Evaluating agricultural non-point source pollution with high-resolution remote sensing technology and SWAT model: A case study in Ningxia Yellow River Irrigation District, China

Agricultural non-point source pollution threatens the quality of the ecological environment, human health, and safety. This study took the Sixth Drainage Ditch of the Yellow River Irrigation Area in Ningxia as the research area, set up a runoff water quality monitoring network, and comprehensively constructed an agricultural non-point source pollution monitoring model by combining the “source-sink” landscape theory, high-resolution remote sensing technology, and soil and water assessment tool (SWAT). The results showed that the simulation results of the flow and total nitrogen met the accuracy requirements. The R2 values of total nitrogen in the calibration and validation periods were both > 0.8, and Ens was > 0.9. The regional applicability of the model was good. Based on the simulation results, the following conclusions were drawn. (1) The temporal distribution of the pollution load was concentrated in May–October, with peaks in June and August, which is consistent with the irrigation period. (2) Spatially, the pollution load was mainly distributed in sub-basins 1 and 5. The area is dominated by cultivated land and has poor conditions that are prone to nitrogen and phosphorus loss. (3) By quantitatively identifying pollution sources, the results showed that agricultural irrigation accounted for approximately 92.88 % of total pollutants. Compared with traditional methods, the monitoring method proposed in this study systematically evaluates the potential for non-point source pollution in the region and builds a relatively complete real-time monitoring network, improving data quality and model reliability. In addition, the relationship between river network density and catchment area threshold was used to optimize the catchment area threshold in the SWAT model, and non-point source pollution parameters suitable for the basin were obtained, providing a data basis and theoretical support for the large-scale application of the model.