Estimate Soil Erosion Research Articles

Soil erosion estimation and risk assessment based on RUSLE in Google Earth Engine (GEE) in Turkiye

ABSTRACT Soil erosion is a critical challenge threatening agricultural productivity and environmental sustainability globally. This study presents the first estimation of water-induced soil loss in Ordu province, Turkey, using the Revised Universal Soil Loss Equation (RUSLE) integrated with Google Earth Engine (GEE) and Geographic Information System (GIS) technologies. Our analysis provides spatial insights into erosion patterns across the region, revealing that soil loss rates range from 0–5 t/ha/yr in stable areas to over 200 t/ha/yr in severely eroded regions. The mean soil loss rate is estimated at 12.58 t/ha/yr. The study identified the LS factor (slope length and steepness) as the most significant contributor to soil erosion, followed by the R factor (rainfall erosivity). These findings offer valuable insights into soil erosion dynamics, supporting sustainable soil management practices and informing erosion control strategies. The results contribute to land use planning and policy development aimed at mitigating soil degradation and enhancing agricultural resilience in Ordu province.

Geospatial technology for assessment of soil erosion prone areas of the Pulappatti Sub-basin, Dharmapuri District, South India

Soil erosion poses serious threats to agricultural production, hydraulic structures, and the world’s ecosystem. Scientific management of soil, water and vegetation resources on a watershed basis is very important for arresting erosion in order to retain the nutrient levels. A number of parametric models have been developed to predict soil erosion prone zones in structural terrains. Normalized Weight is the most widely used empirical equation for estimating soil erosion. A comprehensive methodology using Normalized Weight method and Geographic Information System (GIS) techniques was adopted to identify soil erosion prone zones in parts of the Pulappatti Sub-basin, Dharmapuri District, Tamil Nadu, India. Various thematic maps on soil erosion such as slope (%), geology, geomorphology, drainage density, soil, elevation and land use/land cover maps were prepared and the GIS analyses method used to understand the situation of soil erosion. These parameters under various thematic maps were integrated with ArcGIS. A probability weighted approach has been adopted that allows a linear combination of probability weights of each thematic map (Wt) and ranking for different features in the thematic maps were assigned and final scores were obtained (Sc), depending upon their role in soil erosion. The integrated soil erosion map is reclassified into five zones as very low, low, moderate, high and very high soil erosion prone zones. Terrains associated with moderate slope, high elevation, high drainage density and agricultural land are more prone to soil erosion. This factor increases the vulnerability of land degradation in the region. Qualitative results are generated in this study which would be helpful to generate quantitative intensity and to control soil erosion process. The final output in this study provides important tools for researchers and engineers to plan for control of soil erosion in the study area. They should take active and meaningful decisions to avoid this hazard in high and very high areas prone to soil erosion. The results also revealed that soil conservation measures can be considered in this area for sustainable development.

Spatial and temporal variability in infiltration and soil characterization using ERT

ABSTRACT Infiltration of the aquifer is one of the primary processes of the hydrological cycle. It plays a crucial role in the estimation of artificial groundwater recharge, surface runoff, and soil erosion. The electrical resistivity tomography (ERT) surveys were carried out to characterize the subsurface soil, and double-ring experiments were carried out at different locations in the study area. The results estimated from the double-ring infiltrometer data were compared with the infiltration rates obtained from various models such as Horton, Green-Ampt, Kostiakov, etc. The parameters are to be calibrated under the same hydrological site conditions, and these models’ water content and constants were estimated using the Graphical method. The best-fit models were estimated for each site, and the performance of the infiltration models was assessed based on the correlation coefficient, coefficient of determination, and root mean square error (RMSE). Horton model showed minimum RMSE, maximum correlation coefficient, and coefficient of determination, i.e. 5.24, 0.99, and 0.98, respectively, at most sites. The ERT data showed that sand is the predominant material available in the subsurface, and the saturated infiltration rate lies in the range of the sand. Nonlinear regression modeling was used to develop novel infiltration rate models from infiltration data.

