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1.
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 (QR) 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 QREI30. 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 QR. Here, equations are provided to convert the USLE/RUSLE factors to values suitable for use when the erosivity factor is based on the QREI30 index under these circumstances. At some geographic locations, non linear relationships exist between soil loss from bare fallow areas and the QREI30 index. The effect of this on the slope length factor associated with the QREI30 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   

2.
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   

3.
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   

4.
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   

5.
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   

6.
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   

7.
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   

8.
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   

9.
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   

10.
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   

11.
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   

12.
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   

13.
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   

14.
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   

15.
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.  相似文献   

16.
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   

17.
Studies of soil erosion on small plots present upscaling problems. The results in the literature on the effect of slope length (i.e. scale) on runoff and soil erosion are contradictory. Furthermore, most studies that examine scale effects measured through erosion plots have been conducted in Mediterranean environments. The objective of this study was to assess the effects of plot size on runoff and soil loss in a subtropical environment. Other measurements were taken to appraise the topsoil property changes inside the plots. The soil was ploughed twice, the surface was leveled with a hoe and it was kept bare during the experiment. Data were collected from 10 paired plots, five plots measuring 10 m × 1 m and five plots measuring 1 m × 1 m, installed in the same pedo‐geomorphologic unit. Measurements were carried out from November 2008 to November 2009. During this period, 97 natural storms were registered. The results indicate that the small plots tended to have higher runoff (30% higher) compared to larger plots, especially during periods of greater rainfall volume, duration and intensity. The soil loss was similar in both the 1 m2 plots (6·33 kg/m2) and the 10 m2 plots (6·26 kg/m2). Moreover, the dynamics of the soil loss during the experiment was relatively similar across both plot sizes. The large plots tended to have a greater internal complexity. In these plots, the steps retreat were higher, the overland flow scars were more frequent, and points of rill initiation and protochannels emerged in several parts of the plots. The results of the small plots were comparable to the results obtained on the large plots, especially in relation to soil loss. These plots were useful for short‐term assessments of soil erosion. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

18.
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   

19.
The point measurement of soil properties allows to explain and simulate plot scale hydrological processes. An intensive sampling was carried out at the surface of an unsaturated clay soil to measure, on two adjacent plots of 4 × 11 m2 and two different dates (May 2007 and February–March 2008), dry soil bulk density, ρb, and antecedent soil water content, θi, at 88 points. Field‐saturated soil hydraulic conductivity, Kfs, was also measured at 176 points by the transient Simplified Falling Head technique to determine the soil water permeability characteristics at the beginning of a possible rainfall event yielding measurable runoff. The ρb values did not differ significantly between the two dates, but wetter soil conditions (by 31%) and lower conductivities (1.95 times) were detected on the second date as compared with the first one. Significantly higher (by a factor of 1.8) Kfs values were obtained with the 0.30‐m‐diameter ring compared with the 0.15‐m‐diameter ring. A high Kfs (> 100 mm h?1) was generally obtained for low θi values (< 0.3 m3m?3), whereas a high θi yielded an increased percentage of low Kfs data (1–100 mm h?1). The median of Kfs for each plot/sampling date combination was not lower than 600 mm h?1, and rainfall intensities rarely exceeded 100 mm h?1 at the site. The occurrence of runoff at the base of the plot needs a substantial reduction of the surface soil permeability characteristics during the event, probably promoted by a higher water content than the one of this investigation (saturation degree = 0.44–0.62) and some soil compaction due to rainfall impact. An intensive soil sampling reduces the risk of an erroneous interpretation of hydrological processes. In an unstable clay soil, changes in Kfs during the event seem to have a noticeable effect on runoff generation, and they should be considered for modeling hydrological processes. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

20.
Runoff and erosion processes can increase after wildfire and post-fire salvage logging, but little is known about the specific effects of soil compaction and surface cover after post-fire salvage logging activities on these processes. We carried out rainfall simulations after a high-severity wildfire and post-fire salvage logging to assess the effect of compaction (uncompacted or compacted by skid traffic during post-fire salvage logging) and surface cover (bare or covered with logging slash). Runoff after 71 mm of rainfall across two 30-min simulations was similar for the bare plots regardless of the compaction status (mean 33 mm). In comparison, runoff in the slash-covered plots averaged only 22 mm. Rainsplash in the downslope direction averaged 30 g for the bare plots across compaction levels and decreased significantly by 70% on the slash-covered plots. Sediment yield totalled 460 and 818 g m−2 for the uncompacted and compacted bare plots, respectively, and slash significantly reduced these amounts by an average rate of 71%. Our results showed that soil erosion was still high two years after the high severity burning and the effect of soil compaction nearly doubled soil erosion via nonsignificant increases in runoff and sediment concentration. Antecedent soil moisture (dry or wet) was the dominant factor controlling runoff, while surface cover was the dominant factor for rainsplash and sediment yield. Saturated hydraulic conductivity and interrill erodibility calculated from these rainfall simulations confirmed previous laboratory research and will support hydrologic and erosion modelling efforts related to wildfire and post-fire salvage logging. Covering the soil with slash mitigated runoff and significantly reduced soil erosion, demonstrating the potential of this practise to reduce sediment yield and soil degradation from burned and logged areas.  相似文献   

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