Spatio-temporal aspects of the environmental factors affecting water quality in boreal rivers

Abstract

River water quality is the outcome of multiple processes and factors, which vary spatially and temporally. In this study, the key spatial and temporal scales and the most important environmental factors explaining river water quality at these scales were analysed by generalized additive models (GAMs). Water quality was studied through total phosphorus (median = 61.9 µg l -1 ) and nitrogen (1388.1 µg l -1 ), pH (6.7) and water colour (143.3 mg Pt l -1 ). Environmental factors covered variables from land use/cover, climate and other landscape characteristics. The spatial scales used were the closest 50 m, 100 m, 200 m, 500 m and 1000 m buffer zones around the river channel, in addition to the entire catchment area. Temporality was studied through the entire year and four periods, which were determined by the natural variation in discharge.In the comparison of spatial scales of environmental factors, the variation in phosphorus, nitrogen and water colour was best explained using environmental data from the broadest scale, the entire catchment. In contrast, the variation in pH was best explained using data from the closest (50 m) buffer zone, the riparian area. In modelling temporal scales, the variation in water quality variables was best explained during discharge maximum periods and when the environmental data covering the entire year was considered. Nutrients were related specifically to agriculture, water colour to lake percentage, and pH to pastures. The results showed the suitability of GAMs in water quality studies.

Effect of multiple spatial scale characterization of land use on water quality

“…The close proximity of croplands to urban areas at our sampling sites might be why all the best models with EC appeared at the reach scale. Smaller distances between cropland and rivers reduces the in ltration and retention time compared with agricultural activities that are not in close proximity, and thus more nutrients and ions could be transported into the rivers (Varanka and Hjort 2017). The best model of COD Mn in spring and winter was at the reach scale, which was contrary to a previous study (Ding et al 2016).…”

Global variation in freshwater physico‐chemistry and its influence on chemical toxicity in aquatic wildlife

“…catchment geology, land cover, primary production, soil characteristics and water source contributions) and anthropogenic factors (e.g. eutrophication via farmland irrigation, livestock densities, mining, domestic and industrial sewage) (Rothwell et al ., 2010; Feng et al ., 2017; Varanka & Hjort, 2017). However, more than 95% of surface fresh waters globally are between pH 6 and 9, and 99.8% are between pH 4 and 9.5 (GLORICH database; Hartmann et al ., 2014, 2019; Fig.…”

Snow to Precipitation Ratio Controls Catchment Storage and Summer Flows in Boreal Headwater Catchments

“…They are being increasingly used in environmental research and show high potential for modeling hydrological process‐environment relationships over broad geographical regions. Statistical models are computationally more cost efficient than process models and can readily account for environmental conditions related to soil conditions, topography, and land cover, which can be difficult to parameterize physically (Varanka & Hjort, ). However, there are inferential limits associated with observational studies, depending on the characteristics and accuracy of the data sets used.…”