The purpose of this Special Issue is to provide an opportunity for researchers publish novel results that could help landowners, land-users, farmers, politicians, and other representatives our global society protect, and, if possible, improve the quality quantity precious soil resources. Authors were encouraged submit papers related new ways mapping, showing more detailed input data, modeling r...

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• A methodology to select climate models model future soil erosion is proposed. under land use changes The most likely best- and worst-case scenarios are developed. Results show that will increase the mean of scenarios. Winter wheat an economically viable option reduce erosion. Soil by water from arable poses a serious threat on-field agricultural productivity wider environment through off-site...

Neural networks may provide a user-friendly alternative or supplement to complex physically based models for soil erosion prediction for some study areas. The purpose of this study was to investigate the applicability of using neural networks to quantitatively predict soil loss from natural runoff plots. Data from 2879 erosion events from eight locations in the United States were used. Neural n...

CLIGEN is a stochastic weather generator to produce daily variables to drive process-based runoff and erosion prediction models such as WEPP. Algorithms were developed to compute the R-factor, its monthly distribution, and 10-year storm erosion index (EI) needed to apply the revised Universal Soil Loss Equation (RUSLE) using stochastically generated weather variables. Measured R-factor and 10-y...