Soil Erosion Risk Research Articles

Effects of different tillage and residue management systems on soil organic carbon stock and grain yield of rice–wheat double cropping system

Of late, intensive agricultural practices are often associated with many negative implications for soil systems, such as decline in soil organic matter and biological diversity, and increase in the risk of soil erosion, and degradation of soil physical quality. However, conservation agriculture (CA) offers minimum soil disturbance, improving and creating more efficient use of natural resources, enhancing use efficiency of external inputs and increasing soil functioning, organic matter content, and biodiversity. However, CA practices need to be redefined at the specific field level or growing conditions to promote sustainable production system without deteriorating soil health, and to manage profitability. Therefore, a fixed-site field experiment was conducted to evaluate the effects of different tillage and residue management practices; CT0 (puddled transplanted rice followed by conventional tilled wheat sown with residue removal); CTR (puddled transplanted rice followed by conventional tilled wheat sown with residue retention); NT0 (direct seeded rice followed by zero-tilled wheat sown with residue removal); NTR (direct seeded rice followed by zero-tilled wheat sown with residue retention) on soil organic carbon (SOC) stock and grain yield of rice–wheat double cropping system. The results showed that the mean SOC stock increased by 31%, 21.9% and 15.3% and by 35.2%, 22% and 17% under NTR, CTR, and NT0 compared with CT0. Furthermore, the SOC sequestration rate in NTR was significantly higher than other treatments. Particulate organic carbon (POC) concentration was 58.4% higher in 0–15 cm than 15–30 cm soil layer, and the maximum concentration was recorded under NTR and minimum was under CT0. The highest average rice grain yield was recorded under CTR, which was 5.5%, 16.2% and 24.3% higher than CT0, NTR and NT0, respectively. Both NTR and CTR produced more average wheat yield, which was 8.4% and 8.5% higher than CT0 and NT0, respectively. Principal component analysis (PCA) also showed that cumulative cropping system yield and SOC were increased under CTR, NTR as compared to other treatments. Therefore, it can be drawn that residue retention could be a useful management practice to increase SOC stock and grain yield under both tillage practices in the rice–wheat cropping system.

Response of soil fertility to soil erosion on a regional scale: A case study of Northeast China

Climate change and human activities have increased the risk of soil erosion, resulting in widespread soil degradation in many regions worldwide. In this study, our purpose was to estimate the spatial and temporal changes in the integrated fertility indexes (IFI) of Northeast China from the 1990s to the 2010s from a regional perspective. The soil erosion modulus, calculated using the Revised Universal Soil Loss Equation, was combined with soil nutrient data for four nutrients in two time stages, and the response of soil fertility to soil erosion was analyzed under nine land-use patterns. The results indicated that soil fertility in Northeast China had decreased by 4.46% from the 1990s to the 2010s, with the IFI of cultivated land showing the smallest decline at 1.69%, followed by grassland at 4.81%, and forest land at 6.21%, in that order. The IFI of changed land-use decreased more than that of unchanged land-use. The △IFI (IFI in the 2010s minus IFI in the 1990s) was negatively correlated with the soil erosion modulus, particularly when the average soil erosion modulus exceeded 2 t/(ha/yr). Furthermore, △IFI was mostly negative, indicating obvious soil degradation. The contribution rate of nutrient loss in the 1990s averaged over 70%, which was significantly higher than that in the 2010s, indicating that non-point source pollution control and soil erosion interception measures implemented by most city governments after 2010 had been effective. In most cases, the contribution rates of the total potassium (TK) and soil organic carbon (SOC) in the 1990s were significantly higher than those of the other indicators, with the highest contribution rate of SOC loss being observed for cultivated land and the highest contribution rate of TK loss being observed for forest land and grasslands. Forest land is conducive to maintaining soil fertility, and when other types of land are converted into forest land, the contribution rate of nutrient loss decreases.

