Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation / Statistiques de valeurs extrêmes et estimation de précipitations extrêmes: I. Recherche théorique

By Demetris Koutsoyiannis. 2004

297 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/77f7a9b13f20bdd093c91aff8b47d86a76f3cf27

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Citations in the corpus

Intensity–duration–frequency (IDF) rainfall curves in Senegal (2017, background): “Indeed, such heavy-tailed behavior in daily rainfall samples is generally found: in the African region (e.g., Panthou et al., 2012; Giugni et al., 2015) but also all around the world (e.g., Koutsoyiannis, 2004b; Papalexiou and Koutsoyiannis, 2013).”
An entropic‐stochastic representation of rainfall intermittency: The origin of clustering and persistence (2006, methodology): “…the rainfall depth at scale k, application of the ME principle of Koutsoyiannis [2005b] showed that this variable should have Pareto-type distribution, thus verifying and explaining an earlier investigation [Koutsoyiannis, 2004a, 2004b] based on a large database of rainfall maxima worldwide.”
On the emergence of rainfall extremes from ordinary events (2016, background): “It has been noted that in many applications the number of events from which the maximum value is selected is not nearly sufficient for this asymptotic hypothesis to be valid [Cook and Harris, 2004; Koutsoyiannis, 2004].”
Threshold detection for the generalized Pareto distribution: Review of representative methods and application to the NOAA NCDC daily rainfall database (2016, background): “…problem in engineering hydrology [see e.g., Bernard, 1932; Demar ee, 1985; Coles and Tawn, 1996; Koutsoyiannis et al., 1998; Katz et al., 2002; Koutsoyiannis, 2004a, 2004b; Cooley et al., 2007; Veneziano et al., 2006, 2007, 2009; Veneziano and Yoon, 2013; Deidda, 2010; El Adlouni and Ouarda,…”
Battle of extreme value distributions: A global survey on extreme daily rainfall (2013, methodology): “[14] This analysis indicates that even if the parent distribution of daily rainfall is of exponential type, thus belonging theoretically to the domain of attraction of the Gumbel law, the annual maximum is better described by the Fr echet law [see also Koutsoyiannis, 2004a].”
Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data (2010, background): “…2002; Nguyen et al. 2002; Segal et al. 2002; Crisci et al. 2008; Shane and Lynn 1964; Todorovic and Zelenhasic 1970; Madsen et al. 1997a, b; Alila 1999; Fowler and Kilsby 2003; Koutsoyiannis 2004; Semmler and Jacob 2004; Martins and Stedinger 2000; Morrison and Smith 2002; Glaves and Waylen 1997).”
Statistics of extremes and estimation of extreme rainfall: II. Empirical investigation of long rainfall records / Statistiques de valeurs extrêmes et estimation de précipitations extrêmes: II. Recherche empirique sur de longues séries de précipitations (2004, background): “For appropriate choice of ξ, it can be shown that (see Koutsoyiannis, 2004): G(x) = 1 – 1 + κ x λ – ψ –1/κ x ≥ λψ (4) which is the Pareto distribution.”
How extreme is extreme? An assessment of daily rainfall distribution tails (2012, background): “Additionally, we note that heavy tails tend to be hidden (see e.g. Koutsoyiannis, 2004a,b) especially when the sample size is small.”
Uncertainty, entropy, scaling and hydrological stochastics. 1. Marginal distributional properties of hydrological processes and state scaling / Incertitude, entropie, effet d'échelle et propriétés stochastiques hydrologiques. 1. Propriétés distributionnelles marginales des processus hydrologiques et (2005, methodology): “Thus, knowing that, as a result of the ME principle, the distribution of rainfall at a small time scale is power- rather than exponential-type will lead to the use of EV2, rather than EV1, distribution of maximum rainfall.”
Simulation of the entire range of daily precipitation using a hybrid probability distribution (2012, background): “In the HEG distribution, is forced to be positive, considering the fact that daily precipitation is widely acknowledged being heavy tail distributed [Koutsoyiannis, 2004a, 2004b; Vrac and Naveau, 2007, Furrer and Katz, 2008].”
Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? (2015, unknown): “…that univariate algorithms can be used in a nonlocal context through careful pairing of observed and model grid cells, and further that multi-variate bias corrections often rely on univariate quantile mapping algorithms as a key part of their application (Li et al. 2014; Vrac and Friederichs 2015).”
How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset (2018, methodology): “The cumulative distribution function of the GEV distribution is given by the following equation (Coles 2001, pp.47, 48; Dey et al. 2016, see also Stedinger et al. 1993; Hosking and Wallis 1997; Koutsoyiannis 2004 for equivalent expressions of the GEV).”