Estimate Soil Loss Research Articles

Assessment and mapping of water erosion by the integration of Gavrilovic's "EPM" model in the MENA region: case study of the Admer-Ezem sub-watershed, upper Oum-Rbaa basin, Morocco.

The main goal of the research is to assess soil erosion while analyzing the spatial distribution of its evolution using the EPM (erosion potential model). Situated northwest of the upper Oum-Rbaa watershed in Morocco, the Admer-Ezem watershed is part of the research area. Its climate is Mediterranean, ranging from semi-arid to subhumid bioclimate, which favors fairly scattered vegetation and poor soil. Separating the factors of complexity and interdependence in the analysis of erosion risk was made possible quickly and effectively by the Geographic Information System (GIS) integration of thematic maps of the various Gavrilovic Equation EPM factors with their databases. It also made it possible to evaluate each factor's influence and determine how much it contributed to soil loss. The priority of the various watershed regions was made possible by the use of Gavrilovic's calculations. This basin covers a total area of 197.59 km2, experiences relatively abundant rainfall (800 mm/year), and is subject to significant anthropogenic pressure. This will have an impact on the overexploitation of natural resources in general and soils in particular. Excessive use of agricultural land has led to its fragility and increased susceptibility to erosion. These natural and anthropogenic conditions have induced an intense erosive dynamic, which can be seen in its various forms (badlands, landslides, flows, concentrated active ravines…). The use of the "EPM" model for estimating soil losses approximates the severity of the erosion phenomenon. Soil loss due to water erosion according to the model used is estimated at 32.89 t/ha/year as the highest value and 0.11 t/ha/year as the lowest value. In addition, the analysis of these results made it possible, using GIS, to determine the factors that control water erosion.

Assessing the Global Sensitivity of RUSLE Factors: A Case Study of Southern Bahia, Brazil

Global sensitivity analysis (GSA) of the revised universal soil loss equation (RUSLE) factors is in its infancy but is crucial to rank the importance of each factor in terms of its non-linear impact on the soil erosion rate. Hence, the goal of this study was to perform a GSA of each factor of RUSLE for a soil erosion assessment in southern Bahia, Brazil. To meet this goal, three non-linear topographic factor (LS factor) equations alternately implemented in RUSLE, coupled with geographic information system (GIS) software and a variogram analysis of the response surfaces (VARSs), were used. The results showed that the average soil erosion rate in the Pardo River basin was 25.02 t/ha/yr. In addition, the GSA analysis showed that the slope angle which is associated with the LS factor was the most sensitive parameter, followed by the cover management factor (C factor) and the support practices factor (P factor) (CP factors), the specific catchment area (SCA), the sheet erosion (m), the erodibility factor (K factor), the rill (n), and the erosivity factor (R factor). The novelty of this work is that the values of parameters m and n of the LS factor can substantially affect this factor and, thus, the soil loss estimation.

Spatio-Temporal Changes in Erosion-Accumulation Processes on a Small Watershed in the Northern Part of the Central Russian Upland

First time comprehensive studies of soil erosion were carried out in a 42-hectare catchment area (near the Lomovets, Orel region) in the zone of Luvic Phaeozems with erosion resistance is significantly lower than that of Chernozems. Analysis of archival and historical cartographic materials showed that the duration of plowing of the catchment is 200 ± 10 years. The rates of soil erosion and accumulation over the entire plowing period, post Chernobyl period, last 50 ± 25 years and single erosion event (which occurred on May 31, 2022) were determined based on the use of field methods (soil-morphological, radiocesium, rain rills method, detailed survey using drone) and WaTEM/SEDEM erosion model calculation. The estimates of soil losses and accumulation indicate significant fluctuations in the rates of erosion-accumulative processes over the past 200 years, which are mainly due to the conditions of the formation of melt runoff, the repeatability and distribution of runoff-forming heavy rain within the warm season, the set of crops sown, the frequency and methods of tillage, changes in field boundaries. The average annual estimates of soil erosion for the agricultural period are higher than those for the post-Chernobyl period, since the rates of soil erosion have decreased in the last three decades. Estimates of soil losses using the WaTEM/SEDEM model are generally comparable with the results of determining soil losses based on the soil-morphological method, provided that the redistribution of sediment to the lower boundaries of arable land is taken into account. The spatial structure of a single erosive event turned out to be largely close to the spatial structure of the location of the areas of eroded and aggradational soils formed during the entire agricultural period.

