王颖,杨佳希,杨宝钢,翟盘茂,廖代强,朱浩楠,邹旭恺,肖风劲,陈鲜艳. 2024. 利用短序列高密度台站资料推算暴雨重现期方法研究及应用. 气象学报,82(X):1-12. DOI: 10.11676/qxxb2024.20230136
引用本文: 王颖,杨佳希,杨宝钢,翟盘茂,廖代强,朱浩楠,邹旭恺,肖风劲,陈鲜艳. 2024. 利用短序列高密度台站资料推算暴雨重现期方法研究及应用. 气象学报,82(X):1-12. DOI: 10.11676/qxxb2024.20230136
Wang Ying, Yang Jiaxi, Yang Baogang, Zhai Panmao, Liao Daiqiang, Zhu Haonan, Zou Xukai, Xiao Fengjin, Chen Xianyan. 2024. An estimation method for rainstorm return period based on short-sequence high-density station data and its application. Acta Meteorologica Sinica, 82(X):1-12. DOI: 10.11676/qxxb2024.20230136
Citation: Wang Ying, Yang Jiaxi, Yang Baogang, Zhai Panmao, Liao Daiqiang, Zhu Haonan, Zou Xukai, Xiao Fengjin, Chen Xianyan. 2024. An estimation method for rainstorm return period based on short-sequence high-density station data and its application. Acta Meteorologica Sinica, 82(X):1-12. DOI: 10.11676/qxxb2024.20230136

利用短序列高密度台站资料推算暴雨重现期方法研究及应用

An estimation method for rainstorm return period based on short-sequence high-density station data and its application

  • 摘要: 暴雨重现期是城市排水防涝设计的重要基础,通常基于长年代观测数据进行推算。但在无降水观测或观测时间较短的情况下,如何进行重现期推算和暴雨强度评估是目前亟需解决的科学问题。基于重庆市近11年高密度台站降水观测资料,建立各站逐年日降水极值抽样数据集,以“空间换时间”的思想,对日降水极值样本进行空间抽样,通过与国家级气象站长序列观测数据(>60 a)进行交叉检验,构建试验区目标点最佳百分位合成序列,该方法简称为空间抽样合成法(SBS)。通过重庆地区 34 个测站长年代观测资料计算重现期降水量“真值”与 SBS 、邻近点替换、克雷斯曼(Cressman)空间插值、年多个样法等推算结果进行对比检验,就平均而言,SBS的相对误差最小,其中含目标点样本的SBS相对误差最小为7.2%,邻近点替代法相对误差最大(13.2%),表明SBS可以较好地用于中国复杂地形的重庆地区,利用短序列高密度台站降水资料构建无有效降水观测资料目标点处的长序列极值降水样本,从而开展概率拟合优选及暴雨重现期推算。在对上述方法验证基础上,实现重庆地区2062个高密度气象观测站多年(50 a)一遇重现期降水量推算,提高了日尺度极端降水的空间精细化水平,结果能更好反映山区地形对降水的影响。SBS可以充分利用短序列高密度台站降水观测资料,实现区域内任意目标点重现期降水量推算。

     

    Abstract: The rainstorm return period is an important basis for urban drainage and flood control design, which is usually calculated by long-term observation data. However, under the circumstances of none or short-sequence observations, how to calculate the return period and evaluate rainstorm intensity is an important scientific issue that needs to be solved urgently. Based on high-density precipitation observations in Chongqing over the past 11 years, we establish an annual maximum daily rainfall data set. With the idea of "space trade for time", daily rainfall samples are bootstrapped and used for cross-validation with long-term national station data (more than 60 years) to select optimal percentile synthetic sample set of the target point. This method is referred to as the spatial bootstrap synthesis method (hereafter abbreviated as SBS). Comparing the calculated return period rainfall results between the original sequence and other various methods by using 34 stations with long-term observations in Chongqing on average, the relative error of the SBS is smaller than that of the other three methods including the nearest station replacement, Cressman interpolation and annual multi-sampling method. Among them, the SBS containing target point samples has the smallest relative error of 7.2%, and the nearest station replacement method has the largest relative error of 13.2%. This indicates that the SBS can be used well in Chongqing, a complex terrain area of China, to construct long-sequence extreme rainfall samples by making use of short-sequence high-density data from stations surrounding the target point, while the contrusted sequences can be used to fit the probability distribution function and calculate the rainfall return period. On this basis, the 50 a return period rainfall of 2062 high-density meteorological observation stations in Chongqing is calculated, which improves the spatial refinement level of daily extreme rainfall and better reflects the influence of mountainous terrain. The SBS can make full use of short-sequence high-density station precipitation data to estimate the rainfall return period at any target point in the region, and can be applied in municipal design, rainstorm disaster risk assessment, etc.

     

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