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Multifractal rainfall extremes: Theoretical analysis and practical estimation

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  • Langousis, Andreas
  • Veneziano, Daniele
  • Furcolo, Pierluigi
  • Lepore, Chiara

Abstract

We study the extremes generated by a multifractal model of temporal rainfall and propose a practical method to estimate the Intensity–Duration–Frequency (IDF) curves. The model assumes that rainfall is a sequence of independent and identically distributed multiplicative cascades of the beta-lognormal type, with common duration D. When properly fitted to data, this simple model was found to produce accurate IDF results [Langousis A, Veneziano D. Intensity–duration–frequency curves from scaling representations of rainfall. Water Resour Res 2007;43. doi:10.1029/2006WR005245]. Previous studies also showed that the IDF values from multifractal representations of rainfall scale with duration d and return period T under either d→0 or T→∞, with different scaling exponents in the two cases. We determine the regions of the (d,T)-plane in which each asymptotic scaling behavior applies in good approximation, find expressions for the IDF values in the scaling and non-scaling regimes, and quantify the bias when estimating the asymptotic power-law tail of rainfall intensity from finite-duration records, as was often done in the past. Numerically calculated exact IDF curves are compared to several analytic approximations. The approximations are found to be accurate and are used to propose a practical IDF estimation procedure.

Suggested Citation

  • Langousis, Andreas & Veneziano, Daniele & Furcolo, Pierluigi & Lepore, Chiara, 2009. "Multifractal rainfall extremes: Theoretical analysis and practical estimation," Chaos, Solitons & Fractals, Elsevier, vol. 39(3), pages 1182-1194.
  • Handle: RePEc:eee:chsofr:v:39:y:2009:i:3:p:1182-1194
    DOI: 10.1016/j.chaos.2007.06.004
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    Cited by:

    1. Thangavel, Bhagyaraj & Srinivasan, Sabarathinam & Kathamuthu, Thamilmaran, 2021. "Extreme events in a forced BVP oscillator: Experimental and numerical studies," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    2. Ju-Young Shin & Changsam Jeong & Jun-Haeng Heo, 2018. "A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property," Energies, MDPI, vol. 11(3), pages 1-27, March.

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