Search Results (32)

The booming nature rubber industry has contributed to the extensive expansion of rubber plantations in the Lancang-Mekong River Basin over recent decades. To date, limited research has focused on the assessment of soil erosion caused by this expansion, resulting in a knowledge gap in the systematic and quantitative understanding of its ecological and hydrological impacts. This study evaluates soil erosion within rubber plantations and changes associated with their expansion by modifying the Revised Universal Soil Loss Equation (RUSLE) model in the middle section of the Lancang-Mekong River Basin from 2003 to 2022. The results show that: (1) rubber plantations have expanded rapidly, reaching a total area of 70.391 × 10⁴ ha; (2) over the 20-year period, soil erosion trends within rubber plantations show both slight aggravation (affecting 45.377% of the area) and slight mitigation (affecting 35.859% of the area); (3) soil erosion in rubber plantations shows a pattern of decreasing, then increasing, and then decreasing again with stand age, with the lowest erosion (0.693 t·ha⁻¹·yr⁻¹) observed in plantations aged 10–15 years and the highest (1.017 t·ha⁻¹·yr⁻¹) in those aged 15–20 years; (4) rubber plantation expansion led to a fivefold increase in soil erosion with an average soil loss of 0.148 t·ha⁻¹·yr⁻¹ in the non-expansion areas and 0.902 t·ha⁻¹·yr⁻¹ in expansion areas; and (5) slope had the most significant impact on soil erosion. Interactions between slope and other factors —especially slope and soil type (Q > 0.777)—consistently demonstrated strong explanatory power. This research provides valuable insights for the assessment and management of soil erosion in rubber plantations. Full article

Recent climate change has intensified extreme rainfall events, exacerbating soil erosion and agricultural nonpoint source pollution in South Korea’s steeply sloped farmlands. This study assessed soil erosion reduction measures by applying individual Best Management Practices (BMPs) in cropland and expanding upon existing management efforts through the implementation of additional BMPs aimed at further reducing soil erosion. Furthermore, priority management areas were identified based on soil erosion reduction efficiency within subbasins. For this evaluation, the Soil and Water Assessment Tool (SWAT) was employed, with a spatially distributed Hydrological Response Unit (SD-HRU) module and calibrated Modified Universal Soil Loss Equation (MUSLE) parameters tailored to Korean watershed conditions. Scenarios 1 and 2 were implemented in the study area to evaluate BMP effectiveness in controlling soil erosion and suspended sediment (SS) loads. Scenario 1 applied a set of BMPs already in place, while Scenario 2 involved the addition of supplementary BMPs to enhance soil erosion control. Scenario 1 resulted in a 34.6% reduction in annual soil erosion and a 35.0% decrease in SS concentration, whereas Scenario 2 achieved a 59.3% reduction in soil erosion and a 57.3% decrease in SS concentration. Subbasin-scale evaluations revealed considerable spatial variability in erosion control efficiency, ranging from 1.3% to 70.5%, highlighting the necessity for spatially targeted management strategies. These results underscore the importance of employing spatially adaptive BMP approaches and offer practical guidance for enhancing watershed sustainability, particularly in regions vulnerable to extreme hydrometeorological events. Full article

Soil erosion is a critical factor impacting soil health and agricultural productivity, with soil erodibility often quantified using the K-factor in erosion models such as the universal soil loss equation (USLE). Traditional K-factor estimation lacks spatiotemporal precision, particularly under varying soil moisture and land cover conditions. This study introduces modified K-factor pedotransfer functions (Kmlr) integrating dynamic remotely sensed data on land use land cover to enhance K-factor accuracy for diverse soil health management applications. The Kmlr functions from multiple approaches, including dynamic crop and cover management factor (Cdynamic), high resolution satellite data, and downscaled remotely sensed data, were evaluated across spatial and temporal scales within the Fish River watershed in Alabama, a coastal watershed with significant soil–water interactions. The results highlighted that the Kmlr model provided more accurate sediment yield (SY) predictions, particularly in agricultural areas, where traditional models overestimated erosion by upto 59.23 ton/ha. SY analysis across the 36 hydrological response units (HRUs) in the watershed showed that the Kmlr model captured more accurate soil loss estimates, especially in regions with varying land use. The modified K-factor model (Kmlr-c) using Cdynamic and high-resolution soil surface moisture data outperformed the traditional USLE K-factors in predicting SY, with a strong correlation to observed SY data (R² = 0.980 versus R² = 0.911). The total sediment yield predicted by Kmlr-c (525.11 ton/ha) was notably lower than that of USLE-based estimates (828.62 ton/ha), highlighting the overestimation in conventional models. The identification of erosive hotspots revealed that 6003 ha of land was at high erosion risk (K-factor > 0.25), with an average soil loss of 24.2 ton/ha. The categorization of erosive hotspots highlighted critical areas at high risk for erosion, underscoring the need for targeted soil conservation practices. This research underscores the improvement of remotely sensed data-based models and perfects them for the application of soil erodibility assessments thus promoting the development of such models. Full article

