Estimate Soil Erosion 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.

Estimating soil erosion in response to land use/cover change around Ghibe III hydroelectric dam, Southern Ethiopia

Soil erosion is a hazard in every part of the world. The small-scale farmers often grapple with low agricultural production and food insecurity. This study was conducted to estimate soil loss in response to land use/cover change in 1990, 2000, 2010, and 2020 around Ghibe hydroelectric III dam in Southern Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) model was applied using a geographic information system (GIS). Digital elevation model with 30 m to determine topographic factor and supportive practice (P) factor, rainfall from the National Meteorological Institute to calculate the erosivity (R) factor, a digital soil map of the world for erodibility (K) factor, and Landsat 5TM images of 1990, 2000, 2010, and the Landsat 8 OLI of 2020 to determine trends of land use/cover change and the cover management (C) factor were employed. The raster layers of topography, cover management, rainfall erosivity, soil erodibility, and conservation techniques were processed and multiplied using the GIS platform. The overall accuracy of supervised classification was 89.89. The results showed that the percentage of cropland and built-up areas increased by 11.93 and 32.44%, respectively throughout the three study periods. Conversely, the proportion of forest land, grassland, bare land, and bushland declined by 8.2, 9.3, 10.13, 8.6%, respectively. The average annual soil loss rates increased from 30.95 t ha−1 yr−1 in 1990 to 43.85 t ha−1 yr−1in 2020. This is significantly higher than the maximum threshold rate of erosion for Ethiopian highlands (11 t ha−1 yr−1). The local government officers, non-governmental organizations, and farmers who are trained should strengthen watershed management techniques.

Estimation of soil erodibility for soil conservation in upper godavari sub basin of India

Estimation of soil erodibility for soil conservation in upper godavari sub basin of India

Estimation of Soil Erosion by RUSLE Model Using Geoinformatics Techniques: A Case Study of Mulshi Reservoir Catchment, Pune District, Western Maharashtra.

In the western part of Maharashtra, where the Western Ghat highlands are located, soil erosion is a serious and persistent environmental issue. To make matters worse, the majority of the dams in this region run the risk of sedimenting their reservoirs, which is a serious concern. This research was being conducted on the Mulshi reservoir catchment area, Pune district of western Maharashtra having area of 250.25 km2. It is fact that the surface runoff of seasonal rainfall is more in this region due to the presence of western ghats. In this study, high resolution satellite images were used to estimate average annual soil erosion based on the five factors defined in the Revised Universal Soil Loss Equation (RUSLE) with the help of Geoinformatics techniques. Overlay of five parameters, viz., rainfall erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support & conservations practices factor (P) has been done in ArcGIS (version 10.8) software. Annual average soil erosion of the catchment is 0 to 433.43 tons/hector/year and has been classified into Six categories, defined as very high, high, moderate, low, and very low and negligible according to the severity of erosion. The high values of soil erosion (>20 tons/hector/year) found on the highlands due to the high slope and bare lands. however, low values (<10 tons/hector/year) are found on the area occupied by reservoir and dense vegetation.

Optimizing key parameters (biological-control factor) of soil erosion simulation models at a regional scale– The case of Jiangxi Province, China

