Modified Universal Soil Loss Equation Research Articles

Comprehensive Assessment of Soil Erosion using USLE and Sustainable Management Strategies

Continuous soil erosion from all parts of world through water and winds have become a severe concerns for soil scientists and geologists due to its adverse effect on soil productivity, water bodies siltation and deterioration of water quality. Therefore, it becomes most important to locate erosion-prone regions of any landscape and take the best measures to solve the issues of erosion. USLE and its modified models i.e. RUSLE (Revised Universal Soil Loss Equation) versions and MUSLE (Modified Universal Soil Loss Equation) are most commonly used for estimation of soil erosion. In India, soil erosion is a major serious concern which varies in its intensity depending on the country's different agro-climatic zones. USLE as well known model for evaluating soil deterioration is used in this review to provide an understanding of its parameters and how this approach is useful in soil erosion assessment. In addition, an overview of the current situation of erosion status in India which is being altered by regional variations like climate and land uses. Literature also dictates and discusses the potential advantages of better erosion control approaches, such as increased agricultural output and long-term land sustainability and looks at what might be possible in the future if the things get managed. The study also looks at crop selection and land use systems, emphasizing how crucial it is to choose crops that are suitable for the land vulnerability to erosion in order to prevent the further deterioration. Lastly, it emphasize on a thorough explanation of the USLE's parameters which are essential for precise erosion prediction including slope factors, soil erodibility, rainfall erosivity and conservation techniques. The study summarizes the knowledge on soil erosion in India and the necessity of customized, area-specific soil conservation plans to guarantee long-term agricultural outputs and sustainable agricultural goals.

A modified Soil and Water Assessment Tool Plus (M-SWAT+) model for sediment yield estimation

ABSTRACT The Soil and Water Assessment Tool Plus (SWAT+) model depends on the Modified Universal Soil Loss Equation (MUSLE), which in turn depends on the peak runoff rate for the estimation of soil loss from a catchment. The SWAT+ model generates a random number for each day in the process of peak runoff rate estimation from the catchment. On the other hand, the model provides calibration parameters like the peak rate adjustment factor for sediment routing in the tributary channels and main channels. There is uncertainty that how likely the SWAT+ model estimates the actual peak runoff rates. An improved MUSLE does not depend on the peak runoff rate, and, therefore, it minimizes uncertainty related to the peak runoff rate when estimating soil loss from the catchment in the SWAT+ environment. On the other hand, the effect of the topographic factor of the MUSLE or improved MUSLE on soil erosion and sediment transport is not explicitly explained. Therefore, we modify the source code of the SWAT+ model for sediment yield estimation by replacing the improved MUSLE and the best-fitted equations of the topographic factor in the source code. The modified SWAT+ model showed better performance than the original SWAT+ model.

A Probabilistic Statistical Risk Assessment Method for Soil Erosion Using Remote Sensing Data: A Case Study of the Dali River Basin

Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method.

Combined use of physically based hydrological model and empirical models to improve parameterisation of erosion processes in a flash flood prone catchment

AbstractThis study assesses the effectiveness of a distributed physically based hydrological model (MARINE) to investigate erosion estimation during flash floods compared with other widely used empirical models derived from the Universal Soil Loss Equation (USLE) like Revised Universal Soil Loss Equation (RUSLE) and Modified Universal Soil Loss Equation (MUSLE). It is carried out on a small catchment in south‐eastern France, the Claduègne catchment. To compare the erosion volumes simulated by the three models, MARINE, MUSLE and RUSLE, a sensitivity analysis on the model parameters is carried out. According to physics‐based simulations, flood events fall into two categories: those dominated by raindrop erosion and those dominated by shear stress erosion. The results show that the erosion simulated by the three methods are comparable, except for events dominated by raindrop erosion suggesting that further research is needed to improve raindrop erosion within MARINE. Simulations from the MARINE model provide access to the spatio‐temporal variability of erosion dynamics during the event and can also be used to produce erosion/deposition maps, which are useful for environmental decision‐makers and planners in identifying areas at risk from erosion and deposition hazards.

