Estimate Soil Loss Research Articles

Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks

Soil erodibility (K) is an essential component in estimating soil loss indicating the soil's susceptibility to detach and transport. Data Computing and processing methods, such as artificial neural networks (ANNs) and multiple linear regression (MLR), have proven to be helpful in the development of predictive models for natural hazards. The present case study aims to assess the efficiency of MLR and ANN models to forecast soil erodibility in Peninsular Malaysia. A total of 103 samples were collected from various sites and K values were calculated using the Tew equation developed for Malaysian soil. From several extracted parameters, the outcomes of correlation and principal component analysis (PCA) revealed the influencing factors to be used in the development of ANN and MLR models. Based on the correlation and PCA results, two sets of influencing factors were employed to develop predictive models. Two MLR (MLR-1 and MLR-2) models and four neural networks (NN-1, NN-2, NN-3, and NN-4) optimized using Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) were developed and evaluated. The model performance validation was conducted using the coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE). The analysis showed that ANN models outperformed MLR models. The R2 values of 0.446 (MLR-1), 0.430 (MLR-2), 0.894 (NN-1), 0.855 (NN-2), 0.940 (NN-3), and 0.826 (NN-4); MSE values of 0.0000306 (MLR-1), 0.0000315 (MLR-2), 0.0000158 (NN-1), 0.0000261 (NN-2), 0.0000318 (NN-3), and 0.0000216 (NN-4) suggested the higher accuracy and lower modelling error of ANN models as compared with MLR. This study could provide an empirical basis and methodological support for K factor estimation in the region.

Estimation of erosion rate in a watershed of a deforestation area in the Brazilian Amazon, eastern of Mato Grosso

The central region of Brazil, geographically located in Mato Grosso, presents changes in land cover with replacement of the forest for soybean and cotton cultivation, intensifying soil losses in a hydrographic basin located in the tropical climate. The objective of the research is to estimate the loss of soil in the Curisevo River Basin, from the Universal Soil Loss Equation. The methodologies used were the elaboration of a cartographic base in the QGIS geographic information system, with the spatial information of slopes, soil types and land cover, of the Curisevo River Basin. The rainfall erosivity was estimated with rainfall data from the National Institute of Meteorology and the erodibility of the soils was estimated according to the indications of the State of Mato Grosso. The land cover of the Curisevo River Basin was analyzed based on remote sensing images with the vectorization of the areas of the agriculture, pasture and forest classes. The spatial data of slopes of the Curisevo River Basin were obtained from the Shuttle Radar Topography Mission base, with spatial analysis treatment in QGIS. In the basin with an area of 1.18 million hectares, an erosion rate of 134,526 t/ha/year was estimated, corresponding to 16 million tons of sediments. This study contributes to the geographical studies, and the indices used in the equation to estimate soil losses can be analyzed by climatic, pedological, geomorphological and landscape analysis studies in land cover changes in a watershed.

Prioritization of Sub-Watershed Based on Soil Loss Estimation Using RUSLE Model

Soil erosion is one of the most crucial land degradation problems and is considered the most critical environmental hazard worldwide. The present study uses remote sensing data integrated with the geographical information system (GIS) technique and the revised universal soil loss equation (RUSLE) model for assessing the annual average soil loss of the Digaru watershed of India for 1999 and 2020. The estimated mean gross yearly soil loss from the entire watershed was 102716 t yr-1 in 1999 and 178931.6 t yr-1 in 2020. The overall average soil loss rate increased significantly between 1999 and 2020, rising from 4.73 t—ha-1yr-1 to 8.43 t—ha-1yr-1. The sub-watersheds are prioritized as high (≥ 40 t ha−1yr−1), moderate (20–40 t ha−1yr−1), and low (<20 t ha−1yr−1) based on the spatial distribution of soil erosion. Seven sub-watersheds have been grouped under low priority, followed by seven under moderate priority and one under high priority. This study demands instant attention for soil and water conservation efforts in highly eroded watershed areas.

