The source area of the Liao River is an important grain growing area in China which experiences serious problems with agricultural nonpoint source pollution (NPS) which is impacting the regional economy and society. In order to address the water quality issues it is necessary to understand the spatial distribution of NPS in the Liao River source area. This issue has been investigated by coupling a wavelet artificial neural network (WA-ANN) precipitation model with a soil and water assessment tool (SWAT) model to assess the export of nonpoint source pollutants from the Liao River source area. The calibration and validation of these models are outlined. The WA-ANN models and the SWAT model were run to generate the spatial distribution of nonpoint source nutrient (nitrogen and phosphorus) exports in the source area of the Liao River. It was found that the SWAT model identified the sub-catchments which not only receive high rainfall but are also densely populated with high agricultural production from dry fields and paddy fields, which are large users of pesticides and chemical fertilizer, as the primary source areas for nutrient exports. It is also concluded that the coupled WA-ANN models and the SWAT model provide a tool which will inform the identification of NPS issues and will facilitate the identification of management practices to improve the water environments in the source area of the Liao River.