2012
DOI: 10.1002/hyp.9272
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Selecting the best IDF model by using the multifractal approach

Abstract: In this work, the multifractal properties of hourly rainfall data recorded at a location in Southern Spain have been related to the scale properties of the corresponding intensity–duration–frequency (IDF) curves. Four parametric models for the IDF curves have been fitted to the quantiles of rainfall obtained using the generalized Pareto frequency distribution function with the extreme data series obtained for the same place. The scaling of the rainfall intensity moments has been analysed, and the empirical mom… Show more

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Cited by 34 publications
(28 citation statements)
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“…Improving regional projections (e.g., through variable selection or statistical downscaling) and characterizing natural variability (e.g., irreducible uncertainty at decadal scales: Sutton, 2009, 2011;Branstator and Teng, 2012;Deser et al, 2012a, b;Fischer et al, 2013;Hu and Deser, 2013;Rosner et al, 2014) are necessary for informing adaptation at stakeholder-relevant scales and planning horizons. As climate-related data approaches the scale of hundreds of petabytes (Overpeck et al, 2011) and climate data mining research continues to improve (Smyth et al, 1999;Robertson et al 2004Robertson et al , 2006Khan et al, 2006;Camargo et al, 2007a, b;Gaffney et al, 2007), new opportunities will emerge (e.g., Schneider et al, 2013;Monteleoni et al, 2013;Ganguly et al, 2013). The 2014 Climate Data Initiative (Lehmann, 2014) launched by the White House (United States President's Office) points to big data as a solution for climate adaptation and lends further urgency of the theme discussed in this manuscript.…”
Section: Discussionmentioning
confidence: 86%
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“…Improving regional projections (e.g., through variable selection or statistical downscaling) and characterizing natural variability (e.g., irreducible uncertainty at decadal scales: Sutton, 2009, 2011;Branstator and Teng, 2012;Deser et al, 2012a, b;Fischer et al, 2013;Hu and Deser, 2013;Rosner et al, 2014) are necessary for informing adaptation at stakeholder-relevant scales and planning horizons. As climate-related data approaches the scale of hundreds of petabytes (Overpeck et al, 2011) and climate data mining research continues to improve (Smyth et al, 1999;Robertson et al 2004Robertson et al , 2006Khan et al, 2006;Camargo et al, 2007a, b;Gaffney et al, 2007), new opportunities will emerge (e.g., Schneider et al, 2013;Monteleoni et al, 2013;Ganguly et al, 2013). The 2014 Climate Data Initiative (Lehmann, 2014) launched by the White House (United States President's Office) points to big data as a solution for climate adaptation and lends further urgency of the theme discussed in this manuscript.…”
Section: Discussionmentioning
confidence: 86%
“…However, the value of multimodel averages has been questioned (Knutti, 2010), particularly for regional assessments Kodra et al, 2012). Recent attempts at regional assessments include the development of statistical methods that consider both model performance relative to historical observed data and model ensemble agreement (Smith et al, 2009;Ganguly et al, 2013).…”
Section: Complexity Of Uncertainty Assessmentsmentioning
confidence: 99%
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“…The Weibull PDF and L-Moments parameter estimation are frequently used in hydrological frequency analysis because of their purported benefits relative to other distributions and parameter estimation techniques (Gaál and Kysely, 2009;García-Marín et al, 2013;Sarkar et al, 2010;Yang et al, 2010;Kharin and Zwiers, 2005;Fowler et al, 2007;Leander et al, 2014). However, the benefits were not consistently observed throughout this analysis as Weibull outperformed the Gumbel in terms of CC and SRC, but underperformed in terms of the bias as shown in the results of Experiment 3 (see Fig.…”
Section: Figmentioning
confidence: 87%
“…One of the most widely used in hydrology is the turbulence formalism developed by Schertzer and Lovejoy (1987) (e.g. Schertzer and Lovejoy, 1988;De Lima and Grasman, 1999;De Lima and de Lima, 2009;García-Marín et al, 2012). This approach assumes that the variability of the process could be directly modeled as a stochastic (or random) turbulent cascade process (Schertzer and Lovejoy, 1987;Gupta and Waymire, 1993;Over and Gupta, 1994;Lovejoy and Schertzer, 1995).…”
Section: Multifractality Of Rainfallmentioning
confidence: 99%