Author:

Keywords:

runoff, Science & Technology, Life Sciences & Biomedicine, Soil Science, Agriculture, RUNOFF, 05 Environmental Sciences, 06 Biological Sciences, 07 Agricultural and Veterinary Sciences, Agronomy & Agriculture, 30 Agricultural, veterinary and food sciences, 31 Biological sciences, 41 Environmental sciences

Abstract:

Understanding and quantifying the large, unexplained variability in soil erosion data are critical for advancing erosion science, evaluating soil erosion models, and designing erosion experiments, We hypothesized that it is possible to quantify variability between replicated soil erosion field plots under natural rainfall, and thus determine the principal factor or factors which correlate to the magnitude of the variability: Data from replicated plot pairs for 2061 storms, 797 annual erosion measurements, and 53 multi-year erosion totals were used. Thirteen different soil types and site locations were represented in the data. The relative differences between replicated plot pair data tended to be lesser for greater magnitudes of measured soil loss, thus indicating that soil loss magnitude was a principal factor for explaining variance in the soil loss data. Using this assumption, we estimated the coefficient of variation of within-treatment, plot replicate values of measured soil lass. Variances between replicates decreased as a power function (r(2) = 0.78) of measured soil loss, and were independent of whether the measurements were event-, annual-, or multi-year values. Coefficients of variation ranged on the order of 14% for a measured soil loss of 20 kg/m(2) to greater than 150% for a measured soil loss of less than 0.01 kg/m(2) These results have important implications for both experimental design and for using erosion data to evaluate prediction capability for erosion models.