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The source region of the Yellow River (SRYR), known as the "Chinese Water Tower", is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR's needs. By leveraging soil erosion data measured by 137Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R2 of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha−1·y−1 with an annual total erosion in the SRYR of 225.18 × 106 t·y−1. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development. [ABSTRACT FROM AUTHOR]

Soil erosion has been identified as a major threat to existing soil deposits, which negatively affects ecosystem sustainability, agricultural production and clean water supply. This study covered the eastern part of Kaštela Bay with the aim of developing a model of susceptibility to soil erosion by applying the GIS-based multicriteria analysis, based on nine determinant criteria categorized by the Jenks method in five classes. The largest representation of soil erosion susceptibility was detected on the slopes of the Kozjak and Mosor massifs. According to the calculated weighting coefficients using the analytic hierarchy process (AHP), the slope was the most impactful criterion with 29.9 % importance. It was followed by hydrological criteria of topographic wetness index, standardized precipitation index and LS-factor, with 13.1 % importance each. After the development of the model, zones of very high and high susceptibility to soil erosion were delineated, with a combined coverage of 33.34 % of the study area. These areas were identified as a potential threat from the future occurrence of negative effects of soil erosion, which should be addressed in the future by land policy managers and government managers. [ABSTRACT FROM AUTHOR]

Soil erosion is an obstacle in the process of maintaining and restoring the ecological environment in desert steppe landforms. We studied soil erosion in typical desert steppe landform areas in the northern foothills of the Yinshan Mountains. We used differences in altitude maps from different years to construct a dataset for soil erosion research, combined with data on precipitation, human activities, land cover, topography, and soil properties. We employed a convolutional neural network to learn and predict ASED and SEDD in different regions of the Yinshan Mountains. The trained model provided satisfactory prediction performance. We also determined the contributions of different factors and years to soil erosion prediction. Human activities had a greater impact on ASED prediction, while the impact of various factors on SEDD prediction was more balanced. Topography and precipitation factors had a relatively small effect on ASED prediction but played a prominent role in SEDD prediction. This study provides a new approach to exploring the mechanism of different factors on soil erosion in the Yinshan Mountains' northern foothills. [ABSTRACT FROM AUTHOR]

Rainfall erosivity (RE), a pivotal external force driving soil erosion, is impacted by El Niño/Southern Oscillation (ENSO). Studying the spatiotemporal variations in RE and their response to ENSO is essential for regional ecological security. In this study, a daily RE model was identified as a calculation model through an evaluation of model suitability. Daily precipitation data from 1971 to 2020 from 38 meteorological stations on Hainan Island, China, were utilized to calculate the RE. The multivariate ENSO index (MEI), Southern Oscillation Index (SOI), and Oceanic Niño Index (ONI) were used as the ENSO characterization indices, and the effects of ENSO on RE were investigated via cross-wavelet analysis and binary and multivariate wavelet coherence analysis. During the whole study period, the average RE of Hainan Island was 15,671.28 MJ·mm·ha−1·h−1, with a fluctuating overall upward trend. There were spatial and temporal distribution differences in RE, with temporal concentrations in summer (June–August) and a spatial pattern of decreasing from east to west. During ENSO events, the RE was greater during the El Niño period than during the La Niña period. For the ENSO characterization indices, the MEI, SOI, and ONI showed significant correlations and resonance effects with RE, but there were differences in the time of occurrence, direction of action, and intensity. In addition, the MEI and MEI–ONI affected RE individually or jointly at different time scales. This study contributes to a deeper understanding of the influence of ENSO on RE and can provide important insights for the prediction of soil erosion and the development of related coping strategies. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the major threats to soil sustainability and a global environmental issue causing serious losses of the fertile upper layer of soil, affecting land productivity. Among natural processes and human activity factors, the highest sensitivity of soil loss rate is related to climate changes, as well as land cover/land use transformations. The aim of our paper is to assess the efficacy of various land cover and land use management practices under current climate conditions, as a decision‐making indicator in searching for sustainable soil‐use solutions. The approach is focused on two complementary case studies from the non‐arable hilly area of Romanian Subcarpathians and it is based on aggregating and processing Earth Observation (EO) techniques together with the Revised Universal Soil Loss Equation (RUSLE) equation. The workflow follows three stages: (1) the assessment of the present‐day status of soil erosion, as baseline scenario; (2) the analysis of historical soil erosion dynamics within the last 35 years; and (3) the prediction of soil loss rates in different scenarios of changed conditions related to land cover management and support practices against erosion. The results demonstrate the effectiveness of human interventions in soil erosion prevention, mitigation, or conservation. Soil‐improving management through vegetative measures and soil practices, like grazing management and mulching/manure application, together with forest recovery on eroded slopes may reduce soil loss rates by 50%–70%. However, abandoning the land and allowing the environment to change uncontrollably is a regional‐specific strategy that could accelerate soil erosion on the slopes that are already affected, while decelerating on the others by forest and shrubs regrowth. The significance of our research is related to the identification of the optimal soil use strategies that balance the local communities' economic interests with the effectiveness of sustainable soil management practices, thereby assisting in the achievement of the UN Sustainable Development Goals (SDGs) as indicators for a sustainable future. [ABSTRACT FROM AUTHOR]

Dead storage of reservoir usually designed by soil erosion rate of catchment area result in certain year without considering land use and precipitation dynamic in the catchment and sedimentation distribution analysis at the bottom of the reservoir as well. Development of protocol is needed for more accurate prediction of reservoir sediment transport to describe the real condition and accommodate data limitations. It will give good perspective in design, operation and maintenance strategy for reservoir. The proposed protocol is consisted of long-term soil erosion prediction, modelling long-term sediment discharge, modelling spatial-temporal reservoir sediment transport and predicting reservoir sediment volume and distribution in the future. The case study is in Wonogiri reservoir, Indonesia. Simulation of the proposed protocol at the case study began by obtaining long-term soil erosion prediction from 1993 - 2019. Sediment discharge was modelled from soil erosion and synthetic long-term hydrographs using the FJ Mock method and validated with sediment rate measured directly in the field. Reservoir sediment transport model was using MIKE software. The simulation gives volume and distribution of sedimentation validated by bathymetry result of the reservoir. Prediction of future sedimentation was using the modular regression method. This protocol promotes spatial reservoir sediment transport model and sedimentation prediction by modular regression. The prediction result of reservoir sediment volume in 2069 is 146.7 million m³. [ABSTRACT FROM AUTHOR]

Soil erosion poses a significant threat to sustainable land management and agricultural productivity. Addressing this issue requires advanced predictive models that can accurately identify areas at risk and inform soil conservation strategies. This study focuses on the development and interpretation of well-optimized deep learning (DL) models to predict soil erosion probability, aiming to enhance decision-making in land management. Utilizing the Revised Universal Soil Loss Equation (RUSLE) in conjunction with ground-truthing, we identified critical erosion-prone areas. To predict soil erosion probability, we employed Bayesian optimization to fine-tune Deep Neural Network (DNN), Convolutional Neural Network (CNN), Fully Connected Neural Network (FCNN), and DNN-CNN hybrid models. These DL models were verified using a set of metrics. SHAP value analysis as a means of explainable artificial intelligence (XAI) was used to interpret these DL models for better decision-making. RUSLE estimations and ground truthing highlight that Soil erosion rates in the northeastern and northwestern regions are nearing the highest observed at 25 tonnes per hectare annually, largely due to steep slopes and limited vegetation. In contrast, the southern and southeastern areas have lower erosion rates, due to denser vegetation and gentler slopes. Deep learning models, optimized using Bayesian methods, demonstrate high performance in spatially modeling soil erosion probability. The DNN model achieved an accuracy of 0.93, a precision of 0.92, and an F1-score of 0.94, identifying 222.73 sq. km as highly susceptible to erosion, which indicates its strong ability to detect true erosion events. The CNN model identified 49.68% of the study area (503.30 sq. km) as high-risk, with an accuracy of 0.90 and a precision of 0.91. The FCNN model showed a balanced risk distribution, indicating 37.04% of the land (375.25 sq. km) as very low risk and 36.43% (369.11 sq. km) as very high risk, with an accuracy of 0.91. The DNN-CNN hybrid model highlighted 41.58% of the area (421.20 sq. km) as high risk, demonstrating its effectiveness in capturing spatial patterns of erosion susceptibility. SHAP value analysis indicates that land use and soil type (LULC and K-factor) are crucial in erosion predictions, with LULC having a significant predictive influence in the DNN model. These insights facilitate the prioritization of soil conservation measures, enabling decision-makers to focus on the most impactful factors for mitigating soil erosion. [ABSTRACT FROM AUTHOR]

