Simulation on the effects of non-point source pollution in Qingjiang River basin based on SWAT model and GIS

Abstract

Assessment of the pollution of water bodies from non-point sources (NPS) is a complex data-requiring and time-consuming task. The accuracy of NPS pollution models depends to a great extent on how well model input parameters describe the relevant characteristics of the watershed. It is assumed that promoting the precision of input parameters affects the simulation results of runoff, sediment and nutrients yield from the entire watershed. An integration of simulating model with GIS technique is one of the most efficient methods to NPS pollution quantified research at present. In this study, the basic database, which includes DEM, soil and landuse map, climate data, and land management data, were established for the study purpose using GIS. The generation and formation of non-point source pollution involves great uncertainty, and this uncertainty makes monitoring and controlling pollution very difficult. Understanding the main parameters that affect NPS pollution uncertainty is necessary to provide the basis for the planning and design of control measures. Based on the results of parameter sensitivity analysis, the sensitive parameters of Soil and Water Assessment Tool (SWAT) model were identified, and then model parameters related to stream flow and nutrient loadings were calibrated and validated by the observed value, and the simulation showed that the simulated values were reasonably comparable to the observed data, suggesting the validity of SWAT model. The spatial-temporal distribution features of NPS pollution in the Qingjiang River basin (a case study of this paper which is one main branch of Yangtze River basin in Three Gorges Project area) were revealed. NPS pollution mainly takes place in flood season. The critical risk areas of soil erosion were identified. Stream flow and nutrient loadings (including total nitrogen (TN) and total phosphorus (TP)) in Qingjiang River Basin were simulated. The surface runoff and nutrient yield results indicated that the average annual runoff and output of TN and TP provides better understanding on stream flow and nutrient loadings responding to variations of land use conditions, agricultural tillage operation and natural rainfall etc.