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Information about the watershed is crucial to understanding the formation of river water quality. In this study, focusing on the Asakawa River within the Tamagawa River system, which encompasses diverse watershed environments, we aimed to develop a methodology for comprehensively understanding river water quality and watershed characteristics using both water quality indices for 34 observation points within the watershed and watershed indices for each of those points. We calculated correlation coefficients, performed multivariate analyses (cluster analysis, principal component analysis), and explored the relationships among water quality and watershed factors. The results of the cluster analysis showed similarities in water quality indices and watershed indices, yet differences were observed in certain classification trends, which can be employed to identify unique tributaries and point sources of pollution within the target watershed and to investigate contributing factors. Additionally, the results of the principal component analysis can be used to compare the influences of natural and anthropogenic factors, relative to the findings of the cluster analysis. This analysis can also reveal the extent to which tributaries with distinct watershed indices impact water quality formation. The application of multivariate analysis techniques to both river water quality indices and watershed indices, as demonstrated in this study, contributes to comprehending the influences of watershed factors on water quality and holds potential for enhancing river basin management and conservation efforts.
Information about the watershed is crucial to understanding the formation of river water quality. In this study, focusing on the Asakawa River within the Tamagawa River system, which encompasses diverse watershed environments, we aimed to develop a methodology for comprehensively understanding river water quality and watershed characteristics using both water quality indices for 34 observation points within the watershed and watershed indices for each of those points. We calculated correlation coefficients, performed multivariate analyses (cluster analysis, principal component analysis), and explored the relationships among water quality and watershed factors. The results of the cluster analysis showed similarities in water quality indices and watershed indices, yet differences were observed in certain classification trends, which can be employed to identify unique tributaries and point sources of pollution within the target watershed and to investigate contributing factors. Additionally, the results of the principal component analysis can be used to compare the influences of natural and anthropogenic factors, relative to the findings of the cluster analysis. This analysis can also reveal the extent to which tributaries with distinct watershed indices impact water quality formation. The application of multivariate analysis techniques to both river water quality indices and watershed indices, as demonstrated in this study, contributes to comprehending the influences of watershed factors on water quality and holds potential for enhancing river basin management and conservation efforts.
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