  Flood quantile estimation is of great importance for many engineering studies
and policy decisions. However, practitioners must often deal with small data
available. Thus, the information must be used optimally. In the last decades,
to reduce the waste of data, inferential methodology has evolved from annual
maxima modeling to peaks over a threshold one. To mitigate the lack of data,
peaks over a threshold are sometimes combined with additional information -
mostly regional and historical information. However, whatever the extra
information is, the most precious information for the practitioner is found at
the target site. In this study, a model that allows inferences on the whole
time series is introduced. In particular, the proposed model takes into account
the dependence between successive extreme observations using an appropriate
extremal dependence structure. Results show that this model leads to more
accurate flood peak quantile estimates than conventional estimators. In
addition, as the time dependence is taken into account, inferences on other
flood characteristics can be performed. An illustration is given on flood
duration. Our analysis shows that the accuracy of the proposed models to
estimate the flood duration is related to specific catchment characteristics.
Some suggestions to increase the flood duration predictions are introduced.
