SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Modeling of phosphorus loss from field to watershed: A review 
Authors:Wang, Z., T. Zhang, C.S. Tan and Z. Qi 
Journal:Journal of Environmental Quality 
Volume (Issue):49(5) 
Article ID: 
URL (non-DOI journals): 
Broad Application Category:review/history 
Primary Application Category:phosphorus cycling/loss and transport 
Secondary Application Category:model and/or data comparison 
Watershed Description:none 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Phosphorus (P) losses from nonpoint sources into surface water resources through surface runoff and tile drainage play a significant role in eutrophication. Accordingly, the number of studies involving the modeling of agricultural P losses, the uncertainties of such models, and the best management practices (BMPs) supported by the modeling of hypothetical P loss reduction scenarios has increased significantly around the world. Many improvements have been made to these models: separate manure P pools, variable source areas allowing the determination of critical source areas of P loss, analyses of modeling uncertainties, and understanding of legacy P. However, several elements are still missing or have yet to be sufficiently addressed: the incorporation of preferential flow into models, the modification of P sorption–desorption processes considering recent research data (e.g., pedotransfer functions for labile, active, or stable P, along with P sorption coefficients), BMP parameterization, and scale-up issues, as well as stakeholder–scientist and experimentalist–modeler interactions. The accuracy of P loss modeling can be improved by (a) incorporating dynamic P sorption– desorption processes and new P subroutines for direct P loss from manure, fertilizer, and dung, (b) modeling preferential flow, connectivity between field and adjacent water bodies, and P in-stream processes, (c) including an assessment of model uncertainty, (d) integrating field and watershed models for BMP calibration and scaling field results up to larger areas, and (e) building a holistic interaction between stakeholders, experimentalists, and modelers.