Unsurprising Surprises: The Frequency of Record‐breaking and Overthreshold Hydrological Extremes Under Spatial and Temporal Dependence
Unsurprising Surprises: The Frequency of Record‐breaking and Overthreshold Hydrological Extremes Under Spatial and Temporal Dependence
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不足为奇的惊喜：时空依赖性下破纪录和超阈值水文极端事件的频率
DOI：
10.1029/2018wr023055
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发表时间：
2018
影响因子：
5.4
通讯作者：
C. Kilsby
中科院分区：
文献类型：
--
作者：
F. Serinaldi;C. Kilsby
破纪录（RB）事件是自观测期开始以来给定变量（例如温度和降水量）假设的最高或最低值。最近，对水文气候波动及其与此类极端事件的联系的研究重新引起了人们对 RB 事件的兴趣。然而，RB 事件的实证分析通常依赖于基于过于严格的假设的统计技术，例如独立同分布（i/id）随机变量或非一般数值方法。在这项研究中，我们提出了一些精确的分布以及精确的近似值，描述了一般时空依赖性下 RB 和峰值超阈值 (POT) 事件的发生概率，这使得能够基于更合适的假设进行分析。我们表明，（i）泊松二项分布是 i/id 下 RB 事件数量的精确分布，（ii）等效二项分布是 i/id 下的精确近似，（iii）β 二项分布提供了时空依赖性下 POT 发生的精确分布，（iv）等效 β 二项分布提供了时空依赖性下 RB 发生分布的精确近似。为了执行数值验证，我们还引入了一个空间和时间相关二进制过程的生成器，称为 BetaBitST。作为应用示例，我们研究美国本土每月降水量和温度的 RB 和 POT 发生情况，并重新分析莫纳罗亚山每日温度数据。结果表明，考虑时空依赖性会产生截然不同的结论，使得观察到的 RB 和 POT 事件频率远不如预期那么令人惊讶，并对文献中报道的先前结果提出质疑。
Record‐breaking (RB) events are the highest or lowest values assumed by a given variable, such as temperature and precipitation, since the beginning of the observation period. Research in hydroclimatic fluctuations and their link with this kind of extreme events recently renewed the interest in RB events. However, empirical analyses of RB events usually rely on statistical techniques based on too restrictive hypotheses such as independent and identically distributed (i/id) random variables or nongeneral numerical methods. In this study, we propose some exact distributions along with accurate approximations describing the occurrence probability of RB and peak‐over‐threshold (POT) events under general spatiotemporal dependence, which enable analyses based on more appropriate assumptions. We show that (i) the Poisson binomial distribution is the exact distribution of the number of RB events under i/id, (ii) equivalent binomial distributions are accurate approximations under i/id, (iii) beta‐binomial distributions provide the exact distribution of POT occurrences under spatiotemporal dependence, and (iv) equivalent beta‐binomial distributions provide accurate approximations for the distribution of RB occurrences under spatiotemporal dependence. To perform numerical validations, we also introduce a generator of spatially and temporally correlated binary processes, called BetaBitST. As examples of application, we study RB and POT occurrences for monthly precipitation and temperature over the conterminous United States and reanalyze Mauna Loa daily temperature data. Results show that accounting for spatiotemporal dependence yields strikingly different conclusions, making the observed frequencies of RB and POT events much less surprising than expected and calling into question previous results reported in the literature.
影响因子：
1.8
作者：
Bogachev, M. I.;Bunde, A.
通讯作者：
Bunde, A.