Runoff dependent erosivity and slope length factors suitable for modelling annual erosion using the Universal Soil Loss Equation

Despite revisions and refinements, the Revised Universal Soil Loss Equation (RUSLE), which is the revised version of the Universal Soil Loss Equation (USLE), over predicts small annual soil losses and under predicts large annual soil losses. To some large extent, this results from the equation over estimating small event soil losses and under estimating large event soil losses. Replacing the USLE/RUSLE event erosivity index (EI₃₀) by the product of EI₃₀ and the runoff ratio (Q_R) significantly reduces the errors in estimating event erosion when runoff is measured, but the USLE‐M, the USLE variant that uses the Q_REI₃₀ index, requires crop and support practice factors that differ from those used in the RUSLE. The theory which enables the Q_REI₃₀ index to be used in association with the RUSLE crop and support practice factors is presented. In addition, the USLE/RUSLE approach was developed for conditions where runoff is produced uniformly over a hill slope. A runoff dependent slope length factor that takes account of runoff variations over a hill slope is presented and demonstrated for the situation where runoff from a low runoff producing area passes onto an area where runoff is produced more readily. Copyright © 2007 John Wiley & Sons, Ltd.     

Applying the QREI30 index within the USLE modelling environment

The USLE/RUSLE model was designed to predict long‐term (~20 years) average annual soil loss by accounting for the effects of climate, soil, topography and crops. The USLE/RUSLE model operates mathematically in two steps. The first step involves the prediction of soil loss from the ‘unit’ plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope. Appropriate values of the factors accounting for slope length, gradient, crops and crop management and soil conservation practice are then used to adjust that soil loss to predict soil loss from areas that have conditions that are different from the unit plot. Replacing EI₃₀, the USLE/RUSLE event erosivity index, by the product of the runoff ratio (Q_R) and EI₃₀, can enhance the capacity of the model to predict short‐term soil loss from the unit plot if appropriate data on runoff is available. Replacing the EI₃₀ index by another index has consequences on other factors in the model. The USLE/RUSLE soil erodibility factor cannot be used when the erosivity factor is based on Q_REI₃₀. Also, the USLE/RUSLE factors for slope length, slope gradient crops and crop management, and soil conservation practice cannot be used when runoff from other than the unit plot is used to calculate Q_R. Here, equations are provided to convert the USLE/RUSLE factors to values suitable for use when the erosivity factor is based on the Q_REI₃₀ index under these circumstances. At some geographic locations, non linear relationships exist between soil loss from bare fallow areas and the Q_REI₃₀ index. The effect of this on the slope length factor associated with the Q_REI₃₀ index is demonstrated using data from runoff and soil loss plots located at the Sparacia site, Sicily. Copyright © 2012 John Wiley & Sons, Ltd.     

Comparative analysis of sediment yield estimations using different empirical soil erosion models

The study aims to evaluate the performance of different empirical soil erosion models (EPM, USLE, Koutsoyiannis and Tarla, RUSLE) in mountainous Mediterranean-type catchments. The study area comprises the Arachthos, Kalamas, Upper Acheloos and Venetikos river basins, located in northwestern Greece. The methodology followed includes both qualitative and quantitative analyses. The former refers to the specific attributes of the models and the latter to the estimated sediment yield results. The results were initially validated against observed sediment yield values. The ambiguous reliability of such measurements led to their replacement by simulated ones, estimated using the sediment rating curve methodology. In the latter analysis, the models performed better, with more accurate results. Overall, the RUSLE corresponded best to such basins. Finally, the performance of seven empirical equations (Syvitski, Avendano Salas et al., Dendy and Bolton, Lu et al., Webb and Griffiths, Zarris et al.) was assessed, yielding relatively poor results.     