Modeling of soil erosion risk in a typical tropical savannah landscape

Tropical savannah landscapes are faced with high soil degradation due to climate change and variability coupled with anthropogenic factors. However, the spatiotemporal dynamics of this is not sufficiently understood particularly, in the tropical savannah contexts. Using the Wa municipality of Ghana as a case, we applied the Revised Universal Soil Loss Equation (RUSLE) model to predict the potential and actual soil erosion risk for 1990 and 2020. Rainfall, soil, topography and land cover data were used as the input parameters. The rate of predicted potential erosion was in the range of 0–111 t ha−1yr−1 and 0–83 t ha−1yr−1 for the years 1990 and 2020, respectively. The prediction for the rate of potential soil erosion risk was generally higher than the actual estimated soil erosion risk which ranges from 0 to 59 t ha−1yr−1 in 1990 and 0 to 58 t ha−1yr−1 in 2020. The open savannah areas accounted for 75.8 % and 73.2 % of the total soil loss in 1990 and 2020, respectively. The validity of the result was tested using in situ data from a 2 km2 each of closed savannah, open savannah and settlement area. By statistical correlation, the predicted soil erosion risk by the model corresponds to the spatial extent of erosion damages measured in the selected area for the validation. Primarily, areas with steep slopes, particularly within settlement, were identified to have the highest erosion risk. These findings underscore the importance of vegetation cover and effective management practices in preventing soil erosion. The results are useful for inferences towards the development and implementation of sustainable soil conservation practice in landscapes with similar attributes.

Soil Erosion Risk Assessment in The Niğde Using Corine Model

Soil erosion risk was calculated using the coordination of information on the environment (CORINE) model in this study. The aim of the study is to determine the soil erosion risk of Niğde province, taking into account soil properties, slope and land use. Potential (PSER) and actual soil erosion risks (ASER) were determined using factors which were soil properties, slope, climatic factors, and land cover data. Data were produced using Arc-GIS 10.3 software, and results were obtained on these maps. 34.72% of the soils were classified as moderately actual soil erosion risk, which located in the eastern and southeast part of the study area.51.66% and 13.62% of the soils were classified as low and high actual soil erosion risk, respectively. Areas which have low actual soil erosion risk are located in the middle part, and areas which have high actual soil erosion risk are located in the northwest part of the area. The areas which were categorized as low potential soil erosion risk were increased from 23.52% to 51.66% in the actual soil erosion risk, after combining the land cover map. On the other hand, the total areas classified as high and moderate actual soil erosion risk decreased from 76.48% to 48.34% in the actual soil erosion risk due to land cover types. Soil texture, land cover, and slope are the most important factors that affect erosion risk. This study indicated that the CORINE model integrated with GIS (Geographic Information Systems) and RS (Revised Universal Soil Loss Equation) has a very effective and accurate potential for soil erosion risk assessment.

Spatial assessment of soil erosion by water using RUSLE model, remote sensing and GIS: a case study of Mellegue Watershed, Algeria-Tunisia.

Soil erosion is an important global phenomenon that can cause many impacts, like morphometry and hydrology alteration, land degradation and landslides. Moreover, soil loss has a significant effect on agricultural production by removing the most valuable and productive top soil's profile, leading to a reduction in yields, which requires a high production budget. The detrimental impact of soil erosion has reached alarming levels due to the exacerbation of global warming and drought, particularly in the arid climates prevalent in Tunisia and Algeria and other regions of North Africa. The influence of these environmental factors has been especially evident in the catchment of Mellegue, where profound vegetation loss and drastic changes in land use and cover, including the expansion of urban areas and altered agricultural practices, have played a significant role in accelerating water-induced soil loss between 2002 and 2018. The ramifications of these developments on the fragile ecosystems of the region cannot be overlooked. Accordingly, this study aimed to compare soil losses between 2002 and 2018 in the catchment of Mellegue, which is a large cross-border basin commonly shared by Tunisian-Algerian countries. The assessment and mapping of soil erosion risk were carried out by employing the Revised Universal Soil Loss Equation (RUSLE). This widely recognised equation provided valuable insights into the potential for erosion. Additionally, changes in land use and land cover during the same period were thoroughly analysed to identify any factors that may have contributed to the observed risk. By integrating these various elements, a comprehensive understanding of soil erosion dynamics was achieved, facilitating informed decision-making for effective land management and conservation efforts. It requires diverse factors that are integrated into the erosion process, such as topography, soil erodibility, rainfall erosivity, anti-erosion cultivation practice and vegetation cover. The computation of the various equation factors was applied in a GIS environment, using ArcGIS desktop 10.4. The results show that the catchment has undergone significant soil water erosion where it exhibits the appearance of approximately 14,000 new areas vulnerable to erosion by water in 2018 compared to 2002. Average erosion risk has also increased from 1.58 t/ha/year in 2002 to 1.78 in 2018, leading to an increase in total estimated soil loss of 54,000 t/ha in 2018 compared to around 25,500 t/ha in 2002. Maps of erosion risk show that highly eroded areas are more frequent downstream of the basin. These maps can be helpful for decision-makers to make better sustainable management plans and for land use preservation.

