个人信息Personal Information
副教授
博士生导师
硕士生导师
主要任职:无
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:水利工程系
学科:水文学及水资源
办公地点:综合实验4号楼411
联系方式:0411-84707904
电子邮箱:weiding@dlut.edu.cn
Inherent Relationship between Flow Duration Curves at Different Time Scales: A Perspective on Monthly Flow Data Utilization in Daily Flow Duration Curve Estimation
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论文类型:期刊论文
发表时间:2018-08-01
发表刊物:WATER
收录刊物:SCIE
卷号:10
期号:8
ISSN号:2073-4441
关键字:Kappa distribution; parameter relation; partial gauged basin; power function; ratio curve; ungauged basin
摘要:Modelling flow duration curves (FDCs) has long been a topic of interest since it is widely used in various hydrological applications. Most studies related to the estimation of FDCs in ungauged or partial gauged basins focus primarily on using climate and catchment characteristics to regionalize FDC at some single time scale. However, the relationship of FDCs at various time scales are rarely analyzed or studied. Here, we propose two methods, which are Modelled FDC Parameter comparison (M-FDC-P) and Empirical FDC Ratio comparison (E-FDC-R), to study the quantitative relationship between daily and monthly FDCs. One method M-FDC-P, selects a Kappa (KAP) distribution to represent the characteristics of the FDCs and then analyzes the relationship between KAP parameters of modelled FDCs at different time scales. Results indicate that three out of four parameters have strong correlations between FDCs at daily and monthly time scales. The other method, E-FDC-R, compares the quantitative relationship between daily and monthly empirical FDCs with given exceedance probabilities. The Power function is used for fitting the ratio-exceedance probability curves. In addition, the simulated daily FDC derived from monthly FDC can be very consistent with the observed daily flow records when the two parameters of power function are quantified precisely. These results clearly indicate that there are strong connections between daily and monthly FDCs, and monthly FDC can provide valuable information for daily FDC estimation. Since flow records at a large time scale are easier to obtain, daily FDC could be derived from monthly FDC by considering the inherent relationships between FDCs at different time scales, which is not sufficiently realized in previous studies.