Average Soil Loss Research Articles

Reviewing the Impact of Engineering Measures on Soil Erosion in Jammu and Kashmir Anantnag Watershed

Planners now find that stopping soil loss in India's watersheds must be their top priority. Currently, a wide range of mathematical models are used worldwide to predict soil erosion. The Revised Universal Soil Loss Equation (RUSLE), which is frequently used to predict average soil loss, is one such model. Recently, there has been an increased emphasis on applying USLE/RUSLE in conjunction with Geographic Information System (GIS) technology, enabling grid-based analysis for predicting soil erosion and aiding management methods. This study assesses the efficacy of flood control and erosion management programs launched in 1914. Four different maps representing the possible erosion threats were created for four different time periods, and a comparison study was carried out to assess the changes that occurred. The implementation of various strategies such as terracing, afforestation, and rehabilitation in the watershed led to a noteworthy prediction of decreased soil loss in the watershed. In the temperate region of Jammu and Kashmir and sparsely vegetated areas, the rate of anticipated soil loss decreased from 632 to 378 metric tons per hectare per year between 1990 and 2020, indicating the efficacy of soil conservation measures in mitigating potential soil loss.

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.

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.

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.

Exploring soil erosion and reservoir sedimentation through the RUSLE model and bathymetric survey

The aim of the paper was to compare the soil erosion in a river catchment with the sediment volume accumulated at the river mouth. Firstly, the sediment yield was estimated in GIS based on the soil loss according to the RUSLE model, and then further integrated into the sediment production equation. Following this, we estimated the sediment volume accumulated at the river mouth based on the diachronic overlap of topographic and bathymetric data. This methodology was validated for the Eselnita catchment exiting into the Iron Gates I Reservoir. The LS factor has an average value of 4, with lower values for the forest cover. The C factor has an average value of 0.057 being statistically correlated with the RUSLE result. The average soil loss was estimated at approximately 1.89 t ha-1 yr -1, a value that is validated by previous studies as a low risk of erosion at national scale. The sediment transfer model indicates a distribution of cells sediment production strongly correlated with the time travel to the discharge channels. Overall, the sediment volume obtained by using the RUSLE model corresponds to about 70% of the sediment volume accumulated at the river mouth during 53 years (1970-2022). The difference in sedimentation may be due to human activities along the river mouth’s banks to extend the built-up area and to enjoy the waterscape. This paper is relevant for the topic of reservoir sedimentation and recommends the use of the RUSLE model to predict the sediment contribution, especially for small ungauged catchments.

Harmonizing models and measurements: Assessing soil erosion through RUSLE model.

Soil erosion poses significant ecological and socioeconomic challenges, driven by factors such as inappropriate land use, extreme rainfall events, deforestation, farming methods, and climate change. This study focuses on the Kozhikode district in Kerala, South India, which has seen increased vulnerability to soil erosion due to its unique geographical characteristics, increase in extreme events, and recent land use trends. The research employs RUSLE (Revised Universal Soil Loss Equation), considering multiple contributing factors such as rainfall erosivity (R), slope length and steepness (LS), cover management (C), conservation practices (P), and soil erodibility (K). The study is unique and novel, since it integrates extensive field data collected from agricultural plots across Kozhikode with the RUSLE model predictions, providing a more accurate and context-specific understanding of soil erosion processes and also suggesting management strategies based on risk priority. The study found that Kozhikode experiences an average annual soil loss of 28.7 tons per hectare. A spatial analysis revealed varying erosion risk levels across the district. 52.0% of the area experiences very slight erosion, 10.31% has slight erosion, 6.18% undergoes moderate erosion, 3.88% is moderately severe, 7.34% is at severe erosion risk, 5.6% has very severe erosion, and 14.65% faces extremely severe erosion. Field data collected from agricultural plots across Kozhikode were compared with RUSLE-predicted values, revealing a low root mean square error, indicating a strong correlation between observed and simulated data. Based on these findings, the district was categorized into low, medium, and high-priority regions, with tailored recommendations proposed for each. Implementing these measures could mitigate erosion, preserve soil fertility, and support the long-term sustainability of natural and agricultural ecosystems in Kozhikode. Given the practical challenges in estimating RUSLE factors in Southern India, where data scarcity is a common issue, this preliminary study underscores the need for expanded, long-term field observations to enhance understanding of soil erosion processes at the watershed level.

