RT Journal Article
SR Electronic
T1 Soil Vulnerability Index assessment as a tool to explain annual constituent loads in a nested watershed
JF Journal of Soil and Water Conservation
FD Soil and Water Conservation Society
SP 42
OP 52
DO 10.2489/jswc.75.1.42
VO 75
IS 1
A1 S. Lohani
A1 C. Baffaut
A1 A.L. Thompson
A1 E.J. Sadler
YR 2020
UL http://www.jswconline.org/content/75/1/42.abstract
AB The Soil Vulnerability Index (SVI) was developed by the USDA Natural Resources Conservation Service (NRCS) to identify inherent vulnerability of cropland to runoff and leaching. It is a simple index that relies on the SSURGO database and can be used with basic knowledge of ArcGIS. The goal of this study was to investigate a relationship between constituent (sediment and nutrient) loadings and fraction of the watershed in each SVI class. The SVI maps were developed for each of the seven subwatersheds of the Mark Twain Lake watershed in Missouri, which were similar in soil conditions and climatic variability. The SVI assessment was performed by investigating if the distribution of the SVI for cropland in each subwatershed could help explain measured 2006 to 2010 sediment and nutrient loads better than crop distribution alone. Regression analyses were performed between annual loads of sediment and nutrients exported from the watersheds and a composite number that included either cropland distribution alone, or cropland distribution combined with the SVI. Coefficients of determination and p-values were compared to assess the ability of land use and SVI distributions to explain stream loads. Integrating the SVI in the land cover variable improved the ability to explain constituent loads in the watersheds for sediment, total nutrients, and dissolved nitrogen (N). Regression results with and without the SVI were identical for dissolved phosphorus (P), potentially indicating that SVI was not indicative of dissolved P transport at the current site. Overall, the application of the SVI at watershed scale was not perfect, but acceptable at correctly identifying cropland of greatest vulnerability and linking with transported constituent loads.