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1.
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   

2.
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   

3.
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   

4.
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   

5.
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   

6.
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   

7.
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   

8.
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   

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》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   

11.
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   

12.
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   

13.
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   

14.
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   

15.
The drastic growth of population in highly industrialized urban areas, as well as fossil fuel use, is increasing levels of airborne pollutants and enhancing acid rain. In rapidly developing countries such as Iran, the occurrence of acid rain has also increased. Acid rain is a driving factor of erosion due to the destructive effects on biota and aggregate stability; however, little is known about its impact on specific rates of erosion at the pedon scale. Thus, the present study aimed to investigate the effect of acid rain at pH levels of 5.25, 4.25, and 3.75 for rainfall intensities of 40, 60, and 80 mm h?1 on initial soil erosion processes under dry and saturated soil conditions using rainfall simulations. The results were compared using a two‐way ANOVA and Duncan tests and showed that initial soil erosion rates with acidic rain and non‐acidic rain under dry soil conditions were significantly different. The highest levels of soil particle loss due to splash effects in all rainfall intensities were observed with the most acidic rain (pH = 3.75), reaching maximum values of 16 g m?2 min?1. The lowest levels of particle losses were observed in the control plot where non‐acidic rain was used, with values ranging from 3.8 to 8.1 g m?2 min?1. Similarly, under saturated soil conditions, the lowest level of soil particle loss was observed in the control plot, and the highest peaks of soil loss were observed for the most acidic rains (pH = 3.75 and pH = 4.25), reaching maximum average values of 40 g m?2 min?1. However, for saturated soils with acidic water but with non‐acidic rain, the highest soil particle loss was observed for the control plot for all the rainfall intensities. In conclusion, acidic rain has a negative impact on soils, which can be more intense with a concomitant increase in rainfall intensity. Rapid solutions, therefore, need to be found to reduce the emission of pollutants into the air, otherwise, rainfall erosivity may drastically increase.  相似文献   

16.
Wildfire effects on soil‐physical and ‐hydraulic properties as a function of burn severity are poorly characterized, especially several years after wildfire. A stratified random sampling approach was used in 2015 to sample seven sites representing a spectrum of remotely sensed burn severity in the area impacted by the 2011 Las Conchas Fire in New Mexico, USA. Replicate samples from each site were analysed in the laboratory. Linear and linear indicator regression were used to assess thresholds in soil‐physical and ‐hydraulic properties and functional relations with remotely sensed burn severity. Significant thresholds were present for initial soil‐water content (θi) at 0–6 cm depth between the change in the Normalized Burn Ratio (dNBR) equal to 618–802, for bulk density (ρb) at 3–6 cm between dNBR equal to 416–533, for gravel fraction at 0–1 cm between dNBR equal to 416–533, for fines (the silt + clay fraction) at 0–1 cm for dNBR equal to 416–533, and for fines at 3–6 cm for dNBR equal to 293–416. Significant linear relations with dNBR were present between ρb at 0–1 cm, loss on ignition (LOI) at 0–1 cm, gravel fraction at 0–1 cm, and the large organic fraction at 1–3 cm. No thresholds or effects on soil‐hydraulic properties of field‐saturated hydraulic conductivity or sorptivity were observed. These results suggest that ρb and LOI at 0–1 cm have residual direct impacts from the wildfire heat impulse. The θi threshold is most likely from delayed groundcover/vegetation recovery that increases evaporation at the highest burn severity sites. Gravel and silt + clay thresholds at 0–1 cm at the transition to high burn severity suggest surface gravel lag development from hydraulic erosion. Thresholds in ρb from 3 to 6 cm and in silt + clay fraction from 3 to 6 cm appear to be the result of soil variability between sites rather than wildfire impacts. This work suggests that gravel‐rich soils may have increased resilience to sustained surface runoff generation and erosion following wildfire, with implications for assessments of postwildfire hydrologic and erosion recovery potential.  相似文献   