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.

Controlling soil erosion and landslides through ecosystem-based adaptation interventions in the hilly landscape of western Rwanda

Ecosystem-based Adaptation interventions such as protective vegetation, soil and water conservation measures and re-vegetation of degraded land have shown promise in reducing soil erosion and landslides occurrence. The western province of Rwanda has experienced destructive natural hazards especially soil erosions, floods and landslides. This study aimed to assess the contribution of Ecosystem-based Adaptation interventions on the reduction of soil erosion and landslides in the hilly landscape of Western Rwanda with a case study of Rubyiro sub-catchment in Rusizi District. Methods used in data collection comprised remote sensing techniques combined with a questionnaire survey to collect perceptions of farmers conducting agricultural activities in the catchment where Rusizi District has implemented Ecosystem-based Adaptation (EbA) interventions. The Revised Universal Soil Loss Equation (RUSLE) model combined with Geographic Information System (GIS) and remote sensing tools were used to assess soil loss pre- and post-EbA interventions. The results of the survey indicated that after four years of EbA interventions, the number of landslide occurrences was reduced at a rate of 62.6 %. The respondents who appreciated forest restoration to minimize landslides were 72.4 %, progressive terraces 26.1 % and bench terraces 1.5 %. The estimated average annual soil losses were 11.9 tons/ha/year, 17.6 tons/ha/year, 10.1 tons/ha/year, 9.3 tons/ha/year in 2017, 2018, 2021 and 2022, respectively. This indicates a reduction of 8.3 tons/ha/year from 2018 to 2022 as opposed to an increase of 5.7 tons/ha/year recorded from 2017 to 2018. Thus, EbA interventions made a great contribution to the reduction of soil erosion and landslides in the Rubyiro sub-catchment, and they can be recommended in other similar hilly landscapes.

Assessing monthly dynamics of agricultural soil erosion risk in Poland

Soil erosion in agricultural landscapes poses a significant threat to soil health and productivity. This study investigates the dynamics of soil erosion across agricultural lands in Poland at monthly scale using the Revised Universal Soil Loss Equation (RUSLE) model. The study utilizes the Global Rainfall Erosivity Dataset (GLOREDA), which provides the most up-to-date and highest resolution rainfall erosivity (R) values, derived from 10-min resolution rainfall records. Additionally, satellite imagery spanning 2003 to 2023 was leveraged to estimate the cover-management (C) factor, capturing the spatial and temporal dynamics of vegetation cover and agricultural practices. Results reveal significant spatio-temporal variations in soil erosion rates, with peak erosion occurring during the summer months following crop harvesting, despite not aligning precisely with peak rainfall erosivity. The lowest erosion rates are observed during the winter seasons, attributed to minimal rainfall erosivity. The findings highlight the critical role of agricultural practices, particularly the timing of crop harvesting and temporary exposure of bare soil, in driving soil erosion dynamics in Poland's agricultural landscapes. Specifically, the average annual soil loss for agricultural areas in Poland was determined to be 0.27 t ha−1 yr−1, with peak monthly soil erosion rates reaching up to 0.08 t ha−1 yr−1 in August. Total monthly soil loss from agricultural lands in Poland was estimated to be approximately 4.87 Mt. annually, with 68 % of the total annual soil loss occurring during the summer months. This study contributes valuable insights into understanding and managing soil erosion risks in agricultural systems, aiding in the development of targeted soil conservation strategies and sustainable land management practices.