Soil Loss Estimation and Community Awareness Assessment on Integrated Watershed Management: The Case of Lake Hashenge Watershed, Tigray, Ethiopia

Ethiopia is recognized as one of the most severely affected regions by soil erosion globally, posing significant threats to its national economy. This study investigates soil loss estimation and community awareness within the framework of integrated watershed management in the Lake Hashenge watershed, Tigray, Ethiopia. The mean annual rainfall and total drainage area of Lake Hashenge watershed is 988.7 mm and 129km2 respectively. Utilizing the Revised Universal Soil Loss Equation (RUSLE) model, we quantified soil erosion rates and identified critical areas prone to severe soil loss. Our findings indicate significant soil degradation, exacerbated by both natural and anthropogenic factors. The estimated soil loss yield entering Lake Hashenge in the form of sheet and rill erosion was 61.94 tons/ha/year. According to the universal soil loss factsheet, the soil loss estimated in the watershed was categorized as severe, indicating that the lake is in danger. Concurrently, we assessed community awareness and participation in soil conservation practices through surveys and interviews with a multidisciplinary team of researchers. The results reveal a moderate level of awareness and engagement, highlighting the need for enhanced educational and participatory approaches to foster sustainable watershed management. This research underscores the importance of integrating scientific soil loss assessments with community-based integrated watershed management strategies to mitigate soil erosion, protect the lake from sedimentation, and promote environmental sustainability in the region.

A GIS-RS Approach for RUSLE-Based Method of Mean Estimation of Mean Annual Soil Loss of the Tagoloan River Basin, Philippines

Abstract. Soil erosion is a serious environmental concern in Tagoloan River Basin (TRB), a major watershed in Northern Mindanao, Philippines. It leads to soil loss causing detrimental impacts such as decreased soil productivity, nutrient loss, siltation, and water quality degradation among others. These impacts are better understood by estimating the degree of soil loss in the watershed and visualized in a GIS-based and factor-based approach using the Revised Universal Soil Loss Equation (RUSLE) model. Thematic maps of soil loss were generated with factors for rainfall erosivity (R), soil erodibility (K), topographic (LS) consisting of slope length (L) and slope steepness (S), cover management (C), and support practice (P). Factors were calculated separately and multiplied together to develop combined soil loss maps. Results show that TRB has an actual mean annual soil loss of 153.20 tons/hectare/year with 47.15% of the study area having very high to very severe susceptibility to soil loss. Further, a comparison of the potential (RKLS) and actual (RKLSCP) soil loss maps indicates the significance of cover management and support practices to the resulting mean annual soil loss. The present characterized soil loss level maps and its understanding driving forces of soil erosion for the planning of management practices and mitigating environmental hazards in the watershed.

Estimation of soil loss and sediment yield by using the modified RUSLE model in the Indus River basin, including the quantification of error and uncertainty in remote-sensing images

Context Indus River is the cradle of Pakistani lifeline, and its lower reaches are prone to soil loss owing to bank erosion. Aims The aim was to investigate the sediment yield in the Lower Indus River Basin (LIRB), while addressing challenges related to error or uncertainty in remote-sensing data. Methods We employed a modified revised universal soil loss equation (RUSLE) model, integrating high-resolution digital elevation model (DEM) and calibrated Climate Hazards Group InfraRed Precipitation with station data (CHIRPS). Additional data layers, including land use, soil and cropping data, were also utilised. Key results The extent of actual soil erosion ranges from minimum to maximum erosion; 38.9% area lies in the range >50 Mg ha‒1 year‒1, whereas 23.2% area lies in the range of 0–10 Mg ha‒1 year‒1, and 18.1% area lies in the range of 10–20 Mg ha‒1 year‒1. Conclusions The study identifies critical erosion areas and tackles uncertainties in remote-sensing data. The spatial analysis showed that higher distribution sediment erosion along the channel flow direction from the northern part of LIRB to the Arabian Sea. Implications The findings have provided critical information for policymakers and water managers to implement effective measures to reduce erosion, maintain soil integrity and promote the sustainability of the Indus River system.