Unveiling the spatial information of actual soil erosion is a prerequisite for subsequent soil erosion research and is critical for soil and water conservation as well as ecological resource protection. However, the existing regional-scale soil erosion simulation models often ignore the vertical structure of vegetation, particularly near-surface vegetation information, which leads to discrepancies with the real-world conditions. This study aims to optimize the biological-control factor (B factor) of the Chinese Soil Loss Equation (CSLE) model to improve simulation accuracy. The Global Ecosystem Dynamics Investigation (GEDI) can provide accurate layered vegetation data. Thus, this study focuses on the forest of Jiangxi Province, simulating the understory vegetation structure by vegetation type using a combination of GEDI data and sampled data. By considering the soil and water conservation benefits of different vegetation layers, we developed a B factor optimization method to simulate soil erosion in forests at a regional scale. Compared to previous methods, the optimized B factor mean value for Jiangxi Province’s forested areas increased from 0.0034 to 0.0096. The estimated soil erosion changed from 38 t·km-2a-1 (with 86.6 % of forests exhibiting no soil erosion) to 166.7t·km-2a-1 (with 28.2 % of forests exhibiting no soil erosion). Our findings indicate that the optimized assessment results are more consistent with the actual situation. The optimization method is a good attempt at regional-scale B factor simulation and can provide a more accurate depiction of the spatial information of soil erosion in forest at the regional scale. This study can lay a solid foundation for future regional-scale soil erosion assessments and offer valuable data support and methodological references for the regional-scale dynamic monitoring of soil and water loss, as well as research in soil and water conservation.

Evaluation of WEPP and Its Comparison with USLE and MUSLE in Yozgat-Kadılı Village

The water erosion is a significant environmental issue in arid and semi-arid regions. It leads to soil degradation, reduced agricultural productivity, and desertification. This article used The WEPP, the USLE, and the MUSLE models to estimate the average soil loss in the Yozgat-Kadılı village. Also, The MUSLE model utilized the WEPP model-estimated runoff for soil loss estimation. The USLE model, which estimates soil erosion using six factors (R, K, L, S, P, and C), can be improved by incorporating the Modified Fournier Index (MFI). Results indicated that the MUSLE model (3.66 t/ha) performed well in estimating soil losses close to the observed value (3.15) in the wheat fields between 1986-1996. the MUSLE (5.31 t/ha) and WEPP (5.88 t/ha) models underestimated soil losses to the observed value (8.75 t/ha) in the fallow field for 1986-1996. The WEPP model estimated the highest average soil loss at 5.18 t/ha in a wheat field, while the USLE model yielded the lowest estimate at 1.28 t/ha between 1969 and 2020. The MUSLE model estimated the highest (4.94 t/ha) and The USLE model estimated the lowest (2.53 t/ha) soil loss in the fallow field between 1969-2020. Results also revealed that the WEPP model is needed to calibrate for estimating soil loss in arid and semi-arid regions.

Assessment of soil erosion risk and vulnerability in the transboundary Sio-Malaba-Malakisi watershed in Kenya and Uganda

Persistent soil erosion poses a significant threat to water quality, ecosystem viability and soil health in many regions of the world. Addressing this challenge requires a comprehensive understanding of local soil erosion rates, including the identification of vulnerable areas, to facilitate effective and integrated environmental management. In East Africa, however, many affected regions are data poor and lack measured hydrological data from which soil erosion estimates can be derived. This study used the physically based Revised Universal Soil Loss Equation (RUSLE) model to estimate the annual rate of soil erosion in the transboundary Sio-Malaba-Malakisi catchment, straddling Kenya and Uganda. Soil erosion events were quantified by integrating input factors derived from physical datasets using a Geographical Information System (GIS). The Analytical Hierarchy Process (AHP) technique was then used to assess the importance of selected physical factors influencing the soil erosion process in order to identify regions of increasing environmental vulnerability. The results obtained indicate a wide range of soil erosion rates across the region, with an estimated mean annual rate of 250 t ha−1 yr−1 for the whole catchment. Upstream areas characterized by intensive agricultural activity had erosion rates of up to 2000 t ha−1 yr−1, while downstream areas recorded erosion rates below 600 t ha−1 yr−1. Areas with intensive agriculture, unprotected soils, and a soil loss tolerance value above 12 t ha−1 yr−1 were found to be most vulnerable. Rainfall and vegetation cover were identified as key factors influencing erosion susceptibility. Regions with soil erosion rates above 50 t ha−1 yr−1 were identified as high priority regions for targeted soil and water conservation measures. The identification of vulnerability zones, including the classification of their severity, provides an opportunity to prioritize sustainable environmental management interventions; a process that can be replicated for such affected regions elsewhere.

