Erosion Assessment Research Articles

Assessment of Cavitation Erosion Using Combined Numerical and Experimental Approach

This paper aims to numerically assess the cavitation damage of hydrodynamic machines and hydraulic components and its development in time, based on cavitation erosion tests with samples of used materials. The theoretical part of this paper is devoted to the numerical simulation of unsteady multiphase flow by means of computational fluid dynamics (CFD) and to the prediction of the erosive effects of the collapses of cavitation bubbles in the vicinity of solid surfaces. Compressible unsteady Reynolds-averaged Navier–Stokes equations (URANS) are solved together with the Zwart cavitation model. To describe the destructive collapses of vapor bubbles, the modeling of cavitation bubble dynamics along selected streamlines or trajectories is applied. The hybrid Euler–Lagrange approach with one-way coupling and the full Rayleigh–Plesset equation (R–P) are therefore utilized. This paper also describes the experimental apparatus with a rotating disc used to reach genuine hydrodynamic cavitation and conditions similar to those of hydrodynamic machines. The simulations are compared with the obtained experimental data, with good agreement. The proposed methodology enables the application of the results of erosion tests to the real geometry of hydraulic machines and to reliably predict the locations and magnitude of cavitation erosion, so as to select appropriate materials or material treatments for endangered parts.

Failed radial head arthroplasty treated by removal of the implant.

In patients with a failed radial head arthroplasty (RHA), simple removal of the implant is an option. However, there is little information in the literature about the outcome of this procedure. The aim of this study was to review the mid-term clinical and radiological results, and the rate of complications and removal of the implant, in patients whose initial RHA was undertaken acutely for trauma involving the elbow. A total of 11 patients in whom removal of a RHA without reimplantation was undertaken as a revision procedure were reviewed at a mean follow-up of 8.4 years (6 to 11). The range of motion (ROM) and stability of the elbow were recorded. Pain was assessed using a visual analogue scale (VAS). The functional outcome was assessed using the Mayo Elbow Performance Score (MEPS), the Oxford Elbow Score (OES), and the Disabilities of the Arm, Shoulder and Hand questionnaire (DASH). Radiological examination included the assessment of heterotopic ossification (HO), implant loosening, capitellar erosion, overlengthening, and osteoarthritis. Complications and the rate of further surgery were also recorded. The indications for removal of the implant were stiffness in five patients, aseptic loosening in five, and pain attributed to the RHA in three. The mean time interval between RHA for trauma to removal was ten months (7 to 21). Preoperatively, three patients had overlengthening of the implant, three had capitellar erosion, six had HO, and four had radiological evidence of loosening. At the final follow-up, the mean the flexion-extension arc improved significantly by 38.2° (95% CI 20 to 59; p = 0.002) and the mean arc of prono-supination improved significantly by 20° (95% CI 0 to 72.5; p = 0.035). The mean pain VAS score improved significantly by 3.5 (95% CI 2 to 5.5; p = 0.004). The mean MEPS improved significantly by 27.5 (95% CI 17.5 to 42.5; p = 0.002). The mean OES improved significantly by 9 (95% CI 2.5 to 14; p = 0.012), and the mean DASH score improved significantly by 23.5 (95% CI 7.5 to 31.6; p = 0.012). Ten patients (91%) had HO and osteoarthritis. Two patients underwent further surgery due to stiffness and pain, respectively. Simple removal of the implant at revision surgery following a failed RHA introduced following trauma provides satisfactory mid-term results with an acceptable risk of complications. Osteoarthritis, instability, and radioulnar impingement were not problems in this series.

Soil erosion assessment for prioritizing soil and water conservation interventions in Gotu watershed, Northeastern Ethiopia.

Soil erosion by water is a serious problem in Ethiopia, contributing to diminishing crop yields and food shortages. Apart from understanding the magnitude, risk, and spatial distribution of the problem, identifying erosion hotspot areas is essential for effectively reversing the problem. This study aims to identify erosion hotspots in the Gotu watershed, in northeastern Ethiopia, using the revised universal soil loss equation (RUSLE) and incorporating local farmers' perspectives to prioritize conservation efforts. The RUSLE model reveals that 29,744.3 metric tons of soil is lost annually from the Gotu watershed, with an average loss of 65.3 to t ha⁻1year⁻1. The main contributing factors to soil erosion in the watershed include undulating topography, loss of plant cover, and continuous cultivation. The highest soil loss rates (> 80 t ha⁻1year⁻1) were found in the western, northern, and southern parts of the watershed, where cultivation occurs on moderate to steep slopes with sparse vegetation cover. These areas should be prioritized for conservation interventions. Farmers identified poor crop yields and damaged conservation structures as key indicators of soil erosion prevalence in the watershed. Increasing farmer's understanding of soil erosion and the importance of soil and water conservation is essential for effectively controlling soil erosion and improving food security in the area.