This study assessed soil erosion intensity and soil properties across the Crveni Potok catchment in Serbia, a region of diverse morphology, geology, pedology, and vegetation. Soil samples were collected using a regular grid approach to identify the underlying factors contributing to erosion and the most vulnerable areas. Based on ¹³⁷Cs activities and the profile distribution (PD) model, severe erosion (>10 t ha⁻¹ y⁻¹) was predicted at nearly 60% of the studied locations. The highest mean erosion rates were detected for the lowest altitude range (300–450 m), Rendzic Leptosol soil, and grass-covered areas. A significant negative correlation was found between the erosion rates, soil organic matter, and indicators of soil structural stability (OC/clay ratio and S_t), indicating that the PD model successfully identifies vulnerable sites. The PD and RUSLE (revised universal soil loss equation) models provide relatively similar mean erosion rates (14.7 t ha⁻¹ y⁻¹ vs. 12.7 t ha⁻¹ y⁻¹) but significantly different median values (13.1 t ha⁻¹ y⁻¹ vs. 5.5 t ha⁻¹ y⁻¹). The model comparison revealed a positive trend. The observed inconsistencies were interpreted by the models’ spatiotemporal frameworks and RUSLE’s sensitivity to input data quality. Land use stands out as a significant factor modifying the variance of erosion rate, highlighting the importance of land management practices in mitigating erosion. Full article

The Yellow River Basin (YRB) is an important grain production base, and exploring the spatiotemporal heterogeneity and driving factors of soil erosion in the YRB is of great significance to the ecological environment and sustainable agricultural development. In this study, we employed the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Transport-Limited Sediment Delivery (TLSD) to explore a modified RUSLE-TLSD for use assessing net water erosion. This modification was performed using sediment data, and the explanatory power of driving factors was assessed utilizing an optimal parameters-based geographical detector (OPGD). The results demonstrated that the modified RUSLE-TLSD can accurately simulate the spatiotemporal distribution of net water erosion (NSE = 0.5766; R² = 0.6708). From 2000 to 2020, the net water erosion modulus in the YRB ranged between 1.62 and 5.33 t/(ha·a). Specifically, the net water erosion modulus decreased in the YRB and the middle reaches of the YRB (MYRB), but it increased in the upper reaches of the YRB (UYRB). The erosion occurred mainly in the Loess Plateau region, while the deposition occurred mainly in the Hetao Plain and Guanzhong Plain. The Normalized Difference Vegetation Index (NDVI) and slope emerged as significant driving factors, and their interaction explained 31.36% of YRB net water erosion. In addition, the redistribution of precipitation by vegetation and the slope weakened the impact of precipitation on the spatial pattern of net water erosion. This study provides a reference, offering insights to aid in the development of soil erosion control strategies within the YRB. Full article

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. Full article

Soil loss (SL) and its related sedimentation in mountainous areas affect the lifetime and functionality of dams. Darbandikhan Lake is one example of a dam lake in the Zagros region that was filled in late 1961. Since then, the lake has received a considerable amount of sediments from the upstream area of the basin. Interestingly, a series of dams have been constructed (13 dams), leading to a change in the sedimentation rate arriving at the main reservoir. This motivated us to evaluate a different combination of equations to estimate the Revised Universal Soil Loss Equation (RUSLE), Sediment Delivery Ratio (SDR), and Reservoir Sedimentation (RSed). Sets of Digital Elevation Model (DEM) gathered by the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), AQUA eMODIS NDVI V6 data, in situ surveys by echo-sounding bathymetry, and other ancillary data were employed for this purpose. In this research, to estimate the RSed, five models of the SDR and the two most sensitive factors affecting soil-loss estimation were tested (i.e., rainfall erosivity (R) and cover management factor (C)) to propose a proper RUSLE-SDR model suitable for RSed modeling in mountainous areas. Thereafter, the proper RSed using field measurement of the bathymetric survey in Darbandikhan Lake Basin (DLB) was validated. The results show that six of the ninety scenarios tested have errors <20%. The best scenario out of the ninety is Scenario #18, which has an error of <1%, and its RSed is 0.46458 km³·yr⁻¹. Moreover, this study advises using the Modified Fournier index (MIF) equations to estimate the R factor. Avoiding the combination of the Index of Connectivity (IC) model for calculating SDR and land cover for calculating the C factor to obtain better estimates is highly recommended. Full article

