Estimate Soil Loss Research Articles

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.

Responses of soil and water-related ecosystem services to landscape dynamics in the eastern Afromontane biodiversity Hotspot

The investigation of soil and water-related ecosystem services (ES) and recognizing the spatiotemporal effects of land-use and land cover changes (LULC) are essential for the formulation of conservation strategies. The research employed the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and the Revised Universal Soil Loss Equation (RUSLE) models for the assessment of ES. The study was carried out in the Kaffa Forest Biosphere Reserve in Ethiopia, which is part of the eastern Afromontane biodiversity hotspots. The aim of this investigation was to examine and map the temporal and spatial fluctuations in sediment retention, soil erosion, and water yield resulting from LULC modifications between 1986 and 2019, and to provide forecasts for the next three decades. According to the RUSLE analysis, the landscapes exhibited estimated soil losses ranging from zero to 1.5 tons ha-1 yr-1 in 1986, 2009, and 2019, respectively. The mean annual sediment exports for the years 1986, 1999, and 2019 were estimated to be 12.6, 9.9-, and 28.7-tons ha−1, respectively. The water yield of the site experienced a notable increase from 9.8 × 109 m3 in 1986 to 19.6 × 10 9 m3 in 1999, and subsequently rose to 39.3 × 109 m3 in 2019, which is considered to be a disadvantageous to the site. The study found a significant positive correlation between water yield and the expansion of settlement area (r = 0.99, P = 0.015) as well as agricultural land (r = 0.99, P = 0.05). It was also found that significant positive correlation found between vegetation dense area such as forest (r < 0.999, P < 0.001) and shrub & bamboo (r = 0.998, P = 0.036) with sediment retention service. The investigation discovered that there existed tradeoffs between the ES of sediment retention and water yield as the slope increased. The results may be attributed to the presence of dense vegetation cover on the elevated slope regions, rendering them unsuitable for agricultural activities, and the concurrent expansion of arable lands in the lower slope areas, which are flat terrains more conducive to cultivation. The transition from land with more vegetation density to land with lower or no vegetation coverage resulted in an increase in soil loss and water yield, while simultaneously decreasing the sediment retention service. Therefore, the findings can be used as a document to guide decision-makers to design soil-water conservation technologies to enhance the ecological integrity of the biosphere reserve.

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.

Harnessing the Power of Remote Sensing and Unmanned Aerial Vehicles: A Comparative Analysis for Soil Loss Estimation on the Loess Plateau

This study explored the innovative use of multiple remote sensing satellites and unmanned aerial vehicles to calculate soil losses in the Loess Plateau of Iran. This finding emphasized the importance of using advanced technologies to develop accurate and efficient soil erosion assessment techniques. Accordingly, this study developed an approach to compare sinkholes and gully heads in hilly regions on the Loess Plateau of northeast Iran using convolutional neural network (CNN or ConvNet). This method involved coupling data from UAV, Sentinel-2, and SPOT-6 satellite data. The soil erosion computed using UAV data showed AUC values of 0.9247 and 0.9189 for the gully head and the sinkhole, respectively. The use of SPOT-6 data in gully head and sinkhole computations showed AUC values of 0.9105 and 0.9123, respectively. The AUC values were 0.8978 and 0.9001 for the gully head and the sinkhole using Sentinel-2, respectively. Comparison of the results from the calculated UAV, SPOT-6, and Sentinel-2 data showed that the UAV had the highest accuracy for calculating sinkhole and gully head soil features, although Sentinel-2 and SPOT-6 showed good results. Overall, the combination of multiple remote sensing satellites and UAVs offers improved accuracy, timeliness, cost effectiveness, accessibility, and long-term monitoring capabilities, making it a powerful approach for calculating soil loss in the Loess Plateau of Iran.

