might include, for example, Sl and VL)


organic carbon


(= TO + SO) ozone (= tropospheric and stratospheric ozone)


(= SD + SI) anthropogenic sulfate aerosol direct and indirect effects


anthropogenic sulfate aerosol, accounting only for direct effects


anthropogenic sulfate aerosol, accounting only for indirect effects


solar irradiance


stratospheric ozone


sea salt


tropospheric ozone


volcanic aerosols


  1. Alexander LV et al. (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111:D05109. doi: 10.1029/2005JD006290 Google Scholar
  2. Allan RP, Soden BJ (2008) Atmospheric warming and the amplification of precipitation extremes, vol 321. Science, pp. 1481–1484. doi: 10.1126/science.1160787
  3. Arya DS, Himanshu J, Abbasi S (1994) “developmental trends and their environmental impact in a typical central Indian town with special reference to Roorkee”. Environ Monit Assess 33:135–150CrossRefGoogle Scholar
  4. Dore MHI (2005) Climate change and changes in global precipitation patterns: what do we know? Environ Int 31:1167–1181. doi: 10.1016/j.envint.2005.03.004 CrossRefGoogle Scholar
  5. Ghosh S, Luniya V, Gupta A (2009) Trend analysis of Indian summer monsoon rainfall at different spatial scales. Atmos Sci Lett 10:285–290Google Scholar
  6. Goswami BN, Venugopal V, Sengupta D, Madhusoodanan MS, Xavier PK (2006) Increasing trend of extreme rain events over India in a warming environment. Science 314(5804):1442–1445CrossRefGoogle Scholar
  7. Groisman PY, Rankova EY (2001) Precipitation trends over the Russian permafrost-free zone: removing the artifacts of pre-processing. Int J Climatol 21:657–678. doi: 10.1002/joc.627 CrossRefGoogle Scholar
  8. Hamlet AF, Lettenmaier DP (2007) Effects of twentieth century warming and climate variability on flood risks in the western US. Water Resour Res 43:W06427. doi: 10.1029/2006WR005099 CrossRefGoogle Scholar
  9. Hassanzadeh, E., Nazemi, A., & Elshorbagy, A. (2013). Quantile-based downscaling of precipitation using genetic programming: application to IDF Curves in Saskatoon. J Hydrol Eng, 19(5):943–955CrossRefGoogle Scholar
  10. Lal M, Meehl GA, Arblaster JM (2000) Simulation of Indian summer monsoon rainfall and its intraseasonal variability in the NCAR climate system model. Reg Environ Chang 1(3–4):163–179CrossRefGoogle Scholar
  11. Lenderink G, Van Meijgaard E (2008) Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat Geosci 1:511–514. doi: 10.1038/ngeo262 CrossRefGoogle 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. Liew SC, Liong SY, Raghavan S 2012. A novel approach, using regional climate model, to derive present and future IDF curves for data scarce sites. 18th Congress of International Association of Hydraulics Engineering and Research – Asia Pacific Division, 2012Google Scholar
  14. Mekis E, Hogg WD (1999) Rehabilitation and analysis of Canadian daily precipitation time series. Atmosphere-Ocean 37:53–85. doi: 10.1080/07055900.1999.9649621 CrossRefGoogle Scholar
  15. Mirhosseini G, Srivastava P, Stefanova L (2013) The impact of climate change on rainfall Intensity–Duration–Frequency (IDF) curves in Alabama. Reg Environ Chang 13(1):25–33CrossRefGoogle Scholar
  16. Peck A, Prodanovic P, Simonovic SP (2012) Rainfall intensity duration frequency curves under climate change: city of London, Ontario, Canada. Canadian J Water Res 37(3):177–189CrossRefGoogle Scholar
  17. Prodanovic P, & Simonovic, SP. (2007). Development of rainfall intensity duration frequency curves for the City of London under the changing climate. Department of Civil and Environmental Engineering, The University of Western Ontario.Google Scholar
  18. Rupakumar K, Sahai AK, Kumar KK, Patwardhan SK, Mishra PK, Revadekar JV, Kamala K, Pant GB (2006) High-resolution climate change scenarios for India for the twenty-first century. Curr Sci 90:334–345Google Scholar
  19. Srivastav RK, Schardong A, Simonovic SP (2014) Equidistance quantile matching method for updating IDF curves under climate change. Water Resour Manag 28(9):2539–2562. doi: 10.1007/s11269-014-0626-y
  20. Subash N, Singh SS, Priya N (2011) Extreme rainfall indices and its impact on rice productivity—a case study over sub-humid climatic environment. Agric Water Manag 98:1373–1387. doi: 10.1016/j.agwat.2011.04.003 CrossRefGoogle Scholar
  21. Wang B, Bao Q, Hoskins B, Wu G, Liu Y (2008) Tibetian plateau warming and precipitation changes in east Asia. Geophys Res Lett 35:L14702. doi: 10.1029/2008GL034330 CrossRefGoogle Scholar
  22. Wang B, Zhang M, Wei J, Wang S, Li S, Ma Q, Li X, Pan S (2013) Changes in extreme events of temperature and precipitation over Xinjiang, northwest China, during 1960–2009. Quat Int 298:141–151. doi: 10.1016/j.quaint.2012.09.010 CrossRefGoogle Scholar
  23. Zhang X, Vincent LA, Hogg WD, Niitsoo A (2000) Temperature and precipitation trends in Canada during the twentieth century. Atmosphere-Ocean 38:395–429. doi: 10.1080/07055900.2000.9649654 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Ronit Singh
    • 1
  • D. S. Arya
    • 1
    Email author
  • A. K. Taxak
    • 1
  • Z. Vojinovic
    • 2
  1. 1.Department of HydrologyIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.UNESCO-IHE Institute for Water EducationDelftNetherlands

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