摘要
【目的】分析调水工程水源区和受水区的径流丰枯遭遇特性,可为合理制定调水方案、相机实施工程调水提供科学依据。【方法】以引江济淮工程为例,根据大通站和鲁台子站1956—2022年月径流实测资料,采用相关性系数、标准化径流指数,以及构建边缘分布函数和二维Copula函数的联合概率分布模型,描述单站径流丰枯变化和揭示二维径流互补规律。【结果】结果显示:大通站径流和鲁台子站径流在年、丰水期和枯水期时间尺度下均表现出较弱的相关性,对应的Kendall相关系数分别为0.216、0.273和0.227。4—6月为大通站径流的丰水月份,对应的丰水概率为0.313、0.328和0.373;6月、8月则为鲁台子站径流的枯水月份,对应的枯水概率为0.209和0.179。年、丰水期和枯水期对应的径流组合丰枯异步的概率分别为54.5%、52.5%和52.2%;此外,这三个时间尺度下有利于从水源区向受水区调水的概率分别为72.7%、73.7%、73.9%。【结论】Gamma分布可以较好地拟合不同时间尺度下径流的分布特性,Frank Copula、Frank Copula和Clayton Copula分别是大通站和鲁台子站年、丰水期和枯水期径流组合的最优Copula函数;不同时间尺度下径流组合丰枯异步的概率均大于丰枯同步的概率。
[Objective]Analyzing the runoff wetness-dryness encountering characteristics between water source and water receiving areas is essential for optimizing water diversion schemes and implementing engineering operational strategies in a timely manner.[Methods]Taking the Yangtze River to Huaihe River Diversion Project was taken as an example,according to the monthly measured runoff data of Datong Station and Lutaizi Station from 1956 to 2022,the correlation coefficient,the standardized runoff index,as well as marginal distribution functions and joint probability distribution models based on two-dimensional Copula functions were employed,to describe the characteristics of runoff wetness-dryness for a single station and reveal the complementary patterns of two-dimensional runoff.[Results]The result indicated that weak correlations between these two stations across annual,high-flow and low-flow time scales,with Kendall correlation coefficients of 0.216,0.273 and 0.227,respectively.Datong Station experienced high-flow months from April to June,with corresponding probabilities of 0.313,0.328 and 0.373;while Lutaizi Station encountered low-flow months in June and August,with probabilities of 0.209 and 0.179,respectively.It could also be shown that the sum of synchronous probabilities for runoff combinations were 54.5%,52.5%and 52.2%across annual,high-flow and low-flow time scales,respectively;and the probabilities of being favorable for transferring water from the source area to the receiving area were 72.7%,73.7%and 73.9%during these three time scales.[Conclusion]The conclusion were that the Gamma distribution could well fit the distribution characteristics of runoff under different time scales,and the optimal Copula functions for runoff combinations between these two stations were Frank Copula,Frank Copula and Clayton Copula across annual,high-flow and low-flow time scales.The sum of asynchronous probabilities for runoff combinations were all greater than the sum of synchronous probabilities under these time scales.
作者
王晓颖
宋培兵
徐红霞
张峰
王超
孔令仲
WANG Xiaoying;SONG Peibing;XU Hongxia;ZHANG Feng;WANG Chao;KONG Lingzhong(Anhui Water Conservancy Technical College,Hefei 231603,Anhui,China;China Renewable Energy Engineering Institute,Beijing 100120,China;China Institute of Water Resources and Hydropower Research,Beijing 100038,China;Yangzhou University,College of Hydraulic Science and Engineering,Yangzhou 225009,Jiangsu,China)
出处
《水利水电技术(中英文)》
北大核心
2025年第2期125-136,共12页
Water Resources and Hydropower Engineering
基金
安徽省高等学校科学研究项目(KJ2021A1454)
安徽省自然科学基金水科学联合基金项目(2208085US06)
中国水力发电学会青年人才托举项目(2024015)
国家自然科学基金项目(52009119)。
关键词
径流
调水工程
丰枯遭遇
标准化径流指数
边缘分布
COPULA函数
影响因素
runoff
water diversion project
wetness-dryness encountering
standardized runoff index
marginal distribution
Copula function
influencing factors
