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Kevin P Norton - One of the best experts on this subject based on the ideXlab platform.
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a review of the revised universal Soil Loss equation r usle with a view to increasing its global applicability and improving Soil Loss estimates
Hydrology and Earth System Sciences, 2018Co-Authors: Rubianca Benavidez, Bethanna Jackson, Deborah Maxwell, Kevin P NortonAbstract:Abstract. Soil erosion is a major problem around the world because of its effects on Soil productivity, nutrient Loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind Soil erosion, we can more easily identify erosion-prone areas within a landscape to address the problem strategically. Soil erosion models have been used to assist in this task. One of the most commonly used Soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviews the different sub-factors of USLE and RUSLE, and analyses how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with (R)USLE and related approaches. Within each sub-factor section, the strengths and limitations of the different equations are discussed, and guidance is given as to which equations may be most appropriate for particular climate types, spatial resolution, and temporal scale. We investigate some of the limitations of existing (R)USLE formulations, such as uncertainty issues given the simple empirical nature of the model and many of its sub-components; uncertainty issues around data availability; and its inability to account for Soil Loss from gully erosion, mass wasting events, or predicting potential sediment yields to streams. Recommendations on how to overcome some of the uncertainties associated with the model are given. Several key future directions to refine it are outlined: e.g. incorporating Soil Loss from other types of Soil erosion, estimating Soil Loss at sub-annual temporal scales, and compiling consistent units for the future literature to reduce confusion and errors caused by mismatching units. The potential of combining (R)USLE with the Compound Topographic Index (CTI) and sediment delivery ratio (SDR) to account for gully erosion and sediment yield to streams respectively is discussed. Overall, the aim of this paper is to review the (R)USLE and its sub-factors, and to elucidate the caveats, limitations, and recommendations for future applications of these Soil erosion models. We hope these recommendations will help researchers more robustly apply (R)USLE in a range of geoclimatic regions with varying data availability, and modelling different land cover scenarios at finer spatial and temporal scales (e.g. at the field scale with different cropping options).
Keli Zhang - One of the best experts on this subject based on the ideXlab platform.
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The assessment of Soil Loss by water erosion in China
International Soil and Water Conservation Research, 2020Co-Authors: Baoyuan Liu, Keli Zhang, Yun Xie, Yin Liang, Zhang Wenbo, Shuiqing Yin, Xin Wei, Wang ZhiqiangAbstract:Abstract Soil erosion is a major environmental problem in China. Planning for Soil erosion control requires accurate Soil erosion rate and spatial distribution information. The aim of this article is to present the methods and results of the national Soil erosion survey of China completed in 2011. A multi-stage, unequal probability, systematic area sampling method was employed. A total of 32,948 sample units, which were either 0.2–3 km2 small catchments or 1 km2 grids, were investigated on site. Soil erosion rates were calculated with the Chinese Soil Loss Equation in 10 m by 10 m grids for each sample unit, along with the area of Soil Loss exceeding the Soil Loss tolerance and the proportion of area in excess of Soil Loss tolerance relative to the total land area of the sample units. Maps were created by using a spatial interpolation method at national, river basin, and provincial scales. Results showed that the calculated average Soil erosion rate was 5 t ha−1 yr−1 in China, and was 18.2 t ha−1 yr−1 for sloped, cultivated cropland. Intensive Soil erosion occurred on cropland, overgrazing grassland, and sparsely forested land. The proportions of Soil Loss tolerance exceedance areas of sample units were interpolated through the country in 250 m grids. The national average ratio was 13.5%, which represents the area of land in China that requires the implementation of Soil conservation practices. These survey results and the maps provide the basic information for national conservation planning and policymaking.
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Effects of topographic factors on runoff and Soil Loss in Southwest China
CATENA, 2018Co-Authors: Xingqi Zhang, Xinya Guo, Hong Yang, Zhenke Zhang, Keli ZhangAbstract:Abstract Soil erosion is a threat to sustainable agricultural and regional development in karst regions. In this study, field plot observation method was used to estimate the effects of slope gradient and length on runoff and Soil Loss in Guizhou, Southwest China. The results showed that runoff and Soil Loss is nonlinearly related to slope gradient. The increasing trends of runoff and Soil Loss declined after the slope gradient of 15°. This turning point was affected by both slope gradient and rock outcrops on the 20°–25° slopes, hence it is still unknown whether the slope gradient of 15° is a critical value. Runoff showed a trend of decrease-increase-decrease as slope length increased, and Soil Loss rate showed an increasing trend as slope length increased. There is a significantly positive linear relationship between Soil Loss and slope length (P
Vito Ferro - One of the best experts on this subject based on the ideXlab platform.
