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Equidistance Quantile Matching Method for Updating IDFCurves under Climate Change

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  • Roshan Srivastav
  • Andre Schardong
  • Slobodan Simonovic

Abstract

Recent increase in intensity and frequency of catastrophic hydrologic events is shown to be a major threat to the global economy. These major events have been strongly linked to climate change and are expected to become worse in the future. The intensity-duration-frequency (IDF) curves quantify the extreme precipitations which are commonly used in planning and design of hydraulic structures. Since it is expected that the trends in extreme precipitation events will alter in the future, this will impact the IDF curves and they will have to be updated. In this study we present a methodology based on equidistance quantile matching (EQM) for updating the IDF curves under climate change. The two main steps in the proposed methodology are: (i) spatial downscaling of the maximum daily precipitation values from the global climate model/s (GCM) data to each of the sub-daily maximums observed at a station under consideration; (ii) explicit description of the changes in the GCM data between the baseline period and the future period (temporal downscaling). The main advantage of the proposed method compared to the existing methods which only use the spatial downscaling/disaggregation methods at baseline period, is that the proposed methodology additionally incorporates the changes in the distributional characteristics of the GCM model between the baseline period and the projection period. In addition, the method is simple to adopt and computationally efficient. To demonstrate the utility of the proposed methodology we use: (i) Canadian GCM model CanESM2 and (ii) its Representative Concentration Pathways (RCPs) for greenhouse gas concentration trajectories adopted by the IPCC for its Fifth Assessment Report (AR5) for the description of future conditions. The sub-daily annual maximum intensities are obtained for four stations in Canada located at London (Ontario), Hamilton (Ontario), Calgary (Alberta) and Vancouver (British Columbia). It is observed that the proposed approach is skillful in capturing and replicating the historical intensities and frequencies. The results indicated that for all RCP simulations considered in this study there is increase in precipitation intensities for all return periods. The relative increase in precipitation extremes is consistent with the RCP scenarios, i.e., the intensity of RCP-26 is lower than the RCP-45 which in-turn is lower than RCP-85. The quantile-based modeling without the temporal downscaling consistently underestimates the precipitation intensity when compared to the proposed method. The proposed method offers a valuable contribution to water resources planning and management of future extreme conditions. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Roshan Srivastav & Andre Schardong & Slobodan Simonovic, 2014. "Equidistance Quantile Matching Method for Updating IDFCurves under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2539-2562, July.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:9:p:2539-2562
    DOI: 10.1007/s11269-014-0626-y
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    References listed on IDEAS

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    1. V. Kharin & F. Zwiers & X. Zhang & M. Wehner, 2013. "Changes in temperature and precipitation extremes in the CMIP5 ensemble," Climatic Change, Springer, vol. 119(2), pages 345-357, July.
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    Cited by:

    1. Subhra Sekhar Maity & Rajib Maity, 2022. "Changing Pattern of Intensity–Duration–Frequency Relationship of Precipitation due to Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5371-5399, November.
    2. M. T. Vu & V. S. Raghavan & S.-Y. Liong, 2017. "Deriving short-duration rainfall IDF curves from a regional climate model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(3), pages 1877-1891, February.
    3. Qin, Shanshan & Wu, Yuehua, 2020. "General matching quantiles M-estimation," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    4. Ronit Singh & D. S. Arya & A. K. Taxak & Z. Vojinovic, 2016. "Potential Impact of Climate Change on Rainfall Intensity-Duration-Frequency Curves in Roorkee, India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4603-4616, October.
    5. Fahad Alzahrani & Ousmane Seidou & Abdullah Alodah, 2022. "Assessment and Improvement of IDF Generation Algorithms Used in the IDF_CC Tool," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4591-4606, September.
    6. Ioannis M. Kourtis & Ioannis Nalbantis & George Tsakiris & Basil Ε. Psiloglou & Vassilios A. Tsihrintzis, 2023. "Updating IDF Curves Under Climate Change: Impact on Rainfall-Induced Runoff in Urban Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2403-2428, May.

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