Cities Institute, London Metropolitan Business School, London Metropolitan University, Stapleton House, 277-281 Holloway Road, London N7 8HN, UK.
J Environ Manage. 2013 Jan 30;115:98-105. doi: 10.1016/j.jenvman.2012.11.013. Epub 2012 Dec 12.
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.
防洪设施的设计，如泵站，考虑到了极端降水深度的预测重现期。这些重现期通常通过将广义极值（GEV）或广义帕累托（GP）概率分布拟合到年最大值序列或部分持续时间序列来估计。在本文中，使用一系列概率分布对从英国东南部三个选定站点的日降雨量数据中获得的降水深度年最大值序列进行了分析。这些分析表明，GEV 或 GP 分布并不总是对数据提供最佳拟合，并且对于长重现期（例如 100 年一遇）的极端降雨估计，由于所使用的分布模型不同，差异可能超过 40%。由于英国和其他地方的许多物业目前都受益于使用 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所提供的保护水平是否在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方是否合适。这项工作：（a）提高了对极端降雨分析中常见做法的局限性的认识；（b）提出了一种简单的方法来纳入不确定性，该方法易于适用于全球的当地降雨数据；因此（c）有助于改善洪水风险管理。