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

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

Estimation and assessment of water erosion in the Peixe Angical basin, Brazil

Water erosion causes the displacement of soil particles from higher to lower elevations, and this process intensifies when land use and vegetation cover change, such as through the conversion of forests into pastures or agricultural fields. Identifying priority areas for soil and water conservation practices is essential for promoting sustainable agriculture. Equally important is identifying the most influential factors driving erosion, as understanding these can guide effective land management strategies. Machine learning techniques, such as Random Forest, are valuable tools for analyzing large datasets and assessing the importance of variables. The primary aim of this study was to estimate soil losses due to land-use changes in the Peixe Angical Reservoir drainage basin using the Revised Universal Soil Loss Equation (RUSLE) within a Geographic Information System (GIS) framework, and to identify priority areas for soil conservation. Additionally, the study aimed to evaluate the contribution and importance of the RUSLE model factors (R, K, LS, and C) to soil loss using the Random Forest regression algorithm. Soil losses were computed for the chronological scenarios (1990, 2000, 2010, and 2017), using rasters with 90 m resolution to calculate the product of the R, K, LS, and C factors, along with the P factor. These soil losses were classified into erosion risk categories, ranging from very low (0–2.5 Mg ha−1 yr−1) to extremely high (greater than 100 Mg ha−1 yr−1). Soil losses in the basin increased over time. The Random Forest algorithm was applied to evaluate the importance of each factor. Rainfall erosivity was found to vary spatially, ranging from 7047.64 MJ mm ha−1 h−1 yr−1 to 11,348.5 MJ mm ha−1 h−1 yr−1, while the LS factor exhibited values ranging from near 0 to over 20. Litholic Neosol (Entisol) was the predominant soil type in the drainage basin. In terms of land use, forests accounted for the largest portion of the basin: 55.60% in 1990, 51.31% in 2000, 48.88% in 2010, and 48.21% in 2017. The C factor, which reflects vegetation cover, was the most significant contributor to soil loss, accounting for 44.8% in 1990, 43.5% in 2000, 44.2% in 2010, and 44.4% in 2017, followed by the K factor (soil erodibility). These assessment techniques can be utilized in guiding conservation planning, thereby supporting sustainable land use practices.

Количественная оценка потерь почвы вследствие водной эрозии на сильновыпаханных почвах бассейна верхней Оки с использованием географических информационных систем

The paper presents some results of studying soil losses appearing due to water erosion in an agricultural field with highly degraded plowed soils. The research was carried out on the arable slope of the southern exposure in an experimental area located in the Oryol district of the Oryol Region (upper Oka basin). A set of methods was used: morphometric analysis of the topography relief, radiocaesium method, soil-morphological method, method for determining easily decomposable organic matter (labile organic matter) designed according to Ganzhara and Borisov, agrochemical methods. The study was based upon the authors’ in-situ data of 2016–2023. Maps are compiled to show the caesium-137, humus and labile organic matter spatial distribution, and of the soil degradation degree (in points on the Ganzhara and Borisov scale) in the arable horizon of 0–25 cm. Analysis of the caesium-137 radioactivity spatial distribution made it possible to identify two sectors in an area of highly degraded plowed soil, which differ in surface slope values and in nature of the caesium-137 radioactivity interdependences with catchment area and sign of profile curvature. Developed semi-empirical computational dependences for the delineated sectors allow to compile a gridded map of the soil runoff intensity (in t/ha/year). The intensity of soil runoff in the area with highly degraded plowed soils is ranged from 5 to more than 20 t/ha/year. The algorithm for the intensity of soil delivery outside of the studied area was developed on the basis of layer-by-layer soil sampling data collected in the mouth of the ravine, removing soil from the highly degraded plowed soil area. The algorithm showed that 4.7–6.5 tons of soil per year are removed from 1 hectare of the catchment area basin. The rest of the outwashed soil material is deposited in accumulation areas within the catchment area basin. The authors recommend verifying the findings in other areas of the agricultural field. The proposed methodology for soil loss estimation, developed for highly degraded plowed soil areas in an agricultural field, requires clarification by enlarging the scale of the study.