Spatial Sediment Erosion and Yield Using RUSLE Coupled with Distributed SDR Model

Estimating sediment yield in a river is a challenging task in the water resources field. Different methods are available for estimating sediment erosion and yield, but generally they are not spatially distributed in nature. This paper presents the application of the Revised Universal Soil Loss Equation (RUSLE) for estimating soil erosion and integrates it with spatially distributed Sediment Delivery Ratio (SDR) to calculate sediment yield in a Himalayan river. The study area is Kabeli sub-catchment, located upstream of the Koshi River Basin in the eastern part of Nepal. The Kabeli River is where numerous hydropower projects are envisaged, and sediment-related issues are of major concern. With the use of the RUSLE, the mean annual soil erosion is estimated at 35.96 tons/ha/yr. The estimated specific sediment yield (SSY) from the distributed SDR method is 6.74 tons/ha/yr, which is close to the observed SSY of 7.26 tons/ha/yr using the data records of ~8 years. Based on correlation analysis, the topographic factor (LS) is the most sensitive RUSLE parameter with respect to sediment erosion. The sloping areas near the river hillslope are particularly vulnerable to soil erosion. The results indicate that the approach employed in this study may be potentially applied in other catchments with similar physiographic characteristics for the estimation of sediment yield.

GIS-based RUSLE model for estimating soil erosion and sediment yield in Rushikulya River Basin of Odisha, India

GIS-based RUSLE model for estimating soil erosion and sediment yield in Rushikulya River Basin of Odisha, India

Impact of Land Use and Land Cover Change on Soil Erosion in Dondor Watershed, Blue Nile Basin, Northwestern Ethiopia

Understanding how land use and land cover (LULC) changes affect soil erosion is essential for effective management of watershed areas. This study used Geographic Information Systems (GISs) and the Revised Universal Soil Loss Equation (RUSLE) model to analyze the impact of LULC changes on soil erosion in the Dondor Watershed. Remote sensing data, including Landsat and Sentinel-2 satellite images, alongside field surveys, topographic data, rainfall, and soil data were used. The results showed agricultural land as the primary LULC type, increasing from 43.49% in 2002 to 59.10% in 2023. Forest and built-up areas also expanded, while grassland decreased. Soil erosion estimates revealed that more than 85% of the watershed experienced very slight erosion though the average annual soil loss increased from 4.98 t ha⁻1 year⁻1 in 2002 to 7.96 t ha⁻1 year⁻1 in 2023. Agriculture and built-up areas were identified as the primary contributors to erosion. This study underscores the importance of monitoring LULC dynamics for responsible land management and conservation efforts in the watershed.

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.

Multi-Temporal Assessment of Soil Erosion After a Wildfire in Tuscany (Central Italy) Using Google Earth Engine

The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton × ha−1 × yr−1 to 1.29 ton × ha−1 × yr−1, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton × ha−1 × yr−1, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables.

Using radiotracers 137Cs and 210Pbex to document climate change in mountain areas through the estimate of soil erosion rates

Abstract. During the last few decades, a general increase in heavy rainfall events has been documented in many areas of the world. These events have caused changes in soil erosion rates and strongly affected the human activities in mountain areas by reducing the national income obtained from cultivated land. In this context, the use of fallout radiotracers can be an important tool for better understanding the consequences of climate change in these areas and proposing effective countermeasures in order to reduce soil loss. In this contribution, plot experiments carried out in southern Italy that involve the use of 137Cs and 210Pbex measurements were performed to estimate soil erosion rates during the last few decades. The overall results indicate an increase in soil erosion rates during the last 15–20 years and suggest the use of this technique to detect climate change in mountain areas.

Comprehensive Assessment of Soil Erosion using USLE and Sustainable Management Strategies

Continuous soil erosion from all parts of world through water and winds have become a severe concerns for soil scientists and geologists due to its adverse effect on soil productivity, water bodies siltation and deterioration of water quality. Therefore, it becomes most important to locate erosion-prone regions of any landscape and take the best measures to solve the issues of erosion. USLE and its modified models i.e. RUSLE (Revised Universal Soil Loss Equation) versions and MUSLE (Modified Universal Soil Loss Equation) are most commonly used for estimation of soil erosion. In India, soil erosion is a major serious concern which varies in its intensity depending on the country's different agro-climatic zones. USLE as well known model for evaluating soil deterioration is used in this review to provide an understanding of its parameters and how this approach is useful in soil erosion assessment. In addition, an overview of the current situation of erosion status in India which is being altered by regional variations like climate and land uses. Literature also dictates and discusses the potential advantages of better erosion control approaches, such as increased agricultural output and long-term land sustainability and looks at what might be possible in the future if the things get managed. The study also looks at crop selection and land use systems, emphasizing how crucial it is to choose crops that are suitable for the land vulnerability to erosion in order to prevent the further deterioration. Lastly, it emphasize on a thorough explanation of the USLE's parameters which are essential for precise erosion prediction including slope factors, soil erodibility, rainfall erosivity and conservation techniques. The study summarizes the knowledge on soil erosion in India and the necessity of customized, area-specific soil conservation plans to guarantee long-term agricultural outputs and sustainable agricultural goals.