Impact of soil architecture on the interrill erodibility in clayey subtropical soil

AbstractSoil erosion is a key concern with regard to ecosystem functionality and food, fibre and bioenergy productions worldwide. Therefore, understanding the mechanisms and controls of soil erosion, particularly the link between soil aggregate stability and soil erodibility, is of utmost importance. The use of disturbed samples and sieved soil to determine the involved erodibility and aggregate stability is standard in soil erosion studies. However, soil erodibility estimation based on disturbed‐soil samples can be inaccurate as it involves changes in the architecture of the considered soil, possibly leading to overestimations. Moreover, a necessity for evaluating soil erodibility beyond intrinsic soil characteristics (e.g. texture) exists. The objective of this research was to assess the erodibility impact of soil disturbance. Undisturbed‐soil cores with dimensions of 45 cm (length) × 30 cm (width) × 10 cm (depth) were extracted while preserving their architecture. An A horizon corresponding to brown clayey subtropical oxisol soil from Southern Brazil was used for performing an experiment that involved simulation of 58–mm h−1 rain for 30 min. A total of seven replicate experiments were performed for each soil condition (i.e. undisturbed and disturbed soils). Results show that soil architecture deterioration had a larger impact on the involved soil loss than runoff. Further, soil structure failure did not affect the aggregate stability per se. Notably, the soil erodibility and loss were approximately 10 times larger under the disturbed‐soil condition than under the undisturbed‐soil condition (interrill erodibility: 4.30 × 107 and 4.39 × 106 kg s m−4, respectively; soil loss: 0.925 and 0.094 kg m−2, respectively). Overall, the intrinsic soil characteristics did not change; however the soil architecture deterioration considerably increased the erodibility.

Soil erosion estimation in a catchment with implemented soil and water conservation measures

AbstractThe variations in soil erosion significantly impact regional ecological security. Under rapid urbanisation, extensive ecological restoration and climate change, soil erosion development in the red soil region of southern China is ambiguous. Therefore, this study investigated the current (1980s–2020) and future (2050) erosion characteristics in a typical soil erosion control catchment (Changting section catchment) in this region by using the Cellular Automata Markov model and CMIP6 data to predict future scenarios and the Revised Universal Soil Loss Equation to estimate soil erosion. The results showed significant changes in the vegetation coverage of major land uses from 1980s to 2020, which was mainly caused by continuous soil and water conservation (SWC). The land use subtypes that were obtained by reclassifying land use based on the threshold of vegetation cover on soil erosion control, reflect a continuous transformation from those with poor SWC effectiveness to those with great SWC effectiveness. Therefore, the estimated soil erosion intensity continued to decrease from 1980s to 2020, and the contribution of land use/land cover (LULC) impacts ranged from 74%–195%. However, predictions of land use subtypes indicated that LULC may be stable after 2020; thus, soil erosion changed little when the climate was almost unchanged in 2050. Under climate change scenarios, soil erosion may increase by 111%–121%, and the contribution of precipitation impacts was 63%–66%. The major driving factor of soil erosion changes may shift from LULC to precipitation after 2020. Therefore, in the future, the potential for reducing soil erosion by vegetation restoration may be limited, and more engineering measures should be applied to address the erosion risk caused by climate changes. This study provides prospects for land use/land cover and soil erosion in the red soil region of southern China.