Using Cs-137 measurements and RUSLE model to explore the effect of land use changes on soil erosion and deposition rates in a mid-sized catchment in southern Italy

In some areas of southern Italy, the change in land use over the last 4–5 decades has increased pressure on land and water resources and caused different forms of soil degradation. In order to mitigate the magnitude of soil erosion, different strategies that include construction of flood control structures and reforestation programs have been done in several areas. However, quantifying the effectiveness of these strategies is difficult in absence of direct measurements of soil erosion. To cover this information gap, the use of distributed numerical models coupled with measurements of the radionuclide cesium-137 (137Cs) offers a good alternative to the classic experimental sites (plot, catchments) that, on the contrary, require long term datasets to produce reliable estimates of soil loss. In this paper, measurements of 137Cs in a floodplain area are firstly described for a representative Calabrian catchment as an example to reconstruct the trend of soil deposition rates during the last six decades. These measurements have been integrated with estimates of soil loss obtained with the Revised Universal Soil Loss Equation (RUSLE) model for which land use maps of different periods are available. The final comparison between estimates of soil erosion provided by the RUSLE at catchment scale and sedimentation rates derived from 137Cs measurements on depositional areas allowed interesting information on the trend of soil erosion and deposition rates in these areas to be obtained.

Effects of Different Methods for Calculation of Topographic Factor on Precision of Storm-Wise Soil Loss Estimation

Effects of Different Methods for Calculation of Topographic Factor on Precision of Storm-Wise Soil Loss Estimation

Mapping the vulnerability of irrigation sand traps in a tropical volcanic basin, Indonesia

Sand traps in irrigation networks are typically used in mitigating canal sedimentation. In irrigation networks located in basins of high sediment yield due to the presence of volcanoes, it is essential to assess the vulnerability of sand traps. Using sediment yield at irrigation scheme inlets, sand trap vulnerability can be evaluated. This study aims to understand the vulnerability of irrigation sand traps throughout the Progo–Opak–Serang (POS) Volcanic River Basin, Indonesia, via mapping the sediment yield distributions in the basin. We employed the Revised Universal Soil Loss Equation to estimate soil loss, where the results show that the average soil loss in the POS River Basin is 179.69 tons/ha/year that falls under the category of moderate erosion potential, while the average sediment yield for the whole basin is 51.04 tons/ha/year. Parts of the basin with high yields of more than 180 tons/ha/year were mostly found along the volcanic mountains such as Sindoro, Sumbing, Merapi, Merbabu, and Telomoyo, and the Menoreh Hills. The model demonstrated relatively high performance with R2, NSE, RMSE, and MAE of 0.89, 0.82, 0.14, and 0.11, respectively. Within the POS Basin, Badran, Kalibawang, and Blawong are the three most vulnerable irrigation sand traps, with sediment yield values of 252.83, 178.92, and 63.49 tons/ha/year, respectively; they are all located in sub-watershed outlets. The vulnerability assessment conducted in this study can be used for the decision support system to prioritize irrigation sand traps towards a more effective irrigation system development.

Pixel-Based Soil Loss Estimation and Prioritization of North-Western Himalayan Catchment Based on Revised Universal Soil Loss Equation (RUSLE)

Land degradation is a noteworthy environmental risk causing water quality issues, reservoir siltation, and loss of valuable arable lands, all of which negate sustainable development. Analysis of the effect of land use changes on erosion rate and sediment yield is particularly useful to identify critical areas and define catchment-area treatment plans. This study utilized remote sensing and geographical information system/science (GIS) techniques combined with the Revised Universal Soil Loss Equation (RUSLE) on a pixel basis to estimate soil loss over space and time and prioritized areas for action. The methodology was applied to the Sutlej catchment from the perspective of sedimentation of the Bhakra reservoir, which is leading to the loss of active storage capacity and performance and of the safety and efficiency of many existing hydroelectric projects in the Sutlej and its tributaries that drain the Himalayas. Soil loss estimation using RUSLE was first calibrated using data from three sites, and the calibrated model was then used to estimate catchment soil loss for 21 years (1995–2015). The number of land use/land cover (LULC) classes as 14 and the C factor as 0.63 for agriculture land were optimized using the observed data for the Sutlej catchment. Further, the linkage between soil erosivity and annual precipitation was also established. It was concluded that extensive control treatment would be necessary from the soil and water conservation point of view. Structures like check dams, terraces, bunds, and diversion drains in the upstream can overcome the issue of fragmentation of soil in the Sutlej catchment.

