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

V. Bagarello  V. Ferro  G. Giordano 《水文研究》2010,24(6):789-797

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.  相似文献   

Testing assumptions and procedures to empirically predict bare plot soil loss in a Mediterranean environment          下载免费PDF全文

V. Bagarello  V. Ferro  V. Pampalone 《水文研究》2015,29(10):2414-2424

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.  相似文献   

Comparing theoretically supported rainfall‐runoff erosivity factors at the Sparacia (South Italy) experimental site          下载免费PDF全文

Vincenzo Bagarello  Costanza Di Stefano  Vito Ferro  Vincenzo Pampalone 《水文研究》2018,32(4):507-515

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.  相似文献   

Applying the QREI30 index within the USLE modelling environment          下载免费PDF全文

P. I. A. Kinnell 《水文研究》2014,28(3):591-598

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.  相似文献   

Supporting USLE‐MM reliability by analyzing soil loss measurement errors          下载免费PDF全文

Vincenzo Bagarello  Vito Ferro 《水文研究》2017,31(4):847-853

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.  相似文献   

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

P. I. A. Kinnell 《水文研究》2007,21(20):2681-2689

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.  相似文献   

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

Costanza Di Stefano  Vincenzo Pampalone  Francesca Todisco  Lorenzo Vergni  Vito Ferro 《水文研究》2019,33(18):2422-2433

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.  相似文献   

Modelling event soil losses using the QREI30 index within RUSLE2          下载免费PDF全文

P.I.A. Kinnell 《水文研究》2014,28(5):2761-2771

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.  相似文献   

A comprehensive analysis of Universal Soil Loss Equation-based models at the Sparacia experimental area     

Vincenzo Bagarello  Vito Ferro  Vincenzo Pampalone 《水文研究》2020,34(7):1545-1557

Improving Universal Soil Loss Equation (USLE)-based models has large interest because simple and reliable analytical tools are necessary in the perspective of a sustainable land management. At first, in this paper, a general definition of the event rainfall- runoff erosivity factor for the USLE-based models, REF_e = (Q_R)^b1(EI₃₀)^b2, in which Q_R is the event runoff coefficient, EI₃₀ is the single-storm erosion index, and b₁ and b₂ are coefficients, was introduced. The rainfall-runoff erosivity factors of the USLE (b₁ = 0 and b₂ = 1), USLE-M (b₁ = b₂ = 1), USLE-MB (b₁ ≠ 1 and b₂ = 1), USLE-MR (b₁ = 1 and b₂ ≠ 1), USLE-MM (b₁ = b₂ ≠ 1), and USLE-M2 (b₁ ≠ b₂ ≠ 1) can be defined using REF_e. Then the different expressions of REF_e were simultaneously tested against a data set of normalized bare plot soil losses, A_eN, collected at the Sparacia (south Italy) site. As expected, the poorest A_eN predictions were obtained with the USLE. The observed tendency of this model to overestimate small A_eN values and underestimate high A_eN values was reduced by introducing in the soil loss prediction model both Q_R and an exponent for the erosivity term. The fitting to the data was poor with the USLE-MR as compared with the USLE-MB and the USLE-MM. Estimating two distinct exponents (USLE-M2) instead of a single exponent (USLE-MB, USLE-MR, and USLE-MM) did not appreciably improve soil loss prediction. The USLE-MB and the USLE-MM were recognized to be the best performing models among the possible alternatives, and they performed similarly with reference to both the complete data set and different sub-data sets, only including small, intermediate, and severe erosion events. In conclusion, including the runoff coefficient in the soil loss prediction model is important to improve the quality of the predictions, but a great importance has to be paid to the mathematical structure of the model.  相似文献   