Soil is a fundamental resource with its value vital to the world's ecosystem. Due to the fact that soil is one of the bases of all terrestrial life, humans cannot survive without it. However, soil erosion has jeopardized soil sustainability and affected the environmental quality, leaving a bad impact if these issues were not tackled at an earlier phase. Many research has been done to predict soil erosion susceptibility areas using different methods. This research aims to classify the contributing factors of soil erosion according to the risk and generate a soil erosion risk prediction map in Cameron Highlands. Thus, this research focuses on a knowledge-driven method that uses Analytical Hierarchy Process (AHP) technique to achieve the objectives. This technique consists of weighing the factors adopted by comparing pairs of factors that control erosion in this area through experts' opinions. 15 factors have been chosen to build the prediction map. Result shows that rainfall erosivity is the main factor contributing to soil erosion in Cameron Highlands which is 0.110, followed by land use (0.095), slope steepness (0.089), soil texture (0.079), NDVI (0.079), TWI (0.072), slope length (0.065), slope aspect (0.064), slope altitude (0.062), SPI (0.061), lithology (0.060), slope curvature (0.054), drainage density (0.049), distance to road (0.029) and distance to stream (0.025). The west part of the study area was exposed to a high risk of soil erosion. This research will give the decision-makers, policymakers and planners insight into minimizing the soil erosion problem and suggest better precautions and solutions to overcome this severe environmental problem in the more advanced phase. [ABSTRACT FROM AUTHOR]

Rainfall is one of the factors affecting soil erosion, and rainfall kinetic energy or momentum is used to represent the capability of the precipitation to erode soil, named rainfall erosivity. Accurate measurements of rainfall erosivity are useful for a reliable prediction of soil loss. Rainfall momentum and kinetic energy, both calculated per unit time and area, can be obtained using terminal raindrop velocity and the drop size distribution (DSD) measured by disdrometers, which are instruments expensive and not suitable for large scale use. An innovative patented method to measure the rainfall energy is based on the estimation of DSD by simultaneous detection, in a given time interval, of the rainfall intensity I and the number N of raindrops that hit a specific piezoelectric surface. In this paper, advances of this method are presented. In particular, two new theoretical procedures to estimate the parameters μ and Λ of Ulbrich's distribution, that allow for the calculation of the rainfall kinetic energy and momentum, are proposed. Both procedures (Scenario 1 and 2) are based on the frequency distribution of the momentum M(D) of raindrops detected in a sampling time interval. Specifically, in the Scenario 1, μ and Λ are estimated by using I, N and the standard deviation, s(D), of the drop diameters obtained from the measured momentum distribution. In the Scenario 2 the parameters are estimated using I, N and the mean value, m(D), of the drop diameters deriving from the momentum distribution. The reliability of the proposed procedures was tested using DSD measurements recorded in three different experimental sites. The developed analysis demonstrated that Scenario 2 is the best method to estimate μ and Λ, and to reproduce the DSD, accordingly. The proposed method, associated with a patented device, not yet build, will allow the direct measurement of the rainfall energy characteristics, which are usually roughly estimated from rainfall intensity. The possibility to easily measure these energy variables can support the development of research in the field of soil erosion and, in general, of hydrogeological instability. In particular, the proposed measurement method and the construction of the device could stimulate the scientific community to deepen the study of the effect of the rainfall energy on soil erosion for improving the predictive capability of water erosion models. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the greatest environmental degradation challenges the tropical region and the world in general are facing. Tropical soils are less erodible than those of the temperate region, but climatic erosivity is higher in the tropical region due to high erosive power of rain than those of the temperate region. In the past and present, physical processes of soil erosion and soil erosion control have been assessed using the revised universal soil loss equation (RUSLE) model. This review aims at revisiting the questioned reliability of the RUSLE for soil erosion prediction in the tropics. The RUSLE model estimates the long-term average annual soil loss (A) by multiplying out factors of rainfall erosivity (R) and soil erodibility (K), slope length and slope steepness factors (LS), land cover and management factor (C) and the soil conservation or prevention practices factor (P). The RUSLE model is not as data demanding and so more often used than any other tool available. It is also characterized by high degree of fl exibility, data accessibility and suitability in computer program. As a drawback, the development of the model is site-specifi c and often criticized as being a mere temperate-based empirical erosion model which cannot be applied in tropical soil erosion predictions. However, amongst all other soil erosion prediction tools, the model is dependable and reliable in the tropics especially now that it combines with remote sensing (RS), digital elevation model (DEM) and geographical information system (GIS) to estimate annual soil loss (on a pixel-bypixel basis) and spatial distribution of the soil erosion. [ABSTRACT FROM AUTHOR]

Over half of the approximately 12,000 earthen watershed dams sponsored by the USDA have exceeded their planned 50-year service life. Age, land use changes, extreme weather events, structural deterioration, and sedimentation filling flood pools pose increased risks of dam incidents and potential failures. Among various mechanisms leading to integrity issues, soil erosion is of particular concern due to its potential to occur with little warning. The objective of this research is to determine if soil erosion can be predicted using acoustic emissions. A simulated dam overtopping experiment was replicated in a test flume with dimensions of 0.61 m by 4.27 m (2 ft. by 14 ft.) with a 13.7% slope and a 0.15 m (6 in) layer of inorganic clay (USCS CL) compacted at 17.4% moisture content. A constant flow discharge of 0.07 m3/s (2.37 cfs) was applied to induce erosion. The test was performed until complete failure of the test section occurred. Throughout the experiment, a sonar radar, a 3D scanning total station, and an accelerometer were used to monitor the water level, erosion levels, and vibrations, respectively. The frequency analysis of the water-induced vibrations was compared to measured erosion volumes to determine if in situ vibrations can predict erosion. The results revealed a linear relationship between erosion volume and time, with noticeable changes in the frequency domains as erosion progressed. The outcomes of this research have the potential to provide real-time insights into the integrity of earthen dams concerning erosion, offering a valuable tool for monitoring and maintenance. [ABSTRACT FROM AUTHOR]

Scouring and mass failure are two common mechanisms used to describe soil bed erosion, but their combined effects are often not considered. To better understand how these mechanisms compete and under what conditions they prevail, it is essential to consider infiltration and a more realistic unsaturated soil bed. This study investigates soil bed erosion by considering unsaturated soil mechanics, a wetting front, and both erosion mechanisms of scouring and mass failure. Physical experiments were conducted on model water runoff over an unsaturated sand bed to investigate the effects of soil water content and flow velocity on erosion. Experimental results show that current understanding of soil bed erosion can be enhanced by adopting unsaturated soil mechanics and considering the combined effects of scouring and mass failure. The scouring rate is found to be independent of the bed water content because it only affects the uppermost soil particles, which immediately become saturated once water flows over them. Mass failure, on the other hand, is initiated at the wetting front when the rate of infiltration exceeds that of scouring. The depth of mass failure can be described by the net infiltration depth, which is defined as the difference between the infiltration and scouring depths. The net infiltration depth is jointly governed by the soil water content and flow velocity. The crucial role of the coupled effects of the hydro-mechanical behavior of unsaturated soil in the realistic modeling of soil bed erosion is demonstrated. Outcomes present advancement toward improved hazard assessments of debris flows. Plain Language Summary When water flows over unsaturated soil on a slope, the soil bed can be eroded by either scouring or mass failure. Scouring occurs when the surficial soil particles are transported by the flow, and mass failure occurs when the stress exerted by the flow exceeds the soil strength at a potential failure plane. It is important to study how these two mechanisms compete and the conditions that enable each mechanism to prevail. In turn, improved predictions of soil bed erosion can be obtained. Furthermore, erosion is governed by the infiltration of flow water, which changes the soil water content and the soil strength. In this study, soil bed erosion was investigated by considering different erosion mechanisms and flow water infiltration, and a unique set of physical experiments was carried out. The results show that the rate of scouring is not sensitive to the changes in the soil moisture content but increases with the flow velocity. Mass failure can be assessed with soil water content and flow velocity. The findings of this research provide engineers and scientists with an enhanced understanding of soil bed erosion and the subsequent growth of the debris flow volume. [ABSTRACT FROM AUTHOR]

Recognizing soil as a vital resource for food production and animal habitat, this study employed a comprehensive, nationwide erosion assessment in Bangladesh using the Revised Universal Soil Loss Equation (RUSLE) model. Rainfall, soil data, the Digital Elevation Model (DEM), Landsat 8 imagery, and land use and land cover (LULC) maps were employed as inputs for the prediction of potential soil erosion. Utilizing the zonal statistics tool within a geographic information system (GIS) platform, the study area's most erosion-vulnerable zones were subsequently identified and mapped. These zones were designated as 'erosion hotspots' (EHs) with demonstrably clear connections to river basins and networks. Analysis revealed an annual average erosion rate of 1.3 t ha−1 yr−1, resulting in an alarming loss of 17.68 million tons of topsoil annually. Notably, four distinct EHs were identified within the study area. The southeast region, due to its specific topography and climate, was found to be highly susceptible to erosion. Within this region, 2.22% (2995.93 km2) experience erosion exceeding 10.0 t ha−1 yr−1. RUSLE-based results were validated by comparison with outputs from the Global Soil Erosion Modeling Platform (GloSEM) and Google Earth imagery. In addition, the model's performance was evaluated using the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). The high AUC value of 0.94 achieved in this study reinforces the accuracy and confidence of the soil erosion estimations. [ABSTRACT FROM AUTHOR]

