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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #345613

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Assessing the suitability of the Soil Vulnerability Index (SVI) on identifying croplands vulnerable to nitrogen loss using the SWAT model

Author
item Lee, S. - University Of Maryland
item Sadeghi, Ali
item Mccarty, Gregory
item Baffaut, Claire
item Lohani, S. - University Of Missouri
item Duriancik, L.f. - Natural Resources Conservation Service (NRCS, USDA)
item Thompson, A. - Missouri State University
item Yeo, I.y. - University Of Newcastle
item Wallace, C. - University Of Pennsylvania

Submitted to: Catena
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/14/2018
Publication Date: 5/14/2018
Citation: Lee, S., Sadeghi, A.M., McCarty, G.W., Baffaut, C., Lohani, S., Thomson, A., Yeo, I., Wallace, C. 2018. Evaluating the suitability of the Soil Vulnerability Index (SVI) classification scheme using the SWAT model. Catena. 167:1-12. https://doi.org/10.1016/j.catena.2018.04.021.
DOI: https://doi.org/10.1016/j.catena.2018.04.021

Interpretive Summary: Computer simulation models are valuable tools for identifying areas that contribute most to pollutant loads within agricultural croplands, but their application is limited to locations where data and modeling expertise are available. The USDA Natural Resources Conservation Service (NRCS) has recently developed a Soil Vulnerability Index (SVI) to identify crop fields that have a high risk of pollutant transport through surface runoff and leaching processes. The aim of this study was to evaluate the suitability of the SVI method on two adjacent sub-watersheds within the Choptank watershed in Maryland. These two sub-watersheds were selected as test sites because their water and nutrient cycles are distinctively different due to the differences in their soil characteristics. Outputs from a computer simulation model, the Soil and Water Assessment Tool (SWAT), were used to compare with the SVI classification scheme. Comparison results indicated that the SVI method was less suitable for identifying nitrate vulnerability because nitrate transport is mainly caused by leaching. SVI was better suited for organic nitrogen (N) vulnerability because surface runoff is a major pathway for organic N. Results obtained emphasize the importance of selecting a vulnerable area identification method that is based on the type of pollutant as well as the soil characteristics of the site. State NRCS offices and other local agricultural management agencies can use the SVI method to classify crop fields vulnerable to surface runoff and leaching processes.

Technical Abstract: Conservation practices are effective ways to mitigate non-point source pollution, especially when implemented on critical source areas (CSAs) known to be the areas contributing disproportionately to high pollution loads. Although hydrologic models are promising tools to identify CSAs within agricultural landscapes, their application is limited to areas where data and modeling expertise are available. The Soil Vulnerability Index (SVI) developed by the USDA-Natural Resources Conservation Service is regarded as a potentially powerful tool for CSA identification, but its usefulness has not been fully evaluated. This study evaluated the suitability of the SVI classification scheme for identifying the inherent vulnerability of cultivated soils to nitrate and organic nitrogen (N) transport by surface runoff on two adjacent watersheds with contrasting soil characteristics. We simulated nitrate and organic N loses transported by surface runoff with the Soil and Water Assessment Tool (SWAT) to use as reference data. The results showed that SVI was more suitable for identifying areas vulnerable to organic N transport compared to nitrate based on the differences in their transport behaviors. Organic N losses of (12 – 23 kg/ha) were, on average, more than 8 times greater than the nitrate (0.7 – 3.8 kg/ha), because surface runoff is a primary pathway for organic N but nitrate transport is mainly caused by leaching process. The SVI classification scheme was shown to be most effective for identifying CSAs vulnerable to organic N transport within poorly-drained croplands while the nitrate leaching classification scheme performed better for the well-drained croplands. Therefore, to identify crop fields vulnerable to a high risk of pollutant transport through surface runoff and leaching processes, it is important to select a CSA identification method that is based on site characteristics as well as the type of pollutant.