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

P. I. A. Kinnell 《水文研究》2008,22(16):3168-3175

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.  相似文献   

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

V. Bagarello  V. Ferro 《水文研究》2004,18(1):141-157

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.  相似文献   

Predicting unit plot soil loss in Sicily,south Italy   总被引：2，自引：0，他引：2  

V. Bagarello  G. V. Di Piazza  V. Ferro  G. Giordano 《水文研究》2008,22(5):586-595

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.  相似文献   

Assessing changes in rainfall erosivity in Sicily during the twentieth century     

F. D’Asaro  L. D'Agostino  V. Bagarello 《水文研究》2007,21(21):2862-2871

Changes in rainfall erosivity are an expected consequence of climate change. Long‐term series of the single storm erosion index, EI, may be analysed to detect trends in rainfall erosivity. An indirect approach has to be applied for estimating EI, given that long series of rainfall intensities are seldom available. In this paper, a method for estimating EI from the corresponding rainfall amount, h_e, was developed for Sicily. This method was then applied at 17 Sicilian locations, representative of different climatic zones of the region, to generate a long series (i.e. from 1916 to 1999 in most cases) of EI values. Linear and step (step located at 1970) trends in annual and seasonal erosivity were detected by both classical approaches (Mann–Kendall test, Wilcoxon‐Mann‐Whitney rank‐sum test) and a new empirical approach (quantile approach, QA), based on the determination of the erosivity values corresponding to selected probability levels. A power relationship between EI and h_e with a space‐ and time‐variable scale factor and a time‐variable process parameter yielded the most accurate predictions of EI. However, a simpler model, using a time‐variable scale factor and a constant process parameter, yielded reasonably accurate EI estimates. Annual erosivity did not increase in Sicily during the twentieth century. At the most, it decreased at a few locations (three of the 17 considered locations). Significant trends were observed more frequently for winter erosivity (six locations) than for summer erosivity (two locations), suggesting that the erosive storms of winter determined the occasional occurrence of a negative trend in annual erosivity. In general, the QA compared reasonably well with more classical approaches. The QA appears promising since step trends for different return periods may be detected but efforts are needed to statistically formalize the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

Validation of official erosion modelling based on high‐resolution radar rain data by aerial photo erosion classification          下载免费PDF全文

Franziska Katharina Fischer  Michael Kistler  Robert Brandhuber  Harald Maier  Melanie Treisch  Karl Auerswald 《地球表面变化过程与地形》2018,43(1):187-194

The universal soil loss equation (USLE) is the most frequently applied erosion prediction model and it is also implemented as an official decision‐making instrument for agricultural regulations. The USLE itself has been already validated using 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 × 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 × 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 parametrization 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 parametrization weaknesses but not by a failure of the model itself. Copyright © 2017 John Wiley & Sons, Ltd.  相似文献   

Exploring the effect of different time resolutions to calculate the rainfall erosivity factor R in Calabria,southern Italy   总被引：1，自引：0，他引：1       下载免费PDF全文

Paolo Porto 《水文研究》2016,30(10):1551-1562

The rainfall erosivity factor R of the Universal Soil Loss Equation is a good indicator of the potential of a storm to erode soil, as it quantifies the raindrop impact effect on the soil based on storm intensity. The R‐factor is defined as the average annual value of rainfall erosion index, EI, calculated by cumulating the EI values obtained for individual storms for at least 22 years. By definition, calculation of EI is based on rainfall measurements at short time intervals over which the intensity is essentially constant, i.e. using so‐called breakpoint data. Because of the scarcity of breakpoint rainfall data, many authors have used different time resolutions (Δt = 5, 10, 15, 30, and 60 min) to deduce EI in different areas of the world. This procedure affects the real value of EI because it is strongly dependent on Δt. In this contribution, after a general overview of similar studies carried out in different countries, the relationship between EI and Δt is explored in Calabria, southern Italy. The use of 17 139 storm events collected from 65 rainfall stations allowed the calculation of EI for different time intervals ranging from 5 to 60 min. The overall results confirm that calculation of EI is dependent on time resolution and a conversion factor able to provide its value for the required Δt is necessary. Based on these results, a parametric equation that gives EI as a function of Δt is proposed, and a regional map of the scale parameter a that represents the conversion factor for converting fixed‐interval values of (EI₃₀)_Δt to values of (EI₃₀)₁₅ is provided in order to calculate R anywhere in the region using rainfall data of 60 min. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

Drop size distribution and kinetic energy load of rainfall events in the highlands of the Central Rift Valley,Ethiopia     

Derege Tsegaye Meshesha  Atsushi Tsunekawa  Mitsuru Tsubo  Nigussie Haregeweyn  Enyew Adgo 《水文科学杂志》2013,58(12):2203-2215

Abstract

Knowledge of rainfall characteristics is important for estimating soil erosion in arid areas. We determined basic rainfall characteristics (raindrop size distribution, intensity and kinetic energy), evaluated the erosivity of rainfall events, and established a relationship between rainfall intensity I and volume-specific kinetic energy KE_vol for the Central Rift Valley area of the Ethiopian highlands. We collected raindrops on dyed filter paper and calculated KE_vol and erosivity values for each rainfall event. For most rainfall intensities the median volume drop diameter (D₅₀) was higher than expected, or reported in most studies. Rainfall intensity in the region was not high, with 8% of rain events exceeding 30 mm h^-1. We calculated soil erosion from storm energy and maximum 30-min intensity for soils of different erodibility under conditions of fallow (unprotected soil), steep slope (about 9%) and no cover and management practice on the surface, and determined that 3 MJ mm ha^-1 h^-1 is the threshold erosivity, while erosivity of >7 MJ mm ha^-1 h^-1 could cause substantial erosion in all soil types in the area.