Rock outcrops play a crucial role in affecting hydrological and erosion processes on karst hillslopes. However, due to various difficulties in constructing runoff plots on field rocky slopes, previous studies used small metal flumes to simulate outcropped slopes, which greatly differed from the natural rock outcropped slopes. In this study, the effects of rock outcrops on runoff and erosion were studied on karst slopes with natural soil–rock conditions at a larger plot scale (10 m in length × 2.2 m in width, 25° in gradient). Simulated rainfall experiments were conducted on the four runoff plots with different rock outcrop coverages (0%, 5%, 13%, and 42%) under five rainfall intensities (30, 60, 90, 120, and 150 mm·h−1). The study indicated that the rock outcrops complexed the water erosion processes by playing multiple and opposite roles simultaneously. It positively influences water erosion by serving as impermeable surfaces, providing preferential flow paths, and offering flow barriers to hillslopes. Conversely, it can exacerbate erosion by causing increased runoff, concentrating flow paths, and thus intensifying erosion in specific areas. Besides, the effects of these roles varied with the coverage of rock outcrops and rainfall intensity. The main results showed that (i) limited rock outcrop coverage (5% and 13%) increased runoff and erosion, but the opposite was observed on the severe rocky slope (42%). Both "infiltration‐excess" and "saturation‐excess" mechanisms coexisted by turns on karst slopes during rainfall; the break of "saturation balance" tended to occur on the severe rock outcrop slope with abundant soil–rock interface and underground fissures. (ii) Rock outcrops greatly increased the spatial variability and uncertainty of hydrological and erosion processes on karst slopes. The rock outcrops resulted in a larger range of variation in flow velocity and a greater difference among the three slope positions; it also brought more spatial heterogeneity in the development of rill erosion. (iii) Rock outcrops tended to increase flow velocity, and intensify flow turbulence, stream power, and flow energy. The severe rock outcrops significantly reduced the soil erosion induced by the limited soil detachment. This study contributes to a deeper understanding of the mechanisms behind how rock outcrops influence runoff and erosion and provides insights for improving soil erosion prediction in karst regions. [ABSTRACT FROM AUTHOR]

Soil serves as a reservoir for organic carbon stock, which indicates soil quality and fertility within the terrestrial ecosystem. Therefore, it is crucial to comprehend the spatial distribution of soil organic carbon stock (SOCS) and the factors influencing it to achieve sustainable practices and ensure soil health. Thus, the present study aimed to apply four machine learning (ML) models, namely, random forest (RF), k-nearest neighbors (kNN), support vector machine (SVM), and Cubist model tree (Cubist), to improve the prediction of SOCS in the Srou catchment located in the Upper Oum Er-Rbia watershed, Morocco. From an inventory of 120 sample points, 80% were used for training the model, with the remaining 20% set aside for model testing. Boruta's algorithm and the multicollinearity test identified only nine (9) factors as the controlling factors selected as input data for predicting SOCS. As a result, spatial distribution maps for SOCS were generated for all models, then compared, and further validated using statistical metrics. Among the models tested, the RF model exhibited the best performance (R2 = 0.76, RMSE = 0.52 Mg C/ha, NRMSE = 0.13, and MAE = 0.34 Mg C/ha), followed closely by the SVM model (R2 = 0.68, RMSE = 0.59 Mg C/ha, NRMSE = 0.15, and MAE = 0.34 Mg C/ha) and Cubist model (R2 = 0.64, RMSE = 0.63 Mg C/ha, NRMSE = 0.16, and MAE = 0.43 Mg C/ha), while the kNN model had the lowest performance (R2 = 0.31, RMSE = 0.94 Mg C/ha, NRMSE = 0.24, and MAE = 0.63 Mg C/ha). However, bulk density, pH, electrical conductivity, and calcium carbonate were the most important factors for spatially predicting SOCS in this semi-arid region. Hence, the methodology used in this study, which relies on ML algorithms, holds the potential for modeling and mapping SOCS and soil properties in comparable contexts elsewhere. [ABSTRACT FROM AUTHOR]

Purpose: Hilly areas are highly susceptible to soil erosion. This study aims to discover the drivers of soil erosion, identify soil erosion–sensitive areas, and predict future soil erosion in typical hilly areas of Hubei Province, China, using combined RUSLE and LSTM models. Materials and methods: In this study, soil erosion in hilly areas of Hubei Province from 2000 to 2020 was quantitatively analyzed using the revised universal soil loss equation (RUSLE), and the soil erosion sensitivity evaluation system was constructed, a geographic detector was employed to identify the main drivers of soil erosion sensitivity, and using the long short-term memory neural network model (LSTM) to predict soil erosion in 2025. Results and discussions: The results showed that most areas were dominated by slight and moderate erosion. Slope and vegetation coverage were identified as the core elements influencing the space heterogeneity of soil erosion. Soil erosion sensitivity was mainly composed of moderate sensitivity, accounting for more than 70% of the total area. The strong and extreme sensitivity demonstrated a downward trend with the continued implementation of slope management and forest rehabilitation from slope agriculture, whereas the sensitivity was still higher in the northwest and southwest Hubei Province. Regions with severe soil erosion had high sensitivity, and the spatial distribution of the two is strongly coherent. Areas with surface relief > 300 m and vegetation cover < 30% had the highest sensitivity and should be highly valued. The percentage of moderate and higher soil erosion area in 2025 was 3.77% lower than in 2020, but severe erosion still exists in the northwest and southwest Hubei Province. Conclusions: Soil erosion sensitivity in the western part of the study area was the highest, followed by the southeast, and the overall erosion sensitivity was gradually decreasing during the studied period. In the future, soil erosion intensity will show a downward trend, whereas the deployment of soil and water conservation measures in soil erosion–sensitive areas should still be strengthened. The results are helpful for accurate soil erosion control and prediction in the hilly areas of Hubei Province, China. [ABSTRACT FROM AUTHOR]

Soil erosion and the consequent loss of nutrients have consistently been significant factors contributing to land degradation and nonpoint source pollution. While runoff serves as the primary carrier for nutrient loss, the hydraulic processes governing the mechanisms of nutrient loss remain not entirely clear. This paper aims to investigate the impacts of rainfall intensity and the slope gradient on hydraulic parameters, soil loss rates, and ammonia nitrogen loss rates, with the objective of determining the optimal hydraulic parameters for more accurate predictions of soil erosion and nutrient loss rates. A series of simulated rainfall experiments with three rainfall intensities (25, 50, and 75 mm min−1) and four slope gradients (8.7%, 17.6%, 26.8%, and 36.4%) were conducted on a 5 m × 10 m slope. The results indicated that the flow velocity, shear stress, stream power, unit stream power, and unit energy all increased with the increase in slope gradient or rainfall intensity. The water depth decreased with an increase in the slope gradient but increased with an increase in the rainfall intensity. Laminar flow occurred in all experiments (Reynolds number < 500). Only the overland flow under a 25 mm h−1 rainfall intensity and 8.7% slope gradient was subcritical flow (Froude number < 1). Hydraulic parameters, the soil loss rate, and ammonia nitrogen loss rate could be all expressed as the product of rainfall intensity and slope power function, with R2 ranging from 0.949 to 0.997. The average soil loss rate and process soil erosion rate could both be fitted using the power function of hydraulic parameters, with the optimal fitting parameter being stream power (R2 = 0.980 and 0.909). The average ammonia nitrogen loss rate exhibited a linear relationship with the hydraulic parameters, and the optimal fitting parameter was also stream power (R2 = 0.933). However, there were relatively low correlations between hydrodynamic parameters and the ammonia nitrogen loss rate (R2 = 0.450–0587). Our results contribute to a deeper understanding of the hydraulic processes involved in nutrient loss. [ABSTRACT FROM AUTHOR]