Erosion is a natural process, but it can be accelerated by anthropogenic activities. Two of the predominant types of human-induced erosion are related to agricultural and construction activities. Of the two, construction-induced erosion is more severe because of the simultaneous removal of the land cover, disturbance of the soil, and eventual compaction of the soil by heavy machinery. Eroded materials released from bridge construction sites can alter the sediment regime and geomorphological conditions of receiving streams and may have short- and long-term impacts on aquatic habitats. Several models have been developed to estimate the total amount of soil erosion and sediment yield; however, no predictive model is available to quantify the potential release of sediment during the construction of bridges or to predict the quantity, size fraction, and accumulation depths for the extent of the measurable downstream effect. A GIS-based predictive sediment toolkit is developed to estimate the overland erosion and to determine the potential depositional area and suspended sediment concentration downstream of bridges. The performance of the GIS toolkit in estimating soil erosion was assessed using field data collected from the Wilson Creek bridge construction site in McKenney, Texas, U.S., and it was concluded that it predicted the overland erosion rate and sediment yield within the ranges observed in the field. Full article

The effect of the topographic factor of the Modified Universal Soil Equation (MUSLE) on soil erosion and sediment yield is not clear. Except for the coefficient, soil erodibility, cover, and conservation practice factors of the MUSLE, an individual effect of the exponents and topographic factors of the MUSLE on soil erosion and sediment yield can be seen by applying the model at different watersheds. A primary objective of this paper is to estimate the best exponents and topographic factors of the MUSLE under the hydro-climatic conditions of Ethiopia. For the sake of the calibration procedure, the main factors of the MUSLE that directly affect the soil erosion process, such as cover, conservation practice, soil erodibility, and topographic factors, are estimated based on past experiences from the literature and comparative approaches, whereas the parameters that do not directly affect the erosion process or that have no direct physical meaning (i.e., coefficient a and exponent b) are estimated through calibration. We verified that the best exponent of the MUSLE is 1 irrespective of the topographic factor, which results in the maximum performance of the MUSLE (i.e., approximately 100%). The best exponent that corresponds to the best equation of the topographic factor is 0.57; in this case, the performance of the model is greater than or equal to 80% for all watersheds under our consideration. We expect the same for other watersheds of Ethiopia, while for other exponents and topographic factors, the performance of the model decreases. Therefore, for the conditions of Ethiopia, the original exponent of the MUSLE is changed from 0.56 to 0.57, and the best equations of the topographic factor are provided in this paper. Full article

A primary objective of this paper is to change the input data requirement of the Modified Universal Soil Loss Equation (MUSLE) for the calculation of its runoff factor for possible application in data-scarce areas. Basically, the MUSLE was developed for a small agricultural watershed, where the extent of erosion is from sheet to rill erosion, but we cannot exactly tell whether it considers gully erosion or not. The underlying physical assumption to improve the MUSLE is that the amount of potential energy of runoff is proportional to the shear stress for sediment transport from a slope field and the kinetic energy of the runoff at the bottom of the slope field for gully formation. The improved MUSLE was tested at four watersheds in Ethiopia, and it showed better performance (i.e., the minimum performance is 84%) over the original MUSLE (i.e., the minimum performance was 80%), for all four watersheds under our consideration. We expect the same to be true for other watersheds of Ethiopia. Full article

Climate change induces more extreme precipitation events, which increase the amount of soil loss. There are continuous requests from the decision-makers in the European Union to provide data on soil loss; the question is, which ones should we use? The paper presents the results of USLE (Universal Soil Loss Equation), RUSLE (Revised USLE), USLE-M (USLE-Modified) and EPIC (Erosion-Productivity Impact Calculator) modelling, based on rainfall simulations performed in the Koppány Valley, Hungary. Soil losses were measured during low-, moderate- and high-intensity rainfalls on cultivated soils formed on loess. The soil erodibility values were calculated by the equations of the applied soil erosion models and ranged from 0.0028 to 0.0087 t ha h ha⁻¹ MJ⁻¹ mm⁻¹ for the USLE-related models. EPIC produced larger values. The coefficient of determination resulted in an acceptable correlation between the measured and calculated values only in the case of USLE-M. Based on other statistical indicators (e.g., NSEI, RMSE, PBIAS and relative error), RUSLE, USLE and USLE-M resulted in the best performance. Overall, regardless of being non-physically based models, USLE-type models seem to produce accurate soil erodibility values, thus modelling outputs. Full article