Soil Erosion Risk Assessment Using Corine Model: A Case Study in Shwan Sub basin, Kirkuk, Iraq

Water erosion is one of the most important problems and challenges facing the optimal management of natural resources, especially soil and water resources, because of its impact on soil degradation. This study aimed to classify the risk of soil water erosion and clarify its distribution in the Shwan Basin in Kirkuk Governorate - Iraq based on the Corine model.In the first stage of the work, the soil erodibilty, erosivity and slope factors were determined, classified and mapped according to the degree of their impact on soil erosion.The potential soil erosion risk map was prepared using Arc GIS 10.4.1.In the second stage, the land cover factor was calculated and classified according to the degree of protection. In the last stage, the actual soil erosion risk map was prepared by multiplying the class of land cover with the map of potential soil erosion risk over the entire study area.The study showed that 28.18% of the studied area is classified under a high risk, 39.57% represents as a medium risk and 32.25% is a low risk .The areas of severe soil erosion risk were concentrated in the southwestern part and some northern and central parts, and the areas of medium risk were concentrated in the northern, central and southern parts, while regarding the low actual risk, they were concentrated in the western parts and in random areas in the center and east of the study area.The results also showed that the Corine model for soil water erosion risk mapping is a highly effective and cost-effective approach.

Soil Erosion Risk Assessment Using Revised Universal Soil Loss Equation RUSLE Model: a Case Study in Shwan Sub basin, Kirkuk, Iraq

The revised universal soil loss equation (RUSLE) was used in order to predict the amounts of soil lost by water erosion in the study area. The equation depends mainly on the factors that affect soil erosion represented by soil characteristics, rain fall, topography, vegetation cover, and conservation factor. The study showed that the study area was divided into three classes according to the degree of severity. The class1, the low risk of soil erosion, concentrated in all western parts and some central parts of the study area, occupying an area of 42525.61 hectares, at a rate of 41% of the total area. The class 2, a moderate risk of soil erosion, occupied an area estimated at (40451.19) hectares (39%), spread in the northern, northeastern, some central and southern parts of the study area, while the class 3, the high risk of erosion, had an area of 20744.20 hectares (20% of the area of the region), concentrated in the eastern and southeastern parts of the study area. Generally, the total amounts of soil lost annually due to water erosion in the study area amounted to 1,570,748 tons. The results also showed that the RUSLE model of soil water erosion risk mapping is a highly effective and cost-effective approach.

Soil erosion estimation and erosion risk area prioritization using GIS-based RUSLE model and identification of conservation strategies in Jejebe watershed, Southwestern Ethiopia.

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.

Gully erosion susceptibility maps and influence factor analysis in the Lhasa River Basin on the Tibetan Plateau, based on machine learning algorithms

The environment of the Tibetan Plateau is under stress due to the warming climate and increased human activity, which also increases the risk of soil erosion. Gully erosion is one of the main environmental problems in the Tibetan Plateau. Taking Lhasa River Basin as a representative, investigating the spatial variation and influencing factors in this area will help to understand the current status and mechanism of gully erosion. However, gully erosion factor analysis has not been carried out at the regional scale. Therefore, three machine learning algorithms, including Random Forest (RF), Support Vector Machine, and Logistic Regression, were used to analyze 2224 samples and 18 influencing factors and detect the gully erosion susceptibility in the Lhasa River Basin. Seventy percent of the samples were used to train the model and rank the influencing factors by importance, and 30% were used to assess the prediction ability of the model. Approximately 15%–20% of the study area has a high or very high gully erosion susceptibility. The slopes of the lower Lhasa River and the Nyenchen Tanglha valleys were identified as areas with a high gully erosion susceptibility. The RF model exhibits the best performance, with area under the curve (AUC) values of 0.969 and 0.894 for the training and testing datasets, respectively. The main factors affecting the occurrence of gully erosion were determined to be the elevation, normalized difference vegetation index, relief, human footprint, mean annual temperature, and distance to the residential area. Elevation has the greatest effect on gully erosion, which can be explained as elevation indirectly controls gully erosion by affecting human activities and rainfall. The findings of this study provide insights into the effects of human activities, climate and permafrost on soil erosion and can serve as a basis for regional land use planning and gully erosion prevention and control in the Tibetan Plateau.

