Abstract
The water quality in many Midwestern streams and lakes is negatively impacted by agricultural activities. Although the agricultural inputs that degrade water quality are well known, the impact of these inputs varies as a function of geologic and topographic parameters. To better understand how a range of land use, geologic, and topographic factors affect water quality in Midwestern watersheds, we sampled surface water quality parameters, including nitrate, phosphate, dissolved oxygen, turbidity, bacteria, pH, specific conductance, temperature, and biotic index (BI) in 35 independent sub-watersheds within the Lower Grand River Watershed in northern Missouri. For each sub-watershed, the land use/land cover, soil texture, depth to bedrock, depth to the water table, recent precipitation area, total stream length, watershed shape/relief ratio, topographic complexity, mean elevation, and slope were determined. Water quality sampling was conducted twice: in the spring and in the late summer/early fall. A pairwise comparison of water quality parameters acquired in the fall and spring showed that each of these factors varies considerably with season, suggesting that the timing is critical when comparing water quality indicators. Correlation analysis between water quality indicators and watershed characteristics revealed that both geologic and land use characteristics correlated significantly with water quality parameters. The water quality index had the highest correlation with the biotic index during the spring, implying that the lower water quality conditions observed in the spring might be more representative of the longer-term water quality conditions in these watersheds than the higher quality conditions observed in the fall. An assessment of macroinvertebrates indicated that the biotic index was primarily influenced by nutrient loading due to excessive amounts of phosphorus (P) and nitrogen (N) discharge from agricultural land uses. The PCA analysis found a correlation between turbidity, E. coli, and BI, suggesting that livestock grazing may adversely affect the water quality in this watershed. Moreover, this analysis found that N, P, and SC contribute greatly to the observed water quality variability. The results of this study can be used to improve decision-making strategies to improve water quality for the entire river basin.
Highlights
Nonpoint source (NPS) pollution from agricultural activities has become the main source of contamination in surface waterResponsible editor: Philippe GarriguesEnviron Sci Pollut Res (2019) 26:1487–1506 growing season and after harvest (Zhu et al 2012)
Pairwise comparison of the data acquired during the fall and spring showed that all water quality parameters were statistically different data sets with p < 0.02 for all parameters, which suggests that the timing of water quality sampling is critical
Simple regression analysis of all variables revealed that correlations between independent variables and water quality indicators fluctuated with the season but that the Bpasture/hay^ LULC category was statistically significant for several water quality indicators for both sampling campaigns
Summary
Nonpoint source (NPS) pollution from agricultural activities has become the main source of contamination in surface waterResponsible editor: Philippe GarriguesEnviron Sci Pollut Res (2019) 26:1487–1506 growing season and after harvest (Zhu et al 2012). All types of agricultural practices and land use, including animal feeding operations (AFOs), are treated as agricultural NPS pollution. Due to the features of NPS pollution, field measurements, and the limitations of experiments, NPS pollution management practices depend on spatial-temporal simulation modeling, a key method used to estimate NPS pollution related to spatial uncertainty (Shamshad et al 2008; Huiliang et al 2015). Various approaches have been used to estimate the loads of NPS pollution, including small spatial-scale experiments and watershedscale modeling, which accurately calculates the pollution loads of different land uses through experimental methods (Alberti et al 2007; Pratt and Chang 2012). It is difficult to extend field experimental methods to the watershed scale due to the biological and chemical reactions and the complexity of the transport mechanism in the watershed
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