Classifying Cropland Vulnerability to Pollutant Loads Across South Korea Under Climate Change Conditions Using Soil Vulnerability Index

The soil vulnerability index (SVI) classifies vulnerability to cropland pollutant transport by surface runoff and leaching, helping to categorize areas with a high contribution of pollutant loads. This study aims to 1) adopt the SVI classification scheme in South Korea and 2) suggest a modified SVI classification scheme for surface runoff to anticipate climate change impacts. The precipitation amount was considered in an original scheme to make a modified scheme. The results with an original SVI classification scheme predicted that the SVI surface runoff vulnerability was relatively greater in areas with a steep slope than in those with a plain topography, and a high level of the SVI leaching vulnerability was frequently observed in plain areas. When a modified SVI classification scheme was applied, "High" vulnerability class increased from 44.0% to 57.4% and the greater increase rate was observed in the regions susceptible to heavy precipitation defined as a day with more than 80 mm precipitation. When compared with observational suspended solids, the results with a modified SVI classification scheme were more consistent with observations than those with an original SVI classification scheme. Relative to the baseline period, the SVI surface runoff vulnerability of "High" class increased by 47.1-88.7% during the climate change period. When the number of vulnerability classes was increased, the differences on "Very extremely high" class between the baseline and projection periods were clear with the increase rate of 16.4-343%. The results with an original SVI classification scheme matched well with the topographic characteristics, and a modified SVI clas-sification scheme captured the impacts of regional precipitation patterns and climate change impacts. As a first approach to adopting the SVI classification system to South Korea, increasing the number of classifications in regions with excessive rainfall amounts can improve identifying those most vulnerable areas.