Hydrologic homogeneous regions using monthly streamflow in turkey

Cluster analysis of gauged streamflow records into homogeneous and robust regions is an important tool for the characterization of hydrologic systems. In this paper we applied the hierarchical cluster analysis to the task of objectively classifying streamflow data into regions encompassing similar s...

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Autores:
Kahya, Ercan
Demirel, Mehmet C.
Bég, Osman A.
Tipo de recurso:
Article of journal
Fecha de publicación:
2008
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/33995
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/33995
http://bdigital.unal.edu.co/24075/
Palabra clave:
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Cluster analysis of gauged streamflow records into homogeneous and robust regions is an important tool for the characterization of hydrologic systems. In this paper we applied the hierarchical cluster analysis to the task of objectively classifying streamflow data into regions encompassing similar streamflow patterns over Turkey.The performance of three standardization techniques was also tested, and standardizing by range was found better than standardizing with zero mean and unit variance. Clustering was carried out using Ward's minimum variance method which became prominent in managing water resources with squared Euclidean dissimilarity measures on 80 streamflow stations. The stations have natural flow regimes where no intensive river regulation had occurred. A general conclusion drawn is that the zones having similar streamflow pattern were not be overlapped well with the conventional climate zones of Turkey; however, they are coherent with the climate zones of Turkey recently redefined by the cluster analysis to total precipitation data as well as homogenous streamflow zones of Turkey determined by the rotated principal component analysis. The regional streamflow informationin this study can significantly improve the accuracy of flow predictions in ungauged watersheds.