17.
Soil erosion hinders the recovery and development of ecosystems in semiarid regions. Rainstorms, coupled with the absence of vegetation and improper land management, are important causes of soil erosion in such areas. Greater effort should be made to quantify the initial erosion processes and try to find better solutions for soil and water conservation. In this research, 54 rainfall simulations were performed to assess the impacts of vegetation patterns on soil erosion in a semiarid area of the Loess Plateau, China. Three rainfall intensities (15 mm h‐1, 30 mm h‐1 and 60 mm h‐1) and six vegetation patterns (arbors‐shrubs‐grass ‐A‐S‐G‐, arbors‐grass‐shrubs ‐A‐G‐S‐, shrubs‐arbors‐grass ‐S‐A‐G‐, shrubs‐grass‐arbors ‐S‐G‐A‐, grass‐shrubs‐arbors ‐G‐S‐A‐ and grass‐arbors‐shrubs ‐G‐A‐S‐) were examined at different slope positions (summits, backslopes and footslopes) in the plots (33.3%, 33.3%, 33.3%), respectively. Results showed that the response of soil erosion to rainfall intensity differed under different vegetation patterns. On average, increasing rainfall intensity by 2 to 4 times induced increases of 3.1 to 12.5 times in total runoff and 6.9 to 46.4 times in total sediment yield, respectively. Moreover, if total biomass was held constant across the slope, the patterns of A‐G‐S and A‐S‐G (planting arbor at the summit position) had the highest runoff (18.34 L m‐2 h‐1) and soil losses (197.98 g m‐2 h‐1), while S‐A‐G had the lowest runoff (5.51 L m‐2 h‐1) and soil loss (21.77 g m‐2 h‐1). As indicated by redundancy analysis (RDA) and Pearson correlation results, a greater volume of vegetation located on the back‐ and footslopes acted as effective buffers to prevent runoff generation and sediment yield. Our findings indicated that adjusting vegetation position along slopes can be a crucial tool to control water erosion and benefit ecosystem restoration on the Loess Plateau and other similar regions of the world. Copyright © 2018 John Wiley & Sons, Ltd.  相似文献   

18.
The Soil Conservation Service curve number (CN) method commonly uses three discrete levels of soil antecedent moisture condition (AMC), defined by the 5‐day antecedent rainfall depth, to describe soil moisture prior to a runoff event. However, this way may not adequately represent soil water conditions of fields and watersheds in the Loess Plateau of China. The objectives of this study were: (1) to determine the effective soil moisture depth to which the CN is most related; (2) to evaluate a discrete and a linear relationship between AMC and soil moisture; and (3) to develop an equation between CN and soil moisture to predict runoff better for the climatic and soil conditions of the Loess Plateau of China. The dataset consisted of 10 years of rainfall, runoff and soil moisture measurements from four experimental plots cropped with millet, pasture and potatoes. Results indicate that the standard CN method underestimated runoff depths for 85 of the 98 observed plot‐runoff events, with a model efficiency E of only 0·243. For our experimental conditions, the discrete and linear approaches improved runoff estimation, but still underestimated most runoff events, with E values of 0·428 and 0·445 respectively. Based on the measured CN values and soil moisture values in the top 15 cm of the soil, a non‐linear equation was developed that predicted runoff better with an E value of 0·779. This modified CN equation was the most appropriate for runoff prediction in the study area, but may need adjustments for local conditions in the Loess Plateau of China. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

19.
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   

20.
Soil and nutrient loss play a vital role in eutrophication of water bodies. Several simulated rainfall experiments have been conducted to investigate the effects of a single controlling factor on soil and nutrient loss. However, the role of precipitation and vegetation coverage in quantifying soil and nutrient loss is still unclear. We monitored runoff, soil loss, and soil nutrient loss under natural rainfall conditions from 2004 to 2015 for 50–100 m2 runoff plots around Beijing. Results showed that soil erosion was significantly reduced when vegetation coverage reached 20% and 60%. At levels below 30%, nutrient loss did not differ among different vegetation cover levels. Minimum soil N and P losses were observed at cover levels above 60%. Irrespective of the management measure, soil nutrient losses were higher at high-intensity rainfall (Imax30>15 mm/h) events compared to low-intensity events (p < 0.05). We applied structural equation modelling (SEM) to systematically analyze the relative effects of rainfall characteristics and environmental factors on runoff, soil loss, and soil nutrient loss. At high-intensity rainfall events, neither vegetation cover nor antecedent soil moisture content (ASMC) affected runoff and soil loss. After log-transformation, soil nutrient loss was significantly linearly correlated with runoff and soil loss (p < 0.01). In addition, we identified the direct and indirect relationships among the influencing factors of soil nutrient loss on runoff plots and constructed a structural diagram of these relationships. The factors positively impacting soil nutrient loss were runoff (44%–48%), maximum rainfall intensity over a 30-min period (18%–29%), rainfall depth (20%–27%), and soil loss (10%–14%). Studying the effects of rainfall and vegetation coverage factors on runoff, soil loss, and nutrient loss can improve our understanding of the underlying mechanism of slope non-point source pollution.  相似文献   

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