A regional Bayesian POT model for flood frequency analysis

By Mathieu Ribatet, Eric Sauquet, J. Grésillon, Taha B. M. J. Ouarda. 2007

95 citations recorded by the source corpus.

Flood frequency analysis is usually based on the fitting of an extreme value distribution to the local streamflow series. However, when the local data series is short, frequency analysis results become unreliable. Regional frequency analysis is a convenient way to reduce the estimation uncertainty. In this work, we propose a regional Bayesian model for short record length sites. This model is less restrictive than the index flood model while preserving the formalism of “homogeneous regions”. The performance of the proposed model is assessed on a set of gauging stations in France. The accuracy of quantile estimates as a function of the degree of homogeneity of the pooling group is also analysed. The results indicate that the regional Bayesian model outperforms the index flood model and local estimators. Furthermore, it seems that working with relatively large and homogeneous regions may lead to more accurate results than working with smaller and highly homogeneous regions.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/5c5881f892fd4785511ad50560039db9384e744f

How researchers see this paper

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

In Global and Planetary Change

1. Benjamin Renard
2. V. Garreta
3. Matthew Lang
4. Z. Zahmatkesh
5. Zhongmin Liang
6. Saeed Golian
7. Binquan Li
8. Wenjuan Chang

In Water Science and Technology

1. Z. Zahmatkesh
2. Matthew Lang
3. Saeed Golian
4. Benjamin Renard
5. A. Razmi

Citations in the corpus

• A two-step framework for over-threshold modelling of environmental extremes (2014, unknown): “…approach widely spread in extreme value analyses, together with the application of the GPD, (Davison and Smith, 1990; Simiu and Heckert, 1995; Embrechts et al., 1997; Palutikof et al., 1999; Coles, 2001; Mackay et al., 2001; Pandey et al., 2001; Rosbjerg and Madsen, 2004; Ribatet et al., 2007).”
• An application of Bayesian analysis and Markov chain Monte Carlo methods to the estimation of a regional trend in annual maxima (2006, methodology): “... 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 Pericchi, 2003; Coles et al., 2”
• A Bayesian hierarchical approach to regional frequency analysis (2011, methodology): “…over the years [e.g., Stedinger, 1983; Stedinger and Tasker, 1985, 1986; Hosking and Wallis, 1988; Madsen and Rosbjerg, 1997; Reis et al., 2005; Ribatet et al., 2007; Kjeldsen and Jones, 2009a], most implementations still rely on several hypotheses that complicate the quantification of…”
• Bayesian flood frequency analysis in the light of model and parameter uncertainties (2012, methodology): “Ribatet et al. (2007) developed a regional Bayesian POT model to reduce the estimation uncertainty when the local data series is short.”
• Non-Stationary Frequency Analysis of Extreme Water Level: Application of Annual Maximum Series and Peak-over Threshold Approaches (2017, methodology): “The POT approach, along with the GPD, has been widely used in the extreme value analyses (Coles 2001; Mackay et al. 2001; Pandey et al. 2001; Ribatet et al. 2007).”