Application of the Modified Universal Soil Loss Equation (MUSLE) for the prediction of sediment yield in Agewmariam experimental watershed, Tekeze River basin, Northern Ethiopia

The study utilized the Modified Universal Soil Loss Equation (MUSLE) to predict sediment loss and evaluate the model's performance in the Agewmariam experimental watershed in order to support the planning, management, and appropriate use of the soil and water resources in the watershed. The natural resources conservation service (NRCS) curve number method was used to model runoff energy factor. By overlaying maps of runoff energy, soil erodibility, slope length and steepness, cover management, and support practice factors with assigned values, the cumulative effect of these parameters for the suspended sediment yield was calculated using the ArcGIS raster calculator. The runoff energy factor was the most sensitive parameter, followed by slope length and steepness factor. To improve the model's fit to the local conditions, the initial abstraction to storage ratio (λ) of the runoff energy factor was reduced to 0.023, and the MUSLE model coefficient and exponent were adjusted to 1 and 0.59, respectively. During calibration, the mean observed and estimated suspended sediment yields were 0.2 and 0.23 ton/ha, respectively, while during validation, they were 0.7 and 0.53 ton/ha, respectively. The model evaluation showed that the MUSLE model, without calibration, was not appropriate for estimating runoff and sediment yield. However, with appropriate calibration, the model showed good performance with a coefficient of determination (R2), coefficient of efficiency (E), and index of agreement (d) of 0.85, 0.85, and 0.96 respectively, during calibration and 0.84, 0.65, and 0.83 respectively, during validation. Based on these findings, this study suggests that the calibrated MUSLE model can be used to prioritize soil and water conservation interventions within the watershed or can be extrapolated to neighboring similar watersheds. Further refinement of model input parameters using more data from the watershed is recommended to increase the prediction accuracy of the model.

Evaluating the Sediment Yield and Environmental Impact in Mining Expansion Scenarios in Sta. Cruz – Candelaria, Zambales

The study addressed the environmental concerns arising from industrial activities, such as mining, by focusing on the Candelaria-Santa Cruz region. Nickel mining in the region has raised issues related to water quality and sedimentation patterns. Employing the Modified Universal Soil Loss Equation (MUSLE) model, sediment yield computations were conducted, with consideration on factors like bare soil coverage, slope characteristics, and precipitation rates. The results emphasize the need for sustainable land management practices to mitigate adverse effects. The research covers a 10,000-ha area, simulating overburden removal scenarios ranging from 100 ha to 5,847.78 ha, and assesses sediment yield implications under different precipitation rates. The study also determines optimal relative growth rates and plant cover densities for progressive rehabilitation, highlighting the potential for effective erosion control. The findings underscore the significant impact of mining on sediment yield and water quality, prompting the call for comprehensive monitoring, calibration, and validation of the MUSLE model to guide informed decision-making and promote sustainable practices in the region. This research highlights the crucial role of integrated approaches in understanding and mitigating the consequences of resource exploitation on the environment and local communities.

Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability.

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash-Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation-based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.

Modification of rain and run-off erosivity to predict erosion hazard level on The Manguan Sub-watershed’s Upper Slope

To protect natural resources and maintain the normal functioning of hydrological processes, the upper slope of the Manguan Sub-watershed should be designated as a conservation area. However, agricultural activities in the upstream have disrupted the watershed’s hydrological function, triggering high rates of erosion, sedimentation, and flooding. The aim of this research was to estimate the level of erosion hazard on Manguan Sub-watershed’s Upper Slope and its conservation directives to mitigate it. This study adopted a land unit approach to flow occurrence land units with the prediction of MUSLE (Modified Universal Soil Loss Equation) by integrating rainfall and run-off erosivity. The direction of land conservation will be evaluated through the perspectives of land rehabilitation and soil conservation. Based on the result, the prediction of erosion hazard levels is classified into four groups: Low, Moderate, High, and Extremely High. The Manguan Sub-Watershed’s Upper Slope has a significantly high level of erosion and necessitates soil conservation improvement measures on approximately 79% of its area.

