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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #322226
Title: Best management practices for reducing nutrient loads in a sub-watershed of Chesapeake Bay

Author
item Veith, Tameria - Tamie
item AMIN, MOSTOFA - Pennsylvania State University
item Collick, Amy
item KARSTEN, HEATHER - Pennsylvania State University

Submitted to: Soil and Water Assessment Tool International Conference
Publication Type: Abstract Only
Publication Acceptance Date: 9/30/2015
Publication Date: 10/14/2015
Citation: Veith, T.L., Amin, M., Collick, A.S., Karsten, H.D. 2015. Best management practices for reducing nutrient loads in a sub-watershed of Chesapeake Bay. Soil and Water Assessment Tool International Conference. p. 38.

Interpretive Summary: An interpretive summary is not required.

Technical Abstract: Water quality improvement in the Chesapeake Bay is a grave concern. An initiative to reduce the nutrient loads to stream has been undertaken to attain a target total maximum daily load (TMDL) at Chesapeake Bay. A general guideline with a set of best management practices (BMPs) has been in place for the Chesapeake watershed states to be implemented for the TMDL goal. The Chesapeake watershed states have been directed to sub-divide the allocations of the TMDL by local areas, but for more effective allocations of the BMPs a field-scale implementation plan is needed. Spring Creek watershed of Centre County, Pennsylvania was chosen in this study for investigating the effectiveness of the BMPs and deriving an implementation plan using the Soil and Water Assessment Tool (SWAT). The SWAT model is capable of assessing the effects of field-scale BMPs on watershed discharge estimates under inter-annual and seasonal variations of temperature and precipitation. Recorded weather and streamflow data, 3-m and 10-m digital elevation maps, and SSURGO soil data were used to calibrate and validate the model. Since base-flow is a major component of streamflow in Spring Creek, the most sensitive calibration parameters affecting model performance were base-flow factor, curve number, and surface water to groundwater transport delay factor. The model adequately described hydrologic processes with a Nash-Sutcliffe coefficient of 0.76 and a coefficient of determination of 0.75. The model has finally been used to prepare a set of effective field-scale BMPs to cut 20% of the nutrient loads as allocated for the watershed by 2025.