Modeling of phosphorus loss from field to watershed: A review. Issue 5 (24th August 2020)

• Record Type: Journal Article
• Authors: Wang, Zhaozhi; Zhang, Tiequan; Tan, Chin S.; Qi, Zhiming
• Is Part Of: Journal of Environmental Quality. Volume 49:Issue 5(2020)
• Page Start: 1203
• Page End: 1224
• Publication Date: 2020-08-24
• Subjects: Agricultural ecology -- Periodicals; Environmental engineering -- Periodicals; Pollution -- Periodicals; 630
• DOI: 10.1002/jeq2.20109
• Languages: English
• ISSNs: 0047-2425

Abstract

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.