Defining the hundred year flood: A Bayesian approach for using historic data to reduce uncertainty in flood frequency estimates
Defining the hundred year flood: A Bayesian approach for using historic data to reduce uncertainty in flood frequency estimates
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DOI：
10.1016/j.jhydrol.2016.07.025
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发表时间：
2016-09
影响因子：
6.4
通讯作者：
Brandon Parkes;D. Demeritt
中科院分区：
文献类型：
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作者：
Brandon Parkes;D. Demeritt
本文介绍了一种贝叶斯统计模型，结合不确定的年最大值（AMAX）的数据，从河流测量的洪峰流量从各种历史来源，早于仪器记录期间的估计，估计洪水频率。这些历史洪水记录有望扩大减少极端事件重现期估计的不确定性所需的时间序列数据，但历史记录的异质性和不确定性使其难以与洪水估计手册和其他标准方法一起使用，用于从仪表数据生成洪水频率曲线。以英国坎布里亚郡卡莱尔的伊甸河为例，建立了一个贝叶斯模型，将1800年以来的历史洪水估算值与1967年以来的水位数据相结合，考虑流量估算值的不确定性，估算该地区低频洪水事件的概率。结果表明，减少95%的置信区间约50%的年度洪水概率小于0.0133（重现期超过75年）相比，标准的洪水频率估计方法，仅使用系统的数据。敏感性分析表明，该模型对2个模型参数敏感，这两个参数都与时间序列的历史（前系统）时期有关。这突出了充分考虑历史渠道和洪泛区变化或历史洪水流量估计中可能存在的偏差的重要性。还讨论了在其他站点推出这种贝叶斯方法进行洪水频率估计所需的下一步。
This paper describes a Bayesian statistical model for estimating flood frequency by combining uncertain annual maximum (AMAX) data from a river gauge with estimates of flood peak discharge from various historic sources that predate the period of instrument records. Such historic flood records promise to expand the time series data needed for reducing the uncertainty in return period estimates for extreme events, but the heterogeneity and uncertainty of historic records make them difficult to use alongside Flood Estimation Handbook and other standard methods for generating flood frequency curves from gauge data. Using the flow of the River Eden in Carlisle, Cumbria, UK as a case study, this paper develops a Bayesian model for combining historic flood estimates since 1800 with gauge data since 1967 to estimate the probability of low frequency flood events for the area taking account of uncertainty in the discharge estimates. Results show a reduction in 95% confidence intervals of roughly 50% for annual exceedance probabilities of less than 0.0133 (return periods over 75 years) compared to standard flood frequency estimation methods using solely systematic data. Sensitivity analysis shows the model is sensitive to 2 model parameters both of which are concerned with the historic (pre-systematic) period of the time series. This highlights the importance of adequate consideration of historic channel and floodplain changes or possible bias in estimates of historic flood discharges. The next steps required to roll out this Bayesian approach for operational flood frequency estimation at other sites is also discussed.