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Title: Multivariate statistical analysis for estimating surface water quality in reservoirs

Authors: Matias Bonansea; Raquel Bazán; Susana Ferrero; Claudia Rodríguez; Claudia Ledesma; Lucio Pinotti

Addresses: Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Departamento de Estudios Básicos y Agropecuarios, Facultad de Agronomía y Veterinaria (FAV), Universidad Nacional de Río Cuarto (UNRC), Ruta Nacional 36 Km 601, 5800 Río Cuarto, Córdoba, Argentina ' Departamento de Ingeniería Química y Aplicada, Facultad de Ciencias Exactas Físicas y Naturales, Universidad Nacional de Córdoba (UNC), Instituto Superior de Estudios Ambientales (ISEA-UNC), Argentina ' Departamento Matemática, Facultad de Ciencias Exactas, Físico-Químicas y Naturales (FCEFQyN), Universidad Nacional de Río Cuarto (UNRC), Argentina ' Departamento de Estudios Básicos y Agropecuarios, Facultad de Agronomía y Veterinaria (FAV), Universidad Nacional de Río Cuarto (UNRC), Argentina ' Departamento de Estudios Básicos y Agropecuarios, Facultad de Agronomía y Veterinaria (FAV), Universidad Nacional de Río Cuarto (UNRC), Argentina ' Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Departamento de Geología, Físico-Químicas y Naturales (FCEFQyN), Universidad Nacional de Río Cuarto (UNRC), Argentina

Abstract: Regular water quality monitoring programs are an important aspect of water management. Different multivariate statistical techniques were applied for interpretation and evaluation of the data matrix obtained during a six-year monitoring program (2006 to 2011) in the principal reservoirs of the central region of Argentina. Eleven sampling sites located in two reservoirs were surveyed each climatic season for 18 parameters. Cluster analysis grouped the sampling sites into three clusters and classified the different climatic seasons into two clusters based on their similarities. Principal component analysis/factor analysis showed the existence of five significant varifactors (VF) which account for 79.3% of the variance, related to soluble salts, nutrients, physico-chemical parameters, and non-common source. Source contribution was calculated using multiple regression of sample mass concentration on the absolute VF scores. This study demonstrates the usefulness of multivariate statistical techniques helping managers to get better information about surface water systems.

Keywords: monitoring program; multivariate statistical techniques; pattern recognation; reservoirs; water quality.

DOI: 10.1504/IJHST.2018.088675

International Journal of Hydrology Science and Technology, 2018 Vol.8 No.1, pp.52 - 68

Available online: 11 Dec 2017 *

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