Problems in intelligent farming include soil erosion, pests, diseases, etc. Population intelligence optimization algorithms have a wide range of application prospects in farming environments. While in practical applications, there are problems such as vulnerability to disturbance, tendency to fall into local optimization, and lack of accuracy. Thus, in this study, the Moth Flame Optimization (MFO), Grey Wolf Algorithm (GWO), and Integrated Particle Algorithm (PSO) were combined with improved algorithms for optimization. The Sine Cosine Strategy (SCS) was used to promote the MFO algorithm and was adopted for the land erosion prediction problem based on the Kernel-Based Extreme Learning Machine (KELM) algorithm. The multi-strategy mechanism was used to improve the GWO as a basis for designing an accurate fertilization model. The traditional PSO algorithm was improved by applying elite augmentation and applied to the segmentation of maize disease images. The results showed that the improvement of SMFO-KELM for KELM effectively improved the prediction ability of soil erosion classification. In intelligent agriculture, the performance of multi-strategy GWO was distinctly better than other improved algorithms. In contrast to traditional PSO algorithm, the structure similarity index of the elite enhanced PSO algorithm was improved from 0.88 to 0.95, and the feature similarity index was improved from 0.72 to 0.86 and could obtain better segmentation accuracy than other similar algorithms in solving the overall effect of multi-threshold segmentation for maize rust spot disease. The accuracy of the population intelligence algorithm was improved, and the problem of interference was solved. The use of the population intelligence optimization algorithm realized real-time monitoring and intelligent management of the corn field environment, including the monitoring and regulation of soil moisture, temperature, nutrient status, and other parameters to promote the growth and development of maize and improve the yield and quality of maize, which helped to promote the development of intelligent agriculture and realize the refinement and intelligent management of the agricultural production process. [ABSTRACT FROM AUTHOR]

Soil erosion is a destructive consequence of land degradation caused by deforestation, improper farming practices, overgrazing, and urbanization. This irreversible effect negatively impacts the limited renewable soil resource, causing soil truncation, reduced fertility, and unstable slopes. To address the anticipation of erosion modulus resulting from long-term land use and land cover (LULC) changes, a study was conducted in the Swat District of Khyber Pakhtunkhwa (Kpk), Pakistan. The study aimed to predict and evaluate soil erosion concerning these changes using remote sensing (RS), geographic information systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE) model. We also evaluated the impact of the Billion Tree Tsunami Project (BTTP) on soil erosion in the region. Model inputs, such as rainfall erosivity factor, topography factor, land cover and management factor, and erodibility factor, were used to calculate soil erosion. The results revealed that significant soil loss occurred under 2001, 2011, and 2021 LULC conditions, accounting for 67.26%, 61.78%, and 65.32%, falling within the category of low erosion potential. The vulnerable topographical features of the area indicated higher erosion modulus. The maximum soil loss rates observed in 2001, 2011, and 2021 were 80 t/ha−1/year−1, 120 t/ha−1/year−1, and 96 t/ha−1/year−1, respectively. However, the observed reduction in soil loss in 2021 as compared to 2001 and 2011 suggests a positive influence of the BTTP on soil conservation efforts. This study underscores the potential of afforestation initiatives like the BTTP in mitigating soil erosion and highlights the significance of environmental conservation programs in regions with vulnerable topography. [ABSTRACT FROM AUTHOR]

Soil erosion is a significant problem in the agriculture sector and the environment globally. Susceptible soil erosion zones must be identified and erosion rates evaluated to decrease land degradation problems and increase crop productivity by protecting soil fertility. Therefore, a research study has been carried out in the Ponnaniyar River basin, an ungauged tributary of the Cauvery basin in India, primarily used for agriculture. The main purpose of this study is to assess soil erosion (SE) and sediment yield (SY) for the future in an ungauged basin by utilizing the projected land use/land cover (LULC) map of the study area. Additionally, Landsat 8 satellite dataset was only used for the classification and prediction of LULC to eliminate the variation between the resolution, bands and its wavelength of different satellites datasets. To achieve the goals of this study, three phases were followed. First, the LULC of the study area was classified using a Random Trees Classifier (RTC), a machine learning technique, followed by the projection of land cover using a Cellular Automata-based Artificial Neural Network (CA-ANN) model. The driving factors for this model include digital elevation model (DEM), slope, distance to roads, settlements, and water bodies. The accuracy level of the projected LULC map was determined by comparing it with the classified LULC map of the study area, and the results showed an overall accuracy (OA) of 85.35 percentage and a kappa coefficient (K) of 0.74, respectively. Second, the projected LULC map was used in the land management factor (C) and conversation practice factor (P) of the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion. The model was integrated with the sediment delivery ratio (SDR) to estimate sediment yield within the study area. The accuracy of the generated erosion map based on the classified and projected LULC for the year 2022 was determined using the receiver operating characteristic curve (ROC) curve, and it was found to be in satisfactory agreement. Finally, for effective soil and water conservation measures, the basin was divided into 13 sub-watersheds (SWs) using terrain analysis in geographical information system (GIS). The SWs were prioritized based on the mean soil loss in the 4-year interval from 2014 to 2030 and integrated using the weighted average method to determine the final prioritization. From these findings, SW 11, SW 9, SW 12, and SW 1 are extremely affected by soil erosion, and immediate implementation of water harvesting structures is required for soil conservation. Also, this research might be useful for decision-makers and policymakers in land management. [ABSTRACT FROM AUTHOR]

This study focuses on the suspended sediment delivery of 17 rivers and streams of various sizes to the sea over a wide geographical area covering most of the Greek peninsula, utilizing two Moderate Resolution Imaging Spectroradiometer (MODIS) products. Equal-area polygons ("plume" polygons), were delineated at the mouths of each selected river. These polygons were utilized to estimate the suspended sediment load of each river through the application of suspended sediment indices, ratios, and masks. To achieve this, 669 Level 1B MODIS images (MOD02) and their corresponding MODIS cloud products (MOD35) were downloaded and processed for a 10-water-year period (2004–2014). During this period of 669 days, there were 58 flood events (episodes) ranging in duration from 5 to 45 days. Relative atmospheric correction was applied to the images based on four selected bright invariant areas (PIFs) scattered along mainland Greece. The second product used in this study was MOD09Q1, an atmospherically corrected 8-day composite processed for the entire record period (2000–2019). Suspended sediment indices, ratios, and masks were developed using all three visible channels and near-infrared (NIR) for the MOD02 dataset, while only Red and Near-InfraRed (NIR) channels were available from the MOD09Q dataset. The resulting rankings from the remote sensing analysis were compared with the predictions of soil loss models, and the outcomes were largely consistent. While the remote sensing results can be considered as a type of experimental data or measurements, they come with inherent limitations. These include infrequent access to cloud-free data on stormy days, the influence of wind and currents, and the potential impact of dust storms originating from Africa, among others. On the other hand, soil loss models are sensitive to the parameter values used, and in some cases, the uncertainties are significant. Hence, the ranking derived from remote sensing can serve as a calibration of the models, particularly for the BQART model, which provides information on the catchment's sink capacity. An index of "sediment productivity per square kilometer and mm of rainfall" was developed. This index can be considered a "sediment delivery ratio" and is crucial for accurately quantifying the phenomenon. [ABSTRACT FROM AUTHOR]

Global warming is an essential factor to consider when studying tidal wetlands. The Río Piedras and Flecha de El Rompido salt marsh is one of the main wetlands in Andalusia, Spain. From the mid-1950s to the present day, Land Use Changes (LUCs) have caused significant alterations to the landscape. These changes, along with the effects of climatic variables and human activity, have led to an unprecedented impact on the environment. In this study, a patented method is used to obtain the total cubic meters of eroded soil and the average erosion prediction between 2015 and 2021 in the marshland area. Additionally, the various factors contributing to this phenomenon and the influence of intertidal processes are discussed. The results demonstrate how the enhanced integration of LIDAR technologies, digital elevation models, and Geographic Information Systems (GIS), in conjunction with regression models, has proven highly useful in describing, analyzing, and predicting the volumetric change process in the study area. In conclusion, the methodology used is helpful for any type of coastal marshes influenced by tidal processes and climate change. [ABSTRACT FROM AUTHOR]

Erosion of soil refers to the process of detaching and transporting topsoil from the land surface by natural forces such as water, wind, and other factors. As a result of this process, soil fertility is lost, water bodies' depth is reduced, water turbidity rises, and flood hazard problems, etc. Using a numerical model of erosion rates and erosion risks in the Jejebe watershed of the Baro Akobo basin in western Ethiopia, this study mapped erosion risks to prioritize conservation measures. In this study, the Revised Universal Soil Loss Equation (RUSLE) model was used, which was adapted to Ethiopian conditions. To estimate soil loss with RUSLE, the rainfall erosivity (R) factor was generated by interpolating rainfall data, the soil erodibility (K) factor was derived from the soil map, the topography (LS) factor was determined from the digital elevation model (DEM), cover and management (C) factor derived from the land use/cover data, and conservation practices (P) factor generated from digital elevation model (DEM) and land use/cover data were integrated with remote sensing data and the GIS 10.5 environment. The findings indicated that the watershed annual soil loss varies from nearly 0 on a gentle slope of forest lands to 265.8 t ha−1 year−1 in the very steep slope upper part of the watershed, with a mean annual soil loss of 36.2 t ha−1 year−1. The total annual soil loss in the watershed is estimated to be around 919,886.5 tons per year. To minimize the amount of soil erosion in the watershed that had been most severely affected, we identified eight conservation strategies that could be implemented. These strategies were based on the participatory watershed development (PWD) principles established by the Ethiopian government and the severity of the erosion in the watershed. The study's findings showed that a GIS-based RUSLE soil erosion assessment model can provide a realistic prediction of the amount of soil loss that will occur in the watershed. This tool can also help identify the priority areas for implementing effective erosion control measures. [ABSTRACT FROM AUTHOR]