Hydrological evaluation of conservation bench terrace system in outer foothills of Himalayas using soft computing techniques

Soil erosion has been problematic in the outer foothills of the Himalayas. Therefore, it is necessary to implement suitable conservation measures to check erosion issues. The present study experimented on conservation bench terraces (CBT) as a conservation measure in the outer foot Himalayas in Dehradun, India. The study evaluates the effectiveness of different CBTs of embankment heights 10 cm (CBT-10), 15 cm (CBT-15), and 20 cm (CBT-20) to runoff and soil loss under different rainfall storms. Further, the estimation of Soil loss (Sloss) was evaluated using soft computing techniques (machine learning models). The soft computing techniques were applied to the data obtained from CBT plots for hydrological assessment. The methods used for analysis were support vector machine (SVM), Bagging, and Random Forest (RF) models. The performance of developed models was evaluated using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson's correlation coefficient (PCC), and Wilmott Index (WI) along with graphical representation (Scatter plot and Taylor diagram). The results showed that the CBT techniques were observed to reduce runoff and soil loss. Among CBT, CBT-20 was the most significant as the number of runoff events was less than others. Considering the storm size, the maximum percentage of runoff generated from the storm size is more important than 100 mm. Moreover, soil loss (Sloss) estimation showed that the SVM models perform better in all three scenarios, which are results of three scenarios such as R2 = 0.50, 0.52 and 0.80; MAE = 0.03, 0.03 and 0.10; RMSE = 0.08, 0.07 and 0.27; NSE = 0.39, 0.12 and 0.21; PCC = 0.71, 0.725 and 0.89; and WI = 0.67, 0.794, and 0.49, respectively. Therefore, SVM techniques can be applied to the estimation of Sloss. Results can be recommended for precision farming under climate changes in the hilly and plain areas and similar models can be applied to other areas.

Estimation of Soil Loss by Revised USLE Model using Geospatial Techniques: A Case Study of Sainj Valley, Northwestern Himalaya, India

Soil erosion is a severe environmental problem. The current study is being conducted in Sainj valley of Himachal Pradesh. An increase in soil erosion rates has significant effects on land degradation, biodiversity loss, productivity and other factors. The Revised Universal Soil Loss Equation (RUSLE) model along with, Geographic Information System (GIS) and Remote Sensing (RS), was used in current study to quantify soil loss in the Sainj valley. Five important parameters like, Rainfall erosivity factor (R), Soil erodibility factor (K), Slope length and steepness (LS), Cropping management factor (C), and support practice factor (P) have been used to estimate the amount of soil loss in the study area. All these maps were created in GIS software using a variety of data preparation methods. The rainfall erosivity factor ranged between 501.19 to 1097.23 mt ha-1 cm-1. Spatial distribution of conservation support practice on soil loss indicated the variability (0–1) where lower value represents the higher conservation practice. The results show that average annual predicted soil loss is between 0 to 541.52tons/ha/yr and the predicted Average soil erosion rate was 11.15 tons/ha/Yr. Predicted soil loss was classified into six Erosion intensity classes Negligible (0-5), Low Erosion (5-10), Moderate Erosion (10-25), Moderately high (25-75), High Erosion (75-100), Extremely High Erosion (&gt;100) From soil loss estimation it was observed that 62 % of the area is having very low erosion intensity and 0.37% of the study area having Extremely High erosion intensity. These findings can help the decision makers further in developing a suitable conservation program to prevent soil erosion.

Estimation of Soil Loss using RUSLE, GIS, and Remote Sensing: A Case Study of Sangli District, Maharashtra

Accurate estimation of soil loss is essential for watershed managers and planners to identify the priority areas for soil and water conservation measures. This study was undertaken to estimate the average annual soil loss in the study area of Sangli district, Maharashtra by using the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Geographic Information System (GIS) and Remote Sensing (RS) data. The five potential factors of RUSLE impacting soil erosion were estimated through remote sensing data, enabling a comprehensive and informed assessment of soil erosion. The results of the analysis revealed that the average annual soil loss from the study area varied between 0 t.ha-1.yr-1 and 202.10 t.ha-1.yr-1. Higher annual soil loss was estimated in the western part of the study area, which ranged from 15 t.ha-1.yr-1 to 25 t.ha-1.yr-1 as compared to other parts of the study area. The Sangali district, in general, can be categorised as a low erosion potential district (0-5 t.ha-1.yr-1). The generated information can be utilised for the implementation of soil and water management and conservation measures in the western part of Sangli district, where there is a large area under the forest and agricultural land.

