Spatial uncertainty analysis for mapping soil erodibility based on joint sequential simulation

Soil erodibility (susceptibility of soil to be lost to erosion) is one of the components of the universal soil loss equation (USLE). In the USLE, erodibility is known as the K factor, which in turn is a function of these soil properties: particle size distribution, organic matter content, structure, and permeability. The traditional approach for estimating soil erodibility does not account for spatial variability of individual soil properties or spatial correlation among those properties. Our objectives in this study were to evaluate the use of joint sequential simulation for mapping soil erodibility, as well as to partition the individual and joint variance contribution of soil properties for predicting soil erodibility. We collected 192 usable soil samples across Fort Hood, Texas in the summer of 1999. For each of those samples, we obtained an estimate of particle size distribution, organic matter content, structure, permeability, and calculated soil erodibility. We carried out both independent and joint sequential simulation to generate spatially explicit predictions and variance of all soil properties as well as covariance between pairs of soil properties for each cell within our simulation area. We used the program GCOSIM3D to conduct those simulations. On average, joint sequential simulation resulted in a K factor variance of less than half the variance obtained from independent simulation. Using the results from joint sequential simulation, we partitioned the contribution of each soil property and pair of properties using first-order Taylor series expansion of the soil erodibility function. Individually, Very-Fine-Sand-and-Silt contributed the most (46.19%), whereas Structure contributed the least (6.53%) to the K factor variance. Jointly, Permeability/Structure contributed the most (9.32%), whereas Sand/Very-Fine-Sand-and-Silt caused the largest reduction (−19.19%) in the K factor variance. We conclude that joint sequential simulation provided approximately twice as much precision as independent simulation for the spatially explicit prediction of soil erodibility. Likewise, first-order Taylor series expansion offered an accurate approach for partitioning the individual and joint contribution of soil properties to soil erodibility variance. This partitioning allowed us to identify large sources of uncertainty and suggest efficient approaches for further improving the precision of K value predictions. [Copyright &y& Elsevier]