Engineering Study of Erosion to Protect the Gometseri Alazani and Pirikiti Alazani Catchment Area (Kakheti region, Akhmeta municipality, Tusheti)

Soil erosion is a global environmental issue that reduces soil productivity, affects water quality, leads to sediment deposition, and increases the likelihood of agricultural land degradation. Combating erosion requires both quantitative and qualitative assessment of potential soil erosion on specific sites, along with knowledge of local terrain, soil types, land use systems, and management practices. From a theoretical and practical point of view, erosion processes are widespread and dangerous in Georgia, especially in mountainous areas. Slopes that have not been eroded or have not formed ravines are rare. Intensive landslides and mudslides are also observed here. Changes in factors associated with erosion are of interest, including the impact of climate change and human activity on components of erosional geosystems, especially on soil cover. Modern geoinformation systems (GIS) provide qualitatively new opportunities for research, modelling and optimization of the use of erosion-prone lands. Despite its wide application in many spheres of human activity, its potential in erosion research has not yet been fully realized. This article discusses about the Gometseri Alazani and Pirikiti Alazani (Akhmeta Municipality, Tusheti) catchment areas. The erosion-landslide processes developed in the research area are studied using the Revised Universal Soil Loss Equation (RUSLE). Erosion-vulnerable areas have been identified, where it is necessary to carry out additional engineering protection measures.

Runoff Dynamics and Soil Erosion Assessment using a SWAT Model at Upper Cauvery River Basin

The increasing availability of geospatial datasets on watershed characteristics and hydro-meteorological variables emphasises the importance of integrated hydrological models for effective catchment management. Climatic and physiographic determinants such as topography, land use, soil properties, and anthropogenic interventions substantially influence a catchment's hydrological equilibrium. The SWAT model was used to elucidate rainfall-runoff dynamics in the Upper Cauvery River Basin, Karnataka, India (36,682 km²), applying the SCS Curve Number (CN) method for runoff estimation. Runoff was estimated for 2012–2021 using rainfall data from the Indian Meteorological Department (IMD), soil data from the FAO, and land use/land cover and slope datasets.Soil erosion, exacerbated by intensified agricultural practices, was evaluated using the Revised Universal Soil Loss Equation (RUSLE) model integrated with Geographic Information Systems (GIS). The SWAT model results showed that runoff accounted for 15-20% of the total precipitation, with an annual soil loss of 2027.95 tons. The predominance of agricultural land use, covering 66.29% of the basin, significantly contributed to high runoff, whereas the forested areas (26.48%) demonstrated a low runoff potential. About 38% of the basin exhibited a very low soil loss risk, while 11% was classified as high risk and 9% as very high risk. The SWAT model is recognized for its robustness, and this research aims to leverage its capabilities to validate its effectiveness in estimating runoff and erosion, providing crucial insights for advancing sustainable catchment resource management.

An improved Eulerian-Lagrangian method combined with ductile material model for cavitation erosion assessment

Abstract Cavitation erosion in hydraulic machinery constitutes a multifaceted, instantaneous physicochemical process resulting in material wear and decreased efficiency. This paper employs an enhanced Eulerian-Lagrangian method to evaluate cavitation erosion. The method captures erosive impact loads released by the non-spherical collapse of near-wall bubbles and integrates them with a one-dimensional ductile material mode, a capability lacking in traditional homogeneous mixture methods. A classic axisymmetric nozzle test case is conducted under four different cavitation numbers (s=0.8, 0.9, 1.09, and 1.6) to validate the reliability of the new approach. Qualitative and quantitative analysis demonstrates that the impact load distribution on the lower and upper walls aligns with experimental measurements. Compared with reference works, the new method accurately predicts the maximum wear position and yields a narrower erosion area closer to the experimental data. Moreover, the relative error of the minimum incubation time at s=0.9 on the lower wall calculated by the new method is 4.67%, and the relative error of the maximum wear rate is 36.6%. This method is pivotal for further studying how various materials respond to cavitation wear. Further analysis reveals that material response patterns are similar under cavitation erosion conditions at s=0.8, 0.9, and 1.09. In contrast, the material surface wear rate is reduced by 46.7%, and the incubation time nearly triples at s=1.6.