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Supporting USLE-MM reliability by analyzing Soil Loss measurement errors
Hydrological Processes, 2016Co-Authors: Vincenzo Bagarello, Vito FerroAbstract: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 since 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 since these errors can have a noticeable effect on the calibrated empirical models for Soil Loss prediction.
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Establishing Soil Loss tolerance: an overview
Journal of Agricultural Engineering, 2016Co-Authors: Costanza Di Stefano, Vito FerroAbstract:Soil Loss tolerance is a criterion for establishing if a Soil is potentially subjected to erosion risk, productivity Loss and if a river presents downstream over-sedimentation or other off-site effects are present at basin scale. At first this paper reviews the concept of tolerable Soil Loss and summarises the available definitions and the knowledge on the recommended values and evaluating criteria. Then a threshold Soil Loss value, at the annual temporal scale, established for limiting riling was used for defining the classical Soil Loss tolerance. Finally, some research needs on tolerable Soil Loss are listed.
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Establishing a Soil Loss Threshold for Limiting Rilling
Journal of Hydrologic Engineering, 2015Co-Authors: Vincenzo Bagarello, Costanza Di Stefano, Vito Ferro, Vincenzo PampaloneAbstract:AbstractIn this paper a frequency analysis of event Soil Loss measurements collected in the period 1999–2012 at the microplots and plots of the Sparacia Experimental Area in Sicily, southern Italy, was developed. The analysis was carried out using the annual maximum Soil Loss measurements normalized by the mean Soil Loss measured at a given temporal and spatial scale. The empirical frequency distribution of the normalized variable was well fitted by two Gumbel’s theoretical probability distributions discriminated by a value of the normalized variable equal to 2. This last value discriminates between the relatively low and frequent values of the normalized variable and the high and rare ones. The annual maximum Soil Loss was demonstrated to be representative of the total annual Soil erosion at the Sparacia Experimental Area. Then, a threshold Soil Loss value at the annual temporal scale was calculated by multiplying the frequency factor, equal to 2, by the mean annual maximum Soil Loss values for each give...
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Using plot Soil Loss distribution for Soil conservation design
CATENA, 2011Co-Authors: Vincenzo Bagarello, Costanza Di Stefano, Vito Ferro, Vincenzo PampaloneAbstract:Abstract Soil conservation design is generally based on the estimation of average annual Soil Loss but it should be developed taking into account storms of a given return period. However, use of frequency analysis in Soil erosion studies is relatively limited. In this paper, an investigation on statistical distribution of Soil Loss measurements was firstly carried out using a relatively high number of simultaneously operating plots of different lengths, λ (11, 22, 33 and 44 m) at the experimental station of Sparacia (southern Italy). Using a simple normalization technique, the analysis showed that the probability distribution of the normalized Soil Loss is independent of both the scale length λ and the temporal scale, which are completely represented by the mean Soil Loss calculated for a given event using all replicated data collected in plots having the same length. Then, a comparison between the frequency distribution of Soil Loss and rainfall erosivity index of the USLE was carried out. An estimating criterion of the annual Soil Loss of a given return period was also developed. By this criterion, the frequency distribution of the rainfall erosivity factor can be used to design Soil conservation practices.
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Predicting unit plot Soil Loss in Sicily, south Italy
Hydrological Processes, 2008Co-Authors: Vincenzo Bagarello, Vito Ferro, G. V. Di Piazza, Giuseppe GiordanoAbstract: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.
Rubianca Benavidez - One of the best experts on this subject based on the ideXlab platform.
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a review of the revised universal Soil Loss equation r usle with a view to increasing its global applicability and improving Soil Loss estimates
Hydrology and Earth System Sciences, 2018Co-Authors: Rubianca Benavidez, Bethanna Jackson, Deborah Maxwell, Kevin P NortonAbstract:Abstract. Soil erosion is a major problem around the world because of its effects on Soil productivity, nutrient Loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind Soil erosion, we can more easily identify erosion-prone areas within a landscape to address the problem strategically. Soil erosion models have been used to assist in this task. One of the most commonly used Soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviews the different sub-factors of USLE and RUSLE, and analyses how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with (R)USLE and related approaches. Within each sub-factor section, the strengths and limitations of the different equations are discussed, and guidance is given as to which equations may be most appropriate for particular climate types, spatial resolution, and temporal scale. We investigate some of the limitations of existing (R)USLE formulations, such as uncertainty issues given the simple empirical nature of the model and many of its sub-components; uncertainty issues around data availability; and its inability to account for Soil Loss from gully erosion, mass wasting events, or predicting potential sediment yields to streams. Recommendations on how to overcome some of the uncertainties associated with the model are given. Several key future directions to refine it are outlined: e.g. incorporating Soil Loss from other types of Soil erosion, estimating Soil Loss at sub-annual temporal scales, and compiling consistent units for the future literature to reduce confusion and errors caused by mismatching units. The potential of combining (R)USLE with the Compound Topographic Index (CTI) and sediment delivery ratio (SDR) to account for gully erosion and sediment yield to streams respectively is discussed. Overall, the aim of this paper is to review the (R)USLE and its sub-factors, and to elucidate the caveats, limitations, and recommendations for future applications of these Soil erosion models. We hope these recommendations will help researchers more robustly apply (R)USLE in a range of geoclimatic regions with varying data availability, and modelling different land cover scenarios at finer spatial and temporal scales (e.g. at the field scale with different cropping options).
