Regional frequency analysis (RFA) serves as a useful tool for flood estimation at a regional level, to improve the precision of estimated flood quantiles or for prediction in ungauged basins. RFA is used extensively in hydrologic literature for estimation of the magnitude of the hydrologic variable (annual maximum flows), pooling data from several sites within a homogeneous region. However, in areas that experience a strong seasonality of the climate, the timing of a hazardous event, such as flood, may be equally important. Therefore, in such regions, improved estimation of annual peakflow seasonality is imperative for efficient management of water infrastructure or for undertaking timely preventive measures against floods. In this study, we propose a novel RFA framework for estimation of annual peakflow seasonality based on circular statistics. The framework consists of (a) selection of attributes based on catchment similarity using seasonality descriptors as a response, (b) formation of homogeneous regions based on the region of influence method, (c) homogeneity tests adapted to directional data on timing of annual peakflows, and (d) probabilistic prediction of annual peakflow seasonality using the non-parametric regional circular density. Applicability of the proposed approach is first demonstrated on two synthetically generated homogeneous regions, with unimodal and bimodal annual peakflow seasonality, respectively. The proposed homogeneity test outperforms the existing test yielding H value < 1 for homogeneous regions. Further, real-world applications are illustrated on prediction of flood peak timing in the Northwest USA, revealing nearly 75% of the sites with absolute bias <16 days.

中文翻译：

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基于非参数循环统计的框架，用于预测未测量站点的峰值流量季节性

区域频率分析 (RFA) 可作为区域一级洪水估计的有用工具，以提高估计洪水分位数的精度或在未测量的盆地中进行预测。RFA 广泛用于水文文献中，用于估计水文变量（年最大流量）的大小，汇集来自同质区域内多个站点的数据。然而，在气候具有强烈季节性的地区，洪水等危险事件的发生时间可能同样重要。因此，在这些地区，改进对年度高峰流量季节性的估计对于有效管理水利基础设施或及时采取防洪措施至关重要。在这项研究中，我们提出了一种新的 RFA 框架，用于基于循环统计估计年度峰值流量的季节性。该框架包括（a）基于流域相似性的属性选择，使用季节性描述符作为响应，（b）基于影响区域方法的同质区域的形成，（c）适用于年度峰值流量时间的定向数据的同质性​​测试, 和 (d) 使用非参数区域循环密度对年度高峰流量季节性的概率预测。所提出的方法的适用性首先在两个综合生成的均质区域上得到证明，分别具有单峰和双峰的年度峰值流量季节性。所提出的同质性测试优于现有的测试产量 (c) 适用于年度高峰流量时间定向数据的同质性​​测试，以及 (d) 使用非参数区域循环密度对年度高峰流量季节性的概率预测。所提出的方法的适用性首先在两个综合生成的均质区域上得到证明，分别具有单峰和双峰的年度峰值流量季节性。所提出的同质性测试优于现有的测试产量 (c) 适用于年度高峰流量时间定向数据的同质性​​测试，以及 (d) 使用非参数区域循环密度对年度高峰流量季节性的概率预测。所提出的方法的适用性首先在两个综合生成的均质区域上得到证明，分别具有单峰和双峰的年度峰值流量季节性。所提出的同质性测试优于现有的测试产量均匀区域的H值 < 1。此外，在美国西北部洪水高峰时间的预测中说明了实际应用，揭示了近 75% 的站点的绝对偏差 <16 天。