Soil erosion poses a significant threat to sustainable land management and agricultural productivity. Addressing this issue requires advanced predictive models that can accurately identify areas at risk and inform soil conservation strategies. This study focuses on the development and interpretation of well-optimized deep learning (DL) models to predict soil erosion probability, aiming to enhance decision-making in land management. Utilizing the Revised Universal Soil Loss Equation (RUSLE) in conjunction with ground-truthing, we identified critical erosion-prone areas. To predict soil erosion probability, we employed Bayesian optimization to fine-tune Deep Neural Network (DNN), Convolutional Neural Network (CNN), Fully Connected Neural Network (FCNN), and DNN-CNN hybrid models. These DL models were verified using a set of metrics. SHAP value analysis as a means of explainable artificial intelligence (XAI) was used to interpret these DL models for better decision-making. RUSLE estimations and ground truthing highlight that Soil erosion rates in the northeastern and northwestern regions are nearing the highest observed at 25 tonnes per hectare annually, largely due to steep slopes and limited vegetation. In contrast, the southern and southeastern areas have lower erosion rates, due to denser vegetation and gentler slopes. Deep learning models, optimized using Bayesian methods, demonstrate high performance in spatially modeling soil erosion probability. The DNN model achieved an accuracy of 0.93, a precision of 0.92, and an F1-score of 0.94, identifying 222.73 sq. km as highly susceptible to erosion, which indicates its strong ability to detect true erosion events. The CNN model identified 49.68% of the study area (503.30 sq. km) as high-risk, with an accuracy of 0.90 and a precision of 0.91. The FCNN model showed a balanced risk distribution, indicating 37.04% of the land (375.25 sq. km) as very low risk and 36.43% (369.11 sq. km) as very high risk, with an accuracy of 0.91. The DNN-CNN hybrid model highlighted 41.58% of the area (421.20 sq. km) as high risk, demonstrating its effectiveness in capturing spatial patterns of erosion susceptibility. SHAP value analysis indicates that land use and soil type (LULC and K-factor) are crucial in erosion predictions, with LULC having a significant predictive influence in the DNN model. These insights facilitate the prioritization of soil conservation measures, enabling decision-makers to focus on the most impactful factors for mitigating soil erosion.

Estimating the critical slope angle (CSA) for sheet erosion is important for the precision estimation of sheet erosion and the development of erosion control practices. This study developed mathematical equations considering rainfall intensity and soil infiltration to efficiently estimate both instantaneous (at a given instant during rainfall) and cumulative CSAs, while also providing a valuable explanation for the change in CSA. The mathematical equations were consistent with observations from runoff plots (NSE = −1.01) of loess soils from Zhangjiakou (China) and simulation results (NSE = 0.96) from the Water Erosion Prediction Project model for a loam soil in Montana (USA). Estimated instantaneous CSA determined by the mathematical equations increased as the ratio of rainfall intensity to soil infiltration (I/f) increased, resulting in higher observed cumulative CSA after heavy versus normal rainfall events. Heavy rainfall, compacted soil, and varying rainfall duration affected the CSA by changing the I/f ratio. Maximum instantaneous CSA provided a better prediction of changes in soil erosion dynamics than that obtained from CSAs determined by field observations or experimental simulations. The mathematical equations illustrate the underlying physical mechanisms by which rainfall intensity and soil infiltration affect the CSA through changing the shear stress of overland flow. The results of this study provide critical information for guiding the development of effective soil erosion control strategies. [ABSTRACT FROM AUTHOR]

The issue of regional soil and water loss caused by human activity is particularly severe in coastal regions. Since coastal reclamation areas are a valuable land reserve resource, it is of practical significance to understand the distribution characteristics of rainfall erosion and its impact on soil erosion for the prediction, evaluation, and management of regional soil and water resources. Rainfall erosivity should be updated and estimated from simplified indices. This paper analyzed the observed rainfall data of field runoff plots in Dongtai City, Jiangsu Province, between 2011 and 2017. According to the standard of erosive rainfall in coastal areas, reporting 10.8 mm of rainfall or 7.6 mm·h−1 of I30 (maximum 30 min rainfall intensity), the annual average erosive rainfall frequency in Dongtai City was 37.7 and the annual erosive rainfall was 1082.0 mm on average, which accounted for 51.6% and 90.6% of the total rainfall frequency and the total rainfall, respectively. Moreover, the annual average rainfall erosivity in the region from 2011 to 2017 was 7717.4 MJ·mm·hm−2·h−1. The annual distribution of rainfall erosivity was irregular, with an average monthly erosivity value of 4501.8 MJ·mm·hm−2·h−1. Since the accumulated rainfall erosivity of Dongtai City in the flood season (May to September) accounted for 88.1% of the total rainfall erosivity, it is essential to focus on preventing soil and water loss in the flood season. This paper established a rainfall-based model and a composite model and intensity appropriate for a single event and monthly rainfall erosivity in the region. Both models can be used to calculate the annual rainfall erosivity, but only the composite model based on rainfall amount and intensity is recommended for calculating single and monthly rainfall erosivity levels in Jiangsu coastal areas. The empirical formulas in Jiangsu coastal areas can be updated using more recent rainfall data and assess soil erosion risk accurately. [ABSTRACT FROM AUTHOR]

A reliable prediction of event soil loss at the plot scale can be obtained by Universal Soil Loss Equation (USLE)-type models. For the Sparacia site (South Italy), the USLE-MB model was recently developed, in which the effect of the erosive agent is modeled using the rainfall erosivity index of the USLE by a power b1 > 1 of the runoff coefficient QR. In this investigation, the model is parameterized separately using plot data collected for rill and interrill events that occurred in the Sparacia experimental area. The values b1 = 1.406 and b1 = 1.012 were obtained for the interrill and rill databases, respectively, which revealed a different effect of the runoff coefficient on soil loss due to the two erosive processes. Different relationships expressive of topographic factors were also deduced. The USLE-MB estimation performance significantly improved when operating the distinction between the two databases compared with the model parameterized on the complete database. The model was particularly reliable in estimating the highest event soil loss values, which were associated with the occurrence of rills. Finally, the proposed parameterization procedure lends itself to being tested in the framework of empirical soil loss modeling. [ABSTRACT FROM AUTHOR]

The Hilbert–Huang transform (HHT) has been used as a powerful tool for analyzing nonlinear and nonstationary time series. Soil loss is controlled by complicated physical processes and thus fluctuates with nonlinearity and nonstationarity over time. In order to further clarify the relationship between rainfall, surface runoff, and sediment yield, this study adopted the HHT to analyze these characteristics through multiple time scales and investigated their relationship through time-dependent intrinsic correlation (TDIC) in the time series. A six-year study (2015–2020) was conducted on sloping farmlands to explore the relationships between soil loss and rainfall in southwest China. Time series of soil loss and rainfall were identified as the relevant characteristics at different time scales based on the method of HHT. Local correlation between the soil loss and runoff was carried out by the method of TDIC. The original time series of the rainfall, runoff, and soil loss were decomposed into eight intrinsic mode functions (IMFs) and a residue by ensemble empirical mode decomposition (EEMD). The residue indicated that the rainfall and runoff increased and then decreased during the maize-growing season from 2015 to 2020, whereas the soil loss gradually decreased. IMF1 and IMF2 accounted for nearly 80% of the temporal variations in rainfall, runoff, and soil loss, indicating that the variables varied the most at short time scales. The TDIC analysis showed that strong and positive correlations between the soil loss, rainfall, and runoff prevailed over the entire time domain at the scales of IMF1 and IMF2, indicating the rapid response of the soil loss to rainfall and runoff at short time scales. Time-varying correlations were observed at the IMF3–IMF5 scales. At the IMF7 scale, an evident switchover in the nature of the correlation was identified during the years 2018 and 2019; this could be related to a sudden rainstorm under low vegetation coverage conditions. The EEMD-based TDIC tool is an effective means to clarify the relationship between soil loss, rainfall, and runoff. Our results provide a better understanding of the relationship between soil loss and rainfall varied with time at multiple time scales. Short-term heavy rainfall and rapid surface runoff are the important factors causing serious soil and water loss on a short time scale in a mountainous region with yellow soil, which is of great significance for the construction of a regional soil erosion prediction model. [ABSTRACT FROM AUTHOR]