Event-based soil erosion and sediment yield modelling for calculating long-term reservoir sedimentation in the Alps

ABSTRACT Among the most characteristic offsite effects of soil erosion, reservoir sedimentation is one of the longlasting problems pertaining to storage capacity and dam operations. In this work, sedimentation rates of the Fortezza reservoir in South Tyrol (Italian Alps) are analysed for nine erosive events over 14 months. Sediment rates are calculated through a modified Universal Soil Loss Equation (USLE) combined with a Sediment Delivery Ratio (SDR) module at 2.5 m resolution. The topographic factor is adjusted to the Alpine terrain, and an additional factor is included to consider the nonerodible parts of the basin. The results are validated against a 14-month sediment yield obtained by high-resolution (0.2 m) reservoir bathymetric analyses. The calculated sediment yield has an absolute deviation of 4.8% from the measured one. The results indicate that these methods can be successfully applied for the estimation of reservoir sedimentation rates in the Alps.

Comparison of the sediment yield estimation using bathymetric survey and Modified Universal Soil Loss Equation at Shumburit Reservoir, East Gojjam Zone Amhara Region, Ethiopia

Comparison of the sediment yield estimation using bathymetric survey and Modified Universal Soil Loss Equation at Shumburit Reservoir, East Gojjam Zone Amhara Region, Ethiopia

The Brigalow Catchment Study: A comparison of four methods to estimate peak runoff rate for small catchments before and after land use change in the Brigalow Belt bioregion of central Queensland, Australia

Study regionBrigalow Belt bioregion of north-eastern Australia. Study focusDynamic SedNet is used to model erosion from 42.4 Mha of grazing land in the Great Barrier Reef catchments to guide the $3 billion Reef 2050 Long-Term Sustainability Plan 2021–2025. Improving Dynamic SedNet by incorporating the Modified Universal Soil Loss Equation requires spatially derived peak runoff rate. This study evaluated four simple methods to estimate peak runoff rate at a site representative of the 15 Brigalow Belt bioregion catchments that intersect with the 35 Great Barrier Reef catchments. Performance was assessed against measured data from three long-term catchments of the Brigalow Catchment Study both pre-clearing (1965–1982), when all catchments were virgin brigalow scrub prior to land use change, and post-clearing (1984–2004), after one catchment was converted to cropping and another to grazing. New hydrological insights for the regionUseful estimations were obtained from the scaling technique (R2 = 0.90; NSE = 0.79), multiple regression models (R2 = 0.90; NSE = 0.63), and the variable infiltration rate method (R2 = 0.88; NSE = 0.71). Estimations using the curve number and graphical peak discharge method gave an R2 of 0.85; however, NSE was typically negative because the method systematically underestimated runoff rate. Despite different data requirements and complexity, all four methods were easily applied with parameters derived from widely available rainfall data, measured runoff volume data, and basic physical descriptors of the catchment.

Evaluation of best management practices to reduce sediment yield in the upper Gilo watershed, Baro akobo basin, Ethiopia using SWAT

The increasing sediment yield in the watershed is caused by natural and human activities, which significantly shifts the hydro-meteorological in the watershed. The Modified Universal Soil Loss Equation (MUSLE) equation in the Soil and Water Assessment Tool (SWAT) was used to estimate sediment yields for each hydrological response unit (HRU) based on peak runoff, daily runoff volume, area of hydrological response unit, and other estimated and default hydrological model parameters. The amount of sediment yield from each HRU is then summed to give the total soil erosion for the watershed.The spatio-temporal variations of sediment yield in the Upper Gilo watershed was simulated to identify the hotspot area and select the effective management practices (BMPs) for reducing significant problems. Model calibration and validation were carried out using sediment yield data from 1990 to 2004 and 2005 to 2014. The results indicated that the watershed total sediment yield is 1021.8 tonnes/yr. Furthermore, 17 sub-basins (37.8% of total watershed area) are severely threatened by high soil erosion. According to the simulation results, the filter strips, terraces, and contours reduced the watershed sediment yield by up to 53.2%, 45.4%, and 48%. Overall, the selected BMPs are highly effective in reducing sediment yield in watershed-prone areas.