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

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

A modified Soil and Water Assessment Tool Plus (M-SWAT+) model for sediment yield estimation

ABSTRACT The Soil and Water Assessment Tool Plus (SWAT+) model depends on the Modified Universal Soil Loss Equation (MUSLE), which in turn depends on the peak runoff rate for the estimation of soil loss from a catchment. The SWAT+ model generates a random number for each day in the process of peak runoff rate estimation from the catchment. On the other hand, the model provides calibration parameters like the peak rate adjustment factor for sediment routing in the tributary channels and main channels. There is uncertainty that how likely the SWAT+ model estimates the actual peak runoff rates. An improved MUSLE does not depend on the peak runoff rate, and, therefore, it minimizes uncertainty related to the peak runoff rate when estimating soil loss from the catchment in the SWAT+ environment. On the other hand, the effect of the topographic factor of the MUSLE or improved MUSLE on soil erosion and sediment transport is not explicitly explained. Therefore, we modify the source code of the SWAT+ model for sediment yield estimation by replacing the improved MUSLE and the best-fitted equations of the topographic factor in the source code. The modified SWAT+ model showed better performance than the original SWAT+ model.

The effects of land cover transition and its patch mosaics on soil erosion using geospatial technology in South Wollo, Ethiopia

Soil erosion has been a significant threat to agricultural productivity, dam sustainability, and ecosystem services in the highlands of Ethiopia. Despite some watershed-specific research on this problem, the spatiotemporal distribution of soil erosion risks is still rarely assessed in South Wollo. This study aims to monitor land cover changes, analyze landscape mosaics, and estimate soil loss in South Wollo from 1990 to 2020. The Revised Universal Soil Loss Equation (RUSLE) was utilized to estimate soil loss. Key informant interviews and field observations were conducted to gain further insights into land cover change and soil erosion. The results showed that agricultural land use was dominant, covering 50.75 % of the study area in 1990 and it continued to expand at an annual rate of 1.73 %, primarily on shrubland and grassland. The landscape structure was more heterogeneous in the north compared to the central and southern areas. In 1990, the mean soil loss was 21.13 t ha-1yr−1, totaling 41.53 million tons. By 2020, These figures had increased to 28.97 t ha-1yr−1 and 49.86 million tons, respectively. Areas experiencing very severe soil erosion (>50 t ha-1yr−1) expanded from 10.81 % (1990) to 13.57 % (2020). The magnitude of soil loss also differed among land cover types, with the highest rates observed in bare land (>85 t ha-1yr−1), followed by agricultural land (>30 t ha-1yr−1). Soil loss factors and landscape metrics exhibited a significant correlation with soil loss, though the strength and direction of these interactions varied. Land cover change and its associated soil erosion were primarily exacerbated by policy factors, including tenure insecurity and disregard for farmers’ indigenous conservation knowledge. This study offers valuable insights into the trends of land cover transitions, landscape structure, and soil loss. It will help guide sustainable land management, aimed at increasing green legacy and reducing soil loss.

Estimation of soil loss using RUSLE model and GIS tools: Case study in the Jucusbamba Micro-Watershed (Amazonas, NW Peru)

Human activities have intensified soil erosion globally, negatively impacting the physical, chemical, and microbiological quality of water and soil. This study estimated soil loss in the Jucusbamba Micro-Watershed (Amazonas, northwestern Peru) using the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS) and remote sensing (RS) data. Meteorological data, soil maps, digital elevation models, and land cover and use maps were integrated into a GIS environment to predict the spatial distribution of erosion. The results showed five classes of soil loss severity: slight (93.34%), moderate (3.94%), high (1.82%), very high (0.61%), and severe (0.29%). Areas with high, very high, and severe erosion levels were mainly located in the middle and lower parts of the micro-watershed. The average annual soil loss was estimated at 23.24 t/ha/year. The information obtained is crucial for the formulation of soil management plans and programs to address erosion problems in the Jucusbamba River Micro-Watershed. Future research should focus on validating these models through field studies and implementing soil conservation practices based on these findings. Keywords: Amazonas, hydrology, RUSLE model, water erosion map, watershed.