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 of soil erodibility for soil conservation in upper godavari sub basin of India

Estimation of soil erodibility for soil conservation in upper godavari sub basin of India

Estimation of Soil Erosion by RUSLE Model Using Geoinformatics Techniques: A Case Study of Mulshi Reservoir Catchment, Pune District, Western Maharashtra.

In the western part of Maharashtra, where the Western Ghat highlands are located, soil erosion is a serious and persistent environmental issue. To make matters worse, the majority of the dams in this region run the risk of sedimenting their reservoirs, which is a serious concern. This research was being conducted on the Mulshi reservoir catchment area, Pune district of western Maharashtra having area of 250.25 km2. It is fact that the surface runoff of seasonal rainfall is more in this region due to the presence of western ghats. In this study, high resolution satellite images were used to estimate average annual soil erosion based on the five factors defined in the Revised Universal Soil Loss Equation (RUSLE) with the help of Geoinformatics techniques. Overlay of five parameters, viz., rainfall erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support & conservations practices factor (P) has been done in ArcGIS (version 10.8) software. Annual average soil erosion of the catchment is 0 to 433.43 tons/hector/year and has been classified into Six categories, defined as very high, high, moderate, low, and very low and negligible according to the severity of erosion. The high values of soil erosion (>20 tons/hector/year) found on the highlands due to the high slope and bare lands. however, low values (<10 tons/hector/year) are found on the area occupied by reservoir and dense vegetation.

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.

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.

Assessment of soil erosion risk and vulnerability in the transboundary Sio-Malaba-Malakisi watershed in Kenya and Uganda

Persistent soil erosion poses a significant threat to water quality, ecosystem viability and soil health in many regions of the world. Addressing this challenge requires a comprehensive understanding of local soil erosion rates, including the identification of vulnerable areas, to facilitate effective and integrated environmental management. In East Africa, however, many affected regions are data poor and lack measured hydrological data from which soil erosion estimates can be derived. This study used the physically based Revised Universal Soil Loss Equation (RUSLE) model to estimate the annual rate of soil erosion in the transboundary Sio-Malaba-Malakisi catchment, straddling Kenya and Uganda. Soil erosion events were quantified by integrating input factors derived from physical datasets using a Geographical Information System (GIS). The Analytical Hierarchy Process (AHP) technique was then used to assess the importance of selected physical factors influencing the soil erosion process in order to identify regions of increasing environmental vulnerability. The results obtained indicate a wide range of soil erosion rates across the region, with an estimated mean annual rate of 250 t ha−1 yr−1 for the whole catchment. Upstream areas characterized by intensive agricultural activity had erosion rates of up to 2000 t ha−1 yr−1, while downstream areas recorded erosion rates below 600 t ha−1 yr−1. Areas with intensive agriculture, unprotected soils, and a soil loss tolerance value above 12 t ha−1 yr−1 were found to be most vulnerable. Rainfall and vegetation cover were identified as key factors influencing erosion susceptibility. Regions with soil erosion rates above 50 t ha−1 yr−1 were identified as high priority regions for targeted soil and water conservation measures. The identification of vulnerability zones, including the classification of their severity, provides an opportunity to prioritize sustainable environmental management interventions; a process that can be replicated for such affected regions elsewhere.

Impact of soil architecture on the interrill erodibility in clayey subtropical soil

