ues of treatments and for comparison between experimental treatments. The quantification of variance in soil Understanding and quantifying the large, unexplained variability erosion data is critical to the advancement of erosion in soil erosion data are critical for advancing erosion science, evaluating soil erosion models, and designing erosion experiments. We hy- science. pothesized that it is possible to quantify variability between replicated Unfortunately, however, knowledge of variability in soil erosion field plots under natural rainfall, and thus determine the soil erosion data is quite limited. Only one erosion study principal factor or factors which correlate to the magnitude of the has been conducted with a sufficient number of replivariability. Data from replicated plot pairs for 2061 storms, 797 annual cated erosion plots to allow an in-depth analysis of varierosion measurements, and 53 multi-year erosion totals were used. ability. Wendt et al. (1986) measured soil erosion rates Thirteen different soil types and site locations were represented in on 40 cultivated, fallow, experimental plots located at the data. The relative differences between replicated plot pair data Kingdom City, MO, in 1981. All of the 40 plots were tended to be lesser for greater magnitudes of measured soil loss, thus cultivated and in other ways treated identically. The indicating that soil loss magnitude was a principal factor for explaining coefficients of variation for the 25 storms ranged from variance in the soil loss data. Using this assumption, we estimated the coefficient of variation of within-treatment, plot replicate values 18 to 91%, with 15 of the storms falling in the range of of measured soil loss. Variances between replicates decreased as a less than 30%. The more erosive storms tended to show power function (r 2 5 0.78) of measured soil loss, and were independent the lesser degree of variability. Of the 15 storms with of whether the measurements were event-, annual-, or multi-year mean erosion rates of greater than 0.1 kg/m 2 (1.0 Mg/ values. Coefficients of variation ranged on the order of 14% for a ha), 13 showed coefficients of variation of less than measured soil loss of 20 kg/m 2 to greater than 150% for a measured 30%. The results of the study indicated that “only minor soil loss of less than 0.01 kg/m 2 These results have important implica- amounts of observed variability could be attributed to tions for both experimental design and for using erosion data to any of several measured plot properties, and plot differevaluate prediction capability for erosion models.
[1]
R. Lyman Ott.,et al.
An introduction to statistical methods and data analysis
,
1977
.
[2]
R. H. Myers,et al.
Probability and Statistics for Engineers and Scientists
,
1978
.
[3]
K. C. McGregor,et al.
Evaluation of WEPP Runoff And Soil Loss Predictions Using Natural Runoff Plot Data
,
1996
.
[4]
Gerard Govers,et al.
Rill erosion on arable land in Central Belgium: Rates, controls and predictability
,
1991
.
[5]
Mark A. Nearing,et al.
Error Assessment in the Universal Soil Loss Equation
,
1993
.
[6]
James C. Ascough,et al.
THE WEPP WATERSHED MODEL: III. COMPARISONS TO MEASURED DATA FROM SMALL WATERSHEDS
,
1997
.
[7]
E. E. Alberts,et al.
Variability of Runoff and Soil Loss from Fallow Experimental Plots
,
1986
.
[8]
V. Prasuhn,et al.
Measurement of runoff and soil erosion on regularly cultivated fields in Switzerland — some critical considerations
,
1995
.
[9]
J. Rapp.
Error assessment of the revised universal soil loss equation using natural runoff plot data
,
1994
.