气象学报2024，Vol.82Issue(4)：510-521,12.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
Estimating the rainstorm return period based on short-sequence high-density station data:Meteorology and application
摘要
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 14 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 a)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 are calculated,which improves the spatial refinement level of daily extreme rainfall and better reflects the influence of mountainous terrain.Generally,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.关键词
空间抽样合成法/百分位合成序列/年最大日降水/重现期推算Key words
Spatial bootstrap synthesis method/Percentile of synthetic sequence/Annual maximum daily rainfall/Return period estimation分类
天文与地球科学引用本文复制引用
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..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[J].气象学报,2024,82(4):510-521,12.基金项目
科技创新2030-"新一代人工智能"重大项目课题(2022ZD0119502)、中国气象局创新发展专项(CXFZ2024J071)、中国气象局气候资源经济转化重点开放实验室开放课题(2023009)、国家气候中心气候可行性论证创新团队项目(NCCCXTD002)、重庆市气象局业务技术攻关项目(YWJSGG-202129、YWJSGG-202205)、中央级公益性科研院所基本科研业务费专项基金项目(IUMKY202439). （2022ZD0119502）
