Development of Reliable Erosion Indices for Climate-Informed Soil Conservation in the Southeastern United States

Abstract

A new methodology and corresponding dataset are recommended for a more accurate calculation of erosion index (EI) and erosivity (R) that was more consistent with observations from superior data sources. NOAA NCDC DSI-3260 (quarter-hour) station data from 1970 to 2010 was screened and a water balance was performed to compare measured precipitation with the expected values at each station having matching climate normal data. The results of the water balance were used to select the screening method that most accurately accounts for precipitation at a high spatial resolution (about 3 times more dense than the previous publication of EI values). It was found that most stations have a slight deficit (averaging 5.9%) with a comparable missing data percentage of 5.77%, which might be the reason for the deficit. Updated annual, seasonal, and monthly EI distributions were calculated along with an analysis of single storm EI for 1, 2, 5, 10, and 20-year recurrence intervals. Annual EI values were found to be higher than AH703 by an average of 18.6% for unadjusted data, and values should be increased at least another 4% for the type of recording station being used. Station observations were gridded by geostatistical interpolation for better spatial representation of the data. The effects of limiting the maximum 30-minute intensity and adjusting for known uncertainties was quantified for the preferred screening method within each analysis. Station data was compared to a reliable literature source to validate the new methodology. Results were further analyzed for climate variability influences from ENSO by the statistical method known as joint-rank fit (JRfit). Data was analyzed under various clusters, and ENSO was found to have a significant effect on multiple precipitation parameters. Changes in the distribution of EI throughout the year, based on ENSO phase, was used to highlight general implications for BMPs aimed at soil conservation and reductions of sediment yield. With known variabilities accounted for, observed changes in erosivity from the influence of climate change can be accurately assessed in the future.