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The hydrochemical characteristics of groundwater in Songnen Plain's agricultural area were analyzed based on aquifer types and topography classification to evaluate irrigation suitability and factors influencing groundwater quality. Samples of different groundwater types and topographical conditions within the research area were collected and chemical indices, such as sodium adsorption ratio, %Na, residual sodium carbonate, and magnesium hazard values, were calculated to assess the groundwater suitability for irrigation. The results indicated that groundwater was generally neutral, with low total dissolved solids and slightly high hardness; the dominant anion in groundwater was HCO, while Ca was the relatively stable primary cation found in water samples from the high plain and river valley plain. The nitrate in groundwater significantly exceeded WHO drinking water standards, especially in the unconfined water of the high plain, which was due to the large-scale agricultural production activities in the eastern regions. The main reactions in the groundwater system were weathering and dissolution of carbonates and sulfates and ion-exchange reactions. Horizontal zoning in water chemical characteristics was prominent; from the high plain to river valley plain and low plain, the hydrochemistry gradually transitioned from HCO-Ca-Na to HCO-Na-Ca and HCO-Na. Based on the chemical indices, the majority of samples were suitable for agricultural irrigation except for some in the western area with high salinity and sodium hazards. Treatment measures to groundwater and soil should be taken to reduce the possibility of soil salinization and promote crop growth in these latter regions.

Fluctuations in groundwater depth play an important role and are often overlooked when considering the transport of nitrogen in unsaturated zone. To evaluate directly the variation of nitrogen transport due to fluctuations in groundwater depth, the prediction model of groundwater depth and nitrogen transport were combined applied by least squares support vector machine (LS-SVM) and Hydrus-1D in the western irrigation area of Jilin in China. The Calibration and testing results showed the prediction models were reliable. Considering different groundwater depth, the concentration of nitrogen was affected significantly with the groundwater depth of 3.42–1.71 m while it not affected with the groundwater depth of 5.48–6.47 m. The total leaching loss of nitrogen gradually increased with the continuous decrease of groundwater depth. Furthermore, the limited groundwater depth of 1.7 m is determined to reduce the risk of nitrogen pollution. This paper systematically analyzes the relationship between groundwater depth and nitrogen transport to form appropriate agriculture strategies.

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.