Metastatistical Extreme Value analysis of hourly rainfall from short records: Estimation of high quantiles and impact of measurement errors
Metastatistical Extreme Value analysis of hourly rainfall from short records: Estimation of high quantiles and impact of measurement errors
复制标题
短记录每小时降雨量的元统计极值分析：高分位数的估计和测量误差的影响
DOI：
10.1016/j.advwatres.2018.05.001
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发表时间：
2018
影响因子：
4.7
通讯作者：
E. Morin
中科院分区：
文献类型：
--
作者：
Francesco Marra;E. Nikolopoulos;E. Anagnostou;E. Morin
本研究将亚统计极值(MEV)框架扩展到次日降雨量频率分析，并与极值理论方法在短记录和测量误差下进行了比较。基于每小时数据的时间自相关来识别普通事件，并用威布尔分布建模。在从实际数据(160个雨量计在毗邻的美国有至少60年的记录)和被遥感降雨估计的典型测量误差扰动的合成数据中估计长重现期分位数方面，MEV与极值理论方法进行了比较。当使用5-20年的实际数据时，MEV倾向于低估每小时降雨量的100年重现期分位数，但呈现出减少的不确定性。当普通事件的良好模型和每年足够多的事件可用时，MEV能够提供从10年到20年，甚至是5年的数据的100年重现期分位数的信息，并且不确定性显著降低(对于5年的记录，不确定性为30%)。与极值理论方法相比，基于短记录的100年重现期分位数的MEV估计对加性/乘性误差、估计中存在上限值以及极值缺失的敏感性要低得多。这项研究的结果有力地支持了使用MEV进行基于遥感数据集的降雨频率分析。
This study expands the Metastatistical Extreme Value (MEV) framework to sub-daily rainfall frequency analysis and compares it to extreme value theory methods in presence of short records and measurement errors. Ordinary events are identified based on the temporal autocorrelation of hourly data and modeled with a Weibull distribution. MEV is compared to extreme value theory methods in the estimation of long return period quantiles from actual data (160 rain gauges with at least 60-year record in the contiguous United States) and on synthetic data perturbed with measurement errors typical of remote sensing rainfall estimation. MEV tends to underestimate the 100-year return period quantiles of hourly rainfall when 5–20 years of actual data are used, but presents diminished uncertainty. When a good model of the ordinary events and adequate number of events per year are available, MEV is able to provide information on the 100-year return period quantiles from 10–20, or even 5 years of data with significantly reduced uncertainty (<30% uncertainty for 5-year records). MEV estimates of 100-year return period quantiles from short records are much less sensitive than extreme value theory methods to additive/multiplicative errors, presence of cap values in the estimates, and missing of extreme values. Results from this study strongly support the use of MEV for rainfall frequency analyses based on remotely sensed datasets.