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A review of the (Revised) Universal Soil Loss Equation (R/USLE): with a view to increasing its global applicability and improving Soil Loss estimates
2018Co-Authors: Rubianca Benavidez, Bethanna Jackson, Deborah Maxwell, Kevin NortonAbstract:Abstract. Soil erosion is a major problem around the world because of its effects on Soil productivity, nutrient Loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind Soil erosion, we can more easily identify erosion-prone areas within a landscape and use land management and other strategies to effectively manage the problem. Soil erosion models have been used to assist in this task. One of the most commonly used Soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviewed the different components of USLE and RUSLE etc., and analysed how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with R/USLE and related approaches. We investigate some of the limitations of R/USLE, such as issues in data-sparse regions, its inability to account for Soil Loss from gully erosion or mass wasting events, and that it does not predict sediment pathways from hillslopes to water bodies. These limitations point to several future directions for R/USLE studies: incorporating Soil Loss from other types of Soil erosion, estimating Soil Loss at sub-annual temporal scales, and using consistent units for future literature. These recommendations help to improve the applicability of the R/USLE in a range of geoclimatic regions with varying data availability, and at finer spatial and temporal scales for scenario analysis.
Vincenzo Bagarello - One of the best experts on this subject based on the ideXlab platform.
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Supporting USLE-MM reliability by analyzing Soil Loss measurement errors
Hydrological Processes, 2016Co-Authors: Vincenzo Bagarello, Vito FerroAbstract: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 since 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 since these errors can have a noticeable effect on the calibrated empirical models for Soil Loss prediction.
-
Establishing a Soil Loss Threshold for Limiting Rilling
Journal of Hydrologic Engineering, 2015Co-Authors: Vincenzo Bagarello, Costanza Di Stefano, Vito Ferro, Vincenzo PampaloneAbstract:AbstractIn this paper a frequency analysis of event Soil Loss measurements collected in the period 1999–2012 at the microplots and plots of the Sparacia Experimental Area in Sicily, southern Italy, was developed. The analysis was carried out using the annual maximum Soil Loss measurements normalized by the mean Soil Loss measured at a given temporal and spatial scale. The empirical frequency distribution of the normalized variable was well fitted by two Gumbel’s theoretical probability distributions discriminated by a value of the normalized variable equal to 2. This last value discriminates between the relatively low and frequent values of the normalized variable and the high and rare ones. The annual maximum Soil Loss was demonstrated to be representative of the total annual Soil erosion at the Sparacia Experimental Area. Then, a threshold Soil Loss value at the annual temporal scale was calculated by multiplying the frequency factor, equal to 2, by the mean annual maximum Soil Loss values for each give...
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Using plot Soil Loss distribution for Soil conservation design
CATENA, 2011Co-Authors: Vincenzo Bagarello, Costanza Di Stefano, Vito Ferro, Vincenzo PampaloneAbstract:Abstract Soil conservation design is generally based on the estimation of average annual Soil Loss but it should be developed taking into account storms of a given return period. However, use of frequency analysis in Soil erosion studies is relatively limited. In this paper, an investigation on statistical distribution of Soil Loss measurements was firstly carried out using a relatively high number of simultaneously operating plots of different lengths, λ (11, 22, 33 and 44 m) at the experimental station of Sparacia (southern Italy). Using a simple normalization technique, the analysis showed that the probability distribution of the normalized Soil Loss is independent of both the scale length λ and the temporal scale, which are completely represented by the mean Soil Loss calculated for a given event using all replicated data collected in plots having the same length. Then, a comparison between the frequency distribution of Soil Loss and rainfall erosivity index of the USLE was carried out. An estimating criterion of the annual Soil Loss of a given return period was also developed. By this criterion, the frequency distribution of the rainfall erosivity factor can be used to design Soil conservation practices.
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Predicting unit plot Soil Loss in Sicily, south Italy
Hydrological Processes, 2008Co-Authors: Vincenzo Bagarello, Vito Ferro, G. V. Di Piazza, Giuseppe GiordanoAbstract: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.
