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Addressing uncertainty in extreme rainfall intensity for semi-arid urban regions: case study of Delhi, India

Author

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  • Ranjana Ray Chaudhuri

    (TERI School of Advanced Studies)

  • Prateek Sharma

    (TERI School of Advanced Studies)

Abstract

Classical approaches are used to develop rainfall intensity duration frequency curves for the estimation of design rainfall intensities corresponding to various return periods. The study modelled extreme rainfall intensities at different durations and compared the classical Gumbel and generalized extreme value (GEV) distributions in semi-arid urban region. The model and parameter uncertainties are translated to uncertainties in design storm estimates. A broader insight emerges that rainfall extremes in 1 h and 3 h are sensitive to the choice of frequency analysis (GEV in this case) and helps address anticipated intensification of extreme events for short duration at urban local scale. In comparison with Gumbel, GEV predicts higher extreme rainfall intensity corresponding to various return periods and duration (for 1-h duration the increase in extreme rainfall intensity is from 27 to 33% for return periods 10 years and higher, 3-h and 50-year return period—20%, 3-h and 100-year return period—20.6%, 24 h at similar return periods—10%). The Bayesian posterior distribution has a calibration effect on the GEV predictions and reduces the upper range of uncertainty in the GEV probability model prediction from a range of 16–31% to 10–28.4% for return period varying from 10 to 50 year for 1-h storms. In geographically similar areas these extreme intensities may be used to prepare for the rising flash flood risks.

Suggested Citation

  • Ranjana Ray Chaudhuri & Prateek Sharma, 2020. "Addressing uncertainty in extreme rainfall intensity for semi-arid urban regions: case study of Delhi, India," 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. 104(3), pages 2307-2324, December.
    • Handle: RePEc:spr:nathaz:v:104:y:2020:i:3:d:10.1007_s11069-020-04273-5
    DOI: 10.1007/s11069-020-04273-5
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    References listed on IDEAS

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    1. Stuart G. Coles & Jonathan A. Tawn, 1996. "A Bayesian Analysis of Extreme Rainfall Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(4), pages 463-478, December.
    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. Richard L. Smith & J. C. Naylor, 1987. "A Comparison of Maximum Likelihood and Bayesian Estimators for the Three‐Parameter Weibull Distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 358-369, November.
    4. Andreas F. Prein & Roy M. Rasmussen & Kyoko Ikeda & Changhai Liu & Martyn P. Clark & Greg J. Holland, 2017. "The future intensification of hourly precipitation extremes," Nature Climate Change, Nature, vol. 7(1), pages 48-52, January.
    5. Linyin Cheng & Amir AghaKouchak & Eric Gilleland & Richard Katz, 2014. "Non-stationary extreme value analysis in a changing climate," Climatic Change, Springer, vol. 127(2), pages 353-369, November.
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    1. Mingcheng Du & Jianyun Zhang & Qinli Yang & Zhenlong Wang & Zhenxin Bao & Yanli Liu & Junliang Jin & Cuishan Liu & Guoqing Wang, 2021. "Spatial and temporal variation of rainfall extremes for the North Anhui Province Plain of China over 1976–2018," 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. 105(3), pages 2777-2797, February.

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