Influence of different management systems on surface protection and erodibility of a temperate mixed forest

Unmanaged artificial forests face severe rates of erosion which could also impose serious threats to their adjacent environment. The Soil and Water Assessment Tool (SWAT) is a widely used physically-based model which estimates sediment yield using the empirical Modified Universal Soil Loss Equation (MUSLE). Although SWAT has been proven to obtain satisfactory results in forested catchments, the lack of representation of surface protection in the MUSLE factors still needs to be addressed. Acknowledging this gap, the present study applied a unique method of evaluating the surface protection and erosion susceptibility of four forest groups (poorly-managed private forest, moderately-managed unfenced Government-managed forest, well-managed fenced Government-managed forest, and natural forest) in the upstream catchment of Ogouchi Reservoir in Japan (262.9 km2) which is dominated by steep slopes (35°–45°). While the individual susceptibility to open and throughfall raindrops can be expressed by stand density, surface protection was found to be additionally influenced by slope and management system. With this, a multiple linear regression equation for surface protection (Pr) was derived from the influencing forest properties. Integrating the combined impacts of open and throughfall raindrops with surface protection, the values of total erodibility coefficient (CTEr) were computed which will then be corresponded to the MUSLE management practice factor (MUSLE P) input in SWAT. Poorly-managed private forests were found to be the most susceptible to raindrop splash erosion due to their exposed surface, while the well-maintained fenced Government-managed forests possess the most ideal condition for erosion control. The results of modeling CTEr at various management scenarios confirmed that the erosion susceptibility of poorly-managed Japanese cypress forest could be about 40% greater than the case of well-managed conifer plantation, a ratio that is close to and within the range of the values earlier reported.

Applicability evaluation of agricultural Best Management Practices to estimate reduction efficiency of suspended solids

Nonpoint source (NPS) pollution affects water quality, and the path of leakage and discharge of NPS pollutants is not clear. NPS pollution is heavily influenced by weather conditions such as precipitation, making NPS pollution difficult to manage. The Soyang River watershed, in the upper reaches of the Han River, is dominated by mountain farmland, which causes the water to become turbid due to soil erosion during rainfall. Therefore, South Korea’s Ministry of Environment has designated Mandae District as an NPS pollution management area to install and manage various NPS pollution reduction facilities (NPRF). However, research on the NPS reduction efficiency is insufficient. Therefore, a Soil and Water Assessment Tool (SWAT) model was used to evaluate soil loss and suspended solids (SS) reduction efficiency by NPRF installed in the Mandae District. In this study, hydrologic response units (HRUs) were modified to spatially consider agricultural fields in steep-sloping areas in South Korea, and soil loss, as well as SS reduction efficiency, were calculated reflecting temporal/spatial characteristics of best management practices (BMPs). In order to accurately evaluate the NPRF, this study digitized all fields in the Mandae District and applied them to land use maps where slope and slope length were entered for each agricultural field. To this end, the digitized boundaries of all agricultural fields have been updated into land use maps. In addition, the estimated soil loss was corrected by calibrating the exponent and coefficient of the MUSLE(Modified Universal Soil Loss Equation) formula. Reduction efficiency was assessed by comparing the amount of soil loss and SS before and after the installation of the NPRF with a scenario-based analysis. As a result, the soil loss reduction efficiency of the NPRF in Mandae District was 7.8% and the SS reduction efficiency was 5.8%. Both soil loss and SS were sensitive to slope length in evaluating the effectiveness of reduction. The results showed that the longer the slope length, the greater the soil loss or SS.