Optimizing soil erosion estimates of RUSLE model by analyzing land use/cover dynamics in upper Awash River Basin, Central Ethiopia

The Revised Universal Soil Loss Equation (RUSLE) is the most widely used erosion model for decision making on conservation priority. However, interpreting estimates of the mean annual soil loss alone could not accurately depict the spatial variation of soil erosion severity due to its inherent mathematical errors. This study aims to optimize the model’s outputs through detailed analysis of land use/cover dynamics in Upper Awash River Basin, central Ethiopia (7815.1 km2). The analysis include annual rate of change, net gain or loss, and conversion pathways. Results of the estimated mean annual soil loss in the basin varies between 4.1 t/ha/y and 5.1 t/ha/y during three consecutive decades (1990-2000, 2000-2010, and 2010-2020). The cultivated land cover exhibits slight erosion severity (< 5 t/ha/y), while contributing up to 59% the total annual soil loss (3198.8 Mt/ha). Although the net loss of cultivated land cover outweighing the gain during these decades, it progressively encroaches forests (142.2 km2), shrubs (780.8 km2), and grasslands (274.5 km2). Such net loss of protective land covers will inevitably increase the soil erosion risk. Findings from the study would be useful, enabling conservation practitioners to understand the overall severity of soil erosion and make informed decisions.

Hydric erosion rates in Raouz watershed, Morocco: RUSLE, GIS, and remote sensing

Water erosion significantly contributes to global soil degradation, inducing detrimental consequences. One of the most adverse impacts is the sedimentation buildup in reservoirs, which results in reduced water storage capacity and damage to the agro-pedological environment. In Morocco's western Rif region, the Raouz watershed faces soil losses annually that disrupt the basin's ecosystems and impede socio-economic progress. These erosional losses manifest in the Moulay El Hassan Ben Mehdi (MHBM) Dam's sedimentation, causing substantial financial repercussions of several million dirhams/year. To comprehensively assess and quantify this soil degradation phenomenon, this study evaluates soil losses employing the revised universal soil loss equations (RUSLE), strategically integrating geographic information system (GIS) layers that encapsulate pertinent environmental variables such as climate, topography, soil composition, and land use. By utilizing these empirical equations, the identification of predominant factors responsible for soil water erosion within the Raouz watershed is achieved. The outcomes of this study methodology demonstrate an average soil loss estimate of 16.53 t/ha/yr through RUSLE application. Furthermore, comparing soil loss values from this study using RUSLE to those derived from prior research using the Caesium-137 method reveals relatively minor disparities.

Estimation of Soil Loss for Different Soil Series, Tillage Management and Slopes under Soybean Cropping in Malwa Agro-Climatic Zone

The soil erosion can cause a reduction in agricultural productivity, ecosystem disturbances, and pollution of water. Physical and climatic features of a catchment such as topographic conditions, land use land cover, rainfall intensity, and the soil characteristics are the key significant factors of the soil erosion. The loss of the top fertile soil nutrients is intensely increasing. In present study soil loss for 12 soil series viz., Kheri, Bararia, Junapania, Namali, Dhamaniya Diwan, Dhodar, Surajpura, Paroliya, Ratangarh, Khermaliya, Morwan and Hathipura and 3 different tillage management viz., conventional tillage, ridge and furrow system and no tillage system under soybean crop in Malwa Agro-climatic Zone in central India has been estimated. The soil loss for four different sloes viz., 1%, &gt;1% to &lt; 3%, &gt;3 to &lt;5% and &gt;5% under these soil series and management practices have been studied. The results of the study revealed that soil loss found decreased with decrease in slope and vice-versa. Similarly, the soil loss was found higher under conventional tillage (4.134 t acre-1 y-1) as compared to the ridge and furrow system (1.447 t acre-1 y-1) and no tillage system (1.033 t acre-1 y-1). The Bararia soil series (4.196 t acre-1 y-1) found most vulnerable to the soil loss whereas the Dhodar soil series (1.036 t acre-1 y-1) was found resistant to soil loss under soybean cropping in Malwa Agro-climatic Zone.

