Spatiotemporal characterization of nutrient pollution source compositions in the Xiaohong River Basin, China

Although multiple pollution sources could discharge significant nutrient loads within a river basin, few studies have attempted to analyze the spatiotemporal distribution patterns of nutrient pollution source compositions across a basin. Using the Xiaohong River Basin as a case example, this study aims to establish a feasible framework for characterizing the spatiotemporal patterns of regional pollution source compositions that could be implemented in watersheds worldwide. Firstly, SWAT (Soil and Water Assessment Tool) was used to simulate nutrient loads from all main human activities in the basin. Driven by hourly rainfall, the SWAT model was able to simulate both monthly total nitrogen (TN) and total phosphorous (TP) loads in the basin satisfactorily. Source attribution of TN and TP loads through SWAT scenario analysis combined with the kappa-means clustering analysis was then used to characterize the spatiotemporal distribution patterns in the nutrient pollution source compositions. A total of six types of TN pollution source compositions and five types of TP pollution source compositions have been identified across the region. In general, crop production dominated TN load contributions in 24 sub-basins along the main reach and upstream tributaries, concentrated animal feeding operations (CAFOs) joined crop production as the leading source in eight sub-basins, and wastewater treatment plants (WWTPs) and industry contributed significant TN loads in the remaining three sub-basins. For TP, crop production dominated load contributions in six sub-basins along the upstream tributaries, CAFOs and crop production dominated in 28 sub-basins, while WWTPs were the leading source in the remaining sub-basin. Therefore, implementing effective programs to promote the utilization of organic fertilizers from animal manure for crop production is essential to alleviate the serious nutrient pollution situation in the basin.