SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Assessment of agricultural best management practices using models: current issues and future perspectives 
Authors:Xie, H., L. Chen and Z. Shen 
Year:2015 
Journal:Water 
Volume:7(3) 
Pages:1088-1108 
Article ID: 
DOI:10.3390/w7031088 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:review/history 
Primary Application Category:BMP assessment 
Secondary Application Category:model comparison 
Watershed Description: 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Best management practices (BMPs) are the most effective and practicable means to control nonpoint source (NPS) pollution at desired levels. Models are valuable tools to assess their effectiveness. Watershed managers need to choose appropriate and effective modelling methods for a given set of conditions. This paper considered state-of-the-art modelling strategies for the assessment of agricultural BMPs. Typical watershed models and specific models were analyzed in detail. Further improvements, including simplified tools, model integration, and incorporation of climate change and uncertainty analysis were also explored. This paper indicated that modelling methods are strictly scale dependent, both spatially and temporally. Despite current achievements, there is still room for future research, such as broadening the range of the pollutants considered, introducing more local BMPs, improving the representation of the functionality of BMPs, and gathering monitoring date for validation of modelled results. There is also a trend towards agricultural decision support systems (DSSs) for assessing agricultural BMPs, in which models of different scales are seamlessly integrated to bridge the scale and data gaps. This review will assist readers in model selection and development, especially those readers concerned about NPS pollution and water quality control. 
Language:English 
Keywords:nonpoint source pollution; water quality assessment; best management practices; agriculture; modelling; decision support systems