Claudia Rodríguez

Determinación de indicadores de eutrofización en el embalse Río Tercero, Córdoba (Argentina)

Eutrophication is one of the most important environmental problems in lakes and reservoirs. Rio Tercero reservoir (Argentina) is affected by various human activities that impact negatively on the system. When generating a monitoring program of aquatic systems, one of the most important considerations is to define the most significant variables. Multivariate analysis was presented as an alternative tool. The aim of this study was to determine water quality and trophic status in Rio Tercero reservoir, in order to generate an adequate basis for management of water resources. Sampling campaigns were conducted during the years 2003 to 2006. Water quality parameters were measured. We determined the trophic status of the reservoir. Multivariate analysis was performed, generating a correlation matrix and a principal component analysis (PCA). The reservoir experienced a deterioration in water quality, from mesotrophic to eutrophic state. It was shown that there is positive correlation between chlorophyll-a and total phosphorus (r = 0.83), dissolved oxygen (r = 0.51) and temperature (r = 0.43). The PCA explained the 65.6% of the total variability of the data. Multivariate analysis established that the most significant variables were chlorophyll-a, total phosphorus and temperature, demonstrating that algal blooms should occur in spring and summer seasons. The results obtained allow to generate a preliminary program of monitoring and management applied to other aquatic resources.

Multivariate statistical analysis for estimating surface water quality in reservoirs

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.