Abstract

The Xingu River, one of the most important of the Amazon Basin, is characterized by clear and transparent waters that drain a 509.685 km2 watershed with distinct hydrological and ecological conditions and anthropogenic pressures along its course. As in other basins of the Amazon system, studies in the Xingu are scarce. Furthermore, the eminent construction of the Belo Monte for hydropower production, which will alter the environmental conditions in the basin in its lower middle portion, denotes high importance of studies that generate relevant information that may subsidize a more balanced and equitable development in the Amazon region. Thus, the aim of this study was to analyze the water quality in the Xingu River and its tributaries focusing on spatial patterns by the use of multivariate statistical techniques, identifying which water quality parameters were more important for the environmental changes in the watershed. Data sampling were carried out during two complete hydrological cycles in twenty-five sampling stations. The data of twenty seven variables were analyzed by Spearman's correlation coefficients, cluster analysis (CA), and principal component analysis (PCA). The results showed a high auto-correlation between variables (> 0.7). These variables were removed from multivariate analyzes because they provided redundant information about the environment. The CA resulted in the formation of six clusters, which were clearly observed in the PCA and were characterized by different water quality. The statistical results allowed to identify a high spatial variation in the water quality, which were related to specific features of the environment, different uses, influences of anthropogenic activities and geochemical characteristics of the drained basins. It was also demonstrated that most of the sampling stations in the Xingu River basin showed good water quality, due to the absence of local impacts and high power of depuration of the river itself.

Highlights

  • The Amazon Basin is the largest fluvial system in the world (7,008,370 km2), and comprehends a wide variety of rivers with different physical and chemical characteristics influenced by the geochemical composition and the topology of its particular drainage area (Sioli, 1950; ANA, 2011)

  • The surface water quality in the Xingu River basin showed high spatial heterogeneity, being influenced by the conditions of the surroundings and the areas drained by the water bodies analyzed

  • The correlations among variables indicate the influence of the environment on the water quality

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Summary

Introduction

The Amazon Basin is the largest fluvial system in the world (7,008,370 km2), and comprehends a wide variety of rivers with different physical and chemical characteristics influenced by the geochemical composition and the topology of its particular drainage area (Sioli, 1950; ANA, 2011). One of the main right bank tributaries of the Amazon Basin is the Xingu River, which is characterized by having clear and transparent waters and a watershed that drains 509.685 km including different geological substrates: Precambrian rocks of the upper Paraguay Basin, sediments of the Paraguay and Parecis (Upper Xingu) basins; large tracts of Precambrian rocks of igneous metamorphic complex of southern Amazonian Basin (middle Xingu) and sediments of the Amazon Basin (lower Xingu), besides the alluvial deposits that accompany all large tributaries (Fittkau, 1970; Sioli, 1984; ANA, 2011). Near to the city of Altamira, the river forms a sharp deflection forming the so-called Volta Grande do Xingu, with large rapids of 85 m of drop along 160 km and with varying anastomosing channels (CNEC, 1988; Eletrobrás, 2009; Leme Engenharia, 2009). Conditions of topography and land use are important factors that influence the water quality in lotic environments (Sheldon et al, 2012), requiring the use of methods of study that focus both the ecological and anthropogenic processes occurring in the landscape scale and/or in the watershed as a whole (Likens, 1984; Wiens, 1989; Zalewski, 2000; Turner et al, 2001; Tundisi et al, 2008)

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