Bayesian MCMC flood frequency analysis with historical information

By Dirceu Silveira Reis, Jery R. Stedinger. 2005

320 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/40305d62bce7e24de48b98c9950733ca26102b5e

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In Global and Planetary Change

Citations in the corpus

Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models (2010, unknown): “…Jin and Stedinger (1989), Stevens (1992), Pilon and Adamowski (1993), Frances et al. (1994), Cohn et al. (1997), Jarret and Tomlinson (2000), Martins and Stedinger (2001), O’Conell et al. (2002), England et al. (2003), Naulet et al. (2005), Reis and Stedinger (2005) and Merz and Blöschl (2008).”
A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates (2016, background): “…Haddad et al. (2012), Kwon et al. (2008), Liang et al. (2011), Lima and Lall (2010), Martins and Stedinger (2000), Parent and Bernier (2003), Reis and Stedinger (2005), Renard (2011), Renard et al. (2006), Renard and Lall (2014), Viglione et al. (2013), Steinschneider and Lall (2015), Sun…”
An application of Bayesian analysis and Markov chain Monte Carlo methods to the estimation of a regional trend in annual maxima (2006, background): “As an example, publications dealing with Bayesian inference are now usual in the field of hydrology [Coles and Tawn, 1996; Khodja et al., 1998; Lu and Berliner, 1999; Perreault et al., 1999, 2000a, 2000b, 2000c; Thyer and Kuczera, 2000, 2003a, 2003b; Wang, 2001; Thyer et al., 2002; Coles and Pericch”
Bayesian Nonstationary Frequency Analysis of Hydrological Variables 1 (2011, unknown): “Martins, E.S. and J.R. Stedinger, 2000.”
Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth‐death Markov chain Monte Carlo (2009, methodology): “Reis and Stedinger [2005] used a Bayesian framework to integrate regional and historical information.”
Statistical analysis of extreme events in a non-stationary context via a Bayesian framework: case study with peak-over-threshold data (2006, methodology): “At last, hierarchical Bayesian models, including trends or step-changes parameters, may be useful for regional analysis of extremes (Cooley 2005; Cooley et al. 2005), as well as for measurement uncertainties handling ( Reis and Stedinger 2005 ).”
Generalized maximum likelihood estimators for the nonstationary generalized extreme value model (2007, methodology): “This point was well discussed by Reis and Stedinger [2005] .”
Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation (2005, background): “... Matalas and Gilroy, 1968; Thomas and Benson, 1970; Moss and Kerlinger, 1974; Jennings et al., 1994] and to increase the precision of the statistic of interest at sites with short record lengths by adding regional information [Shane and Gaver, 1970; Vicens et al., 1975; IACWD, 1982; Kuczera, 1982”