Optimal control of nonpoint source pollution in the Bahe River Basin, Northwest China, based on the SWAT model
2021
Li, Shu | Li, Jiake | Xia, Jun | Hao, Gairui
This study conducts in the Bahe River Basin, an agricultural basin in Northwest China. We use the Soil and Water Assessment Tool (SWAT) model to identify the spatial distribution characteristics of non-point source (NPS) pollution and determine the critical source areas (CSA). Then the relationship between landscape pattern and NPS pollution is analyzed by spearman correlation analysis and redundancy analysis (RDA). On this basis, we set up eight landscape management practices in the CSA and evaluate their reduction effects on NPS pollution loads. The results show that the spatial distribution of nitrogen and phosphorus loss intensity has a certain correlation with rainfall and runoff, and the correlation between phosphorus loss intensity and sediment loss intensity is more significant. The NPS pollution load is closely related to the landscape pattern of the river basin, and is affected by the fragmentation, aggregation and complexity of the landscape. Farmland, forest land, and grassland are the main landscape components of the river basin. Farmland is the main source of NPS pollution, whereas forest land and grassland can effectively inhibit the output of NPS pollution, and the reduction effect of forest land is significantly better than that of grassland. The largest patch index (LPI), landscape shape index (LSI), patch density (PD) are the main landscape factors that affect the output of NPS pollution load. Among all the scenarios, the reduction effect of returning farmland to forest land in slopes above 15° is the best, and the reduction rates of total nitrogen (TN) and total phosphorus (TP) loads have reached about 25%. This study provides some reference for the management of NPS pollution in the Bahe River Basin and other similar basins.
Показать больше [+] Меньше [-]Ключевые слова АГРОВОК
Библиографическая информация
Эту запись предоставил National Agricultural Library