Effectiveness of best management practices for non-point source pollution in intensively managed agricultural watersheds

Abstract

Non-point source (NPS) pollution is a significant challenge in watershed water pollution control because of its difficulty in monitoring and regulation. To address this issue, the Storm Water Management Model (SWMM) is employed to quantify the spatio-temporal characteristics of total nitrogen (TN) and total phosphorus (TP) pollution and to simulate the effects of various Best Management Practices (BMPs) in the West Tiaoxi watershed in southeast China, characterized by a mix of forest, agricultural, and urban areas. This study provided an effective means of improving water quality in the watershed. The SWMM modelling results demonstrated that TN and TP pollution was most serious in the lower reaches of the watershed, with average TN and TP concentrations of 2.49 mg/L and 0.17 mg/L, respectively. TN pollution was more serious than TP pollution, and TN and TP pollution in the non-flood season was worse than that in the flood season. Farmland and tea plantations contributed the most to TN pollution, and impervious surfaces contributed the most to TP pollution. Various BMPs have different reduction effects on NPS pollution. The average reduction rates of TN and TP for nutrient management measures and landscape management measures were 8.03%–10.07% and 5.28%–10.26%, respectively. Combining the nutrient and landscape management practices could achieve the maximum reduction in TN and TP pollution levels, with an average reduction rate of 19.34% and 16.34% for TN and TP, respectively. This study not only provides new ideas for the optimization of SWMM for river water quality simulation under heterogeneous watershed conditions, but also expands the applicability of SWMM from urban areas to agricultural watersheds. The results presented herein provide a decision-making basis for controlling NPS pollution and improving water quality in watersheds under high-intensity human activities.