Spatial and Temporal Prediction and Uncertainty Analysis of Rainfall Erosivity for the Revised Universal Soil Loss Equation

摘要

Prediction of soil loss is important when a sustainable environment is expected. The Revised Universal Soil Loss Equation (RUSLE) is widely used to predict longtime average annual soil loss based on rainfall, soil erodibility, slope length and steepness, cover management, and support practice. The rainfall erosivity R factor, defined as the sum of erosivity index values for all rain events in one year, best indicates soil loss due to rainfall. The larger the R factor value, the more the soil loss. In previous military applications, the R factor value, obtained from empirical isoerodent maps, is assumed constant over time. At the same time, it is considered linear and smooth over space. For a specific area, a constant is usually applied. Recent studies suggest, however, that those assumptions may not hold and may result in uncertainty both temporally and spatially in terms of predicting R factor values. This report proposes a strategy for prediction and uncertainty analysis of this factor. The temporal and spatial variability of the R factor is first modeled using semi- variograms in geostatistics. Its prediction and uncertainty analysis is then carried out using sequential Gaussian simulation. The strategy is illustrated in a case study at Fort Hood, TX. Additionally, the report assesses the uncertainty caused by traditional applications of isoerodent maps. The results are also important for overall uncertainty analysis of RUSLE.