Impact of spatial resolution on soil loss estimation: a case study of abandoned quarries in Morocco

Impact of spatial resolution on soil loss estimation: a case study of abandoned quarries in Morocco

Estimating erosion vulnerability within agricultural fields by downscaling the Daily Erosion Project (DEP): the OFEtool

AbstractAgriculture continues to be one of the most important sources of nonpoint source pollution to surface water bodies. Consequently, it is critical to identify and prioritize high‐contributing agricultural fields and sub‐field areas for reducing soil erosion and sediment delivery by implementing best management practices (BMPs). Current erosion risk assessment tools are either complex modelling approaches or rely on a simplified reality and generalized assumption. The Daily Erosion Project (DEP) is a daily estimator of precipitation, hillslope runoff, detachment and soil loss covering ~630 000 km2 across the Midwest United States. These estimations are reported daily and publicly at the hydrologic unit code 12 watershed resolution (approximately 100 km2). The main objective of this study was to develop a new tool (named Overland Flow Element tool [OFEtool]) that downscales the watershed scale of DEP to estimate average runoff and soil displacement within a field, helping to locate erosive hotspots at multiple scales. We also demonstrated the applicability of OFEtool in Bennet Creek‐Sugar Creek in East Central Iowa (the United States) and compared its results with other erosion vulnerability tools such as the Soil Vulnerability Index for Cultivated Cropland (SVI‐cc) and a GIS‐based Revised Universal Soil Loss Equation (RUSLE). The same erosion risk classes and ranges (low, moderate, moderately high and high) were implemented for all indexes. The advantages of the OFEtool compared to the SVI‐cc and RUSLE models are related to the use of an event‐based modelling approach, such as DEP, with updated soil loss estimates based on temporal changes in climate inputs and land use and management. The OFEtool uses a 6‐year time frame and a more up‐to‐date field inputs, while RUSLE provides a long‐term average and SVI‐cc only considers soil and topographical factors for risk assessment. Results indicated that the spatial distribution of vulnerable fields (and parts of the fields) followed a similar trend as other tested indices. However, the risk level associated with each tool differed (SVI‐cc > RUSLE > OFEtool). These differences could arise from intrinsic disparities within the tools (inputs, timing, processes considered, assumptions). While currently limited to the DEP domain and relying on the DEP random sampling scheme, further research is warranted to validate the tool at other Midwest locations and ensure it captures the watershed's landscape variability (combination of terrain, soil, land use and management) required to identifying critical erosion hotspots.

Analysing the capacity of multispectral indices to map the spatial distribution of potential post-fire soil losses based on soil burn severity

