Spatial location identification and effect assessment of best management practices

Non-point source pollution poses a significant threat to global water security. Meanwhile, best management practices have been widely adopted for watershed management to address non-point source pollution. Selecting appropriate farmland management measures at suitable locations is crucial to minimizing the impact of agricultural non-point source pollution on water bodies. Using the Tianmu Lake basin as the study area, this research employed remote sensing and the Soil and Water Assessment Tool (SWAT) model to identify high-risk zones, simulate various management scenarios, and assess water quality improvements. The key findings are as follows: Significant land use changes were observed in the Tianmu Lake watershed, primarily characterized by the conversion of forested areas into tea plantations and farmland. Model calibration based on experimental results met simulation accuracy requirements. Non-point source pollution hotspots were mainly concentrated in the central part of the watershed, particularly in the Daxi and Shahe sub-watersheds, due to the prevalence of tea plantations. Implementation of ecological measures such as ecological ditches, vegetated buffer strips, and nutrient removal wetlands effectively controlled non-point source pollution. These findings provide technical insights for selecting and implementing surface pollution control measures within watersheds, offering practical guidance for sustainable watershed management.