Study On Land Use Optimization Oriented To Non-Point Source Pollution Control

Protecting environment is a basic policy in our country. With the point source pollution has been effective controlled and managed, non-point source pollution has become the main pollution source of water environment deterioration. Among the main influence factors of non-point source pollution(rainfall, terrain, soil etc.), land use is one of the key factors. Exploring the influence mechanism of land use structure and non-point source pollution on basin scale using the SWAT model is of great significance in land use planning cross administrative region and speeding up resource-saving, environment-friendly society construction.Huifa River is the biggest tributary of the Second Songhua River and the main supply of Songhua Lake. According to the water quality monitoring data of Huifa River Basin from 2006 to 2010 provided by the environmental monitoring station of Jilin Province, Huifa River is seriously polluted and the category of the downstream annual water quality is bad V category. In this paper, the Huifa River Basin non-point source pollution database is established based on the population spatial distribution model, remote sensing images, meteorological and hydrological monitoring data of water quality; based on the SWAT model, runoff simulation is conducted with the FY, TRMM and CMORPH remote sensing rainfall data and the non-point source pollution in Huifa River Basin is also simulated; on this basis of the mechanism of non-point source pollution of land use, the pattern of land use is adjusted in the watershed with the multiple objectives linear programming model and the water ecological function regionalization techniques. The main contents and results are as follows:(1) The databases of land use, soil, meteorological and agricultural management are established. To estimate the distribution of population in agricultural and non agricultural population within the basin, a population distribution model is built based on and statistical population data. The impervious surfaces of Huifa River Basin are estimated using remote sensing and GIS technology. Then, the rural life pollution and livestock feces pollution are calculated with the estimated population data in Huifa River Basin.(2) Based on the SWAT model, effects of different spatial distribution of precipitation on runoff simulation are evaluated by establishing 7 precipitation input scenarios using the rain gauge data and remote sensing rainfall data. By runoff simulation accuracy, FY > observed precipitation > TRMM. This research shows that the precipitation data of high spatial resolution remote sensing with subbasin precipitation average values as input performs better than the direct use of the grid point values on stream simulation. Parameter calibration and verification of the model was conducted using the Meihekou and Huinan ammonia monitoring data. The non-point source pollution in Huifa River Basin is simulated. Through the contribution rate analysis of the pollution sources in Huifa River Basin, fertilizer application of agriculture is identified as the main pollution source of water environment deterioration. The total nitrogen, total phosphorus load account for 51% and 36%. The rainfall-runoff process is the main driven factor of non-point source pollution, and the coefficient of determination R2 between total nitrogen, total phosphorus and precipitation are 0.55 and 0.66.(3) According to the water ecological function area dividing theories and principles, the Huifa River Basin is divided into 2 levels of water ecological function areas. With the combination of non-point source pollution sensitivity analysis results, the Huifa River Basin is classified into 5 levels non-point source sensitivity area. The study found that the average annual loss of nitrogen in paddy field(136.57-219.81kg/ha) is generally higher than dry land the amount(72.23-122.1kg/ha).(4) Taking the economic and ecological service value as the objective function, a linear programming model is built for the land use quantity structure optimization. According to the results of land utilization quantity optimization, spatial adjustment of land use is operated in the high sensitivity zones of non-point source pollution. After the optimization of land use, its total nitrogen annual load is reduced by 3.19%-6.09%, and 0.3%-4.89% for TP load reduction.