Climate change effect on soil erosion using different erosion models: A case study in the Naip Dam basin, Türkiye

Soil erosion, expected to increase with the potential effects of climate change, is the main problem that reduces the useful storage capacity of river dam reservoirs. This problem threatens the safety and sustainability of water and food resources. Over the last few years, a large number of numerical models have been developed and used to predict the current, and future state of soil erosion with Geographic Information Systems (GIS) based erosion models. This study aims to estimate the annual average amount of soil loss in the Naip dam basin in Türkiye Thrace by using GIS-based Revised Universal Soil Loss Equation (RUSLE) and Modified Universal Soil Loss Equation (MUSLE) erosion models and analyze the forecast outputs verified by the reservoir sedimentation results within the framework of the effects of climate change. Thus, the effects of future climate change on the spatiotemporal change of erosion rate and reservoir sedimentation were tried to be explored. Study data were organized using the ArcGIS Soil Erosion Tool, and annual average soil loss was calculated based on the dam basin's RUSLE and MUSLE erosion models. Accordingly, it was understood that the average annual soil loss in the basin area was between 0.31 t ha−1 yr−1 (RUSLE) and 0.36 t ha−1 yr−1 (MUSLE). These results were checked by the results of reservoir sedimentation determined by the volume changes of the Naip dam. RUSLE and reservoir sedimentation results were found to be compatible with each other. Therefore, the RUSLE erosion model was rerun with climate change data for 9-year periods between 2021 and 2050. Because of the predicted effects of climate change, it is understood that erosion will increase more in this area in the future. Thus, helpful and guiding information was provided to the decision makers for the dam reservoir sedimentation management, the dam's sustainability, and the extension of the reservoir life. In terms of adaptation to climate change, it is recommended to take measures to reduce or prevent erosion in the basin area.

Estimation of the amount of sediment entering into Shumburit reservoir from the Shumburit watershed, East Gojjam zone, Amhara Region, Ethiopia

Sediment accumulation is a major factor in reducing a reservoir's useful life. As a result, information on any changes in storage capacity is required to reduce the reservoir's risk. Therefore, the goal of this study was to determine the amount of sediment entering the reservoir using the Modified Universal Soil Loss Equation (MUSLE). The sediment entering into the reservoir was calculated by the MUSLE model over the last six years and is estimated after determining the physical parameters (K, LS, C, and P factors) and runoff index (VQ and QP) from the watershed using ArcGIS 10.8 and Excel software. The single event sediment (daily sediment) using MUSLE was a product of physical parameters (K, LS, C, and P factors) and runoff index (VQ and QP). The Modified Universal Soil Loss Equation estimated 256,262.86 tons of sediment yield at the outlet of the watershed (inflow to the reservoir in the last six years). The reservoir's total capacity has been reduced by 5.4 percent over the last six years of operation. The average rate of sedimentation was estimated at 42,710.48 tons/year or 31.64 tons/ha/year. Hence; the annual capacity reduction of the reservoir was 0.9 percent.

Sustainable and Cost-Effective Management of Degraded Sub-Watersheds using Ecological Management Practices (EMPs) for Genale Basin, Ethiopia

Land, water, and air are the most essential resources blessed by nature to humankind, conserved and maintained with steadfast efforts. This study applies the Modified Universal Soil Loss Equation (MUSLE) to identify erosion prone sub-basins and a non-linear optimization algorithm to determine the optimal area combination of Ecological Management Practices (EMPs) control sediment yield within permissible limits at a minimum cost. The model is practiced for critical sub-basins in the Genale watershed to assess the effectiveness of five EMPs individually and as a combination in controlling sediment yields and peak flow. A relative assessment revealed that terracing as an individual (61.8%) and EMPs combinations (78.5%) are better in reducing sediment yield at the sub-basin scale. Considering the environmental and economic viewpoint, the total cost of EMPs (for five critical sub-basins), applied to reduce sediment yield, is 46.101 million USD (United States Dollar) or (1.844 billion Ethiopian birr). EMPs are environment friendly and cost-effective to reduce sediment yield.