Variation of Soil Erosion Estimates when Using Different Maps of Arable Land of the Belgorod Region

Current medium- and small-scale estimates of soil erosion in Russia are very few. At the same time, a favorable situation has now developed for assessing the rates and volumes of soil erosion losses. Erosion models have been developed that are adapted to available digital elevation models, various farmland masks and climate databases have been created. The paper studies the accuracy of erosion estimates using various maps of arable land. Two maps are public (ESA WC, GLCLU), the third is the official Ministry of Agriculture (MA) of the Russian Federation, the fourth map is an author’s reference map of Alekseevsky district. It has been established that the map of the MA gives the most average arable land areas among the first three maps. Public access maps showed maximum and minimum estimates of arable land area. Comparison with the standard showed that the accuracy of the map of the MA does not exceed 90%, the remaining maps – 84 and 83%. The area of arable land in the Belgorod region varies slightly (from 1,445 to 1,586 thousand hectares); so the region is favorable for erosion modelling. Deviations from the average rates of soil erosion calculated using different maps of arable land in the region as a whole amounted to 7%, and in some areas reached 27%. Thus, today assessments of soil erosion at the regional level can be carried out with an error of at least 10–15% only as a result of the uncertainty in mapping the boundaries of arable land. In the Russia as a whole, data on the area of arable land varies significantly, from 80 to 132 million hectares. Consequently, the use of existing maps of arable land can lead to significant uncertainties in soil erosion estimates averaged at the level of districts and above.

Application of fallout radionuclide—137Cs for estimating soil erosion in steep hillslopes with diverse land use of North-western Indian Himalayas

Hilly and mountainous regions are significantly impacted by soil erosion, primarily due to rainfall-runoff processes occurring on the hillslope scale. Assessing soil erosion is crucial for quantifying the loss of soil carbon and nutrients, which diminishes the potential of soil ecosystem services and is critical for mitigating the impacts of climate change on food security. However, the Himalayan landscape poses serious challenges for assessing soil erosion due to its steep and rugged terrain, which hinders the use of conventional and modelling methods. The fallout radionuclide—137Cs has been extensively utilized as an environmental marker for investigating soil redistribution processes. Despite its potential, there is a notable lack of 137Cs-based soil erosion studies in the Himalayan region. In this context, we assessed the applicability of the fallout radionuclide—137Cs method in quantifying soil erosion rates and identifying erosion hotspots on two hillslopes of the Higher Himalayas. On the hillslope scale, we observed that soil erosion rates vary based on slope gradient and land use/land cover. Forested areas exhibited the lowest soil erosion rates compared to cultivated areas, while flat hillslope positions experienced lower erosion rates than steeper positions. The average net erosion rate for the Harsil hillslope varied among different hillslope positions, ranging from – 2.9 to – 15.6 t ha−1 yr−1. Similarly, in the Gangnani hillslope, the net erosion rates varied across different positions, ranging from – 5.6 to – 39 t ha−1 yr−1. Our findings confirm that the middle and lower hillslope positions are the most critical source areas with higher soil erosion rates, while hillslope positions with forest cover demonstrate the lowest erosion rates, identified as helpful in controlling soil erosion. The study has demonstrated the applicability of FRN as a soil erosion measurement method in the complex, rugged, and steep terrain of the Himalayas, highlighting the need for targeted conservation efforts to mitigate soil erosion and preserve ecosystem integrity.

GloRESatE: A dataset for global rainfall erosivity derived from multi-source data

Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.1° × 0.1° spatial resolution) integrating satellite (CMORPH and IMERG) and reanalysis (ERA5-Land) derived rainfall erosivity estimates with gauge rainfall erosivity observations collected from approximately 6,200 locations across the globe. We used a machine learning-based Gaussian Process Regression (GPR) model to assimilate multi-source rainfall erosivity estimates alongside geoclimatic covariates to prepare a unified high-resolution mean annual rainfall erosivity product. It has been shown that the proposed rainfall erosivity product performs well during cross-validation with gauge records and inter-comparison with the existing global rainfall erosivity datasets. Furthermore, this dataset offers a new global rainfall erosivity perspective, addressing the limitations of existing datasets and facilitating large-scale hydrological modelling and soil erosion assessments.