The area burned in Spain exceeded historical records in 2022, when exceptionally warm conditions influenced wildfire events. The predicted intensification of wildfire regimes includes an increase in frequency, severity, and size. Therefore, a study of the wildfires that occurred in 2022 is necessary to understand their behaviour and possible environmental impacts. The objective of this study is to analyse the applicability of using spectral indices and Geographic Information System (GIS) approaches to map the spatial distribution and estimate potential soil losses using Sentinel-2 imagery and fire severity field data. Soil losses were estimated using an empirical model based on soil burn severity data collected in the field after wildfire. The relationship between the Normalized Difference Infrared Index (NDII), Difference Normalized Wildfire Ash Index (dNWAI), and the Blue Normalized Difference Vegetation Index (BNDVI) with the estimated soil losses was then evaluated. In addition, the influence of different time scales of the satellite images was analysed. The first period considered (Date I) ranges from 8 to 20 days after the beginning of the wildfire, which coincides with the field data collection. The second period considered (Date II) ranges from 28 to 35 days after the start of the wildfire. The results obtained showed a significant dependence relationship between the BNDVI index (using satellite images of Date I) and the estimated soil losses (R2 = 0.756), while the results of the NDII (R2 = 0.31) and dNWAI (R2 = 0.061), showed no spatial relationship with the estimated soil losses. Three of the largest wildfires in 2022 in Spain were analysed, and the results showed strong correlations of BNDVI index for Folgoso do Courel (R2 = 0.808), for Carballeda de Valedorras (R2 = 0.906), and for Sierra de la Culebra (R2 = 0.939). In addition, these results allowed the mapping and quantification of potential soil losses in areas where fire severity was high, totalling ∼2,50,000 Mg ha−1 in Folgoso do Courel, ∼3,70,000 Mg ha−1 in Carballeda de Valdeorras, and ∼4,70,000 Mg ha−1 in Sierra de la Culebra. Moreover, BNDVI values for estimating soil loss vary by vegetation type, and there is a positive correlation between severity classes and the BNDVI index. This approach can inform post-fire land management decisions in future wildfires and could be applied to other regions.

Unveiling Meghalaya topography: slope length gradient (LS) factor estimation for insightful soil erosion evaluation

Meghalaya is known for its complex environment due to its hilly terrain and heavy rains, which result in significant soil erosion challenges. The LS factor is the primary factor used to estimate soil loss. Acquiring accurate LS values has always been a significant challenge. The main aim of this research is to compute the LS factor in the Meghalaya region. Three methods are used to calculate the LS factor: (1) Wischmeier and Smith’s (1978) method, (2) Moore and Wilson’s (1992) method, and (3) Desmet and Gover’s (1996) method. All three approaches used the digital elevation model (DEM) in the spatial domain to determine the LS factor. The results of this investigation demonstrated significant variation in LS factor in all three methods. Wischmeier and Smith’s method yielded LS factor values ranging from 0 to 266.538, Moore and Wilson’s method resulted in values ranging from 0 to 112.631, and Desmet and Gover’s method resulted in values ranging from 0 to 683.679. This study concluded that Wischmeier and Smith’s method resulted in LS factor values distributed uniformly across the spatial domain. In contrast, Moore and Wilson’s method only yielded high values along the flow path and low values in other areas. Additionally, Desmet and Gover’s method identified high LS factors in regions with steep slopes near rivers and streamlines. This study can be helpful in anticipating soil erosion well before time and also in developing a slope management plan in the study region.

Spatial analysis and assessment of soil erosion in the southern Western Ghats region in India.

Soil erosion is expected to worsen in the future as a result of climate change, growing population demands, improper land use, and excessive exploitation of natural resources in India. Due to the growing population and changes in land use, it has become increasingly crucial to map and quantitatively assess soil for the purpose of sustainable agricultural usage and planning conservation efforts. The problem of soil erosion is mainly on steeper slopes with intense rainfall in parts of Western Ghats. The 20.17% of geographical area have been converted into wasteland due to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is a highly prevalent and effective technique utilized for estimating soil loss in order to facilitate the planning of erosion control measures. Despite the fact that RUSLE is accurately estimate sediment yields from gully erosion, it is an effective tool in estimating sheet and rill erosions losses from diverse land uses like agricultural to construction sites. The current study is mainly about combining the RUSLE model with GIS (Geographic Information System) to find out how much soil is being lost, particularly in Noyyal and Sanganur watersheds which is located in Coimbatore district of Tamil Nadu, India. This analysis is based on the soil order, with a significant proportion of alfisols and inceptisols being considered. The obtained outcome is contrasted with the established soil loss tolerance threshold, leading to the identification of the areas with the highest susceptibility to erosion. Within the narrower and more inclined section of the watershed, yearly soil loss scales from 0 to 5455 tonnes/ha/year, with an average annual loss of soil of 2.44 tonnes/ha. The severe soil erosion of 100 to 5455 tonnes/ha/year is found along the steep and greater slope length. The generated soil map was classified into six categories: very slight, slight, moderate, high, severe, and very severe. These classifications, respectively, occupied 6.23%, 14.88%, 10.56%, 15.70%, 7.73%, and 6.63% of the basin area. Based on the results of cross-validation, the estimated result of the present study was found to be very high compared to past studies conducted 0 to 368.12 tonnes/ha/year especially in very severe erosion zones. But very slight to severe erosion zones nearly matched with same level of soil loss. To protect the soil in the study area from erosion, more specific actions should be taken. These include micro-catchment, broad bed furrows, up-and-down farming, soil amendment with coconut coir pith composition, streambank stabilization with vegetation, and micro-water harvesting with abandoned well recharge. These actions should be carried out over time to make sure to work.