Sediment delivery from hillslopes and the Universal Soil Loss Equation: some perceptions and misconceptions

The Universal Soil Loss Equation (USLE) or the revised USLE (RUSLE) are often used together with sediment delivery ratios in order to predict sediment delivery from hillslopes. In using sediment delivery ratios for this purpose, it is assumed that the sediment delivery ratio for a given hillslope does not vary with the amount of erosion occurring in the upslope area. This assumption is false. There is a perception that hillslope erosion is calculated on the basis that hillslopes are, in effect, simply divided into 22·1 m long segments. This perception fails to recognize the fact the inclusion of the 22·1 m length in the calculation has no physical significance but simply produces a value of 1·0 for the slope length factor when slopes have a length equal to that of the unit plot. There is a perception that the slope length factor is inappropriate because not all the dislodged sediment is discharged. This perception fails to recognize that the USLE and the RUSLE actually predict sediment yield from planar surfaces, not the total amount of soil material dislocated and removed some distance by erosion within an area. The application of the USLE/RUSLE to hillslopes also needs to take into account the fact that runoff may not be generated uniformly over that hillslope. This can be achieved by an equation for the slope length factor that takes account of spatial variations in upslope runoff on soil loss from a segment or grid cell. Several alternatives to the USLE event erosivity index have been proposed in order to predict event erosion better than can be achieved using the EI₃₀ index. Most ignore the consequences of changing the event erosivity index on the values for the soil, crop and soil conservation protection factors because there is a misconception that these factors are independent of one another. Copyright © 2007 John Wiley & Sons, Ltd.     

Modelling event soil losses using the QREI30 index within RUSLE2

Recently, a USDA Curve Number‐based method for obtaining estimates of event runoff has been developed for use in enhancing the capacity of Revised Universal Soil Loss Equation (RUSLE2) to deal with runoff‐driven phenomena. However, RUSLE2 still uses the EI₃₀ index as the basis for determining the erosivity of the rainfall for sets of runoff producing storms at a location even though the product of the runoff ratio (Q_R) and EI₃₀ index is better at prediction event erosion when runoff is known or predicted well. This paper reports the results of applying the Q_REI₃₀ index using data available from tables within RUSLE2 to predict storm event soil losses from bare fallow areas and areas with continuous corn at Holly Springs, MS, and Morris, MN. In RUSLE2, all rainfall during a calendar year is considered to detach soil material that is flushed from the area if and when runoff occurs. However, the Q_REI₃₀ index is calculated using the EI₃₀ value for the amount of rain in the storm that produces runoff. Consequently, changes were made to the timing of events during the calendar year in order to meet the criteria for using the Q_REI₃₀ index. As a general rule, the peak event soil loss produced using the Q_REI₃₀ index were higher than produced by RUSLE2, and the peak event soil loss for the bare fallow occurred later than for the continuous corn. The results of the work reported here show that the Q_REI₃₀ index may be used to model event erosion produced by a set of storms within RUSLE2 provided that the appropriate mathematical rules upon which the USLE was developed are adhered to. Copyright © 2013 John Wiley & Sons, Ltd.     

Enhancing RUSLE to include runoff‐driven phenomena

RUSLE2 (Revised Universal Soil Loss Equation) is the most recent in the family of Universal Soil Loss Equation (USLE)/RUSLE/RUSLE2 models proven to provide robust estimates of average annual sheet and rill erosion from a wide range of land use, soil, and climatic conditions. RUSLE2's capabilities have been expanded over earlier versions using methods of estimating time‐varying runoff and process‐based sediment transport routines so that it can estimate sediment transport/deposition/delivery on complex hillslopes. In this report we propose and evaluate a method of predicting a series of representative runoff events whose sizes, durations, and timings are estimated from information already in the RUSLE2 database. The methods were derived from analysis of 30‐year simulations using a widely accepted climate generator and runoff model and were validated against additional independent simulations not used in developing the index events, as well as against long‐term measured monthly rainfall/runoff sets. Comparison of measured and RUSLE2‐predicted monthly runoff suggested that the procedures outlined may underestimate plot‐scale runoff during periods of the year with greater than average rainfall intensity, and a modification to improve predictions was developed. In order to illustrate the potential of coupling RUSLE2 with a process‐based channel erosion model, the resulting set of representative storms was used as an input to the channel routines used in Chemicals, Runoff, and Erosion from Agricultural Management Systems (CREAMS) to calculate ephemeral gully erosion. The method was applied to a hypothetical 5‐ha field cropped to cotton in Marshall County, MS, bisected by a potential ephemeral gully having channel slopes ranging from 0·5 to 5% and with hillslopes on both sides of the channel with 5% steepness and 22·1 m length. Results showed the representative storm sequence produced reasonable results in CREAMS indicating that ephemeral gully erosion may be of the same order of magnitude as sheet and rill erosion. Copyright © 2010 John Wiley & Sons, Ltd.     

