Quantitative studies of the relationships between river water quality and environmental variables are needed to improve understanding of the impacts of natural and human factors on aquatic environments. However, multicollinearity between environmental variables can hinder the identification of key factors when water quality–environment relationship is studied using traditional regression methods. This study utilized two alternative statistical methods, variation and hierarchical partitioning, to address these difficulties in studies of river water quality. Using these methods, we explored the effects of catchment physiography, climate and land use variables on total phosphorus and nitrogen, pH, water colour and dissolved oxygen during the years 1995–2005 in 32 boreal rivers in Finland.
Catchment physiography and land use explained most of the variation in water quality, especially in phosphorus, nitrogen and water colour data. The strong correlations (rs > 0.8) between agricultural land use and phosphorus and nitrogen concentrations indicate that water quality is highly affected by agriculture in boreal regions. By determining the environmental drivers of different water quality variables, we can estimate the water quality of different catchments in response to environmental changes and identify areas that are sensitive to global changes. Our study shows that novel statistical methods integrated with geographic information system data and techniques provide deeper insights into water quality–environment relationships than traditional regression, and these should be considered when developing, for example, conservation planning for rivers. Copyright © 2011 John Wiley & Sons, Ltd.