Quantifying effects of conservation practices on non-point source pollution in the Miyun Reservoir Watershed, China.

Non-point source (NPS) pollution, including fertilizer and manure application, sediment erosion, and haphazard discharge of wastewater, has led to a wide range of water pollution problems in the Miyun Reservoir, the most important drinking water source in Beijing. In this study, the Soil and Water Assessment Tool (SWAT) model was used to evaluate NPS pollution loads and the effectiveness of best management practices (BMPs) in the two subwatersheds within the Miyun Reservoir Watershed (MRW). Spatial distributions of soil types and land uses, and changes in precipitation and fertilizer application, were analysed to elucidate the distribution of pollution in this watershed from 1990 to 2010. The results demonstrated that the nutrient losses were significantly affected by soil properties and higher in both agricultural land and barren land. The temporal distribution of pollutant loads was consistent with that of precipitation. Soil erosion and nutrient losses would increase risks of water eutrophication and ecosystem degradation in the Miyun Reservoir. The well-calibrated SWAT model was used to assess the effects of several Best Management Practices (BMPs), including filter strips, grassed waterways, constructed wetlands, detention basins, converting farmland to forest, soil nutrient management, conservation tillage, contour farming, and strip cropping. The removal rates of those BMPs ranged from 1.03 to 38.40% and from 1.36 to 39.34% for total nitrogen (TN) and total phosphorus (TP) loads, respectively. The efficiency of BMPs was dependent on design parameters and local factors and varied in different sub-basins. This study revealed that no single BMP could achieve the water quality improvement targets and highlighted the importance of optimal configuration of BMP combinations at sub-basin scale. The findings presented here provide valuable information for developing the sustainable watershed management strategies.

Development of PLEAD: A Database Containing Event-based Runoff Phosphorus Loadings from Agricultural Fields.

Computer models are commonly used for predicting risks of runoff P loss from agricultural fields by enabling simulation of various management practices and climatic scenarios. For P loss models to be useful tools, however, they must accurately predict P loss for a wide range of climatic, physiographic, and land management conditions. A complicating factor in developing and evaluating P loss models is the relative scarcity of available measured field data that adequately capture P losses before and after implementing management practices in a variety of physiographic settings. Here, we describe the development of the P Loss in runoff Events from Agricultural fields Database (PLEAD)-a compilation of event-based, field-scale dissolved and/or total P loss runoff loadings from agricultural fields collected at various research sites located in the US Heartland and southern United States. The database also includes runoff and erosion rates; soil-test P; tillage practices; planting and harvesting rates and practices; fertilizer application rate, method, and timing; manure application rate, method, and timing; and livestock grazing density and timing. In total, >1800 individual runoff events-ranging in duration from 0.4 to 97 h-have been included in the database. Event runoff P losses ranged from <0.05 to 1.3 and 3.0 kg P ha for dissolved and total P, respectively. The data contained in this database have been used in multiple research studies to address important modeling questions relevant to P management planning. We provide these data to encourage additional studies by other researchers. The PLEAD database is available at .

Modeling the non-point source pollution risks by combing pollutant sources, precipitation, and landscape structure.

Traditional models of nutrient simulation usually focus on the pollutant sources and precipitation, lacking the quantification of landscape structure. We developed a new prediction model of pollution risks by combing pollutant sources, precipitation, and landscape structure, which was defined as the source-precipitation-landscape model (SPLM). The SPLM was applied to simulate the non-point source (NPS) total nitrogen (TN) exports in one of the largest river basins in China (the Haihe River Basin, HRB). TN concentrations of 35 sampling catchments in 2013 were used to test the accuracy of the SPLM. Simulated results showed that (1) the SPLM had a relative high accuracy in the simulation of NPS TN export and intensity, especially for TN intensity. (2) The mean TN export and intensity of all the 1578 catchments in the HRB were 441.97 t and 2.08 t/km2, respectively. (3) The TN export intensities differed greatly among the sub-basins in the HRB, ranging from 0.64 to 6.81 t/km2. On the whole, the TN export intensities of the plain sub-basins (e.g., the Tuhaimajia River, the Heilonggang River, and the Beisihe River) were much higher than those of mountainous sub-basins (e.g., the Yongding River, the Beisanhe River, and the Luanhe River). (4) The contributions to TN exports, from high to low, were land use (38.82%), livestock husbandry (33.57%), and rural population (27.61%). Among all the ten pollution sources, arable land (30.87%), rural population (27.61%), and large livestock (17.73%) had the top three contributions to TN exports. This study provides a feasible tool for policymakers and administrators to develop workable management measures for the mitigation of NPS pollution. This SPLM can be extended to other regions in a rapid urbanization context.

Statistical assessment of nonpoint source pollution in agricultural watersheds in the Lower Grand River watershed, MO, USA.

The water quality in many Midwestern streams and lakes is negatively impacted by agricultural activities. Although the agricultural inputs that degrade water quality are well known, the impact of these inputs varies as a function of geologic and topographic parameters. To better understand how a range of land use, geologic, and topographic factors affect water quality in Midwestern watersheds, we sampled surface water quality parameters, including nitrate, phosphate, dissolved oxygen, turbidity, bacteria, pH, specific conductance, temperature, and biotic index (BI) in 35 independent sub-watersheds within the Lower Grand River Watershed in northern Missouri. For each sub-watershed, the land use/land cover, soil texture, depth to bedrock, depth to the water table, recent precipitation area, total stream length, watershed shape/relief ratio, topographic complexity, mean elevation, and slope were determined. Water quality sampling was conducted twice: in the spring and in the late summer/early fall. A pairwise comparison of water quality parameters acquired in the fall and spring showed that each of these factors varies considerably with season, suggesting that the timing is critical when comparing water quality indicators. Correlation analysis between water quality indicators and watershed characteristics revealed that both geologic and land use characteristics correlated significantly with water quality parameters. The water quality index had the highest correlation with the biotic index during the spring, implying that the lower water quality conditions observed in the spring might be more representative of the longer-term water quality conditions in these watersheds than the higher quality conditions observed in the fall. An assessment of macroinvertebrates indicated that the biotic index was primarily influenced by nutrient loading due to excessive amounts of phosphorus (P) and nitrogen (N) discharge from agricultural land uses. The PCA analysis found a correlation between turbidity, E. coli, and BI, suggesting that livestock grazing may adversely affect the water quality in this watershed. Moreover, this analysis found that N, P, and SC contribute greatly to the observed water quality variability. The results of this study can be used to improve decision-making strategies to improve water quality for the entire river basin.