Predicting unit plot soil loss in Sicily,south Italy

Predicting soil loss is necessary to establish soil conservation measures. Variability of soil and hydrological parameters complicates mathematical simulation of soil erosion processes. Methods for predicting unit plot soil loss in Sicily were developed by using 5 years of data from replicated plots. At first, the variability of the soil water content, runoff, and unit plot soil loss values collected at fixed dates or after an erosive event was investigated. The applicability of the Universal Soil Loss Equation (USLE) was then tested. Finally, a method to predict event soil loss was developed. Measurement variability decreased as the mean increased above a threshold value but it was low also for low values of the measured variable. The mean soil loss predicted by the USLE was lower than the measured value by 48%. The annual values of the soil erodibility factor varied by seven times whereas the mean monthly values varied between 1% and 244% of the mean annual value. The event unit plot soil loss was directly proportional to an erosivity index equal to , being Q_RR_e the runoff ratio times the single storm erosion index. It was concluded that a relatively low number of replicates of the variable of interest may be collected to estimate the mean for both high and particularly low values of the variable. The USLE with the mean annual soil erodibility factor may be applied to estimate the order of magnitude of the mean soil loss but it is not usable to estimate soil loss at shorter temporal scales. The relationship for estimating the event soil loss is a modified version of the USLE‐M, given that it includes an exponent for the Q_RR_e term. Copyright © 2007 John Wiley & Sons, Ltd.     

Comparing theoretically supported rainfall‐runoff erosivity factors at the Sparacia (South Italy) experimental site

Interpreting rainfall‐runoff erosivity by a process‐oriented scheme allows to conjugate the physical approach to soil loss estimate with the empirical one. Including the effect of runoff in the model permits to distinguish between detachment and transport in the soil erosion process. In this paper, at first, a general definition of the rainfall‐runoff erosivity factor REF_e including the power of both event runoff coefficient Q_R and event rainfall erosivity index EI₃₀ of the Universal Soil Loss Equation (USLE) is proposed. The REF_e factor is applicable to all USLE‐based models (USLE, Modified USLE [USLE‐M] and Modified USLE‐M [USLE‐MM]) and it allows to distinguish between purely empirical models (e.g., Modified USLE‐M [USLE‐MM]) and those supported by applying theoretical dimensional analysis and self‐similarity to Wischmeier and Smith scheme. This last model category includes USLE, USLE‐M, and a new model, named USLE‐M based (USLE‐MB), that uses a rainfall‐runoff erosivity factor in which a power of runoff coefficient multiplies EI₃₀. Using the database of Sparacia experimental site, the USLE‐MB is parameterized and a comparison with soil loss data is carried out. The developed analysis shows that USLE‐MB (characterized by a Nash–Sutcliffe Efficiency Index NSEI equal to 0.73 and a root mean square error RMSE = 11.7 Mg ha^?1) has very similar soil loss estimate performances as compared with the USLE‐M (NSEI = 0.72 and RMSE = 12.0 Mg ha^?1). However, the USLE‐MB yields a maximum discrepancy factor between predicted and measured soil loss values (176) that is much lower than that of USLE‐M (291). In conclusion, the USLE‐MB should be preferred in the context of theoretically supported USLE type models.     