Assessment of the extent of soil degradation over different land uses in the Kebbi area, northwestern Nigeria

This paper assessed soil degradation over land use in the Kebbi area, Northwestern Nigeria. The specific objectives entailed identifying major forms of soil loss in the study area, estimating soil loss, and examining the responses of soil physico-chemical properties over dominant land use types. Slope angles were determined using a GPS, a handheld Abney level, ranging poles, and a 30 m measuring tape while gully depths, widths, and lengths were taken at 5 m-30 m intervals. Key soil physico-chemical parameters were determined using standard procedures. Descriptive statistics such as mean, standard deviation, and coefficient of variation were used to summarize the data generated from the study. The analysis of variance and the student’s t-test were used to test for significant association between and within pairs of land uses. The results show that slopes range from 4⁰ - 16⁰, while the magnitude of soil loss ranged between 3580.32 m3 in K1 (Kalgo 1), 3550.89 m3 in G2 (Goru 2) and 161.01 m3 in A1 (Angwar Daji 1). Results show significance at p≤ 0.05 in bulk density values over the different land uses, with the highest bulk density value of 1.78 g/cm3 in badland and the lowest value of 1.35 g/cm3 in plantation land use. Total nitrogen ranges between 0.01 – 0.38%, with the lowest values in badland and scrubland, while soil base saturation is highest over plantation (76.3%) and lowest in badland (50.4%). The study concludes that the dominant geomorphic responses identified in the study area are gully initiation and development and varying amounts of soil degradation over the different land uses. The findings should facilitate policy initiation to rehabilitate degraded lands and adopt sustainable soil management practices like tree planting while linking geomorphological information to infrastructural planning and development.

Modeling the impact of land use land cover change on the estimation of soil loss and sediment export using InVEST model at the Rib watershed of Upper Blue Nile Basin, Ethiopia

Information on soil loss and sediment export is essential to identify vulnerable area of soil erosion and to inform conservation interventions in a given watershed. The goal of this study was to analyze the changes in soil loss and sediment exports in the Rib watershed of Ethiopia's Upper Blue Nile Basin. The study used spatial data by using a variety of data sources, including topographic maps, soil maps, meteorological data, and satellite images. Cultivated land, forests, grazing areas, shrubs, water bodies, and settlements were all identified in the study watershed. Soil loss and sediment export were calculated using the Integrated Valuation of Ecosystem Services and Tradeoff (In-VEST) model. The model was calibrated using the sediment yield data gathered in the sample watersheds. The results reveal that while the equivalent sediment export grew from 6.54 t/ha/year to 11.05 t/ha/year in 2000 and 2020, respectively, the average soil loss increased from 22.37 t/ha/year in 2000 to 33.38 t/ha/year in 2020. The largest rate of soil erosion was seen on cultivated land, which increased from 40.86 t/ha/year in 2000 to 53.9 t/ha/year in 2020. This relates to the expansion of the agricultural land. The soil loss and sediment export rates in sub-watersheds three (SW-3) and five (SW-5) were the highest, at 61.80 and 63.48 t/ha/year and 18.75 and 19.35 t/ha/year, respectively. The least amount of soil loss occurs in sub-watershed twelve (SW-12) (2.56 t/ha/year). This is because SW-12 is situated in the watershed's lower reaches fogera plain parts of the watershed experiencing less erosion. The result concluded that prioritizing those sub-watersheds is important for informed decision-making processes.

GIS based annual soil loss estimation with revised universal soil loss equation (RUSLE) in the upper Meki sub-catchment, rift valley sub-basin, Ethiopia

GIS based annual soil loss estimation with revised universal soil loss equation (RUSLE) in the upper Meki sub-catchment, rift valley sub-basin, Ethiopia

Estimation of Water Yield and Soil Erosion in Samanalawewa Watershed in Sri Lanka using GIS-based InVEST Model

