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| 基于极值水文分区和层次贝叶斯的区域非一致性频率分析 |
| Regional non-stationary frequency analysis based on extreme hydrological regionalization and hierarchical Bayesian framework |
| 投稿时间：2025-05-27 修订日期：2026-01-05 |
| DOI： |
| 中文关键词: 区域非一致性 水文频率分析 极值水文分区 层次贝叶斯 湘江流域 |
| 英文关键词: regional non-stationarity hydrological frequency analysis extreme hydrological regionalization hierarchical Bayesian framework Xiangjiang River Basin |
| 基金项目:国家自然科学基金创新研究群体项目(52121006)；湖南省自然科学基金面上项目(2024JJ5022)；湖南省教育厅科学研究优秀青年项目(24B0329)；湖南省水利科技项目(XSKJ2023059-06) |
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| 中文摘要: |
| 非一致性水文频率分析是变化环境下工程水文设计研究的关键科学问题，现有方法不确定性较高且未考虑站点序列间的空间相关性。本文提出基于极值水文分区和层次贝叶斯的区域非一致性水文频率分析方法，首先基于变差函数F-madogram的围绕中心点划分（PAM）聚类算法对流域划分水文分区，其次针对同一分区构建基于层次贝叶斯的区域非一致性频率分析模型，具体包括无池化、部分池化和全池化3种模型。最后，选择 湘江流域年最大24小时极值降雨为例开展实例研究。结果表明：适用极值理论的空间聚类算法，将湘江流域36个雨量站划分为3个水文分区；以第I水文分区为例，与大部分站点呈显著正相关的气候驱动因子为FMA Nino12。相较单站非一致性模型，部分池化模型模拟表现最优，较好体现气候驱动因子的区域同质响应特征并保留了站点个体差异，各站区域参数的不确定性区间显著降低了10%~35%。随着重现期的不断增大，各站非一致性重现期相较一致性情况不断减小，如50年一遇的重现期，部分站点减小比例接近60%，表明在气候因子驱动下分区内极值降雨事件发生概率增大。文中方法可为丰富非一致性水文频率分析的方法体系、科学确定流域设计暴雨并制订防灾策略提供依据。 |
| 英文摘要: |
| Non-stationary hydrological frequency analysis is a critical scientific research issue for engineering hydrologic design under changing environments. Existing methods exhibit high uncertainty and often fail to account for the spatial dependences among data series of different stations. This paper proposes a regional non-stationary hydrological frequency analysis methodology based on hierarchical Bayesian framework and extreme hydrological regionalization. Firstly, the partitioning around medoids (PAM) clustering algorithm based on the F-madogram variogram is utilized to partition the basin into hydrologically homogeneous regions. And then regional non-stationary hydrological frequency analysis models based on hierarchical Bayesian are constructed. They can be classified into no pooling, partial pooling and full pooling models. Finally, a case study is conducted using the annual maximum 24 hour extreme rainfall data from the Xiangjiang River Basin. Results indicate that based on the spatial clustering algorithm adapting with extreme value theory, the Xiangjiang river basin with 36 rainfall gauges is divided into three hydrological regions. For Region I, FMA Nino12 is identified as a climatic driver significantly positively correlated with most stations. Comparing with the at-site non-stationary model, the partial pooling model exhibits superior performance, effectively capturing the regionally homogeneous response to climatic drivers while preserving individual site characteristics. Simultaneously, the regional models exhibit a great benefit with the uncertainty for regional parameters at each station reduced by about 10% to 35%. Moreover, as the return period increases, the non-stationary return periods consistently decrease, compared with the stationary conditions. For instance, at the 50-year return period, the reductions rate approaches 60% for some stations, indicating an increased probability of extreme rainfall events within the region driven by climatic factors. This methodology enriches the methodological framework of non-stationary hydrological frequency analysis and can provide a scientific basis for determining the design rainstorms in basins and formulating disaster prevention strategies. |
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