An evaluation of the universal soil loss equation and field techniques for assessing soil erosion on a tropical alfisol in western Nigeria

Four techniques for soil erosion assessment were compared over two consecutive seasons for bare-fallow plots and a maize-cowpea sequence in 1985 at IITA, Ibadan, Nigeria. The techniques used were: tracer (aluminium paint), nails (16 and 25), the rill method, and the Universal Soil Loss Equation (USLE). Soil loss estimated by these techniques was compared with that determined using the runoff plot technique. There was significantly more soil loss (P < 0·01) in bare-fallow than in plots under maize (Zea mays) or cowpea (Vigna unguiculata). In the first season, soil loss from plots sown to maize was 40·2 Mg ha^?1 compared with 153·3 Mg ha^?1 from bare-fallow plots. In the second season, bare-fallow plots lost 87·5 Mg ha^?1 against 39·4 Mg ha^?1 lost from plots growing cowpea. The techniques used for assessing erosion had no influence on the magnitude of soil erosion and did not interfere with the processes of erosion. There was no significant difference (P < 0·05) between soil erosion determined by the nails and the runoff plot technique. Soil loss determined on six plots (three under maize, three bare-fallow) by the rill technique, at the end of the season, was significantly lower (P < 0·05) than that determined by the runoff plot technique. The soil loss estimated by the rill method was 143·2, 108·8 and 121·9 Mg ha^?1 for 11, 11, and 8 per cent slopes respectively, in comparison with 201·5, 162·0, and 166·4 Mg ha^?1 measured by the runoff plot method. Soil loss measured on three bare-fallow plots on 10 different dates by the rill technique was also significantly lower (P < 0·01) than that measured by the runoff plot. In the first season the USLE significantly underestimated soil loss. On 11, 11, and 8 per cent slopes, respectively, soil loss determined by the USLE was 77, 92, and 63 per cent of that measured by the runoff plot. However, in the second season there was no significant difference between soil loss determined by the USLE and that determined by the conventional runoff plot technique.     

Testing assumptions and procedures to empirically predict bare plot soil loss in a Mediterranean environment

Empirical prediction of soil erosion has both scientific and practical importance. This investigation tested USLE and USLE‐based procedures to predict bare plot soil loss at the Sparacia area, in Sicily. Event soil loss per unit area, A_e, did not vary appreciably with plot length, λ, because the decrease in runoff with λ was offset by an increase in sediment concentration. Slope steepness, s, had a positive effective on A_e, and this result was associated with a runoff coefficient that did not vary appreciably with s and a sediment concentration generally increasing with s. Plot steepness did not have a statistically detectable effect on the calculations of the soil erodibility factor of both the USLE, K, and the USLE‐M, K_UM, models, but a soil‐independent relationship between K_UM and K was not found. The erosivity index of the USLE‐MM model performed better than the erosivity index of the Central and Southern Italy model. In conclusion, the importance of an approach allowing soil loss predictions that do not necessarily increase with λ was confirmed together with the usability of already established and largely applied relationships to predict steepness effects. Soil erodibility has to be determined with reference to the specific mathematical scheme and conversion between different schemes seems to need taking into account the soil characteristics. The USLE‐MM shows promise for further developments. The evolutionary concept applied in the development of the USLE should probably be rediscovered to improve development of soil erosion prediction tools. Copyright © 2014 John Wiley & Sons, Ltd.     

Plot‐scale measurement of soil erosion at the experimental area of Sparacia (southern Italy)

Obtaining good quality soil loss data from plots requires knowledge of the factors that affect natural and measurement data variability and of the erosion processes that occur on plots of different sizes. Data variability was investigated in southern Italy by collecting runoff and soil loss from four universal soil‐loss equation (USLE) plots of 176 m², 20 ‘large’ microplots (0·16 m²) and 40 ‘small’ microplots (0·04 m²). For the four most erosive events (event erosivity index, R_e ≥ 139 MJ mm ha^?1 h^?1), mean soil loss from the USLE plots was significantly correlated with R_e. Variability of soil loss measurements from microplots was five to ten times greater than that of runoff measurements. Doubling the linear size of the microplots reduced mean runoff and soil loss measurements by a factor of 2·6–2·8 and increased data variability. Using sieved soil instead of natural soil increased runoff and soil loss by a factor of 1·3–1·5. Interrill erosion was a minor part (0·1–7·1%) of rill plus interrill erosion. The developed analysis showed that the USLE scheme was usable to predict mean soil loss at plot scale in Mediterranean areas. A microplot of 0·04 m² could be used in practice to obtain field measurements of interrill soil erodibility in areas having steep slopes. Copyright © 2003 John Wiley & Sons, Ltd.     