Evaluation of soil erosion rate using geospatial techniques for enhancing soil conservation efforts

According to reports, Ethiopia is one of the countries in sub-Saharan Africa with the worst affected by soil erosion. It has both on-site and off-site consequences on biophysical and socioeconomic settings in an area. The study area is heavily affected by soil erosion forming diverse erosion structures, particularly in the upper course of the watershed. Hence, this work seeks to estimate the geographically distributed annual soil loss rate and mapping of soil erosion hazard hotspot areas in the watershed using the Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions. The RUSLE parameters, such as rainfall erosion factor (R-factor), soil erodibility factor (K-factor), slope steepness and slope length factor (LS-factor), land cover factor (C-factor), and conservation practice factor (P-factor) were considered as data input for the analysis to quantify the soil loss rate in the study area. A digital elevation model (DEM) with a 12.5 × 12.5-meter resolution was employed for catchment delineation and determination of the LS factor.The mean yearly rainfall data from the surrounding rain gauge stations was used to analyze the R-factor. The results of the current conditions showed that the average typical soil loss rate from the entire watershed is 23.8 t ha-1 yr-1, and the quantity of soil loss from the study area ranged from 0 to 776.71tan /ha-1 yr-1. Nonetheless, Tiro Afeta experiences mean soil erosion at a rate of roughly 50.2 t ha-1 yr-1, exceeding the acceptable threshold of 11 t ha-1 yr-1. Determining the sustainability of soil production requires assessment, particularly in cases where significant yearly soil erosion occurs. Due to intensive agricultural activities in the Xiro Afeta watershed, significant soil erosion is predominantly occurring in thissteep upper region. Consequently, this area urgently requires appropriate soil protection measures.

Effect of Tillage, Crops Residues and Crops Management Practices on Runoff Erosion, Soil Loss and Soil Properties in Ethiopa: Review

The conducted investigations showed that tillage practices with crop residue and proper cropping systems protect loss of soil from runoff erosion which depletes soil nutrients and affects soil physical and chemical properties. The review was conducted with aim of reviewing the effect of tillage, crops residues and crops management practices on runoff, soil loss and soil properties in Ethiopia. The three years study conducted in the Upper Blue Nile basin of Northwestern Ethiopia showed that reduced tillage reduced soil loss over conventional tillage, row planting reduced soil loss over broadcast planting, without trampling reduced soil loss over with trampling planting, and the sediment concentration was ranged from 0.01 to 5.37g/L and total soil loss was 0.20 to 0.50t/ha. The study conducted in the humid highlands of Ethiopia showed that the lower average soil loss was 16 t/ha.yr under zero tillage with crop residue and maximum was 30 t/ha.yr in conventional tillage without crop residue. The investigation indicated that zero tillage with maize soya bean intercrop, maize rotation, continuous maize and continuous soya bean improved soil properties than conventional tillage system. The investigation which was carried out to evaluate the effects of tillage and cropping system on soil properties showed that enrichment ratio ≤1 under no tillage with intercropping and no tillage with mulch reduce nutrient losses and enrichment ratio. The study conducted at Derashe and Arba Minch Zuriya in Ethiopia showed that some selected properties were statistically significant (P<0.05) and conservation tillage is favored for soil management relative to conventional tillage. Therefore, tillage practices like zero tillage and minimum tillage with crop residue management like mulching and crop management such as intercropping and crop rotation reduce surface runoff erosion, soil loss and soil fertility depletion, but additional continual research is needed to reveal trends in tillage, crops residues and crops management.

Sustainable Land Management for Mitigating Soil Erosion at the Catchment Scale

This study addresses this challenge by focusing on the Revised Universal Soil Loss Equation (RUSLE) to combat excessive soil erosion rates within the Kayacık Dam Basin, located in the semi-arid region of the Tigris-Euphrates Basin in Türkiye. The primary objective is to maintain an average annual soil loss of no more than 10 tons per hectare. To achieve this, the RUSLE model is employed to evaluate sustainable land management (SLM) strategies. These strategies encompass agronomic, vegetative, and structural measures, all of which are intricately linked to RUSLE's C and P factors. Spatial analysis reveals that severe erosion (10-20 t ha-1 y-1) is predominantly concentrated in agricultural areas, spanning 2536.37 hectares. In contrast, very severe erosion (>20 t ha-1 y-1) affects 834.13 hectares of grassland. Statistical analyses underscore the significant contributions of various model factors to predicted soil losses (A). LS, C, K, and R factors account for 80.24%, 44.68%, 0.97%, and 0.27% of the effect, respectively. Remarkably, topography exerts the most substantial influence on agriculture, pasture, and forest/other land uses, contributing 84.46%, 57.29%, and 39.27% to the variation, respectively. These findings highlight the pivotal role of effective agronomic and vegetative practices in ecosystems management, especially within steep-slope landscapes encompassing agriculture, grassland, and forest. Furthermore, the semi-arid regions under investigation contend with the intricate interplay of heightened drought risk and the ramifications of intensive irrigated agriculture on soil erosion. This complex dynamic presents distinct challenges for implementing SLM strategies in the face of evolving climatic conditions and underscores the need for climate-resilient solutions.