Simulating daily sediment transport using the Water Quality and Sediment Model (WQSED)

IntroductionHigh erosion and sediment yield rates continue to pose a significant threat to the environment globally. Information on erosion and sediment rates is key for supporting effective and sustainable mitigation measures. Models that estimate sediment yield are vital in providing information about erosion and sediment yield rates, as empirical studies are prohibitive over large spatial and temporal scales.MethodsIn this study, we simulate daily sediment transport using the WQSED model and assess the effectiveness of the tool in providing crucial estimations of sediment yield. The model structure links the Modified Universal Soil Loss Equation (MUSLE) to a simple sediment storage component. The model was applied to the Odzi River catchment in Zimbabwe and The Rio Tanama River catchment in Puerto Rico, where daily observations of sediment yield exceeding a decade were available for calibration and validation.ResultsIn both catchments, we achieved a coefficient of efficiency and R2 and NSE of > 0.7 during model calibration and > 0.6 during model validation. The percentage bias remained below 45% for both calibration and validation periods.ConclusionThese results indicate that the WQSED model can be applied to provide estimates of sediment yield that are reliable for erosion, sediment yield and water quality management. An effective and relatively simple sediment yield model incorporating sediment storage is essential for catchment management in erosion-prone areas.

An improved MUSLE model incorporating the estimated runoff and peak discharge predicted sediment yield at the watershed scale on the Chinese Loess Plateau

An accurate prediction of sediment yield at the watershed scale is critical not only for sustainable watershed management but also for improving knowledge regarding the relationship between sediment yield and its determinant factors, which often rely on the varying model complexity. In this study, a modified sediment yield formula based on the modified universal soil loss equation (MUSLE) model was developed by introducing a channel factor into the original formula. The reliability of the proposed method was tested using data from 1,341 storm events in 38 watersheds and was applied to 256 storm events in five application watersheds using the optimized parameters. Results indicated that the proposed method is very accurate, as demonstrated by the Nash–Sutcliffe efficiency (NSE) values of 88.18 %, 85.72 %, and 85.51 % during calibration, validation, and application, respectively. The performance of the proposed model was superior to that of the original MUSLE model. Subsequently, the proposed method was used to predict sediment yield from the last five typical watersheds. This prediction utilized the parameters derived from the initial 38 watersheds; the peak discharge predicted by the modified Chemicals, Runoff, and Erosion from Agricultural Management Systems (CREAMS); and the runoff estimated by the modified Soil Conservation Service Curve Number (SCS-CN) method. Elevated NSE (61.88–81.42 %) and low root mean square error values (2.35–11.40 t ha−1) were calculated for the five watersheds. From the results, the proposed sediment yield model, combined with the modified SCS-CN method and CREAMS, was found to accurately predict the event-based sediment yield, peak discharge, and runoff in the Loess Plateau region under varying hydrological and geomorphic conditions.

Correlation between soil erodibility and light penetrometer blows: A case study in Sungai Langat, Malaysia

The soil erodibility (K) factor is one of the key parameters used for predicting and estimating soil erosion in models such as Universal Soil Loss Equation (USLE) and Modified Universal Soil Loss Equation (RUSLE). It is the inherent resistance of soil to survive erosion and reflects the susceptibility of soil to the erosion process. Long-term studies for direct measurement of K factor are time-consuming and expensive, so efforts were made by the researchers to develop simple indices to relate soil properties with K factor for instant and economical estimation of erodibility. Mackintosh Probe is a portable and lightweight dynamic penetrometer that provides a relatively fast, economical, and easy assessment of subsoil. In this study, soil samples were collected from the Sungai Langat site, and a series of laboratory tests, including sieve analysis, hydrometer analysis, and organic matter content, were performed. K factor was determined by employing Wischmeier and Tew equations. The mean values of the K factor for the Sungai Langat site were found to be 0.005 and 0.0045 for Tew and Wischmeier equations, respectively. Multiple regression analysis was performed to analyze the relations between Mackintosh Probe and K factor, which the coefficient of multiple correlation values of 0.345 and 0.352 for the Tew and Wischmeier equation, respectively. Although the analysis represented a weak relation between Mackintosh Probe values and K factor, however, it provides a basis for the research perspective to simplify the estimation of soil erodibility with a minimalistic experimental setup.