Supporting USLE‐MM reliability by analyzing soil loss measurement errors

Sampling the collected suspension in a storage tank is a common procedure to obtain soil loss data. A calibration curve of the tank has to be used to obtain actual concentration values from those measured by sampling. However, literature suggests that using a tank calibration curve was not a common procedure in the past. For the clay soil of the Sparacia (Italy) experimental station, this investigation aimed to establish a link between the relative performances of the USLE‐M and USLE‐MM models, usable to predict plot soil loss at the event temporal scale, and soil loss measurement errors. Using all available soil loss data, lower soil loss prediction errors were obtained with the USLE‐MM (exponent of the erosivity term, b₁ > 1) than the USLE‐M (b₁ = 1). A systematic error of the soil loss data is unexpected for the Sparacia soil because the calibration curve does not depend on the water level in the tank. In any case, this type of error does not have any effect on the b₁ exponent. Instead, this exponent decreases as the level of underestimation increases for increasing soil loss values. This type of error can occur at Sparacia if it is assumed that a soil loss measurement can be obtained by a bottle sampler dipped close to the bottom of the tank after mixing the suspension and assuming that the measured concentration coincides with the actual one. In this case, the risk is to obtain a lower b₁ value than the actual one. In conclusion, additional investigations on the factors determining errors in soil loss data collected by a sampling procedure are advisable because these errors can have a noticeable effect on the calibrated empirical models for soil loss prediction.     

A comparative analysis of sediment yield simulation by empirical and process-oriented models in Thailand / Une analyse comparative de simulations de l'exportation sédimentaire en Thaïlande à l'aide de modèles empiriques et de processus

Abstract

Although soil erosion has been recognized worldwide as a threat to the sustainability of natural ecosystems, its quantification presents one of the greatest challenges in natural resources and environmental planning. Precise modelling of soil erosion and sediment yield is particularly difficult, as soil erosion is a highly dynamic process at the spatial scale. The main objective of this study was to simulate soil erosion and sediment yield using two fundamentally different approaches: empirical and process-oriented. The revised form of the Universal Soil Loss Equation (RUSLE), along with a sediment delivery distributed model (SEDD) and the Modified Universal Soil Loss Equation (MUSLE), which are popular empirical models, were applied in a sub-basin of the Mun River basin, Thailand. The results obtained from the RUSLE/SEDD and MUSLE models were compared with those obtained from a process-oriented soil erosion and sediment transport model. The latter method involves spatial disaggregation of the catchment into homogeneous grid cells to capture the catchment heterogeneity. A GIS technique was used for the spatial discretization of the catchment and to derive the physical parameters related to erosion in the grid cells. The simulated outcomes from the process-oriented model were found to be closer to observations as compared to the outcomes of the empirical approaches.     

Testing the Universal Soil Loss Equation‐MB equation in plots in Central and South Italy

Planning soil conservation strategies requires predictive techniques at event scale because a large percentage of soil loss over a long‐time period is due to relatively few large storms. Considering runoff is expected to improve soil loss predictions and allows relation of the process‐oriented approach with the empirical one, furthermore, the effects of detachment and transport on soil erosion processes can be distinguished by a runoff component. In this paper, the empirical model USLE‐MB (USLE‐M based), including a rainfall‐runoff erosivity factor in which the event rainfall erosivity index EI₃₀ of the Universal Soil Loss Equation (USLE) multiplies the runoff coefficient Q_R raised to an exponent b₁ > 1 is tested by the measurements carried out for the Masse (10 plots) and Sparacia (22 plots) experimental stations in Italy. For the Masse experimental station, an exponent b₁ > 1 was also estimated by tests carried out by a nozzle‐type rainfall simulator. For each experimental site in fallow conditions, the effect of the sample size of the plot soil loss measurements on the estimate of the b₁ coefficient was also studied by the extraction of a fixed number N of randomly obtained pairs of the normalized soil loss and runoff coefficient. The analysis showed that the variability of b₁ with N is low and that 350 pairs are sufficient to obtain a stable estimate of b₁. A total of 1,262 soil loss data were used to parameterize the model both locally and considering the two sites simultaneously. The b₁ exponent varied between the two sites (1.298–1.520), but using a common exponent (1.386) was possible. Using a common b₁ exponent for the two experimental areas increases the practical interest for the model and allows the estimation of a baseline component of the soil erodibility factor, which is representative of the at‐site soil intrinsic and quasi‐static properties. Development of a single USLE‐MB model appears possible, and sampling other sites is advisable to develop a single USLE‐MB model for general use.     

