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Soil erosion rates on six semi-arid loamy upland rangeland sites located in southeastern Arizona were measured using a rainfall simulator and 137Cs fallout methods. Site characteristics that have the greatest effects on soil erosion and runoff were identified. Long term (50years) soil erosion rates as estimated using 137Cs method varied between 5.1 and 11.0Mgha−1y−1 and showed significant differences...
Future changes in precipitation will induce changes in the erosive power of rainfall and hence changes in soil erosion rates. In this study we calculated downscaled mean annual precipitation and USLE rainfall erosivity (R) for time periods 2030 through 2059 and 2070 through 2099 in Northeast China using future precipitation predicted from six GCM models under three emissions scenarios. To accomplish...
The 30-min rainfall erosivity index (EI 30 ) is commonly used in the Universal Soil Loss Equation for predicting soil loss from agricultural hillslopes. EI 30 is calculated from the total kinetic energy and the maximum 30-min rainfall intensity of a storm. Normally, EI 30 values are calculated from breakpoint rainfall information taken from continuous recording rain gauge charts,...
A leaching experiment using a mixture of five REE oxides and gravelly, sandy loam soils was conducted to examine the applicability of the REE oxides as a sediment tracer for coarse-textured soils. The experiment showed little leaching of the oxides and an enrichment of various size classes of soil particles with the oxides, which reflected the strong binding capability of the REE oxides with the gravelly,...
This study investigates potential changes in erosion rates in the Midwestern United States under climate change, including the adaptation of crop management to climate change. Previous studies of erosion under climate change have not taken into account farmer choices of crop rotations or planting dates, which will adjust to compensate for climate change. In this study, changes in management were assigned...
Global climate has changed over the past century. Precipitation amounts and intensities are increasing. In this study we investigated the response of seven soil erosion models to a few basic precipitation and vegetation related parameters using common data from one humid and one semi-arid watershed. Perturbations were made to inputs for rainfall intensities and amounts, and to ground surface cover...
The development of soil conservation plans and evaluation of spatially distributed erosion models require knowledge of rates of soil loss and sedimentation on different landscape elements and slope positions. Characterization of soil erosion rates and patterns within watersheds is important for the understanding of erosion processes and landscape evolution. Experimental data that show spatial translocation...
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 networks...
The understanding of soil erosion processes and the development of accurate erosion prediction models require understanding of detachment, deposition, and sediment transport in rills. The objectives of this study were to determine whether sediment transport capacity is a unique value for given soil, flow rate, and slope, and to determine if equilibrium sediment concentration in the rill obtained by...
Soil erosion by water involves the processes of detachment, transport and deposition of soil materials by the erosive forces of raindrops and surface flow of water. The redistribution of sediment within a field-sized area is important in estimating the effect of erosion and deposition on productivity, in helping the conservation planner to target efforts to reduce erosion, and to evaluate erosion...
Evaluation of various soil erosion models with large data sets have consistently shown that these models tend to over-predict soil erosion for small measured values, and under-predict soil erosion for larger measured values. This trend appears to be consistent regardless of whether the soil erosion value of interest is for individual storms, annual totals, or average annual soil losses, and regardless...
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