Assessment of Soil Erosion by RUSLE Model using remote sensing and QGIS: A Case Study on Sina River Basin

Soil erosion is a significant environmental problem that affects river basins globally, leading to the loss of fertile topsoil, degradation of agricultural lands, and water pollution. The Revised Universal Soil Loss Equation (RUSLE) model is commonly used to assess soil erosion by considering various factors such as rainfall erosivity, soil erodibility, slope length and steepness, vegetation cover, and conservation practices. To obtain the necessary data for accurate predictions, remote sensing techniques and Quantum Geographic Information Systems (QGIS), specifically QGIS software, are utilized. The objectives of this research are to determine to estimate soil loss using the Revised Universal Soil Loss Equation (RUSLE) model for the Sina River watershed using QGIS. This research paper focuses on assessing soil erosion in the Sina River Basin located in Ahmednagar, Maharashtra, India, using the RUSLE model and QGIS, having a drainage area of 12,372 km2 . The parameters of the RUSLE model were estimated using remote sensing data and the erosion was determined using QGIS. The estimated rainfall erosivity, soil erodibility, topographic and crop management factors range from 83 to 84 MJ/mm·ha−1hr−1/year, 0 to 0.25t ha−1·MJ−1·mm−and 0 to 957.083, 0 to 0.19 respectively. The results indicate that the estimated total annual potential soil loss of about 14.37 t/yr. The predicted soil erosion rate due to the increase in a barren area is about 9.76 t/yr. The results can certainly aid in the implementation of soil management and conservation practices to reduce soil erosion in the Sina River Basin.

Erosivity Factor of the Revised Universal Soil Loss Equation (RUSLE) - A Systematized Review

The Revised Universal Soil Loss Equation (RUSLE) is a globally accepted erosion model which has gained good acceptability. Among the five influences of the RUSLE method of soil erosion estimation, the erosivity factor (R) represents rainfall event’s ability to produce erosion. It is mainly affected by rainfall intensity and kinetic energy of the rain. The erosion index represented by EI30 is the most common R-factor estimation method. Due to the non-availability of rainfall intensity data in many watersheds, researchers have developed methods for erosivity estimation using rainfall depth. The Modified Fournier Index method has gained popularity. Recently, different models using machine learning techniques and ANN are also being set up to establish the R-factor for soil loss estimation. These models can estimate the R-factor quickly and more accurately. They can even predict the R-factor for the future to predict soil loss and plan conservation measures accordingly. An attempt has been made here to review different methodologies proposed by scientists across the globe for arriving at the R-factor for soil loss estimation using RUSLE model.

Assessment of soil loss susceptibility in geodetic landmarks for the past and future climate change scenarios

Geodetic landmarks (GLs) are essential for obtaining precise height, horizontal coordinates, and the Earth's gravity field. This study aims to assess the soil susceptibility of GLs for past, present, and future scenarios, considering the projected anthropogenic effect from Coupled Model Intercomparison Project, Version 6 (CMIP6). Therefore, the soil loss estimations were made for all the GLs in the southern region of Santa Catarina state in Brazil using the Revised Soil Loss Equation (RUSLE). Our results showed average soil loss decreasing from 1985 to 2020. There was an increase in GLs in the null class (soil loss = 0 t/ha/year), mainly caused by urban growth. A decrease occurred in the low (0 t/ha/year < soil loss ≤10 t/ha/year), very severe (soil loss >200 t/ha/year), severe (50 t/ha/year < soil loss ≤200 t/ha/year), and moderate classes (10 t/ha/year < soil loss ≤50 t/ha/year). In addition, most future scenarios projected an increase in soil loss susceptibility, which also increased the GLs' susceptibility to soil loss from 2020 to 2100, albeit with lower values than the historical series. The soil loss remained stable in ssp126, slightly increased in ssp245 and ssp370, and increased in ssp585. The future scenarios only take into account changes in rainfall. Thus, the land cover change forecast would also be necessary for better analysis for future studies. Therefore, climate simulations can be used to understand the effects of climate change on soil erosion to support decision-making regarding GLs maintenance and the construction of new ones.