Detection and automatic identification of loess sinkholes from the perspective of LiDAR point clouds and deep learning algorithm

Nowadays, the detection and automatic identification of the three-dimensional structure of sinkholes is extremely lacking, which has resulted in significant gaps in sinkholes mapping, soil erosion estimation and morphological studies. In this study, we discovered 249 sinkholes on a river terrace (about 2050 m long and 100 m wide) in a small watershed of Chinese Loess Plateau. Subsequently, we used the unmanned aircraft systems (UAS) and handheld laser scanner (HLS) to investigate these loess sinkholes in detail. We introduced the PointNet ++ deep learning model to train the point cloud dataset for 50 epochs and then selected the best model. In order to evaluate the identification accuracy and transferability of the model, we input point clouds of the unknown prediction area into the trained model to predict the sinkhole point clouds. The trained model exhibits excellent transferability and can effectively identify the sinkhole point clouds in the predicted area (OA = 0.935, IoU (Sinkhole) = 0.662, mIoU = 0.794, AUC = 0.966, Recognition rate = 82.46 %), and even sinkholes with complex connected structures can be accurately identified. This study provides a new perspective for future large-area LiDAR surveys, mapping, and assessment of sinkholes.

Unveiling the threshold in forest patch shapes to soil retention ecosystem services

Soil erosion is a critical process that leads to landscape degradation, compromising soil fertility and ecosystem functions. Forest ecosystems, with their intricate characteristics, play a pivotal role in mitigating soil erosion and providing soil retention ecosystem services (SRES). This study explores the impact of forest patch thresholds and critical points on soil erosion rates, focusing on 401 catchments in Poland using generalised additive models to identify thresholds and critical points in forest patches.Landscape metrics were applied to measure landscape structure, including shape, fractal dimension, contiguity, related circumscribing circles, and perimeter-area ratio indexes. These metrics, along with slope, rainfall, organic carbon content, water content, and clay ratio variables, were considered dependent variables in the models. The developed models have demonstrated reliable performance in estimating soil erosion rates, with a significant deviation explained from 80.5 to 81.1 for coniferous forest patches, 79.1 to 80.1 for broad-leave forest patches, and 80.9 to 81.4 for mixed forest patches at p < 0.05.In broad-leaved forests, three thresholds are identified in the shape index, which influence soil erosion rates in a complex manner. For coniferous forests, thresholds in the perimeter area ratio, related circumscribing circles, and contiguity indexes exhibit nonlinear relationships with soil erosion rates. Mixed forests show two thresholds in the related circumscribing circle and one in the fractal dimension index, affecting soil erosion rates differently.This research contributes significantly to understanding the interplay between forest patch shapes and soil erosion rates, providing decision support for land use planning. The identified thresholds and critical points offer valuable tools to enhance sustainable landscape functionality, emphasizing the importance of considering forest landscape structure in preserving soil retention ecosystem services.

Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

The Effect of Land Cover Change on Soil Erosion in Awach Kibuon Sub-basin, Kenya

Land cover change is a significant driver of soil erosion. While soil erosion is a natural process, human activities can significantly alter the landscape, making soil more vulnerable to erosion. This erosion reduces a watershed&amp;apos;s capacity to sustain vital natural resources and ecosystem services. This study investigated the impact of these changes on soil erosion within four hydrological units (Awach Kibuon, Awach Owade, Awach Kasipul, and Awach Kabondo) of the Awach Kibuon sub-basin between 2018 and 2023. The specific objective of the study was to quantify the effect of land cover change on soil erosion rate and determine how specific land cover types affect soil erosion in the study area. This study employed a quasi-longitudinal design to assess the influence of land cover changes on soil erosion. Sentinel-2 NDVI satellite imagery provided land cover data. The land cover maps, soil data, rainfall data and the Digital Elevation Model were used in the Revised Universal Soil Loss Equation Model within a GIS environment to estimate soil erosion rates. The study revealed a consistent decline in vegetation cover across all hydrological units, as indicated by a decrease in NDVI. The mean NDVI decreased by 12.88%, 10.92%, 4.78%, and 11.92% in Awach Kibuon, Awach Owade, Awach Kasipul and Awach Kabondo respectively. Conversely, the mean soil erosion rate increased by 23.9% in Awach Kibuon, 17.85% in Awach Owade, 24.43% in Awach Kasipul, and 20.54% in Awach Kabondo. Sediment yield increased by 33% in Awach Kibuon, 18% in Awach Owade, 17% in Awach Kasipul, and 23% in Awach Kabondo. These findings suggest a direct relationship between reduced vegetation and elevated soil erosion. The relationship between land cover and erosion varies depending on the density of vegetation. Areas with dense vegetation cover have an inverse relationship, highlighting the protective role of vegetation cover. However, the study also observed that very dense vegetation areas which were also found in high-sloped areas experienced high soil erosion rates. The erosion rate increases even in areas that have experienced an increase in vegetation cover. This is because these areas are also found in high-sloped areas. The slope factor superseded the ability of vegetation cover to protect against soil loss. In conclusion, the change in land cover has significantly increased soil erosion in the Awach Kibuon Sub-basin, however, the slope factor also accelerated soil loss in the basin. Therefore, a holistic approach that combines promoting vegetation cover with land management techniques like terracing and drainage channels is crucial for mitigating soil degradation and water sedimentation in sub-basin.

Comparison of Several Methods for Analysis Slope Length Index Factor at A Watershed Scale

Slope length and steepness factor index (LS) is one of the parameters for the Universal Soil Loss Equation (USLE) to estimate soil erosion. Currently, several methods for LS analysis, i.e. Wischmeier-Smith, Moore-Nieber, and Desmet – Govers. This study aims to compare the Wischmeier-Smith method, Moore–Nieber method, and Desmet–Govers method to analyze LS in the watershed in Manokwari – West Papua. This research consists of 4 main stages, i.e. data inventory, watershed boundary delineation, LS analysis, and LS comparison. The research showed that the Wischmeier-Smith method gave a higher LS value than the Moore – Nieber method and the Desmet – Govers method. Meanwhile, the Desmet – Gover method gives a lower average LS value than the Wischmeier-Smith method and the Moore – Nieber method. Based on the T-test, the LS produced by the Wischmeier-Smith, Moore-Nieber, and Desmet–Govers methods has significant differences in analyzing LS in the watershed in Manokwari – West Papua. Keywords: Desmet – Govers, Moore – Nieber, Universal Soil Loss Equation, Watershed, Wischmeier-Smith

Estimating Soil Erosion and Sediment Yield Using Geographic Information System in the Central Pre-Rif (Northern Morocco) – The Case of the Oued Lebene Watershed

Estimating Soil Erosion and Sediment Yield Using Geographic Information System in the Central Pre-Rif (Northern Morocco) – The Case of the Oued Lebene Watershed

Correction: Soil erosion estimation in the Bagmati River basin of Nepal using RUSLE modeling to assess priority areas for conservation

Correction: Soil erosion estimation in the Bagmati River basin of Nepal using RUSLE modeling to assess priority areas for conservation

Comparison of predictive modeling approaches to estimate soil erosion under spatially heterogeneous field conditions

The accuracy of soil erosion models in agroecosystems with heterogeneous field conditions is challenging due to uncertainties from soil water fluxes and crop growth. In this study, we coupled two modeling methods (Freebairn and Rose) to represent soil erosion with a process-based crop and runoff models within the SIMPLACE framework. Their accuracy was compared to a statistical model developed using 16 erosion plots (each of 625 cm2) within the same field. Uncertainty analysis showed that runoff and slope angle were the most critical components for predicting sediment yield in both models, followed by soil erodibility in the Freebairn model and entrainment efficiency in the Rose model. However, due to plot size constraints, slope-length effects were not examined. The Freebairn model had a slightly higher accuracy (RMSE = 0.69 t ha−1 d−1) of sediment yield predictions than the Rose model (RMSE = 0.83 t ha−1 d−1). Both models are effective for predicting soil loss with appropriate parameter values.