Predicting soil erosion risk using the revised universal soil loss equation (RUSLE) model and geo‐spatial methods

AbstractAnthropogenic activities like overgrazing, deforestation and mismanaged land use accelerate soil erosion (SE), causing nutritional and organic matter loss. In this study, we predicted the annual rate of soil loss in the Salt Range, extending south from the Pothohar plateau, Pakistan, using the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model parameters and erosion probability zones were estimated using remote sensing and Geo‐Spatial methods. The annual average soil loss rates were calculated by considering five geo‐environmental factors, that is, slope length and steepness (LS), rainfall erosivity (R), cover management (C), soil erodibility (K), and conservation practice (P) range from 0–559 527, 1404–4431, 0–1, −0.14 to 1.64, and 0.2–122 respectively. This research determined that the yearly average rate of SE in the Salt Range varies from over 50 to above 350 . The distribution of land area across different SE probability zones reveals that a small portion (2.11%) is classified as High, a moderate portion (7.13%) falls under the category of Moderate, while the majority (90.7%) is classified as Low in terms of proneness towards erosion. The land devoid of vegetation and characterized by steep slopes is especially prone to SE. The Salt Range is highly vulnerable to SE risk due to climatic variations and improper land use practices. The result provides a spatial distribution of SE across the salt range, utilized for management planning processes and conservation at the policy level among decision‐makers and land‐use planners.

Assessment of soil erosion risk in a hilly zone sub-watershed of Karnataka using geospatial technologies and the RUSLE model

ABSTRACT Human activities and climate change have sped up soil erosion, leading to annual losses of fertile soil and declining crop yields. The biggest concerns in land and water conservation are modeling and monitoring soil loss, as well as measuring the vulnerability and danger of active erosional processes. In the present study, a comprehensive approach used to evaluate the risk of soil erosion in the Ganjigatti sub-watershed of Karnataka, India, using the RUSLE model and GIS techniques. The annual average soil loss estimated using the RUSLE model ranged from 0 to 122.73 t ha−1 yr−1, with an average of 3.48 t ha−1 yr−1 in the Ganjigatti sub-watershed. The quantity of erosion was found to be greatly influenced by topography (LS factor) and land use and land cover (C factor). The results showed that a larger part of the study area, 98.34%, accounted for slight erosion (0–5 t ha−1 yr−1). The spatial pattern of erosion revealed high to very severe risk zones in the main stream of sub-watershed. The total soil loss was calculated to be 15,046.96 t yr−1 in the sub-watershed. The study’s results aid decision-makers, land administrators, environmentalists, and others in implementing effective land and water conservation measures in the Ganjigatti sub-watershed to control soil erosion.

Predicting the Impacts of Land Use/Cover and Climate Changes on Water and Sediment Flows in the Megech Watershed, Upper Blue Nile Basin

