The streamflow drought hazard can be characterized in a variety of ways, including using different indices. Traditionally, percentile‐based indices, such as Q95 (the flow exceeded 95% of time), have been used by the hydrological community. Recently, the use of anomaly indices such as the Standardized Streamflow Index (SSI), a probability index‐based approach adopted from the climatological community, has increased in popularity. The SSI can be calculated based on various (non)parametric methods. Up to now, there is no consensus which method to use. This study aims to raise awareness how the inherent sensitivity of the SSI to the used method influences derived drought characteristics. We compared SSI time series computed with seven different probability distributions and two fitting methods as well as with different nonparametric methods for 369 rivers across Europe. Results showed that SSI time series and associated drought characteristics are indeed sensitive to the method of choice. A resampling experiment demonstrated the sensitivity of the parametric SSI to properties of both the low and high end of the sample. Such sensitivities might hinder a fair comparison of drought in space and time and highlight the need for a clear recommendation which method to use. We could recommend overall suitable methods, for example, from the parametric approaches, the Tweedie distribution has several advantageous properties such as a low rejection rate (2%) and a lower bound at zero. However, the most suitable method depends on the used evaluation criteria. Rather, we stress that shown approach‐specific sensitivities and uncertainties should be carefully considered.

中文翻译：

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基于标准流量指数得出的干旱特征：参数和非参数方法的大样本比较

可以通过多种方式来表征径流干旱的危害，包括使用不同的指标。传统上，水文界一直使用基于百分位的指数，例如Q95（流量超过95％的时间）。最近，越来越多的人使用异常指数，例如标准化流指数（SSI），这是一种从气候学界采用的基于概率指数的方法。可以基于各种（非）参数方法来计算SSI。到目前为止，尚未达成共识。这项研究旨在提高人们对SSI对使用的方法的固有敏感性如何影响衍生干旱特征的认识。我们比较了欧洲369条河流的7种不同概率分布和两种拟合方法以及不同非参数方法计算的SSI时间序列。结果表明，SSI时间序列和相关的干旱特征确实对选择的方法敏感。重新采样实验表明，参数SSI对样品低端和高端特性的敏感性。这种敏感性可能会妨碍对时空干旱的公平比较，并强调需要明确建议使用哪种方法。我们可以推荐总体上合适的方法，例如，从参数方法中，Tweedie分布具有几个有利的属性，例如低拒绝率（2％）和下限为零。然而，最合适的方法取决于所使用的评估标准。相反，我们强调应仔细考虑显示的方法特定的敏感性和不确定性。