Ecosystem services (ES) are a group of tasks performed by the earth's ecosystems that are required to support life and offer benefits to humanity. Thus, this study explores two of the water-related ecosystem services, water yield, and soil retention that were distributed in the Samanalawewa Watershed (SW) and its eight sub-watersheds in Sri Lanka, over 20 years (2000-2020) with five- year time intervals. Soil and water conservation and water yield play an important role in ecosystem management. Hydrological balance is a crucial component of the hydrological ecosystem services. Assessing water yield and soil erosion are pivotal for watershed management. Thus, this study estimated and mapped the water yield and soil erosion, using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.9.2) Annual Water Yield (AWY) and Sediment Delivery Ratio (SDR) model respectively. According to the obtained results, in 2000, 2005, 2010, 2015 and 2020, the estimated mean annual water yield was 2.62x105, 2.6x105, 3.5x105, 3.58x105 and 2.75x105 m3 ha-1year-1, respectively. Furthermore, the estimated mean annual soil erosion rates for the above- mentioned years were 53.2, 52.9, 69.7, 87.7 and 70.2 t ha-1 year-1. The estimated soil loss values of the SW are 10 to 18 times greater than the soil erosion tolerance (5 t ha-1 year-1) in Sri Lanka. The results show that the years 2015 and 2005 have the highest and the lowest water yield and total soil loss values, respectively. The upper part of the watershed has relatively moderate water yield and soil loss values than the lower part. These findings would be beneficial in developing watershed management strategies and in the implementation of suitable soil and water conservation techniques within the watershed. 
 Keywords: Ecosystem services, InVEST, Samanalawewa watershed, Soil erosion, Water yield

A new approach to pedestal differentiation for soil loss estimation—a case study from a burnt area in north-central Portugal

Soil pedestals have long been used as qualitative indicators of soil splash erosion. In rangelands, plant-capped pedestals, generally grass tussocks, have also been used to quantitatively estimate soil loss since the first half of the twentieth century. In agricultural lands, forests, and badlands, stone-capped pedestals have been used as qualitative and semi-quantitative indicators of active, ‘extreme’ erosion. Little work has been reported on using capstone pedestal data for quantifying soil loss. We postulate that three distinct capstone pedestal types may be present in any given location and that a detailed analysis of a pedestal height histogram may be used to recognize their populations. This analysis can subsequently inform if soil loss can be reliably estimated and if so, which of the existing methods using pedestal height data will provide more accurate results. The three proposed capstone pedestal types are: (1) neo-pedestals formed underneath surface stones exposed by (partial) removal of the soil surface cover; (2) endo-pedestals formed underneath stones that were buried in the soil but have been exposed by erosion; and (3) phoenix-pedestals formed underneath stones from collapsed pedestals. In the pedestal height histogram of any given location, a skew to smaller heights may indicate the existence of endo- and/or phoenix-pedestals, which may be revealed as a bi-(or tri) modal distribution when using a smaller bin size. This concept was applied to a case study where soil loss had been monitored for control plots and mulched plots during a 5-year period following wildfire in a eucalypt plantation. We measured pedestal heights and used methods to quantitatively assess soil loss from soil pedestal data in the available literature. Soil pedestal data at the end of the 5-year period under or overestimated soil loss in the control treatment, with results ranging from 60 to 115% of measured soil loss, depending on the method. It is postulated that phoenix- and endo-pedestals may be a driving factor behind the observed discrepancies. We discuss how future research may provide more insight into dominant processes, and how frequency distributions may be used to select the best methods for estimating soil loss from pedestals.

Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model

The existing digital soil maps are mainly characterized by coarse spatial resolution and are not up to date; thus, they are unable to support the physical process-based models for improved predictions. The overarching objective of this work is oriented toward a data-driven approach and datacube-based tools (Soil Data Cube), leveraging Sentinel-2 imagery data, open access databases, ground truth soil data and Artificial Intelligence (AI) architectures to provide enhanced geospatial layers into the Revised Universal Soil Loss Equation (RUSLE) model, improving both the reliability and the spatial resolution of the final map. The proposed methodology was implemented in the agricultural area of the Imathia Regional Unit (northern Greece), which consists of both mountainous areas and lowlands. Enhanced soil maps of Soil Organic Carbon (SOC) and soil texture were generated at 10 m resolution through a time-series analysis of satellite data and an XGBoost (eXtrene Gradinent Boosting) model. The model was trained by 84 ground truth soil samples (collected from agricultural fields) taking into account also additional environmental covariates (including the digital elevation model and climatic data) and following a Digital Soil Mapping (DSM) approach. The enhanced layers were introduced into the RUSLE’s soil erodibility factor (K-factor), producing a soil erosion layer with high spatial resolution. Notable prediction accuracy was achieved by the AI model with R2 0.61 for SOC and 0.73, 0.67 and 0.63 for clay, sand, and silt, respectively. The average annual soil loss of the unit was found to be 1.76 ton/ha/yr with 6% of the total agricultural area suffering from severe erosion (>11 ton/ha/yr), which was mainly found in the mountainous border regions, showing the strong influence of the mountains in the agricultural fields. The overall methodology could strongly support regional decision making and planning and environmental policies such as the European Common Agricultural Policy (CAP) and the Sustainable Development Goals (SDGs).

Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfalldata, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy.

Assessment of soil erosion from rainwater runoff within a small non arable catchment

The article represents the dynamics of erosion-accumulation processes within the test catchment of the Lena basin over two time periods (1985-1990; 2015-2019) from rainfall-runoff, studied using the WaTEM/SEDEM model. The estimate of soil loss from rainfall-runoff was obtained, taking into account the deposition of part of the eroded soil within the catchment. This is one of the first works that estimates the magnitude of soil erosion within a poorly crop developed catchment from rainwater runoff, taking into account the deposition. It was determined that the measured sediment yield from the test catchment decreased over the two studied periods. The decrease in runoff from the studied territory is explained by a decrease in the intensity of agricultural activity in the catchment, as well as the forest area increase and grassland area reduction.

Assessment of soil erosion in the Boussellam watershed, Algeria: Integrated approach using the Erosion Potential Method (EPM) and GIS

Land degradation is a growing concern, exacerbated by recent climate change. Water erosion emerges as a crucial tool to address this issue. This study focuses on estimating soil loss from water erosion in the Boussellam valley watershed, a part of the ex-pansive Soummam basin in northeastern Algeria, characterized by a semi-humid to humid climate. Covering an expanse of 4,301 km2 with a perimeter of 420 Km, the basin's assessment incorporates often-overlooked factors such as temperature. Utilizing Geographic In-formation System (GIS) in conjunction with GAVRILOVIC's EPM (Erosion Potential Method) model, erosion projections for the year 2022 have been generated. The findings reveal that the entire Boussellam watershed experiences an average erosion rate of 8.50 tonnes per hectare annually. However, it is evident that the current protective measures implemented by decision-makers are suboptimal. To pinpoint the most vulnerable areas, GIS was employed to map and subsequently categorize them into five levels of erosion intensity: low, moderate, medium, high, and very high. These detailed maps will enable more precise and tailored interventions by decision-makers to effectively safeguard the regions most impacted by erosion.

Estimation and mapping of water erosion and soil loss: Application of Gavrilovic erosion potential model (EPM) using GIS and remote sensing in The Assif el mal Watershed, Western high Atlas

Estimation and mapping of water erosion and soil loss: Application of Gavrilovic erosion potential model (EPM) using GIS and remote sensing in The Assif el mal Watershed, Western high Atlas

An Estimation of Annual Soil Loss using Integrated Approach of RUSLE and Geospatial Techniques: A Case Study of Upper-Krishna River Basin, India

An Estimation of Annual Soil Loss using Integrated Approach of RUSLE and Geospatial Techniques: A Case Study of Upper-Krishna River Basin, India

Assessment of Soil Loss Using RUSLE Method in Mrica Reservoir Catchment, Central Java, Indonesia

The Indonesian government has identified the Serayu Watershed as a priority area for restoration within the National Mid-Term Development Plan. One of the significant challenges in this region is the high level of soil erosion, which threatens the overall ecosystem. This study aims to estimate the amount of soil loss in the Mrica Catchment using the Revised Universal Soil Loss Equation (RUSLE) Method. Various data sources were utilized, including soil type, rainfall, land cover, Digital Elevation Model, and conservation data. Geographic Information System (GIS) techniques were employed to calculate the critical factors required by the RUSLE Method, including soil erodibility (K), rainfall erosivity (Ri), slope length and steepness factor (LS), and cover management and conservation factor (CP). This research provides critical information for land management in Mrica Catchment. These factors were used to estimate soil loss in the Mrica Catchment, revealing a range between 62,553 tons per year (t/y) and 21,323,311 t/y, with an average value of 443.90 ton per hectare per year (t/ha/y). These results indicate high erosion potential based on the Classification of Erosion Hazard (HER). This study provides critical information for land management and offers suggestions for devising effective strategies to mitigate sedimentation impact in the Serayu Watershed. The highest soil loss values according to the RUSLE Method, both under the Environmental and Management Variable, are observed in the same location, namely, in the north of Mrica Catchment. The findings emphasize the urgent need for erosion control measures and sustainable land management practices in this priority restoration area.

