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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #132243
Title: MODELING PHOSPHORUS MOVEMENT FROM AGRICULTURE TO SURFACE WATERS

Author
item Sharpley, Andrew
item Kleinman, Peter
item GITAU, MARGARET - PENNSYLVANIA STATE UNIV
item Gburek, William
item Bryant, Ray

Submitted to: Cornell Modeling Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 10/20/2002
Publication Date: 11/20/2002
Citation: Sharpley, A.N., Kleinman, P.J., Gitau, M., Gburek, W.J., Bryant, R.B. 2002. Modeling phosphorus movement from agriculture to surface waters. Cornell Modeling Proceedings. p. 1-19.

Interpretive Summary:

Technical Abstract: Modeling phosphorus loss from agricultural watersheds is key to quantifying the long-term water quality benefits of alternative best management practices. Scientists engaged in this endeavor struggle to represent processes controlling phosphorus transport at scales and time frames that are meaningful to farmers, resource managers, and policy makers. To help overcome these challenges, we review salient issues facing scientists that model phosphorus transport, providing a conceptual framework from which process-based phosphorus transport models may be evaluated. Recent advances in quantifying the release of soil phosphorus to overland and subsurface flow show that extraction coefficients relating soil and flow phosphorus are variable but can be represented as a function of land cover or erosion. Existing information on best management effects on phosphorus export can be linked to watershed models to better represent changes in P transport. The main needs of phosphorus transport models are inclusion of flexible coefficients relating soil and overland flow phosphorus, manure management and phosphorus loss, stream channel effects on edge-of-field phosphorus losses prior to water body input, and linkage of watershed and water-body response models. Even so, it is essential that the most appropriate model be carefully selected to meet a user's needs, in terms of level of predictive accuracy needed, input data available, and scale of simulation being considered.