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1.
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 (QR) significantly reduces the errors in estimating event erosion when runoff is measured, but the USLE‐M, the USLE variant that uses the QREI30 index, requires crop and support practice factors that differ from those used in the RUSLE. The theory which enables the QREI30 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   

2.
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 (QR) and EI30 index is better at prediction event erosion when runoff is known or predicted well. This paper reports the results of applying the QREI30 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 QREI30 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 QREI30 index. As a general rule, the peak event soil loss produced using the QREI30 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 QREI30 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   

3.
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 (QREI30)1·47, being QREI30 the runoff ratio (QR) 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   

4.
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 REFe including the power of both event runoff coefficient QR and event rainfall erosivity index EI30 of the Universal Soil Loss Equation (USLE) is proposed. The REFe 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   

5.
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   

6.
Improving empirical prediction of plot soil erosion at the event temporal scale has both scientific and practical importance. In this investigation, 492 runoff and soil loss data from plots of different lengths, λ (11 ≤ λ ≤ 44 m), and steepness, s (14.9 ≤ s ≤ 26.0%), established at the Sparacia experimental station, in Sicily, South Italy, were used to derive a new version of Universal Soil Loss Equation (USLE)‐MM model, by only assuming a value of one for the topographic length, L, and steepness, S, factors for λ = 22 m and s = 9%, respectively. An erosivity index equal to (QREI30)b1, QR and EI30 being the runoff coefficient and the event rainfall erosivity index, respectively, with b1 > 1 was found to be an appropriate choice for the Sparacia area. The specifically developed functions for L and S did not differ appreciably from other, more widely accepted relationships (maximum differences by a factor of 1.22 for L and 1.09 for S). The new version of the USLE‐MM performed particularly well for highly erosive events, because predicted soil loss differed by not more than a factor of 1.19 from the measured soil loss for measured values of more than 100 Mg ha?1. The choice of the relationships to predict topographic effects on plot soil loss should not represent a point of particular concern in the application of the USLE‐MM in other environments. However, tests of the empirical approach should be carried out in other experimental areas in an attempt to develop analytical tools, usable at the event temporal scale, reasonably simple and of wide validity. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

7.
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 QR 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   

8.
Predicting unit plot soil loss in Sicily,south Italy   总被引：2，自引：0，他引：2  
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 QRRe 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 QRRe term. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

9.
P. I. A. Kinnell 《水文研究》2015,29(6):1397-1405
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. KUM, the soil erodibility factor associated with the QREI30 index, varies independently of runoff and the product of KUM and the runoff ratio for the unit plot (QR1) provides an alternative to the temporally varying Ks currently used in predicting storm soil loss in RUSLE2. Comparisons were made between the product of QR1 and KUM 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 QR1 and KUM being 1.7 times that obtained using RUSLE2 Ks. In theory, average annual KUM 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 KUM varies directly with soil properties and in space and time. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

10.
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, REFe = (QR)b1(EI30)b2, in which QR 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 REFe. Then the different expressions of REFe were simultaneously tested against a data set of normalized bare plot soil losses, AeN, collected at the Sparacia (south Italy) site. As expected, the poorest AeN predictions were obtained with the USLE. The observed tendency of this model to overestimate small AeN values and underestimate high AeN values was reduced by introducing in the soil loss prediction model both QR 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   

11.
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, Ae, 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 Ae, 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, KUM, models, but a soil‐independent relationship between KUM 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   

12.
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, Re ≥ 139 MJ mm ha?1 h?1), mean soil loss from the USLE plots was significantly correlated with Re. 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   

13.
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   

14.
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, he, 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 he 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   

15.
For interrill erosion, raindrop‐induced detachment and transport of sediment by rainfall‐disturbed sheet flow are the predominant processes, while detachment by sheet flow and transport by raindrop impact are negligible. In general, interrill subprocesses are inter‐actively affected by rainfall, soil and surface properties. The objective of this work was to study the relationships among interrill runoff and sediment loss and some selected para‐meters, for cultivated soils in central Greece, and also the development of a formula for predicting single storm sediment delivery. Runoff and soil loss measurement field experiments have been conducted for a 3·5‐year period, under natural storms. The soils studied were developed on Tertiary calcareous materials and Quaternary alluvial deposits and were textured from sandy loam to clay. The second group of soils showed greater susceptibility to sealing and erosion than the first group. Single storm sediment loss was mainly affected by rain and runoff erosivity, being significantly correlated with rain kinetic energy (r = 0·64***), its maximum 30‐minute intensity (r = 0·64***) and runoff amount (r = 0·56***). Runoff had the greatest correlation with rain kinetic energy (r = 0·64***). A complementary effect on soil loss was detected between rain kinetic energy and its maximum 30‐minute intensity. The same was true for rain kinetic energy and topsoil aggregate instability, on surface seal formation and thus on infiltration characteristics and overland flow rate. Empirical analysis showed that the following formula can be used for the successful prediction of sediment delivery (Di): Di = 0·638βEI30tan(θ) (R2 = 0·893***), where β is a topsoil aggregate instability index, E the rain kinetic energy, I30 the maximum 30‐minute rain intensity and θ the slope angle. It describes soil erodibility using a topsoil aggregate instability index, which can be determined easily by a simple laboratory technique, and runoff through the product of this index and rain kinetic energy. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