Rainfall erosivity (RE) is an important indicator of the ability of rainfall to cause soil erosion and is linked to ENSO through the transport of rainfall. Accurate assessment of RE and improved understanding of RE are essential for soil erosion prediction, optimization of soil and water conservation measures, and ecological management and restoration. Therefore, the Wuding River Basin, a typical ecologically fragile area, is selected as the research area. The erosivity model based on daily rainfall was first used to calculate RE, and the temporal and periodic characteristics of RE are studied. Then the effects of the Southern Oscillation Index (SOI), the Surface Temperature of the Central East Equatorial Pacific Ocean (SST), and the Multivariate ENSO Index (MEI) on RE are explored by using cross wavelet technique. Results indicated that: (1) the concentration of rainfall leads to the largest RE values in summer compared with other seasons, accumulating about 69% of the annual RE; (2) The overall trend of increasing RE in the Wuding River basin is not significant, but shows an abrupt change in 2015; (3) the influence of the SOI and SST indices of ENSO events on RE is significant, which is shown to be a statistically significant correlation (95% confidence level), indicating that ENSO has a strong influence on the changing pattern of RE. These findings are helpful in predicting soil erosion and are significant for developing further erosion control measures. [ABSTRACT FROM AUTHOR]

Soil is the source of the vast majority of food consumed on Earth, and soils constitute the largest terrestrial carbon pool. Soil erosion associated with agriculture reduces crop productivity, and the redistribution of soil organic carbon (SOC) by erosion has potential to influence the global carbon cycle. Tillage strongly influences the erosion and redistribution of soil and SOC. However, tillage is rarely considered in predictions of soil erosion in the U.S.; hence regionwide estimates of both the current magnitude and future trends of soil redistribution by tillage are unknown. Here we use a landscape evolution model to forecast soil and SOC redistribution in the Midwestern United States over centennial timescales. We predict that present‐day rates of soil and SOC erosion are 1.1 ± 0.4 kg ⋅ m−‐2 ⋅ yr−‐1 and 12 ± 4 g ⋅ m−2 ⋅ yr−1, respectively, but these rates will rapidly decelerate due to diffusive evolution of topography and the progressive depletion of SOC in eroding soil profiles. After 100 years, we forecast that 8.8 (+1.9/−2.1) Pg of soil and 0.17 (+0.03/−0.04) Pg of SOC will have eroded, causing the surface concentration of SOC to decrease by 4.4% (+0.9/−1.1%). Model simulations that include more widespread adoption of low‐intensity tillage (i.e., no‐till farming) determine that soil redistribution, SOC redistribution, and surficial SOC loss after 100 years would decrease by ∼95% if low‐intensity tillage is fully adopted. Our findings indicate that low‐intensity tillage could greatly decrease soil degradation and that the potential for agricultural soil erosion to influence the global carbon cycle will diminish with time due to a reduction in SOC burial. Plain Language Summary: Plow‐based agriculture has accelerated soil erosion rates by one to two orders of magnitude, which diminishes crop productivity and has the potential to impact the carbon cycle. Using a numerical model, we calculate that present‐day rate of soil and soil organic carbon (SOC) erosion are 1.1 ± 0.4 kg ⋅ m−‐2 ⋅ yr−‐1 and 12 ± 4 g ⋅ m−2 ⋅ yr−1, respectively and predict that 8.8 (+1.9/−2.1) Pg of soil and 0.17 (+0.03/−0.04) Pg of SOC will be eroded in the Midwestern United States over the next century. Compared to other measured rates of soil and SOC erosion, the predicted rates are relatively high and have the potential to degrade soil and influence the carbon cycle. With our model, we simulated the more widespread adoption of low‐intensity tillage (i.e., no‐till farming) and found that the full adoption of low‐intensity tillage would reduce soil and SOC erosion by ∼95% over the next 100 years. Therefore, our results indicate that further adoption of low‐intensity tillage has the potential to greatly reduce soil and SOC erosion and contribute to soil sustainability. Key Points: Soil and soil carbon redistribution are predicted across the Midwestern United States over centennial timescales using a landscape evolution modelIn the next century, we predict soil and soil organic carbon (SOC) erosion to be 8.8 (+1.9/−2.1) and 0.17 (+0.03/−0.04) Pg, respectivelyWe predict that the total adoption of low‐intensity tillage practices can reduce soil and SOC erosion by 95% after 100 years [ABSTRACT FROM AUTHOR]

Soil detachment capacity by rill flow (Dc) is sensitive to changes in topsoil properties generated by crop straw incorporation. In the present study, we investigated the effect of crop straw incorporation on the seasonal variation in Dc, the knowledge regarding which is limited currently, and identified the key influencing factors. Three typical crop straw types (wheat, soybean, and maize) were selected for this study. Soil samples incorporated with crop straw and without crop straw were collected over 375 days (from October 1, 2020 to October 10, 2021) and were subjected to a hydraulic flume (length, 4.0 m; width, 0.2 m) to determine Dc. The topsoil properties soil bulk density (BD), soil cohesion (Coh), soil penetration resistance (PR), soil organic matter content (SOM), mean weight diameter (MWD), and straw decomposition rate (SDR) were measured to explain the seasonal variation in Dc. The results showed that crop straw incorporation effectively reduced Dc, and the Dc for the soil incorporated with straw decreased by 25.92%–38.76% compared with that without straw incorporation. Dc exhibited a significant decreasing trend over time, and the Dc at the end stage decreased by 83.00%–88.31%, compared with the initial stage. Seasonal variation in Dc was significantly correlated with BD, Coh, PR, SOM, MWD, and SDR. The measured Dc decreased with BD and Coh as a power function and with SOM and MWD as an exponential function. The influence of straw incorporation on Dc is time‐dependent and can be divided into short‐term and long‐term effects. Natural soil consolidation gradually masked the effect of straw residue with the decomposition of the incorporated straw. The comprehensive response of seasonal variation in Dc to straw incorporation was described by a power combination equation (Dc = 1.23 τ1.65 BD−2.63 Coh−1.33 SOM−0.70; R2 = 0.96). Thus, the seasonal variations in Dc with straw incorporation were mainly controlled by the changes in soil properties triggered by the decomposition of incorporated straw and natural soil consolidation. The results of this study provided a scientific reference for soil erosion prediction and control in sloping farmlands. [ABSTRACT FROM AUTHOR]

First, the writers thank the discussers for their interest in their article and for providing further opportunity to elucidate two fundamental aspects perturbing the contribution of the Froude number (F) in assessing flow resistance in movable-bed rivers. 2 compares calculated HT ht and predicted HT ht . If HT ht , the flow is supercritical, and if HT ht , the flow is subcritical. As, HT ht does not depend only on HT ht and HT ht , these two input parameters in model development cannot be reliable. [Extracted from the article]

Land use affects ecosystem stability and agricultural ecological security in black soil regions. Additional attention is required regarding the impact of different land-use patterns on black soil. However, the construction of sustainable agricultural ecological security in black soil environments is a dynamic process that depends on the reviews of experts and statistical analyses of literature data. This study quantitatively reviewed the past 20 years of the literature regarding black soil. Using the superposition of the expert knowledge map and machine clustering, knowledge regarding land use in black soil fields was classified structurally. Further, studies directly related to the spatiotemporal pattern of land use were identified, and frequently cited works of the literature were screened to build a dynamic knowledge network of black soil research. The results show that (1) the cooperative relationship among China, the United States, and Canada is the strongest, but the density of cooperation networks between other countries is low; (2) land-use research regarding black soil is divided into four research areas: soil microbial community and activity, soil erosion and ecological processes, ecological management of land use, soil organic matter, and element cycling; (3) the monitoring and management mode of land use in black soil areas should be established to include information management that incorporates knowledge of the cultivated land factor potential, grain production capacity assessment, soil erosion evaluation and prediction, and farmland landscape planning. [ABSTRACT FROM AUTHOR]

Biological soil crust (BSC), as a groundcover, is widely intergrown with grass. The effects of grass combined with BSCs on slope hydrology and soil erosion during rainfall are still unclear. In this study, simulated rainfall experiments were applied to a soil flume with four different slope cover treatments, namely, bare soil (CK), grass cover (GC), BSC, and GC + BSC, to observe the processes of runoff and sediment yield. Additionally, the soil moisture at different depths during infiltration was observed. The results showed that the runoff generated by rainfall for all treatments was in the following order: BSC > GC + BSC > CK > GC. Compared with CK, GC promoted infiltration, and BSC inhibited infiltration. The BSCs obviously inhibited infiltration at a depth of 8 cm. When the rainfall continued to infiltrate down to 16 and 24 cm, the effects of grass on promoting infiltration were stronger than those of BSCs on inhibiting infiltration. Compared with CK, the flow velocity of the BSC, GC and GC + BSC treatments was reduced by 62.8%, 32.3% and 68.3%, respectively. The BSCs and grass increased the critical shear stress by increasing the resistance. Additionally, the average sediment yield of GC and both treatments with BSCs was reduced by 80.8% and >99%, respectively, compared with CK. The soil erosion process was dominated by the soil detachment capacity in the CK, BSC and GC + BSC treatments, while the GC treatment showed a transport‐limited process. This study provides a scientific basis for the reasonable spatial allocation of vegetation in arid and semiarid areas and the correction of vegetation cover factors in soil erosion prediction models. [ABSTRACT FROM AUTHOR]

