On the use of Cox regression to examine the temporal clustering of flooding and heavy precipitation across the central United States
On the use of Cox regression to examine the temporal clustering of flooding and heavy precipitation across the central United States
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DOI：
10.1016/j.gloplacha.2017.07.001
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发表时间：
2017-08-01
影响因子：
3.9
通讯作者：
Smith, James A.
中科院分区：
文献类型：
--
作者：
Mallakpour, Iman;Villarini, Gabriele;Smith, James A.
美国中部频发灾难性洪灾，比如1993、2008、2011、2013、2014和2016年的洪灾事件。这项研究的目的是研究是否有可能用气候系统的变化来描述亚季节尺度上的洪水和强降水事件的发生。这项研究使用了美国中部地区(这里定义包括北达科他州、南达科他州、内布拉斯加州、堪萨斯州、密苏里州、爱荷华州、明尼苏达州、威斯康星州、伊利诺伊州、西弗吉尼亚州、肯塔基州、俄亥俄州、印第安纳州和密歇根州)的日径流和降水时间序列。我们使用基于Cox过程的回归模型来模拟洪水和强降水事件随时间的发生/不发生，这可以被视为Poisson过程的推广。考克斯过程不是假设一个事件(即洪水或降水)独立于前一个事件(如泊松过程)的发生而发生，而是允许我们考虑到潜在的时间聚集的存在，这表现为静止期和活动期的交替。在这里，我们使用两个气候指数作为时变协变量：北极涛动(AO)和太平洋-北美型(PNA)来模拟洪水和强降水事件的发生/不发生。我们发现，在我们考虑的78%以上的水系量具中，AO和/或PNA是解释洪水发生的时间聚集的重要预报因子。在处理强降水事件时，也得到了类似的结果。对结果对用于识别事件的不同阈值的敏感性的分析得出了相同的结论。这项工作的结果突出表明，气候系统的变化在解释美国中部亚季节尺度上洪水和强降水事件的发生方面发挥了关键作用。
The central United States is plagued by frequent catastrophic flooding, such as the flood events of 1993, 2008, 2011, 2013, 2014 and 2016. The goal of this study is to examine whether it is possible to describe the occurrence of flood and heavy precipitation events at the sub-seasonal scale in terms of variations in the climate system. Daily streamflow and precipitation time series over the central United States (defined here to include North Dakota, South Dakota, Nebraska, Kansas, Missouri, Iowa, Minnesota, Wisconsin, Illinois, West Virginia, Kentucky, Ohio, Indiana, and Michigan) are used in this study. We model the occurrence/non-occurrence of a flood and heavy precipitation event over time using regression models based on Cox processes, which can be viewed as a generalization of Poisson processes. Rather than assuming that an event (i.e., flooding or precipitation) occurs independently of the occurrence of the previous one (as in Poisson processes), Cox processes allow us to account for the potential presence of temporal clustering, which manifests itself in an alternation of quiet and active periods. Here we model the occurrence/non-occurrence of flood and heavy precipitation events using two climate indices as time-varying covariates: the Arctic Oscillation (AO) and the Pacific-North American pattern (PNA). We find that AO and/or PNA are important predictors in explaining the temporal clustering in flood occurrences in over 78% of the stream gages we considered. Similar results are obtained when working with heavy precipitation events. Analyses of the sensitivity of the results to different thresholds used to identify events lead to the same conclusions. The findings of this work highlight that variations in the climate system play a critical role in explaining the occurrence of flood and heavy precipitation events at the sub-seasonal scale over the central United States.