  Regional flood frequency analysis is a convenient way to reduce estimation
uncertainty when few data are available at the gauging site. In this work, a
model that allows a non-null probability to a regional fixed shape parameter is
presented. This methodology is integrated within a Bayesian framework and uses
reversible jump techniques. The performance on stochastic data of this new
estimator is compared to two other models: a conventional Bayesian analysis and
the index flood approach. Results show that the proposed estimator is
absolutely suited to regional estimation when only a few data are available at
the target site. Moreover, unlike the index flood estimator, target site index
flood error estimation seems to have less impact on Bayesian estimators. Some
suggestions about configurations of the pooling groups are also presented to
increase the performance of each estimator.

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