Assessment of soil erosion in a monsoon-dominated mountain river basin in India using RUSLE-SDR and AHP

The long-term average annual soil loss (A) and sediment yield (SY) in a tropical monsoon-dominated river basin in the southern Western Ghats, India (Muthirapuzha River Basin, MRB; area: 271.75 km²), were predicted by coupling the Revised Universal Soil Loss Equation (RUSLE) and sediment delivery ratio (SDR) models. Moreover, the study also delineated soil erosion risk zones based on the soil erosion potential index (SEPI) using the analytic hierarchy process (AHP) technique. Mean A of the basin is 14.36 t ha^?1 year^?1, while mean SY is only 3.65 t ha^?1 year^?1. Although the land use/land cover types with human interference show relatively lower A compared to natural vegetation, their higher SDR values reflect the significance of anthropogenic activities in accelerated soil erosion. The soil erosion risk in the MRB is strongly controlled by slope, land use/land cover and relative relief, compared to geomorphology, drainage density, stream frequency and lineament frequency.     

Testing alternative erosivity indices to predict event soil loss from bare plots in Southern Italy

Methods for predicting unit plot soil loss for the ‘Sparacia’ Sicilian (Southern Italy) site were developed using 316 simultaneous measurements of runoff and soil loss from individual bare plots varying in length from 11 to 44 m. The event unit plot soil loss was directly proportional to an erosivity index equal to (Q_REI₃₀)^1·47, being Q_REI₃₀ the runoff ratio (Q_R) times the single storm erosion index (EI₃₀). The developed relationship represents a modified version of the USLE‐M, and therefore it was named USLE‐MM. By the USLE‐MM, a constant erodibility coefficient was deduced for plots of different lengths, suggesting that in this case the calculated erodibility factor is representative of an intrinsic soil property. Testing the USLE‐M and USLE‐MM schemes for other soils and developing simple procedures for estimating the plot runoff ratio has practical importance to develop a simple method to predict soil loss from bare plots at the erosive event temporal scale. Copyright © 2009 John Wiley & Sons, Ltd.     

Orders of magnitude increase in soil erosion associated with land use change from native to cultivated vegetation in a Brazilian savannah environment

The Brazilian savanna (cerrado) is a large and important economic and environmental region that is experiencing significant loss of its natural landscapes due to pressures of food and energy production, which in turn has caused large increases in soil erosion. However the magnitude of the soil erosion increases in this region is not well understood, in part because scientific studies of surface runoff and soil erosion are scarce or nonexistent in the cerrado as well as in other savannahs of the world. To understand the effects of deforestation we assessed natural rainfall‐driven rates of runoff and soil erosion on an undisturbed tropical woodland classified as ‘cerrado sensu stricto denso’ and bare soil. Results were evaluated and quantified in the context of the cover and management factor (C‐factor) of the Universal Soil Loss Equation (USLE). Replicated data on precipitation, runoff, and soil loss on plots (5 × 20 m) under undisturbed cerrado and bare soil were collected for 77 erosive storms that occurred over 3 years (2012 through 2014). C‐factor was computed annually using values of rainfall erosivity and soil loss rate. We found an average runoff coefficient of ~20% for the plots under bare soil and less than 1% under undisturbed cerrado. The mean annual soil losses in the plots under bare soil and cerrado were 12.4 t ha^‐1 yr^‐1 and 0.1 t ha^‐1 yr^‐1, respectively. The erosivity‐weighted C‐factor for the undisturbed cerrado was 0.013. Surface runoff, soil loss and C‐factor were greatest in the summer and fall. Our results suggest that shifts in land use from the native to cultivated vegetation result in orders of magnitude increases in soil loss rates. These results provide benchmark values that will be useful to evaluate past and future land use changes using soil erosion models and have significance for undisturbed savanna regions worldwide. Copyright © 2015 John Wiley & Sons, Ltd.     

