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The Universal Soil Loss Equation (USLE) is the most frequently applied erosion prediction model and it is also implemented as official decision-making instrument for agricultural regulations. The USLE itself was already validated by different approaches. Additional errors, however, arise from input data and interpolation procedures that become necessary for field-specific predictions on a national scale for administrative purposes. In this study, predicted event soil loss using the official prediction system in Bavaria (Germany) was validated by comparison with aerial photo erosion classifications of 8100 fields. Values for the USLE factors were mainly taken from the official Bavarian high-resolution (5 x 5 m2) erosion cadastre. As series of erosion events were examined, the cover and management factor was replaced by the soil loss ratio. The event erosivity factor was calculated from high-resolution (1 x 1 km2, 5 min), rain gauge-adjusted radar rain data (RADOLAN). Aerial photo erosion interpretation worked sufficiently well and average erosion predictions and visual classifications correlated closely. This was also true for data broken down to individual factors and different crops. There was no reason to assume a general invalidity of the USLE and the official parameterization procedures. Event predictions mainly suffered from errors in the assumed crop stage period and tillage practices, which do not reflect interannual and farm-specific variation. In addition, the resolution of radar data (1 km2) did not seem to be sufficient to predict short-term erosion on individual fields given the strong spatial gradients within individual rains. The quality of the input data clearly determined prediction quality. Differences between USLE predictions and observations are most likely caused by parameterization weaknesses but not by a failure of the model itself.

The impact of rainfall pattern on the interrill erosion process is not fully understood despite its importance. Systematic rainfall simulation experiments involving various rainfall intensities, stages, intensity sequences, and surface cover conditions were conducted in this study to investigate their effects on the interrill erosion process. Five rainfall patterns designed with the same total kinetic energy/precipitation (increasing, decreasing, rising–falling, falling–rising and constant patterns) were randomly delivered to a pre-wet clay loam soil surface at a 10° slope gradient. Significant differences in soil losses were observed among the different rainfall patterns and stages, but there was no obvious difference in runoff. Kinetic energy flux (KEr) was a governing factor for interrill erosion, and constant rainfall pattern (CST) produced nine times greater soil loss than runs with no KEr. Varied-intensity patterns had a profound effect on raindrop-induced sediment transport processes; path analysis results indicated that said effect was complex, interactive and intensity-dependent. Low hydraulic parameter thresholds further indicated that KEr was the dominant factor in detaching soil particles, while overland flow mainly contributed to transporting the pre-detached particles. This study not only sheds light on the mechanism of interrill sediment transport capacity and detachability, but also may provide a useful database for developing event-based interrill erosion prediction models. Copyright © 2017 John Wiley & Sons, Ltd.

The rate of erosion of hillside gullies depends both on gully flow characteristics and the resistance offered by the gully soil profile to erosion. This paper describes a method for quantifying a physically-based resistance measure, illustrated by application to a gully feeding sediment into the Bremer River, southeast Queensland, Australia. The dynamics of discharge down the gully during runoff events is the driver of erosion, but this was unknown. A new method is described whereby this unmeasured flow can be estimated using data on rainfall rate and river gauge monitoring. The data collected on the gully was the increase in dimensions and volume (and so soil loss) over a two year period. This information was obtained from a digital elevation model (DEM) of the catchment, derived from Light Detection and Ranging (LiDAR) observations made at either end of the two year period. The soil profile resistance characteristic evaluated is the energy required to erode a unit mass of soil from the gully walls, a physically-defined parameter, J, present in flow-driven erosion theory, which was adapted and applied to predict soil loss from the Bremer River gully. The value of J was evaluated by equating predicted to measured gully soil loss over the two year period using two alternative descriptions of gully cross-section. Firstly a realistic gully shape description was used, made possible by LiDAR data, yielding J?=?405.5?J/kg. Secondly, in order to allow use of more widely-available aerial photography for such studies, the simplifying assumption of a semi-circular gully shape was made, yielding J?=?455?J/kg. Allowing a ᳰ% error in estimated effective runoff rate for this ungauged gully, the estimated J value would have an uncertainty of +1%/-7% using the actual gully geometry. The assumptions made in estimating J are discussed, and possible applications of this information listed.

Modifications are made to the revised Morgan–Morgan–Finney erosion prediction model to enable the effects of vegetation cover to be expressed through measurable plant parameters. Given the potential role of vegetation in controlling water pollution by trapping clay particles in the landscape, changes are also made to the way the model deals with sediment deposition and to allow the model to incorporate particle-size selectivity in the processes of erosion, transport and deposition. Vegetation effects are described in relation to percentage canopy cover, percentage ground cover, plant height, effective hydrological depth, density of plant stems and stem diameter. Deposition is modelled through a particle fall number, which takes account of particle settling velocity, flow velocity, flow depth and slope length. The detachment, transport and deposition of soil particles are simulated separately for clay, silt and sand. Average linear sensitivity analysis shows that the revised model behaves rationally. For bare soil conditions soil loss predictions are most sensitive to changes in rainfall and soil parameters, but with a vegetation cover plant parameters become more important than soil parameters. Tests with the model using field measurements under a range of slope, soil and crop covers from Bedfordshire and Cambridgeshire, UK, give good predictions of mean annual soil loss. Regression analysis of predicted against observed values yields an intercept value close to zero and a line slope close to 1·0, with a coefficient of efficiency of 0·81 over a range of values from zero to 38·6 t ha−1. Copyright © 2007 John Wiley & Sons, Ltd.

