Influence of nitrogen inputs, dam construction and landscape patterns on riverine nitrogen exports in the Yangtze River basin during 1980–2015

Abstract

A quantitative understanding of different N sources and the factors controlling riverine N fluxes is critical for optimizing water pollution control strategies. However, the lack of a comprehensive understanding of N pollution legacy effects and human alteration of landscapes (e.g., dam construction and land use/land cover) hinders development of efficient N management strategies. This study integrated long-term (1980–2015) net anthropogenic nitrogen inputs (NANI) and different forms of N exports, hydroclimate data, dam construction and landscape metrics to elucidate N sources and the factors controlling N fluxes in the Yangtze River basin (YRB). Over the 36-year study period, annual NANI in the YRB increased from 4166 to 8571 kg N km−2 yr−1. Riverine concentrations of total nitrogen (TN), dissolved inorganic nitrogen (DIN) and dissolved organic nitrogen (DON) increased ∼ 3-fold, ∼3-fold and ∼ 36-fold during 1980–2015, respectively, whereas riverine particulate nitrogen (PN) decreased by ∼ 90 %. Such long-term riverine N export dynamics were driven by elevated NANI, landscape configuration change and dam construction activities. Inclusion of these variables in multiple linear regression models explained 91 %, 83 % and 96 % of the annual variability in riverine DIN, DON and PN fluxes, respectively. Estimated riverine TN flux from sum of predicted DIN, DON and PN fluxes showed high agreement with measured values (R2 = 0.92, Nash–Sutcliffe coefficients = 0.91), indicating strong efficacy for the developed regression models. Separation of annual riverine N exports into current year NANI versus legacy source contributions indicated that legacy sources contributed ∼ 49 % (26–82 %), 51 % (<1–78 %), 95 % (87–98 %) and 56 % (36–86 %) of riverine DIN, DON, PN and TN exports, respectively. The release of legacy N from the landscape is associated with increases in dam construction, urban density, and the number of small area farm operations between 1980 and 2015. This study highlights the potential of aggregating multiple historical N input datasets, along with changes in land use and river regulation to model long-term riverine N export dynamics. The results of the study provide a foundation for developing integrated watershed N management strategies that address both current and legacy N pollution.