<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">This article deals with the regionalization of low flow regimes lower than Q95 in Slovakia. For the regionalization of 219 small and medium-sized catchments, we used a catchment area running from 4 to 500 km2 and observation periods longer than 20 years. The relative frequency of low flows lower than Q95 was calculated. For the regionalization, the nonhierarchical clustering K-means method was applied. The Silhouette coefficient was used to determine the right number of clusters. The principal components were found from the pooling variables on the principal components. The K-means clustering method was applied. Next, we compared the differences between the two methods of pooling data into regional types. The results were compared using an association coefficient.</abstract>

This article deals with the regionalization of low flow regimes lower than Q95 in Slovakia. For the regionalization of 219 small and medium-sized catchments, we used a catchment area running from 4 to 500 km2 and observation periods longer than 20 years. The relative frequency of low flows lower than Q95 was calculated. For the regionalization, the nonhierarchical clustering K-means method was applied. The Silhouette coefficient was used to determine the right number of clusters. The principal components were found from the pooling variables on the principal components. The K-means clustering method was applied. Next, we compared the differences between the two methods of pooling data into regional types. The results were compared using an association coefficient.

Pooling of low flow regimes using cluster and principal component analysis

Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Radlinského 11, 813 68 Bratislava

Slovak Journal of Civil Engineering

{"article-title":"Pooling of low flow regimes using cluster and principal component analysis"}

This article deals with the regionalization of low flow regimes lower than Q95 in Slovakia. For the regionalization of 219 small and medium-sized catchments,...

Pooling of low flow regimes using cluster and principal component analysis

Publié en ligne: 19 juil. 2012

Pages: 19 - 27

DOI: https://doi.org/10.2478/v10189-012-0010-y

Mots clés
Low flow, Cluster analysis, Principal component analysis

This content is open access.

Pooling of low flow regimes using cluster and principal component analysis

Publié en ligne: 19 juil. 2012

Pages: 19 - 27

DOI: https://doi.org/10.2478/v10189-012-0010-y

Mots clésLow flow, Cluster analysis, Principal component analysis

This content is open access.

Mots clés
Low flow, Cluster analysis, Principal component analysis