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首页> 外文期刊>Journal of hydrometeorology >A Hybrid Precipitation Index Inspired by the SPI, PDSI, and MCDI. Part I: Development of the Index
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A Hybrid Precipitation Index Inspired by the SPI, PDSI, and MCDI. Part I: Development of the Index

机译：由SPI，PDSI和MCDI启发的混合降水指数。 第一部分：发展指数

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摘要

The filtering properties of the standardized precipitation index (SPI), the Palmer drought severity index (PDSI), and the model calibrated drought index (MCDI) are investigated to determine their relations to past, present, and future precipitation anomalies in regions with a wide diversity of precipitation characteristics. All three indices can be closely approximated by weighted averages of precipitation, but with different weighting. The SPI is well represented by one-sided, uniformly weighted averages; the MCDI is well represented by one-sided, exponentially weighted averages; and the PDSI is well represented by two-sided, exponentially weighted averages with much higher weighting of past and present precipitation than future precipitation. Detailed analyses identify interpretational complications and other undesirable features in the SPI and PDSI. In addition, the PDSI and MCDI are each restricted to single regionally specific "intrinsic'' time scales that can significantly differ between the two indices. Inspired by the strengths of the SPI, PDSI, and MCDI, a hybrid index is developed that consists of exponentially weighted averages of past and present precipitation that are implicit in the PDSI and MCDI. The explicit specification of the exponential weighting allows users to control the time scale of the hybrid index to investigate precipitation variability on any time scale of interest. This advantage over the PDSI and MCDI is analogous to the controllability of the time scale of the SPI, but the exponentially fading memory is more physical than the uniform weighting of past and present precipitation in the SPI.
机译：研究了标准化降水指数（SPI）、帕尔默干旱严重度指数（PDSI）和模型校准干旱指数（MCDI）的过滤特性，以确定它们与降水特征多样性地区过去、现在和未来降水异常的关系。这三个指数都可以用降水量的加权平均值近似表示，但权重不同。SPI由单侧均匀加权平均值很好地表示；MCDI由单侧指数加权平均值很好地表示；PDSI由双边指数加权平均值很好地表示，过去和现在降水量的加权比未来降水量高得多。详细分析确定了SPI和PDSI中的解释复杂性和其他不良特征。此外PDSI和MCDI各自仅限于单个特定区域两个指数之间可能存在显著差异的“内在”时间尺度。受SPI、PDSI和MCDI的优势启发，开发了一个混合指数，该指数由过去和现在降水量的指数加权平均值组成，这些平均值隐含在PDSI和MCDI中。指数加权的明确规定允许用户控制混合指数的时间尺度调查任何感兴趣的时间尺度上的降水变化。与PDSI和MCDI相比，这种优势类似于SPI时间尺度的可控性，但指数衰减记忆比SPI中过去和现在降水的均匀加权更具物理性。

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