Stationary hydrological frequency analysis coupled with uncertainty assessment under nonstationary scenarios

Abstract The use of the nonstationary hydrological frequency analysis (HFA) has been prompted when nonstationarity is diagnosed in hydrometeorological data. However, the inconclusive identification of the physical process(es) and driver(s) behind the nonstationarity challenges the identification of an appropriate model structure, and consequently might hinder its reliable implementation. To date, no solid consensus on whether the nonstationary HFA is always superior to the stationary HFA has been reached. Therefore, this paper aimed to advance the understanding of the stationary and nonstationary HFAs under nonstationary scenarios by illustratively comparing their performance in real applications, and examining the effects of the nonstationarity on the stationary HFA through a simulation study, especially from the perspective of the uncertainty. The investigation of the effects of the nonstationarity on the stationary HFA was conducted in two fundamental nonstationary scenarios, namely temporal trends in the mean and variance, in which the degree of nonstationarity was quantifiable and known a priori. The HFAs were conducted using the Particle Filter, a Bayesian filtering technique which was recently employed in the stationary HFA and was further extended for the nonstationary HFA in this paper. The illustrative comparison did not demonstrate a consistent superiority of either HFA approach in terms of both fitting efficiency and uncertainty. This result thus implied that the stationarity HFA could outperform the nonstationary HFA in some cases. Besides, the simulation investigation of the stationary HFA revealed that the increase of nonstationarity degree would lead to the deterioration in the analysis accuracy and the elevation of uncertainty. The uncertainty in the stationary HFA was found to primarily originate from the nonstationarity, while the distribution selection would be the other but secondary source of uncertainty. The results from both the illustrative comparison and the simulation investigation suggested that whether the stationary or nonstationary HFA outperforms the other could be associated with the degree and pattern of nonstationarity. Therefore, it is recommended to consider them when developing a strategic framework to select an appropriate approach to deal with nonstationary hydrometeorological data.