Improved Estimation of Splash and Sheet Erosion in Rangelands: Development and Application of a New Relationship and New Approaches for Sensitivity and Uncertainty Analyses

Abstract

Soil erosion is a key issue in rangelands, but current approaches for predicting soil erosion are based on research in croplands and may not be appropriate for rangelands. An improved model is needed that accounts for the dominant erosion processes that operate in rangelands rather than croplands. In addition, effective application of such a model of rangeland erosion requires improved methods for assessing both model sensitivity and uncertainty if the model is to be applied confidently in natural resources management.I developed a new equation for calculating the combined rate of splash and sheet erosion (Dss, kg/m2) using existing rainfall-simulation data sets from the western United States that is distinct from that for croplands: Dss = Kss I 1.052q0.592, where Kss is the splash and sheet erosion coefficient, I (m/s) is rainfall intensity, and q (mm/hr) is runoff rate. This equation, which accounts for inter-relationship between I and q, was incorporated into a new model, the Rangeland Hydrology and Erosion Model (RHEM). This new model was better at predicting observed erosion rates than the commonly used, existing soil erosion model Water Erosion Prediction Project (WEPP).New approaches for assessing model uncertainty and sensitivity were developed and applied to the model. The new approach for quantifying localized sensitivity indices, when combined with techniques such as correlation analysis and scatter plots, can be used effectively to compare the sensitivity of different inputs, locate sensitive regions in the parameter space, decompose the dependency of the model response on the input parameters, and identify nonlinear and incorrect relationships in the model. The approach for assessing model predictive uncertainty, called "Dual-Monte-Carlo" (DMC), uses two Monte-Carlo sampling loops to not only calculate predictive uncertainty for one input parameter set, but also examine the predictive uncertainty as a function of model inputs across the full range of parameter space. Both approaches were applied to RHEM and yielded insights into model behavior.Collectively, this research provides an important advance in developing improved predictions of erosion rates in rangelands and simultaneously provides new approaches for model sensitivity and uncertainty analyses that can be applied to other models and disciplines.