Evaluating Soil Erosion within the Neyyar Basin in Kerala through RUSLE and Geospatial Analysis

Soil erosion represents a significant issue in the Western Ghats, especially within the Neyyar river basin in the southern region of Kerala. To address this problem, a research project was conducted in three sub-watersheds of the Neyyar river basin, specifically Neyyar, Mullayar, and Chittar, with the primary goals of quantifying annual loss of soil and creating erosion maps. To predict this, researchers employed the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Geographic Information System (GIS). To compute the RUSLE factors viz., R, K, LS, C and P for these sub-watersheds, monthly and yearly rainfall, soil texture and organic matter content, length and steepness of slope, crop cover and conservation practices data were used.The results showed that the extent of soil loss varied among the three watersheds. Soil loss ranged from 0 to 92, 257, and 28 tons per hectare per year for Chittar, Mullayar, and Neyyar sub-watersheds, respectively. In 38% of Mullayar, 41% of Chittar, and 45% of Neyyar watersheds, soil loss was relatively low, being less than 10 tons per hectare per year. However, in 35% of Mullayar, 30% of Chittar, and 25% of Neyyar sub-watersheds, soil loss was moderate to very high, ranging from 15 to over 40 tons per hectare per year. These variations in soil loss were attributed to factors beyond just rainfall, such as slope (LS) and soil erodibility (K), indicating that other elements influenced the soil erosion risk. Moreover, it was observed that Mullayar had the highest erosion risk, followed by Chittar watershed, highlighting the significance of factors like slope and soil erodibility in contributing to the loss of soil.

Mapping Prospects for Artificial Groundwater Recharge Utilizing Remote Sensing and GIS Methods

The indiscriminate use of groundwater and its overexploitation has led to a significant decline in groundwater resources in India, making it essential to identify potential recharge zones for aquifer recharge. A study was conducted to determine such potential recharge zones in the Nandhour-Kailash River watershed. The study area included 1481 streams divided into 12 sub-basins (SWS). The results show that the downstream Saraunj sub-basins (SWS-11) and Odra sub-basins (SWS-12) were high priority and required immediate soil and water conservation attention. Sub catchments Lobchla West (SWS-4), Deotar (SWS-5), Balot South (SWS-8), Nandhour (SWS-9), and Nakoliy (SWS-10) had medium priority and were designated for moderate soil erosion and degradation. In contrast, sub-catchments Aligad (SWS-1), Kundal (SWS-2), Lowarnala North (SWS-3), Bhalseni (SWS-6), and Uparla Gauniyarao (SWS-7) had low priority, indicating a low risk of soil erosion and degradation. Using the existing groundwater level data, the potential map of groundwater was validated to confirm its validity. According to the guidelines provided by the Integrated Mission for Sustainable Development (IMSD), the results of the groundwater potential zones for good to very good zones have been integrated at the slope and stream order. In a 120.94 km2 area with a slope of 0–5% in first-order streams, 36 ponds were proposed, and in a 218.03 km2 area with a slope of 15% in first- to fourth-order streams, 105 retention dams were proposed and recognized as possible sites for artificial groundwater recharge. The proposed water harvesting structure may aid in continuously recharging these zones and benefit water resource managers and planners. Thus, various governmental organizations can use the results to identify possible future recharge areas.

