Non-Stationary Frequency Analysis of Extreme Water Level: Application of Annual Maximum Series and Peak-over Threshold Approaches
Non-Stationary Frequency Analysis of Extreme Water Level: Application of Annual Maximum Series and Peak-over Threshold Approaches
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DOI：
10.1007/s11269-017-1619-4
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发表时间：
2017-03
影响因子：
4.3
通讯作者：
A. Razmi;S. Golian;Z. Zahmatkesh
中科院分区：
文献类型：
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作者：
A. Razmi;S. Golian;Z. Zahmatkesh
洪水频率分析中的数据平稳假设是否合理，这是一个很大的挑战。如果在数据分析中纳入非平稳性，频率分析（FA）的结果可能会有很大不同。在这项研究中，极端水位（年最大值和每日瞬时最大值）在纽约市的沿海地区被认为是FA。年最大值序列（AMS）和峰值超过阈值（POT）的方法被用来建立数据时间序列。使用统计检验（包括Man-Kendall、增强Dickey-Fuller（ADF）和Kwiatkowski-Phillips-Schmidt-Shin（KPSS））检查所得时间序列的潜在趋势和平稳性。利用赤池信息准则（AIC）来选择最合适的概率分布模型。在非平稳假设下，基于AMS和POT方法，对所选数据分别采用广义极值分布（GEV）和广义帕累托分布（GPD）作为概率分布函数。应用最大似然法和惩罚最大似然法两种方法对分布参数进行了估计，并进行了比较。结果表明，在FA中加入非平稳性后，极端水位设计值与平稳性假设下的设计值有显著差异。此外，在非平稳FA中，考虑时间依赖性的分布参数导致设计洪水的变化范围。本研究的结果强调FA的重要性下的数据平稳性和非平稳性的假设，并考虑到最坏的情况下，洪水的情况下，未来规划的流域对可能的洪水事件。有必要更新为静态洪水风险评估开发的模型，以便进行更稳健和更有弹性的水文预测。应用非平稳FA提供了一种先进的方法来外推回报水平，以期望的未来时间前景。
A great challenge has been appeared on if the assumption of data stationary for flood frequency analysis is justifiable. Results for frequency analysis (FA) could be substantially different if non-stationarity is incorporated in the data analysis. In this study, extreme water levels (annual maximum and daily instantaneous maximum) in a coastal part of New York City were considered for FA. Annual maximum series (AMS) and peak-over threshold (POT) approaches were applied to build data timeseries. The resulted timeseries were checked for potential trend and stationarity using statistical tests including Man-Kendall, Augmented Dickey–Fuller (ADF) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS). Akaike information criterion (AIC) was utilized to select the most appropriate probability distribution models. Generalized Extreme Value (GEV) distribution and Generalized Pareto Distribution (GPD) were then applied as the probability distribution functions on the selected data based on AMS and POT methods under non-stationary assumption. Two methods of maximum likelihood and penalized maximum likelihood were applied and compared for the estimation of the distributions’ parameters. Results showed that by incorporating non-stationarity in FA, design values of extreme water levels were significantly different from those obtained under the assumption of stationarity. Moreover, in the non-stationary FA, consideration of time-dependency for the distribution parameters resulted in a range of variation for design floods. The findings of this study emphasize on the importance of FA under the assumptions of data stationarity and non-stationarity, and taking into account the worst case flooding scenarios for future planning of the watershed against the probable flood events. There is a need to update models developed for stationary flood risk assessment for more robust and resilient hydrologic predictions. Applying non-stationary FA provides an advanced method to extrapolate return levels up to the desired future time perspectives.