Consolidation of locally and regionally available hydrological information for probabilistic estimation of the flood regime.

Summary The practitioner, when predetermining flood flows, is often confronted with a limited data set. In our research work, we have proposed three new probabilistic models specially designed for the estimation of flood regime characteristics in a partially gauged context. Among these models, two of them are so-called regional models, i.e. integrating information coming from stations having a behavior considered similar to the one of the studied site. These models, based on Bayesian theory, showed a great robustness to the degree of heterogeneity of the sites belonging to the region. Similarly, it appeared that for the estimation of high quantiles (T > 50 years), the idea of a regional parameter controlling the extrapolation is relevant but must be integrated in a flexible way and not imposed within the likelihood. Since the most valuable information available to the practitioner is that from the study site, the third proposed model reverts to estimating only from data contemporary with the study site. This new model uses richer information than that obtained from a classical sampling of maximum v.a.i.id. since the whole chronicle is exploited. Therefore, even with only five years of record and thanks to a modeling of the dependence between successive observations, the size of the exploited samples is then much more important. We have shown that for the estimation of flood quantiles, this model clearly outperforms the local approaches classically used in hydrology. This result is all the more true when the return periods become important. Finally, by construction, this approach also allows to obtain a probabilistic estimation of the flood dynamics.