Comparing geographic information systems-based fuzzy-analytic hierarchical process approach and artificial neural network to characterize soil erosion risk indexes

Comparing geographic information systems-based fuzzy-analytic hierarchical process approach and artificial neural network to characterize soil erosion risk indexes

SOIL EROSION RISK IN RELATION TO RAINFALL EROSIVITY IN GUNUNG ALAB AND INOBONG OF CROCKER RANGE PARK IN SABAH, MALAYSIA

The rainfall ability to erode and detach soil particles, resulting in soil erosion, is referred to as rainfall erosivity (R). This research aims to estimate the risk of soil erosion from rainfall intensity in Gunung Alab and Inobong of Crocker Range Park, Sabah. Daily rainfall data from 2017 and 2020 where an observed mild La Nina were extracted. The 30-minute Maximum Rainfall Intensity (I30) and Rainfall Kinetic Energy (E) were determined for estimating the R Factor. ROSE Index was then referred to in classifying the soil erosion risk. The estimated monthly R Factor in Gunung Alab ranged from 15282.2 to 32029.2 MJ.mm (ha.h)-1 in 2017 and from 16069.5 to 35401.6 MJ.mm (ha.h)-1 in 2020. Gunung Alab was rated as having an “extremely high” and “critical” risk of soil erosion 80% of the time in 2017 and 50% in 2020. At the Inobong substation, the monthly R Factor in 2017 (16069.5 to 58348.0 MJ.mm (ha.h)-1) and 2020 (16863.6 to 36246.4 MJ.mm (ha.h)-1) translates to an erosion risk (extremely high-critical) throughout 67% and 92% of the time, respectively. Given the possibility of landslides, rainfall erosivity is useful information in assessing soil erosion risk and could be used in soil and land management

Using 2DVD technology to explore droplet characteristic variations of sprinkler irrigation as affected by a maize canopy

Canopy cover influences the water distribution and droplet characteristics of sprinkler irrigation, creating uncertainty in the design optimization of sprinkler irrigation systems. To evaluate the effects of canopy cover on water distribution and droplet characteristics of sprinkler irrigation, the radial distribution of droplet diameters, velocities, impact angles, shear stresses, and kinetic energies were investigated by an indoor experiment using a two-dimensional video disdrometer (2DVD) with Nelson R33LP low-pressure sprinklers at three operating pressures (100, 200, and 300 kPa), three nozzle diameters (4.4, 4.8, and 5.2 mm), and two growing stages of the maize canopy. Additionally, the water application rates were measured by catch-can devices for each treatment. The results show that small droplets passing through the canopy converge into larger-diameter droplets and large droplets passing through the canopy break up into smaller-diameter droplets. The canopy cover significantly affected droplet kinetic energy and velocity and reduced shear stress by 2.48–96.09 %, which could minimize the risk of soil erosion. Meanwhile, an increase in the leaf area index (LAI) reduced the kinetic energy by 0.26–99.00 %. Canopy cover also had an obvious effect on the irrigation uniformity coefficients (CU). The CU without the canopy exhibited a significant linear correlation with the CU with the canopy. When the irrigation CU without the canopy was 62.00 %, the maize canopy at the jointing stage improved it by 16.62 %, and when the CU without the canopy was 88.47 %, the maize canopy cover at the jointing stage reduced it by 4.68 %. An increase in operating pressure improved the uniformity of the sprinkler by 0.57–151.58 % and reduced shear stress by 2.80–95.11 %. An increasing nozzle size increased the application rate and specific power. This study provides technical support for the design of sprinkler irrigation systems.

Soil erosion susceptibility mapping in Bangladesh

This study aims to draw a scientific framework for plotting soil erosion susceptibility in the Chittagong Hill Tracts of Bangladesh by comparing existing approaches. Data-driven machine learning techniques (including Classification and Regression Tree (CART), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF)) and a knowledge-based approach (AHP) are used in this study to pinpoint areas of Chittagong that are particularly susceptible to soil erosion while taking into account 18 soil erosion-regulating parameters. Furthermore, the effectiveness of the selected data-driven machine learning models and knowledge-based models was assessed by utilizing soil erosion and non-erosion sites. When evaluating the fidelity of each model using the ROC and AUC, the RF model was shown to be the most accurate and predictive. There is no poor performer among these models; all have AUCs greater than 67 % (RF = 0.86, ANN = 0.73, SVM = 0.67, CART = 0.67, and AHP = 0.82). According to the findings of the Random Forest model, approximately 71.55 percent of the area exhibited a moderate level of susceptibility to soil erosion. In relation to the land area, the high and low zones accounted for 16.91 percent and 11.54 percent, respectively. The specific area shares of 2256.25, 9548.08, and 1539.67 square kilometers were attributed to the high, moderate, and low danger zones, respectively. The best models' results after comparing models of data-driven and knowledge-based approaches can help to estimate soil erosion risk zones and provide insight into establishing appropriate policies to minimize this issue. In addition, the methods used in this research might be applicable to assessing the vulnerability and risk of soil erosion events in other areas. As they begin long-term planning to reduce soil erosion, local authorities and policymakers will find the study's results on practical policies and management options quite helpful.