The Cross-Verification of Different Methods for Soil Erosion Assessment of Natural and Agricultural Low Slopes in the Southern Cis-Ural Region of Russia

The conventional measuring methods (runoff plots and soil morphological comparison) and models (WaTEM/SEDEM and regional model of Russian State Hydrological Institute (SHI)) were tested with regard to the Southern Cis-Ural region of Russia, along with data from rainfall simulation for assessing soil erosion. Compared with conventional methods, which require long-running field observations, using erosion models and rainfall simulation is less time-consuming and is found to be fairly accurate for assessing long-term average rates of soil erosion and deposition. In this context, 137Cs can also be used as a marker of soil redistribution on the slope. The data of soil loss and sedimentation rates obtained by using conventional measuring methods were in agreement with the data based on the used contemporary modeling approaches. According to the erosion model calculations and data on the fallout of radionuclides in the Southern Cis-Ural (54°50–25′ N and 55°44–50′ E), the average long-term annual soil losses were ~1.3 t·ha−1 yr−1 in moderate (5°) arable slopes and ~0.2 t·ha−1 yr−1 in meadows. In forests, surface erosion is negligible, or its rates are similar to the rate of soil formation of clay–illuvial chernozems. The rates of soil erosion and sediment deposition on the arable land obtained using different methods were found to be very close. All the methods, including the WaTEM/SEDEM, allowed us to measure both soil erosion and intra-slope sedimentation. The regional SHI model fairly accurately assesses soil erosion in the years when erosion events occurred; however, soil erosion as a result of snowmelt did not occur every year, which should be taken into account when modeling. The concentrations of 137Cs in the topsoil layer (0–20 cm) varied from 0.9 to 9.8 Bq·kg−1, and the 137Cs inventories were 1.6–5.1 kBq·m−2, with the highest values found under the forest. The air dose rate in the forest was higher than in open areas and above the average of 0.12 μSv·h−1 on the slope (0.1 μSv·h−1 in the meadow and 0.08 μSv·h−1 on the arable land), with the value increasing from the watershed to the lower part of the slope in all the areas. The γ-background level in the studied ecosystems did not exceed the maximum permissible levels.

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

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

Water erosion processes: Mechanisms, impact, and management strategies

This study rigorously explores water erosion, a critical environmental challenge diminishing land productivity and ecosystem stability worldwide. Addressing a notable gap in comprehensive, region-specific erosion assessments, this research underscores innovative strategies integrating traditional practices with modern technologies. It assesses the interplay between splash erosion, surface runoff, and subsurface flow, which collectively drive significant landscape changes and soil fertility loss. The influence of key factors such as rainfall intensity, soil type, topography, and vegetation is analyzed through case studies from the Loess Plateau, the Mississippi River Basin, and the Ethiopian Highlands, illustrating varying erosion dynamics. Importantly, this work evaluates the implementation of nanotechnology and biotechnological advances in soil stabilization, supported by robust policy frameworks, and demonstrates these technologies in context, ensuring relevancy to the observed erosion processes. By combining empirical research with cutting-edge science and active community participation, this paper champions adaptive and inclusive strategies to effectively manage water erosion, aiming to bolster environmental sustainability and resilience globally.

