Abstract
Abstract In this study, the relationships between the concentration and isotopic compositions (δ15NNO3 and δ18ONO3) of nitrate in river water and the land use and topographic characteristics of the watershed were analyzed for the tributaries of two major inflow rivers (Hokota River and Tomoe River) of Lake Kitaura (Ibaraki Prefecture, Japan). Modelling by the Random Forests machine learning algorithm in combination with statistical analyses (correlation analysis and principal component analysis) revealed that crop farming in dry fields (i.e., application of chemical fertilizers and manure) and swine farming (e.g., addition of swine manure to cropland soils over the basal application rate, leakage of swine manure from the stockyard to groundwater or river) in the watershed are the main drivers for increasing nitrate concentration in the river water of the studied river basins. Based on calculations using the Random Forests models, nitrate derived from swine farming was estimated to account for up to 28% of nitrate in the river water, and its contribution was proportional to the density of farmed swine (log-transformed value). Similarly, the percentage of nitrate removed in the riparian zone and the lotic environment (e.g., bottom sediment, water column and hyporheic zone within the river flow path) was estimated to be up to 34% and was also proportional to the topographic wetness index in the riparian zone (log-transformed value). In addition, significant negative correlations between log-transformed nitrate concentration and δ15NNO3 and δ18ONO3 were recognized in the combined data for all the tributaries as well as the single data for several tributaries, suggesting that biological processes (e.g., denitrification, assimilation) contribute to the removal of nitrate and associated changes in δ15NNO3 and δ18ONO3 of residual nitrate. These findings are applicable for planning measures to control nitrogen load from the watershed and for improving the accuracy in calculating the proportions of nitrate from different sources based on the isotope mixing models.
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