Spatiotemporal patterns of non-point source nitrogen loss in an agricultural catchment

Non-point source nitrogen loss poses a risk to sustainable aquatic ecosystems. However, non-point sources, as well as impaired river segments with high nitrogen concentrations, are difficult to monitor and regulate because of their diffusive nature, budget constraints, and resource deficiencies. For the purpose of catchment management, the Bayesian maximum entropy approach and spatial regression models have been used to explore the spatiotemporal patterns of non-point source nitrogen loss. In this study, a total of 18 sampling sites were selected along the river network in the Hujiashan Catchment. Over the time period of 2008–2012, water samples were collected 116 times at each site and analyzed for non-point source nitrogen loss. The morphometric variables and soil drainage of different land cover types were studied and considered potential factors affecting nitrogen loss. The results revealed that, compared with the approach using the Euclidean distance, the Bayesian maximum entropy approach using the river distance led to an appreciable 10.1% reduction in the estimation error, and more than 53.3% and 44.7% of the river network in the dry and wet seasons, respectively, had a probability of non-point source nitrogen impairment. The proportion of the impaired river segments exhibited an overall decreasing trend in the study catchment from 2008 to 2012, and the reduction in the wet seasons was greater than that in the dry seasons. High nitrogen concentrations were primarily found in the downstream reaches and river segments close to the residential lands. Croplands and residential lands were the dominant factors affecting non-point source nitrogen loss, and explained up to 70.7% of total nitrogen in the dry seasons and 54.7% in the wet seasons. A thorough understanding of the location of impaired river segments and the dominant factors affecting total nitrogen concentration would have considerable importance for catchment management.