Accounting for the influence of runoff on event soil erodibilities associated with the EI30 index in RUSLE2

Soil erodibilities (K) associated with the EI₃₀ index vary not only with soil properties but also with soil moisture as it varies in time and space. In Revised Universal Soil Loss Equation Version 2 (RUSLE2), temporal variations in soil erodibility in the USA are calculated using monthly precipitation and temperature as independent variables. K_UM, the soil erodibility factor associated with the Q_REI₃₀ index, varies independently of runoff and the product of K_UM and the runoff ratio for the unit plot (Q_R1) provides an alternative to the temporally varying Ks currently used in predicting storm soil loss in RUSLE2. Comparisons were made between the product of Q_R1 and K_UM and RUSLE2 Ks for representative storms at four locations representing the north to south variation in climate in the USA. Peak erosion associated with the current approach used in RUSLE2 was slightly higher at two locations and slightly lower at the other two locations. One other location, Morris, MN, provided an exception with the peak loss predicted by using the product of Q_R1 and K_UM being 1.7 times that obtained using RUSLE2 Ks. In theory, average annual K_UM values should be better related to soil properties than the average annual values of K frequently used when the average annual values of EI₃₀ are used to predict soil loss. However, work has yet to be performed to determine how K_UM varies directly with soil properties and in space and time. Copyright © 2014 John Wiley & Sons, Ltd.     

Impact of root architecture on the erosion‐reducing potential of roots during concentrated flow

Many studies focus on the effects of vegetation cover on water erosion rates, whereas little attention has been paid to the effects of the below ground biomass. Recent research indicates that roots can reduce concentrated flow erosion rates significantly. In order to predict this root effect more accurately, this experimental study aims at gaining more insight into the importance of root architecture, soil and flow characteristics to the erosion‐reducing potential of roots during concentrated flow. Treatments were (1) bare, (2) grass (representing a fine‐branched root system), (3) carrots (representing a tap root system) and (4) carrots and fine‐branched weeds (representing both tap and fine‐branched roots). The soil types tested were a sandy loam and a silt loam. For each treatment, root density, root length density and mean root diameter (D) were assessed. Relative soil detachment rates and mean bottom flow shear stress were calculated. The results indicate that tap roots reduce the erosion rates to a lesser extent compared with fine‐branched roots. Different relationships linking relative soil detachment rate with root density could be established for different root diameter classes. Carrots with very fine roots (D < 5 mm) show a similar negative exponential relationship between root density and relative soil detachment rate to grass roots. With increasing root diameter (5 < D < 15 mm) the erosion‐reducing effect of carrot type roots becomes less pronounced. Additionally, an equation estimating the erosion‐reducing potential of root systems containing both tap roots and fine‐branched roots could be established. Moreover, the erosion‐reducing potential of grass roots is less pronounced for a sandy loam soil compared with a silt loam soil and a larger erosion‐reducing potential for both grass and carrot roots was found for initially wet soils. For carrots grown on a sandy loam soil, the erosion‐reducing effect of roots decreases with increasing flow shear stress. For grasses, grown on both soil types, no significant differences could be found according to flow shear stress. The erosion‐reducing effect of roots during concentrated flow is much more pronounced than suggested in previous studies dealing with interrill and rill erosion. Root density and root diameter explain the observed erosion rates during concentrated flow well for the different soil types tested. Copyright © 2007 John Wiley & Sons, Ltd.