Stochastic modeling of flood peaks using the generalized extreme value distribution

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Statistics of extremes in hydrology (2002, background): “A mixture of Gumbel distributions, as might arise when the extremal distributions depends on the time of day or year or on some covariate such as the El Ni~ n no phenomenon , can resemble the GEV [68].”
On the emergence of rainfall extremes from ordinary events (2016, methodology): “…the most efficient and most commonly used techniques: maximum likelihood (ML) [Coles, 2001; Martins and Stedinger, 2000; Gençay et al., 2001], L-moments [Hosking et al., 1985; Hosking, 1990], the peak over threshold approach [Davison and Smith, 1990], and mixed methods [Morrison and Smith, 2002].”
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).”
A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates (2016, methodology): “In this case, the scale factor is the drainage area and it is assumed that a log-log linear relationship of location and scale parameters and the drainage area exists, which has theoretical and empirical support (Gupta and Dawdy, 1995; Gupta et al., 1994; Gupta et al., 2007; Gupta and Waymire, 1990;”