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

Conservation practices are effective ways to mitigate non-point source pollutions, especially when implemented on critical source areas (CSAs) known as the areas contributing disproportionately 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 Resource Conservation Service (NRCS) Conservation Effects Assessment Project (CEAP) is regarded as a potentially powerful tool for supporting initial classification of inherent soil vulner-abilities at field scale and so could be useful for CSA identification. Its usefulness is being fully evaluated in this project and as part of a larger coordinated study. This particular study evaluated the suitability of the SVI classification scheme for identifying inherent vulnerability of cultivated soils to nitrate and organic N transport by surface runoff and nitrate leaching on two adjacent watersheds with contrasting soil drainage characteristics. We used simulated nitrate and organic N fluxes from the Soil and Water Assessment Tool (SWAT) as reference data. The results showed that the SVI runoff classification scheme was more suitable for organic N while the SVI leaching classification scheme was suited for nitrate due to pollutant transport characteristics. In addition, the SVI leaching classification scheme was more suitable for the poorly-drained croplands than the well-drained croplands. The SVI leaching classification scheme and SWAT output consistently classified nitrate leaching vulnerability based on soil drainage characteristics for the poorly-drained croplands, while the well-drained croplands were highly sensitive to a soil water content characteristic (i.e., gravitational water). Depending on the selection of reference data and test sites, however, the suitability of the SVI classification scheme could differ. Therefore, additional evaluation of the SVI using multiple validation data and sites is highly required to demonstrate its usefulness.