A ranking method for regionalization of watersheds

被引:7
作者
Ahani, Ali [1 ]
Nadoushani, S. Saeid Mousavi [1 ]
Moridi, Ali [1 ]
机构
[1] Shahid Beheshti Univ, Fac Civil Water & Environm Engn, Dept Water Resources Management, Tehran, Iran
关键词
Clustering; Flood quantile estimation; Homogeneity; Regionalization; Regional flood frequency analysis; FLOOD FREQUENCY-ANALYSIS; CLUSTER-ANALYSIS; HOMOGENEOUS REGIONS; DRAINAGE BASINS; CLASSIFICATION;
D O I
10.1016/j.jhydrol.2022.127740
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Identification of groups of watersheds with similar flood generation mechanisms and the same hydrological responses is an important step of regional flood frequency analysis which is called regionalization of watersheds. A group of watersheds with similar flood generation mechanisms and the same hydrological responses is called a homogeneous region. Theoretically, the homogeneity of regions is a requirement to fit the regional frequency distribution and estimate flood quantiles. However, it may be observed in some real-world cases that a regionalization with a more appropriate homogeneity condition results in less accurate estimates in comparison with a regionalization with lesszappropriate homogeneity. In this study, a ranking method is proposed to compare various regionalizations and select more suitable ones for regional flood frequency analysis. The proposed method is used to identify a suitable regionalization for regional flood frequency analysis by considering the homogeneity, quantile estimation accuracy, and average region size. The method was applied to Karun-ebozorg basin in the southwest of Iran as a case study. The results show that the method has an acceptable performance in identifying a regionalization that provides suitable homogeneity, quantile estimation accuracy, and average region size. Also, the study results indicate that the ranking based on the homogeneity ratio is relatively compatible with the ranking based on the quantile estimation accuracy. In addition, according to the results, the most appropriate regionalization of 41 watersheds in the study area includes 3 regions provided by the combination of the clustering algorithms average-linkage and K-means.
引用
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页数:10
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