Soil erosion vulnerability and soil loss estimation for Siran River watershed, Pakistan: an integrated GIS and remote sensing approach.

Soil erosion is a problematic issue with detrimental effects on agriculture and water resources, particularly in countries like Pakistan that heavily rely on farming. The condition of major reservoirs, such as Tarbela, Mangla, and Warsak, is crucial for ensuring an adequate water supply for agriculture in Pakistan. The Kunhar and Siran rivers flow practically parallel, and the environment surrounding both rivers' basins is nearly identical. The Kunhar River is one of KP's dirtiest rivers that carries 0.1 million tons of suspended sediment to the Mangla reservoir. In contrast, the Siran River basin is largely unexplored. Therefore, this study focuses on the Siran River basin in the district of Manshera, Pakistan, aiming to assess annual soil loss and identify erosion-prone regions.Siran River average annual total soil loss million tons/year is 0.154. To achieve this, the researchers integrate Geographical Information System (GIS) and remote sensing (RS) data with the Revised Universal Soil Loss Equation (RUSLE) model. Five key variables, rainfall, land use land cover (LULC), slope, soil types, and crop management, were examined to estimate the soil loss. The findings indicate diverse soil loss causes, and the basin's northern parts experience significant soil erosion. The study estimated that annual soil loss from the Siran River basin is 0.154 million tons with an average rate of 0.871 tons per hectare per year. RUSLE model combined with GIS/RS is an efficient technique for calculating soil loss and identifying erosion-prone areas. Stakeholders such as policymakers, farmers, and conservationists can utilize this information to target efforts and reduce soil loss in specific areas. Overall, the study's results have the potential to advance initiatives aimed at safeguarding the Siran River watershed and its vital resources. Protecting soil resources and ensuring adequate water supplies are crucial for sustainable agriculture and economic development in Pakistan.

SOIL EROSION AND IMPACT ON RECREATIONAL RESOURCES IN THE SHYNGYRLAU BASIN, WESTERN KAZAKHSTAN: A MULTI-ANALYTICAL ASSESSMENT

Over the past decades, water erosion has increased significantly in the Shyngyrlau River basin. Recreational development, haphazard construction and operation of roads have led to increased water erosion and the formation of numerous linear erosion forms. Erosion reduces the natural resource status of the region. Water erosion develops in most cases along roads laid along the thalwegs of the ravine-hollow network. The light substrate along with sparse vegetation cover (the sparseness is aggravated by intensive grazing) predetermine its rapid development. Water erosion is an acute problem arising from the present climate change, agricultural intensification and diverse forms of anthropogenic land degradation. Assessment of present and potential soil erosion is useful for landscape preservation as well as development planning. Multi-analytical modeling can provide a quantitative and consistent estimation of soil erosion and sediment yield under locally specific environmental conditions. The soil loss model, Revised Universal Soil Loss Equation (RUSLE), integrated with GIS, has been used to estimate soil loss in the Shyngyrlau Basin (a left-bank tributary of the Ural River, NW Kazakhstan). The RUSLE model is based on remote sensing and field data; erosion probabilities were determined using GIS. The percentage ratio of the soil loss in the Shyngyrlau River basin shows erosion variation of 0.001–2.47 t/ha/yr. The degree of erosion increases with the length of the slope. A new factor Chip curvature (Cu factor), defining the accuracy of the soil loss results, was defined. According to this factor, soil erosion in the study area is 0.007–2.48 t/ha/yr within the low erosion class. The lowest indicator of the K factor is 0.2 in sandy clay soils used as pasturelands. The highest K factor (0.3) relates to clayey arable soils. The research results add to implementation of new strategies in soil management and conservation practices reducing soil erosion in the Shyngyrlau Basin. Both climate and anthropogenic factors are seen behind the activated ground erosion. The development of forms of water erosion leads to a violation of the integrity of modern natural complexes of the West Kazakhstan region, which significantly reduces their stability and recreational potential.

