Probabilistic characterization of drought properties through copulas

By F. Serinaldi, B. Bonaccorso, A. Cancelliere, Salvatore Grimaldi. 2009

214 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c724bf8a3ca9a8fc13f631fdf1c69cf655f9b0d1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology, Evolution, Behavior and Systematics

In Global and Planetary Change

Citations in the corpus

Copulas and Its Application in Hydrology and Water Resources (2018, methodology): “Serinaldi et al. (2009) applied copulas to the probabilistic analysis of drought characteristics.”
Bivariate drought frequency analysis using the copula method (2012, methodology): “Furthermore, the relationship between Kendall’s τ and λu can be exploited for the preliminary selection of a copula (Serinaldi 2008; Serinaldi et al. 2009).”
Upper tail dependence in rainfall extremes: would we know it if we saw it? (2015, methodology): “…assumes that the underlying copula can be approximated by an extreme-value copula, studies have shown that the estimator performs well even if the copula does not belong to extreme value classes (Frahm et al. 2005)’’ (Villarini et al. 2008; Serinaldi et al. 2009; Janga Reddy and Singh 2014).”
Critical drought intensity-duration-frequency curves based on total probability theorem-coupled frequency analysis (2021, background): “When the characterization of the drought process is concerned, determination of the return period becomes an important issue (Fernandez and Salas 1999, Chung and Salas 2000, Shiau and Shen 2001, Bonaccorso et al. 2003, Serinaldi et al. 2009, Cancelliere and Salas 2010).”
Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm (2010, background): “The above review shows that most of the investigations employing copulas have been related to either floods or rainfall, and applications of copulas to drought frequency analyses have been limited (Shiau 2006; Shiau et al. 2007; Serinaldi et al. 2009 ).”
Application of copulas for derivation of drought severity–duration–frequency curves (2012, methodology): “Serinaldi et al. (2009) modelled four-dimensional joint distribution of drought properties, namely, the mean SPI, drought duration, minimum SPI value, and drought areal extent and computed joint return periods using elliptical class of Student’s t copula for different degrees of freedom.”
Increased probability of compound long-duration dry and hot events in Europe during summer (1950–2013) (2019, methodology): “A geometric distribution is generally used for D but the application of copulas requires continuous marginals, and so we employ its continuous counterpart as done in Serinaldi et al (2009).”
Copula based analysis of rainfall severity and duration: a case study (2013, methodology): “In the first study of its type, Shiau and Shen (2001) derived the recurrence period of droughts with a certain severity.”
Bivariate frequency analysis of nonstationary low‐flow series based on the time‐varying copula (2015, methodology): “, 2007) has been widely applied (De Michele and Salvadori, 2003; Favre et al., 2004; Salvadori and De Michele, 2004a; Grimaldi and Serinaldi, 2006; Zhang and Singh, 2006; Zhang and Singh, 2007; Evin and Favre, 2008; Kao and Govindaraju, 2008; Bárdossy and Pegram, 2009; Serinaldi et al., 2009; Wang e”
Multivariate drought characteristics using trivariate Gaussian and Student t copulas (2013, methodology): “Based on SPI index, Serinaldi et al. (2009) proposed a methodology to model drought events and estimate the corresponding return periods, by using bivariate margins of the complete four-dimensional copulas approach to construct the required joint distributions of drought characteristics.”
Bivariate Flood Frequency Analysis with Historical Information Based on Copula (2013, background): “…(a) flexibility in choosing arbitrary marginals and structure of dependence, (b) extension to more than two variables, and (c) separate analysis of marginal distribution and dependence structure (Salvadori and De Michele 2007; Genest and Favre 2007; Zhang and Singh 2007a; Serinaldi et al. 2009).”