It is common knowledge that erosion depends on environmental factors modified by human activity. Erosion within a catchment area can be defined by local lithological, morphometric, hydrological features, etc., and land cover, with spatial distribution described by means of remote sensing tools. The study relied on spatial data for the catchment of the Lower Vistula—the biggest river in Poland. GIS (SAGA, QGIS) tools were used to designate the spatial distribution of independent environmental variables that determined the process of erosion according to land cover types within the Lower Vistula catchment (Corine Land Cover). In addition, soil loss in the catchment area was calculated using the USLE model (Universal Soil Loss Equation). The spatial data was used to determine the predictive power of variables for the process of erosion by applying the maximum entropy model (MaxEnt) commonly used in fields of science unrelated to fluvial hydrology. The results of the study pointed directly to environmental features strongly connected with the process of erosion, identifying areas susceptible to intensified erosion, and in addition positively verified by USLE. This testifies to the correct selection of the proposed method, which is a strong point of the presented study. The proposed interdisciplinary approach to predict erosion within the catchment area (MaxEnt), widely supported by GIS tools, will allow the identification of environmental pressures to support the decision-making process in erosion-prone areas. Full article

Soil erosion-associated sedimentation has become a significant global threat to sustainable land and water resources management. Semi-arid regions that characterise much of southern Africa are particularly at risk due to extreme hydrological regimes and sparse vegetative cover. This study aims to address the need for an erosion and sediment delivery model that successfully incorporates our conceptual understanding of sedimentation processes in semi-arid regions, particularly sediment storage and connectivity within a catchment. Priorities of the Semi-arid Sediment Yield Model (SASYM) were simplicity and practical applicability for land and water resource management while adhering to basic geomorphic and hydrological principles. SASYM was able to represent multiple sediment storages within a catchment to effectively represent a change in landscape connectivity over geomorphic timeframes. SASYM used the Pitman rainfall–runoff model disaggregated to a daily timescale, the Modified Universal Soil Loss Equation (MUSLE), incorporating probability function theory and a representation of sediment storages and connectors across a semi-distributed catchment. SASYM was applied to a catchment in the Karoo, South Africa. Although there were limited observed data, there was a historical dataset available for the catchment through dam sedimentation history. SASYM was able to effectively present this history and provide evidence for landscape connectivity change. Full article

Land use and land cover (LULC) management influences the severity of soil erosion risk. However, crop management (C) is one factor of the Revised Universal Soil Loss Equation (RUSLE) model that should be taken into account in its determination, as it influences soil loss rate estimations. Thus, the present study applied an adapted C-factor estimation approach (CvkA) modified from the former approach (Cvk) to assess the impact of LULC dynamics on soil erosion risk in an agricultural area of Rwanda taking the western province as a case study. The results disclosed that the formerly used Cvk was not suitable, as it tended to overestimate C-factor values compared with the values obtained from t CvkA. An approximated mean soil loss of 15.1 t ha⁻¹ yr⁻¹, 47.4 t ha⁻¹ yr⁻¹, 16.3 t ha⁻¹ yr⁻¹, 66.8 t ha⁻¹ yr⁻¹ and 15.3 t ha⁻¹ yr⁻¹ in 2000, 2005, 2010, 2015 and 2018, respectively, was found. The results also indicated that there was a small increase in mean annual soil loss from 15.1 t ha⁻¹ yr⁻¹ in 2000 to 15.3 t ha⁻¹ yr⁻¹ in 2018 (1.3%). Moreover, the soil erosion risk categories indicated that about 57.5%, 21.8%, 64.9%, 15.5% and 73.8% had a sustainable soil erosion rate tolerance (≤10 t ha⁻¹ yr⁻¹), while about 42.5%, 78.2%, 35.1%, 84.5% and 16.8% had an unsustainable mean soil erosion rate (>10 t ha⁻¹ yr⁻¹) in 2000, 2005, 2010, 2015 and 2018, respectively. A major portion of the area fell under the high and very high probability zones, whereas only a small portion fell under the very low, low, moderate and extremely high probability zones. Therefore, the CvkA approach presents the most suitable alternative to estimate soil loss in the western province of Rwanda with reasonable soil loss prediction results. The study area needs urgent intervention for soil conservation planning, taking into account the implementation of effective conservation practices such as terracing for soil erosion control. Full article

This study compares different methods to calculate erosion and sedimentation processes in the Aviar Basin, a natural peri-urban basin located in Comúd’Encamp (Andorra). The basin area is small, covering less than one square kilometer. Currently, increased densities of houses and buildings under natural basins can cause drainage problems. This is due to the heavy accumulation of eroded solid material in the sewer systems. Therefore, for a given basin condition, accurate estimation of erosion and sedimentation amounts is important. The development of erosion models aims to facilitate the estimation of eroded solid material and the design of possible protective measures to prevent soil losses. Both empirical and physically based erosion models were used to study the Aviar Basin for these purposes. Empirical models include USLE (Universal Soil Loss Equation), RUSLE (Revised USLE) and MUSLE (Modified USLE), while one physically based model, KINEROS 2, was used. The volumes of solid materials produced in the Aviar Basin during the year 2012 were determined using these four different erosion models and then compared between them. The results of this study show that the estimation of soil loss using KINEROS 2 is useful in practice because the results obtained are close to those obtained from the empirical models. Full article