INFLUENTIAL FACTORS IN SURFACE WATER QUALITY IN CATCHMENTS WITHIN THE PAMPA BIOME WITH DIFFERENT LAND USE1

• Autor(a) principal: Valente,Mirian Lago
• Data de Publicação: 2015
• Outros Autores: Cruz,Jussara Cabral, Baggiotto,Carine, Consensa,Catarine Barcellos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Árvore (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622015000601135
• topic: Multivariate analysis; Water resources; Silviculture

The aim of this study was to identify, by multivariate statistical technique, the physic, chemical and biological variables that best characterize the quality of surface waters in two small rural catchments with different land uses (eucalyptus silviculture (SC) vs. pasture and extensive livestock (LC)) located in Rosário do Sul, RS - Brazil. Monitoring was conducted during the months of August 2011 to August 2012 and the following parameters were analyzed: Ca2+, Mg2+, K+, SO42-, Cl-, pH, electrical conductivity, turbidity, alkalinity, suspended and dissolved solids, biochemical oxygen demand , total coliforms, Escherichia coli and temperature, flow and rainfall. Through the use of FA/PCA, it was found that the model best fit to express water quality of in LC that was composed of five factors which account for 83.5% of the total variance, while for SC, four factors accounted for 85.12% of the variance. In LC, the five main factors were, respectively, soluble salts, diffuse pollution, solid, and both anthropogenic and organic factors. In SC, the four factors were namely: soluble salts, mineral, nutritional and diffuse pollution factors. The results of this study showed that by replacing the traditional soil usage (pasture and livestock) with planted forest, diffuse pollution was attenuated but, however, it did not result in major changes in the physical-chemical and biological characteristics of the water. Another point to note is that factorial analysis did not result in a large reduction in the number of variables, once the best model fit occurred with the addition of 15 of 18 analyzed variables (LC) and 17 of 18 analyzed variables (SC).