Erosion caused by concentrated flows in agricultural areas is responsible for important soil losses, and rapid sediment transfer through the channel network. The main factors controlling concentrated flow erosion rates include the erodibility of soil materials, soil use and management, climate and watershed topography. In this paper, two topographic indices, closely related to mathematical expressions suggested by different authors, are used to characterize the influence of watershed topography on gully erosion. The AS1 index is defined as the product of the watershed area and the partial area-weighted average slope. The AS2 index is similar to the AS1 but uses the swale slope as the weighting factor. Formally, AS2 is the product of the watershed area and the length-weighted average swale slope. From studies made using different ephemeral gully erosion databases, it is shown that a high correlation consistently exists between the topographic indices and the volume of eroded soil. The resulting relationships are therefore useful to assess soil losses from gully erosion, to identify the most susceptible watersheds within large areas, and to compare the susceptibility to gully erosion among different catchments. This information is also important in studying the response of natural drainage network systems to different rainfall inputs. Copyright © 2005 John Wiley & Sons, Ltd.

A database composed of 673 natural rainfall events with sediment concentration measurements at the field or plot scale was analysed. Measurements were conducted on similar soil type (loess soils prone to sealing phenomenon) to apprehend the variability and complexity involved in interrill erosion processes attributable to soil surface conditions. The effects of the dominant controlling factors are not described by means of equations; rather, we established a classification of potential sediment concentration domain according to combination of the dominant parameters. Thereby, significant differences and evolution trends of mean sediment concentration between the different parameter categories are identified. Further, when parameter influences interact, it allows us to discern the relative effects of factors according to their respective degree of expression. It was shown that crop cover had a major influence on mean sediment concentration, particularly when soil surface roughness is low and when maximum 6-min intensity of rainfall events exceeds 10 mm h � 1 : mean sediment concentration decreases from 8Ð93 g l � 1 for 0–20 per cent of coverage to 0Ð 97 gl � 1 for 21–60 per cent of coverage. The established classification also indicates that the increase of the maximum 6-min intensity of the rainfall factor leads to a linear increase of mean sediment concentration for crop cover over 21 per cent (e.g. from 2Ð96 g l � 1 to 14Ð44 g l � 1 for the 1–5 cm roughness class) and to an exponential increase for low crop cover (e.g. from 3Ð92 g l � 1 to 58Ð76 g l � 1 for the 1–5 cm roughness class). The implication of this work may bring perspective for erosion prediction modelling and give references for the development of interrill erosion equation. Copyright  2002 John Wiley & Sons, Ltd.

A series of 75 non-bordered plots was used to measure surface erosion on granitic road cuts on forest roads in the mountains of Idaho. Erosion data were collected for four years following road construction. Erosion rates for the first winter period after construction averaged about five times greater than the average of erosion rates for subsequent seasons. Both mass and surface erosion processes were observed on road cuts with mass erosion particularly important during the first season after construction. Regression analysis showed slope gradient, slope aspect, ground cover density and snow-free period rainfall erosivity had statistically significant effects on erosion. Slope gradient was by far the most influential site factor affecting erosion but slope length had no affect. Three erosion control treatments – dry seeding, hydroseeding plus mulch, and terracing with hydroseeding plus mulch – were evaluated. Two treatments – dry seeding and hydroseeding plus mulch –caused statistically significant reductions in erosion. Dry seeding was the most cost-effective treatment on sites with deep alluvial soil. Elsewhere, hydromulching was the most cost-effective treatment. Further testing is needed to evaluate the effectiveness of erosion control treatments during the first period after construction. We were unable to discriminate between erosion rates on the moderately to highly weathered granitic rock included in this study. A discussion of the application of the study results is presented. Copyright © 2001 John Wiley & Sons, Ltd.

The paper describes an attempt to relate patterns of vegetation cover with topography and a set of biological and grazing intensity variables in a mountain and piedmont area of arid central Australia. Vegetation cover, as measured by an index based on data from the Landsat satellite, can also be used as an erosion/deposition surrogate so the results have implications for distributed erosion models. A simple, analytically based erosion model derived from the continuity equation does not reproduce observed patterns of vegetation cover, and neither do various topographically based moisture indices. A regression approach shows that patterns of vegetation cover are related to topography but the most important predictors are biological ones, with percentage of bare ground upslope being the strongest. Tests with variable drainage area show that relationships between cover and topography, bare area upslope and grazing effects change systematically with basin size and that scale effects are present. Distributed erosion models are not yet capable of handling biological processes very well, yet these processes must be incorporated if erosion prediction is to be successful.