This study assessed the impacts of the land use/cover (LULC) and climate changes on the runoff and sediment flows in the Megech watershed. The Geospatial Water Erosion Prediction Project (GeoWEPP) was used to assess LULC and climate changes’ impact on runoff, soil loss, and sediment yield. The QGIS 2.16.3 plugin module for land use change evaluation (MOLUSCE) tool with the cellular automata artificial neural network (CA-ANN) was used for LULC prediction based on historical data and exploratory maps. Two commonly used representative concentration pathways (RCPs)—4.5 and 8.5—were used for climate projection in the 2030s, 2050s, and 2070s. The LULC prediction analysis showed an expansion of cropland and settlement areas, with the reduction in the forest and rangelands. The climate projections indicated an increase in maximum temperatures and altered precipitation patterns, particularly with increased wet months and reduced dry periods. The average annual soil loss and sediment yield rates were estimated to increase under both the RCP4.5 and RCP8.5 climate scenarios, with a more noticeable increase under RCP8.5. By integrating DEM, soil, land use, and climate data, we evaluated runoff, soil loss, and sediment yield changes on only land use/cover, only climate, and the combined impacts in the watershed. The results revealed that, under all combined scenarios, the sediment yield in the Megech Reservoir was projected to substantially increase by 23.28–41.01%, showing a potential loss of reservoir capacity. This study recommends strong climate adaptation and mitigation measures to alleviate the impact on land and water resources. It is possible to lessen the combined impacts of climate and LULC change through implementing best-management practices and adaptation strategies for the identified scenarios.

Assessment of soil erosion and sediment yield in the Peddavagu watershed, India, using a revised universal soil loss equation model (RUSLE) and GIS techniques

ABSTRACT The present investigation was carried out within the Peddavagu watershed, which is located in India. The necessary datasets, including soil, land use land cover, rainfall, and digital elevation model, were processed and analysed within a Geographic Information System framework. To evaluate soil loss within the watershed, the present investigation employed the revised universal soil loss equation (RUSLE) model. Subsequently, the sediment yield is estimated based on the sediment delivery ratio (SDR). The average annual soil loss was estimated at 17.91 tonnes/hectare/year, which is high soil erosion risk. The RUSLE model's accuracy is 82.1%. Moreover, the findings revealed that sub-watersheds (SW) 9 and SW 3 exhibited the maximum and minimum average annual soil loss. The Peddavagu watershed's SDR was 0.210. Annually, 3.76 tonnes/hectare/year of sediment were transported to the Peddavagu watershed outlet. The findings revealed that SW 9 and SW 5 exhibited the maximum and minimum average annual sediment yield. The model's performance was evaluated by comparing its predictions with gauge data for validation. The observed actual data indicated a yield of 3.66 tonnes/hectare/year, while the model predicted a yield of 3.76 tonnes/hectare/year. This resource offers significant insights for policymakers and decision-makers on sustainable watershed management techniques.

Applications of advanced technology for Prioritization of Watershed

Prioritization of sub-watersheds is critical in strategic planning for incorporating management practices in delicate semi-arid regions. Geomorphometric as well as land use/cover data sets are essential for determining subwatershed priorities for integrated watershed management. Prioritizing watersheds entails ranking of sub-watersheds according to their susceptibility based on several variables, including the average yearly soil loss, the depletion of water resources, and ecological deterioration. The sub-watershed prioritization for the study area Muzaffarpur district was done by combining Principal Component Analysis (PCA) and Weighted Sum Approach (WSA). PCA was used to differentiate important parameters but WSA was used to measure compound values for priority ranking and determining weights for significant parameters. The PCA was effective in obtaining the most crucial values (i.e., WB, Dt, Re, and Rb). A load of each significant parameter was successfully defined by the WSA application. Traditional prioritizing procedures uses numerous criteria in a complex manner and presumptively contribute equally but PCA-WSA integration results in more dynamic, effective, and efficient solutions.