Soil erosion assessment using SWAT, in relation withLand use, agricultural practices, and future climate change in a semi-arid catchment in Tunisia

Abstract Soil erosion is a severe environmental concern arising from intensive agricultural uses, land degradation, and anthropogenic activities. This problem threatens agricultural productivity and sustainable development, particularly in emerging countries. Therefore, evaluating soil erosion is essential in conservation, planning, and management on a watershed or basin scale. This study aims to assess the erosion of soil loss in the El Gouazine Watershed, central Tunisia, using the Soil and water assessment tool (SWAT). We define the impact of soil and water conservation management implementation combined with climate change scenario. We identified the spatial distribution of erosion rates based on soil properties, topography, and land use. The observed specific erosion rate of the watershed is estimated at 1.6 t.ha−1.yr−1, whereas according to the SWAT model, the average soil loss rate is 1.4 t.ha−1.yr−1. Furthermore, the obtained results highlight importance of the slope factor in affecting the severity of the soil loss rates in the El Gouazine watershed. It was also demonstrated that it’s urgent to prioritize other measures such as contour cropping or conservation agriculture, to enhance and strengthen the soils’ resistance against the detachment due to discharge water. In this context, this research found that these techniques decrease considerably the soil loss by 22% for the strip cropping, 33% for the No-tillage, and 72% for the bench terracing. Moreover, these farming techniques, contribute at the same time to the amelioration of the water balance by reducing the evapotranspiration and enhancing the soil water storage. To go further in this study a soil erosion forecast using the worst-case scenario for climate change RCP 8.5 was conducted. an overview of the future soil erosion patterns is obtained. We noticed then a decrease of the average annual soil loss rate until 2050 and then a prominent increase from 2051 to 2100.

Assessment of forest disturbance and soil erosion in wind farm project using satellite observations

The construction of wind farms, involving road construction and wind turbine installation, severely disrupts natural landscapes. Wind energy expansion in global forested areas has unclear impacts on local forests and ecosystem services. Due to a lack of information on internal road distribution and deployment dates, few studies have assessed forest disturbances caused by wind farms. Environmental issues like vegetation destruction and soil erosion may be overlooked. To address this, we integrated multi-source spaceborne observations to identify deployment dates and road distributions of forest wind farms and mapped related forest disturbances and soil erosion changes. Six global locations were tested, showing over 80 % accuracy. Disturbance intensity ranged from 1.5 to 6.5 ha/MW, with NDVI decreasing by 0.03 to 0.33 in disturbed forest regions. The average soil erosion increase per unit area due to road construction ranged from 24.74 to 274.33 t/hm−1a−1, while wind turbine construction caused an average soil erosion increase ranging from 26.52 to 26.52 to 263.46 t/hm−1a−1. Road construction is the primary cause of forest disturbance, with greater soil erosion increases in mountainous than in plain forests. This method enhances monitoring and understanding of wind farms' environmental impacts.

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

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

Assessment of soil erosion by integrating RUSLE-SDR-TLA model in Cauvery river basin, India

Assessment of soil erosion by integrating RUSLE-SDR-TLA model in Cauvery river basin, India

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

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

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

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

Refining erosion assessment with NDVI-based modeling: a case study in diverse climatic zones

Refining erosion assessment with NDVI-based modeling: a case study in diverse climatic zones

Soil Erosion Assessment Using the RUSLE Model, Remote Sensing, and GIS in the Woybo Watershed, Ethiopia

Soil Erosion Assessment Using the RUSLE Model, Remote Sensing, and GIS in the Woybo Watershed, Ethiopia

Dental care staff’s experience with risk assessment of dental erosion: a qualitative study

BackgroundThe risk assessment of dental erosion among children and adolescents is an important aspect of dental care, as dental erosion constitutes a rapidly growing, global problem. Dental professionals rely solely on their own perception, as the current risk assessment process is not completely automatized, which affects the risk assessment reliability.AimTo explore dental professionals’ experiences with risk assessment of dental erosion among children and adolescents.MethodIn-depth interview was used as data collection method. A total of 11 dental professionals were interviewed. The interviews were analyzed using qualitative content analysis.ResultsThe findings were summarized in the categories Professionals’ responsibility, Systematic approach , and Collaboration and communication. Dental staff perceived that their basic knowledge regarding erosion should be improved, and skills development was desired to reduce the knowledge gaps around the risk assessment of dental erosion. They alleged that the systematic approach could be improved by reducing workplace stress, implementing a universal dental erosion index, improving the existing risk assessment software, and automating the risk assessment of the condition. Dental professionals also experienced a need to calibrate and collaborate with each other and with other healthcare professionals to improve patient care.ConclusionDental professionals experienced their basic knowledge of dental erosion and their risk assessment as good, but a more advanced skill development was required. Furthermore, they experienced the risk assessment software as a good tool that should be improved to compile more objective risk assessment. A universal erosion index was also requested.

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.