Effects of anthropogenic nitrogen discharge on dissolved inorganic nitrogen transport in global rivers.

Abstract

Excess nutrients from fertilizer application, pollution discharge, and water regulations outflow through rivers from lands to oceans, seriously impacting coastal ecosystems. A reasonable representation of these processes in land surface models and River Transport Models (RTMs) is very important for understanding human-environment interactions. In this study, the schemes of riverine dissolved inorganic nitrogen (DIN) transport and human activities including nitrogen discharge and water regulation, were synchronously incorporated into a land surface model coupled with a RTM. The effects of anthropogenic nitrogen discharge on the DIN transport in rivers were studied based on simulations of the period 1991-2010 throughout the entire world, conducted using the developed model, which had a spatial resolution of about 1° for land processes and 0.5° for river transport, and data on fertilizer application, point source pollution, and water use. Our results showed that rivers in western Europe and eastern China were seriously polluted, on average, at a rate of 5,000-15,000 tons per year. In the Yangtze River Basin, the amount of point source pollution in 2010 was about four times more than that in 1991, while the amount of fertilizer used in 2010 doubled, which resulted in the increased riverine DIN levels. Further comparisons suggested that the riverine DIN in the USA was affected primarily by nitrogen fertilizer use, the changes in DIN flow rate in European rivers was dominated by point source pollution, and rivers in China were seriously polluted by both the two pollution sources. The total anthropogenic impact on the DIN exported to the Pacific Ocean has increased from 10% to 30%, more significantly than other oceans. In general, our results indicated that incorporating the schemes of nitrogen transport and human activities into land surface models could be an effective way to monitor global river water quality and diagnose the performance of the land surface modeling.