Connectivity elements and mitigation measures in policy-relevant soil erosion models: A survey across Europe

The current use of soil erosion models in Europe was investigated through an exploratory survey of 46 model applications covering 18 European countries. This revealed novel information on erosion model applications, their parameterisation, incorporation of landscape elements and mitigation measures with implications for connectivity and their use in decision-making in Europe. The model application predictions were applied at national, regional, catchment or field scale. The majority of model applications used the USLE or versions thereof, but a range of semi-empirical, decision-tree and process-based models were also used. The majority of model applications were used for policy relevant purposes such as erosion risk assessment or mitigation measure implementation at a range of spatial scales. The analysis identified an evident prevalence towards the use of national or regional data sets and a highly varying parameterisation of model applications. Landscape elements and mitigation measures with effects on connectivity were implemented in most model applications, but not with a focus on modelling connectivity within the landscape. Altogether, the results demonstrate a need for improving connectivity modelling in diverse agricultural landscapes across multiple scales. Models should be chosen dependent on their ability to reflect erosion risk at different spatial scales. Albeit, harmonisation of data sets, parameterisation procedures and validation approaches is needed for certain modelling scenarios to ensure comparability of soil erosion risk assessment and suitable mitigation practices. Furthermore, we recommend that policy-relevant erosion risk maps should be verified by empirical data and thresholds derived from erosion risk maps should be adapted to regional conditions when used for policy guidelines. Hence, comparability, comprehensibility and regional adaptation are essential qualities of policy-relevant erosion maps.

Spatiotemporal dynamics and driving factors of soil erosion in the Beiluo River Basin, Loess Plateau, China

Soil erosion reduces soil fertility and land productivity while enhancing desertification, which seriously threatens ecological security, carbon sequestration, and sustainable economic and social development. Therefore, it is fundamental to precisely assess the long-term dynamics of soil erosion and explore its drivers to control the risk of soil erosion. Currently, machine learning-based analyses of the factors driving soil erosion are lacking in the Beiluo River Basin (BRB), China. This paper aimed to investigate the spatiotemporal dynamics of soil erosion by the Chinese soil loss equation coupled with the gully erosion factor, and then reveal its driving factors using the Boosting Regression Tree method (BRT) and estimate the influence of drivers' interactions on soil erosion in the BRB between 1990 and 2017. The findings demonstrated that the expanded Chinese soil loss equation accurately explained the variations in soil erosion in the BRB. The range of average soil erosion rates was 2,852.83–800.35 t km−2 a−1. Steep gully farmland, particularly areas with relatively rough terrain, was more vulnerable to intense erosion. Soil erosion rates for different geomorphologies decreased as follows: hilly–gully areas > gully–plateau areas > alluvial river plains > Rocky Mountains. The major drivers affecting the soil erosion rate were the biomass control factor (B) and slope, followed by slope length (L) and slope steepness (S). B–soil erodibility (1990), B–LS (2000), LS–rainfall erosivity (2010), and slope–rainfall erosivity (2017) interactions had the greatest influence on soil erosion, with interaction intensities of 32.3%, 17.75%, 46.05%, and 35.69%, respectively. The average soil erosion rates according to land classification types decreased as follows: farmland > grassland > forest. The results indicate that water and soil conservation in the study area benefited greatly from implementation of the Grain for Green Program, with forests reducing erosion more effectively than grassland.