The earth observation data and CMIP6 models were used to predict plausible soil loss from the Ghaghara river basin. The decadal prediction of soil loss (28.64 ton/ha/year) was found high for SSP585 of CanESM5 during 2015–2025. However, the lower value was reported as 21.71 ton/ha/year for SSP245 of MRI-ESM2-0 during 2035–2045. The century level future rainfall erosivity factor was found lowest for SSP245, however highest for SSP585 of Access-ESM1-5, CanESM5, and IPSL-CM6A-LR. The SSP585 (Access-ESM1-5, CanESM5, and IPSL-CM6A-LR) have maximum soil erosion rate as 29.07, 28.03, and 28.0 ton/ha/year, respectively. For the SSP585, increments were observed as 35.93%, 31.04%, and 30%, respectively, compared to the baseline year (2014). Whereas, lowest was reported as 21.7 and 24.9 ton/ha/year and consequently the low increment as 1.31% and 16.55% for both scenarios of MRI-ESM2-0 compared to baseline. We observed that the soil erosion rate is aligned with the predicted rainfall erosivity factor. [ABSTRACT FROM AUTHOR]

Soil erosion has posed a serious threat to the ecological balance and food security in the most fragile environment of the Loess Plateau. Frequent human activities are drastically changing the spatial pattern of land use in the economic and social development. Therefore, it is of great significance to predict the soil erosion under different strategies of land use for the regional coordinated development in the future. Taking the Jiuyuangou watershed as the research object, this study aims to propose an improved FLUS-CSLE model for the prediction of the spatial distribution pattern of land use in different scenarios. The vegetation coverage factor (B) and the tillage measure factor (T) were also calculated in the future scenario. The CSLE model was used to evaluate the soil erosion status in the historical period (2010-2020). The vegetation coverage factor and tillage measure factor were combined to predict the soil erosion under various scenarios of land use in 2025. Finally, a systematic analysis was made to determine the response of land use types to soil erosion status. The results show that: 1) The land use types were grassland (62.23%) and forest land (28.41%), followed by cropland, buildings, and water in the study area. There were the great variations in the spatial distribution pattern of land use. Specifically, the areas of forest and grassland increased by 8.36% from 2010 to 2020, whereas, the area of cropland was decreased by 30.3%. 2) The average modulus of soil erosion in the three years of 2010, 2015, and 2020 were 19.49, 15.83, and 20.7 t/(hm2·a), respectively, indicating a trend of first decreasing and then increasing. The soil erosion modulus of land use types was ranked in the descending order of the cropland (40.56 t/(hm2·a)) > grassland (18.79 t/(hm2·a)) > building (10.25 t/(hm2·a)) > forest land (8.02 t/(hm2·a)). 3) Under the positive ecological protection scenario, the area of forest and grassland in 2025 was basically the same as that in the natural development scenario, but the proportion of forest land increased. The area of forest and grassland was increased by 5.06% compared with the economic growth scenario. The area of cropland was increased by 1.20% compared with the natural development scenario and decreased by 14.73% compared with the economic growth scenario. 4) The soil erosion moduli were 24.3, 22.9, and 18.3 t/(hm2·a), respectively, under the natural development, economic growth, and ecological protection scenarios of the watershed in 2025. Therefore, an active ecological protection mode can be expected to appropriately expand the building area in the needs of economic development. Anyway, a rapid and efficient prediction of soil erosion can be given for the management strategies in the various scenarios of land use in the future. The findings can also provide the scientific reference for the decision-making on land use, as well as soil and water conservation in the watershed. [ABSTRACT FROM AUTHOR]

Soil erosion affects agricultural production, and industrial and socioeconomic development. Changes in rainfall intensity lead to changes in rainfall erosivity (R-factor) energy and consequently changes soil erosion rate. Prediction of soil erosion is therefore important for soil and water conservation. The purpose of this study is to investigate the effect of changes in climatic parameters (precipitation) on soil erosion rates in the near future (2046–2065) and far future (2081–2100). For this purpose, the CMIP5 series models under two scenarios RCP2.6 and RCP8.5 were used to predict precipitation and the R-factor using the Revised Universal Soil Loss Equation (RUSLE) model. Rainfall data from synoptic stations for 30 years were used to estimate the R- factor in the RUSLE model. Results showed that Iran's climate in the future would face increasing rainfall, specially in west and decreasing rainfall in the central and northern parts. Therefore, there is an increased possibility of more frequent occurrences of heavy and torrential rains. Results also showed that the transformation of annual rainfall was not related to the spatial change of erosion. In the central and southern parts, the intensity of rainfall would increase. Therefore, erosion would be more in the south and central areas. [ABSTRACT FROM AUTHOR]

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Soft computing techniques, such as artificial neural network (ANN) and multiple linear regression (MLR), have been found useful in the predictive modeling of environmental hazard indicators, even in areas with data scarcity. The detachability and liquefaction potential index (DLPI) is a numerical model that quantifies soil erodibility potential. This paper aims to design ANN and MLR models that can predict the DLPI of erodible soils in southeastern Nigeria. Feedforward back-propagated ANN models (ANN1, ANN2, ANN3 and ANN4) optimized using different algorithms and output layer activation functions were proposed. To implement the four models, the geotechnical data of the eroding soils, which were used for the DLPI calculation, were utilized. Sixteen geotechnical variables analyzed on the soils were used as input variables. While ANN1 and ANN3 were optimized using scaled conjugate gradient algorithm, ANN2 and ANN4 were optimized using gradient descent algorithm. However, the output layers of the ANN1 and ANN2 were activated using identity function while those for the ANN3 and ANN4 were activated using hyperbolic tangent function. The results of the four models showed high performance with determination coefficient (R2) values of 0.971 (ANN1), 0.998 (ANN2), 0.987 (ANN3), and 0.992 (ANN4). Further, low modeling errors were detected from the residual plots, sum of square errors (0.000–0.024), and relative errors (0.003–0.080); thus, confirming the reliability of the models. The MLR gave a perfect model for the DLPI prediction, with its multiple correlation coefficient = 1.000, R2 = 1.000, R2adjusted = 1.000, and standard error of estimate = 0.000. Therefore, it suffices to say that the ANN and MLR algorithms are suitable for DLPI prediction.

Soil erosion substantially implicates global nutrient and carbon cycling of the land surface. Its monitoring is crucial for assessing and managing global land productivity and socio-economy. The Zhuoshui River Basin, the largest catchment, in Taiwan is highly susceptible to soil erosion by water due to extremely high rainfall, rugged terrain, easily eroded soil, and intensively agricultural cultivation over the steep land. Hence, this study examines the annual soil erosion rate for 2005, 2011, and 2019 and the average long-term soil erosion and sediment yield (SY) during 2005–2019. Coupling of the Revised Universal Soil Loss Equation (RUSLE) and sediment delivery ratio (SDR) models is implemented using remote sensing and GIS techniques. The soil erosion rate is classified into five classes, namely mild (0–10 t ha−1 year−1), moderate (10–50 t ha−1 year−1), moderately severe (50–100 t ha−1 year−1), severe (100–150 t ha−1 year−1), and very severe (> 150 t ha−1 year−1). Over one half of the total area is categorized as moderate and moderately severe classes, and one-third of the whole basin as severe and very severe classes. Recently, mild and moderate classes increase, while moderately severe, severe, and very severe decrease. During 2005–2019, the annual soil loss rate ranges from 0.00 to 6,881.88 t ha−1 year−1 with an average rate of 122.94 t ha−1 year−1. Among the SDR models, the RUSLE combined with the SDR model with the length and slope gradient of mainstream shows satisfactory sediment yield estimation. Predictably, the downstream receives a massive sediment delivery from all upper streams (246.06 × 106 t year−1), and the percent bias values for all sub-basins are below ± 39.0%. The study provides a rapid approach to investigate soil erosion and sediment yield, and it can be applied to the other basins in Taiwan. More importantly, information about spatial patterns of soil erosion and SY is critical to establish suitable measures to achieve effective watershed planning and optimize the regional productivity and socio-economy. The proposed approach is potentially to identify risk areas, conduct scenario estimation for management, and perform spatiotemporal comparison of soil erosion, while adjustment in the empirical formulas of the proposed approach may be needed when it is applied to the other regions, especially outside Taiwan. [ABSTRACT FROM AUTHOR]

