Accuracy and sensitivity of soil erosion estimation using 137Cs technology: a statistical perspective

Authors: Zhang, Xunchang; Busteed, Phillip

Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/24/2024
Publication Date: 3/27/2024
Citation: Zhang, X.J., Busteed, P.R. 2024. Accuracy and sensitivity of soil erosion estimation using 137Cs technology: a statistical perspective. Geoderma. 444.116863. https://doi.org/10.1016/j.geoderma.2024.116863.
DOI: https://doi.org/10.1016/j.geoderma.2024.116863

Interpretive Summary: The radioactive materials like isotope cesium-137 has been used to estimate soil erosion rates in the past five decades. Spatial random variation of cesium-137 inventory in soils inhibits the application of the method. A robust experimental design is imperative for obtaining reliable soil erosion estimation by removing the random variation. The objectives are to 1) characterize the effect of replicate number on accuracy of estimated soil erosion rates and 2) compare the minimal soil erosion rates that can be detected by different Cs-137 methods. Two small watersheds were sampled using a soil corer in a grid scheme with the grid box sizes ranging from 2 to 10 yards. For each sampling point, six or ten samples that are closest to the target sample were averaged to evaluate the effect of sample number on soil erosion estimation. The results showed that the Cs-137 methods cannot be used to estimate soil erosion for a single point due to the spatial random variation. Replicate samples must be used to remove the spatial variation. Results also indicates that 7-replicates are sufficient to produce good estimation. Minimal detectable soil erosion rates varied with Cs-137 methods. This work will be useful to soil conservationists and erosion control engineers for better estimating soil erosion rates using the Cs-137 methods. "USDA is an equal opporutnity provider and employer."

Technical Abstract: Random spatial variation of 137Cs inventory is the principal contributor to uncertainty in soil erosion estimation using 137Cs technology. A statistically sound sampling design is imperative for obtaining reliable soil erosion estimation. The objectives of this study are to: 1) characterize the effect of sample number on the estimates of mean inventories; 2) evaluate the sensitivity of the estimated soil redistribution to sample number using a new 137Cs model; 3) compare erosion detection limits among five conversion models; and 4) assess three spatial interpolation methods. Two small watersheds were sampled in an irregular grid design, and 30 samples were taken on a reference site. A moving window scheme was used to compute the 7-point and 11-point means of 137Cs inventories. The Welch’s modified t test was used to test the mean estimates and to compute the detection limits. Five models were selected to convert 137Cs inventories to soil redistribution rates. The insignificant test results for most single samples without replication confirmed that the 137Cs technique cannot be used to estimate soil erosion at a single point due to random spatial variation. With 30 reference samples, 7-replicate samples on the sampling site provided reliable estimations of 137Cs inventories and soil redistribution. The spatial patterns of the estimated 137Cs inventories and soil redistribution became more regular and systematic as sample size increased, which agreed increasingly well with topography and surface hydrology. Replicate samples can be taken from each landform element or slope position where the erosion rate is expected to be uniform. Alternatively, a systematic grid sampling scheme can be used, and the nearest neighbors can be treated as replicates to calculate moving averages. Spline and inverse distance weighting methods performed better than kriging for interpolation due to the lack of spatial autocorrelation in 137Cs inventories. Detection limits and magnitudes of soil redistribution varied substantially with conversion models, depending on their sensitivities to changes in 137Cs inventories. USDA is an equal opportunity provider and employer.