The implementation of nonstationary hydrological frequency analysis (NS-HFA) has often been hampered by the relatively short datasets and the resulting high uncertainty. Most recently, the non-asymptotic Metastatistical extreme value (MEV) and simplified MEV (SMEV) distributions, which rely on the ordinary events rather than the extremes only, have attracted attention in the HFA of both rainfall and streamflow. Despite their use for trend detection/attribution and producing future projections, their practical implementation for the NS-HFA is absent in the literature. This paper therefore implemented these models (called MEV and SMEV-based models) in the NS-HFA and comprehensively assessed their performance from the perspectives of fitting efficiency, accuracy, and uncertainty for both in-sample fitting and out-of-sample prediction purposes. The asymptotic models based on the generalized extreme value (GEV) distribution were used as the benchmark. The assessment employed synthetic and real rainfall datasets that exhibit stationarity in the number of events per year. All the nonstationary ordinary-event datasets followed the Weibull distribution with linearly changing parameters, while their standardized annual maximum series aligned with the GEV distribution. Thus, the MEV, SMEV- and GEV-based models could be fairly assessed and compared. The regula-falsi profile likelihood method was extended to quantify the uncertainty of the MEV and SMEV-based models. The results demonstrated that the MEV model was not advantageous over other models in terms of all three evaluation perspectives. Whereas the SMEV-based models demonstrated superiority due to their higher accuracy, equivalent or better fitting efficiency, as well as lower uncertainty compared to all other models. Therefore, this paper advocates the use of the SMEV distribution to advance the NS-HFA by harnessing the information from the ordinary events.

非平稳水文频率分析（NS-HFA）的实施经常受到相对较短的数据集和由此产生的高不确定性的阻碍。最近，非渐近元统计极值 (MEV) 和简化的 MEV (SMEV) 分布，它们依赖于普通事件而不仅仅是极端事件，在降雨和径流的 HFA 中引起了人们的关注。尽管它们用于趋势检测/归因和产生未来预测，但文献中没有它们对 NS-HFA 的实际实施。因此，本文在 NS-HFA 中实现了这些模型（称为基于 MEV 和 SMEV 的模型），并从样本内拟合和样本外预测目的的拟合效率、准确性和不确定性的角度全面评估了它们的性能. 基于广义极值（GEV）分布的渐近模型被用作基准。该评估使用了合成的和真实的降雨数据集，这些数据集在每年的事件数量上表现出平稳性。所有非平稳普通事件数据集均遵循 Weibull 分布，参数呈线性变化，而其标准化的年最大值序列与 GEV 分布一致。因此，可以公平地评估和比较基于 MEV、SMEV 和 GEV 的模型。这 而他们的标准化年度最大系列与 GEV 分布一致。因此，可以公平地评估和比较基于 MEV、SMEV 和 GEV 的模型。这 而他们的标准化年度最大系列与 GEV 分布一致。因此，可以公平地评估和比较基于 MEV、SMEV 和 GEV 的模型。这regula-falsi轮廓似然法被扩展以量化 MEV 和基于 SMEV 的模型的不确定性。结果表明，就所有三个评估角度而言，MEV 模型并不优于其他模型。而基于 SMEV 的模型由于其更高的精度、等效或更好的拟合效率以及与所有其他模型相比更低的不确定性而表现出优越性。因此，本文提倡使用 SMEV 分布，通过利用来自普通事件的信息来推进 NS-HFA。