Bivariate Statistical Approach to Check Adequacy of Dam Spillway

By Carlo De Michele, G. Salvadori, M. Canossi, A. Petaccia, Renzo Rosso. 2005

333 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Ocean Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/d68d8d52fe0c42a6ca08acaee90143c440325d45

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Citations in the corpus

Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data (2010, background): “In recent years considerable attention has been focused on the use of copulas for multivariate hydrologic analyses (De Michele and Salvadori 2003; Shiau 2003; Favre et al. 2004; Salvadori and De Michele 2004, 2006, 2007; De Michele et al. 2005 ; B ardossy 2006 ;B a´rdossy and Li 2008; Grimaldi and ”
Metaelliptical copulas and their use in frequency analysis of multivariate hydrological data (2007, background): “See Salvadori and De Michele [2004, 2006], De Michele et al. [2005] , Zhang and Singh [2006], or Grimaldi and Serinaldi [2006] for additional examples of applications, as well as Genest and Favre [2007] for a review of inference techniques for copula modeling in a hydrologic setting.”
Impact of copulas for modeling bivariate distributions on system reliability (2013, background): “[4] De Michele C, Salvadori G, Canossi M, Petaccia A, Rosso R.”
Frequency analysis via copulas: Theoretical aspects and applications to hydrological events (2004, methodology): “[80] The second case study of interest is taken from De Michele et al. [2004]. The authors use a Gumbel-Hougaard copula (with upper-unbounded generalized extreme value marginals) to assess the spillway design flood of an existing Italian dam by considering the maximum annual flood volume and flood p”
Multivariate L‐moment homogeneity test (2007, methodology): “The Gumbel logistic model is also considered by De Michele et al. [2005] with Frechet extreme value marginal distributions.”
Copula‐based drought severity‐duration‐frequency analysis in Iran (2009, background): “A significant amount of research (Gupta and Duckstein, 1975; Sen, 1980; Dracup et al., 1980; Zelenhastic and Salvai, 1987; Frick et al., 1990; Kendall and Dracup, 1992; Loáiciga and Leipnik, 1996; Chung and Salas, 2000; Cancelliere and Salas, 2004) has developed probabilistic methods to investigate ”
Weighted likelihood copula modeling of extreme rainfall events in Connecticut. (2010, neutral): “As shown by De Michele et al. (2005), among21 other consequences, failure to take into account the dependence between these variables may22 lead to overestimation and underestimation of the design storm and associated risk.23 Copulas have recently emerged as a practical and efficient method for…”
Multivariate drought characteristics using trivariate Gaussian and Student t copulas (2013, background): “…majority of the researches (De Michele and Salvadori, 2003; Shiau, 2003; Favre et al., 2004; Salvadori and De Michele, 2004a, 2004b, 2006, 2007; De Michele et al., 2005; Grimaldi and Serinaldi, 2006; Shiau et al., 2006; Zhang and Singh, 2006, 2007a, 2007b, 2007c; Genest and Favre, 2007; Genest et…”
Fitting bivariate copulas to the dependence structure between storm characteristics: A detailed analysis based on 105 year 10 min rainfall (2010, methodology): “, 2007], and conditional mixtures [De Michele et al., 2007; Salvadori and De Michele, 2007]. The authors of this work see the vines method [Lewandowski [2008] and references therein] as a promising technique for the construction of multivariate copulas, which will be the subject of further research.”
Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields (2008, methodology): “A useful introduction to this tool can be found in Genest and Favre (2007), while for hydrological applications we refer the reader to some recent works and references therein, such as Favre et al. (2004), De Michele et al. (2005) , Salvadori and De Michele (2004, 2006), Grimaldi and Serinaldi (200”
Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data (2011, methodology): “De Michele et al. (2005) employed the Gumbel–Hoogaurd (GH) copula for generating a large number of pairs of generalized extreme value distributed annual peak flow and volume data in order to assess the adequacy of dam spillway capacity.”
A copula-based joint deficit index for droughts. (2010, background): “…and Serinaldi, 2006b; Kao and Govindaraju, 2007a; Zhang and Singh, 2007; Kuhn et al., 2007), flood frequency analysis (Favre et al., 2004; De Michele et al., 2005; Shiau et al., 2006; Zhang and Singh, 2006; Renard and Lang, 2007), trivariate frequency analysis (Grimaldi and Serinaldi,…”
A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall (2011, background): “Pioneering work in copula‐based bivariate frequency analysis are the studies of De Michele and Salvadori [2003] and Favre et al. [2004], followed by several other contributions [e.g., De Michele et al., 2005; Genest and Favre, 2007; Kao and Govindaraju, 2007; Zhang and Singh, 2007a].”
Asymmetric copula in multivariate flood frequency analysis (2006, methodology): “[10] apply bivariate Archimedean copulas to simulate pairs flood peak-volume to be used to determine synthetic flood hydrographs.”