Abstract Recent studies report that the extreme rainfall characteristics in most parts of the globe exhibit temporal non-stationarity. Therefore, modeling the nonstationary behavior of extreme rainfall for different water resources applications is vital. When modeling non-stationarity in extreme rainfall series, previous studies consider a single threshold value in the peaks over threshold (POT) approach to extract extreme rainfall series. However, extreme rainfall series extracted with different threshold values may have a different degree of non-stationarity. Consequently, it is essential to understand the effect of threshold selection in modeling peaks over threshold based nonstationary extreme rainfall series. This study aims at quantifying the threshold uncertainty (i.e., uncertainty in extreme rainfall return levels due to the choice of the threshold) in modeling peaks over threshold based nonstationary extreme rainfall series using the Generalized Pareto Distribution (GPD). To study the threshold uncertainty, extreme rainfall series over India from the India Meteorological Department’s high-resolution gridded (0.25° Longitude × 0.25° Latitude) daily rainfall dataset is used. For modeling non-stationarity in extreme rainfall series, different indices representing four physical processes, namely, global warming, El Nino–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and local temperature anomaly are linked with the scale parameter of the GPD. Uncertainties in extreme rainfall return levels calculated over India indicate that the uncertainty created due to the choice of threshold is 54% higher under the nonstationary condition when compared to the stationary condition.

中文翻译：

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阈值选择对基于阈值的非平稳极端降雨序列峰值建模的影响

摘要 最近的研究表明，全球大部分地区的极端降雨特征表现出时间上的非平稳性。因此，为不同的水资源应用模拟极端降雨的非平稳行为至关重要。在对极端降雨序列中的非平稳性进行建模时，先前的研究考虑了峰值超阈值 (POT) 方法中的单个阈值来提取极端降雨序列。然而，不同阈值提取的极端降雨序列可能存在不同程度的非平稳性。因此，必须了解阈值选择对基于阈值的非平稳极端降雨序列的峰值建模的影响。本研究旨在量化阈值不确定性（即 在使用广义帕累托分布 (GPD) 对基于阈值的非平稳极端降雨序列进行建模时，由于阈值选择而导致极端降雨返回水平的不确定性。为了研究阈值不确定性，使用了来自印度气象部门的高分辨率网格（0.25°经度×0.25°纬度）日降雨量数据集的印度极端降雨系列。为了模拟极端降雨序列的非平稳性，代表四个物理过程的不同指数，即全球变暖、厄尔尼诺-南方涛动 (ENSO)、印度洋偶极子 (IOD) 和局部温度异常与 GPD 的尺度参数相关联.