## 1. Introduction

[2] Depending of the availability of data, flood quantiles can be estimated using local frequency analysis, regional frequency analysis or a combination of both. Much effort have been spent during the last decades on the study of the statistical properties of flood distributions, but the lack of sufficiently long data series continues to limit the precision of the results [*Bobée and Rasmussen*, 1995]. The regionalization concept, introduced by *Dalrymple* [1960], allows us to mitigate the lack of data by transposing information from gauged sites toward ungauged sites of interest. The concept was continuously developed since, and new approaches were regularly developed by researchers [e.g., *Benson*, 1962; *Matalas and Gilroy*, 1968; *Vicens et al.*, 1975; *Rousselle and Hindie*, 1976; *National Environment Research Council* (*NERC*), 1975; *Tasker*, 1980; *Greiss and Wood*, 1981; *Kuczera*, 1982; *Hosking et al.*, 1985; *Lettenmaier et al.*, 1987; *Stedinger and Lu*, 1995; *Madsen et al.*, 1994, 1995; *Madsen and Rojsberg*, 1997; *Fill and Stedinger*, 1998; *Burn*, 1990; *Groupe de recherche en hydrologie statistique* (*GREHYS*), 1996a, 1996b; *Ouarda et al.*, 2000, 2001; *Chokmani and Ouarda*, 2004]. Regionalization also results in more precise estimates of quantiles and parameters in sites with short records. It is however difficult to decide whether the local data series are long enough to discard regional information. To deal with this issue, *Matalas and Gilroy* [1968] recommend choosing the estimator that has the smallest variance. It would however make more sense to combine systematically all available and relevant information to have a better knowledge of the hydrological quantities to be estimated. Attention should be paid to the fact that, in a highly heterogeneous region, the addition of the regional information may be counterproductive.

[3] We present in this paper a parametric Bayesian method for combining local and regional information for the GEV distribution. In this method, the prior information is specified from the regional data by the probability distribution of a quantile and two quantile differences *q*_{T1}, *q*_{T2} − *q*_{T1}, *q*_{T3} − *q*_{T2} (where *q*_{T} is the T-year annual flood quantile). Guidelines for its extension to other extreme value distributions are also provided.

[4] The paper is divided into six parts. Section 2 presents a literature review on the Bayesian approaches for combining local and regional information. In section 3 the proposed Bayesian model is presented and the approaches for regional estimation and for prior specification are developed. The MCMC algorithm that was used to make inference on parameters and quantiles is also presented. The validation methodology is presented in section 4. The case study is presented in section 5, and the results are discussed in section 6. A conclusion is finally presented in section 7.