Estimating flood recurrence uncertainty for non-stationary regimes

Assuming non-stationarity in flood frequency models is still controversial due to uncertainty in estimates. In this study, a hierarchical Bayesian framework for flood frequency analysis is presented without assuming the stationarity hypothesis. We account data and model uncertainty in all modelling steps and use the Pardo River, Brazil, as study case. Results showed the presence of increasing trends in floods in Pardo River. The stationary model underestimated floods compared to the non-stationary model. Physical-based covariates models performed better than time-based showing the importance of adding physical covariates to explain the trend behavior. The presented model is adaptable to other case. Finally, this study provided guidance for the flood recurrence estimation under non-stationary conditions.