Consequences to flood management of using different probability distributions to estimate extreme rainfall
Consequences to flood management of using different probability distributions to estimate extreme rainfall
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DOI：
10.1016/j.jenvman.2012.11.013
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发表时间：
2013-01-30
影响因子：
8.7
通讯作者：
Esteves, Luciana S.
中科院分区：
文献类型：
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作者：
Esteves, Luciana S.
泵站等防洪设施的设计考虑了极端降水深度的预测重现期。最常见的是，通过将广义极值 (GEV) 或广义帕累托 (GP) 概率分布拟合到年度最大值序列或部分持续时间序列来估计这些值。在本文中，使用一系列概率分布分析了从英国东南部三个选定站点测量的每日降雨量数据获得的年度最大降水深度系列。这些分析表明，GEV 或 GP 分布并不总是能够提供与数据的最佳拟合，并且根据所使用的分布模型，长重现期（例如 100 年一遇）的极端降雨量估计值可能相差 40% 以上。由于英国和其他地方的大量财产目前受益于使用 GEV 或 GP 概率分布设计的防洪设施，因此本研究的结果质疑它们提供的保护水平是否适合于数据清楚表明替代概率分布可能更适合当地降雨数据的地区。这项工作：(a) 提高人们对极端降雨分析中常见做法的局限性的认识； (b) 提出一种纳入不确定性的简单方法，该方法很容易适用于全球各地的当地降雨数据；因此 (c) 有助于改善洪水风险管理。 (C) 2012 Elsevier Ltd. 保留所有权利。
The design of flood defences, such as pumping stations, takes into consideration the predicted return periods of extreme precipitation depths. Most commonly these are estimated by fitting the Generalised Extreme Value (GEV) or the Generalised Pareto (GP) probability distributions to the annual maxima series or to the partial duration series. In this paper, annual maxima series of precipitation depths obtained from daily rainfall data measured at three selected stations in southeast UK are analysed using a range of probability distributions. These analyses demonstrate that GEV or GP distributions do not always provide the best fit to the data, and that extreme rainfall estimates for long return periods (e.g. 1 in 100 years) can differ by more than 40% depending on the distribution model used. Since a large number of properties in the UK and elsewhere currently benefit from flood defences designed using the GEV or GP probability distributions, the results from this study question whether the level of protection they offer are appropriate in locations where data demonstrate clearly that alternative probability distributions may have a better fit to the local rainfall data. This work: (a) raises awareness of the limitations of common practices in extreme rainfall analysis; (b) suggests a simple way forward to incorporate uncertainties that is easily applicable to local rainfall data worldwide; and thus (c) contributes to improve flood risk management. (C) 2012 Elsevier Ltd. All rights reserved.