Spatiotemporal dynamics of soil erosion response to land use land cover dynamics and climate variability in Maybar watershed, Awash basin, Ethiopia

ABSTRACT Soil erosion is a global problem that threatens sustainability and food security by washing away fertile soil, nutrients, and organic matter. Land use changes, land degradation, and soil erosion are increasing, resulting in annual losses of fertile soil and declining crop yields. The objective of this study was to examine how changes in land use and land cover affected soil erosion dynamics and determine which areas should receive priority for soil and water conservation interventions. The study employs remote sensing and Geographic information system techniques to assess the impact of land use and land cover change on soil erosion between 2004 and 2020. The RUSLE model estimates soil erosion rates. Mean annual rainfall, soil data, 2 m DEM, and satellite images are used to input data into the model to determine soil erosion. The study identifies six land use and cover types and shows that significant changes have occurred in bare land and shrubland, decreasing by 25.48% and 6.17%, respectively, from 2004–2020. On the other hand, forest land increased by 20.19%, and cultivated land increased by 3.52%. Additionally, the study evaluated the response of soil erosion to land use and land cover changes, showed that bare land, cultivated land, and grazing land have the highest annual loss. In contrast, forest land, shrubland, and water bodies have the lowest. The analysis of soil erosion risk indicated a decline in the mean annual soil loss rate at the watershed scale, with rates decreasing from 12.8t ha−1 yr-1 in 2004 to 3.21t ha-1 yr-1 in 2020. The watershed is classified into five severity classes, and the study area was divided into five priority categories based on their average annual soil losses. The study emphasized that prioritizing interventions for watershed types with high erosion risks is crucial for reducing on-site soil loss and off-site effects together with conserving water resources. The findings support the importance of long-term watershed management in reducing soil erosion. To effectively reduce soil erosion and improve environmental quality, it is recommended to encourage afforestation and reforestation, develop sustainable land use policies, strengthen soil conservation measures, and improve monitoring and evaluation of land use changes.

GIS based soil erosion assessment using the USLE model for efficient land management: A case study in an area with diverse pedo-geomorphological and bioclimatic characteristics

The complex environmental process of soil erosion is crucial to sustainable land management and conservation. This study uses the Universal Soil Loss Equation (USLE) model to understand the intricate interactions that cause soil erosion in Cluj County, Romania, a region susceptible to complex erosion and landslide phenomenon. The established analysis depicts erosion patterns from localised hotspots to regions of relative stability, providing valuable insights into this critical issue. Spatial distribution maps with color-coded gradients show soil erosion risk and identify vulnerable regions, with temporal investigations depicting how environmental changes affect soil erosion, making it relevant to soil conservation and land management. A careful analysis of the USLE model's parameters (Ls, Cs, C, S, K) shows their soil erosion contributions. The erosion rates were graded in five classes in accordance to general practices of USLE modelling, which range from very-low, low, moderate, high and very-high susceptibility to soil erosion. While a significant majority of the county’s surface is represented by very-low and low erosion risk, several hotspots were identified with intense erosion processes that highlights the critical need to implement soil conservation measures in the area. Identifying erosion hotspots and conservation solutions encourages stakeholders to protect soils, and can provide policymakers with useful information for developing improved guidelines for soil and water conservation.

Accommodating uncertainty in soil erosion risk assessment: Integration of Bayesian belief networks and MPSIAC model

Accommodating uncertainty stands as one of the most salient challenges in the development of soil erosion assessment tools. We presented a novel approach integrating the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model and Bayesian Belief Networks (BBNs) to assess soil erosion in a region of western Iran. The soil erosion status was reckoned based on the nine factors of MPSIAC. We utilized BBNs to produce a causal model for soil erosion, with output probabilities being validated through re-evaluation and sensitivity analysis. We identified erosion types, geological formations, run-off, soil erodibility, soil permeability, soil characteristics, and precipitation intensity as the main determinants of soil erosion. A significant, positive correlation existed between the erosion rate derived from MPSIAC and BBNs model in all land-use/covers over the work units. Overall, this study highlighted the potential of BBNs as a supportive tool for soil erosion prediction as well as a relatively simple and updatable soil erosion model for dealing with the diagnostic, scenario, and sensitivity analysis. Considering the increasing incidence of soil erosion, the BBNs model proposed in this study can be extended to a variety of ecosystems that are subject to soil erosion and changes in the probability of its causal factors.