Soiling consists of the accumulation of dust on the solar panel's surface and has a deleterious effect on solar photovoltaic devices' performance, which varies with location. However, soiling losses and rates are significantly under-reported or underestimated since regional differences and seasonal variations are overlooked. Accurate prediction of PV soiling losses for a particular location can save revenue losses associated with a solar PV system. This research investigated the effect of soiling on PV performance through optical losses by employing a low-cost soiling station. Low iron glass coupons (5 mm × 5 mm) were exposed on three angles (vertical, tilt-45°, and horizontal) in seven sites across Nigeria to collect annual, seasonal and monthly soiling data. Each coupon was then subjected to optical characterisation using a spectrometer and imaging analysis using the SEM/EDX. The finding shows significant optical losses across the country, with all the highest rates recorded on coupons exposed on the horizontal plane, where the maximum loss of 88% was recorded on the Abuja, North Central (ABV) coupon. SEM/EDX finding illustrated minerals with the potential to affect light transmittance, and the pollutant data confirmed the particles. The optical results were further employed to map the soiling distribution across the country. A wide deviation was observed from the data on the Global Solar Atlas, as it disproportionately underestimated the soiling losses across the world. [ABSTRACT FROM AUTHOR]

Soil erosion in the Nangka watershed has always been a matter of concern. Over the last decade, soil erosion has led to continuous environmental problems. A thorough examination of the extent of the problem was required to identify an appropriate soil conservation strategy within the watershed. This study was conducted to observe erosion rates and map out the erosion hazard level. Erosion predictions were analysed by using the Revised Universal Soil Loss Equation (RUSLE) model with the help of ArcGIS software. RUSLE was selected because of its quantitative ability to estimate average annual soil erosion and its compatibility with the GIS interface. The potential hazard of soil erosion was classified and ranked into five class categories as set by the national authority. The results reveal that the Nangka watershed is prone to soil erosion with the annual average values ranging from 1.33 Mg·ha-1·y-1 to 2472.29 Mg·ha-1·y-1. High soil erosion rates of 9.8% are in severe (class IV) and very severe (class V) conditions, primarily in the upper course of the watershed. The low annual average of soil erosion (class I and class II), which accounted for 75.95% of the total erosion, mostly took place in the steepness below 35%. The remaining area of 14.25% within the watershed is in moderate condition (class III). It is expected that the results of this study will help the authority in the implementation of soil conservation measures. [ABSTRACT FROM AUTHOR]

This research was held to estimate rainfall and change in soil erosion vulnerability from 2020 to 2050 in Merawu Sub-Watershed, Banjanegara District with RCP 2.6, 4.5 and 8.5. The RCP is an overview of the concentration trends for greenhouse gases, aerosols and land use change created by the climate modeling community. Rainfall prediction was generated from SDSM Software and combined with USLE to predict soil erosion in ArcGIS 10.4. Changes in rainfall intensity are an important factor in changes of soil erosion rates because the kinetic energy of falling rainwater can cause soil erosion.The results showed rainfall in Banjarnegara Station at 2020-2050 with RCP 2.6,4.5 and 8.5 were increasing by +0,26 %; +0,60 %; +0,52 %, while in Kalisapi Station were decreasing by -1,54 %; -1,65 % dan -2,20 %. The change of soil erosion vulnerability prediction showed that soil erosion in Sub-DAS Merawu at 2020-2050 with RCP 2.6,4.5 and 8.5 in very light category were -0,02 %;-0,02 %;-0,03 %, light category were -0,17 %;-0,17 %;-0,17 %, moderate category -0,05 %;-0,05 %;-0,04 %, heavy category -0,26 %;-0,35 %;-0,37 %, and very heavy category were +1,46 %;+1,88 %;+1,95 %. While the average soil erosion prediction at RCP 2.6, 4.5 and 8.5 were +0,86, +1,19% and +1,03%, respectively.

A reliable prediction of event soil loss at the plot scale can be obtained by Universal Soil Loss Equation (USLE)-type models. For the Sparacia site (South Italy), the USLE-MB model was recently developed, in which the effect of the erosive agent is modeled using the rainfall erosivity index of the USLE by a power b1 > 1 of the runoff coefficient QR. In this investigation, the model is parameterized separately using plot data collected for rill and interrill events that occurred in the Sparacia experimental area. The values b1 = 1.406 and b1 = 1.012 were obtained for the interrill and rill databases, respectively, which revealed a different effect of the runoff coefficient on soil loss due to the two erosive processes. Different relationships expressive of topographic factors were also deduced. The USLE-MB estimation performance significantly improved when operating the distinction between the two databases compared with the model parameterized on the complete database. The model was particularly reliable in estimating the highest event soil loss values, which were associated with the occurrence of rills. Finally, the proposed parameterization procedure lends itself to being tested in the framework of empirical soil loss modeling.

Rainfall erosivity values are required for soil erosion prediction. To calculate the mean annual rainfall erosivity (R), long-term high-resolution observed rainfall data are required, which are often not available. To overcome the issue of limited data availability in space and time, four methods were employed and evaluated: direct regionalisation of R , regionalisation of 5 min rainfall, disaggregation of daily rainfall into 5 min time steps, and a regionalised stochastic rainfall model. The impact of station density is considered for each of the methods. The study is carried out using 159 recording and 150 non-recording (daily) rainfall stations in and around the federal state of Lower Saxony, Germany. In addition, the minimum record length necessary to adequately estimate R was investigated. Results show that the direct regionalisation of mean annual erosivity is best in terms of both relative bias and relative root mean square error (RMSE), followed by the regionalisation of the 5 min rainfall data, which yields better results than the rainfall generation models, namely an alternating renewal model (ARM) and a multiplicative cascade model. However, a key advantage of using regionalised rainfall models is the ability to generate time series that can be used for the estimation of the erosive event characteristics. This is not possible if regionalising only R. Using the stochastic ARM, it was assessed that more than 60 years of data are needed in most cases to reach a stable estimate of annual rainfall erosivity. Moreover, the temporal resolution of measuring devices was found to have a significant effect on R , with coarser temporal resolution leading to a higher relative bias. [ABSTRACT FROM AUTHOR]

Cold areas commonly experienced freeze–thaw cycles and covered with rock fragment. Exploring sediment transport capacity of frozen‐stony soil scoured by concentrated meltwater flow is meaningful for soil erosion prediction and modelling in cold regions. This laboratory experiment is conducted to evaluate the contribution of frozen layer and rock fragment to soil erosion, which involves three slope gradients of 5°, 10°, and 15°, three flow rates of 1, 2, and 4 L min−1, four thawed soil depths of 1, 2, 5, and 10 cm, and four rock fragment contents of 0%, 10%, 20%, and 50% on mass basis with rill length of 12 m. Sediment concentration was determined by the soil in runoff at the outlet. An equation is proposed to estimate sediment transport capacity affected by thawed soil depth and rock fragment content by combining exponential and quadratic functions. Model‐fitting results show that sediment transport capacity is negatively correlated with thawed soil depth and exhibits quadratic relationship with rock fragment which peaks at 20% and significantly decreases at rock fragment of 50% on partially thawed slope. Rock fragment takes the leading role of soil erosion on frozen‐stony slope instead of thawed soil depth. Results also indicate that embedded rock fragment on totally thawed soil presents more apparently positive impact at low content, but offers more protection at high content on partially frozen layer. This research reveals the soil erosion mechanisms on frozen‐stony slope and gives guidance for soil conservation in similar hydrologic conditions and soil type. [ABSTRACT FROM AUTHOR]

Restoring vegetation is an effective way to control regional erosion as well as reduce soil erodibility. However, it is not clear how the vegetation restoration duration affects soil erodibility and how it further influences soil erosion processes. Therefore, the soil physicochemical properties and comprehensive soil erodibility index (CSEI) at five sampling sites with 3, 20, 55, 80 and 100 years of vegetation restoration were investigated in this study. A simulated rainfall with intensities of 60, 90, and 120 mm h−1 was conducted on three slopes with gradients of 10°, 20°, and 30° by using rare earth element oxides (Ho 2 O 3 and Sm 2 O 3) as tracers to quantify interrill and rill erosion. The results revealed a decreasing trend in both the CSEI and sediment concentration with increasing vegetation restoration duration. Compared to that at the site with 3 years of vegetation restoration, the CSEI at the sites with 20, 55, 80, and 100 years of restoration was reduced by 35.2 %, 39.7 %, 92.8 %, and 67.1 %, respectively. Interrill erosion dominated the hillslope erosion processes and contributed more than 76.9 % to the total erosion amount. By comparing the measured and estimated erosion rates using the equations provided by the Water Erosion Prediction Project (WEPP), significant prediction errors were found. Therefore, relationships among the CSEI , slope gradient and rainfall intensity were established for interrill and rill erosion rate estimation in vegetation restoration areas. This study provides a theoretical basis for evaluating the soil and water conservation benefits of vegetation restoration and for improving soil erosion prediction models within the context of vegetation restoration. • The soil erodibility decreased with increasing vegetation restoration duration. • The sediment concentration decreased with increasing vegetation restoration duration. • Interrill and rill erosion processes were distinguished by using rare earth element tracing method. • Equations were developed to predict interrill and rill erosion. [ABSTRACT FROM AUTHOR]