16.
ABSTRACT

The accurate representation of the Earth’s surface plays a vital role in soil erosion modelling. Topography is parameterized in the Universal Soil Loss Equation (USLE) and Revised USLE (RUSLE) by the topographic (LS) factor. For slope gradients of < 20%, soil loss values are similar for both models, but when the gradient is increased, RUSLE estimates are only half of those of USLE. The study aims to assess the validity of this statement for complex hillslope profiles. To that end, both models were applied at eight diverse mountainous sub-watersheds. The USLE and RUSLE indices were estimated utilizing the SEAGIS model and a European dataset, respectively. LS factors were in a 3:1 ratio (i.e. USLE:RUSLE) considering the entire basin area. For areas with slopes <20%, gross erosion estimates of both models converged. Sites of strong relief (>20%) USLE yielded significantly higher values than RUSLE.  相似文献   

17.
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.  相似文献   

18.
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   

19.
Simulations using a mechanistic model of raindrop driven erosion in rain‐impacted flow were performed with particles travelling by suspension, raindrop induced saltation and flow driven saltation. Results generated by both a high intensity storm, and a less intense one, indicate that, because of the effect of flow depth on the delivery of raindrop energy to the bed, there is a decline in sediment concentration, and hence soil loss per unit area, with slope length when particles are transported by raindrop induced saltation. However, that decline is reversed when the critical velocities that lead to flow driven saltation are episodically exceeded during an event. The simulations were performed on smooth surfaces and a single drop size but the general relationships are likely to apply for rain made up of a wide range of drop size. Although runoff is not always produced uniformly, as a general rule, flow velocities increase with slope length so that, typically, the distance particles travel before being discharged during an event increase with slope length. The effect of slope length on soil loss per unit area is often considered to vary with slope length to a power greater than zero and less that 1·0. The simulations show that effect of slope length on sediment discharge is highly dependent on the variations in runoff response resulting from variations in rainfall duration‐intensity‐infiltration conditions rather than plot length per se. Consequently, predicting soil loss per unit area using slope length with positive powers close to zero when sheet erosion occurs may not be as effective as commonly expected. Erosion by rain‐impacted flow is a complex process and that complexity needs to be considered when analysing the results of experiments associated with rain‐impacted flow under both natural and artificial conditions. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

20.
The objective of this research was to characterise annual precipitation extremes in a Mediterranean vineyard region. The number of exceptional events (P > 95th percentile) and annual extreme events (P > 99th percentile), as well as their strength, erosive character and return period were analysed for 2000–2004. The erosive character was evaluated according to the R‐factor (kinetic energy × maximum intensity in 30‐min periods). Soil and nutrient losses caused by these events were evaluated by combining field sampling and a hydrological model to estimate total runoff in a vineyard plot. The results show a clear increase in the number of very wet days and extreme events (P > 95th percentile), which represented up to 88% of annual rainfall. The severity of the extreme events (TS = precipitation event P > 99th percentile) reached values higher than 50 mm almost every year. These values were far exceeded in 2000, when one extraordinary event recorded 50% of the annual rainfall, with TS of 189 mm, about 80% of total rainfall being lost as runoff. Annual erosivity was driven not only by extreme events, but also by short events of less depth but high intensity. During some of the years analysed, rainfall erosivity was two or three times the average in the area. Most soil and nutrient losses occurred in a small number of events: one or two events every year were responsible for more than 75% of the annual soil and nutrient losses on average. Antecedent soil moisture conditions, runoff rates, and events with a return period higher than two years were responsible for the higher erosion rates. Apart from an exceptional event recorded in 2000, which produced more than 200 Mg ha?1 soil losses, annual soil losses up to 25 Mg ha?1 were recorded, which are much higher than the soil loss tolerance. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

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