AbstractSoil erosion is a key concern with regard to ecosystem functionality and food, fibre and bioenergy productions worldwide. Therefore, understanding the mechanisms and controls of soil erosion, particularly the link between soil aggregate stability and soil erodibility, is of utmost importance. The use of disturbed samples and sieved soil to determine the involved erodibility and aggregate stability is standard in soil erosion studies. However, soil erodibility estimation based on disturbed‐soil samples can be inaccurate as it involves changes in the architecture of the considered soil, possibly leading to overestimations. Moreover, a necessity for evaluating soil erodibility beyond intrinsic soil characteristics (e.g. texture) exists. The objective of this research was to assess the erodibility impact of soil disturbance. Undisturbed‐soil cores with dimensions of 45 cm (length) × 30 cm (width) × 10 cm (depth) were extracted while preserving their architecture. An A horizon corresponding to brown clayey subtropical oxisol soil from Southern Brazil was used for performing an experiment that involved simulation of 58–mm h−1 rain for 30 min. A total of seven replicate experiments were performed for each soil condition (i.e. undisturbed and disturbed soils). Results show that soil architecture deterioration had a larger impact on the involved soil loss than runoff. Further, soil structure failure did not affect the aggregate stability per se. Notably, the soil erodibility and loss were approximately 10 times larger under the disturbed‐soil condition than under the undisturbed‐soil condition (interrill erodibility: 4.30 × 107 and 4.39 × 106 kg s m−4, respectively; soil loss: 0.925 and 0.094 kg m−2, respectively). Overall, the intrinsic soil characteristics did not change; however the soil architecture deterioration considerably increased the erodibility.

Soil erosion estimation in a catchment with implemented soil and water conservation measures

AbstractThe variations in soil erosion significantly impact regional ecological security. Under rapid urbanisation, extensive ecological restoration and climate change, soil erosion development in the red soil region of southern China is ambiguous. Therefore, this study investigated the current (1980s–2020) and future (2050) erosion characteristics in a typical soil erosion control catchment (Changting section catchment) in this region by using the Cellular Automata Markov model and CMIP6 data to predict future scenarios and the Revised Universal Soil Loss Equation to estimate soil erosion. The results showed significant changes in the vegetation coverage of major land uses from 1980s to 2020, which was mainly caused by continuous soil and water conservation (SWC). The land use subtypes that were obtained by reclassifying land use based on the threshold of vegetation cover on soil erosion control, reflect a continuous transformation from those with poor SWC effectiveness to those with great SWC effectiveness. Therefore, the estimated soil erosion intensity continued to decrease from 1980s to 2020, and the contribution of land use/land cover (LULC) impacts ranged from 74%–195%. However, predictions of land use subtypes indicated that LULC may be stable after 2020; thus, soil erosion changed little when the climate was almost unchanged in 2050. Under climate change scenarios, soil erosion may increase by 111%–121%, and the contribution of precipitation impacts was 63%–66%. The major driving factor of soil erosion changes may shift from LULC to precipitation after 2020. Therefore, in the future, the potential for reducing soil erosion by vegetation restoration may be limited, and more engineering measures should be applied to address the erosion risk caused by climate changes. This study provides prospects for land use/land cover and soil erosion in the red soil region of southern China.

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.

Application of fallout radionuclide—137Cs for estimating soil erosion in steep hillslopes with diverse land use of North-western Indian Himalayas

Hilly and mountainous regions are significantly impacted by soil erosion, primarily due to rainfall-runoff processes occurring on the hillslope scale. Assessing soil erosion is crucial for quantifying the loss of soil carbon and nutrients, which diminishes the potential of soil ecosystem services and is critical for mitigating the impacts of climate change on food security. However, the Himalayan landscape poses serious challenges for assessing soil erosion due to its steep and rugged terrain, which hinders the use of conventional and modelling methods. The fallout radionuclide—137Cs has been extensively utilized as an environmental marker for investigating soil redistribution processes. Despite its potential, there is a notable lack of 137Cs-based soil erosion studies in the Himalayan region. In this context, we assessed the applicability of the fallout radionuclide—137Cs method in quantifying soil erosion rates and identifying erosion hotspots on two hillslopes of the Higher Himalayas. On the hillslope scale, we observed that soil erosion rates vary based on slope gradient and land use/land cover. Forested areas exhibited the lowest soil erosion rates compared to cultivated areas, while flat hillslope positions experienced lower erosion rates than steeper positions. The average net erosion rate for the Harsil hillslope varied among different hillslope positions, ranging from – 2.9 to – 15.6 t ha−1 yr−1. Similarly, in the Gangnani hillslope, the net erosion rates varied across different positions, ranging from – 5.6 to – 39 t ha−1 yr−1. Our findings confirm that the middle and lower hillslope positions are the most critical source areas with higher soil erosion rates, while hillslope positions with forest cover demonstrate the lowest erosion rates, identified as helpful in controlling soil erosion. The study has demonstrated the applicability of FRN as a soil erosion measurement method in the complex, rugged, and steep terrain of the Himalayas, highlighting the need for targeted conservation efforts to mitigate soil erosion and preserve ecosystem integrity.