Spatio-temporal Soil Erosion Estimation in Sangareddy District, Telangana Using Rusle Model

Soil erosion poses a significant environmental challenge worldwide, causing the depletion of fertile topsoil, reduced crop productivity, and heightened sedimentation in water bodies. The extent of erosion depends on factors such as rainfall intensity, land slope, soil type, land use, and management practices. Accurate assessment of soil loss, considering spatial, temporal, and climate change factors, is vital for effective soil and water conservation planning. In this study, a spatial model for estimating soil loss was developed by integrating GIS with the Revised Universal Soil Loss Equation (RUSLE), and validation showed satisfactory results (R2=0.82). The spatial estimation revealed that most of the area experienced soil loss below 5 t ha-1 yr-1, with only a smaller portion showing soil loss exceeding 20 t ha-1 yr-1. To address this, in-situ soil conservation measures are recommended, such as using erosion-resistant crops (e.g., groundnut) in strip cropping, adopting crop rotations, mulching, and planting grasses for bund stabilization. Practices like deep ploughing, summer ploughing, and mixed cropping should also be adopted for sustainable watershed management. Construction of conservation structures like farm ponds and percolation tanks is highly recommended. The study also observed temporal variations in soil loss for Sangareddy district, with the highest soil loss of 134 t ha−1 yr−1 occurring in 2020, and the lowest of 71 t ha−1 yr−1 in 2018. These findings underscore the need for continuous monitoring and adaptive conservation strategies to mitigate soil erosion and ensure sustainable land use practices in the region.

Tracing soil erosion with Fe3O4 magnetic powder: Principle and application

Magnetic powder is regarded as an effective and economical tracer for estimating soil erosion. However, the principle and application for using magnetic powder to estimate soil erosion are still not fully developed. In this study, magnetic powders with mean diameters of both 30 and 1 μm were mixed into three soils at different mass proportion. The relationship between magnetic susceptibility and the mass proportion of the introduced magnetic powder in the tagged soil, and the binding ability of magnetic powder to soil particles after both dry and wet sieving were investigated. The accuracy of tracking soil loss by using magnetic powder as a tracer was assessed. The results showed that there was a significant linear relationship between the magnetic susceptibility and the mass proportion of the introduced magnetic powder in the tagged soil. The relationship between the amount of soil captured by a magnet and the mass proportion of magnetic powder in the tagged soil indicated that soils were readily magnetized by magnetic powder, especially fine fractions. The magnetic susceptibility of magnetic powder in different sizes of soil aggregates was variable. A majority of magnetic powder of both 30 and 1 μm diameters was strongly bound with fine particles <0.05 mm in dry and wet sieving. Using the estimated tracer mass proportions, the relative errors between measured and estimated soil losses with enrichment correction factor were less than 18.3% under the simulated rain events. This study not only reveal the principle of Fe3O4 powder in soil erosion, but also improve its estimated precision of soil loss, which can make the tracing method by Fe3O4 magnetic powder more useable in future.

Optimization of land management measures for soil erosion risk using GIS in agricultural landscape of western Hararghe highlands, Ethiopia

Soil loss through water is one of the major environmental problems leading to land degradation worldwide. Though soil erosion is an inevitable natural phenomenon, it is possible to reverse and minimize the rate at a local level. This requires spatially unambiguous evidence on rate of soil loss so as to implement site specific land management decisions. The aim of this study was to estimate the mean annual soil loss and optimize land management measures in the Midhagdu watershed of western Hararghe highlands, Ethiopia. The study used five soil loss factors as input variables to estimate the soil loss. A thematic layer map was generated for each factor with spatial resolution of 30 m using Geographical Information System (GIS), and combined them with revised universal soil loss equation (RUSLE) model to estimate soil loss and identify erosion risky area in the watershed. According to the results, the distribution of annual soil loss varied between 0 and 179 t ha−1 yr−1, with a mean annual loss rate of 44.24 t ha−1 yr−1. The total annual soil loss in the entire watershed was 240,250 tons. Of this, 93.48% (224,593 tons) of the loss occurred in 49% (2,674 ha) of the catchment area,which was above the maximum tolerable soil loss limits. Most of the soil erosion affected areas are spatially situated in the upper and hillsides of the watershed. This is due to factors attributed to the steepness of the slope, dominance of Leptosols with shallow soil depth, coupled with absence of supporting practice in these parts of the watershed. Thus, slope length and slope steepness (LS) factor was the primary RUSLE parameters that controlled the soil loss in the studied watershed followed by soil erodibility (K) and support practice (P) factor. Hence, this study optimized land management options for future intervention based on the inherent limitation of the biophysical environment, socioeconomic and cultural conditions of the watershed community.