Morphometric parameters based prioritization of watersheds for soil erosion risk in Upper Jhelum Sub-catchment, India.

Soil erosion is the inherent and destructive threat affecting agricultural production and livelihood of million mouths. The increased frequency of floods and land use/land cover changes has made Upper Jhelum Sub-catchment susceptible to soil erosion risk. Morphometric based watershed prioritization for soil erosion risk may help in sustainable management of natural resources. Thus, this paper endeavors to prioritize watersheds of Upper Jhelum Sub-catchment in India based on morphometric parameters for soil erosion risk using geospatial techniques. Weights to the morphometric parameters were assigned through a multi-criteria decision method. The watersheds in the Sub-catchment have been categorized into low, medium, high and very high priority classes based on prioritization ranks that were determined by computing the compound value for the soil erosion risk, based on prioritization ranks obtained through compound value for the soil erosion risk. The results revealed 1E1D3 and 1E1D8 watersheds accorded very high priority. The watersheds namely IE1D2 and IEID4 were found under high priority. Medium priority for soil erosion risk was determined in IEID5 and IED7 watersheds while 1E1D1 and IE1D6 watersheds were identified for low priority. The study calls for implementing soil conservation practices in the Sub-catchment. The Sub-catchment can be made less hazardous for the soil erosion risk by implementing contour farming, building check dams, terrace farming, afforestation and limiting large scale overgrazing. The findings of this study may offer valuable insights for stakeholders for conservation of soil resource. The approach utilized in the study may be linked with soil loss estimation for effective conservation of natural resources in further future studies.

Spatial assessment of soil erosion by water using RUSLE model, remote sensing and GIS: a case study of Mellegue Watershed, Algeria-Tunisia.

Soil erosion is an important global phenomenon that can cause many impacts, like morphometry and hydrology alteration, land degradation and landslides. Moreover, soil loss has a significant effect on agricultural production by removing the most valuable and productive top soil's profile, leading to a reduction in yields, which requires a high production budget. The detrimental impact of soil erosion has reached alarming levels due to the exacerbation of global warming and drought, particularly in the arid climates prevalent in Tunisia and Algeria and other regions of North Africa. The influence of these environmental factors has been especially evident in the catchment of Mellegue, where profound vegetation loss and drastic changes in land use and cover, including the expansion of urban areas and altered agricultural practices, have played a significant role in accelerating water-induced soil loss between 2002 and 2018. The ramifications of these developments on the fragile ecosystems of the region cannot be overlooked. Accordingly, this study aimed to compare soil losses between 2002 and 2018 in the catchment of Mellegue, which is a large cross-border basin commonly shared by Tunisian-Algerian countries. The assessment and mapping of soil erosion risk were carried out by employing the Revised Universal Soil Loss Equation (RUSLE). This widely recognised equation provided valuable insights into the potential for erosion. Additionally, changes in land use and land cover during the same period were thoroughly analysed to identify any factors that may have contributed to the observed risk. By integrating these various elements, a comprehensive understanding of soil erosion dynamics was achieved, facilitating informed decision-making for effective land management and conservation efforts. It requires diverse factors that are integrated into the erosion process, such as topography, soil erodibility, rainfall erosivity, anti-erosion cultivation practice and vegetation cover. The computation of the various equation factors was applied in a GIS environment, using ArcGIS desktop 10.4. The results show that the catchment has undergone significant soil water erosion where it exhibits the appearance of approximately 14,000 new areas vulnerable to erosion by water in 2018 compared to 2002. Average erosion risk has also increased from 1.58 t/ha/year in 2002 to 1.78 in 2018, leading to an increase in total estimated soil loss of 54,000 t/ha in 2018 compared to around 25,500 t/ha in 2002. Maps of erosion risk show that highly eroded areas are more frequent downstream of the basin. These maps can be helpful for decision-makers to make better sustainable management plans and for land use preservation.

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

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