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia

Soil erosion is a major environmental problem in Ethiopia, reducing topsoil and agricultural land productivity. Soil loss estimation is a critical component of sustainable land management practices because it provides important information about soil erosion hotspot areas and prioritizes areas that require immediate management interventions. This study integrates the Revised Universal Soil Loss Equation (RUSLE) with Google Earth Engine (GEE) to estimate soil erosion rates and map soil erosion in the Upper Tekeze Basin, Northern Ethiopia. SoilGrids250 m, CHIRPS-V2, SRTM-V3, MERIT Hydrograph, NDVI from sentinel collections and land use land cover (LULC) data were accessed and processed in the GEE Platform. LULC was classified using Random forest (RF) classification algorithm in the GEE platform. Landsat surface reflectance images from Landsat 8 Operational land imager (OLI) sensors (2021) was used for LULC classification. Besides, different auxiliary data were utilized to improve the classification accuracy. Using the RUSLE-GEE framework, we analyzed the soil loss rate in different agroecologies and LULC types in the upper Tekeze basin in Waghimra zone. The results showed that the average soil loss rate in the Upper Tekeze basin is 25.5 t ha−1 yr−1. About 63 % of the basin is experiencing soil erosion above the maximum tolerable rate, which should be targeted for land management interventions. Specifically, 55 % of the study area, which is covered by unprotected shrubland is experiencing mean annual soil loss of 34.75 t ha−1 yr−1 indicating the need for immediate soil conservation intervention. The study also revealed evidence that this high mean soil loss rate of the basin can be reduced to a tolerable rate by implementing integrative watershed management and exclosures. Furthermore, this study demonstrated that GEE could be a good source of datasets and a computing platform for RUSLE, in particular for data scarce semi-arid and arid environments. The results from this study are reliable for decision-making for rapid soil erosion assessment and intervention prioritization.

Modelling, quantification and estimation of the soil water erosion using the Revised Universal Soil Loss Equation with Sediment Delivery Ratio and the analytic hierarchy process models

AbstractThis research used the Revised Universal Soil Loss Equation (RUSLE) with Sediment Delivery Ratio (SDR) model. The analytic hierarchy process (AHP) method, while also incorporating the use of a geographic information system (GIS) and remote sensing (RS) to predict the annual soil loss rate and spatialise the processes of water erosion at the scale of the Loukkos Watershed, Morocco. The RUSLE model and AHP parameters were estimated using RS data, and the erosion vulnerability zones were determined using GIS. We used five parameters, including precipitation erosivity, soil erodibility, slope length and steepness, vegetation cover, and soil erosion control practices in the RUSLE. For the AHP technique, we used seven geo‐environmental factors, including annual average precipitation, drainage density, lineament density, slope, soil texture, land use/land cover and landform maps. The results of RUSLE indicated that the average annual soil loss varied from 0 to 2388.27 . The total estimated annual potential soil loss was approximately 40 790 220.11 , and a sediment yield estimated by RUSLE‐SDR was 8 647 526.66 , equivalent to 6.65 Mm3. This value is very close to the measured value of 6.81 Mm3, for a difference of 0.16 Mm3. Furthermore, the results of the AHP indicate that the soil erosion potential index varies from 0 to 0.205315 . Overall, nearly 13.7% of the area suffered from severe soil erosion exceeding 50 . Approximately 80% of the Loukkos Watershed area experienced only slight erosion, while the remaining 6% incurred moderate erosion. Integrating GIS and RS into the RUSLE model and AHP helped us robustly estimate the extent and degree of erosion risk. Territorial decision‐makers should adopt our results to develop soil conservation strategies, water management plans and other necessary soil and water conservation measures for this region.

Estimation of Soil Loss in the Nanoi River Basin using Geospatial Techniques

Objectives: The main purpose of this research is to identify severe soil loss areas in the Nanoi river basin of Assam, India to suggest appropriate soil management planning in the river basin. Methods: The Revised Universal Soil Loss Equation (RUSLE) integrates geospatial technologies to assess overall soil loss in the Nanoi river basin which provides a faster and more accurate estimation. It is possible to understand soil erosion patterns more thoroughly using RUSLE which makes sustainable soil management easier. Findings: According to the RUSLE equation, the Nanoi river basin experiences an estimated total soil loss of 18,562.5 tons per year, with an average annual soil loss of 0.32 t/h/y. This suggests that approximately 0.64 km2 of the area falls into the Moderate, high and Extreme soil erosion sensitivity zones. Furthermore, a final map is generated to display different areas with varying levels of soil loss rates. Novelty: The geospatial approaches used produce precise findings at a reasonable price and demonstrate the severity of soil loss in the river basin. If soil loss continues at the current rate, there is a high likelihood that certain areas on both sides of the river, particularly in the downstream region of the basin, will experience fluvial hazards, such as drainage congestion and floods. Hence, the results generated will be helpful in the management practices of the river basin. Keywords: Soil loss, Soil erosion, Geospatial, Revised universal soil loss equation, River basin