Soil erosion risk assessment of the Lakhmess watershed (northwestern Tunisia) via the SEAGIS model: Inferred prioritization of risky sub-watersheds

Water erosion in Tunisian semi-arid regions causes harmful effects by silting reservoirs and reducing agricultural lands and soil fertility. Several factors are involved in the erosion process: rain erosivity, soil fragility, and degraded land cover on steep slopes associated with the intensification of inappropriate human practices. Thus, identifying erosion vulnerable sub-watersheds based on the assessed soil loss rate is very important to apply suitable conservation measures. The current research aimed to prioritize risky areas in the Lakhmess watershed, north-west Tunisia via the Soil Erosion Assessment using Geographical Information System (SEAGIS) model. To prioritize sub-watersheds vulnerable to soil erosion and sediment yield, the Lakhmess watershed, covering an area of 162 km2, was divided into 16 sub-watersheds (L1–L16), according to the hydrographic network. Then, the mean annual soil erosion rate and the mean annual sediment yield in the watershed were estimated by integrating the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Ratio (SDR) in the SEAGIS model and their spatial distribution was determined. The obtained results indicate that the estimated average annual soil erosion rate is 4.2 t/ha/y and the annual sediment yield is 2.6 t/ha/y. Maner's SDR model was selected as the best model for estimating SY, with standard error, standard deviation, and coefficient of variation values of 0.75%, 0.01, and 0.45%, respectively. The prioritization of the Lakhmess sub-watersheds based on the estimated soil loss rate reveals that among the 16 sub-watersheds, three sub-watersheds (L10, L12, and L15) were identified as being in a very high priority soil erosion class. The high soil erosion rate and sediment yield in these sub-watersheds is explained by the steep slope and a high rainfall erosivity factor. Six sub-watersheds (L2, L4, L5, L6, L7, and L16) were found to belong to a very low priority soil erosion class, as they are characterized by a very gentle slope, which appears to be an extremely determining factor. These findings constitute a basis for decision makers to plan effective conservation measures to conserve agricultural lands, soil, and water resources in northwestern Tunisia.

The Contradictory Issue of the Impact of Antecedent Soil Moisture to Interrill Erosion in Clay Soil: A Two-Year Field Study

The impact of antecedent soil moisture content on soil erosion has been a contradictory issue in erosion research, as well as process-based soil loss estimation models. The objective of this study was to investigate the impact of antecedent soil moisture content on the loss of clay soil through two-year runoff plot experiments under natural rainfall. Volumetric soil moisture sensors were used to monitor soil moisture changes, and readings were used along with rainfall records to quantify the antecedent soil moisture conditions. The results of this study show that the impact of antecedent soil moisture on interrill erosion is conditional, and the impact only exists in erosion events with a low Rainfall–Runoff Erosivity Index. The coefficient of determination between antecedent soil moisture content and soil loss per the Rainfall–Runoff Erosivity Index (Soil Loss/EI30) varies from 0.222 to 0.758, depending on the rainfall duration and Rainfall–Runoff Erosivity. The results of this study also suggest that accumulative rainfall within 48 h (Pp48) prior to an effective erosion event is strongly correlated with Soil Loss/EI30, particularly when the duration of an effective erosion event is either 3~7 h or 10~30 h. Hence, Pp48 can be considered as a replacement for antecedent soil moisture in process-based soil loss simulation models.